text stringlengths 1.23k 293k | tokens float64 290 66.5k | created stringdate 1-01-01 00:00:00 2024-12-01 00:00:00 | fields listlengths 1 6 |
|---|---|---|---|
COMBSS: Best Subset Selection via Continuous Optimization
The problem of best subset selection in linear regression is considered with the aim to find a fixed size subset of features that best fits the response. This is particularly challenging when the total available number of features is very large compared to the number of data samples. Existing optimal methods for solving this problem tend to be slow while fast methods tend to have low accuracy. Ideally, new methods perform best subset selection faster than existing optimal methods but with comparable accuracy, or, being more accurate than methods of comparable computational speed. Here, we propose a novel continuous optimization method that identifies a subset solution path, a small set of models of varying size, that consists of candidates for the single best subset of features, that is optimal in a specific sense in linear regression. Our method turns out to be fast, making the best subset selection possible when the number of features is well in excess of thousands. Because of the outstanding overall performance, framing the best subset selection challenge as a continuous optimization problem opens new research directions for feature extraction for a large variety of regression models.
Introduction
Recent developments in information technology have enabled the collection of high-dimensional complex data in engineering, economics, finance, biology, health sciences and other fields [9].In high-dimensional data, the number of features is large and often far higher than the number of collected data samples.In many applications, it is desirable to find a parsimonious best subset of predictors so that the resulting model has desirable prediction accuracy [26,10,25].This article is recasting the challenge of best subset selection in linear regression as a novel continuous optimization problem.We show that this reframing has enormous potential and substantially advances research into larger dimensional and exhaustive feature selection in regression, making available technology that can reliably and exhaustively select variables when the total number of variables is well in excess of thousands.
Here, we aim to develop a method that performs best subset selection and an approach that is faster than existing exhaustive methods while having comparable accuracy, or, that is more accurate than other methods of comparable computational speed.
Consider the linear regression model of the form y = Xβ + ϵ, where y = (y 1 , . . ., y n ) T is an ndimensional known response vector, X is a known design matrix of dimension n × p with x i,j indicating the ith observation of the jth explanatory variable, β = (β 1 , . . ., β p ) T is the p-dimensional vector of unknown regression coefficients, and ϵ = (ϵ 1 , . . ., ϵ n ) T is a vector of unknown errors, unless otherwise specified, assumed to be independent and identically distributed.Best subset selection is a classical problem that aims to first find a so-called best subset solution path [e.g.see 26,21] by solving, for a given k, where ∥ • ∥ 2 is the L 2 -norm, ∥β∥ 0 = p j=1 I(β j ̸ = 0) is the number of non-zero elements in β, and I(•) is the indicator function, and the best subset solution path is the collection of the best subsets as k varies from 1 to p.For ease of presentation, we assume that all columns of X are subject to selection, but generalizations are immediate (see Remark 2 for more details).
Exact methods for solving (1) are typically executed by first writing solutions for low-dimensional problems and then selecting the best solution over these.To see this, for any binary vector s = (s 1 , . . ., s p ) T ∈ {0, 1} p , let X [s] be the matrix of size n × |s| created by keeping only columns j of X for which s j = 1, where j = 1, . . ., p.Then, for any k, in the exact best subset selection, an optimal s can be found by solving the problem, where β [s] is a low-dimensional least squares estimate of elements of β with indices corresponding to non-zero elements of s, given by where A † denotes the pseudo-inverse of a matrix A. Both (1) and ( 2) are essentially solving the same problem, because β [s] is the least squares solution when constrained so that I(β j ̸ = 0) = s j for all j = 1, . . ., p.
It is well-known that solving the exact optimization problem (1) is in general non-deterministic polynomial-time hard [27].For instance, a popular exact method called leaps-and-bounds [12] is currently practically useful only for values of p smaller than 30 [30].To overcome this difficulty, the relatively recent method by [2] elegantly reformulates the best subset selection problem (1) as a mixed integer optimization and demonstrates that the problem can be solved for p much larger than 30 using modern mixed integer optimization solvers such as in the commercial software Gurobi [14] (which is not freely available except for an initial short period).As the name suggests, the formulation of mixed integer optimization has both continuous and discrete constraints.Although, the mixed integer optimization approach is faster than the exact methods for large p, its implementation via Gurobi remains slow from a practical point of view [17].
Due to computational constraints of mixed integer optimization, other popular existing methods for best subset selection are still very common in practice, these include forward stepwise selection, the least absolute shrinkage and selection operator (generally known as the Lasso), and their variants.Forward stepwise selection follows a greedy approach, starting with an empty model (or intercept-only model), and iteratively adding the variable that is most suitable for inclusion [8,19].On the other hand, the Lasso [32] solves a convex relaxation of the highly non-convex best subset selection problem by replacing the discrete L 0 -norm ∥β∥ 0 in (1) with the L 1 -norm ∥β∥ 1 .This clever relaxation makes the Lasso fast, significantly faster than mixed-integer optimization solvers.However, it is important to note that Lasso solutions typically do not yield the best subset solution [17,37] and in essence solve a different problem than exhaustive best subset selection approaches.In summary, there exists a trade-off between speed and accuracy when selecting an existing best subset selection method.
With the aim to develop a method that performs best subset selection as fast as the existing fast methods without compromising the accuracy, in this paper, we design COMBSS, a novel continuous optimization method towards best subset selection.
Our continuous optimization method can be described as follows.Instead of the binary vector space {0, 1} p as in the exact methods, we consider the whole hyper-cube [0, 1] p and for each t ∈ [0, 1] p , we consider a new estimate β t (defined later in Section 2) so that we have the following well-defined continuous extension of the exact problem (2): where X t is obtained from X by multiplying the jth column of X by t j for each j = 1, . . ., p, and the tuning parameter λ controls the sparsity of the solution obtained, analogous to selecting the best k in the exact optimization.Our construction of β t guarantees that ∥y − X s β s ∥ 2 = ∥y − X [s] β [s] ∥ 2 at the corner points s of the hypercube [0, 1] p , and the new objective function ∥y−X t β t ∥ 2 2 is smooth over the hypercube.
While COMBSS aims to find sets of models that are candidates for the best subset of variables, an important property is that it has no discrete constraints, unlike the exact optimization problem (2) or the mixed integer optimization formulation.As a consequence, our method can take advantage of standard continuous optimization methods, such as gradient descent methods, by starting at an interior point on the hypercube [0, 1] p and iteratively moving towards a corner that minimizes the objective function.See Fig. 1 for an illustration of our method.In the implementation, we move the box constrained problem (4) to an equivalent unconstrained problem so that the gradient descent method can run without experiencing boundary issues.
The rest of the paper is organized as follows: In Section 2, we describe the mathematical framework of the proposed method COMBSS.In Section 3, we first establish the continuity of the objective functions involved in COMBSS, and then we derive expressions for their gradients, which are exploited for conducting continuous optimization.Complete details of COMBSS algorithm 4) for different values of the parameter λ.In each of these three plots, the curve (in red) shows the execution of a basic gradient descent algorithm that, starting at the initial point t init = (0.5, 0.5) T , converges towards the best subsets of sizes 0, 1, and 2, respectively.
are presented in Section 4. In Section 5, we discuss roles of the tuning parameters that control the surface shape of the objective functions and the sparsity of the solutions obtained.Section 6 provides steps for efficient implementation of COMBSS using some popular linear algebra techniques.Simulation results comparing COMBSS with existing popular methods are presented in Section 7. We conclude the paper with some brief remarks in Section 8. Proofs of all our theoretical results are provided in Appendix A.
Continuous Extension of Best Subset Selection Problem
To see our continuous extension of the exact best subset selection optimization problem (2), for t = (t 1 , . . ., t p ) T ∈ [0, 1] p , define T t = Diag(t), the diagonal matrix with the diagonal elements being t 1 , . . ., t p , and let X t = XT t .With I denoting the identity matrix of an appropriate dimension, for a fixed constant δ > 0, define where we suppress δ for ease of reading.Intuitively, L t can be seen as a 'convex combination' of the matrices (X T X)/n and δI/n, because X T t X t = T t X T XT t and thus Using this notation, now define We need L † t in (7) so that β t is defined for all t ∈ [0, 1] p .However, from the way we conduct optimization, we need to compute β t only for t ∈ [0, 1) p .We later show in Theorem 1 that for all t ∈ [0, 1) p , L t is invertible and thus in the implementation of our method, β t always takes the form β t = L −1 t X T t y/n, eliminating the need to compute any computationally expensive pseudoinverse.
With the support of these observations, an immediate well-defined generalization of the best subset selection problem (1) is Instead of solving the constrained problem (8), by defining a Lagrangian function for a tunable parameter λ > 0, we aim to solve By defining g λ (w) = f λ (t(w)), we reformulate the box constrained problem (10) into an equivalent unconstrained problem, where the mapping t = t(w) is The unconstrained problem (11) is equivalent to the box constrained problem (10), because Remark 1.The non-zero parameter δ is important in the expression of the proposed estimator β t , as in (7), not only to make L t invertible for t ∈ [0, 1) p , but also to make the surface of f λ (t) to have smooth transitions from one corner to another over the hypercube.For example consider a situation where X T X is invertible.Then, for any interior point t ∈ (0, 1) p , since T −1 t exists, the optimal solution to min β ∥y − X t β∥ 2 2 /n after some simplification is T −1 t (X T X)X T y.As a result, the corresponding minimum loss is ∥y − X(X T X)X T y∥ 2 2 /n, which is a constant for all t over the interior of the hypercube.Hence, the surface of the loss function would have jumps at the borders while being flat over the interior of the hypercube.Clearly, such a loss function is not useful for conducting continuous optimization.
Remark 2. The proposed method and the corresponding theoretical results presented in this paper easily extend to linear models with intercept term.More generally, if we want to keep some features in the model, say features j = 1, 2, and 4, then we enforce t j = 1 for j = 1, 2, 4, and conduct subset selection only over the remaining features by taking t = (1, 1, t 3 , 1, t 5 , . . ., t p ) T and optimize over t 3 , t 5 , . . ., t p .
Remark 3. From the definition, for any t, we can observe that β t is the solution of , which can be seen as the well-known Thikonov regression.Since the solution β t does not change, even if the penalty λ p j=1 t j is added to the objective function above, with in the future, we can consider the optimization problem as an alternative to (10).This formulation allows us to use block coordinate descent, an iterative method, where in each iteration the optimal value of β is obtained given t using (7) and an optimal value of t is obtained given that β value.
Continuity and Gradients of the Objective Function
In this section, we first prove that the objective function g λ (w) of the unconstrained optimization problem (11) is continuous on R p and then we derive its gradients.En-route, we also establish the relationship between β [s] and β t which are respectively defined by (3) and (7).This relationship is useful in understanding the relationship between our method and the exact optimization (2).
Theorem 1 shows that for all t ∈ [0, 1) p , the matrix L t , which is defined in (5), is symmetric positive-definite and hence invertible.
Theorem 2. For any s ∈ {0, 1} p , ( As an immediate consequence of Theorem 2, we have ∥y − Therefore, the objective function of the exact optimization problem (2) is identical to the objective function of its extended optimization problem (8) (with λ = 0) at the corner points s ∈ {0, 1} p .
Our next result, Theorem 3, shows that f λ (t) is a continuous function on [0, 1] p .Theorem 3. The function f λ (t) defined in (9) is continuous over [0, 1] p in the sense that for any sequence t (1) , t (2) , • • • ∈ [0, 1) p converging to t ∈ [0, 1] p , the limit lim l→∞ f λ (t (l) ) exists and Corollary 1 establishes the continuity of g λ on R p .This is a simple consequence of Theorem 3, because from the definition, g λ (w) = f λ (t(w)) with t(w) = 1−exp(−w⊙w).Here and afterwards, in an expression with vectors, 1 denotes a vector of all ones of appropriate dimension, ⊙ denotes the element-wise (or, Hadamard) product of two vectors, and the exponential function, exp(•), is also applied element-wise.
Corollary 1.The objective function g λ (w) is continuous at every point w ∈ R p .
As mentioned earlier, our continuous optimization method uses a gradient descent method to solve the problem (11).Towards that we need to obtain the gradients of g λ (w).Theorem 4 provides an expression of the gradient ∇g λ (w).Theorem 4. For every w ∈ R p , with t = t(w) is defined by (12), where with , and Figure 2 illustrates the typical convergence behavior of t for an example dataset during the execution of a basic gradient descent algorithm for minimizing g λ (w) using the gradient ∇g λ given in Theorem 4. Here, w is mapped to t using (12) at each iteration.
Subset Selection Algorithms
Our algorithm COMBSS as stated in Algorithm 1, takes the data [X, y], tuning parameters δ, λ, and an initial point w (0) as input, and returns either a single model or multiple models of different sizes as output.It is executed in three steps.
In Step 1, GradientDescent w (0) , ∇g λ calls a gradient descent method, such as the well known adam optimizer, for minimizing the objective function g λ (w), which takes w (0) as the initial point and uses the gradient function ∇g λ for updating the vector w in each iteration; see, for example, [22] for a review of popular gradient based optimization methods.It terminates when a predefined termination condition is satisfied and returns the sequence w path = (w (0) , w (1) , . . . ) of all the points w visited during its execution, where w (l) denotes the point obtained in the lth iteration of the gradient descent.Usually, a robust termination condition is to terminate when the change in w (or, equivalently, in t(w)) is significantly small over a set of consecutive iterations.
Algorithm 1 COMBSS X, y, δ, λ, w (0) 1: w path ← GradientDescent w (0) , ∇g λ 2: Obtain t path from w path using the map (12) 3: M ← SubsetMap(t path ) 4: return M Selecting the initial point w (0) requires few considerations.From Theorem 4, for any j = 1, . . ., p, we have t j (w j ) = 0 if and only if w j = 0 and ∂g λ (w)/∂w j = 0 if w j = 0. Hence, if we start the gradient descent algorithm with w (0) j = 0 for some j, both w j and t j can continue to take 0 forever.As a result, we might not learn the optimal value for w j (or, equivalently for t j ).Thus, it is important to select all the elements of w (0) away from 0.
Consider the second argument, ∇g λ , in the gradient descent method.From Theorem 4, observe that computing the gradient ∇g λ (w) involves finding the values of the expression of the form L −1 t u twice, first for computing β t (using ( 7)) and then for computing the vector c t (defined in Theorem 4).Since L t is of dimension p × p, computing the matrix inversion L −1 t can be computationally demanding particularly in high-dimensional cases (n < p), where p can be very large; see, for example, [13].Since L t is invertible, observe that L −1 t u is the unique solution of the linear equation L t z = u.In Section 6, we first use the well-known Woodbury matrix identity to convert this p-dimensional linear equation problem to an n-dimensional linear equation problem, which is then solved using the conjugate gradient method, a popular linear equation solver.Moreover, again from Theorem 4, notice that ∇g λ (w) depends on both the tuning parameters δ and λ.Specifically, δ is required for computing L t and λ is used in the penalty term λ p j=1 t j of the objective function.In Section 5 we provide more details on the roles of these two parameters and instructions on how to choose them.
In Step 2, we obtain the sequence t path = (t (0) , t (1) , . . . ) from w path by using the map (12), that is, Finally, in Step 3, SubsetMap(t path ) takes the sequence t path as input to find a set of models M correspond to the input parameter λ.In the following subsections, we describe two versions of SubsetMap.
The following theoretical result, Theorem 5, guarantees convergence of COMBSS.In particular, this result establishes that a gradient descent algorithm on g λ (w) converges to an ϵ-stationary point.Towards this, we say that a point w ∈ R p is an ϵ-stationary point of g λ (w) if ∥∇g λ ( w)∥ 2 ≤ ϵ.Since w is called a stationary point if ∇g λ (w) = 0, an ϵ-stationary point provides an approximation to a stationary point.Theorem 5.There exists a constant α > 0 such that the gradient decent method, starting at any initial point w (0) and with a fixed positive learning rate smaller than α, converges to an ϵ-stationary point within O(1/ϵ 2 ) iterations.
Subset Map Version 1
One simple implementation of SubsetMap is stated as Algorithm 2 which we call SubsetMapV1 (where V1 stands for version 1) and it requires only the final point in the sequence t path and returns only one model using a predefined threshold parameter τ ∈ [0, 1).
Due to the tolerance allowed by the termination condition of the gradient descent, some w j in the final point of w path can be almost zero but not exactly zero, even though they are meant to converge to zero.As a result, the corresponding t j also take values close to zero but not exactly zero because of the mapping from w to t.Therefore, the threshold τ helps in mapping the insignificantly small t j to 0 and all other t j to 1.In practice, we call COMBSS X, y, δ, λ, w (0) for each λ over a grid Algorithm 2 SubsetMapV1 (t path , τ ) 1: Take t to be the final point of t path 2: for j = 1 to j = p do 3: s j ← I(t j > τ ) 4: end for 5: return s = (s 1 , . . ., s p ) T of values.When SubsetMapV1 is used, larger the value of λ, higher the sparsity in the resulting model s.Thus, we can control the sparsity of the output model using λ.Since we only care about the last point in t path in this version, an intuitive option for initialization is to take w (0) to be such that t(w T , the mid-point on the hypercube [0, 1] p , as it is at an equal distance from all the corner points, of which one is the (unknown) target solution of the best subset selection problem.
In Appendix B, we demonstrated the efficacy of COMBSS using SubsetMapV1 in predicting the true model of the data.In almost all the settings, we observe superior performance of COMBSS in comparison to existing popular methods.
Subset Map Version 2
Ideally, there is a value of λ for each k such that the output model s obtained by SubsetMapV1 has exactly k non-zero elements.However, when the ultimate goal is to find a best suitable model s for a given k ≤ q such that |s| = k, for some q ≪ min(n, p), since λ is selected over a grid, we might not obtain any model for some values of k.Furthermore, for a given size k, if there are two models with almost the same mean square error, then the optimization may have difficulty in distinguishing them.Addressing this difficulty may involve fine tuning of hyper-parameters of the optimization algorithm.
To overcome these challenges without any hyper-parameter tuning and reduce the reliance on the parameter λ, we consider the other points in t path .In particular, we propose a more optimal implementation of SubsetMap, which we call SubsetMapV2 and is stated as Algorithm 3. The key idea of this version is that as the gradient descent progresses over the surface of f λ (t), it can point towards some corners of the hypercube [0, 1] p before finally moving towards the final corner.Considering all these corners, we can refine the results.Specifically, this version provides for each λ a model for every k ≤ q.In this implementation, λ is seen as a parameter that allows us to explore the surface of f λ (t) rather than as a sparsity parameter.
For the execution of SubsetMapV2, we start at Step 1 with an empty set of models M k for each k ≤ q.In Step 2, for each t in t path , we consider the sequence of indices j 1 , . . ., j q such that t j 1 ≥ t j 2 ≥ • • • ≥ t jq .Then, for each k ≤ q, we take s k to be a binary vector with 1's only at j 1 , . . ., j k and add s k to the set M k .With this construction, it is clear that M k consists of models of size k, of which we pick a best candidate s * k as show at Step 3. Finally, the algorithm returns the set consists of s * 1 , . . ., s * q correspond to the given λ.When the main COMBSS is called for a grid of m values of λ with SubsetMapV2, then for each k ≤ q we obtain at most m models and among them the model with the minimum mean squared error is selected as the final best model for k.
Algorithm 3 SubsetMapV2 (t path )
1: M k ← {} for each k ≤ q 2: for each t = (t 1 , . . ., t p ) T in t path do 3: Let t j 1 , t j 2 , . . ., t jq be the q largest elements of t in the descending order 4: for k = 1 to q do 5: Take s k ∈ {0, 1} p with non-zero elements only at j 1 , . . ., j k 6: end for 8: end for 9: for k = 1 to k = q do 10: Since this version of COMBSS explores the surface, we can refine results further by starting from different initial points w (0) .Section 7 provides simulations to demonstrate the performance of COMBSS with SubsetMapV2.
Remark 4. It is not hard to observe that for each λ, if the model obtained by Algorithm 2 is of a size k ≤ q, then this model is present in M k of Algorithm 3, and hence, COMBSS with SubsetMapV2 always provides the same or a better solution than COMBSS with SubsetMapV1.
Roles of Tuning Parameters
In this section, we provide insights on how the tuning parameters δ and λ influence the objective function f λ (t) (or, equivalently g λ (w)) and hence the convergence of the algorithm.
Controlling the Shape of f λ (t) through δ
The normalized cost ∥y − X t β t ∥ 2 2 /n provides an estimator of the error variance.For any fixed t, we expect this variance (and hence the objective function f λ (t)) to be almost the same for all relatively large values of n, particularly, in situations where the errors ϵ i are independent and identically distributed.This is the case at all the corner points s ∈ {0, 1} p , because at these corner points, from Theorem 2, X s β s = X [s] β [s] , which is independent of δ.We would like to have a similar behavior at all the interior points t ∈ (0, 1) p as well, so that for each t, the function f λ (t) is roughly the same for all large values of n.Such consistent behavior is helpful in guaranteeing that the convergence paths of the gradient descent method are approximately the same for large values of n.
Figure 3 shows surface plots of f λ (t) for different values of n and δ for an example dataset obtained from a linear model with p = 2. Surface plots (a) and (d) correspond to δ = n, and as we can see, the shape of the surface of f λ (t) over [0, 1] p is very similar in both these plots.
To make this observation more explicit, we now show that the function f λ (t), at any t, takes almost the same value for all large n if we keep δ = c n, for a fixed constant c > 0, under the assumption that the data samples are independent and identically distributed (this assumption simplifies the following discussion; however, the conclusion holds more generally).
Observe that where Under the independent and identically distributed assumption, y T y/n, X T y/n, and X T X/n converge element-wise as n increases.Since T t is independent of n, we would like to choose δ such that L −1 t also converges as n increases.Now recall from (6) that It is then evident that the choice δ = c n for a fixed constant c, independent of n, makes L t converging as n increases.Specifically, the choice c = 1 justifies the behavior observed in Figure 3.
Sparsity Controlling through λ
Intuitively, the larger the value of λ the sparser the solution offered by COMBSS using Sub-setMapV1, when all other parameters are fixed.We now strengthen this understanding mathematically.From Theorem 4, ∇f λ (t) = ζ t + λ1, t ∈ (0, 1) p , and for w ∈ R p , where ζ t , given by ( 15), is independent of λ.Note the following property of ζ t .
Proposition 1.For any j = 1, . . ., p, if all t i for i ̸ = j are fixed, This result implies that for any j = 1, . . ., p, we have lim t j ↓0 ∂f λ (t)/∂t j = λ, where lim t j ↓0 denotes the existence of the limit for any sequence of t j that converges to 0 from the right.Since ζ t is independent of λ, the above limit implies that there is a window (0, a j ) such that the slope ∂f λ (t)/∂t j > 0 for t j ∈ (0, a j ) and also the window size increases (i.e., a j increases) as λ increases.
As a result, for the function g λ (w), there exists a constant a ′ j > 0 such that In other words, for positive λ, there is a 'valley' on the surface of g λ (w) along the line w j = 0 and the valley becomes wider as λ increases.In summary, the larger the values of λ the more w j (or, equivalently t j ) have tendency to move towards 0 by the optimization algorithm and then a sparse model is selected (i.e, small number k of variables chosen).At the extreme value λ max = ∥y∥ 2 2 /n, all t j are forced towards 0 and thus the null model will be selected.
Low-vs High-dimension
Recall the expression of L t from (5): We have noticed earlier from Theorem 4 that for computing ∇g λ (w), twice we evaluate matrixvector products of the form L −1 t u, which is the unique solution of the linear equation L t z = u.Solving linear equations efficiently is one of the important and well-studied problems in the field of linear algebra.Among many elegant approaches for solving linear equations, the conjugate gradient method is well-suited for our problem as L t is symmetric positive-definite; see, for example, [13].
The running time of the conjugate gradient method for solving the linear equation Az = u depends on the dimension of A. For our algorithm, since L t is of dimension p × p, the conjugate gradient method can return a good approximation of L −1 t u within O(p 2 ) time by fixing the maximum number of iterations taken by the conjugate gradient method.This is true for both lowdimensional models (where p < n) and high-dimensional models (where n < p).
We now specifically focus on high-dimensional models and transform the problem of solving the p-dimensional linear equation L t z = u to the problem of solving an n-dimensional linear equation problem.This approach is based on a well-known result in linear algebra called the Woodbury matrix identity.Since we are calling the gradient descent method for solving a n-dimensional problem, instead of p-dimensional, we can achieve a much lower overall computational complexity for the high-dimensional models.The following result is a consequence of the Woodbury matrix identity, which is stated as Lemma 2 in Appendix A. Theorem 6.For t ∈ [0, 1) p , let S t be a p-dimensional diagonal matrix with the jth diagonal element being n/δ(1 − t 2 j ) and L t = I + X t S t X T t /n.Then, The above expression suggests that instead of solving the p-dimensional problem L t z = u directly, we can first solve the n-dimensional problem L t z = (X t S t u) and substitute the result in the above expression to get the value of L −1 t u.
A Dimension Reduction Approach
During the execution of the gradient descent algorithm, Step 1 of Algorithm 1, some of w j (and hence the corresponding t j ) can reach zero.Particularly, for basic gradient descent and similar methods, once w j reaches zero it remains zero until the algorithm terminates, because the update of w in the lth iteration of the basic gradient descent depends only on the gradient g λ (w (l) ), whose jth element Because ( 16) holds, we need to focus only on ∂g λ (w)/∂w j associated with w j ̸ = 0 in order to reduce the cost of computing the gradient ∇g λ (w).To simplify the notation, let P = {1, . . ., p} and for any t ∈ [0, 1) p , let Z t be the set of indices of the zero elements of t, that is, Similar to the notation used in Theorem 2, for a vector u ∈ R p , we write (u) + (respectively, (u) 0 ) to denote the vector of dimension p − |Z t | (respectively, |Z t |) constructed from u by removing all its elements with the indices in Z t (respectively, in P \ Z t ).Similarly, for a matrix A of dimension p × p, we write (A) + (respectively, (A) 0 ) to denote the new matrix constructed from A by removing its rows and columns with the indices in Z t (respectively, in P \ Z t ).Then we have the following result.
Theorem 7. Suppose t ∈ [0, 1) p .Then, Furthermore, we have β t 0 = 0, In Theorem 7, (18) shows that for every j ∈ Z t , all the off-diagonal elements of the jth row as well as the jth column of L −1 t are zero while its jth diagonal element is n/δ, and all other elements of L −1 t (which constitute the sub-matrix L −1 t + ) depend only on (L t ) + , which can be computed using only the columns of the design matrix X with indices in P \ Z t .As a consequence, (19) and (20) imply that computing β t and c t is equal to solving p + -dimensional linear equations of the form L −1 t + z = v, where p + = p − |Z t |.Since p + ≤ p, solving such a p + -dimensional linear equation using the conjugate gradient can be faster than solving the original p-dimensional linear equation of the form L t z = u.
In summary, for a vector t ∈ [0, 1) p with some elements being 0, the values of f λ (t) and ∇f λ (t) do not depend on the columns j of X where t j = 0. Therefore, we can reduce the computational complexity by removing all the columns j of the design matrix X where t j = 0.Here we compare running times for COMBSS with SubsetMapV1 using only conjugate gradient (ConjGrad), conjugate gradient with Woodbury matrix identity (ConjGrad-Woodbury), and conjugate gradient with both Woodbury matrix identity and truncation improvement (ConjGrad-Woodbury-Trunc). For the truncation, η = 0.001.The dataset for this experiment is the same dataset used for Figure 2.
Making Our Algorithm Fast
In Section 6.2, we noted that when some elements t j of t are zero, it is faster to compute the objective functions f λ (t) and g λ (t) and their gradients ∇f λ (t) and ∇g λ (t) by ignoring the columns j of the design matrix X.In Section 5.2, using Proposition 1, we further noted that for any λ > 0 there is a 'valley' on the surface of g λ (w) along w j = 0 for all j = 1, . . ., p, and thus for any j, when w j (or, equivalently, t j ) is sufficiently small during the execution of the gradient descent method, it will eventually become zero.Using these observations, in the implementation of our method, to reduce the computational cost of estimating the gradients, it is wise to map w j (and t j ) to 0 when w j is almost zero.We incorporate this truncation idea into our algorithm as follows.
We first fix a small constant η, say at 0.001.As we run the gradient descent algorithm, when t j becomes smaller than η for some j ∈ P, we take t j and w j to be zero and we stop updating them; that is, t j and w j will continue to be zero until the gradient descent algorithm terminates.In each iteration of the gradient descent algorithm, the design matrix is updated by removing all the columns corresponding to zero t j 's.If the algorithm starts at w with all non-zero elements, the effective dimension p + , which denotes the number of columns in the updated design matrix, monotonically decreases starting from p.In an iteration, if p + > n, we can use Theorem 6 to reduce the complexity of computing the gradients.However, when p + falls below n, we directly use conjugate gradient for computing the gradients without invoking Theorem 6.
Using a dataset, Fig. 4 illustrates the substantial improvement in the speed of our algorithm when the above mentioned improvement ideas are incorporated in its implementation.Remark 5. From our simulations over the range of scenarios considered in Section 7, we have observed that the performance of our method does not vary significantly when η is close to zero.In particular, we noticed that any value of η close to or less than 0.001 is a good choice.Good, in the sense that, if s η ∈ {0, 1} p is the model selected by COMBSS, then we rarely observed s η ̸ = s 0 .Thus, the Hamming distance between s η and s 0 is zero when η is close to or smaller than 0.001, except in few generated datasets).This holds when comparing the estimated true model and when comparing the best subsets.
Simulation Experiments
Our method is available through Python and R codes via GitHub1 .The code includes examples where p is as large as of order 10,000.This code further allows to replicate our simulation results presented in this section and in Appendix B.
In Appendix B, we focused on demonstrating (using SubsetMapV1) the efficacy in predicting the true model of the data.Here, our focus is on demonstrating the efficacy of our method in retrieving best subsets of given sizes, meaning our ability to solve (1) using SubsetMapV2.We compare our approach to forward selection, Lasso, mixed integer optimization and L0Learn [17].
Simulation design
The data is generated from the linear model: Here, each row of the predictor matrix X is generated from a multivariate normal distribution with zero mean and covariance matrix Σ with diagonal elements Σ j,j = 1 and off-diagonal elements Σ i,j = ρ |i−j| , i ̸ = j, for some correlation parameter ρ ∈ (−1, 1).Note that the noise ϵ is a ndimensional vector of independent and identically distributed normal variables with zero mean and variance σ 2 .In order to investigate a challenging situation, we use ρ = 0.8 to mimic strong correlation between predictors.For each simulation, we fix the signal-to-noise ratio (SNR) and compute the variance σ 2 of the noise ϵ using We consider the following two simulation settings: • Case 1: The first k 0 = 10 components of β are equal to 1 and all other components of β are equal to 0.
• Case 2: The first k 0 = 10 components of β are given by β i = 0.5 i−1 , for i = 1, . . ., k 0 and all other components of β are equal to 0.
Both Case 1 and Case 2 assumes strong correlation between the active predictors.Case 2 differs from Case 1 by presenting a signal decaying exponentially to 0.
In the low-dimensional setting, the forward stepwise selection (FS) and the mixed integer optimization (MIO) were tuned over k = 0, . . ., 20.In this simulation we ran MIO through the R package bestsubset offered in [15] while we ran L0Learn through the R package L0Learn offered in [18].For the high dimensional setting, we do not include MIO due to time computational constraints posed by MIO.
Low-dimensional case
In low dimensional case, we use the exhaustive method to find the exact solution of the best subset for any subset size ranging from 1 to p.Then, we assess our method in retrieving the exact best subset for each subset size.Figure 5, shows the frequency of retrieving the exact best subset (provided by exhaustive search) for any subset size from k = 1, . . ., p, for Case 1, over 200 replications.For each SNR level, MIO as expected retrieves perfectly the optimal best subset of any model size.Then COMBSS gives the best results to retrieve the best subset compared to FS, Lasso and L0Learn.We can also observe that each of these curves follow a U-shape, with the lowest point approximately at the middle.This behaviour seems to be related to possible p k choices for each subset size k = 1, . . ., p, as at each k we have p k options (corner points on [0, 1] p ) to explore.Similar behaviours are reported for the low-dimensional setting of Case 2 in Figure 6.
High-dimensional case
To assess the performance of our method in retrieving a competitive best subset, we compare the best subset obtained from COMBSS with other methods for two different subset sizes: 5 and 10, over 50 replications.Note that the exact best subset is unknown for the high dimensional case since it is computationally impractical to conduct an exhaustive search even for moderate subset sizes when p = 1000.Hence, for this comparison, we use the mean squared error (MSE) of the dataset to evaluate which method is providing a better subset for size 5 and 10. Figure 7 presents these results over 50 replications for SNR values from 2 to 8. As expected the MSE of all methods is decreasing when SNR is increasing.Overall, COMBSS is consistently same or better than other methods for providing a competing best subset.On the other hand none of the alternative methods is consistent across all the cases.
In this high-dimensional setting, as mentioned earlier, deploying MIO, which is based on the Gurobi optimizer, proves impractical (see [16]).This is due to its prohibitively long running time, extending into the order of hours.In stark contrast, COMBSS exhibits running times of a few seconds for both the cases of the simulation settings: approximately 4.5 seconds with SubsetMapV1 (for predicting the true model) and approximately 7 seconds with SubsetMapV2 (for best subset selection).We have observed that for COMBSS, SubsetMapV1 operates at approximately twice the speed of SubsetMap2.Other existing methods demonstrate even faster running times, within a fraction of a second, but with lower performance compared to COMBSS.In summary, for best subset selection, COMBSS stands out as the most efficient among the methods that can run within a few seconds.Similarly, in predicting the true model, we believe that the consistently strong performance of COMBSS positions it as a crucial method, particularly when compared to other faster methods like Lasso.
Conclusion and Discussion
In this paper, we have introduced COMBSS, a novel continuous optimization method towards best subset selection in linear regression.The key goal of COMBSS is to extend the highly difficult discrete constrained best subset selection problem to an unconstrained continuous optimization problem.In particular, COMBSS involves extending the objective function of the best subset selection, which is defined at the corners of the hypercube [0, 1] p , to a differentiable function defined on the whole hypercube.For this extended function, starting from an interior point, a gradient descent method is executed to find a corner of the hypercube where the objective function is minimum.
In this paper, our simulation experiments highlight the ability of COMBSS with SubsetMapV2 for retrieving the "exact" best subset for any subset size in comparison to four existing methods: Forward Stepwise (FS), Lasso, L0Learn, and Mixed Integer Optimization (MIO).In Appendix B, we have presented several simulation experiments in both low-dimensional and high-dimensional setups to illustrate the good performance of COMBSS with SubsetMapV1 for predicting the true model of the data in comparison to FS, Lasso, L0Learn, and MIO.Both of these empirical studies emphasize the potential of COMBSS for feature extractions.In addition to these four methods, we have also explored with the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD), which are available through the R package ncvreg; refer to [4] for details of these two methods.In our simulation studies, we omitted the results for both MCP and SCAD, as their performance, although somewhat similar to the performance of Lasso, did not compete with COMBSS for best subset selection and for predicting the true model parameters.
In our algorithm, the primary operations involved are the matrix-vector product, the vector-vector element-wise product, and the scalar-vector product.Particularly, we note that most of the running time complexity of COMBSS comes from the application of the conjugate gradient method for solving linear equations of the form Az = u using off-the-shelf packages.The main operation involved in conjugate gradient is the matrix-vector product Au, and such operations are known to execute faster on graphics processing unit (GPU) based computers using parallel programming.A future GPU based implementation of COMBSS could substantially increase the speed of our method.Furthermore, application of stochastic gradient descent [3] instead of gradient descent and randomized Kaczmarz algorithm (a variant of stochastic gradient) [28] instead of conjugate gradient has potential to increase the speed of COMBSS as the stochastic gradient descent methods take just one data sample in each iteration.
A future direction for finding the best model of a given fixed size k is to explore different options for the penalty term of the objective function f λ (t).Ideally, if we select a sufficiently large penalty for p j=1 t j > k and 0 otherwise, we can drive the optimization algorithm towards a model of size k that lies along the hyperplane given by p j=1 t j = k.Because such a discrete penalty is not differentiable, we could use smooth alternatives.For instance, the penalty could be taken to be λ(k − p j=1 t j ) 2 when p j=1 t j > k and 0 otherwise, for a tuning parameter λ > 0.
We expect, similarly to the significant body of work that focuses on the Lasso and on MIO, respectively, that there are many avenues that can be explored and investigated for building on the presented COMBSS framework.Particularly, to tackle best subset selection when problems are ultra-high dimensional.In this paper, we have opened a novel framework for feature selection and this framework can be extended to other models beyond the linear regression model.For instance, recently [24] extended the COMBSS framework for solving column subset selection and Nystr öm approximation problems.
Moreover, in the context of Bayesian predictive modeling, [23] introduced Bayesian subset selection for linear prediction or classification, and they diverged from the traditional emphasis on identifying a single best subset, opting instead to uncover a family of subsets, with notable members such as the smallest acceptable subset.For a more general task of considering variable selection, the handbook edited by [29] offered an extensive exploration of Bayesian approaches to variable selection.Extending the concept of COMBSS to encompass more general variable selection and establishing a connection with Bayesian modelling appear to be promising avenues for further research.
In addition, the objective function in (13) becomes ∥y − X(t ⊙ β)∥ 2 2 /n when both the penalty terms are removed, where note that X t β = X(t ⊙ β).An unconstrained optimization of this function over t, β ∈ R p is studied in the area of implicit regularization; see, e.g., [20,33,35,11,36].Gradient descent in our method minimizes over the unconstrained variable w ∈ R p to get an optimal constrained variable t ∈ [0, 1] p .On the contrary, in their approach, t itself is unconstrained.Unlike the gradient descent of our method which terminates when it is closer to a stationary point on the hypercube [0, 1] p , the gradient descent of their methods may need an early-stopping criterion using a separate test set.
A Proofs
Proof of Theorem 1.Since both X T t X t and T t are symmetric, the symmetry of L t is obvious.We now show that L t is positive-definite for t ∈ [0, 1) p by establishing The matrix In addition, for all t ∈ [0, 1) p , the matrix δ I − T 2 t is also a positive-definite because δ > 0, and which is strictly positive if t ∈ [0, 1) p and u ∈ R p \ {0}.Since positive-definite matrices are invertible, we have L † t = L −1 t , and thus, Theorem 8 is a collection of results from the literature that we need in our proofs.Results (i) and (ii) of Theorem 8 are well-known in the literature as Banachiewicz inversion lemma (see, e.g., Tian and Takane [31]), and (iii) is its generalization to Moore-Penrose inverse (See Corollary 3.5 (c) in [5]).
Theorem 8. Let M be a square block matrix of the form with A being a square matrix.Let the Schur complement S = D − BA † C. Suppose that D is non-singular.Then following holds.
(i) If A is non-singular, then M is non-singular if and only if S is non-singular.
(ii) If both A and S are non-singular, then Proof of Theorem 2. The inverse of a matrix after a permutation of rows (respectively, columns) is identical to the matrix obtained by applying the same permutation on columns (respectively, rows) on the inverse of the matrix.Therefore, without loss of generality, we assume that all the zero-elements of s ∈ {0, 1} p appear at the end, in the form: s = (s 1 , . . ., s m , 0, . . ., 0), where m indicates the number of non-zeros in s.Recall that X [s] is the matrix of size n × |s| created by keeping only columns j of X for which s j = 1.Thus, L s is given by, From Theorem 8 (i), it is evident that L s is invertible if and only if X First assume that X T [s] X [s] is invertible.Then, from Theorem 8 (ii), Now recall the notations ( β s ) + and ( β s ) 0 introduced before stating Theorem 2.Then, we use (27) to obtain is singular, by replacing the inverse with its pseudo-inverse in the above discussion, and using Theorem 8 (iii) instead of Theorem 8 (ii), we can establish the same conclusions.This is because, the corresponding Schur complement for L s is S = n I/δ, which is symmetric and positive definite.
Proof of Theorem 3. Consider a sequence t 1 , t 2 , • • • ∈ [0, 1) p that converges a point t ∈ [0, 1] p .We know that the converges easily holds when t ∈ [0, 1) p from the continuity of matrix inversion which states that for any sequence of invertible matrices Z 1 , Z 2 , . . .that converging to an invertible matrix Z, the sequence of their inverses Z −1 1 , Z −1 2 , . . .converges to Z −1 .Now using Theorem 8, we prove the convergence when some or all of the elements of the limit point t are equal to 1. Suppose t has exactly m elements equal to ).
Further, take with X 1 denoting the first m columns of X.Similarly, we can write We now observe that As a result, and Since t ℓ ∈ [0, 1) p , L t ℓ is non-singular (see Theorem 1), and hence we have Note that the corresponding Schur complement S ℓ = D ℓ − B ℓ A −1 ℓ C ℓ is non-singular from Theorem 8 (i).Furthermore, since and hence, Using (24), and hence, is equal to Now by defining we have Since T t,2 < I, we can see that D is symmetric positive definite and hence non-singular (this can be established just like the proof of Theorem 1).Furthermore, the corresponding Schur complement S = D − BA † C is symmetric positive definite, and hence non-singular.The symmetry of S is easy to see from the definition because A and D are symmetric and B = C T .To see that S is positive definite, for any x ∈ R p−m \ {0}, let z = X t,2 x and thus 1 is a projection matrix and hence positive definite, S is also positive definite.
In addition, using the singular value decomposition (SVD) Similarly, we can show that AA † C = C. Thus, using (25) Since lim ℓ→∞ X t ℓ ,2 = X t,2 and lim ℓ→∞ B ℓ = B, from ( 28) and ( 29), to show that it is enough to show that Since S and each of S ℓ are non-singular, (31) holds from the continuity of matrix inversion.Now observe that To establish (32), we need to show that X = lim ℓ→∞ A −1 ℓ X T 1 is equal to X † 1 .Towards this, define (1/t 2 ℓ,i − 1).
Then, we observe that both η ℓ and ϵ ℓ are strictly positive and going to zero as ℓ → ∞.Thus, where for any two symmetric positive semi-definite matrices Z and Z ′ , we write Now for any matrix norm, denoting as ∥ • ∥, using the triangular inequality, Using the SVD of That is, suppose σ i is the ith singular value of X 1 , then the ith singular value of (A , which goes to zero and thus the first term in (33) goes to zero.The second term in (33) also converges to zero because of the limit definition of pseudo-inverse that states that for any matrix This completes the proof.
For proving Theorem 4, we use Lemma 1, which obtains the partial derivatives of β t with respect to the elements of t.
Lemma 1.For any t ∈ (0, 1) p , the partial derivative ∂ β t ∂t j for each j = 1, . . ., p is equal to where Z = n −1 X T X − δI and E j is a square matrix of dimension p × p with 1 at the (j, j)th position and 0 everywhere else.
Proof of Lemma 1. Existence of β t for every t ∈ (0, 1) p and δ > 0 follows from Theorem 1, which states that L t is positive-definite and hence guarantees the invertibility of L t .Since β t = L −1 t T t X T y/n, using matrix calculus, for any j = 1, . . ., p, where we used differentiation of an invertible matrix which implies Since ∂T t /∂t j = E j , and the fact that L t = T t ZT t + δI/n, we get This completes the proof Lemma 1.
Proof of Theorem 4. To obtain the gradient ∇f λ (t) for t ∈ (0, 1) p , let Consequently, where From the definitions of β t and γ t , which is obtained using Lemma 1 and the fact that ∂T t /∂t j = E j and Z = n −1 X T X − δI .This in-turn yields that ∂γ t ∂t j is equal to where we recall that b For a further simplification, recall that c t = L −1 t (t ⊙ a t ) and d t = Z (t ⊙ c t ).Then, from (36), the matrix ∂γ t /∂t of dimension p × p, with jth column being ∂γ t /∂t j , can be expressed as From (35), with 1 representing a vector of all ones, ∇f λ (t) can be expressed as where Finally, recall that g λ (w) = f λ (t(w)), w ∈ R p , where the map t(w) = 1 − exp(−w ⊙ w) and Then, from the chain rule of differentiation, for each j = 1, . . ., p, ∂g λ (w) ∂w j = ∂f λ (t) ∂t j 2w j exp(−w 2 j ) .
Proof of Theorem 5. A function g(w) is ℓ-smooth if the gradient ∇g λ (w) is Lipschitz continuous with Lipschitz constant ℓ > 0, that is, for all w, w ′ ∈ R p .From Section 3 of [7], gradient descent on a ℓ-smooth function g(w), with a fixed learning rate of 1/ℓ, starting at an initial point w (0) , achieves an ϵ-stationary point in c ℓ (g(w (0) )−g * ) iterations, where c is a positive constant, g * = min w∈R p g(w).This result (with a different constant c) holds for any fixed learning rate smaller than 1/ℓ.
Thus, we only need to show that the objective function g λ (w) is ℓ-smooth for some constant ℓ > 0.
B Additional Simulation Experiments
In this section, through additional simulations, we assess the efficiency of our proposed method, COMBSS using SubsetMapV1, by comparing it with some of the well-known existing methods, namely, Forward Selection (FS), Lasso, L0Learn, and MIO.These comparisons are conducted both in low-dimensional and high-dimensional settings.The simulation designs of Case 1 and Case 2 are provided in Section 7.1 of the main article.
B.1 Performance Metrics
For the comparison between the methods, we consider the prediction error as well as some variable selection accuracy performance metrics.The prediction error (PE) performance of a method is defined as where β is the estimated coefficient obtained by the method and β * is the true model parameters.
The variable selection accuracy performances used are sensibility (true positive rate), specificity (true negative rate), accuracy, F 1 score, and the Mathew's correlation coefficient (MCC) [6].
B.2 Model Tuning
In the low-dimensional setting, FS and MIO were tuned over k = 0, . . ., 20.For the high dimensional setting, FS was tuned over k = 0, . . ., 50.In this simulation we ran MIO through the R package bestsubest offered in [15] and we fixed the default budget to 1 minute 40 seconds (per problem per k).
The dynamic grid of λ values for COMBSS is constructed identical to the description provided in Section 7.1 of the main article.One exception is that for each simulation in this appendix, we call COMBSS with SubsetMapV1 only for one initial point t (0) = (0.5, . . ., 0.5) T .
For each subset size k for FS and MIO and each tuning parameter λ for Lasso and COMBSS, we evaluate the mean square error (MSE) using a validation set of 5000 samples from the true model defined in (19) in the main article.Then, for all the four methods, the best model is the one with the lowest MSE on the validation set.Overall, in the low-dimensional setting, COMBSS outperforms FS, L0Learn, and MIO methods in terms of MCC, accuracy, and prediction error.It also outperforms the Lasso in terms of MCC in both the cases.Note that the Lasso presents lower prediction error and accuracy in general as it tends to provide dense model compared to other methods.As a result, the Lasso suffers with low specificity.
B.3 Simulation Results
Figures 10 and 11 presents the results in the high-dimensional setting for Case 1 and Case 2, respectively.The panels in this figure display average MCC, prediction error, F1-score, Sensibility, and Specificity, over 50 replications, where the vertical bars denote one standard error.We ignored MIO for these simulation due to its high computational time requirement.We do not present accuracy for the high-dimensional setting, because even a procedure which always selects the null model will get an accuracy of 990/1000 = 0.99.
In both the cases, COMBSS clearly outperforms the other three methods (FS, L0Learn, and Lasso) in terms of MCC, prediction error, and F1 score.In this setting, the Lasso again suffers from selecting dense models and thus exhibiting lower specificity.
Figure 1 :
Figure1: Illustration of the workings of COMBSS for an example data with p = 2. Plot (a) shows the objective function of the exact method (2) for s ∈ {0, 1} 2 .Observe that the best subsets correspond to k = 0, k = 1, and k = 2 are (1, 1)T , (1, 0) T , and (0, 0) T , respectively.Plots (b) -(d) show the objective function of our optimization method (4) for different values of the parameter λ.In each of these three plots, the curve (in red) shows the execution of a basic gradient descent algorithm that, starting at the initial point t init = (0.5, 0.5) T , converges towards the best subsets of sizes 0, 1,
Figure 2 :
Figure 2: Convergence of t for a high-dimensional dataset during the execution of basic gradient descent.Solid lines correspond to β j = 0 and remaining 5 curves (with line style − • −) correspond to β j ̸ = 0.The dataset is generated using the model (21) shown in Section 7.1 with only 5 components of β are non-zero, equal to 1, at equally spaced indices between 1 and p = 1000, and n = 100, ρ = 0.8, and signal-to-noise ratio of 5.The parameters λ = 0.1 and δ = n; see Section 5 for more discussion on how to choose λ and δ.
Figure 3 :
Figure 3: Illustration of how δ effects the objective function f λ (t) (with λ = 0).A dataset consists of 10000 samples generated from the illustrative linear model used in Figure 1.For (a) and (b), 100 samples from the same dataset are used.
Figure 4 :
Figure4: Running times of our algorithm at λ = 0.1 for an example dataset using the adam optimizer, a popular gradient based method.These boxplots are based on 300 replications.Here we compare running times for COMBSS with SubsetMapV1 using only conjugate gradient (ConjGrad), conjugate gradient with Woodbury matrix identity (ConjGrad-Woodbury), and conjugate gradient with both Woodbury matrix identity and truncation improvement (ConjGrad-Woodbury-Trunc). For the truncation, η = 0.001.The dataset for this experiment is the same dataset used for Figure2.
Figure 7 :
Figure 7: Ability of COMBSS (using SubsetMapV2) for providing a competing best subset for subset sizes 5 and 10 in comparison to FS, Lasso and L0Learn.The top plots are for Case 1 while the bottom plots are for Case 2.
Figures 8 and 9
Figures 8 and 9 present the results in the low-dimensional setting for Case 1 and Case 2, respectively.The panels in this figure display the average of MCC, accuracy, prediction error, F1-score, Sensibility, and Specificity, over 50 replications, where the vertical bars denote one standard error.
Figure 11 :
Figure 11: Performance results in terms of MCC, prediction error, F1-score, Sensibility, and Specificity for Case 2 in the high-dimensional setting where n = 100, p = 1000, and ρ = 0.8.
1. Using the arguments from the proof of 2, without of loss of generality assume that all 1s in t appear together in the first m positions, that is, t = (1, . . ., 1 Figure 10: Performance results in terms of MCC, prediction error, F1-score, Sensibility, and Specificity for Case 1 in the high-dimensional setting where n = 100, p = 1000, and ρ = 0.8. | 14,970.2 | 2022-05-05T00:00:00.000 | [
"Computer Science",
"Mathematics"
] |
Ship Target Detection Algorithm Based on Improved Faster R-CNN
: Ship target detection has urgent needs and broad application prospects in military and marine transportation. In order to improve the accuracy and e ffi ciency of the ship target detection, an improved Faster R-CNN (Faster Region-based Convolutional Neural Network) algorithm of ship target detection is proposed. In the proposed method, the image downscaling method is used to enhance the useful information of the ship image. The scene narrowing technique is used to construct the target regional positioning network and the Faster R-CNN convolutional neural network into a hierarchical narrowing network, aiming at reducing the target detection search scale and improving the computational speed of Faster R-CNN. Furthermore, deep cooperation between main network and subnet is realized to optimize network parameters after researching Faster R-CNN with subject narrowing function and selecting texture features and spatial di ff erence features as narrowed sub-networks. The experimental results show that the proposed method can significantly shorten the detection time of the algorithm while improving the detection accuracy of Faster R-CNN algorithm.
Introduction
With the development of social economy, inland navigation and ocean shipping are developing rapidly.Moreover, the number of ships and the corresponding loading capacity and the transportation velocity has continuously increased in recent decades [1].On the other hand, frequency of accidents, including collisions with bridges or other ships, has increased, so that automatic detection of sea targets has been widely applied in modern ships.It is of great significance to monitor adjacent ships to prevent collisions.The automatic detection of sea targets helps managers to determine the distribution of surface vessels in time, effectively manage the ship parking and prevent illegal activities such as illegal fishing and illegal ship docking [2].Target visual inspection has great practical value and application prospects.Applications include intelligent video surveillance, robot navigation, automatic positioning and focusing of ships in digital cameras, road detection in aircraft aerial or satellite images and obstacles in vehicle camera images [3].The detection, identification and tracking technology of sea surface moving targets have important applications in preventing collisions, coastal defense safety, customs management, maritime smuggling and port vessel dispatching etc. [4].
Studies show that there are some challenges in the conventional ship target detections.Firstly, the current intelligent video surveillance system is mainly used in the terrestrial environment.It is a very challenging task to achieve target detection under complex water surface backgrounds.This is mainly due to the influence of light, wind, water waves and other factors.In the process of dynamic change, the image contains a large amount of related noise, such as the chaotic and irregular texture of the fish, the irregular texture of the wave etc., while the sky moving clouds, near-shore wave breaking and swaying branches has caused a lot of interference for target detection.These factors are likely to cause the ship's detection accuracy to be low and easy to miss detection and false detection.Secondly, the traditional method of machine learning based on image processing lacks specificity for region selection, which is easy to cause sliding window redundancy and high time complexity [5].
In view of the problems existing in ship target detection methods, many scholars have studied better methods for detecting ship images.Li et al. [6] combined the background-difference method with the maximum between-class variance method (OTSU) to extract the in-situ navigation targets of different rivers under different weather and different angles, and have certain weather adaptability.Shi and An [7] used a background suppression algorithm based on multi-structural element morphological filtering to effectively suppress background noise and sea clutter and quickly detect ship targets.Traditional target detection methods have problems such as low accuracy and poor universality.
Deep learning is a branch of machine learning and can be understood as the development of the neural network.The development of neural networks has driven the development of deep learning, and neural networks have promoted the development of many fields, such as in the prediction of the electricity price.Weron summarized the electricity price forecasting methods in the past 15 years, explaining the complexity, advantages and disadvantages of the available solutions, as well as the opportunities and threats that forecasting tools provide or may encounter and speculating on the direction Electricity Price Forecasting (EPF) will or should take in the next decade or so [8].Cincotti et al. [9] proposed three different methods to model prices time series: a discrete-time univariate econometric model (ARMA-GARCH) and two computational-intelligence techniques (Neural Network and Support Vector Machine).Price series exhibit a strong daily seasonality, addressed by analyzing separately a series for each of the 24 hours.Amjady and Keynia [10] proposed a combination of wavelet transform (WT) and a hybrid forecast method to predict the price of electricity more accurately and more stably.
In recent years, with the rapid development of deep learning theory, target detection has entered a new stage.Hinton et al. [11] first proposed deep learning techniques represented by deep neural networks in 2006, which attracted the attention of academic circles.Krizhevsky et al. [12] constructed a deep convolutional neural network in 2012 and achieved great success in large-scale image classification.Then, in the target detection task, the deep learning method also surpassed the traditional method.In 2013, Region-based Convolutional Neural Network (R-CNN) was born.The MAP value of the VOC2007 test set was increased to 48%.In 2014, the network structure was upgraded to 66%, and the MAP of the ILSVRC2013 test set was also raised to 31.4.In 2015, He et al. [13] proposed Res Net with a depth of up to hundreds of layers, the number of network layers (152 layers) is more than five times higher than that of any successful neural network, and the image classification error rate on the Image Net test set is as low as 3.57%.The R-CNN proposed by Girshick [14] has made breakthroughs in the field of target detection, and successively appeared algorithms such as Spatial Pyramid Pooling (SPP), Fast Region-based Convolutional Neural Network (Fast R-CNN), Faster R-CNN, You Only Look Once (YOLO), Single Shot Multi-Box Detector (SSD) [15][16][17][18].The innovative algorithm of deep learning is applied to the detection of ship targets, and the detection effect is significantly improved.The deep learning frameworks can learn the training image through the convolutional neural network and automatically extracts target features from the image.Furthermore, these frameworks have high learning ability, fast recognition speed and high detection precision.Gu et al. [19] improved the Faster R-CNN and applied it to ship detection.After the first fully connected layer of the regional generation network, a dropout layer was added to improve the detection rate, and the accuracy rate reached 90.4%.Wu [20] filtered a large number of invalid regions through a candidate region optimization network, and the average detection time of the improved algorithm is 7ms less than that of Faster R-CNN algorithm.
Although the improved deep learning algorithm improves the detection speed of ship target detection, it loses the accuracy.In this paper, the image-based ship detection technology in video surveillance is studied.Faster R-CNN watercraft detection algorithm based on narrowing idea is proposed to improve the detection accuracy of Faster R-CNN algorithm and shorten the detection time of the algorithm.
Basic Faster R-CNN Algorithm
The main steps of the basic Faster R-CNN algorithm: (1) Input a picture with arbitrary size M×N to the CNN (Convolutional Neural Network) network VGG-16 (Visual Geometry Group-16).( 2) When the CNN network forwards the image to the last shared convolution layer, On the one hand, the feature map is got which is provided to the input of Regional Proposal Network (RPN).
On the other hand, the forward propagation to the unique convolution layer is continued to generate a feature map with the higher dimension.(3) The feature map of the RPN network is applied to obtain regional recommendations and regional scores, where the regional scores are Non-Maximum value suppression (threshold value is 0.7).The obtained result is handed over to the ROI (Region of Interest) pooling layer.(4) The high-dimensional feature map of step 2 and the region output of step 3 are applied into the ROI pooling layer and the corresponding region is extracted as suggested features.(5) The suggested regional feature of step 4 passes through the fully connected layer and generates the classification score of the region and the bounding-box after the regression.
The specific process is shown in Figure 1: Although the improved deep learning algorithm improves the detection speed of ship target detection, it loses the accuracy.In this paper, the image-based ship detection technology in video surveillance is studied.Faster R-CNN watercraft detection algorithm based on narrowing idea is proposed to improve the detection accuracy of Faster R-CNN algorithm and shorten the detection time of the algorithm.
Basic Faster R-CNN algorithm
The main steps of the basic Faster R-CNN algorithm: 1) Input a picture with arbitrary size M×N to the CNN (Convolutional Neural Network) network VGG-16 (Visual Geometry Group-16).
2) When the CNN network forwards the image to the last shared convolution layer, On the one hand, the feature map is got which is provided to the input of Regional Proposal Network (RPN).On the other hand, the forward propagation to the unique convolution layer is continued to generate a feature map with the higher dimension.
3) The feature map of the RPN network is applied to obtain regional recommendations and regional scores, where the regional scores are Non-Maximum value suppression (threshold value is 0.7).The obtained result is handed over to the ROI (Region of Interest) pooling layer.
4) The high-dimensional feature map of step 2 and the region output of step 3 are applied into the ROI pooling layer and the corresponding region is extracted as suggested features.
5) The suggested regional feature of step 4 passes through the fully connected layer and generates the classification score of the region and the bounding-box after the regression.
The specific process is shown in Figure 1:
Methodology
The method in this paper is shown in Figure 2. The non-red part of the dashed box is the basic structure of the basic Faster R-CNN, and the red part is the improved part of this article.The length and width of the ship image are initially down-scaled in accordance with the definition of the image pixel pyramid and the local correlation between image pixels.Then, the narrowing method is used to establish the ship image characteristics based on the common sense and prior knowledge.The semantic relationship between the reasonable scene and the ship is a hierarchical narrowing network composed of the target area positioning network, ship area generation network and the Faster R-CNN convolutional neural network.It should be indicated that the target area positioning network mainly displays the area, where the ship appears in the image.
In order to separate the target from the background area, the method of narrowing the theme is utilized to summarize the main factors that affect the understanding of image content.Human
Methodology
The method in this paper is shown in Figure 2. The non-red part of the dashed box is the basic structure of the basic Faster R-CNN, and the red part is the improved part of this article.The length and width of the ship image are initially down-scaled in accordance with the definition of the image pixel pyramid and the local correlation between image pixels.Then, the narrowing method is used to establish the ship image characteristics based on the common sense and prior knowledge.The semantic relationship between the reasonable scene and the ship is a hierarchical narrowing network composed of the target area positioning network, ship area generation network and the Faster R-CNN convolutional neural network.It should be indicated that the target area positioning network mainly displays the area, where the ship appears in the image.
the experience theme and the task, the theme narrowing subnet is added to enhance the description ability of the overall network, thereby realizing a more detailed image description under limited resource conditions.
In the present study, it is intended to improve the detection rate and accuracy of the Faster R-CNN scheme.Therefore, different methods are utilized in the regards.It should be indicated that selected methods are related to each other from different aspects.
Image downscaling
Since each image contains huge number of data pixels, analyzing it is a difficult and time-consuming process.In order to reduce the time of image analysis and processing, this paper uses the method of image downscaling.The images before and after downscaling differ in scale, but the key details of the images are approximately equal.The image is downscaled by the length and width of the image.However, downscaling the image decreases the image quality, distorts the image and blurs it so that the image cannot be scaled down arbitrarily.The image downscaling process is performed in accordance with the definition of the image pixel pyramid (the pyramid of an image is a collection of images whose resolution is gradually reduced in a pyramid shape) and the local correlation between the pixel points of the image.The original image prior to the downscaling process is referred to as the Y-image, while the obtained image after the downscaling process is referred to as the Z-image.During the downscaling process, the pixel data in even columns and rows of the Y-image are retained in the Z-image, while that of odd columns and rows of the Y-image are removed.Although the images before and after the downscaling have different dimensions, the key pixel information of the front and back image features have similarities due to the local correlation between pixels.Moreover, part of the noise pixel information will be removed after downscaling [21].
In order to avoid the details of the original image being lost during downscaling, only the pixel information of even rows and even columns of the original image are saved.Equation (1) shows the pixel correlation between the original Y-image and the downsized Z-image, wherein each pixel of the coordinates in the original Y-image generates a pixel of the coordinates in the Z-image and N represents a natural number set [22].
Figure 3 illustrates a comparison between the original the downsized image.Figure 3 (a) indicates that the length and width of the Y-image are 2540 and 1080 pixels, respectively.Moreover, In order to separate the target from the background area, the method of narrowing the theme is utilized to summarize the main factors that affect the understanding of image content.Human visual cognition experience and computer vision are applied to detect ship texture features and spatial difference features.The sub-network is a collaborative deep network with clear functions in the overall black box and subnet.During training the network, the narrowed subnet is trained in advance and parameters of the narrowed subnet are solidified or only fine-tuned during the overall convolutional network training.Based on the black box training and according to the relevance of the experience theme and the task, the theme narrowing subnet is added to enhance the description ability of the overall network, thereby realizing a more detailed image description under limited resource conditions.
In the present study, it is intended to improve the detection rate and accuracy of the Faster R-CNN scheme.Therefore, different methods are utilized in the regards.It should be indicated that selected methods are related to each other from different aspects.
Image Downscaling
Since each image contains huge number of data pixels, analyzing it is a difficult and time-consuming process.In order to reduce the time of image analysis and processing, this paper uses the method of image downscaling.The images before and after downscaling differ in scale, but the key details of the images are approximately equal.The image is downscaled by the length and width of the image.However, downscaling the image decreases the image quality, distorts the image and blurs it so that the image cannot be scaled down arbitrarily.The image downscaling process is performed in accordance with the definition of the image pixel pyramid (the pyramid of an image is a collection of images whose resolution is gradually reduced in a pyramid shape) and the local correlation between the pixel points of the image.The original image prior to the downscaling process is referred to as the Y-image, while the obtained image after the downscaling process is referred to as the Z-image.During the downscaling process, the pixel data in even columns and rows of the Y-image are retained in the Z-image, while that of odd columns and rows of the Y-image are removed.Although the images before and after the downscaling have different dimensions, the key pixel information of the front and back image features have similarities due to the local correlation between pixels.Moreover, part of the noise pixel information will be removed after downscaling [21].
In order to avoid the details of the original image being lost during downscaling, only the pixel information of even rows and even columns of the original image are saved.Equation (1) shows the pixel correlation between the original Y-image and the downsized Z-image, wherein each pixel of the coordinates in the original Y-image generates a pixel of the coordinates in the Z-image and N represents a natural number set [22].
Figure 3 illustrates a comparison between the original the downsized image.Figure 3a indicates that the length and width of the Y-image are 2540 and 1080 pixels, respectively.Moreover, Figure 3b shows that the image size after downscaling reduces to 1270 × 540 pixels.In other words, the size of the graph b is 1/4 of that of the graph a.Although downscaling can reduce the noise problem to some extent, it adversely affects the image feature information more or less.It should be indicated that since the test image is large and the relative detail loss is negligible, the downscaling impact on the image quality is ignored.Since the image size is reduced, the number of pixels of the subsequent processing is greatly reduced so that the detection speed of the target image increases.
Scene Semantic Narrowing
Aiming at certain types of regions and certain geographical locations in the image, it is the focus of attention and a more detailed interpretation can be realized by performing appropriate analysis.For example, aircraft inspection missions are usually carried out in airport type areas.Furthermore, ship inspections are usually performed in three types of areas: ports, docks and high seas.On the other hand, optimized training can be performed for different areas and classification tasks.Considering existing historical images, monitoring tasks in the same geographical area, such as monitoring of a naval formation, can achieve highly accurate target detection, classification and change detection in the region [23].
The narrowing of scene semantics is mainly based on the characteristics of the task and image, the common sense and prior knowledge about the task and establishment of a reasonable correlation between the scene and the semantics of the target [24].The role of scene and semantic narrowing are: 1) Achieving rapid localization from large images to local areas, containing mission targets.
2) Implementing semantic analysis from global to local and achieving target and scene classification that mutually support the semantic correlation.
3) Forming the scene-target correlation in the image to facilitate the analysis and understanding of the image.
In the present study, the video image detection of the ship is considered as an example.It should be indicated that the ship image has a large amount of data, while the ship target occupies small number of pixels in the original image.However, most of the pixel data is not necessary for target detection.Therefore, a detection method is adopted in the present study to reduce the target pixel area from the original image.
Figure 4 indicates that this method uses a target area positioning network, a target area generation network and a hierarchical narrowing network composed of the Faster R-CNN convolutional neural network.The target area positioning network mainly separates the area, where the ship appears in the remote sensing image, from the background area.When the background is removed from the original image, the Faster R-CNN convolutional neural network detects and classifies the target from the ship target area.The semantic algorithm is applied to locate the target and determine whether the detected target is a ship or not.The hierarchical narrowing scheme has
Scene Semantic Narrowing
Aiming at certain types of regions and certain geographical locations in the image, it is the focus of attention and a more detailed interpretation can be realized by performing appropriate analysis.For example, aircraft inspection missions are usually carried out in airport type areas.Furthermore, ship inspections are usually performed in three types of areas: ports, docks and high seas.On the other hand, optimized training can be performed for different areas and classification tasks.Considering existing historical images, monitoring tasks in the same geographical area, such as monitoring of a naval formation, can achieve highly accurate target detection, classification and change detection in the region [23].
The narrowing of scene semantics is mainly based on the characteristics of the task and image, the common sense and prior knowledge about the task and establishment of a reasonable correlation between the scene and the semantics of the target [24].The role of scene and semantic narrowing are: (1) Achieving rapid localization from large images to local areas, containing mission targets.
(2) Implementing semantic analysis from global to local and achieving target and scene classification that mutually support the semantic correlation.(3) Forming the scene-target correlation in the image to facilitate the analysis and understanding of the image.
In the present study, the video image detection of the ship is considered as an example.It should be indicated that the ship image has a large amount of data, while the ship target occupies small number of pixels in the original image.However, most of the pixel data is not necessary for target detection.Therefore, a detection method is adopted in the present study to reduce the target pixel area from the original image.
Figure 4 indicates that this method uses a target area positioning network, a target area generation network and a hierarchical narrowing network composed of the Faster R-CNN convolutional neural network.The target area positioning network mainly separates the area, where the ship appears in the remote sensing image, from the background area.When the background is removed from the original image, the Faster R-CNN convolutional neural network detects and classifies the target from the ship target area.The semantic algorithm is applied to locate the target and determine whether the detected target is a ship or not.The hierarchical narrowing scheme has remarkable advantages over the conventional schemes, including lower computational expenses and lower target detection search scales.Figure 5
Topic narrowing subnetwork
Considering long distance of image shooting and the influence of optical path propagation medium, each single pixel contains high range of features so that mixed elements are a challenge [25].However, an image often contains too much unnecessary information.To realize the expression of all information, it is necessary to use a more complex network than the existing deep learning network architecture.On the other hand, the network size that computers can handle is limited.Different methods have been proposed to improve the description ability of the deep network for
Topic narrowing subnetwork
Considering long distance of image shooting and the influence of optical path propagation medium, each single pixel contains high range of features so that mixed elements are a challenge [25].However, an image often contains too much unnecessary information.To realize the expression of all information, it is necessary to use a more complex network than the existing deep learning network architecture.On the other hand, the network size that computers can handle is limited.Different methods have been proposed to improve the description ability of the deep network for images with limited computing resources.A widely adopted solution is to split the image or down sample, which will inevitably result in loss of information.In order to achieve a more detailed expression of images in limited conditions, a deep convolutional network with the narrowing function of the subject is proposed.Moreover, the main factors affecting the understanding of the image content are investigated [26].The human visual cognitive experience and the computer vision
Topic Narrowing Subnetwork
Considering long distance of image shooting and the influence of optical path propagation medium, each single pixel contains high range of features so that mixed elements are a challenge [25].However, an image often contains too much unnecessary information.To realize the expression of all information, it is necessary to use a more complex network than the existing deep learning network architecture.On the other hand, the network size that computers can handle is limited.Different methods have been proposed to improve the description ability of the deep network for images with limited computing resources.A widely adopted solution is to split the image or down sample, which will inevitably result in loss of information.In order to achieve a more detailed expression of images in limited conditions, a deep convolutional network with the narrowing function of the subject is proposed.Moreover, the main factors affecting the understanding of the image content are investigated [26].The human visual cognitive experience and the computer vision research are utilized in this regards.
Based on the task, an appropriate theme is selected as a narrow sub-network to realize the collaborative deep network with clear functions of the overall black box and subnet [27].During the network training, the narrowed subnet is initially trained and parameters of the narrowed subnet are solidified or only fine-tuned during the overall convolutional network training.Based on the black box training and according to the relevance of the experience theme and the task, the theme narrowing subnet is added to enhance the description ability of the overall network, thereby realizing more detailed image description in limited resource conditions.The topics here mainly include imaging factors and visual characteristics.At present, there is similar research on the narrowing of the theme, mainly focusing on object-oriented topics, such as R-CNN, Fast R-CNN, YOLO and other target detection algorithms.Based on the advantages of the existing methods, the present study will carry out a more in-depth research on narrowing the deep network structure framework of various topics [28].
The outline of the training steps of the deep convolutional network Faster R-CNN with the theme narrowing function is as follows: (1) The meaning of the intermediate feature layer is initially defined in the topic-oriented deep network image feature learning so that the advanced semantic image is initially oriented.Then, features narrowing learning is performed to generate neural networks sensitive to different advanced image features, including texture, lighting, imaging, atmospheric, topographic and other features.Each feature is a manifold dimension of the image that describes a unique component with minimal information redundancy between them.(2) During the theme network training, the solidified topic narrowing subnet part is initialized according to the obtained training result from the aforementioned step.Moreover, the main network of the task is initialized in accordance with the random initialization or pre-training network.It should be indicated that the task labeling database is used for the training and only the theme part is trained at this stage.Then, parameters, including the fully connected parameters of the cured topic network, are optimized without updating other parameters of the solidified topic subnet.(3) In order to update the topic narrowing subnet, the annotation data of each topic is used and each subnet is separately fine-tuned by alternate iteration.When the subnet tuning training is completed, the overall network is fine-tuned to obtain the final optimized network parameters [29,30].
Two collaborative ways have been introduced thus far in the convolutional neural network: the serial collaboration and the parallel cooperation.Figure 6 schematically shows the diagram of the serial cooperation and indicates that there is a specific causal correlation between the narrowing of the two themes.Furthermore, Figure 6 indicates that there is a large difference between the scales.On the other hand, Figure 7 schematically illustrates the diagram of the parallel cooperation.It indicates that when the task does not require two-subject causal correlation, most of the topic collaboration will use parallel collaboration.Studies showed that the parallel cooperation has faster performance compared with the serial cooperation.It should be indicated that different narrowing themes and two collaborative ways can be included in a deep network.The parallel cooperation is used in the present study and the main network and the subnet are simultaneously collaborated in parallel.Furthermore, texture features and spatial difference features are incorporated into the topic framework as subnetworks [31,32].collaboration will use parallel collaboration.Studies showed that the parallel cooperation has faster performance compared with the serial cooperation.It should be indicated that different narrowing themes and two collaborative ways can be included in a deep network.The parallel cooperation is used in the present study and the main network and the subnet are simultaneously collaborated in parallel.Furthermore, texture features and spatial difference features are incorporated into the topic framework as subnetworks [31][32].
Characteristic components of ship images A. Texture features components of ship images
Texture features are important features of ship images and are constant in terms of noise, rotation and illumination parameters.In this section, the narrowing algorithm is added to the RPN network.The texture feature is an indicator to show the performance of the method.The texture analysis can deepen the fusion training of the feature image.Combined with the narrowing of the scene and theme, the convolutional neural network algorithm is accelerated.The texture features studied in this section are indicated by DWT (Discrete Wavelet Transform) in local binary mode [33].
DWT is widely used in the field of digital imaging since it provides spatial and frequency information for digital images.In the present study, the 2-D Wavelet (Daubechies (db)) is utilized in the MATLAB toolbox, which mainly includes scale conversion functions ( , ) x y
A. Texture Features Components of Ship Images
Texture features are important features of ship images and are constant in terms of noise, rotation and illumination parameters.In this section, the narrowing algorithm is added to the RPN network.The texture feature is an indicator to show the performance of the method.The texture analysis can deepen the fusion training of the feature image.Combined with the narrowing of the scene and theme, the convolutional neural network algorithm is accelerated.The texture features studied in this section are indicated by DWT (Discrete Wavelet Transform) in local binary mode [33].
DWT is widely used in the field of digital imaging since it provides spatial and frequency information for digital images.In the present study, the 2-D Wavelet (Daubechies (db)) is utilized in the MATLAB toolbox, which mainly includes scale conversion functions ϕ(x, y) and three wavelet functions, including horizontal ψ H (x, y), diagonal and vertical functions ψ V (x, y).The scale transformation and the wavelet functions are composed of the product of two one-dimensional functions.It should be indicated that three wavelet functions represent horizontal, diagonal and vertical gray scale variations, respectively.The specific definitions are as the following: If there is a point on the input size image, the image discrete wavelet transform can be obtained by Equations ( 4) and ( 5) as follows: where j 0 is the initial arbitrary scale ratio, usually chosen to be zero.When the scale ratio is j 0 , the coefficient W ϕ ( j 0 , m, n) is an approximation of P(x, y).When the scale ratio j ≥ j 0 , the coefficient W i ψ ( j, m, n) Added horizontal, diagonal and vertical detail to the ship.
Figure 8 shows the sample video ship image, which is considered in this section to perform the DWT wavelet transform.Figures 8 (c) and (d) indicate that the ship has four areas, including the upper left, which is the positioning map of the ship, the upper right, which is the horizontal texture feature map, the lower left, which is the diagonal texture feature map, and the lower right, which is the vertical texture feature map.In the texture feature, the horizontal, diagonal and vertical textures of the ship are clearer than the texture of the non-ship area, so the texture feature can be observed as a unique Figure 8c,d indicate that the ship has four areas, including the upper left, which is the positioning map of the ship, the upper right, which is the horizontal texture feature map, the lower left, which is the diagonal texture feature map, and the lower right, which is the vertical texture feature map.In the texture feature, the horizontal, diagonal and vertical textures of the ship are clearer than the texture of the non-ship area, so the texture feature can be observed as a unique component of the narrowing process of the subject.
B. Ship Image Spatial Difference Features Component and Coefficient of Variation Component (1) Spatial difference features
The ship has a unique feature in that it does not maintain a stable color and the area without the ship has a different color even in a small area.In other words, the ship is composed of several different colors in a small area, and this feature is called the spatial difference feature [34].Thus far, the analysis of spatial difference features mainly originates from three methods of filter, variance/histogram and spatial wavelet.The spatial difference features used in this section are analyzed based on histograms by processing and analyzing typical color video frames.The standard deviation σ g of the color component histograms of three different channels obtains the spatial difference characteristics of the ship's target [35].Reviewing the literature indicates that the threshold value of the standard deviation of the green component of the histogram σ g is standard deviation (std) = 55, while if the standard deviation of the green component of the histogram is greater than 55, the foreground target area is the ship and if it is below 55, the background is not detected as ship.
(2) Coefficient of variation The spatial difference is the process of obtaining the standard deviation and the standard deviation is essentially a variant of variance [36].For the standard deviation, in the field of mathematical statistics, the coefficient of variation can express discrete data more reasonably.Therefore, the coefficient of variation (cv) of spatial differences is used as a characteristic parameter of the ship and is used as a unique component of the narrowing process of the subject.The mathematical definition of the coefficient of variation is as follows: where σ I and P(X) indicate the variance and the mean value, respectively.Figure 9 shows an example of a video image.
Electronics 2019, 8, 959 10 of 19 threshold value of the standard deviation of the green component of the histogram g σ is standard deviation (std) = 55, while if the standard deviation of the green component of the histogram is greater than 55, the foreground target area is the ship and if it is below 55, the background is not detected as ship.
2) Coefficient of variation The spatial difference is the process of obtaining the standard deviation and the standard deviation is essentially a variant of variance [36].For the standard deviation, in the field of mathematical statistics, the coefficient of variation can express discrete data more reasonably.Therefore, the coefficient of variation (cv) of spatial differences is used as a characteristic parameter of the ship and is used as a unique component of the narrowing process of the subject.The mathematical definition of the coefficient of variation is as follows: ( ) where I σ and ( ) P X indicate the variance and the mean value, respectively.Figure 9 shows an example of a video image.In Figure 8(a), the original picture is taken as an example.The images in Figure 9 (a), (b) and (c) are extracted and the difference characteristics are analyzed separately.The figure shows the std values of (a) and (b) are above 55, while the std value of (c) is less than 55, thus, (a) and (b) are the ship's target and (c) is the background.Therefore, the feature analysis is accurate.In order to better verify the accuracy of the ship's features, this section exemplifies four groups of ship images and In Figure 8a, the original picture is taken as an example.The images in Figure 9a-c are extracted and the difference characteristics are analyzed separately.The figure shows the std values of (a) and (b) are above 55, while the std value of (c) is less than 55, thus, (a) and (b) are the ship's target and (c) is the background.Therefore, the feature analysis is accurate.In order to better verify the accuracy of the ship's features, this section exemplifies four groups of ship images and extracts the ship area image and the non-ship area image (each ship image has two ship targets and one background), and then analyzes the difference characteristics of images.Table 1 shows that the standard deviation of the ship area image is greater than 55, and the std value of the background image is lower than 55.Therefore, the spatial difference feature can be better as a unique component to become a subject narrowed subnetwork.In addition, the comparison of the coefficient of variation in Figure 10 shows that the coefficient of variation of the target area of the ship and the background area are maintained at 0.6 and 0.21, respectively.Therefore, the coefficient of difference reflects the spatial difference feature and becomes a sub-network with a narrowed theme as a unique component.In addition, the comparison of the coefficient of variation in Figure 10 shows that the coefficient of variation of the target area of the ship and the background area are maintained at 0.6 and 0.21, respectively.Therefore, the coefficient of difference reflects the spatial difference feature and becomes a sub-network with a narrowed theme as a unique component.
Training and experiment
The training set is designed to directly affect the performance of the algorithm.The larger the number of pictures in the training set, the stronger the accuracy and robustness of the algorithm.Since there is currently no special ship image training set, the present study establishes a training set of pictures of the watercraft and labels the pictures in the training set.Moreover, in order to quantitatively analyze the algorithm, a test set of pictures of water ships is also established.
Since there is currently no public ship database, the present study utilizes the method of shooting video to collect ship data.The Yangtze River was selected as a shooting point based on field visits in various places in Jiangsu Province.According to the needs of this work, 34 videos were taken.The pixels are 1980 × 1080 with a total size of 3.58 GB.The program uses VS2015 to frame the video, extracts 30,000 multi-target images and selects 10,000 multi-target images and 2700 single-target images.The MATABLE tool is used to mark and record the position of the top left and bottom right of the image, then it is saved as mat file and txt file for later needs.After labeling, the
Training and Experiment
The training set is designed to directly affect the performance of the algorithm.The larger the number of pictures in the training set, the stronger the accuracy and robustness of the algorithm.Since there is currently no special ship image training set, the present study establishes a training set of pictures of the watercraft and labels the pictures in the training set.Moreover, in order to quantitatively analyze the algorithm, a test set of pictures of water ships is also established.
Since there is currently no public ship database, the present study utilizes the method of shooting video to collect ship data.The Yangtze River was selected as a shooting point based on field visits in various places in Jiangsu Province.According to the needs of this work, 34 videos were taken.The pixels are 1980 × 1080 with a total size of 3.58 GB.The program uses VS2015 to frame the video, extracts 30,000 multi-target images and selects 10,000 multi-target images and 2700 single-target images.The MATABLE tool is used to mark and record the position of the top left and bottom right of the image, then it is saved as mat file and txt file for later needs.After labeling, the MATLAB program is utilized to convert the .txtfile into an .xmlfile.Finally, the data is prepared for deep learning training and the database is well established.Part of the picture labeled data is shown in Figure 11.
Training
In order to verify the effectiveness of the algorithm and the efficiency of parallel optimization, an experimental simulation environment is established.The processor used is an Intel i7 8, the RAM is 16GB, the GPU processor is NVIDIA 1080Ti, the experimental platform is Windows 10 and the software environment is Python3.7,Opencv3.4.1, VS2015, JetBrains PyCharm 2019.1.1,Anaconda3, CUDA8.0,Cudnnv6.0.
Since the identification ships and the identification of common targets are similar in function
Training
In order to verify the effectiveness of the algorithm and the efficiency of parallel optimization, an experimental simulation environment is established.The processor used is an Intel i7 8, the RAM is 16GB, the GPU processor is NVIDIA 1080Ti, the experimental platform is Windows 10 and the software environment is Python3.7,Opencv3.The training set is 10,000 images, the test set is 2700 images and the validation set is 2700 images.The result statistics are shown in Table 2.The original feature extraction network is initialized using ImageNet's classification samples and the remaining new layers are randomly initialized.It should be indicated that each mini-batch contains 256 anchors extracted from one image, and the foreground background sample is 1:1.In the first 6K iterations, the learning rate is 0.001, while after 2K iterations, the learning rate becomes 0.0001.Moreover, the momentum and the weight decay are set to 0.9 and 0.0005, respectively.12 illustrates that the loss function value of the algorithm in the training is significantly lower than that of the unmodified Fast R-CNN algorithm, and can converge to a stable state more quickly.This is because the bounding box size in this study is set corresponding to the data set in the present study, the image is downscaled and the theme and the scene are narrowed, which makes the image redundant information less.Moreover, in the training process of bounding box regression, the candidate region can be adjusted to a position close to the detection target frame, so the framework of the proposed method has a lower function loss value and can converge more quickly.
Experimental results and analysis
In the present study, the ship target detection system V1.0 is developed by using QT5.3 software.The main applications of this system are to detect the ship target on the inland river, use the real-time image acquisition device to collect the image information and play the real-time picture in the host computer system.Then, utilizing the existing algorithm for information in the picture to detect possible ship targets in the image is the most important part of the system.The system interface is shown in Figure 13.
Experimental Results and Analysis
In the present study, the ship target detection system V1.0 is developed by using QT5.3 software.The main applications of this system are to detect the ship target on the inland river, use the real-time image acquisition device to collect the image information and play the real-time picture in the host computer system.Then, utilizing the existing algorithm for information in the picture to detect possible ship targets in the image is the most important part of the system.The system interface is shown in Figure 13.
There are 2700 test pictures in this paper.Faster R-CNN algorithm with narrowing semantics improves the detection speed of the downscaled image, because the number of target pixels in the image decreases and the convolution calculation of the pixels is accelerated.Table 3 shows the specific time comparison.Narrowing includes topic narrowing and scene narrowing.It is concluded that the Faster R-CNN algorithm takes less time to reduce the scale and narrow the image than the original algorithm.Figure 14 and Table 4 indicate that the proposed method is obviously superior to the conventional deep learning algorithms, including SSD and YOLO, and the detection rate is improved.18 images are recognized per second.The conventional Faster R-CNN algorithm can be used to detect and recognize targets in ship images reasonably; however, the speed of target detection is relatively slow and only about 13 images are recognized per second.The improved Faster R-CNN algorithm proposed in this paper has better detection rate and speed rather than the combination of classical algorithms, including SVM + HOG.This method can shorten the training and detection time of the algorithm and the detection rate is higher since it saves the time of generating nine bounding boxes for each feature map element corresponding to the original image, and reduces the scale of the image to the subsequent algorithm.The size of the bounding box generated in the proposed algorithm is more practical for this data set.At the same time, by combining the methods of scene and topic narrowing, the algorithm can process faster, which can improve the speed of ship image detection and recognition as well as its accuracy.
. Experimental results and analysis
In the present study, the ship target detection system V1.0 is developed by using QT5 ftware.The main applications of this system are to detect the ship target on the inland river, us e real-time image acquisition device to collect the image information and play the real-tim cture in the host computer system.Then, utilizing the existing algorithm for information in th cture to detect possible ship targets in the image is the most important part of the system.Th stem interface is shown in Figure 13.There are 2700 test pictures in this paper.Faster R-CNN algorithm with narrowing semantics improves the detection speed of the downscaled image, because the number of target pixels in the image decreases and the convolution calculation of the pixels is accelerated.Table 3 shows the specific time comparison.Narrowing includes topic narrowing and scene narrowing.It is concluded that the Faster R-CNN algorithm takes less time to reduce the scale and narrow the image than the original algorithm.Figure 14 and Table 4 indicate that the proposed method is obviously superior to the conventional deep learning algorithms, including SSD and YOLO, and the detection rate is improved.18 images are recognized per second.The conventional Faster R-CNN algorithm can be used to detect and recognize targets in ship images reasonably; however, the speed of target detection is relatively slow and only about 13 images are recognized per second.The improved Faster R-CNN algorithm proposed in this paper has better detection rate and speed rather than the combination of classical algorithms, including SVM + HOG.This method can shorten the training and detection time of the algorithm and the detection rate is higher since it saves the time of generating nine bounding boxes for each feature map element corresponding to the original image, and reduces the scale of the image to the subsequent algorithm.The size of the bounding box generated in the proposed algorithm is more practical for this data set.At the same time, by combining the methods of scene and topic narrowing, the algorithm can process faster, which can improve the speed of ship image detection and recognition as well as its accuracy.
Conclusions
In the present study, the image downscaling, scene narrowing and theme narrowing methods were combined with the deep learning Faster R-CNN.It was observed that the RPN network in the Faster R-CNN algorithm can be better processed in limited resources.The image downscaling method was used to preprocess the image, which enhanced the useful information of the ship image, reduced the number of pixels of the subsequent image and sped up the detection.The scene semantic narrowing method was used to locate the target area and the Faster R-CNN convolutional neural network was constructed into a hierarchical narrowing network, which reduced the target detection search scale, directly located the ship area, reduced most of the background area and improved the calculation speed of Faster R-CNN.The Faster R-CNN with the narrowing function of the theme was studied.The texture feature and the spatial difference feature were selected as the narrowed sub-network, which realized the deep cooperation between the main network and the subnet.In the network training, the narrowed subnet was trained in advance.When the convolutional network was trained, the parameters of the narrowed subnet were solidified or only fine-tuned, and the network parameters were optimized.Furthermore, it as found that the learning and test speeds of the R-CNN algorithm improved the detection rate of the Faster R-CNN algorithm.
The algorithm in this paper also had a high accuracy rate for detecting ships with small targets at a long distance.In the future, it will still be necessary to further study the fast and regional convolutional neural network algorithm, try new methods and adjust the network structure and parameters to further optimize the algorithm and improve the universality of the algorithm.
Figure 2 .
Figure 2. Flow chart of the method.
Figure 2 .
Figure 2. Flow chart of the method.
Figure 3 Figure 3 .
Figure 3 (b) shows that the image size after downscaling reduces to 1270 × 540 pixels.In other words, the size of the graph b is 1/4 of that of the graph a.Although downscaling can reduce the noise problem to some extent, it adversely affects the image feature information more or less.It should be indicated that since the test image is large and the relative detail loss is negligible, the downscaling impact on the image quality is ignored.Since the image size is reduced, the number of pixels of the subsequent processing is greatly reduced so that the detection speed of the target image increases.
Figure 6 .
Figure 6.Schematic diagram of serial cooperation.Figure 6.Schematic diagram of serial cooperation.
Figure 8 .
Figure 8. Video image ship texture features.
Figure 8 .
Figure 8. Video image ship texture features.
Figure 9 .
Figure 9. Spatial difference characteristic map of video images.
Figure 9 .
Figure 9. Spatial difference characteristic map of video images.
(a) Ship target coefficient of variation (b) Background coefficient of variation
Figure 11 .
Figure 11.data annotation part of the picture.
4.1, VS2015, JetBrains PyCharm 2019.1.1,Anaconda3, CUDA8.0,Cudnnv6.0.Since the identification of ships and the identification of common targets are similar in function and there is currently no picture training set related to ship identification, the present study selects the existing VGG16 network as the feature extraction network and uses it in the existing Image Net general target.It should be indicated that the weights obtained by training on the training set are used as initial values.The training and the test pictures are 10,000 and 2700, respectively.The specific operational steps of the proposed method are as the following: (1) Downscaling the image of the ship.(2) Labeling all of the training sets and saving them in the format of .xmlfile.(3) A hierarchical narrowing network composed of a target area positioning network, a target area generating network and a Faster R-CNN convolutional neural network are trained.(4) During network training, the narrowed subnet is trained in advance; then, texture features and spatial difference features are added, and the parameters of the narrowed subnet are solidified during the overall convolutional network training.(5) By using the migration learning method, the network is initialized with the parameters trained by the ImageNet dataset, and then the RPN network is independently trained with training times of 2000.(6) The candidate area generated in the previous step is trained as an input picture to train a Faster R-CNN network.Until then, no parameters of any layer have been shared, and the number of trainings in this step is set to 1500 times.(7)The RPN network is retrained by using the parameters of the Faster R-CNN network trained in the previous step; however, the parameters of the convolutional layer shared by the RPN network and the Fast R-CNN network are maintained, which is unique to the RPN.The number of training sessions of the convolutional layer is set to 2000.(8)The convolutional layers shared by the RPN network and the Fast R-CNN network are maintained unchanged.However, parameters of the layers that are unique to the Fast R-CNN network are fine-tuned, and finally the function of quickly and accurately detecting and recognizing images is achieved.It should be indicated that the number of training sessions is 1500.(9)The trained frame is detected by the test set image, while the original test image is detected by the conventional Faster R-CNN algorithm and the result is compared.
Figure 12
Figure12shows the change of the loss function of the Fast R-CNN algorithm and the proposed algorithm for the same training set image as the training times increase.The red line with rectangle shows the training result of the conventional Fast R-CNN algorithm, while the blue line with ellipse is the training result of the proposed algorithm.Figure12illustrates that the loss function value of the algorithm in the training is significantly lower than that of the unmodified Fast R-CNN algorithm, and can converge to a stable state more quickly.This is because the bounding box size in this study is set corresponding to the data set in the present study, the image is downscaled and the theme and the scene are narrowed, which makes the image redundant information less.Moreover, in the training process of bounding box regression, the candidate region can be adjusted to a position close to the detection target frame, so the framework of the proposed method has a lower function loss value and can converge more quickly.
Figure
Figure12shows the change of the loss function of the Fast R-CNN algorithm and the proposed algorithm for the same training set image as the training times increase.The red line with rectangle shows the training result of the conventional Fast R-CNN algorithm, while the blue line with ellipse is the training result of the proposed algorithm.Figure12illustrates that the loss function value of the algorithm in the training is significantly lower than that of the unmodified Fast R-CNN algorithm, and can converge to a stable state more quickly.This is because the bounding box size in this study is set corresponding to the data set in the present study, the image is downscaled and the theme and the scene are narrowed, which makes the image redundant information less.Moreover, in the training process of bounding box regression, the candidate region can be adjusted to a position close to the detection target frame, so the framework of the proposed method has a lower function loss value and can converge more quickly.
Figure 12 .
Figure 12.Training loss function line chart.
Figure 12 .
Figure 12.Training loss function line chart.
Figure 12 .
Figure 12.Training loss function line chart.
Figure 14 .
Figure 14.Test diagram of this method.
Figure 14 .
Figure 14.Test diagram of this method.
is a result of narrowing the target scene of the ship.By scene semantic narrowing method, it directly locates the ship area and reduces most of the background area, thus, reducing the search scale of ship target detection.
Video image Convolution layer Feature layer Candidate area Forecast area Image Note: 1.merge candidate area 2.crop image 3.result processing Regional positioning network Goal (foreground) background ...
Table 1 .
Ship image spatial difference eigenvalues.
Table 1 .
Ship image spatial difference eigenvalues.
Table 2 .
Training related data.
Table 4 .
Comparisons of Algorithms.Abbreviated specific definition in the table: MAP: Mean Average Precision; MAPE: Mean Absolute Percent Error; SVM+HOG: Support Vector Machine + Histogram of Oriented Gradient; SSD: Single Shot Multi-Box Detector; YOLO: You Only Look Once; Faster R-CNN: Faster Region-based Convolutional Neural Network. | 12,720.4 | 2019-08-29T00:00:00.000 | [
"Engineering",
"Computer Science"
] |
Anti-inflammatory effects of Edaravone and Scutellarin in activated microglia in experimentally induced ischemia injury in rats and in BV-2 microglia
Background In response to cerebral ischemia, activated microglia release excessive inflammatory mediators which contribute to neuronal damage. Therefore, inhibition of microglial over-activation could be a therapeutic strategy to alleviate various microglia-mediated neuroinflammation. This study was aimed to elucidate the anti-inflammatory effects of Scutellarin and Edaravone given either singly, or in combination in activated microglia in rats subjected to middle cerebral artery occlusion (MCAO), and in lipopolysaccharide (LPS)-induced BV-2 microglia. Expression of proinflammatory cytokines, including tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), and inducible nitric oxide synthase (iNOS) was assessed by immunofluorescence staining and Western blot. Reactive oxygen species (ROS) and nitric oxide (NO) levels were determined by flow cytometry and fluorescence microscopy, respectively. Results In vivo, both Edaravone and Scutellarin markedly reduced the infarct cerebral tissue area with the latter drug being more effective with the dosage used; furthermore, when used in combination the reduction was more substantial. Remarkably, a greater diminution in distribution of activated microglia was observed with the combined drug treatment which also attenuated the immunoexpression of TNF-α, IL-1β and iNOS to a greater extent as compared to the drugs given separately. In vitro, both drugs suppressed upregulated expression of inflammatory cytokines, iNOS, NO and ROS in LPS-induced BV-2 cells. Furthermore, Edaravone and Scutellarin in combination cumulatively diminished the expression levels of the inflammatory mediators being most pronounced for TNF-α as evidenced by Western blot. Conclusion The results suggest that Edaravone and Scutellarin effectively suppressed the inflammatory responses in activated microglia, with Scutellarin being more efficacious within the dosage range used. Moreover, when both drugs were used in combination, the infarct tissue area was reduced more extensively; also, microglia-mediated inflammatory mediators notably TNF-α expression was decreased cumulatively. Electronic supplementary material The online version of this article (doi:10.1186/s12868-014-0125-3) contains supplementary material, which is available to authorized users.
Background
Ischemic stroke constitutes most of all strokes and is caused by obstruction of blood flow to the brain, which would initiate a complex cascade of metabolic alterations, including release of reactive oxygen species and inflammatory cytokines, activation of complement proteins etc. This would then exacerbate neuroinflammation and ultimately cause neuronal death. The innate immune response to induce postischemic inflammation is undoubtedly the hallmark feature for the progression of cerebral ischemia injury [1]. The key cell players in this are the activated microglia which can act as sensors to detect abnormal alterations in response to internal and external insults.
Microglial cells are the resident immune cells that mediate neuroinflammation in the central nervous system (CNS) [2] In neurodegenerative diseases and stroke, they are activated and engaged in different functions such as phagocytizing the toxic cellular debris, producing proinflammatory cytokines and enhancing neuronal survival by release of trophic factors [3]. In chronic activation, microglia are thought to contribute to neuronal damage via release of excessive proinflammatory cytokines and/ or cytotoxic factors, such as nitric oxide (NO), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and reactive oxygen species (ROS) [4,5]. As a corollary, inhibition or suppression of microglia to prevent over-reaction and inflammatory response of microglia may prove to be an efficacious therapeutic strategy to alleviate the progression of the neurological diseases.
In the search for potential drugs that may effectively suppress overt microglial activation, attention has recently been drawn to Edaravone and Scutellarin. Edaravone (3methyl-1-phenyl-2-pyrazolin-5-one), a free radical scavenger that is currently used in the treatment of acute ischemic stroke as a neuroprotective reagent, has been shown to significantly reduce the infarct size, improve neurological scores, and decrease ROS generation [6]. More specifically, it can counteract toxicity from activated microglia [7]. Neuroinflammation in middle cerebral artery occlusion (MCAO) may be attenuated by Edaravone which acts through suppression of expression of proinflammatory cytokines in activated microglia [8].
The above studies have shown that both Edaravone and Scutellarin have anti-inflammatory effects in activated microglia. It remains to be determined which of the two common drugs is more potent in its antiinflammatory effect in activated microglia, and whether there would be a cumulative therapeutic effect when both drugs were used in combination. This study was therefore aimed to investigate the anti-inflammatory effect of Edaravone and Scutellarin used either singly or in combination in experimentally induced cerebral ischemia, and in vitro in the BV-2 microglial cells. We sought to determine if a combination of Edaravone and Scutellarin at appropriate dosage may represent a more efficacious therapeutic strategy for treatment of neurodegenerative diseases in which activated microglia are implicated.
Results
Changes in infarct size in MCAO rats given Edaravone and/or Scutellarin treatment A large infarct area was observed in the ipsilateral cerebrum in the MCAO rats at 3 days after MCAO. Compared with this group, the infarct area of the cerebral cortex was markedly reduced by Edaravone (E) or Scutellarin (S) treatment or a combination of both drugs (Figure 2A). Treatment of MCAO rats with Edaravone along with high dose Scutellarin (E + SH) markedly reduced the infarct volume. There were no apparent differences between Edaravone combined with low dose Scutellarin (E + SL) group and Edaravone group (E) or Scutellarin low dose group (SL). On the other hand, the infarct volume in E + SH group was significantly decreased compared with Scutellarin high dose group (SH) ( Figure 2B). Microglia were activated after MCAO but were reduced in cell numbers following treatment with drugs The infarct size was considerably reduced in MCAO rat brains at 3 days treated with drugs E, S and E + S in comparison to untreated MCAO rats. The activated microglia, in large numbers, were observed in the ipsilateral cerebral cortex of MCAO rat brain without drugs treatment. The incidence of activated microglia was noticeably reduced in MCAO rat brains when treated with drug E, S and E + S and this was accompanied by a decrease in the infarct zone being most pronounced in the last mentioned group (Figure 3).
Edaravone and Scutellarin separately or in combination reduced the expression of inflammatory mediators in MCAO rats
To investigate the anti-inflammatory effects of Edaravone and Scutellarin on activated microglia, we examined the production of inflammatory cytokines (TNF-α and IL-1β) and iNOS in MCAO rats given the treatment of both drugs either separately or in combination by double immunofluorescence staining in MCAO rats given either low or high dose of Scutellarin. Here we show the images of Scutellarin high dose (SH) only. iNOS immunofluorescence in activated microglia in the penumbral zones was Figure 2 Reduction in size of infarct zone was observed in the brain cortices of MCAO rats following treatment with drugs Edaravone (E), Scutellarin (S) and E + S. (A) Triphenyl tetrazolium chloride (TTC) staining showing a marked decrease in the infarct size in brain sections following treatment of MCAO rats with drug E alongside low (SL) and high (SH) dosages of drug S and killed at 3 days after MCAO (n = 5 for each group). (B) Significant differences in infarct volume in MCAO (M) rats compared with other group was expressed as *p <0.05. The infarct volume in MCAO rat brains treated with E + SH shows marked differences compared with that treated with E or SH, #+ p <0.05. Each bar represents mean ± SD. noticeably enhanced after MCAO, but it was markedly reduced at 3 days following treatment with E, SH and E + SH. This was conspicuous in MCAO + SH and especially so in MCAO + E + SH rats in which iNOS expression in activated microglia was virtually abolished (Figure 4). The expression of TNF-α ( Figure 5) and IL-1β ( Figure 6) paralleled with that of iNOS after treatment with E, SH or E + SH. Thus, in MCAO rats given SH or E + SH treatment notably in the latter and killed at 3 days post-operation, TNF-α and IL-1β immunoexpression in lectin labeled activated microglia was obliterated. In MCAO rats killed at 7 days, iNOS and TNF-α immunofluorescence that was augmented following ischemia was attenuated with E, SH or E + SH treatment (see Additional file 1: Figure S1, Additional file 2: Figure S2). An additional feature was the profuse ramification of microglia at this time point. In general, immunofluorescence of iNOS and TNF-α was less intense compared with that at 3 days.
Western blot analysis also showed that the protein expression of iNOS, TNF-α and IL-1β was obviously suppressed at 3 days after treatment with drugs ( Figure 7). It is striking that TNF-α expression in MCAO rats given combined E + SH treatment showed the most drastic reduction when compared with drugs used separately (p <0.05). The expression level of TNF-α in MCAO rats treated with E + SH was reduced by about 35%, compared with 24% of treatment with SH alone, i.e. a further decrease by 11%. Likewise, the expression level of IL-1β and iNOS in combined drug treatment (E + SH) was further suppressed by 7% and 4%, respectively, when compared with rats treated with SH alone.
Cell viability assay of BV-2 cells
We chose to perform our in vitro studies on BV-2 cell line instead of primary cultured microglial cells to ensure that we obtained enough cells for analyzing cell viability following drug treatment and also obtained adequate amounts of protein for Western blot analyses. The cytotoxicity data was obtained by the MTS assay for the effect of Edaravone and Scutellarin on BV-2 cells. A combined concentration of Edaravone (in the range of 50 μM to 200 μM) and Scutellarin (in the range of 0.27 mM to 1.1 mM) did not result in any significant cell death (Additional file 3: Figure S3). In this study we have used Edaravone at 100 μM with Scutellarin at 0.54 mM for all subsequent analysis.
Edaravone and Scutellarin separate treatment or in combination reduced the expression of inflammatory cytokines and iNOS in LPS-induced BV-2 microglia Consistent with results in vivo, changes in the inflammatory mediators, including TNF-α, IL-1β and iNOS, whose immunoexpression was also observed in LPS-induced BV-2 cells. TNF-α, IL-1β and iNOS immunofluorescence intensity was markedly augmented versus controls when the cells were subjected to LPS, but was suppressed in LPSactivated microglia pretreated with E, S and E + S ( Figures 8,9,10). The expression of these inflammatory mediators was clearly diminished in activated BV-2 cells that were pretreated with S or a combination of the both drugs.
The protein expression of iNOS, TNF-α and IL-1β was decreased in BV-2 cells pretreated with drugs. In this connection, the expression of TNF-α was further depressed when E + S were used in combination when compared with the other groups, a phenomenon that is consistent with the result in vivo ( Figure 11). The expression level of TNF-α in LPS-induced BV-2 cells with E + S was reduced by about 49%, compared with 25% reduction in cells given treatment with S alone. This amounted to a further decrease by about 24%. The expression level of IL-1β and iNOS in combined drug treatment was further suppressed by 3% and 27%, respectively, when compared with E treatment alone. The expression level of IL-1β in combined drug treatment was further suppressed by 5% when compared with S treatment alone, but iNOS expression in drug combination group was not significantly lower than that of S used alone.
Edaravone and Scutellarin separate treatment or in combination reduced the expression of ROS and NO in LPS-induced BV-2 cells
Intracellular ROS and NO in LPS-activated BV-2 microglia following treatment with E, S and E + S was measured. ROS production was increased in LPS-induced BV-2 microglia, and significantly decreased in cells pretreated with E, S and E + S ( Figure 12). NO production was markedly increased in LPS-induced BV-2 cells, and was observably reduced when the cells were pretreated with E, S and in combination. The expression of NO in S or E + S treatment group was clearly reduced especially in the latter when compared with E treatment group ( Figure 13).
Discussion
Neuroinflammation is a key contributor in the ischemic cascade after cerebral ischemia that leads to neuron damage and death. Activated microglia in inflammatory response have both beneficial and detrimental functions in the nervous system. During neuroinflammation, activated microglia remove cellular debris or invading pathogens, and release neurotrophic factors that regulate the microenvironment [16]. On the other hand, activated microglial cells are also elicited to produce a plethora of proinflammatory mediators, including TNF-α, IL-1β, ROS etc., which have been implicated in the pathogenesis of different neurodegenerative diseases, including Alzheimer's disease [17], Huntington's disease [18], Parkinson's disease [19], stroke [20] and hypoxic insults [21]. Therefore, a prevalent view is that activated microglia can aggravate the injury and subsequent neurodegeneration and they may serve as a prime therapeutic target in a wide variety of CNS diseases.
Here we examined the anti-inflammatory action of Edaravone and Scutellarin in activated microglia. Both Edaravone and Scutellarin reduced the infarct size in brain cortices of MCAO rats, inhibited production of proinflammatory mediators (TNF-α, IL-1β, NO and ROS) in MCAO rats and LPS-induced BV-2 microglial cells, as well as suppressed the experimentally induced increased expression of iNOS. Activated microglia appeared to emit many thin cytoplasmic processes when treated with Edaravone and Scutellarin. A possible explanation for this would be that both drugs had promoted the ramification of microglia whose ramified phenotype might represent a less active state. On the other hand, the possibility that Edaravone and Scutellarin can suppress the activated microglia by causing the cells to become more resistant to transformation into an amoeboid form, presumably a more active state, is considered. In any event, a significant diminution in distribution of activated microglia was evident in rats treated with both drugs combined. Furthermore, we have also provided the first morphological evidence that in comparison to Edaravone, Scutellarin was more efficacious in suppressing the expression levels of inflammatory mediators in activated microglia with the dosage used. Additionally, the two drugs in combination cumulatively depressed the expression of TNF-α both in vivo and in vitro. The results suggest that Scutellarin is more potent in therapeutic potential for various microglia mediated neuroinflammatory diseases.
Edaravone is a synthetic small-molecule free-radical scavenger. It has been reported to be effective in inhibiting the inflammatory responses [22], brain edema [23], ROS generation, and oxidative tissue damage [24]. Edaravone currently is being used clinically to treat stroke patients. Recently, Edaravone has been shown to possess neuroprotective and antioxidative effects on the brain after traumatic brain injury both in rat models and in patients [25][26][27]. The likely underlying mechanism for this is via inhibiting oxidative stress, leading to a decreased inflammatory response, and thereby reducing neuronal death and improving neurological function. Many in vivo experiments have similarly reported that Edaravone could mitigate microglial activation and suppress the production of proinflammtory meditors by activated microglia [8,28]. Here, we confirmed the efficacy of Edaravone in inhibiting (See figure on previous page.) Figure 4 Treatment of MCAO rats with drugs E, SH and E + SH resulted in the reduction of iNOS expression in activated microglia. Confocal images showing the expression of iNOS (red) in lectin + microglia (green) in the penumbral zones of MCAO rat brain (D-F) and following treatment with E (G-I), SH (J-L) and E + SH (M-O) (n = 5 for each group). Increase in iNOS expression (E) can be observed in the activated microglia (D) in MCAO rat brain. A marked reduction of iNOS expression (H, K) was observed in activated microglia (G, J) 3 days following treatment of MCAO rats with drugs E and SH. Further, iNOS expression (N) was hardly detectable in activated microglia (M) when MCAO rats were treated with a combination of the two drugs. DAPIblue. Scale bars in A-O: 20 μm.
the expression levels of various inflammatory mediators in activated microglia as shown by Western blot analysis and immunofluorescence labeling.
It has been reported that Scutellarin could improve neuronal injury and had protective effect in rat cerebral ischemia at least related to its antioxidant property [29]. The neuroprotective effect of Scutellarin was associated with inhibition of the apoptosis-inducing factor pathway [9]. Recently, some studies have extended that Scutellarin could inhibit production of proinflammatory mediators induced by LPS in rat primary microglia or BV-2 microglial cells [14]. The present results are consistent with this. It is unequivocal from the present results that Scutellarin with the dosage used based on cell viability assay showed a greater potency in comparison to Edaravone in its antiinflammation in activated microglia; hence, it is suggested that Scutellarin is endowed with a better therapeutic potential for various microglia mediated neuroinflammatory diseases.
While both Edaravone and Scutellarin have shown great efficacy in their anti-inflammatory effect in activated microglia, the potency of both drugs either administered separately or in combination has not been explored. We show here that when compared with Edaravone, Scutellarin was more effective in decreasing the infarct volume in MCAO rats. Very strikingly, Scutellarin alone suppressed the expression levels of inflammatory mediators to a greater extent when compared to Edaravone in activated microglia. Interestingly, the two drugs in combination cumulatively depressed the expression of inflammatory mediators notably TNF-α in activated microglia. Moreover, Edaravone when combined with a high dose of Scutellarin decreased the infarct volume extensively in MCAO rats. On the other hand, expression of other markers was depressed to a lesser extent in activated microglia between Scutellarin used separately or when combined with Edaravone. The possible explanation for this would be that expression of inflammatory mediators and changes in cerebral infarct volume are not synchronized and there are other mechanisms involved in response to brain injury. Notwithstanding, Scutellarin and Edaravone when used in combination can produce a cumulative protective effect in ischemia brain injury.
In the present results, the infarct volume of the cerebral cortex in MCAO rats was obviously decreased when treated with Edaravone and Scutellarin separately or in combination. Associated with this was the drastic diminution in numbers of activated microglia. Recent studies suggest that Toll-like receptors (TLRs), especially TLR2, may have a key role in the progression of brain damage induced by cerebral ischemia [30][31][32][33]. It has been documented that a marked long-term induction of TLR2 expression in microglia activation after transient MCAO, suggesting an important role of TLR2 activation after stroke [31,33]. A significant decrease of the infarct volume in TLR2 deficient mice compared to wild type mice and altered microglia activation profiles has been observed [33,34]. The present results showed that the infarct size was considerably reduced in MCAO rat brain treated with drugs in comparison to untreated MCAO rats. Meanwhile, the activated microglia were not only reduced in numbers but many of them had also appeared ramified phenotype in the ipsilateral cerebral cortex of MCAO rat brain notably in rats given the combined drugs. The possibility is that both Edaravone and Scutellarin can cumulatively suppress the upregulation of TLR2.
We show here that Edaravone and Scutellarin when applied in combination cumulatively decreased the expression of inflammatory mediators, being most pronounced for TNF-α in activated microglia. TNF-α contains soluble and membrane-bind TNF-α [35]. Activated microglial cells are the major producers of soluble TNF-α within the first 6 hours after cerebral ischemia [36][37][38]. TNF-α converting enzyme (TACE), also known as a disintegrin and metalloprotease 17 (ADAM17) [39], is involved in multiple cell signaling pathways, including p44 mitogen-activated protein kinase (MAPK)-dependent manner [40]. In light of the above, it needs to be further explored whether pretreatment with Edaravone and Scutellarin would directly suppress the expression and function of ADAM17, or act on p38/p44 MAPK to synergisticly decrease TNF-α expression. Scutellarin and Edaravone might target on different sites of TNF-α expression and this should be considered. Studies have shown that inhibition of TNF-α using its antibody reduces infarct volume after MCAO [41,42]. This may offer an explanation on why Edaravone and Scutellarin in combination can markedly reduce the infarct volume after MCAO. Apart from TNF-α, it is interesting to note that there was lesser reduction in the expression of other inflammatory mediators following Edaravone, Scutellarin or combined treatment. One possible explanation for this may be that the expression of these inflammatory (See figure on previous page.) Figure 5 Treatment of MCAO rats with drugs E, SH and E + SH resulted in the reduction of TNF-α expression in activated microglia. Confocal images showing the expression of TNF-α (red) in lectin + microglia (green) in the penumbral zones of MCAO rat brain (D-F) and following treatment with E (G-I), SH (J-L) and E + SH (M-O) (n = 5 for each group). A drastic increase in the expression of TNF-α (E) can be observed in the activated microglia (D) in MCAO rat brain. A marked reduction of TNF-α expression (H, K) was observed in activated microglia (G, J) 3 days following treatment of MCAO rats with drugs E and SH. Further, TNF-α expression (N) was negligible in activated microglia (M) when MCAO rats were treated with a combination of the two drugs. DAPIblue. Scale bars in A-O: 20 μm. Figure 6 Treatment of MCAO rats with drugs E, SH and E + SH resulted in the reduction of IL-1β expression in activated microglia. Confocal images showing the expression of IL-1β (red) in lectin + microglia (green) in the penumbral zones of MCAO rat brain (D-F) and following treatment with E (G-I), SH (J-L) and E + SH (M-O) (n = 5 for each group). A noticeable increase in IL-1β expression (E) can be observed in the activated microglia (D) in MCAO rat brain. IL-1β expression (H, K), however, was depressed in activated microglia (G, J) 3 days following treatment of MCAO rats with drugs E and SH. Also, IL-1β expression (N) was almost totally abolished in activated microglia (M) when MCAO rats were treated with a combination of the two drugs. DAPIblue. Scale bars in A-O: 50 μm Figure 7 Protein expression of inflammatory cytokines and iNOS was decreased in MCAO rat brains following treatment with E, S and E + S. The expression levels of TNF-α, IL-1β and iNOS in MCAO rat brains are depressed significantly at 3 days following treatment with E, S and E + S when compared with the MCAO ( n = 5 for each group). Significant differences in protein levels between MCAO and drugs used rats are expressed as *p <0.01. TNF-α protein expression level of the E + SH group was most drastically suppressed following MCAO when compared with the other groups. Significant difference is expressed as # p <0.05. The values represent the mean ± SD in triplicate. mediators has reached the basal levels with either Edaravone or Scutellarin treatment. Therefore, further treatment with two drugs in combination could not suppress the expression further except for TNF-α, which may be more sensitive to the drugs.
A similar phenomenon and possible underlying mechanism are also found on NO expression in BV-2 cells treated. The NO expression was markedly reduced in BV-2 cells treated with Scutellarin or Scutellarin + Edaravone as compared with Edaravone alone within the dosage range as determined by cell viability assay. This is consistent with iNOS expression as manifested by inmunofluorescence labeling and Western blot.
The present morphological evidence and protein analysis indicate that Scutellarin is more potent in suppressing neuroinflammation induced by activated microglia with the dosage used, but the underlying molecular mechanism remains uncertain. Studies have shown that Scutellarin is capable of attenuating the expression of not only those proinflammatory molecules whose expression depends on the activation of NF-κB (a major mediator of microglial inflammatory response), but also those via transcription factor signal transducer and activator of transcription 1α (STAT1α) transcription factor [14]. Recent studies by us have shown that the Notch signaling pathway is involved in microglial activation and microglia-mediated cytokine production by promoting the expression of NF-κB [43,44]. Notch-1 signaling was also identified to regulate microglial activation via NF-κB pathway after hypoxic exposure [45]. Indeed, there is evidence that Notch/ NF-κB signaling pathways are involved in inhibition of microglial activation by Scutellarin and production of inflammatory mediators (unpublished data).
Conclusion
We show here that both Edaravone and Scutellarin could decrease infarct volume, frequency and distribution of activated microglia as well as suppress the production of inflammatory mediators in activated microglia in MCAO rats. Scutellarin appeared to be more potent in its antiinflammatory effects when compared with Edaravone with the dosage used. Remarkably, the two drugs in combination cumulatively decreased the infarct size in the cerebrum and diminished the ischemia-induced inflammatory mediators being most pronounced for TNF-α expression. Thus, Scutellarin or along with Edaravone may prove to a more efficacious therapeutic strategy for treatment of microglia mediated neurodegenerative diseases, such as stroke.
Ethics statement
This research work has been carried out within an appropriate ethical framework. While handling and use of rats, ethical guidelines as stated in the National Institutes of Health Guide for the Care and Use of Laboratory Animals were adopted. All experimental protocols and use of animals were approved by Kunming Medical University and all efforts were made to minimize the number of rats used and their suffering.
Anesthesia of the rats was achieved by an intraperitoneal injection of sodium pentobarbital (50 mg/kg). The surgical procedure followed that described previously by us [46]. Briefly, a circular aperture 3 mm in diameter was burred in the right parietal bone with a dental drill, and the main trunk of the middle cerebral artery (MCA) was exposed and cauterized. In the sham-operated rats, the same surgical procedure was followed but the MCA was not cauterized.
Injection of Edaravone and Scutellarin
The rats in the respective groups were given an intraperitoneal injection of Edaravone Figure 10 iNOS expression was drastically suppressed following treatment of LPS-activated microglia with drugs E, S and E + S in vitro. Confocal images show an upregulation of iNOS (E) in LPS-activated BV-2 microglia (D) in comparison to control (A-C). Addition of drug E resulted in decrease in iNOS expression (H) in LPS-activated BV-2 microglia (G). iNOS expression was obliterated in activated microglia treated with drug S (K) notably with a combination of drugs E and S (N). DAPIblue. Scale bars in A-O: 50 μm. Figure 11 Protein expression of inflammatory cytokines and iNOS was decreased in LPS-activated microglial cells following treatment with E, S and E + S. The expression levels of TNF-α, IL-1β and iNOS in LPS-activated microglial cells are depressed significantly following treatment with E, S and E + S. Significant differences in protein levels between LPS and drugs used groups are expressed as *p < 0.05. TNF-α and iNOS protein expression level in LPS + S group and LPS + E + S group was markedly suppressed when compared with the LPS + E group. Furthermore, the expression of TNF-α in LPS + E + S BV-2 cells was significantly lower than other groups. Significant differences in protein levels are expressed as # p < 0.05 and △ p <0.01. The values represent the mean ± SD in triplicate.
TTC assessment of infarct size
Thirty five rats were used for assessing infarct size (n = 5 for each group). The rats were killed at 3 d after MCAO. The brains were rapidly removed, frozen at −20°C for 30 min. A total of six 2 mm thick coronal sections of the brain were then cut in a rat brain matrix starting at the frontal pole. This series of brain sections totaling 12 mm in thickness included the entire infarct area caused by the MCAO. The sections were incubated for 30 min and stained with 1% triphenyltetrazolium chloride (TTC) at 37°C protected from light. After this, sections were fixed in 2% buffered formaldehyde solution for 4 h. The cerebral infarct area as outlined in white in MCAO rats as well as in rats after various drug treatments, is depicted in Figure 2. Infarct areas in each section were measured using Image J software. A correction for edema was Figure 12 ROS expression was reduced in LPS-activated microglial cells following treatment with E, S and E + S. Intracellular ROS in LPS-activated BV-2 microglial cells following treatment with E, S and E + S was measured. The upper panel shows cell counts (y-axis) and log10 expression of fluorescence intensity (x-axis). The lower panel is a bar graph showing a significant change in the fluorescence intensity of intracellular ROS production following the above treatments when compared with the LPS. Note the ROS production, which is increased after LPS stimulation, is significantly decreased after pretreatment with E, S and E + S. Significant differences in protein levels are expressed as * p <0.05. made according to the following formula: infarct area × (area contralateral hemisphere/area ipsilateral hemisphere) [47,48]. Cerebral infarct volume was measured as a percentage of the total contralateral hemisphere, as calculated with the following formula: total infarct volume = sum of infarct volume of all sections measured (corrected infarct area × 2 mm for each section)/total contralateral hemispheric volume × 100.
BV-2 cell culture and treatment
BV-2 murine cells were cultured in Dulbecco's modified Eagle's medium (DMEM), supplemented with 10% fetal calf serum (FCS) at 37°C in a humidified incubator under 5% CO 2 . The cells were divided into control, LPSinduced, LPS + Edaravone, LPS + Scutellarin, and LPS + Edaravone + Scutellarin groups. The cells was pretreated with Edaravone (100 μM), Scutellarin (0.54 mM), and Edaravone + Scutellarin 1 h at 37°C in a humidified incubator under 5% CO 2 . The dosage of Edaravone and Scutellarin used was based on cell viability assay when the drugs were used separately (data not provided). After incubation, the medium was discarded and the cells were washed with PBS, and then incubated with LPS (1 μg/ml, Sigma-Aldrich, MO, USA) for 3 h. The culture medium was replaced with basic DMEM before treatment. For controls, the medium was replaced with basic DMEM incubated in a chamber 95% air 5% CO 2 . Finally, proteins were extracted for Western blot analysis.
Double immunofluorescence labeling in the cerebrum and BV-2 cells
A total of 15 rats of various experimental groups were used for double immunofluorescence labeling: 1, 3, 7 days (n = 5 at each time point). Following deep anesthesia with 6% sodium pentobarbital, the rats were sacrificed by perfusion with 2% paraformaldehyde in 0.1 M phosphate buffer. The brain was removed and paraffin embedded. Coronal sections of 7 μm thickness were cut on a microtome (Model: 2165; Leica, Bensheim, Germany).The sections were rinsed with phosphate-buffered saline (PBS). For blocking of non-specific binding proteins, tissue sections were incubated in 5% normal goat serum diluted in PBS for 1 h at room temperature (22-24°C). After discarding the serum, the sections were incubated in a humidified chamber with primary polyclonal antibody iNOS were incubated, respectively, with fluorescent secondary antibodies: Cy3-conjugated secondary antibody and FITCconjugated lectin (Lycopersicon esculentum) that labels both microglia and blood vessel endothelial cells for 1 h at room temperature. After 3 rinses with PBS, the sections were mounted with a fluorescent mounting medium containing 4' ,6-diamidino-2-phenylindole (DAPI) (Sigma, USA; Cat. No. F6057). Colocalization was observed by confocal microscopy (Fluoview 1000, Olympus Company Pte. Ltd., Tokyo, Japan). Details of antibodies used are given in Table 2. BV-2 cells were fixed with 4% paraformaldehyde in 0.1 M PBS for 20 min. Following rinsing with PBS, the coverslips with adherent cells were used for immunofluorescence staining. In each group, BV-2 cells were incubated with the primary antibodies as described above overnight 4°C. Subsequently, the cells were incubated in FITC/Cy3-conjugated secondary antibodies for 1 h at room temperature. After washing, the coverslips were mounted using a fluorescent mounting medium with DAPI. All images were captured using a confocal microscope. The isotopic control confirmed the specificity of all primary antibodies used (data not shown).
Western blotting analysis for MCAO tissues and BV-2 cells
A total of 105 rats were used for Western blotting analysis. The sham-operated and MCAO rats given saline, Edaravone and/or Scutellarin injections were sacrificed at 1 day (n = 5), 3 days (n = 5) and 7 days (n = 5), respectively. The control or ischemic cortex derived from each group was frozen in liquid nitrogen and stored at −80°C. Tissue samples from various groups were homogenized with protein extraction reagent (Pierce, IL, USA) containing protease inhibitors. For BV-2 cells of each group, the cells were lysed with lysis buffer, mechanically scraped off with a rubber scraper and centrifuged at 13,000 rpm for 25 min. Protein concentrations of both tissues and BV-2 cells were determined by using a protein assay kit (Bio-Rad, Hercules, CA, USA; Cat. No. 500-0002). Samples of supernatants containing 50 μg protein of tissues or 40 μg protein of BV-2 cells were loaded and heated to 95°C for 5 min, and were separated by sodium dodecyl sulfatepoly-acrylamide gel electrophoresis in 10% or 12% gels, in a Mini-Protein II apparatus (Bio-Rad, CA, USA). Protein bands were electroblotted onto polyvinylindene difluoride (PVDF) membrane and blocked with non-fat dried milk for 1 h. The membranes were incubated with iNOS (mouse monoclonal IgG 1:1000) (BD Pharmingen, San Jose, CA, USA; Cat. No. 610432), TNF-α (rabbit polyclonal IgG 1:1000) (Chemicon, International, Temecula, CA, USA; Cat. No. AB1837P), IL-1β (rabbit polyclonal IgG 1:1000) (Chemicon International; Cat. No. AB1832P), and β-actin (mouse monoclonal IgG 1:10000) (Sigma; Cat. No. A5441) primary antibodies diluted in Tris-Buffered Saline-0.1% Tween (TBST) overnight at 4°C. They were then incubated with the secondary antibodies, either with horseradish peroxidase (HRP) conjugated anti-rabbit IgG (dilution 1:3000) (Thermo Scientific; Cat. No. 31460) or anti-mouse IgG (dilution 1:20000) (Thermo Scientific; Cat. No.31430). Protein was detected by chemiluminescence kit (GE Healthcare UK Limited, Bucks, UK) following the manufacturer's instructions and developed on the film. The band intensity was quantified in Image J software (National Institutes of Health, NIH, USA). All experiments were repeated at least in triplicate.
Measurement of reactive oxygen species by flow cytometry
Intracelluar ROS production in BV-2 cells of different groups was evaluated by detecting the fluorescence intensity of 20, 70-dichlorofluorescene, the oxidized product of the fluoroprobe 5-(and 6)-chloromethyl-20, 70-dichlorodihydrofluorescein diacetate (CM-H2DCFDA, Molecular Probes, Invitrogen; Cat. No. C6827) following the manufacturer's instruction. The amount of ROS production was considered to be directly proportional to fluorescence intensity given as cell counts and fluorescence intensity at the y-axis in the flow cytometry.
Real time measurement of free nitric oxide BV-2 cells were treated as described above and the cells were seeded directly onto glass chamber. NO production was measured by NO detection Kit for fluorescence microscopy (Enzo Life Science, NY, USA; Cat. No. ENZ-51013-200) according to the manufacturer's instruction.
Statistical analyses
The data are presented as mean ± standard deviation (±SD). Statistical significance was evaluated by one-way analysis of variance (ANOVA) followed by post-hoc test. The difference was considered statistically significant when p <0.05. SPSS 16.0 statistical software was used to analyze data. | 8,096.2 | 2014-11-22T00:00:00.000 | [
"Biology",
"Medicine",
"Chemistry"
] |
Exploiting Domain-Aware Aspect Similarity for Multi-Source Cross-Domain Sentiment Classification
Article history: Received: 01 May, 2021 Accepted: 15 June, 2021 Online: 10 July, 2021
Introduction
Online shopping becomes more and more popular during the pandemic. Product reviews serve as an important information source for product sellers to understand customers, and for potential buyers to make decisions. Automatically analyzing product reviews therefore attracts people's attention. Sentiment classification is one of the important tasks. Given sufficient annotation resources, supervised learning method could generate promising result for sentiment classification. However, it would be very expensive or even impractical to obtain sufficient amount of labeled data for unpopular domains. Large pre-trained model, such as the Bidirectional Encoder Representations from Transformers model (BERT) [1], could be an universal way to solve many kinds of problems without exploiting the structure of the problem. In [2], the author apply large pre-trained model to handle this problem task, which has sufficient labeled data only in source domain but has no labeled data in target domain, with fine tuning on source domain and predicting on target domain. In [3], the author train the large pre-trained model using various sentiment related tasks and show that the model could directly apply to the target domain even without the fine-tuning stage. However, these large pre-trained models do not consider the structure of the problem and they have certain hardware requirement that might not be suitable in some situations. We focus on smaller models, which have a few layers, in this work in order to handle the constraint of little labeled data 1 . Besides using the gigantic pre-trained model, domain adaptation (or cross-domain) [4,5] attempts to solve this problem by utilizing the knowledge from the source domain(s) with abundant annotation resources and transfers the knowledge to the target domain. This requires the model to learn transferable sentiment knowledge by eliminating the domain discrepancy problem. Domain adversarial training [6,7] is an ef-fective method to capture common sentiment features which are useful in the target domain. Various works using domain adversarial training [8]- [11] achieve good performance for single-source crossdomain sentiment classification. It could be also applied to the large pre-trained model to further boost the performance [12]. Moreover, it is quite typical that multiple source domains are available, the model might be exposed to a wider variety of sentiment information and the amount of annotation requirement for every single domain would be smaller. A simple approach is to combine the data from multiple sources and form a new combined source domain. Existing models tackling single-source cross-domain sentiment classification mentioned above could be directly applied to this new problem setting after merging all source domains. However, the method of combining multiple sources does not guarantee a better performance than using only the best individual source domain [13,14]. Recent works measure the global domain similarity [15]- [17], i.e. domain similarity between the whole source and target domain, or instance-based domain similarity [18]- [21], i.e. domain similarity between the whole source domain and every single test data point. We observe that these approaches are coarse-grained and ignore the fine-grained aspect relationship buried in every single domain. Domain-specific aspects from the source domain might have negative effect in measuring the similarity between the source domain and the target domain, or the single data point. For instance, we would like to predict the sentiment polarity of some reviews from the Kitchen domain and we have available data from the Book, and the DVD domain. Intuitively, the global domain similarity might not have much difference as both of them are not similar to the target. However, reviews related to the cookbook aspect from the Book domain, or reviews talking about cookery show from the DVD domain might contribute more to the prediction of Kitchen domain. Discovering domain-aware latent aspects and measuring the aspect similarity could be a possible way to address the problem. Based on this idea, we introduce the domain-aware aspect similarity measure based on various discovered domain-shared latent aspect topics using the proposed domain-aware topic model. The negative effect of domain-specific aspects could be reduced.
Existing models measuring domain similarity have another drawback. They usually train a set of expert models with each using a single source domain paired with the target domain. Then, the domain similarity is measured to decide the weighting of each expert model. Another way is to select a subset of data from all source domains which are similar to the target data. We argue that these approaches are not suitable under the constraint of little labeled data as each single sub-model is trained using a small portion of the limited labeled data which might obtain a heavily biased observation. The performance under limited amount of labeled data is underexplored for most of existing methods as they require considerable amount of labeled data for training. In [22], the author study the problem setting applying the constraint. However, they assume equal contribution for every source domain. We study the situation under the constraint of little labeled data and at the same time handling the contribution of source domains using fine-grained domain-aware aspect similarity.
To address the negative effect of domain-specific aspects during the domain similarity measure, and also the limitation of the constraint of little labeled data, we propose a novel framework exploiting domain-aware aspect similarity for measuring the contribution of each aspect model representing the captured knowledge of particular aspects. It is capable of working under the constraint of little labeled data. Specifically, the framework consists of the domain-aware topic model for discovering latent aspect topics and inferring the aspect proportion utilizing a novel aspect topic control mechanism, and the topic-attention network for training multiple aspect models capturing the transferable sentiment knowledge regarding particular aspects. The framework makes predictions using the measured aspect proportion of the testing data, which is a more fine-grained measure than the domain similarity, to decide the contribution of various aspect models. Experimental results show that the proposed domain-aware aspect similarity measure leads to a better performance.
Contributions
The contributions of this work are as follows: • We propose a novel framework exploiting the domain-aware aspect similarity to measure the contribution of various aspect models for predicting the sentiment polarity. The proposed domain-aware aspect similarity is a fine-grained measure which is designed to address the negative effect of domainspecific aspects existing in the coarse-grained domain similarity measure.
• We present a novel domain-aware topic model which is capable of discovering domain-specific and domain-shared aspect topics, together with the aspect distribution of the data in an unsupervised way. It is achieved by utilizing the proposed domain-aware linear layer controlling the exposure of different domains to latent aspect topics.
• Experimental results show that our proposed framework achieves the state-of-the-art performance for the multi-source cross-domain sentiment classification under the constraint of little labeled data.
Organization
The rest of this paper is organized as follows. We present related works regarding cross-domain sentiment classification in Section 2. We describe the problem setting and our proposed framework in Section 3. We conduct extensive experiments and present results in Section 4. Finally, we talk about limitations and furture works in Section 5, and summarize our work in Section 6.
Related Works
Sentiment analysis [23]- [25] is the computational study of people's opinions, sentiments, emotions, appraisals, and attitudes towards entities [26]. In this work, we focus on textual sentiment data which is based on review of products, and the classification of the sentiment polarity of reviews. We first present the related works of single-source cross-domain sentiment classification. Next, we further extend to multiple-source case.
Single-Source Cross-Domain Sentiment Classification
Early works involve the manual selection of pivots based on predefined measures, such as frequency [27], mutual information [5,28] and pointwise mutual information [29], which might have limited accuracy.
Recently, the rapid development of deep learning provides an alternative for solving the problem. Domain adversarial training is a promising technique for handling the domain adaptation. In [8], the author make use of memory networks to identify and visualize pivots. Besides pivots, [9] also consider non-pivot features by using the NP-Net network. In [10], the author combine external aspect information for predicting the sentiment.
Large pre-trained models attract people's attention since the BERT model [1] obtains the state-of-the-art performance across various machine learning tasks. Researchers also apply it on the sentiment classification task. Transformer-based models [2,12,3] utilize the amazing learning capability of the deep transformer structure to learn a better representation for text data during the pretraining stage and adapt themselves to downstream tasks (sentiment classification in our case) using fine tuning. However, we argue that the deep transformer structure has been encoded with semantic or syntactic knowledge during the pre-training process which makes the direct comparison against shallow models unfair. It also has certain hardware requirement which hinders its application in some situations.
Methods mentioned above focus on individual source only and they do not exploit the structure among domains. Although we can still directly apply these models to solve the problem by either training multiple sub-models and averaging predictions, or merging all source domains into a single domain, having a performance better than using only the single best source is not guaranteed. Therefore, exploring the structure or relationship among various domains is essential.
Multi-Source Cross-Domain Sentiment Classification
Early works assuming equal contribution for every source domain [30]- [32] could be a possible approach to handle the relationship between source domains and the target. Other solutions try to align features from various domains globally [33]- [22]. However, the source domain with higher degree of similarity to the target domain contributing more during the prediction process is a reasonable intuition. These methods fail to capture the domain relation. Recent works try to measure domain contribution in order to further improve the performance. Researchers propose methods to measure the global domain similarity [15]- [17], i.e. the domain similarity between the whole source and target domain, or the instance-based domain similarity [18]- [21], i.e. the domain similarity between the whole source domain and every single test data point. In [15], the author measure the domain similarity using the proposed sentiment graph. In [17], the author employ a multi-armed bandit controller to handle the dynamic domain selection. In [18], the author compute the attention weight to decide the contribution of various already trained expert models. [20] also utilize the attention mechanism to assign importance weights. They incorporate a Granger-causal objective in their mixture of experts training. The total loss measuring distances of attention weights from desired attributions based on how much the inclusion of each expert reduces the prediction error. Maximum Cluster Difference is used in [19] as the metric to decide how much confidence to put in each source expert for a given example. In [21], the author utilize the output from the domain classifier to determine the weighting of a domain-specific extractor.
These methods measure the coarse-grained domain relation and ignore the fine-grained aspect relationship buried in every single domain. In addition, these methods do not consider the constraint of limited labeled data, which is the main focus of this work.
where n L and n U are the number of data of labeled and unlabeled data respectively, and d j is the augmented domain membership indicator. Note that y i is the sentiment label for the whole review x i and we do not have any fine-grained aspect-level information. The kth source domain can be written as . The data of the target domain has similar structure except that we do not have the sentiment label, i.e.
respectively. n L s k is the number of labeled data and they are the same for all k. We set all d s k * to k and all d t * to m + 1. The objective of the multi-source cross-domain sentiment classification is to find out a best mapping function f so that given the training data T = {D s 1 , D s 2 , ..., D s m , D t }, the aim is to predict the label of the target domain labeled data y t = f (x t ).
Overview of Our Framework
We describe our proposed framework exploiting domain-aware aspect similarity. Specifically, there are two components: i) the domain-aware topic model discovering domain-aware latent aspect topics, ii) the topic-attention network identifying sentiment topic capturing the transferable aspect-based sentiment knowledge. The first component captures both domain-specific and domain-shared latent aspect topics, and infers the aspect distribution of each review. It is an unsupervised model that utilizes only the unlabeled data. It is analogous to the standard topic model which discovers latent topics as well as topic distributions. However, the standard topic model is not capable of controlling discovered latent topics. Our proposed domain-aware topic model is capable of separating discovered latent topics into two groups: we name them as domain-specific aspect topics and domain-shared aspect topics. The topic control is achieved by using the domain-aware linear layer described in the latter subsection. Specifically, the model discover n spec domainspecific aspect topics for every domain, and n share domain-shared www.astesj.com aspect topics which are shared among all domains. Each review has a n spec + n share dimensional aspect distribution with the first n spec dimension corresponding to domain-specific aspect topics and the last n share dimension corresponding to domain-share aspect topics. Discovered aspect topics and inferred aspect distributions have three important functions: • By considering only domain-shared aspect topics, the negative effect of domain-specific aspect topics could be minimized for measuring the contribution during the inference process.
• The overall aspect distribution of the testing data reveals the importance of each discovered aspect topic following the assumption that the topic appearing more frequent is more important for the target domain.
• The aspect distribution of the unlabeled data could be used for picking reviews with a high coverage of a particular set of aspect topics.
Based on the domain-shared aspect distribution of the target domain, we divide discovered domain-shared aspect topics into groups with each group having unlabeled reviews from all domains with high aspect proportion forming the training dataset for the second component. Specifically, we divide domain-shared aspect topics into groups based on the overall aspect distribution of the target domain. We aim at separating aspect topics and train an expert model for each group of aspects. Each aspect model focuses on a particular set of aspects so as to boost the learning capability of that set of fine-grained aspect topics. Therefore, we need to construct the dataset carrying the information related to selected aspect topics. We select the unlabeled data from all domains with high aspect proportion of a particular set of aspect topics to form the aspect-based training dataset.
Each of the aspect-based training dataset guides the next component to focus on the corresponding aspect group and identify the related transferable sentiment knowledge. The obtained training dataset is jointly trained with the limited labeled data using the topicattention network to generate an aspect model for each aspect-based training dataset. The topic-attention network is a compact model which is designed to work effectively under limited training data. The topic-attention network captures two topics simultaneously: i) the sentiment topic and ii) the domain topic. The sentiment topic captures the transferable sentiment knowledge which could be applied to the target domain. The domain topic serves as an auxiliary training task for constructing a strong domain classifier which helps the sentiment topic to identify domain-independent features by using domain adversarial training. These two topics are captured by the corresponding topical query built in the topic-attention layer. These topical queries are learnt automatically during the training process. The limited labeled data works with the sentiment classifier to control the knowledge discovery related to sentiment (sentiment topic captures sentiment knowledge while domain topic does not), while the unlabeled data works with the domain classifier to control the knowledge discovery related to domain. Finally, the framework makes predictions using various aspect models with contribution defined by the aspect distribution of the testing data. For example, if the testing data has a higher coverage regarding aspect group 1, then naturally the prediction made by the aspect model of group 1 should contribute more to the finally prediction as intuitively that aspect model would have more related sentiment knowledge to make judgement. We believe this fine-grained latent aspect similarity would provide a more accurate sentiment prediction than the traditional coarse-grained domain similarity due to the fact that we eliminate the negative effect of domain-specific aspects when measuring the similarity between the testing data and the expert models.
We first describe the architecture of the two components. Then, we describe the procedure of inferring the sentiment polarity of reviews of the target domain. The domain-aware topic model follows the mechanism of the variational autoencoder framework (VAE) [35] which utilizes the encoder for inferring the latent variable (the Dirichlet prior α in our case representing the expected aspect distribution) and the dewww.astesj.com 4 coder for reconstructing the input. Researchers try to apply the VAE network for achieving functionalities of standard topic model in a neural network way, such as inferring the topic proportion of the input and the word distribution of each topic. This provides some advantages such as reducing the difficulty of designing the inference process, leveraging the scalability of neural network, and the easiness of integrating with other neural networks [36]. However, the standard VAE using Gaussian distribution to model the latent variable might not be suitable for text data due to the sparseness of the text data. The Dirichlet distribution used in the topic model [37] has a problem of breaking the back-propagation. Calculating the gradient for the sampling process from the Dirichlet distribution is difficult. Researchers propose approximation methods [38,39,40,41] in order to apply Dirichlet distribution to the neural topic model. We follow the rejection sampling method [42] in this work. Although discovered topics might carry extra information which might be helpful for identifying the hidden structure of the text data, it is not intuitive for applying this information to help the sentiment classification task. We introduce the domain-aware linear layer for controlling the formation of domain-specific and domain-shared aspect topics. To the best of our knowledge, we do not find any similar aspect topic control layer applied for multiplesource cross-domain sentiment classification in related works. The domain-aware linear layer identifies both domain-specific aspect topics and domain-shared aspect topics. We utilize domain-shared aspect topics only which could provide a more accurate measure for calculating the similarity. In addition, the inferred aspect topic proportion is used for constructing the aspect-based training dataset, and determining the level of contribution of each aspect model. Details of the architecture of the model are described below.
Encoder
The input of the encoder is the bag of words of the review. Specifically, we count the occurrence of each vocabulary in each review and we use a vector of dimension V to store the value. This serve as the input representing the review. The encoder is used to infer the Dirichlet prior of the aspect distribution of the input. The bag-ofwords input is first transformed using a fully connected layer with RELU activation followed by a dropout layer.
Domain-Aware Linear Layer
Next, the output is fed into the domain-aware linear layer for obtaining domain-specific and domain-shared features. The domain-aware linear layer has m + 1 sub-layers including m domain-specific sublayers handling the feature extraction of the corresponding domain and 1 domain-shared sub-layer handling all domains as follows: where d x is the domain ID of the input x, and [; ] represents the operation of vector concatenation. The output x DL is batch normalized and passed to the SoftPlus function to infer the Dirichlet prior α of the aspect distribution. To make sure each value in α is greater than zero, we set all values smaller than α min to α min .
We use the rejection sampling method proposed in [42] to sample the aspect distribution z and at the same time it allows the gradient to back-propagate to α.
Decoder
The decoder layer is used for reconstructing the bag-of-word input. The sampled aspect distribution z is transformed by the domainaware linear layer as follows: The output x dec is batch normalized and passed to the log-softmax function representing the log probability of generating the word.
Loss Function
The loss function includes the regularization loss and the reconstruction loss. The regularization loss measures the difference of the log probability of generating the aspect distribution z between two prior, α and α as follows: where α is inferred by the model and α is the predefined Dirichlet prior. The reconstruction loss is the log probability of generating the bag-of-word input calculated as follows: where V is the vocabulary size, y i is the log probability of the ith word generated by the model, and x i is the count of the ith word in the input.
Topic-Attention Network
The topic-attention network aims at capturing the transferable sentiment knowledge from the limited labeled data of various source domains. To achieve this goal, the network is designed to capture two topics simultaneously: i) the sentiment topic, and ii) the domain topic. The sentiment topic identifies the transferable sentiment knowledge from the input data while the domain topic helps to train a strong domain classifier. We use the technique of domain adversarial training [6,7,43] to maintain the domain independence of the sentiment topic. However, instead of using the standard gradient reversal layer, we use the adversarial loss function [22] to achieve the same purpose with a more stable gradient and a faster convergence. The model has two training tasks: i) the sentiment task for identifying the sentiment knowledge, and ii) the auxiliary domain task for training a strong domain classifier. The adversarial loss function is applied to the domain classifier output of the sentiment topic and the sentiment classifier output of the domain topic to hold the indistinguishability property of these two topics. Details of the architecture of the model is described below.
Encoding Layer
Each word is mapped to the corresponding embedding vector and then transformed by a feed-forward layer with tanh activation for obtaining the feature vector h.
Topic-Attention Layer
The feature vector h i of the ith word is re-weighted by the topical attention weight β k i calculated as follows: where k indicates the topic (either sentiment or domain topic), m i is the word-level indicator indicating whether the ith position is a word or a padding, n m is the number of non-padding words, and q k is the topical query vector for topic k learnt by the model. Note that we have two topical query vectors representing two topics. The topical feature vector t k i of the topic k and the review i is obtained by summing feature vectors weighted by the corresponding topical attention weight β k * as follows: where W i is the number of words in review i. t k i represents extracted features of the review by topic k.
Decoding Layer
This layer consists of two decoders with each handling one training task, namely the sentiment decoder and the domain decoder for classifying the sentiment polarity and the domain membership respectively. Note that the review feature vector of labeled data is passed to the sentiment decoder while the unlabeled data of the aspect groups is passed to the domain decoder. Although we use the same t k to represent the input feature vector in the following two equations, they are actually representing the review features captured from the labeled data, and unlabeled data respectively. Specifically, the review feature vector is linearly transformed and passed to the Softmax function for obtaining a valid probability distribution.
p sen,k = Softmax(W sen t k + b sen ) (11) Note that there are four outputs generated by the decoding layer, including two outputs generated by the captured features of two topics passing to the sentiment decoder, and similarly the remaining two generated by the domain decoder. The two topics are sentiment and domain topic, i.e. k = {sen, dom}. Therefore, the four outputs are: p sen,sen and p sen,dom coming from the labeled data passing to the sentiment decoder (the first superscript) having specific features captured by the sentiment and domain topic (the second superscript) respectively, and p dom,sen and p dom,dom coming from the unlabeled data passing to the domain decoder having specific features captured by the corresponding topic.
Loss Function
We use the standard cross entropy loss to measure the classification performance: where s i and d i are the class indicator specifying the sentiment polarity or the domain membership of the ith training data, and p * i,c is the predicted probability regarding the cth class. Therefore, we have four cross entropy losses. The loss generated by the sentiment decoder from the sentiment topic and the loss generated by the domain decoder from the domain topic are used to update all parameters of the model using back-propagation. The remaining two are used to update the parameters of the decoding layer only. We introduce the adversarial loss function for doing adversarial training for both tasks as follows: www.astesj.com where c is the number of classes and p i is the predicted probability for the class i. Note that c for sentiment task is 2 while it is m + 1 for the domain task. We use the probability distributions generated by the sentiment decoder from the domain topic p sen,dom , and by the domain decoder from the sentiment topic p dom,sen , to calculate the adversarial losses, which are used to update the parameters of the encoding layer and the topic-attention layer.
Training Strategy
We first train the domain-aware topic model using the unlabeled data X u from all domains. The model is then used for predicting the aspect proportion of the unlabeled data X u and testing data X t to obtain α u and α t . Note that the domain-aware topic model is an unsupervised model that does not utilize any labeled data from source domains nor target domain. The aspect score θ t of the target domain is calculated using the mean value of the domain-shared aspect part of α t over all testing data: where α t i [−n share :] represents the last n share dimensions of the vector α t i . Therefore, θ t is a n share dimensional vector with each value representing the importance score of the corresponding aspect topic for the target domain. We divide the domain-shared aspect topics into k groups based on their importance score using θ t in descending order. The set topic g k contains the topic indices of the k th aspect group. For each group g k , we select top n unlabeled data from all domains based on the aspect topic score of the k th group ω u k , which is the sum of the corresponding domain-shared aspect proportion of the k th group for the uth review using its discovered aspect proportion: where α u [i] represents the value in the ith dimension of α u . Next, we train k aspect models using the topic-attention network. For each aspect model, the limited labeled data X l , Y l is used for training the sentiment task while the group of selected unlabeled data g k is used for training the auxiliary domain task. The last step is to utilize the obtained models for predicting the sentiment polarity of all testing data x t . Let AM k be the aspect model trained by using the dataset {X l , Y l , g k }, we denote the sentiment prediction of the sentiment topic generated by the model as p t k for the target review x t as follows: Finally, we combine the sentiment predictions of the sentiment topic generated by all aspect models having each contributes according to the aspect proportion of the testing data to obtain the final prediction: where ω t i is the contribution of the ith aspect model to the final prediction.
Experiment Settings
We use the Amazon review dataset [5] for the evaluation of our proposed framework. The Amazon review dataset is a common benchmark for sentiment classification. We use 5 most common domains, namely Book, DVD, Electronics, Kitchen and Video. For each experiment cross, we reserve one domain as the target domain and use others as source domains. There are 5 combinations in total and we conduct experiments on these 5 crosses. For each domain, we follow the dataset setting in [9] collecting 6000 labeled data, with half positive and half negative polarity. We do further sampling to select a subset of the labeled data to fulfill the constraint of little labeled data. We first construct two lists with each having 3000 elements representing the index of the labeled data of positive and negative class respectively. We randomly shuffle the lists and pick first n indices. Next, we select the labeled data based on these indices. In order to have a comparable result for different size of labeled data, we fix the seed number of the random function so that the runs with different size of labeled data would obtain a same shuffle result. Therefore, the run with 20 labeled data contains the 10 labeled data from the run with 10 labeled data, and also another 10 new labeled data. Similarly, the run with 30 labeled data contains the 20 labeled data from the run with 20 labeled data. With this setting, we can directly estimate the effect of adding additional labeled data and compare the performance directly. We continue the process for other source domains. Finally, we construct 5 datasets having 10 to 50 labeled data for each target domain (there are 40 to 200 labeled data in total as there are 4 source domains). The unlabeled dataset includes all unlabeled data from all domains (including the target domain). All labeled data from the target domain is served as the testing data. We run every single run for 10 times and present the average accuracy with standard deviation in order to obtain a reliable result for model comparison.
Domain-Aware Topic Model
The Dirichlet prior is set to 0.01. The minimum of inferred prior is set to 0.00001. We set the number of domain-specific and domainshared topics to 20 and 40 respectively. We divide the domainshared aspect topics into 5 groups. The domain-aware topic model is trained for 100 warm-up epochs, and stopped after 10 epochs of no improvement.
Topic-Attention Network
We use word2vec 2 embedding [44] to represent each word. We do not further train them to prevent overfitting. The batch size is www.astesj.com 8 set to the number of available labeled data. The topic-attention network is trained for 20 epochs. We use Adam 3 optimizer [45] for back-propagation for both models.
Evaluation Metric
We use accuracy to measure the evaluate the performance of various models. The target is a binary class. Therefore, correct cases involve the true positive (TP) and true negative (TN). Incorrect cases involve the false positive (FP) and the false negative (FN). The accuracy is calculated as follows: The average accuracy is calculated by taking the average of accuracy scores of multiple runs.
Main Results
Models used for performance comparison are as follows: • BERT [1]: This is the Bidirectional Encoder Representations from Transformers model, which is the popular pre-trained model designed to handle various text mining tasks. We use the BERT-Large model with fine tuning using the labeled data to obtain the prediction.
• BO [46]: This model employs Bayesian optimization for selecting data from source domains and transfer the learnt knowledge to conduct prediction on the target domain.
• MoE [19]: This is the mixture of expert model. It measures the similarity between single test data to every source domain for deciding the contribution of the expert models.
• EM [22]: This is the ensemble model. It uses various base learners with different focuses on the training data to capture a diverse sentiment knowledge.
• ASM: This is the proposed framework exploiting the domainaware aspect similarity measure for obtaining a more accurate measure to adjust the contribution of various aspect models focusing on different aspect sentiment knowledge.
Results are presented in Figure 3 and Table 1. We use the classification accuracy as the metric to measure the performance. The proposed framework achieves the best average accuracy among all crosses. Its average performance is 71.73%, 78.75%, 80.49%, 81.43%, and 82.02% for 10, 20, 30, 40 and 50 labeled data cases respectively, or 40, 80, 120, 160 and 200 labeled data cases in total respectively.
Discussions
Our proposed framework performs substantially better than the comparison models. The proposed framework has an average of 4%, 7%, 6%, 6% and 6% absolute improvement over the second best result for 10, 20, 30, 40 and 50 labeled data cases respectively, or 40, 80, 120, 160 and 200 labeled data cases in total respectively. The variance of the proposed model is comparable to or better than the second best models. The result proves that our proposed framework is very effective for conducting multi-source cross-domain sentiment classification under the constraint of little labeled data. The model can capture transferable sentiment knowledge for predicting the sentiment polarity of the target reviews.
We also do comparative analysis to test the effectiveness of the proposed fine-grained domain-aware aspect similarity measure. It is based on the discovered aspect topics and also the aspect topic proportion for adjusting the contribution of various aspect models. We try to remove these two components to test the performance of the variants. The results are presented in Table 2. The first variant is rand. select data + avg. pred., which means using the unlabeled data selected in a random way instead of using the aspect-based training dataset constructed by the domain-aware topic model, and combining the predictions of various aspect models by averaging them. In other words, the first variant removes both components. The second variant is avg. pred.. It keeps the first component (train the aspect models using the aspect-based training dataset) and only removes the second component. Therefore, it assumes equal contribution from various aspect models, just like the first variant. The last one is the proposed framework equipped with both components. Results show that the proposed fine-grained domain-aware aspect similarity measure improves the performance in general except the case having very few labeled data. We think the reason is that the aspect model could not locate the correct aspect sentiment knowledge from the limited data. Thus, the simply averaging the prediction of these biased aspect models would be better than relying on some models. Although the second variant (avg. pred.) has a better performance than the full framework in 10 labeled data case, the difference is very small (around 0.18%). Therefore, this comparative analysis could show that the proposed fine-grained domain-aware aspect similarity measure is effective for adjusting the contribution from different discovered aspects.
When comparing with the EM model [22] with similar network architecture but having an equal contribution for the source domains, the result shows that varying the contribution based on the domain-aware aspect similarity leads to a better performance.
We observe that our proposed framework has a small performance gain when giving more labeled training data, besides the case from 10 to 20. The EM model also has similar problem as mentioned in [22]. However, the BERT model [1] has an opposite behavior, which has a steady performance gain. We believe that the reason is due to the compact architecture of the topic-attention network which prevents overfitting the limited labeled data in order to have a better domain adaptation. Increasing the learning capability of the model and at the same time handling domain adaptation could be a future research direction.
Limitations and Future Works
The proposed framework involves two separate models handling their own jobs. These models do not share any learning parameters. Many works report that the single model handling various tasks would have a better generalization and thus leads to a better performance. One possible future work might consider integrating both www.astesj.com 9 www.astesj.com models together forming an unified model to take the advantage of multi-task learning. This might further improve the performance for the sentiment classification task.
Conclusion
We study the task of multi-source cross-domain sentiment classification under the constraint of little labeled data. We propose a novel framework exploiting domain-aware aspect similarity to identify the contribution of discovered fine-grained aspect topics. This fine-grained similarity measure aims at addressing the negative effect of domain-specific aspects appearing in the existing coarsegrained domain similarity measure, and also the limitation caused by the constraint of little labeled data. Aspect topics are extracted by the proposed domain-aware topic model in an unsupervised way. The topic-attention network then learns the transferable sentiment knowledge based on the selected data related to discovered aspects. The framework finally makes predictions according to the aspect proportion of the testing data for adjusting the contribution of various aspect models. Extensive experiments show that our proposed framework achieves the state-of-the-art performance. The framework achieves a good performance, i.e. around 71%, even though there are only 40 labeled data. The performance reaches around 82% when there are 200 labeled data. This shows that our proposed fine-grained domain-aware aspect similarity measure is very effective under the constraint of little labeled data. | 8,724.8 | 2021-01-01T00:00:00.000 | [
"Computer Science"
] |
Lightweight convolutional neural network for aircraft small target real-time detection in Airport videos in complex scenes
Airport aircraft identification has essential application value in conflict early warning, anti-runway foreign body intrusion, remote command, etc. The scene video images have problems such as small aircraft targets and mutual occlusion due to the extended shooting distance. However, the detection model is generally complex in structure, and it is challenging to meet real-time detection in air traffic control. This paper proposes a real-time detection network of scene video aircraft-RPD (Realtime Planes Detection) to solve this problem. We construct the lightweight convolution backbone network RPDNet4 for feature extraction. We design a new core component CBL module(Conv (Convolution), BN (Batch Normalization), RELU (Rectified Linear Units)) to expand the range of receptive fields in the neural network. We design a lightweight channel adjustment module block by adding separable depth convolution to reduce the model’s structural parameters. The loss function of GIou loss improves the convergence speed of network training. the paper designs the four-scale prediction module and the adjacent scale feature fusion technology to fuse the adjacent features of different abstract levels. Furthermore, a feature pyramid structure with low-level to high-level is constructed to improve the accuracy of airport aircraft’s small target detection. The experimental results show that compared with YOLOv3, Faster-RCNN, and SSD models, the detection accuracy of the RPD model improved by 5.4%, 7.1%, and 23.6%; in terms of model parameters, the RPD model was reduced by 40.5%, 33.7%, and 80.2%; In terms of detection speed, YOLOv3 is 8.4 fps while RPD model reaches 13.6 fps which is 61.9% faster than YOLOv3.
Related works
The current mainstream target detection methods mainly include the Faster-RCNN 24 series based on region proposal and the series based on regression algorithm YOLO [25][26][27] , SSD [28][29][30][31][32] , etc. Compared with traditional methods, the detection accuracy and speed are improved. Still, there are shortcomings, such as many regional proposal boxes, which lead to a large amount of model calculation and ample storage space. The training process of the target detection algorithm requires high-performance GPU support, which is challenging to meet the real-time requirements 33 , especially on embedded devices with weak computing power. Especially on embedded devices with weak computing power. It is not easy to achieve real-time applications 34 . Zhang 35 et al. proposed a lightweight deep learning model Slimyolov3, which solved the problem that the deep learning model has many parameters and cannot be deployed on the embedded side. Still, its accuracy is poor in small target detection scenarios and cannot be widely used. The aircraft target detection method based on a neural network has achieved high accuracy. Still, its operation on embedded devices with small video memory and memory is restricted with the continuous improvement of the performance of the neural network model and the increase of model parameters and calculation.
When deploying a target detection model in an aviation scene, we need to consider the textcolorredmodel's computational complexity, parameter quantity and the detection accuracy. MobileNet is a lightweight convolutional neural network proposed by Google in 2017 and subsequently developed into three versions, MobileNetv1 36 , MobileNetv2 37 , and MobileNetv3 38 . Compared with the traditional convolutional neural network, it reduces the model parameters and the amount of computation while ensuring detection accuracy as much as possible. GhostNet 39 was proposed by Huawei and ShuffleNet 40,41 and SqueezeNet 42 presented by Questyle Technology, etc. Building a new network model improves detection accuracy while reducing the model parameters, which is very useful for mobile deployment. Great significance. In addition, deep learning model compression and acceleration technology is also an essential direction in developing network models. Commonly used methods include model pruning 43 , network parameter quantization 44 and parameter optimization of existing network models. Model pruning adopts structured pruning technology to remove the weights of redundant channels in the model. After pruning, the model can bring acceleration effects on general hardware and improve the efficiency of network operation 45 . Network parameter quantization minimizes the space required for network weight storage by reducing the model detection accuracy. YOLOX 46 is optimized based on YOLO by combining model pruning and network parameter quantization technology. Although the parameter calculation amount is reduced, they cannot effectively identify small target detection in complex scenes.
In contrast to the previous models, We propose a lightweight neural network RPD for detecting small objects in airport video in complex scenes. Constructing an RPDNet4 deep convolutional feature extraction network, designing an adjacent scale feature fusion module, and using four-scale feature prediction can effectively identify aircraft and their positions information. The effectiveness of the proposed model is verified by comparing it with YOLOv3, Faster-RCNN, SSD, YOLOX-Tiny, and YOLOX-Nano through the Zhengzhou Xinzheng Airport aircraft image dataset experiment.
Methodology
Build the RPD network. The target detection network proposed in this paper is shown in Fig. 1. It includes four parts: (1) the mage input module, which performs preprocessing operations such as zooming, panning, and random cropping on the input image; (2) the Feature extraction network module (RPDNet4). Among them, CBL and Block are the basic modules of this network. CBL consists of Conv (convolutional layer), BN (batch normalization), and Relu activation function. Block consists of two CBL modules, convolutional, batch normalization composition. (3)the Neck module, which improves the expressive ability of features, in which Concat indicates that image features of different levels complete feature information fusion through downsampling; (4) the Prediction module, used to predict the target: perform 160*160, 80*80, 40*40, 20*20 4-scale target prediction classification and positioning.
Feature extraction backbone network. The input image is subjected to preprocessing operations such as zooming, panning, and random cropping to improve the detection accuracy of small objects and mutual occlusion in the airport video scene. Such as formula (1) and formula (2): (1) f n = F n f n−1 , n > 0 www.nature.com/scientificreports/ In formula (1), f n represents the feature map of the nth layer, and Fn represents the nonlinear mapping relationship between the feature map of the previous layer and the current one. The primary operations are convolution, batch normalization, nonlinear activation function, Etc. When n=1, f 0 represents the input image; g i represents the channel feature pruning and aggregation spatial dimension feature decomposition operation on the i-th feature image, G represents the feature fusion process, and Y represents the feature fusion result. The feature extraction network (RPDNet4) sets four feature extraction layers according to the direction from input to output. Figure 2 shows the first feature extraction layer, the second feature extraction layer, the third feature extraction layer, and the fourth feature extraction layer. Floor: The first feature extraction layer includes a first convolution module, a second convolution module, a first residual module, a third convolution module, a second residual module, and a fourth convolution module, which are arranged in sequence from input to output. www.nature.com/scientificreports/ The second feature extraction layer includes a third residual module and a fifth convolution module arranged in sequence from the input to the output direction.
The third feature extraction layer includes a fourth residual module and a sixth convolution module arranged in sequence from the input to the output direction.
The fourth feature extraction layer includes a fifth residual module. The specific parameter settings of the feature extraction layer are shown in Fig. 3. For the feature extraction layer layer1, the parameter setting steps are as follows.
(1) Pass a frame of 640*640 3-channel input image through the CBL module once (n is 1), the step size s is 1, the number of output channels c is 32, the channel expansion factor e is one by default, and the output is 640*640*32 tensor. (2) Using the output result of step 1, after a CBL module (n is 1), the step size s is 2, the number of output channels c is 64, the channel expansion factor e is one by default, and the output is a 320*320*64 tensor. (3) Using the output result of step 2, after a CBL module (n is 1), the step size s is 2, the number of output channels c is 64, the channel expansion factor e is 1.5, and the output is a 320*320*64 tensor. (4) Using the output result of step 3, after a CBL module (n is 1), the step size s is 2, the number of output channels c is 128, the channel expansion factor e is one by default, and the output is a 160*160*128 tensor. (5) Using the output result of step 4, after two block modules (n is 2), the step size s is 1, the number of output channels c is 128, the channel expansion factor e is 1.5, and the output is 160*160*128 tensor. (6) Using the output result of step 5, after one block module (n is 1), the step size s is 2, the number of output channels c is 256, the channel expansion factor e is one by default 80*80*256 tensor.
The feature extraction layers Layer2, Layer3, and Layer4 are consistent with the parameter setting steps of the feature extraction layer Layer1 (1)CBL module To obtain the richer semantic information of the original images, we need to increase the convolution kernel's size to expand the range of the receptive field in the neural network. However, a larger convolution kernel size will increase the model calculation parameters. VGGNet 47 found that the receptive field range of the extensive convolution kernel mapping can be achieved by stacking multiple convolution kernels and using a 3 × 3 size The convolution kernel is set to 3 × 3 and 1 × 1, and the input image is first after a 3 × 3 convolution operation, the data batch normalization (BN) processing is performed, and the formula is as follows: µ β represents the sample mean, where x i is the i-th sample, σ 2 β represents the sample variance, x ′ i represents the sample normalization processing result, y i different scales and bias Shift operation β i on x ′ i . To enhance the nonlinear expression ability of the neural network and prevent the gradient explosion of the backpropagation of the network, and speed up the convergence speed of the network, the nonlinear function Relu is introduced as the excitation function, and the formula is as follows: (2)Block module To ensure the accuracy of network detection and minimize the computational parameters of the feature extraction network, we designed a lightweight channel adjustment module Block, whose structure is shown in Fig. 4. When the input sample passes through the first CBL module, the convolution kernel size is set to 1 × 1, aiming to map the image features from low-dimensional to high-dimensional space. Factor E is used to expand the dimensional space in Fig. 3. When the features are input to the second CBL module, the high-dimensional spatial convolution is decomposed into a depthwise convolution in a low-dimensional space and a point-bypoint convolution that modifies the number of channels using a 3 × 3 depthwise separable convolution 48 . The convolution of high-dimensional space is decomposed into the depth convolution of low-dimensional space The residual Res formula is as follows: Where x represents the input feature, H(x) represents the neural network learning feature, and F(x) represents the output result after the residual connection. When the residual F(x) = 0, the block module only does the identity mapping, which can keep the network performance unchanged. However, in the experiment, the residual F(x) is not 0, so the block module can continuously learn new features and better performance. Using residual connections can significantly preserve the spatial gradient structure, solve the phenomenon of gradient fragmentation, and facilitate network backpropagation.
Adjacent scale feature fusion and prediction. The adjacent scale feature fusion (Neck) module (Fig. 5) is based on the feature extraction network RPDNet4. It adopts a serialized bottom-up structure design to fuse features of different abstraction levels to adjacent features. The Neck module performs upsampling three times in total. Layer4 is used as the starting feature map. After the CBL module, the feature map Neck4 is obtained, and then it is subjected to 2-fold upsampling, batch normalization, and merged with Layer3 for feature fusion. After the CBL module, the feature map Neck3 is obtained. The method obtains the feature maps Neck1 and Neck2 and appends a CBL block to each merged map to generate the final feature map. The final output feature map has four scales of 160*160, 80*80, 40*40, and 20*20, corresponding to Layer1, Layer2, Layer3, and Layer4 with the same spatial size, respectively. The Prediction module uses the 1 × 1 convolution operation for Neck1, Neck2, Neck3, and Neck4 instead of a real connection, to complete 4-scale target classification and positioning.
LOSS function. Using the Iou Loss loss function to test the lightweight target detection network RPD, the predicted aircraft target position significantly deviates from the accurate position. The common Iou Loss only focuses on the intersection ratio between the predicted and actual frame. When intersecting, the value of Iou Loss is 1, and the network is difficult to converge. When two boxes intersect, the value of Iou Loss is also directional, which cannot guide the network to converge correctly. Therefore, Shortening the centre distance between the target and the natural frame can better reflect the actual deviation between the target and the natural frame. This paper uses GIou Loss 50 to add a penalty based on the original Iou Loss Item to solve the problem, such as formula (9). It can guide the convergence direction of the network when Iou Loss does not play a role in monitoring and can effectively improve the convergence speed during network training.
Among them, s pre represents the predicted target bounding box area, s true represents the natural target bounding box area, A C represents the minimum area enclosed by s pre ands true , and A u represents the area of the intersection of s pre ands true . loss l oc represents the position deviation between the predicted target bounding box and the actual target bounding box. 16, the training data set is iteratively trained 300 times in total, the initial learning rate is 0.001, and the learning decay rate is 0.92. In order to further optimize the model parameters, we use model pruning technology, and the steps are 1. Channel pruning of the network. By setting an appropriate pruning rate, and according to the value of γ , the high-contribution channels are retained, and the low-contribution channels are deleted. 2. Layer pruning of the network. For each CBL and Block of the RPDnet4 backbone network, the average value of each layer is sorted, and then the layer with the smallest average value is selected for layer pruning. 3. After compressing the width and depth of the RPD network through steps 1 and 2, respectively, fine-tune the RPD network to restore the detection accuracy of the network model. The data set used in this paper comes from the video images of Zhengzhou Xinzheng Airport, including different types of single-passenger aircraft, multiple occluded passenger aircraft, and other small target images, a total of more than 11,000 images, covering sunny, foggy, rainy, and other daytime weather conditions. The training, validation, and test sets are made according to 6:2:2.
Ablation experiments. We perform a series of ablation experiments to understand better and analyze our key contributions' impact. Table 2 shows that the PRD detection model adopts different image classification networks as the backbone. We validate our proposed model by comparing and analyzing model parameters, detection accuracy, and inference time, mainly since two modules (i.e., CBL and BLOCK) constitute the backbone network. We just changed the backbone network during the experiment, and other modules remained unchanged. The training strategy of the model remains the same, the data preprocessing steps are the same, and the initialization parameters are the same.
Results and discussion
To effectively evaluate the performance of the network model, the precision P (Precision), the recall rate R (Recall), Inference time and the mean average precision mAP (mean Average Precision) are selected to evaluate the detection ability of the network model. The formula is as follows: www.nature.com/scientificreports/ In the formula, TP represents the correct positive sample detection. For example, the aircraft in the image is detected as an aeroplane. FP represents the negative sample falsely detected as a positive sample. For example, the aeroplane is incorrectly identified as the background. FN represents the positive sample falsely detected as a negative sample, such as the background detected as an aeroplane. P(R) represents the curve drawn with the detection accuracy P , and the recall rate R, the area enclosed by this curve and the coordinate axis is the detection class accuracy AP; C represents the target category, mAP is an average of all categories. This paper compares RPD with Faster-RCNN, SSD, YOLOX-Tiny, YOLOX-Nano and YOLOV3 from four aspects: mAP, FPS (Frame Per Second), Inference time and Param (Parameter), as shown in Table 1.
Faster-RCNN uses VGGNet16 as the feature extraction network, uses the RPN (Region Proposal Network) network instead of the Selective Search method in R-CNN 51 to generate regional proposal windows, and uses the non-maximum suppression algorithm to eliminate proposals with large overlapping areas. The window improves the quality of the proposed window, and the detection accuracy reaches 82.3%. However, Faster R-CNN is a twostage target detection. The RPN network uses the sliding method to detect the process of generating multi-scale anchors, which is time-consuming, and the model parameters are the largest, reaching 350.8M. SSD, YOLOV3, and RPD are all based on single-stage detection, directly classifying and regressing images, and the model parameters are significantly lower, 105M, 117M, and 69.6M in sequence. SSD eliminates the proposal generation phase and the subsequent feature resampling process. Its FPS is 2.5, the detection speed is two times faster than Faster R-CNN, and FPS is 2.5, but in terms of detection accuracy, mAP is 67.7% among all models, the detection accuracy is the lowest.
The SSD prediction target bounding box is an offset relative to the default bounding box position of the network. This prediction is not stable enough at the beginning of training. Yolov3 predicts the offset. The predicted result forces the output value between 0 and 1 through a sigmoid function, improving detection accuracy, reaching 83.8%. YOLOV3 is three times faster than SSD in terms of detection speed, and the FPS gets 8.4.
YOLOX-Tiny and YOLOX-Nano are two lightweight models of YOLOX. Although the model parameters are significantly reduced, only 17.4M and 8.7M, the detection accuracy is far from the detection requirements of airport air traffic.
The RPD model in this paper has the following characteristics: (1)Feature extraction network module (RPDNet4): The core component CBL module uses 11 and 33 convolution kernels to increase the receptive field, separate the critical contextual features, and reduce the network running speed; Separate convolution [50] extracts feature maps. From experience, the effect is almost the same as standard convolution, and the computational cost is significantly lower than that of standard convolution. The RPDNet4 feature extraction network is better than VGG-16 and Darknet-53 in parameter quantity.
(2)Adjacent scale feature fusion and prediction module. Faster-RCNN only uses the feature map of the network's last layer to predict the target. SSD tried to use the pyramid level feature of the convolutional neural network to predict the target, but it gave up Shallow features. Compared with SSD, YOLOV3 uses shallow features, splices different feature maps, increases the number of channels, and predicts targets at three scales, significantly improving the detection ability of small targets. The neck module in this paper adopts the adjacent scale feature fusion technology to fuse the features of different abstraction levels of layers (Fig. 2) and build a low-level to high-level feature pyramid structure (Neck1, Neck2, Neck3, Neck4 shown in Fig. 5), and then use the CBL module to eliminate the aliasing effect of upsampling to generate the feature map required by the Prediction module. The four scales of 160*160, 80*80, 40*40, and 20*20 in the Prediction module can share classification and regression parameters at all levels. This structure enables our mAP to reach 88.6%, higher than YOLOV3. 5.4%, which is 23.6% higher than SSD.
On the whole, compared with YOLOv3, Fast-RCNN, and SSD models, the detection accuracy of the RPD model is 88.6%, an increase of 5.4%, 7.1%, and 23.6%, respectively; in terms of model parameters, the RPD model is 69.6M, a decrease of 40.5%, 33.7%, and 80.2%, which can meet the real-time detection of airport aircraft.
Furthermore, we conduct an ablation study on the RPD model to assess our proposed technique's performance, particularly the two modules (i.e., CBL and BLOCK) that constitute the backbone network. The ablation experiments for our suggested model RPD+RPDNet4 are practical, and the comparison results are provided in Table 2. The results show an improvement in accuracy for each example, showing that the RPD models are all useful. In the first layer of PRDNet4, the size of the convolution kernel of the first two CBL modules is 3*3, which expands the receptive field and textcolorredreduces the parameters as much as possible. The size of the convolution kernel of the CBL module of the remaining layers is 1*1. Upscaling the dimension of the channel greatly increases the nonlinearity while keeping the scale of the feature map unchanged (that is, without losing resolution). The Block module reduces the computational parameters of the feature extraction network as much as possible while ensuring the detection accuracy of the network model. Therefore, the CBL and Block modules can pay more attention to the intricate details of the image and obtain better detection results. In other words, it proves that the RPDNet4 feature extraction network preserves the most critical information in the image and suppresses the unnecessary information, resulting in more discriminative features for surface aircraft recognition. Combining these two modules yields the best results, demonstrating that our approach is feasible and beneficial.
Comparing part (a) of the figures, we can find that SSD has missed detection because the model does not extract enough semantic information to distinguish the background. Faster-RCNN, YOLOV3, and RPD detection effects are sound; part (b) of the figure, YOLOV3 is better than Faster-RCNN, SSD, YOLOX-Tiny and (13) www.nature.com/scientificreports/ YOLOX-Nano for small target detection, but some small targets have missed detection. RPD detects the most significant number of small targets and two more targets than YOLOV3 because our position loss function introduces GIou Loss. Simultaneously, the adjacent scale feature fusion technology is used to fuse the deep semantic information into the shallow features layer by layer, improving the detection accuracy of small objects. It can be found in part (c) that Faster-RCNN, SSD, YOLOX-Tiny and YOLOX-Nano can not recognize the aircraft in the Figure 11. YOLOX-Nano detection renderings in different scenarios. | 5,308.8 | 2022-08-25T00:00:00.000 | [
"Computer Science",
"Engineering"
] |
Complete chloroplast genome studies of different apple varieties indicated the origin of modern cultivated apples from Malus sieversii and Malus sylvestris
Background Apple is one of the most important temperate deciduous fruit trees worldwide, with a wide range of cultivation. In this study, we assessed the variations and phylogenetic relationships between the complete chloroplast genomes of wild and cultivated apples (Malus spp.). Method We obtained the complete chloroplast genomes of 24 apple varieties using next-generation sequencing technology and compared them with genomes of (downloaded from NCBI) the wild species. Result The chloroplast genome of Malus is highly conserved, with a genome length of 160,067–160,290 bp, and all have a double-stranded circular tetrad structure. The gene content and sequences of genomes of wild species and cultivated apple were almost the same, but several mutation hotspot regions (psbI-atpA, psbM-psbD, and ndhC-atpE) were detected in these genomes. These regions can provide valuable information for solving specific molecular markers in taxonomic research. Phylogenetic analysis revealed that Malus formed a new clade and four cultivated varieties clustered into a branch with M. sylvestris and M. sieversii, which indicated that M. sylvestris and M. sieversii were the ancestor species of the cultivated apple.
INTRODUCTION
Malus is one of the most important economic fruit crops in temperate regions. It is composed of approximately 30-35 deciduous trees or shrubs of Rosaceae (Giomaro et al., 2014). Over thousands of years of evolution, thousands of excellent apple varieties have been produced (Morgan et al., 2002), such as Red Delicious, Golden Delicious, Ralls, and Red Fuji. They are becoming increasingly popular worldwide because of their good taste, nutrient-rich value, storability, and convenience. In terms of apple production, China is currently the largest apple producer in the world, with apple planting area and output accounting for more than 50% of the world (Li et al., 2021). In addition to their economic value, apples play an important role in preventing diseases, such as Parkinson's disease and all kinds of cancer, as well as reducing the risk of diabetes and lowering cholesterol (Rupasinghe, Thilakarathna & Nair, 2013). Therefore, the improvement of apple varieties and cultivation of new varieties is particularly important. The essential questions of the origin and evolution of cultivated species ultimately arise from the identification of wild ancestral populations. Studying the relationship between cultivated apples and their possible predecessors is important for studying the origin of cultivated apples. Additionally, the time, place, and mode of origin of cultivated apples are also the core of the study on the origin of cultivated apples.
To better understand the origin of cultivated apples and their relationship with their major wild ancestors, researchers have conducted numerous studies on the origin of cultivated apples and the phylogenetic relationships between cultivated apples and different wild apples. In 1926, Nikolai Vavilov suggested that the wild species and their related species in Central Asia were the ancestors of the modern cultivated apple, and the whole process of apple cultivation and domestication can be traced back to the Almaty region of Kazakhstan (Vavilov, 1926). However, the only wild species in Central Asia is Malus sieversii Roem. Therefore, the wild species referred to by predecessors (Harris, Robinson & Juniper, 2002) should be M. sieversii, which still exists in Central Asia today. Later, Li (1989) found that M. sieversii is the ancestor of cultivated apples through the investigation of wild apples in Central Asia and believed that Ili in Xinjiang is the origin centre of M. sieversii and the source of diversified cultivated apples, whereas Velasco et al. (2010) and Cornille et al. (2012) contended that the primary and secondary contributors of cultivated varieties are M. sieversii and M. sylvestris, respectively. Sun et al. (2020) proved that the two wild ancestors made great genetic contributions to cultivated apples by analysing the genome and the re-sequencing data of cultivated apples and wild apples (M. sieversii and M. sylvestris). It also showed that M. sylvestris and M. sieversii were the common ancestors of cultivated apples. In summary, researchers have conducted extensive research on the origin of cultivated apples and the phylogenetic relationship between cultivated apples and different wild apples. However, in previous studies on the evolution of apple populations, cultivated apples were usually treated as a group while focusing on the genetic relationship between cultivated apples and different wild apples. Almost no one has studied the genetic differences between different cultivated apples, and there are few studies on the population history of apples. With the development of sequencing technologies, biological science research has entered the era of big data. Genome-level traceability and homology analysis can address a number of important scientific questions, such as crop origin and domestication. At the same time, we can also detect the changes that have occurred in the genome structure and sequence of crops during domestication. However, most of the research on genetic breeding and improvement of crops is focused on the nuclear genome and rarely on the organellar genome.
The nucleus, chloroplast and mitochondria are the three main organelles in the cell that contain genomes. They play an important role in plant activities (Yin et al., 2018). Since Ohyama et al. (1986) first obtained the chloroplast genome of Nicotiana tabacum in 1986, this is the first study to observe and study the structure and characteristics of the chloroplast genome at a more micro level, which is of great significance to the in-depth study of the chloroplast genome. A typical chloroplast genome structure consists of four stable parts: two reverse repeat regions, which are separated by a large single-copy (LSC) region, and a small single-copy (SSC) region (Raubeson & Jansen, 2005). Angiosperm chloroplast genomes generally contain 110-130 genes, most of which are protein-coding genes (involving photosynthetic reaction and gene expression), and the rest encode tRNA and rRNA (Green, 2011). Compared with the other two organellar genomes, chloroplast DNA has several advantages, such as maternal inheritance, multiple copies and a simple structure, and chloroplast genome is highly conserved in both gene content and order. In addition, complete chloroplast genome analysis can provide more genetic information than gene fragments. Therefore, chloroplast genome sequencing has a significant contribution to the species identification and phylogeny of angiosperms (Xi et al., 2012;Xie et al., 2019). For example, Nikiforova et al. (2013) showed the genetic relationship between wild apple (M. sieversii and M. sylvestris) and cultivated apple through the phylogenetic analysis of 47 chloroplast genomes and clarified the contribution of wild species to the maternally inherited genome of domesticated species. Shen et al. (2017) revealed a sister relationship between different Asteraceae species through a comparative analysis of chloroplast genome sequences of five Asteraceae species.
Crop domestication is a process of artificial selection that promotes interdependence between humans and crop plants (Duan et al., 2017). The domestication of apples as cultivars has occurred over thousands of years. Studies have shown that crops accumulate some variation in their cellular DNA during domestication and cultivation (Schmid-Siegert et al., 2017). Chloroplasts, the organelles of cellular photosynthesis, also have their own circular DNA, which plays a pivotal role in the life activities of plants. Therefore, starting from the chloroplast genome, it was further demonstrated that M. sieversii and M. sylvestris are the ancestral species of the four cultivated apples. The following scientific problems are yet to be completely solved: Did the apples also accumulate some variation in their organellar DNA during domestication and cultivation mainly through clonal reproduction (grafting)? What is the mutation rate? What structural variants occur in the genome? What structural variations have taken place in the genome? In this study, 24 genomic sequences of four different Malus germplasms were sequenced. DNA variation was detected to reveal the differences in chloroplast genomes between the two progenitor species and cultivated apples of different varieties. Also, a phylogenetic tree was reconstructed using the complete chloroplast genome to show the phylogenetic relationships among modern cultivated apples, M. sieversii and M. sylvestris. This study provides a strong basis for apple evolution and domestication and an important reference for variety breeding and parental selection in the development of the apple industry.
Sampling and DNA extraction
In this study, 24 samples of four representative Malus varieties (Red Delicious, Golden Delicious, Red Fuji, and Ralls) were collected from different provinces in China. All samples were immediately frozen with silica gel and stored at −20 C (Yang et al., 2020). Red Fuji varieties were selected from three provenances with three individuals in each region; for other cultivars, three different provenances were selected for each cultivar (Table 1). We extracted DNA from 24 samples using the new Tiangen plant genomic DNA extraction kit (Tiangen, Urumqi, Xinjiang, China).
Chloroplast genome sequencing, assembly and annotation
The complete chloroplast genome of 24 cultivated apple accessions were sequenced using the next-generation sequencing. The tested DNA samples were randomly interrupted by Covaris ultrasonic crusher, and the whole library preparation was completed via end repair, addition of polyA-tail, addition of sequencing adapters, purification, PCR amplification. The constructed libraries were sequenced using the Illumina MiSeq 2,000 platform. After they passed the library inspection, the different libraries were pooled according to the effective concentration and target downstream data volume and then sequenced using Illumina HiSeq/MiSeq. The raw image data files obtained from high-throughput sequencing were converted into raw sequences (sequenced reads) by CASAVA base identification analysis, which we called raw data or raw reads, and the results were stored in FASTQ file format, which contains sequence information (reads) and their corresponding sequencing quality information. For each sample, approximately 50.0 GB of raw data were generated, which were assembled using the programme NOVOPlasty (Dierckxsens, Patrick & Guillaume, 2017) with M. sieversii (MH890570.1) as the reference. The focus of the assembly is on the NOVOPlasty configuration file, also known as config.txt; the settings included config.txt parameter values, K-mer = 39, The reference sequence was uploaded; forward reads, and reverse reads uploaded that need to be assembled. The best quality of the assembly appeared as a circular assembly file; however, in general, there are two options, both of which are correct owing to the indeterminate gene order of the two reverse repeat regions of the chloroplast. When one wants to determine which one is correct, sequence comparisons or gene annotations must be performed. All annotated genes were manually curated using Sequin. 16.0 (Wyman, Jansen & Boore, 2004), and start/stop codons and intron/exon boundaries were adjusted using Geneious v4.8.5. Finally, we used the online OGDRAW software to generate a circular genome map (Lohse, Drechsel & Bock, 2007).
Characterization of chloroplast genomes in Malus
The total length of the genome and the length of each region, including LSC, SSC, and inverted repeats (IRs; a pair of reverse complementary repeat regions), gene composition (protein-coding genes, transcriptional RNA genes, ribosomal RNA genes, introns, and exons), base composition, and GC (AT) content were analysed using the Geneious v4.8.5 software.
Analysis of the LSC, SSC, and IR border regions
The IRscope (http://irscope.shinyapps.io/irapp/) online analysis software was used to upload the manually annotated Genbank file and generate a boundary map (Ali, Jaakko & Peter, 2018). Finally, the length differences and related genes of the four regions in different apple varieties were analysed.
Codon usage analysis
All protein-coding genes were used for determining the codon usage. We further analyzed the codon usage frequency and relative synonymous codon usage (RSCU) based on sequences of 83 protein-coding genes in the Malus chloroplast genomes. Avoiding the influence of the amino acid composition, we examined the RSCU using MEGA v7.0, and the results are presented as charts (Sharp & Li, 1987).
Variation analysis
Although chloroplast genomes are highly conserved with regard to composition and sequence, there are some internal mutations and structural variations among the chloroplast genomes of different varieties. In this study, the online software mVISTA (http://genome.lbl.gov/vista/index.shtml) was used for sequence alignment and variation analysis of the apple chloroplast genome (Frazer et al., 2004). The degree of variation in the complete sequence can be evaluated by comparing the similarities between the coding region, non-coding region, introns, and exons of the input sequence with the other sequences. To identify SNPs or short insertion/deletions (InDels), we used the Geneious. v4.8.5 software.
Phylogenetic analysis
To study the phylogenetic position of the chloroplast genomes of cultivated apples and other genera of the Rosaceae family, phylogenetic analysis was performed based on the chloroplast genome sequences for four cultivated apples and 16 Rosaceae species obtained from NCBI (National Center for Biotechnology Information (nih.gov))-M. hupehensis The complete chloroplast genome sequences were used to construct the phylogenetic topology using Bayesian inference (BI) (Huelsenbeck, 2012). Based on the results of the phylogenetic analysis, the phylogenetic relationships between different apple germplasms were further clarified.
Features of the chloroplast genome
The chloroplast genome structure and length of four apple cultivars and two wild ancestors were compared, and the results are summarised in Table 2. The complete chloroplast genome maps of M. domestica and wild apples as shown in Fig. 1. Subtle differences were observed among the chloroplast genome sequences. The results showed that the length of the chloroplast genome of four apple cultivars and two wild ancestors was approximately 160 kb. Among them, the genomes of Red Delicious and M. sieversii were the longest, with their lengths ranging from 160,225-160,290 bp, while those of others were shorter, with lengths ranging 160,067-160,069 bp. The chloroplast genome structures consisted of four stable parts: two IR regions (26,323-26,337 bp), which were separated by an LSC region (88,240-88,440 bp) and an SSC region (19,174-19,340 bp).
M. sieversii and M. sylvestris contained 128 genes, including 83 coding sequence (CDS), 35 tRNA, 8 rRNA, and infA and ycf1 genes. Among the 130 genes annotated in cultivated apple varieties, there were 84 CDS, 8 rRNA, 37 tRNA, and ycf1 genes. The LSC region of M. sieversii and M. sylvestris contained 83 genes, accounting for 64.8% of the chloroplast genome, including 21 tRNA genes, 61 protein-coding genes, and infA gene, whereas the SSC region contained 11 CDS genes, accounting for 8.6% of the chloroplast genome. Eight rRNA genes, 12 CDS genes, and 14 tRNA genes were located in the IR region, accounting for 26.6% of the genome. The LSC region of 24 cultivated apples contained 61 CDS genes and 22 tRNA genes, while 11 CDS genes and 1 tRNA gene were located in the SSC region. All eight rRNA genes, 13 CDS genes, and 13 tRNA genes were located in the IR region. The CDS genes in the LSC, SSC, and IR regions accounted for 64.1%, 9.9%, and 26.0% of the total chloroplast genome, respectively. The GC content of the complete chloroplast genome of Malus ranged from 36.5% to 36.6%, with an average content of 36.56%. Specifically, the GC content in the IR region (42.7%) was higher than that in the LSC region (34.1-34.2%) and SSC region (30.3-30.4%) ( Table S1).
The gene with the largest intron (2,497 bp) was trnK-UUU, and the matK gene was included in this gene.
IR expansion and contraction
Different species have different gene sequences in the four junction regions, and the contraction or expansion of the IR region usually leads to length changes in the chloroplast genome. To further analyse the boundary differences between the two wild species and cultivated apple varieties, four apple samples from different varieties were selected. It can be seen from the results that structural variation was still found in the IR-SSC and IR-LSC boundary in Malus. The IR regions of M. sieversii, M. sylvestris, Red Delicious, Golden Delicious, Red Fuji, and Ralls are shown in Fig. 2. The rps19 gene had an extension of 85 bp in the IRa region and 114 bp in M. sieversii and M. sylvestris. Because the ycfl gene was located at the boundary between SSC and IRb, a 1,074 bp long pseudogene fragment was generated in the IRa region. The ndhF gene crossed the IRa/SSC boundary and was located in the IRa region with a length spanning 12 bp. In addition, the trnH-GUG genes in the four cultivated apple varieties and two wild species were all located in the LSC region, and its distance from the IRb/LSC boundary was between 32 and 82 bp. In general, the genus Malus showed similar characteristics in the IR/SC boundary region.
genomes, the number of the four repeat types was similar, and their overall distribution in the chloroplast genome was highly conserved. A total of 633 repeats were detected in the 11 chloroplast genomes, including 337, 220, 60, and 16 forward repeats, palindromic repeats, reverse repeats, and complementary repeats, respectively (Fig. 3A), and four repeats were observed in 52.8%, 35.3%, 9.9%, and 2%, respectively (Fig. 3B). The length of the repeat sequence was 30-65 bp and mainly 30-40 bp among Malus (Fig. 3C). Most of these repeats were located in the intergenic spacer and gene region, accounting for 66% and 27.1%, respectively, while only 6.9% were located in the intron region (Fig. 3D). The types, distribution and number of SSRs in the chloroplast genome of Malus were counted (Table S2). A total of 2,279 SSRs of single nucleotides, dinucleotides, and complex polynucleotides were detected in the chloroplast genome of Malus. Two wild and 11 cultivated apple sequences were selected. The number of mononucleotide, dinucleotide, and complex nucleotide repeats ranged 45-60, 2-3, and 8-9, respectively, in four cultivated apples and two progenitor speices (Fig. 4A). The most common mononucleotide repeats were A/T repeats. SSRs were broadly similar across genomes, compared with SSC and IR regions, most of which were located in the LSC region, and most SSRs were located in the intergenic spacer region (Fig. 4B, Table S3).
Codon usage
A total of 61 codons encoding 20 amino acids were detected (Fig. 5). The minimum value of Relative synonymous codon usage (RSCU) was 0.47 and the maximum value was 1.85. AGA had the highest RSCU value, ranging from 1.85 to 1.97. Methionine and tryptophan were the least common amino acids, whereas leucine, arginine, and serine were the most common amino acids. The RSCU values of 33 and 34 codons exceeded one for M. sieversii and M. sylvestris, respectively; 31 codons of Red Delicious exhibited greater preference (RSCU > 1), and 32 codons of Golden Delicious, Red Fuji, and Ralls showed greater preference (RSCU < 1). Half of the frequently used codons had RSCU value > 1, and all codons ended in A/U, which was consistent with the rich A/T characteristics of the angiosperm chloroplast genome (Table S4). Comparative chloroplast genomic analysis Considering the chloroplast genome sequence of M. sieversii as reference, mVISTA was used to compare the differences in the chloroplast genome sequences of 13 Malus materials. The results are shown in Fig. 6. The whole sequences of 13 chloroplast genomes were highly similar. The sequence variation was mainly concentrated in the non-coding region, and the non-coding region was less conserved than the coding region. In general, the intergenic region was a highly divergent region among these chloroplast genomes, such as psbI-atpA, psbM-psbD, and ndhC-atpE.
Phylogenetic analyses
Phylogenetic analyses revealed strong bootstrap support for each node. All Malus species formed a branch with strong guidance support that the four cultivated apples had a close phylogenetic relationship with M. sieversii and M. sylvestris, in which the species M. sieversii and Red Delicious formed a cluster, while the Golden Delicious, Red Fuji, Ralls and M. sylvestris clustered into another branch (Fig. 7). Therefore, M. sieversii was not the unique parent source of the modern cultivated apple; however, it was a major progenitor.
DISCUSSION
In previous studies on Rosaceae fruit trees, chloroplast gene numbers ranged from 110 to 130 (Daniell et al., 2016). In this study, we obtained the chloroplast genome sequences of M. sieversii and M. sylvestris from the NCBI database and assembled complete chloroplast genomes of four varieties of Malus species. Sequence analysis revealed that the two progenitors had 128 genes and four M. domestica contained 130 genes. Similar to the other higher plants, the chloroplast genome had a highly conserved structure and size; it showed a typical circular DNA structure and usual length of the chloroplast genome sequence, ranging from 160,067 to 160,290 bp in Malus.
In the chloroplast genome, the IR region is the most conserved region among all the regions. The change in chloroplast genome size is related to the expansion and contraction of IRs, and its evolution is very different (Shen et al., 2017;Ravi et al., 2008). To a certain degree, the variation and evolution of the chloroplast genome is related to its contraction and expansion (Yang et al., 2010). From the perspective of terrestrial plant evolution, the IR region tends to expand; there are subtle differences in gene contraction and expansion among species, and the difference between the maximum and minimum length of the IR region in Malus is 32 bp (Fig. 2). The IR expansion/contraction of the genus showed no remarkable phylogenetic significance.
Codon usage bias is believed to occur due to the differential abundance of tRNAs corresponding to different codons in cells. This phenomenon is of great significance because it is related to the translation of DNA and the synthesis of biologically functional proteins (Akashi, 1997;Bulmer, 1991;Zhang et al., 2012;Wong et al., 2002;Ermolaeva, 2001). The codon usage analysis of chloroplast genomes of two progenitors and four apple varieties revealed that among the encoded amino acids, leucine and tryptophan were the most and least abundant amino acids, respectively, implying that the usage bias of codons is uneven. The chloroplast genome of Malus prefers to use codons with A or T at the third position. This phenomenon is also found in the chloroplast genomes of other angiosperms, such as upland cotton, wheat, and gramineous plants (Gao et al., 2017;Clegg et al., 1994;Mader et al., 2018;Meng et al., 2018). This also proves that the AT content in the chloroplast genome of Malus is slightly higher than the GC content.
SSRs are valuable molecular markers with a high degree of variation within species; they are widely used in plant population genetics, polymorphism investigations, and evolutionary research (Powell et al., 1995). Most of the SSRs identified in the chloroplast genomes of four apple varieties and two ancestral species were found in non-coding regions (Table S3), which was not unusual considering the higher number of mutations within these regions compared with highly conserved coding regions (Ebert & Peakall, 2009). Conversely, the number of SSRs in the LSC region was significantly higher than that in the SSC and IR regions, which is consistent with the results of previous research results showing uneven distribution of SSRs in the chloroplast genome (Qian et al., 2013).
Based on the chloroplast genome of M. sieversii as a reference, sequence identification maps of four Malus varieties and two progenitor species were generated. Compared with the IR regions, the LSC and SSC regions showed more differences, and non-coding regions were less conservative than the coding regions. In contrast, similar to the chloroplast genomes of other plants, all rRNA genes were highly conserved (Dong et al., 2013). Comparative analysis showed that different types of DNA fragments had different degrees of sequence variation. In general, the variation in exons was lower than that in introns, and this trend was also observed in other analyses . Three variable regions-psbI-atpA, psbM-psbD, and ndhC-atpE-were found, which may be specific DNA barcodes for effective apple variety identification, or potential molecular markers for apple germplasm resources. Single nucleotide polymorphism (SNP) mainly refers to DNA sequence diversity caused by the variation of a single nucleotide at the genomic level, and such polymorphisms are rich and numerous (Liu et al., 2016). in this study, 135 SNP sites were found. SNPs provide an important basis for studying genetic variation in human families, animals and plants. Therefore, they are widely used to study population genetics (including biological origin, evolution, and migration) and disease-related genes.
It is believed that M. sieversii is the primary progenitor, and M. sylvestris is a major secondary contributor to cultivated apple; however, there is no direct and sufficient evidence to this (Nock et al., 2015). Phylogenetic analysis showed that all Rosaceae species formed a large branch with a strong support value. The four cultivated apples were closely related to M. sieversii and M. sylvestris. M. sieversii and Red Delicious formed a cluster, while the Golden Delicious, Red Fuji, Ralls, and M. sylvestris clustered into another branch. Therefore, M. sieversii was not the only parent source of the modern cultivated apple, and M. sylvestris was also a major progenitor. These results are consistent with those reported by Sun et al. (2020). The results provide important theoretical values for the protection and utilisation of the germplasm resources of the genus Malus and contribute to understanding parent selection and breeding of high-yield varieties.
CONCLUSIONS
In this study, the chloroplast genomes of four cultivated varieties of Malus were sequenced and compared with the two wild species of Malus. The similarities and differences in the chloroplast genomes of Malus and the genetic relationship between cultivated and wild species were revealed. Intraspecific differences among the chloroplast genomes mainly exist in the intergenic spacer, which is the main feature of the observed genome size differences. In addition, phylogenetic relationships of species in the genus Malus were reconstructed based on whole-genome sequences. The tree showed that varieties of M. domestica and wild apples (M. sylvestris and M. sieversii) formed a branch, which indicated that M. sylvestris and M. sieversii had a close relationship with cultivated apple. This supports the view that M. sylvestris and M. sieversii are the common ancestors of cultivated apples. Furthermore, three cpDNA marker sequences-psbI-atpA, psbM-psbD, and ndhC-atpE-were identified that could be used to study the intraspecific genetic structure and diversity of Malus. This study reveals the relationship between cultivated apples and their possible ancestors based on the molecular analysis of the chloroplast genome. The corresponding molecular data can provide theoretical guidance for the use of apple resources and environment protection.
ADDITIONAL INFORMATION AND DECLARATIONS Funding
This study was supported by the Open project of Xinjiang Key Laboratory of Biological Resources and Genetic Engineering (grant 2020D04033 to Xinmin Tian). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. | 6,064.8 | 2022-03-18T00:00:00.000 | [
"Agricultural And Food Sciences",
"Biology"
] |
Babesia ovis secreted antigen-1 is a diagnostic marker during the active Babesia ovis infections in sheep
Ovine babesiosis caused by Babesia ovis is an economically significant disease. Recently, a few B. ovis-specific proteins, including recombinant B. ovis secreted antigen-1 (rBoSA1), have been identified. Immunological analyses revealed that rBoSA1 resides within the cytoplasm of infected erythrocytes and exhibits robust antigenic properties for detecting anti-B. ovis antibodies. This protein is released into the bloodstream during the parasite’s development. It would be possible to diagnose active infections by detecting this secretory protein. For this purpose, a rBoSA1-specific polyclonal antibody-based sandwich ELISA was optimized in this study. Blood samples taken from the naturally (n: 100) and experimentally (n: 15) infected sheep were analyzed for the presence of native BoSA1. The results showed that native BoSA1 was detectable in 98% of naturally infected animals. There was a positive correlation between parasitemia level in microscopy and protein density in sandwich ELISA. Experimentally infected animals showed positive reactions from the first or second day of inoculations. However, experimental infections carried out by Rhipicephalus bursa ticks revealed the native BoSA1 was detectable from the 7th day of tick attachment when the parasite began to be seen microscopically. Sandwich ELISA was sensitive enough to detect rBoSA1 protein at a 1.52 ng/ml concentration. Additionally, no serological cross-reactivity was observed between animals infected with various piroplasm species, including Babesia bovis, B. bigemina, B. caballi, B. canis, B. gibsoni, Theileria equi, and T. annulata. Taken collectively, the findings show that the rBoSA1-specific polyclonal antibody-based sandwich ELISA can be successfully used to diagnose clinical B. ovis infections in sheep at the early stage.
Introduction
Ticks play a significant role in human and veterinary medicine due to their ability to transmit many protozoal, rickettsial, and viral diseases.Notably, protecting the small ruminants against tick infestations in the field is too hard because of their grazing behavior for long periods of the year.Ovine babesiosis is one of the most significant tick-borne protozoan diseases of livestock.Several epidemic and endemic cases caused by ovine babesiosis have been observed in Europe, the Middle East, North Africa, and some Asian countries (Yeruham et al., 1998;Fakhar et al., 2012;Ranjbar-Bahadori et al., 2012;Sevinc et al., 2013;Sevinc et al., 2018;Ceylan et al., 2021a;Ceylan et al., 2021b;Stevanovic et al., 2022).
The most prominent clinical symptoms of B. ovis infections include fever, hemolytic anemia, and hemoglobinuria.In addition to these clinical symptoms; fatigue, loss of appetite, weight loss, and abortions may also occur in clinical cases.During the acute phase of infection, B. ovis merozoites proliferate and develop rapidly in erythrocytes.Then, they break down these erythrocytes and enter new erythrocytes.Some residual substances from the parasite and hemoglobin pass through the plasma.The presence of hemolytic anemia prevents the proper oxygenation of tissues, resulting in the onset of internal organ failures.Inevitably, when the level of parasitemia becomes elevated, it leads to a fatal outcome.Early and accurate diagnosis is the most critical part of disease control strategies.During the acute phase of the infection, the clinical signs may indicate the disease, and the parasites can be diagnosed by a specialist by examining the morphological characteristics of the parasite under a microscope.However, it is tough to diagnose the disease by microscopic and clinical examination methods in cases where the number of parasites is low (Yeruham et al., 1998;De Vos et al., 2000;Sevinc et al., 2013).
Serological diagnostic methods have generally been utilized to detect latent infections (Bose et al., 1995;Homer et al., 2000;Georges et al., 2001;Bock et al., 2004;Ceylan and Sevinc, 2020).Serological methods encompass both antibody-based techniques, which are mainly employed for gathering epidemiological information about diseases, and antigen-detection methods, which are utilized for diagnosing active infections.In cases where there is a low parasite count in the body, antigen detection methods can still identify the proteins secreted by the parasites.Consequently, these methods exhibit higher sensitivity compared to direct microscopy for the accurate diagnosis of ongoing infections (Montealegre et al., 1987;Chen et al., 2008;Luo et al., 2012).
Our group has recently described an immunoreactive protein named rBoSA1 (recombinant B. ovis secreted antigen-1) from B. ovis.We found that this protein had strong antigenic structures to detect anti-B.ovis antibodies.Additionally, we determined that the native BoSA1 protein was abundant in the cytoplasm of infected erythrocytes and corroborated that this protein was also detectable in the plasma of B. ovis-infected animals by western blot analysis (Sevinc et al., 2015).This unique secretory protein is predicted to be released into circulation from infected red blood cells due to intravascular hemolysis during the asexual development of the parasite.Active infections could be diagnosed by detecting this secretory protein via an antigen detection-based serologic method.Therefore, the present study aimed to develop a sandwich ELISA technique to detect native BoSA1 protein in serum and blood samples of sheep with active B. ovis infection.
Blood samples, naturally and experimentally infected sheep
A hundred naturally infected sheep and 15 experimentally infected splenectomized lambs were included in the study.Natural infections were from the clinical cases detected in the central part of Türkiye (Sevinc et al., 2013).The experimental lambs underwent splenectomy utilizing established surgical techniques, as outlined in the previous study (Sevinc et al., 2007).Preimmune sera were collected from the lambs prior to initiating the experimental infection.Of the 15 experimental infections, 13 were performed by intravenous inoculation of B. ovis-infected blood (Sevinc et al., 2007;Sevinc et al., 2014).Imidocarb dipropionate (1.2 mg/kg) was used to treat animals that developed parasitemia during these infections.The remaining 2 experimental infections were carried out by unfed adult Rhipicephalus bursa infected with B. ovis in 5-6 months-old Anatolian Romanov lambs.The lambs were purchased from the Baskil district located in Elaziğprovince of Türkiye, and housed in a closed pen at the Veterinary Medicine animal facility.The blood samples taken from the lambs before the experiment were subjected to microscopic, serologic, and molecular analyses, and it was confirmed that the lambs were free from blood parasites including B. ovis prior to being infested with infected ticks.The infected unfed R. bursa tick line continuing at the Parasitology Department of the Veterinary Faculty, Firat University was used for the experiment.
Experimental infection in lambs by unfed adult R. bursa infected with B. ovis
To establish the timeframe for detecting native BoSA1 in the bloodstream, 90 and 100 adult unfed R. bursa infected with B. ovis were placed on the splenectomized lamb 1 and lamb 2, respectively (Erster et al., 2016).The infected ticks were fed on lambs until repletion in the plastic capsules glued to the backs of the animals (Almazań et al., 2018).After the infected ticks were attached, the lambs were periodically monitored for the progress of the infection.During the experiment, clinical findings and body temperature of each lamb were checked daily.Simultaneously, thin blood smears, EDTA blood samples and sera were collected for microscopic examination, PCR, and rBoSA1-specific polyclonal-antibodybased sandwich ELISA, respectively.
Microscopic detection of Babesia ovis
Animals suspected to have the disease were first examined clinically, followed by a small incision from the ear tip of the animals.A few drops of blood were drawn from these incisions, and the thin blood smears were prepared.The prepared smears were stained with a 10% Giemsa solution for at least 30 min after fixing with methanol for 5 min.The level of parasitemia was determined by examining at least 20 microscope fields.Parasitemia levels were categorized according to relevant literature as follows: 1: Low parasitemia (0.1-0.3%), 2: Moderate parasitemia (0.4-0.9%), 3: High parasitemia (1-2.5%), and 4: Very high parasitemia (>2.5%) (Sevinc et al., 2013).Blood samples were collected from the jugular vein of the lambs using both anticoagulant-coated (EDTA) and non-anticoagulant vacuum tubes.The sera were separated by centrifugation and stored in a -20°C freezer until use.PCR analysis for detection of B. ovis DNA was performed as described previously (Aktas et al., 2005).
Production of recombinant BoSA1 protein
Expression of rBoSA1 protein from E. coli DH5a cells and its purification were prepared as reported in the previous study (Sevinc et al., 2015).
Mice and rabbit immunizations
To produce the capture and detection antibodies, twelve 6week-old specific pathogen-free (SPF) ICR mice (CLEA, Japan) and one white rabbit weighing 2.5 kg were used to generate anti-rBoSA1 polyclonal antibodies.Mice were immunized by injecting 100 micrograms (µg) of purified rBoSA1 intraperitoneally after emulsifying with an equal volume of Freud's complete adjuvant (Sigma-Aldrich, USA).At 14 and 28 days after the first immunization, the same amount of antigen was emulsified with Freud's incomplete adjuvant (Sigma-Aldrich, USA), and the mice were given second and third immunizations by intraperitoneal injection again.In rabbit immunization, 1 mg of purified rBoSA1 antigen was administered subcutaneously to different points on the rabbit's body, with emulsions made with Freud's complete and incomplete adjuvants, as in mice.Rabbit immunizations were performed three times with an interval of two weeks, as in the mice.The whole blood of mice and rabbit immunized with antigen was collected 14 days after the last immunization, and immune sera were extracted by centrifugation.Anti-rBoSA1 polyclonal IgG antibodies in mice and rabbit sera were purified using the Econo-Pac protein A kit (BioRad Laboratories, USA) (Luo et al., 2012).Purified antibodies were stored in a -30°C deep freezer and then used in the sandwich ELISA method.
Determination of detection limit of native BoSA1 protein by sandwich ELISA
To determine the minimum detection limit of the rBoSA1specific polyclonal antibody-based sandwich ELISA, two-fold dilutions beginning from 200 µg/ml concentration of rBoSA1 were tested in sandwich ELISA.
Sandwich ELISA applications
Sandwich ELISA assay was optimized by testing different dilutions of rabbit and mouse anti-rBoSA1 polyclonal IgGs and enzyme-labeled secondary antibody (HRP-conjugated goat-anti mouse IgG, Bethyl lab, USA).Twenty serum and blood samples collected from healthy lambs were used to establish a BoSA1specific cut-off for sandwich ELISA.The cut-off value was calculated according to the formula of the mean optical density of the negative samples plus 2-fold the standard deviation.The ELISA microplate was first incubated at 4°C overnight with 100 ml of rabbit anti-rBoSA1 antibodies diluted with carbonate-bicarbonate buffer (0.05 M carbonate-bicarbonate buffer, pH 9.6) at a concentration of 2 mg/ml and then blocked in a 37°C incubator with phosphatebuffered saline (PBS) containing 5% skim milk powder (Sigma-Aldrich) solution for 1 h.After one washing with PBST (Phosphate buffered saline with Triton-X), 100 µl of the positive/negative control samples and infected animals' serum/blood samples at dilution of 1/5 were added and incubated at 37°C for 1 h.Then, six washes were performed with PBST again.It was incubated with mouse anti-rBoSA1 antibodies diluted with 5% skim milk solution at a concentration of 2 mg/ml at 37°C for 1 h.Six more washes were repeated with PBST, and finally, the plate was incubated with the horseradish peroxidase (HRP)-conjugated anti-mouse IgG (Bethyl, USA) diluted with 5% skim milk solution at a ratio of 1/8000 at 37°C for 1 h.The wells were rewashed, and 100 µl ABTS (2,2'-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid)) substrate (Sigma-Aldrich, USA) was added to each well, and then the plate was kept in the dark for 30 min.The intensity of the enzyme reaction was quantified using the 415 nm filter of the ELISA microplate reader (Rayto RT-2100C, China).Purified recombinant BoSA1 protein was used as a positive control in the sandwich ELISA.
Control of serological cross-reactivity
To determine the specificity of the sandwich ELISA method, various piroplasm-positive serum samples, including B. bovis, B. bigemina, B. caballi, B. canis, B. gibsoni, Theileria equi, and T. annulata were tested in terms of native BoSA1 protein.
Statistical analysis
The correlation between the parasitemia level of B. ovis infection and the optical density (OD value) of native BoSA1 protein in sandwich ELISA was statistically investigated using Spearman Correlation statistical analysis method.The SPSS 25 (IBM Corp. Released 2017.IBM SPSS Statistics for Windows, Version 25.0.Armonk, NY: IBM Corp.) statistical package program was adopted to analyze the data.p-Values were computed to determine the level of statistical significance.The significance level was indicated to be p < 0.05.
Ethical statement
All applications performed on animals were conducted according to the conditions defined in the Selcuk University (Ethical Approval: 2016-35) and Firat University (Ethical Approval: 2021/12) Experimental Medicine Research and Application Center local ethical committee instructions.Rabbit and mice blood was collected under sedation by 5 mg/kg intramuscular xylazine hydrochloride (Rompun, Bayer) + 30-40 mg/kg ketamine hydrochloride (Ketalar, Pfizer) administration.
After the blood was taken, the rabbit was euthanized by cervical decapitation, and the mice were euthanized by cervical dislocation.
Detection limit of BoSA1 by sandwich ELISA
By analyzing 24 serial dilutions starting from 200 µg/ml concentration of rBoSA1, it was found that sandwich ELISA had a very high sensitivity to detect rBoSA1 proteins.The lowest detectable amount of rBoSA1 was 1.52 ng/ml.Detailed information is illustrated in Figure 1.
The presence of native BoSA1 protein in the naturally infected sheep
To determine the method's sensitivity, the samples taken from 100 naturally infected sheep were examined for the presence of the native BoSA1 protein by sandwich ELISA.The cut-off calculated from healthy lambs was 0.477 for sera and 0.396 for blood.Circulating native BoSA1 protein was detected in 98 and 97 of the sera and blood samples, respectively.While OD values above 0.477 and 0.396 in infected sera and blood were detected respectively, it was well below these limits in the negative samples.Sandwich ELISA results of all serum and blood samples taken from naturally infected animals are illustrated in Figure 2.
The dynamics of native BoSA1 protein in experimentally infected lambs
Serum samples from experimentally infected lambs showed positive reactions from the first and second days of the infected blood inoculations.Native BoSA1 protein was detected on the first day following the B. ovis-infected blood inoculation in twelve of the thirteen samples, and all lambs were positive on the second day of inoculation.The density of protein continued till the treatment time with high OD values.Native BoSA1 protein started to disappear on the first day of the drug (1.2 mg/kg imidocarb dipropionate) administration which mostly corresponded to the fourth day of experimental infection, and all samples were negative during the post-treatment period.Detailed information about the dynamics of native BoSA1 protein in the experimentally infected lambs by B. ovis-infected blood inoculation is given in Figure 3.This experiment was added to the study to find out the detection time of native BoSA1 protein in the blood of animals after tick attachment.The information to be obtained from this experiment will be extremely important for the diagnosis of infection under field conditions.Both splenectomized lambs infested unfed adult R. bursa infected with B. ovis developed severe clinical babesiosis, including anemia, hemoglobinuria, high fever, and high parasitemia of 7.1% (lamb 1) and 42.5% (lamb 2).Both lambs died 13 days post tick infestation, within 3-4 days from the onset of parasitemia (Table 1).In the infections carried out by B. ovis-infected R. bursa ticks, native BoSA1 could be detected from the 7 th day of tick attachment by rBoSA1-specific polyclonalantibody-based sandwich ELISA.The time of appearance of the rBoSA1 protein nearly corresponded to the day when B. ovis merozoites began to appear microscopically in the blood.The detection of parasite DNA by PCR was 1-3 days before protein detection time.The OD value was above the cut-off on the seventh day and increased in the following days, except for an experimental infection with low parasitemia.Detailed information concerning sandwich ELISA results of experimental infections carried by R. bursa ticks is given in Table 1.
Cross-reactivity
There was no cross-reactivity against polyclonal anti-rBoSA1 antibodies in the sera from various animals infected with piroplasm species, including B. bovis, B. bigemina, B. caballi, B. canis, B. gibsoni, T. equi, and T. annulata.
Positive correlation between parasitemia level and OD value
A statistically significant relationship was determined between parasitemia levels and OD values of naturally infected animals' serum and blood samples.It was observed that the OD value increased as the parasitemia level increased, and the OD value decreased as the parasitemia level decreased.The graphs obtained from Spearman correlation analysis are shown in Figure 4.
Discussion
Ovine babesiosis is usually associated with mortality and morbidity in small ruminants.The incubation period following tick attachment is around two weeks.At the end of this period, the body temperature increases up to 40-42°C.Loss of appetite and depression are observed in animals.The respiration and pulse rates elevate because of hemolytic anemia due to the breakdown of red blood cells, and hemoglobinemia, hemoglobinuria, and jaundice are observed.Manifestations including the presence of bloody stools, muscle tremors, and leg paralysis can be observed as symptoms of the infection.In cases of acute babesiosis, if left untreated, the disease can lead to fatality, with the primary cause of death typically being anoxia resulting from anemia (Yeruham et al., 1998).Erythrocytes are the first and only settlement area of the Babesia species in vertebrate hosts.Babesia ovis merozoites develop by dividing into two or more in erythrocytes, break down the erythrocytes, and enter other erythrocytes to be able to continue asexual development.As a result of a series of asexual reproduction, most of the erythrocytes become infected, and some residual substances from the parasite pass through the plasma (Yeruham et al., 1998;Uilenberg, 2006;Sevinc et al., 2013).
Secretory proteins found in the parasite structures are the richest resources for new therapeutic drug targets, diagnostic antigens, and vaccine candidates (Bonin-Debs et al., 2004;Ceylan et al., 2022).Parasite proteins secreted into the host cell play a vital role in modifying the host cell and provide the interaction between the host immune system and the apicomplexan parasites including intracellular protozoans such as Toxoplasma, Plasmodium and Theileria (Ravindran and Boothroyd, 2008).During the asexual development of Babesia species in erythrocytes, parasite proteins are released and can be found in both the cytoplasm of erythrocytes and blood plasma.The majority of these parasite proteins are capable of eliciting an immune response.It means that they stimulate the immune system by activating cellular and humoral defense factors when secreted.Therefore, immunoreactive proteins are the target molecules used to diagnose diseases (Sahagun Ruiz et al., 2000;Kumar et al., 2002;Huang et al., 2006;Terkawi et al., 2007;Goo et al., 2008;Ramos et al., 2009;Ooka et al., 2011;Ceylan et al., 2022).
Successful treatment of B. ovis infection depends on an early and accurate diagnosis.During an acute infection, the disease is diagnosed by observing babesiosis-related clinical symptoms and analyzing the morphological structures of the merozoites in the erythrocytes.However, there are some diseases confusing with babesiosis because of the similarity of clinical signs, such as leptospirosis, anaplasmosis, and copper poisoning.Besides, the parasites are not demonstrable in the blood under a microscope, The dynamics of native BoSA1 protein in the experimental infections performed by B ovis-infected blood inoculation.The horizontal axis represents the pre-and post-inoculation days.
Tick attachment
Days especially in the case of subclinical infections.Various serological antibody detection methods have been used to detect latent or subclinical infections with low parasitemia (Bose et al., 1995;Georges et al., 2001;Bock et al., 2004).The indirect fluorescence antibody (IFA) test is a sensitive method for serological diagnosis of babesiosis (Homer et al., 2000).Factors such as the occurrence of cross-reactions between different species (Bose et al., 1995), the lack of automated testing procedures, semi-quantitative evaluation of results, and the need for a specialist and fluorescence microscope to evaluate the results are among the disadvantages of the IFA test.
The literature review showed that there is no commercial diagnostic test based on antigen detection in the diagnosis of ovine babesiosis caused by B. ovis.Our group has recently identified a secretory protein named recombinant B. ovis-secreted antigen 1 (rBoSA1) of B. ovis.It was characterized as an immunoreactive protein that can be used to develop serological methods for diagnosing babesiosis in sheep, and the immunofluorescence assay results revealed that native BoSA1 protein located on the surface and inside of B. ovis merozoites, and the parasite intensely secreted the protein to the cytoplasm of the infected erythrocytes (Sevinc et al., 2015).These circulating B. ovis proteins can be detected earlier than the Relation between OD value and parasitemia level.The relationship between parasite load and OD value indicates there is a positive correlation for serum samples (r: 0.990, p: 0.000) and blood samples (r: 0.980, p: 0.000).For the parasitemia line: 1, 2, 3, and 4 indicate the low (0.1-0.3%), moderate (0.4-0.9%), high (1-2.5%)and very high (>2.5%)parasitemia levels, respectively.
formation of specific antibodies in the blood by a method based on antigen detection, and they can serve as biomarkers in the early diagnosis of B. ovis infection.This study was carried out to detect native BoSA1 protein in the blood and serum samples of the infected animals using the sandwich ELISA technique and eventually diagnose babesiosis in sheep during active infection.The sandwich ELISA technique was applied using polyclonal antibodies specific for recombinant BoSA1 protein.The polyclonal anti-rBoSA1 antibodies detected the circulating native BoSA1 protein with high sensitivity in 98% of the animals, which were positive for B. ovis infection by microscopy and PCR analysis (Figure 2).In ELISA, only two serum samples from naturally infected animals were negative.There was a statistically positive correlation between the OD values of these samples and parasitemia levels.Our previous study (Sevinc et al., 2015) showed that 68.42% of naturally infected sheep were positive for B. ovis-specific antibodies during active infections.It is inferred that the sandwich ELISA technique is more sensitive than indirect ELISA in detecting clinical infections.Antibodies, the basic elements of humoral defense, are usually seen in circulation toward the last stages of acute infections and continue to exist in the body for several years.Therefore, the detection of the parasite-specific antibodies in circulation indicates that the parasite infects the host; however, it does not provide any information about the infection time.On the other hand, the detection of a specific parasite protein in blood indicates that the parasite-originated infection still exists in the body.Hence, antigen detection-based methods should be used to diagnose active infections, rather than antibody detection-based serological methods.Additionally, the sera from 13 lambs experimentally infected with B. ovis-infected blood showed a positive reaction, at OD values above 0.477, from the first or second day of experimental infections.The OD values of native BoSA1 increased progressively in the acute period.At this period, B. ovis merozoites asexually propagated quickly (Figure 3).Then, OD values decreased significantly on the 1 st post-treatment day following imidocarb dipropionate (1.2 mg/ kg) administration and decreased below the cut-off value on the 2 nd post-treatment day (Figure 3).When these results were compared with the previous studies (Sevinc et al., 2007;Sevinc et al., 2014;Sevinc et al., 2015) where B. ovis-specific antibodies could be detected on the 7 th or 8 th days of experimental infections by blood inoculation, sandwich ELISA is more sensitive than indirect ELISA for early diagnosis of clinical infections.On the other hand, B. ovis-infected R. bursa ticks-induced experimental infections mimicking natural infection, native BoSA1 was detected in blood serum from the 7 th day of tick attachment.The detection time of native BoSA1 protein in sera and the appearance of B. ovis merozoites in Giemsa-stained thin blood smears were compared.Accordingly, while the lamb1 was found positive for the presence of the native BoSA1 protein on the day of microscopically detection time of merozoites, lamb2 showed positivity in Sandwich ELISA one day before the detection of B. ovis merozoites (Table 1).The detection of parasite DNA by PCR was one or two days before protein detection.All performed experiments suggest that native BoSA1 is a promising indicator for detecting early-stage of B. ovis infections, and the new sandwich ELISA technique described in the study is a much more sensitive method to diagnose active B. ovis infections when compared to antibody detection assays.
Serial dilutions of rBoSA1 were tested to determine the lowest detection limit of sandwich ELISA.As a result of the test, sandwich ELISA was highly sensitive to detect as little as 1.52 ng of rBoSA1 protein in 1 ml solution (Figure 1).In addition, there was a significantly positive correlation between OD values and parasitemia levels (r: 0.980, p: 0.000 for blood, r: 0.990, p: 0.000 for serum) in the present study (Figure 4).Similar findings were reported by Joshi et al. (2004) in Plasmodium vivax infections of humans, and by Luo et al. (2012) in B. microti infections of hamsters.Luo et al. (2012) revealed that circulating Babesia microti-secreted antigen 1 (BmSA1) overlaps with the parasitemia profile during active infections in a hamster model.It is concluded that rBoSA1-specific polyclonal antibody-based sandwich ELISA may provide information about the severity of acute infections and B. ovis burden in circulation in the clinical evaluation of ovine babesiosis.
The major disadvantage of serological tests is cross-reactions (Jiang et al., 2021).In order to reduce the possibility of crossreaction, antigenic fractions of the parasite in the host circulation should be used in serological methods (Petray et al., 1992;Chen et al., 2008).No cross-reactivity against polyclonal anti-rBoSA1 antibodies in the sera of different animal species which were positive for the apicomplexan parasites, including B. bovis, B. bigemina, B. caballi, B. canis, B. gibsoni, T. equi, and T. annulata was detected in this study.This result indicated that the native BoSA1 protein has no common antigenic determinants with the aforementioned piroplasm species, and it is a strong immunoreactive protein specific for B. ovis.The findings indicate that this new sandwich ELISA technique can successfully diagnose ovine babesiosis caused by B. ovis during active infection without cross-reaction.
In conclusion, this is the first study on the sandwich ELISA technique for diagnosing B. ovis infection in its active phase.By sandwich ELISA, native BoSA1 protein was captured in almost all samples examined with high sensitivity and specificity in sheep.When the ELISA results of the positive and negative samples tested in this study are evaluated, it is seen that the sensitivity is 98.26% (113 of a total of 115 naturally and experimentally infected samples) and the specificity is 100% (all of the 20 negative samples examined for cut-off calculation).Early diagnosis is crucial to control B. ovis infection, which is especially common in tropical and subtropical countries and causes deaths in sheep (Sevinc et al., 2018;Ceylan and Sevinc, 2020).The application of the rBoSA1-specific polyclonal antibody-based sandwich ELISA technique yielded noteworthy findings concerning the timing of native BoSA1 protein detection in experimental infections induced through infected blood inoculation and R. bursa ticks infected with B. ovis.The study findings demonstrate that the rBoSA1-specific polyclonal antibodybased sandwich ELISA method can be employed with success and reliability for the early detection of acute B. ovis infections.Furthermore, it has the potential to be utilized in the evaluation of therapeutic drug effectiveness.
FIGURE 1
FIGURE 1Twenty-four serial dilutions of rBoSA1 to determine the lowest detectable concentration by sandwich ELISA.
FIGURE 2 Sandwich ELISA results in the serum and blood samples of naturally infected sheep.(A) OD values of serum samples, (B) OD values of blood samples.
TABLE 1
Sandwich ELISA results of experimental infections carried by R. bursa ticks. | 6,137.6 | 2023-08-16T00:00:00.000 | [
"Agricultural And Food Sciences",
"Biology"
] |
Characteristic analysis of epileptic brain network based on attention mechanism
Constructing an efficient and accurate epilepsy detection system is an urgent research task. In this paper, we developed an EEG-based multi-frequency multilayer brain network (MMBN) and an attentional mechanism based convolutional neural network (AM-CNN) model to study epilepsy detection. Specifically, based on the multi-frequency characteristics of the brain, we first use wavelet packet decomposition and reconstruction methods to divide the original EEG signals into eight frequency bands, and then construct MMBN through correlation analysis between brain regions, where each layer corresponds to a specific frequency band. The time, frequency and channel related information of EEG signals are mapped into the multilayer network topology. On this basis, a multi-branch AM-CNN model is designed, which completely matches the multilayer structure of the proposed brain network. The experimental results on public CHB-MIT datasets show that eight frequency bands divided in this work are all helpful for epilepsy detection, and the fusion of multi-frequency information can effectively decode the epileptic brain state, achieving accurate detection of epilepsy with an average accuracy of 99.75%, sensitivity of 99.43%, and specificity of 99.83%. All of these provide reliable technical solutions for EEG-based neurological disease detection, especially for epilepsy detection.
can be explored through network topology analysis. In general, the brain network can be inferred by setting the brain electrodes (channels) as nodes, and then the edges between the nodes can be determined by various related metrics. Moreover, multilayer network are the latest development of complex network theory [13][14][15] . The multilayer network has multiple layers and can describe different aspects of the studied system. The multilayer structure makes it possible to describe complex systems more comprehensively and accurately. Some successful applications of multilayer networks can be found in the fields of chemical systems 16 , EEG signal analysis 17,18 and traffic network analysis 19 .
The human brain has obvious multi-frequency characteristics. When multilayer network is introduced into brain research, spatiotemporal characteristics in each frequency band can be mapped into a single layer. MMBN considers the specific information of multiple frequency bands and can be used as an effective feature for epilepsy detection. However, it should be noted that in the analysis process, multilayer network are usually represented as a series of adjacency matrices. Each adjacency matrix corresponds to a single layer. In the face of such multidimensional samples, traditional classifiers, such as support vector machines, cannot be directly used for classification. As the most advanced theory in machine learning, deep learning [20][21][22][23] has received extensive and continuous attention. Specifically, deep learning is an end-to-end learning framework that can extract deeper internal representations from the input itself [24][25][26][27] . So far, deep learning has shown great potential in epilepsy research. For instance, Li et al. 28 proposed a deep learning method combining the fully convolution network and the long short-term memory (LSTM) to automatically detect epilepsy. Specifically, the full connection layer network and LSTM are used to extract EEG-based epileptic features and explore the inherent temporal correlation in EEG signals. Kemal et al. 29 developed a stacking ensemble method to detect epilepsy using five deep neural network (DNN) models. Gao et al. 30 used approximate entropy and recursive quantitative analysis to extract the features of EEG signals, and then establishes convolutional neural networks to detect epilepsy. Zhao et al. 31 developed a novel one-dimensional CNN model to detect epilepsy with raw EEG signals. The model used three convolution blocks including BN layer and dropout layer for feature extraction. Naseem et al. 32 employed a method integrating CWT and CNN to classify EEG data and detect seizures caused by epilepsy and brain tumors. The results show that the deep learning model is conducive to EEG classification and timely prediction of seizures to avoid damage caused by repeated seizures. Some researchers tried to add the attention mechanism module to the CNN model [33][34][35] . The attention mechanism introduces weight on the basis of the original model, and can help the new model focus on informative and important features.
Motivated by the above-described background and progress, we propose an epilepsy detection method combining multilayer brain network and deep learning. In detail, the EEG signals of each channel are decomposed into multiple frequency bands through wavelet packet decomposition and reconstruction. Then, constructing MMBN, where each layer is a spatiotemporal feature topology of EEG signals in a specific frequency band. Compared with single-layer network, this multilayer brain network integrates the information from multiple frequency bands, helping to provide a more comprehensive description of brain states. In addition, considering the strong ability of deep learning to learn structural features, we carefully developed a CNN model based on attention mechanism (AM-CNN) with MMBN as input. Through evaluation on the CHB-MIT dataset, this method achieved excellent performance with an accuracy of 99.75%. The results indicate that this study can effectively characterize the brain state during seizures, extract essential features for precise classification, and is expected to provide reference for other EEG signal based neurological disease detection. The overall structure of our work is shown in Fig. 1.
Results
MMBN analysis of brain-topological characteristic of epileptic. We randomly select eight subjects and calculate the measurement statistics of the MMBN obtained from the normal state and the seizure state respectively, and then t-test was performed on them. Four network measures are introduced, including average clustering coefficient C , clustering coefficient entropy E C , spectral radius R and graph energy E . These statistical measures are defined as the following equations: www.nature.com/scientificreports/ where N is the total number of nodes (channels) in the network, C(ν) means clustering coefficient of node ν , the mathematical expression is where t ν is the total number of closed triangles containing node ν , k ν is the degree of node ν , w νκ represents the edge weight between nodes κ and ν , w να represents the edge weight between nodes α and ν , w κα represents the edge weight between nodes κ and α.
where P C (ν) is expressed as where ν denotes the ν-th eigenvalue of the adjacency matrix(single-layer network).
It can be seen from Table 1 that 93.75% of p-value values are less than 0.001, and 100% of p-value values are less than 0.05. Figure 2 shows two randomly selected 8-layer brain networks. One sub-graph corresponds to one layer of the MMBN and is related to a specific frequency band. The MMBN in the normal state is located in the upper row, and the MMBN in the seizure state is located in the lower row. As can be seen, the network topology shows obvious differences in different frequency bands. All the above indicates that the proposed multilayer Table 1. p-value of MMBN statistical measure between normal state and seizure state ( * : p < 0.05, * * : p < 0.001).
Sub_1
Sub_2 www.nature.com/scientificreports/ brain network can effectively characterize the differences in the topology of brain networks between seizure and normal, and confirms the importance of frequency and electrodes (channels) in epilepsy detection research.
Attention mechanism-based epilepsy detection. Taking MMBN that integrates spatiotemporal features across eight frequency bands as input, the AM-CNN model was trained using Keras through a fully supervised process and used to perform epilepsy detection on 18 selected subjects. The final results indicated that the epilepsy detection scheme combining multilayer brain network and AM-CNN model can effectively distinguish between normal state and epileptic state, with a classification accuracy of 99.75% sensitivity of 99.43%, and specificity of 99.83%. Some existing research results on the CHB-MIT dataset are also listed in Table 2. The proposed method is better than them in terms of accuracy and sensitivity, and is extremely close to Dang's work in terms of specificity but superior to other works. All of these provide new ideas for the characterization of EEG signals, and also provide technical support for the construction of an efficient and accurate epileptic state detection system.
Discussions
Effectiveness of MMBN. The proposed method in this paper achieves an excellent epilepsy detection performance with an average accuracy of 99.75%. This reflects the overall effectiveness of using multilayer brain networks as new feature inputs. At the same time, we also analyze the effectiveness and contribution of each frequency band to epilepsy detection. Specifically, taking a single-layer network as input, epilepsy detection is performed on 18 subjects using the same AM-CNN architecture. The results are shown in Fig. 3. The average detection accuracy ranges from 79.06% to 92.97%. This indicates that all eight frequency bands divided in this study are helpful for epilepsy detection, but the contribution of each frequency band is different. Meanwhile, the contributions of each frequency band are also influenced by individual differences among the subjects. Taking frequency band F 4,3 4 as an example, the detection results of different subjects fluctuate between 70.9% and 94.2%. www.nature.com/scientificreports/ This further confirms that brain function has multi-frequency characteristics, and MMBN fused with information from multiple frequency bands can more effectively decode epileptic brain states.
Effect of attention mechanism. In the methods section, AM-CNN architecture is designed to conduct feature extraction from multilayer brain networks. To further emphasize and illustrate the role of attention mechanism, we construct a comparison model to validate the effectiveness of the attention mechanism using the same inputs. The architecture and test results of comparative model is shown in Table 3. The performance of comparative model with accuracy of 93.54%, sensitivity of 93.18%, and specificity of 92.76% is significantly lower than those of AM-CNN. The complete AM-CNN does achieve the better performance. This indicates that attention mechanisms play a crucial role in enhancing feature extraction processes in this study.
Rationality of frequency band division.
In order to further prove the rationality of frequency band division in this study, we provide classification results of no band division, two bands ( F 2,0 p,2 (0-32 Hz), F 2,1 p,2 (32-64 Hz)), four bands ( F 3,0 p,3 (0-16 Hz), F 3,1 p,3 (16-32 Hz), F 3,2 p,3 (32-48 Hz), F 3,3 p,3 (48-64 Hz)) and eight bands. The relevant results are shown in Fig. 4. As can be seen, the average detection accuracy of multiple frequency bands exceeds 90%. Especially for eight frequency bands, the accuracy reaches 99.75%. It is worth mentioning that when no band division is performed, the classification accuracy is only 82.16%. This is because features in different frequency bands cannot be used specifically, resulting in information confusion. In summary, considering multiple frequency bands, the proposed method achieves excellent results in epilepsy detection and is expected to provide valuable reference for other EEG-based neurological disease detection.
Experiment validation. Taking the designed MMBN as input, the proposed AM-CNN model is trained
and tested on the CHB-MIT dataset which was created and provided by Boston Children's Hospital (CHB) and the Massachusetts Institute of Technology (MIT) and included 24 subjects (5 males, 3-22 years old; 19 females, 1.5-19 years old).
All scalp electroencephalograms in the CHB-MIT dataset were collected using the international 10-20 system electrode placement method. The electrode positions used in the dataset were FP1, FP2, F7, F3, FZ, F4, F8, FT9, FT10, T7, C3, CZ, C4, T8, P7, P3, PZ, P4, P8, O1, O2, and the sampling frequency was 256 Hz. The dataset adopts a bipolar measurement method, where the collected EEG signals are recorded in the form of voltage differences between adjacent electrodes in a longitudinal direction. The number of electrode pairs (channels) contained in different subsets and different signal segments in the same subset varies from 18 to 23. In this paper, considering data integrity, we used 18 subjects, all of whom had 23 channels of EEG signals (involving frontal lobe: F3, F4, F7, F8, FZ; frontal lobe: FP1, FP2; temporal lobe: T7, T8; occipital lobe: O1, O2; parietal lobe: P3, P4, P7, P8; central lobe: C3, C4, CZ; ft9, ft10). Samples were segmented using a sliding window with a length of 1 s. The sliding step is 0.5 s. In this paper, we obtained 11,150 samples in total, including 5410 normal state samples and 5740 seizure state samples. Aim to avoid contingency, ten-fold cross validation is conducted. For one fold, 90% Multi-frequency multilayer brain network. We establish a multilayer brain network based on EEG signals to study epilepsy related brain states, where each layer corresponds to a specific frequency band. Taking p-channel EEG signals x p,l L l=1 p = 1, 2, . . . , N , with a length of L as an example, MMBN is constructed as follows. Firstly, we perform 4-layer wavelet packet decomposition on the EEG signals of each channel to obtain sixteen frequency bands. The mathematical expression of wavelet packet decomposition is defined as: where F j,i p,n represents the sub-frequency band after n-layer wavelet packet decomposition of p-channel EEG signal. (j, i) is the node order of wavelet packet tree. h(·) is a low pass filter. g(·) is a high pass filter. m and n are the number of decomposition layers. The bandwidth of each frequency band is f s 2 2 4 = 8 Hz , where f s = 256 Hz means sampling frequency. Due to the fact that the frequency of EEG signals reflecting epileptic brain state is mainly distributed within 70Hz [42][43][44] , this study used eight frequency bands, including F 4,0 p,4 (0-8 Hz), . The selected wavelet base is dbN, which has been proven by existing research to be able to decompose EEG signals and has fast computational speed 45,46 .
Secondly, we use the function wprcoef (·) to reconstruct an approximation to raw EEG signals from selected nodes in the wavelet packet tree T. The signals for the reconstruction of sub-frequency band F 4,i p,n is Finally, in each frequency band, we define brain electrodes (or channels) as network nodes. The weight of edge between nodes κ and ν is determined via the Spearman rank correlation coefficient. The mathematical expression is where rg Through determining the edge between each channel pair (or node pair) via the above method, the network under this frequency band can be obtained. In different frequency bands, the correlation characteristics between channels are significantly different, making it possible to obtain different frequency-dependent brain networks. By repeating the above process at eight frequency bands, a multilayer brain network can be constructed, which has eight layers with N nodes per layer. For each layer, 30% of the edges with larger weights are reserved for subsequent analysis.
Convolutional neural network model based on attention mechanism.
Here, take the obtained multilayer brain network as input, a convolutional neural network model based on attention mechanism (AM-CNN) is carefully designed for epilepsy detection. Figure 5 shows the detailed architecture. Table 4 lists the corresponding parameters.
The AM-CNN model consists of two blocks. The function of the first block is a feature extraction based on the attention mechanism (AM-FE module), which is used to acquire multilayer network features from eight frequency bands. Note that, each layer of the multilayer brain network can be represented as an adjacency matrix, which is a grid like data. Elements at positions (ν, κ) in the adjacency matrix represent weights w ν,κ . The first module has eight branches, and it exactly matches the structure of the multilayer brain network. Each branch shares the same structure. Specifically, each branch is provided with two convolution layers (layer 1 and layer 2). Convolutional layer realizes feature extraction by designing a certain number of convolution kernels, which shows obvious advantages in processing grid-like data, such as the adjacency matrix here. In the following description, Layer 1 is simplified to L1, and others similarly. L1 and L2 can be described by the following formula: Approximate signal www.nature.com/scientificreports/ where A 0 is the input data (i.e., adjacency matrix), σ 1 k and σ 2 k are k-th output characteristic maps of L1 and L2, respectively. w k and b k represent the weight matrix and deviation term of the k-th convolution kernel, and conv(·) represents convolution operation. In each layer,K = 16 convolution kernels are designed, and the size is set to 3 × 3.
Due to the fact that convolution operations mainly handle local information of features. Directly processing the convolutional output features cannot effectively model the interrelationships between channels in the output features. To address this issue, we introduced channel-based attention mechanism after L2 to exploit the channel dependencies in outputs features σ 2 .
where σ 2 k ∈ R H×W is the feature maps corresponding to the k-th convolutional kernel with the heights of H and widths of W. Specifically, in L3, channel-wise statistic σ 3 = σ 3 1 , σ 3 2 , . . . , σ 3 K is generated by using global average pooling, and the k-th element σ 3 k of σ 3 is defined by www.nature.com/scientificreports/ where σ 2 k (κ, ν) is an element at the position (ν, κ) . In order to fully capture the dependencies between channels, we introduce two dense layers (L4 and L5) to form a bottleneck structure. The outputs of L5 is where W 1 ∈ R K r ×K and W 2 ∈ R K× K r are the weight matrix of L4 and L5, respectively.r represents the reduction ratio, and the value of r is 2 in this study via trade-off between performance and computational cost. In L6, the output of L5 is used to weight each feature map of L2. The mathematical expression is In general, layers 3-6 constitute attention paths, which can enhance the effective features of L2. L7 is a batch normalization (BN) layer that can mitigate overfitting and accelerate the training process. The implementation of BN is as follows: where θ and π are learnable parameters. In order to ensure the distribution consistency of σ 7 k and σ 6 k , σ 6 k is the normalized input data having the following form: where mean(·) and std(·) represent the expected value and standard deviation of σ 7 k . The task of the second block is the feature fusion, which integrates the network features of multiple frequency bands, and then realizes the detection of epilepsy. Firstly, outputs σ 1 k , σ 2 k and σ 7 k from eight frequency bands are concatenated together (L8). Then, we set a convolution layer (L9) with 32 kernels for feature fusion. The kernel size is 3 × 3 . The 32 feature maps of L9 are learned through another convolution layer (L10), and the kernel size is 1 × 1 . The outputs of L10 are flattened in L11. Finally, all learned features are input into a dense layer (L12) for epilepsy detection, using the softmax activation function.
Data availability
The data that support the findings of this study are openly available in CHB-MIT dataset at https:// physi onet. org/ conte nt/ chbmit/ 1.0. 0/. | 4,389 | 2023-07-03T00:00:00.000 | [
"Computer Science"
] |
Experimental Verification of Self-Adapting Data-Driven Controllers in Active Distribution Grids
Lately, data-driven algorithms have been proposed to design local controls for Distributed Generators (DGs) that can emulate the optimal behaviour without any need for communication or centralised control. The design is based on historical data, advanced off-line optimization techniques and machine learning methods, and has shown great potential when the operating conditions are similar to the training data. However, safety issues arise when the real-time conditions start to drift away from the training set, leading to the need for online self-adapting algorithms and experimental verification of data-driven controllers. In this paper, we propose an online self-adapting algorithm that adjusts the DG controls to tackle local power quality issues. Furthermore, we provide experimental verification of the data-driven controllers through power Hardware-in-the-Loop experiments using an industrial inverter. The results presented for a low-voltage distribution network show that data-driven schemes can emulate the optimal behaviour and the online modification scheme can mitigate local power quality issues.
Introduction and Related Work
Modern distribution system operators need to control Distributed Generators (DGs), such as Photovoltaic units (PV), wind turbines, and other distributed energy resources, such as battery energy storage systems and controllable loads, to guarantee safe grid operation, increase their operational flexibility or provide ancillary services to higher voltage levels. Centralised approaches based on optimal control of DGs usually require a communication, remote monitoring and control infrastructure, which current distribution networks (DN) lack due to high costs and complexity. On the other hand, local schemes offer communication-free, robust, cheap, but sub-optimal solutions which do not fully exploit the DG capabilities. Lately, data-driven control algorithms have been proposed, which use historical data, advanced off-line optimization techniques, and machine learning methods, to design local controls that emulate the optimal behaviour without the use of any communication [1][2][3][4].
The state-of-the-art data-driven schemes differ mainly in terms of two aspects. First, with respect to the existence of a feedback in the control method. Open-loop schemes, e.g., [2,4], do not use feedback, i.e., the DG output has no effect on the controller input variable. They are typically stable, and simple to implement. On the contrary, in closed-loop schemes, e.g., [1,3], the output of the controller has an impact on the local measurements and influences its input through a feedback term. These schemes are generally more • First, we propose a self-adapting algorithm for the data-driven controls to improve performance when the operating conditions are not as in the training dataset. • Second, we perform the first, to the best of our knowledge, experimental verification of data-driven local control schemes in inverter-based DGs to assess the performance of Artificial Intelligence (AI)-based controllers and identify hidden problems considering the whole system's response, and not just individual components. Such an experimental verification in the power system society using control schemes that are allowed already today in grid codes i.e., volt/var schemes, can foster real-life field implementation.
The remainder of this paper is organised as follows. In Section 2, we summarise the off-line optimization approach to derive the optimal setpoints and the design of the data-driven local controllers that emulate the optimal response. Then, we present the proposed real-time self-adapting algorithm in Section 3. In Section 4, we present an overview of different testing levels for controllers and hardware, and in Section 5 the experimental results using the typical Cigre European Low Voltage (LV) grid. Finally, we draw conclusions in Section 6.
Data-Driven Control Design
In this section, we briefly review the process for designing the data-driven local controls. In short, a large number of off-line OPF calculations that consider various expected and critical operating conditions are used to derive rules that depend only on local measurements. This is achieved by machine learning algorithms that map the multidimensional space of the OPF setpoints into a reduced space based solely on local features. Thus, in real-time operation, no monitoring and communication infrastructure is required. Interested readers are referred to [1] for more details.
As input data, the grid topology and the installed capacity of the DGs and loads is needed, information that is usually available to the DN operators. As the topology is not always known, one can use topology identification based on the voltage sensitivities [10], and phase identification based on clustering approaches [11]. Thus, even in this situation, we would need an identification step and then the same method should be applied. In case of missing information, e.g., normalised solar radiation data of specific areas, typical load profiles, or the actual line/cable impedances, the operator can use default values from the literature. The impact of such sources of uncertainty can be examined and quantified.
Then, an OPF algorithm is used to process the data and derive the optimal DG behavior. Although any OPF formulation can be used, we present below a formulation from [1] based on the backward-forward sweep (BFS) power flow.
OPF Formulation
In this part, we present the single-phase formulation considering only DGs, e.g., PV units in the LV grid. The Distribution System Operator (DSO) guarantees safe grid operation by minimizing the system losses and operating costs while satisfying the power quality constraints. In this formulation, we penalise the curtailment of active power and the provision of reactive power support by DGs. The objective function is evaluated by considering the DG control cost over all network nodes N b , branches N br and time horizon N OPF , i.e., where u denotes the vector of the available active control measures and ∆t the length of each time period. The curtailed power of the DGs connected at node j and time t is calculated by P c is the maximum available active power and P g j,t the actual active power injection of the DGs. The use of reactive power by the DGs connected at node j and time t is minimised, i.e., Q ctrl j,t = |Q g j,t |, where Q g j,t represents the reactive power injection or absorption. The cost of curtailing active power and providing reactive power support (opportunity cost or contractual agreement) is represented by the coefficients C P and C Q , respectively. Priority is given to the use of reactive power, i.e., we set C Q C P . Finally, the losses are calculated by P loss i,t = |I br i,t | 2 · R br i , where |I br i,t | is the magnitude of the current flow in branch i and R br i its resistance. The power injection at node j and time step t is given by where P l j,t is the node active power demand and cos(φ load ) is the power factor of the load, which is assumed to be constant. We assume loads of constant power in order to model the conservative case, i.e., voltage sensitive loads have a beneficial impact on voltage quality issues.
A single iteration of the BFS power flow problem is considered to represent the power flow constraints. That is: whereV * j,t is the voltage magnitude at node j at time t, * indicates the complex conjugate and the hat indicates that the value from the previous iteration is used (the interested reader is referred to [12,13] for more details in terms of the use of BFS in an OPF framework); I inj t = [I inj j,t , ∀ j] and I br t = [I br i,t , ∀ i] represent the vectors of bus injection and branch flow currents, respectively (I br i,t is the i-th branch current); BIBC (Bus Injection to Branch Current) is a matrix with ones and zeros, capturing the radial topology of the DN; the entries in ∆V t correspond to the voltage drops over all branches; BCBV (Branch Current to Bus Voltage) is a matrix with the complex impedances of the lines as elements; V slack is the voltage in per unit at the slack bus (here assumed to be 1 < 0 • ). Thus, the constraint for the current magnitude for all branches i at time t is given by where I max i is the maximum thermal limit of the branches. For the voltage magnitude constraints V min ≤ |V j,t | ≤ V max , we can approximate the voltage with its real part only, considering that the angles in DNs are very small. This approximation is particularly useful in planning problems which face tractability issues [12]. Thus, the voltage constraints are given by Finally, the limits of the inverter-based PVs are given by P min where P min j,t and P max j,t are respectively the lower and upper limits for active DG injection at time t and node j. The reactive power limits vary depending on the type of the DG and the control schemes implemented. Usually, small inverter-based generators have technical or regulatory [14] limitations on the power factor they can operate at. Here, the reactive power limit depends on the active power injections, and the acceptable power factor is denoted as cos(arctan(φ max )). This formulation does not consider the aspect of fairness in terms of the DG control. In case this is a DSO requirement, it can be easily considered in the mathematical formulation, e.g., by curtailing all DG units with the same p.u. amount according to their installed capacities.
After we obtain the optimal OPF setpoints, we perform an exact power flow calculation to derive an Alternating Current (AC) feasible operating point. The voltages of this point are used in the next OPF iteration, and the loop is repeated until we reach convergence in terms of voltage magnitude mismatch.
Control Design
In this section, we present the procedure to derive the data-driven closed-loop scheme, using the voltage magnitude as a local feature to control active and reactive power of the DGs. The final volt/watt and volt/var curves are similar to the ones used today in modern grid codes but can be composed of an arbitrary number of piece-wise linear segments, and are optimised for each DG based on its location and the DN objectives.
Regarding notation, the real-time response of the jth inverter-based DG (j ∈ [1, 2, . . . , N J ]) in terms of reactive power control q (j) t and active power curtailment c (j) t is derived from the N OPF optimal setpoints (t ∈ [1, 2, . . . , N OPF ]) obtained in the offline calculations. The feature matrix Φ (j) ∈ R N OPF × N K contains as columns the N K features and as rows the N OPF observations of the k th input measurement φ State-of-the-art methods consider multiple measurements, e.g., [2], and complex models, e.g., [3], to derive customised control laws. Intuitively, the more the used features, the better the optimal response can be emulated. The process of selecting the features which carry the most information is addressed in [2]. In [3], we highlighted the importance of using closed-loop schemes in terms of robustness to conditions which were not seen in the training dataset, e.g., topological changes. Furthermore, the focus of this paper lies on the experimental verification of schemes that can be easily embedded into real hardware, and thus, we will study schemes that rely only on local voltage magnitudes, are allowed by modern grid codes, and can be easily implemented within the DG inverters.
The procedure used in this work to derive the piece-wise linear curves is detailed in [1]. In summary, the characteristic curves for reactive power control and active power curtailment are calculated by applying segmented-regression, optimizing also the placement of the break-points. The iterative approach which solves a residual sum-of-squares (RSS) optimization problem inspired by [15], is summarised below.
First, we define the number of break-points n s , initialise them, and solve for each inverter j the following residual sum of squares problem subject tõ where the vectorxī refers to the reactive power control model at the current iterationī, and Φ (j) = [|V j,t |] is the vector of voltage magnitudes used as input to the fitting problem.
We fit the linear model based on the known breakpoints s¯i k , ∀k = 1, . . . , n s at the current iterationī, the left slope β 0 and difference-in-slopes β k . The indicator function I(·) becomes one when the inside statement is true. Finally,x 0 is the model intercept and γ a parameter which updates the location of the breakpoints towards the optimal one. The monotonicity constraint (weakly decreasing for the volt/var case) is imposed by Equation (15). The slope constraints defined by Equation (16) avoid sudden changes of the control actions. After the problem is solved, we update the breakpoints s¯i +1 k = γ k β k + s¯i k and iteration indexī =ī + 1, repeating the procedure until the RSS does not change between two subsequent iterations, i.e., when RSS¯i − RSS¯i −1 ≈ 0.
The same method is used for both the active power curtailment and reactive power control curves, using respectively the PV optimal active and reactive setpoints from the OPF results.
Online Controller Self-Adapting Algorithm
In this work, we propose for the first time a rule-based method to adjust a data-driven control scheme in real-time, without the need for retraining, when the observed behaviour deviates from the expected conditions of the training dataset. This might happen, e.g., when there is a topological change or new units are installed in the DN and the data-driven schemes do not imitate the optimal response anymore. Thus, instead of re-running the off-line methodology, a real-time self-adapting approach can be activated to tackle the power quality issues. The resulting scheme prioritises the local power quality issues over the overall system optimization of the off-line OPF calculations. Hence, modifying heuristically the data-driven schemes in real-time may result in a sub-optimal system response. Reinforcement and online learning has shown recently a lot of potential to control power systems in an adaptive way [16]. However, due to the lack of formal guarantees on constraint satisfaction, and complexity in the design and operational stages, we rely on a simple rule-based method which is rooted more in the power system background, i.e., reactive power sensitivity to voltage.
Algorithm 1 summarises the procedure to adjust the data-driven schemes for the case of the volt/var curves. The algorithm describes the overvoltage case, but the formulation for undervoltage issues, or thermal overloads is similar. It is assumed that only local measurements are available, e.g., the voltage magnitude V j,t of node j at each time step t. The average value of these measurements over a certain period, e.g., T = 5 minutes, is denoted by V j,T , and is used as the indicator to shift the characteristic curve. In the presence of overvoltages, e.g., due to an installation of a nearby PV unit which was not considered in the OPF calculations, the curve is shifted downwards to consume more reactive power than initially. The shifting step value w is chosen such that it results in a reasonable (but not too aggressive) voltage change. The modified controller is activated over the next time period T, and the curve is further shifted if the overvoltage phenomenon persists. However, if the voltage problem is fixed, the initial controller is restored, to avoid suboptimal behaviour without voltage quality problems, or when a temporal event triggered the voltage issue.
Algorithm 1 Real-time adjustment of the data-driven volt/var control scheme for the overvoltage case at t = t 0 Input: Averaging period T, voltage measurements V j,t , t ∈ [t 0 , t 0 − T], shifting step w and initial control modelx. Output: Shifting value shi f t and modified control modelx m . 1: Shift the curve downwards: Restore the data-driven curve: shi f t = 0 6: end if 7: The initial and modified control schemes are bounded to [−1, 1] p.u. The base power for the per unit values refers to each device separately, i.e., the value of 1 p.u. refers to the nominal capacity of each DG inverter. Thus, if the modified controller reaches the normalised bounds, the shifting would not have any effect as the inverter would not be able to contribute more. In this case, a similar approach for the active power curtailment controllers can be used.
Types of Controller Testing
This section lists the different controller testing levels used in this paper. These levels are organised in levels as shown in Figure 1, based on the methodology proposed in [7]. The lower levels are usually less expensive and more flexible, so they are used to detect and fix most flaws early on. The higher levels involve more specific hardware topologies, lower flexibility, and higher cost but are necessary before the product is released.
Purely Digital Simulations (PDS)
The simplest and most common level is the purely digital simulation conducted in one or more software. Typically, the control algorithm is written as a script within the software using specific functions and models. This testing is flexible, safe and can validate numerous algorithms for most power system applications. However, the interfacing between the power devices and the controllers can be difficult, and no simulation model or algorithm can accurately capture the real hardware behaviour.
Software-in-the-Loop Simulations (SIL)
Software-in-the-Loop (SIL) simulations use two or more separate but interfaced software platforms for the power and the control systems. The interconnected software platforms exchange signals in closed-loop, allowing for a more realistic representation of the setup, embedding the standard communication protocols (e.g., TCP/IP, Modbus, CAN bus). In this paper, the software that implements the online self-adapting control algorithm reads signals from the software that focuses on the solution of the power system components and sends output control signals to the other software through the communication link closing the loop. The limitations, apart from increasing cost compared to digital simulations, concern the synchronization requirements, the compatibility of communication protocols, and the initialization [17,18].
Control Hardware-in-the-Loop (CHIL) Simulations
HIL simulations use a Digital Real-Time Simulator (DRTS) to simulate a power system in real-time and connect to real devices through the multiple input/output channels. In essence, a DRTS solves the model equations for one time-step within the same time period of a real-world clock. A summary of existing DRTSs can be found in [19].
CHIL simulations can interface multiple real hardware devices within the same simulation, using the analogue and digital input/output signals that the DRTSs offer. In this scheme, any device that uses analogue and digital signals can be interfaced with the DRTS, exchanging data and control signals according to the functionality of the controller. Noise and time delays can be considered in the exchanged signals, and various aspects of communication can be studied, such as the impact of delays, packet loss and bandwidth limitations. While CHIL simulation is adequate to thoroughly verify the operational functionality of the controller, it cannot guarantee the performance of the power device as a whole.
Power Hardware-in-the-Loop (PHIL) Simulations
PHIL simulations provide the most realistic environment before the real life field implementation. It combines the benefits of the DRTS, i.e., real-time simulation, safety, flexibility and accuracy, with the use of an actual device that can be interfaced with the simulated power system. In PHIL simulations, a power interface is required to connect HuT (the PV inverter in this paper) and the DRTS through the exchange of low level signals, since the analogue (resp. digital) ports of a DRTS operate in a voltage range of ±10 V (resp. 5 V). Typically, the power interface consists of a power amplifier that receives reference variable values from the DRTS and applies them to the HuT. Finally, a sensor measures the response of the HuT according to its control algorithm and communicates it back to the DRTS closing the loop. The inclusion of the power interface is crucial to the experiments, since it can lead to stability and accuracy issues. These problems are considered for example in [8,20] and are outside of this paper's scope.
Real-Life Field Testing
This is usually the last testing level before releasing a product. A real-life field testing is performed to validate the controller behaviour in a real setting.
Experimental Results
In this section, we describe the experimental infrastructure at the Electric Energy Systems laboratory of the National Technical University of Athens (NTUA) that was used for the following experiments. We also present the balanced LV DN used for the experimental verification of the proposed data-driven schemes. Subsequently, we proceed with the experimental validation of the derived data-driven controls with SIL and combined SIL-PHIL simulations. We provide modelling details of the simulated system in the DRTS platform to highlight realistic aspects of HIL setups. The implementation of the off-line method (Section 2) was done in MATLAB (Mathworks Inc., Natick, MA, USA) using YALMIP [21] as the modelling layer and Gurobi (Gurobi Optimization, Beaverton, OR, USA) [22] as the solver. The results were obtained on an Intel Core i7-2600 CPU and 16 GB of RAM.
Laboratory Infrastructure
A detailed description of all the components and capabilities of the microgrid can be found in [9]. The DRTS used at NTUA is a Real Time Digital Simulator (RTDS) [23].
Experimental Setup
We use the benchmark radial Cigre LV grid [24] to experimentally validate the proposed controls. We simplify the system to 11 nodes to reduce the computational burden on the DRTS, as illustrated in Figure 2. The PVs are installed on nodes [8,9,10,11] with capacity [30, 15,3,15] kWp respectively. In this work, we only consider single-phase system operation, due to the technical capabilities of the experimental setup, but extending to unbalanced three-phase operation is straightforward. The operational costs are assumed to be c P = 0.3 CHF kWh and c Q = 0.01 · c P .
SIL Implementation
For the SIL implementation, we use the RSCAD (RTDS Technologies Inc., Winnipeg, Canada) software [23]. Several control blocks define the real-time operation of each unit of the examined LV grid. The MV grid is represented by a Thevenin equivalent, assuming a nominal voltage of V MV = 20 kV at the MV level short-circuit power S sc = 100 MVA. Furthermore, assuming an R/X ratio of 1, the MV impedance Z MV , is calculated to be 828 Ω. Hence, the inductance is given by L MV = X MV 100π = 0.009 H. The single phase quantities for all components, loads, PV units, etc., are derived by dividing the corresponding three-phase values by three, and are modeled as current sources.
As this experimental setup does not include a transformer with on load tap changing capabilities, only the DG inverters can contribute to the voltage regulation through the injection and consumption of reactive power. The experiment is done using optimised local volt/var curves, as described in Section 5.3 implemented in RSCAD.
The self-adapting algorithm described in Section 3 is implemented in MATLAB. Thus, the RTDS calculates in real-time the state of the DN based on the existing loading, solar radiation conditions and local data-driven conditions, and sends the voltage measurements to the MATLAB function that calculates the shifting value in case of overvoltages. The resulting values are passed to the inverter blocks of RSCAD that update their curves and continue to provide real-time reactive power control.
SIL-PHIL Implementation
For the combined SIL-PHIL simulation, the laboratory test environment depicted in Figure 3 is used. In this setup, one of the PV inverters is replaced with a real component that represents the HuT of the PHIL test and the self-adapting algorithms represent the software under test of the SIL simulation. Thus, the self-adapting controllers are implemented in another software which interacts with the real-time simulation through reading local voltage measurements and sending the derived shifting setpoints.
The Sunny Boy 3000 TL inverter from SMA (Niestetal, Germany) [25] is used as the HuT combined with a PV simulator. This commercial inverter is capable of operating using both local control strategies, i.e., closed-loop volt/var curves or the current open-loop scheme implemented in Germany [14], and following a centralised approach receiving specific P and Q setpoints. This allows us to first calculate the shifting from the selfadapting algorithm, apply the measured voltage to the characteristic volt/var curve of the PV, and finally calculate and send specific reactive power setpoints to the inverter.
In this setup, the power interface is composed of a Spitzenbenger Spies PAS5000 linear amplifier (Viechtach, Germany) and the Tektronix A622 current probe (Beaverton, OR, USA). The DN shown in Figure 3 is simulated in the RTDS, and the voltage at the Node 10 is transferred as a reference low level signal to the power interface (via the D/A interface of the RTDS). This reference voltage is amplified and applied to the real PV inverter. The AC current flowing from the inverter is then measured and sent back to the simulation closing the loop (via the A/D interface of the RTDS). Finally, the communication of the RTDS with the external software is implemented with a communication interface, which is based on the TCP-IP protocol. The self-adapting algorithms are again realised in MATLAB and tested in a SIL concept combined with the PHIL simulation. The nodal voltages of the simulated DN are provided to the software and the calculated results are sent back to the simulation to close the loop.
Individual Data-Driven Local Control Schemes
The derivation of the data-driven local controllers is based on a 30-day summer dataset following Section 2. The resulting control curves are shown in Figure 4. All the PV units show a capacitive behaviour at low voltages to increase voltages, optimise losses and to satisfy the local reactive power demands by local injections. As voltage approaches the maximum acceptable value of 1.05 p.u., the units start switching into inductive mode. The higher the voltage, the more reactive power is absorbed by the units. However, the fact that for the maximum voltage value of 1.05 p.u., not all units absorb their maximum reactive power shows that in the underlying optimization problem the capacity was enough to solve the local overvoltage issues.
Experimental Results
In this section, we first present the results under expected conditions (included in the training dataset) and we investigate the suitability of data-driven controllers to emulate the optimal response. Then, we present the behaviour under new conditions due to the installation of a PV unit which was not considered in the offline methodology. The latter reveals the risks from using AI-based controls in real-time and highlights the contributions of the proposed online adjustment algorithm to satisfy the power quality constraint.
Expected Conditions
The real-time behaviour is evaluated using different input data from the training set. More specifically, we use the operating conditions of a summer day in July and provide the power flow results for the time period of 8 h. Figure 5 shows the voltage magnitude evolution of the considered methods. First, we observe that operating without control, i.e., when the PV units inject their maximum available active power, results in overvoltage issues. The real-time OPF method serves as a benchmark case that shows the optimal response that satisfies all network constraints at minimum cost. We observe that the data-driven approach emulates satisfactorily the real-time OPF due to the customised and optimised volt/var curves of Figure 4. The experimental results using the RTDS verify the suitability of the data-driven schemes to emulate the optimal response under expected conditions. The experimental results are closer to the simulation results at high solar radiation hours for the case without reactive power control, and the largest deviation for both cases is 0.7%.
Online Self-Adapting Algorithm
In this part, we assume that a new PV unit is installed at node 6 with the same installed capacity as the PV unit at node 10. This unit is operating according to the standard volt/var curve as indicated in the grid codes [14] and the location is chosen such that its impact on the neighboring nodes is significant (installations closer to the secondary of the transformer where voltage is regulated would have less impact on the system). Figure 6 shows the voltage evolution with (solid lines) and without (dash-dotted lines) the online modification of the volt/var curves. The shifting step is set to 0.1 p.u. and it was adequate to reduce the overvoltage issues of node 11. The combined SIL-PHIL experiment which considers the self-adapting algorithm confirmed the simulation results as can be seen by the voltage measurements at nodes 9 and 11. Upper Limit PHIL: PV 9 PHIL: PV 11 Figure 6. Voltage evolution at PV nodes with (indicated by solid lines) and without (indicated with the dash-dotted lines) using the self-adapting algorithm.
Comparative Evaluation of Optimal, Adaptive and Non-Adaptive Schemes
Finally, in this part, we compare the whole system response in terms of power quality constraint satisfaction, loss minimization and the use of flexibility in terms of reactive power control. Through the different setups, we highlight the inefficiencies of current industrial practices, the emulation of the optimal response via the data-driven controllers, the suboptimality for not updating the optimised curves by rerunning the offline methodology, and the suitability of the online algorithm to solve local voltage issues. More specifically, we consider the following cases: • Method 0: PVs inject the maximum active power at unity power factor. This scheme shows the real-time behaviour when no control measures are taken. • Method 1: PVs operate according to the same standardised volt/var curves from the IEEE grid-codes [26]. The maximum acceptable voltage is set to 1.05 p.u. We use this scheme as the benchmark for the current industrial practice without the possibility for online adjustments. • Method 2: PVs operate according to the German grid-code [14]. DGs become inductive when injecting more than 50% of their installed capacity. The power factor decreases linearly from 1 to 0.95 or 0.9 based on the DG capacity. This scheme is also used as the current open-loop industrial practice without online adjustments. • Method 3: PVs are controlled with a centralised OPF algorithm summarised in Section 2. This scheme is used as the benchmark for the best achievable performance. • Method 4: The offline training methodology is repeated considering the addition of the PV unit. The PV inverters implement the updated volt/var curves which refer to the new conditions, and the self-adapting algorithm in case of unexpected overvoltage issues. • Method 5: The PV units are operating according to the initial local data-driven schemes without re-training, i.e., the PV unit at node 6 is not considered in the design stage. Potential overvoltages are tackled by the online algorithm proposed in Section 3. Table 1 summarises the comparison in terms of the maximum observed voltage magnitude, the total system losses and the use of reactive power m Q , calculated by, Both the current industrial practices, i.e., methods 1 and 2 result in overvoltages and increase the losses compared to the "no control" method 0. Method 2 utilises more reactive power in terms of additional demand, showing the highest losses since more reactive power is needed during times with high solar radiation. The data-driven methods 4 and 5 manage to mimic the optimal centralised response of method 3 closely. Method 4 shows a marginally closer to the optimal behaviour due to the repetition of the offline methodology and the derivation of the updated optimised volt/var curves which consider the added PV unit. The difference between methods 4 and 5 depends on the potential to modify the characteristic curves online and on the location and the size of the added element, i.e., a larger PV unit would provoke larger deviations. Characteristic curves saturated at −1 p.u. do not provide additional flexibility to solve local power quality issues online, when the real-time conditions have changed significantly from the training dataset.
Conclusions
Active distribution grids rely on real-time DG control to ensure a safe and reliable grid operation. Data-driven, purely local, strategies can bridge the gap between optimal (but costly) centralised approaches and robust (but suboptimal) existing local schemes. In this paper, we verified experimentally the behaviour of data-driven controllers and proposed an online self-adapting algorithm to modify the control schemes when local power quality issues are observed. The experimental verification of the results can be used towards the development of new grid codes that will allow the implementation of state-of-the-art methods developed in this paper, such that the operational flexibility provided by active DGs is used to alleviate power quality problems, defer grid investments and optimise the grid use. Future work will focus on experimental studies on ancillary service provision including battery energy storage systems and demand response schemes. The indicator function which becomes one when the statement inside is true.
N b
Total number of network nodes (−).
N br
Total number of network branches (−). cos(φ l ) Power factor of the load (−). ∆t Length of a time interval within the optimization horizon (h). P max g,j,t Maximum available active power of the DER connected at node j, at time t (kW). C P Fixed cost of curtailing active power ( CHF kWh ). C Q Fixed cost of providing reactive power support (DER opportunity cost or contractual agreement) ( CHF kVArh ). V * j,t Voltage magnitude at node j, and time t; the bar indicates that the known value from the previous Backward/Forward Sweep iteration is used (p.u.). V slack Complex voltage at the slack bus (here assumed to be 1 0 • (p.u.).
V min /V max Minimum/Maximum acceptable voltage magnitude (here assumed to be 0.95/1.05 (p.u.). I max i Maximum thermal limit for the i-th branch (p.u.). P min g,j,t /P max g,j,t Upper and lower limits for the active DER power at node j, and time t (kW). Active power injection of the DER connected at node j, at time t (kW). P c j,t Curtailed active power of the DER connected at node j, at time t (kW). P loss i,t Active power losses at branch i, at time t (kW). P l j,t /Q l j,t Active and reactive demand of constant power type at node j, at time t (kW).
Q g j,t Reactive power injection (positive) or absorption (negative) of the DER connected at node j, phase z, at time t (kVAr). Matrix with ones and zeros, capturing the radial topology of the network.
BCBV
Matrix with the complex impedances of the lines as elements.
Φ (j) Feature matrix containing the optimal setpoints which refer to the local measurements (features) that are used for the design of the local controllers. | 8,048.6 | 2021-05-02T00:00:00.000 | [
"Engineering",
"Computer Science"
] |
Electronic decay of core-excited HCl molecules probed by THz streaking
The ultrafast electronic decay of HCl molecules in the time domain after resonant core excitation was measured. Here, a Cl-2p core electron was promoted to the antibonding σ* orbital initiating molecular dissociation, and simultaneously, the electronic excitation relaxes via an Auger decay. For HCl, both processes compete on similar ultrashort femtosecond time scales. In order to measure the lifetime of the core hole excitation, we collinearly superimposed 40 fs soft x-ray pulses with intense terahertz (THz) radiation from the free-electron laser in Hamburg (FLASH). Electrons emitted from the molecules are accelerated (streaked) by the THz electric field where the resulting momentum change depends on the field's phase at the instant of ionization. Evaluation of a time-shift between the delay-dependent streaking spectra of photo- and Auger electrons yields a decay constant of (11 ± 2) fs for LMM Auger electrons. For further validation, the method was also applied to the MNN Auger decay of krypton. Reproduction of the value already published in the literature confirms that a temporal resolution much below the duration of the exciting x-ray pulses can be reached.
I. INTRODUCTION
When an electron of a molecule is resonantly excited to an antibonding orbital, the molecule will start to dissociate, and if the excitation has created a core-hole, the latter will typically relax electronically. Electronic de-excitation via the emission of an Auger electron is usually very fast and occurs within a few femtoseconds, while in most cases, the nuclear rearrangement is much slower. Thus, both processes can often be studied separately. However, for molecules containing a very low mass component such as the hydrates HI, HBr, and HCl, the dissociation is also very fast and may evolve on similar time scales as the Auger-emission. This was first observed in HBr molecules after resonant excitation of 3d electrons to the antibonding 4r à orbital. 1 The emitted Auger electrons were found to exhibit a narrow, atomiclike spectrum, thus indicating emission of the majority of the electrons after the dissociation. This energy-domain technique of deducing the dynamics of a process by relating it to the exponential decay of a core excitation is commonly known as the core hole clock method and has been used for decades to study molecular dissociation rates, 2 charge transfer processes, 3 and wave packet dynamics. 4 The speed of the electronic decay, which sets the clock in these experiments, can be easily obtained for pure atomic Auger electrons where the excited state lifetime can be inferred from spectral widths. This is no longer possible for Auger electrons emitted from molecules where the spectra are broadened due to nuclear motion. For these systems, a time domain approach may remove such limitations.
The first time domain measurement of an Auger decay was performed in krypton atoms. 5 The atoms were ionized by attosecond XUV pump-pulses from a high-harmonic source, and the energy of the emitted Auger electrons was modulated (streaked) by an ultrashort, few femtosecond near-infrared (NIR) laser probe-pulse. By scanning the delay of the pump-and probe-pulse, the Auger lifetime of 7:9 þ1 À0:9 fs was measured. Later, experiments measured Auger lifetimes by using charge state chronoscopy 6,7 and recently by transient absorption spectroscopy. 8 All these experiments rely on attosecond pump-and intense few femtoseconds NIR probe-pulses. The generation of attosecond pulses is experimentally very challenging and limited to the XUV regime, if decent photon flux is required. Another difficulty in these experiments is the high intensity of the NIR probe pulses. On the one hand, these NIR pulses can perturb the system under study 8 and, on the other hand, can lead to a significant background signal due to multiphoton ionization.
Here, we present an alternative way to measure the Auger lifetime by using femtosecond pump-pulses and long-wavelength THz radiation to streak the emitted electrons. The system under study is excited by short soft-x-ray pulses, and the emitted electrons are accelerated by a superimposed THz-light field with an oscillation period much longer than the investigated processes. The interaction with the THz field causes a time-dependent momentum change Dp of the electrons according to where A(t i ) is the vector potential at ionization time t i , u t i ð Þ is the phase of the electric field, and e is the electron charge. When the relative delay between the electron emission time and the THz streak-field is scanned, the evolution of the streak field is mapped onto changes of electron energies. The electron emission time t i is encoded in the phase of the streak curve.
Photoelectrons are emitted instantaneously after absorption of the ionizing photons, and thus, the temporal profile of the photoelectron emission rate represents the temporal profile of the ionizing light pulse. The temporal profile of the Auger emission can be described as a convolution of the temporal distribution of the core hole creation and the evolution of the Auger process. Here, we assume a soft x-ray pulse with a Gaussian intensity envelope I X ¼ e À t 2 2r 2 X and an exponential Auger decay where r X corresponds to the root mean square (rms) pulse duration and A 1 corresponds to the initial number of core vacancies. The parameter t 0 accounts for a delayed starting time for the decay. The Auger emission rate is then given by Figure 1(a) depicts the calculated photo-and Auger electron emission rates generated by a light pulse of 17 fs rms duration (40 fs FWHM) and an Auger lifetime of s AE ¼ 7:9 fs: For x-ray pulse durations much longer than the Auger lifetime, the shape of the Auger emission rate is mainly determined by the shape of the ionizing light pulse. However, in spite of an instantaneous start of the decay (t 0 ¼ 0), due to the finite lifetime s AE , the maximum of the Auger emission is delayed by Dt with respect to the maximum of the photoelectron emission. The shift depends on the Auger lifetime, the x-ray pulse duration, and the onset t 0 of the decay [see Fig. 1 Owing to this shift Dt, the on-average delayed Auger electrons are exposed to a different phase of the streak field than the photoelectrons. This is visible as a phase shift between the photo-and Auger electron streak spectrograms. Phase shifts much smaller than the duration of the ionizing pulses become discernible. Therefore, lifetimes much shorter than the duration of the exciting pulses can be measured. This technique is regularly used in attosecond physics, where, e.g., photoemission delays of (21 6 5) as have been observed after ionization with 200 as XUV pulses and streaking with NIR. [9][10][11] To obtain meaningful streak spectra, streak-fields with oscillation periods long compared to both the processes studied and the ionizing light pulses have to be used. NIR at an 800 nm wavelength has an oscillation period of only 2.5 fs and is suitable to study processes in the attosecond range. By choosing a streak-field in the far infrared (THz) region with an oscillation period of 177 fs, we have transferred this technique to the femtosecond range. 12 We found a THz electric field of only 5 MV/ m to be sufficient for the streaking, which is about three orders of magnitude below the field strengths commonly used in NIR streaking experiments. (a) Emission rates of photo-(black) and Auger electrons (red) generated by an x-ray pulse with a Gaussian envelope and a rms pulse duration of 17 fs (40 fs FWHM). As an example for krypton, the value of the Auger lifetime is here set to 7.9 fs with t 0 ¼ 0. Due to the on average later emission of the Auger electrons, the peak of the Auger emission is shifted by Dt with respect to the maximum of the photoelectron emission. (b) The Auger lifetime s AE as a function of the time shift Dt of the Auger emission peak for different rms x-ray pulse durations. The shift was determined by fitting Gaussian curves to the Auger emission rates calculated using Eq. (3) with t 0 set to zero.
First, to prove the ability and test of the accuracy of the experiment, the lifetime of the well-studied MNN Auger decay in krypton has been measured. Subsequently, the characteristic time constant of dissociating HCl molecules after resonant excitation of 2p Cl electrons to the antibonding r à orbital was investigated, which to our knowledge is the first time-resolved measurement of this process.
II. EXPERIMENT
The experiment has been performed at the free-electron laser in Hamburg FLASH. 13,14 A detailed description of the setup can be found in Ref. 12. Briefly, the soft x-ray pulses provided by FLASH are collinearly superimposed with intense 53 lm THz pulses from the FLASH THz undulator. 15 Both pulses are inherently synchronized due to their generation from the same electron bunch. The 200 eV soft x-ray pulses were focused with a Cr/C multilayer mirror with a focal length of 2 m to a spot of about 100 lm diameter. The peak reflectivity of the mirror was 15% at a wavelength of 6.19 nm (200.3 eV) with a FWHM bandwidth of 2.8 nm (2.4 eV). The spectrum of the soft x-ray pulses was measured with the FLASH VLS spectrometer. 16 The width of the spectrum averaged over 5000 shots was 0.05 nm (1.5 eV) FWHM with a central wavelength within the mirror reflectivity range.
The THz pulses with an initial pulse energy of 1-2 lJ and a pulse duration of 1.8 ps were focused using an off-axis parabolic mirror with a focal length of 200 mm. To filter out harmonics and smooth the evolution of the THz field, a THz bandpass filter was used. The electric field-strength in the THz focus was 5 MV/m, which was determined from the streaking amplitude. The delay between the two pulses was adjusted by a delay stage with a step size of 1 lm. The target gases were provided by a pulsed nozzle (Parker Series 9 solenoid valve). The target density was set carefully in order to avoid space-charge effects with chamber pressure being lower than 5 Â 10 À6 mbar. The electron spectra were recorded by two time-of-flight spectrometers (TOFs) aligned in the plane of the THz polarization. The polarization of the soft x-ray pulses was perpendicular to the polarization of the THz light.
The duration of the soft x-ray pulses was in the range from 30 to 50 fs (FWHM). For each run of measurement, the average x-ray pulse duration was determined by comparing the width of unperturbed electron spectra with those taken close to a zero crossing of the THz vector potential. Assuming a Gaussian envelope of the averaged x-ray pulses, the rms pulse duration can be calculated by 17 where r streak and r 0 correspond to the rms widths of the streaked and the field-free electron energy spectrum, respectively, which were obtained by fitting with Gaussian curves. The streaking speed s is defined by the temporal derivative of the electron energy change DW jj due to the streaking field The streaking speed is assumed to be constant close to zero crossings of the vector potential and was determined by a linear fit to the centers-of-mass of the photo-and Auger electron peaks in the vicinity of the zero crossings. In our case, streaking speed values in the range of 0.01 to 0.04 eV/fs were measured. By comparing streak spectra taken by the two TOFs facing each other, a possible linear chirp of the soft xray carrier frequency can be disclosed. During the measurements, no significant chirp was observed.
A. Auger decay in krypton
In order to prove the capability of our setup, we investigated the well-studied atomic Auger decay in krypton as a benchmark experiment. To this end, krypton atoms were ionized with 200 eV photons. With this photon energy, it is possible to ionize krypton 4p, 4s, and 3d electrons. Upon photoionization of 3d electrons, the generated core hole relaxes via emission of MNN Auger electrons. Figure 2 depicts averaged kinetic energy spectra of the M 4,5 N 1 N 2,3 krypton Auger electrons in the range of 35 eV to 43 eV measured with (solid red line) and without the THz streak field (solid blue line). Here, the delay between the THz and x-ray pulses was set close to a zero crossing of the THz streak field.
The dashed lines represent Gaussian curves fitted to the Auger peaks. Here, as a boundary condition for the fit, the rms widths of the M 5 N 1 N 2,3 (first) and M 4 N 1 N 2,3 (second and fourth) peaks were set to be equal. The third peak contains two lines with an energy difference of 0.1 eV 18 which cannot be resolved. Thus, the position and width of this line were chosen to be free parameters. Also, a constant background was included as a fit parameter. From the streak broadening of the Auger M 5 N 1 N 2,3 line, taken from the fits, a mean duration of the electron distribution of (44 6 5) fs FWHM was determined. As discussed above, the temporal profile of the Auger electron emission consists of a convolution of the temporal profile of the ionizing x-ray pulse and an exponential function describing the Auger decay (see Fig. 1). A deconvolution of the well-known 7.9 fs Auger lifetime yields an x-ray pulse duration of (41 6 5) fs. Figure 3(a) displays a series of streaked electron spectra for different THz/x-ray delay times. The oscillation of the THz vector potential is mapped onto changes of the recorded electron energies. As indicated in Fig. 1, the maximum of the Auger temporal profile is shifted with respect to the maximum of the photoelectrons by Dt. This temporal delay is visible in the streak scan as a phase difference between the streak-oscillations of the direct photo-and the Auger electrons. To FIG. 2. Krypton M 4,5 N 1 N 2,3 Auger spectra measured without (blue) and with (red) the terahertz streak field with fitted Gaussian curves. The spectra were averaged over 100 shots. From the fits, the energy shift DE of the streaked electrons with respect to the field-free spectrum was obtained.
ARTICLE
scitation.org/journal/sdy precisely determine this phase difference, Gaussian curves were fitted to the photo-and Auger electron lines of each spectrum, and their energy shift with respect to the field-free spectrum was calculated (see Fig. 2). Plotted in Fig. 3(b) are the thus obtained energetic shifts due to streaking. Owing to a higher initial kinetic energy of the 3d photoelectrons (W 0,PE ¼ 107.5 eV) compared to the Auger electrons (W 0,AE ¼ 37.7 eV), the streak amplitude is a factor of ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi W 0;PE =W 0;AE p ¼ 1.7 higher for the photoelectrons. By fitting sinusoidal curves to the data, a time difference between the two functions of Dt ¼ (7 6 5) fs was determined as the weighted average of two scanning trace measurements, where both electron spectrometers were evaluated.
In an alternative evaluation procedure, electron spectra measured with both TOFs at three different time delays close to zero crossings of the THz vector potential were analyzed. The shift of the photo-and Auger electron spectra with respect to the field-free spectrum was evaluated for each measurement trace. Here again, the peaks were fitted by Gaussian functions. At the zero crossing, we assume the energy change to be linear in time and calculate the mean ionization time t i ¼ DE=s for each peak, where DE is the energy shift of the investigated spectral line and s is the corresponding streaking speed [see Eq. (5)]. For this evaluation procedure, a time difference of Dt ¼ (6 6 5) fs between the maxima of the M 5 N 1 N 2,3 Auger and the 3d photoelectron peak as the weighted average over all measurements at the three different zero crossings was obtained.
Using Eq. (3), the estimated pulse duration, and the weighted average of the determined time shifts, the values corresponding to an Auger lifetime of s ¼ (8 6 5) fs are in agreement with the literature. 5 The error of the Auger lifetime is dominated by the error in the determination of the shift Dt and could be reduced by improving the measurement statistics.
B. Auger decay in HCl
Owing to the hydrogen's low mass, the dissociation of HCl after resonant excitation of 2p Cl electrons to the antibonding r à orbital is fast compared to the electronic relaxation via Auger decay. Thus, the majority of the Auger electrons are emitted from chlorine atoms rather than HCl molecules. This is reflected in sharp atomiclike Auger lines. 19,20 However, the Auger spectra are not purely atomic because some electrons are also emitted before and during the dissociation. The kinetic energy distribution of these electrons is broad and shifted to smaller energies with respect to the atomic Auger lines. 21,22 The precise shape of the spectrum depends on the dissociation speed and thus on the energy of the exciting photons. 23 While the energy of the emitted Auger electrons is clearly influenced by the nuclear dynamics, we assume the lifetime of the core hole excitation to be independent of the internuclear distance of the Cl and H atoms and to be in good approximation equal to the lifetime of the 2p 3/2 hole in the Cl à atom. 21,24 Figure 4 depicts streaked (red) and THz-field-free (blue) electron spectra of HCl molecules measured after resonant excitation with (200.0 6 1.5) eV photons. These spectra are each integrated over 600 single shots. In the non-streaked spectra, the lines at 179 eV and 180.5 eV correspond to the atomic chlorine Auger lines from the 2p 5 3=2 3p 6 ! 3p 4 ( 1 D) and 2p 5 3=2 3p 6 ! 3p 4 ( 3 P) transitions and are clearly resolved, whereas in the streaked spectrum, these Auger lines overlap due to the broadening induced by the THz field. The feature at 182 eV is attributed to 2p 1/2 ( 3 P) Auger electrons. Due to the broad excitation photon energy of 1.5 eV, this line contributes as well to the average spectrum. The Auger decay from the 2p 5 3=2 3p 6 ! 3p 4 ( 1 S) at 177 eV is not resolved. Here, the atomic Auger peak overlaps with contributions of molecular Auger electrons. The peaks at 184 eV and 188 eV belong to the photoelectrons of the 5r À1 and 2p À1 orbitals. The dashed lines mark Gaussian curves which are fitted to the investigated peaks. As a restriction to the fit parameters, the widths of the atomic Auger peaks are assumed to be equal, and their distance is fixed to dE ¼ 1.5 eV taken from Ref. 20. We estimated the total amount of the molecular Auger electrons by calculating the difference of the measured spectra and the fitted Gaussian curves to (26 6 15) %. From the broadening of the 2p À1 photoelectron peak, a pulse duration of (40 6 5) fs (FWHM) was determined which confirms the pulse duration obtained from the broadening of the krypton Auger line. 3. (a) Series of krypton Auger (upper panel) and photoelectron (lower panel) spectra simultaneously taken for different THz/x-ray delay times. Each spectrum was averaged over 100 shots, and the overall intensity was normalized. The white dots represent the maxima of Gaussian curves fitted to the spectra. The 1.2 eV spin-orbit splitting of the 3p 5/2 and 3p 3/2 photoelectrons is not resolved by the TOFspectrometers. (b) Sinusoidal curves (solid lines) are fitted to the energy shift of the photo-and Auger electron lines of the spectra. The phase shift between the Auger and photoelectron curves reflects the averaged delayed emission of the Auger electrons. For this particular scanning trace, a delay of (7 6 9) fs was determined from the sinusoidal fits.
ARTICLE
scitation.org/journal/sdy Figure 5 shows a series of HCl photo-and Auger electron spectra taken for different THz/x-ray delay times. At each delay position, 100 shots are averaged. The phase-dependent energy-modulation of the 1 D and 3 P Auger electron and the 2p À1 photoelectron lines is clearly visible. The peaks are fitted with Gaussian functions, and the white dots mark the resulting peak centers. For a quantitative analysis, a sinusoidal function is fitted to the center of the 2p 3/2 ( 1 D) Auger and the 2p À1 photo line. A time shift of Dt ¼ (12 6 3) fs is discernible from the difference of the respective fitting parameters. This is the weighted mean of eight scanning traces where both electron spectrometers were evaluated. Again, we additionally determined the time shift by evaluating the energy shift of streaked Auger and photoelectron peaks with respect to the field-free peaks measured close to a zero transition of the THz vector potential. Here, a time shift of Dt ¼ (15 6 4) fs was obtained. The value is a weighted average of data taken at ten different zero-crossing points with TOF spectrometers. The measurement error is dominated by the uncertainty of the streaking speed s. For further calculation, the weighted average of both methods is used.
The time shift given here was evaluated for the atomic Auger lines. As estimated above, (26 6 15) % of the Auger electrons are emitted in the molecular regime. To obtain the lifetime of the Cl à core hole excitation, we follow the model of Menzel et al. 21 and assume an exponential Auger decay rate with a lifetime s Cl independent of the nuclear motion. The energy of the Auger electrons depends on the internuclear distance R at the emission time. The first electrons are emitted while R is small and are energetically shifted with respect to the Auger electrons emitted at later times with large R values. Thus, the emission of Auger electrons with energies corresponding to the atomic lines sets in with a delay t 0,atomic , which can be understood as the dissociation time of the molecules.
Assuming a pure exponential decay as in Eq.
(2), we can calculate t 0;atomic ¼ Àln 1 À 0:26 ð Þ s ¼ 0:3 s. Inserting this as t 0 in Eq. (3), we deduce the corresponding Auger lifetime s Cl ¼ ð1162Þ fs for the determined time shift and pulse duration. The error of the estimated decay time starting point t 0 is rather large (660%); however, the obtained value for s only slightly depends on t 0 . Note that including t 0,atomic in Eqs. (2) and (3) is an approximation which is applicable in the case discussed here, where the main contribution to the error of s is given by the uncertainty in the determination of the time shift Dt.
IV. CONCLUSION AND OUTLOOK
We have introduced the measurement of time-shifts in terahertz streak spectrograms to study ultrafast electronic decays and applied it to investigate the Auger decay in krypton atoms and HCl molecules. The measured lifetime for the krypton MNN Auger decay of s ¼ (8 6 5) fs is in very good agreement with previous studies. 5 For HCl molecules, the lifetime broadening of the 3s3p 5 Auger lines was measured after resonant excitation with 200.85 eV photons to be (96 6 5) 3 P Auger electron peaks (lower lines) are taken from the fitted Gaussian curves. Each spectrum is an averaged over 100 shots and is intensity-normalized. (b) Sinusoidal curves (solid lines) are fitted to the energy shift of the photo-and Auger electron lines of the spectra. The visible phase shift between them reflects the on-average later emission of the Auger electrons with respect to the photoelectrons. For this particular trace, the determined delay is (15 6 7) fs.
FIG. 4.
Photo-and Auger electron spectra from HCl molecules measured without (blue) and with (red) the terahertz-streak field. The dashed lines indicate Gaussian curves fitted to the investigated peaks in order to obtain the energy shift DE of the streaked electrons with respect to the field free case. Each spectrum is averaged over 600 shots.
ARTICLE
scitation.org/journal/sdy meV, which corresponds to a lifetime of 7 fs. 25 In contrast to the 3s 2 p 4 Auger lines investigated here, these lines are almost purely atomic and thus facilitate such measurements. The value obtained by terahertz streaking of s ¼ ð1162Þ fs is significantly longer and also longer than the 2p 3/2 core hole lifetime of isoelectronic argon atoms, which was inferred from the Auger linewidth to be only 5.9 fs. 26 Though, it is in good agreement with the value of 12 fs estimated by Menzel et al. using a quasiclassical model. 21 The lifetime presented in this work is the combined emission time for the 1 D and 3 P Auger electrons. Within our experimental resolution, no difference for the two lines was discernible. The measurement error is dominated by the uncertainty in the determination of the time shift between streaked photo-and Auger electron peaks. It was large in the case of krypton due to low statistics since only two streak traces with a moderate signal to noise ratio were measured in the available time window of approximately 2 h. If the x-ray pulse duration is long compared to the Auger lifetime, as is the case here, a variation of the pulse duration has only a small impact on the determined time constant. For example, in the case of krypton with s ¼ (8 6 5) fs and an x-ray pulse of 40 fs FWHM, a change of 50% of the pulse duration results in a change of only 3.4% of the value determined for the Auger lifetime.
The presented approach based on the evaluation of phase shifts between photo-and Auger-electrons in streaking traces facilitates time resolved measurements with a resolution much better than the duration of the exciting light pulses. This makes it well suited for experiments at free-electron lasers where the pulse durations are often in the range of several tens of femtoseconds. 13,27 Until now, time resolved investigations of Auger electron emission have mainly employed laserbased high-harmonic generation sources delivering ultrashort light pulses in the VUV to XUV range at photon energies usually below 100 eV. Using FEL sources will extend the usable spectrum to the xray regime and thus permit access to deep core-hole excitations. Since spectral shifts are evaluated rather than the width or shape of the Auger electron spectra, the technique is also applicable to the measurement of molecular Auger decay, where the spectrum is broadened due to nuclear motion.
ACKNOWLEDGMENTS
The authors thank the scientific and technical team at FLASH, in particular, the machine operators and run coordinators. This work was financially supported by the Deutsche Forschungsgemeinschaft within the SFB 925. A.D. and U.F. acknowledge financial support from the excellence cluster "The Hamburg Centre for Ultrafast Imaging-Structure, Dynamics and Control of Matter at the Atomic Scale" (DFG)-EXC 1074 project ID 194651731. S.W. acknowledges financial support from the DFG Forschergruppe FOR 1789. N.S. acknowledges financial support from German Academic Exchange Service (DAAD Grant Nos. 5721983923 and 57393513). | 6,307 | 2019-05-01T00:00:00.000 | [
"Physics"
] |
Automatic preform design and optimization for aeroengine disk forgings
To ensure a more uniform forging distribution microstructure and improve the service performance of aeroengine disk parts, an automated preform design method is proposed for integrated preform shape design and optimization based on the non-uniform rational B-spline (NURBS) curve, finite element method, and genetic algorithm (GA). First, the random preform shape graph is automatically constructed by the NURBS curve design criterion. The volume and shape complexity are used as the constraints of the preform. Second, the ratio of the mesh area within the set strain range to the total mesh area is used as the fitness function for the uniformity of deformation, and the GA is used for optimization. Finally, a large disk forging is an example of its optimal design. Results show that the deformation uniformity of the forgings is excellent, its fitness value is as high as 99.59%, and problems, such as folding, underfilling, and limited distribution of flash, do not exist, thereby verifying the effectiveness of the method. In addition, the method has the advantage of strong universality, that is, it can find the preform shape with good deformation uniformity for any shape forgings.
Introduction
In aeroengines, many disk forgings are formed by forging [1,2]. The performance and life of disk forgings determine the performance and life of the engine and indirectly affect the performance of the whole engine [3]. Given that disk forgings need to work under severe service conditions, higher requirements are placed on the organizational and mechanical properties of their forgings [1,4]. A uniform deformation distribution is an essential requirement for aerospace forgings [3]. Deformation uniformity should also be guaranteed while reducing the forming load [5] and eliminating the forming defects, such as underfilling [6][7][8], folds [6,8,9], and cracks [10,11]. The deformation uniformity inside the forging directly determines the uniformity of forging organization [12]. Moreover, forging internal organization is uniformly distributed by the deformation uniformity decision, and non-uniform organization reduces the fatigue life of forgings or lead to premature failure while in use. Mixed crystal and coarse grains are the primary organizational defects [13][14][15]. To ensure high-quality forging, the design and optimization of the preform for disk forgings are critical.
Some methods are used to design preform shapes. The Upper Bound Elemental Technique (UBET) is commonly used in backward simulation. It is widely used for designing preforms for axisymmetric parts because of its simple principle and fast solution speed [16][17][18]. Another method used in the backward simulation is based on the boundary node release criterion and backtracking loading path to determine the appropriate precast design (i.e., backward tracing scheme (BTS)) [19,20], and this method has been successfully applied to design the preform die-shapes of a generic turbine-disk forging process [20]. The equipotential field method is also a preform design technique used in material forming [21]. Its design is based on the similarity principle, which determines the preform shape of the forging based on the equipotential line between the initial and final shapes [22]. In addition, the use of other physical fields is possible to describe the material flow and thus obtain the shape of the preform, for example, by integrating QForm metal forming simulation software with special CAD software that enables isothermal surface extraction to preform modeling [21]. With the development of computer technology, a preform shape design method based on optimal design is widely used to optimize the design variables related to the preform shape size to obtain the optimal solution with a particular index or a specific relative relationship as the objective function, such as response surface method (RSM) [23][24][25], sensitivity analysis [26,27], genetic algorithms (GA) [28][29][30], topological optimization [31,32], and reduced basis technique [33].
The preform shape, regardless of the design method used to obtain it, needs to be verified by the forward forming process [21,25,30,32]. The difficulty of forwarding process analysis is parameter setting and constraint control, which reduce the number of trials and errors and improves optimization efficiency. However, UBET, BTS, sensitivity analysis, and topological optimization require user technicians to consider the structural features and metal flow characteristics of forgings, which are tedious processes that consumes considerable time and effort. Moreover, the aforementioned methods require preliminary guesses on the preform design during the designing of variables. Inevitably, the optimization results have certain user biases and cannot search for potential preform shapes [5]. Based on the stated problems, the need for an engineering method that can be used by simply telling the system basic information about the forgings is growing. The system will automatically complete the design and optimization of the preforms.
After considering the limitations of current preform design methods, an integrated system is developed to obtain satisfactory preforms in this paper. First, the appropriate strain range is selected according to the forging requirements. A suitable fitness function is established. The corresponding solution algorithm is built on the basis of the direct communication between finite element analysis and the GA. Second, the non-uniform rational B-spline (NURBS) curves are used to describe the shape of the preform. The positions of the control points are defined as design variables, and the GA is used as a controller to control these design variables to obtain a satisfactory preform shape by crossover, variation, and competition among all individuals. Finally, the feasibility of the optimization algorithm is verified using two disk forges. In addition, the optimal preform shapes obtained by the algorithm are redesigned to derive shapes that can be used in actual production.
Design of objective function
According to the general forming process, the forgings are formed by preform, and then the forgings are machined to obtain the finished part, as shown in the white box in Fig. 1. In this study, the equivalent strain indicates deformation uniformity. In the actual production, the equivalent strain variation value of the finished parts should be 0.43-1.02 to ensure that the mechanical properties and microstructure of the aeroengine rotary forgings meet the requirements. The value of this variable usually comes from production experience and can be changed according to requirements. To accurately judge the forming effect of forgings using a computer, the equivalent strain field of the finished parts must be quantified. The essence of an equivalent strain field is the combination of the equivalent strain values for each grid element [25]. Through extraction and calculation, the ratio of the grid area within the set strain range to the total grid area can accurately judge whether the forming effect is good or not. The larger the ratio, the better the forming product, and vice versa, as shown in Eq. (1): where F s is the objective function to achieve the ratio of the grid area to the total grid area in the set strain range, n is the number of grids that reach the set strain range, m is the total grid number, and S i is the corresponding grid area under a certain grid number. The range of fitness value F s is from zero to one.
Although die filling must be quantified to apply in the data analysis process, it is considered an optimization objective [29]. A filling ratio parameter was introduced to quantify die filling, as follows: where V forge and V actual are the volume of the designed forging and the actual forging after trimming the flash, respectively. There is no underfilling situation, and the filling rate can be idealized to 1. Here, V actual = V forge .
Flash volume is obtained by Eq. (3): where V pre is the preform volume. Here, the flash volume is only used as a constraint on the fitness function F s .
Design principle
After obtaining the evaluation index, a series of preform shapes must be constructed to find the optimal individual by using the optimistic algorithm. NURBS is a handy tool for geometric modeling, and it has gained increased attention from the engineering community because of its good properties [34]. Given that NURBS has the characteristics of manipulating control points and weighting factors, it can provide sufficient flexibility for the shape design of preforms with various complex shapes. NURBS modeling is always defined by curves and surfaces so that sharp corners will not be generated, which the preform expects. On the one hand, sharp corners will cause the folding of forgings. On the other hand, it will increase the number of remeshing in finite element simulation and reduce computational efficiency. The NURBS curve describes the outline of the preform (Fig. 2). The NURBS curve equation is expressed as where N i,k (u) mainly refers to the basic function of k times B-sample, p i denotes the control point, and i denotes the weight factor of the control vertex p i . The size of i value determines the degree of curve deviation from the control point, see Fig. 2a, where 1 is smaller than 2 and 2 is smaller than 3 . A larger i represents a more significant weight factor, and the closer the curve is to the control point p i means that the shape of the NURBS can be changed to approximate any shapes by adjusting its control points, which are extremely important for various aspects of subsequent graph redesign, analysis, and processing. Figure 2a shows that the x-coordinate of the data point is relatively fixed, and its length is limited by the maximum radius length of the forging. Here, its y-coordinate is defined as a design variable so that the shape of the preform can be changed in a wide range, thereby screening out some potential preform shapes (Fig. 2b). Therefore, the shape optimization problem can be transformed into a parameter optimization problem.
Shape constraint
As shown in Fig. 2, the random preform graph generated by the NURBS curve consists of two curves on the top and bottom and a raised arc on the right side, where the two unexpected curves are divided by the x-axis and are generated by interpolating two or more reference points. Given that the values of the reference points are strongly random, they also need to be subjected to certain constraints.
In the actual forging process, the length of the preform shape is shorter than the forging length. The design length of the preform is highly related to the maximum forging length. This work sets the maximum length of the curve in the x-axis direction to "m times the maximum radius length of the forging (5)." The distance between each reference point is assigned according to the number of reference points (6). The distance between each reference point can be customized.
where x pre in Eq. (5) is equal is the length of forging, l max is the maximum radius length of the forging, and m is the coefficient between them. x distance in Eq. (6) is equal is the distance between each reference point, and n is the number of reference points.
The reference points are random only in the y-axis direction, and the ranges of the upper and lower parts are set to [LB, UB] T , where UB is the "maximum height of the forging (maxh)" and LB is a value greater than 0. The values of LB and UB can be changed according to the requirements, thereby narrowing and increasing the efficiency of the scope of the graphical search. The feasible region of the design variables of the two-stage curve shall be defined as follows: where y 11 , y 12 ⋅ ⋅y 1n are the shape parameters of the upper-part-preform and y 21 , y 22 ⋅ ⋅y 2n are the shape parameters of the lower-part-preform.
The preform volume is a parameter that is worthy of attention in the field of forging. The increment in volume causes production costs and more flash volume to increase in forming load. Thus, the preform volume must be considered. According to the volume invariance principle of plastic forming principle, the volume of the preform should be the same as the volume of the forging plus the flash. Therefore, volume constraints must also be imposed. Specifically, a penalty function is established for the random preform graph. When the accumulated differential volume ( V pre ) is smaller than the forging volume ( V forge ) or larger than the set threshold ( v * V forge ), the combination of such variables or the preform shape is eliminated, and fitness F s = 0.
As we learned from the previous section, the characteristics of NURBS determine whether the graphs constructed by NURBS will not produce a sharp angle structure, so when the upper and lower curves are closed to construct the graphs, a natural rounded section will be created between the two points, which corresponds to the "bulging" phenomenon caused by the pier diameter in the preform, as seen in the rightmost shaded part in Fig. 3, which can be used as a flash control area. Its size could be adjusted to achieve near-net forming in the subsequent design process.
Then, in the investigation process, the generation of preform shape has strong randomness. Very complex shapes easily appear, and such forgings are easy to fold [35], thereby resulting in the scrapping of forgings. As shown in Fig. 3a, b, the shape of the forging fluctuates violently, the height difference between the peak and the valley is large, and the peak and valley regions are prone to folding. The two simulations also show that processing or forging these preforms is impossible. The shape complexity factor is used to design preforms [36], especially for more complex mass distribution parts, which becomes more important. Hence, the shape complexity factor should be considered in the design of preforms. Euclidean distances have also been used to describe the complexity of preforms [35]. However, in the present study, the method is less applicable, based on which the thesis proposes a height difference shape control method. Through the analysis of three consecutive control points, a Δh is calculated, and Δh is used as a complexity factor with following Expression (9): where Δh is related to the values of LB, UB, and x distance , as shown in Fig. 3b, c, d, and the larger the value of |∆h|, the higher the probability of folding (here the value of ∆h is obtained from the calculation of y 2,1 , y 2,2 , y 2,3 ). Δh is calculated by the following empirical formula, which is obtained based on a previous survey.
In summary, the constraint model for preform shape design is established as follows:
FE simulation
The hot forging process was simulated by the Deform-3D commercial package. DEFORM, as a finite element simulation software, is generally operated by the user through the graphical user interface (GUI) of the software. The GUI mode has the advantage of being intuitive and concise.
However, the DEFORM software can also be run in another mode, "Text mode," in which the user can use their edited command stream files to operate DEFORM's secondary simulation software. The text mode allows the graphical data in the '.key' file to be updated and simulation tasks to be submitted, which provides automated simulation and optimization feasibility.
Automatic optimization algorithms for preform
The automatic optimization algorithm takes the reference points [y 11 , y 12 ⋅ ⋅y 1n , y 21 , y 22 ⋅ ⋅y 2n ] T as the optimization variables and the deformation uniformity as the evaluation index. The number of reference points can be set according to the complexity of the forgings. The more complex the forgings are, the more reference points can be set. However, the more complex the random preform shape will be, the longer the computation time is. In addition, a virtually indefinite number of solutions are possible. Thus, selecting an appropriate optimization algorithm is necessary to solve this problem. However, finite element simulation is a typical black box, where each individual generated by the design variables has to be calculated by FEM to obtain the fitness value, which cannot be obtained by solving the mathematical model, and some optimization algorithms, such as gradient descent, Newton's method, and conjugate gradient method, are no longer applicable. The use of GAs is possibly suitable to solve this optimization problem because they are population-based heuristics [37]. They are particularly suitable to deal with black-box problems that do not require auxiliary information: differentiable, derivable, and continuous. GA is also a stochastic optimization algorithm that excels at searching for problems with large search spaces. It can effectively use existing information to search for individuals that show promise for improving the quality of the solution.
GA is similar to natural evolution. It simulates replication, crossover, and mutation in natural selection and inheritance. An automatic, collaborative, real-time dynamic optimization process is implemented by writing the corresponding interface program using the direct communication between the finite element method (FEM) and the GA. The GA module is used as a controller to send design variables to the FEM module and receive the individual fitness returned by the FEM module. As shown in Fig. 4, the core modules in the algorithm flow chart are the preparation, Auto Fitness, and GA module. The Auto Fitness module includes several sub-modules: the preform shape modeling sub-module, the The specific process is presented as follows.
Module_1 Preparation
The forging forming is divided into three work steps, namely, the heat transfer step, forming step, and Boolean cutting step. The heat transfer step represents the calculation of the heat transfer between the forging and the air during the removal of the preform from the furnace and its transfer to the final forging die. The forming step is the forming process of the preform in the cavity of the final forging die. The Boolean cutting step is the cutting down of the finished part from the forming forging part, which is used to observe and calculate the strain. After setting the various parameters, the '.key' file in the first step is exported. Subsequently, the forging contour graph '.dxf' file is imported in MATLAB. Data are extracted to calculate the maximum height, maximum radius length, and volume of the forging to provide constraint data for the random preform shape. After selecting a suitable reference point variable matrix [LB, UB] T , the appropriate preform length coefficient m and height difference Δh are evaluated according to the variable matrix to further constrain the shape of the preform and reduce its complexity.
Module_2 Auto fitness
In this module, an automated integrated program was developed to complete the three steps of graphical modeling, numerical simulation, and feature extraction to obtain the fitness values of individuals. The operation of the whole program is based on the communication between MATLAB and Deform software. The MATLAB core commands for automated integration programs are as follows:
Module_3 GA
The GA is used to create preforms with random characteristics as an initial population, where each individual is described by a set of genes that represents the control variables for the preform shape. Then, the fitness function is calculated using the deformation uniformity parameter, and the termination condition is the preset genetic algebra. In the optimization process, the vectors of individuals with larger fitness values in the retained population are selected by changing the genes of the selected parents using crossover and variation methods, and their variation variable vectors are compared with the original variable vectors and passed to the next generation. Subsequently, the new population is evaluated again until the termination condition is satisfied. The specific algorithmic procedure can be displayed in the GA module in Fig. 4. Finally, all the optimizations have been completed, and the preforms with the above key dimensions will be determined.
Preform optimal process and results
In the following investigation, a disk forging is being used as demonstration parts, as shown in Fig. 5. Then the preform shape of this forging will be designed and optimized automatically. [20,20,20,20,20
Parameter definition
Finite element simulation was performed using Deform-2D. The part temperature was set to 1010 °C, and the material data file IN718 (1650-2200 F (900-1200 ℃)) provided in the Deform database was used. The friction coefficient was set to 0.3, and the heat transfer coefficient is 5 N/sec/mum/C. For all parameters, other parameters not explicitly stated as standard (initial parameters set by Deform) were assumed for the hot forging process.
In this research, an individual consists of 10 genes that represent the design variables. The population size should contain 10 times the number of individual genes [38].
Therefore, the population size is assigned as 100, and the number of evolutionary generations is 20, as a stopping condition to reduce the total simulation time. The crossover rate is set to 0.8, and the mutation rate is set to 0.2.
The forging drawing shown in Fig. 5 is imported, the calculated maximum radius length l is 323 mm, and the maximum height maxh is 173 mm, and the forging volume V forge is 3.5225e + 07 mm 3 . A total of 10 reference points is taken in the example, and the range of the values of the reference point of the NURBS curve [LB, UB] T can be obtained according to maxh. Through preliminary exploration, constraints are defined to improve the convergence rate and avoid folding during the deformation. Considering that : a 1st generation, b 3rd generation, c 5th generation, d 9th generation, e 13th geneation, f 15th generation, g 20th generation, h forming load and flash rate the irregularity of the parts will disappear during the optimization process, the selection range should be appropriately relaxed when setting the constraints to avoid the optimization algorithm from falling into a local optimum and losing many potential combinations of variables. The specific data are shown in Table 1. Figure 6 shows the development of the best-rated and average individuals within the population for 20 generations. A total of 2100 simulations were run for this preform optimization algorithm. Considering the time cost, the optimization was executed on an Intel Core i7 processor with four processors. Parallel operation was implemented by MATLAB software to improve the computing efficiency. The total time consumed from the shape design of the preform to the FEM simulation and then to the completion of the optimization was approximately 55 h. Figure 6 shows that the optimal fitness value converges quickly and surges to a higher fitness value in the 5th generation. In contrast, the average fitness is not equal to the former until after the 15th generation, which indicates that the design parameters of the preforms have a large convergence rate before the 15th generation. Then, after the 15th generation, the variation of the best fitness value and the average fitness tends to a constant value (0.9959), as shown in Table 2. This finding indicates that the convergence rate gradually decreases, and the search range approaches the optimal individual as the number of evolutionary generations increases. Among them, 0.9959 indicates that the area of the grid element satisfying deformation 0.43-1.02 in the finished part accounts for 99.59% of their total area, which shows that the algorithm searches for the preform shape with very good results.
Results and discussions
The best preform shape at each generation is illustrated in Fig. 7a-g to easily understand the evolutionary process. With the increase in the evolutionary generation, the shape of the preform becomes increasingly similar, and all of them show the shape of "bulging in the middle, flat on both sides." The data in Table 2 are not difficult to observe, and the numerical changes in the reference points are also becoming smaller and smaller. From the equivalent strain evolution diagram in Fig. 7a-g, gray, light blue, and green gradually dominate from the 1st generation to the 15th generations, thereby exhibiting a more uniform strain distribution in forging. Moreover, Fig. 7h Fig. 9 Redesign of optimal preform shape: a original optimal preform shape, b data point modification, c control point modification, and d locating structure shows that the flash rate and forming load can be reduced in the continuous evolution process, which again proves that the shape of the preform has been optimized significantly.
The histogram of the distribution of the equivalent strain (Fig. 8a) and the forming load-stroke diagram (Fig. 8b) of the finished part at the 20th generation are presented. The distribution of the equivalent strain of the forging is nearly normal. The strain is mainly concentrated in the range of 0.65-0.85 (Fig. 8a), where the average value of the strain distribution (Avg) is 0.8, and the standard deviation (Stdev) is 0.1. The minimum and maximum values are 0.4 and 1.0, respectively. The maximum load during the forging process is approximately 30,227 tons from Fig. 8b, which meets the production requirements. The optimization results show that the GA can automatically converge to the optimal design parameters and obtain the optimal combination of the design parameters.
Redesign of optimizing the shape of preform
The optimal preform shape is obtained by using the above method, and the numerical simulations are good and satisfy the optimization objectives. However, some problems in practical applications, such as sharp corners caused by control [20,20,20,20,20 points, difficult processing area, and billet positioning structures, must still be considered. Therefore, this study improves the original optimization results based on the concept of the redesign proposed in the literature [39], to find a more feasible preform shape to meet the practical application requirements while ensuring that the optimization objectives will be met.
To eliminate the complexity of the original optimal preform shape, the preform shape is simplified using the approximation method while maintaining the original shape characteristics (Fig. 9). First, the shape of the preform was fine-tuned by changing the NURBS data points to make the preform more machinable (Fig. 9b). Subsequently, the number of control points was increased, and the weight values of the control points were adjusted (Fig. 9c) to avoid severe sharp corners, which can cause mold penetration during simulation and stress concentration during plastic deformation. Finally, the preform shape's locating structure was designed using CAD software at the blue-shaded position in Fig. 9d.
The positioning of the workpiece in the die and the results are shown in Fig. 10a-c; it can be concluded that the deformation uniformity of the redesigned preform inherits the characteristics of the original optimal one, and the equivalent strain in 99% of the area of the finished part reaches 0.43-1.02. More importantly, the complexity of the preform shape is sharply lowered, thereby making it easier to be manufactured.
In our previous work [40], the preform shape of the forging was designed by the sectional design method. A total of 96.55% of the finished product was in the equivalent strain range of 0.43-1.02 after forging. The actual forging production was carried out for the forgings, and the forgings met the quality requirements in terms of physicochemical detection and property analysis. The grain grade in each deformation zone was above grade 10, as shown in Fig. 10d. This observation shows that the preform design method and the integrated system constructed in the paper are accurate and reliable.
Case study
To better reflect the generality of the automation optimization algorithm, an aeroengine disk forging is identified as a case study in this research. The diagram of the forging is shown in Fig. 11.
The operation process is the same as that in the previous section and will not be repeated here. The maximum radius length l of the forging is 297 mm, the maximum height maxh is 94 mm, and the forging volume V forge is 1.8511e + 07mm 3 . Other detailed data are shown in Table 3.
The forging shape contour is simple, so it takes 42 h to complete the design and optimization of the preform. The number of finite element simulations is 2100, and the optimization results are shown as follows.
The preform shape of the aeroengine disk was obtained by the automatic optimization algorithm in the case study. After 20 generations of evolution, the fitness value of the optimal individual reached 98.30% (Table 4). From the evolution curve in Fig. 12, the fitness value changes very little after the 10th generation. The optimization process can be ended at this point to improve efficiency. The shape and equivalent strain of the best individual at each generation is illustrated in Fig. 13a-d.
After obtaining the optimal shape of the preform, the redesign method is used to simplify the shape of the performed part (Fig. 14a). The results show that the uniform deformation of the redesigned preform part is still realized well. The equivalent strain in 96% of the area of the finished part reaches 0.43-1.02 (Fig. 14a,b). Moreover, the 12 Evolution curve for optimization design of preform shape using GA maximum forming load is only 22,082t (Fig. 14d), which is suitable for production.
Conclusion
To improve the quality of forgings, this paper proposes an automated preform design method based on NURBS curve modeling, FEM analysis, and GA optimization.
(1) The proposed preform design method can automatically complete the design and optimization of preform shapes for various types of disk forgings without human intervention during the design and optimization processes. The optimization process and results can be observed in real time.
(2) The design method can optimize the preform according to specific target values. The optimization procedure can be stopped in practical use to reduce the computational cost while meeting the deformation requirements. (3) To verify the effectiveness of the method, two different engine disks are investigated. The results show that the forgings have good deformation uniformity. The equivalent strain in 96.55% and above areas of the finished parts can meet the requirements, thereby proving the practicability and effectiveness of this method in engineering applications. Data availability Not applicable.
Declarations
Consent to participate All authors agreed with the consent to participate.
Consent for publication All authors have read and agreed to the published version of the manuscript.
Competing interests
The authors declare no competing interests. | 6,962.2 | 2023-01-16T00:00:00.000 | [
"Materials Science"
] |
An ab initio study of the polytypism in InP
The existence of polytypism in semiconductor nanostructures gives rise to the appearance of stacking faults which many times can be treated as quantum wells. In some cases, despite of a careful growth, the polytypism can be hardly avoided. In this work, we perform an ab initio study of zincblende stacking faults in a wurtzite InP system, using the supercell approach and taking the limit of low density of narrow stacking faults regions. Our results confirm the type II band alignment between the phases, producing a reliable qualitative description of the band gap evolution along the growth axis. These results show an spacial asymmetry in the zincblende quantum wells, that is expected due to the fact that the wurtzite stacking sequence (ABAB) is part of the zincblende one (ABCABC), but with an unexpected asymmetry between the valence and the conduction bands. We also present results for the complex dielectric function, clearly showing the influence of the stacking on the homostructure values and surprisingly proving that the correspondent bulk results can be used to reproduce the polytypism even in the limit we considered.
Method
In order to model an InP sample in the WZ phase with a low density of ZB stacking faults, we built up an hexagonal supercell with 15 layers of In and P atoms in the AB stacking sequence along the hexagonal c axis of the WZ (4 atoms per layer, 60 atoms in total). On the top of this supercell, we added three layers of In and P atoms using the ABC stacking sequence as along the [111]-direction of their ZB phase (6 atoms per layer, 18 atoms in total). In other words, we built up an hexagonal supercell superimposing 15 InP WZ cells along their c axis and over them we put 3 InP ZB cells where the cubic [111]-direction corresponds to the hexagonal c axis. In Fig. 1 we show an schematic representation of the interface between WZ and ZB regions in our supercell. The A, B and C layers Scientific RepoRts | 6:33914 | DOI: 10.1038/srep33914 whose stacking sequence defines the region symmetry are indicated. The C layer that is present only in the ZB region has a different color scheme for clarity.
We define a low density of ZB stacking faults as a case where the density of states (DOS) for the atoms at the center of the WZ region reproduce the results obtained for the bulk WZ system. This condition intend to guarantee that the WZ bulk is reproduced at the middle between two neighboring ZB regions. Our results show that this condition is achieved for our WZ region formed by 15 cells. On the other hand, since at the WZ-ZB interface the ABC sequence contains an AB region, we need to use at least 3 ZB cells to assure the existence of a well defined ZB region.
The ab initio calculations have been performed using the "Linearized Augmented Plane Wave method" (LAPW) as implemented in the Wien2k code 18 . Our basis functions were expanded up to R mt × K max = 6, where R mt is the smallest of the atomic "muffin-tin" radii and K max is the magnitude of the largest K vector for the plane wave basis functions. The atomic "muffin-tin" radii used here were 2.50 Bohr for In and 2.11 Bohr for the P atoms. We have employed the modified Becke-Johnson exchange potential plus LDA correlation with its original parametrization (P-original) 19,20 . The energy separation for core and valence states was − 6.0 Ry and the spin-orbit coupling was taken into account during the calculation of the electronic properties. The only difference among the supercell and bulks calculations was the K-grid. For the total energy and DOS calculations we employed K-grids with 12, 16 and 10 inequivalent points in the irreducible part of the Brillouin Zone (BZ) for the supercell, WZ and ZB bulks respectively. To obtain the dielectric functions, denser grids were used (see below).
The structural parameters used for the InP cells in the WZ and ZB phases were previously optimized for the bulk cases 21 . The supercell were built up as described above and we considered two cases. In the first case, on top of the 15 optimized WZ cells, we added three ZB cells with the same WZ a parameter. The volume per atom obtained for the optimized ZB bulk was recovered through changes in the atomic distances along the c axis. In the second case, we kept the same atomic distances along the a and c axes found in the optimized WZ bulk and changed only the stacking sequence from AB to ABC.
In both cases we got little forces on atoms along the c axis, proving that we were dealing with relaxed supercells. More specifically, these forces were always lower than 2.9 mRy/Bohr in the second structure and we have chosen this structure for our study. Besides that, these results indicate that strain effects must have a minor role on the electronic properties of the supercell.
Results
In Fig. 2 we show how the top of the valence band (blue circles) and the bottom of the conduction band (red circles) evolve as a function of the atomic relative coordinate along the c axis for two consecutive WZ InP supercells with ZB stacking faults. To obtain these values, we compare the DOS per atom in the supercell with that of the bulk systems. We have observed that at the center of the WZ region, the corresponding bulk DOS was reproduced at the band gap neighborhood. After that, we took from these central atoms the first derivative of the DOS at zero energy (top of the valence band) and at the gap energy of the WZ bulk (bottom of the conduction band) and used them to determine how the two band extrema evolve along the supercell c axis. Obviously, this is not a rigorous approach and Fig. 2 should be taken as a qualitative description. Despite of this, our results show a smooth transition between the WZ and ZB regions, what is a more realistic description than the commonly assumed abrupt transition.
As one can see, our results confirm the type II band alignment with holes concentrated at the WZ side of the WZ → ZB interface, while the electrons tend to be at the ZB side of the ZB → WZ interface. This carriers spatial separation has a significant impact on the exciton recombination time 8 and can be tuned through a strict control of the atomic stacking sequence during the sample growth. We can also notice that the WZ → ZB and ZB → WZ are not equivalent due to the lack of mirror symmetry in the supercell and at the ZB region. Finally, the shape of and C layers whose stacking sequence defines the region symmetry are indicated. The atoms in A and B layers are in gray and yellow. The C layer that is present only in the ZB region presents atoms in green and red.
the band extrema evolution along the supercell c axis is different for the valence and conductance bands. This is in a clear disagreement with the commonly modeled type-II quantum wells, since, in our results, the conduction band profile looks like a square quantum well, while the valence band one is similar to a teeth saw quantum barrier.
In Fig. 3, we show the energy band gap (the difference between the two curves in Fig. 2) as a function of the atomic relative coordinates along the c axis of the supercell. Here, it is important to say that we have reduced the basis size when compared with our previous work 21 . The main change was the reduction of R mt * K max from 9 to 6 in order to decrease the computation time, but keeping an acceptable level for results quality. In order to get a fair comparison with the bulk systems, we repeated the correspondent calculations with the same parameters used for the supercell and we observed a reduction of approximately 23 meV in the bulk gaps when compared with the previously published values 21 . Here we obtain 1.470 eV and 1.373 eV for the WZ and ZB gaps, respectively (dashed lines in Fig. 3).
As previously commented, the calculated DOS at the center of the WZ region reproduced the WZ bulk results and we took their derivative as a marker for the top of the valence band and the bottom of the conduction band. As a result, Fig. 3 shows the exact reproduction of the WZ bulk gap at the center of WZ supercell region.
It is also important to notice that the band gap, as the difference between the two curves in Fig. 2, reinforces the lack of mirror symmetry in the supercell with an almost linear behavior inside the ZB region and very distinct profiles at the ZB → WZ and WZ → ZB interfaces. The impact of this asymmetry in the carriers localization and lifetime has been experimentally investigated 8 . Figure 4 shows our projected density of states results for the WZ InP supercell with ZB stacking faults. We present the calculated values for S (full red line), P x + P y (dashed blue line) and P z (dotted black line) projections. As one can see, the WZ symmetry results are reproduced 21 . This means that the ZB region has practically no influence in the polarization properties of the system as experimentally verified for the limit of low density of ZB stacking faults 22 .
The convergence of the dielectric function was obtained with a more dense K-grid. More specifically, we used 90, 198 and 104 inequivalent points in the irreducible part of the Brillouin Zone for the supercell, WZ and ZB bulks, respectively.
In Fig. 5, we present the real part of the complex dielectric permittivity as a function of the incident photon energy for the WZ InP supercell with ZB stacking faults (full green line with dots), WZ bulk (dashed red line) and ZB bulk (dotted blue line). In the left panel (a), we consider light polarization in the xy plane and, in the right panel (b), the polarization is taken along the z axis. As one can see, the ZB stacking faults have a small effect on the real part of the supercell dielectric function. The most noticeable deviations from the WZ results occur around show that a weighted average between the WZ and ZB bulk results perfectly fits the supercell ones, even for the points where the supercell results exhibit the above mentioned deviation from the WZ values. The full line in both panels represent this average with weight 5 for WZ and 1 for ZB results (the rate between the number of bulk cells in the supercell). This is somehow surprising because we did not guarantee that the ZB region was able to reproduce the correspondent bulk environment. It indicates that strain effects, that should differentiate the stacking faults from the bulk, must have a minor contribution to the supercell properties. Beside this, it is expected that z axis (b). We show the results for the WZ InP supercell with ZB stacking faults (full green line with dots), WZ bulk (dashed red line) and ZB bulk (dotted blue line). We also show the weighted average between WZ and ZB bulk results (5:1) (full black line). the interfaces between the symmetries have a significant role considering the narrow ZB region as reflected in the results for the conduction and valence band edges evolving along the supercell c axis (Figs 2 and 3). In conclusion, we can say that this somehow counterintuitive result indicates that approximations that consider abrupt transitions between the phases can be suitable even in the case of low density of narrow stacking faults regions, opening the possibility of avoiding computationally expensive supercells when modeling dielectric functions in homostructures. Figure 6 is equivalent to Fig. 5 but for the imaginary part of the dielectric function. Here, the main contribution of the ZB stacking faults to the supercell results occurs in the energy interval from 4 to 6 eV for both polarizations, where the WZ and ZB bulk results are significantly different. Once more, the weighted average results perfectly fit the supercell ones even in the energy range where the ZB stacking faults has the most important contribution. This corroborates our previous conclusion that, surprisingly, bulk results can be used to reproduce supercell ones even for low densities of narrow stacking faults regions.
Conclusions
Our ab initio analysis of a WZ InP system with ZB stacking faults in the limit of low density of narrow ZB regions confirmed the type II band alignment between the phases, what can lead to the control of the exciton lifetime in this kind of sample. Our results also show a reliable picture of the band gap smooth transition between the phases, with the ZB region presenting a linear dependence of the band gap with the position along the supercell z axis. Despite of this, the complex dielectric function results indicate the possibility of using bulk results to model the supercell systems. In other words, models that consider abrupt transitions between the phases retain the essential characteristics of this kind of systems. | 3,108 | 2016-09-26T00:00:00.000 | [
"Materials Science",
"Physics"
] |
Innovative Approaches to Management of Virtual Teams Leading to Reliability and Retention
Purpose: The paper focuses on identification of variables affecting management leading to reliability and retention of virtual teams. Methodology/Approach: The data were collected globally from 323 managers working with virtual teams; members were hired and worked fully virtually with team members from different countries and time zones. Respondents were from all continents. The data were evaluated by tested by reliability tests and two and multidimensional statistics (Spearman’s correlation, principal component and factor analysis). Findings: Empowerment and encouraging were proven as variables significantly affecting management of virtual teams’ reliability. Variables leading to employee retention are communication, performance appraisal, career plans, training and leadership/supervision to overcome barriers. Efficient management in virtual environment is significantly related to policies and career possibilities. Over 20% of managers are incompetent to work with virtual teams. The main threat leading to failure of virtual teams is burn out based on social distancing. Research Limitation/Implication: Limitation of the study is the first approach to the virtual teams’ management only focusing on ICT employees. The findings revealed significant relations leading to virtual operations impacting employees’ performance, reliability and retention. Originality/Value of paper: This paper provides an insight into the importance of innovative approach to virtual teams, as virtual employees may strive with low social contact and less support from organization.
INTRODUCTION
Virtual teams are becoming an integral part of modern organizations. After the Covid-19 era, management in virtual environment have been considered a necessity by organizations. Past years, even pre-Covid, global organizations invested significant amounts of money and sources to support smooth operation of virtual teams and virtual managers. The global outreach of Covid and use of communication technology for continued operations has further facilitated the idea of remote workstations where employees are working in the geographically differentiated regions but stay online and work together on organizational goals.
A number of studies show that managing virtual teams is more difficult than managing collocated teams, as leaders have less influence and less information about the status of the team; process management and team dynamics can be impaired; it is difficult to set up practices to uncover and resolve conflicts, motivate team members and monitor members' performance; it is difficult to build trust and team cohesion -see Davis and Bryant (2003), Zaccaro and Bader (2003), Zigurs (2003), Dulebohn and Hoch (2017). These problems are closely related to the reliability of virtual teams and the retention of employees in the organization and management need to focus on specifics of virtual teams (Gilson et al., 2015).
The aim of this paper is to is to test variables affecting management of virtual teams and significantly impact virtual teams' success. The research formulates and tests hypotheses revealing variables affecting virtual teams' reliability and retention.
The study tests model of management in virtual environment. The model is build and analysed based on factor analysis. This paper contains a review of the existing literature, presents methods followed by results, that are further discussed and conclusions are presented.
Theoretical Background
Virtual teams represent a work arrangement where team members are geographically dispersed, have limited face-to-face contact, work interdependently, and use electronic communication media to achieve common goals (Dulebohn and Hoch, 2017). Within virtual teams, knowledge workers collaborate despite time and distance to combine efforts and achieve a set goal (Bell and Kozlowski, 2002). The use of virtual teams holds great promise for the future (Dulebohn and Hoch, 2017). Mobility and flexibility are examples of megatrends that influence everyday life and also intensively change the way we work (Großer and Baumöl, 2017). The use of virtual teams thus represents a new chance in this context. For employees, this is associated with flexibility (regarding location and working hours), for organizations in an increasingly digital environment, it means competitiveness (new technological opportunities, employee retention, cost efficiency). From the point of view of competitiveness on the labour market, it is about offering a work environment that provides time flexibility. The deployment of virtual teamwork is not only supported by technological and societal changes, but also seems relevant for employee retention (Großer and Baumöl, 2017).
Employee retention was described by James and Mathew (2012) and Bidisha and Mukulesh (2013) as a process in which employees are encouraged to remain with the organization for the maximum period of time (or until the project is completed). Mita, Aarti and Ravneeta (2014) defined employee retention as "a technique adopted by businesses to maintain an effective workforce and at the same time meet operational requirements". In the context of virtual teams, these are techniques within an organization that enable effective work teams to be maintained while meeting operational requirements. Based on impact of Covid-19, employers take steps to ensure that employees stay with the organization as long as possible (Alferaih, Sarwar and Eid, 2018;De Smet et al., 2021). According to Anitha (2016), virtual employee retention is not easy as the workforce is becoming more confident and demanding due to changes in markets and demographics. The employee retention process (in the context of virtual teams) represents a strategic tool for the success of the organization (Aburub, 2020). Kossivi, Xu and Kalgora (2016), described factors determining employee retention: management/leadership, conducive work environment, social support, development opportunities, autonomy, compensation, work-life balance and employee training and development. Attention has been paid to manager's leadership style, the organization's commitment to social responsibility, autonomy, work-life balance and technology (Khan and Wajidi, 2019;Valentine and Godkin, 2017;Kim and Stoner, 2008;Koubova and Buchko, 2013;Haar and White, 2013). According to Lee et al. (2022), there are still no studies that examine the effect of all these factors on employee retention and the underlying mechanism of these relationships. There are also no studies focused on this issue in the context of virtual teams. That is why this study was conducted. Based on the above mentioned, the following hypotheses on variables impacting management of retention of virtual teams were stated:
H1 (retention):
Virtual team retention is related to positive perceptions of current employer.
H2 (retention):
Virtual team retention is related to motivation and willingness to stay at current position. Sishuwa and Phiri (2020) identified the main factors influencing employee retention, but also developed a framework based on a causal model and recommended possible solutions. Authors found that job satisfaction, organizational commitment and workplace structures are important for employee retention; however, individual characteristics did not have a significant influence on employee retention. Howard-Grenville (2020) emphasizes the need to focus on research into organizational dynamics among remote workers in order to explore the role of cultural factors in shaping remote workers' interactions.
Pianese, Errichiello and da Cunha (2022) discuss five "control domains"control systems, supervisory management styles, trusting relationships, organizational identification, and work identity. They conclude that the management of remote workers represents a shift from direct supervision to management by objectives, and is linked to a leadership style that emphasizes trust-relationships and the empowerment and self-control of remote workers. Further, the organizational and managerial approach emphasizes the autonomy of remote workers, and empowerment often co-exists with a strict control (Porter and van den Hooff, 2020). Pianese, Errichiello and da Cunha (2022) stated that behavioural control promotes overcoming tensions and misunderstandings in cross-cultural teams, the study has shown the importance of combining technocratic control with socio-ideological control, based on informal sharing of norms, beliefs and values among team members, trusting relationships and team identification, which strengthens alignment of individual and collective goals.
The ability of leaders to support and empower of virtual team members during virtual meetings and through electronically mediated communication is essential for this soft form of control. According to Arunprasad et al. (2022), stressed the need to develop conceptual frameworks related to the influence of culture on the remote work implementation and collaboration. O'Neill et al. (2016) adds that effective communication helps to build reliability and commitment, and interaction plays a crucial role, which was confirmed by Watson-Manheim, Chudoba and Crowston (2012) and Olson and Olson (2013). Based on these studies, the role of reliability in management of virtual teams will be tested by the following hypotheses:
H3 (reliability):
Virtual team reliability is related relevant periodic performance appraisal.
H4 (reliability):
Virtual team reliability is related to clearly communicated policies.
This paper focus on identification of variables affecting efficiency of management of virtual teams. Current research has shown that virtual teams present a number of challenges compared to collocated teams (Newman and Ford, 2021). Appropriate approaches to human resource management (Bulińska-Stangrecka and Bagieńska, 2019), knowledge sharing and collaborative culture (Kim, Billinghurst and Lee, 2018) contribute to building a sustainable competitive advantage through innovation management. These aspects also need to be addressed in the context of remote work management and appropriate systems and procedures need to be designed and implemented (Arunprasad et al., 2022).
In the context of the above findings and relations, this paper defines and tests the main variables affecting quality management and impacts virtual teams' retention.
METHODOLOGY
This study is based on questionnaires investigation of global managers working with virtual teams. The data were collected globally from 323 managers. Companies were selected based on their global operations and focus on ICT. The teams were considered virtual when members were hired and worked fully virtually with other team members from different countries worldwide and through different time zones. The sample was defined using Cochran's formula.
The survey was used due to the fact that it was difficult to reach out to managers in dispersed locations worldwide (Saunders, Lewis and Thornhill, 2015). The questionnaire was designed to monitor actions of managers of virtual teams to lead employees online including focus on their welfare, the quality of interactions, impact of online work on satisfaction, reliability and factors impacting retention in virtual environment. Respondents were asked to provide their insight into their remote management and employee experience, distractions, reliability, performance appraisal and retention. Respondents had to indicate their views on recommending other people to work in their companies, organizational culture, remuneration, satisfaction on current position or possibility of external mobility.
The questionnaire had six identification questions, and ten main sections with 5-10 closed-ended Likert-scale sub-questions per each section. The scale was designed having five points from strong agreement to strong disagreement (see Tab. 1). The whole questionnaire and each sub-section were tested for validity using the Cronbach's Alpha (CA) test. As the whole questionnaire and each subsection reliability reached value over 0.9, it was considered reliable and used for statistical analyses (Sullivan, 2011). The Pearson correlation (r) method was used to test relationships between the variables.
Data and Sample
The data were collected online (CAWI) to employees who are working in virtual teams. The population and sample size was developed according to Cochran (2007) formula. Counting with (p = 0.5) and taking 95% confidence level the required sample size is 384 respondents for unspecified total size of population. This formula was then adjusted to limited sample size. The test finally shown 254 responses. The final sample of presented survey is 323 responses. Therefore, it can be considered as representative.
All respondents were employees between ages of 18-65 years old, working in virtual team and they are managed virtually. Respondents were asked to fill the questionnaire when they were working in different countries than they were located and teams contained members from different countries. Thus, the questionnaire goal was to reached out to maximum diversity of countries to simulate diverse virtual environment. The main business were 18% operations, 11% support services/administrations, 10% IT, 9% finance, 6% sales, manufacturing (5%), quality (5%), supply chain (4%), marketing (4%), legal (5%), R&D (3%), HR (2%) and 18% indicated they work in different business operations that does not match given categories (i.e. education, healthcare, entertainment, agriculture, hospitality and others). According to size of company, 19% were small organizations (1-49 employees), 35% medium-sized organizations (50-999 employees), 46% in large organizations (over 1,000 employees). The questionnaire was anonymous.
Data Analysis
All survey results were processed in SPSS and Excel. Firstly, the data table was checked for missing values and unfinished questionnaires were excluded from the data file. The questions and their constructs were tested for their internal consistency by Cochrans's Alpha. All coefficients were reaching value over 0.9 showing adequate level and therefore the data were used for analyses. To evaluate the data, Spearman's coefficient was used, Pearson's test, and ANOVA. Based on satisfactory results of consistency tests and correlations, a multidimensional analysis was used. The multi-dimensional analysis was conducted using component analysis and factor analysis with Varimax rotation. The process of calculation and interpretation of results was processed according to Anderson, Fontinha and Robson (2019), Mishra et al. (2019) and Bell (2019).
To test relevance of data for factor analysis, the Kaiser-Meyer-Olkin test was used. The resultant value reached over 0.8. Thus, the data could be used for multivariate analysis. Factors explain variability and dependence of considered variables. Theoretical factors were created (see Table 1) and further tested by factor analysis. The final output factors were reduced using the Maximum Likelihood Factor Analysis with Kaiser Varimax Rotation with a goodness of fit. For the selection of substantial factors, the Kaiser-Guttman rule was applied (i.e. substantial factors having a value within the range higher than 1) and subsequently the Sutin test was applied. The correlation coefficients are in the interval from <-1;1>. If the correlation coefficient is positive, it shows a direct proportion; negative shows indirect proportion. For the evaluation, the value of variable correlation higher than 0.3 (moderate correlation) according to Anderson, Fontinha and Robson (2019) was used. The data analysis was run by SPSS Statistics 22.
RESULTS
The results of presented study focus on variables that affect virtual teams' retention and reliability. The Tab. 2 shows first perceptions of managers on virtual management, where 1 mean not important and 6 stands for key emphasis of managers to attract virtual teams' members. According to the results, the most important according to managers working with virtual teams is quality of interaction. On the other hand, the threat of distractions is reported less in the studied sample.
The correlation analysis shown that emphasis has to be paid to quality of management of virtual teams that has to focus on the variables significantly affecting virtual teams' retention and reliability. Variables significantly impacting virtual teams' reliability are quality of interactions (r = 0.714, p = 0.000), homework policies (r = 0.634, p = 0.000), clear goals (r = 0.487, p = 0.000), fair treatment (r = 0.657, p = 0.000), access to trainings (r = 0.658, p = 0.000), clear career path (r = 0.502, p = 0.000), communication and feedback (r = 0.487, p = 0.000).
The presented data show that managers of virtual teams face new approaches of employees (i.e. 17% are unsuitable to fit virtual conditions -see Tab. 2), changed needs and requirements (online supervision, changes in motivation a communication that shift to individualized periodic goals, reskilling and upskilling and performance appraisal) that require new managerial skills. Attention has to be paid to employees' mental state and socialization during distance work (approx. 20% of employees are threatend by mental problems and burn-out syndrome -see Tab. 3), their motivation through possibilities for development and career progression (over 65% of employees are motivated by career opportunities), commitment and job satisfaction which proved to be the most influential areas of employee reliability and retention (r = 0.4 to 0.5).
Factors affecting employee retention are according to the survey results mainly impossibility of career development (33%) and 24% rely on unsatisfactory remuneration, while 22% refers to unreliable manager relationship. Two third of respondents stated that the possibility of career development is crucial for them to stay at current job position. Impossibility to grow makes over 60% of respondents to search for another job elsewhere. Even when teams are meeting only virtually, there is a crucial need for clearly communicated development plans and meetings with manager in order to discuss current and future progress and perspectives.
Retention is significantly related to positive perception of current employer (r = 0.634, p = 0.000; H1 accepted) and motivation and willingness to stay at current position (r = 0.332, p = 0.000; H2 accepted). The analysis shown that management of virtual teams relies heavily on career management. The mean importance of this factor was the highest from all other searched areas (4.329). Main factor leading to employee mobility in virtual teams is lack of career opportunities.
To ensure reliability of virtual employees, managers need to focus on remote working experience, challenges while working from home, virtual evaluation and impact on the performance appraisal, and other impact of remote work on personality. All mentioned areas are playing significant role in reliability in virtual environment (all averages and means were reaching over 4 out of scale where 5 means the highest value (strong agreement). The correlations between reliability and relevant periodic performance appraisal are strong and significant (r = 0.634; p < 0.001) (H3 accepted). The tests also confirmed that clearly communicated policies for virtual teams are statistically significant predictor (F(4, 315) = 54.431, p < 0.001)) of work experience and home office (H4 accepted). Respondents indicated that empowerment and encouraging is the most important strategy to enhance reliability of virtual teams.
The data were further processed by multivariate statistics. The model is significant as Kaiser-Meyer-Olkin (KMO) test value exceeds 0.9, Bartlett's test p-value is 0.000. The correlation analysis provided adequate level of relations among tested variables and their significance. Sutin test was used to calculate final number of resultant factors. The four final factors are in Tab. 3. Totally, 54% of the variance was explained. Factor 4 defines incompetent management given virtual conditions. The main problems are no opportunity to grow, no career path and promotion plan, impossibility to address problems and no supervision nor help, lack of clear communication and lack of trainings and development. The factor analysis points out management failures within management of virtual teams: incompetent recruitment, lack of communication, lack of carrier possibilities. Virtual employees are usually highly qualified and skilled labour force with clear vision of their growing potential. Management should support VT to achieve high performance to get reliable and loyal employees.
DISCUSSION
This paper discusses innovative management practices related to retention and reliability of virtual teams. Four hypotheses were tested; two related to virtual teams' retention and two referring to virtual employees' reliability. All four hypotheses were accepted. Results show that virtual team management heavily relates on positive perceptions of current employer (H1), motivation (H2), clear carrier plans and relevant periodic performance appraisal (H3) and clearly communicated policies for virtual teams (H4 accepted). The results are in line with relevant researches. The detailed results are further discussed in the area of retention and reliability.
The influence of motivation and positive perception of employer on employee retention must be taken into account (Shah and Asad, 2018), which is in line with this study (H2 accepted). In case employees perceive lack of career perspectives, it leads to low retention or low reliability. This study shows that skilled and proactive communicating managers are the key to success of virtual teams. In virtual environment, managers need to focus on how to lead and motivate geographically dispersed team members. Relation-oriented leadership behaviours have been identified as a key factor for effective virtual leadership (Bartsch et al., 2021), as tested and accepted by H1. This study found that managers need to pay attention to personalized periodical appraisal, fair treatment, periodic online trainings and development to improve performance, help to overcome performance gaps and promote an atmosphere of social support.
Employee reliability is impacted by the workplace environment, supervisor, and the opportunity for development (Malinen, Wright and Cammock, 2013;Shuck and Albornoz, 2007;Carnevale and Hatak, 2020), which is in accordance with the results of this study. Aburub (2020) emphasizes that employee engagement is related to company policy, culture, leadership style and strategic human resource management tools, (confirmed by H3). This paper results add factor of communication, in line with Powell, Piccoli and Ives (2004).
To avoid burnout in virtual teams, it is essential to establish an atmosphere of collaboration and provide team with the necessary autonomy (Liao, 2017). Support of employee motivation leads to reliability. Managers are required to adapt their leadership style to the requirements of virtual teamwork (Kauffeld et al., 2022) to training needs of their staff, which was tested and accepted by H4.
CONCLUSION
The findings in this paper tested and confirmed variables affecting reliability and retention of virtual teams. According to respondents, the most important in virtual teams' management is quality of interaction. The correlation analysis shown that quality of management of virtual teams is affected by the variables homework policies and overall organizational policies in relation to virtual work, clear goals, fair treatment, access to trainings, clear career path, communication, and feedback.
Significant portion of virtual employees strive with low social contact and less support, as over one fifth of managers of virtual teams lack competences to manage virtual employees in fully online environment.
The factors affecting virtual teams' reliability is impacted by positive perceptions and references of current employer (H1), motivation and willingness to stay at current position (H2). Virtual teams' retention is affected mainly by clear carrier plans and relevant periodic performance appraisal (H3), and clearly communicated policies for virtual work and home office (H4).
Quality management of virtual teams has to primarily focus on reliable employee-manager relationship. Periodic discussions on plans, updates, performance appraisal and career development are crucial for virtual team retention. Impossibility to grow makes over 60% of respondents to search for another job. There is a crucial need for periodic team and individual meetings with manager in order to discuss current and future progress and perspectives. Respondents indicated that empowerment and encouraging is the most important strategy to enhance reliability of virtual teams. The key to virtual employees' retention and reliability are training and supervision. This innovative management approach also attracts potential workers. Presented factors were confirmed by correlation analysis, ANOVA, regression and factor analysis.
Incompetent management of virtual teams lead to inefficiency and mobility of virtual workers. Proven problems are lack of opportunities, missing or unclear career path and promotion plan, impossibility to address problems and lack of supervision or help, lack of clear communication and lack of trainings.
Limitation of this paper is narrow focus on employees working in IT. On the other hand, the tests proven reliability and representativeness. Future research may explore virtual teams in other business branches and investigate team relations and its impact on reliability and retention. | 5,231.8 | 2022-11-30T00:00:00.000 | [
"Business",
"Computer Science"
] |
Dimethylammonium 3-carboxybenzoate
The asymmetric unit of the title organic salt, C2H8N+·C8H5O4 −, consists of two dimethylammonium cations and two 3-carboxybenzoate anions. The 3-carboxybenzoate anions are linked via strong intermolecular and nearly symmetrical O—H⋯O hydrogen bonds forming infinite chains parallel to [111]. Neighbouring chains are further connected by the dimethylammonium cations via N—H⋯O bonds, resulting in a double-chain-like structure. The dihedral angles of all carboxylate groups with respect to the phenylene rings are in the range 7.9 (1)–20.48 (9)°.
The asymmetric unit of the title organic salt, C 2 H 8 N + Á-C 8 H 5 O 4 À , consists of two dimethylammonium cations and two 3-carboxybenzoate anions. The 3-carboxybenzoate anions are linked via strong intermolecular and nearly symmetrical O-HÁ Á ÁO hydrogen bonds forming infinite chains parallel to [111]. Neighbouring chains are further connected by the dimethylammonium cations via N-HÁ Á ÁO bonds, resulting in a double-chain-like structure. The dihedral angles of all carboxylate groups with respect to the phenylene rings are in the range 7.9 (1)-20.48 (9) .
Data collection: APEX2 (Bruker, 2011); cell refinement: SAINT (Bruker, 2011); data reduction: SAINT; program(s) used to solve structure: SHELXS97 (Sheldrick, 2008); program(s) used to refine structure: SHELXLE (Hü bschle et al., 2011) and SHELXL97 (Sheldrick, 2008); molecular graphics: DIAMOND (Brandenburg, 2001); software used to prepare material for publication: publCIF (Westrip, 2010 The title compound, dimethylammonium 3-carboxybenzoate, was obtained as part of our investigations into the solvothermal synthesis of metal organic frameworks of magnesium with aromatic dicarboxylates. Reaction of magnesium nitrate with isophthalic acid and piperazine at 373 K did not yield an extended metal organic framework, but partial decomposition of the DMF solvent let to formation of the title dimethylammonium organic salt, which was isolated as a minor side product in the form of colourless plate-like crystals. Under the rather harsh solvothermal conditions used for the synthesis of many coordination compounds and metal organic frameworks formamides become unstable towards hydrolysis or Lewis acid catalyzed decarbonylation (Hine et al., 1981;Cottineau et al., 2011). This is also evidenced by the high number of dimethyl ammonium salts reported in the Cambridge Structural Database (CSD; Allen et al., 2002;597 entries up to Feb. 2012), counting only structures that also contain at least one metal ion. With dimethyl amine itself being a gas and being used not extensively as a reagent, it can be safely assumed that most of these structures originated from in situ hydrolysis of a dimethyl amide such as DMF. Twenty eight of these entries in the CSD with dimethyl ammonium ions also contain formate ions, the other product of DMF hydrolysis. The title compound, the dimethylammonium salt of isophtalic acid, is one such example that incorporates ammonium cations formed in situ through decomposition of a formamide.
The asymmetric unit of the title compound is composed of two hydrogen-3-carboxybenzoate anions and two dimethyl ammonium cations ( (Table 1), as is typical for very strong hydrogen bonds with very electronegative donor and acceptor atoms (Gilli & Gilli, 2009). The keto oxygen atoms of the carboxylate units, which are not involved in the O-H···O hydrogen bonds, act as acceptors for N-H···O hydrogen bonds that originate from both of the dimethylammonium cations, which double bridge the carboxylic acid and carboxylate groups of the anions into a bis(dimethylammonium)-bis(COO -···H + ··· -OOC) cluster (Fig. 3). In such a manner parallel infinite 3carboxybenzoate chains are connected into an inversion symmetric double chain like structure (Fig. 4). Supramolecular structures comprising 3-carboxybenzoates have been reported previously (Guo et al., 2010;Liu et al., 2007;Weyna et al., 2009). Similar one-dimensional chain-like structures have been reported by (Ballabh et al., 2005).
Experimental
The compound was synthesized under solvothermal conditions. In a typical synthesis, Mg(NO 3 ) 2 .6H 2 O (0.064 g, 0.25 mmol) was dissolved in a 1:1 mixture of DMF (5.0 ml) and EtOH (5.0 ml). Then, alumina (Sorbent Technologies, Atlanta, GA) (0.051 g, 0.5 mmol), isophthalic acid (0.166 g, 1.0 mmol) and piperazine (0.043 g, 0.5 mmol) were added to the reaction mixture which was stirred for one hour before transferring the mixture into a glass vial. The final mixture was heated to 373 K for 48 h. The vial was then slowly cooled to room temperature. Slow cooling of the reaction mixture yielded colourless plate-like crystals of the title compound as a minor product.
Refinement
Hydrogen atoms were placed in calculated positions with C-H bond distances of 0.95 Å (aromatic H), 0.99 Å (methyl H) or 0.88 Å (N-H). Methyl group H atoms were allowed to rotate around the C-C bond to best fit the experimental electron density. Carboxylic acid hydrogen atoms were located in difference electron density maps, but were placed in calculated positions with fixed C-O-H angles, but with the C-C-O-H dihedral angles and the O-H distances freely refined (AFIX 148 command in SHELXTL (Sheldrick, 2008)). U iso (H) values for all H atoms were constrained to a multiple of U eq of their respective carrier atom (1.2 times for aromatic and ammonium H atoms, 1.5 times for methyl and carboxylic acid H atoms).
Figure 1
View of the asymmetric unit with the atom-numbering scheme and 50% probability displacement ellipsoids. Special details Geometry. All e.s.d.'s (except the e.s.d. in the dihedral angle between two l.s. planes) are estimated using the full covariance matrix. The cell e.s.d.'s are taken into account individually in the estimation of e.s.d.'s in distances, angles and torsion angles; correlations between e.s.d.'s in cell parameters are only used when they are defined by crystal symmetry. An approximate (isotropic) treatment of cell e.s.d.'s is used for estimating e.s.d.'s involving l.s. planes. Refinement. Refinement of F 2 against ALL reflections. The weighted R-factor wR and goodness of fit S are based on F 2 , conventional R-factors R are based on F, with F set to zero for negative F 2 . The threshold expression of F 2 > σ(F 2 ) is used only for calculating R-factors(gt) etc. and is not relevant to the choice of reflections for refinement. R-factors based on F 2 are statistically about twice as large as those based on F, and R-factors based on ALL data will be even larger. | 1,430.8 | 2012-05-19T00:00:00.000 | [
"Materials Science",
"Chemistry"
] |
High-energy resummation in heavy-quark pair photoproduction
We present our predictions for the inclusive production of two heavy quark-antiquark pairs, separated by a large rapidity interval, in the collision of (quasi-)real photons at the energies of LEP2 and of some future electron-positron colliders. We include in our calculation the full resummation of leading logarithms in the center-of-mass energy and a partial resummation of the next-to-leading logarithms, within the Balitsky-Fadin-Kuraev-Lipatov (BFKL) approach.
Introduction
The high energies reached at the LHC and in possible future hadron and electron-positron colliders represent a great chance in the search for long-waited signals of New Physics. They offer, however, also a unique opportunity to test the Standard Model in unprecedented kinematic ranges. A vast class of processes can be studied at high-energy colliders, called semihard processes, characterized by a clear hierarchy of scales, s Q 2 Λ 2 QCD , where s is the squared center-of-mass energy, Q is the hard scale given by the process kinematics and Λ QCD is the QCD mass scale, which still represent a challenge for QCD in the high-energy limit. Here the fixed-order perturbative description, allowed by the presence of a hard energy scale, misses the effect of large energy logarithms, which compensate the smallness of the coupling α s and must therefore be resummed to all orders. The theoretical framework for this resummation is provided by the Balitsky-Fadin-Kuraev-Lipatov (BFKL) approach [1], whereby a systematic procedure has become available for resumming all terms proportional to (α s ln(s)) n , the so called leading logarithmic approximation (LLA), and also those proportional to α s (α s ln(s)) n , the so called next-to-leading approximation (NLA). In both cases, within the BFKL approach, the (possibly differential) cross section of processes falling in the domain of perturbative QCD, takes a peculiar factorized form, whose ingredients are the impact factors describing the transition from each colliding particle to the respective final state object, and a process-independent Green's function. The BFKL Green's function obeys an integral equation, whose kernel is known at the next-to-leading order (NLO) both for forward scattering (i.e. for t = 0 and color singlet in the t-channel) [2,3] and for any fixed (not growing with energy) momentum transfer t and any possible two-gluon color state in the t-channel [4][5][6].
The phenomenological reach of the BFKL approach is limited by the number of available impact factors. So far, only a few of them have been calculated with next-to-leading order accuracy: i) impact factors for colliding quarks and gluons [7][8][9][10], which are at the basis of the calculation of the ii) forward jet impact factors (or jet vertices) in exact form [11][12][13] or in the small-cone approximations [14,15] and of the iii) forward hadron impact factors [16], iv) impact factor for the γ * to light vector meson transition at leading twist [17], v) impact factor for the γ * to γ * transition [18,19].
The forward hadron impact factors were recently used to calculate the cross section and some azimuthal correlations in the inclusive production of two identified hadrons composed of light quarks and separated in rapidity [39,40] which could also be studied at the LHC.
The impact factor for the γ * to light vector meson transition enters the imaginary part of the cross section for the exclusive production of two light vector mesons in the collision of two highly virtual photons [41][42][43][44][45], which could be considered in future linear colliders.
The γ * to γ * impact factor is the ingredient for the γ * γ * total cross section, which is considered to be the gold-plated channel for the manifestation of the BFKL dynamics. A number of predictions for this cross section were built, with partial inclusion of NLA BFKL effects [46][47][48][49] and with full NLA accuracy [50,51], whose comparison with the only available data from LEP2 cannot be conclusive due to the relatively small center-of-mass energy and the limiting accuracy of LEP2 experiment.
In this paper we introduce another process which could serve as a probe of BFKL dynamics: the inclusive production of two heavy quark-antiquark pairs, separated in rapidity, in the collision of two real (or quasi-real) photons, where Q here stands for a charm/bottom quark or antiquark. In Fig. 1 we present a schematic representation of this process, in the case when the tagged objects are a heavy quark with momentum q 1 , detected in the fragmentation region of the photon with momentum p 1 , and a heavy quark with momentum q 2 , detected in the fragmentation region of the photon with momentum p 2 . This process can be studied either at electron-positron or in nucleus-nucleus colliders via collisions of two quasi-real photons. In this first exploratory study, we will focus on the case of electron-positron colliders and will adopt the equivalent photon approximation (EPA) to parametrize the photon flux emitted by the colliding electrons and positrons. The main aim is to show that sensible predictions can be built, within the BFKL approach with NLA, which can be compared with experimental results. For the sake of definiteness, we will consider the center-of-mass energies of LEP2 and of the CLIC future collider.
The totally inclusive two heavy-quark pair production process has much in common with the above discussed inclusive interaction of two virtual photons (the γ * γ * total cross section).
Here the large values of masses of the produced heavy quarks play the role of hard scale, similar to the role that large photon virtualities play in γ * γ * interactions. It is interesting to note that just this observable, the total inclusive cross section for two heavy-quark pairs photoproduction, was calculated first in QCD within BFKL resummation method, see the paper by I. Balitsky and L. Lipatov in [1]. Despite the fact that the BFKL resummation gives formally a finite result for this total cross section, it does not represent an observable that can be directly confronted with the experiment. Indeed, in order to be sure that two heavy-quark pairs are produced in the event, one needs to detect at least one of the heavy quarks in each quark pair. The other reason for the tagging of two heavy quarks is that the knowledge of their momenta (their rapidities) allows one to keep control on the energy of the collision of two quasi-real photons in e + e − experiments. In our present study we restrict/fix the momenta of these two tagged quarks as if they were true final states. As a further step, one needs to include into the theoretical analysis the heavy-quark fragmentation describing the tagging procedure of heavy quarks in the particular experiment.
An attractive idea is to consider also similar experiments which assume the detection of the pair of heavy quarks, separated by a large rapidity interval, in photon-photon interactions via ultra-peripheral (UPC) nucleus-nucleus collisions at the LHC. In [52] the total cross section for the production of two heavy-quark pairs in such collisions was estimated in the LO BFKL approach at a sizeable value. However the kinematics of experiments with a tagged pair of heavy quarks separated by the rapidity interval of a few units requires rather large energies of the colliding quasi-real photons. Unfortunately at the LHC the energies of such photon-photon interactions in the UPC heavy nucleus-nucleus collisions fall in the kinematic range where the quasi-real photon fluxes from the colliding heavy nuclei are greatly suppressed due to electromagnetic nuclear form factors, and therefore such experiments look not feasible.
The paper is organized as follows: in Section 2 we explain the theoretical setup of our calculation; in Section 3 we present our results for the cross sections azimuthal angle correlations in dependence on the rapidity interval between the tagged heavy quarks; in Section 4 we discuss our results and draw conclusions. Figure 1: Diagrammatic representation of the heavy-quark pair photoproduction in the case when a heavy quark with transverse momentum q 1 (q 2 ) from the upper (lower) vertex is tagged.
Theoretical setup
For the process under consideration, given in Eq. (1), we plan to construct the cross section, differential in some of the kinematic variables of the tagged heavy quark or antiquark, and some azimuthal correlations between the tagged fermions. In the BFKL approach the cross section takes a factorized form, schematically represented in Fig. 2, given by the convolution of the impact factors for the transition from a (quasi-)real photon to a heavy quark-antiquark pair (the upper and lower ovals in Fig. 2, labeled by Φ) with the BFKL Green's function G. The crosses in Fig. 2 denote the tagged quarks, whose momenta are not integrated over in getting the expression for the cross section.
In our calculation we will partially include NLA resummation effects, by taking the BFKL Green's function in the NLA, while keeping the impact factors at the leading order, since their next-to-leading order corrections are not yet known. Figure 2: Schematic representation of the BFKL factorization for the process under consideration.
The impact factor
The (differential) impact factor for the photoproduction of a heavy quark pair reads where R and P read Here α and α s denote the QED and QCD couplings, e Q denotes the electric charge of the heavy quark, m stands for the heavy-quark mass, z and z ≡ 1 − z are the longitudinal fractions of the quark and antiquark produced in the same vertex and k, q, k − q represent the transverse momenta with respect to the photons collision axis of the Reggeized gluon, the produced quark and antiquark, respectively. The details of the derivation of this result may be found, for instance, in [53]. Such impact factor differs only by the coupling and overall normalization from the similar QED quantity known since long and used in the calculations of the lepton-pair production.
In the following we will need the projection of the impact factors onto the eigenfunctions of the leading-order BFKL kernel, to get their so called (n, ν)-representation. We get where ζ ≡ q 2 m 2 + q 2 ; the azimuthal angles ϑ and ϕ are defined as cos ϑ ≡ k x /| k| and cos ϕ ≡ q x /| q|.
Kinematics of the process
For the tagged quark momenta we introduce the standard Sudakov decomposition, using as light-cone basis the momenta p 1 and p 2 of the colliding photons, with here q = E, q, q and the rapidity can be expressed as Therefore for the rapidities of the two tagged quarks in our process we have whence their rapidity difference is .
For the semihard kinematic we have the requirement therefore we will consider the kinematic when ∆Y ≥ ∆ 0 ∼ 1 ÷ 2.
In what follows we will need a cross section differential in the rapidities of the tagged quarks, therefore we have to make the following change of variables: which implies dz 1 dz 2 = e y 1 −y 2 m 2 + q 2 1 m 2 + q 2 2 W 2 dy 1 dy 2 .
The BFKL cross section and azimuthal coefficients
Similarly to the Mueller-Navelet jet and the dihadron production processes (see Refs. [23,40]), the differential cross section for the inclusive production of a pair of heavy quarks separated in rapidity (a "diquark" system in what follows) can be cast in the following form: where ϕ = ϕ 1 − ϕ 2 − π, while C 0 gives the cross section averaged over the azimuthal angles ϕ 1,2 of the produced quarks and the other coefficients C n determine the distribution of the relative azimuthal angle between the two quarks.
The scale s 0 can be arbitrarily chosen, within NLA accuracy; in our calculation we made the choice s 0 = √ s 1 s 2 . Equation (7) is written for the general case when two heavy quarks of different flavors with masses m 1 and m 2 are detected.
The e + e − cross section
To pass from the photon-initiated process to the one initiated by e + e − collisions, we must take into account the flux of quasi-real photons dn emitted by each of the two colliding particles, dσ e + e − = dn 1 dn 2 dσ γγ , where x = ω Ee is the fraction of the electron (positron) energy carried by the photon and q denotes now the transverse component of the photon momentum. The emission angle is θ ≈ q ⊥ Ee(1−x) and we will consider the antitag experiment, so that θ ≤ θ 0 whence ( q) max = E e (1 − x) θ 0 .
Integrating in Eq. (8) over q 2 , we get where some terms O(m 2 e /E 2 e ) were neglected.
Therefore the general expression for our observables is with y In order to give predictions to be confronted with experiment, we have to integrate our fully differential cross section over some range of the tagged quarks transverse momenta. In what follows we label such integrated coefficients with C n . As a background contribution for the case when a quark and an antiquark of the same flavor are detected, we have to consider the lowest-order QED cross section for the production of a heavy quark and antiquark in photon-photon collisions. In our notations the corresponding e + e − cross section reads
The "box" qq cross section
where and Λ m 2 .
In the case when the heavy quarks of different flavor, two quarks or two antiquark of the same flavor, are detected, the "box" mechanism does not represent, of course, a background channel.
Results
In this Section we present our results for the dependence on the rapidity separation between the two tagged quarks, ∆Y = y 1 − y 2 , of the ϕ-averaged cross section C 0 and of the ratios R 10 ≡ C 1 /C 0 and R 20 ≡ C 2 /C 0 ratios. We consider here only the case of charm quark and fix the mass m at the value 1.2 GeV/c 2 .
Introducing some reasonable kinematic cuts, we integrate the quark transverse momenta in the symmetric range q min < q 1,2 < q max . We fix q max to 10 GeV and consider below the three cases q min = 0, 1, 3 GeV. We fix the center-of-mass energy to √ s = 200 GeV, typical of LEP2 analyses, and study the behavior of our observables in the rapidity range 1 < ∆Y < 6. For comparison, we give predictions of C 0 and R 10 also at √ s = 3 TeV, characteristic of the future e + e − CLIC linear accelerator. In the last case, we allow for a larger rapidity interval between the two quarks, i.e. 1 < ∆Y < 11. We fix the maximum for the lepton emission angle θ 0 = 0.0835, which is inside of the acceptance range of the OPAL forward detectors [54,55]. All calculations are done in the MS scheme.
Pure LLA and NLA BFKL predictions, together with the "box" qq calculation of C 0 for q min = 0 GeV and √ s = 200 GeV, are shown respectively in Table 1 and 2. GeV. LLA and NLA predictions are compared with the "box" qq cross section. C stands for µ 2 R /(s 1 s 2 ).
Numerical tools and uncertainty estimation
All numerical calculations were done in Fortran. Numerical integrations were performed using routines implemented in the Cuba library [56,57], making extensive use of the Monte Carlo Vegas [58] integrator. The numerical stability of our results was crosschecked by separate calculations performed using both Mathematica and the Dadmul CERNLIB routine [59].
The most important source of uncertainty comes from the numerical six-dimensional integration over the variables | q 1 |, | q 2 |, y 1 , ν, x 1 , and x 2 and was directly estimated by the Vegas integration routine [58]. We checked that other sources of uncertainties, related with the upper cutoff in the integrations over | q 1 |, | q 2 |, and ν, are negligible with respect to the first one. Thus, the error bars of all predictions are just those given by Vegas. The other, internal source of uncertainty of our calculation is related with the scale of the running QCD coupling. Below we quantify this uncertainty studying the renormalization scale dependence. We vary µ 2 R around its "natural" value √ s 1 s 2 in the range 1/2 to two. The parameter C entering Tables 1 and 2 gives the ratio µ 2 R / √ s 1 s 2 .
Discussion
The inspection of results in Table 1 suggests that the cross section C 0 is smaller than the reference "box" cross section. This is similar to what occurred in the calculations of the total γ * γ * cross section, where it was found that the "box" mechanism gives still a very important contribution at LEP2 energies. The situation changes if we pass to the larger energies and therefore larger rapidity differences that are possibly available at future e + e − colliders. Here the BFKL mechanism with the gluonic exchange in the t-channel starts to win over the "box" one with the fermionic t-channel exchange, see our results in Table 2. We stress, however, that for our two heavy-quark (or two heavy-antiquark) tagged process, contrary to the γ * γ * case, the "box" mechanism is not a background. The cross section exhibits the expected trends: it increases when moving from the LEP2 energies to the CLIC ones and decreases when moving from the LLA to the NLA, a typical feature in the BFKL approach. We note also that our partial inclusion of NLA effects leads to results that are less sensitive to the variation of the renormalization scale than the results of LLA BFKL resummation.
Azimuthal correlations are in all cases much smaller than one and decrease when ∆Y increases, as it must be due to the larger emission of undetected partons. The reason for this smallness, with respect to the case of Mueller-Navelet jets or di-hadron systems (see, e.g., Refs. [23,40]) is that in this case there is not any kinematic constraint, even at the lowest order in perturbation theory, between the transverse momenta of the two tagged quarks, since they are produced in two different vertices (each of them together with an antiquark). When the minimum value of the tagged quark transverse momentum q min is increased, azimuthal correlations increase due to the more limited available phase space in the transverse space and the consequently more constrained transverse kinematics. We can see that the inclusion of NLA effects increases the correlations, which can only be explained with the larger suppression of C 0 with respect to C 1,2 when these effects are included.
Summary and outlook
We have considered the inclusive photoproduction of two heavy quarks separated in rapidity, taking into account the resummation to all orders of the leading energy logarithms and the resummation of the next-to-leading ones entering the BFKL Green's function. We have calculated the cross section for this process averaged over the relative azimuthal angle of the two tagged quarks and presented results for the azimuthal angle correlations, considering for definiteness the case in which photons are emitted by electron and positrons colliding at the energies of the LEP2 and the CLIC colliders.
The trends of our results with the energy of the collision beam and the behavior of the considered observables with the rapidity interval ∆Y between the tagged quarks is just as expected: azimuthal correlations decrease with increasing ∆Y . Moreover, just as in the case of Mueller-Navelet jets and dihadron production, the inclusion of next-to-leading order correction reduces the decorrelation. In absolute value, azimuthal correlations are much smaller with respect to Mueller-Navelet jets and dihadrons, a result which is not surprising since here we have a two heavy-quark pair production mechanism and the two tagged quarks are produced in the leading order in different interaction vertices, having independent transverse momenta.
This process extends the list of semihard processes by which strong interactions in the high-energy limit, and in particular the BFKL resummation procedure, can be probed in the future e + e − linear colliders.
There are several obvious developments of this work. One is the calculation of the next-toleading order impact factor for the photoproduction of a heavy quark-antiquark pair, which would allow for the full NLA treatment of the process under consideration. The other is to include into the theoretical analysis heavy-quark fragmentation describing the experimental tagging procedure of heavy quarks.
As we already noted, the possibility for the experimental study of our process in UPC collisions of heavy ions at the LHC kinematics is not feasible, unfortunately. Nevertheless, the study of heavy-quark observables that reveal BFKL resummation effects looks promising in the LHC proton-proton collisions. Recently, in [60] the process of inclusive forward J/Ψmeson and very backward jet production was suggested. The other interesting possibility is to extend the methods used in our work to the case of two detected heavy-quark inclusive hadroproduction, i.e. a process similar to the one considered here, but initiated by quarks and gluons emitted by protons (in collinear factorization) rather than by photons. | 4,981.2 | 2017-09-28T00:00:00.000 | [
"Physics"
] |
Influence of Compression Loading on Acoustic Emission and Light Polarization Features in TeO2 Crystal
Monitoring the processes inside crystalline materials under their operating conditions is of great interest in optoelectronics and scientific instrumentation. Early defect detection ensures the proper functioning of multiple crystal-based devices. In this study, a combination of acoustic emission (AE) sensing and cross-polarization imaging is proposed for the fast characterization of the crystal’s structure. For the experiments, tellurium dioxide (TeO2) crystal was chosen due to its wide use in acousto-optics. Studies were performed under uniaxial compression loading with a simultaneous acquisition of AE signals and four polarized optical images. An analysis of the temporal dependencies of the AE data and two-dimensional maps of the light depolarization features was carried out in order to establish quantitative criteria for irreversible damage initiation and crack-like defect formation. The obtained results reveal the polarization image patterns and the AE pulse duration alteration specific to these processes, and they open up new possibilities for non-destructively monitoring in real-time the structure of optically transparent crystals under their operating conditions.
Introduction
One of the areas in which crystalline materials have become widespread is acoustooptics.The key material for the production of acousto-optical devices is currently tellurium dioxide (TeO 2 ).Its unique properties allow for the building of small-sized and highly efficient devices for photonics and laser techniques [1][2][3][4][5][6][7].These devices operate normally only under certain conditions, which are determined by the mechanical and optical characteristics of its components, among others.To predict the behavior of the crystal in real operating conditions, it is necessary to conduct many studies on its behavior under external stressors.Such studies, also referred to as flaw detection, make it possible to determine the operating conditions' boundaries and to determine the overall failure tolerance of the device with a degree of certainty.
To solve the problem of crack detection in crystals, there are a few experimental methods that can be used based on X-ray radiation [8,9], polarized light [10,11] and other techniques.
In X-ray methods, the proportionality of the inter-atomic distance and radiation wavelength makes it possible to consider the inspected crystal as a three-dimensional diffraction lattice.The analysis of the X-ray diffraction data on such periodic structures allows us to understand the arrangement and type of atoms present, as well as to estimate the interatomic distance.Among all X-ray methods, X-ray diffraction techniques, including two-and three-crystal X-ray diffractometry, are most commonly used for crystal examination.The experimental results obtained with these techniques are compared with the calculations following from the dynamic theory of diffraction, allowing us to come to a clear conclusion about the structure of the crystal, including the presence of defects.These techniques are also used to study crystals under dynamic load-however, in general, the temporal resolution of such techniques is limited.This problem can be solved by using fast diffractometric techniques, such as adaptive X-ray optics [12,13].In [14], studies on the influence of ultrasonic modulation on TeO 2 crystals were conducted, which showed a complex picture of the evolution of the crystal structure under pressure, but, due to the impossibility of time-resolved measurements, no response was received on the nature of the changes observed.In [15], a new method of rapid X-ray diffractometry was proposed, which allowed the authors to observe structural changes in real-time, but the determination of the time of transition from the area of elastic deformation to irreversible structural change using only the proposed methodology remains difficult due to the limited brightness of the laboratory X-ray sources.
Optical microscopy provides observations of macroscopic defect formation in transparent brittle materials during mechanical loading [16][17][18].Quantitative analysis of stressinduced artificial birefringence in isotropic materials allows us to conduct elastic stress assessments [19] with respect to the stress-optic law [20].The application of photoelastic birefringent coatings facilitates shear stress monitoring for opaque materials [21,22].The evolution of optical polarization features under external mechanical load in anisotropic optical crystals is guided by the complex combination of stress-induced birefringence and the intrinsic material anisotropy of the elastic and optical properties [11,[23][24][25].This hampers the application of optical techniques for studying anisotropic materials.One successful method for anisotropic crystal inspection is conoscopic interference pattern analysis [10,26,27].With no external load applied, the conoscopic inspection indicates the inhomogeneity of the refractive indices formed during crystal growth and through mechanical post-growth processing.The precise inspection of thick crystalline specimens requires a stable and uniform laser illumination source [10].At the same time, obtaining data on stress localization from fringe patterns during mechanical loading may be a challenging task.
Despite the obvious advantages of these methods, their use is significantly difficult when it is necessary to analyze the impact of the mechanical stress arising in the process of operation directly in ready-made devices.This issue may be solved by acoustic emission (AE) sensing, which is successfully used to study the condition of structural and functional materials, including the study of dynamic damage in alloys [28] and composite materials [29].Different parameters can be used to estimate crystal defects using the AE method, both primary (e.g., amplitude (u m ), duration (t imp ), activity ( Ń)) and complex, based on the calculations of certain functions (e.g., amplitude distribution (u m ) and energy (E imp )).However, only complex AE data analysis allows us to determine informative AE parameters and their threshold values, indicating irreversible destruction in the inspected crystal.In [30][31][32], it was shown that, at the moment of formation and the development of irreversible damage in the crystal, a change in the parameters of the energy distribution and duration of AE pulses can be observed.This allows us to obtain analytical dependencies E imp (t imp ) and u m (E imp ), which correlate with the degree of damage to the crystals.
In this feasibility study, we propose to analyze AE data in TeO 2 crystals under mechanical load, obtain quantitative criteria of defect appearance and confirm this by simultaneous cross-polarization imaging [33][34][35].We introduce the criteria based on AE pulse duration for the evaluation of crystal damage degree instead of other AE signal parameters [36][37][38].To simplify the polarization features' extraction and conserve the most informative data on the angle and degree of light polarization, highlighting the structural inhomogeneities in crystal samples, we apply a linearly polarized illumination.The combination of these two techniques allows for real-time data acquisition and analysis, and thus the determination of the dependencies between the external load and the obtained AE and optical data.
Crystals
Sample preparation included crystal growth, orientation, quality control and processing.High-purity single α-TeO 2 crystals were grown from a melt using the Czochralski method in a [110] direction (National Research Centre "Kurchatov institute", Moscow, Russia).The crystal boule was 60 mm in diameter and had a 60 mm cylindrical part.The exact orientations of the crystallographic axes were determined using X-ray diffraction patterns.Quality control included spectral chemical analysis, dislocation density measurements by selective etching and inner defect detection by laser conoscopy [10].Crystallinity and grainfree structure (high degree of perfection) were ensured by X-ray topography [39].After the crystal was oriented and checked, samples were cut from it.Their facets were polished and coated to ensure a high level of flatness and optical transmission in the visible range.
In this study, we tested samples of two types (Figure 1).Both of them have dimensions of 10 ± 0.1 mm × 10 ± 0.1 mm × 20 ± 0.1 mm and four polished faces.The samples' faces contacting the testing machine compression platens were beveled to prevent the undesired defect growth.Samples of the first type had polished and coated faces parallel to planes ( 110 formative data on the angle and degree of light polarization, highlighting the structural inhomogeneities in crystal samples, we apply a linearly polarized illumination.The combination of these two techniques allows for real-time data acquisition and analysis, and thus the determination of the dependencies between the external load and the obtained AE and optical data.
Crystals
Sample preparation included crystal growth, orientation, quality control and processing.High-purity single α-TeO2 crystals were grown from a melt using the Czochralski method in a [110] direction (National Research Centre "Kurchatov institute", Moscow, Russia).The crystal boule was 60 mm in diameter and had a 60 mm cylindrical part.The exact orientations of the crystallographic axes were determined using X-ray diffraction patterns.Quality control included spectral chemical analysis, dislocation density measurements by selective etching and inner defect detection by laser conoscopy [10].Crystallinity and grain-free structure (high degree of perfection) were ensured by X-ray topography [39].After the crystal was oriented and checked, samples were cut from it.Their facets were polished and coated to ensure a high level of flatness and optical transmission in the visible range.
In this study, we tested samples of two types (Figure 1).Both of them have dimensions of 10 ± 0.1 mm × 10 ± 0.1 mm × 20 ± 0.1 mm and four polished faces.The samples' faces contacting the testing machine compression platens were beveled to prevent the undesired defect growth.Samples of the first type had polished and coated faces parallel to planes (110) and (1-10), providing optical imaging along the [110] and [1][2][3][4][5][6][7][8][9][10] The optical properties of α-TeO 2 under normal conditions, including their polarization features, are well known [3,7].Linearly polarized light conserves its polarization state when passing through a crystal in an arbitrary direction other than the vicinity of the optical axis [001], where gyrotropy is present.
Considering the mechanical properties of α-TeO 2 , it is necessary to pay attention to Young modulus and compressive strength.Young modulus significantly depends on direction and varies from 8.7 GPa to 112.4 GPa [40].For the two mentioned specimen types, compressive Young modulus is 95.9 GPa along the [001] axis and 112.4 GPa along the [1][2][3][4][5][6][7][8][9][10] axis.The variability in compressive strength values reported in previous works are caused by different crystal growth techniques, initial specimen quality and the vulnerability of this brittle material with respect to the compression loading conditions.Furthermore, the compressive strength is expected not to exceed 150 MPa along the [001] axis and 120 MPa along the other one [41,42].
Before the compressive tests, we inspected the crystalline samples with a standard conoscopic technique [10] in a static mode, i.e., without external mechanical load, to ensure the absence of structural (macroscopic) defects.The examples of conoscopic patterns for the specimens of both types are shown in Figure 2.
tion features, are well known [3,7].Linearly polarized light conserves its polarization state when passing through a crystal in an arbitrary direction other than the vicinity of the optical axis [001], where gyrotropy is present.
Considering the mechanical properties of α-TeO2, it is necessary to pay attention to Young modulus and compressive strength.Young modulus significantly depends on direction and varies from 8.7 GPa to 112.4 GPa [40].For the two mentioned specimen types, compressive Young modulus is 95.9 GPa along the [001] axis and 112.4 GPa along the [1][2][3][4][5][6][7][8][9][10] axis.The variability in compressive strength values reported in previous works are caused by different crystal growth techniques, initial specimen quality and the vulnerability of this brittle material with respect to the compression loading conditions.Furthermore, the compressive strength is expected not to exceed 150 MPa along the [001] axis and 120 MPa along the other one [41,42].
Before the compressive tests, we inspected the crystalline samples with a standard conoscopic technique [10]
Experimental Setup
To achieve simultaneous mechanical loading and a comprehensive visualization of the crystalline samples, we applied a complex experimental setup, which was a combination of an AE sensing system, a cross-polarization imager and a universal testing machine, (UTM) Instron 5982 (Instron GmbH, Darmstadt, Germany), providing the specimens with a uniaxial compressive load (Figure 3).For the precise control of external load and data acquisition synchronization, all three devices were connected to a single PC.The tested specimen was placed vertically on a 10 mm thick steel platen P2.Compressive force was applied by the moving upper platen P1, which was equipped with a floating hinge, providing the coincidence of compressive force F and the chosen crystallographic axis.Since paratellurite crystals have low plasticity, we used a floating hinge to avoid distortions during loading.During every single bench test, the upper UTM traverse moved downwards with a low loading rate (0.01 mm/min) to ensure a reliable recording and the temporal separation of AE signals during loading.The upper value of the test load was not set, and the test was stopped after the appearance and propagation of visible defects in the tested sample.
Experimental Setup
To achieve simultaneous mechanical loading and a comprehensive visualization of the crystalline samples, we applied a complex experimental setup, which was a combination of an AE sensing system, a cross-polarization imager and a universal testing machine, (UTM) Instron 5982 (Instron GmbH, Darmstadt, Germany), providing the specimens with a uniaxial compressive load (Figure 3).For the precise control of external load and data acquisition synchronization, all three devices were connected to a single PC.The tested specimen was placed vertically on a 10 mm thick steel platen P 2 .Compressive force was applied by the moving upper platen P 1 , which was equipped with a floating hinge, providing the coincidence of compressive force F and the chosen crystallographic axis.Since paratellurite crystals have low plasticity, we used a floating hinge to avoid distortions during loading.During every single bench test, the upper UTM traverse moved downwards with a low loading rate (0.01 mm/min) to ensure a reliable recording and the temporal separation of AE signals during loading.The upper value of the test load was not set, and the test was stopped after the appearance and propagation of visible defects in the tested sample.
AE Sensing System
The AE method is based on the phenomenon of elastic wave generation during the formation and propagation of defects in the tested material.Piezoelectric AE transducers connected to the AE data collection and processing system via a pre-amplification unit are used to record elastic waves.As a result of the action of elastic acoustic waves on piezoelectric transducers (direct piezoelectric effect), an analog electrical signal is generated.The conversion of analog signals into a digital data stream is performed using analog-to-digital converters built into the AE diagnostics system.Due to its high sensitivity, the AE method is one of the most widely used methods of technical diagnostics used in the creation of structural health monitoring systems [43].
The AE sensing system is based on the Vallen AMSY-6 (Vallen Systeme GmbH, Wolfratshausen, Germany) monitoring and processing unit.A quasi-resonant piezoelectric AE transducer VS150-RIC with a built-in 34 dB preamplifier was used as a receiving transducer connected to the AE monitoring unit.The bandwidth of the used AE transducer corresponds to 100-400 kHz, with a maximum sensitivity at a frequency of 150 kHz.The AE transducer (designated as "AE" in Figure 3) was fixed on the steel plate P2 with a clamp through a layer of contact grease at a distance of 25 mm from the crystal.
Cross-Polarization Imaging System
The cross-polarization imager consists of an illumination system and a polarizationsensitive camera (Figure 3).The illumination system is the white LED-based wideband light source equipped with a diffusing plate (DP), a collimating Fresnel lens (FL) and a linear polarizer (PF) (laminated film polarizer, extinction ratio 100:1 in a 400-700 nm
AE Sensing System
The AE method is based on the phenomenon of elastic wave generation during the formation and propagation of defects in the tested material.Piezoelectric AE transducers connected to the AE data collection and processing system via a pre-amplification unit are used to record elastic waves.As a result of the action of elastic acoustic waves on piezoelectric transducers (direct piezoelectric effect), an analog electrical signal is generated.The conversion of analog signals into a digital data stream is performed using analogto-digital converters built into the AE diagnostics system.Due to its high sensitivity, the AE method is one of the most widely used methods of technical diagnostics used in the creation of structural health monitoring systems [43].
The AE sensing system is based on the Vallen AMSY-6 (Vallen Systeme GmbH, Wolfratshausen, Germany) monitoring and processing unit.A quasi-resonant piezoelectric AE transducer VS150-RIC with a built-in 34 dB preamplifier was used as a receiving transducer connected to the AE monitoring unit.The bandwidth of the used AE transducer corresponds to 100-400 kHz, with a maximum sensitivity at a frequency of 150 kHz.The AE transducer (designated as "AE" in Figure 3) was fixed on the steel plate P 2 with a clamp through a layer of contact grease at a distance of 25 mm from the crystal.
Cross-Polarization Imaging System
The cross-polarization imager consists of an illumination system and a polarizationsensitive camera (Figure 3).The illumination system is the white LED-based wideband light source equipped with a diffusing plate (DP), a collimating Fresnel lens (FL) and a linear polarizer (PF) (laminated film polarizer, extinction ratio 100:1 in a 400-700 nm wavelength range).An image of the specimen illuminated by linearly polarized light is acquired using the polarization-sensitive camera.To reduce the influence of optical rotatory power dispersion, the camera is equipped with combination of colored glass, absorbing bandpass filters (SF) (CWL 540 nm, FWHM 50 nm).The image is formed using the machine vision lens (35 mm focal length, f/1.65) with an additional 3× extender, providing the overall magnification of 64 pixels per 1 mm in the object plane.An image sensor with an on-chip polarizer array (DZK 33UX250, The Imaging Source Europe GmbH, Bremen, Germany) allows for the simultaneous acquisition of four images formed by the light with different polarization plane orientations (0 • , 45 • , 90 • and 135 • ) in a single frame.During the preliminary adjustment, the camera is aligned with the orientation of the illumination polarization plane provided by the linear polarizer.We analyzed the polarization features of the specimen, considering several criteria calculated using the corresponding single polarization images, I 0 (x,y), I 45 (x,y), I 90 (x,y) and I 135 (x,y).From the intensity values in the single polarization images, one can calculate the Stokes parameters for the linear polarization case and assess the alterations in the light polarization state introduced by different spatial areas of the inspected sample.
Data Processing 2.3.1. AE Signal Processing
Prior to the compression bench tests, the optimal parameters of the Vallen AMSY-6 unit should be determined.The AE pulse discrimination threshold (u th ) is determined according to the condition u th ≥ u n + 6 dB (u th is the AE pulse discrimination threshold, u n is the maximum amplitude of noise signals), and, for the described configuration of the experimental setup, it is u th = 34 dB.To eliminate noise signals arising due to the friction of the UTM upper platen and the crystalline specimen surface, we adjusted the digital filters' bandwidth for the range ∆f p = 95-850 kHz.Also, before each bench test, we checked the quality of the acoustic contact between the specimen and the transducer by pressing the graphite pencil lead against the specimen's side face (Hsu-Nielsen source).The amplitude of the pulses the from pencil lead breakage was in the range of u m = 99.7-99.8dB in all of the tests, indicating the low attenuation of the acoustic signals in the AE sensing system.
For the AE monitoring of TeO 2 single crystals during the compression, we analyzed the primary and complex AE parameters and calculated the high-level quantiles of their empirical distribution functions.The calculation of the empirical distribution functions was carried out using a sliding window function F * M defined as follows: where M is a window size, I is a quantity of the AE parameters satisfying the condition X i < y, X i is the AE parameter value from the sample X = (X 1 , . .., X i , . .., X M ) and y is a threshold value of the AE parameter in the following range: y ∈ [X min , . .., X max ].
At the first stage of data processing, we assessed the evolution of the primary AE data flow, pulse amplitude u m and duration t imp , which we registered during the compression until the visible defect formation occurred.The empirical function of the AE pulse duration distribution may be implemented as the informative diagnostic value indicating the initiation and growth of defects in the TeO 2 single crystals [44].Thus, at the second stage of experimental data processing, we propose to consider the high-level quantile of the empirical distribution function of the AE pulse duration t imp (p = 0.85) as a criterion for the detection of the emerging defects using the AE technique.
Cross-Polarization Image Processing
Each 2 × 2-pixel block of a raw image contains four intensity values (I 0 , I 45 , I 90 and I 135 ).After single-polarization image extraction, they are used for calculating the following maps: non-polarized (NP), angle of linear polarization (AoLP) and degree of linear polarization (DoLP).The NP map is a monochrome image formed by non-polarized light, calculated as the mean intensity value of the 2 × 2-pixel block: Assuming the linear polarization of light after passing through the inspected crystal, the Stokes parameters may be calculated as follows: The S 3 parameter necessary for the circular polarization is not available for this sensor type.However, using the Stokes parameters, values (2) for each pixel block, AoLP and the DoLP spatial maps are derived as follows: The AoLP and DoLP spatial maps are helpful for tracking the changes in polarization features.During the experiments, we acquired raw images with resolutions of 1280 × 960 pixels at 40 fps and calculated 640 × 480 pixel NP, AoLP and DoLP maps.
The joint data processing pipeline for simultaneous AE monitoring and cross-polarization imaging is presented in Figure 4.The S3 parameter necessary for the circular polarization is not available for this sensor type.However, using the Stokes parameters, values (2) for each pixel block, AoLP and the DoLP spatial maps are derived as follows: The joint data processing pipeline for simultaneous AE monitoring and cross-polarization imaging is presented in Figure 4.In the beginning of the compression test (σ ≤ 50 MPa), a low-intensity AE data flow was recorded.The maximum amplitude and duration did not exceed um = 40 dB and timp = 1000 µs.Further compressive stress increases led to a growth in the values of the primary AE parameters.As the load reached σ = 56.8MPa, amplitude and duration grew to um = 100 dB and timp = 60,000 µs.A sharp increase in AE pulse duration may be associated with the friction of the edges of a developing crack during crystal compression, which is confirmed by a decrease in the level of mechanical stress from σ = 56.8MPa to σ = 55.2MPa at τ = 1325.4s. Figure 6 shows the calculated values belonging to the p = 0.85 level quantile of the AE pulse duration empirical distribution function.In the beginning of the compression test (σ ≤ 50 MPa), a low-intensity AE data flow was recorded.The maximum amplitude and duration did not exceed u m = 40 dB and t imp = 1000 µs.Further compressive stress increases led to a growth in the values of the primary AE parameters.As the load reached σ = 56.8MPa, amplitude and duration grew to u m = 100 dB and t imp = 60,000 µs.A sharp increase in AE pulse duration may be associated with the friction of the edges of a developing crack during crystal compression, which is confirmed by a decrease in the level of mechanical stress from σ = 56.8MPa to σ = 55.2MPa at τ = 1325.4s. Figure 6 shows the calculated values belonging to the p = 0.85 level quantile of the AE pulse duration empirical distribution function.In the beginning of the compression test (σ ≤ 50 MPa), a low-intensity AE data flow was recorded.The maximum amplitude and duration did not exceed um = 40 dB and timp = 1000 µs.Further compressive stress increases led to a growth in the values of the primary AE parameters.As the load reached σ = 56.8MPa, amplitude and duration grew to um = 100 dB and timp = 60,000 µs.A sharp increase in AE pulse duration may be associated with the friction of the edges of a developing crack during crystal compression, which is con firmed by a decrease in the level of mechanical stress from σ = 56.8MPa to σ = 55.2MPa at τ = 1325.4s. Figure 6 shows the calculated values belonging to the p = 0.85 level quantil of the AE pulse duration empirical distribution function.Since, for this type of sample, the compressive load is applied along the optical axis [001] with metal platens, optical observation is available only through the polished faces ( 110) and (1-10).Figure 7 Cross-polarization imaging of the samples was carried out along the [001] axis.Thus, the polarization plane orientation ambiguity associated with optical rotatory power dispersion leads to a noise-like pattern in AoLP maps and zero DoLP without external mechanical load, i.e., at t = 0 s.The main stages of crack growth are shown in Figure 11.Cross-polarization imaging of the samples was carried out along the [001] axis.Thus, the polarization plane orientation ambiguity associated with optical rotatory power dispersion leads to a noise-like pattern in AoLP maps and zero DoLP without external mechanical load, i.e., at t = 0 s.The main stages of crack growth are shown in Figure 11.As the compression force increases, the DoLP increases in locally stressed zones.In the example shown in Figure 11, the crack-like defect appears in the field of view after 1747 s.Its initial location is highlighted with the edge of increased DoLP near the moving platen.In the short time interval between two consequent frames acquired after 1776.025s and 1776.05s, the crack length sharply increases and becomes easily notable in the NP image.Between 1776.05 s and 1874.975s, the defect grows gradually.After 1875 s, the crack reaches the other platen, and the specimen breaks into two pieces.When the compression load is removed, the residual deformation leads to inhomogeneous AoLP and DoLP maps in both pieces of the sample.
Compressive load along the [110] axis
Figure 12a demonstrates the temporal dependencies in the selected areas (Figure 12b) located near the initial defect formation zone, at the crack tip after its sharp growth between 1776.025s and 1776.05s, far from the area of the initial crack formation and in the undamaged zone.The DoLP in all selected regions progressively increases until 1776.025s.Then, the response depends on the distance to the area of the initial defect formation.As the compression force increases, the DoLP increases in locally stressed zones.In the example shown in Figure 11, the crack-like defect appears in the field of view after 1747 s.Its initial location is highlighted with the edge of increased DoLP near the moving platen.In the short time interval between two consequent frames acquired after 1776.025s and 1776.05s, the crack length sharply increases and becomes easily notable in the NP image.Between 1776.05 s and 1874.975s, the defect grows gradually.After 1875 s, the crack reaches the other platen, and the specimen breaks into two pieces.When the compression load is removed, the residual deformation leads to inhomogeneous AoLP and DoLP maps in both pieces of the sample.
Figure 12a demonstrates the temporal dependencies in the selected areas (Figure 12b) located near the initial defect formation zone, at the crack tip after its sharp growth between 1776.025s and 1776.05s, far from the area of the initial crack formation and in the undamaged zone.The DoLP in all selected regions progressively increases until 1776.025s.Then, the response depends on the distance to the area of the initial defect formation.
Acoustic Emission
To characterize a crystal damage degree, we propose calculating the weight of the AE data flow meeting the criterion of (timp)p = 0.85 ≥ 4000 µs (W): 100%, where Np is a quantity of the AE data flow meeting the criterion (timp)p = 0.85 ≥ 4000 µs, NΣ is an overall quantity of the calculated AE data flow.
Quantitative Criteria for Irreversible Damage Initiation and Crack-like Defects Formation Acoustic Emission
To characterize a crystal damage degree, we propose calculating the weight of the AE data flow meeting the criterion of (t imp )p = 0.85 ≥ 4000 µs (W): where N p is a quantity of the AE data flow meeting the criterion (t imp )p = 0.85 ≥ 4000 µs, N Σ is an overall quantity of the calculated AE data flow.The examples of the W values calculated for two TeO 2 samples loaded along the [001] and [1][2][3][4][5][6][7][8][9][10] axes are presented in Figure 13.
The notable growth in the W parameter value at τ = 1325.4s by 36.7% matches the mechanical stress decrease from σ = 56.8MPa to σ = 55.2MPa (Figure 13a).A sharp increase in W calculated with the statistical characteristics of the empirical distribution function of the AE pulse duration is associated with crack edge friction.In Figure 13b, the local minima of W(τ) appear at τ = 1087.4s, and its global maximum appears at the exact moment of the visible complete specimen destruction (τ = 1875 s) and does not exceed 6.2%.The first local maxima of W(τ) indirectly point at the initiation of crack-like defect formation before it becomes detectable with the optical imaging techniques.The strong correlation between the intensive crack growth and the W(τ) function seems helpful for monitoring the defect development and damage in TeO 2 crystals.The notable growth in the W parameter value at τ = 1325.4s by 36.7% matches the mechanical stress decrease from σ = 56.8MPa to σ = 55.2MPa (Figure 13a).A sharp increase in W calculated with the statistical characteristics of the empirical distribution function of the AE pulse duration is associated with crack edge friction.In Figure 13b, the local minima of W(τ) appear at τ = 1087.4s, and its global maximum appears at the exact moment of the visible complete specimen destruction (τ = 1875 s) and does not exceed 6.2%.The first local maxima of W(τ) indirectly point at the initiation of crack-like defect formation before it becomes detectable with the optical imaging techniques.The strong correlation between the intensive crack growth and the W(τ) function seems helpful for monitoring the defect development and damage in TeO2 crystals.
Cross-Polarization Imaging
In comparison to conventional NP images, AoLP and DoLP mapping provides additional data, mainly in the case of observation in the vicinity of the crystal's optical axis.In contrast with Figure 7, the maps in Figure 11 demonstrate the evolution of the AOLP and DOLP values, indicating the potential zones of defect formation before visible structural changes occur.Also, in this case, the DoLP and AoLP temporal curves at the last stage of crack-like defect formation (Figure 14) show that fast crack growth is accompanied by the sharp changes in these dependencies.These changes may be noticed not only along the crack path, but also in the undamaged areas located in its neighborhood.The impact of the growing defect on the undamaged areas may be associated with the stress distribution in the volume of the sample.In addition, the obtained NP images, as well as the DoLP maps, provide quantitative data on the transmission coefficient and the polarization features posteriori, i.e., after the structural changes happen.Thus, they are not predictive damage indicators but may be informative for AE data validation and the determination of defect location and type.
Cross-Polarization Imaging
In comparison to conventional NP images, AoLP and DoLP mapping provides additional data, mainly in the case of observation in the vicinity of the crystal's optical axis.In contrast with Figure 7, the maps in Figure 11 demonstrate the evolution of the AOLP and DOLP values, indicating the potential zones of defect formation before visible structural changes occur.Also, in this case, the DoLP and AoLP temporal curves at the last stage of crack-like defect formation (Figure 14) show that fast crack growth is accompanied by the sharp changes in these dependencies.These changes may be noticed not only along the crack path, but also in the undamaged areas located in its neighborhood.The impact of the growing defect on the undamaged areas may be associated with the stress distribution in the volume of the sample.In addition, the obtained NP images, as well as the DoLP maps, provide quantitative data on the transmission coefficient and the polarization features posteriori, i.e., after the structural changes happen.Thus, they are not predictive damage indicators but may be informative for AE data validation and the determination of defect location and type.
Discussion
We addressed the issues of determining the informative AE signals specific for defect formation and growth in anisotropic crystals and assessed the applicability of cross-polarization imaging to localize the sample's areas of increased mechanical stress in situ.We studied the feasibility of joint AE and cross-polarization imaging for monitoring defect
Discussion
We addressed the issues of determining the informative AE signals specific for defect formation and growth in anisotropic crystals and assessed the applicability of crosspolarization imaging to localize the sample's areas of increased mechanical stress in situ.We studied the feasibility of joint AE and cross-polarization imaging for monitoring defect formation in anisotropic crystals with different orientations of crystallographic axes without destruction.In contrast to previous AE studies of anisotropic crystals, we carried out continuous crack growth experiments with TeO 2 crystals of two different crystallographic orientations.We supposed that the weight content of the high-level quantile of the empirical duration distribution function (W(τ): (t i )p = 0.85 ≥ 4000 µs) may be an informative AE parameter, as its dynamic change correlates well with the degree of crystal damage.The moments of crack-like defect appearance in the tested samples determined by AE sensing were precisely confirmed by the results of cross-polarization imaging.The applicability of cross-polarization imaging for the localization of increased mechanical stress depends on the orientation of sample crystallographic axes.However, this mapping technique may be implemented for defect location and characterization, regardless of the sample type.In practice, the combination of these two techniques may become an effective approach to the non-destructive testing of crystals in the real operational conditions of crystal-based devices.This is especially important for devices operating under increased quasi-static, cyclic, vibrational or impact mechanical stresses.A possible workflow chart illustrating this concept is shown in Figure 15.Generally, the AE sensing technique provides in situ information on the presence of damage in the studied crystal, and cross-polarization imaging allows us to verify and quantify it, i.e., determine the defect type, location and dimensions.The dynamics of the changes in the AE parameters and the function W(τ) during AE sensing led us to the assumption that not only are the moments of initial crack formation recorded, but so are the friction processes of the crack faces.Therefore, the proposed technique can be used both to study newly manufactured crystals and to control samples that have already been in use for some time to identify existing (residual) damage that appeared during operation.
Conclusions
In this feasibility study, we found that AE data acquisition and analysis may provide an effective quantitative merit for crack appearance and development under dynamic loading in TeO2 crystals widely used in acousto-optics.From the experiments with crystal samples of two different configurations, we established an informative AE parameterthe weight of the high-level quantile in the duration distribution function.The moment of The dynamics of the changes in the AE parameters and the function W(τ) during AE sensing led us to the assumption that not only are the moments of initial crack formation recorded, but so are the friction processes of the crack faces.Therefore, the proposed technique can be used both to study newly manufactured crystals and to control samples that have already been in use for some time to identify existing (residual) damage that appeared during operation.
) and (1-10), providing optical imaging along the [110] and [1-10] axes and the application of compressive force F along the [001] axis.Specimens of the second type, with compressive force F applied along the [1-10] axis, were prepared for observation along the [110] and [001] axes.
axes and the application of compressive force F along the [001] axis.Specimens of the second type, with compressive force F applied along the [1-10] axis, were prepared for observation along the [110] and [001] axes.
Figure 1 .
Figure 1.Configurations of the inspected samples of the first (a) and second (b) type.
Figure 1 .
Figure 1.Configurations of the inspected samples of the first (a) and second (b) type.
in a static mode, i.e., without external mechanical load, to ensure the absence of structural (macroscopic) defects.The examples of conoscopic patterns for the specimens of both types are shown in Figure2.
Materials 2024 ,
17, x FOR PEER REVIEW 7 of 19polarization (DoLP).The NP map is a monochrome image formed by non-polarized light, calculated as the mean intensity value of the 2 × 2-pixel block: polarization of light after passing through the inspected crystal, the Stokes parameters may be calculated as follows: DoLP spatial maps are helpful for tracking the changes in polarization features.During the experiments, we acquired raw images with resolutions of 1280 × 960 pixels at 40 fps and calculated 640 × 480 pixel NP, AoLP and DoLP maps.
Figure 4 .
Figure 4. Joint AE and cross-polarization imaging data processing pipeline.
3. 1 . 1 .
Figure 5 shows the temporal dependencies of AE pulse amplitude um and duration timp time and compressive stress σ values for the specimen loaded along the [001] axis.
Figure 4 .
Figure 4. Joint AE and cross-polarization imaging data processing pipeline.
Figure 5 .
Figure 5 shows the temporal dependencies of AE pulse amplitude u m and duration t imp time and compressive stress σ values for the specimen loaded along the [001] axis.
Figure 6 .
Figure 6.Values belonging the p = 0.85 level quantile of the AE pulse duration empirical distribution function (timp)p = 0.85 for the specimen loaded along the [001] axis.
Figure 7 illustrates the typical AoLP and DoLP maps at the most significant defect formation stages.This specimen type conserves the polarization state, and compressive load does not induce changes in the AoLP and DoLP patterns.A defect appears between 1325.55 s and 1325.275s, as may be seen in NP images.The significant light intensity reduction caused by the crack leads to noise-pattern formation in the damaged zones of the AoLP and DoLP maps.In defect-free areas, AoLP and DoLP values remain
Figure 5 .
Figure 5. Temporal dependencies of AE pulse amplitude u m (a) and duration t imp (b) time v (dotted) with the compressive stress σ values (solid line) for the sample loaded along the [001] axis.
Figure 5 .
Figure 5. Temporal dependencies of AE pulse amplitude um (a) and duration timp (b) time v (dotted with the compressive stress σ values (solid line) for the sample loaded along the [001] axis.
Figure 6 .
Figure 6.Values belonging the p = 0.85 level quantile of the AE pulse duration empirical distribution function (timp)p = 0.85 for the specimen loaded along the [001] axis.
Figure 7 illustrates the typical AoLP and DoLP maps at the most signifi cant defect formation stages.This specimen type conserves the polarization state, and compressive load does not induce changes in the AoLP and DoLP patterns.A defect ap pears between 1325.55 s and 1325.275s, as may be seen in NP images.The significant ligh intensity reduction caused by the crack leads to noise-pattern formation in the damaged zones of the AoLP and DoLP maps.In defect-free areas, AoLP and DoLP values remain stable.The gradual growth of the crack continues until the end of the bench test.
Figure 6 .
Figure 6.Values belonging the p = 0.85 level quantile of the AE pulse duration empirical distribution function (t imp )p = 0.85 for the specimen loaded along the [001] axis.Before the local decrease in mechanical stress from σ = 56.8MPa to σ = 55.2MPa (τ < 1325.4 s), the AE data flow did not exceed (t imp )p = 0.85 = 10 µs.At the same moment (local decrease), a significant growth in the flow ((t imp )p = 0.85 ≥ 4000 µs) was noted.Since, for this type of sample, the compressive load is applied along the optical axis [001] with metal platens, optical observation is available only through the polished faces (110) and (1-10).Figure7illustrates the typical AoLP and DoLP maps at the most significant defect formation stages.This specimen type conserves the polarization state, and compressive load does not induce changes in the AoLP and DoLP patterns.A defect appears between 1325.55 s and 1325.275s, as may be seen in NP images.The significant light intensity reduction caused by the crack leads to noise-pattern formation in the damaged
19 Figure 7 .
Figure 7. NP, AoLP and DoLP maps of TeO2 sample loaded along [001] axis.To analyze the evolution of DoLP and AoLP values during the bench test, the image of the crystal was divided into 20 × 20-pixel areas.In each of them, we calculated average AoLP and DoLP values.Figure 8a demonstrates the AoLP and DoLP temporal dependencies for the areas shown in Figure 8b.AoLP and DoLP across the entire specimen remain stable until the crack appears.After this, individual areas demonstrate significant reductions in DoLP and noise-like AoLP due to the low light intensity in the damaged areas, as mentioned before.
Figure 8a demonstrates the AoLP and DoLP temporal dependencies for the areas shown in Figure 8b.AoLP and DoLP across the entire specimen remain stable until the crack appears.After this, individual areas demonstrate significant reductions in DoLP and noise-like AoLP due to the low light intensity in the damaged areas, as mentioned before.
Figure 7 .
Figure 7. NP, AoLP and DoLP maps of TeO 2 sample loaded along [001] axis.To analyze the evolution of DoLP and AoLP values during the bench test, the image of the crystal was divided into 20 × 20-pixel areas.In each of them, we calculated average AoLP and DoLP values.Figure8ademonstrates the AoLP and DoLP temporal dependencies for the areas shown in Figure8b.AoLP and DoLP across the entire specimen remain stable until the crack appears.After this, individual areas demonstrate significant reductions in DoLP and noise-like AoLP due to the low light intensity in the damaged areas, as mentioned before.
Figure 8 .
Figure 8. DoLP and AoLP temporal dependencies in TeO2 sample loaded along [001] axis (a) in selected areas, shown in the NP image acquired at the end of the bench test (b).Colors of rectangular areas in (b) correspond to curve colors in (a).
3. 1 . 2 .
Figure 9 shows the temporal dependencies of AE pulse amplitude um and duration timp time, as well as the compressive stress σ values for the TeO2 sample loaded along the [110] axis.
Figure 8 .
Figure 8. DoLP and AoLP temporal dependencies in TeO 2 sample loaded along [001] axis (a) in selected areas, shown in the NP image acquired at the end of the bench test (b).Colors of rectangular areas in (b) correspond to curve colors in (a).
3. 1 . 2 .Figure 8 .
Figure 9 shows the temporal dependencies of AE pulse amplitude u m and duration t imp time, as well as the compressive stress σ values for the TeO 2 sample loaded along the [1-10] axis.
Figure 9 Figure 9 .
Figure 9 shows the temporal dependencies of AE pulse amplitude um and duration timp time, as well as the compressive stress σ values for the TeO2 sample loaded along the [110] axis.
Figure 9 .
Figure 9. Temporal dependencies (dotted) of AE pulse amplitude u m (a) and duration t imp (b) time, and compressive stress σ values (solid) for the sample loaded along the [1-10] axis.During the compression load, evolution patterns of primary AE parameters appear quite similar to the data acquired for the sample loaded along the [001] axis.The amplitude and duration of AE pulses did not exceed u m = 45 dB and t imp = 5000 µs.Due to the friction of the growing crack edges, the amplitude and duration increased sharply at τ = 1087.4s и τ = 1386.5s.For the sample loaded along the [1-10] axis, the p = 0.85 quantile values of the empirical distribution function of the AE pulse duration are shown in Figure 10.
Figure 10 .
Figure 10.Values belonging the p = 0.85 level quantile of the AE pulse duration empirical distribution function (timp)p = 0.85 for the specimen loaded along the [110] axis.
Figure 10 .
Figure 10.Values belonging the p = 0.85 level quantile of the AE pulse duration empirical distribution function (t imp )p = 0.85 for the specimen loaded along the [1-10] axis.
Figure 12 .
Figure 12.DoLP and AoLP temporal dependencies in TeO2 sample loaded along [110] axis (a) in selected areas shown in the NP image acquired at the end of the bench test (b).Colors of rectangular areas in (b) correspond to curve colors in (a).
Figure 12 .
Figure 12.DoLP and AoLP temporal dependencies in TeO 2 sample loaded along [1-10] axis (a) in selected areas shown in the NP image acquired at the end of the bench test (b).Colors of rectangular areas in (b) correspond to curve colors in (a).
Materials 2024 , 19 Figure 14 .
Figure 14.DoLP and AoLP temporal dependencies at the last stage of crack formation in TeO2 samples loaded along the [110] axis in the areas shown in Figure 12b.Curve colors correspond to to Figure 12.
Figure 14 .
Figure 14.DoLP and AoLP temporal dependencies at the last stage of crack formation in TeO 2 samples loaded along the [1-10] axis in the areas shown in Figure 12b.Curve colors correspond to to Figure 12.
Figure 15 .
Figure 15.Proposed workflow chart of combined AE sensing and cross-polarization imaging for detection and characterization of crack-like defects in TeO2 crystals.
Figure 15 .
Figure 15.Proposed workflow chart of combined AE sensing and cross-polarization imaging for detection and characterization of crack-like defects in TeO 2 crystals. | 10,584 | 2024-07-01T00:00:00.000 | [
"Physics",
"Materials Science"
] |
Extension of working life and implications for occupational health in Chile
Chile has one of the highest effective retirement ages among the countries of the Organisation for Economic Cooperation and Development (OECD). This could be associated with retirement at older ages, as low pensions encourage people to remain active in the workforce.People undergo several changes due to the passage of time, and they have an impact on their health from a biological, psychological and social point of view. However, there is not enough knowledge on how these changes impact and interact with working, employment and health conditions of workers as they get older.This article aims to contribute to the critical debate on the extension of working life and its implications for occupational health. Some reflections in this regard are proposed based on a review of the most recent relevant literature.
Existen diversos cambios que se producen en las personas debido al paso del tiempo y que tienen impacto en la salud desde el punto de vista biológico, psicológico y social. Sin embargo, en la actualidad no se tiene suficiente conocimiento respecto de cómo esos cambios impactan e interactúan con las condiciones de trabajo, empleo y salud de las personas a medida que envejecen.
Este artículo pretende hacer un aporte al debate crítico respecto a la extensión de la vida laboral y sus implicancias en la salud laboral. Se proponen algunas reflexiones en la materia con base en una breve revisión de la literatura más reciente.
Introduction
The rapid growth of the elderly segment is one of the most important social transformations in Chile as it will make up nearly a quarter of the population in a few years. Consequently, this phenomenon urgently needs to be addressed from different perspectives, because it has multiple consequences in areas as diverse as health, housing, transport, economy, among others.
With a population of over 18 370 000 according to the most recent statistics, life expectancy in Chile is one of the highest of the continent, reaching 77.3 years for men and 82.1 years for women. (1) According to Marín (2), the increased life expectancy currently found in the population tends to be perceived as a problem from a medical, social and economic point of view, taking into account the costs associated with the complexity and concomitance of various pathologies, the increase in drug costs, the potential for dependency and care needs, etc.
One of the aspects that have been barely studied to date is the impact that ageing has on the working place. Specifically, the Chilean working reality is regulated by multiple norms and laws that are transgressed by companies on a daily basis; in addition, there is little participation of workers in decision making and a low union representation, with great deregulation in matters of health and safety at work. Chile has a quite vulnerable "social floor" and, in this context of tensions and conflicts, it is necessary to accommodate a population distribution with a larger amount of elderly people.
In this scenario, uncertainty increases for those approaching the final stages of their productive lives: Chile's pension system, which includes old-age, disability and survival benefits, is currently experiencing a crisis characterized by low coverage, contribution densities that do not exceed 50% for the entire affiliated population, and an insufficient income replacement rate during the activity stage. (3)(4) The latest political discussions in the country on this regard have paid especial attention to the extension of working life. The process of reforming the Chilean pension system has been under scrutiny in recent years and one of its possible implications -increasing the current retirement age-has focused exclusively on economic discussions and has not had sufficient evidence about other relevant aspects. In particular, this important discussion in the country has not received contributions from scientific knowledge concerning employment, working and health conditions among the working population at all ages; so it is important to characterize the jobs they perform and know what circumstances the country's workers will face with respect to a possible increase in the legal retirement age and, therefore, the extension of their working life.
This reflection article aims to contribute to the critical debate on the extension of working life and its impact on health and safety at work, based on a brief literature review and updated scientific evidence. Some proposals are made to address the changes brought about by ageing in this area.
Age-related changes and their effects on occupational health: evidence summary
Many changes can be observed as people age, and most of them are part of what could be considered as normal ageing of the body, as they are usually associated with functional capacity deficit and decline (decreased sensory acuity, slowed functions, etc.) However, there are also processes related to a positive dimension of ageing: the potential for learning and the increase of wisdom.
The process of ageing involves progressive structural and functional changes, including reductions of bodily performance and changes in psychological disposition, entailing various consequences for working life. (5)(6)(7) This process results in a gradual and progressive deterioration of physical and mental health conditions, and may be associated with an increase in some chronic health problems which, if appropriate preventive measures are not established in a timely manner, may lead to functional limitations and progressive loss of autonomy. (8) Evidence from occupational health research shows that older workers generally have better safety performance, with lower accident rates in some productive sectors; however, these workers are at greater risk of fatal accidents and take longer to recover from serious incidents. (9-11) Although physical and psychological changes occur at ages over 50, there are also large individual differences, and the risks associated with those changes can be reduced if activity is sustained. (12,13) About the positive aspects of ageing, Ilmarinen (14) highlights a series of characteristics in older workers such as wisdom, better control of life events, sharp wit, greater commitment to work, ability to deliberate, greater loyalty to the employer, ability to reason, fewer absences from work, more global capacity for understanding, greater work experience, better verbal command and greater motivation to learn. All these factors would compensate for negative aspects from the point of view of risks and safety at work.
Occupational health and ageing: from "decent work" to "sustainable work" One of the concepts that emerged in recent years within the discussion on promoting better working conditions is "decent work". The International Labour Organization (ILO) has established a Decent Work program that has leaded both promotion of decent work and advisory activities around the world. This concept summarizes the aspirations of people regarding their working lives in relation to opportunities and income; rights, voice and recognition; family stability; personal development; and gender equity and equality. According to the ILO, decent work is a major contribution to helping to reduce world poverty, and is a means to achieving equitable, inclusive and sustainable development. (15) This concept has been questioned and debated because it is considered to be very broad and imprecise -especially, when compared to other technical constructs such as the quality of employmentwhich would make it difficult to carry out measurements on the subject in different countries. (16-18) Researchers from various disciplines have questioned the departure from the concept of the ethos of social justice that defined it at first. Recent contributions from psychology have argued that decent work has not focused on the role of meaning and purpose in a worker's life, so a psychological perspective should help revitalize the decent work agenda by emphasizing on individual experiences, which in turn would reconnect the concept of decent work with its origins in social justice. (19) A key concept for the design of public policies on occupational health at all ages is "sustainable work" as proposed by Docherty et al. (20) The authors compare "sustainable work systems" with "labor-intensive systems" and argue that the latter, in the long term, will have detrimental effects both on individuals and on the quality of products and services.
In 2012, a decade after the publication of the original work by Docherty et al., the European Foundation for the Improvement of Living and Working Conditions used this idea in various studies and in its proposal for monitoring the ageing workforce. From a prospective point of view, the concepts of sustainable work and life cycle contained in various documents (21-24) provide a comprehensive approach to assist policy makers in improving both employment and working conditions for all; this is done by considering both the individual changes that can occur with ageing and the implications of such changes for safety and health in the workplace. (11) Association between gender and ageing: a barely explored field in occupational health The World Health Organization (WHO) states that one of the most important inequalities that should be addressed to achieve equity in health is gender. The area of study that intersects gender and work has an important research tradition in North America and Europe. One of the essential researchers in this field is the Canadian Karen Messing (25), whose contribution to the recognition of the specificity of gender differences and their relationship to occupational health has been highly valued. Messing is one of the authors of a document that summarizes the evidence on gender equity, work and health presented by WHO in the last decade. (26) However, the approach to the process of ageing at work from a gender perspective has been barely explored and constitutes an emerging challenge for occupational health research. (27) Campos-Serna et al. (28) conducted a systematic review in which they concluded that there are a number of gender inequalities in occupational health related to working and employment conditions. Among their findings are that men are more exposed than women to longer working hours, higher levels of physical demands, and more noise. Women, on the other hand, have worse contractual working conditions and psychosocial work environments, show greater job insecurity and report worse self-perceived physical and mental health. In general, women have poorer physical and mental health than men.
Regarding the oldest age group in the workforce, there are many gender disparities. (29-31) Some of the factors causing these differences are: segregation, both horizontal (between sectors of activity) and vertical (between categories of work), which leads to very different work situations for women and men; different career paths for men and women; different opportunities for self-fulfillment at work and recognition at work; and unequal distribution of domestic work and care tasks.
A recent study in Chile based on secondary sources clearly confirms these findings, showing that the presence of horizontal and vertical segregation in the case of women extends into old age, with a concentration in the service sector of the economy and lower levels of employment status (as is the case with domestic service, even among workers over 70). In turn, the double burden and caregiving tasks extend into very old age. (32) The debate on extending working life: why should working life be extended into old age?
The ageing of the population has become a social phenomenon that demands responses from various areas of public policy. Concern about the growing proportion of older people around the world and the costs associated with a considerable number of potential nonworking population, in a critical scenario from the perspective of the sustainability of pension systems, has prompted debates about the need to prolong people's working lives as long as possible.
Alongside the ageing of the workforce, there has been a marked increase in the prevalence of "nonstandard" or "contingent" work (9), that is, work that does not involve a permanent position with any employer, of 35 hours or less per week and of limited duration (temporary or fixed-term). Contingent work is similar to other constructs, such as flexible and precarious work.
For years, European countries have been developing legislative strategies to keep people working until old age (33,34), discouraging early retirement formulas and increasing the legal retirement age, for example. As Alcover (25) points out, there has been a long-standing trend combining the creation of work spaces for people of retirement age, the formulation of policies that encourage the maintenance of older workers in the labor market, and the encouragement of reincorporation once retired. (35) In the case of the United Kingdom, the Department for Work and Pensions (DWP) has been analyzing the issue for the past decade, commissioning various specialists to conduct studies; such is the case of Phillipson & Smith (36) who have been quite critical of the application of these measures without having a clear background of the multiple dimensions that make up such a complex phenomenon. Recently, the DWP has funded extensive research involving social psychologists on the attitudes, knowledge and information preferences of older workers, as well as their broader orientations for extending working life. (37) Various relevant aspects regarding the extension of working life have been addressed by several researchers. From a public policy perspective, it is especially interesting to know which modalities have been stimulated to encourage permanence on a job. The literature points out that current policies along these lines in Europe are oriented towards the development of flexible forms of employment, whether salaried, self-employed or mixed, full-time or part-time, stable or temporary. (38) A concept of growing interest is "bridge employment", which refers to any type of paid work (e.g. part-time, full-time or selfemployment) that employees may have after retirement. It is called bridge employment because it spans over the period of time after retiring from a professional job and before full retirement; however, this idea may be ambiguous: older people may retire from one job and take a second job, but never retire completely from the workforce. In this case, employment is not, strictly speaking, a bridge to anywhere, but rather a bridge to the end of existence, if the person works until before their death. (39) Finnish researchers have analyzed the association of working and health conditions with long-term employment (six months after reaching the retirement age) in a cohort of non-disabled older employees, concluding that the key factors for extending working life into old age would be good mental health combined with the opportunity to control working time. (40) A very interesting contribution to the study of employability in older workers is the concept of work ability. Towards the end of the 1990s, the Finnish Institute of Occupational Health introduced this construct, which was based in part on the results of an 11-year followup of more than 6 500 salaried and office workers (41) and refers to the balance between the worker's perceptions of the demands of their work and ability to cope with those demands. It is a dynamic process that changes a lot for various reasons throughout an individual's working life; one of the main factors affecting this variation is ageing and its effects on people. Another major source of change that ageing workers have to face is the change in the nature of work. The main predictors of this perceived ability may be certain individual factors, job demands and health aspects such as health perception, physical fitness and lifestyle. (42) The Work ability Index-WAI was developed to assess this construct, (43); it is a questionnaire widely used both in occupational health and age management in companies.
With regard to occupational health promotion for older people, current evidence does not show that workplace promotion programs improve the working capacity, productivity or job retention of older workers, as concluded by a systematic literature review on interventions in such programs targeted specifically at older workers. (44) As Phillipson (45) points out, a key dimension of the changing relationship between ageing and work is the tension between policies to extend working life, as well as the increasingly fragmented nature of late working life with the emergence of varied transitions, including bridge employment, second/third career, part-time work, early retirement and others. The same author is in favor of improving quality of work and safety as a precondition for supporting policies that encourage working in later life.
Conclusions
In general, until now, various positions regarding the implications and consequences of ageing converge around the benefits of being physically and intellectually active in order to preserve good health until old age. In that sense, extending working life as much as possible could be seen as a protective factor for most people and would be a sort of "promise" of healthy longevity.
Beyond the discussion of whether it is biologically and psychologically positive or not for older people to continue working, a critical reading of the design of public policies in Chile along these lines questions the pertinence of the application of measures for the legal extension of working life for the entire population. It also demands deep intersectoral reflections and proposals for transformations in the areas of regulation, inspection and management, particularly with regard to health and safety at work.
In the current scenario of the country, where older people are impoverishing due to the low pensions they receive, it is true that a significant proportion of this group works until old age, beyond the legal retirement age: Chile has a high average effective retirement age for both women (70.3 years) and men (70.9 years), one of the highest among the countries that make up the Organization for Economic Cooperation and Development. (46) Gender equity is one of the pending issues in the country in many aspects. The working world shows great inequalities in this sense, because, after analyzing the most recent information available on the labor situation of people as they age, the great differences in labor insertion between men and women, exposure to different health risks and absence of rights (work contracts, vacations, medical leaves) are more notorious in the case of women. The discussion on the delay of the retirement age in the country and the consequent extension of working life must include a deep reflection in its agenda on what it means to be a woman and to live longer with an increasingly worse quality of life.
The accelerated ageing of the Chilean citizens and its repercussions on the work force should inspire the construction of inclusive public policies as a priority challenge for government institutions in charge of the areas of health and labor. In this sense, the regulatory framework that governs health and safety should consider age in the parameters that it incorporates in surveillance and in the specific measures that they establish.
Research on how, where and why people work as they age and how they are protected by regulations on working conditions, employment and health could provide valuable information for making the adjustments needed to think properly about extending working life in Chile.
Conflicts of interest
None stated by the authors.
Funding
None stated by the authors. | 4,366.6 | 2019-10-01T00:00:00.000 | [
"Sociology",
"Medicine",
"Economics"
] |
Treatment planning comparison of IMPT, VMAT and 4π radiotherapy for prostate cases
Background Intensity-modulated proton therapy (IMPT), non-coplanar 4π intensity-modulated radiation therapy (IMRT), and volumetric-modulated arc therapy (VMAT) represent the most advanced treatment methods based on heavy ion and X-rays, respectively. Here we compare their performance for prostate cancer treatment. Methods Ten prostate patients were planned using IMPT with robustness optimization, VMAT, and 4π to an initial dose of 54 Gy to a clinical target volume (CTV) that encompassed the prostate and seminal vesicles, then a boost prescription dose of 25.2 Gy to the prostate for a total dose of 79.2 Gy. The IMPT plans utilized two coplanar, oblique scanning beams 10° posterior of the lateral beam positions. Range uncertainties were taken into consideration in the IMPT plans. VMAT plans used two full, coplanar arcs to ensure sufficient PTV coverage. 4π plans were created by inversely selecting and optimizing 30 beams from 1162 candidate non-coplanar beams using a greedy column generation algorithm. CTV doses, bladder and rectum dose volumes (V40, V45, V60, V65, V70, V75, and V80), R100, R50, R10, and CTV homogeneity index (D95/D5) were evaluated. Results Compared to IMPT, 4π resulted in lower anterior rectal wall mean dose as well as lower rectum V40, V45, V60, V65, V70, and V75. Due to the opposing beam arrangement, IMPT resulted in significantly (p < 0.05) greater femoral head doses. However, IMPT plans had significantly lower bladder, rectum, and anterior rectal wall max dose. IMPT doses were also significantly more homogeneous than 4π and VMAT doses. Conclusion Compared to the VMAT and 4π plans, IMPT treatment plans are superior in CTV homogeneity and maximum point organ-at-risk (OAR) doses with the exception of femur heads. IMPT is inferior in rectum and bladder volumes receiving intermediate to high doses, particularly to the 4π plans, but significantly reduced low dose spillage and integral dose, which are correlated to secondary cancer for patients with expected long survival. The dosimetric benefits of 4π plans over VMAT are consistent with the previous publication.
Background
It is estimated that in the year 2015, there will be around 220,800 new cases of prostate cancer and around 27,540 deaths. Prostate cancer is the second most common cancer and the second leading cause of cancer death for men in the United States [1]. External beam radiation therapy is commonly used to treat prostate cancer. Studies have shown the benefits of 76 Gy or higher conventionally fractionated treatments, although there is a substantial risk of gastrointestinal toxicity, particularly stemming from the rectum dose [2,3]. In these cases, radiation doses better conforming to the prostate are necessary to reduce possible rectal complications. The use of charged particle beams, such as proton, demonstrates strong potential for highly conformal dose distribution. The Bragg peaks of proton beams allow extremely localized dose delivery at a precise depth with no exit dose after the distal tail and secondary particles. However, since most targets are larger than the Bragg peaks, a Spread-Out-Bragg-Peak (SOBP) must be created to homogeneously cover the target laterally and in the beam direction. A range-shifter wheel is typically used to modulate the incident proton energy for varying depths. The proton beams are further broadened by high-Z scatter foils and then collimated to the size of the target. To compensate for the surface contour of the patient, tissue composition and shape of the target, a custom compensator is made for each patient. With these additional devices, passive scattering delivers a number of individual Bragg peaks of different depths and weighted to achieve the SOBP. Although this technique has attracted worldwide interest, it is considered a simple method with considerable limitations [4] including low dose conformity, secondary particles including neutrons that increase patient integral dose and the logistic hurdles associated with devices needed for individual patients.
Active scanning is a development that can be automatically controlled, allowing proton beams to achieve a more efficient complete dose delivery [5]. To cover a target, each beam is scanned laterally across the target using magnetic fields in a technique called Pencil Beam Scanning (PBS) [6]. PBS enables state of the art intensity modulated proton therapy (IMPT), which is analogous to the intensity modulated radiation therapy (IMRT) that inversely optimize all beams to deliver a uniform dose to the target while individual beams only deliver a partial heterogeneous dose [7].
For photon therapy, Volumetric Modulated Arc Therapy (VMAT) is a widely adopted technique with advantages over conventional step-and-shoot Intensity-Modulated Radiation Therapy (IMRT), namely its delivery efficiency at equivalent dosimetry [8][9][10]. VMAT is unable to achieve the organ-at-risk (OAR) dose sparing demonstrated by proton therapy due to proton's advantageous Bragg peaks [11][12][13]. However, photon therapy has the advantage of being a much more cost effective and widespread treatment modality. 4π radiotherapy is a noncoplanar IMRT technique that has demonstrated superior OAR dose sparing compared to VMAT in various tumor sites, including the prostate [14][15][16][17][18]. There is an increasing interest in comparing the non-coplanar 4π treatment to the state-of-the-art proton prostate therapy for relative dosimetric benefit. Here, we study the dosimetric performance of IMPT proton compared to photon VMAT and 4π therapy for prostate cancer.
Patients
This retrospective study was approved by the Internal Review Board of the Willis-Knighton Health System. Ten prostate patients were selected, each with an initial dose of 54 Gy to a clinical target volume (CTV) encompassing the prostate and seminal vesicles, then a boost of 25.2 Gy to a CTV encompassing only the prostate for a total dose of 79.2 Gy delivered in fractions of 1.8 Gy. The patients were then planned using the IMPT technique described as follows.
IMPT planning
The treatment plans for the ten patients were generated with the IBA ProteusOne compact system beam model on the RayStation researh version 4.99.1 (RaySearch Laboratories, Stockholm, Sweden) with automatic spot spacing and spot placing. IMPT plans used two coplanar, oblique beams angled 10°posterior of the lateral beam positions. A pencil beam algorithm was used for proton dose calculation with 3 × 3 × 3 mm 3 dose grid. The IBA ProteusOne compact gantry with C230 cyclotron has a 70-226 MeV energy range. The spot size in air is 3.5 mm at the max energy and 7.6 mm at the lowest energy. Spot size variation with gantry angle is less than 5%. A maximum of 0.5 cm uniform setup error and a range uncertainty of 3.5% were used in the robustness setting for optimization. Since the concept of PTV was not used in IMPT, dosimetric analysis of the target was focused on the clinical target volume (CTV). The dose objectives used for all treatment plans are shown in Table 1, with the CTV dose normalized at 100% of prescription dose delivered to 100% of the volume.
VMAT planning
The IMPT treatments were re-planned using VMAT (RapidArc, Eclipse Treatment Planning System version 13, Varian) with 2.5 × 2.5 × 2.5 mm 3 dose grid. Both photon treatments used the PTVs for planning but then normalized to the CTV to be consistent with the IMPT plans. For the X-ray plans, these PTVs have a 5 mm posterior margin and 6 mm in all other directions. Each plan used two full coplanar arcs to ensure good PTV coverage. To match the proton plan target coverage, VMAT plans were normalized for 100% of the prescription dose covering 100% of the CTVs. With this primary prescription satisfied, on average, 97.3% of PTV is covered by 100% of the prescription dose. The collimator was rotated 90°between the arcs. Optimization objectives for VMAT planning were the same constraints used in IMPT planning (Table 1) or lower, if possible, for normal tissues. PTV hot spots were constrained to be as low as possible.
4π radiotherapy 4π radiotherapy was developed to incorporate noncoplanar beams distributed on the 4π spherical surface, thus the name, in IMRT optimization. 4π optimization begins with a candidate pool of 1162 non-coplanar beams, each 6°apart in the 4π solid angle space. Using a computer assisted design (CAD) model of the Varian TrueBeam machine and a 3D human surface model, each angle is simulated and subsequently eliminated if a collision is predicted between the gantry and the couch or patient [19]. The remaining beams were divided into 5 × 5 mm 2 beamlets, whose dose was calculated using convolution/superposition and Monte Carlo calculated 6MV polyenergetic kernels as described previously [20,21]. The dose calculation resolution is 2.5 × 2.5 × 2.5 mm 3 . Inverse optimization is performed by using a greedy column generation algorithm to iteratively select 30 non-coplanar beams with integrated fluence map optimization [22]. The 30 Fig. 1 Isodose colorwash of a typical patient planned using IMPT, 4π, and VMAT plans beam angles consisted of 24-30 couch kicks for the 10 patients. The beam angles were then imported into Eclipse to recalculate the IMRT dose, creating a clinically deliverable plan that can be directly compared to the IMPT and VMAT plans. The 4π optimization objectives used in Eclipse were identical to VMAT constraints as described above, including normalization for the CTV dose. On average, 99.3% of PTV is covered by 100% of the prescription dose.
Dose comparison
Various dose metrics were evaluated for comparison of the IMPT, VMAT, and 4π plans. Table 1 lists the planning objectives used for all treatment planning methods. Metrics used for planning objectives were calculated and compared between planning techniques, including V40, V45, V60, V70, V75.6, V80 of the rectum, V70 of the bladder, mean, and max doses for organs at risk. Multivariate regression was performed on these OAR metrics to determine the influence of the OAR volume. Because the concept of PTV is no longer used in IMPT prostate planning, CTV coverage was compared using mean, max doses, and CTV homogeneity index, which was evaluated by calculating the D95 to D5 ratio. R50 and R10, which were defined as the 50 and 10% isodose volume to evaluation CTV ratios, were also calculated to examine high dose and low dose spillage, respectively. Since PTV was not used in the IMPT plans, the standard conformity index of the ratio between the 100% isodose volume and the PTV did not apply. Instead, to quantify the 100% isodose volume, we calculated R100, which is the ratio between 100% isodose volume and the CTV.
Results
Isodose and dose volume histogram (DVH) comparisons between the three treatment modalities for a representative example case are shown in Figs. 1 and 2. As one would expect, the lateral beam angles used by the IMPT plans delivered substantially greater dose to the femoral heads than photon plans delivering beams from vastly more beam orientations. It is also worth to note the oblique dose distribution patterns resulted from 4π noncoplanar beams, in comparison to the coplanar VMAT and proton plans. Subsequently, the 4π femoral head doses are also significantly lower than those of VMAT. However, IMPT resulted in more homogeneous CTV coverage, reducing the hot spots visible in the 4π and VMAT dose in Fig. 1 as well as the CTV DVHs in Fig. 2. Mean doses of the bladder and sigmoid colon and max doses for the bladder, rectum, and anterior rectal wall were lowest with IMPT planning. 4π had the lowest mean dose for the anterior rectal wall and the femoral heads and the lowest max dose for the femoral heads. VMAT did not outperform both 4π and IMPT in any OAR mean or max dose (Fig. 3). Of the specific dose metric constraints for the bladder, VMAT had the lowest V70 and V75. 4π achieved the lowest dose volumes for all rectum metrics except for V80 (Fig. 4). IMPT is superior in almost all of the CTV dose metrics, showing more homogeneous dose distribution in the CTV. IMPT had the least intermediate and low dose spillage (R50 and R10), as well as integral dose. However, the 100% isodose volume was lowest with VMAT plans. Figure 6 shows the results of the multivariate regression analysis for dose metrics and OAR volumes. Rectum V40 increases with increasing rectum volume for all three techniques but it appears to increase more with IMPT than the X-ray counter parts. However, rectum V80 of IMPT increases slower than that of VMAT and 4π. As expected, the average bladder doses decrease for all three planning methods but the maximum doses also decrease with increasing bladder volume.
Discussion
Proton therapy is attractive due to the unique physical properties of the heavy charged particles that deliver the majority of dose in sharp Bragg peaks and leave no exit dose. On the other hand, the side by side dosimetric comparison between proton therapy and the best of photon therapy has rarely been performed. In a dosimetric comparison between 3D conformal proton therapy (CPT) and IMRT, Trofimov et al. concluded that IMRT resulted in superior bladder sparing and similar rectum sparing compared to 3D CPT, which is superior in reducing the low dose spillage [23]. The same study also pointed out that the lack of dose conformity in 3D CPT would be overcome with the use of scanning pencil beam and intensity modulated proton therapy (IMPT). With the improvement of proton therapy techniques, PBS proton has gradually replaced passive scatter due to its superior dose shaping capability. In our comparison, state of the art PBS based IMPT was used.
At the same time, VMAT has evolved to be the mainstay therapy method for the prostate because of good dosimetry quality and superior delivery efficiency, compared to static beam IMRT. There has been a notion that VMAT may be the ultimate IMRT method for the prostate [24] and static beam IMRT will be completely phased out. 4π radiotherapy revived non-coplanar IMRT methods by providing a mathematical tool for combined beam orientation and fluence map optimization. This method was shown to be advantageous to coplanar VMAT for almost all disease sites including the prostate and yet is deliverable on existing C-arm linacs. In light of the technical improvement in both photon and proton techniques, revisiting the dosimetric comparison provides interesting insight to the treatment modality selection problem.
In our study, IMPT generally achieved similar dose sparing overall compared to the photon treatment methods, with the exception of the high doses to the femoral heads, due to proton entrance dose. Compared to the photon plans, IMPT is clearly better in PTV dose homogeneity and coverage. It also reduced maximal doses to the bladder, rectum, and anterior rectal wall. However, the advantage disappears when OAR volumes receiving high dose are considered. This is due to several factors. The most important one can be seen in Fig. 1, that the concave CTV is lateral to the anterior portion of the rectum, placing this volume along the proton beam direction and subject it to the increased distal penumbra dose. The second factor contributing to the rectum and bladder dose is the proton spot sizes ranging from 3.5 to 7.6 mm, creating less sharp beam edges in the directions perpendicular to the beams. Variable spot spacing may reduce the spot size related penumbra but has not been implemented in commercial planning systems.
The multivariate analysis shows that the magnitude of difference in dosimetric metrics of treatment modalities may depend on the OAR volume but not the relative relationships. For instance, the relative disadvantages and advantages of IMPT for V40 and V80, respectively, widen for larger rectum volumes. This information may be used to steer patient treatment if confirmed with a larger patient cohort. The bladder mean dose decreasing with increasing bladder volumes is intuitive. However, the similar decrease in the bladder maximum dose is less intuitive. It is possibly due to the distance between the bladder and the CTV also increasing with increasing bladder volume. Between the two photon techniques, 4π plans are superior to VMAT plans with the exception of sigmoid colon dose and small differences in the maximum point doses to the bladder and rectum. The increase in dose to the sigmoid colon in the 4π plans is a result of noncoplanar beams delivering dose to superior and inferior structures. However, the off-plane dose is low and less of a concern in prostate treatment. This is consistent with previous studies comparing VMAT to 4π for a different prostate stereotactic body radiotherapy patient cohort [17]. Putting all three modalities together, one may make the observation that IMPT excels at reducing the maximal point dose to surrounding normal organs, reducing the low dose spillage and achieving a more homogeneous target dose. 4π improves the intermediate dose spillage compared to VMAT and achieves the lowest rectum volume receiving 40-70 Gy.
Different from the higher cost of proton, 4π delivery does not require new expensive equipment. Instead, it requires more sophisticated geometrical modeling to prevent gantry-patient collision. The delivery time of 4π plans involving a large number of beams can be excessively long in the manual mode. This limitation will be overcome using automating non-coplanar plan delivery [19].
Conclusion
In comparison to coplanar VMAT and non-coplanar 4π plans for the prostate treatment, IMPT proton treatment plans showed benefits in integral dose, CTV coverage, homogeneity and maximum point OAR doses. IMPT is inferior in rectum and bladder volumes receiving intermediate to high doses, particularly to the 4π plans. The dosimetric benefits of 4π plans over VMAT are consistent with the previous publication. Specifically, increasing the organ weights of the rectum and bladder forces the plan to use more non-coplanar beams to move dose to the inferior and superior planes while similar increase in the coplanar VMAT plans was ineffective. | 4,068.4 | 2017-01-11T00:00:00.000 | [
"Medicine",
"Physics"
] |
Global Decrease of Histone H3K27 Acetylation in ZEB1-Induced Epithelial to Mesenchymal Transition in Lung Cancer Cells
The epithelial to mesenchymal transition (EMT) enables epithelial cells with a migratory mesenchymal phenotype. It is activated in cancer cells and is involved in invasion, metastasis and stem-like properties. ZEB1, an E-box binding transcription factor, is a major suppressor of epithelial genes in lung cancer. In the present study, we show that in H358 non-small cell lung cancer cells, ZEB1 downregulates EpCAM (coding for an epithelial cell adhesion molecule), ESRP1 (epithelial splicing regulatory protein), ST14 (a membrane associated serine protease involved in HGF processing) and RAB25 (a small G-protein) by direct binding to these genes. Following ZEB1 induction, acetylation of histone H4 and histone H3 on lysine 9 (H3K9) and 27 (H3K27) was decreased on ZEB1 binding sites on these genes as demonstrated by chromatin immunoprecipitation. Of note, decreased H3K27 acetylation could be also detected by western blot and immunocytochemistry in ZEB1 induced cells. In lung cancers, H3K27 acetylation level was higher in the tumor compartment than in the corresponding stroma where ZEB1 was more often expressed. Since HDAC and DNA methylation inhibitors increased expression of ZEB1 target genes, targeting these epigenetic modifications would be expected to reduce metastasis.
Introduction
The epithelial-to-mesenchymal transition (EMT), which converts epithelial cells into an elongated, motile and invasive phenotype, is thought to be a critical step in the dissemination of tumor cells during metastasis [1,2].For example, the loss of E-cadherin and upregulation of vimentin or N-cadherin have been most frequently described during EMT.Other frequent changes include the loss of cytokeratins, increased MMP activity, and increased fibronectin.In epithelial cancers, EMT has been associated with resistance to therapy and a poor prognosis [3,4].In part, EMT is mediated by transcription factors including ZEB1, ZEB2, Snail, Slug, Twist and E12/E47 that bind E-box elements (i.e., CANNTG) in genomic DNA [5,6].In lung cancer, ZEB1 appears to be a major factor in the EMT process [7].In non-small cell lung cancer (NSCLC) cell lines, we and others previously found that loss of E-cadherin was inversely and specifically correlated with ZEB1 mRNA expression [7,8].In addition, we reported that the tumor suppressor gene SEMA3F, coding for a cell guidance and tumor suppressor molecule, was directly repressed by ZEB1 in H358 NSCLC cells [9].
By transcriptomic analyses of 38 NSCLC cell lines, we identified 466 genes that were significantly correlated with ZEB1 expression [10].For a subset of genes, the response to ZEB1, ZEB2, and TGFβ (a natural inducer of EMT) was confirmed.These genes include ST14, encoding for a membrane associated serine protease (matriptase) that processes the hepatocyte growth factor precursor and plays a role in tight-junction maintenance; EpCAM, encoding for an epithelial cell adhesion protein targeted by the therapeutic antibody catamaxumab; and ESRP1, encoding an epithelial splicing factor, which plays an important role in EMT biologic responses [10].However, we did not determine whether ZEB1 binds directly to these genes.Also included in the gene set, but not further explored, was RAB25 encoding an epithelial-specific member of the Rab family of small GTPases, which can act as both a tumor enhancer and suppressor depending on the cellular context.RAB25 is involved in cell migration, invasion, and intracellular vesicle trafficking in the regulation of epithelial polarity and transformation [11][12][13].This may include the recycling of EGF and TGF-β receptors [14].In addition, the reported binding of RAB25 to Smad4 and TGFβR1 suggests that its regulation by ZEB1 may have an important role in TGF-β signaling [15,16].
ZEB1 protein level is negatively regulated by the miR-200 microRNA family, which includes five members (miR-200a, -b, -c, -141, and -429).A positive feedback loop has been described with ZEB1 being able to repress miR-200c and miR-141 expression (for a review see [17]).The transcriptional activity of ZEB1 is mediated through recruitment of co-repressors and co-activators depending on the target gene and tissue [18].This complexity underlies the multiple functions of ZEB1 as a transcriptional activator as well as repressor.For gene activation, ZEB1 associates with the histone acetyltransferases (HATs) p300, PCAF, and Tip60.In contrast, as a repressor, ZEB1 interacts with CtBP [19] and recruits class I and II histone deacetylases (HDACs) in pancreatic tumors to repress the E-cadherin gene CDH1 [20].ZEB1 can also recruit the nicotinamide adenine dinucleotide-dependent HDAC SIRT1 in prostate cancer cells to repress E-cadherin and to induce several EMT markers [21].Partners like repressive histone lysine methyltransferases (KMTs) and the lysine demethylase (KDM) LSD1 bind ZEB1 (for reviews see [22,23]).In addition, ZEB1 interacts via its N-terminal region with BRG1 to repress E-cadherin in colon cancer cells [24].BRG1 is one of the two ATPase subunits in the SWI/SNF chromatin-remodeling complex and has been reported to be frequently mutated or silenced in primary human NSCLC tumors and cell lines (for reviews see [25,26]).Therefore, we would anticipate that the induction of ZEB1 would lead to epigenetic changes shown to be important in cancer causation and progression [27].
In the present study, we tested the hypothesis that ZEB1 directly binds ST14, EpCAM, and ESRP1 genes in H358 non-small cell lung cancer cells.We also validated RAB25 as a ZEB1 target gene.For each of these genes and other ZEB1 targets, we asked whether ZEB1 binding induced changes in histone marks (acetylation and methylation).We found direct ZEB1 binding to target genes associated with decreased histone acetylation.Lastly, human lung tumors were tested for ZEB1 and H3K27 acetylation by immunohistochemical staining.In these tumors, H3K27 acetylation level was higher in the tumor compartment than in the corresponding stroma where ZEB1 is more often expressed.
Cell Lines and Transformants
NSCLC cell lines were obtained from the Colorado Lung Cancer SPORE Cell Repository.Verification of cell lines was carried out by microsatellite genotyping analysis and comparison to ATCC data.Cell lines were grown in RPMI-1640 supplemented with 10% FCS and antibiotics (Invitrogen, Carlsbad, CA, USA) at 37 °C and 5% CO 2 .Non-immortalized (NHBE), telomerase-immortalized (FC6625-2 3KT) and SV40-immortalized (BEAS2B) human broncho-epithelial cells were grown as described [10].
RNA Expression Analysis
Total RNA was extracted using the RNeasy Mini kit (Qiagen, Valencia, CA, USA) with DNase I treatment and quality controlled by electrophoresis on 0.8% agarose gel.RT-PCR was performed on 0.5-1 g total RNA with the Transcription First Strand cDNA Synthesis kit from Roche (Mannheim, Germany).Gene expression was assessed by quantitative real-time PCR with the GeneAmp 7500 system and SYBR-Green chemistry (Applied Biosystems, Foster City, CA, USA).Data were expressed as the percent of GAPDH, 100 × 2 −∆Ct , where ∆Ct = Ct gene of interest − Ct GAPDH .Primer sequences are provided in Table 1.
MiRNA Analysis
Total RNA was extracted by TRIzol (Invitrogen).Reverse transcription for mature miR-200c and RNU6B was performed with 20 ng total RNA with the TaqMan MicroRNA reverse transcription kit containing MuLV reverse transcriptase (Applied Biosystems).The corresponding TaqMan MicroRNA assay was used for quantitative real-time PCR (Applied Biosystems).Results were reported as the percent of RNU6B with the ∆Ct method as above.
Chromatin Immunoprecipitation (ChIP) Assay
ChIP assay was performed with the protocol described by Upstate (Millipore, Saint Quentin en Yvelines, France) with modifications.Briefly, cells (10 7 cells for ZEB1 ChIP and 10 6 cells for histone modifications per assay) were cross-linked with 1% formaldehyde for 10 min at room temperature.Cells were resuspended in SDS lysis buffer [1% SDS, 10 mM EDTA, 50 mM Tris-HCl (pH 8.1)] for 10 min on ice.The lysate was sonicated with a Bioruptor Sonicator (Diagenode Inc, Sparta, NJ, USA) three times for 7 min each with water change.After centrifugation at 13,000 rpm at 4 °C for 10 min, the supernatant was diluted 10-fold in dilution buffer [0.01%SDS, 1.1% Triton ×100, 1.2 mM EDTA, 167 mM NaCl, 16.7 mM Tris-HCl (pH 8.1)] and incubated for 1.5 h on a rotating plateform at 4 °C with Protein-A Sepharose (Millipore, Temecula, CA, USA).Rabbit anti-ZEB-1 antibodies and rabbit polyclonal antibodies for histone modifications, were incubated with the pre-cleared chromatin on a rotating plateform overnight at 4 °C (Table 2).IgG from a non-immunized rabbit was used as a control.Immune complexes were collected with Protein-A Sepharose and washed 3 times with LiCl buffer [0.25 M LiCl, 1% Triton ×100, 1% deoxycholic acid, 1 mM EDTA, 10 mM Tris-HCl (pH 8.1)], then twice with TE buffer [1 mM EDTA, 10 mM Tris-HCl (pH 8)] before phenol-chloroform extraction and ethanol precipitation.Quantitative real-time PCR was performed with SYBR-Green chemistry with primers described in Table 1.The primers were designed to amplify evolutionary conserved regions with predicted ZEB1 binding sites as determined with ecrbrowser as published previously [9].For each PCR primer set, Ct values were obtained for purified input DNA and the immunoprecipitated chromatin.The results were expressed as the percentage of the input: 100 × 2 −ΔCt where ΔCt tested antibody = Ct tested antibody − Ct input .
Immunofluorescence
Cells grown on 8-wells ibidi plates (ibidi GmbH, Martinsried, Germany) were fixed for 15 min with 3.7% formaldehyde, permeabilized with 0.5% Triton X-100, and blocked using 3% goat serum in PBS.Rabbit anti-human ZEB1, mouse anti-human E-cadherin, rabbit anti-acetyl H3, anti-acetyl H3K27, and anti-acetyl H4 antibodies (Table 2) were incubated overnight at 4 °C.Mouse anti-human ZEB1 and rabbit anti-human E-cadherin antibodies were also tested.After three washes with PBS, cells were incubated for one hour with Alexa 488 -conjugated chick anti-mouse or Alexa 549 -conjugated goat anti-rabbit secondary antibodies.DAPI staining was performed for ten minutes.Stained slides were mounted in Dako Fluorescent Mounting Medium (Dako, Glostrup, Denmark).Images were captured with IPLab software on a BD CARVII spinning disc confocal microscope (BD Biosciences, San Jose, CA, USA).
Lung Cancer Tissue Microarray (TMA), Immuno-Histochemistry and Analysis
Commercial TMA slides containing 109 lung cancers and 10 normal lung tissues with evaluation of TNM disease stages were obtained from U.S. Biomax (Rockville, MD, USA, catalog reference BC041115).The detailed immunohistochemistry protocol has been described previously [10].Briefly, slides were deparaffinized by incubations in Histoclear solution (National Diagnostics, Inc., Charlotte, NC, USA), rehydrated by sequential immersions in decreasing concentrations of ethanol and successively incubated in the following buffers: Antigen Unmasking Solution (Vector Laboratories, Burlingame, CA, USA), 0.3% H 2 O 2 , 0.5% Triton in PBS, and 5% rabbit serum in PBS with 0.1% Triton (Invitrogen).Rabbit primary antibodies were anti-human ZEB1, anti-acetyl histone H3 and anti-acetyl H3K27 (Table 2).Secondary antibodies (Vector Laboratories) were biotinylated goat anti-rabbit antibodies.Slides were exposed to Vectastain ABC standard solution (Vector Laboratories), then DAB peroxidase substrate (ThermoFisher Scientific, Inc., Waltham, MA, USA), and finally mounted using Dako Fluorescent mounting medium (Dako, Carpinteria, CA, USA).Slides were scanned with an Aperio system (Aperio Technologies, Inc., Vista, CA, USA) and each sample scored (from 0 to 300) by multiplying the percentage of positive cells in the tumor or stroma (0 to 100%) by the average level of staining intensity (0 to 3).
Statistical Analysis
Data were summarized by median and range for quantitative variables, percentage and confidence intervals when appropriate for qualitative variables.Expression score distributions in tumor, stroma and normal tissue were compared using Wilcoxon-Rank sum test and the Kruskall-Wallis test.Relationship between co-staining status and qualitative parameters (histology, nodes) was analyzed by Fisher's exact test or Chi-square as appropriate.Correlations between score expression and other quantitative parameters were determined with the Spearman rank correlation method.Potential relationships with baseline characteristics were also explored with the use of non-parametric test, as appropriate.
ZEB1 Binding to New Target Gene Promoter Regions
From our previous identification of ZEB1-correlated genes in lung cancer using Affymetrix arrays, we had confirmed regulatory relationships for ST14, EpCAM, and ESRP1 upon ZEB1 gain-and loss-of-function.In addition, RAB25 expression was negatively correlated with ZEB1 (R = −0.73),similar to that of E-cadherin (R = −0.75)[10].Of interest, EGFR inhibitors are currently used in clinics for non-small cell lung cancer patients with EGFR activating mutations [28][29][30][31] and RAB25 expression has been reported to correlate positively with EGFR inhibitor sensitivity in NSCLC cell lines [32].Therefore, we first validated the Affymetrix results by qRT-PCR analyses in 22 NSCLC cell lines and four controls.With the cell lines arranged in increasing RAB25 mRNA levels (Figure 1A, upper), RAB25 expression negatively correlated with ZEB1 expression (Figure 1A, middle), whereas it was positively correlated with expression of the epithelial marker E-cadherin (Figure 1A, lower).We confirmed that RAB25 expression was decreased by doxycycline-induced ZEB1 in H358 NSCLC cells (Figure 1B) and also by TGF-β, a natural inducer of EMT (Figure 1B).RAB25 protein level was decreased by ZEB1 (Figure 1C).Thus, RAB25, an epithelial marker, is repressed during EMT.
Next, we asked whether ZEB1 binds directly to the promoters of RAB25 and the previously identified targets [10].We first verified decreased mRNA levels of the ZEB1-negatively correlated genes after ZEB1 induction in H358 non-small cell lung cancer cells (Figure 2).To determine whether ZEB1 binds directly to regulatory regions of ST14, EpCAM, ESRP1, and RAB25, we identified predicted, evolutionary conserved, ZEB1 binding sites in the promoter regions of these genes, as well as a site in the first intron of ESRP1 (Supplementary Material).
We tested ZEB1 binding to these elements by chromatin immunoprecipitation (ChIP) coupled to quantitative real-time PCR analyses with corresponding positive and negative controls (Figure 3).Enriched ZEB1 recruitment was identified for predicted target sites in ST14, EpCAM, ESRP1, and RAB25.In contrast, no binding was evident for a conserved E-box sequence in Neuropilin 2 (NRP2), which encodes a common receptor for SEMA3F and VEGF [33], and which seems to be upregulated by ZEB1 predominantly at the post-transcriptional level according to our preliminary data.Also, no enrichment was evident for E-box-negative regions located in ST14 intron 16 (ST14-neg), SEMA3F intron 13 (SEMA3F-neg) and Alu sequences [9] (Supplementary Material).Together, these results demonstrate that ZEB1 binds ST14, EpCAM, ESRP1, and RAB25 when these genes are silenced following ZEB1 induction.
Decrease of Histone Acetylation by ZEB1 on Target Genes
Next, we focused our study on histone acetylation because ZEB1, as a repressor, recruits class I and II histone deacetylases (HDACs) in pancreatic tumors and nicotinamide adenine dinucleotide (NAD)-dependent HDAC SIRT1 in prostate cancers, while in colon cancer cells, ZEB1 recruits BRG1 to repress E-cadherin.To our knowledge, the mechanism of ZEB1 repression in lung cancer cells during EMT is not known.In our previous study [9], we found that SEMA3F was repressed by ZEB1 and that SAHA, a HDAC inhibitor, reduced ZEB1 binding.This result suggested that histone acetylation was modified upon ZEB1 binding.In the present study, we checked this hypothesis for SEMA3F as well as the new ZEB1 targets that we identified.Using ChIP assays, a decrease in histone H3 acetylation was found at each of the tested target genes (Figure 4A).Similarly, with the exception of RAB25, decreased histone H4 acetylation was also noticed (Figure 4B).Next, we examined specific histone H3 lysine residues for acetylation.Decreased H3K9 acetylation was found for ST14, ESPRP1 and RAB25, but not for EpCAM (Figure 5A).Loss of H3K27 acetylation was identified for target genes, with EpCAM, ESRP1, and RAB25 being the most responsive (Figure 5B).Even though the E-cadherin, SEMA3F, and ST14 promoter sites had relatively little H3K27 acetylation at baseline, ZEB1 induction caused a further decrease.
In summary, ZEB1 binding to its target gene sequences induces a decrease in histone H3 (H3K9, H3K27) and H4 acetylation, with decreased histone H3 acetylation, especially H3K27, being the most consistent change.
Decrease of Histone H3K27 Acetylation by ZEB1
To determine whether ZEB1 induces changes in histone acetylation at a level, we examined H358 cell lysates by western blot upon ZEB1 induction.Compared to controls, global H3K27 acetylation was decreased upon ZEB1 expression, whereas no global decrease was evident for total H3; total H4 acetylation was minimally affected at best (Figure 6A).By immunocytochemistry, we also found that H3 and H4 acetylation staining was unchanged (data not shown), whereas H3K27 acetylation staining was decreased (Figure 6B).In order to maintain use of the same antibodies for both immunocytochemistry and ChIP assays, we used E-cadherin staining as a marker of ZEB1 induction.We verified decreased E-cadherin staining when ZEB1 was induced (Figure 6C).Even with the use of the mouse anti-ZEB1 antibody, which had a low sensitivity in our hands, we could see that the ZEB1 expressing cells were less positively stained for H3K27 acetylation (Figure 6D).These results suggest that H3K27 loss of acetylation is a mark of ZEB1 induction.
Modulation of Histone H3K27 Methylation by ZEB1
Since HDACs interact with many histone methyltransferases, we performed ChIP experiments for H3K4me2, H3K9me3, and H3K27me3 (Figure 7).H3K4 dimethylation (a mark of active chromatin) did not change substantially (Figure 7A) and either no change or a tendency to increased H3K9 trimethylation (a mark of repression on gene promoter) was noticed when ZEB1 was induced (Figure 7B).Alu sequences were trimethylated on H3K9 as expected.However, a more consistent enrichment for trimethylation of H3K27, a mark of gene repression, was obtained for the target genes.Of note, the initial H3K27me3 levels without ZEB1 induction were different for the tested genes.SEMA3F and ST14 promoters had the highest level of K3K27 trimethylation (Figure 7C) that could reflect their low expression (Figure 2).These results indicate that ZEB1 binding to its target sequences affects H3K27 with some enrichment for tri-methylation.
DNA Demethylation Agent and Histone Deacetylase Inhibitor Increase ZEB1 Target Gene Expression
Since loss of histone acetylation seems to be a mark of ZEB1 induction, we treated three NSCLC cell lines with vorinostat (SAHA), a HDAC inhibitor, either alone or in combination with 5-aza-2'-deoxycytidine (AZA), an inhibitor of DNA methylation.This strategy was chosen because DNA methylation is abnormal in cancer cells [34] and because combination of DNA methylation and HDAC inhibitors has efficacy in patients with advanced non-small cell lung cancer [35].The three cell lines (NCI-H157, NCI-H460, NCI-H661; hereafter H157, H460, H661) have a mesenchymal phenotype compared to H358 cells and express ZEB1 endogenously [10].The SAHA and AZA treatments partially restored mRNA expression of the ZEB1 target genes but the response was cell line-dependent, with H661 cells being the most responsive (Figure 8).The response was also gene specific.Indeed, SEMA3F expression was responsive to SAHA alone, while E-cadherin and RAB25 mRNA levels were most stimulated by SAHA and AZA.The other ZEB1 target genes, ST14, EpCAM, and ESRP1, responded significantly to AZA alone.Lastly, since EMT is under the control of micro-RNAs of the miR-200 family, we examined the expression of miR-200c after SAHA and/or AZA treatment.AZA treatment was efficient by itself to restore miR-200c expression in H157, H460, and H661 cells (Figure 8).However, the induced miR-200c level was less than the endogenous level observed in H358 cells (Figure 2).Of note, ZEB1 mRNA levels were not affected by AZA/SAHA treatment in these cell lines.
Together, these results suggest that combined treatments targeting epigenetic mechanisms would reduce EMT.
Histone H3K27 Acetylation in Lung Cancers
To determine whether a correlation exists in human tumors between ZEB1 expression and H3K27 acetylation, we examined corresponding immunohistochemical stainings on a commercial tissue microarray containing a series of human lung cancers.Because of sample quality, some tumors were excluded leaving, for analysis, 43 adenocarcinomas, 36 squamous cell carcinomas and a group of 20 other lung tumors (called "others") with a limited number of adenosquamous carcinomas, atypical carcinoid large cell carcinomas, and papillary adenocarcinomas.63% of the tumors were positive for ZEB1 with a nuclear staining confirmed to elongated cells in the stroma compartment.Among these tumors, 31% were also ZEB1 positive in the tumor compartment (Figure 9).These results are consistent with ZEB1 being predominantly expressed in the stroma.Histology, tumor grade, and metastatic lymph nodes did not affect ZEB1 scores.In contrast, every tumor was positive for H3 acetylation and H3K27 acetylation in both compartments (Figure 9).Interestingly, H3K27 acetylation scores were statistically higher in the tumor compartment than in the corresponding stroma for all pathological samples with a median ratio of 1.06 (p < 0.0001).This difference confirmed with histology (adenocarcinomas, p = 0.0423; squamous cell carcinomas, p = 0.0004; and others, p = 0.0177), tumor grade, and metastatic lymph nodes.Similar statistically significant results were found for H3 acetylation for all pathological samples (p < 0.0001), and confirmed for squamous adenocarcinomas (p = 0.0257) and others (p = 0.0022), but not for adenocarcinomas (p = 0.0663).These results suggest that H3K27 acetylation is preferentially higher in the tumor compartment, whereas ZEB1 is preferentially expressed in the stroma of the tumor.
Discussion
Emerging data provide evidence for the role of EMT in lung cancers [36], including the description of circulating lung cancer cells with an epithelial-mesenchymal phenotype [37].Although E-cadherin loss is a classic feature of EMT, it has been considered as a late event in tumor progression.The use of additional markers has demonstrated that a majority of NSCLCs have EMT features.Previously, we and others found that ZEB1 was predominantly responsible for the loss of E-cadherin in lung cancer cell lines [7,8].More recently, using transcriptomic data from 38 NSCLC cell lines, we identified genes most correlated with ZEB1 expression [10].For a subset of these genes, we confirmed that their expression was responsive to both overexpression and knockdown of ZEB1, but did not determine whether the changes were the direct consequence of ZEB1 binding.In the present study, this is now demonstrated for ST14, EpCAM, ESRP1 and, in addition, for RAB25.
RAB25, an epithelial-specific member of the Rab family of small GTPases, can act both as a tumor promoter and suppressor.When highly expressed in ovarian cancer cells, RAB25 facilitates invasion [38,39].It is also pro-tumorigenic in human colon cells [13].Conversely, loss of RAB25 is associated with a poor prognosis in ER-negative breast cancer, and RAB25 behaves as a tumor suppressor in breast cancer cell lines by decreasing cell migration/invasion and affecting pathways including the VEGF-A/VEGFR-1 autocrine loop [40].Some of these discrepancies could be due to CLIC3, which is involved in α5β1 integrin trafficking necessary for cell invasion [12].In the absence of CLIC3, RAB25 acts as a tumor suppressor, whereas in the presence of CLIC3, RAB25 increases tumor aggressiveness [12].RAB25 is involved in intracellular vesicle trafficking in the regulation of epithelial polarity and transformation.Changes in the localization of integrin β1, which affects tumor cell invasion/migration, have been associated with both increased and decreased RAB25 [13,38].RAB25 expression has also been reported to affect Ras signaling, the recycling of EGF or TGF-β receptors, and was found to bind Smad4 and TGF-βR1 [14,15], suggesting that RAB25 plays an important role in the EMT phenotype.
Interfering with EMT in cancer requires detailed knowledge of how target genes are affected, including the epigenetic modifications of histones.In H358 NSCLC cells, ZEB1 binding resulted in decreased acetylation of histones H3 and H4, especially a decreased acetylation on residues H3K9 and H3K27 on target genes.By global analyses (Western blot and immunocytochemistry), although ZEB1 induction in H358 cells did not lead to detectable decrease in H3 or H4 acetylation, we did identify a global decrease in H3K27 acetylation.Thus, decreased H3K27 acetylation may be a mark for ZEB1-positive cells undergoing EMT.
Decrease in lysine acetylation on ZEB1 target genes is in accordance with the mechanism of transcriptional repression induced by ZEB1.Indeed, ZEB1 can recruit class I HDACs, HDAC1 and HDAC2 [18,20,41], and the nicotinamide adenine dinucleotide-dependent HDAC SIRT1 for E-cadherin repression [21].Indeed, increased H3K9 acetylation (a mark of active chromatin) and increased RNA pol II occupancy were found on the E-cadherin promoter concomitant with ZEB1 knockdown in DU145 prostate cancer cells.As might be expected, these changes were associated with increased E-cadherin expression [21].Of note, Sirtuins also act as mono-ADP-ribosyltransferase (for review: [42]) and this function suggests that mono-ADP-ribosylation might be affected by ZEB1 binding as well.
Treatment with SAHA (a HDAC inhibitor), either alone or in combination with AZA (an inhibitor of DNA methylation), partially restored epithelial gene expression in three NSCLC cell lines that exhibit a more mesenchymal phenotype than H358 cells.The response was cell line-dependent and gene-specific.SAHA was mostly effective to restore SEMA3F expression but had little effect on other target genes by itself.One explanation could be that on the non-responsive genes, ZEB1 associates with Sirtuins, members of class III HDACs that are insensitive to SAHA.This would explain the lack of response of H157 cells for E-cadherin expression with SAHA and other class I and II HDAC inhibitors [43].AZA was more efficient to restore ZEB1 target gene expression.It could be explained, in part, by the induction of miR-200c expression that is often silenced by DNA methylation in NSCLC cell lines [44,45].miR-200c is a member of the miR-200 family that negatively regulates EMT and affects a multitude of extracellular matrix and cell adhesion molecules [17,46], and its loss is associated with an aggressive, invasive and chemoresistant phenotype in NSCLC [44].Further experiments would be necessary to explore histone acetylation and DNA methylation on ZEB1 target genes.
ZEB1 mediates repression, by interacting with the corepressor CtBP [19] which associates with HDACs and several other partners including the Polycomb complex PRC2 which methylates H3K27.ZEB1 also interacts with the histone methyltransferases G9a (EuHMT2) that mono-methylates H3K27 [47], the co-repressor CoREST, and the histone demethylase LSD1 (for reviews see [22,23]).Indeed, when H3K27 acetylation (a mark of transcriptional activation) is lost, G9a could first mono-methylate H3K27 which could be further di-and tri-methylated by the Polycomb complex PRC2, establishing a mark of stable transcriptional repression [48].Interestingly, within the same region, H3K27 methylation can be found with H3K4 methylation, a mark of active promoter.The presence of both marks defines bivalent domains within regions enriched for genes poised for activation in pluripotent cells [49].Our preliminary results show that ZEB1 increased trimethylation of H3K27 on selected target genes and that H3K4me2 did not change drastically upon ZEB1 binding.They suggest that ZEB1 would recruit PRC2 and would create bivalent domains during EMT.Further experiments, beyond the scope of this study, would be necessary to explore this issue.
In human NSCLC tumors, we restricted our analysis to ZEB1 staining and H3K27 acetylation because loss of H3K27 acetylation is critical for transcriptional repression.Although H3K27 acetylation is found in a large proportion of cells either in the tumor or the stroma compartment, H3K27 acetylation scores were higher in the tumor compartment.In contrast, ZEB1 was more often expressed in a restricted number of cells in the stroma of the tumor.This result suggests that there is a reciprocal relationship between H3K27 acetylation and ZEB1 expression in patient tumor samples.The nature of the ZEB1 positive stroma cells is still unknown.The possibility that these cells represent tumor cells that have undergone EMT is supported by Mink et al. [50] and our previous work [10].In that case, decreased H3K27 acetylation might mark a region where tumor cells have escaped the primary tumor in the metastatic process.
Changes in histone modifications have been described in NSCLC tumors, without focusing on EMT.Gain of H4K5 and H4K8 acetylation, loss of H4K12 and H4K16 acetylation and H4K20 trimethylation were described (for reviews see [51,52]).Levels of H3K4 dimethylation and H3K18 acetylation have also been reported as independent predictors of clinical outcome in adenocarcinomas of different origins (for reviews see [52][53][54]).High level of SIRT1 was found in lung cancer [55] and increased histone methyltransferase G9a, which methylates H3K9 and mono-methylates H3K27 [47], was correlated with poor prognosis [56].Indeed, G9a was reported to be responsible for EpCAM repression with an enrichment for histone H3K9 dimethylation and promoter methylation [56].Deregulation of histone methyltransferases was reported for oncogenic transformation of human bronchioepithelial cells [57] and mutations of the histone methyltransferase, SETD2, were recently identified by large scale DNA sequencing of lung adenocarcinomas [58,59].It is possible that some of these epigenetic modifications might be specifically linked to the development of EMT features.
Conclusions
In summary, we found that ZEB1 binds directly to the target genes we identified: EpCAM, ESRP1, and ST14.We identified RAB25 as a new ZEB1 target gene.ZEB1 binding was associated with reduced histone H3 and H4 acetylation and some increased methylation of H3K27.Decreased H3K27 acetylation could be detected by global analysis.Our results represent a step towards identifying the chain of events leading to changes in gene expression mediated by ZEB1 during the EMT process in lung cancer.An effective therapeutic intervention will require more detailed knowledge and the availability of a wider repertoire of agents, such as those that inhibit EZH2 and other methyl transferases [60] in addition to DNA methylation and HDAC inhibitors recently described for their efficacy at low doses [35].
Figure 1 .Figure 2 .Figure 3 .
Figure 1.RAB25 is a ZEB1 target gene.(A) RAB25 expression positively correlated with E-cadherin but negatively with ZEB1 in a series of lung cancer cell lines.RNA expression was measured by quantitative real-time PCR in 22 NSCLC cell lines, two NHBE cultures, and two immortalized human airway primary cell lines (BAES2B and FC6625-2 3KT).Cells are ranked from left to right with increasing RAB25 mRNA level.Values are expressed as percent of the geometric mean between GAPDH and actin mRNA.The experiment was done twice with qRT-PCR in duplicate.(B) RAB25 mRNA level is decreased by ZEB1 overexpression in H358 FlipIn ZEB1 cells (left) and by TGFβ treatment in H358 EV control cells (right).Values are expressed as percent of GAPDH for three independent experiments with qRT-PCR in duplicate.Bars = SD.(C) Western blot: RAB25 protein level is decreased by ZEB1 overexpression during 1 to 5 days of DOX treatment in H358 FlipIn ZEB1cells.E-cadherin is decreased as well.Actin is the loading control.Protein molecular weights are indicated in kDa on the left.A
Figure 4 .
Figure 4. Decrease of histone H3 and H4 acetylation induced by ZEB1 binding on target genes.ChIP assays were performed with the rabbit anti-acetyl H3 (A), and anti-acetyl H4 (B) antibodies on H358 FlipIn ZEB1 cells grown for 48 h in the absence (white bars) or in the presence (black bars) of DOX.Results are expressed as % of input.Two independent experiments were performed with qPCR reaction in duplicate and one representative experiment is shown.
Figure 5 .
Figure 5. Decrease of histone H3K9 and H3K27 acetylation induced by ZEB1 binding on target genes.ChIP assays were performed with the rabbit anti-acetyl H3K9 (A), and anti-acetyl H3K27 (B) antibodies on H358 FlipIn ZEB1 cells grown for 48 h in the absence (white bars) or in the presence (black bars) of DOX.Results are expressed as % of input (left column).The ratio of the % input obtained without and with DOX for each experiment is shown in the right column with a value 1.0 indicating no change.Values are from three independent experiments with PCR in duplicate.Bars, SD.
Figure 6 .Figure 7 .
Figure 6.Histone H3K27 acetylation: a mark of ZEB1 induction.H358 FlipIn ZEB1cells were grown for 48 h in the absence (−) or in the presence (+) of DOX.(A) Western blot for H3, H3 acetylation, H3K27 acetylation, and H4 acetylation.H3 staining is the loading control.Protein molecular weights are indicated in kDa.This blot is representative of three independent experiments (left panel).Protein staining with Coomassie blue is on the right.(B) Immunocytochemistry for H3K27 acetylation (red) in H358 FlipIn ZEB1cells with E-cadherin (green) as a read-out for ZEB1 induction.E-cadherin staining was performed with the mouse anti-E-cadherin.Blue color indicates DAPI nuclear staining.One image is reported and representative of two independent experiments with three pictures taken for each well.Bar scale: 40 µM.(C,D) E-cadherin, detected with the mouse anti-E-cadherinantibody, is a read-out of ZEB1 expression: (C) Co-immunostaining for E-cadherin and ZEB1(detected with the rabbit ZEB1-antibody) in H358 FlipIn ZEB1 cells without and with DOX.E-cadherin staining was decreased when ZEB1 was induced and detected with the rabbit antibody.(D) Additional control for co-staining of ZEB1 and H3K27ac.H3K27 acetylation (detected with the rabbit H3K27ac-antibody) was performed with ZEB1 stained with the mouse antibody in ZEB1-induced H358 cells.H3K27 acetylation staining is less intense in positive ZEB1 stained cells as indicated by arrows.Six pictures were taken per well and one representative image is shown.
Figure 8 .
Figure8.DNA demethylation and histone deacetylase inhibition increase ZEB1 target gene expression.H157, H460, and H661 NSCLC cell lines were treated by AZA, SAHA, and the combination (A + S).Gene expression was measured by qRT-PCR.For mRNA expression, values are expressed as percent of GAPDH, and for miR-200c they are expressed as % of RNU6B.Two independent experiments were performed with qPCR reaction in duplicate and one representative experiment is shown.
Figure 9 .
Figure 9.Staining for ZEB1, H3K27 acetylation, and H3 acetylation on a TMA.One example of a lung squamous adenocarcinoma is shown.T and S indicate tumor and stroma compartments respectively. | 7,379.4 | 2013-04-03T00:00:00.000 | [
"Biology",
"Medicine"
] |
Interleukin-18 Increases TLR4 and Mannose Receptor Expression and Modulates Cytokine Production in Human Monocytes
Interleukin-18 is a proinflammatory cytokine belonging to the interleukin-1 family of cytokines. This cytokine exerts many unique biological and immunological effects. To explore the role of IL-18 in inflammatory innate immune responses, we investigated its impact on expression of two toll-like receptors (TLR2 and TLR4) and mannose receptor (MR) by human peripheral blood monocytes and its effect on TNF-α, IL-12, IL-15, and IL-10 production. Monocytes from healthy donors were stimulated or not with IL-18 for 18 h, and then the TLR2, TLR4, and MR expression and intracellular TNF-α, IL-12, and IL-10 production were assessed by flow cytometry and the levels of TNF-α, IL-12, IL-15, and IL-10 in culture supernatants were measured by ELISA. IL-18 treatment was able to increase TLR4 and MR expression by monocytes. The production of TNF-α and IL-10 was also increased by cytokine treatment. However, IL-18 was unable to induce neither IL-12 nor IL-15 production by these cells. Taken together, these results show an important role of IL-18 on the early phase of inflammatory response by promoting the expression of some pattern recognition receptors (PRRs) that are important during the microbe recognition phase and by inducing some important cytokines such as TNF-α and IL-10.
Introduction
Interleukin-18 (IL-18) belongs to the fourth member of the IL-1 family and is produced by a wide variety of cells including macrophages, dendritic cells (DCs), neutrophils, adipocytes, Kupffer cells, microglial cells, and certain neurons in the brain. This cytokine presents many unique biological effects, including pleiotropic, multifunctional, and proinflammatory actions [1][2][3][4].
Like IL-1 , the prototype member of the family, IL-18 secretion does not happen via endoplasmic reticulum and Golgi apparatus. The cytokine is produced as a leaderless and biologically inactive 24 kDa precursor protein called pro-IL-18, which is cleaved by IL-1 converting enzyme, called caspase-1, to produce 18 kDa mature and biologically active cytokine [3,5,6]. Caspase-1 is presented in an inactive 45 kDa precursor form whose activation requires assembly of multiunit complexes involving certain nucleotidebinding and oligomerization domain-(NOD-) like proteins, called inflammasomes, that are responsible for recruiting and activating caspase-1 precursor molecules [7][8][9]. So, an increased production of biologically active IL-18 requires two distinct stimuli: one increases IL-18 gene expression at mRNA and protein levels and usually comes from recognition of pathogen products by a pattern recognition receptor (PRR); the second signal causes inflammasome assembly, caspase-1 activation, and secretion of mature IL-18 [10][11][12].
IL-18 was initially described as an IFN--inducing factor that upregulates the IL-12R subunit on T cells and has generally been considered a Th1 type cytokine [13,14]. However, depending on the context of stimulation, the cytokine microenvironment, and genetic predisposition, IL-18 can promote a Th1 or Th2 response [15]. The IL-18R membrane protein is responsible for ligand binding and TIR domains present in the cytoplasmic tails of the receptor chains transduce signals in target cells, which involves MyD88and TRAF6-dependent pathways to activate NF-B and JNK cascades [1,2,4,16]. Although the pleiotropic effects of IL- 18 show an important role in the modulation of Th2 cytokines, when acting independently of the action of IL-12 [15], its main function would be to participate in inflammatory response by inducing production of several proinflammatory cytokines and chemokines including TNF-, IL-8, IL-1 , MIP-1 , NO, MMP, CXCL8, CXCL9, and CXXL10 from a variety of human cells [3,4,17].
The innate immune response is initiated through the activation of pattern recognition receptors (PRRs) by patternassociated molecular patterns or PAMPs and endogenous molecules produced by injured tissue. These receptors regulate many aspects of innate immunity and determine the polarization and function of adaptive immunity [18][19][20][21], but they are also involved in the maintenance of tissue homeostasis by regulating tissue repair and regeneration [10,20,22]. TLRs are the most extensively studied recognition sensors that participate in the initiation of inflammation [20]. TLR2 recognizes peptidoglycan and lipoteichoic acids of Gram-positive bacteria. Besides, TLR2 is involved in the recognition of other bacterial components such as lipoprotein/lipopeptides, lipoarabinomannan, phenol-soluble modulin, porins, and glycolipids [23]. However, TLR4 can recognize lipopolysaccharide, heat shock proteins, flavolipin, mannan, fibrinogen, taxol, glycoinositolphospholipids, retroviral envelope protein, hyaluronic acid, and fibronectin [24].
Tissue-resident macrophages express all TLRs (except TLR3) and are highly responsive to their agonist [20]. In these cells, TLRs are important for each stage of phagocytosis, ranging from engulfment of invading pathogens to antigen processing and presentation of antigenic peptides. TLRs also lead to the production of cytokines such as tumor necrosis factor-(TNF-) and interleukin-(IL-) 1 , and to the release of chemokines that induce endothelial cell activation and drive inflammatory cell recruitment, regulate the generation of vasoactive lipids and reactive oxygen species [20,21,25,26]. In addition, TLR activation regulates the expression of major histocompatibility complex (MHC) molecules and costimulatory molecules [27] and induces the release of IL-12 and IL-10, cytokines which differentially alert DCs to polarize naive T cells and activate specific adaptive immunity [28].
The mannose receptor (MR, CD206) is a member of the MR family, which is a subgroup of the C-type lectin superfamily that comprises transmembrane and soluble proteins such as selectins and collectins and can bind terminal mannose, fucose, or N-acetyl glucosamine and consequently recognizes a wide variety of ligands, including several bacterial, viral, and fungal pathogens [29]. Thus, MR is considered a PRR and pathogens recognized by this receptor include Candida albicans, Leishmania, Mycobacterium tuberculosis, HIV, Pneumocystis carinii, dengue virus, and selected strains of Klebsiella pneumonia, Cryptococcus neoformans, and Streptococcus pneumoniae [29].
Thus, the present study was designed to better elucidate the role of IL-18 on the expression of some PRRs such as TLR2, TLR4, and MR by human monocytes isolated from peripheral blood, and its effect on TNF-, IL-12, IL-15, and IL-10 production by these cells, once IL-18 is involved in the development of various diseases as mentioned above. The results presented herein demonstrate a clear role of IL-18 in directly modulating TLR4 and MR expression and TNFand IL-10 production by these cells.
Donors.
Fifteen healthy blood donors from the Faculdade de Medicina de Botucatu (FMB), UNESP, Brasil (age range 20-50 years), were included in this study. The Research Ethics Committee approved the study, and informed consent was obtained from all the subjects (2513/07).
Monocyte Isolation.
Heparinized venous blood was obtained from healthy adults. Peripheral blood mononuclear cells (PBMC) were isolated by density gradient centrifugation at 400 g for 30 min on Ficoll-Paque Plus (density ( ) = 1.077) (GE Healthcare Bio-Sciences AB, Uppsala). Briefly, heparinized blood was mixed with an equal volume of RPMI-1640 tissue culture medium (Sigma-Aldrich, St. Louis, USA), and samples were layered over 10 mL of Ficoll-Paque Plus in a 50 mL conical plastic centrifuge tube. After centrifugation at 400 g for 30 min at room temperature, the interface layer of PBMC was harvested and washed twice with RPMI-1640 tissue culture medium (Sigma-Aldrich). The PBMC suspension was stained with neutral red (0.02%) which is incorporated by monocytes and allows their identification and counting in a hemocytometer chamber. After counting, the mononuclear cell suspension was adjusted to 1×10 6 monocytes/mL in RPMI-1640 (Sigma-Aldrich) containing 10% heat-inactivated fetal calf serum (Complete Tissue Culture Medium-CTCM), dispensed into Mediators of Inflammation 3 1000 L/well in 24-well flat-bottom plates (TPP, Trasadingen, Switzerland) and used for flow cytometry analysis and for cytokine production. After incubation of cultures for 1 h at 37 ∘ C in 5% CO 2 , nonadherent cells were removed by aspiration and each well was rinsed twice with RPMI-1640. This procedure resulted in cultures with more than 95% of monocytes. The resulting monocyte cultures were treated or not with IL-18 (MBL, Medical & Biological Laboratories Co. Ltda), 100 ng/mL, for 18 h at 37 ∘ C in 5% CO 2 . In some cocultures, anti-IL-18 (MBL), 0.5 g/mL, was used before the IL-18 treatment, to block IL-18 effects. Control groups with negative isotype control were also tested.
Flow Cytometry.
For CD14, TLR2, TLR4, and MR expression, adherent monocytes were detached from wells by putting the plate on ice and using HyQTase Cell Detachment Solution (HyClone Laboratories Inc., Logan, UT, USA). After that, cells were put into polystyrene tubes for cytometric analysis (BD Labware, Franklin Lakes, NJ USA) and were washed and incubated with mouse anti-human CD14-PE/Cy7, mouse anti-human CD206-APC (MR), mouse antihuman TLR2-FITC, and mouse anti-human TLR4-PE (all from BioLegend, Inc., San Diego, CA) according to the instructions of the manufacturer. Nonspecific signals were calculated and attenuated by isotype control (BioLegend) tubes. After incubation for 20 min at room temperature in the dark, cells were washed and a fixative solution consisting of 5% formaldehyde in buffer (Becton Dickinson, San Jose, CA) was added; then cells were analyzed. Control experiments showed that HyQTase Cell Detachment Solution did not affect cell viability nor altered the expression of all receptors evaluated (data not shown).
For TNF-, IL-10, and IL-12 intracellular analyses, monocyte cultures were pretreated with Brefeldin A Solution (BioLegend), six hours prior to harvest. Afterwards, detached monocytes were distributed into polystyrene tubes for cytometric analysis (BD Labware). Cells were washed and incubated with mouse anti-human CD14-PE/Cy7 (BioLegend), according to the manufacturer's instructions. Next, the permeabilization and staining procedures were conducted using a Cell Permeabilization Kit FIX&PERM (ADG, AN DER GRUB Bio Research GMBH, Kaumberg, Austria). Cells were stained with rat anti-human IL-10-PE, mouse anti-human IL-12/IL-23 p40-FITC, and mouse anti-human TNF--APC (all from BioLegend). Nonspecific signals were calculated and attenuated by isotype control (BioLegend) tubes. After incubation for 20 min at room temperature in the dark, cells were washed and a fixative solution consisting of 5% formaldehyde in buffer (Becton Dickinson) was added; then cells were analyzed.
For both, cells were analyzed with a FACSCalibur flow cytometer (Becton Dickinson). Data (an average of 10,000 events per sample) were analyzed with the software CELL QUEST (Cell Quest Software).
Measurement of Cytokines.
After IL-18 treatment, monocyte culture supernatants were separated from cell debris by centrifugation at 1000 g for 15 min and stored at −80 ∘ C.
The TNF-, IL-10, IL-12, and IL-15 concentrations were measured by capture ELISA using BD OptEIA human ELISA Set (BD Biosciences, Franklin Lakes, NJ, USA). IL-18 concentrations were measured by human IL-18 ELISA Kit (MBL). ELISA was performed according to the manufacturer's protocols. Cytokine concentrations were determined with reference to a standard curve for serial twofold dilutions of recombinant cytokines. Absorbance values were measured at 492 nm using a micro-ELISA reader (MD 5000; Dynatech Laboratories).
Statistical
Analysis. Data were analyzed statistically using GraphPad Prism software (GraphPad Prism 5.0, San Diego, CA). The results were compared by Friedman test, followed by Dunn's Multiple Comparison Test, with the significance level set at < 0.05.
Results and Discussion
The innate immune system promptly responds to the invasion of microbes and acts as the first line of defense, whereby innate immune cells such as macrophages or DCs play a central role in the production of proinflammatory cytokines and nitric oxide after recognition of pathogen [44]. This response is triggered by PRRs that interact with pathogen structures and send signals to the host cell.
To better understand the role of IL-18 in the expression of TLR2, TLR4, and MR by purified CD14 + monocytes, cells were treated with IL-18 and subsequently analyzed by flow cytometry. The results showed that IL-18 was able to increase TLR4 and MR expression by CD14 + monocytes ( Figure 1). However, the cytokine treatment did not affect TLR2 expression (Figure 1(d)). The blocking of IL-18 with specific neutralizing antibody showed a reversal on the TLR4 and MR expression results, as shown in Figures 1(e) and 1(f). The treatment with negative isotype control did not affect the response of monocytes (data not shown). These are new data that support the autocrine role of IL-18 by identifying an important direct modulation of TLR4 and MR on human monocytes by this cytokine, seeing that purified human monocytes treated with this cytokine presented higher expression of TLR4 and MR than control cells, whereas the blocking of IL-18 with anti-IL18 reversed this effect.
TLR-2 and TLR-4 are constitutively expressed by various cell members of the immune system including macrophages, neutrophils, and DCs (reviewed [20]). The expression of TLR-2 and TLR-4 is tightly regulated by several proinflammatory cytokines. But until now, the role of IL-18 in the expression of PRRs is not completely understood, and studies have reported an indirect effect of IL-18 on these cells via Th1 activation. Radstake et al. [45] showed that TLR-2 and TLR-4 are expressed in synovial tissue of patients with rheumatoid arthritis, with clinically active disease, and these expressions were associated with the levels of both IL-12 and IL-18. However, IL-12 and IL-18 treatment in vitro did not affect the expression of TLR-2 or TLR-4 on purified monocytes. An upregulation of TLR-2 and TLR-4 was just seen when PBMC were treated with IL-18. This effect was inhibited by the blocking of IFN-, thus showing an indirect role of IL-18 on TLR2 and TLR4 expression via induction of IFN-by T cells [45]. This increased expression of TLR4 by IL-18 that was detected in this study could promote a series of events, after pathogen recognition, trigging the production of cytokines. After ligand binding, TLRs dimerize and undergo conformational changes, which are required for the recruitment of adaptor molecules, via their TIR domains. These adaptor molecules, namely, MyD88, Mal (MyD88 adapter-like)/ TIRAP (TIR-domain-containing adaptor protein), TRIF (Toll-receptor-associated activator of interferon), TRAM (TRIF-related adaptor molecule), and SARM (sterile and armadillo motifs), contribute to the specificity of individual responses to pathogens. Each TLR can mediate a tailored response in association with different combinations of these adaptors. Two major pathways can be activated by TLRs; the MyD88-dependent pathway results in the activation of NF-B and activating protein-1 (AP-1), regulating the transcription, mRNA stability, and translation of numerous proinflammatory cytokine genes, such as TNF-, IL-6, IL-12, and IFNs, while the TRIF-dependent pathway results in the activation of type I interferons (IFNs) [46].
Thus, this direct effect of IL-18 on the increase of TLR4 expression could account on several inflammatory diseases. One of the most important disease, that presents high levels of IL-18 (more than 10,000 pg/mL) is sepsis [43]. It was observed that patients with severe Gram-negative infection (Melioidosis) had elevated levels of IFN-, IL-18, IL-12p40, and IL-15 on admission, with significantly higher levels in blood culture-positive [43]. It was also observed that IFN-production by whole blood stimulated with heatkilled Burkholderia pseudomallei was inhibited by anti-IL-12 treatment more than anti-IL-18 or anti-IL-15, and the effect of anti-IL-12 was further enhanced by anti-IL-18 treatment, suggesting that, during Gram-negative sepsis, IFNproduction is controlled at least in part by endogenous IL-18, IL-12, and IL-15 [43]. Puren et al. [47] in a previous study evaluated a simple 24 h human whole blood culture that was treated with IL-18 in different concentrations, plus low concentration of LPS, showing that only IL-18 did not induce IFN-production. However, the combination of LPS plus increasing concentrations of IL-18 (0.625-10 nM) resulted in an increased IFN-production in a dependant manner. The combination, however, was independent of the concentration of LPS. It was also detected that cultures treated with IL-18 + LPS showed an increased production of IL-6, IL-8, and TNF-, and LPS-induced TNF-production was potentiated by IL-18 [47]. Recently, it was also demonstrated that the exposure of RAW264.7 cells to LPS/ATP triggered the activation of caspase-1 and the cleavage of interleukin-(IL-) 1 , as well as the release of other cytokines, such as IL-18 and IL-33 [48]. Thus, once demonstrated that LPS/ATP triggered the activation of caspase-1 with release of IL-18 [48], as well as the identification of IL-18 effect on TLR4 expression, this could also explain the systemic activation of cells, and amplification of the response observed in sepsis.
Another important PRR is the MR (CD206), a type I transmembrane protein that possesses eight extracellular CTLDs and a short cytoplasmic tail which lacks classical signaling motifs; it is expressed by macrophages, some DCs, and a variety of other cells and tissues [29,49,50]. The MR has been shown to induce a variety of cellular responses, but the molecular mechanisms responsible for transducing the intracellular signals from this receptor are unclear. The recognition of microorganisms by this receptor has been shown to promote the production of a number of cytokines such as TNF-, GM-CSF, IL-12, IL-8, IL-6, and IL-1 , although there is also evidence that the MR can inhibit the production of certain cytokines, including TNF- [29,[50][51][52]. A mechanism that could account for the negative effect of MR ligation on proinflammatory cytokine production is the upregulation of IRAK-M (an inhibitor of TLR signaling that blocks the dissociation of IRAK-1 and IRAK-4 from MyD88), since this regulator could be induced by treatment with the MR ligand mannan [53]. Rajaram et al. [54] reported that virulent Mycobacterium tuberculosis and mannose-capped lipoarabinomannan induce the expression of nuclear receptor/transcriptional factor PPAR (peroxisome proliferator-activated receptor ) in human macrophages and that this upregulation of PPAR expression was mediated by the MR. The induction of this new pathway serves as a negative regulator of macrophage activation by altering the expression of many inflammatory genes [54][55][56], modulating macrophage differentiation and activation through transrepression of the transcription factors NF-B, AP-1, and STAT [57][58][59][60][61], and attenuating the respiratory burst [62]. These attributes have important implications for the control of infections. But although the MR plays a clear role in homeostasis, its role in antimicrobial immunity remains unclear [50]. Besides that, MR has been considered an important marker of M2 macrophages, mainly in adipose tissue macrophages (ATMs) [63], and recent studies showed increase of IL-18 expression in subcutaneous and abdominal adipose tissues of obese subjects or with metabolic syndrome, and in monocyte-derived macrophage cultures exposed to hyperglycaemia [64,65]. Study also showed that circulating levels of IL-18 were higher in obese subjects [64]. Now, it was showed that leptin stimulates caspase-1 activity in monocytes and that leptin-induced IL-18 secretion is dependent on caspase-1 activity suggesting a signalling pathway between leptin and the inflammasome in these cells [66]. The authors suggested that leptin-stimulated IL-18 could be explained by a secondary effect of the upregulation of other cytokines, such as TNF-, as having been described by other studies [67], once leptin had no direct effect on monocyte TNFsecretion [68]. Confirming these results, Esser et al. [69] showed that the metabolically unhealthy obese phenotype seems to be associated with an increased activation of the NLPR3 inflammasome in macrophages infiltrating visceral adipose tissue. Thus, our results suggest that IL-18 production could account for MR expression and induce ATMs into alternative M2 macrophages.
Knowing the ability of IL-18 to induce either Th1 or Th2 responses [15], we also tested the capacity of IL-18 to induce some pro-or anti-inflammatory cytokines by CD14 + monocytes. The IL-18 treatment induced an increase in TNF- (Figures 2(a) and 2(b)) and IL-10 (Figures 2(c) and 2(d)) levels by CD14 + monocytes. The productions of intracellular IL-12 ( Figure 2(e)) and IL-15 (Figure 2(f)) were not induced by IL-18. The IL-12 levels in culture supernatant of CD14 + monocyte controls and in that treated with IL-18 were undetected by ELISA, while IL-15 levels did not differ between groups. The blocking of IL-18 with specific neutralizing antibody reversed the effect of IL-18 on TNF-and IL-10 production by these cells. The treatment with negative isotype control did not affect the production of the quantified cytokines (data not shown). Then, our results showed an important role of IL-18 in increasing the endogenous production of IL-10 and TNFby monocytes.
TNF-is known to induce proinflammatory activities through various cell types including mononuclear and polymorphonuclear phagocytes, in which it is responsible for the activation of cytocidal systems and plays a major role in host defense [70][71][72]. Takahashi et al. [73] demonstrated the increased production of TNF-alpha, IL-12, and IFNby PBMC treated with IL-18. These results were correlated with the upregulation of ICAM-1, B7.2, and CD40 expression on monocytes. Blocking the engagement of these adhesion molecules by antibodies against ICAM-1 and B7.2 reduced the cytokine production by IL-18-treated PBMC [74,75]. The authors suggested that IL-18 induces cytokine production through upregulation of adhesion molecule expression on monocytes [73]. But now, our results show a direct effect of IL-18 on purified monocytes by inducing TNF-production that together with effects shown on the TLR4 receptor could further compromise the response mainly in sepsis.
On the contrary, IL-10 is a cytokine produced by CD4 + T helper type 2 (TH 2 ) cells, CD8 + T cells, monocytes, macrophages, and B cells. It was first described as an inhibitor of activation and cytokine production by TH 1 cells [76]. However, IL-10 suppresses the activity of T and NK cells indirectly, via monocyte and macrophage inhibition, and is considered a macrophage deactivation factor [76]. This IL-10 effect may occur mainly by influencing macrophage recruitment, viability, morphology, phagocytosis, the production of cytokines and expression of their receptors such as the major histocompatibility complex and costimulatory molecules, antigen presentation, generation of reactive oxygen and nitrogen intermediates, and the killing of microbes and tumor cells [76,77]. Studies suggest that IL-10, beyond acting on monocytes/macrophages and lymphocytes, may also exert an important regulatory action on neutrophil functions [78,79].
We would like to point out that, in this study, these new very interesting results regarding the direct effect of IL-18 on human monocytes by inducing both TNF-and IL-10 production and MR expression could indicate that IL-18 participates on the induction of both classically (M1) and alternatively (M2) activated macrophages, once after IL-18 treatment, cultured cells presented higher TNF-, IL-10 production and MR expression. M1 macrophages are characterized by high microbicidal capacity and secretion of proinflammatory cytokines such as TNF-, while M2 macrophages present high expression of mannose, galactose, and scavenging receptors, more phagocytic activity, and a phenotype characterized by high expression of IL-10 and low expression of IL-12 [80,81]. Further investigations are being conducted in our laboratory to better elucidate these mechanisms.
Conclusions
In conclusion, our findings showed that IL-18 affects TLR4 and MR expression on human monocytes, and TNF-and IL-10 production by these cells. Taken together, these results implies that this cytokine may also play an important role in the initiation of innate immune responses, participating in severity or resolution of infections and inflammatory diseases, since monocytes and macrophages are the main components of this response. | 5,163.2 | 2015-03-19T00:00:00.000 | [
"Biology",
"Medicine"
] |
Vault RNAs: hidden gems in RNA and protein regulation
Non-coding RNAs are important regulators of differentiation during embryogenesis as well as key players in the fine-tuning of transcription and furthermore, they control the post-transcriptional regulation of mRNAs under physiological conditions. Deregulated expression of non-coding RNAs is often identified as one major contribution in a number of pathological conditions. Non-coding RNAs are a heterogenous group of RNAs and they represent the majority of nuclear transcripts in eukaryotes. An evolutionary highly conserved sub-group of non-coding RNAs is represented by vault RNAs, named since firstly discovered as component of the largest known ribonucleoprotein complexes called “vault”. Although they have been initially described 30 years ago, vault RNAs are largely unknown and their molecular role is still under investigation. In this review we will summarize the known functions of vault RNAs and their involvement in cellular mechanisms.
Introduction
Non-coding RNAs represent the vast majority of transcriptional product of the human genome [1,2]. The family of non-coding RNAs is composed of 19 different classes; among them transfer RNAs (tRNAs), tRNA-derived RNA fragments (tRFs), ribosomal RNAs (rRNAs), small nucleolar RNAs (snoRNAs), endogenous small interfering RNAs (endo-siRNAs), sno-derived RNAs (sdRNAs), transcription initiation RNAs (tiRNAs), miRNA-offset-RNAs (moRNAs), circular RNAs (circRNAs), vault RNAs, microRNAs (miR-NAs), small interfering RNAs (siRNAs), small nuclear RNAs (snRNAs), extracellular RNAs (exRNAs), piwi-interacting RNAs (piRNAs), small Cajal body RNAs (scaRNAs), transcribed-ultraconserved regions (t-UCRs), long intergenic non-coding RNAs (lincRNAs), and long non-coding RNAs (lncRNAs) . The role and function of tRNAs, rRNAs, microRNAs and lncRNAs, in particular, have been well examined both under physiological and pathological conditions [26]. In general, non-coding RNAs control all levels of genes' regulation in eukaryotes, including the control of chromosome dynamics, splicing, RNA editing, translational inhibition and mRNA degradation [26]. Even transcription itself may be regulated by non-coding RNAs as outlined in several reports [27][28][29]. This is achieved on one hand, by control of chromosome dynamics and modifications and on the other hand, by regulation of RNA polymerase II activity. Therefore, non-coding RNAs are involved in regulation of accessibility of DNA sequences for the transcription machinery, as well as in modulation of the transcription rate of RNA polymerase II [30][31][32][33][34]. Furthermore, splicing of pre-mRNA transcripts, post-transcriptional regulation of expression rate as well as translation of mRNAs in cytoplasm and regulation of mRNA half-life are under control of non-coding RNAs [26,27,35]. In addition, some non-coding RNAs are known to be involved in intercellular communication and cell regulation [36,37]; whereas, others are part of the antiviral defence by stimulating immune response and activating RNA interference pathway [38,39].
In contrast, the molecular functions of vault RNAs are still not completely clear even after more than 30 years since their discovery [40,41]. With a length between 88 and 140 nucleotides vault RNAs are longer than miRNAs, but they 1 3 are still included as members of the short non-coding RNA group [41,42].
In humans, four vault RNAs are encoded on chromosome 5q31 in two different loci. The VTRNA-1 locus (located between zinc-finger matrin-type 2 gene and proto-cadherin cluster) contains the genetic information for three vault RNAs (vault RNA1-1, vault RNA1-2 and vault RNA1-3) and VTRNA-2 locus (located between the genes coding for transforming growth factor beta 1 and SMAD family member 5) codes for vault RNA2-1 also known as pre-miR-886 [43][44][45]. All vault RNA genes are under control of a polymerase III type 2 promoter and they contain a box A and box B motif normally found in tRNA genes [41,42]. Nevertheless, the promoters of the two vault RNA loci are not identical; therefore, expression patterns of the vault RNA genes are different [42]. Furthermore, epigenetic modifications such as promoter methylation are important regulators for vault RNAs expression especially for the VTRNA-2 gene [46,47]. The distant regulatory elements of the VTRNA-1 promoter are characterized by differential CpG accessibility and this might be a hint for a cell-type-specific expression of the three vault RNAs under control of this promoter [48]. The internal promoter sequences box A and box B present in VTRNA-1 and VTRNA-2 enable binding of transcription factors TFIIIC and TFIIIB which facilitate polymerase III binding to the transcription starting site [49]. Vault RNAs transcription is also under control of cAMP response (CRE)-and tetradecanoyl-phorbol acetate response (TRE)like elements [41,42]. These elements represent binding sites for the transcription factors CREB and AP-1, respectively, which adapt key cellular processes such as differentiation, proliferation and survival to nutrient, growth factor and stress signaling [50,51]. This could explain the observed differential vault RNA transcription rate upon viral infection, starvation and cancer [45,[52][53][54]. Furthermore, the short half-life time observed of around 1 h makes vault RNAs suitable as signaling molecules that quickly respond to stimuli [55,56].
Vault RNAs were first identified as a component of vault particles [40] but most of the vault RNAs (around 95%) are not associated with these particles and therefore, vault RNAs are most probably also involved in other cellular processes and interactions [57,58] (Fig. 1).
The main component of vault particles is the major vault protein that is sufficient itself for the structural conformation characteristic of the vault particles [73][74][75][76][77]. The major vault protein has no homology to any other protein known, but it is highly conserved among different species (around 90% identity between mammalians and around 60% with lower organisms) [75,78]. Interestingly, the major vault protein contains two Ca 2+ binding sites at the N-terminal end which are necessary for correct folding and particle assembly but also to interact with other proteins like PTEN, thus connecting the vault particles to cellular signaling pathways [79]. Beside the major vault protein, two other proteins are present in the vaults particles; the poly-(adenosine-diphosphate ribose) polymerase-a member of the PARP family-and the telomerase-associated protein 1 (TEP1) [80][81][82]. In these vault particles, the vault RNAs are associated with the caps [43,57,75,83]. The vast majority (around 90%) of vault particles in unstressed cells are located in the cytoplasm but vault particles are also found to be associated with the nuclear membrane. The distribution of vault particles varies in response to external stimuli and rapidly react towards extracellular changes with translocation to different subcellular compartments. Furthermore, under pathological conditions like cancer, a higher amount of vault particles are associated with the nuclear membrane and up to 5% of them are found within the nucleus [68,[84][85][86][87][88]. Based on this observation and the barrel-like structure of the vault particles, the hypothesis exist that vault particles have an important role in mediating shuttle processes between cytoplasm and nucleus, including nuclear import of tumor-suppressors like PTEN, nuclear hormone receptors as well as drug export. It is speculated that some of the cargos transported in vault particles are bound to the vault RNAs present in these complexes [66,67,[89][90][91]. But up to now, the role of vault particles as transporter is still under discussion and further investigation is urgently needed because most of these studies used either immunoprecipitation of signaling complexes or yeast two hybrid systems, and it cannot be excluded that the found interaction with vault particles and vault RNAs occurred accidentally and is without any biological sense. A verification of vault particles as transporter in humans under physiological and/or pathological conditions (e.g., tumor) is still missing.
Studies on vault particles using knock-out mice
The role of major vault protein and vault particles has been addressed in relevant mice knock-out models [92][93][94]. In TEP1 knock-out mice vault particles were still present, but inside these vault particles, no vault RNAs have been found. Therefore, it was concluded that TEP1 is absolutely required for a stable association of vault RNAs with the vault complex [93]. TEP1 knock-out mice as well as major vault protein knock-out mice are viable, healthy and display no obvious abnormalities [93,94]. The major vault protein knock-out mice express no vaults particles as expected and have been used in different studies to examine the role of vault particles [78,84,94,95]. Surprisingly, embryonic stem cells and bone marrow cells derived from major vault protein knock-out mice showed no change in sensitivity to drugs when compared to wild-type mice cells. In addition, the activities of the multidrug resistance-related transporters P-glycoprotein, multidrug resistance-associated protein and breast cancer resistance protein were not altered in vaultdeficient cells ruling out the possibility that these proteins compensate for the loss of vaults. Also, the response towards doxorubicin treatment was the same in major vault protein knock-out and wild-type mice in in vivo experiments [94]. These observations lead to the conclusion that at least in mice, vaults are not directly involved in drug resistance [78,84]. In another study, the major vault protein knock-out mice were used to address the role of vaults in regard to dendritic cells. Development and function of dendritic cells, derived from mononuclear bone marrow cells, appeared normal in knock-out mice. In-vivo immunization assays showed that neither T-cell-mediated immune response nor T-celldependent humoral response were affected by major vault protein knock-out, indicating intact antigen-presenting and migration capacities of dendritic cells. Obviously, in mice vault particles are not required for primary dendritic cell functions [95]. This observation is in contrast with findings in humans where major vault protein and vault particles are up-regulated during the development of human dendritic cells. Moreover, major vault protein-specific antibodies, presumably interfering with the function of major vault protein or vaults, resulted in reduced expression levels of dendritic cell markers, co-stimulatory molecules and decreased capacity to induce T-cell proliferative and interferon-ɣ-releasing responses [96]. Recently, the major vault protein was identified as a suppressor for NF-κB signaling in macrophages [97]. Global as well as myeloid-specific major vault protein gene knock-out intensified high-fat diet-induced obesity, insulin resistance, hepatic steatosis and atherosclerosis in mice via NF-κB signaling pathway. Furthermore, increased macrophage infiltration and inflammatory responses in the microenvironments have been observed [97]. Another study used peripheral blood mononuclear cells (PBMCs) from major vault protein knock-out mice and evaluated an essential role of major vault protein for the induction of early antiviral cytokines (like IL-6 and IL-8) in the context of double-stranded RNA-or virus-induced pro-inflammatory response [98]. In the following sections, we will focus on the role of vault particles and vault RNAs in humans.
Vault RNAs, vault particles and drug resistance
One of the roles of the vault particles is the contribution to mediate drug resistance mechanisms by transporting the drugs from their intracellular targets to the extracellular compartment and also in drug sequestration [78]. In an elegant experiment, expression of major vault proteins was prevented by a siRNA approach in human bladder cancer cells under doxycycline treatment. This resulted in inhibition of cytosolic doxorubicin sequestration in perinuclear lysosomes and enhanced accumulation of the drug in the nucleus as well as increased cytotoxicity [99]. Based on the fact that nuclear PTEN is involved in the maintenance of chromosomal stability [100], its nuclear transportation by vaults particles could also play a role in drug resistance mechanisms by counteracting drug-induced DNA damage [101].
In most cell lines, vault RNA1-1 has the highest expression level of all vault RNA transcripts [102]. In multidrugresistant cells, the level of vault RNA1-1 is not altered but expression rate of vault RNA1-3 is raised and an increased association of vault RNA1-3 with vaults particles has been observed [43,102]. However, the molecular details behind this observation are still not clear. In general, vault RNAs bound to the vault particles have the capacity to interact with drugs via specific binding sites [103]. For instance, in cancer patients who developed resistance to chemotherapy, the number of vault particles is increased, in agreement with their observed role, in in vitro models [78,[103][104][105]. Another relevant example is given by mitoxantrone resistance in osteosarcoma, glioblastoma and leukemia where drug failure is based on direct binding of the drug to vtRNA1-1 and vtRNA1-2 [103,104].
Besides sequestering drugs, vault RNAs are processed into several small RNAs (Fig. 1). Among them small-vault RNAs, account in a second way for multidrug resistance in cancer patients by down-regulating CYP3A4, the key enzyme in drug metabolism [106]. Interestingly, the introduction of 5-methyl-cytosine by the RNA methyltransferase NSUN2-dependent leads to the cleavage of vault RNAs in a Dicer-dependent mechanism; thus, the resulting small-vault RNAs regulate their target genes in a miRNA-like fashion [106][107][108].
Furthermore, vault RNAs can induce drug resistance in an indirect way by influencing cell proliferation and preventing cell death as described in the following sections.
Vault RNAs and proliferation
Drug resistance can also arise by the increase in cell proliferation rate [109,110]. Vault RNAs have been found to influence cell proliferation in different ways and in a celltype-specific manner without the participation of vault particles (Fig. 1).
In breast cancer, vault RNA1-1 interacts directly with the RNA/DNA-binding protein polypyrimidine tract binding splicing factor (PSF) [105]. PSF is an important regulatory nuclear protein that acts as a component of spliceosomes via the RNA-binding domain and furthermore regulates transcription of genes via the DNA-binding domain; e.g., PSF controls the transcription of P450-linked side-chain cleaving enzyme (CYP11A1) and regulates this by the steroid pathway; in addition PSF inhibits transcription of proto-oncogene G antigen 6 (GAGE6) [111][112][113]. Following the binding of vault RNA1-1 to PSF RNA-binding domain, the transcriptional repression of GAGE6 via the DNA-binding domain is released and transcription of the proto-oncogene proceeds [114]. Induced expression of GAGE6 results in increased cell proliferation and causes drug resistance [105]. Vault RNA2-1 interacts with and blocks the pro-apoptotic interferon-inducible protein kinase R (PKR). PKR is a central protein for cellular response to different stress signals such as pathogens, starvation, cytokines and irradiation. PKR activates different central pathways like JNK, NF-ϰB, PP2A, p38 and inhibits the eukaryotic translation initiation factor eIF2α by phosphorylation [115]. In normal cells, this inhibits further cellular mRNA translation based on AUG initiation codons and in parallel activates the tumor-suppressor PP2A which blocks cell-cycle, as well as proliferation and leads ultimately to cell death [55]. In different cancer cells, active PKR fails to induce phosphorylation of eIF2α and PP2A, so that apoptosis is not triggered but PKR promotes still the pro-survival NF-ϰB pathway [116][117][118]. Therefore, the reduced expression levels of vault RNA2-1 found in cancer cell lines and cancer patients specimens result in activation of PKR and subsequent increased cell proliferation as well as drug resistance [119]. Consequently, vault RNA2-1 seems to act as tumor suppressor in contrast to oncogenic effects of vault RNA1-1 [46,47,[120][121][122][123].
Vault RNAs and apoptosis
Vault RNA1-1 is involved in inhibiting the intrinsic as well as extrinsic apoptosis pathway in several cancer cell lines as demonstrated in in vitro experiments [54,124]. To address the role of vault RNAs in apoptotic mechanisms, cells have been treated with an autophagy inhibitor and cell death induced by serum starvation. Cells with knock-out vault RNA1-1 gene were more susceptible to programmed cell death; whereas, re-expression of vault RNA1-1 restored apoptosis resistance of the cells. The mechanism underlying the blocking of apoptosis seems to be related to a short stretch within the central domain of vault RNA1-1 and cannot be exerted by other vault RNA members. Furthermore, it was demonstrated that regulation of programmed cell death is independent of vault particles and relay only on vault RNA1-1 [54]. The protective effects of vault RNA1-1 against programmed cell death have been observed after triggering the intrinsic (via staurosporine, etoposide) as well as extrinsic (via Fas ligand) apoptosis pathway [54]. Increased vault RNA1-1 expression activates the pro-survival PI3K-/ AKT-and ERK1/2 MAPK-signaling pathways and by this counteract cell death [124]. In Epstein-Barr virus (EBV)infected B-cells, the latent membrane protein 1 (LMP1) of EBV up-regulates the NF-ϰB pathway that results in increased expression of vault RNA1-1. In this context, vault RNA1-1 inhibits the extrinsic and intrinsic apoptotic pathways and enables cell proliferation by further activation of NF-ϰB pathway and up-regulation of the expression of Bcl-xL [54].
Vault RNAs and autophagy
Autophagy is besides apoptosis another catabolic pathway essential in homeostasis of cells [125]. Both mechanisms are interconnected by several molecular nodes and a close cross-talk exists [54]. In direct proximity of the VTRNA-1 locus is the proto-cadherin cluster that encodes for the protocadherin family, which is involved in autophagy [126]. Therefore, it seems indicative that also vault RNAs might be involved in autophagy [52] (Fig. 1). Autophagic process is necessary for cleaning out unnecessary or dysfunctional components in cells and recycle nutrients and energy. All cargos that cannot be degraded by the ubiquitin-proteasome system are cleaved via autophagy in the lysosomes [127][128][129]. In contrast to apoptosis that results in cell death [130], autophagy in cancer can facilitate tumor cell survival in stress conditions (e.g., under hypoxia or starving conditions) by providing energy and nutrients [131]. An established marker for the autophagic state of a cell is the intracellular levels of p62 [132]. The selective autophagy receptor p62 [133,134] is of pivotal importance to the autophagic process by recognizing cargos for the autophagic process, triggering autophagosome formation and exerting a regulatory role in autophagy [127,[135][136][137][138]. Vault RNA1-1 binds directly to p62 preventing its oligomerization, a prerequisite for autophagy. This results in inhibition of p62-dependent autophagy and aggregate clearance [52,139]. Another role of p62 is the cross-talk between autophagy and apoptosis [140,141] and increased levels of monomeric p62, upon autophagy inhibition via vault RNA1-1, could modulate the balance between the two catabolic pathways. In addition, p62 is involved in the regulation of inflammatory pathways, especially the autophagic defence against invading bacteria and viruses [142]. Most probably, viruses target p62 by upregulation vault RNAs to decrease the autophagic processes in parallel with inhibition of interferon responses as outlined further below [53].
Vault RNAs, cellular differentiation and development
It is well established that the levels of non-coding RNAs, including vault RNAs, are highly regulated during development and cellular differentiation since they are essential to these processes [143]. One example is based on the above-mentioned NSUN2-dependent 5-methyl-cytosine modification of vault RNA1.1 and vault RNA1.3 [107,144] which was recently reported to influence cell differentiation [107,108]. The serine/arginine-rich splicing factor 2 (SRSF2) binds to the non-methylated form of vault RNA1-1 with higher affinity and counteracts the processing by NSUN2 [108]. Therefore, the expression level of SRSF2 and NSUN2 and their binding to vault RNA1-1 orchestrates the production of small-vault RNAs. The lack of NSUN2mediated methylation of vault RNA1-1 results in reduced amount of small-vault RNAs and results in changes in epidermal differentiation program of keratinocytes [107,108]. It is well established also that lack of NSUN2-dependent 5-methyl-cytosine modification in other non-coding RNAs modifies the physiologic situation too; e.g., aberrant 5-methyl-cytosine modification of tRNAs impairs the translation machinery and causes neuro-developmental deficits [145,146].
The regulated expression of a small-vault RNA derived from vault RNA2-1 (called small-vault RNA2-1a) has been shown to modulate early developmental processes in the central nervous system and has an important role in human brain development as well as aging. The small-vault RNA2-1a has the highest expression level early in post-natal developmental stages and the amount decreases after 1 year with low levels being detected at the oldest ages examined [147].
Vault RNA-derived small RNAs
Another peculiar characteristic of vault RNAs is that they can be processed into several small RNAs and the cleavage process of vault RNAs is mediated by RNA methyltransferase NSUN2. The introduction of 5-methyl-cytosine by NSUN2 is a prerequisite for DICER-dependent cleavage process of vault RNAs and the resulting small-vault RNAs regulate their target genes in a miRNA-like fashion [106][107][108] as the aforementioned down-regulation of CYP3A4 by small RNAs resulting in altered dug metabolism [106] as well as the role of small-vault RNAs for epidermal differentiation program of keratinocytes [107,108]. In both cases, the small-vault RNAs are processed from vault RNA1-1. Another example for the role of small-vaultdependent RNAs was recently reported in prostate cancer. Vault RNA2-1 produces two small RNAs (snc886-3p and snc886-5p) that are found to be reduced in tumor tissues compared to the surrounding normal tissues. Based on PAR-CLIP (photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation) and knock-out experiments of microRNA biogenesis enzymes, it was demonstrated that vault RNA2-1 cleavage is based on DICER but independent of DROSHA and the resulting small-vault RNAs are associated with argonaute proteins [148] in a similar process of miRNAs biogenesis [149]. As functional proof of action, over-expression of snc886-3p in relevant in vitro and in vivo systems, resulted in down-regulation of mRNAs containing complementary sequences to the seed sequence of the small-vault RNA in their 3′-UTRs. This led to reduced cell cycle progression, increased apoptosis [148,150] and this seems in agreement with the view of vault RNA2-1 as tumor suppressor [46,47,[120][121][122][123]. In Parkinson disease, a smallvault RNA derived from vault RNA2-1 is up-regulated in early stages of the disease and this small-vault RNA is most probably involved in the process of brain development as outlined in detail above [151].
Furthermore, small RNAs derived from vault RNAs and associated with the argonaute complex have been identified also in breast, prostate, lung and lymphoid tissue [106,148].
These findings support the hypothesis of a cleavage of vault RNAs into small RNAs which influence mRNA stability and/or regulate translation like miRNAs. However, the main role of small-vault RNAs need further investigation and it will be of valuable interest if these small RNAs can regulate transcription in a tissue and cell-type-specific fashion as miRNAs [149].
Furthermore, small-vault RNAs are secreted by cells and they are present in high numbers in exosomes (Fig. 1). Therefore, small-vault RNAs are most probably also involved in cell-cell signaling [106,107,152].
Vault RNAs, viral infection and immune system
Viral infections induce vault RNA expression [45,153] and this was observed in in vitro models for different virus families including γ-herpesviridae (Herpes simplex virus 1), paramyxovirus (Sendai and Epstein-Barr virus), Kaposi's sarcoma-associated herpes and influenza-A virus [44,45,53,54]. Most of these viruses are known to reduce the autophagic capacity of their host cells that is a consequence of high expression levels of vaults RNAs as mentioned above [53,154]. In addition, transcriptional induction of vault RNAs upon infection, has been associated with expression of latent membrane protein 1 for EBV and non-structural protein NS1 of influenza virus, respectively, with the aim to prevent cells from apoptosis and suppress PKR-mediated innate immunity [53,54]. Therefore, high expression levels of vault RNAs result in an increased viral load. Viruses are known to hijack cells and their cellular replication machinery to maximize viral replication while inhibiting cellular defence mechanisms [155]. Up-regulation of vault RNA levels seems to be a very efficient way to escape targeted viral degradation via autophagy and subsequent MHC class II antigen presentation [156] and in parallel force the cell to enter a pro-proliferative state that counteracts cellular suicide programs as well as support rapid virus replication [157]. Therefore, it is not surprising that vault RNAs are hijacked and used by viruses. This underlines the important and central role of vault RNAs in regulating cellular processes (Fig. 1).
Another effect of viral infection is the reduction of cellular DUSP11 expression. DUSP11-mediated de-phosphorylation of the 5′-end of vault RNAs initiates the degradation of these RNAs [158,159]. Therefore, an infection-dependent reduction of DUSP11 levels results in accumulation of vault RNAs that in turn trigger an innate immune response via retinoic acid-inducible gene-1 (RIG-1) receptors [160]. By this, at least one of the anti-viral defence mechanisms against RNA virus is activated [161].
Vault RNAs as diagnostic and prognostic markers
In Parkinson disease, down-regulation of miR-7, miR-34b/c and miR-133b [162][163][164] as well as up-regulation of a small-vault RNA derived from vault RNA2-1 is common in brain areas that are affected by this disease [147]. Increased expression of vault RNA2-1 occurs early in the course of disease and could perhaps be used as a diagnostic marker.
Conclusion and perspectives
The old simplistic view that non-coding RNAs only play functional roles in protein synthesis as integral components (rRNA) or reaction substrates (tRNA) of the ribosome has dramatically evolved during the last 2 decades with emerging concepts linking different classes of noncoding RNAs to physiology and disease. The non-coding RNA group of vault RNAs, which is composed of only four members in human, exert an important role within the cell. Although until recently not all functions and processes have been unveiled in detail, it is already clear that vault RNAs add another level of regulation to the network of non-coding and coding RNAs. As outlined in this review, vault RNAs are involved in transferring extracellular stimuli into signals inside the cell; they regulate central signaling pathways and cell-cell communication. Furthermore, vault RNAs play a substantial role in immunity response, influencing proliferation, apoptosis and autophagy as well as being involved in drug resistance mechanisms (Fig. 1). All these functions are under vault RNAs regulation either via direct interaction with proteins or via post-transcriptional regulation of mRNAs. In particular, in the context of cancer, vault RNAs appear to have a critical role and a better understanding of their biology in this disease could offer a new prospect for cancer treatment and prevention of drug resistance.
Author contributions Conceptualization, JCH, AL and NV; writing, original draft preparation, review and editing, JCH, AL and NV. All authors have read and agreed to the published version of the manuscript.
Funding This research received no external funding.
Compliance with ethical standards
Conflict of interest N.V. received speaker honorarium from the companies Bayer, Eli-Lilly, Pfizer and Merck. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
Ethics approval Not applicable.
Consent for publication Not applicable.
Code availability Not applicable.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. | 6,085.8 | 2020-10-15T00:00:00.000 | [
"Biology"
] |
The Use and Effectiveness of Triple Multiplex System for Coding Region Single Nucleotide Polymorphism in Mitochondrial DNA Typing of Archaeologically Obtained Human Skeletons from Premodern Joseon Tombs of Korea
Previous study showed that East Asian mtDNA haplogroups, especially those of Koreans, could be successfully assigned by the coupled use of analyses on coding region SNP markers and control region mutation motifs. In this study, we tried to see if the same triple multiplex analysis for coding regions SNPs could be also applicable to ancient samples from East Asia as the complementation for sequence analysis of mtDNA control region. By the study on Joseon skeleton samples, we know that mtDNA haplogroup determined by coding region SNP markers successfully falls within the same haplogroup that sequence analysis on control region can assign. Considering that ancient samples in previous studies make no small number of errors in control region mtDNA sequencing, coding region SNP analysis can be used as good complimentary to the conventional haplogroup determination, especially of archaeological human bone samples buried underground over long periods.
Introduction
Ancient DNA (aDNA) analysis is very important for understanding the origin and evolution of mankind in history. Of various aDNA studies, analysis on mitochondrial DNA (mtDNA) is one of the best methods to know the phylogeny of archaeologically obtained human samples. mtDNA shows maternal haplotype lineage that is passed down throughout each generation without changing and reshuffling of DNA. In fact, it could be analyzed successfully even in case where nuclear DNA (nDNA) is degraded seriously [1].
Recently, about the determination of East Asian mtDNA haplogroups, Lee et al. [2] showed that Korean mtDNA could be allocated into 15 haplogroups by two different multiplex systems for 21 coding region SNP markers and one deletion motif. As Koreans do have many D4 subhaplogroups, the third set of PCR multiplex systems was also used for defining them in much detail. Authors showed that East Asian mtDNA haplogroups, especially those of Koreans, could be successfully assigned by the multiplex analysis system they developed [2]. As many previously published works exhibited that some of mtDNA data from degraded samples were not sufficiently authentic, the establishment of more tools for detecting possible sequence errors looks valuable to concerned researches.
The multiplex system was originally designed for mtDNA analysis of the degraded samples frequently met in the field of forensic science [2]. However, the technique looks very suggestive to the anthropologists in East Asia as well. Like forensic scientists, the biological anthropologists always tried to analyze the aDNA that is seriously degraded, remaining in archaeological human samples by small amounts. Therefore, sequencing errors in aDNA analysis have always been the researchers' concern. Since archaeological and forensic DNA typing share common subjects to be considered for making their studies on the degraded samples more successful and authentic, many experimental methods developed for forensic science have been also applied to aDNA researches.
In this respect, we wonder if the mtDNA typing by use of coding region SNP analysis could be also applicable to ancient samples from archaeological sites in East Asia. As the technique was proven to be time-, cost-, and target DNA-saving [2], it can be used as a good complimentary to conventional aDNA sequencing if both methods could show well-matching results from archaeologically obtained samples. However, regretfully enough, there were not any previous researches on how perfectly this multiplex system can be applied to archaeologically obtained human samples buried underground for several hundred to thousand years.
For the past several years, we tried to build a skeletal series consisting of human bones collected from 16th to 18th century Joseon tombs in South Korea. Our previous reports on the collection have revealed information concerning the health and disease status of premodern Korean people [3][4][5][6][7][8][9]. Using the same human skeleton collection, we undertook the experiments to compare haplogroup-directed data made by two different methods: conventional control region sequencing and analysis of coding region SNP markers. It determines whether the analysis of the triple multiplex system for coding region SNP analysis, like the forensic cases, could be also useful for the mtDNA analysis of hundred-year-old human bones from archaeological sites.
Materials and Methods
Human skeletons ( = 11) collected from 16th to 18th century Korean tombs (Joseon Dynasty) were used in this study. Sex determination was made on the basis of morphological differences manifest in the pelvic bone, by the examination of greater sciatic notch, preauricular sulcus, ischiopubic ramus, subpubic angle, subpubic concavity, and ventral arc [10,11]. Considered ancillary indicators for sex determination were skull structures, specifically the nuchal crest, the mastoid process, the supraorbital margin, the glabella, and the mental eminence [12,13]. Age was also estimated by auricularsurface degeneration of the hipbone, based on the degree of transverse organization, granularity, apical activity, retroauricular area degeneration, and auricular-surface porosity [14].
The femur fragments from the skeletal remains were used for aDNA analysis in this study. The surfaces of the bones were removed using a sterilized knife, after which they were exposed to UV irradiation for 20 min and subsequently immersed in 5.4% (w/v) sodium hypochlorite. After the samples were washed with distilled water and absolute ethanol, they were air-dried and pulverized to a fine powder using a SPEX 6750 Freezer/Mill (SPEX SamplePrep, Metuchen, NJ) [15,16]. Bone powder (0.5 g) was incubated in 1 mL of lysis buffer (EDTA 50 mM, pH 8.0; 1 mg/mL of proteinase K; SDS 1%; 0.1 M DTT) at 56 ∘ C for 24 h. Total DNA was extracted with an equal volume of phenol/chloroform/isoamyl alcohol (25 : 24 : 1) and then was treated with chloroform/isoamyl alcohol (24 : 1). DNA isolation and purification were performed using a QIAmp PCR purification kit (Qiagen, Hilden, Germany). The purified DNA was eluted in 50 L of EB buffer (Qiagen) [17][18][19][20].
During sampling or lab work, we always wore protection gloves, masks, gowns, and head caps. Our aDNA lab facilities were set up in accordance with the protocol of Hofreiter et al. [21]. The rooms for aDNA extraction or PCR preparation were physically separated from our main PCR lab. The DNA extraction/PCR preparation rooms were equipped with night UV irradiation, isolated ventilation, and a laminated flow hood. The other procedures for authentic aDNA analysis, suggested by Hofreiter et al. [21], were also followed by us.
Multiplex PCR amplification was done in a 20 L reaction volume, containing 40 ng of template DNA, AmpliTaq Gold 360 Master Mix (Life Technologies, USA), and appropriate concentrations of each primer. Thermal cycling was conducted on a PTC-200 DNA engine (MJ Research): 95 ∘ C for 10 min; 45 cycles of 95 ∘ C for 20 s, 58 ∘ C for 20 s, and 72 ∘ C for 30 s; and a final extension at 72 ∘ C for 10 min. To purify PCR products, 5 L of the PCR products was treated with 1 L of ExoSAP-IT (catalogue number 78201; USB, Cleveland, OH, USA) at 37 ∘ C for 45 min. After that, the enzyme was inactivated by incubation at 80 ∘ C for 15 min.
We used twenty-two single base extension (SBE) primers recommended by Lee et al. [2]. SBE reactions were carried out using a SNaPshot Kit (Applied Biosystems, USA) according to the manufacturer's instructions. Thermal cycling conditions for SBE were as follows: denaturation at 96 ∘ C for 10 sec; annealing at 50 ∘ C for 5 sec; extension at 60 ∘ C for 30 sec. SBE was performed using a PTC-200 DNA Engine (Bio-Rad Laboratories, Hercules, CA). For postextension treatment, reaction mixtures were mixed with 1.0 unit of shrimp alkaline phosphatase (SAP), incubated at 37 ∘ C for 45 min, and followed by heat inactivation at 80 ∘ C for 15 min. The reactants were analyzed by an ABI PRISM 3100 Genetic Analyzer (Applied Biosystems, USA), using GeneMapper ID software, v3.2.1 (Applied Biosystems, USA). SNP scoring at each locus was confirmed by sequencing two samples for each of the observed alleles.
We also did direct sequencing of mtDNA control region of the samples. By sequencing of hypervariable regions I, II, and III, we could get haplotype of the bones and further determined haplogroups of them. The results could be compared with haplogroup determination by coding region SNP analysis on the same samples. Briefly, after quantification was done by NanoDrop ND-1000 Spectrophotometer (Thermo Fisher Scientific, MA, USA), 40 ng of aDNA was mixed with premix containing 1X AmpliTaq Gold 360 Master Mix (Life Technologies, USA) and 10 pmol of each primer (Integrated DNA Technology, USA). PCR conditions used in this study were as follows: predenaturation at 94 ∘ C for 10 min; 45 cycles of denaturation at 94 ∘ C for 30 sec; annealing at 50 ∘ C for 30 sec; extension at 72 ∘ C for 30 sec; final extension at 72 ∘ C for 10 min. PCR amplification was performed using a PTC-200 DNA Engine (Bio-Rad Laboratories, Hercules, CA). Primer sets used for this study were as follows: for 267-bp HV1A, The PCR products were separated on 2.5% agarose gel, stained with ethidium bromide, and then isolated using a Qiagen gel extraction kit (Qiagen, Germany). The sequencing of each amplicon was performed by ABI Prism 3100 Genetic Analyzer (Applied Biosystems, USA), using ABI Prism BigDye Terminator Cycle Sequencing Ready Reaction Kit (Applied Biosystems, USA). The obtained DNA sequences were compared with the revised Cambridge Reference Sequence (rCRS; accession number: NC 012920), to identify the sequence differences between them. The resultant control region mutation motifs were imported into program mtD-NAmanager (http://mtmanager.yonsei.ac.kr/), with which most Korean mtDNA haplotypes can be automatically classified into East Asian mtDNA haplogroups and their subhaplogroups [27], or another web-based program for mtDNA haplogroup analysis (http://dna.jameslick.com/mthap/) [28,29].
In order to guard against any modern DNA contamination of ancient samples, the mtDNA profiles of all of the researchers involved in this study were determined (with the permission of the Institutional Review Board of Seoul National University, H-0909-049-295). They were then compared with the mtDNA profiles from the Joseon skeletons to rule out the possibility of modern DNA contamination.
Results
The sex and age of the samples determined in this study are summarized in Table 1. By direct sequencing of mtDNA control region, every Joseon skeleton could be assigned to relevant existing haplo-or subhaplogroups of mtDNA. In multiplex PCR analyses to detect 21 SNP markers in mtDNA coding region, eight (multiplex I) and seven (multiplexes II and III) primer extension peaks for different haplogroups could be observed. Most samples exhibited no missing or extra assignment peaks in the results (Figures 1 and 2).
When the coding region SNP typing data were further compared with control region direct sequencing results, well-matching patterns can be observed between the outcomes of two methods. The mtDNA haplogroup expected by coding region SNP could fall successfully within the same haplogroup that control region sequencing could make (Table 1, Figure 1). However, as far as haplogroup subclades are concerned, the results of direct sequencing on control region mutation motif were far better than SNP marker analysis on coding region. The haplogroup subclades of the cases numbers 024, 034, 036, 038, 040, 045, 100, and 238 were much successfully determined in direct sequencing of control region than in coding region SNP analysis (Table 1, Figure 2).
The absence of modern DNA contamination could be confirmed by the comparison of mtDNA haplotypes obtained from the current Joseon skeleton and participating researchers' samples. As we did not see any identical sequence between them (Table 1), mtDNA obtained from Joseon samples should have been the endogenous DNA of the ancient people but not the outcome of modern DNA contamination from researchers.
Discussion
Coding region SNP analysis attracts forensic scientists' interest because it is a time-, cost-, and target DNA-saving method for mtDNA analysis of degraded samples [2,[30][31][32][33][34][35]. Multiplex PCR system for coding region SNP used in this study was originally designed for the detection of major haplogroups of forensic samples from East Asia. In the study, the triple multiplex system on coding region SNP markers was proven to be very useful for analysis on forensic materials [2]. Briefly, when they tried to do the coding region SNPbased mtDNA analysis on the long bone or molar samples from about 50-year-old skeletal remains of war victims in the Korean War (1950)(1951)(1952)(1953), mtDNA haplogroup could be determined successfully, even with a limited volume of degraded DNA [2]. In fact, as the triple multiplex system makes very high success rate in haplogroup determination, the combined consideration of coding region SNP markers and control region polymorphism can become a very useful tool for the genetic analysis of degraded samples collected from East Asian populations. This technique is also very suggestive for anthropologists who deal with several hundred-to thousand-year-old samples from archaeological sites. Most of the archaeological human bones were maintained under the worst preservation conditions for a long while. To make matters worse, it is very hard for researchers to get authentic outcomes from the ancient samples because only the limited volume of the samples can be allowed for the analysis of archaeologically important cases. We therefore admit that mtDNA analyses with highly degraded ancient samples are sometimes too risky because sequencing errors commonly occurred during analysis, and they could not be corrected easily by a due course of repeated experiments with a sufficient amount of samples.
In this regard, if another time-, cost-, and sample-saving mtDNA analysis could be also established for the assignment of ancient skeleton samples to relevant existing haplogroups, it will become a convenient method indeed for counterchecking the possible errors hidden in the conventional mtDNA sequencing. Our current results showed that most haplogroup results determined by coding region SNP analysis on Joseon archaeological samples can fall successfully within the same haplogroups that were decided by control region sequencing analysis. In fact, the results confirm coding region SNP analysis' error-screening role in the mtDNA haplogroup determination even for the ancient samples.
However, as for the current ancient samples, we must also admit some technical limits of haplogroup determination based on coding region SNP analysis. Briefly, a coding region SNP analysis on a few samples did not show the subhaplogroup results as completely as observed in the analysis of control region mutation motifs. This means that multiplex SNP analysis could not completely replace the conventional control region mtDNA sequencing, at least for the ancient cases from archaeological fields in South Korea. Even so, considering coding region SNP analysis' superb potential for reconfirmation of haplogroups determined by mtDNA control region sequencing in time-, cost-, and target DNAsaving manner, the use of this method can be expedient for making mtDNA haplogroup determination of archaeological samples much authentic.
Conclusion
Obtaining authentic mtDNA outcomes from archaeological bone samples still remains a significant challenge to concerned researchers. The identification of possible errors in conventional sequencing as quickly as possible is thus significant for authentic mtDNA analysis of archaeologically obtained samples. In this study, we can show that mtDNA haplogroup determination can also be successfully carried out by coding region SNP analysis, in a time-, cost-, and target DNA-saving manner. Although the mtDNA subhaplogroups could not be determined by the coding region SNP analysis as completely seen in the control region sequencing, the former can be used as good supplementary to the latter, for making Although mtDNA haplogroup expected by coding region SNP analysis could fall successfully within the same haplogroup that control region sequencing could make, haplogroup subclade made by SNP analysis (D4b) was not as successful as seen in control region sequencing (D4b2b).
the mtDNA typing of archaeological human bones much authentic. | 3,619 | 2015-08-06T00:00:00.000 | [
"Biology",
"History"
] |
Exploring the Relationship between Digital Inclusive Finance and Traditional Finance in China
,
Introduction
Although digital inclusive finance (DIF) is a product of information technology, its realization relies heavily on government promotion and advocacy.Conventional formal credit access is biased toward large enterprises with substantial assets, as it relies on a credit model that favors businesses with significant assets.Consequently, this restricts the opportunities for small and medium-sized enterprises (SMEs) or businesses with asset-light models Provinces also issued "Implementation Opinions on Promoting Inclusive Financial Development" to comply with notification requirements.Since 2017, large and medium-sized commercial banks have established dedicated departments for inclusive finance.The period since 2016 has witnessed significant policy support for the advancement of DIF.According to the "Report on Financial Inclusion Innovation in China ( 2020)", digital finance has emerged as the predominant means of promoting financial inclusion in China (Zeng et al., 2020;Liu et al., 2021).
The implementation of various policies since 2016 not only facilitates the sustainable development of finance but also provides an opportunity for this study to investigate the relationship between DIF and traditional finance.While formal financial institutions are primarily driven by policies, achieving success in the digital transformation of traditional financial institutions requires a substantial investment of time.Financial technology (FinTech) companies have played a pioneering role in advancing digital financial inclusion (Wu, 2015;Xie et al., 2015;Bollaert et al., 2021 ).Consequently, these policies have created a favorable policy and market environment for FinTech companies.Accordingly, we can define "digital inclusive finance" as the financial development propelled by FinTech companies, and "traditional finance" as the financial development dominated by conventional financial institutions during the initial stages of policy implementation (Huang and Huang, 2018;Song et al., 2022).This paper examines the impact of multiple policies on DIF as exogenous factors by employing methodologies proposed by Greenland et al. (2019) and Li et al. (2022).Generalized difference-in-difference (generalized DID) is utilized to examine the impact of policies in regions with varying levels of traditional finance.This approach assumes that the nationwide implementation of a policy will uniformly impact all regions of the country, resulting in a treatment group without a control group.However, considering variations in the degree of policy effects across regions, the DID method can still be employed.Although there are no discernible regional characteristics, the impact of national policies may vary based on varying traditional fiscal levels across different regions.Therefore, this paper incorporates the pre-policy implementation level of traditional finance as a variable to measure regional impacts and assesses changes in DIF before and after policy introduction.This process also clarifies the relationship between DIF and traditional finance.
The main contributions of this paper are as follows: Firstly, the emergence of DIF has ignited extensive discourse in both practice and academia regarding its relationship with traditional finance.This study organizes existing perspectives and conducts further analysis to enhance theoretical research on their relationship.Secondly, this article utilizes the introduction of diverse policies that promote inclusive finance since 2016 as a quasi-natural experiment to study its impact on the regional development of DIF and effectively evaluate the effects of these policies.Thirdly, the discussion on the relationship between DIF and traditional finance mainly remains at a theoretical level, with limited empirical research.Drawing upon panel data from prefecture-level cities in China spanning from 2011 to 2019, this paper employs generalized DID estimation to empirically investigate the extent to which traditional finance influences DIF development, thereby providing empirical evidence for exploring their relationship.
Literature Review
Before evaluating the implementation impact of a series of policies on digital inclusive finance, it is imperative to clarify the theoretical relationship between digital finance and traditional finance.On one hand, the formulation of policies regarding DIF should consider the regional level of traditional finance.On the other hand, existing research has presented diverse perspectives on the "substitution relationship" and "complementary relationship" between DIF and traditional finance, but a consensus regarding their interconnection remains elusive.
In China, the view of "substitution relationship" primarily emerged during the era of Internet finance i .Since the inception of Yu'e Bao ii , there has been a proliferation of Internet financial products, resulting in a substantial outflow of commercial bank deposits to Internet-based financial enterprises (Wang and Zhang, 2015).Gopal and Schnabl (2022) also observed that following the financial crisis, there was a notable surge in loan volume for FinTech enterprises in the United States, while traditional bank credit to small businesses experienced a decline; however, these two shifts in credit nearly balanced each other out.This implies an inverse relationship between digital finance and traditional credit.According to Wolfe and Yoo (2017), peer-to-peer lending has resulted in a decrease in loan volume for small commercial banks, particularly in terms of personal loans.However, Balyuk et al. (2020) suggested that newly established FinTech firms often displace loans from large banks rather than those from small banks.Leveraging information technology and Internet platforms, Internet finance can effectively reduce transaction costs, enhance financial efficiency, and mitigate information asymmetry between transacting parties (Li et al., 2021).In addition to serving as a direct trading platform for capital supply and demand, Internet finance also effectively mobilizes idle funds to meet small-scale financing needs.While competing with banks for high-value customers, it also possesses an inherent advantage in serving the traditionally excluded "long-tail group" (i.e., the large low-income group) (Berg et al. 2019;Liu et al., 2021;Li et al., 2021), thereby presenting significant challenges to traditional finance.Xie and Zou (2012), who first introduced the concept of "Internet finance" in China, contend that modern information technology, epitomized by the Internet, has had a disruptive impact on traditional financial models.They predicted that Internet finance would become an outstanding financial paradigm in the future.Xie (2015), Tang (2019), andBollaert et al. (2021) argued that the Internet has the potential to expand the boundaries of trading, and in the future, Internet finance could facilitate financial transactions without relying on traditional intermediaries.This may result in displacing traditional banks and capital markets, leading to a situation akin to Walras's general equilibrium where financial intermediation becomes redundant.Therefore, following the substitution theory, Internet finance is considered a disruptive financial paradigm that presents significant challenges to traditional models and competes with them for market dominance (Xie, 2015;Wolfe &Yoo, 2017;Erel & Liebersohn, 2020).
A growing number of scholars support the notion of a "complementary relationship," suggesting that the development of DIF is dependent on traditional finance and that the pace of digital finance's advancement corresponds to the initial progress level of traditional finance (Cornelli et al., 2020;Wang et al., 2021).Digital finance represents an innovative paradigm, encompassing advancements and enhancements of financial products and services through the integration of digital technology.It is important to note that DIF operates within the framework of conventional finance without seeking to undermine or supplant it (Jagtiani & Lemieux, 2019).The primary role of FinTech banks is to enhance the supply of credit and address the inherent limitations of traditional finance, particularly in regions facing intense competition from conventional financial institutions (Jagtiani & Lemieux, 2018).Yao and Shi (2017) contended that digital finance represents a gradual evolution of traditional finance rather than a disruptive force, thereby exhibiting path dependence on the latter.From the perspective of financial knowledge spillover, traditional finance can serve as a reservoir of talent and expertise to facilitate financial innovation.The study conducted by Wang et al. (2021) underscores the pivotal role of traditional finance in fostering DIF, encompassing four fundamental dimensions: capital provision, dissemination of financial knowledge, enhancement of infrastructure, and stimulation of financial demand.The provision of financial resources by traditional finance is essential.In well-established regions, a diverse range of financing channels can be offered to facilitate the growth of FinTech enterprises and establish an enabling financial ecosystem (Haddad & Hornuf, 2019).Generally, regions with robust traditional financial systems have greater financial needs, which provide a broader market space for the growth of DIF (Cornelli et al., 2020).According to Xu et al. (2022), DIF has effectively complemented traditional finance in alleviating credit rationing and liquidity constraints for rural residents, based on their research on China's rural financial market.Moreover, it has contributed to narrowing the urban-rural income gap in conjunction with traditional finance (Song et al., 2022), demonstrating complementary functions in financial services.
The perspective that lies between the "substitution relationship" and the "complementary relationship" posits that the primary function of Internet finance development is to alleviate financial risks arising from information asymmetry and enhance financial efficiency, rather than undermining traditional finance (Wang & Zhang, 2015).The rapid expansion of China's Internet finance sector can be attributed to the regulatory arbitrage strategies employed by Chinese Internet finance companies, capitalizing on existing deficiencies in China's financial system such as interest rate controls, credit allocation mechanisms, and discriminatory lending practices toward NSOEs.Moreover, the informal financial sector in China lacks comprehensive regulatory measures (Buchak et al., 2017;Huang & Huang, 2018;Cornelli et al., 2020).Although financial technology enterprises can cater to the financial needs of rural residents and small and micro-enterprises, bridging the gaps left by formal finance, Internet finance may encounter challenges and struggle to establish itself as a mainstream form of finance in the future amidst the deepening of financial system reform and gradual implementation of regulatory measures.
Theoretical Analysis and Research Hypothesis
The substitution view characterizes digital finance as a disruptive financial innovation model that differs significantly from traditional finance.While traditional finance relies on financial institutions or markets for mediation, digital finance is expected to evolve into a financial model that operates without the need for intermediaries or markets, potentially replacing traditional finance in the future.However, this perspective fails to acknowledge the indispensability of financial regulation and the fundamental role played by financial intermediation.
In China, the primary objective of financial regulation is to ensure the maintenance of financial stability and prevent systemic financial risks.To achieve this goal, traditional finance is subjected to stringent regulations that restrict the allocation of financial resources and impact the enhancement of financial efficiency.FinTech enterprises, such as Ant Financial, are quasi-financial institutions that operate without financial licenses and are not directly supervised by regulatory authorities.The rapid emergence of these novel financial institutions can be attributed to the exploitation of regulatory loopholes (Huang & Huang, 2018;Huang & Tao, 2019;Cornelli et al., 2020).However, as their scale expands, ensuring financial security becomes an increasingly pressing concern.This will inevitably lead to the reinforcement of regulatory measures and industry rectification, ultimately undermining their advantage in exploiting regulatory loopholes (Wang & Zhang, 2015).Since 2016, the Chinese government has gradually implemented policies to regulate the development of Internet finance; however, owing to technological and other factors, the regulatory framework for it remains considerably less stringent compared to that of traditional finance (Zetzsche et al., 2019;Bollaert et al., 2021).
In terms of the fundamental functions of financial intermediation, China's traditional financial institutions are predominantly government-controlled, and government endorsement significantly enhances their credit intermediation function.However, in digital finance, FinTech enterprises serve as the primary players with Internet platforms as carriers.While digital technology reduces information asymmetry between lenders and borrowers, it does not necessarily increase trust between them; in fact, it may even lower the threshold for fraud (Bollaert et al., 2017).Therefore, FinTech enterprises can only act as "information intermediaries" rather than "credit intermediaries" (Huang & Tao, 2019).This also implies that digital finance cannot entirely replace traditional finance.
Although the conclusion that the disappearance of regulatory arbitrage advantages will limit the development of digital finance contradicts the substitution view, it fails to acknowledge the integration trend between traditional finance and DIF, thereby overlooking their interdependence.In contrast, the complementarity view emphasizes the relationship between inheritance and development within both digital finance and traditional finance, aligning more closely with current developmental realities and future trends in this domain.
We contend that DIF has been rooted in conventional finance since its inception.First of all, the development of digital technology has been facilitated by the sustained support of traditional finance for science and technology in China.Guided by the principle that "Science and technology constitute a primary productive force," China has consistently increased financial institutions' backing for scientific research, gradually establishing a long-term mechanism for the banking industry to support technological enterprises (Hao, 2017).The emergence and growth of DIF are direct results of the feedback from science and technology in finance.Secondly, digital finance embodies an innovative financial paradigm, necessitating specific prerequisites for its market acceptance (Sandhu & Arora, 2022).In terms of financial infrastructure, the rapid economic growth in China has resulted in a swift expansion of the banking network.The physical branches dispersed throughout the nation have not only enhanced residents' accessibility to financial services (Leyshon & Thrift, 1993), but also progressively enriched their financial knowledge, improved their financial literacy, and fostered positive financial habits.These factors are pivotal in bolstering public acceptance of financial innovation.For instance, as emphasized by Moorthy (2020), customers' perception of security significantly influences their inclination toward adopting mobile payment services.Consequently, this can diminish households' self-exclusion and stimulate their demand for financial products and services (Zhang & Yin, 2016;Guo & Wang, 2020;Song et al., 2022).In terms of information technology, digital finance has emerged based on the achievements made in the electronization and informatization of traditional financial institutions (Shahrokhi, 2008;Sandhu & Arora, 2022).Significant advancements have been achieved in the realm of e-banking services, including online banking and mobile banking, prior to the emergence of Internet finance businesses.Although banks still rely on offline channels for customer acquisition, the primary goal of developing e-banking was to alleviate pressure on bank branches rather than digitize operations.However, e-banking has enabled financial services to withdraw from physical channels and has laid a foundation for the widespread acceptance of digital financial business by the public.
Given the profound integration of finance and technology, the digital transformation of financial institutions has emerged as an overarching trend.To capitalize on this opportunity and gain a competitive edge, traditional financial intermediaries are actively embracing FinTech and leveraging data and digital technology to reshape their business models (Gomber et al., 2017;Drasch et al., 2018;Jagtiani & Lemieux, 2018).Yang (2020) highlighted that bank digital transformation involves two levels: "comprehensive transformation," transitioning from partial breakthroughs to overall development, and "in-depth transformation," shifting from general fields to specific domains.The former refers to the digital transformation of all aspects of business, including its form, management practices, and operational models.The latter focuses on enhancing value by shifting service scenarios from traditional financial settings to "finance + life" contexts and broadening the customer base to include both bank clients and online users.The transformation will take time; however, traditional financial institutions will remain crucial in driving digital finance advancement (Hao, 2017).As stated in the "Report on the Development of China's FinTech and Digital Inclusive Finance (2020)", China's future digital financial system will primarily rely on banking financial institutions, with support from Internet enterprises and complementation from non-bank financial institutions.In 2022, the China Banking and Insurance Regulatory Commission (CBRC) issued "Guiding Opinions on the Digital Transformation of Banking and Insurance Industries", marking it the first authoritative document specifically targeting digital reform in banking.This document effectively presents principles, an operational framework, and guidelines for digitization across financial institutions.Therefore, the substitute view suggests that FinTech companies will play a crucial role in the future development of DIF, disregarding the potential transformative impact on traditional financial institutions and asserting that digital finance and traditional finance are inherently distinct and incompatible.However, this perspective lacks foresight.
Irrespective of perspectives, it is widely acknowledged that the development of regional DIF is closely intertwined with the initial state of traditional finance.Consequently, the efficacy of policies pertaining to DIF will vary across regions based on different levels of traditional finance.Building upon the aforementioned analysis, this study suggests a complementary association between DIF and traditional finance.Given this proposition, the following research hypothesis is proposed.
H1: Where the initial level of traditional finance is higher, the development of digital inclusive finance will be faster, and the impact of digital inclusive finance policies will be more significant.
Sample Selection and Data Sources
The prefecture-level data used in this paper are mainly from the China City Statistical Yearbook, the provincial-level data are from the China Statistical Yearbook, and the digital inclusive finance index is obtained from the Institute of Digital Finance, Peking University.After removing the missing values of the samples, a total of 279 prefecture-level cities are included iii .Since the data on digital inclusive finance started in 2011, the sample period for this paper is from 2011 to 2019.
Construction of Benchmark Model
The issuance of Notice, Principles, and a series of policies since 2016 has become an external driving force for the rapid expansion of digital inclusive finance.Despite the challenges in distinguishing between the treatment group and control group, regional disparities in traditional finance can lead to variations in policy outcomes.Therefore, this study employs the methodologies proposed by Greenland et al. (2019) and Li et al. (2022) to investigate the impact of relevant policies on the development of DIF in regions with varying levels of traditional finance using the generalized DID approach.The model is set as follows: In model (1), the dependent variable digital_fin represents the level of DIF, while the independent variable post×loan_m is an interaction term that measures the policy effects of digital inclusive finance-related policies on prefecture-level cities with varying levels of traditional finance.X serves as a set of control variables.By controlling for fixed effects at the city and time levels, we can account for differences between cities and changes caused by other macro factors before and after policy implementation.This model can not only examine the policy effects but also capture the dependence of DIF on traditional finance.
Variable Selection
1) Explained variable.Digital inclusive finance (digital_fin) is a complex concept that cannot be fully captured through a singular perspective.The Peking University Digital Financial Inclusion Index of China (PKU-DFIIC), compiled by the Institute of Digital Finance, Peking University, utilizes transaction data from Ant Financial Services Group (Ant Financial) iv to establish an index based on three dimensions: breadth of coverage (coverage), depth of adoption (usage), and level of digitalization (digitization).Due to the challenges in obtaining data and matching data from various institutions, only one representative FinTech institution is used as the data source for this index.Nevertheless, it still reflects the development trends and regional differences among regions, making it the most widely utilized digital finance index in China (Guo et al., 2020;Luo et al., 2022).
2) Explanatory variable.The explanatory variable is the interaction term post×loan_m, which is derived as follows: Firstly, considering China's bank credit-dominated financial structure, we utilize "the ratio of urban financial institutions' loan balance to GDP at year-end" as a measure of each region's level of traditional finance.We calculate the average value of this measure (loan_m) before the implementation of policies (2011)(2012)(2013)(2014)(2015).A higher loan_m indicates a higher initial level of traditional finance.Secondly, 2016 marks a significant turning point and the variable post is utilized to indicate the temporal occurrence of this event.Specifically, post=0 denotes the period prior to 2016, while post=1 represents the period from 2016 onwards (including 2016).Thirdly, we utilize the interaction term post×loan_m to quantify the environmental changes encountered by DIF in regions with varying degrees of traditional finance before and after the implementation of policies.Notably, despite the significant growth of China's direct financing market in recent years, due to the dominant position of bank credit in its financial structure and practical limitations on the segmentation of regional stock and bond markets, when evaluating traditional finance at the city level, bank credit still serves as a proxy indicator (Li et al., 2013).
3) Control variables.X represents a set of control variables, and to mitigate the influence of policy changes on these variables, their baseline is established as the initial value in 2011.The control variables consist of six factors: (a) level of economic development (lngdp), which is measured by the logarithm of each city's real GDP deflated by its GDP deflator to eliminate inflation.This variable reflects the principle that financial services serve the real economy and indicates that regions with higher levels of economic development have a stronger demand for finance.(b) Industrial structure (structure), represented by the proportion of tertiary industry in GDP, also affects the financial demand of a region.(c) Regional openness (open) is calculated by dividing the total import and export volume of the province where the city is located by its GDP, due to the unavailability of city-level import and export data.This variable reflects how regional openness influences financial development.(d) The degree of government intervention (fiscal), expressed as budgetary fiscal expenditure divided by GDP, plays an important role in DIF development.Additionally, (e) the mobile payment penetration rate (mobile) is represented by the ratio of mobile phone users to the total population in a given region while (f) the level of Internet development (tele) is measured by the ratio of telecommunications service revenue to GDP.
Descriptive Statistics
The descriptive statistics of the main variables are presented in Table 1.The DIF variable, digital_fin, exhibits a mean value of 165.2668 with a range from 17.02 to 321.6457.Specifically, the mean values of the sub-dimensions coverage, usage, and digitization of the PKU-DFIIC are 155.5299,163.0097, and 201.5243 respectively, which align with the total index but exhibit significant variations among samples.It is evident that the mean values have significantly increased from their initial values (118.0597, 113.3403, 110.8546, and 146.7338) to their subsequent values (224.1483, 208.1531, 228.0628, and 269.8646) with the introduction of relevant policies.This indicates a rapid development in DIF across various dimensions.The variable loan has an average value of 1.3173 during the sample period, with a minimum value of 0.3711 and a maximum value of 9.6221.This suggests significant heterogeneity in the level of traditional finance across regions and implies that regional differences may impact the effectiveness of implementing DIF policies.
Empirical Results
Table 2 presents the regression results of Model (1).Column (1) depicts the impact of policy implementation on DIF development in regions with varying levels of traditional finance.The interaction term is scaled by a factor of 100 to standardize its magnitude and facilitate the interpretation of the regression results.Our findings suggest that, after controlling for other covariates, the coefficient associated with the interaction term post×loan_m exhibits a positive and statistically significant relationship with traditional finance.This implies that the impact of regional policies becomes increasingly significant as the level of development in traditional finance rises.Since 2016, DIF has experienced robust growth due to improvements in policy and market environments.In regions with higher levels of formal credit, the growth of DIF is more rapid, indicating that a city's initial level of formal credit serves as a solid foundation for financial innovation and development.Among the control variables, the level of urban economic development and the degree of regional openness significantly contribute to fostering DIF.
The regression results for the explanatory variable and control variables on each dimension of DIF are presented in columns ( 2), (3), and ( 4) respectively.The coefficient of the interaction term post×loan_m is significantly positive at a 1% confidence level, indicating that an improved development environment greatly enhances the reach of DIF.This effect is particularly pronounced in regions with robust traditional financial systems, where the growth rate of DIF becomes even more substantial.Similarly, Column (3) presents the results for the depth of DIF adoption.The coefficient of post×loan_m in this column is also significantly positive at a 1% confidence level, suggesting that users in regions with higher levels of traditional financial services have exhibited greater acceptance and adoption of DIF since 2016.However, in Column (4), the coefficient of the interaction term is not statistically significant.This suggests that there was no discernible disparity in the level of DIF development among regions after 2016.
Analysis of Results
The breadth of coverage includes the number of Alipay accounts per 10,000 people, the proportion of Alipay users who link their Alipay account to bank cards, and the average number of bank cards linked to each Alipay account.To open an Alipay account, it is necessary to possess a bank account because Chinese FinTech companies currently cannot accept deposits or conduct large-scale transfers.The DIF business aims to offer customers innovative digital financial products and services by leveraging the existing customer bases of banks, necessitating collaboration with traditional financial institutions and relying on the advancement of traditional finance.
The depth of DIF adoption encompasses the frequency of utilizing diverse services, including payment, money market funds, loans, insurance, investment, and credit investigation.Acquiring these services requires not only economic resources but also necessary financial literacy and habits.According to the 2014 World Bank survey, a lack of financial knowledge was identified by 78% of respondents as the primary barrier for households to access financial accounts.In regions with robust traditional financial institutions, users not only gain access to a diverse range of financial accounts and services but also have the opportunity to acquire extensive financial knowledge and gradually cultivate investment habits.Moreover, innovative products and services based on digital technology are more readily embraced by users in these regions.
The degree of digitalization in inclusive finance refers to the mobile, affordable, credit and facilitation of financial services with a primary focus on their reliance on digital technology.The statistical insignificance of this dimension suggests that digitalization in inclusive finance is not dependent on traditional finance.While traditional finance can provide financial support for the development of digital technology, the extent of digitalization in inclusive finance depends more on the integration between information technology and finance rather than being limited by traditional credit levels.In regions with limited growth in digital technology, the inherent influence of traditional finance can actually pose significant obstacles to the establishment of digitally inclusive financial institutions and hinder banks' digital transformation (Tang, 2019).
Based on empirical evidence, a positive correlation has been observed between the development of DIF and traditional finance.In regions with higher initial levels of traditional finance, policy-driven promotion of DIF leads to faster development.The empirical findings are consistent with our theoretical analysis, indicating that regions with higher initial levels of traditional finance will experience accelerated growth in DIF, resulting in a more pronounced effect of DIF policies.This confirms hypothesis H1 and supports the complementary perspective.
Robustness Test
In the empirical section, this paper employs "the ratio of financial institutions' credit balance to GDP" as a metric for assessing the level of traditional finance.To further evaluate China's traditional finance development from various perspectives and test the robustness of regression results by substituting explanatory variables, this paper also utilizes indicators such as "financial correlation rate" and "credit ratio of non-state-owned economic sectors.
The regression results presented in Table 3 demonstrate the utilization of post×fin_m as an explanatory variable, where fin_m represents the average financial correlation rates before 2016 (2011)(2012)(2013)(2014)(2015) in each region.Financial correlation rates are measured by "the ratio of total deposits and loans of financial institutions to GDP at year-end".The interaction term post×fin_m captures variations in policy and market environments between regions with distinct pre-existing financial correlation rates before and after the introduction of policies in 2016.The results for DIF and its dimensions in columns (1) to (4) reveal that the interaction term has a significant positive impact on the overall index as well as its two dimensions: coverage breadth and adoption depth.However, there is no significant relationship between the degree of digitization in DIF and the financial correlation rate.These findings align with previously reported empirical results.In the robustness test results presented in Table 4, this paper employs the ratio of credit to GDP of non-state-owned economic sectors as an indicator of the development level of traditional finance.Following research methodologies utilized by Aziz and Duenwald (2002) and Li et al. (2013), it assumes that the bank credit is allocated proportionally between the state-owned and non-state-owned sectors, with both sectors contributing equally to overall output.The allocation proportion can be estimated based on the correlation between bank credit and the share of assets held by SOEs.The explanatory variable is "the proportion of assets in state-owned economic sectors" (Soe).Due to data availability, we use a proxy variable that represents "the proportion of assets in state-owned and holding industrial enterprises within total industrial enterprise assets" for provinces where prefecture-level cities are located from 2010 to 2019.The explained variable is the ratio of credit balance to GDP in various prefecture-level cities while controlling for fixed effects specific to each city.
To address the issue of fluctuating levels of bank credit resulting from different financial deleveraging policies during the sample period, time-fixed effects are also incorporated into the model.The estimation equation is presented in Formula (2).To address the issue of serial correlation, the formula incorporates a first-order autoregression of the error term.
Among them, βSoe can measure the proportion of credit allocated to the state-owned economic sector in relation to GDP.The ratio of credit allocation to the non-state sector of the economy as a share of GDP denoted as ploan, is constructed using a constant term, regional and time dummy variables, and an error term.
Table 4 presents the regression results using post×ploan_m as an explanatory variable, where ploan_m represents the average credit value of non-state-owned economic sectors in each region before 2016 (2011-2015).
Based on the findings presented in Table 4, it is evident that significant differences exist in the development of the DIF index and its three dimensions among regions with varying proportions of credit for non-state-owned economic sectors prior to and after a policy change in 2016.In contrast to previous findings, regions with a higher proportion of credit in non-state-owned economic sectors have witnessed not only more rapid development in the breadth and depth of adoption but also accelerated progress in digitalization.This can be attributed to the dependence of digitization on information technology advancement.As mentioned in the theoretical section, given that China's FinTech enterprises are predominantly privately owned, an increase in the credit ratio of non-state-owned economic entities can provide significant financial support for private enterprises, especially those operating in FinTech.
Table 4.The interaction term for the share of credit to the non-state sector of the economy is used
Financial Environment
The benchmark regression results reveal significant regional disparities in the policy impact of DIF.Regions with higher levels of traditional finance exhibit a more rapid advancement of DIF, indicating a complementary relationship between the two and highlighting the reliance of DIF on traditional finance for its growth.To enhance our understanding of the policy effects across regions, this study incorporates regional environmental variables and investigates how the regional environment moderates the supportive role of traditional finance in DIF.Given that DIF encompasses both financial and technological dimensions, this study examines how regional financial and technological contexts influence the effectiveness of DIF by establishing Model (3) as an extension of the benchmark model.
The variable envir represents the environment of DIF in prefecture-level cities.The financial environment includes indicators such as the level of social credit (credit), degree of marketization (market), and level of banking competition (compete).Meanwhile, the technological environment includes factors like the level of technological innovation (innovate), Internet penetration rate (netuser), and mobile payment penetration rate (mobile).A detailed description of these variables is provided in Table 5.Firstly, credit serves as a fundamental prerequisite for the execution of all contracts, and the development of finance is inseparable from its driving force, particularly in inclusive finance (Guiso, 2012).Compared to traditional finance, digital finance faces greater financial risks and encounters more challenging regulatory obstacles.Therefore, the progress of regional inclusive digital finance is more sensitive to the credit environment.
Secondly, the level of regional marketization exerts an influence on the extent of support from traditional financial institutions toward DIF, thereby impacting the efficacy of policy.The advancement of financial systems is closely intertwined with economic development, and regions characterized by a high degree of marketization demand elevated standards for financial services, thus fostering an environment conducive to nurturing innovative financial intermediaries (Bos et al., 2013;Mao, 2021).Moreover, regions exhibiting a heightened level of marketization typically possess more transparent and open market mechanisms that facilitate the growth of digital finance (Wang et al., 2021).Thirdly, the impact of competition within the regional banking industry should also be taken into consideration.On one hand, a higher degree of banking competition signifies a robust financial foundation and a relatively balanced financial market structure in a region, thereby fostering an environment conducive to financial innovation (Jagtiani & Lemieux, 2018;Song et al., 2022).On the other hand, intensified competition among commercial banks results in the growth of traditional finance and consolidation of monopoly power, which hinders the survival and expansion of innovative financial models (Bos et al., 2013).
The coefficient value of the interaction term post×loan_m×credit in Column (1) of Table 6 exhibits a significantly positive relationship at the 1% significance level.This finding suggests that regions characterized by higher levels of social credit exhibit accelerated development in DIF, thereby demonstrating the enhanced efficacy of policies.The empirical results in Column (2) demonstrate that the coefficient value of the interaction term post×loan_m×market is positively and significantly associated at the 1% level, indicating that as regional marketization levels increase, traditional finance provides stronger support for DIF, thereby amplifying policy effects.According to the empirical findings presented in Column (3), there is a positive trend observed for the coefficient of post×loan_m×compete, suggesting that regions with weaker bank competition tend to receive stronger support from traditional finance in facilitating digital finance.However, this result lacks statistical significance due to counterbalancing dual effects stemming from bank competition.Successful implementation of digital finance policies is more likely to occur within a financial environment promoting regional trust and inclusiveness., 1983;Hannon & McDowell, 1984).The integration of digital technology into financial services not only substantially decreases information and transaction expenses but also empowers financial institutions to transcend geographical and temporal limitations via Internet platforms.Particularly, the proliferation of smartphones has facilitated financial service providers to reach an unprecedented number of customers, thereby expanding the scope of financial services extensively (Moorthy et al., 2020).In China, mobile payment has emerged as a prominent digital financial business (Huang & Tao, 2019).Building upon this premise, this study investigates the influence of regional levels of scientific and technological innovation, Internet penetration rate, and mobile payment level on the policy efficacy of DIF.
Based on the findings presented in Table 7, specifically in Column (1), the coefficient estimate of the interaction term post×loan_m×invent exhibits a significantly positive association at a significance level of 1%.This implies that regions with higher technological innovation will receive increased support from traditional financial institutions to promote DIF, thus facilitating the more effective implementation of DIF policies.The coefficient value of post×loan_m×netuser in Column (2) also shows a significantly positive association at the 1% level, indicating that regions with higher Internet penetration rates demonstrate greater efficacy in implementing DIF policies and receive stronger support from traditional financial institutions toward DIF.In addition, the interaction term post×loan_m×mobile is found to be significantly positive at the 10% level in Column (3), suggesting that regions with higher levels of mobile payment exhibit superior implementation outcomes for DIF policies.These findings suggest that a favorable regional science and technology environment can have a positive impact on the effectiveness of implementing digital inclusive financial policies.
Conclusions
The relationship between DIF and traditional finance is currently a prominent issue that has garnered significant attention in the academic field.Since 2016, the development of DIF has been included in national strategic plan, contributing significantly to enhancing financial sustainability through relevant policies.This paper employs research methods from Greenland et al. (2019) and Li et al. (2022) to examine changes in the policy environment around 2016, testing variations in the level of DIF across regions with varying levels of traditional finance.
Based on the empirical findings, this study draws the following conclusions: Firstly, the results of this study demonstrate that the initial level of traditional finance in a region plays a supportive role in fostering DIF, thereby substantiating the complementary perspective.Secondly, while traditional finance significantly influences the breadth of coverage and depth of adoption of DIF, its impact on the level of digitization is limited.This may be attributed to the fact that the digitization level relies more on local advancements in financial technology and integration between technology and finance, rather than traditional finance itself.Thirdly, the effectiveness of implementing regional policies on DIF is contingent upon the local financial and technological milieu.The traditional finance plays an important role in enhancing DIF and achieving better policy outcomes in regions with favorable financial and technological conditions.The research findings of this article are consistent with the arguments put forth by Jagtiani and Lemieux (2019), Cornelli et al. (2020), and Wang et al. (2021), thereby providing support for the view of a "complementary relationship."
Implications
The focus of China's financial reform lies in "inclusive finance", with DIF emerging as the predominant form in its current development.This study confirms that the growth of regional DIF is dependent on the level of traditional finance and is influenced by financial and technological environments.This brings us to the following policy implications.In areas where traditional finance is well-established, the government should strategically leverage existing advantages and actively guide traditional financial institutions toward digital transformation.Additionally, it should enhance local credit systems and market mechanisms while continuing to strengthen regional science and technology information infrastructure.In areas lacking adequate traditional financial services, local governments must initiate financial reforms, establish robust financial infrastructure, and create an enabling environment for FinTech advancements.Additionally, they should actively leverage the enthusiasm and drive of financial institutions to develop a comprehensive range of innovative financial services.
Limitations of the Study and Recommendations for Future Research
Due to the challenges in measuring the developmental level of DIF from a financial institution perspective, this paper utilizes a regional-level DIF index to assess its development level.Additionally, we incorporate policy effects to examine the relationship between DIF and traditional finance.While this approach provides valuable insights into their interplay at a regional level, it falls short in uncovering the direct impact and micro mechanisms through which traditional finance influences DIF.In future research endeavors, conducting comparative analyses of these two models of finance at an enterprise level would yield more compelling findings.
In addition, despite uncovering the complementary relationship between traditional finance and DIF, this paper lacks in-depth empirical research on how traditional finance supports the development of DIF.The impact mechanism of traditional finance on DIF remains unclear, necessitating future research to provide more empirical support and delve into mechanistic studies.
Notes
i Note 1.The rapid development and extensive application of digital technology in the financial sector have given rise to various concepts, including Internet finance, FinTech, and digital finance.FinTech emphasizes the technical aspects of financial innovation, while Internet finance focuses on the financial activities of Internet enterprises; conversely, digital finance places greater emphasis on leveraging digital technology for financial applications (Huang & Huang, 2018;Liu et al, 2021).Despite their subtle distinctions, these three definitions share robust inclusive financial characteristics.Recognizing the inherent connection between digital finance and inclusive finance functions, a form of inclusive finance known as "digital inclusive finance" has gradually emerged.
ii Note 2. Yu 'e Bao, an Internet money fund introduced by Ant Financial Service Group in 2013, has fundamentally transformed Chinese people's perception of Internet-based financial management.It has
Table 1 .
Descriptive statistics of main variables
Table 2 .
The impact of traditional finance on digital inclusive finance
Table 3 .
The interaction term for financial correlation rates is used
Table 5 .
Description of environmental variables
Table 6 .
Impact of financial environment on policy effects
Table 7 .
The impact of the technological environment on policy effects | 9,086 | 2024-05-07T00:00:00.000 | [
"Economics",
"Business"
] |
SWITCHES: Searchable Web Interface for Topologies of CHEmical Switches
Bistable biochemical switches are key motifs in cellular state decisions and long-term storage of cellular ‘memory’. There are a few known biological switches that have been well characterized, however these examples are insufficient for systematic surveys of properties of these important systems. Here we present a resource of all possible bistable biochemical reaction networks with up to 6 reactions between 3 molecules, and 3 reactions between 4 molecules. Over 35,000 reaction topologies were constructed by identifying unique combinations of reactions between a fixed number of molecules. Then, these topologies were populated with rates within a biologically realistic range. The Searchable Web Interface for Topologies of CHEmical Switches (SWITCHES, https://switches.ncbs.res.in) provides a bistability and parameter analysis of over 7 million models from this systematic survey of chemical reaction space. This database will be useful for theoreticians interested in analyzing stability in chemical systems and also experimentalists for creating robust synthetic biological switches. Availability and Implementation Freely available on the web at https://switches.ncbs.res.in. Website implemented in PHP, MariaDB, Graphviz, and Apache, with all major browsers supported. Supplementary Information Not applicable.
INTRODUCTION
Biochemical switches are key motifs in cellular state decisions and long-term storage of cellular 'memory' (Angeli et al., 2004, Craciun et al., 2006, Markevich et al., 2004, Tyson et al., 2003. Such switches are bistable, that is, they have the property that they can exist stably in either of two states, with a third state acting as a transition or saddle point. Two broad strategies to characterize bistable systems are the theory-based (e.g., Soranzo andAltafini, 2009, Craciun andFeinberg, 2006) and the catalog/database approach (Ramakrishnan and Bhalla, 2008). In the former, the graph structure of the network is used to determine whether the network has the potential for bistability irrespective of specific rate constants. The theoretical results underlying these methods are sometimes restrictive in their scope (e.g., the chemical reaction network theory methods used in (Soranzo and Altafini, 2009) do not permit zero concentrations at the steady states. Furthermore, these methods only guarantee multi-stationarity, not multi-stability and cannot make a conclusive determination of potential for bistability for some examples. In the current study we adopt the database approach. Here we catalog specific, fully defined chemical networks using linear algebra criteria for bistability, that is, two stable states and a saddle point.
RESULTS
We previously conducted a systematic survey of chemical kinetic models to generate all possible reaction topologies up to a certain size, and assess their ability to form bistable switches (Ramakrishnan and Bhalla, 2008). The current database expands this survey to include over 7 million models sampled from 35,000 reaction topologies. encoded into a relational database form. 3561 of these topologies have at least one set of parameters that form a bistable model. There are over 33,000 bistable models, all of which have been encoded in SBML (Systems Biology Markup Language, Hucka et al., 2003). We provide interfaces for visualizing the chemical topology of each model in SBGN (Systems Biology Graphical Notation, Novère et al, 2009) format, and a way to navigate through relationships.
We generated chemical kinetic models using a small 'alphabet' of basic chemical reactions, assembling permutations of entries from this alphabet with permutations of molecules to participate in the reactions, eliminating isomorphisms, sampling parameters for each system, computing steady states, and then classifying them using linear algebraic criteria.
The database provides access to three key features of chemical space (Fig 1A). First, it organizes reaction systems by their topology and topology class. The topology of the chemical system is the configuration of how molecules react with each other, independent of the rate information. The topology class contains all the topologies with the same number of reactants and reactions. Second, it organizes the relationships between bistables. One of the key findings of (Ramakrishnan and Bhalla 2008) was that bistable chemical systems tend to be derived from simpler systems that are also bistable. In the SWITCHES database, we provide a graphical interface for users to navigate these relationships, both through a family-tree diagram of all switches and through individual records. Third, SWITCHES classifies all reaction systems in terms of their stability properties. For each reaction topology we sample 100 specific models using logarithmic Monte Carlo sampling (Ramakrishnan and Bhalla 2008). In addition, we use latin hypercube sampling (Moza, 2020, Deutsch andDeutsch, 2012) to sample more uniformly in the high-dimensional space of reactionrates.
Each database entry is a model sampled from one of the reaction topologies, and includes an analysis of the three features described above. The entries are described using a unique hierarchical description ID which describes the model in this way: Class ID.Topology ID.Model ID. Thus the first model of the topology in Fig 1B, which has 3 reactants and 3 reactions is described as 3x3.113.1. The entry for each model is associated with the molecular concentration at steady-states, and the stability of each steady state (Fig 1 B-E). Additionally, we report final categories (singly stable, bistable, line, and other), the stable and saddle points, and the eigenvalues of the Jacobian matrix of the reaction rate system at each of these stable and saddle points. Each entry also includes a report on stuck-states. These are a special category of stable states relevant for stochastic chemistry (Fig 1F). The system can fall into such a state with a finite probability, but once in this state, it cannot transition out. For example, if A----> B, catalyzed by B, then there is a stuck state once B becomes zero. This database serves a range of studies that examine stability in chemical systems and evolutionary analyses based on the relationships between reactions. For example (Siegal-Gaskins et al., 2009) have used a similar survey of possible model topologies to classify bistables in gene networks. We have recently used SWITCHES to study robustness in bistable systems (Moza and Bhalla, 2020). By providing searchable, crosslinked, and fully characterized models, the SWITCHES database allows for searches for features and relationships of such systems. Furthermore, by providing models in the standard SBML format, it enables access by over 100 software tools. | 1,416.8 | 2020-08-05T00:00:00.000 | [
"Chemistry",
"Biology",
"Computer Science"
] |
Study on the Distribution Characteristics and Inuencing Factors of Homocysteine in the Physical Examination Population
Background: Homocysteine (Hcy) is considered to be an independent risk factor for cardiovascular and cerebrovascular diseases. No study has evaluated the distribution of Hcy on a large-scale health examination. Accordingly, this study aimed to investigate the level and distribution of Hcy in the healthy physical examination population and the correlation with other biomarkers, and analyzed for cardiovascular and other diseases. The prevention provides an important scientic basis. Methods: From February 2017 to April 2020, 8063 medical examination populations were selected for analysis. Determination of serum Hcy, TC, TG, LDL-c, HDL-c, ALT, ALP, γ-GT, TBIL, GLU, urea, Cr, UA and related metabolic risk factors. According to the multivariate regression model of age, gender, smoking, drinking, body mass index (BMI), systolic blood pressure (SBP) and diastolic blood pressure (DBP), the relationship between Hcy and other biochemical indicators was evaluated. Results: Among 8063 cases, the age, BMI, SBP and DBP of the high-Hcy group were higher than those of the low-Hcy group, the difference was statistically signicant (P<0.05), and the proportion of males, smoking and drinking were higher than the low In the Hcy group, the difference was statistically signicant (P<0.05); the ALT, ALP, γ-GT, TBIL, Urea, Cr, UA, and TG in the high Hcy group were higher than those in the low Hcy group, and the difference was statistically signicant (P<0.05 ); HDL-c in the high-Hcy group was lower than that in the low-Hcy group, and the difference was statistically signicant (P<0.05). There was no statistically signicant difference in TC, LDL-c, and GLU between the high- and low-Hcy groups (P>0.05). In multivariate analysis, lnHDL-C was negatively correlated with lnHcy (β=-0.038, SE=0.016, P<0.05), lnCr model showed that high lnHcy is the occurrence of high TG (OR: 1.870, 95% CI: 1.581-2.212, P<0.05), low HDL-C (OR: 1.803, 95% CI: 1.404-2.316, P< 0.05), abnormal γ-GT (OR: 1.270, 95%CI: 1.028-1.569, P<0.05), high TBIL (OR: 2.456, 95%CI: 1.741-3.464, P<0.05), high UA (OR: 3.106, 95%CI: 2.439-3.956, P<0.05) risk factors. High lnHcy is a protective factor for abnormal ALP (OR: 0.692, 95%CI: 0.531-0.900, P<0.05) and abnormal Cr (OR: 0.737, 95%CI: 0.565-0.960, P<0.05); multivariate logistic regression Model analysis results show that high lnHcy is high TG (OR: 1.281, 95%CI: 1.078-1.523, P<0.05), high UA (OR: 2.008, 95%CI: 1.565-2.575, P<0.05), abnormal TBIL (OR: 1.707, 95% CI: 1.205-2.418, P<0.05) risk factors. High lnHcy is a protective factor for abnormal Cr (OR: 0.663, 95% CI: 0.508-0.866, P<0.05) and high LDL-c (OR: 0.820, 95% CI: 0.699-0.962, P<0.05). For the abnormal Hcy group (Hcy>15μmol/L), single-factor logistic regression showed that high lnHcy is low HDL-C (OR: 1.772, 95% CI: 1.184-2.653, P<0.05), abnormal ALP (OR: 1.940, 95%CI: Low-density lipoprotein cholesterol; High-density lipoprotein cholesterol; ALT: Alanine aminotransferase; ALP: Alkaline phosphatase; γ-GT: γ-glutamyltransferase; TBIL: Total bilirubin; GLU: Blood glucose; Cr: Creatinine; UA: Uric acid; SBP: Systolic
Conclusion: Hcy is closely related to HDL-c, Cr and UA, which indicates that Hcy may affect the metabolism of HDL-c and UA, and can also be used as an auxiliary diagnostic index for kidney injury.
Background
Homocysteine (homocysteine, Hcy) is a sulfur-containing amino acid produced during the metabolism of methionine in cells of the body. Folic acid and vitamin B12 participate in its metabolism. Its pathogenic mechanism is mainly through multiple mechanisms such as vascular endothelial damage, stimulating smooth muscle cell proliferation, affecting coagulation and thrombus activation, and elevated Hcy is considered to be an independent risk factor for cardiovascular and cerebrovascular diseases [1,2] . In this Methods The subjects of this study came from a total of 8063 cases of the physical examination population in our hospital from February 2017 to April 2020. This research protocol was approved by the Ethics Committee of Nantong University A liated Hospital. The distribution characteristics of Hcy, blood lipids and other biochemical indicators, systolic blood pressure, diastolic blood pressure, body mass index, and pulse of the physical examination population were observed. 8063 study subjects were divided into low-Hcy group (Hcy≤15μmol/L) and high-Hcy group (Hcy>15μmol/L) according to their Hcy levels. Male UA>420 umol/L is an abnormal UA group, female UA>360umol /L is the abnormal UA group [3] ; According to the 2016 edition of the "Guidelines for the Prevention and Treatment of Dyslipidemia in Adults in China" [4] , TG>1.7mmol/L is de ned as the abnormal TG group; TC>5.2mmol/L is abnormal TC group; LDL-c>3.4mmol/L is abnormal LDL-c group; HDL-c<1mmol/L is abnormal HDL-c group. According to the "Guidelines for the Prevention and Treatment of Type 2 Diabetes in China" [5] , GLU>6.1mmol/L was regarded as the abnormal GLU group. BMI<18.5 kg/m 2 means lean, BMI between 18.5~24.0 kg/m 2 means normal group, BMI between 24~28 kg/m 2 means overweight, BMI≥28 kg/m 2 is the obesity group [6] ; systolic blood pressure>130mmHg and/or diastolic blood pressure>80mmHg is the abnormal blood pressure group [7] ; have had smoking and drinking behaviors De ned as the abnormal smoking and drinking group; males with ALT between 9-50U/L are regarded as normal group, females with ALT between 7-40U/L are regarded as normal group; outside the range are abnormal ALT group; males with ALP between 45-125U /L is the normal group, women aged 20 to 49: ALP between 35 to 100 U/L is the normal group, women 50 to 79 years old: ALP is between 50 to 135 U/L is the normal group, outside the range is de ned as abnormal ALP Group; male γ-GT between 10~60U/L is the normal group, female γ-GT between 7~45U/L is the normal group, all outside the range is de ned as abnormal γ-GT group [8] ; TBIL≤23.0μmol/L is the normal group, outside the range is de ned as abnormal TBIL group [9] ; 20~59year-old male Cr between 57~97μmoI/L is In the normal Cr group, 60-79-year-old men with Cr between 57-111μmoI/L are considered normal Cr group, 20-59-year-old women with Cr between 41-73μmoI/L are normal Cr group, 60-79-year-old women with Cr between 41~81μmoI/L is the normal Cr group, all outside the range is de ned as the abnormal Cr group; 20-59 year-old males with Urea between 3.1-8.0mmol/L are considered normal group, 60-79 year-old males with Urea between 3.6~ 9.5mmol/L is the normal group, 20-59 year old women with Urea between 2.6 and 7.5mmol/L are normal group, 60-79 year old women with Urea between 3.1 and 8.8mmol/L are normal group. All outside the scope is de ned as the abnormal urea group [10] .
The subjects were fasted for 12 hours and collected 5 mL of venous blood into a vacuum test tube containing separation gel. After the blood coagulated, the serum was separated by centrifugation at 2062g for 10 minutes within 2 hours and tested on the machine. All tests are carried out under the condition that the instrument and reagents are in normal condition and the indoor quality control is under control, and are carried out in strict accordance with the reagent and instrument operating procedures (SOP). All biochemical index testing instruments are American Beckman-Coulter automatic biochemical analyzer testing. Hcy (enzyme cycling method) kit and calibrator were purchased from Qiangsheng Biotechnology Co., Ltd., quality control products were provided by Shanghai Kunlai Biotechnology Company; ALT (lactate dehydrogenase method), ALP (NPP substrate-AMP buffer method) ), γ-GT (rate method), TBIL (diazonium method), TG (GPO-POD method), TC (cholesterol oxidase method), LDL-c (direct method), HDL-c (direct method), GLU (hexokinase method), BUN (urease-glutamate dehydrogenase method), Cr (sarcosine oxidase method), UA (uricase-peroxidase method) are tested by Beckman-Coulter Original kits and calibrators, quality control products are provided by Bio-Rad.
Statistical analysis
Statistical software stata20.0 was used for data analysis, skewness and kurtosis normality test (sktest), the numerical variables of the normal distribution were expressed by the mean ± standard deviation, and the comparison between the two groups was performed by the t test; the numerical variables of the nonnormal distribution The median (interquartile range) was used to express, the Mann-Whiteney U test was used for comparison between the two groups, the count data were all expressed by the number of cases (percentage), and the chi-square test was used for comparison between groups. This study analyzed the relationship between Hcy and TC, TG, LDL-c, HDL-c, ALT, ALP, TBIL, γ-GT, Urea, Cr and UA through single factor, multivariate linear regression and logistic regression models. The skewed numerical variables are analyzed after natural logarithmic transformation, and the multivariate regression is adjusted for age, gender, smoking, drinking, pulse and body mass index. Two-sided P<0.05 was considered statistically signi cant.
General characteristics
This study included 8063 patients with an average age of (50.88±11.92) years old, body mass index (25.02±3.39) kg/m 2 , systolic blood pressure (130.54±18.85) mmHg, diastolic blood pressure (78.66±11.96) mmHg, pulse ( 77.54±11.31) times/min. Among them, 5478 cases were male, accounting for 67.94%, aged 20-79 years old, and 2585 cases were female, accounting for 32.06%, aged 20-79 years old. The body mass index, systolic blood pressure, diastolic blood pressure, and glucose of men were higher than women, the difference was statistically signi cant (P<0.0001), while the pulse rate of men was lower than that of women, the difference was statistically signi cant (P<0.0001). Among men, the proportion of smoking is 23.95% and the proportion of drinking is 64.8%, which is much higher than that of women. Male ALT, γ-GT, TBIL, TG, LDL-c, Urea, Cr, UA are all higher than females, the difference is statistically signi cant (P<0.0001), TC, HDL-C are lower than females (P<0.001 ), there was no statistical difference in ALP between the
Comparison of various indicators grouped by Hcy high and low
The age, body mass index, systolic blood pressure, and diastolic blood pressure of the high-Hcy group were higher than those of the low-Hcy group, the difference was statistically signi cant (P<0.05), and the proportion of men, smoking and drinking was higher than that of the low-Hcy group, and the difference was statistically signi cant Academic signi cance (P<0.05).
The results of blood lipids showed that the TG of the high-Hcy group was signi cantly higher than that of the low-Hcy group, and the difference was statistically signi cant (P<0.05). The HDL-c of the high-Hcy group was signi cantly lower than that of the low-Hcy group, and the difference was statistically signi cant (P<0.05). There was no statistically signi cant difference in TC and LDL-c between the high and low Hcy groups (P>0.05); the liver function results showed that the ALT, ALP, γ-GT and TBIL in the high-Hcy group were higher than those in the low-Hcy group, and the difference was statistically signi cant (P<0.05); Renal function results showed that Urea, Cr and UA in the high-Hcy group were signi cantly higher than those in the low-Hcy group, and the difference was statistically signi cant (P<0.05). See Table 2 for details.
Comparison of high and low Hcy indicators by gender
Among men, the age, systolic blood pressure, and diastolic blood pressure of the high-Hcy group were higher than those of the low-Hcy group, and the difference was statistically signi cant (P<0.05). The GLU between the high-Hcy group was lower than that of the low-Hcy group, and the difference was statistically signi cant (P<0.05); the γ-GT and TBIL of the high-Hcy group were higher than those of the low-Hcy group, and the difference was statistically signi cant (P<0.05); the TG of the high-Hcy group was signi cantly higher than that of the low-Hcy group, and the difference was statistically signi cant (P<0.05). The Cr and UA of the high-Hcy group were higher than those of the low-Hcy group, and the difference was statistically signi cant (P<0.05).
Among women, the age, BMI, systolic blood pressure and diastolic blood pressure of the high-Hcy group were signi cantly higher than those of the low-Hcy group, and the difference was statistically signi cant (P<0.05). The ALP of the high-Hcy group was higher than that of the low-Hcy group, and the difference was statistically signi cant (P<0.05). The TG of the high-Hcy group was signi cantly higher than that of the low-Hcy group, and the difference was statistically signi cant (P<0.05), while the HDL-C was lower than the low-Hcy group, and the difference was statistically signi cant (P<0.05); Both Cr and UA were signi cantly higher than the low-Hcy group, and the difference was statistically signi cant (P<0.05). See Table 3 for details.
Logistic regression model analysis of serum Hcy on each index
The single factor logistic regression model showed that high lnHcy is the occurrence of high TG (OR: Table 5 for details.
Discussion
The baseline data collected in this study showed that the proportion of men smoking and drinking was higher, BMI, systolic blood pressure, diastolic blood pressure, GLU, Hcy, ALT, γ-GT, TBIL, TG, LDL-c, Urea, Cr, UA Both are higher than women, while TC and HDL-c are lower than women. This may be related to multiple factors such as genetics, lifestyle and eating habits. There is no signi cant difference in ALP between the two groups, which may be related to the average age of the subjects we included Too big related. Folic acid, vitamin B12, estrogen, etc. in the human body can promote the metabolism of Hcy.
Generally, the concentration of Hcy in women is lower than that in men [11][12][13][14] . In this study, the average concentration of Hcy was 10.2 (8.3-12.8), and males were much higher than females. In addition, smoking can indirectly lead to the reduction or lack of folic acid and vitamin B12 levels in the blood and affect the decomposition and metabolism of Hcy. This may also be the reason why the level of Hcy in men is higher than that in women.
Studies have found that Hcy is related to early renal damage [15] . High UA enhances oxidation, promotes lipid peroxidation through oxidative stress, and accelerates the production of oxygen free radicals and coronary artery The progression of the disease is related to cardiovascular and cerebrovascular diseases such as hypertension, coronary atherosclerosis, heart failure, and stroke. Many researchers regard UA as an independent risk factor for coronary heart disease. Therefore, in this study, we evaluated Cr and UA as basic data and found that Cr and UA in the high-Hcy group were signi cantly higher than those in the low-Hcy group. Univariate and multivariate analysis of Hcy normal group and abnormal group showed that Hcy was positively correlated with Cr and UA. In the follow-up study, we will follow up the study subjects to further clarify the relationship between Hcy and kidney injury and other related diseases.
TC, TG, HDL-c, LDL-c are involved in the metabolism of lipids and cholesterol in the blood, and are closely related to the occurrence and development of cardiovascular and cerebrovascular diseases. HDL-c is signi cantly different, which is consistent with related literature reports [16,17] . High Hcy can damage blood vessel walls and affect lipid metabolism. In this study, both the univariate and multivariate linear regression model analysis results of Hcy in the normal group showed that Hcy was negatively correlated with HDL-c and positively correlated with TG; while in the high Hcy group, Hcy and HDL-c were still negatively correlated with TC. There is a negative correlation. The existing literature reports that high Hcy is negatively related to HDL-c, but the correlation between TC and TG is not consistent in the literature [18] . This may be related to the source of the research object, the geographical distribution, the degree of fasting before sample collection, the number of samples included in the study, and the factors used for correction in the multivariate analysis.
Hcy is a sulfur-containing amino acid produced during the metabolism of methionine in the body. Its main physiological function is to provide methyl groups for many important physiologically active substances such as DNA, protein and phospholipids in the body. Under normal circumstances, the production and metabolism of Hcy in the body maintain a dynamic balance [19] , so that the concentration of Hcy in the blood is maintained at 5-15mmol/L. There are many factors that affect the level of Hcy. In addition, under certain pathological conditions, taking drugs that interfere with metabolism can affect the metabolism of Hcy. The superoxide and peroxide produced can cause vascular endothelial cell damage and vascular smooth muscle cell proliferation. The structural damage of the wall and the increase of lipid deposits in the blood vessel wall accelerate the process of atherosclerosis. Hcy can also destroy the normal coagulation mechanism, increase the chance of thrombosis, and easily increase the risk of arteriosclerotic diseases such as stroke, coronary heart disease, and peripheral vascular disease. Studies have pointed out that for every 5 µmol/L increase in blood Hcy, the risk of ischemic heart disease increases by 32%, and every 3 µmol/L decrease in Hcy, the risk of disease is reduced by 16% [20] . A large number of studies have shown that hyperHcyemia is closely related to the occurrence, development and prognosis of a variety of cardiovascular and cerebrovascular diseases, hypertension, diabetes, and kidney diseases.
There are still some shortcomings in this study. For example, the fasting state of the study subjects may not be completely consistent, and the liver function is not judged in conjunction with imaging, so detailed evaluation was not performed.
Conclusion
This study shows that Hcy may participate in or affect the metabolism of HDL-c, Cr, UA, etc. The content of Hcy should be paid attention to in clinical work to provide data support for clinical monitoring of cardiovascular and cerebrovascular diseases and renal function.
Availability of data and material
The datasets used and/or analyzed during the current study are de-identifed and available from the corresponding author on reasonable request. Identifying/confdential patient data should not be shared.
Ethics approval and consent to participate | 4,089.4 | 2021-05-19T00:00:00.000 | [
"Biology",
"Medicine"
] |
Integration of the Health Monitoring System with IoT Application in Sports Technology: A Review
Nowadays, monitoring health systems in robust technology has been extensively applied in the sports field. Even though massive utilization of wearable device technologies aims to quantify athlete performance, inconsistent performance still exists between training sessions and competition. The rigorous discussion about the latest research in monitoring technological systems will help trainers obtain accurate data about athlete performance. This paper focuses on the athlete monitoring system in terms of psychological and physiological parameters and applications in individual sports based on Internet of Things (IoT) Technology. The study incorporates three factors: the parameters that affect athlete performance, multiple device sensors in sports health monitoring, and IoT technology’s application for athletes. Based on analysis and observation, efficient sports health monitoring can effectively enhance athlete performance in physiological and psychological conditions. An IoT system encompasses four main aspects: sensing, networking, data processing and application layer. These aspects provide real-time information on the athlete’s body condition during training and games. Therefore, this monitoring system greatly assists coaches in designing practical training and activities for athletes. It is highlighted that wearable health monitoring systems by IoT technology will be further built based on athlete requirements.
INTRODUCTION
The advancement in individual sports achievements has emerged with the development of technologies.A particular necessity in individual sports depends on self-paced sports like archery, rifle shooting, dart, golf, ice skating, running, and diving (Kolayis et al. 2014;Norlander et al. 1999).In today's athlete society, an increasing number of athletes suffering from an inconsistent performance between their training sessions and real game lead be a significant issue in the sports field (Karahan 2020).Some have become a part of athletes' and sports trainers' everyday lives as aiding tools to monitor athletes' conditions.Information and communication technology are also extensively advancements with the explosive growth of monitoring systems in assistive sports devices.Intelligent and personalized assessment of athlete condition through wireless sensor network composed of multi-source sensors, extensive data analysis and Internet of Things (IoT) Technology is at the heart of research in today's sports advancement.In an endeavour to improve athletes' performance in line with sustainable Technology, real-world applications from IoT systems are highly relevant to producing wearable health devices for athletes' benefit and coaches' referrals (Wan Ahmad 2022).
Various monitoring gadgets have been studied and manufactured to improve athlete performance on both psychological and physiological levels (Fry 2019).Various existing health monitoring systems have been discovered, including real-time monitoring of the athlete's health condition (Naranjo-Hernández et al. 2017).Besides that, the health monitoring system can record and compare the previous athlete performance data before, during and after the training session or competition (Taffoni et al. 2018).Significantly, it can be seen from various perspectives, either in physiological and psychological aspects in realtime during training or competition.Zhao and Li (2020), a researcher from China, reported that there are still relatively few studies about wearable health monitoring devices with integration between IoT sensing systems used in sports backgrounds.The existing Technology with IoT devices is more geared toward health monitoring that specifically involves patients and focuses on the medical field (Sec & Shanmugam, 2016).Therefore, this paper wants to review current research on implementing athlete monitoring systems based on IoT for individual sports performance.The purposes concentrate on three critical areas: 1. Study parameters that affect athlete performance; 2. Study on multiple device sensors in sports health monitoring; 3. Application of IoT's system toward athlete progression.Although such applications remain a long way from widespread sports applications as several key technical and fundamental scientific issues are yet to be overcome, notable scientific advancements and applications have been achieved in these areas.This paper aims to summarize the characteristics and applications of monitoring health systems used in sport among sports trainers, coaches, and athletes, present the recent innovation and discuss existing issues with health monitoring systems related to sport application.
PARAMETER MEASUREMENTS IN ATHLETE PERFORMANCE
Sports performance during training and actual games is an essential indicator of an athlete's achievement, whether in the beginner, intermediate or elite categories.Each type of sport has its criteria for getting a win.An individual sport involving high accuracy or concentration like archery, rifle shooting, and dart requires psychological and physiological aspects.Mental strength, known as psychology, is required to ensure that the arrow's precision on the target butt is always on target (Rizal et al. 2019).In contrast, physiology like body movement, sweating skin, and muscle strength are essential in high-accuracy games to maintain a consistent score (Pelana & Winata 2018).Multi physiological parameters required to complete the human health analysis include the combination of heart rate, pulse rate, and blood pressure in real-time reading (Saçakli 2019).Generally, physiological reaction engages with the whole parts of the athletes' physical body.While psychological, it is the reaction that engages with athletes' mental manifestations, such as anxiety, stress, and depression.Both aspects have several parameters that must be clearly emphasized in athlete performance during training and competition.Hence, an assistive monitoring technology device is necessary to collect and store the athlete's data.The obtained parameter reading from the athlete's condition was recorded for further analysis, and the sports trainer was able to arrange compatible training to enhance the athlete's performance.
PSYCHOLOGICAL AND PHYSIOLOGICAL PARAMETER
Emotion and mental manifestation are essential components of psychology in an individual sport.Previous research described athletes who engage in individual sports experience higher anxiety than team sports (Soltani et al. 2016).Individual athletes are more engaged in their skills and abilities without depending on other athletes.A study from Bali (2015) revealed that athlete psychological preparation is 90% significant in sports games to help athletes better deal with the challenge of competition and training.General mental preparation for games in terms of arousal, self-confidence, and focus level should be psychological preparation.Athletes must work on psychological skills to perform well (Ismail, 2019).Psychology in an individual sport such as shooting, dart and archery depends on athlete abilities.Anxiety, motivation, stress, self-confidence, general activation, attention, and team cohesion are all psychological factors that influence athletes' performance and well-being (Liao et al. 2020).
HEALTH MONITORING SYSTEM
The application of health monitoring plays a vital role in advancing sports technology and performance.The monitoring system is essential to sports trainers in optimizing athlete performance and improving their condition throughout their training session or during the actual competition (Asthana et al. 2017).The primary purposes are to monitor the athlete's health condition and help to indicate the precaution of athlete health in the early stage throughout their training or during games.Typically, this health monitoring system consists of two categories that act as a preventive and responsive system.A responsive system can detect health conditions early and provide various health options based on the normal situation.Mosenia et al. (2017) described the responsive health monitoring system as capable of detecting users' health conditions early before reaching the worst stage and providing continuous monitoring.
The most popular preventive health monitoring system types are fitness trackers to measure heart rate, blood pressure, and calories burned (Rao, 2019).This monitoring system can encourage healthy habits and reduce the risk of significant illness by automatically identifying and notifying athletes about unhealthy practices.Utilizing monitoring system technologies in athletes as assistive devices has contributed to incorporating additional devices that improve physical resistance and players' health.Furthermore, Guembe et al. (2021) study revealed that the monitoring system consists of multiple sensors that detect a possible mistake during any movement.The device will alert the player.Thus, this monitoring system could detect possible faults while performing movements, and the device would alert the player throughout the game session.With this, the improvement in the athlete's performance becomes better and more consistent.However, there are limitations in providing direct information about an athlete's condition and requiring accurate readings of athlete data for coach action and supervision from the appropriate sensor selection WEARABLE BIO-SENSING DEVICE Sensor technologies enable the development of real-time information systems based on digital data integration.This section presents a wearable bio-sensing modality used in health monitoring systems in sports applications.It can be described as sensing behaviour emerging from physiological and psychological parameter alterations among body athlete's conditions.Thus, wearable sensors have extensively provided actual sensing capability in daily user activities.The advantage of modern wearable sensors is that they are integrated into the IoT's system and can be monitored in any location, not restricted by time and space.Moreover, wearable sensors can directly obtain data from the athlete's body and transform information into an analytically valuable signal (Garcia-Ceja et al. 2018).Feedback from the sensor's device would be beneficial for real-time monitoring of any reaction and response from the athlete's body and other markers to obtain and explain the athletes' physiological responses during the training or game sessions.A study in netball sport locates the wearable IMU accelerometer and gyroscope in a pocket worn underneath the athlete's dress, as shown in Figure 1 (a).Then, in tennis, a wearable pebble watch sensor is attached to the hand athlete's upper limb to monitor the racquet motion in Figure 1 (b).While in archery, the GSR sensor locates at the archer's fingers to monitor the skin reaction during the gripping and release of the string for the shooting process, as shown in Figure 1 (c).
Table 1 summarizes the bio-signal sensor commonly used in the health monitoring system by the athlete, sportperson, and coach during any sports activities.These sensors are essential in obtaining actual readings from the athlete's parameter condition, both physiologically and psychologically.The common characteristics of these technological sensors should be sensitive, responsive, adaptive, transparent, ubiquitous and unobtrusive (Vigneshvar et al. 2016).The application of health monitoring plays a vital role in advancing sports technology and performance.The monitoring system is essential to sports trainers in optimizing athlete performance and improving their condition throughout their training session or during the actual competition (Asthana et al. 2017).The primary purposes are to monitor the athlete's health condition and help to indicate the precaution of athlete health in the early stage throughout their training or during games.Typically, this health monitoring system consists of two categories that act as a preventive and responsive system.A responsive system can detect health conditions early and provide various health options based on the normal situation.Mosenia et al. (2017) described the responsive health monitoring system as capable of detecting users' health conditions early before reaching the worst stage and providing continuous monitoring.
The most popular preventive health monitoring system types are fitness trackers to measure heart rate, blood pressure, and calories burned (Rao, 2019).This monitoring system can encourage healthy habits and reduce the risk of significant illness by automatically identifying and notifying athletes about unhealthy practices.Utilizing monitoring system technologies in athletes as assistive devices has contributed to incorporating additional devices that improve physical resistance and players' health.Furthermore, Guembe et al. (2021) study revealed that the monitoring system consists of multiple sensors that detect a possible mistake during any movement.The device will alert the player.Thus, this monitoring system could detect possible faults while performing movements, and the device would alert the player throughout the game session.With this, the improvement in the athlete's performance becomes better and more consistent.However, there are limitations in providing direct information about an athlete's condition and requiring accurate readings of athlete data for coach action and supervision from the appropriate sensor selection daily user activities.The advantage of modern wearable sensors is that they are integrated into the IoT's system and can be monitored in any location, not restricted by time and space.Moreover, wearable sensors can directly obtain data from the athlete's body and transform information into an analytically valuable signal (Garcia-Ceja et al. 2018).Feedback from the sensor's device would be beneficial for real-time monitoring of any reaction and response from the athlete's body and other markers to obtain and explain the athletes' physiological responses during the training or game sessions.The application of health monitoring plays a vital role in advancing sports technology and performance.The monitoring system is essential to sports trainers in optimizing athlete performance and improving their condition throughout their training session or during the actual competition (Asthana et al. 2017).The primary purposes are to monitor the athlete's health condition and help to indicate the precaution of athlete health in the early stage throughout their training or during games.Typically, this health monitoring system consists of two categories that act as a preventive and responsive system.A responsive system can detect health conditions early and provide various health options based on the normal situation.Mosenia et al. (2017) described the responsive health monitoring system as capable of detecting users' health conditions early before reaching the worst stage and providing continuous monitoring.
The most popular preventive health monitoring system types are fitness trackers to measure heart rate, blood pressure, and calories burned (Rao, 2019).This monitoring system can encourage healthy habits and reduce the risk of significant illness by automatically identifying and notifying athletes about unhealthy practices.Utilizing monitoring system technologies in athletes as assistive devices has contributed to incorporating additional devices that improve physical resistance and players' health.Furthermore, Guembe et al. (2021) study revealed that the monitoring system consists of multiple sensors that detect a possible mistake during any movement.The device will alert the player.Thus, this monitoring system could detect possible faults while performing movements, and the device would alert the player throughout the game session.With this, the improvement in the athlete's performance becomes better and more consistent.However, there are limitations in providing direct information about an athlete's condition and requiring accurate readings of athlete data for coach action and supervision from the appropriate sensor selection daily user activities.The advantage of modern wearable sensors is that they are integrated into the IoT's system and can be monitored in any location, not restricted by time and space.Moreover, wearable sensors can directly obtain data from the athlete's body and transform information into an analytically valuable signal (Garcia-Ceja et al. 2018).Feedback from the sensor's device would be beneficial for real-time monitoring of any reaction and response from the athlete's body and other markers to obtain and explain the athletes' physiological responses during the training or game sessions.The basic IoT system architecture can be divided into several layers.The three layers (Rizal et al. 2019), four layers (Wan et al. 2018) and five layers (Sethi & Sarangi, 2017).This review focused on individual sports health monitoring systems that followed four layers of the basic architecture of the IoT's system.Hence, we outline the function of the four layers.1.The sensing layer is the physical sensor layer screens, and devices involving multiple sensors' applications.The foremost use of IoT-based monitoring systems in sport application is to monitor the condition of athletes effectively and help coaches get an accurate picture of the condition of athletes more effectively than manual observation (Segura-Garcia et al. 2018).Obtaining actual data from athlete conditions assists the coach in preparing the appropriate training schedule without 1.The basic IoT system architecture can be divided into several layers.The three layers (Rizal et al. 2019), four layers (Wan et al. 2018) and five layers (Sethi & Sarangi, 2017).This review focused on individual sports health monitoring systems that followed four layers of the basic architecture of the IoT's system.Hence, we outline the function of the four layers.2. The sensing layer is the physical sensor layer involving various sports devices and athletes' bodies.It senses the physical parameters or identifies other input signals obtained from the sensors from the attached part of the athlete's body will then be passed next through the sensor-based network layer.3. The networking layer is enabled to connect sensors to other network devices and servers using longdistance communication via the cellular network and short-distance wireless communication technology.Its features efficiently classify and secure data transmitting to corresponding data process units for real-time collection of vast amounts of data generated from the sensing layer.4. The data processing layer is responsible for storing and retrieving valuable data, analyses, and processes data mining in the recent advancement of cloud computing servers.
The application layer is the purpose of developing the Internet of Things.This layer provides a human-computer interaction interface between human feedback information and the application for user demand in the actual production of the device.
The IoT technologies have a great potential to influence the overall health monitoring in sports applications as each device and object can be explicitly recognized within the connection of modern internet infrastructure and provide vast benefits for athlete achievement.These benefits typically consist of advanced systems, monitoring screens, and devices involving multiple sensors' applications.The foremost use of IoT-based monitoring systems in sport application is to monitor the condition of athletes effectively and help coaches get an accurate picture of the condition of athletes more effectively than manual observation (Segura-Garcia et al. 2018).Obtaining actual data from athlete conditions assists the coach in preparing the appropriate training schedule without burdening the athlete physically and mentally (Huifeng et al. 2020).Kos et al. (2019) explained that the interaction between coach and athlete conditions signifies a unique application space for utilizing IoT technology.
The involvement of IoT Technology in sports can improve the performance of athletes in a consistent and controlled manner.Systematic monitoring and data storage can help coaches design and organize training fitness programs accurately based on the current ability and fitness of the athletes to be more excellent (Shah et al. 2019).The trainers can precisely regulate the difficulty level of training activity according to the athlete's capability.That is because the level of ability in every person is different.The collected data from the sensors must be stored and processed intelligently to derive helpful inferences.The communication between IoT devices is mainly wireless.In wearable health monitoring system technology, a wireless component must be used for system interfacing.It acts for real-time or sporadic updating to a remote processing node, downloading the collected, stored data, or transmitting data from a sensor node to the on-body or remote processing unit (Rizal et al. 2019).Health monitoring Internet of Things in sports is a vast system involving various sensors and a massive number of data analyses and processing.Hence, the system enables a broad range of interactions worldwide and leads to ingenious technologies within this high network.In determining victory, every sport has its criteria.An athlete needs multiple preparations to be an expert in the sport he is involved in.Experienced athletes find that physiological and psychological preparation is essential in ensuring that their performance is consistent, whether during training or actual competition.For example, the sport of archery requires a high focus to ensure that each archer is on a set target.Both Psychological (anxiety and concentration, body temperature) and physiological parameters (heart rate, muscle activation, skin conductance) need to be given attention by athletes (Snyder et al. 2021).If these two main parameters are not achieved at optimal levels, the athlete will face inconsistent and debilitative performance and vice versa.Due to that, individual sports are seen as more challenging than group sports (Ismail, 2019).Most coaches are concerned about these basic parameters in determining their athlete's performance.The critical parameters such as heart rate level, muscle activation, skin conductivity, body movements, oxygen levels and blood pressure have been listed in Figure 3.
DISCUSSION
In determining victory, every sport has its criteria.
An athlete needs multiple preparations to be an expert in the sport he is involved in.Experienced athletes find that physiological and psychological preparation is essential in ensuring that their performance is consistent, whether during training or actual competition.For example, the sport of archery requires a high focus to ensure that each archer is on a set target.Both Psychological (anxiety and concentration, body temperature) and physiological parameters (heart rate, muscle activation, skin conductance) need to be given attention by athletes (Snyder et al. 2021).If these two main parameters are not achieved at optimal levels, the athlete will face inconsistent and debilitative performance and vice versa.Due to that, individual sports are seen as more challenging than group sports (Ismail, 2019).Most coaches are concerned about these basic parameters in determining their athlete's performance.The critical parameters such as heart rate level, muscle activation, skin conductivity, body movements, oxygen levels and blood pressure have been listed in Figure 3. 2015) stated that excellent monitoring of the critical parameter reading would assist their athletes in maintaining and consistently performing in every training session.Thus, IoT Technology and the health monitoring system can collect all the essential parameter readings from the bio-signal sensor.This review shows that the sensors can retrieve and analyse digital data (Shah et al. 2019).By comparing and studying the IoT device in sports health monitoring systems, we can better understand that the configuration of the Internet of things for health monitoring systems is different for each purpose.The researchers have focused on implementing and designing IoT sports health monitoring to enhance the current services for athlete performance and helping the coach in analyzing the athlete's condition.However, some open research challenges and issues still need to be considered.In some conditions, the athlete and coach require long-term and continuous monitoring.The types of sports requiring a high concentration level include rifle shooting, archery, golf, dart, and even athletics (running, long jump, disc jump, and high jump).That kind of sport is under the individual sports category that highly depends on the monitoring sport application-seeing that the individual sport entirely relies on the athlete's fitness.Consequently, monitoring individual athletes is essential to ensuring that the coach can monitor the athlete's condition throughout the training or games session.
By comparing and studying the IoT device in sports health monitoring systems, we can better understand that the configuration of the Internet of things for health monitoring systems is different for each purpose.Typically, the existing health monitoring devices are equipped with a small form factor to provide limited battery-powered, which imposes constraints for long-term monitoring from the coach and athlete to obtain factual information during training or competition (Shah et al. 2019).Integrating the device with IoT technology allows the power supply to be transmitted via wireless and Bluetooth from a plug-in charger in the stage of the network layer (Qiu et al. 2021).Hence, the limitation can be overcome when the battery is easily accessible for wireless charging (Kos et al. 2019).In other studies, most of the sport's health monitoring devices have limited built-in memory and computing capacity.Hence, integrating with the Internet of Things (IoT), the memory will be transmitted to another processing server, where cloud computing is commonly adopted.The sensor data from the monitoring device can be transmitted to the cloud server.The users can access the data with any Internet-enabled device anytime from anywhere (Wan et al. 2018).
CONCLUSION
Internet of Things has become a real-life changer in the recent past through its applicability in sports technology.A need for real-time monitoring system for athlete activity recognition with wearable IoT sensors is essential for current assistive paradigms in sports technological advancement.This paper briefly reviews the current health monitoring system that integrates IoT technologies.Secondly, this paper explicates the critical parameters and applicable sensors between physiological and psychological for individual athletes to enhance their performance.Thirdly, the essential characteristics required to enable the real-time monitoring of the athlete users and allow the obtained information to be accessed from the cloud system directly.
To conclude, the outcomes of this research presented beneficial information regarding health monitoring systems for the individual athlete, coach, sports trainer, and policymakers operational in the field of sport and the IoTbased.Based on this review, the researchers recommended that future research upgrade and facilitate the accessibility in terms of readings of various parameters from one device and applied during actual training sessions and competitions.For recommendations, developing a wearable monitoring system may improve the wearable characteristics of the device in terms of accuracy data reading, long-lasting battery, creating an alert system for monitoring critical information, and focusing on the ergonomic design system for athlete comfort.
FIGURE 1 .
FIGURE 1. Types of the wearable sensor for the athlete(Ahmad et al. 2014) FIGURE 1. Types of the wearable sensor for the athlete (Ahmad et al. 2014) Figure 1 indicates a few examples of wearable bio-sensing devices commonly found in sports applications.A study in netball sport locates the wearable IMU accelerometer and gyroscope in a pocket worn underneath the athlete's dress, as shown in Figure 1 (a).Then, in tennis, a wearable pebble watch sensor is attached to the hand athlete's upper limb to monitor the racquet motion in Figure 1 (b).While in archery, the GSR sensor locates at the FIGURE 1. Types of the wearable sensor for the athlete (Ahmad et al. 2014) Figure 1 indicates a few examples of wearable bio-sensing devices commonly found in sports applications.A study in netball sport locates the wearable IMU accelerometer and gyroscope in a pocket worn underneath the athlete's dress, as shown in Figure 1 (a).Then, in tennis, a wearable pebble watch sensor is attached to the hand athlete's upper limb to monitor the racquet motion in Figure 1 (b).While in archery, the GSR sensor locates at the
FIGURE 2 .
FIGURE 2. The basic architecture diagram of the Internet of Things system
FIGURE 3 .
FIGURE 3. Percentage of Individual Sport Monitoring Sensor-IoT Based SystemThe assistance of sensor devices in advancing sports technology can help athletes and coaches accurately obtain data from the parameter.Ogan et al. (2015)
TABLE 1 .
The summary of the bio-signal sensor in the health monitoring system THE INTEGRATION OF IOT TECHNOLOGY IN HEALTH MONITORING APPLICATIONApplications based on IoT devices are getting more attention because of their capabilities to cover many aspects under one system.IoT technologies are intentionally developed to enhance athlete performance through monitoring, evaluating, and analysing sports demand.Integrating IoT technology is vastly better than traditional or manual tracking and monitoring the athlete's performance.The IoT technology Table 2 shows the standard athlete health monitoring sensor integrated with IoT technological system.
TABLE 2. Identification of selected muscle and electrode placement position continue ... FIGURE 3. Percentage of Individual Sport Monitoring Sensor-IoT Based System DISCUSSION | 5,937.4 | 2022-11-30T00:00:00.000 | [
"Computer Science",
"Medicine",
"Engineering"
] |
Neonatal overfeeding induced glucocorticoid overexposure accelerates hepatic lipogenesis in male rats
Background Postnatal overfeeding activates tissue glucocorticoid (GC) activity by up-regulating 11β-hydroxysteroid dehydrogenase 1 (11β-HSD1) and increasing sensitivity to high-fat (HF) diet-induced non-alcoholic fatty liver disease (NAFLD). The present study aimed to evaluate the effects of postnatal overfeeding on GC regulation and lipogenesis in the liver and to observe the impact of GC on hepatocyte lipid metabolism. Methods In vivo, Male Sprague-Dawley rat pup litters were adjusted to litter sizes of three (small litter, SL) or ten (normal litter, NL) on postnatal day 3 and then given standard chow from postnatal week 3 (W3) to W13. In vitro, HepG2 cells were stimulated by GC, mifepristone (Mi) or GC + Mi within 48 h, followed by sodium oleate (OA) intervention (or not) for 24 h. Intracellular lipid droplets, triglyceride (TG) concentrations and gene expression related to lipid metabolism were measured in hepatic tissues or HepG2 cells. Results In vivo, weight gain in the body and liver and TG concentrations in the liver were significantly increased in the SL rats compared to the NL rats at W3 and W13 (p < 0.05); mRNA expression of hepatic 11β-HSD1, acetyl-CoA carboxylase 1 (ACC), stearoyl-CoA desaturase-1 (SCD1), fatty acid synthase (FASN) and their nuclear transcription factor, sterol regulatory element binding protein-1c (SREBP-1c) (p < 0.05), was also increased. In vitro, intracellular lipid droplets and TG content in HepG2 cells increased under stimulation with GC or OA (p < 0.05); the increase was more significant following treatment with GC and OA together (p < 0.05). The ACC, SCD1, FASN and SREBP-1c mRNA expression changes were highly similar to the changes in TG content in cells. All the changes induced by GC disappeared when the glucocorticoid receptor (GR) was blocked by Mi. Conclusions Postnatal overfeeding induced GC overexposure through 11β-HSD1 up-regulation in the liver. GC activated hepatic de novo lipogenesis (DNL) via GR and led to hepatic lipid accumulation, which increased the risk of NAFLD during adulthood.
Background
Non-alcoholic fatty liver disease (NAFLD) and other components of metabolic syndrome (MS) have become increasingly comorbid with the increasing prevalence of obesity in both children and adults [1,2]. Unlike what happens in adults, the onset of paediatric NAFLD is asymptomatic until it progresses into hepatic fibrosis and cirrhosis [3,4]. In order to develop strategies to effectively prevent NAFLD and MS, it is important to better understand the mechanisms by which obesity increases susceptibility to NAFLD. NAFLD is characterized by excessive triglyceride (TG) accumulation in the absence of significant alcohol consumption [1]. It is primarily caused by the imbalance of hepatic lipid homeostasis between the acquisition and removal of TG/fatty acid, which involves increased fatty acid/TG uptake, enhanced de novo lipogenesis (DNL), impaired fatty acid β-oxidation, and/or decreased lipid export in the liver. Several rate-limiting enzymes and transcription factors participate in hepatic lipid metabolism [5].
Glucocorticoid (GC), such as corticosterone and cortisol, affects fat accumulation and lipid and glucose metabolism [19,20]. At the tissue level, GC exposure is determined not only by circulating levels, but also by the tissue-specific GC-activating enzyme 11βhydroxysteroid dehydrogenase type 1 (11β-HSD1) and the GC-inactivating enzymes 5α-reductase type 1 (5αR1) and 5β-reductase (5βR) [21,22]. Previous studies have shown that the overexpression of 11β-HSD1 in tissue amplifies local GC action, which leads to increased accumulation of adipose tissue and metabolic disorders in both humans and rodents [23][24][25][26]. GC is involved in every stage of the pathogenesis of NAFLD [27]. In animal models, GC increases lipid biosynthesis within the liver that can lead to hepatic steatosis and increase circulating TG levels [28,29].
The results of both experimental studies using animal models and clinical investigations have indicated that the early nutrition environment is associated with the development of obesity and MS in later life [30][31][32][33] and that GC is a possible mediator of the permanent programming of obesity, insulin resistance, and other metabolic dysregulations [34][35][36]. Previously, we reported that small litter (SL) rearing induced obesity in adult rats. The animals also had hyperinsulinemia, elevated circulating corticosterone levels, peripheral tissue-specific alterations in 11β-HSD1 expression and activity and 5αR1 and 5βR expression starting at puberty [37]. In addition, SL rats also displayed increased ACC activation in the livers and were more prone to develop NAFLD when challenged with high-fat (HF) diets [38]. Our hypothesis was that local GC activity plays the crucial role in the pathogenesis of hepatic steatosis by regulating lipid synthesis enzymes. Therefore, in this study we examined the expression patterns of 11β-HSD1 and 5αR1 and 5βR in the livers of SL rats, as well as those of lipid metabolism-related genes involved in hepatic DNL and fatty acid β-oxidation and lipid export. Moreover, we wanted to determine the action of GC on hepatic lipid metabolism. We first treated HepG2 cells with GC and then with OA in vitro to mimic hepatic GC overexposure in vivo.
Animals and experimental design
All animal studies were performed following the guidelines established by the University Committee on the Use and Care of Animals and were overseen by the Unit for Laboratory Animal Medicine at Nanjing Medical University (IACUC: 14030102). Male Sprague-Dawley rats were used. They were maintained under a controlled 12/12 h light/dark cycle in temperature (22 ± 2°C) conditions with free access to food and water.
The experimental setup was similar to that described in Boullu-Ciocca [39]. In rats, the weaning period is postnatal week 3, puberty is postnatal weeks 6-8 and adulthood is week 9 and afterward [40]. In our previous studies, we showed that metabolism disorders in SL rats took place during postnatal weeks 13-16 [38,41]. Therefore, postnatal weeks 3 and 13 were selected as two experimental points of this study to examine the effects of early nutrition on adult health. On postnatal day 3 (P3), male pups were randomly redistributed to litter sizes of three (SLs) or ten (normal litters (NLs)) to induce early postnatal overfeeding or normal nutrition, respectively [42]. After weaning (P21, W3), the NL and SL rats were fed a standard diet (NL or SL group) until postnatal week 13 (W13). All rats were housed 3-4 per cage after weaning. Body weight and food intake were monitored weekly throughout life. The animals were killed at W3 and W13 after an overnight fast.
Tissue collection
The rats were anaesthetized with chloral hydrate (300 mg/kg body weight, i.p.) after an overnight fast (12 h). Body weight was recorded. Each rat's liver was dissected and weighed, and the hepatosomatic index (HSI) was calculated as (liver weight/body weight) * 100% [43]. All tissue samples were snap-frozen in liquid nitrogen and stored at − 70°C until gene expression analysis.
Hepatic lipid assays
Concentrations of TG in the liver and cells were determined using TG assay kits (E1013, Applygen, Beijing, China). The hepatic TG concentration was expressed relative to 1 g of liver protein. Hepatic protein concentrations were determined using a Pierce BCA protein assay kit with bovine serum albumin as the standard (Thermo Fisher Scientific, Rockford, IL, USA).
Cell culture
HepG2 cells, obtained from Keygen Biotech (Nanjing, China, ATCC HB-8065), were maintained in DMEM medium containing 10% FBS and 1% P/S at 37°C with 5% CO 2 (Thermo Scientific, CO 2 incubator) in 75 cm 2 flasks. Cells were plated in 6-well plates at 2*10 5 cells per well. The following day, confluent cells were starved for 6 h without FBS. Then, the cells were treated with 2.0 ml of fresh supplemented culture medium containing dexamethasone (active GC, D4902, Sigma), mifepristone (glucocorticoid receptor (GR) antagonist, Mi, M8046, Sigma), both GC and Mi, or vehicle (culture medium) for 48 h, followed by exposure (or not) to sodium oleate (OA, O7501, Sigma), which is rich in fatty acids, for 24 h. To evaluate the possible effects of GC on gene expression related to lipid metabolism, HepG2 cells were incubated with GC at different concentrations (0, 50, 100, 125, 250, 500 and 1000 nM; n = 3 for each concentration) and time (24, 36 and 48 h) and to ascertain the maximal response. The effects of GC (125 nM) combined with Mi at different concentrations (0, 0.1, 1, 5, 10 μM) were then used (n = 3) to evaluate the individual and combined effects on the hepatic lipid homeostasis. The TG content in the cells was determined using commercial kits (E1013, Applygen, Beijing, China).
Oil red O staining
At the end of incubation, the cultured cells were washed with PBS and fixed with 4% formaldehyde for 30 min at room temperature. Then, the cells were stained using Oil red O working solutions containing 6 ml of Oil red O stock solution (0.5 g in 100 ml of isopropanol) and 4 ml of ddH 2 O at 37°C for 30 min. Staining was visualized by bright-field microscopy (BX51, OLYMPUS, Japan).
Total RNA extraction and real-time PCR
Total RNA was extracted from cells or liver tissues using Trizol (Invitrogen) according to the manufacturer's instructions and quantified spectrophotometrically at OD260. The integrity of the total RNA was assessed using agarose gel electrophoresis, and cDNA was synthesized using M-MLV reverse transcriptase (TAKARA) with 0.5 μg of the RNA sample as recommended by the manufacturer. Genes of interest were analysed by real-time PCR using the SYBR GREEN ABI Prism 7500 sequence detector for the target genes, including SREBP-1c, ACC, SCD1, FASN, PPARα, LPL, L-FABP, CPT1 and MTP ( Table 1). Expression of the target genes was normalized to the expression of glyceraldehyde-3phosphate dehydrogenase (GAPDH) ( Table 1).
Statistical methods
Data are expressed as Means ± SEM. Two-ways analysis of variance (ANOVA) tests were used to analyse body weight gain. Two-sided Student's t-test was used to analyse liver weight, hepatic lipid content, mRNA
Food intake and body weight
The food intake of the SL rats increased significantly only at W3 to W5 compared to the NL rats (p < 0.01, Table 2), and there were no significant differences between groups after that time (p > 0.05, Table 2). Body weight increased with age in both groups (p < 0.001), and the SL rats gained more than the NL rats (p < 0.001); there was a significant interaction for weight gain in the SL rats with age (p < 0.001, Fig. 1a).
Liver weight and hepatic TG content
The liver weight was higher in the SL rats compared to the NL rats at W3 and W13 (p < 0.001, Fig. 1b), but there was no significant difference in HSI between groups (p > 0.05, Fig. 1c). Hepatic TG content was higher in the SL rats compared to the NL rats at W3 and W13 (p < 0.001, Fig. 1d).
11β-HSD1, 5αR1 and 5βR mRNA expression in the liver at W3 and W13 Hepatic 11β-HSD1 mRNA expression was higher in the SL rats compared to the NL rats at W3 (p < 0.001, Fig. 2a) and W13 (p < 0.05, Fig. 2b). Compared to the NL rats, hepatic 5αR1 and 5βR mRNA expression was higher in the SL rats at W3 (p < 0.01, Fig. 2a) but decreased significantly in the SL rats compared to the NL rats at W13 (p < 0.01, Fig. 2b).
mRNA expression of rate-limiting enzymes in hepatic tissue at W3 and W13 Hepatic ACC, SCD1, FASN and SREBP-1c mRNA expression was significantly increased in the SL rats compared to the NL rats at W3 and W13 (p < 0.05, Fig. 3), whereas mRNA expression of LPL and L-FABP mRNA only increased in the SL rats at W3 (p < 0.05, Fig. 3a), but not at W13 (p > 0.05, Fig. 3b). There were no significant differences in the expression of PPARα, CPT1 or MTP between the two groups at W3 or W13 (p > 0.05, Fig. 3). Data are expressed as the mean ± SEM. Significant differences between groups of rats at corresponding time points were analyzed by two-sided Student's t-test **p < 0.01, ***p < 0.001 vs. NL rats. n = 6 in each NL and SL group Fig. 1 Body weight (a) from postnatal week 1 to week 13; liver weight (b), hepatosomatic index (c) and hepatic TG content (d) in normal litters (NLs) and small litters (SLs) at week 3 (W3) and week 13 (W13). Data are expressed as the mean ± SEM. Body weight gain was analyzed by two-way ANOVA. # F = 1980, p < 0.001 for effect of age; † F = 363, p < 0.001 for effect of SL; & F = 11, p < 0.001 for interaction of SL and age. Significant differences between groups at W3 or W13 were analyzed by two-sided Student's t-test. ***p < 0.001 vs. NL rats. n = 6 in each NL and SL group
Effects of GC or/and OA on lipid accumulation in HepG2 cells
Oil red O staining showed that little lipid droplets existed in the normal HepG2 cells, but these intracellular lipid droplets were obviously increased in the cells after treatment with GC or OA; there was more significant lipid accumulation after treatment with GC + OA (Fig. 4a). In addition, Mi treatment attenuated the increase of the lipid accumulation induced by GC or GC + OA (Fig. 4a). Like the lipid droplet accumulation, the TG content in the HepG2 cells increased after treatment with GC or OA or GC + OA and decreased when Mi was added compared to the GC or GC + OA treatment (p < 0.05, Fig. 4b).
Gene expression in response to GC and OA in HepG2 cells
To determine whether the regulation of hepatic lipid accumulation by GC was mediated by metabolism enzymes, we next examined ACC, SCD1, FASN and SREBP-1c mRNA expression in the HepG2 cells. As expected, the level of ACC mRNA was dependent on the dose and timing of the GC stimulation (p < 0.05, Fig. 5a, b); the optimum concentration and timing were 125 nM for 48 h. SCD1, FASN and SREBP-1c mRNA expression increased in GC stimulation (p < 0.05, Figs. 6a, b, g), but CPT1 expression decreased (p < 0.05, Fig. 6f), and all these alterations were more significant in the GC + OA treatment (p < 0.05, Fig. 7b, c, g, h), as well as ACC (p < 0.05, Fig. 7a). Mi alone (p > 0.05, Fig. 6) and the Mi + OA treatment (p > 0.05, Fig. 7
Discussion
It has become increasingly recognized that the metabolic programming effects of nutritional modifications in early postnatal life are independently related to the development of obesity and MS in later life [31]. Overnutrition during lactation induces a persistent increase in body weight, hyperinsulinemia, hyperleptinemia and MS in adults, including NAFLD [30,31]. Consistent with our previous reports, we confirmed that early neonatal overfeeding induced increased mRNA expression of 11β-HSD1, decreased expression of 5αR1 and 5βR and abnormal lipid metabolism in the livers of the SL rats compared to NL rats. The new finding of this study Fig. 2 mRNA expression of 11β-HSD1, 5αR1 and 5βR at W3 (a) and W13 (b). Data are expressed as the mean ± SEM. Significant differences between groups at W3 or W13 were analyzed by two-sided Student's t-test. *p < 0.05, **p < 0.01, ***p < 0.001 vs. NL rats. n = 6 in each NL and SL group Fig. 3 mRNA expression of the genes involved in hepatic lipid metabolism at W3 (a) and W13 (b). Data are expressed as the mean ± SEM. Significant differences between groups at W3 or W13 were analyzed by two-sided Student's t-test. *p < 0.05, **p < 0.01, ***p < 0.001 vs. NL rats. n = 6 in each NL and SL group was that exposure to GC increased hepatocyte lipid accumulation by up-regulating the gene mRNA expression of hepatic DNL through GR. We suggest that early postnatal overfeeding induced by SL rearing leads to peripheral GC metabolism activity, which might contribute to the increase in hepatic lipid synthesis in adult rats. Previous studies have shown the presence of higher 11β-HSD1 mRNA and/or activity in the adipose tissue of obese rodents [24,25] or humans [23,26]. 11β-HSD1 is known to be positively associated with features of MS in adults [20]. Transgenic mice with adipose-or liverspecific 11β-HSD1 overexpression exhibit elevated intraadipose and portal corticosterone levels, abdominal obesity, dyslipidaemia, insulin resistance and hypertension [44,45]. In our studies, SL adult rats exhibited obesity and increased hepatic 11β-HSD1 overexpression but decreased 5αR1 and 5βR expression, indicating that there are more active GC in hepatic tissue. The high concentrations of GC in the liver could have important effects on lipid metabolism [28,45,46]. In the present study, small litters displayed significant increases in liver mass and TG contents compared to NL rats.
GC can lead to hepatic steatosis by decreasing lipid export and oxidation [47,48], increasing cholesterol synthesis and fatty acid uptake [49,50] or increasing lipid biosynthesis [28,29]. In addition to the changes of 11β-HSD1, 5αR1 and 5βR in the liver, we also found that postnatal overfeeding induced a significant increase in DNL by SREBP-1c, ACC, SCD1 and FASN overexpression in the liver from weaning to adulthood, which might be an important mechanism underlying the development and progression of NAFLD in adulthood, that is, overexposure to GC through 11β-HSD1 up-regulation in the liver.
In line with our hypothesis that overexposure to GC induces an increase in DNL in hepatocytes, we found that both lipid accumulation and TG content in HepG2 cells were significantly increased by GC treatment via DNL increase through SREBP-1c, ACC, SCD1 and FASN overexpression. Therefore, the augmented active GC induced by the increase in 11β-HSD1 might be an important factor responsible for the increased DNL in the livers of SL-reared rats. Because the effects of GC were mainly mediated via the GR, which is a member of the steroid hormone receptor superfamily [51,52], we used Mi, the GR antagonist [53,54], and confirmed that most of the hepatic lipid metabolism changes induced by GC were inhibited by Mi. Thus, we suggest that GC could increase lipid accumulation by increasing DNL through its receptor in the hepatocytes.
Although postnatal overfeeding can alter lipid metabolism in the liver, a high-fat diet is central to the onset of NAFLD [55,56]. In our previous studies, we found that neonatal overfeeding in rats induced by SL rearing increased their vulnerability to a HF diet from postsuckling to adulthood and promoted early onset and exaggeration of HF diet-induced NAFLD [38]. Moreover, we found that SL and a high-fat diet exhibited a significant interaction with regard to 11β-HSD1 expression, but hepatic 11β-HSD1 expression was not observed in NL-HF rats [57]. We suggest that the the increased activity of the GC induced by 11β-HSD1 and a HF diet have a significant interaction on lipid metabolism in the liver. In the present study, we found that the GC + OA treatment in vitro resulted in the most significant lipid accumulation and DNL increase in HepG2 cells compared to separate GC or OA treatments.
Previous studies have shown that during energy overconsumption, LPL and L-FABP expression increased in the liver [58,59], but CPT1and MTP decreased [60,61]; all these alterations could contribute to the occurrence of NAFLD [27,62]. In the present study, we found that CPT1 decreased after GC treatment in vitro, but it did not change at W13 in the SL rats. Notably, our previous observation indicated that CPT1 decreased at W16 in the SL rats [38], suggesting that long-term overexposure to GC also affected lipid oxidation in the hepatocytes. Furthermore, the transient elevation of LPL and L-FABP mRNA expression in the SL rats (at W3) might be due to the excessive food intake; it did not change after weaning in vivo or after GC overexposure in vitro. There was also no change in MTP or PPARα caused by GC overexposure either in vivo or in vitro. Therefore, we suggest that GC overexposure in the SL rat model and HepG2 cells augmented the hepatic lipid accumulation mainly through DNL increase.
Conclusions
Postnatal overfeeding induced GC overexposure through 11β-HSD1 up-regulation in the liver, and the GC activated the hepatic DNL by GR. This resulted in hepatic lipid accumulation, leading to an increased risk of NAFLD during adulthood. More animal and clinical studies are needed to examine the prolonged effects of manipulating the availability of pre-receptor GC and the mechanisms of GR activation in the liver. Specifically, we suggest that targeting pre-receptor GC activation in the liver may provide a novel approach to the treatment of NAFLD, particularly in childhood.
Availability of data and materials
Data are all contained within the article.
Authors' contributions FY and XL conceived and designed the experiments. FY and CM performed the animal experiment. FY and YD performed the cell experiment. YD and CM performed the analyses. FY and XL wrote the paper. YD and CM reviewed the manuscript. All authors approved the manuscript.
Ethics approval and consent to participate All animal studies were performed following the guidelines established by the University Committee on the Use and Care of Animals and were overseen by the Unit for Laboratory Animal Medicine at Nanjing Medical University (IACUC: 14030102). | 4,880.4 | 2018-05-02T00:00:00.000 | [
"Biology",
"Environmental Science",
"Medicine"
] |
Draft Genome Sequence of Paenibacillus polymyxa 3A-25AI, a Strain Antagonist to Root Rot Causal Phytopathogens
Here, we report the draft genome sequence of the Paenibacillus polymyxa 3A-25AI strain, isolated from the rhizosphere of wild grass. This strain inhibits Phytophthora capsici and Rhizoctonia solani phytopathogens. The genome size is 5.6 Mb, with a G+C content of 45.59%, and contains 5,079 genes, 4,968 coding DNA sequences (CDSs), 35 tRNAs, 3 rRNAs, and 72 unexpected miscellaneous RNA (miscRNA) features.
P aenibacillus polymyxa is a bacterium that helps to improve biocontrol strategies to counteract phytopathogens. It is a Gram-positive, abundant metabolite and a polysaccharide-producing bacterium (1) that produces nonribosomal peptide/ polyketide bioactive compounds with antifungal and antibacterial functions. P. polymyxa is also a plant growth-promoting rhizobacterium (2,3) and improves the microbial richness and diversity of soil (4). A consortium of fungi and oomycete pathogens that cause necrosis commonly affects the roots of solanaceous crops. It is necessary, therefore, to improve phytopathogen biocontrol tools and gradually put aside the use of pesticides.
Here, we report the draft genome sequence of the Paenibacillus polymyxa 3A-25AI strain, isolated from the rhizosphere of Sporobolus airoides (Torr.), sampled in the Morelos Municipality of Zacatecas, Mexico. This bacterial strain grows in King B and LB medium at 27°C, reaching the stationary phase after 18 h.
For sequencing purposes, the bacterium was grown in LB medium for 18 h, and bacterial genomic DNA was extracted with the cetyltrimethylammonium bromide (CTAB) reagent (5). One nanogram of DNA was used for the preparation of libraries by adhering to Nextera kit instructions (Illumina, San Diego, CA, USA). Genome sequencing was performed with a MiSeq sequencer (Illumina) in a 2 ϫ 75-bp paired-end run. The quality of sequencing reads was analyzed in FastQC (6), with a lower threshold for a contig length of 200 bp. Genome assembly was done using SPAdes or Unicycler (7), and the quality of the assemblies was analyzed in QUAST 4.1 (8), including a reference genome (GenBank accession number NZ_CP025957.1). The assembly with SPAdes offered better results, with a contig N 50 value of 16,622 bp and genome coverage of 13.0ϫ. An estimate of the maximum genome completeness and minimal contamination assessed by CheckM (9), including 32 reference genomes and 468 markers, shows 99.09% completeness and 1.25% contamination. Therefore, we have a nearcomplete sequenced genome with low contamination.
Genome annotation was achieved using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) (10) and complemented on the Galaxy server using Prokka (11).
The alignment in NCBI GenBank in the BLAST microbial genomes algorithm unveiled a significant match to Paenibacillus polymyxa SC2, covering 97.5% of contigs, 99.3% of the genome total, and 98.5% identity; the P. polymyxa SC2 strain was isolated from the rhizosphere of pepper in Guizhou, China (12). An evolutionary analysis with the 16S rRNA sequence in MEGA7 (13) confirms this bacterium to be Paenibacillus polymyxa (Fig. 1).
The antifungal and antioomycete activities of this bacterium possibly rest on the biosynthesis of polyketides and/or -glucanase. With the sequenced genome and the described characteristics, this P. polymyxa strain adds to the range of possibilities for assembling bacterial formulations for use in the fight against phytopathogens.
Data availability. The draft genome sequence of Paenibacillus polymyxa 3A-25AI and the raw data generated in this study have been deposited in DDBJ/ENA/GenBank under the accession number MLCZ00000000. The version described in this paper is the first version, MLCZ01000000. The associated BioProject and BioSample accession numbers are PRJNA343056 and SAMN05846293, respectively.
ACKNOWLEDGMENT
This polymyxa strains included in the phylogenetic analysis define a nested clade. The evolutionary history was inferred using the neighbor-joining method (15). The associated taxa clustered together in the bootstrap test (300 replicates). The optimal tree with the sum of branch length of 2.49816535 is shown. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the maximum composite likelihood method and are in the units of the number of base substitutions per site. The analysis involved 20 nucleotide sequences. Codon positions included were 1st ϩ 2nd ϩ 3rd ϩ noncoding. All positions containing gaps and missing data were eliminated. There were a total of 1,246 positions in the final data set. Evolutionary analyses were conducted in MEGA7 (13). | 999.6 | 2020-02-01T00:00:00.000 | [
"Biology",
"Engineering"
] |
‘Animals run about the world in all sorts of paths’: varieties of indeterminism
In her seminal essay ‘Causality and Determination’, Elizabeth Anscombe very decidedly announced that “physical indeterminism” is “indispensable if we are to make anything of the claim to freedom”. But it is clear from that same essay that she extends the scope of that claim beyond freedom–she suggests that indeterminism is required already for animal self-movement (a position recently called ‘agency incompatibilism’ by Helen Steward). Building on Anscombe’s conception of causality and (in)determinism, I will suggest that it extends even further: life as such already requires physical indeterminism. Furthermore, I show that we can, on this basis, arrive at the idea of varieties of (in)determinism, along with a corresponding variety of incompatibilist theses. From this Anscombean vantage point, the free will discussion takes on a quite different outlook. The question whether free agency can coexist with determinism on the level of blind physical forces, which preoccupies the philosopher of free will, turns out to conflate a whole series of compatibility questions: not just whether life is compatible with physical determinism, but also whether animal self-movement is compatible with ‘biological determinism’, and whether free agency is compatible with ‘animal determinism’.
3 Anscombe's libertarianism
Towards the end of her inaugural lecture at Cambridge, entitled 'Causality and Determination', Elizabeth Anscombe decidedly and unambiguously declares herself a libertarian regarding free will: My actions are mostly physical movements; if these physical movements are physically predetermined by processes which I do not control, then my freedom is perfectly illusory. The truth of physical indeterminism is thus indispensable if we are to make anything of the claim to freedom. (C&D: 146) 1 This remark gives voice to a quite fundamental anti-compatibilist intuition, which is well-known in the shape of, e.g., Peter van Inwagen's 'Consequence Argument' and Derk Pereboom's 'Manipulation Argument'. 2 Anscombe made the remark as an afterthought in a lecture which, in its main parts, is not at all directly concerned with freedom or intentional agency, but rather with, indeed, the far more general notions of causality and determination. 3 It will be my aim in this essay to bring out how her views on the latter may be used to develop 'varieties of indeterminism', an idea that helpfully illuminates our very understanding of the free will problematic.
Anscombe comes to express her libertarianism in response to the "severe criticism" that "this 'mere hap'"-physical indeterminism-"is the very last thing to be invoked as the physical correlate of 'man's ethical behaviour'" (C&D: 145). And this, in turn, expresses a fundamental anti-incompatibilist thought; the one underlying the much-discussed 'Luck Objection'. 4 She confidently takes the sting out of that criticism (at least partly) by noting that "[t]he physically undetermined is not thereby 'free'"; freedom is "not to be analysed as the same thing as, or as produced by, the physical haphazard" (C&D: 146). Nevertheless, she insists that there is nothing unacceptable about the idea that that 'physical haphazard' should be the only physical correlate of human freedom of action; and perhaps also of the voluntariness and intentionalness in the conduct of other animals which we do not call 'free'. (C&D: 146; my emphasis).
Clearly, Anscombe was envisaging a form of libertarianism on which 'physical' indeterminism is a necessary ingredient in setting the stage on which we humans can act freely, but no part of the positive conception of free agency. On that point, she offers only the very brief remark that it involves "the power of acting according to an idea" (C&D: 146). 5 Moreover, she apparently holds that a similarly-structured view is apt regarding animal behavior at large. This resonates with Helen Steward's (2012a,b) 'agency incompatibilism', on which it is not the narrower idea of morally responsible, free agency, but rather agency as such which requires indeterminism, where 'agency' is understood to extend significantly into the domain of animal self-movement. Now, this seemingly casual broadening of scope in Anscombe's paper is, as we will see, not casual at all; in fact, it follows quite naturally from the conception of causality developed in C&D.
Further reflection on the picture we can thus extract from Anscombe's work reveals the need for an understanding of the idea of incompatibilism (the requirement of indeterminism) that is much more differentiated than what we find in most of the contemporary literature on agency and free will. Consequently, I will argue, we need to distinguish different 'varieties of indeterminism', and a corresponding variety of incompatibility claims. In effect, these varieties distinguish 'levels' within the natural world, 'levels' which are related in such a way that each 'higher' level requires indeterminism on the 'lower' level(s). Drawing on Anscombe's reflections, I will be working with a provisionary list of such levels in this paper: free agency ('acting according to an idea'), animal behavior, life, the 'physical haphazard'. But it is not this specific list of levels that I aim to be developing and defending here; rather, it is the very idea of such levels, differentiated by varieties of indeterminism and incompatibilism, that I aim to get into focus.
Hence my primary aim in what follows is to bring out this broader, differentiated incompatibilist picture, highlighting along the way the transformed shape that the traditional free will problematic takes on once we put it in this Anscombean light. More specifically, when we position Anscombe's libertarianism against the background of the overall understanding of causality and determination she develops, we can come to see (1) that the requirement of indeterminism does not spring from freedom specifically; (2) that it springs from her understanding of causality as, primarily, a 'local' matter of interacting substances; and (3) that this understanding invites a metaphysical picture that makes room for diverse 'levels'.
Let us, therefore, first have a closer look at what Anscombe exactly has to say on causality.-It should perhaps be kept in mind that I will not undertake to properly defend the Anscombean picture that I develop below; my aim is merely to sketch it in order then to show how it gives rise to the promised varieties of indeterminism and the ensuing reshaping of the classical free will debate. 5 With this mere hint, Anscombe is clearly referring to her action-theoretic reflections in her monograph Intention (Anscombe 1957). She there approaches the topic of intentional action through the famous question 'Why?'. Note that this question (when taken in a different, broader, 'sense') can also be read as the defining question for the topic of causality more broadly. Compare the opening pages of Anscombe (1983).
3 2 Anscombe on causality
Anscombe's self-declared aim in C&D is to reject the habit of associating the idea of causation with necessity and/or universality. Eventually, she isolates the "core, the common feature, of causality", which thus doesn't involve the ideas of necessity or exceptionless generality, as follows: "causality consists in the derivativeness of an effect from its causes … Effects derive from, arise out of, come of, their causes" (C&D: 136).
Moreover, she adds that "analysis in terms of necessity or universality does not tell us of this derivedness of the effect; rather it forgets about that" (C&D: 136). The reason is, in a nutshell, that, although both necessity and universality may be in play in a (or any) given case of causality, this will then be an additional fact, one which places the causal happening here and now in the context of some natural law or true universal generalization. However, while focusing on such an additional fact, we lose sight of the original fact of the causality here and now. 6 Given that Anscombe thus expels the elements of necessity and universality from our conception of causality, it is only natural that she has been claimed as one of the leading advocates of 'singularism', the view that, as Ann Whittle puts it: Causal connections between two relata [don't] depend upon anything extraneous to that relation. Rather, the truthmakers of singular causal statements are entities which are local and intrinsic to those relations. (Whittle 2003: p. 372) And this impression is reinforced when we read, e.g., Anscombe's eventual definition of 'necessitating cause' (on which more below): "a cause C is a necessitating cause of an effect E when (I mean: on the occasions when) if C occurs it is certain to cause E unless something prevents it" (C&D: 144). Anscombe puts very much emphasis indeed on the fact that this definition is to be read as regarding particular 'occasions', and not as introducing a general link that holds between all instances of C and E.
Another metaphysician who appeals to 'singular causation', with reference to Anscombe, is David Armstrong. Indeed, he is so convinced of Anscombe's 'extreme' singularism that he writes: "in opposition to those such as Anscombe (1971) for whom causation is essentially singular, singular causation is not ontologically primitive. It can be given an ontological analysis … as the instantiation of a law of nature" (Armstrong 1997: p. 202).
However, as Ometto (ms.) convincingly argues, it is not necessary to associate Anscombe with singularism in this manner. For, arguably, Anscombe actually believes that there is a general element relevant to causality. She just rejects the notion that this generality takes the shape of necessity or universality (which appear 1 3 Synthese (2021) 199:11945-11961 to be the only forms of generality Armstrong could think of 7 ). Rather, the generality involved is a broadly Aristotelian one, relating to powers possessed by natural kinds.
Here is her most explicit statement on the matter: Suppose we were to call propositions giving the properties of substances "laws of nature". Then there will be a law of nature running "The flashpoint of such a substance is. . . ", and this will be important in explaining why striking matches usually causes them to light. This law of nature has not the form of a generalization running "Always, if a sample of such a substance is raised to such a temperature, it ignites"; nor is it equivalent to such a generalization, but rather to: "If a sample of such a substance is raised to such a temperature and doesn't ignite, there must be a cause of its not doing so." (C&D: 138) There isn't much by way of elucidation of this conception of natural laws in C&D. 8 And although much more would need to be said on the peculiar generality present in such Anscombean laws of nature-i.e., in powers-that is not the topic of this paper. 9 What matters for present purposes is the resulting shift in our understanding of what it is for something to happen as such. In an unduly neglected paper in defense of a 'limited indeterminism', Arthur Prior once characterized this understanding as follows: [T]he world consists not of events, such as headaches, but of things, such as heads, which act and interact and change. … [H]ow things behave-that is, what events occur-is determined partly by their natures or dispositions, and partly by what happens to them. (Prior 1962: p. 58-9) What happens is determined, in part, by the 'natures or dispositions' of the things that happen to be around: this nicely captures the general metaphysical orientation that is present in the background of Anscombe's lecture. 10 Against this background, it is much easier to understand her otherwise rather obscure definitions of necessitating and non-necessitating causes, and of determinism and indeterminism. Let us start with the former: [A] necessitating cause C of a given kind of effect E is such that it is not possible (on the occasion) that C should occur and should not cause an E, given that there is nothing that prevents an E from occurring. 11 A non-necessitating cause is then one that can fail of its effect without the intervention of anything to frustrate it. (C&D: 144) It may strike one as odd, that Anscombe leaves open the possibility of prevention in her definition of necessitating cause. How can a necessitating cause ever be thwarted? The contrast with the non-necessitating cause helps to clarify what is meant. Suppose, to take a less destructive variant of an example Anscombe takes from Feynman (C&D: 144-5), that you put some radioactive material in the vicinity of a Geiger counter, intending to thereby make it register a certain reading. Depending on the specifics of the case, it may very well be that all is set for the desired reading to be registered-and yet it doesn't. Nothing interferes with the process of decay (we imagine that there is radioactive decay going on, just not enough to trigger the relevant reading); it is just that this is a case of 'non-necessitating' causality. By contrast, if you drop a stick in mid-air, it will fall to the ground-unless, indeed, something prevents it. Your dog, for instance, might jump up and catch the stick in mid-air. But it is impossible that the stick should simply hang in the air where you left it of its own accord. Gravity, the power of massive objects to attract each other, acts as a necessitating cause. But whether it will actually result in the corresponding change of position depends not just on the 'laws of gravity' but on the specifics of the situation at hand-on the 'natures or dispositions' of the substances that happen to be around. The presence of dogs makes a difference (although the difference it makes is itself not likely to be necessitating).
Now suppose the thing you are dropping is not a stick, but rather a chicken of exactly the same weight. Neglecting differences in the way air resistance affects stick-shaped and chicken-shaped physical objects, the laws of gravity will indicate an identical outcome. But there is more to the chicken than its weight: it can fly, and may very well start doing so upon being released in mid-air. (And, again, your dog might interfere as well.) This should suffice to illustrate how Anscombe thinks of laws and causes. If we think of the world as fundamentally inhabited by interacting things in this way, then these things include not just the particles of physics or the stuffs of chemistry, but also dogs and chickens and people. And these have powers-such as the power to fly, or the power to act according to an idea-that are completely different from any of the powers possessed by physical particles or chemical stuffs. Yet, as our examples indicate, these powers may interfere with the manifestation of physical or chemical powers. On this basis we can see why Anscombe ends her essay with the remark: "The most neglected of the key topics in this subject are: interference and prevention" (C&D: 147).
Anscombe on indeterminism
There is nothing objectionable about things interfering with exercises of the power of gravity. In fact, gravity itself can be thought of as 'interfering' with gravity: the relative movements towards each other of two heavy objects, as dictated by the laws of gravity, will not materialize if a third heavy object is present in the vicinity. The point is quite trivial once we have shed the tendency to think of causes in terms of necessity or universality: what will actually happen is not determined by the laws of any specific feature or power or force, for there may always be other things present with additional features or powers or forces. Abstractly speaking, then, interference with the power of gravity by a playful dog is no different from interference by another heavy object.
But there is a difficulty when we look more concretely at such a situation. For the dog itself is composed of physical particles and chemical stuffs-and these particles and stuffs presumably behave in accordance with their nature, whether they're inside the dog or not. So how can the dog as a whole make any difference to what its constituent matter is doing?
We must distinguish between two senses of this question. Borrowing Jonathan Lear's apt distinction: we can put the question either 'with a skeptical sneer' or rather 'with a straight face' (see Lear 1984). To answer the latter question, which is a genuine inquiry into how there could be such a difference-making on the part of the dog, we need to distinguish matter from material. The matter inside the dog isn't just matter, for the dog uses it as material. 12 More concretely: if the dog is to interfere, out of its playful desire, with the falling stick, then that desire must be effective in steering the dog's constituent matter accordingly. This in turn means that the constituent matter must leave open a range of physical outcomes, which partly constitute the behavioral options open to the dog, to be narrowed down further by the dog's desires, impulses, and instincts. In this way, then, the dog can make a difference to what its constituent matter does, without of course ever breaking the laws governing that matter. 13 I will elaborate on this answer to the 'straight face' question shortly. But, although this lies strictly outside of the concerns of this paper, let me briefly touch upon the 'skeptical sneer' version of that question as well. It can, for instance, take the following shape: by which mechanism does the dog exert influence on its constituent matter? This question is then not to be answered by merely pointing out that the dog uses the 'mechanisms' of his muscular system, for instance. What the mechanistic inquiry is after is, rather, a mechanism by which the dog makes its constituent matter (including its muscles) do things that that matter would not be doing 'on its own' in the first place. And obviously, the mechanistic inquiry isn't particularly interested in dogs; it wants to know how substances generally can exercise powers of their own by 'using' their materials.
Put in this way, there will be no satisfactory answer-and the reason why there will be no such answer is precisely because it rests on a 'skeptical sneer' towards the Anscombean-Priorian conception of interacting substances as such. Compare a 12 An anonymous referee helpfully pointed out to me that this statement requires qualification. There may be matter 'inside' the dog that is not, and cannot, be used as material in the relevant sense-suppose, e.g., that the dog has swallowed a small rock. Furthermore, it looks like we need to make a difference between the 'using of material' that is purely biological, like the way in which the dog's digestive system takes up nutritive substances, and the agentive 'using of material' that is relevant to my example here, which consists in the dog's moving its body in accordance with its perceptions and desires. This further differentiation foreshadows the 'varieties of indeterminism' I aim to get into view in what follows. 13 Not that it is left completely open what can happen: the laws governing that matter will constrain the possibilities greatly. That is, by the way, why Prior named his view "limited indeterminism." similar mechanistic inquiry in the case of the matter itself: electrons, being negatively charged, repel each other-but by what mechanism do they exert such influence? We must say: by no mechanism; rather, it is through having the power of electric charge (among other powers) that electrons can be invoked in mechanistic explanations in the first place. 14 In short: the categories of substance and power are basic; it is in terms of interacting substances that we can understand what goes on. But this is, to repeat, a highly abstract insight; nothing in the very idea of substance and power prohibits there being composite substances. A dog is such a substance, with powers of its own; these are, on our present picture, metaphysically as well as explanatorily basic. 15 Of course, much can be said concerning the way in which a dog exercises its 'agentive' powers (e.g., by using its muscular system in certain ways), but that more detailed story concerning the dog's agency will simply take its place within the fundamental, metaphysical conception of it as a substance having certain powers. It will answer our straight-face question for the particular case of the dog, not the skeptical-sneer question.
So much for our brief excursus. Notice that we have finally returned, now, to the casual broadening of the requirement of indeterminism from free agency to animal behavior registered in §1 above. Here is another quote to the same effect taken directly from Anscombe's discussion of causality: We could say: of course nothing violates … the laws of the force of gravity. But animals, for example, run about the world in all sorts of paths and no path is dictated for them by those laws …. (C&D: 143; my emphasis) Again, it is not animals per se that Anscombe seems to be interested in ('for example', she writes). Rather, her essay as a whole points towards a more general conclusion: that, on a metaphysical picture of interacting substances, the requirement of physical indeterminism arises as soon as we want to acknowledge substances that have the power to do certain things by using their constituent matter in certain ways-in short, where matter is used as material. And it looks like this is paradigmatically true not just for free agency, and not just for animal behavior either, but for the realm of the living at large. 16 14 Of course, there may be further, more advanced scientific insights into the specifics of interactions between electrons, yet it is clear that, as long as we stick to the substance-power scheme, at the ultimate 'bottom-out' point we will still find certain substances with certain powers. 15 Here the skeptical sneer may take on a different shape: it is fine to have fundamental particles which are 'basic' in the sense described, but surely dogs aren't 'basic' in this sense? Aren't they mere products of evolution, 'derivative' rather than 'basic' beings? I cannot discuss this vexed reductionist objection in detail here (but see Mulder (2016Mulder ( , 2021c); yet, for what it is worth, here is one pertinent consideration: this objection threatens to confuse two senses of being 'basic': a metaphysical and a mereological (or perhaps rather aetiological or ontogenetical) one. The metaphysical category of substance doesn't rule out composite, evolved substances, and so it is perfectly possible that there be metaphysically basic substances which are nevertheless physically composite and biologically evolved. 16 I have argued for such a 'vitalist' incompatibilist claim at length in Mulder (2016Mulder ( , 2021a. Perhaps, the claim must be extended even further, to all composite objects, alive or not. See, e.g., Elder (2011) for such a stance.
Thus we arrive at an incompatibility claim of much broader scope that the one usually found among libertarians in the free will debate. However, that doesn't yet give us the promised varieties of indeterminism and incompatibility. Before we move on to develop those (in §4 below), we need a better grasp on what it means, exactly, to endorse indeterminism, on our Anscombean picture. She herself defines it as follows: I should explain indeterminism as the thesis that not all physical effects are necessitated by their causes. (C&D: 145) Conversely, then, determinism will be the thesis that all physical effects are necessitated by their causes. That still doesn't mean that nothing can interfere with any given case of necessitating causation: it just means that that interference itself then must be the result of a necessitating cause as well. (For instance, a billiard ball's movement towards the pocket may be interrupted by another ball, which got its momentum from a fine hit by a player's cue, which is a necessitating cause of movement.) Given the background metaphysical picture of interacting substances, it is always in principle possible that something interferes with a given necessitating cause. But if all that happens involves necessitating causes, then nothing outside of the actual unfolding of events is possible.
Or, at least, that seems to be what Anscombe has in mind when she defines indeterminism in this way. There is, however, a complicating factor: nothing in Anscombe's definition of 'necessitating cause' prohibits there being necessitating causes the effects of which aren't fully determinate. Take her own example of rabies being a necessitating cause of death (you're sure to die from it without treatment 17 ): the rabies doesn't necessitate exactly when, where and how you're going to die from it-not in every conceivable detail. Thus it may well be that "all physical effects are necessitated by their causes" without it being the case that the future course of events is fully predetermined-i.e., without determinism being true. 18 In fact, I think this observation adds to Anscombe's case for her thesis that "[i] t is the [determinist's claim of] total coverage of every motion that happens, that is a fanciful claim"-which is why she insists that "indeterministic physics … is only culturally, not logically, required to make the deterministic picture doubtful" (C&D: 147). 19 After all, if necessitating causes indeed leave room for a certain range of outcomes (all of which conform to that which is necessitated, of course), then this may be thought to already provide for the leeway I claimed to be required by the vital operations of living beings, the behavior of animals, and the free actions of thinking beings like ourselves. 17 Well, not really-there turn out to be few but significant exceptions. See Gilbert et al. (2012). 18 It may be thought that this complication arises only because we specify the cause too generically: generally speaking (at the type level), it may well be true that rabies doesn't settle the relevant details, but what about this specific instance of it (at the token level); might there not be full coverage of the relevant details there?-To see why this is no option for Anscombe, recall our discussion of 'causal singularism' earlier. 19 In a later paper, she speaks in this connection of a "deterministic itch" (Anscombe 1983: 105).
3
And indeed, many of the typical arguments and examples one finds in Anscombe's essay are intended to show that even if we take the relevant goings-on to be the result of necessitating causes only, still there is no way in which we can conclude that the end result was predetermined (see, for instance, her discussion of the bouncing balls; C&D: 142).
But I do not think Anscombe is entirely clear on this matter in C&D. On the one hand, it looks like her definition of indeterminism, as well as her short discussion of the import of indeterministic physics with regards to the free will problem, assume that unless we have non-necessitating causes, determinism is true. On the other hand, as I just indicated, her overall strategy is one of dissociating the 'fanciful claim' of determinism even from the notion of deterministic laws (i.e., laws governing necessitating causes).
We can amend the situation for Anscombe in two ways. At first sight, it may seem that we should simply redefine 'necessitating cause' so that it only applies to cases where what is necessitated is determinate in every respect. But it is quite doubtful whether there will then be any necessitating causes. (Briefly put, these would then either have to be governed by laws so precise that they exceed limits of accuracy beyond which we cannot really give meaning to their statements 20 , or they would collapse to singular (token) causes, which we discussed earlier.) It thus doesn't look like this solution sits well with Anscombe's philosophical outlook.
The alternative is to redefine 'indeterminism' more specifically as the claim that not every physical effect is predetermined in every respect. Given Anscombe's preference for the term '(un)predetermined' throughout the lecture (a notion she does not explicitly define), this looks to be the better solution. And it also fits better into the basic metaphysical framework of substance and power against which I have been plotting her considerations. Recall that, on that conception, powers embody the element of generality, which Anscombe regains by rejecting the involvement of universality and necessity in the very concept of causality. Powers point towards their manifestation, we could say, but this 'pointing' is not a matter of relating the given, particular situation to a particular manifestation-situation later on. Rather, powers point to their manifestations generically (e.g., rabies leads to death), and such a generic manifestation can materialize in many particular ways. 21 Thus understood, it becomes clear why determinism would be a 'fanciful claim' even if one insisted that there are only ever necessitating causes. Now that we have sketched out Anscombe's conception of causality and (in) determinism, let us return to our strong incompatibilist claim and see how it gives rise to varieties of indeterminism and incompatibilism.
Varieties of indeterminism
My rough sketch of Anscombe's take on causality and indeterminism was meant to reveal the point at which indeterminism becomes a requirement for her. We saw that this point lies wherever something has powers that are exercised by making use of the possibilities offered by its constituent matter. I said that things having such powers are not their matter but rather have matter, which then is their material. 22 Now, in general, wherever matter is used as material for some substance or activity X, X must be allowed by the laws governing that matter, even though it will not spring from those laws. More concretely, I endeavored to claim that this point lies not where human free agency starts, but is to be located much lower on the scala naturae, namely where life starts.
The following comparison may help to see how the relations between matter, material, indeterminism, and determination lie, on our Anscombean picture. Consider the physical matter that composes your house: it allows for the physical configuration it in fact takes on in your house-it allows to be used as material for that purpose-but it doesn't organize itself in that way of its own accord. Rather, the cause of its actually taking on that organization lies in the realm of human agency-houses are, after all, paradigmatically organized in accordance with an idea.
Now it may seem that these two accounts of the origination of your house-in human agency, or rather in the sum of the physical processes leading up to itare rival accounts. In her later essay 'The Causation of Action' (1983), Anscombe reflects on that suggestion in the context of a longer discussion of the question how human action comes about (and here, too, she extends the discussion at points to include animal behavior as well). She discusses the broadly physiological project of tracing out the "causal history" of an action: e.g., tracing back the action of pushing a door open via the muscles and efferent nerves to the afferent nerves and the eardrums via which certain vocal sounds (the order to do so) were transmitted. And she contrasts such a causal history with one of "a different type from the physiological one" (Anscombe 1983: p. 100): one involving beliefs, desires, intentions, orders, etc. Her point eventually is that "The causal histories of the two types aren't rival accounts" (Anscombe 1983: p. 101). And this is just what we should say about the house example. For one can describe what went on, purely physically speaking, with all the little bits of matter that ended up composing your house. This purely 'physiological' causal history of the house will not mention the beliefs, desires, intentions, etc. of human beings. It will, rather, simply mention a wide range of consecutive physical processes and forces-forces by which, we can say, employing the other, intention-involving 'causal history', the various building materials were assembled according to some plan, and finally put together to result in your house. Again, the two accounts aren't rivals. 23 We make clever use of in-principle available physical goings-on for our project of building houses. Now, interestingly, these considerations point to a more fine-grained differentiation of levels than just the dual one of matter and what it is used for by that to which it stands as material. To see how, notice first that the causal history of your house is badly characterized as 'physiological': it is really a physical causal history. The physiological account of what goes on in a human organism when an action is performed, by contrast, is best understood as a biological account, as opposed to an exclusively physical account. It speaks of things like muscles and nerves, after all.
It follows that it is not sufficient to speak of two levels. We do not merely have a general contrast of physical matter and what it is used by as material-e.g., a plant, or a dog, or a human being-but we must acknowledge a further contrast of biological, living tissue and what it is used for. Accordingly, a dog's body, let us say, considered just as a living organism, on the one hand uses inanimate, physical matter as material (and thus requires indeterminism on the level of inanimate matter), but on the other hand, it provides the basis for the dog's animal agency, which in turn requires indeterminism on the biological level. 24 Such a biological indeterminism can be thought of as follows: the dog's body is constantly regenerating itself, keeping itself alive, and while doing so it enables a certain range of possible movements of various of its body parts. But it does not settle how exactly its legs will move-that is left open, to be determined by the dog through its desires, impulses, and instincts.
Put more generically: there is nothing in the overall Anscombean picture we are considering that dictates that only physical matter can play the role of material. For instance, biological substances, tissues, organs, etc. can play the role of material, too. In principle, our background picture of things interacting with each other in accordance with their natures or dispositions leaves open whether determinism holds. It also leaves open whether there are any composite substances, substances that interact with other substances (composite or not) by using their constituent matter in certain ways-but if there be such substances, there must be indeterminism on the lower level. And, we now discover, it even leaves open that that there may be various levels of substances related in this manner, substances that make use of lower-level material. At all lower levels, indeterminism will then be required.
Thus we finally arrive at the idea of varieties of indeterminism: the biological requires physical indeterminism, animal self-movement requires biological indeterminism-and along these lines there appears to be room for the further claim that human agency in turn requires a form of 'animal indeterminism'. For if we characterize animal agency as situation-bound, in that it is fundamentally desire-driven and perception-guided, then "acting according to an idea" may be thought to require a suitable form of indeterminism not just on the physical and biological levels, but on this level of animal desires and drives as well. 25 -This is not to spell out such a position in any detail, but merely to highlight that the framework of varieties of indeterminism that I am suggesting here can provide for such a differentiation between animal and rational agency.
From this vantage point, interesting questions concerning the relations between the levels come into light. As I have just put it, one might think that higher-level substances are like 'layered cakes': in the case of an animal, for instance, we would have its constitutive physical matter, its living body, and the perceiving-desiring animal. I do not think this ultimately makes sense, but I will not argue for that claim here. An alternative conception would hold that the higher-level capacities 'transform' the lower levels: an animal is not a plant with perceptive and agentive faculties added on top; rather, what it is to have a living body in the first place is transformed by those faculties. 26 This makes explicit a dimension to our Anscombean framework that I have not mentioned so far: we do not merely get varieties of indeterminism, but varieties of determinism as well. Each higher level, I said, requires indeterminism on all lower levels. It follows that there can only be one deterministic level; and that if any one level is deterministic, there cannot be any higher levels. But that leaves it open that there may still be lower levels-which must then of course be indeterministic.
In 'The Causation of Action', Anscombe considers the theoretical possibility of such a 'higher-level' determinism: one might think that the descriptions of chemical or animal forms were not merely supervenient. 27 But [one might still hold] that the existence and actions of all chemicals and animals that ever exist were determined-i.e. causally necessitated -from any previous point of time. (Anscombe 1983: p. 105;my addition) 25 See, for instance, Mulder (2018aMulder ( , 2021a. The characterization of animals as 'situation-bound' derives from Sebastian Rödl's characterization of animal behavior as "situation-responsive behavior" (cf. Rödl 2012: p. 69-73). 26 See, e.g., Haase (2013) and Mulder (2021a). But see also Gobsch (2017) for an excellent discussion of the serious issues that such a 'transformative' picture faces. 27 'Supervenient descriptions' are, for Anscombe, descriptions that appear to concern different levels yet do not really do so. An example is talk of water waves interfering with each other: this is a supervenient description, in her sense, of the interaction between water masses. No incompatibilist conclusions concerning the water masses follows from such talk of water waves.
Abstractly speaking, the thought here is that, while on the physical level many future possibilities are left open, those possibilities are brought down to exactly one by the (chemical or animal) factors that operate at the higher level.
Anscombe brings this theoretical possibility up because it bears on a 'fallacious argument' that was prevalent in her days: that microphysical indeterminism still translates into macrophysical determinism, because the indeterministic level is governed by statistical laws which effectively enforce conformity on the macro-level (see also C&D: 146). 28 Now, although she urges that there is no reason for endorsing this view (except for the 'deterministic itch' she complains about), Anscombe does not want to rule it out either: [W]e just do not know whether, for example, the course taken by an animal is predetermined any time it runs about some area where the causal factors are constant. The appearance is otherwise; but that may be illusory-we ought to admit that we do not know." (Anscombe 1983: p. 106) To be sure, this statement should be read as concerning the 'higher-level' form of predetermination I just introduced. Interestingly, even with regards to human agency, Anscombe keeps open the possibility of such a higher-level determinism-she speaks of the "possibility of holding deterministic views in relation to 'human' causality" (Anscombe 1983: p. 106) in just this sense.
It would be interesting to investigate what that could be: an autonomous level of 'human' causality that is, per laws of its own, deterministic. In particular, this raises a question for incompatibilists regarding free will: if free will is thought to be incompatible with determinism tout court, then what to say of such a situation? Of course, the usual incompatibilist argumentation depends precisely on the determining factors not being under our control 29 , so it is open to incompatibilists to argue that such a level-specific determinism, if coherent at all, does not pose a threat to freedom. But then it seems that the issue that is at stake in the free will debate is much better cast in terms of level-specific notions of (in)determinism than in terms of an apparently unitary notion of determinism. (Of course, as we saw, the existence of a level of human agency implies lower-level indeterminism, and insofar the classical rendering of the problem is apt, but simply too coarse-grained.) 28 An anonymous referee pointed out to me that this 'fallacious argument' still finds currency in the contemporary free will debate. See, for instance, Pereboom (2007: 112f), who employs it in his argument against 'agent-causal libertarianism'. In C&D, Anscombe attempts to rebut it by constructing her rather fanciful 'box analogy' (see C&D: 146); see Müller (ms.) for an in-depth discussion showing the cogency and actual possibility of such an This holds, for instance, for van Inwagen's Consequence Argument and Pereboom's Manipulation Argument, which I mentioned in §1 above. (2021) 199:11945-11961 5 Which levels? concluding afterthought I have given a rough sketch of a broadly Anscombean take on causality and determinism. It is primarily characterized by a reorientation: away from the supposed search for patterns in the ongoing train of events (be they necessary or universal or not) and towards an understanding of the ongoing train of events in terms of the concrete things (substances) around, with their capacities or powers. On this metaphysical picture, the laws of nature do not capture necessary or universal cooccurrences, but rather the way in which things respond to certain kinds of circumstances-responses which can in principle always be prevented or interfered with. Now, we saw reason to distinguish, within this overall metaphysical picture, between certain levels, which can be characterized as follows: the higher-level things are such that their interactions with their environment make use of the possibilities provided by their constituent matter. Wherever we find apparent levels to be related in this way, we must conclude that the lower of the levels cannot be deterministic-for if it were, there would be nothing for the higher level to make use of. 30 In my discussion of this framework on the basis of Anscombe's work, I have rather tendentiously spoken of quite specific levels: inanimate nature-life-animal self-movement-rational agency. But although traces of an interest in these specific levels can be found in Anscombe's writings (e.g., her frequent allusions to animal movement), I should stress that she does not endorse any specific list of levels (though she seems quite certain when it comes to the last one in my list). And neither was it my aim in this essay to develop or defend such a concrete proposal. 31 Still, the very possibility of different levels along the sketched lines already makes quite a difference to our understanding of the classical free will debate, since it opens up the possibility of there being varieties of determinism and indeterminism, together with an analogous variety of incompatibility claims.
Synthese
Most centrally, such a variegated picture may help us get away from the stark opposition between the mechanical causality of the physical level and human agency. If we allow ourselves to first attempt an understanding of life, and animal self-movement, as differing relevantly from the physical level, we may very well find ourselves in a position from which our power of 'acting according to an idea' makes much more sense. And more specifically, Anscombe's suggestion that there is the logical possibility of higher-level determinism at the level of human agency brings into focus that it is, in the end, a distraction to focus on some unitary notion of determinism in relation to human agency. What we really need to make progress 30 Compatibilists will object here, of course: compatibilist senses of the idea of substances "making use of material" are easily cooked up, without any need for lower-level indeterminism. When we keep our substance/power scheme in place, however, it is clear that there is then a fundamental distinction between compatibilist 'substances' and 'powers', which make no difference to what goes on, and the substances and powers in which such compatibilist 'substances' and 'powers' are ultimately grounded, which genuinely do make differences. In Anscombe's terms (see fn. 27 above), all such substances are merely supervenient. Anyway, it has not been my aim to squarely confront the compatibilist in this essay; this is meant as a mere gesture towards such a confrontation. 31 But see Mulder (2021a). in the free will debate is, rather, a good understanding of what it means to determine something agentially, of what it is to "act according to an idea". And I think that this recommendation is fully in line with much of Anscombe's work in the relevant philosophical areas, in particular with her take on intentional agency as put forth in her monograph Intention (Anscombe 1957).
What stands in the way of taking seriously the broadly Anscombean picture I have tried to highlight here is, presumably, still the very same picture that Anscombe attempted to combat in her inaugural lecture C&D: a picture on which the only intelligible story on causality must rest upon some sort of (universal, or necessary) pattern among the elements of the ongoing chain of events. The varieties of indeterminism come into sight only if we reject that picture in favor of a picture on which interacting substances comprise our metaphysical bottom line. For then our bottom line already includes the possibility for such varieties of indeterminism: the mere idea of interacting substances covers both those that are and those that are not composed of 'material'. | 10,430 | 2021-08-06T00:00:00.000 | [
"Philosophy"
] |
Synthesized Vortex Beams in the Turbulent Atmosphere
The results of our investigations on the control over the orbital angular momentum (OAM) of a laser beam synthesized through combination of wave fields of a fiber array are reviewed. Peculiarities of OAM formation in the case of control over the value of phase shifters of individual sub-beams are studied theoretically and experimentally. The conditions for formation of vortex beams with the given value of the orbital angular momentum are determined. The information coding by the OAM value is demonstrated in laboratory experiments. Statistical characteristics of vortex Laguerre—Gaussian and synthesized laser beams propagating through the turbulent atmosphere are studied theoretically. Versatile models are proposed for the probability density function of radiation intensity fluctuations in the cross section of an arbitrary-type beam propagating through turbulence of different strength. The spectrum of azimuthal modes of the synthesized vortex beam at its formation and propagation is analyzed.
INTRODUCTION
In the last decades, the great attention is paid to laser beams with orbital angular momentum (OAM) [1][2][3] due to their particular properties, which have found a lot of applications [4][5][6][7][8]. The beams of this kind are referred to as optical vortices or vortex beams due to the presence of the transverse circulation component of the Pointing vector [1]. In particular, the feasibility of using optical vortices for information coding and transmission is intensely studied [7,8]. The generation of vortex beams has become a new field of the new optical science -singular optics [9]. However, the development of relevant technologies requires creation of fast devices for generation of vortex beams with a tunable topological charge or OAM. Generation methods based on spatial light modulators (SLM) [10][11][12] are insufficiently fast and, as a rule, inefficient in conversion of one beam type into another. Their application allows beam OAM to be tuned with a frequency no higher than few kilohertzs. As a result, the development of new high-speed methods and devices for generation of vortex laser beams comes to the forefront. In some tasks, they should operate under conditions of high radiation intensity. In our opinion, the method of formation of vortex optical beams with the changeable orbital angular momentum based on an array of coherent fiber radiators is most adequate to the formulated problem. It implements the approach, whose idea occurred to us upon publication of Lachinova and Vorontsov [13]. The approach is based on the control over the phase of individual radiating subapertures. These subapertures are arranged hexagonally and make up a cluster (array) to provide for the phase progression of 2mπ while circling around the center of the synthesized beam. The main advantage of this approach is the possibility of fast (with a frequency higher than 10 9 Hz) phase shift at subapertures, which provides for a change of OAM. This review analyzes the possibility of creating a system for generation of laser beams, in particular, vortex laser beams, with the spatial structure controllable in real time based on the Coherent Beam Combining principles [13,14]. First, some peculiarities of formation of vortex beams synthesized from the different number of sub-beams are determined [15][16][17][18][19]. Then, the corresponding experimental setup is schematically described, along with its main operating principles and experimental results obtained with it [20,21]. Finally, the results of numerical simulation of the propagation of synthesized beams in the turbulent atmosphere are given [15][16][17]22].
GENERATION OF VORTEX BEAM BASED ON COHERENT COMBINING OF FIELDS FROM ELEMENTS OF THE FIBER CLUSTER. NUMERICAL EXPERIMENT
The complex amplitude of the field of a synthesized vortex beam can be presented in the following form where N a is the number of sub-beams (subapertures) in the array, x sub c and y sub c are coordinates of the centers of sub-beams arranged in a circle or a hexagon, a sub is the sub-aperture radius. It should be noted that the central sub-beam has the coordinates x c = 0, y c = 0. In addition, if the synthesized beam is formed from several sub-beam rings (N a = 18, 36, 60, and so on), internal rings may be absent.
We take the Laguerre-Gaussian beam LG l m E(r, θ, as a reference for the comparison with the synthesized beam given by Equations (1)(2)(3)(4). Here, r = x 2 + y 2 and θ = arctan(y/x) are polar coordinates, a is the beam radius, m is the radial mode index, and l is the value of the topological charge.
The amplitude and phase distributions for the field of this beam with the topological charge l = 3 are shown in Figure 1. It can be seen that the number of subapertures making up the synthesized vortex beam analogous to the fundamental Laguerre-Gaussian beam (5) determines the radius of an individual subaperture. In addition, it is obvious that the number of subapertures also determines the maximal possible value of the topological charge of the vortex beam, which can be achieved in this way. Figure 2 shows the amplitude and phase distribution of the field of the synthesized vortex beam with the topological charge l = 3 upon propagation in the free space to the distance equal to a half diffraction length (k 0 = 2π/λ is the wave number, λ is wavelength) corresponding to the traditional Laguerre-Gaussian beam of the radius a. It can be seen that during the propagation, the amplitude and phase of vortex beams synthesized from 18 or 36 subapertures behave similarly to those of the Laguerre-Gaussian beam -the ring amplitude distribution and the screw phase distribution with the progression of 2lπ while circling around the beam center are observed. However, as the beam synthesized of six apertures propagates, no vortex components appears, and only the interference pattern from the interaction between subaperture Figure 1 during the beam propagation in a free space to a distance z = 0.5 k 0 a 2 [17]. radiation is seen. This is explained by the fact that no less than 3l radiation sources are necessary to generate a field with the nonzero orbital angular momentum l. In this case, for formation of a vortex beam with the topological charge l = 3, the minimal number of subapertures arranged in a circle should be nine.
In the study of the influence of the synthesized beam structure (number of sub-beam rings) on the formation of an optical vortex, it was found that the radiation from subapertures lying in the inner ring of the fiber cluster weakly affects the formation of the orbital angular momentum of the synthesized vortex beam. In this case, the maximal topological charge l max is determined by the number of subapertures in the outer ring (N out a ) and is independent of the number of inner rings (l max =N out a /3). It should be also noted that for the topological charge close to the maximal value l max , the smoothed amplitude and phase distributions of the field form much more quickly (at shorter distances) if there is only one ring (no inner rings). The main property of field (1-4) manifests itself at the propagation in a free space. It consists in the fact that the intensity and phase distributions of the synthesized and ordinary Laguerre-Gaussian beams are widely different in the near zone (at short distances z<<z d ) and close in the far zone (at distances comparable with the Rayleigh diffraction length z d = 0.5 k 0 a 2 and longer) [16,17]. Let us consider the free propagation of the field with initial distribution (1-4) by representing it as an expansion in azimuthal modes and calculate the energy fractions corresponding to the modes of different order n where, rdr a n (r, z) 2 (9) We have succeeded in determining [17] that as the synthesized beam propagates at the initial part of the distance in the nearaxial zone (whose size is comparable with the initial size of the beam), the energy transfers between modes corresponding to different values of n. In the course of the propagation, the energy of the mode n, whose order is equal to the given topological charge l, increases and saturates to the level close to 100% of the total energy. Thus, as the synthesized vortex beam propagates to the distance exceeding several tenth fractions of the Rayleigh diffraction length, the beam properties in the near-axial zone approach the properties of the Laguerre-Gaussian beam (5).
We have studied the orbital angular momentum of the synthesized beam (1), as well as the structure of its wavefront [18,19]. For this purpose, we have calculated the transverse component of the Pointing vector, which can be represented in the following form in the paraxial approximation [1] where I(r, z) and ϕ(r, z) are the intensity and phase of the field E x, y, z . With allowance for Equation (10), we can write the equation for the normalized (specific) density of the orbital angular momentum [1] dr is the power of radiation, n z is the unit vector in the direction of the radiation propagation axis. The orbital angular momentum of the laser beam at the distance z upon normalization to the beam power [1] is calculated by as follows: The calculation of the orbital angular momentum by Equation (12) for the initial field specified by Equations (1)(2)(3)(4) gives zero values for any values of the parameters l and N a both in the initial plane and at any distance from it. It should be reminded that as the number of subapertures in the fiber cluster increases, the transverse phase distribution in the central part of the beam in the course of its propagation becomes closer to the screw structure characteristic of the Laguerre-Gaussian vortex beam. It was assumed in the calculations that all the radiating subapertures form nested hexagonal rings. This structure in the initial plane is shown in Figure 3.
In this configuration of 36 subapertures, according to Equations (1-4), the subaperture phase ϕ sub takes 24 different values. Every three phase values at the subapertures lying at six rays outgoing from the center are equal.
We assume that the propagation of laser beams in the atmosphere is described quite accurately by the parabolic equation for the complex amplitude of the field: where n 1 x, y, z are variations of the refractive index (in this section n 1 (x, y, z) = 0). Our computational schemes employ the method for numerical solution of Equation (13) based on splitting by physical factors [23] implemented in the parallel code [24].
To study the dependence of the received OAM value on the radius of the receiving aperture, the integration over the unlimited plane in Equation (12) was replaced with the integration over a circle with the radius a t and the center at the beam axis where Figure 4 shows the results of calculation of the intensity, phase, and OAM density distributions for the beam formed of 36 subbeams at the distance z/z d0 = 0.4, where z d0 = k 0 a 2 0 /2, a 0 = 8.5a sub is the radius of the synthesized beam as determined in Figure 3A.
The OAM density (Equation 11) distribution is shown in Figure 4C. The light shade in this figure corresponds to the positive density, while the dark one is for the negative density values. It can be seen that the OAM density is nonzero in the central part, where the density is positive. In addition, there are six zones located at the hexagon vertices at the beam periphery, within which the OAM density is nonzero. The OAM density at these zones takes both positive and negative values. Thus, in the interference field of the synthesized beam, the zone of nonzero OAM density is multiply connected and alternatingsign in contrast to the simply connected OAM density of the Laguerre-Gaussian beam [25].
The received power and L z (a t ) as functions of the radius a t are shown in Figure 5. One can see that for the receiving aperture with the radius a t /a 0 ≤ 3 the OAM value is close to unity, while for the receiving aperture with the radius a t /a 0 > 6 the OAM value is close to zero. The behavior of the OAM curve in Figure 5 demonstrates how the presence of positive and negative values of the OAM density described by Equation (11) affects the OAM value within the limited aperture (14). It can be seen that L z (a t ) tends to zero at a t → ∞. Thus, the OAM value of the synthesized beam fully intercepted by the receiving aperture is equal to zero at any point of the path. This result reflects the principle of OAM conservation.
The study of the phase gradient circulation demonstrates [18,19] that this characteristic also reflects the features of hexagonal arrangement of subapertures. In the near-axial part of the beam, there is a limited zone, within which the integral of the OAM density is equal to unity. The circulation of the phase gradient over the perimeter of this zone is equal to 2π.
Thus, in Dudorov et al. [15] and Aksenov et al. [16][17][18][19] we have studied theoretically the influence of the number of radiating apertures N a , and their arrangement on the phase and intensity distribution of the synthesized beam in the far wave field for different given values of OAM. It has been shown that OAM of the beam synthesized by the proposed method is equal to zero at the complete interception by the receiving aperture. However, the aperture limitation of the beam in the receiving area allows us to separate the central part of the synthesized beam and to assign the non-zero OAM to it. In this case, the central ring of the vortex beam carries about 50% of the power emitted by the synthesized aperture at N a = 6, l = 1 and 70% of the power at N a = 18, l = 1.
VORTEX BEAM GENERATION BASED ON COHERENT COMBINING OF FIELDS OF THE FIBER CLUSTER. EXPERIMENTAL SETUP AND LABORATORY STUDIES
Vortex beams with l = 1 and l =2 were obtained experimentally for the first time by us with an array of six coherent Gaussian sub-beams arranged in a circle by setting a fixed phase shift between neighboring sub-beams [20]. In Aksenov et al. [21], the experimental setup was significantly modernized, and new experimental data were obtained. Further on, we follow the paper Aksenov et al. [21].
Consider the experiment and, in the first turn, the experimental setup in detail (Figure 6).
In our experiment, the linearly polarized radiation of narrow-band ( f = 3 MHz) semiconductor laser 1 with the central wavelength λ = 1,064 nm and the output power up to 150 mW was amplified by fiber amplifier 2 with the tunable amplification factor from 0 to 33 dBm. The amplified radiation was split into eight channels (power of every channel up to 15 mW) with fiber splitters 3. Six or seven working channels were used in the experiment. Every channel was connected by an optical fiber with one of seven integral LiNbO 3 phase modulators 4 with the modulation frequency up to 150 MHz with the controllable phase shift in the range from 0 to > 6π in response to the applied voltage. Seven (4), fiber collimators (5), long-focus lens (6), beam splitting plate (7), lenslet (8), beam profiler (9), Shack-Hartmann sensor (10), computer (11), pinhole (12), broadband photodetector (13), optimizing multichannel SPGD processor (14), control computer (15), oscilloscope (16), spiral phase plate (17). Arrangement of subapertures for coherent beam combining: 1) in the experiment on synthesis of a beam with maximal intensity at the axis, 2) in the experiment on synthesis of a vortex beam [21]. fiber collimators 5 (F = 100 mm) arranged hexagonally and forming a cluster of sub-beams [(1) in Figure 6] were installed at the output.
Each collimator formed a sub-beam with the Gaussian intensity distribution and the diameter d sub = 22 mm. The optical axes of all the sub-beams were aligned in parallel to each other thus forming an aperture with the diameter D = 90 mm. The set of sub-beams was focused by lens 6 with the focal length F = 1535 mm. Ophir-Spiricon SP503U beam profiler 9 with 1X10 micro-objective 8 was set in the focal plane of lens 6. The recorded intensity distribution of the synthesized beam was displayed at the monitor of computer 11. To provide for the phase control of the synthesized beam, 50:50 beam-splitting plate 7 was set in the radiation propagation channel. Plate 7 directed a part of the beam to Thorlabs PDA10CF-EC photodetector 13 equipped with pinhole 12. Photodetector 13 recorded the intensity of the interference maximum formed as a result of sub-beam superposition in the plane of pinhole 12. The signal from photodetector 13 was processed by multichannel processor 14 operating according to the stochastic parallel gradient descent (SPGD) algorithm [26]. The multichannel SPGD processor [27] with the clock frequency up to 240 kHz (100 kHz in the experiment) generated control signals for phase modulators 4 to maintain the maximal level of the signal at photodetector 13. Control parameters were set with computer 15. According to the SPGD algorithm, the maximum of the signal recorded by photodetector 13 corresponded to the in-phase state of all the sub-beams. Thus, the feedback loop providing for the phase synchronization of radiation of the constructed fiber array was formed.
To synthesize the vortex beam, it is first necessary to perform the initial synchronization of sub-beam phases, since the system is unstable due to thermal and acoustic fluctuations in the system elements including optical fiber [28].
The phase synchronization of the sub-beams forming the cluster was performed with the SPGD algorithm. As the SPGD was turned on, the in-phase state of the channels was achieved for the time shorter than 10 ms. Then the SPGD controller was turned off, and the signal drop was recorded at photodetector 13. The typical oscillogram of this process is shown in Figure 7. The time for one reading is 0.0325 s. The time for signal drop down to the 1/ √ 2 level upon averaging over 10 realizations is 4.75 s. The signal drop time is caused by random phase progressions in fiber channels under experimental conditions and characterizes the particular system. During this time, the phase state of the system can be considered as frozen and sufficient for the phase modulation. Once the state of phase synchronization was achieved for all the six beams, the SPGD controller applied the voltages corresponding to the necessary phase shift to the phase modulators [20,21]. The intensity distribution recorded by beam profiler 9 is shown in Figure 8.
The intensity distribution has the nearly ring shape at the center and six surrounding rings formed in the zones of peripheral intensity maxima. The additional studies revealed that the stable lifetime of this pattern is no shorter than 2 s.
To check whether the obtained beam is a vortex beam, we used Shack-Hartmann sensor 10 (Thorlabs WFS20-5C-M) installed in place of the beam profiler. It allowed us to record the distribution of local wavefront tilts. Figure 9 shows two versions of the calculated radiation intensity distribution at the photodetector array of the Shack-Hartmann sensor, as well as the measured intensity distribution and measured wavefront tilts of the synthesized beam.
To determine the OAM sign and the degree of correspondence of the obtained intensity distribution to the vortex beam, we used additionally spiral phase plate 17. It was set in front of the beam profiler at the place where the sub-beams do not overlap and do not interfere yet. Its radial phase relief performed the uniformly distributed (over a circle with 32 levels) phase shift of the incident radiation from 0 to 2π (within λ = 1,064 nm) [29].
Thus, each of the six sub-beams corresponded to a special zone of the phase plate, at which the average phase shift differed from the average phase shift at the adjacent zone by 2π/6. Upon the passage through the plate, all the sub-beams acquired the extra phase shift by this value. If the direction of increase of the phase shift of the incident sub-beams coincided with the direction of increase of the phase shift at the spiral plate, then each sub-beam acquired an extra phase shift growing in the direction of increase of the phase shift at the plate. At the same time, the topological charge of the synthesized beam in the zone of overlapping of the sub-beams increased by one, while the profile of the intensity distribution kept the central zero value. However, if the direction of increase of the phase shift of the plate was opposing to the direction of increase of the phase shift of the sub-beams, each sub-beam, upon passage through the spiral phase plate, acquired the same extra phase shift, but opposite in sign. In this case, the phases of the sub-beams became equal to each other, and the resultant profile of the intensity distribution corresponded to the coherent combining of the beams.
Thus, the sigh of OAM of the synthesized vortex beam can be set by turning the spiral phase plate by 180 • around any axis lying in its surface plane.
PROPAGATION OF THE SYNTHESIZED BEAM THROUGH THE TURBULENT ATMOSPHERE
As was already mentioned, we studied the propagation of laser beams in the turbulent medium through the solution of parabolic wave Equation (13) with application of the phase screen method. In this case, the modified Andrews spectrum of fluctuations of the refractive index [29] in the following form was used: Here, C 2 n is the structure characteristic of the refractive index, κ l = 3.3/l 0 , κ 0 = 2π/L 0 , l 0 and L 0 are the inner and outer scales of turbulence.
The turbulent conditions of propagation were specified with the Rytov complex parameter β 2 0 = 1.23C 2 n k 0 7/6 z 11/6 or the Fried radius r 0 = 1.68 C 2 n k 0 2 z −3/5 [30]. Here, z is the path length, k 0 is the wave number. The statistical characteristics of intensity fluctuations were studied in numerical experiments. The following designations were used: the mean intensity I(r) , where the square brackets are for the averaging over realizations of the turbulent medium, the variance of intensity fluctuations B I (r) = I(r) 2 − I(r) 2 , and the relative variance of intensity fluctuations (scintillation index) The scintillation index (17) was calculated under various turbulent conditions of propagation for the synthesized and fundamental beams [15,16]. Statistical characteristics entering into Equation (17) were calculated through numerical simulation of more than 5,000 random realizations of the medium.
The same number of random realizations of the medium was used to calculate the statistical characteristics of azimuthal modal components (9) of the vortex beam propagating in the turbulent atmosphere and OAM of this beam [31]. However, in this case, the beam propagating through the turbulent atmosphere was no longer synthesized. Figure 10 shows random realizations and averaged spectra of angular harmonics of the Laguerre-Gaussian beam with the initial topological charge l = 4 upon propagation of the path length z = 2000 m in the statistically uniform and isotropic medium with the turbulence strength corresponding to the Rytov parameter β 2 0 = 0.743. Now let us dwell on the study of the probability density function (PDF) P(I) of intensity fluctuations of the traditional and synthesized beam [16,17,22].
Comparing the average intensities of the synthesized and Laguerre-Gaussian beams (Figure 11), we can see that they are approximately identical, and the variance of intensity fluctuations of the synthesized beam decreases with the increasing number of subapertures and tends to the corresponding values for the Laguerre-Gaussian beam. Figure 12 compares PDFs of intensity fluctuations of the Gaussian, Laguerre-Gaussian, and synthesized (Na = 36) beams as obtained from the numerical experiment with the lognormal and gamma distributions [30,32]. Atmospheric parameters correspond to the conditions of weak turbulence.
As the observation point shifts along the beam radius from the position of the maximal average intensity (the ring-shaped zone) to the beam periphery, the scintillation index increases and the PDF changes its shape. is the Fried parameter) [17].
Frontiers in Physics | www.frontiersin.org [30], and the dashed curve corresponds to the gamma distribution [32]. Figure 12 [16]. Figure 13 demonstrates the numerical and analytical PDFs of intensity fluctuations of the Gaussian, Laguerre-Gaussian, and synthesized beams at the cross-section points lying at different distances from the center. It follows from the figure that the PDFs obtained in the numerical experiment do not follow the analytical models of PDF, although the propagation conditions correspond to weak turbulence (β 2 0 = 0.1). The numerical PDFs in this case nearly coincide with the gamma distribution for the beams of all types.
The curves demonstrating the PDF behavior in Figure 13 can be well described by the gamma distribution. For the numerically obtained values of the scintillation coefficient, this distribution transforms into the negative exponential one [30], which is characteristic of the so-called strong intensity fluctuations, although the turbulent conditions of propagation still correspond to weak turbulence (β 2 0 = 0.1). Figure 14 shows the numerical and analytical PDFs of radiation intensity fluctuations at the axis (r = 0) of the Laguerre-Gaussian and synthesized beams, as well as at the periphery (r = 1.9a) of the Gaussian beam. It can be seen that at the very close scintillation indices for all the three beams, the shape of the PDFs is nearly identical as well.
As a result of numerical experiments, the analysis of the PDFs obtained for various propagation conditions has shown that, regardless of the beam type and the position of the observation point at the beam axis, the PDF is determined by the scintillation index and the average radiation intensity at a given point. For intensity fluctuations with the scintillation index σ 2 I (r) < 1 , the probability density is well approximated by the gamma distribution. When the scintillation index σ 2 I (r) > 1 , the PDF behavior can be well approximated by the fractional exponential distribution [33]. Similarly to the gamma distribution, this distribution tends to the negative exponential distribution [30], when the scintillation index tends to unity.
SUMMARY AND CONCLUSION
In this paper, we have reviewed the results of numerical simulation, presented schematically the experimental setup, and reported the results of laboratory experiments on generation of a vortex laser beam through coherent combining of laser sub-beams formed by a cluster (array) of fiber radiators. The synthesized wave field is formed as a result of interference of individual sub-beams at the given relations between phase pistons of these sub-beams.
The results obtained mostly by the authors have been described. The requirements on parameters of the vortex beam generator (number and size of subapertures and their mutual arrangement) have been formulated. The spatial evolution of the wave field structure during formation of the synthesized beam has been considered. The problems have been formulated and the methods to achieve the temporal stability of the synthesized beam have been proposed. The feasibility of on-line control of the orbital angular momentum of the beam has been studied. The spatial dynamics of the synthesized beam is analyzed in the comparison with the traditional Laguerre-Gaussian beam in a free space and in the turbulent atmosphere. The statistical characteristics of intensity fluctuations in the cross section of the synthesized beam propagating through the turbulent atmosphere have been investigated. The distribution laws of intensity fluctuations have been examined and new models for their description have been proposed. The theoretical analysis of propagation of these beams in the turbulent atmosphere indicates that the statistical regularities of intensity fluctuations and OAM fluctuations mostly follow the statistical regularities of propagation of already well-studied Laguerre-Gaussian beams [25,[33][34][35][36][37][38].
It should be noted that different aspects of development of the technology for generation of vortex beams through coherent combining of wave fields from an array of coherent radiators and the propagation of synthesized optical beams are also studied by other research groups [39,40].
Our plans for further development of this field of research include the analysis of applicability of the coherent combining technology to generation of vector vortex beams, which are now generated by other methods [41]. In addition, it should be noted that the practical application of this technology, as well as other technologies, for information transfer by a laser beam in open atmospheric channel face the problem of turbulent distortions of a beam. That is why the issues of adaptive compensation of these distortions are quite urgent. Our tentative results demonstrate that the methods of adaptive optics can be included in communication systems based on this technology without significant extra expenses.
AUTHOR CONTRIBUTIONS
VA and VD formulated the concept, form, and subject of the study and wrote sections Introduction and Generation of Vortex Beam Based on Coherent Combining of Fields From Elements of the Fiber Cluster. Numerical Experiment. VK and ML wrote section Vortex Beam Generation Based on Coherent Combining of Fields of the Fiber Cluster. Experimental Setup and Laboratory | 6,799.8 | 2020-05-15T00:00:00.000 | [
"Physics"
] |
Effects of mobile phone-related distraction on driving performance at roundabouts: Eye movements tracking perspective
Modern road infrastructures are complex networks featuring various elements such as roads, bridges, intersections, and roundabouts, with advanced control systems. Roundabouts have gained prominence as a safer alternative to traditional intersections promoting smoother traffic flow and fewer collisions by guiding traffic in one direction, encouraging reduced speed, and minimizing conflict points. This study investigated driver behavior within roundabouts, focusing on gaze behavior, particularly the left-side mirror and window, under mobile phone distraction conditions. In addition, the effects of roundabout specifications (i.e., number of lanes and size of the central island) and the drivers’ characteristics (i.e., driving experience) were examined. In total, 43 participants, aged 19–56 years including 30 males and 13 females, held a valid driving license, drove through a virtual simulated urban road containing four roundabouts, implemented in a static driving simulator, under baseline condition (no distraction) as well as mobile-induced distraction. Driving simulator data were collected and drivers’ gaze direction and fixation on nine areas of interest were captured with an eye tracker. Results showed that experienced drivers exhibit a more fixation on the left-side mirror and window and were less distracted. Moreover, the road environment, i.e., the number of cars and the roundabout size, significantly influenced the drivers’ attention. As regards the driving performance, the number of infractions increased when the drivers diverted focus from the left side of the car. The outcomes of the present study might help to improve traffic safety at roundabouts.
Introduction
Traffic crashes have far-reaching consequences, including health problems, increased rates of impairments and disabilities [1,2] Along with significant losses in productivity, reduced mobility, and adverse effects on economic development and growth prospects [3,4].
Inattentive driving and multitasking are major contributors to road crashes and fatalities with distraction being a common factor in
Material and methods
This section details the general set-up, design, and overview of the study.W. Boulagouas et al.
Apparatus 2.1.1. Driving simulator
To conduct this study, driving performance data was gathered using an on-campus adapted DriveSim driving simulator (Fig. 1).The simulator comprises three connected screens, each sized 39″ with a resolution of 1920 × 1080 resolution providing a proper field of view of the simulated traffic, road environment, and weather conditions.Driving scenes are recorded with an update rate of 40-60 Hz given the hardware's graphics overload capability in the virtual reality environment.
For dynamic simulation, the apparatus is equipped with an accelerator, brake and clutch pedals, gear system, turn signals, a steering wheel, etc., and uses sophisticated artificial intelligence applications to mimic a variety of driving situations under different conditions.
For this study, further devices have been included (Fig. 2): (1) an eye tracker to follow the driver's gaze, (2) a timestamp, and (3) a mobile phone placed to the right of the steering wheel.
Timestamp
To synchronize the simulator data and the eye tracker, a separate timer is added to the simulator.This timer displays the minutes, seconds, and milliseconds of each frame rendered by the simulator to be used in the database.Thus, the time synchronization of the entire system used the records from the 43 experiments in which the timestamp of the simulator was distinctly visible in the records (Fig. 3).
Moreover, the simulator has an internal timer that incorporates time information into the database and guarantees the reliability of recorded data regarding infraction times, and driver positions (inside or outside of each roundabout).Thus, the eye tracker captures the time of each Area of Interest (AOIs, Fig. 4) by displaying the eye tracker timestamp in real-time and facilitates the synchronization of both independent systems.
Eye tracker
To track participants' eye movements in predefined sections of the roundabouts, a Tobii Pro Grasses 2 was used (Fig. 5).The latter consists of two cameras providing an accurate real-time data stream on the participants' gaze behavior at a sampling rate of 100 Hz.To improve the quality of the tracking system, many markers were added to the screen frame to visually connect the Tobii eye tracker to the real environment.This is because the virtual reality environment of the driving simulator is frame-changing and cannot be used as a reference for tracking.
Participants
In order to be selected to take part in the experiment, participants had to have a valid driving license, have no visual impairments, and be physically able to conduct the experiment.
A total of 43 participants signed an informed consent form approved by the Ethics Committee of the University of Burgos (Spain) and took part in this study.Among the 43 participants, there were 30 males and 13 females.The sample included young, middle-aged, and older drivers (ages ranged from 19 to 56 years old), with a mean and standard deviation of 23 and 6.85 years, respectively.In addition, the driving experience of the participants was determined by number of years of driver's license acquisition (ranging from 0 to 38 years).The driving frequency and number of Km the participants drove were also collected (Table 1).
Procedure
Participation in this study was voluntary and participants were informed about the objectives of the study and told that they could quit the experiment at any time in case of any kind of discomfort.Experiments were conducted in several stages.In the first stage, participants were informed about the purpose of the study, briefed on various procedures, and given experimental instructions.
In the second stage, all participants completed a questionnaire regarding their demographics (age and gender), years with a valid driving license, driving frequency, and whether they like driving or not.Afterward, the participants took a practice drive for a few minutes to get familiar and comfortable with the virtual reality environment and different devices.
In the third stage, each participant completed two driving experiences.The first experience was a normal driving situation in which participants drove through a virtual urban road.The second experience was a distracted driving condition in which participants were preoccupied with mobile phone-related distractions.
Experiment setup
All participants drove the same general scenario under the same driving conditions in the driving simulator, clear weather, daytime driving, and random but stable traffic flow.The driving scenario involved navigating a multi-lane urban road with four roundabouts (Fig. 6).Due to the road layout, three of the roundabouts were run twice.The course began at 1, went to 4 which was circled, and then back through the previous three roundabouts (3, 2, and 1).
This study aimed to explore drivers' behaviors at specific sections of roundabouts while experiencing distractions induced by mobile phone.The selected sections of roundabouts for the present experiment are shown in Fig. 7.Among the roundabouts examined, roundabouts (1), (2), and (4) are smaller in size, while the roundabout (3) is larger.Moreover, roundabout (3) features three lanes, whereas the others have only one lane.
Previous research [20,27,32] showed that traffic crashes at roundabouts often result from drivers' failure to yield in entry lanes.To accurately capture the moments when drivers enter roundabouts, five triggers on Locations of Interest (LOIs) were integrated along each roundabout's entry and exit routes within the simulator (Fig. 8).These triggers on the LOIs serve to pinpoint the precise instant when a driver enters a roundabout.The first trigger was set approximately 50 m before the roundabout, providing an early indication.The second trigger was placed just before the entrance lane, alerting drivers to their imminent entry.The third trigger marked the beginning of the entrance lane, while the fourth trigger was positioned after the entrance lane to capture the transition onto the roundabout.Finally, the last trigger was placed at the exit line, marking the point of departure from the roundabout.The placement of these triggers was carefully chosen to provide comprehensive data on driver behavior at various critical points of roundabout navigation.The route of the experiment was designed with consideration of these LOIs.
Data collection procedure 2.4.1. Experiment procedure
Before each driving session, an experimenter instructed the participants, mounted the eye tracker device onto their heads to track the eye movements and gaze patterns, and initiated the recording using the Tobii Glasses Controller software.At the end of each driving session, the experimenter stopped the recording and removed the eye tracker.
During the distracted driving session, the experimenter called the participants and exchanged conversation with them.Participants also had to respond to several WhatsApp messages and use Instagram.Similar questions and topics were discussed with all participants.These tasks were performed using a specific mobile phone provided for the experiment.The latter was placed in the simulator cockpit to the right of the steering wheel where it is usually mounted while driving.
Study variables
Given the experiment design, the Tobii Pro Lab was configured with nine (09) AOIs, namely, left-and right-side windows, windshield, left-and right-side mirrors, rearview mirror, dashboard, steering wheel, and mobile phone.
In this study, from the data gathered, only changes in fixation on the left side mirror and window were considered.Fixation refers to the visual gaze focused on a particular location for a period of time while processing visual information [33,34].In the particular case of this study, to homogenize the raw fixation data among the different types of roundabouts, the percentage of the total time spent in the roundabout, including all trigger points (shown in Fig. 8), was calculated.
The simulator records the telemetry data for each experiment into a SQLite database, allowing gathering a large amount of valuable data on the drivers' driving performance.In fact, the simulator covers 28 different telemetry records related to the vehicle conditions at any given time, for instance, speed, control status, accelerations, etc.In this regard, there are up to 87 different conditions in which the simulator registers a penalty, i.e., exceeding speed limits, flashing lights, incorrect use of lights, crossing over continuous lines, going to the side of the road, etc. Infractions and driving violations of the drivers were extracted from the simulator records.
According to the study objectives, additional dependent variable sets were considered, namely: socio-demographic variables (age and gender), roundabout size, number of cars in the roundabout, and mobile-induced distractions.
Data analysis
In order to analyze the differences between the mean values, several alternatives to the standard Student's t-test [35] were considered to take into account the sample size (z-test, [36]), the applicability of the ANOVA Bartlett [37]; Arsham & Lovric [38] and the existence of two [39] or more (Kruskal-Wallis test, [40]) groups.In all cases, p-values were estimated using the functions included in the Statistics R package [41] to determine the statistical significance of the results obtained.
In this sense, Table 2 shows the results of comparisons among the variances of the different groups defined for the analysis proposed in this study to fulfill the hypothesis required for the application of the ANOVA analysis.In most cases, the null hypothesis can be rejected with the conclusion that, from a statistical point of view, the variances of each group considered are distinctly different.As a result, it wouldn't be appropriate to apply an ANOVA in this case and other tests should be used such as Wilcoxon and/or Kruskal-Wallis tests, depending on the number of groups considered.Based on these results the Kruskal-Wallis has been finally used to compare the means.
Note that the probability of rejecting the null hypothesis as true is 0.05 considering the number of hypothetical tests used and the significance level considered (95 %).In that sense, it might be found that one of the 24 tests listed in Tables 2 and 3 would be rejected if the null hypothesis is true.For this reason, the compared means are also included in Table 3 to recognize this possibility.
Two types of figures were considered to present the results.On the one hand, the boxplot represents a box that gives the range between the 25th and the 75th percentiles for each value of the X-axis plot when the subsample is associated with the corresponding Xvalue while the Y-axis represents the fixation percentage.The horizontal line inside the box reflects the median value whilst the vertical lines outside of the box define the range established for the outliers.Points outside of these ranges are the subsample outliers.On the other hand, a heat map with the p-values is used to show the statistical significance of the differences between the means of the different parameters considered.In particular, the fixation on the left window and mirror is usually considered according to different parameters (i.e., number of penalizations, license years, etc.), leading to a symmetrical matrix.In the case of distractions, comparing the left window/mirror fixation means of two experiments with and without mobile phone use yields an asymmetric heat map.Note that 95 % statistical significance corresponds to the first color defined in the heat map.Finally, the effect of the roundabout size on the average AOIs' fixation was considered across samples, with and without mobile phone use.In particular, the largest roundabout (Fig. 7 panel c) was compared with the remaining roundabouts.
Results and discussion
First, the applicability of the ANOVA for the study analysis was studied obtaining that the variance of the fixation on the 9 AOIs of each group defined for the different comparisons considered in this work was, for most of the cases, statistically different, reflecting a great heterogeneity of the sample among groups.As a result, the Kruskal-Wallis test was considered instead other more common options as the standard Student's t-test.Note that, since two groups were consistently compared, the degrees of freedom for this test remained consistently equal to one.
Second, changes in the AOIs fixation, based on driving conditions, were analyzed considering the number of cars and the size of roundabouts.
Fixation on left mirror and window and number of cars
The results indicated that fixation on the left mirror and window tended to escalate in tandem with the increasing number of cars inside the roundabout.However, it's is crucial to note that the statistical significance of obtained differences was somewhat constrained by the sample variability, as depicted in Fig. 9. Furthermore, upon closer examination of the fixation values, it became evident that drivers paid more attention to the left window than to the left mirror.
These findings could be explained by the fact that, while the left mirror primarily serves as a tool for monitoring adjacent lanes and blind spots, the left window offers a direct and a broader view of the road environment including incoming flow of traffic inside the roundabout.Therefore, drivers prioritized their visual attention towards this area to maintain situational awareness and anticipate any risks as it provided richer perceptual information.
Fixation on left mirror and window and roundabout size
Table 3 shows the results (H-statistic and p-value) of the Kruskal-Wallis' test for the comparison of the AOIs' fixation mean value of the largest roundabout (Fig. 7 panel c) and the smaller ones.Note that degrees of freedom associated with the test are constant and equal to 1 since all comparisons involve two groups.Therefore, this parameter was not included in Table 3.In order to better interpret the p-values, Table 3 shows the AOIs fixations for the largest roundabout and smaller ones for the three cases (all the sample and laps with and without distractions).Significant differences are obtained for the central and right parts of the car, with p-values less than 0.05 in the three cases for the right mirror, right window, rear-view mirror, and the wheel, which are more relevant for smaller roundabouts.Moreover, although not statistically significant, in all cases, the fixation on the left mirror is higher for the largest roundabout than for the smaller ones, whilst the differences in the left window fixation increase when there are no distractions in favor of the largest roundabout.
Table 3
Kruskal-Wallis' test results (H-statistic and p-value) for the two samples comparison of the largest roundabout (Fig. 7 panel c) and the remaining roundabouts considering the whole sample (first block), the laps without distractions (center block), and the laps with mobile use.Right elements of the car garnered more visual attention in smaller roundabouts, in both baseline and distraction conditions.This could be explained by the fact that the drivers might adjust their scanning patterns based on the spatial constraints and traffic dynamics inherent to small roundabouts.Moreover, in smaller roundabouts, maneuvers were typically executed with less room for errors; therefore drivers allocated less attention to left window and left mirror.Furthermore, the fixation on the windshield and mobile phone was closely related to the use of a mobile phone as the differences obtained faded away when there were no distractions.This behavior could be expected as the drivers were switching their view between the mobile phone and the road, paying less attention to the rest of the AOIs.
These findings relate to Dong et al. [42] study comparing the effect of the mobile phone position on the drivers' fixation and driving performance.The authors found that placing the mobile above the AC vent prompts drivers to switch their heads between the road ahead and the mobile phone.This behavior increases the glance range and prolongs fixation times.
Fixation on the left mirror was higher for the largest roundabout compared to smaller ones, particularly in the presence of mobile phone distractions, offers insights into how cognitive and visual load and environmental factors influenced the gaze behavior of the drivers.Indeed, induced distractions related to the mobile phone use reduced driver's awareness to incoming traffic flow inside the roundabout and blind spot and limited their ability to comprehend visual scanning information resulting in decreased fixation on critical areas (i.e., left mirror and window).Moreover, the heightened fixation to left window in larger roundabout, in the absence of mobile phone distraction, suggested that non-distracted drivers adopted a more cautions and a proactive approach to visual monitoring to cars inside the roundabout to ensure a safer navigation.
These findings relate to Rasanen & Summala [43] study exploring car drivers' adjustments to cyclists at roundabouts.The main findings confirmed that the drivers' behaviors depend on the size of the central island.Interestingly, the authors found that the drivers approach the roundabouts, with large central islands, at a lower speed, which in fact allows them to adequately adjust their visual search pattern.Similarly, Vetturi et al. [44] reported that drivers slow down when traveling through large roundabouts and the level of attention is greater, i.e., up to 91 %.
Fixation on left mirror and window and number of infractions
The analysis of the number of infractions and gaze fixation reveals a significant relationship between the AOI and infractions.To ensure a sufficient sample size for each class, infractions committed at all roundabouts were grouped into dozens (1 mean value from 0 to 12, 1 from 13 to 24, and so on).
Results in Fig. 10 showed the statistical significance that the more participants fixed their gaze into the left mirror while driving in roundabouts, the fewer infractions they committed.Note that the case with more penalizations has only two valid observations, 0.00 and 12.71, leading to the anomalous behavior observed.The left mirror serves as a key tool for monitoring adjacent lanes and blind spots, enabling drivers to make informed decisions and execute maneuvers safely in roundabouts.This is coherent with the literature related to drivers' behaviors when traveling through roundabouts.A previous study conducted by Verma et al. [45] showed that dual-task driving leads to poor driving performance in terms of poor lane keeping.Similar conclusions were drawn from a simulator study of the effects of mobile phone use on the driving performance of young drivers [21].This study revealed significant differences in vehicle control (i.e., lateral distance and hard shoulder line violations) between baseline and mobile phone distraction conditions.
Moreover, drivers who allocated more visual attention to the left mirror were likely to demonstrate greater attention to roundabout features and heightened vigilance regarding surrounding traffic.Thus they were less likely to commit infractions, such as failing to yield or give way [46].
Fixation on left mirror and window and driving experience
The study results uncovered an important finding pertaining to the driving experience of the participants.Remarkably, the older a driver's license is, the more often participants demonstrated a tendency to look more frequently at both the left window and mirror.This trend was consistently evident and statistically significant across numerous cases as depicted in Fig. 11.Moreover, despite the variability within the sample which affects the statistical significance of the results, similar conclusions were obtained when considering other variables related to the drivers' experience, including frequency of driving, distance driven, and love for driving.
The heightened situational awareness and anticipation skills of experience drivers resulted from the adaptive nature of visual scanning strategies of experienced drivers gained with more years of driving experience, Moreover, with time and practice, drivers often refine their driving habits and adopt more efficient gaze behavior.Furthermore, experienced drivers had the ability to instinctively prioritize their visual attention towards left mirror and window, the most relevant areas, to verify neighboring vehicles and traffic flow in the roundabout and pay less attention to other in-vehicle elements and distractions.Consequently, they maintained a high level of awareness and responsiveness to navigate roundabouts safely.These findings are consistent with Konstantopoulos et al. [47] study that tracked gaze behavior and driving performance of driving instructors.The outcome of this latter showed that experienced drivers have a longer fixation period on side mirrors, shorter processing time, better sampling rate, and wider scanning of the environment.In line with these conclusions, Falkmer & Gregersen [48] noted that inexperienced drivers focus their attention on in-vehicle objects and limited areas inside and outside the vehicle.
Gaze behavior of the drivers under baseline and distraction conditions
In the context of analyzing gaze behavior under baseline and distraction conditions, the findings underscored the relevance of drivers' visual attention in the absence of mobile phone distractions.Moreover, the analysis revealed that drivers exhibited a statistically significant higher frequency of glances towards their left mirror, although there was greater uncertainty for the left window.However, when examining left window fixation during the distraction's lap, the main conclusion drawn from Fig. 12 is that a decrease in infractions coincides with an increase in left window fixation.For this analysis, the same seven groups as those used in previous penalizations analysis were used.
Mobile phone use behind the wheel diverted drivers' attention away from the driving task, and adversely impacted their gaze behavior particularly their focus on the left window and mirror when navigating roundabouts.This potentially increased the risk of errors, infractions, or lapses in judgment.Indeed, in the baseline condition, the drivers maintained vigilance towards the left mirror which allowed them to gather essential information about the road environment, including the presence of other vehicles or pedestrians.This heightened situational awareness to left mirror and window facilitated early detection of potential risks, and aided proactive hazard avoidance strategies, thereby reducing the likelihood of committing infractions.
These findings relate to a previous study by Haque et al. [20] compared drivers' behavior with and without mobile phone-induced distractions, when traveling through roundabouts.The authors reported that distracted drivers accepted smaller safety margins than non-distracted drivers.Likewise, Azimian et al. [32] compared the total fixation of drivers at roundabouts, under baseline and mobile phone distractions, and concluded that mobile phone use reduced fixation duration in all rearview mirror, windshield, left-side mirror, and window and passenger-side mirror and window.
Study limitations and directions for future research
In summary, this study was designed to safely and efficiently collect a very large amount of data on gaze behavior and driving performance of drivers at roundabouts under baseline and mobile phone distractions conditions.However, there are potential limitations due to the nature of the present study which used a driving simulator that cannot capture all possible drivers' behaviors under distraction in a real-world driving environment.Moreover, the present study selected only four, one and three-lanes, roundabouts.The design of driving simulator parameters plays an important role in research results.In fact, the nature, type, and specifications of roundabouts directly affect the stimuli presented to the drivers and, consequently, the level of realism.Further research should look into drivers' performance at multilane and spiral roundabouts and consider different simulator configurations of traffic and road users to gain further insight into the severity of mobile phone use while entering, driving in, and exiting a roundabout.In addition, as the experience gained by the driver on the first lap may influence their behavior on the second lap, future studies should consider a random selection of the lap order (with and without mobile phone distractions) to avoid bias and obtain more robust conclusions.Furthermore, this study's design was limited to only analyzing the drivers' experience.In fact, driving behavior and driving style depend on the driver's particularities, for instance, risk perception, gender, age, personality traits, emotional and behavioral conditions, attention, eye-gaze dynamics, body movement, and gestures [49,50].Future studies should broaden the context of the current paper and examine other factors that may affect the drivers' performance.Particular attention could be paid to differences in cognitive control between young and older drivers and impacts on their driving performance.
Finally, given the fact that roundabouts are designed to congregate different road users, steering safely through roundabouts requires drivers to consider many aspects other than checking their left side mirror and window.Thus, future research may look into other behaviors such as the use of turn signals, yielding rate, gap acceptance, and braking behavior, and may explore the influence of other distraction conditions (texting, operating a music player or radio, and eating while driving).distracciones del conductor en la seguridad vial.Diseño de un sistema integrado: simulador de conducción, "eye tracker" y dispositivo de distracción.
Fig. 3 .
Fig. 3. (1a) eye fixation in a video recorded by the eye tracker; (1b) position around the vehicle's cockpit as detected by the Tobii Pro Lab software, in this case, the left mirror; (2) timestamp within the simulator that enables the synchronization with the eye tracker timestamp; (3) eye tracker timestamp that allows computing the difference between both systems.
Fig. 6 .
Fig. 6.Aerial view of the virtual urban road.
Fig. 8 .
Fig. 8. Spatial distribution of Locations of Interests (LOIs)'s of the third roundabout.
Note:
The mean values for the large roundabout (Mean L) and the small roundabouts (Mean S) were also included.The statistically significant pvalues were highlighted in bold.The nine AOIs have been included in the table: Left Mirror (L.M.), Left Window (L.W.), Right Mirror (R.M.), Right Window (R.W.), Rear-View Mirror (RV.M.), Windshield (WS), Wheel (W), Dashboard (D), and Mobile phone (M.P).The degrees of freedom (DoF) corresponding to the Kruskal-Wallis' test were not included since they are a constant parameter (DoF = 1) as the mains of the two groups were compared throughout the test.
Fig. 9 .
Fig. 9. Fixation on the (a) left mirror and (b) window depending on the number of cars.Note that the title of each figure describes the specific fixation represented by the Y-axis.
Fig. 10 .
Fig. 10.(a) Infractions related to the fixation on the left mirror (b) p-values for the Kruskal-Wallis test comparing the fixation on the left mirror at different numbers of infractions.
W
.Boulagouas et al.
Fig. 11 .
Fig. 11.(a) License years associated with left mirror fixation (b) p-values for the Kruskal-Wallis test comparing the left mirror fixation by license years (c) license years associated with left window fixation (d) p-values for the Kruskal-Wallis test comparing the left window fixation by license years.
Table 1
Summary of participants' characteristics.
Table 2 Bartlett
's test results (p-values) for different comparisons (i.e., with distractions vs. without distractions, large vs. small roundabouts, number of cars in roundabouts), and the different AOIs considered in this study.Cases in which the null hypothesis cannot be rejected are highlighted in bold.Note: L.M: Left Mirror, L.W: Left Window, R.M: Right Mirror, R.W: Right Window, RV.M: Rear-View Mirror, WS: Windshield, W: Wheel, D: Dashboard, M.P: Mobile Phone.W. Boulagouas et al. | 6,395.2 | 2024-04-10T00:00:00.000 | [
"Engineering",
"Psychology",
"Computer Science"
] |
RFim: A Real-Time Inundation Extent Model for Large Floodplains Based on Remote Sensing Big Data and Water Level Observations
: The real-time flood inundation extent plays an important role in flood disaster preparation and reduction. To date, many approaches have been developed for determining the flood extent, such as hydrodynamic models, digital elevation model-based (DEM-based) methods, and remote sensing methods. However, hydrodynamic methods are time consuming when applied to large floodplains, high-resolution DEMs are not always available, and remote sensing imagery cannot be used alone to predict inundation. In this article, a new model for the highly accurate and rapid simulation of floodplains, called “RFim” (real-time inundation model), is proposed to simulate the real-time flooded area. The model combines remote sensing images with in situ data to find the relationship between the inundation extent and water level. The new approach takes advantage of remote sensing images, which have wide spatial coverage and high resolution, and in situ observations, which have continuous temporal coverage and are easily accessible. This approach has been applied in the study area of East Dongting Lake, representing a large floodplain, for inundation simulation at a 30 m resolution. Compared with the submerged extent from observations, the accuracy of the simulation could be more than 90% (the lowest is 93%, and the highest is 96%). Hence, the approach proposed in this study is reliable for predicting the flood extent. Moreover, an inundation simulation for all of 2013 was performed with daily water level observation data. With an increasing number of Earth observation satellites operating in space and high-resolution mappers deployed on satellites, it will be much easier to acquire large quantities of images with very high resolutions. Therefore, the use of RFim to perform inundation simulations with high accuracy and high spatial resolutions in the future is promising because the simulation model is built on remote sensing imagery and gauging station data.
Introduction
Floods are one of the most common and harmful natural disasters in the world, having caused direct economic losses exceeding $1 trillion and killing more than 220,000 people over the last forty years [1]. The flood risk may not be reduced and may even increase in many regions of the world in the future because of climate change [2]. Knowing the real-time flood inundated extent during a flood event is an important way to respond quickly and reduce disaster impacts [3]. Great efforts have been made to study flood inundation, and the methods can be roughly divided into three types: Hydrodynamic methods, DEM-based (digital elevation model-based) inundation methods, and remote sensing methods.
Based on hydrodynamic models, hydrodynamic methods mathematically express the physical laws of flood movement and the inundation extent. From the perspective of dimension, the hydrodynamic in Section 5. The discussions are presented in Section 6. The conclusions and future work are summarized in Section 7.
Study Area
East Dongting Lake, which is connected to the Yangtze River, is the largest lake in the Donging Lake system [17]. It covers the region from approximately 28°59″ to 29°38″ and 112°43″ to 113°15″ [18], and drains an area of 1900 km 2 , including a water area of 364 km 2 [19]. The location of East Dongting Lake is shown in Figure 1. Since it is a flood basin of the Yangtze River and precipitation is imbalanced during the year, East Dongting Lake fluctuates dramatically between the wet and dry seasons, with the maximum area in August and the minimum area in January or February [20]. The elevations of East Dongting Lake are lower than 35 m in Huanghai Datum 1965, and slopes are less than 3° [21]. In recent years, under the impact of economic development and human activities, flood disasters have occurred frequently around East Dongting Lake. Hence, it is significant to predict the inundation extent in this area for flood disaster preparation and mitigation.
Data Selection
The size of East Dongting Lake is always changing throughout the year. To assess its extent as much as possible, Landsat images of the lake with a dense time series were chosen. Generally, large-scale flood inundation models are run at a 25-100 m resolution [22]. Landsat images with a 30 m resolution were chosen. In this paper, the archives of Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) orthorectified, top-of-atmosphere reflectance images acquired between 2001 and 2016 were used. The 97 images acquired from 2002 to 2011 that were used for constructing the simulation model in the study are shown in Table 1. The images acquired from 2013 to 2016 that were used for validation or application are also shown in Table 1. Landsat 5 was launched on 1 March 1984, to collect imagery of the Earth until 5 June 2013. Landsat 8, launched on 11 February 2013, still works. Landsat 5 and 8 revisit the same scene every 16 days, with a 30 m resolution. Landsat 7 Enhanced Thematic Mapper-plus (ETM+) images were not used in the study because of the scan line corrector (SLC) failure [23], which led to the loss of more than 20% of each single scene acquired after 23 May 2003 [24]. The measurements of the water level were provided by the Chenglingji gauging station (29.41°, 113.12°), which is located at the confluence of East Dongting Lake and the Yangtze River, and is near East Dongting Lake. This gauging station provides the daily water level of East Dongting Lake. The collected daily water levels at the Chenglingji gauging station from 1 January 2001 to 31 December 2016 were used in this study.
Data Selection
The size of East Dongting Lake is always changing throughout the year. To assess its extent as much as possible, Landsat images of the lake with a dense time series were chosen. Generally, large-scale flood inundation models are run at a 25-100 m resolution [22]. Landsat images with a 30 m resolution were chosen. In this paper, the archives of Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) orthorectified, top-of-atmosphere reflectance images acquired between 2001 and 2016 were used. The 97 images acquired from 2002 to 2011 that were used for constructing the simulation model in the study are shown in Table 1. The images acquired from 2013 to 2016 that were used for validation or application are also shown in Table 1. Landsat 5 was launched on 1 March 1984, to collect imagery of the Earth until 5 June 2013. Landsat 8, launched on 11 February 2013, still works. Landsat 5 and 8 revisit the same scene every 16 days, with a 30 m resolution. Landsat 7 Enhanced Thematic Mapper-plus (ETM+) images were not used in the study because of the scan line corrector (SLC) failure [23], which led to the loss of more than 20% of each single scene acquired after 23 May 2003 [24]. The measurements of the water level were provided by the Chenglingji gauging station (29.41 • , 113.12 • ), which is located at the confluence of East Dongting Lake and the Yangtze River, and is near East Dongting Lake. This gauging station provides the daily water level of East Dongting Lake. The collected daily water levels at the Chenglingji gauging station from 1 January 2001 to 31 December 2016 were used in this study.
Data Preparation
To pre-process the images quickly, Google Earth Engine (GEE), a free cloud-computing platform, was used in this study. The GEE provides Internet-based application programming interfaces (APIs) that enable users to process and analyze large quantities of remote sensing images at a time. Additionally, many geographical datasets could be found in the GEE, including Earth surface observations from Landsat, SPOT, MODIS, and Sentinel-1 [25]. The main part of data preparation is cloud removal. More than five hundred Landsat 5 and 8 images were acquired in the research area from 2001 to 2016, but most of them were partly or entirely covered by clouds. It is necessary to remove the clouds to make use of the acquired images. There is an API called 'simpleCloudScore', provided by the GEE, that calculates the cloud likelihood for every pixel in the range from 1 to 100 by using the normalized difference snow index (NDSI), and the brightness and temperature from the Landsat imagery [26]. The details of simpleCloudScore could be found in the GEE documentation. The simpleCloudScore is easy to operate and saves time, and some studies used the API to remove the clouds [26,27]. Therefore, we also chose to use it to remove the clouds. For this study, more than 40% of the images covered by clouds were deleted. A threshold of 20 for the cloud score was used to remove clouds based on the visual interpretation of the Landsat images [27]. After cloud removal, only 97 images were left that were suitable for further processing.
Method
The basic idea of RFim is to find the relationships between water levels and inundation extents. Following the main idea of RFim, the method of the real-time flood inundation simulation and prediction model based on remote sensing data is shown in Figure 2. The method consists of six steps: (1) Discretizing the study area and resampling the images; (2) extracting historical flood extents based on remote sensing images; (3) relating the water level values to pixels; (4) forming inundation records for every grid cell in the study area; (5) establishing the relationship between inundation extent and water level; and (6) simulating and predicting flood extent with the relationship between inundation extent and water level. Figure 2. The flowchart of the approach in this paper.
Discretizing the Study Area and Resampling the Images
The proposed method requires pixels covering the same position to be completely coincident in different images. Therefore, it is necessary to discretize the research area, as shown in Figure 3. Then, the remote sensing images used in the experiment are resampled, using nearest-neighbor interpolation according to the results of grid cell division, as shown in Figure 4. After that, the pixels
Discretizing the Study Area and Resampling the Images
The proposed method requires pixels covering the same position to be completely coincident in different images. Therefore, it is necessary to discretize the research area, as shown in Figure 3. Then, the remote sensing images used in the experiment are resampled, using nearest-neighbor interpolation according to the results of grid cell division, as shown in Figure 4. After that, the pixels covering the same location are completely coincident in different resampled images. For example, the study area, East Dongting Lake, is divided into 1560 rows and 1907 columns to discretize the area into 30 m grid cells, the amount of which is 2,974,920 (1560 × 1907). the study area, East Dongting Lake, is divided into 1560 rows and 1907 columns to discretize the area into 30 m grid cells, the amount of which is 2,974,920 (1560 × 1907).
Extracting the Historical Flood Extents Based on Remote Sensing Images
Water extent extraction is a common topic in object classification, and experts have developed many models to successfully extract surface water, such as tasselled cap transformation, the normalized differences of water index (NDWI) [28], the modified normalized differences of water index (mNDWI) [29], and support vector machines (SVMs) [30]. In this research, the NDWI is selected to detect the water extents from the images because of its simplicity and high accuracy in large open water [31]. The NDWI separates the water from the background by the different reflections of the green band (p(Green)) and near infrared band (p(NIR)) on different objects. It should be stressed that the water extraction method is not fixed, and any method could be used if it is suitable for the experimental conditions. The mathematical model of the NDWI is as follows: Determining the threshold is one of the most important steps in using the NDWI to distinguish the study area, East Dongting Lake, is divided into 1560 rows and 1907 columns to discretize the area into 30 m grid cells, the amount of which is 2,974,920 (1560 × 1907).
Extracting the Historical Flood Extents Based on Remote Sensing Images
Water extent extraction is a common topic in object classification, and experts have developed many models to successfully extract surface water, such as tasselled cap transformation, the normalized differences of water index (NDWI) [28], the modified normalized differences of water index (mNDWI) [29], and support vector machines (SVMs) [30]. In this research, the NDWI is selected to detect the water extents from the images because of its simplicity and high accuracy in large open water [31]. The NDWI separates the water from the background by the different reflections of the green band (p(Green)) and near infrared band (p(NIR)) on different objects. It should be stressed that the water extraction method is not fixed, and any method could be used if it is suitable for the experimental conditions. The mathematical model of the NDWI is as follows: NDWI = (p(Green) − p(NIR))/(p(Green) + p(NIR)) (1) Determining the threshold is one of the most important steps in using the NDWI to distinguish water from non-water areas [32]. Unfortunately, the threshold values between water and other objects were unstable and varied with scenes and locations [33], and it is quite impractical to find the threshold for every image by hand. Otsu thresholding [34] is widely used to determine the optimal
Extracting the Historical Flood Extents Based on Remote Sensing Images
Water extent extraction is a common topic in object classification, and experts have developed many models to successfully extract surface water, such as tasselled cap transformation, the normalized differences of water index (NDWI) [28], the modified normalized differences of water index (mNDWI) [29], and support vector machines (SVMs) [30]. In this research, the NDWI is selected to detect the water extents from the images because of its simplicity and high accuracy in large open water [31]. The NDWI separates the water from the background by the different reflections of the green band (p(Green)) and near infrared band (p(NIR)) on different objects. It should be stressed that the water extraction method is not fixed, and any method could be used if it is suitable for the experimental conditions. The mathematical model of the NDWI is as follows: Determining the threshold is one of the most important steps in using the NDWI to distinguish water from non-water areas [32]. Unfortunately, the threshold values between water and other objects were unstable and varied with scenes and locations [33], and it is quite impractical to find the threshold for every image by hand. Otsu thresholding [34] is widely used to determine the optimal threshold for water detection with the NDWI [35]. From the image histogram, the Otsu method decided the ideal threshold to make the between-class variance maximum and the within-class variance minimum. Thus, Otsu thresholding was selected to determine the threshold automatically in this paper. The accuracy of water extraction this study using NDWI and Ostu thresholding is shown in Appendix A.
Relating the Water Level Values to Pixels
To find the relationship between water levels and inundation, it is necessary to relate the right pixels to corresponding water levels according to the date when the image is acquired. If there is one gauging station that can provide water level data, the procedure of relating water level values to images is illustrated in Figure 5. As shown in the right part of Figure 5, each pixel in blue with the water level value in it means that the pixel was submerged based on observations at that water level, and each pixel in white with the water level value in it means that the pixel was not submerged based on observations at that water level. Furthermore, if there are two or more gauging stations, each pixel will be related to the water level value which is calculated from water level values in the gauging stations, based on the inverse distance weighted interpolation method and the distances between gauging stations and the center of the pixel. threshold for water detection with the NDWI [35]. From the image histogram, the Otsu method decided the ideal threshold to make the between-class variance maximum and the within-class variance minimum. Thus, Otsu thresholding was selected to determine the threshold automatically in this paper. The accuracy of water extraction this study using NDWI and Ostu thresholding is shown in Appendix A.
Relating the Water Level Values to Pixels
To find the relationship between water levels and inundation, it is necessary to relate the right pixels to corresponding water levels according to the date when the image is acquired. If there is one gauging station that can provide water level data, the procedure of relating water level values to images is illustrated in Figure 5. As shown in the right part of Figure 5, each pixel in blue with the water level value in it means that the pixel was submerged based on observations at that water level, and each pixel in white with the water level value in it means that the pixel was not submerged based on observations at that water level. Furthermore, if there are two or more gauging stations, each pixel will be related to the water level value which is calculated from water level values in the gauging stations, based on the inverse distance weighted interpolation method and the distances between gauging stations and the center of the pixel. Figure 5. The procedure of relating water levels to pixels: (1) Find the water level value at the specific date when the image was acquired; (2) relate the water level value to pixels in that image, and reserve the pixel values that represent dry or wet (thus, every pixel in that image will have two kinds of data at the same time, one of which represents dry or wet, while the other represents water level).
Forming Inundation Records for Every Grid Cell in the Study Area
Based on the previous step, where all the pixels in the images have been labelled with water level values, pixels from different images but in the same grid cell that the study area is divided into are placed together to form a set of inundation information and corresponding water level values for that grid cell, as shown in Figure 6. The set of inundation information and water level values for that grid cell are called its "inundation records" in this article. After obtaining inundation records for a grid cell, we can easily determine whether the grid cell is inundated or not under the water level Figure 5. The procedure of relating water levels to pixels: (1) Find the water level value at the specific date when the image was acquired; (2) relate the water level value to pixels in that image, and reserve the pixel values that represent dry or wet (thus, every pixel in that image will have two kinds of data at the same time, one of which represents dry or wet, while the other represents water level).
Forming Inundation Records for Every Grid Cell in the Study Area
Based on the previous step, where all the pixels in the images have been labelled with water level values, pixels from different images but in the same grid cell that the study area is divided into are placed together to form a set of inundation information and corresponding water level values for that grid cell, as shown in Figure 6. The set of inundation information and water level values for that grid cell are called its "inundation records" in this article. After obtaining inundation records for a grid Remote Sens. 2019, 11, 1585 8 of 18 cell, we can easily determine whether the grid cell is inundated or not under the water level from the inundation records. In Figure 7, a schematic diagram of the inundation records, sorted by the water level value, in one grid cell is displayed, which looks like a column of cells.
a. Find the minimum value under which a grid is submerged or wet, and the maximum one under which a grid is unsubmerged or dry in the 'inundation records' of that grid. If the minimum value and the maximum value do not exist at the same level, there is no need to remove 'abnormal records', or in other words, there is no 'abnormal record'; b. Compare the minimum and the maximum value which are found in step a. If the minimum value under which the grid is submerged is lower than the maximum one under which the grid is unsubmerged, remove these 'abnormal records' from the 'inundation records' of that grid. If the minimum is larger than the maximum, there is no 'abnormal record', and so we do not need to remove 'abnormal records'; c. Repeat step a and step b on the rest of 'inundation records' of that grid in step b.
Figure 6.
Relating water levels to images and forming inundation records for every grid cell in the study area. Ideally, the minimum value of the water level under which the grid cell is inundated is always larger than the maximum value of the water level where the grid cell is not flooded. However, in some cases, the minimum value under which the grid cell is submerged is lower than the maximum value under which the grid cell is unsubmerged, as shown in the middle of Figure 8, due to errors when detecting water. Similar to other classification methods, the NDWI could not separate water from no-water with 100% accuracy. This means that a grid cell that is not submerged may be classified as an inundation area when extracting the water surface from an image because of the error associated with detecting water. When this error occurs, the minimum value or maximum value with the corresponding inundation information is called an "abnormal record" in the article. To prevent such anomalies from affecting the subsequent steps, these abnormal records should be removed from the inundation records. Inundation records: The cell in blue with a water level value means that the grid cell is submerged from an observation under that water level value, and the cell in white with a water level means that the grid cell is observed to be unsubmerged under that water level.
Establishing the Relationship between the Inundation Extent and Water Level
The essence of establishing the relationship between flood extent and water level is to find out the threshold for every grid cell that determines whether it would be submerged when compared to the water level.
It is obvious from Figure 7 that the inundation threshold of a grid cell is the value between the lowest water level under which the grid cell is submerged and the highest water level under which the grid cell is unsubmerged. However, any value in that range might be the true threshold. We choose the mean value of the lowest water level under which the grid cell is submerged plus the highest level under which the grid cell is unsubmerged. Although the mean value might not equal the exact threshold, it would be near the true threshold when the observations are dense enough to make that range small. The impacts of the mean value will be explained in the analysis section. The equation for calculating the threshold is as follows: The procedure that determines abnormal records is as follows: a. Find the minimum value under which a grid is submerged or wet, and the maximum one under which a grid is unsubmerged or dry in the 'inundation records' of that grid. If the minimum value and the maximum value do not exist at the same level, there is no need to remove 'abnormal records', or in other words, there is no 'abnormal record'; b. Compare the minimum and the maximum value which are found in step a. If the minimum value under which the grid is submerged is lower than the maximum one under which the grid is unsubmerged, remove these 'abnormal records' from the 'inundation records' of that grid. If the minimum is larger than the maximum, there is no 'abnormal record', and so we do not need to remove 'abnormal records'; c. Repeat step a and step b on the rest of 'inundation records' of that grid in step b.
Establishing the Relationship between the Inundation Extent and Water Level
The essence of establishing the relationship between flood extent and water level is to find out the threshold for every grid cell that determines whether it would be submerged when compared to the water level.
It is obvious from Figure 7 that the inundation threshold of a grid cell is the value between the lowest water level under which the grid cell is submerged and the highest water level under which the grid cell is unsubmerged. However, any value in that range might be the true threshold. We choose the mean value of the lowest water level under which the grid cell is submerged plus the highest level under which the grid cell is unsubmerged. Although the mean value might not equal the exact threshold, it would be near the true threshold when the observations are dense enough to make that range small. The impacts of the mean value will be explained in the analysis section. The equation for calculating the threshold is as follows: where u is the lowest water level under which the grid cell is submerged and b is the highest water level under which the grid cell is unsubmerged.
Simulating and Predicting Flood Extent with the Relationship between Inundation Extent and Water Level
After calculating threshold values for each grid cell in the inundating threshold part, the inundation extent could be predicted easily by comparison with the water level on the desired date, as shown in Figure 9.
where u is the lowest water level under which the grid cell is submerged and b is the highest water level under which the grid cell is unsubmerged.
Simulating and Predicting Flood Extent with the Relationship between Inundation Extent and Water Level
After calculating threshold values for each grid cell in the inundating threshold part, the inundation extent could be predicted easily by comparison with the water level on the desired date, as shown in Figure 9.
Results
The remote sensing images were divided into two parts: One part was used for modelling acquired from 1 January 2002 to 31 December 2011 by calculating the water level threshold in every grid cell, and the other part was used for validation or application acquired from 1 January 2013 to 31 December 2016.
Due to the location of East Dongting Lake and data losses when removing clouds in images, at least two images were required to cover the study area. Hence, "composite images" were used for validation in our study, and composite image in this article refers to mosaicking several images. We Figure 10. The accuracies and kappa coefficients were determined using a cell-to-cell comparison strategy between the predictions and observations. The accuracies and kappa coefficients are shown in Table 2. The flooded area obtained from Landsat imagery was regarded as the real and correct flood extent. The prediction accuracy is equal to the percentage of cells correctly predicted by the approach, and the formulation is shown in Equation (3):
Results
The remote sensing images were divided into two parts: One part was used for modelling acquired from 1 January 2002 to 31 December 2011 by calculating the water level threshold in every grid cell, and the other part was used for validation or application acquired from 1 January 2013 to 31 December 2016.
Due to the location of East Dongting Lake and data losses when removing clouds in images, at least two images were required to cover the study area. Hence, "composite images" were used for validation in our study, and composite image in this article refers to mosaicking several images. Figure 10. The accuracies and kappa coefficients were determined using a cell-to-cell comparison strategy between the predictions and observations. The accuracies and kappa coefficients are shown in Table 2. The flooded area obtained from Landsat imagery was regarded as the real and correct flood extent. The prediction accuracy is equal to the percentage of cells correctly predicted by the approach, and the formulation is shown in Equation (3): where w is the number of correct water pixels, n is the number of correct non-water pixels, and s is the number of pixels in the study area. Prediction accuracy = (w + n)/s where w is the number of correct water pixels, n is the number of correct non-water pixels, and s is the number of pixels in the study area.
Application
The model is easy to set up for being used by users with relatively little hydraulic modelling experience. This model could simulate real-time flood extent, but also reproduce the flood extents in the past when the images on the floodplain were not available. Therefore, inundation extent on every day of 2013 was simulated based on the model and water level values collected during that period. The daily water level variations from 1 January 2013 to 31 December 2013 are shown in Figure 11, and the daily variations in the inundation area from 1 January 2013 to 31 December 2013 in East Dongting Lake are shown in Figure 12.
From Figure 12, it is clear that the variations in the inundation area during 2013 exhibit a first-rise-and-then-fall pattern that is similar to that for the changes in water level during 2013.
Application
The model is easy to set up for being used by users with relatively little hydraulic modelling experience. This model could simulate real-time flood extent, but also reproduce the flood extents in the past when the images on the floodplain were not available. Therefore, inundation extent on every day of 2013 was simulated based on the model and water level values collected during that period. The daily water level variations from 1 January 2013 to 31 December 2013 are shown in Figure 11, and the daily variations in the inundation area from 1 January 2013 to 31 December 2013 in East Dongting Lake are shown in Figure 12.
From Figure 12, it is clear that the variations in the inundation area during 2013 exhibit a first-rise-and-then-fall pattern that is similar to that for the changes in water level during 2013. The largest inundation area in 2013 was 1069.8 km 2 when the water level at Chenglingji station was 29.83 m, and the lowest in 2013 was 9.1 km 2 when the water level was 20.41 m. The variations in the inundation extent every four days from 5 May 2013 to 21 May 2013 are displayed in Figure 12.
Discussions
Among the predictions during the different time periods, there was the best agreement between the inundation prediction and the observation for the composite image from 1 July 2014 to 30 September 2014. Up to 96% of the cells or grid cells were predicted correctly based on the composite from 1 July 2014 to 30 September 2014, and the kappa coefficient was approximately 0.92, which represents almost perfect agreement. The worst performance of the approach was the prediction on the composite image from 1 January 2016 to 31 March 2016, with 93% of the cells predicted correctly, and the kappa coefficient was approximately 0.67, which represents substantial agreement. Although 93% was the lowest accuracy compared to those of the other three predictions, it was acceptable in the flood simulation. The accuracies of the four predictions were all approximately 94%, and the kappa coefficients ranged from 0.67 to 0.91.
There is a main reason that could explain why the accuracy of prediction from 1 January 2016 to 31 March 2016 was relatively low. The number of remote sensing images with good quality during that period was small compared to those in the other three time periods. The number of images during each month used to build the prediction model is shown in Figure 13. From 2001 to 2011, only 11 images, which were acquired from January to March, were considered good observations and used for modelling. The fewer the number of good observations that are acquired, the less the historical water extent can be accessed. The shortage of good observations made the model perform
Discussions
Among the predictions during the different time periods, there was the best agreement between the inundation prediction and the observation for the composite image from 1 July 2014 to 30 September 2014. Up to 96% of the cells or grid cells were predicted correctly based on the composite from 1 July 2014 to 30 September 2014, and the kappa coefficient was approximately 0.92, which represents almost perfect agreement. The worst performance of the approach was the prediction on the composite image from 1 January 2016 to 31 March 2016, with 93% of the cells predicted correctly, and the kappa coefficient was approximately 0.67, which represents substantial agreement. Although 93% was the lowest accuracy compared to those of the other three predictions, it was acceptable in the flood simulation. The accuracies of the four predictions were all approximately 94%, and the kappa coefficients ranged from 0.67 to 0.91.
There is a main reason that could explain why the accuracy of prediction from 1 January 2016 to 31 March 2016 was relatively low. The number of remote sensing images with good quality during that period was small compared to those in the other three time periods. The number of images during each month used to build the prediction model is shown in Figure 13. Apart from the number of remote sensing images, three factors affected the model performance.
The first factor was the failure to extract a narrow water body when using the NDWI. Although the difference between water and other objects could be enhanced by the NDWI, its formulation is inherently sensitive to imagery noise [36]. In addition, mixed water pixels usually appear in narrow rivers and shallow water [37]. This made it difficult to delineate the narrow water bodies. The lake expanded and merged many narrow rivers and small ponds into a large single unity during the flood season, which reduced the narrow water bodies that were hard to detect using the NDWI. During the dry season, the lake shrank, and the narrow water bodies appeared, which led to much confusion regarding water extraction. Then, the results of water delineation, with failure to detect narrow water bodies, were applied to simulate the inundation extent and violated the predictions to some degree. Since the water extraction in the images during the wet season did not interfere with the small water surface, the inundation simulation from 1 July 2014 to 30 September 2014 had the highest accuracy in the experiment.
The second factor that had a negative effect on the model performance was including only one gaging station, the Chenglingji water station, for the measurements of water stages. Although the Chenglingji water station could represent the daily water level of East Dongting Lake using a single value, the height of the whole water surface actually varied slightly from place to place, especially when many water bodies shrank and separated from the lake during the dry season.
The third factor was using the mean value to represent the real threshold in each grid cell. This impacts the performance of RFim, as the mean value and the real threshold are not equal in every grid cell. If the gap, which is the difference between the lowest water level under which the grid cell is submerged and the highest level under which the grid cell is unsubmerged, is small, RFim is likely to obtain the mean value equivalent the real threshold in that grid cell. The grid cells were collected, of which corresponding thresholds were calculated from water level values from July to September, and the distribution of the gaps from these grid cells is shown in Figure 14a. Additionally, the distribution of the gaps from which the corresponding thresholds were calculated from the water levels from January to March is shown in Figure 14b. From these figures, the gaps for the water levels from July to September were smaller than the gaps for the water levels from January to March. Thus, this observation can partly explain why the prediction from 1 July 2014 to 30 September 2014 was better than the prediction from 1 January 2016 to 31 March 2016. Apart from the number of remote sensing images, three factors affected the model performance.
The first factor was the failure to extract a narrow water body when using the NDWI. Although the difference between water and other objects could be enhanced by the NDWI, its formulation is inherently sensitive to imagery noise [36]. In addition, mixed water pixels usually appear in narrow rivers and shallow water [37]. This made it difficult to delineate the narrow water bodies. The lake expanded and merged many narrow rivers and small ponds into a large single unity during the flood season, which reduced the narrow water bodies that were hard to detect using the NDWI. During the dry season, the lake shrank, and the narrow water bodies appeared, which led to much confusion regarding water extraction. Then, the results of water delineation, with failure to detect narrow water bodies, were applied to simulate the inundation extent and violated the predictions to some degree. Since the water extraction in the images during the wet season did not interfere with the small water surface, the inundation simulation from 1 July 2014 to 30 September 2014 had the highest accuracy in the experiment.
The second factor that had a negative effect on the model performance was including only one gaging station, the Chenglingji water station, for the measurements of water stages. Although the Chenglingji water station could represent the daily water level of East Dongting Lake using a single value, the height of the whole water surface actually varied slightly from place to place, especially when many water bodies shrank and separated from the lake during the dry season.
The third factor was using the mean value to represent the real threshold in each grid cell. This impacts the performance of RFim, as the mean value and the real threshold are not equal in every grid cell. If the gap, which is the difference between the lowest water level under which the grid cell is submerged and the highest level under which the grid cell is unsubmerged, is small, RFim is likely to obtain the mean value equivalent the real threshold in that grid cell. The grid cells were collected, of which corresponding thresholds were calculated from water level values from July to September, and the distribution of the gaps from these grid cells is shown in Figure 14a. Additionally, the distribution of the gaps from which the corresponding thresholds were calculated from the water levels from January to March is shown in Figure 14b. From these figures, the gaps for the water levels from July to September were smaller than the gaps for the water levels from January to March. Thus, this observation can partly explain why the prediction from 1 July 2014 to 30 September 2014 was better than the prediction from 1 January 2016 to 31 The new approach described in the paper could determine the flood extent in a large floodplain thanks to the wide spatial coverage and short revisit cycle of remote sensing images. It successfully simulated the inundation extent in the approximately 2000 km 2 region in this study. Furthermore, hydrodynamic models need detailed parameters as inputs, which may be not available. Since the data for this approach are temporally continuous and easy to access, the new method could simulate the flooded area in real time if the real-time water level was given.
If the first five steps have been finished and the inundation thresholds of the grid cells are already known, it will not take much time to obtain a real-time inundation simulation according to the real-time measurements of the water stage in the final part. The final part of the approach is, essentially, a simple matrix operation that is just comparing the elements in the matrix with the given value. Real-time inundation simulation can be achieved with this method.
This method does have the capabilities to use remote sensing big data with huge volume and big complexity, although only Landsat archived data were used in this article. If the user intends to apply this method with remote sensing big data, the inundation simulation can be made through the same six steps of the method, which were illustrated in this article. The only adjustment of the method when using remote sensing big data is the way of extracting water bodies according to the resolutions and wave bands of images.
From the application section, the method also shows that it could simulate real-time flood extent, and also reproduce the flood extents from the past when the images on the floodplain were not available.
With the increasing number of Earth observation satellites equipped with higher-resolution mappers, the ability to predict the real-time inundation extent at higher spatial resolutions by using remote sensing imagery and in situ measurements of water height is promising. Over the last twenty years, an increasing number of high-resolution sensors have been operating in space. For example, Ikonos, Worldview, Quickbird, and RapidEye have the ability to provide imagery at the metre or submetre level.
There are some drawbacks of the new method: First, it could not model flood movement. However, if the flood extent is predicted at a daily time step, flood movement is relatively unimportant on such a temporal scale.
Second, the new method may not predict the inundation area correctly under water levels higher than the historical maximum water level, or lower than the minimum. The RFim is built on images and the corresponding water levels among the acquired dates of the images. Therefore, the range of water levels under which RFim could simulate the corresponding inundation area is limited. If a flood occurred with a water level higher than the historical maximum level, the model could not perform well. The new approach described in the paper could determine the flood extent in a large floodplain thanks to the wide spatial coverage and short revisit cycle of remote sensing images. It successfully simulated the inundation extent in the approximately 2000 km 2 region in this study. Furthermore, hydrodynamic models need detailed parameters as inputs, which may be not available. Since the data for this approach are temporally continuous and easy to access, the new method could simulate the flooded area in real time if the real-time water level was given.
If the first five steps have been finished and the inundation thresholds of the grid cells are already known, it will not take much time to obtain a real-time inundation simulation according to the real-time measurements of the water stage in the final part. The final part of the approach is, essentially, a simple matrix operation that is just comparing the elements in the matrix with the given value. Real-time inundation simulation can be achieved with this method.
This method does have the capabilities to use remote sensing big data with huge volume and big complexity, although only Landsat archived data were used in this article. If the user intends to apply this method with remote sensing big data, the inundation simulation can be made through the same six steps of the method, which were illustrated in this article. The only adjustment of the method when using remote sensing big data is the way of extracting water bodies according to the resolutions and wave bands of images.
From the application section, the method also shows that it could simulate real-time flood extent, and also reproduce the flood extents from the past when the images on the floodplain were not available.
With the increasing number of Earth observation satellites equipped with higher-resolution mappers, the ability to predict the real-time inundation extent at higher spatial resolutions by using remote sensing imagery and in situ measurements of water height is promising. Over the last twenty years, an increasing number of high-resolution sensors have been operating in space. For example, Ikonos, Worldview, Quickbird, and RapidEye have the ability to provide imagery at the metre or submetre level.
There are some drawbacks of the new method: First, it could not model flood movement. However, if the flood extent is predicted at a daily time step, flood movement is relatively unimportant on such a temporal scale.
Second, the new method may not predict the inundation area correctly under water levels higher than the historical maximum water level, or lower than the minimum. The RFim is built on images and the corresponding water levels among the acquired dates of the images. Therefore, the range of water levels under which RFim could simulate the corresponding inundation area is limited. If a flood occurred with a water level higher than the historical maximum level, the model could not perform well.
Third, there should be at least one gauging station or other device which could measure and represent the water levels of rivers or lakes in the target area.
Fourth, the new method works well only if the topography of the study area is relatively stable. If the terrain changes greatly, the inundation extent will be different under the same water level, which will affect the performance of the model. If many other satellites are selected, such as SPOT, Ikonos, and GF-1, more information about water level and inundation extent can be obtained in a short period, during which the terrain is likely to be relatively stable, avoiding the impact of topographic changes in the study area. Fifth, if the water level data are scarcer, the performance will be affected. Scarcer water level data mean that fewer water levels could be related to flood extents according to whether the water level and the flood extent were acquired at the same date, which is necessary to establish the relationship between flood extent and water level.
Conclusions and Further Work
In this paper, a new approach was proposed for flood extent simulation and prediction. The new approach tries to find a relationship between remote sensing big data and in situ data to build a real-time flood prediction model, called RFim, taking advantage of the wide spatial coverage and high resolution of remote sensing images, and the continuous temporal coverage and easy accessibility of in situ observations. RFim was validated in East Dongting Lake. The prediction accuracy was approximately 94%, and the kappa coefficients ranged from 0.67 to 0.91. With an increasing number of Earth observation satellites operating in space and equipped with high-resolution mappers, the approach in this study has great potential for real-time flood simulation, since RFim is based on remote sensing big data.
There are some points that need to be considered in future work. Firstly, how to balance the cost of using remote sensing big data and the performance of the model needs to be considered. Using large quantities of images can improve the model performance, but the performance may be improved only a little with a heavy price of computational resources. Secondly, how to reduce the negative effect from scarcer water level data needs to be investigated. This may solve the problems of finding and establishing more relationships between other factors and the flood extent. That is, when water level data cannot be accessed, other factors can be options as inputs to simulate flood extent. Thirdly, how to extend the model capabilities to predict not just flood extent, but also flood duration and water volume, is worth exploring.
Author Contributions: Z.C. and J.L. conceived and designed the research, processed the data, and wrote the manuscript. N.C. conducted the fieldwork and reviewed the manuscript. R.X. and G.S. contributed materials.
Funding: This work was supported by the National Natural Science Foundation of China (nos. 41771422, 41890822).
Conflicts of Interest:
The authors declare no conflict of interest.
Water Extraction Accuracy Using NDWI
Here, we will calculate the accuracy of water extraction for the composite image from 1 July 2014 to 30 September 2014. Since it was impossible to verify more than 100 images, the composite image from 1 July 2014 to 30 September 2014 was selected for verification. There were two purposes for choosing the composite image from 1 July 2014 to 30 September 2014 for verification: One was to show that it is reasonable to use NDWI to detect water; and the other was that water extraction using NDWI can be treated as the real water distribution, which will be compared with the results of the flood simulation.
The method of verification is as follows: a. Select 250 samples randomly from the image; b. By visual object interpretation, identify the classes of the 250 samples; c. Compare the samples that have been identified with the results of water extraction based on the image, and then calculate the accuracy of extraction.
The accuracy of the composite image from 1 July 2014 to 30 September 2014 is 98.00%, with 130 samples classified correctly as water, 115 classified correctly as non-water, and only five classified incorrectly. | 11,457.6 | 2019-07-04T00:00:00.000 | [
"Environmental Science",
"Mathematics"
] |
Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder
As a complex field-circuit coupling system comprised of electric, magnetic and thermal machines, the permanent magnet synchronous motor of the electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency and communication characteristics make it difficult to diagnose faults. Meanwhile, the common intelligent diagnosis methods have low accuracy, poor generalization ability and difficulty in processing high-dimensional data. This paper proposes a method of fault feature extraction for motor based on the principle of stacked denoising autoencoder (SDAE) combined with the support vector machine (SVM) classifier. First, the motor signals collected from the experiment were processed, and the input data were randomly damaged by adding noise. Furthermore, according to the experimental results, the network structure of stacked denoising autoencoder was constructed, the optimal learning rate, noise reduction coefficient and the other network parameters were set. Finally, the trained network was used to verify the test samples. Compared with the traditional fault extraction method and single autoencoder method, this method has the advantages of better accuracy, strong generalization ability and easy-to-deal-with high-dimensional data features.
Introduction
As one of the important parts of an electric vehicle, the permanent magnet synchronous motor (PMSM) has the advantages of small volume, high-efficiency and highpower density. However, most electric vehicle motors work in closed, narrow, complex and harsh environments [1,2]. Under the combined action of electric field force and magnetic field force, the load changes greatly, and a variety of faults are prone to occur, such as stator inter-turn winding short circuit [3], shafting misalignment, permanent magnet loss of excitation and so on [4]. The occurrence of one kind of fault may induce the occurrence of another kind of fault and even lead to the coupling effect of various faults. The generation of coupling fault will cause irreversible damage to the performance of the motor itself, especially in the high-temperature working environment, which will seriously affect the normal operation of the motor and electric vehicle. Therefore, the fault diagnosis analysis of electric vehicle PMSM is of great significance to the development of electric vehicles and motors [5].
At present, many scholars have carried out fault diagnosis and analysis of permanent magnet synchronous motor. In reference [6], the positive envelope of bus current and threephase current is taken as the signal extraction object, and the wavelet packet algorithm is used as the bearing fault signal extraction method to identify the bearing fault information in the DC motor. In reference to [7], the multi-loop mathematical model of Asynchronous motor is established according to the circuit method. The finite element analysis method is used to simulate the motor with multiple fault types, and the obtained current spectrum is analyzed to verify the fault mechanism characteristics of the motor. In reference [8], the mathematical model of the motor is established in MATLAB/Simulink to simulate various faults. The current, torque and speed waveforms of the motor under normal operation are compared and analyzed, and the basic characteristics information of the permanent magnet synchronous motor faults are obtained. However, the traditional identification methods of motor fault diagnosis based on a mathematical model and electrical signal are highly dependent on the accuracy of the model, and the selection of signal wave base has certain limitations, so the accuracy of motor fault feature extraction and analysis still needs to be improved.
At present, with the continuous development of artificial intelligence, machine learning has been cross-applied in various fields [9], such as the recovery and prediction of missing data [10], the judgment of stock price changes [11], and the detection of urban road obstacles [12]. These areas span biology, medicine, machinery, finance, etc.; [13][14][15][16], which has become the future development trend. The deep learning algorithm proposed by Hinton [17] and others is increasingly used in the fields of pattern recognition and deep feature extraction [18], which has a good application prospect in the faults detection of various mechanical equipment. In view of the inaccuracy of traditional manual fault feature extraction of vehicle motor [19], large capacity of fault data, many types of data and slow transmission speed [20], multilevel network analysis structure and adaptive learning process can be used to extract faults data features more accurately [21][22][23]. Reference [24] constructs a transformer fault recognition framework based on block training of the Adaboost-RBF algorithm. Reference [25] uses the wavelet packet analysis method to extract features of vibration data collected from motor experiments and then inputs the decomposed data as test samples into the support vector machine for classification diagnosis. Reference [26] adopts the sparse autoencoder algorithm to extract features of motor bearing vibration signal so as to achieve fault diagnosis. Reference [27], the denoising autoencoder is used to extract the features of the aero-engine gas path fault and combined with FBRF classifier. The features extracted for aero-engine fault analysis have good robustness. However, in the actual motor experiments, the collected data often have noise error compared with the real value and cannot accurately collect all the monitored signal values. Most of the above methods using deep neural networks for mechanical equipment faults detection do not consider the data with noise, and the processing of interference signal is weak.
As an unsupervised learning algorithm, autoencoders (AE) can accurately learn the internal characteristics of complex signals from unlabeled data, which has obvious advantages for high-dimensional motor data processing [28]. Based on the above research on autoencoder and the application of deep learning method in various fields, this paper proposes a fault diagnosis method based on the stacked denoising autoencoder (SDAE) algorithm for permanent magnet synchronous motor (PMSM) used in an electric vehicle. This method is mainly composed of two parts: first, it uses SDAE to extract the features of the collected operation data of PMSM and then inputs the extracted data into the support vector machine (SVM) for classification calculation, so as to identify the motor fault types, and finally achieve the purpose of fault detection. The main purpose of the SDAE algorithm is to extract the features of the collected PMSM data, which is equivalent to label the data and facilitate the subsequent SVM to classify it. Moreover, the SDAE used in this paper adds noise processing in the data input, which has better adaptability for the actual incomplete data.
The paper will discuss the proposed method according to the following aspects: in the second part, the principle of a single AE algorithm is introduced first, and gradually the working principle of the SDAE and SVM algorithm is introduced; in the third part, the specific steps of motor diagnosis method proposed in this paper are described; in the fourth part, the feasibility of the diagnosis method proposed in this paper is verified by the bearing data set, and then the motor operation data obtained from the experiment is used for practical verification. The experimental results show that the method has better accuracy and faster running speed. Finally summarizes the whole paper. This method can enhance the self-adaptive diagnosis ability of stacked denoising autoencoder by artificially adding noise to the input data to simulate the damage data collected and can effectively extract and classify the motor fault features.
Principle
The fault diagnosis of permanent magnet synchronous motor (PMSM) based on the stacked denoising autoencoder algorithm includes two parts: stacked denoising autoencoder and support vector machine (SVM) classification. A stacked denoising autoencoder can be regarded as the superposition of multiple denoising autoencoders. The collected motor running signals are taken as the input. The input data are randomly set to 0 or 0.2-0.3 times of Gaussian noise is added to simulate the damaged data to predict the output result of the original undamaged data. The fault characteristics are obtained by minimizing the reconstruction error. Finally, the nonlinear transformation of the support vector machine is used to classify the extracted fault features and output the final results.
Autoencoder Network Principle
An autoencoder is a kind of neural network proposed by Yann Lecun, which can realize the BP backpropagation algorithm. After training, it can copy the input to the output. The autoencoder can analyze the data characteristics well by reducing the dimension of the data, so it is often used to realize the functions of data anomaly analysis, data denoising, image analysis and data retrieval. Figure 1 shows the network structure of the single hidden layer autoencoder, in which the coding layer encodes the input data as the representation, while the decoding layer decodes the representation as to the output with a minimum loss, and the reconstruction error is the basis for measuring the learning effect [29]. specific steps of motor diagnosis method proposed in this paper are described; in th fourth part, the feasibility of the diagnosis method proposed in this paper is verified b the bearing data set, and then the motor operation data obtained from the experiment used for practical verification. The experimental results show that the method has bette accuracy and faster running speed. Finally summarizes the whole paper. This method ca enhance the self-adaptive diagnosis ability of stacked denoising autoencoder b artificially adding noise to the input data to simulate the damage data collected and ca effectively extract and classify the motor fault features.
Principle
The fault diagnosis of permanent magnet synchronous motor (PMSM) based on th stacked denoising autoencoder algorithm includes two parts: stacked denoisin autoencoder and support vector machine (SVM) classification. A stacked denoisin autoencoder can be regarded as the superposition of multiple denoising autoencoder The collected motor running signals are taken as the input. The input data are randoml set to 0 or 0.2-0.3 times of Gaussian noise is added to simulate the damaged data to predi the output result of the original undamaged data. The fault characteristics are obtained b minimizing the reconstruction error. Finally, the nonlinear transformation of the suppo vector machine is used to classify the extracted fault features and output the final result
Autoencoder Network Principle
An autoencoder is a kind of neural network proposed by Yann Lecun, which ca realize the BP backpropagation algorithm. After training, it can copy the input to th output. The autoencoder can analyze the data characteristics well by reducing th dimension of the data, so it is often used to realize the functions of data anomaly analysi data denoising, image analysis and data retrieval. Figure 1 shows the network structur of the single hidden layer autoencoder, in which the coding layer encodes the input dat as the representation, while the decoding layer decodes the representation as to the outpu with a minimum loss, and the reconstruction error is the basis for measuring the learnin effect [29]. There are n groups of training samples X = {X(1), X(2), X(3), . . . , X(N)}, each group of samples is an n-dimensional vector, the coding process can be expressed as: Among them, hidden layer h{h1(1),h2(1),h3(1), . . . , hm(1)} is the m-dimensional vector (m ≥ 1), which can be regarded as m neurons, W is the weight matrix of order m × n, b is the hidden layer bias vector of dimension m, S is the sigmoid activation function.
The decoding process can be expressed as follows: where W is the weight matrix of order m × n and b is the output bias vector of dimension m. For the purpose of minimizing the reconstruction error of autoencoder, let the reconstruction error be: where the loss function is the loss function of the reconstruction error. In this paper, the mean square error method is used to calculate: where λ is the weight adjustment coefficient. After iterative training, the reconstruction error can reach a smaller value, and the accuracy of data feature extraction can be improved.
Stacked Denoising Autoencoder Structure
In order to avoid the overfitting phenomenon of autoencoder in the process of data processing, the denoising autoencoder (DAE) adds "damage noise" or random zeroing to the input data on the basis of the original simple autoencoder to simulate the input of damaged data, which can effectively improve the robustness of model learning [30]. Figure 2 is the network structure diagram of the denoising autoencoder. In the input part, the original data becomes the missing or damaged impurity data after a specific destruction processing as the new input, and the input replaces the original data for automatic coding and learning process. In this case, the minimization objective function of denoising autoencoder becomes: (m ≥ 1), which can be regarded as m neurons, W is the weight matrix of order m × n, b is the hidden layer bias vector of dimension m, S is the sigmoid activation function. The decoding process can be expressed as follows: where W' is the weight matrix of order m × n and b' is the output bias vector of dimension m. For the purpose of minimizing the reconstruction error of autoencoder, let the reconstruction error be: J(θ), θ = {W,b}, ( ) = ∑ ( , ) , where the loss function is the loss function of the reconstruction error. In this paper, the mean square error method is used to calculate: where λ is the weight adjustment coefficient. After iterative training, the reconstruction error can reach a smaller value, and the accuracy of data feature extraction can be improved.
Stacked Denoising Autoencoder Structure
In order to avoid the overfitting phenomenon of autoencoder in the process of data processing, the denoising autoencoder (DAE) adds "damage noise" or random zeroing to the input data on the basis of the original simple autoencoder to simulate the input of damaged data, which can effectively improve the robustness of model learning [30]. Figure 2 is the network structure diagram of the denoising autoencoder. In the input part, the original data becomes the missing or damaged impurity data after a specific destruction processing as the new input, and the input replaces the original data for automatic coding and learning process. In this case, the minimization objective function of denoising autoencoder becomes: Among them is the processed damage input. Generally, a Gaussian noise or dropout method can be used to denoise the original data. Due to the limitations of the single-layer network model for complex data processing, multiple denoising autoencoders are introduced to form a stacked Denoising autoencoder. The input and output of each layer can be seen as a separate network structure. Equations (5) and (6) are the encoding and decoding process of the SDAE layer 1: Among them is the processed damage input. Generally, a Gaussian noise or dropout method can be used to denoise the original data.
Due to the limitations of the single-layer network model for complex data processing, multiple denoising autoencoders are introduced to form a stacked Denoising autoencoder. The input and output of each layer can be seen as a separate network structure. Equations (5) and (6) are the encoding and decoding process of the SDAE layer 1: After the first layer training, the weight W and bias b are updated by gradient descent method: The output of the first hidden layer can be used as the input of the next layer to continue the iterative training until the training of all layers is completed. When there are L hidden layers in common, the denoising autoencoder network of each layer can be expressed as: Through the single-layer successive updating transformation of the features by stacked denoising autoencoder, the high-dimensional feature expression of the data can be realized, and the method has better robustness and accuracy for the feature extraction of the data.
SVM Classifier
As a supervised learning algorithm, the support vector machine mainly uses the idea of the maximum interval to solve the problem of data classification in the field of pattern recognition. It can be regarded as an optimization algorithm for solving convex quadratic programming and has a good classification effect for both linear and nonlinear problems. Compared with the deep learning classification method, SVM is easy to operate in the program, and it can get higher accuracy without using much data, which is suitable for the situation of fewer motor data collected in this paper, and it is not easy to appear overfitting phenomenon. Furthermore, the addition of kernel function makes SVM can accurately reflect the nonlinear characteristics, so this paper selects SVM as the classifier of motor diagnosis research.
In the SVM algorithm, let the training data set in the given feature space be: T = {(x 1 , y 1 ), (x 2 , y 2 ), . . . , (x n , y n )}, x i ∈ R n , y i ∈ {+1, −1}, i = 1,2, . . . ,n, x i is the i-th eigenvector and y i is the x i class marker. The separation hyperplane equation is: where ω is the weight vector and b is the offset. The constrained optimization problem of separating hyperplane and classification decision function by maximum interval can be transformed into solving the minimum value of the following equation: The constraint conditions of Equation (14) are as follows: Where C is the penalty coefficient and ζ i is the relaxation coefficient. By introducing Lagrange function and taking Radial Basis Function (RBF) K x i , x j as the inner product kernel function of the algorithm, the interval maximization problem can be obtained: where α i is the Lagrange multiplier corresponding to x i . And the constraint condition of Equation (16) is as follows: The decision function is as follows: According to the principle of statistics, the accuracy of the SVM classifier can be expressed as the ratio of the number of samples correctly classified on a given test set to the total number of samples, as shown in Equation (17): where N' is the capacity of training sample; I is the indicator function, when y =f (x i ), I = 1; Otherwise, I = 0.
Method
As an electromechanical coupling component, a permanent magnet synchronous motor (PMSM) often works in a harsh working environment during its operation. After the motor has a tendency to damage, the coupling effect of the electromagnetic field will make the faults happen faster and more obvious, especially inter-turn short circuits. For example, when the motor has a slight short circuit fault, if it is not checked and repaired in time, it will lead to an increase in the motor operating temperature. That will cause the change of the working temperature field, which will increase the degree of the motor inter-turn short circuit fault, and even cause the demagnetization of the permanent magnet. Moreover, the change of the working magnetic field caused by demagnetization will aggravate the degree of short circuit and other faults eventually.
The original feature extraction method uses artificial discrimination to operate, the accuracy of the diagnosis results depends on the technical level and practical experience of the diagnosis personnel, its self-learning ability is weak, and the intelligence level is low, while the fault diagnosis method of signal analysis has a strong dependence on data and poor generalization ability. Aiming at the shortcomings of traditional manual or signal processing methods, such as the inaccurate and slow speed of motor fault feature extraction, this paper proposes a method of feature extraction of permanent magnet synchronous motor fault using the SDAE method according to the principle of stacked denoising autoencoder, and combined with support vector machine to complete the classification of fault features. Figure 3 shows the main process of motor fault diagnosis based on stacked denoising autoencoder.
SDAE Diagnostic Process
In the stacked denoising autoencoder part, the data processing generally includes the following steps: 1.
The vibration and speed signals collected from the PMSM fault experiment are divided into training samples and test samples, and the data samples of known fault types are packaged to establish the motor fault signal database; 2.
The vibration data are normalized and preprocessed according to the (0,1) standardized formula, and the dimensional vibration signal is transformed into the 3.
The training samples are randomly set to 0, or Gaussian noise is added to realize the "damage noise" addition to simulate the fault data collected in the actual test and determine the network structure, such as the number of the SDAE input layer nodes, the number of hidden layer nodes and the number of nodes in each layer; 4.
The single hidden layer feedforward neural network is used as the basic model to construct multiple autoencoders, and the pseudo-inverse learning algorithm is used to train each autoencoder separately to obtain the connection weight and offset of the i-layer autoencoder. The hidden layer output of the former autoencoder is used as the input of the latter autoencoder, and the above steps are repeated to train the new autoencoder step-by-step; 5.
Fine-tune the parameters of the SDAE network according to the known types of faults, complete the sample feature extraction, and use the SDAE output data as the input of the support vector machine for training, diagnosis and classification.
SDAE Diagnostic Process
In the stacked denoising autoencoder part, the data processing generally includes the following steps: However, When the amount of data are too large, it is easy to overfit, and other parameters need to be modified, like hyperparameter. Because the amount of data collected in this paper is not large, so it is not necessary to carry out the step in this experiment.
SVM Classification
After fault feature extraction, a support vector machine is used to classify the fault types, as shown in Figure 4: 23, 339 8 of 14 collected in this paper is not large, so it is not necessary to carry out the step in this experiment.
SVM Classification
After fault feature extraction, a support vector machine is used to classify the fault types, as shown in Figure 4: The SVM learning model takes the fault features extracted by the SDAE algorithm as input. According to the trained model, the fault type is judged. If the fault is not a single mode, the concrete discriminant model is further input to analyze the coupling fault. The output function of fault probability in the SVM is shown in Equation (19): where and are the shape parameters of the function. The final output of the SVM is the probability value of a motor fault between [0, 1].
Results
The motor fault data collected in this experiment is limited; the verification effect is not universal. To verify the feasibility and accuracy of the fault diagnosis method described in this paper, the bearing data set of Case Western Reserve University is used for simulation verification [31]. In the experiment, the SKF2605 drive end bearing of SKF2605 was selected, the sampling frequency was 12 Hz, the motor load was 0 horsepower, the speed was approximately 1797 r/min, and the single point damage of the bearing was carried out by electric spark to simulate the fault. The faults include nine kinds of single-point faults and normal working conditions with diameters of 0.1778 mm, 0.3556 mm and 0.5334 mm at the inner ring, outer ring and bearing roller, respectively. In the simulation, 400 groups of data are randomly selected from the above ten bearing states as the training set, and 60 groups of data are selected as the test set. The description of bearing fault data is shown in the Table 1 below. The SVM learning model takes the fault features extracted by the SDAE algorithm as input. According to the trained model, the fault type is judged. If the fault is not a single mode, the concrete discriminant model is further input to analyze the coupling fault. The output function of fault probability in the SVM is shown in Equation (19): where A and B are the shape parameters of the function. The final output of the SVM is the probability value of a motor fault between [0, 1].
Results
The motor fault data collected in this experiment is limited; the verification effect is not universal. To verify the feasibility and accuracy of the fault diagnosis method described in this paper, the bearing data set of Case Western Reserve University is used for simulation verification [31]. In the experiment, the SKF2605 drive end bearing of SKF2605 was selected, the sampling frequency was 12 Hz, the motor load was 0 horsepower, the speed was approximately 1797 r/min, and the single point damage of the bearing was carried out by electric spark to simulate the fault. The faults include nine kinds of singlepoint faults and normal working conditions with diameters of 0.1778 mm, 0.3556 mm and 0.5334 mm at the inner ring, outer ring and bearing roller, respectively. In the simulation, 400 groups of data are randomly selected from the above ten bearing states as the training set, and 60 groups of data are selected as the test set. The description of bearing fault data is shown in the Table 1 below. The optimal parameters in the program were obtained after many experiments. Set the number of the input layer and hidden layer nodes of the stacked denoising autoencoder learning model as 250 and 150, respectively, the number of hidden layers as 3, the denoising parameter used to improve the accuracy of the algorithm to judge the damaged data, set as 0.2, and the training learning rate is used to control the convergence of the algorithm, set as 0.6, other parameters of the SDAE, such as hyperparameters, are mainly used to solve the problem that the algorithm is easy to cause overfitting. However, due to the small amount of data in this paper, the over-fitting phenomenon is not easy to occur, so such parameters are not added. Using SVM classifier in MATLAB Libsvm toolbox for simulation. In the test, SDAE was first used to extract the features of motor fault data and then input the extracted features into the SVM for classification to realize the process of fault diagnosis. In the program, RBF is selected as the kernel function of the SVM. This is because when the parameters of the RBF kernel function are adjusted to a certain value, it can be regarded as a linear kernel function, and the influence of the adjustment of the RBF parameters on the experimental results will not have a large deviation. At the same time, it can deal with nonlinear problems, which is suitable for the data dimension of this paper. After many experiments, it is found that the convergence effect can be achieved when the number of iterations is about 50, so the number of iterations is set to 60. Moreover, inputs the preprocessed data into the constructed denoising autoencoder for training. The total running time is 12 min, and the accuracy of the SDAE training is shown in Figure 5. The precision and recall of the SDAE are 7 and 7.5. With the increase of the number of iterations, the training accuracy gradually improves and shows a general convergence trend. For the RBF method, the accuracy can be significantly improved when the number of iterations is small, but the final accuracy is not as good as the method proposed in this paper. For a single autoencoder method, there is a small fluctuation in the iterative process; the stability is poor. Select a motor for experimental data acquisition and subsequent analysis. and bench used in the experiment are shown in Figure 6. By changing the numb of the stator winding, the short circuit fault of the permanent magnet synchron can be simulated, and the negative sequence current can be used as the cha quantity of the inter-turn short circuit. Some motor data collected are shown below: Select a motor for experimental data acquisition and subsequent analysis. The motor and bench used in the experiment are shown in Figure 6. By changing the number of turns of the stator winding, the short circuit fault of the permanent magnet synchronous motor can be simulated, and the negative sequence current can be used as the characteristic quantity of the inter-turn short circuit. Some motor data collected are shown in Table 2 below: and bench used in the experiment are shown in Figure 6. By changing of the stator winding, the short circuit fault of the permanent magnet can be simulated, and the negative sequence current can be used quantity of the inter-turn short circuit. Some motor data collected a below: The right side of the table is the fault state, 1 is normal, 2 is the occurrence of inter-turn short circuit fault. Two hundred groups of data were used as training samples, and 100 groups were used as test samples. Figure 7 is an accurate image of PMSM fault diagnosis using the SDAE + SVM method.
RBF is also a neural network algorithm for classification [23], and here it is to compare it with the method described in this paper. Different algorithms are used to set the same network structure parameters, and the motor data are analyzed, as shown in Table 3: It can be seen from the above table, the accuracy of the fault diagnosis classification method, which combines stacked denoising autoencoder and support vector machine is 94.2%, compared with the traditional algorithm RBF, single SVM and the autoencoder algorithm are only 86.6%, 89.1%, and 90.6%, respectively, which means SDAE has higher diagnosis accuracy in the actual training of motor; from the standard deviation, RBF, single SVM and autoencoder algorithm are 2.21, 0.89, 1.49, respectively; however, the standard deviation of the method proposed in this paper is only 0.88. This shows that the fluctuation of the diagnosis result using SDAE + SVM is more stable, and the robustness is better. Although the standard deviation of the single SVM algorithm is small, its accuracy is also lower than the method of this paper. The right side of the table is the fault state, 1 is normal, 2 is the occurrence of interturn short circuit fault. Two hundred groups of data were used as training samples, and 100 groups were used as test samples. Figure 7 is an accurate image of PMSM fault diagnosis using the SDAE + SVM method. RBF is also a neural network algorithm for classification [23], and here it is to compare it with the method described in this paper. Different algorithms are used to set the same network structure parameters, and the motor data are analyzed, as shown in Table 3: It can be seen from the above table, the accuracy of the fault diagnosis classification method, which combines stacked denoising autoencoder and support vector machine is 94.2%, compared with the traditional algorithm RBF, single SVM and the autoencoder It can be seen from the above experiments that the RBF has a good convergence rate for PMSM fault extraction and classification of electric vehicles, but the accuracy is not very high, and the data integrity is required to be high. However, the RBF algorithm has the advantages of simple and good generalization ability, which is suitable for classification with complete data and low requirements; when using a single SVM to classify faults, although the fluctuation of accuracy is small, it still does not reach the ideal accuracy, and when the amount of data are large, a single SVM classifier does not have obvious advantages, which means it may cause a long operation time. Compared with a single SVM classifier, the accuracy of DAE for motor fault diagnosis fluctuates less and is more stable. The proposed SDAE + SVM method combines the single SVM and DAE algorithm as a new PMSM diagnosis method and increases the number of hidden layers on the basis of DAE to form the SDAE algorithm. It improves the learning effect of the program. Experiments show that the method has better accuracy and convergence speed, but the generalization performance needs to be further verified.
Conclusions
Based on the principle of stacked denoising autoencoder, this paper proposes a fault diagnosis method of electric vehicle PMSM based on the SDAE. The feature extraction of motor fault based on stacked denoising autoencoder algorithm has better generalization ability and diagnostic accuracy and has better recognition advantage in unsupervised learning. Compared with the traditional diagnosis method, this method can effectively avoid artificial error and diagnosis time; under the same network structure, compared with a single diagnosis algorithm, this method has better robustness and more stable diagnosis results. The research of this paper provides a new idea for the diagnosis of permanent vehicle magnet synchronous motor on the basis of intellectualization and provides a certain basis for the fault diagnosis of PMSM under the complex operation of vehicles in the future.
The diagnosis results show that under the same sample characteristics, compared with other classification diagnosis algorithms, the test accuracy of the SDAE for motor fault diagnosis can reach 94.2%; however, the accuracy rates of the RBF, single SVM and DAE are only 86.6%, 89.1% and 90.6%, respectively, and the standard deviation of the SDAE diagnostic algorithm is also small, only 0.88%, which means that the test results of the SDAE algorithm are relatively stable and have no big fluctuation.
However, there are still some defects in the above research: the fault experiments of permanent magnet synchronous motor are carried out in the normal physical field environment, without considering the influence of temperature, load and other factors on the motor work; in addition, because the motor fault in this paper is artificially set, the data results are limited, and it does not reach the ideal sample size, so there are still of some test limitations in statistics.
Based on the above shortcomings, this paper will study the operation and fault conditions of permanent magnet synchronous motor under a complex environment in the future, learning and analyzing the characteristics of coupling fault phenomena and carry out more experiments to expand the data sample size of the results; in the aspect of the classification algorithm, learn and use the improved SVM to analyze the diagnosis results more accurately, and continue to perfect the experimental program to improve the generalization performance and recognition advantages of th method that discussed in this paper. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement:
The data presented in this study are available on request from the corresponding author. | 7,980 | 2021-03-01T00:00:00.000 | [
"Computer Science"
] |
Ky i v-Moh y l a Hu m a n i t ies Jou r na l Nechui’s Aesthetic Code: Repetition, Pacing, and Non-Purposeful Narration
Traditional and modernist comments on the mechanics of Nechui’s prose style are largely critical, focusing on what are assumed to be errors or infelicities in writing. This article examines these presumed errors and proceed to focus on three central qualities of Nechui’s writing: repetition, pacing, and the absence of purposeful construction. The intention here is not to make judgments about the strengths and weaknesses of his writing but rather to point out its essential features. Two central features of Nechui’s writing that are explored are deliberate repetition and non-purposeful plot structure.
The figure of Ivan Nechui-Levytsky is, for most students of Ukrainian litera tu re, an exemplary image of everything that is firmly rooted in the old realist aesthetic, essentially representative of what needed to be swept aside in order to move forward into the modernist era. He is the embodiment of "not modern." But the status he holds is not firmly rooted in the actual character of his writing. In this essay, I shall survey some of the critical examinations of Nechui's technique and then explore his actual technique with a particular view towards placing Nechui's writing in the development of Ukrainian prose technique through the realist and into the modernist era.
The most important and influential critic of Nechui is Serhy Iefremov, generally regarded as a preacher of populist realism. Аs many obser vers have noted, Nechui frequently uses simple epithets (usually just adjectives) or extended comparisons that derive, "primarily from the sphere 221 Pidmohylny, who wrote an introduction to the 1927 edition of Nechui's Selected Works. In this little-known essay Pidmohylny first discusses Nechui's "feeble dramaticality." He then goes on to his most damning remarks: "The first true sin of our author is the uncultivated shape of his expression. His works give the impression, as if once having written them, he never read them over. The rough and untidy character of his sentences hurts the eye." 4 As an example, Pidmohylny then goes on to quote a passage from Kaidasheva simia (Kaidash's Family) emphasizing particular words that get repeated in it.
"All the people who sat by the church got up and began to cross themselves. Kaidash could see the entire hill on which the church stood, all the people who stood beside the church. He took off his hat and began to cross himself." Even an illiterate would figure out to say it this way: "All the people who sat by the church got up and began to cross themselves. Kaidash could see the entire hill on which the church stood and all the people beside it. He took off his hat and also began to cross himself." The use of pronouns and adverbs is an elementary, a childish step in the organization of an expression, not just a literary one, but any decent expression. 5 This is a very serious indictment. There can be no such thing as a decent writer who fails an elementary test of clear writing. So, is Nechui a bad writer unworthy of our attention or is Pidmohylny wrong in his assessment?
In the example quoted above and in a few of the other examples he gives, the basic issue is repetition. In another set of examples, Pidmohylny demonstrates that sometimes Nechui makes no particular effort to smooth out the narrative flow from one sentence to another. One short sentence follows another without the familiar conjunctions, adverbs, or other connecting devices that facilitate the reader's comprehension. In a third and final set of examples, Pidmohylny complains that Nechui relies too heavily on comparisons that lose their vitality because they 222 K y i v -M o h y l a H u m a n i t i e s J o u r n a l › 1 ( 2 014) are repetitious and annoyingly familiar. What's clear in all of this is that Pidmohylny is responding to a particular style that is evident in Nechui's writing. The key ingredients of this style, in the context of Pidmohylny's criticism, are a very deliberate, slow pacing and repetition. Pidmohylny assumes that these are symptoms of poor writing. Perhaps they are merely elements of a style that Pidmohylny (and many a like-minded reader) doesn't like. Whatever the verdict, they are not accidents from the pen of a careless and inattentive writer. They are very deliberate and conscious choices that Nechui makes.
One of the most revealing examples of rhetorical repetition in Nechui's works occurs on the opening page of his novel Mykola Dzheria: Near the town of Vasylkiv, the small Rastavytsia River quietly flowed across a wide valley between two rows of gently sloping hills. Clumps of lush, tall willows dotted the valley where the village of Verbivka lay engulfed in their greenery. A high, whitewalled, three-domed church was clearly visible in the sun, and beside it a small bell tower seemed entangled in the green branches of old pear trees. Here and there, whitewashed cottages and black roofs of big barns peeped out from among the willows and orchards.
Communal vegetable fields and meadows stretched across the village on either side of the river. There were no fences; plots were separated only by bound aries or rows of willows. A footpath wound its way through Verbivka along the grassy riverbank. Looking around from that path, one could only see a green, green sea of willows, orchards, hemp, sunflowers, corn and thick-growing sedge. 6 In this opening landscape of the novel, within the eight sentences that constitute the first two paragraphs, the words verba and Verbivka (Willow-ville) occur a total of eight times. 7 Perhaps Pidmohylny would find this excessive and objectionable, but the passage is aesthetically effective and the repetition of a key word helps create a particular effect 223 on the reader. Nechui is attempting something similar to the famous repetition of the word "fog" in the second paragraph of the first chapter of Charles Dickens' Bleak House. Just as Dickens' fog describes both the actual wea ther in London and the metaphorical lack of clarity in the High Courts of Chancery, so, too, Nechui's willows are more than just the predominant tree in this central Ukrainian village. They are a symbol of the qualities of this place -verdant, luxurious, and healthy. They are also, as the village name indicates, a symbolic component of its human dimensions. They stand as metaphorical surrogates of the inhabitants to whom the natural qualities are thus ascribed. This becomes particularly evident in the second paragraph, where the gardens and meadows are described as being without fences, divided only by the willows themselves. Of course, Nechui wants to emphasize the harmony that characterizes village residents in their relations. Unlike Robert Frost's twentieth-century unfriendly New Englander, they don't need fences. But the willows that do separate these garden plots are not there accidently. As Nechui explains on the next page: "Usi vulytsi v Verbivtsi niby zumysne obsadzheni vysokymy verbamy: to porosly verbovi kilky tyniv." 8 ("All the streets in Verbivka were lined with tall willows that seemed to have been planted there on purpose. Actually, they were willow fence posts which had taken root.") As Nechui and most village boys know very well, a willow stick pushed into the ground might easily take root and grow into a tree. It turns out that the willows in Verbivka are not only the natural ornament of this valley; they are also a living monument to human activity, an enduring sign of human civilization. They offer testimony of the naturalness and appropriateness of the human presence in this valley. Like the willows that surround them, the residents of Verbivka have taken root in this place, they belong to it, although it certainly does not belong to them, since they are serfs. Verbivka's willows, its inhabitants, its buildings, and its stream and mill pond are all part of a simple natural order. 224 K y i v -M o h y l a H u m a n i t i e s J o u r n a l › 1 ( 2 014) The rootedness of the willows and the peasants is, of course, an important theme in the novel. The story line of the novel depicts Mykola's uprooting, his enforced alienation from his family and the place where he belongs. The repetition of the word willow on these opening pages serves to call attention to this natural rootedness. Repetition thus functions as a form of emphasis, which further combines with a metaphorical interpretation of the significance of the repeated image to highlight an important thematic motif in the novel. This emphatic function is, essentially, a product of the reader's awareness of the fact of repetition. 9 This awareness is a form of disturbance in the otherwise smooth flow of a reader's appreciation of the text. Because this disturbance takes place in a temporal dimension, repetition also has a rhythmic function. The reader perceives it as a temporal pattern of events. Nechui makes very specific use of this rhythmic function of repetition. He uses it to alter the tempo of his narration and to reinforce the reader's sense of familiarity with the characters and setting of the story.
Unlike a musical rhythm, which sets a basic, underlying pattern, the rhythmic function of rhetorical repetition is a singular phenomenon that the reader perceives against a backdrop of underlying patterns established by other features of the text or the story. However, the first 9 J. Hillis Miller asserts that "The reader's identification of recurrences may be deliberate or spontaneous, self-conscious or unreflective," in his Fiction and Repetition: Se ven English Novels (Cambridge, MA: Harvard University Press, 1982), 2. This may be true, but nevertheless there must be an identification of the recurrence. The unreflective identi fi cation of repetition cannot be understood as a total unawareness of the recurrence. There can be no emphatic function without this recognition. The subjective nature of this recognition also helps to explain the variability of the effect of repetition on readers and of their judgment of its rhetorical efficacy. A very attentive reader may find an instance of repetition annoying because the emphasis it provides was already evident. A very inattentive reader may not notice the repetition at all, or may fail to appreciate the relevance of the emphasis in a particular text. For a wide discussion of repetition as a linguistic and rhetorical device, see the essays collected in Repetition, ed. Andreas Fischer, Swiss Papers in English Language and Literature 7 (Tübingen: Gunter Narr Verlag, 1994), particularly Jean Aitchison "'Say, Say it Again Sam': The Treatment of Repetition in Linguistics," 15-34, and Brian Vickers, "Repetition and Emphasis in Rhetoric: Theory and Practice," 85-114.
225
repetition of the word "verba" in Mykola Dzheria occurs in the first two paragraphs of the text, before there is much of an opportunity to establish any other rhythm. In what is, by its genre, an introductory, scene-setting landscape description the reader is bombarded with a long sequence of recurrences that highlight and dramatize the passage. The rhetorical rhythm is somewhat at odds with the bucolic languor of the serene river valley. This perception is reinforced by other rhetorical devices, such as the alliteration of the "r" sound in the first sentence. Verbivka and the Ros River valley get a somewhat surprisingly staccato introduction. In sub sequent paragraphs there are fewer recurrences. The reader feels the tempo subside, the tension of the narrative diminishes. The willows still appear in the text, recalling the earlier paragraphs, but their frequency is reduced and they are explicitly referenced as repetitious elements: "Na hrebli znov u dva riadky vydyvliaiutsia v vodi duzhe stari, tovsti, duplynasti verby." 10 ("On the dam once again two rows of old, thick, hollow-ridden willows were reflected in the water.") The technique has a curious effect on the reader. The sequence of recurrences is apparently not finished, but its character has changed. The repetition itself now seems familiar, the emphatic effect is therefore reduced. The tempo is diminished. The passage suggests an incompleteness. Something is missing. The reader expects either an abandonment of the repetition -its function is already established -or an elaboration that leads to closure. But in the third and fourth paragraph, Nechui deliberately holds back, teasing the reader, as it were, with a very unhurried narrative style that draws the reader even further into what will eventually turn out to be a very simple and familiar image of a Ukrainian village. The lethargy and familiarity are, of course, qualities of the village that Nechui thus passes to the reader as a sensation embodied in the text. Eventually, the author takes pity on the reader and at the beginning of paragraph five, explains the fence-post origins of the willows lining 226 K y i v -M o h y l a H u m a n i t i e s J o u r n a l › 1 ( 2 014) the streets of the village. 11 This recurrence of willows has a different character than the previous ones -it offers a rational explanation of the significance of the image that has been elaborated. Because it explains, this recurrence gives the reader a sense of finality, of closure. After the deliberate delay of the preceding two paragraphs, the rhetorical device and the importance of the image are now complete. But the closure is potentially disappointing. The explanation is so simple. The reader had fully accepted such a reading even before being offered this additional guidance. Nechui's use of repetition is sometimes elaborate, but it is not complicated. The apparent purpose of the device is to give emphasis, but that emphasis is neither surprising nor profound. A more significant function of the device is to control the rhythm of the narration and to enhance the aesthetic qualities of the text. It is a verbal, narrative device used as much for its rhetorical, artistic function in the shaping of the narrative as for its potential to enhance the articulation of thematic material. For the most part, repetition is decoration, it adds aesthetic qualities to the text. Nechui uses the device constantly. Even as he brings the recurrences of willows to a close, he ends the fifth paragraph with a doubled epanalepsis: "Dyvyshsia i ne nadyvyshsia, dyshesh i ne nadysheshsia." 12 Nechui repeats, and he cannot repeat enough. It's a central feature of the rhythm and folksy flavor of his prose. It is an instrument of his technique that controls the tone and tempo of the writing.
And the device is not limited to any particular narrative mode or style. It occurs in the language of the characters. It occurs in the narrator's focalized and unfocalized voice. It occurs between the language of the characters and the language of the narrator. It occurs as a major element in extended passages, and it occurs as a simple oddity in single sentences.
11 It must not go unnoticed that in this fifth paragraph, Nechui introduces a new and different image of the valley as a space flooded with sea water that has suddenly crystalized in tall waves of green. This image belongs to a different kind of non-rhetorical repetitive sequence that points forward to Mykola's sojourn on the shores of the Black Sea as a fisherman. 12 Kovalenko's translation fails to capture the tone: "One never tired of that view and could never breathe one's fill of that hot, fragrant air." Kovalenko, trans., Mikola Dzherya, 5.
227
The reader is frequently faced with verbal constructions that highlight the recurrence of a word without the elaborate choreography that was shown in the passage analysed above. For example, in the novel Neodnakovymy stezhkamy (Not the Same Paths) from 1902, Taisa Andriivna, in a moment of self-contentment, consumes "dorohyi zapashnyi chai z varenniam ta krykhkymy krendeliamy, do choho tsia vypeshchena lasiika bula duzhe lasa." 13 ("Expensive aromatic tea with jam and crisp pastry, for which this spoiled, craving woman had a strong craving.") A reader with a taste for only the most elegant, lean, and simple linguistic pastry may well find this craving for repetition repetitious, as Pidmohylny does. But the device occurs with such frequency, regularity, and, occasionally, with such clear purpose, that it is simply impossible to dismiss it as the unconscious product of a careless writer. For better or worse, Nechui employs this device very deliberately throughout his works. Also, its use is tied to a number of other features of his writing, particularly plot development and character delineation. Another example Pidmohylny gives speaks directly to the role of repetition in the development and tempo of the story line. He mentions two episodes in Kaidasheva simia in which an anticipated repetition is delayed. In the first example between the narrator's announcement that Marusia Kaidash has stepped out of her door to call her family to lunch, using the much favored vechirnii pruh (evening arc) expression, to when she actually calls them in to eat, an entire paragraph intervenes with a lengthy characterization of this pompous woman who served in the master's kitchen when she was young and now behaves as if she were better than the other villagers. 14 When only Lavrin comes to eat, Marusia repeats the invitation and Nechui mentions the arc of the sun again. Late in the afternoon, as Kaidash sets off for church, the sun's position is mentioned once more. Pidmohylny is apparently annoyed that the narrator does not move directly from Marusia at the door to her calling the men for lunch. The effect here is similar to a cinema flashback: we learn Marusia's biography as she stands in the sunlight, framed by the door of her house. The tableau is not unlike the one that framed Kaidash just inside his barn door on the second page of the novel. Nechui likes to bring his characters on stage and then -slowly, deliberately, expansively, exploringly, exhaustively, annoyingly -to stop the action for a moment while he gives them a character profile. Pidmohylny, a psychological realist who portrays characters through their actions and words, does not favor this kind of old-fashioned description while the action of the story is arrested. What Pidmohylny does not note, but might have, is that this scene is not only slowed down by the descriptive digression that delays the act of inviting the men to lunch, its dramatic impact is enhanced by this digression. Through the delay, lunch seems to acquire a greater importance. Actually, of course, Kaidash, who fasts on Fridays, doesn't come home for lunch, only his sons do. Marusia's unusually drawn-out invitation builds a contrast between her pretentious, formal expectations and Kaidash's foolish religious fervour. The day ends with the hungry old zealot wasting his money and his evening at the village tavern, where his day-long fast has finally landed him for some decidedly unhallowed relief. Repetition thus frames a pattern of digression and return that is an important component of Nechui's storytelling.
Something similar occurs in the second example of delay that Pidmohylny offers. At the beginning of chapter two of Kaidasheva simia Karpo goes to visit his sweetheart Melashka, who is engaged in the quintessentially ethnographic activity of whitewashing and decorating her house. Her materials are two jugs of clay, one red and the other white. The girl has the red jug in her hands, and the second jug is on the ground by the doorsill. 15 Pidmohylny elaborates: "We read on for a page -there's nothing about this second jug. In the middle of the second page, angry at the author for introducing irrelevant details, we finally forget about the second jug with the white clay, until suddenly, on the third page we 229 see 'Karpo turned around to avoid soiling his boot and struck the second jug with white clay with the heel of his foot.'" 16 What Pidmohylny doesn't mention is that the jug with the red clay has already spilled. Karpo and Melashka have been engaged in a very familiar scene of slapstick romantic courtship that would not be out of place in a Chaplin film comedy. The fact that the white jug goes unmentioned for three pages while the red jug is at the centre of the comic action is, once again, very basic comic technique. As Pidmohylny admits, the reader is waiting for the white clay to spill as well as the red clay. Since the joke will end there, the white jug is delayed until the events have played out to their maximum duration. The real issue here is that Nechui's estimate of the maximum length of a comic scene -that is, of the best rhythm for comic material -is different from Pidmohylny's.
Nechui's use of repetition for narrative rhythm and the framing of narrative digressions is a component of a larger issue concerning the shaping of narrative and the structure of plot in his works. This is a difficult subject in literary studies. The nature of what constitutes an effective plot and, as a corollary, what constitutes an ineffective plot, is a highly contentious issue.
The most damning formulation of this concern for plot structure occurs in an introduction by Andry Nikovsky to a popular edition of Nechui's Mykola Dzheria published in 1926. In this lengthy essay Nikovsky discusses the difference between works of literature that are based on plot and works that are based on character. Nikovsky, although not a conservative Marxist ideologue, adopts in this introduction a Marxist position on the value of literary works. He insists that the value of literature is tied to reality, to the depiction of actual issues that affect living people (or those who lived at other times). He distinguishes between two modes of storytelling: one focused on characters, which he terms a portrait approach, and the other focused on events, which he calls a plotted (siuzhetnyi) approach. In his view, the portrait or psychological approach is distinctly inferior. He argues that "only a high level of artistry in developing the fundamental universal plots (and only partially one's own national and local plots) will lead this or that literature out of the limits of domestic usage onto the free expanse of world literature." 17 Nikovsky sees Mykola Dzheria as an example of a psychological type of writing and he wonders how a European reader, accustomed to the masterpieces of world literature, would respond to this novel. After asserting that such a reader would see the work as a weak variant on the plot of Tristan and Isolde in Uncle Tom's Cabin, Nikovsky asserts: There is no point in continuing a literary debate with our European listener, because, aside from misunderstanding, nothing good will come of it: he will start to complain about the deficient and lame dramatic tension in the scenes or he will admonish Levytsky for the gray and forlorn destiny of his heroes. And he will be right, because in life and in literature, only what ends clearly (whether for better or worse) is good. But here it turns out that plenty of things in the novel (the romance with Nymydora, with Mokryna, relations with the master, etc.) do not end in any way at all. So let's leave our foreigner with the suggestion that he read the entire novel and gain a wider familiarity with Ukrainian literature. Let's agree that there is some kind of plot in Nechui-Levytsky's novel, that it's poorly developed but nevertheless interesting; that the internal dialectic of the novel is very weak because all the logical possibilities that arise from the given combination of relations are not developed, and because the psychology of the characters who are drawn into the plot is treated rather monotonously; but a number of the structural defects, faults (but not mistakes!) can be explained by the theme of the novel and by the conscious political tendencies of this author. 18 Despite the confusing and backhanded manner of his presentation, Nikovsky is making a familiar and comprehensible point. Nechui's fictional works generally share two qualities of construction: they are built 231 around a very simple plot that lacks dramatic tension, and they are not built around logical or emotional arguments for a particular thematic idea or position. These qualities of construction are evident on various levels of Nechui's works, from the overall structure of the works to the structure of individual scenes and chapters.
As we have seen in some of the examples of repetition above, Nechui often relies on a circular narrative direction that brings the exposition back to the point from which it started. This circularity is most evident in the large canvas of some of his plots. At the beginning of the novel, Mykola Dzheria gets married and leaves his village. At the end, he returns to his village, but his wife is no longer alive and he is as solitary in his old age as he was during the bulk of his life, which he lived away from his village. The plot has the protagonist actually return to his village and his family, but the chief quality of this plot line, and most of Nechui's plots, is not so much in the actual return to a condition defined at the beginning of the work, but in the absence of any linear progress, the failure (in the development of the plot) to resolve the major issues that were presented at the beginning of the story. This is Nikovsky's major complaint about Mykola Dzheria.
There is no thematic advancement. Whether the action is judged to be circular, repetitive, or simply static, Nechui's plots and thematic constructions generally end up in the same place where they began or, more precisely, they do not reach any particular dramatic or thematic goal. They are non-purposeful.
In Mykola Dzheria, for instance, Nechui does not actually focus on the social problems that critics, particularly Soviet critics, invariably mention as the thematic center of the novel. As Nikovsky points out, 19 the novel was written a decade and a half after the abolition of serfdom. In 1878, Nechui could no longer adopt the abolitionist tone that characterizes the work of writers such as Marko Vovchok. The novel does indeed depict the inhumanity of serfdom, but these scenes are limited to the first two 232 K y i v -M o h y l a H u m a n i t i e s J o u r n a l › 1 ( 2 014) chapters, a mere quarter of the book as a whole. After that, Mykola and the runaways experience another form of exploitation, industrial labor, but this, too, lasts for only two chapters. The third section of the novel, again two chapters, depicts a life of relative peace and tranquillity, although far from home. Chapter seven is a digression about the life of Nymydora and those left behind in the village. The suffering here is largely a result of the absence of Mykola, rather than the underlying social conditions. Finally, the last chapter accelerates the action of the plot, events reach a climax but, in an act of apparently divine intervention, serfdom is abolished and Mykola returns home, only to find new loneliness and a new regimen of social inequality. The text ends with the image of an elderly Mykola telling youngsters stories about the adventures he experienced. Beyond any doubt, the work is held together by its titular protagonist rather than an interest in depicting social conditions. Nechui's novel is often juxtaposed with Panas Myrny's Khiba revut voly iak iasla povni? (Do the Oxen Bellow, when Their Mangers are Full?), a novel that takes a very broad historical survey of both social and family history. But Myrny's novel is focused at every turn on the influence of social injustice -historically and in the present -on the behaviour of its protagonist. Nechui's novel is very different. Here, there is hardly any sense of causal relationships. Serfdom is a despicable institution that ruins people's lives, but in the chapters set in Bessarabia, Mykola has in fact escaped its reach, though not very happily. It is the personality of Mykola that is central to the story. He is a rebel, a hothead who responds angrily and violently against injustices of all kinds. But he is not a hero. His rebellions appear sooner as instinct than as purposeful activity. They accomplish very little of value. On the contrary, when he returns his family and his village are suspicious of him and only grudgingly accept him back. Nechui's novel thus never reaches a meaningful thematic statement. Nechui has not produced an expose of social injustice, he has not produced a portrait of noble suffering, and he has not created a model of heroic struggle. Like Faulkner's Yoknapatawpha County, Nechui's Verbivka and its inhabitants merely endure, but unlike Faulkner's characters, Nechui's do not acquire the stature of exemplary 233 human beings, symbols of the moral and philosophical importance of the human condition. Nechui avoids the elements of plot and structure that would ennoble his characters or provide the reader with abstract ideas that give meaning or explanation to the dilemmas he portrays.
In Mykola Dzheria, this avoidance is most apparent in the deliberate unwillingness to explain a key event. In chapter two, as Mykola and the other serfs are leaving the village, the night sky is illuminated. The master's stackyard and barns have been set ablaze. Nechui depicts the scene in a beautiful, extended passage full of colour and extraordinary detail. But he never explains who was responsible. The men watching on the hillside raise this question and one of them, Kavun, says that the arsonist will be revealed by the image of his soul flying in the sparks of the fire. Mykola rebuffs the superstitious idea, but in the next scene, as noted earlier, Nymydora, losing her rational faculties, sees Mykola in the flames. The matter ends there. As Nikovsky suggests, 20 perhaps Nechui is using this image to reveal who the arsonist is. But there is no certainty here. Nechui clearly does not want to reveal who set the fire. Even without Nymydora's hallucination, readers would consider Mykola a primary suspect. The connection between Kavun's remark and Nymydora's vision is not emphasized, and it is not self-evidently plain. Responsibility for the crime remains uncertain. Analytical readers might suggest various reasons for Nechui's reticence. Perhaps he felt an attribution for the crime would be seen as an endorsement of violent revolt against social order -something censors in both Russia and Austria would view unfavourably. Perhaps he felt an attribution to Mykola would lead readers to turn away from his protagonist and judge him too harshly. But these potential arguments are very weak. Far more likely is the simple fact that such an attribution would clarify what Nechui means to keep vague; it would add rational purpose to what is meant to remain indeterminate, it would alter the character of the fiction he is producing, pointing it toward drama, social significance, and explanation (as in Myrny's novel), rather than perception, sensibility, portrait, and landscape -the core elements of Nechui's non-purposeful writing style.
In most of Nechui's other novels, this non-purposeful approach is even more evident. Starosvitski batiushky ta matushky (Old-World Priests and Their Wives) is unabashedly structured as a chronicle of the way clergymen lived in the first half of the nineteenth century. As already noted, the plot follows the careers of two priests, Kharytin Mossakovsky and Marko Balabukha. The former is a local boy without much education who has been elected parish priest by his community. The latter is a seminary-educa ted careerist. Nechui switches focus, alternating the two men in their relations with women, their relations with parishioners, their relations with church and secular authorities. In some cases, entire chapters are juxtaposed, each presenting parallel events in the life of one of the priests. Nechui satirizes both men. Balabukha turns out to be more successful, but nevertheless unhappy. Kharytin is a hopeless bumpkin, but a far more personable and likable man. While Nechui makes no particular effort to suggest any conclusions on the basis of the juxtaposition of these two men, the events in the novel follow a simple logic of comparison. But at the beginning of chapter nine Kharytin dies and the focus switches to his widow, Onysia. The last two chapters then focus on domestic affairs in the Balabukha household, particularly the role of his wife, Orysia. The balanced comparison of the two priests is thus partially unbalanced in the final pages of the book. The novel concludes with the marriages of the children in both families, that is, it returns to the same issues with which it began, the disposition of parishes and the marriages of clergymen's daughters, which are often one and the same matter. Nechui brings the events full circle to the next generation of characters, with very little purpose other than depicting the life, the habits, the characters, and the setting. Nechui's readers would no doubt have recognized that church reforms in mid-century had introduced changes into the life of the rural clergy that brought to an end the manners and customs described here, but this fact is nowhere specifically addressed in the text. The juxtaposition (that is, repetition highlighting differences) of the two priests is neither an anti-clerical satire nor a particular endorsement of the old ways. It is certainly not a justification of the impending institutional reforms intended to professionalize the clergy. Aside from a nostalgic gratification in witnessing the mundane events in the lives of these characters, Nechui does not convey any special sentiment or judgment regarding the social setting he depicts. The plot is built in a circular pattern with repetition used for contrast. The events with which Nechui builds his plot, both here and in most of his novels, consist of courtship, marriage, and domestic family relations as well as the daily rituals that distinguish people by their professions. Both in its overall structure and in the construction of individual scenes or chapters, the action and the narrative are not designed to convey a particular judgment. For example, Onysia browbeats the metropolitan in Kyiv to assign her late husband's parish to the orphaned children, and her daughters hastily marry young seminarians. But despite keeping the parish in her own hands, Onysia is not particularly fortunate, nor are her daughters. In contrast to this, Balabukha's wife, Orysia, aspires to a great social future for her daughter, Nastia, whom she is matchmaking with the son of the foreign director of the sugar refinery. But the director leaves town after an argument with the local landlord, and Nastia ends up marrying a colourless widower with children who is a local administrative official. Nechui infuses both ends of this comparison with rich satiric details and wonderful comic situations, but there is no larger lesson hiding in the juxtaposition. These are merely fascinating characters with delightful peculiarities in intriguing situations.
The quality of non-purposeful storytelling is evident in the general plot of all of Nechui's novels. Kaidasheva simia, like Starosvitski batiushky ta matushky, is a family chronicle except there is only one family involved (but contrast is developed through juxtaposing the love stories of the two sons). The story begins with discussions of the marriage prospects of the two sons. It ends with the two sons taking over their late father's property and continual quarrels between their two families. The only events along the way are the matrimonial enterprise and foolish domestic quarrels. Of course, this is satire, but the aim of this satire is too broad to have specific targets. Readers generally see this novel as a glorious, rollicking monument to the idiosyncrasies of life in a Ukrainian village. Kaidasheva simia is satire without scorn, ridicule without contempt. It's comedy without instructive purpose.
The glue that binds this novel lies in the relations between the charac ters and in the accumulation (repetition?) of incidents that depict the personalities of the characters in the story. Kaidash is shown to be a weak-willed religious obscurantist. His wife, Marusia, is pretentious and proud. Time and again we see these traits without significant expansion or development. The qualities Marusia Kaidash displays on her visit to the Dovbyshes are no different from the qualities on view during the visit to the Balashes. The jokes may be different, but there is no advancement in the development of her character or in the reader's understanding of it. What there is, however, is a wonderfully colourful interplay of familiar personalities in a slow dance of anecdotal merriment. Works such as Neodnakovymy stezhkamy, Afonskyi proidysvit (The Vagabond from Athos), and Kyivski prokhachi (The Kyivan Beggars), have a somewhat sharper focus because they concentrate on a single idea (respectively, social changes resulting from the disappearance of an agricultural economy, the hypocrisy of Orthodox monks, and charity as a corrupt industry). But even here the organization of the episodes and the overall plot do not lead to specific conclusions or to a thematic closure. The ideas presented at the beginning of these works are not significantly elaborated or explored in the course of the presentation.
The most telling examples of Nechui's non-purposeful construction are found in those works that depict the issues with which his writing is intimately concerned: the nationality question and marital relations. Marital relations are an abiding theme of Nechui's writing. The relations between husband and wife -or more generally, between men and women -are surely the most frequently encountered topic in his works. Occasionally, however, this topic assumes a greater significance in his works, as it does in Ne toi stav ([He] Changed), Na hastroliakh v Mykytianakh (On a Tour in Mykytiany), and Hastroli (On a Tour). These works are central 237 in any understanding of Nechui's depiction of women but in terms of their structure and plot, they avoid projecting a strong thematic idea. In the first of these, Ne toi stav, a woman struggles to find marital happiness with a husband who becomes a fanatically devoted religious scholar and abandons the normal joys and responsibilities of domestic life. Nechui makes clear the dimensions of the problem, but stops short of actually analysing it. His story includes a variety of instruments for comparison and analysis. Solomia is compared to Zinka, Roman is juxtaposed to his friend Denys and his father in law, Fylon, but in the last chapter Nechui has Roman abandon religion and turn to drink, and Solomia dies helping rescue neighbours from a house fire. The ending seems very contrived and discontinuous. The events and themes of the story lead nowhere, Solomia's death simply brings the story to an end, with no thematic closure, no catharsis, no insight. Solomia is neither heroine nor victim.
Nechui's two variants of the "hastroli" story have a similar structure. In both versions, Sofia takes a lover while her husband, an opera singer, is away from home. In both works (though more elaborately in the longer Na hastroliakh v Mykytianakh) Nechui reveals the incompatibility of the personalities of husband and wife and thus provides motivation for the wife's love affair. In both works, however, the love affair, after developing in a traditional manner that corresponds to the reader's expectations, ends without a morally or dramatically satisfying conclusion. In Hastroli, the station master, Nykolaidos, is suddenly forced to quit his job. He leaves the area and abandons Sofia, who moves to Kyiv and finds a new lover. In the other version, Flegont has an angry confrontation with his faithless wife. Her young lover leaves and she returns to her husband. But the story continues for another five paragraphs, detailing the fate of Flegont's cousin, Levko, who also pursues a career as a singer but ends up taking his own life when an unfortunate disease robs him of his voice and his income. The connection between this anti-climactic ending and the events of the story is accidental and thematically obscure. The presumed reconciliation of husband and wife is not elaborated or explored. The melodramatic suicide of a secondary character creates a dramatic coda, but one whose tone seems peculiarly out of sync with the larger plot. Nechui's understanding of the basic form of his story seems disconnected from its plot. Levko's death at the end of the story is neither poetic justice nor tragic irony. Nechui seems explicitly to avoid the expected judgment and its appropriate dramatic exposition around which he has constructed his story.
This non-purposeful approach to storytelling lies at the heart of many readers' disaffection with Nechui's works. Among the earliest negative reactions to Nechui were those provoked by works that focused on what should be his signature theme: the development of Ukrainian national consciousness. Pavlo Radiuk, the presumed hero of the novel Khmary (Clouds), was criticized by Drahomanov, Konysky, and others for the weakness of his active commitment to the Ukrainian cause, for being merely a spokesperson rather than an activist. But all of these criticisms are built on the highly dubious assumption that Nechui set out to depict an activist hero. In fact, Radiuk -like all of Nechui's heroes from Mykola Dzheria to Andrian Hukovych (Neodnakovymy stezhkamy) and including Viktor Komashko, the schoolteacher in Nad Chornym Morem (On the Black Sea Coast) -is a product of a non-purposeful approach to story construction that does not presume to offer answers, display essential features, or provide analysis and judgment. Nechui builds his works on a measured, repetitive depiction of Ukrainians and Ukraine, of people and place, of characters and setting. He is not focused on ideas, on analysis, or goals. His characters are not heroes, his settings are not metaphors. His writing is meant to offer a reflection of the beauty and reality of Ukraine. It is not directed at a social, political, moral, or even national purpose. In the culinary metaphor that Nechui used to describe his writing, the meal he prepares has no motive beyond good taste.
Nechui's works no doubt embodied many sins. He was certainly not the European intellectual modernist that younger writers saw as the literary ideal. But he was also not quite the urban, industrial, and politically engaged realist that western European fiction had established as the previous ideal. His writing was simultaneously simple and unadorned yet | 9,875.6 | 2014-07-08T00:00:00.000 | [
"Philosophy"
] |
Characterization and Control of a Multi-Primary LED Light Lab
A new light lab facility has been commissioned at Rochester Institute of Technology with the research goal of studying human visual adaptation under temporally dynamic lighting. The lab uses five-channel LED luminaires with 16 bits of addressable depth per channel, addressed via DMX. Based on spectral measurements, a very accurate multiprimary additive color model has been built that can be used to provide “colorimetric plus” multi-primary channel intensity solutions optimized for spectral accuracy, color fidelity, color gamut, or other attributes. Several spectral tuning and multi-primary solutions are compared, for which accuracy results and IES TM-30-15 color rendition measures are shown. © 2017 Optical Society of America OCIS codes: (000.2170) Equipment and techniques; (230.3670) Light-emitting diodes; (330.1690) Color; (330.1710) Color, measurement; (330.1715) Color, rendering and metamerism; (330.7320) Vision adaptation. References and links 1. M. J. Murdoch, M. G. M. Stokkermans, and M. Lambooij, “Towards perceptual accuracy in 3D visualizations of illuminated indoor environments,” J. Solid State Lighting 2(1), 12 (2015). 2. C. Miller, Y. Ohno, W. Davis, Y. Zong, and K. Dowling, “NIST spectrally tunable lighting facility for color rendering and lighting experiments,” in Proceedings of Light and Lighting Conference with Special Emphasis on LEDs and Solid State Lighting (CIE, 2009), pp. 5–9. 3. IEC, Default RGB colour space – sRGB, International Standard IEC 61966–2-1 (IEC, 1999). 4. M. Fairchild and D. Wyble, “Colorimetric characterization of the Apple studio display (Flat panel LCD),” Munsell Color Science Laboratory Technical Report (1998). 5. M. J. Murdoch, M. E. Miller, and P. J. Kane, “Perfecting the color reproduction of RGBW OLED,” in Proceedings of International Congress on Imaging Science 2006 (IS&T, 2006), pp. 448–451. 6. T. Ajito, K. Ohsawa, T. Obi, M. Yamaguchi, and N. Ohyama, “Color Conversion Method for Multiprimary Display Using Matrix Switching,” Opt. Rev. 8(3), 191–197 (2001). 7. H. Motomura, “Color conversion for a multi-primary display using linear interpolation on equi-luminance plane method (LIQUID),” J. Soc. Inf. Disp. 11(2), 371–378 (2003). 8. A. Žukauskas, R. Vaicekauskas, P. Vitta, A. Tuzikas, A. Petrulis, and M. Shur, “Color rendition engine,” Opt. Express 20(5), 5356–5367 (2012). 9. Y. Ohno, “Spectral design considerations for white LED color rendering,” Opt. Eng. 44(11), 111302 (2005). 10. H. Ries, I. Leike, and J. Muschaweck, “Optimized additive mixing of colored light-emitting diode sources,” Opt. Eng. 43(7), 1531–1536 (2004). 11. F. Zhang, H. Xu, and Z. Wang, “Optimizing spectral compositions of multichannel LED light sources by IES color fidelity index and luminous efficacy of radiation,” Appl. Opt. 56(7), 1962–1971 (2017). 12. M. C. Chien and C. H. Tien, “Multispectral mixing scheme for LED clusters with extended operational temperature window,” Opt. Express 20(Suppl 2), A245–A254 (2012). 13. N.-C. Hu, Y.-C. Feng, C. C. Wu, and S. L. Hsiao, “Optimal radiant flux selection for multi-channel lightemitting diodes for spectrum-tunable lighting,” Light. Res. Technol. 46, 434–452 (2014). 14. S. Afshari, L. Moynihan, and S. Mishra, “An optimisation toolbox for multi-colour LED lighting,” Light. Res. Technol. 0, 1–15 (2016). 15. Illuminating Engineering Society, IES Method for Evaluating Light Source Color Rendition. IES TM-30–15 2015; ISBN 978–0-87995–312–6 16. A. David, P. T. Fini, K. W. Houser, Y. Ohno, M. P. Royer, K. A. G. Smet, M. Wei, and L. Whitehead, “Development of the IES method for evaluating the color rendition of light sources,” Opt. Express 23(12), 15888–15906 (2015). 17. International Commission on Illumination, “CIE 13.3-1995: Method of Measuring and Specifying Colour Rendering Properties of Light Sources.” (CIE, 1995). 18. Philips Lighting, SkyRibbon Intellihue, http://www.colorkinetics.com/ls/IntelliHue/skyribbon-wall-washing/ 19. ENTTEC, “DMX USB Pro Widget API Specification 1.44,” https://dol2kh495zr52.cloudfront.net/pdf/misc/dmx_usb_pro_api_spec.pdf Vol. 25, No. 24 | 27 Nov 2017 | OPTICS EXPRESS 29605 #305977 https://doi.org/10.1364/OE.25.029605 Journal © 2017 Received 30 Aug 2017; revised 1 Nov 2017; accepted 5 Nov 2017; published 13 Nov 2017 20. DMXKing, ultraDMX Micro, https://dmxking.com/usbdmx/ultradmxmicro 21. Photo Research, SpectraScan® Spectroradiometer PR-655, http://www.photoresearch.com/content/spectrascan%C2%AE-spectroradiometer 22. MathWorks, MATLAB 2014b, https://www.mathworks.com/products/new_products/release2014b.html 23. International Commission on Illumination, “CIE 15: Colorimetry, 3ed” (CIE, 2004).
Introduction
Dynamic lighting is light that changes over time. Natural light is typically dynamic, including daylight's diurnal variation in color and intensity, the effects of weather, and the variation seen in daylight filtered through trees or reflected from water. Artificial light is becoming ever more dynamic as digital addressable LED systems make changes in color and intensity easy and responsive. Already, dynamic lighting systems are being designed to influence circadian rhythms, to dim in response to occupancy or daylight entrance, to attract attention, and for specific tasks like focused reading or calming effects. Programming these new dynamic lighting systems mostly falls to lighting designers or product managers, who rely on their experience and limited market research. With the exception of nonvisual, circadian effects, there is not much scientific research on the human visual system's capability for adaptation under dynamic lighting. It is exactly this field of research for which the Dynamic Visual Adaptation Lab (DVA Lab) was recently commissioned in RIT's Munsell Color Science Laboratory (MSCL). Describing the DVA Lab's design and performance builds on several different background areas, including illuminators and light labs, additive color system modeling, and multispectral control algorithms.
Color scientists, graphic artists, and others have long relied on viewing booths, daylight simulators, and other illuminators to provide standard illumination for visual tasks. Recent LED-based viewing booths provide stable illumination, typically manually switchable between a variety of spectral power distributions. Room-scale illumination systems or light labs have been built for research purposes, including at Philips Research [1] and at NIST [2]. The Philips lab was primarily used for the assessment of perceived atmosphere in static scenes. The NIST facility was designed for spectral tuning, meaning a wide range of spectral power distributions enabled by a large number (22) of LED spectral channels. Some recent commercial viewing booths and illuminators have begun to offer spectral tuning capability. The DVA Lab was intended primarily for careful temporal control and secondarily for spectral tuning, at least in terms of overall object color saturation. Preference was given for commercial light fixtures for reasons of robustness and a standard control interface.
Creating a desired light output with an illumination system, be it a colorimetric specification or a spectral match, requires an invertible model of the system's behavior. Recognizing the similarity between multi-channel (or multi-LED) lighting systems and multiprimary displays, a starting point is the colorimetric model often employed in 3-primary (RGB) display systems, with an accounting for a system nonlinearity and a matrix representing the linear combination of basis primaries that are assumed to be independent and colorimetrically stable. The structure of this model is the basis of standard RGB encodings like sRGB [3], and can be used to model most display systems [4]. Importantly, a 3x3 matrix is invertible, meaning it is possible to directly and unambiguously compute required RGB values from desired XYZ colorimetry. Setting the nonlinearity aside, the forward additive colorimetric model is: Where the vector XYZ est represents the estimated XYZ tristimulus values for a given input vector RGB, which comprises the primaries' relative intensity values, each of which lies in the closed interval [0, 1]. The 3x3 primary matrix contains the XYZ colorimetry of the three color primaries, and the vector XYZ flare is an offset to account for non-zero light output when the RGB intensities are set to zero (this is generally needed for displays, but less likely for lighting systems unless there is ambient light). This model is essentially a linear combination of the primaries' colorimetry. The matrix-based additive system model can be extended to any number of primaries, but of course in so doing, colorimetric invertibility is lost, meaning there are multiple primary intensity solutions for a specified XYZ. Primaries beyond three correspond to additional degrees of freedom, so a multi-primary solution requires additional constraints beyond colorimetric matching; such a solution may be either direct or iterative. Examples of direct computations include a red, green, blue, and white (RGBW) OLED solution that directly computes the most power efficient solution by white replacement [5], a multi-primary display algorithm that selects among multiple matrix solutions [6], and a "virtual primary" solution that reduces the problem to an invertible 3-primary system [7]. A lighting-specific example using red, green, blue, and amber (RGBA) LEDs uses weighted combinations of colorimetric RGB and AGB solutions to modulate the perceived color rendition characteristics [8]. Iterative multi-primary solutions, discussed below, typically rely on nonlinear optimization techniques.
It is important to realize that the colorimetric matrix model is an integrated representation of an underlying spectral matrix model, in which the primaries' XYZ tristimulus values are replaced by their spectral power distributions. This model goes a step beyond metameric solutions (XYZ matches), and allows consideration of spectral solutions. For example, a forward additive spectral model (with m spectral bands) for n primaries is given in Eq. (2). The vector S(λ) est , the estimated spectral power distribution over spectral bands λ, is a linear combination of the primaries' spectral power distributions S(λ) j , weighted by the primary intensities P j and, if necessary, an additive offset to account for flare: which may be equivalently expressed (and is seen in other literature) as: An unconstrained inversion of this model, such as a [non-negative] pseudo-inverse, results in a least-squares spectral match, and thus not necessarily -and not likely -a colorimetric match. Well-chosen constraints can take advantage of the available degrees of freedom in smarter ways.
Much literature has addressed the need for constrained optimized inversions. Some work on this topic has focused on selecting LEDs for illumination system design, rather than the range of solutions possible in a multi-primary LED system, but the objectives are often the same. Linear and nonlinear approaches were proposed that maximize efficacy and/or color rendering characteristics [9][10][11]. Similarly, though additionally modeling the current and temperature dependence of LEDs, efficacy and light quality were maximized for given colorimetric goals [12]. As multi-primary systems became more common, researchers addressed solutions such as matching standard illuminants. A general solution applied to a 12-LED multi-primary system was used to compute max-flux solutions with colorimetric values matching several standard illuminants [13]. Bringing together many aspects of these research approaches, a computational toolbox was described that allows multi-primary optimizations with different constraints [14].
Color rendition of light sources is a common thread in many of these papers, and it is one of the most important performance characteristics of lighting systems. For the sake of the present document, however, only the basic color rendition scores defined by IES TM-30-15 (TM-30) [15] are discussed. As explained in [16], TM-30 was designed to supersede and improve the legacy CIE General Color Rendering Index (CRI) [17] with a combination of summary scores and plotted graphics. TM-30 defines the computation of two measures that compare the color rendition of a given test light source to that of a standard illuminant selected to match its correlated color temperature (CCT). TM-30 R f , or fidelity, is an average of color similarity between objects under the test and reference light sources, and R g , or gamut, is an average relative color saturation of objects under the test and reference light sources. Condensing the generally-complex characteristics of color rendition to a twomeasure plane provides a useful, but very simplified summary, so TM-30 also provides huedependent R f and R g bar charts, along with diagrams of hue and chroma distortion characteristics. One detail to point out is that typical light sources, with fixed chromaticity and spectral characteristics, typically plot as a point in TM-30 (R f , R g ) space; however, multiprimary, spectrally-tunable, color-changing light sources can plot as a set of points or a locus in this space, depending on how they are driven, as will be shown later in this paper. Color rendition is an important characteristic that can be an objective of "colorimetric plus" multiprimary control solutions, solving the inversion of Eq. (2), that are relevant for the DVA Lab.
DVA lab & lighting design
The DVA Lab was built in a 3.66 x 4.27 m room in the Munsell Color Science Laboratory at RIT. One of the long walls is made up of three sliding wooden panels that form a moveable wall, which can be opened 2.4 m wide. On the 2.44 m high ceiling, 14 Philips SkyRibbon IntelliHue Wall-Washing fixtures [18], which are 5-primary (Red, Green, Blue, Mint Green, White: RGBMW) LED luminaires, are arranged in a rectangle 60 cm from the walls. The intention was to create a space with smooth vertical luminance, accepting some natural nonuniformity as more ecologically valid than a ganzfeld for testing visual adaptation to lighting in real architectural spaces. The light-to-dark gradient in illumination is approximately 3:1. Pictured in Fig. 1, the DVA Lab was finished with white ceiling grid and tiles, walls painted with a matte white paint with an average reflectance of 93%, and floor carpeted in variegated gray with average reflectance of 7%.
The LED fixtures are controlled via DMX, a digital protocol originally designed for theatre lighting control, which allows data transfer at 40 Hz of one-byte intensity values to up to 512 logical addresses. The 14 SkyRibbon fixtures are each made up of two addressable 30 cm sections, each of which has 5 channels (RGBMW), each of which is controllable at 16-bit depth by using two DMX bytes; these total 280 DMX addresses. The system can be driven either by a programmable Philips controller or, for easier integration with custom software used for psychophysical testing, using the ENTTEC DMX-USB API [19] on a computer via a USB-to-DMX interface such as the DMXKing ultraDMX Micro [20]. The latter control was used for all the measurements described herein.
LED system measurement & modeling
Comprehensive spectral measurements were made of the DVA Lab's LED system. Because the lab is designed for visual adaptation, we are more interested in the radiance of the walls, which will fill an observer's field of view during experiments, than of the irradiance onto lab surfaces. Thus, tele-spectroradiometric measurements were made of the brightest part of the rear wall, e.g. in Fig. 1 approximately ¼ of the way from the ceiling to the floor, above the round wall-cover and under the center of one of the LED luminaires. Unlike in the photo in Fig. 1 The characteristic spectral power distributions (SPDs) of each LED channel are shown in Fig. 2. The RGB SPDs are relatively narrow-band discrete LEDs, while the M and W SPDs are characteristic of phosphor-converted LEDs with a small blue-pump peak around 450 nm and a broadband phosphor emission, peaking about 550 nm for M and about 600 for W. With all five channels at maximum intensity, the measured luminance of the wall is 837 cd/m 2 . Each LED channel was measured at 61 different intensity levels, repeated 8 times, and the spectral shape is remarkably consistent due to the PWM drive. This consistency satisfies one of the requirements for a matrix-based additive color model, colorimetric stability, meaning the chromaticities of the primaries do not vary with intensity. Plotted in 1964 u'v' chromaticity coordinates in Fig. 3, the five color primaries are shown as the vertices of the black polygons. The RGB primaries define the outer boundaries of the chromaticity gamut, and the M and W primaries plot within it. The W primary lies near a 4000 K CCT white, and the M is slightly yellow-green of it. Also shown in the chromaticity diagram are the 1,024 colors used to verify the additive color model, described below. The LED system also satisfied the second requirement for the additive color model, channel independence, meaning the intensity of a given LED channel is not affected by the intensity of any other channel. This was tested by comparing the sum of channels' separately-measured XYZ 10 values with measured XYZ 10 of all five channels on simultaneously, and the absolute difference was less than 0.25% for all intensity levels.
With these requirements met, the LED system was modeled as described above as a spectral additive color model using a 101 x 5 matrix (101 spectral bands by 5 primary channels) in the form of Eq. (2) -the matrix is comprised of the SPDs plotted in Fig. 2 Flare for the system (meaning dark-room luminance) was essentially zero, so that term of the model was not used. The nonlinearity of the LED system was measured using the ramps mentioned. Look-up tables (LUTs) were computed to relate 16-bit digital drive value to intensity (fraction of the maximum luminance) for each primary channel.
One negative performance attribute was observed: thermal droop, the well-known LED drop in efficiency and output as they warm up. The LED luminaires do not actively control their temperature, so droop of up to 6% loss in luminance was observed over a time period of about an hour. Accounting for this directly is not trivial, as it would involve either modeling the thermal behavior and keeping track of the recent history of each LED or a closed-loop control strategy. The present tactic is to simply warm up the LEDs by setting all channels to about 60% intensity for an hour. All measurements and verifications were made in repeated sets over the course of about a week, each set after one-hour warm-up periods.
Verifying the accuracy of the spectral additive system model consisted of comparing actual measured spectra with model-estimated spectra, for a set of verification colors. In the 5-D input primary intensity space, a 4-level grid was created of 5 4 or 1,024 colors. Two sets of measured and estimated spectra were converted to XYZ, u'v', and CIELAB using a reference white of the chromaticity coordinates of the W primary (4000K CCT) at the luminance of the all-channel maximum. The differences, in Table 1, were quite small; notably, the maximum error in CIEDE2000 (Delta E 00 ) [23] was less than 1, with a mean of 0.26; and, the mean luminance error was 0.51 cd/m 2 , compared to the average luminance over all measured verification colors of 338 cd/m 2 and maximum system luminance of 837 cd/m 2 . Qualitatively speaking, these are quite small errors; the largest of these small errors were mostly found in darker colors with v' less than 0.45 -barely visible in Fig. 3, which plots the expected colors as dots connected to measured colors by thin lines.
LED multi-primary solutions
Several multi-primary channel intensity solutions were implemented for comparison. A set of 101 equi-luminant (175 cd/m 2 ) colors, from 2000K to 9000K, were selected as target spectral power distributions. In the same way that TM-30 defines reference colors, spectra at the warm end of the set are Planckian blackbody radiators, at the cool end are daylight sources, and between 4000K and 5000K are defined by a smooth linear transition between. The u'v' and XYZ colorimetric values of these target SPDs are shown in Fig. 4; note the slight wiggle in the path in u'v' due to the transition between the Planckian and daylight spectral loci. In the following section, examples of different spectral tuning variations for reproducing these target SPDs are explained. A 3-primary additive color system has an unambiguous solution for any colorimetric target within its gamut. Using only the RGB LEDs, thus setting the M and W primary intensities to zero, Eq. (4) simplifies to a 3 x 3 matrix model that can be solved by matrix inversion. RGB solutions for each of the XYZ target colors shown in Fig. 4 were computed in this way, and are shown in the upper frame of Fig. 5, plotted versus CCT.
As is expected, the amount of R decreases from warmer, low-CCT to cooler, high-CCT colors, and the amount of B increases over the same range. In the lower frame of Fig. 5 is an RGBW solution, computed via white replacement (utilizing as much W primary as possible in place of a metameric combination of RGB, as explained in [5]). The RGBW solutions still has more R at the low-CCT end, and more B at the high-CCT end, but R, G, and B all go to nearly zero around a CCT of 4000K, replaced nearly entirely by the W primary, which on its own produces 4000K white light. Both the RGB and RGBW solutions provide colorimetric matches to the target colors.
Colorimetric plus spectral match and max-R f
An obvious objective for spectral tuning is a spectral match. With only 5 LED channels, there is not enough spectral variation to make excellent spectral matches, but indeed for any given target SPD, the spectral error can be minimized with a least-squares fitting algorithm. However, for the purposes of the DVA Lab, accurate color is critical: thus, the idea of "colorimetric plus" solutions, meaning that the optimization must be constrained to solutions that firstly are colorimetric matches to the target, and secondarily minimize an objective function, such as spectral error. MATLAB provides such a constrained optimization with the function fmincon, which allows a linear constraint, essentially the no-flare version of Eq. (1). Comparing the spectral match solutions with the RGB-only solutions, Fig. 6 shows the aim SPD and two solution SPDs at three example CCT values. This illustrates that the spectral match is a better match than the RGB solution to the aim SPD, but still not excellent.
The colorimetric-plus spectral match solutions for all 101 target SPDs in the 2000 -9000K CCT range are shown in the upper frame of Fig. 7. Spectral minimization favors the broader SPDs of the M and W primaries over RGB, but of course the R and B with some G are necessary at the extreme CCT values. Apparently, M with R makes a better spectral match to lower-CCT Planckian SPDs, and W with B improves the spectral match for higher-CCT daylight SPDs. Another example of spectral tuning is to affect the color rendition of the light source, for example by maximizing TM-30 R f . Again, this is a "colorimetric plus" constrained optimization, which is highly nonlinear thanks to the complexity of the TM-30 computation, but is still possible with MATLAB's fmincon function. Colorimetric plus max-Rf solutions for the target SPDs are plotted in the lower frame of Fig. 7. The similarity between it and the spectral match should not be a surprise, because a perfect spectral match to a Planckian or daylight SPD would by definition score a maximum R f value of 100. It is only because the spectral matches are not excellent that a slightly different weighting of primary intensities can maximize the R f score beyond that of the spectral match. A few wiggles in the plotted intensity versus CCT can be seen (especially in the W near 5000K, where the balance between W and M appears slightly unstable); this is due to the minimization of each target CCT being independent from all others, and hints that a secondary smoothness constraint might be useful.
Colorimetric accuracy
The above plots show the LED channel drive values that are modeled to match the target CCT colorimetric and spectral values. They were tested in the DVA Lab to see how closely they came to their targets, measured with the PR-655 as described previously, but several months later. The average errors over the 101 target CCT colors in CIE u'v' are listed in Table 2.
Overall the numbers are small, but it is worth noting that the error levels are about three times higher for target CCT solutions in which RGB are used heavily than for solutions in which M and W are used. Mean errors for the spectral match and max-R f solutions of less than 0.001 in delta u'v' are very good and expected to be below the threshold for visibility.
Differences in color rendition
Each of the above multi-primary solutions produce colorimetric matches, but they have different SPDs, which means they may render object colors differently. White RGB LEDs typically have low-fidelity, high-gamut color rendition characteristics, while white phosphorconverted LEDs typically have higher fidelity, moderate-gamut characteristics. Each multiprimary solution uses the primaries differently, and for all solutions, over the set of target CCT colors, the changing blends of LED primaries means that the color rendition characteristics are not constant. Recognizing that TM-30 (R f , R g ) plots do not tell a complete story, it is nonetheless instructive to compare the results of the different multi-primary solutions. where R f , or fidelity, indicates how similarly, on average, a light source renders object colors relative to a reference source; and R g , or gamut, indicates the average chroma of rendered object colors relative to the reference. The point (100, 100) corresponds to a perfect match in color rendition to the reference. Plot A shows the colorimetric RGB-only solution, B shows the colorimetric plus Max-R f solution, and C shows the colorimetric plus goal of (85, 105).
In Fig. 8, plot A shows the (R f , R g ) scores of the colorimetric RGB solution, which wander slightly in the neighborhood of (60, 110). Plot B shows the colorimetric plus max-R f solution -here it is clear that R f is highly dependent on CCT. At all CCTs, this solution is higher in R f than the RGB only solution, but R f varies from about 68 to 92, while at the same time going from R g 112 to 92. Such a variation in color rendition, even if incompletely described by the averaging that results in these plots, might be distracting in a visual adaptation experiment that involves any colored objects. For this reason, another colorimetric plus solution was computed with an optimization function the distance in TM-30 (R f , R g ) space to a goal of (85, 105), a point chosen because it is achievable for most CCTs, even if it has lower R f than the system can reach for some CCTs. The result is in Plot C, which shows that while the lower-R f 2000K persists, points in the CCT range from about 2500 to 8000K remain within about 5 units of the goal. This level of spectral tuning, where an excellent spectral match remains difficult, but important color rendition characteristics can be controlled, on average, is very valuable for the DVA Lab and for LED systems with a few (approx. 4-8) spectral channels.
Future work
One topic of interest is to generalize the above results to non-white colors. The target colors described herein are all along the Planckian and daylight loci, meaning they are all perceptually whitish, ranging from warm white to cool white. Target SPDs are clearly defined, and TM-30 measures of color rendition are all valid. However, for non-white colors, CCT is undefined, target SPDs may not exist, and there are no measures for color rendition (TM-30 is explicitly not defined for non-white colors). Yet, many non-white colors remain interesting for adaptation research, not to mention for application in architectural lighting. A measure for color fidelity for non-white colors would be extremely valuable; until it exists, a rule-of-thumb approach of a colorimetric plus solution that also constrains the SPD to minimize local slope will be used.
As mentioned, a main objective of the DVA Lab is the study of dynamic lighting, thus accurate temporal control is very important. The measurements included in this paper were all conducted with static light settings, which excepting thermal droop effects are not expected to be different for dynamic lighting. Visually, temporal transitions made with the target CCT colors described appear very smooth. However, we do not yet have measurements of dynamic stimuli that objectively confirm there are no flicker or transient color or luminance errors.
One possibility for future development is closed-loop control; a faster spectrometer than the PR-655 used for these measurements would allow rapid response to color errors, for example caused by thermal droop. The use of a control loop would have to be balanced with the need for dynamic lighting settings, so it would have to be fast and smooth in its effect. Literature includes control loops, but they generally take seconds to stabilize [2], much too slow for the experiments desired for the DVA Lab.
A final implementation detail is worth mentioning, related to the optimizations used to generate some of the colorimetric plus solutions. Optimizations need only be performed once for a given target color, and saved for later use. A 3D-to-5D LUT, that for example stored the 5-primary intensity required for given XYZ target input, for a given colorimetric plus solution strategy, would be one flexible, practical implementation. For some tasks, like a CCT series, simply storing the 101 5-primary intensities calculated above would be sufficient for later playback in the lab.
Conclusion
The performance of the newly-constructed Dynamic Visual Adaptation Lab, in terms of colorimetric accuracy and spectral tuning for lighting characteristics, has been confirmed. The lab is designed for controlled visual experiments studying dynamic visual adaptation, meaning chromatic and luminance adaptation while the lighting changes over time.
The lab contains 14 5-channel LED fixtures controlled via DMX, which have been comprehensively measured and modeled. The overall accuracy of the LED additive system model is about 0.25 Delta E 00 for the entire color gamut, or about 0.0007 Delta u'v', which is excellent for visual adaptation research. The LED additive system model is represented in both colorimetric matrix terms, similar to a display model, and in spectral terms, allowing inverse solutions via matrix and via nonlinear optimizations. Using "colorimetric plus" multiprimary solutions, in which a first constraint of colorimetric accuracy is met, and a second objective function minimization leads to a primary intensity solution for a given target color, shows success and flexibility.
Multi-primary solutions using RGB-only and RGBW colorimetric solutions have been shown, along with colorimetric plus solutions including spectral match, max-R f , and an (R f , R g ) goal, using TM-30 measures of color rendition. It is useful to plot loci of color transitions in TM-30 (R f , R g ) space. Future work will include variations on these multi-primary solutions as well as temporal smoothness constraints. Psychophysical experiments to measure dynamic visual adaptation and temporal color perception will commence imminently. | 7,000 | 2017-11-27T00:00:00.000 | [
"Physics"
] |
ZnO/Cu2O/Si Nanowire Arrays as Ternary Heterostructure-Based Photocatalysts with Enhanced Photodegradation Performances
Abstract Ternary ZnO/Cu2O/Si nanowire arrays with vertical regularity were prepared with all-solution processed method at low temperature. In addition to the detailed characterizations of morphologies and crystallographic patterns, the analyses of photoluminescence and photocurrents revealed the sound carrier separation owing from the established step-like band structures. By modeling the photodegradation process of the prepared heterostructures through kinetic investigations and scavenger examinations, the photocatalytic removal of MB dyes was found to follow the second-order kinetic model with reaction constant more than 15.3 times higher than bare Si nanowires and achieved 5.7 times and 3.4 times than ZnO/Si and Cu2O/Si binary heterostructures, respectively. Moreover, the highly stable photoactivity of ZnO/Cu2O/Si photocatalysts was evidenced from the repeated photodegradation tests, which demonstrated the robust photocatalytic efficiency after cycling uses. The facile synthesis along with in-depth mechanism study of such ternary heterostructures could be potential for practical treatment for organic pollutants. Keywords HeterostructurePhotocatalystSilicon nanowire arraysKinetic study Electronic supplementary material The online version of this article (10.1186/s11671-019-3093-9) contains supplementary material, which is available to authorized users.
Background
Heterostructure-based nanomaterials have attracted substantial attention owing to the remarkable optical, optoelectronic, and photochemical properties [1][2][3], which benefited many practical applications including photovoltaics, hydrogen production, energy storage, optical sensing, and photocatalysis [4][5][6][7]. So far, considerable efforts have been made to fabricate heterostructure-based nanomaterials, such as thermal evaporation [8], hydrothermal method [9], chemical vapor deposition [10], and electrospinning process [11]. Nevertheless, these methods essentially required a high processing temperature, long reaction time, vacuum environment, or complex operating procedures [12]. In contrast, the solution-processed synthesis emerged as the facile, inexpensive, and rapid process to form the heterostructures with high-quality structural configuration that might hold great promise for many functional applications.
So far, the synthesis compatibility of constituted materials along with the design strategy for the formation of nanoscale heterostructures based on solution synthesis remained to be the critical issue for practical use.
Cuprous oxide, Cu 2 O, with a direct bandgap of 1.9-2.2 eV, has been considered a potential candidate for practical photocatalytic applications in view of its low toxicity, sound environmental compatibility, sustainable availability, and high activity under visible illuminations [13]. Nevertheless, the employment of stabilizers was required owing to the fact that the oxidation state of Cu 2 O turned unstable and might be subject to variation in the ambient environment. The supporters with high aspect ratio that enabled to load the great number of visible-responsive Cu 2 O photocatalysts could readily benefit their photocatalytic performances with reliability and robustness. These features enabled to further prevent from the unwanted aggregation of nanosized photocatalysts upon operation, which might cause significant degradation of photoactivity. In this regard, one-dimensional silicon nanowires (SiNWs) with controllable surface wettability and high mechanical strength could act as the supporting skeletons that allowed the incorporated photocatalysts exhibiting the spatially distributed uniformity on activating the photodegradation reaction [14].
Aside from that, in view of enhancing the photochemical effectiveness at short wavelength regions so as to actively utilize the wide-band illuminations, crystalline ZnO nanostructures held sound potentials because of their wide band-gap energy (3.1-3.3 eV), reliable chemical stability, low growth temperature, and facile synthetic conditions [15]. In this regard, incorporation of wide-band ZnO crystals with supporting Cu 2 O nanoparticles featuring the visible-activated photocatalytic behaviors might potentially impact the photocatalytic applications originating from the achievement of both improved chemical stability and enhanced photocatalytic activation covering the broad wavelength regions. Taken together, the compelling heterostructures constituted ZnO/Cu 2 O/Si architectures were synthesized with a facile and inexpensive method. The benefited features originated from the established step-like band structures, which facilitated the separation of photogenerated charges that were found to improve the photodegradation efficiency, and further presented the robust performance under cycling photocatalytic tests. In addition, the underlying photochemical mechanism for dye removal was revealed.
Fabrication of Si Nanowire Arrays
The silicon substrates (Resistivity = 1-10 Ω-cm and thickness = 525 μm) with a fixed area of 1.5 cm by 1.5 cm were carefully rinsed with acetone, IPA, and DI water for several cycles. The as-cleaned Si substrates were immersed in the mixture containing 0.02 M of AgNO 3 and 4.8 M of HF, and the etching reaction was operated at room temperature (25°C) [16][17][18][19][20][21]. Afterwards, the grown Ag dendrites during etching reaction covering on the SiNWs were removed with dipping in the concentrated HNO 3 (63%) for 10 min and followed by rinsing with DI water.
Synthesis of Heterostructures
The well-dispersed Cu nanoparticles grown on the SiNW surfaces were prepared with an electroless deposition method. In general, the as-prepared SiNWs were immersed in the mixed solution with 0.047 g (0.015 M) of CuSO 4 powders and followed by gently introducing HF (4.5 M) solution for operating the reduction reaction of Cu 2+ ions to Cu 0 . The above reaction could result in the formation of well-dispersed Cu nanostructures on SiNWs surfaces. Subsequently, the samples were heated in the air at 90°C for 30 min in order to prepare Cu 2 O nanoparticles. To grow the ZnO nanoparticles on the Cu 2 O/Si heterostructures, the samples were sandwiched with a slide of glass under normal pressure of 100 g cm − 2 , and then the mixture of 0.02 M Zn(OAc) 2 in ethanol (40 ml) was dropped in between the samples and covered glass. Afterwards, the samples were switched onto a hot plate at 90°C for 10 min. These processes were repeated 10 times in order to successfully decorate the samples with ZnO nanoparticles. Eventually, the asprepared structures were annealed at 300°C for 2 h.
Characterizations
The morphology of samples was characterized with scanning electron microscope (SEM; HITACHI SU6000). X-ray diffraction (XRD) was performed with a Bruker AXS Gmbh using Cu Kα (λ = 0.15405 nm) radiation at 30 kV and 10 mA with a scanning range of 300-550. The microstructures and compositional examination were investigated using transmission electron microscopy (TEM; JEM-2100F) equipped with energy dispersive X-ray spectroscopy (EDS). The spectral reflection measurements were conducted by UV-Vis-NIR spectrophotometer (Varian, Cary 5000, Australia). Photoluminescence (PL) spectroscopy was performed with a home-made spectrophotometer using a LED light source with a center wavelength of 365 nm. Investigations of current-voltage results were performed with a standard semiconductor characterization system (Keithley 2400). Pohotcatalytic experiments were performed based on the PanChum multilamp photoreactor (PR-2000) with a light source at center wavelength of 580 nm (power = 13.7 Wcm − 2 ). In each experiment, 20 mL (0.1 mM) of methylene blue (MB) was used as the tested target. The samples with the size of 1.5 cm by 1.5 cm were subjected to the dark condition for 40 min in order to establish the adsorption equilibrium. After that, 0.5 mL of the solution was withdrawn from the vial at regular time intervals and immediately centrifuged at 7000 rpm for 3 min to remove the suspended particles or impurities. The dye concentrations were monitored using a UV/visible spectrophotometer (Shimadzu UV-2401 PC). Figure 1 a-c displayed the morphologies of three various heterostructures, and the dimensions of corresponding deposited nanoparticles on SiNWs were presented in the inserted figures, respectively. It indicated that the uniform coating of ZnO nanoparticles on the NW sidewalls was created based on a pressure-induced deposition, and the position of formed ZnO nanoparticles distributed throughout NWs with average size of 24.8 nm, as shown in Fig. 1 a. On the other hand, Fig. 1 The reduction of Cu 2+ ions was achieved preferentially on Si surfaces through the galvanic displacement with Si. The introduction of HF etchants that was responsible for removing the oxidized Si was required for the complete decoration of Cu seeds on the exposed surfaces of SiNWs. These nucleated Cu seeds were subsequently oxidized under the anneal treatment at 90°C, transforming to the visible-responsive Cu 2 O seeds with average dimension of 13.4 nm. By incorporating Cu 2 O deposition and followed by ZnO growth, the ternary heterostructures emerged as the ZnO/Cu 2 O/Si nanowire arrays were generated. The dual features on evaluating the average dimensions of decorated nanostructures could be found (13.8 nm and 25.2 nm) due to the coexistence of ZnO and Cu 2 O nanoparticles. These features also suggested the closely packed ZnO with Cu 2 O as coexisted nanoparticles supported by the SiNW arrays with large aspect ratio. In addition, XRD characterizations of as-formed heterostructures were performed, as shown in Fig. 1 d. The results revealed the characteristic diffraction patterns of ZnO crystals with preferentially crystallographic plane of (002) appearing in ZS NWs. In addition, the multiple XRD patterns featuring the (111) and (200) of crystalline Cu 2 O planes could be observed in CS nanowires. These correlated plane indexes all presented in the ZCS-heterostructure NWs, explicitly identifying the successful formation of ternary nanostructures based on the synthetic solution procedures. To further examine the microstructures and related chemical composition of the ZCS NW arrays, TEM investigations were conducted, as presented in Fig. 2 a. It could be clearly observed that the NW surfaces were covered with densely packed nanoparticles. In addition, EDS mapping was conducted to analyze the chemical compositions of formed heterostructure NW arrays, as presented in Fig. 2 b. The spatial position of characteristic Si, Cu, and Zn compositions were displayed, indicating that Zn and Cu compositions corresponded to the decorated nanoparticles in SiNW arrays that were uniformly distributed throughout the NW sidewalls, which essentially revealed the successful formation of ternary heterostructures.
Results and Discussion
Explorations of light reflectivity from these three fabricated NW-based samples along with sole NWs were This could be understood by the fact that the appearance of ZnO enabled to reduce the mismatch of refractive index in between highly light-reflective Cu 2 O and surrounding medium [22].
In addition, the photoluminescence spectra of samples were inspected, as shown in Fig. 3 b. Compared with three various heterostructure arrays, the sole SiNW arrays displayed the highest PL intensity centered at 520 nm. It has been reported that the PL behavior correlated with the radiative recombination of photogenerated electrons and holes, and hence the strong PL intensity explicitly reflected the facilitated carrier recombination that might quench the possible photodegradation reactions on organic dyes. By contrast, the ZCS heterostructures possessed the lowest PL intensity, which suggested the involvement of reduced recombination from photoexcited carriers by effectively separating the electrons toward the Cu 2 O seeds through a created heterojunction. Besides, the measured photocurrents from four various samples under the 580-nm illuminations were displayed in Fig. 3 c. The results indicated that the ZCS heterostructures possessed the highest excited photocurrents under the sweeping bias from − 1 to 5 V. This could be supported to the well incorporation of ZnO/Cu 2 O/Si into the regulated NW features that responded to the increased photocurrents collected by the external electrodes. In particular, the photocurrent enhancement of ZCS heterostructures, defined as I photocurrent − I dark current [23,24], presented approximately an order of magnitude greater than that of sole SiNWs.
The comparable low reflectivity of visible illuminations suggested that both of these two nanostructures could effectively interact with the incoming lights rather than directly reflecting them. Thus, the distinct photocurrent enhancement could be attributed to the preferential recombination of carriers prior to turning into the photocurrents in the case of bare SiNWs with indirect band-gap nature. Nevertheless, by introducing the heterostructure architectures on SiNWs, the separation pathway of photogenerated electrons and holes might be created and thus displayed the substantial gain of photocurrents collected by the electrodes. These findings could be further elucidated by the band structures of constructed ternary heterostructures, as illustrated in Fig. 3 d. The photogenerated electrons and holes were readily separated due to the creations of Si/Cu 2 O and Cu 2 O/ZnO interfaces with step-like band diagram on both sides of conduction band and valence band, respectively. These could essentially benefit the photocatalytic reactions of organic pollutants in solutions because the photogenerated electrons actively participated in the possible oxidative degradation of organic molecules rather than being dissipated by the recombination with holes.
The photodegradation capability was investigated through monitoring the concentration of MB dyes in the presence of tested photocatalysts, including three various heterostructure-based photocatalysts, as shown in Fig. 4 a. In addition, the sole SiNWs were tested under the similar condition as a control sample. It could be found that the remaining concentration of dyes as a function of illumination time for sole SiNWs, ZS, CS, and ZCS heterostructures were 81.6%, 55.1%, 46.2%, and 23.0%, respectively. It evidenced that the photoactivity of three various heterostructures was greatly improved compared with bare SiNW samples and among them, photodegradation efficiency of ZCS samples reached above 3 times superior than that of sole SiNWs. In addition, the photodegradation results of ZCS photocatalysts were also presented in Additional file 1. To quantitatively compare the photocatalytic activity, the reaction kinetics of dye degradation were further explored, where three possible kinetic models, including first-order kinetic model, second-order kinetic model, and Langmuir-Hinshelwood kinetic model were investigated, as presented below.
First-order kinetic model [24], Second-order kinetic model [25], 1/C t = k 2 t + 1/C 0 (4) Langmuir-Hinshelwood kinetics model [26], ln(C t /C 0 ) + k ab (C 0 − C t ) = − k 3 k ab t (5) in which C 0 and C t represented the instantaneous concentrations of MB dyes at illumination time = 0 and time = t, respectively. k 1 , k 2 , and k 3 were the rate constants of first-order, second-order, and Langmuir-Hinshelwood, respectively. In addition, k ab was indicated as the Langmuir constant.
By examining the photodegradation results of ZCS photocatalysts with these three kinetics models, the corresponding correlation coefficients were correspondingly evaluated, which evidenced that the involved photodegradation process matched the second-order kinetic model with the highest R 2 (0.98) among three examined models, as shown in Fig. 4 b. The photochemical degradation rate was extracted on fitting the measured degradation results with the kinetic model, and the evaluated reaction constants from various NW samples were presented in Fig. 4 c. The results indicated that the extracted k 2 of ZCS NWs achieved more than 5.7 and 3.4 times higher than that of Si nanowire incorporated with ZnO and Cu 2 O nanoparticles, respectively.
In addition to the improved photocatalytic efficiency, the repeated photodegradation tests of ZCS heterostructures were conducted, as shown in Fig. 4 d. The results verified the remarkable stability for conducting dye removal under illuminations with less than 2.3% of efficiency loss after conducting the repeated tests for four times, reflecting the sound structural robusticity of such ternary photocatalysts. Aside from that, the wetting characteristics of photocatalyst surfaces were analyzed, as shown in Fig. 5. Among three tested heterostructures, it was found that the ZCS heterostructures particularly possessed the highly hydrophilic property with the contact angle of 26.3°, which was lower than CS (contact angle = 33.5°) and ZS (contact angle = 63.8°) nanostructures. Such feature correlated with the comparably close contact with the dye solutions that assisted the occurrence of possible photochemical interactions, and thus might contribute to the improved photocatalytic performance.
Examinations of possible photocatalytic mechanism of ZCS heterostructures were carried out based on the trapping tests of active scavengers [27,28]. Na 2 -EDTA, AgNO 3, and IPA were engaged to trap the photogenerated holes (h + ), electrons (e − ) and hydroxyl radicals (·OH) in the solutions, respectively, as demonstrated in Fig. 6 a. The results indicated that the degradation rate of MB dyes in the presence of Na2-EDTA was 37.6%, close to the degradation results realized without introducing the scavenger molecules, interpreting the photoexcited holes responded to a trivial contribution for initiating the photodegradation of MB dyes. Nevertheless, the presence of either AgNO 3 or IPA reagents significantly reduced the degradation rates of MB dyes toward 86.6% and 81.4% after a 120-min reaction, suggesting that photogenerated electrons and ·OH radicals acted as decisive roles on activating the photocatalytic removal of MB dyes.
On the basis of kinetic investigations and scavenger examinations, the mechanism diagram of ZCS heterostructures that contributed to the photodegradation process was schematically displayed, as shown in Fig. 6 b. In this configuration, the highly light-absorptive SiNWs was capable of absorbing broadband visible lights owing from the inherently small band gap (1.1 eV) as well as remarkable light-trapping characteristics. Thereby, the transport pathway of photo-generated electrons was established from the conduction band of Si, through Cu 2 O and toward the outer ZnO layer due to the involved e − þ O 2 →O − 2 (6) Such highly oxidative species were more likely to actively initiate the photocatalytic degradation of MB dyes as long as the ZCS photocatalysts were excited under light illuminations. Apart from that, the photo-generated holes from either Cu 2 O or ZnO sides were collected and trapped within the valence band of Si following the illustrated scheme shown in Fig. 3 d that facilitated the carrier separation. Thus, the recombination of photoexcited electrons and holes was greatly inhibited, which additionally promoted the formation of hydroxyl radicals through the following reactions, (8) The created hydroxyl radicals were also involved with the photodegradation process in the presence ZCS heterostructures by driving the oxidative removal of MB molecules. Overall, these two pathways were believed to contribute to the response of photocatalytic removal of MB dyes by taking advantage of minimizing the charge recombination from the established heterojunctions, where the proposed mechanism was in accordance with the scavenging tests shown in Fig. 6 a.
Conclusion
Incorporations of ZnO/Cu 2 O nanoparticles with SiNW arrays was presented through a facile and inexpensive all-solution processed method that allowed the formation of uniform and large-area production of ternary heterostructures. The combined photoluminescence and photoexcitation studies revealed the greatly improved charge separation, suppressing radiative recombination of photogenerated carrier losses in SiNW host. This Fig. 6 a Scavenging analysis of photodegradation process in the presence of ZCS nanowires. b Schematic illustration for the photocatalytic mechanism on dye degradation under ZCS heterostructure-based photocatalysts enabled to the excellent photocatalytic degradation of MB dyes using ternary heterostructures, which could reach 15.3 times higher than that of sole SiNWs, and more than 5.7 and 3.4 times higher than that of ZnO/Si and Cu 2 O/Si binary heterostructures, respectively. The in-depth kinetic studies along with unveiling the photodegradation mechanism were further presented, which might benefit the practical applications on the photocatalytic treatment of wastewater or organic pollutants with an efficient and sustainable route.
Additional file
Additional file 1: Figure S1
Availability of Data and Materials
The datasets supporting the conclusions of this article are included within the article. | 4,330.2 | 2019-07-23T00:00:00.000 | [
"Materials Science",
"Chemistry",
"Environmental Science"
] |
The ω3 scaling of the vibrational density of states in quasi-2D nanoconfined solids
The vibrational properties of crystalline bulk materials are well described by Debye theory, which successfully predicts the quadratic ω2 low-frequency scaling of the vibrational density of states. However, the analogous framework for nanoconfined materials with fewer degrees of freedom has been far less well explored. Using inelastic neutron scattering, we characterize the vibrational density of states of amorphous ice confined inside graphene oxide membranes and we observe a crossover from the Debye ω2 scaling to an anomalous ω3 behaviour upon reducing the confinement size L. Additionally, using molecular dynamics simulations, we confirm the experimental findings and prove that such a scaling appears in both crystalline and amorphous solids under slab-confinement. We theoretically demonstrate that this low-frequency ω3 law results from the geometric constraints on the momentum phase space induced by confinement along one spatial direction. Finally, we predict that the Debye scaling reappears at a characteristic frequency ω× = vL/2π, with v the speed of sound of the material, and we confirm this quantitative estimate with simulations.
The manuscript by Yu et al. employs a series of inelastic neutron scattering and molecular dynamics simulations to study the scaling behavior in amorphous solids under nanoconfinements at low frequency. The authors find a clear cubic scaling of the VDOS at low frequencies, as opposed to Debye's scaling of omega^2. The experiments are carefully performed by sandwiching the samples in graphene oxide membranes, which can provide soft confinements in a controlled manner. The authors also provide a theoretical ration to observed scaling with regards to geometric considerations of the system. While I believe in general the work warrants publication in Nat. Comm., some clarifications and additional information may be required before publication: 1-MD simulations have been performed, which in general of great in boosting one's confidence in observed scaling in neutron scattering. However, the results are very briefly discussed, with a single graph presented and some snapshots of the bulk ice, as opposed to confined geometry. Having access to all spatiotemporal configurations in the simulations, presenting the actual structure can be quite informative. Also, some anomalous behavior is observed in Fig. 4D for the bulk ice, which should be clearly described. Fig. 3B show a transition from scaling of 2 to 3, rather gradually while changing the confinement layer; nonetheless, I'm not sure that I agree with the slopes presented on the graph, specially with GOM 0.4, the slope is very similar to 3. If so, some discussion will be necessary to clarify the transition from 2 to 3 scaling. Along the same line, Fig. 3 D for the hydration level shows 6 different samples (data points), but all results are presented for 4 samples. Some clarification would be useful. 3-Details of matching the mechanics of the GOM in MD simulations with the experiments will be informative. 4-Minor comment on some grammatical error throughout; but this should not affect the overall assessment of the manuscript as I'm confident all those will be carefully looked at prior to publication.
-The experimental VDOS measurements in
Considering the comments above, I believe that the work contributes fundamentally to a wide range of areas, and the results presented are synergistically confirming the hypotheses put forward by the authors. As such, I am happy to recommend publication upon addressing the comments above Reviewer #2 (Remarks to the Author): This paper reports the scaling of vibrational density of states in a quasi-2D confined solids. The sample was amorphous ice confined by graphene oxide membranes (GOMs). The confinement size L is controlled by hydration level. Inelastic neutron scattering measurements were employed to determine the vibrational density of states (DOS). The authors found that DOS scales with w^3 when L is small, and gradually approaches w^2 when L increases. Molecular Dynamics simulations were carried out to confirm the findings from inelastic neutron scattering experiments. Finally, a theoretical analysis was carried out with the aim to demonstrate why the DOS behaves in such a way in a confined system. Phonon DOS in glasses has long attracted a lot of interests. In spite of extensive studies, however, there are still wide-range of disagreements between experiments and simulations, and also amongst different simulation studies themselves. In this regard, the present study, which was carefully orchestrated, is interesting and could make an important contribution to our understanding of the phonon dynamics in glass.
However, I have some concerns which I would like the authors to address. 1. A numerical study was recently published in Phys. Rev. Lett. [PRL, 127, 248001 (2021)] on lowfrequency excess vibrational modes in a 2D model glass, where the authors found that the DOS scales with w^2 in a typical 2D glass but the scaling changes to w^3 for a small system. I would like the authors to consider this paper and address how their findings reconcile or differ from this paper. 2. The authors attempted a theoretical analysis to show that there are extra modes at low energies due to the peculiarities of the confinement. The argument hinges on Eq. (4), but I cannot see how this was derived. The authors referred to ref. [79], but this is an unpublished result posted online. Furthermore, I had trouble to access the file (invalid link). Given that Nature Communications does not impose a page limit, I suggest that the authors elaborate on the development of Eq. (4) and incorporate the key parts of ref. 79 in Supplemental Materials. 3. Along the same line, please also show analytically how the scaling transitions from w3 to w2 as L increases. 4. Can the simulation also confirm a transition from w3 to w2 as L increases? 5. The authors discussed the data in the low-energy regime leading up to the Boson peak. What is the energy of the Boson peak in these materials?
Reviewer #3 (Remarks to the Author): This is a nice combination of experimental and theoretical work. The topic is of general interest. Therefore, the manuscript should be published. Nature Commication seems to be a good place for that.
In the abstract it is written that the vibrational density of states and the specific heat is well described by the Debye theory. This is only true for crystalline materials where a huge variety of materials like glasses deviations are found. The authors should be more specific with that. The same is true for the first part of the introduction. The Debye assumption is fulfilled at low enough frequencies seems to be trivial for bulk systems because the dispersion relation could be always approximated by a linear law.
I fully agree with the authors that in confined and/or fractal systems a linear approximation is not appropriate because some states cannot be realized and reach under such circumstance. The presented experimental data seems to be nice examples for that. Nevertheless, other numerical approaches to the low frequency density of states show that also under confinement situation becomes quadratic in the frequency dependence. See Phys. Rev. B: Condens. Matter Mater. Phys., 2010, 81, 054208. At least this paper should be cited.
In figures 2,3 and 4 at the x-axis omega is used. General in physics omega is used as a symbol for the frequency (see also equ. 2). In these figures energy values are given. This is not consistent.
Minor comment: In figure 7 it should be THz. Temperature differences should be given in K not in C in accordance with international regulations.
Response to the Referees for manuscript NCOMMS-21-37774-T thanks a lot for reviewing our manuscript and for the positive assessment of our work. In the following, we reply point by point to the comments of all the Referees. We have thoroughly revised and improved the manuscript (see below for details). The changes in the manuscript are left in red color to facilitate the job of the Referees.
Moreover, inspired by the comments of the Referees, we have added additional material in the manuscript. In particular, (I) we have added the analytic prediction from theory of the crossover frequency from the ω 3 scaling to the Debye ω 2 scaling and (II) we have performed a further analysis of the DOS in the simulations for different confinement sizes and confirmed the theoretical prediction in point (I).
The point-by-point response to the Referees' questions are provided below with the original Referees' questions marked in blue Italic. We hope the Referees and the Editor will find now our work suitable for publication in Nature Communications.
Referee 1
The manuscript by Yu et al. employs a series of inelastic neutron scattering and molecular dynamics simulations to study the scaling behavior in amorphous solids under nanoconfinements at low frequency. The authors find a clear cubic scaling of the VDOS at low frequencies, as opposed to Debye's scaling of ω 2 . The experiments are carefully performed by sandwiching the samples in graphene oxide membranes, which can provide soft confinements in a controlled manner. The authors also provide a theoretical ration to observed scaling with regards to geometric considerations of the system. While I believe in general the work warrants publication in Nat. Comm., some clarifications and additional information may be required before publication: We thank Referee 1 for his/her positive evaluation of our work. In the following we address all the comments of Referee 1 point-by-point.
MD simulations have been performed, which in general of great in boosting one's confidence in observed scaling in neutron scattering. However, the results are very briefly discussed, with a single graph presented and some snapshots of the bulk ice, as opposed to confined geometry. Having access to all spatiotemporal configurations in the simulations, presenting the actual structure can be quite informative. Also, some anomalous behavior is observed in Fig. 4D for the bulk ice, which should be clearly described.
We thank Referee 1 for the comments and suggestions.
Additional details about the MD simulations, the systems used and the corresponding structures have been added in the Supplementary Material (Fig.R-1(a)-(f) in this Reply). The pair distribution function analysis in the radial direction, revealing additional information about the spatial structure, has been added as well to confirm the difference between the crystalline and amorphous setups.
In Fig.4D of the manuscript, a sharp peak in the VDOS of bulk ice is observed around ≈ 1.5 THz (6.2 meV). In order to investigate its origin further, in Fig.R-1(g),(h), we have computed the velocity auto-correlation function in different coaxial directions to obtain the vibrational density of states in the various directions. From there, we can conclude that such a sharp peak comes from the vibration perperdicular to the [0001] basal plane of the hexagonal ice. In both the amorphous and slab-crystalline samples, this peak is broadening and moves slightly to lower frequencies, ≈ 1.1 Thz (4.5 meV). Such effect can be explained by an increase of the linewidth corresponding to this mode. In the case of the amorphous systems, this broadening of the linewidth naturally arises because of structural disorder. In the case of the slab-crystalline sample, the spatial confinement on the z direction reduces the constructive interference and thus decreases the peak intensity. Regarding the nature of this peak, there is yet no consensus in the literature. Some works identify this excess mode as a genuine boson peak (PhysRevLett.85.3185; Physica B: Condensed Matter, 316, 493-496.), others (PhysRevLett.85.4100; PhysRevB.78.064303,PhysRevB.77.104306) attribute this excess to a specific optical mode of the ice structure. Despite being an interesting question, it is only tangential to the scope of the present work; therefore, we are not in the position of saying more about this peak. We have added the above discussion and The experimental VDOS measurements in Fig. 3B show a transition from scaling of 2 to 3, rather gradually while changing the confinement layer; nonetheless, I'm not sure that I agree with the slopes presented on the graph, specially with GOM 0.4, the slope is very similar to 3. If so, some discussion will be necessary to clarify the transition from 2 to 3 scaling.
Following the suggestion of the Referee, we have revisited and improved the fitting. Additionally, the details of the fitting procedure were not totally clear in the previous version of manuscript,but were now clarified in the revised one. Briefly, the power law reported is obtained by choosing a proper energy range for the fitting. More precisely, we have fixed this range by referencing the results in bulk ice, which were Fig.3(a) of the original manuscript and presented in Fig.R-2(a) of this reply. There, one can notice that the Debye scaling law is robust only up to ≈ 4 meV (indicated by a vertical dashed line in Fig.R-2(a)), as the shoulder of the strong vibrational peak discussed before enters at higher energy. Therefore, this value is taken as the upper bound for the energy window of the fit. For consistency, all the data are fitted in the range of 2.4mev to 4meV,where the lower bound is set as 2.4 meV. Using this more conservative procedure, the power law of the sample at h = 0.4 is around 2.6 (see Fig.R-2(b)) and indeed lower than that of the sample at h=0.1,which is 3. As shown in Fig.R-2(c), within the experimental error the power law fittings well modeled the experimental data. The previous value for h = 0.4 was overestimating the power because higher energy points were used in the fitting and the shoulder of the strong vibrational peak was affecting the final result. We have improved the fitting procedure and added in the text more details about how it is performed. Additionally, the corresponding figure ( Fig.3(b),(d)) in the manuscript has been modified.
The continuous transition between the ω 2 to ω 3 scaling in the experimental setup can be understood as follows. When the hydration level is low, the water molecules are mostly confined between the GOM layers and form confined amorphous ice, showing the ω 3 scaling derived in the manuscript. By increasing the hydration level, part of the water molecules at low temperature moves to some large voids or even to the external surface of the GOM to form bulk crystalline ice, contributing to the total DOS with the standard ω 2 Debye scaling. Therefore, at any given hydration level, the system contains two separate components, a 2D-confined disordered phase and 3D bulk ice, which contribute with different scaling laws to the total density of states. As a result of summing these two terms, and using a single power-law fitting function, one obtains a continuous crossover between the two powers which is mainly the manifestation of how the corresponding prefactors change upon hydration. The relative mass ratio between the two components can be roughly determined by using Differential scanning calorimetry. As shown in Fig.R-2(d), the mass proportion of bulk ice gradually increases from 0 at h=0.1 to ≈ 67% at h=1.2, implying that the higher the hydration level the less the confined disordered phase and the more the bulk crystalline ice. Because of this competition, and the unavoidable bulk ice component in the experimental setup, a single effective power law fitting results into a graduate change of the scaling from 3 to 2 upon increasing h. We have expanded and improved the discussion about this point in the revised manuscript.
Along the same line, Fig. 3 D for the hydration level shows 6 different samples (data points), but all results are presented for 4 samples. Some clarification would be useful.
The discrepancy on the number of data points between neutron and DSC in Fig.3D of the main text results from the different cost of these two methods. The beamtime of the inelastic neutron scattering is limited, and we collected data for 9 hours per sample at each temperature to get sufficient statistics. The DSC measurement was conducted after the neutron scattering, and it was performed by using a household instrument for 1.5 hour per sample per temperature. Therefore, we were able to measure more samples covering the range of hydration levels studied by neutron scattering. We have clarified this difference in main text. Details of matching the mechanics of the GOM in MD simulations with the experiments will be informative.
We have performed additional MD simulations on GOM-sandwiched water (see the snapshot in Fig.R-3(b)). The obtained DOS is in qualitative agree-ment with that from the slab setup without GOM. In the main text, we still use the slab simulation without GOM for our analysis and discussion. The reasons are the following: first, the slab simulation is easy to set up for both crystalline and amorphous phases, and thus one can make a fair comparison of the two systems under the same confinement. In contrast, GOM has a rather rough and curvy surface (see Fig.R-3(b) below), and thus the simulation for confined crystalline ice becomes difficult. Secondly, precise control of the thickness of the sample is needed in order to test the theoretical derivation for the crossover scale between the ω 3 and ω 2 scalings (see Fig.R-4 and the related reply to Referee 2 for more details about the crossover scale). Again, the rough and curvy surface of GOM prevents such an analysis. In order to explain in more detail this point, we have added the above discussion and Minor comment on some grammatical error throughout; but this should not affect the overall assessment of the manuscript as I'm confident all those will be carefully looked at prior to publication.
8
We have revised carefully the text and fixed the spotted grammatical errors. Thanks for pointing this out.
Considering the comments above, I believe that the work contributes fundamentally to a wide range of areas, and the results presented are synergistically confirming the hypotheses put forward by the authors. As such, I am happy to recommend publication upon addressing the comments above We would like to thank Referee 1 once again for the helpful comments and for the positive evaluation of our work. We have carefully considered the points above and improved our manuscript accordingly. We hope that Referee 1 will find it now suitable for publication in Nature Communications.
Referee 2
This paper reports the scaling of vibrational density of states in a quasi-2D confined solids. The sample was amorphous ice confined by graphene oxide membranes (GOMs). The confinement size L is controlled by hydration level. Inelastic neutron scattering measurements were employed to determine the vibrational density of states (DOS). The authors found that DOS scales with ω 3 when L is small, and gradually approaches ω 2 when L increases. Molecular Dynamics simulations were carried out to confirm the findings from inelastic neutron scattering experiments. Finally, a theoretical analysis was carried out with the aim to demonstrate why the DOS behaves in such a way in a confined system. Phonon DOS in glasses has long attracted a lot of interests. In spite of extensive studies, however, there are still wide-range of disagreements between experiments and simulations, and also amongst different simulation studies themselves. In this regard, the present study, which was carefully orchestrated, is interesting and could make an important contribution to our understanding of the phonon dynamics in glass.
We thank Referee 2 for the positive evaluation of our work and for finding our study interesting. Some of his/her questions prompted us to improve the theoretical analysis and to add new simulations in support of it.
However, I have some concerns which I would like the authors to address. 1. A numerical study was recently published in Phys. Rev. Lett. [PRL, 127, 248001 (2021)] on low-frequency excess vibrational modes in a 2D model glass, where the authors found that the DOS scales with ω 2 in a typical 2D glass but the scaling changes to ω 3 for a small system. I would like the authors to consider this paper and address how their findings reconcile or differ from this paper.
We thank Referee 2 for pointing us to this recent interesting paper. In the PRL paper mentioned [PRL, 127, 248001 (2021)], the author found that the low-frequency excess vibrational modes, obtained by subtracting the Debye contribution from the overall density of states, in a 2D model glass scales with ω 2 , but it changes to ω 3 for a small system. Despite the apparent similarities, our system is quite different from that discussed in the reference mentioned above. The authors of the PRL consider a purely 2D system without a third direction. Our system is not a 2D system, but rather a 3D system with the third dimension strongly confined. Importantly, there is vibrational dynamics also along the third confined dimension, which is the key for the ω 3 scaling observed here. In addition, using simulations, we are able to show that this ω 3 scaling is not an exclusive feature of amorphous systems but it appears also in ordered structures under confinement, while the PRL studies glassy systems only. Moreover, the density of states studied in the present work is the total one obtained without subtracting the Debye contribution. Finally, our physical interpretation of the ω 3 scaling is quite different since it does not involve any low-frequency excess vibrational modes. In Summary, at this point we believe that our scaling is not of the same nature of the one reported in the PRL mentioned. In the revised manuscript, we have cited the PRL paper and added the above discussion.
The authors attempted a theoretical analysis to show that there are extra modes at low energies due to the peculiarities of the confinement. The argument hinges on Eq. (4), but I cannot see how this was derived. The authors referred to ref. [79], but this is an unpublished result posted online. Furthermore, I had trouble to access the file (invalid link). Given that Nature Communications does not impose a page limit, I suggest that the authors elaborate on the development of Eq. (4) and incorporate the key parts of ref.
in Supplemental Material.
Let us clarify our interpretation further. We do not attribute the appearance of this novel scaling to extra modes at low energies, as usually done in the context of amorphous systems. On the contrary, we show that the geometric confinement, together with the absence of periodic boundary conditions, modifies, by itself, the Debye contribution (below a certain frequency scale, see more below) changing the scaling from quadratic to cubic at low energy. "Eq.(4)" mentioned by the referee is now published in Phys. Rev. Materials 5, 035602 and it has been updated in the manuscript. Nevertheless, for the sake of clarity, we are happy to provide a few more details of the derivation in the manuscript. We have expanded the theoretical derivation with more details in the revised manuscript and updated the citation with the published version.
Along the same line, please also show analytically how the scaling transitions from ω 3 to ω 2 as L increases. Can the simulation also confirm a transition from ω 3 to ω 2 as L increases?
We would like to thank Referee 2 for these two questions since they urged us to greatly improve the exposition of the theory part and to add additional simulations providing further and clear evidence for its validity.
Let us start by explaining in more detail the theoretical framework. We consider a cylindrical system confined to length L in the z direction and for simplicity we assume the other directions (x, y) to be infinitely extended. 1 We use spherical polar coordinates, measuring the polar angle θ from the z axis (see Fig.R-4(a)). If measured at an angle θ from the z confinement axis, the extent of the confined medium is L/ cos θ (Fig.R-4(a)). Taking this value to be the maximum allowed wavelength in that direction, and using the absence of hard-wall boundary conditions (which is proved with simulations in the Supplementary Material), we obtain k max = 2π cos θ/L. In the range 0 ≤ θ ≤ π, this equation describes two spheres with radius π/L, centred at (0, 0, ±π/L) in k-space as shown in Fig. R-4 (b). This implies that the phase space of allowed momenta k ≡ k becomes angle dependent below k × = 2π/L. In particular, momenta lying within the two spheres, white region in Fig. R-4(b), are not allowed. Notice that the geometric confinement has no effects for k > k × and that k < k × would not be allowed in presence of hard-wall boundary conditions. By now inverting the constraint on k max in terms of a minimal angle θ min = arccos (k/k × ), we arrive at Eq.4 in the main text from which we derived the novel ω 3 scaling, after converting the results from momentum k to frequency ω, using the dispersion relation ω = vk. We emphasize that the novel scaling is expected to roughly appear at frequencies below ω × = vk × with v being the characteristic speed of sound of the material. In summary, our theoretical model predicts that: • Above ω × = 2πv/L, there is no effect of the geometric confinement and the density of states of the solid system is expected to follow the standard Debye law g(ω) ∼ ω 2 .
• Below ω × , a novel ω 3 scaling appears because of the geometrical effects of confinement on the phase space of the low energy vibrational modes.
From a theory point of view, this crossover is sharp, meaning the density of states is continuous at ω × but its derivative is not. Obviously, this feature is a result of the simplifications required to keep the theoretical model analytically tractable, and in the realistic situation it will be smeared out by several effects, including thermal fluctuations.
In order to answer the second question of Referee 2, and test the validity of the predictions from our theory, we have performed more simulations by varying the confinement scale L from small (nanoconfined system) to large (bulk material). The results are presented using the Debye reduced density of states g(ω)/ω 2 in Fig.R-4(c). The bulk sample (yellow points therein) shows a clear plateau at low frequencies confirming the validity of Debye law, g(ω) ∼ ω 2 . By decreasing the confinement scale L, we notice a gradual appearance of a novel ω 3 regime at low frequencies, up to a crossover scale indicated with × in the plot. The crossover scale ω × moves to larger frequencies by reducing L, as expected from the theoretical considerations described above. In order to quantitatively validate our theory, we have tracked the position of the crossover frequency ω × as a function of the inverse confinement scale 2π/L. In Fig.R-4(b), we report a linear scaling between the two quantities, confirming the theory predictions. A numerical fitting gives a value: which is in good quantitative agreement with the actual value of sound velocity extracted from the dispersion relation, v ≈ 1300 m/s within a 14% error(see the inset of Fig.R-4d). Here, the reference sound velocity (inset of Fig.R-4d) is the transverse mode. If one used the average speed of transverse and longitudinal modes, 3/v 3 = 2/v 3 T A + 1/v 3 LA , with v LA ≈ 3900 m/s, an even better agreement with the value dervied from Eq.(R-1) will be obtained, asv ≈ 1480 m/s. In summary, the new simulations analysis confirms the validity of our theoretical framework not only in predicting the novel low frequency scaling ∼ ω 3 under confinement but also in estimating the crossover frequency to the Debye scaling.
We have re-written the theory part, added the new results from the simulations together with a lengthy discussion regarding the confirmation of the validity of the theory and its predictions. Fig.R-4(b)-(d) have also been added to the revised text ( Fig.5(b),(c) and (d)).
tions are found. The authors should be more specific with that. The same is true for the first part of the introduction. The Debye assumption is fulfilled at low enough frequencies seems to be trivial for bulk systems because the dispersion relation could be always approximated by a linear law.
The Referee is certainly right. Our presentation in the abstract and in the introduction was sloppy. The Debye law does not apply to amorphous materials in which important deviations are found. Following the suggestion of the Referee, we have rephrased parts of the abstract and the introduction to render our statements more precise.
I fully agree with the authors that in confined and/or fractal systems a linear approximation is not appropriate because some states cannot be realized and reach under such circumstance. The presented experimental data seems to be nice examples for that. Nevertheless, other numerical approaches to the low frequency density of states show that also under confinement situation becomes quadratic in the frequency dependence. See Phys. Rev. B: Condens. Matter Mater. Phys., 2010, 81, 054208. At least this paper should be cited.
The reference (Phys. Rev. B: Condens. Matter Mater. Phys., 2010, 81, 054208.) indicated by Referee 3 was already cited in the first version of our manuscript but we do agree that it deserves more elaboration. The reference mentioned, among other things, emphasizes the importance of the boundary conditions on the dynamics of the VDOS under confinement. Despite our data reported in Fig.3(a) are probably not enough to make a definitive statement nor conduct a complete analysis, the behaviour of the DOS seems compatible with the case of soft confinement reported in the reference mentioned above. The peak found in bulk ice around ∼ 6 meV is gradually shifted to lower frequency and it broadens upon confinement. This seems compatible with our structure in which the GOM membrane, as a soft material, does not induce hard-type confinement. Let us emphasize also that our setup is slightly different from the one considered in the reference above. Our work is not only focused on amorphous materials but also crystalline ones under confinement. In any case, the paper mentioned by the Referee is definitely important in relation to our analysis and it is now discussed in the revised manuscript.
In figures 2,3 and 4 at the x-axis omega is used. General in physics omega is used as a symbol for the frequency (see also equ. 2). In these figures energy values are given. This is not consistent.
We are sorry for this inconsistency. It has been fixed in the revised version of the manuscript. We now usehω (whereh is the Planck constant) instead of E. We thank the Referee for pointing this out.
Minor comment: In figure 7 it should be THz. Temperature differences should be given in K not in C in accordance with international regulations.
Thanks for pointing this out. We have corrected these inaccuracies in the revised manuscript. | 7,159.6 | 2021-08-17T00:00:00.000 | [
"Materials Science"
] |
EnzymeNet: residual neural networks model for Enzyme Commission number prediction
Abstract Motivation Enzymes are key targets to biosynthesize functional substances in metabolic engineering. Therefore, various machine learning models have been developed to predict Enzyme Commission (EC) numbers, one of the enzyme annotations. However, the previously reported models might predict the sequences with numerous consecutive identical amino acids, which are found within unannotated sequences, as enzymes. Results Here, we propose EnzymeNet for prediction of complete EC numbers using residual neural networks. EnzymeNet can exclude the exceptional sequences described above. Several EnzymeNet models were built and optimized to explore the best conditions for removing such sequences. As a result, the models exhibited higher prediction accuracy with macro F1 score up to 0.850 than previously reported models. Moreover, even the enzyme sequences with low similarity to training data, which were difficult to predict using the reported models, could be predicted extensively using EnzymeNet models. The robustness of EnzymeNet models will lead to discover novel enzymes for biosynthesis of functional compounds using microorganisms. Availability and implementation The source code of EnzymeNet models is freely available at https://github.com/nwatanbe/enzymenet.
Introduction
Enzymes are used with a wide range of industrial chemicals, pharmaceuticals, antibiotics, and food additives, and are essential mediators of metabolic pathways to biosynthesize functional substances using engineered microbes (Choi et al. 2015, Basso andSerban 2019).However, microbial metabolic pathways and enzymes are not necessarily optimal.Novel enzyme discovery is required to increase the production of target compounds (Otte andHauer 2015, Ali et al. 2020).Moreover, the number of unannotated protein sequences is explosively increasing (Bateman et al. 2021).Therefore, a valid computational method to predict enzyme functions with high accuracy from sequence information is needed to help to discover novel enzymes within a huge number of unannotated sequences in the future.
Of these methods, one of the most basic approaches is machine learning which can learn various data and is suitable for mass predictions.Machine learning methods have been applied to predict various protein annotations (Almagro Armenteros et al. 2017, Kulmanov andHoehndorf 2020).Then, several studies have been reported to predict Enzyme Commission (EC) numbers, one of the enzyme annotations (Dalkiran et al. 2018, Nursimulu et al. 2018, Ryu et al. 2019, Shi et al. 2023, Yu et al. 2023).EC numbers consist of four digits and are used to classify enzymes based on enzymatic reaction type.Yu et al. have recently proposed a contrastive learning based model, CLEAN, and the model can classify EC numbers and predict multiple functions for each sequence.Shi et al. have also developed a model, ECRECer, using multiple embedding representations extracted from protein sequences and a bidirectional gated recurrent unit neural network with an attention mechanism.The model can also predict non-enzymes in addition to the same features as CLEAN.
However, these studies have not discussed the evaluation of the sequences with numerous consecutive identical amino acids observed within unannotated sequences.The proteins with numerous consecutive identical amino acids might not have protein activity.Therefore, prediction models need to exclude the exceptional sequences from enzyme candidates for comprehensive enzyme annotation prediction.Without the operation, the sequences might be regarded as enzymes by prediction models and might remain in enzyme candidates in mass prediction of protein sequences.Moreover, the existing prediction models might not completely correctly predict more than a few thousand EC numbers, and a more valid model for EC number prediction is required to annotate enzyme features for a vast number of unannotated protein sequences.
Here, EnzymeNet models using residual neural networks (ResNet) were developed to predict EC numbers while removing proteins except for enzymes from sequence candidates used in enzymatic reaction prediction (He et al. 2016).ResNet which includes multiple convolutional neural network (CNN) layers has been demonstrated in protein structure and ligand-binding site predictions (Hanson et al. 2019, Shi et al. 2020) and can address vanishing gradient problem occurring in deep learning models with deeper layers.Moreover, several CNN models built from the image-like features which were transformed to one-hot encoding from sequence information have been demonstrated in various enzyme annotation predictions (Ryu et al. 2019, Kulmanov andHoehndorf 2020).Enzyme sequence information might consist of structural information because several reports enabled to predict protein structures from sequence information using deep learning (Baek et al. 2021, Tunyasuvunakool et al. 2021).Therefore, EnzymeNet models were built using ResNet which can learn sequence data while capturing extensive enzyme features.
The EnzymeNet models predict EC numbers in two steps: (i) EC number first digit or negative and (ii) complete EC number prediction (Fig. 1).Moreover, the models exclude the exceptional sequences with numerous consecutive identical amino acids in the first step.Therefore, the optimized condition of EnzymeNet models to remove such sequences was determined using several different negative datasets.The models were more accurate for extensive enzyme sequences with lower similarity to our training data than four previously reported models based on machine learning and sequence similarity methods (Dalkiran et al. 2018, Nursimulu et al. 2018, Ryu et al. 2019, Sanderson et al. 2023).The experimental evaluation of enzyme candidates predicted by EnzymeNet in the future can help to discover novel enzymes.
Data for prediction of EC number first digits and negative sequences
To build positive data, 5 610 630 enzyme sequences for each EC class were collected from Kyoto Encyclopedia of Genes and Genomes (KEGG) GENES (Kanehisa and Goto 2000) released on July 2019 by KEGG FTP Academic Subscription.KEGG data are freely available for academic users.The enzyme sequences are also registered in the other databases (Supplementary Fig. S1) and the part of the sequences have been annotated by KEGG (Kanehisa et al. 2002).There are seven first digit EC number classes referred to as EC 1 to EC 7. EC 7 enzymes were not included in any of the data because too few enzymes were registered in KEGG.Enzyme sequences that were duplicated, with multiple EC numbers, or included non-canonical amino acids were removed and the length of amino acid residues is limited from 100 to 1000.
To keep data balanced, highly similar enzyme sequences were omitted by clustering at 90% identity using CD-HIT (Li and Godzik 2006) and then only a single enzyme sequence from each cluster was included.More than 80% of the EC numbers consisted of fewer than 800 sequences.Therefore, similar sequences were removed by decreasing the identity until the number of sequences within each EC number was fewer than 800.As a result, 1 049 807 unique enzyme sequences were used to build and evaluate EnzymeNet models.
To remove non-enzyme protein sequences and the exceptional sequences in the first prediction of EnzymeNet, negative data were built in three ways as follows: (i) Non-enzyme, (ii) random substitution, and (iii) consecutive substitution.Three random substitution and three consecutive substitution datasets were built to optimize the models for the first prediction.
Non-enzyme
Proteins except for enzyme sequences which are freely available were collected from Swiss-Prot released in 2021 (Bateman et al. 2021).The sequences that were duplicated or included non-canonical amino acids were removed and the length of amino acid residues was limited from 100 to 1000.Only a single enzyme sequence from each cluster was used after clustering at 90% identity to remove the sequence redundancy in the data.As a result, 142 378 non-enzyme sequences were used.
Random substitution
About 16 964 sequences were randomly extracted from the enzyme sequences included in the positive data.For each sequence, 20% of the random amino acids of the sequence were substituted with the other amino acids (Supplementary Fig. S2a).The position and type of the substituted amino acids were randomly selected.This strategy was inspired by masked language models, such as Bidirectional Encoder Representations from Transformers (BERT).The BERT is pretrained by randomly masking some of the tokens from input data, and the objective of the training is to predict the original vocabulary of the masked word based only on its context (Devlin et al. 2019).Therefore, to make EnzymeNet models understand original amino acid patterns of enzymes and the other sequence patterns, the artificial random substitution sequences were built.Moreover, 10% and 40% random substitution datasets were generated to evaluate the effect of the rate of substituted amino acids on this prediction.
Consecutive substitution
About 16 964 sequences were randomly extracted from positive data.For each sequence, 50%�80% of the amino acids in the sequence were substituted with consecutive identical amino acids (Supplementary Fig. S2b).The position, type, and rate of the substituted amino acids were randomly selected.Previously reported models were not evaluated using such sequences, which are found within unannotated sequences.Therefore, the current models enabled to remove the sequences.Moreover, 1%�25% and 26%�49% consecutive substitution datasets were generated to explore the relationship between prediction accuracy and the rate of substituted amino acids.All positive and negative data were merged.All data were randomly split into training, validation, and test data at an approximate ratio of 8:1:1 (Table 1).Training, validation, and test data were used for building models, evaluating all models in training and evaluating all models after training, respectively.Most of the enzyme sequences in positive data are also registered in National Center for Biotechnology Information and UniProt (Supplementary Fig. S1).Common test data consisted of the test data of positive data and nonenzymes for prediction of EC number first digits, and the data for the same number of artificial negative test data extracted from each condition of building artificial negative data (Supplementary Table S1).The common test data were used to evaluate six EnzymeNet models and to determine the optimal models and to compare the models to previously reported models in the first prediction.
Data for prediction of complete EC numbers
Positive data for EC number first digit prediction were separated by each EC number fourth digit.Highly similar enzyme sequences were omitted by clustering at 90% identity to decrease sequence redundancy.Moreover, the sequences with EC numbers that contained much fewer sequences in each EC number fourth digit were removed.The data were randomly split into training, validation, and test data at an approximate ratio of 8:1:1 (Table 1).
Model construction
EnzymeNet models which were built using ResNet50v2 (He et al. 2016) consisted of two predictions: (i) prediction of EC number first digits and negative and (ii) prediction of complete EC numbers.The model structure in the first prediction is shown in Fig. 2. In Embedding Postprocessor layer (Lan et al. 2020), each amino acid included in each sequence was transformed into tokens which could be treated by deep learning.Zero padding was used for the sequences with less than 1024 residues.The tokens were transformed to (n, 1024, 128) feature maps.The positional information of each amino acid which is important for protein activity was added to the feature maps by Positional Embedding, and (n, 1024, 1024) feature maps were outputted.Next, in ConvertImg layer, feature maps were transformed to image-like (n, 256, 256, 3) feature maps, which were passed through ResNet50v2.Several studies have reported various biological predictions using CNN which has been often used in image recognition (Ryu et al. 2019, Kulmanov andHoehndorf 2020).ResNet can address vanishing gradient problem occurring in deep learning models with deeper CNN layers.Therefore, ResNet which was an expanded CNN model was used to build EC number prediction models.From the final layer, the scores for seven classes were then outputted.Moreover, six models referred to as EnzymeNet version 01 to 06 (v_01 to v_06) models were built from the same positive and non-enzyme datasets, and different artificial datasets obtained under different conditions of random and consecutive substitutions to explore the optimal condition in the first prediction (Table 2).
The EnzymeNet models in the second prediction were built applying transfer learning for the first step's EnzymeNet models which predicted with higher accuracy in the common test evaluation.For each model, six models for EC 1 to EC 6 were built.When EnzymeNet models predict EC numbers for a sequence, EC number first digit is predicted by the first prediction model, and then complete EC number is predicted by one of the six models selected from the first results (Fig. 1).If a result of the first prediction is negative, the second prediction is not performed.The all models in this study were built using TensorFlow (Abadi et al. 2016).A categorical crossentropy loss function was used to train the models, and trainable parameters were updated for each batch.
Model evaluation
EnzymeNet models were evaluated in three ways as follows.First, EnzymeNet v_01 to v_06 models for EC number first digit and negative predictions were evaluated using test data.All models were also evaluated using common test data to determine the optimized models for these predictions.Second, for only complete EC number prediction, the EnzymeNet models were evaluated using the test data for the second prediction.The EC 1 to EC 6 models of each EnzymeNet model EnzymeNet were evaluated using each EC fourth digit dataset in the complete EC number prediction, respectively.The evaluation was not used for evaluating the EnzymeNet models to the other EC number prediction models in the complete prediction.Then, the EnzymeNet models combining the first and the second prediction models were evaluated to confirm the ability for both predictions by Continuous Test, in which the models firstly predicted EC number first digits or negative using the second prediction's test data, and predicted complete EC numbers using only correctly predicted test data in the first prediction.The incorrect test samples in the first prediction were not predicted in the next prediction.Accuracy, F 1 score, Precision, Recall, and Matthews correlation coefficient (MCC) were used for the evaluations.The detailed information of evaluation parameters is shown in Supplementary Data.
Moreover, EnzymeNet models were compared with four EC number prediction models, DeepEC (Ryu et al. 2019), DETECT v2 (Nursimulu et al. 2018), ECPred (Dalkiran et al. 2018), and ProteInfer (Sanderson et al. 2023) using same test datasets.All models were evaluated using three ways.First, for simple EC number prediction, the common test data for EC number first digit prediction was used in the evaluation of EC number first digit and negative predictions, while the test data for complete EC number prediction was used in the evaluation of complete EC number prediction.In the complete prediction, the EnzymeNet models were evaluated in the same way as the Continuous Test because of the two-step prediction.The other models which had already been trained in each report were used.DeepEC, ECPred, and Proteinfer were based on machine learning methods while DETECT v2 was based on sequence similarity strategy (Camacho et al. 2009).DETECT v2 and ProteInfer could not predict negative samples and therefore, the test samples whose scores were not outputted by these models were regarded as negative.The test samples whose scores were not outputted by DeepEC and ECPred were regarded as incorrect because these models could predict negative samples.Moreover, EnzymeNet models were compared to two ensemble methods combined with four previous models in only complete EC number prediction.The first ensemble method (Ensemble 1) was evaluated using a majority rule.If the four models predicted EC 1.1.1.1,EC 1.1.1.2,EC 1.1.1.3,and EC 1.1.1.2for a test sample, respectively, the prediction result of the first ensemble method was EC 1.1.1.2.If the two models predicted one EC number (EC 1.1.1.1 and EC 1.1.1.1)and the other models predicted another EC number (EC 1.1.1.2and EC 1.1.1.2) for a test sample, or if all models predicted different EC numbers, the result was randomly selected.In the second ensemble method (Ensemble 2), the test samples correctly predicted by at least one of the four models were regarded as correct, while the other cases were regarded as incorrect.The performances of all models were evaluated using Accuracy, Macro F 1 score, Macro Precision, and Macro Recall.To compare the accuracy for prediction of all EC numbers in test data to all models, these values for all EC numbers were calculated using the number of EC numbers.
Second, to evaluate these models for only specific EC number prediction, these models were evaluated using two test data, which the enzyme sequences with high similarity to the training data are removed from, by lowering the sequence identity threshold using CD-HIT.One data was the enzyme and non-enzyme sequences extracted from common test data for prediction of EC number first digits and the other data was test data for prediction of complete EC numbers.Finally, since all EC numbers included in our test datasets could not be necessarily predicted by the previously reported models, all models in addition to EnzymeNet models were also compared the results of the only predictable EC numbers which were outputted from the test datasets using each model.
EC number first digit and negative predictions using EnzymeNet models
Supplementary Fig. S3 shows loss function curves for training and validation in the first prediction.The validation loss function decreased as epochs proceed.The results indicated that all EnzymeNet models for the prediction do not overfit.Test results are shown in Supplementary Table S2.The model performances of all versions increased as epochs proceeded.The models were built using 1500, 1300, 1400, 1500, 1400 and 1500 epochs, respectively, where the MCCs were highest and the other values were relatively higher.Prediction accuracies showed no significant differences among the models.
Next, the models were evaluated using common test data (Table 3 and Supplementary Table S3).The results of the overall first step prediction maintained constant high accuracy among EnzymeNet models, while the prediction results of the negative samples varied.EnzymeNet v_03 model was more accurate for negative sequences than the other models.EnzymeNet v_01 model which learned only non-enzyme dataset as negative data predicted artificial negative samples with much lower accuracy.The EnzymeNet v_03 and v_05 models were regarded as optimized models in the first step prediction because the models more correctly predicted both all test sequences and artificial sequences.Supplementary Table S4 shows the common test results using the two models for each class.EnzymeNet v_06 model was not selected as an optimized model because the model learned the more different artificial sequences from original enzyme sequences and more easily classified the sequences than the other models.All models predicted consecutive substitution samples with higher accuracy than random substitution samples.
Complete EC number prediction using EnzymeNet models
Supplementary Fig. S4 shows loss function curves for training and validation in the second prediction using EnzymeNet v_05 models.The results of EnzymeNet v_03 models were similar to that of EnzymeNet v_05 models.Unlike the first prediction, the validation loss functions of s models for EC 1 to EC 6 insufficiently decreased in comparison to the training loss.However, all models were not regarded as overfitting, because all validation loss functions did not significantly increase.The EnzymeNet v_05 models for EC 1 to EC 6 were built using 400, 500, 400, 400, 90, and 300 epochs, respectively (Supplementary Table S5).On the other hand, the EnzymeNet v_03 models were built using 500, 500, 400, 350, 90, and 450 epochs, respectively.Both models also predicted test data with high accuracy in the second prediction although the accuracies were lower than that of EC number first digit prediction.
Continuous Test results of EC number first digits and complete EC numbers for the models are shown in Fig. 3.The data of complete EC number prediction was used in this evaluation.The incorrect test samples in the first predictions were not performed in the next predictions.As a result, the prediction accuracies were slightly lower than in only complete EC number prediction but remained high.As with the first prediction, EnzymeNet v_03 models were more accurate than EnzymeNet v_05 models.
Comparative evaluation of EC number prediction
As a benchmark, the EnzymeNet models were compared with DeepEC, DETECT v2, ECPred, and ProteInfer using common test data, and test data for prediction of complete EC numbers.Figure 4a and Supplementary Fig. S5 and Table S6 show the comparative results of common test data.Both EnzymeNet models exhibited higher test prediction accuracy and higher both Macro Precision and Macro Recall.The accuracies of DeepEC and DETECT v2 were lower than those of other models and the Macro Recalls were lower than the Macro Precisions.Moreover, the ability to classify non-enzyme and random substitution sequences using EnzymeNet models was lower than that of DETECT v2 and ProteInfer (Supplementary Fig. S5 and Table S6).Random substitution sequences tended to be more incorrectly predicted than consecutive substitution sequences.Both EnzymeNet models predicted correctly more consecutive substitution sequences than the other models.
Next, the prediction results of complete EC number prediction are shown in Fig. 4b.EnzymeNet models were also compared to two ensemble methods as described in Section 2.3.Both EnzymeNet models showed higher prediction accuracy EnzymeNet with Macro F 1 scores up to 0.850 than the other models and ensemble methods.The conditions of negative artificial datasets in EnzymeNet v03 models were more suitable for EC number prediction than EnzymeNet v_05 model because of the higher accuracies of all evaluations.On the other hand, the accuracies of the other models in the second prediction decreased much more than those of the first prediction.The test enzyme sequences included 2591 EC numbers and some EC numbers were easy to predict using the previously reported models.DETECT v2 predicted 468 EC numbers with higher accuracy than EnzymeNet models and the other models.For example, the reactions involved in polymer, protein, RNA, and DNA, which EnzymeNet models predicted with low accuracy, were also included.However, the F 1 scores of 1953 of 2591 EC numbers for EnzymeNet v_03 or v_05 models were higher.All EC number results of the benchmark evaluation are shown in Supplementary Sheet.
Figure 5 shows the results of the two datasets, which similar sequences to the training datasets are removed from, in EC first digit and complete EC number predictions.The lower sequence identity threshold was, the more difficult the predictions were not depending on prediction models.In the first prediction, both EnzymeNet models predicted more correctly in 70 and 80 sequence identity thresholds.However, ECPred was the most accurate between all models.On the other hand, both EnzymeNet models were more accurate in the complete EC number prediction not depending on the value of sequence identity thresholds.All models in these evaluations showed the decreases in prediction accuracy as more similarity sequences were removed.Moreover, Fig. 6 shows the results of the predictable EC numbers outputted by each model using the test data of the second prediction to fairly compare the results.This is because the predictable EC numbers in the other models were not shown.DETECT v2 and EnzymeNet v_03 models are almost the same high accuracy although the number of predictable EC numbers in DETECT v2 was small.Finally, Supplementary Table S7 shows the Macro F 1 score results of the EC numbers which could be more correctly predicted in EnzymeNet models and other models using F 1 score of each EC number as a threshold.The previously reported models exhibited higher prediction accuracies in the limited EC number than EnzymeNet models.
Discussion
We present EnzymeNet models to predict complete EC number for each amino acid sequence in two-step prediction while removing non-enzyme proteins and exceptional sequences.
To discover novel enzymes within a vast number of unannotated protein sequences, enzyme prediction models for enzyme functions need to efficiently learn the patterns of amino acids for each enzyme sequence.Therefore, EnzymeNet models were built to enable to remove the sequences with numerous consecutive identical amino acids, which are found within unannotated sequences, as well as non-enzyme proteins.The conventional EC number prediction models have not considered such sequences.Moreover, EnzymeNet models deeply learned various patterns of amino acid sequences by adding the random substitution sequences, which were similar to the original enzymes, to the datasets.To characterize more enzyme features, Positional Embedding layer which was included in relatively new models such as Transformer and Generative Pre-Training models was used in addition to ResNet structure (Vaswani et al. 2017, Radford et al. 2018).Therefore, EnzymeNet models learned position features of each amino acid of a protein which are important for protein activity, secondary structure, and protein-ligand interaction.
First, the methods of generating artificial negative sequences in the first prediction were optimized.All EnzymeNet models in the evaluations of test and common test data maintained high prediction accuracy.However, the prediction results of artificial negative sequences using common test data were significantly different depending on the models.EnzymeNet v_01 model which did not learn the artificial sequences did not predict almost the sequences.This is because machine learning models generally have difficulty predicting the data which is so different from training data.
Considering the results of the positive and negative data, two complete EC number prediction models were built based on EnzymeNet v_03 and v_05 models, which exhibited higher prediction accuracy of the overall sequences.Moreover, the artificial negative condition of EnzymeNet v_03 model is suitable for the prediction because the model predicted the consecutive substitution sequences constructed by all conditions with higher accuracy.The results of EnzymeNet v_03 models in the complete EC number prediction and the Continuous Test predictions were almost as accurate as those of EnzymeNet v_05 models.This indicates that the conditions of generating artificial negative samples do not have a significant influence on the overall prediction accuracy in both predictions.Moreover, the accuracies of EnzymeNet v_03 and v_05 models did not depend on the number of training data and the number of similar sequences (Supplementary Figs S6 and S7).These results show that the EnzymeNet models do not necessarily predict only easy EC numbers with high accuracy.
Next, the EnzymeNet models were compared with four previously reported EC number prediction models (Fig. 4a and Supplementary Fig. S5 and Table S6).In the prediction of common test data, EnzymeNet models exhibited higher prediction accuracy.Furthermore, the previously reported models could not predict the sequences with consecutive identical amino acids which are apparently non-enzyme.The results and common test results of EnzymeNet v_01 model The number of predictable EC numbers in EnzymeNet models, DeepEC, DETECT v2, ECPred, and ProteInfer in fact is 2591, 4669, 764, 732, and3411, respectively (Ryu et al. 2019;Sanderson et al. 2023).
EnzymeNet
indicate that prediction models cannot predict the exceptional sequences without learning them.However, the EnzymeNet models could not classify non-enzyme and random substitution sequences with the highest accuracy.Even though the number of non-enzyme sequences is clearly larger than that of enzyme sequences, EnzymeNet models learned more enzymes.The number of no-enzyme training data in ProteInfer was about 200 000 and was larger than that of our models.This is why the accuracies of the models for nonenzyme sequences were lower.
Moreover, EnzymeNet models had difficulty classifying the random substitution sequences because the models learned both random substitution sequences and the pre-substituted enzyme sequences (the enzyme sequences before substituting) which were similar to each other.Since the other models except for DeepEC were built from fewer enzyme sequences than EnzymeNet models, it is assumed that the other models did not learn the pre-substituted enzymes and were able to predict them without confusion.However, to improve the ability of our models to predict non-enzyme and random substitution sequences, the optimization of these datasets is required.
For complete EC number prediction, EnzymeNet models showed much higher prediction accuracy than the other models and the ensemble methods (Figs 4b and 6).Ensemble 1 using a majority rule of the four models was not improved, and Ensemble 2, combining the prediction ability of the models, improved prediction accuracy, yet the accuracy was lower than that of the EnzymeNet models.The results suggest that EnzymeNet models can predict the EC numbers which the other previously reported models have difficulty predicting.On the other hand, DeepEC and ProteInfer correctly classified positive enzymes because the Macro Precisions were much higher than Macro Recalls.Therefore, the putative enzymes which are predicted as positive by these models can be assigned new annotations.Moreover, the test enzyme sequences included in 271 EC number seem to be a core set that all models can predict with high accuracy according to Supplementary Table S7.For the prediction of this core set, DETECT v2 and the other reported models are superior to EnzymeNet models although EnzymeNet can predict extensive EC numbers combining the core set and the other enzyme sequences.
In addition, CLEAN (Yu et al. 2023), which has recently been developed, can predict single or multiple EC numbers for each sequence, although it cannot predict non-enzymes.Hence, EnzymeNet models were only compared to CLEAN in complete EC number prediction using the same test data.The test samples were regarded as correct in CLEAN if one of the multiple EC numbers output by CLEAN was successfully predicted.The test results are shown in Supplementary Fig. S8.
As a result, EnzymeNet models exhibited higher accuracy in complete EC prediction than CLEAN.However, the accuracy of CLEAN is higher in comparison to the other models.EnzymeNet models can predict a single enzyme function for each sequence.Therefore, the accuracy of CLEAN might have been lower because the prediction targets of CLEAN are a little different from that of EnzymeNet models.Considering the ability of CLEAN, which cannot predict non-enzyme, EnzymeNet firstly removes non-enzymes and exceptional sequences and predicts single enzyme functions when predicting enzyme functions for unannotated protein sequences.Next, CLEAN predicts detailed enzyme functions for the candidate enzyme sequences output from EnzymeNet model prediction.Therefore, candidate enzyme sequences for synthesis of target functional compounds can be selected using EnzymeNet and CLEAN.
The other EC number prediction models (Zou et al. 2019, Khan et al. 2021) were built from various enzyme features while EnzymeNet models, DeepEC, and ProteInfer need so simple features, namely, one-hot encoding, token, and positional embedding.These simple feature extractions do not have a large effect on prediction results, which depend on only amino acid pattern information.Moreover, the number of training data in DeepEC was almost as large as that of EnzymeNet models.This suggests that prediction accuracy does not necessarily rely on the number of training data for each model.Building optimized model structure to match prediction target is required.
Furthermore, EnzymeNet and the four models are evaluated using the datasets which similar sequences to the training datasets are removed from.EnzymeNet models also exhibited higher prediction accuracy for difficult enzyme sequences in EC number complete prediction even though the models did not show the highest accuracy in the first prediction.The results suggest EnzymeNet model v_03 model can more correctly predict EC numbers for more extensive sequences in comparison to reported models.However, EnzymeNet models cannot correctly predict some enzymes with lower similarity to training data and some EC numbers which DETECT v2 could predict with high accuracy.To improve the abilities of EnzymeNet models for the difficult positive, non-enzyme, and random substitution predictions further, updated methods to build training data and model structure are needed.The common decreases (Fig. 5) in prediction accuracy of all machine learning models except for DETECT v2 as lowering the threshold indicates that the evaluation of the difficult enzymes in the predictions may be insufficient.
In summary, EnzymeNet models can exclude the exceptional sequences from the sequence candidates in addition to the EC number prediction, which were more accurate for extensive enzyme sequences than the reported models.Moreover, up to 4000 sequences are predicted using our models in about 10 min at one time.Therefore, EnzymeNet models enable to apply to find available enzymes from metagenomics registered in sequences databases (Agarwala et al. 2018, Mitchell et al. 2020).In this case, to quickly predict EC numbers for a huge number of protein sequences, decreasing the training sequences included in some EC numbers whose number of sequences is larger is required.Moreover, for the putative enzyme sequences predicted using EnzymeNet models, the Substrate-Enzyme-Product models developed in our previous study (Watanabe et al. 2022) can predict corresponding substrates and products, namely, detailed enzymatic reaction annotations.The robustness of EnzymeNet models will lead to predict enzyme annotations related to enzymatic reactions for mass unannotated protein sequences and discover novel enzymes for biosynthesis of functional compounds using microorganisms.
Figure 1 .2
Figure 1.Scheme of two-step EC number prediction using EnzymeNet.
Figure 3 .
Figure 3. Continuous Test results of (a) EnzymeNet v_03 models and (b) EnzymeNet v_05 models.In the Continuous Test, the models firstly predicted EC number first digits or negative using the second prediction's test data and predicted complete EC numbers using only correctly predicted test data in the first prediction.
Figure 4 .6
Figure 4. Comparative results of (a) EC number first digit and negative predictions and (b) complete EC number prediction.
Figure 5 .
Figure 5. Macro F 1 scores of (a) EC number first digit prediction and (b) complete EC number prediction using the common test and test sequences removing similar sequences to our training enzyme sequences for each sequence identity threshold using CD-HIT.
Figure 6 .
Figure 6.Macro F 1 scores of the predictable EC numbers which each model outputted using the test data of complete EC number prediction.The number of predictable EC numbers in EnzymeNet models, DeepEC, DETECT v2, ECPred, and ProteInfer in fact is 2591, 4669, 764, 732, and 3411, respectively (Ryu et al. 2019; Sanderson et al. 2023).
Figure 2. EnzymeNet structure in the first prediction using ResNet50v2.
Table 2 .
Type of artificial negative datasets used in six EnzymeNet versions in the first prediction.
Table 3 .
Common test results of the first step using six EnzymeNet models. | 7,558.6 | 2023-11-24T00:00:00.000 | [
"Computer Science",
"Biology"
] |
On-Demand Generation of Entangled Photon Pairs in the Telecom C-Band with InAs Quantum Dots
Entangled photons are an integral part in quantum optics experiments and a key resource in quantum imaging, quantum communication, and photonic quantum information processing. Making this resource available on-demand has been an ongoing scientific challenge with enormous progress in recent years. Of particular interest is the potential to transmit quantum information over long distances, making photons the only reliable flying qubit. Entangled photons at the telecom C-band could be directly launched into single-mode optical fibers, enabling worldwide quantum communication via existing telecommunication infrastructure. However, the on-demand generation of entangled photons at this desired wavelength window has been elusive. Here, we show a photon pair generation efficiency of 69.9 ± 3.6% in the telecom C-band by an InAs/GaAs semiconductor quantum dot on a metamorphic buffer layer. Using a robust phonon-assisted two-photon excitation scheme we measure a maximum concurrence of 91.4 ± 3.8% and a peak fidelity to the Φ+ state of 95.2 ± 1.1%, verifying on-demand generation of strongly entangled photon pairs and marking an important milestone for interfacing quantum light sources with our classical fiber networks.
List of Figures
S7 Laser state tomography measured for vertical input polarization into our analysis setup, (a) real part and (b) imaginary part of the resulting density matrix.S11 S8 Density matrices of the raw data (a, real part) and (b, imaginary part) and of the compensated data (c, real part) and (d, imaginary part). . . . . . . . S14 S9 Fidelities to Φ + for different measurement bases. The open circles represent the data, the bold solid lines correspond to fits to the data. (a) Fidelity of Φ meas to Φ + with fit (blue) and Φ meas with fit (turquoise) (b) Fidelity of Φ comp to Φ + with fit (green open circles) andΦ meas to Φ max (gray stars). . . . . . . S15 S10 Concurrence as a function of coincidence percentage in the center peak. . . . S16
Sample growth
The sample was grown by metal-organic vapor-phase epitaxy (MOVPE) on Si-doped GaAs (001)-oriented substrates in an Aixtron 200/4 low-pressure (100 mbar) horizontal reactor with H 2 as carrier gas and trimethylgallium (TMGa), trimethylaluminium (TMAl), trimethylindium (TMIn), and arsine (AsH 3 ) as precursors. The epitaxial layer structure is given Table 1. The distributed Bragg reflector (DBR) and compositionally graded InGaAs metamorphic buffer layer (MMBL) were first grown at 670°C (calibrated wafer surface temperature) after which the growth was stopped and the temperature was reduced to 515°C for quantum dot growth. Next, a 10 s ripening step was used and the low-temperature part of the capping layer was grown. Finally, the temperature was increased to 670°C and the structure was completed with the high-temperature part of the capping layer. A three-lambda cavity is formed between the DBR and the semiconductor-air interface with the MMBL and capping layer thicknesses chosen to optimize the extraction efficiency. The lattice relaxation of the MMBL layer allows for the growth of large QDs with an emission wavelength of around 1550 nm, which is significantly longer than what can be obtained from the coherent growth on the GaAs substrate (typically <1300 nm) 1 . In our previous work, using similar growth conditions, we estimated the QD density to be in the 1 × 10 7 cm −2 range 2 .
Rabi oscillations of QD1
It has been reported in several articles in the literature that additional white light (or in our case above-band laser) can help to stabilize the charge environment 3,4 by saturating charges in the vicinity of the quantum dot. Unsaturated charges can in turn lead to fluctuating electric fields resulting in the quantum dot jumping in and out of resonance again and not yielding highest occupation probability in the π-pulse, visible in the power-dependence in The population of the excited state is determined via the following fit to the data 5 : Here, c 2 (t) is the probability amplitude to find the quantum system in the excited state.
The following relations apply: . Ω 0 is the Rabi frequency and Γ 1 corresponds to the decay rate of the excited state. Ω 0 = | − eE 0 µ 12 /h| is the Rabi frequency and E 0 the electrical field. The dipole matrix element µ 12 is proportional to the excitation power Ω 0 ∝ E 0 ∝ √ P . This allows us to express the population of the excited S7 state depending on the pulse area via: ∞ −∞ E 0 (t)dt is the excitation pulse envelope. Via fits we obtain a population of 82.6 ± 1.6 % for the biexciton and 84.6 ± 2.7 % for the exciton. By multiplying these probabilities, we obtain a photon pair generation efficiency of 69.9 ± 3.6 %. Another way of determining the photon pair generation efficiency is discussed in the supplementary material of reference 6 .
Here, the authors suggest calculating the pair generation efficiency by comparing the peak areas of the center peak A center in co-polarized cross correlation measurements (e.g. HH) and the corresponding side peak areas A side . By taking the ratio of both quantities, the result is no longer dependent on the optical path efficiencies for biexciton and exciton. The pair generation efficiency p is related to the peak areas as follows: Here, theĀ indicates that we average over all 6 co-polarized measurements. An excited-state preparation and radiation probability of 66.4 ± 1.8 % is found, which is in good agreement S8 with the value obtained via fitting the Rabi oscillations.
Rabi oscillations of QD2
Power-dependent measurements for QD2 are shown in Fig. S4. During the measurement, we suffered from spectrally impure laser pulses after our pulse slicer, which did not allow us to perform pure two-photon resonant excitation. While we attempted to excite via the two-photon resonance, the effect due to the spectral profile of the laser can be described more as a two-photon excitation scheme with additional phonon contribution. As the laser pulses were not spectrally pure, there were additional spectral components partially detuned towards the phonon resonance energy. This results in the power-dependent measurement shown in Fig. S4 (a) that is not displaying the damped Rabi oscillations expected for two-photon resonant excitation. The additional and unwanted spectral components were visible in the spectrum and much broader than the bandwidth of our notch filters. This again highlights the robustness of the phonon-assisted scheme that is not relying on precise overlap of a laser spectrum with the quantum dot resonance. To be able to estimate the state population, we subtract the phonon background (see Fig. 2 (c) in the main text), obtaining the data points shown in Fig. S4 (b). A fit to the data yields a state population of 80.1 ± 9.5 % in the π-pulse.
Lifetimes of QD2
We examine the influence of the excitation scheme on the radiative lifetimes of the biexciton of QD2. A decay time measurement under above-band excitation with 2 ps pulses at 1200 nm is shown in Fig. S5 (a). We find a decay time of τ above = 794±3 ps. The used fitting function is similar to the one in the supplementary material of Ref. 7 for the charged exciton. We would like to note that this is not the biexciton lifetime, as discussed in the appendix of Ref. 8 .
In comparison, by using the phonon-assisted two-photon resonance excitation scheme the
Exciton autocorrelation
In Figure S6 we show the autocorrelation measurements performed on the exciton of QD1 under two-photon resonant excitation in (a) and QD2 under phonon-assisted two-photon excitation in (b). We determine g (2) TPE (0) = 0.07 ± 0.004 for two-photon excitation and g (2) Phonon (0) = 0.068 ± 0.004 for phonon-assisted excitation. To make sure our quantum state tomography setup is carefully aligned, we perform a state tomography of the excitation laser, which is coupled into the analysis setup (see main text Fig. 1(e)), without passing the cryostat. The polarization of the laser is set to vertical using a half and a quarter waveplate, which is verified with a polarimeter. We perform all 36 measurements of the quantum state tomography with the vertically polarized laser, yielding the density matrix shown in Fig. S7 (a) and (b). From the matrix, we can infer that the waveplate angles are well calibrated, since there is only a peak for the |VV VV| state. The density matrix element of the vertical states amounts to 0.99, while the absolute values of all other elements are no larger than 0.03. From the imaginary part shown in Fig. S7 (b) we can infer that our analysis setup is not introducing significant additional phases.
Two-photon quantum state reconstruction
As mentioned in the main text, we perform a transformation from our birefringent mea- The applied transformation is keeping the orthogonality of the polarizations. Furthermore, the fidelity of our state to |HH + |ṼṼ in theHṼ-basis is the same as the fidelity to |HH + |VV in the HV-coordinate system. In Fig. S9 (a) we show how the fidelity to Φ + is evolving over time for different initial states. The fidelity of the stateΦ meas in the Figure S8: Density matrices of the raw data (a, real part) and (b, imaginary part) and of the compensated data (c, real part) and (d, imaginary part).
the oscillation of the emitted quantum state between Φ + and Φ − due to the finestructure splitting. After applying the transformation to the HV-coordinate system, the maximum fidelity of our state compared to Φ + is increased to 95.2 ± 1.1 % shown in turquoise. Now an almost perfect visibility in the oscillation between the two states is achieved. Finally, we calculate the fidelity of the measured state to Φ + after applying an optimal waveplate to each individual time bin compensating for the FSS. This yields a nearly flat fidelity to Φ + , with a maximum fidelity of 95.4 %, which is shown in Fig. S9 (b) with green open circles.
In comparison, we plot the fidelity ofΦ meas to a maximally entangled state in theHṼ-basis,
S14
showing full overlap within measurement uncertainty. igure S9: Fidelities to Φ + for different measurement bases. The open circles represent the data, the bold solid lines correspond to fits to the data. (a) Fidelity ofΦ meas to Φ + with fit (blue) and Φ meas with fit (turquoise) (b) Fidelity of Φ comp to Φ + with fit (green open circles) andΦ meas to Φ max (gray stars).
Concurrence
To demonstrate that the concurrence is only decreasing over time due to an increased noise level for time delays larger than 3 ns, we add a different way of plotting the data. In Figure S10, we plot the concurrence not as a function of time but instead as a function of already detected coincidences of the center peak. To do so, we sum up all coincidences in the center peak and normalize to the total amount of coincidences. In this representation it is apparent S15 that the concurrence is only decreasing distinctly after more than 90 % of the coincidences in the center peak have been measured, emphasizing that the decrease is only related to the noise level at this point in the measurement and not dephasing effects. | 2,643.2 | 2021-07-15T00:00:00.000 | [
"Physics"
] |
Giant Enhancement of Electron–Phonon Coupling in Dimensionality‐Controlled SrRuO3 Heterostructures
Abstract Electrons in crystals interact closely with quantized lattice degree of freedom, determining fundamental electrodynamic behaviors and versatile correlated functionalities. However, the strength of the electron–phonon interaction is so far determined as an intrinsic value of a given material, restricting the development of potential electronic and phononic applications employing the tunable coupling strength. Here, it is demonstrated that the electron–phonon coupling in SrRuO3 can be largely controlled by multiple intuitive tuning knobs available in synthetic crystals. The coupling strength of quasi‐2D SrRuO3 is enhanced by ≈300‐fold compared with that of bulk SrRuO3. This enormous enhancement is attributed to the non‐local nature of the electron–phonon coupling within the well‐defined synthetic atomic network, which becomes dominant in the limit of the 2D electronic state. These results provide valuable opportunities for engineering the electron–phonon coupling, leading to a deeper understanding of the strongly coupled charge and lattice dynamics in quantum materials.
S3. Parameters used for the two-temperature model (TTM) analysis
In solving the so-called rate equations introduced as Eq. 1 and 2 in the main text, several parameters should be pre-determined. Among them is the electron heat capacity e of the SrRuO3 (SRO) layer. e of the SrTiO3 (STO) layer is ignored because of its insulating nature, and that for SRO is taken into account to be varied depending on the SRO thickness. We perform the density functional theory (DFT) calculation for the SRO system, and estimate e from the density of states at the Fermi level for the given SRO thickness as shown in Fig.S3(a). Our DFT calculation indicates that the SRO layer becomes insulating at the 2 unit-cell (uc) thickness. However, the [2|6]10 SRO/STO superlattice (SL) exhibits a metallic character at room temperature 4 . We therefore take e for the 2 uc SRO layer from a linear extrapolation of the thickness-dependent e. By the way, we use the bulk value for the SRO films thicker than 10 uc.
For the thermal conductivity , although of the SRO layer is taken as a fit parameter in our analysis as demonstrated in Fig. 2c in the main text, we consider that of the STO layer to be pre-determined based on the Boltzmann's transport model given as κ SRO = κ bulk 1 + (2β l MFP )/x , where is a thicknessdependent parameter. Note that this prediction works well in describing the thickness-dependent thermal conductivity of the SRO layer as shown in Fig. 2(c). For the STO layer, the bulk thermal conductivity ( bulk ) and phonon mean free path are 11 Wm -3 K -1 and 2.35 nm 5,6 , respectively, and Fig. S3(b) shows an effective thermal conductivity ( eff ) of the STO layer which is taken as a pre-determined parameter in the simulation. In our previous research 4 , our DFT calculation within the GGA+U scheme explains well the metal-insulator transition in SRO/STO SL consistently with experimental observations. Therefore, we believe that our DFT calculation can provide reasonable values of the density of states or the electron specific heat for ruthenates, at least to some extent.
To solve the rate equations (1) and (2) introduced in the main text, we adopt a finite difference method.
As depicted in Fig. S4, Eq. S1 and S2 are discretized by using the Euler method and the box method as Here, a spatial grid size (a) is set to be 0.4 nm, and each grid node is indexed by a subscript i. A temporal grid size (t) is set to be 10 fs, and a temporal evolution is indicated by a superscript j. We consider also a thermal boundary conductance which plays an important role in a thermal diffusion in the superlattice system. At the interface between SRO and STO, a boundary condition is given by the Fourier's law where q is a heat flux and is a thermal boundary conductance. At the surface, we simply choose the Neumann boundary condition. Figure S4. Schematic of the finite difference method for the two-temperature model analysis. Heat flux (q) is calculated by two adjacent grid points. We also consider the thermal boundary conductance between different thermal media.
S4. Sensitivity analysis of fitting parameters used in the two-temperature model
A transient reflectivity change is described by three different processes, namely (i) electron-phonon thermalization, (ii) thermalization between SRO-STO superlattice layers (SL thermalization), and (iii) heat diffusion from superlattice to substrate ( Fig. S5(a)). Each of these three processes is related to the electron-phonon coupling constant (Gep), the thermal boundary conductance (TBC) and the thermal conductivity of SRO (κSRO), respectively. To figure out whether these parameters can be determined reliably from our experimental results, we perform the sensitivity analysis for each parameter 7 .
Sensitivity function S of a fitting parameter is given by Here, Y is a fitted function which is the transient reflectivity in this work. And, S dictates how much the fitting function Y is influenced by the fitting parameter . Figure S5
S5. Role of the electron-phonon thermalization in the hot carrier cooling process
The transient reflectivity becomes relaxed faster as the SRO layer becomes thinner, and this is attributed to the faster cooling of hot carriers. In understanding such results, although we have considered the contribution of the electron-phonon thermalization process, one may think about a possibility of the contribution of the thermal diffusion which would work more efficiently in a thinner layer of metal 8 . Indeed, the transient reflectivity change in SrVO3/SrTiO3 SLs could be explained by considering only the thermal diffusion without the electron-phonon thermalization 9 .
In SRO/STO SLs, however, we confirm that the electron-phonon thermalization process is essential in understanding the hot electron cooling process. As shown in Fig.S6, if electron and lattice subsystems would be immediately thermalized (Gep = inf.) after the photoexcitation, although the response after 2 ps can be reasonably fitted with the contribution of the thermal diffusion, an initial fast decay after pumping cannot be explained. Indeed, RMS error which is a difference between experiment and fitting results is larger before 2 ps when we ignore the electron-phonon thermalization ( Fig. S4(b)).
S7. Pump-power-dependent hot carrier cooling time
The electron-phonon coupling constant Gep may be given differently when an initial electron temperature is extremely high 10 . In our two-temperature model analysis, however, we assume that the hot carrier temperature is not high enough to have such a nonlinear effect. In the two-temperature model, the relaxation time ep of the hot carrier temperature via the electron-phonon thermalization is given Here, Ti is an initial temperature. Therefore, provided that Gep remains the same, ep should be in proportion to the pump power as it linearly increases Te.
S8. State-filling effect
An interpretation of the reflectivity change right after the photo-excitation is complicated due to an existence of non-thermalized photo-carriers which can have contributions to polarization grating, statefilling, anisotropic distribution, and so on 12 . Among them, we figure out the state-filling effect explicitly and analyze our results by separating out such contribution.
To extract the state-filling effect, we designed two different situations having an energy relationship of pump and probe beams, namely, Epump>Eprobe and Epump<Eprobe as depicted in Fig. S9(a). When Epump>Eprobe, the probe beam can respond to a rapid relaxation of non-thermalized carriers into the band edge; as the optical transition is less allowed due to the Pauli exclusion principle, the reflectivity of probe beam shows a drastic change. In particular, such state-filling effect will disappear as nonthermalize carriers become thermalized carriers via an optical phonon scattering and a carrier-carrier scattering 13 . When Epump<Eprobe, this process will not appear.
S9. Substrate effect for electron-phonon coupling in SrRuO3 superlattices
Figure S10(a) shows transient reflectivity changes of the SRO/STO superlattices prepared on various substrates. Transient reflectivity increased right after pumping rapidly drops on a similar timescale for all the cases. After 5 ps, the slope of reflectivity change is slightly different depending on the substrate used, and it is attributed to different thermal conductivity values of substrates.
S10. Temperature-dependent electron-phonon coupling
In the electron-lattice non-equilibrium state, the heat equation is described by Here, Ce is an electron specific heat, and H is an electron-to-lattice energy transfer rate per unit volume which is given as a function of an electron temperature Te and a lattice temperature Tl. As in conventional metals, an interaction between electron and acoustic phonons is first considered for the energy transfer process. In the two-temperature model, the energy transfer rate is described in a more detail as 11 Here, ∞ is an intrinsic electron-phonon coupling constant, and is the Debye temperature.
Temperature-dependent electron-phonon coupling constant Gep(T) is simply given by the first derivative of energy change rate, U(T).
If the electron would be coupled with optical phonons, the modification should be made for Eq. (S9) about the energy change rate U(T) as Here, the optical phonon is assumed to have no dispersion near the zone center. In this case, unlike the interaction with acoustic phonons, the energy change rate exponentially decreases as temperature decreases. Figure S11 summarizes the electron-phonon coupling strength of SRO films investigated in this work together with various noble metals. For noble metals, Gep shows an increase in proportion to Ce/T. This general trend can be naturally understood as the higher electron density of states at the Fermi level gives rise to the larger el-ph coupling strength. For SRO films, however, the relationship between Gep and Ce/T are largely different; although the SRO films in the thin film limit seem to show the general trend of noble metals, the major Gep enhancement starting from the bulk SRO largely deviates from such trend. This implies that the large Gep variations observed in SRO films should be understood by considering not only the electron density of states but also the variation in the phonon contribution as discussed in the main text. | 2,298.8 | 2023-04-13T00:00:00.000 | [
"Materials Science",
"Physics"
] |
The Yang-Mills gradient flow and lattice effective action
Recently, the Yang-Mills gradient flow is found to be a useful concept not only in lattice simulations but also in continuous field theories. Since its smearing property is similar to the Wilsoninan"block spin transformation", there might be deeper connection between them. In this work, we define the"effective action"which generates configurations at a finite flow time and derive the exact differential equation to investigate the flow time dependence of the action. Then Yang-Mills gradient flow can be regarded as the flow of the effective action. We also propose the flow time dependent gradient, where the differential equation becomes similar to the renormalization group equation. We discuss a possibility to regard the time evolution of the effective action as the Wilsonian renormalization group flow.
I. INTRODUCTION
Recently, the Yang-Mills gradient flow (or the Wilson flow) [1][2][3][4] is found to be a useful concept in lattice simulations and widely applied to various issues. Since it is regarded as a continuous stout smearing of the link variables, effects coming from the lattice cut-off are reduced while the IR (or long-range) physics is kept unchanged. Moreover, the fact that the smeared link variables look closer to the smooth renormalized fields enables us to measure the topological charge and energy momentum tensor even on the lattice. Not only in lattice simulations but also in continuous field theories, the gradient flow shows an attractive property. In the 4-dimensional pure Yang-Mills theory, any correlation function in terms of the gauge fields at a finite flow time (= t) are finite without additional renormalization, once the gauge coupling is renormalized suitably [5]. The 2-dimensional O(N) non-linear sigma model has also been studied and shown its renormalizability [6,7].
Since the smearing property of the gradient flow is similar to the Wilsoninan "block spin transformation", we want to reveal deeper relation between them, or eventually construct the Wilsonian renormalization group (RG) using good properties of the gradient flow. One of the advantages of this approach is that the gradient flow is compatible with the gauge symmetry. Thus there is a possibility to carry out the Wilsonian RG transformation in a gauge symmetric way, which is usually difficult in functional RG approaches with a momentum cut-off.
As a first step to achieve the above final goal, it could be worth studying the "effective action" of the 4-dimensional lattice SU(3) pure Yang-Mills theory, which generates the configurations smeared by the Wilson flow. This effective action is defined by changing the integration variables, where S W is the Wilson plaquette action andV t [U] is the solution of the Wilson flow equation with the initial conditionV 0 [U] = U.
In our previous work [8], the t-dependence of S t has been investigated by the lattice simulation. S t is truncated so that it contains the Wilson plaquettes and the rectangular loops only and their corresponding couplings are determined by the demon method [9,10].
The result shows that the coupling of the plaquette grows while that of the rectangular tends to be negative with the flow time as the known improved actions [11][12][13][14][15][16]. However the obtained trajectory in 2-coupling theory space travels in the opposite direction to the renormalization trajectory investigated by QCD-TARO collaboration [16].
In this work, we propose a different method to study the effective action defined by Eq. (1).
We derive the exact differential equation for S t starting from the Wilson flow equation for the link variables. The solution of this proposed equation tells us the t-dependence of couplings. Unlike the previous work, we do not require implementing lattice simulations in our method. Moreover, the differential equation is just a linear inhomogeneous first-order differential equation and its solution can be obtained analytically. For an application, we give a solution in a truncated 8-coupling theory space. Note that the truncation of the action is just for a practical reason and our differential equation itself is exact without any truncation. The result agrees with that of Ref. [8] on the negativeness of the coupling of the rectangular. On the other hand, the coupling of the plaquette shows the different behavior.
In contrast to the monotonic increase observed in Ref. [8], it increases at a small t region and tends to decrease at around √ 8t ≃ 1 in our result. We find that this difference can be understood as the difference of the truncation.
We also propose an extension of the differential equation to more generic cases: it is not limited to the Wilson flow but can take any t-dependent lattice action for the gradient flow equation. For example, the "trivializing action" [2] which gives the vanishing S t at a fixed t = t 0 could be a good candidate. In this work, S t itself is taken as a concrete example of the t-dependent action, where the extended differential equation is quadratic in S t and becomes similar to the RG equation. We also give a solution in the same truncated theory space as the case of the Wilson flow. In this case, the coupling of the plaquette shows the similar behavior as explained in the previous paragraph while the other 7 couplings rapidly grow with increasing the flow time. Then we discuss a possibility to regard this time evolution as the Wilsonian RG flow.
The rest of our paper is organized as follows. First, we define the exact differential equation for the effective action and show its solution in a truncated theory space in Sect. II.
Then we extend the equation to the gradient flow with arbitrary t-dependent action in Sect. III. The case where S t itself is chosen as a concrete example of the t-dependent action is considered in Sect. III B. We discuss similarities and differences of the time evolution compared to the Wilsonian RG flow in Sect. III C. A summary is given in Sect. IV.
II. EXACT DIFFERENTIAL EQUATION FOR THE EFFECTIVE ACTION
In this section, we derive the exact differential equation for the action starting from the Wilson flow equation for the link variables. The basic idea for the derivation has been proposed in the context of the exact renormalization group equation [17,18]. In this work, we concentrate on the 4-dimensional lattice SU(3) pure Yang-Mills theory, but it is expected to be applied to various field theories. We also give a solution of the obtained equation in truncated 8-coupling theory space.
A. The effective action and its differential equation Let us define the effective action S t by the change of variables: where S W [U] is the Wilson plaquette action, Here we take t to be a dimensionless parameter. The differentiation with respect to the link and ∂ x,µ = T a ∂ a x,µ , where T a are the anti-hermitian SU(3) generators whose normalization is given by With Eq. (5) and Eq. (6), To derive the differential equation for S t , we differentiate both hand sides of Eq. (2) with respect to t, The differentiation of the delta function in Eq. (8) is evaluated as where ∂ a x,µ and∂ a x,µ denote the differentiation with respect to V µ (x) andV µ (x; t) respectively. Substituting Eq. (9) for Eq. (8), we finally obtain or equivalently, Note that this equation is independent of g 2 0 since S W ∝ 1/g 2 0 and the overall g 2 0 on the right hand side of Eq. (11) is canceled.
B. Solution in truncated theory space
We demonstrate computing S t by solving Eq. (11) in this subsection. To this end, we truncate S t , as is often the case in studies of a functional renormalization group. Note that this truncation is just for a practical reason and Eq. (11) itself is exact without any truncation. In the following, we take the truncation so that S t is in the 8-coupling theory space: where each W i is defined by the sum of the trace of the associated Wilson loops over the whole lattice, Here U(C) denotes the the ordered product of the link variables along the loop C. The Wilson plaquette is associated with C 0 . The other classes C i≥1 of Wilson loops and pairs of them are constructed by the contraction of two Wilson plaquettes with a common link variable (see Fig. 1). Hereafter we omit the V -independent term in S t . We also take the initial condition as S 0 = S W , specifically, for simplicity.
Note that the truncation is reasonable in a small t regime, since contributions from the loops which occupy larger area of the lattice is not negligible in a large t regime. In fact, the solution under the above truncation scheme are same as the exact one up to O(t 2 ). For higher orders in t, the number of the required Wilson loops grows factorially and analytic calculation becomes harder. Now let us rewrite Eq. (11) as the differential equations for β i (t). The left hand side of where the differentiation is performed at constant V .
For computing the right hand side of Eq. (11), we make use of an identity for the SU (3) generators, Some algebraic manipulations yield x,µ x,µ x,µ Comparing the coefficient of W i on the both hand sides of Eq. (11), we finally obtain where M ij is the 8 × 8 real matrix given by within our truncation scheme. Note that since the right hand side of Eq. (11) We plot these solutions as the functions of √ 8t in Fig. 2. β = 6 is taken for the initial condition. Note that β 1 (t) = M 10 γ(t) = −γ(t) and β i≥2 (t) are proportional to γ(t). Two dot-dashed lines in Fig. 2 represent β 0 (t) and β 1 (t) obtained by the numerical fit in Ref. [8] respectively, where S t is truncated so that it has the Wilson plaquettes and rectangular loops only.
From Fig. 2, the two approaches agree on the negativeness of β 1 (t) at t > 0, which has been already reported by Ref. [8], however the flow time dependence is clearly different.
This can be understood as the difference of the truncation of S t , since we obtain is taken to be 6 as same as that in Ref. [8]. The solid lines represent β 0 (t) and β 1 (t) = −γ(t) in Eq. (28), the dashed lines represent those in Eq. (29) and the dot-dashed lines represent those determined in Ref. [8] by the lattice simulation.
when we set β i≥2 (t) to be 0 in Eq. (26). These solutions are also plotted as two dashed lines in Fig. 2. The similar exponential dependence in t has been observed in Ref. [8], although the value of the exponent still does not coincide.
For our solution, the flow time dependence of β 0 (t) looks interesting. Since the expectation value of the plaquette at a finite t is considered to be larger than that of the plaquette at t = 0, we naively expect the coupling of the plaquette to increase monotonically with t. On the other hand, our result shows that it increases at a small t region due to the second term on the right hand side of Eq. (26), then turns to decrease at around √ 8t ≃ 1. This apparently looks inconsistent with our naive guess. However we may not discuss the value of the plaquette based only on β 0 (t) because the inverse of the squared "effective gauge coupling" is not given by β 0 (t) but given by a suitable linear combination of β i (t). Algebraically, the second derivative of β 0 (t) with respect to t satisfies in this truncation, which causes such a behavior of β 0 (t). Note that d 2 β 0 (t)/dt 2 < 0 is just a consequence of the algebraic manipulation and truncation effects on these solutions should be kept in mind.
III. EXTENSION TO FLOW TIME DEPENDENT GRADIENT FLOW
In this section, we consider the extension of Eq. (11) to more generic cases. Specifically, the flow equation is not limited to the Wilson flow equation (Eq. (4)) but can be taken as a flow equation with an arbitrary chosen action. For example, the "trivializing action" [2] which gives the vanishing S t at a fixed t = t 0 could be a good candidate. S t itself can also be chosen as the seeding action generating S t+dt as similar as a conventional RG transformation.
A. Extension of Eq. (11)
To extend Eq. (11) to more generic cases, let us consider an infinitesimal change of the flow time at t as follows, Suppose thatV dt [V ] is given bȳ where an initial action can be any lattice action which consists of Wilson loops and products of them. When we choose S t = g 2 0 S W , Eq. (33) is equivalent to Eq. (11) in which the flow of S t is generated by the Wilson flow.
In this subsection, we analyze Eq. (33) in the case of S t = g 2 0 S t , namely with the same truncation (Eq. (12) and Fig. 1) and initial condition (Eq. (14)) of S t as in Sect. II B.
The computation is almost same as in the previous section. For this case, we additionally need the relations in App. A in order to derive the following differential equation for β i (t), the value of |β i≥1 (t)|. Then β 0 (t) receives the effect of this growth in |β i≥1 (t)| and decrease less than its initial value. These explain such an interesting time evolution of couplings as seen in Fig. 3.
C. Similarities and differences compared to the Wilsonian RG flow
As noted in Sect. I, our final goal is to construct a scheme of gauge-invariant renormalization group. Here we discuss on this subject based on the result in Sect. III B.
Let us assume that the flow time dependence of β 0 (t) described above is common to any β i (t): we assume that a coupling β i (t) associated with a class of Wilson loops or their products whose "extent" is roughly around √ 8t ≃ n becomes relevant at (n − 1) √ 8t n and turns to be irrelevant at √ 8t n. This implies that Eq. However, since the gradient flow is just a smearing of the link variables, the time evolution of S t does not have a coarse-graining step. S t is defined on the fine lattice even at a large t regime. Therefore, we need a coarse-graining step to eventually construct a RG scheme in this approach, for example to obtain the beta function of the gauge coupling. This could be one of future perspectives of this work. Note that a coarse-graining step on the lattice could define a discretized RG equation rather than continuous (differential) one like Eq. (33).
IV. SUMMARY
In this work, we have first proposed the exact differential equation for the effective action For the case of the Wilson flow, the differential equation of couplings becomes a linear inhomogeneous first-order differential equation with any truncation, therefore it can be solved analytically. The solutions are shown in Eq. (28) and plotted in Fig. 2 as the functions of √ 8t. We have found that the coefficient of the rectangular loop (β 1 ) tends to negative at t > 0, which agrees with the result of Ref. [8] and known improved actions [11][12][13][14][15][16]. However the flow time dependence of couplings is different from that of Ref. [8]. For our solution, the coefficient of the Wilson plaquette (β 0 ) increases at the beginning, then turns to decrease at around √ 8t ≃ 1.
For the case of S t = g 2 0 S t , the differential equation of couplings is no longer integrated analytically, since it is quadratic in couplings. The numerical solutions are shown in Fig. 3 | 3,752 | 2015-10-28T00:00:00.000 | [
"Physics"
] |
Social Inquiry Field Work Based Instruction Model To Improve Understanding Environmental Friendly Activities
This research is motivated by economic learning conditions that have not involved the value of sustainability. Sustainability values need to be developed in economic learning because economic activities have a great opportunity to behave in an environmentally unfriendly manner that has a broad impact. The application of sustainable values to students is realized through meaningful learning, one of which is by applying the inquiry work field learning model that emphasizes direct experience through field research. Thus this study aims to design learning models with the ADDIE approach (analysis, design, development, implementation and evaluation). To design the design of this learning model, a needs analysis was conducted and design was designed by conducting surveys, observations and literature studies on the learning model. The results of this study reveal the current conditions of economic learning, the results of the needs analysis for the preparation of learning models and the design of learning models. It is hoped that from this research, it can be an input for meaningful economic learning through direct experience that is able to foster environmentally friendly behavior in the long term. Keyword. learning, inquiry; economics; fieldwork. Article history. Received Agustus, 2018. Revised Oktober, 2018.Accepted December, 2018 Corresponding Author. Email<EMAIL_ADDRESS><EMAIL_ADDRESS>How to cite article. Kurniawati, S., & Dirgantari, P. D. (2018). Social Inquiry Field Work Based Instruction Model To Improve Understanding Environmental Friendly Activities. The International Journal of Business Review (The Jobs Review), 1(2), 109–114. https://doi.org/https://doi.org/10.17509/tjr.v1i2.14475 INTRODUCTION The long-term sustainability of resource use is one of the things that must be considered in efforts to explore and exploit resources. This is what is called the concept of consumption sustainability. Behaviors that are counter to the concept of sustainability are wasteful use of energy, use of non-environmentally friendly products and actions that cause waste and pollution. Non-environmentally friendly behavioral phenomena can be seen from the use of ozone-depleting substances (BPO), B3 materials (hazardous and toxic materials), high amounts of waste that are difficult to recycle, high deforestation and high use of goods produced in ways that are not environmentally friendly like, paper, tissue, cotton, tampons and herbal items. In daily activities students often find various environmentally unfriendly behaviors and the effects of these behaviors. To be able to be environmentally friendly, students must be able to criticize good and bad conditions of environmentally unfriendly behavior and environmental conditions that have not supported their lives such as unhealthy environments. At the tertiary level, especially in the Economics, exploitation and resource use study programs, it is reviewed in the Resource Economics course. The thing that needs to be DISMAN, SUSANTI KURNIAWATI,, PUSPO DEWI DIRGANTARI/Social Inquiry Field Work Based Instruction Model To Improve Understanding Environmental Friendly Activities 110 | The International Journal of Business Review (The Jobs Review) Vol.1 | No.2 | 2018 improved on ESD learning now is the lack of understanding of students on environmental issues which is a global issue. The negative impact of economic activity on the environment has been at a critical point where there is often scarcity of resources as input to production. In addition, the delivery of ESD material is less varied / monometode. In fact, ESD material is very closely related to natural and social conditions, so learning based on field research is very suitable for this research. One effort in the field of education to overcome this problem is to provide an understanding of the environment in the concept of green economy. This concept is packaged in the learning of Resource Economics with the material of the Green Economy. Learning model that is expected to be able to develop an understanding of new concepts. Therefore, the inquiry learning model is used based on field work to improve students' understanding. Inquiry learning is one of the learning methods adapted from science learning that aims to increase the ability of students to make decisions and solve problems in accordance with learning objectives (Fiedel et. Al: 2008). Inquiry based learning acquires knowledge from research (Bransford, Brown & Cocking: 2000). RESEARCH METHOD The research method that will be used is the experimental method used is a quasi experiment, which is experimentation conducted with intact group subjects and not randomly taken subjects to be treated. The design that will be used is Control Group PretestPosttest Design, consisting of a group of experimental students with learning inquiry-based fieldwork learning models and control groups with conventional models (lecture method). RESULTS AND DISCUSSION In applying the learning method that will be used, it is necessary to ensure that the learning method that will be designed is really needed by the user. To find out that this method is needed, a needs analysis is carried out, the results of the needs analysis are as follows: Table 1. Need Assesment of Inquiry Based Field Work Method of Instruction Source : Primar 2018 Based on needs analysis, this method is needed to improve understanding through direct experience. This immediate experience can change the mindset and preach the behavior of students. No Statement % 1. Lecturer responses regarding the importance of developing inquiry based field work models in learning Resource Economics (ESD) to provide meaningful learning experiences . 71% 2. Lecturer responses regarding the importance of developing inquiry based field work models in learning Resource Economics (ESD) to provide meaningful learning experiences 62% 3. Students' responses to the need for learning that provide direct experience to facilitate understanding 84% 4. The response of students is the need for meaningful learning through the inquiry approach 78% 5. The response of students is that the development of ESD learning methods needs to be more meaningful . 88% 6. The response of students is the need for sustainability values in economic learning so that they contribute more to the environment. 83% 7. The response of students is the need for sustainability values in economic learning so that they contribute more to the environment.. 52% 8. The response of students that learning with field research is able to change the mindset 83% 9. The response of students is that learning from field research that provides direct experience can change behavior. 75% THE INTERNATIONAL JOURNAL OF BUSINESS REVIEW (THE JOBS REVIEW), 1 (2), 2018, 109-114 111 | The International Journal of Business Review (The Jobs Review) Vol.1 | No.2 | 2018 Furthermore, the development of the learning material with ADDIE was compiled. The results of the method development obtained the following results: Figure 1. Inquiry Based Fieldwork Model Of Instruction After applying twice the treatment in the control class and experimental class, twice and evaluated, the results obtained as shown in Figure 1 below: INQUIRY BASED FIELDWORK MODEL Of INSTRUCTION Landasar Teoretis 1. The philosophical framework of social constructivist education 2. Theory of social development of cognition (Vygotsky) 3. Developmental Zone Vigotsky Learning Principles 1. Constructivism 6.Integrative 2. Active 7. Interactive 3. Direct Experience 8. Meaningul 4. Collaborative 9.Value Based SYNTAX MODEL BASIC LITERATION • Students identify, observe, describe eco-friendly economic activities through solar research • Students analyze, categorize activities Economy is environmentally friendly ORIENTATION •Students know and understand the learning objectives • Student students assess things related to environmentally friendly economic activities ASSOCIATION • Students collaborate to analyze, explain and categorize eco-friendly economic activity concepts • Students communicate the findings in the field in front of the class REFLECTION • Students re-explain the concepts obtained during the learning process • Students assess and evaluate the benefits of the learning process that has been done SUPPORT SYSTEM 1. Method : Inquiry Based Fieldwork 2. Learning Resources : Hand Out 3. Media : Film Strip SOCIAL SYSTEM Collaborative between students and lecturers LECTURERS ROLE 1. As a student facilitator in building understanding 2. The teacher keeps the students on the ZPD 3. The teacher giving "Scaffolding" to students ASSESMENT 1. Understanding data collection through test assessment 2. Understanding environmentally friendly activities through tests UNDERSTANDING OF ECONOMIC GREEN ACTIVITIES DISMAN, SUSANTI KURNIAWATI,, PUSPO DEWI DIRGANTARI/Social Inquiry Field Work Based Instruction Model To Improve Understanding Environmental Friendly Activities 112 | The International Journal of Business Review (The Jobs Review) Vol.1 | No.2 | 2018 Figure 2. Increasing Student’s Understanding Score on Economics Green Activities (1st treatment) Figure 3 : Increasing Student’s Understanding Score on Economics Green Activities (2nd treatment) In Figure 1 and Figure 2, there appears to be an increase in students' understanding after treatment 1 and 2. After t test, it can be stated that there is a significant increase in the experimental class with the inqiry based field method with the control class using the lecture method. This increase indicates that inquiry-based field methods effectively improve students' understanding abilities. Theoretically this happens because this method is based on the research of students to locations related to environmentally friendly economic activities such as markets, factories, public facilities and others; CONCLUSION One effort in the field of education to overcome this problem is to provide an understanding of the environment in the concept of green economy. This concept is packaged in the learning of Resource Economics with the material of the Green Economy. Learning model that is expected to be able to develop an understanding of new concepts. Therefore, the inquiry learning model is used based on field work to improve students' understanding. The development of this learning model was carried out using the ADDIE method, which resulted in the conclusion that inquiry-based field methods effectively enhance the ability to understand students' environmentally friendly activities. 0 20 40 60 80 100 120 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 GAIN KE 1 GAIN KK1 Responden 0 20 40 60 80 100 120 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 GAIN KE 2 GAIN KK2 Responden THE INTERNATIONAL JOURNAL OF BUSINESS REVIEW (THE JOBS REVIEW), 1 (2), 2018, 109-114 113 | The International Journal of Business Review (The Jobs Review) Vol.1 | No.2 | 2018 REFERENCES Abdi, Ali.(2014).The Effect of Inquiry-based Learning Method on Students’ Academic Achievement in Science Course Research 2(1): 37-41, 2014 http://www.hrpub.org DOI: 10.13189/ujer.2014.020104 Ali Abdi Department of Educational Sciences Payame noor University , PO BOX 19395-3697 Tehran, Iran Abigail L. Kuhn and Lynne M. O’Hara (2015). Promoting Inquiry-Based Learning through National History Day Social Education 78(3), pp 138–142 ©2014 National Council for the Social Studies Bern, Robert G. dan Patricia M. Erickson. (2001). Contextual Teaching and learning: Preparing Students for the New Economy. Tersedia dalam http://eric.ed.gov/?id=ED452376 diakses pada 12 Desember 2016. Bloom et al. (1956). Taxonomy of Educational Objectives: The Classification of Educational Goals. New York: McKay. Chan, Y. F.1*, Sidhu, G. K.1, Suthagar, N.1, Lee, L. F.1 and Yap, B.(2016) .Relationship of Inquiry-based Instruction on Active Learning in Higher Education. Pertanika J. Soc. Sci. & Hum. 24 (S): 55 – 72 (2016). Ralf R. Greenwald1 & Ian J. Quitadamo, (2014). Mind of Their Own: Using Inquiry-based Teaching to Build Critical Thinking Skills and Intellectual Engagement in an Undergraduate Neuroanatomy Course. The Journal of Undergraduate Neuroscience Education (JUNE), Spring 2014, 12(2):A100-A106 JUNE is a publication of Faculty for Undergraduate Neuroscience (FUN) www.funjournal.org ARTICLE A 1Department of Psychology, Central Washington University, Ellensburg, WA 98926; 2Departments of Biological Sciences and Science Education, Central Washington University, Ellensburg, WA 98926. Sehgal, Preety, Neha Singh. (2010) Impact on Eco Friendly Products on Consumer, CBS E Journal, Biz n bytes, Vol. 6 Dec, 2010. ISSN 0076-0458 ShahneynShilla.(2012). The Impact of Individual Differences on Green Purchasing of Malaysian Consumers, International Journal of Business and Social Science, Vol. 3 No. 16 2012. Sharkness, Jesica, Linda De Angelo.(2011). Measuring Studengt Involvement : A Comparison of Classical Test Theory and The Response Theory in The Construction of Scale from student survey. Res-High Educ (2011) 52: 480-507 DOI : 10.1007/5 III 62 – 010-920 US EPA (United States Environmnetal Protection Agency).(2011).STORET/WQXCommomly Ask Questions (http://www.epa.gov/faq,html#101) diakses pada 4 Juni 2016 DISMAN, SUSANTI KURNIAWATI,, PUSPO DEWI DIRGANTARI/Social Inquiry Field Work Based Instruction Model To Improve Understanding Environmental Friendly Activities 114 | The International Journal of Business Review (The Jobs Review) Vol.1 | No.2 | 2018 Yvonne, Bayech Rebecca.(2016). Exploratory Study of MOOC Learner’s Demographicsand Motivation : The Case of Student Involved in Group. Open Praxis Vol. 8 Issue 3 JuliSept 2016 pp 223-233 International Council for Open and Distance Education.
INTRODUCTION
The long-term sustainability of resource use is one of the things that must be considered in efforts to explore and exploit resources. This is what is called the concept of consumption sustainability. Behaviors that are counter to the concept of sustainability are wasteful use of energy, use of non-environmentally friendly products and actions that cause waste and pollution. Non-environmentally friendly behavioral phenomena can be seen from the use of ozone-depleting substances (BPO), B3 materials (hazardous and toxic materials), high amounts of waste that are difficult to recycle, high deforestation and high use of goods produced in ways that are not environmentally friendly like, paper, tissue, cotton, tampons and herbal items.
In daily activities students often find various environmentally unfriendly behaviors and the effects of these behaviors. To be able to be environmentally friendly, students must be able to criticize good and bad conditions of environmentally unfriendly behavior and environmental conditions that have not supported their lives such as unhealthy environments.
At the tertiary level, especially in the Economics, exploitation and resource use study programs, it is reviewed in the Resource Economics course. The thing that needs to be improved on ESD learning now is the lack of understanding of students on environmental issues which is a global issue. The negative impact of economic activity on the environment has been at a critical point where there is often scarcity of resources as input to production. In addition, the delivery of ESD material is less varied / monometode. In fact, ESD material is very closely related to natural and social conditions, so learning based on field research is very suitable for this research. One effort in the field of education to overcome this problem is to provide an understanding of the environment in the concept of green economy. This concept is packaged in the learning of Resource Economics with the material of the Green Economy. Learning model that is expected to be able to develop an understanding of new concepts. Therefore, the inquiry learning model is used based on field work to improve students' understanding. Inquiry learning is one of the learning methods adapted from science learning that aims to increase the ability of students to make decisions and solve problems in accordance with learning objectives (Fiedel et. Al: 2008). Inquiry based learning acquires knowledge from research (Bransford, Brown & Cocking: 2000).
RESEARCH METHOD
The research method that will be used is the experimental method used is a quasi experiment, which is experimentation conducted with intact group subjects and not randomly taken subjects to be treated. The design that will be used is Control Group Pretest-Posttest Design, consisting of a group of experimental students with learning inquiry-based fieldwork learning models and control groups with conventional models (lecture method).
RESULTS AND DISCUSSION
In applying the learning method that will be used, it is necessary to ensure that the learning method that will be designed is really needed by the user. To find out that this method is needed, a needs analysis is carried out, the results of the needs analysis are as follows: Based on needs analysis, this method is needed to improve understanding through direct experience. This immediate experience can change the mindset and preach the behavior of students. The response of students is the need for meaningful learning through the inquiry approach 78% 5.
The response of students is that the development of ESD learning methods needs to be more meaningful .
88%
6. The response of students is the need for sustainability values in economic learning so that they contribute more to the environment.
The response of students is the need for sustainability values in economic learning so that they contribute more to the environment..
The response of students that learning with field research is able to change the mindset 83% 9.
The response of students is that learning from field research that provides direct experience can change behavior.
THE INTERNATIONAL JOURNAL OF BUSINESS REVIEW (THE JOBS REVIEW), 1 (2), 2018, 109-114
Furthermore, the development of the learning material with ADDIE was compiled. The results of the method development obtained the following results: After applying twice the treatment in the control class and experimental class, twice and evaluated, the results obtained as shown in Figure 1 In Figure 1 and Figure 2, there appears to be an increase in students' understanding after treatment 1 and 2. After t test, it can be stated that there is a significant increase in the experimental class with the inqiry based field method with the control class using the lecture method. This increase indicates that inquiry-based field methods effectively improve students' understanding abilities. Theoretically this happens because this method is based on the research of students to locations related to environmentally friendly economic activities such as markets, factories, public facilities and others;
CONCLUSION
One effort in the field of education to overcome this problem is to provide an understanding of the environment in the concept of green economy. This concept is packaged in the learning of Resource Economics with the material of the Green Economy. Learning model that is expected to be able to develop an understanding of new concepts. Therefore, the inquiry learning model is used based on field work to improve students' understanding. The development of this learning model was carried out using the ADDIE method, which resulted in the conclusion that inquiry-based field methods effectively enhance the ability to understand students' environmentally friendly activities. | 4,159.4 | 2018-12-22T00:00:00.000 | [
"Economics"
] |
Vimentin: from a cytoskeletal protein to a critical modulator of immune response and a target for infection
Vimentin is an intermediate filament protein that plays a role in cell processes, including cell migration, cell shape and plasticity, or organelle anchorage. However, studies from over the last quarter-century revealed that vimentin can be expressed at the cell surface and even secreted and that its implications in cell physiology largely exceed structural and cytoskeletal functions. Consequently, vimentin contributes to several pathophysiological conditions such as cancer, autoimmune and inflammatory diseases, or infection. In this review, we aimed at covering these various roles and highlighting vimentin implications in the immune response. We also provide an overview of how some microbes including bacteria and viruses have acquired the ability to circumvent vimentin functions in order to interfere with host responses and promote their uptake, persistence, and egress from host cells. Lastly, we discuss the therapeutic approaches associated with vimentin targeting, leading to several beneficial effects such as preventing infection, limiting inflammatory responses, or the progression of cancerous events.
Introduction
Intermediate filaments are composed of approximately 70 different types of proteins such as keratin, vimentin, desmin, and lamin. The proteins that compose the intermediate filament depend on the cell type and its localization. Intermediate filaments are approximately 8-12 nm wide; they are called intermediate because they are in between the size of microfilaments and microtubules (1). Besides lamins, which are found in the nucleus and help support the nuclear envelope (2), intermediate filaments are mainly found in the cytoplasm although nestin and vimentin can be found in the nucleus (3,4). In the cytoplasm, intermediate filaments maintain the cell shape and tension and provide structural support to the cell.
Vimentin is expressed in mesenchymal cells, including fibroblasts, endothelial cells, macrophages, melanocytes, Schwann cells, and lymphocytes (5). It is known to be implicated in a dynamic, flexible network that plays an important role in several cell events. Most of the knowledge regarding the role of vimentin comes from studies on vimentin-deficient mice. While the phenotype of such mice is rather mild (6), detailed analyses have shown that vimentin deficiency affects cell adhesion, migration, and cell signaling and, therefore, that vimentin plays key roles in several physiological processes (5).
One of the processes that implicate vimentin is the epithelial-tomesenchymal transition (7), which allows polarized cells to revert to a mesenchymal phenotype, granting the cells a greater migratory ability and a more resistant cell type (8). Vimentin also regulates cell adhesion by interacting with and regulating integrin function (9). In addition, as a major intermediate filament protein in leukocytes, vimentin plays a critical role in leukocyte migration by regulating cell attachment to vascular endothelium and transmigration (10).
However, while vimentin is a cytoskeletal protein, several reports have shown that in macrophages and microvascular endothelial cells, it can be expressed at the cell surface or secreted, suggesting a role during innate immune responses (11)(12)(13)(14). Thus, it is not surprising that some pathogens have evolved strategies in order to subvert vimentin function and interfere with host responses.
In this review, we will highlight the different roles that vimentin can play during pathogen infection of the host cell.
Vimentin structure
The basic structure of vimentin consists of a central a-helical rod domain flanked by unstructured head and tail domains. Vimentin monomers pair up into coiled-coil dimers, which then align in a staggered, antiparallel fashion to form tetramers; groups of eight tetramers make up the unit-length filaments (ULFs) that join end-to-end and subsequently undergo radial compaction to form the mature vimentin intermediate filaments (15). Interactions between vimentin molecules are regulated by posttranslational modifi cations, including O-linked glycosylation and phosphorylation (16). O-GlcNAcylation of Ser49 residue (and Ser34, Ser 39 to a lesser extent) in the head domain promotes interactions between vimentin molecules and the assembly and/or maintenance of mature vimentin filaments (17). The formation of vimentin filaments is dynamic although ULFs are released from growing filaments at low rates. The equilibrium between the abundance of free ULFs and assembled filaments favors polymerization, leading to the formation of a dynamic, complex, and insoluble network of filaments ( Figure 1) that plays an important role in several cellular events. Disassembly and assembly of vimentin filaments are mainly orchestrated by serine phosphorylation (18, 19), which regulates severing and annealing events (20, 21). Inhibition of type-1 and type-2A protein phosphatases (PP1 and PP2A) results in the disassembly of vimentin filaments into soluble vimentin tetramers (19), whereas several serine/threonine kinases, including protein kinase A and C (PKA and PKC) (19), calmodulin-dependent protein kinase II (CaMKII) and Rho-associated protein kinases (ROCK1 and R O C K 2 ) ( 2 2 -2 4 ) , h a v e b e e n i n v o l v e d i n v i m e n t i n phosphorylation and linked to diverse biological processes. In addition, it has been shown that tyrosine phosphorylation and dephosphorylation by Src and SHP2, respectively, are also involved in the reorganization of vimentin filaments during migration in response to growth factors (25).
During mitosis, vimentin filaments are dramatically reorganized and may appear as a filament cage around the mitotic spindle or may disassemble, depending on the cell type. Vimentin disassembly is mediated by phosphorylation by various kinases, including Cdk1 (maturation/M-phase promoting factor, MPF; p34 cdc2 /cyclin B) and Plk1 from prometaphase to metaphase (26-28) and by Aurora-B and Rho-kinase from anaphase to the end of mitosis (24, 29). Interestingly, vimentin disassembly or persistence as a filament cage appears dependent on nestin, an intermediate filament protein that cannot form filament by itself but that can form copolymers with vimentin (30), thereby promoting its phosphorylation and disassembly (31). In nestinnegative cells, vimentin persists as filaments that closely interact with the actomyosin cortex and redistribute to the cell periphery (32).
Vimentin and organelle anchorage
Vimentin forms a vast intracellular network surrounding the nucleus and spanning toward the cell periphery. The vimentin distribution throughout the cell allows the structural maintenance of the cell organelles.
It has been previously shown that vimentin is able to interact with different organelles such as the Golgi apparatus, mitochondria, or even vacuoles via associated proteins.
The formimidoyltransferase cyclodeaminase (FTCD), an enzyme located on the Golgi membrane plays the role of a linker protein, promoting the binding of vimentin to the Golgi apparatus as revealed by the colocalization of vimentin with GM130, a marker protein of the Golgi apparatus (33). The interaction between FTCD and vimentin allows the remodeling of the Golgi and represents an interaction interface between the Golgi and the rest of the cytoskeleton (33). In addition, it was recently shown that the trans-Golgi network coiled-coil protein GORAB interacts with vimentin (34), suggesting that vimentin filaments contribute to the structural stability of the Golgi apparatus through GORAB, although it cannot be excluded that vimentin binds Golgi via other proteins.Vimentin has also been shown to be associated with proteins implicated in the sorting of the endosomal-lysosomal machinery (35). One of the key proteins implicated in the sorting of the proteins present in distinct vesicles secreted by host membranes is the adaptor protein (AP) complex (36). More specifically the AP-3 complex implicated in the sorting of proteins present on lysosomes interacts directly with vimentin (35), suggesting the potential role that vimentin plays in the scaffolding of lysosome vesicles throughout the cell and also the regulation of the sorting ability of AP-3 complex (35).
Finally, vimentin is implicated in the cellular movement of the mitochondria. Plectin 1b, a cytolinker, ensures the anchorage of the mitochondria to vimentin filaments (37) but it also seems that vimentin can bind directly to the mitochondria via its N-terminal domain (38). The binding of vimentin filaments to the mitochondria modulates the motility of these organelles and limits the movements of the mitochondria throughout the cells.
Vimentin and cell shape and motility
Besides the roles that vimentin plays in organelle anchoring, it also confers the cell's general rigidity and shape.
Polarized epithelial cells undergo epithelial-to-mesenchymal transition (EMT), which is a process that confers the cells the capacities of migration and to become more resistant. EMT often occurs during wound healing where the polarized epithelial cells detach from the base and migrate to the wound site, reverting to their initial state. Vimentin governs the healing process by regulating fibroblast proliferation, extracellular matrix (ECM) accumulation, and EMT processing (39). This process is mostly mediated by inflammatory signals and ECM proteins, including collagens, laminins, elastin, and tenacins (8). Cells undergoing EMT have increased vimentin expression (7), which confers the cell its elasticity to navigate to rigid domains. Vimentin also protects against compressive stress ( 40 ) and is involved in mechanosensing during migration by enhancing cell spreading (41).
It has been shown that migratory cells powered by EMT utilize vimentin to generate cell extension and direct migration. This process is controlled by vimentin phosphorylation by p21activated kinase 1 (42) after interaction with the actin-binding protein filamin A (43, 44). The mature phosphorylated vimentin filaments are stable and maintain the cell extensions that are directed by the detection of the inflammatory signals or ECM complex (45).
Other cells such as lymphocytes also utilize the reorganization of vimentin to initiate their movements through a dense environment or during transmigration (10).
During cancer progression, epithelial cells can undergo EMT to migrate, invade, and proliferate and vimentin overexpression can be a sign of the progression of cancerous events (46), including breast
Extracellular and membrane-associated vimentin
Besides its role in maintaining the cell organelles, giving the cell its global shape and rigidity, and acting as a moving scaffold to direct the migration of the cells, several studies have shown that vimentin can be expressed at the cell membrane, secreted to the extracellular environment and even be secreted via exocytosis of particles.
Indeed, both vimentin and desmin, another type III intermediate filament protein, can be located at the cell surface of cardiomyocytes and vascular smooth muscle cells (50). Interestingly, the rod II domain ( Figure 1) present on both vimentin and desmin has a carbohydrate-recognition activity that binds b-N-acetylglucosamine (GlcNAc). It was shown that this lectin-like activity was required for the internalization of GlcNAcconjugated liposomes by cardiomyocytes, suggesting that cell surface vimentin and desmin are involved in the clearance of GlcNAc-conjugated proteins and cellular debris (50).
It was also shown that in certain conditions, including infection, immune cells such as monocytes or macrophages can express vimentin at the membrane (12, 51) or even secrete vimentin (11), suggesting a role during the immune response. Interestingly, the secretion of vimentin is triggered by the pro-inflammatory cytokine TNF produced by macrophages, whereas it is inhibited by the inhibitory cytokine IL-10 (11). Finally, astrocytes, neutrophils, and endothelial cells have also been shown to secrete vimentin (52-55). Likewise, tumor endothelial cells overexpress and secrete vimentin through type III unconventional secretion mechanisms (56), although cancer cells can also secrete vimentin via the use of exosomes (57). As mentioned above, tumorous cells overexpress vimentin and the presence of vimentin in exosomes is most likely due to the overexpression of vimentin and this might likely accelerate the transformation of the target cells into an uncontrollable EMT (46).
Vimentin is also involved in several inflammatory and autoimmune diseases including, among others, rheumatoid arthritis, systemic lupus erythematosus, sarcoidosis, or ankylosing spondylarthritis (58). Vimentin is a substrate of peptidylarginine deiminase type 2 (PADI2), which deiminates arginine residues into citrulline residues, resulting in a mutated citrullinated vimentin (59). Vimentin citrullination results in a loss of vimentin's normal functions but also triggers the production of anti-citrullinated protein antibodies (ACPAs) (60), which have been shown to induce osteoclastogenesis and bone loss (61,62). As a consequence, anti-citrullinated antibodies and more generally ACPAs represent a valuable diagnostic and prognostic marker of rheumatoid arthritis (63,64).
Vimentin and immune response
To detect and neutralize pathogens, the host cells mobilize various mechanisms. One of the mechanisms is the use of sensor proteins or pattern recognition receptors that recognize conserved motifs expressed by microbes and that include Toll-like receptors (TLRs) (65), C-type lectin receptors (CLRs) (66), Nucleotidebinding and oligomerization domain (NOD)-like receptors (NLRs) (67). One of the major responses from these signaling proteins is the alteration of gene expression through the activation of the nuclear factor-kB (NF-kB), mitogen-activated protein kinase (MAPK), or interferon (IFN)-regulatory factor (IRF) pathways. The genes affected by these alterations in transcription are mostly those encoding pro-inflammatory cytokines and IFN-stimulated genes and they play a major role in cell-intrinsic control of pathogens and activation of adaptive immunity. NLRs detect bacterial infection and assemble signaling structures called inflammasomes (67), large oligomeric multiprotein complexes that act as signaling platforms to catalytically activate pro-caspase 1 to promote the maturation of IL-1b and IL-18 pro-inflammatory cytokines (68).
Several studies have revealed that the vimentin network plays an essential role in the detection of pathogens as well as in the mobilization of antimicrobial responses. Indeed, it has been demonstrated that recognition of muramyl-dipeptide (MDP), the minimal motif of bacterial peptidoglycan by the intracytoplasmic sensor NOD2, required membrane targeting of NOD2 for proper activation of NF-kB after MDP recognition (69). Unexpectedly, vimentin is directly implicated in this process (Figure 2A). NOD2 interacts with vimentin at the plasma membrane through its leucine-rich repeats domain and this is critical for NF-kB activation since vimentin inhibition with Withaferin A relocalizes NOD2 in the cytosol and inhibits downstream activation of NF-kB as well as NOD2-dependent autophagy induction (69). Interestingly, in light of card15, which encodes the NOD2 protein as a susceptibility gene for Crohn's disease, polymorphisms in the vim gene may also be associated with Crohn's disease, although further genetic studies are required (69).
Vimentin also regulates the NLRP3 inflammasome. Vimentindeficient mice are protected against acute lung injury and fibrosis (70). This is because vimentin binds NLRP3 in macrophages and this interaction may facilitate the transport and the assembly of other proteins implicated in the inflammasome, including caspase-1, which is implicated in the maturation of IL-1b (70) (Figure 2A).
Finally, it has been shown that vimentin could be an endogenous ligand for the CLR Dectin-1 (71) (Figure 2A) and mediate chronic inflammation leading to atherosclerosis. Although Dectin-1 is implicated in the recognition of yeast and fungal pathogens (72), it can also bind extracellular vimentin that is released during necrosis in atherosclerotic lesions, leading to NADPH oxidase activation and O − 2 production and low-density lipoprotein oxidation, thereby contributing to the chronicity of the disease (71). This is in agreement with other findings that showed that vimentin deficiency attenuates atherosclerosis in mice (73). However, the precise role of vimentin in inflammation remains to be further clarified since it was also shown that macrophages deficient in vimentin are characterized by increased oxidative and inflammatory responses (73).
Vimentin was shown to regulate host antiviral immune responses. Indeed, in vitro, vimentin controls type-I IFN expression induced by TLR, retinoic acid-inducible gene-I (RIG-I)-like receptors (RLR), and the cytosolic DNA sensor cGAS by directly interacting through its N terminus domain with both TBK1 and IKKe and thus preventing IRF3 phosphorylation (74). These data are strengthened by the fact that mice deficient in vimentin are more resistant and display milder symptoms following infection by the encephalomyocarditis virus or herpes simplex virus (HSV-1) (74).
Besides its involvement in various signaling pathways that controls host responses, vimentin can also participate directly in the immune response by acting as a ligand when expressed at the cell surface. For example, vimentin present at the surface of infected monocytes is used as a recognition pattern for Natural killer (NK) cells (12). NK cells naturally utilize the activating receptors NKp30 and NKp46 for the recognition and lysis of cells expressing their ligands to initiate their lysis (75). Interestingly, monocytes infected by M. tuberculosis (H37Ra) upregulate vimentin at the cell surface and NK cells are able to detect infected cells by interacting with vimentin through NKp46, which in turn induces the lysis of the infected cells (12) ( Figure 2B). In addition, surface expression of vimentin may also participate in the engulfment and clearance of apoptotic cells by interacting with O-GlcNAc proteins present in apoptotic cells (76) (Figure 2B). Finally, as already noted above, activated macrophages secrete vimentin, and extracellular cellsurface vimentin is mainly localized at the rear extremity of the macrophage, in the opposite direction of the migration (51).
Diversion of vimentin by pathogens
Given the multiple roles that vimentin plays as a structural entity maintaining the overall cell integrity, its role in the regulation of the immune response, and its availability at the cell surface in certain conditions, it is not surprising that certain pathogens make use of vimentin to facilitate their entry into the host cell or allow their maintenance or to disseminate to different organs (77,78).
Escherichia coli is a common Gram-negative bacterium found in the gastrointestinal tract, which is in most cases harmless. However, strains expressing virulence factors such as Ibe proteins (IbeA, IbeB, IbeC, IbeR, and IbeT) can cause neonatal bacterial sepsis and meningitis (NSM) in immunocompromised infants, with high morbidity and mortality (79). Most cases of NSM are caused by E. coli K1 (79), which expresses IbeA. IbeA is required for invasion of human brain microvascular endothelial cells (HBMEC) where it interacts with surface-expressed vimentin (Figure 3) at the head domain (80). IbeA-vimentin interaction then induces a signaling cascade involving the phosphorylation of vimentin and also the activation of the MAPK signaling pathway that mediates the invasion of E. coli K1 into HBMECs and the activation of NF-kB, which are required for bacteria-mediated pathogenicity (81,82).
Other bacteria utilize cell surface vimentin as a means of entry. Streptococcus pyogenes (group A streptococcus, GAS) is a Grampositive coccus, member of the skin microbiota. Under certain circumstances, it may be responsible for several human diseases ranging from mild skin infections and pharyngitis to necrotizing fasciitis and myonecrosis (83). In patients lacking a portal of entry, GAS is suspected to spread from the oropharynx to the site of prior muscle injury. There, GAS binds vimentin that is overexpressed by injured muscle cells. Adhesion of GAS at the cell surface ( Figure 3) in sufficient numbers seems to lead to the initiation of infection of the host cell (83). In addition, S. pyogenes expresses SyaA, an ADPribosyltransferase that modifies vimentin at the head domain (84). Ribosylation of vimentin at the head domain leads to the disruption of the vimentin network around the nucleus, and this disruption may interfere with wound healing, play a role in reducing the microbicidal activity of macrophages, and contribute to further bacterial dissemination.
Once inside the cell, some intracellular bacteria, including Chlamydia trachomatis, use vimentin for maintaining the replicative vacuole. C. trachomatis are obligated intracellular Gram-negative bacteria that mainly infect ocular and genital epithelia leading to conjunctivitis, salpingitis, and urethritis (85). Vimentin has been shown to contribute to the establishment of replicative niches (Figure 3). Indeed, C. trachomatis maintains a large, stable membrane-bound vacuole that is stabilized by coopting the function of F-actin and vimentin. The bacterium mobilizes the GTPase RhoA functions to assemble an actin ring around the vacuole. In addition to F-actin rings, a cage of intermediate filaments, including vimentin, is assembled on the mature chlamydial inclusion (86) and vimentin is necessary for the inclusion expansions because it provides scaffolding support. CPAF, a protease synthesized by C. trachomatis, can cleave the head domain of vimentin, allowing the expansion of the inclusion (86). Of note, the ability of vimentin to form cages may be reminiscent of the ring like-structures that form in the initial steps of cell adhesion/ spreading as well as during mitosis (87).
M. tuberculosis is another pathogen that targets vimentin to favor infection. M. tuberculosis is a facultative intracellular bacterium that infects macrophages and monocytes and persists in vacuoles. M. tuberculosis downregulates both the reactive oxygen species (ROS) production and the pro-inflammatory response (88) to allow its persistence in the host cell. Interestingly, during M. tuberculosis infection, vimentin is also downregulated ( Figure 3) and evidence suggests that the downregulation of vimentin is associated with that of ROS and depends on ESAT6 expression by M. tuberculosis (89).
Numerous studies have also shown that some viruses can use vimentin as a receptor/coreceptor, including dengue virus (90), enterovirus 71 (91), influenza A (92), severe acute respiratory syndrome coronavirus (SARS-CoV) (93), and SARS-CoV-2 (94-97) (Figure 3). Regarding betacoronaviruses, it was first determined that vimentin acted as a co-receptor alongside angiotensinconverting enzyme 2 (ACE2) on epithelial cells during the binding of the spike protein of SARS-CoV (93), which in turn allowed the entry of SARS-CoV into the host cell. It has been shown that the recently emerged SARS-CoV-2, highly similar to SARS-CoV (98), is also able to utilize vimentin at the cell surface as an attachment receptor (Figure 3) to facilitate the binding to ACE2. We have previously shown that inhibition of vimentin during SARS-CoV-2 infection reduced viral uptake, favored the protection against virus-mediated cell cytotoxicity, and reduced the pro-inflammatory response (95).
Influenza A virus, the causative agent of seasonal flu epidemics, reorganizes the vimentin network after infection of the host cell (99) (Figure 3). A recent study demonstrated that vimentin-deficient cells showed a massive decrease in the production of viral RNA and viral protein production (92). This was due to the lack of transportation of the endosomal vesicles containing the viral genomes to the nucleus, which is mediated by the vimentin scaffold throughout the cell (92).
Finally, during viral infection, vimentin can also form vimentin cages that facilitate viral replication and protein production of viruses. For example, dengue virus (DENV) and coxsackievirus B3 utilize vimentin to concentrate their viral factories to the perinuclear area (Figure 3), whereas, in vimentin-deficient cells, the viral factories are dispersed throughout the cell (100,101). Similar findings were observed during Zika virus (ZIKV) infection (102). However, besides its structural role in maintaining the integrity of ZIKV replication complexes, vimentin additionally interacts with and regulates RNA-binding proteins to facilitate viral replication (102). Vimentin is also required for viral replication of the alphacoronavirus transmissible gastroenteritis virus (TGEV). Although the precise implication of vimentin needs to be further explored, it has been shown that vimentin interacts with the nucleocapsid N protein and TEGV replication is severely impaired in vimentin-deficient cells (103).
Vimentin may also be required for viral egress. This is the case for infection with bluetongue virus, whose capsid viral protein 2 (VP2) binds directly to cytosolic vimentin (104). The neo-formed mature viral particles of bluetongue virus utilize the interaction between vimentin and VP2 to migrate to the surface and allow its egress (104) (Figure 3). In another case, DENV-2 non-structural protein 1 (NS1), which is an indicator of viral replication, interacts with vimentin, and the disruption of vimentin results in a decrease of NS1 expression and decreased viral replication and viral egress (105). Lastly, the foot and mouth disease virus non-structural protein 2C interacts directly with vimentin, which forms a cage around viral factories (106). The interaction with vimentin and non-structural protein 2C seems to be implicated in viral replication and viral release since non-structural protein 2C mutant-expressing viruses have decreased viral replication and release (106).
Vimentin targeting as a therapeutic option
As mentioned above, vimentin is involved in numerous physiological and pathophysiological processes. Hence, given the multiple localizations of vimentin, several therapeutic strategies have been developed to target vimentin in light of tumorigenesis and viral and bacterial infections.
Several studies have shown that cell surface vimentin can be detected and potentially blocked by using monoclonal antibodies directed against vimentin (107)(108)(109)(110). The overexpression of vimentin in most cases is a marker of EMT, and uncontrolled EMT leads to the initiation of cancerous cell establishment. Hence, targeting extracellular vimentin with the monoclonal 86C antibody induces the apoptosis of glioblastoma multiforme cancer stem cells (109). In addition, the monoclonal SC5 antibody has been used to identify target cells in the case of the cutaneous T cell lymphoma Seźary syndrome (107). Pritumumab is another anti-vimentin monoclonal antibody used in glioma patients that binds to malignant cells expressing vimentin at their surface and is able to distinguish physiological and malignant vimentin (108, 110). Besides monoclonal antibodies, a polyclonal response induced by vaccination against extracellular vimentin seems very promising as it appears effective and safe in two different syngeneic preclinical mouse models of melanoma and colorectal carcinoma, as well as in FIGURE 3 Subversion of vimentin by bacterial and viral pathogens. Vimentin expressed at the cell surface can be utilized by pathogens as an attachment factor to initiate entry into the host cell. Vimentin can also be recruited for the establishment and maintenance of replicative niches and the vimentin network may also facilitate the egress of newly formed viral particles (see text for details).
dogs with spontaneous transitional cell carcinoma of the bladder (56). Cell surface vimentin has also been shown to be increased after an infection of the host cell by SARS-CoV-2 (95). This increase probably facilitates viral entry and targeting vimentin with the vimentin-targeting small molecule ALD-R491 (111,112) seems to decrease viral entry (113). The subsequent decrease in entry is also associated with an increase in host cell survival and an alteration of the host response to the infection, which is still not completely understood (95). Thus, targeting extracellular vimentin seems to be an effective way to target specific cells undergoing abnormal modifications or to decrease viral uptake.
Intracellular vimentin could also be targeted as a therapeutic option by reorganizing its availability throughout the cell. Cytoplasmic vimentin is used by various pathogens (86,106,114) besides those cited above that can establish their niches or allow easier recruitment of host effectors to favor the pathogen cycle. Therefore, disruption of intracellular vimentin seems to be an effective way to limit the establishment or replication of pathogens within the host cell.
To target intracellular vimentin, several molecules [which have been reviewed extensively by Ramos et al. (115)] have been shown to have a disrupting effect on vimentin. One of the molecules is Withaferin A (WFA) (116), a natural compound extracted from Withania somnifera. WFA can bind directly to vimentin, leading to a disruption of the vimentin network throughout the cell. Interestingly, WFA has also a potent anti-tumoral and antiangiogenic effect (116). Similarly, the natural compound Ajoene, which is extracted from garlic cloves, can bind to vimentin and disrupt the intermediate filament network (117). The disruption of vimentin leads to a decrease in cell migration and in the case of cancer cells, this decrease in migration is associated with a reduction of invasion of cancerous cells, giving it an antimetastatic activity (117). Finally, simvastatin, which is primarily used to lower blood cholesterol levels, has also been shown to bind and reorganize vimentin to one side of the nucleus, which later induces the apoptosis of the cells (118), thereby contributing to neutralizing cancerous cells (118). Hence, inhibition of vimentin seems to have various beneficial effects. Therapeutic targeting of vimentin is promising in cases of vimentin hijacking by pathogenic mechanisms, including infection and tumorigenesis.
Conclusion
Initially considered a redundant, non-essential intermediate filament protein, vimentin has quickly been demonstrated to be a key player in a variety of cell features. Vimentin functions range from a scaffolding network anchoring cell organelles, which can rearrange to allow cells to migrate and adapt their flexibility, to a critical regulator of several immune processes. The rearrangement of vimentin can also relocate vimentin to the cell surface, where the newly available vimentin is exposed to external ligands such as pathogens or surrounding cells. Vimentin can also be found in the extracellular environment. Vimentin engagement can potentially modulate immune signaling pathways but also facilitate the entry of a pathogen. Some pathogens have the ability to hijack or remodel the vimentin network to favor their life cycle. Over the years vimentin has been determined as a suitable target for therapies where compounds are able to disrupt the vimentin network and in turn block the effects of either pathogens or tumorous events.
Author contributions
JA searched the literature and developed the first draft under the supervision of BD. BD conceptualized and revised the manuscript. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Funding
This work was supported by the French Government under the Investissements d'avenir (Investments for the Future) program managed by the Agence Nationale de la Recherche (ANR, fr: National Agency for Research), no. 10-IAHU-03. This work was supported by Reǵion Provence Alpes Cote d'Azur and European funding FEDER PRIMI. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | 6,756.4 | 2023-07-05T00:00:00.000 | [
"Medicine",
"Biology"
] |
Improvement of the Primary Education using the Evidential Reasoning
Primary education is one of the important social sectors in the developing countries. It plays a gigantic role in promoting the social development of the concerned countries. More specifically, not the only education but the quality primary education which depends on such factors will contribute a lot for its smooth acceleration. In primary education, if the quality will maintain then automatically all the problems in primary education i.e. enrollment, completion, drop-out and so on will be robotic and gradually solved. The improvement primary education is influenced by both multiple qualitative and quantitative factors. This paper presents evidential reasoning(ER) approach to find out significant factors that are aggregated in assessment of performance of primary education. A case study of Dhaka, Chittagong, Rajshahi and Khulna districts in Bangladesh is provided to illustrate the implementation process of the ER approach for finding strong factors of primary education. For that reason, firstly we assess the performance of four districts then we determine the weakness and strength of specific factors of particular district. In this paper we also show the relation of lowest or best performing districts to its specific factors.
INTRODUCTION
A Primary education in Bangladesh that is the largest unitary authorities in the world plays an important role in education system.In order to improve primary education Bangladesh government has undertaken a lot of necessary steps.The Primary and Mass Education Division (PMED) prepared in 1997 a comprehensive Primary Education Development Program (PEDP) which aimed at enhancement of education planning and management capacity, increasing equitable access to primary schooling and improvement of the quality of primary education through its several projects [10].In the World Education Forum held at Dakar, Senegal in April 2000, the government of Bangladesh has committed to achieve of Education for All goals and every citizen by the year 2015 [12].Now 75% of total schools are controlled by the government and around 83% of the total children enrolled in the primary level educational institution go to these schools [10], [12].The quality of primary education is evaluated in terms of the classroom climate, teaching style, classroom management, and understanding the subject matters during the lesson in the class [13].PEDP-II defines 14 key monitoring indicators, the Key Performance Indicators (KPI) and the Primary School Quality Level (PSQL) indicators which act as the basis for the sector programme performance report, setting expectations that will instill purpose to ongoing monitoring and evaluation activities for the benefit of the planning process [1], [2], [10].
A number of survey reports were published to reflect the performance of primary education of Bangladesh based on some statistical measurement.According to case study of two district of Tamil Nadu it is observed that they assess the primary education with taking few major factors such as completion, repetition, and dropout rates.After analyzing these factors they identify the weaken areas which contribute to the lack of acceptable quality schools and focus the learning environment, school governance and management issues [11].But this assessment procedure do not follow systematic computational methods.For this reason the result is reflected by unwanted uncertainties.At that case, Evidential Reasoning approach is very effective which enable both qualitative and quantitative measurement under the multiple attributes decision analysis [3], [4], [5], 6].
In this paper we select four focus districts as our problem areas where two significant factors of KPI such as enrollment and outcomes are selected for performance measuring factors.The main objective of this paper is to find out the strong factor that influence the performance of primary education best district using ER approach by aggregating basic attributes of these two factors.Finally we show the ranking of district wise primary education as well as their factors.
We organize the research activities as follows.In section 2, we explain the ER approach for ADPE outlined and illustrated by subsequent sub-sections 2.1, and 2.2.The experimental result is outline by section 3.Finaly we concluding our remarks at section 4 in which we show the outcomes of evaluation with the discussion of suggestion of future work.www.ijacsa.thesai.org
Identification of Assessment Factors and Evaluation Grades
We apply the evidential reasoning approach to analyze the performance of four main districts wise primary education including Dhaka, Chittagong, Rajshahi, and Khulna.Here only qualitative performance attributes are considered for demonstrating purpose.The major performance attributes are considered as enrollment and outcomes.For facilitating the assessment these attributes are further classified basic factors such as gross enrollment, net enrollment, survivable rate and coefficient of efficiency which are shown in the figure 1.
According to School Survey Report 2007 we draw the scenario of primary education of four districts as the following table [1].Considering the data of three years of the following table 1 we define the evaluation scale as follows: In the following table 2 we summarize the whole assessment problem where I, A, G, and E indicate the evaluation grades Indifferent, Average, Good and Excellent respectively, and the number in a bracket denotes the degree of a belief to which an attribute is assessed to a grade.Now we consider the primary education of Dhaka district where we use the grades as defined before and represent the following distribution as follows [3] , [4], [5], [6], [7], [8]: The gross enrollment rate of Dhaka is complete because the sum of belief is 1 but the coefficient of efficiency is incomplete because .8<1.
Computational steps of aggregating assessment
Firstly we show the total calculation for assessment of enrollment of Dhaka primary education .The enrollment (e 1 ) is assessed by two basic attributes: gross enrollment rate (e 11 ) and net enrollment rate(e 12 ).
From (1a) and (1b), we have On the basis of importance on the performance of primary education suppose the hypothetical weights for two attributes are: ω 11 =0.55 and ω 12 =0.45.
We get the basic and combined probability masses ( m n,i ) by using following recursive equations [4], [5], [6], [7], [8]: Now the combined degrees of belief are calculated by using equation as follows [4], [5], [6], [7], [8]: Finally the performance of Dhaka district primary education is assessed by enrollment (e 1 ) and outcomes (e 2 ) as shown in table 3. www.ijacsa.thesai.orgdistrict assessment process the relative importance of each attribute is also measured by identifying the strengths and weakness of it's on each district.In this paper we identify that the enrollment f the strong factor of Khulna district primary education.For that reason it is clear that if we increase the enrollment rate particular district then the performance of the primary education of this district is stronger.We present the result of an individual district in the form of interval from minimum utility to maximum utility in a systematic and effective way.When a set of necessary steps to increase the performance of such weaken factors of weaken districts then the expected quality of primary education is achieved. | 1,593.2 | 2012-01-01T00:00:00.000 | [
"Computer Science",
"Education"
] |
An Efficient Quantum Jump Method for Coherent Energy Transfer Dynamics in Photosynthetic Systems under the Influence of Laser Fields
We present a non-Markovian quantum jump approach for simulating coherent energy transfer dynamics in molecular systems in the presence of laser fields. By combining a coherent modified Redfield theory (CMRT) and a non-Markovian quantum jump (NMQJ) method, this new approach inherits the broad-range validity from the CMRT and highly efficient propagation from the NMQJ. To implement NMQJ propagation of CMRT, we show that the CMRT master equation can be casted into a generalized Lindblad form. Moreover, we extend the NMQJ approach to treat time-dependent Hamiltonian, enabling the description of excitonic systems under coherent laser fields. As a benchmark of the validity of this new method, we show that the CMRT-NMQJ method accurately describes the energy transfer dynamics in a prototypical photosynthetic complex. Finally, we apply this new approach to simulate the quantum dynamics of a dimer system coherently excited to coupled single-excitation states under the influence of laser fields, which allows us to investigate the interplay between the photoexcitation process and ultrafast energy transfer dynamics in the system. We demonstrate that laser-field parameters significantly affect coherence dynamics of photoexcitations in excitonic systems, which indicates that the photoexcitation process must be explicitly considered in order to properly describe photon-induced dynamics in photosynthetic systems. This work should provide a valuable tool for efficient simulations of coherent control of energy flow in photosynthetic systems and artificial optoelectronic materials.
Introduction
During the past decade, much progress has been achieved in both experimental and theoretical explorations of photosynthetic excitation energy transfer (EET), e.g., EET pathways determined by pump-probe as well as two-dimensional electronic spectroscopy (2DES) [1,2,3], coherent EET dynamics revealed by 2DES [4,5,6], and theoretical studies to elucidate mechanisms of EET [7,8,9,10]. It is intriguing to consider the possibility of using laser pulse to coherently control energy flow in photosynthetic complexes. Notably, coherent light sources were adopted to control energy transfer pathways in LH2 [11]. The phases of the laser field can efficiently adjust the ratio of energy transfer between intra-and inter-molecule channels in the complex's donoracceptor system. In addition, theoretical control schemes were put forward to prepare specified initial states and probe the subsequent dynamics [12], and the optimal control theory has been adopted to optimize the effects of electromagnetic field's polarization and structural and energetic disorder on localizing excitation energy at a certain chromophore within a Fenna-Matthews-Olson complex of green bacteria [13].
However, although the breakthroughs in experimental techniques have provided much insight into the coherent dynamics in small photosynthetic systems [14,15], efficient theoretical methods that can describe quantum dynamics in realistic photosynthetic antenna are still lacking, because the typical size of an antenna (> 100 chromophores) and the warm and wet environment make the accurate description of coherent EET in these systems a formidable task. Methods for simulating coherent EET dynamics in photosynthetic systems have been put forward (see [9] for a comprehensive review). For example, Förster theory directly provides EET rates, however it neglects quantum coherence effects that have been shown to be critical in photosynthetic systems [7]. In contrast, Redfield theory describes coherent EET dynamics, but it is valid only when the system-bath interactions are sufficiently weak [16,17]. In order to bridge the gap between these two methods, recent advances in theoretical methods have focused on new approaches valid in the intermediate regime. For example, the small-polaron master equation approach [18,19,20,21] should provide a reasonable description of the EET dynamics in the intermediate regime, and the hierarchical equation of motion (HEOM) actually yields a numerically exact means to calculate EET dynamics [22,23,24].
Nevertheless, it is still difficult to apply these theoretical methods to a realistic photosynthetic antenna, which often exhibits > 100 chromophores and static disorder, due to the formidable computational resources required. Moreover, to understand the fundamentals of light-matter interactions in photosynthetic light harvesting and realize laser control of energy flow in photosynthetic systems, explicit treatment of photoexcitation processes would be necessary. Furthermore, it was suggested recently that the photoexcitation condition determined by the nature of the photon source could strongly affect the appearance of EET dynamics in photosynthetic complexes [25,26,27,28,29]. As a result, the interplay between the photoexcitation process and ultrafast EET dynamics may be highly nontrivial, and it would be important to explicitly consider system-field interactions in a theoretical description of light harvesting. Therefore, an accurate yet numerically efficient method for simulating quantum dynamics in photosynthetic systems in the presence of coherent laser fields is highly desirable.
In this work, we combine a coherent modified Redfield theory (CMRT) [30] and a non-Markovian quantum jump (NMQJ) method [31,32,33] to develop an efficient approach for coherent EET dynamics in photosynthetic systems under the influence of light-matter interactions. The modified-Redfield theory [34,35] yields reliable population dynamics of excitonic systems and has been successfully applied to describe spectra and population dynamics in many photosynthetic complexes. [36,37,3] As a generalization of the modified-Redfield theory, the CMRT approach provides a complete description of quantum dynamics of the system's density matrix. Furthermore, we adopt a quantum jump method to achieve efficient propagation of the CMRT equations of motion. Using quantum jumps or quantum trajectories to unravel a set of equations of motion for the density matrix into a stochastic Schrödinger equation has proven to be a powerful tool for the simulation of quantum dynamics in an open quantum system [38,39,40,41,31,32]. In particular, Piilo et al. have developed a non-Markovian quantum jump (NMQJ) method that can be implemented for simulating non-Markovian dynamics in multilevel systems [31,32]. To make use of the NMQJ method, which has been shown to be efficient for simulating quantum dynamics in EET [33,42], we recast the CMRT equation into a generalized Lindblad form [43]. In addition, by transforming to a rotating frame, we extend CMRT to describe a time-dependent Hamiltonian, which is necessary to describe systems in the presence of laser fields. To examine the validity of the combined CMRT-NMQJ approach, we simulate the EET dynamics in the FMO complex. Moreover, we investigate a simple dimer system under the influence of laser pulses. We aim to shed light on how the photoexcitation process affects the coherence of EET dynamics in light harvesting. This paper is organized as follows. In the next section, we briefly describe CMRT for EET in a dimer system with time-independent Hamiltonian. Then, we extend the CMRT to treat time-dependent case by including interactions with a laser pulse applied to the dimer. Furthermore, in order to make the CMRT suitable for time-propagation by the NMQJ method, we show that the CMRT quantum master equation can be rewritten in a generalized Lindblad form. In section 3, we detail the implementation of the NMQJ method to solve the CMRT master equation. To verify the applicability of the new method, in section 4, we apply it to simulate the EET dynamics in FMO and compare the results to those from numerically exact simulations. Furthermore, in section 5, we calculate the dynamics of site populations and entanglement in a dimer system after it has been excited by a laser pulse to investigate the interplay between laser excitation and ultrafast EET in the system. Finally, the main results of this work are summarized in section 6. Figure 1. Energy diagram of a dimer for a time-independent case. The site energy for the single-excitation state |n is E n and the energy of the ground state |G is E 0 . The electronic coupling J between the single-excitation states is also considered.
Coherent Modified Redfield Theory
For the sake of self-consistency, we briefly describe the CMRT method in this section. Note that the focus of this work is the combined CMRT-NMQJ approach. The details in the derivation and validity of the CMRT approach is outside of the scope of this paper and will be published elsewhere [30].
Time-Independent Hamiltonian
Before proceeding to the case with a time-dependent Hamiltonian, we briefly review the CMRT approach for a time-independent Hamiltonian. The CMRT approach is a generalization of the original modified Redfield theory [34,35] to treat coherent evolution of the full density matrix of an excitonic system. For simplicity, we investigate the quantum dynamics in a dimer system (figure 1) described by the following excitonic Hamiltonian: where |n is the product state of the excited state on the nth site and the ground state on the other site, i.e., |1 = |e 1 |g 2 , |2 = |g 1 |e 2 , E n is the corresponding site energy, J is the electronic coupling between these two sites. Here, when there is no pulse applied to the system, the ground state |G = |g 1 |g 2 with energy E 0 is decoupled to the single-excitation states |n . Note that although the Hamiltonian for a dimer system is considered in this work, the results can be straightforwardly generalized to treat multi-site systems, e.g. the FMO with seven sites as shown in section 4. Furthermore, the environment is modeled as a collection of harmonic oscillators described by the Hamiltonian where p nq and x nq are respectively the momentum and position for qth harmonic oscillator on the nth site with mass m q and frequency ω q . Without loss of generality, we have assumed an independent bath for each site. Nevertheless, the CMRT can deal with a general system with correlated baths, whose effects on EET dynamics have been investigated in detail previously [44,21,45].
To describe exciton-bath interactions, we assume linear system-bath couplings described by where the collective bath-dependent coupling is where nq denotes the displacement of the qth harmonic mode when the nth site is excited.For the electronic part, we diagonalize the system Hamiltonian in the exciton basis [7] H (1) where each exciton basis state is a superposition of site-localized excitations with exciton energy ε ′ k . Since there is no interaction between the ground state and the single-excitation states, we have with In other words, Hereafter, for the sake of simplicity, we shall label |G as |0 .
Transforming H SB to the exciton basis, the system-bath interactions contain both diagonal and off-diagonal terms. Following the essence of the modified Redfield theory [34], we include the diagonal term of H SB into the zeroth-order Hamiltonian in the exciton basis to be treated nonperturbatively, and we only treat the off-diagonal term of H SB in the exciton basis as the perturbation. In the case, the unperturbed and perturbation Hamiltonians read respectively where a (1) Notice that a (1) kk ′ (n) = 0 as long as k = 0 or k ′ = 0 as a result of (9). We then apply the second-order cumulant expansion approach using V (1) as the perturbation followed by secular approximation to derive a quantum master equation for the reduced density matrix of the electronic system [34,46,30]: and the reorganization energy is Here ε (1) k can be viewed as the eigen value of the electronic Hamiltonian shifted by bath reorganization.The CMRT master equation (13) describes the coherent dynamics driven by the exciton Hamiltonian, the population transfer dynamics, and dephasing dynamics including pure-dephasing induced by diagonal fluctuations and populationtransfer induced dephasing. The dissipation rate R where g (1) is the line-broadening function evaluated from the spectral density of the system-bath couplings [46]: In addition, the pure-dephasing rate R (1)pd kk ′ (t) is given by The CMRT equation (13) can be considered as a generalization of the original modified Redfield theory to treat full coherent dynamics of the excitonic system. By treating the diagonal system-bath coupling in the exciton basis nonperturbatively, the CMRT considers multi-phonon effects and generally interpolates between the strong system-bath coupling and the weak system-bath coupling limits, in contrast to the Redfield theory [35,3]. Despite this, our method still shows its promising validity as compared to the numerically exact HEOM approach shown in section 4.
Functions required to propagate the CMRT master equation can be readily calculated if the system Hamiltonian H S and the spectral densities J n (ω) are provided. Generally speaking, the spectral densities used in theoretical investigations of photosynthetic systems are obtained by fitting to available experimental data, e.g., linear absorption lineshape or nonlinear spectra [7,47]. For self-consistency, the same theoretical method should be used in the fitting process of the parameters and dynamical simulations. Therefore, it is important that a dynamical method can also describe spectroscopy within the same framework. For example, the absorption lineshape defined as the Fourier transform of the dipole-dipole correlation function can be calculated, i.e., where the dipole-dipole correlation function is Within the framework of the CMRT under the same second-order approximation, the dipole-dipole correlation function reads [48,36] It corresponds to several peaks around the level spacings between the exciton states and the ground state, i.e., ε 0 . The second term in the exponential g kkkk (t) is the exchange-narrowed line-shape function [48], which is proportional to the participation ratio n C (1) kn 4 . Generally speaking, for a delocalized state n C (1) kn 4 is much smaller than that for a completely-localized state. Therefore, the widths of the peaks in the absorption line shape are significantly narrowed due to exciton delocalization. The third term in the exponential is the relaxation-induced broadening. It originates from the population relaxation within the single-excitation subspace when the molecule is excited by light, and has been demonstrated to be important in the spectra of molecular assemblies [36].
Coherent Modified Redfield Theory in the Presence of Laser Fields
To treat system-field interactions in the framework of the CMRT, we need to extend the CMRT equations of motion to include influences of laser fields. As shown in figure 2, during the time-evolution, we consider a dimer system interacting with a monochromatic pulse whose frequency is ω, thus inducing couplings between the ground state ε (1) 0 and delocalized exciton states ε (1) k (k = 1, 2). In this case, the electronic Hamiltonian reads where 2g k is the laser-induced coupling strength between the ground state ε . Transformed to a rotating frame with where we have applied the rotating-wave approximation and dropped the fast-oscillating terms with factors exp (±i2ωt). The above Hamiltonian can be diagonalized as where is the electronic eigen state with eigen energy ε ′′ k . In the basis of ε (2) k in the rotating frame, the unperturbed and perturbation Hamiltonians read respectively where a (2) Following the CMRT approach, we could obtain the master equation where ρ T (t) = U † ρ(t)U is the density matrix in the rotating frame, Here ε (2) k can be viewed as the eigen value of the electronic Hamiltonian In (31), the dissipation rate R (2)dis kk ′ (t) and pure-dephasing rate R (2)pd kk ′ (t) can be obtained following (17) and (21) by substituting ε Thus, the influence of the laser field can be considered as modifying the electronic part of the Hamiltonian by pulse induced couplings between ground state and singleexcitation states in the rotating frame. Notice that due to the transformation, the density matrix in the original frame ρ(t) is transformed to ρ T (t) = U † ρ(t)U. Hence, after solving the master equation, the obtained density matrix should be transformed back to the original frame as ρ(t) = Uρ T (t)U † . We further remark that (31) enables us to simulate field-induced and dissipative dynamics for an excitonic system due to a step-function pulse. However, this is not a limitation because of the time-local nature of the equations of motion. Within any propagation scheme based on discrete time steps, this formalism can be easily generalized to arbitrary pulse shapes as discussed in Appendix A.
Lindblad-form Coherent Modified Redfield Theory
We have obtained the CMRT master equation that governs the dynamics of an excitonic system in the presence of laser fields. For a system with M chromophores, the CMRT master equation corresponds to a set of M 2 ordinary differential equations. As a consequence, it would be a formidable task to propagate the dynamics if the system has hundreds of sites, just as in a typical photosynthetic complex, e.g., 96 chlorophylls in PSI. We can apply the NMQJ method [31,32] to reduce the computational complexity to the order of M to achieve efficient simulation of the CMRT dynamics. The NMQJ method propagates a set of equations of motion in the generalized Lindblad form [49]: Thus, in order to solve the master equation (cf. (13) and (31)) by means of the NMQJ method, we should rewrite them in the generalized Lindblad form. To this end, we define the following jump operators where |ε k (t) is kth eigen state of the system Hamiltonian H e (t), and the matrix elements of the population transfer and dephasing rates are defined respectively as where Γ k s are determined by the pure-dephasing rates in the CMRT master equation: where the matrix elements of A and B are respectively Note that since there are M(M − 1)/2 independent pure-dephasing rates R pd kk ′ in the CMRT master equation and only M Lindblad-form dephasing rates Γ k , the problem is overdetermined. We found that a least-square fit of Γ k to R pd kk ′ preserves the CMRT dynamics extremely well and is also numerically straightforward to implement. The details of the numerical determination of Lindblad-form dephasing rates are presented in Appendix B.
Non-Markovian Quantum Jump Method
We apply the non-Markovian quantum jump (NMQJ) approach proposed by Piilo et al. to simulate the CMRT equation of motion for multilevel systems [31,32]. The original implementation of the NMQJ approach only considers a time-independent Hamiltonian. In order to apply the NMQJ method to simulate CMRT dynamics under the influence of laser fields, here we briefly describe the NMQJ method and show how to explicitly implement the method for the time-dependent case to propagate the quantum dynamics of an M-level system under the influence of external time-dependent fields.
In our theoretical investigation, we consider the system to be initially in the ground state |ψ M (0) = |G and thus the initial density matrix is Then, the laser is turned on and applied to the system for a duration τ starting at t = 0. According to the NMQJ method, for an M-level system, there are M + 1 possible states for the system to propagate in, i.e., ε (2) k for k = 0, 1, · · · M − 1, in addition to the deterministic evolution where the non-Hermitian Hamiltonian is If N ensemble members are considered in the simulation, the NMQJ method describes the number of ensemble members in each of the individual states at time t, N (I) α (t) (α = 0, 1, · · · M), with the initial value given as During the pulse duration, the Lindblad-form master equation (31) is solved by the NMQJ method to obtain the distribution of N (I) α (t). Therefore, the density matrix in the rotating frame is straightforwardly given by At the end of the pulse, we transform the density matrix back to the original frame by where β k s are the expanding coefficients in the basis ε (1) k . Following the end of the pulse, the laser field is switched off and the system then evolves free of laser pulse influence, yet still follows the CMRT master equation describing population relaxation and dephasing in the exciton basis. In this case, since ε Thus, to describe both laser-induced dynamics and dissipative dynamics of an excitonic system with M chromophores, we must consider 2M + 1 possible states for the system to propagate in, i.e., ε Finally, the density matrix can be calculated from the time-dependent distribution of numbers of ensemble members in states N (II) We remark that the above procedure can be generalized to simulate arbitrary pulse shape as long as the laser pulses only induce transitions between the ground state and single-excitation states. In this case, the eigen states in the rotating frame are always the superpositions of the ground state and single-excitation states. Therefore, once the instantaneous eigen states are changed due to the change in system-field interaction, M new trajectories will be initialized as the previous eigen states are replaced by the eigen states of the new Hamiltonian. In Appendix A we present the numerical details for simulating the interaction between an excitonic system and a laser pulse with arbitrary pulse profile.
The generalization of the combined CMRT-NMQJ method for a system in the presence of laser fields can be easily extended to describe multiple pulses to enable the simulation of N-wave-mixing optical experiments. For example, the method described in this work can be applied to calculate third-order response functions [46]. Additionally, since the combined CMRT-NMQJ approach describes the full laser-driven dynamics of a dissipative excitonic system, it can be used together with the density matrix based approach for the calculation of photon-echo signals [50,51] to evaluate nonlinear spectra, including 2D electronic spectra and photon-echo peakshift signals. Although 2D electronic spectra can not be calculated without 2-exciton manifold, it would be straightforward to generalize the results presented in this work to include dynamics in the two-exciton manifold [34,50]. As a result, nonlinear spectra for coupled systems, which are often difficult to calculate due to the need to average over static disorder, can be calculated by using the combined CMRT-NMQJ approach. We emphasize that in addition to the favorable system-size scaling due to dynamical propagation in the wavefunction space, this approach benefits from the fact that the average over static disorder can be performed simultaneously with the average over quantum trajectories. Therefore, the combined CMRT-NMQJ method should provide a much more numerically efficient means for the evaluation of nonlinear spectra in large condensed-phase systems.
Note that in addition to the NMQJ method, quantum trajectory methods such as the non-Markovian quantum state diffusion approach [39,40] offer alternatives to the unraveling of quantum dynamics in light-harvesting systems [33,52]. In that approach, an integration of memory kernel and a functional derivative of the wavefunction with respect to the noise are required to obtain the stochastic Schrödinger equation. Recently, de Vega has applied the quantum state diffusion approach within the socalled post-Markov approximation to describe non-Markovian dissipative dynamics of a network mimicking a photosynthetic system [52]. However, generally speaking it is not straightforward to unravel a non-Markovian quantum master equation using the quantum state diffusion approach. In this work we aim to focus on the CMRT method, which is a time-local quantum master equation with a generalized Lindblad form. As a result the quantum jump approach proposed by Piilo and coworkers seems to be a natural way to implement the propagation of the time-local CMRT equation. A detailed comparison of the numerical efficiency and domains of applicability of the various quantum jump and quantum trajectory based methods is given in Refs. [41] and [32]. We believe these methods could provide superior numerical performance as well as useful physical insights for EET dynamics in complex and large systems.
EET Dynamics of FMO
In the previous sections, by combing the CMRT with the NMQJ method, we have proposed an efficient approach to simulate the EET dynamics of photosynthetic systems in the presence of laser fields. In order to implement the NMQJ method, the master equation obtained by the CMRT is revised in the Lindblad form. Since several approximations are made in order to efficiently simulate the quantum dynamics of a large quantum open system for a broad range of parameters, it is necessary to verify the validity of this new approach for photosynthetic systems. [24]) is used in both calculations. Solid curves are obtained by the CMRT-NMQJ approach, and dashed curves depict results from the HEOM method. Red curves are for site 6, green curves for site 3, blue curves for site 4, and black curves for site 5. In order to make the figure clear, population dynamics for other sites whose populations always stay less than 0.1 are not shown here.
As a numerical demonstration, in figure 3, we compare the population dynamics of EET in FMO simulated by two different approaches, i.e., the combined CMRT-NMQJ approach and the numerically exact HEOM method from Ishizaki and Fleming [24]. Clearly, the results from the two methods are in good agreement. The combined CMRT-NMQJ method does not only reproduce the coherent oscillations in the shorttime regime but is also consistent with the HEOM in the long-time limit. In the shorttime regime the oscillations are somewhat suppressed in the case of CMRT-NMQJ approach. This is likely due to the simplification of the pure-dephasing process by the direct projection operations. If more realistic dephasing operators as implemented in [53] is applied to simulate the pure-dephasing process, we would expect a better accuracy from the CMRT-NMQJ approach. However, this additional effort may require much more computational time with only marginal improvements for EET dynamics in photosynthetic systems. Therefore, it is a reasonable tradeoff for numerical efficiency that we adopt the Lindblad-form CMRT and make use of the NMQJ method to simulate quantum dynamics of realistic photosynthetic systems Note that because the NMQJ is a numerical propagation scheme, the accuracy of the relaxation dynamics calculated by this method actually depends on the Lindbladform CMRT, which is effectively a generalization to the widely used modified-Redfield theory in terms of population dynamics. Modified-Redfield approach has been proven to provide excellent results for biased-energy systems in photosynthesis [3], therefore we expect the combined CMRT-NMQJ method described in this work to be broadly applicable to EET in photosynthetic systems.
Dimer Model
To demonstrate the combined CMRT-NMQJ simulation of laser-driven dynamics, we investigate the behavior of a model dimer system under the influence of laser excitation in this section. Note that in previous simulations of EET dynamics in photosynthetic systems the photoexcitation process was often overlooked and the instantaneous preparation of a given initial condition at zero time is normally assumed. Given that the selective preparation of a specific excitonic state is a highly non-trivial task (in particular the site-localized states often assumed in coherent EET simulations), it is crucial that an experimentally realistic photoexcitation process can be considered in a complete dynamical simulation [27,28]. Here we show that the combined CMRT-NMQJ approach is capable of describing the excitation process. We further investigate the behavior of a model dimer system (figure 2) under the influence of laser excitation to study the interplay between the laser excitation and ultrafast EET dynamics.
Absorption Spectrum
Before we examine the laser-induced dynamics we demonstrate that the absorption spectrum can be obtained by the CMRT method (cf. (24)). In figure 4, we show numerically calculated absorption lineshape for dimer system with ground state energy E 0 = −12800cm −1 , site energies E 1 = 120cm −1 and E 2 = 100cm −1 , electronic coupling J = 300cm −1 , and the electronic transition dipole moments in the eigen basis are set to be µ 10 = 10D and µ 20 = 5D, respectively. We further assume identical Ohmic baths described by the spectral density with the reorganization energy λ = 35cm −1 and the cut-off frequency ω c = 50cm −1 , and temperature T = 300K. The simulated spectrum exhibits two peaks located at ε 2 . Figure 4 shows that the absorption lineshape is well captured by the CMRT approach, which is important if a comparison to experimental data is to be made [36,47]. The relative peak heights are given by the ratio of |µ k0 | 2 . Here, the widths of the two peaks are nearly the same as the effect of relaxation-induced broadening is not significant in this particular parameter set. Physically speaking, the pure-dephasing process is much faster than the energy relaxation from the high-energy state ε (1) 2 to the low-energy state ε (1) 1 when the dimer system is excited. For other cases, the contribution from the relaxation-induced broadening might play a more important role [36].
Laser-induced Dynamics
In this section, we apply the CMRT-NMQJ approach to investigate the quantum dynamics of a dimer system after laser excitation. As shown in figure 5, the dimer system initially in the ground state is applied with a square laser pulse during the time period [0, t 1 ]. Note that a square pulse is used here for the sake of simplicity (see Appendix A). Due to the coupling induced by the laser pulse, the dimer is coherently excited into a superposition between the ground state and the single-excitation states. After the laser is turned off at t = t 1 , the dimer is left alone to evolve under the influence of system-bath couplings. The energy diagrams for the case with and without a laser pulse are depicted in figures 2 and 1, respectively. In our numerical simulation, we adopt the following parameters to model photosynthetic systems: the ground state energy E 0 = −12800cm −1 , the site energies E 1 = 200cm −1 and E 2 = 100cm −1 . Moreover, we consider two specific cases J = 120cm −1 and J = 20cm −1 to explore the electronic coupling's influence on the dynamics. We further assume identical Ohmic spectral densities (cf. (52)) for each site with a reorganization energy of λ = 35cm −1 , cut-off frequency ω c = 50cm −1 , and temperature T = 300K. In all cases, the laser duration t 1 is set to 100fs. In addition, in order to make the results converge within a reasonable computational time, we use a moderate time step dt = 1fs and average over a sufficiently-large number of trajectories, i.e., N = 10 5 . Figures 6(a) and 6(b) show the timeevolution of the site populations initialized by a laser pulse for strongly (J = 120cm −1 ) and weakly (J = 20cm −1 ) electronically coupled dimers, respectively. The laser carrier frequency is tuned at ω = 13000cm −1 , close to both transition energies. Therefore we expect the laser pulse to induce significant coherence between the two exciton states. As a result, during the pulse duration (from t = 0fs to t = 100fs), the populations on both sites oscillate with large amplitudes, for the laser field is nearly on resonance with the transitions between the ground state |ε After the laser is turned off at t = 100fs, the population on the ground state remains invariant since there is no relaxation from the single-excitation states to the ground state in our model (cf. (3)), however, dynamics in the single-exciton manifold show strong J dependence. A comparison between figures 6(a) and 6(b) shows that although both systems evolve to reach a thermal equilibrium due to the system-bath couplings, the relaxation dynamics are qualitatively different due to the different electronic coupling strengths. In the case with strong electronic coupling (figure 6(a)) coherent oscillations persist for up to 400fs, and the system then quickly relaxes to the equilibrium. Clearly, the coherent energy relaxation is extremely efficient in this case and the photoexcitation dynamics and the coherent dynamics are intertwined [54,42]. We conclude that in strongly coupled systems the nature of the photoexcitation process must be explicitly considered in order to reasonably describe the photon-induced dynamics, as a fictitiously assumed initial condition prepared instantaneously at t = 0 could not correctly capture the photoexcitation process. In contrast, for the weak electronic coupling case in figure 6(b), the coherent oscillations stop immediately after the pulse is turned off, and the system relaxes incoherently and also slowly. In this case a clear time-scale separation exists and a specific consideration of the photoexcitation process may not be necessary.
Laser-induced Population dynamics
To investigate the effects of laser excitation condition, we explore the effect of laser detuning on the quantum dynamics in figure 7. In this case, the laser carrier frequency is tuned at ω = 13400cm −1 . Because the laser is largely detuned from the transition between |ε can not be effectively excited when the pulse is present [55]. Indeed, figure 7(b) shows that in this weak electronic coupling case, site 1 is selectively excited within the pulse duration, since it is the higher-energy site in our dimer model. Consequently, figure 7(b) indicates that specific initial state, e.g., a specific site being excited, may be prepared if a proper pulse is applied for an appropriate duration. In contrast, figure 7(a) shows that the oscillation amplitudes of site populations, P 1 and P 2 , are nearly the same in the strong electronic coupling case, as in this case both sites contribute significantly to the delocalized exciton state |ε (1) 2 . Noticeably, the excitation-relaxation dynamics presented in figures 6(a) and 7(a) are markedly different, i.e. no coherent evolution in figure 7(a) after t = 100fs. Clearly, the dynamical behavior of the strongly coupled dimer depends critically on the excitation conditions. Our results further emphasize the notion that the photoexcitation process must be explicitly considered [27,28]. Note that we aim to demonstrate the validity of the combined CMRT-NMQJ method developed in this work. It is clear that much more efforts must be paid to elucidate the effects of light source in natural light-harvesting processes. In this regard, the methodology developed in this work appears to be valuable for the study of coherent EET dynamics and quantum coherent control in photosynthetic systems.
Entanglement dynamics
In the population dynamics, coherent evolutions are present when the laser excitation or the electronic coupling is strong. To more effectively follow coherent EET dynamics, it has been shown that entanglement is a excellent probe for coherent dynamics in photosynthetic systems [56,57,58,59,60,61,62]. Here, we investigate the time-evolution of entanglement in the dimer system as a demonstration of the combined CMRT-NMQJ method for coherent EET dynamics. Because each pigment in the dimer is a natural qubit, we especially focus on the evolution of concurrence [63]. For a two-qubits system with arbitrary density matrix ρ, the concurrence is defined as [63] where λ j s are the square roots of eigen values of the matrix ρρ in the descending order. Here,ρ = (σ y ⊗ σ y ) ρ * (σ y ⊗ σ y ) with ρ * being the complex conjugate of the density matrix ρ. For a purely classical system, its concurrence vanishes, while it would be unity for the maximum entangled states, e.g., Bell states. For other states, the concurrence monotonously increases as the state is more quantum-mechanically entangled. An analytical expression that yields C(t) from the matrix elements of ρ(t) can be derived. In our model, since we restrict the evolution of the system within the sub-space without the double-excitation state, the density matrix is of the block type where the basis states are |0 = |g 1 |g 2 , |1 = |e 1 |g 2 , |2 = |g 1 |e 2 , and |3 = |e 1 |e 2 . Thus, the concurrence is simplified as Because ρ 11 +ρ 22 ≥ 2 |ρ 21 |, the more population is excited to the single-excitation states, the larger the concurrence could be. In addition, at some time, when ρ 21 vanishes, we would expect the system evolves into the disentangled state as the concurrence disappears. In figure 8, we plot concurrence dynamics for the condition that the excitation pulse is in near resonance with both transitions. For both strong and weak electronic coupling cases, there exists significant entanglement induced by the optical excitation. However, the concurrence dynamics after the laser is switched off at 100fs are markedly different. Figure 8(a) shows that for the strongly coupled dimer the concurrence quickly evolves to reach a steady state with a considerable value as a result of the large electronic coupling. In contrast, for the weak electronic coupling case shown in figure 8(b), the concurrence rapidly decays to near zero and then follows by a continuous but slow rise. These results are consistent with the characteristics of population dynamics as shown in figure 6, indicating that the concurrence provides an effective tool to describe coherent energy transfer process.
Moreover, the evolution of concurrence with only one exciton state selectively excited is shown in figure 9. In the duration of pulse, the maximum entanglement generated by the field is significantly smaller than that by a resonance field. This is because less coherence between the two transitions can be created as a consequence of the laser de-tuning. In addition, because the laser is only in close resonance with one of the transitions, as shown in figure 9(a), the concurrence at the steady state is less than that achieved in figure 8(a), as less population can be excited to the single-excitation states by the weaker pulse. This discovery again emphasizes the importance of the state preparation by photoexcitation.
Besides, we notice that for all cases there is an abrupt change at t = 100fs due to the discontinuity of the system-field interaction, which is an artifact of the stepfunction pulse shape. We remark that this problem can be solved by using a smooth pulse profile. In a realistic experiment, a gaussian pulse should be applied to the system. The combined CMRT-NMQJ approach can be generalized to consider a gaussian pulse by decomposing it into a series of rectangular sub-pulses with equal width (see details in Appendix A). Again, these results indicate that the photoexcitation process plays an important role in the induction of initial coherence in excitonic systems, therefore system-field couplings must be explicitly considered in order to elucidate electronic quantum coherence effects in photosynthesis [28,14].
Conclusions
In this paper, we have combined the CMRT [30] approach and the NMQJ method [31,32] to develop a formalism that provides efficient simulations of coherent EET dynamics of photosynthetic complexes under the influence of laser fields. In order to implement the NMQJ propagation of the CMRT master equation, the original CMRT master equation is revised in the Lindblad form. In addition, the NMQJ approach is generalized to be suitable for the case with a time-dependent Hamiltonian due to interactions with laser fields. These new developments allow the efficient calculation of quantum dynamics of photosynthetic complexes in the presence of laser fields.
To demonstrate the effectiveness and efficiency of this new approach, we apply the CMRT-NMQJ approach to simulate the coherent energy transfer dynamics in the FMO complex, and compare the result to the one obtained by the numerically exact HEOM method. We show that both results are consistent in the long-time and short-time regimes. Furthermore, we investigate photon-induced dynamics in a dimer system. For strongly coupled dimers, coherent oscillations are observed in the population dynamics during the pulse duration as well as the free evolution as predicted by theory. In addition to the quantum dynamics in the excited states, we also consider coherent effects induced by the laser detuning in the stage of state preparation. We show that the dynamical behavior of a strongly coupled dimer depends critically on the excitation conditions, which further emphasizes the point that the photoexcitation process must be explicitly considered in order to properly describe photon-induced dynamics in photosynthetic systems. In addition, we investigate the evolution of concurrence of a dimer in the presence of laser fields. These results demonstrate the combined CMRT-NMQJ approach is capable of simulation of EET in photosynthetic complexes under the influence of laser control. Note that although the phases of the fields are fixed in our calculations, the combined CMRT-NMQJ method is fully capable of describing quantum coherent control phenomena by shaping phases of the laser fields.
Compared to other numerical simulation methods, this new approach has several key advantages. First of all, because it is based on the CMRT, its validity in a broad range of parameters has been well characterized [3,30], which is confirmed in simulating the energy transfer dynamics of the FMO complex. Note that the CMRT might fail in the regime where the exciton basis is not a suitable choice [3], e.g., λ > J > ∆ε. In that case, a combined Redfield-Förster picture could be used to provide accurate simulations of EET dynamics [64,65]. Because the generalized Förster dynamics only add diagonal population transfer terms between block-delocalized exciton states, which directly fit into a Lindblad form, an extension of the methods describe here to simulate EET dynamics in the Redfield-Förster picture will be straightforward. Second, as this approach incorporates the NMQJ method, it effectively reduces the calculation time [31] and therefore is capable of simulating the quantum dynamics in large photosynthetic systems. For an M-level system in the absence of laser fields, the effective ensemble size N eff = M + 1 in the CMRT-NMQJ approach can be sufficiently smaller than that needed in the non-Markovian quantum state diffusion, e.g. N = 10 4 for a two-level system in Ref. [40]. In addition, the problem of positivity violation, which may be encountered by master equation approaches, can be resolved by turning off the quantum jump once the population of the source state vanishes. Third, it can make use of parallel computation, because the sampling of trajectories can be efficiently parallelized, and it can take the average over static disorder at the same time as the average over trajectories. Finally, since the absorption spectrum [36] and other nonlinear optical signals should be efficiently simulated within the same theoretical framework, all the parameters used in the simulation can be self-consistently obtained by fitting the experimental data using this theoretical approach.
Our motivation to extend the CMRT theory to include time-dependent fields and NMQJ propagation is to provide a tool for the study of quantum control of photoexcitation and energy flow. In order to simulate EET dynamics in a coherent control experiment, it is necessary to consider a time-dependent Hamiltonian that includes the influence of control laser fields. Quantum coherent control lies at the heart of human's exploration in the microscopic world [66,67,68]. Its amazing power manifests in manipulating quantum dynamics by systematic control design methods [69,70,71,72]. The strong motivation to apply quantum coherent control to EET lies in not only the aspiration to knowledge but also the energy need to learn from the highly-efficient photosynthetic systems to improve artificial light harvesting. We believe the methodologies presented in this work would be useful for further investigation of using coherent quantum control methods to direct energy flow in photosynthetic as well as artificial light-harvesting systems. For example, we are currently using the theoretical framework described in this work to investigate how laser pulse-shape and -phase affect EET dynamics in photosynthetic systems. where a = 1, 2, . . . M. After simplification, we obtain a system of linear equations for the Lindblad-form dephasing rates, that is where the coefficient on the right hand side is Or equivalently, (B.6) can be written in a matrix form as with the matrix elements of B given by as given in (37). | 9,968 | 2014-04-08T00:00:00.000 | [
"Physics"
] |
EDUCATION MANAGEMENT RESEARCH DATA ANALYSIS: COMPARISON OF RESULTS BETWEEN LISREL, TETRAD, GSCA, AMOS, SMARTPLS, WARPPLS, AND SPSS FOR SMALL SAMPLES
Abstract - The purpose of this study is to compare the results of quantitative research data processing in the field of education management using Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS software for small samples or respondents. This research method is quantitative and research data analysis uses the four types of software to obtain a comparison of the results of the analysis. The analysis in this study focuses on the analysis of hypothesis testing and regression analysis. Regression analysis is used to measure how much influence the independent variable has on the dependent variable. The field of this research is education management and the research data uses quantitative data derived from questionnaire data for a small sample of 32 respondents with three research variables, namely the independent variable of transformational leadership and job satisfaction, while the dependent variable is teacher performance. Based on the results of the analysis using Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS software, the results showed that for a small sample there was no significant difference in the significance value of p-value and t-value. There is also no significant difference in the determination value, and the correlation value in the resulting structural equation also has no significant difference in results, while for CB-SEM represented by Lisrel, Tetrad cannot process data with a small sample size.
INTRODUCTION
Many researches in the field of education management use statistical software tools such as Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS. Many researchers in the field of education management are still hesitant in choosing which software to use. There are many researches in the field of education that use SmartPLS, such as those conducted by Budi Santoso, P., Asbari, M., Siswanto, E., & Fahmi, K. (2021). examines the role of job satisfaction and organizational citizenship behavior on performance. Putra, F., Asbari, M., Purwanto, A., Novitasari, D., & Santoso, P. B. (2021) investigated linking social support and performance in college. Johan, M. (2021) examines the effect of knowledge sharing and interpersonal trust on innovation. Purwanto, A., Santoso, PB, Siswanto, E., Hartuti, H., Setiana, YN, Sudargini, Y., & Fahmi, K. (2021) the effect of hard skills, soft skills, Nidhomul Haq, Vol 6, Issue 2, 2021 Agus Purwanto et al 384 organizational learning and innovation capability on performance lecturer. Novitasari, D., Asbari, M., Purwanto, A., Fahmalatif, F., Sudargini, Y., Hidayati, L. H., & Wiratama, J. (2021). examined the effect of social support factors on the performance of elementary school teachers. Anas Ahmadi, E., Herwidyaningtyas, F.B., & Fatimah, S. (2020) examined the influence of organizational culture, work motivation, and job satisfaction on lecturer management performance. Jayus, J.A. (2021). Examining the effect of distributive justice, procedural justice and interactional justice on teacher involvement and teacher performance. Ahmed, U., Umrani, W. A., Qureshi, M. A., & Samad, A. (2018). examined the relationship between teacher support, academic efficacy, academic resilience, and student engagement in Bahrain. There are many researches in the field of education that use SPSS, such as those conducted by ebjan, U., & Tominc, P. (2015) on the impact of teacher support and conformity with learning needs on the use of SPSS by students. Murtiningsih, M., Kristiawan, M., & Lian, B. (2019)
METHOD
This research method is quantitative, research data analysis uses Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS software to obtain a comparison of the results of the analysis. The analysis in this study
Result and Discussion
A. Testing the Significance of t-Value The first stage of data analysis is testing the significance of the relationship between the independent variable transformational leadership (X), job satisfaction (Y1) with the dependent variable teacher performance (Y2) by looking for t-Value using Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS, the decision criteria if the t-Value value is greater than 1.96 or > 1.96 then the relationship is significant, if less than 1.96 or < 1.96 then the relationship is not significant. For WarpPLS does not produce a t-statistic value, the significance test can be seen on the p-value, so that the tstatistic value will be obtained. The test results with 4 software for a direct relationship can be seen in Table 1 Relationship between transformational leadership (X) and job satisfaction (Y1) Based on the results of the software analysis, the results of the t-Value using Amos data cannot be processed. The t-Value using SmartPLS is 89,509, which is greater than 1.96, so it can be concluded that the relationship between X and Y1 is significant. The result of t-Value using SPSS is 21.424 which is greater than 1.96 so that it can be concluded that the relationship is significant so that it can be concluded that SmartPLS and SPSS give the same results.
Relationship between transformational leadership (X) and performance (Y2). Based on the results of the software analysis, the results of the t-Value using Amos using Amos data cannot be processed. The t-Value using SmartPLS is 1.960, which is greater than 1.96, so it can be concluded that the relationship between X and Y2 is significant. The results of the t-Value using SPSS of 2.125 are greater than 1.96 so that it can be concluded that the relationship between X and Y2 is significant, so it can be concluded that SmartPLS and SPSS give the same results.
The relationship between transformational leadership (X) and performance (Y2) through job satisfaction (Y1) Based on the results of the software analysis, the results of the t-Value using Amos data cannot be processed. The result of t-Value using SmartPLS is 0.822 which is smaller than 1.96 so that it can be concluded that the relationship between X and Y2 through Y1 is not significant. The result of t-Value using SPSS of 1.051 is smaller than 1.96, so it can be concluded that the relationship between X and Y2 through Y1 is not significant, so it can be concluded that SmartPLS and SPSS give the same results.
B. Testing the Significance of p-Value
The second stage is data analysis, namely testing the significance of the relationship between the independent variable transformational leadership (X), job satisfaction (Y1) with the dependent variable teacher performance (Y2) by looking for pvalue using SPSS, Amos, SmartPLS, WarpPLS and SPSS software. The decision is that if the p-value is less than 0.050 or <0.050 then the relationship is significant, if it is more than 0.050 or >0.050 then the relationship is not significant. The test results with 4 software for direct connection are as follows: Relationship between transformational leadership (X) and job satisfaction (Y1) Based on the results of the software analysis, the p-value results using Amos data cannot be processed. The p-value using SmartPLS is 0.000 less than 0.050 so it can be concluded that the relationship between X and Y1 is significant. The p-value using WarpPLS is 0.000 less than 0.050 so it can be concluded that the relationship is significant. The p-value using SPSS is 0.000 less than 0.050, so it can be concluded that the relationship between X1 and Y1 is significant, so it can be concluded that WarpPLS, SmartPLS and SPSS give the same results.
The relationship between transformational leadership (X1) and performance (Y2) Based on the results of the software analysis, the p-value results using Amos data cannot be processed. The p-value using SmartPLS is 0.046 which is smaller than 0.050, so it can be concluded that the relationship between X and Y2 is significant. The result of p-value using WarpPLS is 0.410 less than 0.050 so that it can be concluded that the relationship between X and Y2 is significant. The result of the p-value using SPSS is 0.042 less than 0.050 so it can be concluded that the relationship between X and Y2 is significant t so that it can be concluded that WarpPLS. SmartPLS and SPSS give the same result. The relationship between transformational leadership (X1) and performance (Y2) through job satisfaction (Y1) Based on the results of software analysis, the p-value using SmartPLS was 0.411 more than 0.050, so it was concluded that the relationship between X and Y2 through Y1 was not significant. The result of p-value using WarpPLS is 0.450 which is greater than 0.050 so that it can be concluded that the relationship between X and Y2 through Y is not significant. The results of the p-value using SPSS of 0.302 is greater than 0.050 so that it can be concluded that the relationship between X and Y2 through Y is not significant.
Coefficient of Determination Test
Testing the coefficient of determination to calculate the influence of the independent variable on the dependent variable. In this study, the R Square termination coefficient was calculated for the independent variables of transformational leadership (X), Job Satisfaction (Y1) and Performance (Y2 Table 4, the R Square value for Performance (Y2) using Amos cannot be run. The value of R Square for Performance (Y2) using SmartPLS is 0.852 or 85.2%, meaning that the performance variable (Y2) is influenced by transformational leadership variables (X) and job satisfaction (Y1) is 85.2% while the remaining 14.8% is influenced by other variables not discussed in this study. The R Square value for Performance (Y2) using SmartPLS is 0.85 or 85%, meaning that the performance variable (Y2) is influenced by transformational leadership variables (X) and job satisfaction (Y1) by 85% while the remaining 15% is influenced by other variables. which were not discussed in this study. The value of R Square for Performance (Y2) using SmartPLS is 0.844 or 84.4%, meaning that the performance variable (Y2) is influenced by transformational leadership variables (X) and job satisfaction (Y1) is 84.4% while the remaining 15.6% is influenced by other variables not discussed in this study.
Correlation Coefficient Test
The correlation coefficient shows the strength of the linear relationship and the direction of the relationship between variables. If the correlation coefficient is positive, then the two variables have a unidirectional relationship (Purwanto et al, 2020). This means that if the value of the variable X is high, then the value of the variable Y will be high as well. Conversely, if the correlation coefficient is negative, then the two variables have an inverse relationship. This means that if the value of the variable X is high, then the value of the variable Y will be low and vice versa. According to Hair et al (2017) to make it easier to interpret the strength of the relationship between two variables, the following criteria are provided: The results of structural equations using Lisrel, Tetrad and Amos software cannot be run. The results of the structural equation using SmartPLS software obtained the equation is Y2 = 0.642X1+0.287X2+e, meaning that the correlation coefficient value of the influence of transformational leadership variable (X) on performance (Y2) is 0.642, meaning that there is a strong correlation and indicates that if the value of transformational leadership ( X) increases by 1 unit, while the value of job satisfaction (X2) remains, the performance value (Y2) will increase by 0.642 units. This means that the partial effect of transformational leadership on performance is 64.2%. The correlation coefficient value of the influence of job satisfaction variable (Y1) on performance (Y2) is 0.287, meaning that there is a sufficient correlation and shows that if the value of job satisfaction (Y1) increases by 1 unit, while the value of transformational leadership (X) remains, the performance value (Y2 ) will increase by 0.287 units . This means that the effect of job satisfaction (Y1) on performance partially is 28.7%.
The results of the structural equation using WarpPLS software obtained the equation is Y2 = 0.658X1+0.271X2+e, meaning that the correlation coefficient value of the influence of transformational leadership The results of the structural equation using SPSS software obtained the equation is Y2=0.219+ 0.642X1 + 0.324Y1 + e, meaning that the correlation coefficient value of the influence of transformational leadership variable (X) on performance (Y2) is 0.642, meaning that there is a strong correlation and indicates that if the value of leadership transformational (X) increases by 1 unit, while the value of job satisfaction (X2) remains, the performance value (Y2) will increase by 0.642 units plus a constant of 0.219 units. This means that the partial effect of transformational leadership on performance is 64.2%. The correlation coefficient value of the influence of job satisfaction variable (Y1) on performance (Y2) is 0.324, meaning that there is a sufficient correlation and indicates that if the value of job satisfaction (Y1) increases by 1 unit, while the value of transformational leadership (X) remains, the performance value (Y2 ) will increase by 0.324 units plus the constant 0.219 units . This means that the effect of job satisfaction (Y1) on performance partially is 32.4%.
The results of the structural equation using the GSCA software obtained the equation is Y2=a+ 0.703X+ 0.229Y1 + e, meaning that the correlation coefficient value of the influence of transformational leadership variable (X) on performance (Y2) is 0.704, meaning that there is a strong correlation and shows that if the value of transformational leadership ( X) increases by 1 unit, while the value of job satisfaction (X2) remains, the performance value (Y2) will increase by 0.703 units n. This means that the partial effect of transformational leadership on performance is 70.3%. The correlation coefficient value of the influence of job satisfaction variable (Y1) on performance (Y2) is 0.229 meaning that there is a sufficient correlation and indicates that if the value of job satisfaction (Y1) increases by 1 unit, while the value of transformational leadership (X) remains, the performance value (Y2 ) will increase by 0.229 units plus the constant 0.229 units . This means that the effect of job satisfaction (Y1) on performance partially is 22.9%.
CONCLUSION
Based on the results of the analysis using GSCA, SPSS, SmartPLS and WarpPLS software, the results showed that for a small sample there was no significant difference in the significance value of p-value and t-value. There is also no significant difference in the determination value produced, and the correlation value in the resulting structural equation also has no significant difference in results, while for CB-SEM represented by | 3,301.2 | 2021-08-01T00:00:00.000 | [
"Education",
"Computer Science"
] |
An Open Cloud Model for Expanding Healthcare Infrastructure
with the rapid improvement of computation facilities, healthcare still suffers limited storage space and lacks full utilization of computer infrastructure. That not only adds to the cost burden but also limits the possibility for expansion and integration with other healthcare services. Cloud computing which is based on virtualization, elastic allocation of resources, and pay as you go for used services, opened the way for the possibility to offer fully integrated and distributed healthcare systems that can expand globally. However, cloud computing with its ability to virtualize resources doesn't come cheap or safe from the healthcare perspective. The main objective of this paper is to introduce a new strategy of healthcare infrastructure implementation using private cloud based on OpenStack with the ability to expand over public cloud with hybrid cloud architecture. This research proposes the migration of legacy software and medical data to a secured private cloud with the possibility to integrate with arbitrary public clouds for services that might be needed in the future. The tools used are mainly OpenStack, DeltaCloud, and OpenShift which are open source adopted by major cloud computing companies. Their optimized integration can give an increased performance with a considerable reduction in cost without sacrificing the security aspect. Simulation was then performed using CloudSim to measure the design performance.
INTRODUCTION
Although much research effort has been put into the design and development of novel e-Health services and applications, their interoperability and integration remain challenging issues.Health services deal with large amount of private data which needs to be both fully protected and readily available to clinicians.However, health care providers often lack the capability to commit the financial resources for either the research or the infrastructure required.In addition, the public is often skeptical about whether IT systems can be trusted with private clinical information.Past failures have fuelled a culture to restrict any innovation by both members of the public and the politicians responsible for health services [1].
Cloud computing has recently appeared as a new computing paradigm, which promises virtually unlimited resources.Customers rent resources based on the pay-as-yougo model and thus are charged only for what they use.Opposite to other service models, in-house Picture Archiving and Communication System (PACS) and application service provider PACS [2], cloud computing offers relatively lower cost, higher reliability and scalability as shown in table 1.The cloud system can be divided mainly into three main layers.The infrastructure as a service (IaaS) is the lowest level which delivers computing infrastructure as a service to end users.IaaS is typically provided as a set of APIs that offer access to infrastructure resources.The APIs allow creating virtual machine instances, or to store or retrieve data from a storage or database.The main benefit of virtualized infrastructure, which is offered as a service, is scalability.The commercial model is to pay for the infrastructure that is actually used.This means the designer doesn't have expensive servers idling, and if there is a spike in the visitor numbers, the designer doesn't have to worry if the hardware will cope.He simply scales the infrastructure up and down as needed.Since the servers are virtual, it's much easier to create a new one than it was to add a new box to a server farm.IaaS provides users with a way to monitor, manage, and lease resources by deploying virtual machine (VM) instances on those resources.Amazon EC2, Eucalyptus, Nimbus, OpenStack and Open www.ijacsa.thesai.org Nebula are examples of cloud infrastructure implementations [3].
In Platform as a Service (PaaS), we move a step further.We no longer have to deal with every element of the infrastructure; instead, we can regard the system as one solid platform.For example, in the case of IaaS, if the designer has a website that suddenly requires more capacity because the amount of visitors increased, he would typically fire up more virtual machine instances.With PaaS, this is no longer the case; the platform will scale up and down as necessary and takes care of the implementation details.
The platform is typically represented as a single box.Since the platform usually acts as if it were a single box, it's much easier to work with, and generally there is no need to change much in the application to be able to run on a PaaS environment.PaaS doesn't only offer cpu, memory or file storage; but also offers other parts of the infrastructure, such as databases, either in the form of a scaling traditional RDBMS system, or one of the 'NoSQL' databases that are currently gaining momentum due to its ability to distribute large amount of data over the cloud infrastructure [4].The third available service model goes another step further in the realm of abstraction.We no longer care about infrastructure as with IaaS, nor do we care about the platform, as with PaaS.Where in the past, software was installed on the desktop, now with software as a service (SaaS); the user just creates an account and is ready to use the applications, in the comfort of the web browser.As with SaaS, we only need to worry about the application we're dealing with.Good examples of SaaS are cloud PACS utilities which can offer services for imaging centers, reading physicians, primary care clinics, and hospital management [5].As shown in Figure 1, there is a spectrum of use cases where one end consists of use cases that manipulates public data, hence have very low risk associated with them, while the other end consists of use cases that manipulates credit cards, Social Security Numbers, or ultra sensitive data like nuclear tests.
For the low end of the spectrum, Cloud Computing is an obvious choice, while for the high end; Cloud Computing might never be used.However, instead of public and private cloud, Hybrid architectures provide another choice, a middle ground.
For example, hybrid architecture could move computations to the cloud while keeping sensitive data in a secure database that resides in the private network [6].
In this paper, we provide a closer look into the implementation of healthcare infrastructure using a private cloud based on OpenStack.That was accompanied by setting a PaaS on OpenShift for connecting the private cloud to other health care services, mobile and web applications for clinicians and patients, and resources provided by different cloud providers.That hybrid design was assessed using CloudSim to measure its performance.
The rest of this paper is organized as follow; section 2, introduces a brief note about private cloud implementation, section 3, explains the potentials of expanding the private cloud infrastructure by incorporating other public cloud providers, section 4, discusses a medical application that took advantage of the implemented hybrid cloud, section 5, provides a simulation analysis to assess the performance benefits behind the proposed implementation, and finally we concluded the paper by a discussion section.
II. PRIVATE CLOUD IMPLEMENTATION
A private cloud implementation aims to avoid many of the objections including control over hospital and patients' data, worries about security, and issues connected to regulatory compliance.Because a private cloud setup is implemented safely within the corporate firewall, it remains under the control of the IT department.However, the hospital implementing the private cloud is responsible for running and managing IT resources instead of passing that responsibility on to a third-party cloud provider.Hospitals initiate private cloud projects to enable their IT infrastructure to become more capable of quickly adapting to continually evolving healthcare needs and requirements.
Launching a private cloud project involves analyzing the need for a private cloud, formulating a plan for how to create a private cloud, developing cloud policies for access and security, deploying and testing the private cloud infrastructure, and training employees and partners on the cloud computing project.To create a private cloud project strategy, a hospital identifies which of its healthcare practices can be made more efficient than before, as well as which repetitive manual tasks can be automated via the successful launch of a cloud computing project.By creating a private cloud strategy, the resulting cloud will be able to deliver automatic, scalable server virtualization, providing the benefits of automated provision of resources and the optimal use of hardware within the IT infrastructure [7].It is important for the private cloud implementation process to analyze and ensure the proper processes and policies are in place to successfully build a secure private cloud.Research and acquire the private cloud infrastructure and cloud-enabling software that will be used, such as OpenStack, CloudStack, and Eucalyptus.Ensure the hypervisor that will manage the virtual machines and virtualized storage are available or can be purchased and installed.www.ijacsa.thesai.orgCloud management is the software and technologies designed for operating and monitoring applications, data, and services residing in the cloud as shown in figure 2. Cloud management tools help ensure hospital's cloud computingbased resources are working optimally and properly interacting with users and other services.Cloud management strategies typically involve numerous tasks including performance monitoring (response times, latency, uptime, etc.), security and compliance auditing and management, and initiating and overseeing disaster recovery and contingency plans.With cloud computing growing more complex and a wide variety of private, hybrid, and public cloud-based systems and infrastructure already in use, a hospital's collection of cloud management tools needs to be just as flexible and scalable as its cloud computing strategy.Choosing the appropriate cloud platform, however, can be difficult.They all have pros and cons.
A. The cloud management software
We have decided to compare the capabilities of CloudStack, Eucalyptus, and OpenStack as the most notable open source systems available.Both Eucalyptus and CloudStack's application programming interface (API) provides compatibility with Amazon Web Services' Elastic Compute Cloud (EC2), the world's most popular public cloud.While OpenStack, supports public clouds built by its major vendors.OpenStack is backed by Dell, IBM, RackSpace the second leading IaaS provider after Amazon, NASA, HP the supplier of ARM cloud servers, Canonical the supplier of ubuntu which is tightly integrated with OpenStack in every release and the main operating system for ARM cloud servers [8].The simple implementation and support of OpenStack for ARM cloud servers favored its choice for our private cloud implementation as it increases the possibility for a lesser cost and an ever growing performance [9,10].
OpenStack refers to a collection of open-source software packages designed for building public and private clouds.OpenStack is implemented as a set of Python services that communicate with each other via message queue and database.Figure 3 shows a conceptual overview of the OpenStack architecture, with the OpenStack Compute components (which provides an IaaS) bolded and OpenStack Glance components (which stores the virtual machine images) shown in a lighter color.The nova-api service is responsible for fielding resource requests from users.Currently, OpenStack implements two APIs: the Amazon Elastic Compute Cloud (EC2) API, as well as its own OpenStack API [11].The nova-schedule service is responsible for scheduling compute resource requests on the available compute nodes.The nova-compute service is responsible for starting and stopping virtual machine (VM) instances on a compute node.The nova-network service is responsible for managing IP addresses and virtual LANs for the VM instances.The nova-volume service is responsible for managing network drives that can be mounted to running VM instances.The queue is a message queue implemented on top of RabbitMQ [12] which is used to implement remote procedure calls as a communication mechanism among the services.The database is a traditional relational database such as MySQL used to store persistent data shared across the components.While, the dashboard implements a web-based user interface.
B. The hypervisor
A hypervisor, also called a virtual machine manager, is a program that allows multiple operating systems to share a single hardware host.Each operating system appears to have the host's processor, memory, and other resources all to itself.However, the hypervisor is actually controlling the host processor and resources, allocating what are needed to each operating system in turn and making sure that the guest operating systems (virtual machines) cannot disrupt each other.Most OpenStack development is done with the KVM and XEN hypervisors.KVM however is free and easier to deploy than free XEN, well supported by every major distribution and is adequate for most cloud deployment scenarios (for example, EC2-like cloud with ephemeral storage) while storage support is improving for KVM.It is also simpler and more stable to use with OpenStack and is fully packed by ubuntu 12 Linux that is why KVM was chosen over XEN for our private cloud [13].www.ijacsa.thesai.org
C. Legacy migration
Legacy physical server could be migrated into OpenStack cloud using a persistent volume so that the migrated VM is backed by persistent storage.The function provided by the legacy server was the same afterwards and had the manageability benefits from running on an infrastructure platform.
Two things were created: a volume that would contain the data from legacy server and a 'working' VM as a way to work with that volume throughout the migration process.Horizon dashboard has been used to create volumes.The volume size had to be large enough to store all the file systems from legacy system, with appropriate growth, and included any swap-space that might be needed.The 'working' instance was then launched to set up the new volume.This instance had port 22 open under access and security, and ssh key pair was placed onto the system.Then, under 'volumes' the volume was attached to the working instance.The rsync was used to synchronize files between the legacy system and the new VM.The rsync tool was perfect for this for a few reasons: If interrupted, it can resume where it left off.Further, it can be throttled so that data can trickle over to the new volume without impacting the performance of the legacy server.For minimal downtime two separate rsyncs were done.The first one would sync over the vast majority of the data from the legacy server to the volume.The second one was done while both servers were down in order to sync the final changes from the legacy server right before the replacement VM was booted.
The next step was to make the volume bootable by adding a bootloader at the beginning of the volume.OpenStack provides firewalling, so we needed to set up a special security-group for VM that allowed all expected traffic to reach the server.This was done under "Access & Security" in horizon.Before committing to the change-over we had to verify that newlycreated bootable volume did indeed work as expected.Once VM was setup correctly, the volume was unmount from the working VM, and legacy server was powered down.New VM system was finally launched.
D. Private cloud security measures
Dome9 secures OpenStack cloud servers and makes them virtually invisible to hackers.That automation closes firewall ports like RDP and SSH, and enables on-demand secure access with just one click.With Dome9, OpenStack Security Groups, centralizing policy management were automated within Dome9 Central.It has as well an SaaS management console for the entire cloud infrastructure.In addition, inter-and intra-group security rules gave ultimate flexibility and granularity.With Dome9 Cloud Connect, setting up Dome9 with OpenStack took less than a minute.Using Dome9 Account, we could add OpenStack supported region, and enter credentials.It was then connected to the private via API to manage all of Security Groups.
III. HYBRID CLOUD IMPLEMENTATION
Public clouds involve the use of third party servers where the user is typically charged on the usage basis.It helps cut the user's capital expenditure significantly, while providing the user with greater flexibility and scalability.However, the advantages of the public cloud come at the cost of poorer performance and increased risks to data and applications.Private clouds attempt to solve the problem by providing cloud installation on-site with better performance and security, coming at increased capital expenditure and reduced flexibility.Therefore, hybrid clouds attempt to bridge the gap by providing the best of both worlds.Enterprises that use the hybrid cloud typically have a private cloud that handles the performancesensitive core applications, while using the public cloud for scaling and non-core applications.For instance, the mail server and collaboration related components can be kept on the public cloud, while keeping the patient's database and large files in the private cloud.Another reason is that some applications are highly suited for public cloud, while some other legacy applications might not.Private clouds could be less robust than a public cloud managed by a reputed service provider.If the designer have a natural or manmade disaster attacking his hospital site, his private cloud infrastructure might become crippled.The designer can use the public cloud as a fail-over in that case.Thus, the designer might want to have a hybrid approach [14].
Although OpenStack integrates well with almost all IaaS providers, that doesn't stop our implementation from taking full advantages from those IaaS provider that don't support OpenStack.The key was to use a PaaS that could communicate with many different IaaS through a cloud interface.The cloud interface is the holygrail of cloud computing as it bridges the gap between different IaaS suppliers.On the higher level there is a unified API that could be called from a PaaS while on the lower level there are drivers specific to each IaaS and implemented with the vendor without compromising their codes.That cloud interface and the closely integrated PaaS are now reality with deltacloud and openshift thanks to the ever growing competition between cloud services providers to control the cloud computing market [15].
PaaS was initially conceived as a hosted solution for web applications.However the conceptual design forces the Cloudcompliant applications to accept several restrictions: (1) use the APIs exposed by PaaS owners; (2) use the specific programming paradigm that is adequate for the type of applications allowed by the PaaS; and (3) use the programming languages supported by the PaaS owner.These constraints are still valid for most current PaaS offers (e.g., Google AppEngine, Azure, Heroku, Duostack, XAP, Cast, CloudBees, and Stackato).A first step towards the developer freedom in building new PaaS independent applications was done by DotCloud which lets developers build their own software stack for a certain application.This solution is unfortunately not free and is currently based only on EC2 [16].
On the other hand, Deltacloud is an API developed by Red Hat and the Apache Software Foundation that abstracts differences between clouds.So, Deltacloud provides one unified REST-based API that can be used to manage services on any cloud.While each IaaS cloud is controlled through an adapter called "driver" and provides its own API.Drivers exist for the following cloud platforms: Amazon EC2, Fujitsu Global Cloud Platform, GoGrid, OpenNebula, Rackspace, OpenStack, RHEV-M, RimuHosting, Terremark and VMware vCloud.Next to the 'classic' front-end, it also offers CIMI and EC2 www.ijacsa.thesai.orgfront-ends.Deltacloud is used in applications such as Aeolus to prevent the need to implement cloud-specific logic [17].
Deltacloud has some dependencies that need to be installed before its installation as Deltacloud server relies on a number of external rubygems and other libraries.However, once all the dependencies have been installed the Deltacloud server installation is done in one step [18].
OpenShift takes care of all the infrastructure, middleware, and management and allows the developer to focus on what they do best: designing and coding applications.It takes a No-Lock-In approach to PaaS by providing built-in support for Node.js,Ruby, Python, PHP, Perl, and Java.In addition, OpenShift is extensible with a customizable cartridge functionality that allows enterprising developers to add any other language they wish such as Clojure and Cobol.In addition to this flexible, no-lock-in, language approach, OpenShift supports many of the popular frameworks that make development easier including frameworks ranging from Spring, to Rails, to Play.OpenShift is designed to allow Developers to work the way they want to work by giving them the languages, frameworks and tools they need for fast and easy application development [19].Healthcare generates a tremendous amount of data each day (CT, MRI, US, PET, SPECT, Mammography, X-Ray … etc) and consumes quickly the hospitals storage space.For the reason of combining private and public cloud resources, a web application has been designed to store medical images on the private cloud using Mongodb which automatically migrate images older than one month to the public cloud.The subjects information were removed by stripping out the PHI (Protected Health Information) to conform to HIPAA standard and a sharding key was selected for our database that combines exam date and exam id.Compression, upload, delete, retrieval, and viewing were all integrated into the web application.Hibernate OGM; java and SWING were used to manage the backend on OpenShift with the ability to detect which device is trying to access the database.That allows smart phones as well as desktops to access the data for consultation purposes as shown in figure 4. We now detail the design of the Medical Image Web Application (MIWA) to guarantee the Atomicity, Consistency, Isolation, and Durability properties.Each of the properties is discussed individually [20].
A. Atomicity
The Atomicity property requires that either all operations of a transaction complete successfully, or none of them does.To ensure Atomicity, for each transaction issued, MIWA is using shards for actually storing data, mongos processes for routing requests to the correct data, and config servers, for keeping track of the cluster's state.As soon as an agreement to "COMMIT" is reached, the mongos processes can simultaneously return the result to the web application and complete the second phase.
B. Consistency
The consistency property requires that a transaction, which executes on a database that is internally consistent, will leave the database in an internally consistent state.Consistency is typically expressed as a set of declarative integrity constraints.We assume that the consistency rule is applied within the logic of transactions.Therefore, the consistency property is satisfied as long as all transactions are executed correctly.
C. Isolation
The Isolation property requires that the behavior of a transaction is not disturbed by the presence of other transactions that may be accessing the same data items concurrently.The MIWA decomposes a transaction into a number of sub-transactions, each accessing a single data item.Thus, the isolation property requires that if two transactions conflict on any number of data items, all their conflicting subtransactions must be executed sequentially, even though the sub-transactions are executed in multiple mongos processes.
D. Durability
The Durability property requires that the effects of committed transactions cannot be undone and would survive server failures.In our case, it means that all the data updates of committed transactions must be successfully written back to the back-end cloud storage service.The main issue here is to support mongos processes failures without losing data.For performance reasons, the commit of a transaction does not directly update data in the cloud storage service but only updates the in-memory copy of data items in the shards.Instead, each mongos process issues periodic updates to the cloud storage service.During the time between a transaction commit and the next checkpoint, durability is ensured by the replication of data items across several shards.After checkpoint, we can rely on the high availability and eventual consistency properties of the cloud storage service for durability.
E. Security
Cryptographic modules supplied by HP Atalla were used in the medical image web application to encrypt healthcare data and reduce the risk of data encryption and reputation damage without sacrificing performance using high-performance hardware security modules.Those Data Security solutions meet the highest government and financial industry standardsincluding NIST, PCI-DSS and HIPAA/HITECH-protect sensitive data and prevent fraud.HP Enterprise Secure Key Manager (ESKM) and Atalla Network Security Processors (NSP) provided robust security, high performance and www.ijacsa.thesai.orgtransparency while ensuring comprehensive, end-to-end network security.
V. SIMULATION Evaluation of alternative designs or solutions for Cloud computing on real test-beds is not easy due to several reasons.Firstly, public Clouds exhibit varying demands, supply patterns, system sizes, and resources (hardware, software, network) [19].Due to such unstable nature of Cloud resources, it is difficult to repeat the experiments and compare different solutions.Secondly, there are several factors which are involved in determining performance of Cloud systems or applications such as user's Quality of Service (QoS) requirements, varying workload, and complex interaction of several network and computing elements.Thirdly, the real experiments on such large-scale distributed platforms are considerably time consuming and sometimes impossible due to multiple test runs in different conditions.Therefore, a more viable solution is to use simulation frameworks which will enable controlled experimentation, reproducible results and comparison of different solutions in similar environments.Despite the obvious advantages of simulation in prototyping applications and developing new scheduling algorithms for Cloud computing, there are a few simulators for modeling real Cloud environments.For evaluating a scheduling algorithm in a Cloud computing environment, a simulator should allow users to define two key elements: (i) an application model specifying the structure of the target applications in Clouds, typically in terms of computational tasks and data communication between tasks; (ii) a platform model of Cloud computing data centers specifying the nature of the available resources and the network by which they are interconnected.Clouds currently deploy wide variety of applications both from industrial enterprises and scientific community [21].In terms of the platform, Cloud computing is quite different from traditional distributed computing platforms defined by serviceoriented features such as resource elasticity, multiple-level of services and multi-tenancy of resources.The experiments in this research were performed on the CloudSim cloud simulator which is a framework for modeling and simulating the cloud computing infrastructures and services [22].The CloudSim simulator has many advantages: it can simulate many cloud entities, such as datacenter, host and broker.It can also offer a repeatable and controllable environment.And we do not need to take too much attention about the hardware details and can concentrate on the algorithm design.The simulated datacenter and its components can be built by coding and the simulator is very convenient in algorithm design [23].The main parts which relate to the experiments in this research and the relationship between them are shown in Figure 5 while the functions of those components are explained in table 2. The application simulates an IaaS provider with an arbitrary number of datacenters.Each datacenter is entirely customizable.The user can easily set the amount of computational nodes (hosts) and their resource configuration, which includes processing capacity, amount of RAM, available bandwidth, power consumption and scheduling algorithms.The customers of the IaaS provider are also simulated and entirely customizable.The user can set the number of virtual machines each customer owns, a broker responsible for allocating these virtual machines and resource consumption algorithms.Each virtual machine has its own configuration that consists of its hypervisor, image size, scheduling algorithms for tasks (here known as cloudlets) and required processing capacity, RAM and bandwidth.
The simulation scenario models a network of a private and a public cloud (HP's cloud).The public and the private clouds were modeled to have two distinct data centers.A CloudCoordinator in the private data center received the user's applications and processed (queue, execute) them in a FCFS basis.To evaluate the effectiveness of a hybrid cloud in speeding up tasks execution, two test scenarios were simulated: in the first scenario, all the workload was processed locally within the private cloud.In the second scenario, the workload (tasks) could be migrated to public clouds in case private cloud resources (hosts, VMs) were busy or unavailable.In other words, second scenario simulated a CloudBurst by integrating the local private cloud with public cloud for handing peak in service demands.Before a task could be submitted to a public cloud (HP), the first requirement was to load and instantiate the VM images at the destination.The number of images www.ijacsa.thesai.orginstantiated in the public cloud was varied from 10% to 100% of the number of hosts available in the private cloud.Task units were allocated to the VMs in the space-shared mode.Every time a task finished, the freed VM was allocated to the next waiting task.Once the waiting queue ran out of tasks or once all tasks had been processed, all the VMs in the public cloud were destroyed by the CloudCoordinator.The private cloud hosted 20 machines.Each machine had 2 GB of RAM, 10TB of storage and one CPU run 1000 MIPS.The virtual machines created in the public cloud were based on an HP's small instance (2 GB of memory, 2 virtual cores, and 60 GB of instance storage).We considered in this evaluation that two virtual cores of a small instance has the same processing power as the local machine.The workload sent to the private cloud was composed of 2,000 tasks.Each task required between 20 and 22 minutes of processor time.The distributions for processing time were randomly generated based on the normal distribution.Each of the 2,000 tasks was submitted at the same time to the private cloud.Figure 6 shows the makespan of the tasks that were achieved for different combination of private and public cloud resources.The pricing policy was designed based on the HP's small instances (U$ 0.042 per instance per hour) business model.It means that the cost per instance is charged hourly.Thus, if an instance runs during 1 hour and 1 second, the amount for 2 hours (U$ 0.084) will be charged [25].This experiment showed that the adoption of a hybrid public/private Cloud computing environments could improve productivity of the healthcare organization.With this model, organizations can dynamically expand their system capacity by leasing resources from public clouds at a reasonable cost.
VI. DISCUSSION AND CONCLUSION
Cloud computing is quickly becoming a dominant model for end-users to access centrally managed computational resources.Through this work in extending OpenStack, we have demonstrated the feasibility of providing healthcare users with access to heterogeneous computing resources using a hybrid cloud computing model.Open cloud computing is not only a low cost choice for implementation but also provides the designer with a vast number of choices.That low cost solution for resources allocation can solve both the limited storage space and the lack of full utilization of computer infrastructure that healthcare always suffered from.We have to be aware that users' requirements may be very different and so the optimal infrastructure will vary.The ability to select suitable resources from different cloud providers can increase performance and lower cost considerably.That was achieved using Deltacloud and OpenShift which offers a communication layer between web applications, mobile applications and the different cloud providers.Simulation was an important step to measure the feasibility of our design and showed better makespan when public cloud took a higher workload share.
Fig. 1 .
Fig. 1.The relation between security restrictions against the benefits of cloud adoption.
Fig. 2 .
Fig. 2. The cloud ecosystem for building private clouds.(a) Cloud consumers need flexible infrastructure on demand.(b) Cloud management provides remote and secure interfaces for creating, controlling, and monitoring virtualized resources on an infrastructure-as-a-service cloud.(c) VM managers provide simple primitives (start, stop, suspend) to manage VMs on a single host.
Fig. 6 .
Fig. 6.The relation between cost in USD and the public cloud percentage (up).The relation between makespan in seconds and public cloud percentage (down).
TABLE I .
STRENGTHS AND WEAKNESS OF IN-HOUSE PACS, ASP PACS, AND CLOUD PACS. | 7,075.6 | 2013-01-01T00:00:00.000 | [
"Computer Science",
"Engineering",
"Medicine"
] |
A Multiple Power Reachable Sliding Mode Control Approach for Guidance of Miniature Laser Beam Riding Steered Munition
For a two-stage launch miniature shoulder munition steered with a laser beam riding system, the laser beam points to the target with a limited field radius and approximate straight-line spatial path; thus, the munition could not fly into the laser information field after the powered flight. The miniature munition dynamics are established firstly; then, an adaptive multiple power reachable sliding mode controller is presented and adopted to constrain both the trajectory inclination and pitch angle, which makes the munition enter the field and fly under control in the field with desired attitude angles, respectively. Considering the constraints of the incidence angle and the radius of the laser information field, an arctangent function curve is selected as the expected trajectory, and an adaptive multiple power reachable integral sliding mode guidance law is detailed, which makes the munition trajectory approach and converges to the expected curve fastly with limited acceleration. Therefore, the miniature munition flight trajectory is planned and optimized. Convergence and stability are analyzed based on the Lyapunov method. The numerical simulation against the stationary target is performed to fully demonstrate the efficacy of the proposed method.
Introduction
In recent years, infantry shoulder munitions are becoming smaller and lighter with the development of advanced materials and MEMS technology. In order to meet the need for urban and close combats in complex environments, various types of lightweight guided munitions have been put forward, such as Spike (U.S. Navy), Pike (Raytheon), and Spike SR (Rafael). These munitions adopt strapdown electrooptical seeker for target acquisition and guidance and enable forces to shoot-and-scoot without exposing their location. The background munition in this paper is different and adopts a laser beam riding steered (LBRS) system, which has more high guidance precision and better antijamming capability [1]. The essence of the LBRS guidance is to measure the position errors of the munition relative to the central point of the laser information field and generate command to control the munition flying along the centerline of the information field [2]. Usually, the munition should be powered during the flight envelope to maintain a stable altitude, which brings a great challenge to the miniature munition that will not enter the field during the initial two-launch stage. The typical guidance mechanism for the beam riding system generally adopts a three-point method, which has the disadvantage that the miss distance will increase because of the lag of measurement deviation error.
Aiming at the uncertainty in the guidance system, the state feedback control technology is presented in [3], and a controller based on global sliding mode control is proposed in [4] to improve the robustness and stability. Aiming at the synchronization problem of uncertain nonlinear systems, a composite nonlinear feedback control method is detailed in [5], and an adaptive global terminal sliding mode control scheme is shown in [6]. Sliding mode control-(SMC-) based guidance and control design methods can be seen in literature for its good performance in response and robustness to parameter change and disturbance [7,8]. For intercepting a nonmaneuvering target, an optimal sliding mode guidance law is proposed combining optimal theory in [9]. For the impact angle constraint problem, an impact angle control guidance law, a finite-time convergent SMC guidance law, and guidance law considering both impact time and impact angle constraints are presented in [10][11][12][13][14], respectively.
In order to track the expected line-of-sight rate with uncertainty, a robust second-order SMC combined with the backstepping method is designed in [15]. A guidance law for intercepting a constant-velocity target with a desired impact angle is proposed in [16]. In [17], a guidance law based on SMC is used to intercept stationary, uniform, and maneuvering targets at the desired impact angle. In order to design an impact angle constraint terminal guidance law to improve the warhead effect of air-to-ground guided weapons in [18], a variable structure guidance law with pitch/yaw angle constraints is derived, and saturation function is introduced in the reachable guidance law to weaken the system chattering. In [19], a design scheme for integrated guidance and control (IGC) with terminal impact angle constraint based on SMC is proposed. In [20], a continuous robust impact angle constraint guidance law with finite-time convergence is adopted for maneuvering targets with unknown acceleration boundary. In [21], the SMC-based guidance law is used to adaptively control the unknown uncertainty boundary. In [22], an advanced guidance law based on the nonsingular fast terminal SMC scheme and adaptive control is analyzed. To solve the terminal interception of a maneuvering target, an adaptive integral SMC (ISMC) guidance law and the case considering terminal angle constraint are detailed in [23,24]. Aiming at the high precise guidance problem of a hypersonic vehicle in the gliding phase, considering the complex multiconstraints and uncertainty, a guidance law based on global ISMC is discussed in [25].
Due to the radius limitation of the laser information field and the upper arc trajectory during two-stage launch, the miniature munition with LBRS guidance cannot fly into the laser information field directly at the initial flight stage; thus, the traditional three-point guidance mode is not applicable. In this paper, to overcome the limitation of the traditional LBRS guidance mechanism, based on the above research results, a novel guidance law combined SMC with adaptive multiple power reachable (AMPR) method is proposed to constrain the ballistic inclination and pitch angle and make the munition fly into the laser information field with desired attitude angles. Then, the ISMC with AMPR method is presented to shape the trajectory and approach to the desired curve. The convergence and stability are proved based on the Lyapunov stability theorem.
The remainder of this paper is organized as follows. In the second part, the munition dynamics and the expected flight trajectory are described. In the third part, the piecewise guidance law and the control command based on the adaptive multiple power reachable method and SMC method is detailed. In the fourth part, the stability analysis of the proposed guidance law is performed using the Lyapunov theorem. The simulation results and analysis are discussed in the fifth part, and the conclusion is given finally.
Miniature Munition Dynamics and Model
The conventional guidance strategy of the LBRS system is a three-point method; that is, the launch point, munition, and target are always kept in a straight line [26], but it is difficult to achieve for the miniature munition detailed in this paper. Considering the flight trajectory constraint, the flight inclination angle and pitch angle are selected and combined with SMC to design and optimize the guidance law for the whole flight process. The geometric relations for munition, target, and trajectory are described in Figure 1.
Start-Up and Control
Conditions. According to Figure 1, the control of miniature LBRS munition is divided into two stages.
In the first stage, the judgment conditions are where Δr m is the effective radius of the laser information field and Δy m is the centerline height of the laser beam.
In the second stage, the guided munition flies into the laser beam and toward the target, and the judgment conditions are dy dt < 0, 2.2. Munition Dynamics. The coordinate and attitude of a theoretical miniature LRBS munition model are shown in Figure 2, where the coordinate origin is set in the center of the munition gravity, OXYZ is the inertial coordinate, OX 1 Y 1 Z 1 is the ballistic coordinate, OX 2 Y 2 Z 2 is the munition body coordinate, and OX 3 Y 3 Z 3 is the flight velocity coordinate; all obey the right-hand rule. The typical diameter and length of this model are 40 mm and 550 mm, respectively. The translational dynamics of the munition can be expressed as ð3Þ International Journal of Aerospace Engineering where α is the angle of attack (AoA), β is the sideslip angle, θ m is the ballistic inclination angle, ψ V is the ballistic declination angle, and γ V is the velocity inclination angle. The drag X, lift Y, and lateral force Z can be written as The rotational dynamics of the munition can be written as where The kinematic equation of the munition can be also written as Derivation of Equation (8) gives x :: : cos θ m , z :: The firstand second-order derivatives for the inclination angle can be obtained from Equation (3): Δy m m ϑ m Figure 1: Relations of munition, target, and trajectory. where where Similarly, the pitch angle derivatives can be obtained from Equation (7): ϑ :: Substituting both Equations (5) and (6) into Equation (16) yields ϑ :: where 2.3. Expected Trajectory Model. Inspiring from Figure 3, an arctangent function is proposed with the form as This curve decreases rapidly and tends to be gentle with time, where the intersection with the longitudinal axis, the descending height, and the slope of the zero point of the coordinate are determined by A 0 , B 0 , and k 0 , respectively. Thus, the shape of the curve can be shaped using different parameter sets, and the part where the positive transverse axis of arctangent function is a well choice for the terminal trajectory of the miniature LBRS munition.
The expected trajectory curve has the form below after the munition enters the laser beam: where y 0 = Δy m + Δr m is the initial height, k n1 = −Δr m is the final height to be lowered, k n2 = θ n π/180 is the initial trajectory inclination angle, and x n = x m is the transverse position. International Journal of Aerospace Engineering The derivatives of Equation (20) can be calculated as follows: :
Guidance Law Design
The adaptive multiple power reachable (AMPR) method proportional differential SMC is used to constrain the angle, and the adaptive multiple power reachable integral sliding mode guidance law (AMPR-ISMGL) is used to constrain the trajectory.
Angle Constraint SMC Guidance Law.
In order to make the LBRS munition fly into the laser information field at a constant angle, the inclination angle θ should be constrained, while the pitch angle ϑ should be also limited for small AoA. Thus, select ϑ n and θ n as the constraint. Inspired by Reference [27], the sliding mode surface S = ½s 1 , s 2 is selected as where c 1 , c 2 > 0 for adjusting the state approaching speed, which can be determined according to the pole arrangement on the premise that the state dynamic characteristics are far less than the control loop dynamic characteristics. The derivation expression has the form below: To avoid the chattering, slow convergence, and unsmooth response of the traditional SMC method, an AMPR guidance law is proposed as follows: where k i > 0, i = 1, 2, 3, 4, α r > 1, 0 < β r < 1, and the value of γ r is Due to the performance of the exponential function, Equation (24) is mainly affected by −k 1 jsj α r sgn s − k 3 jsj γ r sgn s and −k 2 ½1/ðjsj + 1Þjsj β r sgn s − k 3 jsj γ r sgn s when the system state satisfies jsj ≥ 1 and jsj < 1, respectively. When jsj is very small, Equation (24) is mainly determined by −k 4 ½1/ðjsj + 0:1Þs. γ r in Equation (25) ensures that the system adaptively changes the exponential parameters once the state satisfies jsj > α r or jsj < β r , thus yielding a faster convergence rate.
Trajectory-Keeping SMC Guidance
Law. The fundamental control strategy of miniature LBRS munition is to use the AMPR-ISMGL to restrain the trajectory to approach the centerline of laser beam quickly and fly along the centerline until hitting the target.
Derivation of Equation (33) gives Substituting Equation (32) into Equation (34) obtains Then, the guidance command a n is given as Considering the practical application, the main variables in Equation (36) can be analyzed as follows. Wherein, rate ω z , velocity V, and acceleration a can be directly measured by an inertial measurement unit (IMU). Position information x n = x m and y m can be obtained by IMU navigation integration or measured directly by the onboard laser receiver; y n can be obtained by Equation (20). Similarly, for the variables involved in the angle constraint commands Equations (26) and (29), the ballistic inclination angle θ m is obtained by the tangent value of the velocity component, and the rudder deflection angle δ y can be obtained using the feedback data of the rudder system. Besides, the approaching speed is mainly determined by sliding surface coefficients α ei , i = 1, 2, c i > 0, i = 1, 2, 3, 4, and reaching law coefficients α r , β r , γ r , and k i > 0, i = 1, 2, 3, 4. Proof. According to the reachable law expression, the following equation holds: That is, s s : ≤ 0, if and only if s = 0, there is s s : = 0.
According to the existence and accessibility conditions of sliding mode reachable law for continuous systems in Ref. [29], if s_ s ≤ 0 satisfied, the designed control law is existing and reachable; that is, the system state s can reach the equilibrium point s = 0 under the control law Equation (24).
Lemma 2.
Lyapunov stability in finite time [30]. For a continuous nonlinear system, where f ð0Þ = 0 and U is an open neighborhood set containing the origin. Given Lyapunov function VðxÞ: U → ℝ and real number k, α r that satisfies k > 0 and α r ∈ ð0, 1Þ, it makes where V : ðxÞ = ∂VðxÞ/∂xf ðxÞ; then, the origin of the nonlinear system is Lyapunov stable; that is, it is finite-time stable, and the time to reach the origin satisfies t < Vðxð0ÞÞ 1−α r /kð1 − α r Þ.
Let the Lyapunov function as V = 1/2s 2 : where k 1 , k 2 , k 3 , and k 4 are all positive, α r > 1, 0 < β r < 1. s is satisfied regardless of the value of V : ≤ −k 1 jsj α r +1 . There is a positive constant Q ∈ ð0, k 1 Þ and δ ∈ ð0, 1Þ, so that Considering V = 1/2s 2 , thus V : + QV δ ≤ 0 is established. According to Lemma 2, the proposed control law can guarantee the system finite-time stability. In addition, according to Theorem 1, the sliding surface variable s can converge to zero in finite time under the action of the proposed reachable law.
Stability Analysis of Guidance Law.
Based on the aforementioned proof and Lyapunov stability criterion, the solved guidance and control commands can make the designed sliding mode surfaces in Equations (22) and (33) asymptotically stable and further ensure the system converges to stability during finite time in the presence of uncertainty.
Accordingly, it can be seen before the miniature LBRS munition passes through the highest trajectory point and does not enter the laser information field, its attitudes satisfy ϑ m → ϑ n , ϑ : m → ϑ : n , θ m → θ n , θ : m → θ : n . Then, when the munition flies in the laser information field until it hits the target, its attitudes satisfy ϑ m → ϑ n1 , ϑ :
Simulation Results and Analysis
The simulation conditions include the guidance parameters shown in Table 1 and the initial munition flight data shown in Table 2.
The saturation function satð⋅Þ is used instead of sgn ð⋅Þ for smooth consideration: where Δ is the boundary layer, Δ = 0:0001. The flight trajectory, velocity, trajectory inclination angle, and normal acceleration are shown in Figures 4 and 5. It can be seen that the trajectory inclination angle quickly approaches the set desired value after passing the highest trajectory point, and the normal acceleration converges to zero after a short oscillation.
The AoA, pitch angle, and pitch rate are shown in Figure 6. The AoA of the miniature LBRS munition is small during the unguided stage and converges to zero quickly once the pitch angle is constrained to the set value and the pitch rate converges to zero quickly, which validates the effectiveness of the proposed adaptive multiple power reachable sliding mode guidance law. The curves of pitch angle deviation Δϑ = ϑ m − ϑ n and trajectory inclination deviation Δθ = θ m − θ n are shown in Figure 7.
The curves of pitch angle deviation change rate Δ ϑ : and trajectory inclination angle deviation change rate Δ θ : are shown in Figure 8, both of which quickly converge to zero.
In order to objectively evaluate the ballistic trajectory constraint performance of the algorithm in this paper, the proportional differential sliding mode guidance law (PDSMGL) is also simulated under the same initial conditions. The trajectory comparison curve of guided munition after entering the laser information field is shown in Figure 9. Compared with the traditional PDSMGL, the trajectory using the proposed guidance law in this paper approaches the desired trajectory faster and stably.
The curves of trajectory deviation Δy = y m − y n and rate Δ y : between real trajectory and desired trajectory are shown in Figure 10. It can be seen that both methods can ensure that the trajectory error tends to zero. The proposed AMPR-ISMGL in this paper has a faster response speed for large sliding mode surface error, while the oscillation near the sliding mode surface is smaller.
Conclusion
In this paper, a multiple-power adaptive reachable guidance law based on the SMC method is proposed for a two-stage launch miniature LBRS munition. The pitch angle and trajectory inclination angle are converged to the expectation value quickly once the munition achieves the highest trajectory point, which guarantees the munition entering the desired laser information field. During flight in the laser information field, the ballistic trajectory with the laser beam radius and the terminal parallel constraint is planned based on the arctangent curve, and an AMPR-ISMGL is detailed to shape the trajectory into the desired curve. The trajectory is smooth with deviation converging to zero at the end; compared with PDSMGL, AMPR-ISMGL has faster convergence speed and smaller oscillation, which proves that the guidance law can effectively restrain trajectory and angle. The existence and accessibility of the proposed guidance method are analyzed using the Lyapunov theorem. The simulation results validate that the presented multiple-power sliding mode guidance law is feasible for the guidance and control of the miniature guided munition. To the best of the authors' knowledge, there are few results of complex guidance laws for the LBRS system. Because the guidance and control time of the miniature munition system is very short, subsequent research could be focused on the optimization for guidance parameters and approaching speed, as well as the further hardware-inloop simulation implementation.
Data Availability
There is no website with available data yet.
Conflicts of Interest
The authors declare that they have no conflicts of interest. | 4,481.4 | 2021-05-29T00:00:00.000 | [
"Physics"
] |
A Self-Test , Self-Calibration and Self-Repair Methodology of Thermopile Infrared Detector
: To improve the reliability and yield of thermopile infrared detectors, a self-test, self-cali-bration and self-repair methodology is proposed in this paper. A novel micro-electro-mechanical system (MEMS) infrared thermopile detector structure is designed in this method with a heating resistor building on the center of the membrane. The heating resistor is used as the stimuli of the sensing element on chip to achieve a self-test, and the responsivity related with ambient temperature can be calibrated by the equivalent model between electrical stimuli and physical stimuli. Fur-thermore, a fault tolerance mechanism is also proposed to localize the fault and repair the detector if the detector fails the test. The simulation results with faults simulated by the Monte Carlo sto-chastic model show that the proposed scheme is an effective solution to improve the yield of the MEMS thermopile infrared detector.
Introduction
MEMS non-contact infrared radiation (IR) temperature detectors have been an effective means on monitoring and detecting body temperature during the COVID-19 pandemic [1][2][3][4][5], which increased the demand of MEMS IR sensors rapidly. High demand and high output mean that there is a greater requirement for product reliability than before [6], including a fast and low-cost test method, mature calibration strategy and related fault analysis and fault repair mechanisms that need to be further explored.
For the testing of MEMS, the most common tool is automatic test equipment (ATE), which is designed for MEMS 3D micromechanical structure devices and provides a peculiar test stimulus such as the shock stimulus, the pressure stimulus, the vibration stimulus [7][8][9][10][11], etc. For the high-precision MEMS infrared thermopile detector, a black body radiation is needed as the stimuli, and a readout circuit is used to obtain signals and evaluate the performance of the device [12,13]. The above test methods are too complicated and costly since each kind of detector needs a specific test stimulus, though those methods can obtain comprehensive performance parameters of devices.
In recent years, built-in self-test (BIST) and built-in self-calibration (BISR) mechanisms of MEMS have been proposed to improve the test efficiency and sensor accuracy and lower test cost [14]. Researchers have investigated a variety of approaches to realize testing and calibration on chips. In 2001, Benoit Charlot summarized how to test sensors with electronical stimuli instead of physical stimuli in a parallel plate capacitance structure, a micro-beam piezoresistive structure and a cantilever beam thermocouple structure, respectively [15]. An interesting structure was designed for a convective accelerometer with a heater built in the middle of two sensing beams, providing the symmetrical tem-perature gradient, which can detect the bias when there is lack of acceleration [16]. Generally, the MEMS BIST can be summarized into four classes based on different MEMS sensors principles: the test of symmetry, the test of sensitivity, parameter extraction and direct test [17]. Besides, several researchers have attempted to realize calibration on chip. In 2014, Jia et al. proposed an on-chip scheme to calibrate the responsivity of infrared thermopile temperature sensor with digital control signals [18]. Based on this scheme, a machine learning algorithm robust heteroscedastic probabilistic neural network (RHPNN) was also proposed to calibrate the responsivity with the parameters of a read circuit by Kuan et al. [19].
Though the above BIST or BISR methods can achieve testing and calibration of a MEMS detector on-chip, the response of the closed-loop test achieved by electric stimulus can also be further used for the fault analysis and defects repair which has not been addressed well in previous works.
In this paper, a self-test, self-calibration and self-repair scheme for infrared thermopile detectors is established, and the device reliability testing, failure analysis and fault repair are closed-loop processes from a broader perspective. First, the built-in heating resistor is used to generate internal quantitative stimulus, the response signal is analyzed, and the threshold method is used to determine whether the device is faulty. Second, the equivalent relationship between physical stimulus and electrical stimulus can be fitted by the simulation data, so that the thermopile responsivity at different ambient temperatures is corrected by it. Last, the self-repair of thermopile detector can be fault tolerant through the redundancy method, which partitions the thermopile into M identical modules, the fault type and location can be predicted by BP neural network, and the fault module is isolated to keep the detector work normal. The response of electrical stimulus can not only be used to implement testing on-chip, but also to realize the fault tolerance of some fault types to repair detectors. Simultaneously, the responsivity related with ambient temperature can be calibrated by the equivalent model between electrical stimuli and physical stimuli. This ultimately improves the reliability of the device. This paper is organized as follows: Section 1 is an introduction to the test and calibration of infrared thermopile detectors, and there will present some past solutions for this question. Section 2 introduces the basic principle of MEMS infrared thermopile, and the derivation process of one-dimensional thermal steady-state model for built-in thermal resistor. In Section 3, the design and theoretical basis of self-testing, self-calibration and fault repair are introduced in detail. Section 4 introduces the verification simulation design for the scheme proposed in this article. The simulation results are described in Section 5 and summarized in Section 6.
The Principle of Infrared Thermopile Detector
Based on Kirchhoff's radiation law, energy can transmit by absorbing and emitting the spectrum. In this course, the transmission efficiency relies on the wavelength of the spectrum and the temperature of objects. The thermopile temperature detector is designed based on the Seebeck effect. This effect describes the generation of voltage between the ends of two joint materials (thermocouple) placed on a temperature gradient which has different Seebeck coefficients, as shown in Figure 1.
where, , are the Seebeck coefficients of two different materials respectively, is the temperature of the object and is the ambient temperature. According to the Seebeck effect, the temperature change of the thermocouple junction end can lead to the change of voltage at the output end, so an infrared thermopile detector can be designed to obtain the temperature of object whose energy can transfer through infrared radiation and increase the temperature of the absorption zone on which the hot end of the thermopile is placed.
According to the Stefan-Boltzmann law, the relationship between blackbody radiation power and its temperature is described, as shown in Equation (2): where A is the area of the absorption region, ε is the emissivity of the grey body and σ is the Stefan-Boltzmann constant, the value of the constant is: where k is the Boltzmann constant, h is Planck's constant, and c is the speed of light in a vacuum.
Defined the responsivity of thermopile R: where P is the power of absorbed radiation. The temperature of object can be calculated by the following formula: where ∆ is obtained from readout circuit, is the ambient temperature calculated by the value of the thermistor isolating from the thermopile and R changes with .
Physical Stimulus and Electrical Stimulus Mechanism
It can be seen from Equation (1) that the principle of the infrared thermopile detector is to convert infrared radiation energy into electrical energy through the Seebeck effect, so the temperature can be characterized by output electrical signals. Therefore, the infrared thermopile detector is designed based on this theory.
Thermopile infrared detectors usually consist of three parts: thermocouple, dielectric support layer and heat dissipation substrate. Usually the thermopile infrared detectors have three structures: 1. Closed membrane structure; 2. Cantilever beam structure; 3. Suspended structure. From the performance comparison of the three structures, the thermopile infrared detector with the closed membrane structure has the smallest thermal resistance, and its response time is the shortest compared with the other two structures; the thermopile infrared detector with the suspended structure has the highest thermal resistance and the longest response time; the performance of the cantilever structure is between these two structures. From a process point of view, the preparation of the closed membrane structure detector is the easiest, and the preparation process of the cantileverbeam structure detector is the most complicated. The thermopile infrared detectors with closed membrane structure currently on the market have a simple structure, but the yield rate is low. In summary, the closed membrane structure of the thermopile detector is used as the research object of this article.
In the closed membrane thermopile structure, as shown in Figure 2, the infrared radiation of the object produces a temperature distribution in the absorption layer, and the temperature gradient can be detected by the thermopile to calculate the object temperature by Equation (4). For infrared thermopile detectors, the process of the temperature gradient generated in the absorption layer of the device caused by the infrared radiation of objects is called the physical stimulation process. The process of temperature gradient on the absorption layer generated by the on-chip thermoelectric effect with a resistor is called the electrical stimulation process. The two kinds of stimulation methods were simulated and compared, and the results are shown in Figure 3. Figure 3 shows the temperature distribution on the film caused by the physical stimulus and electrical stimulus, respectively, by COMSOL simulation. In the physical stimulus, the heat flux of infrared radiation across the membrane was set to 5000 W/m 2 . The heat was mainly concentrated in the area on the film where the cavity was located, and the isotherm was like to rectangles. In the stimulation, we used aluminum as the material of the heating resistor, and the heating resistor input voltage was 100 mV the temperature distribution generated on the film was like to the physical stimulus, but the isotherm was approximately an ellipse. The other materials and size parameter settings of the above simulation are shown in Section 4. The difference in temperature distribution is mainly due to the different heat generation mechanisms of the two stimuli. The temperature variation range under two stimuli is shown in Table 1, and both are within the operating temperature range of the device.
One-Dimensional Thermal Steady-State Analysis Model of Self-Test
To quantitatively analyze the change of energy in this process with a built-in heater, a one-dimensional thermal steady-state model for converting electric energy to heat energy was derived based on the structure as shown in Figure 2. The double-ended doublelayer beam thermopile structure was placed on the closed membrane structure, and a resistor was built in the middle of the infrared radiation absorption area as the thermal stimulus. The corresponding derivation is as follows: The heat balance equation for the process is: Taking the heating resistor strip with a length of ∆ (∆ → 0) as the analysis object, the width of the heating resistor strip is w, the thickness is d, the resistivity is , and the current through the heating resistor is I, then: There are three ways to dissipate heat: heat radiation, heat conduction and heat convection [20]. The heat convection is zero in a vacuum environment, so there is: Heat radiation is: where, is the Stefan-Boltzmann constant (5.66 × −8 W/( 2 × 4 )); λ is the emissivity; ( ) is the temperature function of resistor bar and is the ambient temperature. Heat conduction is: where, is the thermal conductivity.
Then the heat balance equation can be written as: To simplify the calculation model, it is assumed that the temperature distribution of substrate is uniform, and the value is equal to the ambient temperature , the heat gradient in the z-axis direction of the membrane is ignored and the temperature difference between the hot and cold ends of the thermocouple is much smaller than . Then there is: Divide both sides of the Equation (10) by ∆ and substitute Equation (11) into it, then the new heat balance equation can be obtained: The boundary conditions are: Then, the temperature distribution of the heating resistor can be obtained as: The thermal effect of thermopile with the built-in resistor was analyzed by COMSOL simulation and the results are shown in Figure 4. The simulation conditions are the same as described in Section 2.2. The results indicate that the temperature distribution on the membrane is concentrated in the region where the cavity is located. The overall temperature distribution is axis symmetric along x = 1/2 length of the substrate and y = 1/2 length of the heating resistor as shown in Figure 4d, and the temperature distribution of resistor is shown in Figure 4c, which has a similar to second power relationship with the position of the heating resistor on the y-axis.
Self-Test
In the self-test mode, the voltage of heating resistor is provided by the digital-to-analog converter (DAC) circuit as shown in Figure 5. The power of the heating resistor is [21]: is the voltage of heating resistor, is the resistance and ε is the conversion efficiency of electric power and heat. The responsivity is: The responsivity can be calculated by reading ∆ twice with different .
Under different , different responsivities are obtained. According to Equations (16) and (17), we can calculate whether the error is within the allowable range. The use of multiple measurements is mainly due to the low-detection efficiency under a single measurement, which can be explained by the posterior Bayesian probability. It is assumed that the device is detected as a fault as event A, and the device is faulty as event B, then the probability that the detected fault is the actual fault is: where P(B) is the probability of device failure, P(A|B) is the probability that there is a fault and the device is detected as faulty, P(A|B) is the probability that there is no failure, but the device is detected as faulty. During fault detection, P(A|B) ≫ P(B) ≈ P(A|B), so the Equation (18) can be simplified to: It is not difficult to prove that P(B|A) < P(A|B). Suppose P(B) = 0.1%, p(B|A) = 99%, P�A�B� = 0.5%, and the posterior probability P(B|A) is reduced to 19.43%. That it shows a high failure detection rate does not mean high fault detection accuracy. Calculating the responsivity twice under different is an effective means to improve the detection effect.
Self-Calibration
As already explained in Section 2, a built-in resistor can produce the thermal gradient on the film. Therefore, an equivalent model between the electrical stimulus and physical stimulus needed to be built for the calculation of the right responsivity. The relationship between the absorption power under the two stimuli is defined as follows.
Where the is the absorption power of infrared radiation, and is the absorption power from the heating resistor. f is the mapping relationship between and which can be fitted by the stimulation of COMSOL in Section 4.
The temperature of object can be calculated by Equation (5). Since the computation is too complex and power consuming, a binary table is used to describe the correspondence relationship between , and as shown in Figure 6. The temperature of the object calculated by the equivalent model is:
Analysis of Thermopile Faults and Defects
The double-end beam thermopile structure is composed of two materials, N-Polysilicon and P-Polysilicon. This structure can reach a responsivity of 220 V/W in the stimulation. However, the compact structure layout also brings more hidden troubles. Besides, the compact structure of the double-end beam thermopile is more prone to be polluted and adhered to by particles carried with the process of surface cleaning, metal deposition, annealing and packaging, resulting in a short circuit and other defects. Moreover, the thermocouple can be corroded, and the circuit can break because of water vapor and a humid environment. Situations of common corrosion and particle adhesion are shown in Figure 7.
Redundant Repair Yield Model
Suppose a group of defects may occur in N positions of the infrared thermopile detector, that is, there may be N types of defects in the device. Assume that the occurrence of each defect is an independent event, and the probability of each defect occurring is q [22]. The defect described in Section 3.3.1 can thus be expressed as a binomial distribution: Assuming that N is large enough, the binomial distribution evolves into a Poisson distribution, and the average probability of occurrence of defects scattered on the thermopile structure can be obtained.
The distribution estimation by Equation (22) is too pessimistic because of the lack of consideration of the clustering effect, which is the situation where defects occur in the same area. Therefore, the defects distribution function ( ) can be updated as follows: where A is the area in where the device may have defects, b is the defects density coefficient, is the average probability of defects, γ is the clustering parameter and Γ(k) is the gamma function.
The probability of x defects occurring in the thermopile structure can be expressed as: The probability of no defect in the detector is: When there is a fault detected by the self-test method, the failure redundancy mechanism will work. According to the above analysis, the thermopile structure can be divided into M modules as shown in Figure 8 According to the above analysis, the double-end beam thermopile structure can be divided into 4, 6 and 8 modules, which can tolerate 1, 2, 3 and 4 faults, respectively.
Fault Module Identification
First, fault types of infrared thermopile sensors are discussed according to Section 3.3.1. Faults can be divided into three types: parallel connection of thermocouple, corrosion of thermocouple and disconnection of thermocouple, according to the causes of the defects. There is only a loss for the defects with the parallel connection of thermocouple and corrosion of thermocouple because of the change of thermopile responsivity, but a broken result for disconnection of thermopile defect. Therefore, defects can be classified into parametric defect and catastrophic defect; the former can be corrected by self-calibration and part of the latter can be fixed by self-repair.
Next, we showed the way to find a fault in M identical modules. The temperature on the composite dielectric film is distributed unevenly, as introduced in the Section 2, which results in different output voltage of each module and makes the fault module identification complicated. To solve this problem a back propagation (BP) neural network is designed and utilized for locating the fault in M identical modules, as shown in Figure 9.
The BP neural network is a multilayer feedforward neural network trained according to the error back propagation algorithm, which can be used for regression and classification problems. In the problem of the M module predicting the fault type and fault location, the BP neural network is used to detect the fault type and fault location. Take the model of M = 6 as an example. The model is divided into two layers of BP network for training. The output of the first layer of BP network is the fault type. Different fault types will choose to enter different second layer BP networks, and there is a total of the types of failures that can be repaired; the second layer of the network is to predict the location of the above-mentioned redundant failure types. Due to the simple data structure, a threelayer network structure is adopted (too many hidden layers will cause the over-fitting phenomenon of the neural network, resulting in insufficient network generalization ability). The hidden layer of the first layer of network is 9, and the hidden layer of the second layer of network is 6, and the activation function selects the sigmoid function.
Recalculate the Responsivity
The location of the fault module can be detected by the BP neural network, then the fault module data is isolated, and the thermopile output voltage is updated: After fault tolerance, the responsivity calculated according to Equation (3) also needs to be updated. The power absorbed can be calculated as follows.
Therefore, the temperature distribution of the thermopile hot end under different ambient temperatures is fitted by simulation, and the temperature difference ratio of each module is obtained, which is approximated as the M mode power distribution coefficient .
The power can be recalculated by Equation (14), and the responsivity can be updated accordingly.
Establishment of the Equivalent Model of Electrical Stimulus and Physical Stimulus
To establish an equivalent model between the electrical stimulus and physical stimulus under the same simulation conditions, the response of the thermopile detector under electrical stimulus and physical stimulus was obtained by COMSOL simulation, and then the conversion relationship was fitted by the least-square method. The specific steps are as follows: (i) Set the ambient temperature to = 293.15 and the heat flux from 0 W/m 2 to 20,000 W/m 2 , then the relationship between the heat radiation and the output voltage can be obtained; (ii) Set the ambient temperature to = 293.15 and the voltage of heating resistor from 0 V to 2 V with a step of 0.1, then the relationship between the electrical stimulus and the output voltage can be obtained; (iii) Modulate the relationship between the radiation and electric power with the same output voltage.
The relevant simulation parameters are shown in Tables 2 and 3.
Verification of Fault-Repair and Error Calculation
The prediction of fault type and fault location using the BP neural network described in Section 3.3 is an important step to realize fault repair, and its detection accuracy affects the classification and fault tolerance of faults. Thus, this part verifies the BP neural network's ability to predict fault types. The neural network training data comes from the COMSOL simulation data. The training output is manually labeled to train the weights of the neural network. Part of the training data is retained as a test set to test the performance of the neural network. The specific verification method is as follows: (i) Obtain the fault data set with N defects with simulation, including M modules output data and location of defects; (ii) Train the BP neural network with the outputs data of M modules as the inputs and the fault location as the outputs; (iii) Calculate the accuracy of the predicted faults under different numbers of modules with the test samples; (iv) Calculate the average responsivity error after fault repair with M = 6 modules.
Calculation Yield
Monte Carlo is a common method to generate random distribution. It is used to simulate the situations of random failures occurring in many MEMS sensor devices which is also used in this paper to simulate the calculation of yield in the production process. The randomly distributed particles constructed on the thermopile structure can be represented as faults by Monte Carlo. The number of particles is generated from 0 to 3, and the particle size is randomly generated from 1 to 10, representing the impact of different types of defects, corresponding to the simulation output data. Defects cause abnormal thermopile output, which can be manifested as no impact, accuracy impact and failure. The corresponding expressions can be catalogued into three types: good, parametric and catastrophic. Figure 10 shows a flowchart of the process of random fault generation with the self-test, self-calibration and fault-repair models accordingly. Figure 10. Flowchart of self-test, self-calibration and fault-repair model.
Establishment of the Equivalent Model of Electrical Stimulus and Physical Stimulus
The equivalent model of infrared radiation stimulus and electric stimulus was established by Step 1 of the simulation as introduced in the previous section. As can be seen in Figure 11, the output voltage is linearly related to the electrical stimulus and the radiation stimulus, respectively. The conversion efficiency of the linear fitting is given in Equation (30). Since the energy transfer by radiation is proportional to the 4th square of temperature, and the energy transfer by heat conduction depends on the temperature gradient.
Prediction Accuracy of Location of Faults and Responsivity Error Calculation
The train data and test data come from the simulation of COMSOL in different failure models, as shown in Figure 6. The BP neural network is trained with the output data of the COMSOL simulation of the thermocouple strip under different assumptions such as The results, as shown in Figure 12, indicate that predictive ability decreases as the number of failures increases. It indicates that for the M modules' redundancy, the fault location is easier to identify with fewer faults, and the predictions are more accurate. The fewer modules of M, the higher the recognition rate is. In addition, the larger M is, the more abundant samples are needed, and the number of hidden layer units required by the neural network increases, so the cost will also increase accordingly. The fault-repair model of the thermopile structure can be divided into six modules with high accuracy and appropriate cost. The average temperature distribution of the hot end of the thermopile under different ambient temperatures in M = 6 is shown in Figure 13. Furthermore, the electric power weight values with each module number can be seen in Table 4, which can be used to recalculate the responsivity with fault repair. The responsivity error calculated in Table 4 is the average error of a single fault under different modules. The total average error is 9.7 × 10 −4 and the mean square error is 0.0694. The above results verify that the error caused by the fault-repair model has little effect on the accuracy of the responsivity.
Faults Simulation by Monte Carlo and Yield Calculation
With the fault dataset obtained by the Monte Carlo method, the self-test, self-calibration and fault-repair were conducted according to the process as shown in Figure 10, and then the simulation results were obtained and are given in Table 5. It can be seen from Table 5 that in mass production, the greater the number of defects is, the greater the possibility that the devices will fail. The yield can be improved with the self-test, self-calibration and fault-repair processes, especially when there are more parametric defects. However, there is a limit to how much yield can be achieved when there are too many catastrophic defects, since only part of catastrophic defects can be repaired. Therefore, there is a significant correlation between the improved yield and the defect types and numbers. On the other hand, with the increase of the number of defects, the neural network recognition accuracy decreases, which is negative for the yield improving. Overall, these results show the effectiveness of the self-test, self-calibration and fault-repair models.
Conclusions
In this article, to enhance reliability and high yield, a novel MEMS infrared thermopile detector construction is designed that builds in a heating resistor on the membrane central and partitions the thermopile into M identical modules. The establishment of both an equivalent model of the electrical stimulus and physical stimulus and a faults analysis model based on M identical modules is fundamental to achieve the self-test, self-calibration and self-repair in this issue. Based on that, the major work is to build an effective model to achieve test, calibration and repair for faults on chip, and finally to imply the improvement of reliability and high yield for detectors. Therefore, a dual standard suggested for the self-test is to have an equivalent model and lookup table used to achieve the calibration and a redundant and fault-tolerant mechanism designed for fault repair with M identical modules when detector fails the test. To verify the validity of the model, the Monte Carlo method was used to simulate the fault output model generated by random defects, and the proposed model was used to process the detectors data. The results suggest that when the number of thermopile modules M = 6, the model has excellent prediction accuracy and an appropriate cost. And the comparison of the findings with different number of defects indicates the improvement of the yield dependent on the proportion of fault types. In summary, these results show the proposed model has an effective improvement in yield. Overall, this paper provides a wide insight into design for testability in combination with a self-test, self-calibration and self-repair model which is significative for future works.
Conflicts of Interest:
The authors declare no conflict of interest. | 6,514.2 | 2021-05-13T00:00:00.000 | [
"Engineering",
"Physics",
"Materials Science"
] |
A Novel Method to Limit the Adverse Effect of Fine Serpentine on the Flotation of Pyrite
A novel method to limit the adverse effect of fine serpentine on the flotation of pyrite was investigated in this paper. The flotation results showed that coarser serpentine possessed a weaker depression effect on the pyrite flotation process, and the use of KAl(SO4)2·12H2O could efficiently limit the detrimental effect of fine serpentine on pyrite with a maximum increase of pyrite recovery from 14% to 86% at pH 9.0. The results of particle size measurements and rheological measurements exhibited that the addition of KAl(SO4)2·12H2O increased the particle size of serpentine buta hrdly affected the particle size of pyrite, then limited the formation of serpentine-pyrite aggregates. Adsorption test results showed that the adsorption density of potassium butyl xanthate (PBX) onto pyrite regained with the addition of KAl(SO4)2·12H2O, thereby achieving good flotation improvement. It can be concluded that KAl(SO4)2·12H2O is likely to be an effective pyrite flotation reagent, especially in the presence of fine serpentine.
Introduction
Serpentine, as a typical magnesium silicate, often associates with many sulfide ore deposits [1].Plenty of studies have demonstrated that serpentine can adsorb on the surface of sulfide minerals as "slime coatings" through electrostatic attraction in the flotation process of sulfide ores [2][3][4].The coatings of serpentine can reduce the adsorption of collectors and increase the hydrophilicity of valuable minerals [5,6].
In order to eliminate the adverse influence of serpentine on sulfide minerals flotation, chemical additives such as sodium silicate, sodium hexametaphosphate, carboxymethyl cellulose (CMC), and N-carboxymethyl chitosan have been used to prevent the formation of "slime coatings" on valuable mineral surfaces by changing the surface potential of serpentine [7][8][9][10].However, in practice, the results are not so satisfactory due to the high dosage and the lack of selectivity of the dispersant.In addition, physical methods such as ultrasonic treatment and high-intensity conditioning have been usually employed to remove the slime coatings from valuable mineral surfaces [11][12][13].However, it appears that ultrasonic treatment and high-intensity conditioning are not efficiently economical methods.
Previous studies have shown that the formation of slime coatings on sulfide mineral surfaces is closely related to the particle size of serpentine: Finer serpentine obtained a greater depressant effect on the flotation of sulphide minerals [14].Thus, changing the apparent particle size of fine serpentine by adding coagulants may be a possible method to improve the flotation performance of sulfide minerals.Aluminum potassium sulfate dodecahydrate (KAl(SO 4 ) 2 •12H 2 O), which is commonly known as potassium alum, has been most commonly used as an inorganic coagulant for decades due to its characteristics of nontoxicity, cheapness, and wide sources [15,16].KAl(SO 4 ) 2 •12H 2 O hydrolyzes to form an amorphous floc of Al(OH) 3 (s) to coagulate small particles into large flocs by its high adsorption affinity in aqueous solution [17,18].However, the utilization of KAl(SO 4 ) 2 •12H 2 O to remove the depressant of fine serpentine on the flotation of sulphide minerals has not been studied previously, and thus it was chosen as a potential coagulant in removing slime coatings of fine serpentine from sulphide minerals in this study.Pyrite is the most widespread and abundant of naturally occurring metal sulphides, and is always associated with magnesium silicate minerals [19,20].Therefore, it was chosen as the valuable mineral in this paper.The effect of KAl(SO 4 ) 2 •12H 2 O on the flotation process was evaluated through single mineral flotation, particle size measurements, rheology measurements, and adsorption measurements.
Samples and Reagents
The serpentine and pyrite minerals used for all the experiments were obtained from Donghai, Jiangsu Province, and Yunfu, Guangdong Province of China, respectively.Serpentine and pyrite samples were crushed to −1 mm in a laboratory roll crusher and grounded using an agate mortar and pestle to the designed fraction.Here, −150 + 74 µm, −74 + 38 µm, and −10 µm serpentine and −150 + 74 µm pyrite particles were used for the flotation tests; the −10 µm serpentine and −150 + 74 µm pyrite particles were used for particle size measurements and rheology measurements; and the −10 µm serpentine and −38 µm pyrite particles were used for the adsorption measurements.According to the results of X-ray diffraction (XRD), shown in Figure 1, the serpentine sample constituted 98% serpentine and 2% chlorite, and the purity of pyrite was higher than 98%.by adding coagulants may be a possible method to improve the flotation performance of sulfide minerals.
Aluminum potassium sulfate dodecahydrate (KAl(SO4)2•12H2O), which is commonly known as potassium alum, has been most commonly used as an inorganic coagulant for decades due to its characteristics of nontoxicity, cheapness, and wide sources [15,16].KAl(SO4)2•12H2O hydrolyzes to form an amorphous floc of Al(OH)3(s) to coagulate small particles into large flocs by its high adsorption affinity in aqueous solution [17,18].However, the utilization of KAl(SO4)2•12H2O to remove the depressant of fine serpentine on the flotation of sulphide minerals has not been studied previously, and thus it was chosen as a potential coagulant in removing slime coatings of fine serpentine from sulphide minerals in this study.Pyrite is the most widespread and abundant of naturally occurring metal sulphides, and is always associated with magnesium silicate minerals [19,20].Therefore, it was chosen as the valuable mineral in this paper.The effect of KAl(SO4)2•12H2O on the flotation process was evaluated through single mineral flotation, particle size measurements, rheology measurements, and adsorption measurements.
Samples and Reagents
The serpentine and pyrite minerals used for all the experiments were obtained from Donghai, Jiangsu Province, and Yunfu, Guangdong Province of China, respectively.Serpentine and pyrite samples were crushed to −1 mm in a laboratory roll crusher and grounded using an agate mortar and pestle to the designed fraction.Here, −150 + 74 µm, −74 + 38 µm, and −10 µm serpentine and −150 + 74 µm pyrite particles were used for the flotation tests; the −10 µm serpentine and −150 + 74 µm pyrite particles were used for particle size measurements and rheology measurements; and the −10 µm serpentine and −38 µm pyrite particles were used for the adsorption measurements.According to the results of X-ray diffraction (XRD), shown in Figure 1, the serpentine sample constituted 98% serpentine and 2% chlorite, and the purity of pyrite was higher than 98%.Potassium butyl xanthate (PBX, purchased from Macklin Biochemical Co., Ltd., Shanghai, China) with 98% purity and methyl isobutyl carbinol (MIBC, obtained from Tianzhuo Flotation Reagent Co., Ltd., JiAn, Jiangxi, China) were used as the collector and frother, respectiTvely. he coagulant of KAl(SO4)2•12H2O with 99.5% purity was obtained from Fuchen Chemical Reagents Factory, Tianjin, China.Hydrochloric acid (HCl) and sodium hydroxide (NaOH) were used as pH regulators, and both of them were obtained from Zhuzhou Flotation Reagent Co., Ltd., Hunan province of China.All of the above reagents were of analytical grade, and deionized water with a Potassium butyl xanthate (PBX, purchased from Macklin Biochemical Co., Ltd., Shanghai, China) with 98% purity and methyl isobutyl carbinol (MIBC, obtained from Tianzhuo Flotation Reagent Co., Ltd., JiAn, Jiangxi, China) were used as the collector and frother, respectively.The coagulant of KAl(SO 4 ) 2 •12H 2 O with 99.5% purity was obtained from Fuchen Chemical Reagents Factory, Tianjin, China.Hydrochloric acid (HCl) and sodium hydroxide (NaOH) were used as pH regulators, and both of them were obtained from Zhuzhou Flotation Reagent Co., Ltd., Hunan province of China.All of the above reagents were of analytical grade, and deionized water with a resistivity of 18.2 MΩ•cm at 25
Flotation
The single mineral flotation was carried out in an XFG-type mechanical agitation flotation machine with a 40 mL cell [21].The pyrite suspension was prepared by adding 2.0 g of pyrites, which was treated by 5 min ultrasonic pretreatment to 35 mL solutions.When needed, 0.2 g serpentine was added at the beginning of the conditioning period.The pH regulator, KAl(SO 4 ) 2 •12H 2 O, and collector were added into the pulp in sequence, and a 3 min conditioning period was conducted for each reagent.Then frother was added to the pulp and conditioned for 1 min before the commencement of flotation, and the flotation process was conducted for 3 min.Following this, the concentrates and tailings were collected, filtered, dried, and weighed, and the flotation recovery was calculated based on solid weight distributions between the two products.Each microflotation test was duplicated three times, and the average value and the standard deviation bar were presented in the results of flotation tests.
Particle Size Measurements
Particle size measurements were carried out in a Malvern Mastersizer 2000 (Malvern Instruments Ltd., England) by light scattering.The pulp samples for measurements were prepared by adding pH regulator, KAl(SO 4 ) 2 •12H 2 O.Each pulp sample as wsubsampled twice, the particle size distribution of each pulp sample was measured three times, and the average of six resulting measurements was used for a particle size distribution curve.
The apparent size of serpentine and pyrite samples that were added with a certain amount of KAl(SO 4 ) 2 •12H 2 O were evaluated by the particle size distribution D 50 (value of the particle diameter at 50% of the sample volume that existed) and particle size distribution D 90 (value of the particle diameter at 90% of the sample volume that existed), which were directly offered by a Mastersizer instrument [22].
Rheology Measurements
The rheological properties of flotation pulp were measured in apparent viscosity by control shear rate (0-400 s −1 ) mode, whereas the shear yield stress was measured by control shear stress (0.02-20 Pa) mode.For each rheology test, 40 mL of slurry sample with a designed concentration was prepared in an agitating flotation cell.Then the pH regulator and KAl(SO 4 ) 2 •12H 2 O were added in sequence as a flotation scheme, and 3 min of conditioning was conducted before adding the next reagent.The pulp prepared was poured into a sample cup for rheology measurements.Rheological measurements were conducted in an Anton Paar MCR102 rheometer (Anton Paar, Shanghai, China) with a vane impeller probe.A 38 cm 3 sample cup (diameter = 27 mm) and an impeller (diameter = 24 mm) with six outer blades were used.All rheological measurements were performed at an ambient temperature of around 25 • C.
Adsorption Measurements
For adsorption measurements, 1 g of pyrite powder was added into the PBX solution with a desired concentration in the presence and absence of serpentine in a 250 mL Erlenmeyer flask.When needed, KAl(SO 4 ) 2 •12H 2 O was added into the mixed pyrite and serpentine suspension.The total volume of the suspension was 100 mL, and the pulp pH was adjusted to a desired value by adding NaOH and HCl.Then the suspension was conditioned for 30 min, ensuring that the adsorption process reached equilibrium.After that, the suspensions were centrifuged and filtered, and the filter liquor was collected for adsorption measurements.The adsorption measurements were conducted on a UV-2001 ultraviolet spectrophotometer (Rayleigh, Beijing, China) with the absorbance at 300 nm.The amount of PBX adsorbed on pyrite was calculated though the PBX initial concentration (C 0 ) and residual concentration (C 1 ), and the computational formula was shown as:
Flotation
Single mineral flotation experiments were first conducted to evaluate the effect of the particle size of serpentine and KAl(SO 4 ) 2 •12H 2 O on the flotation process of the pyrite-serpentine system.
Figure 2 exhibits the effect of serpentine with different particle sizes on the flotation of pyrite as a function of pulp pH.It is evident from Figure 2 that at pH 3-9, PBX had excellent collecting ability for bare pyrite.When above pH 9, the flotation recovery of pyrite decreased due to the formation of Fe(OH) 3 species on the pyrite surface [19,23].Compared to the flotation recovery of bare pyrite, the pyrite recovery decreased in the presence of serpentine due to the formation of serpentine slimes onto pyrite.It also can be seen that the flotation recovery of pyrite was related to the particle size of serpentine.The finer the serpentine was, the lower the flotation recovery of pyrite was.The results shown in Figure 2 are consistent with previous studies by Feng and Li [14,24].
Flotation
Single mineral flotation experiments were first conducted to evaluate the effect of the particle size of serpentine and KAl(SO4)2•12H2O on the flotation process of the pyrite-serpentine system.
Figure 2 exhibits the effect of serpentine with different particle sizes on the flotation of pyrite as a function of pulp pH.It is evident from Figure 2 that at pH 3-9, PBX had excellent collecting ability for bare pyrite.When above pH 9, the flotation recovery of pyrite decreased due to the formation of Fe(OH)3 species on the pyrite surface [19,23].Compared to the flotation recovery of bare pyrite, the pyrite recovery decreased in the presence of serpentine due to the formation of serpentine slimes onto pyrite.It also can be seen that the flotation recovery of pyrite was related to the particle size of serpentine.The finer the serpentine was, the lower the flotation recovery of pyrite was.The results shown in Figure 2 are consistent with previous studies by Feng and Li [14,24].Figure 3 shows the flotation recovery of pyrite as a function of the concentration of KAl(SO4)2•12H2O in the presence and absence of −10 µm serpentine at pH 9.0.It is obvious that the flotation recovery of pyrite slightly decreased with the increase of KAl(SO4)2•12H2O concentration without serpentine.A maximum decrease was obtained with the pyrite flotation recovery of 87% at the concentration of 9.375 × 10 −4 mol/L KAl(SO4)2•12H2O, whereas higher KAl(SO4)2•12H2O concentration did not further reduce the flotation recovery of pyrite.James et al. [25] proposed the view that metal ions could adsorb on minerals surface via forming metal hydroxide precipitation, because the solubility product of metal hydroxide at the interface is less than the solubility product in the solution, and the concentration of metal ions in the interface area is much higher than that of metal ions in the solution phase.Thus, it can be deduced that the slight decrease of pyrite recovery was caused by the adsorption of aluminum hydroxyl onto pyrite surfaces, which decreased the hydrophobicity of pyrite [25,26].In contrast, the flotation recovery of pyrite was rather low, with a value of about 14% in the presence of serpentine at pH 9, which shows that fine serpentine had an adverse influence on pyrite flotation due to the serpentine slime coatings [13].Interestingly, the Figure 3 shows the flotation recovery of pyrite as a function of the concentration of KAl(SO 4 ) 2 •12H 2 O in the presence and absence of −10 µm serpentine at pH 9.0.It is obvious that the flotation recovery of pyrite slightly decreased with the increase of KAl(SO 4 ) 2 •12H 2 O concentration without serpentine.A maximum decrease was obtained with the pyrite flotation recovery of 87% at the concentration of 9.375 × 10 −4 mol/L KAl(SO 4 ) 2 •12H 2 O, whereas higher KAl(SO 4 ) 2 •12H 2 O concentration did not further reduce the flotation recovery of pyrite.James et al. [25] proposed the view that metal ions could adsorb on minerals surface via forming metal hydroxide precipitation, because the solubility product of metal hydroxide at the interface is less than the solubility product in the solution, and the concentration of metal ions in the interface area is much higher than that of metal ions in the solution phase.Thus, it can be deduced that the slight decrease of pyrite recovery was caused by the adsorption of aluminum hydroxyl onto pyrite surfaces, which decreased the hydrophobicity of pyrite [25,26].In contrast, the flotation recovery of pyrite was rather low, with a value of about 14% in the presence of serpentine at pH 9, which shows that fine serpentine had an adverse influence on pyrite flotation due to the serpentine slime coatings [13].Interestingly, the flotation recovery of pyrite was significantly improved with the addition of KAl(SO 4 ) 2 •12H 2 O, and a maximum increasement of pyrite recovery from 14% to 86% was obtained with 2.5 × 10 −3 mol/L KAl(SO 4 ) 2 •12H 2 O in the presence of serpentine.
Minerals 2018, 8, x FOR PEER REVIEW 5 of 11 flotation recovery of pyrite was significantly improved with the addition of KAl(SO4)2•12H2O, and a maximum increasement of pyrite recovery from 14% to 86% was obtained with 2.5 × 10 −3 mol/L KAl(SO4)2•12H2O in the presence of serpentine.Figure 4 shows the flotation recovery of pyrite under different conditions as a function of the pulp pH.It is evident that the adverse effect of serpentine on the flotation of pyrite was related to pulp pH.With the increase of pulp pH, the depression effect decreased.According to previous reports [9,27], the decrease of the depression effect of serpentine was caused by the change of the surface charges of minerals.Besides, it also can be seen that the depression effect of serpentine on pyrite was limited with the addition of 2.5 × 10 −3 mol/L KAl(SO4)2•12H2O.At pulp pH 5-9, the recovery of pyrite almost reached 90%.However, when pulp pH was below 5, the elimination effect of KAl(SO4)2•12H2O on serpentine slimes decreased, which may have been caused by the restriction of the formation of Al(OH)3, which requires a pH above 4.3 [28].
Particle Size Measurements
As a coagulant, the addition of KAl(SO4)2•12H2O brought an influence on the coagulation behavior of mineral particles, and this may also have been the main reason for the regain of pyrite Figure 4 shows the flotation recovery of pyrite under different conditions as a function of the pulp pH.It is evident that the adverse effect of serpentine on the flotation of pyrite was related to pulp pH.With the increase of pulp pH, the depression effect decreased.According to previous reports [9,27], the decrease of the depression effect of serpentine was caused by the change of the surface charges of minerals.Besides, it also can be seen that the depression effect of serpentine on pyrite was limited with the addition of 2.5 × 10 −3 mol/L KAl(SO 4 ) 2 •12H 2 O.At pulp pH 5-9, the recovery of pyrite almost reached 90%.However, when pulp pH was below 5, the elimination effect of KAl(SO 4 ) 2 •12H 2 O on serpentine slimes decreased, which may have been caused by the restriction of the formation of Al(OH) 3 , which requires a pH above 4.3 [28].
Minerals 2018, 8, x FOR PEER REVIEW 5 of 11 flotation recovery of pyrite was significantly improved with the addition of KAl(SO4)2•12H2O, and a maximum increasement of pyrite recovery from 14% to 86% was obtained with 2.5 × 10 −3 mol/L KAl(SO4)2•12H2O in the presence of serpentine.Figure 4 shows the flotation recovery of pyrite under different conditions as a function of the pulp pH.It is evident that the adverse effect of serpentine on the flotation of pyrite was related to pulp pH.With the increase of pulp pH, the depression effect decreased.According to previous reports [9,27], the decrease of the depression effect of serpentine was caused by the change of the surface charges of minerals.Besides, it also can be seen that the depression effect of serpentine on pyrite was limited with the addition of 2.5 × 10 −3 mol/L KAl(SO4)2•12H2O.At pulp pH 5-9, the recovery of pyrite almost reached 90%.However, when pulp pH was below 5, the elimination effect of KAl(SO4)2•12H2O on serpentine slimes decreased, which may have been caused by the restriction of the formation of Al(OH)3, which requires a pH above 4.3 [28].
Particle Size Measurements
As a coagulant, the addition of KAl(SO4)2•12H2O brought an influence on the coagulation behavior of mineral particles, and this may also have been the main reason for the regain of pyrite
Particle Size Measurements
As a coagulant, the addition of KAl(SO 4 ) 2 •12H 2 O brought an influence on the coagulation behavior of mineral particles, and this may also have been the main reason for the regain of pyrite recovery.In order to investigate the coagulation of mineral particles at different concentrations of KAl(SO 4 ) 2 •12H 2 O in the flotation process, particle size measurements were subsequently carried out, and the results are shown in Figures 5 and 6. Figure 5 shows the changes of the particle size distribution of pyrite particles in the absence and presence of KAl(SO 4 ) 2 recovery.In order to investigate the coagulation of mineral particles at different concentrations of KAl(SO4)2•12H2O in the flotation process, particle size measurements were subsequently carried out, and the results are shown in Figures 5 and 6. Figure 5 shows the changes of the particle size distribution of pyrite particles in the absence and presence of KAl(SO4)2•12H2O.The average particle size barely changed (from 153,799 µm to 148,254 µm) as 2.5 × 10 −3 mol/L KAl(SO4)2•12H2O was added, indicating that KAl(SO4)2•12H2O could not coagulate pyrite particles.Figure 6 exhibits the changes in the apparent size of serpentine particles under different concentrations of KAl(SO4)2•12H2O.It can be seen that significant changes in particle size of serpentine particles occurred with the addition of different concentrations of KAl(SO4)2•12H2O.The D50 and D90 of serpentine particles were 4401 µm and 9836 µm, respectively, in the absence of KAl(SO4)2•12H2O.When the concentration of KAl(SO4)2•12H2O increased, the dominant peak of fine size fractions decreased, and new peaks generated in the coarse size fractions range.The D90 of serpentine particles increased from 9836 µm to 304,532 µm, and the D50 increased from 4.401 µm to 6.178 µm, as the concentration of KAl(SO4)2•12H2O increased from 0 to 2.5 × 10 −3 mol/L.When the concentration of KAl(SO4)2•12H2O continued to increase, the D90 and D50 of serpentine particles remained stabilized, corresponding to the variation tendency of flotation results, which are shown in Figure 3.The difference of the changes of apparent particle size between pyrite and serpentine may have been caused by the distinction between the particle size fraction of two minerals.Beyond that, the bridge connection between serpentine slimes and Al(OH)3(s) by hydroxyl also played an important role [29].
Rheological Measurements
Pulp rheology, which provides much information about the heterocoagulation between The difference of the changes of apparent particle size between pyrite and serpentine may have been caused by the distinction between the particle size fraction of two minerals.Beyond that, the bridge connection between serpentine slimes and Al(OH) 3 (s) by hydroxyl also played an important role [29].
Rheological Measurements
Pulp rheology, which provides much information about the heterocoagulation between particles, has been extensively used to investigate the particle interactions in slurries of fluorite and quartz, galena, and clay minerals [30][31][32].In order to investigate the influence caused by serpentine and KAl(SO 4 ) 2 •12H 2 O on single pyrite pulp, rheological tests were conducted by adding serpentine and KAl(SO 4 ) 2 •12H 2 O under different conditions.In Figure 7a, there are obvious changes in apparent viscosity values at certain shear rates after the serpentine was added at relatively low concentrations (3 wt % and 8 wt %), which means that there was some mutual interaction between these mixed minerals [33].As a result, the separation of pyrite and serpentine became difficult, thereby deteriorating the flotation, the same result as in Figure 2. In contrast, the coagulant KAl(SO 4 ) 2 •12H 2 O brought about a slight decrease in the influence on pulp viscosity.Figure 7b shows the shear yield point in the same conditions.It concludes that the raised amount of serpentine could significantly increase the shear yield point of mixed pulp.For example, the shear yield stress was as high as 0.57 Pa when the mass concentration of serpentine reached 8 wt %.Ancey and Jorrot [34] also found that the yield stress sharply increased and approached an infinity value when the solid density reached its maximum value.The increase of shear yield stress with the addition of serpentine indicates that the serpentine-pyrite aggregates were much more difficult to break than single pyrite.However, a maximum decrease of shear yield stress from 0.57 Pa to 0.14 Pa was obtained with the addition of 0.025 mol/L KAl(SO 4 ) 2 •12H 2 O, suggesting that the addition of KAl(SO 4 ) 2 •12H 2 O could decrease the formation of serpentine-pyrite aggregates.
Adsorption Measurements
To further demonstrate the fact that the addition of KAl(SO4)2•12H2O could coagulate fine serpentine particles and consequently limit the formation of "slimes" on pyrite surface, adsorption measurements were conducted to detect the adsorption density of PBX on mineral surfaces under different conditions.Figure 8 shows the results of adsorption isotherms of PBX onto pyrite and serpentine.The results show that the adsorption density of PBX onto pyrite surface increased with the increase of PBX.However, the adsorption density of PBX onto serpentine surface was always extremely low, illustrating that PBX could not adsorb onto a serpentine surface, which was similar to earlier observations [27].After the addition of 0.2 g of serpentine, the adsorption density of PBX on pyrite surface decreased significantly, which indicated that the formation of "slimes" consisted of serpentine on the pyrite surface and prevented the adsorption of PBX.This may have been the main reason for the decrease of pyrite recovery in the presence of fine serpentine.With the addition of 2.5 × 10 −3 mol/L KAl(SO4)2•12H2O, the adsorption density of PBX onto pyrite could be regained, indicating that the fine serpentine slimes were limited effectively.
Adsorption Measurements
To further demonstrate the fact that the addition of KAl(SO 4 ) 2 •12H 2 O could coagulate fine serpentine particles and consequently limit the formation of "slimes" on pyrite surface, adsorption measurements were conducted to detect the adsorption density of PBX on mineral surfaces under different conditions.Figure 8 shows the results of adsorption isotherms of PBX onto pyrite and serpentine.The results show that the adsorption density of PBX onto pyrite surface increased with the increase of PBX.However, the adsorption density of PBX onto serpentine surface was always extremely low, illustrating that PBX could not adsorb onto a serpentine surface, which was similar to earlier observations [27].After the addition of 0.2 g of serpentine, the adsorption density of PBX on pyrite surface decreased significantly, which indicated that the formation of "slimes" consisted of serpentine on the pyrite surface and prevented the adsorption of PBX.This may have been the main reason for the decrease of pyrite recovery in the presence of fine serpentine.With the addition of 2.5 × 10 −3 mol/L KAl(SO 4 ) 2 •12H 2 O, the adsorption density of PBX onto pyrite could be regained, indicating that the fine serpentine slimes were limited effectively.
serpentine particles and consequently limit the formation of "slimes" on pyrite surface, adsorption measurements were conducted to detect the adsorption density of PBX on mineral surfaces under different conditions.Figure 8 shows the results of adsorption isotherms of PBX onto pyrite and serpentine.The results show that the adsorption density of PBX onto pyrite surface increased with the increase of PBX.However, the adsorption density of PBX onto serpentine surface was always extremely low, illustrating that PBX could not adsorb onto a serpentine surface, which was similar to earlier observations [27].After the addition of 0.2 g of serpentine, the adsorption density of PBX on pyrite surface decreased significantly, which indicated that the formation of "slimes" consisted of serpentine on the pyrite surface and prevented the adsorption of PBX.This may have been the main reason for the decrease of pyrite recovery in the presence of fine serpentine.With the addition of 2.5 × 10 −3 mol/L KAl(SO4)2•12H2O, the adsorption density of PBX onto pyrite could be regained, indicating that the fine serpentine slimes were limited effectively.
Mechanism Analysis
It can be seen obviously from Figures 5 and 6 that the addition of KAl(SO 4 ) 2 •12H 2 O could not change the apparent size of pyrite particles.However, as a coagulant, the addition of KAl(SO 4 ) 2 •12H 2 O hydrolyzed and formed Al(OH) 3 (s) when pulp pH was above 4.3, and they existed as amorphous flocs with strong adsorption capacities [17,28].The amorphous flocs of Al(OH) 3 (s) could adsorb the fine serpentine particles and form flocs, which obtained bigger D 90 than pyrite did.In the separation process of pyrite and serpentine, the depressant effect of serpentine on pyrite was significantly influenced by the particle size [24].According to the flotation results shown in Figure 2, finer serpentine particles were easily forming slimes and preventing the adsorption of collectors on pyrite.However, the depressant effect of serpentine decreased as the serpentine particle size increased, which was further confirmed by the flotation results shown in Figures 3 and 4. The results of rheology measurements also indicated that the formation of serpentine-pyrite aggregates was limited with the addition of KAl(SO 4 ) 2 •12H 2 O.The results of the adsorption measurements showed that the adsorption density of PBX on pyrite surface was regained in the presence of serpentine, which further confirmed that the formation of serpentine slimes could be limited through increasing the apparent particle size of serpentine with the addition of KAl(SO 4 ) 2 •12H 2 O. Therefore, KAl(SO 4 ) 2 •12H 2 O could efficiently eliminate the adverse effect of fine serpentine on the flotation of pyrite.
Based on the abovementioned analysis, the schematic illustration of the effect of KAl(SO 4 ) 2 •12H 2 O in the pyrite-serpentine system is shown in Figure 9.In Figure 9, the serpentine slimes coating was formed on the pyrite surface without KAl(SO 4 ) 2 •12H 2 O.With the addition of KAl(SO 4 ) 2 •12H 2 O, fine serpentine particles could be coagulated and obtained a bigger apparent particle size.Subsequently, the formation of serpentine slimes onto pyrite was prevented, and the adsorption density of PBX on the surface of pyrite was regained, which was consistent with the results of particle size measurements, rheology measurements, and adsorption measurements.One could explain the result as the use of KAl(SO 4 ) 2 •12H 2 O being able to eliminate the adverse effect of fine serpentine on the flotation process of pyrite.KAl(SO4)2•12H2O, fine serpentine particles could be coagulated and obtained a bigger apparent particle size.Subsequently, the formation of serpentine slimes onto pyrite was prevented, and the adsorption density of PBX on the surface of pyrite was regained, which was consistent with the results of particle size measurements, rheology measurements, and adsorption measurements.One could explain the result as the use of KAl(SO4)2•12H2O being able to eliminate the adverse effect of fine serpentine on the flotation process of pyrite.
Conclusions
The use of KAl(SO 4 ) 2 •12H 2 O to limit the adverse effect of fine serpentine in the pyrite flotation process was investigated in this paper.From the results of the flotation tests, particle size measurements, rheology measurements, and adsorption measurements above, conclusions could be reached as follows.
Fine serpentine possessed a strong depressant effect on the flotation of pyrite in the presence of PBX, which could be efficiently solved by the utilization of KAl(SO 4 ) 2 •12H 2 O.The results of particle size measurements and rheology measurements showed that the addition of KAl(SO 4 ) 2 •12H 2 O increased the apparent particle size of serpentine remarkably through the flocculation of Al(OH) 3 (s), in contrast, had little influence on the pyrite particle size.Therefore, the formation of serpentine-pyrite aggregates was prevented.The coarser the serpentine particle in the flotation system of pyrite and serpentine, the lesser the depressant effect on pyrite was.The results of adsorption measurements also proved that the adsorption density of PBX on pyrite regained with the addition of KAl(SO 4 ) 2 •12H 2 O. Therefore, KAl(SO 4 ) 2 •12H 2 O is likely to be a reagent of great significance in eliminating the adverse effect of fine serpentine on the pyrite flotation process.
Figure 5 .
Figure 5.Effect of the presence and absence of KAl(SO4)2•12H2O on the particle size distribution of pyrite (pH = 9.0).
Figure 5 .
Figure 5.Effect of the presence and absence of KAl(SO 4 ) 2 •12H 2 O on the particle size distribution of pyrite (pH = 9.0).
Figure 6 Figure 6 .
Figure 6 exhibits the changes in the apparent size of serpentine particles under different concentrations of KAl(SO 4 ) 2 •12H 2 O.It can be seen that significant changes in particle size of serpentine particles occurred with the addition of different concentrations of KAl(SO 4 ) 2 •12H 2 O.The D 50 and D 90 of serpentine particles were 4401 µm and 9836 µm, respectively, in the absence of KAl(SO 4 ) 2 •12H 2 O.When the concentration of KAl(SO 4 ) 2 •12H 2 O increased, the dominant peak of fine size fractions decreased, and new peaks generated in the coarse size fractions range.The D 90 of serpentine particles increased from 9836 µm to 304,532 µm, and the D 50 increased from 4.401 µm to 6.178 µm, as the concentration of KAl(SO 4 ) 2 •12H 2 O increased from 0 to 2.5 × 10 −3 mol/L.When the concentration of KAl(SO 4 ) 2 •12H 2 O continued to increase, the D 90 and D 50 of serpentine particles remained stabilized, corresponding to the variation tendency of flotation results, which are shown in Figure 3. Minerals 2018, 8, x FOR PEER REVIEW 7 of 11
Figure 6 .
Figure 6.Effect of the presence and absence of KAl(SO 4 ) 2 •12H 2 O on the particle size distribution of serpentine (pH = 9.0).
Figure 7 .
Figure 7.The (a) apparent viscosity-shear rate curves and (b) the shear strain-shear stress curves of pyrite and serpentine under different conditions (pH = 9.0).
Figure 7 .
Figure 7.The (a) apparent viscosity-shear rate curves and (b) the shear strain-shear stress curves of pyrite and serpentine under different conditions (pH = 9.0).
Figure 9 .
Figure 9. Schematic illustration of the effect of KAl(SO4)2•12H2O on the flotation of pyrite in the presence of serpentine.
Figure 9 .
Figure 9. Schematic illustration of the effect of KAl(SO 4 ) 2 •12H 2 O on the flotation of pyrite in the presence of serpentine.
• C obtained from a Laboratory Water Purification System (Smart-s15, Hitech Instruments Co., Ltd., Shanghai, China) was used for all the experiments. | 7,856.8 | 2018-12-10T00:00:00.000 | [
"Materials Science"
] |
Keyword occurrences and journal specialization
Since the borders of disciplines change over time and vary across communities and geographies, they can be expressed at different levels of granularity, making it challenging to find a broad consensus about the measurement of interdisciplinarity. This study contributes to this debate by proposing a journal specialization index based on the level of repetitiveness of keywords appearing in their articles. Keywords represent one of the most essential items for filtering the vast amount of research available. If chosen correctly, they can help to identify the central concept of the paper and, consequently, to couple it with manuscripts related to the same field or subfield of research. Based on these universally recognized features of article keywords, the study proposes measuring the specialization of a journal by counting the number of times that a keyword is Queryrepeated in a journal on average (Sj). The basic assumption underlying the proposal of a journal specialization index is that the keywords may approximate the article’s topic and that the higher the number of papers in a journal based on a topic, the higher the level of specialization of that journal. The proposed specialization metric is not invulnerable to a set of limitations, among which the most relevant seems to be the lack of a standard practice regarding the number and consistency of keywords appearing in each article.
Introduction
This paper aims to contribute to the debate on the specialization and compartmentalization of academic journals and, indirectly, on their degree of interdisciplinarity.An interdisciplinary journal is an academic publication that publishes articles and reviews covering themes and approaches from different disciplines (Augsburg, 2016).These journals seek to overcome the boundaries of individual disciplines and promote collaboration and integration of research approaches from different fields.Articles published in interdisciplinary journals often address complex issues that require understanding and solutions that cannot be provided by a single discipline.Interdisciplinary journals focus on promoting research and academic debate that goes beyond the limits of individual disciplines and promotes a more holistic approach to analysing and understanding problems.These journals are often sought out by those looking for a broader perspective on a topic or issue and are used as a source of information for interdisciplinary research and academic projects.In contrast to interdisciplinarity, we have journals with a strong degree of disciplinarity, which we identify here with the concept of specialization.A specialized journal is an academic source whose contributions are strongly concentrated in a field bodies of knowledge or research practice.The specialization level and interdisciplinarity of journals may be considered complementary terms: the higher the interdisciplinarity of a journal is, the lower the level of specialization and vice versa.
This paper intends to contribute to developing a debate about the disciplinarity level of journals by introducing an index of journal specialization (Sj index).
The literature on the degree of disciplinarity of journals is scant in formal terms, but it is extensive in substantive terms.In fact, if we consider specialization as a complementary term to interdisciplinarity, it is straightforward to count the bulk of studies on interdisciplinarity as indirect references to the topic of journal specialization.
From the analysis of the literature on interdisciplinarity, it emerges that while the attention on interdisciplinary research has grown in recent decades (Leydesdorff, 2007;Glänzel & Debackere, 2022), the academic literature on this topic has shown a lack of consensus on the most appropriate measurement approach for interdisciplinary research (Leydesdorff & Rafols, 2011;Wang & Schneider, 2020).Moreover, the categorization of scientific articles and journals by topic is one of the most difficult and essential problems of information science.
Specifically, and in a quantitative context, there are at least two different methods to classify the interdisciplinarity of research: journal-level and paper-level categorization.The debate on which of these two methods of categorization provide the most appropriate solution is still open (Abramo et al., 2018;Milojević, 2020).The most common research categorization approach is the journal-level approach, based on the assumption that journal subject categories (SCs) identify disciplines (Carpenter & Narin, 1973, Zwanenburg et al., 2022).
However, using an a priori discipline structure is not invulnerable to criticism.The unambiguous categorization of journals through subject categories appears to be rare due to the fuzziness of journal sets (Bensman, 2001).Further, Journal-level categorization cannot assign a single or few categories to interdisciplinary journals (such as Nature or Science), ad other concerns about the risk of disambiguation have been raised. 1o overcome these limitations, it seems reasonable to introduce a paper-level categorization approach based on one of the most granular items in an article, such as the title words, as suggested in their final remarks by Eck and Waltman (2012) or keywords, as proposed by this study.
This paper tries to produce a measure of specialization (and therefore indirectly of interdisciplinarity) of journals using paper-level categorization that uses research keywords as an element of analysis.Article keywords are generally considered a crucial element in understanding the contents of an academic paper (Callon et al., 1986;Choi et al., 2011;Hartley & Kostoff, 2003;He, 1999;Whittaker, 1989).
As these keywords summarize and represent the paper's topic, they represent carefully selected terms that are deemed essential and significant.This is because scholars can quickly identify papers by scanning through article keywords, which are known to consist of the most important terms that represent papers.Additionally, research keywords can reveal related terms that may have previously been unknown and expand search queries.
The use of keywords seems to be one of the most effective ways authors and editors enhance the chances of potential readers finding their articles.Since keywords are not limited to predetermined categories, as they can be freely selected by the authors, they permit readers to make certain that the research article is relevant and support editors and research databases in enhancing the relatedness of articles on a specific topic (Hartley & Kostoff, 2003).Furthermore, keywords selected by authors have been shown to be useful for understanding a discipline (Li, 2018;Onyancha, 2018;Xu et al., 2018), even though Tsai et al. (2011) have shown that expert authors tend to select keywords that better represent the content of their article compared to those chosen by novices.
Moreover, it has also been found that some statistical characteristics of keywords (e.g., number of keywords and percentage of new keywords) have significant relations with citation counts (Uddin et al., 2016).
Based on the assumption that keywords are able to identify the content of the article, the approach proposed in this study identifies the frequency of keywords in a journal as a possible indicator of its specialization: the higher (the lower) the occurrences of the keyword among articles published by a single journal, the higher its discipline specialization (its interdisciplinarity).
By using a dataset consisting of 88,583 articles published in 50 journals, we propose the use of a specialization index, Sj, interpreted as the average frequency at which a keyword appears in the j th journal.
The main peculiarity of this index is its extreme simplicity and replicability due to the availability of freely accessible bibliometric software (i.e., VOSviewer, Eck & Waltman, 2010) that quite easily collects keywords from a dataset of papers.
However, analysing the keyword dynamics requires caution in several aspects.The number of keywords collected in a dataset of articles can be correlated with many factors, such as the number of articles collected, the presence or absence of a publisher's policy that requires the inclusion of a minimum or a maximum number of keywords, the degree of specialization of the article, and the degree of attention given by a researcher to the selection of their keywords.The analysis of the dynamics of keywords still seems to be an unexplored theme, while their use as a possible key factor to screen the specialization of journal research is a novelty.
The essential contribution of this study is to enhance the literature related to publication-level classification systems by introducing a new method of investigation about the specialization of academic journals based on the resource of keywords in a journal that attempts to approximate the level of concentration of articles on a specific topic.By assuming that keywords approximate the article's topic, this study suggests that the higher the occurrences of keywords, the higher the level of specialization of that journal.
The remaining paper is structured as follows: Sect."Related literature" provides context for the proposed measure by comparing it to other journal disciplinarity/interdisciplinarity measures.Sect."Methodology" reports the methodology of the specialization index.Sect."The dataset" describes the way in which the sample of journals was composed.Sect."Keyword dynamics across journals."describes some unobserved dynamics of keywords of journals, while Sect."Results" reports the calculation of the metrics for the identified sample.Finally, Sect."Conclusions and limitation" concludes and outlines the implications of this study.
Related literature
To our knowledge, this is the first study that attempts to propose a metric of specialization by using author keywords.At the same, time it is not the first study that propose an index of specialization.Boyack and Klavans (2011) propose to measure the journal specialization (indicated with the term "journal specificity") by introducing more than one quantity including a textual coherence indicator that is the most close to the index presented in this manuscript.By leveraging the title and abstract information of papers, the authors employed word probability vector techniques to generate clusters and structural insights.Specifically, the index of journal specificity proposed is based on the Jensen-Shannon divergence since it measures the similarity (and then the divergence) between two probability distributions: the likelihood of occurrence of a word in a document (an article) and the likelihood of occurrence of the same word in a Journal (where the article has been published).This approach enables to capture meaningful data about the scientific domain of academic article and it follows other studies in which quite similar text analysis have been used (Boyack & Klavans, 2010;Braam et al., 1991;Glänzel & Czerwon, 1996;Jarneving, 2001).
By comparing the Boyack and Klavans (2011) metric with the Sj index, it becomes apparent that while Sj offers insights into the frequency of specific keywords used within a specific journal, the other index determines the textual coherence of individual journals by comparing word probability distributions.In summary, these two metrics differ both in terms of the data used (keywords versus title and abstract) and the methodological approach (probability of occurrence versus actual occurrence).
Other studies discuss, in some case partially, of journal specialization even though without a proposal of metric.The concept of journal specialization has been discussed by Glänzel et al., (1999) who recognizes that the demarcation of subject areas through journal assignment is inherently less precise compared to using subject headings from individual publications.In these circumstances, keywords can be the considered as a flexible means of tracing the dissemination and trajectories of knowledge, as they are highly indicative of the concepts and subjects covered in articles (Xu et al., 2018).By using keywords extracted through an automatic method (Frantzi et al., 2000 2 ), rather than author's keywords as reported in our study, Xu et al. (2018) examine the formation of interdisciplinary knowledge through the lens of keyword evolution.By doing so, it is possible to gain insights into the specific developmental characteristics of interdisciplinarity, which provide a potential timeline for the interdisciplinary formation process.Specifically, the formation of an interdisciplinarity approach around a specific topic of research progresses through some significant phases (a latent phase, an embryo phase, and a mature phase) and this evolution may be captured through the evolution of keywords related to a topic in different domains of science.In summary, while keywords may be considered as a valuable method to analyze the concentration of subjects and concepts inside a single journal, if collected in different journals and referred to a specific subject, they can also be considered tools for analysis of the interdisciplinarity attitude of that specific subject and also to predict knowledge evolution (Choi et al., 2011).Griffiths and Steyvers (2004) introduce a statistical inference algorithm for Latent Dirichlet Allocation (LDA), a generative model that considers documents as a combination of topics.The implementation of this algorithm allows them to explore the topic dynamics and the identification of words' significance in the semantic content of documents.This results is in part related to our study to the extent that it represents a valuable example of how to calculate the frequency of topic in a cluster of documents (including academic journals).In any case, the most distinguished characteristic of the Sj Index when compared with other explicit or implicit indicators of journal specialization rely on the circumstances that it is not based on probability calculations neither in a more complex algorithm while it represents a metric expressed by a deterministic process.
Methodology
The content of a paper may be described by many features, such as title, abstract, keywords, and discipline classification coding.Keywords represent a "subject heading" that should help readers understand the central concept of the paper and its fields of concern (Hartley & Kostoff, 2003).
Based on this attitude, the underlying rationale of the recourse to keywords to analyse the topic specialization of a journal is that the more times a keyword is replicated in a journal, the higher the number of papers related to the same or similar concepts.
To formalize this idea, we indicate with K j (with K j > 0) the total number of unique keywords contained in a set of m articles of journal j, and with OCC ij (with OCC ij > 0 and where i = 1,..,K j ) the occurrences of each i th unique keyword appearing in journal j.The term "unique" means that, for example, if K j = 10, there are m articles of journal j containing 10 keywords, each of which is replicated OCC ij times.
Since OCC ij represents the number of times a unique keyword is found in a single journal j and since keywords are items unique to each paper, OCC ij should be positively related to the number of articles focused on a specific field of research.In other words, it is possible to consider OCC ij as the number of papers that use the i th keyword in the j th journal.
Based on this specification, we propose measuring the specialization of the j th journal through the level of density of keywords Sj, which can be written in the following form: where OCC ij and K j have already been defined, while OCCj represents, by construction, the total number of keywords (including duplications) selected by all authors of journal j. 3 (1) 3 Suppose that a journal has only the following three author keywords: red, white, green.Then, suppose that the keyword red has an occurrence of 4 (i.e., there are four papers that use this keyword), white has an occurrence of 5, and green has an occurrence of 6.In these conditions OCC j is equal to 15 (4 + 5 + 6), and S j = 5 (15/3), meaning that, on average, a keyword is repeated 5 times in the journal.The higher this number, the higher the number of papers in the journal on the same topic.To clarify further the concept of OCC j and S j , suppose that the journal has now also a fourth keyword, blue, and that this keyword has an occurrence of 1 (i.e., the keyword blue is present in only one paper).In these new conditions, OCC j is equal to 16 (4 + 5 + 6 + 1), while S j is equal to 4 (16/4, where the denominator is the number of unique keywords that passes from 3 to 4).
By expressing the index in this way, Sj appears to be immediately interpreted as the number of times that a generic keyword on average appears in the j th journal.
The simplicity of the index and its immediate comprehensibility represent its primary properties.
However, as discussed in the remainder of the paper, the level of Sj may be affected by a special link between OCC j and K j .Basically, when someone compares the number of unique keywords appearing in journal j (K j ) with the total number (including duplicates) of keywords selected by authors of journal j (OCC j ), it is reasonable to expect that OCC j increases in Kj.This relationship could be explained by probability theories.In particular, assume that selecting a keyword is a mechanism analogous to drawing a card from a deck of W cards with replacement, where W is the (unknown4 ) size of the vocabulary from which researchers choose their keywords and where any drawing is independent of the others.Under these circumstances, the number of times a researcher selects a keyword (i.e., takes a card from the deck) already chosen by another researcher should theoretically be approximated by the binomial distribution X ~ B(n, p) with parameters n ∈ W and p ∈ [0,1], where the probability of success is positively related to the number of trials.In other words, the positive link between the number of keywords in a journal (K j ) and the number of times a keyword may be selected more than one time in that journal (OCC ij ) is because with a larger number of keywords selected, there is a higher chance that two or more researchers will select the same keyword by chance.5Consequently, since journals with a higher number of keywords (K j ) tend to show a higher number of occurrences (OCC j ), these journals may show a higher value of the specialization index Sj.In other words, if we calculate the Sj of two journals with a very different number of keywords, the journal with the higher number of keywords tends to be overestimated.
The positive linkage between K j and OCC j then makes a correction of Sj necessary for those cases where K j is higher to avoid overestimating Sj.
Thus, we propose an adjusted version of Sj that can be written as follows: Using the logarithm of variables represents an attempt to reduce the impact of outliers and extreme values that may skew the analysis results.While the correction proposed by [2] should mitigate the bias of the K-OCC linkage, it has the drawback of losing the immediate meaning of Sj (i.e., the number of times a keyword is on average repeated in a journal) and is only one of the possible corrections.In reality, one could also skip the use of such a measure of correction if the Sj calculation is limited to a set of journals with approximately the same number of keywords. (2)
The dataset
The data used to calculate the journal specialization measure proposed in this study (Sj) were based on keyword frequencies obtained from 50 journals selected from the Scopus source title list (updated in March 2023).More specifically, we selected 50 active journals from the Scopus journal list in alphabetical order containing a minimum number of unique keywords 6 .For each journal, we retrieved all their articles from the Scopus database.The maximum threshold of 20,000 journal articles imposed in the Scopus algorithm has not represented a problem since no one of the 50 journals has more than 20,000 documents to export.Each journal is classified according to the Scopus All Science Journal Classification, ASJC (Franceschini et al., 2016).The keywords were collected through the function available in VOSviewer (version 1.6.18), a freely available bibliometric software explicitly developed to create and display bibliometric maps (Eck & Waltman, 2010, 2014).This function permits the collection of unique keywords and their occurrences at the article level.For keywords, this study intends only author keywords, the terms selected by authors to accurately represent the content of their document.The indexed keywords-that represent keywords selected and standardized by Scopus for indexing purposes using vocabularies derived from Elsevier-owned or licenced thesauri-are excluded from the analysis even though they represent a separate vector of data that deserves further investigation in future research. 7 The number of keywords and occurrences of each journal and some additional details are reported in Table 1.We collected 119,775 unique keywords 8 in total.At a single journal level, the number of keywords varied from a minimum of 43 (for the journal AAO Journal) to a maximum of 13,112 (for the journal Accident Analysis and Prevention).
Using a data cleaning function able to merge different keyword variants in unique forms (a VOSviewer thesaurus file), the keywords collected in this study were submitted to a filter to delete too generic or ambiguous keywords that do not consent to specify in detail the content of an article.The reasons for the ambiguousness vary from the extreme generalization of keywords (genetics, health, chemistry) to an incorrect specification of the article content (e.g., case, report, note, etc.) up to confounding terms (e.g., united states, adult, procedures). 9In making this correction, it is necessary to clarify that it is not meant to label these keywords as inappropriate.Rather, the purpose of the correction is to highlight 6 The minimum number has been arbitrarily set at 40.It should be noted that not all journals have retrievable keywords.In some cases, there may be a lack of author keywords, index keywords, or both.Since the topic of missing keywords remains unexplored, it could be a worthwhile area for future research. 7More specifically, index keywords are keywords not entered by authors but from the reference database (e. g.Scopus or WoS).Scopus, for example, has index keywords that use a controlled vocabulary to describe the contents of a study, such as MeSH (medical subject headings), Emtree (life sciences & health science), or Compendex (engineering). 8In this study the term "unique" is referred to the single journal j and not to the set of 50 journals since it is not possible to exclude the possibility that a unique keyword in one journal also appears in another journal.Table 1 Keywords and occurrences of Journals analysed. 9The list of the keywords qualified as ambiguous and then deleted from our data is the following: covid-19, Covid19, COVID/19, covid_19, adult, aged, animal, animals, article, case report, chemistry, clinical article, clinical trial, conference paper, controlled study, diagnosis, diseases, editorial, female, genetics, health, human, humans,letter, major clinical study, male, methodology, nonhuman, note, priority journal, procedures, review, short survey, therapy, united kingdom, united states, europe.We also removed the keyword "covid-19" or similar terms (i.e.Covid19, COVID/19, covid_19) due to the massive impact of the COVID-19 pandemic on scientific production (Riccaboni and Verginer, 2022) and the related possible bias in keyword distribution.the importance of correcting keywords during estimation exercises to prevent overestimation of the specialization index.Through the function provided by Bibliometrix (Aria & Cuccurullo, 2017), we also counted the average number of coauthors per document for each journal (see NC in Table 1).The number of papers retrieved on Scopus (NP j ) has been calculated with Bibliometrix and was not the same for all journals and varied from 111 (the number of papers for A e C-Revista de Direito Administrativo e Constitucional and for AAO Journal) to 14,803 (for Academic Medicine).The period of collection also differed, as the briefest time span was 4 years, from 2019 to 2022 (for A e C-Revista de Direito Administrativo e Constitucional), and the longest was 1922-2023 (for Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg).
Keyword dynamics across journals
This section analyses the dynamics of the number of occurrences of keywords in a journal.At first glance, the occurrence of keywords (OCC ij , the number of times the i th keyword appears in the journal j th ) is a function of the number of keywords.Specifically, by using the analogy of a deck of cards (i.e., an actual finite population from which samples can be randomly drawn) and considering that the topics of articles published in a volume of a journal should be random, we can approximate the choice of a keyword as a card taken from a deck of W j cards with replacement (where W j is the unknown and many possible keywords), while the occurrence should be approximated as success in selecting a keyword already extracted by another author.Under these circumstances, the number of occurrences is increasing in the number of keywords, as the drawing with replacement analogy is positively associated with the number of trials.
The positive linkage between the number of occurrences and the number of keywords is empirically tested in Fig. 1.The special connection between keywords and occurrences is observed in all journals, independent of the ASJC macrodiscipline.Since the number of keywords seems, at this point, to reasonably act as a factor that affects the number of occurrences, one wonders what are the main factors that determine the number of keywords appearing in a set of papers.The first possible answer is to connect the number of keywords to the size of the set of papers analysed since one should expect that a high number of papers analysed corresponds to a high number of topics investigated and hence a high number of keywords.As shown in Fig. 2a, this relationship is observed in our sample.
However, analysing the dynamics of keywords is more complex, and it seems to represent a still underexplored subject.For example, the keyword dynamics should be a function not only of the number of collected papers but also of the journal submission settings to the extent that some journals may require a minimum number and/or a maximum number of keywords.
Additional factors that could explain a high number of keywords for a single paper could be the number of article pages, as the number of pages could reasonably be correlated with the article's depth (and then to the number of keywords selected).Not being able to directly correlate the number of pages with the number of keywords for each article, this study considered the average number of authors for each journal since a relationship between article length and the number of authors was found, albeit weak and limited to a single discipline (Papatheodorou et al., 2008).The image shown in Fig. 2b supports this idea and suggests considering this relationship in future research related to inspecting keyword dynamics across academic journals.
Results
This section shows the results of calculating the level of concentration of keywords in a set of articles of journal j, with the Sj index exhibited in [1] that we use as a proxy of journal specialization.
Recalling that the Sj index indicates the number of times a keyword appears on average in the j th journal, the value of the Sj index for each of the 50 journals included in the sample is shown in Table 2.The highest level of Sj is 4,19, which is attributed to Academic Medicine, while the lowest level (Sj = 1) is related to AAO Journal.
Since a pattern of dependence of keywords on the number of papers was observed and discussed in the previous section, the journals with the higher Sj are not surprisingly almost coincident with the journals with the higher number of papers collected.
In particular, if we qualify as high-frequency journals (hfj) the journals with numerous collected papers higher than the 75 th percentile of NP j (equal to 1850), we observe that 7 out of 10 journals with the higher Sj are in fact hfj.Moreover, the correlation between Sj and NP j is 0.74 (highly statistically significant at the 1% level), indicating a positive relationship that can be graphically observed in Fig. 3a.
The positive relationship between the number of papers and the specialization index also emerges if we observe the dynamic of Sj inside the same journal.Specifically, if we analyse the Sj of a single journal, we observe a tendency of the Sj index of that journal to be positively correlated with the number of papers of the sample from which to collect the number of keywords.Figure 3b reports the case of the two journals 2D Materials and 3 Biotech (the first and the second journals of the sample in alphabetical order), and it is possible to observe that Sj is also positively correlated with the number of papers in this case.The positive relationship between the index of specialization of a journal and the number of articles retrieved to collect the keywords raises concerns about the appropriateness of calculating the Sj index among journals with different sizes of article collection.
One possible solution could be to limit the comparison of any keyword-based metric, such as Sj, only to journals for which the same amount of research output (i.e., NP j ) was collected.
An alternative approach could be to normalize the Sj index for the natural logarithm of K j, as shown by equation [2].
The adjusted version of the keyword density level, Sj-adj and the relative rank were then calculated for each journal, and the results are presented in columns 5 and 6 of Table 2, respectively.
The adoption of the correction changes the rank of 40 out of 50 journals, while the other 10 (20,0% of the sample) remain unaltered, including the journal positioned in the top rank (i.e., Academic Medicine).However, the amplitude of change in ranks after the adoption of the Sj-adj measure is minimal.Specifically, 64% of journals show a change in the ranks of only ± 3 ranks, while approximately 16% of journals show a change higher than 3 notches with respect to the Sj-based ranking.A large change is observed for 3 Biotech, which scaled down the ranking by 7 positions.Taken together, these findings suggest that using Sj-adj does not radically alter the previous Sj-based results, as is also revealed by Fig. 4, which compares the rank of each journal according to the two measures discussed above.
Conclusions and limitation
The number of academic journal titles has grown progressively during the last century, and the scholarly publication system is experiencing new growth because of Internet technology and open access options (Gu & Blackmore, 2016).This study introduces a new methodology for constructing an index of journal specialization.Based on the collection of keywords appearing in a predefined collection of articles published by a journal, the study proposes to measure journal specialization through the number of occurrences of keywords appearing in a journal.In this section, we discuss the strengths and limitations of the proposed methodology.Important strengths are the extreme simplicity of the methodology and its immediate comprehensibility, as the Sj index could be interpreted as the number of times that a keyword appears on average among the articles published by a specific journal.The higher this number is, the higher the fraction of papers about a specific research subject.The underlying assumption is that the keywords are chosen carefully by researchers to adequately represent the content of manuscripts and to be specific to a field or subfield of research.
The methodology is fully documented in this paper and accompanied by an analysis of keyword dynamics that, to our knowledge, has not been explored before.To determine journal specialization, the methodology relies exclusively on unique keywords and occurrences that can be easily retrieved through VOSviewer (Eck & Waltman, 2010).The data observed for our sample of 50 journals suggest considering the number of papers (NP j ) collected for the j th journal as a crucial variable since the number of keywords K j is dependent on the amount of research analysed.Furthermore, K j positively affects the occurrence of OCC j , which affects the same Sj index (since it is calculated as OCC j /K j ).
To avoid any overestimation of the Sj index, the study proposes an adjustment of Sj for the number of keywords; the corrected version of the index seems not to be changed overall to the extent that more than 80% of the papers collected do not show a significant variation in specialization ranks.Nevertheless, the findings of this study must be seen in light of some limitations.First, the Sj index cannot definitively conclude without caution the specialization of a journal in a specific discipline.The index only indicates the level of overlapping articles' contents, assuming that they are reasonably approximated by the keywords.From an abstract perspective, a journal could show a high level of Sj , while its articles may cover different disciplines.In a basic example regarding the recent or structural events, a journal could have published many papers with the keyword "COVID-19" or "climate" even though the contents cover various research fields (medicine, economics, engineering, sport science, etc.).
A second limitation is that the Sj index could be extremely affected by an underestimation of the number of occurrences OCC j , as the matching of keywords could fail due to the different ways in which two authors can write the same keyword.The number of examples is indefinite, as the discoupling of two similar keywords may occur due to recourse to the use of singular or plural, or different uses of special characters (e.g., fibrillin 1 rather than fibrillin-1), or synonyms.10Third, to conclude about the reliability of the Sj index, one should consider how it is affected by many aspects related to the size and the quality of the keywords retrieved that may depend on many factors, such as (i) the attitude of the researchers towards inserting detailed article-specific keywords, (ii) the choice of the journal to impose a minimum (and/or a maximum) number of keywords per article (Golub et al., 2020) and (iii) the lack of a standard for the length of keywords. 11he ranking's high sensitivity to multiple factors highlights that the purpose of this work is not to determine the ranking of specialized journals but rather to contribute to the ongoing discussion on specialization and interdisciplinarity metrics.Importantly, the sample size used in this study is arbitrary and limited to only 50 journals out of a total of approximately 44,000 journals listed in Scopus as of March 2023 (active and inactive journals).This study could also help address future research that intends to use keywords (and lemmatization of keywords) to enhance the journal specialization debate.For example, an issue that needs to be addressed in future works is the introduction of measures that also consider the characterization of the distribution of keywords (e.g., skewness and kurtosis).In this regard, there is room for improvement in the approach proposed here by adapting metrics such as the Herfindahl-Hirschman index (HHI) to the use of keywords and occurrences.
Funding Open access funding provided by Università Parthenope di Napoli within the CRUI-CARE Agreement.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.To view a copy of this licence, visit http:// creat iveco mmons.org/ licen ses/ by/4.0/.
This figure displays the connection between the number of unique keywords and their occurrences among the 50 journals constituting the sample: subpicture a) reports the case of all journals; subpicture b) groups journals according to the macrodiscipline categorization reported by the Scopus All Science Journal Classification (ASJC).The grey area indicates the 95% confidence interval for the predicted values.
Fig. 1
Fig. 1 Keyword and occurrences relationship Subpicture a) reports the scatter plot of the connection between the number of unique keywords and the average number of papers collected.The long-dashed line indicates the area's border containing the 95% confidence interval for the predicted values.Subpicture b) reports the scatter plot between the number of unique keywords and the average value of the number of coauthors per document; each circle represents a journal, while the grey area indicates the 95% confidence interval for the predicted values.
Fig. 2
Fig. 2 Keywords, number of papers, and number of coauthors
Fig. 3
Fig. 3 Index of specialization and number of papers retrieved This figure reports in graphic form the values reported in Table2.Note: a) the left dot graph indicates the dot graph of Sj and Sj-adj values, while the right scatter plot shows the correlation between the ranks obtained with Sj and Sj-adj for each journal.
Fig. 4
Fig. 4 This figure reports in graphic form the values reported in Table 2. Note: the left dot graph reported in subpicture a) indicates the dot graph of Sj and Sj-adj values, while the right scatter plot reported in subpicture b), shows the correlation between the ranks obtained with Sj and Sj-adj for each journal
Table 1
Keywords and occurrences of Journals analysed
Table 1 (
This table reports the number of keywords (variable K j , column 3) retrieved from Scopus for the 50 journals listed in column 1.The number of articles collected for each journal is indicated in column 2 by NP.The variable OCC (column 4) is the total number of occurrences of keywords appearing in the journal.Variable NC reported in column 5 indicates the average number of coauthors per document.In column 6 are reported the time-span of the articles collected for each journal.Data have been retrieved in Scopus at the date of May 1st 2023
Table 2
Journal ranking by specialization
Table 2
Sj the number of times that a keyword on average appears in the j th journal.Sj-adj is a version of Sj corrected for the number of keywords.Rank Sj and Rank Sj-adj represent the position of a journal in the ranking order for Sj and Sj-adj, respectively.Δ Rank is the difference between Rank Sj and Rank Sj-adj.Column (2) marks high-frequency journals (hfj) and low-frequency journals (lfj): journals indicated with (hfj) have numerous articles equal or higher than 1850 (the 75th percentile of column 2 of Table1); journals indicated with (lfj) have numerous articles less than 1850 | 8,764 | 2023-09-03T00:00:00.000 | [
"Economics",
"Education"
] |
Least-squares Problems
The multilinear least-squares (MLLS) problem is an extension of the linear least-squares problem. The difference is that a multilinearoperator is used in place of a matrix-vector product. The MLLS ...
Introduction
Consider the following multilinear least-squares (MLLS) problem in which u • v denotes the component-wise product of vectors u and v.Given a vector b ∈ R m and matrices A i ∈ R m×n i , i = 1, 2, . . ., L, find x * ∈ R N that solves the problem where x i ∈ R n i , i = 1, 2, . . ., L, N = n 1 + n 2 + . . .+ n L and x = (x T 1 , x T 2 . . . ., x T L ) T .For the sake of simplicity, we consider here the standard Euclidean norm, although the subsequent reasoning holds also for the weighted Euclidean norm.Note that if L = 1, then (1) is a linear least-squares problem.
The MLLS problem occurs, for instance, in factor analysis, chemometrics, psychometrics [7,8,9,13,15].We will study this problem in relation to the design of filter networks [1,11,14], specifically the sequential connection of sparse sub-filters presented by Fig. 1.In this case, x i stands for individual characteristics (design parameters) of sub-filter i, whose frequency response is A i x i .The ideal (desired) frequency response of the sub-filter sequence and the actual one are represented in (1) by b and (A 1 x 1 ) • (A 2 x 2 ) • . . .• (A L x L ), respectively.It is common for the design of filter networks that N << m.
MLLS is a non-convex, typically large-scale, optimization problem with a very large number of local minimizers.Each of the local minimizers is singular and non-isolated.The most typical approach to solving the MLLS problem consists in generating randomly a number of starting points for their further refinement with the use of local optimization methods.One major shortcoming of this approach is that a very large number of starting points is required to be generated in order to find a reasonably good fit in problem (1).Another major shortcoming is that the convergence of local methods is too slow in this problem.
The most popular of the local algorithms used for solving the MLLS problem is called alternating least squares (ALS).It exploits the feature of problem (1) that, if to fix all the vectors x T 1 , . . ., x T L but one, say x i , then the resulting sub-problem of minimizing over x i is a linear least-squares problem.In the ALS algorithm, the linear least-squares sub-problems are solved for the alternating index i.This algorithm is also known as block-coordinate relaxation or nonlinear Gauss-Seidel algorithm [12].The mentioned major shortcomings of the local search algorithms are inherent in ALS.
The main aim of our work here is to develop an effective global optimization approach to solving the MLLS problem and justify it theoretically.
Our work is organized as follows.In Section 2, we consider a new constrained optimization problem introduced in [11].It is similar, in some sense, to the MLLS problem, and its solution gives a good starting point for running the local search in the MLLS problem.Global optimality conditions for the new problem are derived in Section 3.These conditions are then used in Section 4 for constructing a global search algorithm.In Section 5, we report and discuss results of applying our global search algorithm to solving MLLS problems related to the design of filter networks.In Section 6, we draw conclusions and discuss future work.
Problem reformulation
Problem (1) can be written in the equivalent form where Following [11], we consider a similar, conceptually close, problem in which b = y 1 • . . .• y L , and A i x i ≈ y i , i = 1, . . ., L.
We formulate it as: After solving this problem in x, we obtain min where P i is the matrix of orthogonal projection defined by A i .In the numerical implementation, it may not be reasonable to compute P i explicitly, but instead, it can be treated as a linear operator defined by A i in one of the standard ways [2].Moreover, since this problem may admit trivial asymptotic solutions, it must be regularized.This can be done by adding µ y i 2 with a small µ to each term in the objective function.We assume further that all matrices P i have been slightly perturbed in this way, and hence they are positively definite.
Observe that the regularized objective function in ( 2) is strictly convex with a unique minimizer in the origin.Unlike (1), this problem does not suffer from the bad property of having non-isolated minimizers.However, it inherits the multi-extremal nature of problem (1).
Without loss of generality, we can assume that b ≥ 0 in (1) and (2).Indeed, if any component of b is negative, the change of its sign to positive can be compensated by the change of sign in the corresponding row of, for instance, A 1 .In [11], it is discussed how to treat the case of zero components.From now on, we assume that b > 0.
Note that the feasible set in problem (2) consists of disjoint subsets.Each of these subsets is connected.It is characterized by a certain feasible combination of signs of y 1 , . . ., y L .The total number of the subsets is determined by the number of the feasible combinations of signs that equals 2 m(L−1) .
Consider how to solve problem (2) on a given isolated subset of the feasible set, for instance, the subset associated with the positive orthant R mL ++ = {y ∈ R mL : y > 0}.The problem in this case takes the form min The substitution y i = exp(w i ) reduces this problem to: min where exp(•) and ln(•) are component-wise operations.This linear equality constrained problem can be efficiently solved by the conventional methods [10] that are able to take advantage of using the easily available derivatives of the objective function and the simple structure of the linear constraints in (4).In [11], the computational time for solving this problem was approximately half the time for one run of the ALS algorithm on problem (1).
To study the general case of sign combinations, we divide problem (2) into an outer binary problem to deal with the signs of y and an inner subproblem, similar to (4), in which the minimization is performed on the corresponding subset of the feasible set.Notice that the feasible vectors y 1 , . . ., y L in (2) have no zero components, because b > 0. Following [11], we present problem (2) as a specially enumerated set of subproblems of the form (3). We will use the following notations: ( Let S m be the set of all vectors in R m whose elements equal +1 or −1.If y i is feasible, then s i ∈ S m and ȳi > 0. Furthermore, for all feasible vectors y 1 , . . ., y L in (2) we have with the objective function Here, the dependence of Pi on s i is given by (5).Note that the substitution s L = s 1 • . . .• s L−1 is able to eliminate the equality constraint in outer problem (6), which is a binary problem with 2 m(L−1) feasible points.This number of feasible points defines the number of all inner problems (7).
The important feature of problem ( 6) is that it performs a partitioning of the feasible set in (2) and reduces this problem to a finite number of easy-to-solve inner problems (7) of the same form as (3).This allows us to capture the nature of the local minimizers of problem (2) and to enumerate them efficiently by combining the signs.
Any optimal or close to optimal solution y to problem (6), or equivalently problem (2), can be used as an initial point in problem (1), to be further refined by local search algorithms like ALS.Given y, the initial point x is computed by the formula where . In our numerical experiments [11], we compared the performance of the ALS for the initial point generated by our approach and for randomly generated points.It was required to run the ALS from at least 500 random points in order to get a local minimizer in (1) with the approximation error comparable with only one run of the ALS from the point generated by solving problem (6).Thus, the approach introduced in [11] achieved the overall network design speedup factor of several hundreds.Moreover, the randomly generated initial points did not guarantee any success.This speaks for the robustness of the approach.
It should be emphasized that binary problems are, in general, difficult to solve, but fortunately, the nature of signs in the sub-filter outputs are often well understood.Prior knowledge of the filter characteristics and its structure helps to facilitate substantially the solution process of the outer problem by focusing on a relatively small number of sign combinations (see [11] for details).
Theoretical background for global search
Given an approximate solution to problem (2), our global search is aimed at finding a new combination of signs in (6) with a better value of the objective function defined by (7).It is based on solving problem (2) under the assumption that all components of given feasible vectors y 1 , . . ., y L are fixed, except for their kth components denoted here by u 1 , . . ., u L , respectively.The value of k changes in the process of global minimization.
To justify our approach, we will consider problem (2) rewritten in terms of these components.Let ŷi coincide with y i in all the components, but the kth one which equals zero in ŷi .Let (P i ) k and (P i ) kk stand for the kth column and diagonal element of the matrix P i , respectively.It can be easily verified, for i = 1, . . ., L, that Thus, the minimization over u = (u 1 , . . ., u L ) T in (2) results in the problem: where c denotes the kth component of b.It is worth noting that this problem has at least one global minimizer, because the level sets of the objective function are compact and the function defining the constraint is smooth.Let U * stand for the set of all global minimizers in problem (9).For this problem, the following notations will be used: Let the multivariate function σ(•) be defined as the product of the signs of all the variables, for instance, Note that the feasible set in problem (9) consists of disjoint subsets.Each of these connected subsets belongs to the corresponding orthant R L s determined by sign(u).Since such subsets are characterized by σ(u) = 1, their total number is 2 L−1 .It grows exponentially with L. This is indicative of a highly multi-extremal nature of problem (9).The result presented in Theorem 1 allows one to effectively locate the optimal combination of signs S * or, equivalently, to find the orthants that contain the connected subsets of the feasible set on which the global optimum of problem ( 9) is attained.
Theorem 1 Let the coefficients c and α in problem (9) be positive.Then, s * ∈ S * if and only if σ(s * ) = 1 and one of the following conditions holds: (i) σ(β) ≥ 0 and s * i = sign(β i ), for all i such that β i = 0; (ii) σ(β) = −1 and there exists i * ∈ I such that with the set Proof.We start by proving the "if" part.Suppose that s * ∈ S * .Let u * ∈ U * be such that sign(u * ) = s * .The feasibility of u * implies that σ(s * ) = 1.
Consider the linear space transformation given by the formula This nonsingular transformation is aimed at easing our analysis because, in the new space, the objective function takes the form of a squared Euclidean distance between the two points v = (v 1 , . . ., v L ) T and a = (a 1 , . . ., a L ) T = s * • β.Note that Arg min Another important feature of the transformation is that it does not change the multiplicative type of the constraint.The problem in the new space takes the form: where c = c • α 1 • . . .• α L .Thus, the reformulated problem (10) is to find the shortest distance from a to the feasible set.Let v * = (v * 1 , . . ., v * L ) T be the image of u * in the new space, i.e., v * = α • s * • u * .Clearly, v * is a global minimizer for problem (10).Then, in the view of the fact that v * > 0, conditions (i) and (ii) can be reformulated in the new space as follows: (ii ′ ) if σ(a) < 0, then there exists i * ∈ I such that a i > 0, for all i = i * , and a i * < 0.
We first show that there is no more than one negative component of a. Suppose, to the contrary, that at least two components of a are negative, say, a i and a j .It can be verified easily that the open linear segment (v * , a) intersects the hyperplane where λ ∈ (0, 1) is given by the formula Consider the point v ′′ = (v ′′ 1 , . . ., v ′′ L ) T defined as follows: This point is obviously feasible.The triangle inequality gives since v ′ ∈ (v * , a).Hence, the feasible point v ′′ gives a better objective function value in (10) than v * .This contradicts the assumption that v * is a global minimizer for problem (10) and proves that a can have at most one negative component.This result immediately proves (i ′ ) for σ(a) = 1.For σ(a) = 0, suppose, contrary to (i ′ ), that there exists a i < 0. Such component must be unique.There must exist index j such that a j = 0.For these indices i and j, consider the points v ′ and v ′′ defined by formulas ( 11), ( 12) and ( 13).One can show, as above, that ( 14) holds for the two points.This contradicts the assumption that v * is a global minimizer.Thus, statement (i ′ ), and consequently part (i) of the theorem, hold.
Consider now the case σ(a) < 0. As shown above, exactly one component of a must be negative, say, a i < 0. Suppose, contrary to (ii ′ ), that i / ∈ I.For this i and any j ∈ I, consider the point v ′′ defined by (13).For the point v ′ defined by ( 11) and ( 12), the condition λ ∈ (0, 1) is satisfied, because a i + a j < 0. For v ′ and v ′′ one can show, as above, that ( 14) holds, which contradicts that v * is a global minimizer.This proves (ii ′ ) and accomplishes the proof of the "if" part of the theorem.
For the "only if" part, let s * satisfy the sufficient conditions.We choose any u ∈ U * and construct a point u * = (u * 1 , . . ., u * L ) T individually for each of the cases (i) and (ii).Suppose that (i) holds.Consider u * defined as follows: Obviously, sign(u * ) = s * and u * is a feasible point.As proved in the "if" part, sign(u) must satisfy (i).Thus, s * i = sign(u i ), for all i such that β i = 0. Therefore, u * has the same objective function value in (9) as u.
Suppose now that (ii) holds.Let i * ∈ I be such that s * i * = −sign(β i * ).It must satisfy (ii).Suppose j ∈ I is the index for which sign(u j ) = −sign(β j ).This means that then we define u * = u.Otherwise, we define u * as follows: It can be easily seen that sign(u * ) = s * and also that u * is feasible and has the same objective function value in (9) as u.
In each of the two cases, u * ∈ U * , and consequently s * ∈ S * .This completes the proof of the theorem.This result suggests ways in which the sign combinations intrinsic in the global minimizers of problem ( 9) can be effectively constructed for any given β.Our algorithm presented in the next section is based on this result.
Global search algorithm
Theorem 1 implies that s * is not unique when any of the following two cases occurs: • β has more than one zero component.
• σ(β) = −1 and the set I consists of more than one element.Given β, the set S * can be constructed based on the optimality conditions as follows.
• If σ(β) = 0, then S * is composed of all vectors s * ∈ S L whose components s * i = sign(β i ), for all i such that β i = 0, and the rest of the components ensure σ(s * ) = 1.
• If σ(β) = −1, then S * is composed of the same number of elements as I.Each i * ∈ I determines s * ∈ S * in such a way that s * i * = −sign(β i * ), and the remaining components of s * are the same as in sign(β).Note that it is not necessary to construct the whole set S * when it is required to find only one s * ∈ S * .The same principles as above can be employed in this case.
We propose below a global search algorithm.It uses procedures optsign, local and als.Procedure optsign(y, k) computes β by formula (8), and then it returns an s * arbitrarily chosen from the set S * .Another task of this procedure is to verify if a given s ∈ S L is optimal for problem (9).The optimality conditions given by Theorem 1 can be used for checking if s ∈ optsign(y, k) holds.Procedure local(s 1 , . . ., s L ) returns y that solves problem (7) for a given sign combination s 1 , . . ., s L .Procedure als(x 0 ) returns the result of running the ALS algorithm from a given starting point x 0 .
The derived optimality conditions open the way to a successive improvement of the sign combination in outer problem (6).The resulting global search strategies admit various implementations.
The one that we present below is based on a sequential checking of the components in s 1 , . . ., s L for a possible improvement.It starts from a given sign combination s 1 , . . ., s L , and it returns an approximate solution for problem (1) In Algorithm 1, an initial sign combination s 1 , . . ., s L is required to be given.For this purpose, the choice of signs proposed in [11] can be used.An alternative is to choose as the initial sign combination the best one produced by ALS, starting from a number of randomly generated points.
Numerical experiments
For generating MLLS test problems of the form (1), we considered two-dimensional filters of the monomial class [6] with the lognormal [3,4] and logerf [5,11] radial parts.We shall use the abbreviations LP, BP and HP standing for the Low-Pass, first-order Band-Pass and first-order High-Pass filters, respectively.They were approximated by a sequence of L = 5 sub-filters.The total number of coefficients N of the sub-filters and the number of components m of the discretized ideal frequency responses b are specified in Table 1 for each filter.
Our numerical experiments were performed on a PC with a 2.27GHz Intel Xeon E5520 processor and 32-bit Windows XP operating system.The results are shown in Table 1.The Matlab routine fmincon was used to solve problem (4) which is a reformulation of (7).As mentioned earlier, the objective function in (2) is required to be regularized.For the regularization parameter value, we used µ = 0.5.We shall use the term approximation error to refer to the objective function value in (1) and denote it by ε.In Table 1, min(ε j ) stands for the best approximation error obtained by running ALS from 500 randomly generated starting points.The CPU time (in seconds) spent on performing these 500 runs is denoted by t als .We shall use the term local search to refer to solving problem (7) only once for the sign combination chosen as proposed in [11].The approximation error ε loc is the result of one run of ALS from the starting point produced by the local search.Our global search strategy is aimed at improving the local search results.To initialize it, we used the same sign combination as in the local search.The set of filters used in our experiments included also zero-and second-order bandpass and high-pass filters.For these filters, the initial sign combination proposed in [11] was nearly optimal in the sense that there was practically no difference between the approximation errors ε loc and min(ε j ).For this reason, our global search strategy was unable to improve the initial sign combination.
The results presented in Table 1 refer to the filters for which the global search strategy was able to improve local search solutions in terms of objective function values in problems (7) and (1).In the case of high-pass and band-pass filters, the solution produced for problem (1) was as good as the best of those produced by 500 runs of ALS, but for achieving this, the global search required a CPU time that was over 50 times shorter.These results demonstrate the efficiency of our global search strategy and its capability for substantially speeding up the filter design process.
Conclusions
The derived optimality conditions open up possibilities to perform a global search for a better sign combination.The implemented global search strategy is not a very computationally demanding procedure.Its efficiency was demonstrated by the results of numerical experiments.For some filters, our global search ensured a faster process of optimizing sub-filter parameters with an overall speedup factor of over fifty.
We plan to extend our approach to solving optimal filter design problems having more general sub-filter network structures. | 5,303.2 | 2012-04-01T00:00:00.000 | [
"Mathematics"
] |
Tuning of liver circadian transcriptome rhythms by thyroid hormone state in male mice
Thyroid hormones (THs) are important regulators of systemic energy metabolism. In the liver, they stimulate lipid and cholesterol turnover and increase systemic energy bioavailability. It is still unknown how the TH state interacts with the circadian clock, another important regulator of energy metabolism. We addressed this question using a mouse model of hypothyroidism and performed circadian analyses. Low TH levels decreased locomotor activity, food intake, and body temperature mostly in the active phase. Concurrently, liver transcriptome profiling showed only subtle effects compared to elevated TH conditions. Comparative circadian transcriptome profiling revealed alterations in mesor, amplitude, and phase of transcript levels in the livers of low-TH mice. Genes associated with cholesterol uptake, biosynthesis, and bile acid secretion showed reduced mesor. Increased and decreased cholesterol levels in the serum and liver were identified, respectively. Combining data from low- and high-TH conditions allowed the identification of 516 genes with mesor changes as molecular markers of the liver TH state. We explored these genes and created an expression panel that assesses liver TH state in a time-of-day dependent manner. Our findings suggest that the liver has a low TH action under physiological conditions. Circadian profiling reveals genes as potential markers of liver TH state.
While the general effects of TH on liver metabolism are well-characterized, it remains largely unknown how the thyroid state interacts with the circadian regulation of physiological processes in an organ.Most species have developed endogenous time-keeping mechanisms that allow them to keep track of (day-) time and adjust physiology and behavior in anticipation of regularly recurring events.In mammals, a central clock residing in the hypothalamic suprachiasmatic nucleus (SCN) is reset by the external light-dark cycle and coordinates molecular oscillators in central and peripheral tissues including the liver.At the molecular level, a series of oscillatory interlocked transcriptional-translational feedback loops comprised of clock genes and proteins oscillate throughout the day, adjusting cellular functions across the 24-h day cycle.Rhythmic factors such as body temperature, hormones (e.g., cortisol and melatonin), and autonomic nervous stimuli are known pathways through which the SCN pacemaker regulates peripheral clocks and rhythms 9,10 , but it is still up for discussion how low amplitude rhythmic or arrhythmic signals interact with circadian functions at the tissue level.
We have recently shown that, in mice, a high-TH state leads to marked time-of-day specific alterations in energy metabolism such as increased energy expenditure during the active (i.e., the night) and higher body temperature during the inactive phase (i.e., the day).In the liver, T 3 treatment leads to a rewiring of the diurnal liver transcriptome with strong effects on energy metabolism-associated genes, especially those involved in glucose and lipid metabolism.Transcriptional and metabolite data suggest higher triglyceride biosynthesis during the inactive phase followed by increased lipolysis rates in the active phase in T 3 -treated mice.Interestingly, these effects are independent of the liver clock gene machinery itself, which is rather insensitive to T 3 treatment 11 .
While the effects of high-T 3 in the liver have been characterized, we investigated here how a low thyroid state affects the circadian regulation of energy metabolism and the liver transcriptome.Low TH levels reduced systemic energy turnover in line with human hypothyroid conditions.At the same time, only modest effects were observed on the liver transcriptome.Circadian transcriptome profiling allowed for more fine-grained characterization of low-TH effects revealing changes in lipid and cholesterol metabolism.Our approach identified several temporally stable TH state-responsive genes that may serve as livers-specific biomarkers of the TH state.
TH state tunes systemic energy metabolism
To model hypothyroid conditions in mice, we supplemented their drinking water with methimazole and potassium perchlorate (MMI group), which suppresses TH biosynthesis through inhibition of thyroperoxidase in the thyroid gland and inhibits iodine uptake 12 .MMI treatment strongly reduced T 3 and T 4 levels and led to strong Tshb mRNA induction in the pituitary (Supplmentary Fig. S1A).T 3 levels showed circadian rhythms in the control (CON) and MMI groups peaking in the late and mid-dark phase, respectively (Fig. 1A).T 4 , on the other hand, was arrhythmic in both conditions (Fig. 1B; Supplementary file 1).
Reduced food and water intake were observed in MMI compared to CON mice (Supplementary Fig. 1B,C).As had been reported before 13 -and unlike hypothyroid humans-MMI mice had reduced body weight compared to CON mice (Fig. 1C).Compared to CON mice, locomotor activity rhythms were similar in MMI mice, but activity was reduced specifically during the dark phase (Fig. 1D; Supplement Fig. 1D).Body temperature was reduced in MMI during the dark phase (Fig. 1E; Supplementary Fig. 1E).Energy turnover, assessed by oxygen consumption, was slightly lower throughout the day (Fig. 1F; Supplementary Fig. 1F) while respiratory quotient profiles were largely unaltered in MMI mice (Fig. 1; Supplementary Fig. 1G,H).
By averaging profile data over the whole day and comparing data to high-T 3 conditions 11 , clear systemic effects of TH state on metabolic homeostasis became apparent.TH state-dependent effects were observed for locomotor activity (r = 0.62, p = 0.0191), food intake (r = 0.85, p = 0.0002), body temperature (r = 0.75, p = 0.0042), and oxygen consumption (r = 0.79, p = 0.032, Fig. 1G-J).In sum, our findings confirm that a low TH state decreases systemic energy turnover, but this effect is more pronounced during the dark (active) phase.
Low TH levels have moderate effects on liver transcription
In face of the marked effects of TH state on systemic energy metabolism, we focused our attention on the liver due to its major role as a metabolic organ.We collected tissues every 4 h over the whole day to allow the evaluation of time-of-day dependent effects of TH state on liver transcription.To assess T 3 state effects, we determined differentially expressed genes (DEGs) in T 3 and MMI conditions irrespective of sampling time.Surprisingly, T 3 mice showed a fivefold higher number of DEGs compared to MMI mice (Fig. 2A; Supplementary file 2).DEG analysis was performed separately for each sampling time, and the number of DEGs per timepoint is represented in UpSet plots.DEGs identified for at least one time point yielded 95 DEGs in MMI livers compared to 2200 DEGs (a factor of ca.23) in high-T 3 mice 11 (Fig. 2B,C; Supplementary file 2).We previously identified 37 robust DEGs, i.e., genes consistently differentially expressed across all time points in high TH liver compared to CON 11 .At a low TH state (MMI), however, no robust DEG was identified (Fig. 2D).Analysis of established liver TH/ THR target genes and modulators confirmed a much stronger effect of high-T 3 than low TH conditions on liver TH state (Fig. 2E; Supplementary Fig. 2).
Together, these data show that despite marked systemic effects, MMI treatment had much lower effects on liver transcription.Vice versa, this suggests that, under physiological conditions (CON), the liver is already in a low-TH action.
Diurnal profiling reveals transcriptional effects of a low-TH state in the liver
Evaluation of core clock gene expression profiles showed little effect on rhythmic parameters between MMI and CON mice (Fig. 3A) in line with what was previously observed under high-T 3 conditions 11 .To fully assess the rhythmic liver transcriptome, the JTK cycle algorithm 14 was used.A total of 3,329 and 3,383 genes (3,354 and 3,397 probe sets) were classified as rhythmic (p < 0.05) in CON and MMI, respectively (Supplementary Fig. 3; Supplementary file 3).1412 genes (1417 probes) were significantly rhythmic in both groups (Supplementary Fig. 3; Supplementary file 3).An average phase delay of 0.24 h was observed for these genes in MMI livers (Fig. 3B) compared to a 1 h phase advance in high-T 3 conditions 11 .Expression peak phases of rhythmic genes were widely distributed, and amplitudes were overall similar between conditions (Supplementary Fig. 3).
To investigate the difference in rhythmic characteristics of the transcriptome regulation under low TH conditions, we performed differential circadian rhythm analysis using CircaCompare 15 .Of the 5297 genes (5334 probes) showing significant 24-h rhythmicity in at least one group, 1882 genes showed changes in mesor, 403 in amplitude, and 391 in phase between low-TH and control conditions (Fig. 4A,B; Supplementary file 4).Processes associated with cellular proliferation, mRNA processing, and macromolecule complex assembly were enriched in genes with mesor DOWN while processes associated with response to oxidative stress and xenobiotic metabolism processes were found in the mesor UP genes.Genes associated with response to hypoxia, exocytosis, and lipid transport showed increased amplitudes in MMI livers.Conversely, the expression of genes involved in oxidative stress and detoxification processes (cellular oxidant detoxification, hydrogen peroxide catabolism, glutathione, and ethanol metabolism) were phase-delayed in MMI mice (Fig. 4C; Supplementary file 4).A-F) Serum levels of T 3 and T 4 , body weight across the experiment, 24-h profiles of locomotor activity, body temperature, and O 2 consumption are shown.Rhythmicity was assessed using CircaCompare algorithm.Presence (R) or absence (NR) of significant circadian rhythmicity is depicted.In the presence of significant 24-h rhythmicity, a sine curve was fit to the data.(G-J) 24 h average data for locomotor activity, food intake, body temperature, and O 2 consumption for MMI, CON, and T 3 11 groups.One-way ANOVA was performed (p value is shown) followed by Tukey's post-test comparisons (depicted with asterisks).In Lipid metabolism-associated processes were regulated for all three rhythm parameters (Fig. 4C).A dual effect (up-and down-regulation) on mesor was observed while most lipid metabolism genes showed reduced amplitudes and phase delays in MMI mice (Fig. 4D).The identified genes were manually inspected and only those genes participating directly in lipid metabolism pathways were included.In this refined gene set for mesor UP, approximately 50% of the genes were associated with acyl-CoA degradation (Acot1, 2, 3, 4, 7, 9, 13) and biosynthesis (Acsm 1, 3, 5) while others were associated with lipolysis (Lpl, Lipa) and TAG uptake (Vldr).In the mesor DOWN group, ca.40% of the genes were involved in cholesterol biosynthesis (Aacs, Dhcr7, Hmgcr), uptake (Ldlr, Lrp10, Npc1, Lrp5, Pcsk9), and bile acid secretion (Abcb11, Cyp7a1, Abcg5, Slc10a1, Slc10a5, Slc10a2).Moreover, 35% of the genes were associated with FA biosynthesis (Acacb, Fasn), elongation (Elovl1, 2, 3, 5, Hacd1), transport (Cpt1a, Fabp5), and uptake (Slc27a2, Slc27a5).Around 30% of amplitude DOWN genes were associated with cholesterol biosynthesis (Aacs, Hsd17b7) and secretion (Akr1d1, Nr1h5) while genes with a gain in amplitude were linked with cholesterol uptake (Abcb1b) and internal trafficking (Stard3, Npc1) as well as TAG uptake (Vldlr).Genes with expression profile phase effects were more diverse in their functions.Phase-delayed genes were associated with cholesterol metabolization (Cyp8b1) and bile acid secretion (Cyp7a1, Abcb11), FA biosynthesis (Fads2), transport (Cpt1a) and elongation (Elovl2) (Supplementary file 4).
Focusing on cholesterol metabolism, we divided this pathway into three categories: uptake, biosynthesis, and bile acid secretion.Averaged diurnal gene expression from both groups was rhythmic with a strong mesor DOWN effect in MMI mice (Fig. 4E).Total liver cholesterol was reduced and arrhythmic in MMI mice compared to the CON group.On the other hand, serum total cholesterol was elevated in MMI compared to CON mice, indicating impairment in liver cholesterol uptake-as previously suggested.Liver TAG was less responsive to a low-TH state and showed a mesor DOWN effect (Fig. 4F).
Our findings show that a low-TH state results in distinct alterations in liver transcriptome rhythms.Rhythms in lipid and cholesterol metabolism-associated gene programs are affected by low TH levels and translate into differences mostly in cholesterol levels.
Identification of liver TH response genes
When assessing liver TH state across studies-particularly those based in clinical settings-it is not always possible to consider temporal dynamics in liver physiology.Therefore, it would be helpful to identify molecular markers that indicate liver TH state largely independent of sampling time.However, unlike what we had previously shown for high-TH conditions, no robust DEGs were identified in livers of MMI mice (Fig. 2B).While this would preclude reliable data analysis independent of sampling time, we circumvented this limitation by filtering our dataset for transcripts with marked TH state-dependent mesor effects in expression.This would allow for reliable detection of TH state at any time point if sampling time were consistent across experimental conditions.We identified 516 genes that showed consistent mesor effects in response to changes in TH state for both MMI and T 3 conditions (Fig. 5A).Gene Set Enrichment Analysis (GSEA) for genes in the DOWN-MMI/UP-T 3 group showed enrichment for metabolic processes such as steroid, cholesterol, FA, and bile acid metabolism.Conversely, genes in the UP-MMI/DOWN-T 3 group were associated with processes involved in FA, lipoprotein, carbohydrate, and xenobiotic metabolism, amongst others.Interestingly, glucose Glucose/glycogen metabolism was exclusively enriched in the UP in MMI/down in T 3 group (Fig. 5B,C; Supplementary file 5), suggesting a TH-driven overall inhibition of these pathways in line with our previous observations 11 .Averaged expression of all genes pertaining to similar biological processes across the day showed that lipid metabolism-mostly comprised of FA biosynthesis, TAG uptake and biosynthesis-genes were highly responsive to TH levels, being up-and downregulated in T 3 and MMI mice, respectively.Genes associated with FA catabolism were up-and downregulated in MMI and T 3 mice, respectively.As already indicated in the GSEA, cholesterol and bile acid metabolism were markedly affected as MMI and T 3 mice showed down-and upregulation, respectively (Fig. 5C; Supplementary file 5).Validation of selected metabolic genes identified in the microarray was confirmed by qPCR (Supplementary Fig. 4).
Absolute mesor change was used to rank each gene for each comparison (CON vs. T3 and CON vs. MMI).The top-4 genes (Tlcd2, Stim2, Sdr9c7, Akr1c19) are shown (Fig. 5D) recapitulating low or high TH states at any fixed timepoint across the day.Independent qPCR validation confirmed the array findings (Supplementary Fig. 4).The fact that these genes are robustly rhythmic across conditions further emphasizes that sampling time should be kept consistent even in one-timepoint sampling studies (Fig. 5D; Supplementary file 5).To test the predictive efficiency of our approach, we used the published T 3 atlas that combines transcriptome and TH receptor ChipSeq data from different tissues 16 .We identified an overlap of ca.70% (161 of 224 genes) between the two datasets, further supporting the validity of our approach.We further validated the predictive power of our tuning approach by comparing it against two studies.The first one 17 consisted of rending mice hyperthyroid by supplementing T 4 in the drinking water for two weeks, followed by two weeks of recovery.From the TH-responsive DEGs identified, 42 overlapped with our tuning DEGs (20% overlap).The second study 18 rendered THRβ WT and THRβ KO mice hypothyroid for four weeks, followed by one week of T 3 supplementation.This experiment design allowed the identification of THRβ-responsive genes.From our DEGs tunning set, a total of 128 genes were identified, representing 60% of overlap (Supplementary file 5).
Collectively, the predictive power of the tuning DEG set was validated in public datasets and represents an interesting gene set to estimate TH liver state quantitatively.
Discussion
In line with the role of TH in metabolic regulation, reducing the TH state by MMI treatment decreased energy turnover in mice.Contrasting these systemic effects, transcriptional responses in the liver were surprisingly subtle with only a few temporally stable DEGs compared to animals with normal or elevated TH levels.Temporal profiling of transcriptome responses, however, revealed time-of-day dependent changes in metabolic pathways in MMI livers.An altered gene signature associated with TAG and cholesterol metabolism was identified.Interestingly, cholesterol levels were more sensitive to a low TH state than TAGs.Compiling our circadian low-and high-TH datasets genes, several biomarker genes for the TH state were identified and could be used to assess the liver TH state robustly.
In MMI mice, T 3 and T 4 levels were markedly suppressed, whereas a strong induction (ca.40-fold) of pituitary Tshb mRNA expression was identified, confirming the success of our experimental approach.The circadian rhythm of T 3 in the blood was retained at low-TH conditions.In line with previous studies in rodents and observations in hypothyroid patients, we identified TH dose-and time-dependent effects on systemic energy metabolism 13,19,20 .Notably, the effects of a low-TH state on energy metabolism were largely restricted to the active phase.MMI mice showed decreased body weight throughout the experiment, contrasting with hypothyroid conditions in humans.These differences were recently confirmed in genetic hypothyroidism mouse models and www.nature.com/scientificreports/are associated with food intake suppression, higher skeletal-muscle adaptive thermogenic and fatty acid oxidation in low-TH mice 13 .Liver transcriptome analyses showed 23-fold fewer DEGs in MMI-compared to T 3 -treated (high-TH) mice.This reduced liver responsiveness to a low TH input is further reflected in decreased effects on the established TH/THR target genes 2,4 .Further, THR (Thra and Thrb) expression in response to TH state alterations was only seen in T 3 -but not in MMI-treated animals.In contrast, the expression of Dio1, the main deiodinase in the liver 21 , showed a clear TH dose-dependent effect.Knockdown of liver Dio1 favored the development of fatty liver disease (NAFLD/MASLD) due to reduced fatty acid oxidation 22 .Our data suggest that reducing TH action under physiological conditions would be of little consequence as the liver is already a low-TH action organ.An alternative explanation for our findings of low liver responses to reduced TH state would be that intra-liver TH levels could be stabilized due to high DIO1 activity.Dio1 mRNA was downregulated in the MMI group, which speaks against this idea, but evaluating DIO1 activity or directly measuring liver TH levels would be needed to fully exclude this option.
While these conclusions arise from a simple overall gene expression analysis, circadian profiling of transcriptome data allows for a much more fine-grained evaluation 23 .Previously undetected alterations in several genes revealed novel affected biological processes in response to a low TH state such as cellular proliferation, hypoxia, and oxidative stress, which may serve as hypothesis-generating data for follow-up experiments.We here focused our efforts on lipid metabolism because of the putative role of TH in NAFLD/MASLD regulation 24,25 , a condition that also has been linked to circadian rhythms 26,27 .
Gene signatures of lipid and cholesterol metabolism showed alterations in all three rhythm parameters (mesor, phase, and amplitude) in MMI-treated mice.Serum cholesterol was highly sensitive to TH state.Increased serum cholesterol levels in MMI mice agreed with previous observations of murine models for sub-clinical 28 or severe hypothyroidism 29 .Diurnal transcriptome profiling showed a mesor DOWN effect in genes associated with cholesterol uptake, biosynthesis, and bile acid secretion.A low TH state is known to reduce bile acid secretion in gall bladder, ileum, and feces compared to euthyroid mice 30 and an increased incidence of cholesterol gallstone formation has been associated with decreased circulating TH levels 31,32 .In contrast, liver TAG levels were less responsive to a low TH state despite marked related alterations at the transcript level.A murine study using a severe hypothyroidism model (low-iodine plus Slc5a5 knockout) found a protective effect against NAFLD/ MASLD.In this model, the hampered TH signaling impairs the adrenergic adipose-derived lipolysis, which reduces fatty acids shuttling to the liver.The reduction of fatty acid uptake by the liver prevents NAFLD/MASLD development.However, a mild hypothyroidism model, achieved by a low iodine diet for 12 weeks, increased liver TAG levels as the adrenergic adipose-derived fatty acid shuttling was not impaired 29 .Increased liver TAG has been reported in a subclinical model of hypothyroidism, achieved by low doses of MMI in drinking water after 16 weeks 28 .However, a recent study using propylthiouracil for 2 and 4 weeks found no changes in TAG and cholesterol levels despite changes in the serum 33 .
Our findings show that a low TH state produces transcriptional changes that impair cholesterol metabolization by reducing liver cholesterol uptake, biosynthesis, and bile acid secretion.We suggest that the low cholesterol clearance of MMI mice results in increased serum cholesterol that can be classified as dyslipidemia and an early consequence of a low TH state.Although transcriptional changes associated with TAG metabolism were observed, liver TAG showed a less pronounced alteration and is suggestive of being a late consequence of hypothyroidism.
The systemic changes observed at low thyroid hormone (TH) levels extend beyond the alterations detected in the liver.MMI mice exhibit a decrease in metabolic activity, likely stemming from disrupted metabolic networks across multiple organs.Such a decline in metabolism may reduce the transport of free fatty acids from adipocytes to the liver, impacting lipid synthesis and beta-oxidation processes.Additionally, diminished locomotor activity may lead to lower energy expenditure and reduced consumption of energy resources, contributing to an overall metabolic slowdown.This is mirrored by the lower body temperatures observed in MMI mice.Further research is imperative to understand the responses of other metabolic tissues to a low-TH state and to elucidate the influence of the circadian clock on these interconnected metabolic pathways.
Considering that liver metabolism changes over the course of the day 34,35 and many metabolic genes showed time-of-day dependent responses to low-(this study) or high- 11 TH state, time emerges as an important variable in characterizing metabolic footprints in liver and, potentially, other tissues.We have recently modeled the impact of accounting for time in identifying DEGs in a mouse model of NASH motivated by low consistency in DEG identification in published studies.Accounting for time and using circadian analytical methods led to a ca.sevenfold higher DEG yield 23 .
Circadian profiling may not often be feasible-or simply too expensive-particularly in clinical settings.Therefore, we used our data to identify genes that consistently respond to changes in TH state at all times of day as potential robust markers of liver TH state.None of the previously identified 37 high-TH response genes 11 showed altered expression after MMI treatment.In this new approach, time was fully considered yielding markers of TH state in the liver.Rhythmic genes with mesor changes are associated with several metabolic processes known to be TH regulated such as lipid, glucose, steroid, and xenobiotic metabolism.We further explored the mesor-affected genes as possible candidates to estimate liver TH status.
We restricted our analysis to genes that only showed mesor changes to avoid possible confounding factors caused by amplitude and/or phase.In this subset, 224 genes were identified as only being affected at the mesor level.Importantly, out of the 37 robust DEGs identified at high TH conditions, only two genes (Gstt1 and Alt2) were present in MMI mice.Such findings are expected as in this new analysis both rhythmicity and mesor differences were considered.Using public datasets of different experiments that manipulated systemic TH levels, we obtained a good overlap between tuning DEGs and published data, thus demonstrating that our experimental approach can be useful in future experiments to identify and compare liver TH states.Importantly, we did not www.nature.com/scientificreports/identify a 100% overlap between our tuning DEGs and other datasets.This could be associated with using different experimental protocols of low and high TH state induction, sampling time, and/or methodological aspects (ex.RNAseq vs. microarray).In addition, our study corroborates these findings by demonstrating that not all genes are equally responsive to TH and can show a gradual response to TH across the day.This is particularly detected in our diurnal analysis and could explain the distinct overlap across the studies.Our findings show that several thousand T 3 -induced DEGs are affected by time, which can directly affect DEG detection if sampling time is inconsistent.However, our findings further refine this dataset and provide a selective set of genes that can be used to establish liver TH state.We suggest that this gene panel can be used to estimate liver TH state if sampling is time-controlled in experimental and possibly clinical studies.
As previously observed in a high-TH condition 11 , the circadian core clock was largely unaffected by either low or high T 3 conditions.These findings suggest that TH effects on transcriptome rhythms are downstream of the liver circadian clock and warrant further investigation.Metabolic changes evoked by a low TH state emerged only upon temporal profiling.We also identified a subset of genes that respond in a TH dose-dependent manner with changes in expression mesor.We suggest these genes can be used for follow-up validations of TH liver state in experimental conditions containing limited time points.Understanding the circadian effects of TH state may prove useful for identifying therapeutic targets and optimizing existing treatment strategies for metabolic liver diseases.
Limitations
Our conclusions are drawn from a pharmacological hypothyroidism model.Further validation using genetic models of hypothyroidism to investigate low TH action in the liver should be performed.Due to methodological limitations, all differential rhythm analyses were performed using CircaCompare in a pair-wise fashion without correcting for multiple testing, which could result in increased false positives.We mitigated this limitation by using two different circadian methods to estimate and assess rhythmicity.Our findings suggest that differences can be reliably detected if sampling time is kept consistent.However, performing a circadian profile improves the detection of affected genes and pathways beyond what is achievable through single-timepoint analysis.The hypothyroidism model was successful as evaluated by reduced serum TH levels and increased pituitary Tshb expression.Our conclusion that the liver is a low TH action organ arises from the gene signature of known TH targets (e.g., Thrsp, Dio1, Cyp7a1), which suggests a low effect of low compared to a high TH condition.We base our claims on an action standpoint (target gene expression) and not on specifically low intrahepatic TH levels themselves.Further investigation of intrahepatic TH levels is required.Importantly, how male and female mice differ in response to a low and high TH state is a matter for future investigation.
Mouse model and experimental conditions
Two-to three-months-old male C57BL/6 J mice (Janvier Labs, Germany) were housed in groups of three under a 12-h light, 12-h dark (LD, ~ 300 lx) cycle at 22 ± 2 °C and a relative humidity of 60 ± 5% with ad-libitum access to food and water.Mice were treated with methimazole (MMI, 0.1%, Sigma-Aldrich, USA) potassium perchlorate (0.2%) and sucralose (1 tablet per 50 ml, Tafelsüss, Borchers) for three weeks.
During the treatment period, mice were monitored for body weight individually and food and water intake per cage.All in vivo experiments were ethically approved by the Animal Health and Care Committee of the Government of Schleswig-Holstein and were reported according to the ARRIVE international guidelines.All methods were carried out in accordance with relevant guidelines and regulations.The sample size was calculated using G-power software (version 3.1) and is shown as biological replicates in all graphs.
Euthanasia was carried out using cervical dislocation and tissues were collected every 4 h.Night experiments were carried out under dim red light.Tissues were immediately placed on dry ice and stored at − 80 °C until further processing.Blood samples were collected from the trunk, and clotting was allowed for 20 min at room temperature.Serum was obtained after centrifugation at 2500 rpm, 30 min, 4 °C, and samples stored at − 20 °C.
Total T 3 and T 4 evaluation
Serum quantification of T 3 and T 4 was performed using commercially available kits (NovaTec, Leinfelden-Echterdingen, DNOV053, Germany for T 3 and DRG Diagnostics, Marburg, EIA-1781, Germany for T 4 ) following the manufactures' instructions.
Triglycerides and cholesterol evaluation
TAG and total cholesterol evaluation were processed according to the manufacturer's instructions (Sigma-Aldrich, MAK266 for TAG and Cell Biolabs, San Diego, USA, STA 384 for cholesterol).
Telemetry and metabolic evaluation
Core body temperature and locomotor activity were monitored in a subset of single-housed animals using wireless transponders (E-mitters, Starr Life Sciences, Oakmont, USA).Probes were transplanted into the abdominal cavity of mice 7 days before starting the drinking water treatment.During the treatment period, mice were recorded once per week for at least two consecutive days.Recordings were registered in 1-min intervals using the Vital View software (Starr Life Sciences).Temperature and activity data were averaged over two consecutive days (treatment days: 19/20) and plotted in 60-min bins.
An open-circuit indirect calorimetry system (TSE PhenoMaster, TSE Systems, TSE Systems, USA) was used to determine respiratory quotient (RQ = carbon dioxide produced/oxygen consumed) and energy expenditure in a subset of single-housed mice during drinking water treatment.Mice were acclimatized to the system for one Vol:.(1234567890
Microarray analysis
Total RNA was extracted using TRIzol (Thermofisher, Waltham, USA) and the Direct-zol RNA Miniprep kit (Zymo Research, Irvine, USA) according to the manufacturer's instructions.Genome-wide expression analyses was performed using Clariom S arrays (Thermo Fisher Scientific) using 100 ng RNA of each sample according to the manufacturer's recommendations (WT Plus Kit, Thermo Fisher Scientific).Data were analyzed using Transcriptome Analyses Console (Thermo Fisher Scientific, version 4.0) and expressed in log 2 values.Sample MMI_ZT06_a and MMI_ZT18_d were removed from the data due to low quality.
Quantitative PCR (qPCR)
cDNA was generated by reverse transcription of total RNA using the high-capacity cDNA reverse transcription kit (Applied Biosystems, USA).Gene expression was determined by qPCR using the Go Taq qPCR master mix kit (Promega, USA) and a CFX-96 thermocycler (Bio-Rad, USA).Relative mRNA levels were obtained by analyzing gene expression with the Pfaffl Eq. 37 .Eef1α was used as a normalizer due to its robust expression across time and groups.Primer sequences are provided in supplementary file 5.
Differentially expressed gene (DEG) analysis
To identify global DEGs, all temporal data from each group was considered and analyzed by Student's t test and corrected for false discovery rates (FDR < 0.1).Up-or downregulated DEGs were considered when a threshold of 1.5-fold (0.58 in log 2 values) regulation was met.As multiple probes can target a single gene, we curated the data to remove ambiguous genes.To identify DEGs at specific time points (ZTs-Zeitgeber time; ZT0 = "lights on"), the procedure described above for each ZT was performed separately.Time-independent DEGs were identified by finding consistent gene expression pattern across all ZTs.
Rhythm analysis
To identify probes that showed diurnal (i.e., 24-h) oscillations, we employed the non-parametric JTK_CYCLE algorithm 14 in the Metacycle package 38 with a set period of 24 h and an adjusted p-value (ADJ.P) cut-off of 0.05.Phase and amplitude parameter estimates from CircaSingle were used for rose plot visualizations 15 .To directly compare rhythm parameters (mesor and amplitude) in gene expression profiles between T 3 and CON, CircaCompare fits were used irrespective of rhythmicity thresholds.Phase comparisons were only performed when a gene was considered as rhythmic in both conditions (p < 0.05) as previously described 11 .Temporal profiles were made using geom_smooth (ggplot2 package), method "lm", and formula = y ~ 1 + sin(2*pi*x/24) + cos(2*pi*x/24).Small differences in rhythmic parameters can be present due to the different sine curve fitting between CircaCompare and ggplot curve fit.
Gene set enrichment analysis (GSEA)
Functional enrichment analysis of DEGs was performed using the Gene Ontology (GO) annotations for Biological Processes on the Database for Annotation, Visualization, and Integrated Discovery software (DAVID 6.8 39 ).Processes were considered significant for a biological process containing at least 5 genes (gene count) and a p-value < 0.05.To remove the redundancy of GSEA, we applied the REVIGO algorithm 40 using default conditions and a reduction of 0.5.For enrichment analyses from gene sets containing less than 100 genes, biological processes containing at least 2 genes were included.Overall gene expression evaluation of a given biological process was performed by normalizing each timepoint by CON mesor.
Data handling and statical analysis of non-bioinformatic related experiments
Samples were only excluded upon technical failure.Data from ZT0-12 were considered as light phase and from ZT 12-24 as dark phase.Day vs. night analyzes were performed by averaging the data and comparing using unpaired Student's t test with Welch correction.Time-course data were analyzed by Two-way ANOVA for main treatment effects followed by Bonferroni post-test.When applicable, Two-Way for repeated measures was applied.Single timepoint data were evaluated by unpaired Student's t test with Welch correction or Mann-Whitney test for parametric or non-parametric samples, respectively.ANOVA One-Way followed by Tukey was used for single timepoint data when CON, MMI, and T 3 groups were evaluated.Spearman's correlation was used for all correlational analyzes.Analyzes were done in Prism 9.4 (GraphPad) and a p-value < 0.05 was used to reject the null hypothesis.
Data handling and statical analysis of bioinformatic experiments
Statistical analyses were conducted using R 4.0.3(R Foundation for Statistical Computing, Austria) or in Prism 9.4 (GraphPad).Rhythmicity was calculated using the JTK_CYLCE algorithm in meta2d, a function of the Meta-Cycle R package v.1.2.0.Rhythmic features were calculated and compared pairwise among the groups using the CircaCompare R package v.0.1.1.Data visualization was performed using the ggplot2 R package v.3.3.5, eulerr R package v.6.1.1,UpSetR v. 1.4.0,pheatmap v. 1.0.12, and Prism (GraphPad).
Figure 1 .
Figure 1.Dose-dependent effects of thyroid hormone (TH) state on systemic energy metabolism.(A-F) Serum levels of T 3 and T 4 , body weight across the experiment, 24-h profiles of locomotor activity, body temperature, and O 2 consumption are shown.Rhythmicity was assessed using CircaCompare algorithm.Presence (R) or absence (NR) of significant circadian rhythmicity is depicted.In the presence of significant 24-h rhythmicity, a sine curve was fit to the data.(G-J) 24 h average data for locomotor activity, food intake, body temperature, and O 2 consumption for MMI, CON, and T 3 11 groups.One-way ANOVA was performed (p value is shown) followed by Tukey's post-test comparisons (depicted with asterisks).In (A) and (B), n = 4-6 animals per group or timepoint.In (C), n = 24.In (D), n = 4 and 5 for CON and MMI groups, respectively.In (E) and (F), n = 4 for each group.*, **, ***, **** represent a p value of < 0.05, 0.01, 0.001, and 0.0001, respectively.
Figure 1.Dose-dependent effects of thyroid hormone (TH) state on systemic energy metabolism.(A-F) Serum levels of T 3 and T 4 , body weight across the experiment, 24-h profiles of locomotor activity, body temperature, and O 2 consumption are shown.Rhythmicity was assessed using CircaCompare algorithm.Presence (R) or absence (NR) of significant circadian rhythmicity is depicted.In the presence of significant 24-h rhythmicity, a sine curve was fit to the data.(G-J) 24 h average data for locomotor activity, food intake, body temperature, and O 2 consumption for MMI, CON, and T 3 11 groups.One-way ANOVA was performed (p value is shown) followed by Tukey's post-test comparisons (depicted with asterisks).In (A) and (B), n = 4-6 animals per group or timepoint.In (C), n = 24.In (D), n = 4 and 5 for CON and MMI groups, respectively.In (E) and (F), n = 4 for each group.*, **, ***, **** represent a p value of < 0.05, 0.01, 0.001, and 0.0001, respectively.
Figure 2 .
Figure 2. Lowering thyroid hormone state has subtle effects on liver transcriptome rhythms.(A) Global DEG analysis (disregarding sampling time) is represented as a Venn diagram.(B) UpSet plots represent DEG analysis for each ZT separately.(C) Venn diagram represents all temporal DEGs (i.e., showing different expression levels of at least one ZT) identified in MMI and T 3 groups versus CON mice.(D) Selected examples of robust DEGs (37 in total) previously identified in T 3 -treated mice.Absolute fold change comparison of all 37 DEGs in T 3 and MMI mice are shown.Absolute fold changes were used as some genes were up-or downregulated across the groups.(E) Selective TH output genes and fold change of these genes.Comparisons were performed using two-way ANOVA (main treatment effect, p < 0.05).n = 3-4 for all ZTs and groups.Pair-wise comparisons were performed by Student's t test with Welch correction.Presence (R) or absence (NR) of circadian rhythm by JTK cycle (p value < 0.05).***, **** represents a p value of < 0.001, and 0.0001, respectively.
Figure 3 .
Figure 3. Lowering thyroid hormone state does not affect the rhythmic expression of core clock genes and has a slight phase effect on robustly rhythmic genes.(A) Diurnal expression profile of core clock genes is shown.Presence (R) or absence (NR) of significant circadian rhythm by JTK cycle (p value < 0.05) is depicted.(B) Rose plot and heatmap of robustly rhythmic genes are shown.A minor phase delay of 0.24 h was identified in the MMI group compared to CON (test against zero, p < 0.001).n = 3-4 for all ZTs and groups.
Figure 4 .
Figure 4. Differential rhythm analysis reveals changes in liver transcriptome rhythms that affect lipid and cholesterol metabolism in MMI mice.(A) Differential rhythm analysis was performed using CircaCompare and is represented as Venn diagrams.(B) UpSet plots show alterations in diurnal rhythm parameters (mesor, amplitude, phase).(C) Gene set enrichment analysis (GSEA) of the genes with either mesor, amplitude, or phase alterations was performed.Top-5 biological processes for each category are shown.(D) In-depth diurnal lipid metabolism analysis in response to low thyroid hormone state.Heatmaps show genes with mesor changes.Volcano plot and rose plot show alterations in amplitude and phase, respectively.(E) Normalized gene expression of selected genes participating in cholesterol uptake, biosynthesis, and degradation (bile acid secretion) in CON and MMI mice.(F) Quantification of cholesterol and TAG in serum or liver.Presence (R) or absence (NR) of significant circadian rhythm by CircaCompare (p value < 0.05) is depicted.n = 3-4 for all ZTs and groups.
Figure 5 .
Figure 5. Identification of thyroid hormone-responsive genes by mesor comparison.(A) Heatmap shows the 516 TH-dose responsive genes with differential mesor expression among each group.(B) Gene set enrichment analysis (GSEA) of mesor-altered genes is shown for each condition.(C)Genes pertaining to similar biological pathways identified in B were normalized by CON mesor and plotted.FA biosynthesis pathway comprises lipid metabolic process, unsaturated fatty acid biosynthetic process, long-chain fatty-acyl-CoA biosynthetic process, diacylglycerol biosynthetic process, negative regulation of fatty acid biosynthetic process, and lipid storage.FA catabolism pathway is comprised of acyl-CoA metabolic process, lipid metabolic process, fatty acid metabolic process, very-low-density lipoprotein particle assembly processes.Cholesterol and bile acid metabolism pathways comprise cholesterol homeostasis, cholesterol metabolic process, steroid metabolism, and bile acid signaling pathway.Carbohydrate metabolism pathways are comprised of carbohydrate metabolic process, glycogen metabolic process, ATP metabolic process, and glucose homeostasis.(D) Selected biomarkers for TH state at all time points (additional genes can be found in Supplementary file 5).Presence (R) or absence (NR) of circadian rhythm by CircaCompare (p value < 0.05) is depicted.n = 3-4 for all ZTs and groups. https://doi.org/10.1038/s41598-023-50374-z week prior to starting the measurement.Monitoring of oxygen consumption, water intake as well as activity took place simultaneously in 20-min bins.VO 2 and RQ profiles were averaged over two consecutive days (treatment days: 19/20) and plotted in 60-min bins.Energy expenditure was estimated by determining the caloric equivalent according to Heldmaier 36 : heat production (mW) = (4.44 + 1.43*RQ)*VO 2 (ml O 2 /h). | 8,801.8 | 2023-09-24T00:00:00.000 | [
"Medicine",
"Biology"
] |
On weighted exponential-Gompertz distribution: properties and application
In this paper, we introduce a new distribution generated by an integral transform of the probability density function of the weighted exponential distribution. This distribution is called the weighted exponential-Gompertz (WE-G). Its hazard rate function can be increasing and bathtub-shaped. Several statistical properties of the new model are obtained, such as moment generating function, moments, conditional moments, mean inactivity time, mean residual lifetime and Rényi entropy. The maximum likelihood estimation of unknown parameters is introduced. A real data application demonstrates the performance of the new model.
Introduction
Numerous extended distributions have been extensively used over the last decades for modelling data in several areas. Recently, there has been an increased interest in defining new families of distributions by adding one or more parameters to the baseline distribution which provide great flexibility in modelling data in practice. For example, Eugene et al. [1] proposed the beta generated method that uses the beta distribution with parameters a and b as the generator. The cumulative distribution function (cdf) of a beta generated random variable X is defined as (1) where G(x) is the cdf of any random variable X.
Zografos and Balakrishnan [2] have presented a new family of distributions generated by a gamma random variable. This family has the following cdf (2) whereḠ(x) is a survival function which is used to generate a new distribution. Also, Ristic' and Balakrishnan [3] introduced a new family of distributions with survival function defined as On the same line, we provide a new family of distributions generated by the weighted exponential distribution.
A random variable X has a weighted exponential (WE) distribution if its pdf is given by (4) where α is the shape parameter and β is the scale parameter.
The corresponding cdf is More details on the WE distribution can be founded in Gupta and Kundu [4].
In this paper, we introduce a new family of distributions generated by an integral transform of the pdf of a random variable T which follows WE distribution. The survival function of this family is defined bȳ and the pdf f (x; α, β, ζ ) = α + 1 α βg(x; ζ ) G β−1 (x; ζ ) In the following sections, we study the properties of a special case of this family, when G(·) is the cdf of the Gompertz distribution. In this case, the random variable X is said to have the weighted exponential-Gompertz distribution.
The reminder of this paper is organized as follows. In Section 2, the weighted exponential-Gompertz distribution is studied in detail. In Section 3, we provide expansions for weighted exponential-Gompertz cumulative and density functions. In Section 4, we present various properties of the new model such as moment generating function, moments and conditional moments. Also, some reliability properties including mean inactivity time function, mean and variance of (reversed) residual lifetime of the our model are discussed in Section 5. In Section 6, Rényi entropy is justified for our proposed model. Order statistics are obtained in Section 7.
In Section 8, the maximum likelihood estimator of the parameters of our model is obtained. Section 9 gives an application to a real data set.
Weighted exponential-Gompertz distribution
The Gompertz (G) distribution has the pdf and its cdf is The Gompertz distribution plays an important role in modelling reliability, human mortality and actuarial data that have hazard rate with an exponential increase. An extension version of Gompertz is the weighted Gompertz distribution discussed by Bakouch and Abd El-Bar [8].
Definition:
and the cdf F(x; α, β, λ, σ ) The survival and the hazard rate functions corresponding to (10) are, respectively, defined by S(x; α, β, λ, σ ) and Figure 1 illustrates the shapes of the pdf of the WE-G distribution for some various values of the shape parameters σ and α in the case of λ = 2 and β = 1 for graphs (a), (b), (c) and in the case of λ = 1 and β = 2 for graph (d). It can be summarized some of the shape properties of our model as: • The pdf is monotonically decreasing when σ < 1 and α < 1 [graph (a)]. • The pdf is reversed-J when σ ≥ 1 and α < 1 [graph (b)]. • The pdf is left-skewed when σ < 1 and α ≥ 1 [graph (c)]. • The pdf is right-skewed when σ > 1 and α ≥ 1 [graph (d)]. Figure 2 gives some of the possible shapes of the hazard rate function of the WE-G distribution for some various values of the shape parameters σ and α in the case of λ = 1 and β = 2 for graph (a) and in the case of λ = 0.5 and β = 0.01 for graph (b). It can be summarized some of the shape properties of the hazard rate function of WE-G as: • The hazard rate function is an increasing function for σ < 1 and α < 1 [graph (a)]. • hazard rate function is bathtub shaped for σ ≥ 1 and
Expansions for the cdf and pdf
In this section, we discuss some useful expansions for the cdf and pdf of the WE -G distribution.
We also obtain another expansion of the cdf of WE-G as: From Equation (11) and expanding the term [1 − exp(−σ (e λx − 1)) ] β (i α+1) , the cdf of WE-G can be rewritten as Using power series expansion for exp(− σ j(e λx − 1)) and binomial expansion for (e λx − 1) k , the cdf admits the following expansion where
Expansion for the pdf
Here, we provide simple expansions for the WE-G pdf. Firstly, expanding the term Using again binomial expansion for (e λx − 1) k , we can express the pdf of WE-G whose weighted coefficients are We also obtain the expression for the pdf of WE-G as a linear combination of Gompertz density function as: where g(x; λ , σ (j + 1)) denotes the density function of Gompertz distribution with parameters λ and σ (j + 1) . Therefore, the density function of WE-G can be expressed as an infinite linear combination of Gompertz densities.
Statistical properties of WE-G
In this section, we derive the main statistical properties of WE-G model for instance, the moment generating function, moments, central moments and conditional moments.
Moment generating function
where υ i,j,k,l defined by Equation (17).
Proof:
The mgf of a continuous random variable X is defined as Then, the mgf for WE -G with density function given in Equation (16), we have Solving the above integral, we have which completes the proof.
Theorem 4.2:
The WE-G random variable has the rth moment function about the origin is Proof: The rth moment about origin is defined by By using the expansion form of pdf that given in Equation (16) yields Since, (r) = ∞ 0 y r−1 e -y dy, then the above integral yields the rth moment given by Equation (20).
In particular, the first four moments of X are Hence, the skewness (γ 1 ) and kurtosis (γ 2 ) can be obtained using the following relations,
Proposition 4.1: Let X be a random variable following the WE-G distribution, then the central moments is
Proof: By the definition of central moments, we have Substituting by the Equation (20) into Equation (23) after some simple calculations, we obtain
Remark 4.1:
The variance of WE-G model is obtained from Equation (22) for n = 2.
Note: In the next sections, we will make use of the following lemma.
Proposition 4.2: The conditional moments of the WE-G distribution is
where E(X r ) is defined by Equation (20) and ϕ(t; r, α, β, λ, σ ) is obtained by Lemma 1.
Proof:
The proof follows from the following definition
Reliability measures of WE-G
Here, we derive the expression for the mean and strong mean inactivity time functions, mean and variance of residual lifetime and reversed residual lifetime of the WE-G model.
Mean inactivity time function
The mean inactivity time (MIT) and strong mean inactivity time (SMIT) functions are an important characteristic in many applications to describe the time, which had elapsed since the failure. Many properties and applications of MIT and SMIT functions can be found in Kayid and Ahmad [9], Izadkhah and Kayid [10] and Kayid and Izadkhah [11]. Let Xbe a lifetime random variable with cdf F(·). Then the MIT and SMIT respectively, are defined by and
Proposition 5.2: The SMIT function of X with WE-G distribution is
From Tables 1 and 2 it is observed that the MIT and SMIT are increasing for decreasing values of α and β, respectively.
From Figures 3 and 4, it is note that MIT and SMIT functions of the WE-G distribution are increasing for decreasing values of α and β in the case of λ = 0.5, σ = 1 and t = 1.
Residual lifetime function
The residual life is the period from time tuntil the time of failure and defined by the conditional random variable R (t) := X − t|X > t, t ≥ 0.
Proposition 5.3: The mean and variance of R (t) for the WE-G distribution are
respectively, where E(X) and E(X 2 ) can be obtained using (20),S(t) defined by (12) and ϕ(t ; 2 , α , β , λ , σ ) is defined by Lemma 1 for r = 2.
Proof:
The proof follows directly from the definitions:
Reversed residual life function
The reversed residual life is the time elapsed from the failure of a component given that its life X ≤ t and defined as the conditional random variable − R (t) and respectively, where F(t) defined by Equation (11).
Proof: The proof follows using the following definitions
Rényi entropy
The entropy of a random variable X is a measure of variation of the uncertainty. The Rényi entropy (Rényi [12]) defined as ⎠ , γ > 0 and γ = 1.
We expand the following term as Now, applying the power series in the last term of the above equation, we obtain the form of f γ (x) as One can evaluate the integral of f γ (x) as Then, the Rényi entropy is given by
Order statistics
Let X 1 , X 2 , . . . , X n be a random sample from the WE -G distribution, and let X i:n denote the ith order statistic. The pdf of ith order statistic for (i = 1, 2, . . . , n), is where f (·) and F(·) are the pdf and cdf of the WE-G, respectively. Using the definition of binomial expansion for the term: [1 − F(x)] n−i , then f i:n (x) can be expressed as (30) We can write from Equation (11) Inserting Equations (10) and (31) in Equation (30), then the pdf of X i:n reduces to where Finally, the pdf of X i:n can be expressed as is the exp-G pdf with power parameter a > 0. Hence, the pdf of X i:n of a WE-G is a mixture of exp-Gompertz density. So, the moments and mgf of WE-G order statistics follow directly from linear combinations of those quantities for exp-Gompertz distributions, where El-Gohary et al. [13] studied the generalized Gompertz distribution.
Estimation and inference
Let x 1 , x 2 , . . . , x n be the random sample from the WE-G with parameters α, β , λ and σ . Then The log-likelihood function is Setting the first derivatives of Equation (34) with respect to α, β, σ and λ, respectively, to zero, we have The maximum likelihood estimates α, β, σ and λ can be obtained by solving the non-linear Equations (35-38) numerically for α, β, σ and λ by using the statistical software Mathematica package.
where the elements of the matrix I n (α, β, σ , λ) are given in the Appendix. The variance-covariance matrix would be I −1 is the inverse of the observed information matrix.
Data application
In this section, we provide a practical example to illustrate the perform of the new model. To illustrate the good performance of our model, we use package Mathematica software. For comparison purpose, we consider the following distributions: Gompertz (G(σ , λ)) distribution: • Shifted Gompertz (S G (a,b)) distribution (Bemmaor [14]): x a, b > 0.
• Weighted exponential-log logistic (WE -LL (α,β,η,κ)) distribution: • Weighted exponential-Weibull (WE-W(α,β,θ,γ )) distribution: The data set represents the failure time of 50 devices (Aarset [15]) and listed in Table 3. For Aarset data, we obtain the maximum likelihood estimates (MLE's) and the respective standard errors of each distribution. Further, we use the goodness-of -fit statistics in order to provide the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Hannan-Quinn Information Criterion (HQIC), Kolmogorov-Smirnov (K-S) test, the p-value of K-S, Cramer-von Misses (W * ) and Anderson Darling (A * ). Table 4 provides the MLE's and the respective standard errors for comparison distributions. Tables 5 and 6 provides the values of AIC, BIC, HQIC, K-S, p-value, W* and A* of the comparison distributions. Hence, we conclude that our model provides the better fit. Figure 5 shows the estimated densities and estimated survival functions for the considered distributions of data set. We note that the proposed model is more appropriated to fit the data, again. We also, plot the profiles of the log-likelihood function in Figures 6-9 to show that the likelihood equations have a unique solution in the parameters of WE-G distributions. Some descriptive statistics of the Aarset data can be found in Table 7, that indicates negative skewness and kurtosis. The observed information of the Aarset data and the variance-covariance matrices are respectively The 90% confidence intervals (CIs) for the parameters α, β , σ and λ are (0, 5.75831 ], (0, 1.3415], (0, 0.0012579) and [0.0384075, 0.132916 ], respectively.
Concluding remarks
In this paper, a new extension version of the Gompertz distribution generated by integral transform of the pdf of the weighted exponential distribution is introduced with its important properties. Estimation using the method of maximum likelihood is straightforward. Moreover, the new model with other distributions is fitted to real data set and it is shown that this model has a better performance among the compared distributions. Some issues for future research may be considering different estimation methods of the unknown parameters. In addition, the new model properties can be compared with process based on two-piece distributions (Maleki and Mahmoudi [16] and Hoseinzadeh et al. [17]). | 3,396.4 | 2019-05-13T00:00:00.000 | [
"Mathematics",
"Computer Science"
] |
Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique
4mC is a type of DNA alteration that has the ability to synchronize multiple biological movements, for example, DNA replication, gene expressions, and transcriptional regulations. Accurate prediction of 4mC sites can provide exact information to their hereditary functions. The purpose of this study was to establish a robust deep learning model to recognize 4mC sites in Geobacter pickeringii. In the anticipated model, two kinds of feature descriptors, namely, binary and k-mer composition were used to encode the DNA sequences of Geobacter pickeringii. The obtained features from their fusion were optimized by using correlation and gradient-boosting decision tree (GBDT)-based algorithm with incremental feature selection (IFS) method. Then, these optimized features were inserted into 1D convolutional neural network (CNN) to classify 4mC sites from non-4mC sites in Geobacter pickeringii. The performance of the anticipated model on independent data exhibited an accuracy of 0.868, which was 4.2% higher than the existing model.
Introduction
Alterations in DNA play a significant role in gene expression and regulation, DNA replication, and transcriptional regulation. Methylcytosine is a key epigenetic trait at 5cytosine-phosphate-guanine-3 site. Methylcytosine is precisely correlated with cell growth and chromosomal protection [1,2]. 5-Hydroxymethylcytosine (5hmC), 5-methylcytosine (5mC), and 4-methylcytosine (4mC) are the familiar cytosine methylations in multiple genomes of prokaryotes and eukaryotes [3,4]. 5mC is a frequent type of methylcytosine and responsible for many neurodegenerative and cancerous diseases [5]. 4mC is a significant alteration that protects genomic knowledge from weakening by restriction enzymes [6].
Precise identification of 4mC sites can give important signs to understand the method of gene regulation. At present, there are several techniques to recognize 4mC sites, for example, single-molecule real-time sequencing [7], mass spectrometry [8], and bisulfite sequencing [9], but these techniques are time-consuming and expensive when utilized on next-generation sequencing data. Hence, a computational model to identify 4mC sites is needed on an urgent basis. Currently, a few computational and mathematical methods have been introduced to predict 4mC sites in multiple species. In 2017, Chen at al. [10] introduced the first computational model to predict 4mC sites in multiple species on the basis of confirmed 4mC dataset. Subsequently, Wei at al. [11] designed the novel iterative feature illustrative algorithm for the prediction of 4mC sites. Tang et al. [12] introduced the new linear integration method by merging the existing models for the identification of 4mC sites. Afterwards, Manavalan et al. [13] established the new tool Meta-4mCpred to recognize 4mC sites in six different species. Khanal et al. [14] introduced the first deep Int. J. Mol. Sci. 2022, 23, 1251 2 of 10 learning model 4mCCNN by utilizing numerous feature combinations [15][16][17] for the prediction of 4mC sites in multiple genomes [18]. Although the prediction model 4mCCNN can yield good outcomes, there is still space for more improvement.
To tackle these hitches, we constructed a 1D CNN model to recognize 4mC sites in Geobacter pickeringii. Figure 1 illustrates the flowchart of the whole study. Binary and k-mer nucleotide composition descriptors were used to encode DNA sequences of Geobacter pickeringii into feature vectors and then these features were optimized by using a correlation and gradient-boosting decision tree (GBDT)-based algorithm with incremental feature selection (IFS) method. After this, these optimized features were inserted into 1D CNN-based classifier using 10-fold cross-validation and we attained the finest model to classify 4mC from non-4mC. troduced the new linear integration method by merging the existing models for the identification of 4mC sites. Afterwards, Manavalan et al. [13] established the new tool Meta-4mCpred to recognize 4mC sites in six different species. Khanal et al. [14] introduced the first deep learning model 4mCCNN by utilizing numerous feature combinations [15][16][17] for the prediction of 4mC sites in multiple genomes [18]. Although the prediction model 4mCCNN can yield good outcomes, there is still space for more improvement.
To tackle these hitches, we constructed a 1D CNN model to recognize 4mC sites in Geobacter pickeringii. Figure 1 illustrates the flowchart of the whole study. Binary and kmer nucleotide composition descriptors were used to encode DNA sequences of Geobacter pickeringii into feature vectors and then these features were optimized by using a correlation and gradient-boosting decision tree (GBDT)-based algorithm with incremental feature selection (IFS) method. After this, these optimized features were inserted into 1D CNN-based classifier using 10-fold cross-validation and we attained the finest model to classify 4mC from non-4mC.
Performance Evaluation
We constructed a 1D CNN-based model named Deep-4mCGP for the identification of 4mC sites in Geobacter pickeringii. In the first step, we converted the sequence data in to feature vectors by using k-mer nucleotide composition and binary encodings. Subsequently, these feature vectors were improved by means of correlation and GBDT-based algorithm with IFS method. Initially, correlation and then GBDT with IFS were utilized to pick the finest features. Figure 2A,B displays the IFS curve of top features. Afterward, these finest features were inserted into 1D CNN by using 10-fold cross-validation to classify 4mC sites from non-4mC sites in Geobacter pickeringii. In this work, 10-fold cross-validation was employed to examine the efficiency of the model. The data were arbitrarily divided into 10 segments of equal proportion. Each segment was independently tested by
Performance Evaluation
We constructed a 1D CNN-based model named Deep-4mCGP for the identification of 4mC sites in Geobacter pickeringii. In the first step, we converted the sequence data in to feature vectors by using k-mer nucleotide composition and binary encodings. Subsequently, these feature vectors were improved by means of correlation and GBDT-based algorithm with IFS method. Initially, correlation and then GBDT with IFS were utilized to pick the finest features. Figure 2A,B displays the IFS curve of top features. Afterward, these finest features were inserted into 1D CNN by using 10-fold cross-validation to classify 4mC sites from non-4mC sites in Geobacter pickeringii. In this work, 10-fold cross-validation was employed to examine the efficiency of the model. The data were arbitrarily divided into 10 segments of equal proportion. Each segment was independently tested by the model, which was trained on the outstanding nine segments. Thus, 10-fold cross-validation technique was executed 10 times, and the average of the outcomes was the ultimate result. AUROC of the anticipated model was 0.986, which was 6.5% higher than the existing model.
The accuracy, precision, recall, and F1 are shown in Table 1, and the ROC curve is shown in Figure 2C.
the model, which was trained on the outstanding nine segments. Thus, 10-fold cross-validation technique was executed 10 times, and the average of the outcomes was the ultimate result. AUROC of the anticipated model was 0.986, which was 6.5% higher than the existing model. The accuracy, precision, recall, and F1 are shown in Table 1, and the ROC curve is shown in Figure 2C. Table 1. Outcomes of single encodings and their fusion based-models on training and independent data by using different classification algorithms. Bold is used to highlight the best results.
Comparison on the Basis of Independent Data
Features fusion were inserted into LSTM [21], GBDT [22], and RF [23,24] to compare with the CNN-based model [25]. Ultimately, on the basis of AUROC, we achieved a perfect model for each predictor, which is shown in Table 1 and Figure 2F. Comparison of anticipated model with 4mCCNN by using 10-fold cross-validation is shown in Figure 2E. On the independent data (200 Pos. seq and 200 Neg. seq) the efficiency of Deep-4mCGP was checked and then compared with the existing 4mCCNN. The accuracy, precision, recall, F1, and AUROC of the 4mCCNN were 0.826, 0.818, 0.823, 0.825, and 0.920, respectively. The accuracy, precision, recall, F1, and AUROC of Deep-4mCGP were 0.868, 0.876, 0.773, 0.859, and 0.961, respectively. The performance of the anticipated Deep-4mCGP on independent data exhibited the accuracy of 0.868, which was 4.2% higher than the 4mCCNN. The performance comparison is shown in Table 2.
Materials and Methods
Authentic data are a significant requirement for the construction of a machine learningbased model [26,27]. Thus, we acquired the data of 1138 (569 Pos. seq and 569 Neg. seq) sequences of Geobacter pickeringii from the work of Chen et al. [10] for training and testing the model. Moreover, we attained the data of 400 sequences (200 Pos. seq and 200 Neg. seq) from the work of Manavalan et al. [13] for the sake of independent testing.
k-mer
k-mer composition has the ability to show interactions between nucleotides of DNA sequences [40]. The residues of nucleotides can be attained by setting the size of window and steps. A random sample F with n sequence length can be designated as where S i indicates the i-th nucleotide of the DNA sequences and can be converted in to 4 k D features vector with the help of k-mer.
where d 1 k-tuple denotes the incidence of i-th k-mer and T represents the transposition. If the value of k is equal to 1, then DNA sequence will be decoded in to 4D features vector, and if the value of k is equal to 2, then DNA sequence will be 16D features vector. In this work, k was set as 1, 2, 3, 4, 5, 6. Consequently, DNA sequences were converted into (4 1 + 4 2 + 4 3 + 4 4 + 4 5 + 4 6 = 5460D) formulated as
Binary
Binary encodings such as 0s and 1s have the ability to illustrate any information. Therefore, we can transform DNA sequence in the form of 0s and 1s. In this work, DNA sequences of Geobacter pickeringii with length of 41bp was encoded into the (4 × 41 = 164D) features vector.
Correlation
Correlation is a familiar comparison amongst two different features, e.g., if the features are un-correlated, then the correlation will be zero; otherwise, it will be ±1. Two complete modules named classical linear correlation and correlation on the basis of information theory were implemented to compute the correlation amongst the two unique variables. Linear correlation coefficient is the most acquainted and utilizable. The linear correlation coefficient 'r' for a pair of (p, q) variables is specified as Correlation generates good results in smaller datasets, but the performance of correlation coefficient is not up to the mark on gigantic amounts of data. Therefore, it is necessary to determine the substantial relationship amongst the features. Thus, we utilized the t-test to investigate the statistical correlation between the features and picked the significant features. The value of 't' can be computed as where 'r' signifies the coefficient of correlation and 'n' represents the occurrences. 'n−2 denotes the degree of freedom. Probability of the significance relation is 0.05. If 't' is greater than the probability of the significance relation 0.05, then the feature will be selected.
GBDT with IFS
GBDT is a popular machine learning-based classifier that has been utilized in various mathematical, cheminformatics, and bioinformatics tools [41,42]. It has the ability to establish a scalable and reliable prediction model by utilizing non-linear joints of weak learners [43].
{(x 1 , y 1 ) . . . ( x n , y n )} (∴ x i x ⊆ S n , and y i y ⊆ S) q k (x):= where θ k is minimal risk of the decision tree and D k (x; θ k ) is the decision tree.
GBDT also computes the concluding evaluations in an advancing mode.
Negative gradient loss function q k−1 is applied for residual computation.
Hence, we trained the anticipated model through S ki to compute the minimal risk θ k . This kind of trees rationally represents the relations between variables, e.g., plotting the input X into J fragments S 1 . . . S J , and output is Z J for area S J .
The IFS [44,45] method was implemented in this work to pick the finest feature. IFS estimates the performance of the best q-ranked features repetitively for q (1, 2, 3, . . . n), where 'n' is the overall number of the features. IFS frequently stops at the first scrutiny of performance. In IFS, features were picked incrementally from a randomly taken initial feature and the finest result from several randomly re-instated IFS processes were outputted. A brief explanation of the IFS technique can be found in [46]. for i = 1 to k do 8 t = to calculate the significance (r, ρ) for L i (∴ by utilizing the t-test value from Equation (5)) 9 if t > critical value 10 Q best = Q list 11 end 12 return Q best Algorithms 1: Cont.
Convolutional Neural Network
LeCun at al. [47] introduced convolutional neural network, and now it has been roughly utilized in many biological and bioinformatics advances [48][49][50]. The fundamental principle of CNN is to create abundant filters that have the ability to produce hidden topological features from data by executing pooling procedures and layer-wise convolutions. The performance of CNN on 2D data of images and matrices is exceptional [51]. Subsequently, 1D CNN has been used to tackle the difficulties of biomedical sequence data identification and the research associated with natural language processing [41,52]. In this work, we implemented 1D CNN to identify 4mC sites in Geobacter pickeringii. We employed Keras 2.3.1 [53], TensorFlow 2.1.0, and Python 3.5.4 to perform this experiment. The best tuning parameters are recorded in Table 3.
Metrics Evaluation
Precision, accuracy, recall, and F1 [54][55][56] were employed to examine the effectiveness of the anticipated prediction model and formulated as (11) where 'TP' symbolizes the accurately predicted 4mC sequences, 'TN' represents the perfectly predicted non-4mC sequences, 'FP' indicates the non-4mC sequences predicted as 4mC sequences, and 'FN' indicates the 4mC sequences predicted as non-4mC sequences.
Conclusions
4mC is a type of DNA alteration that has the ability to synchronize multiple biological movements for example DNA replication, gene expressions, and transcriptional regulations. Accurate prediction of 4mC sites can provide exact information to their hereditary functions. Currently, several machine learning models have been used to predict 4mC sites in multiple genomes [10,12,13,[57][58][59][60]. However, there is only one deep learning-based model, 4mCCNN [14], that exists for Geobacter pickeringii. In this work, a deep learning model was constructed to recognize 4mC sites in Geobacter pickeringii. In the anticipated model, two kinds of feature descriptors, namely, binary and k-mer composition were used to encode the DNA sequences of Geobacter pickeringii. The obtained features from their fusion were optimized by using correlation and GBDT-based algorithm with IFS method. Then, these optimized features were inserted into a 1D CNN-based classifier using 10-fold cross-validation, and we attained the finest model to classify 4mC from non-4mC. The performance of the anticipated Deep-4mCGP on independent data exhibited an accuracy of 0.868, which was 4.2% higher than the 4mCCNN. The source code and data are available at GitHub: https://github.com/linDing-groups/Deep-4mCGP (accessed on 19 January 2022). In future work, we have a plan to release a web-based application to make our anticipated model more convenient for the users without programming and statistical knowledge. | 3,480.4 | 2022-01-23T00:00:00.000 | [
"Biology",
"Computer Science",
"Environmental Science"
] |
Quantum spin models for the SU(n)_1 Wess-Zumino-Witten model
We propose 1D and 2D lattice wave functions constructed from the SU(n)_1 Wess-Zumino-Witten (WZW) model and derive their parent Hamiltonians. When all spins in the lattice transform under SU(n) fundamental representations, we obtain a two-body Hamiltonian in 1D, including the SU(n) Haldane-Shastry model as a special case. In 2D, we show that the wave function converges to a class of Halperin's multilayer fractional quantum Hall states and belongs to chiral spin liquids. Our result reveals a hidden SU(n) symmetry for this class of Halperin states. When the spins sit on bipartite lattices with alternating fundamental and conjugate representations, we provide numerical evidence that the state in 1D exhibits quantum criticality deviating from the expected behaviors of the SU(n)_1 WZW model, while in 2D they are chiral spin liquids being consistent with the prediction of the SU(n)_1 WZW model.
Before constructing the wave functions, let us briefly review the SU(n) 1 WZW model [67]. This rational CFT has n primary fields, denoted by Λ a , with a = 0, 1, . . . , n − 1, corresponding to particular SU(n) irreducible representations. The primary field Λ 0 is an SU(n) singlet, which is also the identity field with conformal weight h(Λ 0 ) = 0. The next primary field Λ 1 is the SU(n) fundamental representation, corresponding to a single box when the SU(n) irreducible representations are represented as the Young tableaux. In general, the primary field Λ a corresponds to a Young tableau with a single column and a rows. Accordingly, Λ a consists of dim Λ a = n a components, and we write these components as Λ a,α , where α ∈ {1, 2, . . . , dim Λ a }.
As we shall discuss further below, the SU(n) 1 WZW model has a free-field representation with n − 1 free bosons. In this representation, the primary fields are conveniently realized using vertex operators.
To build lattice wave functions, we consider N T spins sitting at the fixed positions z j (j = 1, 2, . . . , N T ) in the complex plane. Following Ref. [56], we define lattice wave functions |Ψ = α1,α2,...,αN T 0|Λ a1,α1 (z 1 )Λ a2,α2 (z 2 ) . . . Λ aN T ,αN T (z NT )|0 |α 1 , α 2 , . . . , α NT (2) that are chiral correlators of primary fields. Here, |0 is the vacuum of the CFT and |α j are the basis vectors of the internal state of spin number j. CFT states of the form (2) can be seen as a special type of matrix product states in which the finite-dimensional matrices have been replaced by infinite-dimensional conformal fields. They are therefore sometimes referred to as infinite-dimensional-matrix product states (IDMPS). Regarding the wave function (2), there are several comments in order. First, choosing the primary field Λ aj at site j requires that the spin at this site also transforms under the SU(n) irreducible representation corresponding to a Young tableau with one column and a j rows. Note that the SU(n) 1 WZW model does not have primary fields corresponding to a Young tableaux with more than one column. Secondly, the fusion rules in (1) always have a unique fusion outcome, which ensures that the wave function (2) is a unique function. Lastly, to have a nonvanishing wave function, the N T primary fields in (2) must fuse into the identity Λ 0 (i.e. the SU(n) singlet), Λ a1 ⊗ Λ a2 ⊗ · · · ⊗ Λ aN T = Λ 0 .
In this work, we shall focus on the case, where each of the spins belong either to the SU(n) fundamental representation Λ 1 or to the SU(n) conjugate representation Λ n−1 . We shall denote the sublattice of spins transforming under the fundamental (conjugate) representation by A (B), A : Fundamental representation, B : Conjugate representation, (4) and we shall let N (N ) denote the number of spins in A (B) such that N +N = N T . The condition (3) then gives that (N −N )/n must be an integer, and we shall assume this to be the case throughout. Note that the fundamental and conjugate representations are the same for n = 2, so that there is only one state in this particular case. For n ≥ 3, however, they are different. Before we continue with the above case, let us note that other choices for the primary fields are possible. For instance, for even n, one could use the primary field Λ n/2 (self-conjugate representation) to build the wave function, according to the fusion rule Λ n/2 ⊗ Λ n/2 ⊗ · · · ⊗ Λ n/2 = Λ 0 (N T even). For the SU(4) case, one has SU(4) 1 ≃ SO(6) 1 and the SU(4) self-conjugate primary field Λ 2 becomes the vector representation of SO(6) with conformal weight h(Λ 2 ) = 1/2, which can be interpreted as a Majorana field and has been considered in Ref. [59]. Although we only consider states constructed from the fundamental and conjugate representations below, we note that the formalism we develop is general and that other cases can be treated in a similar way.
In the following, we shall find it convenient to use the notation ϕ αj (z j ) = Λ 1,αj (z j ) for j ∈ A Λ n−1,αj (z j ) for j ∈ B .
Since we shall often refer to the wave function, for which all the primary fields belong to the fundamental representation, we shall give this wave function a particular name: |Ψ F . Explicit representations of |Ψ F and |Ψ will be discussed in Secs. III and V, respectively. In the next two subsections, we shall use their abstract forms to derive relevant null fields and their corresponding decoupling equations, which are our starting point for deriving parent Hamiltonians.
B. Null vectors
As a rational CFT, the SU(n) 1 WZW model has null vectors in its Verma modules of the Kac-Moody algebra. According to Ref. [56], identifying proper null vectors and deriving decoupling equations for the chiral correlators are the key for constructing parent Hamiltonians of the wave functions. In this subsection, we derive the null vectors relevant for (7).
The SU(n) 1 Kac-Moody algebra is defined by where J a m = 0 dz 2πi z m J a (z) is the mth mode of the Kac-Moody current J a (z) and f abc are the structure constants of the SU(n) Lie algebra. Here and later on, we shall always assume that repeated indices are summed over. The operator product expansion (OPE) between the Kac-Moody currents and a primary field is [67] where the matrices t a with elements (t a ) αβ are the generators of SU(n) in the representation of the primary field. Let us note here that the generators in the fundamental and conjugate representations are related though a complex conjugation and a multiplication by a minus sign, i.e., SU (2) SU (3) SU(4) where τ a are the generators in the fundamental representation (see Appendix A).
To the primary field ϕ α (z), one associates a primary state |ϕ α satisfying the following properties [67]: and descendant states are obtained by multiplying |ϕ α by any number of current operators J a n with n < 0. A null state is a state that is at the same time a descendant and a primary state. Since the wave function (7) only involves primary fields belonging to the fundamental or the conjugate representation, we shall here only need to deal with the two Verma modules formed by the corresponding primary states, as well as their descendants.
Let us first consider the primary field Λ 1,α (z) belonging to the fundamental representation. In Virasoro level m = 1, we look for null vectors with the following form: where W q,aα can be interpreted as Clebsch-Gordan coefficients satisfying aα W * q,aα W q ′ ,aα = δ qq ′ . They come from the tensor product decomposition of the (n 2 − 1)-dimensional SU(n) adjoint representation (carried by J a −1 ) and the fundamental representation (carried by |Λ 1,α ), where the irreducible representations are denoted by their dimensions (they are not distinguished with their complex conjugate representations). Fig. 1(a) shows the tensor product decomposition (13) for n = 2, 3, 4, using the Young tableaux. We have found that, for SU(n) 1 WZW model with all n, null vectors indeed exist in Virasoro level m = 1, and they belong to the SU(n) representation with dimension 1 2 n(n− 1)(n+ 2) in (13). In practice, the Clebsch-Gordan coefficients W q,aα in (12) can be determined by requiring the null vector condition For our purpose, we redefine the null vectors as [56] |χ a,α = q W * q,aα |χ q where (K F ) aα bβ is given by K F can be viewed as a matrix with its entries being (K F ) aα,bβ = (K F ) aα bβ , and it is a projector (i.e. K 2 F = K F ) onto the SU(n) irreducible representation with dimension 1 2 n(n − 1)(n + 2). This also lead to an additional equation, These two equations are sufficient for determining the explicit form of (K F ) a b . For general n, we obtain where d abc is a totally symmetric tensor (see Appendix A). If we build the null vector at Virasoro level m = 1 using the primary state |Λ n−1,α in the conjugate representation, the representations appearing in (13) would be their complex conjugate representations. See Fig. 1(b) for this tensor product decomposition for n = 3 and 4. As a result, the Clebsch-Gordan coefficients in (12) for obtaining the null vectors would be their complex conjugate. Then, the corresponding null vectors can be written as where (K C ) aα bβ = (K * F ) aα bβ . Utilizing (10), we can combine (14) and (17) into a single expression where K aα bβ are the matrix elements of Here, r = +1 for the fundamental representation, r = −1 for the conjugate representation, and t c are the generators in the considered representation.
C. Decoupling equations
Following Ref. [56], a set of decoupling equations can be derived for the chiral correlator (7) using the null vectors (18). These decoupling equations provide operators annihilating the wave functions, which can be used to build parent Hamiltonians.
The null state (18) corresponds to the following null field: By definition of the null field, substituting it into the wave function (7), one obtains a vanishing expression After deforming the integral contour and using the OPE (9) between the Kac-Moody currents and primary fields, we arrive at where (K (i) ) a b denotes the operator K a b in (19) acting on spin number i and (t b j ) αj α ′ j denote the matrix elements of the operator t b acting on spin number j. (Note that the representation chosen for t b j is the same as the representation of spin number j.) Thus, the resulting decoupling equation yields a set of operators which annihilate the wave function |Ψ , i.e. P a i (z 1 , . . . , z N )|Ψ = 0 ∀i, a. Together with the fact that |Ψ is a global SU(n) singlet, T a |Ψ = 0 with T a = i t a i , we obtain where C a i (z 1 , . . . , z N ) is given by and w ij = (z i + z j )/(z i − z j ). For SU(2), we have d abc = 0 and (25) recovers the result in Ref. [56]. Utilizing the formulas in Appendix A, we get which is a convenient form for constructing parent Hamiltonians.
D. Vertex operator representation
After working out the decoupling equations for (7) using an abstract form of the primary fields, we now turn to an explicit representation of these primary fields, using chiral vertex operators. This is possible, since SU(n) 1 WZW model is equivalent to a free theory of n − 1 massless bosons.
For our purpose, it is convenient to label the spin states in each site by their weights (eigenvalues of the Cartan generators). The state |α , α ∈ {1, 2, . . . , n}, in the fundamental representation is therefore characterized by n − 1 quantum numbers, which we collect into the vector m α given explicitly by , . . .
In the conjugate representation, the state |α , α ∈ {1, 2, . . . , n}, is characterized by the quantum numbers − m α . The SU(3) and SU(4) weight diagrams are shown in Fig. 2 as examples. Using the weights, the primary field ϕ α (z) can be expressed as where r = +1 for the fundamental representation and r = −1 for the conjugate representation as above. The colons denote normal ordering and φ(z) is a vector of n − 1 independent fields of free, massless bosons. The factor κ α is a Klein factor, commuting with the vertex operators and satisfying Majorana-like anticommutation relations Here, (m1, m2, m3) is shorthand notation for the components of the vectors mα and − mα, respectively.
Note that κ α is the same in the fundamental and in the conjugate representation. At this moment, the meaning of these Klein factors is not clear. In fact, their role is to ensure that the wave function (7) is an SU(n) singlet. We will go back to this point when discussing the wave functions in Sec. III and Sec. V. Let us note that the vertex operators in (28) have the anticipated conformal weights, since Another quantity, which will be used in later sections, is m α · m α ′ with α = α ′ . It is easy to convince ourselves that this value does not depend on the states we choose. For α = α ′ , we find Altogether, we thus conclude
III. QUANTUM STATES FROM THE FUNDAMENTAL REPRESENTATION OF SU(n)
In this section, we analyze the wave function (7) in detail, both theoretically and numerically, for the case where all spins transform under the fundamental representation. First, the chiral correlator can be evaluated and expressed in terms of a product of Jastrow factors [67] Ψ F (α 1 , α 2 , . . . , α N ) = χ(α 1 , α 2 , . . . , α N )δ where χ(α 1 , α 2 , . . . , α N ) = κ α1 κ α2 · · · κ αN is a z j -independent phase factor to be determined below and the Kronecker delta function δ i mα i =0 , which is 1 for i m αi = 0 and zero otherwise, ensures charge neutrality. Referring to Eq. (27), we observe that the charge neutrality forces the number of spins N α in the state |α to fulfill N 1 = N 2 = . . . = N n . This gives N α = N/n for all α, and we shall therefore assume N/n to be an integer whenever we consider states constructed from only the fundamental representation of SU(n). Utilizing (32), we note that (33) simplifies to We shall also find it useful to express the state |Ψ F in another notation. For a given spin configuration j , where j = 1, 2, . . . , N/n, be the position within the ket of the jth spin in the state |α . For example, if we choose n = 3 and N = 9 and consider the state ket |1, 2, 1, 3, 2, 3, 3, 2, 1 , we would have x . We can then express |Ψ F as where S N is the symmetric group over the elements {1, 2, . . . , N } and Let us next determine χ from the condition that |Ψ F should be an SU(n) singlet. We shall find below that the wave function |Ψ F is proportional to the ground state of the SU(n) HS model if we choose z j = e 2πij/N and where the right-hand side of (37) is the sign of the permutation needed to transform x Since the ground state of the SU(n) HS model is an SU(n) singlet, it follows that (37) is the correct choice of χ for all choices of z j . The result (37) can be obtained from χ = κ α1 κ α2 · · · κ αN by choosing the factors κ α to be Klein factors, which satisfy the Majorana-like anticommutation relation (29), and choosing to work in a sector, in which κ 1 κ 2 · · · κ n = 1. This follows from The proof given in Appendix B shows directly that the state (34) with χ given by (37) and z j arbitrary is an SU(n) singlet without referring to the SU(n) HS model.
A. Wave functions in the hardcore boson basis
In order to compare the state (33) to known models in particular limits, we shall now express the state in a hardcore boson basis. In this picture, the coordinates z j are lattice sites that can be empty or occupied by at most one hardcore boson. A spin in the state |n is interpreted as an empty site, and a spin in the state |α , with α ∈ {1, 2, . . . , n − 1}, is interpreted as a site occupied by a hardcore boson with color α.
Referring to (27), we observe that the (n − 1)th component m αj ,n−1 of the vector m αj can be written as where p j is one if α j ∈ {1, 2, . . . , n − 1} and zero if α j = n. In other words, we can use this component to distinguish between occupied sites and holes, and we shall use this observation to eliminate the coordinates of the unoccupied sites from the Jastrow factor in (33). The part of this factor that includes the contribution from m αj ,n−1 can be written as The second factor in the above expression can be simplified as [57,60] i<j where Let us next consider the part of the Jastrow factor that includes the contributions from m αj ,l with l = 1, 2, . . . , n − 2.
Utilizing (27) and (32), we find If α or α ′ is equal to n, we instead get n−2 l=1 m α,l m α ′ ,l = 0 as follows immediately from (27). The part of the Jastrow factor that includes the contributions for m αj ,l with l = 1, 2, . . . , n − 2 can therefore be written as Combining (40) and (44), we get the expression for the Jastrow factor. We would like to also remove the hole coordinates from the sign factor χ. Doing so gives rise to a sign factor that compensates the factor j (−1) (j−1)pj in the wave function. The remaining sign factor is then sgn(x N/n ). We note, however, that this factor can be absorbed by rearranging the ordering in the Jastrow factor. Putting everything together, we thus conclude that the state (35) can also be written as where the sum is over all possible distributions of the (n − 1)N/n colored bosons on the N lattice sites with at most one boson per site, and with α, β ∈ {1, 2, . . . , n − 1} and i, j ∈ {1, 2, . . . , N/n}. We shall now comment further on (47) for particular choices of the lattice.
Jastrow wave functions for the uniform 1D lattice
We first consider a uniform lattice in 1D with periodic boundary conditions, which is achieved by choosing z j = e 2πij/N . For this particular case, we have the simple expression f N (z [56]. Inserting this in (47), we see that the wave function for the particular case of a uniform 1D lattice reduces to the ground state of the SU(n) HS Hamiltonian [42][43][44] Let us next consider a regular lattice in 2D. We shall assume that the area of each lattice site (defined as the area of the region consisting of all points that are closer to the given lattice site than to any other lattice site) is the same for all lattice sites. In this case, it has been shown in [60] that (49) for N large. The state (47) can therefore be written as in the thermodynamic limit, where Up to a local phase factor that can be removed with a simple transformation (of both the wave function and the parent Hamiltonian), we thus observe that the wave function (34) reduces to the lattice version of the Halperin state [69], which appeared in the context of the multilayer FQH effect. For example, the SU(3) state corresponds to Halperin's 221 double-layer spin-singlet state. One consequence of this interesting connection is that the wave function (34) describes an SU(n) chiral spin liquid state, supporting Abelian anyonic excitations (the same as those in Halperin states). Another consequence is that the particular series of Halperin FQH states in (50) have a hidden enhanced SU(n) symmetry. For instance, one may expect that the chiral gapless edge excitations of these states are described by the SU(n) 1 WZW model.
B. Numerical results
Since the properties of the uniform 1D SU(n) HS state are already well-known, we shall here only investigate the states in 2D. We compute the TEE −γ by considering the state on an R × L square lattice on the cylinder and using the formula [62,63,70] for the entanglement entropy of half of the cylinder. In (52), we assume the cut to be perpendicular to the cylinder axis, L is the number of spins along the cut, and the formula is valid asymptotically for large L and R. The mapping of the IDMPS (36) to a cylinder is done through a conformal transformation, which amounts to choosing and considering r j and l j as the coordinates rather than Re(z j ) and Im(z j ). This will also change the chiral correlator by a constant factor, but we can ignore this, since the factor does not depend on the state of the spins. The square lattice is then obtained by choosing r j ∈ {−R/2+1/2, −R/2+3/2, . . ., R/2−1/2} and l j ∈ {1, 2, . . . , L} and N = RL.
Since it is easier to compute numerically, we choose to consider the Renyi entropy with index 2, which is defined as S and interpreting as a classical probability distribution. The results of the computations are shown as a function of the number of spins along the cut in Fig. 3. The figure provides evidence for n = 3 and n = 4 that the TEE is −γ = − ln(n)/2. This is consistent with the prediction that the states in 2D are chiral spin liquid states, with the SU(n) 1 WZW model being their corresponding chiral edge CFT: According to the fusion rule (1) of the SU(n) 1 WZW model, the states support n types of Abelian anyons with quantum dimension 1, giving rise to a total quantum dimension √ n.
IV. PARENT HAMILTONIANS FOR THE STATES FROM THE FUNDAMENTAL REPRESENTATION
In this section, we derive parent Hamiltonians of the states Ψ F in Eq. (33). In 1D, we obtain two-body parent Hamiltonians, including the SU(n) HS model as a special case, and for 2D lattices they are parent Hamiltonians of the SU(n) chiral spin liquid states.
Our starting point is the fact that the operator C a i in (26) annihilates the state (33) as derived above, C a i |Ψ F = 0. It follows that the positive semi-definite Hermitian operator is therefore a parent Hamiltonian of (33), H|Ψ F = 0. Inserting (26) in (56) and utilizing the formulas listed in Appendix A, we obtain the explicit expression which is valid for general z j .
A. Exchange form of the parent Hamiltonian
As we shall now show, H can also be expressed in terms of the exchange operator P ij , which swaps the spin states at sites i and j, i.e., P ij = n α,β=1 |α i , β j β i , α j |. To do so, we define the following fermionic representation of the SU(n) generators with the local constraint n α=1 c † iα c iα = 1 for all i. Using Fierz identity (A9), we can then express the SU(n) Heisenberg interaction in terms of P ij . Inserting this into (57), we get
B. SU(n) Hamiltonian in 1D
In this subsection, we restrict ourselves to 1D systems. This is done by restricting all z j to lie on the unit circle in the complex plane, i.e. |z j | = 1 ∀j. When this is the case, we have w * ij = −w ij , and using (A5), the 1D Hamiltonian therefore takes the form For the particular case n = 2, the three-body term vanishes because d abc = 0, and we recover the Hamiltonian in Eq. (70) of [56].
In the following, we simplify (61). First, by using the cyclic identity w ij w ik + w ji w jk + w ki w kj = 1, we find and i =j =k Inserting these relations into (61), utilizing that T a = i t a i and w 2 ij = 1 + 4zizj (zi−zj ) 2 , the parent Hamiltonian (61) for the state (33) with |z j | = 1, ∀j, can be written as where E 1D is given by Here let us remind that (64) directly comes from (56) and H 1D |Ψ F = 0. Since |Ψ F is an SU(n) singlet, we have d abc T a T b T c |Ψ F = T a T a |Ψ F = 0. Thus, we could get rid of the three-body and two-body Casimirs in (64) and define a pure two-body parent Hamiltonian which has (33) as its ground state with ground-state energy The Hamiltonian (66) is an inhomogenous generalization of the SU(n) HS model. For n = 2, it reduces to the SU(2) inhomogenous HS model derived in [55].
C. 1D uniform Hamiltonian and the SU(n) HS model
We now further restrict z j to be uniformly distributed on the unit circle by choosing z j = e 2πij/N . This gives a uniform 1D lattice with periodic boundary conditions. In this case, i =j The 1D uniform parent Hamiltonian is therefore whose ground-state energy is given by We note that the first term in (70) is given by where H HS is the 1D SU(n) HS model In the thermodynamic limit N → ∞, we have ( N π ) 2 sin 2 π N (i − j) → 1/(i − j) 2 and the strength of SU(n) exchange interaction in (73) is inversely proportional to the square of the distance between the spins.
Then, we can write the uniform 1D parent Hamiltonian as Since T a |Ψ F = 0, the ground-state energy of H HS is given by The 1D uniform parent Hamiltonian thus practically reduces to the 1D SU(n) HS model.
Energy spectra of the SU(n) HS model
For the SU(n) HS model, it has been shown [72] that it has a hidden Yangian symmetry, generated by the total spin operator T a and the operator We note that Λ a = n+1 n+2 i C a i , which thus annihilates |Ψ F as well. It is known [72] that T a and Λ b both commute with H HS , but they do not mutually commute, which is responsible for the huge degeneracies in the spectra of H HS .
The eigenvalues of the SU(n) HS model have been obtained in [72]. Combining (59) and (72), we rewrite the SU(n) HS Hamiltonian as where It has been shown [72] that the complete set of eigenvalues of H Haldane can be obtained by the simple formula where Here m i are distinct integer rapidities satisfying m i ∈ [0, N ] ∀i. Physically, the sum of these rapidities is proportional to the lattice momenta of the energy eigenstate |{m i } [72] According to [72], there is a simple rule for finding physically allowed sets of rapidities Using (79) and (81), the energy and lattice momenta of the ground state are therefore given by and Note that the ground-state energy E Haldane determined in this way is consistent with (75) by taking into account the constant term in (77).
Identifying CFT from finite-size spectra
CFT gives a powerful prediction for the spectra of 1D critical spin chains. In particular, it is known that the eigenenergies of a critical quantum chain with N sites and with periodic boundary conditions are given by [73,74] where ε ∞ is the ground-state energy per site in the thermodynamic limit, v is the spin-wave velocity, c is the central charge, h andh are conformal weights of the primary fields, and n l and n r are non-negative integers. For the SU(n) HS model, the spin-wave velocity and the conformal weights of the primary fields can be determined directly by the finite-size spectra obtained from (79). To show this, we consider the SU(n) HS Hamiltonian in (77). Let us start with an excitation defined through the rapidities which is obtained by removing the particle "1" in the ground-state configuration (82). Using (79) and (81), we obtain the excitation energy E ′ and the lattice momentum P ′ of this excitation and Comparing to the CFT prediction of the finite-size spectra (85), this excited state corresponds to h =h = n r = 0 and n l = 1. Thus, we obtain the spin-wave velocity However, let us note that the central charge c cannot be obtained using (85). The reason is that the SU(n) HS Hamiltonian has long-range interactions, which allow an N -dependent constant term and the ground-state energy as a function of N could violate the CFT prediction (85).
where [qn + a], with q = 0, . . . , N n − 1, denotes the missing rapidities in the rapidity set. By using (79) and (81), we obtain the excitation energies and lattice momenta of the corresponding excited states and Note that these excitations are gapless in the thermodynamic limit N → ∞. Compared to the CFT prediction (85), these excited states correspond to h a =h a and n l = n r = 0. Comparing with (85), we obtain the conformal weights h a = a(n−a) 2n , which correspond to the primary fields Λ a of the SU(n) 1 WZW model. This also agrees with the known results [72,75,76] that the SU(n) 1 WZW model describes the low-energy physics of the SU(n) HS model.
Regarding the excited states of the SU(n) HS model, one remaining interesting question is to obtain their explicit form and to relate them with the rapidity description in (79). Some of these excited states have already been obtained in Refs. [77,78]. As a further remark, we note that the gapless excitations at lattice momenta P = a 2π n with a ∈ {1, . . . , n} are also known to exist in the SU(n) ULS model [39][40][41], which belongs to the same SU(n) 1 WZW universality class [79][80][81][82].
D. SU(n) Hamiltonian in 2D
In this subsection, we discuss the parent Hamiltonian in 2D. After multiplying by an overall constant 2(n+1) (n−1)(n+2) , the 2D Hamiltonian in (57) can be written as Note that this Hamiltonian can be defined on any 2D lattice (both regular or irregular) and does not rely on a particular lattice geometry. For SU(2), we have d abc = 0 and f abc t a i t b j t c k = t i · ( t j × t k ). Then, (93) reduces to the parent Hamiltonian in [57] for the ν = 1/2 lattice Laughlin state. This state is also known as the Kalmeyer-Laughlin state [64,65], whose parent Hamiltonian has been extensively studied [57,[83][84][85][86][87][88][89]. From the parent Hamiltonian, it becomes transparent that the chiral three-spin interaction term t i · ( t j × t k ), which explicitly breaks time-reversal and parity symmetries, stabilizes the spin-1/2 Kalmeyer-Laughlin state. Recently, it has been found [61,90,91] that Hamiltonians with short-range chiral three-spin interactions can already stabilize the Kalmeyer-Laughlin state. This is very encouraging, as such short-range Hamiltonian might be realized in cold atomic systems in optical lattices [61,92].
The SU(n) parent Hamiltonian (93) also has three-body interactions. Compared to the SU(2) case, one remarkable feature is that, the three-body coupling is suppressed by a factor of 1/(n − 1). This gives us a hint that, for large n, one may have a chance to drop the three-body terms and the (long-range) Hamiltonian with two-body Heisenberg interactions may stabilize the lattice Halperin state (50) as its ground state. However, as the number of terms in the three-body interactions d abc t a i t b j t c k and f abc t a i t b j t c k also increases with n, it is unclear whether the parent Hamiltonian can be adiabatically connected to the long-range Heisenberg model without closing the gap. Clarifying whether the gap closes in this interpolation is an interesting problem and certainly deserves further investigation.
Finally, for the 2D SU(n) Heisenberg model on a square lattice with only nearest-neighbor interactions, it has been argued [93,94] that chiral spin liquid supporting Abelian anyons becomes stable in the large n limit. Thus, it would be interesting to further explore its possible connection with our wave function (33).
V. QUANTUM STATES FROM THE FUNDAMENTAL AND CONJUGATE REPRESENTATIONS OF SU(n)
In this section, we turn to the more general situation, where we use both the fundamental and the conjugate representation to construct IDMPSs. In this case, the chiral correlator (7) evaluates to where χ(α 1 , α 2 , . . . , α N +N ) = κ α1 κ α2 · · · κ α N +N is a z j -independent phase factor that we shall determine below and By using (32), we can also express the chiral correlator in the simpler form Considering (27), we observe that the charge neutrality condition δ N +N i=1 ri mα i =0 yields . . . where N α (Nᾱ) is the number of spins in the fundamental (conjugate) representation in the state |α . Together with the conditions we thus conclude that must hold for all nonzero terms in the wave function. This is consistent with our previous observation that (N −N )/n must be an integer. χ is determined from the requirement that |Ψ must be a singlet state, i.e. T a |Ψ = 0, where T a = N +N i=1 t a i . We show explicitly in Appendix B that this condition is fulfilled for where x (α,ᾱ) i is the position within the ket of the ith spin that is in the state |α without distinguishing between the fundamental and the conjugate representation. As for the case, where only the fundamental representation is used, we can obtain (103) by demanding κ α in (28) to be Klein factors.
A. Numerical results
We next investigate the states numerically for lattices with alternating fundamental and conjugate representations. We start with the uniform 1D case, where we use the fundamental representation on all the odd sites and the conjugate representation on all the even sites (see Fig. 4). Let us consider the entanglement entropy of a block of L consecutive spins, where L is even. We compute this quantity by Monte Carlo simulations as explained in Sec. III B, and the result is shown in Fig. 5. We observe that the entanglement entropy grows logarithmically. The CFT prediction for the entanglement entropy of a critical 1D system is [95][96][97] and by using this formula as a fit, we obtain the central charges c = 1.5 for n = 3 and c = 1.7 for n = 4, respectively. Next we compute the correlation function by using the Metropolis Monte Carlo algorithm. Here, t 3 is the third SU(n) generator, which we choose such that with c = n − 1. The numerically estimated critical exponents of the two-point correlation function also differ from 2(n − 1)/n, the expected value for critical spin chains described by the SU(n) 1 WZW model. One possibility for these deviations is that the system is still described by the SU(n) 1 WZW model, but in the presence of marginally irrelevant perturbations. Another possibility is that the system belongs to another universality class which is sharply different from the SU(n) 1 WZW model. In the present framework, it is rather difficult to distinguish these possibilities. In Sec. VI C, we propose a short-range Hamiltonian where critical ground states belonging to the same universality class are likely to appear and which is easier to analyze in practice and may shed light on the correct critical theory. Another integrable U q [sl(2|1)] superspin chain with alternating representations 3 and3 has been studied in [98], which exhibits several critical theories depending on the parameters of the Hamiltonian. There could be a connection between these results and our results.
We now turn to the 2D state on a square lattice on the cylinder, with fundamental and conjugate representations in a checkerboard pattern (see Fig. 4). In Fig. 7, we compute the TEE following the same approach as in Sec. III B. The results are in agreement with −γ = − ln(n)/2 within the precision of the computation. Similar to the SU(n) which is valid for general z j . Note that this reduces to our previous result (57) for r j = +1 ∀j. We also observe that (106) does not depend on r j for n = 2. This happens because the fundamental representation and the conjugate representation are the same representation for n = 2.
A. 1D parent Hamiltonian
We now specialize to 1D by forcing all z j to fulfill |z j | = 1. This gives w * ij = −w ij . We therefore obtain the 1D parent Hamiltonian By using (A5) and (A7), we find and by using (62) and the definition of T a , we get Inserting these expressions in the expression for the Hamiltonian leads to where
B. 1D uniform parent Hamiltonian
For the 1D uniform case, z j = exp(i 2π NT j). By using (68) and (69), the parent Hamiltonian therefore simplifies to where We plot examples of spectra of H 1D uniform in Fig. 8. The spectra show that the ground state is unique.
One important motivation of studying long-range parent Hamiltonians is that they may shed light on the physics of some short-range realistic Hamiltonians. As we already mentioned, the SU(n) HS Hamiltonian with inversesquare interactions and the SU(n) ULS model with only nearest-neighbor interactions belong to the same SU(n) 1 WZW universality class. For other long-range parent Hamiltonians constructed for the SU(2) k and SO(n) 1 WZW models [56,59,99,100], the corresponding short-range Hamiltonians are the SU(2) spin-k 2 Takhtajan-Babujian models [101,102] and the SO(n) Reshetikhin models [103,104], respectively. Regarding the SU(n) parent Hamiltonian (112) with both fundamental and conjugate representations, the natural question one may ask is whether there exist shortrange Hamiltonians belonging to the same universality class. In fact, finding such short-range Hamiltonians can also be very useful for clarifying the unsolved issue in Sec. V A on identifying the critical theory of these models.
To address this problem, we restrict ourselves to the 1D uniform case with alternating fundamental and conjugate representations (see Fig. 4). Following the strategy in [61], we truncate the long-range interactions in (112) by keeping only two-body interactions between nearest-neighbor and next-nearest-neighbor sites, as well as three-body interaction terms among three consecutive sites. In the thermodynamic limit, N T → ∞, this procedure yields the following Hamiltonian: By using (A4) and (A6), the three-body interaction term can be rewritten as and then the truncated Hamiltonian is expressed as There is no guarantee that the truncated Hamiltonian with precisely the coupling constants in (116) has the same physics as the long-range parent Hamiltonian (112). However, the form of (116) suggests that a candidate short-range Hamiltonian which shares the same physics might be found in the J 2 − J 3 SU(n) spin chain with J 2 , J 3 being close to the couplings in (116). We have performed an exact diagonalization of the Hamiltonian in (117) for n = 3 and N T = 10 sites. Fig. 9 shows the overlap | Ψ J2−J3 |Ψ | between the ground state |Ψ J2−J3 of (117) and the state |Ψ defined in (96). The maximum overlap (marked with a circle in Fig. 9) is 0.9998 and occurs for J 2 = 0.557 and J 3 = −0.536. These values are quite close to J 2 = 0.467 and J 3 = −0.400 predicted by the truncated Hamiltonian (116).
Let us also mention several solvable cases in (117), which are useful for understanding the phase diagram and are also interesting on their own right. One known solvable point in (117) is the pure SU(n) Heisenberg chain with J 2 = J 3 = 0, which has gapped dimerized ground states for n ≥ 3 [105,106]. In Fig. 9, this Heisenberg point is marked with a plus sign. Motivated by a recent work [107], we have also identified another class of solvable cases in (117), which have perfectly dimerized ground states and can be viewed as SU(n) generalizations of the spin-1/2 Majumdar-Ghosh model [108]. These SU(n) Majumdar-Ghosh Hamiltonians are written as where K 1 , K 2 > 0 and which, on a periodic chain with even N T sites, have ground-state energy E MG = [(n + 1)(n − 2)K 1 + (n − 1)(n + 2)K 2 ]N T /(2n 2 ). In Fig. 9, the Majumdar-Ghosh Hamiltonian (118) is shown as a straight line terminated at J 2 = −3, J 3 = 3 and J 2 = 6/5, J 3 = −3/5. This line seems to be at a phase boundary between two different phases. Fully clarifying the phase diagram of (117) requires extensive numerics. This is beyond the scope of the present work and we leave it for a future study.
VII. CONCLUSION
In summary, we have constructed a family of spin wave functions with SU(n) symmetry from CFT, and we have used the CFT properties of the states to derive parent Hamiltonians in both 1D and 2D. The states are defined on arbitrary lattices, and each of the spins transforms under either the fundamental or the conjugate representation of SU(n). For the case, where all spins in the model transform under the fundamental representation, our results provide a natural generalization of the SU(n) HS model from a uniform lattice in 1D to nonuniform lattices in 1D and to 2D. For the nonuniform 1D case, the Hamiltonian can be chosen to consist of only two-body terms. In 2D, the states reduce to Halperin type wave functions in the thermodynamic limit. This suggests that these states are chiral spin liquids with Abelian anyons, and we find numerically that the total quantum dimension is close to √ n. It also shows that a class of Halperin states have an SU(n) symmetry and provides parent Hamiltonians that can stabilize these topological states.
We have also investigated the case with alternating fundamental and conjugate representations numerically. In 1D, our results suggest that the state is critical, but the central charges and the exponents of the correlation functions deviate from the results expected for the SU(n) 1 WZW model. In 2D, we find a nonzero TEE, and the extracted total quantum dimension is √ n, which is consistent with the SU(n) 1 WZW model predictions. For the case with alternating fundamental and conjugate representations, we have proposed a short-range Hamiltonian for the 1D uniform case and solved it exactly for particular choices of the parameters. Given that it is possible in many related models with long-range Hamiltonians to find short-range Hamiltonians that describe practically the same low-energy physics, it is likely that the proposed short-range Hamiltonian has a ground state in the same universality class as the constructed SU(n) wave functions for certain choices of the parameters.
Note added.-During the preparation of this manuscript, we learned that related results have been obtained by R. Bondesan and T. Quella [109]. where all elements that are not shown are zero. For the A sites (fundamental representation), we therefore have and for the B sites (conjugate representation), we have Altogether, Let us define The term in |Ψ ′ having N 1 + 1 spins in the state |1 in the fundamental representation, N1 spins in the state |1 in the conjugate representation, N 2 − 1 spins in the state |2 in the fundamental representation, and N2 spins in the state |2 in the conjugate representation at given positions has coefficient 1→N2−1 }, . . . , {x 1→N1 , x 1→j−1 , x j ) is the index of the jth spin in the state |α in the fundamental (conjugate) representation. We define the order operator O as (B5) Note that 1→N2−1 }, . . . , {x 1→N2 }, . . .) 1→N2−1 }, . . . , {x 1→N2 }, . . .). | 10,742.2 | 2014-05-12T00:00:00.000 | [
"Physics"
] |
A search for the decays of stopped long-lived particles at $\sqrt{s}=13$ TeV with the ATLAS detector
A search for long-lived particles, which have come to rest within the ATLAS detector, is presented. The subsequent decays of these long-lived particles can produce high-momentum jets, resulting in large out-of-time energy deposits in the ATLAS calorimeters. These decays are detected using data collected during periods in the LHC bunch structure when collisions are absent. The analysed dataset is composed of events from proton-proton collisions produced by the Large Hadron Collider at a centre-of-mass energy of $\sqrt{s}=13$ TeV and recorded by the ATLAS experiment during 2017 and 2018. The dataset used for this search corresponds to a total live time of 579 hours. The results of this search are used to derive lower limits on the mass of gluino $R$-hadrons, assuming a branching fraction $B(\tilde{g} \rightarrow q \bar{q} \tilde{\chi}_1^0)=100$%, with masses of up to 1.4 TeV excluded for gluino lifetimes of $10^{-5}$ to $10^3$ s.
Introduction
This paper presents a search for long-lived particles which have come to rest within the ATLAS calorimeters and decay at a later time when no proton-proton ( ) collisions occur.
Although absent in the Standard Model (SM), exotic metastable particles are featured in many beyond-the-SM (BSM) theories. These include R-parity-conserving supersymmetry (SUSY) [1][2][3][4][5][6][7] models such as split-SUSY [8,9] and gauge-mediated SUSY breaking [10][11][12], as well as other scenarios such as universal extra dimensions [13,14]. In the example of split-SUSY, it is assumed that the naturalness problem is solved via a small amount of fine-tuning, and gauge coupling unification and dark matter are used to guide the theory. Here, SUSY-breaking can occur at a very high energy scale, leading to correspondingly heavy squarks and sleptons. This results in a suppression of gluino decay via a heavy off-shell squark, creating the possibility for the gluino to acquire a non-negligible mean proper lifetime (˜).
If long-lived gluinos are produced by proton collisions at the Large Hadron Collider (LHC), these strongly produced SUSY particles would hadronise with SM quarks and gluons, forming new composite states known as -hadrons [1]. This scenario was chosen as the benchmark for this search. The constituent gluino or squark can be regarded as a heavy particle surrounded by a cloud of interacting, coloured SM particles. As the -hadron traverses the detector it interacts with the detector material via the exchange of constituent partons, thereby altering its composition. During this process the -hadron can flip between neutral, charged or even doubly charged states. This search is robust against these charge flips. Since in most models the lightest -baryon state is charge neutral [15], a significant fraction of -hadrons that exit the ATLAS calorimeter would be charge neutral. Detector-stable -hadrons are therefore likely to not leave charged tracks in the muon spectrometer system.
Assuming that the gluinos are produced near threshold at the LHC, the -hadrons they form are expected to be slow-moving, and some fraction will lose sufficient momentum while traversing the detector to come to rest. In such cases a stopped gluino could decay significantly later than the bunch crossing in which it was produced, depending on (˜), leaving a significant energy deposit within the ATLAS calorimeter system. This search makes use of data collected in so-called empty bunch crossings, where the proton buckets in the crossing beams are unfilled, providing a clean environment in which to identify these delayed decays. This approach provides sensitivity to gluino lifetimes across several orders of magnitude, in the range of 10 −5 to 10 3 s. In the absence of proton collisions, events containing muons from cosmic rays or the interaction of beam protons with upstream collimators, residual gas within the beam pipe, or the beam pipe itself, become the dominant background. [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. This analysis represents the first search for stopped long-lived particles at ATLAS using √ = 13 TeV collision data, with an integrated luminosity of 111 fb −1 . The analysis significantly expands the limits on such signatures given by previous ATLAS analyses, which exclude gluinos with mass (˜) < 832 GeV for (˜) from 10 −5 to 10 3 s. Novel strategies for the estimation of non-collision background processes have been developed, making use of additional non-collision datasets collected by the ATLAS experiment to derive background templates.
ATLAS detector
The ATLAS detector [22] at the LHC covers nearly the entire solid angle around the collision point. 1 It consists of an inner tracking detector surrounded by a thin superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer incorporating three large superconducting toroidal magnets.
The inner-detector system is immersed in a 2 T axial magnetic field and provides charged-particle tracking in the range | | < 2.5. The high-granularity silicon pixel detector covers the vertex region and typically provides four measurements per track, the first hit normally being in the insertable B-layer installed before Run 2 [23,24]. It is followed by the silicon microstrip tracker, which usually provides eight measurements per track. These silicon detectors are complemented by the transition radiation tracker (TRT), which enables radially extended track reconstruction up to | | = 2.0. The TRT also provides electron identification information based on the fraction of hits (typically 30 in total) above a higher energy-deposit threshold corresponding to transition radiation.
The calorimeter system covers the pseudorapidity range | | < 4.9. Within the region | | < 3.2, electromagnetic calorimetry is provided by electromagnetic barrel and endcap high-granularity lead/liquidargon (LAr) calorimeters, with an additional thin LAr presampler covering | | < 1.8 to correct for energy loss in material upstream of the calorimeters. Hadronic calorimetry is provided by the steel/scintillator-tile (Tile) calorimeter, segmented into three barrel structures within | | < 1.7, and two copper/LAr hadronic endcap calorimeters (HEC). The solid angle coverage is completed with forward copper/LAr and tungsten/LAr calorimeter modules optimised for electromagnetic and hadronic measurements respectively.
The muon spectrometer (MS) comprises separate trigger and high-precision tracking chambers measuring the deflection of muons in a magnetic field generated by the superconducting air-core toroids. The field integral of the toroids ranges between 2.0 and 6.0 T m across most of the detector. A set of precision chambers covers the region | | < 2.7 with three layers of monitored drift tubes (MDTs), complemented by cathode-strip chambers (CSCs) in the forward region, where the background is highest. The muon trigger system covers the range | | < 2.4 with resistive-plate chambers in the barrel, and thin-gap chambers in the endcap regions. Interesting events are selected to be recorded by the first-level (L1) trigger system implemented in custom hardware, followed by selections made by algorithms implemented in software in the high-level trigger (HLT) [25]. The L1 trigger accepts events from the 40 MHz bunch crossings at a rate below 100 kHz, which the HLT reduces in order to record events to disk at about 1 kHz.
Dataset and reconstruction
During collision data-taking the LHC circulates two counter-rotating proton beams constructed from bunches of protons (∼10 11 protons per bunch). The radio frequency (RF) cavities providing particle acceleration at the LHC operate at 400 MHz, which corresponds to an RF bucket spacing of 2.5 ns. A group of ten RF buckets are assigned a unique bunch-crossing identifier (BCID) within which only one RF 1 ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the -axis along the beam pipe. The -axis points from the IP to the centre of the LHC ring, and the -axis points upwards. Cylindrical coordinates ( , ) are used in the transverse plane, being the azimuthal angle around the -axis. The pseudorapidity is defined in terms of the polar angle as = − ln tan( /2). Angular distance is measured in units of bucket can contain a proton bunch, for each beam. Proton bunches are spaced at 25 ns intervals, such that a filled RF bucket is separated from the next by at least 25 ns. There are 3564 available BCIDs in which a filled bunch can reside around the LHC circumference, with each corresponding to a time window of 25 ns. Following LHC injection not all BCIDs contain a filled bunch, with the number of unfilled bunches depending on the LHC filling scheme [26].
To identify potential signals in the calorimeter that originate from delayed decays of stopped exotic particles, this analysis makes use of events recorded during empty bunch crossings (BXs), where the ten crossing RF buckets in each beam are unfilled. Events selected for analysis are taken from empty BXs during stable beam periods, when data-taking for physics purposes is underway, and are required to pass standard data-quality requirements [27]. The use of empty bunches minimises detector activity from collision events, reducing contamination from SM processes in a bid to identify out-of-time decays. As a result, the main backgrounds for this search are from non-collision sources, which, to leading order, scale with live time as opposed to the integrated luminosity of the proton-proton data-taking campaign. Here 'live time' refers to the total amount of time during which the trigger was able to select and accept signal-like events in empty BXs in the √ = 13 TeV data-taking. In contrast to the background, the number of signal events in a given dataset approximately scales with both the live time, governing the acceptance of potential signal events, and the integrated luminosity of the collisions, which governs their production.
This analysis includes data collected during 2017, with an empty-BX live time of 298 hours, and during 2018, with an empty-BX live time of 281 hours. These are the live times for this analysis after imposing requirements based on beam conditions, detector conditions and data quality. The 2017 dataset corresponds to an integrated luminosity of 49.0 fb −1 of √ = 13 TeV collisions delivered by the LHC to the ATLAS experiment, while the 2018 dataset corresponds to 62.1 fb −1 . The uncertainty in the 2017 (2018) integrated luminosity is 2.4% (2.0%) [28], obtained using the LUCID-2 detector [29] for the primary luminosity measurements. The analysis sensitivity is higher for the 2018 dataset, which has the larger integrated luminosity and a slightly lower live time than the 2017 dataset. The higher live time during 2017 results in a larger contribution from non-collision backgrounds, while the lower integrated luminosity of the 2017 dataset leads to fewer expected signal events. For 2015 and 2016 the even lower integrated luminosities and higher live times reduce the sensitivity of the analysis further, motivating the use of the 2017 and 2018 datasets alone in this search. The exception to this is a single dataset from 2016, taken during a period of time when there were no beams circulating within the LHC machine, which is used to study backgrounds induced by cosmic rays (a background component referred to herein as 'cosmics').
Analysis data samples and triggers
Dedicated triggers are used to select empty-BX events with significant calorimeter activity. For the 2017 data, these triggers require that the empty bunch in each beam is separated from a filled bunch by at least five unfilled BCIDs both before and after. This effectively enforces a 150 ns buffer between the empty BXs considered in this analysis and a collision event. Due to the introduction of additional trigger acceptance, in the 2018 dataset this buffer is reduced to 100 ns before the considered BX, but for BXs following the considered BX the buffer remains at 150 ns. 'Signal-like' events forming the search sample for this analysis are selected in empty BXs by a trigger requiring missing transverse momentum miss T > 50 GeV and at least one jet with transverse momentum T > 55 GeV and | | < 2.4 at the HLT. In order to be selected by this trigger the event must contain at least one jet with T > 30 GeV at L1. To ensure the trigger is fully efficient relative to the analysis selection, events selected for analysis are required to contain at least one jet with T > 90 GeV. Additional datasets are used to study background processes. Cosmic rays can result in energetic muons traversing the detector, which can in turn induce energetic jets in the calorimeters, presenting an important background for this search. To obtain a pure sample of cosmic-ray events free of beam-induced backgrounds (BIB), a cosmic run (taken without beam in the LHC machine) recorded during 2016 provides a cosmic sample. Beam-induced backgrounds in this search arise as a result of stray protons interacting with material upstream from the detector, producing energetic particles that traverse the detector in the horizontal plane. Jets induced by energetic muons produced in these interactions present the other important background process for this search. An additional BIB sample for studying beam-induced backgrounds is taken from unpaired BX data. Here only one of the crossing LHC beams contains a filled bunch (with the corresponding bunch in the other beam remaining unfilled), reducing contributions from beam-beam collisions. Events are selected from these datasets using a trigger requiring the presence of a jet with T greater than either 12 GeV or 50 GeV at L1. Because of its high rate, only a fraction of the events passing the 12 GeV trigger is recorded. This trigger, therefore, is only used to select events in which the highest jet T is under 120 GeV. The contribution from events selected by this trigger is scaled by the inverse of the corresponding fraction, to ensure a smooth jet-T distribution when transitioning between events selected using the 12 GeV and 50 GeV L1 trigger (the latter trigger is used to select events in which the highest jet T is ≥120 GeV). An additional cavern background sample is formed using events recorded by a random trigger, which was active during empty BXs. This is used to provide an unbiased sample from which to derive the amount of noise expected in the ATLAS detector due to effects such as activity from errant neutrons and photons commonly called 'cavern background'. A summary of the data samples used in this analysis is given in Table 1. The same T requirements are placed on each data sample used in the analysis in order to make the samples selected using different triggers consistent. The exception to this is the cavern background sample, where an unbiased sample is obtained by making no kinematic selection. and cosmic reconstruction (right) configurations. The red line represents the extrapolated muon path based on the hits induced in the MDTs (blue). For readability, only the MDT chambers with associated hits are shown. A cut-away view of the LAr and Tile calorimeters is included for orientation. By using cosmic reconstruction mode the muon-segment track reconstruction requirements are loosened, increasing the efficiency for identifying muon segments induced by cosmic rays. With collision reconstruction settings, only the lower half of the cosmic-ray muon track is reconstructed, whereas in cosmic reconstruction mode the upper leg is reconstructed as well.
Data reconstruction
The signal decays targeted by this search would be expected to originate from within the detector material, and be displaced from the interaction point (IP). The decay of a stationary long-lived particle would be isotropic, and so the decay activity would likely not point directly towards or away from the IP, nor would resulting energy deposits necessarily be projective with respect to the expected positions of beam-beam collision vertices. In order to increase the efficiency with which out-of-time and non-pointing signals can be reconstructed, and to increase the identification (and subsequently rejection) efficiency of background events from cosmic rays, a cosmic reconstruction configuration [30] is applied to reconstruct all data used in this search. This configuration loosens the pointing and impact parameter requirements on muon segments [31], such that muon tracks are not required to point towards the IP. Special timing procedures are applied to cosmic muon reconstruction in the MDT detectors to account for the different timing of the recorded hits relative to the nominal LHC bunch crossing. These modifications increase the efficiency with which the upper muon segments produced by a cosmic-ray muon are reconstructed. This is illustrated in Figure 1, where both the upper and lower muon segments induced by a traversing cosmic-ray muon are reconstructed when using the cosmic reconstruction mode, but when using a collision reconstruction configuration only the lower segments are reconstructed.
The LAr calorimeter reconstruction method remains unchanged when reconstructing the data in cosmic mode, while the Tile calorimeter reconstruction methods are altered to use an iterative method that more accurately reconstructs the energy of out-of-time energy deposits [32]. Standard reconstruction methods are employed to reconstruct collision vertices from tracks in the inner detector in order to veto contributions from beam-beam collisions.
Jets are reconstructed using the anti-algorithm [33,34] with a radius parameter of = 0.4. Jets are required to have T > 20 GeV and reside within | | < 4.5. Due to low average energy deposition in the environment studied in this search, jet four-momentum calibration is derived from a sample of collision events with a low number of additional interactions per BX, known as pile-up. As such, no pile-up corrections are applied to data collected during empty BXs.
To reject backgrounds, reconstructed track segments in the MS are used in the signal region definitions. Reconstructed muon segments [35] are required to have at least three hits, except for CSC segments, which must have at least three hits and three hits. For chambers in which there are two inactive layers, only two hits and two hits are necessary. Muon segments with a failed track fit, or that have activity overlapping with adjacent events, are rejected by including only those segments with a time-of-first-hit between 0 and 750 ns (where 750 ns corresponds to the maximum drift time in the MDTs).
Stand-alone muons [35] are used to select cosmic-ray-enriched control regions and to reject cosmic-rayinduced backgrounds in signal-enriched regions within the search sample. Stand-alone muon tracks are formed by requiring at least two matched segments (except in the transition region between the MS barrel and endcap, where a single good-quality segment can be used). Using this configuration, approximately 85% of events selected in the cosmic sample have at least one stand-alone muon reconstructed.
Signal models
The results of this search are interpreted in the context of a split-SUSY-inspired simplified model in which gluinos are produced, forming -hadrons, with the gluinos decaying as˜→¯˜0 1 . Signal samples were generated with gluino masses (˜) in the range 400 to 1800 GeV. Three different mass scenarios for the lightest neutralino,˜0 1 , are considered in order to probe a range of possible kinematic signatures. In one case the mass of the lightest neutralino (˜0 1 ) is fixed at 100 GeV, in another more 'compressed' scenario the mass difference Δ ≡ (˜) − (˜0 1 ) is fixed at 100 GeV, and in the third scenario Δ = 500 GeV. The way in which -hadrons would interact with matter is not certain, but many of the uncertain aspects do not have a major impact on this analysis. The details of the -hadron mass spectrum, the formation of bound states that include gluons, the rehadronisation, and the treatment of heavy flavour and strangeness in the initial and final states, for example, must all be treated with a specific numerical hadronic interaction model. In this analysis the -hadron simulation uses the Regge model [36,37], which makes assumptions about the mass spectra of the -hadron states and their production rates. The precise configuration used in the generation and simulation of the signal samples considered herein follows that documented in Ref. [15]. The details of the stopping mechanisms and the probability of stopping are also discussed in this reference.
In the configuration detailed in Ref. [15], a little over half of the produced -hadrons are electrically neutral. This fraction is sensitive to the probability of producing gluino-gluon bound states, since this so-called gluinoball state is electrically neutral. There is only a 10% decrease in -hadron stopping rate with increasing gluinoball probability over the full range of probabilities. As such, in contrast to most searches for very long-lived particles, the sensitivity of the analysis presented herein remains robust against variations in the gluinoball probability, being sensitive to both neutral and charged -hadron states.
Each decay is locally isotropic as the -hadron would be stationary within the detector. The spectator partons in the final -hadron do not modify the decay kinematics significantly. Since stopped -hadron decays generally occur inside the calorimeter system, the energy from the decay products tends to be deposited near the point of the decay. Jet algorithms subsequently cluster the resulting energy depositions into one or two high-T jets.
As this search is carried out using empty-BX data, signal samples were simulated without pile-up. Any potential impact of residual pile-up on the energy measurement of signal jets is expected to be negligible, since a pile-up jet would have to overlap geometrically with the signal jet in order to contaminate the energy reconstruction. This assumption is supported by signal-jet energy comparisons made using signal samples simulated with and without pile-up, where no significant difference in jet energy was observed in signal events with and without pile-up. Even in this non-collision environment, spurious detector activity as a result of non-collision backgrounds or residual radiation in the ATLAS cavern can occur. Such activity is not modelled in the signal simulation. To account for this effect, simulated signal events are overlaid with events from the cavern background data sample indicated in Table 1. About 92% of these random events contain muon segment activity, less than 0.5% contain a reconstructed muon track, and less than 0.03% contain a reconstructed jet.
Potential signal decays will be recorded only if the trigger accepts the event. The sensitive time window for the trigger is estimated to be [−10, +15] ns relative to the bunch crossing time, so -hadron decays are placed in this time window in simulated event samples. The detector is timed in for particles moving at the speed of light, so that particles from the collision are assigned to detector elements in the same bunch crossing. This requires calorimeter cells further from the interaction point to have a later readout time than those closer to the interaction point. In order to account for this effect, the decay times of the -hadrons are offset by the propagation time of a particle traveling at the speed of light from the interaction point to the decay position.
The (˜) dependence of the interpretation is evaluated using the signal live fraction as a function of (˜). The signal live fraction defines the temporal acceptance of the trigger for a given signal scenario and depends on (˜). The live fraction for the empty-BX data considered in this analysis is shown as a function of (˜) in Figure 2. Typically, ATLAS records data continuously during periods of time in which the LHC is delivering collision data. Each of the datasets taken while ATLAS is continuously recording is referred to as an ATLAS run [27]. Taking both run and bunch structure into consideration, there are two (˜) regimes for the live-fraction calculation.
In the short lifetime ( (˜) < 1 s) regime, -hadrons would usually originate, stop, and decay within a single luminosity block (LB) [27], a data-taking interval during which detector conditions can be considered stable and that is usually about one minute in length. For these lifetimes a simple algorithm sums the product of the probability for -hadrons to be produced in each BCID within the LB and the probability for them to decay in a subsequent empty BCID. Per-BCID instantaneous luminosities are included in this calculation, with the assumption that the instantaneous luminosity for a given BCID is constant within an LB. This probability, multiplied by the trigger-only live fraction, summed and weighted by integrated luminosity over all the LBs in a data-taking period, is the live fraction for a short lifetime gluino. The live fraction becomes significant for gluino lifetimes of (˜) ∼ 100 ns, corresponding to the minimum time between an empty BX considered in this analysis and the previous colliding BX. The turn-on is faster for 2018 data, where the inclusion of empty BXs closer to the last filled BX boosts sensitivity in this small (˜) regime. For longer lifetimes of the order of a minute, gluinos being produced in one LB and decaying in a subsequent LB is an important effect. The signal live fraction becomes a weighted sum of the probability of the decay being captured in the live time of a subsequent LB over all possible production LBs in a given run. For lifetimes approaching 1 hour, most gluinos will decay either during the same run, or otherwise before the next run begins, with the signal live fraction decreasing as a result of the latter cases. For even longer lifetimes, the number of potentially stopped gluinos present in the detector at any given time depends on the run structure, and the integrated luminosity delivered during previous ATLAS runs becomes more important in accounting for potential gluino production in earlier runs. This search considers the potential production of gluinos during the 2017 and 2018 data-taking periods only, as including the decays of gluinos produced in preceding years would only have an effect for lifetimes above 10 9 s, where this search has no significant sensitivity. For these lifetimes the impact on analysis sensitivity is negligible due to the exponential nature of the particle lifetime distribution, and consequently small acceptance in the available 2017-2018 live time for potential signal production pre 2017.
The expected number of signal events falling in the signal regions (SRs) for this search can be calculated as where int corresponds to the total integrated luminosity delivered,˜˜is the gluino production crosssection and SR is the signal selection efficiency for the signal regions. For a signal model with (˜,˜0 1 ) = (1400, 100) GeV, SR ranges between 25% and 34% for the signal regions considered in this search. The stopping fraction stopping corresponds to the fraction of produced -hadrons that are expected to come to a stop within the detector. For gluino -hadrons this fraction varies from 4% to 8% for gluino masses between 0.6 and 2.6 TeV, with the stopping fraction increasing as a function of gluino mass. There is a factor of two to account for the fact that the gluinos are pair-produced. The gluino decays are unlikely to happen simultaneously, hence there are potentially two chances to detect the decay per produced gluino pair. The signal models used in the interpretation of the search results are normalised following this procedure.
Event selection
Events used in this analysis must pass the following preselection requirements. In each event the jet with the highest T must satisfy a set of cleaning criteria to reject events triggered by detector noise or non-collision backgrounds. Additionally, this leading jet is required to have T > 90 GeV and to reside within | | < 2.4 to be within the geometric acceptance of the search sample trigger.
To avoid selecting events where most of the energy associated with a jet could be produced by localised noise, events are rejected if the leading jet has >90% of its energy associated with a single constituent cluster or layer within the LAr calorimeter. Potential background from noise bursts [38] in the LAr calorimeter is rejected via a veto on events where the leading jet's largest energy deposit is located in the EM endcap calorimeter, where noise bursts originate. Noise-induced jets in the HEC are removed by imposing the HEC-specific cleaning of the BadLoose selection criteria described in Ref. [39]. These cleaning requirements reject approximately 20% of events in the signal samples used in this analysis.
To effectively remove collision backgrounds from the data used in this analysis, events selected in the search sample, BIB sample and cosmic sample are required to contain no primary vertex. A primary vertex is defined as a reconstructed vertex that is associated with two or more tracks of transverse momentum T > 500 MeV. In empty BXs in 2018 (2017), less than 0.0005% (0.00002%) of events passing the search sample trigger and standard data-quality requirements were found to contain a primary vertex. In contrast, over 80% of events passing the BIB sample triggers contained a primary vertex, which are mostly due to 'ghost' collisions (where protons in the filled bunch in one beam collide with de-bunched protons -or ghost charge -in the nominally empty bunch [40]). There are no events containing a reconstructed primary vertex in the cosmic sample.
Events that enter the signal regions (SRs) are selected from the search sample, and the leading jet in the event is required to have T > 150 GeV. Two SRs, each divided into three leading-jet T ranges, are defined: • The central SR (SRC) has an additional requirement that the leading jet reside within | | < 0.8.
These SRs are optimised for the split-SUSY-inspired scenarios targeted by this search, which tend to contain central decays.
• The inclusive SR (SRIncl) accepts all events in which the leading jet resides within | | < 2.4, corresponding to the acceptance dictated by the trigger. These SRs are included to preserve acceptance for potential signal scenarios with more forward topologies [41][42][43].
Events containing a reconstructed muon are rejected from the signal regions to remove events seeded by cosmic-ray-induced jets. Further cosmics rejection is provided by vetoing events in which the leading jet overlaps with a trajectory between two reconstructed muon segments -an indication that the jet is likely induced by a cosmic ray. This is achieved via the use of the variable, which represents the minimumdistance between the leading jet and a putative cosmic-ray muon (inferred from a combination of upperand lower-hemisphere muon segments) as it passes through the calorimeter. For cosmic-ray-induced jets, takes on small values, and events are required to have > 0.2 to enter the SRs. In cases where cannot be defined (typically > 50% of signal events and 8% of cosmic-ray events), events are accepted into the SRs. The construction of is described in detail in Section 6.1.
To reject BIB the narrow BIB-jet width in the -plane, resulting from the relatively narrow showers expected from BIB traversing the detector parallel to the beam, is exploited via the use of the variable. This is defined as where, in the sums, runs over the constituents of the jet and Δ (jet, ) is the distance in the dimension between a constituent energy cluster associated with the leading jet in the event and the reconstructed centroid of that jet. While signal events tend to have an isotropic topology, the parallel-to-the-beam jets from BIB tend to have small values, and events entering the SRs are required to have > 0.02. The characteristics and estimation of BIB are described in detail Section 6.2.
The SRs are binned by leading jet T , with bin boundaries defining the ranges 150 < T < 300 GeV, 300 < T < 500 GeV and T > 500 GeV. Each jet T bin offers sensitivity to signal scenarios with different Δ . The lowest T bin offers sensitivity to scenarios with Δ = 100 GeV, while the higher jet-T bins offer coverage for scenarios with larger mass splittings. An overview of the signal regions, separated by jet T bin boundaries, is given in Table 2. In order to provide production cross-section upper limits for the signal models described in Section 4, all bins in leading jet T are included in a profile-likelihood [44] fit. To form six discovery regions (DRs), each SR T -bin is made inclusive of larger leading jet TṪ hese DRs are used individually to probe the existence of BSM physics or to assess model-independent upper limits on the number of possible signal events. Because the sensitivity of the 2018 dataset to generic signal models is expected to dominate, the discovery regions comprise 2018 data only. The six DRs are also summarised in Table 2.
Background estimation
The main backgrounds considered in this analysis are as follows: • Cosmic rays: Muons from cosmic rays can induce energetic jets in the calorimeter while traversing the detector. The methods used to minimise this background and predict the remaining contribution to the SRs are discussed in Section 6.1.
• Beam-induced backgrounds: Beam protons can interact with upstream collimators, residual gas within the beam pipe, or the beam pipe itself, resulting in energetic muons traversing the detector in the horizontal plane. The methods used to reduce and estimate the extent of these backgrounds are detailed in Section 6.2.
In both the cosmic-ray-and beam-induced backgrounds, the T distribution of the leading jet falls steeply, and the background estimation strategy relies on the modelling of the shapes of these distributions. Jet T templates are extracted for both cosmics and BIB in dedicated control regions (CRs). The background estimation strategy and validation is described in detail in Sections 6.1 and 6.2, but the control and validation regions are outlined below.
To model the cosmics background in the SRs, a 'cosmic-tag' selection is applied to data collected in both the search and the BIB samples to construct cosmics-enriched control regions. The BIB and search samples are combined to provide the highest possible number of events in the template construction. A cosmic-tag is enforced by the requirement that events contain at least one central muon (| | < 1.4) and have < 0.2.
Although the cosmic sample provides a pure sample of cosmic-ray-induced events, the sample size is not sufficiently large to extract a statistically robust jet-T template. Instead, a cosmics CR is defined for each of the SRs, with a selection otherwise matching that of the SR as closely as possible. These CRs are denoted CRC-cos, corresponding to SRC, and CRIncl-cos, corresponding to SRIncl. The shape of the jet T distribution is taken from each cosmics CR and is extrapolated to the SR of interest via a transfer factor obtained from a cosmic sample, as described in detail in Section 6.1. The level of signal contamination in the 2016 cosmic sample from potential -hadron production in earlier collision data-taking was found to be negligible.
For BIB, the jet T template is derived from the BIB sample. BIB CRs are constructed with a selection closely matching that of SRC (to form CRC-bib) and SRCIncl (to form CRIncl-bib), but with the requirement lowered to > 0.01 to accept more background events. The level of signal contamination in the BIB CRs for a 1 TeV gluino is found to be negligible. The BIB-jet T templates are then normalised in dedicated normalisation regions (NRs) using search sample data. Each normalisation region has a selection identical to that of the SR of interest, but requires a leading jet T between 90 and 150 GeV. The control and normalisation region definitions are given in Table 3. Table 3: Overview of the control and normalisation regions used to estimate the contribution of background processes to the SRs. The main requirements that distinguish the control and normalisation regions from the signal regions are indicated in boldface. Events for which cannot be determined are included in the > 0.2 selection CR(Incl/C)-bib and NR(Incl/C)-bib.
Region
Data
Cosmic-ray muon background
Events containing cosmic-ray muons can enter into the event selection of this analysis when a traversing muon induces an energetic jet in the calorimeter. This background is dominated by the emission of a highly energetic bremsstrahlung photon from a high-momentum muon. The T distribution of cosmic-ray-induced jets has a longer high-T tail than the corresponding distribution of jets from beam-induced backgrounds, making it the dominant background process in the highest jet-T range targeted by this search.
As a cosmic-ray muon traverses the detector from top to bottom it passes through the MS. The presence of reconstructed muon segments in opposite hemispheres of the detector can be an indication of a cosmic-ray muon event, although energetic jets produced in gluino decays could punch through from the calorimeter into the MS, also resulting in reconstructed segments. Spurious muon segments can also occur independently of potential signal decays as a result of cavern background, contributing muon segment activity to potential signal events that is uncorrelated with the signal decays. This makes relying on muon segment information alone impractical, as it results in decreased signal selection efficiency. The variable uses the relative geometric location of a pair of reconstructed muon segments combined with the location of the leading jet in the event (assumed to be seeded by the same traversing cosmic-ray muon as the muon segments). This concept is illustrated in Figure 3. Here the left and middle panels show a jet positioned within the calorimeter, near the path of a traversing cosmic-ray muon. The right panel shows how the variable is defined, based on geometric information associated with these objects.
To calculate , reconstructed muon segments in an event are grouped into pairs. A pair is formed if the trajectory measured in at least one of the two muon segments points in the direction of the other, with each pair containing one upper-and one lower-hemisphere muon segment. In order to recover inefficiencies due to the MS gap at = 0, the direction vector of each segment is also extrapolated to the opposite side of the detector in to determine whether it crosses the = 0 plane within the radial range otherwise instrumented by the MS. Should this be the case, a 'dummy' segment is inserted at that position and used in the calculation of , to acknowledge that a traversing cosmic-ray muon could escape through the gap without seeding a muon segment.
For each pair of muon segments in an event, a Δ value is calculated for the pair of points where the axis of the leading jet and the 3D straight line connecting the muon segment pair are closest to each other. The variable is defined as the minimum of these 'points of closest approach' Δ values in the event. This is illustrated in the right panel of Figure 3 in the -plane. The points of closest approach must correspond to a location consistent with being within the volume of the calorimeter. If this is not the case, or if no suitable pair of muon segments is identified, is undefined and the event is not rejected. This occurs in 8% of the events in the cosmic sample, 56% of the events in the signal model with Δ = 1300 GeV, and 78% of the events in the signal model with Δ = 100 GeV. For events where the leading jet is induced by a cosmic-ray muon, takes on small values, as shown in Figure 4, and events are rejected from the SRs if they satisfy < 0.2. For signal events, a value of may be defined due to the leakage of hadronic activity out of the calorimeter and the cavern background effects described above. To estimate the remaining cosmics background expected in the SRs, T templates for cosmic-ray-induced jets are extracted from the cosmics CRs (CRIncl-cos and CRC-cos). The selection applied in these CRs provides a highly pure sample of cosmic-ray events in data, with negligible contamination from BIB processes and potential signal decays. The shape of the resulting jet T template is validated by comparison with the same distribution taken directly from the cosmic sample. The shapes of the distributions are consistent within statistical uncertainties.
The cosmic sample is used to derive a transfer factor between the control and signal regions. The same transfer factor is used to extrapolate between the CR and SR for both the inclusive and central SRs, as the differing requirements have no significant impact on the transfer factor. The transfer factor is derived from the | | < 2.4 selection as follows: transfer = cosmic sample events (SRIncl) cosmic sample events (CRIncl-cos) where cosmic sample events (SRIncl) and cosmic sample events (CRIncl-cos) correspond to the total number of events passing SR-and CR-like selections, respectively, in the cosmic sample. The T template for cosmic-ray-induced jets is first normalised to the total number of events entering the CR from the search sample. A transfer factor of 0.018 ± 0.006 is then applied to the leading-jet T template extracted from the corresponding cosmics CR. To account for differences in the shape of the jet T distribution when extrapolating from < 0.2 to > 0.2, a loose selection is applied to the cosmic sample used to derive per-jet T bin weights that can be used to reweight the jet T template taken from the cosmics CRs when extrapolating to the SRs. The impact of this reweighting is small at low T , with reweighting factors reaching 1.6 in the highest jet-T bin. The normalisation of the template remains constant upon applying these weights, so as to preserve the total expected event counts in the SR. Following this procedure, the expected contribution from cosmics in the relevant SR is obtained.
Beam-induced background
The primary component of BIB is beam halo, which occurs as a result of beam protons interacting with upstream collimators, residual gas inside the beam pipe, or the beam pipe itself, resulting in energetic muons traversing the detector in the horizontal plane. The contribution from beam-gas interactions inside the length of beam pipe within the detector volume is particularly small in empty BXs, and these events are further reduced by the requirement that there be no reconstructed vertex in selected events. In order for beam halo to contribute to this search, the interactions must take place sufficiently far upstream from the IP to allow such muons to reach relatively large radii in the ATLAS detector, reaching the calorimeters and seeding energetic jets. Owing to bending via the dipole magnets of the LHC, beam-halo muons tend to arrive in ATLAS in the horizontal plane [45], with the resulting jets being of larger width in than in . The width of BIB-jets in the direction tends to be small, due to the trajectory of the traversing muons being approximately constant in and the fact that the resulting energy deposition is primarily electromagnetic (yielding narrow showers). This is illustrated in Figure 5. A requirement of > 0.02 is applied to reject BIB in the SRs.
Events from unpaired BXs, where only one of the two counter-rotating beams has a filled bunch, provide a BIB-enriched data sample in which the contribution from cosmic rays is small compared to that in empty-BX data. The relative intensity of BIB as a function of time within the 25 ns BCID time window differs between unpaired-and empty-BX data. In unpaired-BX data the dominant BIB contribution comes from BIB that is in-time with the 'nominal' RF bucket, i.e. the 2.5 ns bucket within the filled BCID that contains the proton bunch. In contrast, BIB in empty-BX data tends to be evenly distributed across all RF buckets, although the production mechanism remains the same. A dedicated systematic uncertainty is applied to the BIB prediction to account for differences in BIB characteristics between empty-and unpaired-BX data, as described in Section 7.
To estimate the contribution from BIB to the SRs, a leading-jet T template is taken from the corresponding BIB CR, which is constructed using the BIB sample. To facilitate this, the leading-jet requirement is loosened to >0.01, since the SR > 0.02 cut significantly reduces the size of the BIB sample. The BIB template obtained in this region is consistent with the distribution obtained using a tighter > 0.02 selection within statistical uncertainties. In order to remove contamination from cosmic-ray-induced events the cosmics background contribution must be estimated and removed from the BIB CRs. This is done by taking the cosmics template from a cosmics CR-like region, but with a lowered threshold of 0.01, and following the steps outlined in Section 6.1 to estimate the cosmics background in the corresponding BIB CR. The resulting cosmics template is then subtracted from the BIB template to yield a BIB-only jet-T template, which is then normalised in the BIB NR corresponding to the SR of interest. The cosmics contribution to this normalisation region is first subtracted using the 90-150 GeV jet-T bins of the SR cosmics template, derived as described in Section 6.1. Since BIB rates and characteristics depend on machine and beam conditions, and LHC operating conditions differed between 2017 and 2018, this procedure is performed separately for 2017 and 2018 data. The BIB normalisation factors for 2017 data are 0.70 ± 0.06 (SRIncl) and 0.65 ± 0.10 (SRC), while for 2018 data they are 0.55 ± 0.03 (SRIncl) and 0.61 ± 0.06 (SRC).
Background estimate validation
The background estimation procedures outlined in Sections 6.1 and 6.2 are tested using dedicated validation regions (VRs). A summary of the kinematic selections used in the validation regions is given in Table 4. In each of these validation regions, the BIB templates are normalised in the 90-150 GeV leading-jet T range. The BIB normalisation regions used for each VR are also summarised in Table 4 The VRs (VRIncl-and VRC-) are designed to test the extrapolation and reweighting of the cosmics T template from the cosmics CRs to > 0.2. The central-muon requirement ensures a region dominated by cosmic-ray-induced events in order to test this. The BIB VRs (VRIncl-bib and VRC-bib) have an requirement identical to that of the SR, but a lower requirement in order to obtain a data sample dominated by BIB events to validate the BIB modelling at high . The VRs (VRIncl-and VRC-) are constructed with the same leading-jet requirement as the SR, but with the requirement inverted. This results in a region where the modelling of both the cosmics and the BIB can be checked at high . In this region, no reweighting is applied to the cosmics template, as there is no extrapolation over . For VRIncl-and VRIncl-(VRC-and VRC-) the cosmics template is extracted from CRIncl-cos (CRC-cos). For VRIncl-bib (VRC-bib) the selection of CRIncl-cos (CRC-cos) is modified to match that of the BIB VRs (0.01 < < 0.02). The BIB templates for VRIncl-and VRIncl-bib (VRC-and VRC-bib) are taken from CRIncl-bib (CRC-bib), but for VRIncl-(VRC-) the CRIncl-bib (CRC-bib) requirement is inverted to match that of the VRs ( < 0.2). For a 1 TeV gluino the level of signal contamination in the VRs is <10%. The exceptions to this are the single bins above 500 GeV in VR-(<20% for all gluino masses) and VR-bib. In the bin above 500 GeV in VR-bib, potential signal contamination is <25% for models with a 1.4 TeV gluino and a 100 GeV neutralino, with the signal population decreasing significantly for 1.4 TeV gluino models with smaller mass-splittings. The signal contamination can be up to 100% for models with lower gluino masses and large mass-splittings. Table 4: Overview of the validation regions and the low-T regions used to normalise the BIB templates for the corresponding validation region. A central set and an inclusive set of these regions are defined to test the background modelling in both SRC and SRIncl. The VRs are also checked separately using data from 2017 and from 2018 to ensure consistent background modelling in both years. The main requirements that distinguish the validation regions from the signal regions, and the normalisation regions from the validation regions, are indicated in boldface. Events for which cannot be determined are included in the > 0.2 selection in VR(Incl/C)-, VR(Incl/C)-bib and the corresponding NRs. The resulting background predictions are compared with the observed data in each of the VRs in Figure 6. The predicted distributions are in good agreement with the observed data in the VRs.
Systematic uncertainties
The systematic uncertainties applied to the background estimates and the simulated signal samples are summarised in this section.
For the cosmics background, an uncertainty is assigned to the cosmics transfer factor to account for the limited number of events in the relevant regions in the cosmic sample. An uncertainty is applied to the reweighting of the T distribution for the cosmics template, corresponding to 100% of the impact of the reweighting. An additional 20% uncertainty is applied to account for differences between the shapes of the leading-jet T distribution in the cosmic sample and the cosmics CRs.
For the BIB prediction, an uncertainty is assigned to the BIB normalisation factor to account for the limited number of events in the relevant NR. The uncertainties in the cosmics background template used to subtract the cosmics contribution from the BIB template are propagated to the BIB estimation. In order to take into account differences between the leading-jet T distributions of BIB-jets in unpaired-and empty-BX data, a shape uncertainty is derived using the observed shape difference in an inclusive region with > 0.2. This shape uncertainty is treated as uncorrelated between 2017 and 2018 data, owing to the different bunch schemes and sample sizes in the comparison.
The magnitude of each uncertainty contribution relative to the total predicted background in each jet T bin of SRC for 2017 and 2018 data is illustrated in Figure 7. The T > 500 GeV jet bin in each region dominates the analysis sensitivity, and the largest uncertainty contribution in this region is due to the shape uncertainties of the cosmics background prediction. This includes the uncertainty due to both the reweighting procedure and the shape comparison with the cosmic sample. Of these contributions, the reweighting uncertainty is the dominant contributor in this jet T range. Uncertainties associated with the BIB prediction become more important in the lower jet-T bins, where the relative contribution from BIB to the total background estimate is more significant. For 2017 data, differences in the shape of the BIB-jet T distribution when comparing empty-and unpaired-BX data are the largest (second largest) source of uncertainty for the 300 < T < 500 GeV (150 < T < 300 GeV) jet bin. This is partly due to beam instabilities within the LHC, which necessitated frequent changes to the LHC filling scheme (and therefore the bunch configuration) during 2017 data-taking operations. The magnitude of the differences observed between BIB-jet T distributions in the search and BIB samples in 2017 are consequently larger than those observed in 2018 data.
For simulated signal samples, uncertainties are applied in order to take into account the non-projective and out-of-time nature of jets resulting from -hadron decays. To account for the impact on jet energy reconstruction of jets arriving out-of-time, an uncertainty is derived from variations in the jet T spectrum of cosmic-ray-induced jets as a function of jet time-of-arrival. Since the T spectrum for these jets should be independent of time-of-arrival, the variation in T as a function of time is taken as a T -dependent uncertainty to account for biases as a result of late or early arrival of the jets relative to the LHC clock. This procedure results in a T -dependent uncertainty, with an impact ranging from 5% to 50% in the highest jet-T bin. To account for the impact of jet non-projectivity on the jet energy reconstruction, the variation in the leading-jet T distribution for cosmic-ray events as a function of is used. Since cosmic-ray muons approach the detector in a downwards direction, by looking at the full detector range all projective possibilities are considered. This results in an uncertainty of up to 12% in the highest jet-T bin. For both the timing and the projection uncertainty, the maximum deviation upwards and downwards from the mean in each T bin is taken as an upwards and downwards uncertainty in the jet energy, respectively. No further experimental systematic uncertainties are applied to the predicted signal yields. Signal cross-sections are calculated to approximate next-to-next-to-leading order in the strong coupling constant, adding the resummation of soft gluon emission at next-to-next-to-leading-logarithm accuracy (approximate NNLO+NNLL) [46][47][48][49][50][51][52][53]. The nominal cross-section and its uncertainty are derived using the PDF4LHC15_mc PDF set, following the recommendations of Ref. [54].
Results
The integrated yields in the central and inclusive signal regions are compared with the expected background in Tables 5 and 6, respectively, for 2017 and 2018 data. The binned jet-T distributions are shown for these regions in Figure 8. The central control and normalisation regions are used to provide the background estimates in SRC, and the inclusive control and normalisation regions are used to provide the predicted yields in SRIncl.
Assumptions about the mass difference Δ impact the point at which the jet T distributions of various Table 5: Breakdown of the expected and observed data yields in SRC in 2017 and 2018, integrated over the jet T spectrum. The quoted uncertainties include statistical and systematic contributions. The expected event yields from two example signal models with a gluino mass of 1.4 TeV and neutralino masses of 100 GeV and 900 GeV are also included, where the yields are calculated assuming a gluino lifetime between about 10 −5 and 10 3 s (within the range of the live-fraction plateau in Figure 2). Table 6: Breakdown of the expected and observed data yields in SRIncl in 2017 and 2018, integrated over the jet T spectrum. The quoted uncertainties include statistical and systematic contributions. The expected event yields from two example signal models with a gluino mass of 1.4 TeV and neutralino masses of 100 GeV and 900 GeV are also included, where the yields are calculated assuming a gluino lifetime between about 10 −5 and 10 3 s (within the range of the live-fraction plateau in Figure 2). signal models are expected to peak. Any potential excess of events must therefore be searched for across the range of the leading-jet T distribution. To do this the three exclusive jet T bins in each SR are made inclusive to form the discovery regions, used to make model-independent statements about the possible presence of new physics. Because the signal cross-section sensitivity of the 2018 dataset is expected to be roughly 20% larger than the 2017 dataset, only 2018 data is used to make these model-independent statements. The results in the central and inclusive DRs are summarised in Tables 7 and 8, respectively. The data are consistent with the expected background across the full jet T range.
Inclusive signal regions
Model-independent upper limits at 95% confidence level (CL) on the number of events ( 95 ) that could be attributed to BSM physics processes are derived using the CL S prescription [55], implemented using HistFitter [56]. This procedure is carried out using the DRs summarised in Table 2, and neglects potential signal contamination in the CRs. Pseudo-experiments are used to set these upper limits. The expected ( 95 exp ) and observed ( 95 obs ) upper limits on the number of possible BSM events are provided in Tables 7 and 8. The -values, which represent the probability of cosmic-ray-or beam-induced backgrounds alone to fluctuate to the observed number of events or higher, are also provided.
Exclusion limits are set at 95% CL as a function of the gluino mass and mean proper lifetime, for different gluino-neutralino mass-splitting hypotheses, and can be found in Figure 9. For the signal models considered in this interpretation the central signal regions dominate the sensitivity, so the limits presented here are based on the results in SRC alone. SRC in this context is implemented in the fit as two separate three-bin regions, with one region for 2017 data and the other for 2018 data (these regions are shown in the top panel of Figure 8). The normalisation region, NRC-bib, is included as two separate single-bin regions, again with one region per year. The hypothesis tests include the expected signal yield and its associated uncertainties in the normalisation regions and SRs, so potential signal contamination in NRC-bib is taken into account in the exclusion fit.
Gluinos with (˜) = 100 ns are excluded up to a mass of 1.0 TeV for a neutralino mass of 100 GeV, with this lifetime corresponding to the minimum time between the last collision and the first empty BX used in this analysis. The live-fraction efficiency plateau is reached for (˜) approximately equal to a quarter of the time taken for a single full LHC revolution. While on the plateau, gluino masses of up to 1.4 TeV are excluded for lifetimes up to a few hours (10 −5 to 10 3 s), after which point the signal efficiency decreases. Gluino masses of 1.0 TeV are excluded for lifetimes of up to 10 7 s. The sensitivity is significantly weaker Table 7: Central (jet | | < 0.8) discovery regions in 2018 data. The lower lines of the table show the 95% CL upper limits on the number of signal events ( 95 obs ), the 95% CL upper limit on the number of signal events given the expected number (and ±1 excursions of the expectation) of background events ( 95 exp ), the value, i.e. the confidence level observed for the background-only hypothesis, and the discovery -value ( ( = 0)). The -value is reported as 0.5 if the observed yield is smaller than the predicted.
Central discovery regions
Jet T > 150 GeV Jet T > 300 GeV Jet T > 500 GeV ), the 95% CL upper limit on the number of signal events given the expected number (and ±1 excursions of the expectation) of background events ( 95 exp ), the value, i.e. the confidence level observed for the background-only hypothesis, and the discovery -value ( ( = 0)).
Inclusive discovery regions
Jet T > 150 GeV Jet T > 300 GeV Jet T > 500 GeV when smaller mass splittings are considered (Δ = 100 GeV) due to a reduced selection acceptance. In these cases, the sensitivity is driven by the expected signal yields in NRC-bib. Limits on the signal production cross-section are also shown as a function of gluino mass in Figure 10, assuming a gluino lifetime of (˜) = 1 ms.
Conclusion
This paper presents a search for long-lived particles that have come to rest within the ATLAS detector at the LHC, using data collected in empty bunch crossings during 2017 and 2018 √ = 13 TeV collision data-taking. The data are found to be consistent with the background prediction, and the results are interpreted in the context of simplified models of gluino pair-production, where the gluinos form -hadrons. Gluino masses up to 1.4 TeV are excluded for gluino lifetimes of 10 −5 to 10 3 s, assuming a neutralino mass of 100 GeV. This analysis represents the first search for stopped long-lived particles at ATLAS using √ = 13 TeV collision data, significantly expands the limits on such signatures given by previous ATLAS analyses, and includes new methodologies for the estimation of non-collision background processes. The ATLAS Collaboration | 13,657 | 2021-04-07T00:00:00.000 | [
"Physics"
] |
heidegger and blumenberg on modernity
The debate surrounding the way in which Heidegger and Blumenberg understand the modern age is an opportunity to discuss two different approaches to history. On one hand, from Heidegger’s perspective, history should be understood as starting from how Western thought related to Being, which, in metaphysical thinking, took the form of the forgetfulness of Being. Thus, the modern age represents the last stage in the process of forgetfulness of Being, which announces the moment of the rethinking of the relationship with Being by appealing to the authentic disclosure of Being. On the other hand, Blumenberg understands history as the result of the reoccupation process, which means replacing old theories with other new ones. Thus, to the historical approach it is not important to identify epochs as periods of time between two events, but to think about the discontinuities occurring throughout history. Starting from here, the modern age will be thought of not as an expression of the radicalization of the forgetfulness of Being, but as a response to the crises of medieval conceptions. For the same reason, the interpretation of history as a history of the forgetfulness of Being is considered by Blumenberg to subordinate history to an absolute principle, without taking into account its protagonists’ needs and necessities.
determining hiStoriCal ePoChS and the Problem of modernity
Raising the issue of approaching modernity from Heidegger and Blumenberg's different perspectives represents an opportunity to discuss two ways of approaching history.This means, on one hand, the interrogation with respect to how historical eras were constituted and how they legitimate themselves.Heidegger's perspective of identifying the ontological fundaments of historical eras is challenged by the idea that claims that historical events cannot be understood outside history, but only from the perspective of continuities 1 Faculty of Philosophy and Social-Political Sciences.Al.I. Cuza University of Iasi<EMAIL_ADDRESS>discontinuities with the historical tradition.On the other hand, explaining the essential phenomena of modern age (science, technology, new condition of man, secularisation) from the two perspectives offers the possibility to discuss the expectations we have from the expectations we should have from the thinking area that opened after the end of modernity.The announcement of a new era characterised by its separation from the traditional metaphysical thinking and marked by an authentic thinking of the Being is considered to subordinate the diversity of history to an absolutist principle.In this context, the dialogue between Heidegger's and Blumenberg's conceptions opens a new space of reflection on the attitude we should have toward the modern era and toward the mechanism according to which changes happen in history.
According to Heidegger, history should be understood starting from how Western thinking relates to the Being (Sein).Beginning with Plato, this relation took the form of metaphysics, to which the understanding of the Being meant grasping the permanent given.However, such a way of questioning is not adequately to the Being because while searching for the durable and stable, we actually question beings (Seiende), leaving the question regarding the Being unthought.According to this line of thinking, history is no longer merely a succession of some events through time (Historie).But it should be understood starting from what opens in such events, which is from the Being (Geschichte).History becomes the destiny of the Being, where the term destiny should mean "sending" (schicken), a way of offering of the Being that discloses (geschehen) in each epoch in a determined way.Epochs are configured according to a certain understanding of the Being and, therefore, the delimitation from the perspective of historical events is considered irrelevant.
The history of Being means destiny of Being in whise sending both the sending and It which sends forth hold back with their self-manifestation.To hold back is, in Greek, epoché.Hence, we speak of the epochs of destiny of Being.Epoch does not mean here a span of time in occurence, but rather the fundamental characteristic of sending, the actual holding-back of itself in favor if the discernibility of the gift, that is, of Being with regard to the grounding of beings [...] The epochs overlap each other in their sequence so that the original sending of the Being as presence is more and more observed in different ways.(HEIDEGGER, 2002, p. 9).
As a process of the Being, history actually means forgetfulness of Being (Seinsvergessenheit), and the historical epochs represent various ways where the Being withdraws or discloses unauthentically as being.The history of the forgetfulness of the Being becomes synonymous with the "conversion of truth," to the extent that the truth is understood as the disclosure of the Being.The three epochs Heidegger speaks about, the antiquity (after the pre-Socratics), the Middle Ages, and the modern age, should be analysed from the perspective of forgetting the Being, meaning the more and more distancing from aletheia, from the pre-Socratic experience of the truth as unconcealment (Unverborgenheit) and from the original experience of the Being.These epochs should not be thought of as a continuous process as they form an "independent string" leading together to the consolidation of the forgetfulness of the Being and to the transition from the existence of man in the light of truth to the conception of truth as depending on human perception.
The history of forgetfulness of Being starts with the platonic interpretation of the Being as ίδέα.The shine emanating from ίδέα makes visible the field of beings.This shine is what "[...] brings about presencing, specifically the coming to presence of what a beings in any given instance.A being becomes present in each case in its whatness" (HEIDEGGER, 1998, p. 173).In other words, in the Idea, being is grasped in its "visible form" (ειδος), by gazing.Therefore, "formation" is important as well as "education" (παιδεια) through which gaze should become adequate in order to grasp the Idea correctly.The adequacy of the gaze and its correctness become criteria of the truth, which is now understood as the resemblance (όμοιωσιξ) or correspondence (adaequatio) between assertion or representation and a thing.The understanding of the Being as ίδέα marks the transition from a new conception of the truth that is no longer understood as "unconcealment", but as "[...] όρϑότης, the correctness of apprehending and asserting" (HEIDEGGER, 1998, p. 177).Hence, starting with cu Plato, the truth is no longer regarded as unconcealment of the Being, but, to the extent that access to what is concealed is made by gaze, the correctness of such an act and its correct direction toward the Idea become the criteria of truth.
With Aristotle, the Being acquires a new determination, as it is understood as ένέργεια.The meaning of the term ένέργεια should be clarified starting from the root εργον, which means work, an efficient action presencing (anwesen) in unconcealment.Ένέργεια is not the efficient reality resulting from a productive action, but it is "[...] the presencing, standing there in unconcealment, of what is set up" (HEIDEGGER, 2003, p. 5).Approaching the Being as consistency (Ständige) makes thus a step forward as compared to thinking the Being as visible form, in crystallising the metaphysics of the permanence that will wrongly orient toward knowing beings and not the Being.
The Middle Ages come with a new interpretation of the Being and of the truth from the perspective of the concept of empire, which builds reality on order and commandment.According to "imperial" essence of romanity, the truth is the right (rectus), meaning compliant with what is orderly.The Being has transformed now from ένέργεια into actualitas, referring to the real as such or truly real (actus purus), which fulfils itself in God, the supreme being.This is the meaning of the Being that would dominate the western thinking until the modern era.
The latest great epoch of the history (of forgetfulness) of the Being is the modern age, characterised by five essential phenomena: emergence of science, machine technology, aesthetics ("[...] the art work becomes the object of mere subjective experience, and that consequently art is considered to be an expression of human life" (HEIDEGGER, 1977a, p. 33), culture as man's supreme activity and desacralization (understood as a process of leaving aside the Christian conception of the world, in parallel with the transformation of the Christian vision with a view to adapting it to the reality of the modern age).In the modern age, the truth is understood as certainty, as awareness of knowledge, as its representation (Vorstellung).This conception of the truth offers a privileged position to man -as a subject, meaning the basis the entire reality relies upon.Reality and the subject are thought as being opposed (Gegensändige) to one another, and the presencing of the Being is only possible by means of its transformation into object, which requires a subject to represent it.This is the last but one step in the forgetfulness of the Being, which will culminate with delivering the Being as availability by Technology.Technology represents the fulfilment of all metaphysics, as it offers the means to dominate nature as standing-reserve (Bestand) man can manipulate.
To Blumenberg, any attempt to uniformise history, either in terms of interpreting it from the perspective of the event of the forgetfulness of the Being, or in terms of classical historiography of delimiting some historical eras, represents a failure in the understanding of the fact that history represents a plurality of questions and answers reflecting people's needs.History, now understood as history of ideas (Geistesgeschichte) no longer has to focus on "[...] the individualization of historical periods as complex unities of events and their consequences and the preference given to states rather then actions, to configurations rather then figures" (BLUMENBERG, 1999, p. 459).This is also suggested by the meaning of the Greek term "epoché," which refers to a break of a movement or even a reversed direction.In astronomical language, "epoché" refers to a privileged point wherefrom movement of a celestial body can be observed.To the historical approach, it is not important to identify epochs as periods of time between two events, but thinking the discontinuities occurring throughout history.Historical events are important not by their scope but insofar as they "[...] contain deep radical changes, re-evaluation and turnings, which affect the entire structure of life" (BLUMENBERG, 1999, p. 464).
Discussing historical inquiry, in Thomas Kuhn' terms, it should attach more importance to understanding how paradigms of thinking change, rather than to their consolidation.Paradigms wear out; they reach puzzles impossible to solve that lead eventually to forming other theories that replace the old thinking system.However, the theory of "scientific revolutions" does not explain how these mutations occur and what does happen within the framework of such changes.
In order to understand the transitions of history, we should accept the fact that all changes unfold due to a constant matrix of needs, which operates along the historical stages.This means that the quest for absolute beginnings in history is wrong, as the questions are forever the same.The interest for some of them may disappear in a period only to re-emerge later within a favourable context.What vary are the answers, which, owing to the contradictions reached by their latest consequences, determine the occurrence of the "reoccupation" process, i.e. replacing some theories with others.
The concept of "re-occupation" designates, by implication, the minimum of identity that it must be possible to discover, or at least to presuppose and to search for, in even the most agitated movement of history.In the case of systems of «notions of man and the world» "reocupation" means that different statements can be understood as answers to identical questions.(BLUMENBERG, 1999, p. 466).
The reoccupation process is not a gradual one.Even if it is heralded by some ideas, explored in isolation before the transformation proper takes place, the occurrence of the change as such remains imperceptible.The term of threshold of the epoch explains, on one hand, how the old theory exists before the threshold, and, on the other hand, we have the new theory that represents stepping over the threshold, without any intermediate steps in-between.This transition, Blumenberg highlights, is not guided randomly, but it is based on the needs system that may be found on both sides of the threshold.There is even an indicator of stepping over the threshold that consists of the formation of a new seriousness, by the attention paid to certain previously considered secondary questions.In the Middle Ages this seriousness referred to the preoccupation for reaching beatitude via faith, which had not existed in Antiquity; the seriousness of the modern age, contrary to medieval beliefs, was oriented toward cancelling traditional prejudice and revaluing experience as a source of knowledge.
The consciousness of a new seriousness puts the totality of the preceding attitudes, sympathies, and actions under the suspicion of frivolity; one had not yet found it necessary to take things so to heart, to be so particular, to want real knowledge."(BLUMENBERG, 1999, p. 473).
Starting from this perspective on history, whose moments are formed of convergent lines coming from the past, Blumenberg opposes the interpretation of modernity as an absolute beginning, meaning a complete break with the past with a view to creating novelty.Such a perspective cancels the legitimacy of the modern age, concealing the true reasons why it appeared.
[...] the program of the modern age cannot be assumed as a contingent "spontaneous generation"; the unfolding of its conceptual presuppositions already reflects the singular structure of the needs that had emerged, compellingly, in the self dissolution of the Middle Ages.(BLUMENBERG, 1999, p. 467).
The legitimacy of the modern age can neither be found in its interpretation as a result of a process of correcting the errors of the past, as it happens in its understanding from the perspective of the secularisation process.In this interpretation, the importance attached to certainty in the modern age is a secularisation of the Christian issue of the certainty of salvation; the modern ethics of labour is a secularisation of sanctity and of the different forms of ascetics; postulating political equality of all people is a secularisation of the idea of people's equality before God; the idea of progress is a transformation of the "salvation idea," and last, but not least, science is a secularisation of the vision of the world and of the intended actions of the original Christianity.
To this line of thinking, meaning the need to correct the negative consequences of the past, which influence the present, the interpretation of history as "history of Being" belongs as well.Even if Heidegger's approach of history avoids the mythology of the absolute beginning, it fails to legitimate the modern era by "[...] withdrawal into the comforting solidity of what was there all the long" (BLUMENBERG, 1999, p. 193).The consequence of this line of thinking of history is a negative idealisation of the modern era that relies on an a priori typification of the epoch, from whose perspective it is interpreted as the latest version of the "forsakenness of Being".Thus, the possibility to legitimate the modern era from a historical point of view is cancelled, preferring instead its characterisation as bearing [...] the stigmata of domination, of the serviceability of theory for technicity, of man's self-production, precisely not as an "answer" to a provocation (bequeathed to it in whatever manner) but rather as one of the un-"graced" confusions surrounding the "Being" that has been withdrawn and concealed since the time of Pre-Socratics.(BLUMENBERG, 1999, p. 192).
The modern age cannot be explained merely as the result of a generalising process, such as the radicalisation of the inauthentic thought about Being, nor can it be treated as an absolute beginning without precedent in history.Its origin should be looked for in the dissolution of the medieval world, which, due to the contradictions of the theological absolutism doctrine -developed within the medieval nominalism -it generated the need for reoccupation of the scholastic thinking system.Self-assertion is a consequence of the contradiction expressed within the theological absolutism, between God's absolute will and man's will (which claimed to be free).Science is the result of a naturally human attitude -curiosity -that was arbitrarily excluded from the human authentic activities and which, objectivising itself, took the opportunity to manifest.Progress expresses confidence in the human being's abilities that by means of science and technology may understand nature and even master it.Hence, the modern age becomes legitimate by its discontinuities as compared to the past.It cannot be separated from the way the dissolution of the Middle Ages created the background for new answers to be formulated to the unsolved questions of the past.
modern SCienCe and teChnology
Despite Heidegger including desacralization on the list of modern age features, he does not consider it defining for modern thinking.From the historial (Geschichtlichkeit) point of view, secularisation does not grasp the Being's understanding specific to the modern age.It is not a way to bring to presence the Being; it is merely a consequence of understanding the world in a way where religious explanations cannot find their place any longer.
The loss of the gods is a twofold process.On the one hand, the world picture is Christianized inasmuch as the cause of the world is posited as infinite, unconditional, absolute.On the other hand, Christendom transforms Christian doctrine into a world view (the Christian world view), and in that way makes itself modern and up to date.The loss of the gods is the situation of indecision regarding God and the gods.( HEIDEGGER, 1977a, p. 116-117).
The essence of modern thinking lays in its relation to the Being by means of science and technology.What distinguishes modern science from the ancient and medieval ones is not any deep truth, but the approach of beings under the guise of research.Research implies the opening of a field of beings by projecting a new determined ground plan (Grundriss) on nature.By means of this ground plan, each process introducing itself as natural should be determined in spatial-temporal terms, i.e. it should be determined by number and calculation.The specific feature of the modern relation to nature is given by its mathematical character, whereby the essence of things is grasped from the outside, via the data we already possess.Thus, mathematics appears to be a way to acknowledge something setting-before.Therefore, calculation, which forms the essence of modern research, discloses beings as setting-before, as something that can be subjected at any time to re-presentation because it has been set before.
Τα μαϑήματα means for the Greeks that which man knows in advance in his observation of whatever is and in his intercourse with things: the corporeality of bodies, the vegetable character of plants, the animality of animals, the humanness of man.( HEIDEGGER, 1977a, p. 118).
Another characteristic feature of modern science, as a historial process, is methodology (Verfahren), which implies clarifying the real by applying rules and laws that lead to objectifying the facts.This means looking for the necessities that guide changes in nature.From this perspective, one of the important consequences of science, understood as research, is that it is experiment-oriented, where by experiment we understand not only merely observing some facts, but a calculation questioning of the facts from the perspective of a law with a view to validating or invalidating such principle.
Lastly, the last determination of science under the guise of research is ongoing activity (Betrieb), which implies delimiting each field of beings, where each science is to be practiced.An effect of specialization is institutionalised research whereby science methodology asserts the prevalence over any being.Hence, science appears as a whole where the differences between natural sciences and humanities seem to fade due to the importance of methodology.
The historial consequence of modern science is not man's detachment from the framework of medieval thinking, but the transformation of the relation with the Being and beings which now takes the form of the certainty of representation.If during the Greek times, Being was the one that would open, and within this opening the encounter with man also occurred, in the modern age, beings exist only as man's subjective perception, which "[...] means to bring what is present at hand before oneself as something standing over against, to relate it to oneself, to the one representing it, and to force it back into this relationship to oneself as the normative realm" (HEIDEGGER, 1977a, p. 131).Modern science is built up on standing-against (Vergegenständlichung) being, that is on the separation between subject and object.A consequence to such delimitation is that the subject by representing the object with a view to its immediate availability objectivises the entire beings, including itself.
The last stage of the forgetfulness of the Being is represented by modern technology, whose essence does not consist of producing (ποιησις) as with the Greeks, but in setting-upon (stellen) nature with a view to its immediate challenging forth (Herausfordern).
The revealing that rules throughout modern technology has the character of a setting-upon, in the sense of a challenging forth.That challenging happens in that the energy concealed in and what is distributed is switched about ever anew.Unlocking, transforming, storing, distributing, and switching about are ways of revealing.( HEIDEGGER, 1977b, p. 16).
Taking out from unconcealment, as "standing-reserve," is specific to technology.This means that Being does no longer reach presencing, not even as an object, but as something that is to be subjected to command.The character of standing-against of being has as a consequence the approach of being as standing-reserve, which should subject to command (bestellen).The subject-tocommand character that dominates the essence of modern technology and is referred to as Ge-stell (Enframing) is a result of understanding the reality as representation and of transforming nature by calculation, as setting-before (Vor-stellen).This means that modern technology is based on modern science, which contains this desire to deliver the Being with a view to subjecting it to command.However, technology represents no only the fulfilment of modern science, but the desire to subject the real as well, such desire is manifest in the entire metaphysics as it is the most radical way of the forgetfulness of the Being.
To Blumenberg, modern science is the result of freeing theoretical curiosity from the artificial constraints occurred throughout time for nature inquiry.Even if the theoretical attitude can be considered a constant in history, dating back to the beginnings of the Greek philosophy, Blumenberg refuses to turn it into the "destiny" of the western history.It is not the relation to the hidden principle of the Being what will clear the emergence of modern science, but understanding the historical circumstances, which determined the emergence of a self-conscious curiosity.In this case, understanding modern science is no longer carried out starting from the fact that it challenges forth nature to deliver itself in an inauthentic manner, but from how curiosity, as a "base instinct," pursuing the inessential and superficial matters, transformed under the constraint of the theories that had condemned that drive, in a reflexive attitude oriented to knowing the things and phenomena in this world.
In Antiquity, when man's inquiry prevailed all nature inquiry, theory was still seen as a way to reach happiness in life.However, this thing changed in the medieval era, when alongside the desire to secure happiness in the afterlife, the interest in salvation of the soul became more important than the one in nature inquiry.Rehabilitating the drive for nature inquiry was only possible when the possibility of the knowledge of the world we live in was asserted.In this case, it is no longer about asserting naïve curiosity, which characterises any human being, but it is about a "self-conscious" curiosity, which involved passing beyond the apparent things in order to inquiry methodically what happens in a universe open to man's eyes.From this perspective, the modern age represents the triumph of theoretical curiosity over other attitudes or ways of achieving fulfilment in life.
Just as "purity" as a quality of the theoretical attitude could only be formulated in the circumstances of Plato's opposition to the Sophists' instrumentalization of theory, so also the "right" to an unrestricted cognitive drive contituted itself and was united with the self-consciousness of an epoch only after the Middle Ages has discriminated against such intellectual pretensions and put them in a restrictive adjunct relation to another human existential interest posited as absolute.The rehabilitation of theoretical curiosity ar the beggining of the modern age is just not the mere renaissance of a life ideal that had already been present once before and whose devaluation, through the interruption of its general acceptance, had only to be reversed.(BLUMENBERG, 1999, p. 233).
The theoretical attitude appeared in the classical Greek epoch as a way man related to reality, without its being a result of a choice, as Husserl thought, from among many ways of understanding reality.The pre-Socratic man finds himself in the position of observer of the world, meaning that the universe opens before his eyes and he is invited to enjoy its contemplation.This contemplation of celestial bodies determines emancipation of the Greek spirit from the mythological explanations and the emergence of philosophy.Nevertheless, pre-Socratic thinking also contains the germ of the limits of theory claiming that what lies beyond appearance can only understood by the initiated.Moreover, Thales's story, who fell in a well while studying celestial bodies, heralds the conflict between the preoccupation for theory and the citizens' daily obligations, which in patristic literature would take the form of preoccupation for salvation instead of worrying about public duties.
In this context, Socrates sets the priority of inquiring man versus inquiring nature, which would distract the attention from the more important issue of man's integration in cosmos.If Socrates sets forth that philosophy should focus on the study of logic and ethics, Plato makes a step forward, by formulating the anamnesis theory, renouncing the difference between what should concern man and what distracts him from knowledge.Knowledge means discovering the truths existing in man, which secures his integration in the cosmic order.Aristotle refuses to associate the drive to know with selfknowing or with the moral action, considering it a natural drive coming from the perceptual access to the natural world, and not being related to any vital need of the human being.In this case, we are talk about knowing for one's own sake, which has no other goal than one's self-awareness, in compliance with man's natural instinct of recognising and understanding the truth.
In the Hellenistic period, curiosity was understood as a superfluous preoccupation, which extends man's cognitive capacities beyond what is possible to be known by man and useful to man.To the Stoics, curiosity was a disposition in agreement with our nature, which we cannot be ignored, but which also offers the drive to inquiry those vague and obscure objects.This idea appears in Seneca as well, who considers that owing to curiosity man wants to know more and more things succeeding thus to know the skies, as the highest object of knowledge.However, he recommends at the same time selfrestricted curiosity, which implies the risk of transforming into intemperance pushing us toward inquiring the objects that we cannot know.To Epicurus, who represents the second Hellenistic school, the appetite for knowledge is an important source of fear and hope, which cancels the chance to obtain happiness.Therefore, the preoccupation of philosophy will not be the basis of objective knowledge, but to remove all negative influences of the drive to know, which induce man's uncertainty.Philosophy becomes thus therapy to the extent that it assumes the role of correcting man's drives, which led him to an unhappy life.This idea can be found in the third Hellenistic school as well, scepticism, to which philosophy is a technique to remove obstacles that prevent us from being happy.In other words, happiness is what remains after we succeed in removing pain, curiosity, and the cognitive drive.
Augustine's conception is decisive to how man's natural propensity to knowledge was perceived in the Middle Ages.Against Gnosticism, which regarded knowledge as a condition of salvation, Augustine considers the cognitive drive a consequence of man's condition after the fall, including thus this drive among vices.Hence, curiosity becomes a temptation of the physical world, against which we must fight with temperance (in an expression he borrowed from Cicero).Curiosity as vice is described as exercising itself by means of the sensory organs, finding satisfactions in the most trivial objects, and that it consists of the appetite for sensual experience.Curiosity is repudiated because it is not an activity that would contribute neither to man's salvation nor to knowing God, but it is directed toward the things that nature does not make accessible to man.The theological conception of the universe is subordinated to a theological conception where the interest in man's salvation through faith emerges in the foreground.
The restriction of curiosity in medieval thinking is illustrated by the status of astronomy, as a subject that focuses on nature inquiry, as a liberal art, meaning a subject that says nothing about God.This idea is defended among others by Peter Damian as well, who considers that access to the divine truth cannot come from the philosophers' wisdom reflected in the liberal arts.The desire to know conceals man's desire to be God's equal and it is an attitude that results from self-asserted reason, which is only interested in securing "[...] the metaphysical conditions of the possibility of his objects" (BLUMENBERG, 1999, p. 328).Albert the Great supports the existence of some incongruence between the subjective cognitive drive and the objective need to know, asserting that inquiry into the objects that are irrelevant to man is a wrong intention in itself.In other words, the quest for knowledge of nature is due to the limited cognitive capacities of man, which determine him to ask questions about his needs and orient his cognitive drive toward accordingly.Thomas d'Aquino considers that theoretical curiosity reaches its ultimate fulfilment in knowing God, as a form of knowledge subordinating all other sciences.Nevertheless, curiosity, as knowledge of nature, is one of the forms taken by man's renunciation, after the fall, in knowing God.This feeling of acedia (indifference, apathy) characterising the man who ceased to believe in God, materialises in the diversion from activities proper to the human being, such as cura, actio, et labor (care, action, and labour).The re-evaluation of the theoretical curiosity at the end of the Middle Ages will start from this very theory of indifference to the absolute.
The vice of disregarding the preliminary character of this life was to be replaced by the conception of man's theoretical/technical form of existence, the only one left to him.From melancholy over the unreachability of the transcedent reservations of the Diety there will emerge the determined competition of the immanent idea of science, to which the infinity of nature discloses itself as the inexhaustible field of theoretical application and raises itself to the equivalent of the transcedent infinity of the Diety Himself, which, as the idea of salvation, has become problematical.(BLUMENBERG, 1999, p. 336).
The way curiosity was reflected in the medieval times can be also analysed from the perspective of the descriptions Dante and Petrarch offered of nature.In his Inferno, Dante describes the Odysseus's journey beyond Hercules's Columns, which will transform into the modern age's symbol of heralding a new beginning, which would surpass everything that had been known before.Petrarch tells of the emotions felt after reaching the peak of Mont Vertoux.In both cases, curiosity does not take the modern objective form of science.Dante's tale is nothing but a version of the quest for salvation, which marked the entire scholasticism, where the desire for knowledge requires transcendent legitimacy to be found beyond itself.Whereas Petrarch's approach on curiosity is done from the perspective of the distinction between what is necessary and what is superfluous as he claims to be "[...] stupefied and is angry with himself for his admiration of earthly things; he rests content with what he has been and turns his inner attention to himself " (BLUMENBERG, 1999, p. 341).
The rehabilitation of curiosity could be carried out only after renouncing the interpretation of the cognitive drive as the "care" manifested for superfluous issues, which consequently entailed re-positioning in the issue of salvation and the need to know the natural world.
The self -assertive character of the theoretical attitude eradicated the immediacy of contemplation, the meaningfulness of watching the world from an attitude of respose, and required the aggressive cognitive approach that goes behind appearances and proposes and verifies at least their possible constitution.Theoretical curiosity, and the confirmations that it was to provide for itself when it eas constituted as "science", could no longer appropiately be disqualified as superfluous.The question, which had become open in every respect, what one had to expect from reality did not (for instance) repress the medieval concern for salvation; rather it took over the position of the concern for salvation as the one thing left in which man could center his interest and form which he could derive attitudes.(BLUMENBERG, 1999, p. 346).
This was due to two preconditions.The former was the importance given by the nominalistic voluntarism to predestination, to the idea that man cannot do anything with respect to his salvation.The latter is the assumption that the world is no longer directly accessible to the man's knowledge, but we need to appeal to the hypotheses and concepts of reason.In both cases, the consequences of the nominalistic idea of the existence of a hidden God were important as they determined an active behaviour of man with a view to occupy the place held by divinity.
The plans of this hidden God, William Ockham said, cannot be known by means of reason, which consequently has no longer a role in man's salvation.Reason is the starting point of some knowledge, which should operate by hypotheses and give up any ideal of adequacy to the concepts and standards used.Still, the preparation for the legitimation of knowledge of nature is not completed in the nominalistic view by the acceptance of measurement and calculation as instruments of nature inquiry.The ancient idea that God laid out nature according to measure, number, and weight is now interpreted in terms of the hidden God: "[...] according to His measure, according to magnitudes reserved to Him and related to His intellect alone" (BLUMENBERG, 1999, p. 349).The idea that the world can only be known by means of God's measures, which cannot be known by man, lay at the basis of the non-assertion of science ever since the fourteenth century.Nevertheless, the ideal of the quantitative description of objects and the development of mathematical instruments and methods to carry out such description -instruments and methods -which would represent the basis of the scientific inquiry of nature in the seventeenth century -was heralded ever since that period.
The rehabilitation of measures and calculation begins with Nicolaus Cusanus, who considers them instruments specific to human knowledge that bring to light the constant proportions of things.However, they do not grasp the essential characteristics of things, but they discover the heterogeneous feature of things they inquiry, together with the inaccuracy of the spirit using them.The quantitative approach to nature remains but a human endeavour that can never reach nature's precision, the more so that numbers and geometrical figures are not the result of the contemplation of Ideas and Perfect Shapes, as with Plato, but products of our mind.Hence, Cusanus proclaims the autonomy of the theoretical endeavour against all purposes that, in the scholastic conception, would justify it.Thus, Cusanus heralds the thematisation of the method as an immanent means of justifying the theory, offering at the same time a positive interpretation of curiosity.
Another important moment of legitimising curiosity was represented by Copernicus, who, by introducing the possibility of universal knowledge, liberated the curiosity drive from the cognitive restrictions.The world Copernicus described is a world made according to man's rational principles, which means that it can be known by means of science.Thus, theoretical knowledge has a positive role, i.e. to orient human spirit toward understanding the world.Curiosity widens thus its scope of activity from what is above us to the terrestrial world and to what is beyond the surface of the earth.This extension is illustrated by Leonardo da Vinci, considered a promoter of pure curiosity owing to his interest in inquiring the varied creations of nature and his preoccupation for the proliferation of human inventions to the detriment of "ancient" explanations and submission to God.
Legitimacy of the modern age consists rather in accepting the manifestation of curiosity as scientific research of nature than in rehabilitating curiosity.This is due to the replacement of medieval prejudice as to knowing the world with other assumptions that have increasingly legitimised the theoretical attitude.Giordano Bruno celebrates knowledge as a form of deliverance of truth from this world where it lies imprisoned.Moreover, to him knowing nature and possessing happiness become identical.To Bacon, man's orientation toward science is one of the attributes bestowed by divinity whereas the self-restriction imposed by the Middle Ages is an underestimation of man's capacities.In this context, Bacon identifies the vice of indolence to be the one that determined man to content himself with the explanations offered by the tradition authority without nature, i.e. without progressing according to his own nature.The ideal of knowledge is no longer contemplation but we now speak of knowledge as an on-going process in need of constant progress.
If Bacon does not make the step toward mathematisation of nature, with Kepler and Galileo, nature's laws can be encompassed in mathematical formulae, which are the "essence of necessity."Consequently, God's mediation between man and reality, which guarantees the certainty of man's knowledge of reality, is therefore cancelled.Thus, they go further than Descartes, securing truth not by divine guarantee, but by means of mathematics, which offers evidence and access in knowing nature's and God's laws.In this case, we speak of man's knowledge equal to God's, knowledge that no longer needs transcendent justification as it can self-justify since it is the expression of necessity to which both man and God related.
Starting from this point, Galileo will show that it is not the programme of knowledge that legitimates the cognitive drive, but "[...] the function of consciousness of what lies before it at any given time, which gives everything that has been achieved the mark of finitude and provisionality" (BLUMENBERG, 1999, p. 395).Hence, Galileo objectivises curiosity, he detaches it from man's personality, he moves it away from the motivational forces of the psyche, and transforms it into the sign of science imperfection.In contrast with Descartes, Galileo considers that truth can be found outside the methods as well by accidental discoveries or by removing prejudice, wrongly considered definitive.Another way of asserting the legitimacy of unrestricted curiosity is represented by Galileo's use of the telescope in nature inquiry.Penetrating areas hidden from man's eyes until then determined a new position of man and a new perspective on nature.Thus, the telescope becomes [...] a factor in the legitimation of theoretical curiosity precisly because, unlike any experimental intervention in the objects of nature, it could be adapted to the classical ideal of the contemplation of nature.The phenomena newly revealed by the telescope nourished and gave wings to the imagination, which sought to provide itself, by means of the "plurality" of the worlds, with continually self-surpassing limit conceptions of what was as yet undisclosed.(BLUMENBERG, 1999, p. 375).
Descartes' merit was to take the process of objectivising theoretical curiosity through, insisting on the necessity that some certain fundamentals be found to ground knowledge.He also insisted on the importance of methodology whose role is to secure all cognitive acts.Descartes dissociates curiosity from life and happiness -which later on will constitute the basis of the critique to the absolutism of the Cartesian theory -transforming it into a mere cognitive realisation that is carried out by science.
Modern science is not the result of a process unfolded throughout the entire western history.It is a result of the way curiosity was understood as a consequence of the restrictions imposed by the Middle Ages.Competing with other interests of man, curiosity could not be understood as it was in the ancient times, as a contemplation of the world, but, in order to explain what happens in nature, it had to become an objective enterprise compliant with the laws of reason.The history of these transformations shows that the dominating character of science, which Heidegger considers a consequence of the entire history of forgetfulness of Being, not represents the fulfilment of a way of relating to this world that had manifested in the previous eras.Subjecting nature is the sign of a new anthropological condition, the condition of the man freed from medieval constraints, who now can exercise his power over the real.
human Condition in the modern age
The historial consequence of modern science was the introduction, via the idea of representation, of oppositive thinking, which separates subject from the object.Unlike the medieval times, the new position of man is not determined by a preset hierarchy, but it defined by its subject condition.Thus, beginning with Descartes, we witness a transformation of the meaning of the terms subject and object against the background of the introduction of mathematics as instrument in order to understand the reign of the Being of beings.
Until Descartes every thing present-at-hand for itself was a "subject"; but now the "I" becomes the special subject, that with regard to which all the remaining things first determinate themselves such [...] The word objectum now passes through a corresponding change of mening.For up to them the word objectum denited what was thrown up opposite one's men imaginery.I imagine a golden mountain.This thus represented -an objectum in the language of the Middle Ages -is according to he usage of the language today, merely something, "subjective".(HEIDEGGER, 1967, p. 105).
In this new significance, the subject should be understood starting from the Greek term ύπο -κειμενον, which translates as "[...] that-whichlies-before, which, as ground, gathers everything onto itself " (HEIDEGGER, 1977a, p. 128).The modern subject, owing to the fact that it is given beforehand as subjacent, is the one that will be the ground of the entire beings, or, according to well-known phrase, it is the measure of all things.This does not mean it has the same privileged relationship with the Being as the Greek used to have during the Classical period.To the latter, the relationship with the Being was a direct one because it was opened to man, which means that the submission of physis to its subjectivity was not necessary.Protagoras' assertion should be interpreted in this sense of moderation in approaching the unhidden, of limitation only to knowledge of what is human.In the modern age, one cannot speak of measure as calculating thinking, which established truth as a certainty guaranteed by method, wishes to grasp the entire reality in an image and make it available to the subject.
Man understood as subject is a result of the establishment, in the modern age, of mathematics as a measurement unit of all thinking.Nevertheless, mathematical thinking is an axiomatic thinking, which imposes the existence of some privileged sentences upon which the knowledge system is built.The subject appears thus as a privileged axiom as an absolute fundament all beings are built upon.The Cartesian principle "cogito, ergo sum" expresses precisely the characteristic of primordial being of man, who can dispose of the other beings.Moreover, man is grounded on certainy, which gives this characteristic to the entire world built thereupon.Even if Heidegger agrees that this certainty is the result of the secularisation of the salvation certainty in the Middle Ages, he says that it is defining for man only in the historial sense as a determined way of understanding being.This thing is also valid for understanding man as subject, which thus turns into a moment of the destiny of Being originating in the ancient times.
The name subjectivity names the unified history of Beings, beginning with the essential character of Being as idea with competition of the modern essence of Being as the will of power.(HEIDEGGER, 2003, p. 48).
The historial modern man reveals himself as subjectivity, as the will of will, his destiny is to disappear with the domination of Technology over nature.
To Blumenberg, the interpretation given in the modern age to man's position as master of nature is not a consequence of the forgetfulness of Being, but it is the result of re-occupying a conception that could no longer be sustained.The modern age should be regarded as the second attempt to overcome of Gnosticism, after the failure of the first attempt in the Middle Ages.Gnosticism represented an answer to the problem of the existence of the evil in the world as it was inherited from the ancient times.The Gnostics replied that God's omnipotence cannot be claimed because this would mean to reach a contradiction between God's will to destroy the material world, which is evil in itself, and the divinity's preoccupation with man's salvation; hence, the postulation of two divinities, a good one and an evil one, which would explain the antagonistic dualism of forces in the world.
The official doctrine of Christianity did not agree with this idea, sustaining God's omnipotence and considering matter a result of creation, which had no co-existed with God.The answer to the problem of evil was the doctrine of free will, as Augustine formulated it, aimed at increasing man's responsibility for all the evil in the world rather than instituting man's freedom.The evil in the world is in fact a reflex of man's wickedness and does not exist in itself, independent from man.Man's wickedness comes from the original sin of which he can be pardoned by divine grace, i.e. predestination.The idea of predestination also contains the idea that man is responsible for the entire corruption in the world because, even if some may be saved, there are still many more that will be responsible for the evil.Thus, the price for removing the Gnostic dualism was God's transformation into a hidden God who is the absolute sovereign.In this world dominated by evil, which comes from man, man is discouraged in any attempt of correcting it while still being urged to focus his attention toward the salvation of his soul.This is the reason why selfassertion is not a consequence of the first attempt to overcome Gnosticism.
Considering the modern age the second overcoming of Gnosticism is based on the disappearance of the medieval order and the assertion that the evil is a result of "facticity" that can be defined in relation with everything that does not let man live.In this context, self-assertion is defined as [...] an existential program, according to which man posits his existence in historical situation and indicates to himself how he is going to deal with the reality surrounding him and what use he will make of possibilities that are open to him.In man's understanding of the world, and in the expectations, assessments, and significations that are bound up with that understanding, a fundamental change takes place, which represents not a summation of facts of experience but rather a summary of things taken for granted in advance, which in their turn determine the horizon of possible experiences and their interpretation and embody the "a priori" of the world's significance for man.(BLUMENBERG, 1999, p. 138).
The destruction of the old order is due to nominalism, which took the medieval conception of the world to the ultimate consequences.This determined the formulation of the self-assertion presuppositions by abandoning the idea of cosmos as creation meant for man and the return to the atomist theory, that hold the neutral character of the creation of the world as well as by reoccupying the divine will with matter developing according to its own pre-established laws, which determined the formulation of the mechanicist theory.
There were multiple consequences to nominalism: accepting the doctrine of creation out of nothing, the nominalists promoted the idea that God is not like a demiurge, carrying out a pre-established plan, but that His absolute will creates things from the infinite possibilities.Nevertheless, man can understand this diverse reality only by reducing it to "classificatory concepts" (BLUMENBERG, 1999, p. 153), whereby he imposes upon it his own order.Self-assertion originates in this "economy" of concepts man uses to understand the world (and not God, who can only be understood through faith).Meanwhile, the consequences of creation out of nothing were that man could no longer find salvation either in escaping from the transcendent (as the Gnostics) or in indifference (as the Epicureans) or in the simple inquiry of nature, which now seems infinite.Science and philosophy have contributed to offer the means to remove man's uncertainty from the world: science, on the one hand, by presenting itself as a means to exercise man's power over nature, and philosophy, on the other hand, by providing a "[...] method of assuring the material adequacy and competence of man's possession of the world" (BLUMENBERG, 1999, p. 158).
Another consequence of Nominalism's accepting of the divine omnipotence was to claim the existence of the plurality of worlds.The possibility of God's conception of several worlds, of which but one is actually carried out, led to cancelling the conception that the universe was created for man.Removing man from the privileged position he used to hold within creation triggered the increase of awareness of man's insecurity in this world.This feeling grew due to the consequences of the assertion of God's absolute omnipotence.To fully carry out such absolute omnipotence, God cannot restrict His forces no even to benefit man.Hence, the conception of the biblical God interested in man, who sacrifices Himself to save the latter, is replaced by the image of God defined only by His omnipotence and who became man not to save the man but to prove His omnipotence.
After asserting the teleological absolutism and the cancellation of the anthropocentric character of the universe, man's only solution was to find an immanent ground for reality to rely upon.The Cartesian cogito and the certainty Descartes looked for represent the final point of the process of securing man in the world and of searching for an alternative to salvation, which is no longer accessible to man.
The God Who had never owed man anything and still owed him nothing, the God Who in Augustine`s theodicity left to man the entire burden of the blame for what is wrong in the world and kept man`s justification concealed in the degrees of His grace, was no longer the highest and the necessary, nor even the possible point of reference of the human will.On the contrary, He left to man only the alternative of his natural and rational self-assertion, the essence of which Luther formulated as the "program" of antidivine self-deification.(BLUMENBERG, 1999, p. 178).
Hence, the Cartesian endeavour does not produce a secularisation of the certainty of salvation but it stands for a solution to overstep the bounds of this idea.
Asserting the divine omnipotence has as consequence the awakening of the interest in man's status in the world.Compared to God's greatness, the man is reduced to a few qualities, which become defining to man and assert his unique character among the other living creatures.The Cartesian cogito is the consequence of the reduction of man to his essential qualities, which also opens the ways for man to assert himself in this world.This is also proved by the importance given, within the Cartesian system, to freedom even from the certainty of knowledge.Introducing the hypothesis of the evil genius, as a radicalisation of the theological absolutism, by transforming God in the philosophical hypothesis of a spirit that can deceive us, Descartes wants to highlight that under these extreme circumstances, man has a minimum liberty, which consists of refraining from any form of theoretical expression.Man's freedom does not consist of the possibility to find grounds for any enterprise, but of giving up all grounds when he thinks they are misleading.Hence, error is an expression of man's liberty and not of God's will.
A god can prevent man from knowing a single truth, but he cannot himself bring about error, unless man for his part freely runs the risk of being deceived.So man is not free in that he has grounds for his action but rather in that he can dispense with gounds.Absolute freedom would be readiness and the ability to resign all interest in truth so as not to risk error.The structure of consciousness appears both transparent and at the disposal of its possesor, so that the dimension of prejudice can be suspended.For this approach great disappointments and corrections were in store, from historicism to psychoanalysis.(BLUMENBERG, 1999, p. 185).
Man's freedom to know means renouncing the deduction of the truths about the world from God's intentions, whose realisation can be seen all over in nature.This means "disappearance of inherent purposes," postulated by nominalism and building up knowledge on hypotheses, whereby "[...] the methodical freedom of arbitrarily chosen conditions" (Blumenberg, 1999, p. 184) is asserted.The hypothesis signifies objectivising the relationship with reality by creating the conditions for the hypothesis to be validated.Such conditions aim at searching the certainty of the conjectures we make about the world.Thus, the theory acquires autonomy, liberating itself from the status of contemplation of the world from the divine point of view, which aims at reaching happiness.
Giving up the qualitative approach of the cosmos also contributed to consolidating this process by reducing the universe merely to its material aspects, which can be explained by the mathematical instruments.Accepting the materiality of the world as well as its mathematisation are the expression of the search for some universal means in order to know the possible worlds, whereby natural phenomena and processes can be interpreted objectively by means of the same standards.
The mathematisation of science started with the abandonment of the astronomical claim to explain the causes of the celestial bodies' movement.By assuming the status of a liberal art, astronomy moved closer to mathematics creating thus the premises for physics to be approached from the geometrical and arithmetical perspective.Moreover, it created the possibility of greater freedom of expression, which would be freed from the rigorous constraints of the Aristotelian science and to offer explanations about the way the celestial mechanism works.
Nature's materiality is a response to the doctrine of creation from nothing, which renders God's goals hidden to man.Facing a world that is no longer created for him and whose metaphysical principles cannot be known, man cannot but represent nature to himself according to his reason in order to serve his own needs.Hence, a new anthropological consciousness is born whereby man is offered the possibility of exercise his demiurgic activity on the world.Whether the world is the result of the development of original matter, which can be known and manipulated to benefit man, it means that man can assert his independence from the allegedly immanent goals of nature whose author is God.However, the teleological image of the world begins to be questioned together with the assertion of the world materiality, which suggests the idea that physical processes unfold incessantly.The idea of an "unfinished" world (Kant) contributes to strengthening the idea of man's independence from any imposed goals, suggesting thus that the self-assertion task, in this world at man's disposal, is endless.
The "unfinished world" is no longer on the way, of its own accord, to ever greater perfection, with the aim of bringing forth man at its point of culmination, who as the witness of its immanent power registers its history in the result only and does not experience and push forward the process.Progress now becomes a category with a noncosmic status, a structure of human history, not of natural development.The "unfinished world" becomes the metaphor of a teleology that discovers reason as its own immanent rule that up untile then had been projected onto nature.Only the mecanism of this projection is exposed does the history of the disappearance of inherent purposes enter the phase of conscious and deliberate destruction.(BLUMENBERG, 1999, p. 214).
Hence, there is a horizon of possibilities that now opens which man is invited to explore assisted by science and technology.Modern science is a modality of man's self-assertion by asserting the possibility of foreseeing and anticipating events as well as of the ability to alter or produce such events.Modern technology is the expression of these new circumstances man finds himself under situation, of reaching a "[...] new quality of consciousness" (BLUMENBERG, 1999, p. 135), which would allow him to act on the world.Both are means of man's self-assertion in a world freed from the inevitable goals introduced by the medieval order and not the expression of western thinking which by distancing itself from the Being undertook as its goal to dominate nature.The self-asserting task becomes thus infinite and implies finding that pre-established structure of the world, which may offer safe grounds to build the world rationally with a view to transforming the reality we live in.
ConCluSion
To Heidegger, history acquires meaning only when relating to the Being and transforming its moments in landmarks of access to the Being.The quest for the destiny of the Being means approaching history as the destruction of the history of ontology (Destruktion der Geschichte der Ontologie) by means of which the way to conceal the Being along the centuries is revealed.This quest aims at announcing the moment of the rethinking the relationship with the Being by appealing to the authentic disclosure of the Being.
To Blumenberg, Heidegger's understanding of history leaves unquestioned the very principles of the development of history.Making appeal to a sense of history, which is beyond history itself, transforming all events in expressions of the forgetfulness of the Being, means to reduce history to a singular fact and to overlook the plurality of questions and answers, which gives dynamism to history.Equally, the emergence of authentic thinking of the Being, which would be the liberating consequence of some many epochs of perverting the understanding of the Being, appears to be a renunciation to history and to the quest for its legitimating questions.Heidegger's Being presents himself as the medieval hidden God, who subordinates the entire reality as a principle that eludes our knowledge.This position is characterised by Blumenberg as pseudo-theology that hides the continuation of the medieval absolutism by other means.
History does not mean the simple sequence of facts throughout time, but the questions and answers associated to such facts, which generate changes of the way to interrogation the world.Legitimating the historical epochs is done from the perspective of such questions and answers whose consequences determine the process of reoccupation.Therefore, it would be wrong to consider henceforth that changes of the perspective on the world occur by virtue of attaining a certain goal or that the process of reoccupation contains in itself the idea of an unavoidable end.The existence of a logic, which would explain all the mutations of ideas, not only cannot be sustained, but it is also dangerous in itself as it contains the hazard of a totalitarian view on history.
The epoch appears as an absolute fact -or better: as a "given"; it stands, sharply circumscribed, outside any logic, adapted to a state of error, and in spite of its immanent pathos of domination (or precisely on account of it) finally permits only the one attitude that is the sole option that the "history of Being" leaves open to man: submission.(BLUMENBERG, 1999, p. 192).
History is not a monolithic process; it is formed of discontinuities, which reflect the heterogeneous and autonomous character of the human beings participating to it.However, at the same time, historical events unfold within the same history; therefore, they cannot be interpreted as absolute beginnings without continuity with what happened before.Any idea comes to occupy a position released from other idea, either due to a contradiction reached or to a prioritised need.The game of reoccupation is what allows history to rid of its dominating character and deliver it rather as an expression of people's spontaneity and not of a hidden transcendent principle.
From this perspective on history, modernity cannot represent the moment of heralding a new epoch altogether different from what was before.Just as modernity did not represent, despites its claims, an absolute novelty, what comes after the modern age is not the beginning of a perfect future where history ended and alongside the reoccupation process as well.The ill-fated consequences of the quest for universality in the modern age have led to its dissolution and to activating the process of reoccupying those ideas, which had been looking for the subordination of reality to some unique principles, with ideas open to the understanding of the plurality and fragmentation of the world.
From this perspective, Heidegger's idea that history is a process oriented to its own fulfilment, transforms modernity into a moment that needs to be overcome in order to achieve a bright future, whose promise is immanent to history.To Blumenberg, who denounces the Heideggerian approach of history as absolutist, history is formed according to the ever-changing ideas that correspond to its protagonists' needs.Therefore, the legitimacy of the epochs is achieved by thinking of the discontinuity of ideas and of the process of reoccupation underlying the replacement of ideas.Modernity in this case is no longer a moment in fulfilling history, but it should be regarded as the starting point in thinking the discontinuities that legitimate the contemporary era.Marília, v. 35, n. 2, p. 93-120, Maio/Ago., 2012.RESUMO.O debate em torno da maneira como Heidegger e Blumenberg entendem a idade moderna é uma oportunidade para discutir duas abordagens diferentes sobre a história.Por um lado, do ponto de vista de Heidegger, a história deve ser entendida partindo do modo como o pensamento ocidental se relacionava com o Ser, o que, no pensamento metafísico, tomou a forma do esquecimento do Ser.Assim, a idade moderna representa a última etapa no processo do esquecimento do Ser, que anuncia o momento de repensar a relação com o Ser apelando para a autêntica divulgação do Ser.Por outro lado, Blumenberg entende a história como o resultado do processo de reocupação, o que significa substituir antigas teorias com teorias novas.Logo, para a abordagem histórica não é importante identificar épocas como períodos de tempo entre dois eventos, mas de pensar sobre as descontinuidades que ocorrem ao longo da história.A partir dai, a idade moderna é pensada não como uma expressão da radicalização do esquecimento do Ser, mas como uma resposta as crises de concepções medievais.Pela mesma razão, a interpretação da história como a história do esquecimento do Ser é considerada por Blumenberg para subordinar a história a um princípio absoluto, sem levar em conta as necessidades de seus protagonistas. | 14,432.6 | 2012-08-01T00:00:00.000 | [
"Philosophy"
] |
Peeking into the future: Transdermal patches for the delivery of micronutrient supplements
Adhesive transdermal delivery devices (patches) are the latest advancement in the delivery of micronutrients. A common challenge in this mode of delivery includes surpassing the physical barrier of the skin, while the use of microneedle (MN) arrays, or pretreatment of the skin with MNs can be used for a more successful outcome. Limited evidence from human non-randomized trials point to a sub-optimal delivery of iron through skin patches, although no MNs were used in those trials. Moreover, the use of patches proved inefficient in reducing the prevalence of micronutrient deficiencies in post-bariatric surgery patients. The delivery of minerals was tested in animals using reservoir-type patches, gel/foam patches, MNs and iontophoresis. Results from these studies indicate a possible interplay between the dietary manipulation of mineral intake and the trandermal delivery through patches, as reduced, or regular dietary intake seems to increase absorption of the delivered mineral. Moreover, intervention duration could be an additional factor affecting absorption. Possible adverse events from animal studies include redness or decolorization of skin. In vitro and ex vivo studies revealed an increase in vitamin K, vitamin D and iron delivery, however a variety of methodological discrepancies are apparent in these studies, including the models used, the length of the MNs, the duration of application, temperature control and total micronutrient load in the patches. Data indicate that pre-treating the skin with MNs might enhance delivery; however, a source of variability in the observed effectiveness might include the different molecular weights of the nutrients used, skin factors, the ideal tip radius and MN wall thickness. Non-human studies indicate a potential benefit in combining MN with iontophoresis. Presently, the transdermal delivery seems promising with regard to nutritional supplementation, however limited evidence exists for its efficacy in humans. Future research should aim to control for both intervention duration, possible deficiency status and for the dietary intake of participants.
Introduction
The evolution of the science of nutrition in parallel to the pharmaceutical industry has led to the development of novel methods for micronutrient delivery. Micronutrient deficiencies currently affect 2 billion of the total world population, and for this, this universal problem is named "hidden hunger" by the World Health Organization [1]. However, although oral nutrient supplements (ONS) might be required, individual characteristics, age and health status particularities, often demand an alternative mode for the delivery of micronutrients. Buccal sprays [2], gums [3], sublingual tabs [4], oral drops, even creams and ointments [5] are often recruited for the delivery of micronutrients, all aiming in enhancing absorption and improving utilization. More recently, the use of adhesive transdermal delivery devices (patches) was suggested for optimal delivery, making use of the body's largest organ, the skin [6].
Within patches, the compounds are stored in a reservoir which is adhesive to the skin on one side, and enclosed with an impermeable backing on the other side [7,8]. The compound is either dissolved in a gel or liquid-based reservoir (allowing for the use of enhancers), or into a solid polymer matrix [7]. The second generation of delivery systems focused on skin permeability enhancement through the use of chemical enhancers (prodrugs, liposomes, microemulsions, etc.) [7,9,10], ultrasound, or iontophoresis. In the latter, charged compounds of small molecules are directed into the stratum corneum via electrophoresis, whereas weakly charged and uncharged compounds are moved by electroosmotic water flow [11]. The third generation of delivery systems includes hypodermic microneedles (MNs) for the enhanced delivery of macromolecules [7].
As with drugs, only a handful of micronutrients are currently delivered via patches [7]. In comparison to the topically applied products, transdermal patches target the systemic circulation of an individual, whereas topically applied compounds target different skin layers, the skin appendages and underlying tissues [12].
Surpassing the skin barrier
Only drugs with a suitable lipophilicity and a molecular weight <500 Da can be delivered passively through the skin [13]. Moreover, according to some researchers [14,15], to avoid clearance of the particles by macrophages, a size smaller than 500 nm should be sought, with particles smaller than 100 nm tending to move along the edge of the blood stream. Successful compounds delivered via transdermal patches have small molecular masses (some hundred Da), fewer hydrogen bonding sites, a low melting point, require small daily doses, demonstrate moderate lipophilicity, or exhibit octanol-water partition coefficients favoring lipid compounds [7,12,[16][17][18]. With diffusivity being inversely related to the molecular size of the examined compound, the use of large compounds through micron-scale disruptions is most likely to be unsuccessful [19]. Therefore, many compounds do not possess the required physico-chemical characteristics to permeate the skin in adequate quantities, narrowing down the transdermal market [12].
To overcome the skin barrier and reach the intact dermis, alternative pathways mainly for hydrophilic compounds, include blood and lymph vessels, nerve endings, hair follicles and sweat glands [20,21]. Moreover, technologies promoting passive permeation utilize penetration enhancers and as a result, a variety micro and nano-systems have been developed [12,22,23]. On the other hand, active permeation technologies for macronutrient delivery make use of external drivers including electrical (iontophoresis and sonophoresis) [24], and mechanical approaches, with a focus on MN arrays [12,25]. Other existing active delivery technologies like the use of thermal ablation or ultrasound have not yet been examined with regard to micronutrient supplementation [25].
Nevertheless, the topical application of MNs prior to the adhesion of patches consists of a common technique in transdermal patch research, especially in patches lacking MNs themselves. The application of MNs increases the potential of drug delivery through the skin by disrupting the skin layer, creating micro-pathways and leading the compound to the epidermis, thus entering the systemic circulation by surpassing the upper skin layers [26].
Studies performed on humans
Only two studies to date have tested the micronutrient delivery via transdermal patches on human participants [27,28] (Table 1). However, none of the studies applied a randomized controlled trial (RCT) design. The Saurabh et al. study [21] was retrospective and the McCormick [28] one, a non-randomized clinical study. Saurabh and associates [21] examined the efficacy of a transdermal multivitamin (MV) patch against ONS in gastric bypass patients, post-operatively. Their results revealed that participants in the patch group were more likely to demonstrate at least one micronutrient deficiency at 12 months post-operatively, as compared to those receiving ONS in a pill form. In parallel, using the patch for a year was associated with lower serum concentrations of vitamins D, B 1 , and B 12 [21]. McCormick et al. [28] tested the efficacy of iron patches compared to oral iron administration among endurance-trained runners with suboptimal iron stores. In parallel, they [28] were the only ones to record the diet of participants through 4-day food diaries. In the trial, the patch group failed to demonstrate differences in hemoglobin levels post-intervention, whereas at week 6, the per os supplementation arm exhibited greater ferritin levels compared to the patch arm participants. In neither of the human studies [27,28] did the patches have MNs, nor was any information on the use of penetration enhancers included in the publications. Moreover, both studies [27,28] used commercially available products from the same company (Patch MD, USA) and this is troubling in extrapolating valid conclusions.
Animals were either fed a regular standard diet [31], a low iron diet [33], a controlled low iron diet otherwise based on the American Institute of Nutrition guidelines [30], or, the dietary intake of the animals was not accounted for [27,29,32,34].
In one prospective non-RCT, healthy, non-pregnant Jersey heifers were used as a population [34] and calcium was the administered nutrient, in the form of calcitriol or calcitriol with concomitant dodecylamine delivery, using fabricated transdermal reservoir-type patches and without controlling for the cattle's diet.
With regard to the delivery of iron, Modepalli and associates [29] failed to record changes in the hematological parameters of the sample, as their study was mostly a feasibility one. In studies where the applied patches did not incorporate MNs, the results did not appear to different significantly from the baseline [31][32][33]. When patches with dissolving MNs were employed [30], improvements were noted in the Hb, RBC, Ht and serum Fe levels [30] of the participating rats.
On the other hand, when rat skin was pretreated with MNs [31,33], ambiguous results were noted. Modepalli and associates [31] failed to induce a significant improvement in either the hematological parameters or the morphology of RBC of the participating rats, suggesting that possibly, the amount of administered FPP was suboptimal. On the other hand, Juluri [33] reported improved hematological parameters 2-3 weeks post-trial initiation. However, in the first [31], rats were fed a regular diet, whereas in the second [33], a low-iron diet. Thus, it is possible that in the Modepalli trial [31], dietary iron intake might have compromised the induced efficacy of the intervention, whereas on the other hand, in the Juluri intervention [33], the constant low dietary iron intake might have allowed for a greater hematological improvement during the study.
When IN was applied [31,32] in the interventions, an acute increase in serum Fe and % transferrin saturation (TS) levels was noted when FPP was delivered via skin surface chambers, with IN at a current of 0.5 mA/cm 2 [32]. When the duration of FPP patches application lasted for 4 weeks in total with concurrent IN at a constant current strength of 0.15 mA/cm 2 and the skin was pretreated with MNs [31], no significant improvements were recorded in the hematologic parameters of participating rats. However, in the latter study [31], the intervention was long-term, the current strength was much lower as compared to the first study, and rats were fed a regular, uncontrolled diet.
In the calcium-intervention trial, Yamagishi and associates [34] demonstrated increased serum calcium levels in both groups receiving either calcitriol, or calcitriol with concomitant dodecylamine intake, through fabricated reservoir-type patches, as compared to control vehicles. The rise was noted on the 3rd day of delivery initiation and remained similar throughout the 3-week trial.
Adverse events of in vivo studies
Among the studies using human populations, McCormick [28] reported few gastro-intestinal adverse events associated with the pill administration and none in the patch arm. By design, physiological adverse events could not be reported in animal studies, however, a mild redness of the skin was observed in rats receiving FPP through patches [32], as well as a skin decolorization among rats being pre-treated with MNs before an ID patch was applied transdermally [33].
Ex vivo and in vitro studies
In vitro [29,33,35] and ex vivo [14,30,31] studies examining the transdermal delivery of micronutrients through patches are described in Table 2. All studies utilized porcine [14,35], or rat [30,31,33] skin models, with the exception of Modepalli [29], who used human models. Although Park and associates [24] also tested the transdermal delivery of micronutrients (retinol, niacin, and glutamic acid) using the dorsal skin of mini-pigs in vitro, the aim of their study was not dietary supplementation, but cosmetic. Subsequently, that study was not considered as relevant to the present review.
According to Juluri and associates [33], MN pre-treatment leads to the delivery of a substantial amount of ID across the skin and the colloidal ID does not appear to penetrate or permeate across the intact skin in detectable amounts.
A variety of methodological differences are apparent in the published research using animal models, including the washing of skin with water prior to the treatment, as well as the methods used to assess the dissolution of MNs. Moreover, length (height) of MNs varied greatly, ranging from 467.59 ± 15.23 mm [30] to 700 μm [14]. Patch-application duration was also different in most research, with Hutton and associates [33] reporting 24 h (30 s of constant finger pressure and a 5.0 g circular stainless steel weight then placed on top), Modepalli [29] and Juluri [33] applying the patch for 2 min in total, Maurya [30] and Kim [14] reporting a 5 min application, and Modepalli [29] applying the patch on the model for 1 h. Thus, depending on the model used, the duration and mode of the application and the height of the MNs, differences were also observed in the reported micronutrient load in the skin post-application, ranging from 35% of the total MN initial load [35] to 81.08% [14].
To microneedle or not?
In most in vivo studies with the exception of those performed on humans and cattle [27,28,34], either the patches applied contained MNs, or the skin of the subjects was pre-treated with MNs. In the human studies [27,28], iron was the delivered micronutrient of interest with both studies revealing a greater improvement in the ONS arm as compared to the patch-receiving participants. Although none reported the form of iron used in the patches (both used a commercially available product), in the case of FPP which is the most common form of iron used, the molecular weight reached 745 Da, which might in part explain the poor permeation of Iron [31]. Мoreover, as already explained, none of the human studies used MN technology, which might have increased micronutrient delivery.
When FPP patches with MNs were applied in rats without a comparator arm [29,30], improvements were noted in hematological (Hb, RBC, Ht and Fe levels) parameters [30] and the concentration of free-FPP in the dermal interstitial fluid [29] as compared to the baseline. When patches with MNs or skin pre-treated with MNs were compared against patches without MN [31,33], penetration and delivery of FPP/ID was enhanced in the first groups, as compared to the latter. Similar findings were also reported in ex vivo experiments [31], with MN pretreated skin inducing a greatest enhancement in FPP uptake by the skin as compared to the application of passive patches alone. Researchers [30,31] also noted that poor delivery might also be the result of MNs penetrating only the upper layers of the skin, given that the length of MNs is short enough to avoid possible stimulation of pain receptors [36].
In the case of calcitriol delivery via patches without MNs [34], an improvement was noted in the cattle with regard to plasma calcitriol and Ca concentrations compared to the controls (no calcitriol delivery), however, the molecular weight of calcitriol is 416.64 Da, thus, a greater passive permeation is expected as compared to the FPP.
With regard to the delivery of vitamin D 3 , coated MN patches induced improved delivery performance (5-fold) ex vivo, as compared to ointment with a similar vitamin D 3 content [14]. According to an in vitro study of vitamin K delivery without a comparator arm, delivery of vitamin K with MN patches was optimal, reaching 35% of the administered dose [35]. Nevertheless, all in vitro and ex vivo studies lack the assessment of hematological parameters which would either prove, or refute the clinical efficacy of the intervention.
MNs have been suggested to deliver a variety of compounds in a less invasive and painless manner as compared to the hypodermic needles [37], while their composition may vary greatly. Moreover, differences also arise depending on the use of patches with MNs, or pretreating the skin with MNs prior to the application of patches. Although this issue was not addressed in any of the studies reviewed herein, we are unsure of which method is more efficient in drug delivery, while carrying the fewer adverse events. Often, when polymers are used to create MNs, possible discharge into the skin is another issue of concern, which can be surpassed with the use of biodegradable polymeric MNs [37]. Other issues regarding the use of MNs include the optimal ratio of MN fracture force/skin insertion force, the ideal tip radius and MN wall thickness required to induce an improved delivery [38]. In this manner, great variability is observed in all studies reported herein, with many researchers failing to report relevant and immediately comparable data.
Effects of iontophoresis
To further enhance the transdermal delivery of FPP, iontophoresis has been suggested as a complementary practice, provided that the stratus corneum is compromised [31]. Nanoparticles with a negative charge have been considered as more efficientin entering the blood circulation and avoiding the phagocytic procedures [14,38].
In vitro experiments, indicated that cathodal iontophoresis in MNpre-treated skin enhanced the delivery of FPP considerably, as compared to MNs alone, or passive transdermal delivery [31]. In vivo animal experiments comparing iontophoresis to MNs, reported a lack of significant improvement regarding the hematologic parameters and morphology of RBC, indicating that possibly the amount of FPP delivered was suboptimal [31]. As compared to passive transdermal delivery [32], iontophoresis induced acute improvements in the total serum Fe and TS, within hours of the patch application. Moreover, a combination of MN pretreatment with iontophoresis resulted in significant improvements concerning the hematologic and biochemical parameters of rats (RBC, MCV, MCH and MCHC), within four weeks of intervention in anemic rats [31]. Similar observations were also reported in ex vivo experiments [31], with a combination of MNs application and iontophoresis producing the greatest enhancement (44-fold) in the FPP skin uptake. b Given the small number of participants and the plethora of interventions and comparators, a cross-over design is more likely to occur.
Research in the pipeline
At the moment, three studies testing the delivery of micronutrients via transdermal patches on humans are in the pipeline ( Table 3). Two of these are being conducted in the USA using patch MD products and one is being conducted in Ireland. Of these, one is aiming in reducing deficiencies in bariatric surgery patients and the rest are using general population samples, testing the efficacy of transdermal patches compared to patches of smaller surface, or other modes of micronutrient delivery (chewable, quick dissolve ONS, etc.) in comparative effectiveness designs.
Unfortunately, the D3forME trial (NCT02174718), was discontinued due to manufacturing issues regarding the patch used.
Overview of the advantages of using the transdermal patches technology
According to Isaac and Holvey [39], the delivery of drugs via transdermal patches entails a variety advantages for the patient (Fig. 1), encouraging patient compliance [40]. First, the use of patches is associated with increased patient and carer satisfaction, due to the reduced frequency of doses, the ease of use and tolerability (depending on the adverse events) [21,39,41]. When children are the patients using the patches, the transdermal technology offers the opportunity to surpass the often unpleasant and inconvenient parental administration associated with ONS [39]. In parallel, the delivery of drugs/compounds through patches is circumventing the hepatic first metabolism, which might lead to a reduced compound dose as compared to a per os administration [39,42]. In the traditional ONS administration, an increased rate of gastro-intestinal-related adverse events is noted and often, poor stability of the compounds inside the gastrointestinal tract is also apparent [43]. On the other hand, the use of transdermal delivery technology is associated with reduced systemic side effects [44]. In terms of maintaining a constant balanced delivery, patches appear superior compared to the per os administration, in maintaining constant blood levels instead of episodic peaks [39]. As compared to the IV delivery, patches can be administered and used outside the hospital setting, by patients themselves, or by their carers. Patches are ideal for the delivery of micronutrients in patients with swallowing difficulties, or gastro-intestinal issues, as well as for those with cognitive impairment, likely to miss doses of traditional ONS. Moreover, they also form a good solution for children, travel and vocation, which typically requires everyday parental administration in ONS form.
Finally, patches can potentially reduce the risk of overdose and overvitaminosis, as the removal of the patch immediately discontinues compound delivery, avoiding any intake exceeding the upper levels of specific nutrients [39].
Limitations of the transdermal delivery of micronutrients and the related-research
Limitations of the transdermal delivery (Fig. 1) include the difficulty to surpass the skin barriers, especially when lipophilic compounds are concerned, like vitamin D [45]. On the other hand, as compared to topical solutions or passive delivery, patches using MN appear to deliver a greater amount of micronutrient to the epidermis or upper dermis region, and from there into the circulation [26]. As a result, the transdermal patches technology is associated with slower time towards peak blood concentrations, thus, this model is not suited for emergency treatments requiring the rapid release of nutrients and a fast peak in blood concentrations [39].
The prerequisites for the improved bioavailability of nutrient have already been discussed (molecular weight, few hydrogen bonding sites, low melting point, moderate lipophilicity, etc.) [7,12,[16][17][18]. These cumulatively limit the choices of nutrients that can be delivered using the transdermal format [39].
Moreover, according to Isaac and Halvey [39], good patch adherence to the skin is required for the increased effectiveness of patches. The presence of sweat, scars, hair or oil, on the application site might reduce adherence and limit absorption [39]. Thus, specific guidance must be provided in the commercial patches packages in order to guide proper use and increase effectiveness. Moreover, research on the transdermal delivery of micronutrients has not yet assessed variations in the delivery efficacy as a result of inadequate patch adherence, nor has a specific application site been identified as more effective.
Gender differences exist in the human skin, including the keratinocyte size, with male skin samples tending to be larger than those from obtained from female donors [46]. In parallel, men have larger skin pores sizes, more active sebaceous glands and a lower skin pH compared with the women [46][47][48]. Moreover, ethnicity-and age-based differences are also apparent, with Afro-Caribbean skin demonstrating a reeduced permeation compared to the Caucasian [49,50], and younger skin exhibiting increased permeability in contrast to the older one, possibly increasing the efficacy of transdermal therapy [46]. Thus, it appears that one size does not fit all and it is possible that the application of the same patch might induce different efficacy on different subjects.
An additional limitation is the nature of animal studies as the majority of in vitro studies employ rat models, given that the use of human skin is costly and raises a variety of ethical concerns [51]. According to van Ravenzwaay and Leibold [52], rat skin dermal penetration in vitro is higher than in vivo. In parallel, rat skin is more permeable to all substances as compared to the human skin [52]. On the other hand, based on the literature [53], mean thickness of rat skin is much lower to that of humans and great inter-individual variation has also been reported in human skin samples, depending on the age, body site, and skin type, pigmentation, gender, blood content, and lifestyle [54]. Moreover, the metabolic, surveillance, and transport processes taking place in the deep skin layers can also alter permeability and efficacy of transdermal products [55]. Therefore, the efficacy of MNs depends greatly on the diverse mechanical properties of the skin between the species [53]. Given that quite often researchers noted differences in the permeability of animal skin, translating the possible efficacy of animal studies for human use consists of findings extrapolation [53].
Transdermal delivery via ointments and creams also carries a variety of bottlenecks (depending on the nutrients used), and has been criticized [56]. In parallel, it is challenging as each nutrient has a different molecular weight, and side effects, when used topically.
Moreover, it has been argued that the use of transdermal delivery devices for micronutrients might be costlier compared to the traditional ONS [39], although no research has evaluated this yet. Questions have also been raised with regard to the cumulative effects of long-term use and the possible risks associated with the use of multiple patches simultaneously [39]. As seen in animal studies, the use of transdermal devices often triggers skin allergic reactions, cutaneous irritation, erythema or swelling [44], although this has not been verified on humans.
Finally, ethical and legal dilemmas are apparent in cases when consent to treatment cannot be provided by the patient [39], or when the patient refuses to consume traditional ONS.
Conclusions
The transdermal delivery of micronutrients is an ambitious domain in clinical research with important ramifications for public health. Postulated advantages of delivering micronutrients transdermally include avoiding the first-pass effect of the liver, reducing gastrointestinal related side-effects and providing a stable release rate for a longer time [7]. In parallel, transdermal delivery provides a highly convenient and pain-free administration platform for patients [44], limiting non-compliance associated with pain, swallowing, age or other individual particularities. Subsequently, patient acceptability of all transdermal products appears high [44].
Apart from enhanced skin penetration, continued evolution of the drug industry for topical and transdermal delivery focuses on novel technologies controlling doses, site-targeted delivery, multiplying the range of compounds that can be delivered via skin patches [12]. On the other hand, novel systems including pharmaceutical jewelry [57] have been incorporated in the transdermal delivery science and are expected to expand their application in the micronutrient market as well.
The present review indicates the limited number of studies conducted on humans and the variability in the design and methodology observed in animal research. Thus, it appears that research is still in premature stages and although promising and important, we cannot yet conclude on the efficacy of the transdermal micronutrient delivery on humans.
Consent to publish
Not applicable.
Availability of data and materials
Not applicable as this is a review study.
Funding
No funding was obtained for the present study. | 5,877.2 | 2021-07-13T00:00:00.000 | [
"Biology",
"Medicine"
] |
Morphology, Mechanical Properties and Shape Memory Effects of Polyamide12/Polyolefin Elastomer Blends Compatibilized by Glycidylisobutyl POSS
Small amounts of glycidylisobutyl polyhedral oligomericsilsesquioxane (G-POSS) (up to 10 phr) were added into a immiscible polyamide12 (PA12)/polyolefin elastomer (POE) blend (70 wt%/30 wt%) by simple melt mixing. The effects of the G-POSS on phase morphology and mechanical properties were investigated by FE-SEM, tensile testing, Izod impact test and dynamic mechanical analysis. FE-SEM analysis revealed that domain size of the dispersed POE phase in matrix PA12 is decreased significantly by adding the G-POSS, indicating a compatibilization effect of the G-POSS for the immiscible PA12/POE blend. The PA12/POE blend compatibilized with POSS showed simultaneous enhancement in mechanical properties including tensile modulus, strength and toughness. Further, thermally triggered shape memory effect was observed in this compatibilized blend.
Introduction
Polymer blending is an efficient and simple way to develop new polymeric materials possessing desirable physical properties and specific functionalities for various end-use applications. Most of the polymer blends are immiscible and need proper compatibilization in order to obtain proper phase separated morphologies which determine the performance properties. The compatibilization induces finer phase morphology by improving interfacial adhesion between the component polymers in the blend, which promotes their synergistic combination [1,2].
Blends of polyamides (PA) with polyolefins (PO) are important class of polymer blends because of their balanced properties of strength, toughness and moisture resistance. For the compatibizations of the immiscible PA/PO blends, functionalized polyolefins, such as maleic anhydride (MA)-grafted polyolefins, have been investigated [3][4][5][6][7][8]. Recently, it has been reported that phase morphology and properties of the immiscible PA/PO blends can be influenced by the addition of inorganic nanoparticles such as clay [9], silica [10], carbon nanotube [11], alumina [12] and POSS [13]. The nanoparticles are located at the interface between the component polymers or in one component selectively, which affect the phase morphology of the blends.
Among various inorganic nanofillers, polyhedral oligomeric silsesquioxane (POSS) is unique which features a well-defined nanosized Si-O cage structure (Si 8 O 12 ) with additional organic functional groups covalently bonded to each vertex Si on the cage. Due to the organic functional groups, the POSS cage is naturally compatible with polymers, and can be chemically bonded to polymer chain [14][15][16]. The POSS can be added into various thermoplastics such as polyolefins [17][18][19], polyamides [20][21][22] and others [23][24][25] blending using conventional high-shear mixer and induced improved thermal stability and mechanical properties of the matrix polymer. As compared to the POSS nanocomposites with a single polymer matrix, relatively less studies on those with immiscible polymer blends have been reported [13,26,27].
by melt
Polyamide12 (PA12) is one of the important engineering plastics with a broad range of applications including oil resistant tubes for automotive, cables, food packaging films and powder coatings for metals. For the improvement in toughness of the PA12, blending it with various rubbers such as styrene/ethylene-butylene/styrene block (SEBS) rubber [28], epoxidized natural rubber (ENR) [29] and natural rubber [30] has been explored However, this decreases the tensile modulus and strength. Polyolefin elastomer (POE), a commercially important rubber developed by Dow Elastomers under the brand name Engage ® , has good processibility and thermo-oxidative resistance. To the best of our knowledge, studies on PA12/POE blend with simultaneously improved toughness and strength, which endow the material to have advantages in practical applications, have not been reported yet.
Hence, this work employed glycidylisobutyl POSS (G-POSS) having a cage structure with one glycidyl and seven isobutyl groups at each corner of the cage ( Figure 1) as a compatibilizer and reinforcing nanofiller for immiscible PA12/POE blend. The G-POSS was expected to act as a compatibilizer for the blend due to its affinity with both component polymers via reaction of its glycidyl group with end reactive groups (-NH 2 or -COOH) of PA12 during melt mixing and van der Waals interaction of isobutyl groups with POE, respectively. The compatibilization and reinforcing effects of the G-POSS for the PA12/POE (70 wt%/30 wt%) blend were investigated by observation of phase morphology and mechanical properties. A thermally-triggered shape memory effect of this blend is also reported.
Materials 2021, 14, x FOR PEER REVIEW 2 of 10 thermoplastics such as polyolefins [17][18][19], polyamides [20][21][22] and others [23][24][25] by melt blending using conventional high-shear mixer and induced improved thermal stability and mechanical properties of the matrix polymer. As compared to the POSS nanocomposites with a single polymer matrix, relatively less studies on those with immiscible polymer blends have been reported [13,26,27]. Polyamide12 (PA12) is one of the important engineering plastics with a broad range of applications including oil resistant tubes for automotive, cables, food packaging films and powder coatings for metals. For the improvement in toughness of the PA12, blending it with various rubbers such as styrene/ethylene-butylene/styrene block (SEBS) rubber [28], epoxidized natural rubber (ENR) [29] and natural rubber [30] has been explored However, this decreases the tensile modulus and strength. Polyolefin elastomer (POE), a commercially important rubber developed by Dow Elastomers under the brand name Engage ® , has good processibility and thermo-oxidative resistance. To the best of our knowledge, studies on PA12/POE blend with simultaneously improved toughness and strength, which endow the material to have advantages in practical applications, have not been reported yet.
Hence, this work employed glycidylisobutyl POSS (G-POSS) having a cage structure with one glycidyl and seven isobutyl groups at each corner of the cage ( Figure 1) as a compatibilizer and reinforcing nanofiller for immiscible PA12/POE blend. The G-POSS was expected to act as a compatibilizer for the blend due to its affinity with both component polymers via reaction of its glycidyl group with end reactive groups (-NH2 or -COOH) of PA12 during melt mixing and van der Waals interaction of isobutyl groups with POE, respectively. The compatibilization and reinforcing effects of the G-POSS for the PA12/POE (70 wt%/30 wt%) blend were investigated by observation of phase morphology and mechanical properties. A thermally-triggered shape memory effect of this blend is also reported.
PA12/POE (70 wt%/30 wt%) blends with G-POSS content of up to 10 part per hundred (phr) of the polymers were prepared by melt mixing at 200 °C in a Haake internal mixer equipped with a cam rotor (Haake Polylab Rheomix 600, Karlsruhe, Germany) at a rotor speed of 60 rpm. PA12 was first melted for 1 min followed by addition of POE and mixed for another 1 min, then desired amount of G-POSS was loaded and mixed into the mixture and mixing continued for 10 min till the mixing torque was stabilized. The obtained PA12/POE/G-POSS mixture was formed into a sheet by compression molding at
PA12/POE (70 wt%/30 wt%) blends with G-POSS content of up to 10 part per hundred (phr) of the polymers were prepared by melt mixing at 200 • C in a Haake internal mixer equipped with a cam rotor (Haake Polylab Rheomix 600, Karlsruhe, Germany) at a rotor speed of 60 rpm. PA12 was first melted for 1 min followed by addition of POE and mixed for another 1 min, then desired amount of G-POSS was loaded and mixed into the mixture and mixing continued for 10 min till the mixing torque was stabilized. The obtained PA12/POE/G-POSS mixture was formed into a sheet by compression molding at 200 • C in an electrically heated press (Carver 2518, Wabash, IN, USA) for the property measurements.
FE-SEM Analysis
Phase morphologies of the blend were investigated by a field emission-scanning electron microscope (FE-SEM, S-900, Hitachi Co., Tokyo, Japan) at an accelerating voltage of 15 kV. To avoid ductile deformation during fracture, the samples were chilled in liquid nitrogen before breaking to initiate a brittle fracture. The cryogenically fractured surface was sputter-coated with platinum prior to SEM observation.
Tensile Test
Tensile properties were measured at 25 • C using a universal testing machine (STM-10E, United Co., Fullerton, CA, USA) at a crosshead speed of 50 mm/min with an initial gauge length of 20 mm. At least five specimens were used for the test. Elastic moduli were obtained from the initial slope of the stress-strain curves (up to about 1% strain). Tensile toughness of each sample was obtained by calculating the area of the stress-strain curves.
Izod Impact Test
Notched Izod impact strength was measured using a sample with a thickness of 4 mm and width of 10 mm at 25 • C using an impact tester (DYC-103C, Daeyeong MTC, Hwaseong, South Korea) according to ISO 180. The test was conducted using 10 specimens, and the average values are presented here.
Dynamic Mechanical Analysis
Dynamic storage modulus and tan δ as a function of temperature were determined using a dynamic mechanical analyzer (DMA2980, TA Instruments, New Castle, DE, USA) under a cyclic tensile strain with an amplitude of 10% at a frequency of 1 Hz. The temperature increased at a heating rate of 2 • C min −1 from −100 to 200 • C.
Evaluation of Thermally-Triggered Shape Memory Effects
In order to evaluate thermally-triggered shape memory behavior of the samples, temporarily-fixed sample obtained by uniaxial deformation of dogbone-shaped specimen with a thickness of ca. 1 mm to 50% at 70 • C (which is just above T g and well below T m of the blend) followed by cooling the deformed shape to 0 • C was heated to 70 • C. The shape fixing ratio (R f ) and shape recovery ratio (R r ) of samples were determined by following equations, respectively.
Shape fixing (%) = ε u /ε m × 100 (1) where ε m is an initial strain imposed onto the sample (50% in this case), ε u is a strain measured upon cooling the deformed sample at 0 • C and ε p is a strain measured upon heating the temporarily deformed shape at 70 • C.
Phase Morphology
SEM images of the blend with various amounts of POSS are shown in Figure 2. It can be seen that all blends have phase-separated morphologies, in which POE forms dispersed domain in PA12 matrix, and the dispersed domain size decreased with addition of a small amount of POSS. The dispersed domain size of POE phase with the amount of POSS are shown in Figure 3. It can be seen that the domain size decreased from 34 µm for unfilled blend to about 10 µm for the blend with 5 phr POSS, and then a slow but gradual decrease was observed with further increasing amount of the POSS. Similar trend was also observed in immiscible blend compatibilized with a block (or graft) copolymer [3] which are located at the interface between the component polymers. The G-POSS nanoparticles might have been located at the interface between PA12 and POE through reaction between glycidyl group of G-POSS with amine end group of PA12 during high shear mixing [13] and van der Waals interactions between alkyl group of POSS with POE [17]. This can prevent the coalescence of the dispersed rubber phase, thereby decreasing the rubber diameter. At G-POSS of higher than 5 phr, the competition between break-up and coalescence was maintained at the same level.
blend to about 10 μm for the blend with 5 phr POSS, and then a slow but gradual decrease was observed with further increasing amount of the POSS. Similar trend was also observed in immiscible blend compatibilized with a block (or graft) copolymer [3] which are located at the interface between the component polymers. The G-POSS nanoparticles might have been located at the interface between PA12 and POE through reaction between glycidyl group of G-POSS with amine end group of PA12 during high shear mixing [13] and van der Waals interactions between alkyl group of POSS with POE [17]. This can prevent the coalescence of the dispersed rubber phase, thereby decreasing the rubber diameter. At G-POSS of higher than 5 phr, the competition between break-up and coalescence was maintained at the same level. was observed with further increasing amount of the POSS. Similar trend was also observed in immiscible blend compatibilized with a block (or graft) copolymer [3] which are located at the interface between the component polymers. The G-POSS nanoparticles might have been located at the interface between PA12 and POE through reaction between glycidyl group of G-POSS with amine end group of PA12 during high shear mixing [13] and van der Waals interactions between alkyl group of POSS with POE [17]. This can prevent the coalescence of the dispersed rubber phase, thereby decreasing the rubber diameter. At G-POSS of higher than 5 phr, the competition between break-up and coalescence was maintained at the same level.
Mechanical Properties
Stress-strain curves of PA12/POE blend with various amounts of G-POSS are presented in Figure 4, and Young's modulus, tensile strength, elongation-at-break and tensile toughness obtained from the curves are summarized in Table 1. The blend compatibilized by G-POSS reveals higher values in the modulus, tensile strength and elongation at break, as well as tensile toughness as compared to the blend without the POSS. The Izod impact strengths of the blend are also shown in the Table 1. It can also be seen that the Izod impact strength improved upon the loading of G-POSS into the blend. Tensile modulus, tensile toughness and Izod impact strength of the blend with 10 phr POSS increased by about 115%, 60% and 20% as compared to those of the blend without the POSS, respectively.
Stress-strain curves of PA12/POE blend with various amounts of G-POSS are presented in Figure 4, and Young's modulus, tensile strength, elongation-at-break and tensile toughness obtained from the curves are summarized in Table 1. The blend compatibilized by G-POSS reveals higher values in the modulus, tensile strength and elongation at break, as well as tensile toughness as compared to the blend without the POSS. The Izod impact strengths of the blend are also shown in the Table 1. It can also be seen that the Izod impact strength improved upon the loading of G-POSS into the blend. Tensile modulus, tensile toughness and Izod impact strength of the blend with 10 phr POSS increased by about 115%, 60% and 20% as compared to those of the blend without the POSS, respectively. Such improved toughness of the blends compatibilized by the POSS is attributed to decrease in dispersed rubber particle sizes as observed in the SEM image as well as enhanced interfacial adhesion due to the G-POSS located at the interface between the component polymers. In addition, the rigid POSS nanoparticles acted as a reinforcing filler for the PA12/POE blends and led to enhancement in strength and modulus. In other words, compatibilizing action of the rigid POSS nanoparticles resulted in outstanding simultaneous enhancement in strength and toughness. Similar simultaneous enhancements in reinforcement and toughness were also observed in other rubber toughened polymer blends containing inorganic nanoparticles [31,32]. Figure 5a,b show temperature dependence of dynamic storage moduli and tan δ of the samples, respectively. As shown in Figure 5a, the storage moduli of the blend compatibilized by POSS are higher than those of the neat blend over the whole temperature range examined here, and the modulus increased with increasing G-POSS content. The storage Such improved toughness of the blends compatibilized by the POSS is attributed to decrease in dispersed rubber particle sizes as observed in the SEM image as well as enhanced interfacial adhesion due to the G-POSS located at the interface between the component polymers. In addition, the rigid POSS nanoparticles acted as a reinforcing filler for the PA12/POE blends and led to enhancement in strength and modulus. In other words, compatibilizing action of the rigid POSS nanoparticles resulted in outstanding simultaneous enhancement in strength and toughness. Similar simultaneous enhancements in reinforcement and toughness were also observed in other rubber toughened polymer blends containing inorganic nanoparticles [31,32]. Figure 5a,b show temperature dependence of dynamic storage moduli and tan δ of the samples, respectively. As shown in Figure 5a, the storage moduli of the blend compatibilized by POSS are higher than those of the neat blend over the whole temperature range examined here, and the modulus increased with increasing G-POSS content. The storage modulus at 30°C, for example, was enhanced from 296 MPa for unfilled blend to 564 MPa (about 100% increase) for the blend loaded with 10 phr POSS. Such enhancement is correlated with an increase in tensile modulus, which is attributed to finer dispersion of rubber particle due to the compatibilization by the G-POSS and rigid nature of the POSS nanoparticles in the blend. It is also to be noted that all samples exhibit persistent plateau before the temperature reaches 170 • C at which crystalline domains of PA12 are melted. modulus at 30 ℃, for example, was enhanced from 296 MPa for unfilled blend to 564 MPa (about 100% increase) for the blend loaded with 10 phr POSS. Such enhancement is correlated with an increase in tensile modulus, which is attributed to finer dispersion of rubber particle due to the compatibilization by the G-POSS and rigid nature of the POSS nanoparticles in the blend. It is also to be noted that all samples exhibit persistent plateau before the temperature reaches 170 °C at which crystalline domains of PA12 are melted. Variation of tan δ with temperature is shown in Figure 5b. The two peaks appeared in all samples, which are attributed to glass-to-rubber transition of the POE (lower temperature) and PA12 (higher temperature), respectively. The glass transition temperatures (tan δ peak maximum) of the blend samples are shown in Table 2. It can be seen from the table that Tg of PA12 phase was shifted towards the lower value with the incorporation of POSS into the blend, from 47.1 °C in the neat PA12/POE blend to 41.8 °C in the blend with POSS content of 10 phr. The lowering of Tg of PA12 phase is due to interfacial compatibilization between the PA12 with POE by the G-POSS, which resulted in increased amorphous portion of PA12 [3,4]. Lowering of Tg of POE phase in the presence of POSS was probably due to the plasticization effect of isobutyl group of the G-POSS.
Shape Memory Effects
We observed a shape memory effect in these blends, as demonstrated in Figure 6 and in Table 3. All the temporarily elongated samples shrank and recovered to their original shape upon heating above Tg of the PA12 phase. The recovery ratio increased with increasing POSS content from 47.5% for the blend without the POSS to 94% for the blend with 10 phr POSS. Variation of tan δ with temperature is shown in Figure 5b. The two peaks appeared in all samples, which are attributed to glass-to-rubber transition of the POE (lower temperature) and PA12 (higher temperature), respectively. The glass transition temperatures (tan δ peak maximum) of the blend samples are shown in Table 2. It can be seen from the table that T g of PA12 phase was shifted towards the lower value with the incorporation of POSS into the blend, from 47.1 • C in the neat PA12/POE blend to 41.8 • C in the blend with POSS content of 10 phr. The lowering of T g of PA12 phase is due to interfacial compatibilization between the PA12 with POE by the G-POSS, which resulted in increased amorphous portion of PA12 [3,4]. Lowering of T g of POE phase in the presence of POSS was probably due to the plasticization effect of isobutyl group of the G-POSS.
Shape Memory Effects
We observed a shape memory effect in these blends, as demonstrated in Figure 6 and in Table 3. All the temporarily elongated samples shrank and recovered to their original shape upon heating above T g of the PA12 phase. The recovery ratio increased with increasing POSS content from 47.5% for the blend without the POSS to 94% for the blend with 10 phr POSS. Shape memory polymers have two structural features, i.e., the cross-links that determine the permanent shape and the reversible segments acting as a switching phase [33,34]. Blend of semicrystalline PA12 with elastomer studied here have these structural features, in which PA12 crystallites with Tm of about 175 °C act as crosslink points while amorphous chain of PA12 with Tg of about 40~47 °C act as the reversible phase. When the blend was deformed under tensile load above its Tg, amorphous regions are oriented and elastic energy is stored during the deformation. The stored elastic energy is released when the blends are heated above its Tg, which renders the molecules to have an activity and to recover to its original shape instantaneously upon heating above the Tg and well below crystalline melting temperature. The same structural features have been reported for semicrystalline polymers like PLA [35,36] and PVDF [37] with thermally triggered shape memory effects. Amorphous portions of these semicrystalline polymer molecules can be oriented in direction to applied force at temperature higher than its Tg and much lower than its Tm, and the deformed shape can be fixed as a temporary shape upon cooling below T < Tg due to trapping the entropy of the chains. When the temporarily fixed sample is heated above Tg, the shape recovery occurs due to the release of stored entropic energy and the relaxation of the polymer molecular chains to a higher entropic state. Therefore, the driving force of shape recovery mainly stems from the elastic resilience of the elongated polymer molecular chains. This is the molecular mechanism for the inherent shape memory properties of these semicrystalline polymers. It is also to be noted that blending of these semicrystalline polymers with elastomers along with proper compatibilization can promote the orientation and reorganization of polymer chains.
The improved shape recovery of the PA12/POE blend compatibilized by POSS is correlated with the improved interfacial adhesion of PA12 with the elastomer as discussed above. The dispersed elastomer domains having good interfacial adhesion with the matrix PA12 allows the amorphous chains of the PA12 to have higher activity than those in the Shape memory polymers have two structural features, i.e., the cross-links that determine the permanent shape and the reversible segments acting as a switching phase [33,34]. Blend of semicrystalline PA12 with elastomer studied here have these structural features, in which PA12 crystallites with T m of about 175 • C act as crosslink points while amorphous chain of PA12 with T g of about 40~47 • C act as the reversible phase. When the blend was deformed under tensile load above its T g , amorphous regions are oriented and elastic energy is stored during the deformation. The stored elastic energy is released when the blends are heated above its T g , which renders the molecules to have an activity and to recover to its original shape instantaneously upon heating above the T g and well below crystalline melting temperature. The same structural features have been reported for semicrystalline polymers like PLA [35,36] and PVDF [37] with thermally triggered shape memory effects. Amorphous portions of these semicrystalline polymer molecules can be oriented in direction to applied force at temperature higher than its T g and much lower than its T m , and the deformed shape can be fixed as a temporary shape upon cooling below T < T g due to trapping the entropy of the chains. When the temporarily fixed sample is heated above T g , the shape recovery occurs due to the release of stored entropic energy and the relaxation of the polymer molecular chains to a higher entropic state. Therefore, the driving force of shape recovery mainly stems from the elastic resilience of the elongated polymer molecular chains. This is the molecular mechanism for the inherent shape memory properties of these semicrystalline polymers. It is also to be noted that blending of these semicrystalline polymers with elastomers along with proper compatibilization can promote the orientation and reorganization of polymer chains.
The improved shape recovery of the PA12/POE blend compatibilized by POSS is correlated with the improved interfacial adhesion of PA12 with the elastomer as discussed above. The dispersed elastomer domains having good interfacial adhesion with the matrix PA12 allows the amorphous chains of the PA12 to have higher activity than those in the uncompatibilized blend and to recover to its original shape with the stress releasing instantaneously upon heating its T g .
It should be emphasized that the PA12 blend with shape memory effect and improved mechanical properties can be processed using conventional methods such as extrusion, melt spinning, injection molding and found diverse applications including heat shrinkable fibers, films, tubes as well as self-deployable and actuating devices. These materials in the form of powder are particularly suitable in metal coatings for corrosion protection as well as in powder-based 3D printing processes such as selective laser sintering (SLS) and highspeed sintering (HSS) for precise manufacturing of parts with complicated shape [38,39]. Further, this blend material can be processed with recently developed modern processing technologies, such as centrifugal spinning and pressurized gyration [40][41][42] to fabricate nonwoven shape memory webs in mass production scale for their applications in smart wearable devices, intelligent tools for biomedical applications, sensors, etc.
Conclusions
This study demonstrated that a POSS having both a glycidyl group and several alkyl groups on its cage can act as a compatibilizer for an immiscible PA12/POE (70 wt%/30 wt%) blend system. The incorporation of a small amount of G-POSS reduced the dispersed POE domain size in PA12 matrix of the blend. The combination of the decreased rubber particle size and the presence of rigid POSS nanoparticles in the blend led to interesting simultaneous improvement in modulus and toughness. Moreover, the PA12/POE blend embedded with POSS represented excellent thermally triggered shape memory effect. The PA12 blends with good mechanical properties and shape memory effects may have diverse applications such as smart packaging films, sensors, fast deployable and actuating devices. Further studies on the use of this material for modern processing technologies will be performed.
Data Availability Statement:
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to technical or time limitations at this time.
Conflicts of Interest:
The authors declare no conflict of interest. | 6,060.6 | 2020-12-23T00:00:00.000 | [
"Materials Science"
] |
Fine map of the Gct1 spontaneous ovarian granulosa cell tumor locus
The spontaneous development of juvenile-onset, ovarian granulosa cell (GC) tumors in the SWR/Bm (SWR) inbred mouse strain is a model for juvenile-type GC tumors that appear in infants and young girls. GC tumor susceptibility is supported by multiple Granulosa cell tumor (Gct) loci, but the Gct1 locus on Chr 4 derived from SWR strain background is fundamental for GC tumor development and uniquely responsive to the androgenic precursor dehydroepiandrosterone (DHEA). To resolve the location of Gct1 independently from other susceptibility loci, Gct1 was isolated in a congenic strain that replaces the distal segment of Chr 4 in SWR mice with a 47 × 106-bp genomic segment from the Castaneus/Ei (CAST) strain. SWR females homozygous for the CAST donor segment were confirmed to be resistant to DHEA- and testosterone-induced GC tumorigenesis, indicating successful exchange of CAST alleles (Gct1CA) for SWR alleles (Gct1SW) at this tumor susceptibility locus. A series of nested, overlapping, congenic sublines was created to fine-map Gct1 based on GC tumor susceptibility under the influence of pubertal DHEA treatment. Twelve informative lines have resolved the Gct1 locus to a 1.31 × 106-bp interval on mouse Chr 4, a region orthologous to human Chr 1p36.22.
Introduction
Human granulosa cell (GC) tumors are sex cord-stromal ovarian tumors that are divided into two clinicopathologic subtypes, adult-type and juvenile-type, based historically on histology and age at tumor diagnosis. Adult-type GC tumors are the more common subtype and typically occur in peri-and postmenopausal women (Björkholm and Silfverswärd 1981). Juvenile-type GC tumors can appear in girls from the time of infancy through adolescence and present immediate endocrinological and reproductive complications in addition to malignant potential (Young et al. 1984). A significant advance in our understanding of the GC tumorigenic mechanism has come with the introduction of high-throughput sequencing technology and the identification of a somatic missense mutation (p.C134W) in the FOXL2 gene that is common and specific for adult-type GC tumors (Shah et al. 2009;Al-Agha et al. 2011). The FOXL2 somatic mutation is not strongly associated with the juvenile subtype, suggesting alternate pathways for tumorigenic initiation in the juvenile-onset cases. The development of spontaneous juvenile-onset GC tumors in females of the SWR/Bm (hereafter SWR) inbred mouse strain is a model genetic system to identify pathways leading to juvenile-onset GC tumorigenesis.
The SWR inbred mouse strain and related recombinant inbred (RI) strains are unique for their heritable and spontaneous GC tumor phenotype that affects 1-25 % of the young female population, depending on strain background and hormonal stimulation (Beamer et al. 1985). In affected females, GC tumors develop during a restricted initiation window that coincides with puberty (3-4 weeks of age) as preneoplastic blood-filled follicles that are macroscopically visible on the ovarian surface (Tennent et al. 1990). By 6-8 weeks of age, the tumors progress to highly vascularized cystic or hemorrhagic masses that are homogeneously composed of GCs and enclosed within the ovarian bursa (Beamer et al. 1985;Tennent et al. 1990). Those females that progress through the pubertal transition unaffected remain tumor-free, retain fertility with average litter sizes, and age normally without significant predisposition for the development of other primary tumors. Those females with initially benign proliferative tumors have a high risk for development of malignant disease, with a time course for progression between 6 and 10 months of age (Beamer et al. 1985;Tennent et al. 1990).
The mouse GC tumors are endocrinologically active, secreting high levels of estrogen and inhibin (Beamer et al. 1988a;Gocze et al. 1997), similar to their human juvenile GC tumor counterparts (Young et al. 1984;Sivasankaran et al. 2009). Spontaneous GC tumor initiation in SWR mice is also endocrine-sensitive, as demonstrated by tumor grafting studies that have determined that an intact hypothalamic-pituitary-gonadal axis is required for GC tumor development ). In the intact SWR female mouse, tumor frequency is significantly increased from \1 to C20 % with exogenous androgen supplementation such as dehydroepiandrosterone (DHEA) and testosterone (Beamer et al. 1988a, b;Tennent et al. 1993;Dorward et al. 2007). In accordance with the lack of spontaneous GC tumor development outside the restricted susceptibility window, postpubertal treatment with these hormones does not stimulate GC tumor initiation . Furthermore, short-term exposure to 17b-estradiol before, but not after, the appearance of preneoplastic follicular lesions suppresses GC tumor incidence (Dorward et al. 2007), indicating that the window of tumor initiation overlaps with that of tumor prevention in the mouse model. The restricted pubertal window also suggests that the first wave of maturing follicles have a distinct response to endocrine stimulation relative to subsequent waves of maturing follicles. A recent report by Mork et al. (2012) describes temporal differences in GC specification in the embryonic ovary, which may contribute to phenotypic differences and developmental sensitivity of the ovarian follicle responses in SWR tumorsusceptible females at puberty.
Three previous mapping studies designed to identify genes associated with GC tumor development in SWR mice determined that tumor susceptibility is a polygenic trait involving multiple GC tumor (Gct) susceptibility loci. The first mapping strategy examined the tumor susceptibility traits of 14 lines of the SWXJ RI strain set, which carry different homozygous chromosomal combinations of the progenitor SWR and SJL/Bm (hereafter SJL) inbred strains (Svenson et al. 1995;Shultz et al. 1996). Female mice that retained spontaneous or androgen-induced GC tumor susceptibility always retained the SWR-derived Gct1 locus on Chr 4 (Beamer et al. 1988b;Tennent et al. 1993). It was also determined from the SWXJ strain set that the influence of DHEA at Gct1 is genetically distinguishable from the action of testosterone at Gct4 on Chr X, suggesting that Gct1 is a discrete target for DHEA in female mice that carry the Gct1 SW alleles (Beamer et al. 1988b). An F 2 intercross mapping study using the SWXJ RI parental strains SWR and SJL confirmed significant linkage of GC tumor susceptibility with SWR alleles at Gct1 on Chr 4, along with Gct2 on Chr 12, Gct3 on Chr 15, and Gct5 on Chr 9, and confirmed the modifier influence of SJL alleles at Gct4 on Chr X (Beamer et al. 1998). To improve genetic resolution, an N 2 F 1 backcross mapping strategy between the GC tumorsusceptible SWXJ-9 RI strain and the evolutionarily divergent Mus musculus castaneus (CAST) strain confirmed Gct1 as the fundamental locus for tumor initiation contributed by SWR and identified three novel autosomal loci, Gct7 on Chr 1, Gct8 on Chr 2, and Gct9 on Chr 13, and two epistatic interactions between loci on Chrs 17 and 18 and between loci on Chrs 2 and 10 (Dorward et al. 2005). In this backcross, tumor susceptibility alleles were consistently contributed by the Swiss-derived strains (SWR or SJL) that contribute to the SWXJ-9 genome, with no GC tumor susceptibility alleles contributed by the CAST strain.
The required contribution of SWR-derived susceptibility alleles at Gct1 (Gct1 SW ) to support both spontaneous and androgen-induced GC tumorigenesis across strain backgrounds underscored the Gct1 SW allele as an essential driver for this complex and developmentally restricted ovarian tumor phenotype. To map Gct1 independently from the other autosomal loci, we constructed a congenic mouse strain that has a SWR genomic complement with the exception of a homozygous Chr 4 segment derived from the CAST strain in the region of Gct1. The congenic line was resistant to GC tumor development under androgenic influence, and therefore it was useful to support a subcongenic mapping strategy to refine the interval harboring Gct1. In this report, we present a high-resolution genetic map for the Gct1 ovarian tumor susceptibility locus and a list of candidate genetic determinants for early-onset GC tumorigenesis.
Mouse housing and nutrition
Mice were maintained in the specific-pathogen-free barrier facility in the Faculty of Medicine at Memorial University of Newfoundland and housed under a 12:12-h light/dark cycle. All mice were provided Laboratory Autoclavable Rodent Diet 5010 food (27.5 % protein, 13.5 % fat, 59 % carbohydrate; PMI Nutrition International, Richmond, IN, USA) and autoclaved water ad libitum. Females were weaned at 20-23 days of age and housed in groups of two to five animals per cage, which were 27.9 cm (L) 9 17.8 cm (W) 9 12.7 cm (H) with high-profile filtered lids and contained sterilized Bed-O-Cobs Ò corn-cob bedding material (The Andersons, Maumee, OH, USA). All animal procedures were approved by the Institutional Animal Care Committee of Memorial University of Newfoundland.
SWR.SJL-X.CAST-4 congenic strain development SWR.SJL-X 5 is a previously reported congenic strain that carries the Gct1 SW alleles on Chr 4 and Gct4 alleles on Chr X derived from the SJL strain (Beamer et al. 1998;Dorward et al. 2003). SWR.SJL-X 5 female mice exhibit increased penetrance for spontaneous GC tumor initiation (*20 %) with the same pubertal initiation timing as SWR females, without the requirement for exogenous androgen administration. A large segment of Chr 4 from the CAST strain was transferred onto the SWR.SJL-X 5 congenic strain background through ten consecutive backcross generations followed by brother-sister intercross generations to create the unique strain SWR.SJL-X.CAST-4T (hereafter Line 4-T). The CAST homozygous donor segment of Line 4-T spans the region D4Mit31-D4Mit256, a 47.6 9 10 6 -bp interval that includes the confidence intervals previously determined for the Gct1 locus (Beamer et al. 1998;Dorward et al. 2003). The polymorphic CAST strain was chosen as a donor strain in the congenic mapping scheme to facilitate high-resolution genetic mapping of Gct1. The N 10 backcross scheme also replenished the SWR.SJL-X 5 genomic background (estimated return of [99 % host genomic material). Established mating pairs of Line 4-T were confirmed to possess SWR allelic background at other mapped Gct susceptibility loci on Chrs 9, 12, and 15 and SJL allelic background at Gct4. Recombinant subcongenic lines were produced by mating Line 4-T females with SWR.SJL-X 5 males to produce progeny heterozygous for CAST and SWR on Chr 4 between D4Mit31 and D4Mit256. Subsequent backcrossing to SWR.SJL-X 5 generated N 2 F 1 mice with unique combinations of the CAST and SWR genomes across the Gct1 locus via meiotic recombination. Selected recombinants were chosen as founders for nested and overlapping subcongenic lines that were fixed to homozygosity through brother-sister matings prior to phenotype assessment for GC tumor susceptibility.
Phenotyping
Granulosa cell tumor initiation is a spontaneous, lowpenetrance trait in female SWR mice that is sensitive to the androgenic environment (Beamer et al. 1985). Both DHEA and testosterone treatments increase trait penetrance in SWR females and GC tumor-susceptible SWXJ strains with the expectation for different genetic targets on Chr 4 and Chr X, respectively (Beamer et al. 1988b). To test the GC tumor initiation phenotype of Line 4-T females, DHEA or testosterone was administered to prepubertal females (age = 20-24 days) in the form of a subcutaneous capsule. DHEA (5-androsten-3b-ol-17-one) or testosterone (4-androsten-17b-ol-3-one) (Steraloids Inc., Newport, RI, USA) was packed into 1.0-cm capsules made from Silastic tubing (1.98-mm inner diameter 9 3.18-mm outer diameter; Dow Corning, Midland, MI, USA) capped with glass beads, as previously described (Beamer et al. 1988a). Capsules were implanted subcutaneously on the back at the time of weaning under isoflurane anesthesia (Baxter Corporation, Mississauga, ON, Canada) with postoperative carprofen (5 mg/kg body weight [bw]) analgesic (Pfizer Canada, Kirkland, QC, Canada). For phenotypic examination, female mice were necropsied at 8 weeks of age and ovaries were examined for GC tumors. At this age, tumors present as unilateral or bilateral, cystic or solid hemorrhagic masses of 5-10 mm 3 that are macroscopically identifiable. Females with either unilateral or bilateral GC tumors were counted as one affected animal. Following confirmation of the GC tumorresistant phenotype in Line 4-T females, all subcongenic line females were administered the DHEA capsule treatment to categorize the lines as tumor-susceptible or tumor-resistant.
To facilitate fine mapping of Gct1, additional genetic polymorphisms between the CAST and SWR genomes were determined to supplement annotated DNA markers. Primer sequences were designed around known single nucleotide polymorphisms (SNPs) annotated in dbSNP (Sherry et al. 2001), or made use of unique SNPs identified during preliminary candidate gene sequencing (Supplementary Table 1). Because the SWR strain sequence is not publicly available, annotated SNPs were chosen on the basis of differences between CAST alleles and the alleles of other common inbred strains, particularly Swiss-derived strains that are phylogenetically related to SWR (Petkov et al. 2004). Each PCR reaction contained 22.5 lL of Accuprime TM Pfx Supermix, 0.25 lL each of 10 lM forward and reverse primers, and 2 lL of salt-extracted kidney DNA template. PCR conditions were as follows: 95°C for 5 min; 35 cycles of 95°C for 15 s, 55°C for 30 s, and 68°C for 45 s; 72°C for 10 min. The amplicons were sequenced in both directions using a BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, Foster City, CA, USA), on a 3730 DNA Analyzer and aligned with published sequences using Sequencher ver. 4.10.1 (Gene Codes Corporation, Ann Arbor, MI, USA).
Statistical analysis
A sufficient sample size per subcongenic line was determined to be 50 individual females based on statistical power calculations using an estimated DHEA-induced GC tumor incidence of 20 % in susceptible lines relative to a tumor-resistant phenotype of 0 % (Beamer et al. 1988b). Type I and Type II error probabilities were set to a = 0.05 and b = 0.20, respectively. The GC tumor incidence of each subcongenic line was individually compared to the SWR inbred line incidence using the v 2 analysis for proportions, with a chosen significance level of P \ 0.05. All statistical analyses were performed with GraphPad Prism ver. 5.00 (GraphPad Software, San Diego, CA, USA).
GC tumor incidence in SWR.SJL-X.CAST-4 founder congenic and subcongenic lines
Following the construction of the homozygous congenic Line 4-T, female mice were tested for GC tumor susceptibility under the influence of testosterone or DHEA, as per previous studies with SWR inbred and SWXJ RI strain females. Neither DHEA nor testosterone administration in the form of subcutaneous capsule implants triggered GC tumor development in Line 4-T females (Table 1). In direct contrast, SWR females exhibited a GC tumor incidence of 18 % with DHEA capsule administration, in keeping with historical incidence following androgen supplementation in the diet (Table 1; Beamer et al. 1993). The GC tumor-resistant phenotype of Line 4-T females provided two key findings in support of the chosen congenic mapping strategy to resolve Gct1: (1) confirmation that the transferred genomic segment from the CAST strain encompassed the Gct1 locus, such that the Gct1 SW tumor susceptibility alleles were replaced by Gct1 CA resistance alleles, and (2) confirmation that Gct1 SW is the driver for the ovarian GC tumor susceptibility phenotype, despite the accumulated genetic evidence for multiple genetic modifiers of susceptibility on Chr X and several autosomes.
Twelve informative SWR.SJL-X.CAST-4 recombinant subcongenic lines (Lines 4-1 through 4-12), with unique recombinations of the Gct1 SW and Gct1 CA alleles across the Gct1 locus, were successfully derived from the founder congenic Line 4-T and tested for GC tumor susceptibility with subcutaneous DHEA-filled capsules. The genotypes of the individual lines across the Gct1 interval and the phenotype results for the groups of females examined for each line are represented in Fig. 1. Females from Lines 4-7 through 4-12 were GC tumor susceptible compared to Lines 4-1 through 4-6, which are GC tumor-resistant and therefore phenocopy Line 4-T. GC tumor incidences of the individual susceptible Lines 4-7 through 4-12 (18,11,19,18,27, and 16 %, respectively) were not different from SWR females treated with the same lot of DHEA (18 %), while the lines with a resistant phenotype (0 %) were significantly different (P \ 0.0001). This indicates that the Line 4-T recombinant subcongenic Lines 4-7 through 4-12 carry the Gct1 SW alleles, whereas Lines 4-1 through 4-6 retain the donor Gct1 CA tumor-resistance alleles. The phenotype-driven mapping process refined the Gct1 SW locus to lie between rs27633106 and D4kns1, a 1.31 9 10 6 -bp interval defined by Line 4-10 at the proximal boundary and Lines 4-5 and 4-6 at the distal boundary. Of note, Line 4-7 was found to be heterozygous for SWR and CAST alleles in the region between markers D4Mit70 and D4Mit179 following fine-genotyping, but this region did not interfere with the Gct1 boundary determinations.
Short list of candidate genes for the Gct1 tumor susceptibility locus The 1.31 9 10 6 -bp Gct1 interval contains 16 unique annotated genetic determinants, including 10 proteincoding genes, 2 processed transcripts, 4 noncoding RNA genes, and 23 pseudogenes (Ensembl Mouse Genome Browser release 67; NCBI m37 May 2012; Flicek et al. 2012). The majority of the annotated features are located at the distal end of the Gct1 interval, which is particularly pseudogene-rich and contains repetitive genetic elements. Table 2 summarizes the features of the 16 annotated genes in the Gct1 interval.
Discussion
Granulosa cell tumors of the sex cord-stromal class can affect women at either end of the reproductive spectrum and have generally been classified as adult-or juvenile-type based on age of onset and histology. The latest classification paradigm incorporates molecular genetic strategies, with the report of a specific association for a somatic missense (p.C134W) mutation in the FOXL2 transcription factor in adult human GC tumors (Schrader et al. 2009;Shah et al. 2009). FOXL2 is a member of the forkhead/hepatocyte nuclear factor 3 gene family, which is involved in normal ovarian development and GC differentiation in several species (Pisarska et al. 2011). The mechanism of the p.C134W FOXL2 somatic mutation to support adult-type GC tumorigenesis is under investigation (Benayoun et al. 2010;D'Angelo et al. 2011;L'Hôte et al. 2012), but the curious lack of association of this genetic variant with juvenile-type GC tumors of the ovary supports the likelihood for an alternate genetic etiology in juvenile cases. Identification of Gct1 in the SWR mouse model would provide another avenue of genetic investigation for juveniletype GC tumors in human patients that may be dependent or independent of FOXL2 activity. Granulosa cell tumorigenesis in the SWR mouse recapitulates inherent susceptibility to early-onset GC tumors, Females from the SWR.SJL-X.CAST congenic sublines 4-7 through 4-12 have significantly increased GC tumor incidence compared with individuals from line 4-T and lines 4-1 through 4-6 (P \ 0.0001). Tumorsusceptible strains share common regions of SWR genetic background between markers rs27633106 and D4kns1, for a minimal 1.31 9 10 6 -bp genetic interval containing Gct1 that is defined by line 4-10 at the proximal boundary and lines 4-5 and 4-6 at the distal boundary. Line 4-7 was found to be heterozygous for SWR and CAST alleles in the region between markers D4Mit70 and D4Mit179 following phenotyping. However, the heterozygous region lies outside of the Gct1 interval and does not influence tumorigenesis. Mb 10 6 -bp and genetic investigations for this polygenic ovarian trait has identified Gct1 on distal mouse Chr 4 as a fundamental locus for GC tumor initiation during the restricted window of susceptibility at the pubertal transition. This study employed a subcongenic mapping strategy to refine the Gct1 locus using androgen supplementation to increase trait penetrance. DHEA administration facilitated genetic mapping of Gct1 and reinforced the extent of steroid hormone influence upon the mechanism of GC tumor initiation in the mammalian ovary. Previous mapping studies were unable to resolve the Gct1 interval to a practical list of candidate genes, despite strong genetic evidence for the existence of a unique tumorigenic driver determinant on distal Chr 4 in the SWR strain. The congenic mapping strategy described herein has resolved Gct1 to a 1.31 9 10 6 -bp interval, a defined candidate gene list, and a congenic strain resource for mechanistic studies related to early-onset GC tumorigenesis. The refinement of Gct1 was aided by the empirical identification of novel SSLP-and SNP-based genotyping DNA markers that are polymorphic between SWR and CAST strains because the published DNA marker resources had been exhausted. The 1.31 9 10 6 -bp interval currently contains annotations for 16 known protein-coding genes, known and novel processed transcripts, noncoding RNA genes, and 23 annotated pseudogenes. Until Gct1 is identified and validated, all DNA polymorphisms unique to the SWR strain within the mapped Gct1 interval are under consideration as causative of the trait. In SWR mice, the DHEA-responsive nature of the Gct1 locus is an opportunity to further interrogate the list of candidate genes; however, no candidate genes within the narrowed interval are currently known to be responsive to DHEA in terms of gene expression or ligand-binding activities.
Four characterized protein-coding genes within the Gct1 interval are conserved between mouse and human based on current genome annotations: Vps13d, Tnfrsf8, Tnfrsf1b, and Dhrs3. Given our hypothesis that mouse GC tumor-susceptibility candidates will provide translational information for juvenile-onset GC tumors of the ovary in young female patients, these genes have been given priority for further investigation based on sequence and expression analysis. A comparison of fully differentiated tissue-specific expression patterns is available for these four candidates based on the Gene Expression Atlas initiative (Su et al. 2004) accessed through the BioGPS web portal (www.biogps.gnf.org; Wu et al. 2009). The four genes show evidence of expression in both the mouse and the human ovary, in agreement with our qualitative transcript assessment in the SWR mouse ovary (data not shown). Since it is our goal to identify the AS novel antisense, LINC lincRNA, PC protein coding, ND not determined, NMI novel miRNA, NPT novel processed transcript, ? forward,reverse a Excluded from this list are the 23 known pseudogenes present within the Gct1 interval mechanism of GC tumorigenesis in the SWR mouse, these genes are of significant interest, given their reported biological functions and relevant expression patterns.
Vps13d is a complex mouse gene with multiple isoforms. It has not been well characterized but is a member of the vacuolar-protein-sorting 13 (VPS13) gene family that is conserved across several species. In yeast, VPS proteins are involved in the trafficking of membrane proteins between the trans-Golgi network and the prevacuolar compartment. Velayos-Baeza et al. (2004) predicted through in silico analyses that the human VPS13D protein may have two putative domains: a ubiquitin-associated domain that confers protein target specificity in the ubiquitination pathway (Hofmann and Bucher 1996), and a ricin-B-lectin domain, which is present in many carbohydrate-recognition proteins and can bind simple sugars (Hazes 1996). It is hypothesized that if these putative domains are present, VPS13D is involved in the trafficking of ubiquitin-tagged proteins and/ or carbohydrates (Velayos-Baeza et al. 2004).
Tnfrsf8 is a member of the TNF-receptor superfamily which is expressed mainly in activated cells of the immune system (Berro et al. 2004). Tnfrsf8 signaling can have proliferative and survival or antiproliferative and apoptotic effects depending on the cellular and stimulatory context (Gruss et al. 1994;Mir et al. 2000). Serum Tnfrsf8 levels have been found to be increased in patients with autoimmune diseases and those infected with hepatitis B, hepatitis C, Epstein-Barr, and HIV, and Tnfrsf8 expression is upregulated in hematological malignancies, including Hodgkin's and non-Hodgkin's lymphomas (reviewed by Oflazoglu et al. 2009).
Tnfrsf1b, a member of the TNF-receptor superfamily, is a type I transmembrane receptor that binds tumor necrosis factor alpha (TNFa). Tnfrsf1b contains a short, C-terminal intracellular region that is involved in binding TNF receptor associated factor 2 (TRAF2). TRAF2 binding triggers the recruitment of cellular inhibitor of apoptosis 1 (c-IAP1) and c-IAP2, which leads to Jun N-terminal kinase and NF-jB activation (reviewed by Carpentier et al. 2004). When signaling through Tnfrsf1b, TNFa negatively regulates ovarian folliculogenesis, as Tnfrsf1b-null mice had increased numbers of growing follicles compared to strain controls (Greenfeld et al. 2007); however, it is not clear if this is through an alteration of follicle growth rate, reduced follicular atresia, or some other mechanism. In humans, the p.M196R TNFRSF1B variant has been associated with hyperandrogenism and polycystic ovary syndrome in women (Peral et al. 2002). An established role for Tnfrsf1b in the ovary and its activation of pathways resulting in proliferation and cell survival strengthen its standing as a candidate for Gct1 and the existence of a unique allele in the SWR strain.
The Dhrs3 gene encodes a short-chain dehydrogenase/ reductase that is induced by retinoic acid and reduces all-trans-retinal, a storage form of vitamin A, in a process necessary for photoreception (Haeseleer et al. 1998;Cerignoli et al. 2002). Dhrs3 is expressed in multiple differentiated tissues besides the retina, and DHRS3 expression has been found to be upregulated in human papillary thyroid carcinomas, although it is negatively correlated with subsequent lymph node metastasis (Oler et al. 2008). Dhrs3 was found to be a downstream target of bone morphogenic protein 2, a TGFb family member expressed in GCs of antral follicles that regulates follicle-stimulating hormone receptor and aromatase expression and the prevention of premature follicle luteinization (Bachner et al. 1998;Shi et al. 2011). Dhrs3 is under investigation as a candidate for shared identity with Gct1, as we continue to examine all candidate genes in the interval using a whole-locus customized sequencing approach, gene expression analysis, and in vivo functional strategies in GC tumor-susceptible SWR mice. We anticipate that the combination of strategies will reveal unique SWR polymorphisms, the endocrine-sensitive tumorigenic mechanism, and an explanation for trait sensitivity to DHEA. The SWR mouse model for GC tumorigenesis is a unique and realistic model for genetically complex and stochastic cancer risk and a well-described model for juvenile GC tumors in pediatric patients. Pursuit of GC tumor susceptibility genes in the mouse will provide specific candidates for further investigation in juvenile GC tumor cases to elucidate a genetic etiology for this unique ovarian tumor class. The interval for Gct1 is orthologous to human Chr 1p36.22, a region that has been implicated in a number of disorders through genome-wide association and cytogenetic studies. Kreisel et al. (2011) identified copy number alterations at Chr 1p36.22 in a subset of diffuse large B-cell lymphomas, an aggressive form of non-Hodgkin's lymphoma. Human Chr 1p36.22 has also been identified as a region frequently lost in human hepatocellular carcinoma (Nishimura et al. 2006) and is a susceptibility locus for hepatocellular carcinoma in patients with hepatitis B virus infection (Zhang et al. 2010). Deletions at human Chr 1p36.22 are also frequent in infiltrating ductal carcinoma of the breast (Hawthorn et al. 2010) and premenopausal breast cancers (Varma et al. 2005). Loss of heterozygosity at the Chr 1p36.2 region is associated with neuroblastoma (Mora et al. 2000), and multiple putative tumor suppressor genes within the same region have been identified (Krona et al. 2004;Liu et al. 2011). Despite these associations, Chr 1p36.22 has not been linked with adultor juvenile-type GC tumor susceptibility, although the rarity of cases in the human population has precluded genetic linkage studies. The identification of Gct1 in the mouse model will permit cross-examination of the role for this ovarian GC tumor susceptibility allele in other malignancies genetically associated with Chr 1p36.22. | 6,190.8 | 2012-11-18T00:00:00.000 | [
"Biology",
"Medicine"
] |
Jolie Static Type Checker: a prototype
Static verification of a program source code correctness is an important element of software reliability. Formal verification of software programs involves proving that a program satisfies a formal specification of its behavior. Many languages use both static and dynamic type checking. With such approach, the static type checker verifies everything possible at compile time, and dynamic checks the remaining. The current state of the Jolie programming language includes a dynamic type system. Consequently, it allows avoidable run-time errors. A static type system for the language has been formally defined on paper but lacks an implementation yet. In this paper, we describe a prototype of Jolie Static Type Checker (JSTC), which employs a technique based on a SMT solver. We describe the theory behind and the implementation, and the process of static analysis.
Introduction
The microservice architecture is a style inspired by service-oriented computing that promises to change the way in which software is perceived, conceived and designed [20]. The trend of migrating monolithic architectures into microservices to reap benefits of scalability is growing fast today [10,11]. Jolie [23] is the only language natively supporting microservice architectures [8] and, currently, has dynamic type checking only.
Static type checking is generally desirable for programming languages improving software quality, lowering the number of bugs and preventing avoidable errors. The idea is to allow compilers to identify as many issues as possible before actually run the program, and therefore avoid a vast number of trivial bugs, catching them at a very early stage. Despite the fact that, in the general case interesting properties of programs are undecidable [27], static type checking, within its limits, is an effective and well established technique of program verification. If a compiler can prove that a program is well-typed, then it does not need to perform dynamic safety checks, allowing the resulting compiled binary to run faster. A static type system for Jolie has been exhaustively and formally defined only on paper [25], but still lacks an implementation. The obstacles of programming in a language without a static type analyzer have been witnessed by Jolie developers, especially by newcomers. However, implementing such system is a non trivial task due to technical challenges both of general nature and specific to the language. In this paper, we introduce and describe the Jolie Static Type Checker (JSTC), building on top of the previous work on the Jolie programming language [23]. Our approach follows the formal derivation rules as defined in [25]. The project is built as a Java implementation of source code processing and verification via Z3 SMT solver [9] and it has to be intended as our community contribution to the Jolie programming language [4].
Section 2 recalls the basic of Jolie and section 3 discusses related work. The description of the static type-checking and the system architecture can be found in Section 4, while Section 6 draws conclusive remarks and discusses open issues.
Background
Microservices [12] is an architectural style evolved from Service-Oriented Architectures [15]. According to this approach, applications are composed by small independent building blocks arXiv:1702.07146v5 [cs.SE] 18 Oct 2017 that communicate via message passing. These composing parts are indeed called microservices. This paradigm has seen a dramatic growth in popularity in recent years [24]. Microservices are not limited to a specific technology. Systems can be built using a wide range of technologies and still fit the approach. In this paper, however, we support the idea that a paradigm-based language would bring benefit to development in terms of simplicity and development cost.
Jolie is the first programming language constructed above the paradigm of microservices: each component is autonomous service that can be deployed separately and operated by running in parallel processes. Jolie comprises formally-specified semantics, inspired by process calculi such as CCS [21] and the π-calculus [22]. As for practical side, Jolie was inspired by standards for Service-Oriented Computing such as WS-BPEL [2] and the attempts of formalizing it [18]. The composition of both theoretical and practical aspects allows Jolie to be the preferred candidate for the application of modern research methodologies, e.g. runtime adaptation, process-aware web applications, or correctness-by-construction of concurrent software.
The basic abstraction unit of Jolie is the microservice [12]. It is based on a recursive model where every microservice can be easily reused and composed for obtaining, in turn, other microservices. Such approach allows distributed architecture and guarantees simple management of all components, which reduces maintenance and development effort. Microservices communicate and work together by sending messages to each other. In Jolie, messages are represented in tree structure. A variable in Jolie is a path in a data tree and the type of a data tree is a tree itself. Equality of types must therefore be handled with that in mind. Variables are not declared, wherefore the manipulation of the program state must be inferred. Communications are type checked at runtime, when messages are sent or received. Type checking of incoming messages is especially relevant, since it could moderate the consequences of errors.
The Jolie language is constructed in three layers: The behavioural layer operates with the internal actions of a process and the communication it performs seen from the process point of view, the service layer deals with the underlying architectural instructions and the network layer deals with connecting communicating services.
Other workflow languages are capable of expressing orchestration of (micro)services the same way Jolie can do, for example WS-BPEL [2]. WS-BPEL allows developers to describe workflows of services and other communication aspects (such as ports and interfaces), and it has been also shown how dynamic workflow reconfiguration can be expressed [17]. However, WS-BPEL has been designed for high-level orchestration, while programming the internal logic of a single micro-service requires fine-grained procedural constructs. Here it is were Jolie works better.
Related work
The implementation of a static type checker for Jolie is part of a broader attempt to enhance the language for practical use. Previous work on the type system has been done, however focusing mostly on dynamic type checking. Safina extended the dynamic type system as described in [29], where type choices have been added in order to move computation from a process-driven to a data-driven approach.
The idea to integrate dynamic and static type checking with the introduction of refinement types, verified via SMT solver, has been explored in [32]. The integration of the two approaches allows a scenario where the static verification of internal services and the dynamic verification of (potentially malicious) external services cooperates in order to reduce testing effort and enhancing security.
The idea of using SMT Solvers for static analysis, in particular in combination with other techniques, has been successfully adopted before for other programming languages, for example LiquidHaskell and F*. LiquidHaskell [14] 3 is a notable example of implementation of Liquid Types (Logically Qualified Data Types) [28]. It is a static verification technique combining automated deduction (SMT solvers), model checking (Predicate Abstraction), and type systems (Hindley-Milner inference). Liquid Types have been implemented for several other programming languages. The original paper presented an OCaml implementation. F* [1] instead an ML-like functional programming language specifically designed for program verification.The F* type-checker uses a combination of SMT solving and manual proofs to guarantee correctness Another direction in developing static type checking for Jolie is creating the verified type checker 4 by means of proof assistant instead of SMT solver [3]. Proof assistant is a software tool needed to assist with the development of formal proofs by human-machine collaboration and helps to ascertain the correctness of them. The type checker is expressed as well-typed program with dependent types in Agda [26]. If the types are well formed, all required invariants and properties are described and expressed in the types of the program meaning that the program is correct. This work is currently in progress and evolves in parallel with ours.
Static type-checking implementation
This paper builds on top of Julie Meinicke Nielsen's work [25] at the Technical University of Denmark implementing the type system of the Jolie language. The thesis represents the theoretical foundation for the type checking of the core fragment of the language, which excludes recursive types, arrays, subtyping of basic types, faults and deployment instructions such as architectural primitives. The work of Nielsen presents the first attempt at formalizing a static type checker for the core fragment of Jolie, and the typing rules expressed there are the core theory behind our static checker.
In Nielsen's work typing rules are represented in the style of type theory where type rules are inference rules describing how a type system assigns a type to a syntactic construct of the language [7]. The rules are then applied by the type system to determine if a program is well typed or not. The main typing rules will be presented in the following of this paper.
The implementation of JSTC consists of two system components. Firstly, a Java program accepts the source code of a Jolie program, builds an abstract syntax tree (AST), visits it and produces a set of logical assertions written in SMT Lib [5] language. At the second phase, the generated assertions are feed into Z3 solver. The basic idea is to implement, for each Jolie node 5 , methods containing statements expressed in the SMT Lib syntax. These statements can then be processed via a solver. In Figure 1 the overall process is pictorially represented and details are described in section 4.4.
The concept of SMT solvers is closely related to logical theorems. Logic, especially in the field of proof theory, considers theorems as statements of a formal language. Existence of such logical expressions allows to formulate a set of axioms and inference rules to formalize the typing rules for each of Jolie syntax nodes and then perform the validation of the nodes using constructed theorems. Consequently, the Jolie typing rules are the specific cases of logical theorems, that are used in the project. The concept is implied from software verification fundamentals [6].
Since Jolie program may contain complex expressions with function calls, it is also necessary to consider data structures representing a match between names and expressions, in order to be able to avoid inconsistency and redundancy, that are likely to cause conflicts during type-checking. The project implementation considers using a stack during the recursive checking of the nodes as illustrated in section 4.4.
The decision of using an SMT-solver, instead of more lightweight techniques, was made in order to allow a future straightforward integration of Refinement Types into the type checker, objective on which our team is already working [29,32]. Furthermore, relying on a solid existing technology allowed us to prototype and release a proof of concept of the type checker in a shorter period of time.
Jolie verifier
The Java program reuses an existing structure of a Visitor pattern that was used in a previous project for formatting Jolie source code 6 . It accepts processed Jolie program source code in the form of AST and performs traversing. For each kind of node the system creates one or more logical formulas written using SMT-LIB [5] syntax, which are then stored into a file on disk. At the current implementation state the theorems are collected in a single data element.The verifier targets assignments, conditions, and other cases of variables usage where type consistency can be violated.
SMT Solver
Z3 carries out the main functionality of program verification. Z3 is an SMT solver from Microsoft Research [9]. It is targeted at solving problems that arise in software verification and software analysis. Given a set of formulas that was previously created by the verifier in Java, Z3 processes it and returns whether this set is satisfiable or not. In case of any contradiction in the set, the solver will signal that the overall theorem is not satisfiable, therefore alerting that the input program is not consistent in terms of types usage.
Typing rules
Our objective is to accurately translate Jolie typing rules into SMT statements, therefore allowing static type checking 7 . The foreground activity so far is producing the set of statements for the construct of the behavioural layer of Jolie. The layer describes the internal actions of a process and the communications it performs seen from the process' point of view. The layer is chosen for the first phase of the development because of being the foundation of the syntactical structures of Jolie. Also there is a similarity of the layer with common programming languages in a sense of the abstraction level. So these facts make the behavioural level to be the first entry in the world of Jolie language capabilities.
All statements at the behavioural layer of Jolie are called behaviours. We write Γ B B Γ to indicate a behaviour B, typed with respect to an environment Γ , which updates Γ to Γ during type checking [25].
There are some core rules presented and described below: T-Nil. The typing rule for a nil behaviour is an axiom. In the conclusion the typing environment is not changed, since the nil statement doesn't affect the typing environment.
The rule for typing an if statement is standard: An if statement is typable if its condition has type bool, and if the type checking of its branches perform the same updates to the environment. We require the branches to perform the same updates because we do not know which branch will be taken. The else part may also be omitted and B2 may be replaced by an empty behaviour. The conditional typing statement is the following: T-While. The rule for typing a while statement is standard: A while statement is typable if its condition has type bool, and if type checking its body has no influence on the typing environment.
Γ e : bool Γ B B Γ Γ B while(e)B Γ Above, it is required that the body of the while loop does not change the typing of variables because we do not know whether the body will be executed at all, and for how many times. We also require that expression e is type checked against type bool.
T-Seq. A sequence statement typed with respect to an environment is typable if its first component is typable with respect to the environment and its second component is typable with respect to the update of the environment performed by the first component. The update of the environment performed by the sequence statement is the update performed by the second component with respect to the update performed by the first component.
Thus, fundamental typing rules of the behavioral layer of Jolie programming language are presented and explained for further topic revelation.
Typing rules to SMT translation
Here we will illustrate an example of the conditional rule translation in order to understand the procedure in detail.
The typing rule of the if statement does not contradict intuition. The statement is typeable when its condition expression is boolean, and the execution of both its branches brings the same updates to the environment. This means that the set of matches between expressions and variables with their types remains the same with no difference from a branch choice. This is necessary since it is not possible to predict what branch will be executed at runtime 8 .
The full implementation is available on github. 9 Below we show the Java fragment that builds the corresponding SMT statement. The code structure represents basic steps to achieve a record with corresponding SMT statements of the block as a result. Firstly, a condition of the if statement is separated from the body. Then the condition is sent to be checked using the same visitor class. Eventually after the last 'recursion' step the condition is put in the stack of terms, which contains any terms (expressions, variables etc.) processed during the checking. So the term corresponding to the condition is expected to be on top of the stack. Then an assertion that says the condition term is boolean is written. Afterwards the body is processed using one of the other overloads of the visitor. These steps can be repeated in case of existence of nested conditional statements. In the end of the method the else branch body of the very first if is processed if it is present. There is also an important note is that the conditional statement does not impose any other direct type restrictions besides the condition term that is confirmed by the mentioned typing rule. Other implemented nodes can be seen in the source mentioned above.
The Jolie verifier takes some input for processing. Let us consider a simple piece of Jolie code with a conditional statement.
In the case everything works, none of the typing rules is violated. Z3 agrees with the opinion and results in 'sat', that means the program state is satisfiable. We list here the SMT statements representing the condition processing: (declare-const $$__term_id_10 Term) (assert (hasType $$__term_id_10 bool)) (assert (hasType $$__term_id_10 bool)) The first assertion is made based on an expression type determination: the expression a > b is boolean. The second one is imposed by the typing rule: the condition expression must be boolean. In this case there is no contradiction between these two assertions.
If the condition would be replaced with some other type expression the typing rule may be violated. The corresponding example case with a replacement of a > b is shown below: And the constructed SMT statements for the condition expression are given here: (declare-const $$__term_id_10 Term) (assert (hasType $$__term_id_10 int)) (assert (hasType $$__term_id_10 bool)) Now the contradiction between the assertions is notable. The parser decided the expression to be an integer, which is correct. But the restriction on a condition type from the typing rule simply contradict with the actual type. Consequently Z3 results in 'unsat'. This means that the program state representing the assertion unsatisfiable and incorrect in terms of the considered static type checking analysis.
Evaluation
The question of how to prove correctness of verification tools has always been widely discussed. How can we be sure that the output of such tool is correct? It can be poorly written, or the hardware could malfunction. However, in most cases we tend to trust verification tools, and in our project we have to make sure that this tool is as trustworthy as any other. The general solution is testing. Verification of the written code correctness was continuously performed during the development process. The Jolie Team created a collection of examples of Jolie programs. 10 The verification results of some of these are presented in this section.
An unsatisfiable model
The general purpose of the type checker is to find inconsistency in types usage. The program listed below is the most basic example of a program with inconsistent types. The variable myInt is assigned an integer first, and then a string. The current design of the type checker disallows this behavior.
The resulting set of SMT theorems is listed below.
A satisfiable model
In case everything in the program code is correct in terms of type consistency, the type checker should evaluate the resulting SMT model as satisfiable. It also should ignore cases that have not being processed properly yet, without giving any false positives on any inconsistency. The program listed below has, in fact, an inconsistency in types usage. The line 8 reassigns a variable to be of type integer, whereas at the line 5 the same variable was introduced as a variable of type string. However, as long as this assignment include a statement with a dynamic key, the type checker ignores it. The reason for this is inability to determine which variable this variable path will point to at the moment of execution. main { key = "cat"; animals.cat = "I am a cat"; animals.
(key) = 13 } The resulting set of SMT theorems is listed below.
Conclusions and future works
Jolie is the first programming language specifically oriented to the microservice architecture. It has been shown how software attributes such as extensibility, modifiability and consistency can significantly benefit from a migration into the microservice paradigm [10,11]. Projects run by our team demonstrated the efficacy of the paradigm and of the Jolie programming language in the field of ambient intelligence and smart buildings [31,30]. Social networks implementation would also benefit from a reorganization of the software architecture [19]. Local projects, and beyond that a number of projects worldwide involving the use of Jolie, would immensely benefit from a fully stable implementation of the Jolie Static Type Checker.
Static type checking allows compilers to identify certain programming mistakes (that violate types) at compile time, i.e. before actually running the program. Therefore a vast number of trivial bugs can be caught and fixed at a very early stage of the software lifecycle. In this paper we described JSTC, a static type checker for the Jolie programming language which natively supports microservices. A static type system for the language has been exhaustively and formally defined on paper, but so far still lacked an implementation. We introduced our ongoing work on a static type checker and presented some details of the implementation. The type checker prototype, at the moment, consists of a set of rules for the type system expressed in SMT Lib language. The actual implementation covers operations such as assignments, logical statements, conditions, literals and comparisons. JSTC is already able to validate programs, as it has been shown in this paper. However, it works with certain assumptions. The main assumption is that programs do not contain implicit type casts. The Jolie language allows implicit type casts, however, their behavior is very complex. Handling such situations is an open issue left for future development and future versions. Two other major issues have not been addressed.
Variable types can be changed at runtime. This strictly depends on the approach that has been chosen. Generally, static typing guarantees that a variable has a type that cannot be changed after declaration or assignment. However, Jolie allows this operation. We need to determine which behavior we expect from the type checker, thus deciding how to process type changes.
Implicit type casts in Jolie are ambiguous. This is a major problem, and further research is required in order to find a solution. While Jolie allows implicit type casts, sometimes the result of a cast is not obvious. For example, casting a negative Integer to Boolean will result in a False. This is an unexpected behavior when compared to other programming languages. There may be a solid rationale for this, however, we need to investigate all cases and make sure that the type checker works accordingly to the Jolie actual behavior, and not to the expected one.
JSTC future releases will need to be validated in real-life applications. The plan is to use the Jolie programming language and the type checker as a basis for the development of future research projects, the same way was done in [31] and [30]. Potential application scenarios are cognitive architecture [33], automotive systems [13] and smart houses [16]. | 5,363.4 | 2017-02-23T00:00:00.000 | [
"Computer Science"
] |
Reliable Over-the-Air Computation by Amplify-and-Forward Based Relay
In typical sensor networks, data collection and processing are separated. A sink collects data from all nodes sequentially, which is very time consuming. Over-the-air computation, as a new diagram of sensor networks, integrates data collection and processing in one slot: all nodes transmit their signals simultaneously in the analog wave and the processing is done in the air. This method, although efficient, requires that signals from all nodes arrive at the sink, aligned in signal magnitude so as to enable an unbiased estimation. For nodes far away from the sink with a low channel gain, misalignment in signal magnitude is unavoidable. To solve this problem, in this paper, we investigate the amplify-and-forward based relay, in which a relay node amplifies signals from many nodes at the same time. We first discuss the general relay model and a simple relay policy. Then, a coherent relay policy is proposed to reduce relay transmission power. Directly minimizing the computation error tends to over-increase node transmission power. Therefore, the two relay policies are further refined with a new metric, and the transmission power is reduced while the computation error is kept low. In addition, the coherent relay policy helps to reduce the relay transmission power by half, to below the limit, which makes it one step ahead towards practical applications.
I. INTRODUCTION
Many sensor nodes will be deployed to sense the environment so as to support context-aware applications in the future smart society. These sensors will be connected to the Internet via techniques such as NB-IoT and LoRa [1]. In the data collection process, generally, the sink node has to collect data from each node, one by one, which will take a long time when there are millions of nodes in the coverage of a sink node. In addition, many nodes share a common channel, and the increase in the number of nodes will lead to more transmission collisions.
On the other hand, in some tasks, people are only interested in the statistics of sensor data, e.g., the average temperature or moisture in an area, instead of their respective values. For these cases, it is possible to exploit a more efficient method called over-the-air computation (AirComp) [2]. Typically, all nodes simultaneously transmit their signals in the analog wave [3], and the data collection and processing are integrated in one slot. Then, their fusion (sum) is computed by the superposition of electromagnetic waves in the air, at the antenna of the sink. An essential feature of AirComp is the uncoded analog transmission, which seems inferior to digital transmissions. Actually, it is proved that the computation error in AirComp based estimation is exponentially smaller than the digital schemes when using the same amount of resources [4]. Besides the sum operation, AirComp can support any kind of nomographic functions [5], [6], [7], if only proper preprocessing is done at the sensor nodes and post-processing is done at the sink. Recently, deep AirComp is studied, using deep neural networks in the pre-processing and postprocessing, which enables more advanced processing of sensor data [8].
To ensure an unbiased data fusion, it is required that signals from all nodes arrive at the sink, aligned in signal magnitude. This is usually achieved by transmission power control at sensor nodes [9], [10]. Specifically, each node uses a transmission power inversely proportional to the channel gain so as to mitigate the difference in channel gains. Obviously, for nodes far away from the sink with a low channel gain, even using the largest transmission power cannot equalize the channel, and the misalignment in signal magnitude unavoidably occurs under the constraint of transmission power, which can be regarded as an outage.
Path diversity by a relay is a conventional and effective method to reducing the outage probability. The decodeand-forward (DF) method applies codes to protect signals. Amplify-and-forward (AF) is simpler, where a relay node simply amplifies the received signal (together with noise). There have been many literature on relay for the unicast communication, either AF [11], [12], DF [13], or their comparison [14]. In addition, network coding-based relay also has been studied for the bidirectional communication [15] and the multiple access channel [16]. In the multiple access channel, compute-and-forward [17] works in a similar way as network coding, where a relay node decodes the linear combination of multiple received messages and forwards it towards the sink. The sink, with efficient equations of messages, can solve each message separately. But these relay methods cannot be directly applied to AirComp. Recently, intelligent reflecting surface (IRS) is suggested for AirComp [18], where a reflection surface, as a passive relay node, is used to shape the phase of each signal. This may be possible for the mmWave band (or higher frequencies) where electromagnetic wave can propagate directionally along desired paths, but it is difficult to apply IRS in a reflection-rich environment for the typical IoT frequency bands (e.g., 920MHz, 2.4GHz).
In this paper, we will investigate how to use relay, more specifically, AF-based relay, to improve the performance of AirComp. To the best of our knowledge, this is the first work on AF-based AirComp. AF is considered because signals in AirComp are transmitted in the analog wave. In the communication, the relay node will amplify signals from many nodes and forward them to the sink, and the whole process should try to ensure the alignment of signal magnitudes at the sink so as to reduce the computation error.
The contribution of this paper is three-fold, as follows: • We first present the general relay model for AirComp, and investigate a simple relay (SimRelay) policy, in which a node either directly transmits its signal to the sink or via the relay, but not both. Then, we point out the problem: relay transmission power increases with the number of nodes that use the relay node. • To reduce relay transmission power, we propose a coherent relay (CohRelay) policy, in which a node can divide its power to transmit its signal to both the relay and the sink, and the replicas of its signal are coherently combined together at the sink. We also investigate the impact of the number of nodes using the relay node. • We discuss the tradeoff between computation error and transmission power. The computation error is composed of signal part and noise part. We find that directly minimizing the computation error may lead to a large increase in node transmission power when the noise part is dominant. Therefore, we further refine the relay policies, avoiding over-reducing the noise part. This has little impact on the computation error, but greatly reduces node transmission power. Extensive simulation evaluations confirm the effectiveness of the proposed methods. Especially, CohRelay greatly reduces the relay transmission power to below the limit, which makes it one step ahead towards practical applications.
In the rest of this paper, in Sec.II, we review the AirComp model and previous work on improving its performance. Then, in Sec.III, we present the relay model for AirComp, and investigate two relay policies. With some simulation results, we illustrate the problem of over increase in node transmission power. Then, the two relay policies are refined and evaluated in Sec.IV. Finally, in Sec.V, we conclude this paper and point out future work.
II. RELATED WORK
Here, we review the AirComp method and previous solutions to channel fading.
A. Basic over-the-air computation
We first introduce the basic AirComp model [9]. A sensor network is composed of K sensor nodes and 1 sink. The sensing result at the k th node is represented by the signal x k ∈ [−v, v] ∈ C, which has zero mean and unit variance (E(|x 2 k |) = 1). The sink will compute the sum of sensing data from all nodes. Both the nodes and the sink have a single antenna. To overcome channel fading, the k th node pre-amplifies its signal by a Tx-scaling factor b k ∈ C. The channel coefficient between sensor k and the sink is h k ∈ C. The sink further applies a Rx-scaling factor a ∈ C to the received signal, as follows where n ∈ C is the additive white Gaussian noise (AWGN) at the sink with zero mean and power being σ 2 . It is assumed that channel coefficient h k is known by both node k and the sink. Then, in a centralized way, the sink can always adjust b k to ensure that h k b k is real and positive. Therefore, in the following, it is assumed that h k ∈ R + , b k ∈ R + , and a ∈ R + for the simplicity of analysis.
The computation error is defined as the mean squared error (MSE) between the received signal sum s and the target signal K k=1 x k , as follows (by using the facts that signals are independent of each other and independent of noise, With the maximal power constraint, |b k x k | 2 should be no more than P , the maximal power. Let P max denote P /v 2 . Then, we have b 2 k ≤ P /v 2 = P max . By sorting the channel coefficient (h k ) in the increasing order, the optimal solution (under the power constraint |b k | 2 ≤ P max , k = 1, · · · , K) depends on a critical number, i [9]. A node whose index is below i uses the maximal power P max , and otherwise uses a power inversely proportional to the channel gain. Then, the computation MSE is computed as follows: This computation MSE may be caused by channel fading or noise. The former decides the error in the signal magnitude of i weak signals and the latter decides the term σ 2 |a| 2 .
B. Previous improvement on AirComp
When some nodes are far away from the sink, the magnitudes of their signals cannot be aligned with that of other signals from nearer nodes. Some efforts have been devoted to solving this problem. The work in [9] studies the power control policy, aiming to minimize the computation error by jointly optimizing the transmission power and a receive scaling factor at the sink node. Generally, the principle of channel inversion is adopted. Specifically, with the common signal magnitude being α (α = 1/a), the transmission power of node k is computed as b k = min{α/h k , √ P max }, being the former if α/h k is below the power constraint, and otherwise, using the maximal power. In [10], the authors further consider the timevarying channel by regularized channel inversion, aiming at a better tradeoff between the signal-magnitude alignment and noise suppression. Antenna array was also investigated in [19], [20] to support vector-valued AirComp.
AirComp is an efficient solution in federated learning, where the model update is to be transmitted from each node to the common sink, aggregated there, and then sent back to each node for future data processing. Specific consideration on AirComp is also studied. Because information from some of the nodes is sufficient, node selection based on the channel gain is suggested in [21], although this does not apply to general AirComp where signals from all nodes are needed. Sery et al. further suggests precoding and scaling operations to gradually mitigate the effect of the noisy channel so as to facilitate the convergence of the learning process [22].
III. AIRCOMP WITH AF-BASED RELAY
A wireless signal attenuates as the propagation distance increases. With a single antenna, the effect of transmission power control in dealing with path loss and channel fading is limited. Therefore, we try to exploit relay, which has been proven to be effective in conventional unicast communications.
A. System framework
The network consists of K sensor nodes, a relay r and a sink d. Sink d will compute the sum of sensing data from all nodes, via the help of relay r. All nodes, relay r and sink d use a single antenna. Each node has a constraint of the maximal transmission power. But we assume that the relay has no constraint of transmission power, and investigate how much power is required for relaying signals. Nodes near to the sink can directly communicate with the sink, while nodes farther away can rely on the relay to help. Then, all nodes are divided into two groups. A node k is either a neighbor of r (k ∈ N r ) and will use relay r, or a non-neighbor of r (k ∈ N d ) and will directly transmit its signal to sink d. Fig.1 shows an analogy to a conventional relay network. The difference is that there are more than one node in N r and N d .
We assume that (i) the sensor network is fixed without node mobility, and channel coefficients (h k,r ∈ C and h k,d ∈ C, representing channel coefficients from node k to relay r and sink d, respectively) are constant within a period of time, (ii) each node (and relay r) knows channel coefficients of its links to sink d and relay r, and (iii) channel coefficients of all links are known to sink d 1 , which decides the node grouping policy (decides which nodes to use the relay) and other parameters. Similar to the conventional AF method, the whole transmission is divided into two slots. But the transmission powers (Tx-scaling factor b k,1 ∈ C and b k,2 ∈ C in two time slots) are adjusted per node per slot. The detailed process is shown in Fig.2.
In the first slot, a neighbor node (k ∈ N r ) of relay r transmits its signal using a Tx-scaling factor b k,1 . The signals received at relay r and sink d are where a r,1 ∈ C and a d,1 ∈ C are Rx-scaling factors, and n r,1 and n d,1 are AWGN noises with zero mean and variance being σ 2 .
In the second slot, all nodes transmit their signals to sink d, and node k uses a Tx-scaling factor b k,2 . Meanwhile relay r also forwards its received signal, using a Tx-scaling factor b r,2 . Signals arriving at sink d are composed of 3 parts, as follows: where s d,2 is the signal from k ∈ N r , s d,2 is the signal from k ∈ N d , and s d,2 is the relayed signal. Then, the overall signal at the second slot is where a d,2 ∈ C is a Rx-scaling factor, and n d,2 is AWGN noise with zero mean and variance being σ 2 . Sink d adds the signals received in the two slots. For a signal from a neighbor (k ∈ N r ) of relay r, its overall coefficient at the sink is Its first term corresponds to the signal directly received in the first slot, its second term corresponds to the signal directly received in the second slot, and its third term corresponds to the relayed signal. For a signal from a node not a neighbor ( k ∈ N d ) of relay r, its coefficient at sink d is The overall noise is n a = a d,1 n d,1 + a d,2 n d,2 + a d,2 h r,d b r,2 · a r,1 n r,1 .
It is difficult to directly solve this problem. In the following, we discuss its solution under several relay policies.
B. Nodes grouping and relay transmission power
Different from a conventional relay method, relay r has to amplify |N r | signals, and the overall signal to be relayed is k∈Nr a r,1 h k,r b k,1 x k .
To ensure that all signals are aligned in magnitude at sink d, the magnitude of the relayed signals (a d,2 h r,d b r,2 ·a r,1 h k,r b k,1 , k ∈ N r ) should approach that of directly received signals (a d,2 h k,d b k,2 , k ∈ N d ). Here, relay r will use a Tx-scaling factor b r,2 which depends on node transmission power (b k,1 ), channel gains (h r,d , h k,r ), and alignment with other nodes (a d,2 , a r,1 ). The transmission power required at the relay node is Obviously, the relay transmission power linearly increases with the number of nodes using the relay, which is a big problem. Therefore, it is impractical to use relay for all nodes.
To solve this problem, we propose that nodes far away from sink d while near to relay r should use the relay. Therefore, nodes are sorted in the ascending order of |h k,d | 2 − |h k,r | 2 , the difference of channel gain to sink d and relay r. The top nodes will use relay r, and the percentage of nodes using relay r is a parameter.
C. Simple Relay Policy
We first consider a simple relay (SimRelay) policy. s d,1 is neglected (a d,1 = 0) and s d,2 is not transmitted (b k,2 = 0, k ∈ N r ). In other words, in the first slot, signals from k ∈ N r are sent to relay r, and in the second slot, signals from k ∈ N d are directly sent to sink d and signals from k ∈ N r are forwarded to sink d by relay r. This is the most simple relay method: the direct link is neglected once the relay is used.
With a d,1 = 0, b k,2 = 0 (k ∈ N r ), and a d,2 h r,d b r,2 = c, the computation MSE in Eq.(13) can be rewritten as Because c can be merged into a r,1 , we denote their product as a r,1 = ca r,1 , and the computation MSE can be computed as the sum of Because M SE r and M SE d depend on different nodes and different parameters, the relay problem is equivalent to two AirComp problems, one from k ∈ N r to relay r in the first slot, and the other from k ∈ N d to sink d in the second slot. Each can be solved by using the power control algorithm suggested in [9]. Because b k,1 and b k,2 can be adjusted to ensure h k,r b k,1 and h k,d b k,2 are positive real numbers, in the analysis, h k,r ∈ R + , h k,d ∈ R + , b k,1 ∈ R + , b k,2 ∈ R + , a r,1 ∈ R + , a d,2 ∈ R + are assumed for the simplicity of analysis. In SimRelay, relay r has to amplify the whole signals from |N r | nodes, which requires much transmission power.
D. Coherent Relay Policy
To reduce relay transmission power, we consider a coherent relay (CohRelay) policy. A node using relay divides its power into two parts, and transmits its signal twice. Sink d receives two coherent copies of the same signal and adds them together.
In this case, s d,1 is neglected (a d,1 = 0) but s d,2 (k ∈ N r ) is transmitted. Compared with SimRelay, the difference is that in the second slot, nodes k ∈ N r transmit their signals again. With a d,1 = 0, a d,2 h r,d b r,2 = c, and a r,1 = ca r,1 , the computation MSE in Eq.(13) can be rewritten as Because a d,2 also appears in the first sum, this cannot be simply divided into two AirComp problems like SimRelay. But h k,r ∈ R + , h k,d ∈ R + , b k,1 ∈ R + , b k,2 ∈ R + , a r,1 ∈ R + , a d,2 ∈ R + can be assumed in the analysis. Then, a d,2 h k,d b k,2 in the first sum is a positive real number. Without this term, like SimRelay, an initial estimation of a r,1 and a d,2 can be computed, by minimizing M SE r and M SE d in Eq.(17), respectively. Next consider the presence of a d,2 h k,d b k,2 in the first sum of Eq. (18). Assume originally some a r,1 and b k,1 make a r,1 h k,r b k,1 equal to 1.0 (or approach 1 under the maximal power constraint). If b k,1 is fixed, the presence of a d,2 h k,d b k,2 (a positive number) makes it possible to use a smaller a r,1 to make a d,2 h k,d b k,2 + a r,1 h k,r b k,1 reach 1.0. Meanwhile, the term σ 2 |a r,1 | 2 also decreases. In other words, it is possible to decrease a r,1 in a certain range to reduce the first sum in Eq. (18). Therefore, a heuristic algorithm is to use the initial estimation of a r,1 as a seed, and then gradually decrease it to find the minimum while fixing a d,2 (ensuring the minimum of the second sum in Eq. (18)).
Actually, b k,1 and b k,2 depend on the setting of a r,1 and a d,2 . In addition, to ensure a fair comparison with SimRelay, it is assumed that the overall power, |b k,1 | 2 + |b k,2 | 2 = P , should be no more than P max . Then, the power allocation for b k,1 and b k,2 (k ∈ N r ) is to maximize the term a d,2 h k,d b k,2 + a r,1 h k,r b k,1 , under the power constraint. According to the Cauchy-Schwarz inequality [23] (a r,1 h k,r ) and the equality holds if and only if Then, with |b k,1 | 2 + |b k,2 | 2 = P ≤ P max , ρ k can be computed as On this basis, b k,1 and b k,2 are computed from Eq. (20), and the value of a d,2 h k,d b k,2 + a r,1 h k,r b k,1 is computed as If γ k (P max ) is greater than 1.0, setting γ k (P ) = 1 can find ρ k (P ) and the powers (b k,1 and b k,2 ) that lead to 0 error in the signal magnitude. The whole process of finding optimal parameters and the corresponding computation MSE is described in Algorithm 1.
In CohRelay, a d,2 h k,d b k,2 +a r,1 h k,r b k,1 ≈ 1 so a r,1 h k,r b k,1 is less than 1. This helps to reduce the relay transmission power in Eq.15.
33:
return M SE rd 34: end procedure
E. Simulation Evaluation
Here, we evaluate the relay methods discussed in the previous sections, by comparing them with the AirComp method [9] that only exploits the direct link. Figure 3 shows the simulation scenario. 50 sensor nodes are randomly and uniformly distributed in a rectangle area. The path loss model uses a hybrid free-space/two-ray model and each link experiences independent slow Rayleigh fading (channel gains are the same in two slots). We assume that the link rd does not experience fading by properly selecting a relay node not in fading (the relay selection itself is left as future work). The noise level is -90dBm. It is assumed that both sink d and relay r amplifies the signal with a gain of 90dB. The simulation is run 10,000 times using the Matlab software. Main parameters are listed in Table I. Figure 4 shows the cumulative distribution functions (CDF) of computation MSE, average node transmission power and relay transmission power in different methods. Obviously, AirComp using only direct links has much larger computation MSE than relay methods. SimRelay and CohRelay have almost the same performance in reducing the computation MSE, but CohRelay has much smaller relay transmission power than SimRelay. Surprisingly, both CohRelay and SimRelay require more node transmission power than AirComp.
IV. REFINEMENT OF THE RELAY METHODS
In the following, we analyze the computation MSE in CohRelay and refine the parameters. A similar analysis applies to SimRelay.
In Eq.(18), the computation MSE is composed of the signal part and the noise part. When reducing the computation MSE, at first mainly MSE of the signal part is reduced to align signal magnitudes. When MSE of the signal part gets small and the noise becomes dominant, MSE of the noise part is also reduced, which leads to smaller a r,1 and a d,2 , and increased power (b k,1 and b k,2 ).
In the original AirComp, MSE of the noise part is a 2 σ 2 . To avoid over-reducing MSE of the noise part in CohRelay, we restrict ((a r,1 ) 2 + d 2 d,2 )σ 2 to be no less than γ · a 2 σ 2 , where γ is a parameter (later it is set to 1.0 based on simulation results). In addition, MSE of the signal part is not a continuous function. Actually it is a constant when a r,1 and a d,2 change within a range, because the variation is absorbed by adjusting b k,1 and b k,2 , which change continuously. Then, a small decrease in the computation MSE may lead to a large increase in transmission power. To capture this feature, we consider a new metric, involving both the computation MSE and node transmission power (TxP), as follows, where θ is a parameter. argmin a r,1 ,a d,2 ,(a r,1 ) With each candidate a d,2 in a certain range, a r,1 is computed from γ · a 2 − a 2 d,2 . With a r,1 and a d,2 , transmission power (b k,1 , b k,2 ) for node k ∈ N r is computed using the function OneIter in Algorithm 1. For node k ∈ N d , b k,2 is computed from a d,2 . Then, the computation MSE and average TxP are computed. Because their values are of the same order of magnitude, θ is set to 0.5.
With this new metric, we evaluate MSEs of the signal part and the noise part, average transmission power of nodes in N r and N d , and the metric in Eq. (23). The results are shown in Meanwhile a r,1 decreases, which leads to a quick increase in transmission power of nodes in N r when a d,2 is large (a r,1 is small). The overall MSE of the signal part decreases. Then, the metric, as a weighted sum of the overall MSE and average power, reaches a minimum somewhere, which prefers to use a smaller transmission power when the computation MSE has no significant change. Next, we evaluate the computation MSE, node transmission power, and relay transmission power, by changing the parameter γ. Hereafter, the refined relay methods are renamed as SimRelay+ and CohRelay+, respectively. The results are shown in Fig.6. At γ = 200%, it is equivalent that noise at the relay and the sink are directly added together. When γ decreases from 200% to 100%, MSE of the noise part is also reduced, so the overall MSE gradually decreases, meanwhile node and relay transmission power increases, although slowly. When γ further decreases, the decrease in the computation MSE becomes smaller while the increase in node transmission power becomes larger. Therefore, in the following evaluation, γ is set to 100%.
With γ = 100%, we re-evaluate the computation MSE, node and relay transmission power. The results are shown in Fig.7. The reduction of the computation MSE in CohRelay+ compared with AirComp, decreases from 35.6% (Fig.4(a)) to 25.0%. But the reduction of average node transmission power in CohRelay+ is greatly improved from -10.0% (Fig.4(b)) to 37.0%.
We further investigate the impact of the percentage of nodes using relay. As shown in Fig.8, when only a small percentage of nodes use the relay, the reduction of the computation MSE is limited. Node transmission power is relatively large but the relay transmission power is small. When more nodes use relay, the computation MSE is further reduced, so is node transmission power, but relay transmission power increases. In all cases, CohRelay+ achieves almost the same (or a little smaller) computation MSE as SimRelay+, but reduces node transmission power, and especially reduces the relay transmission power by half or more when the percentage of nodes using relay is less than 30%. In this range, the relay transmission power in CohRelay+ is less than P max , which makes it practical to use a relay node.
In the above evaluation, node transmission power and relay transmission power are separately evaluated, and a relay is not used in AirComp. For a fair comparison, we further investigate the overall transmission power of all nodes and the relay. The result is shown in Fig.9. With the increase of the percentage of nodes using relay, the overall transmission power in SimRelay+ and CohRelay+ decreases at first, because using relay helps to reduce node transmission power, and then increases because of the large transmission power at the relay. When the percentage of nodes using relay is no more than 50%, CohRelay+ consumes less overall transmission power than AirComp.
In sum, using a relay, SimRelay+ and CohRelay+ reduce node transmission power and have a similar performance in reducing the computation MSE, compared with AirComp. This is achieved at the cost of one more slot, and potentially more transmission power. As for the overall transmission power, SimRelay+ may consume more power, but CohRelay+ always consume less power than AirComp in the typical range (the percentage of nodes using relay is less than 50%). Compared with SimRelay+, CohRelay+ reduces the relay transmission power by half or even more, to below the limit when the percentage of nodes using relay is no more than 30%, which facilitates the practical application of AirComp.
V. CONCLUSION
AirComp greatly improves the efficiency of data collection and processing in sensor networks. But its performance is degraded when signals of nodes far away from the sink cannot arrive at the sink, aligned in signal magnitude. To address this problem, this paper investigates the amplify and forward based relay method, and discusses practical issues such as the large relay transmission power and the over-increase of node transmission power. As for the two relay polices, SimRelay+, being simple, effectively reduces the computation MSE and node transmission power. With coherent combination of direct signals and relayed signals, CohRelay+ further reduces the node transmission power and relay transmission power.
Although it is impractical for all nodes to use the relay, CohRelay+ helps to reduce the computation MSE meanwhile keeping the relay transmission power below the limit by adjusting the percentage of nodes using relay. In the future, we will further study the relay selection problem. | 7,193.2 | 2020-10-23T00:00:00.000 | [
"Engineering",
"Computer Science"
] |
Drivers of marine fishery dependence: Micro-level evidence from the coastal lowlands of Kenya
Abstract Poverty and inequality remain a development challenge for most fishery-dependent households. This has prompted the current interest in the ocean-based economic model as Kenya became the first to host a global conference on the sustainable blue economy which was held in Nairobi in November 2018. To provide a more comprehensive understanding of the challenges surrounding the blue economy approach, we used a fractional response model to analyze drivers of marine fishery dependence. Our case study, involving 384 randomly selected households from Coastal lowlands of Kenya, specifically Kilifi County, revealed that marine fishery is the highest livelihood option exhibiting gender differences. The livelihood participation rates were approximately 68%, 36%, 31%, 25%, and 9% for marine fishery and related activities, agriculture, self-employment (excluding agriculture and marine fishery), wage employment, and remittances respectively. Many livelihood options were pursued independently implying a low level of diversified livelihood strategies in the region. The analysis of the determinants of the marine fishery dependence indicated that education level, agricultural productive assets, access to credit, group membership, security of tenure, flood shock, and fish price shock significantly influenced marine fishery dependence. The study therefore recommends government intervention in marine governance and development programs such as infrastructural development, capital availability and extension services. This will enable the dependent households to diversify to alternative livelihood options and contributes to sustainable marine fishery dependence.
PUBLIC INTEREST STATEMENT
Marine fishery is one of the major economic activities along the coastal region of Kenya. Despite its vast potential to sustaining livelihoods and contributing to national economy, majority of the marine fishery-dependent households are poor. Therefore, it was of great interest to explore factors associated with marine fishery participation. This paper established that participation in marine fishery dependence is influenced by socioeconomic, institutional and climatic factors. To attain sustainable livelihood option among fishing communities the study recommends government intervention in marine resource governance, capital availability, and infrastructural development.
Introduction
Marine resources contribute to food security, livelihood, and mitigation of climate change as well as enhanced economic growth through trade (Amevenku et al., 2019;Ding et al., 2017;Funge-Smith & Bennett, 2019;Satumanatpan & Pollnac, 2020). They also serve as the safety net for the poor, providing nutrition and income, particularly in the period of financial hardships. Estimates show that marine resources generate employment for millions of coastal inhabitants and nearly 1 billion people worldwide (Fabinyi et al., 2019;Salas et al., 2019;Teh & Sumaila, 2013). Therefore, the contribution of marine fishery activities to the national economy is inevitable and multifaceted. Apart from improving food security, they contribute to the gross domestic product (GDP) and they are the source of hard currency (FAO, 2014).
In Kenya, marine resource generates employment to over 2 million Kenyans through fishing activities, boat building, fish processing, equipment repair, and tourism (CGOK, 2018;Muigua, 2018). The fishing activities in Kenya's coastal lowlands are mainly small scale and rely heavily on traditional and simple methods. Also, since the marine fishery-dependent households have low literacy levels and the fishing proceeds are affected by seasons, they are more vulnerable to shocks. The higher vulnerability in fishing-communities results in a poverty-environment trap, which is higher among the poor compared to non-poor (Kiwanuka-Tondo et al., 2019;Narain et al., 2008;Nguyen et al., 2018;Soltani et al., 2014). More importantly, marine fishery dependence is under the threat of climatic and idiosyncratic shocks. These include fluctuating prices, changing economic policies, rising sea levels, aridity of the climate, and biological constraints . Therefore, poverty in fishery could be explained by biological limits and natural shocks of the marine fishery resource.
Many of the coastal residents depend on marine resource due to landlessness and insecure land right systems (Van Hoof & Steins, 2017). The squatter problem has become a development concern in the coastal region of Kenya, which traces its way back to the colonial administration. It is worth noting that the enactment of the Registration of Documents Ordinance, together with Crown Land Ordinances of 1902 and 1915 redefined crowned land owned by natives. This resulted in a declined community land. With the lack of efficient ownership rights, Africans at the coast remained dispossessed and have been forced to live as tenants at will. Further, individualization of land tenure and other post-independence laws and policies created rural elite phenomenon and intense squatter problem. The land transfer was grounded within the willing seller-willing buyer principle that favored politicians leading to the unfair allocation of land and land grabbing concept of the 1980s (Githunguri, 2017). Kenya has begun to embrace the blue economy concept for sustainable development. Therefore, an effective legislative framework that enhances the restoration of an individual's entitlement to land is inevitable. Through this, marine fishery-dependent households can diversify to alternative livelihood strategies and contribute to a sustainable fishery.
As the Kenyan population increases, the availability of agricultural land struggles to keep up with its demand. As a result, the importance of the marine resource base for sustainable development and income generation for the households to meet their basic needs has increased. This has inspired a wide range of empirical studies in Kenya to understand marine resource opportunities and challenges facing the blue economy approach Benkenstein, 2018). However, these studies failed to explore fishery dependence and how the shift to blue revolution affects fish biomass concerning both the incidental and target catch. More so, the coastal lowland of Kenya is affected by historical land injustices. Nevertheless, the previous studies failed to take into account the nexus between marine fishery dependence and land tenure. Given the higher incidence of shocks among natural resourcedependent households, over-dependency on marine resources may make households vulnerable to poverty . Therefore, this paper aims at contributing to a better understanding of factors influencing marine fishery dependence, with a special focus on land tenure and shocks. The information obtained is critical to comfort policies pioneered towards promoting inclusive economic growth through sustainable and diversified livelihood options. Further, in natural resource literature, we are not aware of any previous study using the fractional response model (FRM) for analysis of dependency. FRM offered a vital framework for handling the proportional dependent variable and overcoming the observed limitations in linear regression models, Tobit model and non-linear models.
The remainder of the article is structured as follows; the second section presents literature review; the third section presents material and methods for the study; the fourth section presents the discussion of the econometric results, and the fifth section presents the conclusions and policy implications of the study.
Literature review
This section presents a review of the past research and findings on the determinants of marine fishery dependence. Degen et al. (2010) and Cinner et al. (2012) found that fishers' number has increased gradually in Kenya. This is because of the poverty that is linked to poor education, inadequate employment opportunities, lack of access to credit, poor agro-ecological conditions, and unfavourable climate. In this sense, failure to diversify to alternative livelihood options is usually observed especially in regions where there is marginalization and lack of social protection of the fishing communities. This implies that weak institutional framework to capacitate the rural livelihoods may be the missing link towards diversified and sustainable livelihood options . Further, Daw et al. (2012) established that failure to exit marine fishery is attributed to the occupational attachment, ethnicity, household size, education, age and attitudinal factors.
A growing body of empirical literature suggests that natural resource dependence is associated with socio-economic characteristics, distance to the extracting ground, and asset value (Amevenku et al., 2019;Nguyen et al., 2015Nguyen et al., , 2018Tamale et al., 2017). In addition, market access, financial capital, access to health, and membership to formal or informal institutions are likely to affect the dependence on these resources (Béné & Friend, 2011;Soltani et al., 2012). Hence, given climate variability, institutional bottlenecks, inadequate infrastructure, and land degradation (Neiland & Béné, 2013), the households may be attracted to the fishery resource as a result of both the short-term and long-term shocks. These conditions create a downward spiral of overexploitation, which leads to poverty, and poverty results in overexploitation Somoebwana et al., 2021;Stanford et al., 2013).
Description of the study area
The study was carried out in Kilifi County, which is among the six counties in Kenya's coastal region. Kilifi County lies in the East and South of the Indian Ocean and Mombasa County, respectively ( Figure 1). It is characterized by a bimodal rainfall pattern averaging between 300 mm and 1,300 mm. The main topographic features of the County include coastal plain, foot plateau, coastal range, and Nyika plateau (CGOK, 2013). With poor agro-ecological zones and massive blue economy investment potential arising from the Indian Ocean coastline of 265 km, fishing and tourism have emerged as the dominant economic activities in the area. Most importantly, the marine fishery dependence has been long linked to the Bajun ethnic group that is being regarded as the fishers' par excellence. However, since the 1960s, Mijikenda has engaged in this economic activity (Degen et al., 2010). This is due to a significant level of poverty-related to historical injustices of land and social exclusion. Therefore, in an effort to improve the welfare of the dependent households, studying the dimensions of the marine fishery dependence in the County is vital. This will help in formulating appropriate policy options that take into account socioeconomic constraints, land tenure, and multiple shocks observed. As a result, the wealth generated from fishery can contribute to rural development through fishing proceeds and employment multiplier effects due to the cash crop nature of fish (Béné et al., 2007).
Research design, sampling, and data management
The cross-sectional research design was used in this study to explore the drivers of marine fishery dependence among households in Kilifi County. Apart from enabling assessment of the link between covariates and the outcome, this approach allowed a simultaneous comparison of different variables in the study sample and is relatively faster and inexpensive.
The selection of the respondents for this study was arrived at using a multistage sampling technique. Kilifi County was purposively selected in stage one due to its large Indian Ocean coastline, the higher rate of historical land injustices in the region, and significant poverty level (CGOK, 2018). The second stage involved a purposive sampling method of four Sub-Counties (Malindi, Kilifi North, Magarini, and Kilifi South) since they are located along the Indian Ocean. Later four wards were purposively selected from the four Sub-Counties (Shella-Malindi, Watamu-Kilifi North, Gongoni-Magarini, and Shimolatewa-KilifiSouth) from a population of 35 because they offer important ground for artisanal fishing. In the last stage, simple random sampling was used to select 384 households spread over the 4 wards (Table 1) . The household sample size was determined usingEq. (1 (Cochran, 1977) Source: Geography Department, Egerton University-Kenya where n 0 is the minimum estimated sample size, z is the value of the t-distribution, which equals to the alpha of 0.05 = 1.96, p represents population proportion estimate, and e is the margin of error. When the population proportion is unknown, p is put at 0.5 and e at 0.05 (Cochran, 1977).
The primary data were collected through face-face interviews with the household heads. However, this was after the questionnaire was subjected to a pre testing procedure to determine its validity and competency. We ensured quality control in data by carefully selecting and training the enumerators to equip them with knowledge sufficient to portray the research objectives. More so, to avoid biased data collection process, respondents who took part in the pre-test exercise were considered ineligible in the actual household survey. Further, the descriptive and fractional response models were applied in this study to gain clear insights into the dimensions of marine fishery dependence (Chegere, 2018). Data were analyzed using Stata econometric software.
Marine fishery dependence
The level of marine fishery dependence of a particular household was expressed as the proportion of the income derived from the ocean fishery and related (fishing, fish trading and processing, boatbuilding, and selling of fish equipment) to the total household income, as described in Eq. (2).
where Y* is the marine fishery dependence ranging between 0 and 1 with a higher value indicating high dependence (Nguyen et al., 2018). The numerator represents household income generated from marine fishery and related activities, while the denominator reflects total household income; (net income from crop production, livestock rearing, fishery and related activities, business), wages, salary, and remittances (Mathenge et al., 2010).
Analytical framework
In determining the factors influencing marine fishery dependence, the present study used a fractional response model (FRM). Most importantly, FRM was preferred to linear regression models such as Ordinary Least Square (OLS) because they are not fit to estimate fractional dependent variables and, therefore, may produce estimates outside the unit interval (Chegere, 2018). The non-linear models such as logit and probit transformations may be applicable in this case; however, they do not consider observations that lie at the boundaries and hence may result in truncation problem. Further, the Tobit model may seem appropriate for bounded dependent variables; however, in proportional data, the values outside the unit interval are not feasible. Therefore, FRM remains the best model for handling the proportional dependent variable and to overcoming the observed limitations in other econometric models. According to Papke and Wooldridge (1996), the FRM model is a synthesis and an extension of quasi-maximum-likelihood (QML) methods and generalized linear models (GLM) as described below where y i is the dependent variable defined as (0 ≤ y i ≤ 1), x i represents explanatory variables for household i, and G(.) is the logistic regression function that will be estimated directly using QML based on Bernoulli log-likelihood function defined as Given Bernoulli distribution is a linear, exponential family (LEF), the estimator of QML, θ will be given by Therefore, θ is normally distributed, and the method can generate consistent estimates of the fractional response variable. The variables in the fraction response model are presented in Table 2 from the review of past studies;
Descriptive statistics on marine fishery dependency
The total number of households that participated in marine fishery and related activities was approximately 68%, about 36% pursued agriculture, and 31% participated in self-employment (non-farm and non-fishery). Further, approximately 25% of the households participated in wage employment (nonfarm and non-fishery), while households with income from remittances were about 9%. In terms of the level of dependency, marine fishery and related activities, agriculture, self-employment, wage employment, and remittances had about 0.63, 0.02, 0.12, 0.19, and 0.01, respectively. Diversification is a critical approach to ensure a consistent flow of income. However, most of the households who participated in marine fishery and related activities find it difficult to find the next best diversification livelihood option due to insecure land ownership rights and lower education levels. This is clearly indicated by a higher dependence on marine fishery compared to other livelihood options.
The willingness to pursue additional livelihood options among the households was low. Cultural preference and the time-consuming nature of the fishing activities hampered the households to practice diversified strategies. Most importantly, the dependency on marine fishery extends beyond personal income and employment. The conducted interviews suggest that there is a working culture in this particular livelihood strategy defined by reciprocity and interpersonal relationships, where individuals engage in marine fishery through social ties, and form a significant part of their identity. Additionally, most of the households that were dependent on the ocean fishery were Bajuns and were culturally bounded by self-employment. They felt that marine fishery provided a sufficient form of self-employment and did not like wage employment due to too much control from employers. These elements offered a critical pathway in the survival and growth of the fishing industry in the coastal region (Carter & Garaway, 2014). With the government at the heart of transforming the sector and organizing beach management units (BMUs), reciprocal interdependencies have become more evident.
Further, gender disaggregated analysis on the livelihood options was conducted. Results indicated that marine fishery and related activities is a male dominated livelihood option with males taking the largest share at 79.69% while females at 20.31%. This could have been attributed to the cultural belief that fishery suits males since it requires higher strength. Males were also more likely to engage in wage employment than females at 78.57% and 21.43%, respectively. Further, the participation rate of males and females in self-employment was 81.51% and 18.49% accordingly. The higher likelihood of males to engage in wage employment and self-employment is because of their exposure to education and entrepreneurial behaviors than females who for a long time have been marginalized. However, it is worth mentioning that females were more likely to participate in agriculture at 79.14% compared to males at 20.86. This is because of higher tendency of females to provide unpaid farm labor and ensuring food security for households. Table 3a shows descriptive statistics for the dependents and non-dependents households in the marine fishery for continuous variables. There are significant differences with regard to some of the covariates. On average, dependent households have a lower level of education at a mean of about 7.9 compared to 12.5 for non-dependent households. Majority of the dependent households tend not to value education and usually drop out of school because marine fishery serves as an immediate and direct source of income (Ndhlovu et al., 2017;Nguyen et al., 2018), which reduces the individual's incentive to pursue further education for future employment.
Descriptive statistics of the variables used in the econometric model
Participating households in marine fishery and related activities have significantly fewer agricultural productive assets averaging KES 10,417.2 compared to non-participating households, KES 71,321.3 as can be seen in Table 3a. Asset poverty usually compels individuals to diversify into common pool resources (Nguyen et al., 2020), and as such, they pursue the livelihood option as a safety net in response to idiosyncratic and climatic shocks. Further, there are significant differences with respect to distance to the marine and distance to the fishery market. Households that are closer to the ocean and market tend to engage more in the marine fishery and related activities due to social identity and reduction in transaction costs. On the other hand, an increase in the distance lowers the expected net economic value, which ultimately discourages the decision to participate (Kyando et al., 2019). Table 3b reports findings for the socioeconomic characteristics of households stratified by participation in marine fishery for categorical variables. The results indicate that there are also significant differences in credit access. Households that have poor access to credit are more likely to participate in marine fishery and related activities. The logic follows that lack of access to credit limits capital availability and investment in entrepreneurial activities, making common pool resource only viable livelihood option for the affected households. This can be further affirmed by lower proportion of security of tenure for the participating households, 36%, preventing the right to land usage, loan acquisition and long-term investment. Further, group membership was higher among the participating households compared to those that do not pursue the livelihood option at about 86% and 4%, respectively. Participation in local institutions affects the decision to participate in fishery resource due to peer influence and awareness of economic surplus extracted from the commons.
The differences in shocks relating to rainfall, flood, and price are also significant between the two groups. Price uncertainty and the fluctuation nature of the marine fishery returns due to seasons and climatic conditions expose these households to financial and weather shocks. In response to these shocks, households usually take advantage of their right of opportunity-freedom of the commons by increasing fishing activities (Jentoft et al., 2010). In such instances, coping strategies such as adopting illegal gears, exploiting threatened and protected species, or extending fishing periods are deployed. This results in overexploitation, which exposes poor households to heavy risks given that majority of the dependent households do not enjoy the insurance offered by land ownership against a sudden loss of livelihood option. Efforts have been put to organize the marine fishery under beach management units (BMU) with the primary objective of achieving capacity building and reduce sensitivity to shocks through information sharing and provision of marketing facilities. However, more than half of the conducted interviews publicly criticized BMUs due to their failure to mobilize resources and instead focusing on restrictive fishing regulations that are not even equally implemented.
Econometric analysis of the factors influencing marine fishery dependence
To determine the factors influencing marine fishery dependence, the fractional response model was estimated. The results are presented in Table 4. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were 208 and 271, respectively. The likelihood ratio test of the model had a p-value of less than 0.001, indicating a better fit (Shin & McCann, 2018). Pseudo R 2 of 65% was also higher compared to the set statistical threshold of 20%, and hence marine fishery dependence is well explained by the proposed covariates (Power et al., 2015). The result in Table 4 indicates that several factors significantly influenced the level of marine fishery dependence among households. They include education level, group membership, credit, the security of tenure, flood, fish price, and value of agricultural productive assets. The education level of the household head was negatively associated with marine fishery dependence at 1% significant level. Lower education level limits opportunity in formal employment and fishing provides a livelihood option as it does not require higher levels of education. This finding is consistent with previous studies (Amevenku et al., 2019;Daw et al., 2012;Garekae et al., 2017;Selig et al., 2019), where education had a negative effect on both relative and absolute environmental income attributed to higher education opening up alternative employment opportunities in public and private sector jobs. Moreover, education also enhances income diversification and labour diversity (Do et al., 2019;Jin et al., 2018), since it enriches access to information and skills and hence off fishery-related employment opportunities.
The amount of agricultural productive assets had a positive and significant effect on the marine fishery dependence at 5% significant level. The results have been attributed to the majority of the households diversifying into agricultural production. However, agriculture was pursued in this region, mainly for subsistence with pockets of commercialized agricultural production. This is to serve as a coping strategy against the perceived risk and a livelihood diversification approach in sustaining consumption. The finding is consistent with prior evidence (Ndhlovu et al., 2017;Nguyen et al., 2018), where a positive relationship between asset values and natural resource dependence implies an attempt by the households to build adaptive capacity. This is because the fishery is associated with a variety of disruptions, which could result in fluctuations in the income derived. Therefore, increased asset value is critical as a coping strategy and in sustaining consumption among dependent households.
Group membership in agricultural-related activities positively and significantly influenced the level of marine fishery dependence at 1% significant level. Beach management units (BMU) are the main groups in the region related to agriculture and, therefore, positively influenced marine fishery dependence. The reason for this is that beach management units are directly related to marine fishery, and its membership is critical in providing information, skills, knowledge, fishing gears, and market resources that enable fishers to be more efficient. Additionally, membership to beach management units enhanced repeated social cohesion and peer influence, which influences members' behaviour to participate in common economic activity (Alexander et al., 2018).
Access to credit negatively and significantly influenced marine fishery dependence at 5% significant level. Credit access increases financial resources, reduces cash constraint, and enhances participation in off-fishery investment opportunities, reducing dependence on marine fishery. The results are in line with the work of Kimengsi et al. (2019) and Amevenku et al. (2019), who found that lack of access to credit increases dependence on natural resources. The reason for this is that access to credit prompts households to pursue more lucrative livelihood strategies that are not labour intensive. It has been established that households that have efficient access to credit tend to promote nonfarm activities rather than increasing investment in fishing (Amevenku et al., 2019). Similarly, Kleih et al. (2013) reported that small-scale and medium actors in the fishery sector in Egypt have poor access to credit due to the information gap and lack of suitable collateral.
Security tenure of land negatively and significantly influenced the level of marine fishery dependence at 10% significant level. Ceteris paribus, the security of tenure, decreases marine fishery dependence by the probability of 0.5424%. Land ownership right prompts households to invest in agriculture and off-fishery employment opportunities. Security of tenure provides the right to usage, which encourages individuals to engage in entrepreneurial livelihood strategies outside the marine fishery such as business investment and intensive agricultural production. Another possible explanation relates to the land and squatter issue facing the coastal community. The problem was contributed by the colonial era, non-recognition of the land tenure security system, and hence dispossession of indigenous people by successive regimes. Despite the enactment of the 2010 constitution, the squatter problem continues to persist due to a lack of independent enforcement mechanisms and political goodwill (Githunguri, 2017;Klopp & Lumumba, 2017). The political destabilizing effect in land reforms in Kenya's coastal region has increased land inequality among households. As a result, local individuals are exposed to land-related violence, social division, and impeded economic growth, which has subsequently enhanced dependence on natural livelihood options. The result is consistent with the work of Teshager Abeje et al. (2019), who established that dependence on one particular livelihood option is significantly influenced by insecure land ownership rights.
Marine fishery dependence was further influenced by shocks related to flood at 10% significant level. The results indicated that households that experienced floods have a higher likelihood of dependence on marine fishery and related activities. This is particularly true in Malindi during the rainy season when river Sabaki moves into the Indian Ocean. Mixing of waterfronts with different density raises the sea level and result in high and strong tides disrupting fishing activities. Similar findings were found by Nguyen et al. (2018), who founded that households that are dependent on natural resources have faced a significantly Note: *significant at 10%, **significant at 5%, ***significant at 1% higher number of shocks, such as floods. Ndhlovu et al. (2017) argued that the small-scale sector is vulnerable to climate change and through flooding events and fluctuating water levels.
Further, Price shock had a positive and significant effect on marine fishery dependence at 1% significant level. The reason for this is due to the seasonality nature of the livelihood option. Marine fishery catches depend on seasons with kusi season, which occurs from April to July, being the poorest. During this period, rough seas and heavy rainfall hamper the supply of fish and subsequently result in a higher price. On the contrary, kaskaz season, which occurs from August to March, is suitable for fishing activities since the ocean is relatively calm. However, due to the higher supply of fish, price decreases, which ultimately reduces both absolute and relative income from marine fishery and related activities. The findings indicated how the vicious cycle between weather shocks and price uncertainty results in natural resource dependence. Given credit constrain, majority of the households in the region used risk-sharing strategies such as advance payment to cope with flood events and price fluctuation in the fishing industry .
Conclusion and policy implications
This study examined the drivers of marine fishery dependence among households in the coastal lowlands of Kenya using the fractional response model. The key factors influencing marine fishery dependence were education level, access to credit, security of tenure, group membership, agricultural productive assets, flood shock, and fish price shock. Given that poor households mainly depend on marine fishery and related activities there is the need for government intervention in non-formal education programs, financial support, beach management units, and infrastructural development. Public policies built around these aspects will enable adequate marine conservation, good financial management, loan acquisition, and entrepreneurial behaviours. As a result, the dependent households will be able to diversify to alternative livelihood options that will subsequently increase their adaptive capacity and achievement of the blue economy approach.
Further, efficient land right system is considered critical for sustainable livelihood options. Slow adjudication has been observed to jeopardize settlement programs that have subsequently contributed to insecure land tenure in the Country. Therefore, the establishment of the administrative and legal framework that addresses historical claims and regions with squatter problems is critical to solving the landlessness problem. More generally, reducing or alleviating chronic poverty in fishery requires public services, social protection, and enhancing of individual and collective assets. This implies that more comprehensive governance option remains significant for responsible fishery and improving household welfare in the coastal lowlands of Kenya.
This study can be extended to other regions in Kenya to allow for generalization of the research findings for effective policy recommendation critical to contribute to the achievement of blue economy approach. Areas for further research include determining factors influencing vulnerability of the coastal fishing communities and evaluating the effect of migrant fishers on local fishers' income and wellbeing. | 6,777.4 | 2021-01-01T00:00:00.000 | [
"Environmental Science",
"Economics"
] |
Dynamical properties and path dependence in a gene-network model of cell differentiation
In this work, we explore the properties of a control mechanism exerted on random Boolean networks that takes inspiration from the methylation mechanisms in cell differentiation and consists in progressively freezing (i.e. clamping to 0) some nodes of the network. We study the main dynamical properties of this mechanism both theoretically and in simulation. In particular, we show that when applied to random Boolean networks, it makes it possible to attain dynamics and path dependence typical of biological cells undergoing differentiation.
complex web of interactions among genes, RNA, proteins, other gene products and other molecules in the cell.
There are beautiful models of various parts of this system; for example, some describe in detail the steps which lead from DNA to mRNA to ribosomes, and some describe the details of the regulation processes. They are very well suited to describe in-depth the translation and transcription processes, but here we are rather interested in global activation patterns of thousands of heterogeneous entities. Therefore, it is mandatory to introduce some simplifications to keep the model manageable and meaningful. One interesting possibility (pioneered by Stuart Kauffman about 50 years ago) is that of simplifying the picture to that of a network of interacting genes only, without explicitly taking into account the other entities-whose influence is subsumed by the interaction rules among the genes.
Following this approach, we will consider a dynamical system of n "genes", which can take different activation values. The time change of the activation of a gene is ruled by a differential (or difference) equation; the rules can be different for different genes. As time passes, the system will tend to one of its stable steady states-to be associated to a cell type.
This framework is attractive, due to its simplicity, yet it has to be reconciled with the crucial process of cell differentiation, i.e. the development of pluripotent types into fully differentiated ones, through various intermediate stages. If we associate a pluripotent type with a stable or metastable attractor, then it is necessary to understand what can make this attractor unstable. One might be tempted to identify the process of cell maturation with transients, while attractors would be associated to mature cell types only. However, pluripotent intermediate types may be long lived, so it is difficult to devise satisfactory models along this line. It seems more appropriate to associate attractors also to the long-lived intermediate states, but in this case it is necessary to identify mechanisms which can actually modify the dynamical system, making these attractors unstable and driving the system towards the other attractors. This is possible since the cell is not a fully autonomous system, but it is coupled with the environment in which it lives. Various molecules can indeed interfere with the gene expression mechanisms, switching on or off single genes or groups of genes. However, one has to resist the temptation of simplistic explanations based only upon detailed properties of external influences, because the gene network (or rather, the whole dynamical system it represents) is always present. Switching one gene on or off does not only lead to the synthesis of a specific protein or gene product, but it may also affect the expression of other genes. The activation patterns must be coherent with the whole dynamics of the network Huang and Kauffman (2013).
In this paper, we explore the behaviour of a control mechanism, which affects the attractors of a gene network and their stability. This mechanism, which is simple enough to be amenable to large-scale simulation, is directly inspired by biological observations and experimental knowledge about the effects of DNA methylation; refer to Sect. 2 for a brief introduction on biological methylation and its role in the gene expression process.
While the model is described in detail in Sect. 2, let us mention here that we will make the simplifying assumption that nodes are either off or on, i.e. that they take Boolean values, neglecting intermediate values. Moreover, let us suppose that the time evolution of the network is synchronous and deterministic in discrete steps. The attractors of any finite network are therefore either cycles or fixed points (which can be regarded as period-1 cycles). The silencing effects of methylation will therefore be modelled by keeping constantly equal to zero the activation of some nodes (which will be called "externally frozen", briefly e-frozen, or sometimes "blocked"). The network is assumed to be in one of its attractors at time t < 0, while at time t = 0 some nodes are externally frozen. The clamping to zero of a node may affect other nodes, inducing in time a propagation of freezing (i.e. an increase in the number of constant nodes) and the attractor landscape will be modified.
In Sect. 3, we will present a simplified, mean-field description of this process of propagation in time of freezing. In Sect. 4, we will describe the results of several simulations of the effects of freezing on the attractor landscape, investigating the effects of the choice of some key parameters. A particularly important question concerns the importance of the order (in time) of gene silencing: if gene A is silenced at a certain time step, and gene B is silenced at a later stage, will the attractors and their basins be the same as in the case where silencing of B precedes that of A? Interestingly, one can prove that the answer is (sometimes) no, so the system described here can show a strong form of path dependence. This is described in detail in Sect. 5. The final section is devoted to a discussion of the results and of their biological implications.
Methylation model
Epigenetic mechanisms significantly contribute to the determination and maintenance of cell fates in biological organisms. Methylation, in particular, can occur to both DNA regions and proteins. DNA methylation typically occurs at CpG sites 1 changing the activity of DNA sequences, e.g. blocking gene promoter activities and hence their ability to induce transcription, without requiring mutations. Differently, histone methylations change the degree of compactness of the chromatin by adding methyl groups to histone proteins: proteins on which DNA of eukaryotic cells wrap around to form structural units called nucleosome. Changes the degree of compactness of chromatin have implications on the expression of genes belonging to the DNA regions involved: condensed DNA (heterochromatin condition) prevent transcription by polymerases, while loosely packed regions (euchromatin condition) allow transcription. Differential methylation is therefore a mechanism exploited by biological cells to modulate gene regulation and expression during development and differentiation.
Taking into account histone methylation, we observe that although its effects depend on the particular positions on histones on which it acts, it most often leads to heterochromatin conditions (Gilbert and Barresi 2016;Perino and Veenstra 2016;Schuettengruber and Cavalli 2009). In addition, along lineages, the attained configurations of DNA methylation are inherited and progressively extended as cells become more specialised Kim and Costello (2017). Therefore, methylation has a prominent role in the maintenance and stabilisation of the attained gene expressions that ultimately characterise the identities of the various cell states. It is also worth mentioning that methylation is tightly regulated by complex interactions, and that its dysregulation can be the causa prima of a lot of disorders, from cognitive, neurological and chronic diseases to cancer. It is precisely due to the complexity of these mechanisms that the adoption of models can support the analysis of the role of methylation, and more in general of epigenetics, in (patho)physiological processes.
Several works making use of mathematical models of specific aspects of epigenetic processes are present in the literature (Bull 2014;Miyamoto et al. 2015;Turner et al. 2017Turner et al. , 2013; however, to the best of our knowledge, there is no systematic attempt to study the capabilities of reproducing differentiation dynamics by methylation-like mechanisms in modelling. A noteworthy model of differentiation, based on an entirely different mechanism (i.e. intracellular noise), had been presented elsewhere Villani et al. 2011;Villani and Serra 2013). It is likely that the mechanism described here might flank the previous one in giving rise to the complex phenomena of differentiation. In a previous work conducted by us Braccini et al. (2019), we began to investigate the effects of a simplified model of methylation in the dynamics of Boolean networks. Boolean networks (BNs) are a well-known model of biological genetic regulatory network introduced by Kauffman (1969), frequently employed for the investigation of the causes of the rich dynamics arising in complex systems, such as biological cells. Indeed, despite their simplifications, they proved to be suitable systems to represent the dynamics of biological GRNs to many level of abstractions Graudenzi et al. (2011), Serra et al. (2006, Serra et al. (2007), Shmulevich et al. (2005).
Formally, BNs are discrete-time and discrete-state dynamical systems. Following their original formulation, they can be represented by a directed graph with n nodes each having associated a Boolean variable x i , i = 1, . . . , n and a Boolean function g i = (x i 1 , . . . , x i k ) which depends on k other nodes. One among the most prominent classes of BNs is that of random BNs (RBNs), in which functions and connections are chosen according to pre-defined distributions. A special case is the one in which nodes receive exactly k distinct inputs chosen at random (avoiding self-loops) and Boolean functions are defined by choosing for each of the 2 k entries of the truth table value 1 with probability p, being p called the bias. RBNs exhibit a phase transition between order and chaos depending on the values of k and p Derrida and Pomeau (1986), Bastolla and Parisi (1997). For 2 p(1 − p)k < 1, RBNs have on average an ordered behaviour, whilst for 2 p(1 − p)k > 1 the networks show extreme sensitivity to initial conditions and very long cyclic attractors, which denote a chaotic behaviour. A critical regime is attained for 2 p(1 − p)k = 1. Critical RBNs have proven to show properties typical of real cells Nykter et al. (2008), Roli et al. (2018), Serra et al. (2007), Serra et al. (2004), Villani et al. (2018). The abstract methylation model to which we will refer has been introduced in our work mentioned above and it will be summarised in its main concepts in the following. It is inspired by the idea of progressive methylation of chromatin along the development and differentiation of biological The specific expression patterns of nodes in the ennuples that represent the state of the BN over time have no other meaning than to exemplify the increase in methylated nodes, characteristic of the methylation process introduced cells. So, by analogy with the heterochromatin status, the methylation process in Boolean networks has been modelled by blocking the expression of some BN nodes to value 0; these nodes will be referred to as e-frozen in the following (this process is sketched in Fig. 1). This model is based on the hypothesis that-even though it is not the only phenomenon in place-the progression of frozen nodes imposes the arrow of time of the differentiation process. A differentiation model, and therefore also this methylation-based one, should accommodate, at least some, properties related to the differentiation phenomenology. We have already shown that the model in question can give rise, progressively, to reduced alternatives and an increment in stability along the differentiation process-representing cell types by means of the system's attractors Huang et al. (2005), Huang et al. (2009), Huang andIngber (2000). Moreover, we assessed its ability to maintain diversity in terms of possible asymptotic states originating from different combinations of frozen nodes, during all the differentiation process described by the progressive freezing itself.
In this work, we provide a theoretical predictionsupported by a mean-field model-on the progression of the number of fixed genes as a consequence of perturbations in terms of externally frozen genes. The model predictions will be validated through simulations of ensembles of RBNs. This step highlights the twofold role of the model: its applicability in the determination of prediction in finite-size model's instances and its role as a means to generalise the obtained results.
Furthermore, we experimentally investigate, by employing ensembles of RBNs, the presence of a stronger version of the classical path dependence, i.e. the possibility of reaching different asymptotic states (cell types) by freezing the same set of nodes (genes), but in different temporal orders.
Related models
Since the seminal works by Kauffman (1969), Kauffman (1993), Boolean networks have become one among the most used models of GRNs Albert and Thakar (2014), as also proven by the availability of Boolean models in the Cell Collective repository Helikar et al. (2012), hence our first motivation for studying the effect of a methylation-like mechanism in RBNs. Besides this, BNs provide a suitable level of description for cellular dynamics, as they both make it possible to reify the notion of gene activity, and enable to apply the ensemble approach Kauffman (2004) by in silico simulations of BN models with given characteristics.
However, several variants of BNs exist and alternative or complementary models are available De Jong (2002). An overview of these models is out of the scope of this contribution; anyway, here we provide a succinct list of the ones we may suggest as a starting point for the interested reader. Apart from BNs subject to asynchronous dynamics (Harvey and Bossomaier 1997;Di Paolo 2000) and mixed dynamics Darabos et al. (2007), a prominent variant of classical BNs is that of Probabilistic BNs Shmulevich and Dougherty (2010), which extend the original model by allowing for multiple functions per node, each associated to a probability of being chosen for the update. Multi-valued logic networks have also been proposed Thomas et al. (1995). Nonlinearity can be still preserved also in models including continuous variables, as in piecewise linear differential equations (Glass and Pasternack 1978;Kappler et al. 2003;Roli et al. 2010). We conclude this summary by mentioning a couple of recent BN extensions that allow for formal verification of some dynamical properties by means of modal logics. The first is that of Reactive BNs Figueiredo and Barbosa (2018), which introduces the notion of reactive frames Gabbay and Marcelino (2009a) into BNs. The second one builds upon Abstract BNs Yordanov et al. (2016)-whereby update functions might be partially known-and provides a model checking tool for the verification of network dynamical properties Goldfeder and Kugler (2018).
A mean-field description of the spreading of freezing
The external freezing of some nodes can modify the dynamics of the network, since the values of other nodes may be affected. Moreover, at successive time steps, more nodes will be frozen in cascade (locked at either 0 or 1 as a consequence of the external freezing), so there will be a spreading of freezing in the system. In order to understand the main features of this process a simple, mean-field model can be studied. Of course, specific networks can behave in a way very different from the average one.
Note that we will consider here only RBNs with two inputs per node, which suffice to provide information about the most relevant properties of the model. Generalisations are formally straightforward but calculations can become complicated. We will also assume that all the Boolean functions are allowed, with the same probability, so there is a symmetry between 0's and 1's. As discussed above, these assumptions imply that the network starts in a critical state.
Let us suppose to start in attractor A at time t < 0: There is an initial set V i of nodes which can take different values in A, and a set F i of fixed nodes in A. The union of these two sets is of course the whole set of nodes V i ∪ F i = U . Note that here we do not care about the value of a fixed node, but we focus on the distinction between fixed and oscillating nodes. Let f i and v i be the fractions of fixed and variable nodes in A, with At time t = 0 some nodes are externally blocked to 0. If they are constant nodes, the balance of fixed versus variable nodes does not change, so we will consider only those nodes which were previously variable. So at time t = 0 let a fraction b 0 of nodes (which were in V ) be externally blocked to 0, and let B 0 be their set. At time t = 0, the total fraction of constant nodes jumps to In the evolution from t = 0 new nodes can become constant due to spreading of freezing. Now we consider how many nodes will be frozen at the following time step t = 1 (excluding from this count those which are already constant at time 0). Each node has two inputs, therefore a randomly chosen node in V can become frozen iff one of the following conditions apply: (i) its two inputs were in V and are now both in B, due to novel freezing; or (ii) only one input is in B due to novel freezing, while the other was already in F due to the network dynamics; or (iii) only one input is in B due to novel freezing, while the other one is variable, and the Boolean functions are such that the daughter node which was in V now becomes frozen (i.e., when the function of the node is canalizing 2 in that particular value assumed by the frozen parent).
Note that it is also possible that the daughter of two nodes in V be in F, and that this might be altered by the blocking of one node at t = 0. The probability of this event depends quadratically upon v i , which decreases along the freezing process. Therefore, we decided to neglect this term. The probabilities associated to the previous situations are the following (i) two parents move to B: b 2 0 ; (ii) one moves to B, the other was in F: The factor 2 is due to the fact that the node originally in V can be either parent; (iii) one moves to B, the other was in V : 2ηb where the spreading constant η is the probability that a randomly chosen node becomes frozen (at time t + 1 etc.) if one and only one of its parent nodes becomes frozen (at time t) while the other parent node was not frozen at time t. In the case k = 2, all the Boolean functions allowed with equal probability it turns out that η = 1/2. Therefore 2ηb Of course, in order to have a change in the fraction of fixed nodes it is necessary that the target node was a variable one (an event whose probability is v 0 ), so the above values must be multiplied by v 0 . Therefore, summing the contributions from (i) to (iii), the fraction of newly frozen nodes at time t = 1 is where one has taken into account that f 0 + v 0 = 1. Summarising, at time t = 1 At the following time step, the situation is basically the same, except that now the system starts from the values of f and v given by Eq. 4, and the newly frozen nodes are b 1 . Applying the same procedure one gets This is true in general, so This formula can be applied at the following time steps. However, dealing with finite networks it is necessary to observe that nothing changes if less than one node has to be added, so the freezing necessarily halts when b k < 1/N . It is interesting to note that the freezing dynamics described above tends to finite asymptotic values, as shown in Fig. 2.
Simulation results against model predictions
We set up the following in silico experimental setting to compare the RBNs dynamics against the model predictions concerning the spreading of freezing induced by external perturbations-in terms of nodes clamped to 0 values-to system's attractors. For assessing the freezing progression in RBNs, we considered different experimental conditions: ensembles of RBNs with 100 nodes in critical dynamical regime-k = 2, bias= 0.5-and with b 0 ∈ {1, 3, 5, 10}. In accordance with the methylation-inspired model previously formulated, we used a synchronous updating scheme for state update of BNs. For each parameter configuration, we drew 100 samples (RBNs) and for each of them we started from one of its attractor (let's call it A 0 ), chosen at random; with reference to the mean-field model, this represents the situation at t < 0. Then, we clamp to value 0 a number b 0 of nodes; otherwise, if the attractor does not present a number of variable nodes greater than or equal to b 0 we replace the network with another one that satisfies this requirement. After having perturbed the attractor A 0 3 we follow its trajectory recording the number of nodes that are fixed on a value (regardless of whether they are 0 or 1) and which remain so until the dynamic relaxes to its new attractor. In this way, we were able to keep track of the trend of the fixed nodes along all 100 trajectories, as shown in Fig. 3 (see also Figs. 15,16,17 in Appendix): with the blue dots we denote the fixed nodes at t < 0, while with the red triangles we denote the final number of frozen nodes in the new stable dynamic condition.
The figures regarding the progression of fixed nodes show considerable variability. As predicted by the mean-field model, in each replica we observe the tendency to converge to a value smaller than the maximum, i.e. 100 nodes for these experiments. However, to perform a comparison between the experimental and theoretical results, we must take into consideration the progression of the number of the mean fixed nodes predicted by the model and compare itstep by step-with the average of the experimental values of fixed nodes. This comparison has been done for all the experimental conditions reported above. The steps reported, including standard deviation and standard error of the mean, are presented for the number of steps for which at least 30 samples have been found. The results for b 0 = 3 are shown in Fig. 4 (see also Figs. 12,13,14 in Appendix for the other comparisons). As starting condition for computing the model predictions, we have used an initial value of fixed nodes equal to the average value of the fraction of fixed nodes found in 3 We choose the last state in ascending lexicographic order, to be almost sure to perturb the attractor's state also in cyclic attractors effectively. In the plot, only the steps in which there was a number of samples equal to or greater than 30 have been reported. For these steps also the sample standard deviation (grey area) and the standard error of the mean (green area) have been computed and reported random attractors chosen as initial condition in our ensembles of RBNs at time t < 0 (i.e. in their wild condition). In particular, we used f 0 ∈ {0.6866, 0.6903, 0.6419, 0.6424} for values of b 0 ∈ {1, 3, 5, 10}, respectively. The comparison shows, in general, a high agreement between the values predicted by the model and the experimental results. On the one hand, as pointed out in the previous sections, this supports the validity of the mean-field model for the prediction of fixed nodes spreading and allows us to generalise the results obtained even for larger networks in size, which would be otherwise computationally too expensive to simulate. On the other hand, these results provide further support to the validity of this specific methylationinspired mechanism as a driving mechanism for reproducing differentiation phenomenology in Boolean networks. Indeed, a bounded spreading of fixed nodes in response to externally frozen genes is a necessary condition for the existence of a progression of different (meta)stable activation patterns of gene expressions, and so for the existence of differentiation lineages.
Path dependence
The notion of path dependence is expressed as the property of attaining different outcomes from an initial common condition, depending on the path taken by the process Desjardins (2011). The role of path dependence in biological systems has been thoroughly discussed (see, e.g. Longo (2018), Szathmáry (2006)) and is a core ingredient in cell Fig. 5 A graphical representation of the specific formulation of the path dependence taken into account throughout this work. σ 1 and σ 2 labels represent different sets of freezing genes. We ascertain the presence of path dependence if the attractors A 4 and A 5 , reached by different temporal sequences of the same freezing sets of genes, are different differentiation Huang (2009). In this work, we focus on a specific definition of path dependence that refers to the capability of reaching different attractors under different freezing sequences of the same genes, i.e. the attractor reached by freezing first gene σ 1 and then gene σ 2 can be different than the attractor reached by first freezing σ 2 and then σ 1 . In a sense, this is a stronger version of the classical path dependence, as it refers to the possibility of reaching a different outcome by acting on the same set of control variables, but in different temporal order. This property is particularly important because it makes it possible to manoeuver a limited number of control variables to lead the system towards target asymptotic states.
To assess to what extent RBNs exhibit path dependence as a function of the order of gene freezing, we run experiments in which two genes (or two small groups of genes) σ 1 and σ 2 are successively frozen in both orders starting from the same state, and the final attractor reached is compared. A graphical representation of this process is depicted in Fig. 5: let us suppose to select two sets σ 1 and σ 2 of nodes to be externally frozen; starting from a state in attractor A 1 4 we freeze the nodes in σ 1 (resp. σ 2 ) and let the network relax to the asymp- Table 1 Parameters of the experiments for assessing path dependence n = 100 , k = 2, bias = 0.5 |σ i | ∈ {1, 3}, i = 1, 2 100 randomly sampled pairs σ 1 , σ 2 m ∈ {10, 40, 70} totic condition represented by attractor A 2 (resp. A 3 ); once in A 2 (resp. A 3 ), the other group of nodes is frozen and the final attractor A 4 (resp. A 5 ) is stored. If the two final asymptotic states are different, then we can say that the network shows path dependence under the condition that the starting attractor is A 1 and the pair of sets to be frozen is σ 1 , σ 2 . By replicating this comparison with random samples of σ 1 and σ 2 , we can estimate the tendency of a RBN to exhibit path dependence. The comparison between two asymptotic states reached after freezing two different groups of nodes requires some care, because one cannot simply compare the two attractors tout court, i.e. on all the nodes, for they are anyway very likely to differ in the frozen nodes by construction (they might coincide only in the rare case in which in both the attractors A 2 and A 3 , reached by, respectively, freezing the two different groups of nodes σ 1 and σ 2 , all the nodes in σ 1 are constant at 0 in A 3 and all the nodes in σ 2 are constant at 0 in A 2 ). Furthermore, a comparison based on the set of nodes complementing σ 1 ∪ σ 2 would miss an important modelling assumption: The observed cell types are a function of a limited number of coding genes, which are in turn controlled by the remaining part of the network (Borriello et al. 2018;Espinosa-Soto et al. 2004;Fumiã and Martins 2013). Therefore, we compared the asymptotic states on the basis of the values assumed by the nodes in a subset M, that we name attractor projection of size m = |M|. Referring to Fig. 5, proj(A 4 |M) = proj(A 5 |M) if the network states of the two attractors restricted to the nodes in M are the same. The nodes to be frozen are chosen outside M and excluding those nodes always at 0 along the attractor.
The assessment of the general tendency towards path dependence of RBNs has been undertaken in different experimental conditions, summarised in Table 1. For each parameter configuration, 1000 RBNs were generated and for each of them the experiment has been replicated for 100 random samples of σ 1 and σ 2 and from a different initial number of externally frozen nodes. In this way, it is possible to ascertain the progressive tendency of exhibiting path dependence, while the number of externally frozen nodes is incremented. A representative example of the results we obtained is depicted in Fig. 6-the results of the other configurations are qualitatively the same and can be found in the Appendix. For each RBN, the fraction of pairs σ 1 , σ 2 that led to differing asymptotic states (y-axis) is shown w.r.t. the number of initially frozen nodes (x-axis). The area below the segmented line is coloured so as to have a pictorial view of the density of this property across RBNs: the darker the colour of a point , the higher the fraction of RBNs with that amount of path dependence. We first observe the darker area, corresponding to the majority of replicas: The impact of path dependence is about the 20% in the case without initially frozen nodes and decreases linearly to a smaller yet nonnegligible fraction. This trend is qualitatively the same by considering the distribution of this fraction across the RBNs; nevertheless, we observe that path dependence is still quite intense even for about one fourth of initially frozen nodes in a considerable fraction of replicas.
Conclusion
In the previous sections, we have shown that RBNs subject to (possibly progressive) external clamping of some nodes to a fixed value exhibit the properties of (i) keeping the avalanche of frozen nodes bounded-so that on average the asymptotic fraction of frozen nodes is less than 1-and (ii) allowing the possibility of reaching two different asymptotic states by exerting the external freezing of the same genes but in different temporal order. The first property guarantees that in general the network has still multiple fates after e-freezing, i.e. the external intervention can constrain and control the future asymptotic states but does necessarily imply a final unique asymptotic state. The second property-i.e. strong path dependence-shows that it is possible to reach different fates by simply permuting the sequence of external node freezing. This overall outcome supports the use of external freezing in analogy with methylation mechanisms in real cells, enabling us to address some questions on cell differentiation by looking for generic properties in RBNs. For example, one may ask how many control actions are available to reach fates with given characteristics, or, conversely, whether there exist fates that can be reached by specific or fragile sequences of external freezing interventions. Future work is indeed planned to assess in more detail the controllability of RBNs under e-freezing in terms of control theory. A question may arise as to whether the choice of externally freezing the nodes to 0 might impact the results. In RBNs with k = 2 and Boolean functions uniformly distributed, the average distribution of 0s and 1s is symmetrical, therefore the choice of either 0 or 1 simply breaks the symmetry towards one of the two values but the results are the same. Of course, results might differ if the RBNs considered have a different number of inputs per node and an asymmetric distribution of Boolean functions, but they are not expected to undergo qualitative changes.
In addition, we remark that our results are the outcome of models and experiments characterised by the hypothesis that networks are random, therefore one may ask to what extent these results can be valid also for real cells, which are supposed to be characterised by genes with non-random relations. First of all, some questions can be addressed in terms of generic properties-in the frame of the ensemble approach Kauffman (2004)-that can be investigated also in random models. Second, if some properties are generally found in random networks, even if with a mild tendency, it means that selection could easily exploit and enhance those properties during evolution. So ensembles of networks that are the result of evolutionary processes, or have different architectures than the one tested here (different degree of connectivity, scale-free, modular, etc.), or are subject to other updating mechanisms (asynchronous), or can accommodate some kind of expression noise (extrinsic and intrinsic ones), could better match the statistical features of real cells. On the other hand, the simple control mechanism we have discussed in this work may be also applied in the case of Boolean models of real gene regulatory networks. Furthermore, other models or formalisms that can be applied to the study of cellular differentiation can extend our proposed model. For this purpose, reactive graphs Gabbay and Marcelino (2009b), or more specifically reactive Boolean networks Figueiredo and Barbosa (2019), can assume the role of the control module and so represent the causal interactions that ultimately produces the progression of e-frozen nodescurrently externally controlled-that drive the differentiation process. Future work is planned in these directions.
Funding Open access funding provided by Alma Mater Studiorum -Universitá di Bologna within the CRUI-CARE Agreement.
Compliance with ethical standards
Conflicts of interest The authors declare that they have no conflict of interest.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecomm ons.org/licenses/by/4.0/.
Appendix
Pictorial view of the results concerning path dependence. For each RBN, the fraction of pairs σ 1 , σ 2 that led to differing projected asymptotic states (y-axis) is shown w.r.t. the number of initially frozen nodes (x-axis). The area below the segmented line is coloured so as to have a pictorial view of the density of this property across RBNs: the darker the colour, the higher the fraction of RBNs with that amount of path dependence (Figs. 7,8,9,10,11,12,13,14,15,16 and 17). | 8,199.8 | 2020-11-02T00:00:00.000 | [
"Computer Science"
] |
Health-risk assessment based on an additive to paints made from isobutyric aldehyde condensation products
Solvents are primarily used for making protective coatings. Considering their chemical nature, there are a great variety of coatings, including those based on liquid hydrocarbons and organic chloroderivatives. These products are a serious load to the environment because of their physicochemical properties, therefore, they have for some time been replaced with more-environmentally friendly, new generation products. One of them is the hydroxyester HE-1: made from isobutyric aldehyde condensation products, it is an alternative to those coalescents for paints and varnishes which are intended to be replaced or their use restricted. The results of selected toxicological tests relating to the human health risk effect of the hydroxyester HE-1 – environmentally-friendly additive to paints and varnishes are presented. The test results indicate that HE-1 causes skin irritation in rabbit only when used at its maximum concentrations. No lesions in the cornea or iris were observed in any of the test rabbits after the application of the hydroxyester HE-1. In the mutagenic effect test of HE-1 on the bacteria Salmonella typhimurium, the result was negative. Based on the test results, it was found that the hydroxyester HE-1 may only have a human health risk effect when used at its maximum concentrations.
Introduction
Nearly one-half of the volume of globally used solvents is in the production of protective coatings.These solvents are volatile products, made by the processing of natural gas and petroleum.They include liquid hydrocarbons and organic chloroderivatives which are a serious load to the environment [1,2].This is regulated in the Decopaint Directive (2004/42/EC) which came into force on 1 January 2007 [3].It relates to the reduction of emissions of solvents from decopaints and automotive varnishes, other than those covered by the limitations of Directive No. 1999/13/EC concerning solvent emissions [4].According to the Decopaint Directive, any substance having its initial boiling point at 250 o C or lower, as measured at a pressure of 101.3 kPa, is a Volatile Organic Compound (VOC).Lower VOC levels in paints can be obtained by developing paint formulations which contain coalescents not classified as VOC, or which contain coalescents and glycols classified as VOC in amounts not higher than the limits referred to in the applicable laws [5].
Phthalates were the prior choice as additives to paints and varnishes.Considering their physicochemical properties, there are two groups of phhthalates: highmolecular and low-molecular products.The highmolecular phthalates, which include di-isononyl phthalate (DINP) and di-isodecyl phthalate (DIDP), represent 80% of the consumption of phthalates in Europe alone.The low-molecular phthalates include dibutyl phthalate (DBP), benzyl-butyl phthalate (BBP), and di-2-ethyl-hexyl phthalate (DEHP), which are classified as products with high human health risk [1].Based on animal tests, it was found that the lowmolecular phthalates are toxic, therefore, they may not be used for manufacturing toys, articles for children, cosmetics, and medical devices [6].The adverse effect of phthalates on human health has been confirmed in a number of other research reports [2,7,8].
Most of the conventional coalescents, which compete with one another on the market, have their boiling points below 250˚Cthe criterion for being classified as VOC thus falling into the category of VOC with applicable quantitative limitations.Some new products on the market have their boiling points above the criterion for VOC (Table 1).The products with boiling points below 250 o C are approved for use on the condition that their total VOC emissions are not higher than the limits set out in the Decopaint Directive [3].The withdrawals in the sector of coalescents for paints and varnishes or products with limited use can be replaced, as an alternative, with the hydroxyester HE-1 (Fig. 1).
The aim of the study was to determine a human health risk effect of hydroxyester HE-1 and another advanced additives to paints and varnishe.Table 1.Structural formulae and boiling temperatures of advanced additives to paints and varnishes [5,9].
Structural formula
Propane-
Material
Hydroxyester HE-1 is obtained in a sequence of chemical reactions, where isobutyric aldehyde is the basic starting material in the process of aldol condensation with the subsequent Cannizaro and Tiszczenko reaction, as shown in the diagram in Fig. 1.HE-1 is hydrophobic solvent and its major application is a coalescent in water-based architectural paints.
The product has a boiling point of 255 o C and is not classified as a VOC [10,11].Since a HE-1 production plant of a capacity of more than 100 Mg per year is going to be launched, an assessment of the product in terms of its toxicological and ecotoxicological properties is required [12].In this paper, the results of the human health risk tests of HE-1 are discussed.The toxicological tests were carried out in accordance with Good Laboratory Practices at the Institute of Industrial Organic Chemistry, Pszczyna, Poland.
Methods
The results of three selected toxicological tests of the hydroxyester HE1 are presented in this paper.The objective of the test was to obtain information on the health-risk effect of potential dermal exposure to the test material.An initial test was carried out using one animal.A single 0.5 cm 3 dose of the test material was applied to a hairless skin site of one animal (rabbit 1) and secured with asuitable tape.The exposure time was 4 hours.
After the examination of the exposed skin, the test material was applied to the skin of two more animals (rabbits 2 and 3) for a period of 4 hours in order to confirm the presence or absence of irritant effect.The procedure was the same as in the application of the test material to the skin in rabbit 1.
For the duration of the experiment, all the test animals were subjected to general clinical observations for morbidity and mortality.Detailed clinical observations of the exposed skin in the test animals were conducted at 1, 24, 48 and 72 hours after exposure as well as at 7 and 14 days after exposure [14].
The results of the detailed clinical observation of the test animals were reported using the classification referred to in the OECD Test Guideline No. 404/ Method B.4. (Table 2).According to [15], a substance or mixture applied to the skin in rabbit is classified as irritant after developing a clearly inflammatory condition which persists for at least 24 hrs after exposure.The skin condition is clearly inflammatory if: -mean grading for either the erythema and eschar formation or for the oedema is at least 2, -both the erythema and eschar formation and the oedema formation were graded individually in two or three test animals as being equivalent to the mean score of at least 2 for the respective animals.
In both of the above cases, the mean values were calculated from all the reactions graded after 24, 48 and 72 hrs.
A clearly inflammatory condition of the skin will persist in at least two animals until the end of the observation.The following specific effects are taken into consideration: cell growth, desquamation, discoloration, cracking, eschar formation, hair loss [15].
Acute eye irritation/corrosion test in rabbit according to OECD Test Guideline No. 405 [16]
The objective of the acute eye irritation/corrosion test was to obtain information on the health-risk effect of potential exposure of the eyes to the hydroxyester HE-1.
According to the test procedure, 0.1 cm³ of the hydroxyester HE-1 was placed in the conjunctival sac of one eye of each test animal whereas the other eye, which remained untreated, served as control.In order to confirm the presence or absence of irritation, the procedure was applied to three test animals.For the duration of the experiment, all the test animals were under general clinical observation, conducted daily for morbidity and mortality.Detailed clinical observations of the exposed eyes, regarding lesions of the cornea, iris, and conjunctiva were conducted at 1, 24, 48 and 72 hours after exposure.
For grading the acute eye irritation/corrosion effect, the following classification of lesions in the eye was applied (Table 3).The grading relates to the lesions in the cornea, iris and conjunctiva [17].According to [15], a substance or preparation has irritant effect on the eye if, following test substance E3S Web of Conferences 19, 02029 (2017) application in the conjunctival sac of the test animal, it produces obvious lesions arising within 72 hours following application, which persist for at least 24 hours.
Ocular lesions are graded as obvious if mean scores correspond to any one of the following values: -corneal opacitygrade 2 or higher than 2 but lower than 3; -iridial lesiongrade 1 or higher than 1 but not higher than 1.5; -circumcorneal hyperaemiagrade 2.5 or higher; -conjunctival swellinggrade 2 or higher.
If three test animals were used in the test, the substance or preparation is considered to be irritant to the eye if ocular lesions in two or three test animals correspond to one of the grades referred to above, except for iridial lesion, where grade higher than 1 and lower than 2 applies, and for circumcorneal hyperaemia, where grade 2.5 or higher applies [15].
Bacterial reverse mutation test according to OECD Test Guideline No. 471 according to [18]
It was the objective of the test to determine the potential genotoxic effects produced by HE-1 in a standard Ames testshort-term mutagenicity test, recommended as a screen for genotoxic activity [19].The bacterial reverse mutation test uses amino-acid requiring strains of Salmonella typhimurium to detect point mutations, which involve substitution, addition, or deletion of one or a few DNA base pairs.This test detects chemical substances producing mutations which revert mutations present in the test strains and restore the functional capability of the bacteria to synthesize an essential amino-acid.The revertant bacteria are detected by their ability to grow in the absence of the amino acid required by the parent test strain.
Two essential mutagenicity tests were used in the tests described in this paper: plate incorporation (without metabolic activation) and preincubation (with metabolic activation).In the plate incorporation method, components are mixed with an overlay agar and plated immediately onto minimal medium.In the preincubation method, the test mixture (bacteria + test substance) is incubated before being added to an overlay agar and plated onto minimal medium.In both techniques, after 2 or 3 days of incubation, revertant colonies are counted and compared with the number of spontaneous revertant colonies on solvent control plates.Mutagenicity of the test substance is shown in the growing count of the revertant colonies [20].
The test substance has a mutagenic effect if the concentration-related (over the range tested) and reproducible increase at one or more concentrations in the number of revertant colonies per plate with or without metabolic activation, M f > 2 [20].In order to determine mutagenicity, the result was also compared with the negative control, dimethyl sulfoxide (DMSO).
Initially, the dose range was selected by testing HE-1 at concentrations up to 5 mg/platethe maximum test concentration according to the OECD Test Guideline No. 471.This preliminary experiment was performed to determine the HE-1 concentrations for the proper test.
The following HE-1 test concentrations were used in the proper test: 0.0001, 0.001, 0.01, 0.1, 0.5, 1 mg HE-1/plate.The proper test assessed HE-1 for mutagenicity using the bacterial strains of Salmonella typhimurium TA 100, TA 98, TA 97, TA 1535 and TA 102.The tests were carried out in the absence of an external metabolic activation systemin the standard Ames plate test (without modification and with preincubation), and with a S9 fraction derived from the Sprague-Dawley rat and treated with Aroclor 1254in the standard plate test without modification.Three experiments without metabolic activation and two experiments with metabolic activation were performed.
Acute dermal irritation/corrosion in rabbit according to OECD Test Guideline No. 404
Table 4 shows the results of the acute irritation/corrosion of the skin in two rabbits, graded from 0 to 4. The reaction of the test animals to the hydroxyester HE-1 was assessed by grading the skin reaction (Table 2).The acute irritation of the skin was assessed based on mean scores at 24, 48 and 72 hours.
At 1 hour after exposure, observation of the skin in the test site in rabbit 1 and rabbit 2 indicated a very slight erythema (barely perceptible).At 24 hours after exposure, the erythema was more intense and was graded as moderate to severe in the two rabbits.Moreover, a very slight (barely perceptible) oedema was observed in rabbit 1.
At 48 and 72 hours after exposure, the erythema in rabbit 1, which had lost some of its intensity by that time, was graded as well defined.Moreover, no oedema was found on the exposed skin in rabbit 1.During both observations, rabbit 2 continued to have a moderate to severe erythema and a very slight (barely perceptible) oedema.Moreover, epidermal desquamation was detected in that animal (Table 4).At 7 days after exposure, the skin of the two rabbits showed a very slight (barely perceptible) erythema and epidermal desquamation in the test site.The skin of rabbit 2 showed no oedema in the test site.14 days after exposure, both rabbits (1 and 2) showed no pathological dermal changes in the test site (Table 4).At 24, 48 and 72 hours after exposure, mean value for erythema was 2.3 for rabbit 1 and 3.0 for rabbit 2. For oedema, mean values were 0.3 and 0.7, respectively (Table 4).Therefore, on the basis of the test results and according to [15], it is justified to say that the hydroxyester HE-1 causes skin irritation in rabbit.
Acute eye irritation/corrosion in rabbit according to OECD Test Guideline No. 405
The results of acute eye irritation/corrosion in rabbit are shown in Table 5.The effect of the hydroxyester HE-1 on the cornea, iris and conjunctiva was evaluated by scoring the degree of lesions in the eye in rabbit (Table 3).Following test substance application, lesions were observed in the cornea, iris and conjunctiva of the eye in rabbit.At 1 hour following test substance application, no changes were observed in the cornea or iris of the eye in the three test rabbits.The conjunctiva of the eye in the three test rabbits showed a diffuse, crimson redness with individual vessels not easily discernible.Some swelling of the conjunctiva was observed in rabbits 1 and 3 and swelling with partial eversion of lids in rabbit 2. The three test rabbits were observed to have hyperaemia with swelling of the nictitating membrane, circumcorneal injection and an exudate on the eye lids and lid hairs.
At 24 hours following test substance application in the three test rabbits, no changes were detected in the cornea, the iris was hyperaemic, and reaction of the pupil to light was correct.Diffuse beefy redness was observed in the conjunctiva in the three test rabbits.The test rabbits were observed to have the same conjunctival swelling as at 1 hour, that is, some swelling above normal in rabbits 1 and 3 and obvious swelling with partial eversion of the lids in rabbit 2.Moreover, each of the three test animals continued to have a hyperaemic and swollen nictitating membrane, and circumcorneal injection.An exudate on the eye lids and lid hairs was detected in the test rabbits 2 and 3 and a small amount of discharge in the test rabbit 1.
Based on the test results and [15], it is safe to say that the hydroxyester HE-1 is not an eye irritant/corrosive in rabbit.
Bacterial reverse mutation test according to OECD Test Guideline No. 471
For the bacteria Salmonella typhimurium TA 100, mean range of spontaneous mutations was 184-297 revertant colonies per plate.The ratio between the number of revertant colonies per plate and the number of revertant colonies per control plate was not observed to exceed 2. At a ratio of 1 mg/plate in the experiments with and without activation system, the number of revertant colonies was significantly reduced (M f < 0.8).
For Salmonella typhimurium TA 1535, mean range of spontaneous mutations was 19-25 revertant colonies per plate.In one experiment without metabolic activation, a concentration of 1 mg/plate statistically significantly reduced the number of revertant colonies (M f < 0.6).Another experiment, with metabolic activation, a concentration of 0.01 mg/plate produced a statistically significant decrease in the mutation frequency in comparison with the positive control, though without biological significance (M f = 0.78).
For the bacteria Salmonella typhimurium TA 97, mean range of spontaneous mutations was 114-193 revertant colonies per plate.In one experiment without metabolic activation at a concentration of 1 mg/plate, a statistically significant increase in the mutation frequency in respect of the controls was detected, though without biological significance (M f = 0.66).
E3S Web of Conferences 19, 02029 (2017)
In the experiment with metabolic activation at a maximum concentration of 5 mg/plate, the level of frequency of revertant colonies was reduced in comparison with the negative control (M f = 0.35), and concentrations of 1 and 0.5 mg/plate significantly reduced the number of revertant colonies, though without biological significance.
For Salmonella typhimurium TA 102, mean range of spontaneous mutations was from 248 to 350 revertant colonies per plate.In the experiments without metabolic activation, the frequency range of revertant colonies was not higher in comparison with the negative control.In all the experiments, the number of revertant colonies was statistically significantly reduced at a concentration of 1 mg/plate (M f < 0.77), comparably to 0.5 mg/plate in one experiment.It was observed that the toxicity of the concentrations 0.5 and 1 mg/plate was related to the duration of contact of the test substance with the test bacterial strain before plating it onto the Petri dishes.In the experiments with metabolic activation, HE-1 was not observed to increase the number of revertant colonies above the frequency range of spontaneous mutations.For the bacteria Salmonella typhimurium TA 98, mean range of spontaneous mutations was 22-53 revertant colonies per plate.In the experiments without metabolic activation, the frequency range of revertant colonies was not higher in comparison with the negative control.In the experiments with metabolic activation, the mutagenic potential of HE-1 was not confirmed in the experiment with the fraction derived from the rat liver.
In the dose range from 0.0001 mg/plate to 1 mg/plate, HE-1 was not observed to increase the number of revertant colonies above the frequency range of spontaneous mutations.Figures 2 and 3 show the number of revertant colonies per plate for 5 different strains of Salmonella typhimurium with and without metabolic activation.All the results were compared with the negative control (DMSO).For the strain TA 102, at a HE-1 dose of 0.0001 mg/plate in the tests with and without metabolic activation, the number of revertant colonies was not determined.
From the data shown in Fig. 2 and Fig. 3, it was observed that the number of revertant colonies per plate was lower than that for the negative control regardless of the HE-1 dose in a majority of cases.For the bacterial strains TA 100 and TA 97 in the experiments without metabolic activation for a HE-1 dose of 0.001 mg/plate, the numbers of revertant colonies determined for the negative controls were exceeded, although not very highly: the percentages were 0.5 and 3%, respectively.A similar observation was made in the experiments with metabolic activation for a HE-1 dose of 0.0001 mg/plate and the strain TA 97 and for a HE-1 dose of 0.1 mg/plate and the strain TA 102, where the numbers of revertant colonies determined for the negative controls were slightly exceeded, by 1.4 and 1.8%, respectively.
The mutagenic effect test results indicate that HE-1 causes no statistically significant, dose-related increase in the number of revertant colonies and no statistically significant reproducible positive response to any of the test points.Based on the test results, the hydroxyester HE-1 was not found to produce a mutagenic effect in the test system.
Comparison of toxicological properties of hydroxyester HE-1 and advanced additives to paints and varnishes
Table 6 shows the results of tests of the essential toxicological properties of HE-1 and advanced additives to paints and varnishes which are described in the literature and of which the names and structures were provided in Table 1.Information about the toxicological properties of advanced additives to paints and varnishes was collected from the ECHA website [9].The following properties were compared: dermal and ocular irritation and mutagenic effect.For the compounds with the CAS numbers: 34590-94-8 and 6846-50-0, the mutation test was carried out using mammalian cells according to the OECD Test Guideline No. 476 [21].Only three of the advanced additives to paints and varnishes shown in Table 6, namely those with the CAS numbers: 55934-93-5 and 6846-50-0 and HE-1, are not classified as VOC.When comparing their toxicological properties, it can be observed that HE-1 may have an irritant effect only when used at high concentrations.None of the compounds produces a mutagenic effect and none is an irritant.Although the other additives to paints and varnishes in
Conclusions
The results of toxicological tests are discussed on the basis of three selected examples: acute dermal irritation test, acute eye irritation/corrosion test, and mutagenic effect test.
For the acute dermal irritation/corrosion test in rabbit, mean score for erythema was 2.3 and 3.0, respectively, in rabbit 1 and rabbit 2. For oedema, mean score was 0.3 and 0.7, respectively, in rabbit 1 and rabbit 2. Therefore, based on the skin reaction grading, test results and [15], it is justified to say that the hydroxyester HE-1 is a skin irritant in rabbit at its high concentrations only.
In the acute eye irritation/corrosion test in rabbit, no lesions were detected in the cornea or iris in any of the test animals.Only erythema was observed in the conjunctiva in the three test rabbits, which manifested itself as injected blood vessels and congestion of the nictitating membrane.Based on the test results and [15], it is safe to say that the hydroxyester HE-1 does not produce eye irritation in rabbit.
In the mutagenic effect tests, HE-1 did not produce any statistically significant, dose related increase in the number of revertant colonies, and no statistically significant, reproducible positive response to any of the test points.The results of the mutagenic effect tests indicate that HE-1 is not mutagenic in the test system used.
When comparing the results of toxicological tests for the hydroxyester HE-1 and advanced additives to paints and varnishes, it is safe to say that HE-1 is an environmentally friendly alternative to those additives which either are classified as VOC or are involve human health risk.
Poland National Centre for Research and Development in the years 2007-2010.
Table 3 .
Grading of lesions in the eye.
Table 4 .
Grading of acute dermal irritation/corrosion in rabbit.
Table 5 .
Grading of acute eye irritation/corrosion in rabbit.
Acute skin irritation/corrosion Acute eye irritation/corrosion Mutagenic effect
Table 6 do not show any human health risk effect, their boiling temperatures are below 250 o C, E3S Web of Conferences 19, 02029 (2017) | 5,237.4 | 2017-10-01T00:00:00.000 | [
"Chemistry"
] |
ZfA special issue “Digital Human Modeling”—an opening message from the IEA Technical Committee Digital Human Modeling and Simulation
“Quo Vadis, Homo Sapiens Digitalis? The human in the digitized world of work” is the leading topic for this year’s autumn conference of the German Ergonomic Association. It is not the first time this question has evolved in the community of digital human modelers in Human Factors & Ergonomics—in fact it has been a guide for the Technical Committee (TC) Digital Human Modeling (DHM) and Simulation (TC DHMS) of the International Ergonomics Association (IEA) over the last 13 years. The TC DHMS was founded in 2009 by Thomas Alexander and Gunther Paul and is currently one of 25 TC of the IEA. The TC has a non-formal flexible membership structure; it is chaired by three academics and practitioners, Gunther Paul (Australia), Sofia Scataglini (Belgium) and Gregor Harih (Slovenia). Any delegates of national ergonomic societies contributing to the TC’s annual digital human modeling symposia may be considered members of the TC. In another perspective the TC counts all the registered and globally distributed 612 members of its managed and members only LinkedIn social network group (IEA TC Digital HumanModelling and Simulation, https://www.linkedin.com/groups/2401911/) which was founded in October 2009. The IEA presents the committee scope in a historic point of view (https://iea.cc/member/digital-human-modelingand-simulation/):
"Nowadays, digital modelling of physical humans and their behavior has in many ways and application areas matured from research into industrial application. Nevertheless, a large potential for further development remains. With regard to human simulation, digital human models (DHM) have become commonly used tools in virtual prototyping and human-centered product design. They support human-in-the-loop (HITL) ergonomic evaluation of new product designs during the early design stages of a product, by means of modeling anthropometry, posture, forces, motion, muscular effort or predicted discomfort. While DHM are currently still largely stand-alone applications, future DHM will be dominated by fully integrated CAE methods, realistic 3D design, and musculoskeletal and soft tissue modelling all the way down to the microscale molecular processes within single muscle fibers. Important aspects of current DHM research are functional analysis, model integration, and task simulation. Digital ('virtual'; 'analytic') humanoids provide streamlined and efficient support of product testing and verification, allowing for task-dependent performance and motion simulation. Beyond rigid body mechanics, soft tissue modeling will become a standard in future DHM. When addressing advanced issues beyond anthropometry and biomechanics in a holistic perspective, human mental modeling, behaviors, abilities, and skills must be considered in DHM. Recent projects have proposed a more comprehensive approach to human modelling by implementing perceptual, cognitive, and performance models, representing human behavior on a non-physiologic level. Through integration of algorithms from the artificial intelligence domain, the vision of a virtual human will become reality." The IEA TC DHMS aims to increase and foster the awareness of ergonomics in the development of DHM technology, identify and share research needs in the field of digital human modelling, share data, algorithms, tools, and methods in the digital human modelling community, inform ergonomists and the general public about DHM technologies, and promote the relevance of ergonomics to stakeholders in domains relevant to digital human modeling and simulation, such as product design, interaction design, human-machineinterface (HMI) engineering, marketing, and management. The TC organizes an annual DHM symposium as a marketplace for basic and applied DHM research and promotes commercial DHM solutions and related technologies. The TC supports the IEA Triennial Congress through delivery of a DHM thematic track of research presentations. The TC meets once a year, typically in connection with the DHM symposium.
For over 10 years and until 2009, DHM had been globally "owned" by automotive engineers and their ergonomists, and the Canadian SAFEWORK system, now fully integrated into Dassault's various digital engineering software suites, may be considered the first DHM platform aspiring to overcome this unhelpful limitation. In 2009 the International Society of Automotive Engineers (SAE) had called for their annual SAE 2009 "Digital Human Modeling for Design and Engineering" conference, with nominated areas of interest "biomechanics, cognition and perception, dynamics and impact, size and shape analysis, and applications"-then the grand financial crisis struck, and it should not happen. SAE decided to discontinue their investment into DHM and the IEA TC DHMS was born at the IEA triennial conference in Beijing.
"Quo Vadis, Homo Sapiens Digitalis?" was also the topic of my invited introductory lecture for my first professorial appointment at the University of South Australia in 2009. At the time I had just accepted a call from this young University in Adelaide, resigned from my position in manufacturing engineering at Mercedes Benz in Stuttgart and relocated to Australia. I titled my presentation "Towards realistic digital human models-where are we, and how far do we still have to go?". My lecture concluded with the following statement: "So the future in digital human modelling has already started. This future will be dominated by CAE methods, realistic 3D design, musculoskeletal and soft tissue modelling down to the micro-scale molecular activity within single muscle fibers, functional analysis, integration, simulation, digital/virtual/analytic prototypes (DMU), better support of testing and verification and the consideration of task dependent performance and motion. The future will also overcome the human-manikin and manikin-manikin variability in simulation results. Beyond rigid body mechanics, soft tissue modelling will become normal in DHM.
Fascinating examples for what can be achieved here may be found in the work of Prof. Karen Reynolds at Flinders University. As the past has shown, progress often requires Standardization. In DHM, a first step has been done with the provision of the standard EN ISO 15536-Ergonomics: Computer Manikins and Body Templates, Part 1: General Requirements. Through Standards Australia, ISO and the International Ergonomics Association technical committee on digital human modelling, Ergolab will continue to drive future standards in this domain. We have to and we will focus on a holistic approach to overcome system incompatibility, an example of this are the different file formats that are used; to provide common protocols, seamless integration with process management software systems, including product and process data. In manufacturing, such a solution needs to interface to assembly sequencing, product development, process & production planning, production performance analysis, workplace & workload analysis, and last but not least, incident and risk analysis. While in the past digital human models were dominated by Realism, a characteristic which expresses that the human model physically resembles a human but actually behaves non humanlike, at the Ergolab we believe that future DHM must embrace Likeness: this means that a potentially strange creature behaves like a human in motion, body language and expressions. Naturally, future models must strive for both, and Avatar has provided a fantastic vision of immersive technology to come." Much of this has been accomplished over the last 13 years in breathtaking developments.
In 2018 we felt the need to summarize the current stage of DHM science, technology and development with a focus on the body outer and posture analysis-some might already consider this to be the past of DHM in 2022! With the generous support from the TC and contributions from the larger DHM community, Sofia Scataglini and I eventually produced "DHM & Posturography" in 2019 (Scataglini and Paul 2019), a comprehensive 823-page compendium of DHM published by Elsevier Academic Press. The book provides an overview of the current stage of digital human models in the world, including developments in America, Asia, Australia and Europe, and across various disciplines such as the military, automotive, product design, and health sciences. It also discusses challenges in digital human modeling, including standardization and integration.
It only took three years and already developments in DHM have moved significantly beyond the content covered in this book. Strategic and significant funding from the European Union have accelerated the genesis of "insilico medicine" with stunning advances in DHM over the last ten years. The "Digital Twin", initially a NASA concept which found its way into the core philosophy of Industry 4.0 has immensely expanded the community of researchers and practitioners developing DHM technology.
To keep pace with these ever-accelerating advances in DHM, I joined forces with a biomedical specialist, Mohamed Doweidar in Spain and started a new project in 2020, a collection called "DHM & Medicine-The Digital Twin" (Paul and Doweidar 2022) to be published later this year-again by Elsevier Academic Press. The Digital Twin (DT) concept very much epitomizes our vision for the future of DHM well into the 2030s.
While previously a niche specialist discipline, with universities, start-ups and SME dominating the market offering, large industrial players have now entered the market of DHM, including Siemens, Dassault Systems and Philips.
Siemens Healthcare GmbH (2019) describe the value of a DT in a healthcare environment by saying that "A digital twin can help healthcare enterprises identify ways to enhance and streamline processes, improve patient experience, lower operating costs, and increase higher value of care. The digital twin creates models of physical spaces and processes. Then, cost and quality optimization parameters are examined and ultimately selected based on the insights gained from simulations leveraging the digital twin. Digital twin insights can be further enhanced with complimentary technologies like Real-Time Locating Systems (RTLS), which provides a robust data source and a means to test changes in layout, process, etc." Similarly, Philips have found interest in the DT because of the "importance of getting the whole picture", where "different data may point to different conclusions when viewed in isolation. Medical practitioners should ideally have an integrated understanding of a person's health", asking, "Could the 'digital patient'-a digital twin of the human body-be the means to this end?" and finding that "digital twins are proving to be a powerful paradigm for personalizing healthcare and making it more effective and efficient". They provide the examples of medical device testing and organ models that may support diagnosis and management. However, the digital patient concept goes beyond having "isolated models of different organs, a digital patient or 'health avatar' integrates every relevant piece of medical knowledge about you. A digital patient is a lifelong, integrated, personalized model of a patient that is updated with each measurement, scan or exam, and that includes behavioral and genetic data as well" (van Houten 2018).
The European EDITH project (https://www.edith-csa. eu/), which stands for 'Ecosystem for Digital Twins in Healthcare' is a strategic approach to develop a taxonomy of DT in healthcare and plan for their development and implementation in medical practice. EDITH mentions in silico medicine, health data and High-Performance Computing (HPC) as DT elements, however given its early days, does not yet provide the complete definition or visionary framework for the future of DHM. It aims to develop a roadmap and cloud-based repository of resources and best practice, controlled by a governance context of standards, regulations and meta-data. The project eventually includes an infrastructure component in the form of a simulation platform to "realize the vision of the integrated digital twin for personalized healthcare".
Digital Twin developments have obviously propelled digital human modeling into a next age, involving the next generation of scientists, bigger industry, higher politics, and a much broader cross-section of our societies.
The IEA TC DHMS thanks the German Ergonomic Society for making DHM their topic for this autumn conference and we thank ZfA for producing a worthy special issue-hopefully this effort will attract more young ergonomists to find interest in this exciting discipline.
Funding Open Access funding enabled and organized by CAUL and its Member Institutions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4. 0/. | 2,839.2 | 2022-11-29T00:00:00.000 | [
"Engineering",
"Computer Science"
] |
Estimation of Prediction Intervals for Performance Assessment of Building Using Machine Learning
This study utilizes artificial neural networks (ANN) to estimate prediction intervals (PI) for seismic performance assessment of buildings subjected to long-term ground motion. To address the uncertainty quantification in structural health monitoring (SHM), the quality-driven lower upper bound estimation (QD-LUBE) has been opted for global probabilistic assessment of damage at local and global levels, unlike traditional methods. A distribution-free machine learning model has been adopted for enhanced reliability in quantifying uncertainty and ensuring robustness in post-earthquake probabilistic assessments and early warning systems. The distribution-free machine learning model is capable of quantifying uncertainty with high accuracy as compared to previous methods such as the bootstrap method, etc. This research demonstrates the efficacy of the QD-LUBE method in complex seismic risk assessment scenarios, thereby contributing significant enhancement in building resilience and disaster management strategies. This study also validates the findings through fragility curve analysis, offering comprehensive insights into structural damage assessment and mitigation strategies.
Introduction
The uncertainty quantification (UQ) is an emerging domain, alongside artificial intelligence in natural events where accurate prediction is extremely difficult.To compensate for this uncertainty, a considerable margin over and above the actual requirement of natural disasters is added in structure design which results in a huge cost investment.The nominal design plans and point estimations having insufficient information are unable to address the uncertainty challenges [1].The main causes of uncertainty include data mismatch, input, and parameter uncertainty.Instead of relying on point forecast value, incorporating an uncertainty margin such as the prediction interval (PI) can help make decisions more credible and reliable [2].
One specific example where the challenges of uncertainty are evident is in the design of high-rise buildings situated even in intra-plate regions that face threats from longperiod ground motions originating from distant earthquakes.The slow attenuation of long-period waves coupled with potential amplification by soft soil sites renders these structures susceptible to resonance-induced seismic damage.These vulnerabilities have been evidenced during seismic events, such as the 1985 Michoacán earthquake [3], 2011 Tohoku earthquake [4], and 2015 Nepal earthquake [5], whereby high-rise buildings experienced excessive vibrations and severe damage to their non-structural components, notably in Mexico City and Tokyo.This underscores the critical importance of understanding and mitigating the impact of long-period ground motions on high-rise buildings, both structurally and functionally.Given the complex nature of long-period ground motions and the dearth of dependable seismic records, accurate prediction of the structural response of high-rise buildings remains a challenge without real-time monitoring systems supported by sensors and a robust communication infrastructure.The deployment of an early warning system (EWS), as discussed in [6], becomes imperative with access to reliable building data, to ensure decisions are based on the certainty of both the data and the model to mitigate casualties and losses.
The increasing frequency and intensity of earthquakes worldwide highlight the urgent need for advanced methodologies in seismic analysis and SHM.While historically droughts and floods have accounted for significant casualties, the rise in seismic activity, particularly in densely populated urban areas with vertical housing and rapid urbanization, has emerged as a primary concern.Records since the early 1900s indicate a consistent occurrence of major earthquakes, with an average of 16 significant events annually, including one of magnitude 8.0 on the Richter or Mercalli standard scales or greater.The United States Geological Survey (USGS) reports approximately 20,000 earthquakes globally each year with an average of 55 per day [7].In the past 40-50 years, USGS records show that on average, long-term major earthquakes occurred more than a dozen times every year.Notably, in 2011 alone, 23 major earthquakes of magnitude 7.0 on the Richter or Mercalli standard scales or higher occurred, surpassing the long-term annual average.In other years, the total was well below the annual long-term average of 16 major earthquakes.The lowest-ranking year is 1989 with only 6 major earthquakes followed by 1988 with 7 only.Table 1 shows the top-ranked earthquake countries.These seismic events pose severe threats to structures, particularly those situated near fault lines and seismic zones, resulting in substantial casualties and property losses, amounting to tens of billions of dollars annually.Post-earthquake probabilistic performance assessment (PPPA) is crucial for promptly and accurately evaluating building safety, particularly in ensuring safe shelter after seismic events.Typically, this assessment is time-consuming and is carried out by licensed engineering experts [9].Buildings are categorized into safety levels such as inspected, restricted use, and unsafe, based on these assessments [10,11].However, the scarcity of experts poses challenges, as exemplified by the Tokyo metropolitan government's 110,375 certified experts tasked with assessing over 1.9 million buildings [11,12].This shortage becomes more acute during aftershocks or subsequent earthquakes, as demonstrated by the two intense earthquakes that struck the Kyushu area within 28 hours [13].Thus, the swift and reliable post-earthquake assessment of building structures becomes even more critical in safeguarding human lives.Occupants must be promptly notified of the assessed damage state of the building to facilitate safe evacuation.
Background and Related Works
In recent research, a novel model for sensor-based EWS and PPPA has been introduced [6], employing the Vanmarcke approximation based on a two-state Markov assumption for extreme value detection.This approach outperforms previous heuristic techniques, demonstrating its superiority.Moreover, advancements in artificial intelligence (AI), particularly machine learning (ML) techniques employing artificial neural networks (ANNs), have garnered significant attention in seismic analysis.These techniques exhibit remarkable accuracy in predicting the transient behavior of buildings, facilitating real-time applications such as EWS and PPPA, as well as informing performance-based design strategies for buildings [14,15].Notably, AI methodologies have been employed for nonlinear mapping in data modeling, utilizing bootstrapped ANNs for rapid seismic damage evaluation of structural systems [16].Additionally, AI techniques have been instrumental in stripe-based fragility analysis of multi-span concrete bridges [15] where uniform design-based Gaussian process regression was implemented.
While significant strides have been made in SHM utilizing AI techniques, the crucial aspect of UQ remains largely unexplored in seismic analysis.The oversight of uncertainty can lead to substantial misinterpretations in real-world applications, particularly in scenarios where sudden severe hazards occur.Addressing this gap, researchers have proposed modifications to neural networks (NNs) to account for uncertainty [17,18].Further advancements include the development of lower upper bound estimation (LUBE) method [19] which integrates delta, Bayesian, bootstrap, and mean-variance estimation (MVE) techniques directly into the NN loss function.While the LUBE technique has gained traction across various domains, such as energy demand and wind speed forecasting, challenges arise during simulation and implementation phases, notably with the risk of converging to a global minimum when all high-quality prediction intervals for deep learning PIs are diminished to zero.To mitigate this issue [17] introduces the quality-driven PI method (QD) and quality-driven ensemble (QD-Ens.),employing gradient descent (GD) standard training methods for NNs, thereby enhancing robustness and reliability in predictive modeling.
Utilizing QD and QD-Ens.methods, estimating the characteristics of extreme value distribution functions becomes more convenient.Typically, deterministic hazard analysis specifies mean-plus-one-standard-deviation [20].The QD-LUBE method, known for its high accuracy, rapid convergence, and robustness, is applied to extreme engineering demand parameters (EDPs) like inter-storey drift (IDRs), acceleration (A), and base shear (V) providing prediction intervals (PIs) for observed extreme values.Case studies on a three-storey European laboratory for structural assessment (ELSA) model demonstrate the applicability of this approach.This work signifies a new dimension in assessing building structures in/during post-extreme events, such as earthquakes, enhancing the reliability of probabilistic performance analysis.Correctly estimating value bounds based on analyzed data aids disaster management decision-makers in resource allocation, prioritizing life mitigation, and formulating rehabilitation plans.
Proposed Method: QD-LUBE-Based Prediction Interval Analysis
The conventional ML techniques like nonlinear mapping in data modeling, utilizing bootstrapped ANNs for rapid seismic damage evaluation, and stripe-based fragility analysis only provide point predictions, which means single output for every single target, and are incapable of monitoring the sampling error, prediction accuracy, and uncertainty of the model.For important decisions and design plans, point estimations cannot provide sufficient information [1].In seismic analysis, the minimum and maximum values or upper and lower bound values are important for both PPPA and real-time warning systems.Estimating a credible maximum value is crucial as the risk costs the human and capital loss.Furthermore, the uncertainty sources evolved in earthquake prediction must be precisely quantified to provide essential information for decision-makers.
Hence, in model-based forecasting, specifically the ANN or ML models of natural phenomena, decisions are not solely dependent upon the accurate forecasting the certain variables but also on the uncertainty of data associated with the forecast.The main causes of uncertainty are model and data mismatch, input uncertainty, and parameter uncertainty.Incorporating the uncertainty margins termed as prediction interval (PI) in the determined point forecast value can help to make the decision more credible and reliable [2].The proposed technique for earthquake damage assessment begins with the data generation and acquisition where response spectra from moderate earthquakes are modeled using the CSI SAP2000 v22 software generating engineering demand parameters (EDPs) such as maximum inter-storey drift (MIDR), acceleration, and base shear.MIDRs are pre-processed for extreme value analysis using the peak-over-threshold method and then prepared for the QD-LUBE method.In the threshold and extreme values detection phase, the generalized pareto distribution (GPD) is employed to identify threshold values, crucial for analyzing extreme values during earthquakes.The QD-LUBE method is then applied to predict the upper and lower bounds of these extreme EDPs, enhancing UQ.The fuzzy inference system (FIS) is subsequently used to assess performance-based damage by associating EDPs with fuzzy membership functions and evaluating them against predefined rules.This process involves fuzzification, rule evaluation, and defuzzification to produce crisp output values.Finally, local and global damage states are assessed using these outputs, with fragility curves and cost estimations based on the upper bounds of EDPs providing a comprehensive framework for earthquake damage assessment and real-time warning systems.An overview of the workflow of the paper has been discussed with minor details.A flow diagram is given in Figure 1.
the determined point forecast value can help to make the decision more credible reliable [2].The proposed technique for earthquake damage assessment begins with data generation and acquisition where response spectra from moderate earthquake modeled using the CSI SAP2000 v22 software generating engineering demand param (EDPs) such as maximum inter-storey drift (MIDR), acceleration, and base shear.MI are pre-processed for extreme value analysis using the peak-over-threshold method then prepared for the QD-LUBE method.In the threshold and extreme values detec phase, the generalized pareto distribution (GPD) is employed to identify threshold va crucial for analyzing extreme values during earthquakes.The QD-LUBE method is applied to predict the upper and lower bounds of these extreme EDPs, enhancing The fuzzy inference system (FIS) is subsequently used to assess performance-b damage by associating EDPs with fuzzy membership functions and evaluating t against predefined rules.This process involves fuzzification, rule evaluation, defuzzification to produce crisp output values.Finally, local and global damage state assessed using these outputs, with fragility curves and cost estimations based on upper bounds of EDPs providing a comprehensive framework for earthquake dam assessment and real-time warning systems.An overview of the workflow of the pape been discussed with minor details.A flow diagram is given in Figure 1.
Data Generation and Acquisition
In the first step, a response spectrum generated from CSI SAP 2000 software w 475-years' return period earthquake for a three-storey ELSA model was obtained considered the population of moderate earthquakes of the 475-year return period.model was trained on fifteen moderate-intensity earthquakes to obtain EDPs inclu IDR, A, and V, collectively called DAV.Further, EDPs data were pre-processed to m
Data Generation and Acquisition
In the first step, a response spectrum generated from CSI SAP 2000 software with a 475-years' return period earthquake for a three-storey ELSA model was obtained.We considered the population of moderate earthquakes of the 475-year return period.The model was trained on fifteen moderate-intensity earthquakes to obtain EDPs including IDR, A, and V, collectively called DAV.Further, EDPs data were pre-processed to make them compatible with extreme values analysis using peak-over-threshold (EVA-POT) and subsequently prepared for the QD-LUBE method.
Threshold and Extreme Values Detection
In the case of earthquakes or other natural disasters, extreme values are the main points of interest as they have the worst impact.Being the tail-end values, normal distributions cannot capture them well and the output of the system is normally biased to the overall behavior of the data.For this purpose, GPD was used to select the value of the behavior of data.The approach is used to attain the threshold values for all the earthquakes under analysis.Section 3.3 discusses complete results with certain examples.
QD-LUBE-Based PI Analysis and Uncertainity Framework
In the predictive performance-based earthquake analysis, the concept of UQ is not extensively evolved yet; however, it is gathering fame in other natural hazards like flood and prediction of energy demand.The QD-LUBE method proposed by [17] with fast processing speed, higher accuracy, ease of handling big data, and other competitive benefits over the previous techniques to attain the upper bound of the extreme values has been applied and the model has been trained on the selected earthquake data, i.e., EDPs.The upper bound of EDPs extreme values premeditated in this step have been used in the global and local damage state assessment and global cost estimation.
Pearce et al. [17] proposed the uncertainty framework with QD-LUBE loss function using GD.In the model, if the data generating function f (x) exist and are combined with additive noise, they produce observable target values y: where 'ϵ' is termed as irreducible noise or data noise.Some models, for example the delta method, assume ϵ is constant across the input space (homoscedastic), while others allow for it to vary (heteroskedastic).The term "quality-driven" means that the framework incorporates a gradient descent method, designed through qualitative assessment, and includes model uncertainty, which is different than the conventional lower-upper boundary estimation (LUBE) approach.The loss function used in the proposed framework is distribution-free.In other words, it does require any assumption with a specific distribution for the dataset.The loss function (i.e., the objective function) that needs to be minimized to obtain the optimal neural network for a specific dataset can be defined as follows [17]: where MPIW capt is the captured mean prediction interval width, PICP is the prediction interval coverage probability, λ is a Lagrangian multiplier that controls the relative importance of the width compared to the coverage of the prediction interval, n is the number of data points, (1 − α) is the desired proportion of coverage, and α is commonly assumed 0.01 or 0.05.The prediction interval (PI) should be bounded by the predicted upper bound, y, and lower bound, ŷ, such that: where y i is the target observation of an input covariate y i , and 1 ≤ i ≤ n.The PI of each point should be calculated such that MPIW capt is minimum, while maintaining PICP < (1 − α).
To quantify the MPIW capt and PICP mathematically, the following equations can be used: where c is the total number of data points captured by PI(c = ∑ n i=1 k i ), ŷU i , ŷL i , and ŷU i and ŷL i are the upper and lower bounds of the point under consideration, and k i is a binary variable (k i ϵ 0, 1) that represents the occurrence of the data point within the estimated PI, such that: It is assumed that k can be represented as a Bernoulli random variable (i.e., ki Bernoulli (1 − α)).In addition, k i is assumed to be an independent and identically distributed variable.The former assumption can be used to justify that c can be represented by a binomial distribution (i.e., c binomial (n, (1 − α))).Utilizing the likelihood-based approach, θ, the optimum neural network parameters are optimized to maximize, L ′ θ = L ′ (θ|K, α), where K is the vector of length n with each element in the vector is represented by K i .Based on the probability, the mass function can be calculated using the central limit theorem and the negative log likelihood.
Fuzzification and Performance-Based Assessment
The fuzzy inference system (FIS) serves a dual purpose in nonlinear mapping within fuzzy logic, addressing inherent fuzziness and vagueness in limit states while relating earthquake damage parameters to multiple limit states simultaneously [21].Overall, FIS process flowchart is shown in Figure 2. Utilizing the Mamdani procedure, the FIS is established with fuzzification as its initial step, associating each EDP limit state with specific membership functions and determining the degree of association for each EDP value within these functions [22].Fuzzy operators, employing T-norm (minimum) and S-norm (maximum), are then employed to form fuzzy rules, with the number of rules contingent upon the limit states associated with member functions, which is 27 in our case.The antecedent of each rule is evaluated through fuzzy operators to derive a consequent, a unique number between 0 and 1 [23].The inference engine assigns implication relations for each rule, allowing for different rule weights to represent their relative importance.This weight assignment is crucial, especially when certain EDPs are of greater significance, necessitating higher weights for corresponding rules.Although in this study, all rules are given equal weight.The maximum composition operator aggregates the output fuzzy numbers from each rule which are then transformed into crisp output numbers through the center of the area (CoA) defuzzification process.This involves dividing the total area of membership function distributions into sub-areas, with the de-fuzzified value (z*) of a discrete fuzzy set calculated based on sample elements and their associated membership functions [22]: where z i is the sample element, µ(z i ) is the membership function, and n is the number of elements in the sample [22].The concept of evaluation ratio (ER) and its classification into "recommended", "moderate", and "not recommended" classes based on certain threshold values is utilized to assess building damage assessment based on structural characteristics and the earthquake response spectrum.It explains how different ranges of ER correspond to different levels of system performance and suitability for design.These classes are mapped to three different levels of the evaluation ratio as (ER > 0.7), (0.35 > ER > 0.7), and (ER < 0.35), respectively.The ER of each system is labeled "not recommended" if ER < 0.5.The "recommended" class means that system responses are within the recommended limits and vice versa [23,24].This study aims to compare the fuzzified original, without incorporating the PI uncertainty, on EDPs such as MIDR, A, and V with the enhanced value of EDPs based on PI results.
(ER < 0.35), respectively.The ER of each system is labeled "not recommended" if ER < 0. The "recommended" class means that system responses are within the recommende limits and vice versa [23,24].This study aims to compare the fuzzified original, witho incorporating the PI uncertainty, on EDPs such as MIDR, A, and V with the enhance value of EDPs based on PI results.In this step, local damage state assessment has been performed.The fragility curv of FEMA P-58 PACT software (version 3.1.1)were used as the basis for comparison.Glob damage assessment of structures passing the maximum values of DAV through the fuzz inference system (FIS) is executed, followed by the global cost estimation.
In the local damage state assessment on the maximum inter-storey drift values use the probability of damage states for post-earthquake performance assessment and rea time warning systems is calculated using the upper bound of extreme values of 1 earthquakes.Global cost estimation is calculated based on D values passed through FI Similarly, the global damage state of structures has been calculated using the maximu upper bound values of DAV passing through the FIS.
Experimental Evaluation: Case Study Model and Validation
All the steps named in the previous section have been elaborated in depth with th ELSA model.The ELSA model is a well-known standard model used as benchmark f most of the structural design software.Components and material details of the ELS three-storey model are given in Table 2 and Figure 3.In this step, local damage state assessment has been performed.The fragility curves of FEMA P-58 PACT software (version 3.1.1)were used as the basis for comparison.Global damage assessment of structures passing the maximum values of DAV through the fuzzy inference system (FIS) is executed, followed by the global cost estimation.
In the local damage state assessment on the maximum inter-storey drift values used, the probability of damage states for post-earthquake performance assessment and real-time warning systems is calculated using the upper bound of extreme values of 15 earthquakes.Global cost estimation is calculated based on D values passed through FIS.Similarly, the global damage state of structures has been calculated using the maximum upper bound values of DAV passing through the FIS.
Experimental Evaluation: Case Study Model and Validation
All the steps named in the previous section have been elaborated in depth with the ELSA model.The ELSA model is a well-known standard model used as benchmark for most of the structural design software.Components and material details of the ELSA three-storey model are given in Table 2 and Figure 3.
Data Acquisition
Non-linear time history analysis (NLTHA) of fifteen moderate-intensity earthquakes with a 475-year return period was used.All these earthquakes have many similar parameters, and huge infrastructure lies on their fault lines.These moderate earthquakes occurred during the years 1956 to 1980 and provide sufficient data.Important parameters of selected earthquakes are given in Table 2.The nonlinear time series data for all the earthquakes have been formulated, pre-processed, and visualized to make it compatible with the model and for other mathematical operations.The nonlinear time series data o MIDR for the first to second floor during the San Ramon-Eastman Kodak (1980 earthquake is plotted in Figure 4.All fifteen earthquakes, as shown in Table 3, were run on this building model in a single degree of freedom (SDOF), i.e., in the Y direction only to attain the maximum DAV values.
Data Acquisition
Non-linear time history analysis (NLTHA) of fifteen moderate-intensity earthquakes with a 475-year return period was used.All these earthquakes have many similar parameters, and huge infrastructure lies on their fault lines.These moderate earthquakes occurred during the years 1956 to 1980 and provide sufficient data.Important parameters of selected earthquakes are given in Table 2.The nonlinear time series data for all the earthquakes have been formulated, pre-processed, and visualized to make it compatible with the model and for other mathematical operations.The nonlinear time series data of MIDR for the first to second floor during the San Ramon-Eastman Kodak (1980) earthquake is plotted in Figure 4.All fifteen earthquakes, as shown in Table 3, were run on this building model in a single degree of freedom (SDOF), i.e., in the Y direction only, to attain the maximum DAV values.
Extreme Values Detection
Hu et al. in [6] used the Poisson's assumption and compared the results based on the Vanmarcke assumption and Monte Carlo simulation using Kalman Smoother to attain extreme values.However, the extreme values shoot out due to non-flexible, static assumptions only linked with the mean deviation method, leading towards overestimations.
Due to these shortcomings of log-normal distribution and Vanmarcke assumption, the GPD method is used to fit the POT.This allows a continuous range of possible shapes
Extreme Values Detection
Hu et al. in [6] used the Poisson's assumption and compared the results based on the Vanmarcke assumption and Monte Carlo simulation using Kalman Smoother to attain extreme values.However, the extreme values shoot out due to non-flexible, static assumptions only linked with the mean deviation method, leading towards overestimations.
Due to these shortcomings of log-normal distribution and Vanmarcke assumption, the GPD method is used to fit the POT.This allows a continuous range of possible shapes that includes both the exponential and Pareto distributions as special cases.The distribution allows us to "let the data decide" [25] which distribution is appropriate; hence, the highest level of adaptability and accuracy is achieved.
When fitting the excess with the GPD, the primary problem is the selection of threshold λ.If λ is too large, few excesses and insufficient data lead to excessively large estimator variance.If λ is too small, a large deviation between an excess distribution and GPD leads to a biased estimation.Therefore, a compromise between bias and variance is needed for λ selection.By adopting the straightforward graphic methods including the mean residual life plot and shape and scale parameters stability plots to determine λ based on the average excess function, an optimal threshold value can be calculated separately at each node and for every earthquake.Figure 5 shows the plots of the extreme values data against mean excesses and shape parameters for mean inter-storey drift between the roof and third floor for the Imperial Valley-07 earthquake.Similarly, the are plotted in respect to the cumulative distribution function and probability density function in Figure 6.The threshold selection has been calculated considering the mean value of data for each earthquake.
LUBE-Based Prediction Interval Analysis 4.3.1. Preparation of Training Sets
Datasets are the relative joint acceleration, joint displacements, and the sheer force for the 15 earthquakes as explained in Section 2. SAP 2000 produces a nonlinear time series.Pre-processing of data to make them readable to ANN is performed after the extreme values analysis.Absolute values, sorted from the minimum to the maximum value, were used.A sample shape of data is shown in Figure 7.Moreover, GPD distribution estimation for IDR values calculated at nodes '113' and '112' for 'EQ25'("Imperial Valley-07", "El Centro Array #7") is given in Table 4.
Preparation of Training Sets
Datasets are the relative joint acceleration, joint displacements, and the sheer force for the 15 earthquakes as explained in Section 2. SAP 2000 produces a nonlinear time series.Pre-processing of data to make them readable to ANN is performed after the extreme values analysis.Absolute values, sorted from the minimum to the maximum value, were used.A sample shape of data is shown in Figure 7.Moreover, GPD distribution estimation for IDR values calculated at nodes '113' a '112' for 'EQ25'("Imperial Valley-07", "El Centro Array #7") is given in Table 4.
Preparation of Training Sets
Datasets are the relative joint acceleration, joint displacements, and the sheer for for the 15 earthquakes as explained in Section 2. SAP 2000 produces a nonlinear tim series.Pre-processing of data to make them readable to ANN is performed after t extreme values analysis.Absolute values, sorted from the minimum to the maximu value, were used.A sample shape of data is shown in Figure 7.The dataset was further refined and scaled to make it compatible with the model and the loss function of QD-LUBE.The key advantages of the QD-LUBE method are its intuitive objective, low computational demand, robustness to outliers, and lack of distributional assumption.The model used is the Python TensorFlow library Keras
Setting up the Model
The dataset was further refined and scaled to make it compatible with the model and the loss function of QD-LUBE.The key advantages of the QD-LUBE method are its intuitive objective, low computational demand, robustness to outliers, and lack of distributional assumption.The model used is the Python TensorFlow library Keras sequential model with the input layer and one intermediate layer both having 100 neurons first with RELU activation functions and the output layer having two neurons with the LINEAR activation function.The Adam optimizer was used as a compiler and the confidence level was set at 95%.
Predicting the Upper and Lower Bounds for DAV
Finally, the model was run to make the prediction of the upper and lower bounds.The absolute values were used; hence, the peak values information lies in the upper bound only.The upper bounds of the selected earthquakes are shown in 8. From the graph, we can see the outliers which are abnormal from the distribution; however, PI can determine the upper bound on these outliers using the accumulative behavior of the distribution, and prediction can be calculated for any next value.The same procedure was used to attain the upper bound values of the base sheer and MIDR.Table 5 shows the acceleration values of the maximum value of distribution treated as point prediction, and after fitting the QD-LUBE, an upper bound is calculated for every earthquake.This upper bound adds to the margin of uncertainty while trading itself from the behavior of the input data which is above the threshold value.For comparison, the model was also trained with values more than the median, and it was found that the threshold section method using GPD provides a very good approximation of the extreme events or the tail-end events.The LUBE method further adds to the margin of error as the confidence level is specified in the model (in our case 95%).Therefore, the combination of POT and QD-LUBE provides a very robust hybrid combination of UQ.
Setting up the Model
The dataset was further refined and scaled to make it compatible with the model and the loss function of QD-LUBE.The key advantages of the QD-LUBE method are its intuitive objective, low computational demand, robustness to outliers, and lack of distributional assumption.The model used is the Python TensorFlow library Keras sequential model with the input layer and one intermediate layer both having 100 neurons first with RELU activation functions and the output layer having two neurons with the LINEAR activation function.The Adam optimizer was used as a compiler and the confidence level was set at 95%. 4.3.3.Predicting the Upper and Lower Bounds for DAV Finally, the model was run to make the prediction of the upper and lower bounds.The absolute values were used; hence, the peak values information lies in the upper bound only.The upper bounds of the selected earthquakes are shown in Figure 8. From the graph, we can see the outliers which are abnormal from the distribution; however, PI can determine the upper bound on these outliers using the accumulative behavior of the distribution, and prediction can be calculated for any next value.The same procedure was used to attain the upper bound values of the base sheer and MIDR.Table 5 shows the acceleration values of the maximum value of distribution treated as point prediction, and after fitting the QD-LUBE, an upper bound is calculated for every earthquake.This upper bound adds to the margin of uncertainty while trading itself from the behavior of the input data which is above the threshold value.For comparison, the model was also trained with values more than the median, and it was found that the threshold section method using GPD provides a very good approximation of the extreme events or the tail-end events.The LUBE method further adds to the margin of error as the confidence level is specified in the model (in our case 95%).Therefore, the combination of POT and QD-LUBE provides a very robust hybrid combination of UQ.The two main parameters used to evaluate the performance of any model based on a statistical model (e.g.,: LUBE method) given in the literature [26] are the normalized mean prediction interval width (NMPIW), which should be minimized, and PICP, which is considered as good as it is near to 1. QD-LUBE is proven to be better than that of Bootstrap or the LUBE method, and this fact was verified by this work.The PICP of the base shear is given in Table 6.In the final step, the building performance assessment was performed for the following aspects: 1.
Local damage state assessment using the FEMA P-58 PACT fragility specification manager, using the MIDR values calculated in Section 3.3.
2.
Global damage state assessment using the DAV values after passing them through the FIS system of the set limit state member functions.
Local Damage State Assessment
The local damage assessment was performed using FEMA P-58 PACT software.Table 7 shows a comparison of the MIDRS point maximum values and the upper bound calculated by the QD-LUBE model.Figure 9 also shows the same graphically.The model provided an uncertainty margin to accommodate for the noise, data, and model uncertainties, closely following the behavior of tail-end data.The structural component B1044.102slender concrete wall, 18" thick, 12' high, 20' long, was evaluated on three earthquakes (Imperial Valley-06 at "Chihuahua" station (EQ20), "El Centro Array #12" station (EQ22), and Livermore-01 at "San Ramon-Eastman Kodak" station) as shown in Figure 10.The damage states which were on the borderline were moved to the next damage state after adding the uncertainty margins using the upper bound of the predicted model for each earthquake.The structural component B1044.102slender concrete wall, 18" thick, 12' high, 20' long, was evaluated on three earthquakes (Imperial Valley-06 at "Chihuahua" station (EQ20), "El Centro Array #12" station (EQ22), and Livermore-01 at "San Ramon-Eastman Kodak" station) as shown in Figure 10.The damage states which were on the borderline were moved to the next damage state after adding the uncertainty margins using the upper bound of the predicted model for each earthquake.Table 7 shows the values of MIDRs simulated by the SAP model and prediction intervals upper bound.
Global Damage Assessment
For the global cost assessment, the value of Dmax is fuzzified.It is clarified that the highest impact is that of EQ27, followed by EQ17.The ELSA three-storey model has very minor to no damage in the case of other earthquakes.Results of fuzzification are given in Table 8.
Global Damage Assessment
For the global cost assessment, the value of D max is fuzzified.It is clarified that the highest impact is that of EQ27, followed by EQ17.The ELSA three-storey model has very minor to no damage in the case of other earthquakes.Results of fuzzification are given in Table 8.For UQ, the global damage assessment of DAV based on PI has been fuzzified.The evaluation ratio (ER) of EQ-17 and EQ-27 shows the highest damage.Structures' evaluation for these earthquakes resulted in "not recommended".Hence, structural parameters need to be modified to attain performance assessment results in the acceptable range.Table 9 provides normalized and un-normalized output values and evaluation ratios after fuzzification.
Conclusions
The distribution-free ensemble approach, the QD-LUBE method, has been used for uncertainty quantification, in assessing the critical parameters of structural models like inter-storey drift, peak ground acceleration, and base shear, which has been proven as powerful ML tool.In this study, the QD-LUBE was applied to the ELSA model, focusing on a three-storey building subjected to 16 well-known earthquakes within a single degree of freedom framework.
Key aspects of the methodology included leveraging the Vanmarcke's assumption for extreme value detection, which allowed us to extract peak-over-threshold determinations.The upper bounds of prediction intervals for certain parameters were accessed by training the model and testing on given datasets.The process yielded robust results and demonstrated superior performance compared to the bootstrap method in terms of accuracy and reliability.
Furthermore, the findings were validated through fragility curve analysis, specifically evaluating the impact of three earthquakes on the effective drift and transitioning between damage states.To assess the overall structural damage, a fuzzy inference system was integrated, providing a comprehensive evaluation of the global damage state.
The research not only contributes to advancing the field of uncertainty quantification in structural engineering, but also showcases the efficacy of the QD-LUBE method in handling complex scenarios and providing actionable insights for seismic risk assessment and mitigation strategies.
Future Works
The LUBE method can be used to enhance the reliability of a real-time warning system by predicting the upper bound of point prediction.In the event of an earthquake, the system will take the data from sensors and ICT infrastructures.The data samples from multiple buildings can help to make the decision for all the buildings of the same kind and the model trains itself with the extreme values behavior to provide certain values.Moreover, novel ML models are considered to improve the seismic performance assessment of buildings by incorporating additional details and methodologies [27,28].Determining a threshold in peak-over-threshold modeling using mean residual life and threshold stability plots involves significant subjectivity.The identification of linear portions in these plots is challenging due to the vague definition of linearity, leading to potential errors in selecting constant scale and shape parameter estimates.An objective method like a segmentation approach is needed to accurately determine the constant portion of these parameters [29].
Figure 1 .
Figure 1.Workflow of the proposed technique.
Figure 1 .
Figure 1.Workflow of the proposed technique.
Figure 5 .
Figure 5. Mean residual life plot and shape parameter stability plot for MIDR third floor to roof for Imperial Valley-07 earthquake.
Figure 6 .
Figure 6.CDF and PDF for mean residual threshold selection.
Figure 5 .Figure 5 .
Figure 5. Mean residual life plot and shape parameter stability plot for MIDR third floor to roof for Imperial Valley-07 earthquake.
Figure 6 .
Figure 6.CDF and PDF for mean residual threshold selection.
Figure 6 .
Figure 6.CDF and PDF for mean residual threshold selection.
Figure 7 .
Figure 7. Plot showing IDR extreme values for Livermore-Morgan Terr Park (1980) earthquake at second to third storeys of the ELSA model.4.3.2.Setting up the Model
Figure 7 .
Figure 7. Plot showing IDR extreme values for Livermore-Morgan Terr Park (1980) earthquake at second to third storeys of the ELSA model.
Figure 7 .
Figure 7. Plot showing IDR extreme values for Livermore-Morgan Terr Park (1980) earthquake at second to third storeys of the ELSA model.
Figure 8 .
Figure 8.The upper and lower bound of two earthquakes using QD-LUBE method.Figure 8.The upper and lower bound of two earthquakes using QD-LUBE method.
Figure 8 .
Figure 8.The upper and lower bound of two earthquakes using QD-LUBE method.Figure 8.The upper and lower bound of two earthquakes using QD-LUBE method.
Figure 9 .
Figure 9. Plot of MIDRs and their upper bound.
Figure 10 .
Figure 10.Fragility analysis using the maximum value and upper bound for three earthquakes.
Table 2 .
Steel (a) and concrete (b) properties of ELSA model.
Table 2 .
Steel (a) and concrete (b) properties of ELSA model.
Table 3 .
List of the selected earthquakes.
Table 3 .
List of the selected earthquakes.
Table 5 .
Maximum point prediction and upper bound of the acceleration values.
Table 6 .
PCIP of the base shear of all the selected earthquakes.
Table 7 .
MIDR values and upper bound for 15 earthquakes.
Table 7 shows the values of MIDRs simulated by the SAP model and prediction intervals upper bound.
Table 7 .
MIDR values and upper bound for 15 earthquakes.
Table 9 .
ER for engineering demand parameters (DAV) values fuzzification based on PI. | 8,794.8 | 2024-06-28T00:00:00.000 | [
"Engineering",
"Environmental Science",
"Computer Science"
] |
A Generic Framework for Depth Reconstruction Enhancement
We propose a generic depth-refinement scheme based on GeoNet, a recent deep-learning approach for predicting depth and normals from a single color image, and extend it to be applied to any depth reconstruction task such as super resolution, denoising and deblurring, as long as the task includes a depth output. Our approach utilizes a tight coupling of the inherent geometric relationship between depth and normal maps to guide a neural network. In contrast to GeoNet, we do not utilize the original input information to the backbone reconstruction task, which leads to a generic application of our network structure. Our approach first learns a high-quality normal map from the depth image generated by the backbone method and then uses this normal map to refine the initial depth image jointly with the learned normal map. This is motivated by the fact that it is hard for neural networks to learn direct mapping between depth and normal maps without explicit geometric constraints. We show the efficiency of our method on the exemplary inverse depth-image reconstruction tasks of denoising, super resolution and removal of motion blur.
Introduction
High-quality depth maps are required in a wide variety of tasks in computer vision and graphics, such as RGB-D scene reconstruction [1,2], augmented reality [3][4][5] and autonomous driving [6][7][8]. Compared to standard RGB-sensors, depth sensors often produce noisy images, which makes depth-reconstruction tasks especially challenging, since every task also has to account for the different task-specific depth uncertainties or deficiencies. Some classes of sensors have types of artifacts that are not common in that form for typical color sensors. For example, artifacts from motion relative to the camera are a particular problem for Time-of-Flight (ToF) cameras because they capture multiple phase images in sequence. Solutions for these problems require specialized algorithms such as the ones outlined in [9].
Even though approaches that are well known in the realm of color-image enhancement, such as energy minimization methods or deep learning, can often be translated one-to-one to depth enhancement tasks, usually by just interpreting the depths as grayscale values. This fails to incorporate the inherent geometric structure of depth maps. While research on depth-only enhancement exists [10,11], a majority of recent work has focused on some form of intensity or RGB-guided depth enhancement, e.g., for super resolution [12][13][14][15], denoising [16,17] or motion blur removal [18,19]. While this greatly improves the quality of the resultant depth images, these additional RGB sensors are not always available. Moreover, none of the examples above explicitly incorporate surface normal information, which is geometrically tightly linked to the depth map information. However, in the area of depth estimation from a single RGB-image, there have recently been works that not only produce normal maps as an additional output, but also successfully use them to enhance the quality of the final depth map [20][21][22][23][24]. Most notably, Qi et al. [20,21] introduce the GeoNet/GeoNet++ network architecture to estimate a depth and a normal map from a single RGB image. Their approach toggles between depth-to-normal that utilizes a least squares approach, and normal-to-depth estimation based on kernel regression to enforce geometric consistency between the two domains. Their approach can be seen as a weak coupling between normals and depth, as the two stages operate independently. Still, GeoNet++ outperforms standard CNN approaches that learn direct mapping between depth and surface normals, both in terms of accuracy and normal-depth consistency. In an ablation, the authors show that CNNs have problems to learn a direct mapping between depth and surface normals in general [21]. Since it is already hard to learn this mapping in a supervised setting with normals as output, we hypothesize that neural networks also have difficulties including surface normal information in their latent representations without explicit geometric constraints.
In this paper, we develop a generic depth refinement scheme that takes surface normals into account but makes no assumptions about the specific task that is to be solved, except that the output is a depth map. Based on the GeoNet/GeoNet++ concept, our approach computes high-quality normal maps in an intermediate step, which are then used to refine an initial depth estimate provided by the backbone method. Contrary to GeoNet/GeoNet++, we do not utilize the original input to the backbone method, making our approach generic to many existing reconstruction methods. Moreover, we use a tighter coupling between the depth and the normal domain by linking both stages using skip connections, making full normal and depth information available in both stages.
Our experiments show that this approach improves the quality compared to existing methods in a variety of different tasks, namely depth-only super-resolution, RGB-guided super-resolution, additive Gaussian noise removal and deblurring.
Related Work
In this section, we will give a brief overview of research in different areas of depth reconstruction. We will roughly split the methods into classical variational methods and deep-learning-based methods.
Specialized variational and classical non-learning-based approaches for depth reconstruction generally aim to improve depth data with additional sensor data like color images. Huhle et al. [25] use a non-local means (NLM) approach to remove outliers from depth data by computing an additional color-based weight in their NLM formulation. Ferstl et al. [26] use a variational approach to compute higher-resolution depth images with the help of already high resolution intensity images. Some approaches specialize in specific sensor types: Shen and Cheung [27] introduce a probabilistic model using a Markov random field for denoising and completing depth maps from structured light sensors. Another work on structured light sensors was presented by Fu et al. [28], who specifically target the spatiotemporal denoising of the Microsoft Kinect camera.
In recent years, like in any other field of computer vision and graphics, there has been substantial amounts of deep-learning research for depth reconstruction. Sterzentsenko et al. [16] used self-supervision to train a deep autoencoder to combat the lack of real world datasets with noise-free ground truth depths. The work from Tourani et al. [18] deals with the removal of motion artifacts from rolling shutters, which are common in structured sensors such as the Kinect. Li et al. [19] use a two-branched CNN to simultaneously remove motion blur from a color and a depth image. The problem of depth-only super-resolution, i.e., without additional color data, was tackled by Li et al. [11] who extend ideas from deep Laplacian pyramid networks [29], which were originally proposed for RGB superresolution, to depth. They put their work into the context of 3D reconstruction, which they show can greatly benefit from higher-resolution depth-maps. Research in the area of color-guided depth super-resolution is more widespread. Zhao et al. [30] jointly upscale depth and color images by using a generative adversarial neural network (GAN). Another deep-learning-based approach was proposed by Kim et al. [13] in the shape of deformable kernel networks (DKN) for joint image filtering. Apart from guided depth image superresolution their approach can also be applied to saliency map upsamling, cross modality image restoration and texture removal. Recently Tang et al. [14], inspired by progress in neural implicit representations, introduced joint implicit image functions (JIIF) and interpreted the problem of guided depth super-resolution as a neural implicit interpolation task. Another recent deep-learning-based approach is by Zhong et al. [31] who used an attention-based network design to fuse the most important features from depth and color images and then used those features to guide an upscaling network. There have also been hybrid methods which combine classical approaches with deep-learning techniques, e.g. Riegler et al. [10] who combined traditional variational methods with a deep neural network to improve the accuracy of depth super-resolution without the need for additional color sensors.
Even though the experiments in our manuscript do not include depth prediction from single-color tasks, works from this field that use explicit surface normal information are also related to our approach. Apart from GeoNet by [20,21], which our work directly extends and we will discuss in more detail in the upcoming sections, we will list some other research in that direction. Eigen and Fergus [22] tackle the task of depth and normal prediction and semantic segmentation from RGB images in a single deep neural network. Xu et al. [23] first predict initial depth, surface normal, semantic segmentation and contour maps and then fuse them into a final depth-map. However in both of these works, there is no enforcement of consistency between the predicted normal and depth images. A more tightly coupled approach was proposed by Wang et al. [24], who introduced an orthogonal compatibility constraint between normals and surface points that lie in a common planar region. However, their computations are very costly and the method might fail in nonplanar regions of the scene.
Method
In this section, we introduce our generalized depth-enhancement framework for arbitrary image-reconstruction tasks. First, we will briefly review the main ideas from Qi et al. [20,21] in Section 3.1. In Section 3.2, we introduce our general depth-enhancement network. Finally, we discuss the loss functions used in Section 3.3 and implementation details in Section 3.4.
GeoNet
Originally, GeoNet is a method for estimating a normal and a depth map from a single RGB image. In the following explanations of GeoNet, it is assumed that initial normal and depth estimates, by whichever means, e.g., another CNN, have already been computed. The initial normal at pixel i is denoted as n initial i and the initial depth at pixel i as z initial i . Further following the notation of Qi et al. [20,21], we denote pixel coordinates as (u i , v i ) and corresponding 3D coordinates as (x i , y i , z i ). The mapping between the the two is determined by the perspective projection equations where f . and c . are the intrinsic camera parameters.
The main idea of [20] is now to refine the initial normal map by using the geometric constraints given by the depth map, and vice versa. This is motivated by the fact that both representations have an inherent geometric relationship with each other that is hard to learn directly through a network. We will now discuss both paths-depth refinement using normals and normal refinement using depth-separately.
Normal Refinement
To refine the initial normal map n initial , first, an additional normal map that is consistent with the initial depth-map is computed. To avoid confusion, we will denote normals from this auxiliary normal map as n depth i . By using the assumption that surface points in a local neighborhood approximately lie on the same plane, n depth i can be computed from z initial i by first projecting the local neighborhood back into 3D using Equation (1) and then computing the normal using least squares. The neighborhood of size β around i is defined as where γ is a parameter to filter out depths which deviate too much from the center depth.
Writing the points of this neighborhood into a matrix enables the calculation of the normals as the least squares solution Here 1 is the K-dimensional constant vector with only 1 s. Since this normal is prone to noise, it is further refined by a residual network that also takes n initial as input. In [21], it is defined as where N 1 and N 2 are CNNs and • means concatenation along the channel dimension. The output of this network n f inal i is the refined normal map. In our experiments we additionally tried to replace the least squares normals with cross product normals which unfortunately resulted in very high noise and unsatisfactory results. All methods in this paper therefore use least squares normals, as seen in [20] as described above.
Depth Refinement
Analogous to the previous section, the first step is to compute a depth map z normal that is consistent with the initial normal map. The assumption is the same: points in a close neighborhood lie on the same plane. The neighborhood around pixel i is defined as Instead of filtering out large depth deviations, normals with a large angular difference to the center normal are filtered out. Given only the center depth, the depth for each point in the neighborhood can now be estimated as These depth estimates are then aggregated by weighting them with the angular difference of their normal to the center normal by kernel regression Again, these rough estimates are further refined with a CNN Note that all operations above, particularly computing least squares solutions and kernel regression, are differentiable, which means all networks, including the upstream RGB-todepth network, can be trained end-to-end.
General Depth Enhancement Network
We will now explain how we extend the ideas from GeoNet [21] from its RGB-to-depth estimation task to arbitrary depth-to-depth refinement tasks. We assume that we have some generic algorithm G (such as a neural network) that maps the input x (e.g., a low-resolution depth-map) to an initial depth-map estimate of its specific task (such as super-resolution).
We refer to G as backbone (network), but note that, despite our experiments only including neural networks as choices for G, we make no assumptions on the structure or differentiability, i.e., it could in theory also be a classical image-reconstruction method such as non-local means or energy minimization. Unlike GeoNet, which also requires an additional backbone for initial normal computation, we only require a generic backbone that maps x to an initial depth estimate. Moreover, our approach does not utilize the original input data x to the backbone network G, making it independent from the underlying refinement task. Instead of having the two independent depth and normal refinement branches, we propose a single sequential refinement scheme in which we first compute a high-quality normal map from the initial depth-map and then use this normal map to refine the depth map again.
We use Equation (4) to calculate a rough normal estimate n depth . Unlike in Equation (5) we also concatenate the initial depth to the refinement network and add additional skip connections. Compared to GeoNet++, these skip connections enforce a tighter handling of depth and normal information in both stages.
These normals are then used to refine the depth map again. The idea here is that first guiding the network to learn accurate normals might help it to find geometric structure that it would have otherwise missed. We use Equations (7) and (8) to compute a intermediary depth estimation z normal which is further refined into our final result by applying a CNN. Again, we add additional skip connections and concatenate the normal map to improve results: The overall architecture of our scheme is visualized in Figure 1. Most parts of the architecture are fixed weight and not learnable, which makes the training converge quickly. The concrete implementation of the CNNs like the number of layers of kernel sizes will be discussed in Section 3.4.
Loss Functions
Analogously to GeoNet, every operation from the initial depth estimate z initial to the refined estimate z f inal is differentiable. This means all networks (including the backbone, if it is also a neural network) can potentially be trained in an end-to-end fashion. However in the experiments in this paper, to showcase the generality of our approach, we pretrain the backbones and freeze their weights before training our remaining network. We compute loss functions on the intermediate results and sum up the individual losses to the total loss function l = l normal + l depth . More specifically, our normal loss function is the same as in [21]: For the depth loss, we make a few modifications. We do not include a loss function on the direct output of the backbone, since its weights are frozen. Instead, we also compute a loss on z normal . Even though there is only the kernel regression step with no learnable parameters between the computations of n f inal and z normal , we found in our experiments that it is still beneficial to have this additional loss function to pass gradients to the upstream networks. We use the Charbonnier loss-function [32] instead of L2 loss: To pretrain the backbone networks, we use the same Charbonnier loss-function (here of course only with one summand). Note that even though we need ground truth normal maps during training, at no point do we need normal map inputs during inference. This allows us to put our network on top of any arbitrary backbone as long as it outputs depth images.
Implementation Details
We use the same network architecture on top of each backbone. Each CNN in our scheme (see Figure 1) consists of just four convolutional layers with kernel size 3 and hidden dimension 64, which results in 235K additional learnable parameters. Table 1 shows the parameter and runtime overhead of our network compared to different backbones. We use ReLUs as our activation functions. We choose η = 0.5 and λ = 10 −3 for the loss-weighting hyperparameters and = 10 −6 as the parameter of the Charbonnier loss. We set the neighborhood size to 9 × 9 in Equation (2) and Equation (6) and choose γ = 0.05 and α = 0.95. We center crop images to a size of 256 × 256, randomly flip images along the vertical axis for data augmentation and train with a batch size of 16. As mentioned before, we freeze the weights of the backbones in all our experiments. In general, only 2-3 additional epochs are needed for our model to converge.
Training and Tasks
We demonstrate the effectiveness of our method on a variety of classical imagereconstruction tasks. To show the general nature of our approach, we add it and compare it to several different state-of-the-art backbone networks. All backbone networks were trained from scratch using code provided by the authors, using the training data provided by Qi et al. [20]. The dataset is based on the NYU v2 dataset [33] and contains 30,816 frames with real-world depth and color images taken with a Microsoft Kinect, as well as highquality normal maps that we used as ground truth. For more details on this training set, refer to [20]. The input to the networks was simulated from the ground truth images with the respective forward operators of the different tasks and will be further detailed in the following sections.
Denoising
The first task we used for comparison was the removal of additive Gaussian noise with known variance. We compared it against the two state-of-the-art deep-learning methods DnCNN [34] and the attention-based ADNet [35]. We added randomly sampled Gaussian noise with a moderate standard deviation of 0.5 m to our ground truth depth images and trained the networks with default parameters.
Deblurring
We convolved the ground truth depth with a 25 × 25 blur kernel that contained zeros everywhere except on the main diagonal, where it was constant 1/25. This roughly simulated motion blur of a far-away scene when the camera was rotated diagonally from the top left to the bottom right. We used a 17-layer ResNet as backbone, with a similar architecture to DnCNN [34].
Super-Resolution
We covered methods from both depth-only super-resolution as well as color-guided super-resolution in our experiments. For the former, we used DLapSRN [11], which itself is based on Laplacian pyramid networks [29]. Our backbone for color-guided superresolution is the recent deformable kernel network (DKN) [13]. We used bilinear filtering to sub-sample the ground truth depth images to a factor of 1/4. Again, we trained the networks with default parameters until convergence.
In order to gauge how our network deals with inputs of lower quality, we also trained it together with a simple bilinear interpolation backend. This also showcases how our method is not limited to learning-based backends.
Results
We evaluated the different methods on a separate 654 image subset of the common benchmark dataset NYU v2 [33], which is often used to evaluate super-resolution tasks [10,13,14]. To the best of our knowledge, there are no such commonly used benchmark datasets for depth-map Gaussian denoising and deblurring. For this reason, we evaluated all tasks on the same datasets. Quantitative results can be seen in Table 2. Our add-on network consistently improved the results of all backbone networks both in terms of rootmean-square-error (RMSE) and mean-absolute-error (MAE). Since we used the exact same backbone as a stand-alone network in the comparison, this improvement has to be a result of our depth-refinement scheme. The improvements of our network ranged from 3% for ADNet to 20% for DLapSRN in terms of average RMSE and from 6% to 20% in terms of MAE. This discrepancy could be explained with the quite challenging noise level of 0.5 m in our denoising experiments. Since the outputs of the backbones still included many defects, our initial normal computation could output low-quality normals that are not as helpful to the depth refinement network. Note that our add-on-like approach with a skip connection between the backbone output and the final result helps our method to be at least of the same quality as the backbone output, because in the worst case the network could just learn to output the initial depth-map. In terms of the structural similarity index (SSIM) [36], the deblurring experiment is slightly worse than the baseline, but in general the margins are lower, with the exception of the DnCNN experiments. Note that we did not explicitly optimize the networks for perceptual quality.
To show that our network is able to generalize to new datasets, we also evaluated 30 images of the Middlebury stereo dataset [37] without fine tuning our networks. The Middlebury dataset contains pixel disparity images which other authors [10,11,13,14] directly interpret as depth values before feeding them into their method. Since we needed to reproject depth values in order to compute our initial normal maps, we first needed to convert the disparity images into real depth images before inputting them into the backbones. To make our results comparable to other methods, we converted the final depth-maps back into disparity values before computing evaluation metrics. The results in Table 3 show that our network consistently outperformed the baseline methods. Since the Middlebury uses stereo images, as opposed to the training set, which uses structured light [33], we conclude that all tested networks can generalize to different types of sensors. Table 3. Quantitative comparison of depth-map reconstruction for different tasks on the Middlebury dataset [37]. Values are given in pixel disparity as provided by the dataset. We show qualitative results in Figures 2-5. For better visualization, we show pixelwise absolute difference to ground truth depth inside the insets. The areas of the highest improvement differ between tasks. Our method improved the denoising backbones mostly in planar regions (Figure 2). We assume that here, our windowed least-squares normal computation acted as an additional low-pass filter. Nevertheless, sharp edges were still preserved by our network. In contrast, the deblurring ( Figure 3) and super-resolution backbones (Figures 4 and 5) already output high-quality planar regions and the improvements of our network were predominantly located at the edges. The differences in results for RGB-guided super-resolution in Figure 5 are more subtle. DKN can already achieve very sharp edges by utilizing color-image information, and our method seemed to mostly improve some outliers at those edges. To verify our normal refinement module, we show exemplary normal map visualizations from our denoising results using the ADNet backbone in Figure 6. As suggested above, the normal computation for the denoising task is more challenging than for the other tasks. Our final normal maps are less noisy than the normal map that was directly computed with least squares. Especially at the edges, the initial normals show high levels of noise. They are also better in terms of mean angular error. Note that we focused more on high-quality depth-maps when we fine tuned our hyperparameters, and in general, treat the normal maps as auxiliary data to improve those depth maps. Since our loss function is a weighted average of depth and normal loss and depth information can also propagate through the normal refinement module, the network could learn to output slightly lower-quality normals if it, in turn, helps to improve the depth map and lead to a lower local minimum. (
Conclusions
In this paper, we have introduced a generic depth enhancement framework for a potentially wide variety of depth-reconstruction tasks. Our method is able to improve on several state-of-the-art deep-learning-based methods by adding just a few additional learnable parameters. Our approach has the nice side effect that it also computes highquality normal maps that can be utilized in some tasks, e.g., 3D reconstruction.
There are multiple possible directions for future work. Since we froze the weights of all pretrained backbone networks while training our depth-enhancement network, it would be interesting to see if improvements could be made by training them in tandem. We also speculate that our model can be rather easily used for transfer learning because the output of the backbones is already very similar. Another possible direction is to apply our depth-enhancement scheme iteratively, similar to the considerations from Qi et al. in the GeoNet++ paper [21]. In essence, the enhanced depth-map can again be used to compute a higher-quality normal map, which in turn can be used to get an even more improved depth-map, and so on. | 5,870.2 | 2022-05-01T00:00:00.000 | [
"Computer Science"
] |
Evaluation of Hysteresis Response in Achiral Edges of Graphene Nanoribbons on Semi-Insulating SiC
Hysteresis response of epitaxially grown graphene nanoribbons devices on semi-insulating 4H-SiC in the armchair and zigzag directions is evaluated and studied. The influence of the orientation of fabrication and dimensions of graphene nanoribbons on the hysteresis effect reveals the metallic and semiconducting nature graphene nanoribbons. The hysteresis response of armchair based graphene nanoribbon side gate and top gated devices implies the influence of gate field electric strength and the contribution of surface traps, adsorbents, and initial defects on graphene as the primary sources of hysteresis. Additionally, passivation with AlOx and top gate modulation decreased the hysteresis and improved the current-voltage characteristics.
Introduction
Graphene has been widely discussed and gained significant attention in recent decades owing to its unique fundamental low-dimensional physical properties and potential applications [1,2]. Nonetheless, the existence of the Dirac point, directed to a zero bandgap semiconductor [2], limits its integration into advanced digital electronic applications. However, it has been predicted to exhibit band gaps if the large area of graphene is constrained into a quasi-one-dimensional structure in subnanometer dimensions, making graphene suitable for many field-effect transistor applications at room temperature [3]. Many researchers stem from this significant property of graphene and are motivated to work on small constrictions like graphene nanoribbons (GNRs). In past decades these systems have been investigated at various levels of aspects. According to the studies, the properties of these systems can be tuned to form semiconductors to spin-polarized half metals [4,5]. Generally, graphene has five edge structures: zigzag, armchair, cove, gulf, and fjord [6]. In contrast to the zigzag-edged graphene structure, which typically exhibits spin-polarized edge states with energy levels close to Fermi level, most other graphene edges show semiconducting properties [4,6,7]. Moreover, several studies have tried to experimentally prove the semiconducting properties of GNRs with field-effect devices [8,9]. Both theoretical and experimental models revealed that the bandgap tunability is dependable on the width of GNRs [2,3,9]. However, the GNRs possess rough edges and widths along their ribbon length, which can further alter the bandgap depending upon the edge configurations (thus upon the chirality and number of carbon atoms) and doping [10,11]. Furthermore, the field-effect studies on GNR based FETs show a hysteresis behavior in the conductance, which varies with sweeping voltage and sweep rate in ambient conditions [12,13]. However, the hysteretic current-voltage (I-V) characteristics are not beneficial for transistors and integrated circuits. In contrast, the conductance hysteresis in graphene-related two-dimensional materials devices has significant possibilities and excellent potential in nonvolatile memory-based devices. Hence, many researchers have investigated and proposed various mechanisms and factors involved in graphene's origin of hysteresis behavior. However, most of the critical sources of hysteresis are charge transfer or trapping effects, mainly due to absorbates, interface traps, and bulk dielectric traps [12,14,15]. Moreover, considering the possible applications, making a systematic study of the physical mechanisms involved in the hysteresis properties of GNRs is a prerequisite for further device development. Therefore, insight into these fundamental properties and functionalities of GNRs is essential for developing GNR-based memory devices. Consequently, this study proposes a systematic evaluation of the hysteresis response of graphene nanoribbon structures in epitaxial graphene on semi-insulating SiC. The hysteresis response of GNRs on the armchair and zigzag directions of epitaxial graphene and dependent factors like width, measurement conditions, etc., were evaluated and studied. Moreover, the hysteresis response of armchair based side and top gated GNR FET devices were evaluated.
Experimental
Graphene has been prepared epitaxially with an orientation on-axis (0001) Si-face on semiinsulating 4H-SiC substrates at substrate temperatures between 1800 and 1900°C by thermal decomposition in an argon ambient [16][17][18]. The samples have a dimension of 10 x 11 mm 2 . Initially, the samples were cleaned and spin-coated at 4000 rpm with a high-resolution negative resist, hydrogen-silsesquioxane (HSQ) (XR-1541-4%) and immediately baked on a hot plate at 90°C for structuring and fabrication of GNRs. The final resist thickness was about 30 nm. The GNRs were fabricated on epitaxial graphene by electron beam lithography in Raith 150 equipment. For highresolution patterning and fabrication of nanoribbon structures on graphene, the electron beam lithography exposure was carried out at an acceleration voltage of 20 kV and an aperture of 7 µm. Subsequently, the exposed samples were developed with a two-step process in a solution of 3:1 (DI: TMAH) and 9:1(DI: TMAH) for 1 min at room temperature. Then the samples were etched in oxygen for 30 s using an inductively coupled plasma etching technique. The resist was removed by buffered oxide etching (BOE) for 1 min and rinsed in DI for another 1 min. The developed methodology allows the fabrication of GNRs down to sub 10 nm resolutions [8,19]. The patterning of electrodes was defined by a positive tone electron beam resist PMMA (AR-P 617). The drain and source electrodes were metalized by an electron beam evaporation (Ti=10 nm/Au= 80 nm). In side-gate GNR-FET devices, both channel and side gates are made on graphene itself. For the top gate fabrication, initially a thin layer of Al (8 nm) is deposited using e-electron beam evaporation and oxidized thereafter. The top electrodes were metalized by an electron beam evaporation (Pd=10 nm/Au= 80 nm). The morphology and evaluation of critical dimensions and etching step heights of structured GNRs were measured using scanning electron microscopy (SEM) and atomic force microscopy (AFM) which uses active microcantilevers. The fabricated devices were characterized using SEM and measured using a Keithley 4200 system under a nitrogen environment at ambient conditions.
Results and Discussions
A non-contact AFM image of the surface morphology and profile obtained from pristine epitaxial graphene grown on Si-face of semi-insulating 4H-SiC is depicted in Fig. 1. As illustrated in Fig. 1, graphene is provided with wide micro-step structures. These micro steps like terraces are formed during the graphitization on the surface of SiC. These structures are spread along the edges of SiC due to the step bunching process and often cause inhomogeneity in graphene layer thickness [20,21].
Silicon Carbide and Advanced Materials
Terraces formed on the surface of graphene have an average width of 1 µm and a step height of 5 to10 nm. Fig. 2a shows a typical SEM image of the fabricated lateral device. All fabricated lateral devices have a channel length of 1 µm. As shown in the SEM image (see Fig. 2a), Ti/Au contacts were deposited above the GNRs from a two-terminal lateral device. Fig. 2b shows the AFM image of the device's active area with the profile obtained from the channel region. Since the channel region covers the terrace edges, the GNRs formed in such parts of the sample surface has varied graphene thickness. These inhomogeneities in the graphene layer thickness in the terraces and step region further change the electronic transport properties and result in mobility and channel conductance alterations of the graphene channel [22]. Fig. 4 (a) shows the DC hysteresis response obtained from a 20 nm GNR lateral device in the armchair direction, and the insets show the hysteresis response in the zigzag direction.
Furthermore, the hysteresis factor, the difference of the areas underneath the upper and lower branches of the hysteresis curves, was used here to quantify hysteresis using the Eq. 1 as follows: Fig. 1. Surface topography AFM images of epitaxial graphene on semi-insulating 4H-SiC and its respective height profile. Fig. 4a illustrates the hysteresis obtained from both lateral devices on the armchair and zigzag directions. The drain to source voltage (UDS) was applied by the voltage sweep from 0 V to 4 V, then from 4 V to -4 V, and back to 0 V. It is noted that the GNR in the armchair direction shows more pronounced hysteresis response with symmetric behaviour in both sweeping directions. In contrast, no significant hysteresis response of GNR devices in the zigzag direction was observed. Also, the area obtained for lateral devices in the zigzag direction is less when compared to the armchair (See Fig. 4a insets). This dependence in the hysteresis response might arise from the semiconducting and metallic nature of the armchair and zigzag edges of graphene nanoribbons, respectively [4,5,9]. Moreover, the hysteresis might be either attributed to the charge trapping and de-trapping into the surface or interface traps of the GNR device or the charge transfer from the absorbents like water molecules [12,15].
Furthermore, we have evaluated the hysteresis response dependence and the hysteresis factor on GNR strip width. Fig. 4b illustrates the dependence of the hysteresis response on the stripe width obtained from the GNRs in armchair direction for equal channel length. It is noted that with an increase in the strip width, we could observe a moderate increase of the hysteresis factor. This behaviour be attributed to an increase in the absolute value of traps with an increase in the surface Silicon Carbide and Advanced Materials area of the channel [12,15] when the trap density is assumed to be independent on the area. Moreover, due to the increase in the surface area, more additional terrace edges cover the channel region (See Fig. 2b), which further introduces more defect density in the channel [23]. These initial defect sites in the graphene channel promote the charge trapping process and may further influence the conductivity hysteresis of the GNR devices [23,24]. We further evaluated the hysteresis response of the GNR channel by modulating the carrier density using a side gate. Similar methods of side gate engineering of graphene channels for graphene fieldeffect devices have already been reported and studied [25,26]. Therefore, side gates could be an alternative to the top-gating scheme in graphene field effect transistors (GFETs) to modulate the graphene channel using a lower number of lithographic steps. However, this device type often experiences high leakage current caused by the current flow through the dielectric/air interface [27]. Fig. 5a and 5b illustrate SEM images of the general structure of side-gated armchair based GNR-FET. In these devices, graphene is used for both channel and side-gate. To understand the hysteresis behavior in these devices, measured in direct sweep conditions, (UDS was varied from 0 V to 4 V, then from 4 V to -4 V and back to 0 V). The side gate voltage step was varied from 0 V up to 3 V with 1 V step.
Typical output hysteresis response obtained from side gate GNR-FET is shown in Fig. 5d. The FET shows typical n-type behavior of graphene on SiC. In addition, it is noted that the UDS sweep in GNR-FETs exhibits an increase in the hysteresis factor with increasing positive gate voltage. A similar behavior in GFETs has already been studied [12,15]. These hysteresis responses possibly originate from the charge injection into the channel and side gate graphene substrate interface [14,15]. Therefore, with the substantial influence of the side-gate voltage sweep, the hysteresis is triggered by a redistribution of charges at the interface and further causes charge transfer or trapping. As a second source of the conductivity hysteresis adsorbates and processing residues may act. The effect of the side gate-source voltage sweeping range (UGS) and corresponding hysteresis area obtained from the GNR-FET is illustrated in Fig. 5e inset. Here, the UGS voltage varies from 0 V to 3 V. A small hysteresis response was observed for UGS at 0 V. However, a higher hysteresis response was observed when the UGS increased. This behavior infers that the trapping of charges has increased during the increase of side gate electric field strength sweeps which further contribute to the wider hysteresis.
We further investigated the effect of top gate modulation by passivating graphene channel with a high -k dielectric layer like AlOx. The channel region is deposited with 20 nm of AlOx. Subsequently, the top gate electrodes were deposited. Fig. 5b illustrates a SEM image of the top gated GNR-FET with 20 nm AlOx. The devices were further measured with the side gate under ambient conditions. The UDS voltage varied from 0 V to 4 V, then from 4 V to -4 V and back to 0 V. After each direct sweep measurement, the top gate voltage UGS was increased by 2 V from -4 V to 4 V. Fig. 5e illustrates the hysteresis factor of the device under top gate modulation. It is noted that the current has been improved, and hysteresis response in the device was minimized with top gate modulation. This behavior further infers that deposition of a high-k dielectric material like AlOx in the active device area of the GNR-FET modifies and passivates the contribution of hysteresis promoting sources like water molecules and other adsorbents [14,28]. Moreover, it desorbs molecules from the surface of the graphene channel and retards the chemical reaction reducing the H2O molecules at the interface of the channel [14,29]. Another possible explanation for this behavior might be due to a limited supply of oxygen on the surface of the graphene channel, as it suppresses the surface reactions [28].
Summary
In conclusion, we have fabricated and evaluated the hysteresis response of GNR devices in both armchair and zigzag directions of epitaxial graphene on semi-insulating 4H-SiC. The difference of the area underneath the upper and lower branches of the hysteresis curves was used to quantify hysteresis. In addition, the influence of the orientation of fabrication of GNR in both armchair and zig directions and its dimensions on the hysteresis effect was examined and studied. Minimal hysteresis response in devices oriented in zigzag direction, possibly due to the metallic nature of the GNRs with zigzag edges was found. Moreover, the significant increase in the hysteresis factor with increasing channel width was observed stemming from an increase in the absolute value of traps with an increased surface area. Furthermore, the increase in the gate electric field strength substantially increases the hysteresis factor of armchair oriented GNR-FET devices. Additionally, passivation of the active device area with AlOx as a high-k dielectric material and top gate device operation could reduce the defects, resulting in a lower hysteresis factor and enhancing the devices' current. | 3,249.4 | 2023-05-26T00:00:00.000 | [
"Physics",
"Materials Science"
] |
Omariniite, Cu8Fe2ZnGe2S12, the germanium analogue of stannoidite, a new mineral species from Capillitas, Argentina
Abstract Omariniite, ideally Cu8Fe2ZnGe2S12, represents the Ge-analogue of stannoidite and was found in bornite-chalcocite-rich ores near the La Rosario vein of the Capillitas epithermal deposit, Catamarca Province, Argentina. The mineral is associated closely with three other Ge-bearing minerals (putzite, catamarcaite, rarely zincobriartite) and bornite, chalcocite, digenite, covellite, sphalerite, tennantite, luzonite, wittichenite, thalcusite and traces of mawsonite. The width of the seams rarely exceeds 60 μm, their length can attain several 100 μm. The mineral is opaque, orange-brown in polished section, has a metallic lustre and a brownish-black streak. It is brittle, and the fracture is irregular to subconchoidal. Neither cleavage nor parting are observable in the sections. In plane-polarized light omariniite is brownish-orange and has a weak pleochroism. Internal reflections are absent. The mineral is distinctly anisotropic with rotation tints varying between brownish-orange and greenish-brown. The average result of 45 electron-microprobe analyses is Cu 42.18(34), Fe 9.37(26), Zn 5.17(43), In 0.20(6), Ge 11.62(22), S 31.80(20), total 100.34(46) wt.%. The empirical formula, based on Σ(Me + S) = 25, is Cu8.04(Fe2.03In0.02)Σ2.05Zn0.96 Ge1.94S12.01, ideally Cu8 +Fe2 +Zn2+Ge2 4+S12 2-. Omariniite is orthorhombic, space group I222, with unit-cell parameters: a = 10.774(1), b = 5.3921(5), c = 16.085(2) Å, V = 934.5(2) Å3, a:b:c = 1.9981:1:2.9831, Z = 2. X-ray single-crystal studies (R 1 = 0.023) revealed the structure to be a sphalerite derivative identical to that of stannoidite. Omariniite is named after Dr. Ricardo Héctor Omarini (1946–2015), Professor at the University of Salta, for his numerous contributions to the geology of Argentina.
According to Höll et al. (2007) Cu-Fe-Zn-Gesulfides like germanite and renierite are characteristic for the carbonate-hosted Kipushi-type polymetallic deposits (e.g. Tsumeb, Namibia, and Kipushi, Democratic Republic of Congo) but also occur within high-sulfidation epithermal Cu-Au deposits (e.g. Chelopech, Bulgaria). On the contrary, argyrodite is typically observed in bonanza-grade silver mineralization of the Bolivian "Ag-Sn-belt" (e.g. Porco and Colquechaca Ag-Zn-Pb-Sn deposits, Bolivia; Paar and Putz, 2005) and in vein-type Ag-Pb-Zn (-Cu) deposits (e.g. Freiberg district, Saxony, Germany; Höll et al., 2007). The mineralogy and distribution of germanium at Capillitas is thus unique on a worldwide scale and represents a new mode of the occurrence of this rare metal in epithermal systems. In addition to the six mentioned Ge minerals, Ge contents up to 0.2 wt.% have been found in colusite-nekrasovite . This germanium anomaly was discovered during the PhD project of one of the authors (HP), which included a detailed study of the complex mineralogy at Capillitas.
Omariniite is named in honour of Dr. Ricardo Héctor Omarini (1946, Professor at the University of Salta, for his outstanding contributions to the geology of Argentina, especially to the Precambrian basement of the "Formación Puncoviscana". The mineral and the mineral name have been approved by the IMA CNMNC (2016-050). Holotype material is housed within the Systematic Reference Series of the National Mineral Collection of Canada, Geological Survey of Canada, Ottawa, Ontario, under catalogue number NMCC 68096. Cotype material is deposited within the collections of the University of Firenze (Italy), the Natural History Museum of London (UK) and in the private collections of two of the authors (HP and WHP).
Location, geology and mineralization
The Capillitas mining district is part of the Farallón Negro Volcanic Complex, which is located in the Province of Catamarca, Argentina. It consists of Miocene extrusive and intrusive subvolcanic rocks within the crystalline basement (metapelites and schists of the Suncho Formation and Capillitas granitic batholith). Porphyry copper-gold deposits (e.g. Bajo de La Alumbrera, Agua Rica) and epithermal vein-type deposits (e.g. Capillitas, Farallón Negro -Alto de la Blenda) are associated with the intrusive rocks, which range from andesitic to dacitic to rhyolitic in composition (Sasso, 1997;Sasso and Clark, 1998).
The Capillitas diatreme represents one of the volcanic centres within the granitic basement block of the Sierra de Capillitas. It is composed of intrusive and volcaniclastic rocks (ignimbrite, rhyolite porphyry, dacite porphyry and tuffs) and is host to the epithermal vein-type Capillitas deposit, which is known mainly for the occurrence of gem-quality rhodochrosite. The epithermal veins are hosted in rhyolite, ignimbrite and granite and show different types of host-rock alteration and both high-sulfidation and intermediate-sulfidation stage mineralization. The polymetallic character of the veins is reflected by its very complex mineralogy with the participation of Cu, Pb, Zn, Fe, As and Sb, associated with W, Bi, Sn, Te, Ag and Au as well as Ge, Cd, In, V, Ni and Tl. Further details on ore deposit geology, alteration and mineralization styles and ore mineralogy are presented elsewhere (e.g. Márquez-Zavalía, 1988, 1999Paar et al., 2004, Putz et al., 2006, Putz et al., 2009. Cementation processes led to the formation of high-grade copper ores (bornite-chalcocitedigenite) which are restricted to the central part of the deposit in the vicinity of the La Rosario vein. As the old mine workings ("Mina La Rosario" and "Pique Rosario") dating back to the 19th century are completely collapsed, material of this ore type is restricted to the old dumps nearby. Interestingly, only a few samples of this type of ore contain the unique assemblage of Ge minerals, such as putzite, catamarcaite, zincobriartite and omariniite (putzitecatamarcaite-omariniite-zincobriartite-paragenesis).
Intermediate-sulfidation stage veins hosted within the granitic basement (e.g. the La Argentina vein) are rich in galena, sphalerite and abundant rhodochrosite as the dominating gangue mineral. At the Santa Rita mine, where rhodochrosite-bearing ore of the La Argentina vein is currently mined for ornamental purposes, small-sized bonanza-grade ore bodies carry a Agrich assemblage composed of proustite, pearceite, acanthite, native silver and argyrodite. The latter is locally intergrown with spryite, Ag 8 (As 3þ 0:5 As 5þ 0:5 )S 6 (Putz et al., 2002;Bindi et al., 2016).
The Ge-bearing minerals at Capillitas occur in two genetically different environments. The ore of the intermediate sulfidation stage was dictated by a high activity of silver and arsenic, which led to the formation of argyrodite-spryite. The increased activity of copper within the ores created by supergene enrichment processes, however, led to the formation of copper-dominated phases, amongst them putzite. Further details dealing with the crystallization sequence of the omariniite-bearing ore and the possible conditions of formation are presented in Paar et al. (2004) and Putz et al. (2006).
Appearance and physical properties
Omariniite is fairly abundant in those bornitechalcocite ores which also carry putzite and/or catamarcaite. Within the putzite holotype material, the new mineral frequently, but not exclusively, occurs as: (1) a rim or seam at the contact between putzite and chalcocite, rarely exceeding a width of 60 µm but attaining a length of several hundreds of micrometres (Fig. 1a); and (2) as an envelope around the anhedral to subhedral inclusions of catamarcaite within putzite and chalcocite (Fig. 1b), where it is sometimes also associated with zincobriartite. The rims are usually composed of a mosaic of intensely twinned, anhedral subgrains (Figs. 1c and 1d); individual grains without twinning are rare and measure up to 30 µm × 70 µm. An exceptional single grain measures 200 µm × 100 µm. Wittichenite, tennantite, thalcusite and rare grains of mawsonite are also observed within this ore type but never in contact with omariniite. The holotype material of catamarcaite also contains omariniite. Within this material, the new mineral occurs as (3) an envelope (generally <10 µm in thickness) around catamarcaite inclusions within chalcocite/digenite/covellite/sphalerite in association with luzonite (see fig. 3a in Putz et al., 2006).
Omariniite is distinctly orange-brown, has a metallic lustre, a reddish-brown streak and is opaque. It is brittle and has an irregular to subconchoidal fracture. Neither cleavage nor parting could be observed. Microhardness was determined with a Miniload 2 hardness tester using loads of 25 and 100 g, respectively. While testing with a load of 100 g resulted in a VHN of 202 (range 190-215) kg mm -2 , a distinctly higher VHN (534; 488-532 kg mm -2 ) was obtained with a load of 25 g. The higher hardness at lower loads is related to elastic features of the tested material (Young and Millman, 1964). The calculated Mohs hardness using VHN 100 and the equation of Young and Millman (1964) is 3½.
The density could not be determined because of paucity of available material and the penetrative intergrowth with other phases. Using the unit-cell parameters from X-ray single crystal work and the ideal chemical formula, the calculated density is 4.319 g cm -3 . It is distinctly lower than the calculated density of stannoidite (4.68 g cm -3 ; Kato, 1969).
Optical properties
In polished sections using plane-polarized light the colour of omariniite is orange-brown which is enhanced in oil. Pleochroism is weak, as also is the bireflectance. Between crossed polars omariniite is distinctly anisotropic, with rotation tints changing from brownish-orange to greenish-brown. No internal reflections were observed.
Reflectance measurements were made using a J&M TIDAS diode array spectrometre between 400 and 700 nm at intervals of 0.8135 nm. The data were reduced to 20 nm intervals and are summarized in Table 1. A comparison of omariniite with stannoidite ( Fig. 2) shows characteristic differences between the two species which allows them to be easily distinguished using optical methods. Both species show an increase of the reflectance values with increasing wavelength. The maxima for these minerals are towards the red end of the visible spectrum with dominant wavelengths (Λ d ) relative to the C illuminant of 590-597 nm (omariniite) and 577-578 nm (stannoidite) which explains the orange-brown colours of the species in reflected light. However, the reflectance values for omariniite are generally markedly lower than those observed for stannoidite.
Chemical composition
Omariniite was analysed with a JEOL Superprobe JXA-8600 (controlled by an LINK-eXL system, WDS mode, operated at 25 kVand 35 nA, with 20 s and 10 s as counting times for peak and background, respectively), installed at the University of Salzburg, Austria. The following standards and X-ray lines were used: chalcopyrite (CuKα, FeKα), synthetic ZnS (ZnKα, SKα), Ge metal (GeKα) and synthetic InAs (InLα). Tin was sought but not detected. The raw data were processed with the on-line ZAF-4 program. Several aggregates of omariniite in various polished sections of the putzite holotype material were analysed and found to be homogeneous. The results show only minor variation of the chemical composition (
X-ray crystallography and crystal-structure determination
A few omariniite crystals were handpicked from a polished section under a reflected light microscope and examined by means of an Oxford Diffraction Xcalibur 3 CCD single-crystal diffractometer using graphite-monochromatized MoKα radiation (see Table 3 for details). Single-crystal X-ray diffraction intensity data were integrated and corrected for standard Lorentz-polarization factors with the CrysAlis RED package (Oxford Diffraction, 2006). The program ABSPACK in CrysAlis RED (Oxford Diffraction, 2006) was used for the absorption correction. A total of 1238 unique reflections was collected. The statistical tests on the distribution of |E| values (|E 2 -1| = 0.697) indicated the absence of an inversion centre. Systematic absences were consistent with the space group I222. Given the similarity in the unit-cell values and in the space groups, the structure was refined starting from the atomic coordinates reported for stannoidite (Kudoh and Takéuchi, 1976) using the program Shelxl-97 (Sheldrick, 2008). The site occupation factor (s.o.f.) at the seven cation sites was allowed to vary (Zn vs. structural vacancy) using scattering curves for neutral atoms taken from the International Tables for Crystallography (Wilson, 1992). After several cycles and a careful check of the bond distances at the different sites, the positions were found fully occupied by the atomic species given in Table 4 and their s.o.f. were fixed to 1. The electron density was only found higher than 26 ( pure occupation by Fe) at the M5 site, and all the In 3+ found by electron microprobe was thought to occur at this site. Table 3 reports details of the selected crystal, data collection and refinement. Final atomic coordinates and equivalent isotropic displacement parameters are given in Table 4, whereas selected bond distances are shown in Table 5. A list of the observed and calculated structure factors and crystallographic information file are deposited with the Principal Editor of Mineralogical Magazine at http://www.minersoc. org/pages/e_journals/dep_mat.html. Description of the structure and relationships with other species The crystal structure of omariniite (Fig. 3) can be considered as a sphalerite derivative. The omariniite structural topology is also derivable from the stannite structure by substituting Cu atoms for a set of Ge atoms in stannite and adding excess Cu atoms in a set of tetrahedral vacancies (Fig. 4). A comparison of the bond distances for the different tetrahedra occurring in stannoidite and omariniite is presented in Table 5. It appears evident that the Getetrahedron (i.e. M6) is much smaller (mean bond distance 2.248 Å) than the Sn-homologue in stannoidite (mean bond distance 2.40 Å; Kudoh and Takéuchi, 1976) and it is close to that observed Goh et al., 2006) and the corresponding bond-valence sums (BVS) at Cu and Fe sites are 1.18 and 3.15 vu, respectively, with corresponding bond distances of 2.312 and 2.248 Å (Knight et al., 2011). Consequently, the bondvalence sums at Cu sites (M2, M3, M4, M7) in both the stannoidite and omariniite structures probably agree with the presence of Cu + . Such an oversaturation at Cu tetrahedral sites have been observed in other sulfides and sulfosalts and it could probably be related to the non-accuracy of the bond parameter for Cu-S bond given by Brese and O'Keeffe (1991) and to the covalent nature of the bonds. On the contrary, the observed strong undersaturation at M1 (in omariniite) and M5 (both stannoidite and omariniite) have to be taken into account. They could be either related to some limits in the crystal structure refinement or to a different (BVS 3.72) and Cu + at M7 (BVS 1.28), giving rise to the formula Cu þ 8 Fe 3þ 2 ZnGe 4þ 2 S 12 (Z = 2). Calculated X-ray powder diffraction data (d in Å) for omariniite are given in Table 6. Intensities and d hkl values were calculated using the Powdercell 2.3 software (Kraus and Nolze, 1996) on the basis of the structural model given in Table 4.
A solid solution series probably exists between stannoidite and omariniite, as stannoidite from Tsumeb and Khusib Springs carry significant Ge (0.5-0.7 wt.% in Khusib Springs and up to 2.2 wt.% in Tsumeb, respectively; Melcher, 2003). (Kraus and Nolze, 1996) on the basis of the structural model given in Table 5; only reflections with I calc > 1 are listed. The strongest reflections are given in bold. | 3,371.2 | 2017-10-01T00:00:00.000 | [
"Geology"
] |
Electrospinning of Ethylene Vinyl Acetate/Poly(Lactic Acid) Blends on a Water Surface
The electrospinning of an ethylene vinyl acetate (EVA) copolymer with a vinyl acetate content of 28 wt.% is limited due to the solubility of the copolymer in standard laboratory conditions. Poly(lactic acid) (PLA) is a biodegradable polymer that can be electrospun easily. However, PLA has limited applicability because it is brittle. Blends of these polymers are of interest in order to obtain new types of materials with counterbalanced properties originating from both polymeric compounds. The fibers were electrospun on a water surface from a solution mixture containing various weight ratios of both polymers using a dichloromethane and acetone (70:30 v/v) mixture as solvent. The morphologies of the prepared non-woven mats were examined by scanning electron microscopy (SEM), and the chemical composition was investigated by X-ray photoelectron spectroscopy (XPS) and by Fourier Transform Infrared Spectroscopy (FTIR). The fibers’ thermal properties and stability were examined, and the mechanical properties were tested. The results showed that the strength and flexibility of the blend samples were enhanced by the presence of PLA.
Introduction
Ethylene vinyl acetate (EVA) is a random copolymer, consisting of ethylene and vinyl acetate (VAc) units. While the polyethylene units are partially crystalline and stiff, the amorphous vinyl acetate units are flexible and soft. EVA is commercially available, with VAc content varying from 3 to 50 wt.% [1]. The VAc content has two major effects that influence the properties of EVA copolymers; disruption of the crystalline regions formed by the polyethylene segments of the copolymer and the overriding effect of VAc content resulting from the polar nature of the acetoxy side chain [2]. EVA copolymers are transparent, flexible materials with high tensile strength. The disadvantages of EVA copolymers are a reduced chemical resistance compared to LDPE, and reduced barrier properties and creep resistance [1]. EVA copolymers are mainly used in packaging and adhesive applications. Recently, an EVA copolymer was tested as a material for drug release because it is also biocompatible [3,4].
Poly(lactic acid) (PLA) is aliphatic polyester. PLA can be prepared from renewable resources and is biodegradable and biocompatible [5]. PLA has good mechanical properties, such as high strength and stiffness. However, the main drawback of using PLA for most applications is its poor toughness, manifested in its impact strength and elongation at break properties. Under laboratory conditions, PLA is a glassy polymer and is highly brittle. In the food industry, applications of PLA include buffering agents, acidic flavoring agents, acidulates, and bacterial inhibitors [6]. PLA has low melt strength and viscosity, making it difficult to apply in certain processing techniques.
One possibility for improving the mechanical properties of PLA is blending it with other polymers. This method is widely applied due to its feasibility in industry. A number of polymers have been blended with PLA, including various poly(hydroxyalkanoate)s [7,8] and poly(ε-caprolactone) (PCL) [9]. and dichloromethane p.a. (DCM), were supplied by CentralChem, Slovakia. Distilled water was used as a collector for fibers.
Preparation of Electrospun Fibers
The 12 wt.% solutions in the solvent mixture were prepared by dissolving various weight ratios of EVA and PLA granules (100:0, 80:20, 60:40, 40:60, 20:80, 0:100) in DCM. According to a previous report [21], acetone was added for the enhanced conductivity of the solutions. The DCM/acetone ratio was set at 70:30 v/v. The solutions were intensively stirred on a magnetic plate for few hours. Polymers solutions were electrospun from a 5 mL syringe with a metallic needle at the flow rate of 1.5 mL/h. The electrospinning parameters were fixed. A high voltage of 14-15 kV was applied, and the fibers were collected on a distilled water surface containing a copper counter electrode at a distance of 15 cm from the needle tip. The EVA copolymer is not highly soluble at laboratory temperature; therefore, an infrared lamp ( Figure 1) was used to heat the polymer solution, as previously described [15,22]. The temperature was controlled by a thermocouple and adjusted to 37 • C.
Characterization of Prepared Fibers
The scanning electron microscope (SEM) used was a JSM Jeol 6610 microscope (Jeol Ltd., Tokyo, Japan), an accelerated voltage of 10 kV was used for checking the morphology of the electrospun fibers. The samples were sputtered with a thin layer of gold. AzTec software was used for the collection of the micrographs. The images were post-processed using the ImageJ software. ImageJ was also used to measure the average diameter of the fibers in the nonwoven fabrics (the program is available on https://imagej.nih.gov/ij/). For each sample, 100 measurements were randomly made to determine the mean nanofiber diameter.
The samples were analyzed using Fourier Transform Infrared Spectroscopy (FTIR) with a NICOLET 8700™ spectrophotometer (Thermo Scientific, Madison, WI, USA) and an ATR (Attenuated Total Reflection) accessory. The FTIR spectra were analyzed using the OMNIC™ 8.1 software. The analyzed surface was approximately 3.14 mm 2 (the contact area of the Ge crystal, which was used for ATR measurements), and used range of 4000 to 600 cm −1 . Three measurements were used for each sample at three different locations.
Thermo Scientific K-Alpha XPS system (Thermo Fisher Scientific, East Grinstead, UK) equipped with a micro-focused, monochromatic Al Kα X-ray source (1486.6 eV) was used to collect X-ray photoelectron spectra (XPS) of electrospun fibers and also pure EVA and PLA pellets. The constant analyzer energy mode with the pass energy of 200 eV was applied for obtaining the survey scan, narrow regions were collected with the pass energy of 50 eV. For the charge compensation, a flood gun was used. For digital acquisition and data processing, Thermo Scientific Avantage software was used (version 5.988, Thermo Fisher Scientific, East Grinstead, UK).
Mechanical properties were measured at room temperature using the universal testing machine Instron 3365 (Instron, High Wycombe, UK) using a rate of deformation of 50 mm min −1 . Seven specimens were tested for each sample, and the final values of tensile strength and elongation at break were averaged.
The thermal degradation of the samples was investigated using a Mettler-Toledo DSC 821 (Mettler Toledo GmbH, Greifensee, Switzerland) differential scanning calorimeter (DSC). The heat of fusion calibration standard was indium. A temperature interval from −20 to 220 • C was used for samples characterization with a heating rate of 10 • C min −1 in airflow. The data were collected during the first heating run, and transition temperatures were taken as peak maxima. Mettler-Toledo 851e thermogravimetric analyzer (Mettler Toledo GmbH, Greifensee, Switzerland) was used for thermogravimetric analysis (TGA). Analysis was carried out in airflow of 50 cm 3 min −1 at the heating rate of 5 • C min −1 . For both methods, the data were evaluated as average value taken from three samples with SD.
Results and Discussion
On the basis of our former work, where the EVA copolymer with 28 wt.% VAc was used for the preparation of composites with carbon nanotubes [23] for actuators, we wanted to design nanofibers of EVA and later incorporated carbon-based fillers. As was found in the first experiment, the electrospinning of pure EVA with vinyl acetate content 28 wt.% is highly difficult in standard laboratory conditions. Solutions have to be continuously heated during the electrospinning process to stay in liquid form. The electrospun EVA fibers diameter was in the range of micrometers. Various changes in the experimental conditions (e.g., polymer concentration in solution and voltage) did not provide EVA fibers in the nanometer scale. One of the solutions for preparing nanofibers is the electrospinning of a blend with another polymer, e.g., biodegradable poly(lactic acid). The other problem with electrospinning of EVA is that the electrospun samples tend to be sticky, and if they are collected on aluminum foil, they are destroyed during the necessary removal process for further characterization. The fibers can be collected on the water surface to ease manipulation of the samples without risk of their destruction. From the first study, where various concentrations of EVA in DCM were prepared and electrospun, 12 wt.% concentration in solution was later used for electrospinning because fibrous mats were not produced at lower concentrations. Either electrosprayed droplets or fibers with morphological defects in the form of beads were formed. Similar results were found when EVA was electrospun with clays [19]. The higher concentrations resulted in fibers with higher diameters. The 70:30 solvent mixture of dichloromethane (DCM) and acetone was finally used. DCM is a suitable solvent for both PLA and EVA because some of the traditional solvents used for EVA (e.g., cyclohexane), tend to be selective for VAc or ethylene segments [24]. However, DCM has a lower conductivity (4.3 × 10 −5 µS/cm), dielectric constant (ε = 9.1), and boiling point (b.p. = 39.6 • C) than acetone (dielectric constant ε = 20.6, conductivity is 0.2 µS/cm, b.p. = 56 • C) [25]. The addition of acetone to the solvent mixture, as well as its amount, was based on previously published reports [19]. It was found that the diameter of the fibers decreased as the boiling point of the second solvent increased [26]. The effect is caused by slower evaporation of the second solvent, thereby leading to a change in viscoelastic properties, which enhances jet stretching and produces thinner fibers [19].
In all of the experiments, the electrospinning conditions were constant, as described in the previous section. Therefore, the study of the electrospun fibrous mats will be discussed regarding EVA to PLA ratios. SEM images of the samples prepared by electrospinning are presented in Figure 2. The solutions of EVA do not have good conductivity, since EVA has low conductivity [1]. This property resulted in fibers with a high diameter (5906 ± 2958 nm). As the content of EVA in the electrospinning solutions of EVA/PLA decreased the diameter of prepared fibers decreased accordingly, but the diameter distribution remained wide ( Figure 3). Partial separation of polymers during electrospinning caused a wide distribution of the diameter, resulting in heterogeneous fibers containing blended fibers, as well as EVA and PLA fibers. The bad miscibility of EVA and PLA produced these outcomes. Their miscibility depends on the VAc content. The higher the VAc content (>40 wt.%) is, the better the resulting miscibility is [7,10]. The lower the VAc content is, the higher the interfacial tension is [7]. The results are supported by XPS measurements discussed later (Figure 6e). PLA fibers produced from a solvent mixture of DCM/acetone have broad diameter distributions (328 ± 192 nm) with structural defects in the form of beads (Figure 2b). This result is caused by lower conductivity of the PLA solution compared to that of the solvents [21]. In certain cases of electrospun fibers from EVA [15] and low-density poly(ethylene) (LLDPE) [22], rough angular formations were observed. The rapid crystallization process caused these formations during electrospinning. However, similar structures were not found, and the prepared fiber surface was smooth. From Figure 2a-f, it can be noted that the prepared fibers are randomly oriented. Histograms of the diameter distribution calculated from the diameters of 100 random fiber measurements from all the prepared samples are depicted in Figure 3. The SEM image was divided into 8 equal areas, and the diameters of the fibers were measured using ImageJ software. The average diameter of the blends fibers EVA/PLA 60:40, EVA/PLA 40:60, and EVA/PLA 20:80 (Figure 3d,e,f) is nearly the same and reached values from 571 to 510 nm with a notably high standard deviation (SD). The blends EVA/PLA 60:40 and 40:60 contained some beads (Figure 2d,e); however, fibers produced from the EVA/PLA 20:80 mixture are without these defects. Figure 4a shows the ATR FT-IR spectra of the prepared mats and the spectrum of pure electrospun EVA, as well as the spectrum of PLA. Two peaks, which appeared near 2920 cm −1 and 2850 cm −1 , are mainly stretching vibrations of CH and CH 3 groups [5,27]. Details of selected samples spectra in wavenumbers from 970 to 1280 cm −1 and from 2790 to 3050 cm −1 are shown in Figure 4b,c respectively. The peak near 1735 cm −1 belongs to the carbonyl C=O stretching from both vinyl acetate and PLA, respectively [12]. The intense peak from C-O stretching near 1182 cm −1 belongs to PLA [20]. The slightly visible peak near 1453 cm −1 is assigned to the bending vibrations of CH 3 groups [9]. The absorption peak observed at 1366 cm −1 is attributed to C-H deformation, including asymmetric and symmetric bending [19]. The peak at 1240 cm −1 resp. 1268 cm −1 (Figure 4b) is assigned to -C=O bending from EVA and PLA [19,20]. The intense peaks at 1186 and 1087 cm −1 belong to C-O stretching from PLA (Figure 4b) [20]. In the FTIR spectra of the electrospun samples, no new peaks or bands appeared. The resemblance between the spectra of the electrospun polymers and blends is noticeable at the first look, which is not surprising, and a similar trend was reported by Moura et al. [28]. An XPS study was conducted in order to evaluate the surface of prepared samples ( Figure 5). XPS is a surface-sensitive quantitative spectroscopic technique that provides information about the elemental composition and chemical state of investigated samples up to 10 nm depth. Table 1 summarizes the elemental composition of the sample surfaces. Nitrogen N1s (centered at~399.5 eV), carbon C1s (at~285 eV), and oxygen O1s (at~532 eV) signals were detected. The small amount of detected nitrogen is possibly from the ambient air during the preparation and collection of the fibers on the water surface. The high resolution C1 peaks were deconvoluted and fitted with five components. O1s peaks were fitted with two components, C=O and C-O, centered at 532.1 eV and 533.4 eV, respectively. The first step of the XPS investigation was comparison of the chemical composition for the pure polymers prepared by electrospinning with the original samples in the form of pellets. In the original EVA pellets, 90.1 at.% of C and 9.9 at.% of O was detected. EVA typically has a higher carbon content which is connected to the higher amount of ethylene (C-C signal at~285 eV) compared to vinyl acetate, which contains carboxyl groups OC=O (C1s centered at~289 eV, O1s (C=O) centered at~532 eV) [29]. Electrospun EVA contains more oxygen, 12.4 at.%., when compared to EVA pellets, which indicates oxidation during the fiber processing. This oxidation most likely occurs on the polyethylene chain of EVA, which confirms the increase of signal at a 285.8 eV corresponding to a C-O/C-OH signal (see Figure 6a,b) [29]. From the comparison of the composition of EVA and PLA, PLA contains more oxygen. The oxygen content is connected to the polymer structure containing a C-O group (C1s centered at 286 eV, O1s centered at~533 eV) and OC=O group (C1s centered at~289 eV and O1s (C=O) centered at~532 eV), which is typical for PLA [29]. In the PLA pellets, 62.8 at.% of C and 37.0 at.% oxygen was found. The chemical composition of electrospun PLA fibers is highly similar, as only a small amount of nitrogen was found in this sample. In the case of PLA, there was no change in oxygen content before and after electrospinning. After electrospinning, the ratio of C-C/C-O/OC=O is closer to the stoichiometry of PLA, which could be attributed to the sample being less contaminated by adventitious carbon in the case of electrospun PLA (Table 1, and Figure 6c,d). However, after electrospinning, the signals of C-O (~286 eV) and OC=O (~289 eV) appear a slightly broader, which indicates more variations of the carbon-oxygen species, and although the overall oxygen has not increased, a degree of degradation/oxidation of PLA occurred. In this case, we have not attempted to deconvolute some of the additional signals, as in the case of EVA. This finding is observed because the signals are almost at the same positions and are only slightly broader. From the elemental composition in Table 1, it is obvious that the three EVA/PLA samples prepared from different ratios of both polymers have a similar composition to pure PLA (C1s, O1s), only one, EVA/PLA 60:40, is different. This finding indicates that PLA dominates on the surface in the other three EVA/PLA fiber mats. From the results obtained in Table 1, significant difference in the surface composition of the EVA/PLA 60:40 sample was found compared with the results from the other samples. Figure 6e shows a comparison of C1s peaks of PLA and EVA/PLA fibers prepared with the ratios of 60:40 and 80:20. In the case of EVA/PLA 80:20 comparison to pure PLA, pure PLA is more visible, which signifies that PLA is dominant on the surface. There is no change in oxygen content, and only a small decrease in the C-O/OC=O portion is indicated in Figure 6e. In the case of EVA/PLA 60:40, the contribution of the polyethylene chain from EVA (strongest signal at ca. 285 eV corresponding to C-C relative to C-O/OC=O signals) is clearly observed, which confirms EVA is on the surface.
The mechanical properties of a prepared material is essential data for its application. EVA is a flexible elastomer; however, it is not easily electrospun. The mechanical testing of pure electrospun EVA was not possible due to the infirmity of the sample. The weakness of the electrospun EVA compared to the blended EVA/PLA could be caused by partial degradation on the ethylene groups as revealed by XPS. It is also known that PLA is a brittle polymer when it is prepared in the form of a film or electrospun mats. The brittleness of PLA, compared to the blend and the presence of beads in the sample (Figure 2), leads to an impossibility for mechanical testing. From the literature, it is known that with a combination of EVA with PLA at a ratio 80:20, it is possible to create a tough blend [7]. In this case, samples in the form of films were investigated. It can be explained as strong interfacial adhesion between EVA and PLA [30] caused by the elastomeric nature of EVA. The enhanced mechanical properties due to the higher content of PLA can be explained as enhancing the flexibility of PLA by EVA, while reducing the tensile values of PLA (Figure 7). In these cases, the flexible EVA acts as an energy absorber during deformation and leads to the higher toughness of the samples. The samples with higher content of EVA are almost two times weaker with tensile stress around 0.15 MPa than the sample EVA/PLA 20:80 (1.71 ± 0.3 MPa) and have significantly lower flexibility. The tensile strain of EVA/PLA 20:80 was 53.0 ± 10.1% compared to other samples with values close to 35%. This result is caused by the miscibility of the polymers and with a lower amount of PLA, a decreased the number of chain entanglements and covalent bonds [10] are observed, as confirmed by SEM (Figures 2c-e and 3c-e ) and XPS (Figure 6e). The tensile properties of the prepared electrospun EVA/PLA sheet are enhanced with increasing PLA content. Thermal behaviors of the electrospun non-woven mats were investigated by TGA and DSC analysis. The results of the DSC investigation of PLA and electrospun blends are shown in Figure 8. The essential transition temperatures, glass transition temperature (T g ), and melting point (T m ) were determined from the first heating, and the cold crystallization temperature (T cc ) from cooling. All the results obtained are summarized in Table 2. The T g of the pure PLA fibers was determined to be 61.4 • C. As it can be seen from measured data, the amount of EVA present in the blend samples influenced the blends T g . With increasing the EVA amount in the blends, the shifting of T g to higher temperatures was achieved. The T g of EVA/PLA blends containing 20 wt.% of EVA is 62.8 • C, while for blend with the inverse ratio, T g = 66.0 • C was determined. The T m of pure PLA is 146.7 • C. According to the EVA datasheet, melting temperature of this copolymer is in the range of 60 to 90 • C; therefore, the presence of EVA in blends did not significantly change the melting temperature. The small change of T g and T m is caused by low miscibility of the samples with higher concentrations of EVA. The molecular chains in electrospun fibers are highly oriented and aligned, and this behavior is erased in the cooling process, during which the crystallization is finished [31]. The weak cold crystallization peak corresponds to the slow crystallization rate of PLA. For EVA/PLA 80:20, T cc was assigned to 92.2 • C. The T cc peak is shifted to higher temperatures with increased PLA content. The difference of T cc between blend samples is almost 15 • C. Figure 9 shows TGA curves of neat EVA, PLA, and EVA/PLA mats. The degradation temperatures at 10 and 50% of mass losses for all the studied samples and maximum decomposition temperature (T max ) were determined from derivation of the obtained TGA curves (selected DTG curves are shown in Figure 9b). The thermal degradation of EVA is a two-step process. The first part is a loss of vinyl acetate content in the form of acetic acid [32] followed by degradation of the unsaturated polyethylene [33,34]. For the studied EVA fibers, first T1 max was located at 334.5 ± 0.8 • C and T2 max at 431.2 ± 1.5 • C. Pure PLA degrades in a single step with the main peak centered approximately 270-385 • C. This degradation is due to the intramolecular transesterification reaction [30]. For the electrospun PLA fibers, the maximum decomposition temperature was determined at 351.4 ± 0.3 • C. The electrospun blends showed a two-stage decomposition process. The first step is in the range of 250 to 389 • C and corresponds to PLA degradation. The T1 max for the first degradation step is from 322.0 ± 2.0 • C for the sample with highest EVA content to 345.0 ± 0.4 • C for blend with lower EVA content. The T2 max for the blends fibers is very near to the T2 max of the pure EVA. The final stage of decomposition is the degradation of polyethylene (400-520 • C). For the EVA/PLA 20:80 blends fibers, the second decomposition maximum was not clearly detected. The addition of PLA into the blends leads to a less intense second peak. The degradation temperatures of all the prepared samples have been compared at two points, 10 and 50% mass losses, and the results are shown in Table 3. There is a visible stabilizing effect caused by the presence of PLA, with an increasing amount of PLA, T 10% is shifted from 301 to 316 • C. The T 50% is influenced by presence of PLA, and almost the same temperature, approximately 345 • C, was determined for blended fibers samples.
Conclusions
Electrospinning of EVA copolymer with 28 wt.% vinyl acetate content in laboratory conditions is possible only when the solution is continuously heated, but the prepared fiber's diameter was in the range of 6 microns. To obtain fibrous mats with fiber diameters in the nanometer range, EVA was blended with polylactic acid (PLA) in a DCM/acetone solution using various weight ratios of EVA/PLA: 80:20, 60:40, 40:60, and 20:80. Pure PLA fibers mats were also prepared for comparison. The properties of the electrospun EVA/PLA blends depend on the polymers ratios. Morphology studies revealed the presence of small portions of beads in blended samples. Only fibers produced from a blend of EVA/PLA 20:80 were without these defects and reached a diameter of approximately 510 nm, which is ten times less than the diameter of the prepared EVA fibers. The XPS study showed that electrospun EVA contained more oxygen compared to EVA pellets, which indicates a slight oxidation during the fiber processing. The XPS also detected that EVA/PLA blends contained PLA on the surface. Only at the 60:40 ratio was EVA detected in the outer fibers. The prepared blends, however, showed better mechanical properties than pure PLA and reached better thermo-oxidation stability than EVA at a temperature of 10% mass loss. The electrospun EVA/PLA 20:80 blend ideally combined the properties of both polymers, which resulted in better mechanical properties than the other samples. The obtained results showed that electrospinning was a suitable method for producing EVA/PLA blends, which can be potentially used in various biomedical applications. This will be a topic for future study. | 5,674.2 | 2018-09-01T00:00:00.000 | [
"Materials Science"
] |
Suppressing epidemic spreading in multiplex networks with social-support
Although suppressing the spread of a disease is usually achieved by investing in public resources, in the real world only a small percentage of the population have access to government assistance when there is an outbreak, and most must rely on resources from family or friends. We study the dynamics of disease spreading in social-contact multiplex networks when the recovery of infected nodes depends on resources from healthy neighbors in the social layer. We investigate how degree heterogeneity affects the spreading dynamics. Using theoretical analysis and simulations we find that degree heterogeneity promotes disease spreading. The phase transition of the infected density is hybrid and increases smoothly from zero to a finite small value at the first invasion threshold and then suddenly jumps at the second invasion threshold. We also find a hysteresis loop in the transition of the infected density. We further investigate how an overlap in the edges between two layers affects the spreading dynamics. We find that when the amount of overlap is smaller than a critical value the phase transition is hybrid and there is a hysteresis loop, otherwise the phase transition is continuous and the hysteresis loop vanishes. In addition, the edge overlap allows an epidemic outbreak when the transmission rate is below the first invasion threshold, but suppresses any explosive transition when the transmission rate is above the first invasion threshold.
Introduction
An outbreak of such diseases as SARS [1] and H5N1 [2,3] puts at risk the lives of countless people. During the first nine months of the recent Ebola epidemic there were 4507 confirmed or probable cases of infection and 2296 deaths [4]. Increasing the investment of public resources to control a disease pandemic can be a serious economic burden, especially in developing countries [5,6]. Many researches have been done on how to optimize scarce public health care and immunization resources when attempting to control an epidemic [7,8,9,10], the goal being to minimize the number of infected individuals by determining that optimal allocation [11].
A complex network science approach is now being widely used to determine the impact of resource investment on spreading dynamics. Böttcher et al. [12] studied the impact of resource constraints on epidemic outbreaks and found that when the resources generated by the healthy population cannot cover the costs of healing the infected population the epidemics go out of control and discontinuous transitions [13,14,15,16,17] occur. Chen et al. [18] explored the critical influence of resource expenditure on constraining epidemic spreading in networks and found that public resources can affect the stability of the disease outbreak. At a certain disease transmission rate there is a critical resource level above which a discontinuous phase transition in the infected population occurs. Böttcher et al. [19] assumed that only the central nodes in a network can provide the necessary care resource, and they found that a discontinuous transition in infected nodes occurs when the central nodes are surrounded by infected nodes. All of these researches focus on how public resource investment affects the spread of disease.
In real-world scenarios only a small percentage of patients are assisted by public resources. The majority depend on help from family and friends who provide economic [20,21,22] and emotional support [23,24]. We thus study how social support from family and friends affects the dynamics of disease spreading. In a social network, a node has different connections in different settings. We can thus regard friendship ties (virtual contacts) and co-worker ties (physical contacts) as two different network layers. Although economic and medical resources and sources of information usually propagate through social relationships, diseases usually propagate through physical contacts. Thus we use a multiplex network of two-layers [25,26,27,28] to study how resource allocation in the social layer affects the spreading dynamics in the contact layer.
We use the susceptible-infected-susceptible (SIS) model in a multiplex network of twolayers to mimic the coupling dynamics between disease spreading and resource support. The disease propagates through the layer of physical contacts, but infected nodes seek help from their neighbors through the layer of social relations. Infected nodes receive resources from healthy neighbors and do not generate resources. We analyze the process using a dynamic message passing (DMP) approach [29,30,31,32]. We examine how degree heterogeneity affects the dynamical process and find that the infected density in the steady state (ρ) increases continuously at the first epidemic threshold and then jumps suddenly at the second threshold. Hysteresis loops exist in the phase transition of the infected density, and the size of the hysteresis region and the value of the invasion threshold decrease with the degree heterogeneity. Examining how edge overlap between the two layers affects the dynamics of spreading we find that the overlap has a critical value. When the overlap is below the critical value, the infected density first increases continuously and then discontinuously with disease transmission rate, and there are hysteresis loops. When the overlap is above the critical value, the phase transition of ρ is continuous and there is no hysteresis loop. We also find that when the transmission rate is below the first invasion threshold the disease outbreaks more easily for a large edge overlap, but when the transmission rate is above the first invasion threshold the edge overlap suppresses the disease spreading and the second invasion threshold increases as the overlap increases.
Epidemic model with social-support
In a multiplex network of two-layers, each layer has N nodes and each node in the first layer has a counterpart in the second layer. Here the upper layer is the social relationship network (e.g., Facebook friends and family members) from which healthy nodes allocate resources to infected neighbors (see layer S in Fig. 1). The lower layer is the physical contact network through which the disease spreads (see layer C in Fig. 1). Variables A and B are the adjacency matrices of layer S and C with elements a ij and b ij . If nodes i and j are connected by one edge in layer S, a ij = 1, otherwise a ij = 0. The same is true in layer C. We denote by s υ the node state variable of node υ, and if it is in the susceptible state s υ = 0, otherwise s υ = 1. We assume that each healthy individual has a certain resource level r per unit time, which for simplicity we set at r = 1. Resources are distributed equally to infected neighbors. Figure 1 shows that node X distributes one resource unit to three infected neighbors in layer S, and that node Y distributes one resource unit to one infected neighbor. For the sake of analytical tractability, we assume that the total resource is not cumulative in the system, and if healthy nodes do not allocate their resources to neighbors they consume these resources themselves. In addition, infected nodes consume all of the received resources at the current time step, and each healthy individual generates a new one-unit resource at the next time step. Using this definition, the resources that node j gives to node i in layer S is Without resource support a node recovers spontaneously at a rate µ 0 [35], and for simplicity we assume µ 0 = 0. The recovery rate of i at time t is where µ i (t) ≡ µ(R i (t)), and R i (t) is the expected resources that node i receives from healthy neighbors. The µ r value is the coefficient that represents the efficiency of resource support from neighbors, µ r ∈ [0, 1], and k S i is the degree of i in layer S. The recovery rate of infected nodes is assumed to be positively related to the resource received from healthy neighbors in layer S. In real-world setting the cost of repairing a vital node in a complex system is much higher than the cost of repairing a common node. For example, because hub airports in airline networks play a vital role in connecting a large number of countries and regions, the repairing cost when they fail is much higher than that for lower-degree airports [33]. Similarly, the cost of repairing hub nodes in brain networks is much higher than the cost of repairing common nodes [34]. The same is true in epidemic spreading. Individuals exposed to viruses over a long period of time, e.g., medical staff members who are in constant contact with infected individuals, have large degrees in physical contact networks. Community leaders are also hub nodes in high-degree physical contact networks. In both cases the cost of curing these hub nodes being infected is much higher than other infected nodes in the contact networks. Thus we assume that the recovery rate of an infected node is negatively related to its degree.
We use the classical SIS model to investigate the spreading process in multiplex networks. Each individual can be either infected or susceptible. Susceptible individuals are healthy and are then infected by an infected neighbor at a rate β. Infected individuals recover at a rate µ i (t), which is assumed to be independent of the availability of social resources in previous researches [36,37].
Dynamic message-passing method
We use dynamic message-passing method to analyze the spreading dynamics. In this method a variable "message" passes through the directed edges of the network and does not backtrack to the source node. Our message is θ j→i , the probability that node j is infected by its neighbors other than i. In addition, ρ i (t) is the probability that node i is in the infected state at time t. The probability that an infected node i will connect to a healthy node j in layer S is a ij (1−θ j→i (t)), and the expected number of infected neighbors of node j is ℓ =i a jℓ θ ℓ→j (t) + 1, where the plus one takes into account that node i is infected. Thus the resource R i (t) that node i receives from healthy neighbors is Using this definition, the discrete-time version of evolution of ρ i (t) [38] is where ∆t is the time increment, which we set at ∆t = 1, and q i (t) is the probability that i is not infected by any neighbor in layer C, which is given by where N C i is the neighbor set of i in layer C. Note that to exclude any contribution of node i to the infection of j, we adopt θ j→i (t) instead of ρ j (t) in Eq. (5). Similarly, the discrete-time version of evolution of θ j→i (t) is Here (1 − φ j→i (t)) is the probability that j is infected by at least one neighbor other than i.
Here N C j \ i is the neighbor set of j excluding i, and the fraction of infected nodes at time t is where ρ i (∞) ≡ ρ i and µ i (t) ≡ µ i at the steady state t → ∞. Solving Eqs. (4) and (6) at the stationary state and we obtain the phase diagram of the model. We use iteration to numerically compute the evolution of the state of network nodes. Due to nonlinearities in Eqs. (3)-(7) they do not have a closed analytic form, and this disallows obtaining the epidemic threshold β c . If β > β c , ρ > 0, otherwise ρ = 0 in the steady state. When β → β c , ρ i → 0, θ j→i → 0, and the number of infected neighbors of each healthy node in layer S is approximately zero in the thermodynamic limit, prior to reaching the epidemic threshold (1 − θ j→i ) → 1. If we add these assumptions to Eq. (3) resource R i becomes R i → k S i , we will obtain the recovery rate µ i → µ r in the steady state [see Figs. 4(a) and 7(a)].
To solve Eq. (14) we define a 2E × 2E matrix J, where E is the number of edges and the elements of J are The system enters a global epidemic region in which the epidemic grows exponentially when the largest eigenvalue of J is greater than zero [31,37,32]. Thus we can obtain the epidemic threshold as where Λ J is the largest eigenvalue of J.
Numerical and simulation results
To examine how resource support affects epidemic dynamics, we perform numerical computations and stochastic simulations in the networks. Because many real-world complex networks have a highly skewed degree distribution, e.g., Facebook [40] and the World Wide Web [41], we focus on networks with a heterogenous degree distribution. We assume that the two layers of the network have the same degree sequences (k S i = k C i ). Thus for simplicity we denote k i to be the degree of node i in both layers S and C.
To build our multiplex network we use an uncorrelated configuration model (UCM) [42] with a given degree distribution P (k) ∼ k −γ in which γ is the degree exponent. Here a smaller γ implies a more heterogeneous degree distribution. The maximum degree is determined by the structural cut-off k max ∼ √ N [43] and we set the minimum degree at k min = 3. In addition we disallow multiple and self-connections and set the network size as N = 10000. When studying resource support from neighbors, we eliminate any possibility of spontaneous recovery, i.e., µ 0 = 0, and assume that node recovery is solely dependent on the amount of resources received. Here we set the efficiency parameter at µ r = 0.6 and the µ r value does not affect the result [44,37].
To determine the epidemic threshold, we use a susceptibility measure [45,46] where . . . is the ensemble averaging, and χ exhibits peaks at the transition points. We now examine how degree heterogeneity and edge overlap between the two layers of the network affect its dynamic features.
Effects of degree heterogeneity
To investigate how degree heterogeneity affects spreading dynamics, we disallow any edge overlap between the two layers, i.e., nodes are randomly connected by edges in layer S and layer C, and the amount of edge overlap m e is approximately 0 in the thermodynamic limit.
To examine ρ as a function of β, we randomly select one percent of the nodes to be seeds (ρ(0) = 0.01). Figure 2(a) shows the epidemic spreading for γ = 2.4 and γ = 3.2. Note the hybrid phase transition in ρ that exhibits properties of both continuous and discontinuous phase transitions. As β increases ρ grows continuously at β I inv .
Then an infinitely small increase in β induces an sudden jump of ρ at β II inv , where β I inv and β II inv are the first and second invasion thresholds. The ρ transition type indicates that there are three possible system states, (i) completely healthy, (ii) partially infected, and (iii) completely infected. This differs significantly from the classical SIS model. In addition, we find hysteresis loops in the phase transition of ρ when γ = 2.4 and γ = 3.2 [see Fig. 2(a)]. When the seed density is initially low, e.g., ρ(0) = 0.01, the disease breaks out at the invasion threshold β I inv , but when it is initially high, e.g., ρ(0) = 0.9, the disease breaks out at the persistence threshold β per . The arrows in Fig. 2(a) indicate the direction of the hysteresis loops. We determine critical points β I inv and β II inv and persistence threshold β per using the susceptibility χ shown in Fig. 2(b). The theoretical results obtained from the numerical iterations agree with the simulation results [see the lines in Fig. 2(a)].
We next determine how degree heterogeneity (i.e., parameter γ) influences the spreading dynamics. Figure 3(a) shows the two-parameter (β, γ) phase diagram. The parameter space is partitioned into three regions according to ρ value. When β < β I inv , the system falls into the no-epidemic regime, i.e.,the green and part of the purple area below β I inv . When β I inv ≤ β < β II inv , it falls into the low-epidemic regime (bounded by two critical lines) in which ρ increases slowly with β. Finally, above β II inv , ρ suddenly jumps to the high epidemic regime (red) in which approximately all nodes are infected. The regime between invasion threshold β II inv and persistence threshold β per is the hysteresis region (purple). The values of β I inv and β II inv both increase with γ. Although we can obtain the theoretical value of β I inv from Eq. (16), we cannot obtain the theoretical value of β II inv and β per by linearizing the equations around ρ i → 0 and θ j→i → 0 and thus we must apply numerical methods using Eqs. (4) and (6). We first define a judgment value ǫ that is linear with system size N. Without loss of generality we set ǫ = 0.3. We then define the jump size ∆ρ to be where ∆β is an infinitesimal increment in β, which we set at ∆β = 0.001, and ρ(β) is the infected density in the steady state when the transmission rate is β. We obtain the threshold The high epidemic region with large value of ρ (denoted by red color) in steady state, the no epidemic region with zero value of ρ (denoted by green color), and the low epidemic region with small value of ρ (part of the purple region bounded by the two critical lines). The hysteresis region (denoted by purple color) is bounded within β II inv and β per (denoted by red squares). The two invasion thresholds β I inv (denoted by lower blue circles), β II inv (denoted by upper blue circles) and persistence thresholds β per are determined by the susceptibility measure χ. Theoretical results obtained from the DMP method are denoted by dotted lines in the figure. (b) The thresholds interval β II inv − β I inv is plotted as a function of system size N for three different values of γ: γ = 2.0 (red triangles), γ = 2.2 (blue circles), and γ = 2.8 (dark grey squares). Error bars are smaller than the symbols used for the data points.
when ∆ρ ≥ ǫ is at a certain β value in the thermodynamic limit [47,16]. Using the numerical method, we obtain the second invasion threshold β II inv and the persistence threshold β per . Figure 3 shows that the theoretical values marked by dotted lines agree with the simulation results. The change in the system state among the three regions indicates that the phase transitions of ρ are hybrid. Figure 3(a) shows that the low epidemic and hysteresis regions expand as γ increases.
To demonstrate that there are two invasion thresholds in networks with heterogeneous degree distribution, we use a finite-size scaling analysis [48]. Figure 3 as a function of N for γ = 2.0, γ = 2.2, and γ = 2.8, where • is the norm operator. Figure 3(b) shows the values of β II inv − β I inv converging asymptotically to positive constant values in the thermodynamic , which implies the two invasion thresholds do not merge when γ ≤ 2.2 and the two are always present in networks with a heterogeneous degree distribution.
To analyze the sudden jump of ρ and the hysteresis loops, we examine the transmission process analytically using mean-field approximation in random regular networks (RRNs), which corresponds to the limit γ → ∞. Through a bifurcation analysis we account for the existence of the sudden jump of ρ and the hysteresis loops (see appendix information). Note that the first threshold β I inv disappears in the RRNs and the transition of ρ is discontinuous when it is not hybrid [see Fig. 9(a)].
To explain the hybrid transition when γ is finite, i.e., when γ ≤ 3.2, we investigate the number of susceptible neighbors around each infected node in layer S and their recovery rates as a function of β. In the steady state the number of each infected node's susceptible neighbors in layer S is n s i and their fraction n s i /k i . Here the recovery rate is µ i . To evaluate the collective state, we examine the average quantity n s /k of n s i /k i and the average quantity µ of the recovery rate. Figure 4(a) shows plots of n s /k and µ as functions of β for γ = 2.8. We find that both n s /k and µ are constant when β < β I inv , which implies zero values for ρ. They then slowly decrease until they reach the β II inv , at which point an infinitesimal increase in β causes a jump in n s /k and µ . Figures 4(b)-4(d) show the time dependence near β I inv and β II inv . Figure 4(b) shows the time evolution of the infected density ρ(t) around β I inv ≃ 0.023 for γ = 2.8. The difference in ρ(∞) for β just below and above threshold β I inv is ∆ρ [see Eq. (18)]. Note that ρ(∞) increases slowly at β I inv , i.e., a small increment ∆ρ ≃ 0.022. We next examine the time evolutions of the average resources of the infected nodes R(t) and the hub nodes R h (t) . Note that without loss of generality we can assign hub node status to nodes with a degree larger than k = 30. Note also that when β is just below β I inv both R(t) and R h (t) increase until t = t * , which implies that all infected nodes have acquired sufficient resources to recover and ρ(t) drops to zero. In contrast, when β is just above β I inv and the transmission rate is low, the promotion effect of the hub nodes allows the disease to spread on a finite scale and infected nodes have sufficient resources for recovery. Thus infection and recovery processes are balanced, and the values of R(t) and R h (t) fluctuate around a finite value when t → t ∞ [see Fig. 4(b)]. As β smoothly increases Figures 4(c) and 4(d) show a critical time t * ≃ 220 at which β is approximately β II inv ≃ 0.033. At the early stage of the propagation process, i.e., when t < t * , the disease spreads through the local seed nodes. Because most of neighbors of the infected nodes in layer S remain healthy, they have a sufficient resource level to recover. Here the infection and recovery processes are balanced. As the ρ(t) value increases slowly the available resources levels R h (t) and R(t) for β ≃ β II inv slowly decrease [see Figs. 4(c) and 4(d)]. When β < β II inv the infection and recovery processes remain balanced when t → ∞, thus the density of infection fluctuates around a small finite value when t → t ∞ (ρ(∞) ≃ 0.18) [see Fig. 4(c)]. Because infection and recovery processes are balanced in β I inv ≤ β < β II inv , the value of ρ increases slowly. Note that as hub nodes disappear in the RRNs the disease is suppressed until β reaches a threshold at which point it jumps discontinuously, the balance disappears (see Appendix), and only one threshold remains. When β > β II inv the transmission rate is relatively large and the balance between infection and recovery is broken. Infecting the healthy nodes in layer C decreases the resources available to the nodes in layer S and delays the recovery of infected nodes. This recovery delay increases the effective transmission probability in layer C, more healthy nodes are infected, and both the available resources and the recovery rate decrease. This causes a cascading infection in system nodes that is accelerated when hub nodes are surrounded by infected nodes, and this can cause total system failure. Figure 4(d) shows an abrupt drop of R h (t) and R(t) at t * when β > β II inv . Figure 4(c) shows a rapid increase in the density of infection from a small value ρ(t * ) ≃ 0.18 to a high value ρ(∞) ≃ 1.0. Note that in the steady state the large difference ∆ρ between β < β II inv and β > β II inv causes explosive transitions. This explains the hybrid transition in networks with a heterogeneous degree distribution. Figure 4(d) shows the evolution of the resource level in the hub nodes. This explains the decrease in the two invasion thresholds and the gap that appears between the two thresholds with the increase of degree heterogeneity. A more heterogeneous network has more hub nodes and is more sensitive to increases in β. Thus increasing the degree heterogeneity reduces the gap between the two thresholds [see Fig. 3].
These numerical and simulation results differ greatly from the classical SIS model. In the multiplex networks with a heterogeneous degree distribution, degree heterogeneity enhances disease spreading and the phase transition is hybrid. Besides, there are hysteresis loops in the phase transition of ρ, and the interval between the two invasion thresholds and the hysteresis region decreases as degree heterogeneity increases. When γ → ∞ the network is approximately a RRN, β I inv disappears as hub nodes disappear, and the transition is discontinuous.
Effects of edge overlap
In social networks two individuals can be friends in the social relation layer and coworkers in the physical contact layer. In transportation networks two cities can be connected by both an expressway and a railway. Thus edge overlap is essential in the science of complex networks, especially when studying percolation in multiplex networks [49]. Here we examine how the amount of edge overlap m e between the two layers affects the spreading dynamics. To eliminate the effect of structure, we fix the values γ = 2.2 and < k >= 9. We then use UCM to build a multiplex network with two identical layers m e = 1.0. To generate a variety of m e values, with a probability q = 1 − m e we rewire pairs of links in layer S. Figure 5(a) shows a plot of ρ as a function of β with two typical values m e = 1.0 and m e = 0.2. Note that when the edges between the two layers overlap completely (m e = 1.0) the infected density ρ smoothly increases from 0 to 1 and there is no hysteresis loop. When the rate of edge overlap between two layers is lowered, i.e., when m e = 0.2, a hybrid phase transition appears. The infected density ρ smoothly increases at β = β I inv and then the system acquires a low epidemic region (β I inv < β < β II inv ) in which ρ slowly increases. Subsequently at β = β II inv an infinitesimally small increase in β causes an abrupt jump in ρ and the disease suddenly spreads throughout the entire system. Hysteresis loops appear in the transition process and the arrows indicate their direction. Figure 5(b) shows that the invasion thresholds (i.e., β I inv ) and β II inv and the persistence threshold β per are determined by the susceptibility χ. Note that the hysteresis loop disappears when m e = 1.0, and it no longer satisfies the definition of ǫ > 0.3 at β L . Thus β L is an inflection point at which the increase in ρ accelerates. The theoretical results from the DMP method agree with the simulation results.
To determine how the amount of edge overlap between the two layers affects the spreading dynamics, we perform simulations for values of m e from 0 to 1 and obtain the space in the plane (β, m e ) shown in Fig. 6. The parameter space is separated into phase regions I and II by a critical value of edge overlap m c e ≃ 0.8. When m e < m c e the system falls into phase I in which the phase transition of ρ is hybrid and the space is again separated into three regions by two invasion thresholds β I inv (lower blue circles) and β II inv (upper blue circles). When β < β I inv the system has a no-epidemic region (green) in which all nodes are healthy and in a steady state. When β I inv ≤ β < β II inv the system has a low-epidemic region (orange) in which the infected density ρ increases continuously from 0 to a finite value until it reaches the second invasion threshold β II inv . Figure 3(b) shows a small low-epidemic region when m e ≤ 0.2 such that when γ = 2.2 and m e = 0.0 the value of β II inv − β I inv converges to a non-zero constant value when N → ∞. When β ≥ β II inv the system jumps abruptly to a high epidemic region (red) in which the disease spreads throughout the entire system. The hysteresis loops (purple) appear in phase I. In contrast, when m e ≥ m c e the system falls into phase II in which the phase transition of ρ is continuous. The value of ρ smoothly increases from 0 to 1 and the hysteresis loops disappear. Figure 6 shows that when β < β I inv the value of β I inv decreases as the amount of edge overlap increases. Here edge overlap promotes disease spreading. When β ≥ β I inv the value of β II inv increases as the amount of edge overlap increases. Here edge overlap suppresses disease spreading. We obtain the theoretical value of β I inv using Eq. (16) and β II inv and β per using the method in Section IV.A. Figure 6 shows that the theoretical values marked by the dotted lines agree with simulation results.
To clarify these results, Figs. 7(a) and (b) show a plot of the average recovery rate µ and the number of susceptible neighbors around each infected individual n s /k as functions of β. Note that when the two layers overlap completely (m e = 1.0), n s /k and µ decrease at the first threshold β I inv to a certain value and then decrease continuously to zero, indicating that the infected density in the steady state increases continuously up to 1 as β increases. Time evolution of ρ(t) for β close to β II inv , ∆ρ is the jump of ρ in the steady state for β is close below β II inv and close above β II inv . (c). Time evolution of average resource of all infected nodes R(t) , hub nodes R hub (t) , and infected density ρ(t). t * is the moment when all the neighbors of the infected nodes are in healthy state (there is no definition of R hub (t) and R(t) ). (d). The average resource of R(t) and R hub (t) as functions of t.
In contrast, when m e = 0.5 there are two abrupt jumps of n s /k and µ at β I inv and β II inv , respectively. Here µ jumps sharply to zero at β II inv [see Fig. 7 Figure 7(c) shows the average resource of all infected nodes R(t) and the average resource of hub nodes R h (t) as a function of t for m e = 1.0. Note that when β is immediately below β I inv both R h (t) and R(t) increase continuously until t = t * . When t > t * there is no definition of resource because all infected nodes recover [see ρ(t) for β < β I inv at t = t * ]. When β ≥ β I inv the infection and recovery rates are balanced, and both R h (t) and R(t) fluctuate around a finite value when t → t ∞ . Thus all the infected nodes recover with a certain probability and ρ(t) also fluctuates around a finite value when t → t ∞ [see ρ(t) for β > β I inv ]. With an increase in β, resource availability decreases continuously as the number infected nodes increases until the disease spreads throughout the system and no available resources remain [see Figs. 7(a) and 7(c)]. This accounts for the continuous increase in ρ when m e = 1.0. Figure 7(d) shows the time evolution of the infected density and available resource level for m e = 0.5. Note that when β is immediately below β II inv at the early stage the disease propagates within the local range of seed nodes, and there are sufficient healthy neighbors in layer S to temporarily suppress the spread. This causes a brief increase in available resources at the beginning of the propagation process and a slight decline in the density of infection. Subsequently the disease rapidly spreads along the edges in layer C. When edges in layer S link out (m e = 0.5), with a high probability that infected nodes in layer S infect their neighbors, R h (t) and R(t) rapidly decline, and ρ(t) rapidly increases. Eventually infection and recovery become balanced, and ρ(t), R h (t) , and R(t) converge to finite values. When β > β II inv there is also a temporary increase in both the available resources and the density of infection. However, when the propagation begins, unlike when m e = 1.0 [see Figs. 4(c) and 4(d)] there is no balanced period at the beginning of the process. The infection of the S-state nodes reduces the resource available to a large number of infected nodes in layer S and delays their recovery. This recovery delay further increases the transmission probability in layer C. Thus R h (t) and R(t) ) decline sharply to zero, the density of infection rapidly increases to 1, and cascading infection occurs.
An increase in the overlap between two layers indicates an increase in the local social circle of an individual. When an individual's colleagues (those frequently in contact, defined as the contact layer) and friends (the social relations, defined as the social layer) are the same group of people, the links in these two layers largely overlap. When β < β I inv , seed nodes initially transmit the disease only to immediate neighbors with whom they are in frequent contact. This high-value local effect causes infected nodes to have a higher probability of linking with other infected nodes in layer S and lowers the level of resources available from neighbors. Thus the overlap between two layers increases network fragility against the invasion of the disease, and increases the probability of an epidemic breakout, and thus lowers the epidemic threshold β I inv . In contrast, a lower value of overlap rate between the two layers indicates a more global social circle, neighbors of nodes in the social layer differ from neighbors in the contact layer. The infected nodes in the contact layer can acquire resources from healthy neighbors in the social layer. Thus the network is more robust against the invasion of the disease, and there is a relatively high epidemic threshold β I inv . This is the reason β I inv decreases as m e increases, as shown in Fig. 6. When β I inv ≤ β < β II inv , hub nodes promote disease transmission, the disease breaks out in a finite range, a sufficient number of healthy neighbors are present in layer S to help infected nodes to recover, and infection and recovery remain balanced.
Conclusions
We have investigated how the level of social support affects spreading dynamics using the susceptible-infected-susceptible model in social-contact coupled networks. Links in the social layer represent relationships between friends or families through which healthy nodes allocate recovery resources to infected neighbors. Links in the contact layer represent daily physical contacts through which the disease can spread. Infected nodes do not have resources, and their recovery depends on obtaining resources in layer S from healthy neighbors. We assume the recovery rate of an infected node to be a function of the resources received from healthy neighbors. We use the DMP method to analyze the spreading dynamics. We first examine how degree heterogeneity impacts disease spreading. We find that degree heterogeneity enhances disease spreading, and due to the existence of hub nodes there is a balanced interval β I inv < β < β II inv in which the infection and recovery processes remain balanced. The value of ρ increases continuously from 0 to a finite value at the first invasion threshold β I inv , increases slowly in β I inv < β < β II inv , then suddenly jumps at β II inv . Thus the transition of ρ is hybrid. In addition, increasing the degree exponent γ in the network increases the gap between the two thresholds and the hysteresis region. To analyze the sudden jump of ρ and the hysteresis loops, we examine the spreading process analytically using mean-field approximation in RRNs. Through a bifurcation analysis we account for the existence of the sudden jump of ρ and the hysteresis loops. In addition, in the RRNs the balanced interval disappears when there is a lack of hub nodes. The first invasion threshold β I inv thus disappears. We next fix the degree heterogeneity and investigate the effect of edge overlap between the two layers. We find that there is a critical value m c e . When m e < m c e there is a second invasion threshold β II inv that increases with m e . The value of ρ smoothly increases at β I inv and then suddenly jumps at β II inv , revealing the transition of ρ to be hybrid with the presence of hysteresis loops in this region [see Fig. 6]. In contrast, when m e > m c e the phase transition of ρ is continuous and the hysteresis loops disappear. In addition, when β < β I inv seed nodes can only transmit the disease locally at the early stage. Here an increase in global connectivity with a lower rate of overlap in the social layer (layer S) increases the probability of linking to healthy neighbors and increases the probability that infected nodes will recover. Thus the first invasion threshold β I inv decreases as the overlap rate m e increases. When β > β I inv , increasing the transmission rate increases the fraction of infected nodes, and an increase in global connectivity in layer S increases the probability of linking to infected neighbors and lowers the recovery rate. Thus the second invasion threshold β II inv increases with m e when m e < m c e .
Although researchers in different scientific fields have focused on ways of constraining disease epidemics in human populations, most scientific literature has been devoted to questions concerning the optimum allocation of public resources or the impact of government investment on spreading dynamics. There has been little examination of how social supports affect spreading dynamics, and our novel model fills this gap. In future research on the impact of social supports on spreading dynamics we will focus on such elements as how degree correlation and the clustering coefficient affect epidemic spreading. Other related topics will include the effect of preference-driven resource allocation on spreading dynamics and the interplay between disease dynamics and resource dynamics. The steady state of the spreading process corresponds to conditions dρ(t)/dt = 0 and dθ(t)/dt = 0. We denote θ(∞) as θ and obtain We also define g(θ) as the function of θ in the steady state, which is Here g(θ) is tangent to the horizontal axis at θ c (∞), which is the critical value in the limit t → ∞. The critical condition is dg(θ) dθ | θc = 0.
Solving Eq. (25) we also obtain the critical transmission rate. From Eq. (23) we see that θ = 1 and θ = 0 are two trivial solutions. Figure 8 shows that the number of solutions for Eq. (24) is dependent on β and there is a critical value of β at which three roots of Eq. (24) emerge, implying that a cusp bifurcation occurs. A bifurcation analysis [50] of Eq. (24) indicates that the physically meaningful stable solution of θ will suddenly increase, and there is an alternate outcome-explosive growth in ρ. Whether the unstable state stabilizes to an outbreak state (θ > 0,ρ > 0) or an extinct state (θ = 0,ρ = 0) depends on the initial infection density ρ(0), thus a hysteresis loop emerges. To distinguish the two thresholds of the hysteresis loop, we denote β per as the persistence threshold corresponding to the nontrivial solution θ c > 0 of Eq. (24) at which the disease initially has a large ρ(0) value. Here β inv is the invasion threshold corresponding to the nontrivial solution θ c = 0 of Eq. (24) at which the disease initially has a small ρ(0) value. The interval [β per , β inv ) is the hysteresis region. Figure 8 shows an example illustrating the relationship between ρ and β when k = 10. Note that g(θ) is tangent to the horizontal axis at θ = 1.0 when β per ≃ 0.0066 and at θ = 0.0 when β inv ≃ 0.066 respectively. When 0.0066 ≤ β < 0.066 three roots of Eq. (24) emerge, indicating that a saddle-node bifurcation occurs and the physically meaningful stable solution of θ increases suddenly to 1. If the disease initially has a relatively small infection density, e.g., ρ(0) = 0.01, the system converges to the stable state ρ = 0, which corresponds to θ = 0. On the other hand if the disease initially has a relatively large infection density, e.g., ρ(0) = 0.9, the system converges to the stable state ρ = 1, which corresponds to θ = 1. When β ≥ 0.0066 and β < 0.0066, ρ(0) has no effect on the stable state of the system. Thus β = 0.0066 is the persistence threshold β per and β = 0.066 is the invasion threshold β inv . Figure 9(a) shows the numerical and simulation results in RRNs with a degree k = 10. In RRNs the first invasion threshold β I inv disappears and the transition of ρ is discontinuous, i.e., not hybrid, due to the lack of the hub nodes. In addition the hysteresis loops exist in the transition of ρ. The orange dashed line and the blue line correspond to the theoretical results for ρ(0) = 0.01 and ρ(0) = 0.9, respectively, obtained from Eqs. (21) and (22). Figure 9(b) shows the susceptibility measurement χ vs β for ρ(0) = 0.01 and ρ(0) = 0.9. From these results we find that the theoretical results obtained from the mean-field approximation agree with the simulation results in RRNs. | 10,088.4 | 2017-08-08T00:00:00.000 | [
"Computer Science",
"Environmental Science",
"Mathematics"
] |
Reinforcement learning in discrete action space applied to inverse defect design
Reinforcement learning (RL) algorithms that include Monte Carlo Tree Search (MCTS) have found tremendous success in computer games such as Go, Shiga and Chess. Such learning algorithms have demonstrated super-human capabilities in navigating through an exhaustive discrete action search space. Motivated by their success in computer games, we demonstrate that RL can be applied to inverse materials design problems. We deploy RL for a representative case of the optimal atomic scale inverse design of extended defects via rearrangement of chalcogen (e.g. S) vacancies in 2D transition metal dichalcogenides (e.g. MoS2). These defect rearrangements and their dynamics are important from the perspective of tunable phase transition in 2D materials i.e. 2H (semi-conducting) to 1T (metallic) in MoS2. We demonstrate the ability of MCTS interfaced with a reactive molecular dynamics simulator to efficiently sample the defect phase space and perform inverse design—starting from randomly distributed S vacancies, the optimal defect rearrangement of defects corresponds a line defect of S vacancies. We compare MCTS performance with evolutionary optimization i.e. genetic algorithms and show that MCTS converges to a better optimal solution (lower objective) and in fewer evaluations compared to GA. We also comprehensively evaluate and discuss the effect of MCTS hyperparameters on the convergence to solution. Overall, our study demonstrates the effectives of using RL approaches that operate in discrete action space for inverse defect design problems.
Introduction
Defect dynamics and optimization in 2D transition metal dichalcogenides (TMDs) significantly impact the observed electronic, optical, mechanical and chemical properties [1][2][3][4]. A myriad of structural defects can either pre-exist or be introduced in these 2D materials during sample preparation, processing and transfer processes [5,6]. One finds a significant variation in the nature of the observed defects in these TMDC's-from point (e.g., chalcogen vacancies) to extended (e.g., dislocations and boundaries) defects, which in turn impact their functionality. For example, electrical properties for thin sheets of MoS 2 almost universally reveal n-type field effect transistor (FET) characteristics due to chalcogen vacancies, impurities, and metal-like antisite defects pinning the Fermi level of the metal at the metal/TMD contact interfaces [7]. In some cases, the defects are considered detrimental (e.g. electronics) whereas in others they are often beneficial (e.g. catalysis). Exercising precise control over the density and distribution of defects is therefore highly desirable.
The most abundant type of defect in TMDs, such as MoS 2 , are the chalcogen (sulphur) mono-vacancies. During sample processing, transfer as well as during the operation of a device, these defects undergo significant reorganization. It has been shown that the S monovacancies can align together to form an extended line defect of Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. vacancies, which can mediate the cross-over between 2H (semiconductor) and 1T (metallic) phases of MoS 2 [8,9]. Structurally, 2H phase possesses a trigonal prismatic arrangement of molybdenum (Mo) atom sandwiched between two sulphur (S) atoms, while 1T phase exhibits an octahedral coordination. The transition between 2H and 1T phases involve local rearrangement of S atoms with respect to their central Mo atom. From the perspective of tunable 2H-1T transition, it is desirable to attain a fundamental understanding of the atomicscale structure and dynamics of defects in 2D TMDs, as well as their role in driving such structural phase transitions. A first step towards achieving this goal is to identify the optimal configurations of the defects in 2D materials, which is the central aim of this paper.
Time scale of defect dynamics in TMDs varies from seconds to hours depending on experimental conditions. Therefore, studying such a long-time process via standard atomistic molecular dynamics simulation is not tractable. Here, we attempt to employ data driven advanced optimization methods such as evolutionary computing and AI to tackle such long timescale phenomena in materials system. Our objective is to develop computational approaches that are suitable for capturing structural phase transitions that involve largescale spatiotemporal rearrangement of atoms and molecules. In particular, we employ and compare the performance of an AI algorithm viz., Monte Carlo Tree Search (MCTS) [10] and an evolutionary optimizer viz., genetic algorithm (GA) [11] in searching the most energetically favorable distribution of atomic defects starting from random defects in a MoS 2 monolayer. We use optimization of the S vacancies in MoS 2 as a representative example. In both the cases, the energy evaluation of the individual candidate structures (leaf nodes for MCTS or genes for evolutionary algorithm) are done using a reactive force field. We discuss the performance and limitations of the two methods in the broader context of inverse materials design problems.
Monte Carlo tree search
The MCTS is as a powerful global optimization method that has found wide-spread applications in computer games such as Alpha Go, Bridge, Poker and many other video games [12,13]; and shows promises to tackle materials science problems [14][15][16][17][18]. It is a probabilistic and heuristic search algorithm that integrates a tree search algorithm with reinforcement learning. It builds a shallow tree of nodes (called leaf nodes) where each node represents a point in the search space and downstream pathways are generated by a rollout procedure. Here, each node represents a distinct arrangement of defeats on the top layer of a MoS2 film. The MCTS workflow for exploring the search space of 2D MOS 2 is shown schematically in figure 1. Our objective is to identify the optimal sulphur vacancy in MoS 2 i.e. the vacancy distribution that corresponds to the minimum energy configuration. We conduct MCTS calculations for a constant defect density. The MCTS begins with randomly distributing the vacancies in the top S layer in MoS 2 . This candidate serves as the root node of the search tree. The search tree is built in an incremental and iterative way as shown in figure 3 until a pre-defined termination criterion is reached. Once the termination criteria is reached, the search is stopped and the best performing candidate is returned. Our termination criteria is set to be the minimum energy (remaining constant for>5000 MCTS evaluations).
In each MCTS iteration, four steps-selection, expansion, simulations and back-propagation are carried out. A child node is selected during the selection process based on the upper confidence bound (UCB) score [19]. The UCB of a node is defined as Here, z i is the accumulated merit of the node (i.e., the sum of the immediate merits of all the downstream nodes), and v i is the visit count of the node, v p is the visit count of the parent node and C is a constant for balancing exploration and exploitation. The value of C is controlled adaptively at each node as Here, J is the meta parameter which is set to be one and it increases whenever the algorithm reach a 'dead end' node to allow more exploration. At a dead-end node, the number of possible structures narrows to one. This happens when the numbers of k−1 candidate structures reach the limit. Here, the J is updated as where T is the total number of candidates to be evaluated, and t is the number of candidates for which the surface tension is already evaluated. Whenever a new node is added, the variables are initialized as Next, we perform the expansion of the tree by adding child nodes to the selected node. In the simulation step, a playout is performed from each of the added children. We roll out 10 structures randomly during a playout from a child node. These roll outs are performed via two moves, a small vacancy shift and a large vacancy shift. In the small shift empty sites are swapped with neighboring filled sites to translate the empty site along the lattice. In the last shift an associate/dissociate paradigm is used where either an empty site is moved next to another empty site to created neighbored vacancies or two neighbored vacancies are separated by taking one of the two and randomly placing it throughout the lattice. The energy of the 10 structures are evaluated based on a reactive force field [20]. In this study, no neural networks were used for policy evaluation. Instead of a NN model, the objectives were evaluated based on an atomistic model. The objective in this case is min (f(r ij )), where f(r ij ) is the total energy of the minimized system configuration.
The optimization is carried out via the MCTS algorithms coupled to an atomistic simulator such as LAMMPS (acronym for Large-scale Atomic/Molecular Massively Parallel Simulator), which evaluates the energetics using the atomistic model [20]. In principle, a NN model could also be used for evaluation of the configurational energies. Finally, in the back-propagation step, the visit count of each ancestor node of i is incremented by one and the cumulative value is also updated to keep consistency. Note that the backpropagation here refers to the update of information in the nodes on the path from the child to root node.
Evolutionary algorithm
Evolutionary algorithm seeks to mimic the process of 'survival of the fittest' to design or optimize a system's variables to identify chosen target properties [21,22]. This involves establishing a reversible mapping of every possible candidate (in this case, a distinct arrangement of atomic defects in a MoS 2 layer) to a genomic representation, selecting a random initial population of candidates, and determining the fitness -a measure of the degree to which a candidate's property match to the target property. Here we employ such an evolutionary algorithm, viz., genetic algorithm (GA) to identify energetically most favorable arrangements of vacancies in one of the S planes of a MoS 2 layer. In GA, candidate materials structures are mapped to a genome upon which the evolutionary operations are employed. To describe the vacancy distribution in a given candidate structure, we define a 2D binary genome. This genome is a string containing the current state for each site in the 2D triangular lattice; each site can be in one of the two states, namely: (a) '0' representing the absence of a S atom (vacancy), and (b) '1' indicating that site is occupied by a S atom. For a prescribed vacancy density, the evolutionary search begins with a random population of 32 candidates, each with an arbitrarily chosen but distinct genome, (i.e., S-vacancy distribution). The fitness of each candidate is calculated as the potential energy of the MoS 2 monolayer containing a distribution of S-vacancies as defined by its 2D genome. In each generation, genetic operations, namely, selection, mutation, and crossover are performed on the current population to generate 32 new candidates. The mutation was performed by swapping a vacancy site with a neighboring filled site. The crossover operations were performed using a typical cut and paste approach. Additional details of these genetic operations can be found in [8] and [11]. The candidates are then ranked by their fitness, and the best (fittest) 32 candidates are passed to the next generation. This procedure is iterated until the GA run converges, i.e., the potential energy of the fittest candidate does not change over a long period of time (∼100 generations). We note that several case studies are conducted with varying number of population size, and 32 is found to be sufficiently large to ensure necessary structural diversity in the population (in the initial stages), as well as convergence to low energy configurations with reasonable computational costs. Our evolutionary searches using population sizes (>32) have resulted in identical lowest energy configurations, as that from runs with 32 candidates in the gene pool.
Reactive force-field for objective evaluation The atomic interactions in the defective MoS 2 structures are modeled using the reactive force field (ReaxFF) [20]. All energy calculations are performed using LAMMPS [23] package. Periodic boundary conditions are employed in the plane of the MoS 2 sheet. LAMMPS is interfaced with an optimizer such as MCTS or GA-the energy evaluations using ReaxFF model of the defective MoS 2 sheets are used to determine the objective function. We note that in a single layer MoS 2 , a plane of Mo atoms is sandwiched between two planes of S atoms; in each S plane, the atoms are organized in a 2D triangular lattice. As the interlayer vacancy migration is associated with a very high energy barrier (>5 eV), we restrict our search space to the top layer of S atoms. All the calculations are performed for a defect density of 0.03, which is experimentally realizable. We note that the defect density is defined as the ratio of vacant S sites to the total S cites in the top layer of a MoS 2 film.
Results and discussion
A schematic of the MCTS workflow used for defect optimization is shown in figure 1. Our objective is to minimize the configurational energy of the system for any given distribution of the defects i.e. S vacancies. For a given N i.e. number of S vacancies, we initiate the search by randomly distributing the N vacancies amongst the lattice position for the top S layer in MoS 2 . This candidate serves as the root node of the search tree. A search tree is built in an incremental and iterative way as shown in figure 1. The objective evaluation is carried out by performing a structure minimization using LAMMPS.
To assess the performance of the MCTS search algorithm, we track the evolution of the best candidate i.e. one with the lowest energy is the search as a function of the number of search evaluation. We note from figure 2 that there is a slow initial drop in the configurational energy for evaluations<1000, when the search plateaus out and remains constant at ∼5.042 eV/atom until∼2000 evaluations. This is followed by a slow drop until ∼5000 evaluation, when a sudden and significant drop to the optimal solution is observed. The optimal solution corresponds to the S vacancies arranged in the form of an extended line defect. The objective does not change for the subsequent 5000 evaluations and hence the search is terminated. We next attempt to understand the MCTS configurational search process and the evolution of the objective shown in figure 2. The initial slower decay in the configurational energy for evaluations<3000 is attributed to the search being primarily in the exploratory stage. During the early stages, the tree expansion is preferred and the search algorithm tends to explore the various defect configurations, which correspond to widely different configurational space. The initial search has high degree of stochasticity and is random-as a result the energetic drops are rather small. Beyond 3000 evaluations, the search is focussed on exploitation of the configurational regions identified by the tree-during this phase, drop is significant and convergence to solution is much more rapid. We note that the exploratory and exploitation stages may differ amongst the different MCTS runs depending on the choice of the hyperparameters. The influence of a few representative MCTS hyperparameters on the search is discussed later in the manuscript.
The snapshots shown in figure 3 tracks the best candidate identified during the search process. Snapshots in figures 3(a)-(e) show the configurations obtained during the exploration stage. We can note that configurations sampled primarily have mono-vacancies with a few S di-and S tri-vacancy. During the exploitation stage as shown by snapshots in figures 3(f)-(l), we observe that the configurations sampled have clusters of S vacancies-MCTS explores more in regions where vacancy clustering is preferred signifying that the energetics favor the various mono-vacancies to cluster together. Finally, we see that the most preferred orientation for the S vacancies is to align themselves as a single line defect. This is consistent with previous experimental observation by M. Terrones and co-workers. Their HRTEM studies showed that structural defects in 2D MoS 2 evolve from randomly distributed sulphur monovacancies to distributed line defects to extended coupled line defects induced structural disorders. Such HRTEM observations validate the MCTS prediction and indicate a large variation in the size of low energy extended defects formed in 2D MoS 2 .
An important aspect of the MCTS workflow used for defect design is the explicit use of minimization step. We note that that during the playouts, we observe a significant energy/structural deviation of the final minimized configuration compared to the initial (Fig. 4). The number of minimization steps required to converge varies depending on the nature of the defect-we observe ∼400 to ∼15000 minimization steps are required for convergence (Fig. 4). The energy values of the final minimized configurations are used for evaluating the objective function. Thus, even though MCTS moves to manipulate the S vacancies are performed on the MoS 2 lattice, significant structural variations might result upon relaxation even for subtle defect variations. To ensure appropriate mapping between the MCTS configurational moves and the corresponding energies, the objective function should be based on minimized energies to ensure convergence.
We next compare the performance of MCTS with that of an evolutionary approach. We perform GA runs with an initial population starting with randomly distributed S-vacancies, at the desired vacancy density i.e. N=10. The configurational energy of the most stable structure obtained at each generation is tracked and the evolution of the minimum energy configuration is shown as a function of the number of GA evaluations (population * generation) in figure 5. We observe that the GA-identified lowest energy configuration (after 600 generations or 18000 evaluations) for = N 10, consist of a combination of lines (of varying sizes) and isolated vacancies, which results in a somewhat higher energy∼−5.047 eV/atom. Ordering all the vacancies in a single line corresponding to typical line defects seen in experiments, for = N 10 yields an energy value of −5.054 eV/ atom. Thus, the GA run for = N 10 does not reach the global energy minimum even after 18000 evaluations. Defect optimization is often non-trivial and optimization algorithms have difficulties in surmounting suboptimal solutions and tend to slow down near the optimal points as observed in the case of the GA based search. The major advantage the MCTS algorithm has is that it is able to generate a large number of dissimilar structures and balance the relative exploitation and exploration of each of the unique structures. Information from every structure that has been sampled is retained during the optimization and used to determine the next trial. It is able to obtain an approximate idea how much of the phase space is still left to explore and determine if it is worth exploring or if it should instead choose to exploit what it already knows. GA is more localized in scope in that it tends to generate structures that look similar to the ones already in the pool which will generally create solutions that are perhaps one or two minima well over in the phase space. A major advantage of the MCTS, as evidenced in the case of defect optimization, is that if the search gets trapped in a metastable or suboptimal point, it is expected to quickly find another pathway by growing other branches of the tree utilizing the trade-off mechanism between exploration and exploitation. MCTS is therefore able to reach the optimal solution i.e. the line defect with energy value of ∼−5.054 eV/atom in <5000 evaluations.
We evaluate the effect of MCTS hyperparameters on the search efficiency. First, we evaluated the effect of MCTS exploration constant. The search is initiated with randomly generated vacancies on the top S monolayer of the MoS 2 sheet. Mainly 4 kind of moves are used for generating a new structure (i.e. a child node) from previously obtained structures. These are swap, associate, dissociate and shift. The MCTS searches through the action space and selects the best possible child based on the upper confidence bounds (UCB formula shown in equation 1). UCB works on balancing between a more explorative randomly sampled search (depends on the exploration) with the quality of the node objective (more exploitation). In the MoS 2 system, the energy of the qualitatively good and the bad structures are very close in terms of magnitude and only differ by few meV/atom, even though the total cohesive energy of the system is on the order of 5 eV/atom. In addition to this subtle energetic difference between the various sampled configurations, it is possible to generate structures that are identical in shape, but may be slightly shifted or rotated from another or can have multiple energetically similar structure with different arrangement of vacancies. For the search to work well, it should initially explore the search space enough and have enough samples to make an optimal selection that can further be exploited. The balance between exploration and exploitation is critical to ensure faster converge to the optimal structure-an increase in tree width improves exploration whereas the tree depth increases the exploitation tendencies of the MCTS.
If the exploration constant is too small, the tree will greedily pick the current best rewards until it hits a local minima. On the other hand, selecting a constant too large will make it that makes the search to be effectively random and reduces the convergence probability significantly to all but nothing. So, a proper selection of exploration constants can make the search converge efficiently in a relatively few number of expensive objective function evaluations. For the sake of illustration, we start with two different explorations constant, each with a fixed number of playouts∼5. For an exploration constant of 200, it took 300 evaluations before the MCTS found energies that are comparatively higher than the one with an exploration constant of 50. The main reason behind this is the search is still exploring even after finding potential structures that can be exploited to find a low energy structure. In contrast, the exploration constant of 50 has swiftly moved towards a local minima. After 300 evaluations, the search with an exploration constant of 200 slowly moves towards the exploitation stage (2nd term in the UCB equation starts to decrease). This is manifested in figure 6(a)-the configurational energy tends to fall quickly as the exploited structures are more likely to produce a good vacancy alignment upon perturbation.
Next, we study the effect of playouts on the MCTS search. Here, we set the exploration constant to a fixed value of 50 and tried different number of playouts to observe its effect on the convergence rate. Since the playouts are a random measure of the local configuration space around a given node, a larger number of random samples in theory improve the MCTS algorithm's understanding of what potential solutions may exist nearby a search node. Insufficient playouts can lead to an inefficient branch selection because very little information is being discovered per test. But there is a declining rate of return by doing more playouts than is absolutely necessary as this ultimately increases the search time significantly. In such a situation, much of the search is spent on computing redundant or degenerate information. For defect design, a balanced playout is extremely crucial since each structure takes an average of∼5000 energy steps for minimization, which slows down the search significantly. We tried two representative playouts for the search as shown in figure 6(b). From the initial progression of the search, it appears that 10 playouts is more efficient than the 5 one and reduces the time to solution. In this MoS2 system most of the time the energy barrier between a parent and potentially very good child that can be obtained from the parent upon perturbation is considerably high in the context of energy ranges considered. Sometimes it becomes very crucial for MCTS to perform a minimum number of playouts at a node to get a qualitative understanding of the node. But in the context of the given fact that more playout leads to computational inefficiency the maximum playout was kept to 10 which seems to perform a better job in comparison to the search with 5 playouts. For the playout 10 case figure 5(b) the search performs adequate number of playouts at selected node and gets a better qualitative idea of the node based on UCB equation. The chances of a selection of a potentially good parent becomes higher. Thus, the search tends to quickly moves towards a zone where it samples higher number of good offspring and moves quickly towards convergence as compared to the search with playout 5.
Finally, we note that the problem outline above is particularly challenging owing to the subtle differences in the defect energetics. The energetic differences are small because it is easy to generate one of the local minima of the system through naïve random sampling. When the defects are not lined up, the next lowest energy state we have found is to have the defects near each other, but with one filled chalcogen site in between. These are also seen in the snapshots in figure 3. These spotted defects are the easiest minima to access as they can be created even with only a few of the defects (S vacancy) being close to each other.
Benefits of MCTS optimization and related work
Many of the optimization approaches, we looked at such as GA, Bayesian etc., would quickly form these spotted defects and fail to try the line defect of S vacancies. Higher energy configurations do exist even in the case of randomly distributed vacancies. For example, one can simply spread out all the defects away from each other, but there's a high statistical chance that one will get these spotted defects even through naïve or random sampling. As such, our initial design for both the GA and MCTS quickly created one of these configurations. The hard task for sampling, however, is escaping these sub-optimal minima. A major problem with any random based approaches is that they are significantly more likely to generate these local minima than the line defect.
A major advantage the MCTS algorithm has is that it is able to generate a large number of dissimilar structures and balance the relative exploitation and exploration of each of the unique structures [16,24,25]. Information from every structure that has been sampled is retained during the optimization and used to determine the next trial. It is able to obtain an approximate idea how much of the phase space is still left to explore and determine if it is worth exploring or if it should instead choose to exploit what it already knows. In this respect, our strategy outlined seems to clearly perform better compared to local and other global evolutionary searchess
Conclusion
Defect design and optimization in 2D materials is important for a wide range of applications from catalysis to nanoscale electronics. For example, the nature of atomic scale rearrangement of chalcogen vacancies in 2D TMDCs influences the 2H←→1T phase transitions, which in turn has a profound effect on its electronic properties. We perform reinforcement learning in a discrete action space by using Monte Carlo Tree Search interfaced with a reactive molecular dynamics simulator to find optimal arrangement of S vacancies in MoS 2 . Based on our MCTS search of the exorbitant defect search space, we find that the S vacancies tend to form an extended line defect of vacancies-these findings are corroborated by previous experimental studies. By making suitable comparison with an evolutionary approach, we demonstrate the power of RL search-MCTS attains a significantly lower objective in far fewer evaluations compared to GA. We also perform a detailed evaluation on the effect of different MCTS hyperparameters on the defect inverse design. We believe our RL approach is quite general and can be broadly applied to several materials problems that involve search in discrete action space. | 6,427.4 | 2021-02-11T00:00:00.000 | [
"Computer Science"
] |
Numerical Investigation and Parameter Sensitivity Analysis on Flow and Heat Transfer Performance of Jet Array Impingement Cooling in a Quasi-Leading-Edge Channel
: In this study, numerical simulations were carried out to investigate the flow and heat transfer characteristics of jet array impingement cooling in the quasi-leading-edge channel of gas turbine blades. The influence laws of Reynolds number ( Re , 10,000 to 50,000), hole diameter-to-im-pingement spacing ratio ( d/H , 0.5 to 0.9), hole spacing-to-impingement spacing ratio ( S/H , 2 to 6), and Prandtl number ( Pr , 0.690 to 0.968) on flow performance, heat transfer performance, and comprehensive thermal performance were examined, and the corresponding empirical correlations were fitted. The results show that increasing the d/H and reducing the S/H can effectively reduce the pressure loss coefficient in the quasi-leading-edge channel. Increasing the Re , reducing the d/H, and increasing the S/H can effectively enhance the heat transfer effect of the target wall. When d/H = 0.6 at lower Reynolds numbers and S/H = 5 at higher Reynolds numbers, the comprehensive thermodynamic coefficient reaches its maximum values. The average Nusselt numbers and comprehensive thermal coefficients of the quasi-leading-edge channel for steam cooling are both higher than those for air cooling. The pressure loss coefficient of the quasi-leading-edge channel is most sensitive to the change in d/H but is not sensitive to the change in Re . The average Nusselt number of the quasi-leading-edge channel is most sensitive to the change in Re and is least sensitive to the change in Pr . The comprehensive thermal coefficient of the quasi-leading-edge channel is most sensitive to the change in Re . The findings may provide a reference for the design of a steam-cooling structure in the leading edge channel of high-temperature turbine blades.
Introduction
The rotor inlet temperature of an advanced gas turbine is as high as 2000 K [1], which is far beyond the heat resistance limit of metal materials such as stainless steel and titanium. Consequently, effective cooling measures must be applied to gas turbine blades. The leading edge region of a turbine blade has the largest heat load [2], therefore the design of a cooling structure for a turbine blade leading edge needs more attention. Jet impingement cooling is one of the most effective methods to improve the local heat transfer coefficient [3] and is widely used in the cooling of the turbine blade leading edge.
In the study of impingement cooling, the impingement target walls have generally been simplified as flat walls to facilitate the research. A volume of research has been published regarding the flow and heat transfer performance of jet impingement on flat target walls. Experimental and numerical studies on jet impingement cooling are reported in a large number of works. Bradbury et al. [4] previously analyzed the velocity distribution of a typical single-hole jet and the attenuation of jet axial velocity. Kercher et al. [5] previously fitted the heat transfer correlation of a square array jet impinging on a flat target wall related to Reynolds number, hole spacing, and hole diameter based on experimental data. Liu et al. [6] explored the influence law of a tangential jet on the impingement heat transfer of the turbine blade leading edge and pointed out that the heat transfer enhancement effect of tangential jet impingement cooling was mainly related to the nozzle position and Reynolds number of the jet. Long et al. [7] measured the flow vortex structure and heat transfer characteristics of an elliptical jet hole impacting a cylindrical convex target wall and obtained the distribution laws of momentum thickness and wall shear stress. Nguyen et al. [8] investigated the flow field distribution of an under-expanded jet impinging on a flat wall and pointed out that the impingement distance and pressure ratio have a great influence on the transition of energy-contained eddies and the large-scale flow structures generated by the jet impingement. Xu et al. [9] numerically studied the flow and heat transfer performance of a 45° angled swirling jet impinging on a flat wall and fitted the heat transfer correlation for the 45° angled swirling jet impinging cooling at 6000 ≤ Re ≤ 30,000. Dutta et al. [10] reviewed the development trend of jet impingement cooling in gas turbine blades in recent years. They pointed out that the shapes of jet hole outlet, target wall, impingement channel, and the influence of cross-flow are the focus of current jet impingement cooling research. Deng et al. [11] studied the cooling performance of a wall jet with the advantages of swirl cooling and impingement cooling by using the conjugate heat transfer method. They stated that the overall cooling effectiveness of the wall jet cooling is 19% to 54% higher than that of the traditional methods for the cooling of a gas turbine blade. Yang et al. [12] researched the distribution characteristics of flow and heat transfer for a jet impingement cooling scheme with extended jet holes. Their results showed that the heat transfer effect and heat transfer uniformity of the extended jet holes are both better than those of the traditional jet impingement holes.
Although much work has been conducted on the flow and heat transfer performance of a jet impinging on flat target walls, because the wall surface of a turbine blade is curved, researchers began to pay attention to the research of jet impingement on concave target walls to improve the accuracy and practicability of impingement cooling for turbine blades. At present, many studies have also been carried out on the flow and heat transfer performance of a jet impinging on a concave target wall. Patil et al. [13] studied the effects of nozzle aspect ratio and target curvature on the flow and heat transfer characteristics of a jet impinging on a concave target wall and pointed out that small target curvature and large nozzle aspect ratio have the best comprehensive thermal performance when the heat transfer and pressure loss are comprehensively considered. Wang et al. [14] studied the heat transfer characteristics of a single-row jet impinging on a concave target wall of a turbine blade leading edge and found that the tangential jet provided more uniform heat transfer distribution than the conventional jet. Li et al. [15] experimentally investigated the flow behavior of sweeping jets impinging on a confined concave target wall and reported that the most important factors affecting flow behavior are the shape of the concave target wall and the inlet Reynolds number. Lyu et al. [16] examined the influence law of curvatures on the heat transfer capacity of a single-row chevron-jet impinging on concave walls. Their results showed that the influence level of concave curvature on the impingement heat transfer capacity of the chevron jet is significantly affected by the Reynolds number and impingement distance of the jet. Then, they [17] compared the heat transfer capacity of a tri-dimensional lobed jet impinging on a flat target wall and a concave target wall and pointed out that the stagnation heat transfer coefficient on the concave target wall is 20% to 30% lower than the flat target wall. Kura et al. [18] conducted a numerical investigation on the flow and heat transfer performance of single jet impingement on concave and convex target wall by using OpenFOAM (V9, ESI-OpenCFD Ltd, Bracknell, UK). They stated that the shape of the target wall affects the stagnation area size and leads to the difference in flow characteristics of jet impingement cooling. Xu et al. [19,20] studied and compared the heat transfer performance of swirling jet impingement cooling and circular hole jet impingement cooling on a semi-cylinder concave target by the large-eddy simulation method. They reported that the wall average Nusselt number of the 45° angled swirling jet impingement cooling improves by 5% to 10% when compared with circular hole jet impingement cooling. Qiu et al. [21] conducted a numerical investigation on the flow and heat transfer features of jets impinging on a concave target wall with pin-fins. They stated that the existence of a pin-fin can effectively enhance the heat transfer capacity and heat transfer uniformity of the concave target wall. Tepe et al. [22] numerically studied the cooling performance of a staggered jet array impinging on a semi-circular concave target wall. They stated that lengthening the jet holes can effectively improve the heat transfer effect of the semi-circular concave target wall under the impingement of the staggered jet array. Forster et al. [23] researched the heat transfer effect of a jet impinging on the concave target wall of a gas turbine blade leading edge by using experimental and numerical methods. The results showed that the crossflow reduces the local Nusselt number of the stagnation point as well as the average Nusselt number of the target wall and increases the distribution uniformity of the local Nusselt number on the entire target wall.
In addition to the abovementioned flat and concave target walls, there are two important research topics in jet impingement cooling, these being turbulence model and cooling medium. For the turbulence model, this is very important for the accuracy of a numerical simulation of jet impingement cooling. Different turbulence models may lead to different calculation results. Therefore, it is necessary to select an appropriate turbulence model for the numerical simulation of jet impingement cooling. At present, the selection of a turbulence model for a numerical simulation of a jet impinging on a concave target wall has been examined by many investigators. Seifi et al. [24] used the k-w-v 2 -f model to numerically study the heat transfer characteristics of a jet impinging on a concave target wall. The results show that the k-w-v 2 -f model has better prediction performance than other models when the impingement distance is small. Hadipour et al. [25] studied the heat transfer and flow characteristics of a jet impinging on a concave target wall at a small impingement distance. They pointed out that the SST k-ω turbulence model can capture the distribution features of static pressure and heat transfer coefficient on the concave target wall well. Then, they [26,27] studied the influence of elliptical pin-fins on the flow and heat transfer performance of a circular hole jet impinging on a concave target wall; the results again proved the accuracy of the SST k-ω turbulence model in predicting the heat transfer performance of a jet impinging on a concave target wall. Huang et al. [28] reported that the modified SST k-ω turbulence model with the curvature correction can significantly improve the prediction accuracy of flow and heat transfer behaviors for the jet impinging on the concave target wall.
For the cooling medium, the physical properties of the medium have a great influence on the flow and heat transfer performance of jet impingement cooling. The more common media used for turbine blade cooling are air, steam, and mist. In the last decade, several studies have been carried out on the flow and heat transfer capacity of jet impingement cooling with different cooling mediums. Li et al. [29] and Wang et al. [30,31] proposed to apply the mist/steam jet impingement cooling to closed-loop steam-cooled gas turbine blades. They stated that the heat transfer effect of the steam jet with 0.5% mist impinging on a concave target wall is enhanced by up to about 200% at the stagnation point. Xu et al. [32] conducted a numerical study on the total steam jet impingement cooling of a gas turbine blade and pointed out that steam jet impingement cooling can effectively improve the cooling effect of a gas turbine blade. Alhajeri et al. [33] researched the heat transfer characteristics of mist/steam jet impingement on an unconfined target wall and reported that the addition of mist significantly improves the heat transfer capacity of the steam jet impingement cooling. Diop et al. [34] studied the heat transfer features of a jet impinging with a few amounts of mist. Their results showed that the average Nusselt number of the target wall for a jet with 3 mg/s and 6 mg/s mist is 21% and 32% higher than that of a jet without mist.
According to the above literature review, although much research has been devoted to the flow and heat transfer performance of a jet impinging on flat and concave target walls, little research has been conducted on the cooling performance of steam jet impingement cooling for the leading edge of closed-loop steam-cooled gas turbine blades. Therefore, it is necessary to study the flow and heat transfer characteristics of steam jet impingement cooling in turbine blade leading edge channels and obtain the influence laws of structural parameters and working conditions on steam jet impingement cooling.
For the design of a steam-cooling structure of gas turbine blades, the authors of the present study previously carried out experimental and numerical studies on the cooling performance of a steam-cooled turbine blade of a heavy-duty gas turbine [32,35]. However, the cooling effectiveness of the leading edge region for the abovementioned blade was relatively poor. Therefore, further studies need to be carried out to solve the problems of poor cooling effect and high heat load of the leading edge channel in the steam-cooled gas turbine blade reported in the literature [32,35]. A feasible solution would be to arrange steam jet array impingement cooling in the leading edge channel of the abovementioned turbine blade.
The purpose of this study is to understand the flow and heat transfer characteristics of steam jet array impingement cooling in the quasi-leading-edge channel and obtain the influence laws of various parameters on the flow and heat transfer performance of the quasi-leading-edge channel. Therefore, in this study, a type of quasi-leading-edge channel with a jet array impingement cooling structure was established and simplified according to the blade's leading edge geometry. The flow and heat transfer characteristics of jet array impingement cooling in the quasi-leading-edge channel were analyzed in detail. The effects of Reynolds number (Re, 10,000 to 50,000), hole diameter-to-impingement spacing ratio (d/H, 0.5 to 0.9), hole spacing-to-impingement spacing ratio (S/H, 2 to 6), and Prandtl number (Pr, 0.690 for air to 0.968 for steam) on the flow and heat transfer performance of the quasi-leading-edge channel were discussed. The empirical correlations of pressure loss coefficients, heat transfer coefficients, and comprehensive thermal coefficients for jet array impingement cooling in the quasi-leading-edge channel were fitted within the parameters of the study. Finally, parameter sensitivity analysis of pressure loss coefficients, heat transfer coefficients, and comprehensive thermal coefficients to Re, d/H, S/H, and Pr was implemented. The results reveal the flow and heat transfer characteristics as well as the influence laws of various parameters for jet array impingement cooling in the quasileading-edge channel. The findings may provide a reference for the design of a steamcooling structure in the leading edge channel of high-temperature turbine blades.
Physical Model
In this investigation, the impingement cooling structure was designed for the leading edge channel of a steam-cooled gas turbine blade proposed by our research team [32,35]. The structural diagram of the steam-cooled gas turbine blade is shown in Figure 1a. The height, pitch, and chord length of this turbine blade were 83 mm, 96.4 mm, and 126 mm, and the installation angle and exit angle were 35.7° and 17°, as introduced in reference [35]. For the convenience of research and application, the leading edge channel of the steam-cooled gas turbine blade was taken out separately and simplified appropriately. The simplified leading edge channel, i.e., the quasi-leading-edge channel, is shown in Figure 1b. The quasi-leading-edge channel consists of a semi-cylindrical concave wall, two straight walls on both sides, and a bottom wall. The diameter of the semi-cylindrical concave was 9 mm, the length of the two straight walls was 9.5 mm, and the length of channel L was 40 mm. A plug-in channel was placed in the quasi-leading-edge channel to arrange the jet holes and act as the coolant supply plenum. The shape of the plug-in channel was the same as the shape of the quasi-leading-edge channel. The plug-in channel was connected to the bottom wall of the quasi-leading-edge channel through a stiffener. Due to the space limitation of the leading edge channel in the gas turbine blade, the jet impinging distance H was selected as 1 mm. Therefore, the calculated equivalent diameter D of the plug-in channel was 9 mm. Five columns of jet holes were arranged equidistantly along the channel circumference on the semi-cylindrical concave wall, and the angle between two adjacent columns of jet holes was 45°. Two columns of jet holes were arranged at equal intervals on each of the two straight walls, and the distance between two adjacent columns of jet holes was 3 mm. The process of jet array impingement cooling is as follows: the cooling medium enters from the inlet of the plug-in channel, then it impinges and cools the wall of the quasi-leading-edge channel through the jet array holes on the plugin channel, and finally, it flows out from the outlet of the quasi-leading-edge channel. During the study, the jet hole diameter d and the axial jet hole spacing S were changed to analyze the flow and heat transfer performance of the quasi-leading-edge channel and obtain the optimal configuration of the jet array impingement cooling structure. Due to the difficult processing, the easy blockage of jet holes, and the high strength requirements of the real gas turbine blade, the jet hole diameter, and jet hole spacing should not be too small. Meanwhile, due to the requirements of high cooling effect and low pressure loss of the real gas turbine blade, the jet hole diameter and jet hole spacing should not be too large. Therefore, based on the jet hole diameter and jet hole spacing of the impingement cooling structure of a real gas turbine blade, the jet hole diameter and jet hole spacing in this study were selected as 0.5 mm to 0.9 mm and 2 mm to 6 mm, respectively. Then, the impinging distance H was used to perform the dimensionless treatment on the parameters d and S. The dimensionless d/H and S/H ranged from 0.5 to 0.9 and from 2 to 6, respectively, which are also similar to the parameters used in most of the literature.
Data Reduction
The inlet Reynolds number Re, which directly reflects the cooling flow rate of the blade leading edge channel, can be expressed as: where u is the inlet velocity of the coolant; v is the kinematic viscosity of the coolant. The wall Nusselt number Nu can be written as: where q is the heat flux of the target wall; Tw is the temperature of the target wall; Tin is the inlet temperature of the coolant; λ is the thermal conductivity of the coolant. The pressure loss coefficient Cp of jet array impingement cooling in the quasi-leadingedge channel can be defined as: where pin is the channel inlet average pressure; pout is the channel outlet average pressure. Generally, for the internal cooling design of a gas turbine blade, higher heat transfer performance under equal pump power (i.e., under equal Cp 1/3 ) is hoped to be obtained. At the same time, in general, the pressure loss coefficient increases with the increase in wall Nusselt number. Therefore, a comprehensive coefficient is needed to evaluate the comprehensive thermal performance of the internal cooling channel. Consequently, the comprehensive thermal performance coefficient G, which is the ratio of Nu and Cp 1/3 , was defined to comprehensively consider the flow and heat transfer performance of jet array impingement cooling in the quasi-leading-edge channel in this study. The expression of G can be written as:
Numerical Model
The numerical calculation model of jet array impingement cooling in the quasi-leading-edge channel is demonstrated in Figure 2. For the convenience of research, the stiffener that has little effect on the flow and heat transfer of the quasi-leading-edge channel was removed in the numerical model. As shown in Figure 2a, the calculation domain of the numerical model mainly includes the fluid in the plug-in channel, the jet array, and the fluid in the quasi-leading-edge channel. The cooling steam or air in the plug-in channel flows through the jet holes and impinges on the target wall heated by constant heat flux. In order to eliminate the calculation problems caused by the backflow at the outlet, a 40 mm long transition channel was added at the outlet of the quasi-leading-edge channel. Then, the calculation domain was divided into hexahedral-structured grids by the ICEM software, as shown in Figure 2b. The grid near the wall was refined to simulate the flow and heat transfer phenomena in the boundary layer. The height of the first layer of the near-wall grid was 0.001 mm, the growth ratio of the near-wall grid was 1.2. This grid generation strategy can ensure that the y+ value near the wall is less than 1, as required by the turbulence models of standard k-ω and SST k-ω. The grids of the jet holes and their surrounding area were also refined to improve the accuracy of the simulation for jet array impingement cooling. Moreover, the same grid strategy was used in the coupling surfaces of each calculation domain to minimize the transfer error. Verification of grid independence was executed to guarantee the reliability and economy of the numerical methods for the simulation of jet array impingement cooling in the quasi-leading-edge channel. Six sets of grids were divided for the calculation model; the total grid numbers were 2.0 million, 2.6 million, 3.2 million, 3.8 million, 4.4 million, and 5.0 million, respectively. The values of the parameters selected for the grid independence verification were Re = 30,000, d/H = 0.7, S/H = 4. The results of grid independence verification are shown in Figure 3. As can be seen from Figure 3, when the total grid number is small, the average Nusselt number of the target wall increases with the increase in the total grid number. When the total grid number reaches about 4.4 million, the change in the average Nusselt number on the target wall is very small. This indicates that the requirement of grid independence is satisfied when the total grid number reaches 4.4 million. Therefore, the same grid generation strategy as the third set of grids with a total grid number of 4.4 million was adopted for different configurations of jet holes in the quasileading-edge channel in this study.
Numerical Calculation Method
In this investigation, the flow and heat transfer characteristics of jet array impingement cooling in the quasi-leading-edge channel were simulated by CFX software. The cooling fluid in the quasi-leading-edge channel was assumed to be 3D, compressible, and free of gravity. The governing equations were discretized by the bounded central difference scheme of the finite difference method. The Reynolds-averaged Navier Stokes equations were solved based on the fully implicit coupled multigrid. The High Resolution Scheme was selected for the options of Advection Schemes and Turbulence Numerics in the CFX-Pre [1]. The turbulent transport in the channel was simulated by different turbulence models, such as RNG k-ε, standard k-ω, and SST k-ω. The physical properties of the cooling steam were modified according to the IAPWS-IF97 material library. The physical properties of the cooling air were modified by the fitted correlation cp = 0.2348T + 936.95 for specific heat capacity, and the Sutherland formulae λ = λ0(T/T0) 3/2 (T0 + Su)/(T + Su) for thermal conductivity and μ = μ0(T/T0) 3/2 (T0 + Su)/(T + Su) for dynamic viscosity. In the Sutherland formulae, Su is the Sutherland constant of 110.56 K and T0 is the reference temperature of 273.16 K for λ0 and μ0. The condition for stopping the numerical simulation is that the residual level of each item converges below 10 −6 .
The corresponding governing equations [1,36] are as follows. The continuity equation: The momentum equation: where τ is the stress tensor.
The total energy equation: where htot is the total enthalpy.
The form of advection schemes is: where ip is the integration point; up is the upwind node; r is the vector from up to ip. Different θ and ∇ results in different schemes. When θ is close to 1, the advection schemes have a high resolution.
According to the experimental conditions in reference [32,35], the boundary conditions for jet array impingement cooling in the quasi-leading-edge channel were set as follows. The uniform velocity calculated by the Reynolds number of 10,000 to 50,000 and the total temperature of 474 K were assigned to the inlet of the plug-in channel. The turbulence intensity of the coolant at the inlet was set as 5%. The average static pressure of 244 kPa with a pressure fluctuation of 5% was assigned to the outlet of the quasi-leading-edge channel. The target wall of the quasi-leading-edge channel was set as the heated wall with a uniform heat flux of 10,000 W·m −2 . The other walls were set as the adiabatic non-slip wall. In addition, the initialization conditions of the numerical simulation in this study were set as follows. For steam cooling, the initial inlet temperature and pressure were 474 K and 244 kPa, and the initial inlet normal velocity ranged from 15.90 m·s −1 to 79.51 m·s −1 , according to the Reynolds number of 10,000 to 50,000. The density, specific heat capacity at constant pressure, thermal conductivity, dynamic viscosity, and Prandtl number of steam at channel inlet were 1.129 kg·m −3 , 2.030 kJ·kg −1 ·K −1 , 0.0339 W·m −1 ·K −1 , 0.0000162 Pa·s, and 0.968, respectively. For air cooling, the initial inlet temperature and pressure were also 474 K and 244 kPa, and the initial inlet normal velocity varied from 16.10 m·s −1 to 80.49 m·s −1 , according to the Reynolds number of 10,000 to 50,000. The density, specific heat capacity at constant pressure, thermal conductivity, dynamic viscosity, and Prandtl number of air at channel inlet were 1.794 kg·m −3 , 1.026 kJ·kg −1 ·K −1 , 0.0387 W·m −1 ·K −1 , 0.0000261 Pa·s, and 0.690, respectively.
Verification of Numerical Method
The experimental data of a single row of a circular jet impinging on the semi-cylindrical concave wall in reference [20] were used to verify the numerical method in this study. The parameters selected were Re = 10,000 and d/H = 0.2. Three turbulence models, RNG k-ε, standard k-ω, and SST k-ω, were used for the numerical simulations. Then, the numerical results were compared with the experimental data. Figure 4 shows the comparison results between the experimental values of local Nu on the target wall and the corresponding simulation values of local Nu calculated by the three turbulence models. It can be seen that the distribution trends of the local Nu on the target wall calculated by the three turbulence models are consistent with the distribution trend of the experimentally measured local Nu. However, in terms of prediction accuracy, the simulation values of local Nu calculated by the RNG k-ε and standard k-ω turbulence models are obviously lower than those of the experimentally measured local Nu. The maximum prediction deviations of the RNG k-ε and standard k-ω turbulence models are about 17.79% and 15.03%, respectively. While the simulation values of local Nu calculated by the SST k-ω turbulence model are very close to the experimental values [25][26][27][28], the maximum prediction deviation of the SST k-ω turbulence model is about 6.36%. The above results show that the numerical method with the SST k-ω turbulence model used in this study can accurately and reliably simulate the heat transfer characteristics of the jet impinging on the semi-cylindrical concave wall. Therefore, the SST k-ω turbulence model was adopted to calculate the flow and heat transfer performance of jet array impingement cooling in the quasi-leadingedge channel in this investigation. Figure 5a is the three-dimensional flow field and Figure 5b is the two-dimensional flow field. As can be seen from Figure 5a, the flow field distribution in the impinging area of each row of jet holes is very similar along the circumferential direction of the channel but changes greatly along the axial direction of the channel. For the first two rows of jet holes near the entrance of the channel, the flow field distribution in the impinging area of each hole is similar to the flow field distribution in the core impinging area of the typical single-hole jet impingement cooling. That is, a symmetrical radial flow pattern is formed in the area between each jet hole and the target wall. With the development of the flow, from the third row of jet holes the flow field in the impinging area of each row of jet holes inclines downstream due to the influence of the axial flow of the upstream fluid. From the seventh row of jet holes, the flow field in the impinging area of each row of jet holes is completely distributed in the downstream area of the holes and displays a V-shaped pattern. At the same time, due to the increase in fluid from upstream, the fluid velocity in the core impinging area of the downstream rows of jet holes continuously increases. Then, the fluid velocity of the impinging jets near the outlet of the channel reaches the maximum. As can be seen from Figure 5, the first two rows of impinging jets near the entrance of the channel impinge the target wall at a nearly perpendicular angle. Therefore, the first two rows of impinging jets yield high heat transfer regions with nearly circular pattern on the target wall. Then, with the increase in row number, the cross-flow formed by the front rows of impinging jets causes the back rows of impinging jets to not impinge the target wall vertically but incline downstream. Therefore, the pattern shape of the high heat transfer regions formed on the target wall downstream gradually becomes flat. In addition, due to the influence of the cross-flow formed by the upstream jets, the size of the high heat transfer region on the target wall gradually decreases along the axial direction of the channel, and the corresponding local heat transfer gradually weakens. Figure 7a, with the increase in Reynolds number, there is no obvious change in the flow field structure in the quasi-leading-edge channel; only the flow velocity at the corresponding position is significantly increased. As can be seen from the heat transfer distributions in Figure 7b, the local Nusselt number in each region of the target wall significantly increases with the increase in Reynolds number. This is because the jet velocity of each hole also increases with the increase in Reynolds number, which causes more heat to be taken away by the jet impinging on the target wall and then completely improves the heat transfer effect of the target wall. At the same time, the increase in the Reynolds number makes the attenuation speed of the high heat transfer area on the target wall weaker along the axial direction of the channel. This may be because the increase in jet velocity reduces the influence of the crossflow from the upstream on the jet from the downstream. Figure 8a that the change in Re has relatively little effect on the pressure loss coefficient of jet array impingement cooling in the quasi-leading-edge channel. The differences in the pressure loss coefficients are 2.4% to 6% when Re varies from 10,000 to 60,000 under different d/H and S/H. This is because the pressure loss coefficient is inversely proportional to the square of the inlet velocity and directly proportional to the pressure drop in the quasi-leading-edge channel, as shown in Equation (3). At the same time, the inlet velocity and the pressure drop both increase with the increase in Re, and the change rates of these with Re are close to each other. Thus, the change in the pressure loss coefficient with Re is very small. It can be observed from Figure 8b that, dissimilar to the effect of Re on the pressure loss coefficient of the quasi-leadingedge channel, the increase in Re can significantly improve the wall Nusselt number of jet array impingement cooling in the quasi-leading-edge channel. The quantitative result shows that the average Nusselt number of the quasi-leading-edge channel increases by 1.59 to 1.91 times under different d/H and S/H when Re increases from 10,000 to 60,000. It can be deduced from Figure 8c that the variation law of the comprehensive thermal coefficient with Re is different from that of the pressure loss coefficient with Re but is similar to that of the wall average Nusselt number with Re. That is, the increase in Re can effectively improve the comprehensive thermal performance of jet array impingement cooling in the quasi-leading-edge channel. The quantitative result shows that, when Re increases from 10,000 to 60,000, the comprehensive thermal coefficient of the quasi-leading-edge channel increases by 1.62 to 1.86 times under different d/H and S/H. In conclusion, increasing Re can greatly elevate the heat transfer effect and comprehensive thermal performance of jet array impingement cooling in the quasi-leading-edge channel but has little influence on the pressure loss coefficient of this channel. Figure 10a that the pressure loss coefficient of the quasi-leading-edge channel is significantly reduced by increasing the d/H under various Reynolds numbers. This is because, as the d/H increases, the flow velocity at the jet stagnation point and its surrounding area significantly decreases, resulting in a decrease in local pressure loss in these areas. The calculation results show that the pressure loss coefficient of the quasi-leading-edge channel decreases by 76% to 79% when the d/H varies from 0.5 to 0.9 at different Reynolds numbers. It can be observed from Figure 10b that the increase in d/H significantly reduces the average Nusselt number of the target wall at various Reynolds numbers, and this trend is more obvious when the Reynolds number is higher. The quantitative results show that the average Nusselt number of the target wall decreases by 45% to 49% when the d/H increases from 0.5 to 0.9 at different Reynolds numbers. It can be deduced from Figure 10c that the comprehensive thermal coefficients of the quasi-leading-edge channel first increase and then decrease with the increase in d/H under lower Reynolds numbers, and they rapidly decrease with the increase in d/H under higher Reynolds numbers. It is worth noting that when the Reynolds number is low, the optimal value of d/H is 0.6, which makes the comprehensive thermal coefficients of the quasi-leading-edge channel reach the maximum values. In conclusion, increasing the jet hole diameter can effectively reduce the pressure loss coefficient of jet array impingement cooling in the quasi-leading-edge channel; reducing the jet hole diameter can effectively enhance the heat transfer effect of the target wall of the quasi-leadingedge channel. The optimal value of d/H is 0.6 for improving the comprehensive thermal performance of the quasi-leading-edge channel at lower Reynolds numbers. Figure 11 presents the flow field distributions and heat transfer distributions of steam jet array impingement cooling in the quasi-leading-edge channel under different S/H at Re = 30,000 and d/H = 0.7. As can be seen from the flow field distributions in Figure 11a, due to the constant channel length, increasing the hole spacing serves to reduce the number of jet hole rows that can be arranged along the axial direction of the channel. Although the number of jet hole rows changes at different hole spacings, the flow field distribution on the mid-section of the channel is similar under different hole spacings. The difference is that when the hole spacing is smaller, the size of the vortexes around the jet holes is smaller, the fluid velocity at the stagnation point is smaller, and the vortexes upstream of the jet holes disappear earlier along the axial direction. It can be seen from the heat transfer distributions in Figure 11b that with the increase in jet hole spacing, the number of high heat transfer areas on the target wall decreases but the size of each high heat transfer area greatly increases. This is because the increase in jet hole spacing reduces the number of jet holes and increases the velocity of the jets, which improves the effect of impingement heat transfer of each jet on the target wall. On the whole, increasing the jet hole spacing improves the overall heat transfer effect of the target wall significantly. Figure 12a that the pressure loss coefficient of the quasi-leading-edge channel increases with the increase in S/H at various Reynolds numbers. The reason is that the number of jet holes decreases with the increase in jet hole spacing, and the coolant velocity of each stagnation point and its surrounding area greatly increases. This results in the increase in local pressure loss in the impingement area, and then increases the whole pressure loss coefficient of the quasi-leading-edge channel. The calculation results show that the pressure loss coefficient of the quasi-leading-edge channel increases by 1.64 to 1.92 times when the S/H increases from 2 to 6 at different Reynolds numbers. Moreover, from the analysis of Figure 9a, Figure 10a, Figure 11a, and Figure 12a, it can be concluded that the velocity of the coolant at the stagnation point and its surrounding area has the greatest influence on the pressure loss coefficient of the channel. It can be seen from Figure 12b that when the S/H is lower, the average Nusselt number of the target wall rapidly increases with the increase in S/H. When the S/H is higher, the average Nusselt number of the target wall changes slowly, especially when the Reynolds number is lower than 20,000 and the S/H is higher than five; the average Nusselt number of the target wall remains basically unchanged along with the S/H. The quantitative results show that the average Nusselt number of the target wall increases by 54% to 64% when the S/H increases from 2 to 6 at different Reynolds numbers. As can be seen from Figure 12c, the comprehensive thermal coefficients of the quasi-leading-edge channel first increase and then remain unchanged with the increase in the S/H at lower Reynolds numbers, and they first increase and then decrease with the increase in the S/H at higher Reynolds numbers. When the Reynolds number is high, the comprehensive thermal coefficients of the quasi-leading-edge channel reach maximum values at S/H = 5. In summary, reducing the jet hole spacing can effectively reduce the pressure loss coefficient of jet array impingement cooling in the quasi-leading-edge channel, and increasing the jet hole spacing can significantly enhance the heat transfer effect of the target wall of the quasi-leadingedge channel. The optimal value of S/H for improving the comprehensive thermal performance of the quasi-leading-edge channel at higher Reynolds numbers is five. In summary, the flow and heat transfer characteristics of jet array impingement cooling in the quasi-leading-edge channel with uniformly arranged jet holes of equal jet hole diameter were discussed in detail in Section 4.1 to Section 4.4 in this study. In future research, the influence laws of non-uniform jet hole diameter and jet hole spacing on the flow and heat transfer performance of jet array impingement cooling in the quasi-leadingedge channel will additionally be studied in detail. This is because the issue of non-uniform jet hole diameter and jet hole spacing is interesting and meaningful for the application of jet array impingement cooling in the leading edge channel of turbine blades. Figure 13 demonstrates the flow field distributions of jet array impingement cooling in the quasi-leading-edge channel under steam cooling and air cooling at Re = 30,000, d/H = 0.7, and S/H = 4. As can be seen in Figure 13a, the flow field structures of jet array impingement cooling in this channel for steam cooling and air cooling are very similar to each other. The difference is that the sizes of the counter-rotating vortex pairs formed on both sides of the first seven rows of jet holes for steam cooling are larger than those for air cooling. Meanwhile, the cores of those counter-rotating vortex pairs for steam cooling are farther away from the target wall when compared with air cooling. Additionally, the fluid velocity at the stagnation point and its surrounding area for air cooling is slightly higher than that for steam cooling. This is because the kinematic viscosity of air is greater than that of steam at the same temperature and pressure. This may result in a slightly higher pressure loss coefficient in the quasi-leading-edge channel for air cooling than for steam cooling. As can be seen in Figure 13b, the heat transfer distribution characteristics of jet array impingement cooling in the quasi-leading-edge channel for steam cooling are very similar to those for air cooling. The difference is that the distribution patterns of the local Nusselt number at the impingement areas of the first five rows of jet holes are kidneyshaped for steam cooling and circular for air cooling. This is because the sizes and distribution positions of the counter-rotating vortex pairs formed on both sides of the jet holes are different for steam cooling and air cooling. Furthermore, the local Nusselt number on the target wall of the quasi-leading-edge channel for steam cooling is obviously higher than that for air cooling. This is because the Pr of steam at a temperature of 474 K and a pressure of 244 kPa is 0.968, which is significantly higher than the Pr of air, which is 0.690 under this condition. It is well known that the wall Nusselt number of a heated channel is roughly proportional to Pr 4 . Therefore, the heat transfer effect of jet array impingement cooling in the quasi-leading-edge channel for steam cooling is significantly better than that of air cooling. Figure 14a that the variation trends of the pressure loss coefficients with Re for jet array impingement cooling in the quasi-leading-edge channel are basically the same for steam cooling and air cooling. As with steam cooling, the increase in Re also has little effect on the pressure loss coefficient of the quasi-leading-edge for air cooling. It is worth noting that the pressure loss coefficient in the quasi-leading-edge channel for steam cooling is slightly less than that for air cooling. The maximum difference in pressure loss coefficients in the quasileading-edge channel between steam cooling and air cooling is only −0.64%. It can be observed from Figure 14b that the average Nusselt numbers on the target wall of the quasileading-edge channel increase with increasing Re for both steam cooling and air cooling. Meanwhile, the increase rate of the average Nusselt number of the quasi-leading-edge channel for the air cooling greatly reduces with the increase in Re, which is obviously lower than that for the steam cooling. Moreover, the average Nusselt numbers of the quasi-leading-edge channel for the steam cooling are 17.19% to 36.36% higher than those for the air cooling under different Re. It can be established from Figure 8c that the comprehensive thermal coefficients of the quasi-leading-edge channel for steam cooling and air cooling both increase with the increase in Re. Meanwhile, increasing Re makes the gap between the comprehensive thermal coefficients of the quasi-leading-edge channel for steam cooling and air cooling become larger. Additionally, the comprehensive thermal coefficients of the quasi-leading-edge channel for steam cooling are 18.78% to 38.35% higher than those for air cooling when Re increases from 10,000 to 60,000. In summary, when compared with air cooling, steam cooling does not increase the pressure loss coefficient of jet array impingement cooling in the quasi-leading-edge channel but can effectively improve the heat transfer performance and comprehensive thermal performance of the quasi-leading-edge channel.
Correlation Fitting
In this section, the empirical correlations of heat transfer coefficients and pressure loss coefficients for jet array impingement cooling in the quasi-leading-edge channel are fitted to facilitate the application of the research results of this study. According to the analysis in Sections 4.1 to 4.3, the pressure loss coefficient and the average Nusselt number of jet array impingement cooling in the quasi-leading-edge channel monotonically decrease with the increase in d/H, and monotonously increase with the increase in S/H. Meanwhile, the change in Cp with the increase in Re is very small, and the Nu monotonically increases with the increase in Re. The comprehensive thermal coefficient G of jet array impingement cooling in the quasi-leading-edge channel roughly increases with the increase in Re and S/H, and roughly decreases with the increase in d/H. In addition, the Pr of the cooling medium corresponding to steam cooling and air cooling was also considered when fitting the correlations. Therefore, the correlations of Cp, Nu, and G with Re, d/H, S/H, and Pr were assumed to be power functions. The general form of the power function is: where f represents Cp, Nu, and G; a, b, c, d, and e are the parameters needed to be fitted. Based on the results in Figures 6,7,[9][10][11][12], as well as Equation (9) The ranges of parameters were: 10,000 ≤ Re ≤ 50,000, 0.5 ≤ d/H ≤ 0.9, 2 ≤ S/H ≤ 6, and 0.690 ≤ Pr ≤ 0.968. The above empirical correlations are applicable to the cooling of the leading edge regions of gas turbine blades with the requirements of high cooling efficiency and low pressure loss. Figure 15 shows the error distributions of the fitted empirical correlations. As can be seen from Figure 15, the maximum relative errors of Cp correlation, Nu correlation, and G correlation are 15.06%, 13.89%, and −13.41%, and the mean relative errors of Cp correlation, Nu correlation, and G correlation are 7.02%, 6.61%, and 4.72%, respectively. Therefore, the empirical correlations of Cp, Nu, and G fitted in this paper can provide a relatively accurate reference for the prediction of the average Nusselt number, pressure loss coefficient, and comprehensive thermal performance of jet array impingement cooling in the quasi-leading-edge channel.
Parameter Sensitivity Analysis
Sensitivity analysis is a method for studying the degree of influence of changes in input parameters on output parameters. The importance of input parameters to output parameters can be obtained through sensitivity analysis. The greater the sensitivity coefficient, the more important the input parameters are. In this investigation, the Sobol method, which is a widely used global sensitivity analysis method based on variance decomposition, was used to accomplish parameter sensitivity analysis for jet array impingement cooling in the quasi-leading-edge channel. The principle of the Sobol method is to calculate the contribution of each input parameter to the variance of each output parameter so as to determine the relative influence level of each input parameter on the output parameters. Therefore, this method can robustly and reliably calculate the sensitivity caused by the interaction effect between input parameters in the highly nonlinear model [37]. The Sobol method can calculate the first-order, second-order, and total sensitivity coefficients of each input parameter. This investigation focused on the overall influence level of each input parameter of the quasi-leading-edge channel, therefore only the analysis of the total sensitivity coefficient ST of each input parameter was carried out. The calculation formula of the total sensitivity coefficient STi for the ith input parameter is [37]: (13) where N' is the number of sample points; both A and B are the N' × I matrices, which are generated by the Sobol method in Python; n represents the nth row in the matrix; I is the number of input parameters; is the variance of the output parameter. Based on Equations (10)-(13), the parameter sensitivity analysis of flow performance, heat transfer performance, and comprehensive thermal performance of jet array impingement cooling in the quasi-leading-edge channel to Re, d/H, S/H, and Pr was carried out. The results of the sensitivity analysis are shown in Figure 16. As can be seen from Figure 16, the pressure loss coefficient of the quasi-leading-edge channel is most sensitive to the change in d/H, followed by the changes in S/H and Pr, while it is not sensitive to the change in Re. This is because the pressure difference between the channel inlet and outlet as well as the square of the inlet velocity both increase with the increase in Re, which results in a small change in the pressure loss coefficient with the increase in Re. The average Nusselt number of the quasi-leading-edge channel is most sensitive to the change in Re, followed by the changes in d/H and S/H, and is least sensitive to the change in Pr. The comprehensive thermal coefficient of the quasi-leading-edge channel is also the most sensitive to the change in Re, followed by the change in Pr, and is least sensitive to the changes in d/H and S/H. It can also be seen from Figure 16 that the sensitivity coefficients of the wall average Nusselt number and the comprehensive thermal coefficient of the quasi-leading-edge channel to Re are both relatively high, and are much larger than the sensitivity coefficient of the pressure loss coefficient to Re. This indicates that the change in the Re has much greater influence on the heat transfer performance and comprehensive thermal performance of the quasi-leading-edge channel than on the flow performance of the channel. The sensitivity coefficients of the wall average Nusselt number and the comprehensive thermal coefficient of the quasi-leading-edge channel to the d/H and S/H are obviously smaller than the sensitivity coefficients of the pressure loss coefficient to the d/H and S/H. It shows that the changes in the d/H and S/H have a smaller influence on the heat transfer performance and comprehensive thermal performance of the quasi-leading-edge channel than on the flow performance of the channel. The sensitivity coefficients of the pressure loss coefficient, wall average Nusselt number, and comprehensive thermal coefficient of the quasi-leading-edge channel to the Pr are all small, implying that the influence levels of steam cooling on the flow performance, heat transfer performance, and comprehensive thermal performance of the quasi-leading-edge channel are relatively small when taking air cooling as the reference. In summary, when the heat transfer performance of jet array impingement cooling in the quasi-leading-edge channel is expected to be enhanced, the two parameters of Re and d/H are the most important. When the pressure loss of jet array impingement cooling of the quasi-leading-edge channel is expected to be reduced, most attention should be paid to the parameters of d/H and S/H. When the comprehensive thermal performance of the quasi-leading-edge channel is expected to be improved, Re is most important. Figure 16. Distribution of the total sensitivity coefficients.
Conclusions
In this investigation, the flow and heat transfer characteristics of jet array impingement cooling in the quasi-leading-edge channel of a gas turbine blade were numerically analyzed. The main findings are as follows: (1) The change in Re has relatively little effect on the pressure loss coefficient of jet array impingement cooling in the quasi-leading-edge channel. When Re increases from 10,000 to 60,000 under different d/H and S/H, the average Nusselt number of the quasi-leading-edge channel increases by 1.59 to 1.91 times and the comprehensive thermal coefficient of the quasi-leading-edge channel increases by 1.62 to 1.86 times. (2) When the d/H changes from 0.5 to 0.9 at different Re, the pressure loss coefficient in the quasi-leading-edge channel decreases by 76% to 79% and the average Nusselt number of the target wall decreases by about 45% to 49%. When the S/H increases from 2 to 6 at different Re, the pressure loss coefficient in the channel increases by about 1.64 to 1.92 times and the average Nusselt number of the target wall increases by 54% to 64%. (3) The pressure loss coefficient in the quasi-leading-edge channel can be reduced by increasing the jet hole diameter and reducing the jet hole spacing. The heat transfer effect of the target wall can be improved by reducing the jet hole diameter and increasing the jet hole spacing. The comprehensive thermodynamic coefficient reaches its maximum values at d/H = 0.6 for lower Reynolds numbers and at S/H = 5 for higher Reynolds numbers. (4) The pressure loss coefficient in the quasi-leading-edge channel for steam cooling is slightly less than that for air cooling. The average Nusselt numbers and comprehensive thermal coefficients of the quasi-leading-edge channel for steam cooling are 17.19% to 36.36% and 18.78% to 38.35% higher than those for air cooling under different Re. | 12,097.4 | 2022-02-09T00:00:00.000 | [
"Physics",
"Engineering"
] |
A novel high-low-high Schottky barrier based bidirectional tunnel field effect transistor
In this work, we proposed a novel High-Low-High Schottky barrier bidirectional tunnel field effect transistor (HLHSB-BTFET). Compared with previous technology which is named as High Schottky barrier BTFET (HSB-BTFET), the proposed HLHSB-BTFET requires only one gate electrode with independent power supply. More importantly, take an N type HLHSB-BTFET as an example, different from the previously proposed HSB-BTFET, due to that the effective potential of the central metal is increased with the increasing of drain to source voltage (Vds), built-in barrier heights maintain at the same value when the Vds is increased. Therefore, there is no strong dependence between built-in barrier heights formed in the semiconductor region on the drain side and the Vds. Besides that low Schottky barrier formed on the interface between the conduction band of silicon regions on its both sides and the central metal (while high Schottky barrier formed between the valence band of silicon regions on its both sides and the central metal) have been designed for preventing the carriers in valence band from flowing into the central metal induced by thermionic emission effect. Thereafter, the proposed N type HLHSB-BTFET has a natural blocking effect on the carriers flowing in valence band, and this blocking effect is not significantly degraded with the increasing of Vds, which is a huge promotion from the previous technology. The comparison between the two technologies is carried out, which exactly agrees with the design assumptions.
Introduction
The development of integrated circuit technology depends on the reduction of device size, the improvement of device performance and the enrichment of device functions. For sub-30nm technology, short-channel effect of MOSFET with planar gate becomes serious. Therefore, multi-gate FET has been proposed and replaced planar MOSFET and significantly reduced the impact of short channel effect on the device subthreshold performance. The subthreshold swing (SS) of multi gate FET can be maintained at about 63mV/dec at room temperature, just like the planar MOSFET with micron channel length [1,2]. However, in order to further break through the bottleneck of the switch characteristics of MOSFET, tunnel field effect transistor (TFET) with band to band tunneling (BTBT) effect as the device conduction principle was proposed. Due to the more sensitive dependence between the tunnel current and the intensity of band bending, TFET achieves smaller SS than conventional MOSFET [3][4][5]. Both TFET and MOSFET are devices based on ion implantation and other doping processes. Since diffusion as the basic law of nature exists between heterogeneous mediums, and it is significantly accelerated in an environment with higher temperature, the manufacturing process used to form abrupt junction becomes complex for doping based devices in nanoscale. It requires highly difficult annealing process in millisecond. Expensive equipment for ion implantation is also required in accurate doping process. This significantly increases the necessary expenses for production. Comparing to the abrupt junction of MOSFET based on doping technology, Schottky Barrier MOSFET (SB-MOSFET) uses metal materials as the source and drain (S/D) regions of the device [6][7][8]. Due to that the enhancement of on state current can be achieved by adopting different alloy electrodes to form Schottky barrier with lower heights [9][10][11][12][13], the Schottky barrier height between the S/D electrodes and the conduction band of semiconductor region (φ Bn ) is usually much lower than that between the S/D electrodes and the valence band (φ Bp ) for n type SB-MOSFET [14]. Due to that the thermionic emission efficiency is decreased by the Schottky barrier and is decreased with the increasing of the Schottky barrier height, comparing to doping based MOSFET, It has been proved that SB-MOSFET can not achieve the same SS as doping based MOSFET [15]. More than that, the reverse leakage current of SB-MOSFET induced by BTBT is large [16]. An n type HSB-BTFET is proposed which adopt metallic junctions to form a higher Schottky barrier between source/drain contact and conduction band of silicon region [17]. Different from SB-MOSFET, it utilizes higher Schottky barrier to eliminate thermionic emission current as much as possible for off state and realize sharper abrupt metallic junctions to generate BTBT current as much as possible which is the generation mechanism of on state current. However, in order to block the formation of reversely biased leakage current, an assistant gate has to be designed in the central part and sets to work at a constant bias to form a potential barrier to prevent the electron-hole pairs generated by the BTBT phenomena from forming reversely biased leakage current. However, we found that if the drain to source voltage (V ds ) is largely increased, the potential barrier generated by the assistant gate will be largely reduced and eventually lose the blocking effect to the electron hole pairs generated by the BTBT phenomena in reversely biased state, and finally lose the controllability of the leakage current. In this paper, we proposed a novel High-Low-High Schottky barrier bidirectional tunnel field effect transistor (HLHSB-BTFET). Comparing to HSB-BTFET, the proposed HLHSB-BTFET requires only one gate electrode with independent power supply. More importantly, take an N type HLHSB-BTFET as an example, different from the previously proposed HSB-BTFET, due to that the effective potential of the central metal is increased with the increasing of V ds , built-in barrier heights maintain at the same value when the V ds is increased. Therefore, there is no strong dependence between built-in barrier heights formed in the semiconductor region on the drain side and the V ds . Besides that low Schottky barrier formed on the interface between the conduction band of silicon regions on its both sides and the central metal (while high Schottky barrier formed between the valence band of silicon regions on its both sides and the central metal) have been designed for preventing the carriers in valence band from flowing into the central metal induced by thermionic emission effect. Thereafter, the proposed N type HLHSB-BTFET has a natural blocking effect on the carriers flowing in valence band, and this blocking effect is not significantly degraded with the increasing of V ds , which is a huge promotion from the previous technology. The comparison between the two technologies is carried out, which exactly agrees with the design assumptions. Fig. 1(a) shows a schematic top view of HLHSB-BTFET, Fig. 1(b) is a cross view of HLHSB-BTFET along cut line A in Fig. 1(a). The S/ D regions are symmetrical and can change with each other. A significant difference between the proposed HLHSB-BTFET and the previous HSB-BTFET is that the central part of the device is replaced by a central metal instead of an assistant gate. The Schottky barriers are formed not only on the interface between source/drain electrode and silicon but also formed on the interface between central metal and silicon regions which are on each side of the central metal, respectively. However, it is worth noting that, taking ntype devices as an example, unlike the high Schottky barrier (barrier height larger than half of the energy band gap of silicon) formed between the source drain electrode and the conduction band of silicon regions, the Schottky barrier formed between the central metal and the conduction band of silicon regions are low Schottky barriers (barrier height smaller than half of the energy band gap of silicon). This makes the binding force of the central metal on electrons weaker than that of the semiconductors on both sides, resulting in some electrons in the central metal flowing to the semiconductors on both sides. Therefore, the potential of the part of semiconductors on both sides close to the central metal area will be higher than the part where the semiconductors on both sides are close to the source and drain electrodes. That is to say, a built-in potential difference is formed in the semiconductor areas on both sides. The formation of this potential difference helps to prevent holes on both sides of the source and drain from flowing to the central metal area, and also helps to prevent more electrons in the central metal from flowing to both sides of the source and drain. L M is the length of the central metal. The other parts of the proposed HLHSB-BTFET are similar to the previously proposed HSB-BTFET. L i represents the length of the undoped semiconductor region between the S/D contacts and the central metal. L AG represents the length of the assistant gate. L SD is the length of S/D contacts. t ox represents the gate oxide thickness. t tunnel represents the intrinsic tunnel layer thickness between the gate oxide and the S/D contacts. W SD is the width of S/D contacts. W represents the width of the semiconductor region. H represents the height of the semiconductor region. The performance of HLHSB-BTFET has been analyzed and verified through simulation work by SILVACO [18]. Fermi-Dirac statistic model, SRH recombination model, auger recombination model, mobility models, band gap Fig. 2 shows the energy band diagram of an n-type HLHSB-BTFET with V ds equals to 0.6 V. qφ Bns and qφ Bps are the Schottky barrier heights for the conduction band and valence band of silicon on the source side, respectively. qφ Bnd and qφ Bpd are the Schottky barrier heights for the conduction band and valence band of silicon on the drain side, respectively. qφ Bncm and qφ Bpcm are the Schottky barrier heights for the conduction band and valence band of silicon in the central part, respectively. qφ Bps and qφ Bpd are set to equal to be 0.2eV. Therefore, qφ Bns and qφ Bnd are both set to be high Schottky barrier, which can strongly prevent thermionic emission current flow from source/drain electrode into the conduction band of silicon, a much lower qφ Bncm is set to prevent the holes current from flowing through the central metal in the reversely biased state. Fig. 3 shows the comparisons of transfer characteristics of HLHSB-BTFET with different qφ Bncm s. It can be clearly seen that when qφ Bncm decreases, qφ Bpcm increases at the same time. Therefore, the central metal gradually enhances the inhibition of the thermionic emission current generated in the valence band, so the static leakage hole current gradually decreases with the decreasing of qφ Bncm . Unlike the HSB-BTFET, which requires a constant assistant gate operating in the forward bias state, once qφ Bncm is less than half the band gap width (about 0.5 eV), a good control effect on holes leakage current can be obtained. Fig. 4 shows the comparisons of holes current density of HLHSB-BTFET with different qφ Bncm s in silicon region. As the qφ Bncm is increasing, the qφ Bpcm is decreasing at the same time. The Schottky barrier formed between the valence band of the silicon regions and the central metal is decreasing and the central metal gradually loses the ability to block the holes from flowing through the valence band. Therefore, the holes current density is increasing with the increasing of qφ Bncm . s. It can be clearly seen that when V ds is low, HSB-BTFET and HLHSB-BTFET have relatively similar transfer characteristics. However, with the increase of V ds , HSB-BTFET begins to gradually lose its ability to control the holes leakage current. When V ds rises to 0.8 V, the HSB-BTFET can hardly be turned off. On the contrary, the HLHSB-BTFET proposed in this paper is almost unaffected by the change of V ds . Fig. 5(b) shows the relationship between SS and V gs of HLHSB-BTFET. Similar to other types of TFET, HLHSB-BTFET has obtained a lower subthreshold swing, which increases with the increase of gate voltage. In the entire subthreshold region, the average subthreshold swing of HLHSB-BTFET is 49mV/dec, which is lower than the subthreshold swing of MOSFET. Fig. 6(a) shows the Potential distribution of HLHSB-BTFET from source to drain under different V ds s, and Fig. 6(b) shows Potential distribution of HSB-BTFET from source to drain under different V ds s. It can be clearly seen that, for HLHSB-BTFET, when V ds increases, the potential of the central metal also increases, so the built-in potential difference formed in the semiconductor between the central metal and the drain electrode also does not change significantly. However, for HSB-BTFET, since the potential of the central silicon region is controlled by the assistant gate, when the assistant gate voltage is fixed to be a constant, The built-in potential difference inside the silicon near the drain side will decrease with the increase of V ds difference, which will cause the assistant gate to lose its blocking effect on the holes flow from the drain side to the source side, thus generating a large amount of leakage current for higher V ds s. Fig. 7(a) shows the distributions of Electrons and holes'concentration of HLHSB-BTFET in silicon between the source electrode and the drain electrode with different V ds s, and Fig. 7(b) shows the distributions of Electrons and holes' concentration of HSB-BTFET in silicon between the source electrode and the drain electrode with different V ds s. In Fig. 7(b), it can be seen that a path of holes is formed in HSB-BTFET due to lose of hole blocking ability of the assistant gate for a higher V ds. On the contrary, for HLHSB-BTFET, the electron concentration near the central metal is always much higher than the hole concentration, so the P-N-P carrier distribution is formed in the direction from the source to the drain. Since the PN junction on the source side is always in the reverse bias state for a forward biased V ds , this also explains the physical reason for the low static leakage current of HLHSB-BTFET from another perspective. A brief fabrication flow of the proposed HLHSB-BTFET is shown from Fig. 8(a)-(n). As shown in Fig. 8(a)-(c), prepare a SOI wafer, the bottom of the SOI wafer is the silicon substrate. The top of the SOI wafer is the silicon film. The buried oxide layer is sandwiched between them. Remove the central area of the silicon film through the photolithography and etching process, and then deposit the first kind of metal material through the deposition process. After flattening the surface, the central metal is formed. As shown in Fig. 8(d)-(f), remove some areas around the silicon film and the central metal area to expose the buried oxide layer by photolithography and etching process. As shown in Fig. 8(g)-(i), through the deposition process, the insulating dielectric material used to form the grid oxide layer is deposited. After flattening the surface of the insulating dielectric material to expose the silicon film, the part of the area around the insulating dielectric material is removed to expose the buried oxide layer through the photolithography and etching process to form the gate oxide. As shown in Fig. 8(j)-(l), through the deposition process, deposit metal or poly silicon, flatten the surface to expose the silicon film, then remove part of the metal or poly silicon area above and below through the photolithography and etching process. Deposit insulating dielectric materials through the deposition process, flatten the surface again to expose the silicon film, thereafter, the gate electrode and spacer layer are formed through the above steps. As shown in Fig. 8(m) and (n), through photolithography and etching process, part of the area of the silicon film on the left and right sides is etched to expose the buried oxide layer. Then the second kind of metal material is deposited through the deposition process, and then the surface is flattened to expose the silicon film, and the metal source/drain interchangeable regions are formed through the above steps.
Conclusions
In this work, a novel High-Low-High Schottky barrier bidirectional tunnel field effect transistor (HLHSB-BTFET) is proposed. Compared with previous technology which is named as High Schottky barrier BTFET (HSB-BTFET), the proposed HLHSB-BTFET requires only one gate electrode with independent power supply. Due to that there is no strong dependence between built-in barrier heights formed in the semiconductor region on the drain side of the central metal and the V ds , besides low Schottky barrier heights formed between the central metal and the conduction band of silicon regions on its both sides have been designed for preventing the carriers in valence band from flowing into the central metal induced by thermionic emission effect, thereafter, the proposed N type HLHSB-BTFET has a natural blocking effect on the carriers flowing in valence band, and this blocking effect does not degrade significantly with the increasing of V ds , which is a huge promotion from the previous technology. The principle of the proposed HLHSB-BTFET has been explained through analysis on energy band theory. The influence of Schottky barrier heights has been quantitatively analyzed. Once qφ Bncm is less than half the band gap width (about 0.5eV), a good control effect on holes leakage current can be obtained. And the holes current density in static state can be reduced to less than 10 − 5 A/cm 2 . The minimum SS is reduced to less than 25mV/dec, and the average SS in the entire subthreshold region is 49mV/dec. The physical mechanism that the proposed HLHSB-BTFET can better reduce static power consumption and reverse leakage hole current compared with HSB-BTFET is carefully analyzed by comparison of potential distributions and carrier concentration distributions. A brief fabrication flow of the proposed HLHSB-BTFET also has been given.
Author contribution statement
Xiaoshi Jin; Xi Liu: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.
Shouqiang Zhang: Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.
Mengmeng Li: Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.
Meng Li: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data. | 4,142.6 | 2023-02-01T00:00:00.000 | [
"Engineering"
] |
Amperometric Applications to The Assay of Some Biocompounds of Interest in Food and Clinical Analysis: An Editorial
Copyright: © 2016 Pisoschi AM. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Amperometric Applications to The Assay of Some Biocompounds of Interest in Food and Clinical Analysis: An Editorial Aurelia Magdalena Pisoschi*
At a constant potential value, the electron transfer occurs at elevated rate, so the processs becomes controlled by mass transfer. Hence, the intensity of the diffusion current Id depends on: the thickness of the diffusion layer δ, the diffusion coefficient of the analyte D, the number of electrons transferred n, the electrode surface A, the analyte concentration S, and the Faraday number F (96480 C mol −1 ), following the equation [2,3].
Id = nAFDS/δ
The measured current intensity generated by the electrochemical reaction is directly correlated to the analyte concentration [5,6].
Kumar and Narayan have investigated a method for vitamin C determination, relying on a cobalt hexacyanoferrate modified graphite electrode. The cobalt hexacyanoferrate modified electrode presented good electrocatalytic activity towards ascorbic acid oxidation with a linear response ranging from 5.52 × 10 −5 M to 3.23 × 10 −2 M, a detection limit of 3.33 × 10 −5 M and stable analytical response [7].
Lowering the applied potential in the case of amperometric assays based on anodic oxidation, is possible with the use of mediators, such as ferrocene derivatives [8], or redox couples (ferrocyanide/ferricyanide) [9]. The use of ferricyanide immobilized by sol-gel technique on a screen printed electrode, allows for ascorbic acid determination at +0.3 V vs Ag/AgCl, with linearity up to 300 μM, sensitivity and detection limit of 2.85 nA/μM and 46 nM, respectively, at a signal per noise ratio of 3 [9], whereas the use of unmodified Pt electrodes results in ascorbic acid detection at +0.6 V vs. Ag/AgCl, after separation on an anionexclusion column [10].
O'Connell et al. [11] developed a sensor for ascorbic acid assay in foodstuffs and pharmaceutical preparations, by aniline electropolymerization on the surface of glassy carbon or screen-printed electrodes. The low applied potential of 100 mV results in selective electro-oxidation of L-ascorbic acid minimizing interferences from commonly present electroactive species such as 4-acetamidophenol (paracetamol), uric acid and citric acid [11].
Modified amperometric sensors have also been applied to the assessment of several antioxidant compunds such as butylated hydroxyanisole, whose determination has been achieved at a silver ferricyanide-based graphite-wax composite electrode. The amperograms showed that the oxidation current increased linearly with butylated hydroxyanisole concentration within the range of 7.4 × 10 −6 to 8.3 × 10 −4 M. The limit of detection was 3.7 × 10 −6 M [12].
Amperometric biosensors rely on the oxidation/reduction of an electroactive compound generated/consumed in an enzyme reaction. The biocatalyst consumes/generates an electroactive species (such as O 2 or H 2 O 2 ) stoichiometrically related to the substrate concentration. Glucose, ascorbic acid, lactate, chlolesterol, oxalate, malate, can be determined by such biosensors relying on the corresponding oxidase as biocatalyst, and amperometric detection of either consumed molecular oxygen or generated hydrogen peroxide.
Various immobilization procedures involving electropolymerization and the use of mediators have been applied recently, aiming at optimizing the analytical parameters.
A glucose biosensor has been developed, by glucose oxidase entrapment in an electropolymerized polyaniline-polyvinylsulphonate film. Electropolymerization of aniline was performed on a Pt electrode, by applying a constant potential value, 0.75 V vs. Ag/AgCl. The prepared glucose biosensor preserved 80.6% of initial activity after 40 days when stored in phosphate buffer solution 0.1 M at 4°C [13].
In another study, an enzymatic biosensor was constructed, relying on TiO 2 nanotube arrays for transducing, and immobilization of both Prussian Blue and glucose oxidase. A very good sensitivity of the sensor was obtained, namely 248 mA M −1 cm −2 , and a detection limit of 3.2 μM [14].
A novel glucose biosensor integrated in a flow injection system was developed, by coating a tin oxide-modified screen printed electrode with glucose oxidase entrapped in Nafion. The amperometric response of the biosensor was linear up to 200 mg/L with a detection limit (3σ) of 6.8 mg/L. The relative standard deviation for the repeated measurements of 100 mg/L glucose was 2.9%. All tested interferent effects from electroactive compounds (uric acid, acetaminophen, xanthine, hypoxanthine and ascorbic acid) could be hampered by employing the standard addition method. This biosensor allowed for viable determination of glucose in human blood plasma [15]. L-ascorbic acid content was analysed in fruit juices and effervescent tablets, by a biosensor with ascorbate oxidase from Cucurbita sp., immobilized on a screen-printed carbon electrode, using poly(ethylene glycol) diglycidyl ether as a crosslinking agent. Bovine serum albumin was also immobilized on the surface of this electrode, due to the presence of poly(ethylene glycol) diglycidyl ether. The linear range of analytical response for L-asco rbic acid corresponded to 5 -150 μmol/L [16].
In Figure 1, a chronoamperogram obtained by the author of this
Page 2 of 2
Editorial is presented, illustrating an ascorbic acid amperometric signal, applying the conditions described at [6].
Amperometric sensitive assay of cholesterol and H 2 O 2 was possible with a biosensor based on a hybrid material derived from nanoscale Pt particles and graphene. Enzymes cholesterol oxidase and cholesterol esterase were used as biocatalysts [17].
The bienzyme amperometric electrode was characterized by a sensitivity respect to cholesterol ester of 2.07 + 0.1 µA/µM/cm 2 and a limit of detection of 0.2 µM. The assessed apparent Michaelis-Menten constant was 5 mM. The sensor's response was stable and was not affected by interferences from other common electroactive compounds [17].
A dimethylferrocene-modified linear poly(ethylenimine) hydrogel was employed to both immobilize and mediate electron transfer from lactate oxidase immobilized at a carbon anode, and an enzymatic cathode, aiming at amperometric lactate biosensing. The biosensor operated at 300 mV vs. SCE. When coupled with the bilirubin oxidasebased biocathode, the biosensor detected lactate between 0 mM and 5 mM, with a sensitivity of 45 ± 6 μA cm −2 mM −1 [18].
The analytical characteristics (sensitivity, low detection limit, specificity) and validation by numerous applications in various sample types (foodstuffs, biological media), confirm the viability of the amperometric chemical sensors and biosensors, for the assessment of key biocompounds. | 1,471.4 | 2016-12-07T00:00:00.000 | [
"Chemistry",
"Agricultural and Food Sciences"
] |
Rating the significance of the factors influencing shortage of skilled labours for sustainable construction: a perception of Makkah construction practitioner
The construction industry is one of the main economic sectors that contribute significantly to social and economic development. However, the lack of skilled labour is one of the factors affecting the success of projects in the construction industry. Skilled labours are the potential for enhancing overall delivery of construction projects to achieve sustainability. Building and construction industry in Makkah has continued to respond to shortages of skilled labours. Hence, the purpose of this study is to evaluate the importance of factors influencing the shortage of skilled labours from the perceptions of construction practitioners. In order to achieve the aims and objectives of this study, the quantitative descriptive method was adopted for this research study, and data for this study was collected through a stratified random sample of construction practitioners in Makkah. Data were first analysed by way of Analysis of Variance (ANOVA), followed by Relative Importance Index (RII) for ranking comparison among the construction practitioners. The results of RII indicates that the significant factors influencing the shortage of skilled labours in Makkah include delay in salary payment, low wages structure, lack of motivation system, health and safety issues on construction sites, lack of job security, bad relations between skilled labours and management team, poor living conditions, Slow recruitment of skilled labours, restricted government regulations, and exposure of climate conditions. The findings revealed that the factors rated are required for organisational performances which are essential in achieving sustainable building and construction in Makkah.
Introduction
Sustainable construction highlights the need to balance economic, social and environmental goals to address human needs [1,26]. This is due to the fact that construction industry is dynamic and contributes to production of most amenities which is necessary to improve the living standard of any nation [8]. Though, the performance of construction industry to achieve sustainable development depends on its human resources [25,39]. This includes professionals, skilled and unskilled labour [12]. In most countries, these human resources cover 30-50% of the overall project cost, depending on the nature of the project [32]. Kazaz and Acikara [23] and Kumarasinghe and Hadiwattege, [24] emphasised that skilled labour has the biggest impact on construction operations through the task performed. The task performed can be tied to particular skills possessed. These skills trades involve masonry, joinery/ carpentry, tiling, fabrication, plant/equipment operator, thermal/acoustic insulation, electrical services, plumbing and painting. Though, various studies have reported that there is a shortage of skilled labour in Makkah construction industry [4,5]. This poses concern as Saudis' vision 2030 will necessitate increased provision of infrastructure to attain sustainable development. In view of this concern, factors influencing skilled labours have been perceived to characterised issues regarding skilled labours supply to the construction industry. From the review, it was informed that previous studies on factors influencing shortage of skilled labour appear to consider the view from project managers and construction managers [4]. It is in this light, this study takes into account building and construction practitioners' (architects, civil engineers, mechanical engineers, electrical engineers and building engineers) perceptions to be expedited to align with the Makkah construction industry.
Literature review
The few studies that have focused on the need for more varied supply of skilled labour in the construction industry, and have proposed a comprehensive and concise list of factors that match those found in the construction industry of developing countries [4,9,19,23,27,28,40,[44][45][46]. These factors offer an opportunity to understand the shortage of skilled labour in the construction industry. For example, workers poor living condition induced an unemployed skilled worker to look for opportunities in other industries, and when the economy recovers the skilled workers are reluctant to return to the construction labour market, thus influencing shortage of skilled workers [4]. Also, lack of job security has been considered a major factor influencing shortage of skilled workers where skilled workers enjoy little or no social protection [28]. For instance, during the bumper periods, construction industries enjoyed an influx of capital leading to a builder's market with abundant construction work than during economic recession which leads to retrenchment of skilled workers, and when the economy recover the skilled workers are reluctant to return to the construction labour market, thus influencing shortage of skilled labour [44]. Besides, in most developing countries, there is no regulation guiding minimum wage for construction workers. Bilau et al. [9] put forward that different wages are paid in across construction organisations which can be erratic depending on construction projects. In addition, skilled workers potential is boundless but it requires motivation in order to excel [46]. Ironically, the majority of construction firms in developing countries do not motivate their skilled workforce for improved performance and productivity [19,40]. Besides that, poor working conditions of skilled workers have influenced shortage of skilled workers where they have no clear career paths, thus, enjoy no promotion opportunities [45]. Thus, skilled workers seek permanent jobs with clear career paths of promotion opportunities and insurance protection as a guarantee to their life for the future [41,43].
Construction has been one of the most hazardous industries as measured by the number of accidents. Thus, it is being labelled as 3D images of dirty, dangerous and difficult. Similarly, the poor image of the construction industry has unfavourably affected popularity as a career choice [38]. The image is low among workers themselves as the majority of construction skilled workers of various ages and experience would never recommend their trade to their children [37]. Other factors influencing shortage of skilled labour identified by previous researchers include long working hours for more than ten working hours per day, delay in salary payment, restricted government regulations, exposure of climate conditions, long distance from family, bad relations between skilled labours and management team, poor site accommodation, geographic location of the project, slow recruitment of skilled labour and frequent changes of skill requirement [4,23,40,46].
Nature of construction industry in Makkah and type of involved labours
Saudi Arabia's construction market is thought to be the largest and fastest-growing in the Gulf [6]. The country is worldly wide known by the biggest oil exporter resulting in increased government investment on infrastructure and construction projects. Because of this, the Saudi economy is constantly expanding, and activity in the construction sector in particular has increased significantly. According to Alrashed et al. [6], the Saudi Arabian government has allotted a significant amount of money for the building sector. Construction is presently valued at USD 600 billion till 2020, according to Saudi Arabia's Industrial Sector Overview Report from 2016. Due to Saudi Arabia's among the fastest-growing populations, there is a greater need for housing, healthcare, education, and other services. The construction industry, on the other hand, is experiencing financial difficulties as a result of rising military spending, a drop in oil prices, the country's deteriorating financial situation, youth unemployment, and declining financial reserves. Additionally, the Saudi Arabian construction industry is plagued by persistent issues such cost overruns, delays, poor safety, and low quality [13,34,35].
(If a construction project is finished on time, under budget, and with good quality, it can be deemed successful [30]. The construction sector in Makkah was subjected to a modeling investigation by Rahman et al. [34] that J. Umm Al-Qura Univ. Eng.Archit. (2023) 14:13-25 | https://doi.org/10.1007/s43995-023-00013-5 Original Article 1 3 examined 37 delay factors. Findings showed that the most important group to influence construction projects in Makkah, Saudi Arabia, was the client and consultant group. Additionally, Saudi Arabian construction workers face a number of problems, including low productivity, a lack of competent labour, bad living conditions, low compensation, delayed salary payments, inadequate transportation, injuries, and fatalities [7]. Consequently, the performance of the entire project is impacted by these issues, both directly and indirectly. Only foreign male employees make up the majority of Saudi Arabia's construction labourers [7]. They are either immigrants who were born domestic workers or foreign employees. These construction site personnel are divided into three groups based on their talents and academic level. Unskilled site workers make up the lowest class, semi-skilled site workers make up the middle class, and skilled site workers make up the highest class [7]. Therefore, all three of these worker types are necessary for the construction sector, but competent workers are preferred because they can complete construction duties correctly.
Saudi construction industry prefers foreign workers due to the high cost of Saudi labour, negative social and cultural perceptions and attitudes toward manual and low status jobs, the fact that expatriates are more disciplined than Saudis, the fact that Saudis have less English language proficiency and technical skills than expatriates, and finally the fact that Saudi employees are less willing to relocate for work. As a result, employers favor hiring international labour for their companies over local ones. According to the 2016 Saudi Arabia Manpower and Employment Report, 4.4 million expatriates are employed in the construction sector overall [3].
The most lucrative industries for this development are those related to real estate, infrastructure, hospitality, and retail. In Makkah, there is a huge demand for hotel rooms, particularly during the holy months of Ramadan and Hajj. During these times of year, the hotels are typically completely booked. Despite the presence of various hotels, Makkah city still lacks enough rooms to house tourists. According to Mostafa and Al-Buzz [29], more than USD100 billion has been set aside for the building projects being undertaken in Makkah city, including the construction of the Grand Mosque (Al-Haram mosque), in an effort to enhance the services provided to the millions of pilgrims who travel there to perform the Hajj and Umrah.
Elawi estimates that in just six years, the government has spent around USD10.6 billion on the expansion of the Holy Mosque (2010-2015). The Abraj Kudai, Abraj Al-Bait (Makkah Royal Clock Tower Hotel), the extension of the Grand Mosque, the Jabal Omar development project, King Abdul Aziz Road (KAAR), and other large and wellknown projects are available and presently being built in Makkah City. These construction projects encountered failures in terms of timely, affordable, high-quality, and safety-required completion. As a result, the focus of this essay is on the reasons why large-scale construction projects in Makkah, Saudi Arabia, fail [4].
In an attempt to provide a significant overview and evaluating the factors influencing shortage of skilled labours for sustainable building and construction in Makkah, the studies have identified seventeen factors influencing shortage of skilled labour from international studies, as presented in Table 1. It implies that understanding of this aspect is important as it is perceived to characterised issues regarding shortage of skilled labour to the building and construction industry. Hence, it is this gap of determining the significant factors influencing shortage of skilled labour that is the main stimulus for this study.
Research methodology
Research methodology is considered as a systematic approach, which mainly focused on finding answers for all research inquiries and to produce effective results of a specific study [11]. In order to achieve the aims and objectives of this study, descriptive methodology with a systematic approach is adopted to provide a full background with the most knowledge that explains the phenomenon (perceptions of perceptions of Makkah construction practitioners in rating the significance of the factors influencing shortage of skilled labours for sustainable construction), then it will be circulated after the confirmation of results in order to lead the researcher to obtain the targeted results [11].
Study tool
The current study aims to evaluate the significance of the factors influencing shortage of skilled labours for sustainable construction: A perceptions of Makkah construction practitioners, therefore, the questionnaire was used as a tool for this study, with reference to a group of literature and previous studies, to measure the variables arising from the hypotheses and model of the study and to answer their questions. The questionnaire is the most widely used scientific research tool, and it is considered one of the best means of collecting information about the study community and for its relevance to the nature of this study in terms of effort and capabilities and the spread of the study community members in far and different places.
Questionnaire
The data required for this study was gathered through the use of questionnaire survey before carrying out quantitative analysis. Using survey method, questionnaires were administered on respondents with adequate knowledge of the skills shortage issue in the construction industry, these include engineers, architects, civil engineers, mechanical engineers, electrical engineers, and building engineers in region of Makkah in Saudi Arabia.
The main reason for selecting these building and construction practitioners is due to their experience in construction activities, they are believed to have better insight and provide better evaluation and necessary information about the significance of the factors influencing shortage of skilled labours for sustainable construction and how to alleviate skills shortage in the construction industry. The questionnaire survey were centered on literature reviews which revealed seventeen (17) factors influencing shortage of skilled labour by previous researchers in the last 10 years as shown in Table 1. Consequently, a pre-test study was carried out in the form of expert validation to ensure clarity of the questions and ease of completing the questionnaire. Five (5) academics having a strong background of construction participated in this process and suggested a few changes to the questionnaire regarding the wordings of the questions. The questionnaire was then modified based on the feedback before it was finally used at the data collection stage. The questionnaire consists of respondents' personal particulars such as membership of professional bodies, years of experience, academic qualification, and numbers of projects executed (Section A). In section B, each respondent was asked to rate the factors influencing shortage of skilled labour on a five-point Likert scale ranging from 1 to 5, where 1 represents "Insignificant" and 5 represents "Very significant".
Sample structure
The population of this study were targeted on 400 drawn building and construction experts with more than 15 years of experience in the Makkah construction industry. This was intended to gather data from respondents who are considered more experienced and hold a higher position in their respective construction organisations. This is in agreement with Pradhan and Jena, [33] who emphasised that more in-depth understanding can be gathered from highly experienced experts. Consequently, a list of experts was compiled through the building and construction experts' directory. This made the task of finding each expert achievable. Thus, the questionnaire was delivered to the experts through email to ensure easy targeting specific segments from the population. Table 2 shows a summary of the sample responses of the questionnaire survey. From Table 2, the response rates were 112(74.67%), 78(78.0%), 33(66.0%), 27(54.0%) and 27(54.0%) for architects, civil engineers, mechanical engineers, electrical engineers and building engineers respectively. This was considered adequate for analysis based on the assertion by Spillane et al. [42] that the result of a survey could be measured as biased if the return rate was lower than 30%.
Sample size of this study was determined using the following formula [15]. Zaki et al. [45] where SS = Sample Size, Z = Z value (1.96 for 95% confidence level), P = percentage picking a choice, expressed as a decimal (0.5 used for sample size needed), C = margin of error (9%).
Data analysis and discussion
The data was analyzed using the following methods;
Analysis of variance (ANOVA)
The mean scores of the responses from the building and construction experts were analysed using ANOVA to test whether the responses from the five groups of experts could be treated equally. This will provide the basis which supports the opinions of the building and construction experts. The parameters used in the ANOVA are the level of significance (5%) in the mean scores of each variables and the strength of the difference otherwise called effect size for making decisions. A test result in the mean scores of each variable shows a statistically significant difference where the p-value is less than 5% level of significance. This determines the decision rule for accepting or rejecting a hypothesis. While, the effect size was calculated using the Eta squared, which classify the effects as small (0.01), medium (0.06) and large (0.14). Accordingly, the results in Table 4 revealed that there was no significant difference at p > 0.005 in the mean scores for health and safety issues on construction sites, lack of motivation system, poor living conditions, delay in salary payment, restricted government regulations, exposure of climate conditions, bad relations between skilled workers and management team, poor site accommodation, slow recruitment of skilled workers, lack of job security, and frequent changes in skill requirement. The strength of the mean difference was also small, based on the effect size calculated using Eta squared. However, there was a significant difference at p < 0.005 in the mean scores for long working hours for more than ten working hours per day, low wages structure, long distance from ss = Z 2 * P(1 − P)∕C 2 family, poor image of construction jobs, geographic location of the project and poor working conditions. Despite the significance difference, the strength of the mean difference ranges from quite small to medium with a value of 0.07, 0.06, 0.04, 0.08, 0.04 and 0.09 respectively based on the effect size calculated. Overall, this implies that the means did not differ among the building and construction experts on the factors influencing shortage of skilled workers in Makkah construction industry. Thus, the null hypothesis was accepted (Table 3).
Relative importance index (RII)
The data was analyzed using the Relative Importance Index (RII) comparison of ranking among building and construction experts. The values of the RII range from 0 to 1. The closer the RII to 1, the more important. Thus, the output of each variable was rated on the basis of the importance weighting or the proposed efficiency of each variable. Hence, the item with the highest index is the first in the rank order. This type of scale has been found acceptable in several construction management studies as an excellent approach for gathering the respondent's responses of the variables rated on an ordinal scale [14,16,22,36]. From the responses, RII was calculated using the following formula, where n1 represents the number of respondents who answered 'very significance' , n2 represents the number of respondents who answered 'significance' , n3 represents the number of respondents who answered 'moderately significance' , n4 represents the number of respondents who answered 'less significance' , n5 represents the number of respondents who answered 'insignificance' . Table 4 shows that RII values from architects ranged between 0.893 and 0.657. While, Table 5 shows civil engineers RII values ranged between 0.0.931 and 0.615. Also, Table 6 shows mechanical engineers RII values ranged between 0.872 and 0.672. Table 7 revealed that RII = 5n 1 + 4n 2 + 3n 3 + 2n 4 + n 5 5 n 1 + n 2 + n 3 + n 4 + n 5 While, low wages structure was ranked as topmost by electrical engineers, mechanical engineers and building engineers, second by architects and civil engineers. Also, lack of motivation system was ranked third by architects, civil engineers, mechanical engineers and building engineers, and second by electrical engineers. Lack of job security was ranked third by mechanical engineers and building engineers, fourth by architects and electrical engineers, and fifth by civil engineers. Besides, health and safety issues on construction sites was ranked third by the mechanical engineers and building engineers, fourth by civil engineers, and fifth by architects and electrical engineers. Though, poor image of construction jobs was ranked fourteenth by architects, fifteenth by mechanical engineers, sixteenth by building engineers, and seventeenth by civil engineers and electrical engineers. Consequently, the ranking of each building and construction experts were evaluated to have a general agreement. This is in line with the suggestion by Chan and Kumaraswamy, [10] as more convenient for grouping the respondents' ranking of the variables. Following Table 9.
Hence, the mean of RII values was derived by dividing the sum of RII values by the total number of factors to establish the level of agreement. This is expressed below mathematically; Mean;13.443 = 0.791 In relation to the level of agreement presented above, Akadiri, [2] marks the cut-off points for interpretation as follows,Strongly disagree (SD)-below the mean but less than 0.10; Disagree (D)-below the mean but greater than 0.10; Moderately agree (MA)-equal to the mean; Agree (A)-above the mean but less than 0.80, and Strongly agree (SA)-above the mean but greater than 0.80). Therefore, the results which fall in the first and second levels should be dismissed, as it does not represent the mean perception of the building and construction experts. Accordingly, from the above mentioned levels, RII values of factors such as; poor image of construction jobs (0.659), long distance from family (0.659), poor working conditions (0.712), geographic location of the project (0.720), poor site accommodation (0.733), long working hours for more than ten working hours per day (0.753), frequent changes in skill requirement (0.770), were below the mean, and their level of agreement ranges from strongly disagree to disagree. The results showed that there are many common factors among the respondents regarding the factors affecting the shortage of skilled labour in construction projects, such as health and safety issues, low wages structure, delay in salary payment at construction sites and other mentioned factors. But there are some factors that are important to some and unimportant to some of the respondents, such as long distance from family, poor site accommodation and other factors. It is expected that the reasons for the differences between the respondents are that these factors vary in importance according to the job and location of each of the respondents in construction projects.
Based on the outcome, ten factors; delay in salary payment (0.908), low wages structure (0.906), lack of motivation system (0.869), health and safety issues on construction sites (0.853), lack of job security (0.851), bad relations between skilled labour and management team (0.827), Poor living conditions (0.823), Slow recruitment of skilled labour (0.808), restricted government regulations (0.797), and exposure of climate conditions (0.975) are above the mean, the results which was considered by evaluation as the factors influencing shortage of skilled labour in Makkah construction industry. Accordingly, the findings shows delay in salary payment as a significant factor influencing shortage of skilled labour. Al-Emad and Rahman, [4] and Sangole and Ranit, [40] have identified poorly saving of part of advance payment from the beginning of the project and lack of efficient control in the project resources as the causes of delay in salary payment. Low wages structure was also revealed as a significant factor influencing shortage of skilled labour. The result is in agreement with Ismail and Yuliyusman, [21] who affirmed that low wages prompt construction skilled workers to pursue another career for better remuneration. Lack of motivation of skilled labour was also revealed as a significant factor, which may be due to absence clear career paths and promotion opportunities. Norhidayah et al. [31] acknowledged that the issue of motivation prompt construction skilled labour to pursue other careers for better incentives. The result shows health and safety issues on construction sites as a significant factor influencing shortage of skilled labour. This may be due to existence of dirty, difficult and dangerous image that has always been associated with this industry has indirectly discouraged many workforces from staying away from entering the construction industry [17,18]. This finding agrees with the contention of Al-Emad and Rahman [4] asserting that most of construction projects in Makkah are high rise projects and workers are not well train to strictly follow the safety requirements to avoid injuries and fatalities at sites. Based on the outcome, ten factors; delay in salary payment (0.908), low wages structure (0.906), lack of motivation system (0.869), health and safety issues on construction sites (0.853), lack of job security (0.851), bad relations between skilled labour and management team (0.827), Poor living conditions (0.823), Slow recruitment of skilled labour (0.808), restricted government regulations (0.797), and exposure of climate conditions (0.975) are above the mean, the results which was considered by evaluation as the factors influencing shortage of skilled labour in Makkah construction industry. Accordingly, the findings shows delay in salary payment as a significant factor influencing shortage of skilled labour. Al-Emad and Rahman, [4] and Sangole and Ranit, [40] have identified poorly saving of part of advance payment from the beginning of the project and lack of efficient control in the project resources as the causes of delay in salary payment. Low wages structure was also revealed as a significant factor influencing shortage of skilled labour. The result is in agreement with Ismail and Yuliyusman, [21] who affirmed that low wages prompt construction skilled workers to pursue another career for better remuneration. Lack of motivation of skilled labour was also revealed as a significant factor, which may be due to absence clear career paths and promotion opportunities. Norhidayah et al. [31] acknowledged that the issue of motivation prompt construction skilled labour to pursue other careers for better incentives. The result shows health and safety issues on construction sites as a significant factor influencing shortage of skilled labour. This may be due to existence of dirty, difficult and dangerous image that has always been associated with this industry has indirectly discouraged many workforces from staying away from entering the construction industry [17,18]. This finding agrees with the contention of Al-Emad and Rahman [4] asserting that most of construction projects in Makkah are high rise projects and workers are not well train to strictly follow the safety requirements to avoid injuries and fatalities at sites.
Government policies on construction organisations is also one of the significant factors influencing shortage of skilled labour. This is in agreement with Al-Emad and Rahman [4] found that government restrictions to skilled non-Muslim foreign workers, restriction to form union/ association and unfriendly authoritative rules has pushed many skilled labour away from the industry. This policy is a significant source of concern as skilled labour are of the view that their skills are not appreciated by the government. The result also shows exposure of climate conditions as a significant factor influencing shortage of skilled labour in Makkah construction industry. Hearly et al. [20] acknowledged that this issue is rooted in skilled labour working without requisite Personal Protective Equipment (PPE) and protection required despite prolonged exposure to climate conditions.
Conclusion and recommendations
The city of Makkah's construction sector has a variety of difficulties that make it difficult to complete projects successfully. The city of Makkah's construction sector has a variety of difficulties that make it difficult to complete projects successfully. The stakeholders in the construction industry need to pay greater attention to this in order to boost performance and guarantee the completion of construction projects. The importance evaluating the issues affecting the shortage of skilled labours for sustainable building has been identified in this article. The results of this study will assist researchers better understand the complex problems affecting construction projects in Makkah city as well as the construction community in Makkah and throughout Saudi Arabia. The problem of skill shortage in the construction industry has affected the quality and productivity of construction projects over the years in Makkah. The boom in customer demand for projects is putting pressure on the construction industry and the industry is struggling to meet the increasing demand for its services. Rework is another effect of the adoption of unskilled labour force in the construction industry, due to poor quality of work and low productivity. Lack of skills can lead to rework as a result of inadequate supervisors, ratios of supervisors, low level of skilled workers, unclear instructions from supervisors, non-compliance with set specifications, and poor coordination of resources. Foundation failure, which is common in start-up construction companies, is another consequence of skills shortages.
The stakeholders in the construction industry need to pay greater attention to this in order to boost performance and guarantee the completion of construction projects. Evaluation of the issues affecting the shortage of skilled laboures for sustainable building has been identified in this article. The results of this study will assist researchers better understand the complex problems affecting construction projects in Makkah city as well as the construction community in Makkah and throughout Saudi Arabia.
This study presented the significant factors influencing shortage of skilled labours in Makkah construction industry from building and construction practitioners perceptive that have been working for more than 1 3 15 years in Makkah. Although, some of the significant factors follow the same trend as those found by other researches on the literature, new attributes are emerged in this study including delay in salary payment; low wages structure; and lack of motivation system. This is aligned with the perceived low productivity of construction sector in Makkah.
This study offers some recommendations for professionals and academics researching skilled labuors in the construction sector. The first implication is that the performance of building projects may be impacted by a lack of skilled labour. The performance of the construction project has been favourably impacted by the skilled labour scarcity, as evidenced by the findings of our proposed framework. This study backs up the assertion that having access to trained labour is crucial to the success of building projects. The second implication is that human capital and project-related factors might have an impact on the availability of skilled workers in the construction sector. Both causes are equally capable of contributing to a lack of trained workers in the construction sector. Therefore, while developing qualified human resources for the construction business, these issues should receive careful consideration.
Accordingly, the following recommendations were proposed to minimize shortage of skilled labours in construction projects of Makkah: 1. Construction organisations should resolve delay in salary payment by efficiently controlling project resources. 2. Construction organisations should secure sufficient cash flow throughout the project lifecycle. 3. Wages should be distributed fairly based on skills and experience and not based on nationality. 4. Construction organisations should resolve motivation issues by giving some incentives, reward or bonus to workers, and also to allow overtime motivation to improve workers financial status. 5. The contractor must manage its finances and cash flow planning by utilizing phasing effectively and efficiently to avoid any disruption to the project's financial progress. 6. The customer must make an advance payment to the contractor on time to enable the contractor to finance the project. 7. An appropriate coordination system should be established between the project parties to increase project performance. 8. The contractor should establish a dedicated team to plan and monitor the progress of the work on a daily basis and the pending issues.
9. It is essential to establish appropriate formal communication channels between all parties to the project throughout the project life cycle. 10. The contractor must employ the competencies and professionalism of his organization in order to prevent the project from facing construction delays. 11. The contractor must employ a sufficient number of skilled workers and motivate them to improve the productivity of construction activities. 12. The production of the design documents must be done on time, otherwise the delay in the production of the design document will affect the slow implementation of the site.
Consent for publication
The participants has been asked and they all agreed in the statement "I understand the general purposes, risks and methods of this research. I consent to participate in the research project and the following has been explained to me: the research may not be of direct benefit to me. My participation is completely voluntary. " Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. | 7,541.8 | 2023-01-20T00:00:00.000 | [
"Business",
"Economics"
] |
Local Integral Estimates for Quasilinear Equations with Measure Data
Local integral estimates as well as local nonexistence results for a class of quasilinear equations −Δp u = σP(u) + ω for p > 1 and Hessian equations F k[−u] = σP(u) + ω were established, where σ is a nonnegative locally integrable function or, more generally, a locally finite measure, ω is a positive Radon measure, and P(u) ~ expαu β with α > 0 and β ≥ 1 or P(u) = u p−1.
Introduction and Main Results
Let Ω ⊂ R be a bounded domain, let be a nonnegative locally integrable function or, more generally, a locally finite measure on Ω, and let be a nonnegative Borel measure. In this paper, we consider the following nonlinear partial differential equations with measure data: where ( ) = ℓ, , ( ) ∈ 1 ,loc (Ω) is defined, following [1,2], as ℓ, , ( ) = ℓ ( ) (2) and ℓ-truncated exponential function ℓ ( ) is given by Here, Δ is the -Laplacian of defined by Δ fl div(| | −2 ) ( > 1). For convenience, here and elsewhere in the paper, we assume that ℓ > − 1. We will understand (1) in the following potential-theoretic sense using -superharmonic functions (see Section 2).
Recently, Quoc-Hung and Véron [2] obtained twosided estimates on the solutions in terms of -truncated -fractional maximal potential of , which is suitable for dealing with exponential nonlinearities: The Scientific World Journal Some analogous estimates for Hessian equations also are given in this paper. In this paper, firstly, we will establish a priori estimates of (1) with exponential reaction ℓ, , ( ), defined by (2) and (3). One of our main results are the following theorems. (1) in Ω with > 1 and ℓ > − 1. Suppose that 4 ( 0 ) ⊂ Ω. Then, there exists a constant = ( , , , , ℓ) such that for all 0 < ≤ /4.
As a consequence of Theorem 1, we have the following nonexistence results of local solutions to quasilinear equations.
Theorem 2.
Let be a solution of (1) in an open connected set Ω ⊂ R . Suppose that 1 < < , = | | − with > , and 0 ∈ Ω. Then, ≡ 0. Now, we consider (1) with natural growth terms; that is, the ( ) term in (1) is replaced by −1 . It is worthwhile to point out that this problem turns out to be more complex than the supercritical case. The interaction between the differential operator −Δ and the lower order term was investigated by Jaye and Verbitsky [6,7].
Similarly, we have the following.
The plan of the paper is as follows. In Section 2, we collect some elements notions and potential estimates forsuperharmonic. Theorems 1 and 2 will be proved in Section 3. In this section, we also discuss the relations of and provided that there exist solutions of (1). After this, Section 4 presents the proof of Theorems 3 and 4 by a new iteration scheme. Section 5 is devoted to considering fully nonlinear analogues of the Dirichlet problem (1) for Hessian equations without proof.
Preliminaries
In this section, we first recall some notations and definitions. In the following, we denote by a general constant, possibly varying from line to line, to indicate a dependence of on the real parameters , , , , ℓ; we will write = ( , , , , ℓ). We also denote by ( 0 , ) = { ∈ R : | − 0 | < } the open ball with center 0 and radius > 0; when it is not important or clear from the context, we shall omit denoting the center as = ( 0 , ).
For > 0, > 1, such that < , the -truncated Wolff 's potential W 1, ( ) of a nonnegative Borel measure on R is defined by We also denote by W 1, [ ]( ) the ∞-truncated Wolff 's potential.
In this paper, all solutions are understood in the potentialtheoretic sense. A lower semicontinuous function : Ω → (−∞, +∞] is called -superharmonic if is not identically infinite in each component of Ω, and if for all open sets such that ⊂ Ω, and all functions ℎ ∈ ( ), -harmonic in , the implication holds: ℎ ≤ on implies ℎ ≤ in . Note that -superharmonic function does not necessarily The Scientific World Journal 3 belong to 1, loc (Ω), but its truncation ( ) = min{ , } does for every integer ; therefore, we will need the generalized gradient of a -superharmonic function defined by = lim →∞ ∇( ( )). For more properties of -superharmonic, see [12].
The following lower pointwise estimates for superharmonic functions play an important role in our estimate.
The following lemma was also proved in [13].
The following theorem is an analogue of the above theorems for -Hessian equations. For more details, see [14].
Proof of Theorems 1 and 2
In this section, we will give the proof of our main theorem. Firstly, we prove the following integral estimate for solutions of quasilinear equations (1), which shows that if (1) has a nontrivial -superharmonic supersolution, then is absolutely continuous with respect to . The fact can be used to obtain a characterization of removable singularities for the homogeneous quasilinear equation: for all balls such that 2 ⊂ Ω.
Proof. Define Integrating both sides of (21) against over , we find which combined with (20) implies that This inequality is equivalent to which, together with (20), leads to (19).
Proof of Theorem 1. For fixed 0 ∈ Ω, let > 0 be such that 4 The Scientific World Journal view of the lower pointwise potential estimate (14), we find that, for all ∈ ( 0 ), where depends on , .
Restrict the integration on ( 0 ) and let = ; thus, taking into account (25), we obtain in view of which combined with (26) leads to the fact that, for all ∈ ( 0 ), where ( 0 , ) is defined as Thus, taking into account (26) and (28) and arguing by induction, we find where M is a nonlinear integral operator defined by M = W 1, ( ℓ ). The iterates of M are denoted by M = M(M −1 ). It is then easy to see from Proposition 8 that, for all > 0, where ( ) appears in Proposition 8. Consequently, and this yields lim sup Here, we use the fact that In the following, we will divide the proof into two cases.
The proof of inequality (8) is completely similarly and more details are omitted.
Proof of Theorems 3 and 4
In this section, we will prove Theorem 3. It is interesting to note that, in order to prove this theorem, we should give a new iterative process.
The Scientific World Journal 5 Proof of Theorem 3. This proof will be divided into two parts according to the value of .
Case 1 (1 < ≤ 2). For nonnegative measurable functions , define
Obviously, N is a homogeneous superlinear operator acting on nonnegative functions. Assume that is a solution of (1); then, for all ∈ ( 0 ), where depends on , . Iterating (39) times yields Here, we use the fact that N is a homogeneous superlinear operator and th iterate of N is defined by N ( ) = N(N −1 ( )) for > 1.
In the following, we will estimate the iterates of N. Recall = ( 0 ) ; thus, in view of where ( 0 , ) is defined in (29). Consequently, for all ∈ ( 0 ), where ( ) appears in Proposition 8. Obviously, Here, the following fact has been used in this inequality: Note that here is arbitrary; this fact, together with (40) and (44), leads to which, combined with (36), leads to (9) provided that ( 0 ) < ∞. In a similar way, we can prove (9) if ( 0 ) = ∞; more details are omitted.
Case 2 ( > 2). A point worth emphasizing is that the operator N defined by (38) does not fall within this framework since it is not a superlinear operator. Therefore, define In this case, we have Thus, by Minkowski's inequality, where depends on , . It is clear that where 0 , ( 0 , ) appears in (29). Using (49) and (50), we find The Scientific World Journal Therefore, By reverse Hölder inequality, we get The following proof is similar to that of (46), so it is clear. This finishes the proof of Theorem 3.
The proof of Theorem 4 is standard and will be omitted.
A Fully Nonlinear Analogue: The -Hessian
We now move to -Hessian operator and present fully nonlinear counterparts of the results obtained in the previous theorems. More precisely, consider fully nonlinear -Hessian operator , introduced by Trudinger and Wang [15][16][17]: where [ ] denotes the -Hessian ( = 1, 2, . . . , ), The proof of the following theorems is completely analogous to that of (1). One only needs to use Propositions 9 and 10 in place of Propositions 6 and 7, respectively, and argue as in Sections 3 and 4 with W 2 /( +1), +1 in place of W 1, . Therefore, the proof is omitted. | 2,207.8 | 2016-05-17T00:00:00.000 | [
"Mathematics"
] |
Jupiter's Multi‐Year Cycles of Temperature and Aerosol Variability From Ground‐Based Mid‐Infrared Imaging
We use a long‐term record of ground‐based mid‐infrared (7.9–24.5 μm) observations, captured between 1984 and late 2019 from 3‐m and 8‐m class observatories (mainly NASA's Infrared Telescope Facility, ESO's Very Large Telescope, and the Subaru Telescope), to characterize the long‐term, multi‐decade variability of the thermal and aerosol structure in Jupiter's atmosphere. In this study, spectral cubes assembled from images in multiple filters are inverted to provide estimations of stratospheric and tropospheric temperatures and tropospheric aerosol opacity. We find evidence of non‐seasonal and quasi‐seasonal variations of the stratospheric temperatures at 10 mbar, with a permanent hemispherical asymmetry at mid‐latitudes, where the northern mid‐latitudes are overall warmer than southern mid‐latitudes. A correlation analysis between stratospheric and tropospheric temperature variations reveals a moderate anticorrelation between the 10‐mbar and 330‐mbar temperatures at the equator, revealing that upper‐tropospheric equatorial temperatures are coupled to Jupiter’s Equatorial Stratospheric Oscillation. The North and South Equatorial Belts show temporal variability in their aerosol opacity and tropospheric temperatures that are in approximate antiphase with one another, with moderate negative correlations in the North Equatorial Belt and South Equatorial Belt changes between conjugate latitudes at 10°–16°. This long‐term anticorrelation between belts separated by ∼15° is still not understood. Finally we characterize the lag between thermal and aerosol opacity changes at a number of latitudes, finding that aerosol variations tend to lag after thermal variations by around 6 months at multiple latitudes.
Plain Language Summary Jupiter's atmosphere displays a wide variety of perturbations in its temperatures, clouds and aerosols.In this study, we use a large set of ground-based observations captured in the mid-infrared between 1984 and 2019 to characterize long-term changes in the temperatures and aerosols.This long-term analysis show a number of cyclic disturbances, and allows us to distinguish between seasonal and non-seasonal changes in Jupiter's atmosphere.In particular, we observe that the northern mid-latitudes above 30° are continuously warmer than their counterpart latitudes in the south at 10 mbar pressure level (the stratosphere), potentially due to differences in the polar haze in Jupiter, which extends to lower latitudes in the north compared to the south.Additionally, our study reveals for the first time that the thermal oscillation present in Jupiter's equatorial stratosphere at the 10-mbar pressure level (known as Jupiter's Equatorial Stratospheric Oscillation) is also observed to descend to higher pressures (330 mbar), meaning that it is not confined to the stratosphere.Finally, we also discuss the lag between temperature and aerosol changes at diverse latitudes to try to identify the mechanisms responsible for the different atmospheric disturbances observed on Jupiter.
• Ground-based multi-wavelength images are used to compute stratospheric and tropospheric temperature and tropospheric aerosol opacity maps • Results reveal that upper-tropospheric equatorial temperatures are coupled to Jupiter's Equatorial Stratospheric Oscillation • Stratospheric temperatures at 10 mbar show a permanent hemispherical asymmetry at mid-latitudes, with northern mid-latitudes overall warmer than their counterparts
Supporting Information:
Supporting Information may be found in the online version of this article.
Ground-based 5-μm time-series spanning 34 years, sensing thermal emission from the 2-7 bar region modulated by overlying clouds (Bjoraker et al., 2015;Giles et al., 2015), enabled the discovery of a rare cyclic disturbance in Jupiter's Equatorial Zone (EZ,±7° latitude).A comparison between the periodic brightening found in the EZ at 5 μm and long-term changes at visible wavelengths (Antuñano et al., 2018), showed that these disturbances occur contemporaneously with (a) visible reddening events of the EZ (Rogers, 1995), (b) a decrease of the ultraviolet reflectivity at 410 nm (Simon-Miller & Gierasch, 2010), and (c) a decreased aerosol opacity at 600-800 mbar (Antuñano et al., 2020), suggesting a clearing of the NH 3 clouds, and potentially hinting at changes in the ammonia upwelling from the deeper tropospheric levels.A long-term analysis of Jupiter's zonal winds by Simon-Miller and Gierasch (2010), using observations captured by the HST between 1994 and 2008, and later extended to 2017 and 2019 by Tollefson et al. (2017) and Wong et al. (2020) respectively, revealed similar periodic changes in the equatorial zonal-winds at cloud level, with faster winds observed near-contemporaneously to the 5-μm brightness change (Antuñano et al., 2018), potentially due to tracking faster clouds located deeper in the troposphere during the disturbances.
These long-term studies of Jupiter's atmosphere also reported non-periodic changes in the zonal winds (Wong et al., 2020) related to large convective outbreaks at the SEB and NTB, helping researchers to predict future events, and revealed an anticorrelation of the 5-μm brightness changes between the NEB and the SEB, suggesting a potential interhemispheric connection between Jupiter's prominent equatorial belts (Antuñano et al., 2019).
In this study, we aim to extend the investigations of Orton et al. (1991Orton et al. ( , 1994)), Antuñano et al. (2019Antuñano et al. ( , 2021)), and Orton et al. (2023) to a larger number of mid-infrared wavelengths to explore Jupiter's temperature and aerosol variability over long spans of times, from the NH 3 -ice clouds to the stable mid-stratosphere, enabling 10.1029/2022JE007693 3 of 34 us to separate seasonal from non-seasonal variability.We aim to show that, although Jupiter's troposphere and stratosphere exhibit myriad dynamical phenomena, there exist quasi-periodic patterns (e.g., Rogers, 1995) that may aid in future predictions of planetary-scale changes to the banded structure.We will use a large dataset of ground-based mid-infrared observations spanning 36 years at eight wavelengths between 7.9 and 24.5 μm, allowing us to derive latitude-and time-resolved temperature profiles and aerosol distributions via spectral inversion.This extends the analysis of Orton et al. (1991Orton et al. ( , 1994) ) and Simon-Miller et al. (2006), which investigated tropospheric and stratospheric thermal variations between 1978 and 1992, and was the original motivation for our longer-term study.It also uses a larger filter set than the study by Orton et al. (2023) over a similar time frame.
The paper is organized as follows.In Section 2, we describe the observations, image reduction process, and data processing techniques applied in this study.Long-term variations in brightness temperature at each wavelength are explored independently in Section 3 and are compared to previously reported meteorological events in Jupiter.
Optimal estimation inversions, performed to characterize the zonally averaged stratospheric and tropospheric temperatures and aerosol opacity between 1983 and 2019, are described in Section 4. The results are shown in Section 5 and discussed in Section 6.Finally, we summarize our conclusions in Section 7.
Observations and Image Reduction
This study uses ground-based mid-infrared images captured between 1983 and 2019 at wavelengths between 7.9 and 24.5 μm using a number of different instruments (Table 1): BOLO-1 (1983BOLO-1 ( -1993)), the Mid InfraRed Array Camera (MIRAC, 1993(MIRAC, -1999)), MIRLIN (1996MIRLIN ( -2003)), and the Mid-Infrared Imager and Spectrometer (MIRSI, 2003(MIRSI, -2011) instruments mounted at the 3-m NASA Infrared Telescope Facility (IRTF) in Hawai'i; the Very Large Telescope (VLT) Imager and Spectrometer for mid-InfraRed (VISIR (2006(VISIR ( -2011(VISIR ( , 2016(VISIR ( -2018) ) instrument mounted at the 8-m VLT in Chile; and the Cooled Mid Infrared Camera and Spectrometer (COMICS, 2005(COMICS, -2019) ) instrument on the 8-m Subaru Telescope in Hawai'i.A summary of the wavelength range, detector, and plate-scale of these instruments is given in Table 1, and more detailed information on these instruments can be found in the references given in the table.Figure 1 summarizes all the observations used in this study as a function of wavelength and full information on the exact dates, instruments, wavelengths and central meridian longitude coverage is given in Table S1 in Supporting Information S1.As described later in Section 2.2.1, wavelengths shown in Figure 1 are approximate due to differences between instrument filters, and correspond to the wavelengths used during the calibration process to enable coverage over longer time spans.We note that mid-infrared observations at a wider range of wavelengths (e.g., 9.8 μm, 11-12 μm) and captured by other instruments (e.g., MICHELLE and T-ReCS mounted on the Gemini telescope in Hawai'i) are also available.However, these cover shorter time-frames (from months to less than 20 years) or were captured at epochs that were very scattered, impeding a long-term analysis.All 18.72 and 20.5 μm data used in this study are the same as those used in (Orton et al., 2023).
All images used in this study, except those captured by the BOLO-1 instrument, are reduced using the Data Reduction Manager pipeline described by Fletcher et al. (2009).This process consists of subtracting the sky and telescope background from each image to be able to detect Jupiter's weak emission via a chopping-nodding technique, correcting detector non-uniformities and bad-pixels by the application of flat-fields and bad-pixel masks to each image, geometrically calibrating (i.e., navigating) the data by limb-fitting, and projecting them into cylindrical maps at the desired spatial resolutions.As BOLO-1 images were captured using raster-scan techniques sampling the planet in a 1″ regular grid, the previous steps cannot be followed to process these data.Here, we use the reduced and projected BOLO-1 images as described by Orton et al. (1991Orton et al. ( , 1994)).
Jupiter images captured by the VISIR instrument are partially obscured by the negative beam when chopping the telescope for sky subtraction.This is due to the 38 × 38″ field of view of the VISIR instrument and the maximum chopping amplitude of 25″ of the VLT, which is smaller than Jupiter's typical angular diameter of 40″.In this study, no attempt is made to correct the obscured regions and instead, we remove them from each image by cropping the affected latitudes before the navigation step.Additionally, images captured by the VISIR instrument from 2016 onward display a pattern of vertical and horizontal stripes across the detector that cannot be entirely removed by a flat-field.Here, we remove the striping pattern following the steps in Donnelly (2021), which corrects independently the horizontal and vertical stripes by using a Gaussian smoothing for the former, and low-pass and high-pass filtering for the latter.This correction, however, does not entirely remove the central stripe where the two halves of the VISIR detector meet.For this reason, we perform an additional step, which consists of removing the central burst by fitting it to a Gaussian function and subtracting it from the brightness profile.Finally, due to the detector sensitivity and the brightness of the telluric atmosphere at 17.65 and 18.72 μm, the field of view of the VISIR observations between 2016 and 2018 at these wavelengths is smaller than at other wavelengths, meaning that Jupiter images are windowed, cutting off the high-latitudes.
Examples of reduced 8.6-μm Jupiter images captured during each of the four decades analyzed in this study are shown in Figure 2a, showing the evolution of the quality of ground-based mid-infrared observations.Figures 2b-2i shows examples of fully reduced and mapped images of Jupiter captured in May 2018 in the eight filters shown in Figure 1 by the VISIR (left column, 7.9-13.04μm) and COMICS (right column, 17.6-24.5μm) instruments, and correspond to some of the highest quality ground-based mid-infrared observations of Jupiter obtained to date.The filters shown in this figure probe the following pressure levels: 7.9 μm probes the stratosphere at around 10 mbar; 8.6 and 10.77 μm probe temperatures, ammonia and aerosol near the NH 3 cloud level at around 600-800 mbar; and the upper troposphere and tropopause are probed by the 13.04-24.5μm filters.VISIR images shown in this figure have been corrected to remove the artificially obscured regions mentioned above, by adding back in the subtracted flux (Antuñano et al., 2020).This correction, however, leaves a "residual arc" (shown in Note that the wavelength of the observations might not exactly match the wavelength annotated here.See Section 2.2.1 for information on the wavelength shifting performed in this study.A summary of these data is given in Table 1 and a full description is available in Table S1 of Supporting Information S1. 10.1029/2022JE007693 6 of 34 black in Figure 2) at the edge of the obscured regions that is more apparent at wavelengths with limb-brightening or weak limb-darkening (Donnelly, 2021).As mentioned above, this correction is only performed to show the quality of the observations and it is not used in our subsequent quantitative analysis.
Radiometric Calibration
Each image and each BOLO-1 map of the disk is radiometrically calibrated following Antuñano et al. (2020) and Orton et al. (2023), with Q-band (i.e., 17.2-24.5μm) and N-band (7.9-13.04μm) images scaled in radiance to match Voyager IRIS and Cassini CIRS profiles, respectively, within ±50° latitude of the equator.CIRS and IRIS were both Fourier transform spectrometers calibrated in space by viewing a warm black body and cold deep space, and using the difference to calibrate the resulting interferograms.Although separated in time by decades, the CIRS and IRIS spectra of Jupiter are self-consistent in spectral regions of overlap.They remain our most reliable reference sources for in-space calibration.Scaling our long-term observations to Cassini CIRS and Voyager IRIS assumes that Jupiter's overall brightness has not varied significantly with time, resulting in this technique being insensitive to any global-scale change in the mid-infrared brightness over these long spans of time.However, relative changes in brightness from latitude to latitude are robust.The selected scaling region differs from Antuñano et al. (2020), where data were scaled to the average radiance over ± 20°-60° latitude to avoid the equatorial and off-equatorial latitudes, which are more variable than the higher latitudes.However, as mentioned above, VISIR Jupiter images between 2016 and 2018 at 17.6 and 18.7 μm are windowed, cutting off the high-latitudes (i.e., above ∼50° latitude) and preventing the use of the same scaling region as in Antuñano et al. (2020).The choice of the scaling region used in this study was made after several experiments, and was found to be the best method to calibrate the data without compromising temporal changes at any latitude.
As the instruments used in this study have slightly different filters, BOLO-1, MIRAC, MIRLIN, MIRSI and COMICS filters are shifted in wavelength to match those of the VISIR instrument and COMICS 19.5-μm filter (all in Figure 1).This is done before scaling the data to match IRIS and CIRS, to treat all filters equally over the entire time series (e.g., shifting the MIRLIN 18.67-μm filter to treat it as the VISIR 18.72-μm filter).Additionally, due to the higher spatial resolutions of VISIR and COMICS observations compared to the observations acquired with the 3-m IRTF telescope, VISIR and COMICS images are smoothed before the calibration to match the spatial resolution of MIRSI observations and are used throughout the subsequent analysis.We follow Orton et al. (1994Orton et al. ( , 2023) ) in scaling the 17.80-μm images captured by the BOLO-1 instrument in 1980BOLO-1 instrument in -1982BOLO-1 instrument in and 1984BOLO-1 instrument in -1985 as if it was a 18.72 μm filter, as the difference in the peak contribution functions at these wavelengths are relatively small and the Q-band BOLO-1 filter was extremely broad (16-26 μm according to the IRTF photometry manual from April 1988).This process ignores potential small variations of the radiance due to the slightly different filters.However, these variations are expected to be within the absolute-calibration uncertainty, which is as large as 14% in the Q-band data and around 4%-5% in the N-band data (Antuñano et al., 2020).
Zonal-Mean Radiance and Smoothed Radiance Profiles
Latitudinal radiance profiles as a function of date and wavelength are obtained by longitudinally averaging the radiance of each image within 30° longitude of the minimum emission angle in 1° latitudinal bins.For the chop-obscured regions of VISIR images, only latitudes where the obscured region does not fall within 15° longitude east and west of the central meridian were considered.This choice is introduced to ensure that an adequate region around the central meridian is used for the zonal-mean radiance.Images where the Great Red Spot was centered on the central meridian were avoided to exclude anomalous regions that do not represent the zonal radiance.We note that other anomalous regions such as NEB plumes, 5-μm hot-spots are relatively well distributed in longitude and therefore, could still be representative of the zonal mean.In addition, in the CIRS or IRIS data, as well as in ground-based observations in the N-band, 5-μm hot spots do not appear any more prominent than warm areas elsewhere.
Potential longitudinal variability at each latitude and wavelength is given by the standard deviation of the zonalmean radiance.This is usually an order of magnitude smaller than the absolute calibration uncertainty in the Q-band, but can be as large as the absolute calibration error in most of the N-band filters.Furthermore, the longitudinal variability at 8.59 μm (see Figure 2), sensing tropospheric aerosol contrasts, can be up to an order of magnitude larger than the calibration uncertainty.In this study, we include the longitudinal variability in the measurement errors used in our spectral retrievals (see Section 4), adding the standard deviation of the zonal average in quadrature to the absolute calibration uncertainty.Zonal-mean brightness temperature maps, converted from the zonal-mean radiance, are shown in Figure 3 as a function of time and latitude, showing the temporal and latitudinal variability of Jupiter at different wavelengths between 1983 and late 2019.This variability is fully described in Section 3.
To obtain radiance profiles that better represent the full dataset, and to fill in gaps when instruments were unavailable, zonally averaged radiance profiles are smoothed as a function of date for each latitude and wavelength.This is performed using a Savitzky-Golay (SG) smoothing filter (Savitzky & Golay, 1964), which is defined as a moving average weighted by a polynomial of a certain order.This technique allowed us to fit the data with lower uncertainty compared to simple smoothing and interpolation of the data.In this study, a 24-point wide (covering 1,440 days) and fourth-order polynomial SG filter is used.This technique requires data to be on a regular grid, so zonal-mean profiles are first linearly interpolated using a 60-day window in 1° latitudinal bins.Larger window sizes resulted in excessively smoothed profiles, while smaller windows showed an artificially wavy profile.We tested the sensitivity of our results to the chosen smoothing function and found that the Lomb-Scargle periodograms remain invariant for window sizes that better reproduce the observed trend (e.g., ranges between 1,000 and 1,800 days, see Figure S1 in Supporting Information S1).The SG smoothing is repeated 200 times for each latitude and wavelength, using randomized values of the zonal-mean radiance within the estimated measurement errors to consider the longitudinal variability and the uncertainties introduced during the radiometric calibration.
Finally, the 200 smoothed profiles for each latitude and wavelength are averaged together at each date to obtain averaged and smoothed radiance profiles in Figure 4 (with the corresponding standard deviation).This technique is identical to that used in Orton et al. (2023).
These smoothed time series are used to analyze the temporal variability of the brightness temperature at each wavelength, and also to retrieve temperature and aerosol opacity maps on a regular temporal grid (see Sections 3 and 4).Examples of average smoothed brightness temperature profiles at 8.6 μm and two different latitudes (red solid lines), compared to the measured zonal-mean brightness temperatures (black dots) as a function of time, are shown in Figure 4.This demonstrates that the average smoothed profiles adequately reproduce the trends seen in the observations, enabling us to represent the data on a regular temporal grid.These smoothed profiles also reduce our sensitivity to outliers, where discrete meteorological features may influence our approximate zonal mean on each date.
In particular, we use the "Scargle" function written in IDL, setting a false-alarm probability of 0.02 (or 98% significance) and a minimum of 1 year periodicity.The uncertainty of the obtained periods is assumed to be equal to the FWHM of the power spectrum peak.Results are shown in Sections 3 and 5, where only periods with a significance higher than 98% are represented.
Cycles of Variability in the Mid-Infrared
Brightness temperature anomaly maps between ±48° latitude, computed by subtracting the average brightness temperature over all latitudes and dates from the smooth brightness temperature profiles at each latitude, are shown in Figures 5 and 6 as a function of date.This technique enables investigation of potential temporal changes with respect to an average state of Jupiter, and provides a clearer view of the changes than Figure 3.The temporal variance of the brightness temperatures and Lomb-Scargle periodograms (Scargle, 1982) are also presented in Figures 5 and 6, showing the regions of highest variability and the timescales of these changes, respectively, at each of the eight wavelengths analyzed in this study.Brightness temperature anomaly maps show remarkable cyclic activity, which will be discussed in the following subsections.
Jupiter's Equatorial Stratospheric Oscillation
Jupiter's Equatorial Stratospheric Oscillation (JESO, also known as the Quasi-Quadrennial Oscillation or QQO, Leovy et al., 1991;Orton et al., 1991) can be observed at 7.9 μm in Figure 5a as a warm and cold temperature pattern at the equator, with the off-equatorial latitudes at ∼12°-14° changing in anti-correlation to the equator (Cosentino et al., 2017).Our 7.9-μm brightness temperatures also show the JESO disruptions -a change in the phase of JESO at the equator by 180°-from 1992 to 2008 reported by Antuñano et al. (2021), where the brightness temperatures display early brightness temperature maxima compared to the 4-4.5-year period inferred in previous studies (e.g., Simon-Miller et al., 2006).Our brightness temperature data, however, do not show the early temperature increase at the equatorial latitudes in mid 2017 suggested in Giles et al. (2020).This difference is further discussed in Section 5.
At wavelengths between 17.6 and 24.5 μm, sounding upper tropospheric temperatures at 100-300 mbar pressures (Fletcher et al., 2009), the brightness temperature at the EZ also varies in time.The Lomb-Scargle periodograms in Figures 6a-6d hint at two potential periodicities at the equatorial latitudes -a 4-year period mainly present in 17.6, 18.7, and 24.6 μm (indicated by green squares), and a 8-9-year period (indicated by blue squares) observed in all Q-band wavelengths -in agreement with Orton et al. (2023).The 4-year period observed in this tropospheric emission does coincide with the periodicity of the stratospheric JESO seen in Figure 5a, suggesting that this phenomenon could extend downwards into the upper troposphere, as predicted by Leovy et al. (1991).We discuss the potential coupling between stratospheric and upper-tropospheric temperatures in Section 6.
Equatorial Zone Disturbance in the Mid-Infrared
At 8.6 μm, sensing tropospheric temperatures and aerosol opacity at around 600-800 mbar pressure level (Fletcher et al., 2009), Figure 7 shows a periodic change in brightness temperature (i.e., a decrease in the aerosol opacity or increase in the kinetic temperature) of 7 years at the equatorial latitudes (indicated by pink square in Figure 5) related to the EZ disturbances.These disturbances have been previously observed with a ∼7-year periodicity at the NH 3 cloud tops (Antuñano et al., 2020;Rogers, 1995); below the NH 3 clouds sounded by 5-μm observations (Antuñano et al., 2018); in the upper tropospheric haze as brightness changes at blue wavelengths (Simon-Miller & Gierasch, 2010); and in the zonal wind field (Simon-Miller & Gierasch, 2010;Tollefson et al., 2017;Wong et al., 2020).The increase in the brightness temperature in 1992, 2000, 2006-2007, and 2018-2019 match all the coloration events at visible wavelengths since 1983 (e.g., Rogers, 1995) in agreement with a thinning of the NH 3 clouds suggested by Antuñano et al. (2018).
At 10.7 and 13.0 μm (the former sensing tropospheric ammonia gas and temperature near 500 mbar, and the latter sounding tropospheric temperatures at similar pressure levels, Fletcher et al., 2009) Figure 7 shows subtle increases of the brightness temperatures of <1 K at the EZ nearly contemporaneous to the 8.6-μm increases (shown by black arrows), suggesting either small warming of the equatorial latitudes during the EZ disturbances and/or changes on the ammonia gas distribution at the ∼500-mbar pressure level (see Figures 5c and 5d).This is in agreement with the small changes observed in the brightness temperature during the 2006-2007 EZ disturbance analyzed in Antuñano et al. (2020) and the 7-year periodicity seen in Figures 5c and 5d (shown by pink squares).A correlation analysis between brightness temperature variations at 8.6 and 10.7 μm at the equator shows that changes at 8.6 μm lag 360 days behind those at 10.7 μm.Similarly, 8.6-μm changes lag 180 days behind those at 13 μm (as indicated in Figure 7, where the dashed black lines indicate the 13.0 μm brightness temperature minima).
The 8-year period found at the equator in the Q-band wavelengths is not related to the EZ disturbances, as a comparison of the variability at the EZ in Figures 6a-6d with the EZ disturbances in Figures 5b-5d shows that the changes observed in the upper troposphere are not influenced by the EZ disturbances, confirming previous findings of Antuñano et al. (2020).
Off-Equatorial and Mid-Latitudes
The NEB and SEB display the largest temporal variance in the 8.6-μm brightness temperature (see Figure 5b), indicating a highly variable aerosol distribution in these two belts.The NEB displays overall higher brightness temperatures than the SEB mainly at 8.6 μm, 10.7 and 13.0 μm, in agreement with the stronger depletion in ammonia and aerosols found at the NEB compared to the SEB (de Pater et al., 2019;Fletcher et al., 2016, Fletcher, Kaspi, et al., 2020;Fletcher, Orton, et al., 2020;Li et al., 2017).At longer wavelengths (i.e., 17.6-18.72μm), the NEB displays higher temporal variance compared to the SEB, while at 20.5 and 24.5 μm no significant differences are observed between the NEB and the SEB.The Lomb-Scargle periodogram shows a subtle ∼4.5-year period at 17°-20°N latitude at 8.6 and 10.7 μm (indicated by yellow squares), coinciding with the average time intervals between the NEB expansions (Fletcher, Orton, Sinclair, et al., 2017;Rogers, 1995) and confirming the change in tropospheric aerosols and temperatures during these events.
Finally, at latitudes higher than 20°, all wavelengths show a periodic 12 ± 2-year oscillation of their brightness temperatures (indicated by white squares in Figures 5 and 6).These variations are more prominent in the Q-band wavelengths, where the brightness temperatures are observed to change in anti-phase between the northern and southern hemispheres.The similarity between the observed periodicity and the jovian year (11.8 Earth years), together with the anti-correlation between hemispheres, suggests that these observations are dominated by changes to our viewing geometry and are showing the changing emission angle (difference between the sub-observer point and the local normal at the latitude/longitude of interest) as Jupiter moves around the Sun.A comparison between variation of the minimum emission angle between 1983 and late 2019 to 20.5-μm brightness temperature changes at 40°N, show a clear anticorrelation (see Figure 8), suggesting that the observed variations at mid-latitudes are not real and should disappear when accounting for Jupiter's weak seasons.Spectral inversions, which properly account for this effect by calculating radiance for a path at the specific emission angle, will be presented in Section 5 and discussed in Section 6.
In the next section, we describe the retrievals performed to quantify the changes in stratospheric and tropospheric temperatures and aerosol opacity during the cyclic atmospheric events described above.
In this study, spectra represented by the 5-point and 8-point image cubes are inverted independently using the radiative-transfer and retrieval code called NEMESIS (Irwin et al., 2008) to provide estimations of the stratospheric and upper tropospheric temperatures, tropospheric ammonia, and aerosol opacity between 1983 and 2019 and 1996 and 2019, respectively.In both cases, NEMESIS calculates synthetic spectra for a given atmospheric profile using pre-tabulated k-distributions for each gases, along with additional "continuum" opacity sources, such as aerosol cross sections and collision-induced absorption.NEMESIS then fits the crude 5-or 8-point spectra with a non-linear Levenberg-Marquardt method, iteratively changing the free atmospheric parameters to obtain a final optimal fit that does not deviate significantly from a prior (see Sections 4.1 and 4.2).The retrievals do not account for reflected sunlight and we assume pure thermal emission at these mid-infrared wavelengths.A comparison between the retrieved results from the 5-point and 8-point spectral cubes provides an understanding of potential degeneracies inherent in the retrievals due to the low number of wavelengths used in both cases.
Additionally, although the 8-point spectra provide more information to better constrain the retrievals, the 5-point spectral image cubes allow us to extend our study back to 1983.For both retrievals, Jupiter's atmosphere is divided in 80 regular layers in log(p) between pressures of 10 and 10 −6 bar.Descriptions of the reference atmospheric models used in each case are given in the following subsections.
Retrievals With the 5-Point Spectra
In this case, we use a reference atmospheric model where tropospheric ammonia, phosphine, the vertical temperature profile (T(p)) and stratospheric hydrocarbons (methane, ethane, and acetylene) come from a low-latitude Figure 9. Functional derivative profiles dR/dx, where R is the spectral radiance and x is the atmospheric temperature, for each of the 8 filters used in this study, showing that our dataset provides sensitivity between ∼10 and ∼600 mbar.These were calculated at nadir emission angle; the peaks would move to lower pressures with higher emission angles.
The sources of spectral line data used are listed in Fletcher, Orton, et al. (2018).The reference aerosol profile corresponds to a thick cloud of 10±5-μm radius NH 3 ice particles with base at 800 mbar, top at 400 mbar and with a fractional scale height of 0.2 times the gas scale height.However, assuming particles with a 1 ± 0.5-μm radius does not change the retrieved temperatures and gas abundances.
With only five spectral points to define the mid-IR spectrum, the retrieval suffers from significant degeneracy between tropospheric temperatures and the ammonia distribution.In particular, low brightness temperatures at 10.7 μm could result from an increased NH 3 abundance, or a decreased temperature at 500 mbar, and without the added constraint of the 8-filter retrieval we are unable to distinguish between these cases.For this reason, we model our observations using a two-step approach: (a) first, we retrieve temperature, ammonia, and aerosol opacity simultaneously by allowing the vertical temperature profile to vary while scaling the reference aerosol and ammonia distributions, holding para-H 2 fixed at equilibrium; second (b) we use the average of the previously retrieved latitudinal ammonia profile over all dates as the prior profile and retrieve temperatures and aerosol opacity simultaneously whilst assuming that the ammonia gas does not vary with time.Comparisons of the quality of the fits between method (a) and (b) (χ 2 , shown in Figure 10a) and the retrieved temperatures and aerosol opacity profiles for the Equator (Figure 10b) and for 10°N (Figure 10c) obtained from these two approaches show improvements of the fit at 10°N in 1993, 2007, and 2011, but do not show any notable differences at the equator, suggesting that either the ammonia gas distribution has not changed significantly at this latitude in the last 36 years or that these five filters alone are insufficient to be able to characterize its latitudinal and temporal variability.We believe it is the latter, as changes in ammonia distributions are expected to occur at the equatorial and off-equatorial latitudes during the EZ disturbances, NEB expansions, and SEB fading and revival events (Antuñano et al., 2018(Antuñano et al., , 2020;;Fletcher et al., 2011;Fletcher, Orton, Rogers, et al., 2017;Fletcher, Orton, Sinclair, et al., 2017), although no such change has been yet reported at the equatorial latitudes.In this study, therefore, we show and discuss the results obtained from the second approach.
Retrievals With the 8-Point Spectra
A similar reference atmospheric profile to that described above is used for the 8-point spectral retrievals, with the difference that the a priori temperature profile comes now from an average of the retrieved temperatures from the 5-point spectra between 1983 and 2019.This is done to ensure that the reference atmospheric model used represents the average thermal state of Jupiter's atmosphere during the period analyzed in this study.The a priori aerosol profile is the same as in Section 4.1 -a cloud of 10±5-μm radius NH 3 ice particles in the 400-800 mbar pressure level with a fractional scale-height 0.2 times the gas scale-height.However, unlike the 5-point spectra, using a reference aerosol profile with 1 ± 0.5-μm-radius NH 3 particles does not fit the observations at 10.77 and 13.04 μm, particularly at latitudes with high aerosol opacity.
As in the 5-point spectral analysis, we investigate potential degeneracies inherent in the retrievals by comparing the retrieved atmospheric parameters from three different retrieval approaches: Model (1) retrieves stratospheric and tropospheric temperatures and tropospheric ammonia gas distribution and aerosol opacity simultaneously by allowing temperatures to vary freely and scaling ammonia and aerosol opacity profiles; Model (2) is the same as (1) but assumes that ammonia remains unchanged with time, retrieving only stratospheric and tropospheric temperatures and tropospheric aerosol opacity; Model (3) is the same as (2) but also allows para-H 2 to vary as well (due to changes of the opacity of the S(0) and S(1) lines potentially controlling the thermal infrared continuum sensed by the Q-band filters, that is, 17.6-24.5μm Fletcher et al., 2009).
Residual maps of χ 2 , computed by subtracting the goodness-of-fit χ 2 of model 1 from those of model 2 (Figure 10d), and model 3 from to those of model 2 (Figure 10g), show that there is no significant difference on the fits performed in model 1 and 2, except at a narrow region around 10°N (Figure 10f), where ammonia is required to deviate substantially from the assumption of a spatially and temporally uniform prior.The poor fits of the observations at the southern edge of the NEB compared to other latitudes, even when our model enables ammonia to vary during the fitting process (see Figures 10e and 10f), suggests a lack of ammonia gas at these latitudes, in agreement with the large depletion of ammonia observed at the NEB in the microwave, radio and thermal infrared (e.g., Fletcher, Kaspi, et al., 2020;Fletcher, Orton, et al., 2020;Li et al., 2017).The small differences in the goodness of fits between model 1 and 2 indicates that, as with the 5-point spectral analysis, the low number of spectral points prevents us from confidently retrieving the ammonia gas distributions with this multi-filter method.Finally, a comparison of the goodness of fits between models 2 and 3 is presented in Figure 10g, showing that due to the low number of filters used to cover the Q-band, we cannot reliably identify any variability in para-H 2 (i.e., NEMESIS can adequately fit the 8-point spectra by assuming a para-H 2 fraction that remains static with time, see Figures 10h and 10i).Therefore, we neither retrieve ammonia gas nor the para-H 2 fraction in this study, focusing the remainder of this discussion on changes to aerosols and temperatures.Long-term spectroscopic observations, rather than filtered imaging, are likely to be needed to assess variability in NH 3 and para-H 2 .
Results: Retrieved Temperatures and Aerosol Opacity
Retrieved aerosol opacity at 600-800 mbar (constrained by the 8.6-μm filter) and temperatures in the stratosphere (i.e., 10 mbar, constrained by the 7.9-μm filter), upper troposphere (330 mbar, constrained by 18.7-24.5μm filters) and mid-troposphere (i.e., 500 mbar, constrained by the 10.77 and 13.04 μm filters) are shown in Figure 11.These are accompanied by the temporal variance of these temperatures and the corresponding Lomb-Scargle periodograms, showing potential cyclic behaviors of the temperature and aerosol opacity variations.These temperature and aerosol opacity maps are built by combining the 5-filter retrieval results between 1983 and 1996 and the 8-filter retrievals between 1996 and 2020, to obtain a map covering the full 36 years of our dataset.
Due to the different number of filters used in both retrievals, the retrieved temperatures and aerosol opacity using 5 or 8 filters differ in absolute values, although similar temporal tendencies are observed in both cases (see Figure S2 in Supporting Information S1).Differences in the absolute values between the 5-filter and the 8-filters retrievals are corrected in Figure S2 of Supporting Information S1 by shifting the results between 1983 and 1995 up or down depending on the latitude so that the absolute values of the 5-filter and the 8-filter retrievals are consistent in 1996 (when Jupiter was regularly observed with all 8 filters).The applied corrections fall within the retrieval uncertainties (see Figure S3 in Supporting Information S1 where the differences between the 8-filter and 5-filter retrievals are shown as a function of date and latitude).We therefore note that absolute values in Figure 11 are highly uncertain, due to the degeneracies inherent in imaging retrievals, and we only focus on the relative temporal and meridional variability of the temperatures and aerosol opacity, which can be considered to be robust.
Hemispherical Asymmetry
The northern hemisphere stratosphere at 10 mbar, poleward of 30°N, appears to be warmer than the corresponding southern latitudes, irrespective of the solar longitude (see Figure 12), in agreement with the warmer northern mid-latitudes reported in 2000 and 2014 by Fletcher et al. (2016), the preliminary results from brightness-temperature measurements (e.g., Antuñano et al., 2021;Orton et al., 2023) and the 1-D seasonal radiative seasonal model by Guerlet et al. (2020).At 40° latitude, for example, the northern region is on average 3.1 ± 2.6 K warmer than 40°S, while this temperature asymmetry weakens toward the equator, reaching differences of 2.7 ± 2.5 K on average between 30°N and 30°S.This apparently permanent asymmetry, with the warmer northern mid-latitudes, may correspond to the higher number density of aerosols found poleward 30°N at pressures smaller than 50 mbar compared to the southern latitudes, which could provide additional radiative heating (Zhang et al., 2013).This asymmetry could also arise from the combination of the former and the differences in hemispheric solar insolation (Guerlet et al., 2020) because the northern hemisphere receives 21% greater solar energy inflow due to Jupiter's northern summer solstice (solar longitude L s = 90°) occurring close to Jupiter's perihelion (L s = 57°).The northern hemisphere often exhibits enhanced stratospheric wave activity at northern mid-latitudes that may be connected to tropospheric disturbances (Fletcher, Orton, Sinclair, et al., 2017), which could provide additional mechanical forcing to drive this hemispheric asymmetry.
Figure 10.Maps of residual χ 2 values (a, d, g), showing differences in the quality of the spectral fits achieved by NEMESIS between model ii (i.e., retrieving stratospheric and tropospheric temperatures and tropospheric aerosol opacity simultaneously using the 5-point spectra) and model i (retrieving stratospheric and tropospheric temperatures and tropospheric ammonia gas and aerosol opacity simultaneously using the 5-point spectra) in panel a; model 2 (i.e., retrieving stratospheric and tropospheric temperatures and tropospheric aerosol opacity simultaneously using the 8-point spectra) and model 1 (retrieving stratospheric and tropospheric temperatures and tropospheric ammonia gas and aerosol opacity simultaneously using the 8-point spectra) in panel d; and comparing model 2 and model 3 (retrieving stratospheric and tropospheric temperatures, tropospheric aerosol opacity and para-H 2 abundances simultaneously) in panel (g).Actual χ 2 values of the fits using models i and ii (green and black dots, respectively) and models 1-3 (blue, black and red dots, respectively) for the equator (b, e, and h) and 10° north (c, f, and i), showing small variations between models in all panels except (f), where ammonia gas deviates substantially from the prior.The equator and 10° north are highlighted in (a), (d), and (g) by white dashed lines for reference.
Stratospheric temperatures at mid-latitudes are also observed to oscillate with a ∼12-year period in anti-phase between the northern and southern hemisphere, as reported by Simon-Miller et al. (2006).A Pearson correlation analysis of associate conjugate latitudes between 26° and 40° returns large negative correlations, with a mean correlation coefficient of −0.67, confirming the anticorrelated nature of the stratospheric temperature variations at temperate and mid-latitudes (Pearson coefficients as a function of latitude are displayed in Figure S4 in Supporting Information S1).
The top panel of Figure 12 shows a very complex variability of the temperatures with the solar longitude, suggesting that stratospheric temperatures are not solely modulated by radiative forcing.This is reinforced by the lag of around 1 year observed between the temperature maxima and the solar forcing between 1983 and 2008 (in agreement with Simon-Miller et al. (2006)), which is around half than the expected value from radiative response models (Li et al., 2018).Here we speculate that stratospheric temperatures at mid-latitudes are modulated by a combination of radiative and mechanical forcing from meteorological activity at the deeper levels (Fletcher et al., 2016;Guerlet et al., 2020;Simon-Miller et al., 2006).
NTB Disturbances
At lower latitudes -between 20° and 25°-similar stratospheric temperatures are observed in both hemispheres, with average differences of 1.3 ± 2.5 K and 0.2 ± 1.9 K at 24° and 20° latitude, respectively (see Figure 12).However, unlike in the southern region, a large temporal variance is found at the north temperate domain, suggesting the northern stratosphere to be more dynamic than the south.
At 20° and 25° latitude, stratospheric temperatures are observed to oscillate in time with a 5.5-6-year period (indicated by blue squares in Figure 11) in both hemispheres (in agreement with the brightness temperature maps in Figure 5a), displaying a maximum variation of ∼5-6 K at the north hemisphere and ∼3-4 K in the south (see Figures 11 and 12).This period is slightly longer than that of the NTB disturbances occurring deeper in the troposphere (i.e., ∼5-year period, Barrado-Izagirre et al., 2009;Rogers, 1992Rogers, , 1995;;Sánchez-Lavega et al., 2008, 2017), where the eruption of one or more strong convective plumes rising from the deep water cloud layer interact with the background flow forming wakes and vortices (Hueso et al., 2002), to finally completely disturb the ∼22°-27° latitude band at visible wavelengths (Sánchez-Lavega et al., 2017).These convective plumes have been previously observed to reach the lower stratosphere at ∼60 mbar (Sánchez-Lavega et al., 2008).However, no sign of the plumes have been observed at higher altitudes so far.
Figure 12c shows a stratospheric warming around a year after the NTB outbreaks marked in Figure 12c with stars.However, understanding whether the NTB disturbances could be affecting the stratospheric temperatures is challenging.NTB outbreaks could spawn waves that propagate to higher altitudes, warming the stratospheric temperatures at 10 mbar.This is in agreement with the large longitudinal variance (indicating the presence of waves) observed in 7.9-μm images at 20°-25°N in 1992 (Antuñano et al., 2021).However, we note that large temporal longitudinal variance is also found at epochs where no NTB outbreaks were observed (e.g., 1996 and 2002), and little variance is found after the 2008 NTB outbreak, although the latter could be due to the lack of global coverage in ground-based images (Antuñano et al., 2021).Monitoring and characterizing future NTB outbreaks at visible and mid-infrared wavelengths, as well as new numerical model studies will be crucial to understand whether stratospheric temperature variations are influenced by NTB outbreaks.
Jupiter's Equatorial Stratospheric Oscillation
Figure 11a shows the distinct signature of the JESO, with the equatorial and off-equatorial (i.e., 12°-14°) latitudes displaying the largest temporal variance of the stratospheric temperatures.The off-equatorial and equatorial latitudes present warm and cool temperature patterns in anti-phase.As in the 7.9-μm brightness temperature maps shown in Figure 5a, the JESO disruptions from 1992 to 2008 reported by Antuñano et al. (2021) are also observed in the retrieved stratospheric temperatures.The retrieved temperature profile at 13.5 mbar shown in Cosentino et al. (2020) and (Giles et al., 2020) from TEXES spectroscopic mapping (around the pressure level sensed by the 7.9-μm filter) suggests an early temperature increase at the equatorial latitudes in early to mid 2017, although our retrieved temperatures and brightness temperature profile do not show this increase (see Figures 5a and 11a).However, the retrieved temperatures do show that the latest temperature maximum happened in early 2019, ∼0.5 year earlier than expected from a 4-year period, as the previous maximum happened in late 2015 (see Figure 11a).This is potentially related to the phase shift found at higher altitudes in Giles et al. ( 2020) and the 10.1029/2022JE007693 20 of 34 differences between these studies are potentially due to the 7.9-μm imaging filter blending together the flux from a range of altitudes, compared to the superior vertical resolution provided by the TEXES observations.The Lomb-Scargle periodogram shows a 4-year period (indicated by green squares in Figure 11) at the equatorial and off-equatorial latitudes, in agreement with Figure 5a and previous studies (Cosentino et al., 2017;Giles et al., 2020;Orton et al., 1991;Simon-Miller et al., 2006).Antuñano et al. (2021) showed that, although a ∼4-year period is found when fitting all the 7.9-μm data simultaneously, this does not adequately fit the observations prior to 1992 (where a 5.7-year period is found instead) and after 2008 (where the observations do not show any particular periodicity).The Lomb-Scargle periodogram does not hint at the longer period previously reported in the observations prior to 1992, obtained using a wavelet-transform analysis, but it can be clearly observed in Figures 5a and 11a, where an almost 6-year interval is observed between the brightness temperature maxima in 1984 and 1990.A wavelet transform analysis of the retrieved stratospheric temperatures similar to that performed by Antuñano et al. (2021) returns a statistically significant 4-year periodicity between ∼1994 and 1996, but does not show either the longer period reported in Antuñano et al. (2021) between 1980 and 1988 (see Figure S4 in Supporting Information S1) or the 4-year period between 2012 and 2019 (Giles et al., 2020).
Differences in the wavelet transform periodogram analysis between this study and Antuñano et al. (2021) come from the fact that this study does not include 7.9-μm observations from 1980 to 1982, unlike in Antuñano et al. ( 2021), as we could not create 5-point spectral cubes due to the lack of data in some of the filters used in this study during those years.This results in only one oscillation being identified between 1983 and 1990, instead of the 1.5 oscillations seen in Antuñano et al. (2021).Similarly, differences between the periodogram results in Giles et al. (2020) and ours are potentially due to the lack of 7.9-μm observations between early 2013 and early 2016, where our smoothing technique (see Section 2.2.3) might not be representing the real state of Jupiter's atmosphere.Retrieved stratospheric temperatures from 1984 to 1990 and 2016-2019 shown in Figure 11, however, do show the ∼6-year and 4-year time intervals, respectively, between temperature maxima.These confirm the change in the JESO's periodicity after the 1992 disruption, and reveal that the 2017 disruption of the JESO did not alter its periodicity.
Tropospheric Temperatures and Aerosol Opacity
Retrieved tropospheric temperature profiles as a function of time at 330 mbar (solid lines) and 500 mbar (dashed lines) are shown in Figure 13 for four different conjugate latitudes, representative of the temperature variations of the diverse regions described in this paper.The simultaneous representation of the temperatures at both tropospheric altitudes and conjugate latitudes enables us to perform a correlation analysis of changes, both in altitude and latitude at the same time (discussed in Section 6).
Jupiter's upper troposphere at 330 mbar displays temperature differences at mid-latitudes of ∼1 K at around 40°, although this falls within the formal uncertainties on our retrievals (see Figure 13a).This asymmetry is opposite and much weaker than that observed at the stratospheric mid-latitudes, and it is not observed at 500 mbar, where temperatures are largely symmetric at latitudes >20°, as shown in Figure 13b.At both altitudes tropospheric temperatures at latitudes >20° are less variable than those in the stratosphere.The off-equatorial tropospheric temperatures at 500 mbar exhibit the largest temporal variance, mostly due to the large variability observed in the NEB and SEB compared to other belts in Jupiter.
Tropospheric aerosol opacity in Jupiter's belts and zones, shown in Figure 11, is correlated with the albedo at visible wavelengths, and anti-correlated with the overall tropospheric temperatures.High aerosol opacity (see Figure 11) is found in the cold and typically white zones, whereas lower values are found in the usually warm and low-albedo belts, in agreement with volatiles condensing at cool temperatures and forming cloudy regions.The aerosol opacity is highest at the EZ, followed by the north temperate latitudes between ∼20°-40° and the south temperate regions at ∼20°-40°S.The NEB displays the lowest aerosol opacity and the highest tropospheric temperatures, with a difference in aerosol optical depth between the NEB and the SEB of −0.3 (at 10 μm), and maximum temperature differences between these two belts that could reach 1.8 K at 330 mbar and 5 K at 500 mbar (see Figures 11 and 13).The north and south temperate latitudes display approximately twice the aerosol opacity of the NEB.
Multiple periods are found in Figures 11b-11d, both in the temperature and aerosol opacity variations.The mid-latitude tropospheric temperatures poleward of 30° show 4-4.5-year periods (shown by red squares), while the north temperate latitudes (i.e., 20°-30°N) display a 7-year period for the temperature variations and 5-year cycle for changes in the aerosol opacity (indicated by orange squares).These periodicities differ from those found in the stratosphere (see Section 5.1).
Additionally, the ∼12-year periods that we originally found in the brightness temperatures at all wavelengths between 10.7 and 24.5 μm (see Figure 4) are not observed in the retrieved tropospheric temperatures at 500 mbar nor in the aerosol opacity, indicating that these were largely due to the change of the minimum emission angle over the 36 years (Orton et al., 2023), which are now properly accounted for in the temperature inversion.However, at 330 mbar retrieved temperatures continue to show a 10-14-year period for latitudes larger than 40° (yellow squares), in agreement with (Orton et al., 2023) at 20-30°.
The absence of a 5-5.5-year periodicity in the tropospheric temperatures at 20°-30°N suggests that although the NTB outbreaks darken the southern edge of the north temperate latitudes at visible wavelengths during the NTB disturbances (e.g., Rogers, 1995;Sánchez-Lavega et al., 2017), they do not affect the tropospheric temperatures, as expected from the mid-infrared images, where the NTB disruptions do not appear to have significant signatures.The lack of temperature variations during the NTB disturbances contrasts with the SEB revivals (Fletcher et al., 2011;Fletcher, Orton, Rogers, et al., 2017;Pérez-Hoyos et al., 2012;Rogers, 1992;Sanchez La Vega, 1989) and NEB expansions (described in the following sections), where the troposphere warms, accompanied by a removal of aerosols, allowing the 5-μm emission to escape from the deeper levels.This difference between the NTB disturbances and the SEB revivals, both triggered by convective storms, could come from various sources, such as the underlying environmental differences between the SEB and NTB, with the SEB notably depleted in NH 3 compared to the NTB, or differences in the accumulated convective available potential energy at these latitudes, which would result in differences between the magnitudes of the convective storms observed at the NTB and the SEB, where the former seem to reach higher altitudes (Sánchez-Lavega et al., 2017).The faster wind velocities at the NTB (Barrado-Izagirre et al., 2009) could also produce differences in the interaction of the storms with the background at the NTB and the SEB.
Thermal and Aerosol Opacity Variations During NEB Expansions
The periodograms of the aerosol opacity and tropospheric temperatures at 500 and 330 mbar peak at a 4-5-year period (see white squares in Figure 11), coinciding with the periodicity of the NEB expansions (shown as black lines in Figures 13c, 14d, and 14e and white dashed lines in Figures 14a-14c), where the low opacity of the NEB seems to expand poleward reaching the NTrZ(S) (e.g., Fletcher, Orton, Sinclair, et al., 2017;Rogers, 1995).This is in agreement with the tropospheric warming at 650 mbar reported during these events in previous studies (e.g., Figure 2c in Fletcher, Orton, Sinclair, et al. (2017)) and the brightness temperature maps at 10.7 and 13.0 μm shown in Figure 5.
The NTrZ (17°-22°N) displays no evidence of thermal changes related to NEB expansions at 330 mbar.However, temperatures at 500 mbar in Figures 13c and 14d, suggests increases of <1.5 K accompanied by a subtle decrease in the aerosol opacity (Figure 14c) during some (1988, 1994, 2009-2011, 2012, and 2016), but not all (1996 and 2004-2007) NEB expansions.Although there exists a degeneracy between tropospheric temperature and aerosol opacity due to the low number of filters used in this study, the reduction of the aerosol opacity during the NEB expansions is in agreement with the lower reflectivity at 890 nm shown in Fletcher, Orton, Sinclair, et al. (2017).
A correlation analysis between temperature and aerosol opacity variations at 16°N, suggests a temporal lag of 240 ± 60 days between these two, with aerosol opacity changes happening before the variation in the tropospheric temperature (see also Figures 14d and 14e).This suggests removal of the white aerosols at the NTrZ to reveal the darker chromophore at deeper levels during the NEB expansions.However, we note that this study cannot constrain the altitude of the chromophore, which is still an open question (e.g., Braude et al., 2020;Dahl et al., 2021;Pérez-Hoyos et al., 2020;Sromovsky et al., 2017).
Thermal and Aerosol Opacity Variations During SEB Fading and Revival Events
Since 1983, the SEB has undergone three fade and revival events (1989-1990, 1992-1993, and 2009-2011) and a partial fading (in 2007), where the SEB transforms from being the darkest and broadest belt on Jupiter to a faded and white zone, and restores its normal dark brown coloration after several years (e.g., Rogers, 1995).These fading and revival events are represented in Figure 15 by horizontal black lines and vertical dashed white lines.
The retrieved aerosol opacity at 400-600 mbar shows significant variability related to the aforementioned SEB fading and revival events at all latitudes between 8° and 18°S.The aerosol opacity displays large increases during the SEB fading of 1989-1990 and 2009-2010, with optical depth increases of ∼1 on average at 12°-18°S, and around 0.85 on average at 8°-10°S.During these two whitening events our aerosol opacity retrievals show that, although the aerosol opacity seems to start increasing almost contemporaneously at all latitudes, the most equatorward latitudes of the SEB reach their peak in aerosol opacity months before the poleward latitudes of the SEB.This is not in complete agreement with the observed evolution of the SEB fading at visible wavelengths, where the whitening of the SEB seems to start at mid-SEB latitudes and rapidly expand into lower latitudes (e.g., Rogers, 2017).We note that our study deals with zonally averaged profiles and that at some dates we do not have complete global coverage, leading to a potential loss of the start of the SEB fading.
In 2007, the SEB underwent a partial fading in the visible and at 5-μm wavelength (the later sensing the radiance below the ammonia cloud) that lasted around half a year and where the most equatorward latitudes of the SEB remained undisturbed (e.g., Rogers, 2007).Our retrievals show that the aerosol opacity at 12°-18°S started to increase in early 2007, reaching its maxima in early-to mid-2007 and displaying its lowest aerosol opacity value in early 2009, right before it started to increase again for the 2009-2010 fading event.At the northern latitudes of the SEB between 8° and 10°S, the aerosol opacity seems to stay almost invariant between 2006 and mid-2007 and then decreases following the tendency seen at 12°-18°S.
Considering the anticorrelation between aerosol opacity and tropospheric temperature changes, one would also expect to observe significant changes in the tropospheric temperatures during the SEB fading and revival events.In fact, Fletcher, Orton, Rogers, et al. (2017) reported a 2-4 K localized warming of the troposphere at 500 mbar during the revival of the faded SEB in 2010-2011, although no significant changes were reported during the fading episode (Fletcher et al., 2011).Our zonally averaged tropospheric temperatures at 330 and 500 mbar show a very complex temporal variability, with some epochs (2009 to mid-2010) hinting at a 0.5-1 cooling of the SEB during the fading, while others showing the complete opposite, a cooling of the temperatures during SEB revivals.These perplexing results demonstrate that further analysis using spectroscopy, fully accounting for longitudinal variability, is needed to be able to disentangle temperature and aerosol changes during SEB fading and revival cycles.
The Equatorial Zone
At the equatorial latitudes (i.e., ±7° of the equator), tropospheric temperatures vary with time, with maximum temperature contrasts of ∼1.5 K, both at 330 and 500 mbar (see Figure 13d).The Lomb-Scargle periodograms in Figure 11 display both 4 and 8-year periods for the oscillations of the upper-tropospheric temperatures at 330 mbar (marked by green squares), while a 8-to 9-year periodicity is found for the temperature variations at 500 mbar.The periodicities at 330 mbar are in agreement with the brightness temperature changes at 17.6-24.5μm (Figure 4) and (Orton et al., 2023).As mentioned in Section 3, the 4-year periodicity observed in the upper-tropospheric temperatures at 330 mbar coincides with the period of JESO, suggesting a potential coupling between Jupiter's equatorial upper troposphere and stratosphere.This coupling in further discussed in Section 6.
Brightness temperatures at 10.77-μm and 13.0-μm shown in Section 3, revealed a ∼7-year period at the equatorial latitudes, coinciding with the EZ disturbances (see Figure 4).This period, however, is not observed in the retrieved temperatures at 500 mbar, indicating that the equatorial troposphere might not sufficiently warm during the remarkable EZ disturbances to show up as clearly in our retrievals.Although hard to confirm due to the lack of data during some of the EZ disturbances (e.g., 1992 or after mid-2019), the 500-mbar tropospheric temperatures seem to increase before the EZ disturbances appear at 5 μm, mainly in 2006 and 2018 (see Figure 13d), and decreases again right after the EZ disturbances finish at 5 μm.However, this temperature pattern is not observed in all the EZ disturbances known so far; we note that a further analysis using spectroscopy rather than photometry will surely improve our understanding of the temperature variations during these rare disturbances.Nevertheless, if temperatures do increase before the EZ disturbances, this increase would be smaller than 1.5 K. Whether such a variation could clear the ammonia clouds by sublimation is still unclear.
At these latitudes, the aerosol opacity in Figure 16 is observed to start decreasing around 1-1.5 years before the brightening of the EZ at 5 μm.It usually takes around 4 years to restore the typical equatorial aerosol opacity.This is in agreement with the observed duration of the coloration events at visible wavelengths (Antuñano et al., 2018;Rogers, 1995) and the 8.6-μm brightness temperature changes in Figure 5b, confirming that EZ disturbances start at the ammonia cloud level and expand downward with time to finally brighten the EZ at 5 μm.The contemporaneous decrease of the albedo and aerosol opacity also agrees with the hypothesis of the EZ disturbances being the result of a decrease in the ammonia upwelling due to a potential change in the deep ammonia column beneath the EZ (Bolton et al., 2017;Ingersoll et al., 2017;Li et al., 2017), or due to strong precipitation surrounding the large plumes observed at the northern edge of the NEB (Antuñano et al., 2020).Our study cannot distinguish between these two cases.A correlation analysis between aerosol opacity and tropospheric temperature changes returns the maximum correlation factor (0.41) for lag of 180 ± 60 days between tropospheric temperature variations and aerosol opacity changes.This means that a moderate correlation is found between these two magnitudes, with temperature variations leading to the changes we observe in the aerosol opacity.This clarifies that the 8.6-μm changes that we observe are first due to temperature changes at around 500-600 mbar and later due to aerosol opacity changes.Finally, the aerosol opacity decrease observed in 2012-2013 did not lead to 5-μm brightening of the equatorial latitudes nor to strong coloration events as did the decreases in 1992, 2000, and 2007.Figure 16 also shows a secondary decrease of the aerosol opacity, usually observed halfway between EZ disturbances.This secondary decrease of the aerosol opacity at the equator is more subtle than during the EZ disturbances, with decreases of around half of those found during the EZ disturbances.These shorter-period (3.5-4 years, shown with a light blue square in Figure 11) of aerosol opacity changes at the EZ are somewhat surprising, in that they have no counterpart in the tropospheric temperatures at 500 mbar, nor are they related to notable coloration changes or tropospheric wind speed changes (Tollefson et al., 2017;Wong et al., 2020).This suggests that the aerosol opacity at the equator is modulated not only by the EZ disturbances, but also by other dynamical forces.
These shorter-period changes in the equatorial aerosol opacity do not follow the anticorrelation observed in the tropospheric temperatures at the NEB and SEB, which present a longer period (see Section 6.3), confirming that the potential connection between the NEB and SEB originates deeper in the atmosphere and does not appear to influence the equatorial latitudes.Understanding how ammonia varies with time, both at the ammonia cloud tops sensed via mid-infrared spectroscopy and the deeper levels sensed by Juno's MWR, will be essential to investigate the nature of the subtle aerosol opacity decreases found between the EZ disturbances.
Finally, an asymmetry in the aerosol opacity is also observed at the EZ, with higher aerosol opacity found at 0°-5°S (EZ(S)) compared to 0°-5°N (EZ(N)) overall (see Figure 11d).This asymmetry is present at visible wavelengths too, where the EZ(S) sometimes displays higher albedo than the EZ(N) (e.g., Rogers, 1995).However, the asymmetry, both at the visible wavelengths and aerosol opacity, is opposite to the observed NH 3 distribution Bolton et al. (2017), and could be an effect of the richer cloud morphology observed at the EZ(N) related to the NEB hot spots and plumes (Fletcher, Orton, et al., 2020;Orton et al., 1998;Westphal, 1969) compared to the more quiescent and calm EZ(S).
Discussion
The previous sections have described significant variability in Jupiter's stratospheric and tropospheric temperatures and aerosol opacity, with strong correlation/anticorrelation to previously known cyclic activity and hemispherical changes.A summary of the observed variability is given in Table 2.In this section, we further discuss the most remarkable temperature and aerosol opacity variations, focusing on understanding their nature by characterizing the potential connections between tropospheric and stratospheric changes.
Stratospheric Temperature Asymmetry at Mid-Latitudes
The consistent calibration and analysis approaches to the entire mid-infrared imaging time series have revealed that (a) the northern mid-latitude stratospheric temperatures at 10-20 mbar pressure level are overall 2.7-3.1 K warmer on average than the southern mid-latitude stratospheric temperatures; and (b) temperature variations are highly anticorrelated between hemispheres, with periodicities close to a Jovian year.The consistency of higher brightness temperatures at 7.9 μm and retrieved stratospheric temperatures at the northern mid-latitudes independent of the solar longitude, provides compelling evidence of a hemispherical thermal asymmetry that is weakly modulated by the jovian seasons.This is in agreement with the warmer northern mid-to high-latitudes predicted from the 1-D radiative-convective models of Guerlet et al. (2020), where they attribute the north-south asymmetry to the strong asymmetry in the polar haze abundance as constrained by Zhang et al. (2013).The hemispherical asymmetry in the stratospheric temperatures, aerosol number density, C 2 H 2 and C 2 H 6 could be related to the auroral oval, which is larger and extends further south in the northern hemisphere compared to the more compact auroral oval in the south.However, to date, there has been no systematic study of the time variability of polar stratospheric aerosols to confirm this.
Predicted temperature variations from the 1-D radiative-convective model of Guerlet et al. (2020) are shown in Figure 17 as dashed lines for 40°N and 40°S.The southern mid-latitude stratospheric temperatures do not follow the radiative climate predictions of Guerlet et al. (2020), which expected a smaller amplitude of temperature variations in the south, and that the warmest temperatures would be encountered during mid-northern (rather than southern) summer, because Jupiter is closer to perihelion during southern winter.The temperature time series in our southern dataset in Figure 17 is apparently more random than in the north.Nevertheless, the data reveal the opposite trend to the model (warmer southern temperatures are experienced during southern spring and summer), suggesting that Jupiter's orbital eccentricity plays a smaller role than that accounted for in the model, in agreement with Orton et al. (2023).Furthermore, our data generally have temperature contrasts twice as large as predicted by the model, indicating that radiative heating of the southern hazes may continue to dominate the temperature trends.Future studies of stratospheric aerosol variability with latitude and time would greatly improve the ability of radiative climate models to predict Jupiter's stratospheric temperatures.
Finally, the strong anticorrelation of the temperature variations between hemispheres is still a mystery.Quasi-seasonal variations are observed also at other latitudes, such as JESO and at other planets like Saturn and Earth.However, these are usually observed at low latitudes.Additionally, the anticorrelation found at changes in the NEB and SEB could be potentially explained by deep connections via cylinders parallel to the rotation axis (see Section 6.3).However, these are most effective at latitudes lower than 16°.Further analysis and models are needed to understand the mechanisms responsible of such anticorrelation.
Stratosphere-Troposphere Coupling
Many studies from the past decades have shown that, in the Gas Giants, deep tropospheric meteorological and/or convective activity can affect the temperature and aerosols in the upper troposphere at ∼100-330 mbar and lower stratosphere at 5-60 mbar (e.g., Li & Ingersoll, 2015;Sugiyama et al., 2014).In most cases, this is thought to be related to wave activity that carries internal heat flux from the deeper levels into the stratosphere, either due to deep convective activity or generated by wind-shear instabilities at the cloud level.
The power spectrum analysis of the retrieved stratospheric and upper-tropospheric temperatures at 10 and 330 mbar, respectively, shows a 4-year period for changes in their temperature profiles at the equator.As mentioned in previous sections, this hints at a potential connection between temperature changes at these two pressure levels.However, the power spectrum alone cannot determine the kind of connection between temperature changes at 10 and 330 mbar.For this reason, in Figure 18 we compare the temporal variability of the retrieved temperatures at 10 and 330 mbar, which enables us to investigate whether such a connection is really seen in the temperature profiles.
Figure 18 shows that the retrieved temperatures at 330 mbar are observed to be anticorrelated to those at 10 mbar, even during the epochs when the JESO was disrupted potentially due to energetic "Global Upheaval" events (Antuñano et al., 2021).A Pearson correlation analysis returns a low-to-moderate anticorrelation with a coefficient of −0.47 at the equator.The low correlation found in the Pearson correlation analysis is likely related to the lack of observations at some epochs.During these epochs the retrieved thermal profiles might not represent the real state of the atmosphere and therefore, one needs to be cautious when analyzing the results.The anticorrelation observed in Figure 18 is likely to be real as it also matches deviations from the usual state of JESO during the disturbed epochs, suggesting a potential tropospheric-stratospheric coupling.This is in agreement with the anticorrelation observed between brightness temperature changes at 7.9 μm and temperatures at 330 mbar between 1981 and 2011 (Orton et al., 2023).This anticorrelation between temperature temporal variability at 10 and 330 mbar does not extend deeper into the troposphere, as retrieved temperature and aerosol opacity at the 500 mbar level do not show any correlation between their variability and JESO.
A study of Jupiter's stratospheric temperatures between 2012 and late 2019 using high-vertical resolution spectra from TEXES (Giles et al., 2020) shows the descending pattern of warm and cold temperature anomalies and their evolution with time (their Figure 3).Using that figure, and assuming a mean scale-height of 23 km at around 10 mbar (Leovy et al., 1991), we obtain a descending rate of ∼13.6 km/year.A ∼13.6 km/year descending-rate of the JESO means that the temperatures at 10 mbar take ∼5.25 years to reach the 330 mbar level.A Pearson correlation analysis between stratospheric and upper tropospheric temperatures shows a correlation maxima of 0.4 for a lag of 2.5 years.This is roughly half of the descending time of a particular temperature anomaly, and if genuine, it could indicate that more than one temperature anomaly (positive or negative) are present between 10 and 330 mbar.Therefore, we believe that our results reveal for the first time that Jupiter's upper-tropospheric temperature variations are influenced by the equatorial stratospheric temperature oscillation, with the vertically stacked chain of warm/cool airmasses propagating downward into the upper troposphere.By changing the equatorial temperature contrasts, this could also influence the shear on the equatorial jets near the tropopause via the thermal wind equation (although this has not yet been observed due to the challenges of measuring winds at these pressure levels) and the aerosol condensation.This, however, has previously been observed in Earth's Quasi-Biennial Oscillation, while Saturn's Equatorial Stratospheric Oscillation has been observed to almost reach the tropopause (Schinder et al., 2011).
NEB-SEB Anticorrelation
The aerosol and thermal variability of the SEB and NEB have been characterized by numerous studies, focusing on observations during SEB fade/revivals and NEB expansion events.However, the lack of a consistent long-term analysis approach made it difficult to investigate the long-term behavior of the clouds, aerosols and temperatures.2019) reported a potential anti-correlation between the 5-μm brightness temperature changes at these two belts, suggesting a potential coupling between belts that are separated by ∼15° latitude.
Although difficult to detect in Figure 11, our retrieved aerosol opacity and tropospheric temperatures at the NEB and SEB, also seem to vary in antiphase with one another.This is more clearly observed in Figure 13c, where the tropospheric temperatures at 500 mbar (dashed line) and 330 mbar (solid line) are shown for 16° latitude (north and south), and Figure 19, where the tropospheric temperature and aerosol opacity profiles are represented between 10° and 16° latitude.
A Pearson correlation analysis of associate conjugate latitudes at the SEB and NEB (shown in Figure S5 in Supporting Information S1) returns a moderate negative correlation of the aerosol opacity and tropospheric temperatures, with maximum correlation coefficients of −0.53 at 14° latitude in the aerosol opacity, −0.52 at 12° latitude in the tropospheric temperatures at 500 mbar and between −0.6 and −0.67 at 14° and 16° latitude in the upper-tropospheric temperatures at 330 mbar, hinting in a potential connection between these two belts as found at 5 μm.
The continued long-term connection between belts separated by ∼15° latitude is still not understood.Rogers (1995) describes epochs where a number of belts and zones in Jupiter (mainly the SEB, EZ, NEB, and NTB) seem to display a disturbed appearance at visible wavelengths almost contemporaneously.These are called "Global Upheavals."During these events, Jupiter's equatorial and temperate latitudes display major disturbances as if one of the perturbations would trigger the rest of the disturbances, hinting in some kind of deep connections between these latitudes.Although one could think that the NEB-SEB anticorrelation are related to the Global Upheavals, these seem to occur only at specific epochs and are thought to be more frequent during SEB revival events Rogers (2007), unlike the long-term coupling of the NEB and SEB.
A potential cause for an anticorrelation in the tropospheric temperatures and aerosols opacity between belts situated in the same hemisphere, could be the meridional propagation of tropospheric waves, following a similar mechanism to that observed in Saturn during 2011-2014 (Fletcher, Guerlet, et al., 2017).At that epoch, stratospheric waves emanating from a hot and large stratospheric vortex near 40°N disrupted Saturn's Equatorial Stratospheric Oscillation (Fletcher, Guerlet, et al., 2017;Guerlet et al., 2018).However, meridional propagation of waves between hemispheres is unlikely due to the change in the Coriolis force.
Here we speculate that the NEB-SEB anticorrelation observed between 2 and 5 bar and 330 mbar originates in the deep troposphere, via a deep connection of these belts via cylinders parallel to the rotation axis.These would be most effective equatorward of ±16° where the cylinders do not intersect with the dynamics of a region of metallic hydrogen (Cao & Stevenson, 2017;Liu et al., 2008), in agreement with the highest negative correlation coefficients found at ∼14° latitude.Future studies analyzing the temporal variability of Jupiter's deep atmosphere using Juno Microwave Radiometer could shed some light on the causes of the NEB and SEB anticorrelation.
Conclusions
The continued monitoring of Jupiter in the mid-infrared from ground-based observatories during the last four decades provides essential long-term context of Jupiter's atmospheric variability.In this study, we made use of a large dataset of images captured between 7 and 25 μm between early 1984 and late 2019 to explore Jupiter's climate over three jovian years.In particular, we (a) characterize the long-term variability of Jupiter's atmosphere in each of the wavelengths analyzed; (b) retrieve stratospheric and tropospheric temperatures, as well as tropospheric aerosol opacity, to explore hemispherical asymmetries, both seasonal and non-seasonal changes, and equatorial oscillations; and (c) investigate thermal and aerosol opacity changes during cyclic and non-cyclic disturbances of the belt/zone structure.Although some of the mid-infrared images used in this study have been previously published (e.g., Antuñano et al., 2021;Fletcher, Orton, et al., 2018;Fletcher, Orton, Sinclair, et al., 2017, Orton et al., 1994, 2023), this is the first study that analyses simultaneously long-term ground-based images captured in 5 and 8 filters in a systematic fashion.The conclusions of this study are summarized below: • Hemispherical asymmetry: Stratospheric temperatures at 10 mbar retrieved from the 5-point and 8-point spectral cubes show persistent warmer northern latitudes poleward of 30°, compared to their conjugate latitudes in the south.The stratospheric temperature asymmetry between hemispheres increases with latitude, reaching differences of 3.1 ± 2.6 K at 40° and 2.7 ± 2.5 K at 30°.The nature of this asymmetry is still unknown, although it could be related to (a) the higher number density of aerosols found poleward of 30°N at 50 mbar (Zhang et al., 2013) leading to stronger radiative heating, and (b) the differences in hemispheric solar insolation (Guerlet et al., 2020).In contrast, the southern upper troposphere at 330 mbar appears to be overall warmer than the northern latitudes poleward of 30°.However, thermal differences between hemispheres at this pressure are only around 1 K.At deeper levels this asymmetry is no longer detected.• The temporal variability of stratospheric and upper tropospheric temperatures also display a remarkable asymmetry between hemispheres at latitudes poleward of 30°.At these pressures and latitudes, temperatures are observed to oscillate with a 10 to 14-year period (i.e., very close to the jovian year) in anti-phase between hemispheres, as reported by Orton et al. (2023).Comparisons of these thermal oscillations to seasonal variations reveal that they are unlikely to be solely related to radiative heating as temperature peaks do not coincide with solstices nor with the expected seasonal lag (Orton et al., 2023).Numerical radiative-climate models (Guerlet et al., 2020) predict smaller peak-to-peak thermal oscillations in the southern hemisphere, suggesting that the thermal oscillation that we observe is not strictly seasonal.• Belt/zone structure: Our results confirm the anticorrelation between the aerosol opacity and tropospheric temperatures at 500 mbar -typically white and cold zones present high aerosol opacity, while usually warm and low-albedo belts display low aerosol opacity.The NEB displays the lowest aerosol opacity and the largest tropospheric temperatures, while the contrary is true for the EZ.
• In this study we also confirm thermal and/or aerosol opacity changes during NEB expansion and SEB fading events.During some, but not all, NEB expansions, the NTrZ at 17°-22°N displays subtle increases of <1.5 K at 500 mbar accompanied by small decreases in the aerosol opacity, in agreement with Fletcher, Guerlet, et al. (2017).During SEB fading events, our retrievals show that, although the aerosol opacity seems to start increasing almost contemporaneously at all latitudes in the SEB, the most equatorward latitudes (8°-10°S) reach their maxima in aerosol opacity months before the poleward latitudes of the SEB (12°-18°S).Retrieved tropospheric temperatures at 330 and 500 mbar do not show a clear variability during SEB fading and revival events.• At the EZ, this study confirms the variability of the aerosol opacity observed during the EZ disturbances in Antuñano et al. (2018Antuñano et al. ( , 2020)).However, unlike in previous studies, tropospheric temperatures at 500 mbar appear to increase by less than 1.5 K immediately prior to disturbances at 5 μm, although this is hard to confirm due to the lack of data during some of the EZ disturbances.Changes in aerosol opacity appear to lag 180 ± 60 days behind tropospheric temperature changes.Finally, the aerosol opacity at the EZ shows a secondary decrease halfway between disturbances, with a periodicity of 3.5-4 years.This unexpected secondary decrease does not follow the coloration events nor changes in the NEB and SEB.• NEB-SEB anticorrelation: Antuñano et al. ( 2021) reported a continued anticorrelation in the variability of the 5-μm radiance at the NEB and SEB.In this study, we confirm that this long-term anticorrelation is also present higher up in the atmosphere, with maximum Pearson coefficients of −0.53 in the aerosol opacity between conjugate latitudes at 14° at 500 mbar, −0.52 in the tropospheric temperatures at 12° at 500 mbar and −0.6 in the upper-tropospheric temperatures at 14°.This long-term coupling between the NEB and SEB spans all the dates analyzed in this study but, perplexingly, not all of the temperature/aerosol variations are directly related to the significant meteorological events (fades, revivals, expansions, etc.) characterized in visible light.Put another way, the deep-seated anticorrelated connection between the NEB and SEB may be a triggering mechanism for global upheavals, but they do not always lead to upheavals of the same magnitude.• Stratosphere-troposphere coupling: Retrieved stratospheric and upper tropospheric temperatures reveal, for the first time, that JESO extends down to the 330-mbar pressure level.In particular, retrieved temperatures at 330 mbar are overall observed to be moderately anticorrelated to those at 10 mbar, with a Pearson correlation coefficient of −0.47.The Pearson correlation analysis also returns a correlation maxima of 0.4 for a lag of 2.5 years between stratospheric and upper-tropospheric temperature maxima.This lag is half of the expected descending rate of ∼13.6 km/year derived from Giles et al. (2020), which would indicate a lag between 10 and 330 mbar of ∼5.25 years.
These long-term records of thermal variability help to place snapshots from visiting spacecraft (Galileo, Cassini, Juno, etc.) into a broader context, revealing the uniqueness of the jovian climate at a particular moment.Further ground-based spectroscopic data sets will allow us to better disentangle temperature, ammonia and aerosol changes.By extending this time series into the era of the James Webb Space Telescope and the forthcoming JUICE and Europa Clipper missions, new atmospheric discoveries in the 2020s and 2030s can be understood in terms of the meteorological variability observed since the 1980s.
Figure 1 .
Figure 1.Temporal coverage of observations as a function of wavelength.Colors represent different instruments.Note that the wavelength of the observations might not exactly match the wavelength annotated here.See Section 2.2.1 for information on the wavelength shifting performed in this study.A summary of these data is given in Table1and a full description is available in TableS1of Supporting Information S1.
Figure 2 .
Figure 2. Four examples of Jupiter images captured 8.6 μm, showing the evolution of ground-based mid-infrared imaging over the past 4 decades (a).From left to right, images in (a) were captured by IRTF/BOLO-1 (1989), IRTF/MIRAC (1999), and Very Large Telescope (VLT)/VISIR (2007, 2018).Cylindrical maps of Jupiter at 7.9-24.5 μm captured by VISIR and Cooled Mid Infrared Camera and Spectrometer instrument on 25-27 May 2018 (b-i).At 20.5 μm (i) images from April 1 and 21 August 2018 were also used.VISIR images in this Figure are reconstructed to remove the obscured regions resulting from the small field of view of VISIR and the chopping amplitude limitation of 25″ of the VLT.This correction leads to unrecoverable lost signal in thin regions seen as black arcs or strips in the maps (b-i).
Figure 3 .
Figure3.Zonal-mean brightness temperature maps of Jupiter as a function of time between ±48° latitude at eight different wavelengths, showing the atmospheric variability at each of the wavelengths analyzed in this study.Note that the spectral coverage increases in the mid-1990s with the introduction of the MIRAC instrument.Gray shadowed regions represent epochs where no data are available for 2 years or longer.In these cases, we use the value of the brightness temperature of the last available epoch and assume it remains constant during the period without any available data.We note that this is only for the representation and in our analysis we use the averaged and smoothed profiles shown in Figure4.
Figure 4 .
Figure 4. Examples of the average smoothed 8.6-μm radiance profiles smoothed over 3.95 years (red solid line) at the equator (top) and 16° south (bottom), compared to the 8.6-μm zonal-mean radiance (black dots).Black error bars represent measurement errors computed by adding calibration uncertainties and the standard deviation of the zonal mean profiles in quadrature.Pink shadowed regions represent the 1σ uncertainty of the averaged smoothed radiance profiles, as described in the main text.
Figure 5 .
Figure 5. Brightness temperature anomaly maps of Jupiter in the N-band as a function of time between ±48° (a-d, left).Middle and right panels represent the temporal variance and the Lomb-Scargle periodogram of the brightness temperatures, respectively.Note that only periods with 98% of significance or higher are shown.Brighter periods in the right panel correspond to higher spectral powers.Different boxes represent the periods analyzed in this study.Residual brightness temperatures at each wavelength are computed by subtracting the average brightness temperature over all latitudes and dates from the smoothed zonal-mean brightness temperature profile (see Section 2.2.3).Gray shadowed regions represent epochs where no data are available for 2 years or longer.
Figure 6 .
Figure 6.Same as Figure 5, but for the Q-band wavelengths.
Figure 7 .
Figure 7. Smoothed brightness temperature profiles at 8.6 μm (pink), 10.7 μm (gray), and 13.0 μm (yellow) for the equator at 0° latitude, averaged over 30° longitude of the minimum emission angle.Pink, yellow and gray shadowed regions represent the 1σ uncertainty.Blue shadowed regions represent gaps in our dataset.Dashed black lines represent the 13.0-μm brightness temperature minima.10.7 and 13.0 μm brightness temperature profiles are offset by 10 and 12 K, respectively, for clarity.Note the increase in the 8.6-μm brightness temperature during the Equatorial Zone disturbances in 2000, 2007 and late 2018, indicated by black arrows.The increase in 2012-2013 also indicated by a black arrow did not lead to a disturbance at 5 μm.
Figure 8 .
Figure 8.Comparison of the minimum emission angle at 40°N as a function of date (dashed line) and the 20.5-μm brightness temperature variations at the same latitude (solid line), showing a clear anticorrelation between them.The shadowed gray region represent the measurement errors described in Section 2.2.
Figure 11 .
Figure 11.Retrieved temperatures, their variance, and the Lomb-Scargle periodograms at (a) 10 mbar, (b) 330 mbar, and (c) 500 mbar and aerosol opacity at (d) 400-600 mbar as a function of time and latitude.The middle panel in (a-d) shows the temporal variance of the temperatures and aerosol opacity, while right panel in (a-d) represents the Lomb-Scargle periodogram, showing Jupiter's atmospheric variability between June 1983 and November 2019 and potential cyclic changes.Note that only periods with 98% of significance or higher are shown.Brighter periods in the right panel correspond to higher power spectrum.
Figure 12 .
Figure 12.Retrieved stratospheric temperature profiles at 40° (top), 30° (middle), and 24° (bottom) for the north (dashed lines) and south (solid lines) as a function of time.Light and dark gray shadowed regions represent the formal uncertainties of the retrieved temperatures.Black dots and stars in the bottom panel indicate epochs of North Equatorial Belt expansions and North Temperate Belt outbreaks, respectively.
Figure 13 .
Figure 13.Retrieved tropospheric temperature time series at 330 mbar (solid lines) and 500 mbar (dashed lines) for latitudes (a) 40°, (b) 24°, (c) 16°, and (d) 2° in the northern (red) and southern (green) hemisphere as a function of time.Green and red shadowed regions represent the formal uncertainties of the retrieved temperatures.Black dots in (b) indicate the eruption of a convective storm (start of the North Temperate Belt disturbances).Black and red horizontal lines in (c) represent the epochs of North Equatorial Belt expansions and South Equatorial Belt fadings, respectively.The blue horizontal lines in (d) represent the epochs of Equatorial Zone (EZ) disturbances at 5 μm (Antuñano et al., 2018), while the red horizontal line in the same panel indicates a reddening of the EZ without a 5-μm brightening.
Figure 14 .
Figure 14.Retrieved upper-tropospheric temperature contours at (a) 330 mbar, (b) 500 mbar, and (c) aerosol opacity at 400-600 mbar showing the North Equatorial Belt (NEB) and the NTrZ.Retrieved tropospheric temperature at 500 mbar and aerosol profiles (d and e), respectively, are also shown for clarification.White dashed lines in (a-c) and the horizontal black lines in (d and e) represent the beginning and end of the NEB expansions described in Fletcher, Orton, Sinclair, et al. (2017).
Figure 15 .
Figure 15.Retrieved upper-tropospheric temperature contours at (a) 330 mbar, (b) 500 mbar, and (c) aerosol opacity at 400-600 mbar showing the South Equatorial Belt (SEB).Retrieved tropospheric temperature at 500 mbar and aerosol profiles (d and e), respectively, are also shown for clarification.White dashed lines in (a-c) and the horizontal black lines in (d and e) represent the beginning and end of the SEB fading described in Antuñano et al. (2019).
Figure 16 .
Figure 16.Retrieved aerosol opacity profiles at 400-600 mbar for the Equatorial Zone (EZ), showing the aerosol opacity decreases during the EZ disturbances (represented by the dashed red squares) and a secondary peak found around 3.5-4-years after the EZ disturbances.The aerosol opacity decreases in 2012-2013 did not lead to severe coloration events and 5-μm brightening.
Figure 17 .
Figure 17.Retrieved temperature profiles at 10 mbar (solid lines) for 40°N (top) and 40°S (bottom) compared to predicted temperatures in the "all aerosols" model (dashed lines) in Guerlet et al. (2020) and the seasonal insolation cycle (dotted lines).To match the retrieved temperatures, model temperatures in the northern and southern hemispheres have been decreased by 1.0 K and increased by 0.3 K, respectively.
Figure 18 .
Figure 18.Retrieved temperature profiles at 10 mbar (top) and 330 mbar (bottom) for the equator, showing a clear anticorrelation between changes at these pressures.Dark and light gray shadowed regions indicate the uncertainty in the retrieved temperatures.Red shadowed regions indicate epochs without data.
Figure 19 .
Figure 19.Retrieved aerosol opacity (dashed-dotted line), temperatures at 500 mbar (dashed line) and at 330 mbar (solid line) profiles for the North Equatorial Belt and South Equatorial Belt, showing a clear anticorrelation in the changes at these two belts, mainly at 12°-16° latitude.
Table 1
Summary of the Instruments, Date of Observation, and Wavelength Range and Plate Scale of Each Instrument
Table 2
Summary of the Periodicities Described in Section 5 | 19,938.8 | 2023-12-01T00:00:00.000 | [
"Environmental Science",
"Physics"
] |
A Class of Algorithms for Continuous Wavelet Transform Based on the Circulant Matrix
: The Continuous Wavelet Transform (CWT) is an important mathematical tool in signal processing, which is a linear time-invariant operator with causality and stability for a fixed scale and real-life application. A novel and simple proof of the FFT-based fast method of linear convolution is presented by exploiting the structures of circulant matrix. After introducing Equivalent Condition of Time-domain and Frequency-domain Algorithms of CWT, a class of algorithms for continuous wavelet transform are proposed and analyzed in this paper, which can cover the algorithms in JLAB and WaveLab, as well as the other existing methods such as the cwt function in the toolbox of MATLAB. In this framework, two theoretical issues for the computation of CWT are analyzed. Firstly, edge effect is easily handled by using Equivalent Condition of Time-domain and Frequency-domain Algorithms of CWT and higher precision is expected. Secondly, due to the fact that linear convolution expands the support of the signal, which parts of the linear convolution are just the coefficients of CWT is analyzed by exploring the relationship of the filters of Frequency-domain and Time-domain algorithms, and some generalizations are given. Numerical experiments are presented to further demonstrate our analyses.
Introduction
In recent years, different Time-frequency representations, such as, empirical mode decomposition [1], wavelet transform [2] and its variants, empirical wavelet transform [3,4], synchrosqueezed wavelet transforms [5], have been used for analyzing nonlinear and non-stationary signals.To name only a few, the method of fused empirical mode decomposition and wavelets is applied to detection-location of damage in a truss-type structure [6]; Wavelet transform is used for the pattern recognition for diagnosis, condition monitoring and fault detection [7][8][9].It is also used to design an algorithm for Brain-computer interfacing [10], to detect the exact onset of chipping of the cutting tool from the workpiece profile [11], to determine the length of piles [12]; Synchrosqueezed wavelet transform is used for global and local health condition assessment of structures [13], for modal parameters identification of smart civil structures [14].
This paper gives priority to the computation of continuous wavelet transform (CWT) for Morlet-type wavelets.However, for this type of wavelets, it is not possible to use a multiresolution framework for the computation of CWT [15].Michael Unser [16] used Exponentials or B-spline window to approximate Gabor window, which can achieve O(N) complexity per scale.Another kind of method for the computation of CWT with Morlet-type wavelets concerns directly discretizing the integral expression of Wavelet transform.These methods allow us to use arbitrary values for the scale variable, and require the explicit expression of wavelet function and cannot deal with those wavelets without analytic expressions such as Daubechies wavelets [17].This kind of method include Time-domain methods [17] and Frequency-domain methods [18].
Frequency-domain methods for CWT are also widely used in the freewares such as JLAB (available online http://www.jmlilly.net),Wavelab (available online http://www-stat.stanford.edu/$\sim$wavelab), which can achieve satisfactory precision while the edge effect, defined in [18], may occur and the complexity of those methods is O(N log(N)) where N is the length of the signal.
The function cwt in wavelet toolbox of MATLAB, which has poorer precision than Frequency-domain methods mentioned above, is a representation of the Time-domain method [17].For the details of the comparison of the different methods, we refer to [17].
The computation of linear convolution can be realized by using the circular convolution, while the computation of circular convolution can be computed by using the FFT-based fast method [19].In this paper, a novel and simple method is given to prove the FFT-based fast method of linear convolution by exploiting the structures of circulant matrix.After introducing Equivalent Condition of Time-domain and Frequency-domain Algorithms of CWT, a class of algorithms for continuous wavelet transform are proposed and analyzed, which can cover the algorithms in JLAB and WaveLab, as well as the other existing methods such as the cwt function in the toolbox of MATLAB.In this framework, two theoretical issues for the computation of CWT are analyzed.
Firstly, by using Equivalent Condition of Time-domain and Frequency-domain Algorithms of CWT to design Frequency-domain Algorithm, the edge effect, defined in [18], can be avoided.To be specific, in [18], the time series is padded with sufficient zeroes to bring the total length N up to the next-higher power of two, thus limiting the edge effects and speeding up the Fourier transform.In fact, the same number of zeros is padded for all scales in [18], which may cause some troubles, for example, increasing the amount of data to process, and consequently the computational complexity.In this article, the time series (the data) is padded with 2aT zeros while a is the scale.These two zero padding methods are compared in Remark 2 of this paper.
Secondly, due to the fact that linear convolution expands the support of the signal, which parts of the linear convolution are just the coefficients of CWT is analyzed by exploring the relationship of the filters of Frequency-domain and Time-domain algorithms (see Theorem 4), and some generalizations are given (see Theorem 5).
This paper is organized as follows.Section 2 gives some definitions and theorems concerning circulant matrix and linear convolution.Section 3 analyzes algorithms of continuous wavelet transform.Section 4 presents numerical experiments to demonstrate our results and finally, we end this paper with conclusions and discussions in Section 5.
Primary Definitions and Theorems
Definition 1. Circulant matrix: An N × N circulant matrix C takes the form [20,21] A circulant matrix is fully specified by one vector, c, which appears as the first column of C.
Definition 2. Discrete Fourier transform(DFT):
N is the N-th root of unity.
Equation ( 2) can be written as where F N is a N × N matrix, defined as It is easy to verify that where " * " means conjugate transposition.Then, the inverse discrete Fourier transform (IDFT) is given as Theorem 1. References [20,21] The matrix C defined in (1) can be diagonalized by the DFT matrix F N , namely, where c is the first column of C, i.e., c = [ Proof.It is easy to verify that the normalized eigenvectors of C are given by where ω j = e 2πij N with i = √ −1 is the N-th root of unity.The corresponding eigenvalues are then given by From (2), ( 3) and ( 9), we have Using {v j } N−1 j=0 defined in Equation ( 8) as column vectors to form a then we have Furthermore, we have 4), ( 5) and (11).Then we have Definition 3. Linear convolution [23]: A time-invariant linear operator L can be represented as a linear convolution.To be specific, we denote by δ[n] the discrete Dirac Any signal f [n] can be decomposed as a sum of shifted Diracs: Let Lδ[n] = h[n] be the discrete impulse response.Linearity and time invariance implies that where " " represents linear convolution.
Definition 4. Causality and Stability [23]:
One can verify that the filter is stable if and only if h ∈ l 1 (Z).
Proposition 1. Assume that the length of f and h are finite.To be specific, If f = [ f 0 , f 1 , . . ., f n−β−1 ] T , and h = [h 0 , h 1 , . . ., h β ] T , then the linear convolution of f and h defined in ( 14) can be written as where H ∈ R n×(n−β) , and Proof.We refer the reader to [19] for details.
Suppose we wish to compute the polynomial product c(x) = a(x) • b(x), the ordinary product expression for the coefficients of c(x) involves a linear convolution.
Definition 5. Circular convolution [19]:
The circular convolution of a signal {x k=0 is defined as a matrix vector multiplication as follows. y where C is defined in equation ( 1), and " " represents circular convolution.
Proposition 2. The computation of circular convolution can be realized by using FFT-based fast method.
In other word, Equation ( 17) can be written as Proof.The matrix involved in ( 17) is a circulant matrix, thus ( 18) is obtained by using Theorem 1.
The equivalent condition of circular convolution and linear convolution will be given in the following theorem.
then linear convolution g = f h of f and h defined in ( 14) can be computed as circular convolution f h of f and h, namely, where F N , F N −1 are defined by ( 4), ( 5) respectively; ". * "means componentwise product of two vectors.
Proof.From Proposition (1), we get g = H f , where H ∈ R n×(n−β) is defined in (16).Now extend H to a circulant matrix [21] thus the first column of C is h.Therefore, by using Definition 5 and Proposition 2.
From Theorem 2, the algorithm of linear convolution of g = f h is given as follows (See Algorithm 1).
Algorithm 1: FFT-based fast method for linear convolution. Input: Appending an array of zeros to the end of h to obtain h so that the length of h is n.
3. Appending an array of zeros to the end of f to obtain f so that the length of f is n Additionally, the asymptotic time complexity is O(nlog 2 (n)) for Algorithm 1.Note the condition n = length( f ) + length(h) − 1 plays a pivotal role in the equivalence of linear convolution and circular convolution.This condition will also be used in the following part of this paper.
Definition 6. Equivalent Condition of Linear Convolution and Circular Convolution
Equivalent Condition of Linear Convolution and Circular Convolution.
A Class of Algorithms for Continuous Wavelet Transform
where ψ(t) is a continuous function called the mother wavelet and the overline represents the operation of complex conjugate.
For real-life applications, the length of f (t) is finite and the support of ψ(t) is compact.Without loss of generality, assume the sampling rates of the signal and the wavelet are equal to 1, then Equation ( 21) can be written as , where m ∈ Z.
Assume the sampled signal is { f [i]} N−1 i=0 and the support of the mother wavelet then the support of ψa (t) is [0, 2aT].Now, we can get the following result.
Theorem 3. Assume the length of f (t) is finite and the support of ψ(t) is compact, and the sampling rates of the signal and the wavelet are equal to 1, then CWT of { f [i]} N−1 i=0 can be written as where we assume aT is an integer for the convenience of analysis.Furthermore, the corresponding filter is causal and stable.
Proof.By using Proposition 1 and Equations ( 23) and ( 24) is deduced from (22).Furthermore, define then the discrete filter {h a [i]} i∈Z is causal and stable since h a [i] = ψa (i) = 0 if i < 0 and h a ∈ l 2 (Z).Furthermore, Equation ( 24) can be written as can be implemented with linear convolution [17].From Algorithm 1, we can get the algorithm of CWT (see Algorithm 2).
Algorithm 2: Time-domain fast algorithm for CWT.
, where the overline represents the operation of complex conjugate;
Frequency-Domain Algorithm for CWT
The algorithm can be further optimized by taking advantage of the analytic expression of ψ(ω), the Fourier transform of the mother wavelet ψ(t).In fact, the wavelet transform defined in (21) can also be written as a frequency integration by applying the Fourier Parseval formula [23].
Assume the sampled signal is { f [n]} N−1 n=0 .Discretizing (27) and considering the periodic property of discrete Fourier transform yields It is seen by comparing (28) and ( 18) that (28) represents a circular convolution.By considering Equivalent Condition of Linear Convolution and Circular Convolution, It is reasonable to let In this case, the values of { f ( 2π M k)} M−1 k=0 can be computed as the discrete Fourier transform of As for the computation of { ψ( 2π M ka)} M−1 k=0 , we can make use of the analytic expression of ψ(ω).For example, the Morlet wavelet is defined as [24] where σ 2 is shape parameter, and η is center frequency.Furthermore, the Morlet wavelet is approximately analytic and therefore ψ( 2π M ka) ≈ 0 for k < 0. So by the periodic property of discrete Fourier transform.Equation (32) will be used to design algorithm of CWT (see the 4th-6th steps of Algorithms 3).29) is called Equivalent Condition of Time-domain and Frequency-domain Algorithms of CWT.
Remark 1.
In freeware such as Wavelab, the parameter M in Equation (29) takes value N, where N is the length of signal.In this case, { f ( 2π M k)} M−1 k=0 can be obtained as the discrete Fourier transform of the data.Furthermore, the length of the result of ifft is just N.However, the method will produce artificial periodicity, which is called edge effect by Torrence [18], in the wavelet coefficients if signal is not periodic.In order to limit the edge effect, in [18], the data is padded with sufficient zeros before doing the CWT, then the first N coefficients of the corresponding convolution are just the coefficients of the CWT.Nevertheless, the reason for this is not answered in previous papers in the author's knowledge.Put another way.Why the last step of Algorithm 3 is dissimilar from the last step of Algorithm 2? The answer will be given in the following Theorem 4.
Lemma 1.The filter used in Equation ( 27) is Proof.We refer the reader to [23] for details.
Theorem 4. The wavelet coefficients of the CWT of { f [n]} N−1 n=0 is the first N coefficients of (28).While the length of the total coefficients of (28) is M.
Theorem 5. Assume that (Without loss of generality, aT is assumed to be an integer.) Proof.If m = 0, Equation ( 34) is simply Equation ( 28), the conclusion is deduced from Theorem 4. Since , the conclusion for the m = 2 case can also be proved in the same way.Now, Frequency-domain algorithm of CWT with Morlet wavelet as the mother wavelet is given as follows.
Algorithm 3: Frequency-domain fast algorithm for CWT (In real application, the case m = 0 is enough for the calculation of CWT). Input: , the Fourier transform of the Morlet wavelet ψ(t) T, where [−T, T] is the support of ψ(t).Output: 2. Define ϕ m (ω) = e −imaTω ψ(aω), m = 0, 1, 2, where the overline represents the operation of complex conjugate; 3. Let M = 2aT + N; Remark 2. Note that the previous data preparation method takes M = 2 q , where q = min n∈Z {2 n > N} [17,18].However, this method may fail for some real life data.We propose M = N + 2aT, Equivalent Condition of Time-domain and Frequency-domain Algorithms of CWT, then the edge effect, defined in [18], that may occur in JLAB, Wavelab and [25] can be avoided.See Figures 6 and 7 for details.
Numerical Experiments
The experiments are conducted on two types of data, one for synthetic data, another for real-life data.Entropy can be used to measure the sparsity of wavelet coefficients [17].In order to define entropy, {|W f (a i , b j )| 2 } i,j are rearranged as {c k } k=1,...,M , then are normalized to obtain: The wavelet entropy is calculated as with the convention 0 log 0 = 0 by definition.Experiments for Data 1: Data 1 is a synthetic signal with length N. The first half contains sinusoidal signal superimposition with three different frequencies,namely a 4 sin(30πt) + a 3 sin(60πt) + a 1 sin(120πt); The latter half contains the 60 Hz sinusoidal signal with amplitude a 2 , namely a 2 sin(120πt), where a 4 = 1, a 3 = 1.2, a 1 = 1.4, a 2 = 0.6, and the sampling frequency is 400 Hz.Data 1 with length N = 1024 is presented in Figure 1a. Figure 1b, the absolute values of CWT coefficients of Figure 1a, computed with the awt function in Wavelab, where the shape parameter σ 2 = 1 and the center frequency η = 8, manifests edge effects.Figure 1c without edge effects is computed by using the case 0 of Algorithm 3. Table 1 gives the computational results of Data 1 with wavelet parameter σ 2 = 1, η = 8 by using different methods."cwt" means the cwt function in wavelet toolbox of Matlab."Zhao's method" is an improved version of cwt [26]."Direct" means the computation method of linear convolution by using Equation (15).If the length of signal is far larger than the length of the filter, an "Overlap-add" procedure for the calculation of linear convolution is faster than Direct method and Algorithm 1 [23].The "wavelet entropy" shown in Table 1 can measure the sparsity of wavelet coefficients [17]."Err_i" means the relative error of a i and the corresponding maximum amplitude of CWT coefficients.
From Table 1, we know that the precision, wavelet entropy of "Direct", Algorithms 2 and 3, "Overlap-add" are almost the same and are more optimal to cwt function in toolbox of Matlab.In fact, the precision of two methods in [17], "FFT based method" and the proposed method is almost the same.This phenomenon indicates some equivalence of these two methods.As a matter of fact, these two methods can be categorized as Algorithms 2 and 3 respectively.It is seen from Table 2 that the wavelet entropy for Algorithm 2 is the smallest of all the methods.Therefore, Algorithm 2 is the most suitable method for this temperature data.
Figures 6 and 7 are calculated with different data preparation methods.To be specific, Figure 6 is computed with the frequency-domain method of CWT with data preparation method given in previously published papers, namely, M = 2 q , q = min n∈Z {2 n > N} while Figure 7 is computed with the case 0 of Algorithm 3 with M = N + 2aT.Note the different structures of contours in the upper edge of these two figures.The contour is not closed in the upper edge of Figure 6, which means that there will be a valley and a peak in the left and right part of this edge.So the edge effect is obvious in Figure 6. .This contour, computed with frequency-domain method of CWT with data preparation method given in previously published papers, namely, M = 2 q , q = min n∈Z {2 n > N}, manifests edge effect.
Conclusions
The continuous wavelet transform of a signal with finite length for a fixed scale is considered as a linear time-invariant operator.Furthermore, the filter with causal and stability is constructed.The algorithm of linear convolution constitutes a unifying framework to the continuous wavelet transform methods previously published in [17].The precision of these methods based on this framework is almost the same, no matter what method is used, and higher than other methods that use the approximation of wavelet, for example, the cwt function in wavelet toolbox of Matlab.The edge effect is also easily handled by using Equivalent Condition of Time-domain and Frequency-domain Algorithms of CWT.
The algorithms of CWT consist of two methods, time-domain method and frequency-domain method.For time-domain method, by constructing the causal filter ψa (t), we know the wavelet coefficients are just the middle part of the corresponding linear convolution.For frequency-domain method, by exploring the relationship of ψa (t) and ψa (t), we know the wavelet coefficients are just the first N coefficients of the corresponding convolution.Furthermore, by constructing the different filters, the wavelet coefficients can be the first N coefficients, or the middle N coefficients, or the last N coefficients of the corresponding convolution for frequency-domain method.
There are three methods for the calculation of linear convolution.The first one is to directly implement the definition of linear convolution.The second one is known as the FFT-based method, for example, Algorithm 1. Lastly, if the length of the signal is far larger than the length of the filter, an overlap-add procedure for the calculation of linear convolution is faster than the previous two methods [23].How to combine the Frequency-domain method and an overlap-add procedure for the computation of CWT is a question for future research.
3. 1 .
Time-Domain Algorithm for CWT Definition 7. Continuous wavelet transform: The continuous wavelet transform of a function f (t) at a scale a ∈ R + and translational value b ∈ R is expressed by the following integral [17] W f (a, b)
Figure 1 .
Figure 1.(a) is Data 1 with N = 1024.(b) the absolute values of CWT coefficients of (a), computed with the awt function in Wavelab, where the shape parameter σ 2 = 1 and the center frequency η = 8, manifests edge effects.(c) without edge effects is computed with Algorithm 3.
Figure 2
is computed by using the case 1 of Algorithm 3. The wavelet coefficients of Figure2bcan exactly characterize the time-frequency local properties of data of Figure1a.At the same time, Figure2a,c fail to do so.In fact, it is known from the case 1 of Algorithm 3, the wavelet coefficients should be chosen as the middle part of the coefficients of the convolution.However, the wavelet coefficients of Figure2a,c are respectively chosen as the first N and the last N part of the coefficients of the convolution.
Figure 2 .
Figure 2.Figure 2 is computed by using the case 1 of Algorithm 3. The wavelet coefficients of (b) can exactly characterize the time-frequency local properties of data of (a).At the same time, (a,c) fail to do so.
Figure 2 Experiments for Data 2 :
Figure 2.Figure 2 is computed by using the case 1 of Algorithm 3. The wavelet coefficients of (b) can exactly characterize the time-frequency local properties of data of (a).At the same time, (a,c) fail to do so.
Figure 5 .Figure 6
Figure 5.This contour is computed by using CWT function in wavelet toolbox of Matlab with the Morlet wavelet parameter σ 2 = 1, η = 5 for Data 2.
Figure 7 .
Figure 7.This contour, computed with the case 0 of Algorithm 3 with M = N + 2aT, manifests no edge effect.
Table 1 .
Comparisons between the different CWT methods for Data 1.
Table 2 .
The wavelet entropy corresponding to different wavelet transform methods. | 5,171.6 | 2018-02-27T00:00:00.000 | [
"Computer Science",
"Mathematics"
] |
Energy Dissipation of Rock with Different Parallel Flaw Inclinations under Dynamic and Static Combined Loading
: Deep surrounding rocks are highly statically stressed before mining (excavating) and will inevitably experience disturbances from unloading, mining, stress adjustment or their combinations during mechanical or blasting excavation, which actually suffer from a typical coupled static-dynamic stress. A split Hopkinson pressure bar was used to carry out dynamic-static loading test on rock specimens with different fracture angles. The results show that the change law of energy utilization efficiency is similar to the energy absorption rate that they increase first and then decrease with the increasing of axial pressure. The elastic energy of specimens would also increase first and then decrease with the increasing of axial pressure, while the plastic energy generally decrease overall. Both the energy utilization efficiency and energy absorption rate increase with the growth of dynamic compressive strength under impact loading, which indicate that the energy dissipation exhibits a positive with the dynamic strength. The energy absorption density and energy utilization efficiency gradually increase linearly with the increasing of the average strain rate, while the relationship between energy utilization efficiency and incident energy basically follows the exponential function increasing law. The rock burst of pre-flawed rock is related to the static load level under dynamic-static loading, it occurs obviously under the action of medium energy when the axial pressure is high. Based on the energy dissipation theory, the damage variable model was further established, the damage variable can reasonably describe the damage evolution of crack granite under dynamic-static loading.
Introduction
In the fields of resource extraction, transportation, and underground protection engineering, the construction of deep rock mass engineering is becoming more and more frequent, and the research on deep rock mass mechanics has received extensive attention [1][2][3]. In addition to the high static load of self-weight stress and tectonic stress, deep fractured rock mass is often affected by engineering disturbances such as blasting vibration, mechanical excavation, drilling, and rock caving [4]. Body fissures and engineering disturbances are both important factors that affect the mechanical properties of deep high-stress rock mass, especially deep hard rock has significant brittleness characteristics, which are more likely to cause engineering disasters such as rock burst and rock mass engineering instability and damage. Therefore, it is of great significance to father ensure the safety and stability of rock mass engineering by carrying out research on the energy dissipation law of fractured mass under combined dynamic and static loading.
Up to now, a lot of work has been carried out to study the impact of mining on the rock mass and to recommend measures for reducing strain changes of rock mass and minimising the stress on the surface. Since mining production has a significant effect on the stress-strain behaviour of rock mass, the issues of reducing this influence are very relevant and scientists around the world are trying to minimize it. For example, the stress-strain behavior control in rock mass were studied by using different-strength backfill [5], and the proposed approach to the stress-strain analysis of rock mass in rock-back-fill contact zones is recommended for the applied geotechnical assessment in the conditions of mining with manmade support of mined-out area. Through analysing the impact of underground mining on the undermined mass, this impact is investigated using FLAC 3D modelling software. Mining operations cause destruction of the mass above, bringing this disturbance to the surface and producing subsidence and sinkholes. Ways to minimise the impact of underground mining on the surface are suggested [6]. Some researcher proposed to minimize the change in the stress-strain behavior of rock mass using a different-strength backfill. The assessment of the stress-strain behavior of the undermined mass allows to set the stress values boundaries depending on the formation of an artificial mass [7]. A methodology of selecting a mineral deposit development technology and a rational backfill material includes fuzzy models and algorithms, and it can provide processing of large amounts of information and form the significance of environmental factors [8]. Affected by long-term geological tectonic movement and human activities, various types of defects such as cracks, holes and joints are often formed in the rock mass. As an important rock mass structural plane, cracks are widely distributed in deep rock mass engineering.
Some important achievements have been made in the research on the mechanical properties of rock with cracks. Many scholars have studied the strength properties, deformation properties, energy transfer laws, etc. of rock under quasi-static or dynamic loading, and they also found that there are large differences in the energy dissipation laws under quasi-static or dynamic loading [9][10][11]. These results show that the mechanical properties and crack propagation mode of crack rocks are different under static or dynamic loads, but both of them are affected by the number and angle of cracks. Some scholars have studied the influence of crack angle and strain rate on dynamic cracking behaviours and energy evolution of multi-crack rock under dynamic loading, they found that the dynamic strength increases with the increase of strain rate, decreases first and then increases with the increase of crack angle, showing evident rate-dependence [12]. To get insitht into rock unloading failure, some researchers have used the discretized virtual internal bond model in conjunction with element partition method to study the dynamic fracturing process. First of all, the fracturing pattern exhibits the tensile-shear transition with the stress level increasing, and shear cracks are moe likely to occur when the flaw angle is closer to 45 • . Then, the results shows that the inclination angle of the parallel flaws has a large effect on the unloading failure of rock by simulating the unloading failure processof rock with multiple flaws [13,14]. Subsequently, the DIC was used to investigate crack initiation and propagation of rock with different angles, some scholars have presented a simple calibration method, which involves adjusting the size and spacing of subsets of pixels within images of a specimen's surfance, and the crack opening displacements and displacement vectors can be determined at all stages of loading. Besides, different ranges of load contact configurations are also considered, for both the flat platen and samll wooden cushion, they observed that the cracks initiates far from the disc centres for 0 • and 90 • anisotropy angles, the development of the fracture process zone is also identified after the major crack initiation [15]. Some scholars have studied the compressiom behavior of Kuru Gray granite at a wide range of confining pressures by using the servo-controlled hydraulic testing machine, and the tests were divided into low strain rate compression test and dynamic test. The results show that the rock strength increases significantly with the increse of strain rate and confining pressure. At confinements higher than 20 MPa, the strength of the material increases faster at the lower strain rate while at confinements lower than this, the effect of confining pressure is weaker at the higher strain rate [16]. Additionally, to study the fragmentation characteristic and burst behavior of coal under impact load, some researchers found that coal samples have high peak stress, pulverized fragmentation, and intensive burst energy under impact load. And the fragments induced by the impact load have a relatively consistent distribution mode, which can be characterized by a fractal model [17,18]. Since rocks in underground engineering constructions are likely to be confronted with static stress and dynamic disturbance simultaneously, it is of great scientific significance and engineering application value to study the excavation damage caused by blasting, mechanical drilling and other impact dynamic loads of crack rock mass in the stratum or the effect of dynamic natural disasters such as seismic wave and rock burst on th stability of surrounding rock in deep rock engineering [19,20], the coupled static-dynamic loading on flawed rocks has also been performed [21,22]. Due to the different failure mode caused by static and dynamic loads, some studies have been carried out on engineering materials under coupled static and dynamic loading [23,24]. Under uniaxial compression, britle solids are frequently observed to fail by splitting parallel to the direction of loading. Although this failure has long been recognized, considerable confusion regarding how such splitting mechanisms develops still remains [25], based on this, some researchers used the Brazilian disk method and high -speed camera to study the dynamic tensile mechanical properties of granite rock under dynamic and static combined loading, they have found that a slowing rising stress wave woule be created in the dynamic and static combined loading test through wave analyses, stress measurement and crack photography, which allows the stress in the sample gradually accumelatesd, whilst the loads at both ends of the sample remained in a balanced state. The tests results showed that the tensile strength of the granite decreases with the increase of pre-stress under the dynamic and static loads, which might lead to the major repair of the blasting design or support design in deep underground projects. In addition to it, the failure patterns of specimens under dynamic and static loads have been investigated [26,27]. Based on the Braziliza disk method to study the dynamic strength under coupled dynamic and static loads, an experimental method is used to study the dynamic failure process of pre-stressed rock specimen with a circular hole. The researchers have found that the rock debris ejected at the surrounding circular hole because of the high static pre-stress coupled with dynamic loading. while the rock failure can not be induced by the lower static pre-stress coupled dynamic loading. Besides, the dynamic stress concentration occurred when the half-sine wave was generated around the circular hole [28]. Cyclic impact load tests of rock with a single hole were carried out by using Split Hopkinson Pressure Bar device under different impact loads and impact methods. The results showed based on one-dimensional stress wave theory and interface continuity conditions, an improved damage calculation formula is obtained suitable for rock specimen. Damage accumulation of granite increases in a power function with the increase of strain rate. Moreover, cumulative specific energy absorption value increase gradually with cyclic impact. [29] The split Hopkinson pressure bar(SHPB) was used to carry out the crack development and damage patterns under dynamic and static loads [30][31][32], and DIC is a non-contact monitoring device, some scholars have used it to studied the strain and displacemengt field, failure mode and the crack trend of doublefractured specimens, the results showed that the fractures had a promotional effect on final failure of specimens with different angles [33]. The damage to the specimens was caused firstly by large principal and shear strains near the fissure tips, which illustrated that the parallel double fractures was different from the single crack rock. In addition to it, there were two main types of cracks discovered under the dynamic and static loading: tensile cracks and shear cracks in the ultimate failure mode. In addition to it, some experts and scholars have conducted research on the energy dissipation of single-fracture rock mass under combined dynamic and static loading [34,35]. For example, some researchers have studied the rockburst characteristics and tendency indicators under the combined dynamic and static loading conditions, and reached a meaningful conclusion: the rockburst tendency was not only related to the characteristics of its sample, but related to the static load level and dynamic load energy density under different axial pressures and impact numbers [36,37]. In order to study the mechanical properties and failure laws of intact primatic sandstone specimens and specimens with a prefabricated internal circular cavity under coupled static and dynamic loads, coupled static and dynamic loads tests were carried out with a modified split Hopkinson pressure bar(SHPB) apparatus, and the high-speed camera was also applied to record and analyze the fracturing and damage evolution of specimens. The tests results showed that the strength of the fractured specimen is obviously lower than that of the intact specimen, and that, with increasing of the axial pre-stress, the dynamic strength and dynamic elastic modulus generally increased first and then decreased, the combined strength generally increased while the dynamic strain generally decreased [38]. To reveal the mechanical characteristics and failure modes of rock samples with a mini-tunnel under combined static-dynamic loads, some scholars have used the digital image correlation technique to monitor the fracturing in real time, fracture evolution and energy consumption characteristics were further summarized. The results showed that: the pre-stress level decided on the dynamic crack initiation stress, failure mode and dynamic crack velocity of the specimen under otherwise similar dynamic and static disturbance conditions [39].
The research on mechanical properties of rock materials with defects is mainly focused on static loads, dynamic loads test and numerical simulation. At present, only some scholars have carried out the research on mechanical properties of rock mass with defects undr dynamic-static combined loading. In addition to it, the combined static-dynamic SHPB tests are mainly limited to the mechanical responses and fracturing characteristics of pre-flawed rocks and the energy dissipation of the intact rocks. Since the energy dissipation and damage variable of flawed rocks significantly differ from that of intact rocks, it is essential to acquire the energy dissiapation and damage variable of flawed rocks under combined static-dynamic loading.
In this paper, double-flawed granite specimens were tested under dynamic-static loading with an modified SHPB system, and the influences of the crack angle and axial pressure on the energy dissiapation and damage variable of pre-flawed rocks were revealed. The strcuture of this paper is: Section 2 presnets sample preparation, testing machine and data processing. Section 3 illustrates the experimental results, including the influences of axial pressure, compressive strength, average strain rate and incident energy on energy absorption density and energy utilization efficiency, as well as the influences of axial pressure on elastic energy, palstic energy and rock-burst. Section 4 discusses the new damage variable model and the influences of absorbed energy and pre-stress on damage degree. Sections 5 and 6 concludes the discussions and the whole study.
Sample Preparation
By virture of its isotropy in mechanics and homogeneous in texture, the test material is granite with relatively good integrity and uniformity, as shown in Figure 1 (made by author). In order to ensure the accuracy of experimental results, all test specimens were manufactured according to the International Society for Mechanics and Rock Engineering [40]. First, intact rocks were cut with a nominal size of 45 × 45 × 20 mm 3 . Then, diamond saw is used to cut the flaws with different diffsrsnt inclina-tion angles α (the angle is between horizontal direction and flaw orientation as shown in Figure 1), which were divided into 0 • , 45 • , 90 • . Finally, we polished the parallel-flawed rocks until the surfance roughness was less than 0.02 mm, the depth of the cracks is about 1 mm, the crack length is 10 mm and the width is 1 mm. The average density of the rock samples is 2580 kg/m 3 , the average elastic modulus is about 9.86 GPa. Before dynamic loading test, it is essential to use the INSTRO 1346 test system to conduct uniaxial compression test on rock specimens. Three specimens are tested for the specimen configuration and the average com-pressive strength is 139.66 MPa. This experiment included two kinds of specimens: intact specimens and pre-flawed specimens with different angles, a total of 66 samples were required. Among them, there were 6 intact specimens and 18 samples for other 3 kinds of crack angles.
Three specimens are tested for the specimen configuration and the average strength is 139.66 MPa. This experiment included two kinds of specimen mens and pre-flawed specimens with different angles, a total of 66 samples Among them, there were 6 intact specimens and 18 samples for other 3 angles.
Testing Machine
In the test, a modified Split Hopkinson Pressure Bar (SHPB) loading sy to perform the dynamic-static loading test. The system was mainly compo dent bar, transmitted bar, buffer bar, spindle shaped punch, pressure devi quisition system, the axial pressure was providedby the axial pressure sy the transmitted rod. Strain signals were collected by strain gauges pasted o and transmitted bar, and relevant test data were processed by oscilloscope of incident and transmitted bar was 50 mm. The anisotropic punch was u constant strain loading, as shown in Figure 2. Before the test, a layer of butt applied at both ends of the rock specimen to ensure good contact between of the sample and incident and transmitted bar, it would also reduce the in between the rock specimen and elastic rod. The sample was sandwiched b cident and transmitted bar, before the dynamic and static loading, the axia slowly applied to the set value first and then the axial pressure loading pum The oscilloscope was adjusted to receive the signal transmitted through th After all the preparation was ready, the nitrogen switch was strated. The i striked the pulse shaper, and then impacted the incident bar to generate a when the incident wave reached the rock surfance, a part of incident wave w mitted to the transmitted bar through the rock, the others formed the refle reflected back to the surface of rock specimen. At this time, the strain gagu signals of three stress wave at the same time.
Testing Machine
In the test, a modified Split Hopkinson Pressure Bar (SHPB) loading system was used to perform the dynamic-static loading test. The system was mainly composed of an incident bar, transmitted bar, buffer bar, spindle shaped punch, pressure device and data acquisition system, the axial pressure was providedby the axial pressure system at end of the transmitted rod. Strain signals were collected by strain gauges pasted on the incident and transmitted bar, and relevant test data were processed by oscilloscope. The diameter of incident and transmitted bar was 50 mm. The anisotropic punch was used to achieve constant strain loading, as shown in Figure 2. Before the test, a layer of butter was evently applied at both ends of the rock specimen to ensure good contact between the two ends of the sample and incident and transmitted bar, it would also reduce the interface friction between the rock specimen and elastic rod. The sample was sandwiched between the incident and transmitted bar, before the dynamic and static loading, the axial pressure was slowly applied to the set value first and then the axial pressure loading pump valve closed. The oscilloscope was adjusted to receive the signal transmitted through the strain gauge. After all the preparation was ready, the nitrogen switch was strated. The impact bar first striked the pulse shaper, and then impacted the incident bar to generate an incident bar when the incident wave reached the rock surfance, a part of incident wave would be transmitted to the transmitted bar through the rock, the others formed the reflected wave and reflected back to the surface of rock specimen. At this time, the strain gague recorded the signals of three stress wave at the same time.
Data Processing
The half-sinusoidal stress loading wave generated by the spindle-shaped punch can enable the constant strain rate loading. At the same time, the system is equipped with ultra-dynamic strain gauge, oscilloscope and data processing device, which can realize the functions of stress wave signal acquisition, recording and data processing. Based on the one-dimensional stress wave theory, the mechanical parameters such as the average dynamic stress ( ), strain ( ) and strain rate ( ) of the specimen can be obtained by the following equation [41]: In the above equations, σI(t) is the incident wave strain; σR(t) is the reflection wave strain; and σT(t) is the transmitted wave strain; Ae is the sectional area of the elastic rod; Ce is the velocity acoustic wave; ρe is the density of the rod; As is the sectional area of the sample; and Ls is the length of the sample.
Data Processing
The half-sinusoidal stress loading wave generated by the spindle-shaped punch can enable the constant strain rate loading. At the same time, the system is equipped with ultra-dynamic strain gauge, oscilloscope and data processing device, which can realize the functions of stress wave signal acquisition, recording and data processing. Based on the one-dimensional stress wave theory, the mechanical parameters such as the average dynamic stress σ(t), strain ε(t) and strain rate . ε(t) of the specimen can be obtained by the following equation [41]: .
In the above equations, σ I (t) is the incident wave strain; σ R (t) is the reflection wave strain; and σ T (t) is the transmitted wave strain; A e is the sectional area of the elastic rod; C e is the velocity acoustic wave; ρ e is the density of the rod; A s is the sectional area of the sample; and L s is the length of the sample.
According to the loading principle of SHPB and the law of conservation of energy, the curves of incident wave σ I (t), reflected wave σ R (t) and transmitted wave σ T (t) obtained from the test were used to indirectly calculate the incident energy E I , reflected energy E R and transmitted energy E T [41]: The energy absorbed by the rock specimen E A , the energy utilization efficiency E d defined in this study, and the energy absorption density N d of the rock specimen can be determined as follows [41]: where V S is the volume of the rock specimen. The absorbed energy can be mainly divided into: (1) For crack initiation, propagation, penetratin and final failure of the sample.
(2) Elastic energy stored inside the rock. (3) The kinetic energy and other forms of energy required for flying fragments. The energy absorption rate and energy absorption density can reflect the tensile damage and deformation failure of the specimens during the dynamic and static loading.
Dynamic Stress Equilibrium Check
In order to ensure the accuracy of SHPB test results, both ends of the specimen must reach dynamic stress equilibrium before rock failure under dynamic loading. The curve of dynamic stress wave at end of the specimen S 0 -flaw0 • -2 is shown in Figure 3. It can be seen that the transmitted stress wave σ T (t) is basically coincident with the superposition wave of incident and reflected stress wave, which indicates that the balance condition of dynamci stress can be achieved and maintained during the dynamic-static loading, and it also verifies the validity of the test results. Figure 3b depicts the typical evolution curve of the strain rate and stress of S 0 -flaw0 • -2. When the stress reaches the peak stress, there is a gentle stage in the curve of strain rate-time for a period time, and the strain rate hardly changes with the time, which indicates that the deformation rate of rock is constant at this stage. In this study, the average strain rate is defined at the constant deformation rate stage.
Dynamic Deformation Characteristics
The SHPB device was used to perform the impact tests on the rock samples under different axial (0, 14, 27.9, 41.9, 69.8 and 83.8 MPa), respectively corresponding to 0%, 10%, 20%, 30%, 50%, 60% of the uniaxial compressive strength of the standard sample. Besides on Equations (1)-(9), the incident energy, reflected energy, transmitted energy, energy absorbed by the samples, energy absorption rate and the energy absorption density. The results of the dynamic and static combined loading test were listed in Table 1. In the test, the specimen with cracks failed under all loading conditions, and the dynamic stress-strain curve was shown in Figure 4.
Dynamic Deformation Characteristics
The SHPB device was used to perform the impact tests on the rock samples under different axial (0, 14, 27.9, 41.9, 69.8 and 83.8 MPa), respectively corresponding to 0%, 10%, 20%, 30%, 50%, 60% of the uniaxial compressive strength of the standard sample. Besides on Equations (1)-(9), the incident energy, reflected energy, transmitted energy, energy absorbed by the samples, energy absorption rate and the energy absorption density. The results of the dynamic and static combined loading test were listed in Table 1. In the test, the specimen with cracks failed under all loading conditions, and the dynamic stressstrain curve was shown in Figure 4.
The Relationship between Energy Utilization Efficiency and Energy Absorption Density and Axial Pressure
Due to space limitations, Figure 5 shows the energy evolution curve of 0 • fracture under different axial pressures, the four kinds of enenrgy increase first and then remain unchanged with the increase of time. At the beginning of the energy change, the reflected energy is almost equal to the absorbed energy, and the transmitted energy is nearly zero. This is because the signal of incident wave can only be received by the strain collection system after multiple reflections and transmissions. Therefore, the arrival time of the incident wave was delayed but then subsequently increased. At the same time, the reflected and transmitted wave continued to increase at a faster speed. During the dynamic and static combined test, the reflected energy was consistently the largest, followed by the absorbed energy, and the transmitted energy accounted for the minimum proportion. It can be seen from the Figure 4, the energy evolution curve is obviously affected by the axial static pressure. As the axial pressure increased, the absorbed energy gradually decreased, and it accounted for the largest proportion at 10%UCS.When the axial pressure was 50%UCS and 60%UCS, the absorbed energy was clearly lower than the reflected energy. When the axial pressure is 20%UCS and 30%UCS, the absorbed and reflected energy of different crack specimens were almost the same level. The evolution curves can be divided into three stages: (1) compaction stage. The micro-cracks are compacted under the action of axial pressure, which is similar to the stress-strain curve in the dynamic compression test; (2) Linear stage. The consumption of energy increases linearly, and the crack further initiates and expends; (3) Stable stage. The absorbed energy remains nearly constant because the accumulative strain energy is sufficient to cause the specimen to collapse.
The Relationship between Energy Utilization Efficiency and Energy Absorption Density and Axial Pressure
Due to space limitations, Figure 5 shows the energy evolution curve of 0°fracture under different axial pressures, the four kinds of enenrgy increase first and then remain unchanged with the increase of time. At the beginning of the energy change, the reflected energy is almost equal to the absorbed energy, and the transmitted energy is nearly zero. This is because the signal of incident wave can only be received by the strain collection system after multiple reflections and transmissions. Therefore, the arrival time of the incident wave was delayed but then subsequently increased. At the same time, the reflected and transmitted wave continued to increase at a faster speed. During the dynamic and static combined test, the reflected energy was consistently the largest, followed by the absorbed energy, and the transmitted energy accounted for the minimum proportion. It can be seen from the Figure 4, the energy evolution curve is obviously affected by the axial static pressure. As the axial pressure increased, the absorbed energy gradually decreased, and it accounted for the largest proportion at 10%UCS.When the axial pressure was 50%UCS and 60%UCS, the absorbed energy was clearly lower than the reflected energy. When the axial pressure is 20%UCS and 30%UCS, the absorbed and reflected energy of different crack specimens were almost the same level. The evolution curves can be divided into three stages: (1) compaction stage. The micro-cracks are compacted under the action of axial pressure, which is similar to the stress-strain curve in the dynamic compression test; (2) Linear stage. The consumption of energy increases linearly, and the crack further initiates and expends; (3) Stable stage. The absorbed energy remains nearly constant because the accumulative strain energy is sufficient to cause the specimen to collapse. Using Equations (4)-(7), the energy partitions of the confined double-flawed specimens under different axial pressure conditions are determined. Figure 6 presents the incident energy of confined double-flawed specimens under different axial pressure in each crack angle group. The abscissa at the bar grasp bottom represents different crack angle groups, and the axial pressure increases successively from left to right in each crack angle group. Under the same incident energy and impact velocity, the axial pressure makes rocks deform more difficulty. As the axial pressure increases, specimen require more incident energy to obtain a similar strain rate. The reflected energy, transmitted energy, and dissipated energy are normalized by the incident energy, which are shown in Figure in the form of a percentage. As the axial pressure increased, the proportion of reflected energy decreases gradually, which changes from 55% to 44%, and the transmitted energy decreases from 21% to 14%, while the dissipated energy first increases and then decreases, the maximum value is reached when the axial pressure is 0.1 MPa. The higher axial pressure improves the bearing capacity of the specimen, and more energy is used to fracture the rock. When the axial pressure is small, the micro-cracks inside the rock are closed, the dynamic strength increases, and the energy used to destroy the rock will increase. As the axial pressure continues to increase, the micro-cracks inside the sample grow and expand, the energy storage inside the rock gradually increases, and the energy required for rock failure also decreases. Therefore, the proportion of transmitted energy is negatively correlated with the axial static pressure. Under a similar axial pressure, the proportion of reflected energy first decreases and then increases with the increasing of crack angle, while the proportion of transmitted energy and absorbed energy increases first and then decreases, which indicates that there is no obvious relationship between crack angle and energy utilization efficiency of rock under combined dynamic and static loading.
Mathematics 2022, 10, x FOR PEER REVIEW 10 of 23 Figure 5. Energy evolution curves of tested specimens.
Using Equations (4)- (7), the energy partitions of the confined double-flawed specimens under different axial pressure conditions are determined. Figure 6 presents the incident energy of confined double-flawed specimens under different axial pressure in each crack angle group. The abscissa at the bar grasp bottom represents different crack angle groups, and the axial pressure increases successively from left to right in each crack angle group. Under the same incident energy and impact velocity, the axial pressure makes rocks deform more difficulty. As the axial pressure increases, specimen require more incident energy to obtain a similar strain rate. The reflected energy, transmitted energy, and dissipated energy are normalized by the incident energy, which are shown in Figure in the form of a percentage. As the axial pressure increased, the proportion of reflected energy decreases gradually, which changes from 55% to 44%, and the transmitted energy decreases from 21% to 14%, while the dissipated energy first increases and then decreases, the maximum value is reached when the axial pressure is 0.1 MPa. The higher axial pressure improves the bearing capacity of the specimen, and more energy is used to fracture the rock. When the axial pressure is small, the micro-cracks inside the rock are closed, the dynamic strength increases, and the energy used to destroy the rock will increase. As the axial pressure continues to increase, the micro-cracks inside the sample grow and expand, the energy storage inside the rock gradually increases, and the energy required for rock failure also decreases. Therefore, the proportion of transmitted energy is negatively correlated with the axial static pressure. Under a similar axial pressure, the proportion of reflected energy first decreases and then increases with the increasing of crack angle, while the proportion of transmitted energy and absorbed energy increases first and then decreases, which indicates that there is no obvious relationship between crack angle and energy utilization efficiency of rock under combined dynamic and static loading. Figure 7 shows the relationship between the energy utilization efficiency of four types of specimens and different axial static pressures. It can be seen that with the increasing of the axial pressure, the energy utilization efficiency of specimen first increased slightly and then it shows a downward trend, and the energy utilization efficiency reaches Figure 7 shows the relationship between the energy utilization efficiency of four types of specimens and different axial static pressures. It can be seen that with the increasing of the axial pressure, the energy utilization efficiency of specimen first increased slightly and then it shows a downward trend, and the energy utilization efficiency reaches the maximum when the axial pressure is 14 MPa. The energy utilization efficiency of sample is always positive during the whole loading process, it is indicated that during the loading process, the rock continues to absorb energy for the initiation and propagation of internal cracks. The initial loading causes the micro-cracks inside the sample to become dense and hard, and the energy required for rock failure will increase, so the energy utilization efficiency rises briefly; As the axial pressure continues to increase, the micro-cracks in the sample initiate and gradually increase, the micro-cracks in the sample initiate and gradually expand, the elastic strain energy stored in the rock gradually increase, the energy required for rock failure decreases, and the energy utilization efficiency also decreases. Figure 7 shows the relationship between the energy utilization efficiency of four types of specimens and different axial static pressures. It can be seen that with the increasing of the axial pressure, the energy utilization efficiency of specimen first increased slightly and then it shows a downward trend, and the energy utilization efficiency reaches the maximum when the axial pressure is 14 MPa. The energy utilization efficiency of sample is always positive during the whole loading process, it is indicated that during the loading process, the rock continues to absorb energy for the initiation and propagation of internal cracks. The initial loading causes the micro-cracks inside the sample to become dense and hard, and the energy required for rock failure will increase, so the energy utilization efficiency rises briefly; As the axial pressure continues to increase, the microcracks in the sample initiate and gradually increase, the micro-cracks in the sample initiate and gradually expand, the elastic strain energy stored in the rock gradually increase, the energy required for rock failure decreases, and the energy utilization efficiency also decreases. Figure 8 shows the relationship between energy absorption density of four types of specimens and different axial static pressures. The energy absorption density of samples first increases and then decreases with the increasing of the axial pressure. when the axial pressure is 14 MPa, the energy absorption rate reaches the largest, its characteristic law is similar to Figure 6, the three kinds of fracture samples continuously absorb energy from the outside during the dynamic and static combined loading test process, but do not release energy.
Mathematics 2022, 10, x FOR PEER REVIEW 12 of 23 first increases and then decreases with the increasing of the axial pressure. when the axial pressure is 14 MPa, the energy absorption rate reaches the largest, its characteristic law is similar to Figure 6, the three kinds of fracture samples continuously absorb energy from the outside during the dynamic and static combined loading test process, but do not release energy. Figure 9 presents the relationship between the various energy rations and the axial pressure, the energy ratios of three crack samples are similar, and the energy absorption rate increases with axial pressure, at the same time, energy reflectance has a rise. However, Figure 9 presents the relationship between the various energy rations and the axial pressure, the energy ratios of three crack samples are similar, and the energy absorption rate increases with axial pressure, at the same time, energy reflectance has a rise. However, there is no significant change in energy transmittance. Take 0 • crack as an example (similar to other fracture angles), as the axial pressure increases, the reflective energy is used significantly, and the energy absorption rate is gradually reduced. Figure 9 presents the relationship between the various energy rations and the axial pressure, the energy ratios of three crack samples are similar, and the energy absorption rate increases with axial pressure, at the same time, energy reflectance has a rise. However, there is no significant change in energy transmittance. Take 0°crack as an example (similar to other fracture angles), as the axial pressure increases, the reflective energy is used significantly, and the energy absorption rate is gradually reduced.
The Relationship between Elastic Energy and Plastic Energy and Axial Pressure
According to the law of thermodynamics, the elastic energy density uk and plastic energy density up can be obtained according to the dynamic stress-strain curve. In the following equations, * is the dynamic peak stress, * is the dynamic peak strain, Ed is the dynamic elastic modulus, EK and EP are the elastic energy and plastic energy in the loading process, as shown in Figure 10.
The Relationship between Elastic Energy and Plastic Energy and Axial Pressure
According to the law of thermodynamics, the elastic energy density u k and plastic energy density u p can be obtained according to the dynamic stress-strain curve. In the following equations, σ* is the dynamic peak stress, ε* is the dynamic peak strain, E d is the dynamic elastic modulus, E K and E P are the elastic energy and plastic energy in the loading process, as shown in Figure 10.
As shown in Figure 11a, the elastic energy stored by the rock under the combined dynamic and static loading is generated by the axial pressure and the impact load. When the impact load is constant, the elastic energy stored in the rock first increases and then decreases with the increasing of the axial pressure, this is because the impact loading time is very short and the rock sample instantly completes the two stages of energy storage and energy release. When the axial pressure reaches a certain value, the damage of the rock sample increases sharply, and the internal micro-cracks develop rapidly. The damage degree of the rock sample is small in the initial stage of the impact, and the ability to resist the impact load is not obviously, resulting in elastic energy of the rock sample increases rapidly, it indicates that the rock sample has a tendency to transform from brittleness to ductility. When the axial pressure is more than 41.9 MPa, the elastic energy decreases with the increasing of the axial pressure, it proves that the high axial pressure aggravates the internal damage, reduces its strength, and weakens the ability of energy storage. In addition to the change of elastic energy, the rock often has a change of plastic energy during dynamic and static combined loading. As shown in Figure 11b, the plasticity energy generally decreases with the increasing of the axial pressure, it is indicated that irreversible structural changes have occurred inside the rock sample when the rock sample is irreversibly deformed under impact loading, and studying the change law of plastic energy can reveal the evolution characteristics of the internal structure of sample. When the axial pressure is relatively low, the original cracks or formed due to the pre-axial pressure are not fully closed, and they continue to close and consume more energy under impact loading. The crack of rock closes more sufficient with an increasing of the axial pressure, and the energy consumed for micro-crack closing is correspondingly reduced. The new cracks caused by the impact load continue to expand and the energy consumed at this time increases slightly with an increasing of axial pressure. As shown in Figure 11a, the elastic energy stored by the rock under the combined dynamic and static loading is generated by the axial pressure and the impact load. When the impact load is constant, the elastic energy stored in the rock first increases and then decreases with the increasing of the axial pressure, this is because the impact loading time is very short and the rock sample instantly completes the two stages of energy storage and energy release. When the axial pressure reaches a certain value, the damage of the rock sample increases sharply, and the internal micro-cracks develop rapidly. The damage degree of the rock sample is small in the initial stage of the impact, and the ability to resist the impact load is not obviously, resulting in elastic energy of the rock sample increases rapidly, it indicates that the rock sample has a tendency to transform from brittleness to ductility. When the axial pressure is more than 41.9 MPa, the elastic energy decreases with the increasing of the axial pressure, it proves that the high axial pressure aggravates the internal damage, reduces its strength, and weakens the ability of energy storage. In addition to the change of elastic energy, the rock often has a change of plastic energy during dynamic and static combined loading. As shown in Figure 11b, the plasticity energy generally decreases with the increasing of the axial pressure, it is indicated that irreversible structural changes have occurred inside the rock sample when the rock sample is irreversibly deformed under impact loading, and studying the change law of plastic energy can reveal the evolution characteristics of the internal structure of sample. When the axial pressure is relatively low, the original cracks or formed due to the pre-axial pressure are not fully closed, and they continue to close and consume more energy under impact loading. The crack of rock closes more sufficient with an increasing of the axial pressure, and the energy consumed for micro-crack closing is correspondingly reduced. The new cracks
The Relationship between Energy Utilization Efficiency and between Energy Absorption Density and Compressive Strength
It can be observed from Figure 12, as the dynamic compressive strength of the sample gradually increased, the energy absorption density and energy utilization efficiency also increase. This is because the impact load is very short in the crack rock, the elasticity can not be done in time, therefore, both of the energy absorption density and energy utilization efficiency increase with the improvement of dynamic compressive strength. When
The Relationship between Energy Utilization Efficiency and between Energy Absorption Density and Compressive Strength
It can be observed from Figure 12, as the dynamic compressive strength of the sample gradually increased, the energy absorption density and energy utilization efficiency also increase. This is because the impact load is very short in the crack rock, the elasticity can not be done in time, therefore, both of the energy absorption density and energy utilization efficiency increase with the improvement of dynamic compressive strength. When the axial pressure is small, microfitting of the rock closes, the evolution of energy absorption rate makes the intensity of the crack rock deteriorate, eventually result in structural destruction. With an increase in the axial pressure, the higher the compressive strength is, the faster the small crack of the sample expands, the remaining carrying capacity of the moving load gradually decreases, the more energy the sample absorbs before it breaks, the more reflected and transmitted energy the sample coverts during destruction, and the damage of the sample is more serious, thus the energy absorption density and energy utilization efficiency are directly related to the damage and strength deterioration of the fractured granite.
It can be observed from Figure 12, as the dynamic compressive strength of the sample gradually increased, the energy absorption density and energy utilization efficiency also increase. This is because the impact load is very short in the crack rock, the elasticity can not be done in time, therefore, both of the energy absorption density and energy utilization efficiency increase with the improvement of dynamic compressive strength. When the axial pressure is small, microfitting of the rock closes, the evolution of energy absorption rate makes the intensity of the crack rock deteriorate, eventually result in structural destruction. With an increase in the axial pressure, the higher the compressive strength is, the faster the small crack of the sample expands, the remaining carrying capacity of the moving load gradually decreases, the more energy the sample absorbs before it breaks, the more reflected and transmitted energy the sample coverts during destruction, and the damage of the sample is more serious, thus the energy absorption density and energy utilization efficiency are directly related to the damage and strength deterioration of the fractured granite. Figure 13 presents the relationship between energy absorption density, energy utilization efficiency and average strain rate. The average strain rate of the specimen is defined by choosing the moment when the specimen reaches the stress uniformity to failure (or the stress peak value). The energy absorption density and energy utilization efficiency gradually increase linearly with the increasing of the average strain rate under a certain impact load, which shows an obvious rate effect. The correlation coefficient of the linear fit between the two is relatively high, it shows that among the energy absorption density, energy utilization efficiency and average strain rate have a positive correlation. When the average strain rate is approximately the same, the larger the axial compression ratio is, the smaller the energy absorption density and energy utilization efficiency are. This is because the increase in axial pressure means that the small cracks inside the sample increase and expand gradually, the sample reduces its own energy storage limit, the energy absorption density and energy utilization efficiency decrease accordingly.
Relationship between Energy Absorption Density and Energy Utilization Efficiency and Incident Energy
In order to qualitatively analyze the relationship under combined dynamic and static loading, the energy absorption density, energy utilization efficiency and incident pressures can be fitted during the different axial. Figure 14 and Table 1 show incident energy decreases with an increasing of axial pressure, and the energy absorption density of sample increases first and then decreases with increasing of the axial pressure. It can be seen that energy absorption density and energy utilization efficiency of rock sample basically follow the exponential function increasing law with the increasing of incident energy by nonlinear fitting, and the overall trend is on the rise. impact load, which shows an obvious rate effect. The correlation coefficient of the linear fit between the two is relatively high, it shows that among the energy absorption density, energy utilization efficiency and average strain rate have a positive correlation. When the average strain rate is approximately the same, the larger the axial compression ratio is, the smaller the energy absorption density and energy utilization efficiency are. This is because the increase in axial pressure means that the small cracks inside the sample increase and expand gradually, the sample reduces its own energy storage limit, the energy absorption density and energy utilization efficiency decrease accordingly.
Relationship between Energy Absorption Density and Energy Utilization Efficiency and Incident Energy
In order to qualitatively analyze the relationship under combined dynamic and static loading, the energy absorption density, energy utilization efficiency and incident pressures can be fitted during the different axial. Figure 14 and Table 1 show incident energy decreases with an increasing of axial pressure, and the energy absorption density of sample increases first and then decreases with increasing of the axial pressure. It can be seen that energy absorption density and energy utilization efficiency of rock sample basically follow the exponential function increasing law with the increasing of incident energy by nonlinear fitting, and the overall trend is on the rise.
Energy Dissipation of Rock under Coupled Static-Dynamic Loads
Under the action of static pre-stress, assuming that the elastic energy stored inside the rock is Es, and the disturbance energy is Ed, when it is disturbed by the outside, then the surface energy required for rock failure under Es conditions is Ec, without considering the thermal energy and radiation energy generated in the process of rock failure, Ec = γSR (γ is the surface energy per unit area; Sr is the total area of new crack surfaces generated by rock failure, which is an increasing function of Ed),that is, EC = γSR = Rf (Ed). When the Ed is larger, the fragmentation of the rock increases, and the consumption of Ec is larger. According to it, we propose a rock-burst occurrence criterion based on dynamic and static energy indicators (Es and Ed) whether there is internal elastic energy storage and release.
Under impact loading, the excited internal elastic energy is greater than the surface energy required for rock fracture, the residual elastic energy is converted into the kinetic
Energy Dissipation of Rock under Coupled Static-Dynamic Loads
Under the action of static pre-stress, assuming that the elastic energy stored inside the rock is E s , and the disturbance energy is E d , when it is disturbed by the outside, then the surface energy required for rock failure under E s conditions is E c , without considering the thermal energy and radiation energy generated in the process of rock failure, E c = γS R (γ is the surface energy per unit area; S r is the total area of new crack surfaces generated by rock failure, which is an increasing function of E d ), that is, E C = γS R = R f (E d ). When the E d is larger, the fragmentation of the rock increases, and the consumption of E c is larger. According to it, we propose a rock-burst occurrence criterion based on dynamic and static energy indicators (E s and E d ) whether there is internal elastic energy storage and release.
Under impact loading, the excited internal elastic energy is greater than the surface energy required for rock fracture, the residual elastic energy is converted into the kinetic energy of rock that needs to be broken and it also leads to the rock burst. The rock burst in deep rock is affected by the dual effects "Internal elastic energy and External impact "kinetic energy" according to the energy angle. It can be seen that whether there is a occurrence of rock-burst based on dynamic and static energy indicators (E S and E c ) that released by internal elastic energy storage. And the premise of the rock-burst is that the elastic energy of the rock is partially redundant that is used for the ejection of the rock no matter how large the incident energy outside the world, the stress status and energy storage of rock can be. Therefore, the elastic energy inside rock is greater than the surface energy which is required for the rock fracture under the effect of impact loading, so there is [42], Figure (b, e, h) in Table 2 describe the relationship between incident energy and the unused energy. It can be seen that, the energy partition can be separated into two regions: (1) Region I is linear and very close to 1:1, where almost all incident energy is dissipated in the form of transmitted and reflected energy (unused energy), but hardly any damage is caused to the rock sample; (2) In region II, when the incident energy is close to the critical value, the sum of transmitted energy and reflected energy increases with the increasing of incident energy, and deviates from the 1:1 line. The growth rate becomes larger, denoting that the sample absorbs less energy, eventually the unused energy reaches the upper limit. When the axial compression ratio exceeds 25% of the crack damage threshold (25% of UCS), almost all samples with different angles occur rock-burst under medium and high axial pressure. Figure (a, d and g) illustrate relationship between the absorbed energy and incident energy, it can be observed that the total absorbed energy increases with the increasing of incident energy under medium and low axial compression, while at high axial pressure, the absorbed energy gradually decreases, especially for rock samples with 45 • cracks, when the axial compression is 69.8-83.8 MPa, the final absorbed energy of the sample during the experiment is negative (releasing energy),this is because the rock first accumulates a large amount of strain energy under the action of axial pressure, which is mainly used for rock spalling and formation of expansion cracks, and then continues to release the strain energy after dynamic loading, a small fraction of stored strain energy is dissipated for the growth and inter-action of cracks, while the remaining part predominately delivers into elastic bars as released energy. with the increasing of incident energy under medium and low axial compression, at high axial pressure, the absorbed energy gradually decreases, especially for roc ples with 45°cracks, when the axial compression is 69.8-83.8 MPa,the final absorb ergy of the sample during the experiment is negative (releasing energy),this is becau rock first accumulates a large amount of strain energy under the action of axial pre which is mainly used for rock spalling and formation of expansion cracks, and the tinues to release the strain energy after dynamic loading, a small fraction of stored energy is dissipated for the growth and inter-action of cracks, while the remainin predominately delivers into elastic bars as released energy. with the increasing of incident energy under medium and low axial compression, while at high axial pressure, the absorbed energy gradually decreases, especially for rock samples with 45°cracks, when the axial compression is 69.8-83.8 MPa,the final absorbed energy of the sample during the experiment is negative (releasing energy),this is because the rock first accumulates a large amount of strain energy under the action of axial pressure, which is mainly used for rock spalling and formation of expansion cracks, and then continues to release the strain energy after dynamic loading, a small fraction of stored strain energy is dissipated for the growth and inter-action of cracks, while the remaining part predominately delivers into elastic bars as released energy. with the increasing of incident energy under medium and low axial compression, while at high axial pressure, the absorbed energy gradually decreases, especially for rock samples with 45°cracks, when the axial compression is 69.8-83.8 MPa,the final absorbed energy of the sample during the experiment is negative (releasing energy),this is because the rock first accumulates a large amount of strain energy under the action of axial pressure, which is mainly used for rock spalling and formation of expansion cracks, and then continues to release the strain energy after dynamic loading, a small fraction of stored strain energy is dissipated for the growth and inter-action of cracks, while the remaining part predominately delivers into elastic bars as released energy.
Rock Damage Characteristics
Theoretically, the definition of the damage value of the rock samples based dissipated energy of crushing absorbed energy is closer to the nature of rock da Therefore, this paper defines a new damage variable form based on the study of g energy absorption.
Rock Damage Characteristics
Theoretically, the definition of the damage value of the rock samples based on the dissipated energy of crushing absorbed energy is closer to the nature of rock damage. Therefore, this paper defines a new damage variable form based on the study of granite energy absorption.
Rock Damage Characteristics
Theoretically, the definition of the damage value of the rock samples based on the dissipated energy of crushing absorbed energy is closer to the nature of rock damage. Therefore, this paper defines a new damage variable form based on the study of granite energy absorption.
Rock Damage Characteristics
Theoretically, the definition of the damage value of the rock samples based dissipated energy of crushing absorbed energy is closer to the nature of rock da Therefore, this paper defines a new damage variable form based on the study of g energy absorption.
Angle/(°C) Incident Energy and Unused Energy
Absorbed Energy and Incident Energy Absorbed Energy and Pre-Stress 0°flaw 45°flaw 90°flaw
Rock Damage Characteristics
Theoretically, the definition of the damage value of the rock samples based on the dissipated energy of crushing absorbed energy is closer to the nature of rock damage. Therefore, this paper defines a new damage variable form based on the study of granite energy absorption.
Angle/(°C) Incident Energy and Unused Energy
Absorbed Energy and Incident Energy Absorbed Energy and Pre-Stress 0°flaw 45°flaw 90°flaw
Rock Damage Characteristics
Theoretically, the definition of the damage value of the rock samples based on the dissipated energy of crushing absorbed energy is closer to the nature of rock damage. Therefore, this paper defines a new damage variable form based on the study of granite energy absorption.
Rock Damage Characteristics
Theoretically, the definition of the damage value of the rock samples based on the dissipated energy of crushing absorbed energy is closer to the nature of rock damage. Therefore, this paper defines a new damage variable form based on the study of granite energy absorption.
In the above formula, U b i is the energy absorption density of fractured rocks with different axial compression under the action of dynamic and static loading, and U b m is the maximum value of energy absorption density. Figure 15 shows the damage value of the rock samples increases continuously with the progress of the dynamic and static combined loading test, there is an obvious nonlinear relationship between the damage value and axial strain, the damage value increases withthe increasing of axial pressure. And the peak damage value generated by sample in high axial pressure is faster, indicating that a certain axial preload can enhance the failure strength of rock. The damage value develops relatively gently in the initial compaction and elastic sections, increases significantly in the crack development stage, decreases slowly at the early stage of pre-peak unstable fracture and gradually approaches to 1. It can be seen that the damage value does not appear to be abnormally reduced or even "negative damage" at each stage. The damage development reflected in different stages is the same as that reflected by the stress-strain curve characteristic analysis, it can be inferred that the damage degree of rock under static and dynamic loading is reasonable. strength of rock. The damage value develops relatively gently in the initial compaction and elastic sections, increases significantly in the crack development stage, decreases slowly at the early stage of pre-peak unstable fracture and gradually approaches to 1. It can be seen that the damage value does not appear to be abnormally reduced or even "negative damage" at each stage. The damage development reflected in different stages is the same as that reflected by the stress-strain curve characteristic analysis, it can be inferred that the damage degree of rock under static and dynamic loading is reasonable. Figure 16 shows the relationship between the damage value and the absorbed energy of the specimen during the combined dynamic and static loading. The damage value decreases with an increase of the axial pressure ratio, but an abnormal phenomenon occurs that the damage value increases when the axial pressure is 69.8-83.8, this is because the micro-cracks inside the sample expand seriously under high axial pressure, and the damage value increases again. It can be seen from the changing curve during the loading procedure that the damage value of the sample decreases with a decrease of the absorbed energy, which proves that there is a similar change law between the damage value variable of the sample and the absorbed energy during the loading procedure, and both of them are in a proportional relationship. From this, it can be concluded that the damage value of the three different fracture specimens generally decreases with an increase of the axial pressure, the energy absorbed by the specimen during the loading process is used to initiate and expand the cracks inside a sample and gradually damages the sample until it fails. Figure 16 shows the relationship between the damage value and the absorbed energy of the specimen during the combined dynamic and static loading. The damage value decreases with an increase of the axial pressure ratio, but an abnormal phenomenon occurs that the damage value increases when the axial pressure is 69.8-83.8, this is because the micro-cracks inside the sample expand seriously under high axial pressure, and the damage value increases again. It can be seen from the changing curve during the loading procedure that the damage value of the sample decreases with a decrease of the absorbed energy, which proves that there is a similar change law between the damage value variable of the sample and the absorbed energy during the loading procedure, and both of them are in a proportional relationship. From this, it can be concluded that the damage value of the three different fracture specimens generally decreases with an increase of the axial pressure, the energy absorbed by the specimen during the loading process is used to initiate and expand the cracks inside a sample and gradually damages the sample until it fails.
Discussions
Energy consumption characteristics and damage degree are the key components to study rock samples with different fractures under combined dynamic and static loading at a certain incident energy. In this study, the fracture angle and axial pressure of the rock sample have a certain influence on the energy dissipation and damage degree, which is similar to the analytical and experimental results [23,39], they showed that energy utilization efficiency and energy absorption density both decrease with the increasing of the axial pressure. The energy utilization efficiency and energy absorption density increase linearly with the increasing of mean strain rate and increase exponentially with the increasing of incident energy [43], this is because fractured rocks have relatively with good homogeneity, and rocks can absorb more energy for crack propagation and failure. In addition, we define the sum of the reflected energy and the transmitted energy as the unused
Discussions
Energy consumption characteristics and damage degree are the key components to study rock samples with different fractures under combined dynamic and static loading at a certain incident energy. In this study, the fracture angle and axial pressure of the rock sample have a certain influence on the energy dissipation and damage degree, which is similar to the analytical and experimental results [23,39], they showed that energy utilization efficiency and energy absorption density both decrease with the increasing of the axial pressure. The energy utilization efficiency and energy absorption density increase linearly with the increasing of mean strain rate and increase exponentially with the increasing of incident energy [43], this is because fractured rocks have relatively with good homogeneity, and rocks can absorb more energy for crack propagation and failure. In addition, we define the sum of the reflected energy and the transmitted energy as the unused energy of the rock. Regardless of the axial pressure, the unused energy in the dynamic and static combined loading process is divided into two stages with the change of the incident energy, while the absorption energy of rock first increases and then decreases with the increasing of axial pressure. We find that rock burst occurs when the sample is under medium and high axial pressure and exceeds 25% of the crack damage stress, this is because part of the elastic strain energy will be stored in the sample under high preload, under the action of a certain impact, the rock absorbs another part of the energy, resulting in a rock burst phenomenon.
The damage variable is an important index to define the damage of rock with the change of energy during the loading process. In this paper, we define a new damage degree form, that is, the ratio of the energy absorption density to the maximum energy absorption density of the rock during the combined dynamic and static loading process. We find that the newly defined damage variable exhibits an obvious nonlinear relationship with the axial strain, and the damage degree increases with the increasing of the axial strain. Figure 17 shows the coupled effects of axial pressure and energy absorption density on rock damage. As shown in Figure 16b, for a certain crack angle, with the increasing of axial pressure, the rock damage degree shows an obvious decreasing trend, which verifies that the axial pressure has a certain inhibitory effect on the final rock damage degree. Figure 16c shows that the damage degree increases with the increasing of energy absorption density, which indicates that the energy absorbed under impact loading is further converted into elastic strain energy required for rock failure. This study may contribute to some rock engineering practices., underground protective structure design, rock blasting and rock excavation. For deep rock structures, the failure process is controlled by the combined effects of strain energy stored in rock mass (in situ stress) and the external dynamic disturbance, in which the strain energy storage dominates the growth and interaction of micro-cracks within the rock, while the dynamic disturbance only participates as triggering source for rock dynamic response and energy supply for rock fragmentation. Based on the analysis of energy feature, the failure of rock in underground engineering can be categorized as follows: (1) The rocks without pre-stress need to absorb external energy to break them. (2) When the pre-stress is moderate and the crack density is very low, the elastic strain energy stored under the impact loading is suddenly released, resulting in the occurrence of rock-burst. (3) When the pre-stress exceeds the crack damage threshold, the rock experiences large deformation and has considerable strain energy storage capacity.
Conclusions
In this study, we performed combined dynamic and static SHPB tests on granites with different fractures to investigate the coupling effects of fracture angle and axial pressure on rock energy dissipation and damage characteristics. Under certain incident energy and impact velocity, six groups of granites with axial pressure in the range of 0% UCS, 10% UCS, 20% UCS, 30% UCS, 50% UCS and 60% UCS were tested. Main conclusions can be drawn as follows: (1) It was found that the energy absorption density and energy utilization efficiency of the sample first increase and then decrease with the increase of axial pressure, and both of them increase first decrease and then increase with increase of the flaw angle.
(2) As a result of this study, the energy absorption density and energy utilization efficiency increase linearly with an increase of the average strain rate, which belongs to the deterioration effect of rock dynamic mechanical properties during the dynamic and static combined loading process. This study may contribute to some rock engineering practices., underground protective structure design, rock blasting and rock excavation. For deep rock structures, the failure process is controlled by the combined effects of strain energy stored in rock mass (in situ stress) and the external dynamic disturbance, in which the strain energy storage dominates the growth and interaction of micro-cracks within the rock, while the dynamic disturbance only participates as triggering source for rock dynamic response and energy supply for rock fragmentation. Based on the analysis of energy feature, the failure of rock in underground engineering can be categorized as follows: (1) The rocks without pre-stress need to absorb external energy to break them. (2) When the pre-stress is moderate and the crack density is very low, the elastic strain energy stored under the impact loading is suddenly released, resulting in the occurrence of rock-burst. (3) When the pre-stress exceeds the crack damage threshold, the rock experiences large deformation and has considerable strain energy storage capacity.
Conclusions
In this study, we performed combined dynamic and static SHPB tests on granites with different fractures to investigate the coupling effects of fracture angle and axial pressure on rock energy dissipation and damage characteristics. Under certain incident energy and impact velocity, six groups of granites with axial pressure in the range of 0% UCS, 10% UCS, 20% UCS, 30% UCS, 50% UCS and 60% UCS were tested. Main conclusions can be drawn as follows: (1) It was found that the energy absorption density and energy utilization efficiency of the sample first increase and then decrease with the increase of axial pressure, and both of them increase first decrease and then increase with increase of the flaw angle. (2) As a result of this study, the energy absorption density and energy utilization efficiency increase linearly with an increase of the average strain rate, which belongs to the deterioration effect of rock dynamic mechanical properties during the dynamic and static combined loading process. (3) The energy absorption density and energy utilization efficiency increase with the increasing of the incident energy, it can also be seen from the curve fitting that both of them increase exponentially with the incident energy. (4) The dependence of plastic and elastic energy and axial pressure was obtained, the elastic energy first increases and then decreases with the increase of axial pressure, while the plastic energy decreases with the increase of axial pressure. (5) When the axial compression ratio exceeds 25% of the crack damage stress (accounting for 25% of the UCS), the dynamic strength of rock decreases because of the impact loading, and a small part of strain energy inside the rock is released. Overall, the fractured rock generally absorbs energy in rock bursts. (6) As the absorbed energy decreases, the damage variable of the specimen decreases overall. The damage variable defined based on the energy absorption density can reasonably describe the damage evolution process of fractured rock under static and dynamic loading. The damage variable is relatively gentle in the initial compaction and elastic deformation stages under different axial pressures, it slightly increases in the crack development stage. After that, it decreases and gradually becomes stable. | 16,107 | 2022-11-02T00:00:00.000 | [
"Engineering"
] |
THE INFLUENCE OF EARLY MARRIAGE ON MONETARY POVERTY IN INDONESIA
ARTICLE INFO Introduction/Main Objectives: The aim of this study is to analyze the influence of early marriage on monetary poverty in Indonesia. Background Problems: Recent studies on early marriage show that the prevalence of early marriage in Indonesia reached 13.5 percent (Marshan et al., 2013) and that early marriage exacerbates poverty, which causes an increase in the economic burden on the family (Djamilah, 2014), an increase in family harassment, divorce and individuals not continuing with their schooling (Putranti, 2012), and an increase in the chance of poverty by 31 percent in the United States (Dahl, 2010). However, most studies are qualitative studies. Research Methods: This study uses recent data from the Indonesian Family Life Survey (IFLS), year 2014; with the sample being women who get married for the first time at less than 18 years old as a proxy for early marriage; and monthly per capita income as a measurement of monetary poverty. This study employs a binary method for the binary dependent variable which is whether the women experience monetary poverty. Findings/Results: The result shows that the prevalence of early marriage in Indonesia has reached 16.36 percent. Among those, 46.61 percent of the women who marry in their teens (before 18 years old) do not complete the mandated nine years of basic education, and 52.35 percent of the women who were married at an early age do not have a health insurance card. The results of the binary probit model show that early marriage does not affect the possibility of a woman experiencing monetary poverty. It means that early marriage does not influence the monthly per capita income of the women. Conclusion: The results of this study imply that other measurements of poverty may need to be considered. Therefore, the policies that are aimed at reducing early marriage should consider the impact of other factors on poverty. Article history: Received in 4 January 2019 Received in revised form 13 June 2019 Received in revised form 25 February 2020 Accepted 28 February 2020
INTRODUCTION
Poverty is a complex issue facing all countries. Although many studies and research have been conducted, the standard definition of poverty is hard to find (Arsyad, 2010: 299). This is due to the complexity of the topic. Almost all social discipline sciences pay attention to this issue, such as economics, sociology, anthropology, psychology, and politics (Austin, eds, 2006: 3).Therefore, poverty can be analyzed from many aspects.
Poverty is divided into structural poverty and cultural poverty, according to the causes of poverty (Arsyad, 1992). According to the structural view, the economic system that develops in society and strategies to boost development sometimes cannot touch all the layers of society, therefore there are some people who cannot access the factors of production, and this causes poverty. Meanwhile, cultural poverty occurs because the community has not been able to utilize its production factors effectively (Arsyad, 1992). However, fundamentally the causes of structural and cultural poverty cannot be separated due the interaction between both of them.
The concept of absolute (monetary) poverty measures the amount of poverty by comparing the level of income or expenditure of a person with the minimum level of income that a person needs to meet his or her basic needs. Poverty is seen as an economic inability to meet the basic needs for food and not food as measured by expenditure. Furthermore, the minimum expenditure to meet the basic needs is what is called the poverty line. The poor are residents who have an average monthly per capita expenditure below the poverty line (BPS, 2015).
Early marriage is the marriage of people who have not yet reached the age of 18 years old. It is also referred to as one of the social pathologies that cause or exacerbate poverty (UNICEF, 2001). According to Jordan (2004), teen pregnancy which is identified with early marriage, divorce, and crime, are all forms of cultural poverty, a social dysfunction or deficiency experienced by individuals that causes them to be economically weak.
Research into the impact of early marriage on poverty has not been widely conducted. It is caused by the limited data on early marriage, and the theories for structural poverty are more developed than those for cultural poverty (BKKBN, 2012). Dahl's study (2010) provides empirical evidence showing that early marriage has a significant effect on poverty. Dahl (2010) used panel data from 41 states in the United States, and concluded that early marriage increased the likelihood of being poor in the future by 31 percent. Meanwhile, Jordan's research (2004) showed that pregnancy in adolescence did not significantly affect the number of poor people.
In Indonesia, research into the impact of early marriage has been undertaken, for example by Djamilah (2014). The results indicate that early marriage has an impact on increasing the family's economic burden, the divorce rate, domestic violence, reproductive health problems as well as maternal and child mortality. In 2011, the Center for Policy Studies and Population Universitas Gadjah Mada and Plan Indonesia conducted research in six regions of Indonesia. The results of this study indicate that early marriage leads to continued poverty, increased incidences of domestic violence, divorce, and dropping out from school (Putranti, 2012).
Almost all the research into early marriage refers to women as the object of the studies. This is because the incidence of early marriage is more common for women, and women are more vulnerable to the negative impacts of early marriage (eg. Oyortey and Pobi, 2003;Putranti, 2012;Djamilah, 2014). Existing research indicates that a woman who had an early marriage will experience more pregnancy risks and lower levels of education than a woman who has not had an early marriage (Putranti, 2012;Djamilah, 2014).
Previous studies show that an early marriage has impacts on many aspects of life, especially the quality of life for the women who experience an early marriage. Therefore, this paper analyzes the impact of early marriage on monetary poverty. Since previous studies use qualitative methods, this study uses a comprehensive quantitative method to analyze the impact. Previous qualitative studies have not been able to quantify the magnitude of the impact; therefore the study of the impact of an early marriage on monetary aspects by this paper can contribute to the knowledge of the impact of an early marriage on the quality of life for people.
Monetary Poverty
The monetary approach is one form of onedimensional poverty measurement (Alkire and Foster, 2011). The calculation of monetary poverty uses the concept of absolute poverty, which is determined based on the inability of the individual to meet the minimum basic needs necessary to live and work. This minimum requirement is translated into a financial measure in the form of money, where the value of the minimum basic needs is known as the poverty line. Furthermore, people whose expenditure is below the poverty line are classified as poor (BPS, 2015).
The monetary poverty approach is still used by the Central Bureau of Statistics (BPS) to determine who is poor. According to BPS (2015), poverty is seen as an economic inability to meet basic needs for food and non-food.
According to Dartanto and Nurkholis (2011), poverty is influenced by human capital, geographic conditions, demographic factors, and employment status. Dartanto and Nurkholis (2011) found that the increase in human capital shown by having an education has a negative effect on poverty. Geographical conditions, in the form of the location of a person's residence, also determine poverty. A person living in a village is more vulnerable to poverty. Furthermore, demographic factors, in the form of an increase in the number of family members, have a positive effect on poverty. Finally, employment status also determines poverty. A person who is unemployed is more at risk of experiencing poverty than someone with a job.
Early Marriage
Early marriage or child marriage refers to a marriage conducted before both parties are adults, or a marriage where at least one of the parties is still a child or under the age of 18 years old (BKKBN, 2012). This is in accordance with Article 1989 of the United Nations Convention on the Rights of the Child (CRC) which defines children as all persons under the age of 18. More than 100 countries in the world have declared under-18 marriage to be a form of early marriage (eg The Inter-African Committee-IAC, Ghana's Children's Act of 1998, Resolution of the Council of the European Parliament and The Convention on the Elimination of All Form of Discrimination Against Women-CEDAW). However, CRC has not yet taken effect in Indonesia. This causes Indonesia to lag behind the majority of countries in the world, in terms of child protection and the prevention of early marriage (BKKBN, 2012).
The provisions of Article 6 paragraph (2) of Law Number 1 Year 1974 on Marriage has actually set the age limit for marriage at 21 years for both men and women. However, Article 7 paragraph (1) of Law Number 1 Year 1974 mentioned that with the permission of the parents, a marriage can be held before the age of Journal of Indonesian Economy and Business, Vol. 35, No. 1, 2020 33 21 years, ie at 16 years for women and 19 years for men. Furthermore, Article 7 paragraph (2) of Law Number 1 Year 1974 stated that this minimum age limit can be exempted if a dispensation from a religious court is obtained, which can be requested by the parents. With this paragraph, it means there is no minimum age limit for marriage in Indonesia, because according to Mark andBurn (2014, in UNICEF, 2015) 90 percent of the requests for a dispensation are granted, and the number of applicants for this continues to grow. This is in accordance with the results of Hastutiningtyas's (2015) study which showed that there is an increasing level of requests for marriage dispensations submitted to the Religious Court of Yogyakarta.
The Government of Indonesia (GoI), in an effort to prevent child marriage, has actually set forth fresh rules for this in Law No. 23 of 2002 on Child Protection. Article 1 paragraph (1) of Law no. 23 of 2002 states that the definition of a child is someone who has not reached the age of 18 years. Furthermore, Article 26 paragraph (1) mentioned that parents are obliged to prevent a child from being married. But in reality the number of child marriages in Indonesia is still significant.
Impact of Early Marriage on Poverty
Almost all the family members of early marriage actors will be adversely affected. However, this negative impact has the greatest effect on women. The negative influence of an early marriage can be seen in the education, health, economy, and empowerment of women, which will all lead to poverty.
For the educational aspect, it has been widely demonstrated that early marriage is associated with low levels of education (Maertens, 2013;Field and Ambrus, 2008). The publication by UNICEF (2015) on the occurrence of early marriage in Indonesia shows that women who were married at the age of 15 have lower education levels than those of women married at the age of 18. In addition, it was also found that many girls who undergo early marriages drop out of school because they have to take care of the household (Putranti, 2012). Jensen and Thornton (2003) said that marriage is a limitation on women getting a higher education. Therefore, many studies recommend increasing school participation as one way to reduce early marriage (Smith et al., 2012).
For the health aspect, early marriage will impact on a range of health problems, such as depression caused by forced sexual intercourse, sexual trauma, high risk of pregnancy at a very young age, high maternal and infant death rates, high risk of HIV transmission, sexually transmitted diseases, and cancer (Fadlyana and Larasaty, 2009;Smith et al., 2012). In the case of early marriage, adolescents are still growing, so if a pregnancy occurs, there will be a competition with the fetus for nutrition (Unicef, 2015;Oyortey and Pobi, 2003). This condition results in the occurrence of anemia and nutritional deficiencies that can cause a low birth weight. Research undertaken by Fadlyana and Larasaty (2009) showed that 14 percent of babies born to teenage mothers aged less than 17 years old experienced a premature birth. In turn, psychologically, the children of teenage parents (who have no experience of life) are at risk of mistreatment, developmental delay, low IQ, learning difficulties, and behavioural disorders (Fadlyana and Larasaty, 2009).
For the economic aspect, women who are married early will lose the opportunity to improve their skills and have good jobs (Singh and Samara, 1996;UNICEF, 2015). As well as losing the opportunity to continue their education, women lose job opportunities because they have to take care of the household. With their low educational background, knowledge, and skills, the employment opportunities which exist for these girls are only in low wage jobs (Damayati, 2015). Early marriage also affects the fertility of the women, prolonging the period of sexual activity, which has implications for high numbers of offspring (Oyortey and Pobi, 2003). This condition will cause a population explosion that may increase the burden on the family's economy.
Divorce is also one of the consequences of early marriage (Singh and Samara, 1996). Jones's research (2001) in Madura, East Java shows that many divorces occur within a short span of time after the marriage. This is in accordance with Damayati's (2015) research in the Sungai Keruh Sub-district of Musi Banyuasin Regency that shows 38 percent of early marriages end in divorce, even many divorce occur at the age of marriage is below 5 years. The impact of early marriage is also felt when the age gap between husband and wife is large (Jensen and Thornton, 2003). If the husband dies, the girl/young woman must bear the burden of the family's life.
Early marriage also makes women lose their independence. Many women who get married early lose the opportunity to participate in determining household decisions. This means women cannot refuse if their husband wants sex, they also cannot determine when they will have children. In addition, women also face the risk of losing their close friends, networks, and community (Jensen and Thornton, 2003;Singh and Samara;1996).
Several studies into early marriage in Indonesia show that people who have experienced early marriage have a high risk of experiencing a low socioeconomic status for their families (e.g Savitridina, 1997;Putranti, 2012;Djamilah, 2014, andDamayati, 2015). However, the existing research has rarely measured the effect of early marriage on monetary poverty quantitatively. Therefore, this study is designed to measure the effect of early marriage on monetary poverty in Indonesia.
DATA AND METHOD
This study uses secondary data from the Indonesian Family Life Survey, 2014 (IFLS5). IFLS data provide comprehensive longitudinal survey data on individuals, households, and communities in Indonesia. The respondents are from 13 provinces in Indonesia and represent more than 83 percentage of the population in Indonesia. IFLS wave 5 year 2014 is the latest data published by RAND in cooperation with the Center for Policy Studies and Population Universitas Gadjah Mada and Survey METRE. The data sources for this study are samples of married women aged 15 and older.
The dependent variable in this research is poverty, which is measured from the monetary aspect. The poverty line used in this study refers to the measurement conducted by the Central Body of Statistics or BPS (2015). BPS established the total urban and rural poverty line for the period of March 2015 to be 330,776 rupiah. With this measurement, the monetary poverty is defined as individuals who have an average monthly expenditure below 330,776 rupiah. The monetary poverty variable in this study is in the form of a dummy variable, which is equal to one if a person is classed as poor (an average monthly expenditure below 330,776 rupiah), and zero otherwise.
The key independent variable is the incidence of an early marriage. In this study, early marriage refers to a marriage for individuals under the age of 18 years old (BKKBN, 2012). This age limit is chosen because it is the most used measurement by international agencies such as UNICEF, The Inter-African Committee-IAC, as well as the European Parliament. Based on this definition, the source for the data on early marriage information in the IFLS is obtained from the question of what year did people marry and what year were they born. The calculation of how old someone was when they first married can be obtained by deducting the year of marriage from their year of birth. From this calculation we can identify individuals who experience early marriage. Then, the dummy variable is created, a score of one for individuals who were married before they were 18, and zero otherwise.
The control variables in this research are the women's education level, age, place of residence, dummy unemployment, and number of family members. Table 1 explains the definition of each variable used in this paper.
In this study, the dependent variables are qualitative, being poor is worth one and not poor is worth zero. Using this model, we use an estimation of the qualitative response model, whose purpose is to find the probability of an event, which in this research is the possibility of someone being poor or not. The most widely used models for qualitative response regressions are the logit and probit models (Gujarati and Porter, 2013: 173). These models are able to guarantee probability estimation values ranging within logical limits, ie. between zero and one.
Logit and probit models use forms of the cumulative distribution function (CDF). The difference is that the logit model uses the logistic distribution function, while the probit uses a normal distribution function. The logit equation's form can be written: Pi is the probability of a successful event (Y = 1), or a person's probability of being poor. Parameter values are symbolized by β2. The equation can then be written: Zi is known for its logistics distribution function. If Pi is the probability of a person's success being unlikely, the likelihood of an event failing or someone not being poor is (1-Pi). This can be written as: P i /1-P i is an odds ratio (probability ratio) for the possibility of a woman who married young being poor. Odds ratios are a measure of the tendency to experience a successful event, which is also the ratio between the probability of being poor or not being poor. This value is obtained by performing an antilog on the logit's estimation result. If it is transformed into a natural logarithm, the equation is obtained: L i = ln = β 1 + β 2 X 1 + u i Equation 7 is linear in X and in the parameter, and it is called a logit equation. In contrast to the linear regression model which uses the OLS method as a parameter estimator, a logistic regression model uses the maximum likelihood estimation method to estimate the parameters. Maximum likelihood estimates show a value for each parameter that gives the greatest possibility, and are asymptotically normal, natural, consistent, and effective (Wooldridge, 2013: 564).
Meanwhile, the probit model, also known as the NORMIT model equation, is one of the cumulative distribution function (CDF) models. This model is used for determining the probability of individuals experiencing a successful event based on the value of the group of independent variables that are used in this study.
The probit model is used by following several assumptions: 1) The probability of a successful event depends on the latent variable or the unobserved variable, which is determined by the explanatory variable, where the greater the unobserved value is, the greater the chance of success is. 2) There is a critical value for the unobserved variable, so that if the unobserved variable passes this critical level, the event is a success and vice versa. The unobserved critical value equals the unobserved variable, with the assumption that a normally distributed critical value has the same mean and variance (Suwardi, 2011).
Using the assumption of normality, the probability of a critical value I i * less than or equal to an unobserved variable can be calculated by the CDF. The chance of success is determined by the unobserved utility value I i variable. The probit model is: If P is a probability of success, the normal default value is between -∞ to I i . The estimation of I i is obtained by inversing the normal cumulative function, so that it is obtained: Similar to the logistic regression analysis model, the probit's parameter (β) also uses the maximum likelihood (ML) method. The ML β estimator is an unbiased estimator and approximates the normal distribution for a large sample. Similarly, the value of the probit's coefficient also cannot be interpreted directly, because the probability value follows the normal distribution Z. Direct interpretation can only be done for the coefficient signs of the independent variables. The way to interpret the probit model's coefficient is to calculate the change in the probability value with the marginal effect (ME), which calculates the change in probability if there is a change in the independent variable. The binary regression model has several statistical test steps to determine the effect of the explanatory variables in the model, such as the simultaneous likelihood ratio test (LR Test) and the partial significance test of the Z statistics or the Wald test. The LR statistical test is used to test the simultaneous influence of the independent variables on the dependent variable (Wooldridge, 2013;Nawangsih and Bendesa, 2013). The statistical value of G or LR follows the chi square's distribution (X2) with the degree of freedom (df) as the number of independent variables. The hypothesis of the statistical tests of LR or G is: Simultaneously there is no influence of the independent variable on the dependent variable; H 1 : at least 1 βj ≠ 0, j = 1, 2, 3, ...i.
Simultaneously there is at least one independent variable that affects the dependent variable.
The result shows that H 0 is rejected, thus it can be concluded that the independent variables simultaneously and significantly affect the dependent variable.
The partial significance of the independent variables on the dependent variable can be determined by Wald or Z statistical tests (Wooldridge, 2013, Nawangsih andBendesa, 2013). The hypothesis of the Z test is: H 0 : β j = 0, j = 1, 2, 3, …i H 1 : β j ≠ 0, j = 1, 2, 3, …i This concept can be done by assuming that the Wald test follows the distribution of chi square with degrees of freedom equal to one. The result indicates that H 0 is rejected. Therefore, it can be said that the independent variables partially and significantly affect the dependent variable.
According to Dahl (2010), and Dartanto and Nurkholis (2011), the models built in this research are: The best model (logit or probit) to choose is determined by the maximum likelihood value (Cameron and Trivedi, 2010: 456). Furthermore, to check the robustness of the results, regression interaction terms will be added, which are early marriage and the level of education.
RESULTS AND DISCUSSION
The identification of the respondents who were experiencing monetary poverty was done using the BPS (2015) poverty line. About 22.02 percent of the respondents have an average expenditure of under Rp330,776.00 per month, which is classified as experiencing monetary poverty. Furthermore, as many as 77.98 percent of the respondents have an average monthly expenditure which is greater than the poverty line, therefore they are included in the non-poor category.
Probit Significance Test
The significance tests are performed using a simultaneous significance test with a G test or statistical LR test. The LR statistic test results show that the prob score is > chi2 which is 0.000 or smaller than α 1 percent, therefore H 0 is rejected at the 1 percent significance level. This means that the independent variables in this study simultaneously and significantly affect the monetary poverty at the 1 percent level of significance.
The partial significance tests for the early marriage variables that are used in the statistical Z test or the Wald test show that the p value is 0.375, therefore H 0 is not rejected. These results indicate that there is no significant impact of the early marriage variables on monetary poverty. In other words, early marriage has no effect on the likelihood of a person being poor, from either the financial or the average monthly expenditure aspects. This is in line with the findings of Jordan (2004), which compared the causes of poverty from the structural and cultural aspects. Using aggregate estimates at the national level, Jordan (2004) concluded that there is no effect of pregnancy in adolescence on poverty. Pregnancy in adolescence may indicate that there was an early marriage. According to Jordan, structural aspects such as unemployment, income distribution, and the Gini index are more influential than cultural aspects such as crime rates, and pregnancy in adolescence.
In line with Jordan (2004), Hotz et al., (1999), found that there was no significant impact between pregnancy in adolescence and poverty. According to Hotz et al., (1999) adolescents who get pregnant early have the ability to adapt to the situation. This brings short-term impacts in the form of low levels of education, but over the long term this is compensated for by a longer working life. In the end, adding more working years will provide a higher wage rate. In addition, Hotz et al. (1999) showed that after getting a spouse, early pregnant adolescents earned an income from the spouse, and there was no evidence to suggest that the income of the spouse was lower than when pregnancy was delayed.
Information about the excessive work hours and earnings of husbands in early marriage in this study is difficult to obtain, because most of the respondents did not answer the questions related to work hours. Brown's (1982) study shows that government welfare programs can meet the minimum needs of young pregnant teenagers. All respondents in this study provided answers relating to their participation in welfare assistance programs. Furthermore, as many as 25.72 percent of marriage offenders receive assistance from the government's welfare programs, in the form of Direct Cash Assistance (BLT), the Family Hope Program (PKH), as well as Direct Community Support (BLSM).
Information on working hours, the husbands' income, and the acceptance of government welfare programs are actually very important to explain the absence of the effects of early marriage on monetary poverty, on the per capita expenditure side. However, in this study the data about working hours, husbands' income, and the use of credit is very limited.
Furthermore, the results of the probit's regression in this study also indicate that the variables of the level of education, where they reside, unemployment, and the number of family members have significant effects on monetary poverty. Living in a village, being unemployed and a large number of family members all have a positive effect on monetary poverty, while the level of education negatively affects poverty. Meanwhile, the age variable has no effect on monetary poverty. This finding is consistent with the studies by Sumaryati (2013), Wibowo (2015), and Indriyani (2015), who all found that poverty is a rural phenomenon. In addition, this is also in accordance with Jordan's (2004) study which indicated that unemployment has a positive effect on poverty. Brown's research (1982) also shows that the greater the number of family members there are, the greater the economic burden is, increasing the numbers in poverty. The influence of the level of education on monetary poverty is also in accordance with research by Artha and Dartanto (2014), Erwansyah (2011), and Idorway (2009) which showed that education has a negative effect on poverty. The magnitude of the influence of each variable will be explained through the estimated value of the marginal effect, which is explained in the next section.
Marginal Effect
According to Cameron and Trivedi (2010: 462), in the nonlinear regression model, the value of the marginal effect is more informative than the coefficient of estimates. This study used the average marginal effect (AME) as a reference, for the reason that it accommodates the change of dummy variables at the discrete level. The results show that when the education level rises by one year, the chances of a woman experiencing monetary poverty fall by 1.9 percentage points. This is in accordance with the study by Artha and Dartanto (2014) which states that the higher the level of education is, the higher the possibility of not being poor is. Furthermore it was found that if a person lives in a village, the probability of experiencing monetary poverty increases by 11.9 percentage points. This is in accordance with research by Artha andDartanto (2014), Indriani (2015), and Wibowo (2015) who all stated that people living in villages are more vulnerable to poverty. An unemployed person also had a significant positive effect on the 5 percent degree of confidence in the occurrence of monetary poverty. If someone is unemployed, the likelihood of monetary poverty rose by 2.8 percentage points. Furthermore, if the number of family members rose by one person, the chances of experiencing monetary poverty rose by 4.4 percentage points. This is consistent with Brown's (1982) study, which states that as the population increases, the poverty level increases.
Interaction Term
Robustness testing of the model to check the influence of early marriage on monetary poverty was done by including the interaction term between the variable of early marriage with the level of education. This is done to find the difference in the influence of the level of education on monetary poverty between those who marry young, and those who remain unmarried. The result of the regression with the interaction term indicates that the interaction variable between early marriage and level of education has no significant effect on monetary poverty. This shows that, at the same level of education, there is no difference in the effect of married women with young unmarried women on the possibility of women entering monetary poverty. This reinforces the first model's regression findings that there is no effect of early marriage on monetary poverty.
CONCLUSION
This study analyzes the impact of early marriage on monetary poverty. The result shows that early marriage is not proven to influence monetary poverty. This suggests that the incidence of early marriage does not affect the difference in the average per capita spending. Given that there has been very little quantitative research into the effect of early marriage on poverty in Indonesia, this research is able to provide an updated picture of the effect of early marriage on poverty in Indonesia. However, there are some limitations that need to be addressed in the future. The limitations are related to the lack of information on women's incomes, working hours, the husbands' income, and the use of credit, making it difficult to get an explanation of the impact of these variables on monetary poverty in Indonesia. This study suggests that future research may consider these variables when analysing monetary poverty. | 7,236.6 | 2020-03-23T00:00:00.000 | [
"Economics",
"Sociology"
] |
Rapidly and exactly determining postharvest dry soybean seed quality based on machine vision technology
The development of machine vision-based technologies to replace human labor for rapid and exact detection of agricultural product quality has received extensive attention. In this study, we describe a low-rank representation of jointly multi-modal bag-of-feature (JMBoF) classification framework for inspecting the appearance quality of postharvest dry soybean seeds. Two categories of speeded-up robust features and spatial layout of L*a*b* color features are extracted to characterize the dry soybean seed kernel. The bag-of-feature model is used to generate a visual dictionary descriptor from the above two features, respectively. In order to exactly represent the image characteristics, we introduce the low-rank representation (LRR) method to eliminate the redundant information from the long joint two kinds of modal dictionary descriptors. The multiclass support vector machine algorithm is used to classify the encoding LRR of the jointly multi-modal bag of features. We validate our JMBoF classification algorithm on the soybean seed image dataset. The proposed method significantly outperforms the state-of-the-art single-modal bag of features methods in the literature, which could contribute in the future as a significant and valuable technology in postharvest dry soybean seed classification procedure.
and eight shape features of perimeter, area, circularity, elongation, compactness, eccentricity, elliptic axle ratio and equivalent diameter as the input of BP artificial neural network and set up a three layers classifier for sorting six categories -mildewed, insect-damage, broken, skin-damaged, partly detective and normal soybean kernels 18 . These previous methods used global visual characteristics of color, morphology, and texture to describe the soybean seeds. The global features usually contain an amount of invalid background information, and the local detailed information is easy to be masked by using them. The introduction of invalid features and the loss of effective detailed discrimination information will inevitably affect the performance of the classification model, thus affecting the final recognition accuracy. The defective soybean seed features often appear in the local image, even in the small local ranges. Compared with using the global features to describe the defective soybean seeds, effective local image features can be used as a key means to distinguish the quality of soybean seeds. Therefore, it is necessary to develop a new local feature algorithm to further improve the classification accuracy of soybean seeds.
In recent years, the state-of-the-art technologies of low-level local visual feature representation based on the bag-of-feature model showed great potential in object recognition. The BOF method who is derived from the document analysis method converts the low-level local image features to visual word features to represent the image property. Murat Olgun et al. (2016) used the BoF of dense scale invariant features to represent the wheat grain varieties 19 . Xiao et al. (2018) introduced a support vector machine (SVM) classifier for classifying four kinds of important southern vegetable pests based on scale-invariant feature transform (SIFT) BoF visual vocabulary 20 . The above investigations only used one kind of BoF visual dictionary, which is hard to fully express the complex agricultural objects. Abozar Nasirahmadi et al. presented a bag of feature model joining Harris, Harrise-Laplace, Hessian, Hessian-Laplace and maximally stable extremal regions key point detectors along with a scale invariant feature transform descriptor for classification of sweet and bitter almond varieties 21 . Although several local feature algorithms have been implemented for improving the performance of the agricultural product quality detection system, there is little study aimed at the detection quality of soybean seed.
In this paper, we intend to use the BoF-based algorithms to validate the effect of soybean seed image classification. Besides, the simple combination of multiple features will inevitably lead to redundancy of features to represent an image, and to a certain extent affect the performance of the classifier in the final process of feature recognition. To further improve the performance of intelligent recognition systems, this paper proposes a low-rank representation (LRR) algorithm 22,23 to find the lowest rank representation among the long and distinct kinds of features in subspace. The method can organically merge the distinct category of semantic dictionaries by a generation of the new low-dimensional descriptors in low-rank subspace and eliminate the influence of irrelevant semantic dictionary information in such space.
The objective of this study was to exploit a low-rank representation of jointly multi-modal BoF (JMBoF) classification framework for exactly, non-destructively and fast identifying the quality of soybean seeds. The rest of the paper is organized as follows: firstly, the experimental materials and devices used to capture the images are introduced; secondly, the JMBoF-related methods for inspecting the dry soybean seed quality are presented; thirdly, the experimental results are shown and discussed; Finally, the conclusions are drawn. experiments experimental materials. The soybean seeds for the experiment were purchased from the local market.
There are ten kinds of cotyledon-lacked, physically damaged, naturally cracked, testa-damaged, coat shriveled, cotyledon-atrophic, worm-bitten, testa-decayed, cotyledon-moldy and heteromorphic defective soybean seed samples (see Fig. 1), where the physically damaged means that the seed coat and cotyledons are split after the seed kernel is physically or mechanically squeezed. The cotyledon-lacked, physically damaged, naturally cracked and testa-damaged seed kernels without the protection of the outermost hull are prone to mildew after long-term storage. The severely hull-shriveled and cotyledon-atrophic seed kernels are considered to be malnourished, thus affecting human health and related product quality. Ingestion of the worm-bitten, testa-decayed, cotyledon-moldy Two good soybean seed sample images (a1,a2) are shown on the left side. Other two kinds of (b1) cotyledonlacked, (b2) physically damaged, (b3) naturally cracked, (b4) testa-damaged, (b5) hull-shriveled, (b6) cotyledon-atrophic, (b7) worm-bitten, (b8) testa-decayed, (b9) cotyledon-mouldy and (b10) heteromorphic soybean seed images are shown on the right side. and heteromorphic seeds can impair the health of humans and animals. Therefore, it is necessary and important to grade soybean seeds in terms of their appearance features. In this experiment, we attempt to automatically discriminate three grades of good, moderate and unhealthy soybean seeds in terms of their appearance quality. A good soybean seed comprises approximately 8% seed coat (or hull, or testa), 90% cotyledons and 2% Embryonic axis (including plumula, hypocotyl, and radicle) 24 . The good appearance features indicate that the seed coat is intact and smooth, as well as the cotyledons are plump, which will be good for the health of humans and animals (see Fig. 1(a1,a2)). The moderate one indicates that the seed coat is broken, the cotyledon is cracked, or the cotyledon is slightly shriveled, but it does not harm the health of humans and animals (see Fig. 1(b1-4)); The unhealthy one indicates that the seed coat or cotyledon is severely shriveled, cotyledon-atrophic, worm-bitten, testa-decayed, cotyledon-moldy or heteromorphic, which will damage the health of humans and animals after consumption (see Fig. 1(b5-10)). There are 843 soybean seeds used for the test. Each type has 281 samples. The training set contains 70% randomly selected samples, and the remaining 30% is used for test purposes. experimental devices. The visual spectral imaging device (Perfection V850 Pro, Epson, US) is used to capture the image of the soybean seeds. The principal parts of the imaging system comprise black absorption cover, transparent flat glass plate, motor-driven shifting electronic platform, charge-coupled imaging device (CCD), black box, communication cable, and computer machine. Each soybean sample is laid at equal intervals on the transparent glass panel. Then the black absorption cover is placed horizontally above the samples as the image background. The motor-driven shifting platform carries the shifting linear light and shifting mirror. The shifting linear source emits the linear beams to the sample surface through the transparent flat glass plate. The sample reflects the light beams to the shifting mirror, and then the beams from the shifting mirror are reflected to the fixed mirror. Finally, the CCD collects the linear sample spectra transmitted from the fixed mirror. Compared with the traditional camera shooting technology, the motor-driven shifting electronic shooting platform to capture the photograph can ensure that each soybean seed in any position of the photograph is uniform. The imaging devices are fixed in a closed black box which can block the effects of external lighting. A communication cable is used to connect with the outside computer machine with the inside imaging devices. Each original captured photograph contains 20 kernels, in which each kernel image is automatically separated and stored to the disk. Methods color space conversion. L*a*b* is a color space specified by the International Commission on Illumination (CIE) 25 , where L* is for the lightness and a* and b* are for the green-red and blue-yellow color components, respectively. The L*a*b* color space not only contains all the gamut of RGB color space but also expresses a part of color space that the RGB can not do. The RGB color space can not be directly transferred to L*a*b* color space. It takes two steps to implement the conversion. The RGB color space firstly must be transformed into a specific CIE XYZ color space 26 The L*a*b* color space is further defined relative to the tristimulus values of the reference white point (X n , Y n , Z n ) of the XYZ space from which they were converted: An instance of color space conversion from RGB to L*a*b* is shown in Fig. 2.
SURf feature space descriptors. SURF is a successful algorithm for feature detection introduced by Bay 27 .
The goal is to define unique and robust space descriptors of an image. The algorithm consists of the following three main steps: (1) Detect interest points (see Fig. 3(a)). It takes advantage of an integer approximation of the determinant of the Hessian blob detector, which can be calculated by three predefined integral operators. Its feature descriptor is based on the sum of the Haar wavelet response around the point of interest; The category of feature area can be determined by the sign (denoted by −1 and +1) of the Laplacian (i.e. the trace of the Hessian matrix) 28 .
(2) Obtain gradient information in the subregion. The interest region is split into smaller 4 × 4 squared subregions aligned to the selected orientation (see Fig. 3(c)), and for each one, the Haar wavelet responses are extracted at 3 × 3 regularly spaced sample points (see Fig. 3(b)). The responses are multiplied by Gaussian gains to resist the deformations, noise, and translation. (3) Generate feature space descriptors. Concatenate 64-dimensional gradient related feature descriptor from each 4 × 4 local neighborhood subregion underlying intensity structure from each detected interest point to form the feature space descriptors (see Fig. 3(d)).
www.nature.com/scientificreports www.nature.com/scientificreports/ Bag of feature model. BoF is a technique adapted to image categorization from the area of document categorization. Rather than using actual words as in document categorization. BoF algorithm uses image features like the visual words which are finally combined as the visual dictionary to represent an image 21,29 . To achieve this, it includes the following two main steps: (1) Extract a 'bag' of independent features. In this study, we extract two bags of features of the L*a*b* color features and the SURF features, where the L*a*b* color feature composes of two portions of the average of color components within 16 × 16 subregions of image and the corresponding spatial coordinates in an image where it was extracted. (2) Generate visual dictionary. The k-means clustering method is performed to cluster the feature vectors obtained in Step 1. The cluster center is defined as the visual word. All the visual words are collected to generate the visual dictionary. The number of clustering centers is the visual dictionary size. Thus, the low-level image features are quantized as the high-level semantic information to express the image content through the distribution of visual words. www.nature.com/scientificreports www.nature.com/scientificreports/ Jointly low-rank feature representation. In the aforementioned BoF method, the visual dictionary size will affect the features constituted by visual words on the interpretability of image content: the small size dictionary may not fully describe the image features, and too large size dictionary may cause redundant semantic expression. Besides, the new joint-modal features of SURF and L*a*b* will multiply the dimensionality of the dictionary, which will further increase the redundant semantic representation of features. To solve the issue, we firstly extract a large size of visual dictionary from images by setting a large number of dictionaries (numbers = 800) and then used the LRR method to eliminate the redundant semantic information to effectively express the image content. LRR supposes the high-dimensional data Y has low intrinsic dimensionality. In order to alleviate the curse of dimensionality, the original Y can be into two components of low-rank matrix X and sparse error matrix E: where, ||⋅|| ⁎ denotes the nuclear norm, ||⋅|| 1 denotes L1-norm and λ is a regularization parameter. The above optimization problem is essentially to find the optimal projection of high-dimensional data in a low-dimensional subspace. After removing the residual E, the compact visual dictionary set X will be used as the effective expression of the raw image 22,23 .
Support vector machine regression. The algorithm of SVM 30,31 is used to transform the input space into a high-dimensional Hibert space by nonlinear transformation, and then implement linear classification in this space. The SVM method assumes a set of training data Λ for a given set of points N: where the predicted value y i corresponds to the independent variable x i . The SVM method uses the kernel function ϕ to project the independent variable x into the high-dimensional feature space to establish the linear fitting The equation can be set up by solving the following optimization problem: (3) Apply the BoF algorithm to individually constructing the L*a*b*-and SURF-related visual dictionary by reducing the number of features through quantization of feature space using K-means clustering; (4) Form the hybrid semantic information by concatenating the L*a*b*-and SURF-related visual dictionaries. (5) Employ the LRR method to project the multiplicative-dimensional visual dictionary to low-rank space to eliminate redundant semantic information. (6) Perform the SVM algorithm to constructs the splitting hyperplanes in the high-dimensional kernel space to divide the jointly LRR data into three categories.
Results and Discussion color feature evaluation. As shown in Fig. 1, those appearance color properties of three kinds of soybean seed kernels differ from each other. The exposed yellow colors of the cotyledon-exposed soybeans ( Fig. 1(b1,b2,b4)) are brighter than the good ones. The cotyledon surface colors of the naturally cracked soybean (Fig. 2(b3)) are a little bit more white and darker. The images of other unhealthy beans (Fig. 2(b5-10)) are mixed with other color features. Thereby, the color features can be adopted to discriminate the quality of soybean seeds. The collected raw soybean image data are stored in the RGB color format by using the customized imaging device. The RGB color model is device-dependent, which is initially designed to model the output of physical display or data acquisition devices. The image classification of soybean seeds is actually based on the human visual comprehensive perception of soybean seed color features. The L*a*b* color space mimics the nonlinear response of the eye. It can preserve the broad gamut of the color features of soybean seed image. All of the color which can be perceived by human eyes can be discovered in the L*a*b* color model 25 . The distribution of color features in the L*a*b* color space is more uniform than the RGB (see the light part in Fig. 2(c3)). The RGB color space contains too many transitional colors between blue and green, and lacks yellow and other colors between green and red (see Fig. 2(b1-3,c1-3)). As shown in Fig. 2(b1-3), the components of the RGB color space have small differences between their intensity values, so the visual perception of three components of RGB soybean image is extremely close. After color space conversion from RGB to L*a*b*, the components of L*a*b* color space have a significant difference between three distinct color channels, so Fig. 2(c1-3) show three extremely distinct types of images of visual perception effect. It enables the algorithm to easily quantify the visual differences between colors because it is more consistent with the Euclidean space structure. The converted distinguishable features are more suitable for the subsequent color dictionary generation procedure, which is based on k-means 32 . In order to more accurately express the color feature of soybean seed kernel images, we convert the RGB color descriptors to the CIEL*a*b*.
SURf feature evaluation. The SURF algorithm can estimate the placement angle by measuring the dominant orientation from the image. The soybean kernels are arbitrarily placed in the imaging panel, however, the www.nature.com/scientificreports www.nature.com/scientificreports/ extracted effective features are not affected by the placement angle, because the SURF feature is invariant to image rotation. As shown in Fig. 5(a), there are a total of 60 feature points detected by the SURF algorithm. The detected feature points are mainly distributed at the edge of the soybean image. The category of feature points corresponds to the sign of the Laplacian 27 . The Laplacian detected on the outside edge such as at the locations of 1, 46, 56 and 58 is −1, which are marked with blue solid circles; the Laplacian on the inside edge such as at the locations of 34, 57, 59 and 60 is +1, which are marked with red broken circles; Each feature point is numbered. The green radius indicates the dominant direction. The dominant direction is related to the feature area. The dominant direction around the inner and outer edge area is perpendicular to the tangential direction of the soybean outline. The surface color of good soybean kernel is relatively smooth and uniform, so the gradient change is relatively small, while the color around the edge area changes significantly, so the gradient change at the edge is larger relative to the inner smooth area. The SURF algorithm is mainly based on the gradient algorithm, so the characteristic points are detected on the edge of the soybean kernel.
As shown in Fig. 5(b), there are a total of 62 feature points detected by the SURF algorithm. Each feature point is numbered. The detected feature points mainly distribute in the edge, cracked (see the location A in Fig. 5(b)) and shriveled (see the location B in Fig. 5(b)) area of soybean kernel. The Laplacian detected on the outside of the edge such as at the locations of 47, 48, 52 and 53 is −1, which are marked with blue solid circles; the Laplacian on the inside edge such as at the locations of 11, 16, 35 and 51 is +1, which are marked with red broken circles; the detection center such as at the locations of 9, 26 and 57 has a Laplacian of 1 at the cracked area, which marked with red broken circles; the detection center such as at the locations of 38 and 40 has the Laplacian value of +1 in the shriveled area, which are marked with red broken circles. In the cracked and shriveled area, the dominant directions are approximately perpendicular to the cracked and shriveled direction, respectively. The darker image regions formed at the locations of the cracked and shriveled area relative to the yellow background region, so the gradient change at the edge is relatively large at these sites. The SURF method is mainly based on the gradient algorithm, so these feature locations are obviously perceived. The gradient-related information at the detected cracked and shriveled area can be used as the distinguishing feature for estimating the soybean quality.
Jointly multi-modal feature evaluation. The confusion matrix diagram 33 is utilized to summarize and visualize the results of the performance of the proposed JMBoF + SVM algorithm. As shown in Fig. 6, the rows of indicate confusion matrix the predicted results and the columns show the actual results. The correct classification results are shown on the green diagonal cells. For the training set, 197, 179 and 189 objects are correctly identified as the good, moderate and unhealthy soybeans, respectively. These correspond to 33.3%, 30.3% and 32.0% of all 591 training soybean instances, respectively. Similarly, for the test set, 84, 53 and 70 objects are correctly classified as good, moderate and unhealthy instances, respectively. These correspond to 33.3%, 21.0% and 27.8% of all 252 test soybean images, respectively. The red non-diagonal elements show where the model has made the wrong prediction. For the training set, 5 moderate and 1 unhealthy species are incorrectly classified as the good species, which correspond to 0.8% and 0.2% of all 591 good instances, respectively. 7 unhealthy species are mistakenly considered as the moderate species, which correspond to 1.2% of all 591 good instances. 13 moderate species are incorrectly classified as the unhealthy species, which correspond to 2.2% of all 591 good instances. Similarly, for the test set, 5 moderate and 1 unhealthy species are incorrectly classified as the good species, which correspond Figure 7 shows the tradeoff between the precision and recall for different thresholds, namely the tendency for the recall to increase as the precision to decline. It is obvious that the JMBoF + SVM model basically holds higher percentage of precision rates than the RGB + BoF + SVM, HSI + BoF + SVM, L*a*b* + BoF + SVM and SURF + BoF + SVM models at the different thresholds of recall. The mean average precision (mAP) score which is the area under the precision-recall curve 35 can be used as the integrated evaluation of algorithm performance. The JMBoF + SVM model yields the highest mAP scores of 0.973 and 0.918 on the training and test soybean image dataset, respectively, and outperforms the RGB + BoF + SVM of 0.904 and 0.787, HSI + BoF + SVM of 0.905 and 0.791, L*a*b* + BoF + SVM of 0.914 and 0.811 as well as the SURF + BoF + SVM of 0.962 and 0.893 (See Table 1).
As shown in Table 1, the RGB + BoF + SVM model results in 78.1% and 62.8% accuracy, HSI + BoF + SVM model results in 80.3% and 59.6% accuracy, and L*a*b* + BoF + SVM model results in 80.4% and 63.1% accuracy on the training and test soybean image dataset, respectively. The L*a*b*-based single-modal algorithm outperforms the other two. It might be because several discriminated color features of soybean samples can be highlighted in the L*a*b* color space (see Fig. 2). The color-based single-modal method mainly distinguishes the soybean classes according to the overall characteristics of appearance. The damaged parts of defective soybean sometimes occupy a small proportion of the soybean surface. The corresponding extracted features also occupy a small ratio in the whole feature vector. It will result in the discriminated information ignored during the following classification process, so the low recognition rate is generated. Unlike the RGB, HSI and L*a*b*-based global color model, the SURF + BoF + SVM model does not apply the global color information from the soybean image. It attempts to detect the potential characteristic points and construct the gradient-based descriptor from the interest region. These feature points are mainly distributed at the edge and defective parts of the soybean kernel. Due to gaining the effective discriminated features, the SURF + BoF + SVM model improves the classification accuracy, which is 9.4% and 15.1% higher than the L*a*b* + BoF + SVM model on the training and test soybean image dataset, respectively. However, one potential drawback of this approach is that the relationships between the defective and intact patches and global image structure are ignored. This can be partially compensated by sampling the global L*a*b* features from the whole images. The JMBoF + SVM model takes advantage of local interest-region gradient-based features and global color feature information to further improve the classification www.nature.com/scientificreports www.nature.com/scientificreports/ accuracy. The L*a*b* + BoF + SVM model results in the highest 95.6% and 82.1% classification accuracy on the training and test soybean image dataset, which is 5.7% and 3.9% better than the SURF + BoF + SVM model, respectively.
conclusions
The paper firstly evaluates the appearance color properties for classifying the soybean seed kernels. The visual perception of three components of the RGB soybean image is extremely close. After color space conversion from RGB to L*a*b*, the components of L*a*b* color space show the significant visual difference between three distinct color channels. The extremely distinct types of images of visual perception effect enable to easily form distinguished features. The SURF feature is invariant to image rotation. The SURF algorithm can estimate the placement angle byout measuring the dominant orientation from the image. Though the soybean kernels are arbitrarily placed in the imaging panel, the extracted effective features are not affected by the placement angle. The dominant direction is related to the feature area. The dominant direction around the inner and outer edge area is perpendicular to the tangential direction of the soybean outline. In the cracked and shriveled area, the dominant directions are approximately perpendicular to the cracked and shriveled direction, respectively. The gradient change at the edge is larger relative to the inner smooth area, and the darker image regions formed at the locations of the cracked and shriveled area relative to the yellow background region, so the gradient change at the edge is relatively large at these sites. The SURF method is mainly based on the gradient algorithm, so these feature locations are obviously perceived.
Five different algorithms of RGB + BoF + SVM, HSI + BoF + SVM, L*a*b* + BoF + SVM, SURF + BoF + SVM and JMBoF + SVM are applied to classification of soybean quality. The multi-modal-based method of JMBoF + SVM outperforms than the other four single-modal-based algorithms, probably because the JMBoF + SVM model synthetically takes advantage of the global color feature and local interest-region gradient-based features (SURF) information. The JMBoF + SVM model results in the highest 95.6% and 82.1% accuracy on the training and test soybean image dataset, respectively. The proposed algorithm has the potential to be applied to the intelligent automated soybean grading machines for exactly, non-destructively and fast screening out the poor kernels. In the further, we intend to merge more effective discriminated feature elements from the soybean appearance to boost the accuracy of the classification algorithm. Precision-recall curves of discriminating three kinds of good, moderate and unhealthy soybean seeds in terms of their appearance quality using the methods of RGB + BoF + SVM, HSI + BoF + SVM, L*a*b* + BoF + SVM, SURF + BoF + SVM and JMBoF + SVM on the training (a) and test (b) dataset, respectively. Table 1. Accuracy and mean average precision (mAP) scores of grading 3 kinds of good, moderate and unhealthy soybean seeds in terms of their appearance quality using the RGB + BoF + SVM, HSI + BoF + SVM, L*a*b* + BoF + SVM, SURF + BoF + SVM and JMBoF + SVM models on the training and test dataset, respectively. | 6,241.2 | 2019-11-06T00:00:00.000 | [
"Computer Science"
] |
BLT2 is expressed in PanINs, IPMNs, pancreatic cancer and stimulates tumour cell proliferation
Pancreatic cancer has an abysmal prognosis. Targets for early detection, prevention and therapy are desperately needed. Inflammatory pathways have an important impact on tumour growth and progression. Expression of BLT2 (the second leukotriene B4 receptor) was investigated by real-time RT–PCR and immunohistochemistry. Cell proliferation was studied after stable transfection with BLT2, after treatment with siRNA and Compound A as an agonist. BLT2 is expressed in all pancreatic cancer cell lines. Results from real-time RT–PCR revealed significant overexpression of BLT2 in malignant intraductal papillary mucinous neoplasias (IPMNs) and pancreatic adenocarcinoma. Intense staining was evident in IPMNs, infiltrating tumour cells and advanced pancreatic intraepithelial neoplasias (PanINs) but not in normal ductal cells. Overexpression of BLT2 as well as stimulation of Colo357, Panc-1 and AsPC1 cells with Compound A caused a significant increase in tumour cell proliferation, an effect reversed after siRNA treatment. This study demonstrates for the first time the expression of BLT2 in the pancreas and overexpression in pancreatic cancers and malignant IPMNs in particular. Upregulation of BLT2 is already evident in precursor lesions (PanINs, IPMNs). Overexpression of this receptor leads to significant growth stimulation. Therefore, we suggest BLT2 as a new target for chemoprevention and therapy for pancreatic cancer.
Pancreatic cancer is the fourth leading cause of cancer death (after lung, colon and breast) in the United States and the incidence of this disease has not declined (Jemal et al, 2006). Indeed it has increased in African Americans as well as in the Japanese over recent years (Oomi and Amano, 1998;Lin et al, 1998;Stat bite, 2002;Qiu et al, 2005;Luo et al, 2007). The mortality of pancreatic cancer almost equals its incidence and most patients die within 6 months because of late diagnosis and lack of effective therapies (Howard, 1996;Jemal et al, 2006). Potentially curative surgery is an option only in 9 -20% of patients, because of existing liver metastases or the invasion of major blood vessels (Chua and Cunningham, 2005). However, even in this selected group 2-and 5-year survival rates are at about 40 and 20%, respectively and the median survival time is 20 months when patients receive adjuvant chemotherapy (Neoptolemos et al, 2004;Stocken et al, 2005). Many patients have recurrent disease 12 months after surgery because of tumour recurrence or metastatic tumour progression (Ahrendt and Pitt, 2002;Chua and Cunningham, 2005). Therefore, identification of risk factors and early diagnostic markers as well as new therapeutic approaches are desperately needed so that the disease can be prevented or detected at an early, non-invasive stage. It is believed that ductal pancreatic adenocarcinomas originate from ductal cells. Pancreatic intraepithelial neoplasias (PanINs) are histologically defined lesions in the small ducts and ductules that are thought to be the precursors of pancreatic cancer (Hruban et al, 2001). PanINs are potentially an ideal target for chemoprevention and so a specific marker for these lesions is likely to be useful for early diagnosis (Hruban et al, 2000(Hruban et al, , 2001. Epidemiological and animal studies suggest that a high intake of polyunsaturated fatty acids (PUFAs) is associated with an increased incidence and growth of tumours at several specific organ sites including pancreas, colon, breast and prostate (Rose, 1997;Woutersen et al, 1999a, b). A recent review pointed out the important role of cyclooxygenase and lipoxygenase pathways in fat metabolism and in the regulation of pancreatic cancer cell proliferation and survival (Ding et al, 2003). Cyclooxygenase-2 (COX-2) plays an important role in carcinogenesis, is upregulated in 56 -90% of pancreatic adenocarcinomas and 65% of PanINs and frequent use of aspirin seems to decrease the risk of pancreatic cancer (Hitt, 2002;Maitra et al, 2002;Ding et al, 2003). The 5-lipoxygenase (5-LOX) pathway seems to have an even more important function in pancreatic carcinogenesis (Ding et al, 2003). Leukotriene B 4 (LTB 4 ) is a downstream metabolite of 5-LOX and stimulates pancreatic cancer cell growth through ERK1/2 phosphorylation, which can be inhibited by an LTB 4 receptor antagonist . However, LY293111 is not specific as it inhibits 5-LOX activity and is also a PPARg agonist. Recently, we have reported that the LTB 4 receptor 1 (BLT1) is overexpressed in pancreatic cancer cells and tissues as well as islets adjacent to the tumour . LTB 4 receptors are G-proteincoupled receptors which belong to the chemoattractant receptor group (Yokomizo et al, 2001). BLT1 was isolated and cloned by Yokomizo's group in 1997(Yokomizo et al, 1997. Three years later they identified a second, low-affinity receptor for LTB 4 (BLT2), with 45% homology with BLT1 at the amino acid level (Yokomizo et al, 2000). The open reading frame (ORF) of BLT2 is localised upstream of BLT1 and contains the promoter region of BLT1. This represents a very rare case of a 'promoter in ORF' in higher eukaryocytes, the physiological significance of which has not been determined (Yokomizo et al, 2000). BLT1 (352 amino acids) is almost exclusively expressed in peripheral leukocytes with low level expression in human spleen and thymus (Yokomizo et al, 1997). However, BLT2 (358 amino acids) is expressed in spleen, liver, ovary and leukocytes and low levels of its mRNA have been demonstrated in many tissues (Yokomizo et al, 2000). Recently, expression of BLT2 in dendritic cells was described, speculating on a regulatory role in dendritic cell trafficking during induction of immune responses (Shin et al, 2006). Moreover, BLT2 was found in mast cells, possibly mediating recruitment and accumulation of mast cells in areas of inflammation (Lundeen et al, 2006). Further studies showed by in situ hybridisation that expression of BLT2 is significantly upregulated in a variety of human cancers (Yoo et al, 2004). In addition, it has been suggested that BLT2 is responsible for LTB 4 -induced generation of reactive oxygen species (ROS) through Rac/ERK and that this receptor is involved in cytokineinduced differentiation and expansion of haematopoietic stem cells (Woo et al, 2002;Chung et al, 2005). Stem cell factor and TNF-a are suggested to regulate BLT2 expression (Lundeen et al, 2006;Qiu et al, 2006).
There is a strong relationship between chronic inflammation and development of cancers of the gastrointestinal tract, including oesophagitis with Barrett's metaplasia and ultimately oesophageal adenocarcinoma, inflammatory bowel disease with colon cancer, and chronic pancreatitis with pancreatic cancer (Orlando, 2002;Itzkowitz and Yio, 2004;Whitcomb, 2004). The lipoxygenase pathway in particular has been linked with development and growth of pancreatic adenocarcinoma, whereas inhibitors of this pathway, including LY293111, inhibit growth of the cancer Tong et al, 2002;Ding et al, 2003). As the function of the second LTB 4 receptor in pancreatic cancer is not known, we investigated the expression and biological importance of BLT2 in human pancreatic cancer cells and tissues. This study follows our previous work investigating the function of the 5-lipoxygenase pathway in pancreatic carcinogenesis.
Immunohistochemistry for BLT2
Tissue samples from 10 patients with chronic pancreatitis and 10 with pancreatic ductal adenocarcinoma were examined. As shown in Table 1, 12 of these surgical pancreatic specimens showed PanIN lesions. Six specimens showed PanIN-1a and 1b lesions, five had PanIN-2 and six had PanIN-3 lesions. Ten pancreas specimens from multi-organ donors were included as controls. However, one of them contained PanIN-1a lesions.
In addition, seven specimens with benign intraductal papillary mucinous neoplasias (IPMN), 12 with borderline IPMN and nine with malignant IPMN were subjected to immunohistochemistry. Fixation, sectioning and immunohistochemistry were carried out as described earlier . The primary polyclonal antibody to BLT2 from Cayman Chemicals (Ann Arbor, MI, USA) was used at 1 : 50. The stained tissue samples were examined by two pathologists. Controls included incubation in the absence of primary antibody and quenching of the primary antibody with the respective blocking peptide for 1 h at room temperature before application to the tissue.
Cell lines and cell culture
The cell lines used, AsPC-1, Colo357, and Panc-1 were established from patients with pancreatic adenocarcinoma. The entire human pancreatic cancer cell lines were purchased from the American Type Culture Collection (ATCC, Rockville, MA, USA) and grown in RPMI-1640 medium (Invitrogen Life Technologies, Karlsruhe, Germany), supplemented with 10% FBS (PAN-Biotech GmbH, Aidenbach, Germany) and 100 U ml À1 penicillin -streptomycin (Invitrogen Life Technologies, Karlsruhe, Germany).
Real-time light cycler s quantitative polymerase chain reaction (QRT -PCR) All reagents and equipment for mRNA/cDNA preparation were purchased from Roche Applied Science Diagnostics (Mannheim, Germany). mRNA extractions were prepared by automated isolation using the MagNA Pure LC instrument and isolation kits I (for cells) and II (for tissue). cDNA was prepared using the first strand cDNA synthesis kit (AMV) according to the manufacturer's instructions. Real-time PCR was performed with the Light Cycler Fast Start DNA SYBR Green kit. All primers were obtained from Search-LC (Heidelberg, Germany). Primers for BLT1 were: 5 0 -AC TGCCTCCAGCCCTCTCAA-3 0 (forward) and 5 0 -TAGCATTCTGCC AGGAGGAAA-3 0 (reverse). Primers for BLT2 were: 5 0 -ACC TGTAGGCCCAGAAGGATGT-3 0 (forward) and 5 0 -GAAGTCTTC CAGCTCAGCAGTGT-3 0 (reverse). The calculated number of specific transcripts was normalised to the housekeeping genes cyclophilin B and hypoxanthine guanine phosphoribosyltransferase, and expressed as number of copies per microlitre of input cDNA.
siRNA transfection
Three different sets of siRNA were tested and the most effective set knocking down BLT2 was used for our experiments. AsPC-1 and Panc-1 BLT2-transfected pancreatic cancer cells were seeded into 6-well microplates at a concentration of 1 -2 Â 10 4 in RPMI-1640 supplemented with 10% FBS (complete medium). After 24 h, BLT2 siRNA (sense 5 0 CCACGCAGUCAACCUUCUGtt 3 0 , antisense 5 0 CAGAAGGUUGACUGCGUGGta 3 0 ) or negative control siRNA (AATTCTCCGAACGTGTCACGT) were added using RNAiFectTH Transfection Kit (Qiagen, Hilden, Germany) to give a final concentration of 5 mg siRNA per well according to manufacturer's instructions.
Medium was changed 24 h after transfection, and cells were grown in complete culture medium.
Proliferation studies: in tumour cells treated with BLT2 ligands
In further studies cells were seeded into 12-well microplates in complete medium (30 -50% confluence). After 24 h, cells were cultured in serum-free media with or without different concentrations of LTB 4 , Compound A and Compound B (control) for 24, 48, 72, 96, and 120 h. Medium was changed after 48 h. At the end of each time point, the cells were trypsinised to produce single cell suspension and the viable cell number in each well counted using Guava PC (Guava Technologies, Hayward, CA, USA). For MTT assay cells were seeded into 12-well microplates in complete medium at a concentration of 2 Â 10 4 cells per well. After 24 h, cells were cultured in serum-free media with or without different concentrations of LTB 4 , Compound A and Compound B (control) for 24, 48, 72 and 96 h. Medium was changed after 48 h. At the end of each time period 5 mg ml À1 MTT was added.
Proliferation studies: in tumour cells treated with BLT2 siRNA
For the siRNA proliferation studies, cells were trypsinised and the viable cell number was counted at 24, 48, 72, 96 and 120 h after cell seeding using Guava PC (Guava Technologies, Hayward, CA, USA).
Proliferation studies: in tumour cells treated with BLT2 ligands and siRNA
In an additional experiment, cells were seeded into 12-well microplates in complete medium (30 -50% confluence). After 24 h, cells were cultured in serum-free medium with or without Compound A and Compound B (control) for 48 h. At this time point, the medium was changed and BLT2 siRNA (sense 5 0 CCACGCAGUCAACCUUCUGtt 3 0 , antisense 5 0 CAGAAGGUU GACUGCGUGGta 3 0 ) or negative control siRNA (AATTCTCC GAACGTGTCACGT) were added using RNAiFectTH Transfection Kit (Qiagen, Hilden, Germany) to give a final concentration of 5 mg siRNA per well according to manufacturer's instructions. The medium was changed 12 h after transfection, and the cells were left to grow in complete culture medium for another 12 h. Twenty-four hours after siRNA transfection, the medium was changed and cells were grown in complete culture medium with or without Compound A and Compound B (control) for another 24 and 48 h under siRNA effect and then counted using Guava PC (Guava Technologies, Hayward, CA, USA).
STATISTICAL ANALYSIS
Proliferation studies have been repeated at least three times independently. Data on BLT2 expression by immunohisto-chemistry in humans were analysed by Kruskal -Wallis one-way ANOVA with the Dunn's method as post hoc test. Results from QRT -PCR were analysed using one-way ANOVA with the Student -Newman -Keul's Method as post hoc test for multiple paired comparisons. Paired t-test and Friedman repeated measures analysis of variance on ranks (with Tukey's as post hoc test for pairwise multiple comparisons) were used to analyse cell proliferation (cell counting).
RESULTS
Expression of BLT2 in human pancreatic tissues BLT2 were found to be markedly upregulated in PanIN lesions and cancer cells, but were not expressed in islet cells, except in four specimens obtained from patients with chronic pancreatitis (Figure 1). PanIN-1a lesions and normal ductal cells showed absolutely no staining for BLT2, however, we observed strong positive staining in all PanIN-1b, two and three lesions which were found in 8 of 10 pancreatic adenocarcinoma tissues and in two specimens from patients with chronic pancreatitis (Figure 1). The other eight chronic pancreatitis tissues contained either normal ducts and/or PanIN-1a lesions, which did not stain for BLT2. Furthermore, infiltrating tumour cells showed strong positive staining in pancreatic adenocarcinoma tissues and in a lymph node metastasis ( Figure 1). Acinar cells in normal pancreas, chronic pancreatitis and pancreatic adenocarcinoma sporadically showed BLT2 expression in the cytoplasm adjacent to the basolateral membrane. Details are shown in Table 1.
In addition, immunohistochemistry was performed in a variety of pancreatic tissues showing IPMN lesions. Moderate (12 of 28) or strong (13 of 28) cytoplasmic staining for BLT2 was detected in 25 of 28 tissues with IPMNs regardless of whether they were benign or malignant or if they are main or branch duct type lesions (Figure 1). Acinar cells did not stain for BLT2. Islet cells, when applicable, showed weak (16 of 28) or moderate (3 of 28) expression of BLT2 in their cytoplasm. Details are shown in Table 2. Negative controls for BLT1 and BLT2 (first antibodies quenched with the appropriate blocking peptides) showed no staining (Figure 1). In most sections, inflammatory cells stained positive for BLT2 and this provided a valuable internal control. In normal tissues and tissues containing only PanIN1a lesions, these were the only cells with significant staining. It is likely that these were mast cells, but we did not confirm this by double staining.
The results obtained from QRT -PCR confirmed the immunohistochemical findings. BLT1 and BLT2 mRNA were upregulated in chronic pancreatitis and malignant pancreatic tissues. BLT1 expression is approximately fivefold higher than BLT2. Furthermore, although BLT2 is significantly overexpressed only in malignant pancreatic tissues, BLT1 is also significantly upregulated in tissues from patients with chronic pancreatitis (Figure 2).
Expression of BLT1 and BLT2 in human pancreatic cancer cells
Both, BLT1 and BLT2 were found to be expressed at the mRNA level in all human pancreatic cancer cell lines (MiaPaCa2, AsPC-1, T3M4, Panc-1, Su8686, Capan1, BxPC3, Colo357) tested. Real-time RT -PCR revealed that expression levels of both receptors differed between cell lines, with approximately 2-fold higher BLT1 mRNA expression compared with BLT2 expression (Po0.001). MiaPaCa2 cells showed the highest expression for both receptors compared with the other cell lines (BLT1 all Po0.005; BLT2 all Po0.05) (Figure 3).
The effect of BLT2 overexpression on tumour cell proliferation was inhibited by siRNA transfection. After 48 h BLT2 mRNA levels decreased from 2583 to 802 transcripts per microlitre in Panc-1 and from 2198 to 830 transcripts per microlitre in AsPC-1 cells, causing significant growth inhibition of Panc-1-BLT2 and AsPC-1-BLT2 cells (Po0.001). Despite several attempts, we were not able to successfully transfect Colo357 cells with BLT2 siRNA.
Transfection of scrambled siRNA as control did not change the proliferation of Panc-1-BLT2, AsPC-1-BLT2, Panc-1-hfMLPR and AsPC-1-hfMLPR cells. Data are illustrated for Panc-1 in Figure 4. In tumour cells that do not overexpress BLT2, we observed significant growth inhibition by BLT2 siRNA when BLT2 was stimulated with Compound A but not without stimulation ( Figure 5).
Effects of BLT2 agonists on tumour cell proliferation
The selective synthetic BLT2 agonist, Compound A, caused significant growth stimulation of pancreatic cancer cells in a concentration-and time-dependent manner. The final dose of 1000 nM we used in our time-dependent experiments might be high, but is reasonable as the metabolism and half-life of Compound A are not known. Compound B, its methyl ester derivative without any stimulatory effects on BLT2 (Iizuka et al, 2005) used as a negative control, did not alter proliferation of these tumour cells. Treatment of pancreatic cancer cells with the common BLT1 and BLT2 agonist LTB 4 caused an additional growth advantage when compared with cells treated with Compound A or B. Proliferation was measured by cell counting and MTT assay. Data for the treatment of Colo357 and Panc-1 cells are shown in Figure 5.
DISCUSSION
Arachidonic acid is the precursor of eicosanoids, which are important mediators involved in inflammation as well as in the growth of different cancers, including colon, prostate, breast, lung and pancreatic adenocarcinoma (Ding et al, 2003). There are two major groups of eicosanoids: (1) prostaglandins, that are produced by cyclooxygenases and (2) leukotrienes, that are generated by 5-lipoxygenase (5-LOX). Leukotrienes are potent pro-inflammatory mediators that have important functions in inflammatory disorders and allergies (Sampson, 2000;Shao et al, 2006). Recently, we and others have demonstrated that leukotrienes may also be important in cancer development, metastases and cachexia (Paruchuri et al, 2002(Paruchuri et al, , 2006Tong et al, 2002). In the BOP-hamster model leukotriene concentration was significantly increased in pancreatic carcinoma when compared with tumour-free tissue (Heukamp et al, 2006). Moreover, fish oil (rich in n-3 PUFAs) decreased the incidence of liver metastases, possibly because of decreased intrametastatic leukotriene concentration (Heukamp et al, 2006). Expression of the leukotriene D 4 receptor (CysLT 1 ) is increased in colorectal adenocarcinomas and leukotriene D 4 (LTD 4 ) and LTB 4 stimulate colon cancer cell proliferation by activating ERK1/2 (Paruchuri et al, 2002(Paruchuri et al, , 2006Nielsen et al, 2003Nielsen et al, , 2005Ohd et al, 2003). Furthermore, inhibitors of leukotriene production can effectively prevent the lung cancer development in mice (Gunning et al, 2002). We have recently shown, that LTB 4 stimulates pancreatic cancer cell growth by activating ERK1/2; an effect inhibited by the unspecific LTB 4 receptor antagonist, LY293111 (Tong et al, , 2007. We previously demonstrated the expression of BLT1 in human pancreatic cancer tissues with strong staining in cancer cells and the islets surrounding these tumours . BLT2 mRNA expression was demonstrated in murine and human mast cells responsible for recruitment and accumulation of mast cells in areas of inflammation (Lundeen et al, 2006). Another study described a TNFadependent expression of BLT2 in human umbilical vein endothelial cells which may be important during early vascular responses to inflammation (Qiu et al, 2006). In addition, greatly increased BLT2 mRNA levels were found in a variety of human cancers by in situ hybridisation (Yoo et al, 2004). Pancreatic specimens were not a subject of this study (Yoo et al, 2004). Yoo et al (2004) suggested that a LTB 4 -BLT2-linked cascade has a crucial mediatory function in cell transformation induced by oncogenic Ha-Ras V12 . LY255283 has been described as a selective BLT2 antagonist (Qiu et al, 2006;Shin et al, 2006 expression of BLT2 in all human pancreatic cancer cell lines tested and overexpression in all the human pancreatic tissues obtained from patients with pancreatic adenocarcinoma and chronic pancreatitis, when PanIN lesions are present. BLT2 is also upregulated in IPMNs. Strong BLT2 staining was seen in all grades of PanINs, except PanIN-1a. BLT2 staining was also intense in benign and malignant IPMNs; another progression model for pancreatic cancer. BLT2 was significantly upregulated in malignant IPMNs when compared with benign IPMNs and normal pancreatic specimens. However, all IPMNs, including the benign lesions showed specific staining for BLT2. This might be explained by the fact that complete tumour specimens and not microdissected IPMNs were subjected to quantitative RT -PCR and benign IPMNs are often smaller than malignant IPMNs. Overexpression of BLT2 in three different human pancreatic cancer cell lines resulted in increased proliferation compared with control cells transfected with either hfMLPR or empty vector. This is consistent with our previous findings that LTB 4 stimulates pancreatic cancer cell growth . Pancreatic cancer cells secrete LTB 4 and produce growth factors such as epidermal growth factor (EGF). In addition, EGF is able to stimulate LTB 4 production and secretion in these cells. Therefore, LTB 4 may be involved in the autocrine growth stimulation of pancreatic cancer cells acting on both receptors. This might explain why recombinant overexpression of BLT2 in cancer cells already expressing endogenous BLT2 have a growth advantage without any further treatment, because more receptor-binding sites (BLT2) can be stimulated by LTB 4 . Moreover, serum contains so far unspecified lipid factors stimulating BLT2. Furthermore, LTB 4 has previously been linked to insulin secretion. As we observed that BLT1 is upregulated in islets adjacent to pancreatic adenocarcinoma, it is tempting to speculate that the tumours may induce additional paracrine growth stimulation by augmenting local insulin secretion in the pancreas (Pek and Walsh, 1984;Hennig et al, 2002).
Selective BLT2 stimulation with Compound A and inhibition with siRNA caused increased tumour cell proliferation or growth inhibition, respectively. However, siRNA treatment of cancer cells expressing endogenous BLT2 did not affect their proliferation rate. This is difficult to fully explain, because if endogenous LTB 4 stimulates growth through BLT2 receptors, then receptor knockdown would be expected to reduce proliferation. However, the answer may lie in the fact that both receptor subtypes respond to this ligand. Reduction in copy number of BLT2 may merely allow the LTB 4 to signal through the BLT1, which have higher affinity for the ligand. However, in cells with upregulated BLT2, knockdown will reveal the growth effects of this receptor. Evidence that this explanation is correct comes from the experiments with Compound A in cells (a compound that is relatively specific for BLT2) without overexpression of BLT2. Here, the BLT2 knockdown did block proliferation.
As BLT2 are not or only weakly expressed in normal ductal cells, their marked upregulation in PanINs, IPMNs and infiltrating cancer cells suggests a beneficial role of this receptor for the cancer cells. To our knowledge, this is the first report linking BLT2 to pancreatic cancer. We may have found a selective PanIN and IPMN marker. Maitra et al (2002) reported that COX-2 is overexpressed in 65% of PanIN lesions and suggested the use of COX-inhibitors for chemoprevention in patients at high risk. However, COX-2 is not expressed in all PanIN lesions and is also expressed in islets (Maitra et al, 2002). BLT2 may prove to be a superior target, because of the consistent and selective expression in PanINs and IPMNs. We speculate that upregulation of BLT2 is associated with ductal changes. However, we do not claim BLT2 as a specific marker for pancreatic carcinogenesis. BLT2 could be considered as an imaging target for early neoplastic lesions, for example, in patients at high risk for pancreatic cancer. The expected background signal in normal pancreas should be sufficiently low, because acinar and islet cells only showed weak positive staining in some tissues and BLT2 mRNA levels in whole pancreas are very low compared with other organs. Finally, development of a combined BLT1 and BLT2 antagonist (Hicks et al, 2007) could be a valuable approach for the treatment and chemoprevention of pancreatic and perhaps other cancers. | 5,468.4 | 2008-09-09T00:00:00.000 | [
"Medicine",
"Biology"
] |
Phcogj.com Human Umbilical Cord Blood-derived Secretome Enhance Endothelial Progenitor Cells Migration on Hyperglycemic Conditions
Cardiovascular disease, especially coronary heart disease has been always correlated with the longterm prognosis of patients with uncontrolled hyperglycemia, especially diabetes mellitus patients. Uncontrolled hyperglycemic patients have a higher mortality risk of heart disease ranging from 2 to 4 times higher compared to non-hyperglycemic patients.1 Vascular endothelial impairment has been well observed and found as the main culprit of coronary heart disease in uncontrolled hyperglycemic patients.2-6 In uncontrolled hyperglycemic patients, endothelial dysfunction is caused by low levels and dysfunction of circulating endothelial progenitor cells (EPCs). Disruption of EPC function was found and observed in both of DM types, DM type 13 and 2. This finding proved that there is a correlation between uncontrolled hyperglycemia conditions and EPC dysfunction.4
INTRODUCTION
Cardiovascular disease, especially coronary heart disease has been always correlated with the longterm prognosis of patients with uncontrolled hyperglycemia, especially diabetes mellitus patients. Uncontrolled hyperglycemic patients have a higher mortality risk of heart disease ranging from 2 to 4 times higher compared to non-hyperglycemic patients. 1 Vascular endothelial impairment has been well observed and found as the main culprit of coronary heart disease in uncontrolled hyperglycemic patients. [2][3][4][5][6] In uncontrolled hyperglycemic patients, endothelial dysfunction is caused by low levels and dysfunction of circulating endothelial progenitor cells (EPCs). Disruption of EPC function was found and observed in both of DM types, DM type 1 3 and 2. This finding proved that there is a correlation between uncontrolled hyperglycemia conditions and EPC dysfunction. 4 After being recruited from the bone marrow, the circulating EPCs have the capability to differentiate into mature endothelial cells and also promote endothelial repair. It has been documented that EPCs plays major roles in both the stimulation of angiogenesis and vasculogenesis in human body. 5 Hence, improvement in EPCs function may be very beneficial in reducing cardiovascular complications, especially coronary heart disease in uncontrolled hyperglycemic patients. 6 Human mesenchymal stem cells (hMSCs) are multipotent stem cells that can be isolated from various tissue, such as bone marrow, adipose tissue, and postnatal tissue 7 as used on this study is the human umbilical cord blood (hUCB-MSCs). The therapeutic effect of hMSCs is believed to originate from the soluble factors or cytokines known as secretome, which shown to increase neovascularization and angiogenesis. [8][9][10][11] But its capability under hyperglycemia conditions has not yet been illustrated.
This study aims to investigate the migration capability of endothelial progenitor cells (EPCs) under hyperglycemia conditions, which is expected to mimic the metabolic disturbance that occurs in patients with type 2 diabetes mellitus.
Sample criteria
This is an in-vitro study, with true experimental posttest only control group design. The blood sample was obtained from CAD patients in Dr. Soetomo General Hospital, Surabaya, Indonesia. The inclusion criteria were male, aged 40-59 years old, history of chronic ischemic heart disease as proven by coronary angiography results that showed >50% stenosis of left main coronary artery or >70% of other coronary arteries. The exclusion criteria were a history of the percutaneous coronary intervention procedure, coronary artery bypass grafting surgery, acute coronary syndromes, diabetes mellitus and anaemia. This study protocol has an ethical clearance from the Health Research Ethics Committee of Dr. Soetomo General Hospital, Surabaya (No.1567/KEPK/X/2019, approved on October 8 th , 2019). The included subject has signed informed consent before subject recruitment.
The hUCB-MSCs-derived secretome preparation
Preparation of hUCB-MSCs-derived secretome was conducted as described in a previous study. 12 HUCB-MSCs (3H Biomedical AB, Uppsala, Sweden) was cultured in Mesencult media (StemCell Technologies Inc., Vancouver, Canada) containing penicillin and streptomycin. Upon reaching 80% confluency, the media was replaced with supplement-free media and incubated for 24 hours. The media was collected and centrifuged. Supernatant was used as a conditioned medium contained with hUCB-MSCs-derived secretome.
EPCs isolation and culture
EPCs were isolated from mononuclear cells (MNCs) of the peripheral blood of CAD patient. 40 ml of blood was withdrawn and put into conical tubes and diluted with Phosphate buffer saline with 2% fetal bovine serum. Afterwards, Ficoll histopaque was added to the mixtures. And then centrifugation was done until peripheral blood MNCs layer was formed. And then 5 x 105 cells/mL PBMCs were cultured with basal Stemline II hematopoietic stem cell expansion medium (Sigma-Aldrich, St. Louis, MO, USA) supplemented with 15% fetal bovine serum and several growth factors in the fibronectin-coated 6-well plates. The culture was maintained at 37 C with 5% CO2 in a humidified atmosphere. Non-adherent cells were discarded, and fresh medium was added, after two days of culture. After three days of EPCs isolation and culture, EPCs were confirmed using FITC-labeled antihuman CD34 antibody (Biolegend, USA) staining and examined with immunofluorescence microscopy.
Migration assay
This experiment was performed using Costar® Transwell® Permeable Support (Corning, USA) with a 3.0 µm pore size membrane. EPCS migration was calculated using a Boyden chamber assay method. Isolated EPCs were detached using Trypsin EDTA solution (Sigma-Aldrich, USA). A total of 5×105 EPCs were incubated with high glucose (25 mmol/L D-Glucose, Sigma-Aldrich, USA). After 5 days, cells were placed in the modified Boyden chamber at the upper chamber with basal media. The lower chamber was supplemented with basal media and secretome at 2%, 10%, and 20% concentration. The culture was incubated at 37 o C for 24 hours. The non-migratory cells were removed manually. Meanwhile, the migratory EPCs below the upper chamber were fixed with 3.7% paraformaldehyde and permeabilized with methanol. Migrated EPCs were stained with Crystal Violet stain and calculated.
Statistical analysis
Statistical analyses were conducted using SPSS Statistics 24.0 from IBM to detect significance level at p<0.05. Data were evaluated for normal distribution using the Kolmogorov Smirnov test and compared between groups using a one-way ANOVA test for migration and proliferation. Correlation between variables was obtained using Spearman correlation followed by a linear regression test.
EPCs characteristics
EPCs were successfully confirmed from peripheral blood MNCs, marked by positive CD34 on immunofluorescence. Light microscope view showed spindle-shaped cells which also characterize EPCs (Figure 1).
Effect of hUCB-MSCs-derived Secretome on EPC migration
hUCB-MSCs-derived secretome under non-glucose conditions induced a higher migration rate of EPC in a dose-dependent manner, but when compared to VEGF treated group, the migration was not significant.
Treatment with secretome under high glucose concentrations group had a moderate correlation with EPCs migration (r=0.401; p<0.000). The linear regression test showed an r-square of 0.638. This suggested that the treatment with secretome under high glucose concentrations is responsible for 63.8% of EPCs migration.
DISCUSSION
In this study, we show that hUCB-MSCs-derived secretome has the capability of inducing the migration of EPC in a dose dependent manner under none and high glucose concentrations. Surprisingly, this effect statistically significant only with 20% concentrations of secretome and under high glucose concentrations, compared to VEGF treated group as a control.
The impaired migratory capacity of EPC in diabetic patients compared to healthy subjects has been well demonstrated in the previous study 3,13 , it was also demonstrated the upregulated VEGF expression as a response of the low level of circulating EPCs. Unfortunately, the increased expression of VEGF was not followed by the improvement of EPCs number, thus therapeutic angiogenesis strategies by intensifying plasma VEGF may not be effective. 13 The survival, migration, and angiogenesis capability of EPCs are known to be related by the expression and phosphorylation of endothelial nitric oxide synthase (eNOS). 14-17 Nitric oxide derived from eNOS plays a major role in vascular integrity by upregulating VEGF expression. 15 Hyperglycemia state was associated with inadequate production of eNOS activation, thus increasing the production of reactive oxygen species (ROS), which further impaired EPCs function. [14][15][16] However, the pathophysiology behind the impairment of EPC in diabetic patients, have been described by Chen et al., which showed the reversal of EPC function after NO donor, not by antioxidants. 14 Cytokine gradient has also been proposed as an essential mechanism behind the impairment of EPCs mobilization. 8 The stromal cell-derived factor 1 (SDF-1α), through stimulation of its receptor (CXCR4) on the EPCs membrane, is a noteworthy chemokyne that responsible for the mobilization of EPC from bone marrow to ischemic tissues. 8,15 Diabetes has been associated with the impaired production of SDF-1α in the ischemic tissue. In contrast, production increased in bone marrow, which leads to a reduced chemoattractant gradient to mobilized EPCs from the bone marrow. 15 Incorporating findings from previous studies, we hypothesize that improving the production of NO and increasing the concentration of chemoattractants, especially SDF-1α, may improve the EPCs migration capability.
The hUCB-MSCs exceptionally secrete higher amounts of important cytokines and growth factors, via either direct secretion or exosomes and microvesicles, compared to bone marrow derived MSCs. 18 In terms of improving EPCs migration, secreted factors like G-CSF, GM-CSF SDF1, insulin-like growth factor 1 (IGF1), placental growth factor (PlGF), PDGF-β, 8,18,19 and enhancing expression of iNOS through the secretion of extracellular vesicles, 20 may play a major role in the mechanism of the hUCB-MSCs secretome capability.
CONCLUSION
In this study, we report for the first time, the paracrine effect associated with secreted factors from hUCB-MSCs, collectively known as secretome, have the capability of inducing EPCs migration under high glucose concentrations, thus may be of relevance for cell-free and regenerative therapeutic modality for a diabetic patient with CAD. | 2,147.6 | 2020-06-18T00:00:00.000 | [
"Medicine",
"Biology"
] |
Research on Marketing Strategy of Chinese Painting and Calligraphy Art Based on Wireless Communication Network Resources
China has a thousand years of cultural heritage, and its famous paintings and calligraphy works are all representatives of the works of art. Calligraphy and painting have changed from works of art to commodities. *is is undoubtedly caused by the changes of the times and society. Calligraphy and painting have gradually formed in this process. *e most basic feature that distinguishes the market from other markets is the difference in marketing strategies. Aiming at the current problems in the online marketing of Chinese paintings and calligraphy works, a marketing strategy for Chinese paintings and calligraphy works based on wireless communication network resources is proposed. In view of the lack of accurate analysis of the current Chinese painting and calligraphy and artwork marketing and the uneven quality of the artwork, the above-mentioned problems can be improved through the allocation of wireless communication network resources. *e experimental results show that the scope of dissemination of Chinese paintings and calligraphy works using this marketing strategy is wider, and the marketing transaction rate and profit are greater.
Introduction
Calligraphy and painting have become a commodity from their appearance to the present. It is the choice of the times. e calligraphy and painting market formed after painting and calligraphy became a commodity.
is market is a market with a strong cultural atmosphere. Without the permission of certain abilities and strengths, it is not qualified to enter the painting and calligraphy market. Since the painting and calligraphy market exists, it must have its own marketing strategy, just like other commodity markets, but its marketing strategy is still based on cultural atmosphere. In the internet age, if the calligraphy and painting market wants to continue to survive and achieve better development, it must keep pace with the times, reflect the advantages of the Internet, do a better job in the calligraphy and painting market, and use the Internet to promote the advantages of calligraphy and calligraphy [1]. e connotation of historical culture allows people to understand Chinese painting and calligraphy culture and historical civilization. At the same time, it encourages everyone to make self-creation. Do not blindly imitate. Only by creating works with their own style can they have a place in the painting and calligraphy circles. At the same time, the development of appraising programs can also be done. Enhancing everyone's ability to distinguish the authenticity of calligraphy and painting works, this kind of calligraphy and painting market has an artistic soul and can reflect its value not only as a commodity. e establishment of online painting and calligraphy trading venues must be built on many foundations. First of all, the policy is the most basic, so the establishment of online painting and calligraphy trading venues needs a period of precipitation. China's calligraphy and painting market is relatively mature and stable, and value can be reflected in this market [2].
At present, relevant scholars have done research on the marketing strategy of Chinese painting and calligraphy. Literature [3] mainly describes the network business strategy and business model of painting art from the art network marketing strategy. Now, the sale of calligraphy and painting art is not limited to the sales in the real market. e development of online e-commerce provides new ideas for the marketing of painting and calligraphy art. e possibility of painting and calligraphy art is roughly divided into highand low-priced art in terms of price. E-commerce websites have a sufficient supply of low-priced art, and consumers of painting and calligraphy art also have a very large potential demand for low-priced art. To make full use of online marketing strategies, find a marketing model suitable for calligraphy and painting art, regulate the prices of lowpriced artworks, and improve the visibility and integrity of websites. e online market for calligraphy and painting art will be very huge. Literature [4] proposed an e-commerce product marketing strategy based on the ordinary least squares model. Although there is a negative correlation between e-commerce and corporate performance, it is positively correlated under the intermediary effect of certain types of online sales channels. In particular, when companies use commercial websites and online marketplaces, the benefits of e-commerce in terms of higher sales are more obvious. On the other hand, the interaction between e-commerce and search engines has no significant impact on corporate performance. Literature [5] has experienced that market operators can detect the marginal price of abnormal nodes (LMP) during the real-time (RT) operation of the traditional attack model. Because such an attack model ignores the characteristics of LMP itself, resulting in price increase, abnormal signals can be easily detected. Literature [6], by understanding the specific needs of users and clarifying the design principles of digital museums, uses 3ds Max and virtual reality (VR) software platform Unity3D to develop QAU's museum roaming system of ancient high imitation paintings. e system realizes the dissemination of traditional classical art culture and value through new technical means, which has reference significance for other fields of research.
In the internet environment, every industry sells differently, this article assumes that the impact of e-commerce on business performance is not direct, and intermediary factors need to be used for testing. e ordinary least squares (OLS) model was used with data from the Flash Eurobarometer 439 survey titled "SMEs' use of online markets and search engines." e obtained research results provide support for the mediation hypothesis. More precisely, although there is a negative correlation between e-commerce and corporate performance, it is positively correlated under the intermediary effect of certain types of internet sales channels. In particular, when companies use commercial websites and online marketplaces, the benefits of e-commerce in terms of higher sales are more obvious. On the other hand, the interaction between e-commerce and search engines has no significant impact on corporate performance.
is research advances the research on e-commerce by emphasizing the importance of intermediary effects. Online sales methods have brought a lot of benefits to merchants. However, it is impossible for the painting and calligraphy market to change from offline to online because good paintings and calligraphy are precious Chinese paintings and calligraphy works of art. ere are many fake and inferior products that can only be traded offline, but the Internet can be used for publicity. It is one of the ideas of marketing strategy transformation. is article will start from wireless communication network resources to study the transformation of Chinese painting and calligraphy art marketing strategy.
Our contribution is threefold: (1) Aiming at the current problems in the online marketing of Chinese paintings and calligraphy works, a marketing strategy for Chinese paintings and calligraphy works based on wireless communication network resources is proposed. (2) In view of the lack of accurate analysis of the current marketing of Chinese painting and calligraphy artworks and the uneven quality of artworks, this paper aims to improve the above problems through the allocation of wireless communication network resources.
(3) e experimental results show that the scope of dissemination of Chinese paintings and calligraphy works using this marketing strategy is wider, and the marketing transaction rate and profit are greater. e remainder of this paper is organized as follows. Section 1 introduces the marketing status of Chinese painting and calligraphy art. Section 2 discusses the wireless communication network resource allocation method. Section 3 discusses the marketing strategy of Chinese calligraphy and painting based on wireless communication network resources. Section 4 discusses the experiment and analysis. Section 5 presents the conclusions of the study.
Analysis of the Marketing Environment of Chinese
Paintings and Calligraphy Works. e main core of the calligraphy and painting market is calligraphy and painting works. Although calligraphy and calligraphy are traded as commodities, their value has never changed. Its historical value will always be there. And everyone pursues it not only because it is a representative of history but also because it is a representative of history. Respected by calligraphy and painting authors, many famous calligraphy and painting masters are sought after by many people in modern times. e most prominent feature of calligraphy and painting is the precipitation of history and culture, which is the embodiment of the social status quo of the time in which the author lives and is the witness of history. In the modern painting and calligraphy auction market, the characteristics of the painting and calligraphy market are a mixture of investment, speculation, collection, consumption, and gifts [7]. As modern Chinese painting and calligraphy, painting and calligraphy are consumer products from which people pursue spiritual enjoyment. In terms of its historical value and artistic connotation, its cultural value is extremely high-end among modern spiritual consumer goods. It is not only the embodiment of the writer's artistic level but also the embodiment of the modern people's appreciation level. Painting and calligraphy have risen from Chinese painting and calligraphy to consumer goods. It has produced a series of effects, the most obvious of which is the wealth effect. e current calligraphy and painting industry has attracted a lot of investment through the Internet. ese investments are undoubtedly for the pursuit of profit. Some paintings and calligraphy works were given to others as gifts and circulated in the gift market [8].
In addition to the modern auction market, there is a gallery market in the calligraphy and painting market. e calligraphy and painting works sold in the gallery can only be sold for the first time. e second sale is very difficult, so some buyers who need decoration will buy it. is part of the group has no investment intentions, just consumer groups. Painting and calligraphy work as Chinese painting and calligraphy works of art; their price is not fixed, unlike ordinary commodities; and it has a fixed selling price. e price of painting and calligraphy works is for the buyer, who likes to attach importance to a painting and calligraphy work. It can be purchased at a high price. If the same work is given to another person who does not need it or appreciate it, the convincing work will have no value in its eyes, and there will be no price at all. e off-season and peak season of the calligraphy and painting market are mainly determined by the festival. When the Mid-Autumn Festival and the Spring Festival are approaching, the peak season of the calligraphy and painting market will appear. During this time, there are many holidays, and many people have time to visit friends and relatives. It will be accompanied by gifts. Painting and calligraphy are two of the alternative gifts. At the same time, many house decorations are also placed in free time such as holidays.
erefore, painting and calligraphy will also be popular as decorations during holidays. e premise of buying and selling in the calligraphy and painting market is that people with knowledge and culture are needed to identify the true and false. erefore, many appraisers have appeared, and now, there are many appraisal programs and appraisal companies. ese are all economic effects brought by the calligraphy and painting market. In fact, Chinese paintings and calligraphy works do not have much effect on the national economy and people's livelihood. e reason why it can become a "famous person" in the eyes of the public is mainly because of the media's attention after its economic effects are discovered. e media reports after the auction of the paintings and calligraphy works. It will arouse everyone's attention so that the calligraphy and painting works will appear to have more economic value in everyone's eyes.
Macro Environment.
Economic and political environment: art as spiritual food is derived from people's material foundation to meet certain requirements. Only in the environment of social harmony, economic stability, and civilization development can people's ideas progress and the market for calligraphy and painting art develop. ese two factors are also important reasons that affect the prosperity or decline of the Chinese painting and calligraphy art market.
Social and cultural environment: society is an allencompassing overall environment. To flourish the Chinese painting and calligraphy art market, first of all, the society must have a relaxed and free atmosphere, which can provide creators with an environment for creating art, and it must be inclusive and open.
Attitude: if the social environment is a narrowly restricted environmental space, the Chinese painting and calligraphy art market can only go to extinction. If the social restrictions are too narrow, then the Chinese calligraphy and painting artworks created by the artists do not have their own style characteristics and cannot express their artistic creations according to their own ideas.
Legal environment: from an artistic point of view, Chinese paintings and calligraphy works do not seem to be closely related to the law. One is a symbol of freedom, and the other is a representative of rigor. But when Chinese painting and calligraphy works as a commodity, the law is inevitably linked with it. When Chinese painting and calligraphy works are closely linked with the law, we need a complete and reasonable system to restrict and protect the Chinese painting and calligraphy art market.
Microenvironment.
Creative producer: as a person who creates and produces Chinese painting and calligraphy works of art, he must first have high-quality aesthetics and mature creative skills. As the creator of Chinese painting and calligraphy, creators must have keen observation and insight. First of all, we should discover our own essence and use skilled techniques to create a piece of Chinese painting and calligraphy and then choose the appropriate marketing channels to turn Chinese paintings and calligraphy into a commodity.
Exhibition: the exhibition is equivalent to an intermediary in the marketing process of Chinese painting and calligraphy art. It has a fixed venue for planning and then exhibiting Chinese painting and calligraphy art. In this type of environment, most of the exhibitions are mostly Chinese calligraphy and painting artworks created by new and young artists. At present, all kinds of exhibitions are emerging in cities, and they are scattered in every corner of the city.
Auction house: as a professional organization for marketing Chinese paintings and calligraphy works, auction mainly provides venues and related workers. e Chinese paintings and calligraphy works on display will be auctioned publicly, and the higher bidder will get the money.
Problems in the Marketing of Chinese Calligraphy and Painting Art.
(1) Lack of accurate analysis: In the marketing of Chinese paintings and calligraphy, people often cannot accurately analyze the environment. When marketing Chinese calligraphy and painting works of art, Security and Communication Networks it is only treated as a commodity, without distinguishing the differences between different Chinese calligraphy and painting works of art. For example, sculpture and painting have different styles, and their marketing methods and strategies are often very different. (2) e quality of Chinese paintings and calligraphy works is uneven: When Chinese paintings and calligraphy works are marketed as a commodity, their fame and style will be paid attention to. Generally, when we look at a piece of Chinese painting and calligraphy, we also pay attention to its creativity. But creation does not mean to be unconventional, but it cannot be imitated all the time. e current Chinese painting and calligraphy art market often follows the trend. If the Chinese painting and calligraphy art is well marketed, there will be hundreds of such things appearing, and the hype becomes popular, and the counterfeit products are rampant.
Wireless Communication Network Resource Allocation Method
3.1. Priority Setting. In the internet era, the marketing strategies of commodities have undergone earth-shaking changes. It is one of the common ways to change from offline to online [9]. e access of wireless communication network resources is through the division of different time slots on the dedicated wireless network. So that different types of wireless communication services can select different time slots according to the set priority, then share a frequency band on the corresponding time slot, and use the time slot to isolate the wireless communication network resources on the physical layer in the multiservice air interface [10]. e initial access flow chart of wireless communication network resources is shown in Figure 1: e specific implementation process of its access is to set the priority of different services according to the transmission requirements of different services when multiple service terminals send access and transmission applications [11]. e formula for priority setting is as follows: Here, k represents the priority of the service, T k represents the access delay threshold of wireless communication network resources, and S k represents the amount of resources to be accessed. en, the idle time slot is allocated according to the priority from the channel resources, and the time slot occupation table is generated [12]. e table is sent to each service terminal, and the terminal analyzes the time slot in which the respective transmission service is located and performs access and transmission of wireless communication network resources on the corresponding frequency band. When there are many applications for network resource services, low-priority services will access fewer but relatively fixed time slot resources, and a larger number of resources will be reserved for higher-priority services [13].
Forwarding of Wireless Communication Network
Resources. After the central base station accesses the wireless communication network resources, when the network resource node is far from the assigned service node, the smooth distribution of wireless communication network resources is realized by adding relay nodes. e main task of the relay node is to complete the wireless communication network [14]. e forwarding of resources in order to forward the network resources. In order to ensure the smooth completion of the wireless communication network resource forwarding, the decoding forwarding method is adopted. At the network service end, each source sending end sends out its own resource information in the form of broadcast, and the network resource destination receiving end and the relay node listen to and receive this signal at the same time. e calculation formula of the resource energy value received by the relay node is as follows: Here, E r is the received resource energy value, τ 0 is the energy receiving time, P s is the resource signal sent from the central base station, h br is the signal transmission time, a is the energy receiving efficiency of the relay node, and b is the received energy e percentage of resources that can be used to forward information. Next, the relay node decodes and evaluates the received signal after receiving the signal. en the relay node forwards the estimated network resource signal to the resource receiving destination. e information of the two channels is judged and received separately through the resource receiving destination. In this forwarding process, the relay node performs simple detection and judgment processing on the received network resource signal, which does not have high requirements on the wireless communication system, and facilitates subsequent wireless communication network resource allocation.
Allocation of Wireless Communication Network
Resources. e allocation of wireless communication network resources is mainly based on the network resources that are accessed from the central base station and forwarded through the relay node. e proportional fairness algorithm is used for allocation. Under the premise of ensuring a certain wireless communication network capacity, the fairness of resource allocation is fully considered., Maximizing the logarithm sum of the average distribution rate of each service user in the wireless communication network is called proportional fair distribution scheduling P, which is specifically expressed as follows: Here, P k (t) is the allocation rate of network resources, r k (t) is the instantaneous allocation rate of service k at time t, and R k (t − 1) is the allocation rate of service k before time t. e specific steps of the algorithm are as follows: the first step is to equally allocate the allocated power of the wireless communication network resources; the second step is to substitute the service user k into formula (3) to calculate the corresponding network resource allocation rate. e third step is to compare the network resource allocation rates of different services, and the one with the highest allocation rate is substituted into the algorithm set for cumulative calculation. erefore, in the process of wireless communication network resource allocation, under the premise of ensuring a certain network resource capacity, the fairness of different services can be taken into consideration, and the allocation of wireless communication network resources can be realized.
Marketing Strategy of Chinese Calligraphy and Painting Based on Wireless Communication Network Resources
In the internet age, the calligraphy and painting market can also follow its development and open up an online trading market. e online market is convenient and fast, can facilitate the transaction, and save a lot of steps. It is chosen by many commodity exchanges, but it has many in the calligraphy and painting trading market [15]. e most important thing is that the authenticity of the works cannot be guaranteed. erefore, if the online painting and calligraphy trading market wants to exist, it must ensure the authenticity of the works and not deceive consumers. is requires the strong support of the national government to proceed. It needs strict state supervision to continue to exist. e purchaser must also make a sincere purchase. It cannot be a secret exchange. After completing an online transaction, return the goods for reasons other than the goods. is increases the risk of the authenticity of the goods. It is also quite unfair to the sellers in the painting and calligraphy market transactions. erefore, if you create an online trading venue, national supervision is not only to supervise the seller but also to supervise the buyer. Transactions are what you want and you can trade when the two parties agree, but the transaction must comply with the national legal system. erefore, it is necessary to open up online painting and calligraphy market transactions, and supervision is the control point of every transaction [16].
rough the allocation and utilization of wireless communication network resources, the basic characteristics of painting, calligraphy, and art are analyzed, and possible problems existing in the establishment and operation of the current network marketing system are discussed. Manufacturers and open e-commerce platforms belong to the same supply chain. If, in this supply chain, the manufacturer chooses to establish its own exclusive network platform for sales, then the manufacturer will have the leadership and power to decide how to determine the wholesale price of the product and how much.
is situation conforms to the Stackelberg game model. In this model, the market for manufactured products is shared by professional e-commerce platforms and manufacturers. But the manufacturer will dominate. e model is shown in Figure 2.
In Figure 2, X 1 represents the wholesale price at which the manufacturer wholesales the product to the open e-commerce platform. P 1 represents the price at which the open e-commerce platform sells products to consumers. P 2 represents the price at which the manufacturer directly sells the product to the consumer. Determine and analyze the game sequence according to the model. First, the final purpose of the manufacturer's establishment of its own network platform for sales is also for the benefit and to maximize profits. In this case, in order to maximize profits, two variables need to be determined. One is the price at which the manufacturer wholesales the product to the open e-commerce platform, that is, what X 1 is, and the other is the manufacturer's own establishment. What is the price of the product sold through online channels? at is, what is P 1 ? Second, for open e-commerce platforms, profits must also be maximized. erefore, the open e-commerce platform also needs to determine two variables: one is the wholesale price given by the manufacturer, which is X 1 . e second is the price you sell to consumers, which is P 1 .
If the manufacturer establishes its own network platform for sales, it also needs to bear the operating expenses, assuming that the cost is F. e manufacturing cost of the product is expressed as CM. Other fixed costs are ignored. e profit of the manufacturer can be expressed as: (X 1 − CM)Q 1 + (P 2 − CM)Q 2 − F. Knowing the various variables, the profit of the manufacturer can be calculated. For a professional e-commerce platform, the profit is expressed as: (P 2 − X 1 )Q 1 . Once the manufacturer determines two variables, one is the price X 1 at which the manufacturer wholesales the product to the open e-commerce platform, and Security and Communication Networks 5 the other is the price P 1 at which the manufacturer sells the product through the network channel established by the manufacturer. e optimal response function of the open e-commerce platform can be obtained as follows: Finally, the profit expression function for the manufacturer and the Hessian matrix related to the two variables can be obtained, expressed as follows: If a > b, for the manufacturer, both the profit expression and the Hesse matrix are negative definite. erefore, when a > b, the optimal sales price P 2 and the optimal wholesale price X 1 that maximize the profit can be obtained according to the characteristics of the quadratic concave function. e best selling price is expressed as follows: e optimal wholesale price can be expressed as follows: e best wholesale price and the best selling price are expressed as above. For the manufacturer, the optimal demand Q 2 can also be obtained, which is expressed as follows: According to the above optimal wholesale price X 1 , optimal demand Q 2 , and optimal sales price P 2 , the manufacturer's maximum profit can be obtained. e above analysis systematically analyzes the basic characteristics of calligraphy and painting and discusses the possible problems existing in the establishment and operation of the current network marketing system. Starting from the problem, effective planning can be formed in the following three aspects to form an effective online marketing system for calligraphy and painting.
First of all, a marketing environment with multiple channels needs to be established. It is necessary to use modern technology to build the environment of marketing channels. In fact, there are two main environmental factors that plague internet marketing at this stage: one is the need to solve the problem of customer trust; the other is the problem of commodity circulation. e solution of the first environmental problem depends on the development of technology, such as VR technology, real-time video technology, remote internet of things technology, and so on, through the integration of related technologies to solve the effectiveness of customers in appraising and appreciating works, so as to improve credibility. e second problem requires effective cooperation with third-party logistics companies to form a dedicated logistics channel for calligraphy, painting, and art. On the other hand, due to the relatively high added value of artworks, a self-operated professional logistics system can also be constructed in an attempt [17].
Secondly, do a good job in the diversion of art marketing platforms. Targeted and effective publicity should be aimed at the people who adapt to the calligraphy and painting works. Make full use of big data information to identify target groups and form a marketing propaganda network on the basis of identification. It is possible to comprehensively use the foundation of offline platforms and the method of letting third parties to expand the scope and effect of publicity. Pay attention to the characteristics of calligraphy and painting works themselves in the drainage and form a variety of ways of the display, such as online explanations by artists, public classrooms, public auctions, and so on, so as to achieve a better marketing system and strategy construction.
Finally, establish an effective marketing path. With the necessary technical support, it is necessary to change the existing single network channel to form a combined online and offline marketing path. Specifically, use the construction foundation of the offline system to form an effective customer group and brand support, thereby increasing the credibility of the network marketing system. In the process of network marketing platform and path construction, it is necessary to follow the basic model from the shallower to the deeper, that is, to market general paintings and calligraphy works and to conduct large-scale artwork transactions after having a certain flow basis and brand effect. rough this step-by-step approach, the success of the integrated network marketing system is achieved.
On this basis, a promotion strategy for the international market of Chinese traditional painting artworks is proposed, as shown below.
International Sales Promotion.
International marketing personnel engaged in calligraphy, calligraphy and art should have professional experience in international marketing of calligraphy and art and be familiar with the consumer psychology of target customers and background knowledge of calligraphy and art. Different salespersons are responsible for different painting and calligraphy themes and match salespersons according to the needs of target customers. International marketers are professional and are a source of profit in the international market. Due to the development of e-mail and mobile media, the personnel sales model has become more convenient.
International Event Promotion and Public Relations.
To organize international promotion activities for calligraphy, calligraphy and art, it is necessary to cooperate with overseas hotels, attractions, travel agencies, and other institutions to hold experiential calligraphy and art appreciation, free calligraphy and calligraphy art training, Chinese traditional culture lectures, and other forms of cultural elegance. Set activities. Cultural enterprises can invite local customers, calligraphy and calligraphy artists, and artists to create on-site calligraphy and calligraphy. Participants can experience the fun of calligraphy and painting and feel the charm of calligraphy and calligraphy or design interactive games, where participants can get famous on-site calligraphy and painting.
is can not only carry out customer relationship management but also send out potential customers and at the same time improve the company's social visibility and reputation.
International Advertising Promotion.
In international advertising and promotion, media means should be selected according to the expected effect of the advertising, the financial strength of the company, and the characteristics of target customers. e content of advertising should not conflict with local laws and culture. In view of this, cultural companies can outsource advertising to advertising companies in the target market. Because they are familiar with the local social and cultural environment and are highly recognized by local consumers, they can receive better market coverage and penetration. Or choose a domestic local advertising company to use its branches in foreign target markets to display advertising and promotional activities.
Experimental Research
In order to analyze the effectiveness of the Chinese calligraphy and painting art marketing strategy based on wireless communication network resources, a comparative experiment is designed. Literature [3] and [4] are used as experimental comparison methods to verify the effectiveness of the designed strategy from the three aspects of the spread of Chinese paintings and calligraphy, the marketing transaction rate, and the profit.
Experimental Environment and Parameters.
e experiment was carried out in MATLAB simulation software, and literature [3] and [4] were used as experimental comparison methods to simulate the marketing process of Chinese paintings and calligraphy works and to draw conclusions on marketing effects.
Analysis of Experimental Results.
Compare the spread of the three methods for Chinese calligraphy and painting, and the comparison results are shown in Figure 3.
Analyzing Figure 3, it can be seen that when traditional methods are used to spread Chinese calligraphy and art, the initial level of communication is acceptable, but as the number of platform marketing days increases, the degree of communication has a downward trend, indicating that traditional methods cannot meet the requirements of Chinese calligraphy and art communication. e method in this paper has a relatively high degree of communication during dissemination, and the dissemination ability gradually increases with the increase of time, indicating that the use of wireless sensor network resources to determine target needs has increased the spread of Chinese paintings and artworks and deepened people's understanding of Chinese paintings and calligraphy.
On this basis, the three methods are tested for the marketing transaction rate of Chinese paintings and calligraphy works, and the comparison results are shown in Figure 4.
Analyzing Figure 4, it can be seen that the average marketing transaction rate of the method in literature [4] is 40%, the average marketing transaction rate of the method in literature [7] is 30%, and the average marketing transaction rate of the method in this paper is 69%. With the increase of marketing time, the marketing transaction rate is also higher. Because the method in this paper increases the spread of Chinese calligraphy and artworks and classifies Chinese calligraphy and artworks through wireless sensor network resources, it can help target users accurately find their favorite Chinese calligraphy and artworks and increase the marketing transaction rate. On this basis, the three methods are tested for the marketing profit of Chinese paintings and calligraphy works, and the comparison results are shown in Figure 5.
Analyzing Figure 5, it can be seen that the average profit rate of the method in literature [4] is 46%, the average profit rate of the method in literature [7] is 38%, and the average profit rate of the method in this paper is 66%. e art dissemination of the method in this paper is wider; the art marketing transaction rate is higher; and the marketing profit is higher, which can effectively promote the marketing of contemporary calligraphy and painting.
Conclusion
In the internet age, if the calligraphy and painting market wants to continue to survive and achieve better development, it must keep pace with the times, reflect the advantages of the Internet, and do a better job in the calligraphy and calligraphy market. e connotation of history and culture allows people to understand Chinese painting and calligraphy culture and historical civilization. At the same time, they encourage everyone to make self-creation. Do not blindly imitate. Only by creating works with their own style can they have a place in the painting and calligraphy circles. At the same time, the development of appraisal programs can also be done. Enhancing everyone's ability to distinguish the authenticity of calligraphy and painting works, this kind of calligraphy and painting market has an artistic soul and can reflect its value not only as a commodity. e establishment of online painting and calligraphy trading venues must be built on many foundations. First of all, the policy is the most basic. erefore, the establishment of online painting and calligraphy trading venues requires a period of precipitation.
is article uses wireless communication network resources to study the marketing strategy of Chinese paintings and calligraphy works and accurately locates Chinese paintings and calligraphy works through the allocation of wireless communication network resources, classifies the quality of the works, and realizes precision marketing.
e experimental results show that after switching to this strategy, the spread of Chinese paintings and calligraphy works will be wider, and the marketing transaction rate and profit will be greater, which can provide certain help to the marketing of Chinese paintings and calligraphy works.
Data Availability
e data used to support the findings of this study are available from the corresponding author upon request. | 8,191.4 | 2022-02-12T00:00:00.000 | [
"Business",
"Art",
"Computer Science"
] |
Cohomology of Coxeter arrangements and Solomon's descent algebra
We refine a conjecture by Lehrer and Solomon on the structure of the Orlik-Solomon algebra of a finite Coxeter group $W$ and relate it to the descent algebra of $W$. As a result, we claim that both the group algebra of $W$, as well as the Orlik-Solomon algebra of $W$ can be decomposed into a sum of induced one-dimensional representations of element centralizers, one for each conjugacy class of elements of $W$. We give a uniform proof of the claim for symmetric groups. In addition, we prove that a relative version of the conjecture holds for every pair $(W, W_L)$, where $W$ is arbitrary and $W_L$ is a parabolic subgroup of $W$ all of whose irreducible factors are of type $A$.
Introduction
Suppose V is a finite-dimensional, complex vector space.A linear transformation t in GL(V ) is called a reflection if it has finite order and the fixed point set of t is a hyperplane in V , or equivalently, if t is diagonalizable with eigenvalues 1, with multiplicity dim V − 1, and ζ, where ζ is a root of unity, with multiplicity 1. Suppose that W is a finite subgroup of GL(V ) generated by a set of reflections T .For each t in T , let H t = Fix(t) denote the fixed point set of t in V and set A = { H t | t ∈ T } and M W = V \ ∪ t∈T H t .Then (V, A) is a hyperplane arrangement and the complement M W is an open, W -stable subset of V .
The action of W on M W determines a representation of W on the singular cohomology of M W .For p ≥ 0 let H p (M W ) denote the p th singular cohomology space of M W with complex coefficients and let H * (M W ) = p≥0 H p (M W ) denote the total cohomology of M W , a graded C-vector space.It follows from a result of Brieskorn [10] that dim H * (M W ) = |W | and so a naive guess would be that the representation of W on H * (M W ) is the regular representation of W .A simple computation for the symmetric group S 3 shows that this is not the case.
In 1987, Lehrer [24] determined the character of the representation of W on H * (M W ) when W = S n is a symmetric group by explicitly computing the "equivariant Poincaré polynomials" P Sn (g, t) = p≥0 trace(g, H p (M W ))t p , for g in S n (here t is an indeterminate).Subsequently, equivariant Poincaré polynomials were computed case-by-case for other groups by various authors.In 2001, Blair and Lehrer [8] showed that for any complex reflection group, the equivariant Poincaré polynomials have the form P W (g, t) = x∈Z W (g) f g (x)(−t) c(x) where f g : Z G (g) → C is explicitly given and c(x) is the codimension of the fixed point space of x in V .Felder and Veselov [13] have found an elegant description of the character of H * (M W ) when W is a Coxeter group that precisely describes how the character of H * (M W ) differs from the regular character ρ of W . Specifically Felder and Veselov show that the character of H * (M W ) is given as where the sum runs over a set of "special" involutions t in W .
In contrast, while the representation of W on H * (M W ) is well-understood, much less is known about the representations of W on the individual graded pieces H p (M W ) for p ≥ 0. When W is a symmetric group Lehrer and Solomon [25] have described these representations as sums of representations induced from linear characters of centralizers of elements in W .They conjecture that a similar decomposition exists in general.
For the symmetric group S n , Barcelo and Bergeron [1] construct an explicit S n -stable subspace of the exterior algebra of the free Lie algebra on n letters that affords the character of H * (M W ) tensored with the sign character.Their construction could be used to study the characters of the individual cohomology spaces H p (M W ).
For the hyperoctahedral group W (B n ), the first author [11] extended Lehrer and Solomon's construction and expressed each H p (M W ) as a sum of representations induced from linear characters of subgroups.However, the subgroups appearing are not always centralizers of elements of W (B n ).At the same time, Bergeron used the free Lie algebra on 2n letters to construct a representation of W (B n ) analogous to the representation of S n constructed in [1].The character of this representation of W (B n ) is again the character of H * (M W ) tensored with the sign character.He then uses this construction to study the characters of the individual cohomology spaces H p (M W ).
In this paper we state a conjecture for a finite Coxeter group W (Conjecture 2.1) that both refines the conjecture of Lehrer and Solomon [25,Conjecture 1.6] and directly relates the representation of W on H p (M W ) to a subrepresentation of the right regular representation of W .It is straightforward to see that Conjecture 2.1 holds for W if and only if it holds for every irreducible factor of W . Thus, to prove the conjecture we may assume that W is irreducible.Conjecture 2.1 is proved for symmetric groups in this paper (Theorem 6.3).The conjecture has been proved for all rank two Coxeter groups [12] and has been checked using the computer algebra system GAP [31] and the package CHEVIE [15] for all Coxeter groups with rank six or less [6], [7].More generally, in §7 we extend the constructions used in the proof of Theorem 6.3 and prove a "relative" version of Conjecture 2.1 for pairs (W, W L ), where now W is any finite Coxeter group and W L is a parabolic subgroup of W , all of whose irreducible factors are of type A. If the conclusion of Theorem 7.1 were to hold for every parabolic subgroup W , not just those that are products of symmetric groups, then Conjecture 2.1 would hold for W .The statement of the conjecture, along with an expository review of the background material from the theories of Coxeter groups and hyperplane arrangements we use later in the paper, is given in §2.
The subrepresentations of the right regular representation of W that we consider arise from a decomposition of a subalgebra of the group algebra of W , known as "Solomon's descent algebra," into projective, indecomposable modules.Projective, indecomposable modules in an artinian C-algebra are generated by idempotents.The idempotents in the descent algebra we use in this paper were discovered by Bergeron, Bergeron, Howlett, and Taylor [4]; we call them BBHT idempotents.In §3 we study the relationships between BBHT idempotents for W and BBHT idempotents for parabolic subgroups.We also compare the BBHT idempotents for W with those of its irreducible factors when W is reducible.In §4 we show that the right ideals in the group algebra of W generated by BBHT idempotents afford induced representations.It then follows that the BBHT idempotents give rise to a decomposition of the group algebra that is the direct analog of the decomposition of H * (M W ) given by Brieskorn's Lemma [10, Lemme 3].
The proof of Conjecture 2.1 for symmetric groups is given in §5 and §6.As a consequence, we obtain a decomposition of the group algebra CS n as a direct sum of representations induced from one-dimensional representations of centralizers, one for each conjugacy class.Similar decompositions of the group algebra of the symmetric group have been proved independently by Bergeron, Bergeron, and Garsia [3], Hanlon [18], and more recently, Schocker [32], all using different methods.
For readers familiar with the literature on the free Lie algebra and its connections with the descent algebras and group algebras of symmetric groups, Theorems 5.1(a) and 6.3(a) will seem familiar.Indeed, Theorem 5.1(a) was likely known in some form to Brandt [9], Wever [38], and Klyachko [21], and a proof of Theorem 6.3(a) can be extracted from results in [3, §4], [30,Theorem 8.24], and [14, §4].In contrast with these references, where the methods are combinatorial and the emphasis is on the connections between the group algebra CS n and the free Lie algebra on n letters, our methods are mainly group-and representation-theoretic, using the theory of Coxeter groups in conjunction with special features of symmetric groups, and our focus is on the connections between the group algebra CS n and the representation of S n on the cohomology spaces H p (M W ).Moreover, our line of reasoning, which is motivated by the arguments in Lehrer and Solomon [25], demonstrates a striking parallelism between the Orlik-Solomon algebras and group algebras of symmetric groups that to our knowledge has not been observed before.We hope that the approach taken in this paper will lead to a deeper understanding of the topological and geometric properties of general Coxeter arrangements.For example, as we show in §7, the constructions in §5 and §6 have natural extensions when the focus is shifted from considering not just a single symmetric group to considering a product of symmetric groups embedded as a parabolic subgroup in a larger finite Coxeter group W , where the type of W is arbitrary.
Bergeron and Bergeron conjecture in [2] that there might be a decomposition of the group algebra CW (B n ) analogous to the decomposition of CS n studied by Bergeron-Bergeron-Garsia [3] and Hanlon [18].In [5] Bergeron gives a decomposition of the group algebra of a hyperoctahedral group as a direct sum of induced representations induced from linear characters of subgroups.Unfortunately, this decomposition is not the decomposition proposed in Conjecture 2.1, it is the analog for group algebras of hyperoctahedral groups of the decomposition of H * (M W ) found in [11].
2. The Orlik-Solomon algebra and Solomon's descent algebra In the rest of this paper we assume that V is a finite-dimensional, complex vector space and W ⊆ GL(V ) is a finite Coxeter group with Coxeter generating set S. Then each s in S acts on V as a reflection with order two and W is generated by S subject to the relations (st) ms,t = 1, where m s,s = 1 and m s,t = m t,s > 1 for s = t in S. Let T denote the set of all reflections in W .
We assume also that a positive, definite, Hermitian form • , • on V is given and that W is a subgroup of the unitary group of V with respect to this form.It is known that Fix(W ) ⊥ , the orthogonal complement of the space of fixed points of W on V , has a basis ∆ = { α s | s ∈ S } so that α s , α t = − cos(π/m s,t ) for s and t in S. Then s acts on V as the reflection through the hyperplane orthogonal to α s and Φ = { w(α s ) | w ∈ W, s ∈ S } is a root system in Fix(W ) ⊥ with base ∆.
2.1.Shapes and conjugacy classes.We begin by recalling a parameterization of the conjugacy classes in W due to Geck and Pfeiffer (see [17, §3.2]) in a form compatible with the arrangement (V, A) of W .
The lattice of A, denoted by L(A), is the set of subspaces of V that arise as intersections of hyperplanes in A: to be the pointwise stabilizer of X in W .It follows from Steinberg's Theorem [36] that W X is generated by {t ∈ T | X ⊆ H t }.It then follows that X = Fix(W X ), and so the assignment X → W X defines an injection from L(A) to the set of subgroups of W . Notice that W X is again a Coxeter group.
The action of W on A induces an action of W on L(A).Obviously wW X w −1 = W w(X) and so for X and Y in L(A), the subgroups W X and W Y are conjugate if and only if X and Y lie in the same W -orbit.Thus, the assignment X → W X induces a bijection between the set of orbits of W on L(A) and the set of conjugacy classes of subgroups W X .
By a shape of W we mean a W -orbit in L(A).We denote the set of shapes of W by Λ.For example, if W is the symmetric group S n , then Λ is in bijection with the set of partitions of n, and with the set of Young diagrams with n boxes.When λ is a shape and X is a subspace in λ we say λ is the shape of W X .
It is shown in [29, §6.2] that the assignment w → Fix(w) defines a surjection from W to L(A).Composing with the map that sends an element X in L(A) to its W -orbit, we get a map sh : W → Λ.
We say sh(w) is the shape of w.Thus, sh(w) is the W -orbit of Fix(w) in L(A).Clearly, sh is constant on conjugacy classes and so we can define the shape of a conjugacy class to be the shape of any of its elements.
An element w in W , or its conjugacy class, is called cuspidal if Fix(w) = Fix(W ).For example, if W is the symmetric group S n , then the conjugacy class consisting of n-cycles is the only cuspidal class.In general, there is more than one cuspidal conjugacy class.Cuspidal elements and conjugacy classes are called elliptic by some authors.
Suppose that λ is a shape, X in L(A) has shape λ, and C is a conjugacy class in W with shape λ.If w is in C, then Fix(w) is in the W -orbit of X and so C ∩ W X is a non-empty union of cuspidal W X -conjugacy classes.).In the following we use the presentation of this algebra given by Orlik and Solomon [27].
Recall that the set T of reflections in W parametrizes the hyperplanes in A. The Orlik-Solomon algebra of W is the C-algebra, A = A(A), with generators { a t | t ∈ T } and relations • a t 1 a t 2 = −a t 2 a t 1 for t 1 and t 2 in T and The algebra A is a skew-commutative, graded, connected C-algebra that is isomorphic as a graded algebra to H * (M W ). Let A p denote the degree p subspace of A. Then See [29, §3.1] for details.
The action of W on A extends to an action of W on A as algebra automorphisms.An element w in W acts on a generator a t of A by wa t = a wtw −1 .With this W -action A is isomorphic to H * (M W ) as graded W -algebras.
Orlik and Solomon [28] have shown that the normalizer of W X in W is the setwise stabilizer of X in W , that is For X in L(A), define A X to be the span of Proofs of the following statements may be found in [29, Corollary 3.27 and Theorem 6.27].
• If codim X = p, then A X ⊆ A p .
• There are vector space decompositions A p ∼ = codim X=p A X and A ∼ = X∈L(A) A X .
• For w in W , wA X = A w(X) .Thus, A X is an N W (W X )-stable subspace of A.
For a shape λ in Λ, set A λ = X∈λ A X .Suppose X is a fixed subspace in λ and that codim X = p.Then A λ is a W -stable subspace of A p and we have isomorphisms of CWmodules (see [25]).
2.3.Solomon's descent algebra.In contrast with the Orlik-Solomon algebra A, which is defined for every complex reflection group, Solomon's descent algebra is defined using the Coxeter generating set S of W and so has no immediate analog for complex reflection groups that are not Coxeter groups.
Suppose that I is a subset of S. Define Then X I is in L(A) and codim X I = |I|.It follows from Steinberg's Theorem [36] that Then X I is the orthogonal complement of the span of ∆ I .
Orlik and Solomon (see [29, §6.2]) have shown that each orbit of W on L(A) contains a subspace X I for some subset I of S. For subsets I and J of S define I ∼ J if there is a w in W with w(∆ I ) = ∆ J .Then ∼ is an equivalence relation.It is well-known that W I and W J are conjugate if and only if I ∼ J (see [34]).It follows that the assignment I → X I induces a bijection between S/∼, the set of ∼-equivalence classes, and Λ, the set of shapes of W .
Next, let ℓ denote the length function of W determined by the generating set S and define Then W I is a set of left coset representatives of W I in W . Also, define x I = w∈W I w in the group algebra CW .Solomon [33] has shown that the span of { x I | I ⊆ S } is in fact a subalgebra of CW .This subalgebra is denoted by Σ(W ) and called the descent algebra of W .It is not hard to see that { x I | I ⊆ S } is linearly independent and so dim Σ(W ) = 2 |S| .Notice that x S = 1 is the identity in both CW and its subalgebra Σ(W ).
Bergeron, Bergeron, Howlett, and Taylor [4] have defined a basis { e I | I ⊆ S } of Σ(W ) that consists of quasi-idempotents and is compatible with the set of shapes of W .For λ in Λ define Then each e λ is idempotent and { e λ | λ ∈ Λ } is a complete set of primitive, orthogonal idempotents in Σ(W ).(See §3 for more details.)In particular, λ∈Λ e λ = 1 in CW .
Define E λ = e λ CW .In §3 we show that e I CW I affords an action of N W (W I ) and that if I is in S λ , then E λ is induced from e I CW I .Thus, in analogy with the decomposition in §2.2 of the Orlik-Solomon algebra A, we have isomorphisms of CW -modules 2.4.Centralizers and complements.The last ingredient we need in order to state the conjecture is a certain set of characters of centralizers of elements of W .These characters, together with the sign character, should quantify the difference between the representation of W on H p (M W ) and a subrepresentation of the regular representation.They naturally arise in work of Howlett and Lehrer [20] and in recent results of the second author [22] that describe the structure of the centralizer of an element in W .
Suppose that I is a subset of S and C is a conjugacy class in W such that C ∩ W I is a cuspidal conjugacy class in W I .Howlett [19] has shown that W I has a complement, N I , in for z in Z W (c).
2.5.
Relating the Orlik-Solomon algebra and the descent algebra.We now have all the concepts we need in order to state the conjecture.
Let ǫ denote the sign character of W .For c in W , define X c = Fix(c), rk(c) = codim X c , and Associated with each λ in Λ we have the W -stable subspace A λ of the Orlik-Solomon algebra A, the right ideal E λ in CW , and the set of conjugacy classes with shape λ.We conjecture that A λ and E λ are related to the set of conjugacy classes with shape λ as follows.
where ǫ c denotes the restriction of ǫ to Z W (c).
In particular, As stated in the introduction, we prove Conjecture 2.1 for symmetric groups in §5 and §6.
The conjecture is known to be true for all Coxeter groups with rank up to six [12], [6], [7].
We in fact prove more than is stated in the conjecture.First, we show that the character ϕ c of Z W (c) may be chosen to be a trivial extension of a character of Z Wc (c).Second, we construct explicit CW -module isomorphisms In §7 we extend the constructions in §5 and §6 and show if W is any finite Coxeter group, λ is in Λ, c is in W with sh(c) = λ, and the irreducible components of W c are all of type A, then the character ϕ c of Z Wc (c) constructed in §6 extends to a character ϕ c of Z W (c).Moreover, we construct explicit CW -module isomorphisms Note that with the given assumptions we have |C λ | = 1, and so the sums in Conjecture 2.1 (a) and (b) reduce to a single summand.Also, in contrast with the case when the ambient group W is a symmetric group and ϕ c is the trivial extension, in the general case, ϕ c may not be the trivial extension of ϕ c .
A direct proof of the conjecture involves finding suitable linear characters ϕ c of Z W (c).The computations in [6] and [7], as well as calculations in type B, show that some natural guesses about the characters ϕ c are not true.For example, Z W (c) acts on the eigenspaces of c in V and so the powers of the determinant character of Z W (c) acting on an eigenspace of c are linear characters of Z W (c).An example in [6, §4] shows that it is not always possible to choose ϕ c to be one of these characters for any eigenspace.
On the other hand, suppose that c is an involution and Then in all the cases that have been computed so far, it turns out that ϕ c may be chosen to be the "trivial" extension of ǫ Wc to Z W (c) in the sense that ϕ c (wn) = ǫ Xc (w) for w in W Xc and n in N Xc .The representations Ind W Z W (c) ϕ c play a role in understanding the characters of finite reductive groups [23], [16], and the corresponding representations of the Iwahori-Hecke algebra of W play a role in the representation theory of complex reductive groups [26].The fact that these representations seem to be closely related to the representation of W on H * (M W ) is quite mysterious.
BBHT idempotents
In this section we collect several preliminary results about the descent algebra of W and the BBHT idempotents.
For subsets I, J, and K of S define Then W IJ is the set of minimal length (W I , W J )-double coset representatives in W . Solomon [33] has shown that Let 2 S denote the power set of S and fix a function σ : where Φ + is the positive system determined by ∆.Thus, for J ⊆ K and w in W K we have w(∆ J ) ⊆ Φ + .For subsets J and K of S define Because σ(I) > 0 for all subsets I of S we have m σ JJ = 0 for all J and so the system of equations Then e σ K is in Σ(W ), n σ KK = (m σ KK ) −1 for all subsets K of S, and n σ JK = 0 when J ⊆ K. Bergeron, Bergeron, Howlett, and Taylor have shown [4, §7] that e σ I is a quasi-idempotent in Σ(W ).Precisely, for λ in Λ define σ(λ) = I∈S λ σ(I).Then In the special case when the function σ(I) = 1 for all subsets I of S we do not include it in the notation.Thus, (3.4) Notice that the quantities m σ JK , e σ J , m JK , e J , . . .are defined relative to an ambient Coxeter system (W, S).Below we also consider the analogous quantities defined relative to a parabolic subsystem (W L , L).To help keep things straight, in this section and the next we use the following conventions.
• σ always denotes a function from 2 S to R >0 .
• When σ(I) = 1 for all I ⊆ S, the BBHT quasi-idempotents in CW defined with respect to σ are denoted by e J .
• τ always denotes a function from 2 L to R >0 where L is a subset of S.
• When τ (I) = 1 for I ⊆ L, the BBHT quasi-idempotents in CW L defined with respect to τ are denoted by e L J .Thus, e S J = e J .For example, {S} is a shape of W and {L} is a shape of W L .Then e σ {S} = σ(S)e σ S , e {S} = e S , and e L L = e τ L = e τ {L} when τ (I) = 1 for all subsets I of L. The following lemmas give some translation properties for the quantities defined above.Lemma 3.5.Suppose that K ⊆ S and d is in ) for all L ⊆ K and so This proves (b).
Using (a) and (b) we see that for J ⊆ K, Recall that we have fixed a positive, definite, Hermitian form on V such that W is a subgroup of U(V ), the unitary group of V .Define Notice that if n is in N(W ), then nSn −1 = S. Thus, N(W ) acts on S and on 2 S , and Lemma 3.6.Suppose that n is in N(W ) and that σ(I n ) = σ(I) for all I ⊆ S. Then e σ I n = n −1 e σ I n for I ⊆ S. In particular, n centralizes e σ S in CW .
and so W J ∩ P = ∐ J⊆K (Y K ∩ P ) for every subset P of W .For P ⊆ W define Then p P J = J⊆K q P K and f P J = J⊆K g P K , and so by Möbius inversion, (3.8) Taking J = ∅ and P = W in (3.8), we have Y ∅ = {w 0 } and so (3.9) , where w J is the longest element in W J , and so g P J = 1 and f P K = m JK .Therefore, Using (3.9) and (3.10), we have Now suppose that J ⊆ S and µ ∈ Λ are such that J ∈ S µ .By (3.3), we have e µ e J = e J and so w 0 e J = λ∈Λ (−1) d λ e λ e µ e J = (−1) dµ e µ e J = (−1) |J| e J .
For subsets J and K of S we have m JK = m J w 0 K w 0 .Thus, n JK = n J w 0 K w 0 and it follows from (3.1) that w 0 e J w 0 = e J w 0 .Finally, e J w 0 = w 0 (w 0 e J w 0 ) = (−1) |J w 0 | e J w 0 = (−1) |J| e J w 0 .
This completes the proof of the lemma.It is straightforward to check that for a in CW L and wn in W L N L , the assignment (a, wn) → a • wn = n −1 awn defines an action of the group and so left multiplication by x L defines an N W (W L )-equivariant embedding of CW L into CW .For later reference we record this fact in the following lemma.
and so (b) holds.
Suppose n is in N L .Then using (b) and Lemma 3.5 we have J n and e σ L J n are both in CW L and x L (n −1 e σ L J n) = x L e σ L J n , so we conclude from Lemma 3.11 that n −1 e σ L J n = e σ L J n .This proves (c).
We conclude this section with a description of the quasi-idempotents e σ K in the case when W is reducible.
Suppose that W is reducible, say W ∼ = W 1 × W 2 and S = S 1 ∐ S 2 is the disjoint union of S 1 and S 2 where W 1 = S 1 and Every element in W has a unique expression as a product w 1 w 2 with w 1 in W 1 and w 2 in Now suppose that σ : 2 S → R >0 has the property that σ(J 1 ∐ J 2 ) = σ(J 1 )σ(J 2 ) for J 1 ⊆ S 1 and J 2 ⊆ S 2 .Then for J ⊆ K ⊆ S we have where σ i is the restriction of σ to 2 S i for i = 1, 2. Conversely, if we are given functions σ i : 2 S i → R >0 for i = 1, 2 and define σ : With σ as above, set and so f σ J = e σ J .This proves the following proposition.
E λ is an induced representation
Suppose λ is in Λ and σ : 2 S → R >0 .Define E σ λ = e σ λ CW to be the right ideal in CW generated by e σ λ .Similarly, using the notation in (3.4), define We have seen in §2.
In this section we show that E σ λ has a similar description as an induced representation and we analyze how E σ λ depends on the choice of σ.In particular, we show in Corollary 4.8 that for L in S λ , The next lemma follows immediately from (3.3).
Lemma 4.1.Suppose that λ is in Λ and I is in S λ .Then E σ λ = e σ I CW .
The next proposition shows that up to isomorphism, E σ λ does not depend on σ.Proposition 4.2.Suppose that λ is in Λ and that σ and σ 1 are functions from 2 S to R >0 .Then there is a unit u in Σ(W ) such that left multiplication by u defines an isomorphism of right CW -modules Proof.Let rad(Σ(W )) denote the Jacobson radical of Σ(W ) and let θ denote the natural projection from Σ(W ) to Σ(W )/rad(Σ(W )).Bergeron, Bergeron, Howlett, and Taylor [4, §7] have shown that θ(e σ λ ) = θ(e σ 1 λ ) is a primitive idempotent in Σ(W )/rad(Σ(W )).Thus, it follows from [37, Theorem 3.1] that there is a unit u in 1 + rad(Σ(W )) such that ue σ λ = e σ 1 λ u.Then left multiplication by u defines an isomorphism of right CW -modules e σ λ CW ∼ = e σ 1 λ CW .
Suppose that N is a subgroup of N(W ), so W N is a subgroup of N U(V ) (W ).Then as in Lemma 3.11, W N acts on CW by a • wn = n −1 awn for a in CW , w in W , and n in N. If N centralizes e σ λ , then clearly E σ λ is a W N-submodule of CW .It follows from Lemma 3.6 that if σ is constant on N-orbits in 2 S , then N centralizes e σ {S} = e σ S .More generally, if σ is constant on N-orbits in 2 S and S λ is N-stable, then N centralizes e σ λ .Let Σ(W ) N denote the algebra of N-invariants in Σ(W ).Proposition 4.3.Suppose that λ is in Λ and that σ and σ 1 are functions from 2 S to R >0 such that S λ is N-stable, N centralizes e σ λ , and σ 1 is constant on N-orbits in 2 S .Then there is a unit v in Σ(W ) N such that left multiplication by v defines an isomorphism of right CW N-modules E σ λ and E σ 1 λ .
Proof.We saw in the proof of Proposition 4.2 that there is a unit u in 1 + rad(Σ(W )) such that ue σ λ = e σ 1 λ u and we observed in the proof of Lemma 3.6 that n −1 x I n = x I n for I ⊆ S and n in N. It follows that Σ(W ) and rad(Σ(W )) are stable under conjugation by N and so ) and hence is a unit.By assumption, N centralizes e σ λ and e σ 1 λ and it follows that ve σ λ = e σ 1 λ v. Therefore, left multiplication by v defines a N U(V ) (W )-module isomorphism e σ λ CW ∼ = e σ 1 λ CW .
In the next proposition, ℓ x denotes left multiplication by x.
Proposition 4.4.Suppose L is a subset of S, σ : 2 S → R >0 , τ : 2 L → R >0 , and τ is constant on N L -orbits.Then there is a unit Proof.It follows from Lemma 3.12(c) and Lemma 3.6 that e σ L L CW L and e τ L CW L are N W (W L )stable right ideals of CW L , where N W (W L ) acts on CW L as in Lemma 3.11.By Lemma 3.12(b) we have e σ L = x L e σ L L and so it follows from Lemma 3.5(a) that e σ L CW L and x L e τ L CW L are stable under right multiplication by N W (W L ).It now follows from Lemma 3.11 that the vertical maps are isomorphisms of N W (W L )-modules.
The hypotheses of Proposition 4.3 are satisfied with L, {L}, and σ L in place of S, S λ , and σ.Thus, there is a unit v in Σ(W L ) N L such that ℓ v : e σ L L CW L → e τ L CW L is an isomorphism.The conclusion of the proposition is now clear as The decomposition 1 = λ∈Λ e σ λ gives a decomposition |, the number of elements in W with shape λ.In the next lemma we compute |sh −1 (λ)| in terms of cuspidal elements in a parabolic subgroup of W with shape λ.Lemma 4.5.Suppose that λ is a shape in Λ, X is a subspace in λ, and C is a conjugacy class in W with shape λ.Then Proof.Notice that C ∩ W X is a cuspidal conjugacy class in W X .Thus, it follows from (1) and ( 2 This proves (a).Statement (b) follows from (a) and the observation that sh −1 (λ) is the union of those conjugacy classes in W whose intersection with W X is a cuspidal conjugacy class in W X .
Corollary 4.6.Suppose λ is in Λ, J is in S λ , σ : 2 S → R >0 , and τ : This proves (b).It remains to show that dim e σ J CW J = dim e τ J CW J = |sh −1 (λ) ∩ W J |. Using Lemma 3.12(b), Lemma 3.11, Proposition 4.2, and (b) applied to the shape {J} of W J , we have The next proposition and its corollary are the main results in this section.Proposition 4.7.Suppose that σ : 2 S → R >0 , λ is in Λ, X is in λ, and L is in S λ .
(a) N W (W L ) acts on e σ L CW L by right multiplication and We prove (a).
It was shown in Proposition 4.4 that e σ L CW L is stable under right multiplication by N W (W L ).By Lemma 4. 1, E σ λ = e σ L CW .Therefore, to prove that This map is obviously a surjection.Using Lemma 4.5 and Corollary 4.6, we have CW and so the multiplication map is indeed a bijection.
We saw in Proposition 4.4 that e σ L CW L , e σ L L CW L , e τ L CW L , and x L e τ L CW L all afford equivalent representations of N W (W L ) when τ : 2 L → R >0 is chosen only subject to the restriction that it is constant on N L -orbits.Thus, Proposition 4.7(a) implies the following corollary.
In particular, if σ(I) = 1 and τ (J) = 1 for all I ⊆ S and J ⊆ L, then 5. Symmetric groups: λ = (n) In this section and the next we prove Conjecture 2.1 for symmetric groups.In these two sections we take W to be the symmetric group on n letters with n ≥ 2 and we identify W with the subgroup of GL n (C) that acts on the basis {v 1 , v 2 , . . ., v n } as permutations.Here, v i is the column vector whose j th entry is 0 for j = i and 1 for j = i.For 1 ≤ i ≤ n − 1 let s i denote the matrix in W that interchanges v i and v i+1 and fixes v j for j = i, i + 1.Then S = {s 1 , s 2 , . . ., s n−1 } is a Coxeter generating set for W .
By a partition of n we mean a non-increasing finite sequence of positive integers whose sum is n.
It is well-known that for W = S n we may identify Λ with the set of partitions of n.We make this identification precise as follows.Suppose that λ is a partition of n with p parts.Define partial sums τ i for i = 0, 1, . . ., p by τ 0 = 0 and Then W λ is isomorphic to the product of symmetric groups S λ 1 × • • • × S λp , where the factor S λ i acts on the subset Then X λ is in L(A) and W X λ = W λ .We have seen in Proposition 4.7 that It is well-known and straightforward to check that { X λ | λ is a partition of n } is a complete set of orbit representatives for the action of W on L(A) and that { I λ | λ is a partition of n } is a complete set of representatives for S/ ∼.
Notice that in the extreme case when all parts of λ are equal 1 we have I λ = ∅ and W λ = W ∅ = {1}.At the other extreme, when λ = (n), we have I λ = S and W λ = W S = W .We first prove Conjecture 2.1 when λ = (n).
For the rest of this section we take λ = (n).Then W λ = N W (W λ ) = W and so is the top, non-zero graded piece of A. To simplify the notation, we denote A (n) , E (n) , and e I (n) by A n , E n , and e n respectively.
Define c 1 = 1 in W and for 1 < i ≤ n define c i = s i−1 • • • s 2 s 1 , so c i acts on the basis {v 1 , v 2 , . . ., v n } as an i-cycle.Also, set c = c n .Then, • c is a cuspidal element in W , • the set of cuspidal elements in W is precisely the conjugacy class of c, and [25].However, the character ϕ of Z W (c) is the same as in [25].
Theorem 5.1.With the preceding notation we have that Statement (b) has been proved by Stanley [35,Theorem 7.2] and by Lehrer and Solomon [25,Theorem 3.9].As mentioned in the introduction, a proof of (a) may be extracted from classical results about the representation of S n on the free Lie algebra on n letters.In contrast, our proof below that the character of W on E n is Ind W Z W (c) (ϕ) follows the Lehrer-Solomon argument and demonstrates a parallelism between the group algebra and the Orlik-Solomon algebra that we expect will apply in some form to all finite Coxeter groups.In addition, our argument is valid not only for E n and e n , but more generally for E σ (n) and e σ (n) for any function σ : 2 S → R >0 .
To emphasize and differentiate the parallel arguments, we use the convention that the superscript + denotes quantities associated with E n and the superscript − denotes quantities associated with A n .Notice that with the notation of §2, we have X c = {0} and so α c = det | Xc is the trivial character.
Suppose t is an indeterminate.For 0 respectively (the k th factor in the product on the left-hand side of the last equation is Lehrer and Solomon [25, §3] prove the following statements. (iii) Consider the homomorphism of left CW -modules from CW to A n given by right multiplication by a n .The kernel of this mapping is the left Next we show that the analogous statements hold with A n replaced by E n and b − (n, k) replaced by b + (n, k).
For this, we consider elements in W as acting on {1, . . ., n}.That is, we identify the vector v j with j for 1 ≤ j ≤ n.Then . If j ≥ i k , then w(j) ≤ j < j + 1 = w(j + 1).Suppose that k < j < i k .Choose r minimal such that Proof.Using the definition and Lemma 5.2 we have Proposition 5.4.The following analogs of (i)-(iv) above hold.
(c) Consider the endomorphism of CW considered as a right CW -module given by left multiplication by e n .The kernel of this mapping is the free, right Proof.The first statement follows immediately from the definitions.
We prove (b) by recursion.It is clear that . It follows from [4,Theorem 7.8] that e n x J = 0 unless J = S. Thus, it follows from Corollary 5.3 that On the other hand, it follows from the definition that Therefore, Next, consider the endomorphism of CW given by x → e n x.Let K denote the kernel of this mapping and let Therefore K 1 = K.This proves (c).
Finally, define idempotents f + and f − in CZ W (c) by Obviously, the lines Cf + and Cf − in CW are stable under left and right multiplication by Z W (c) and afford the characters ϕ and ǫϕ of Z W (c) respectively.Moreover, Ind W Z W (c) (ϕ) is afforded by the right CW -module f + CW and ǫInd W Z W (c) (ϕ) = Ind W Z W (c) (ǫϕ) is afforded by the left CW -module CW f − .Thus, to prove Theorem 5.1 it is enough to find CW -isomorphisms Lemma 5.5.The idempotent f + acts invertibly by right multiplication on e n and the idempotent f − acts invertibly by left multiplication on a n .
Proof.Lehrer and Solomon [25, §3] show that f − acts invertibly on a n .Their argument is easily modified to show that f + acts invertibly by right multiplication on e n as follows.
We have (1
Multiply both sides on the left by
1 n e n and use Proposition 5.4(b) to get Proof of Theorem 5.1.(See [25, §3].)Consider the mapping from f + CW to E n given by x → e n x.It follows from Lemma 5.5 and the discussion preceding it that e n f + = 0, that Z W (c) acts on the line Ce n f + in E n as the character ϕ, and that the mapping is a surjection.Since dim f + CW = |W : Z W (c)| = (n − 1)! = dim E n , the mapping is also an injection.Thus, we have an isomorphism of right CW -modules, E n ∼ = f + CW .
As in [25, §3], similar reasoning applies to the mapping from CW f − to A n given by x → xa n and shows that A n ∼ = CW f − .
Symmetric groups: arbitrary λ
In this section we consider the case of an arbitrary partition of n and complete the proof of Conjecture 2.1 for symmetric groups.
Suppose λ = (λ 1 , λ 2 , . . ., λ p ) is a partition of n.Recall that I λ = S \ {s τ 1 , s τ 2 , . . ., s τ p−1 } and that W λ = I λ is isomorphic to the product of symmetric groups S λ 1 × • • • × S λp , where the factor S λ i acts on • the set of cuspidal elements in W λ is precisely the conjugacy class of c λ , and With λ as above, for 1 ≤ i ≤ p, define ϕ λ i to be the character of g λ i with ϕ λ i (g −1 λ i ) = e 2πi/λ i .Then ϕ λ i is the analog of the character ϕ in §5 for the factor S λ i of W λ .Next, define the character Note that this notation is not consistent with that of Lehrer and Solomon; our character ϕ λ corresponds to the character ϕ λ ǫ in [25].Applying the special case λ = (n) considered in §5 to each factor S λ i of W λ , for 1 ≤ i ≤ p define Finally, define idempotents f + λ and f − λ in CZ λ by Obviously the lines Cf + λ and Cf − λ in CW are stable under left and right multiplication by Z λ and afford the characters ϕ λ and ǫϕ λ of Z λ respectively.Now consider the canonical complement N X λ of W λ in N W (W λ ).Set N λ = N X λ .If λ has m j parts equal j, then N λ is isomorphic to the product of symmetric groups j S m j (see [19] or [25]).In particular, N λ has one Coxeter generator, say r i , for each i such that λ i = λ i+1 .The generator r i acts on the set {v 1 , v 2 , . . ., v n } by interchanging v τ i−1 +j and v τ i +j for 1 ≤ j ≤ λ i , and fixing v k for k ≤ τ i−1 and k > τ i+1 .
Proof.Suppose that i is such that λ i = λ i+1 and consider the generator r i of N λ .Then r i is an involution and it follows from the description of the action of r i on the basis {v 1 , . . ., v n } of V that Since ϕ λ (g λ i ) = ϕ λ (g λ i+1 ), it follows that r i stabilizes ϕ λ and ǫϕ λ .
The group N λ is generated by { r i | λ i = λ i+1 , } and so N λ stabilizes the characters ϕ λ and ǫϕ λ of Z λ .Moreover, N λ acts on {g λ 1 , . . ., g λp } by conjugation as a group of permutations.Thus, it follows from the definition of f + λ i and f − λ i that conjugation by N λ permutes {f + λ 1 , . . ., f + λp } and {f − λ 1 , . . ., f − λp }.Since the f + λ i 's pairwise commute and the f − λ i 's pairwise commute, we see that Set α λ = α X λ .Then α λ is a character of N W (W λ ) and α λ (r i ) = −1.Note that this notation is not consistent with that of Lehrer and Solomon; our character α λ corresponds to the character α λ ǫ in [25] as ǫ(r i ) = (−1) λ i .Theorem 6.2.Suppose that λ is a partition of n.Then the N W (W λ )-modules e I λ CW λ and A X λ , and the character ϕ λ of Z W (c λ ), are related by (a) the character of the right N W (W λ )-module e I λ CW λ is Ind Proof.Statement (b) has been proved by Lehrer and Solomon [25,Theorem 4.4].Their argument may be rephrased as follows.Extending the definition of the element a n in A n when λ = (n), Lehrer and Solomon define an element a λ in A X λ on which f − λ acts invertibly.Then: To prove (a) we first note that by Proposition 4.4, e I λ CW λ and e I λ I λ CW λ are isomorphic right N W (W λ )-modules and so it suffices to prove that e I λ I λ CW λ affords the character Ind Z W (c λ ) (ϕ λ ).We argue as for A X λ with e I λ I λ in place of a λ .For the rest of this proof we fix a partition λ = (λ 1 , . . ., λ p ) of n.To simplify the notation, set I = I λ and e = e I I .It suffices to show that the line Cef + λ in the right N W (W λ )-module eCW λ satisfies properties analogous to (i), (ii), and (iii) above.
. For 1 ≤ j ≤ p, the idempotent e τ I j in CW λ j is defined using the partition (λ j ) of λ j as in §5 and so the idempotent f + λ j acts as a unit on e τ I j by Lemma 5.5.Therefore, f + λ acts invertibly by right multiplication on e and so ef + ) that the mapping is surjective.Moreover, using Corollary 4.6 we have and so the mapping is an isomorphism.This completes the proof of the theorem.
The proof of Conjecture 2.1 for symmetric groups now follows from Proposition 4.7, Theorem 6.2, and transitivity of induction.Theorem 6.3.For each partition λ of n there is a linear character , where ǫ λ denotes the restriction of ǫ to Z W (c λ ).
In particular,
Parabolic subgroups of type A
In this section we return to the case when W is an arbitrary finite Coxeter group and prove a relative version of Theorem 6.3.
Suppose that λ is in Λ, c is in W with sh(c) = λ, and that all the irreducible components of W c are of type A. Without loss of generality we may assume that W c = W L is a standard parabolic subgroup.Suppose that L = p i=1 L i where We assume that l 1 ≥ • • • ≥ l p , L i = {s i,1 , . . ., s i,l i }, and ∆ L i = {α i,1 , . . ., α i,l i }, where the labeling is such that s i,j and s The rest of this section is devoted to the proof of the following theorem.
Theorem 7.1.The character ϕ c of Z W L (c) extends to a character ϕ c of Z W (c) such that The proof follows the same outline as in §6: We find lines in e L L CW L and A X L such that the analogs of statements (i), (ii), (iii) and (i ′ ), (ii ′ ), and (iii ′ ) in the proof of Theorem 6.2 hold, and so Z W (c) (ǫα c ϕ c ).We then apply Ind W N W (W L ) to both sides of both equations and the theorem follows from Proposition 4.7 by transitivity of induction.The argument in this section is complicated by the fact that the subgroup N L is not necessarily contained in Z W (c).
In case W is a symmetric group, it was shown in 6.1 that ϕ c is the trivial extension of ϕ c .In the general case, this is no longer so.Formulas for ϕ c are given in the proof of Lemma 7.3 below.Notice that c is an involution if and only if l 1 = 1 for 1 ≤ i ≤ p, and that in this case ϕ c is the sign character of Z W L (c) and ϕ c is the trivial extension of ϕ c to Z W (c).
Although N L is not necessarily contained in Z W (c), Konvalinka, Pfeiffer, and Röver [22] have shown that Z W L (c) does have a complement, N c , in Z W (c), and N c is also a complement to By [19], the group N L is generated by { r i , g j | 1 ≤ i ≤ p − 1, 1 ≤ j ≤ p }, where r i and g i act on W L as follows.
In particular, r i is an involution, and r i is in Z W (c).
• Either g i = 1 or g i acts on L by In particular, g i is an involution and if g i = 1, then Notice that if W is a symmetric group, then g i = 1 for 1 ≤ i ≤ p.
For 1 ≤ i ≤ p, let w i denote the longest element in W L i and define Then h i is in Z W (c j ) for 1 ≤ i, j ≤ p.It is shown in [22] that As in §6 define Then the lines Cf + L and Cf − L in CW are stable under left and right multiplication by Z W L (c) and afford the characters ϕ c and ǫϕ c of Z W L (c), respectively.Because h i centralizes c j for 1 ≤ i, j ≤ p, the proof of the second statement in Lemma 6.1 applies word-for-word to N c and proves the next lemma.Proof.The argument in statement (i ′ ) in §6 applies verbatim to f + L and shows that f + L acts invertibly by right multiplication on e L L and so e L L f + L = 0. Similarly, the argument in Lehrer and Solomon [25, §3] shows that f − i acts as a unit on a i for 1 ≤ i ≤ p.It follows that f − L acts invertibly by left multiplication on a L and so f − L a L = 0. Therefore, Z W L (c) acts on Ce L L f + L and Cf − L a L by ϕ c and ǫϕ c , respectively, and ϕ c extends ϕ c .If n i = n i+1 , then as in §6 we have (7.4) L g i w i )f + L = (e L L w i )f + L = (−1) l i e L L f + L , (the last equality follows from Lemma 3.7), and (7.7) since h i centralizes L. It follows that Z W (c) acts on the lines Ce L L f + L and Cf − L a L .Moreover, from (7.4) and (7.6) we see that ϕ c (r i ) = 1 and ϕ c (h i ) = (−1) l i .
To complete the proof we need to show that Z W (c) acts on the line Cf − L a L as ǫα c ϕ c .For w in Z W L (c) we have α c (w) = 1 and ϕ c (w) = ϕ c (w). Hence For n in N L it is shown in [12, Lemma 2.1] that ǫ(n)α c (n) is the sign of the permutation of L induced by conjugation by n.
Therefore, using (7.7) we see that (3.2) e σ I e σ J = σ(λ) −1 e σ J when I and J are in S λ .Thus, if we set e σ λ = I∈S λ σ(I)e σ I , it follows from (3.2) that e σ λ is an idempotent in Σ(W ) and hence an idempotent in CW .We call the quasi-idempotents e σ I BBHT quasi-idempotents and the idempotents e σ λ BBHT idempotents.By definition we have 1 = x S = J⊆S n σ JS e σ J and so 1 = λ∈Λ e σ λ in Σ(W ) and CW .It follows that { e σ λ | λ ∈ Λ } is a set of pairwise orthogonal idempotents in CW and that (3.3) e σ λ e σ I = e σ I and e σ I e σ λ = σ(λ) −1 e σ λ for I ∈ S λ .
Thus, I m σ IJ (e σ I d) = I m σ IJ e σ I d .Now fix a subset L of K, multiply both sides by n σ JL , and sum over J, to get e σ L d = e σ L d .(Note that n σ JL = 0 unless J ⊆ L.) This proves (c).
For a subset L
of S, the pair (W L , L) is a Coxeter system.Because W L is a complete set of left coset representatives of W L in W , left multiplication by x L defines an embedding of CW L into CW .For I ⊆ L define W I L = W L ∩W I and x L I = w∈W I L w. Then { x L I | I ⊆ L } is a basis of Σ(W L ).It is well-known and easy to prove that W L W I L = W I , and so x L x L I = x I .If n is in N L , then n(∆ L ) = ∆ L and so by Lemma 3.5(a) we have x L n = x L .It follows that x L CW L is stable under right multiplication by elements of N W (W L ).
Lemma 3 . 11 .
Suppose that L is a subset of S. Then N W (W L ) acts on CW L by a • wn = n −1 awn, for a ∈ CW L , w in W L , and n ∈ N L , and left multiplication by x L defines an N W (W L )-equivariant embedding of CW L into CW .Recall that the quasi-idempotents e σ I are defined relative to the ambient set S and the function σ.Define σ L : 2 L → R >0 by σ L (I) = m σ IL .Then for J ⊆ L we have the quasi-idempotent e σ L J = I⊆L n σ L IJ x L I in CW L defined relative to the set L and the function σ L .Lemma 3.12.Suppose that I, J, and L are subsets of S with I, J ⊆ L. Then (a) m σ L IJ = m σ IJ and n σ L IJ = n σ IJ ; (b) x L e σ L J = e σ J ; and (c) n −1 e σ L J n = e σ L J n for n in N L .Proof.It is shown in [4, Theorem 7.5] that m σ L IJ = m σ IJ .It then follows from the definitions that n σ L IJ = n σ IJ .This proves (a).Now is the cyclic group of order n generated by c. Set ζ = e 2πi/n in C and define ϕ : Z W (c) → C by ϕ(c −1 ) = ζ.The elements we have denoted by c i are denoted by c −1 i by Lehrer and Solomon (i ′ ) Z W (c λ ) acts on the line Cef + λ via the character ϕ λ : We have seen in Lemma 3.6 that N λ centralizes e and in Lemma 6.1 that N λ centralizes f + λ .Thus, N λ centralizes ef + λ and Z W (c λ ) = N λ Z λ acts on the line Cef + λ via the character ϕ λ if ef + λ = 0. Let τ : 2 I → R >0 be the function that takes the constant value 1.We have I = p j=1 I j where W λ j = I j and so e = e τ I = e τ I 1 • • • e τ Ip by Proposition 3.13.Therefore, ef
Lemma 7 . 2 . 2 . 7 . 3 .
The subgroup N c of Z W (c) centralizes the idempotents f + L and f − L in CZ W L (c).For 1 ≤ i ≤ p, define a i = a s i,1 • • • a s i,l i .Set a L = a 1 • • • a p .Then a L is in A X L .The next lemma is the analog of statements (i) and (i ′ ) in the proof of Theorem 6.LemmaThe lines Ce L L f + L in e L L CW L and Cf − L a L in A X L are non-zero and Z W (c)-stable. Let ϕ c be the character of Z W (c) acting by right multiplication on the line Ce L L f + L .Then ϕ c is an extension of ϕ c and the character of Z W (c) acting by left multiplication on the line Cf − L a L is ǫα c ϕ c .
This completes the proof of the lemma.Because f + λ acts invertibly on e L L , N c acts on the line Ce L L f + L by scalars, andN W (W L ) = N c W L , we see that (7.8) e L L CW L = e L L f + L CW L = e L L f + L CN c W L = e L L f + L CN W (W L ).Lehrer and Solomon [25, Proposition 4.4(ii)] have shown that A X L = CW L a L .Thus, because f − λ acts invertibly on a L , N c acts on the line Cf − L a L by scalars, andN W (W L ) = W L N c , we see that (7.9)A X L = CW L a L = CW L f − L a L = CW L N c f − L a L = CN W (W L )f − L a L .Equations (7.8) and (7.9) are the analogs of statements (ii) and (ii ′ ) in the proof of Theorem 6.2.The dimension computation in the proof of statement (iii ′ ) now applies to show that the analogs of statements (iii) and (iii ′ ) both hold in the present situation: The multiplication maps(7.10)Ce L L f + L ⊗ CZ W (c) CN W (W L ) → e L L CW L and CN W (W L ) ⊗ CZ W (c) Cf − L a L → A X L are isomorphisms of CN W (W L )-modules.It follows from (7.10) and Lemma 7.3 thate L L CW L ∼ = Ind N W (W L ) Z W (c) ( ϕ c ) and A X L ∼ = Ind N W (W L ) Z W (c) (ǫα c ϕ c ). Apply Ind W N W (W L )to both sides of these last two equations and use transitivity of induction to getInd W N W (W L ) e L L CW L ∼ = Ind W Z W (c) ( ϕ c ) and Ind W N W (W L ) (A X L ) ∼ = Ind W Z W (c) (ǫα c ϕ c).By Proposition 4.7 and Corollary 4.8 we haveE λ ∼ = Ind W Z W (c) ( ϕ c) and A λ ∼ = Ind W Z W (c) (ǫα c ϕ c ).This completes the proof of the theorem.
Geck and Pfeiffer [17, §3.2]have shown that in fact C ∩ W X is a single W X -conjugacy class.It follows that C → C ∩ W X defines a bijection between the set of conjugacy classes in W with shape λ and the set of cuspidal conjugacy classes in W X .Fix a set {X λ | λ ∈ Λ } of W -orbit representatives in L(A).Summarizing the preceding discussion we see that conjugacy classes in W are parametrized by pairs (λ, C λ ), where λ is a shape and C λ is a cuspidal conjugacy class in W X λ . | 15,491 | 2011-01-11T00:00:00.000 | [
"Mathematics"
] |
Semiparametric Tests for the Order of Integration in the Possible Presence of Level Breaks
Abstract Lobato and Robinson developed semiparametric tests for the null hypothesis that a series is weakly autocorrelated, or I(0), about a constant level, against fractionally integrated alternatives. These tests have the advantage that the user is not required to specify a parametric model for any weak autocorrelation present in the series. We extend this approach in two distinct ways. First, we show that it can be generalized to allow for testing of the null hypothesis that a series is for any δ lying in the usual stationary and invertible region of the parameter space. The second extension is the more substantive and addresses the well-known issue in the literature that long memory and level breaks can be mistaken for one another, with unmodeled level breaks rendering fractional integration tests highly unreliable. To deal with this inference problem, we extend the Lobato and Robinson approach to allow for the possibility of changes in level at unknown points in the series. We show that the resulting statistics have standard limiting null distributions, and that the tests based on these statistics attain the same asymptotic local power functions as infeasible tests based on the unobserved errors, and hence there is no loss in asymptotic local power from allowing for level breaks, even where none is present. We report results from a Monte Carlo study into the finite-sample behavior of our proposed tests, as well as several empirical examples.
Introduction
It is well known that if not accounted for, level shifts in a weakly autocorrelated (or short memory) process, denoted I(0), can induce features in the autocorrelation function and the periodogram of a time series that can be mistaken as evidence of long memory (see, e.g., Diebold and Inoue 2001;Gourieroux and Jasiak 2001;Granger and Hyung 2004;Mikosch and Stȃricȃ 2004;Qu 2011;Iacone, Leybourne, and Taylor 2019). To avoid the possibility of spurious inference being made about the memory properties of a time series, it is therefore important to develop tests on the fractional integration (memory) parameter of a time series which are robust to level shifts. As a consequence, Iacone, Leybourne, and Taylor (2019) generalized the parametric Lagrange multiplier (LM) time domain based fractional integration tests of Tanaka (1999) and Nielsen (2004) to allow for the possibility of a single break in the deterministic trend function at an unknown point in the sample. These tests are equivalent to analogous extensions of the frequency domain tests of Robinson (1994) to allow for breaks in the deterministic trend function. Iacone, Leybourne, and Taylor (2019) showed that this approach delivers an LM test which, regardless of whether a break occurs or not, is a locally most powerful test and has a χ 2 1 limiting null distribution. However, a significant practical disadvantage of the tests of Iacone, Leybourne, and Taylor (2019) is that, like the tests of Robinson (1994), Tanaka (1999), and Nielsen (2004) from which they are derived, they are based on fitting a full parametric model to the data. Crucially, the short run component of this model must be correctly specified under the null hypothesis for the resulting test to be correctly asymptotically sized. This requirement is clearly problematic in practice, and is likely to be further complicated in the case where level breaks are present as this would likely interfere with any preliminary model selection stage used to specify the form used for the short memory component. It therefore seems worthwhile developing long memory tests analogous to those of Iacone, Leybourne, and Taylor (2019) but which do not require the user to specify a parametric model for the short memory component of the series.
Our contribution in this article is therefore to develop semiparametric analogues of the parametric tests of Iacone, Leybourne, and Taylor (2019). We will base our approach on an extension of the semiparametric frequency domain based fractional integration tests of Lobato and Robinson (1998). This approach is based on the use of a low frequency approximation provided by the local Whittle (LW) likelihood, which obviates the need to explicitly model any short range dependence present in the data. To account for the possibility of level breaks, the Lobato and Robinson (1998)-type statistics we propose are constructed from data which have been de-trended allowing for the possibility of level breaks, the locations of which are estimated by a standard residual sum of squares estimator applied to the levels data. The tests proposed in Lobato and Robinson (1998), again based on the LM testing principle, are specifically designed for testing the null hypothesis that a time series is I(0). We show that, as conjectured in Lobato and Robinson (1998, p. 478), their approach can be generalized to provide a valid test for the null hypothesis that the series is integrated of order δ, for any δ lying in the stationary and invertible region of the parameter space (−0.5 < δ < 0.5). It is also possible to test orders of integration outside the stationary and invertible region using data transformations. For example, the null hypothesis of an autoregressive unit root can be obtained by testing for the null hypothesis of short memory in the first differences of the series; as such this is then a test in the levels data for a unit root allowing for the possibility of trend breaks. Because the tests are based on the LM testing principle, no preliminary estimation of the memory parameter is required.
Our focus on the Lobato and Robinson (1998) testing approach is due, at least in part, to results in Shao and Wu (2007a) who showed that the standard Lobato and Robinson (1998) tests are, for a suitable choice of the bandwidth parameter m used in the local Whittle loss function, considerably more powerful than other semiparametric tests for testing the null of I(0) against the alternative of fractional integration that are available in the literature. In particular, they showed that tests based on the rescaled range and rescaled variance statistics and tests based on the well-known KPSS statistic of Kwiatkowski et al. (1992) have power against local alternatives of order (ln(T)) −1 , where T denotes the sample size. On the other hand, the Lobato and Robinson (1998) tests have power against local alternatives of order m −1/2 , where the bandwidth parameter m is typically of the type m = T α for some 0 < α < 4/5. Moreover, these other approaches have only been developed to test the null hypothesis of I(0) against the alternative of fractional integration, whereas we wish to maintain the flexibility to test a more general I(δ) null hypothesis. Busetti andHarvey (2001, 2003) developed extensions of the KPSS test that allow for a single level break at an unknown point in the sample, although their approach is based on the assumption that a level break is known to occur.
We establish that, regardless of whether level breaks occur or not, the large sample properties of the tests we propose are identical to those which obtain for the standard Lobato and Robinson (1998) tests for δ = 0 in the case where no level breaks occur. In particular, our proposed LM-type test has a χ 2 1 limiting null distribution and the corresponding t-type test a N(0, 1) limiting null distribution, regardless of the value of δ being tested under the null hypothesis, and each attains the same asymptotic local power function as the corresponding infeasible test based on the unobserved errors. Moreover, these asymptotic local power functions do not alter between the break and no break cases and so there is no loss in asymptotic local power from allowing for level breaks, even where no breaks are present. Although based on different and hence not directly comparable models, these large sample properties contrast with those of most popular unit root tests, such as that of Dickey and Fuller (1979), and stationarity tests, such as that of KPSS. In particular, the limiting null distributions of unit root and stationarity test statistics tend to be nonstandard and depend on the functional form of the fitted deterministic, differing between the no break and break cases, and dependent on the locations of the breaks. Moreover, where breaks are fitted but not actually present in the data, these tests show a considerable decline in asymptotic local power relative to the case where no break is fitted.
The remainder of the article is organized as follows. Section 2 sets out the fractionally integrated level break model within which we work. Section 3 describes our tests in the infeasible case where the errors are observable. Our proposed semiparametric statistics for the case of unknown level breaks are described in Section 4, where we also establish their large sample properties. Section 5 summarizes the results from a Monte Carlo simulation study into the finite sample size and power properties of our proposed tests and compares with the nonparametric KPSS-type tests of Busetti andHarvey (2001, 2003). Illustrative empirical examples of the methods developed in this article to bitcoin returns data, VIX market volatility, U.S. CPI inflation, and U.S. real GDP growth are considered in Section 6. Section 7 concludes. Proofs of our main results are provided in a mathematical appendix. A supplementary appendix contains full details of the Monte Carlo design and results.
The Fractionally Integrated Model With Level Breaks
Consider the scalar time series process, y t , satisfying the data generating process (DGP), In (1), β := (β 1 , β 2 ) is a vector of fixed parameters and DU t (τ * ) := (DU t (τ * 1 ), . . . , DU t (τ * k )) is a vector of k level break terms, where DU t (τ ) is defined for a generic argument τ as DU t (τ ) := I(t ≥ τ T ), I(·) denotes the usual indicator function, · denotes the integer part of its argument, and where A := B and B =: A is used to denote that A is defined by B. The formulation in (1) therefore allows for up to k level breaks where τ * := (τ * 1 , . . . , τ * k ) is the vector of (unknown) putative level break fractions and β 2 := (β 2,1 , . . . , β 2,k ) the associated break magnitude parameters, such that a level break occurs at time τ * i T when β 2,i = 0 for i = 1, . . . , k. The true but unknown number of level breaks present, k * say, is then given by the number of nonzero elements of the vector β 2 . The (putative) level break fractions are assumed to be such that τ * i ∈ [τ L , τ U ] =: for all i = 1, . . . , k, where ⊂ (0, 1) is compact and the quantities τ L and τ U are trimming parameters below and above which, respectively, a level break is deemed not to occur. We also make the standard assumption that |τ * i − τ * j | ≥ λ > 0 for all i = j, such that there are at least λT observations between breaks. Note that these conditions imply that the number of breaks that can feasibly be calculated, In the context of (1) the shocks, u t , are assumed to follow a stationary and invertible process which is fractionally integrated of order δ, denoted u t ∈ I (δ). For our purposes, we define fractional integration for u t as where η t is a zero mean I(0) process. We define I(0) to be such that η t has spectral density f (λ) with f (λ) → G for some G ∈ (0, ∞) as λ → 0; formal assumptions on η t required for our large sample theory results will be delayed until Section 3.
The assumption that u t is stationary and invertible entails that the long memory parameter, δ, is such that δ ∈ (−0.5, 0.5). A process satisfying the conditions just stated for u t is often referred to in the literature as a Type I fractionally integrated process.
Our interest focuses on testing the null hypothesis that u t , and hence y t , is I(δ 0 ) for some δ 0 ∈ (−0.5, 0.5); that is, H 0 : δ = δ 0 in (1). Note that the extension to allow δ 0 = 0 is nontrivial, in the sense that testing δ = δ 0 on y t , as we do in this article, is different from testing δ = 0 on δ 0 y t , as is done, for example, in Iacone, Leybourne, and Taylor (2019), since the latter process has a different unconditional mean thereby changing the model and its interpretation. Based on the familiar LM testing principle we will develop tests against two-sided alternatives of the form H 1 : δ = δ 0 (y t is not I(δ 0 )) together with corresponding t-type tests against one-sided alternatives of the form H 1 : δ > δ 0 (y t is more persistent than an I(δ 0 ) series) or H 1 : δ < δ 0 (y t is less persistent than an I(δ 0 ) series).
Next, in Section 3, we discuss the tests proposed in Lobato and Robinson (1998) which were developed for testing the specific null hypothesis that y t is short memory. These tests apply to the case where either u t in (1) is observable or where it is known that β 2 = 0 (so that no level breaks are present). We show that this approach can be readily extended to develop tests for the null hypothesis that y t is I(δ 0 ) for some δ 0 ∈ (−0.5, 0.5). Then, in Section 4, we show how these tests can be generalized to allow for the possibility that β 2 = 0 in (1), such that level breaks could potentially occur in the data. The testing approach we outline in Section 4 does not assume knowledge of whether level breaks genuinely occur; that is, we do not assume knowledge of whether β 2 = 0 or β 2 = 0.
3. Tests of H 0 : δ = δ 0 when it is known that β 2 = 0 Suppose for the purposes of this section that it is known to be the case that β 2 = 0 in (1). Under this restriction we can also set β 1 = 0 with no loss of generality because, as discussed in Lobato and Robinson (1998, p. 477), the statistics we will discuss in this article are invariant to β 1 in the case where β 2 = 0. The restriction that β 2 = 0 is therefore equivalent to the case where β 1 , β 2 and τ * are all known, such that u t in (1) is observable. We may therefore proceed as if u t were observable. We will discuss the application of the tests to u t , although in the context of this section they could equally be applied to y t because no meancorrection is required (provided the mean is constant) due to invariance to β 1 .
For observable u t , semiparametric inference on δ based on the approximation of the Whittle likelihood at low frequencies was proposed by Künsch (1987) and analyzed further in Robinson (1995b). This approach is semiparametric as it does not require the specification of a parametric model for f (λ) and, within the class of semiparametric methods, it has the advantage of being based on a (local) likelihood, and it is therefore considerably more efficient than other semiparametric estimates such as the log-periodogram regression of Geweke and Porter-Hudak (1983) and Robinson (1995a).
For a generic series a t , let w a (λ) := 1 √ 2π T T t=1 a t e iλt denote the Fourier transform of a t , and let I a (λ) := |w a (λ)| 2 denote the periodogram. Then, as discussed in Robinson (1995b), for the observable series u t , the local Whittle estimate of δ is obtained by minimizing the loss function R (d) with respect to d, where and m denotes the bandwidth, satisfying the rate condition that 1/m + m/T → 0 as T → ∞. Recall that λ j := 2π j T for integer j are the Fourier frequencies. Applying the LM principle to the objective function in (3) yields the LM-type statistic to test H 0 : the LM * m (δ 0 ) statistic can be equivalently rewritten in terms of the Fourier frequencies and the periodogram ordinates at those frequencies as The null hypothesis H 0 that u t is I(δ 0 ) can then be rejected for large values of LM * m (δ 0 ), while a large positive (negative) value of t * m (δ 0 ) would allow rejection against the one-sided alternative H 1 : δ > δ 0 (H 1 : δ < δ 0 ). It will turn out that standard critical values can be employed in the context of these decision rules. Lobato and Robinson (1998) analyzed the special case of the t * m (0) and LM * m (0) statistics in (4) and (5), respectively, which obtain setting δ 0 = 0, such that one is testing the null hypothesis of short memory, H 0 : δ = 0. For the purpose of later sections, we need to also define the Lobato and Robinson (1998) t-and LM-type test statistics for the hypothesis H 0 : δ = δ 0 applied to the observed data, {y t }, and which do not account for the possibility of level breaks; we will denote these as t m (δ 0 ) and LM m (δ 0 ), respectively. These differ from the infeasible statistics t * m (δ 0 ) and LM * m (δ 0 ) for the hypothesis H 0 : δ = δ 0 which are applied to the unobserved innovations, {u t }. In the context of this section, where it is known that β 2 = 0, then t m (δ 0 ) and t * m (δ 0 ) coincide, as do LM m (δ 0 ) and LM * m (δ 0 ). Lobato and Robinson (1998) established that, under certain regularity conditions (see Assumption 1), t * m (0) and LM * m (0) have N(0, 1) and χ 2 1 limiting null distributions, respectively. Shao and Wu (2007a) subsequently demonstrated that under local alternatives of the form H c : and, hence, LM * m (0) d → χ 2 1 4c 2 , where χ 2 1 4c 2 denotes a noncentral χ 2 1 distribution with noncentrality parameter 4c 2 . Before progressing to consider the case where u t is not observable, that is where it is not known for sure that β 2 = 0 in (1), we first show that the properties established for the LM * m (0) and t * m (0) statistics in Lobato and Robinson (1998) and Shao and Wu (2007a) carry over to the general case of the LM * m (δ 0 ) statistic in (5) and corresponding t * m (δ 0 ) statistic in (4) for testing H 0 : δ = δ 0 for any δ 0 ∈ (−0.5, 0.5). To do so we first introduce sufficient conditions for establishing these large sample justifications. We will discuss two sets of possible assumptions under which our large sample results obtain. The first set, given in Assumption 1, coincides with the conditions adopted by Robinson (1995b). The second set, given in Assumption 2, coincides with those employed by Shao and Wu (2007a).
ii. The weights ψ j are such that ∞ j=0 ψ 2 j < ∞. iii. The spectral density of η t , f (λ), is twice boundedly differentiable in a neighborhood of λ = 0 and satisfies, as Remark 3.1. The conditions on η t detailed in Assumption 1 coincide with those given in Robinson (1995b) and are slightly stronger than those in Lobato and Robinson (1998). A full discussion of these conditions is given in Robinson (1995bRobinson ( , pp. 1634Robinson ( and 1641 and Lobato and Robinson (1998, p. 478). Assumption 1 includes all stationary and invertible finite-order ARMA models for η t . Assumption 1 allows for nonlinearity via the martingale difference assumption on the innovations, but is otherwise linear. Notice also that Assumption 1 requires f (λ) to be smooth only around λ = 0 and so does not rule out long memory behavior at frequencies other than λ = 0 (although this needs to be strengthened in Assumption 3 to obtain results for our feasible tests).
The assumption of conditional homoscedasticity imposed by part (i) of Assumption 1 may be considered unacceptable for many data applications, in particular those involving financial data. Wu (2007a, 2007b) showed that this can be weakened to allow for a wide class of stationary, causal nonlinear processes. To that end, suppose that where ε t are independent and identically distributed (IID) random variables and F is a measurable function such that η t is well defined as a stationary, causal, ergodic process. For a random variable ξ and p > 0, write ξ ∈ L p if ξ p := (E(|ξ | p )) 1/p < ∞.
Remark 3.2. Assumption 2 includes a number of widely used nonlinear time series models for η t such as bilinear models, threshold models, GARCH and ARMA-GARCH models; see Shao and Wu (2007a, p. 254) and Shao and Wu (2007b) and the references therein for further discussion of this assumption and further examples of classes of nonlinear processes which satisfy it. While Assumption 2 weakens, inter alia, the conditional homoscedasticity restriction of Assumption 1, this comes at the cost of a stronger assumption on the bandwidth, that is restricted to be such that m = o(T 2/3 ). Moreover, as discussed in Shao and Wu (2007b, Remark 3.1), Assumption 2(ii) implies continuous differentiability of f (λ) for all frequencies, whereas, as discussed in Remark 3.1 and Robinson (1995b), Assumption 1 only imposes conditions on f (λ) in a local-to-zero band. There is therefore a clear trade-off between the conditions imposed on η t by Assumptions 1 and 2.
In Theorem 1, we now derive the large sample properties of the LM * m (δ 0 ) and t * m (δ 0 ) statistics, obtained for the case where it is known that β 2 = 0 in (1). To facilitate discussion of asymptotic local power, we consider the local alternative H c : δ = δ 0 + cm −1/2 . Theorem 1. Let y t be generated according to (1) with β 2 = 0, and let either Assumption 1 or Assumption 2 hold on η t . Then, for any δ 0 ∈ (−0.5, 0.5), under H c : δ = δ 0 + cm −1/2 : Remark 3.3. Theorem 1 shows that the results obtained for the limiting null distributions of the LM * m (0) and t * m (0) statistics in Lobato and Robinson (1998) apply more generally to the LM * m (δ 0 ) and t * m (δ 0 ) statistics for testing the null hypothesis that u t is I(δ 0 ) for any δ 0 in the usual stationary and invertible region. Theorem 1 also shows that tests based on the LM * m (δ 0 ) and t * m (δ 0 ) statistics possess the same local power functions as tests based on the LM * m (0) and t * m (0) statistics. Moreover, these results hold regardless of whether u t is conditionally homoscedastic or conditionally heteroscedastic (satisfying Assumption 2). Finally, note that the result in Theorem 1 was anticipated without proof by Marinucci and Robinson (2001, sec. 4), at least under H 0 and Assumption 1.
Feasible Tests of H 0 : δ = δ 0 Allowing for up to k Level Breaks
Recall that the LM-and t-type tests discussed in Section 3 are based on the assumption that β 2 = 0, such that the LM m (δ 0 ) and t m (δ 0 ) statistics calculated on the observed data {y t } will coincide with the LM * m (δ 0 ) and t * m (δ 0 ) statistics based on the shocks, {u t }, even if β 1 = 0 such that {u t } are unobservable (because the statistics are invariant to β 1 ). However, where β 2 = 0 this is no longer the case, and we cannot proceed as if the tests were based on the unobservable shocks, {u t }. Moreover, where β 2 = 0 the LM m (δ 0 ) and t m (δ 0 ) statistics constructed from the observed data, {y t }, are nonsimilar tests and will diverge. For example, if δ 0 = 0 it can be shown that the (m)) under H 0 , so that both statistics will diverge with the sample size, even under the null hypothesis. As a consequence, therefore, the Lobato and Robinson (1998) tests will spuriously reject the null with probability tending to one as the sample size diverges. That is, tests based on LM m (δ 0 ) or t m (δ 0 ) are uninformative if it is unknown whether β 2 = 0 or not. In this section, we will therefore discuss how feasible versions of the tests discussed in Section 3 can be derived for the case where it is not known for certain whether β 2 = 0 or not.
In the context of (1), the disturbances u t are not observable and so they must be estimated. For a generic vector of (putative) break locations, τ = (τ 1 , . . . , τ k ) , we can use ordinary least squares (OLS) estimators of the parameters β 1 and β 2 in (1). To that end, let β := (β 1 , β 2 ) , and let y : For a given value of τ we then have the corresponding estimated residuals Based on I u(τ ) (λ j ), we can then define analogues of the LM * m (δ 0 ) statistic of (5) and the corresponding t-type statistic t * m (δ 0 ) in (4), for testing H 0 : δ = δ 0 as follows If the true vector of break fractions, τ * , were known then one would simply evaluate LM m (δ 0 ; τ ) and t m (δ 0 ; τ ) at τ = τ * . Our focus, however, is on the case where τ * is unknown and so will need to be estimated from the data. An obvious candidate is the minimum residual sum of squares (RSS) estimator considered in Lavielle and Moulines (2000, pp. 38-39), which can be written as where it is recalled that τ L and τ U are trimming parameters such that [τ L , τ U ] ⊂ (0, 1). Given the RSS estimator τ in (9), tests for H 0 : δ = δ 0 can then be based on LM m (δ 0 ; τ ) and t m (δ 0 ; τ ). For these tests to be operational, we will need to establish the large sample behavior of the LM m (δ 0 ; τ ) and t m (δ 0 ; τ ) statistics under the null hypothesis, H 0 : δ = δ 0 , and show that unique asymptotic critical values (in the sense that they do not depend on any nuisance parameters) for the tests can be obtained from these distributions. In fact, we will be able to show in what follows that these statistics have the same limiting null distributions as were obtained for their infeasible counterparts LM * m (δ 0 ) and t * m (δ 0 ) in Theorem 1. To do so, however, we must impose some additional regularity conditions on η t . In particular, Assumptions 1 and 2 must be strengthened to Assumptions 3 and 4, respectively, as follows: Assumption 3. Let Assumption 1 hold. Assume further that: Assumption 4. Let Assumption 2 hold, and define the projection . Then we assume further that: Remark 4.1. Both Assumptions 3 and 4 impose the additional moment condition that q > 1/ (1 + 2δ) moments exist. This condition is needed so that we can appeal to the functional central limit theorem (FCLT) for fractional processes for which the moment condition is necessary; see Theorem 2 of Johansen and Nielsen (2012). The fractional FCLT also requires that q > 2, but this is implied in Assumptions 1 or 2 so is not stated explicitly here. The condition placed on the weights ψ j in Assumption 3(ii) is quite standard for the (fractional) FCLT and is met by all stationary and invertible finite-order ARMA models. This condition also implies continuity of the spectral density of η t and hence rules out long memory at other frequencies, see Remarks 3.1 and 3.2. The condition that 0 < ∞ j=0 ψ j < ∞ (and a similar condition for the nonlinear process) is again omitted because it is implied by the assumption 0 < f (0) < ∞. The additional condition required to hold on the bandwidth in part (iv) of Assumptions 3 and 4 is not restrictive in practice because much larger bandwidths will typically be used.
We are now in a position to state our main result in Theorem 2 which details the large sample behavior of the feasible statistics LM m (δ 0 ; τ ) and t m (δ 0 ; τ ) under local alternatives of the form H c : δ = δ 0 + cm −1/2 . We will first state our main result and then provide some discussion around this result. We will also provide further insights into this result through the case of k = 1 where results are easier to explain.
Theorem 2. Let y t be generated according to (1), and let either Assumption 3 or Assumption 4 hold on η t . Then, for any δ 0 ∈ (−0.5, 0.5), under H c : δ = δ 0 + cm −1/2 , and regardless of Remark 4.2. A comparison of the results in Theorem 2 with those given previously in Theorem 1 yields the following immediate consequence. Regardless of whether any particular ele- (1) is zero or nonzero, the tests based on LM m (δ 0 ; τ ) and t m (δ 0 ; τ ) attain exactly the same asymptotic local power functions as obtained by the infeasible tests based on LM * m (δ 0 ) and t * m (δ 0 ), respectively. Moreover, , so that standard critical values can be used for both tests, again regardless of whether β 2,i = 0 or β 2,i = 0, i = 1, . . . , k, holds in (1).
A proof of Theorem 2 is provided in the appendix. The proof strategy is to consider the distances between the feasible statistics LM m (δ 0 ; τ ) and t m (δ 0 ; τ ) and the infeasible LM * m (δ 0 ) and t * m (δ 0 ) statistics, respectively, in large samples. Inherent in doing so is to analyze the distance between u t and u t ( τ ), the latter given by u t (τ ) evaluated at τ = τ , and establish how this affects the distance between the feasible and infeasible statistics. The behavior of both LM m (δ 0 , τ ) and t m (δ 0 , τ ) clearly depend on the large sample properties of the estimates τ in (9) and β( τ ), the latter given by β(τ ) evaluated at τ = τ . For the properties of τ we apply a result of Lavielle and Moulines (2000), and we combine this with a fractional FCLT for u t to obtain results for β( τ ).
Remark 4.3. To give some insight into the mechanics behind the proof, it is instructive to specialize our discussion to the case where k = 1. Accordingly, and with an obvious notation, we redefine τ , τ , β 2 , and β 2 as τ ,τ , β 2 , andβ 2 , respectively. The proof proceeds by establishing that two key results hold under the conditions of Theorem 2. The first result is that if β 2 = 0 (so that no level break occurs), then (1), in each case uniformly in τ . This result establishes that when no level break occurs, the differences between the statistics based on u t and u t (τ ) are asymptotically negligible, and that this holds uniformly in τ and, hence, holds for τ . To prove this, we first establish uniformly in τ results for β(τ ). It is at this stage that the fractional FCLT is used. We can then derive properties of the estimated residuals u t (τ ) and analyze the distance between the Fourier transforms (and hence the periodograms) of u t (τ ) and of u t . The second result is that if That is, when β 2 = 0, such that a level break occurs, the differences between the statistics based on u t and u t ( τ ) are asymptotically negligible. In this case, we first establish the properties of the estimate of the break fraction, τ , using results from Lavielle and Moulines (2000). These properties allow us to bound the distance between β( τ ) and β(τ * ), and use this to analyze the distance between the Fourier transforms (and the periodograms) of u t ( τ ) and of u t (τ * ).
Remark 4.4. As discussed in Remark 4.3, the difference between the feasible and infeasible test statistics is shown to be o p (1) in Theorem 2. However, these remainder terms are nonetheless functions of δ 0 , or equivalently of δ because of the local asymptotic framework (see, e.g., (A.17), (A.24), and (A.25) in Appendix A.2). This finite sample dependence on δ 0 can also be observed in the Monte Carlo results; see point (v) in Section 5.
Remark 4.5. The result in Theorem 2 shows that there is no loss in asymptotic local power from allowing for k breaks when the true number of breaks, k * say, is smaller than k. However, as the simulation results in Section 5 show, the finite sample size and power properties of the feasible LM-type test, LM m (δ 0 ; τ ), deteriorate somewhat if k is chosen to be larger than k * . On the other hand, if k is chosen to be smaller than k * then we know from the discussion at the start of Section 4 that the LM m (δ 0 ; τ ) statistic will diverge, even under the null hypothesis. In practical applications it would therefore seem sensible to select the number breaks used in constructing the LM m (δ 0 ; τ ) statistic according to a consistent information criterion. Theorem 9 of Lavielle and Moulines (2000, pp. 49-50) provides the conditions required on the penalty function such that an information criterion-based approach will consistently select the true number of breaks in the context of the DGP in (1) under the conditions of Theorem 2. Their result shows that, provided the maximum number of breaks allowed, k, is at least as large as the true number of breaks, k * , then the commonly used Bayes information criterion (BIC) of Schwarz (1978) and Hannan-Quinn information criterion (HQIC) of Hannan and Quinn (1979) will both deliver consistent estimates of k * . We recommend the use of the HQIC as this is less parsimonious than the BIC, and hence constitutes a safer choice in practice, given the severe implications of fitting too few level breaks. We will illustrate the use of the BIC and HQIC in the empirical applications in Section 6.
Monte Carlo Evidence
We begin this section by investigating how well the large sample predictions of Theorem 2 hold in finite samples for a DGP that has either zero or one level break and we accordingly set k = 1 so that the notation of Remark 4.3 applies. To that end, Figures 1 and 2 graph simulated finite sample power functions of the feasible LM-type test, LM m (δ 0 ; τ ), proposed in Section 4 and the corresponding Lobato and Robinson (1998) test, LM m (δ 0 ), that does not allow for the possibility of a level break. In the context of the LM m (δ 0 ; τ ) statistic, we set the trimming parameters to be τ L = 0.15 and τ U = 0.85. Also graphed are the power functions of the corresponding infeasible tests, LM m (δ 0 ; τ * ), defined just under (8), and LM * m (δ 0 ) defined in (5). The former assumes knowledge of the true break location, τ * , but not the innovations, u t , and the latter assumes knowledge of the innovations.
The simulated data used to construct the power curves in Figures 1 and 2 were generated according to the DGP in (1)-(2) for T = 512 and T = 1024 setting k = 1 and with η t ∼ NIID(0, 1), and where β 1 was set equal to zero with no loss of generality. All of the reported tests are for testing H 0 : δ = 0 at the nominal asymptotic 5% level. The graphs depict the simulated power functions of the tests under the local alternative H c : δ = cm −1/2 for a range of values of c and with the corresponding values of δ shown on the horizontal axes. Results are reported for two bandwidth choices, namely m = T 0.65 and m = T 0.8 . The results in Figure 1 relate to the case considered in Theorem 2 with no level break, that is, β 2 = 0, while the results in Figure 2 relate to Theorem 2 for the specific case of a level break with β 2 = 2 at τ * = 0.5, that is, a break equal to two standard deviations of the innovation process occurring midway through the sample. The simulated power curves were computed using 10,000 Monte Carlo replications using the RNDN function of Gauss 20. As a benchmark, we also include in each graph the corresponding asymptotic local power curves obtained directly from the noncentral χ 2 1 (4c 2 ) distribution, where c = δ √ m. Consider first the results in Figure 1 for the no break case. Here, given knowledge that no level break was present, the best possible test to use among the three considered would be the basic Lobato and Robinson (1998) test, LM m (δ 0 ) = LM * m (δ 0 ). Against positive values of δ this test has power closest to the asymptotic local power function and is somewhat more powerful than the infeasible LM m (δ 0 ; τ * ) test, which in turn is more powerful than the feasible LM m (δ 0 ; τ ) test. These differences are, however, reduced for T = 1024 vis-à-vis T = 512 and for m = T 0.8 vis-à-vis m = T 0.65 ; indeed for T = 1024 and m = T 0.8 the differences between the three tests are quite small with all three lying close to the asymptotic local power curve. For negative values of δ there are only very slight differences between the three tests. Overall, when β 2 = 0, the large sample predictions from Theorem 2 appear to hold reasonably well in finite samples, particularly so for the larger bandwidth considered.
Consider next the results in Figure 2 for the case where a level break of magnitude β 2 = 2 occurs. Here the infeasible LM * m (δ 0 ) test no longer coincides with the feasible Lobato and Robinson (1998) test, LM m (δ 0 ). In this case the divergence of the LM m (δ 0 ) test is clearly seen, regardless of whether the null hypothesis holds or not, with the test rejecting essentially 100% of the time even for the smaller sample size considered. The power functions of the infeasible LM m (δ 0 ; τ * ) and feasible LM m (δ 0 ; τ ) tests essentially coincide regardless of the sample size or bandwidth considered, suggesting that τ * is very accurately estimated by τ in this case. As with the results for the no break case in Figure 1, for positive values of δ the power curve of the feasible LM m (δ 0 ; τ ) test lies only slightly below that of the infeasible LM * m (δ 0 ) test, which in turn lies close to the asymptotic local power curve, with the differences between the power curves reducing as T and/or m is increased. For negative values of δ the power curves of the LM m (δ 0 ; τ ) and LM * m (δ 0 ) tests are almost indistinguishable regardless of m or T. Again the large sample predictions from Theorem 2 would appear to hold reasonably well in finite samples.
In the remainder of this section, we summarize the results from an large set of Monte Carlo experiments designed to investigate the finite sample size and power properties of the semiparametric long memory tests proposed in Section 4. Specifically, we compare the empirical size and power properties of the LM m (δ 0 ; τ ), LM m (δ 0 ; τ * ), and LM m (δ 0 ) tests along with the corresponding t-type tests, t m (δ 0 ; τ ), t m (δ 0 ; τ * ) and t m (δ 0 ), respectively. Results are reported for DGPs with either zero, one or two level breaks. In the case where a maximum of one level break is allowed, comparison is also made with the KPSS stationarity test, denoted KPSS, together with the generalizations thereof proposed in Busetti andHarvey (2001, 2003) which allow for a level break at either a known or unknown location, denoted KPSS(τ * ) and KPSS( τ ), respectively. The full set of results together with details of the experimental design can be found in the supplementary appendix.
We considered models for {y t } of the form given in (1) with either k = 1 or k = 2: • For the k = 1 (so that up to one level break is allowed) case the DGP had either no level break or a level break at the sample midpoint with magnitude β 2 ∈ {0.5, 1, 2}. Results are reported related to testing H 0 : δ = 0; both where δ = 0 (empirical size) and where δ ∈ {−0.15, 0.15} (empirical power). The empirical size properties of tests for H 0 : δ = 0.3 and H 0 : δ = −0.3 were also explored. For the empirical size results the error process η t was allowed to follow either an IID process, an AR(1) process or an ARCH(1) process, while for empirical power IID and ARCH(1) processes were considered. • Forthek = 2 (so that up to two level breaks are allowed) case the DGP had either no level break or was such that two level breaks occurred with the level shifting from 0 to β 2 to 2β 2 at 1/3 and 2/3, respectively, of the way through the sample with β 2 ∈ {0.5, 1, 2}. Results are again reported related to testing H 0 : δ = 0; both where δ = 0 (empirical size) and where δ ∈ {−0.15, 0.15} (empirical power).
All of the tests were implemented for both a range of fixed bandwidths and using data-driven bandwidth rules. Again we set the search set as = [0.15, 0.85]. The principal findings of our Monte Carlo results can be summarized as follows, where comments (i)-(vi) relate to results in Tables S.1-S.18 for the single (putative) level break case, and comment (vii) relates to results in Tables S.19-S.24 for the double (putative) break case: i. As with the findings in Lobato and Robinson (1998) our results demonstrate that the bandwidth m has a significant impact on the finite sample properties of the tests, with a clear trade-off seen between size and power. In particular, for a given sample size, excluding those tests which are nonsimilar (i.e., excluding the LM m (δ 0 ) and t m (δ 0 ) tests when β 2 = 0), we observe the following general patterns: (a) for a given pattern of weak dependence and a given bandwidth, m, the observed distortions from the nominal (asymptotic) significance level are greater the larger is m, and (b) empirical power against a given fixed alternative increases as the bandwidth, m, increases. Generally, a range of bandwidths between m = T 0.5 and m = T 0.65 provides reasonable finite sample size control across the cases considered. ii. Our results suggest that the automatic bandwidth, m LR , of Lobato and Robinson (1998) delivers a reasonable tradeoff between finite sample size and power considerations, at least when the data are conditionally homoscedastic. In the conditionally heteroscedastic ARCH(1) case, the empirical size of tests based on m LR do not improve, other things equal, as the sample size is increased. This is perhaps not surprising given that the m LR bandwidth rule is not consistent with the bandwidth rate imposed on m by Assumption 2, and we therefore recommend caution in using the m LR bandwidth rule with data which are suspected to display conditional heteroscedasticity. For the KPSS-type tests, the automatic bandwidth rule recommended in Lobato and Robinson (1998) also appears to deliver a reasonable sizepower trade-off. iii. Overall, our results suggest that it may be helpful in practice to consider the automatic bandwidth, m LR , together with a range of bandwidths between m = T 0.5 and m = T 0.65 . This is what we will do in the empirical examples in Section 6. iv. As expected, where a level break occurs (β 2 = 0), the nonsimilar LM m (δ 0 ), t m (δ 0 ), and KPSS tests are highly unreliable displaying severe oversize (excepting the left-tailed t m (δ 0 ) test which is correspondingly undersized), and hence spurious evidence of long memory. The observed size distortions seen with these tests are higher, other things equal, the larger is the sample size or the level break magnitude. v. Although asymptotically equivalent under both the null and local alternatives (cf. Theorem 2), differences are observed between the finite sample size and power properties of the pairs of tests LM m (δ 0 ; τ ) and LM m (δ 0 ; τ * ), and t m (δ 0 ; τ ) and t m (δ 0 ; τ * ). The LM m (δ 0 ; τ * ) and t m (δ 0 ; τ * ) tests are based on knowledge of whether a level break occurs or not (i.e., whether β 2 = 0 or β 2 = 0) and, where a break occurs, also knowledge of the level break location τ * , while LM m (δ 0 ; τ ) and t m (δ 0 ; τ ) do not assume knowledge of either. The differences between the finite sample properties of these pairs of tests are seen to diminish as either the sample size or, in the case where a level break occurs, the break magnitude increases; indeed, for the largest magnitude considered, β 2 = 2, these differences are largely eliminated even for the smaller of the two sample sizes considered. The observed differences between the empirical power properties of these pairs of tests are seen to be slightly larger, other things equal, in the case where the errors are ARCH(1) vis-à-vis the IID case. Moreover, the finite sample differences between the pairs of tests are smallest for the tests of H 0 : δ = −0.3 and largest for the tests of H 0 : δ = 0.3; cf. Remark 4.4. Where no level break is present, the finite sample differences between the LM m (δ 0 ; τ ) test and LM m (δ 0 ) (which assume no level break is present) are again relatively small, other things equal, particularly for the larger sample size considered. This is also broadly true for a comparison between the t m (δ 0 ; τ ) and t m (δ 0 ) tests, although the differences are larger than for the LM-type tests. Overall, the asymptotic theory presented in Theorem 2 appears to provide a reasonable prediction of the finite sample behavior of the LM m (δ 0 ; τ ) and t m (δ 0 ; τ ) tests. vi. For a given DGP, the one-sided t-tests have more power (in the correct tail) than the corresponding two-sided LM tests, as would be expected. Moreover, and consistent with both the discussion concerning theoretical power rates against local alternatives in Shao and Wu (2007a) and the simulation findings in Lobato and Robinson (1998), the KPSS-type tests have considerably lower power to detect departures from short memory than do the corresponding LM-and t-based fractional integration tests discussed in this article, at least provided reasonable bandwidths m are chosen. vii. The results for the case where two putative breaks are allowed for (k = 2) are qualitatively similar to the corresponding results discussed above for the case of a single (putative) level break. However, as might be expected, the patterns seen for k = 1 are somewhat magnified for k = 2.
Empirical Examples
Throughout the empirical examples in this section, we set the trimming parameters equal to the same values as were used in the Monte Carlo experiments in Section 5, that is, τ L = 0.15 and τ U = 0.85. Where multiple breaks were estimated, we set the minimum spacing parameter λ defined in Section 4 to λ = 0.10, except for the VIX example where we set λ = 0.05 to allow larger values of k. For k ≥ 4, a complete enumeration of all possible break date combinations is infeasible, so the break dates are estimated by numerical (integer) optimization of the RSS function using a Matlab program which is available in the supporting material.
Bitcoin Returns
We apply the semiparametric long memory tests described in this article to the daily returns of Bitcoin over the period 17 September 2014 to 31 December 2019, giving a total of T = 1932 daily observations. The data were retrieved from Yahoo Finance. The logarithm of the closing price of Bitcoin in USD is graphed in Figure 3 along with the returns series, defined as first differences of the (log) closing price series. A visual inspection of the data suggests the plausibility of changes in slope, implying changes in level at the same point in the returns series, with the most obvious case being at around the beginning of 2018. The red line on the graphs shows the fitted deterministic trend/level of the series allowing for two breaks, the locations of which are estimated by applying the RSS-based estimator discussed in Section 4 to the returns data setting k = 2. The estimated break dates are March 24, 2017 and December 16, 2017. Evidence of long memory in returns would of course be in strong violation of the efficient market hypothesis, and so it is of interest in the context of the Bitcoin returns data to test H 0 : δ = 0 against the alternative H 1 : δ > 0. We do so using both the test based on the t m (0) statistic of Lobato and Robinson (1998), which does not allow for a level break, and the analogues of this test based on the t m (0; τ ) and t m (0; τ ) statistics allowing for the presence of either one or two level breaks, respectively, in each case occurring at unknown points in the sample. Following the recommendations from our Monte Carlo study we computed the statistics for a range of values of the bandwidth parameter, m, lying between T 0.5 = 43 and T 0.65 = 137, inclusive, as well as for the automatic bandwidth rule, m LR of Lobato and Robinson (1998) with the value that this takes reported in parentheses below the outcome of the statistics. The results are summarized in Table 1. Here, and also in Tables 4 and 5, the superscripts * , * * and * * * denote outcomes which are statistically significant at the 10%, 5%, and 1% level, respectively, while the superscripts HQ and BIC indicate the number of breaks chosen by the HQIC and BIC, respectively; cf. Remark 4.5.
Using Lobato and Robinson's t m (0) test, we can reject H 0 at the 10% level when using the data-dependent bandwidth rule, m LR , and for all but the smallest and largest of the other bandwidths considered. The null can also be rejected at the 5% level for m = 75 and m = 93. On balance we surmise from the results for the standard Lobato and Robinson test that the short memory null hypothesis is rejected in favor of long memory in the Bitcoin returns data. On the other hand, for the test based on t m (0; τ ), which fits a level break to the data, the evidence against the null hypothesis is considerably weaker and, in particular, H 0 can only be rejected at the 10% level for bandwidths m ∈ {75, 93, m LR }. Allowing for two breaks, which is the number chosen by our preferred HQIC, no choice of bandwidth results in a rejection at even the 10% level for the t m (0; τ ) test. This suggests that the finding of long memory in Bitcoin returns by the Lobato and Robinson (1998) test is likely attributable to the presence of at least one level break in the returns data.
VIX Market Volatility
In the next example, we consider market volatility, measured by VIX, using daily data from January 1, 2000 to December 31, 2019 for a total of T = 5031 observations. The data were downloaded from Yahoo Finance and are graphed in Figure 4. The red step function on the graph shows the fitted deterministic level of the series allowing for 10 level breaks. It has been argued by several authors that long memory in volatility is an important stylized fact (see, e.g., Andersen et al. 2001 and references therein). Furthermore, long memory in volatility is relevant in asset pricing. For example, Baillie, Bollerslev, and Mikkelsen (1996) used asset pricing as motivation for their FIGARCH model, and Christensen and Nielsen (2007) discussed implications of long memory in volatility in the context of stock pricing. Other authors, however, suggest volatility might be a short memory process with the statistical evidence for long memory disappearing once level shifts in the data are accounted for; see, among others, Granger and Hyung (2004).
NOTE: All statistics in this table are significant at the 1% level, excepting those with a superscript a which are significant at the 5% level, those with a superscript b which are significant at the 10% level, and those with a superscript c which are not significant at the 10% level. Superscripts HQ and BIC indicate the number of breaks chosen by the HQIC and BIC, respectively.
To investigate this further, we test the short memory null hypothesis H 0 : δ = 0 against the long memory alternative H 1 : δ > 0 in the VIX data. We report the outcomes of the t m (0) statistic, the t m (0; τ ) statistic which allows for the presence of up to one level break, and the t m (0; τ ) statistic which allows for up to k level breaks for each of k = 2, . . . , 10. We again computed these statistics for a range of values of the bandwidth parameter, m, between T 0.5 = 70 and T 0.65 = 254, inclusive, together with the automatic bandwidth rule, m LR . The results are summarized in Table 2. Following Andersen et al. (2001), we also conducted the analysis using logarithmically transformed VIX data, and the results were nearly identical to those reported in Table 2.
It is seen from the results in Table 2 that the short memory null hypothesis is easily rejected at the 1% significance level for all of the bandwidths considered, other than m LR , regardless of how many level breaks we fit to the data. The tests based on m LR provide weaker evidence of long memory in the VIX data where 5 or more levels breaks are fitted; for example the HQIC selects 10 breaks and here the t m (0; τ ) test is only able to reject at the 10% level when using m LR . In conclusion, though, the results of these tests strongly suggest that long memory is a feature of the VIX data, and that this would not appear to be spurious long memory due to unmodeled level breaks. Table 3 repeats the analysis of Table 2, but testing null hypothesis H 0 : δ = 0.4 against the two-sided alternative H 1 : δ = 0.4. The value δ = 0.4 is very commonly found to characterize volatility data in empirical work (e.g., Andersen et al. 2001;Christensen and Nielsen 2007), and thus seems like a natural null hypothesis. For bandwidths m ≥ 125, including m LR , the null hypothesis is rejected at the 1% level regardless of the number of breaks allowed for. However, for m ≤ 108 the evidence against the null hypothesis becomes weaker. Using the number of breaks selected by either HQIC or BIC, the null cannot be rejected at the 10% level for any m ≤ 100, but can be rejected for larger m. On balance, unless a relatively small bandwidth is used, we conclude that the VIX is more persistent than an I(0.4) series (because rejection is in the right tail). The latter finding is in line with some recent empirical work (e.g., Frederiksen, Nielsen, and Nielsen 2012).
U.S. CPI Inflation
We next consider U.S. CPI inflation, defined as the first differences of the logarithm of the price index. Specifically, we used the series CPIAUCSL from the FRED database, which is the CPI for all items, Urban consumers, seasonally adjusted, base year 1984. We used monthly observations spanning January 1970 to December 2019, for T = 599 observations on the first differences. The log-CPI data along with the inflation data, the latter multiplied by 1200 to return a measure that is compatible with the commonly reported inflation rate, are both plotted in Figure 5. U.S. inflation is widely argued to have gone through several different policy regimes over the sample period considered here, most notably the Great Inflation period of the 1970s, the subsequent Volcker-Greenspan era of inflation rate targeting by the U.S. Federal Reserve starting in the early 1980s, and the response to the financial crisis of 2008. Figure 5 is indeed suggestive of the possibility of several level breaks in the inflation data. The red step line on the graphs again shows the fitted deterministic trend/level of the series allowing for up to four breaks. The estimated break dates are August 1977, July 1982, January 1991, and July 2008, broadly consistent with the regimes discussed above.
We again test the short memory null hypothesis, H 0 : δ = 0, against the alternative of (positive) long memory in the U.S. inflation data. We consider both the test based on the t m (0) statistic of Lobato and Robinson (1998), and the corresponding tests based on the t m (0; τ ) and t m (0; τ ) statistics allowing for the presence of up to k = 1, . . . , 4 level breaks, in each case at unknown points in the sample. The results are reported in Table 4 again for a range of values of the bandwidth parameter, m, lying between T 0.5 = 24 and T 0.65 = 63, inclusive, and the data-dependent bandwidth rule, m LR .
Lobato and Robinson's t m (0) test overwhelmingly rejects short memory at any conventional significance level for all of the bandwidths considered. Allowing for the presence of level breaks considerably reduces the magnitude of the test statistics. The test outcomes are generally still strongly significant when allowing for one or two level breaks, but when three level breaks are allowed for (the number chosen by BIC), the null cannot be rejected at the 5% level for bandwidths up to m = 40. When Table 3. Tests of H 0 : δ = 0.4 versus H 1 : δ = 0.4 in VIX volatility data. allowing for four level breaks (the number chosen by HQIC) only the tests based on bandwidths of m = 50 and m = 63 are significant at the 5% level. Consequently, while the standard Lobato and Robinson (1998) test presents very strong evidence in favor of long memory in the U.S. inflation rate, tests which allows for different policy regimes within the sample period are more suggestive that U.S. inflation is a short memory series.
Real U.S. GDP Growth Rate
Finally, we consider U.S. GDP growth rates obtained as the first difference of the logarithm of real U.S. quarterly GDP (seasonally adjusted) over the period 1947Q1 to 2019Q4 obtained from the FRED database (series GDPC1), for a total of T = 292 quarterly observations. The data for U.S. (log) GDP and the GDP growth rates are both graphed in Figure 6. The red line on the graphs again shows the fitted deterministic trend/level of the series allowing for up to three breaks. The estimated break dates are 1973Q2, 1982Q3 and 2000Q2, broadly consistent with the first oil crisis, changes in the Fed policy (discussed in the context of the U.S. CPI data in Section 6.3) and the end of the dot-com bubble. In particular, we will test the null hypothesis that growth rates are short memory, H 0 : δ = 0, such that the log- NOTE: * , * * , and * * * denote outcomes which are statistically significant at the 10%, 5%, and 1% level, respectively, while superscripts HQ and BIC indicate the number of breaks chosen by the HQIC and BIC, respectively. level of GDP follows an I(1) process, against the alternative of negative long memory (antipersistence) in growth rates, H 1 : δ < 0, such that the log-level of GDP is less persistent than an I(1) process. As in the previous examples, we consider the test of Lobato and Robinson (1998) based on the t m (0) statistic, and the corresponding tests based on the t m (0; τ ) and t m (0; τ ) statistics allowing for up to k = 1, 2, 3 level breaks, in each case at unknown points in the sample. The results are reported in Table 5, again for a range of values of the bandwidth parameter, m, lying between T 0.5 = 17 and T 0.65 = 40, inclusive, and the data-dependent bandwidth rule, m LR .
With only a few exceptions, the tests reported are unable to reject the null hypothesis that GDP growth rates are short memory against H 1 : δ < 0 at conventional significance levels. The results from these tests do not therefore appear to support the conjecture of Perron (1989) that U.S. GDP is I(0) about a broken linear trend, particularly when recalling that our test is of the null hypothesis that U.S. GDP is I(1) around a broken trend. NOTE: * , * * , and * * * denote outcomes which are statistically significant at the 10%, 5%, and 1% level, respectively, while superscripts HQ and BIC indicate the number of breaks chosen by the HQIC and BIC, respectively.
Conclusions
We have developed semiparametric tests, based on the Lagrange multiplier testing principle, for the fractional order of integration of a univariate time series which may be subject to the presence of level breaks. This is of significant practical importance as it is well known that long memory and level breaks can be mistaken for one another, with unmodeled level breaks rendering standard fractional integration tests highly unreliable. Our approach generalizes the tests for the null hypothesis of weak dependence (I(0)) developed in Lobato and Robinson (1998). These tests are based on the local Whittle approach, and therefore do not require the user to specify a parametric model for any weak autocorrelation present in the data, which is a considerable practical advantage where the confounding effects of long memory and level breaks are present. We also show how, as conjectured in Lobato and Robinson (1998, p. 478), their testing approach can be generalized to develop tests of the null hypothesis that a series is I(δ) for any δ lying in the usual stationary and invertible region of the parameter space, not just δ = 0. In spite of these generalizations, our tests are shown to attain the same standard asymptotic null distributions and asymptotic local power functions as the corresponding tests in Lobato and Robinson (1998); hence, there is no loss of asymptotic local power from allowing for level breaks, even where no level breaks are present. Monte Carlo simulations suggest that the tests perform well and that the predictions from the asymptotic theory appear to hold reasonably well in finite samples. The practical relevance of our proposed tests was highlighted with a number of empirical examples relating to macroeconomics and finance.
Appendix A: Mathematical Proofs
In this appendix, we provide proofs of Theorems 1 and 2.
A.1. Proof of Theorem 1
We use the notation δ c := cm −1/2 , so that, under H c , we have δ = δ 0 + δ c . Consider first the proof under Assumption 1. We rewrite t * m (δ 0 ) in (4) as Letting I ε (λ j ) denote the periodogram of ε t , (4.8) of Robinson (1995b) shows that, for r ≤ m, = O p r 1/3 (ln(r)) 2/3 + r 3 T −2 + r 1/2 T −1/4 . (A.5) Then, letting b j := ν j j −2δ c and proceeding as in Robinson (1995b) it follows that the remainder term (A.2) is o p (1). This involves using summation by parts, (A.5), and the bound |b j −b j+1 | = O(j −1 ), which follows by elementary calculations. From (4.11) of Robinson (1995b) it follows directly that (A.3) converges in distribution to N(0, 1). Next, by a Taylor series expansion and by definition of (A.6) Writing ln j = ν j + m −1 m k=1 ln k, the first term of (A.6) is Noting that 2cm −1 m j=1 ν 2 j 2π E(I ε (λ j )) = 2cm −1 m j=1 ν 2 j → 2c, the first term converges in probability to 2c by a law of large numbers. Using the result for (A.3) and the fact that m −1 m k=1 ln k = O(ln m), the second term is O p (m −1/2 ln m) = o p (1). Next, the expectation of the absolute value of the second term of (A.6) is where the last equality follows because m 1/2 ≥ ln m, which implies m 2|δ c | ≤ m 2|c|/ ln m = e 2|c| . This shows that the second term of (A.6) converges to zero in L 1 -norm and hence in probability.
The denominator of t * m (δ 0 ) in (A.1) may be analyzed in the same way to establish the result that m −1 m j=1 λ 2δ j j −2δ c 2π I u (λ j ) → p G. The claim of Theorem 1 under Assumption 1 follows by combining these results.
Next, we prove the theorem under Assumption 2. Instead of the bound (A.5) from (4.8) of Robinson (1995b), we let α T (λ) := (1 − e iλ ) −(δ 0 +δ c ) and use Lemma 4 of Shao and Wu (2007a), where it is shown that, under Assumption 2, where the last equality follows by using bounds for the low-frequency approximation of the ratio of f (λ j ) to G, see Assumption 2(iii), and of |α T (λ j )| 2 to λ −2δ j as in Robinson (1995b).
For the leading term in (A.8), we let b j := ν j |α c (λ j )| 2 (with slight abuse of notation), and rewrite it as As in the analysis of (A.2) it holds that (A.9) is o p (1) using (A.7). The term (A.10) is asymptotically normal as shown in Shao and Wu (2007a). As in the previous case, the same arguments also give m −1 m j=1 λ 2δ j 2π I u (λ j ) → p G, and the claim of Theorem 1 under Assumption 2 follows combining these results.
A.2. Proof of Theorem 2
Proof of Lemma 1. Given that δ 0 < 1/2, for m large enough there is δ = 1/2 − such that δ 0 < δ and δ < δ. Using a mean value theorem expansion, where |δ mvt − δ 0 | < |δ − δ 0 | and k is an integer to be chosen. From the fractional FCLT, the first term on the right-hand side of (A.11) satisfies in the Skorohod metric (see, e.g., Hosoya 2005;Wu and Shao 2006). Moreover, because the jumps in the partial sums take place at fixed points in time, and the limit W(τ ; δ) is a.s. continuous, the weak convergence also takes place in the uniform metric.
By the same argument, including a slowly varying function, it follows that 1 ln(T) ; in both cases uniformly in τ . The k − 2 remaining terms in the expansion of −δ η t in (A.11) can be analyzed the same way.
For the last term on the right-hand side of (A.11), notice that where we recall that f (λ) is the spectral density of η t , which is bounded, uniformly in λ, under either Assumption 3 or Assumption 4. Then, by the Cauchy-Schwarz inequality, and note that this is uniform in τ . So, upon choosing k finite but sufficiently large, T −(1/2+δ 0 ) m −k/2 T → 0 by Assumption 3(iv) or Assumption 4(iv), and consequently Combining these arguments we obtain the desired result.
In what follows, results for stochastic functionals of τ are to be considered as uniform in τ , unless otherwise specified. We omit the reference to uniformity in τ for brevity.
We divide the remainder of the proof into three parts for readability. Recall that k * is the true number of breaks, that is, the number of nonzero elements of β 2 .
A.2.1. Proof of Theorem 2 When k > k * = 0
In this case β 2,i = 0 for all i = 1, . . . , k. To lighten the notation, we give the proof for the case with k = 1 and k * = 0; see also the notation in Remark 4.3. The proof for the general case is the same, but with vectors and matrices replacing scalar quantities. Thus, we prove that, when β 2 = 0, t m (δ 0 ; τ )−t * m (δ 0 ) = o p (1) uniformly in τ . It is sufficient to show that We give only the proof of (A.12). The proof of (A.13) is almost identical leaving out the factor ν j and noting the different normalization. | 16,986.2 | 2021-01-20T00:00:00.000 | [
"Mathematics",
"Economics"
] |
Res-NeuS: Deep Residuals and Neural Implicit Surface Learning for Multi-View Reconstruction
Surface reconstruction using neural networks has proven effective in reconstructing dense 3D surfaces through image-based neural rendering. Nevertheless, current methods are challenging when dealing with the intricate details of large-scale scenes. The high-fidelity reconstruction performance of neural rendering is constrained by the view sparsity and structural complexity of such scenes. In this paper, we present Res-NeuS, a method combining ResNet-50 and neural surface rendering for dense 3D reconstruction. Specifically, we present appearance embeddings: ResNet-50 is used to extract the appearance depth features of an image to further capture more scene details. We interpolate points near the surface and optimize their weights for the accurate localization of 3D surfaces. We introduce photometric consistency and geometric constraints to optimize 3D surfaces and eliminate geometric ambiguity existing in current methods. Finally, we design a 3D geometry automatic sampling to filter out uninteresting areas and reconstruct complex surface details in a coarse-to-fine manner. Comprehensive experiments demonstrate Res-NeuS’s superior capability in the reconstruction of 3D surfaces in complex, large-scale scenes, and the harmful distance of the reconstructed 3D model is 0.4 times that of general neural rendering 3D reconstruction methods and 0.6 times that of traditional 3D reconstruction methods.
Introduction
The objective of 3D reconstruction is to extract accurate information regarding the geometric structure of a scene from multiple images observed from varying viewpoints.The geometric structure information of the scene can be applied to a virtual reality scene representation or creating complete organ models in the medical field.At the same time, multi-view-based 3D reconstruction technology can be used in applications such as the digital reconstruction of cultural relics [1], traffic accident analysis [2], and other building site reconstructions [3].
The traditional approach to multi-view 3D reconstruction involves combining Structure from Motion (SFM) [4] with Multi-view Stereo Matching (MVS) [5][6][7][8].Although impressive reconstruction results have been achieved, due to the cumbersome steps involved, cumulative errors are inevitably introduced into the final reconstructed geometric structure information.Moreover, an inherent limitation of this traditional algorithm is its inability to handle sparse, blurred views, such as areas with large areas of uniform color, complex texture areas, or remote sensing scenes captured from afar.
The latest 3D reconstruction methods represent scene geometric structure information as neural implicit surfaces and use volume rendering to optimize the surface to reduce biases caused by traditional multi-view reconstruction methods because volume rendering has greater robustness compared to surface rendering.Compared to the impression performance of indoor datasets (DTUs [9]) or some outdoor small-scene datasets taken at close range (we list some data from BlendedMVS [10]), the bias generated by traditional methods is partly optimized.However, when using only color information obtained via volume rendering to optimize the surface structure of a scene, challenges remain, specifically processing data in extreme weather conditions (cloudy or foggy, dark or daytime) and remote sensing scene data with distant, sparse views.
To overcome these challenges and apply neural rendering techniques to the above situations, we present a novel solution, Res-NeuS, for the high-fidelity surface reconstruction of multi-view complex scenes.We used the Signed Distance Function (SDF) [11][12][13][14][15] network to locate the zero-level set of a 3D surface and forward-optimized the volume-rendering color network through image appearance embedding [16].We also added surface rendering to improve the original single-rendering framework to make the rendering process approximately unbiased and reversely optimize the SDF network by reducing the disparity between the rendered color and the actual color.Next, to address the issue of geometric ambiguity in that optimizing the scene geometry uses only color information, our method integrates multi-view stereo matching to constrain the geometry.Furthermore, to efficiently utilize computing resources and view dependency [17], we designed a coarse sampling scheme for automatically filtering interesting point clouds.
In summary, our contributions encompass the following: (1) we theoretically analyzed the biases in volume rendering, (2) based on the theoretical analysis, we present appearance embedding to optimize the color function, (3) we combine surface rendering and volume rendering, making the rendering results close to unbiased, (4) we integrate a multi-view stereo matching mechanism to constrain the 3D geometric structure, and (5) we present a novel geometric coarse sampling strategy.Compared to previous research work, we have improved the 3D geometric blur problem and further enriched colors to optimize the 3D model while simplifying the 3D reconstruction process.
Related Work 2.1. Multi-View Surface Reconstruction
Multi-view surface reconstruction is a complex process.For multi-view reconstruction with missing parts, the multi-view clustering method [18,19] can be used to restore image information, and then a 3D reconstruction of the scene can be performed.The purpose of multi-view surface reconstruction is to recover the exact geometric surface of a 3D scene from a multi-view image [20].We summarize the merits and limitations of the multi-view 3D reconstruction method according to different representations, as shown in Table 1.In the initial stages of image-based photogrammetry techniques, a volumetric occupancy grid was employed to depict the scene.This process involves visiting each cube, or voxel, and designating it as occupied when there is strict adherence to color constancy among the corresponding projected image pixels.However, the feasibility of this approach is limited by the assumption of photometric consistency because auto-exposure and non-Lambertian materials would cause color inconsistency.Subsequent approaches commonly initiate with 3D point clouds derived from multiview stereo techniques, followed by a dense surface reconstruction.However, reliance on point cloud quality often leads to missing or noisy surfaces because point clouds are usually sparse.Recently, learning-based approaches have argued for carrying out the point cloud formation process by training neural networks.These approaches improve the quality and density of point clouds by learning image features and constructing cost volumes.However, they are limited by the cost volume resolution and fail to recover the geometric details of complex scenes.
Surface Rendering and Volume Rendering
Surface rendering [12,[21][22][23]: The rendered color depends on the predicted color from the point at which the ray intersects with the surface geometry.When propagating backward, the gradients exit only at the local regions near the intersection.Hence, surfacebased reconstruction methods encounter challenges in reconstructing complex scenes marked by significant self-occlusion and abrupt depth changes.Additionally, such methods typically necessitate object masks for supervision.
Volume rendering [24][25][26][27]: This is an image-based rendering method that renders a 3D scalar field into a 2D image.This method projects rays along a 3D volume.For example, NeRF [28] renders images by integrating the color of the sampling points on each ray, a process which can handle scenes with abrupt depth changes and synthesize high-quality images.However, achieving high-fidelity surface extraction from learned implicit fields [29] poses a challenge.Density-based scene representations face limitations due to insufficient constraints on their level sets.Therefore, the problem with photogrammetric surfaces is more direct to surface reconstruction.
Neural Implicit Surface Reconstruction
The neural implicit field is a new approach to representing the geometry of scenes by training a neural network to fit an implicit function on reconstruction.The inputs to this function are 3D coordinates, and the outputs are the characteristic values of scenes, such as distance or color.Meanwhile, the implicit function can be regarded as an implicit representation of the 3D scene.Therefore, to define the scene representation of 3D surfaces accurately [11,24,[30][31][32][33][34][35][36], implicit functions such as occupancy grids [23,37] or signed distance functions are favored over straightforward volume density fields.
NeuS [24] is a classical neural implicit surface reconstruction method which applies volume rendering [24,25,28,[37][38][39] to learn implicit SDF representation.However, applying standard volume rendering directly to the density values of Signed Distance Functions (SDFs) can lead to significant geometrical bias in scenes.Because the pixel weight is not on or near the object's surface when the volume density is maximum, NeuS constructs a new volume density function and weight function to satisfy the above bias.When the volume density is the same and the distance from the camera is different, the weighted pixel of the point should be different.
Improvements in and Drawbacks of Neural Implicit Surface Reconstruction
Numerous experiments have shown on NeuS that volume rendering based on SDF is very beneficial for surface restoration from 2D images, particularly for some indoor small-scene datasets.Nonetheless, achieving high-quality 3D surface reconstruction remains a challenging task, particularly in the context of outdoor and large-scale scenes characterized by low visibility because the sparsity of view features can cause serious geometric deformation or distortion.Furthermore, the biases of the volume rendering paradigm (such as sample bias and weight bias) are greatly amplified when applied to such scenes.
Background
Our work extends NeRF [28] and its derivative NeuS [24].In this summary, we encapsulate the pertinent aspects of these methods.For a more in-depth understanding, we recommend referring to the original papers.
NeRF and NeuS Preliminaries
The surface S of the scene is represented as follows: where f (p) is the signed distance function that maps a spatial position p ∈ R 3 , and f (p) = 0 represents a point on the surface of the observed object.This function can be represented by a neural network.It is called an SDF network in NeuS and is associated with NeRF in NeuS to optimize the SDF network using NeRF's loss function.
For a specific pixel and a camera position o, we present a ray emitted by a camera and passing through a pixel as p(t) = o + tv, t ≥ 0, where v is the unit direction vector of the ray and t is the depth along the ray starting at o.The volume rendering formula of classical NeRF is To accurately describe volume density, the volume density must be at a maximum at or near the surface (when f (x) = 0, σ(x) also reaches the maximum value, where the view direction x ∈ R 3 points to a color value), so NeuS redefined the expression of the volume density ϕ s (u) = se −su /(1 + e −su ), where u = f (x), the volume density expression ϕ s ( f (x)) is called the S-density, and the rendering formula is Let w(t) = T(t)ϕ S ( f (p(t))), and the w(t) function must be satisfied when the volume density is the same and the distance from the camera is different; the point Pixel weights should be different, otherwise there will be ambiguity.Furthermore, the weight function is normalized because of the influence of T(t): Let w(t) = T(t)ρ(t) and T(t) = exp(− t 0 ρ(u)du), Therefore, T(t) and ρ(t) are solved.Meanwhile, NeuS completes the perfect combination of NeRF and surface reconstruction.
View Dependent on Sparse Feature Bias
NeuS's scene representation is a pair of Multi-layer Perceptrons (MLPs).The first MLP receives sparse 3D points and camera position information x, outputs the S-density and a feature vector, and sends the feature vector with the 2D viewing direction, d, to the second MLP and outputs the color.The architectural design guarantees that the output exhibits distinct colors when observed from various viewpoints, using color to constrain the geometry, but the underlying shape representation is only a function of position.Therefore, only the feature encoding corresponding to sparse 3D points is considered, and the interval length between sampling points is ignored (sampling bias).This leads to missing finer details in appearance encoding.
Color Weight Bias
In volume rendering, when a ray traverses a scene, direct optimization involves the color integral of the sampling points to compute the rendered color.It is noteworthy that for indoor simple geometry datasets like DTU, the maximum of the color weight is typically concentrated on or near the surface position.However, in the case of remote sensing scenes, the color integration occurs along the entire ray rather than just at the surface intersection point.This distinction becomes particularly pronounced in scenes characterized by low visibility, long-distance, sparse views, and complex geometric shapes.The maximum of the color weight tends to deviate from the signed distance function (SDF) and is 0. Consequently, this color weight bias inevitably undermines the geometric constraint capability.
We define C S as the color at the point where the ray intersects with the object's surface, and C V as the color of the volume rendering, t * = argmin{t|o + tv = p, p ∈ ∂Ω, t ∈ (0, ∞)}, where ∂Ω represents the geometric surface.For neural rendering, we often obtain the SDF value through one MLP network inference and obtain the color field through another MLP network, which can be expressed mathematically as The volume-rendered color of the pixel is written in discrete form as We presume that the initial intersection point of the ray and the surface is denoted as p(t * ) with sd f (t * ) = 0; the surface color at p(t * ) along the direction v, i.e., the surface rendering color can be expressed as For compositing new views, our goal is to make the color of the composite view consistent with the target color, so p(t j ) is the nearest sampling point p(t * ), ε sample represents the deviation caused by the sampling operation, and ε weight represents the deviation caused by volume rendering weighting.
Geometric Bias
In many neural-rendering pipelines, geometry is commonly constrained by color loss obtained from a single view in each iteration.However, this approach lacks consistency across different views in the geometric optimization direction, introducing inherent ambiguity.As the input views become sparser, this ambiguity intensifies, leading to inaccuracies in the reconstructed geometry.Addressing this inherent ambiguity becomes especially challenging in the context of large-scale scenes, where views are frequently sparse.
Method
With a set of multi-view images and known poses at our disposal, our objective is to reconstruct surfaces that amalgamate the benefits of neural rendering and volume rendering, all without relying on mask supervision.We leverage the zero-level set of the signed distance function (SDF) to extract the scene's surface in rendering to optimize the SDF.Firstly, we present a novel 3D geometric appearance constraint method known as image appearance embedding: this method involves extracting feature information directly from the images and feeding it into the color MLP, enhancing the disambiguation of geometric structures.Secondly, we perform interpolation on the sampling points of the volume rendering.Additionally, we apply weight regularization to eliminate color bias, as discussed in detail in Section 3.3, enhancing the overall rendering quality.Thirdly, we introduce display SDF optimization.This optimization is instrumental in achieving geometric consistency across the reconstructed scene, contributing to the overall accuracy of the 3D model.Lastly, we present an automatic geometric filtering approach aimed at refining the reconstructed surfaces.This method plays a crucial role in enhancing the precision and visual fidelity of the 3D model.Our approach overview is shown in Figure 1.
reconstruct surfaces that amalgamate the benefits of neural rendering and volume rendering, all without relying on mask supervision.We leverage the zero-level set of the signed distance function (SDF) to extract the scene's surface in rendering to optimize the SDF.Firstly, we present a novel 3D geometric appearance constraint method known as image appearance embedding: this method involves extracting feature information directly from the images and feeding it into the color MLP, enhancing the disambiguation of geometric structures.Secondly, we perform interpolation on the sampling points of the volume rendering.Additionally, we apply weight regularization to eliminate color bias, as discussed in detail in Section 3.3, enhancing the overall rendering quality.Thirdly, we introduce display SDF optimization.This optimization is instrumental in achieving geometric consistency across the reconstructed scene, contributing to the overall accuracy of the 3D model.Lastly, we present an automatic geometric filtering approach aimed at refining the reconstructed surfaces.This method plays a crucial role in enhancing the precision and visual fidelity of the 3D model.Our approach overview is shown in Figure 1.[40] into the network architectures of previous neural implicit surface learning methods.Subsequently, we interpolate the sampled points, estimate the color for all points, and optimize the color weights.Finally, we introduce the SDF loss derived from sparse 3D points and the photometric consistency loss from multi-view stereo to supervise the SDF network explicitly, additionally efficiently implementing coarse geometric sampling.
Appearance Embedding
To mitigate the sparse feature bias discussed in Section 3.2 and account for potential variations in environmental conditions during data capture [41], we extract appearance latent features from each image to subsequently optimize the color MLP.This process is illustrated in Figure 2. [40] into the network architectures of previous neural implicit surface learning methods.Subsequently, we interpolate the sampled points, estimate the color for all points, and optimize the color weights.Finally, we introduce the SDF loss derived from sparse 3D points and the photometric consistency loss from multi-view stereo to supervise the SDF network explicitly, additionally efficiently implementing coarse geometric sampling.
Appearance Embedding
To mitigate the sparse feature bias discussed in Section 3.2 and account for potential variations in environmental conditions during data capture [41], we extract appearance latent features from each image to subsequently optimize the color MLP.This process is illustrated in Figure 2. In our model, the initial MLP is denoted as ( ) F x , predicting the SDF for a spatial position x .Additionally, the network also generates a feature vector which is combined with the viewing direction d and an appearance embedding r .These amalgamated components are then fed into a second MLP denoted ( ) F c which produces the color corresponding to the given point.Therefore, the appearance embedding also further enriches the color information of the neural surface rendering, preparing for further accurate reconstruction.
During model training, considering that latent features typically diminish after repeated convolutions, ResNet-50 is employed to counteract this effect.Unlike conventional setups, ResNet-50 continuously incorporates previous latent features during the back- In our model, the initial MLP is denoted as F(x), predicting the SDF for a spatial position x.Additionally, the network also generates a feature vector which is combined with the viewing direction d and an appearance embedding r.These amalgamated components are then fed into a second MLP denoted F(c) which produces the color corresponding to the given point.Therefore, the appearance embedding also further enriches the color information of the neural surface rendering, preparing for further accurate reconstruction.
During model training, considering that latent features typically diminish after repeated convolutions, ResNet-50 is employed to counteract this effect.Unlike conventional setups, ResNet-50 continuously incorporates previous latent features during the backward training process [40,42] thereby enhancing the global representation of features.
In addition, compared with ResNet-18 and ResNet-34, ResNet-50 not only improves the model's accuracy but also significantly reduces the number of parameters and computations.The reason we did not choose ResNet-101 or ResNet-152 was because they require more computer memory.In the field of feature extraction, DenseNet [43] and MobileNet [44] have also produced impressive results.DenseNet directly merges feature maps from different layers to achieve feature reuse and improve efficiency, which is also the main difference from ResNets.However, the inherent disadvantage of DenseNet is that it consumes a lot of computer memory and cannot handle more complex images.In addition, the accuracy of MobileNet v3 large may decrease when dealing with complex scenarios, and the design of MobileNet v3 small is relatively simple, making it difficult to apply in complex scenarios.In summary, we chose ResNet-50 to extract the depth features of the image.
Consequently, we crop the multi-view image of the scene to 224 × 224 and input the cropped image into ResNet-50 to extract useful features, and the output is a feature vector denoted as r = [1 × 1 × 256].This vector is then fed into the color MLP to accomplish appearance embedding.The convolution results of each image input to ResNet-50, known as ImageNet are detailed in Table 2, and a bottleneck in ResNet-50 is illustrated in Figure 3.
We assessed the surface reconstruction performance and view synthesis performance of NeuS and NeuS with embedded appearance features on the BlendedMVS dataset.As shown in Figures 4 and 5 and Tables 3 and 4. We assessed the performance of surface reconstruction using the distance metric.The chamfer distance is illustrated in Section 5.1.2.And the view synthesis performance was evaluated by PSNR/SSIM (higher is better) and LPIPS (lower is better) is illustrated in Section 5.1.2.We assessed the surface reconstruction performance and view synthesis performance of NeuS and NeuS with embedded appearance features on the BlendedMVS dataset.As shown in Figures 4 and 5 and Tables 3 and 4. We assessed the performance of surface reconstruction using the distance metric.The chamfer distance is illustrated in Section 5.1.2.And the view synthesis performance was evaluated by PSNR/SSIM (higher is better) and LPIPS (lower is better) is illustrated in Section 5.1.2.ResNet-50 introduces a "Bottleneck" structure in the residual structure to reduce the number of parameters (multiple small-size convolutions replace a large-size convolution).This Bottleneck layer structure first goes through a 1 × 1 convolutional kernel, then a 3 × 3 convolutional kernel, and finally through another 1 × 1 convolutional kernel.The 256dimensional input passes through a 1 × 1 × 64 convolutional layer, followed by a 3 × 3 × 64 convolutional layer, and finally through a 1 × 1 × 256 convolutional layer.Each convolutional layer undergoes ReLU activation, resulting in a total parameter count of 256 × 1 × 1 × 64 + 64 × 3 × 3 × 64 + 64 × 1 × 1 × 256 = 69,632.
We assessed the surface reconstruction performance and view synthesis performance of NeuS and NeuS with embedded appearance features on the BlendedMVS dataset.As shown in Figures 4 and 5 and Tables 3 and 4. We assessed the performance of surface reconstruction using the distance metric.The chamfer distance is illustrated in Section 5.1.2.And the view synthesis performance was evaluated by PSNR/SSIM (higher is better) and LPIPS (lower is better) is illustrated in Section 5.1.2.
Volume Rendering Interpolation and Color Weight Regularization
To eliminate ε sample caused by the sampling operation mentioned in Section 3.3, first, identify two neighboring sampling points near the surface.Beginning at the camera position denoted as o,we move along the ray's direction v, and their SDF values satisfy The initial point of intersection between the ray and the surface, denoted as P(t * ), is approximated through linear interpolation as p( t * ): Then, we incorporate the point set p( t * ) into the initial point set P(t i ), resulting in a new point set P = p( t * ) ∪ P(t i ).This combined set P is utilized to generate the final volume rendering color: where w(t * ) represents the weight of P(t i ), c(t i ) represents the pixel value of P(t i ).w( t * ) represents the weight of p( t * ), c( t * ) represents the pixel value of P(t * ), and n denotes the number of points.Then, the color bias becomes Following interpolation, we obtain ε interp , signifying the bias introduced by linear interpolation.Importantly, ε interp is at least two orders of magnitude smaller than ε sample .
Meanwhile, we also alleviate the weight bias to regularize the weight distribution: L weight is utilized to eliminate anomalous weight distributions, specifically those located far from the surface yet exhibiting substantial weight values.This indirectly promotes the convergence of the weight distribution toward the surface.Theoretically, as the weight approaches δ(t − t * ), a delta distribution centered at t * , ε weight− f inal will tend towards 0.
Geometric Constraints
In the scenario of geometric ambiguity outlined in Section 3.4, we introduce photometric consistency loss and point constraints to illustrate the 3D representation of the supervised Signed Distance Function (SDF).
Photometric Consistency Constraints
For a small area S on the surface, its small pixel patch on the projection of the source view is q.The patches associated with S are expected to exhibit geometric consistency across various source views except for occlusion instances.We use the camera coordinate of the reference image pixel I r to represent S, as follows: We introduce a homography matrix H to local the pixel value of a point x i in the reference image.And corresponding to the points x in other images, we have where K r and K i are the internal calibration matrices, R r and R i are rotation matrices, t i and t r are translation vectors of the source view I i and other views I r respectively.
To measure the photometric consistency of different views, we introduce normalization cross-correlation between the reference image and source view where Cov denotes covariance and Var denotes variance, we use the rendered image as the reference image.We calculate Normalized Cross-Correlation (NCC) scores between the sampled patches and their corresponding patches in all source images.To address occlusions, we identify the top four computed NCC scores for each sampled patch [45] and leverage them to calculate the photometric consistency loss for the respective view:
.2. Point Constraints
In the previous data-processing process, acquiring images with known camera poses was imperative.The position information of these images is estimated using Structure from Motion (SFM).SFM is also responsible for reconstructing sparse 3D points, and while these points unavoidably contain noise, they maintain a certain level of accuracy.Therefore, we represent these sparse 3D points P 0 to directly supervise f (P): where N represents the number of points contained within P k .SFM reconstructs these points as P k .We assume that any point P within P k is on the surface and its corresponding SDF value is denoted as f (P).
Point Cloud Coarse Sampling
In most scenarios, the majority of a scene is characterized by open space.In consideration of this, our objective is to strategically identify the broad 3D regions of interest before engaging in the reconstruction of intricate details and view-dependent effects, which typically demand substantial computational resources.This approach allows for a sig-nificant reduction in the volume of points queried along each ray during the subsequent fine-stage processing.
In the handling of input datasets, conventional methods involve manual filtration to eliminate irrelevant point clouds.In contrast, DVGO [17] accomplishes the automatic selection of the point cloud of interest, representing a notable advancement in streamlining this process.To determine the bounding box, rays emitted by each camera intersect with the nearest and farthest points in the scene, as shown in Figure 6.
Point Cloud Coarse Sampling
In most scenarios, the majority of a scene is characterized by open space.In consideration of this, our objective is to strategically identify the broad 3D regions of interest before engaging in the reconstruction of intricate details and view-dependent effects, which typically demand substantial computational resources.This approach allows for a significant reduction in the volume of points queried along each ray during the subsequent fine-stage processing.
In the handling of input datasets, conventional methods involve manual filtration to eliminate irrelevant point clouds.In contrast, DVGO [17] accomplishes the automatic selection of the point cloud of interest, representing a notable advancement in streamlining this process.To determine the bounding box, rays emitted by each camera intersect with the nearest and farthest points in the scene, as shown in Figure 6.Due to the limitations and excessive size of the 3D point cloud regions selected by DVGO, precise localization of fine scene structures is not achieved.Therefore, we introduce a novel point cloud automatic filtering method.Leveraging camera pose information, we identify the point cloud center and compute the average distance from the center to the camera position.Using this average distance as the radius, we select a point cloud region of interest encompassing 360 • around the center.The radius r defining the surrounding area is determined based on the camera's capture mode, whether it is capturing a panoramic view or covering a distant scene, as shown in Figure 7. Due to the limitations and excessive size of the 3D point cloud regions selected by DVGO, precise localization of fine scene structures is not achieved.Therefore, we introduce a novel point cloud automatic filtering method.Leveraging camera pose information, we identify the point cloud center and compute the average distance from the center to the camera position.Using this average distance as the radius, we select a point cloud region of interest encompassing 360° around the center.The radius r defining the surrounding area is determined based on the camera's capture mode, whether it is capturing a panoramic view or covering a distant scene, as shown in Figure 7.
Loss Function
The total loss is characterized as the weighted summation of individual losses:
Dataset
We used the BlendedMVS dataset and the DTU dataset to verify the effectiveness of our method.This dataset encompasses scenes with a focus on large-scale scenes, as well as scenes featuring diverse categories of objects.The images in the dataset have a resolution of 768 × 576, and the number of views varies from 56 to 333.The evaluation of the reconstructed surfaces on the BlendedMVS dataset was conducted using chamfer distances in 3D space.Additionally, for the DTU dataset, we present the visual impact of the reconstructed surfaces.
Evaluation Metrics
We assessed the performance of surface reconstruction using a distance metric.The We used the BlendedMVS dataset and the DTU dataset to verify the effectiveness of our method.This dataset encompasses scenes with a focus on large-scale scenes, as well as scenes featuring diverse categories of objects.The images in the dataset have a resolution of 768 × 576, and the number of views varies from 56 to 333.The evaluation of the reconstructed surfaces on the BlendedMVS dataset was conducted using chamfer distances in 3D space.Additionally, for the DTU dataset, we present the visual impact of the reconstructed surfaces.
Evaluation Metrics
We assessed the performance of surface reconstruction using a distance metric.The chamfer distance in 3D space is mainly used for reconstruction work and is defined as follows: In the provided formula, S 1 denotes the ground truth sampling point, and S 2 represents the sampling point on the reconstructed surface.The evaluation metric for reconstruction accuracy (Acc) is defined as the chamfer distance from S 1 to S 2 .Conversely, the evaluation metric for reconstruction completeness (Comp) is determined by the charmful distance from S 2 to S 1 .The overall score is then computed as the mean of accuracy and completeness.A smaller distance implies a superior reconstruction effect.
Additionally, we assessed the performance of view synthesis akin to NeRF using image quality assessment metrics, including the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS).
Baselines
For a more comprehensive evaluation of our method, we conducted a comparative analysis by benchmarking it against the state-of-the-art learning-based method NeuS and the traditional multi-view reconstruction method COLMAP.This comparison is based on both the reconstruction effect and the evaluation indicators of the model.
Implementation Details
Similar to [12], the SDF network and the color network were modeled by an eight-layer MLP and a four-layer MLP with 256 hidden units, respectively.Assuming that the target reconstruction area was confined within a sphere, we employed a batch size of 2048 rays during the sampling process.For each ray, we first sampled 32 points uniformly and then sampled 96 points hierarchically.The model was trained on a single NVIDIA GeForce RTX 4090 GPU, the learning rate was set to 5 × 10 −4 , and the training process spanned 50,000 iterations, taking approximately 4 h to fulfill memory constraints.After completing the network training, a mesh can be generated from the SDF within a predefined bounding box.This was achieved using the Marching Cubes algorithm [25] with a specified volume size of 512.
Experimental Results
First, we used the reconstruction methods mentioned in Section 5.1.3to test two indoor scenes in the DTU data set and two small scenes in the BlendedMVS data set and compared the reconstruction results; as shown in Figure 8, the test results show that our method is largely better than baselines.
Given the effectiveness of our method in reconstructing small scenes, we proceeded to apply the approach to larger scenes characterized by low visibility and sparse feature views typical of remote sensing scenes in the BlendedMVS dataset.The resulting reconstruction outcomes were compared and analyzed; qualitative surface reconstruction results are depicted in Figure 9 and quantitative surface reconstruction results are depicted in Table 5. Notably, the surfaces reconstructed by COLMAP exhibited noticeable noise, while NeuS, relying solely on color constraints, displayed severe deformations, distortions, and holes in the geometric surface structure.In contrast, our method excels in reconstructing accurate geometric structures while effectively eliminating smooth surface noise.For instance, it successfully reconstructs the geometry of scene 7 with low visibility and restores depth variations in scene 8.
Experimental Results
First, we used the reconstruction methods mentioned in Section 5.1.3to test two indoor scenes in the DTU data set and two small scenes in the BlendedMVS data set and compared the reconstruction results; as shown in Figure 8, the test results show that our method is largely better than baselines.Given the effectiveness of our method in reconstructing small scenes, we proceeded to apply the approach to larger scenes characterized by low visibility and sparse feature views typical of remote sensing scenes in the BlendedMVS dataset.The resulting reconstruction outcomes were compared and analyzed; qualitative surface reconstruction results are depicted in Figure 9 and quantitative surface reconstruction results are depicted in Table 5. Notably, the surfaces reconstructed by COLMAP exhibited noticeable noise, while NeuS, relying solely on color constraints, displayed severe deformations, distortions, and holes in the geometric surface structure.In contrast, our method excels in reconstructing accurate geometric structures while effectively eliminating smooth surface noise.For instance, it successfully reconstructs the geometry of scene 7 with low visibility and restores depth variations in scene 8.We tested three methods using 14 challenging scenes from the Blended MVS dataset.The original picture of the scene is given in Appendix A. All three methods were performed without mask supervision, and the experimental setup of NeuS [24] We tested three methods using 14 challenging scenes from the Blended MVS dataset.The original picture of the scene is given in Appendix A. All three methods were performed without mask supervision, and the experimental setup of NeuS [24] was shown in the original paper.The details of the Res-NeuS implementation are shown in Section 5.1.4.We used the point cloud coarse sampling strategy mentioned in Section 4.4 to select the bounding box, which greatly saved the time of manually obtaining the bounding box, to facilitate the subsequent efficient reconstruction work.The bounding box applied to the different methods is the same for each scene processed.And the surface produced by COLMAP is trimmed with a trimming value of 0.
The quantitative results of the reconstruction integrity of COLMAP in scene 6 and scene 7 were better than our methods.But their visualizations are not very good; a reasonable explanation for this contradiction is that there were plenty of redundant surfaces located on the back of the visible surfaces in all cases, as shown in Figure 9.The redundant surfaces severely reduced the Comp value for scene 6 and scene 7. Except for scene 6 and scene 7, the visualization surface and Comp values of our method are better than those of NeuS and COLMAP.And the Comp value of our method is about 0.6 times that of COLMAP and 0.4 times that of NeuS.
Ablation Study
For the ablation experiments, we utilized the dome church data from the BlendedMVS dataset, with NeuS serving as the baseline.We sequentially incorporated additional modules, and qualitative surface reconstruction results are illustrated in Figure 10.In the baseline, the geometric structure is distorted, the surface exhibits significant noise, and the reconstruction area is incomplete.Model A achieves coverage of the entire area but still contends with substantial surface noise.Model B not only completes the reconstruction of the entire area but also notably enhances the geometric structure.Model C further refines the geometric structure, with errors comparable to Model B. In contrast, the Full model demonstrates outstanding results by accurately reconstructing geometric structures and reducing surface noise.Results of the ablation study are reported in Table 6.
In summary, the appearance embedding module appears to be more inclined toward capturing scene details, geometric constraints contribute to improving the quality of geometric reconstruction to a certain extent, and weight constraints effectively enhance model accuracy.
Figure 1 .
Figure 1.Overview of Res-NeuS.We incorporate ResNet-50[40] into the network architectures of previous neural implicit surface learning methods.Subsequently, we interpolate the sampled points, estimate the color for all points, and optimize the color weights.Finally, we introduce the SDF loss derived from sparse 3D points and the photometric consistency loss from multi-view stereo to supervise the SDF network explicitly, additionally efficiently implementing coarse geometric sampling.
Figure 1 .
Figure 1.Overview of Res-NeuS.We incorporate ResNet-50[40] into the network architectures of previous neural implicit surface learning methods.Subsequently, we interpolate the sampled points, estimate the color for all points, and optimize the color weights.Finally, we introduce the SDF loss derived from sparse 3D points and the photometric consistency loss from multi-view stereo to supervise the SDF network explicitly, additionally efficiently implementing coarse geometric sampling.
Figure 2 .
Figure 2. Integration of appearance embedding and neural implicit surface rendering.
Figure 2 .
Figure 2. Integration of appearance embedding and neural implicit surface rendering.
Figure 4 .
Figure 4.An illustration of the performance of NeuS and NeuS with appearance embedding on BlendedMVS.In comparison to NeuS, only embedding appearance demonstrates a substantial reduction in surface noise and a marked improvement in reconstruction accuracy.
Figure 4 .
Figure 4.An illustration of the performance of NeuS and NeuS with appearance embedding on BlendedMVS.In comparison to NeuS, only embedding appearance demonstrates a substantial reduction in surface noise and a marked improvement in reconstruction accuracy.
Figure 5 .
Figure 5.An illustration of rendering results; appearance embedding significantly enhances NeuS's performance in view synthesis.
Figure 5 .
Figure 5.An illustration of rendering results; appearance embedding significantly enhances NeuS's performance in view synthesis.
Figure 8 .
Figure 8. Qualitative surface reconstruction results for the DTU dataset and BlendedMVS dataset.
was shown in the original paper.The details of the Res-NeuS implementation are shown in Section 5.1.4.We used the point cloud coarse sampling strategy mentioned in Section 4.4 to select the bounding box, which greatly saved the time of manually obtaining the bounding box, to facilitate the subsequent efficient reconstruction work.The bounding box applied to the different methods is the same for each scene processed.And the surface produced by
Figure A1 .
Figure A1.The scenes in the Blended MVS dataset that we used in our work, from left to right and from top to bottom, are scene 3 to scene 14, respectively.
Table 1 .
Summary of multi-view 3D reconstruction methods.
Table 3 .
Quantitative results for surface reconstruction of the sculpture on BlendedMVS.
Table 4 .
Quantitative results for the neural rendering of the sculpture on BlendedMVS.
Table 3 .
Quantitative results for surface reconstruction of the sculpture on BlendedMVS.
Table 4 .
Quantitative results for the neural rendering of the sculpture on BlendedMVS.
Table 5 .
Quantitative results for BlendedMVS scenes.The evaluation metric for reconstruction completeness (Comp) is being displayed. | 8,958.6 | 2024-01-29T00:00:00.000 | [
"Computer Science"
] |
Signs of Composite Higgs Pair Production at Next-to-Leading Order
In composite Higgs models the Higgs boson arises as a pseudo-Goldstone boson from a strongly-interacting sector. Fermion mass generation is possible through partial compositeness accompanied by the appearance of new heavy fermionic resonances. The Higgs couplings to the Standard Model (SM) particles and between the Higgs bosons themselves are modified with respect to the SM. Higgs pair production is sensitive to the trilinear Higgs self-coupling but also to anomalous couplings like the novel 2-Higgs-2-fermion coupling emerging in composite Higgs models. The QCD corrections to SM Higgs boson pair production are known to be large. In this paper we compute, in the limit of heavy loop particle masses, the next-to-leading order (NLO) QCD corrections to Higgs pair production in composite Higgs models without and with new heavy fermions. The relative QCD corrections are found to be almost insensitive both to the compositeness of the Higgs boson and to the details of the heavy fermion spectrum, since the leading order cross section dominantly factorizes. With the obtained results we investigate the question if, taking into account Higgs coupling constraints, new physics could first be seen in Higgs pair production. We find this to be the case in the high-luminosity option of the LHC for composite Higgs models with heavy fermions. We also investigate the invariant mass distributions at NLO QCD. While they are sensitive to the Higgs non-linearities and hence anomalous couplings, the influence of the heavy fermions is much less pronounced.
Introduction
The LHC Higgs data of Run 1 suggest that the scalar particle observed by the LHC experiments ATLAS and CMS in 2012 [1,2] is compatible with the Higgs boson of the Standard Model (SM). The non-vanishing vacuum expectation value (VEV) v of the SU (2) Higgs doublet field φ in the ground state is crucial for the mechanism of electroweak symmetry breaking (EWSB) [3]. It it is induced by the Higgs potential Introducing the Higgs field in the unitary gauge, φ = (0, In the SM the trilinear and quartic Higgs self-couplings are uniquely determined in terms of the Higgs boson mass M H = √ 2λv, with v ≈ 246 GeV. The experimental verification of the form of the Higgs potential through the measurement of the Higgs self-couplings is the final step in the program aimed to test the mechanism of EWSB. The Higgs self-couplings are accessible in multi-Higgs production processes [4][5][6][7]. While previous studies [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] showed that the probe of the trilinear Higgs self-coupling in Higgs pair production should be possible at the high-luminosity LHC, although it is experimentally very challenging, the quartic Higgs self-interaction is out of reach. The cross section of triple Higgs production giving access to this coupling suffers from too low signal rates fighting against a large background [5,7,27]. The relations in Eq. (3) do not hold in models beyond the SM (BSM), and this would manifest itself in the Higgs pair production process. In general, however, new physics (NP) not only affects the value of the Higgs self-coupling, but also other couplings involved in the Higgs pair production process. 1 An approach that allows to smoothly depart from the SM in a consistent and model-independent way is offered by the effective field theory (EFT) framework based on higher dimensional operators which are added to the SM Lagrangian with coefficients that are suppressed by the typical scale Λ where NP becomes relevant [29][30][31][32][33]. These higher dimensional operators modify the couplings involved in Higgs pair production, such as the trilinear Higgs self-coupling and the Higgs Yukawa couplings. Additionally they give rise to novel couplings, like a 2-Higgs-2-fermion coupling, that can have a significant effect on the process. While the trilinear Higgs self-coupling has not been delimited experimentally yet 2 , the Higgs couplings to the SM particles have been constrained by the LHC data and in particular the Higgs couplings to the massive gauge bosons. An interesting question to ask is, while taking into account the information on the Higgs properties gathered at the LHC, if it could be that despite the Higgs boson behaving SM-like, we see NP emerging in Higgs pair production? And if so, could it even be, that we see NP before having any other direct hints e.g. from new resonances or indirect hints from e.g. Higgs coupling measurements?
Previous works have applied the EFT approach to investigate BSM effects in Higgs pair production. 3 A study of the effects of genuine dimension-six operators in Higgs pair production can be found in Ref. [65]. Anomalous couplings in Higgs pair production have been investigated in [66][67][68][69]. In [70][71][72] the EFT was applied to investigate the prospects of probing the trilinear Higgs selfcoupling at the LHC. Reference [73] on the other hand addressed the question on the range of validity of the EFT approach for Higgs pair production by using the universal extra dimension model.
The dominant Higgs pair production process at the LHC is gluon fusion, gg → HH, which is mediated by loops of heavy fermions. It can be modified due to NP via deviations in the trilinear Higgs self-coupling, in the Higgs to fermion couplings, via new couplings such as a direct coupling of two fermions to two Higgs bosons, new particles like e.g. heavy quark partners in the loop, or additional (virtual) Higgs bosons, splitting into two lighter final state Higgs bosons. The purpose of this paper is to address the question of whether it will be possible to see deviations from the SM for the first time in non-resonant Higgs pair production processes by considering explicit models. It has been found that large deviations from SM Higgs pair production can arise in composite Higgs models, which is mainly due to the novel 2-Higgs-2-fermion coupling [36,74]. In this paper, we will hence focus on this class of models. We assume that no deviations with respect to the SM are seen in any of the LHC Higgs coupling analyses, i.e. that the deviations in the standard Higgs couplings due to NP are below the expected experimental sensitivity, for the case of the LHC highenergy Run 2 and for the high-luminosity option of the LHC. Additionally, we assume that no NP will be observed in direct searches or indirect measurements. The prospects of NP emerging from composite Higgs models for the first time in non-resonant Higgs pair production from gluon fusion are analyzed under these conditions. Our analysis is complementary to previous works [46,75], which focused on deviations in Higgs pair production due to modifications in the trilinear Higgs coupling. In Ref. [75] the question is investigated on how well the trilinear Higgs coupling needs to be measured in various scenarios to be able to probe NP. The main focus of Ref. [46] is on how to combine a deviation in the trilinear Higgs coupling with other Higgs coupling measurements to support certain BSM extensions.
Gluon fusion into Higgs pairs exhibits large QCD corrections. In Ref. [4], the next-to-leading order (NLO) QCD corrections were computed in the large top mass approximation and found to be of O(90%) at √ s = 14 TeV for a Higgs boson mass of 125 GeV. The effects of finite top quark masses have been analyzed in [37,[76][77][78][79][80]. While the m t → ∞ approximation exhibits uncertainties of order 20% on the leading order (LO) cross section at √ s = 14 TeV for a light Higgs boson [74,81,82] and badly fails to reproduce the differential distributions [9], the uncertainty on the K-factor, i.e. the ratio between the loop-corrected and the LO cross section, is much smaller due to the fact that in the dominant soft and collinear contributions the full LO cross section can be factored out. The next-to-next-to-leading order (NNLO) corrections have been provided by [83][84][85] in the heavy top mass limit. The finite top mass effects have been estimated to be of about 10% at NLO and ∼ 5% at NNLO [86]. Soft gluon resummation at next-to-leading logarithmic order has been performed in [87] and has been extended recently to the next-to-next-to-leading logarithmic level in [88]. First results towards a fully differential NLO calculation have been provided in [78,80]. For a precise determination of the accessibility of BSM effects in gluon fusion to a Higgs pair, the NLO QCD corrections are essential and need to be computed in the context of these models. They have been provided in the large loop particle mass limit for the singlet-extended SM [58], for the 2-Higgs-doublet model [48] and for the MSSM [4,63]. 4 In the same limit, the NLO QCD corrections including dimension-6 operators have been computed in [89]. In this work, we calculate for the first time the NLO QCD corrections in the large loop particle mass limit for models with vector-like fermions such as composite Higgs models.
The paper is organized as follows. In Section 2 we briefly introduce composite Higgs models. In section 3 we present the NLO QCD corrections to the gluon fusion process in the framework of composite Higgs models including vector-like fermions. In the subsequent sections we analyze whether a possible deviation from the SM signal could be seen or not at the LHC Run 2 with an integrated luminosity of 300 fb −1 and/or the high-luminosity LHC with an integrated luminosity of 3000 fb −1 for different models: in section 4 for the composite Higgs models MCHM4 and MCHM5, and in section 5 for a composite Higgs model with one multiplet of fermionic resonances below the cut-off. In section 6 we discuss the invariant mass distributions with and without the inclusion of the new fermions. We conclude in section 7.
Composite Higgs Models
In composite Higgs models the Higgs boson arises as a pseudo-Nambu Goldstone boson of a strongly interacting sector [90][91][92][93][94][95][96]. A global symmetry is broken at the scale f to a subgroup containing at least the SM gauge group. The new strongly-interacting sector can be characterized by a mass scale m ρ and a coupling g ρ , with f = m ρ /g ρ . An effective low-energy description of such models is provided by the Strongly Interacting Light Higgs (SILH) Lagrangian [97], which, in addition to the SM Lagrangian, contains higher dimensional operators including the SM Higgs doublet φ to account for the composite nature of the Higgs boson. Listing only the operators relevant for Higgs pair production by gluon fusion, the SILH Lagrangian reads 5 with the Yukawa couplings y q = √ 2m q /v (q = u, d), where m q denotes the quark mass, λ the quartic Higgs coupling and α s = g 2 s /(4π) the strong coupling constant in terms of the SU (3) c gauge coupling g s . 6 Here Q L denotes the left-handed quark doublet. The effective Lagrangian accounts for several effects that can occur in Higgs pair production via gluon fusion in composite Higgs models: a shift in the trilinear Higgs self-coupling and in the Higgs couplings to the fermions, a novel coupling of two fermions to two Higgs bosons and additional new fermions in the loops. The latter effect is encoded in the effective operator with the gluon field strength tensors G µν coupling directly to the Higgs doublet φ. While the SILH Lagrangian Eq. (4) is a valid description for small values of ξ = (v/f ) 2 , larger values require a resummation of the series in ξ. This is provided by 4 Reference [63] also shows how the provided results can be adapted to the Next-to-Minimal Supersymmetric extension of the SM. 5 We have not included the chromomagnetic dipole moment operator which modifies the interactions between the gluons, the top quark and the Higgs boson and can be expected to be of moderate size [98]. 6 The relation between the coefficients c and the coefficients c in Eq. (2.1) of Ref. [89] is cx = cxξ (x = H, 6), cu = cuξ and cg = α2/(16π)y 2 t /g 2 ρ cgξ with ξ = v 2 /f 2 and α2 = √ 2GF m 2 W /π in terms of the Fermi constant GF and the W boson mass mW .
MCHM4
MCHM5 Table 1 we report the modifications of the Higgs couplings to the SM particles with respect to the corresponding SM couplings in the SILH set-up and in the MCHM4 and MCHM5. The last two lines list the novel couplings not present in the SM, i.e. the 2-Higgs-2-fermion coupling and the effective single and double Higgs couplings to a gluon pair, as defined in the Feynman rules derived from the SILH Lagrangian, where k 1,2 denote the incoming momenta of the two gluons g a µ (k 1 ) and g b ν (k 2 ). The effective gluon couplings are not present in MCHM4 and MCHM5.
In composite Higgs models fermion mass generation can be achieved by the principle of partial compositeness [101]. The SM fermions are elementary particles that couple linearly to heavy states of the strong sector with equal quantum numbers under the SM gauge group. In particular the top quark can be largely composite. But also the bottom quark can have a sizeable coupling to heavy bottom partners. For gluon fusion this not only means that new bottom and top partners are running in the loops but mixing effects also induce further changes in the top-and bottom-Higgs Yukawa couplings. In addition to the MCHM4 and 5 models involving only the pure non-linearities of the Higgs boson in the Higgs couplings, we consider a model with heavy top and bottom partners based on the minimal SO(5) × U (1) X /SO(4) × U (1) X symmetry breaking pattern. The additional U (1) X is introduced to guarantee the correct fermion charges. The new fermions transform in the antisymmetric representation 10 of SO(5) in this model MCHM10, given by with the electric charge-2/3 fermions u, u 1 , t 4 and T 4 , the fermions d, d 1 and d 4 with charge −1/3, and the χ, χ 1 and χ 4 with charge 5/3. The coset SO(5)/SO(4) leads to four Goldstone bosons, among which three provide the longitudinal modes of the massive vector bosons W ± and Z, and the remaining one is the Higgs boson. The four Goldstone bosons can be parameterized in terms of the field with the generators Tâ (â = 1, ..., 4) of the coset SO(5)/SO(4) The generators of the SU (2) L,R in the fundamental representation read (a, b, c = 1, 2, 3, i, j = 1, ..., 5), The non-linear σ-model describing the effective low-energy physics of the strong sector is given by the Lagrangian with the covariant derivative in terms of the SU (2) L and U (1) Y gauge fields W a µ and B µ , respectively, with their corresponding couplings g and g . The bilinear terms in the fermion fields lead to mass matrices for the 2/3, −1/3 and 5/3 charged fermions, when the Higgs field is shifted by its VEV H , H = H + h. The mass matrices can be diagonalized by means of a bi-unitary transformation. The 2-fermion couplings to one and two Higgs bosons are obtained by expanding the mass matrices in the interaction eigenstates up to first, respectively, second order in the Higgs field, and subsequent transformation into the mass eigenstate basis. The mass matrices and the coupling matrices of one Higgs boson to two bottom-like and top-like states can be found in Ref. [102]. In the Appendix A we give the coupling matrices for the 2-Higgs-2-fermion couplings and, for completeness, repeat the matrices given in Ref. [102].
Next-to-leading Order QCD Corrections to Higgs Pair Production in Composite Higgs Models
The NLO QCD corrections to Higgs pair production in the SM have been computed in Ref. [4] by applying the heavy top approximation, in which the heavy fermion loops are replaced by effective vertices of gluons to Higgs bosons. These can be obtained by means of the low-energy theorem (LET) [103,104]. The Higgs field is treated here as a background field, and the field-dependent mass of each heavy particle is taken into account in the gluon self-interactions at higher orders.
The LET provides the zeroth order in an expansion in small external momenta. Since in Higgs pair production the requirements for such an expansion are not fulfilled sufficiently reliably, it fails to give accurate results for the cross section at LO [81]. In the context of composite Higgs models, the discrepancy between the LO cross section with full top quark mass dependence and the LO cross section in the LET approximation is even worse [74]. For relative higher order corrections the LET approximation should, however, become better, if the LO order cross section is taken into account with full mass dependence. This is because the dominant corrections given by the soft and collinear gluon corrections factorize from the LO cross section generating a part independent of the masses of the heavy loop particles relative to the LO cross section. This was confirmed in Ref. [76] by including higher terms in the expansion of the cross section in small external momenta. Based on these findings, in this section we will give the NLO QCD corrections for Higgs pair production in composite Higgs models in the LET approach.
The expression of the LO gluon fusion into Higgs pairs in a composite Higgs model with heavy top partners has been given in [74]. It can be taken over here, by simply extending the sum to include also the bottom quark and its partners. We summarize here the most important features and refer to [74] for more details. The generic diagrams that contribute to the process at LO are depicted in Fig. 1. Besides the new 2-Higgs-2-fermion coupling f f hh the additional top and bottom partners in the loops have to be taken into account. These lead also to new box diagrams involving off-diagonal Yukawa couplings, with, respectively, the top and its heavy charge-2/3 partners or the bottom and its heavy partners of charge −1/3. The hadronic cross section is obtained by convolution with the parton distribution functions f g of the gluon in the proton, where s denotes the squared hadronic c.m. energy, µ F the factorization scale and in terms of the Higgs boson mass m h . The partonic LO cross section can be cast into the form with the strong coupling constant α s at the renormalization scale µ R . We have introduced the Mandelstam variableŝ in terms of the scattering angle θ in the partonic c.m. system with the invariant Higgs pair mass Q and the relative velocity The integration limits at cos θ = ±1 are given bŷ The form factors read The triangle and box form factors F ∆ , F , F ,5 , G and G ,5 can be found in the appendices of [74,105]. 7 The sum runs up to n t = 5 for the top quark and its charge-2/3 partners and up to n b = 4 in the bottom sector. The couplings are defined as and 7 The form factors F∆, F and G relate to those given in Ref. [82] for the SM case as where G hqq,ij and G hhqq,ij denote the (ith,jth) matrix elements of the coupling matrices in Eq. (58) of the appendix. The triangle factor C i,∆ reads in the MCHM10 as given in the MCHM5. In the SM and in the composite Higgs models MCHM4 and MCHM5 involving solely the Higgs non-linearities and no heavy fermionic resonances, no sum over heavy top and bottom partners contributes and only a sum over the top and bottom running in the loop has to be performed, i.e. n t = n b = 1, with m i = m j = m q and q = t, b, and hence also g hq i q j ,5 = 0 for SM, MCHM4 and MCHM5.
The Yukawa couplings read and for the 2-Higgs-2-fermion coupling we have while the Higgs self-coupling becomes The Feynman diagrams contributing to Higgs pair production at NLO QCD are shown in Fig. 2. The blob in the figure marks the effective vertices of gluons to Higgs boson(s). The first three Feynman diagrams show the virtual contributions. The remaining Feynman diagrams of Fig. 2 display the real corrections generically ordered by the initial states gg, gq and qq. At NLO the cross section is then given by σ NLO (pp → hh + X) = σ LO + ∆σ virt + ∆σ gg + ∆σ gq + ∆σ qq .
The individual contributions in Eq. (30) read with the Altarelli-Parisi splitting functions given by [106] P gg (z) = 6 and N F = 5 in our case. The real corrections ∆σ gg , ∆σ gq and ∆σ qq have straightforwardly been obtained from Ref. [4] by replacing the LO cross section of the SM with the LO cross section for composite Higgs models. The calculation of ∆σ virt is a bit more involved. While the first two diagrams factorize from the LO cross section and can hence directly be taken over from the SM, the third diagram in Fig. 2 does not factorize and needs to be recalculated for the composite Higgs case. The virtual coefficient C is then found to be with The first line in Eq. (37) corresponds to the NLO contribution from the first two diagrams in Fig. 2, while the second line corresponds to the NLO contribution from the third diagram of Fig. 2. The factor (g eff hgg ) 2 stems from the two effective Higgs-gluon-gluon vertices in diagram 3 of Fig. 2. This vertex is obtained by integrating out all heavy loop particles in the loop-induced Higgs coupling to gluons defined in Eq. (6) with g hgg ≡ g eff hgg and The first term is the sum over the normalized top quark and top partner couplings and the second term the sum over the normalized bottom partner couplings to the Higgs boson, excluding consistently the light bottom quark contribution from the loop. The composite Higgs cross sections for MCHM4, MCHM5 and for the composite Higgs model with heavy top and bottom partners, including the NLO corrections have been implemented in HPAIR [107]. In order to exemplify the impact of the NLO QCD corrections, we consider the simple case with the pure Higgs non-linearities only and the fermions transforming in the fundamental representation, i.e. the benchmark model MCHM5, see Table 1. The coupling g eff hgg then reduces to g MCHM5 hgg = (1 − 2ξ)/ √ 1 − ξ and the remaining couplings are given in Eqs. (26)- (29). We define the K-factors for the total cross section and the individual contributions as The cross section at LO is computed with the full quark mass dependences. As the NLO cross section in the LET approximation only includes top quark contributions 8 The renormalization and factorization scales are set to µ R = µ F = m hh /2, where m hh denotes the invariant Higgs pair mass. contributions. As can be inferred from the plot, the real and virtual corrections of the gg initial state make up the bulk of the QCD corrections. The qg and the qq initiated real radiation diagrams only lead to a small correction. The K-factor is almost independent of ξ. In the real corrections, the Born cross section, which shows the only dependence on ξ, almost completely drops out numerically. For the virtual contributions some dependence on ξ may be expected. The virtual correction due to the constant term in C, i.e. the first line in Eq. (37) does not develop any dependence on ξ, however, as it factorizes from the LO cross section. The dependence of ξ can only emerge from the second line in Eq. (37), which, however, is numerically suppressed. This is already the case in the SM, where the corresponding term contributes less than 3% to ∆σ virt . This result also holds true for the case were the heavy quark partners are explicitly included. In composite Higgs models, the NLO QCD corrections to Higgs pair production can hence well be approximated by multiplying the full LO cross section of the composite Higgs model under consideration with the SM K-factor. Figure 3 can also be obtained by using the results of Ref. [89]. Note however, that the effects of heavy top and bottom partners in the effective field theory computation of Ref. [89] have to be added to the top quark contribution, encoded into the Wilson coefficients in front of the operators hG µν G µν and hhG µν G µν .
Numerical Analysis of New Physics Effects in Higgs Pair Production via Gluon Fusion
Having derived the NLO QCD corrections, we can now turn to the analysis of NP effects in Higgs pair production. We assume that no NP is found before Higgs pair production becomes accessible. This means that we require deviations in the Higgs boson couplings with respect to the SM to be smaller than the projected sensitivities of the coupling measurements at an integrated luminosity of 300 fb −1 and 3000 fb −1 , respectively. For the projected sensitivities we take the numbers reported in Ref. [109]. Similar numbers can be found in Refs. [110]. In our analysis we focus on the most promising final states, given by bbγγ and bbτ + τ − [8][9][10]13].
We call Higgs pair production to be sensitive to NP if the difference between the number of signal events S in the considered NP model and the corresponding number S SM in the SM exceeds a minimum of 3 statistical standard deviations, i.e.
with β = 3 for a 3σ deviation. The signal events are obtained as where BR denotes the branching ratio into the respective final states, L the integrated luminosity and A the acceptance due to cuts applied on the cross section. The acceptances have been extracted from Ref. [13] for the bbγγ and bbτ + τ − final states. The acceptance for the BSM signal only changes slightly, as we explicitly checked.
In specific models, the correlations of the couplings will lead to stronger bounds on the parameters. In particular in the MCHM4 and MCHM5 as introduced in section 2, the only new parameter is ξ. The value of ξ can hence strongly be restricted by Higgs coupling measurements [111]. Based on these estimates, we give in Table 2 the maximal values for the cross section times branching ratio. In the fourth and sixth columns we report whether the process within MCHM4, respectively, MCHM5 can be distinguished from the SM cross section by more than 3σ according to the criteria given in Eq. (42) for β = 3. In the check of Eq. (42) we took into account the slight change in the acceptance of the signal rate for the composite Higgs models. Due to the coupling modifications and the new diagram from the 2-Higgs-2-fermion coupling the applied cuts in the analysis of Ref. [13] affect the cross section in a slightly different way.
The table shows, that with the projected precision on ξ at high luminosities Higgs pair production in both MCHM4 and MCHM5 leads to cross sections too close to the SM value to be distinguishable from the SM case. Although with the present bounds on ξ Higgs pair production in MCHM5 differs by more than 3σ from the SM prediction, the corresponding cross section is too small to be measurable, so that first signs of NP through this process are precluded.
Numerical Analysis for MCHM10
We consider the MCHM10 with one multiplet of fermionic resonances below the cut-off. In this model, with more than one parameter determining the Higgs coupling modifications, there is more freedom and a larger allowed parameter space (see Ref. [102] for a thorough analysis). This implies, Table 2: Values of the cross section times branching ratio in the MCHM4 and MCHM5 for the maximal allowed values of ξ at 95% C.L.
[112] and for the projected values at L = 300 fb −1 and L = 3000 fb −1 of Ref. [109]. The fourth and sixth columns decide whether the Higgs production cross section will develop a deviation to the SM Higgs pair production cross section of more than 3σ according to the criteria of Eq. (42).
that the sensitivity on the Higgs couplings is less constrained. The numbers of the projected sensitivities are taken from Table I in Ref. [109]. Additionally, we need to take into account the bounds from the direct searches for new fermions. Currently, exotic new fermions with charge 5/3 are excluded up to masses of m χ ≤ 840 GeV [113], bottom partners up to masses of m B ≤ 900 GeV [114] and top partners with masses of m T ≤ 950 GeV [115]. Note that the latter two limits on the masses depend on the branching ratios of the bottom and top partner, respectively. These limits are based on pair production of the new heavy fermions. First studies for single production of a new vector-like fermion were performed in Refs. [116] and can potentially be more important at large energies [117] but are more model-dependent. Due to this model dependence it is difficult to estimate the LHC reach on single production for our case. Hence we will only use the estimated reach on new vector-like fermions in pair production. In Refs. [118,119] the potential reach of the LHC for charged-2/3 fermions, depending on their branching ratios is estimated. Following [119] we use the reach m T 1.3 TeV for L = 300 fb −1 and m T 1.5 TeV for L = 3000 fb −1 . The potential reach for bottom partners is m B 1 TeV for L = 300 fb −1 and m B 1.5 TeV for L = 3000 fb −1 [120]. We estimate the additional sensitivity for the reach of exotic new fermions by multiplying the excluded cross section at √ s = 8 TeV with [121] r = σ BKG (14 TeV) σ BKG (8 TeV) where L LHC8 and L LHC14 denote the integrated luminosities of the LHC run at √ s = 8 and 14 TeV, respectively. This implies a reach of m χ ≈ 1370 GeV at L = 300 fb −1 and m χ ≈ 1550 GeV at L = 3000 fb −1 . For the background estimate we only considered the dominant background ttW ± [122]. The background cross section was computed with MadGraph5 [123]. Although the assumption of stronger projections on the reach of new fermion masses of up to 2 TeV [124] will lead to a reduced number of points allowed by the constraints we are imposing, it will not change our final conclusion, as we checked explicitly. Note also that in composite Higgs models there is a connection between the Higgs boson mass and the fermionic resonances [125,126]. Reference [126] finds that the mass of the lightest top partner m T lightest should be lighter than with N c = 3 denoting the number of colors. This bound automatically eliminates large values of ξ. In our analysis we allow for finetuning, hence small values of ξ, and we will not apply this bound. For the analysis we performed a scan over the parameter space of the model by varying the parameters in the range 9 We excluded points that do not fulfill |V tb | > 0.92 [127] and the electroweak precision tests (EWPTs) at 99% C.L. using the results of Ref. [102].
In Fig. 4 we show the NLO Higgs pair production cross section via gluon fusion as a function of ξ. The color code in the plots indicates whether the points are distinguishable from the SM according to the criteria given in Eq. (42), with the blue points being distinguishable and the grey points not. The upper plots are for the bbτ + τ − final state, the lower plots for the bbγγ final state, for L = 300 fb −1 (left) and L = 3000 fb −1 (right), respectively. The upper branch in the plots corresponds to the parameters y < 0 and 0 < R < 1 with R = (M 10 + f y/2)/M 10 . This means that at LO of the mass matrix expansion in v/f , the lightest fermion partner originates from the SU (2) bi-doublet. The lower branch corresponds to the cases y < 0 and R < 0 as well as y > 0 implying R > 1.
The plots only show points for which we cannot see new physics anywhere else meaning we require that their deviations in the Higgs couplings that can be tested at the LHC are smaller than the expected sensitivities and that the masses of the new fermionic resonances are above the estimated reach of direct searches. 10 The requirement for only small deviations in the Higgs couplings directly restricts the possible values of ξ to be smaller than 0.071 and 0.059 for L = 300 fb −1 and L = 3000 fb −1 , respectively. The value of ξ is restricted more strongly than in the MCHM4 due to the different coupling modifications, which, considering pure non-linearities, are for the Higgs-fermion couplings (1 − 2ξ)/ √ 1 − ξ in MCHM10 and √ 1 − ξ in the MCHM4. Although the interplay of the various additional parameters in MCHM10 allows for some tuning in the Higgs-bottom (and also Higgs-top) coupling, this is not the case for the Higgs-tau coupling. Comparing the MCHM10 with the MCHM5, the Higgs-fermion couplings are modified in the same way, barring the effects from the additional fermions. The increased number of parameters due to the heavy fermions, however, allows for more freedom to accommodate the data, so that here the constraint is weaker in the MCHM10. The plots show that at L = 300 fb −1 we cannot expect to discover NP for the first time in Higgs pair production in the bbγγ final state, while in the bbτ + τ − final state NP could show up for the first time in Higgs pair production. For L = 3000 fb −1 , we could find both in the bbτ + τ − and the bbγγ final state points which lead to large enough deviations from the SM case to be sensitive to NP for the first time in Higgs pair production. These results can be explained with the increased signal rate in the cases that are sensitive, as can be inferred from Fig. 5. The plots show for the parameter points displayed in Fig. 4 the corresponding number of signal events for Higgs pair production in the bbτ + τ − and bbγγ final states, respectively, after applying the acceptance of the cuts and multiplication with the two options of integrated luminosity. The blue points clearly deviate by more than 3σ from the SM curve.
Invariant Mass Distributions
Finally, in this section we discuss NP effects in invariant Higgs mass distributions. The measurement of distributions can give information on anomalous couplings [69] or the underlying ultraviolet source of NP [128]. Even though they are difficult to be measured due to the small numbers of signal events, they are important observables for the NP search. In the following we will show the impact of composite Higgs models on the distributions. Note, however, that the shape of the invariant mass distributions hardly changes from LO to NLO, since in the LET approximation the LO cross section mainly factorizes from the NLO contributions, as discussed in section 3. The parameters have been chosen such that in the left plot we allow for a larger value of ξ, while the mass of the lightest top partner of m T,lightest = 5441 GeV is much larger than compared to the case shown in the right plot with m T,lightest = 1636 GeV. As can be inferred from these plots, the largest effect on the distributions originates from the pure non-linearities, i.e. the value of ξ, while the influence of the fermionic resonances on the shape of the invariant mass distributions is small. Note also that the main effect on the distributions emerges from the new tthh coupling.
Conclusions
We presented the NLO QCD corrections to Higgs pair production via gluon fusion in the large quark mass approximation in composite Higgs models with and without new heavy fermionic resonances below the UV cut-off. We found that the K-factor of ∼ 1.7 is basically independent of the value of ξ and of the details of the fermion spectrum, as the LO cross section dominantly factorizes. The K-factor can hence directly be taken over from the SM to a good approximation. The size of the absolute value of the cross section, however, sensitively depends on the Higgs non-linearities and on the fermion spectrum.
With the results of our NLO calculation, we furthermore addressed the question of whether NP could emerge for the first time in Higgs pair production, taking into account the constraints on the Higgs couplings to SM particles and from direct searches for new heavy fermions. We focused on composite Higgs models and found that in simple models where only the Higgs non-linearities are considered, we cannot expect to be sensitive to NP for the first time in Higgs pair production. In models with a multiplet of new fermions below the cut-off, it turned out that there are regions where NP could indeed be seen for the first time in Higgs pair production. The subsequent study of the NLO invariant mass distributions demonstrated, that while there is some sensitivity to the Higgs non-linearities mainly due to the new 2-Higgs-2-fermion coupling, the effect of the heavy fermions on the shape of the distributions is much weaker. By applying optimized cuts the sensitivity to new physics effects may possibly be increased in future. the terms bilinear in the quark fields of Eq. (12) read and where U (t/b/χ) L,R denote the transformations that diagonalize the mass matrix in the top, bottom and charge-5/3 (χ) sector, respectively. Expansion of the mass matrices Eqs. (50)- (52) in the interaction eigenstates up to first order in the Higgs field leads to the Higgs coupling matricesG htt to a pair of charge-2/3 quarks andG hbb to a quark pair of charge −1/3, respectively, given by Expansion up to second order in the Higgs field yields the 2-Higgs-2-fermion coupling matrices G hhtt andG hhbb , that can be cast into the form The coupling matrices in the mass eigenstate basis are obtained by rotation with the unitary matrices defined in Eq. (53), i.e. (q = t, b) | 9,023 | 2016-02-18T00:00:00.000 | [
"Physics"
] |
Remotely Supervised Cranial Electrical Stimulation and Clinical Pain for Older Adults With Knee Osteoarthritis
Abstract Knee osteoarthritis (KOA) is one of the most prominent causes of chronic pain, functional impairment, and disability in older adults. The current standards of care for KOA are aimed toward reducing pain and are largely comprised of analgesic medications, but existing pharmacologic approaches often produce significant adverse effects. Moreover, recent evidence suggests that KOA pain is characterized by alterations in pain-related brain mechanisms. Cranial electrical stimulation (CES), which delivers a low-amplitude alternating electric current to the brain, can facilitate the reversal of maladaptive brain function. Portable CES devices can be used at home with real-time monitoring through a secure videoconferencing platform to facilitate high adherence. Thus, the purpose of this pilot clinical study was to examine the preliminary efficacy of remotely supervised CES on clinical pain severity in older adults with KOA. Thirty participants with KOA were randomly assigned to receive 10 daily sessions of remotely supervised CES with 0.1 mA at a frequency of 0.5 Hz for 60 minutes (n=15) or sham CES (n=15). We measured clinical pain severity using the numeric rating scale (NRS; range, 0 – 100). Participants (67% female) had a mean age of 59 years. Active CES significantly reduced scores on the NRS (Cohen’s d = 1.43, P < 0.01). Participants tolerated CES well without any adverse events. Our findings demonstrate the promising clinical efficacy of remotely supervised CES for older adults with KOA. Future studies with larger-scale randomized controlled trials with follow-up assessments are needed to validate and extend our findings.
PAIN ASSESSMENT IN IMPAIRED COGNITION (PAIC15) INSTRUMENT: CUTOFFS AGAINST THREE STANDARDS
Jenny van der Steen, Margot de Waal, and Wilco Achterberg, Leiden University Medical Center, Leiden, Zuid-Holland, Netherlands Observational pain scales can help identify pain in persons with impaired cognition including dementia who may have difficulty expressing pain verbally.The Pain Assessment in Impaired Cognition-15 (PAIC15) observational pain scale covers 15 important items that are indicative of pain, but it is unclear how likely pain is for persons with each summed score (theoretical range 0-45).The goal of our study was to determine sensitivity and specificity of cut offs for probable pain on the PAIC15 against three possible standards.We determined cut offs against (1) self report when able, (2) the established Pain Assessment in Advanced Dementia (PAINAD) cut off of 2, and (3) observer's overall estimate based on a series of systematic observations.We used data of 238 nursing home residents with dementia who were observed by their physician in training or nursing staff in the context of an evidence-based medicine (EBM) training study, with 137 residents assessed twice.The area under the ROC curve was excellent against the PAINAD cut off (□ 0.8) at both assessments, but acceptable or less than acceptable for the other two standards.Across standards and criteria for optimal sensitivity and specificity, cut offs at the PAIC15 could be 3 or 4. Guided by self report we recommend PAIC15 scores of 3 and higher to represent probable pain with sensitivity and specificity in the 0.5 to 0.7 range.Adequate pain management is important to post-acute care functional recovery, yet persons with Alzheimer's disease and related dementias (ADRD) are often undertreated for pain.The objectives of this study were to examine in Medicare post-acute home health (HH) recipients with daily interfering pain 1) if analgesic use at home is related to functional outcome, and 2) if ADRD is related to the likelihood of analgesic use at home.We analyzed data from the Outcome and Assessment Information Set, Medicare claims, and electronic medical records of 6,039 Medicare beneficiaries ≥ 65 years who received care from a large HH agency in New York in 2019 and reported daily interfering pain.Analgesic use was identified in medication reconciliation of HH visits and categorized into any analgesics or opioid(s).ADRD was identified from ICD-10 codes and significant cognitive impairment.Functional outcome was measured as change in the composite score of Activity of Daily Living (ADL) limitations from HH admission to HH discharge.Use of any analgesics at home was associated with greater ADL improvement from HH admission to HH discharge (β= -0.20 [greater improvement by 0.2 ADLs], 95% Confidence Interval [CI]: -0.37, -0.04; p=0.017).Compared with patients without ADRD, those with ADRD were less likely to use any analgesics (Odds Ratio [OR] = 0.66, 95% CI: 0.49, 0.90, p=0.008) or opioids (OR=0.53,95% CI: 0.47, 0.62, p<0.001) at home.Adequate pain management is essential to functional improvement in post-acute HH care.Patients with ADRD may be under-treated for pain in post-acute HH care.
REMOTELY SUPERVISED CRANIAL ELECTRICAL STIMULATION AND CLINICAL PAIN FOR OLDER ADULTS WITH KNEE OSTEOARTHRITIS
Hyochol Ahn, 1 Hongyu Miao, 2 and Yumna Ali, 2 1.University of Texas Health Science Center School of Nursing, Houston, Texas, United States, 2. The University of Texas Health Science Center at Houston, Houston, Texas, United States Knee osteoarthritis (KOA) is one of the most prominent causes of chronic pain, functional impairment, and disability in older adults.The current standards of care for KOA are aimed toward reducing pain and are largely comprised of analgesic medications, but existing pharmacologic approaches often produce significant adverse effects.Moreover, recent evidence suggests that KOA pain is characterized by alterations in pain-related brain mechanisms.Cranial electrical stimulation (CES), which delivers a low-amplitude alternating electric current to the brain, can facilitate the reversal of maladaptive brain function.
Portable CES devices can be used at home with real-time monitoring through a secure videoconferencing platform to facilitate high adherence.Thus, the purpose of this pilot clinical study was to examine the preliminary efficacy of remotely supervised CES on clinical pain severity in older adults with KOA.Thirty participants with KOA were randomly assigned to receive 10 daily sessions of remotely supervised CES with 0.1 mA at a frequency of 0.5 Hz for 60 minutes (n=15) or sham CES (n=15).We measured clinical pain severity using the numeric rating scale (NRS; range, 0 -100).Participants (67% female) had a mean age of 59 years.Active CES significantly reduced scores on the NRS (Cohen's d = 1.43,P < 0.01).Participants tolerated CES well without any adverse Our findings demonstrate the promising clinical efficacy of remotely supervised CES for older adults with KOA.Future studies with largerscale randomized controlled trials with follow-up assessments are needed to validate and extend our findings.
PATIENT, CAREGIVER, AND PHYSICIAN BARRIERS TO HOME-BASED PALLIATIVE CARE: FINDINGS FROM A TERMINATED STUDY Chair: Susan Enguidanos Discussant: Stephanie Wladkowski
Despite two decades of palliative care services, there remains numerous barriers to patient and caregiver use of palliative care.For many years, policymakers believed lack of funding for palliative care was the primary obstacle to accessing palliative care services.In 2017, we undertook a randomized controlled trial to test the effectiveness of a home-based palliative care (HBPC) program within accountable care organizations and in partnership with an insurance company that covered the cost of HBPC.After 20 months, we had recruited just 28 patients.This symposium will: (1) describe outcomes from various approaches undertaken to engage primary care physicians and recruit patients and their caregivers into this trial; (2) present barriers to HBPC referral identified from a qualitative study of primary care physicians; (3) present findings from a qualitative study of patient-and caregiver-identified barriers to HBPC; (4) describe physician and patient barriers to research participation; and (5) discuss implications of these findings for researchers and healthcare providers.Information presented in this symposium will inform researchers and policy makers about challenges and facilitators to recruiting patients, caregivers, and physicians to participate in research studies as well as inform healthcare practitioners of potential obstacles to increasing patient access to HBPC.
TRIALS AND TRIBULATIONS: PALLIATIVE CARE TRIAL RECRUITMENT APPROACHES AND CHALLENGES
Anna Rahman, Sindy Lomeli, and Susan Enguidanos, University of Southern California, Los Angeles, California, United States In 2017, we received funding form the Patient-Centered Outcomes Research Institute to conduct a large, state-wide, randomized controlled trial to test the effectiveness of a homebased palliative care (HBPC) program within accountable care organizations.Participants were randomized to either HBPC or enhanced usual care, where physicians were provided added training and support in core palliative care practices.Originally, we planned to obtain patient referrals to the trial from primary care physicians, however we were unable to engage primary care physicians in patient identification processes.In this session we will describe the numerous trial modifications made to our trial recruitment methods and the success of each approach.Ultimately, after 20 months of trial recruitment, we had recruited just 28 patients and 10 of their caregivers.Findings from this terminated trial may inform other researchers in development of participant recruitment methods.To understand primary care providers' (PCPs) experiences with referring patients to home-based palliative care (HBPC), we conducted individual, key-informant interviews with 31 PCPs.About half participants were male (54.8%), White (42.5%),US-born (58.1%), and were 57 years old (SD=9.17),on average.About one-third of participants (32.3%) indicated they refer 10+ patients annually to HBPC, while most (80.7%) reported "strong" comfort discussing palliative care with patients.Qualitative analysis revealed three prominent thematic categories, each related to barriers PCP experienced when referring patients to palliative care: (1) PCP-level (lack of knowledge and comfort); (2) perceived patient-level (culture, family disagreement, need, home-based aspect); and (3) HBPC program-level (need to close the loop with PCP, insurance coverage, program availability, and eligibility).PCP recommendations for overcoming identified barriers will be discussed.Findings hold important implications for timely patient-referrals to palliative care by PCPs and for sustaining palliative programs that rely on these referrals. | 2,165.8 | 2021-12-01T00:00:00.000 | [
"Medicine",
"Engineering"
] |
Nonunitary Lagrangians and unitary non-Lagrangian conformal field theories
In various dimensions, we can sometimes compute observables of interacting conformal field theories (CFTs) that are connected to free theories via the renormalization group (RG) flow by computing protected quantities in the free theories. On the other hand, in two dimensions, it is often possible to algebraically construct observables of interacting CFTs using free fields without the need to explicitly construct an underlying RG flow. In this note, we begin to extend this idea to higher dimensions by showing that one can compute certain observables of an infinite set of unitary strongly interacting four-dimensional $\mathcal{N}=2$ superconformal field theories (SCFTs) by performing simple calculations involving sets of non-unitary free four-dimensional hypermultiplets. These free fields are distant cousins of the Majorana fermion underlying the two-dimensional Ising model and are not obviously connected to our interacting theories via an RG flow. Rather surprisingly, this construction gives us Lagrangians for particular observables in certain subsectors of many"non-Lagrangian"SCFTs by sacrificing unitarity while preserving the full $\mathcal{N}=2$ superconformal algebra. As a byproduct, we find relations between characters in unitary and non-unitary affine Kac-Moody algebras. We conclude by commenting on possible generalizations of our construction.
Introduction
Free fields in two spacetime dimensions are versatile: operators, correlation functions, and partition functions of interacting conformal field theories (CFTs) can often be constructed algebraically from free bosons via the Coulomb gas formalism, and the simplest unitary minimal model-the Ising model-has a free Majorana fermion underlying it (see [1] for a review). Free fields in higher dimensions seem less powerful: in order to have something useful to say about an interacting CFT, one must usually labor to connect such free fields to the CFT in question through a suitably "smooth" path in the space of couplings [54].
However, one may hope to overcome these obstacles in d > 2 spacetime dimensions whenever there are relations between quantum field theories (QFTs) in d dimensions and QFTs in 2D. In the case of 4D superconformal field theories (SCFTs) with at least N = 2 supersymmetry (SUSY), one such relation was given in [2]: the sector of so-called "Schur" operators of the 4D SCFT (briefly reviewed in the supplementary material) is isomorphic to a 2D chiral algebra living on a plane, P ⊂ R 4 . On the chiral algebra side of this relation, one of the most basic quantities we can compute is the torus partition function where the trace is over the Hilbert space of states associated with the chiral algebra, c 2d is the chiral algebra central charge, M ⊥ = j 1 − j 2 is the spin transverse to P (j 1,2 are Cartans of SO(4)), q ∈ C is a fugacity, and L 0 gives the holomorphic scaling dimension, h. On the 4D side of the relation, (1) is mapped to a particular refined Witten index, called the Schur index [3] (see the supplementary material for further details), that counts the Schur operators weighted by certain quantum numbers where c 4d is the 4D c central charge, F is fermion number, E is the scaling dimension, and R is the su(2) R weight (clearly, the holomorphic scaling dimension satisfies h = E − R while c 2d = −12c 4d [2]). Note that both (1) and (2) can be refined by additional flavor fugacities (i.e., fugacities for symmetries that commute with N = 2 SUSY in 4D), but such modifications will not play a role in our discussion below.
While we believe that many of the ideas we will present are quite broadly applicable (with suitable modifications), in this note we specialize to a particular infinite set of strongly coupled SCFTs whose simplest member is the so-called (A 1 , D 4 ) theory [55]. In this class, the manipulations we use are particularly simple.
The Schur index for the (A 1 , D 4 ) theory was computed in [6][7][8][9] and was shown to equal the vacuum character of su(3) − 3 2 (as conjectured in [10]). More recently, the authors of [11] proposed that this unflavored Schur index takes the following simple form and this formula was proven in [12] (see also the discussion in [13]) to be equivalent to the vacuum character of su(3) − 3 2 [56]. Interestingly, under the rescaling q → q where the righthand side (RHS) is just the index of eight free half-hypermultiplets (i.e., the T 2 theory [16]) or, equivalently in 2D, the vacuum character of four symplectic bosons.
While the derivation in [12] proved (3) [57] along with various generalizations we will encounter below, we would like to give a physical argument for why this index is so closely related to the index of free fields. One hint comes from the study in [17] (building on [18]) that shows the (A 1 , D 4 ) theory plays a role in a particular S-duality that is reminiscent of the role played by free hypermultiplets in the S-duality of [19]. Moreover, by thinking of (4) as a manifestation of a weak-strong "duality" [58] we, in collaboration with T. Nishinaka, speculated that this connection might be related to modularity [17].
As we will see below, this intuition is morally correct, although the free fields that are more closely related to modularity are actually non-unitary (wrong statistics) rather than the unitary fields appearing on the RHS of (4). A strong indication that this idea is correct comes from noting that (3) satisfies the modular differential equation [20] where D (2) q is a modular differential operator, and E 4 is an Eisenstein series (we refer the interested reader to [20] for more details). The characters of so(8) 1 satisfy the same modular differential equation [21]. Since so(8) 1 is unitary and has a representation in terms of eight free Majorana fermions, it is reasonable to imagine that the 4D ancestor of this theory is a non-unitary free theory (recall that, as discussed above, c 4d = − 1 12 c 2d , so c 4d < 0 in this case). Clearly, these free fields then reproduce some of the observables in the Schur sector of the (A 1 , D 4 ) SCFT.
I. MODULAR S-TRANSFORMATIONS AND AN AKM RELATION
In order to understand the modular properties of (3), it is useful to re-write it as follows where q = e 2πiτ , η(τ ) is the Dedekind eta function, and θ i (τ ) are the Jacobi theta functions (see the supplemen-tary material). Under a modular S-transformation, we have In particular, we see that applying a modular Stransformation to (6) yields (8) We immediately recognize the expression on the RHS as also counting (with a (−1) F weighting) the so(8) 1 fields generated by acting on the so (8) [59] (hence, this theory is related to eight decoupled Ising models). These fields have the following singular OPE At the level of characters, we have the relation where, in the second equality, we have used our observation above and, in the first equality, we have used the modular S matrix acting on the characters of the four There are four admissible representations of su ( ), and we find the bijection of finite unrefined characters [60] where the relations hold up to overall constants (see [22] for character relations between other pairs of unitary and non-unitary theories).
II. A 4D INTERPRETATION
We would like to give a 4D interpretation for the unitary so(8) 1 theory described in the previous section by using the relation discovered in [2] (although, apriori, it is not clear such an interpretation must exist). As discussed above, this theory should be non-unitary since Moreover, from the results in (2) and (8), we see that an obvious candidate for our 4D theory is a collection of 8 half-hypermultiplets with wrong statistics (i.e., a "ghost" T 2 theory) [61]. Indeed, the a and c anomalies for such a theory are just minus the corresponding anomalies for the T 2 theory since the wrong statistics leads to an insertion of a factor of −1 in any quantum loop. In particular, we have Note that a 4d − c 4d is then consistent with the q → 1 "Cardy" limit of the index [6,20,23,24], and the full (unrefined) Schur index is precisely what we want (see the previous footnote).
To get a map of operators, the correspondence in [2] requires us to take 4D Schur operators, fix them in a plane (with coordinates z,z), and then twist the globalz conformal transformations with su(2) R . Working in the cohomology of a particular supercharge, É, then gives a map to 2D chiral algebra operators. This procedure is naturally implemented in the operator product expansion (OPE).
For the case at hand, we can build all Schur operators as arbitrary (non-vanishing) products of the su(2) R highest weight anti-commuting scalars of the non-unitary free hypermultiplets, q I , and their derivatives. These fields are organized as q i = Q i and q i+4 =Q i (with i = 1, · · · , 4) and live in the following su(2) R doublets We can write a simple Lagrangian for this non-unitary theory (note that the spinors in the hypermultiplets commute while the scalars anti-commute) Related Lagrangians have been considered in different contexts in [25,26].
The non-vanishing singular OPEs are then (in an appropriate normalization to eliminate a common overall constant factor) According to the discussion in [2], we should twist the hypermultiplets with vectors u i = (1,z) having su(2) R indices i = 1, 2. In particular, we have twisted fields with the following singular OPEs Passing to É cohomology gives the same OPEs as above (the identity operator is É-closed but clearly cannot be É-exact). In particular, we reproduce the free Majorana OPEs of (9).
The theory also has conserved currents sitting as leveltwo descendants in multiplets with Schur operators of the form where i, j = 1, · · · , 4. More covariantly, we can define these operators to form part of a 28-dimensional adjoint representation with µ IJ = iq I q J and I, J = 1, · · · , 8 (this operator is anti-symmetric in I and J). The charges arising from real currents sitting as descendants of linear combinations of the above satisfy an so * (8) ≃ so(6, 2) Lie algebra, which is a real form of so(8, C). On the other hand, the operators in (21) are related to currents that are not real. However, these currents give rise to charges that act in accordance with the reality condition in two dimensions Relabeling the moment maps with an adjoint index of so (8), we obtain the following twisted OPE where f AB C are the structure constants of so (8). Dropping the É-exact terms then leads to the standard so(8) 1 current-current OPE. As a result, we see that a generalization of the procedure of [2] applied to our non-unitary 4D theory yields the desired unitary theory in 2D.
III. INFINITELY MANY GENERALIZATIONS
One can imagine generalizing our discussion above in many directions. Here we choose the simplest direction: the (A 1 , D 4 ) theory is part of an infinite family of SCFTs called the D 2 [SU (2N + 1)] theories [27,28] (where D 2 [SU (3)] ≡ (A 1 , D 4 )). The corresponding chiral algebras were found in [11] and were argued to be su(2N + 1) − 2N +1 2 . The generalization of (3) is and one finds that, upon taking q → q 1 2 , the index (23) reduces to the index of 4N (N + 1) free halfhypermultiplets.
For N > 1, the modular properties of the theory are somewhat different. For example, the modular differential equation in (5) for the N = 1 case becomes third order for all N > 1. However, we can proceed as before and write Then, performing a modular S-transformation yields This result generalizes the N = 1 result discussed above, since we recognize (25) as also counting (with a (−1) F weighting) the so(4N (N + 1)) 1 fields generated by acting on the so(4N (N + 1)) 1 vacuum with the h = 1 2 Majorana fermion in the vector representation, ψ I (its singular self-OPE is the obvious generalization of (9) with I = 1, · · · , 4N (N + 1)).
The so(4N (N + 1)) 1 algebra has four representations for all N : the vacuum, the h = 1/2 representation discussed above, and two h = N (N + 1)/4 representations. The latter two representations have identitcal unrefined characters which we denote as χ It is straightforward to check that These results are simple consequences of the fact that our two chiral algebras satisfy the same modular differential equation for all N .
The 4D generalization of the N = 1 case is straightforward. For example, we have that This anomaly is precisely what we expect for 4N (N + 1) half-hypers with wrong statistics (i.e., N (N + 1)/2 "ghost" T 2 theories). Similarly, a 4d and the superconformal index are compatible with this interpretation. In particular, our 4D Lagrangian is just the obvious generalization of (16) where now I = 1, · · · , 4N (N + 1). Note that the real flavor currents in 4D generate an so * (4N (N + 1)) algebra, but the N (2N − 1) Schur operators that are the generalizations of (21) give rise to the so(4N (N + 1)) 1 AKM algebra in 2D [62].
IV. DISCUSSION AND CONCLUSIONS
We have seen that the simple non-unitary 4D Lagrangian (28) allows us (through manipulations in two dimensions) to exactly compute the unrefined Schur indices for the D 2 [SU (2N + 1)] SCFTs. Clearly, we are also able to compute other (linear combinations of) characters of the associated chiral algebras via the, to our knowledge, novel mathematical identities in (26). Based on known relations between chiral algebras in 2D and 3D QFT, it is reasonable to expect that aspects of the physics of the non-vacuum modules of the chiral algebras are captured by (worldvolumes of) 4D objects that have non-trivial braiding statistics as in [29]. Indeed, there is considerable evidence that this intuition holds [30,31], and we hope to return to a detailed discussion of surface and line defects in our setup soon. In particular, the Lagrangian in (28) seems to compute the Schur indices of the D 2 [SU (2N + 1)] SCFTs in the presence of certain surface defects [63], while we presumably need to introduce defects in our non-unitary theory in order to compute-directly in 4D-the other Schur indices of the D 2 [SU (2N + 1)] theories.
As another future direction, we may hope to find information about new observables that are closely related to the chiral algebra as in [32][33][34][35]. Moreover, since we have a Lagrangian description of certain Schur observables, it is tempting to see what (if anything) the corresponding correlation functions / OPE coefficients compute in the original strongly interacting theory. Even more simply, it would be interesting to understand if it is possible to map flavor symmetries between our two sets of theories.
Moreover, we expect that our procedure of starting with a unitary 4D theory, mapping to 2D using [2], conjugating / permuting the characters, reinterpreting the characters as objects in a unitary 2D theory, and then lifting to a non-unitary theory in 4D will generalize (with certain modifications) to many (and perhaps all) N = 2 theories. While we know that the non-unitary 4D theories will not always have a completely free description in terms of hypermultiplets, we expect gauge fields and perhaps the constructions in [36] to play a role (possibly when the original 4D theory has a conformal manifold [37][38][39][40][41]). Indeed, we expect non-unitary Lagrangians to be a more diverse and flexible group of objects than their unitary counterparts, and so we expect them to describe "more" theories.
Still, we should point out that our non-unitary 4D theories described above have an avatar of 2D unitarity: a modified notion of reflection positivity exists in our theories. Related 2D constructions have played a role in recent work on non-unitary extensions of Zamolodchikov's c-theorem [42]. Such structures, involving "hidden" unitarity, may also shed more light on the question of which non-unitary theories in 2D are able to encode the unitary 4D physics in the original construction of [2]. We hope to return to this question soon.
It would also be interesting to understand any relation between our construction and the Lagrangians appearing in [43][44][45][46][47][48][49][50]. While our Lagrangians govern only a particular sector of the theories we study (and perhaps only a particular set of observables in such a sector), they are considerably simpler than the "full" Lagrangians in these latter works [64]. Also, our approach is different: we sacrifice unitarity instead of the N = 2 superconformal algebra. More generally, it would be interesting to find connections between our discussion and other effective Lagrangian descriptions of sectors of QFTs (e.g., as in [51]).
Finally, we hope to understand if our work is related in any way to supersymmetric localization (see [31] for some interesting work in this direction from a chiral algebra perspective), to understand if our work is related to the free fermion description of the Schur index for quiver gauge theories [52], to see how our procedure might work in 6D, to understand if the theories we have studied here contain some sectors that play a role in the dS/CFT correspondence, and to understand the role our Lagrangians might play in the physics of 3D SCFTs as in [48,49,53].
Supplementary Material
In this appendix, we briefly remind the reader of various formulae and constructions used in the main text: • Schur Operators: For a more comprehensive review, we refer the reader to [2,3]. Here we simply recall that a Schur operator, O, sits in a short multiplet of the 4D N = 2 superconformal algebra (SCA) and is annihilated by the following Poincaré supercharges where the numerical indices are spin-half indices of the su(2) R ⊂ u(1) R × su(2) R part of the SCA and the sign indices are for the Lorentz group (note that we have dropped su(2) R and Lorentz indices from O for simplicity).
The constraints in (29) guarantee that the Schur operators have scaling dimensions and u(1) R charges that are fixed in terms of the su(2) R and Lorentz weights. Moreover, these operators contribute to the Schur limit of the 4D superconformal index In this expression, the trace is over the Hilbert space of local operators, ∆ = Q 2− , Q 2− † , f i are flavor charges, x i are corresponding flavor fugacities, and the remaining quantities have been described in the main text. In this note, we have simply set x i = 1 so that the flavor dependence drops out, and we obtain the unrefined Schur index. Moreover, by the usual arguments of index theory, only states annihilated byQ 2− and Q 2− † contribute to the index (in particular, ∆ = 0 for these operators).
We will not review the detailed taxonomy of Schur operators here, but we note that any local theory in 4D has a Schur operator: the su(2) R and Lorentz highest-weight component of the su(2) R current (this current sits in the same multiplet with the 4D stress tensor). Moreover, any theory in 4D with a local continuous flavor symmetry (in the sense of having a corresponding Noether current) has an additional set of Schur operators: the su(2) R highest weight components of the corresponding moment maps. This universality of the Schur sector is one of the origins of its power.
Finally, we note that under the chiral algebra mapping alluded to in the main text and explained further in [2], we have the following relation between 4D Schur operators and chiral algebra generators where J 11 ++ is the component of the su(2) R current described above, T is the 2D holomorphic stress tensor, µ I is a moment map of the type we have discussed in the previous paragraph, J I is an AKM current, ∂ ++ is a derivative in the z direction of the chiral algebra plane, and ∂ is the 2D holomorphic derivative.
• Modular Quantities: In the main text we have used several quantities that have nice modular properties. These include the Dedekind η function where q = e 2πiτ , and the Jacobi theta functions In the main text, we have set z = 0 and defined θ i (0, τ ) ≡ θ i (τ ).
• so(2n) 1 Characters: The characters of so(2n) 1 are given by [1]: As mentioned in the main text, when n = 4 the last three characters are all equal to each other. | 4,879 | 2017-11-27T00:00:00.000 | [
"Physics",
"Mathematics"
] |
Imaging dose and secondary cancer risk in image-guided radiotherapy of pediatric patients
Background Daily image-guided radiotherapy (IGRT) can contribute to cover extended body volumes with low radiation dose. The effect of additional imaging dose on secondary cancer development is modelled for a collective of children with Morbus Hodgkin. Methods Eleven radiotherapy treatment plans from pediatric patients with Hodgkin’s lymphoma were retrospectively analyzed, including imaging dose from scenarios using different energies (kV/MV) and planar/cone-beam computed tomography (CBCT) techniques. In addition to assessing the effect of imaging dose on organs at risk, the excess average risk (EAR) for developing a secondary carcinoma of the lung or breast was modelled. Results Although the variability between the patients is relatively large due to the different target volumes, the additional EAR due to imaging can be consistently determined. For daily 6MV CBCT, the EAR for developing a secondary cancer at age 50 is over 3 cases per 104 PY (patient-years) for the female breast and 0.7–0.8 per 104 PY for the lungs. This can be decreased by using only planar images (< 1 per 104 PY for the breast and 0.1 for the lungs). Similar values are achieved by daily 360° kV CBCT (0.44–0.57 per 104 PY for the breast and 0.08 per 104 PY for the lungs), which is again reduced for daily 200° kV CBCT (0.02 per 104 PY for the lungs and 0.07–0.08 per 104 PY for the breast). These values increase if an older attained age is considered (e.g., for 70 years, by a factor of four for the lungs). Conclusions Daily imaging can be performed with an additional secondary cancer risk of less than 1 per 104 PY if kV CBCT is applied. If MV modalities must be chosen, a similar EAR can be achieved with planar images. A further reduction in risk is possible if the imaging geometry allows for sparing of the breast by a partial rotation underneath the patient.
Background
Modern radiotherapy offers ever improved techniques for optimal target coverage associated with utmost sparing of neighbouring tissues and organs at risk (OAR's). As dose conformity and dose gradients are increasing, image-guided radiotherapy (IGRT) is a prerequisite, and frequent to daily image-guided positioning verification is common. Since most IGRT systems rely on ionizing radiation for EPID-based (electron portal imaging device) projection or cone-beam computed tomography (CBCT) imaging, an evaluation of the contribution of imaging dose to the treatment plan should be performed, and has been presented for a number of indications in the recent literature. This need is particularly pronounced since there exists a variety of different imaging systems using different photon energy (kV or MV), with 2D or 3D imaging, the dose of which is generally not included in the treatment planning system (TPS). While it is undisputed that the general benefit of image-guided positioning surmounts the possibly deteriorating effects of additional imaging dose on plan quality, particularly sensitive patient collectives such as pediatric patients with good prognosis should not receive excessive imaging dose to avoid OAR complications orin the even lower dose regimerisk of developing secondary malignancies.
The aim of this study is therefore to analyse and compare several common linac-based imaging scenarios (6MV and 121 kV energies for planar and CBCT imaging with daily or non-daily frequency) with regard to their influence on OAR dose and secondary cancer risk in children with Morbus Hodgkin irradiated at the Saarland University Medical Centre. This collective was chosen because of good long-term survival of children with Hodgkin's lymphoma, so that there is a high probability that they will live long enough so that a secondary cancer might develop. Furthermore, secondary cancer induction by irradiation of Morbus Hodgkin patients has been extensively studied for the same reason, so that mathematical model parameters for secondary cancer risk are better known for this type of irradiation.
To assess imaging dose contribution, the summation dose from the complete treatment including different imaging scenarios is calculated and secondary cancer risk is estimated. Although other studies have previously estimated secondary cancer risk for the irradiation of children, they have mainly concentrated on the imaging dose or treatment dose separately. This approach is problematic since the secondary cancer risk from these two contributions cannot be expected to be additivein fact, the secondary cancer risk estimated for imaging dose would normally use a linear model, which only applies in the low-dose regime. Conversely, the secondary cancer risk model for higher doses of the order of a radiotherapy treatment protocol is generally expected to fall out of the linear range, where either bell-shaped or plateau-like models are assumed to account for cell sterilization. In these cases, adding a linearly estimated secondary cancer risk from imaging dose to the risk calculated for the treatment plan separately might yield quite a different result than directly estimating the total risk from the combined dose distribution. A priori, it cannot be known how the two effects interact, which will also be discussed in this study.
Patient collective
We included in the analysis all children treated for Hodgkin's lymphoma at the Department of Radiotherapy and Radiation Oncology, Saarland University Medical Centre, in the years 2011 through 2015, which are seven children aged 5-17 years (mean age 14 years) with 11 different target sites (analysed separately, see Table 1 for details). After obtaining written informed consent of the patients and the patients' parents, the patients received involved-field irradiation with 19.8-29.8 Gy in fractions of 1.0-1.8 Gy using an intensity modulated radiotherapy technique (IMRT) with 6 MV photons. In total 134 treatment fractions were delivered, with image-guidance in 63 fractions.
For image-guidance, the following systems are available at our department:
All systems can be used to acquire planar or CBCT images. Planar MV axial images are taken using 1 monitor unit (MU) each from two orthogonal views. For MV CBCT images, either a full 360°or a shortened arc (200°) can be used with different MU settings depending on the patient anatomy and geometry (7)(8)(9)(10)(11), with a square field of 27.4 cm width at source-to-surface distance (SSD) 100 cm [1]. The kVision system applies a "pre-shot" to automatically optimize the exposure (the mAs value is displayed and protocolled, generally less than 10 mAs for planar images and between 200 and 700 mAs for CBCT in our collective). While the MV CBCT gantry rotates above the head of the patient, the kV CBCT geometry with a reduced arc is inverted, rotating below the back of the patient because the X-ray tube is installed opposite the treatment head. The field size at SSD 100 cm is 28 × 28 cm 2 for the kV system [4].
The realistic imaging scenarios applied for the patients depended to some degree on the availability of the techniques for imagingin 2011, only the 6 MV energy was available, the kVision system was installed last (end 2012). When available, the lower-energy systems were preferentially used for imaging; the percentage use of each imaging system is shown in Fig. 1a. A no-action-level protocol of online positioning correction was followed, so that all deviations observed in pre-treatment imaging (unless smaller than 1 mm) were always corrected for. An analysis of the performed couch shifts after imaging (Fig. 1b) agrees with a normal distribution, although the patient collective is too small to allow for statistical significance. It was checked in a phantom study that the different imaging modalities are in agreement regarding the detected positioning errors [8].
Dose calculation and secondary cancer risk model
The three imaging systems are all commissioned in the Philips Pinnacle treatment planning system (Philips Healthcare, Koninklijke, Netherlands) [1,4,9], so that the imaging dose can be calculated together with the patient treatment plan. For each patient, the dose distribution of the original treatment plan (as it was accepted for treatment = Scenario 1) without inclusion of imaging dose is compared with the following imaging scenarios: Scenario 2: treatment plan with real imaging performed for the patient, with imaging device and frequency differing between the individual patients (details listed in Table 1) Scenario 3: treatment plan with daily 121 kV 200°CBCT Scenario 4: treatment plan with daily 121 kV 360°CBCT Scenario 5: treatment plan with daily 6 MV planar imaging Scenario 6: treatment plan with daily 360°6 MV CBCT.
The hypothetical scenarios 3-6 are chosen to analyse the effect of daily imaging, which is more and more becoming the norm [10]. For the kV and 6 MV CBCT scenarios, the average mAs or MU per rotation was calculated for all patients and applied for the hypothetical scenarios: 360°kV CBCT with 377 mAs, 200°kV CBCT with 99 mAs, 6 MV CBCT (360°) with 11 MU. Evidently, kV imaging is the most desirable scenario; however, this is not available at every clinic. This is why we include 6 MV imaging to see how daily imaging can be achieved if only this modality is available. While it is clear that daily 6 MV CBCT would entail too high imaging dose, we include this scenario as the maximum possible imaging dose; contrarily, daily planar images with 6 MV are included as example of the lowest achievable imaging dose for daily MV images. Reality will range in between these two scenarios as occasional CBCT may be used to acquire 3D views. As kV planar images involve very little dose in comparison with all other modalities (lower than kV CBCT or planar MV images by at least an order of magnitude), this modality is not included in our analysis.
Dose calculation of the summation plans was carried out in the Philips Pinnacle TPS Version 9.0-9.6 on a 2 mm dose grid using the collapsed cone algorithm (for a detailed explanation of the procedure, compare [11]). Dose-volume histogram (DVH) values are considered to assess dose to the organs at risk (OARs). For secondary cancer risk calculation, the dose distribution was exported together with the regions of interests (ROIs) of the OARs and imported into Matlab R2015a (MathWorks, Natick, Massachusetts, USA). An in-house software was created to calculate the organ-specific risk for developing secondary carcinoma using Schneider's mechanistic model [12]. This gives the organ excess absolute risk as EAR org age x ; age a À Á ¼ β org •OED•μ age x ; age a À Á ; where age x and age a are the age at exposure and age attained (when developing the secondary malignancy), respectively, is a modifying function for adjusting the risk to the ages age x and age a and β is a model scaling parameter between the organ effective dose (OED) and the EAR. The organ effective dose is calculated according to [12] as the risk-equivalent-dose (RED)-weighted average of the total volume V T with the organ-specific dose-response relationship given by the mechanistic model for carcinoma induction including cell killing and fractionation effects: Fractionation is taken into account using the linear quadratic model with parameters α and β and a fractionation with target volume prescribed dose D T in fraction doses of d T : The main two organs at risk for secondary cancer development in our patient collective are the lungs and the breast, for which the model parameters are given in Table 2.
The software uses the DICOM (Digital Imaging and Communications in Medicine) structure sets, RT dose and CT data set as input together with the organ specific model parameters to calculate the excess absolute cancer risk. In each case, the age of the patient at exposure was included; the secondary cancer risk was modelled for an attained age of 50 years.
A statistical analysis was carried out in Origin Pro 2015G (OriginLab, Northampton, Massachusetts, USA) for descriptive statistics. To assess differences between the planning scenarios, the plans were pair-wise compared against the original plan ("gold standard" without imaging dose) using the Wilcoxon signed-rank test. Please note that "gold standard" is taken to mean the optimal dose distribution (no deterioration from imaging dose), as it was accepted for treatmentthis is not suggested as the optimal verification scenario, just as a baseline for dose comparisons (see Discussion).
Results
Visualization of dose distributions -Example Figure 2 shows an example of the imaging dose distribution and the planned dose, where differences between the imaging scenarios become apparent. We selected this patient example because several issues can be observed here: Firstly, the dose distribution from imaging only differs strongly from the original treatment plan. Secondly, the dose distributions of the different imaging scenarios are quite different from each other, both in absolute dose (all imaging dose distributions are scaled to their respective maximum dose, ranging from 11.1 cGy for daily kV CBCT to 240.9 cGy in total for daily 6 MV CBCT) and in dose distribution. For this patient (patient 6), two target volumes were irradiated (lymphatics and os ileum), so that two series of imaging scenarios were simulated. The overlapping region between both imaging sets creates a higher-dose "belt" in the simulated scenarios. In the actual verification, most sessions only imaged the larger cranial planning target volume (PTV), so that most imaging dose is accumulated here. Furthermore, the actual verification used a combination of axial and CBCT imaging, which explains the square shape of the isodoses in this scenario. If the combined dose from the radiotherapy treatment and imaging is considered, the differences between the scenarios are less obvious. Some small increases in the lower-dose isolines and an increased maximum dose are observed, particularly for those imaging scenarios with higher additional dose (e.g., 6 MV). For this patient, the actual imaging performed contributed rather high dose because this patient was treated among the first in the collective, when kV imaging was not yet installed at our department.
Dose to organs at risk -DVH analysis
An example DVH for the patient shown above is given in Fig. 3. It is obvious that the dose is markedly increased by daily 360°6MV CBCT, as well as (to a smaller degree) by the actual mixed CBCT-planar 6 MV imaging performed. The other imaging scenarios do not result in a visible increase in the DVH dose. The fact that neither dose to the parotids nor the pharynx appear to change with the imaging scenarios is due to the imaging geometry visible in Fig. 2, in which the imaging beam does not reach up as far cranially to involve these organs.
All DVH parameters are listed in Table 3; an example for the left lung is displayed in Fig. 4. The statistical tests for significance using Wilcoxon's signed-rank test for paired data gave positive results for all organs for which more than 5 patient cases with DVH values existed, because the comparison of any plan with imaging vs. the original plan without imaging always gave positive ranks. Only for those DVH measures such as the D20Gy for the parotid could significance not be reached because of the variability of the plans, which meant that more than half of the patients had D20Gy = 0% for this organ.
As the target volumes and hence treatment plans vary considerably between the patients, the DVH parameters are also very different. Figure 4 shows the variation in DVH parameters for the left lung for the whole patient collective only for the original treatment plan without imaging. The additional dose from imaging creates less absolute difference than the variation between the individual patients, as can be seen for the example of the left lung values in Figs. 5 and 6. For the parotid mean and D2% values, even the extreme imaging scenario with daily 6 MV CBCT only increases dose by less than 1 Gy. For the spinal cord, the D2% dose increases from 19.7 Gy without imaging to at most 21.2 Gy (daily 6 MV), and by only 0.1 Gy for scenarios with kV imaging. Similar results are obtained for the pharynx and maximum (D2%) and mean lung doses. For the lower-dose DVH values, the differences increase somewhat, e.g. by more than 5% for the lung V20Gy and V5Gy. If the individual patient values (range) are considered rather than the average over the patient collective, strong variations appear, with V20% for the lung ranging from 0% to over 46% and V5Gy from 56 to 100% within the collective.
Imaging effect on secondary cancer risk
Secondary cancer risk reflects the behavior of DVH values in varying more between individual patients than between imaging scenarios (Fig. 6, Table 4). Considering the original plan only (no imaging), the absolute excess cancer risk from treatment for the lungs is of the order of 5-6 cases per 10 4 PY (range 1.3-10 per 10 4 PY) and for the breast ranges between 2.4 and 26 per 10 4 PY.
To concentrate on the effect of imaging rather than the inter-individual differences in target volume, we consider the differences between the original plan and the plans including imaging. The absolute excess risk from imaging only (disregarding the "baseline risk" from radiotherapy treatment) is given in Table 5 and shown in Fig. 7 for the left lung. Comparing this with Fig. 6 shows that the variability between the patients is drastically decreased, so that the systematic effect from imaging becomes visible. We can therefore now calculate the average additional risk over the patient collective (Fig. 8), which is considered in the following.
As was expected, daily 6 MV CBCT causes most additional cancer risk (more than 3 cases per 10 4 PY for the female breast and 0.7-0.8 cases per 10 4 PY for the lungs). If only 6 MV imaging is available, the secondary cancer risk can be drastically decreased by preferentially performing planar images; in the case of daily planar 6 MV imaging the additional excess absolute cancer risk for the breast is decreased to 0.4-0.8 cases per 10 4 PY (a factor of 3.9-5.8) and to 0.1 cases for the lungs (more than a factor of 5). Slightly lower values are achieved by daily 360°kV CBCT (0.44-0.57 per 10 4 PY for the breast and 0.08 per 10 4 PY for the lungs), which is again reduced for daily 200°kV CBCT. For this scenario, the additional excess absolute cancer risk for the lungs is 0.02 per 10 4 PY (lower than 360°by a factor of 4) and 0.07-0.08 per 10 4 PY for the breast (decreased by a factor of 6-7 with respect to the 360°kV CBCT scenario). The reason for this more pronounced improvement in the secondary cancer risk for the breast as compared with the lung is the geometry of the imaging system: The 200°kV CBCT is taken rotating the X-ray source under the back of the patient, so that the breast only receives the much attenuated exit dose. As the lungs are located more centrally, the difference between a full CBCT rotation and a partial one does not produce such a pronounced effect. On average, the real imaging performed contributed about as much to secondary cancer risk as daily kV CBCT with mixed 200°and 360°kV CBCT would have.
Discussion
We have mathematically estimated the risk for developing secondary cancers of the lung and female breast for a collective of eight pediatric patients (11 radiotherapy treatment plans) with Morbus Hodgkin based on the dose distribution from the treatment plan and several clinically relevant IGRT scenarios. Although there is relatively large variability between the patients, the additional risk from imaging could be determined. Depending on the imaging scenario used, the excess average risk at age 50 for developing a secondary cancer of the lungs ranged between 1.3 and 10 cases per 10 4 PY (0-2.3 per 10 4 PY only from imaging) and 2.4-27.5 cases per 10 4 PY (0.1-4.8 per 10 4 PY from imaging) for a secondary cancer of the female breast.
Influence of assumed age at secondary cancer incidence
Our estimates for the EAR of secondary cancer development depend on the age attained included in the model.
We have opted for a relatively young age of 50 years, since this is already more than 30 years after treatment (age of exposure was set to the real age of the patients at treatment) and since this would be an age at which all possible therapeutic measures would most probably still be taken to cure the secondary malignancy.
The value assumed for the age attained only influences the risk estimate via the modifying function μ, which directly scales with the EAR. For our patient collective and an assumed age attained of 50 years, μ for the lungs is of the order of 0.23, but strongly increases with age attained to 0.95-0.97 for 70 years and about 2.8 for 90 years. This explains to some degree the relatively small EAR values obtained in our study. By considering an attained age of 70 rather than 50 years, all presented risk values would be increased about fourfold, and up to a factor 12 if a long life expectancy as 90 years is assumed. For breast cancer, μ varies more between the patients, as the dependence on the age at exposure is stronger. For an attained age of 50 years, μ is between 0.95 and 1.32, increasing to 1.7-2.3 for an attained age of 70 years and to 2.5-3.6 for 90 years. Here, the age attained only increases the estimated EAR by a factor of less than 3.
Influence of modelling approach
All models for secondary cancer induction are simplified estimates based on the available evidence, which is complicated by the long latency periods and the existence of a large number of confounding factors influencing the risk of secondary cancer induction (e.g., additional chemotherapy, life-style and other exposures such as smoking, etc.). While a numbers of models are available with parametrizations for different OAR's, it is as yet unclear which class of model (linear-non-threshold, plateau or bell-shaped) should be considered the most realistic. The full model by Schneider et al. [12] has the intuitive plausibility that high doses leading to cell kill would not be expected to be as cancerogenetic as lower doses resulting only in DNA damage without cell death. However, this hypothesis is not unanimously adopted, as some studies have also supported a linear relationship (compare Filippi et al. for a recent review [13]).
If the assumption of a bell-shaped model should hold true, however, it is highly relevant that all dose contributions should be summed together for secondary cancer estimation, rather than considering the effects individually. If the secondary cancer risk from imaging and from treatment were assessed separately from one another, the linear model would be applied to the small doses implicated from imaging. In principle, this approach would be invalid if the treatment dose is far from the linear dose-risk regime, so that the contributions from imaging and treatment are not additive in the simple sense. However, in some cases there is no way of performing a combined imaging plus treatment dose assessment for secondary cancer risk, e.g. if no information on the treatment dose exists and just general conclusions about different imaging systems and their comparison with each other are drawn [14].
While emphasizing that this approach should be avoided because of the presumed non-linear shape of the dose-risk Fig. 4 Dose metrics for the left lung (all patients). Shown are 8 separate data sets because one patient received two separate planning CTs for the two target sites Fig. 5 Spread of the V20Gy dose metric for the left lung across the patient collective. The box gives the 25% and 75% quartile, the horizontal line the median, the square the average. Whiskers reach to the maximum curve, we perform this estimation to demonstrate in how far the results would be influenced by following this approach, as it is sometimes adopted in the literature. Table 6 gives the average dose to the lung and breast from imaging only for the different scenarios, and the resulting secondary cancer risk from the linear model (RED(D) = D). The estimated risk from this method is considerably higher (by a factor 3-4) than the risk resulting from the clinically realistic scenario in Fig. 6 Excess absolute cancer risk for the left lung for the patient collective modelled for different imaging scenarios (each colour bar corresponds to an individual patient CT data set) Fig. 7 Additional EAR from imaging only (difference between total EAR as shown in Fig. 6 and EAR from the original plan, each colour bar corresponds to an individual patient CT data set) which imaging dose and treatment dose act together simultaneously. The possible non-linearity in the risk curve thus plays a significant role and different coinciding contributions to dose should be analysed together, if this is possible.
Comparison with previous studies
A number of studies have estimated imaging dose for MV and kV modalities in either planar or CBCT geometries, although relatively few have considered pediatric patients. For the Varian On-Board Imager (OBI) system (Varian Medical Systems, Palo Alto, California, USA), Ding et al. [15] obtained doses to soft tissue of the order of 4-10 cGy (half-fan) and 3-9 cGy (full-fan mode) for pediatric patients with imaging in the head-and-neck region using the Vanderbilt-Monte-Carlo-Beam-Calibration algorithm. Similar dose values (1.9-10.5 cGy depending on the organ) were reported by Deng et al. [16] for 125 kVp voltage based on Monte Carlo methods. Measurements in an anthropomorphic phantom (adult and pediatric) [17] confirm this order of magnitude. Our calculated average organ doses for the kV imaging scenarios are larger than the original treatment plan dose by up to 20 cGy for daily imaging (up to 16 fractions, which means a few cGy per image). This is in good agreement with previous studies, which can be expected since dose calculations for our system also agreed well with those from Varian and Elekta (Elekta, Stockholm, Sweden) for other tumor indications.
Regarding secondary cancer risk estimates, the results from previous studies are rather varied depending on the assumed target localization, dose distribution and secondary cancer model (for reviews, see [18,19] and references therein). Comparing our predictions with clinical data, Dörffel et al. [20] observed an absolute excess risk of developing secondary malignancies at the breast of 14.9 per 10,000 PY in a cohort study of 2548 patients treated for Hodgkin's Lymphoma within 30 years' follow-up, which agrees well with the range of values obtained in our study (2-26 cases per 10,000 PY). In their review, Kamran et al. [21] list an absolute risk for breast cancer after Hodgkin lymphoma of 37 per 10,000 PY; Schaapveld et al. [22] estimate an EAR of 20.5-29.3 per 10,000 PY for the lung and 44.7-65.0 per 10,000 PY for the breast. Since our prediction is for an attained age of 50 years and increases with higher age, the agreement is still plausible.
Only few studies have focused on secondary cancer risk from imaging in radiotherapy. Zhang et al. [23] presented imaging dose from Varian kV CBCT as a function of patient chest circumference, where organ mean dose per scan was of the order of 0.8-3 cGy (in agreement with our results), but used the BEIR VII model to transfer these values into relative risks for secondary cancer. As we have pointed out, the non-linear behaviour of the cancer risk curve should be accounted for by considering the combination of treatment and imaging dose where this is possible. For this high-dose regime, it is more common to use Schneider's full mechanistic model; however, this is usually applied to treatment plans rather than IGRT scenarios [24]. To our knowledge, our study is the first one to include imaging scenarios in the radiotherapy treatment plan for an accurate assessment of additional cancer risk in the dose regime used for treatment.
Limitations and implications of this study
In addition to the relatively small patient collective available for our study, it must be born in mind that the treatment plans were not particularly optimized to minimize secondary cancer risk, neither for optimal sparing of the female breast. Although these organs are taken into account as an organs at risk in our planning process, a compromise was made for best sparing of all healthy organs, so that the two sites at most risk for secondary cancer incidence were not prioritized over the other OAR's. While all patients were treated with the involved field technique, the target volumes showed strong variation (PTV volumes 54-2419 cm 3 , average 1145 cm 3 ). Most patients were treated using step-and-shoot IMRT (4-14 beams with between 30 and 70 segments), only very small target volumes (e.g., case 9, os ileum) were treated using 3D conformal radiotherapy (3D CRT with 2 beams). Volumetric modulated arc therapy (VMAT) plans were never used. The number of beams and segments used in the IMRT plans varied individually. In addition to setting objectives for the organs at risk, ring and normal tissue structures were created to form a dose gradient around the target volume. Despite the large variability between the patients, the DVH metrics correspond to those reported by other authors. For example, Maraldo et al. [25] assessed normal tissue doses for different radiotherapy targets for patients with Hodgkin's lymphoma. For the lungs, they found mean doses in the range of 30-52% of the prescribed dose (30 Gy in their study, i.e. 11.7-15.6 Gy) for mantle field and mediastinal irradiation and 2.2-4.0 Gy for neck and axilla irradiation, which agrees with the range of doses from our collective (1.6-18.7 Gy mean lung dose). Similarly, their values for the female breast were between 1.4 and 17 Gy depending on the treatment fields, corresponding to our dose range between 0.7 and 11.5 Gy.
Regarding margins for the PTV, in a previous unpublished study (similar to [8]) we analyzed positioning errors for a collective of children, resulting in a systematic population setup error of 0.7/1.0/− 0.2 mm in the anterior-posterior (AP)/left-right (LR) and superior−/inferior (SI) directions, a variation of population setup error of 1.7/2.6/2.5 mm (AP/ LR/SI) and a population random error or 3.9/4.9/3.9 mm (AP/LR/SI), which yields a CTV-PTV margin of 8.2/11.4/ 10.2 mm according to the margin recipe by van Herk et al. [26]. In the clinical practice, between 10 and 15 mm margin are used at our department. In fact, the use of daily imaging would be expected to reduce the required margins, as the daily statistical error could be corrected before treatment and position close to the planned "ideal" position could be attained. In this case, a margin reduction would allow for better sparing of organs at risk and a possible decrease in secondary cancer risk. Therefore, the increase in dose from daily imaging might be counterbalanced by an sparing in dose due to improved positioning accuracy. Such a trade-off would be interesting to perform, but remains outside the scope of this study as a number of combinations (margin vs. imaging scenario) would need to be compared. Similar approaches have been presented for other treatment sites [11,27]. As a rule-of-thumb, dose from kV imaging is relatively small and it might be expected that the positive influence of dose sparing from margin reduction would be dominant in this scenario. Regarding daily MV imaging, however, this might no longer be the case, and it is generally believed that a daily 6 MV CBCT scenario should be avoided unless in very limited indications (e.g., direct vicinity of critical OAR and high-risk target volume). Finally, the present study focusses on a highly vulnerable collective (children with good long-term prognosis treated with relatively large fields and wide margins), so that the resulting estimated risk can be expected to be larger than for the most frequent radiotherapy treatments (adult patients with small fields), where improved positioning accuracy and small margins achieved by daily (kV) imaging might considerably reduce normal tissue complications without detrimentous effects on secondary cancer risk.
Conclusions
If daily imaging is required, this can be performed with less than 1 case per 10 4 PY additional cancer risk using kV CBCT orif no other option exists -6 MV as long as planar images are taken. Depending on the geometry of the X-ray tube (e.g., opposite to the treatment head), a further reduction on dose can be achieved for superficial organs such as the breasts by applying only a reduced-arc rotation (in our case, behind the patient's back). When analyzing secondary cancer risk from imaging modalities, this should in the best case be combined with the treatment plan, as a separate analysis of only imaging dose in the linear range leads to an overestimation of the secondary cancer risk. | 7,539 | 2018-09-05T00:00:00.000 | [
"Medicine",
"Physics"
] |
The Roles of Sodium-Independent Inorganic Phosphate Transporters in Inorganic Phosphate Homeostasis and in Cancer and Other Diseases
Inorganic phosphate (Pi) is an essential nutrient for the maintenance of cells. In healthy mammals, extracellular Pi is maintained within a narrow concentration range of 0.70 to 1.55 mM. Mammalian cells depend on Na+/Pi cotransporters for Pi absorption, which have been well studied. However, a new type of sodium-independent Pi transporter has been identified. This transporter assists in the absorption of Pi by intestinal cells and renal proximal tubule cells and in the reabsorption of Pi by osteoclasts and capillaries of the blood–brain barrier (BBB). Hyperphosphatemia is a risk factor for mineral deposition, the development of diseases such as osteoarthritis, and vascular calcifications (VCs). Na+-independent Pi transporters have been identified and biochemically characterized in vascular smooth muscle cells (VSMCs), chondrocytes, and matrix vesicles, and their involvement in mineral deposition in the extracellular microenvironment has been suggested. According to the growth rate hypothesis, cancer cells require more phosphate than healthy cells due to their rapid growth rates. Recently, it was demonstrated that breast cancer cells (MDA-MB-231) respond to high Pi concentration (2 mM) by decreasing Na+-dependent Pi transport activity concomitant with an increase in Na+-independent (H+-dependent) Pi transport. This Pi H+-dependent transport has a fundamental role in the proliferation and migratory capacity of MDA-MB-231 cells. The purpose of this review is to discuss experimental findings regarding Na+-independent inorganic phosphate transporters and summarize their roles in Pi homeostasis, cancers and other diseases, such as osteoarthritis, and in processes such as VC.
Introduction
Inorganic phosphate (Pi) is an essential nutrient for the formation of ATP skeletal mineralization and the constituents of DNA, RNA, phospholipids and a variety of phosphorylated metabolic intermediates [1,2]. Due to the ability of Pi to donate H + through OHgroups, it contributes to the buffer system in the blood (pH 7.4), modulating the ratio of monovalent to bivalent phosphate based on pH [3].
Pi absorption is possible due to transporter-mediated translocation across cell membranes. Pi absorption through Na + /Pi cotransporters has been established in mammalian cells. These cotransporters constitute two large families of inorganic phosphate transporters: SLC20 and SLC34 [4,5]. The SLC20 family comprises two members, i.e., PiT-1 (encoded by SLC20A1) and PiT-2 (encoded by SLC20A2), both of which are sodium-phosphate cotransporters that preferably carry monovalent inorganic phosphate (H 2 PO 4 − ) together with two sodium ions. These transporters are expressed almost exclusively in the kidney [4,5]. The SLC34 family contains three members, namely NaPi-IIa (SLC34A1), NaPi-IIb (SLC34A2) and NaPi-IIc (SLC34A3), all of which are sodium-phosphate cotransporters; however, they vary in their biochemical kinetics. These proteins transport divalent inorganic phosphate (HPO 4 2− ) together with two or three sodium ions [5,6]. A Na + -dependent Pi transporter family (NaPi-I, NPTI) also exists, though it also carries anions other than Pi. NPTI is predominantly expressed in the proximal brush border tubular membrane and acts more as an intrinsic Pi transport modulator than as a Na + /Pi cotransporter [7,8].
Although Pi Na + -dependent transporters have been investigated in detail, another class of sodium-independent Pi transporters has been identified in intestinal [9] and renal cells [10], osteoclasts [11], chondrocytes [12], endothelial cells [13], blood-brain barrier (BBB) capillaries [14] and tumor cells [15]. Studies have reported the kinetic component as being Na + -independent with a Pi transporter, not as a nonspecific Pi transport, showing that it is not merely a consequence of increased diffusion. This transport exhibits specific characteristics of saturation, pH dependence and inhibition, and along with sodium-dependent Pi, plays a significant role in Pi homeostasis.
In healthy mammals, extracellular Pi is maintained within a narrow range of concentrations: between 0.70 and 1.55 mM [16,17]. Phosphate metabolism in the body involves complex interactions among several factors that can affect the digestion, absorption, distribution and excretion of this element, maintaining Pi homeostasis [8]. In this work, we review the mechanisms of Na + -independent phosphate transporters present in some tissues and discuss their possible roles in the regulation of Pi homeostasis and the development of some diseases.
Pi Transport System in Intestinal Pi Absorption
The physiological luminal phosphate concentrations in the human jejunum range from 0.7 to 12.7 mM, depending on the food type ingested [18]. However, it has been demonstrated that phosphate uptake is critically dependent on sodium, which has an affinity for phosphate of~0.1 mmol/L and is responsible for only 50% of transepithelial phosphate transport across the small intestine. The remaining 50% of transepithelial phosphate transport is Na + -independent ( Figure 1) [19]. Na + -independent Pi transport facilitates the absorption of Pi in intestinal cells [9,19] and proximal tubule cells [10] and the reabsorption of Pi in bone by osteoclast-like cells derived from RAW264.7 cells (H+-dependent Pi transport) [11] and in capillaries of the blood-brain barrier (BBB) (anion exchange) [14].
A study using the human cell line Caco2BBE as a model of intestinal cells was performed to biochemically characterize the transport of Pi of cells grown at 1 mM Pi (control condition) or 4 mM Pi, the latter representing the high concentration of Pi in the intestinal lumen [9]. The cells in all conditions exhibited Na + -independent Pi transport, suggesting that type II and III Pi transporters are not involved. At 1 mM Pi, Na + -independent Pi transport was proton-activated, as evidenced by its inhibition by two proton ionophores (Carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone-FCCP and Carbonyl cyanide-m-chlorophenyl hydrazine-CCCP) [9]. In addition, increased Pi uptake was observed to be insensitive to FCCP and CCCP at 4 mM Pi (Table 1) [9]. Candeal et al. attempted to identify potential transporters involved in Na + -independent Pi transport in several models by analyzing either RNA expression differences between cells maintained at 1 or 4 mM Pi or the siRNA-induced downregulation of specific transporters. None of these approaches were successful, but they helped to eliminate SLC26, SLC20, SLC17 and SLC34 family members as possible candidates [9].
In other animal models, Na + -independent Pi transport has been studied; however, no conclusions about sodium-independent Pi transporters have been possible, although much speculation has been offered. In intestinal brush border membrane vesicles (BBMVs) prepared from rat jejunum, the Na + -independent, diffusional component of intestinal Pi transport represents approximately 40-50% of the total uptake [21]. This component is significantly higher in the rat ileum than in the rat jejunum. Intestinal Pi transport occurs by both a sodium-independent, unsaturated process and an active, sodium-dependent component of phosphate absorption, mainly in the duodenum and jejunum [21].
The relative contributions of Na + -dependent and Na + -independent phosphate transport along the rat intestine have been characterized using duodenal, jejunal and ileal regions of the small intestine and proximal and distal colon. These contributions suggest that transepithelial phosphate absorption in vivo is predominately Na + -independent, with Na + -dependent (presumably NaPi-IIb-mediated) transport playing a lesser role than currently thought [22]. The authors responsible for these findings discussed two possibilities regarding sodium-independent Pi transport: (1) some tight junction proteins, such as claudin, might function as channels that provide a selective paracellular route for passive ion flow. Apparently, it plays a role in the absorption of calcium in the small intestine, but paracellular phosphate transport has not been investigated [22,23]. (2) A yet-to-be identified pathway for Na + -independent phosphate transport may exist [22].
Another research group studying the small intestine of rats found substantial sodium-independent Pi uptake. The authors responsible for this finding argue that at least some of this sodium-independent Pi transport could be explained by the activity of PiT-2, as this transporter is still capable of functioning in the absence of sodium, although with minimal activity [24]; however, this hypothesis has not yet been tested.
Some studies of ruminant intestines have shown the additional participation of Pi Na + -independent transporters in Pi absorption [25]. Shirazi-Beechey et al. showed the existence of H + -rather than Na + -coupling for Pi transport in the ruminant intestine [26]. This finding was achieved by the use of compounds that are capable of dissipating the imposed H + gradient (4,5,6,7-tetrachloro-2-trifluoromethylbenzimidazole, TTFB) and inhibiting the uptake of phosphate into ovine duodenal brush-border membrane vesicles. The pH at the site of Pi transport in the ovine intestine is 3 to 4. At this pH range, the majority (98%) of the orthophosphate is in the monovalent form, suggesting that the species to be transported is H 2 PO 4 [26]. In goats, it was demonstrated that two different transport systems mediate intestinal Pi transport. In goat duodenum, an H + -dependent and Na + -sensitive Pi transport system was identified that does not belong to the NaPi type II family. In contrast, in the goat jejunum, Na + -dependent, H + -sensitive Pi transport is mainly mediated by NaPi-IIb [27].
Bone Resorption and Pi Transport Coupled to the Proton Gradient
The skeleton is continually changing in its mass or form via the activities of osteoblasts (the cells responsible for bone formation) and osteoclasts (the cells responsible for bone resorption) [28]. Bone resorption depends on the ability of osteoclasts to generate extracellular acid compartments containing vacuolar-type H + -adenosine triphosphatase (V-type ATPase), which degrades hydroxyapatite ([Ca 3 (PO 4 )2]3Ca(OH) 2 ) into Ca + , water and phosphate [28].
Researchers have characterized Pi transport in osteoclast-like cells derived from RAW264.7 cells after treatment with receptor activator of NF-κB ligand (RANKL). Osteoclast cell differentiation was confirmed by tartrate-resistant acid phosphatase (TRAP) and calcitonin receptor (CTR) staining. The addition of K 2 HPO 4 induced a slight decrease in intracellular pH. The results suggested that H + flowed into osteoclast-like cells along with Pi. The osteoclast-like cells that were exposed to bone particles showed an increase in H + -dependent Pi transport [11]. One possible explanation for these findings is that HCl secretion by osteoclasts produces Ca 2+ , water and phosphate from the CaPO 4 salt hydroxyapatite. Osteoclast-like cells with a Pi transport system capable of H + -dependent stimulation at acidic pH are necessary for bone resorption or production of the massive amounts of energy required by V-type ATPase for acidification of the extracellular environment [11].
The Pi Transport System in Proximal Renal Tubule
In the kidney, a considerable number of functional nephrons play a significant role in Pi homeostasis, where 75% of the filtered Pi is reabsorbed in the proximal tubule; in the distal tubule, only 10% is reabsorbed, and the remaining 15% is lost in the urine [29]. The proximal tubule is an intensively active region because two-thirds of the glomerular filtrate is reabsorbed as ions, water and other molecules within the lumen of the proximal tubule by brush-border membrane microvilli (BBM). The molecules absorbed by the BBM pass into the blood plasma through the cells of the basolateral membrane (BLM) of the proximal tubule [30].
The electrochemical potential of sodium across the BBM provides the driving force for intracellular phosphate accumulation [31]. In renal membranes from the rat kidney cortex, a sodium-phosphate cotransport system localized in the BBM and sodium-independent Pi transport through the BLM have been demonstrated [32].
Phosphate is taken up by the BBM by a sodium-phosphate cotransporter (usually as 2Na + −HPO 4 −2 ), which has a high affinity for Pi (0.1-0.2 mM) and concentrates phosphate in the cytosol [32].
It is speculated that phosphate exits across the BLM by moving down an electrical gradient; as the cytosolic free phosphate concentration is approximately 1.0 mM and the plasma Pi concentration is 2.5-3.0 mM, the transmembrane voltage plays an essential role in BLM transport [10]. Azzarolo et al. analyzed BLM vesicles isolated from porcine kidneys and demonstrated that Pi transfer across the cytoplasm at the basolateral cell side is facilitated by a form of sodium-independent phosphate transport that is specific for phosphate with low affinity (Figure 1 and Table 1) [10].
Pi Uptake by Capillaries of the Blood-Brain Barrier
The BBB restricts the free diffusion of nutrients, hormones and pharmaceuticals between the blood and brain in either the blood-to-brain direction or the brain-to-blood direction [33]. The capillaries of the brain are formed by a specialized endothelium, the function of which is to regulate the movement of solutes between the blood and brain. The concentration of inorganic phosphate in the interstitial fluid of the brain is maintained between 0.5 and 1.0 mM [14].
In bovine brain capillaries, Pi transport is a high-affinity process not regulated by sodium and is not coupled to cations for the translocation of phosphate across capillary membranes. The uptake of phosphate in the capillaries of the BBB reflects transport via a high-affinity system (Table 1) [14].
In addition, Pi uptake has been found to be sensitive to inhibition by arsenate and phosphonoformic acid (PFA). Phosphate transport by isolated capillaries was found to be partially inhibited by inhibitors of anion exchangers, DIDS (4,4 -diisothiocyanatostilbene-2,2 -disulfonate), SITS (4-acetamido-4-isothiocyanostilbene-2,2-disulfonate), and competitively inhibited by low concentrations of various anions, pyruvate, acetate, citrate, glutamate and sulfate, which are subsequently metabolized by the cell in the Krebs cycle to produce ATP [14]. No correlation was shown suggesting the contribution of NaPi-I to Pi transport/anion exchange. Together with these results, Na + -independent Pi transport is concluded to be an anion exchanger (Figure 1). Such transport would provide phosphate ions to the cell to supply energy demands and phosphorylation processes and help maintain a low concentration of inorganic phosphate in the brain interstitial fluid [14].
For the same group, it was examined whether BBB phosphate transport is influenced by hormonal and nonhormonal stimuli. Three distinct pathways involving various receptors and second messengers were identified, wherein (1) the stimulation of adenylate cyclase decreases Pi transport, (2) the activation of phospholipase C stimulates Pi transport and (3) the stimulation of tyrosine kinases reduces Pi uptake [34].
Extracellular Pi and Tumorigenesis
In 2007, Pi was shown to be a limiting factor for biological growth; it is a limiting factor mainly because it is one of the fundamental elements necessary for the synthesis of nucleic acids, such as DNA and RNA [35]. Elser et al. published a mathematical calculation based on the growth rate hypothesis (GRH), which predicts a three-quarter reduction in tumor size if the patient halves phosphate intake [35].
Because Pi is an essential nutrient for energy metabolism and because high levels of Pi promote signaling aberrations in tumor cells, much research has focused on the association between hyperphosphatemia and cancer development [17,36]. A clinical study by Papaloucas et al. showed that patients diagnosed with head, neck, lung and cervical cancer had a high serum Pi concentration of 2.52 (±0.72), a concentration twice as high as that in healthy patients without cancer: 1.09 (±0.19) mM [37]. However, the authors of that study did not clarify whether the increase in serum phosphate was a cause or consequence of the disease [37].
Similar to cytokines and various growth factors, phosphate can induce the growth of cancer cells through various growth-promoting signals [37]. In skin cancer cells (JB6), high concentrations of Pi (3 mM Pi) have been found to promote cell transformation and stimulate cell proliferation by activating the N-Ras signaling pathway ERK1/2 [16]. However, by adding an inhibitor of Pi transport (PFA or foscarnet), activation of the N-Ras pathway was blocked at high Pi concentrations. In a mouse model of tumorigenesis induced by a highly carcinogenic chemical agent (7,12-dimethylbenz (a) anthracene12-O-tetradecanoylforbol-13-acetate), administration of a high-Pi diet (3.25 ± 0.58 mM Pi) versus a normal-Pi diet (2.17 ± 0.19 mM Pi) accelerated the induction of papilloma formation [16].
In a mouse model of lung cancer induced by K-rasLA1 mutation, researchers observed that the incidence of lung tumors and tumor diameter were larger in mice fed a high (1.0%)-Pi diet for four months than in those fed a normal (0.5%)-Pi diet for four months [38]. In addition, the same group found that an excessive-Pi diet or Pi restriction can decrease the expression of phosphatase and tensin homologue (PTEN), activate the Akt pathway and increase the expression of the NaPi-IIb transporter at high Pi [39].
Because the serum Pi range reaches up to 1.2 mM [37], one year later, it was demonstrated that there is another manifestation of Michaelis-Menten kinetics of Km = 1.38 ± 0.16 mM, indicating low-affinity Pi transport acting in a sodium-independent manner [15]. In addition to the lack of dependence of sodium, H + -dependent Pi transport stimulated by acidic conditions was verified, suggesting that protons present in the extracellular environment might be transported together with inorganic phosphate by an H + -dependent Pi cotransporter [15]. Consistent with these observations, FCCP (an H + ionophore), bafilomycin A1 (an inhibitor of vacuolar H + -ATPase) and SCH28080 (an H + , K + -ATPase inhibitor), which deregulate the intracellular levels of protons, inhibited H + -dependent Pi transport. Notably, no effect was observed when anions and anion-carrier inhibitors (DIDS) were tested [15].
Recently, in mammary gland tumors of mice, an acidic region and a high concentration of Pi in the tumor microenvironment (1.8 ± 0.2 mM) compared to that in normal mammary gland (0.84 ± 0.07 mM) were identified as markers of tumor progression [43]. Decreased Na + -dependent Pi transport and NaPi-IIb expression in the presence of 2 mM Pi were observed, concomitant with an increase in H + -dependent Pi transport [15]. These observations led the researchers involved to suggest the occurrence of a compensatory mechanism for Pi transport in situations where the transport of Na + -dependent Pi is compromised [15]. The excess Pi might be able to compensate for the energetically expensive biochemical features of tumor cells. The H + -dependent Pi transporter could confer on tumors a biological advantage by endowing cells with the ability to incorporate extra Pi even when sodium-dependent Pi transport is saturated by high extracellular Pi in the tumor microenvironment (approx. 2 mM) [15].
Na + -Independent Pi Transport and Metastasis
Based on the growth rate hypothesis, it has been hypothesized that metastasis establishes and forms secondary tumors at organs with higher Pi content than that of the organ containing the original tumor [44]. Lin et al. sought to evaluate the effects of elevated Pi on metastasis and angiogenesis in lung cancer cells (A549) and breast cancer cells (MDA-MB-231). They observed that elevated Pi enhanced cell migration and the expression of angiogenic markers such as VEGF, osteopontin and "Forkhead box protein C2", a transcription factor related to vasculogenesis and angiogenesis [45].
In a study of breast cancer, a sodium-dependent Pi transport inhibitor (PFA) was found to yield an approximately 50% inhibition of cell adhesion and migration, suggesting the participation of this Pi transporter in cellular motility processes [42]. Recently, Lacerda-Abreu et al. demonstrated the inhibition of the Pi H + -dependent transporter by phosphonoacetic acid (PPA), which is able to inhibit cell adhesion and migration by approximately 40% [15].
In fact, these two studies show that the two Pi transporters are important for metastatic processes; however, the biochemical behavior of these transporters differs, and when the Pi concentration in the tumor microenvironment changes, the roles of these Pi transporters in tumor processes might change as well (Figure 2). For example, Lacerda-Abreu et al. used cells that migrated through the Transwell membrane for their Pi transport assays. The migrating cells displayed higher H + -dependent Pi transport activity than nonmigrating cells. In contrast, there was no difference between these two conditions when Pi transport activity was measured at 100 µM Pi, a suitable condition for high-affinity Na + -dependent Pi transporters [15]. Figure 2. Schematic of the role of sodium-independent Pi transport in the development of breast cancer. Na + -independent Pi transport and Na + -dependent Pi transport in breast cancer cells promote cell adhesion and migration, which are important for maintaining cancer metastasis [15,42].
The migration cascade and metastatic invasion are regulated by a multistage process called the epithelial-mesenchymal transition (EMT), in which transformed epithelial cells acquire mesenchymal characteristics, including motility and invasiveness [46]. During EMT, epithelial cells lose epithelial markers (E-cadherin, occludin and cytokeratins) and begin to express mesenchymal markers (vimentin and fibronectin) [46]. Triple-negative breast cancer cells, such as MDA-MB-231 cells, have an EMT morphology (presenting low expression of E-cadherin and high expression of vimentin) and a greater invasive capacity than luminal breast cancer cells (MCF7 and T47-D cells) [15,46]. MDA-MB-231 cells grown in the presence of phosphonoacetic acid (PAA) for 24 h present induced expression of E-cadherin. These observations strongly suggest that when H + -dependent Pi transport is inhibited, MDA-MB-231 cells could revert from a mesenchymal phenotype to an epithelial phenotype and consequently exhibit low migratory capacity [15].
Pi Transport Stimulated by [H + ] in Ehrlich Ascites Tumor Cells
In Ehrlich ascites tumor cells, Na + -dependent Pi transport plays a principal role in the maintenance of intracellular Pi concentration. However, a Na + -independent component of Pi transport comprises approximately 12% of the total Pi flux [47]. Therefore, Na + -dependent and Na + -independent Pi transport processes appear to involve, at least functionally, different transporters.
Furthermore, H + -stimulated Na + -independent Pi transport with saturation kinetics with respect to [H + ] has been observed. Additionally, it appears that the stimulation of Pi Na + -independent transport by H + decreases the intracellular pH (below approximately 6.5), which affects the inhibition of Pi Na + -dependent transport, consistent with the interaction of H + with an intracellular site that regulates Na + -dependent Pi transport [47].
Disease Development Related to Hyperphosphatemia and Hypercalcemia
In general, Pi homeostasis and the regulation of renal and intestinal Pi transport are crucial for the normal functioning of human organs. For example, when the renal and intestinal transport of Pi is compromised, hypophosphatemia is promoted, which leads to the dysfunction of several organ systems, including the musculoskeletal system. On the other hand, the dysregulation of the renal and intestinal transport of Pi can also result in hyperphosphatemia, leading to impaired cardiovascular function and soft tissue calcification [5].
Hyperphosphatemia is defined as a serum phosphorus concentration >4.5 mg/dL (1.45 mM). It is a major cause of morbidity and mortality in patients with chronic kidney disease (CKD) and can also be a cause of acute kidney injury (AKI). A decline in renal function leads to phosphate retention, high levels of parathyroid hormone (PTH) and low levels of 1.25-dihydroxy vitamin D [48]. The most common clinical manifestation of hyperphosphatemia is hypocalcemia due to the precipitation of calcium phosphate in soft tissue, which can lead to clinical manifestations of hypocalcemia [7].
Vascular Smooth Muscle Calcification
Vascular calcifications (VCs) are actively regulated biological processes that involve cellular ossification and the participation of factors that either promote or inhibit the organized deposition of hydroxyapatite in vascular smoothing [49]. Susceptibility to VC is genetically determined and actively regulated by many factors. One of these factors is hyperphosphatemia, which promotes VC and is a nontraditional risk factor for cardiovascular disease mortality in end-stage renal disease patients [50].
Vascular smooth muscle cells (VSMCs) in rats were kinetically characterized for phosphate transport. VSMCs exhibit both Na + -dependent and Na + -independent Pi transport components with similar kinetic behavior and high affinity (Table 1). Both components contribute almost equally to the total uptake of Pi by VSMCs. These kinetic characteristics are very relevant to VC as they are associated with increasing levels of serum phosphate due to hyperphosphatemia, consistent with the theory that Pi influx works as an exact sensor of calcifying conditions (Figure 3) [13]. . Na + -independent and Na + -dependent Pi transport (PiT-1 and PiT-2) in vascular smooth muscle cells (VSMCs) contributes to hydroxyapatite deposition in these cells [13,51].
A study of Pi transport in rat VSMCs revealed that the sodium-independent Pi uptake system is competitively inhibited by sulfate, bicarbonate and arsenate and weakly inhibited by DIDS, SITS and phosphonoformate. These findings indicated that the Pi transport system is most likely coupled to the exit of anions [51]. In addition, an exit pathway from the cell that is partially chloride-dependent and unrelated to the known anion exchangers expressed in VSMCs has been shown to be resistant to DIDS/SITS [51].
To clarify which transporter is responsible for sodium-independent Pi transport, several genes were silenced (SLC4a2, SLC4a3, SLC4a7, SLC26a2, SLC26a6, SLC26a8, SLC26a10 and SLC26a11) using siRNA transfections. None of the interfering transporters were found responsible for a significant part of the sodium-independent influx or efflux of Pi in VSMCs [51].
Crystal Formation in Articular Cartilage and Osteoarthritis Development
Many joint diseases, such as osteoarthritis, are characterized by the eventual destruction of the articular cartilage. Some arthritic conditions are associated with the deposition of crystals, such as hydroxyapatite crystals, in the synovial fluid of articular cartilage of the joint. These crystalline materials include calcium pyrophosphate dihydrate and other forms of calcium phosphate [52]. Calcium and inorganic phosphate are taken up by matrix vesicles (MVs) derived from chondrocytes to form hydroxyapatite crystals, which propagate on collagen fibrils to mineralize the extracellular matrix [53].
Although previous studies of phosphate transport in articular chondrocytes have described high-affinity Pi transport mediated by a PiT system [54], inorganic phosphate transport has been identified in bovine articular chondrocytes, and it appears that high-affinity Na + -independent processes contribute to Pi uptake [12]. Regarding Pi transporter inhibitors, PAA and arsenate exhibit low affinities for the Na + -dependent component, but markedly higher affinities for the Na + -independent component (Table 1) [12]. In addition to chondrocytes, which uptake Pi and Ca 2+ , matrix vesicles have Pi and Ca 2+ transporters [20]. Solomon et al. characterized the Pi transporters in matrix vesicles (Table 1) [20].
Solomon et al. detected not only high-affinity Pi Na + -dependent transport, but also high-affinity Na + -independent Pi transport [20]. They proposed that Pi transport systems in chondrocytes or matrix vesicles could contribute to the deposition of minerals in cartilage, thus promoting degenerative joint disorders such as osteoarthritis. Hence, the pathways identified here constitute potential targets for pharmacological intervention to prevent crystal formation and osteoarthritis ( Figure 4) [12,20]. Na + -independent and Na + -dependent Pi transport (PiT-1 and PiT-2) and annexins (Ca 2+ channels) in chondrocytes or matrix vesicles promote cartilage mineralization and consequently osteoarthritis [12,20].
Conclusions
Pi is essential for many, if not all, living organisms because of its roles in several biochemical processes, such as kinase/phosphatase signaling; ATP formation; and lipid, carbohydrate and nucleic acid biosynthesis. Although the importance of sodium-dependent Pi transporters (NaPi type II and NaPi type III) in different tissues and their physiological functions have been demonstrated, an increasing number of studies have reported the presence of another kind of sodium-independent Pi transporter that aids in Pi absorption in intestinal cells and proximal tubule cells as well as reabsorption of Pi by osteoclasts in bone and by capillaries of the BBB. Sodium-independent Pi transporters also contribute to the accumulation of Pi and other minerals in cartilage and vascular smooth muscles, causing osteoarthritis and VC, respectively. Additionally, Pi is absorbed by a sodium-independent Pi transporter (H + -dependent) in breast cancer cells, which enhances the migration and adhesion processes that lead to the development of metastases. The studies included in this review have improved our understanding of Pi homeostasis and have clarified the development of Pi-related diseases. | 6,335 | 2020-12-01T00:00:00.000 | [
"Medicine",
"Biology"
] |
Calcium channel gating
Tuned calcium entry through voltage-gated calcium channels is a key requirement for many cellular functions. This is ensured by channel gates which open during membrane depolarizations and seal the pore at rest. The gating process is determined by distinct sub-processes: movement of voltage-sensing domains (charged S4 segments) as well as opening and closure of S6 gates. Neutralization of S4 charges revealed that pore opening of CaV1.2 is triggered by a “gate releasing” movement of all four S4 segments with activation of IS4 (and IIIS4) being a rate-limiting stage. Segment IS4 additionally plays a crucial role in channel inactivation. Remarkably, S4 segments carrying only a single charged residue efficiently participate in gating. However, the complete set of S4 charges is required for stabilization of the open state. Voltage clamp fluorometry, the cryo-EM structure of a mammalian calcium channel, biophysical and pharmacological studies, and mathematical simulations have all contributed to a novel interpretation of the role of voltage sensors in channel opening, closure, and inactivation. We illustrate the role of the different methodologies in gating studies and discuss the key molecular events leading CaV channels to open and to close. Electronic supplementary material The online version of this article (10.1007/s00424-018-2163-7) contains supplementary material, which is available to authorized users.
Introduction
The plateau of the cardiac action potential, contraction of muscle cells, generation of pace maker potentials, the release of hormones and neurotransmitters, sensory functions, and gene expression are all mediated by fine-tuned calcium entry through voltage-dependent calcium channels [27,49,96,120]. Mutations in calcium channels that disturb the channel gating lead consequently to diseased states called calcium channelopathies (Table 1) [20,94].
In this review, we focus on the molecular determinants of voltage-dependent opening and closure of the highly homologous CaV1.2 (for mechanism of calcium-dependent inactivation see i.e. Ben-Johny et al. [11]). Quantification of current kinetics and steady state activation of CaV1.2 by Beyl et al. [16] revealed the following sequence of events: At rest, the voltage sensors (VSs) are pulled into a down position by the electrical field. In their down state, the VSs lock the channel in its closed state. Membrane depolarization releases the VSs, resulting in their almost voltage-independent upward movement, which in turn releases the closed channel gates. The pore at first stays closed until the S6 gates disengage and the channel opens. Channel opening and inactivation are enabled when all four S4 segments have left their resting position (see also Horn et al. [51]). Here, we discuss how each of the four S4 segments is linked to channel activation and inactivation and propose a refined gating model for CaV1.2.
Architecture of the CaV1. 1
and CaV1.2 channel
The CaV1.1 α subunit is composed of four domains concatenated in a single polypeptide chain. Each domain consists of a voltage-sensing domain (helices S1 to S4) and the pore-forming module (helices S5 and S6, the P1 and P2 helices, Fig. 1). The selectivity filter, located at the extracellular side of the transmembrane domain (TMD), contains the EEEE locus [39,106,107,111]. The selectivity filter extends to the channel cavity, which is surrounded by a tetrameric arrangement of the S5 and S6 helices (see Fig. 1). The S6 helices form the activation gate at the intracellular end of the TMD [108]. In the closed state, the pore-lining S6 helices converge at the intracellular side to obstruct ion permeation. Modeling of CaV1.2 based on the CaV1.1 crystal structure by Wu et al. [106] shows that the cavity is occluded towards the intracellular side starting at the amino acids V430, F778, F1191, and F1501 from IS6 to IVS6, (see Fig. 2), including a hydrophobic region extending towards the intracellular side.
Upon opening, the intracellular ends of the S6 helices diverge from one another and thereby open wide enough to enable ions to pass. Computational and experimental studies on potassium and sodium channels propose a pivoting motion of the S6 helix, starting at a hinge-point in the middle of the helix [38,62,64,83,116,122]. Although it is widely agreed that such a movement takes place, the extent of channel [4,7,8,10,42,60,66,97,115,123]. A glycine that is frequently found at this hinge-point position (which allows a wider range of phi angles) provides bending flexibility [23,37,52,64,101]. However, the S6 mutation G770P in domain II of CaV1.2 (corresponding to the location of Bgating-hinges^in MthK (G83, [54]) and NaChBak (G219, [122])) affects neither the current kinetics nor the position of the activation curve, which suggests that the mechanism is distinct from potassium channel gating (see Fig. 2c, [50]). Individual VSDs are composed of four membrane spanning α-helices. The actual VSs are the S4 segments, in the case of the CaV1.1 structure a 3 10 helix [106]. These helices contain positively charged arginine or lysine residues at every third or fourth position [73, 85, 104-107, 110, 121]. Consistent with the crystallography and cryo-EM environment, the S4 segment of the VSs of all reported calcium, sodium, and potassium voltage-gated ion channel structures is in the upstate. However, the Blevels^of the upstates do vary in different structures, within some structures also between different domains. These Blevels^are measured by the number of positively charged amino acids that are above a bulky hydrophobic residue of the so-called charge transfer center (CTC). The CTC is formed by conserved negative or polar residues as well as the highly conserved occluding bulky hydrophobic residue on the S2 and an invariant aspartate residue on the S3 [18,79]. These residues, in addition to another negative or polar residue on S2, are important for sequential charge-charge interactions to the positively charged amino acids in S4 to catalyze its transmembrane movement [28,33,34,61,100,102,114]. In the latest CaV1.1 structure [106], the electron density map quality of the VSDIII does not allow assignment of the residues on S3 and S4. The authors propose the VSs are in a depolarized or upstate. In VSDI, four charges, R1 to R4, are above the occluding hydrophobic residue, in this case a phenylalanine, of the CTC. In VSDII, only three charges, R2 to R4, are above the occluding phenylalanine, suggesting induvidual and asynchronous movements of S4 segments.
The S4 and S5 helices are connected via the S4-S5 linker (see Fig. 1c, d)-a helix which runs parallel to the intracellular side of the membrane and almost surrounds the pore domain at its intracellular side (see Fig. 1c). Each voltage-sensing domain is adjacent to the neighboring pore domain (e.g. see [35]), with the S4 of one domain forming hydrophobic interactions with the S5 of the adjacent domain (Fig. 1d). This clockwise assembly not only allows the VSDs to influence the gate of their own [106,107]). CaV1.1 and CaV1.2 are highly homologous (see sequence alignment in Supplementary Fig. 1). The helices are represented as cylinders. Domains I to IV are shown in green, gray, purple, and yellow, respectively. a The top view arrangement of the channel with the voltage-sensing domains (VSDI to VSDIV) and pore domain. b The side view shows the structural elements of opposing domains. The bundle-crossing region at the lower third of the S6 segments forms the activation gate. c The bottom view with a highlight on the S4-S5 linker ring (in red). The S4-S5 linker helices run in parallel to the intracellular side of the membrane. d One domain of the α subunit of the voltage-gated calcium channel. S1 to S3 (green) and S4 (blue) represent the voltagesensing domain. Segment S5, the P-helices (green), the selectivity filter (dark blue), and the S6 (yellow) form the pore domain. S4 is connected to the S5 via the S4-S5 linker (red). The S4 is further in close proximity to the S5 of the adjacent subunit (light pink). The S6 as well as the P-helices of the adjacent subunit are shown in light gray. The G/A/G/A position is highlighted in orange (Depil et al. [36]), close to the loop between the S4-S5 linker and the S5 helix domains but potentially also the neighboring domains [110]. The impact of this interaction is currently unknown. In comparison, in Kv11.1 [104] and Kv10.1 [105], the voltage-sensing S4 segments are connected to the pore of the same domain by a short linker loop and every S4 has hydrophobic interactions with only the S5 of the same domain.
Localization of the activation gate
In the closed state, the pore-lining S6 helices converge at the intracellular side and obstruct ion permeation. A retinal disorder caused by a point mutation in segment IIS6 (I745T) of the CaV1.4 α1 subunit yielded insights into the gating mechanism [44]. Figure 2 illustrates that mutation I781T in IIS6 of the CaV1.2 α1 subunit (corresponding to I745T in CaV1.4) is located within the S6 helix bundle-crossing region. This threonine substitution (or mutation to proline) shifted the activation curves to the left [50], indicating reduced stability of the closed state and/or increased stability of the open state. Residue I781 is part of a cluster of hydrophobic residues, where proline substitutions cause prominent left shifts of the activation curve and marked slowing in current in the lower third of segment IIS6 (L779-A782, called LAIA motif, Fig. 2, [50]). A similar gating sensitive hydrophobic stretch of amino acids (VAVIM motif) in the lower third of segment IVS6 was identified in CaV2.3 by Raybaud et al. [82] (see also Zhao et al. [122] for slow gating phenotypes in NaChBak S6 mutants). These functional data are in agreement with the structural information. As illustrated in Fig. 2d, residues in the lower Fig. 2 Location of the activation gate: hydrophobic residues form a sealing region at the intracellular gate. a The permeation path of the pore domain (modified from Wu et al. [106]). The pore regions including S6 and selectivity filter between CaV1.1 and CaV1.2 are highly homologous (see Fig. S1 in supplemental materials). The figure displays the pore radii of CaV1.1 along the pore. The closest part of the hydrophobic area (a, position around 0) formed by V430, F778, F1191, and F1501 is emphasized with a red bar. The helix bundle crossing region is highlighted with a red dotted box. The blue dotted box marks the selectivity filter. b Cartoon representation of the pore domain. The third domain is omitted for clarity. Domain 2 (DII) is colored in gray, DIII in purple, and DIV in yellow. The dotted rectangles from a are extended to this figure to show the corresponding areas in the protein. The occluding residues V430, F778, F1191, and F1501 from the cavity facing the intracellular side are represented as spheres. c Effects of proline substitutions in positions F778-A782 and G770 on the voltage dependence of CaV1.2 activation. Solid lines represent fits to Boltzmann functions. Mutations causing large shifts and corresponding activation curves are shown in red. This research was originally published in the Journal of Biological Chemistry. Hohaus et al. [50]. d Detailed view on the helix bundle crossing region. The S6 helices are represented as cartoon with the third domain set transparent for clarity. Domain DI, DII, DIII, and DIV are colored in green, gray, purple, and yellow, respectively. The occluding residues V430, F778, F1191, and F1501 from the cavity side are represented as spheres. Please pay attention to the extended hydrophobic cluster of residues below the narrowest occluding positions that are represented as sticks. It is tempting to speculate that the hydrophobic cluster contributes to closed state stability to the activated not open state A (Fig. 3, see also del Camino et al. [24]) third of the S6 gates form a tight hydrophobic cluster. This cluster seals off the inner cavity from the intracellular environment. Disturbance in this region (e.g., by proline-induced bending of the helix) might induce small structural changes leading to wetting of this area, which would destabilize the closed state (Fig. 2d).
Taken together, functional and structural studies support a key role of I781 and three neighboring residues (L779, A780, and A782) in gating of the CaV1.2 channel, indicating that this region forms part of the channel's activation gate (for corresponding gating determinants in CaV2.3, see Raybaud et al. [82]). Interactions with neighboring residues in all four S6 segments apparently contribute to stabilization of the
The four-state gating cycle of CaV activation
As illustrated in Fig. 3, VSs can dwell in two states, resting (down) and activated (up) states, and in addition, the pore has two states: open or closed [16]. Correspondingly, the channel dwells in 2 × 2 = 4 states ( The VSs move in response to a depolarization from Bresting down^to an Bactivated up^position [2,3,90,91,112,113]. The channel thus switches from state R to A with voltage-dependent rate constants x(V) and y(V). When the VSs are in the activated position, the pore opens and closes with the voltage-independent rate constants α and β (channel transfers between states A and O). When the channel is in the open state, a hyperpolarization first induces a downward movement of the VS (transition O ↔ D) which is followed by pore closure (transition D ↔ R).
This model differs from traditional kinetic models of activation [117,118] [16]) Activation gating is determined by two functionally separate processes: a voltage-sensing mechanism (++++) and the conducting pore. Each functional unit can dwell in two states: the VS in the resting (down) and activated (up) states and the pore in the open or closed states. The entire molecule therefore dwells in 2 × 2 = 4 states: R, pore is closed and voltage-sensing mechanism locks the pore; A, voltage-sensing mechanism is activated and releases the pore, which, however, remains closed; O, the pore is open; D, the deactivated voltage-sensing mechanism is in the down position while the pore is still open. Rate constants of the pore opening and closure (α, β, γ, δ) are assumed to be voltageindependent. Rate constants of voltage-sensing mechanism (x, y, u, and w) are voltage dependent Although the model outlined in Fig. 3 fits most of the functional data on calcium current activation, it is still a simplification. Indeed, activation of the VSs comprising four charge-carrying S4 segments can be imagined as a multi-exponential process. During activation, each of the four VSs can dwell in either the resting (down) or activated (up) state, resulting in 2 4 = 16 individual combinations (IS4-up/IIS4-down/IIIS4-down/IVS4-down, IS4up/IIS4-up/IIIS4-down/IVS4-down, etc.). The activation of CaV1.2 is, however, predominantly mono-exponential and lumping together potential transitions between intermediate states seems justified. This model differs from descriptions of activation of the potassium channels, where the current develops with a significant delay (Cole-Moore delay) resulting from sequential transitions of individual VSs [72,87].
The advantage of this four-state model is that it enables the quantification of the VS activation and pore opening from current kinetics with widely accepted constraints: (i) rate constants of the pore opening α and β are voltageindependent and (ii) the voltage dependence of the rate constants of activation is described by Eyring functions: of the inverse problem approach yielded the parameters of the model that, in turn, allowed an interpretation of [17]. © the American Society for Biochemistry and Molecular Biology changes in steady state and kinetics of activation induced by mutations either on the pore or in VS segments [16].
State R
CaV channels remain in the R state at hyperpolarizing voltages (Fig. 3). In this conformation, the channel gates are closed. S4 segments in their down position lock the gate and prevent opening. Structural and functional studies revealed that closed channel gates prevent not only the inflow of calcium ions but can also inhibit dissociation of drug molecules from binding sites within the channel pore [17]. Crystallographic analyses of phenylalkylamine binding to the bacterial homotetrameric CaVAb channel showed how a Br-verapamil is trapped in the central cavity by closed S6 gates [98] (Fig. 4a). Studies of use-dependent CaV1.2 inhibition by the permanently charged phenylalkylamine (−)devapamil ((−)qD888) revealed unrestricted access of this large molecule to its binding determinants in the open state [12,17,47]. Recovery from block was voltage dependent with faster channel unblock at hyperpolarized voltages. This supports a scenario where negative voltages Bpull^positively charged (−)qD888 from its binding site on segments IIIS6 and IVS6 [94] through completely or partially closed gates [17]. From recovery kinetics, it was estimated that a membrane potential fraction of 0.56 affects drug dissociation (Fig. 4b, c; [17]). Assuming a quasi-linear distribution of the potential within the closed channel yields a localization of (−)qD888 within the central pore region. Together, these structural (Fig. 4a) and functional studies (Fig. 4b, c) confirm a binding site for phenylalkylamines within the central cavity.
R ↔ A transition
During this transition, the S4 shuffles its charges above the CTC, thereby releasing the gate. Quantification of CaV1.2 kinetics in terms of the four-state model (Fig. 3) revealed strong voltage dependence of the downward transitions of S4 (rate constant y(V)) compared to weak voltage dependence of the upward movement (x(V)) [16]. This suggests that the membrane voltage is more efficient in pushing the CaV1.2 into the closed state R than Bpulling^the gate open (see also Fedida and Hesketh [40]).
A scenario in which the S4 segments release the gates rather than pulling them open is also evident from other voltage-gated channels. For example, KCNH potassium channel constructs in which BS4 pulling^was excluded by splitting the S4-S5 linker, still opened in a voltage-dependent manner [63].
State A
Studies on Shaker ILT mutants revealed that cysteines engineered into S6 gates are accessible mainly in an activated not open state which provides evidence for the existence of state A [24]. The available CaV1.1 structures with all four VSs presumably in an up position resemble this conformation (A) [98,106,107] (Figs. 1 and 3). The question arises: What keeps an unlocked gate structure shut?
A variety of S6 mutations in the bundle crossing region shift the activation curve (ΔV 0.5act , Fig. 2c) in the hyperpolarizing direction (A) and decelerate current activation and deactivation (exemplified for I781P) indicating closed state destabilization and/or stabilization of the open channel conformation.
Insights into the molecular determinants of closed and open state stability can be obtained if we examine the relation between the physicochemical parameters of gate-forming amino acids (descriptors of residues in the bundle crossing region) and the shifts of the midpoints of the activation curve (ΔV 0.5act , Fig. 5).
Hydrophobicity emerged as the leading determinant of closed gate stability in position 781 (illustrated in Fig. 5b). However, hydrophobic interactions [58] are not the only interactions that contribute to closed state stability. Combined descriptor analysis addressing several amino acid properties simultaneously can substantially improve the correlation indicating that a combination of different amino acid properties [70]. A gating scheme deduced from these experiments is illustrated in Fig. 12c contributes to the stability of open or closed conformations (see [14]).
A ↔ O transition
During continuous depolarization, the S6 gates disengage and CaV channels move from the activated (A) to the open state (O, Fig. 3). This disengagement of the bundle crossing region (concerted pore opening) is a dynamic process. Molecular dynamic simulations illustrate that S6 gates may flicker between engaged and free positions [53]. The concerted pore opening occurs when all four S6 gates disengage and are stabilized in an open position.
CaV1.2 remains in the open state during depolarization.
There is evidence that the channel gates open wide enough to enable unrestricted access of a large molecule like (−)qD888 to its binding site in the cavity [17].
State D, O↔ D, and D ↔ R transitions
Little is known about state D and corresponding transitions. State D was first postulated to describe the process of CaV channel deactivation (Fig. 3 in [16]). A three-state model (Rest ↔ Activated ↔ Open) failed to describe CaV1.2 current kinetics Barium currents were evoked during depolarization starting from − 40 with 10 mV increments from a holding potential of − 100 mV. Right panel shows representative tail currents. Currents were activated during a 20-ms conditioning depolarization to 0 mV. Deactivation was recorded during subsequent repolarizations with 10 mV increments starting from − 100 mV. c Left panel, averaged activation curves of wild type and IIS4N channels. Right panel, voltage-dependent time constants of channel activation/deactivation. Adapted from Pflügers Archiv -European Journal of Physiology. Beyl et al. [13]. © The Authors and in particular was unable to reproduce the acceleration of channel deactivation kinetics that is experimentally observed (see faster channel deactivation at hyperpolarized voltages illustrated by the bell shaped curves in Figs. 7c, 9f, and 10e). This discrepancy is caused by the slow O ↔ A transition, which is a rate-limiting step in a 3 state model and prevents faster deactivation at stronger hyperpolarization (see simulation in [16]).
Further evidence for state D came from simulation studies. Jensen et al. [53] used all-atom molecular dynamics simulations to observe the conformational changes in a potassium channel during closure. The deduced mechanistic model comprises a transition from an activated to a deactivated channel conformation where the VSs moved towards a down position while the pore is still open.
So far, there is no structural evidence for a calcium channel in state D. This is not surprising since in the crystallography and cryo-EM environment, no electrical field is applied. The deactivated state is expected to occur exclusively at hyperpolarized voltages after the channels have passed the open conformation. Thus, observations of state D remain a particular challenge and will require studies under hyperpolarized (i.e., physiologically relevant) conditions.
Voltage sensors move at different rates
In order to track the conformational transitions of individual VSs, Pantazis et al. [71] labeled the extracellular flank of each S4 segment (IS4-IVS4) of the α1 subunit of CaV1.2 with a fluorophore serving as an optical reporter (Fig. 6) [71]. The resulting ΔF-V curves were interpreted to reflect conformational changes associated with individual S4 movements. As illustrated in Fig. 6, each S4 segment activates at different voltages. Pantazis et al. [71] demonstrated that voltage dependence and fast kinetic components in the activation of IIS4 and IIIS4 were compatible with the kinetics of the ionic currents (Fig. 6b). Making use of an allosteric gating model, the authors concluded that IIS4 and IIIS4 supply most of the energy for stabilization of the open state (Fig. 12c).
Up-movement of IS4 (and IIIS4) is a rate-limiting state for pore opening Studies with CaV1.2 constructs where S4 charges were partially or completely neutralized provided further insights: If IIS4 and IIIS4 each contributed about 40% of the energy required for channel opening [71], then neutralization of all IIS4 charges is expected to affect CaV1.2 activation. Beyl et al. [13] observed the opposite of what was expected: Neutralization of all IIS4 charges resulted in a channel construct (IIS4N) which opened and closed with kinetics similar to wild type (Fig. 7). This suggested at first glance that IIS4 is hardly participating in gating (but see Fig. 10). In contrast, neutralization of IS4 (and to a lesser extent IIIS4) significantly decreased the slope of the steady state activation curve (Fig. 8). Calculating the effective charge from the slope of the Boltzmann distribution revealed that IS4 and IIIS4 carry most of the effective charge required for channel activation [15]. We conclude that the upward movement of segment IS4 (and IIIS4) is rate-limiting for releasing the pore gates (Fig. 8). A crucial role of IS4 and IIIS4 in CaV1.2 activation is in line with early work of Garcia et al. [41] and Yamaguchi et al. [109]. Remarkably, activation of IS4 in CaV3 was proposed to represent a rate-limiting stage in activation suggesting a general role of this segment in CaV channel gating [55]. Neutralization of single IVS4 charges has no significant effect on the slope (Fig. 8). The role of IVS4 remains currently unknown, because most constructs with more than one charge neutralized were not functional.
The lack of kinetic effects of IIS4 and IVS4 neutralization does not exclude their participation in gating but is likely to be obscured by the rate-limiting IS4 movement. Fig. 9 Slowly gating IS6 mutant G432W reveals role of segment IIS4: Neutralization of IIS4 accelerates current kinetics and shifts activation curve. a Schematic representation of α1 subunit domains I and II. The cylinders inside represent the S4 or S6 helices. Arg or Lys are shown as (+) while zeros (0) indicate charge neutralizations by Gln. Mutation G423W is highlighted on IS6. b Detailed view of position G432 of CaV1.2. The protein is represented as cartoon. Domain I is shown in green and domain IV in yellow. Glycine 432 is represented as orange spheres and the side chains of the closest residues (P297 and L298, both within 3 Å) are labeled and illustrated as green spheres. These two residues are located at the loop between the S4-S5 linker and the S5 helix. It shows tight packing of this gating sensitive area. This might indicate that even minor changes of the packing can lead to changes in the gating behavior due to necessary rearrangements within this region. For an overview on this position in other channels, see Supplementary Fig. 2. c-f Neutralization of all IIS4 charges (IIS4N) shifts the activation curve of a slowly gating IS6 mutant G432W in the depolarizing direction and accelerates current kinetics (arrows in c and d). Rightward shifts of the activation curves and acceleration of current kinetics by S4 neutralizations are exclusive for mutations in G/A/G/A positions (Beyl et al. [13]). c, d Representative currents through G432W and G432W/IIS4N highlighting slow activation (c) and deactivation of G432W and accelerated activation and tail currents in G432W /IIS4N (d). e, f Averaged activation curves (e) and voltage dependence of the activation/deactivation time constants (f) of WT, G432W, IIS4 N , and G432W/IIS4N. c-f Adapted from Pflügers Archiv -European Journal of Physiology. Beyl et al. [13]. © The Authors . The side chain of A780 and the closest residues (S680 and I681, both within 3 Å) are represented as orange and gray spheres, respectively. S680 and I681 are located at the loop between the S4-S5 linker and the S5 helix. Similar to Fig. 9b, this shows the tight packing of the residues at this critical location. Changes in size or property of the A780 are likely to lead to changes in the dynamic behavior of the channel since this perfect arrangement can be important for the stabilization of certain states. c Schematic representation of the mutations in domain II. The cylinders inside represent the IIS4 and IIS6 helices. Plus stands for Arg or Lys, zero stands for Gln. d Currents of the indicated channel constructs (colors correspond to the illustrations in c). Channels were activated by conditioning pulses from − 80 to 10 mV, deactivation was induced by hyperpolarizing steps to voltages between − 70 and − 100 mV. e Voltage dependence of time constants of activation/deactivation. f Bar graphs illustrating the maxima of the bell shaped curves (shown in e) reflecting the slowest kinetics of activation/deactivation Coupling points between VS and channel pore VSs and the pore are coupled. S4-S5 linkers directly interact with all four S6 segments via G432 (IS6), A780 (IIS6), G1193 (IIIS6), and A1503 (IVS6) designated as the G/A/G/A ring (exemplified for domain I in Fig. 9b, [15,36,103], see also Fig. 12a). Quantification of current kinetics revealed that mutations of G/A/G/A residues affect the movement of the VSs. A VS equilibrium constant Kvs = X(0)/Y(0) (see Fig. 3) increased in these mutants between 6-and 45-fold compared to wild type [13]. Effects on the VSs were exclusively caused by mutations of G/A/G/A residues and not observed for mutations of residues in neighboring positions ( Table 1 in Beyl et al. [13]). Remarkably, G403R in CaV1.3 (corresponding to position G432 in CaV1.2) [84], A749G in CaV1.3 (corresponding to position A780 in CaV1.2) [77], as well as G369D in CaV1.4 (corresponding to position G432 in CaV1.2) [48] all cause channelopathies, highlighting the key role of these residues in channel gating of different CaV families (see Table 1).
G/A/G/A mutants unravel VS movements
While complete charge neutralization of segment IIS4 CaV1.2 did apparently not affect current kinetics (Fig. 7), the situation changed dramatically when S4 charges were neutralized in combination with any of the G/A/G/A mutations. This is exemplified in mutation G432W(IS6) combined with a partially or fully neutralized IIS4 segment (Fig. 9). Mutation G432W shifts the activation curve to the left (Fig. 9e) and strongly decelerates the activation/deactivation (Fig. 9f). Neutralization of all IIS4 charges (examplified for G432W/ IIS4N) shifted the curve back to the right and accelerated current kinetics (Fig. 9f).
At least four conclusions can be drawn: First, each individual S4 segment modulates a concerted ensemble of four tightly interacting (interlinked) S6 segments and is thus not restricted to the S6 gate of its domain (Fig. 12b, Bcooperative gating model^ [13]). Second, neutralization of charges in segments IS4-IIIS4 reduces the stability of the open state (evident from accelerated deactivation, exemplified in Fig. 9e). Third, even a single charge on S4 enables its movement to a down position. Fourth, not all four VSs are obligatory for CaV1.2 deactivation. Hence, IIS4N evidently does not reach its down position but this does not prevent channel deactivation (Figs. 7 and 9, see also [51]).
Stabilization of the open state requires all S4 charges
Neutralization of even a single charge prevents the corresponding S4 segment from contributing to the stability of the open state irrespective of its position on the S4 helix. This is exemplified for mutant A780T, in which all five IIS4 charges were replaced by glutamine one by one (Fig. 10). Removal of any one of these single IIS4 charges shifts the activation curve rightwards and accelerates the current kinetics reflecting a reduction of open state stability [15]. We speculate that partially neutralized S4 segments are retained in a hypothetical Bintermediate^state where they are unable to contribute to open pore stability.
A single charge is sufficient for S4 contribution to pore closure Complete neutralization of S4 charges (IIS4N, Fig. 10) makes this segment voltage-independent and thus prevents any pushing force on S6 towards its closing position. However, when a single charged amino acid remains on IIS4, this segment facilitates channel closure (evident from accelerated deactivation kinetics in A780T/IIS4N+K662, Fig. 10f). In this way, a single VS can modulate all four pore-forming elements (as observed in experiments [13]). Upward movement of IS4 (and IIIS4) is a rate-limiting step for activation [15]. c In a gating model proposed by Pantazis et al. [71], the authors estimated the energy (W1 to W4) contributed by individual S4 segments to pore opening based on fluorescence changes of CaV1.2 constructs with individually labeled S4 segments during voltage clamp steps. Calculated different energies (W1 to W4) suggest that IIS4 and IIIS4 provide most of the energy for pore opening. This figure is modified from Proceedings of the National Academy of Sciences. Pantazis et al. [71] Conformational changes in an S4 segment carrying only one elementary charge may be understood when we consider that a membrane voltage of 100 mVacross a membrane with a thickness of 100 Å corresponds to tremendous 100 kV/cm (which is about an order of magnitude larger than the 14 kV/ cm which causes a lightning discharge in air during a thunder storm).
Voltage sensors have individual impacts on voltage-dependent inactivation
The upward movement of S4 segments during a membrane depolarization enables not only conformational rearrangements at the inner S6 helix bundle (resulting in pore opening, Fig. 3) but also causes a conformational change called voltage-dependent Binactivation.^Voltage-dependent inactivation of CaV channels is evident from the current decay with increasing membrane depolarizations. Inactivation in CaV channels is usually investigated with barium as the charge carrier to avoid the development of calcium-dependent inactivation (see [49] for review).
Although voltage-dependent inactivation is likely to involve structural changes at the outer channel mouth near the selectivity filter [29-31, 56, 80], a number of point mutations in pore lining S6 and adjacent segments of CaV α subunits have been shown to modulate this process [46,49,93]. This includes a cluster of hydrophobic residues located close to the inner channel mouth on IS6 and IIS6 [36,50,58]. Other key inactivation determinants in CaV channels have been identified in intracellular loops [1,46,88,92,76]. Changes in inactivation caused by S6 mutations on CaV channels may be very substantial: a 75-fold acceleration of inactivation by a single point mutation was reported for M1811Q on IVS6 of Cav2.1 [12], while the Timothy syndrome mutation G402S in CaV1.2 prevents voltage-dependent inactivation almost completely [89]. Andranovits et al. [6] identified a key role of segment IS4 in voltage-dependent inactivation. Impairing IS4 function by charge neutralization had the largest and regular (chargedependent) effects on voltage-dependent inactivation (Fig. 11), compared to no or less impact of equivalent charge neutralizations in segments IIS4 and IIIS4.
A gradual reduction of the slope factor of the inactivation curve on stepwise neutralization of IS4 charges is shown in Fig. 11. Apparent enhancement of inactivation caused by IS4 neutralization is also evident from the accelerated time course of current decay. This steeper voltage dependence (Fig. 11a) can be understood assuming that IS4 moves through substates that are stabilized by interactions of IS4 charges with surrounding residues [32]. In this scenario, neutralization of these charges reduces the number of interactions (e.g., salt bridges) and correspondingly the number of sub-states that IS4 can occupy between full activation and inactivation ( [19], see also supplemental Fig. 2 in Andranovits et al. [6]).
Conclusions and outlook
Upward movement of S4 segments (at depolarization) is almost voltage-independent while downward movement (at hyperpolarization) is strongly voltage dependent. This suggests that upward movement is driven by intramolecular forces while downward movement is driven by membrane potential acting on the VS. Even a single charge is sufficient for downward movement of the S4 segment. S4 segments in their down position lock the pore gates in the closed state. Gates are unlocked only when all four segments leave the down position. Movement of IS4 (and IIIS4) is rate-limiting. S4 segments move to the up position via sub-states where their positively charged residues interact with residues of the Bcharge transfer centre.^Neutralization of any S4 charge (i.e., replacement by neutral glutamine) prevents its arrival in a final up position (Blike in a broken zipper^). A ring of small residues (G/A/G/A ring) on the lower third of S6 segments interacts with the S4-S5 linkers. Constructs carrying mutations in these positions are extraordinary sensitive to charge neutralization, making them interesting tools for studying electro-mechanical coupling. Only a completely (fully) charged IIS4 contributes to the stability of the open state. In other words, the specific number of S4 charges (between 5 in IS4 and IIS4 and 6 in IIIS4 and IVS4) is likely to be essential for a given stability of the open state. We speculate that evolution effectively titrated the number of S4 charges (and interacting negative counter charges) to fine tune calcium entry. Future studies on quantitation of inactivation will have to answer the principle question: From which state (R, A, and/or O) does the majority of channels enter the inactivated conformation?
Acknowledgements The research was funded by the Austrian Science Fund (FWF) grants W1232 and P27729.
Funding Information Open access funding provided by Austrian Science Fund (FWF).
Open Access This article is distributed under the terms of the Creative Comm ons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | 8,211 | 2018-06-27T00:00:00.000 | [
"Physics"
] |
Financial Confidence in Financial Satisfaction Through Financial Behavior for Ciputra School of Business Makassar Students
Financial literacy can lead to financial satisfaction (Henager & Anong, 2014). However, the majority of educated people are less content (Grable, J. E., Britt, S., & Cantrell, J., 2007). A person with greater financial literacy is more likely to excercise caution when making financial decisions and, therefore, less likely to feel confidence with their finances. This is also supported by studies by Hira, Fawslow, and Mugenda (cited in Robb & Woodyard, 2011). This study aims to empirically test the causal relationship of financial confidence and financial satisfaction use in relation to financial behavior. This study is an explanatory study aimed at discovering and explaining the causal relationship between variables (Sujarweni, 2019). The results of the study partially indicate a significant effect of financial satisfaction on financial behavior. Financial satisfaction has a positive relationship with financial behavior, Thus, it is concluded that hypothesis one is supported and tested by data (received) that financial satisfaction has a positive effect on financial behavior.
A person's ability to manage their
The findings of Herawati (2015) finances is one of the most important factors of success in life.When someone has the skills and abilities to manage money and deal with financial issues in a positive way, financial happiness will prevail.This financial thing will lead to financial satisfaction.Someone who is more financially literate is more likely to be cautious when it comes to making financial decisions, and therefore less likely to feel confidence with their finances.
and Agustina (2016) indicate that financial literacy plays a positive role in financial life.In addition, Nababan, Sadilia (2013) and Octavio (2016) found that improving financial literacy does not unevenly lead to financial well-being.Someone is confident when they are satisfied by their own behavior (Lopez-Garrido, G., 2021).Thus, financial behavior creates satisfaction with wellused money, and satisfaction with money can gradually increase when a person acquires good financial results (Coşkuner, 2016).Effective financial orientation towards achieving goals and objectives, one by one achieving traditional financial objectives, will lead to financial satisfaction (Patrisia, D., & Fauziah, M., 2019, September).
LITERATURE REVIEW Financial
Financial is one of systems that have relation to the cycle of money in nation or world economy.It is absolutely the concepts that related the terms of the buildness, management, and the investment.As same as (Hasrina, 2015) opinion about the financial, which are meant of the discipline or knowledge about management or the arts of life skill academic.It is also relating to the efforts of economic activities, like the process of marketing, the planning, and the management of money which are affected to student life in general or in the social life.
Financial Confidence
Financial confidence is selfassurance things that necessarily needed to make well-informed financial decisions (Palameta et al., 2016).Another perspective comes from Atlas et al. (2019), that argued the impact of financial confidence based on financial knowledge on financial decision making is short-lived and very dependent on financial confidence.Thus, financial confidence is no less important than other components.Without financial confidence, people will not be able to make healthy financial choices.
Financial Satisfaction
Life satisfaction and well being as the overall component of financial satisfaction (Plagnol, 2011).According to Rob and Woodyard (2011), financial satisfaction can be measured through satisfaction level of assets, debt, and savings.Research by Robb and Woodyard (2011) also indicates that financial behavior influences financial satisfaction, as financial behavior is valued and serves as an important part of financial satisfaction.Hence, there is correlation between behavior and financial satisfaction.
Financial Behavior
Everyone's actions that are related to managing money or cash are referred to as having financial conduct, and the most common financial behaviors include cash, credit, and saving behavior (Xiao, 2008).How to make the right decisions on the flow of student's cash, preventive measure, and also manage budget planning is part of financial behavior.This is way financial literacy is very important to make better and healthier financial behavior that will lead to better financial security for the future (Lusardi, A., Michaud, P. C., & Mitchell, O. S., 2011;Lusardi & Mitchelli, 2007).
METHODS
Regarding to financial behavior and financial satisfaction, this study intends to empirically investigate the causal relationship (causation) between a number of variables, including financial literacy and financial technology use.This kind of research is an explanatory study intended to identify and clarify the causal connection between various variables (Sujarweni, 2019).In addition, this study is designed to answer the said problems, set goals and test hypotheses.
To obtain information, find answers to formulated problems, set goals and hypotheses to be tested on the descriptive quantitative data derived from measurement data, this study used test and questionnaire methods by distributing questionnaires.A test is a series of questions categorized to measure the abilities of individuals in financial literacy.Survey is a method of data collection that is conducted by giving respondents answers to a set of questions or written information.This study uses a questionnaire to measure the association between financial confidence and financial behavior, and financial satisfaction using a quisioner.This study used a closed questionnaire.A closed question is a question that asks the respondent to choose an answer based on their own knowledge and implementation of financial life.
Probability sampling was the sampling technique employed in this study.The probability sampling method employed is purposive proportional random sampling.The selection criteria in this study are University of Ciputra's students who live with their parents or not and also those who have income periodically.This research is conducted at the University of Ciputra Makassar.The students are from many different financial backgrounds.Some students still stay with their parents or guardians, there are also those who come from far away lands to learn at University of Ciputra Makassar.Using Slovin's Formula Sampling Techniques, 136 out of 206 student's responses are needed.Among them are those who already have their own business and are still using full support from their parent.Many of them are still finding their financial way, hence this makes them a good candidate to be our respondents.
In this study the measurement of financial literacy variables is measured by tests of simulated conditions about financial confidence, satisfaction and behavior.Scoring methods 1 to 5 are used to determine the level of financial literacy.Respondents can strongly disagree to strongly agree to the condition given in the survey.
Realibility Test
This test value of the instrument variable (X1) of the 4 questions is 0.721 which means that the realibility value of 0.721 > 0.60, meaning that the instrumental variable (X1) financial satisfaction high reliability.
Normality Test
The normality test used for this data was Kolmogorov Smirnov, because the sample used was 139 respondents.The following is a normality test that has been carried out using SPSS.Significance results of 0.001 < 0.05, it can be said that financial satisfaction (X1) has proven to have a significant effect on financial behavior.So it can be concluded that hypothesis 1 is supported by data (accepted), proving that financial satisfaction has a positive effect on financial behavior.
Variable Coefficients
Sig.
X2 dan Y 0.354 0.001 Based on the results of the study, there is a partial influence between the variables of financial confidence in financial behavior.The close relationship between financial confidence (X2) and financial behavior (Y) is 0.354, meaning that the relationship between financial confidence and financial behavior has a strong relationship.And the nature of the relationship + (positive).The result of its significance of 0.001 < 0.05, it can be said that financial confidence (X2) has proven to have a significant effect on financial behavior.So, it can be concluded that hypothesis 2 is supported by the data received, proving that financial confidence has a positive effect on financial behavior.Thus, it can be said that if the higher a person's financial confidence, it is very positively affecting his financial behavior.Based on the table above, the regression equation can be obtained as follows.Y = 3,884 + 0,359X1 + 0,286X2 A constant value of 3.884 is obtained, which means that statistics without X1 and X2, the magnitude of Y is 3.884.
The
The value of the variable coefficient X1 is 0.359 which means that the magnitude of the influence of X1 on Y is weak because it has a percentage of 35.9%, while the value of the variable coefficient X2 is 0.286 which means that the magnitude of X2's influence on Y is weak.
The sig value of the variable X1 is 0.007 which means 0.007 < 0.05, then X1 has a significant effect on Y, while for the sig value of variable X2 of 0.002 which means 0.007 < 0.05, then X2 also has a significant effect on Y.
CONCLUSION
Money plays a significant role in student's day to day activities.Everything from getting transportation to campus, having brunch in the cafeteria or even personal spending.Student who came from many different backgrounds also have various kinds of needs and income sources.Financial helps manage spending.
How one student behaves by using their money is affected by how much they feel satisfied financially.The study's findings suggest that factors affecting financial satisfaction have a major impact on financial behavior.Financial satisfaction (X1) has a positive relationship with financial behavior (Y), which is 0.329, meaning that there is a positive relationship between financial satisfaction and financial behavior.And the nature of the relationship is + (positive).Significance results 0.001 < 0.05, we can say that financial satisfaction (X1) has a significant impact on financial life.Thus, it is concluded that hypothesis 1 is supported and tested by data (received) that financial satisfaction has a positive effect on financial behavior.
Financial Confidence in Financial Satisfaction Through Financial Behavior.
The impact of financial satisfaction and financial confidence on financial behavior.Thus, it can be said that when a person has a higher degree of financial dependence, it has a significant impact on his or her financial behavior.This results in a constant value of 3.884, which means that in statistics without X1 and X2, the value of Y is 3.884.
The value of the coefficient of variable X1 is 0.359, which means that the magnitude of influence of X1 on Y is weak, which has the value of 35.9%, while the value of the coefficient of variable X2 is 0.286, which means that the magnitude of the influence of X2 on Y is weak.The sig value of variable X1 is 0.007, meaning 0.007 < 0.05, and X1 has a significant effect on Y, whereas the value 0.002 of variable X2, meaning 0.002 < 0.05, and X2 has a significant effect on Y.
Variable of financial behavior only have 17% correlation on financial confidence and financial satisfaction, which means there are many other variables that could have a direct effect on financial behavior.These variables could vary to financial; technology, self-efficacy, autonomy, community-trust, community-support.In hope of having a deeper understanding about financial, we suggest these variables to be used in the next research.
Based on the previous findings and the explanation of these part that related to the financial are causing the implementation or the application of recommendations are: 1) Enough consideration and financial knowledge are needed to build the perspective of financial confidence. | 2,569 | 2022-09-26T00:00:00.000 | [
"Economics",
"Business"
] |
Editorial: Advanced Therapies for Cardiac Regeneration
<EMAIL_ADDRESS>https://eprints.whiterose.ac.uk/ Reuse This article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: https://creativecommons.org/licenses/
Advanced Therapies for Cardiac Regeneration
Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for ∼18 million deaths annually (WHO data). The term CVD gathers a group of different disorders involving the heart, its constituent structures, and the blood vessels. Among CVDs, coronary heart disease and stroke are responsible for four out of five CVD-related deaths, and one third of these deaths occur prematurely in people aged 70 or younger. The progressive or sudden obstruction of the coronary arteries is responsible for the onset of myocardial infarction which initiates a detrimental cascade of events finally leading to heart failure. More in detail, heart failure results from the continuous remodeling of the scar tissue replacing the beating heart muscle in the infarcted region, and represents a chronic condition in which the heart muscle progressively loses its ability to pump enough blood to fulfill the needs of all the body's compartments. Heart failure thus represents the main cause of morbidity and mortality of myocardial infarcted patients in the long term. Within this context, cardiac tissue engineering/regenerative medicine (TERM) strategies could arise as cutting-edge therapies in the management of myocardial infarcted patients, opening the way to the possibility to replace the damaged heart tissue and recover its functionality. Such an approach could effectively represent a valid alternative to the gold standard heart transplantation, encompassing all issues related to donor shortage and the need for life-long administration of immunosuppressive therapies. Among other common cardiovascular diseases, we also recall valve heart diseases and cardiomyopathies. It must be highlighted the strong associations existing between different cardiovascular diseases, such as coronary heart disease and valvular heart disease, cardiomyopathy and heart failure. This observation indicates that a multiple regenerative medicine approach, which considers different diseases, could be an effective strategy in the management of CVDs. The Research Topic "Advanced Therapies for Cardiac Regeneration" aims at presenting a series of articles summarizing the latest research updates on cardiac TERM approaches which combine cells, biomaterials, hydrogels, tissue engineered scaffolds/patches and physico-chemical stimuli to achieve the ultimate goal of regenerating the injured heart tissue. The issue is comprised of 19 peer-reviewed manuscripts (nine reviews, two perspectives, seven original research articles, and one Brief Research Report) derived from the many fields involved in the topic, namely (bio)materials science and engineering, biology, biotechnology, and biomedical engineering.
To better contextualize the Research Topic, the review by Montero et al. presents an in-depth overview of the specific characteristics of the myocardium that determine the needs and requirements cardiac TERM has to meet, with particular emphasis on heart tissue components, architecture and biophysical properties. The authors also briefly revise the key components required to design new cardiac TERM approaches, namely cells, materials, maturation stimuli, and scaffold fabrication techniques. Given the central role of biomaterials in the establishment of cardiac TERM therapies, Bar and Cohen focus their review on the current application of biomaterials in the field of cardiac regeneration, mainly discussing their use as forming materials for nano-carriers and matrices for cardiac regeneration induced by biomolecule release, injectable hydrogels for cell delivery, and cardiac patches. Further and more detailed insight on the use of specific biomaterials in cardiac TERM are provided by Cattelan et al. and Gonzalez De Torre et al., with particular emphasis on their application as hydrogel constituents. Being threedimensional highly hydrated networks showing mechanical properties similar to soft tissues, hydrogels hold great promise in cardiac TERM. Cattelan et al. elucidate the promising properties of alginate in cardiac regeneration strategies and the outcomes of clinical trials in which this material has been tested to treat myocardial infarcted patients. An additional demonstration of the suitability of this material for cardiac TERM is provided by Bloise et al. who develop alginate hydrogels for the controlled release of immunomodulatory and reparative cytokines (antiinflammatory interleukins 4/6/13 and colony-stimulating factor) to direct immune cell fate and control the wound healing process in the ischemic heart. The therapeutic ability of the proposed treatment has been proved in rat models by macrophage polarization toward healing and the improved global cardiac functionality. Differently, Gonzalez De Torre et al. discuss on the potential of elastin-based biomaterials as constituents of hydrogel scaffolds, injectable systems, or complex devices (e.g., heart valves, stents) to treat CVD-affected patients. In this regard, Fernàndez-Colino et al. investigate the use of elastin-like recombinamers as forming materials of small caliber compliant vascular grafts. The authors describe material processing into macroporous three-dimensional structures favoring cell homing, extracellular matrix (ECM) deposition and endothelium development, while exhibiting non-thrombogenicity and elastic properties mimicking the native elastin. Graft textile components are finely designed to confer proper suture retention, longterm structural stability, burst strength and compliance. The proper selection of the biomaterials used as constituents of cardiac scaffolds/patches/devices or hydrogels thus represents the first step toward the engineering of successful regenerative strategies for the management of CVD-affected patients. Indeed, biomaterials strongly affect the possibility to provide the resulting devices with proper biological signals and physical stimuli to achieve the final goal of recovering the initial organ/tissue functionality. In this regard, Belviso et al. report on the potential of decellularized human skin as scaffold to restore the native ECM which plays a pivotal role in guiding heart compliance, cardiomyocyte maturation, and function. The authors argue that a dermal matrix could effectively represent a viable alternative biological scaffold in cardiac TERM, with the key advantages of being autologous, easily accessible, and cost-effective. On the other hand, decellularized organ-derived ECM (dECM) can also be exploited to design hydrogels, thus combining the ECM characteristic pro-regenerative properties with the possibility to shape the material according to specific requirements. Liguori et al. investigate the differences among dECM hydrogels prepared starting from cardiovascular tissues of different origin, namely left ventricle, mitral valve, and aorta. The authors demonstrate that the source of dECM plays a pivotal role in determining cell differentiation and vascular network formation, because of the different molecular and biomechanical cues provided to cells. For instance, ventricular dECM hydrogels exhibit more robust vascular network formation, while aorta-derived dECM drives adipose-derived stromal cell differentiation toward a myogenic phenotype in the absence of TGF-β1 supplement to the culture medium. The readers can also find a thorough survey on the use of decellularized cardiac ECM as biomaterial for cardiac TERM in the review by Maghin et al. Besides the physical stimuli provided by the designed cell-culture platforms through their architecture and their mechanical properties, the possibility to mechanically stimulate cellularized constructs by mimicking the native milieu also promotes the development and maturation of cardiac tissue analogs, as described by Massai et al. The authors develop an automated bioreactor platform for tuneable cyclic stretch of cellularized samples coupled with a specific set-up for the in situ monitoring of the mechanical response of in vitro engineered cardiac tissues. Another key form of stimulus playing a central role in cardiac regeneration is represented by biochemical cues, as thoroughly surveyed by Cassani et al. Particularly, the authors mainly gather their attention on the crucial need of specific carriers (e.g., nanoparticles) to deliver such biochemical cues, which would ensure an efficient delivery of the therapeutic cargo, inhibit its metabolic inactivation, and avoid potential side effects. According to a different approach, proper biochemical stimuli driving cardiac repair and regeneration can be also applied by cell-secreted soluble factors. The literature review by Maghin et al. reports on the therapeutic profiling of stem cell secretome, with regard to the cardio-active ability of cell-released extracellular vesicles, including exosomes carrying cardioprotective and regenerative RNA molecules. An in-depth analysis of the potential and the mechanisms of action of miRNAs as mediators of cardioprotection is provided by Nazari-Shafti et al. The authors highlight the crucial need to identify all miRNAs involved in cardioprotection and remodeling, as well as their composition and exact mechanism of action in view of their application in clinical practice. A class of miRNAs has also been reported to drive the direct reprogramming of fibroblasts into cardiomyocyte-like cells. In the perspective of a clinical translation of this approach, Paoletti et al. test the capability of miRcombo (i.e., a combination of four microRNA mimics) to guide the direct reprogramming of adult human cardiac fibroblasts into induced cardiomyocytes, as previously observed with murine fibroblasts. Other repair and regenerative strategies currently explored in literature target different aspects of the cascade initiated by myocardial insults. For instance, Seclì et al. concentrate on the different role exerted by chaperone proteins in the healthy and injured heart. In detail, in the healthy heart chaperone proteins contribute to the regulation of myocardial physiology, while under stress conditions or during cell damage events, they are secreted by myocardial cells and induce inflammation and cardiomyocyte apoptosis. Blocking chaperone activity has been already demonstrated to produce beneficial effects on heart function in preclinical models. Hence, combining the release of specific antibodies blocking extracellular chaperones with TERM approaches may represent a future innovation to treat infarcted patients. Differently, Bigotti et al. discuss the possible exploitation of agrin as mediator of cardiac regeneration. Recently published works have demonstrated that agrin can promote heart regeneration in murine models through cardiomyocyte de-differentiation and proliferation. Moving from these data, the authors survey the currently available literature on this topic, and critically discuss on the potential clinical translation of agrin-based therapies.
Last but not least, a new chapter in the TERM field has been opened over the last two decades with the discovery of induced pluripotent stem cells (iPSCs). These cells offer the advantage of being of autologous origin, while retaining the proper characteristics of embryonic stem cells, thus completely overcoming both ethical issues and rejection risks. Mazzola and Di Pasquale provide an overview on the differentiation potential of iPSCs toward cardiomyocytes and their application in cell-based therapies, highlighting the typical drawbacks of such approaches in terms of poor cell retention and risks of occurrence of adverse effects (e.g., arrhythmias). A possible route to overcome these issues consists in combining the use of iPSCs with bioengineering technologies which can retain cells in the therapeutic target while favoring their phenotypical maturation and integration in the host tissue. iPSCs can also be successfully used as cell source in the establishment of cardiac tissue models. In addition, their autologous origin can be exploited to recapitulate in vitro human cardiac pathophysiology. In this regard, Jelinkova et al. report the design of an in vitro human model of the cardiac tissue of Duchenne muscular dystrophy (DMD)-affected patients. DMD is a severe genetic disorder associated with progressive dilated cardiomyopathy which culminates with heart failure onset, finally leading to patient death. The authors demonstrate that the designed cardiac models effectively recapitulate DMD-induced functional defects and cardiac wasting, thus offering a promising tool for a better understanding of DMD-associated cardiomyopathy and its treatment.
Finally, to complete our survey on the topic, Taghizadeh et al. exhaustively review the different types of biomaterials applied in the treatment of valvular heart diseases which represent another CVD with high incidence in developed countries, and in particular among the elderly. In the U.S. about 28,000 deaths are due to heart valve disease every year, with approx. the 60% of deaths due to disorders involving the aortic valve (data from the Centers for Disease Control and Prevention). Strict requirements in terms of material physico-chemical properties should also be fulfilled when vascular stents are engineered. Learning from a recent FDA report dealing with the adverse cardiac events and thrombosis observed at the 2-year followup on the first FDA-approved bioresorbable vascular stents, Pacharra et al. characterize a series of polyethylene glycolfunctionalized poly-L-lactide-co-ε-caprolactone copolymers as a new generation of stent-forming materials, with improved cytoand hemo-compatibility. As an outcome of the research work, the authors identify promising candidates for cardiovascular implant fabrication, with the additional advantage of being suitable for an easy translation of the synthesis procedure (a one-step synthesis strategy) toward industrial practice.
The reader can easily appreciate from the brief summaries of the contributions comprised in this issue that the addressed Research Topic is broad in nature and represents a still open question in the TERM field. The definition of regenerative approaches to fight the detrimental consequences of CVDs is still in its infancy. However, the cross-contamination of new ideas coming from complementary but not overlapping fields, as highlighted in the papers published within this issue, will likely lead in the future to the effective and successful repair and regeneration of injured myocardial tissue.
AUTHOR CONTRIBUTIONS
MB wrote the editorial, which was revised, proof-read, and approved by all authors. | 2,973.6 | 2021-03-04T00:00:00.000 | [
"Biology"
] |
Wildfire Smoke Classification Based on Synthetic Images and Pixel- and Feature-Level Domain Adaptation
Training a deep learning-based classification model for early wildfire smoke images requires a large amount of rich data. However, due to the episodic nature of fire events, it is difficult to obtain wildfire smoke image data, and most of the samples in public datasets suffer from a lack of diversity. To address these issues, a method using synthetic images to train a deep learning classification model for real wildfire smoke was proposed in this paper. Firstly, we constructed a synthetic dataset by simulating a large amount of morphologically rich smoke in 3D modeling software and rendering the virtual smoke against many virtual wildland background images with rich environmental diversity. Secondly, to better use the synthetic data to train a wildfire smoke image classifier, we applied both pixel-level domain adaptation and feature-level domain adaptation. The CycleGAN-based pixel-level domain adaptation method for image translation was employed. On top of this, the feature-level domain adaptation method incorporated ADDA with DeepCORAL was adopted to further reduce the domain shift between the synthetic and real data. The proposed method was evaluated and compared on a test set of real wildfire smoke and achieved an accuracy of 97.39%. The method is applicable to wildfire smoke classification tasks based on RGB single-frame images and would also contribute to training image classification models without sufficient data.
Introduction
The frequency and size of wildfires have increased dramatically worldwide over the past few decades [1], posing a significant threat to natural resources while damaging the lives and property of individuals. Smoke detection can provide an earlier warning of a fire, as smoke usually appears earlier than flame can be detected in the early stages of a fire and is less likely to be obscured from view.
With the widespread use of deep convolutional neural networks in computer vision in recent years, more and more researchers have started to combine this method with smoke recognition tasks [2][3][4][5]. In deep learning-based smoke recognition, obtaining diverse wildfire smoke data as a positive sample is challenging due to the episodic nature of fire events, while it is relatively easy to collect forest environments as a negative sample. Under such conditions, the trained models are prone to false positive smoke detection, making it difficult to obtain satisfactory results. In addition, most of the visible images of wildfire smoke are acquired from RGB cameras carried by drones or webcams set up on lookouts. Therefore, the quality of the camera shots will greatly affect the quality of the raw image data, which will be worse if the lens becomes wet or dirty. In summary, acquiring a rich and diverse set of high-quality data is essential for deep learning-based smoke recognition tasks.
In order to increase the diversity of a dataset, the current mainstream approaches can be broadly divided into two categories, one based on GANs [6] to generate the data and the other using synthetic images to overcome the shortage of wildfire smoke data. GANs (generative adversarial networks) [6] have been used for data enhancement of wildfire smoke, which are trained with the idea of adversarial training and have achieved remarkable results in face generation and many other areas [7][8][9][10]. Namozov et al. [11] used a GAN network to generate fire images with winter and night backgrounds by the original images and added different seasons and times of the day. However, this method made it difficult to provide fire smoke with rich morphological variation, as the background of the original image and the shape of the fire smoke are retained during the generation process. Minsoo Park et al. [12] attempted to use CycleGAN [13] to learn a mapping from images of fire-free mountains to pictures of mountains with wildfires, thereby generating many pictures of wildfires. However, the class of fire-free mountain images used to train the model in this method is relatively limited. The missing alarm rate of smoke by the model trained on the generated data still needs to be further reduced. Previous approaches to generating wildfire image data based on GANs require a large amount of rich real wildfire image data or real wildland background image data as support, so the diversity of the generated data is largely limited by the diversity of the real data. This is the drawback of this approach.
There are two broad approaches using synthetic images to supplement the data, namely by generating smoke through indoor ignition experiments or 3D modeling software and then compositing the smoke image with the background image by linear overlay or direct rendering [14][15][16]. However, smoke and environment in ignition experiments differ significantly from that in wildland fires due to ignition environmental limitations. Therefore, in this study, we chose to simulate the smoke in 3D modeling software. However, as there is a difference in appearance between the synthetic smoke and the real smoke, this difference can cause the model to perform less well on real data. This problem arises when there is a difference in the statistical distribution between the synthetic training data and the real test data, i.e., a domain shift [17,18].
Researchers have proposed many domain adaptation methods to address the abovementioned problem [19][20][21][22][23]. CycleGAN [13] is a representative type of pixel-level domain adaptation method, which transforms the source domain data in the original pixel space into a style in the target domain, capturing pixel-level and low-level domain shifts. However, using only pixel-level domain adaptation methods can result in the loss of high-level semantic features in the image during transformation. ADDA [24] is a classical feature-level domain adaptation method that aligns the features extracted by the network in the source and target domains, adapting high-level semantic features relevant to the specific task. Therefore, in this study, we used a combination of these two methods.
In this paper, a method was proposed to train deep convolutional neural networks for real smoke recognition through synthetic smoke data. Firstly, we used 3D modeling software to set various stochastic conditions to simulate virtual smoke, and then, the synthetic smoke image was obtained by rendering virtual smoke under virtual wildland background. After compression using the framework in [25], CycleGAN was used as the pixel-level domain adaptation to convert the synthetic smoke images into photorealistic smoke images in order to better solve the domain shift problem between the synthetic smoke and the real smoke. After this, all the data were split into a source and a target domain. The source domain data consisted of photorealistic smoke and real non-smoke, and the target domain data consisted of real smoke and real non-smoke. Following this, the domain-invariant features of the source and target domain data were learned by the feature adaptation: ADDA incorporated with DeepCORAL [26]. The proposed method is applicable to the task of wildfire smoke classification based on RGB single-frame images.
The full paper is structured as follows. We describe the building process of the synthetic smoke dataset, the CycleGAN-based pixel-level domain adaptation process, and the feature-level domain adaptation process based on ADDA with DeepCORAL in Section 2; Section 3 makes the experiment results using both models and the analysis of the experiments; and Section 4 makes the conclusion.
Data Collection
Existing public datasets of forest fires tend to lack diversity [16]. In this paper, blender [27] was used to generate smoke with various appearances and resolutions by setting different lighting, wind, airflow, gravity, etc., and rendering the virtual smoke under any background image to produce synthetic images, as shown in Figure 1.
The full paper is structured as follows. We describe the building process of the synthetic smoke dataset, the CycleGAN-based pixel-level domain adaptation process, and the feature-level domain adaptation process based on ADDA with DeepCORAL in Section 2; Section 3 makes the experiment results using both models and the analysis of the experiments; and Section 4 makes the conclusion.
Data Collection
Existing public datasets of forest fires tend to lack diversity [16]. In this paper, blender [27] was used to generate smoke with various appearances and resolutions by setting different lighting, wind, airflow, gravity, etc., and rendering the virtual smoke under any background image to produce synthetic images, as shown in Figure 1. For the synthetic images suitable for subsequent pixel-level domain adaptation, the style of each image's foreground and background should be uniform to ensure visual harmony. Therefore, virtual background images were collected in a video game, Red Dead Redemption 2, to match the virtual smoke images. Considering the diversity of the environment, we gathered 1500 images containing the scenes from different periods, different forest types, different environments, and different seasons. The samples of background image data are presented in Figure 2. For the synthetic images suitable for subsequent pixel-level domain adaptation, the style of each image's foreground and background should be uniform to ensure visual harmony. Therefore, virtual background images were collected in a video game, Red Dead Redemption 2, to match the virtual smoke images. Considering the diversity of the environment, we gathered 1500 images containing the scenes from different periods, different forest types, different environments, and different seasons. The samples of background image data are presented in Figure 2. The full paper is structured as follows. We describe the building process of the synthetic smoke dataset, the CycleGAN-based pixel-level domain adaptation process, and the feature-level domain adaptation process based on ADDA with DeepCORAL in Section 2; Section 3 makes the experiment results using both models and the analysis of the experiments; and Section 4 makes the conclusion.
Data Collection
Existing public datasets of forest fires tend to lack diversity [16]. In this paper, blender [27] was used to generate smoke with various appearances and resolutions by setting different lighting, wind, airflow, gravity, etc., and rendering the virtual smoke under any background image to produce synthetic images, as shown in Figure 1. For the synthetic images suitable for subsequent pixel-level domain adaptation, the style of each image's foreground and background should be uniform to ensure visual harmony. Therefore, virtual background images were collected in a video game, Red Dead Redemption 2, to match the virtual smoke images. Considering the diversity of the environment, we gathered 1500 images containing the scenes from different periods, different forest types, different environments, and different seasons. The samples of background image data are presented in Figure 2. We built a synthetic smoke dataset containing 2000 images of 256 × 256 size by rendering each virtual background image with a virtual smoke image. Figure 3 shows the sample of the synthetic image dataset. We built a synthetic smoke dataset containing 2000 images of 256 × 256 size by rendering each virtual background image with a virtual smoke image. Figure 3 shows the sample of the synthetic image dataset. For real smoke data, wildfire smoke images were mainly collected from the internet, the public dataset from State Key Laboratory of Fire Science, USTC [28], and the public dataset from Yuan Feiniu [29]. After data cleaning, we obtained a total of 2500 images of wildfire smoke. In addition, a large number of real wildland background images were collected from the internet and selected, resulting in 4000 wildland background images. Of the images above, 1040 images were selected to build the test set-520 smoke and 520 non-smoke images-and Figure 4 shows some samples of the test set.
Pixel-Level Based Domain Adaptation
The pixel-level domain adaptation was made based on CycleGAN, as shown in Figure 5. ℎ _ is the source domain, and _ is the target domain; the data in the source domain are synthetic smoke images, and the data in the target domain are real smoke images. The generator would learn a mapping function from the For real smoke data, wildfire smoke images were mainly collected from the internet, the public dataset from State Key Laboratory of Fire Science, USTC [28], and the public dataset from Yuan Feiniu [29]. After data cleaning, we obtained a total of 2500 images of wildfire smoke. In addition, a large number of real wildland background images were collected from the internet and selected, resulting in 4000 wildland background images. Of the images above, 1040 images were selected to build the test set-520 smoke and 520 non-smoke images-and Figure 4 shows some samples of the test set. We built a synthetic smoke dataset containing 2000 images of 256 × 256 size by rendering each virtual background image with a virtual smoke image. Figure 3 shows the sample of the synthetic image dataset. For real smoke data, wildfire smoke images were mainly collected from the internet, the public dataset from State Key Laboratory of Fire Science, USTC [28], and the public dataset from Yuan Feiniu [29]. After data cleaning, we obtained a total of 2500 images of wildfire smoke. In addition, a large number of real wildland background images were collected from the internet and selected, resulting in 4000 wildland background images. Of the images above, 1040 images were selected to build the test set-520 smoke and 520 non-smoke images-and Figure 4 shows some samples of the test set.
Pixel-Level Based Domain Adaptation
The pixel-level domain adaptation was made based on CycleGAN, as shown in
Pixel-Level Based Domain Adaptation
The pixel-level domain adaptation was made based on CycleGAN, as shown in Figure 5. synthetic_S is the source domain, and real_T is the target domain; the data i S in the source domain are synthetic smoke images, and the data i T in the target domain are real smoke images. The generator G S−T would learn a mapping function from the synthetic smoke images (the source domain) to the real smoke images (the target domain), the generator G T−S , and vice versa. G S−T and G T−S are used to generate generated_T and generated_S, respectively. Our goal is to convert the source domain image into the style of the target domain image to obtain the data in generated_T, i.e., photorealistic smoke images. For mode collapse [6] and loss of structural information in the source domain images [30], the training process introduced cycle consistency loss to regularize. Specifically, for i S and i T , one of our goals is The other goal is an inverse process for i T . Equation (1) shows the whole cycle consistency loss: The overall loss of CycleGAN is defined as Equation (4): where is the weight of cycle consistency loss. To improve the training efficiency, the CycleGAN model was compressed using the general-purpose compression framework in this paper [25], reducing the computational cost of the generator and the model size.
Feature-Level Based Domain Adaptation
To learn high-level semantic information about the smoke images and further reduce the feature distribution difference between the photorealistic smoke images and the real In addition, two discriminators D S and D T are trained to distinguish real and fake images. The adversarial loss [6] is formulated as Equations (2) and (3): The overall loss of CycleGAN is defined as Equation (4): where λ is the weight of cycle consistency loss. To improve the training efficiency, the CycleGAN model was compressed using the general-purpose compression framework in this paper [25], reducing the computational cost of the generator and the model size.
Feature-Level Based Domain Adaptation
To learn high-level semantic information about the smoke images and further reduce the feature distribution difference between the photorealistic smoke images and the real smoke images, the feature-level domain adaptation method, which combined ADDA [24] with DeepCORAL [26], was proposed in this paper. In this section, the smoke images in the source domain are the photorealistic smoke images obtained by pixel-level domain adaptation, but all the non-smoke images in the source domain are real non-smoke images. This differs from the setting in traditional domain adaptation because the two categories in the source domain have different sources of data. However, our goal is to achieve the binary classification of smoke and non-smoke images, so we only need to focus on the generalized features of smoke and not too much on the features of non-smoke images. Experimentally, this setup did not affect the performance of the model. The images in the target domain are made up of real smoke images and real non-smoke images.
The source domain images X S are used with the label Y S throughout the feature-level domain adaptation process, while the target domain images X T are used without the label. The aim of feature-level domain adaptation is to train a target representation M T and classifier C T that can accurately classify the target images into two classes, including smoke and non-smoke, even in the absence of domain annotations. Since it is not possible to perform supervised training directly on the target domain, a source representation mapping M S and a source classifier C S were trained using the source domain images in the pre-training phase, as shown in Figure 6, where the source classifier is trained using the standard cross-entropy loss: In the adversarial adaptation phase, the main objective is to regularize the source mapping M S and the target mapping M T training, thus minimizing the feature distributions extracted by the source and target mappings: M S (X S ) and M T (X T ). Under such conditions, the source classifier C S can be used directly in the target representation, i.e., C = C S = C T .
Based on the idea of adversarial training, the training losses of the domain classifier D and the target mapping M T are optimized in an alternating minimization process, as shown in Figure 7. First, for domain classifiers, the training goal is to accurately distinguish whether the data come from the source or target domain. The domain classifier D is, therefore, optimized using standard supervised loss, defined as follows: When training the target mapping M T , the standard cross-entropy loss is also used, as defined in Equation (7): In such a setting, the optimization of the target mapping M T and the domain classifier D is performed in adversarial training. In addition, to further align the correlation of features in the source and target domains, the calculation of the CORAL loss [26] was added to ADDA [24]. This is to align the second-order statistics of the source and target domain feature distributions, which helps to confuse the feature distributions. The definition of a CORAL loss is as follows: where CM S and CM T are the covariance matrices of d-dimensional features in the source domain and the target domain, respectively. The CORAL loss is also computed in the adversarial adaptation phase, as shown in Figure 7, where the learning rate of the classifier C was adjusted to 0 when performing backpropagation so that the CORAL loss only trains the target mapping M T . Therefore, the overall loss function of the target mapping M T is shown below: where λ denotes the weight of CORAL loss during training, which varies from 0 to 1 with training epochs. In summary, the overall process of feature-level domain adaptation is as follows: first, a Source CNN and classifier are trained using the source domain images. After the training, the parameters of these two parts are no longer updated. In the adversarial training phase, the initial weights of the Target CNN are the same as those of the Source CNN, and the source and target domain images are used as inputs to the Source CNN and Target CNN, respectively. The features obtained after mapping are used to calculate L D and L adv . At the same time, the source images and the target images are jointly used as input to the Target CNN, and the mapped features are then used as input to the pre-trained classifier, and the final output is used to calculate L CORAL .
C was adjusted to 0 when performing backpropagation so that the CORAL loss only trains the target mapping . Therefore, the overall loss function of the target mapping is shown below: where denotes the weight of CORAL loss during training, which varies from 0 to 1 with training epochs.
In summary, the overall process of feature-level domain adaptation is as follows: first, a Source CNN and classifier are trained using the source domain images. After the training, the parameters of these two parts are no longer updated. In the adversarial training phase, the initial weights of the Target CNN are the same as those of the Source CNN, and the source and target domain images are used as inputs to the Source CNN and Target CNN, respectively. The features obtained after mapping are used to calculate ℒ and ℒ . At the same time, the source images and the target images are jointly used as input to the Target CNN, and the mapped features are then used as input to the pre-trained classifier, and the final output is used to calculate ℒ .
Dataset
For the pixel-level domain adaptation (PDA), 2000 synthetic smoke images and 1800 real smoke images were used, as shown in Table 1. After pixel-level domain adaptation, C was adjusted to 0 when performing backpropagation so that the CORAL loss only trains the target mapping . Therefore, the overall loss function of the target mapping is shown below: where denotes the weight of CORAL loss during training, which varies from 0 to 1 with training epochs.
In summary, the overall process of feature-level domain adaptation is as follows: first, a Source CNN and classifier are trained using the source domain images. After the training, the parameters of these two parts are no longer updated. In the adversarial training phase, the initial weights of the Target CNN are the same as those of the Source CNN, and the source and target domain images are used as inputs to the Source CNN and Target CNN, respectively. The features obtained after mapping are used to calculate ℒ and ℒ . At the same time, the source images and the target images are jointly used as input to the Target CNN, and the mapped features are then used as input to the pre-trained classifier, and the final output is used to calculate ℒ .
Dataset
For the pixel-level domain adaptation (PDA), 2000 synthetic smoke images and 1800 real smoke images were used, as shown in Table 1. After pixel-level domain adaptation,
Dataset
For the pixel-level domain adaptation (PDA), 2000 synthetic smoke images and 1800 real smoke images were used, as shown in Table 1. After pixel-level domain adaptation, the 2000 synthetic smoke images were converted into photorealistic smoke images. The 2000 photorealistic smoke images were subjected to a series of data augmentations such as horizontal flip, gamma correction, color dithering, and contrast enhancement, and a total of 5000 images were selected as smoke samples in the source domain for featurelevel domain adaptation (FDA). For smoke samples in the target domain, 5000 real smoke images were obtained by data augmentation of the original real wildfire smoke images. For both the non-smoke samples in the source and target domains in FDA, real wildland background images were used after data augmentation, resulting in 5000 real non-smoke images in the source domain and 5000 real non-smoke images in the target domain. The Table 2. The test set included 520 real wildfire smoke images and 520 wildland non-smoke images, as shown in Table 3. Table 1. Image datasets for pixel-level domain adaptation.
Synthetic Images Real Images
Smoke images 2000 1800 Table 2. Image datasets for feature-level domain adaptation.
Smoke Images Non-Smoke Images
Source images 5000 5000 Target images 5000 5000 Table 3. Testing set.
Real Smoke Images Real Non-Smoke Images
Test set 520 520
Implementation Details
In the PDA (pixel-level domain adaptation) phase, the batch size was set to 1. The Adam optimizer was used for training with an initial learning rate of 0.0002. After 100 epochs, the learning rate decayed linearly to zero in the following training process for another 100 epochs.
For FDA, the feature extraction part of ResNet-50 [31] was used as the network structure for Source CNN and Target CNN. As shown in Figure 6, first, the Adam optimizer was used for training in the pre-training phase, with the initial learning rate set to 0.0001. During the training process, the learning rate was adjusted every 10 epochs, with each learning rate being 0.5 times the previous one, for a total of 100 epochs.
As shown in Figure 7, in the adversarial training phase, the parameters of the pretrained Source CNN were fixed and shared weights with the Target CNN and trained the Target CNN on this basis. The learning rate was set to 0.00001 for the Target CNN part, 0.0001 for the Discriminator, and 0 for the Classifier in order to keep the classifier parameters constant, thus allowing the CORAL loss to be used to train only the Target CNN. A total of 200 epochs were trained in this phase.
Lastly, we fixed the trained Target CNN and Classifier and tested the performance of the model with data from the test set, as shown in Figure 8.
Evaluation Metrics
To better measure the performance of the final trained model, we referred to the evaluation metrics defined in [16], namely CD (correct detection rate), ED (error detection rate), and MD (missed detection rate), where CD denotes the proportion of samples that were correctly predicted in the entire test set, ED denotes the proportion of non-smoke images in the samples that were predicted as smoke, and MD denotes the ratio of the number of samples that were incorrectly detected as non-smoke to the number of all non-
Evaluation Metrics
To better measure the performance of the final trained model, we referred to the evaluation metrics defined in [16], namely CD (correct detection rate), ED (error detection rate), and MD (missed detection rate), where CD denotes the proportion of samples that were correctly predicted in the entire test set, ED denotes the proportion of non-smoke images in the samples that were predicted as smoke, and MD denotes the ratio of the number of samples that were incorrectly detected as non-smoke to the number of all nonsmoke samples. These three metrics can better evaluate the classification effectiveness of the model on the test set and are defined as follows: where N TP indicates the number of wildfire smoke images predicted to be wildfire smoke, N TN indicates the number of non-smoke images identified as non-smoke, N FP indicates the number of non-smoke images predicted to be wildfire smoke, and N FN indicates the number of wildfire smoke images identified as non-smoke. The overall performance evaluation will be carried out using the wildfire smoke and non-smoke test sets.
Results and Discussion
To verify and compare the effectiveness of each component of the domain adaptation method, ablation experiments were carried out. From the experimental results, we found that when training the ResNet-50-based classification model using only source images (synthetic smoke and real non-smoke) or target images (real smoke and real non-smoke) in Table 2, the CD on the test set was below 0.7000 and the MD was above 0.4500, which reflects serious overfitting of the model. Essentially, this overfitting may be due to the difference in distribution between the training data and the test data. However, when the domain adaptation method was applied to train the classification model, the results on the test set were improved.
When using only the pixel-level domain adaptation method, the CD can be boosted to 0.7042, but the ED and MD were still high. In contrast, as shown in Table 4, the feature-level domain adaptation completely outperformed pixel-level domain adaptation on this dataset. However, we found that performing pixel-level domain adaptation before feature-level domain adaptation helped improve model performance. The CD increased to 0.7918 when using only DeepCORAL for feature-level domain adaptation and to 0.8569 when using only ADDA for feature-level domain adaptation. In both cases, there was some increase in the ED and MD. This suggested that using either ADDA or DeepCORAL alone would degrade the performance of the model, but ADDA performed better in comparison. A combination of ADDA and DeepCORAL achieved better results. This suggested that adding the calculation of CORAL loss to the structure of ADDA was helpful to further confuse the distribution of features. Based on this, by combining pixel-level domain adaptation with full feature-level domain adaptation, the CD was increased to 0.9739, and both the ED and MD were reduced to below 0.0400. In order to further evaluate the performance of the model proposed in this paper, a confusion matrix was used to represent the smoke classification ability of the model. Each column represents the predicted value, and each row represents the actual category. The confusion matrix of the model is shown in Figure 9. In order to further evaluate the performance of the model proposed in this paper, a confusion matrix was used to represent the smoke classification ability of the model. Each column represents the predicted value, and each row represents the actual category. The confusion matrix of the model is shown in Figure 9. Figure 10 shows a partial sample of each of these cases. As can be seen by the samples in Figure 10c,d, false positive mostly occurs in exceptional weather in which the sky is heavily clouded or foggy, and false negative tends to occur when fire smoke overlaps with cloud cover or overly bright sky. However, the proposed method has been able to reduce the false positive rate and false negative rate more effectively in the above cases than models trained without deep domain adaptation. To demonstrate this, we collected 100 wildland images of the cloud-smoke hybrid scenario as positive samples and selected 100 non-smoke wildland images containing clouds or fog as negative samples, as shown in Figure 11. We used such data to test the performance of the proposed model in the cloud-smoke hybrid case and compared it with the model trained using only the target images in Table 2. The backbone of both models is still Res- As can be seen by the samples in Figure 10c,d, false positive mostly occurs in exceptional weather in which the sky is heavily clouded or foggy, and false negative tends to occur when fire smoke overlaps with cloud cover or overly bright sky. However, the proposed method has been able to reduce the false positive rate and false negative rate more effectively in the above cases than models trained without deep domain adaptation. To demonstrate this, we collected 100 wildland images of the cloud-smoke hybrid scenario as positive samples and selected 100 non-smoke wildland images containing clouds or fog as negative samples, as shown in Figure 11. We used such data to test the performance of the proposed model in the cloud-smoke hybrid case and compared it with the model trained using only the target images in Table 2. The backbone of both models is still ResNet-50, and the comparison results are shown in Table 5. From the results, the proposed method improved the CD to 0.9382 and reduced both the ED and MD to below 0.0600 in the case of hybrid cloud-smoke. In summary, the proposed method can increase the recognition accuracy and reduce the false negative rate and false positive rate in the case of hybrid cloud-smoke.
Conclusions
A new method was proposed to address single scenes and poor richness prevalent in samples from wildfire smoke image datasets. First, many morphologically rich smoke samples are simulated in 3D modeling software. These smoke samples are synthesized with a virtual 3D wildland background with rich environmental diversity, resulting in a synthetic wildfire smoke dataset. By successively performing domain adaptation in image pixel and feature spaces, the synthetic smoke dataset is better able to be used to train smoke classification models with higher generalization. The validity of the idea was experimentally verified, an accuracy of 97.39% was obtained on the test set, and the impact of different modules in the domain adaptation structure on performance was investigated.
The proposed method would be further extended to apply in image classification tasks with data shortage in various fields. In addition, we will try to train additional models with confusion-prone fire smoke targets and apply the idea of deep domain adaptation to the wildfire smoke object detection task, which will allow for the more accurate location of relatively small-scale smoke and also better alleviate the problem of poor recognition accuracy in the case of cloud-smoke hybrid. To evaluate the recognition speed of the classification model, we calculated the average recognition time for test images on GPU and CPU, respectively, as shown in Table 6. The GPU is an NVIDIA GeForce RTX 2080 SUPER (NVIDIA, Santa Clara, CA, USA), and the CPU is an Intel Core i5-9600KF (Intel, Santa Clara, CA, USA). The single image recognition is faster on the GPU than on the CPU, and further improvements can be made by using better hardware or optimizing the network structure.
Conclusions
A new method was proposed to address single scenes and poor richness prevalent in samples from wildfire smoke image datasets. First, many morphologically rich smoke samples are simulated in 3D modeling software. These smoke samples are synthesized with a virtual 3D wildland background with rich environmental diversity, resulting in a synthetic wildfire smoke dataset. By successively performing domain adaptation in image pixel and feature spaces, the synthetic smoke dataset is better able to be used to train smoke classification models with higher generalization. The validity of the idea was experimentally verified, an accuracy of 97.39% was obtained on the test set, and the impact of different modules in the domain adaptation structure on performance was investigated.
The proposed method would be further extended to apply in image classification tasks with data shortage in various fields. In addition, we will try to train additional models with confusion-prone fire smoke targets and apply the idea of deep domain adaptation to the wildfire smoke object detection task, which will allow for the more accurate location of relatively small-scale smoke and also better alleviate the problem of poor recognition accuracy in the case of cloud-smoke hybrid. | 7,918.4 | 2021-11-23T00:00:00.000 | [
"Environmental Science",
"Computer Science"
] |
Single top-quark production measurements with ATLAS at the LHC
We present cross-section measurements of single top-quark production in the t-channel and associated Wt production modes using pp collision data recorded with the ATLAS detector at the LHC. The t-channel total cross-section is measured at √ s=7 TeV and at √ s=8 TeV with an integrated luminosity of 1.04 fb−1 and 5.8 fb−1, respectively. The t-channel top-quark and top-antiquark production cross sections and their ratio are also measured at 7 TeV with 4.7 fb−1 of data. Evidence for the Wt associated production is found at 7 TeV in the dileptonic channel using a luminosity of 2.05 fb−1. The measured cross-sections as well as the top-antitop ratio are in good agreement with the Standard Model predictions. The coupling strength |Vtb| is determined from the ratio of the measured to the theoretically predicted cross sections assuming that |Vts| and |Vtd | are small. The extracted values are all compatible with the Standard Model and with previous measurements.
Introduction
At hadron colliders top-quarks are predominantly produced in pairs (top-antitop) via the strong interaction.Alternative production modes proceed via the weak interaction involving a Wtb vertex, leading to a single top-quark intermediate state.Three subprocesses contribute to single top-quark production: the exchange of a virtual W boson in the t-channel or in the s-channel and the associated production of a top-quark and an on-shell W boson.The single top-quark final state provides a direct probe of the Wtb coupling and is sensitive to many models of new physics.The measurement of the production cross section constrains the absolute value of the quark-mixing (CKM) matrix element V tb without assumptions about the number of quark generations.
The observation of single top-quark production was first reported at the Tevatron by both DØ [1] and CDF [2] experiments.The observations are consistent with the Standard Model (SM) expectation for the t-channel and schannel processes.The third single top-quark production mechanism (Wt) has an expected cross-section too small at the Tevatron to be observed.
At the Large Hadron Collider (LHC), first measurements of single top-quark production in the t-channel and Wt modes have been recently reported by the AT-LAS [3,4] and CMS [5] collaborations at a centre-of-mass (CM) energy of 7 TeV.They also show good agreement with the SM expectation which is at approximate next-tonext-leading order (NNLO) σ t = 64.6 +2.7 −2.0 pb for the leading t-channel process and σ Wt = 15.7 ± 1.1 pb for the (sub-leading) Wt production mode.These values are calculated for a top-quark mass of 172.5 GeV and the uncera e-mail<EMAIL_ADDRESS>come from the renormalisation and factorisation scales and from the parton distribution functions (PDFs).At the energy of 8 TeV, a cross section σ t = 87.8+3.4 −1.9 pb is calculated at NNLO for the dominant t-channel process.
In this contribution, we present the results obtained for the t-channel and Wt single top-quark production using the LHC pp collisions data recorded at √ s=7 and √ s=8 TeV with the ATLAS detector.The cross-sections are extracted and values of the magnitude of the CKM matrix element |V tb | are derived.Complete lists of references for these results can be found in the original publications [3,4,6,7].
Data and simulation samples
The analyses reported here use the LHC proton-proton collision data at centre-of-mass energies of 7 and 8 TeV collected in 2011 and 2012 with the ATLAS detector [8].The selected events were recorded based on single electron and muon triggers.For the t-channel measurements, the analyzed data samples correspond to integrated luminosities of 1.04±0.04fb −1 (total cross section) and 4.71±0.18fb −1 (top and antitop cross sections) at 7 TeV and 5.8±0.2fb −1 at 8 TeV.The Wt analysis is based on a data sample recorded at 7 TeV and corresponding to an integrated luminosity of 2.05±0.08fb −1 .
Samples of Monte Carlo (MC) simulated events for all three single top-quark processes and for the top-quark pair process (tt) are produced using dedicated generators (leading-order (LO) AcerMC and next-to-leading order (NLO) MC@NLO) and normalized using NNLO crosssections.The background contribution from Gauge boson (W/Z) production in association with jets is simulated using the LO ALPGEN generator and the diboson processes (WW, WZ, ZZ) using the ALPGEN or HERWIG generators.The hadronization is performed by PYTHIA or with HERWIG in connection with the JIMMY underlying event model.After event generation, all samples are passed through the full simulation of the ATLAS detector based on GEANT4 and are reconstructed using the same procedure as collision data.More details on the used simulation samples are given in [3,4].
Object definition
Electron candidates are reconstructed using a clusterbased algorithm and are required to have a transverse energy E T > 25 GeV and a pseudorapidity for the calorimeter cluster |η cl | < 2.47.Events with electrons falling in the calorimeter barrel-endcap transition region (1.37 < |η cl | < 1.52) are rejected.High-quality electron candidates are selected using a set of cuts which include stringent requirements on the matching between the track and the calorimeter cluster.Isolation criteria are in addition required by applying cuts on the calorimeter transverse energy of all cells and on the transverse momentum of all tracks within cones of radius ∆R=0.2 or 0.3 around the electron direction.
Muon candidates are reconstructed by combining track segments found in the inner detector and in the muon spectrometer and are required to have a transverse momentum p T > 25 GeV and |η| < 2.5.Selected muons must additonally satisfy a series of cuts on the number of track hits present in the various tracking sub-detectors.Muon candidates are required to be isolated using equivalent criteria as applied to electron candidates.
Hadronic jets are reconstructed from calorimeter clusters using the anti-k t [9] algorithm with a radius parameter of 0.4.The response of the calorimeter is corrected by p T -and η-dependent factors which are applied to each jet to provide an average energy scale correction.Jets are required to have E T > 25 GeV (at 7 TeV) or 30 GeV (8 TeV) and |η| <2.5 (Wt) or 4.5 (t-channel).Jets overlapping with selected electron candidates within ∆R < 0.2 are removed.For the t-channel analyses, jets originating from bottom quarks are tagged in the region |η| < 2.5 by reconstructing secondary and tertiary vertices from the tracks associated with each jet and combining lifetime-related information with a neural network.A threshold is applied to the btagging algorithm output corresponding to a b-tagging efficiency of about 50-60% and a light-quark jet rejection factor of about 500.The missing transverse energy (E miss T ) is calculated using clusters of adjacent cells and corrected for the presence of electrons, muons and jets.
Event selection
For the t-channel measurements, the event selection requires exactly one electron or muon candidate, exactly two or three jets and E miss T > 25 GeV (7 TeV) or 30 GeV (8 TeV).A trigger matching requirement is applied where the lepton must lie within ∆R < 0.15 of its trigger-level object.Contribution from multijet background is additionally reduced through a requirement on the transverse mass of the lepton-E miss T system: m T (W) > (60 GeV−E miss T ) or m T (W) > 30/50 GeV depending on the luminosity and on the CM energy.The final signal sample is obtained by requiring in addition that exactly one jet is b-tagged.
For the Wt analysis, events with exactly two charged leptons (electron or muon) with opposite signs are selected and classified in the ee, µµ and eµ categories.Only events with at least one jet without any b-tagging requirement are considered and in addition the missing transverse momentum of the event is required to be greater than 50 GeV.In the ee and µµ channels, the invariant mass of the lepton pair (m ℓℓ ) is required to satisfy m ℓℓ < 81 GeV or m ℓℓ > 101 GeV in order to reduce the contamination from Z boson decays.Furthermore, in all channels, the Z to ττ background is reduced by applying a cut on the sum of the two angles in the transverse plane between each lepton and the missing transverse momentum direction.
Background estimation
For single top final states, the main background contributions originate from top pair production, QCD-multijet events (events with a fake lepton from jet misidentification) and from W boson production in association with jets (this process contributes mainly as background of the tchannel events).Smaller background contributions come from Z+jets and diboson production as well as from the other single top processes.These smaller backgrounds and the tt contribution are normalised to their NLO or NNLO theoretical predictions.For the W+jets background, the kinematic distributions are taken from samples of simulated events while the normalisation of the flavour composition is derived from data.
For the t-channel measurements, the multijet background normalisation is obtained through a binned maximum-likelihood fit to the E miss T distribution in the data using a data-derived template for the multijet background and templates from Monte Carlo simulation for all other processes.The multijet template is created using collision events triggered by a single low-p T jet.These events are then selected by replacing the electron requirement by a jet requirement.
In the Wt analysis, the multijet background contamination is normalised using the so-called matrix method which relates the observed sample composition in terms of selected leptons of different qualities to its true composition in terms of real and fake leptons.
Signal and background discrimination
To separate t-channel or Wt signal events from background, several kinematic variables are combined into one discriminant by employing a multivariate analysis based on a neural network (NN) (t-channel) or on the boosted decision trees technique (BDT) (Wt), both exploiting correlations between the variables.The highest-ranking variables are chosen for the training of the NN and BDT classifiers.
The fifteen highest-ranking variables defined from an optimisation procedure are used as inputs to the t-channel NN.For the 2-jet selection, the most discriminant observable is the invariant mass of the b-tagged jet, the charged lepton and the neutrino, which is an estimator of the topquark mass for signal events (the transverse momentum of the neutrino is given by the x-and y-components of the E miss T vector while the unmeasured z-component is inferred by imposing a W boson mass constraint on the leptonneutrino system).For the 3-jet selection, the two most dis- criminant variables are the invariant mass of the two leading jets and the absolute value of the difference in pseudorapidity of the leading and lowest p T jets.For Wt discrimination, twenty-two variables with significant separation power are used as input to the BDT.The most powerful variable is the magnitude of the vectorial sum of the p T of the leading jet, leptons and missing transverse momentum.
The NN output distributions obtained at 7 TeV are shown in Figures 1 and 2 for events with two and three jets, respectively.Figures 3 and 4 display the BDT output distributions for selected Wt events with one and two jets, respectively.For both channels, a good agreement is obtained between the distributions for data and MC simulation.
Cross-section and |V tb | measurements
To extract the signal cross-section, a maximum-likelihood fit is performed to the complete multivariate output distributions for the datasets corresponding to the different jet multiplicities: the 2-jet and 3-jet samples for the t-channel measurements and the 1-jet, 2-jet and ≥3-jet events for the Wt observation are simultaneously fitted.
Systematic uncertainties on the normalisation of the individual backgrounds and on the signal acceptance as well as uncertainties on the shape of each individual prediction affect the measured cross-sections.The impact of the systematic uncertainties is estimated using a frequentist method based on the generation of a large number of correlated pseudo-experiments.The distribution of the extracted cross-section is an estimator of the probability density function of all possible outcomes of the measurement and it is used to estimate the uncertainty on the actual measurement.Different sources of uncertainties are taken into account: object modeling (residual differences between data and MC simulations for the reconstruction and energy calibration of jets, electrons and muons), Monte Carlo generators (modeling of the single top signal and tt background, of the parton showers and hadronisation, and of the amount of initial-state and final-state radiation (ISR/FSR)), parton distribution functions (PDFs), theoretical cross-section normalisation, background normalisation to data (QCD-multijet and W+jets) and luminosity.The NN-based analysis of the t-channel events yields the following total cross sections: σ t = 83 ± 4(stat) +20 −19 (syst) pb at √ s=7 TeV, σ t = 95 ± 2(stat)±18(syst) pb at √ s=8 TeV.At 7 TeV, the main sources of systematic uncertainties come from ISR/FSR (14%), the b-tagging modeling (13%) and jet energy scale (6%).At 8 TeV, the ISR/FSR and btagging uncertainties have a reduced impact on the measurement (8-9%).
The cross-sections of the top-quark and top-antiquark production in the t-channel and their ratio R t measured at 7 TeV are: σ t (t) = 53.2± 1.7(stat)±10.6(syst)pb, σ t (t) = 29.5 ± 1.54(stat)±7.3(syst)pb, R t = σ t (t)/σ t (t) = 1.81 ± 0.10(stat) +0.21 −0.20 (syst).Adding the measurement of the top-quark and antitopquark cross-sections results in a total cross-section σ t = 82.7 ± 2.3(stat)±17.9(syst)pb which is in excellent agreement with the direct measurement based on 1.04fb −1 of data (value given in the previous paragraph).The main sources of systematic uncertainty for R t come from the background normalisation (5%), ISR/FSR (4%) and the jet energy scale (4%).The measured value of R t is compared to the predictions obtained with different PDF sets in Figure 5.It is in agreement with the predictions that vary between 1.86 and 2.07 depending upon the choice for the u-quark and the d-quark PDFs.
For the Wt cross-section measurement at 7 TeV, the result of the fit of the BDT output distribution is: σ Wt = 16.8 ± 2.9(stat)±4.9(syst)pb.This result is found to be incompatible with the background-only hypothesis at the 3.3σ level, the expected sensitivity assuming the Standard Model production rate being 3. −0.19 from the Wt cross-section measured at 7 TeV.These three results are compatible between them and with the direct measurements performed at the Tevatron [10,11].
Conclusions
This contribution summarizes measurements of the crosssection of single top-quark production in the t-channel with the ATLAS detector in pp collisions at √ s=7 and 8 TeV.Evidence for the production of single top-quark events in the Wt production mode in the dileptonic decay channel is also reported.The measurements are based on neural network and boosted decision tree discrimination to separate signal events from background contaminations.All extracted cross-section values, plotted in Figure 6
Figure 1 .
Figure 1.Neural network output distribution for the 2-jet samples at 7 TeV [4].All component distributions are normalised to the likelihood fit result except the multijet normalisation which is derived from the fit to the E miss T distribution.
Figure 3 .
Figure 3. BDT output distribution for the 1-jet samples at 7 TeV[3].The Wt signal is normalised to the theory prediction.
Figure 5 .
Figure 5. R t values calculated at √ s=7 TeV for different NLO PDFs [6].The error contains the uncertainty on the renormalisation and factorisation scales.The black line indicates the central value of the measured R t and the combined statistical and systematic uncertainty is shown in green (the yellow error band represents the statistical uncertainty).
4σ.A direct determination of the CKM matrix element |V tb | can be done from the measured single top crosssections assuming that |V tb | ≫ |V ts |, |V td | (σ is proportional to |V tb | 2 ).In the Standard Model |V tb | is close to one.The value of |V tb | 2 is extracted by dividing the observed single top cross-section by the SM expectation and the experimental and theoretical uncertainties are added in quadrature.The results obtained for |V tb | are the following: |V tb | = 1.13 +0.14 −0.13 from the t-channel cross-section measured at 7 TeV, |V tb | = 1.04 +0.10 −0.11 from the measurement at 8 TeV and |V tb | = 1.03 +0.16 , are in good agreement with the Standard Model predictions.A direct determination of the CKM matrix element |V tb | is performed from the observed cross-sections and the values are compatible with the Standard Model and with previous measurements from the different colliders and experiments. | 3,764.6 | 2013-05-01T00:00:00.000 | [
"Physics"
] |