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we altered the final step of the data generating mechanism in Equation [(8)](#sim7930-disp-0008){ref-type="disp-formula"}, so that the final outcome was calculated by $$\begin{matrix}
Y_{\mathit{Fij}} & {= \beta_{i} + \left( {\theta + u_{2i}} \right)\textit{treat}_{\mathit{ij}} + e_{\mathit{ij}}} \\
& {\beta_{i} \sim \left( {\textit{Beta}\left( {15,3} \right)} \right) \times 220} \\
& {\mspace{45mu} u_{2i} \sim N\left( {0,\tau^{2}} \right)} \\
& {\mspace{45mu} e_{\mathit{ij}} \sim N\left( {0,\sigma^{2}} \right).} \\
\end{matrix}$$ Therefore, the intercept term β ~i~ was now derived from a beta distribution with shape parameters of 15 and 3, which represent a negatively skewed distribution that was then scaled by 220 to give sensible values for systolic blood pressure (the outcome upon which the hypothetical data is based). An example density plot of this beta distribution for modeling the intercept term is shown in Web Figure A.1.
Secondly, we also considered a data generating mechanism with a common (fixed) treatment effect (ie, τ ^2^ = 0). Here, the fitted stratified and random intercept models were also modified to have a common treatment effect.
3.2. Results {#sim7930-sec-0010}
------------
Simulation results are shown in Tables [2](#sim7930-tbl-0002){ref-type="table"} and [3](#sim7930-tbl-0003){ref-type="table"}, covering most of the scenarios under the normal and beta distribution intercept data generating mechanisms, across all options for specifying and estimating the intercept. These tables show the mean percentage bias of the summary treatment effect estimate $\left( \hat{\theta} \right)$ (Table [2](#sim7930-tbl-0002){ref-type="table"}) and the median percentage bias in its heterogeneity $\left( {\hat{\tau}}^{2} \right.$) (Table [3](#sim7930-tbl-0003){ref-type="table"}). Figure [2](#sim7930-fig-0002){ref-type="fig"} graphically depicts the percentage coverage of the summary treatment effect estimate $\left( \hat{\theta} \right)$.
######
Mean percentage bias of the summary treatment eff
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0.030 0.022
R2 Between 0.904 0.938 0.877 0.912
R2 Within 0.085 0.143 0.085 0.143
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Note. Unstandardized Coefficients. Standard errors in parentheses. MLR.
\* *p* ≤ 0.05,
\*\* *p* ≤ 0.01,
\*\*\* *p* ≤ 0.001.
Models 6 and 8 account also for mediation of institutional trust at the individual level. Sources: ESS, EUROSTAT, QoG regional data.
In Models 6 and 8, we assess to what extent these correlations are due to institutional trust. Results support H2 and illustrate that the impact of both Corruption and Impartiality are mediated by confidence in institutions, suggesting that widespread unfairness will lead people to trust less each other because of a lower confidence in the institutions (Model 6 and 8). As a matter of fact, Model 8 shows that about 61% of the total effect of the Corruption Pillar on social trust passes through institutional trust. Similar results can be observed for the Impartiality Pillar in Model 6 (about 42% of the total effect is mediated). However, Impartiality maintains a strong and significant direct impact on social trust (p \< 0.001) even in the MSEM accounting for the intervening role of institutional trust (Model 6). This indicates that the mediation mechanism works in a weaker way for this dimension of
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N OPTIONS {#sim7930-sec-0002}
===================================================================
Consider that IPD have been obtained from *i* = 1 to *K* related randomized trials, each investigating a treatment effect based on a continuous outcome *Y* (say, blood pressure); that is, the mean difference in outcome value between a treatment and a control group. Suppose that there are *n* ~*i*~ participants in trial *i*. Let *Y* ~*Fi j*~ be the end‐of‐trial (F used to denote final) continuous outcome value, for participant *j* in trial *i*, and *Y* ~*Bi j*~ (B to denote baseline) be the pre‐treatment outcome value. Let *treat* ~*i j*~ take the value 1 or 0 for participants in the treatment or control group, respectively.
Given such IPD, there are several ways in which researchers can use a one‐stage meta‐analysis to model the summary treatment effect across trials. We focus initially on presenting one‐stage analysis of covariance (ANCOVA) mixed models, which either use a stratified intercept or a random intercept to account for clustering of participants within trials. We also assume a random treatment effect since heterogeneity is usually expected.
2.1. Model (1): stratified intercept {#sim7930-sec-0003}
------------------------------------
With the following approach, a stratified intercept is used to account for within‐trial clustering. $$\begin{matrix}
Y_{\mathit{Fij}} & {= \beta_{i} + \lambda_{i}\left( {Y_{\mathit{Bij}} - {\bar{Y}}_{\mathit{Bi}}} \right) + \left( {\theta + u_{i}} \right)\textit{treat}_{\mathit{ij}} + e_{\mathit{ij}}} \\
& {\mspace{99mu} u_{i} \sim N\left( {0,\tau^{2}} \right)} \\
& {\mspace{94mu} e_{\mathit{ij}} \sim N\left( {0,\sigma_{i}^{2}} \right)} \\
\end{matrix}$$ Here, *β* ~*i*~ denotes the intercept term for trial *i* (expected final outcome value for participants in the control group in trial *i* who have the mean baseline outcome value), and the distinct intercept for each trial is used to account for within trial clustering. The term *λ* ~*i*~ denotes
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a trial‐specific adjustment term for the baseline outcome value (here, centered at the mean for each trial ( ${\bar{Y}}_{\mathit{Bi}}$) to aid interpretation of the trial‐specific intercepts). For example, when there are *K* = 10 trials, there would be 10 *β* ~*i*~ terms and 10 *λ* ~*i*~ terms. Of main interest is an estimate of the model parameter θ, as this denotes the summary (average) treatment effect. The random effect, , indicates that the true treatment effects in each trial are assumed to arise from a distribution of true effects with mean and between‐trial variance *τ* ^*2*^. This assumption could be constrained if considered appropriate, with a common (fixed) treatment effect (ie, constrain *τ* ^*2*^ = 0). Lastly, $\sigma_{i}^{2}$ denotes a distinct residual variance per trial.
The flexibility of the one‐stage IPD approach allows us to make further modifications by considering, for example, a common baseline adjustment term (ie, *λ*) across trials, or common residual variances (ie, $\sigma_{i}^{2} =$ *σ* ^*2*^) if necessary([5](#sim7930-bib-0005){ref-type="ref"}, [10](#sim7930-bib-0010){ref-type="ref"}, [11](#sim7930-bib-0011){ref-type="ref"}); however, this should be justified (eg, based on computational reasons or estimation problems), and sensitivity analysis to the choice of assumptions is often sensible.
2.2. Model (2): random intercept {#sim7930-sec-0004}
--------------------------------
When there are a large number of trials to be synthesized, a stratified intercept approach to clustering can be computationally intensive (as Equation [(1)](#sim7930-disp-0001){ref-type="disp-formula"} requires estimation of 3* K + *2 parameters).[4](#sim7930-bib-0004){ref-type="ref"} An alternative approach for dealing with clustering, which is preferred by some researchers,[12](#sim7930-bib-0012){ref-type="ref"} is to use a random intercept term. $$\begin{matrix}
Y_{\mathit{Fij}} & {= \left( {\beta + u_{1i}} \right) + \lambda_{i}\left( {Y_{\mathit{Bij}} - {\bar{Y}}_{\mathit{Bi}}} \ri
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[Figure 2](#fig2-0192513X17710773){ref-type="fig"}, both paths are significant. First, as posited in Hypothesis 2, transnational parents report to be less happy than nontransnational parents. Second, although transnational parenting is significantly associated with family-to-work conflict as postulated in Hypothesis 1, it is not in the expected direction. Instead of transnational parents reporting family-to-work conflict less often they report family-to-work conflict more often. And this is a significant difference.
![Mediation model job instability with binary mediators.\
*Note*. Indirect effects (a1 \* b1 and a2 \* b2); direct effect (c'); total effect \[(a1 \* b1) + (a2 \* b2) + c'\]; percentage of total effect mediated = indirect effects/total effect (20%); Pseudo *R*^2^ = .20; Unstandardized ordinary least squares coefficients presented, paths a1 and a2 are unstandardized logit coefficients.\
Standard errors in parentheses.\
*Source*. TCRAf-Eu Angolan parent survey, The Netherlands 2010-2011.\
\**p* \< .05. \*\**p* \< .01. \*\*\**p* \< .001 (one-tailed test).](10.1177_0192513X17710773-fig2){#fig2-0192513X17710773}
######
Test of Mediation With Bootstrapped Results for Job Instability.
Mediator Β coefficient Bias-corrected CI
------------------------------------- --------------- ------------------- ------
Indirect effect
Happiness 0.07 0.01 0.15
Conflict −0.02 −0.08 0.03
Total 0.05 −0.03 0.13
Direct effect 0.18 0.05 0.33
Total effect 0.23 0.09 0.38
Proportion of total effect mediated 0.20
*Note*. The way to interpret the confidence intervals (CIs) is
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as caused to age restrictions of the questionnaires, we assumed that the data are missing completely at random (MCAR). Therefore, listwise deletion was used. The ANOVA table was inspected to check for significant main and interaction effects and specific hypotheses were tested. Satterthwaite's approximation was used to obtain the degrees of freedom ([@B44]). Model assumptions of linearity, independence, normality and homogeneity of variance were checked. Significance was evaluated at the 5% significance level. To get insight into the magnitude of the effects, 95% confidence intervals (CI) are reported.
Results
=======
[Table 2](#T2){ref-type="table"} shows the means, standard deviations, and observed range for the variables in our study.
######
Descriptive statistics of the study variables.
Patient Mother Father Sibling
------------------------- ------------------------ --------- -------- -------- --------- ------ -------- ------- ------ -------- ------- ------- --------
Cancer appraisal 18.81 5.31 8--28 21.03 6.55 9--39 17.97 6.28 5--32 20.82 6.19 10--36
Family functioning Family relation index 56.22 7.91 37--68 53.76 7.99 28--68 52.66 7.78 26--68 54.82 8.04 37--68
Family structure index 54.09 7.73 39--68 49.68 7.55 20--64 49.34 8.41 18--64 51.06 8.34 35--65
Cancer-related emotions Loneliness 5.91 3.63 1--14 7.82 6.81 0--30 5.34 5.13 0--22 5.49 4.70 0--18
Uncertainty 5.65 3.78 0--15 8.88 4.26 0--18 7.40 3.82 0--15 7.29 5.56 0--24
Helplessness 12.87 4.70 1--23 13.36 4.67 3--21 11.23 4.51 1--21 13.37 5.14 1--21
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002){ref-type="fig"}).
{#gcbb12419-fig-0002}
######
Statistical analyses of non‐structural carbohydrates (NSC). The effect of harvest date and genotype on NSC. Tests are a two‐way [anova]{.smallcaps} with date and genotype as factors. *P* = ≤ 0.05
*F* pr
------------------ -------- -------- --------
Mixed population
Genotype \<0.01 0.012 \<0.01
Date \<0.01 0.062 \<0.01
Geno × Date 0.001 \<0.01 0.02
Mapping family
Genotype \<0.01 \<0.01 \<0.01
Date \<0.01 \<0.01 \<0.01
Geno × Date \<0.01 \<0.01 \<0.01
John Wiley & Sons, Ltd
Biomass yield {#gcbb12419-sec-0021}
-------------
The highest yielding plants in spring 2014 (following the 2013 growing season when plants were sampled for NSC in July and October) were the four hybrid genotypes of the mixed population at 3--5 kg DW plant^−1^ (Fig. [3](#gcbb12419-fig-0003){ref-type="fig"}). The highest yielding hybrids of the mapping family were similar in final yield to Sin 1--5 of the mixed population. The lowest yielding plant was Hyb 21 at 0.07 kg (70 g) DW plant^−1^. The *M. sacchariflorus* genotypes were also generally low yielding, especially Sac 2--4 (Fig. [3](#gcbb12419-fig-0003){ref-type="fig"}).
{#gcbb12419-fig-0003}
The samples used for the analysis of carbohydrates were taken from single stems harvested in July and October 2013. To project the yields of total carbohydrate in July and October, sequential harvests were taken from a separate field site over a two‐year period (Table
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k National University in 2015.
Peer review under responsibility of King Saud University.
{#f0005}
{#f0010}
{#f0015}
{#f0020}
{#f0025}
{#f0030}
{#f0035}
######
Effect of low shear modeled microgravity on antibiotic resistance of *S. pyogenes* to various antibiotics (NG: normal gravity, MMG: low shear modeled microgravity).
Antibiotic Concentration of the antibiotic (μg/disc)
-------------- ------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------
Streptomycin 16.0 ± 1.0 18.0 ± 1 19.3 ± 1.1 18.6 ± 1.1 19.6 ± 1.1 19.6 ± 1.1 21.3 ± 1.5 20.3 ± 1.5
Penicillin 23.6 ± 2.3 24.3 ± 2.0 25.6 ± 1.5 26 ± 1.7 26.6 ± 1.5 27.3 ± 2.3 28.3 ± 0.5 29.3 ± 2.5
Kanamycin 18.0
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ring sites
*wR*(*F*^2^) = 0.089 H-atom parameters constrained
*S* = 1.14 *w* = 1/\[σ^2^(*F*~o~^2^) + (0.0341*P*)^2^ + 13.8363*P*\] where *P* = (*F*~o~^2^ + 2*F*~c~^2^)/3
19520 reflections (Δ/σ)~max~ = 0.009
950 parameters Δρ~max~ = 1.20 e Å^−3^
0 restraints Δρ~min~ = −1.36 e Å^−3^
------------------------------------- --------------------------------------------------------------------------------------------------
Special details {#specialdetails}
===============
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Geometry. All esds (except the esd in the dihedral angle between two l.s. planes) are estimated using the full covariance matrix. The cell esds are taken into account individually in the estimation of esds in distances, angles and torsion angles; correlations between esds in cell parameters are only used when they are defined by crystal symmetry. An approximate (isotropic) treatment of cell esds is used for estimating esds 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
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ng characteristic (ROC) curve to evaluate the multiple ML approaches on the same dataset ([Table 4](#table4){ref-type="table"}). We found that adaptive boosting neural networks achieved the biggest ROC area under the curve on the air quality data, tree bag on the climate data, and random forest on weather and air quality data. In general, we discovered that the predictive performance of the ML approaches improves as data variables increase.
######
Evaluation of machine learning approaches using receiver operating characteristic.
Machine learning approaches Weather, AUC^a^ Air quality, AUC Weather and air quality, AUC
---------------------------------- ----------------- ------------------ ------------------------------
Generalized linear model 0.538 0.682 0.758
Support vector machine 0.500 0.494 0.621
Adaptive boosting neural network 0.611 0.698 0.734
Tree bag 0.714 0.680 0.780
Random forest 0.669 0.692 0.809
^a^AUC: area under the curve.
Discussion
==========
Clinical Significance
---------------------
Recent studies have shown that weather and air pollution have been a major problem leading to an increase in daily deaths and hospital admissions for chronic respiratory illnesses \[[@ref3]-[@ref5],[@ref27],[@ref28]\]. We focused the distribution of daily patient visits for 2 years (ie, 2016 and 2017) ([Figure 2](#figure2){ref-type="fig"}). It is worth noting that peak days are more dominant from October to March, which indicates that the haze is a strong predictor, as these months are mostly colder in Guangzhou. Thus, it is important to recognize the peak OED visits for respiratory conditions.
{#figure2}
Previous studies mainly focused on the peak event forecasting ED visits for patients with one
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ght) + \left( {\theta + u_{2i}} \right)\textit{treat}_{\mathit{ij}} + e_{\mathit{ij}}} \\
& {\mspace{140mu} u_{1i} \sim N\left( {0,\tau_{\beta}^{2}} \right)} \\
& {\mspace{140mu} u_{2i} \sim N\left( {0,\tau^{2}} \right)} \\
& {\mspace{144mu} e_{\mathit{ij}} \sim N\left( {0,\sigma_{i}^{2}} \right)} \\
\end{matrix}$$ Parameters are as in Equation [(1)](#sim7930-disp-0001){ref-type="disp-formula"}, except that within‐trial clustering has now been accounted for by a random (instead of stratified) intercept term, with $\ \tau_{\beta}^{2}\ $ denoting the between trial variance in the intercept about the mean intercept (). Equation [(2)](#sim7930-disp-0002){ref-type="disp-formula"} assumes independence of the two random effects (ie, a covariance of zero), but their correlation could be accounted for assuming a bivariate random effect distribution; indeed, this might be of special interest when evaluating the relationship across trials of mean baseline in the control group and true treatment effect.[13](#sim7930-bib-0013){ref-type="ref"}
Compared to Equation [(1)](#sim7930-disp-0001){ref-type="disp-formula"}, the number of parameters to be estimated has been reduced, with only *β* and for the intercept, instead of *K* separate terms. Therefore, fewer estimation problems might be anticipated than in Equation [(1)](#sim7930-disp-0001){ref-type="disp-formula"}. On the downside, Equation [(2)](#sim7930-disp-0002){ref-type="disp-formula"} makes a strong and potentially unnecessary assumption that control group means are drawn from a normal distribution with a common mean and variance. Furthermore, the estimation of an additional random effect term might increase computational intensity.
2.3. Options for estimation and CI derivation {#sim7930-sec-0005}
---------------------------------------------
The parameters in models (1) and (2) are typically estimated using either a ML or REML approach. ML is known to produce downwardly biased estimates of between trial variance when there are few trials,([14](#sim7930-bib-
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18
FP4_Bowtie 0.58 0.331 8 0.60 0.356 10 0.330
FP4_MAQ 0.58 0.335 9 0.60 0.361 12 0.334
FP4_Soap2 0.58 0.333 9 0.60 0.357 11 0.331
Density Chen Eland 0.53 0.281 10 0.58 0.334 12 0.282
FP4_Eland 0.57 0.324 10 0.60 0.358 12 0.325
FP4_Bowtie 0.59 0.342 9 0.61 0.366 10 0.340
FP4_MAQ 0.59 0.346 9 0.61 0.370 10 0.345
FP4_Soap2 0.59 0.344 9 0.61 0.366 12 0.342
TF interactions wired with epigenetic effects
---------------------------------------------
To investigate the cooperative effects among TFs and epigenetic patterns in gene regulation, we exhaustively searched significant interaction terms from our regression model. First, a subset of ESC-specific genes that are co-bound by a specific TF pair is prepared. Then, the saturated model for the genes is constructed. The model involves 469 variables; 14 main effect terms (11 TFs and 3 epigenetic states) and 455 higher-order interaction terms (all the possible pairwise and triplewise interactions). Finally, our pipeline greedily identifies important variables (see *methods*). This procedure is independently performed with each of five peak datasets.
In total, 215 models were identified in which the predictive power is higher than the models without higher-order terms. These models contained 6-30 variables including at least one interactive term. As an example, the regression model for genes co-bound by Oct4 and Sox2, a well-known pluripotent complex \[[@B9],[@B25
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+ 36 = 0, a - t - 2*t - 10 = i. What is the un
- 0 - 8/(-2). Suppose 1251 = -2*b + 1307. Calculate b*z(i) + h*f(i).
-4*i
Let p(h) = 4*h - 3. Suppose b - 4 = -0*u - 2*u, 2*b - 5*u = 53. Let t(s) = 9*s - 7. Calculate b*p(g) - 6*t(g).
2*g
Let w(n) = -5*n**2 + 4*n + 4. Let d(v) = -6*v**2 + 3*v + 3. Suppose 32 + 36 = 17*y. Give y*d(h) - 3*w(h).
-9*h**2
Let z(u) = 2*u - 141 + 75 + 71 - 6*u. Let x = 8 - 11. Let y(o) = -o**3 - 8*o**2 + 21*o + 14. Let g be y(-10). Let s(q) = -5*q + 6. Determine g*z(c) + x*s(c).
-c + 2
Let i(q) = 20*q + 7. Let a(g) be the second derivative of 5*g**3/3 + 2*g**2 - 16*g. Let c(f) = -5*a(f) + 2*i(f). Let n(o) = 3*o + 2. Determine 2*c(u) + 7*n(u).
u + 2
Let a(n) = 3*n + 30. Let t(f) = -3*f - 31. Determine -6*a(x) - 7*t(x).
3*x + 37
Suppose 0 = -4*d + 4*i - 32, -5*d - i - 10 = -0*i. Let y(v) = v + 1. Let c(r) = -4*r - 6. What is d*y(a) - c(a)?
a + 3
Let o(x) = -x - 5. Let d(k) = 414*k + 450. What is -d(b) - 90*o(b)?
-324*b
Let m(d) = -2*d**2 + d + 1. Let b(j) = -3*j**2 - 2*j + 10. Give b(c) - m(c).
-c**2 - 3*c + 9
Suppose -2*q + 5 + 1 = 0. Suppose 4*z + 5*t = 65, -t + 13 = 2*z - 18. Let h(b) = 1. Let a(y) = -y - 5. Give q*a(j) + z*h(j).
-3*j
Let k(u) = -15. Let w(r) = r - 58. What is 9*k(v) - 2*w(v)?
-2*v - 19
Let u(p) = 9*p**3 + 10*p - 1. Let c(s) = 8*s**3 + s**2 + 9*s - 1. Let j(g) = 5*c(g) - 4*u(g). Let k(b) = 6*b**3 + 7*b**2 + 7*b - 2. Calculate 7*j(t) - 5*k(t).
-2*t**3 + 3
Let g(b) = b**3 + b**2 + 1. Let f(w) = 2*w**3 + 2*w**2 + w + 3. Let n(y) = -y**2 + 7*y + 496. Let x be n(26). Give x*f(q) - 6*g(q).
-2*q**3 - 2*q**2 + 2*q
Let z(f) = -1365*f**2 - 112*f - 112. Let h(d) = 124*d**2 + 10*d + 10. Determine -56*h(o) - 5*z(o).
-119*o**2
Let l(x) = 0 + 67*x - 56*x - 8 - 9*x**2 + 0. Let h be (8/(-5))/((-2)/10). Let b(c) = -6*c**2 + 7*c - 5. Calculate h*b(i) - 5*l(i).
-3*i**2 + i
Let h(b) = 2*b + 1. Suppose 7*d + 23 = 2. Let u(r) = -r. Let i(f) = f - 10. Let c be i(6). Calculate c*u(m) + d*h(m).
-2*m - 3
Let d(t) = -7*t**2 + 7*t + 2. Let b(u) = 9*u**2 - 8*u - 2. Calculate 5*b(j) + 6*d(j).
3*j**2 +
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om the cross flow to the other components. Many weaker causal influences (*NT \<* 0.7) are omitted in the description above, the most significant one being the lift-up mechanism. This can be identified, for example, as the causal influence of the long *υ* to the straight streaks, that is also present and of the order \~ 0.3.
Finally, the self-sustaining process of the logarithmic layer is summarized in [Figure 6](#F6){ref-type="fig"} and interpreted in the context of different known mechanisms. The process is mostly unidirectional, emanating from the time-varying mean velocity profile, which generates straight streaks in a characteristic time scale of 10*S*^−1^. We refer to this process as a parametric instability. The effect of the straight streaks on the flow is twofold. First, it generates long velocity rolls which interact weakly (*NT \<* 0.7) with the straight streaks through the lift-up mechanism. Secondly, straight streaks meander, marking the onset of its instability. Both processes take place at a time scale of 5*S*^−1^. The instability results in the breakdown of the streamwise streaks into short rolls, in a relatively fast process that spans along the time period of 2*S*^−1^.
It is important to remark that the causality discussed above is a measure based on the reduction of the statistical uncertainty of the variables, and it is not directly linked to energy transfer. Furthermore, the current method detects direct causality only if all the intermediate causal variables are accounted for. Otherwise, the causality may flow indirectly through other signals not taken into consideration. For example, the dynamic equation for ${\hat{v}}_{01}$ is $$\frac{\partial{\hat{v}}_{01}}{\partial t} = - \sum\limits_{n,m}ik_{x_{n}}{\hat{v}}_{nm}{\hat{u}}_{( - n)(1 - m)} - \sum\limits_{n,m}\frac{\partial{\hat{v}}_{nm}}{\partial y}{\hat{v}}_{( - n)(1 - m)}$$ $$- \sum\limits_{n,m}ik_{z_{m}}{\hat{v}}_{nm}{\hat{w}}_{( - n)(1 - m)} + \nu\left( {\frac{\partial^{2}{\hat{v}}_{01}}{\partial y^{2}} - k_{z_{1}}^{2}{\hat{v}
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.33 −32.61
**B2‐A2** −36.66 −4.98 −14.36 −5.39 −50.99 −4.84 −12.86 −5.35
**B3** −100.00 −28.68 −61.86 −24.74 −100.00 −17.42 −81.05 −48.75
**C1** −100.00 −16.72 −39.54 −14.38 n/a n/a n/a n/a
**C2** −100.00 −16.86 −40.56 −15.94 n/a n/a n/a n/a
**D1** −100.00 −19.20 −66.97 −24.63 −100.00 −33.67 −99.98 −62.07
**D2** −100.00 −11.65 −30.04 −11.79 −100.00 −10.50 −37.84 −22.19
See Table [1](#sim7930-tbl-0001){ref-type="table"} for full data generation details relating to each scenario. True value for is 7.79, except scenarios D1 and D2 where is equal to 3.9 and 15.6, respectively.
n/a = not applicable, since there is no τ ~β~ ^2^ to vary when a beta distribution is used for the intercept data generating mechanism. Options: ML, maximum likelihood estimation; REML, restricted maximum likelihood estimation.
{ref-type="fig"}A) and beta distributions (Figure [2](#sim7930-fig-0002){ref-type="fig"}B) for the intercept data generating mechanisms, for stratified (left) and random (right) intercept models, under each of th
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tudy because of the global open chromatin conformation in ESC \[[@B19]\]. Other possibilities include additional epigenetic patterns and homeostatic regulation, further investigations are required.
Epigenetic patterns improve the prediction of gene expression
-------------------------------------------------------------
To further understand the epigenetic effects in gene regulation, we add three epigenetic states to the regression models; histone mark (HistM), DNA methylation (Methy), and CpG island (CpGI). Thus, 14 explanatory variables are used. To identify effective variables in the prediction, we reduced the regression model by using the stepwise model selection. Also, 100 runs of computer simulation that randomly assign the epigenetic states were performed.
All models with the epigenetic effects improved CV-*R*^2^ with one to three more variables compared with the models without the epigenetic effects (Table [2](#T2){ref-type="table"}). The additional variables are the epigenetic effect terms. The results of simulation support that the improvements are not by the chance. In particular, the density-based models with the epigenetic effects are significantly better when remapped peak datasets are used. Furthermore, overall regression coefficients gathered from all the density-based models in Table [2](#T2){ref-type="table"} show the relative importance of epigenetic effects except CpGI (Figure [2F](#F2){ref-type="fig"}). Note that the positive-biased activities are consistent with the previous study \[[@B24]\].
######
Effects of epigenetic patterns in reduced regression models
Model Peaks 11 TFs 11TFs + 3 epigenetic effects Simulation
------------- ------------ -------- ------------------------------ ------------ ------- ------- ------- -------
Exponential Chen_Eland 0.53 0.282 9 0.58 0.333 12 0.283
FP4_Eland 0.57 0.319 10 0.59 0.351 12 0.3
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ified intercept model was 88.8%, using REML+standard, but 95.8% using REML+KR.
Using REML+Satterthwaite gave very similar results to REML+KR. Occasionally, there was some over‐coverage using REML+KR or REML+Satterthwaite, particularly when using a low number of trials (*K* = 5). For example, coverage was close to 99% (regardless of which model was used), in a setting of *K* = 5 trials with an equal number of participants per trial (scenario A1; *n* ~*i*~ = 100), and in a setting of *K* = 5 trials with some small‐sized and some large‐sized trials (scenario B2‐A1; 2 small trials where *n* ~*i*~ *∼*U(30, 100), and 3 large trials where *n* ~*i*~ *∼*U(900, 1000)).
*(ii) Under a beta distribution intercept generating mechanism*
For the beta distribution intercept generating mechanism (Figure [2](#sim7930-fig-0002){ref-type="fig"}B and Web Table C.III), using REML+standard again gave better coverage than using ML, and using REML+KR or REML+Satterthwaite generally further improved upon this coverage (ie, moved it closer to 95%), especially with scenarios concerning at least 10 trials that had a large variation in sample sizes.
As before, under ML estimation, the random intercept model showed better estimates of between‐trial variance and improved coverage (closer to 95%) than the stratified intercept model. However, differences between the two models were generally small for estimation under REML (with or without a 95% CI correction).
### 3.2.6. Common treatment effect data generating mechanism {#sim7930-sec-0016}
Results based on a common (fixed) treatment effect data generating mechanism are shown in Web Appendix B. All fitted models assumed a common treatment effect and converged every time (ie, 100% convergence), and there was negligible difference in mean percentage bias of $\hat{\theta}$ between ML and REML estimation options for either model (stratified or random intercept), or between either model (Web Table B.1). The percentage coverage results were stable across all comparisons, ran
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1.06 (0.25) 0.92 (0.17)
Nitrogen dioxide 60.05 (26.09) 46.43 (17.67)
8-hour average ozone slip in a day 74.28 (54.90) 90.24 (52.46)
Data Analysis
-------------
Since the effect of weather and air quality on respiratory conditions in humans was not instantaneous, representative lags were applied to variables based on the work done previously in this area \[[@ref3],[@ref24]-[@ref26]\]. To simplify the delayed impact of respiratory conditions, we considered a 3-day lag for all variables.
We removed records with less than 10 people on weekends to eliminate weekend effects, bringing the total number of samples collected to 559. To create a meaningful feature vector for training and cross-validation, the date field was removed to obtain a (*X*, *y*), where *X* was a matrix with the dimensions (*m* × *n* = 559 × 9) representing values of variables, and *y* was a vector of length (m=559) representing the output class of the examples (ie, events). Analysis of the data suggested that the output class was highly imbalanced with 413 examples of nonpeak and 146 examples of peak events.
Machine Learning Approaches
---------------------------
In this section the ML algorithms are presented and discussed; details of the updating and classification processes are described in the following algorithms.
### Generalized Linear Models
1. Construct the common linear model from the original training set: *f*  = *w^T^ x* + *b*, where *w* is the weight vector and *b* is the bias, both of which are only determined by the training samples
2. Identify the contact function *f* ^-1^
3. Build the GLMs:  = *f* ^-1^ (*w^T^ x* + *b*)
4. Calculate the classification on the test set
### Support Vector Machine
1. Convert the sample space into linearly separable space with polynomial core functions *K* (*x*~i~, *y*~i~)
2. Calculate the support vectors with the foll
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thermore, randomly and uniformly select 2000 sets of data with irradiance between *G* = 150 W/m^2^--1000 W/m² and temperature between 15--40 °C as the test set.
Two layers of MLP structure are used and the activation function is set as LeakyRelu. In the MLP model, the learning rate is set as 1.75 × 10^-3^, the learning decay is 0.9, the training generation is 30, the small batch size is 500, the first layer of neurons is set as 150 and the second layer of neurons is set as 100.
Two layers of CNN structure are used, and the activation function is also set as LeakyRelu. In the CNN model, the learning rate is set as 3.5 × 10^-4^, the learning decay is 0.9, the training generation is 30, the small batch size is 25, the convolution kernel dimension is 50, the convolution kernel size is 3 and the hidden layer dimension is 100.
MLP and CNN models are trained according to the program flow as shown in [Figure 3](#sensors-20-02119-f003){ref-type="fig"}. The training process is illustrated in [Figure 4](#sensors-20-02119-f004){ref-type="fig"}. [Figure 4](#sensors-20-02119-f004){ref-type="fig"}a,b indicate the changes of loss function and accuracy in MLP training while [Figure 4](#sensors-20-02119-f004){ref-type="fig"}c,d indicate the changes of loss function and accuracy in CNN training. In [Figure 4](#sensors-20-02119-f004){ref-type="fig"}, *Acc* is defined as $$Acc = 1 - \frac{\left| {y_{i}^{\prime} - y_{i}} \right|}{y}$$
Curves 1 and 2 in [Figure 4](#sensors-20-02119-f004){ref-type="fig"}b,d show the accuracy in training set and test set respectively. It can be seen from [Figure 4](#sensors-20-02119-f004){ref-type="fig"} that the CNN model has a fast convergence rate and a stable process while the MLP model has a relatively slow convergence rate and has a series of oscillating links. However, the final convergence values of the two models are similar.
4. Results Analysis {#sec4-sensors-20-02119}
===================
4.1. The Accuracy Analysis {#sec4dot1-sensors-20-02119}
--------------------------
MLP and CNN a
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the joints are always well detected. We use the distance proposed by Raptis et al. \[[@B43-sensors-19-03503]\] to group Dense Trajectories of an action around joints. Given a pair of dense and joint trajectories, respectively, $\mathcal{P}^{m}$ and $\mathcal{Q}^{j}$, which co-exist in the temporal range $\tau$, the spatiotemporal distance between two given trajectories is expressed using:$$d{({\mathcal{P}^{m},\mathcal{Q}^{j}})} = \max\limits_{t \in \tau}s_{t} \cdot \frac{1}{L}\sum\limits_{t \in \tau}r_{t},$$ such that $s_{t} = ||\mathtt{p}_{t}^{m} - \mathtt{q}_{t}^{j}{||}_{2}$ is the spatial distance and $r_{t} = ||{(\mathtt{p}_{t}^{m} - \mathtt{p}_{t - 1}^{m})} - {(\mathtt{q}_{t}^{j} - \mathtt{q}_{t - 1}^{j})}{||}_{2}$ is the velocity difference between trajectories $\mathcal{P}^{m}$ and $\mathcal{Q}^{j}$. Then, an affinity matrix is computed between every pair of trajectories $(\mathcal{P}^{m},\mathcal{Q}^{j})$ using Equation ([3](#FD3-sensors-19-03503){ref-type="disp-formula"}) as:$$b{({\mathcal{P}^{m},\mathcal{Q}^{j}})} = \exp{( - d{({\mathcal{P}^{m},\mathcal{Q}^{j}})})},$$ where the measure $d(\mathcal{P}^{m},\mathcal{Q}^{j})$ penalizes trajectories with significant variation in spatial location and velocity. After a hierarchical clustering procedure which is based on the affinity score \[[@B43-sensors-19-03503]\], a membership indicator function specifies the cluster $\mathsf{G}^{j^{*}}$ of joint $j^{*}$ each trajectory belongs to. $$\mathsf{G}^{j^{*}} = \{\mathcal{P}^{m},\forall m \in {\{ 1,\ldots,M\}}{and}\operatorname{arg\ min}\limits_{j \in J}b{({\mathcal{P}^{m},\mathcal{Q}^{j}})} = j^{*}\}.$$
Furthermore, trajectories that are above a certain threshold of distance are rejected. This condition ensures that irrelevant and noise-resulting trajectories will not be considered, e.g., background motion.
Feature Representation
----------------------
As discussed in \[[@B7-sensors-19-03503]\], features can be computed along each trajectory and BoWs can be used to aggregate and encode the information. In suc
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45.900, J44.001, J44.101, J44.803, and J98.801). The duration of the collected data lasted from January 1, 2016, to December 31, 2017, which is 731 days of continuous data. For statistical purposes, the days where the daily volume was less than 24 were labeled as nonpeak events, and the rest were labeled as peak events.
[Table 1](#table1){ref-type="table"} describes the Pearson correlation coefficient between OED visit numbers and input indicators. We found that OED visit numbers showed positive correlations with wind speed, atmospheric pressure, carbon monoxide, sulphur dioxide, nitrogen dioxide, and PM25. However, OED visit numbers showed negative correlations with outdoor temperature, relative humidity, and ozone. The weather and air quality data distribution of patients with acute exacerbations of COPD from peak and nonpeak groups was shown in [Table 2](#table2){ref-type="table"}.
{#figure1}
######
The Pearson correlation coefficients between outpatient and emergency department visit numbers and input indicators.
Variable WS^a^, r TP^b^, r AP^c^, r RH^d^, r PM25^e^, r SO~2~^f^, r CO^g^, r NO~2~^h^, r O~3~\_8h^i^, r Number of visits, r
------------------ ---------- ---------- ---------- ---------- ------------ ------------- ---------- ------------- ---------------- ---------------------
WS 1 --0.32 0.27 --0.4 --0.34 --0.33 --0.26 --0.42 --0.24 0.15
TP --0.32 1 --0.88 0.35 --0.23 0.03 --0.24 --0.25 0.39 --0.38
AP 0.27 --0.88 1 --0.5 0.31 0.09 0.21 0.29 --0.18 0.39
RH --0.4 0.35 --0.5 1 --0.18 --0.27 0.2 0.03 --0.28
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="table"}** for the descriptive data of latency and error rate.
######
Working memory capacity and attentional control in Experiment 1 (means, with standard deviations in parentheses).
Indicators Low WMC High WMC
------------------------- ------------------------- ------------------ ------------------ ------------------
**WMC**
OSPANs 11.679 (3.418) 22.875 (5.319)
**Attentional control**
Latency 391.094 (43.065) 407.443 (44.490) 354.965 (38.808) 370.109 (43.580)
Error rate 0.233 (0.194) 0.261 (0.194) 0.213 (0.155) 0.252 (0.160)
WMC, working memory capacity; SA, state anxiety; WM training, the working memory training group; Control, the control group; OSPANs, operation-word span task scores; Latency, the latency of first correct saccade; Error rate, the percentage of incorrect saccades
.
The results of 2 × 2 ANOVA for latency and error rate showed that the main effects of SA Condition were significant for both latency, *F*(1,54) = 12.988, *p* = 0.001, $\eta_{p}^{2}$ = 0.194, and error rate, *F*(1,54) = 6.199, *p* = 0.016, $\eta_{p}^{2}$ = 0.103, that is, there were significant increases in high-SA condition compared with low-SA condition for both latency (see **Figure [2A](#F2){ref-type="fig"}**) and error rate (see **Figure [2B](#F2){ref-type="fig"}**), which was consistent with H1-1, demonstrating that high-SA impairs attentional control. Furthermore, the main effects of WMC Group were significant for latency, *F*(1,54) = 12.246, *p* = 0.001, $\eta_{p}^{2}$ = 0.185, but not for error rate, *F*(1,54) = 0.103, *p* = 0.749, $\eta_{p}^{2}$ = 0.002, that is, there was a significant decrease in high-WMC group compared with low-WMC group for latency (see **Fig
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e was a significant main effect of *family member* \[χ^2^(3) = 33.99, *p* \< 0.001\], as will be explained below (see section "Similarities and Differences Across Members Within One Family"). There was also a significant effect of the *ill child's age at diagnosis* \[χ^2^(1) = 5.07, *p* = 0.02\]: the older the ill child was at diagnosis, the less all family members reported to experience positive emotions. None of the other variables were significantly related to positive emotions (all χ^2^ \< 3.30, all *p* \> 0.07). Of note, when excluding the non-significant interactions (interaction with FSI, interaction with cancer appraisal, interaction between family functioning and cancer appraisal), the interaction effect between *FRI* and *family member* did no longer reach significance \[χ^2^(3) = 6.60, *p* = 0.09\].
Family Functioning, Cancer Appraisal and Quality of Life
--------------------------------------------------------
The final models for the associations between family functioning, cancer appraisal and quality of life for mothers and fathers on the one hand and patients and siblings on the other hand are shown in [Table 4](#T4){ref-type="table"}.
######
Final models for the associations between family functioning, cancer appraisal and reported quality of life.
QoL mothers and fathers (*N* = 157; 90 mothers, 67 fathers) QoL patients and siblings (*N* = 48; 20 patients, 28 siblings)^1^
------------------------------------------------------------ ------------------------------------------------------------- ------------------------------------------------------------------- --------------- -------- ------------------- ---------------
**Variables of interest**
FES -- FRI
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trial 1/0 indicator) and *treat* ~*ij*~ (treatment group 1/0 indicator) value for each of 100 participants in each of 10 trials.
Next, based on the previous meta‐analysis,[22](#sim7930-bib-0022){ref-type="ref"} we set the true parameter values for this simulation to be as follows: θ = −9.66 (summary treatment effect; negative value favors treatment group), = 7.79 (between trial variation in the treatment effect), *β* = 159.73 (mean blood pressure response in control group), $\tau_{\beta}^{2}$ = 233.99 (between trial variation in the intercept), and *σ* ^*2*^ = 333.74 (residual variance).
We then used these parameter values to generate further terms, beginning with using *σ* ^2^ to generate an error term *e* ~*i j*~, for the *j*th participant from the *i*th trial $$e_{\mathit{ij}} \sim N\left( {0,\sigma^{2}} \right).$$ Then, we generated the trial level values for the random parts of the intercept and treatment effect terms, *u* ~1*i*~ and *u* ~2*i*~, respectively, $$\begin{matrix}
{u_{1i} \sim N\left( {0,\tau_{\beta}^{2}} \right)} \\
{u_{2i} \sim N\left( {0,\tau^{2}} \right).\mspace{900mu}} \\
\end{matrix}$$ Finally, with all the parameters defined (*β*, *u* ~1*i*~, θ, *u* ~2*i*~, *treat* ~*i j*~, and *e* ~*i j*~), we generated the end‐of‐trial continuous outcome value *Y* ~*Fi j*~, under the random intercept model (2) (with no baseline adjustment term and assuming a common residual variance) $$Y_{\mathit{Fij}} = \left( {\beta + u_{1i}} \right) + \left( {\theta + u_{2i}} \right)\textit{treat}_{\mathit{ij}} + e_{\mathit{ij}}.$$ This gave one complete IPD meta‐analysis dataset of 1000 total participants, containing 100 participants in each of 10 trials, consisting of the following data for each individual: a trial indicator (*trial* ~*i*~), a treatment group indicator (*treat* ~*i j*~), and an end‐of‐trial continuous outcome value (*Y* ~*Fi j*~).
*Step 2: Model fit and replication*
Using the generated data, we fitted a stratified intercept model (1) and a random intercept mo
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layer and can be described as (9) and (10) $$y_{i^{l + 1},\ j^{l + 1},d} = \frac{1}{HW}{\sum\limits_{0 \leq i < H,0 \leq j < W}x_{i^{l + 1} + i \times H + i,j^{l + 1} \times W + j,d^{l}}^{l}}$$ $$y_{i^{l + 1},j^{l + 1},d} = \underset{0 \leq i < H,0 \leq j < W}{MAX}x_{i^{l + 1} + i \times H + i,j^{l + 1} \times W + j + j,d^{l}}^{l}$$
The first layer of CNN is pool as **p**^l^∈$\mathbb{R}$ ~l~^W×H×D^, then the pooling results of each layer cover the original results.
The core space of pooling is 2 × 2 matrix and the input data size is *W*~2~ × *H*~2~ × *D*~2~, the output size is *W*~3~ × *H*~3~ × *D*~3~, size of the core space is *f*, the step size is *s*, then the shape of the input and the output meet the following equation $$w_{3} = (w_{2} - f)/s + 1$$ $$h_{3} = (h_{2} - f)/s + 1$$ $$d_{3} = d_{2}$$
The neurons of CNN in the full connection layer are all connected to the activation data in the previous layer, and Adam algorithm is also used to optimize the backward propagation of CNN \[[@B31-sensors-20-02119],[@B32-sensors-20-02119]\].
3.3. Model Training {#sec3dot3-sensors-20-02119}
-------------------
The ANN and CNN model presented are used to predict the I--V curve, and the voltage and five environmental conditions described above are adopted as input conditions. The loss function for MLP and CNN model is defined as *MAE* $$MAE = \frac{1}{N}{\sum\limits_{i = 1}^{N}\left| {y_{i}^{\prime} - y_{i}} \right|}$$ where, *N* is the total data set from NREL, *y*'~i~ is the predicted data, *y*~i~ is the measurement data. Actually, *MAE* is the average absolute error. Back propagation (BP) algorithm is used for the model optimization in this study. The training process of MLP and CNN is illustrated in [Figure 3](#sensors-20-02119-f003){ref-type="fig"}.
Remove missing data items and randomly and uniformly select 5000 sets of data with irradiance between *G* = 150 W/m^2^--1000 W/m² and temperature between 15--40 °C, and select the first 90% as the training set and the last 10% as the validation set. Fur
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re trained to obtain the optimal training models. After the test set verification of the optimal training model, the results obtained are shown in [Figure 5](#sensors-20-02119-f005){ref-type="fig"}. [Figure 5](#sensors-20-02119-f005){ref-type="fig"}a,b indicate the linear fitting effect of I--V curves predicted by MLP model and CNN model. Compared with MLP, there are fewer cusps in the nonlinear fitting curves of CNN model, thus it shows that the fitting effect of CNN model is better than that of MLP model. The cusps will greatly reduce the prediction accuracy and increase the error and the degree of difficulty in finding the maximum power for PV modules.
The evaluation terms for the I--V curves are defined as Mean Absolute Error (MAE, seen in equation (14)) and Root Mean Square Error (RMSE) and *RMSE* is $$RMSE = \sqrt{\frac{1}{N}{\sum\limits_{i = 1}^{N}{(y_{i}^{\prime} - y_{i})}^{2}}}$$ where, *N* is set as 50 in this study. *MAE* shows the distance between the predicted value and the measured value, which is close to zero in ideal situations, while *RMSE* further demonstrates the degree of dispersion between the predicted value and the measured value. Obviously, smaller *RMSE* means higher aggregation of the errors. *RMSE* and *MAE* of the eight groups of I--V curves are shown in [Table 2](#sensors-20-02119-t002){ref-type="table"}.
It can be seen from [Table 2](#sensors-20-02119-t002){ref-type="table"} that, when the irradiation intensity is lower than 500 W/m^2^, *MAE* and *RMSE* of MLP and CNN are relatively low, while *MAE* and *RMSE* of MLP become high when he irradiation intensity is higher than 500 W/m^2^.Thus, accuracy of the CNN model is obviously higher than the MLP model. In addition, it indicates that the aggregation of the errors of CNN is higher than that of MLP.
4.2. Analysis of the Fitting Degree {#sec4dot2-sensors-20-02119}
-----------------------------------
The fitting degree of the I--V curves is analyzed in the following. As we all know, Euclidean distance is the distance between two po
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d* *P*
------------ -------- ----- ------------------- ------ ------ ------
Weekly Female 60 7494.09 (3268.83) 0.23 0.04 0.07
Male 57 7358.60 (3147.58)
Weekdays Female 60 8301.11 (3721.95) 0.02 0.00 0.05
Male 57 8290.00 (3468.30)
Weekend Female 57 5551.72 (3409.37) 1.21 0.23 0.32
Male 48 4753.99 (3318.72)
M, mean; SD, standard deviation; T, T-values (Independent Samples t-test); d, Cohen's d; P, observed power.
Regarding the number of activities carried out by each sex (Mann--Whitney *U* test: *Z* = −4.36, *p* \< 0.001), however, differences in terms of sex were more pronounced, with female subjects reporting an average of 3.29 (*SD* = 1.88) activities per week, compared to 4.73 for their male counterparts (*SD* = 1.75).
A chi-squared test was used to assess the relationship between sex and level of autonomy in PA. In view of the low number of respondents in the "organized PA" group, these subjects were grouped together with the "mixed" participants to permit a comparison between subjects whose PA habits include unstructured activities only and subjects whose PA habits include some organized activities. The results reveal the influence of sex on autonomy of PA (chi-squared = 5.94, *p* \< 0.05, *w* = 0.26), with girls tending to be more autonomous in their practice and boys more likely to opt for organized activities only or a combination of both types.
The study thus reveals a distinct pattern of PA practice among girls, consisting of fewer and less varied (mostly unstructured) types of activities, yet similar overall levels of PA participation across both sexes.
Basic Psychological Needs and Autonomy in PA {#S3.SS2}
--------------------------------------------
As [Table 3](#T3){ref-type="table"} shows, satisfaction of basic psychological needs through exercise (organized or unstructured) is consistently high, while results for phys
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*r + r - 159. Give j(m(h)).
-26*h
Let y(a) = -3*a**2 - 19*a - 910. Let d(s) = s**2 + 6*s + 303. Let x(i) = -19*d(i) - 6*y(i). Let p(h) = 3*h**2. What is p(x(u))?
3*u**4 + 1782*u**2 + 264627
Let u(g) = -2*g**2. Let r be (-138)/(-8) - ((-7)/4 + 2). Let t(d) = 57*d**2 - 43 + 19*d + 43 - r*d. Give u(t(m)).
-6498*m**4 - 456*m**3 - 8*m**2
Let n(k) = 2*k**2 - 1235*k. Let x(l) = 34668*l. Give n(x(u)).
2403740448*u**2 - 42814980*u
Let a(n) = -3*n**2. Suppose 3*q = 9 + 3. Let d(s) = 2 - 4*s - s**2 - 6 + 3 + 5. Let h(r) = -r + 1. Let m(l) = q*h(l) - d(l). Give a(m(f)).
-3*f**4
Let b(c) = -c**2 + c - 205. Let f(a) = a + 11. Calculate f(b(r)).
-r**2 + r - 194
Let z(c) be the second derivative of 11*c**4/24 + 301*c**2/2 - 7*c + 6. Let i(v) be the first derivative of z(v). Let o(t) = -8*t**2. What is i(o(r))?
-88*r**2
Let y(s) = -332*s + 2. Let r(n) = -6*n - 63 - 7*n + 63 + 11*n. Give r(y(k)).
664*k - 4
Let p(c) be the third derivative of c**4/12 + c**2. Let b(f) = -39*f**2 - 2*f - 156. Let o(s) = s**2 + 26. Let r(d) = b(d) + 6*o(d). Give r(p(g)).
-132*g**2 - 4*g
Let i(n) = -48*n**2. Let t(x) = 11*x**2 + 179. Let u(c) = -11*c**2 - 223. Let g(r) = -5*t(r) - 4*u(r). Determine i(g(p)).
-5808*p**4 - 3168*p**2 - 432
Let r(t) = t. Let l(v) = 220620290*v**2. Calculate r(l(p)).
220620290*p**2
Let q(m) be the third derivative of m**5/120 + 19*m**3/3 - 7*m**2. Let h(y) be the first derivative of q(y). Let j(t) = -110*t**2. Give j(h(s)).
-110*s**2
Let j(k) = -3*k - 14. Let y(f) = -32809*f**2 - f. Give j(y(m)).
98427*m**2 + 3*m - 14
Let b(k) = -154*k**2 + 112*k + 224. Let m(n) = 4*n**2 - 3*n - 6. Let u(h) = 3*b(h) + 112*m(h). Let x(j) = -15*j**2 - 2. Determine x(u(s)).
-2940*s**4 - 2
Let y(d) = d + 101. Let u(b) = -3*b**2 + 3*b + 188. Calculate u(y(t)).
-3*t**2 - 603*t - 30112
Let k(v) = -308*v**2 - 26. Let t(x) = -3*x - 79. Give t(k(d)).
924*d**2 - 1
Let q(f) = f. Suppose 4*p - 4*g + 192 = 0, -4*g + 2 = 18. Let l(u) = 99*u + 30. Let m(c) = 1040*c + 312. Let t(z) = p*l(z) + 5*m(z). Determine t(q(v)).
52*v
Let x(j) = -5*j. Let o(m) = -308*m
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**Sliding window (SW)** **Most significant result**
------------------------- ----------------------------- -------- -------- -------------- --------------
**SNPs/SW** **No. of SW** **SW** **SW** ***P*value** ***P*value**
1 4 \- \- \- \-
2 3 S1.S2 S3.S4 0.0208\* 0.0546
3 2 S2.S4 S2.S4 0.0540 0.1099
4 1 S1.S4 S1.S4 0.0397\* 0.1585
S1: rs10907185.
S2: rs6603797.
S3: rs4648727.
S4: rs12126768.
The overall global test, details, and haplotype frequencies are listed in Table [8](#T8){ref-type="table"}. In HCV-1 infected patients, haplotype AC, the window S1-S2, gave the most impressive *P* value for the omnibus test. However, it did not play a significant role in HCV-2 infected patients. Haplotype-specific analyses showed that the CAT haplotypes (S2-S3-S4) might increase the rate of RVR (*P* = 0.0265; OR = 4.50) when compared to the RVR (−) groups, especially in the HCV-2 infected population. The window S1-S2-S3-S4 with the ACAT haplotypes was significantly positively associated with a higher rate of RVR in both HCV-1 and HCV-2 infected patients (OR = 2.01, *P* = 0.0261 and OR = 4.54, *P* = 0.0253, respectively). Furthermore, the results showed that HCV-1 and HCV-2 infected patients with therapeutic responses had the ACAT haplotypes, and thus the ACAT haplotype appeared more frequently in RVR (+) patients than in RVR (−) patients. Therefore, in HCV-1 or HCV-2 infected individuals, haplotype-specific analysis showed that the haplotype ACAT (S1-S2-S3-S4) was associated with an increase in the RVR rate. This observation suggests that the haplotype ACAT may play a role in the response to PEG-IFNα-RBV treatment.
######
Details of sex-adjust
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960 (11.99)
\ Other^c^ 1441 (18.00)
\ Do not know 240 (3.00)
--------------------------------------------------------------------------------
^a^Average of the percentage for all four countries.
^b^Other education corresponds to vocational education, matriculation, or other types of education.
^c^Other occupation corresponds to other types of jobs or status such as at-home mother/father.
Research Question Results
-------------------------
The majority of respondents were willing to share their PHD under specific conditions as shown in [Table 2](#table2){ref-type="table"}. The results differed according to the country the respondents were from. [Figure 1](#figure1){ref-type="fig"} shows the country-wise distribution of the participant responses. Gender of the participants did not impact the results, although men (2234/3922, 56.96%) were slightly more willing to share their health data compared with women (2239/4002, 55.95%). Regarding age, young people were more willing to share their data than older people, as shown in [Figure 2](#figure2){ref-type="fig"}. [Figure 3](#figure3){ref-type="fig"} shows that participants living in cities and urban areas were more willing to share their PHD compared with those living in the countryside. [Figure 4](#figure4){ref-type="fig"} presents the results per education level of participants. [Figure 5](#figure5){ref-type="fig"} presents the results according to the respondent occupation type.
######
Participant responses about the conditions of sharing their personal health data (N=8004).
Responses Value^a^, n (%)
--------------------------------------------------------- -----------------
No 2384 (29.78)
Information is used for scientific research 1811 (22.63)
I would be paid for it 1139 (1
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PTSD symptom clusters and CSB among those with a PTSD diagnosis. The results of this model are displayed in [Table 3](#T3){ref-type="table"}. The re-experiencing symptom cluster was the only cluster significantly associated with CSB (*p* \< 0.05). A 1-standard-deviation increase in the re-experiencing symptoms was associated with 87% greater odds of CSB.
######
Associations between specific PTSD symptom clusters and compulsive sexual behavior among those with a PTSD diagnosis
OR (95% CI) *p*-value
------------------- ------------------- -----------
Re-experiencing 1.87 (1.05, 3.31) **0.032**
Avoidance 1.09 (0.71, 1.68) 0.684
Emotional numbing 0.96 (0.59, 1.53) 0.849
Hyper-arousal 0.71 (0.46, 1.12) 0.142
*Note:* Statistically significant values in bold. Based on GEE modeling, specifying binomial family, logit link, AR 1 correlation structure, and robust standard errors. All symptom cluster variables were standardized prior to analyses.
DISCUSSION {#S4}
==========
This study is the first to examine CSB in a longitudinal sample of male veterans recently returning to civilian life after deployment. Several important conclusions can be drawn from these analyses. First, the prevalence of CSB, although it dropped over the course of follow-up, appeared considerably higher than published population estimates for CSB, suggesting that male veterans may be at particularly high risk for CSB. Secondly, increasing age and traumatic experiences, particularly childhood sexual and physical trauma as well as PTSD symptoms resulting from either combat or other trauma exposure, were significantly associated with CSB. Finally, among those with PTSD, re-experiencing symptoms specifically were associated with CSB. CSB may be an important clinical target in its own right, and improving PTSD symptoms may be beneficial for reducing CSB. It may also be important to address CSB in the context of PTSD symptoms, if CSB is used as an avoidance coping strategy ([@B19])
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etlength{\oddsidemargin}{-69pt}
\begin{document}$$\begin{aligned} {{\mathcal {L}}}B^{(h)}(\Psi ,0,A)=\sum _{\omega , s, \mu }\int \frac{d\mathbf{p}}{(2\pi )^3}\int \frac{d\mathbf{k}'}{(2\pi )^{3}}\, {{\hat{A}}}_{\mathbf{p},\mu }{{\hat{\Psi }}}^+_{\mathbf{k}'+\mathbf{p},s,\omega } {{\hat{W}}}^{(h)}_{2,1;\mu ,\omega }(\mathbf{0},\mathbf{0}){\hat{\Psi }}^-_{\mathbf{k}',s,\omega }. \end{aligned}$$\end{document}$$By the symmetries of the model,$$\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
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\begin{document}$$\begin{aligned} {{\mathcal {L}}}V^{(h)}(\Psi )= & {} \sum _{\omega , s}\int \frac{d\mathbf{k}'}{(2\pi )^{3}}\, \Big [2^h\xi _{\omega ,h}{\hat{\Psi }}^+_{\mathbf{k}',s,\omega } {{\hat{\Psi }}}^-_{\mathbf{k}',s,\omega }+ 2^h\delta _{\omega ,h}{{\hat{\Psi }}}^+_{\mathbf{k}',s,\omega } \sigma _3 {\hat{\Psi }}^-_{\mathbf{k}',s,\omega } \nonumber \\&+{{\hat{\Psi }}}^+_{\mathbf{k}',s,\omega } \begin{pmatrix} -i z_{1,\omega ,h} k_{0}&{} -u_{\omega ,h}(-i k_{1}' +\omega k_{2}') \\ -u_{\omega ,h}(i k_{1}' + \omega k_{2}') &{} -i z_{2,\omega ,h} k_{0} \end{pmatrix} {\hat{\Psi }}^-_{\mathbf{k}',s,\omega }\Big ], \end{aligned}$$\end{document}$$where $\documentclass[12pt]{minimal}
\usepackage{amsmath}
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\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\xi _{\omega ,h}, \delta _{\omega ,h},z_{\rho ,\omega ,h}, u_{\omega ,h}$$\end{document}$ are real constants and $\documentclass[12pt]{minimal}
\usepackage{amsmath}
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------------------ ------------------
**WMC**
OSPANs 14.656 (5.036) 14.938 (5.147) 24.719 (11.312) 19.969 (9.177)
**Attentional control**
Latency 359.179 (33.018) 379.813 (45.241) 369.916 (52.576) 387.399 (51.719) 333.286 (32.058) 338.738 (30.633) 382.246 (58.466) 370.708 (49.867)
Error rate 0.168 (0.101) 0.200 (0.095) 0.191 (0.138) 0.271 (0.114) 0.174 (0.115) 0.200 (0.164) 0.171 (0.131) 0.205 (0.124)
WMC, working memory capacity; SA, state anxiety; WM training, the working memory training group; Control, the control group; OSPANs, operation-word span task scores; Latency, the latency of first correct saccade; Error rate, the percentage of incorrect saccades
.
### Re-examination for the Effects of SA and WMC on Attentional Control
The manipulation check of SA was conducted first: the heart rate, skin conductance and MRF-3 scores were all significantly increased in the high-SA condition (all *ps* \< 0.002 for both pre- and post-training), which implied that SA manipulation was successful for both pre- and post-training. We used the change of latency \[i.e., post-training latency minus pre-training latency. The average change of latency under low-SA condition was -25.894 (*SD* = 20.702) for WM training group, and 12.330 (*SD* = 47.953) for control group, whereas under high-SA condition was -41.075 (*SD* = 36.636) for WM training group, and -16.691 (*SD* = 33.313) for control group\] and the change of error rate \[i.e., post-training error rate minus pre-training error rate. The average change of error rate under low-SA condition was 0.006 (*SD* = 0
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}
Table [4](#sim7930-tbl-0004){ref-type="table"} shows IPD meta‐analysis results for a random sample of 5 and 10 trials that investigated exercise interventions and for 20 trials (using all 15 exercise trials, plus 5 additional trials that investigated mixed interventions).
######
Results from baseline weight adjusted individual participant data meta‐analysis of i‐WIP data: summary treatment effect estimate ( $\left. \hat{\theta} \right)$ with 95% confidence interval and between‐trial variance of treatment effects estimate ( $\left. {\hat{\tau}}^{2} \right)$. From meta‐analysis with different numbers of trials (K = 5, 10, or 20), and assuming a random treatment effect and a common residual variance throughout
$\hat{\mathbf{\theta}}$ (95**%** CI); ${\hat{\mathbf{\tau}}}^{2}$
-------- ------------------------------------------------------------------- ------------------- ------------------- ------------------- -- ------------------- ------------------- ------------------- -------------------
**5** −1.172 −1.172 −1.172 −1.172 −1.170 −1.171 −1.171 −1.171
(−1.811, −0.534); (−1.815, −0.530); (−3.114, 0.770); (−2.712, 0.367); (−1.811, −0.529); (−1.813, −0.528); (−3.072, 0.731); (−2.681, 0.340);
8.58E−17 3.94E−15 3.94E−15 3.94E−15 2.95E−14 4.51E−12 4.51E−12 4.51E−12
**10** −0.972 −0.972 −0.972 −0.972 −0.972 −
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.41)
Number of friends in the Netherlands (log) −0.06 (0.04) 0.08 (0.08)
Number of family members in the Netherlands (log) 0.03 (0.06) −0.05 (0.12)
Dutch proficiency 0.06 (0.07) −0.18 (0.12)
Child \< 8 years of age^[e](#table-fn3-0192513X17710773){ref-type="table-fn"}^ 0.48 (0.16)[\*\*](#table-fn6-0192513X17710773){ref-type="table-fn"} 0.33 (0.31)
*R* ^2^ .24 .13
*Note*. Superscripts indicate reference categories that include (a) nontransnational parent; (b) male; (c) married/in a relationship; (d) room, student housing, institution, other; (e) No children \< 8 years of age.
Standard errors in parentheses
*Source*. TCRAf-Eu Angolan parent survey, The Netherlands 2010-2011.
*p* \< .05. \*\**p* \< .01. \*\*\**p* \< .001 (one-tailed test).
[Figure 2](#fig2-0192513X17710773){ref-type="fig"} presents the results of the mediation analysis graphically and presents us with the relevant coefficients for each step of the mediation analysis. [Table 3](#table3-0192513X17710773){ref-type="table"} displays the indirect, direct, and total effects and the proportion of the total effect mediated with bias-corrected confidence intervals after bootstrapping. Although not presented, the model includes the same control variables as in [Table 2](#table2-0192513X17710773){ref-type="table"}. The second step of mediation requires the independent variable to be related to the mediating variable. Path a1 represents the association between transnational parenting and happiness and a2 between transnational parenting and family-to-work conflict. As graphically evidenced in
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mogorov--Smirnov test, the residual gutta-percha and sealer data were not normally distributed. Therefore, a nonparametric Kruskal--Wallis and post hoc Dunn's tests were used, at P=0.05 to compare the mean area of residual gutta-percha and sealer. All the statistical analysis were performed with SPSS 21.0 (IBM Corp., Armonk, NY, USA) software.
RESULTS {#sec1-3}
=======
The results for the mean area of residual gutta-percha and sealer are shown in [Table 1](#T1){ref-type="table"}. There was a significant difference regarding the total residual gutta-percha and sealer among groups (P\<0.001). The mean area of the gutta-percha and sealer remnant in the XP group (0.80±0.25) was significantly lower than that in the other groups (P\<0.001). The mean area of gutta-percha and sealer remnant in the CI group (1.84±0.50) was significantly greater than that in the other groups (P\<0.001).
######
Mean and Standard Deviations of Residual Gutta-percha and Sealer on Canal Walls (mm^2^)
Group Apical Middle Coronal P value Total
------------------------- ----------------- ------------------- ------------------ --------- --------------
XP-endo Finisher 0.80±0.25^x\ a^ 0.80±0.32^x\ a^ 0.79±0.18^x\ a^ \>0.05 0.80±0.25^x^
EndoActivator 0.93±0.28^x\ a^ 1.21±0.25^yz\ bc^ 1.20±0.36^y\ ac^ \<0.05 1.11±0.32^y^
IrriSafe 0.82±0.33^x\ a^ 1.07±0.47^xz\ ac^ 1.25±0.37^y\ bc^ \<0.05 1.05±0.43^y^
Conventional Irrigation 2.05±0.49^y\ a^ 1.50±0.40^y\ b^ 1.97±0.42^z\ a^ \<0.001 1.84±0.50^z^
P value \<0.001 \<0.001 \<0.001 \<0.001
\*Different superscript letters indicate a significant difference between groups (abc; for rows and xyz; for columns)
When comparing the root canal regions, the apical third of the CI group had significantly more residual gutta-percha and sealer when compared to that of the other groups (P\
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tted as red dots. Four genes that received high regulation criterion values, yet were not found to be cell cycle regulated by the original Spellman analysis, are labeled.](ABI2009-284251.005){#fig5}
######
Percentages of correctly classified genes in the simulation whose results are depicted in [Figure 1](#fig1){ref-type="fig"}.
Temporal variance Regulation criterion
----- ------------------- ---------------------- -------------- -----------------
1% (0/10) 0% (970/990) 98.0% (10/10) 100% (980/990) 99.0%
5% (2/50) 4.0% (932/950) 98.1% (19/50) 38% (949/950) 99.9%
10% (7/100) 7% (887/900) 98.6% (19/100) 19% (899/900) 99.9%
20% (7/200) 3.5% (787/800) 98.4% (20/200) 10% (800/800) 100%
######
The "goodness of rank" (GR) measure results for gene ranking obtained by applying the KM-algorithm to the complete and partitioned data sets with *n* = 2000 genes and *T* time point observations.
*T* 20 40 100
--------------------- -------- -------- --------
Complete data set 0.0421 0.1543 0.6434
Partitioning method 0.2012 0.2982 0.5349
[^1]: Recommended by Zhongming Zhao
Introduction {#s1}
============
Due to advances in operation techniques and novel treatments (targeted therapy and PD-L1 immunotherapy) for patients with non-small cell lung carcinoma (NSCLC), the prognosis of NSCLC is improved. However, the 5-years survival rate of NSCLC patients is still \<20% and NSCLC remains a major health problem worldwide. Therefore, it is urgent to discover novel targets to regulates the carcinogenesis and metastasis of NSCLC and to develop new therapeutic agents for the treatment of NSCLC ([@B1], [@B2]). Recently, accumulating evidence indicates that abnormal regulation of acetylation processes plays a vital role in lung carcinogenesis. For example, histone deacetylases (HDACs) and histone acetyltransferases (HATs) can significantly change the nucleosome co
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ategorical variables, odds ratios, odds of CSB among comparison group vs. odds of CSB among reference group.
Prior to adjusting for covariates, there was a significant association between time and CSB, whereby the proportion of respondents with CSB was significantly lower at 6 months post-deployment than at baseline (*p* \< 0.05). This difference was no longer significant after the inclusion of other covariates in our model (*p* = 0.061). The final multivariable model is displayed in [Table 2](#T2){ref-type="table"}. Only three variables were statistically significant. Age was positively associated with CSB; every increase of 1 standard deviation in age was associated with a 30% increase in the odds of CSB (*p* \< 0.05). Those with a history of CST had more than 3 times greater odds of CSB than did those without such trauma (OR = 3.17, 95% CI = 1.27--7.93). Finally, each standard deviation unit of increase in the PCL score (i.e., PTSD severity) was associated with a 55% increase in the odds of CSB (*p* \< 0.05).
######
Results from multivariable modeling: Factors associated with compulsive sexual behavior
OR (95% CI) *p*-value
-------------------------------------- ------------------- -----------
Time
Baseline Ref.
3 months 1.14 (0.3, 1.78) 0.560
6 months 0.53 (0.27, 1.03) 0.061
Age (standardized) 1.30 (1.07, 1.57) **0.007**
Childhood sexual trauma 3.17 (1.27, 7.93) **0.014**
Childhood physical trauma 2.38 (0.97, 5.82) 0.058
PTSD symptom severity (standardized) 1.55 (1.12, 2.12) **0.006**
*Note:* Statistically significant values in bold. Based on GEE modeling, specifying binomial family, logit link, AR 1 correlation structure, and robust standard errors.
In our final model, we examined associations between specific
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nd choline performed. Paired sampled from 20 dogs were available for urine selenium analysis. Those 20 samples were selected from the original 31 dogs in order of percentage weight lost (i.e., the 20 dogs with the greatest percentage of weight loss had their samples analysed). Weight loss characteristics for dogs included in the study did not differ from those also completing but not ultimately being included in the study (data not shown).
In the 31 dogs finally included in the study, percentage weight loss was 28.3% (16.0-40.1%) starting body weight (SBW), over a period of 250 days (91--674 days); therefore, median rate of weight loss was 0.8% (0.3-1.4%) SBW/week (Table [2](#T2){ref-type="table"}). Neither the signalment nor weight loss outcomes differed between the 26 dogs having choline and amino acid analysis, and those where it was not measured (*P* \> 0.12 for all; data not shown); similarly, signalment and weight loss outcomes did not differ between the 20 dogs where urinary selenium was measured and those where it was not measured (*P* \> 0.28 for all, data not shown).
######
Summary of weight loss in the study dogs
***Criterion*** **Result**
--------------------------------------- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
*Age (months)* 66 (12 to 132)
*Sex* 17 NM, 2 F, 12 NF
*Breed*
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W/m^2^; (**c**) MLP model at *G* = 909.0 W/m^2^; (**d**) CNN model at *G* = 153.7 W/m^2^; (**e**) CNN model at *G* = 653.4 W/m^2^; (**f**) CNN model at *G* = 909.0 W/m^2^.](sensors-20-02119-g006){#sensors-20-02119-f006}
{#sensors-20-02119-f007}
{#sensors-20-02119-f008}
sensors-20-02119-t001_Table 1
######
The selected working conditions.
Working Conditions Irradiance *G* (W/m^2^) Temperature of PV Module Back-Surface *T*~1~ (°C) Ambient Temperature *T*~2~ (°C) Relative Humidity *H*~a~ (%) Atmospheric Pressure *P*~a~ (hPa)
-------------------- ------------------------- --------------------------------------------------- --------------------------------- ------------------------------ -----------------------------------
1 153.7 16.8 21.9 83.2 1002.9
2 237.5 23.3 24.6 42.3 1001.4
3 328.7 26.9 24.7 69.7 997.1
4 445.5 22.7 24.8 58.4 1007.6
5 537.9 28.2
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rovided is a step‐by‐step guide to our simulation study. For simplicity, and to considerably speed up the many simulations, we removed the baseline adjustment term in models (1) and (2), such that it does not exist in any of the data generating mechanisms or models fitted in our simulations. In other words, we generate data without baseline imbalances and thus analyze the data according to a final score IPD meta‐analysis model, which is appropriate in this situation~.~ [21](#sim7930-bib-0021){ref-type="ref"} For similar reasons of simplicity and computational complexity, we assumed a common residual variance across trials (both in data generation and models fitted). Extension to different residual variances is considered in our discussion (Section [5](#sim7930-sec-0019){ref-type="sec"}). To inform the true parameter values for the simulation, we used a previous IPD meta‐analysis of treatment for lower blood pressure outcomes.[22](#sim7930-bib-0022){ref-type="ref"}
All analyses were conducted using Stata v.14.2 (Stata Corporation, TX, USA).[23](#sim7930-bib-0023){ref-type="ref"}
### 3.1.1. Scenario 1 (base case) {#sim7930-sec-0008}
The simulation process is now explained, in the context of an initial base case scenario with IPD from 10 trials and a relatively simple data generating mechanism. Extensions to other more complex scenarios are described afterwards.
*Step 1: Data generating mechanism for one IPD meta‐analysis of 10 trials*
Consider that an IPD meta‐analysis of *i* = 1 to *K* related trials is of interest, with the goal to summarize a treatment effect on a continuous outcome. To generate such data for the base case of this simulation study, we started by setting the number of trials, *K*, to 10. We set a fixed number of participants, *n* = 100 in each trial, and assumed a fixed randomization of 1:1 in each trial; that is, on average, 50% of participants within any given trial are allocated to a treatment group, and the remaining 50% to a control group. This gave us a *trial* ~*i*~ (
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2*j + 2
Let a(y) = 3*y**3 + y. Let w(f) = 11*f + 7. Let t(g) = 3*g + 2. Let l(r) = -7*t(r) + 2*w(r). Calculate a(x) - l(x).
3*x**3
Let k(s) = -3*s - 2. Suppose 5*i - 6 = -26. Let m = 10 + -21. Let g(n) = 147 + 8*n + 149 - 291. Determine i*g(q) + m*k(q).
q + 2
Let u(r) = -973*r. Let g(y) = 730233*y. Determine 5*g(w) + 3752*u(w).
469*w
Let h be -2 + (-14)/(-2) + -3. Suppose a - h = 0, -a + 2*a + 4 = y. Let c(q) = q**2 + 5*q - 8. Let s(l) = -l**2 - 4*l + 7. Give y*s(v) + 5*c(v).
-v**2 + v + 2
Let w(a) = -51*a**3 - 14*a**2 + 9*a + 14. Let u(f) = -26*f**3 - 7*f**2 + 5*f + 8. Determine 7*u(o) - 4*w(o).
22*o**3 + 7*o**2 - o
Let q(a) be the first derivative of -7*a**2 + a**2 - 21 - 2*a + 3 + 5*a**2. Let d(h) = -h. Determine -d(g) - q(g).
3*g + 2
Let g(d) = 223*d**3 - 2*d**2 + 7*d. Let u(i) = 112*i**3 - i**2 + 4*i. Give -4*g(j) + 7*u(j).
-108*j**3 + j**2
Let w(d) = 189*d**2 + 12*d - 20. Let p(m) = 65*m**2 + 4*m - 7. What is 11*p(z) - 4*w(z)?
-41*z**2 - 4*z + 3
Let g(b) = b. Let l(d) = -14*d - 2. Suppose 0 = h - 4 + 2. Suppose -15 = h*x - 3*i, -i + 0 + 5 = 4*x. Suppose -j + x + 1 = 0. Calculate j*l(q) + 12*g(q).
-2*q - 2
Let p(w) = 7*w - 3. Suppose 4 = 2*j - 5*n - 16, -5*j + 21 = 2*n. Let f(q) = 6*q - 3. Calculate j*p(c) - 6*f(c).
-c + 3
Let z(r) = 3*r**2 + 3*r - 3. Let u(h) be the third derivative of -3*h**5/20 - h**4/3 + 4*h**3/3 - 38*h**2 - 4*h. Suppose 4*d = -3 - 29. What is d*z(k) - 3*u(k)?
3*k**2
Let m(z) = -3*z**3 - 4*z - 44. Let n(y) = 8*y**3 + 11*y + 132. Calculate -11*m(r) - 4*n(r).
r**3 - 44
Let l(r) = -r**2 - 6*r. Let u(b) = 2*b**2 + 8*b. What is 4*l(i) + 3*u(i)?
2*i**2
Let a(k) = k**3 + 5*k**2 + 6*k + 2. Let b(r) = -4*r + 0*r**2 + 4*r**2 + r**3 + 2 + 9*r. Let l = 24 + 40. Suppose 0 = -14*v + 8 - l. Give v*a(t) + 5*b(t).
t**3 + t + 2
Let j(q) = -q**3 - 1. Let n(x) = -1. Let f = -3 + 5. Suppose 0*m + 4*b - 18 = m, m + 4*b = 14. Let t be (-4)/(-16)*0 + m. Calculate f*j(r) + t*n(r).
-2*r**3
Let v(d) = -1 + 0 + 0. Let r(l) = -l. Let c = 15 + -16. Let p(q) = -3*q. Let w(t) = c*p(t) + r(t). What is 2*v(j) + w(j)?
2*
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DM Mathematics
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terial cost reduction potential.^\[^ [^55^](#amp210060-bib-0055){ref-type="ref"} ^\]^ Besides the manufacturing costs, the final sale price of the vaccine is also expected to include R&D costs, costs of clinical trials, marketing and supply chain costs and a profit margin.
######
Parameters influencing RNA vaccine production performance and cost
+------------------------------------------------------------------+------------------+-------------+---------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Parameter | Range | Unit | Influencing and determining factors | Reference |
+:=================================================================+:=================+:============+:==============================================================+:============================================================================================================================================================================+
| RNA amount per dose | 0.1‐10 for saRNA | μg/dose | Clinical trials | [3](#amp210060-bib-0003){ref-type="ref"}, [10](#amp210060-bib-0010){ref-type="ref"}, [11](#amp210060-bib-0011){ref-type="ref"}, [54](#amp210060-bib-0054){ref-type="ref"} α |
| | | | |
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B2‐A1** −0.41 −0.43 −0.37 −0.41 0.46 0.52 0.05 −0.17
**B2‐A2** −0.45 −0.41 −0.44 −0.42 −0.45 −0.37 −0.37 −0.34
**B3** 0.19 0.24 0.21 0.25 1.36 1.34 1.20 1.20
**C1** −0.03 −0.02 −0.03 −0.02 n/a n/a n/a n/a
**C2** −0.01 0.07 −0.01 0.07 n/a n/a n/a n/a
**D1** −0.10 −0.13 −0.10 −0.13 0.26 0.34 0.24 0.32
**D2** 0.13 0.12 0.13 0.12 0.46 0.49 0.45 0.46
See Table [1](#sim7930-tbl-0001){ref-type="table"} for full data generation details relating to each scenario. True value for θ is −9.66.
n/a = not applicable, since there is no τ ~β~ ^2^ to vary when a beta distribution is used for the intercept data generating mechanism. Options: ML, maximum likelihood estimation; REML, restricted maximum likelihood estimation.
######
Median percentage bias of the between‐trial variance of treatment effects ( $\left. {\hat{\tau}}^{2} \right)$, under different scenarios for the random treatment effect with normal and beta distributions for the intercept data generating mechanisms. Results shown separately for stratified and random intercept models, under each of the estimation options considered
Median Percentage Bias of ${
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}
For either model (stratified or random intercept), under any given scenario and data generating mechanism, using ML always produced more downwardly biased estimates than REML (Table [3](#sim7930-tbl-0003){ref-type="table"}), as expected.([14](#sim7930-bib-0014){ref-type="ref"}, [15](#sim7930-bib-0015){ref-type="ref"}, [16](#sim7930-bib-0016){ref-type="ref"}, [17](#sim7930-bib-0017){ref-type="ref"}, [18](#sim7930-bib-0018){ref-type="ref"}) For example, for the base case scenario with the random intercept model, under the normal intercept data generating mechanism, the median percentage bias using REML estimation was −15.9% compared to −41.5% using ML estimation. The bias was worse when using a stratified intercept model (due to the extra number of parameters to estimate), as ML estimation often produced a downward median bias of 100%.
When using REML estimation, there were generally only small differences between random and stratified intercept models in terms of bias of the between‐trial variance of treatment effects; however, while better than ML, downward bias was not removed entirely with REML. Furthermore, the overall size of the bias was typically greater in the beta distribution intercept case than in the normal distribution intercept case, regardless of which model was used.
### 3.2.4. Empirical SE and MSE of summary treatment effect estimate {#sim7930-sec-0014}
There were negligible differences in empirical SE or MSE of $\hat{\theta}$ between the two models (stratified or random intercept), under any given scenario and data generating mechanism (Web Tables C.VIII to C.X).
### 3.2.5. Coverage of summary treatment effect estimate {#sim7930-sec-0015}
There were marked differences observed in the coverage of $\hat{\theta}$ across the different estimation approaches (ML or REML) and CI derivations (standard, KR, or Satterthwaite), as now explained.
*(i) Under a normal distribution intercept generating mechanism*
We consider first the normal distribution intercept generating mechanism (Figure
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erthwaite corrected CI is given by $$\hat{\theta} \pm t_{\upsilon;1 - \frac{\alpha}{2}}\sqrt{{Var}\left( \hat{\theta} \right)},$$ where $t_{\upsilon;1 - \frac{\alpha}{2}}$ is as in the KR correction, but the original (unadjusted) variance of $\hat{\theta}$ is used. Note that, while the denominator degrees of freedom calculated from the KR and Satterthwaite corrections are the same for single hypothesis tests, the KR correction uses a bias‐adjusted variance; therefore, CIs derived using Equations [(4)](#sim7930-disp-0004){ref-type="disp-formula"} and [(5)](#sim7930-disp-0005){ref-type="disp-formula"} will potentially differ, with the one using the KR correction (Equation [(4)](#sim7930-disp-0004){ref-type="disp-formula"}) leading to slightly wider intervals.[19](#sim7930-bib-0019){ref-type="ref"}
Although Schaalje et al[20](#sim7930-bib-0020){ref-type="ref"} recommend KR over Satterthwaite in special cases when the sampling distribution of the test statistic is known, there remains debate over the best method, and a lack of literature in this area in regard to IPD meta‐analysis for estimation of a parameter of interest.
3. SIMULATION STUDY {#sim7930-sec-0006}
===================
We now perform a simulation study to examine the statistical performance of the summary treatment effect estimate ( $\hat{\theta}$) from a one‐stage IPD meta‐analysis across a range of scenarios. Our aim is to assess the different model specifications, parameter estimation methods and CI derivation options described in Section [2](#sim7930-sec-0002){ref-type="sec"}. That is, we compare the following: stratified or random intercept specifications; ML or REML estimation options; and, for REML estimation, 95% CIs based on asymptotic formula (Equation [(3)](#sim7930-disp-0003){ref-type="disp-formula"}) or with either KR or Satterthwaite corrections (Equations [(4)](#sim7930-disp-0004){ref-type="disp-formula"} and [(5)](#sim7930-disp-0005){ref-type="disp-formula"}, respectively)).
3.1. Methods {#sim7930-sec-0007}
------------
P
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rs, in my experience, are Read/Write and are used to give parameters to the controller.
You should download a Modbus Master program from the internet, there are hundreds. The program will use the 232 port to send the modbus commands to the controller and wait for a response.
A typical Modbus Master message looks like this:
Read Holding Registers 100-105:
Device Address Command 1st Reg Number of Registers Checksum
100 0x03 0x64 6 CRC
(our starting
register + 5)
Q:
Not getting values from jQuery loop
I'm trying to get the text and the link of an element on a website using Nightmare.js and jquery.
I'm looping through every element with the same class, I get no error, but the text and link are empty.
Here is what I tried:
nightmare
.goto("https://myanimelist.net/anime/season")
.wait(2000)
.evaluate(() => {
let links = [];
$('.link-title').each(() => {
item = {
"title": $(this).text(),
"link": $(this).attr("href")
};
links.push(item);
});
return links;
})
.end()
.then(result => {
console.log(result);
})
.catch(function (error) {
console.error('Failed', error);
});
The output in the console looks like this:
[ { title: '' },
{ title: '' },
{ title: '' },
... 99 more items
]
A:
$('.link-title').each(() => {
item = {
"title": $(this).text(),
"link": $(this).attr("href")
};
links.push(item);
});
Inside each() method, use normal function instead. Arrow functions make this not work as you expected
Q:
Do you need to know JSTL in JSF or Spring
Hi I wanted to know how important is it to learn JSTL ? I mean I could accomplish everything using scriptlet tags
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arxiv_plus_top10pct_by_avg
|
EA~ clade dissemination in east Africa.\
We provide snapshots of the dispersal pattern for the years 1960, 1965, 1970, 1975, 1980, 1985, 1990 and 2000. Lines between locations represent branches in the Bayesian MCC tree along which location transition occurs. Location circle diameters are proportional to square root of the number of Bayesian MCC branches maintaining a particular location state at each time-point. The white-green color gradient informs the relative age of the transitions (older-recent). The maps are based on satellite pictures made available in Google^™^ Earth (<http://earth.google.com>).](pone.0041904.g004){#pone-0041904-g004}
10.1371/journal.pone.0041904.t002
###### Estimated number of migration events of HIV-1 C~EA~ clade among east African countries.
{#pone-0041904-t002-2}
From/To Burundi Ethiopia Kenya Tanzania Uganda
---------- --------- ---------- ------- ---------- --------
Burundi \- 4 5 8 8
Ethiopia 0 \- 0 0 1
Kenya 0 0 \- 1 1
Tanzania 0 0 3 \- 7
Uganda 0 0 0 0 \-
The Bayesian analysis also supports an important phylogeographic subdivision within the C~EA~ lineage. Consistent with the ML topology ([Figure S2](#pone.0041904.s002){ref-type="supplementary-material"}), most subtype C sequences from Ethiopia, Kenya, Tanzania and Uganda branched in country-specific monophyletic sub-clusters that most probably (*PP*≥0.93) had a Burundian origin ([Fig. 3](#pone-0041904-g003){ref-type="fig"}). The C~ET1~ and C~ET2~ lineages, that correspond to the so called Ethiopian-C clade, comprise 44% of all Ethiopian sequences here included and were almost exclusively composed by sequences from this country. The C~KE~ and C~UG~ lineages comprise 33% and 37% of all sequences from Kenya and Uganda, respectively, and their circulation seems to be mainly restricted to thos
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arxiv_plus_top10pct_by_avg
|
d web application.
level=warn ts=2019-08-02T18:23:48.658364708Z caller=scrape.go:932
component="scrape manager" scrape_pool=batch_web
target=https://example.com:443/metrics msg="Error on ingesting samples
that are too old or are too far into the future" num_dropped=6
Any pointers on why this could be happening and how can I solve this?
A:
The error message appears to be quite clear|precise.
The metrics that its trying to scrape appear to be timestamped (possibly the problem!) and are either too old or too far into the future.
Generally, prometheus metrics don't include a timestamp.
If you can drop the timestamps, that would solve the problem.
If you can't drop the timestamps, then correct them so that they're ~current.
You may have solved this issue more quickly by searching Google for the error message.
Q:
Showing that Fourier coefficients tend to zero
Let $f \in L^2[0,1].$ The $n$th Fourier coefficient of $f$ is given by
$$\hat f(n) = \int_0^1 f(t)e^{-2\pi int} \, dt$$
The Fourier exponentials $u_n(t) = e^{2\pi int}$ are orthonormal in $L^2[0,1].$
Using Bessel's inequality, we have
$$\sum_n|\hat f(n)|^2=\sum_n\lvert \langle f,u_n\rangle \rvert^2 \leq \lVert f\rVert^2=\int_0^1
\lvert f \rvert^2 < \infty$$
so $\lvert \hat{f}(n)\rvert ^2 \to 0$ as $\lvert n \rvert \to \infty$ by Cauchy's criterion, which in turn implies that the $\hat{f}(n)$ go to zero.
I additionally want to show that for measurable $A \subset [0,1]$, we have that $$\int_Ae^{2\pi int} \to 0 \text{ as } n \to \infty$$
What is the correct way to approach this? Do we treat this as the Fourier transform of the characteristic function $\chi_A?$
Thank you!
A:
\begin{align}
& \int_Ae^{2\pi int} \, dt = \int_0^1 f(t) e^{2\pi int} \, dt \quad \text{if } f(t) = \begin{cases} 1 & \text{if } t\in A, \\ 0 & \text{if } t\notin A. \end{cases} \\[10pt]
& \text{And } f\in L^2[0,1].
\end{align}
Q:
pointer to value in array?
So I need to have a pointer to a value in a const char array. But I can't quite get it to work without error
| 49
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| 0.968844
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r Step 3, the statistical properties of $\hat{\theta}$ under the different model specification and estimation options were assessed by summarizing the 1000 results obtained using the following metrics: mean percentage (%) bias, empirical standard error (SE), MSE, coverage (separately for each CI method), convergence, and mean run time (separately for each CI method). Additionally, we considered the median percentage bias in the heterogeneity (τ ^2^) of the true treatment effects also. Definitions of these performance measures are provided in Web Appendix A.
### 3.1.2. Extended set of 38 scenarios changing number of trials, participants, between‐trial distributions, and data generating mechanisms {#sim7930-sec-0009}
The base case scenario defined in Section [3.1.1](#sim7930-sec-0008){ref-type="sec"} was extended to further settings, leading to an extensive range of 38 scenarios in total (see Table [1](#sim7930-tbl-0001){ref-type="table"}), which we now summarize.
######
Summary of the different simulation scenarios\*
Scenario Data Generation Details Modification From Base Case Scenario
--------------- ------------------------------------------------------------------------------------ -------------------------------------------------------------------------------
**Base Case** \(i\) Number of trials, K = 10 ‐
\(ii\) Number of participants in trial i, n ~i~ = 100 (fixed across all trials)
\(iii\) Fixed treatment exposure of 50%
\(iv\) θ = −9.66 (summary treatment effect; negative value favors treatment group)
\(v\) τ ^2^ = 7.79 (between trial variation in θ)
\(vi\) β = 159.73 (mean response in control group)
\(vii\) τ ~**β**~ ^2
| 50
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arxiv_plus_top10pct_by_avg
|
return sb.toString();
}
private void setDefault() {
try {
Config config = Config.getInstance();
// If key not present, returns Integer.MIN_VALUE, which is
// almost all zero.
int options = config.getIntValue("libdefaults",
"kdc_default_options");
if ((options & KDC_OPT_RENEWABLE_OK) == KDC_OPT_RENEWABLE_OK) {
set(RENEWABLE_OK, true);
} else {
if (config.getBooleanValue("libdefaults", "renewable")) {
set(RENEWABLE_OK, true);
}
}
if ((options & KDC_OPT_PROXIABLE) == KDC_OPT_PROXIABLE) {
set(PROXIABLE, true);
} else {
if (config.getBooleanValue("libdefaults", "proxiable")) {
set(PROXIABLE, true);
}
}
if ((options & KDC_OPT_FORWARDABLE) == KDC_OPT_FORWARDABLE) {
set(FORWARDABLE, true);
} else {
if (config.getBooleanValue("libdefaults", "forwardable")) {
set(FORWARDABLE, true);
}
}
} catch (KrbException e) {
if (DEBUG) {
System.out.println("Exception in getting default values for " +
"KDC Options from the configuration ");
e.printStackTrace();
}
}
}
}
/*
* Copyright 2016 WebAssembly Community Group participants
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing per
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arxiv_plus_top10pct_by_avg
|
in LVAD patients, as shown in [Table 2](#t0002){ref-type="table"}. The pooled odds ratio (OR) of 30-day mortality was 3.66 (95% CI, 2.00--6.70, *I*^2^ = 71%, [Supplementary Figure S3](https://doi.org/10.1080/0886022X.2020.1768116)) and the pooled OR of 1 year mortality was 2.22 (95% CI, 1.62--3.04, *I*^2^ = 0%, [Supplementary Figure S4](https://doi.org/10.1080/0886022X.2020.1768116)) in LVAD patients with AKI, compared with no AKI. The pooled OR of 30-day mortality was 7.52 (95% CI, 4.58--12.33, *I*^2^ = 73%, [Supplementary Figure S5](https://doi.org/10.1080/0886022X.2020.1768116)) and the pooled OR of 1-year mortality was 5.41 (95% CI, 3.63--8.06, *I*^2^ = 0%, [Supplementary Figure S6](https://doi.org/10.1080/0886022X.2020.1768116)) in LVAD patients with severe AKI requiring RRT, compared with no RRT.
######
AKI associated Mortality in LVAD Patients.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Study Year Outcomes Confounder adjustment Quality assessment
----------------------------------- ------ ------------------------------------------- ----------------------------------------------------------------------------------------------------------------------------------------- --------------------
Kaltenmaier et al. \[[@CIT0040]\] 2000 30 days mortality\ None Selection: 4\
RRT: 2.54 (1.36--4.74)\
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| 0.020198
|
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arxiv_plus_top10pct_by_avg
|
503]\]. They are constructed by densely tracking sampled points over an RGB video stream and constructing representative features around the detected trajectories. As mentioned in [Section 1](#sec1-sensors-19-03503){ref-type="sec"}, Dense Trajectories have been proven to be very effective in action recognition. They mainly owe their success to the fact that they incorporate low-level motion information. Below, we overview the Dense Trajectories approach.
Let $\mathcal{V}$ be a sequence of *N* images. Subsequently, representative points are sampled from each image grid with a constant stepping size---we denote each sampling grid position at frame *t* as $\mathtt{p}_{t} = {(x_{t},y_{t})}$. The point $\mathtt{p}_{t}$ is then estimated in the next frame using a motion field $(u_{t},v_{t})$, derived by the optical flow estimation \[[@B42-sensors-19-03503]\] such that:$$\begin{array}{r}
{\mathtt{p}_{t + 1} = \mathtt{p}_{t} + \kappa \cdot \mspace{600mu}{(u_{t},v_{t})},} \\
\end{array}$$ where $\kappa$ is a median filter kernel at the position $\mathtt{p}_{t + 1}$. As a result, large motion changes between subsequent frames are smoothed. Furthermore, to avoid drifting, trajectories longer than the assigned fixed length are rejected. Applying Equation ([1](#FD1-sensors-19-03503){ref-type="disp-formula"}) on *L* frames results a smoothed trajectory estimation of the point $\mathtt{p}_{t} = {(x_{t},y_{t})}$. We denote the *m*th dense trajectory as:$$\begin{array}{r}
{\mathcal{P}^{m} = {\{\mathtt{p}_{t_{0}}^{m},\ldots,\mathtt{p}_{t_{0} + L}^{m}\}},} \\
\end{array}$$ with $\tau = \left\lbrack t_{0},t_{0} + L \right\rbrack \subset \left\lbrack 1,N \right\rbrack$, $m \in \{ 1,\ldots,M\}$, $t_{0}$ the first frame of the sequence $\mathcal{V}$ and *M* the total number of generated trajectories.
The set of *M* trajectories generated in Equation ([2](#FD2-sensors-19-03503){ref-type="disp-formula"}) is used to construct descriptors aligned along a spatiotemporal volume. In \[[@B7-sensors-19-03503]\], four types of descriptors are
| 53
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| 0.970681
| 0.985341
| 0.014659
|
PubMed Central
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arxiv_plus_top10pct_by_avg
|
data set. A value of 40 has a converted probability of 0.0001 incorrect reads. In contrast, the position is homogeneous in the isogenic reference genome. The same phenomenon was observed at other heterogeneity sites, including those at the genes encoding for the sulfatase family protein, the lipoate-protein ligase A family protein, the penicillin-binding protein 3, the lantibiotic epidermin biosynthesis protein EpiC, the oxacillin resistance-related FmtC protein, and the putative fibronectin/fibrinogen binding protein. Furthermore, a majority of the heterogeneity sites are located in the single-copy DNA fragment in the isogenic reference genome.
######
Characterization of the genetic heterogeneity Sites and SNP in large gene families
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Maq Chrom Position Genotype profile at selected loci of SRX007711 Genotype profile at selected loci of FPR3757 Read Depth SRX007711 Mean Phred Values Max Phred Value Functional Description
----- ----------------------------------------------------------------------------------------------------------------------- ------------------------------------------------ ---------------------------------------------- ------------ ----------------------------- ----------------- ---------------------------------------------------------
I. Heterogeneity sites that passed the SNPfilter and have an average of per-base Phred value greater than 13
| 54
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| 0.968882
| 0.984441
| 0.015559
|
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|
19.74, \< 0.01 45.11 (42.1, 50.19) 2.08 22.18, \< 0.01
Census median centered 0.32 (0.28, 0.36) 0.02 15.62, \< 0.01 0.82 (0.73, 0.82) 0.04 19.88, \< 0.01
A2D 0.13 (0.10, 0.16) 0.02 8.08, \< 0.01 0.14 (0.10, 0.18) 0.02 6.65, \< 0.01
PIT (post vs pre) −21.61 (−23.78, −19.44) 1.11 19.44, \< 0.01 −18.45 (−21.37, −15.52) 1.50 12.36, \< 0.01
LOSD
Intercept 260.7 (251.27, 270.13) 4.81 54.23, \< 0.01 223.47 (216.42, 230.52) 3.59 62.2, \< 0.01
Census median centered 0.66 (0.53, 0.78) 0.06 10.67, \< 0.01 0.90 (0.80, 1.01) 0.05 16.91, \< 0.01
A2D 0.25 (0.19, 0.31) 0.03 8.52, \< 0.01 0.21 (0.13, 0.28) 0.04 5.08, \< 0.01
PIT (post vs pre) −29.83 (−38.03, −21.68) 4.17 7.16, \< 0.01 −11.45 (−16.16, −4.77) 2.40 4.77, \< 0.01
*ED*, emergency department; *AED*, tertiary care academic emergency department; *CED*, community emergency department; *D2P*, arrival to being seen by physician; *LOSD*, total length of stay for discharged patients; *A2D*, admit request to departure for boarded patients awaiting hospital admission; *PIT*, physician in triage; *95% CI*, 95% confidence interval; *SE*, standard error.
Introduction {#Sec1}
============
Reproductive history, like parity, age at first birth and number of births, has consistently been shown to be associated with breast cancer risk \[[@CR1]\]. Women who have undergone a full time pregnancy before 20 years of age, for example, have a 50% reduced lifetime risk of developing breast cancer when compared to nulliparo
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| 5,370
| 0.904734
| 0.952367
| 0.047633
|
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|
arxiv_plus_top10pct_by_avg
|
66.0% 59.8%
Pairwise joint distance \[[@B50-sensors-19-03503]\] 63.3% --
Orderlet \[[@B50-sensors-19-03503]\] 71.4% --
Motion decomposition \[[@B66-sensors-19-03503]\] 80.9% --
Dense Trajectories \[[@B7-sensors-19-03503]\] 64.3% 43.8%
2D Localized Trajectories (ours) 67.4% 59.8%
3D Localized Trajectories (ours) 64.5% 38.4%
sensors-19-03503-t004_Table 4
######
Mean accuracy of recognition (%) on Watch-n-Patch in both kitchen and office settings for Dense Trajectories and 2D Localized Trajectories approaches.
Method Mean Accuracy
--------------------------------------------------------- ---------------
Dense Trajectories---office \[[@B7-sensors-19-03503]\] 68.8%
Dense Trajectories---kitchen \[[@B7-sensors-19-03503]\] 56.2%
2D Localized Trajectories---office (ours) 71.1%
2D Localized Trajectories---kitchen (ours) 81.5%
sensors-19-03503-t005_Table 5
######
Mean accuracy of recognition (%) of Dense Trajectories and 2D Localized Trajectories approaches on KARD dataset.
Method Mean Accuracy
----------------------------------------------------- ---------------
JTMI, LBP and FLD \[[@B67-sensors-19-03503]\] 98.5%
JTMI and Gabor features \[[@B68-sensors-19-03503]\] 96.0%
HOJ3D \[[@B69-sensors-19-03503]\] 95.3%
EigenJoints \[[@B35-sensors-19-03503]\] 96.2%
Dense Trajectories \[[@B7-sensors-19-03503]\] 97.8%
2D Localized Trajectories (ours) 98.2%
[^1]: This paper is an expanded version of "Enhanced trajectory-based action recognition using human pose" published in 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, 17--20 September 2017.
Introduction {#s1}
============
Human immunodeficiency virus
| 56
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| 0.953083
| 0.976541
| 0.023459
|
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|
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|
st-restricts.1.1", new Object[]{baseTypeStr.rawname, refName}, elm);
return null;
}
// if it's a complex type, or if its restriction of anySimpleType
if (baseType == SchemaGrammar.fAnySimpleType &&
baseRefContext == XSConstants.DERIVATION_RESTRICTION) {
// if the base type is anySimpleType and the current type is
// a S4S built-in type, return null. (not an error).
if (checkBuiltIn(refName, schemaDoc.fTargetNamespace)) {
return null;
}
reportSchemaError("cos-st-restricts.1.1", new Object[]{baseTypeStr.rawname, refName}, elm);
return null;
}
if ((baseType.getFinal() & baseRefContext) != 0) {
if (baseRefContext == XSConstants.DERIVATION_RESTRICTION) {
reportSchemaError("st-props-correct.3", new Object[]{refName, baseTypeStr.rawname}, elm);
}
else if (baseRefContext == XSConstants.DERIVATION_LIST) {
reportSchemaError("cos-st-restricts.2.3.1.1", new Object[]{baseTypeStr.rawname, refName}, elm);
}
else if (baseRefContext == XSConstants.DERIVATION_UNION) {
reportSchemaError("cos-st-restricts.3.3.1.1", new Object[]{baseTypeStr.rawname, refName}, elm);
}
return null;
}
return (XSSimpleType)baseType;
}
// check whethe the type denoted by the name and namespace is a S4S
// built-in type. update fIsBuiltIn at the same time.
private final boolean checkBuiltIn(String name, String namespace) {
if (namespace != SchemaSymbols.URI_SCHEMAFORSCHEMA)
return false;
if (SchemaGrammar.SG_SchemaNS.getGlobalTypeDecl(name) != null)
fIsBuiltIn = true;
return fIsBuiltIn;
}
// find if a datatype validator is a list or has list datatype member.
private boolean isListDatatype(XSSimpleType validator) {
if (validator.getVariety() == XSSimpleType.VARIETY_LIST)
| 57
| 8,003
| 0.891292
| 0.945646
| 0.054354
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Github
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arxiv_plus_top10pct_by_avg
|
Vascular invasion 0.472 0.492
Absent 72 33 39
Present 32 17 15
Cirrhosis 2.143 0.143
Yes 61 33 28
No 43 17 26
Recurrence 1.135 0.287
Yes 43 18 25
No 61 32 29
Expression of CLU and its association with clinical features of HCC patients {#sec3-2}
----------------------------------------------------------------------------
Relative expression of *CLU* mRNA in patients with HCC was determined via qRT-PCR. Accordingly, plasma *CLU* expression was higher in HCC cases than in normal controls (1.48 ± 0.22 vs. 0.22 ± 0.12, *P* \< 0.001, [Figure 1](#F1){ref-type="fig"}).
{#F1}
In addition, the expression of CLU protein in HCC tissues and non-malignant tissues was also estimated using IHC method. The results suggested that the expression of CLU protein in HCC tissues was significantly higher and the percentage of positively stained cells was as high as 94.2% (98/104); while CLU protein expression in non-malignant tissues was relatively weaker and the proportion of positively stained cells was only 14.4% (15/104). The difference between two sides was significant (*P* \< 0.001, [Figure 2](#F2){ref-type="fig"}).
{#F2}
| 58
| 3,904
| 0.913608
| 0.956804
| 0.043196
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PubMed Central
|
arxiv_plus_top10pct_by_avg
|
\begin{document}$$\begin{aligned} i\partial _{x_{0}}\langle \mathbf{T}\, j_{0, \mathbf{x}}\,; j_{\nu , \mathbf{y}} \rangle ^{\text {R}}_{\beta ,L}= & {} \langle \mathbf{T}\, i\partial _{x_{0}}j_{0, \mathbf{x}}\,; j_{\nu , \mathbf{y}} \rangle ^{\text {R}}_{\beta ,L} + i\langle [ j_{0, {\vec {x}}}\, , j_{\nu , {\vec {y}}} ] \rangle ^{\text {R}}_{\beta ,L} \delta (x_{0} - y_{0})\nonumber \\= & {} -\langle \mathbf{T}\, \text {div}_{{\vec {x}}} {\vec {\jmath }}_{\mathbf{x}}\,; j_{\nu , \mathbf{y}} \rangle ^{\text {R}}_{\beta ,L} + i\langle [ j_{0, {\vec {x}}}\, , j_{\nu , {\vec {y}}} ] \rangle ^{\text {R}}_{\beta ,L} \delta (x_{0} - y_{0})\;. \quad \end{aligned}$$\end{document}$$Let us now take the Fourier transform of both sides: integrating by parts w.r.t. $\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$x_0$$\end{document}$ and using ([3.7](#Equ31){ref-type=""}), we find$$\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\begin{aligned} p_{0} {\widehat{K}}^{\beta , L; \text {R}}_{0,\nu }(\mathbf{p})= & {} -\frac{1}{\beta L^2} \int _{0}^{\beta } dx_{0} \int _{0}^{\beta } dy_{0}\, \sum _{{\vec {x}}, {\vec {y}}\in \Lambda _L} e^{-ip_{0}(x_{0} - y_{0})}e^{-i{\vec {p}} \cdot ({\vec {x}}-{\vec {y}})} i\partial _{x_{0}}\langle \mathbf{T}\, j_{0, \mathbf{x}}\,; j_{\nu , \mathbf{y}} \rangle ^{\text {R}}_{\beta ,L}\nonumber \\= & {} \sum _{i=1,2} (1 - e^{-i{\vec {p}}\cdot {\vec {
| 59
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| 0.975493
| 0.987747
| 0.012253
|
PubMed Central
|
arxiv_plus_top10pct_by_avg
|
ome (32% for males and 47% for females).
{#pone.0153583.t003g}
------------------------- ------------------------- -------------------
**Males (N = 69)**
**4-MA4-MM** \<0.6 m/s (dismobility) ≥0.6 m/s (normal)
\<0.6 m/s (dismobility) 6 (8.7%) 0
≥0.6 m/s (normal) 13 (18.8%) 50 (72.5%)
**Females (N = 103)**
**4-MA4-MM** \<0.6 m/s (dismobility) ≥0.6 m/s (normal)
\<0.6 m/s (dismobility) 14 (13.6%) 8 (7.8%)
≥0.6 m/s (normal) 16 (15.5%) 65 (63.1%)
------------------------- ------------------------- -------------------
The correlations of 4-MM and 4-MA with other measures of functional performance are also shown in [Table 2](#pone.0153583.t002){ref-type="table"}. A significant positive correlation with 6MWT was found for both 4-MM and 4-MA in men and women (4-MM men r = 0.59, p\<0.001; women r = 0.49, p\<0.001; 4-MA men r = 0.50, p = 0.0004; women r = 0.22, p = 0.048).
4-MA showed a significant positive correlation with handgrip strength (r = 0.40, p = 0.005 in men; r = 0.29, p = 0.01 in women), as also 4-MM (r = 0.51, p\<0.001 in men; r = 0.38, p = 0.0001 in women).
Discussion {#sec016}
==========
In a cohort of community-dwelling older individuals, we found a significant correlation between the assessment of gait speed using a manual (i.e., stopwatch) and technological (i.e., accelerometer) technique. However, our results suggest that the concordance of two tests is less strong than anticipated and might be suboptimal in the classification of single subjects. This is the first study investigating the correlation between these two assessment modalities of gait speed.
There is a wide range of methods available for assessing physical function in both research and clinical practice \[[@pone.0153583.ref024]\]. The final choice on the best measurement should take into account the inter-rater and
| 60
| 1,787
| 0.930346
| 0.965173
| 0.034827
|
PubMed Central
|
arxiv_plus_top10pct_by_avg
|
an age was 34 years and participants had a mean body mass index (BMI) of 24.1 ± 2.78 kg/m^2^. A summary of the baseline characteristics of the participants is presented in [Table [1](#tbl1){ref-type="other"}](#tbl1){ref-type="other"}.
###### Demographic Data of Participants Included in the Study[a](#t1fn1){ref-type="table-fn"}
characteristics male (*n* = 4) female (*n* = 5)
-------------------- ---------------- ------------------
age (years) 34 ± 9 35 ± 14
weight (kg) 77.55 ± 7.91 66.00 ± 12.24
height (m) 1.76 ± 0.08 1.68 ± 0.08
BMI (kg/m^2^) 25.1 ± 2.6 23.2 ± 2.9
waist to hip ratio 0.87 ± 0.05 0.82 ± 0.09
All values shown are mean ± SD.
Examination of the principal component analysis (PCA) revealed that the interindividual variation was the dominant source of variation on the dataset. The samples of the individuals were grouped together in the PCA scores plot ([Figure [1](#fig1){ref-type="fig"}](#fig1){ref-type="fig"}A). To explore the impact of storage, we employed a row-wise centering of the data and this resulted in separation of the samples according to the storage type ([Figure [1](#fig1){ref-type="fig"}](#fig1){ref-type="fig"} B).
{#fig1}
To examine the impact of the storage further, a univariate analysis approach was employed. A total of 14 compounds from the fecal water analysis were significantly different across the three storage conditions. Significant metabolites from repeated measures ANOVA corrected by the false discovery
| 61
| 1,755
| 0.93073
| 0.965365
| 0.034635
|
PubMed Central
|
arxiv_plus_top10pct_by_avg
|
T_CODE AND A.HOURS_SUMMARY = B . HOURS_SUMMARY
WHERE A . EM_NUMBER = EMPLOYEE_ID OR B . EM_NUMBER = EMPLOYEE_ID
UNION
SELECT COUNT ( * ) AS ERROR_COUNT
FROM MPRLIB . V_REQHOURSUMM A EXCEPTION JOIN MPRLIB . V_TSHOURSUMM B
ON A . EM_NUMBER = B . EM_NUMBER AND A . TIMESHEET_CODE = B . TIMESHEET_CODE AND A . HOURS_SUMMARY = B . HOURS_SUMMARY
WHERE A . EM_NUMBER = EMPLOYEE_ID OR B . EM_NUMBER = EMPLOYEE_ID ) TABLE
It seems to work, but seems... excessive. Thoughts? Is there a better way?
Q:
Independence of functions of order statistics when the random variables are uniformly distributed
Let $X_1$,$X_2$,…,$X_n$ be $n$ i.i.d. random variables with $f(x)$ as the pdf and $F(x)$ as the cdf in interval $[0,1]$. Let $F$ be uniformly distributed. Let $X_{i:n}$ be the $i^{th}$ order statistic such that $X_{1:n} \leq X_{2:n} \leq ... \leq X_{n:n}$. I wish to compute the expected value $\mathbb{E} [\frac{X_{(k-1):n} X_{i:n}}{X_{k:n}} ]$ for any $ k < i \leq n$. So the question is are $\frac{X_{(k-1):n}}{X_{k:n}}$ and $X_{i:n}$ independent? Because if they are not, then the problem is non-trivial. Due to a standard result in theory of order statistics, we already know that for any $i \leq n$, $\frac{X_{(i-1):n}}{X_{i:n}}$ and $X_{i:n}$ are independent.
A:
It is easy to show that given $X_{i:n} = x$, the order statistics $X_{1:n}, \dots, X_{(i-1):n}$ have the same joint distribution as the order statistics $X_{1:(i-1)}, \dots, X_{(i-1):(i-1)}$ of a sample from the uniform distribution on $[0,x]$, which, in turn, have the same distribution as $x$ times the order statistics of a sample from $[0,1]$. It follows, in particular, that for $k<i$, $\frac{X_{(k-1):n}}{X_{k:n}}$ is indeed independent of $X_{i:n}$ and
$$
\mathrm{E}\Big[\frac{X_{(k-1):n} X_{i:n}}{X_{k:n}} \Big] = \mathrm{E}\Big[\frac{X_{(k-1):n} }{X_{k:n}} \Big]\mathrm{E}[X_{i:n}] = \frac{k-1}k \cdot \frac{i}{n+1}.
$$
Q:
javascript delete object safe for memory leak
this is my code, I do not know if it good for prevent leaking memory ? help and how can I test
| 62
| 84
| 0.963842
| 0.981921
| 0.018079
|
StackExchange
|
arxiv_plus_top10pct_by_avg
|
tion data for a set of standard and crosslinked/remelted UHMWPE (GUR 1050), at increasing gamma radiation doses. All crosslinked samples were remelted at 170 °C for 4 h and subsequently annealed at 125 °C for 2 days. Adapted from \[[@B60-materials-10-00791]\] with permission.
Control 50 kGy 100 kGy 200 kGy
------------------------------------------ -------------- -------------- ------------- --------------
**Crystallinity (%)** 50.1 ± 0.5 45.6 ± 0.7 46.3 ± 0.8 47.1 ± 0.4
**Lamellar thickness (nm)** 20.0 18.1 18.7 19.1
**Elastic modulus (MPa)** 495 ± 56 412 ± 50 386 ± 23 266 ± 30
**Yield stress (MPa)** 20.2 ± 1.0 19.9 ± 0.8 18.9 ± 0.7 20.2 ± 1.0
**True stress at break (MPa)** 315.5 ± 31.6 237.6 ± 12.3 185.7 ± 7.5 126.0 ± 14.0
**Decrease in true stress at break (%)** \- 24 41 60
**ΔK~incep~ (MPa√m)** 1.41 0.91 0.69 0.55
**Decrease in ΔK~incep~ (%)** \- 35 51 61
1. Introduction {#sec1}
===============
Novel gene expression technologies (e.g., microarrays, next-generation sequencing, etc.) make it possible to study the simultaneous expression of an ever increasing number of genes \[[@B1], [@B2]\]. As these technologies become more available and affordable, the size and complexity of gene expression experiments will continue to increase. In time series studies, for example, microarray-based gene transcript measurements have historically been recorded at several time points over the course of a biological process (e.g., development, response, etc.). Well-known examples of microarray time series experiments include studies on the yeast cell cycle \[[@B3]\], the reaction of mice to acute corneal trauma \[[@B4]\], the life-cy
| 63
| 3,089
| 0.918922
| 0.959461
| 0.040539
|
PubMed Central
|
arxiv_plus_top10pct_by_avg
|
ng.
Path headerPath =
CxxDescriptionEnhancer.getHeaderSymlinkTreePath(
getProjectFilesystem(),
getBuildTarget().withFlavors(),
headerVisibility,
flavor);
args.add(INCLUDE_FLAG.concat(headerPath.toString()));
}
}
return args.build();
}
public ImmutableList<Arg> getAstLinkArgs() {
if (!useModulewrap) {
return ImmutableList.<Arg>builder()
.addAll(StringArg.from("-Xlinker", "-add_ast_path"))
.add(SourcePathArg.of(ExplicitBuildTargetSourcePath.of(getBuildTarget(), modulePath)))
.build();
} else {
return ImmutableList.<Arg>builder().build();
}
}
ImmutableList<Arg> getFileListLinkArg() {
return FileListableLinkerInputArg.from(
objectPaths.stream()
.map(
objectPath ->
SourcePathArg.of(
ExplicitBuildTargetSourcePath.of(getBuildTarget(), objectPath)))
.collect(ImmutableList.toImmutableList()));
}
/** @return The name of the Swift module. */
public String getModuleName() {
return moduleName;
}
/** @return List of {@link SourcePath} to the output object file(s) (i.e., .o file) */
public ImmutableList<SourcePath> getObjectPaths() {
// Ensures that users of the object path can depend on this build target
return objectPaths.stream()
.map(objectPath -> ExplicitBuildTargetSourcePath.of(getBuildTarget(), objectPath))
.collect(ImmutableList.toImmutableList());
}
/** @return File name of the Objective-C Generated Interface Header. */
public String getObjCGeneratedHeaderFileName() {
return headerPath.getFileName().toString();
}
/** @return {@link SourcePath} of the Objective-C Generated Interface Header. */
public SourcePath getObjCGeneratedHeaderPath() {
return ExplicitBuildTargetSourcePath.of(getBuildTarget(), headerPath);
}
/**
* @return {@link SourcePath} to the directory containing outputs from t
| 64
| 10,700
| 0.877462
| 0.938731
| 0.061269
|
Github
|
arxiv_plus_top10pct_by_avg
|
large as those based on *F*, and *R*- factors based on ALL data will be even larger.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Fractional atomic coordinates and isotropic or equivalent isotropic displacement parameters (Å^2^) {#tablewrapcoords}
==================================================================================================
------ --------------- --------------- --------------- -------------------- --
*x* *y* *z* *U*~iso~\*/*U*~eq~
W1 0.685156 (8) 0.527538 (12) 0.590934 (6) 0.00959 (5)
W2 0.828154 (8) 0.329261 (13) 0.458854 (7) 0.01383 (5)
Ag1 0.701283 (16) 0.55717 (2) 0.490031 (13) 0.01578 (7)
Ag2 0.797387 (15) 0.41531 (2) 0.598751 (13) 0.01418 (7)
Ag3 0.776781 (15) 0.20725 (2) 0.532723 (12) 0.01373 (7)
Ag4 0.693052 (15) 0.34050 (2) 0.401396 (12) 0.01168 (7)
S1 0.60897 (5) 0.52790 (8) 0.52055 (4) 0.0157 (2)
S2 0.77015 (5) 0.59208 (8) 0.57931 (4) 0.0129 (2)
S3 0.70064 (5) 0.37936 (8) 0.62017 (4) 0.0136 (2)
S4 0.65859 (5) 0.61359 (8) 0.64489 (4) 0.0154 (2)
S5 0.75460 (5) 0.37745 (8) 0.49394 (4) 0.0136 (2)
S6 0.84065 (6) 0.17432 (9) 0.46535 (5) 0.0247 (3)
S7 0.79958 (5) 0.36970 (9) 0.37903 (4) 0.0156 (2)
S8 0.91157 (6)
| 65
| 634
| 0.94658
| 0.97329
| 0.02671
|
PubMed Central
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arxiv_plus_top10pct_by_avg
|
36 months (follow-up rate of 64.5%). [Table 1](#T0001){ref-type="table"} lists the number of infants followed at birth and at 6, 12, 18, 24, and 36 months as well as their mean weight, height, and head circumference. [Figure 1](#F0001){ref-type="fig"} shows that the weight, height, and head circumference from birth to 36 months were all higher in male than in female infants. All measurements were within the normal ranges based on comparison with the Korean National Growth Curves ([Fig. 1](#F0001){ref-type="fig"}) and WHO Child Growth Standards ([@CIT0009], [@CIT0011]). The levels of maternal antioxidant vitamins and oxidative stress and the general characteristics of the mothers, fathers, and infants, are shown in [Table 2](#T0002){ref-type="table"}.
![Comparison of data with Korean National Growth Curve.\
Source: Korean Pediatric Society ([@CIT0009]).\
The black solid line represents the data of the present study.](FNR-58-20207-g001){#F0001}
######
Anthropometric parameters of infants at birth and at 6, 12, 18, 24, and 36 months[a](#TF0001){ref-type="table-fn"}
Parameter At birth 6 months 12 months 18 months 24 months 36 months
------------------------------- ----------------- ----------------- ----------------- ----------------- ----------------- -----------------
Weight (kg) 3.3±0.4 (383) 8.3±1.0 (250) 10.0±1.2 (259) 11.4±1.4 (196) 12.6±1.3 (171) 15.2±1.8 (124)
Height (cm) 49.4±2.1 (383) 68.9±3.1 (173) 76.8±3.2 (221) 82.6±3.7 (155) 87.8±3.5 (142) 98.3±4.6 (124)
Head circumference (cm) 34.4±1.4 (382) 43.6±1.8 (80) 46.0±1.7 (128) 47.6±1.4 (67) 48.6±1.6 (83) 50.0±1.5 (119)
Weight percentile 45.0±24.6 (382) 62.5±28.8 (250) 58.9±28.0 (259) 57.9±28.0 (196) 57.8±26.7 (171) 67.4±26.2 (124)
Height percentile 51.2±22.9 (383) 58.8±28.7 (173) 57.6±28.8 (221) 57.4±27.8 (155)
| 66
| 6,406
| 0.899068
| 0.949534
| 0.050466
|
PubMed Central
|
arxiv_plus_top10pct_by_avg
|
1
Note that only 48 children could be included in the analyses, due to the age restrictions of some of the questionnaires (FES and PSS).
2
Obtained by fitting a second model, including the subscales of the FES, instead of the FRI and FSI.
∗
p
\< 0.05,
∗∗
p
\> 0.001.
### Loneliness
The interaction effects between *family functioning (FRI and FSI)* and *family member* \[*FRI:* χ^2^(3) = 5.54, *p* = 0.14; *FSI:*χ^2^(3) = 2.79, *p* = 0.43\], between *cancer appraisal* and *family member* \[χ^2^(3) = 5.34, *p* = 0.15\] and between *family functioning* and *cancer appraisal* \[*FRI:*χ^2^(1) = 1.13, *p* = 0.29; *FSI:*χ^2^(1) = 2.30, *p* = 0.13\] were not significant and were subsequently left out of the final model. In the final model, 32% of the variance in *loneliness* was attributable to differences between family members (regardless of which family one belonged to) and 36% was attributable to differences between families. Within the same family, there was a correlation of 0.53 between the different family members in their reports of loneliness.
A significant effect of *FRI* upon loneliness was found \[χ^2^(1) = 9.03, *p* = 0.003\]: higher emotional closeness within the family (more cohesion and expressiveness, less conflict) was related to lower levels of loneliness in all family members. In addition, when refitting the model with the FES subscales instead of the two composite scores, there was a significant effect of *expressiveness* \[χ^2^(1) = 7.26, *p* = 0.007\]. In other words, when a participant perceived his/her family as more expressive, s/he reported to feel less lonely. None of the other FES subscales were significantly related to loneliness (all χ^2^ \< 3.7, all *p* \> 0.05). Furthermore, there was a significant effect of *cancer appraisal* \[χ^2^(1) = 81.83, *p* \< 0.001\]: the more one perceived the illness as uncontrollable and the less as manageable, the more s/he reported t
| 67
| 3,176
| 0.918316
| 0.959158
| 0.040842
|
PubMed Central
|
arxiv_plus_top10pct_by_avg
|
hemaDoc);
if (annotation != null ) {
if(annotations == null) {
annotations = new XSAnnotationImpl [] {annotation};
}
else {
XSAnnotationImpl [] tempArray = new XSAnnotationImpl[2];
tempArray[0] = annotations[0];
annotations = tempArray;
annotations[1] = annotation;
}
}
content = DOMUtil.getNextSiblingElement(content);
}
else {
String text = DOMUtil.getSyntheticAnnotation(child);
if (text != null) {
XSAnnotationImpl annotation = traverseSyntheticAnnotation(child, text, contentAttrs, false, schemaDoc);
if (annotations == null) {
annotations = new XSAnnotationImpl [] {annotation};
}
else {
XSAnnotationImpl [] tempArray = new XSAnnotationImpl[2];
tempArray[0] = annotations[0];
annotations = tempArray;
annotations[1] = annotation;
}
}
}
// get base type from "base" attribute
XSSimpleType baseValidator = null;
if ((restriction || list) && baseTypeName != null) {
baseValidator = findDTValidator(child, name, baseTypeName, refType, schemaDoc);
// if its the built-in type, return null from here
if (baseValidator == null && fIsBuiltIn) {
fIsBuiltIn = false;
return null;
}
}
// get types from "memberTypes" attribute
List<XSObject> dTValidators = null;
XSSimpleType dv = null;
XSObjectList dvs;
if (union && memberTypes != null && memberTypes.size() > 0) {
int size = memberTypes.size();
dTValidators = new ArrayList<>(size);
// for each qname in the list
for (int i = 0; i < size; i++) {
| 68
| 7,980
| 0.891394
| 0.945697
| 0.054303
|
Github
|
arxiv_plus_top10pct_by_avg
|
ual";
}
else {
return "select 1";
}
}
/**
* A class that identifies the type of the database that Jive is connected
* to. In most cases, we don't want to make any database specific calls
* and have no need to know the type of database we're using. However,
* there are certain cases where it's critical to know the database for
* performance reasons.
*/
public enum DatabaseType {
oracle,
postgresql,
mysql("rank"),
hsqldb,
db2,
sqlserver,
interbase,
unknown;
private final HashSet<String> identifiers;
DatabaseType(final String ... identifiers) {
this.identifiers = new HashSet<>(Arrays.asList(identifiers));
}
public String escapeIdentifier(final String keyword) {
if (identifiers.contains(keyword)) {
return String.format("%1$s%2$s%1$s", DbConnectionManager.getIdentifierQuoteString(), keyword);
} else {
return keyword;
}
}
}
}
function y = vl_aibcutpush(map, x)
% VL_AIBCUTPUSH Quantize based on VL_AIB cut
% Y = VL_AIBCUTPUSH(MAP, X) maps the data X to elements of the AIB
% cut specified by MAP.
%
% The function is equivalent to Y = MAP(X).
%
% See also: VL_HELP(), VL_AIB().
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
y = map(x) ;
9
# (void)walker command line interface
# Copyright (C) 2012 David Holm <dholmster@gmail.com>
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without eve
| 69
| 9,116
| 0.885815
| 0.942907
| 0.057093
|
Github
|
arxiv_plus_top10pct_by_avg
|
60*b**3/3 - 110*b**2 - 858*b. Suppose v(l) = 0. Calculate l.
-22, 1, 2
Suppose -73*t = -4*m - 68*t - 17, -2*m = t - 9. Let y(k) be the first derivative of -1/8*k**4 + 0*k - 1/6*k**3 + 0*k**m + 15. Solve y(d) = 0.
-1, 0
Let x(w) = 20*w - 240. Let z be ((-24)/(-10))/(14/(-280)*-4). Let r be x(z). Factor 0*m + 2/9*m**3 + 2/9*m**4 + r*m**2 + 0.
2*m**3*(m + 1)/9
Let g be 4/(-42) + (-4571)/(-147). Suppose 2*b**2 - 3*b + b + 3*b + 2*b + 30*b**3 - g*b**3 = 0. Calculate b.
-1, 0, 3
Let j(f) be the second derivative of f**6/30 - 11*f**5/15 + 13*f**4/3 - 32*f**3/3 + 14*f**2 + 2*f - 3. Let n(l) be the first derivative of j(l). Factor n(u).
4*(u - 8)*(u - 2)*(u - 1)
Let r(f) = -6*f**3 + 578*f**2 - 15120*f + 132214. Let g(p) = -3*p**3 + 291*p**2 - 7560*p + 66108. Let d(z) = -11*g(z) + 6*r(z). Factor d(s).
-3*(s - 36)**2*(s - 17)
Let w(l) be the first derivative of -5/3*l**3 + 1/5*l**5 - 1/4*l**4 + 0*l - 3/2*l**2 + 88. Determine u, given that w(u) = 0.
-1, 0, 3
Let i(d) be the first derivative of -d**5/25 - 9*d**4/10 + 139*d**3/5 + 217*d**2/5 + 5310. Find c, given that i(c) = 0.
-31, -1, 0, 14
Let q be 40/1*((-3)/(-28) + 0/3). Factor 2/7*c**3 + 18/7*c**2 - 50/7 + q*c.
2*(c - 1)*(c + 5)**2/7
Let z(f) be the third derivative of -f**5/160 + 9*f**4/8 + 225*f**3/16 - 41*f**2 + 13. Factor z(n).
-3*(n - 75)*(n + 3)/8
Let a(l) be the third derivative of -l**5/20 + 779*l**4/4 - 606841*l**3/2 - 22*l**2 + 2*l + 3. Find n such that a(n) = 0.
779
Let r(i) be the second derivative of 1/5*i**5 - 1/3*i**4 + 0*i**2 + 11*i - 1 + 0*i**3 - 1/30*i**6. Factor r(n).
-n**2*(n - 2)**2
Let f = 64 + -58. Suppose -3*q + 5*v + 37 = 0, f*q + 21 = 10*q - v. Factor -16*y**3 - 20*y**2 + 3*y**4 - 2*y**q - 2*y**4 - 3*y**4 - 8*y.
-4*y*(y + 1)**2*(y + 2)
Let l = 199 + -202. Let p(o) = -o**2 + 7*o - 6. Suppose -q - 12 = 9. Let u(b) = -b + 1. Let i(t) = l*p(t) + q*u(t). Factor i(w).
3*(w - 1)*(w + 1)
Determine v, given that -26/3*v**5 - 8/3*v - 36*v**4 - 146/3*v**3 - 24*v**2 + 0 = 0.
-2, -1, -2/13, 0
Let g = 357 + -355. Factor 2*h + 6*h**2 + 32*h**2 - 4 - 36*h**g.
2
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74.0 ± 1.2 69.0 ± 1.3 100.8 ± 2.2 108.7 ± 1.1
Genotype 0.105 0.476 \<0.001 0.812 \<0.001 0.963
Date \<0.001 \<0.001 \<0.001 \<0.001 \<0.001 0.002
Geno × Date 0.052 0.12 0.1 0.973 0.239 0.767
John Wiley & Sons, Ltd
The % digestibility of cell wall glucose significantly declined between the two time points for both sets of plants whereas the % digestibility of xylose showed a nonsignificant change between July and October. The average difference in % digestibility of glucose was −16% of July levels in October for the mixed population and −7% of July levels in October for the mapping family. However, as biomass increased by 70% between July and October, yields of digestible sugars in October will still greatly exceed yields in July.
Nutrient remobilization {#gcbb12419-sec-0024}
-----------------------
The nitrogen (N), phosphorous (P) and potassium (K) of the total above‐ground material was analysed at six time points over 2 years: July (2011 & 2012), November (2011), December (2012) and January (2011 & 2012; Fig. [7](#gcbb12419-fig-0007){ref-type="fig"}). The climate data are shown in Fig. [1](#gcbb12419-fig-0001){ref-type="fig"}. Significant differences were observed between the harvest dates for all nutrients in both years (*P* =\< 0.01; Fig. [7](#gcbb12419-fig-0007){ref-type="fig"}). In July 2011 and 2012, N concentration was 13--21 g kg^−1^ but by January this had declined three‐ to fourfold to be only 5 g kg^−1^. A similar fourfold decline was also seen in P
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| 0.925104
| 0.962552
| 0.037448
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nitiated \[[@B9],[@B23]\], using a high protein high fibre weight loss diet (Table [1](#T1){ref-type="table"}), and fed according to manufacturer's instructions. The initial food allocation for weight loss was determined by first estimating maintenance energy requirement (MER = 440 kJ \[105 Kcal\] × body weight \[kg\]^0.75^/day) \[[@B26]\] using the estimated target weight. The exact level of restriction for each dog was then individualised based upon gender and other factors (i.e. presence of associated diseases such as osteoarthritis and other orthopaedic disorders), and was typically between 50-60% of MER at target weight \[[@B23]\]. Owners also implemented lifestyle and activity alterations to assist in weight loss. Dogs were reweighed every 7--21 days and changes were made to the dietary plan if necessary \[[@B9],[@B23]\].
######
Average analysis of the diet used for weight loss in the study dogs
**Nutrient** **Per Mcal** **Per 100 g as fed**
------------------------------ -------------- ----------------------
Kcal/kg Metabolizable Energy 2900^\*^ \-\--
Crude protein (g) 104.0 30.2
Arginine (g) 5.4 1.6
Histidine (g) 2.0 0.6
Isoleucine (g) 3.8 1.1
Met and Cys (g) 3.6 1.0
Leucine (g) 7.7 2.2
Lysine (g) 4.1 1.2
Phe and Tyr (g) 9.6 2.8
Threonine (g) 3.3 1.0
Tryptophan (g) 0.9 0.3
Valine (g) 4.4 1.3
Total fat (g) 33.0 9.6
Linoleic acid (g) 7.3 2.1
Calcium (g) 3.1 0.9
Phosphorus (g) 2.4 0.7
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Having two classes, denoted as $Cl_{1}$ and $Cl_{2}$, the formula of the decision function to classify a new input instance is: $$f\left( \mathbf{x} \right) = \sum\limits_{i = 1}^{N_{SV}}y_{i}\alpha_{i}K\left\langle \mathbf{x},\mathbf{s}_{i} \right\rangle - \rho\,\,\,\,\,\left\{ \begin{array}{r}
{f\left( \mathbf{x} \right) > 0,\mathbf{x} \in Cl_{1}} \\
{f\left( \mathbf{x} \right) < 0,\mathbf{x} \in Cl_{2}} \\
\end{array} \right.$$ where $\mathbf{x} \in \mathbb{R}^{N_{F}}$ is the vector if input features, $\mathbf{s}_{i} \in \mathbb{R}^{N_{F}}$, $i = 1,...,N_{SV}$ are the support vectors, $\alpha_{i}$ are the support values, with $y_{i}$ denoting the class they reference ($y_{i} = + 1$ for $Cl_{1}$, $y_{i} = - 1$ for $Cl_{2}$) and $K\left\langle \, \cdot \,,\, \cdot \, \right\rangle$ denotes the kernel function. In our application, the input for the SVM classifier is the 4-dimensional vector of the EMG signals acquired by the electrodes ($N_{F} = 4$) and we used the Radial Basis Function (RBF) kernel.
Since the training of a SVM classifier is computationally demanding for a low power microcontroller and it should be performed only at the setup of the recognition algorithm, it is possible to perform it offline on a PC. This allows the use of a graphical interface to visualize the training data and perform an accurate segmentation without imposing particular limitations on the system architecture. The calculated models are then stored on the MCU where the classification algorithm is executed for a real time recognition of the performed gestures. A diagram of the SVM training and recognition phases is illustrated in [Figure 3](#sensors-17-00869-f003){ref-type="fig"}a.
The libSVM \[[@B60-sensors-17-00869]\] is a popular open source multiplatform library implementing the SVM algorithm, which includes the training and classification functions \[[@B60-sensors-17-00869]\]. The library implementation is adapted for the embedded platform, where the dynamic allocation of the memory structures is not recommended in the des
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sed in prosthetics, and can be seen as another post-processing step: $$\hat{\mathbf{z}}\left( t \right)\mathbf{~} = \mathbf{~}\hat{\mathbf{z}}\left( {t - 1} \right) + {\delta\hat{\mathbf{y}}}^{\mathbf{'}}\left( t \right),$$ where *δ* is the velocity coefficient that determines the movement velocity of the cursor and **ẑ**(*t*) is the final cursor position in 2D space, controlled by the users' EMG in real time. Since the two parameters interplay together to control the smoothness of the cursor and its speed, a proper combination of those two parameters should be determined. Based on the results of the preliminary study (see section 3.1.), both system parameters were determined to 1/25, with which none of the subjects perceived a delayed system response. Also, the post-processed final regression output **ẑ**(*t*) was restricted not to exceed the limitation of the unit-circle. If the final output **ẑ**(*t*) exceeded the boundary of the unit-circle, the position of the cursor was restricted to the circle boundary.
2.4. Experimental protocol and procedures {#sec010}
-----------------------------------------
### 2.4.1. Calibration run {#sec011}
In the calibration run, a visual synchronization training approach was employed to acquire training data X and Y and a linear regression model **W**, where the subject was instructed to perform wrist contractions based on the position of moving visual targets. The training data X and Y consisted of the RMS features extracted from each electrode and their corresponding positions of visual targets, respectively. Because it was confirmed in our previous study \[[@pone.0186318.ref019]\] that a high decoding accuracy of combined wrist movements of two DoFs can be obtained by a linear regression model trained with single DoF movements, only single motions of each DoF were used in the calibration run. [Fig 2(a)](#pone.0186318.g002){ref-type="fig"} shows a screenshot of a calibration run, where the subject was prompted to make wrist contractions based on the position of the la
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| 0.025469
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r1 >::type fwd_state2;
typedef typename mpl::next<iter1>::type iter2;
typedef typename apply2< ForwardOp,fwd_state2,iter2 >::type fwd_state3;
typedef typename mpl::next<iter2>::type iter3;
typedef fwd_state3 bkwd_state3;
typedef typename apply2< BackwardOp,bkwd_state3,iter2 >::type bkwd_state2;
typedef typename apply2< BackwardOp,bkwd_state2,iter1 >::type bkwd_state1;
typedef typename apply2< BackwardOp,bkwd_state1,iter0 >::type bkwd_state0;
typedef bkwd_state0 state;
typedef iter3 iterator;
};
};
template<> struct reverse_iter_fold_chunk<4>
{
template<
typename First
, typename Last
, typename State
, typename BackwardOp
, typename ForwardOp
>
struct result_
{
typedef First iter0;
typedef State fwd_state0;
typedef typename apply2< ForwardOp,fwd_state0,iter0 >::type fwd_state1;
typedef typename mpl::next<iter0>::type iter1;
typedef typename apply2< ForwardOp,fwd_state1,iter1 >::type fwd_state2;
typedef typename mpl::next<iter1>::type iter2;
typedef typename apply2< ForwardOp,fwd_state2,iter2 >::type fwd_state3;
typedef typename mpl::next<iter2>::type iter3;
typedef typename apply2< ForwardOp,fwd_state3,iter3 >::type fwd_state4;
typedef typename mpl::next<iter3>::type iter4;
typedef fwd_state4 bkwd_state4;
typedef typename apply2< BackwardOp,bkwd_state4,iter3 >::type bkwd_state3;
typedef typename apply2< BackwardOp,bkwd_state3,iter2 >::type bkwd_state2;
typedef typename apply2< BackwardOp,bkwd_state2,iter1 >::type bkwd_state1;
typedef typename apply2< BackwardOp,bkwd_state1,iter0 >::type bkwd_state0;
typedef bkwd_state0 state;
typedef iter4 iterator;
};
};
template< long N >
struct reverse_iter_fold_chunk
{
template<
typename First
, typename Last
, typename State
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pread) of results, standard deviation equals to: 6.12%, 5.90% and 3.12%, respectively, and the differences between boundary values (*max* − *min*) are 27.41%, 24.13% and 13,20%.
Since the spread of the desired speed in experiment 3 is quite high (see [Table 2](#pone.0201732.t002){ref-type="table"})---$min = 2.569\frac{m}{s}$, $max = 5.760\frac{m}{s}$, $\sigma = 0.719\frac{m}{s}$, using this data we analyze the dependency between the speed in smoke and the desired speed. [Fig 11](#pone.0201732.g011){ref-type="fig"} illustrates how many percent of desired speed were remained during movement in the main tunnel. There is only weak, positive correlation between this data on account of the Pearson Correlation Coefficient = 0.2852, *p* = 0.046997 (*p* \< 0.05). This suggests that in the considered conditions, a percentage speed decrease due to smoke is similar for all desired velocities (from range $2.5 - 5.7\frac{m}{s}$).
{#pone.0201732.g011}
On the other hand, due to security and organizational reasons the number of examined scenarios as well as the number of participants in each scenario is limited. Therefore, the results described in this (sec. 4.5) and previous subsection (sec. 4.4) can be disturbed by the small size of samples. In order to address this issue in this paper, we focused only on the clearest dependencies. Nevertheless, further research is required to investigate the dependency between movement speed, and the desired speed in different smoke density.
4.6 Influence of initial position in bus on total evacuation time {#sec013}
-----------------------------------------------------------------
We analyze how initial position in a bus influenced total evacuation time (*hot seat* analysis). The overall dependence is presented in [Fig 12](#pone.0201732.g012){ref-type="fig"}. Results for experiment 1 are presented in [Fig 12A](#pone.0201732.g012){ref-type="fig"}, while for experiment 2 in [Fig 12B
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e classification of tissue degeneration {#Sec11}
--------------------------------------------------
Variable reduction and classification based on PCA show that the samples can be grouped into two linearly separable classes based on variations in their NIR spectral data using the 1^st^ and 2^nd^ principal components scores (PC~1~ and PC~2~). The scores can be observed to group the samples according to level of degeneration along PC~1~, while samples within each group cluster along both PC~1~ and PC~2~.
"Class 1" consists of samples with low Mankin score (\<=2) and relatively high GAG content (\>23 μg/mg), and is representative of cartilage with mild degeneration (sham, weeks 1 and 2); while "class 2" consists of samples with relatively high Mankin score (=\>3) and low GAG content (\<23 μg/mg), indicative of advanced tissue degeneration (weeks 4 and 6) (Fig. [3](#Fig3){ref-type="fig"}). Although PCA was performed on the spectra with and without pre-processing (multiplicative scatter correction (MSC) and derivative (1^st^ and 2^nd^)), optimal classification was obtained without pre-processing. SVM shows that a decision boundary that optimally demarcates both classes in the PCA score plot can be obtained, since the classes are linearly separable (Fig. [3a](#Fig3){ref-type="fig"}). The SVM model classified all samples with advanced degeneration correctly, but misclassified two samples with mild degeneration (Fig. [3b](#Fig3){ref-type="fig"}). No significant difference (p = 0.0588) in tissue degeneration (via the Mankin score) was observed between the samples in class 1; however, statistically significant difference (p = 0.009) was observed between the samples in class 2.Figure 3(**a**) PCA score plot of the NIR spectral data of the samples showing classification into two distinct groups. "Class 1" consists of sham and samples from weeks 1 and 2 post-injury, class 2 consists of samples from weeks 4 and 6 post-injury. (**b**) SVM decision boundary showing the optimal demarcation of both classes.\[w1
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zation or dye-swap normalization. Although measurement errors on gene expression are assumed to be uncorrelated, thus Σ~*ϵ*~ is a diagonal matrix, this does not mean, nor imply, that the observed gene expression values themselves are uncorrelated. In fact, the observed gene expression values are modeled as linear combinations of regulators which are themselves correlated. The system matrix *F* describes the temporal development of the regulators, and the gene regulation matrix *G* is identifiable only if the dimension *m* of the state space is smaller than the number of observed time points *T*. Typically, in biological experiments the number of relevant regulators is small, and therefore this issue is not anticipated as problematic. Something that is dealt with later, but worth noting now, is that the gene regulation matrix *G* and the system matrix *F* are not unique, and any renumbering of the hidden regulators will most likely lead to different gene regulation and system matrices. We chose this model because it provides a large degree of flexibility in the hidden regulators which could be cell internal or external components. On the other hand, the simplifying assumptions of time independent regulation matrices *G* and *F* and a linear relationship between regulating elements and gene expressions are biologically reasonable.
2.2. KM-Algorithm {#sec2.2}
-----------------
A modified EM-algorithm is employed to estimate the parameters of the state space model. The parameters that are of interest, and that need to be estimated in the state space model ([1](#EEq1){ref-type="disp-formula"}), consist of the gene regulation matrix *G*, the system matrix *F*, the covariance matrices of the biological error Σ~*δ*~, the measurement error Σ~*ϵ*~, and the mean and covariance matrix for the Gaussian distribution of the initial regulator state *Y* ~0~ ∼ *N*(*μ*, Σ).
The KM-algorithm starts with random initial values for the model parameters and then alternates between the Kalman smoothing (KS) estimates of the
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del (2) (without the baseline adjustment term and assuming a common residual variance) separately to this simulated IPD, under all the combinations of estimation and CI derivation methods outlined in Section [2](#sim7930-sec-0002){ref-type="sec"}. Figure [1](#sim7930-fig-0001){ref-type="fig"} provides a flow diagram summarizing the possible combinations. Each time a model was fitted (under a particular combination of estimation and CI derivation methods), we stored the following: the summary treatment effect estimate, $\hat{\theta}$; its corresponding 95% CI; a binary indicator variable for coverage of $\hat{\theta}$ (ie, the value 1 if the 95% CI of $\hat{\theta}$ contained the true θ, and 0 otherwise); estimates of any variance parameters; model run time (from start of model fit to end of post estimation); and model convergence (1/0 for convergence within 100 iterations/nonconvergence, respectively).
{#sim7930-fig-0001}
For each model (stratified or random intercept) fitted to the data, this enabled us to obtain two estimates of θ (one each for the models fitted using ML and REML estimation, respectively) and four 95% CIs for $\hat{\theta}$ (one for ML estimation with a standard CI derivation, and then one each for REML estimation with the standard, KR‐corrected, and Satterthwaite‐corrected CI derivations).
Step 3: Simulation replications
Steps 1 and 2 were repeated until 1000 IPD meta‐analysis datasets had been generated using the true parameter values and procedure as outlined thus far, followed by application of the various intercept option, estimation, and CI methods to each of the 1000 replicated datasets (note: 1000 simulations were chosen to give a Monte Carlo error of 0.7% on a coverage of 95%).
Step 4: Summarizing performance
Using the results obtained afte
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4/3*s**2.
-(s - 2)*(4*s + 1)/3
Let m be -1 - (-4 + 0) - 2. Let g be m + 1 + -1 + -1. Factor -4/3*v**2 + 2/3*v**5 + 0*v**3 + 4/3*v**4 - 2/3*v + g.
2*v*(v - 1)*(v + 1)**3/3
Find y, given that 42*y**2 - 35*y**3 + 28*y - 60 + 83*y**2 - 108*y = 0.
-3/7, 2
Let z(g) = 9*g**2 + 24*g - 3. Let q(s) = -10*s**2 - 23*s + 4. Let j(l) = -3*q(l) - 2*z(l). Let j(k) = 0. What is k?
-2, 1/4
Suppose -10*l + 12 = -6*l. Factor o + l*o**2 - 2*o + 0*o**2 + 7*o.
3*o*(o + 2)
Let s(h) be the third derivative of -h**8/672 + h**6/120 - h**4/48 - 17*h**2. Suppose s(y) = 0. What is y?
-1, 0, 1
Let h(v) be the second derivative of -v**9/15120 + v**8/3360 - v**7/2520 - v**4/6 - 4*v. Let l(o) be the third derivative of h(o). Let l(u) = 0. What is u?
0, 1
Let z(m) be the first derivative of -m**6/3 + 3*m**5/5 - m**4/4 - 8. Factor z(a).
-a**3*(a - 1)*(2*a - 1)
Factor -40/7*h**3 + 128/7*h - 4/7*h**4 - 96/7*h**2 + 512/7.
-4*(h - 2)*(h + 4)**3/7
Let d(k) = -k**3 - 13*k**2 + 13*k - 2. Let h be d(-14). Find c, given that 7*c**2 - 3*c**3 + 8*c**2 - c - h*c**2 + c**4 = 0.
0, 1
Let r(b) be the first derivative of -2*b**5/25 + b**4/5 - 2*b**3/15 + 5. Factor r(f).
-2*f**2*(f - 1)**2/5
Let u be 4/9*10/64. Let g(r) be the third derivative of u*r**4 + 1/360*r**6 + 1/45*r**5 + 0 + 0*r + 2*r**2 + 1/9*r**3. Factor g(b).
(b + 1)**2*(b + 2)/3
Let w(n) be the second derivative of 1/105*n**6 + 5*n + 0 + 1/70*n**5 + 0*n**2 - 1/42*n**4 - 1/21*n**3. Factor w(v).
2*v*(v - 1)*(v + 1)**2/7
Suppose 5*p - 5*o = 20, -2*p - 3*o - 8 = -3*p. Factor -5 + s + 2*s + 3 - s**2 + 0*s**p.
-(s - 2)*(s - 1)
Determine t, given that 4*t + 5*t**2 - 3*t**2 - 2*t - t**2 = 0.
-2, 0
Suppose -35 = 4*m + 3*w - 46, -w = -1. Factor -4/9*r - m*r**3 + 14/9*r**2 + 10/9*r**4 - 2/9*r**5 + 0.
-2*r*(r - 2)*(r - 1)**3/9
Suppose -4*g + 12 = -4*c + 4, -3*g + 4*c = -4. Determine p, given that g - p**2 + 4 - 3 - p - 3 = 0.
-2, 1
Factor -p + 0 - 1/2*p**3 + 7/4*p**2 - 1/4*p**4.
-p*(p - 1)**2*(p + 4)/4
Let -1176/5*v**2 + 5488/5*v - 9604/5 + 112/5*v**3 - 4/5*v**4 = 0. Calculate v.
7
Let v = -7/15 - -17/15. Determin
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|
such ethical approval is not mandatory for experimental studies that do not involve any risk or discomfort for the participants as long as anonymity is preserved (Spanish Law 15/1999 for Personal Data Protection) and participants are fully informed about the procedures of the study and give written informed consent to participate. The current experiment is in line with this regulation and further complies with the international standards of experimental economics research. The participants did not learn the identity of the other participants they interacted with and the identity of the participants cannot be inferred from the data which is entirely anonymous. Finally, the experimental protocols were approved by the LINEEX (University of Valencia), the institution hosting the experiment (see their webpage for further details about their data protection policy).
Results {#sec005}
=======
Descriptive statistics {#sec006}
----------------------
We summarize the data in [Table 1](#pone.0204392.t001){ref-type="table"}. Panel A presents the decision of the 48 investors for each possible distribution of endowments. The data for allocators is summarized in Panel B. We note that allocators can only make a decision if they have received any transfer from investors, thus Panel B reports the behavior of those allocators who received a positive transfer from investors. This, in turn, implies that the number of observations may differ across distribution. We report the correlation between the investor's transfer (*X*) and the allocator's returned share (*y*) in Panel C.
10.1371/journal.pone.0204392.t001
###### Summary of the data.
{#pone.0204392.t001g}
*e*~*i*~ = 40 *e*~*i*~ = 10
---------------------------------------------------- --------------- --------------- -------------- --------------
**A. Investor's behavior**
Amount sent (*X*)
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rn)
return match ? {raw: pattern} : false
}
)
/**
* 正则匹配
*
* @param {Walker} walker 表达式字符串读取工具
* @param {RegExp} pattern 正则表达式
* @return {ASTNode|false} 解析得到的 AST，匹配失败时返回 false
*/
export const regexp = memoize(
(walker, pattern) => {
let match = walker.matchRegExp(pattern)
return match ? {raw: match[0]} : false
}
)
/**
* 字符串词法分析类
*/
export default class Lexer {
constructor () {
this.types = {}
}
/**
* 定义规则
*
* @param {Descriptor} descriptor 规则描述对象
* @return {Rule} 结构化规则对象
*/
set (descriptor) {
let item = [def, descriptor]
this.types[descriptor.type] = item
return item
}
/**
* 读取规则
*
* @param {string} 规则名称
* @return {Rule} 结构化规则对象
*/
get (type) {
return this.types[type]
}
}
/*
This samples shows the creation of a multi-level directory which
has the same name for each of the directory level. Before this date
multi-level directory with same name on each level will only create
the first level
Example::
dir = new Directory("../testfiles/dir_test/dir_test/dir_test/dir_test/dir_test")
dir.Create()
The above example will only create the directory ``"../testfiles/dir_test/"``
neglecting all the sub level directory with the same name **dir_test**
This issue has been resolve in the following commit
https://github.com/simple-lang/simple/commit/f45af071ee49ef37d80e88b141adae1e82a1cac0
:copyright: 2018-2019, Azeez Adewale
:license: MIT License Copyright (c) 2018 simple
:author: Azeez Adewale <azeezadewale98@gmail.com>
:date: March 23 2019
:filename: create_directory.sim
*/
from simple.core.Object
from simple.util.Console
from simple.io.Directory
block main()
dir = new Directory("../testfiles/dir_test/dir_test/dir_test/dir_test/dir_test")
stdout.Println(dir)
dir.Create()
#ifndef _MBEDTLS_DEBUG_H_
#define _MBEDTLS_DEBUG_H_
#include "osapi.h"
#defi
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e most dangerous area---the main tunnel, is mostly determined (besides smokiness level) by evacuees' familiarity with the situation, scenario and layout of the tunnel.
4.4 Movement speed {#sec011}
------------------
We calculated movement speed for experiments 1-3. Movement speed in the main tunnel and the evacuation tunnel was calculated separately, since the differences in conditions in these tunnels are significant---there is no smoke in the evacuation tunnel. Due to the size of the tunnel and the heavy smoke, determination of exact trajectories (and exact speed respectively) would be extremely hard. Thus, we calculated evacuees' speed in a simplified way, as the distance to travel divided by walking time. Fridolf et al. propose to call this method *modeling speed* \[[@pone.0201732.ref012]\]. However, this method is widely used in similar experiments \[[@pone.0201732.ref004], [@pone.0201732.ref031]\]. The calculated movement speeds for the main and the evacuation tunnel in consecutive experiments are shown in [Table 2](#pone.0201732.t002){ref-type="table"}.
10.1371/journal.pone.0201732.t002
###### Movement speed for the main and the evacuation tunnel for experiments 1-3.
First 9 persons, who stopped and discussed after leaving the bus during experiment 1 were excluded from the statistics.
{#pone.0201732.t002g}
Experiment section Minimum Maximum Mean Std. deviation
------------------------------------ --------- --------- ------- ----------------
experiment 1 the main tunnel 0.895 1.211 1.056 0.083
experiment 1 the evacuation tunnel 1.542 1.808 1.706 0.058
experiment 2 the main tunnel 0.917 2.422 1.321 0.375
experiment 2 the evacuation tunnel 1.489 1.953 1.635 0.081
experiment 3 the main tunnel 0.893 2.044 1.221 0.295
experiment 3 the evacuation tunnel 2.569 5.760 3.835 0.719
Movement speed in the evacuation tunnel ([Table 2](#pone.0201732.t002){ref-type
| 83
| 1,014
| 0.940468
| 0.970234
| 0.029766
|
PubMed Central
|
arxiv_plus_top10pct_by_avg
|
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