text stringlengths 1.23k 293k | tokens float64 290 66.5k | created stringdate 1-01-01 00:00:00 2024-12-01 00:00:00 | fields listlengths 1 6 |
|---|---|---|---|
Semantic representation of scientific literature: Bringing claims, contributions and named entities onto the Linked Open Data cloud
Motivation: Finding relevant scientific literature is one of the essential tasks researchers are facing on a daily basis. Digital libraries and web information retrieval techniques provide rapid access to a vast amount of scientific literature. However, no further automated support is available that would enable fine-grained access to the knowledge 'stored' in these documents. The emerging domain of Semantic Publishing aims at making scientific knowledge accessible to both humans and machines, by adding semantic annotations to content, such as a publication's contributions, methods, or application domains. However, despite the promises of better knowledge access, the manual annotation of existing research literature is prohibitively expensive for wide-spread adoption. We argue that a novel combination of three distinct methods can significantly advance this vision in a fully-automated way: (i) Natural Language Processing (NLP) for Rhetorical Entity (RE) detection; (ii) Named Entity (NE) recognition based on the Linked Open Data (LOD) cloud; and (iii) automatic knowledge base construction for both NEs and REs using semantic web ontologies that interconnect entities in documents with the machine-readable LOD cloud. Results: We present a complete workflow to transform scientific literature into a semantic knowledge base, based on the W3C standards RDF and RDFS. A text mining pipeline, implemented based on the GATE framework, automatically extracts rhetorical entities of type Claims and Contributions from full-text scientific literature. These REs are further enriched with named entities, represented as URIs to the linked open data cloud, by integrating the DBpedia Spotlight tool into our workflow. Text mining results are stored in a knowledge base through a flexible export process that provides for a dynamic mapping of semantic annotations to LOD vocabularies through rules stored in the knowledge base. We created a gold standard corpus from computer science conference proceedings and journal articles, where Claim and Contribution sentences are manually annotated with their respective types using LOD URIs. The performance of the RE detection phase is evaluated against this corpus, where it achieves an average F-measure of 0.73. We further demonstrate a number of semantic queries that show how the generated knowledge base can provide support for numerous use cases in ABSTRACT Motivation: Finding relevant scientific literature is one of the essential tasks researchers are facing on a daily basis. Digital libraries and web information retrieval techniques provide rapid access to a vast amount of scientific literature. However, no further automated support is available that would enable fine-grained access to the knowledge ‘stored’ in these documents. The emerging domain of Semantic Publishing aims at making scientific knowledge accessible to both humans and machines, by adding semantic annotations to content, such as a publication’s contributions, methods, or application domains. However, despite the promises of better knowledge access, the manual annotation of existing research literature is prohibitively expensive for wide-spread adoption. We argue that a novel combination of three distinct methods can significantly advance this vision in a fully-automated way: (i) Natural Language Processing (NLP) for Rhetorical Entity (RE) detection; (ii) Named Entity (NE) recognition based on the Linked Open Data (LOD) cloud; and (iii) automatic knowledge base construction for both NEs and REs using semantic web ontologies that interconnect entities in documents with the machine-readable LOD cloud. Results: We present a complete workflow to transform scientific literature into a semantic knowledge base, based on the W3C standards RDF and RDFS. A text mining pipeline, implemented based on the GATE framework, automatically extracts rhetorical entities of type Claims and Contributions from full-text scientific literature. These REs are further enriched with named entities, represented as URIs to the linked open data cloud, by integrating the DBpedia Spotlight tool into our workflow. Text mining results are stored in a knowledge base through a flexible export process that provides for a dynamic mapping of semantic annotations to LOD vocabularies through rules stored in the knowledge base. We created a gold standard corpus from computer science conference proceedings and journal articles, where Claim and Contribution sentences are manually annotated with their respective types using LOD URIs. The performance of the RE detection phase is evaluated against this corpus, where it achieves an average F-measure of 0.73. We further demonstrate a number of semantic queries that show how the generated knowledge base can provide support for numerous use cases in managing scientific literature. Availability: All software presented in this paper is available under open source licenses at http://www.semanticsoftware.info/semantic-scientific-literature-peerj-2015-supplements. Development releases of individual components are additionally available on our GitHub page at https://github.com/SemanticSoftwareLab.
. This diagram shows our visionary workflow to extract the knowledge contained in scientific literature by means of natural language processing (NLP), so that researchers can interact with a semantic knowledge base instead of isolated documents.
INTRODUCTION
1 In a commentary for the Nature journal, (Berners-Lee and Hendler, 2001) predicted 2 that the new semantic web technologies "may change the way scientific knowledge 3 is produced and shared". They envisioned the concept of "machine-understandable 4 documents", where machine-readable metadata is added to articles in order to explicitly 5 mark up the data, experiments and rhetorical elements in their raw text. More than a 6 decade later, not only is the wealth of existing publications still without annotations, 7 but nearly all new research papers still lack semantic metadata as well. Manual efforts 8 for adding machine-readable metadata to existing publications are simply too costly 9 for wide-spread adoption. Hence, we investigate what kind of semantic markup can 10 be automatically generated for research publications, in order to realize some of the 11 envisioned benefits of semantically annotated research literature. 12 As part of this work, we first need to identify semantic markup that can actually help 13 to improve specific tasks for the scientific community. A survey by (Naak et al., 2008) 14 revealed that when locating papers, researchers consider two factors when assessing the 15 relevance of a document to their information need, namely, the content and quality of the 16 paper. They argue that a single rating value cannot represent the overall quality of a given 17 research paper, since such a criteria can be relative to the objective of the researcher. For 18 example, a researcher who is looking for implementation details of a specific approach 19 is interested mostly in the Implementation section of an article and will give a higher 20 ranking to documents with detailed technical information, rather than related documents 21 with modest implementation details and more theoretical contributions. Therefore, 22 a lower ranking score does not necessarily mean that the document has an overall 23 lower (scientific) quality, but rather that its content does not satisfy the user's current 24 information need. 25 Consequently, to support users in their concrete tasks involving scientific literature, 26 we need to go beyond standard information retrieval methods, such as keyword-based
Manuscript to be reviewed
Computer Science is to offer support for semantically rich queries that users can ask from a knowledge 29 base of scientific literature, including specific questions about the contributions of a 30 publication or the discussion of specific entities, like an algorithm. For example, a user 31 might want to ask the question "Show me all full papers from the SePublica workshops, 32 which contain a contribution involving 'linked data'." 33 We argue that this can be achieved with a novel combination of three approaches: 34 Natural Language Processing (NLP), Linked Open Data (LOD)-based entity detection, 35 and semantic vocabularies for automated knowledge base construction (we discuss these 36 methods in our Background section below). By applying NLP techniques for rhetorical 37 entity (RE) recognition to scientific documents, we can detect which text fragments 38 form a rhetorical entity, like a contribution or claim. By themselves, these REs provide 39 support for use cases such as summarization (Teufel and Moens, 2002), but cannot 40 answer what precisely a contribution is about. We hypothesize that the named entities 41 (NEs) present in a document (e.g., algorithms, methods, technologies) can help locate 42 relevant publications for a user's task. However, manually curating and updating all 43 these possible entities for an automated NLP detection system is not a scalable solution 44 either. Instead, we aim to leverage the Linked Open Data cloud (Heath and Bizer, 2011), 45 which already provides a continually updated source of a wealth of knowledge across 46 nearly every domain, with explicit and machine-readable semantics. If we can link 47 entities detected in research papers to LOD URIs (Universal Resource Identifiers), we 48 can semantically query a knowledge base for all papers on a specific topic (i.e., a URI), 49 even when that topic is not mentioned literally in a text: For example, we could find 50 a paper for the topic "linked data," even when it only mentions "linked open data," or 51 even "LOD", since they are semantically related in the DBpedia ontology. 1 But linked 52 NEs alone again do not help in precisely identifying literature for a specific task: Did 53 the paper actually make a new contribution about "linked data," or just mention it as an 54 application example? Our idea is that by combining the REs with the LOD NEs, we can 55 answer questions like these in a more precise fashion than either technique alone. 56 To test these hypotheses, we developed a fully-automated approach that trans-57 forms publications and their NLP analysis results into a knowledge base in RDF 2 58 format, based on a shared vocabulary, so that they can take part in semantically 59 rich queries and ontology-based reasoning. We evaluate the performance of this writing scientific articles. Indeed, according to a recent survey (Naak et al., 2008), 76 researchers stated that they are interested in specific parts of an article when searching 77 for literature, depending on their task at hand. Verbatim extraction of REs from text 78 helps to efficiently allocate the attention of humans when reading a paper, as well as 79 improving retrieval mechanisms by finding documents based on their REs (e.g., "Give 80 me all papers with implementation details"). They can also help to narrow down the 81 scope of subsequent knowledge extraction tasks by determining zones of text where 82 further analysis is needed.
83
Existing works in automatic RE extraction are mostly based on the Rhetorical 84 Structure Theory (RST) (Mann and Thompson, 1988) that characterizes fragments of 85 text and the relations that hold between them, such as contrast or circumstance. (Marcu,86 1999) developed a rhetorical parser that derives the discourse structure from unrestricted 87 text and uses a decision tree to extract Elementary Discourse Units (EDUs) from text.
88
The work by (Teufel, 2010) identifies so-called Argumentative Zones (AZ) from 89 scientific text as a group of sentences with the same rhetorical role. She uses statistical 90 machine learning models and sentential features to extract AZs from a document. 91 Teufel's approach achieves a raw agreement of 71% with human annotations as the 92 upper bound, using a Naïve Bayes classifier. Applications of AZs include document 93 management and automatic summarization tasks.
94
In recent years, work on RE recognition has been largely limited to biomedical and The JISC-funded ART project aimed at creating an "intelligent digital library," where 103 the explicit semantics of scientific papers is extracted and stored using an ontology- Prior to the analysis of scientific literature for their latent knowledge, we first need 122 to provide the foundation for a common representation of documents, so that (i) the 123 variations of their formats (e.g., HTML, PDF, L A T E X) and publisher-specific markup 124 can be converted to one unified structure; and (ii) various segments of a document 125 required for further processing are explicitly marked up, e.g., by separating References 126 from the document's main matter. A notable example is SciXML (Rupp et al., 2006), 127 which is an XML-based markup language for domain-independent research papers. It In stand-off annotation style, the original text and its annotations are separated into two different parts and connected using text offsets. 5 Entity linking is a highly active research area in the Semantic Web community. A high-level overview of our workflow design, where a document is fed into an NLP pipeline that performs semantic analysis on its content and stores the extracted entities in a knowledge base, inter-linked with resources on the LOD cloud.
233
We designed a text mining pipeline to automatically detect rhetorical entities in sci- Our RE detection pipeline extracts such statements on a sentential level, meaning 240 that we look at individual sentences to classify them into one of three categories: Claim, 241 Contribution, or neither. If a chunk of text (e.g., a paragraph or section) describes a Claim 242 or Contribution, it will be extracted as multiple, separate sentences. In our approach, we 243 classify a document's sentences based on the existence of several discourse elements 244 and so-called trigger words. We adopted a rule-based approach, in which several rules 245 are applied sequentially on a given sentence to match against its contained lexical and 246 discourse elements. When a match is found, the rule then assigns a type, in form of a Manuscript to be reviewed Computer Science
322
Using the rules described above, we can now find and classify REs in a scientific 323 document. However, by using REs alone, a system is still not able to understand the 324 topics being discussed in a document; for example, to generate a topic-focused summary.
325
Therefore, the next step towards constructing a knowledge base of scientific literature 326 is detecting the named entities that appear in a document. Our hypothesis here is that we will discard any tagged entity that does not fall within a noun phrase chunk. This 342 way, adverbs or adjectives like "here" or "successful" are filtered out and phrases like 343 "service-oriented architecture" can be extracted as a single entity. URIs linked to their LOD resources. Figure 7 shows example RDF triples using our 367 publication model and other shared semantic web vocabularies.
368
The most similar vocabulary to our PUBO vocabulary would have been the Open Manuscript to be reviewed Computer Science and its attributes, such as its features, as the object. Table 1 summarizes the shared 393 vocabularies that we use in the annotation export process. The pre-processed text is then passed onto the downstream processing resources. Manuscript to be reviewed
Computer Science
{ "Resources": [{ "@URI": "http://dbpedia.org/resource/ Software prototyping", "@support": "3235", "@types": "", "@surfaceForm": "prototype", "@offset": "1103", "@similarityScore": "0.9999996520535356", "@percentageOfSecondRank": "0. Manuscript to be reviewed Computer Science The documents in these corpora are in PDF or XML formats, and range from 3-43 477 pages in various formats (ACM, LNCS, and PeerJ). We scraped the text from all files, 478 analyzed them with our text mining pipeline described in the Implementation section, 479 and stored the extracted knowledge in a TDB-based triplestore. 30 30 The generated knowledge base is also available for download on our supplements page, http://www. semanticsoftware.info/semantic-scientific-literature-peerj-2015-supplements. 31 The table is automatically generated through a number of SPARQL queries on the knowledge base; the source code to reproduce it can also be found on our supplementary materials page, http: //www.semanticsoftware.info/semantic-scientific-literature-peerj-2015-supplements. in a document, the total occurrence would be two, but since they are both grounded to 493 the same URI (i.e., <dbpedia:Linked data>), the total distinct number of NEs is one.
494
This is particularly interesting in relation to their distribution within the documents' 495 rhetorical zones (column 'Distinct DBpedia NE/RE'). As can be seen in Table 2, the 496 number of NEs within REs are an order of a magnitude smaller than the total number of 497 distinct named entities throughout the whole papers. This holds across the three distinct 498 corpora we evaluated.
499
This experiment shows that NEs are not evenly distributed in scientific literature.
500
Overall, this is encouraging for our hypothesis that the combination of NEs with REs 501 brings added value, compared to either technique alone: As mentioned in the example 502 above, a paper could mention a topic, such as "Linked Data", but only as part of its 503 motivation, literature review, or future work. In this case, while the topic appears in 504 the document, the paper does not actually contain a contribution involving linked data.
505
Relying on standard information retrieval techniques hence results in a large amount
510
We assessed the performance of our text mining pipeline by conducting an intrinsic 511 evaluation i.e., comparing its precision and recall with respect to a gold standard corpus.
513
In an intrinsic evaluation scenario, the output of an NLP pipeline is directly compared 514 with a gold standard (also known as the ground truth) to assess its performance in a
Intrinsic Evaluation Results and Discussion
536 Table 4 shows the results of our evaluation. On average, the Rhetector pipeline obtained 537 a 0.73 F-measure on the evaluation dataset. 538 We gained some additional insights into the performance of Rhetector. When The system will then show the query's results in a suitable format, like the one shown in 594 Table 5, which dramatically reduces the amount of information that the user is exposed 595 to, compared to a manual triage approach.
596 Table 5. Three example Contributions from papers obtained through a SPARQL query.
The rows of the table show the paper ID and the Contribution sentence extracted from the user's corpus.
Paper ID Contribution
SePublica2011/ paper-05.xml "This position paper discusses how research publication would benefit of an infrastructure for evaluation entities that could be used to support documenting research efforts (e.g., in papers or blogs), analysing these efforts, and building upon them." SePublica2012/ paper-03.xml "In this paper, we describe our attempts to take a commodity publication environment, and modify it to bring in some of the formality required from academic publishing." SePublica2013/ paper-05.xml "We address the problem of identifying relations between semantic annotations and their relevance for the connectivity between related manuscripts." Retrieving document sentences by their rhetorical type still returns REs that may 597 concern entities that are irrelevant or less interesting for our user in her literature review 598 task. Ideally, the system should return only those REs that mention user-specified topics.
599
Since we model both the REs and NEs that appear within their boundaries, the system 600 can allow the user to further stipulate her request. Consider the following scenario: The results returned by the system, partially shown in Manuscript to be reviewed Computer Science Table 6. Two example Contributions about 'linked data'. The results shown in the table are Contribution sentences that contain an entity described by <dbpedia:Linked data>.
Paper ID Contribution
SePublica2012/ paper-07.xml "We present two real-life use cases in the fields of chemistry and biology and outline a general methodology for transforming research data into Linked Data." SePublica2014/ paper-01.xml "In this paper we present a vision for having such data available as Linked Open Data (LOD), and we argue that this is only possible and for the mutual benefit in cooperation between researchers and publishers." in reading, but it also inferred that "Linked Open Data", "Linked Data" and "LOD" So far, we showed how we can make use of the LOD-linked entities to retrieve 620 articles of interest for a user. Note that this query returns only those articles with REs 621 that contain an NE with a URI exactly matching that of dbpedia:Linked data. However, 622 by virtue of traversing the LOD cloud using an NE's URI, we can expand the query 623 to ask for contributions that involve dbpedia:Linked data or any of its related subjects.
624
In our experiment, we interpret relatedness as being under the same category in the 625 DBpedia knowledge base (see Fig. 8). Consider the scenario below: The results from the extended query that show Contribution sentences that mention a named entity semantically related to <dbpedia:Linked data>.
Paper ID Contribution
SePublica2012/ paper-01.xml "In this paper, we propose a model to specify workflow-centric research objects, and show how the model can be grounded using semantic technologies and existing vocabularies, in particular the Object Reuse and Exchange (ORE) model and the Annotation Ontology (AO)." SePublica2014/ paper-01.xml "In this paper we present a vision for having such data available as Linked Open Data (LOD), and we argue that this is only possible and for the mutual benefit in cooperation between researchers and publishers." SePublica2014/ paper-05.xml "In this paper we present two ontologies, i.e., BiRO and C4O, that allow users to describe bibliographic references in an accurate way, and we introduce REnhancer, a proofof-concept implementation of a converter that takes as input a raw-text list of references and produces an RDF dataset according to the BiRO and C4O ontologies.." SePublica2014/ paper-07.xml "We propose to use the CiTO ontology for describing the rhetoric of the citations (in this way we can establish a network with other works)." The system can respond to the user's request in three steps: (i) First, through a federated 629 query to the DBpedia knowledge base, we find the category that dbpedia:Linked data 630 has been assigned to -in this case, the DBpedia knowledge base returns "Semantic web", 631 "Data management", and "World wide web" as the categories; (ii) Then, we retrieve all 632 other subjects which are under the same identified categories (cf. Fig. 8) ORDER BY ?paper 646 The system will return the results, shown in Table 7, to the user. This way, the user 647 receives more results from the knowledge base that cover a wider range of topics 648 semantically related to linked data, without having to explicitly define their semantic 649 relatedness to the system. This simple example is a demonstration of how we can exploit Manuscript to be reviewed Computer Science on their REs. To demonstrate the feasibility of these ideas, we developed an NLP 660 pipeline to fully automate the transformation of scientific documents from free-form 661 content, read in isolation, into a queryable, semantic knowledge base. In future work, 662 we plan to further improve both the NLP analysis and the LOD linking part of our 663 approach. As our experiments showed, general-domain NE linking tools, like DBpedia 664 Spotlight, are biased toward popular terms, rather than scientific entities. Here, we 665 plan to investigate how we can adapt existing or develop new entity linking methods 666 specifically for scientific literature. Finally, to support end users not familiar with 667 semantic query languages, we plan to explore user interfaces and interaction patterns, 668 e.g., based on our Zeeva semantic wiki (Sateli and Witte, 2014) system. 669 | 5,254.2 | 2015-12-09T00:00:00.000 | [
"Computer Science"
] |
Six-Dimensional Correlators From a Five-Dimensional Operator Product Expansion
In this letter we discuss the operator product expansion of scalar operators in five-dimensional field theories with an $SU(1,3)\times U(1)$ spacetime symmetry. Such theories arise by a novel conformal null reduction of six-dimensional Lorentzian conformal field theories. Unlike Lorentzian conformal field theories, three-point functions of generic operators in such theories are not completely fixed by $SU(1,3)\times U(1)$ symmetry. However, we show that in a special case the functional form of the OPE coefficients can be fully determined, and we use them to fix the form of the three-point function. The result is shown to agree with correlation functions obtained by reduction of six-dimensional conformal field theories.
An alternative approach arises by considering a certain conformal mapping of sixdimensional (6D) Minkowski space that brings one of the two light cone coordinates, say x + , into a finite range: x + ∈ (−πR, +πR) [16], where R is a constant with dimensions of length that is introduced by the coordinate transformation along with an anti-symmetric and self-dual tensor Ω ij with Ω ij Ω jk = −R −2 δ ik .This can be done in such a way that ∂ + remains a Killing direction.In this way we can perform a Kaluza-Klein reduction along x + while still studying the non-compact six-dimensional theory.The resulting five-dimensional (5D) theory has an SU(1, 3) × U(1) spacetime symmetry group that commutes with the momentum generator P + (which generates the U(1) factor) [17].Due to the novel coordinate transformation used in the null compactification one finds that the SU(1, 3) × U(1) symmetry is an Ω-deformed version of the more familiar z = 2 Schrödinger group of conventional non-relativistic conformal field theories [18].In particular the generators of SU(1, 3) are M i+ , K + , P − , P i , B, C I , T , which are linearly related to the familiar generators Mµν , Kµ , Pµ , D of SO (2,6).Here, P − is the Hamiltonian, and P i generates spatial translations.B is the rotation generated by Ω ij , while C I are the remaining spatial rotations that commute with Ω ij .Finally, T is Lifshitz scaling symmetry, M i+ is an Ω-deformed Galilean boost, and K + is a special conformal generator.
Unlike correlation functions with Schrödinger symmetry, those constrained by SU(1, 3)× U(1) have power-law suppression in space and time.Indeed, by assembling five-dimensional operators into Fourier series along the compact null direction, one can recover the correlation functions of six-dimensional CFTs in Minkowski space without having to take the limit R → ∞ [19].A key benefit of the theories with SU(1, 3) × U(1) symmetry is that they admit Lagrangian descriptions, including theories with a large number of supersymmetries as appropriate for reduction of six-dimensional CFTs with (2, 0) or (1, 0) supersymmetry [16,21].These are rather non-standard gauge theories and the role of the P + eigenvalue is given by the instanton number.They admit a Lifshitz scaling property and as such might be embedded into UV complete theories [22].Thus there is some hope that these Lagrangians give well-defined path integrals and can be used to compute correlation functions in six-dimensional CFTs and also elucidate the relation of abstract CFT to gauge field theories.
One of the most important concepts in conformal field theory is the operator product expansion (OPE), which expresses the product of two local operators located at different points as an infinite series of local operators located at one of the two points.The existence of such an expansion is a basic assumption of Lorentzian CFTs like the 6D (2, 0) theory, and we will assume that the same holds for local scalar operators in the 5D theories which arise from them via the reduction described above.Note that this may be a nontrivial assumption since such operators correspond to Fourier modes along the null direction and are therefore non-local from a 6D perspective.Using the 5D OPE, we then show that if one of the operators in a three-point function satisfies the constraint p + = ±∆/2R, where p + is the P + eigenvalue and ∆ is the scaling dimension, then the three-point function can be fully determined.Moreover, we show that this agrees with the result obtained from Fourier decomposing a six-dimensional Lorentzian CFT correlator along the compact null direction.Note that for generic operators, the 5D three-point functions are not completely fixed by SU(1, 3) × U(1) [19], so it appears that the 5D OPE can be used to deduce 6D correlators at least for certain operators.
Before proceeding, let us briefly point out an alternative interpretation of the correlation functions studied in this letter.Most generally, they can be understood as the correlation functions L 1 L 2 . . .L n of a collection of line operators L i in a six-dimensional Lorentzian relativistic CFT, each extended along an integral curve of the same null conformal Killing vector field.This vector field is fixed by the parameter R, and in particular as we take R → ∞, the lines follow parallel null geodesics.We expect our results to generalise to SU(1, n) theories, corresponding to correlation functions of null line operators in 2n-dimensional relativistic conformal field theory.This letter is organised as follows.In section 2, we review some basic properties of the 5D theories and their symmetries.In section 3, we consider the OPE of scalar operators in the 5D theories and show that SU(1, 3) × U(1) symmetry can be used to fix the OPE coefficients for all the descendants of a primary operator appearing the expansion in terms of the coefficient of that operator, i.e. the leading OPE coefficient.In general, the leading OPE coefficient is an unfixed function, however we find that when p + = ±∆/2R all the OPE coefficients can be determined.In section 4 we show that scalar three-point functions are determined by the leading coefficient in the OPE of two of the operators.Hence, we can fix the form of a three-point function if one of the operators in a threepoint correlator satisfies p = ±∆/2R, and we show that this form arises from Fourier expanding 6D Lorentzian correlators.In section 5 we give a short conclusion.
Review
The five-dimensional theories we consider arise from placing the 6D Lorentzian CFTs on a manifold with metric where i ∈ {1, 2, 3, 4}, −πR ≤ x + ≤ πR, and Ω is an anti-self-dual 2-form satisfying Ω ik Ω jk = R −2 δ ij .This metric is conformally flat and can be obtained from a standard 6D Minkowski metric ds 2 = dx µ dx µ via a change of variables and Weyl transformation [19].
Reducing along x + then gives rise to a five-dimensional theory that admits a Lagrangian description which can in principle be used to compute 6D observables non-perturbatively via path integrals [20].Although we will not explicitly need the Lagrangians, for the interested reader we display the bosonic part below [16]: where ∇ i = D i − 1 2 Ω ij x j D − , with D − and D i being standard covariant derivatives for the gauge fields A − and A i , G ij is a self-dual Lagrange multiplier, F ij is a field strength constructed from a linear combination of the the field strengths F ij and F −i , and X I are scalars.Here, I is an R-symmetry index.
The Lagrangian enjoys an SU(1, 3)×U(1) spacetime symmetry, which is the isometry group of the metric in (1) after reducing along the x + direction.From the 6D perspective the U(1) is generated by P + = ∂ + while in the 5D Lagrangian theory it corresponds to the instanton number [20].Among the 15 generators of SU (1,3), the ones that we will primarily make use of in this paper are where |x| 2 = x i x i .In the limit R → ∞ these generators reduce to Galilean boosts, special Schrödinger transformations, and a Lifshitz scaling, respectively.For more details of the symmetry algebra, see [17,20].
Primary operators are labelled by their Liftshitz dimension ∆, and P + eigenvalue p + (in what follows we drop the subscript + to clear up the notation), as well as their irreducible representation They are defined by the following transformation properties: We will only look at scalar operators so we can drop the R O [C I ] and R O [B] terms.Such operators then satisfy the conditions Note that conservation of P + implies that the sum over all the U(1) charges of the operators in a given correlator should vanish, i.e.K p K = 0.Although we will not need to be explicit about the scalar operators which appear in correlation functions, to get an idea of the kind of operators we have in mind, recall that in the 6D (2, 0) theory one can construct protected operators by taking the trace of a product of scalar fields X I which are symmetrised and traceless in the R-symmetry indices.Upon reduction to 5D, we once again get a trace of a product of X I fields but it will be dressed with an instanton operator which encodes the mode number along the compact null direction [20].
As shown in [19], the two-point scalar correlators of the theory are completely fixed by the SU(1, 3) × U(1) symmetry and take the form where for any two points which satisfies zab = −z ba .Moreover, three-point scalar correlators are constrained by the SU(1, 3) Ward identities to take the form for an unknown function H IJK of the single variable ζ = z 12 z 23 z 31 /z 12 z23 z31 .
It was shown in [19] that the above forms for the 2-point ( 6) and 3-point functions (8) are consistent with the correlation functions of local operators in the initial SO(2, 6)invariant 6D theory, with the p I corresponding to momentum along a conformally compactified sixth direction, as they must be.That is, we can dimensionally reduce the well-known form of 2-and 3-point functions to obtain 5D correlators of the above form.In particular, the functions H IJK are entirely fixed, up to an overall factor of the relevant 6D OPE coefficient, as we review in detail in Section 4.1.Conversely, it was shown that we can perform a Fourier resummation of such 5D correlators-taking care of certain ordering ambiguities-to recover their 6D counterparts.This is the essential mechanism by which one might study 6D correlation functions from a 5D perspective.
5D Operator Product Expansion
In general, the operator product expansion of two primary operators can be written as a sum over primaries and descendants, which are obtained by acting with derivatives on the primaries.The coefficients in this sum are known as OPE coefficients.Our goal in this section will be to show that SU(1, 3) symmetry can be used to fix all the descendent OPE coefficients in terms of the primary OPE coefficients.This is a standard result in relativistic CFT, and was extended to theories with Schrödinger symmetry in [23,24].Following those references, we consider an OPE of scalar primary operators of the form where the sum is over primary operators O K as well as their descendants Here n is short hand for a string of partial spatial derivatives with length | n| and m = 0, 1, 2, ... counts the number of ∂ − derivatives.In the second line we have explicitly written out the first four terms for illustration.
The idea is to commute T , M i+ and K + given in section 2 with the left and right hand side of (9) and then evaluate the left hand side using (9).This leads to a set of differential equations that relate the various coefficients C K,i,0 IJ , C K,kl,0 IJ etc. to C K, 0,0
For example we can consider T .Using ( 4) and ( 5) on the left hand side of (9) we find Note that (T ) ∂ involves derivatives with respect to x − , x i and hence does not act on . On the other hand on the right hand side we find T, Comparing the two sides leads to the infinite series of differential equations satisfied by each of the coefficients C K, n,m IJ (x): which simply tell us how the coefficients C K n,m IJ (x) need to scale as functions of x − , x i .
Let us now turn evaluate the commutator with M i+ .Since [M i+ , O J (0)] = 0 the left hand side is On the other hand To evaluate this we can first consider and then set x = 0.One can evaluate the first two terms to find Comparing the coefficients of O K (0) and ∂ k O K (0) we can read off the equations We could keep going by including higher order descendants and their coefficients but this becomes increasingly tedious.It is clear that in general these equations can be used to determine C K, n,m IJ in terms of C K, 0,0 IJ .
A Special Case
Let us consider special case where Then we have and hence from (17) we find the equation The reason for this reduction is that for is also a primary operator if O K is a scalar primary.Said another way, an O K with ( 18) is the highest weight state of a special, short conformal multiplet.
Thus we find a single first order differential equation for C K, 0,0 IJ alone.To solve this we can assume C K, 0,0 IJ is of the form C K, 0,0 IJ = C K, 0,0 IJ (x − , |x| 2 ).Indeed, this follows from the consistency of the OPE with the rotational symmetries.If we define then the differential equation for C K, 0,0 IJ reduces to z∂C K, 0,0 These are solved by for any (anti) holomorphic function CK, 0,0
IJ
. However we note that C K, 0,0 IJ must satisfy (12) which, in terms of z, z is and this fixes CK, 0,0 IJ up to a multiplicative constant c K IJ .In particular we find where we introduced the notation Hence, when a primary operator appearing in an OPE satisfies the constraint in (18), we can determine the functional form of its OPE coefficient.In [23], a similar argument was used to deduce the OPE coefficients of primary operators satisfying the unitarity bound ∆ = d/2 in theories with Schrödinger symmetry, where d is the number of spatial dimensions.While the method we used is basically the same, the physical interpretation of our result is very different.First of all, the constraint in (18) leads to a first-order differential equation for the OPE coefficients, whereas the unitarity bound leads to a second order differential equation.Noting that our correlators reduce to Schrödinger correlators with d = 4 when Ω → 0, it would be interesting to investigate how the Ωdeformed version of the unitarity bound constrains OPE coefficients, which is a different constraint than the one in (18).
Correlators from OPEs
Let us see how we can use the OPE coefficients to learn about correlation functions.First we will demonstrate the that the solution in ( 26) can be used to derive the two-point scalar correlators reviewed in section 2. Next we will show that any three-point scalar correlator can be determined in terms of a certain OPE coefficient in the OPE of two operators in the correlator.If one of the operators in the correlator satisfies the constraint in (18), it is then possible to determine the functional form of the three-point function and we will show that it agrees with the result of dimensionally reducing 6D Lorentzian correlators.Along the way, we will also derive an analogue of crossing symmetry for three-point correlators.
We start be deriving scalar two-point functions from the OPE coefficients computed in (26) by simply noting that Here we have used the fact that only the identity operator, denoted by O 0 , has a nonvanishing one-point function; O 0 (0) = 1.Furthermore the identity operator satisfies p 0 = ±∆ 0 /2R = 0 so we can impose both signs in (20) (indeed the left-hand-side vanishes without the projector) and hence we know from ( 26) that Lastly we note that since either choice of sign must work this requires ∆ I = ∆ J , as is well-known for two-point functions.In this way we recover (6), which was previously derived by solving the conformal Ward identities [19].Now let us consider three-point correlators.Taking the OPE of the first two operators in a three-point correlator gives where the derivatives are with respect to x 2 .Thus, formally, there is a differential operator that acts on two-point functions to produce three-point functions.We have argued above that in principle all the C K, n,m IJ can be determined from C K, 0,0 IJ .On the other hand, the general solution to the conformal Ward identities in (8) contains an unfixed function H IJK .Thus the form of the three-point functions are fixed by symmetries of the theory and the unknown function H IJK is determined by the leading OPE coefficient C K, 0,0 IJ .More generally the operator appearing in the last line of (30) acts on n-point functions to produce (n + 1)-point functions and we recover the result, familiar from Lorentzian CFTs, that all the n-point functions are in principle determined by the OPE coefficients C K, n,m IJ .Of course this is a formidable task in general.
Recall that two and three-point functions in Lorentzian CFTs are completely fixed by conformal symmetry while four-point functions are determined up to an unknown function of conformal cross ratios.In this sense, we see that three-point functions of SU(1, 3) × U(1) theories are analogous to four-point functions in Lorentzian CFTs.In Lorentzian CFTs, four-point functions are further constrained by crossing symmetry, which forms the foundation of the conformal bootstrap [25][26][27][28].We may therefore expect a similar constraint to play a role for three-point functions of SU(1, 3) × U(1) theories.To see how this works, we first take the limit x 1 → x 2 in (30) keeping z 12 /z 12 and z 23 finite and arbitrary.If we write where x n is shorthand for a string of coordinates x i x j . . . of length | n| (it could also involve contributions from Ω jk x k in place of x j ) then it follows from (12) that for an unknown function F K, n,m JK (z 12 /z 12 ).Thus the most singular term comes from C K, 0,0 IJ and we get lim where the ellipsis are less singular terms.
On the other hand taking the same limit of ( 8) and noting that z 31 = −z 13 we find lim The first term is diverging but it matches in both expressions.So do the second and fourth terms (since p I + p J + p K = 0).The p-conserving delta-functions also match in both expressions as p L = p I + p J .Thus comparing (33) with (34) we read off Alternatively we can consider a different limit x 2 → x 3 : lim Again taking the same limit of (8) we find lim This time matching gives us However we must find the same function H IJK in both limits and so we require that in general where ζ is the SU(1, 3)-invariant cross-ratio defined below (8).We can think of this as a form of crossing symmetry for scalar three-point functions of SU(1, 3) × U(1) theories.We expect that this relation holds for any consistent set of OPE coefficients.Indeed we will be able to check this in the special case below where both the first and third operators satisfy (18) and hence the OPE coefficients are determined by (26).Conversely in the general case it would be interesting to know if this constraint can be used to restrict or even determine the OPE coefficients.We leave this for future work.
Comparison with Dimensional Reduction
Let us compare the above results with what we find from dimensional reduction.After Fourier expanding three-point scalar correlators of a 6D Lorentzian CFT along a compact null direction and comparing to (8), one finds that the functionH IJK takes the following form [19]: where h is a constant and assume that all ∆ I are even integers.Note that the formally infinite sum is in fact finite as the binomial coefficients vanish when the lower entry is negative.
One case we can check is p K = −∆ K /2R.In this case H IJK (ζ) simplifies to Since all the operators that appear in (35) have p L = −p K and ∆ L = ∆ K we know that p L = ∆ L /2R also.Therefore F L IJ must take the simple monomial form found in (26): Thus we find, from (35), in agreement with (41).
As another case we can take p I = ∆ I /2R where H IJK in (40) also takes the form (41). Now we use the crossed form (38) We see that now p L = −p I and ∆ L = ∆ I so p L = −∆ L /2R and F L JK has just one term: and hence in agreement with (41).And hence it follows that if both p I = ∆ I /2R and p K = −∆ K /2R then the crossing symmetry relation (39) is satisfied (as one can also check directly).
Conclusions
In this letter we have considered the operator product expansion for scalar operators in 5D field theories with an SU(1, 3) × U(1) spacetime symmetry.In particular we showed that if a primary operator O K appearing an OPE of primary operators O I and O J satisfies p K R = ±∆ K /2 (where ∆ K is its scaling dimension and p K is its U(1) charge), then its OPE coefficient C K, 0,0 IJ can be determined in terms of a single constant.Furthermore, following the argument presented in [23] (see also [24]), we showed how the unknown function H IJK (ζ) that appears in the general solution to the three-point Ward identities can be determined from C K, 0,0 IJ .In the special case p K R = ±∆ K /2 we were therefore able to determine H IJK (ζ) and we found that it agrees with dimensional reduction of 6D Lorentzian conformal correlators.Thus at least one special class of three-point functions in 5D SU(1, 3) theories are necessarily those of a 6D Lorentzian conformal field theory.This provides some hope that these 5D theories can be used to compute some quantities in more traditional, but non-Lagrangian, 6D theories such as the famous (2, 0) theory.
In future work we hope to extend our results to more general classes of operators.As a first step, it would be important to better understand the physical interpretation of the 5D OPE studied in this paper.Indeed, since local operators in the 5D theory correspond to Fourier modes along an internal null direction they are non-local operators in six dimensions.Moreover their Kaluza-Klein numbers in six dimensions correspond to instanton numbers in five dimensions.If we construct 5D operators by taking traces of products of fundamental scalar fields X I dressed with instanton operators, the constraint p K R = ±∆ K /2 corresponds to attaching a single instanton or anti-instanton to each scalar field (which has classical scaling dimension equal to two).In [19], this was shown to be the minimal number of instantons needed to have nonzero two-point functions.More generally, we could relax this constraint by attaching more instantons to each scalar field, which would correspond to probing higher modes along the null direction.We may then explore how to generalise the solution for C K, 0,0 IJ when p K R = ±∆ K /2 to other cases, for example when an operator in the OPE satisfies an Ω-deformed analogue of the unitarity bound ∆ = d/2, which was shown to fix OPE coefficients in theories with Schrödinger symmetry in [23].It would also be interesting to explore whether the crossing symmetry relation for three-point functions found in (39) provides the starting point for a non-relativistic conformal bootstrap.We hope to address these exciting questions in the future. | 5,661.6 | 2024-01-15T00:00:00.000 | [
"Physics"
] |
Bridging the climate mitigation gap with economy‐wide material productivity
Projections of UK greenhouse gas emissions estimate a shortfall in existing and planned climate policies meeting UK climate targets: the UK's mitigation gap. Material and product demand is driving industrial greenhouse gas emissions at a rate greater than carbon intensity improvements in the economy. Evidence shows that products can be produced with fewer carbon intensive inputs and demand for new products can be reduced. The economy‐wide contribution of material productivity and lifestyle changes to bridging the UK's mitigation gap is understudied. We integrate an input‐output framework with econometric analysis and case study evidence to analyse the potential of material productivity to help the UK bridge its anticipated emissions deficits, and the additional effort required to achieve transformative change aligned with 2 and 1.5°C temperature targets. We estimate that the emissions savings from material productivity measures are comparable to those from the Government's planned climate policy package. These additional measures could reduce the UK's anticipated emissions deficit up to 73%. The results demonstrate that material productivity deserves greater consideration in climate policy.
accumulation of emissions in materials and products to meet society's needs does not enter decision making processes. For example, while only 9% of emissions produced in the EU are emitted from service and manufacturing sectors directly -and therefore would not be highlighted as a priority for mitigation in these models -they embody 22% and 26% of emissions respectively to satisfy EU consumption as they are significant procurers of GHG-intensive materials and products along their respective supply chains (Scott et al., 2018). Taking a supply chain or embodied perspective therefore identifies further intervention opportunities in the way products are designed, sold, used and disposed of (Scott et al., 2018), which we define in this paper as material productivity measures.
Economic-based analyses tend to reduce behaviour changes to a maximising utility function which assumes agents 'carry out an exhaustive ranking of their preferences over all possible products in all existing markets' (Mercure, Pollitt, Bassi, Viñuales, & Edwards, 2016), without considering other intrinsic attitudes and habits or external societal and institutional factors (Jackson, 2005). Consumers (both intermediate and final) are considered as rational actors (Levine, Chan, & Satterfield, 2015;Spence & Pidgeon, 2009;Wong-Parodi, Krishnamurti, Davis, Schwartz, & Fischhoff, 2016). For example, firms are assumed to 'minimise their costs by choosing an optimal combination of intermediate inputs' (Duarte et al., 2016), which presumes companies always make fully rational choices holding all the relevant information in front of them to exhaust all options to maximise the efficiency/ reduce the impacts of their supply chains. Households are assumed to behave as a single homogenous agent, acting to maximise their utility (i.e. buying as much stuff as they can at the least cost), holding full information on all goods offered on the market to make rational choices, yet we know real world decision making deviates from the principles of rational choice (Mccollum et al., 2017;Mercure et al., 2016;Scrieciu, 2007). Such simplified assumptions have resulted in policies falling short of achieving their technical potential because of unrealistic assumptions around human behaviour (Whitmarsh et al., 2011;Wong-Parodi et al., 2016). Therefore, current dominant models are unlikely to adequately model the mitigation potential of material productivity measures as they largely fail to understand how emissions become embodied in material and products and they have unrealistic representations of consumer behaviour.
To overcome the methodological drawbacks of climate assessments described here some scholars apply qualitative or case study evidence to simulate consumer behaviour based on quantitative consumption-based modelling techniques such as footprinting (Barrett & Scott, 2012;Wood et al., 2018b). A GHG footprint is: "the direct and indirect greenhouse gas emissions … required to satisfy a given consumption. This can be a product, an activity or a set of products or activities" (Minx et al., 2009).
GHG footprinting is a complementary indicator to monitor and assess progress towards meeting climate targets and can appraise the mitigation potential of demand-side measures (Barrett et al., 2013;. Studies using footprinting or life-cycle based techniques (e.g. material flow, life-cycle and input-output analysis) have demonstrated that improved material productivity is worth pursuing, if not a necessary precondition, for achieving global climate mitigation goals (Barrett & Scott, 2012;Cooper et al., 2017;Girod, Van Vuuren, & Hertwich, 2014;Liu, Bangs, & Muller, 2013;Milford, Pauliuk, Allwood, & Müller, 2013;Pauliuk and Müller, 2014). In spite of this, interventions to improve material productiv- ity have yet to be seriously considered as an effective policy response . Options being discussed in the literature include infrastructures being utilised more effectively through shared assets (increased asset utilisation); using products for longer (product longevity); designing products with fewer material inputs (product design); and reducing absolute levels of consumption (reducing consumption) and waste (waste reduction). These require both redesign in industry and lifestyle changes.
Input-output (IO) models, which calculate GHG footprints at the macro scale, offer a framework to assess the economy-wide effects of changes in technologies and consumption patterns (Wood et al., 2018b). IO models trace emissions along supply chains from production to the final demand for products by following the monetary purchases and sales of sectors (Skelton, Guan, Peters, & Crawford-Brown, 2011;Wiedmann, 2009). This process monitors the pull and push effects of sectors on an economy (Wu & Zhang, 2005). Pull effects describe the consumption of a large amount of intermediate sector inputs and push effects describe the demands, from both intermediate sectors and final consumers, for a sector's output.
Using IO analysis each unit of emissions from production activity is uniquely attributed to a region of final demand, through complex supply chains and avoiding double counting (Skelton et al., 2011;Skelton, 2013). The analysis is able to reflect the actual emissions intensity of industries in different countries and allocate production activity in one country by intersectoral and trade monetary transactions to final demand in another country (Giljum, Bruckner, & Martinez, 2015). Whilst it would be more accurate to trace physical flows in the transformation of goods to services, data limitations have meant that IO analysis uses aggregate monetary transactions, which are collected as part of national accounting systems (Giljum et al., 2015), as a proxy of material and product flows between economic sectors and regions (Skelton, 2013).
Some researchers have developed dynamic IO models to simulate material use and emissions to 2050 by endogenously mapping economic structures as a response to price changes (Distelkamp & Meyer, 2019;Giljum, Behrens, Hinterberger, Lutz, & Meyer, 2008;Lutz & Meyer, 2009).
For example, a reduction in material inputs to a selected sector changes the price and profits, which are redistributed through the economy, or a tax is applied to material extraction industries which increases their costs and reduces demand for them. In this paper we focus on non-price simulations, which Dietz, Gardner, Gilligan, Stern, and Vandenbergh (2009) suggests introduces a behavioural realism that is lacking in technology and economic assessments. Wood et al. (2018b) summarise the three main options for exogenously modelling consumption-based interventions using an IO framework: (1) changing consumption patterns including a reduction in overall consumption; (2) modifying the inputs required for production in the industry (e.g., modifying the recipes of production); and (3) reducing direct emissions through, for example, pollution control or improved efficiency. They use case study evidence to identify the potential reduction of annual flows to and from EU clothing and food sectors. Lekve Bjelle, Steen-Olsen, and Wood (2018) assess to what extent Norwegian households can lower their GHG footprint through implementing a set of behavioural actions evidenced in the literature. Emissions and economic impacts of current behaviour are compared to a better performing alternative, then scaled up to yearly savings per household. They include an analysis of the rebound effect whereby any monetary savings to households from demand reduction is re-spent and therefore diminishes the emissions saving without this re-spend. Cooper et al. (2017) analyse industrial energy demand reductions achieved across product supply chains through a range of circular economy opportunities applied to a 2007 baseline. They investigate different strategies which either reduce the need for high impact inputs to produce products ('putting less in') or reduce the need for products ('getting more out'). All these studies analyse one point in time, and do not account for the evolving impacts of economic strategies and decarbonisation from climate policies.
In contrast, Barrett and Scott (2012) incorporate projections of key IO variables (demand, production recipes and carbon efficiencies) into their analysis of the contribution of resource efficiency measures to meeting UK 2050 climate targets. They model changes to the material demands of both production systems and consumption patterns using IO, while reducing the emissions intensity of the electricity sector. De Koning et al. (2018) extrapolate IO variables to analyse future material demands based on technical and socio-economic considerations including economic growth, material demands and efficiency improvements. Similarly, Wilting, Faber, and Idenburg (2008) projected the production structure of the economy by using trend analysis combined with expert opinion to identify how the production inputs of sectors might change in the future. However, none of these studies have included an analysis of the full suite of climate mitigation policies alongside resource efficiency options. This has prevented an effective policy comparison.
RESEARCH AIMS
We use an IO framework to assess the economy-wide mitigation potential of material productivity and lifestyle measures for the UK towards meeting its climate goals. The UK sets five yearly carbon budgets to ensure it is on track to meet an 80% reduction in GHG emissions produced within its territory by 2050 (Commitee on Climate Change, 2008), and has a suite of climate policies focusing on energy used directly in the power sector, industry, buildings and transport to meet these. At the time of analysis five carbon budgets had been set from 2008 to 2032. We measure the (1) potential to help achieve the UK's fourth and fifth carbon budgets (2022-2032), which are anticipated to have a shortfall given existing and planned climate policies (Department for Business Energy and Industrial Strategy, 2018), and (2) how much of the UK's carbon budget to achieve transformative change aligned with alternative 2 • C and 1.5 • C temperature-related targets will be exhausted by 2032. The novelty of our approach lies in measuring the time dependency of emissions savings. We incorporate emission reductions from existing and planned climate policies as they are deployed, enabling us to identify real additional emissions savings and the contribution to meeting longer term cumulative climate goals. We compare the savings of the material productivity strategies to the suite of energy-focused UK climate policies.
METHOD
We develop a time series of emissions flows associated with the production and consumption of material and product demand in the UK using an IO framework. We incorporate changes in the carbon intensity of UK production sectors; disposable household incomes; government and capital spend; and export demand using UK Government economic and emissions forecasts that are constructed using macro-econometric models combined with policy interventions. In addition to this we then model changes in the way products are designed (production recipe) and the consumption patterns of final consumers using case study evidence. We vary the ambition of material productivity strategies and the level of adoption to explore uncertainty in potential emissions savings.
Analysis boundaries
Temporal -we analyse the potential for material productivity strategies to deliver UK GHG emission reductions in addition to existing and planned climate policies, concentrating on the 4th (2023-2027) and 5th (2028-2032) carbon budget periods, which need further policies to bridge the anticipated mitigation gap. We include emissions from 2013, the start of the second carbon budget which is yet to be concluded, projected to 2032.
At the time of analysis only five carbon budgets had been set in legislation. We also calculate the remaining carbon budget the UK has to emit from 2032 to 2050 in line with international climate objectives, and how soon these could be exhausted without further policy intervention. Our analysis takes into account changes in carbon intensities according to planned timings of technological and policy implementation; however, we assume the material productivity measures are linearly implemented from 2013 to their maximum in 2032 as there is limited information on how quickly these strategies can be deployed. Hence, we have explored different rates of ambition in material reductions and adoption informed by our case studies.
The case studies demonstrate the feasibility of material productivity measures within a specific (often very local) context and are not only hindered by technology availability, but also institutional and societal barriers including a lack of policy incentives and public acceptance.
Geographic -to measure the contribution of material productivity strategies to meeting UK carbon budgets we are only interested in emissions savings within the UK. This is not to say we don't think the UK is responsible for reducing emissions in other countries to satisfy its consumption demands -the UK could take on greater responsibility to reflect its global economic status (Meinshausen et al., 2015;Steininger et al., 2014). However, we want to reflect the current political reality that UK carbon budgets will be met by reducing domestic emissions. While by definition GHG footprints include emissions embodied in imports and exclude those embodied in exports destined for final consumption elsewhere, we only include emissions produced in the UK embodied in products consumed domestically, and emissions embodied in products for export. In other words, we model changes to the way UK products are made and the demand for UK products. In our model we set imported emissions to zero and therefore only emissions within the scope of domestic targets are considered, albeit emissions would be reduced along product supply chains outside the UK. These further emission reductions in supply chains outside the UK would substantially increase the calculated domestic emission reductions.
Products/ sectors -in terms of material productivity strategies we focus on the design and demand for materials and goods with a high embodied GHG content: textiles, food and drink, vehicles, construction, electronics and packaging. According to our analysis, these sectors contribute 14% of the industrial emissions released within the UK territory in 2013, yet embodied nearly a quarter of UK emissions (23%) (see the supporting information available on the Journal's website, sheet A). Underlying material productivity strategies are improvements in the carbon intensity of production from existing and planned UK climate policies, which includes residential demand for heating and travel. We do not target service sectors directly, however, we do model the use of products in the provision of services (i.e. along their supply chains).
Emissions embodied in the final consumption of UK products
Instead of allocating emissions to the sector in which they are physically produced ('emissions by source'), we use the UK multiregional input-output model (MRIO) to allocate UK emissions for the year 2013 to the final product they become embodied in. These final products are consumed both in the UK and abroad by households and governments or represent large capital spend.
Goods and services are classified by 106 sectors (also referred to as product groups) according to the UK Standard Industrial Classification system (Office for National Statistics, 2009) and we aggregate the global economy into a two region model of the UK and the Rest of the World (RoW) reflecting how the UK trades in goods and services. By retaining a two-region structure we are able to capture emissions that were exported and then reimported to the UK across international supply chains. Embodied emissions are calculated using the standard Leontief demand-pull model. GHGs emitted directly by UK sectors are reallocated to final consumers (including exports) by following products through multiple trade and transformation steps using Equation (1): Where q is a vector of embodied emissions by sector, e the GHG intensity of UK production sectors (RoW intensities are set to zero), I represents an identity matrix, A is the technical coefficients matrix andŶ is a diagonalised vector of the total household, government and capital final demand in the UK and RoW, including UK goods exported to RoW. The technical coefficients matrix (A) accounts for the proportion of intermediate inputs, both domestic and foreign, that a sector within a country requires to produce one unit of output, also known as a production recipe. The term (I − A) −1 is known as the Leontief inverse (L), which calculates the extent to which output rises in each sector derived from a unit increase in final demand.
Projections
We project the (1) carbon intensity of UK production; (2) level of UK household demand; (3) government and capital expenditure; and (4) demand for UK exports, using macroeconometric modelling; and (5) changing production recipes and demand patterns using case study evidence, similar to Wilting et al. (2008) and (Wood et al., 2018b). See Table 1 for a summary of data sources.
UK energy, economy and emissions projections
Since the late 1970s, the UK Government has published projections of energy demand and supply, and in the 1990s these were extended to include projected carbon dioxide (CO 2 ) and other GHG emissions (Department for Business, Energy and Industrial Strategy, 2017). The Department for Business, Energy & Industrial Strategy (BEIS) is responsible for publishing these projections annually. Within their model, demand for energy is projected using a series of econometric equations that relate energy demand to its key drivers such as economic growth, international fossil fuel prices, carbon prices, population, disposable income and the number of households. Electricity producers meet demand through aiming to maximise their returns on investment. Emissions factors convert energy demand by energy source into emissions. Demand is adjusted to take account of the policy impacts where energy demand is reduced. The energy and emissions projections estimate cumulative emissions savings from an appraisal of climate policies and are used to monitor progress towards achieving carbon budgets. Policies are categorised as expired, implemented, adopted or planned. To calculate the carbon intensity of UK production sectors (e), the UK's sectoral emissions (f) are divided by economic output (x) of each sector (i) (Equation (2)): For future carbon intensity projections e t+1 i ,we use official production emissions projections by sector (i) at five year time intervals (t + 1). A change in output (x) is determined by a change in final demand, 2 using Equation (3): BEIS climate policy projections are underpinned by central demographic, economic and price estimates (detailed in Department for Business Energy and Industrial Strategy (2018)). The econometric trends of household disposable income (y j ) are used to project levels of household spending to 2032 in the IO framework. At this stage the pattern of spend is held constant. Government and capital spend (y j ) follows estimated economic growth in the UK whereas demand for UK exports follows estimates of world economic growth. Increased spending (y t+1 ) is modelled by the Hadamard product of the vector of the original final expenditure (y t ) and a vector of rate of growth for the new year (g t+1 ) 3 (Equation (4)): The Leontief equation is used to reallocate the emissions projections by source sector to the final products they become embodied in for the two climate policy scenarios, using Equation (5): 1 The IPCC requires only the reporting of CO 2 -related GHGs 2 IO analysis is a demand driven model and the size of the economy adjusts to meet an increase in demand 3 g is a matrix representing growth across four final demand column's spending on 106 sectors. The growth rate within each consumer's final demand for products is the same, however the growth rate varies by final consumer TA B L E 2 Summary of material productivity strategies
SECTOR PUTTING LESS IN (PRODUCTION) GETTING MORE OUT (CONSUMPTION)
CLOTHING & TEXTILES Reduce supply chain waste through efficiency improvements in fibre and yarn production, dyeing and finishing
e therefore we include savings in the overall emissions, but do not show them in the sector level analysis.
Where q t+1 climate is the new embodied emissions by product vector calculated at five-year intervals from 2013 up to and including 2032 from the revised improvements in carbon intensities and growth in final demand. Annual emissions between the five-year time periods are linearly interpolated. Changes to production recipes and the pattern of final demand are described in the next sub section and determined by the material productivity case studies.
Material productivity scenarios
We gathered evidence from 43 case studies across the six manufactured products to indicate how they could be redesigned using less carbon intensive inputs ('putting less in'), or the demand for new products reduced so that we get more use out of them ('getting more out') (Table 2) The level of change of the transactions flow is determined by two variables: the reduction level of material/ product use (m) (an indication of the material ambition of a strategy) and the rate of adoption by the consumer (c). A low, medium and high scenario was modelled for each case study to reflect an uncertainty range in the ambition and adoption of a given strategy. The high estimate reflects a maximum technical potential in the case of redesigning products, or demand reduction levels higher than seen in existing case studies with 100% adoption in most cases. The lower level estimate reflects case studies of proven potential with relatively lower levels of adoption in the region of 33% in most cases. The mid-estimate reflects best case estimates with 66% adoption rate. This is similar to the technical penetration and implementation variables modelled in Wood et al. (2018b), however we model a scale on which we would expect mitigation results to sit given uncertainties in ambition and deployment, and to indicate potential beyond that which is found in current case studies. A low carbon transition will require radical reductions in the way we produce and consume materials and products and we could have been even more ambitious in some of our case studies.
For each input (row i) to an intermediate production recipe (column j) vector a ij of the A matrix affected by an intervention is defined by
Equation (6): where a t+1 ij is the new production recipe at time (t + 1)(2032); m s ij is the unique level of material/ product use of a given strategy, s; and c s ij is the adoption rate of policies of a particular strategy. Each element in a column of the A matrix represents the portion of the production recipe that each industry makes to the total product. We assume that when a product is made differently and requires less spend on a particular industry, this spend is effectively reallocated to value added to ensure that the row and column sums in the IO are maintained. Each strategy has a unique factor that is a combination of an industry and product interaction. m and c are on a scale of 0 to 1, with 0 representing no change and any number higher than this represents a reduction in the current material use and adoption rate. For example, an m value of 0.1 equates to 10% material reduction, for example 10% less steel inputs to manufacture cars. This follows for the adoption rate. Likewise, the same approach applies for each sector input (row i) to a final consumer (column j) for final demand (Equation (7)), for example an 0.2 m value equates to 20% reduction in material/ product demand, for example a 20% reduction in clothes purchased by final consumers: Embodied emissions are calculated individually for each material productivity case study, in addition to climate policies, using the standard IO equation (Equation (8)): and emissions savings, v, are calculated by subtracting the new embodied emissions results from the embodied emissions of the climate policy scenario (Equation (9)): The emissions saving is calculated for 2032 (t + 1) and results are linearly interpolated between 2013 and 2032. This assumes the material productivity strategies are implemented incrementally, reaching maximum implementation in 2032, whereas we were able to implement climate policies at five year intervals because the official government emissions projections had the temporal detail.
The cumulative emissions savings of 'putting less in' and 'getting more out' material productivity scenarios are calculated by implementing all strategies in one calculation for differing material/ product use and implementation rates using the standard IO calculation to avoid double counting (see the supporting information on the Web, sheet F). There are additional material productivity options that we have not been able to model, due to a lack of extrapolatable case study evidence. These include strategies to extend the lifetimes of buildings and packaging. We have not provided a comprehensive list, nor an upper bound of potential reductions from material productivity, but an estimate based on available case study evidence that can be applied within our modelling framework.
We chose not to model the rebound effect, where cost savings from reduced demand are re-spent on additional products (Arvesen, Bright, & Hertwich, 2011;Sorrell, 2010;Sorrell, 2015), as we would expect the pricing structures to change as a result of the implementation of the demand reduction strategies, adding an additional layer of uncertainty. By allocating money saved from reducing intermediate spending on inputs to value added allows the emissions intensities of the industries to remain the same and isolates the emissions effect of a change in production recipe without considering further rebounds.
Progress towards longer term international climate objectives
Carbon budgets will need to be set beyond 2032 in line with global climate agreements. We compare the cumulative emissions across our climate and material productivity emissions scenarios to 2032, with cumulative emissions budgets associated with three alternative temperature-related 2050 carbon targets: • 66% chance of 1.5 • C -Global emissions converge to an average global per capita emissions point in 2050 which does not exceed the total cumulative budget to keep average global temperature rise to less than 1.5 • C • 66% chance of 2 • C -Same as above but for 2 • C • UK 80% target -the existing UK 2050 climate target is equivalent to a 50% chance of exceeding 2 • C average global temperature rise, but is not reconciled with a 2 • C global cumulative budget This calculation tells us how much of the 2050 carbon budgets the UK will have emitted by 2032 according to the different policy implementation rates, and by assuming that 2032 emisson levels prevail we calculate the years till the 2050 budgets will be exhuasted.
Scenario summary
In summary, we model five scenarios (Table 3). Two relate to the implementation of existing and planned climate policies of the UK government, and three introduce material productivity strategies across different levels of material use (varying from low to high ambition) and adoption. The material productivity scenarios are intended to present a range of emissions savings related to uncertainty in the ambition and adoption of them.
RESULTS AND DISCUSSION
Most existing climate policies focus on the power sector. If the UK implements its existing and planned climate policies, emissions embodied in the power sector will reduce to 8% by 2032, yet the UK won't have met its legislated 4 th and 5 th carbon budgets. While energy used in manufacturing is decarbonising, additional measures that reduce demand for materials and products is needed.
We present the scenario results in five sections. These in turn show: (1) The economy-wide emissions savings over the four yet to be completed carbon budget periods (2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024)(2025)(2026)(2027)(2028)(2029)(2030)(2031)(2032). This indicates whether implementing material productivity strategies, in addition to existing and planned climate policies, can meet the UK's economy-wide carbon budgets; (2) A comparison of the cumulative emissions savings of economy-wide material productivity strategies across the four carbon budgets with existing and planned climate policies. This indicates whether the scale of reductions are comparable to climate policies; (3) The emissions savings from combined 'using less' and 'getting more' strategies in the 4 th and 5 th carbon budgets (2023-2032) compared to savings from planned (i.e. not savings from existing) climate policies. This indicates whether combined material productivity measures can save more carbon than planned climate policies; (4) Emissions savings by sector in 2032. These are presented both in absolute terms and relative to each sector's total emissions. This indicates which sectors have the highest absolute mitigation potential, and also highlights sectors that may have comparatively low absolute emissions but save a higher proportion of them through material productivity measures; (5) The percentage of carbon budgets associated with alternatively ambitious 2050 climate targets expended by 2032, and the years left till they are exhausted, assuming emissions remain at 2032 levels. This indicates the additional emissions reductions required in future depending on the ambition of longer term targets. Figure 1 compares the scale of potential emissions savings from material productivity strategies to those modelled by BEIS for specific climate policies over the second to fifth carbon budgets. We find that the aggregate emissions savings from material productivity strategies are comparable to those from existing climate policies and are therefore worth pursuing.
Comparison with climate policies
Maximum savings from the material productivity strategies we assessed across the 2 nd to 5 th carbon budgets (
Strategy emissions savings
We assessed strategies for reducing the inputs and demand for products in six sectors and compared this with emissions savings from planned climate policies (i.e. not the savings from existing climate policies) over the 4 th and 5 th carbon budget which are shown to have a deficit. This enables us to compare savings from new policies yet to be legislated. Collectively the projected savings from material productivity strategies are greater than those from planned climate policies (Figure 2). Savings from the individual strategies are given in the supporting information on the Web, sheet B, however, we focus on the cumulative savings from production ('putting less in') and consumption ('getting more out') and the 6 key sectors.
A range of material productivity strategies will be needed and we are primarily exploring the economy-wide potential of material productivity to bridge the UK's mitigation gap.
To bridge the mitigation gap there will need to be changes to both the design of products and their consumption. This is subject to consumer preferences, business practices and policies (Barrett, Cooper, Hammond, & Pidgeon, 2018). Technical obsolescence has been designed into products for decades as a means for businesses to cut production costs and increase sales (Sherif & Rice, 1986). The low cost of products is currently a major barrier to designing and using products for longer (Cox, Griffith, Giorgi, & King, 2013). While evidence suggests that consumers exhibit preferences towards durable goods (Gnanapragasam, Cole, Singh, & Cooper, 2018), systems of production with planned (Satyro, Sacomano, Contador, & Telles, 2018) and perceived (Wieser and Tröger, 2018) obsolescence needs to be overcome. 4 On the one hand designers can lack the expertise to create lighter or longer-lasting products (Bakker, Wang, Huisman, & Den Hollander, 2014) whereas on the publics side shifts away from ownership F I G U R E 2 Comparison of cumulative savings from material productivity scenarios and planned climate policies for the 4 th and 5 th carbon budgets Note: Cumulative emissions savings from production and consumption side material productivity strategies for the 4th and 5th carbon budgets are relative to the planned climate policies scenario and savings from planned climate policies are relative to the existing climate policies scenario. All savings are UK territorial emissions only.
F I G U R E 3
Range of territorial emissions savings by sector in 2032. a) shows absolute emissions savings and b) shows the percentage saving relative to each sector's overall embodied emissions of products to sharing schemes requires public acceptance of access-based costs, yet consumers share concerns about risks and responsibilities of entering contract-based agreements (Cherry & Pidgeon, 2018). Figure 3 shows the range of emissions savings embodied in our 6 key sector outputs in 2032. Emissions savings are a proportion of territorial emissions only, and not emissions embodied in imported supply chain components of these goods. In this chart we assume that decarbonisation happens in line with existing and planned climate policies. Hence, the carbon intensity of the power used to manufacture these goods is significantly reduced from today. The stacked bars represent the different deployment rates. Planned climate policies are expected to save an additional 31Mt in 2032 compared to today's policies. The material productivity strategies would further reduce these between 2 and 21 Mt CO 2 e (Figure 3a). This includes a 43%, 24% and 23% reduction in emissions embodied in vehicles, construction, and clothing and textiles respectively (Figure 3b).
Remaining carbon budget
To calculate cumulative budgets associated with the alternatively ambitious 2050 targets we assume a linear reduction in emissions from 2032 until 2050 to achieve each target. Table 5 shows the percentage of budget exhausted by 2032 for different mitigation targets and years left till exhausted assuming emissions remain at 2032 levels going forward. When compared with the UK's legally-binding 80% reduction target, adopting high deployment material productivity strategies, including savings from BEIS' climate policies, will exhaust 69% of the UK's cumulative budget by 2032, leaving 4,193 MtCO 2 e to emit from 2032 to 2050. If the UK emits the same level from 2032 onwards this budget will be used up in 10 and a half years, seven and a half years short of 2050. If the UK adopts the aspirations of the Paris Agreement to limit global temperature rise to 1.5 • C, 79-80% of the UK's 2050 budget by 2032 will have been exhausted. The budget will be used up in less than six and a half years if the UK continues to emit as it does in 2032, nearly 13 years short of 2050. Future carbon budgets will therefore need to restrict emissions greater than existing budgets.
Introducing material productivity measures in addition to climate policies only gives the UK extra months, not years, until these carbon budgets are exhausted. For example, if the UK adopts a 66% probability of remaining under 1.5 • C the budget will be exhausted in five years eight months with the current climate package in place. Implementing maximum material productivity strategies would only buy the UK an additional eight months. This shows the enormity of the challenge and the need to think about transforming material and product use considerably more than we have modelled here.
CONCLUSIONS
The rationale behind this modelling was to estimate the potential for material productivity strategies to bridge the UK's territorial mitigation gap and that associated with international climate objectives. An IO framework enables us to calculate savings at the economy-wide level, compared to more detailed bottom up studies looking at one particular product. Econometric analysis was used to project economic and emissions variables in the IO framework, allowing us to assess the interactions between material productivity measures alongside climate policies which set cumulative targets into the future. Case studies with ranges for ambition and adoption were used to simulate material productivity gains to introduce a behavioural realism, investigate uncertainty and overcome limiting behaviour assumptions related to rational choice theory.
We measured the (1) potential to help achieve the UK's fourth and fifth carbon budgets (2022-2032), which are anticipated to have a shortfall given existing and planned climate policies, and (2) how much of the UK's carbon budget to achieve transformative change aligned with alternative 2 • C and 1.5 • C temperature-related targets will be exhausted by 2032. Emissions savings from the case studies that we modelled were comparable to those from the UK government's existing and planned climate policy package and could reduce deficits in the 4th and 5th carbon budgets by up to 73% and 47% respectively. The stricter the climate goal in the future, the earlier we will exhaust a remaining carbon budget deemed fair for the UK. Without further changes to those we have modelled here, the UK budget for an 80% reduction target will be exhausted in as little as 10 years, which could reduce to 6 years under a 1.5 • C scenario. This analysis demonstrates that material productivity deserves greater consideration in climate policy. | 8,377.6 | 2018-12-05T00:00:00.000 | [
"Economics"
] |
RELEVANT ASPECTS TO PROMOTE TEACHER AWARENESS IN THE PEDAGOGICAL ORCHESTRATION OF LEARNING ACTIVITIES WHERE STUDENTS MOVE
The pedagogical monitoring of activities involving spatial movement of students represents a teaching challenge due to events taking place outside the visual reach of the teacher. We present a literature review in which observational aspects are relevant to promote teacher awareness in these activities, from a perspective of awareness within the field of CSCW Computer Supported Cooperative Work. We also identified instruments and situations associated with those aspects of awareness. Results are presented via thematic analysis, clarifying the relevance of aspects related to Motivation/Engagement, Location/Path, Execution of the Activity, Interaction/Cooperation, and Results/Feedback. This clarification may contribute towards the development of learning orchestration protocols and information systems which support teachers in the collection and analysis of awareness-supporting data, contributing to teacher awareness of what occurs in these activities and allowing greater freedom and assertiveness in the pedagogical orchestration.
. 3 those aspects and which instruments are used for that purpose. This aim contributes to solving the challenge teachers face when orchestrating such pedagogical activities (Dillenbourg, 2013;Pishtari, G. et al., 2019;Wild, 2010) , promoting teacher awareness (Schmidt, 2002) of the course of the activities, through digital technologies (Munoz-Cristobal et al., 2015;Prieto, 2012;Rojas et al., 2012;Roschelle et al., 2013) to support the management of learning activities where students move.
The ability to orchestrate learning activities where students move is crucial for their feasibility, because teachers need to know what students are doing in different spaces: where they are located, what activities have been completed, the time required for them, the difficulties, the activity status, and other critical information for pedagogical orchestration. This awareness of what is happening in the activity enables teachers to intervene. Teacher awareness of the progress of the learning activities is the main aspect to be improved in the relationship between awareness and learning (Munoz-Cristobal et al., 2015) and the monitoring of these activities is essential in the orchestration (Prieto, 2012;Prieto et al., 2011;Roschelle et al., 2013) .
When students move in learning activities, the orchestration difficulties increase, since visual contact between teachers and students is substantially reduced or even absent, depending on the geographical space and the distance between the actors involved in the activity. Digital technologies can help, but for that it is necessary to know which aspects to monitor, in order to develop models and systems that efficiently contribute to teacher awareness for pedagogical orchestration. This is one of the research problems in the field of awareness in cooperative environments (Schmidt, 2002) which is particularly relevant for the field of education.
[Running head] Teacher awareness in pedagogical orchestration of mobile learning . 4 Therefore, we carried out a systematic literature review combining the concept of awareness in the context of Computer Supported Cooperative Work (CSCW) with the field of education. This combination identified which aspects are relevant to promote teacher awareness of the ongoing process of learning activities where students move. Specifically, we proceeded to 1) collect, analyse, and categorize these aspects, 2) identify the situations (real time, post-activity or a combination of them) when the teacher becomes aware of the aspects, and 3) identify the instruments used by teachers for this.
Section 2 presents the theoretical framework, section 3 describes the systematic methodology review, and section 4 presents the results. These results are analyzed and discussed in Section 5. Section 6 presents the conclusions of this paper and suggestions for future research.
THEORETICAL FRAMEWORK
Awareness is the way in which cooperating actors, while acting individually, pay attention to the context, aligning and integrating their actions towards the joint work effort (Schmidt, 2002) . By keeping themselves aware of the actions of others, the actors adjust their own activities (Gutwin & Greenberg, 2002) without interrupting their line of action in an apparently effortless way (Dourish & Bly, 1992) . Besides this adaptation, when coordinating cooperative activities, and the adoption of technology by individuals or groups in a specific situation, awareness is one of the central dimensions of the CSCW theoretical framework (Bannon, 1993) . The strict understanding of awareness as practices through which cooperative activities are aligned and integrated in a seamless blended way, is specific to CSCW. This field studies systems, technologies, and prototypes (and their design) which aim is to expand the cooperation between [Running head] Teacher awareness in pedagogical orchestration of mobile learning . 5 the actors in their distributed environments, and recognizes the critical importance of awareness in the development of support systems for cooperation (Schmidt, et al., 2011) .
Under this perspective, awareness is not a distinct mental state that exists independently of the action, but rather related to a person's notion of something, the attention they pay to the context of joint effort: an integrated state of practice, which should be inquired as such (Schmidt, 2002) . Awareness as " [...] understanding of the activities of others, which provides a context for their own activity" (Dourish & Bellotti, 1992, p. 107) considers that individuals working together need to gain some level of knowledge about the sharing and progress of joint activities. The fluidity and naturalness of cooperation based on awareness of the progress of work between actors is something tacit, which enables monitoring the work of others without requiring them to provide deliberate feedback (Gutwin & Greenberg, 2002) .
Awareness is not the product of passively acquired information, but a highly active and qualified practice: competent professionals are able to align and integrate their activities because they are aware of the scenario (Heath et al., 2002;Robertson, 2002) . It is not paradoxical to be involved in a line of action and, at the same time, make sense of and pay attention to what goes on beyond it. To do something and to be aware of other relevant events are not two separate lines of action, but specific ways of pursuing the same objective (Schmidt, 2002) . When an actor perceives a colleague doing something in a shared environment, it monitors the work of that other person and exhibits aspects of its own work by non-intrusive interaction that is appropriate to the situation, the so-called "appropriate intrusion". This way, actors are not ignorant or uninterested expectators, but rather actively engaged in the activities and objectives to be achieved.
[Running head] Teacher awareness in pedagogical orchestration of mobile learning .
6
Awareness studies represent a challenge in the field of design and orchestration of learning activities, which can take advantage of computer systems which enable extrapolating direct observations towards new possibilities for monitoring emerging and sustaining of awareness, stimulating engagement (Gaver, 2002) , feedback (Mark, 2002) , and social interaction (Heath et al., 2002) . Regarding the attention actors pay to the social context where both action and interaction occur, informal meetings are useful for bonding, forming alliances, and sharing, generating interactions and shared culture, which are important for politics and management, and fundamental for education (Dourish & Bly, 1992) , where interaction, socialization and cooperation thriving learning.
The realization which it is not a miracle that actors understand each other's actions turns awareness a researchable problem (Schmidt, 2002) . To apply it to activities where students move, it is necessary to determine which aspects of these activities are relevant to the teacher's awareness, enabling them to understand and support cooperative work with students: monitoring for pedagogic orchestration; processes for productive coordination of interventions within learning activities at different levels (individual, group, class), contexts (classroom, home, laboratory, field trips, etc.) and types of media (with or without digital technologies, images, videos, etc.) (Dillenbourg et al., 2009) .
Under this perspective, monitoring constitutes a central aspect in the management of activities where students move (Lima et al., 2020) . This can be enhanced through digital technological devices to support data collection and processing (Dillenbourg, 2013;Schmidt, 2002) , and thus assisting the teacher in pedagogical orchestration. Through it, teachers and other [Running head] Teacher awareness in pedagogical orchestration of mobile learning . 7 actors design, manage, adapt, and evaluate learning activities, aiming to maximize learning within contextual constraints (Prieto, 2012) .
Orchestration helps teachers create, execute, monitor, and manage learning situations in educational environments (Prieto et al., 2011;Roschelle et al., 2013) . In activities where students move throughout large spaces, it is not uncommon for teachers to lose awareness of where students are and what they are doing. In addition to monitoring, it is important to keep students motivated and engaged. Motivation appears as an indication of willingness to participate in the proposed tasks, and engagement as a relationship between this willingness and active participation in the proposed activities, stimulated by extrinsic (rewards) or intrinsic (excitement, fun) aspects (Chou, 2015). Engagement involves aspects such as behavioral: (interest, involvement, concentration, attention, and participation in the activity), cognitive (motivation, immersion, mental effort, and concentration aiming at appropriating information), and emotional (enthusiasm, fun, satisfaction, and belonging). Engagement is an essential component for the success of educational processes (Fredriks et al., 2004).
Once students are motivated and engaged, teachers need to provide support and feedback during the activities. Regarding group behavior, feedback is essential for accommodating processes and encouraging learning and cooperation. Notifications are effective reminders of group plans during the activities, reinforcing positive actions and occasional help when in doubt (Mark, 2002) . Formative feedback contributes in pedagogical orchestration, guiding students towards appropriation and learning, focusing on promoting student's knowledge and learning (Shute, 2007) .
[Running head] Teacher awareness in pedagogical orchestration of mobile learning . 8 These monitoring needs in learning activities where students move require teachers to pay attention to diverse aspects of learning and contexts. Since many actions occur during such activities, teachers must realize which aspects are relevant for awareness, and which are not. The purpose of this research was to determine which are these aspects.
METHOD
We conducted a systematic literature review (Kitchenham & Charters, 2007) aiming to determine which aspects are relevant for teacher awareness in learning activities where students move, and hence prioritized for monitoring. We used Google Scholar with the search terms "outdoor activities management", "outdoor learning management", "mobile learning management", and "mobile activities management", validating the results with the inclusion and exclusion criteria in Table 1. The choice of Google Scholar, to the detriment of other aggregators of scientific production, was meant to obtain a wider range of results, by covering a greater diversity of sources. However, it also required the EC4 exclusion criterion (exclusion of non-peer reviewed articles), for the sake of data quality. After applying the search and inclusion/exclusion protocol, the resulting corpus of articles was submitted to a compliance check, verifying incorrect data source, referencing data, and merging duplicated articles. All papers were then read in their entirety, extracting: a) the aspects observed in learning activities where students move; b) the instruments used for monitoring those aspects; c) the occasions in which the teacher became aware of those aspects (T1: real-time, T2: post-activity, and T3: some combination of T1 and T2).
RESULTS
The result of applying the inclusion and exclusion criteria of Table 1 is presented below (Table 2), with the number of papers resulting from each stage, followed by the resulting size of the paper corpus. The final corpus is presented in Table 3 by decreasing order of year of publication. After a complete reading of the 32 papers, a total of 121 aspects were extracted. These are aspects in support of teacher awareness of learning activities where students move. They were grouped by similarity to eliminate overlaps, resulting in a total of 49 aspects (Table 4): Table 4 Aspects to observe in mobile activities and its absolute frequency [available in the requested attached file ] The resulting aspects were also classified according to the situations to which the teacher became aware of (Graphic 1). Those were: T1: real-time (awareness occurred during the activity) T2: post-activity (awareness occured after the activity ended) T3: mixed (awareness occured in some combination of T1 and T2).
Teacher awareness occasions for the resulting aspects [available in the requested attached file ]
The instruments used to monitor the aspects were also grouped by similarity, to eliminate overlap, into eight categories, shown in Table 5 and Graphic 2.
ANALYSIS AND DISCUSSION
The literature review resulted in 49 relevant aspects to be monitored to support the awareness of the teacher during the pedagogical orchestration of activities in movement (Table 6). Seeking to structure the analysis of these aspects and present a guideline of aspects to be observed in mobile learning activities, they were grouped by thematic analysis into five themes of aspects to be monitored (table 6): Table 6 Thematic grouping of aspects
[available in the requested attached file ]
To identify the prevalence of aspects by theme (graphic 3), two major groups are observed: higher prevalence of the themes Execution (T3) and Location and Path (T2), representing together more than half of the aspects (52%), and lower prevalence of the themes [Running head] Teacher awareness in pedagogical orchestration of mobile learning . 12
Motivation and Engagement (T1), Interaction and Cooperation (T4) and Feedback and Results
(T5), representing together 48% of the issues raised.
Prevalence of thematic aspects to be observed in mobile activities
[available in the requested attached file ] The T2 and T3 themes represent the main concern in the pedagogical orchestration (Prieto, 2012;Prieto et al., 2011;Roschelle et al., 2013) of mobile learning activities, highlighting being crucial that the teacher be aware of activity execution by the students and their location and path. To know where students are during the activity (T2), their trajectory and digital footprints left by them in their browsing promotes teacher awareness (Schmidt, 2002) , meaning more freedom and assertiveness in the teacher pedagogical orchestration. In the activity execution (T3) it is necessary to be aware if activities were or not executed, the duration of execution, the status of task execution and the activity (as a whole), which information students had access to or collected executing the activity, which technologies were appropriated, which difficulties were found and needs of assistance.
The themes T1, T4 and T5, with less prevalence, represent aspects less present in the literature review presented here, which may mean less relevant aspects, or else, areas that demand more research effort. In theme T1, motivation (Chou, 2015) expresses the student's willingness to carry out the activity, while engagement (Fredriks et al., 2004;Gaver, 2002;Piaget, 1979;Rand et al., 2009) promotes involvement and active agency in carrying out activities. The awareness of [Running head] Teacher awareness in pedagogical orchestration of mobile learning . 13 the teacher is promoted by aspects of interest, motivation, participation, concentration, fun, immersion, engagement and students' perspectives or preferences.
In the T4 theme, the interactive-cooperative aspects are present in the area of the CSCW (Dillenbourg, 2013;Gaver, 2002;Heath et al., 2002;Schmidt, 2002) as well as educational area, with the interaction (Rand et al., 2009;Warneken & Tomasello, 2007) and the cooperation (Rand et al., 2009;Rand & Nowak, 2013;Warneken & Tomasello, 2007) . The awareness of the teacher in these aspects is important because interaction and cooperation are closely related to the way the actors interact and cooperate to achieve the proposed objectives which in education correspond to learning.
Teachers awareness allows these interactive-cooperative processes to be carried out in a tacit and discreet way, without affecting the fluidity and naturalness of the activity's execution (Gutwin & Greenberg, 2002;Schmidt, 2002) . The teacher being aware of these aspects is important because it makes it possible to identify the intervention-mediation point in the pedagogical orchestration of the activity, and this awareness is promoted by observing aspects of teamwork, interaction, cooperation, information sharing, communication and connection.
The T5 theme presents feedback and results as a set of closely related aspects. Feedback appears as an important reinforcement of cooperative actions (Mark, 2002) , either by emphasizing positive results in students, or punctually assisting in doubts. It should not be taken in the prescriptive sense, but as a follow-up of the student in promoting learning (Shute, 2007) ).
In addition to the results themselves, aspects such as assessment, effectiveness, and quality of learning, and a sense of victory and accomplishment help promote the teacher's awareness in terms of results.
[Running head] Teacher awareness in pedagogical orchestration of mobile learning . 14 In relation to the temporal moment in which teachers become aware of these aspects (graphic 3), the data shows that more than half (56,3%) of the aspects were observed in real-time (T1), and it indicates that digital systems and technologies may help real-time mobile learning monitoring: this option covers the pedagogical concern in monitoring the mobile activity as a whole, and not only the result. Aspects related to appreciation of activities by students can be analyzed in the post-activity (T2), but the ideal is to be collected, processed, and informed to the teacher in real-time, since students may need feedback and teachers may not be aware of it, the learning opportunity could be compromised. The third possibility is the mixed temporal moment (T3), in which aspects are monitored in real-time and confirmed in post-activity.
Regarding the instruments used for the monitoring of relevant aspects for the teacher awareness in mobile learning (Table 4), it is possible to detect that data coming from digital systems are the majority (54%), a result which is closely related to temporality observations T1, or real-time (56%). The instruments used to observe aspects are specific systems, web dashboards or sheets, 3D avatars, and geolocation. Secondly, manual observation and mapping emerge, with prevalence of 15%. Observation is an important element of monitoring, but it becomes less efficient in mobile activities in wide open spaces, and performed by many students, individually or in teams. Likely, manual mapping, which works in small groups and spaces, is not an option in wide spaces. Currently, with a lot of digital technological resources, which allow to automatize the mapping in real time, it would be expected the appropriation of them would promote the awareness of teachers regarding mobile activities.
The instruments: digital systems/web dasboard/web tools/avatar3D/geolocation (I1 inTable 4) appear as data collection instruments pre and post-activity, and, therefore, not very [Running head] Teacher awareness in pedagogical orchestration of mobile learning . 15 efficient to observe the activity process itself, as they will interrupt the students learning action sequence. Aside from this kind of instruments, as well videos, texts, and images produce results which only come for the teacher after the fact occurrence and in raw format, demanding time for the analysis. We agree that when this data is received in a consolidated form, the time for the analysis is reduced contributing to teachers to offer information and assistance for the students at the appropriate moment.
CONCLUSÕES
The orchestration of mobile learning activities represents a challenge for teachers and the research question sought to identify which aspects are to be observed to promote the awareness of the teacher in this type of activity. The issue emerges both from the observation of monitoring as a central problem in monitoring these activities (Prieto, 2012;Prieto et al., 2011;Roschelle et al., 2013, Lima et al., 2020 as well as more specific problems in monitoring these aspects to inform stakeholders of the educational process, notably teachers . The identification of these aspects advances knowledge and discussion in the field by enabling greater assertiveness and direction in the monitoring of activities and in their orchestration, whether using analog solutions, conventional digital technology or emerging digital technologies.
The awareness of the teacher regarding what is happening at the time of the activity may provide them with the possibility to provide help or feedback promptly, promoting learning and avoiding demotivation or drop outs, turning the students monitoring on the mobile learning activities essencial. After extracting and classifying the aspects into themes, as well as the [Running head] Teacher awareness in pedagogical orchestration of mobile learning . 16 temporality in which teacher awareness occurred and the instruments used in this observation, resulted in 5 themes and respective aspects to be observed in the learning activities in movement: • T1 -Motivation and engagement: motivation, interest, participation, concentration, immersion, fun, engagement, perspectives, preferences, students behavior; • T2 -Location and path: location, path, footprint, exploration, navigation, hotspot, triggering information • T3 -Activity execution: activity execution, execution time, task status, progress status, data/information collection, content access, lab experiences, help, difficult, technological appropriation, usability, artifacts use, app use, scientific exploration) • T4 -Interaction and Cooperation: interaction, cooperation, team report, sharing information, communication, connection; • T5: Results and Feedback: results score, assignment, assessment, peer assessment, learning effectiveness, learning quality, learning satisfaction, knowledge construction, perception, post-test information, feedback, victorious feeling, achievement).
We emphasize that the aspects that compose the themes are not definitive and may receive aspects not mentioned here, but they may emerge from future experiences of mobile learning activities. As long as the description of each theme is respected, new aspects may be framed in the presented themes, or, eventually, constitute new themes with aspects not addressed, increasing knowledge in the area. We emphasize that, as a result of this research and the literature review, these were the themes that emerged.
[Running head] Teacher awareness in pedagogical orchestration of mobile learning .
17
Concerning temporality, about half of the aspects promote awareness in real-time, such as on-site observation or supported by specific systems, mobile devices, dashboards, web tools, and virtual worlds. Observation and manual mapping persist, but represent greater workload and have limited effectiveness in monitoring moving activities. Aspects that promote post-activity teaching awareness are useful in the evaluation of the process by students and future improvements but less beneficial in real-time pedagogical awareness and orchestration, which would enable the pedagogical orchestration during the activity itself.
It is noteworthy that the analysis of these aspects in a practical mobile learning experience is already an ongoing study by the authors. Future research may focus on the design and execution of field activities to deepen the observation of the themes/aspects mentioned here and others. In terms of automation of this monitoring, technological solutions may focus on covering all aspects mentioned through a single system with a lot of resources, but subject to usability, integration, and scalability issues, or in simpler and more focused systems, though would cover fewer aspects. Later and/or complementary advances are configured in the exploration of solutions with simpler technology or emerging digital technologies, among them Internet of Things (IoT). IoT technologies, due to their characteristics of size, connection, and distributed processing in the cloud may cooperate with useful data for the promotion of teacher awareness in the orchestration of mobile learning activities.
ACKNOWLEDGEMENT
[Running head] Teacher awareness in pedagogical orchestration of mobile learning .
18
This research was carried out with the support of the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Financing Code 001. The authors are grateful for the support received.
Informed Consent
Not applicable | 5,308.8 | 2021-02-17T00:00:00.000 | [
"Education",
"Computer Science"
] |
A Phenomenological Investigation of the Presencing of Space
In this paper the author explores certain fulfilling personal experiences that he describes as the presencing of space, i.e. the way in which an individual’s spatial involvement may put him or her in contact with reality as a whole. These experiences are investigated from a phenomenological perspective, and the differences between them and other similar experiences, such as that of the sublime or topophilia, are highlighted. A neologism is introduced: topoaletheia (from the Greek topos , space understood as region, and aletheia , disclosure) to name a distinctive type of spatial experience. This concept may enrich the discussion about our involvement with space in our built environments.
Preface
When I think of buildings and cities, I find that most discussions in the literature leave out something that is important. On many occasions, my involvement with certain spatial realities has provided me with a sense of fulfillment or intellectual enjoyment of such a special quality that it is difficult to describe. It is on those spatial involvements that I want to focus.
I start with a few examples, my initial aim being to articulate the breadth and uniqueness of the kinds of experiences just described and their relation to phenomenological inquiry. Besides, these are not phenomena that only I have experienced. For years, I have shared comments on the sense of plenitude triggered by such spatial experiences to other people, including students and colleagues, and have realized that most of them have somehow lived similar moments. Also, one may find several similar instances described in the literature of geography, especially that of the 1970s and 80s, when there was widespread use of phenomenology in humanistic geography thanks to the works of Relph (1976), Tuan (1974Tuan ( , 1975Tuan ( , 1977, Seamon (1979Seamon ( , 1982Seamon ( , 1993, Seamon and Mugeraurer (1985), and Pickles (1985). 2 In this essay I will follow Spiegelberg's (1960) definition of the stages of phenomenological methodology. Thus, I will firstly investigate particular phenomena and their description by intuiting and analyzing them. In this stage the focus is on identifying how the phenomena appear in one's consciousness. Descriptions of personal experiences can be readily found in the literature 3 as starting points for inquiry into the essence of the object of investigation. Secondly, I will investigate their general essence through a form of eidetic intuiting. Thirdly, I will apprehend the essential relationship among essences of similar experiences, in order to understand the nuances between those experiences. Lastly, I will draw some conclusions about the understanding of those experiences.
I
Phenomenology begins in silence. Only he who has experienced genuine perplexity and frustration in the face of the phenomena when trying to find the proper description for them knows what phenomenological seeing really means. (Spiegelberg, 1960) Describing the kind of fulfilling experiences of space that I aim to analyze has never been an easy task. I have certainly had such experiences since I was a very young boy. And yet, all my life I have struggled to put them into words; words that somehow always managed to escape the point that, ultimately, seemed to hold the key. Probably words have always failed me because they can only establish correspondences within the limited realm of previously established concepts or words, and the experiences I aim to describe seem to refuse that measure. However, when I experienced them, I was certain of their distinctive character.
What is more, as happened to the fictional Lord Chandos 4 in Hofmannsthal's "Letter", the moment I attempt to give a few examples of these phenomena, they strike me as supremely foolish.
But without examples I cannot hope to understand and be understood. 2 For an in-depth explanation of the formulation of humanistic geography, and its commitment to the multi-dimensional nature of the lived-world, see Seamon and Lundberg (2015). Understanding the meaning of space and place for individuals has always been at the core of phenomenological inquiries carried out by humanistic geographers. 3 Descriptions of personal experiences can be found in the literature as starting points of inquiry into the essence object of investigation. See, for instance, Kim (2002) on the experience of nostalgia; Adnams (2002) on the experience of singing together in mass, and Wu (2002) on being a foreigner. Also, Relph (1976) and Tuan (1974) are identify essences from personal experiences. 4 The Letter of Lord Chandos is a fictional letter written by Hugo von Hofmannsthal in 1902 about a writer named Lord Philip Chandos who is experiencing a crisis of language. Having achieved great literary accomplishments in the past, Lord Chandos had recently lost his ability to express himself adequately after he experienced a series of epiphanies and moments of transcendence triggered by everyday life events. He made efforts to fight the deterioration of his writing skills, and thus he returned to the classics, but he found them of little help. Soon the epiphanies were lost in his inability to express them, and his life turned stagnant and grey, as his self-confidence and creativity were gone (see Hofmannsthal, 2008). I remember when, being a little boy, no more than five or six years old, my father took me to the soccer stadium for the first time to attend an important match. When we went through the tunnel and accessed the bleachers, I suddenly saw the great volume of space contained in the stadium, much bigger than any other space I had so far entered. And not only its size overwhelmed me: it was filled with strong lights (it was at night) and with the loud roar of nearly ninety thousand people singing the same tune as if they were one. All of a sudden, I felt I was no longer confined to my body, but had somehow expanded to the limits of the stadium and merged with it in a moving experience that I still remember to this day. I remember another time, when as teenagers and unable to find any vacancy in camping sites, my friends and I were forced to sleep through the night out on a remote wide, empty beach. That night, unable to fall asleep since I could not find comfort on the sand, I looked up to the sky, and saw an enormous number of stars. I never had seen so many. That beach was so remote and devoid of any human interference that the stars managed to literally light it up. Also, I witnessed the wonderful spectacle of shooting stars: dozens of them-hundreds I would say-filled the infinite sky that night. The sea breeze on my face and the distant roaring of the waves transported me to all the remote limits of that wide beach-and I felt at home, secure, confident, full of energy. Those thousands of stars that I could see were obviously very far away, and yet I felt no distance. Something inside me leapt to the stars, and that giant elevation, combined with the feeling of being in unison with the whole extension of the beach, made me weep in joy. I transcended my limited world and found myself floating in a space that, despite being almost infinite, was so near to meeven nearer than my friends, who were sleeping just a few meters away. I have never felt more fulfilled than that night.
Yi-Fu Tuan describes what seems to me a rather similar experience when he visited the Death Valley for the first time and was forced to sleep out in the open: When I woke up, the sun had risen high enough to throw its rays on the range of mountains across the valley and presented me with a scene, totally alien to my experience up to that time, of such unearthly beauty that I felt transported to a supernatural realm and yet, paradoxically, also at home, as though I had returned after a long absence. (Tuan, 1974, p. xi) One may also experience similar phenomena in more prosaic landscapes, even those that may seem most unlikely, such as in a large metropolis. To give a personal example, during my first visit to the United States, when I was 16, I went to Los Angeles, and the few days I spent there provided me with this elusive experience of plenitude. Although I never actually managed to see the ocean, the shoreline was continuously present in my intuition, somehow enclosing the vast volume of space I was witnessing. The view of the mountains on one side and my sense of the ocean on the other-with the massive city spread out in between-provided me with a sense of joy as I felt something inside me was growing. I became aware of that vast volume of space, so full of life, full of activity around me.
Architect Le Corbusier describes a somewhat similar experience that he had when he was first confronted with what it was a new phenomenon for him, fast traffic: On that first of October 1924, I was assisting in a titanic rebirth of a new phenomenon: traffic. Cars, cars, fast, fast! One is seized, filled with enthusiasm, with joy… they joy of power. The simple and naïve pleasure of being in the midst of power, of strength. One participates in it, one takes part in the society that is just dawning. One has confidence in this new society: it will find a magnificent expression of its power. One believes in it. (Le Corbusier, as quoted in Dear 2000) The same enthusiasm at being flooded with energy that Le Corbusier describes is what I felt in that trip to Los Angeles, and have subsequently felt when visiting bustling cities in exotic environments. Nevertheless, in his experience there is something missing that I believe might be important: either spatial points of reference or boundaries that may help us become aware of the space contained between them and ourselves.
Retrospectively, I can now say that it was not only the view of natural landmarks that triggered the experience; the skyscrapers of the downtown area also provided me with what Norberg-Schulz (1979) would call anchors of reference. They provided me with a sensation as if my mind was extended up to them, hence heightening my awareness of the whole volume of space. However, the expression "mind being extended" may be misleading. I was not drawn to those natural or man-made landmarks as to objects standing over against a subject; what I experienced was rather a sense of being at one with the volume of space in between those landmarks and myself. That large volume of space seemed to both separate me from and join me to those landmarks: this space was as if, in its size and fullness, it was enveloping subject and object in unison.
Apart from the mountains, the oceanfront and the downtown, many other places made me embrace that volume of space: symbolic anchors of reference, i.e. places that reminded me of Hollywood movies which I was so used to watching. Not only specific places, such as the Beverly Hills Town Hall did that, but also ubiquitous things such as those typical tall palm trees that seemed so exotic to me at that time. One object in particular triggered in me the elusive phenomenon I aim to analyze: the Hollywood sign. My first sight of what I had watched innumerable times on TV gave me goose bumps, and no matter where I went, its location remained very much alive in my intuition-together with that of the downtown, the mountains and the ocean. Being aware of all those places in between the entire volume of space structured my awareness of the whole space and gave me the sense of fulfillment that I mean to analyze here.
Interestingly, I have heard many accounts by my students and other people about similar experiences they had whenever they visited other landmarks for the first time, regardless of their function, beauty or age (e.g. the Eiffel Tower, the Giza Pyramids, the Taj Mahal, the stadium of their favourite sports team, Disneyland, etc.). It seems that one's relation to them-i.e. how present they had been in one's life up to that moment-is more important than their inherent characteristics.
I could provide several other examples of similar experiences but, as Spiegelberg (1960) points out, it is more important to find the appropriate genus or class for the phenomenon 5 and describe what it is about. The phenomenon we are dealing with is one of fulfillment, something deeply moving when it is experienced to its fullest-as in the case of the beach under the starssuggesting it may be a phenomenon that has a range of intensities. While having these experiences, one may feel a sense of plenitude, an elevated joy, and feel in harmony with the world, whose totality seems able to embrace one's singularity. 6 This may be what Hofmannsthal's Lord Chandos felt when his mind was absorbed by the sweet and foaming nourishments of his milk and his book, and felt that his life was expanding in all directions: "Everywhere I was in the centre of it, never suspecting mere appearance." It is difficult to express these and many other similar experiences, let alone define them. In my own struggle to name them during my youth, I initially adopted the term horizontality, unaware of the use Bollnow (1961Bollnow ( , 1971 and Jager (1971) had made of it. There seem to be a few commonalities between all the examples given above and the phenomenon of what we may call horizontality for now. In all examples, the spatial situation heightens awareness. This occurred as a consequence of the contrast these spatial situations presented with the everyday experiences of space. As Heidegger would put, once we are thrown out of the ordinary, we see more clearly, and indeed those dislocations give a sense of existing more fully. We seem to be expanding to embrace a massive volume of space, and yet we may also feel at home, joyful. This suggests that our experience of space is personal. In these moments, one's personal experience of space is clearly different from Cartesian space, and its impersonal equidistance between units of measure. 7 The other aspect of this point is that such a dislocation can be triggered by a spatial reality-be it a natural or man-made landmark, by a structure's interior or even the stars. Indeed it seems that almost anything could trigger them. The exalting and moving character of the experience does not come from the intrinsic properties of the situations per se, but rather from one's relation to one's spatial reality. Thus, the literal material value of some world famous landmarks (such as the case of the Hollywood sign I mentioned above) may not be much, whereas the value of the experience they trigger may be vast. In other words, the meaning those places may have for the individual discovering them seems to be more important than their intrinsic characteristics-or their socially accepted meaning, for that matter.
Moreover, there seems a relation between the phenomena in question here and the experience of light. It seems that the larger the volume of space one can embrace and the brighter the light that fills it up, the easier it may be to experience the exaltation I am discussing. However, light here is relative to expectation, not measurable in absolute terms. In fact, some of the most moving phenomena, such as the night on the beach I described above, can only occur in relative darkness, at night.
Perhaps there are actually two metaphorical directions in which we may become enveloped by space: horizontally and synchronically, in "space" itself, what I previously called horizontality; but also diachronically, through time, resulting in a sort of tension between the two. 6 This relates to the experience of "flow" popularized by Csikszentmihalyi (1992), which he describes as those times where action and awareness merge. This occurs when music moves us, or when reading books whose stories involve us so much that we get lost and time passes with us being aware of it (Adnams, 2002). The experiences I have described seem to fall into this class. 7 Bollnow's (1961) concept of lived-space seems related to the phenomena we are addressing, as they both refer to an intuitive experience of space that causes an emotional response. Nevertheless, one might say that Bollnow utilizes the term horizontality to refer to a quality of the category of space that may be experienced equally by anyone. Conversely, I was looking for a term to describe an individual feeling of expansion-expansion of one's awareness, a plenitude produced by being exposed to certain spatial contexts.
Consider the case of Kyoto's Golden and Silver pavilions (figures 1 and 2). The walls of the upper floors of the Golden Pavilion (Kinkaku-ji) are covered in gold leaf and shine very strikingly, making the reflection of the pavilion on the lake an astonishing view. The Silver Pavilion (Ginkaku-ji), instead, is not covered in silver foil. In fact, it is hidden behind very deep vegetation and is not easily visible. These two pavilions present both similarities and differences in their ability to disclose space and make us part of it. Both pavilions rest within carefully designed landscapes, crafted according to traditional Japanese gardening techniques. The Golden Pavilion is surrounded by a garden suitable for strolling, and the Silver Pavilion has a rock (Zen) garden next to it. These gardens are all significant in traditional Japanese architecture; in fact, the building is less important than the landscape that surrounds it or the views that the combination of building and park can provide.
The experience of these enclaves is likely to unfold in terms of horizontality, as one becomes part of the landscape with the help of the figures that are scattered across the garden: from the temple, which becomes just another element of the landscape, to the lake, to the carefully pruned tree to the last stone of the rock garden, which breaks the waves of gravel. If the awareness of the in-between is a necessary condition to experience the joyful feeling of being at one with space, being able to enter these Japanese gardens, with all their elements disposed in careful balance, truly helps one achieve that feeling. The beauty of these spaces makes us feel embraced by them, making us feel them as very liveable. This is in clear contrast to the self-assertive character of Western monumental architecture, with its towers, spires, naves and pinnacles always aiming high-protruding into the sky, rather than embracing us on the earth.
But it is the differences between these two pavilions in the manner they disclose space that should be pointed out. The view of the Golden Pavilion is striking, even breathtaking. Visitors standing on the other side of the pond may easily become aware of the volume of space comprised between the conspicuous walls of the pavilion and themselves, making it easy for them to feel in unison with the whole landscape. But after the first realization of that space, however strong it may be, there are limited opportunities to expand. The pavilion is closed to visitors, hence preventing any possible disclosure of the interior space. In short, the intensity of our experience of this pavilion, the space it discloses, may diminish with time.
The Silver Pavilion, in contrast, does not show itself so rapidly, and instead seems to be trying to conceal its being. It is nothing extraordinary at first in any sense: no striking color, no special material, and no outstanding size. Nothing. In fact, it even seems to be unfinished. 8 The building, however, roots us in the soil, from which it seems to have emerged as naturally as the vegetation that surrounds it and the moss that covers the pavilion's wooded grounds.
Having been denied the kind of quick disclosure one had in front of the Golden pavilion, the patient visitor eventually gets his reward, as the Silver pavilion discloses such important meanings in piecemeal such as the central role it occupied in the development of Japanese culture at the time of Yoshimasa (15th century). This is revealed not directly, but through interpretive signs one encounters on one's way to the pavilion. After building this pavilion, former shogun Yoshimasa spent his retirement in it, a time during which he greatly contributed to the development of what Keene (2003) dubs "the soul of Japan." He developed the art of cultivating Japanese gardens, as well as encouraged Noh Theater, as well as the arts of flower arrangement and ink painting. Most importantly, one small room of this pavilion witnessed the origin of the tea ceremony.
Learning about these important meanings of the Silver Pavilion makes one extend one's intuition by reaching out to the past (i.e. to the time those significant meanings came into existence), while being anchored in the present by means of the perception of the authenticity of the pavilion, which itself seems organically grown out of the soil together with the gardens. This can result in an extended presence-of space. It is not that the disclosing ability of this pavilion lasts longer (while one is learning about those historical meanings); or at least not only. One's intuition may be stretched out from a well-rooted present to a past that-by virtue of its meaning -is as conspicuous as the gold-leaf walls of the Golden Pavilion. This reinforces one's experience of identification with that space.
The more one finds out about the Silver Pavilion, the more one may identify with its space, especially in its symbolic side, by means of its duration. This occurs as the pavilion opens up rich meanings that have been bestowed upon it without disrupting the continuity of meanings through a reduction down to the original one, when the logos of its architecture named its being. The realization of this continuity, and the space that is disclosed in the process, is what we could call the diachronic dimensions. We used the adjective diachronic, as it implies a development through time.
In summary, we can distinguish between two kinds of expansion: horizontal, i.e. expansion through becoming aware of the space in between distant places and myself-space that both separates us from and joins us to those places; and diachronic, which means expansion by understanding the ethos of the place through the different layers of signification. 9 In other words, this diachronic expansion supposes becoming aware of the duration of the meaning of a place.
In conclusion: space may present itself in both directions, hence producing a certain tension within which there is room to manoeuvre. For instance, think of the great possibilities that a new theme park may strike within us, hence dislocating us from our everyday world and making space emerge. But a new theme park will not be able to disclose itself diachronically in this way. At least it will not be able to do this, for example, an old hut that may not be conspicuous at all. An old hut will make us think in a different way, by reference to the person who lived there. The world of that person will in turn be composed of a whole range of meanings that will seem to have arisen spontaneously out of the landscape, out of the soil. In contrast, a theme park is a space of simulacra, which borrows meanings from the lives of other people, from other worlds, to create a selfcontained imagined-place, and therefore its meanings are not rooted in its soil. In any case, it seems that it is in the hands of the architect or city planner to design, alter or respect spaces that may provide us with opportunities to disclose space in either directions or both, with the subsequent fulfillment it provides.
Another implication of these reflections is that, since some of the phenomena we are considering imply more than just a form of 'horizontal' expansion, the term horizontality may not do justice to those phenomena and might in fact be misleading. We therefore need to find a new name for these fulfilling phenomena triggered by space.
II
Some parts of our description of these fulfilling experiences of space may have reminded the reader of Yi-Fu Tuan's (1974) concept of topophilia. Indeed, Tuan also focused his geographical investigations on the question of how the environment provides the individual with joy and contentment, and came up with the term topophilia, which he defined as "the affective bond between people and place or setting" (1974, p. 4). As an example: one may experience topophilia when visiting one's hometown, and being surrounded by environmental elements that remind one of very fond childhood memories.
The joyful phenomena we are addressing and topophilia present themselves together in many situations-e.g. a young child going to Disneyland for the first time. Additionally, it is very reasonable to think that places that triggered an immense joyful experience will become very dear in one's memory. However, and despite that they may overlap in numerous occasions, the phenomena we aim to analyze and topophilia present sharp contrasts.
In the first place, and crucially, the role time plays in the experiences we are describing is very different from its role in topophilia. For instance: one may experience topophilia in a certain place during all of one's life-but this is one in which time might enhance that experience. In contrast, habituation seems to work against the fulfilling phenomena we are dealing with.
Habituation caused by a prolonged exposure to a certain spatial situation seems to diminish the joyful phenomena one experiences, and ultimately leads one to indifference towards the very locale that had triggered the phenomena in the first place. Let me give an example: a beautiful starry night sky like the one described above will always be beautiful, but were we to look at it continuously in an endless night spanning days and days, our initial response may wane and we would no longer feel ourselves to be expanding, and therefore we would no longer feel such exaltation.
The rationale is that if it is precisely the contrasting character of our involvement in certain spatial situations that triggers those joyful phenomena, once those spatial situations gradually become part of our everyday life, they may lose their revelatory character. This may occur along time (i.e. prolonged exposure) as well as in space, for in case there was no contrast between the stars and the darkness of the night sky (i.e. if we were witnessing a plenum of stars in the sky), the lack of contrast would prevent our spatial involvement with each star, thus rendering the joyful spatial phenomena perhaps less likely. Besides, it seems that there is room for different intensities, and thus not all spatial situations trigger pleasing phenomena to the same extent. Those that provide us with more intense joyful experiences seem to last longer; it seems intensity is correlated to the time they will continue to produce a contrast to everyday life. Hence, variations of one's involvement with space seem to be necessary if one is going to experience that sense of exaltation or joy.
It seems important to point out that habituation caused by repetition has a very different effects. Repetition is used for example, in rituals or festivals as a re-enactment of the meaning of the festival. It is repetition itself that reminds us of the importance (the intensity, or the meaning) of what is being celebrated (Prat-Ferrer, 2008). Hence, repetition strengthens a kind of projection, the thinking ahead of the event to come, which is part of the extended presencing of the event. This presencing envelopes the memory of the past event, the projection of the one to come, and the perception of the one being re-enacted, stretching one's involvement with the event in what Bergson (1913) would call flow (durée). Therefore, habituation to the repetition of certain events may indeed help provide those joyful phenomena (think of anniversaries), whereas habituation due to prolonged exposure may diminish their effect. 10 Furthermore, and to highlight other differences between topophilia and the phenomenon we are addressing, one could wonder whether topophilia is somehow related to beauty. We feel at home in those places with which we have affective bonds (i.e. topophilia), as well as we experience positive emotions when exposed to the beautiful. We were saying that we also feel at home being exposed to those spaces we were describing above. However, we mentioned that the meaning that a place has for us seemed to be more important than its intrinsic value in its ability to provide us with this sense of plenitude (e.g., remember the example of the Hollywood sign). Therefore, an explanation of the subtle differences between topophilia, beauty and the phenomenon we are analyzing may be helpful.
A discussion about a positive, affective bond between person and place, topophilia, is related to the traditional consideration of beauty. When we experience the beautiful, we feel drawn to the revelatory object. It attracts us, makes us care for it. In fact, Edmund Burke (1757) defines beauty as "that quality or those qualities in bodies by which they cause love, or some passion similar to it". Burke's understanding is in turn based on Plato's definition of beauty as the object of love (or eros). To define beauty as the object of eros is to understand it as the object of our deepest concern. Hence, the strong emotional connection between places and ourselves defined by topophilia means that those places are the objects of our concern. This points to a significant difference between the phenomenon we are exploring and topophilia: no relationship of concern, and certainly no affective bond, between an individual and a place is required to experience the joyful phenomenon we are analyzing. For instance, I had no relationship of concern whatsoever with Los Angeles. Rather, my experience of that phenomenon occurred with no apparent reason, 10 We may remember here Alice in Wonderland, where they celebrated un-birthdays so that they could make merry all days a year bar one, which seemed to them a better idea than the usual birthday celebrations, as these only happen once a year. However, one could easily argue that habituation would render all those un-birthday celebrations almost useless, as participants would find no event to celebrate and in the end those parties would become part of their daily lives' routine.
often triggered by places that were not of explicit interest. This phenomenon occurred as if it were a gift to me 11 -a kind of presentness.
One may argue that our joyful experience per se was the beginning of a relation of concern, and that one is probably no longer indifferent to that particular locale after that experience, since one's memory of that place becomes meaningful henceforth. This being so does not reduce the experiences of horizontality to those of topophilia, mainly due to a twofold reason: on the one hand, the joyful experience may begin at first sight, before any emotional bond has been established between the location and the observer. On the other hand, the longer the time that passes after such a bond is established, the less acute will be the effect of the phenomenon that triggered the experience in the first place. One becomes habituated to the phenomenon, and this necessarily diminishes the feeling of plenitude or expansion in the sense we are exploring.
The experience of topophilia is associated with relationships of care and concern, and thus it is bound to time. "Care and concern usually place us beyond the present; we are ahead of ourselves in hope or fearful anticipation, behind ourselves in anxious reconsideration of what has happened" (Harries, 1990, p. 7). Conversely, when one experiences what we were calling horizontality (i.e. the first dimension of the phenomenon we are exploring), time stands still while one is in the presence of something: one can be said to be "presencing" space. Due to its existence outside of lived time, it is very difficult to capture the significance of this presence from our finite perspective (i.e. within time). However, it seems that one could approach presence by indirection: through the spatial elements that surround one in those situations. They allow us to hold the oneness of reality in our intuition, for one realizes how all entities share the features that make us experience presence as space in the first place (i.e. our involvement within a volume contained by spatial realities). It is as if the spatial relationships of other entities among themselves also came to the fore, allowing us to participate in them. Hence we are able to intuit still further and further relationships of concern with farther entities that in turn may disclose space up to infinity.
We have mentioned the term presence and its derivatives presencing and presentness, but the term itself is in need of further discussion. In fact, understanding the meaning of presence is crucial to the description of our spatial experiences of plenitude. Presence is the most fundamental experience of reality, and yet it is so close to our being as humans that it is extremely difficult to consciously apprehend it in its pure state. It is not easy to define this term, and Harper (1991) describes it as follows: When I am moved by a painting or by music, by clouds passing in a clear night sky, by the soughing of pines in early spring, I feel the distance between art and nature dissolve to some degree, and I feel at ease. I then feel that there is no past and no future, and I am content.
[…] It is not monism or dualism; it is a unitary experience and an experience of totality in the midst of shattering differences. (pp. 6-7) There are a number of ways we can understand presence. It may be defined as the state of being present, meaning immediateness. Or it can also refer to a sense of transcendence, of something beyond to what is actually being presented to us. I hope to explain that these two understandings of the term are not at odds with each other.
Presence is characterized in the literature as implying immediateness. As Fried (1998) asserts, presence (or presentness) occurs as a kind of instantaneousness that one experiences. This refers to an instantaneousness of meaning beyond the burden of time that binds us in our everyday life. This experience may be associated with a sense of intellectual enjoyment. To cite an example from Hofmannsthal's Letter (2008, p. 76), the revelatory experience of immediateness when seeing everyday gives him a sense of an immense sympathy, a flowing over into these creatures, or a feeling that an aura of life and death, of dream and wakefulness, had flowed for a moment into them-but whence?
He suggests that the source of this immense sympathy is the presence of the Infinite, which gives him a shudder-"running from the roots of [his] hair to the marrow of [his] heels"-that may remind the reader of some of my experiences described above.
Another possible explanation for this "immense sympathy" can be found in Kant's approach to beauty. For Kant, the beautiful is associated with a disinterested satisfaction that is, first of all, disinterested because it disrupts our relation of concern to the object we consider beautiful. Concern, or interest, is linked to desire, to will, and therefore the real existence of the object (i.e. as regarded in the "natural attitude") that triggers this interest is not itself a matter of interest. In contrast, in the beautiful we lose ourselves to the presence; we enjoy the presence without further qualms. Secondly, beauty may lead to satisfaction because this is a feeling that arises on the achievement of a purpose, or at least the recognition of a purposiveness (finality). Presence as rose, to follow the example above, is completed in itself: "A rose is a rose is a rose", as Gertrude Stein said, which reminds one both of Heraclitus' assertion that the ground of existence belongs intimately together with Being, and also of the "is-ness" (Istigkeit) of Meister Eckhart. This finality is what produces satisfaction, following the Kantian approach to beauty. The Platonic sense of beauty as an object of love or eros, however, cannot lead to satisfaction since, as mentioned earlier, loving something is making it the object of our deepest concern.
When we are concerned with something, we are bound to time and, thus, presence will slip between our fingers, rendering us unable to experience the presentness of the object of our concern, hence leaving us dissatisfied. Therefore, having strong emotional interests in a place, like the affective bonds Tuan defined as the essence of topophilia, prevents us from experiencing presentness.
However, the question of time requires further explanation in relation to the phenomena we are exploring. Fried argues that presence saves us from the burden of everyday existence, which he identifies with the burden of time. For him, presence is out of time. It implies timelessness, and makes us realize of our own finitude through the interaction of our present attention and our projections. This makes presentness endless in its duration. This endless duration is in turn understood in contrast to the actual passage of time as one stands in front of the object that presents itself. "It is this continuous and entire presentness, amounting, as it were, to the perpetual creation of itself, that one experiences as a kind of instantaneousness" (Fried, as quoted in Harries, 1989, p. 27).
If presence is outside of time, there must however be a conflict with the phenomenon we are exploring, since time does seem to matter in the experience of the diachronic dimension of a phenomenon. This diachronic side implies an understanding of the meaning of a place, and it is related to the meaning that has been incarnated into the spatial entities that provoke the experience. This incarnation of meaning in matter is akin to what Walter Benjamin (1968) understands as aura: an incarnation of spirit in matter so complete that there is no distance between the two. This incarnation necessarily needs time to emerge, marking a difference between the phenomenon we are exploring and presentness-including the presentness of beauty: the diachronic dimension of this phenomenon is not tied up with presence as just defined.
III
We mentioned above that with the beautiful we feel at home, as we establish affective bonds that make us experience it as if it had been made for us. The experience of the beautiful thus "places" us, and it is difficult not to become attached to it. Once we fall into the temptation of becoming attached, familiarity ends up breeding indifference. Familiarity makes even the most beautiful object lose its conspicuous character; it renders the object unable to stand out of the ground that other objects and spatial involvements embody. In short, it makes us too closely bound to the object.
Having explored the relations between the concepts of beauty and presence and the phenomena we are analyzing, one could argue that it is necessary to tackle those exalting experiences of space from the view that is opposed to topophilia. This view must signify the opposite of being bound to objects, and thus it must deal with such an unrepressed search for freedom that one exposes oneself to the unknown and even to the threatening.
We have mentioned above that one may long for bigger volumes of space to be disclosed to oneself. The bigger the volume of space we could become at one with, the more fulfilling our experience might be, and the more intense our feeling of freedom might also be. "A spacious horizon is an image of liberty," as Addison (1712) states, an expression that Harries (2001) interprets as follows: What matters in this context is how the vast expanse observed […] becomes a figure of an even vaster interior space. Spatial extension becomes a metaphor for the boundless extension of the spirit, which leaves behind not only every here but also every now. (p.
155)
Indeed, in my own experience, the larger the volume of space I become aware of, the more empowered I felt. The presentness of this space was so immediate to me that it is as if my spirit was being stretched out to the limits of that space-i.e. to the spacious horizon Addison mentions. Rather than making me feel liberty, or rather free, in a strict sense, these large spaces made me feel confident, full of energy.
However, this search for freedom or empowerment can be frightening. Whenever one is searching for what we used to call horizontality, and puts oneself in situations in which one may perceive larger volumes of space, one runs the risk of losing sight of the limits of such a volume, in which case one will likely feel kenophobic. 12 One may have this fearful experience since one has no anchors of reference and, therefore, one is unable to become aware of any volume of space. For example, when out at sea, departing from the coastline, and heading farther and farther into the ocean, one comes to be in the midst of a vast extension of limitless water. Kenophobia is in fact the opposite of being placed-the being at home that comes with topophilia.
There is a tension between our longing for freedom and our desire to be placed; between our care for the beautiful, familiar places that surround us in everyday life and the lure but also horror that comes with exposure to the unknown. Therefore, in those joyful instances it seems one achieves a certain point of equilibrium in this tension between one's desire for freedom and the fear of reaching a state in which one might feel kenophobia.
If those joyful spatial experiences that we have described bring topophilia to mind, mentioning the threatening character of the unknown makes us think of the concept of the sublime. The Oxford Dictionary defines the sublime in nature and art as that "affecting the mind with a sense of overwhelming grandeur or irresistible power; calculated to inspire awe, deep reverence, or lofty emotion, by reason of its beauty, vastness or grandeur." So far, it seems that the phenomena we are exploring are not too far off from this description, although we already explained how beauty does not necessarily correlate to the nature of those phenomena-to their relative importance of size or grandeur (e.g. think of the Hollywood sign).
Let us now be more specific. For Burke, the sublime experience unleashes the strongest emotion in human beings, since it consists of a sort of terror and pain caused by one's exposure to the violent, uncontrollable might of Nature, which renders one weak and insignificant. He argues that the passion caused by the experience of the sublime in nature is "astonishment." Astonishment is that state of the soul, in which all its motions are suspended, with some degree of horror. In this case the mind is so entirely filled with its object, that it cannot entertain any other. (Burke, 1757) Burke argues that whereas the beautiful attracts our passion of love (i.e. one feels at home with or drawn to the other), in the experience of the sublime we feel homeless, defenceless. We can see this in "The Monk by the Sea," a painting by Caspar David Friedrich (1774-1840) that captures the defenceless feeling we may experience when our will is in collision with the might of Nature (see figure 3). Friedrich himself comments: "Close your eyes and your inner eye will see all the more clearly".
The main difference between the phenomena we are analyzing and the experience of the sublime, as described by Burke, does not seem difficult to grasp. For Burke, the sublime experience arouses awe and respect in the face of Nature's power and vastness. This renders one humbled; one feels frail and insignificant before the might of a world or reality that defies one's will-and yet it is this threatening aspect that is a key part of the aesthetic experience. On the contrary, the experiences we are exploring are fulfilling, for one feels in communion with the spatial situation one is experiencing. Far from horror, in these experiences we feel at one with our surroundings. We feel as if they are not separated from us, but joined with us in a continuous spatial realitysomething impossible to achieve in a scene like the one depicted in "The Monk by the Sea." It is in those situations that one feels confident, as we discussed above. the neologism "space phobia" (also called pseudoagoraphobia) seems to be gaining popularity, and it is also used to describe the fear of big, empty spaces (see Gelder, 1982).
Kant, however, regards the homelessness into which the sublime displaces us in a more positive way than Burke. In fact, one might say that for Kant such homelessness is even exhilarating. He looks at this homelessness as an unbounded freedom that encourages that which is most human: our reason. Kant provides us with a significant revision in one understanding of the sublime: it is our reason which becomes the object of the sublime, and not vast boundless spaces, huge mountains or fierce storms. To explain: what is properly sublime are ideas of reason, such as the ideas of absolute totality or absolute freedom. However huge the size of the sea or the mountain, they are greatly outsized by an absolute totality. However powerful the storm, it is puny compared to absolute freedom. 13 One might point out that, in realizing the totality of space by means of sublime spaces, Kant is dealing with the disclosure of space, which we put at the core of the phenomenon we are addressing. In a sense, he is also expanding himself to the extent of the space of the sublime, since he is apprehending within his reason the totality of space, which is necessarily larger than the sublime space.
However, there is an important difference between the effects sublime places have upon us and the fulfilling experiences of space we are discussing: sublime places appear boundless. One of the features Burke enumerates of sublime places is that they are "apparently infinite, because of the uniformity and succession of their elements." Likewise, Kant argues that whereas beauty is connected with the form of the object which has boundaries, the sublime is to be found in a formless object, represented by a boundlessness (Critique of Judgement, §23). Conversely, one may experience the fulfilling phenomena we described at the beginning of this essay when one perceives-or intuits-the limits of a volume of space.
A limit is "what first makes a being into a being as differentiated from a non-being. Limit and end are that wherewith a being begins to be" (Heidegger, 1959, p. 60). The limit is, therefore, that from which something begins its presencing: limits make it simultaneously possible and impossible for us to feel at one-while retaining our being-with the volume of space they delimit. Therefore, we must distinguish between the type of disclosures enabled by the sublime and those at the essence of the phenomenon that we are analyzing. Following Kant's approach to the subject, sublime places represent a disclosure of space-of the endless, unlimited or empty space 13 For Kant (1790) the experience of the sublime results in the realization of the ideas of totality and reason. There are, however, several things that are experienced before one gets to that point. These initial movements of the mind-like encountering an object that cannot be comprehended by reason or imagination-do seem more related to the spatial realities associated with the joyful phenomena we are exploring. that ancient Greeks named κενóν. In turn, one may experience that exalting experience that we are exploring when one is able to disclose the space contained within boundaries-i.e., space as τόπος, or region.
Dwelling spaces are always finite, bounded. Space in its original meaning of 'place cleared or freed for settlement' is cleared and free…within a boundary,' a boundary being 'not where something stops but…that from which something begins its presencing.' (Heidegger, 1971, p. 154) The experience of space as τόπος, or region, is to be positive and enjoyed; it allows us to dwell not only because we feel somewhat protected by virtue to its limits, but also because it is these limits that, following Heidegger, allows us to experience space as presence. In contrast, the vastness or infinity of experiencing space as κενóν may lead one to feel kenophobic-perhaps the sort of terror Burke described as coming with the experience of the sublime.
Let us look once more at the scene depicted in "The Monk by the Sea." The monk, one can speculate, cannot perceive the limits of the nature that he is contemplating. The vastness of the sea and heaven in front of him, enhanced by their obscurity, renders the monk free and homeless. The vast expanse observed, as was quoted above, "becomes a figure of an even vaster interior space. Spatial extension becomes a metaphor for the boundless extension of the spirit" (Harries, 2001, p. 155). But the monk is not at one with the space that surrounds him, since he cannot perceive or intuit its limits.
There is a further difference between the experience of the sublime and that of the phenomenon we aim to understand. The meanings one associates with a place can enhance or hinder this fulfilling phenomenon-e.g. the enhancing effect that the symbolic images Los Angeles had for me as a teenager. In contrast, the experience of the sublime at a given location may be more easily experienced by different individuals, as it is often the intrinsic characteristics of a place, its boundlessness, that make it sublime.
One may point out that there is a major similarity between the sublime experience and the features one may find out about the phenomenon we are studying: both suggest openness to transcendence 14 . For instance, in The Monk by the Sea, Friedrich is showing us a monk whose spiritual self can be interpreted to be expanding precisely due to the features of the landscape he is involved in: In the sublimity of nature human beings recognize their own sublimity, recognize that within that allows them to transcend all that is finite and thus puts them in touch with the infinite. (Harries, 2001, p. 150) 14 It is worth pointing out that we are considering the sublime here in its Romantic notion, which is the one explored in this essay as based on Addison (1712), Burke (1757), and Kant (1790), and as interpreted by Harries (2001), Weiskel (1976) and to some extent also in Newman (1948). Contemporary discussion on the sublime has taken another direction, regarding it more as an issue of immanence-see Lyotard's (1994) Lessons on the Analytic of the Sublime. Also see Freeman (1995) for a related feminist approach to the sublime, as well as Jameson (1991) for a postmodern approach.
Sublime places make us realize that our lives are not the measure of all things. Likewise, in the phenomena we are exploring, one feels at one with those entities with which one establishes spatial relationships.
Similarly, the experiences we are exploring have some resemblance to the concept of the numinous, as popularized by Otto (1923) to describe the non-rational, non-sensory experience that, according to him, underlies all religion. Both experiences produce an exhilaration of noticeable quality, and lead one to experience what seems to be a communion with a "wholly Other", as Otto put it. This experience of believing that one is coming into communion with the "wholly Other" gives one a sense of transcendence very close to the experience of the sacred, understood in the way Eliade (1957) describes it when he studied the features of sacred space. One might argue that Eliade's notion of the sacred is related to the diachronic dimension of the phenomena we are studying, as sacredness irrupts into and across landscapes by virtue of the collective narratives bestowed upon them over time. This implies an involvement with the ethos of a community, which facilitates the provision of the diachronic dimension of the phenomena. Along this line, in his wonderful book The Landscapes of the Sacred, Belden Lane (2000) describes sacred space as "storied space" that requires poetic insight and analysis. Most relevant to our discussion is Lane's description of how space is transformed from a static locale into an energy field that captivates the imaginary by means of unique, significant events (kairos) that break into the temporal process (chronos). This intersection of time and space in the experience of the sacred corresponds with the intuition of those who may have experienced the phenomena we are discussing. 15 The numinous, however, is an experience that is both terrifying and fascinating at the same time-one could possibly summarize it with the word "uncanny." This makes the experiences we are exploring different from those of the numinous. The phenomena we described can be associated with intellectual joy. One's awareness is heightened, and one finds oneself feeling more lucid, having a sense of existing more fully through the experience of a spatial location. This spatial experience, despite not being ordinary, is not uncanny-let alone terrifying-but one that gives one a sense of plenitude; it is a positive experience.
The concept of the numinous, however, does seem to have certain similarities with the experiences we are describing, especially due to its connotation of feeling wonder and awe. 16 Wonder and awe are feelings that one may experience when exposed to certain landscapes that, due to their out-of-the-ordinary features, make one feel at a loss; making one unable to express the new sensation one may be having, exposing a sense of inadequacy, even vulnerability (Hove, 1996). Hence, resorting to religious vocabulary is a common response, because our identity seems to be challenged; we may feel the need to feel rooted again by naming what we feel.
Awe is indeed a common initial response to the dislocation of our everyday spatial relations we were discussing above. Wonder, however, is a more reflective feeling, which occurs while one tries to comprehend a new situation (see Verhoeven, 1972). Awe motivates wonder, and wonder "suspends our habitual views of things, revealing them in a 'new light', and as a 15 Other interesting sources exploring the relation between the sacred and space are: Barrie (1996), Brown (2004), Jones (2000), Turner (1979) and White (1995). For more general studies on the phenomenology of the sacred and religion, see Flood (1999), Hick (1989), O'Hear (1984) and Smart (1973). 16 There are numerous phenomenological investigations on the phenomena of wonder and awe and on the evocative nature of certain landscapes that seem to bring with them. See, for instance, Bonner andFriedman (2011), Elkins (2001), Elkins et al. (1988), and Parsons (1969).
consequence, propels us into, and establishes anew, our relations with the world/other" (Hove, 1996, p. 437). Attentiveness to wonder, and to the many dimensions of experience that it reveals in our lives, can cultivate a sensitivity to the emergence of wonder in others. Thus, it has significant implications for the way in which we can be pedagogically oriented towards students 17 (Hove, 1996).
However, the phenomena we are exploring are shaped by a second response: the expansion of one's awareness to the extent of the landscape we are exposed to, and hence to the fulfilling experience of plenitude. The spatial relation one establishes with the surroundings in those situations induces in many a sense of awe, and wonder. However, the emphasis is on this third movement: fulfillment.
The upshot of this discussion is that, as Jaspers (1971) points out, one acquires knowledge of one's perceived transcendence by contemplating the evanescent ciphers of transcendence, which signify the limits of human consciousness. In the case of the experiences which we are exploring, these ciphers, as Jaspers explains, can be encountered in nature, in art, in religious symbolism. They are the spatial realities (places) that function as the limits of volumes of space. Without them, no perceived expansion is possible and, in fact, one may succumb to the kind of horror associated with the sublime experiences described by Burke.
Epilogue
We need to find a term to describe the fulfilling phenomenon that is the subject of this essay. The term "horizontality" is incomplete and even misleading; topophilia and the sublime are related to it, and are even overlapping at times. However, they also do not capture it. The presentness of space is also subtly different, for the experience in these cases is taken to be out of lived time. After investigating this phenomenon, it is my understanding that its essence is the disclosure of the space contained within a region. Thus, I would like to put forward the neologism topoaletheia: from the Greek τόπος, space as contained within limits, i.e. region or place; and ἀλήθεια, discloure, or truth. I speak, however, of a different kind of understanding of truth than as correspondence between a proposition and its object. Aletheia is composed of the privative prefix a-(un-or dis-) and the root lethe (hiddenness, concealment, closure). It thus refers to the truth that first appears when something is revealed, whatever proposition we may use afterwards to refer to it-in short, it is the truth of bringing things out of concealment (see Heidegger, 1959). To disclose something is to momentarily rescue it from its prior unavailability and to witness its presencing. Similarly to light, that presents itself by allowing us to see other objects, Being presents itself to us in making all things seem present. In topoaletheia, Being presents itself to us making the space contained within a region emerge.
The neologism topoaletheia presents two sides of the same coin: a) topo-aletheia as the truth of the space contained within a region-i.e., the presencing of a region as it emerges for the human being to experience it. And b) disclosure of Being through the space contained within a region-i.e. region as a means to know Being; as means for a human being to be exposed to the spatial availability of Being that can be disclosed.
I believe that introducing this neologism to name the distinctive type of spatial experience that is the subject of this essay may enrich the discussion about space. Perhaps bearing the concept of topoaletheia in mind may also prove useful when designing built environments, as it points at a kind of fulfilment experienced through our spatial involvement. This possible practical implication, however, should be developed in further research, as it goes beyond the scope of this essay. | 13,643 | 2016-06-28T00:00:00.000 | [
"Philosophy"
] |
Particle Track and Trace during Membrane Filtration by Direct Observation with a High Speed Camera.
A methodology was developed for direct observation and analysis of particle movements near a microfiltration membrane. A high speed camera (1196 frames per second) was mounted on a microscope to record a hollow fiber membrane in a filtration cell with a transparent wall. Filtrations were conducted at varying pressure and crossflow velocities using synthetic core–shell particles (diameter 1.6 μm) of no and high negative surface charge. MATLAB scripts were developed to track the particle positions and calculate velocities of particle movements across and towards the membrane surface. Data showed that the velocity of particles along the membrane increases with distance from the membrane surface which correlates well with a fluid velocity profile obtained from CFD modelling. Particle track and trace was used to calculate the particle count profiles towards the membrane and document a higher concentration of particles near the membrane surface than in the bulk. Calculation of particle velocity towards and away from the membrane showed a region within 3–80 μm from the membrane surface with particle velocities higher than expected from the velocity of water through the membrane, thus the permeation drag underpredicts the actual velocity of particles towards the membrane. Near the membrane, particle velocities shift direction and move away. This is not described in classical filtration theory, but it has been speculated that this is an effect of particle rotation or due to membrane vibration or change in flow pattern close to the membrane.
Introduction
Crossflow microfiltration is an established unit operation in several applications from water and wastewater treatment to food and pharmaceutical industry. However, membrane performance is limited by membrane fouling. For microfiltration, the drag of particles and colloids with the permeate flow results in membrane pore blockage and cake layer formation, reducing the permeability of the membrane. Hence, higher transmembrane pressure (TMP) and frequent cleaning (physical or chemical) is required to maintain flow through the membrane [1]. Therefore, the mechanisms of cake formation and behavior has been studied in several studies in lab and pilot scale to understand fouling mechanisms and through that alleviate fouling. From that, mathematical models have been developed based on extended DLVO theory describing particle-particle and particle-membrane interactions [2], theories for cake filtration [3], and cake formation has been described with particle transport theories. The latter has been described for crossflow microfiltration by Ripperger and Altmann [4], dividing the forces acting on a particle or colloid: • F G is the gravitational force of particles transported towards the membrane by permeation, • F D , a drag force of particles being dragged across the membrane by the crossflow, • F F , a frictional force of particles moving along the membrane acting against the crossflow, hence slowing down the particles as the move across the membrane, and • F L , lifting force, acting opposite to the permeate flow as pressure increase when the water velocity decrease near the surface.
The mentioned models have been described theoretical and compared to filtration data in terms of permeate flux, TMP and "post mortem" analysis of membranes after filtration, e.g., SEM [5,6]. However, the development of on-line fouling monitoring techniques have added extra dimensions into understanding the mechanisms of fouling layer structure and formation [7]. Ultrasonic and laser based methods have been developed for indirect measurements of fouling layer thickness during filtration [8][9][10], while a more recent method, fluid dynamic gauging, can also estimate the cohesive strength of fouling layers [11,12]. Direct observation (DO) techniques have been developed to directly monitor the formation of fouling with videos recording the membrane through a microscope and follow the evolution of thickness [13][14][15][16] and to observe single layer formation [17]. However, the existing method is limited to observe deposited particles and thus cannot observe how the particles approach to the membrane. With high speed cameras it will be possible to observe the particles as they approach the membrane also at high cross-flow velocities. This enables the observation of particle distribution (concentration polarization), particle speed with µm precision and particle acceleration. This can contribute to a new understanding of the forces acting on particles before the membrane fouls, and thereby be used to study how crossflow velocity, TMP, particle and membrane characteristics affect particle transport and risk of fouling formation.
In this study, a high crossflow microfiltration cell is designed and connected with a high speed camera and a microscope to study and understand the transport of particles along and towards the membrane prior to membrane fouling. A MATLAB script will be developed to track particles and calculate concentration and velocity profiles along the membrane. Filtrations will be carried out at varying TMP, crossflow velocity and particle surface charges to understand their influence on particle behavior near the membrane. For this, synthetic microparticles with a hard core and a water swollen shell, developed in a previous study, are selected [17]. The particles are selected as they are spherical and monomodal in size distribution (mean diameter of 1.6 µm) and variable surface charge, making them ideal for controlled studies of microfiltration foulants behavior.
Direct Observation Setup
A DO apparatus was developed. The DO apparatus consisted of a custom crossflow membrane module, a microscope (10× objective lens, Carl Zeiss, Oberkochen, Germany) and a Nikon 1 J5 camera. The feed solution was circulated from the feed container to the filtration module using an OSMO inspector system (CONVERGENCE, NL) which records pressure and flow data. A schematic drawing of the system with microscope and camera is presented in Figure 1.
The crossflow module is depicted in Figure 2 and was made of a custom CNC'ed aluminum block with a flow volume of 160 × 15 × 5 mm. In the top and bottom of the membrane module a 3 mm glass pane was fastened to allow the microscope to see through the assembly. A single 150 mm long piece of hollow membrane fiber (SFX 2860xp 30 nm with polyvinylidene fluoride active layer) was placed lengthwise in the module. To ensure the best observation conditions, the membrane was pulled taught to minimize movement. The membrane was sealed on one end allowing permeate withdrawal from one side. The videos were taken using a 10× objective lens at a resolution of 400 × 144 px at 1200 fps and the scale of the video was calibrated using a micro ruler.
Filtrations
Monodisperse core-shell particles with a polystyrene core and with a hairy shell of polyacrylic acid (negatively charged) or hydroxypropyl cellulose (neutral) was synthesized with the method described in Lorenzen et al. [17]. The particles were used as model foulants and had an average
Filtrations
Monodisperse core-shell particles with a polystyrene core and with a hairy shell of polyacrylic acid (negatively charged) or hydroxypropyl cellulose (neutral) was synthesized with the method described in Lorenzen et al. [17]. The particles were used as model foulants and had an average
Filtrations
Monodisperse core-shell particles with a polystyrene core and with a hairy shell of polyacrylic acid (negatively charged) or hydroxypropyl cellulose (neutral) was synthesized with the method described in Lorenzen et al. [17]. The particles were used as model foulants and had an average diameter of 1.6 µm, a density of 1050 kg/m 3 and a zeta potential of −1.31 mV (low/no charge) and −40.47 mV (high negative charge). The solution was made daily by adding 0.03 g particles to 5 L of tap water. The solution was stirred and then circulated 15 min in the filtration system by pumping prior to the filtration analysis.
Filtrations were carried out on suspensions of both low charge and high charge particles. Each suspension type was filtered at room temperature (21 • C), 1 bar and 2 bar TMP and crossflows of 60 kg/h and 160 kg/h, corresponding to average crossflow velocities of 0.13 and 0.33 m/s in the membrane module, respectively. For each experiment, three repetitions were conducted to find the reproducibility of the results. The permeate was collected in a beaker on a balance (Kern PCB 6000-1, Kern & Sohn GmbH, Balingen, Germany) for on-line data collection. From the measured permeate mass, the permeate flow and flux was calculated.
Microscopy and Video Analysis
The recorded video was converted in VLC Media Player to MP4.h265 format with an added sharpness and graduation filter to make it easier to find the particles using MATLAB. A detailed description of the particle track and trace procedure is presented in Appendix A. The particles were identified using an area comparison function in MATLAB, and the center points were then saved. A geometric overlay function was used on each particle in each frame to test if there was a particle close by in the next frame. If they were within the specified geometry it was considered a trace. A trace can be multiple frames long. If multiple particles were in the geometric target area the last one found were saved. Traced data was filtered to allow for data analysis, by (1) removing traced articles that were detected in less than 5 frames, (2) removing the last registered position of a traced particle, and (3) by removing traces that did not move more than 20 µm in the length direction (in order to remove erroneously tracked membrane).
Particle positions put into length coordinates along the membrane (x coordinate) and height coordinates from the membrane (y coordinate), where lengths and heights has been calculated from pixel positions, using a 1.26 µm distance between particles.
The velocity of particles moving along the fiber, v x , and velocity of particles moving towards the fiber, v y , was calculated using the following equations.
where v y,i and v x,i are the velocities along and towards the membrane (longitudinal and perpendicular velocities, respectively) for the particle tracked in frame i. x i , x i+1 , y i and y i+1 , are the positions of tracked particles in frame i and i+1 and ∆t = 1/1196 fps = 0.84 µs is the time difference between each frame. Accordingly, the acceleration of particles towards the membrane, a y , was calculated using the following equation:
Computational Fluid Dynamics Simulation
A three dimensional method to model the flow in the filtration cell was set up using the commercial software Comsol Multiphysics 5.4. The 3D renderings shown in Figure 2a,b were used for simulations. The water flow velocity through the cell was simulated at T = 20 • C and 160 kg/h flow of water through the cell. The boundary condition for the membrane was set as a fixed flow outlet with the flow set at the measured permeate flow of 0.037 mL/s. The outlet was set as a pressure outlet at 2 bar and the inlet was the inlet velocity which was 0.33 m/s. The mathematical model used was the κ-ε shear stress transport (SST) model using Low Reynolds wall treatment. To make the model solvable with the flow resolution needed the mesh was split into 3 parts. At the observable area, a 1 × 1 × 1 mm cube was placed with an extremely fine mesh to get the best possible wall resolution. The membrane fiber had a less fine mesh and the bulk flow and other walls had an optimized mesh. This was necessary because of the limited hardware available. Multiple different passes were done with different meshes to ensure that the meshing was adequate to resolve the flow. The membrane surface was set to smooth.
Flow Simulations
The water velocity profile of the one half of the flow cell obtained from CFD simulation at 0.33 m/s mean crossflow velocity is shown in Figure 3.
Membranes 2020, 10, x FOR PEER REVIEW 5 of 17 optimized mesh. This was necessary because of the limited hardware available. Multiple different passes were done with different meshes to ensure that the meshing was adequate to resolve the flow. The membrane surface was set to smooth.
Flow Simulations
The water velocity profile of the one half of the flow cell obtained from CFD simulation at 0.33 m/s mean crossflow velocity is shown in Figure 3. The figure shows that the velocity approaches 0.40 m/s in the cell and decreases towards the walls of the cell and the membrane. The velocity increases with the distance from the membrane and reaches 0.35 m/s at a distance of 1 mm from the membrane surface.
Particle Track and Trace
Core-shell particles were filtered and the filtration process monitored with DO. The videos were analyzed in MATLAB. Figure 4 shows a representative map of particles tracked near a membrane during filtration of lowly charged particles at TMP = 2 bar and crossflow velocity 0.13 m/s. The plot shows a collection of all the identified particles during 3 s filtration. The same particle can be recorded several times. The position is given in distance from the membrane, y, vs the length coordinate along the membrane, x. The particles moved with the crossflow from left to right i.e., from low to high x.
As observed from Figure 4 there seem to be less particles near the membrane (less than 20 μm) than far from the membrane (more than 40 μm from the membrane). Close to the membrane, the particles can easily be identified and tracked as they move along the membrane, and it is observed how they oscillate in distance from the membrane, y. At distances higher than 20 μm from the membrane there is a more chaotic map of particles. In the video uploaded under supplementary material it can be observed how particles approach the membrane. Once they reach a critical distance of approximately < 5 μm, from the membrane, the particle velocity decline and the particle falls into the membrane and deposits. This is illustrated in Figure 5 showing frames captured from the video with 0.2 s intervals. The figure shows that the velocity approaches 0.40 m/s in the cell and decreases towards the walls of the cell and the membrane. The velocity increases with the distance from the membrane and reaches 0.35 m/s at a distance of 1 mm from the membrane surface.
Particle Track and Trace
Core-shell particles were filtered and the filtration process monitored with DO. The videos were analyzed in MATLAB. Figure 4 shows a representative map of particles tracked near a membrane during filtration of lowly charged particles at TMP = 2 bar and crossflow velocity 0.13 m/s. The plot shows a collection of all the identified particles during 3 s filtration. The same particle can be recorded several times. The position is given in distance from the membrane, y, vs the length coordinate along the membrane, x. The particles moved with the crossflow from left to right i.e., from low to high x.
As observed from Figure 4 there seem to be less particles near the membrane (less than 20 µm) than far from the membrane (more than 40 µm from the membrane). Close to the membrane, the particles can easily be identified and tracked as they move along the membrane, and it is observed how they oscillate in distance from the membrane, y. At distances higher than 20 µm from the membrane there is a more chaotic map of particles. In the video uploaded under supplementary material it can be observed how particles approach the membrane. Once they reach a critical distance of approximately < 5 µm, from the membrane, the particle velocity decline and the particle falls into the membrane and deposits. This is illustrated in Figure 5 showing frames captured from the video with 0.2 s intervals. The graph shows how the five different particles' positions are tracked and mapped as they move along the fiber. Particles seem to appear and disappear, e.g., Particle 2 is no longer observed at x > 180 μm, while Particle 4 appears at 190 μm. This is explained by particles moving in the third dimension, z, towards or away from the camera, hence it comes out of focus to be captured. The MATLAB script filtrates data to show only particles that have been tracked for at least five frames. The graph shows how the five different particles' positions are tracked and mapped as they move along the fiber. Particles seem to appear and disappear, e.g., Particle 2 is no longer observed at x > 180 μm, while Particle 4 appears at 190 μm. This is explained by particles moving in the third dimension, z, towards or away from the camera, hence it comes out of focus to be captured. The MATLAB script filtrates data to show only particles that have been tracked for at least five frames. The graph shows how the five different particles' positions are tracked and mapped as they move along the fiber. Particles seem to appear and disappear, e.g., Particle 2 is no longer observed at x > 180 µm, while Particle 4 appears at 190 µm. This is explained by particles moving in the third dimension, z, towards or away from the camera, hence it comes out of focus to be captured. The MATLAB script filtrates data to show only particles that have been tracked for at least five frames.
As observed from Figure 6a, the particles far from the membrane surface (e.g., Particle 4 and 5) move across the membrane with the same distance from the membrane. Particles moving closer to the membrane alternate from advancing towards and retreating from the membrane surface. During the 3 s video recordings, the particles near the membrane are detected a higher number of times than particles far from the membrane. Also, the distance between particle positions between each frame seems lower for particles near the membrane surface, showing that the velocity of particles along the membrane is slower, the closer they are to the membrane.
In Figure 6b, the position of Particle 1 (from Figure 6a), velocity along the membrane, v x , and towards the membrane, v y , is plotted as function of the length coordinate along the membrane. This shows that the particle longitudinal velocity decreases as the particle advances towards the membrane. As the particle retreats, the longitudinal velocity increases again. Hence, the tracking methodology developed with high speed DO shows a clear influence of particle position on the longitudinal particle velocity. The particle's velocity in the y-dimension changes from being positive, i.e., the particle advances towards the membrane, to being negative, i.e., the particle retreats from the membrane, and then again moves away from the membrane. It is observed that v y << v x , which is a consequence of the high crossflow velocity (0.13 m/s in the experiment behind Figure 5) compared to the permeate flux, which was measured to be 351.8 ± 13.7 LMH, i.e., 9.8 · 10 −5 ± 0.38 · 10 −5 m/s, during filtration at 2 bar TMP. At 1 bar TMP filtrations, the permeate flux was measured to 263.0 ± 1.9LMH, i.e., 7.3 · 10 −5 ± 0.05 · 10 −5 m/s. The permeate flux was not affected by crossflow velocity, as almost no fouling was formed with the low concentration of particles.
Longitudinal Velocity Profiles
The longitudinal velocity of particles along the membrane was calculated using Equation (1) for each particle tracked in the video from filtration experiments. The velocities were averaged within 1 µm intervals in distance from the membrane, y, and plotted against y. The velocity profiles of lowly charged particles during filtrations at 2 bar TMP and crossflow velocities of 0.13 m/s (three filtrations) and 0.33 m/s (three filtrations) are plotted in Figure 7.
Membranes 2020, 10, x FOR PEER REVIEW 8 of 17 As observed from Figure 6a, the particles far from the membrane surface (e.g., Particle 4 and 5) move across the membrane with the same distance from the membrane. Particles moving closer to the membrane alternate from advancing towards and retreating from the membrane surface. During the 3 s video recordings, the particles near the membrane are detected a higher number of times than particles far from the membrane. Also, the distance between particle positions between each frame seems lower for particles near the membrane surface, showing that the velocity of particles along the membrane is slower, the closer they are to the membrane.
In Figure 6b, the position of Particle 1 (from Figure 6a), velocity along the membrane, vx, and towards the membrane, vy, is plotted as function of the length coordinate along the membrane. This shows that the particle longitudinal velocity decreases as the particle advances towards the membrane. As the particle retreats, the longitudinal velocity increases again. Hence, the tracking methodology developed with high speed DO shows a clear influence of particle position on the longitudinal particle velocity. The particle's velocity in the y-dimension changes from being positive, i.e., the particle advances towards the membrane, to being negative, i.e., the particle retreats from the membrane, and then again moves away from the membrane. It is observed that vy << vx, which is a consequence of the high crossflow velocity (0.13 m/s in the experiment behind Figure 5) compared to the permeate flux, which was measured to be 351.8 ± 13.7 LMH, i.e., 9.8٠10 −5 ± 0.38٠10 −5 m/s, during filtration at 2 bar TMP. At 1 bar TMP filtrations, the permeate flux was measured to 263.0 ± 1.9LMH, i.e., 7.3٠10 −5 ± 0.05٠10 −5 m/s. The permeate flux was not affected by crossflow velocity, as almost no fouling was formed with the low concentration of particles.
Longitudinal Velocity Profiles
The longitudinal velocity of particles along the membrane was calculated using Equation (1) The longitudinal velocity of particles increases with higher distance from the membrane surface and the velocity of particles tracked during the 0.13 m/s crossflow experiments is lower than the velocity of particles tracked during the 0.33 m/s crossflow experiment (Figure 7). The results are reproducible, as the three experimental runs of each setting showed similar velocity profiles.
CFD simulations were used to calculate the profile of fluid velocity as function of distance from the membrane during filtration at 2bar TMP and crossflow 0.33 m/s. There was good correlation with theoretically modelled fluid flow and with measured particle velocity with some underprediction of flow close to the membrane and overprediction far from the membrane. The longitudinal velocity profiles obtained at a crossflow velocity of 0.33 m/s did not reach the expected bulk particle flow velocity within a distance of 100 µm from the membrane, as also predicted from the CFD simulation.
Close to the membrane there is no data for measured longitudinal velocity for particles (<3 µm). This is in accordance with the observation from Video S1 in Supplementary Material that when particles move close to the membrane, they reach a position where they deposit instead of being transported with the longitudinal crossflow. This may be observed as a stagnant layer in which crossflow does not affect particle movement. The extent of the concentration polarization layer can also be determined theoretically as described in Appendix B using the approach described in Christensen et al. [18]. Using the measured flux at 2 bar TMP, 352 LMH, a particle diameter of 1.6 µm, and a crossflow velocity of 0.33 m/s, the theoretical thickness is determined to δ = 0.13 µm, which is well below the observed thickness of the stagnant layer. Figure 8 shows the longitudinal velocity profiles for tracked particles during filtration of high charge particles at TMP = 2 bar and crossflow velocities of 0.13 and 0.33 m/s. These show the similar trends as for the low charge particles. Hence, particle interactions (repulsion and attraction) do not influence flow profiles along the membrane in the 4-100 µm distance range analyzed in this study. This is well in accordance with DLVO theory, as the Debye length can be estimated to be only 2.6 nm by assuming an ionic strength of the tap water of 0.013 mol/L. The assumed ionic strength was based on water analyses by the local water utility company.
Velocity profiles of particles tracked under 1 bar TMP filtrations (data not shown) showed the same tendencies as for 2 bar TMP filtrations, hence neither TMP nor particle surface charge affected particle's longitudinal flow profiles.
Membranes 2020, 10, x FOR PEER REVIEW 9 of 17 The longitudinal velocity of particles increases with higher distance from the membrane surface and the velocity of particles tracked during the 0.13 m/s crossflow experiments is lower than the velocity of particles tracked during the 0.33 m/s crossflow experiment (Figure 7). The results are reproducible, as the three experimental runs of each setting showed similar velocity profiles.
CFD simulations were used to calculate the profile of fluid velocity as function of distance from the membrane during filtration at 2bar TMP and crossflow 0.33 m/s. There was good correlation with theoretically modelled fluid flow and with measured particle velocity with some underprediction of flow close to the membrane and overprediction far from the membrane. The longitudinal velocity profiles obtained at a crossflow velocity of 0.33 m/s did not reach the expected bulk particle flow velocity within a distance of 100 μm from the membrane, as also predicted from the CFD simulation.
Close to the membrane there is no data for measured longitudinal velocity for particles (<3 μm). This is in accordance with the observation from Video S1 in Supplementary Material that when particles move close to the membrane, they reach a position where they deposit instead of being transported with the longitudinal crossflow. This may be observed as a stagnant layer in which crossflow does not affect particle movement. The extent of the concentration polarization layer can also be determined theoretically as described in Appendix B using the approach described in Christensen et al. [18]. Using the measured flux at 2 bar TMP, 352 LMH, a particle diameter of 1.6 μm, and a crossflow velocity of 0.33 m/s, the theoretical thickness is determined to δ = 0.13 μm, which is well below the observed thickness of the stagnant layer. Figure 8 shows the longitudinal velocity profiles for tracked particles during filtration of high charge particles at TMP = 2 bar and crossflow velocities of 0.13 and 0.33 m/s. These show the similar trends as for the low charge particles. Hence, particle interactions (repulsion and attraction) do not influence flow profiles along the membrane in the 4-100 μm distance range analyzed in this study. This is well in accordance with DLVO theory, as the Debye length can be estimated to be only 2.6 nm by assuming an ionic strength of the tap water of 0.013 mol/L. The assumed ionic strength was based on water analyses by the local water utility company.
Velocity profiles of particles tracked under 1 bar TMP filtrations (data not shown) showed the same tendencies as for 2 bar TMP filtrations, hence neither TMP nor particle surface charge affected particle's longitudinal flow profiles.
Perpendicular Velocity Profiles
The perpendicular velocities, i.e., the speed of particles directly towards or away from the membrane, was calculated using Equation (2). Figure 9a,b show the particle velocities towards the membrane at varying particle positions near the membrane during filtration of lowly charged particles at TMP = 2bar and 0.13 m/s and 0.33 m/s crossflow velocity, respectively. filtration at TMP = 2 bar. The line represent modelled water velocity along the membrane fiber at 0.33 m/s crossflow velocity and 2 bar TMP using COMSOL.
Perpendicular Velocity Profiles
The perpendicular velocities, i.e., the speed of particles directly towards or away from the membrane, was calculated using Equation (2). Figure 9a,b show the particle velocities towards the membrane at varying particle positions near the membrane during filtration of lowly charged particles at TMP = 2bar and 0.13 m/s and 0.33 m/s crossflow velocity, respectively. Figure 9a show data for an experiment with a crossflow velocity of 0.13 m/s. The measured velocities towards the membrane (−0.004 m/s-0.004 m/s) are orders of magnitude lower than the measured velocities of the particles along the membrane (up to 0.1 m/s), which prevents rapid deposition. Secondly, it is observed that the velocity of particles moving towards the membrane from the permeation drag is counteracted by particles moving out from the membrane towards bulk suspension. The velocity of particles moving towards the membrane is measured to maximum 0.004 m/s, which corresponds to a permeate flux of up to 14400 L/m 2 /h (LMH). This is higher than the actual permeate flux measured at TMP = 2 bar, which were only 353 ± 14 LMH. Although particles move with a high velocity towards the membrane, a low amount of deposition on the membrane is observed in the videos, which is in accordance with the stable flux throughout filtrations. If particles deposited on the membrane, a lower drag of particles towards the membrane would be observed due to reduced permeability. The lower-than-expected observed velocities of particles moving towards the membrane suggests that (1) the hydrodynamic around the membrane disturbs the fluid flow eventually due to vibration of the membrane or (2) the forces acting on the particles change during the particle transportation, e.g., due to particle rotation. The surface of the membrane was monitored during the experiment and no vibration was observed, still the fluid flow may be disturbed around the filter and cause small fluctuation of the particle velocity. Also, forces acting on the particle may change. If this is correct, the permeation drag used in literature to quantify foulant transport to a membrane surface significantly underestimates the real rate of transport and the forces may vary more than expected. Another explanation might be that the permeation drag can only describes particle transport rate towards the membrane close to the membrane, e.g., a distance < 5-10 μm which is where the particle starts deposition as described in Section 3.2. Figure 9b shows data obtained at filtration with a crossflow velocity of 0.33 m/s. There is only particles moving towards and away from the membrane within a distance of up to 80 μm (critical distance) from the membrane surface. At this critical distance, the velocity of particles along the membrane is approximately 0.075 m/s (Figure 7). Comparing with the perpendicular velocities obtained at 0.13 m/s crossflow filtrations in Figure 9a shows that the apparent critical distance has not been reached, which may be a consequence of the crossflow not reaching a critical level to Figure 9a show data for an experiment with a crossflow velocity of 0.13 m/s. The measured velocities towards the membrane (−0.004 m/s-0.004 m/s) are orders of magnitude lower than the measured velocities of the particles along the membrane (up to 0.1 m/s), which prevents rapid deposition. Secondly, it is observed that the velocity of particles moving towards the membrane from the permeation drag is counteracted by particles moving out from the membrane towards bulk suspension. The velocity of particles moving towards the membrane is measured to maximum 0.004 m/s, which corresponds to a permeate flux of up to 14400 L/m 2 /h (LMH). This is higher than the actual permeate flux measured at TMP = 2 bar, which were only 353 ± 14 LMH. Although particles move with a high velocity towards the membrane, a low amount of deposition on the membrane is observed in the videos, which is in accordance with the stable flux throughout filtrations. If particles deposited on the membrane, a lower drag of particles towards the membrane would be observed due to reduced permeability. The lower-than-expected observed velocities of particles moving towards the membrane suggests that (1) the hydrodynamic around the membrane disturbs the fluid flow eventually due to vibration of the membrane or (2) the forces acting on the particles change during the particle transportation, e.g., due to particle rotation. The surface of the membrane was monitored during the experiment and no vibration was observed, still the fluid flow may be disturbed around the filter and cause small fluctuation of the particle velocity. Also, forces acting on the particle may change. If this is correct, the permeation drag used in literature to quantify foulant transport to a membrane surface significantly underestimates the real rate of transport and the forces may vary more than expected. Another explanation might be that the permeation drag can only describes particle transport rate towards the membrane close to the membrane, e.g., a distance < 5-10 µm which is where the particle starts deposition as described in Section 3.2. Figure 9b shows data obtained at filtration with a crossflow velocity of 0.33 m/s. There is only particles moving towards and away from the membrane within a distance of up to 80 µm (critical distance) from the membrane surface. At this critical distance, the velocity of particles along the membrane is approximately 0.075 m/s (Figure 7). Comparing with the perpendicular velocities obtained at 0.13 m/s crossflow filtrations in Figure 9a shows that the apparent critical distance has not been reached, which may be a consequence of the crossflow not reaching a critical level to eliminate transport towards and away from the membrane within the first 100 µm from the membrane (Figure 7).
It was expected that a higher crossflow velocity, which was reached at longer distances from the membrane, would counteract the relatively low transport of particles towards the membrane, hence that perpendicular transport would decrease with distance from the membrane, but the abrupt absence of perpendicular transport at distances > 80 µm was not expected. Alternatively, the lack of transport towards the membrane of particles further from the membrane than 80 µm could also be a result of an inadequate amount of frames of tracked particle a high distances from the membrane and at high crossflow velocity, e.g., there is not enough captured particles to determine a perpendicular velocity.
The measured perpendicular velocity profiles suggest that particle movement is more complex and chaotic and underestimates local velocities of particles moving towards and away from the membrane. With the dimensions and flows along the membrane used in this study, the results can be transferred to e.g., flat sheet and spiral wound microfiltration, whereas the flow around close packed fibers in hollow fiber modules may differ from the flows investigated in this study. This puts demand for further understanding of the mechanisms of particle movement in membrane filtration at lab scale (idealized conditions) and full scale (real conditions), which should be described in detail in further studies.
The measured force of particles moving towards and away from the membrane can be determined by first finding the acceleration of each particle moving towards and away from the membrane by use of Equation (3). The mass of particles was estimated from the volume of a sphere with diameter of 1.6 µm and density 1050 kg/m 3 relative to the density of water (998 kg/m 3 ). Accordingly, the force of particle movement can be found as the product of the particle mass and acceleration and plotted against distance from membrane in Figure 10.
Membranes 2020, 10, x FOR PEER REVIEW 11 of 17 eliminate transport towards and away from the membrane within the first 100 µ m from the membrane (Figure 7). It was expected that a higher crossflow velocity, which was reached at longer distances from the membrane, would counteract the relatively low transport of particles towards the membrane, hence that perpendicular transport would decrease with distance from the membrane, but the abrupt absence of perpendicular transport at distances > 80 μm was not expected. Alternatively, the lack of transport towards the membrane of particles further from the membrane than 80 μm could also be a result of an inadequate amount of frames of tracked particle a high distances from the membrane and at high crossflow velocity, e.g., there is not enough captured particles to determine a perpendicular velocity. The measured perpendicular velocity profiles suggest that particle movement is more complex and chaotic and underestimates local velocities of particles moving towards and away from the membrane. With the dimensions and flows along the membrane used in this study, the results can be transferred to e.g., flat sheet and spiral wound microfiltration, whereas the flow around close packed fibers in hollow fiber modules may differ from the flows investigated in this study. This puts demand for further understanding of the mechanisms of particle movement in membrane filtration at lab scale (idealized conditions) and full scale (real conditions), which should be described in detail in further studies.
The measured force of particles moving towards and away from the membrane can be determined by first finding the acceleration of each particle moving towards and away from the membrane by use of Equation (3). The mass of particles was estimated from the volume of a sphere with diameter of 1.6 μm and density 1050 kg/m 3 relative to the density of water (998 kg/m 3 ). Accordingly, the force of particle movement can be found as the product of the particle mass and acceleration and plotted against distance from membrane in Figure 10. The measured particle forces are in the range −1٠10 −14 N to 1٠10 −14 N, positive if they accelerate towards the membrane and negative if the accelerate away from the membrane. However, Figure Figure 10. Measured kinetic forces of lowly charged particles towards the membrane plotted vs distance from the membrane surface during filtration at 2 bar TMP and crossflow velocity of 0.13 m/s (a,c) and 0.33 m/s (b,d).
The measured particle forces are in the range −1 × 10 −14 N to 1 × 10 −14 N, positive if they accelerate towards the membrane and negative if the accelerate away from the membrane. However, Figure 10c,d shows that most particle forces are in the range −4 × 10 −16 N to 4 × 10 −16 N. The trend in forces is the same as for velocities, as there for the 0.33 m/s filtrations also is a critical distance at around 80 µm above which there are no forces acting on the particles towards or away from the membrane.
The theoretical lift force, F L , transporting particles away from the membrane can be estimated by the following Equation (4): in which a is the particle diameter (1.6 µm), ρ is the density of water (998 kg/m 2 ), and η is the dynamic viscosity of water (1.002 × 10 −3 Pa · s). The wall shear stress (τ w ) was determined to τ w = 1.28 Pa by multiplying the gradient in fluid velocity with distance from the membrane (du/dy = 1280 1/s within 0-10 µm) at a crossflow of 0.33 m/s (determined from CFD simulations). The resulting lift force is 1.43 × 10 −13 N, i.e., a factor 10 larger than the measured forces of which particles move away from the membrane (Figure 10b,d). Thus, lifting forces may also play a role in the particle velocity and change of particle velocity, but are counteracted by the gravity force, i.e., the drag of particles by permeation.
Particle Count Profiles
The number of particles during 3 s of filtration was counted and plotted against distance from the membrane in Figure 11 for lowly (a,b) and highly (c,d) charged particles filtrated at 2 bar TMP and crossflow velocities of 0.13 m/s (a,c) and 0.33 m/s (b,d). It should be noted that particles far from the membrane will be replaced faster than particles close to the membrane.
Membranes 2020, 10, x FOR PEER REVIEW 12 of 17 10c,d shows that most particle forces are in the range −4٠10 −16 N to 4٠10 −16 N. The trend in forces is the same as for velocities, as there for the 0.33 m/s filtrations also is a critical distance at around 80 μm above which there are no forces acting on the particles towards or away from the membrane. The theoretical lift force, FL, transporting particles away from the membrane can be estimated by the following Equation (4): FL= 0.761τw 1.5 ٠a 3 ٠ρ 0.5 /η (4) in which a is the particle diameter (1.6 μm), ρ is the density of water (998 kg/m 2 ), and η is the dynamic viscosity of water (1.002٠10 −3 Pa٠s). The wall shear stress (τw) was determined to τw = 1.28 Pa by multiplying the gradient in fluid velocity with distance from the membrane (du/dy = 1280 1/s within 0-10 μm) at a crossflow of 0.33 m/s (determined from CFD simulations). The resulting lift force is 1.43٠10 −13 N, i.e., a factor 10 larger than the measured forces of which particles move away from the membrane (Figure 10b,d). Thus, lifting forces may also play a role in the particle velocity and change of particle velocity, but are counteracted by the gravity force, i.e., the drag of particles by permeation.
Particle Count Profiles
The number of particles during 3 s of filtration was counted and plotted against distance from the membrane in Figure 11 for lowly (a,b) and highly (c,d) charged particles filtrated at 2 bar TMP and crossflow velocities of 0.13 m/s (a,c) and 0.33 m/s (b,d). It should be noted that particles far from the membrane will be replaced faster than particles close to the membrane. Comparing the plots in Figure 11a,b shows a steady distribution of lowly charged particles from the membrane for the lower crossflow velocity, while for the higher crossflow velocity, the number of particles declines towards reach zero near y = 100 µm. For the higher charge particles there seems to be a higher total count of particles (Figure 11c), suggesting a higher concentration of particles. However, there is a decline in count with distance from the surface even at the low crossflow velocity, which may be due to repulsion between the charged particles.
Conclusions
A procedure was developed to record and analyze particle distribution and movements along and towards a membrane during crossflow microfiltration. The particles were tracked and traced using video recordings through a microscope with a high speed camera. Particle velocity along the membrane was measured and showed good correlation with fluid velocity along the membrane determined by CFD simulations and showed higher velocities for experiments conducted at higher crossflow velocities. The particle velocity was independent of TMP and particle surface charge.
The velocity of particles towards and away from the membrane was significantly higher than the measured permeate flux, which in literature is frequently used to quantify foulants transport towards the membrane. This was only observed within a region close to the membrane. The extent of this region decreases with higher crossflow velocity. The high velocity towards the membrane is counteracted by similar particle velocities away from the membrane. This may be due to distribution of the fluid flow close to the fiber. Close to the membrane, the particle velocities shift direction, which is not described in classical filtration theory. An explanation may be that the shift in velocity is an effect of particle rotation, membrane vibration or shift in flow pattern close to the membrane. Furthermore, the lifting force may also have an influence but was significantly higher than the particle force estimated from the acceleration of the particles. Hence, the results suggest that the mechanism of particle deposition is more complex than described in literature today, as the particles move in a highly chaotic manner, even at low crossflow velocities with local velocities towards the membrane exceeding that predicted by the permeation drag. This calls for a revised understanding of particle behavior during crossflow filtration.
Finally, particle counting enabled the determination of particle distribution profiles away from the membrane surface, which confirms and quantifies higher particle accumulation near the membrane for lower crossflow velocities. The determination of particle distribution and velocity profiles with the procedure developed in this study is a promising tool to study the behavior of particles near the membrane before they deposit and form fouling layers.
MATLAB Particle Tracking
To analyze what is considered a particle in MATLAB, the videos were first converted with a gradient and sharpness filter in VLC Video Player. Figure A1 shows an example of a post filter frame. Figure A1. Representative filtered frame with visible particles.
To track the particles in MATLAB the individual frames were converted to grayscale using the "imbinarize" function and anything below a set threshold were set to black. Then using "imdilate" anything that is not background is dilated so all particles are a solid color. "imopen" is then used to draw circles around the particles making them more regularly shaped. This is done in the following lines of code: img = img(:, :, 3); img = ~imbinarize(img, 'adaptive','Sensitivity',0.7); img = imdilate(img, strel('disk', 1)); img = imopen(img, strel('disk', 1)); Afterwards the center of the particles is found and it is determined what is a particle and what is noise. The function "regionprops" is used to find the center of all circles and measure their area. All circles above a certain minimum area then have their center coordinate saved as a result. This is done in the following code: p = regionprops(img, {'Centroid', 'Area'}); ind = [p.Area] > minsize; cent = reshape([p.Centroid], 2, length(ind)); cent = cent(:, ind); This code is repeated for every single frame in a single video. To help find the correct minimum area a plot of the frame and what is shown for testing purposes however that is too slow for actually doing a whole video and is commented out normally. Additionally, rotation and cropping was added in the code to align the membrane horizontally and to minimize runtime.
MATLAB Particle Tracing
To trace the particles and find them in all frames of the video the center points given from the particle analyzing script was used. Around any given particle center point a polar coordinate system is made and a function is drawn around it. Through testing the following functions were used: L = linspace(0,2*pi,6); -sets up graph space xv = ParPos(frame).xy(1,particlenumber) -30 + cos(L)*50'; -x part of polar function yv = ParPos(frame).xy(2,particlenumber) + 2 + sin(L)*5'; -y part of polar function To track the particles in MATLAB the individual frames were converted to grayscale using the "imbinarize" function and anything below a set threshold were set to black. Then using "imdilate" anything that is not background is dilated so all particles are a solid color. "imopen" is then used to draw circles around the particles making them more regularly shaped. This is done in the following lines of code: img = img(:, :, 3); img =~imbinarize(img, 'adaptive','Sensitivity',0.7); img = imdilate(img, strel('disk', 1)); img = imopen(img, strel('disk', 1)); Afterwards the center of the particles is found and it is determined what is a particle and what is noise. The function "regionprops" is used to find the center of all circles and measure their area. All circles above a certain minimum area then have their center coordinate saved as a result. This is done in the following code: p = regionprops(img, {'Centroid', 'Area'}); ind = [p.Area] > minsize; cent = reshape([p.Centroid], 2, length(ind)); cent = cent(:, ind); This code is repeated for every single frame in a single video. To help find the correct minimum area a plot of the frame and what is shown for testing purposes however that is too slow for actually doing a whole video and is commented out normally. Additionally, rotation and cropping was added in the code to align the membrane horizontally and to minimize runtime.
MATLAB Particle Tracing
To trace the particles and find them in all frames of the video the center points given from the particle analyzing script was used. Around any given particle center point a polar coordinate system is made and a function is drawn around it. Through testing the following functions were used: L = linspace(0,2*pi,6); -sets up graph space xv = ParPos(frame).xy(1,particlenumber) -30 + cos(L)*50'; -x part of polar function yv = ParPos(frame).xy(2,particlenumber) + 2 + sin(L)*5'; -y part of polar function Notice in the "linspace" function it is only drawing 6 points, effectively drawing a hexagon. Increasing this number gets it closer and closer to a circle. However, it also increases computation time. The function for x (−30 + cos(L)*50) and y (+2 + sin(L)*5) was chosen to draw the polygon that had the best chance to include only the correct particle in the following frame.
The "inpolygon" function is used on the just made polygon to test if any particle centers from the next frame is inside the polygon. If there are, they are saved. This is done in the following code: Xq = ParPos(frame+1).xy (1,:); -x position for all particles in the next frame yq = ParPos(frame+1).xy(2,:); -y position for all particles in the next frame in = inpolygon(xq,yq,xv,yv); -Here the particles are tested if they are inside the polygon The xq and yq is the particle center positions for all particles in the following frame. The results of the test are either a true or false for all particles tested and are saved in the vector "in".
The testing polygon shape was decided through testing and for that a figure was made to show the polygon and particles in the next frame. However, that was commented out when running the script normally to make it possible to actually complete the script.
MATLAB Scripts and Their Functions
The following MATLAB scripts are used to analyze particle flow and distributions. | 11,443.6 | 2020-04-01T00:00:00.000 | [
"Engineering",
"Physics",
"Biology"
] |
Illegal Use of Loading Bays and Its Impact on the Use of Public Space
: Loading bays are public spaces reserved for the operation of freight vehicles, and it is well known that there are significant problems concerning their use due to non-compliance with existing regulations. Unlawful use of loading bays leads to double parking, or to parking on the pavement or in restricted areas. This article has two objectives: Firstly, the study and analysis of the use of loading bays (type of demand, parking duration, illegal use, etc.), as well as their use according to their morphology. Secondly, the quantitative assessment of the influence of illegal use with regard to the e ffi cient use of public urban space. Illegal use is quantitatively assessed by calculating the number of loading bays that are used inappropriately and the surface area (m 2 ) of public space used incorrectly. In the analysis carried out in the city of Santander (Spain), it can be observed that the urban morphology of loading zones influences their use: The greater the capacity of the loading zone, the less e ffi cient is its use. Moreover, it is observed that the degree of illegal use within loading zones is very high and that illegally excessive parking durations have a greater impact on the use of the ground space than vehicle type.
Introduction
The effects of urban freight transport on city mobility and congestion are significant, giving rise to such additional concerns as increases in air and noise pollution and accidents, among other things. Numerous research papers have attempted to minimize the negative impact this has, not only on traffic and congestion, but also on urban space consumption [1][2][3][4]. Thus, several measures have been proposed aimed at regulating freight vehicle access to cities [5,6], optimizing delivery routes in real time [7] through urban consolidation centers [8], regulating freight vehicle off-hour deliveries [9][10][11] and providing logistics infrastructure and facilities [12,13]. De Marco et al. [14] analyzed a dataset of 70 European cities that have been piloting or rolling out these and other measures, with the objective of providing an updated indication of the status of those measures. In this context, most urban areas reserve part of their public space to facilitate the loading and unloading of materials and/or goods, and these are called loading zones (L/U zones). Each loading zone is composed of a finite number of loading bays (LBs). In other words, the number of loading bays of one loading zone is equal to its capacity. The types of vehicle that may use loading zones in cities are determined by local regulations. The characteristics that such vehicles have may include vehicle weight, for example, in some Spanish cities [15], vehicle size or the levels of pollution emitted by the vehicle, as in some Italian cities [16].
Alho and e Silva [43] evaluated the service level of loading bays as well as the illegal parking rates in Lisbon and observed that non-freight vehicles accounted for 80% of demand.
As stated before, one of the consequences of the illegal use of loading bays is that, due to the lack of available space, freight vehicles opt to double park or to park on the pavement or in restricted areas, thereby using urban space inappropriately. Disturbances in city traffic flow and pedestrian mobility are a result of this incorrect use [37]. Alho et al. [44] performed a dynamic simulation with multiple configurations in loading zones, and achieved the restriction of loading zone use to authorized vehicles only to various degrees, so as to be able to compare the resulting disturbances to traffic flow.
Most of the above analyzed research focused on planning loading zone location, design or capacity and on analyzing the effects of illegal loading zone use on traffic. However, as far as the authors know, there is a lack of studies analyzing its effect on the use of public space (efficient use of loading zones implies an efficient use of public space) and how the different compliance rates to usage regulations may affect this efficiency.
For this reason, this article has two objectives: First, to analyze loading zone use in an urban context through the exhaustive analysis of representative loading zones based on the Santander (Spain) city center. The indicators analyzed include: Arrival time, parking duration, vehicle type and parking location, among others. The use of these loading zones is thereby studied according to their morphology in order to understand the degree and type of illegal use taking place, among other data. Based on this analysis, the second objective of this article is to quantitatively assess the influence of such illegal actions on the use of urban public space. To this end, the number of loading bays and the surface area of incorrectly used urban public space (in m 2 ) is obtained for multiple scenarios (different degrees and types of illegal use).
The introduction and state of the art have been presented in this section. Section 2 describes the methodology developed. The data collected and the analysis and comprehension of loading zone usage is carried out in Section 3. With this analysis, the quantitative assessment of the influence of illegal use is developed in Section 4. Finally, the main conclusions are set out in Section 5.
Methodology and Case Study Description
The methodology proposed is based on an exhaustive analysis of the data collected on the vehicles using the studied loading zones. Both the characteristics of the loading zones themselves and their use, meaning who uses them and how they are used, are analyzed, differentiating between their legal and illegal use. A discrete event modeling approach is then applied to simulate differing private user and freight vehicle compliance. These data are later applied in order to quantitatively assess the consequences of incorrect loading zone use, both legal and illegal, and to determine the degree of influence exerted by each non-compliance with existing regulations ( Figure 1).
As loading zone use may vary not only across cities and/or countries, but also within a city according to the loading zone type, quantity, location, etc., a representative sample of the use of loading zones in the area to be analyzed is essential. Firstly, it should be verified that both freight and passenger vehicles have parking problems in the area. Secondly, data from a representative sample of loading zones should be collected. Studied areas should have different characteristics, both within the loading zone itself and concerning the street where it is located.
The loading zones of the Santander city center were analyzed following this approach. This area, like most urban centers, has significant parking problems [45]. The main land use is commercial and retail use, as well as areas used by restaurants, hotels and offices, and, to a lesser extent, residential and educational use. The Santander city center has both pedestrian and traffic streets. There are 48 loading zones, all of them are curb-side parking spots, i.e., the parking is parallel to the pavement. The 48 loading zones were grouped according the following attributes: • Location and size of establishments obtained from the Santander Town Hall. • Location, capacity and regulation of the loading zones, obtained from the Santander City Council and field data.
•
Existence and characteristics of on-street parking, pavement and physical obstacles.
•
Characteristics of the Santander transportation network, e.g., types of streets and intersections, provided by the Santander City Council and field data.
Sustainability 2020, 12, x FOR PEER REVIEW 4 of 17 Characteristics of the Santander transportation network, e.g., types of streets and intersections, provided by the Santander City Council and field data. Four groups were built according to the attributes described above. Then, 652 surveys were done on the loading zones of the center of Santander. The proposal was to collect general data about the uses of the loading zones and their surroundings, not only from the surveys given to carriers, but also from the observations collected while the surveys were done. The general collected data confirmed that the loading zones were well classified by the four groups. Furthermore, it indicated what the representative loading zone of each group was based on the behavior of the carriers and the vehicles forbidden in the loading zones (e.g., cars).
Therefore, four loading zones underwent in-depth analysis within this study because they had different characteristics encompassing all of the variety existing in the city center. Information on these loading zones, which covers 19 loading bays, is summarized in Table 1. The locations of the analyzed loading zones are shown in Figure 2. Loading zones are identified throughout the article using the name of the street where they are found. Furthermore, their capacity is stated as "capacity", understood as the number of loading bays in each loading zone. Four groups were built according to the attributes described above. Then, 652 surveys were done on the loading zones of the center of Santander. The proposal was to collect general data about the uses of the loading zones and their surroundings, not only from the surveys given to carriers, but also from the observations collected while the surveys were done. The general collected data confirmed that the loading zones were well classified by the four groups. Furthermore, it indicated what the representative loading zone of each group was based on the behavior of the carriers and the vehicles forbidden in the loading zones (e.g., cars).
Therefore, four loading zones underwent in-depth analysis within this study because they had different characteristics encompassing all of the variety existing in the city center. Information on these loading zones, which covers 19 loading bays, is summarized in Table 1. The locations of the analyzed loading zones are shown in Figure 2. Loading zones are identified throughout the article using the name of the street where they are found. Furthermore, their capacity is stated as "capacity", understood as the number of loading bays in each loading zone. The characteristics of both the loading zones and the streets where they are located is shown in the following table: The characteristics of both the loading zones and the streets where they are located is shown in the following table: The use of loading bays is regulated by the Traffic Ordinances issued by the Santander City Council [15]. These ordinances establish the following regulations in the four loading zones studied:
•
Type of freight vehicles permitted: Vehicles with a gross vehicle weight rating (GVWR) between 1.8-8 tons. Finally, the data collection was carried out by: • Recordings from seven business days (from Monday to Saturday) between 7:00 a.m.-2:00 p.m. in the four loading zones studied. They were undertaken with the aim of knowing how vehicles that use the loading bays behave, their types and usage habits.
•
Six hundred and fifty-two surveys during a single day in 10 different loading zones, which covered 56 loading bays, in the center of Santander. These surveys were used to verify that the sample of the four loading zones was indeed a representative sample of the city center.
Vehicle Type and Arrival Rate
The records collected in both loading zones provided information on the type of vehicles that used these loading bays. Vehicles were classified by their characteristics exactly as shown in the following table (Table 2). It was observed that the freight vehicles whose characteristics did not match the permitted span did not use the loading bays. No illegal use of this kind was observed. Conversely, cars did use the loading bays despite the fact that this was prohibited ( Figure 3). It was observed that a large number of cars used the loading bays, at a rate reaching 49%, in the Jesus de Monasterio loading zone. This means that the number of available spaces for freight vehicles was reduced.
Sustainability 2020, 12, x FOR PEER REVIEW 6 of 17 Recordings from seven business days (from Monday to Saturday) between 7:00 a.m.-2:00 p.m. in the four loading zones studied. They were undertaken with the aim of knowing how vehicles that use the loading bays behave, their types and usage habits. Six hundred and fifty-two surveys during a single day in 10 different loading zones, which covered 56 loading bays, in the center of Santander. These surveys were used to verify that the sample of the four loading zones was indeed a representative sample of the city center.
Vehicle Type and Arrival Rate
The records collected in both loading zones provided information on the type of vehicles that used these loading bays. Vehicles were classified by their characteristics exactly as shown in the following table (Table 2). It was observed that the freight vehicles whose characteristics did not match the permitted span did not use the loading bays. No illegal use of this kind was observed. Conversely, cars did use the loading bays despite the fact that this was prohibited ( Figure 3). It was observed that a large number of cars used the loading bays, at a rate reaching 49%, in the Jesus de Monasterio loading zone. This means that the number of available spaces for freight vehicles was reduced. Regarding the arrival rate, it was therefore analyzed during the time period when loading bays were reserved for freight vehicles (7:00 a.m.-2:00 p.m.). The arrival of freight vehicles is shown in Figure 4, calculated as an average of seven business days. As can be seen, the arrival of freight vehicles was slightly reduced throughout the four loading zones during the earliest hours. In the La Leña Square and Cervantes Street loading zones, demand was more or less constant throughout the morning. The Jesus de Monasterio and Vargas loading zones, in contrast, demonstrated an increase in demand between 11:00 a.m. and 12:00 p.m. Regarding the arrival rate, it was therefore analyzed during the time period when loading bays were reserved for freight vehicles (7:00 a.m.-2:00 p.m.). The arrival of freight vehicles is shown in Figure 4, calculated as an average of seven business days. As can be seen, the arrival of freight vehicles was slightly reduced throughout the four loading zones during the earliest hours. In the La Leña Square and Cervantes Street loading zones, demand was more or less constant throughout the morning. The Jesus de Monasterio and Vargas loading zones, in contrast, demonstrated an increase in demand between 11:00 a.m. and 12:00 p.m.
Parking Duration and Loading Zone Occupation
The ordinance that regulates the loading bays analyzed states that vehicles may not be parked for more than 30 min. When the percentage of vehicles that violate this regulation on parking time
Parking Duration and Loading Zone Occupation
The ordinance that regulates the loading bays analyzed states that vehicles may not be parked for more than 30 min. When the percentage of vehicles that violate this regulation on parking time limits was analyzed, we observed that nearly 30% of the vehicles in Cervantes Street exceeded the 30-min limit: 16% were parked for more than 60 min, equivalent to more than double the duration allowed. In consequence, vehicle rotation in the loading bays decreased. The reasons why the time limits were exceeded are unknown, however, it was observed that less than 5% of the vehicles that were parked for more than 30 min were due to loading and unloading activities.
Parking duration is shown by vehicle type in Figure 5. The average of the four loading zones analyzed is shown because the differences among them were minimal. The probability that light freight vehicles (LFs) exceeded the permitted time limit was approximately 30%. The probability of exceeding 60 min was approximately 15%.
Parking Duration and Loading Zone Occupation
The ordinance that regulates the loading bays analyzed states that vehicles may not be parked for more than 30 min. When the percentage of vehicles that violate this regulation on parking time limits was analyzed, we observed that nearly 30% of the vehicles in Cervantes Street exceeded the 30-min limit: 16% were parked for more than 60 min, equivalent to more than double the duration allowed. In consequence, vehicle rotation in the loading bays decreased. The reasons why the time limits were exceeded are unknown, however, it was observed that less than 5% of the vehicles that were parked for more than 30 min were due to loading and unloading activities.
Parking duration is shown by vehicle type in Figure 5. The average of the four loading zones analyzed is shown because the differences among them were minimal. The probability that light freight vehicles (LFs) exceeded the permitted time limit was approximately 30%. The probability of exceeding 60 min was approximately 15%. The occupation of loading zones and their surrounding areas (double parking, parking in restricted areas and on the pavement) throughout the morning (7:00-14:00) is shown in Figure 6. The average occupation of each zone can be seen in the stacked line chart (average of the seven days analyzed). The bars display maximum occupation. The occupation in L/U zones could be by any type of vehicle (freight vehicle or cars). Both the maximum and average occupation in the L/U zones are shown in blue. The surrounding areas could be also be illegally occupied by any type of vehicle, and in this case, this is represented in green. Furthermore, the capacity of the L/U zones is represented with a yellow line for quick comparison with the current occupation.
The average occupation of each loading zone, in terms of capacity, is observed in the graphs found in Figure 6. In other words: The occupation of loading zones and their surrounding areas (double parking, parking in restricted areas and on the pavement) throughout the morning (7:00-14:00) is shown in Figure 6. The average occupation of each zone can be seen in the stacked line chart (average of the seven days analyzed). The bars display maximum occupation. The occupation in L/U zones could be by any type of vehicle (freight vehicle or cars). Both the maximum and average occupation in the L/U zones are shown in blue. The surrounding areas could be also be illegally occupied by any type of vehicle, and in this case, this is represented in green. Furthermore, the capacity of the L/U zones is represented with a yellow line for quick comparison with the current occupation.
The average occupation of each loading zone, in terms of capacity, is observed in the graphs found in Figure 6. In other words:
•
Average loading zone occupation exceeded capacity in the case of Jesus de Monasterio Street, where the maximum occupation during the better part of the morning was one vehicle more than capacity.
•
Average loading zone occupation never reached capacity in the case of Vargas Street, where the maximum was one vehicle less.
•
Average loading zone occupation was slightly less than capacity in Cervantes Street.
•
Average loading zone occupation was approximately equal to capacity in the case of La Leña Square.
Based on the data collected, the following issues were observed to contribute to the inefficient use of loading zones: • The greater the zone capacity, the less efficient its use is. This is due to the fact that freight vehicles do not always park in an organized way to optimize zone use. It was observed that when capacity was greater than five vehicles, the loading zone rarely reached capacity.
•
The zone morphology, that is, the type of obstacles on either side of the loading zone, if applicable, influences its use. In sum, the most optimal use of the loading zone is observed when there are no As has been demonstrated above, the current use made of loading bays did not comply with the ordinance that regulates them and the number of infractions committed in loading bays was high. Infractions in loading bays occur due to the types of vehicle that use them and/or the time that said vehicles are parked in them. As a result of the inappropriate use of loading bays, freight vehicles do not have the space they need in order to carry out their activities. Other illegal uses of space are chosen to this end, such as double parking or parking on pavements or in restricted areas. The percentage of vehicles that used loading bays and their surroundings illegally during the study period (7:00 a.m.-2:00 p.m.) is shown in the following table (Table 3) by illegal use.
Scenario Definition and Simulation
The data collected in loading zones confirms the expected: loading bays are illegally used to a large degree, with illegal use observed as follows: • Use of loading bays by unauthorized vehicles. • Non-compliance with the maximum time limits.
As a result of these two unlawful actions, vehicles double park, park on the pavement or park in restricted areas, thereby committing another illegal action. In order to quantitatively assess the consequences of the inappropriate use of loading bays, the following scenarios were analyzed, comprising various degrees of compliance and types of illegal use (Figure 7 Scenarios D, E and F represent those scenarios in which loading bays are monitored but the application of regulations is not strict. The consequences of the illegal use of loading bays was quantitatively assessed for these seven scenarios through the study of their effect on the use of public space. This study was performed through the calculation of the number of loading bays used inappropriately at peak times.
All the scenarios were applied to the four loading zones analyzed. This analysis was carried out based on the simulation of the movement of vehicles in loading zones through discrete event simulation; these events were defined as the presence of freight vehicles in loading bays. Rockwell Arena software, a discrete event simulator, was used. This software has already been applied in a wide range of research, including traffic simulation [44]. It allows for simulating various scenarios with different stochastic variables, such as the loading time by merchandise type and the arrival time distribution. The simulation process went as follows: Step 1: Vehicles are characterized by type.
Step 2: Vehicles arrive at the loading zone according to an arrival distribution.
Step 3: If space is available, they carry out their operations for a specified duration of time. Loading times vary according to the vehicle type and scenario being analyzed.
Step 4: If the loading zone is not available, vehicles will double park or park on pavements or in restricted areas. Scenarios D, E and F represent those scenarios in which loading bays are monitored but the application of regulations is not strict. The consequences of the illegal use of loading bays was quantitatively assessed for these seven scenarios through the study of their effect on the use of public space. This study was performed through the calculation of the number of loading bays used inappropriately at peak times.
All the scenarios were applied to the four loading zones analyzed. This analysis was carried out based on the simulation of the movement of vehicles in loading zones through discrete event simulation; these events were defined as the presence of freight vehicles in loading bays. Rockwell Arena software, a discrete event simulator, was used. This software has already been applied in a wide range of research, including traffic simulation [44]. It allows for simulating various scenarios with different stochastic variables, such as the loading time by merchandise type and the arrival time distribution. The simulation process went as follows: Step 1: Vehicles are characterized by type.
Step 2: Vehicles arrive at the loading zone according to an arrival distribution.
Step 3: If space is available, they carry out their operations for a specified duration of time. Loading times vary according to the vehicle type and scenario being analyzed.
Step 4: If the loading zone is not available, vehicles will double park or park on pavements or in restricted areas.
The seven scenarios discussed were simulated by applying this methodology. First, the model was calibrated to replicate the current situation; the validation step was based on a reasonable adjustment test (Pearson's chi-squared test), confirming that the results obtained in the simulation were adapted to the values obtained with a confidence interval of more than 95%. Then, the other scenarios were simulated based on the current situation with their respective schemas. Performance indicators wwere obtained from the simulations, such as average loading zone occupation.
Loading zone capacity, or the number of loading bays needed for each of the scenarios, was obtained from the results of the simulations. The number of loading bays obtained from the The seven scenarios discussed were simulated by applying this methodology. First, the model was calibrated to replicate the current situation; the validation step was based on a reasonable adjustment test (Pearson's chi-squared test), confirming that the results obtained in the simulation were adapted to the values obtained with a confidence interval of more than 95%. Then, the other scenarios were simulated based on the current situation with their respective schemas. Performance indicators were obtained from the simulations, such as average loading zone occupation.
Loading zone capacity, or the number of loading bays needed for each of the scenarios, was obtained from the results of the simulations. The number of loading bays obtained from the simulation was compared with the current number of loading bays. The difference between them gave the number of vehicles that were using public space incorrectly. These vehicles could be found in the loading bays themselves or in adjacent lanes (vehicles that were double parked) or parked on pavements or in restricted areas. Using this information, as well as the average space that vehicles occupied, we were able to estimate the space that was being used incorrectly due to the varied illegal uses taking place in loading bays.
Scenarios Assesment
This methodology was applied to four loading zones that were analyzed for the seven study scenarios. The data collected and analyzed in Section 3.1 were reused and analyzed for this purpose, these being vehicle type, demand, occupation, parking duration by vehicle type, etc.
The data collected in the loading zones were gathered and analyzed, and the methodology was applied, obtaining the following indicators: -Average loading zone occupation for each of the seven scenarios. -Average number of vehicles illegally parked (double parked (DP), parked in a restricted area (RA), parked on the pavement (OP)) for each of the seven scenarios, taking into account that the number of loading bays is equal to the current number.
-Number of loading bays necessary for there to be no vehicles parked illegally (DP, RA, OP). -Number of loading bays being used inappropriately or not used efficiently due to illegal actions.
In order to simulate scenarios C and D, in which vehicles do not park for longer than 30 min, vehicles that exceeded this limit were hypothesized as parked for between 25 and 30 min. The seven aforementioned scenarios were simulated according to these conditions. One hundred (100) replications were run in each simulation scenario in order to account for random scenario factors. In the proposed model, the CPU time on an Intel Core i5 processor is about 30 s for each scenario. In the following graphs, (Figure 8) the average occupation of each loading zone and its surroundings is presented for the seven scenarios and the four loading zones.
The data collected in the loading zones were gathered and analyzed, and the methodology was applied, obtaining the following indicators: -Average loading zone occupation for each of the seven scenarios. -Average number of vehicles illegally parked (double parked (DP), parked in a restricted area (RA), parked on the pavement (OP)) for each of the seven scenarios, taking into account that the number of loading bays is equal to the current number. -Number of loading bays necessary for there to be no vehicles parked illegally (DP, RA, OP). -Number of loading bays being used inappropriately or not used efficiently due to illegal actions.
In order to simulate scenarios C and D, in which vehicles do not park for longer than 30 min, vehicles that exceeded this limit were hypothesized as parked for between 25 and 30 min. The seven aforementioned scenarios were simulated according to these conditions. One hundred (100) replications were run in each simulation scenario in order to account for random scenario factors. In the proposed model, the CPU time on an Intel Core i5 processor is about 30 s for each scenario. In the following graphs, (Figure 8) the average occupation of each loading zone and its surroundings is presented for the seven scenarios and the four loading zones. As observed in Figure 8, in general, the same excessive parking duration existed in scenario B as in the current situation even though it did not allow cars to use the loading bay; this scenario hardly improved the current situation. The only loading zone in which a significant improvement occurred As observed in Figure 8, in general, the same excessive parking duration existed in scenario B as in the current situation even though it did not allow cars to use the loading bay; this scenario hardly improved the current situation. The only loading zone in which a significant improvement occurred was in the Jesus de Monasterio loading zone. This is owed to the high percentage of illegal use in connection with vehicle type (cars), compared to illegal use in terms of excessive parking duration, as well as compared to the other three zones, as described in Table 3. This is why it can be deduced that the effect of illegal use by vehicles due to excessive duration is greater than the effect of illegal use due to vehicle type, taking into account current parking durations ( Figure 5).
If we observe scenarios D, E, and F, we see that the degree of illegal use was close to zero. Differences among the three scenarios were minimal, similar to the difference compared to the ideal scenario, scenario G, in which compliance with existing regulations was strictly observed. The Jesus de Monasterio loading zone continued to be the exception for the same reason as mentioned above: the high degree of illegal use due to vehicle type, which was triple the amount of illegal use due to excessive parking duration.
Once the average loading zone occupation in the seven scenarios was obtained, we could derive the average loading bays that were being used inappropriately during peak times in each loading zone, as well as the 95% confidence interval half-width (Table 4). A loading bay has to be used whole, in other words, a freight vehicle needs one loading bay and less of one is not useful for it because it does not have space to park. For this reason, the rounded-up number of loading bays that were being used inappropriately and an estimation of the surface area of public space (in m 2 ) are also presented. The surface area measurement in m 2 wasestimated based on the dimensions of the vehicles discussed above (Table 2) and the vehicle types that use each zone (Figure 3), generating an average vehicle occupation of 9.6 m 2 . Inappropriately used space is understood as the space that is not being used efficiently or that is used for a purpose for which it was not designed, whether it is a space in a loading zone or one where double parking, parking on the pavement or parking in a restricted area take place. When the results obtained were analyzed, we observed that, in the current situation, an estimated total of 76.8 m 2 were incorrectly used throughout the total loading zones analyzed and their surroundings. Moreover, even in an ideal scenario, space within the Vargas loading zone will always be used inappropriately for the reasons discussed previously: This area never achieves capacity (eight freight vehicles) because the vehicles are not able to organize themselves and optimize the use of space, and therefore, there is always unused space. Therefore, this space could be available for other purposes, such as parking spaces for bikes, cars or motorbikes, or for underground rubbish bins, wider sidewalks or additional street furniture.
Furthermore, the percentage of illegalities, by duration and type, were similar-except in the case of Jesus de Monasterio Street, in which the duration was less than type illegalities (Table 3). It was observed that illegalities by parking duration (scenario B) had an equal or a greater impact than illegalities by type of vehicle (scenario C).
These results can be generalized to the whole city center, where there is very little public space, in order to estimate the public space that is currently being used inappropriately. The city center has 48 loading zones, which have been classified into five groups: Four of these were grouped due to their similarities with the four loading zones analyzed in terms of the type of illegal use that would occur there if the loading zone was full. In the fifth group are loading zones in which illegal use of this kind never occurs due to the characteristics of the street. Furthermore, it is known that 10 loading zones have a capacity greater than five. Based on the data analyzed and the results obtained, it is believed that vehicles are unable to organize in loading bays with a capacity of more than five bays, and one of the bays is not used properly. The number of public spaces that are used inappropriately and the average m 2 of inappropriately used public space throughout the city center can thereby be estimated. The space used incorrectly in the surroundings of loading zones is specified (Table 5). From this analysis, it can be estimated that a total of 622 m 2 of the Santander city center is currently being used incorrectly or inadequately due to loading bay use only. Moreover, even if there are no illegalities, the area of ten loading bays would be unused-96m 2 -one loading bay unused in each loading zone with a capacity greater than 5 m 2 . Furthermore, it has been seen that, as a consequence of the characteristics of the surroundings of the loading zones, inappropriate use varies in each zone. The average road that is inappropriately used by the vehicles that double park reaches 125 m 2 in the peak times, which may cause traffic disruption. There is more inappropriately used space that impacts on traffic than on pedestrians. The average pavement area that is incorrectly used is only 38 m 2 , nevertheless, is important for pedestrians. In addition, restricted area that is incorrectly used can generate problems for other users, for example, for buses or taxis, if the restricted area has bus or taxi stops, or for private vehicles if it is an exit from a garage.
Finally, knowing that the city center loading zones comprise 2121 m 2 , approximately 30% of the urban public space reserved exclusively for the loading and unloading of goods is being used incorrectly. It is possible that the incorrect use is not directly in the loading and unloading zones, but also in surroundings, but it is also an incorrect use.
Conclusions
The analysis carried out in this article has highlighted the illegal use of loading bays in an urban context through the analysis of the use and characteristics of loading zones of representative loading zones in the city of Santander (Spain). Using the information obtained, different regulation compliance scenarios have been simulated to assess the consequences of illegal loading bay use and its implication on efficient urban space use.
From the analysis of the use of the four loading zones, it has been observed that the degree of illegal use is very high: between 58% and 44% of vehicles make some kind of unlawful use of the loading bay, whether due to type and/or excessive time durations. Hence, between 34% and 55% of vehicles commit other illegal acts in the spaces adjacent to loading zones, such as double parking or parking on the pavement or in restricted areas. On the other hand, with respect to the quantitative assessment of the inappropriate use of loading bays, it was observed that illegal use due to excessive duration has a greater impact than illegal use due to vehicle type (provided that the percentage of illegal use is similar).
Additionally, the following conclusions have been reached, which may help to define loading zone capacity and characteristics: • The larger the loading zone capacity, the less efficient its use is. This is due to the fact that the bigger the capacity, the less probable it is that vehicles will park there in an organized way.
•
With regard to loading zone characteristics, it was observed that the characteristics of the space on either side of the loading zone influence the use of the zone itself.
The conclusions obtained in this investigation provide several recommendations for the city authorities. These recommendations or strategies for the design of loading zones are:
•
The capacity of the loading zone has to be five at most, in other words, five medium freight vehicles parked at the same time.
•
Avoid loading zones without pavements or physical objects. If it is necessary to place a physical object on either side of a loading zone in order to protect street furniture, such is waste bins, authorities should use a concrete slab instead of bollards or any other item.
•
Authorities should implement measures to control loading zones. Not only to control the type of vehicles that use it, but also to control the duration of parking in order to promote vehicle rotation.
•
It is advisable for regulations to be strict so that inappropriate use of urban public space is reduced, both in and around loading zones. Funding: This study and the development of future research are possible thanks to financing from the Santander City Council from the MOBIDATA-SDR project.
Conflicts of Interest:
The authors declare no conflict of interest. | 8,806 | 2020-07-23T00:00:00.000 | [
"Materials Science"
] |
Channel Attention-Based Approach with Autoencoder Network for Human Action Recognition in Low-Resolution Frames
,
Introduction
Human activity recognition includes a wide range of real-life applications, such as monitoring human activities, detecting abnormal or suspicious activity, retrieving video based on various actions, semantic video recognition, and observing patients in health centers [1,2].To date, several solutions have been proposed for monitoring actions using video images, such as a visual review of events in videos [3,4].While some have performed well, various body parts, such as hands and legs, can also be used to detect movement [5].
Still, images alone cannot depict the full action.Our ability to recognize complete actions in video data is based on analyzing human body movements in-frame and their interactions with the environment [6].
Te system should function in a fraction of a second, which has unfortunately not received much attention in previous research.Although a compromise between accuracy and time is required, real-time processing is still regarded as one of the top benchmarks in information processing.Human activity recognition systems can process video frames based on frame rate per second and real-time monitoring of nonstatic environments, according to statistics [7,8].It remains one of the most difcult aspects of video processing to track multiple goals in a chain of online videos.Tis is especially true when it comes to topics such as recognizing human activity.Databases contain movements in everyday life.Tese movements are considered normal, and some are considered anomalous [9][10][11].Because of this, recognition under dense conditions is crucial in those multiple activities.In addition, accuracy is compromised when movements overlap, such as jumping and diving together.Terefore, we plan on developing an action recognition system based on a network of video sensors in diferent dynamic environments.Tis will apply to several multispectral control videos.In order to recognize data, feature extraction and classifcation algorithms are required, regardless of the type of data.Support vector machines (SVM) and neural networks (NNs) can be utilized as primary classifers in handcrafted feature extraction-oriented systems like those described in [12].Deep learning (DL), mainly convolutional neural networks (CNNs), based on the hierarchical system of the human visual cortex, has advanced considerably in image classifcation [13].By using feature extraction and classifcation models, CNNs can learn categorical information from their features.Analyzing action representations and extracting features could signifcantly improve action recognition.
Human activity recognition is a challenging research feld today.Video frames were analyzed to identify human activity.Te demand for more precise and efcient frameworks for a variety of contexts grew, as did the demand for more information, images, and video frames.In this feld, deep learning is a highly efective and powerful technique.Recently, several approaches have been presented to recognize human activity in video using CNN, also known as automatic methods.Nevertheless, such systems may not process multiple video frames accurately in real time.Consequently, the requirement for large volumes of realtime and ofine data has led to creative ideas in the feld of motion and activity recognition through video.Some general goals are as follows: (1) Our goal is to develop easy-to-use methods for our leading action recognition research.For various applications of human activity recognition, this is the most accurate estimate.
(2) Our model has been trained and can be used in action recognition applications like hybrid deep learning.Te network can thus extract information from several datasets and generalize it to other datasets, resulting in improved accuracy.Our model is, therefore, more efcient, faster, and more suitable for big data applications.Te proposed model can be implemented as a ready deep learning architecture in action recognition applications due to its rapid convergence and updating.
Video processing should incorporate deep learning techniques, which uses several feature extraction models.CNN with autoencoder [11,14,15] (CNN-AE) characterizes features well.CNN-AE extracts and classifes features based on improved attention mechanisms.As most methods for recognizing human actions rely on the quality of the frames, recognition errors may occur when the resolution or dimensions of the image change.Figure 1 illustrates how a decrease in quality can adversely afect recognition.
Despite the loss of some frame information, decreasing the size or resolution of video frames can have benefts when sending them to data centers.Tese benefts include preventing unnecessary operations like compression and decompression and online analysis of information received from the environment.Additionally, they reduce the complexity of computation.A suitable and fast structure, such as deep learning, can process low-size or low-resolution frames, reducing computation costs.DL-based structures can function in real time depending on how many layers they have.Tis reduces the decision-making component's computing complexity.As a result of the architecture proposed, it will be easier for an architecture based on generalizability, uncertainty, and evaluation criteria to be developed.A computational method for monitoring human activity is developed in this study using video frames of small size and a low number of frames.Smart city social systems can beneft from adopting and utilizing the proposed approach.To identify human actions in a video with increased accuracy, our research uses a hybrid structure combining a CNN structure and an AE network with a deep hybrid structure.
Tis study aims to improve human activity identifcation in video.Computational complexity is reduced by processing a small number of lower-resolution and lowernumber frames in a short period of time.Our research is innovative in that it combines the improved CNN network with the channel attention module and the AE structure.Tis is for action recognition in high class numbers and in low-resolution videos.A structure like this has never been proposed in a similar study before.
Tis article considers the following contributions.
1.1.Generalizability and Robustness.Te developed CNN with CAM and autoencoder (CNN-AE) model with attention mechanism (AM) is much more robust and helps the decision structure work more efciently.Te proposed method is considered robust since it has low dispersion and low accuracy against large frame quality changes.However, the diversity of datasets used and the ability of the method to make accurate decisions about unknown data demonstrate its generalizability.Due to its robustness, the proposed system recognizes human actions.On the other hand, the approach is capable of processing a random range of video frames of poor quality, indicating that it is sufciently generalizable.
Monitoring Human Action.
It is also possible to detect individual behavior.Monitoring unusual activities can serve many purposes.Recognition of human activity on video has a substantial impact on environmental deterrence and urban crime prevention, resulting in a more sustainable city.
2 International Journal of Intelligent Systems
Real-Time Decision-Making.
A CAM-CNN architecture with AE architecture retains reliable recognition even when no frames or low-size videos are present, unlike end-to-end (e2e) and traditional deep learning models.According to some experiments, the proposed method represents a realtime method.As a single-processor, it has a sufcient frame rate of frames per second (FPS), and each frame takes less than a second to process.It is estimated that the proposed action recognition method can work in real time or close to real time.In other words, a fast structure that can recognize human actions quickly is relied upon during the decisionmaking process to aid in the processing of videos with small sizes and a low number of frames.
Our research is described below.Section 2 provides a brief overview of related studies.A newly developed feature extraction and learning technique is presented in Section 3 that uses the optimized CNN structure and AE described in Section 3. Section 4 reports the experimental outcomes generated by the proposed video frame analysis method.Following the conclusion of this study is a summary of the major points discussed in Section 5.
Related Work
In recent years, computer vision has gained interest in video comprehension and action recognition.Tis is due to its potential applications, such as robots, autonomous driving, camera monitoring, and human behavior analysis.Te earliest video sequence encoding techniques used handcrafted features [16][17][18][19][20][21][22][23][24][25].With its rich trajectory features, AR with increased trajectories [19,26] achieved remarkable performance and has become one of the most popular hand-designed systems today.In this section, we discuss two signifcant topics: deep learning-based action recognition approaches and low-resolution activity recognition methods.
It is possible to divide the remaining techniques for video action recognition into two categories.To enhance their temporal modeling capabilities, the frst group of models uses a conventional two-stream structure [18,41].Spatial 2D International Journal of Intelligent Systems CNNs learn semantic features from optical fow, while spatiotemporal 2D CNNs analyze motion content from video.Te fnal predictions are determined by averaging the scores of the two streams trained simultaneously.Data combinations for spatiotemporal analysis were examined in studies [37,[42][43][44][45]. Using sparse frames from evenly divided video clips, spatiotemporal segment networks (TSNs) [46] capture long-range relationships.Dual-path methods require optical fow computations because they are timeconsuming and storage-intensive.Te proposed technique, however, can operate without an optical fow mode, which reduces network complexity.3D-CNN-based systems and 2 + 1D CNN systems comprise the second group of action recognition algorithms.3D convolutions were used for the frst time to defne spatial and temporal data simultaneously in C3D [47].As part of I3D [48], 2D convolutional kernels are supposed to be stretched into 3D to capture spatiotemporal features.Tere are, however, many parameters involved in 3D-CNNs, which makes them not suitable for all applications.A variety of strategies have been adopted to manage the costly calculations of a 3D-CNN using the 2D + 1D paradigm.By decomposing 3D convolution into a pseudo-3D convolutional block, pseudo-3D (P3D) [49] produces a pseudo-3D convolution.3D convolution is factorized by R (2 + 1) D [50] and S3D-G [51] to improve precision and reduce complexity.A relational module can be viewed as an alternative to pooling using a time relation network (TRN) [52].A spatiotemporal shift module (TSM) [53] shifts a proportion of features along the temporal dimension, giving the network the performance of a 3D-CNN while maintaining the complexity of a 2D CNN.With nonlocal neural networks [54], it was possible to capture long-range temporal dependencies between video frames and be more efcient.A dual-path network with an interactive fusion of mid-level elements was used in SlowFast [55] to model spatiotemporal data at two distinct temporal rates.Using the knowledge distillation procedure, our method also approximates the spatiotemporal representation at the feature level.Te spatiotemporal representation capacity and transferability of 2D CNN and 3D-CNN models were determined [56].Action recognition efectiveness can be enhanced by maximizing selected frames via dynamic knowledge propagation [57].Elastic semantic networks (Else-Net) [58] and memory attention networks (MAN) [59] have shown improvement in recognition precision in recent years.
Frame ordering has been discussed in several previous works [60][61][62].While these previous eforts partially addressed some aspects of order prediction, their results only provided limited supervision, i.e., a binary label for inorder or out-of-order events [60,61] or subclip-based order prediction [62].Furthermore, there is no explicit technique to encourage the model to prioritize motion data over background data.
Transformer-based techniques [63] signifcantly improve accuracy while conserving processing power.Using ViViT, a pure-transformer method for factorizing spacetime dimension inputs, we handled spatiotemporal tokens from a long series of frames efectively.By separating spatial and temporal focus within each block, TimeSformer [64] minimizes training time while maintaining test efectiveness.Spatial-temporal transformer (ST-TR) networks were constructed for skeleton-based action identifcation [65,66].In comparison with previous state-of-the-arts, Trear [67] has shown a signifcant improvement in egocentric RGB-D action recognition.Multiscale pyramid networks, MViT, were presented in [68] to extract information from low-level to high-level attention.Comparatively to other successful applications, transformers have not fully realized their potential in action recognition.
Te human action recognition method has been employed for abnormal events and abnormal behaviors in some studies [69][70][71][72].Additionally, it enhances safety and security by monitoring activities.Furthermore, it can be used to detect suspicious activity as part of a criminal investigation.Classical learning methods were used in some cases, while deep learning methods were utilized in others.
Low-Resolution-Based Action Recognition Methods.
Kawashima et al. [73] developed a deep learning-based method for identifying actions from extremely lowresolution thermal images.Tey distinguish between common and rare human actions (such as walking, sitting, and standing).Individual privacy protection is a strength of their work, which can be applied to Internet of Tings (IoT) platforms.Low-resolution thermal images are difcult to compute feature points and build a precise contour of the human body, even if privacy concerns are overlooked.Termal images, their frame diferences, and the center of gravity of people's areas are used as inputs to their deep learning method for learning the spatiotemporal representation.
Te application of deep neural networks to video action recognition follows their widespread adoption for image classifcation [47,48,74].According to C3D [47], one of the most well-known deep networks, 3D convolution is more suited to extracting spatiotemporal features from video.Analysis of deep ResNet [27] structure options for action recognition [74] has demonstrated desirable performance on common benchmarks using I3D architecture [48].Te approaches to low-resolution (LR) single-frame applications include domain adaptation, feature learning, and superresolution [48,75].
Privacy protection has infuenced earlier research on this topic [76][77][78].Te model in [77] identifes several transformations that produce LR videos based on the highresolution (HR) training set.As a result of training on the LR dataset, action classifers should gain a more precise decision boundary.Te concept of inverse super-resolution (ISR) was introduced by Ryoo et al. [77] after they found distinct pixels in downsampled frames.Using this method, additional data can be extracted from low-resolution frames after learning how to alter images properly.To improve the acquisition of information inherent in low-resolution frames, Ryoo et al. [78] developed multi-Siamese loss.Ryoo's achievements have established the standard for recovering lost visual information from constrained pixels.International Journal of Intelligent Systems According to Chen et al. [79], LR and HR networks could share some flters in a semicoupled two-stream structure.It provides high-quality training frames.Xu et al. [80] found that leveraging HR videos efectively improved LR recognition performance signifcantly.A two-stream structure incorporating HR frames as inputs was demonstrated.A fully linked two-stream network that shares all convolutional flters with an LR network outperforms previous methods marginally.CNN-based action classifers are trained simultaneously [79,80] to ensure equal representation of HR and LR frames.
Action recognition [81] is examined in super-resolution.Optical fow-guided training was developed to improve existing image-and video-driven super-resolution architectures.Tey demonstrate their performance on genuine, minute actions by downsampling HMDB-51 and UCF-101 to 80 × 60, but their performance on genuine, minute datasets difers greatly.
Novel models address the practical difculties associated with extremely low-resolution activity [82][83][84][85].Demir et al. [86] have also developed a natural LR benchmark called TinyVIRAT and an approach that employs a progressive generative method to enhance LR quality.By using these models in HR frames, visual information lost over time with a limited number of pixels can be retrieved [87].
Even though LR frames were used in most of these methods, it is unclear why more optimal architectures were not used.Conversely, similar methods have difculty recognizing states like "falling," "sitting," and "lying down" because many action classes are not considered.Furthermore, some methods cannot be implemented in the real world as a model.
Despite the previous action recognition models, the paper presents an improved CNN that incorporates the structure with attention mechanisms and AE architecture.Tis will increase accuracy while using less information than previous models.In addition, we will test the method's suitability for low-latency and real-time scenarios.Based on feature learning, we developed a dataset for short-term human action recognition using low-quality video.Similar action recognition models require scanning the entire length of a video sequence to classify large temporal sequences.Trough this method, we can create a new and enhanced machine-learning tool for testing models that recognize human motions quickly and with minimal latency.
Methodology
Figure 2 illustrates how our model recognizes various actions in video frames using the introduced method.We describe this method in the following sections.
Preprocessing.
In various environments captured on video surveillance, we use a deep learning network to recognize human actions and detect unusual activities or abnormal behavior.In addition to increasing accuracy, deep learning architectures are more capable of handling large datasets.Video input comes from a mix of existing and newly developed sources.Te process of preprocessing involves removing frames from previously captured videos.A subfolder named after each video is established and maintained along with the frames.JPG images are created from the video frames.
To conform to the enhanced integrated deep learning architecture, the data are compressed and saved in 224 × 224 dimensions.Prior to being stored in the folder, the testing video is also converted to frames and scaled to 224 × 224.Te preprocessing is performed using MATLAB functions.Te bilinear method was also used for large, medium, and lowresolution or low-size images (i.e., 100, 50, and 10% of the original frame resolution).For downsizing images, a rapid reduction of dimensions or resolutions is preferred.Its bilinear frame downsizing accuracy and its speed are signifcant reasons for choosing it.
Random sampling is used to generate a few frames in an action video.By using frame sampling to reduce video volume, unnecessary data processing can be saved.Based on dataset characteristics, diferent videos have diferent numbers of shot segments.In order to reduce the number of images available for each segment, we randomly select one frame.Video captures almost all the actions with a small amount of information.As shown in Figure 3, we present a method for capturing dynamically sampled shots.
Proposed Hybrid Model. Tis paper describes a method
for low-resolution action recognition and abnormal behavior from sample frames that consists of four sections: convolution, maximum integration, sampling, and fully connected.Te following are parts of the proposed combined method to recognize human actions in video.
Multilayer Convolution.
CNN architecture is depicted in Figure 4. Multilayer convolution has four types of operations: fully connected layer (gray color modules), upsampling layer (light yellow modules), max-pooling layer (light green modules), and convolutional layer (light blue modules).Te permeability of porous materials was predicted using a CNN (see Figure 4).Tere are two convolutional layers and one max-pooling layer in the CNN architecture.Max-pooling reduces the number of parameters in the network and expands its receptive feld by halving the size of the feature map.As a result, the CNN structure is essentially the design of the network, while the autoencoder (AE) is the core of the network [14].
For AE and CNN, we provide frames of low-resolution 128 × 128 × 1 size.Te size of the detail matrix is reduced to 64 × 64 × 2 after the frst CNN layer.It is the number of kernels that determines the number of channels in the feature map when convolution is performed.Using the CNN architecture, a low-resolution 128 × 128 × 1-sized frame is converted to 4 × 4 × 32, 8 × 8 × 16, 16 × 16 × 8, 32 × 32 × 4, and 64 × 64 × 2-sized feature map.According to the most recent attribute map, each integer represents the highest level of a feature.To fatten and connect 3-D map layers, we used 1-dimensional feature lines with 512 features.AE creates a 4 × 4 × 64 feature map, which is then transformed into a 1024-dimensional feature line.As shown in Figure 4, International Journal of Intelligent Systems the AE-CNN will be discussed below.In addition, two feature maps are examined in an interconnected network.Input layers contain nodes that facilitate the transfer of lowresolution image output from one frame to the next.
Instances of a node may display regional characteristics, such as various parts of pixel picture information at diferent activity locations.Global characteristics can also be displayed in another instance.Training determines characteristics automatically.Te fully connected network consists of nodes linked at the upper and lower layers.We use the nodes in the previous layer to calculate each node, which is expressed as follows: Te current layer is indicated by s, the number of neurons in the layer by r, and the number of layers with full connectivity by w and b.A common machine learning strategy for evaluating, choosing, and utilizing high-level data to estimate valuations is a fully connected network.For instance, as depicted in Figure 4, it decreases in size from 400 to 150 due to classes.A frame can be used to deduce the actions to be taken in the upper half of the tree.If the input image has poor resolution, the reconstructed features will be inappropriate, common in feature engineering scenarios.Low-resolution frames lack comprehensive information, resulting in confusion during training and accuracy drops.Low-level characteristics are needed to detect activities.CNN cannot forecast high-resolution properties based on low-resolution images.To support the trained network, lowresolution frames and high-resolution features can both be used.Te hybrid CNN combines low-resolution images with features, while the AE module creates high-resolution images.
Autoencoder.
To train AE procedures, we do not need to recognize every frame in the dataset.Relabeling, on the other hand, prevents low-detail frames from appearing and enables more accurate training.AE is signifcantly easier to collect training datasets due to labeled data independence.As a result, the dataset containing the greatest number of pairs of low-and high-resolution frames is selected as a starting point.Te fgure shows that the AE module contains an encoder (upper branch) and a decoder (lower branch).An encoder consists of three convolution layers and a max-pooling layer (distant branch).A decoder layer consists of one up-sampling layer and two convolution layers.Figure 4 illustrates in yellow how the aforementioned sampling approach has the opposite efect on the maximum collection operation.Te small map is transformed into a large, high-resolution image using a sampling method that doubles its width and height.Te encoder transforms low- International Journal of Intelligent Systems max-pooling layers, increasing the map size of the ultimate feature to 32 × 32 × 8. Tis network's parameters have also been designed and trained.Trough repetition, each encoder and decoder consist of fve layers.Te initial conditions are a low-resolution frame (L), a high-resolution or image (H), and a newly generated high-resolution or original-size image (newH).Four components of AE training are examined: (1) Te encoding process begins with the convolution layer, which transforms L input data into features.Te following relationship between the F feature and the L input can be specifed after an encoding layer [15]: (2) Unlike the previous step, the decoding procedure converts the F feature into a high-resolution newH image.Input newF and output newH are related through the following equation [15]: Te encoding and decoding convolution layers are identical with the exception of the last decoding layer.By improving the activation performance of the last complexity layer, the output result is transformed to the range 0-1.
(3) Te adaptive moment estimation technique reduces cross-entropy error for N data in AE (N AE ) by using a network that changes the network's settings [15].
(4) During training, the number of encoding and decoding convolution layers increases.In both the encoder and the decoder, each layer is initialized one by one.Each encoder or decoder layer is added in three steps, up to fve encoder layers.
An encoder can achieve high-resolution recording of human actions by using the above training approach.Tis trains it to distinguish between low-and high-resolution frames from video frames.Te decoder can produce highquality images using this data.CNN's kernel was incorporated into an image processing module to extract features from low-resolution images.Both CNN and AE are provided as a fully connected layer for the ultimate prediction of actions from low-resolution images of distinct areas, with AE acting as a parallel branch line to the original CNN branch.Since the encoding features prevent defection accumulation, we use them instead of high-resolution frames.For high-resolution frames, we need encoders and decoders before CNN, resulting in a 15-layer convolution layer instead of the 5-layer layer proposed in this study, which increases parameters, overftting, and enhancement.Accuracy decreases when degradation occurs.
In equation ( 4), the LOSS metric function is diferent from the loss function representing the entire combined network and its convergence.For this study, the LOSS function was used for the AE.However, in general, for the entire combined network and to guide the network to train all the parameters, the mean square error (MSE) was used as the loss function.Te MSE can be expressed as follows: In this context, for N data, the variable y i ′ represents the recognized action of the i-th low-resolution video image, while y i represents the observed action of the corresponding high-resolution image as determined through the utilization of the lattice Boltzmann technique.
Channel Attention.
Channel attention modules (CAMs) are CNN modules focused on channel-based attention.Te channel attention map is generated by leveraging the interchannel relationship among features.Te concept of channel attention arises from the understanding that each channel within a feature map detects specifc features.Consequently, channel attention aims to determine the signifcance or relevance of the detected features in relation to the input frames.It is necessary to compress the spatial dimension of the input feature map to calculate channel attention efectively.A squeeze block and an excitation block were used in the feature channel domain.CNN extracts spatial features as a ftted decision system.By adjusting several feature maps in the channel domain, discriminating features can be selected.
Its performance can be maximized without adding new features by combining dense block and transmission layers with channel attention.Channel attention networks are small in size, and their assisting parameter is just 0.22 M, preventing overftting.To minimize the size of the feature map, a transition layer with the 1 × 1 convolution layer and International Journal of Intelligent Systems a middle integration with stride 2 can be used.Combining the channel attention module with the transfer layer results in adaptive sampling.In Figure 5, the channel-based attention mechanism processes feature channels, such as "excitation" and "squeeze," in two stages.
In the squeeze step, a one-dimensional vector of input characteristics is compressed into a length proportional to the number of input channels.In the original input feature, W × H × C, there are C channels in the spatial domain and U channels in the size domain.
Te 1 × 1 × C vector is generated by compressing each spatial domain W × H into a single value by pooling global averages.Te formal determination of the c th component, z c , of the squeeze output is given by the following equation [33,59]: Gate mechanisms consisting of two nonlinear, fully connected layers can capture channel dependence during the excitation phase.
As a result of the model's low computational complexity, the two fully connected layers are just C/16 and C, respectively.s c are used to represent the excitation output to decrease model complexity [33,59]: In the presence of W 1 and W 2 , which are the C/16 and C layer parameters, σ is the sigmoid function, and δ is the ReLU function.Furthermore, the z is the squeeze output.Finally, a weight is assigned to each feature channel.For each feature map, the weight vector s c and the initial feature maps u c are used as inputs.Te channel-wise multiplication of feature maps produces the fnal product, the u c ′ feature maps [33,59].
Te channel attention module allocates adaptive weights to features by expanding and squeezing feature channels.Te attention model for feature maps is the only parameter in this module that has a limited number of parameters.
Experimental Results
In this section, we analyze the results based on the implementation parts of the study methodology.We begin by examining the video frames.
Datasets.
Datasets utilized in the analysis include HMDB51 [88], UCF50 [75], and UCF101 [76].Dataset HMDB51 [70] is one of the most complex and difcult to analyze video image datasets related to human action recognition.Human facial interaction includes movement of body parts, physical contact with objects, and exercise.From YouTube, 6849 action samples were collected and categorized into 51 categories.Each category contains approximately 100 videos.Datasets are complicated when samples are collected from diferent participants performing the same task under diferent lighting and perspective settings.Considering the variety of camera movement, view and position of objects, object scale, perspective, cluttered background, and ambient light, the UCF50 [75] shows a wide range of human behaviors.Te action groups are divided into several groups with some characteristics in common, such as a person who plays the piano four times from diferent perspectives.
It contains 13,320 YouTube videos from 101 action classes in AVI format from UCF101 [76].Every action takes between 2 and 7 seconds, and 100 to 130 samples are evenly distributed across all categories.UCF101 analysis is difcult due to the large number of action classes involving human interaction with objects, musical instruments, and body parts.A few frames from the UCF50 dataset are depicted in Figure 6.
Implantation Details.
Te features of the computer system that allowed us to develop our approach are as follows: Intel (R), Core (TM), and Core i7 processors come with a single processor and 8 GB of RAM and a 64 bit operating system.MATLAB programming tools were used for the analysis of quantitative.Te default learning rate for this model is 0.001.Te improved model uses CNN and autoencoder between 200 and 1000 learning periods, and SGD applied CNN and autoencoder to further enhance the optimized structure.A single CPU processor was utilized to train the improved CNN model and autoencoder for about six to 10 hours for diferent learning structures.
All of our models are built based on transition learning models and fne-tuned convolutional networks.Te training and validation process involved the calculation of errors, estimation of training parameters, convergence, and fnally accuracy calculation.Error minimization during validation
Evaluations.
Based on the confusion matrix confguration, the multiclass status is estimated based on the accuracy criterion.In this study, three modes of all video frames were analyzed.In these modes, the frame was created at 70, 40, and 20% of the quality.Te proposed model was used to identify human actions.An analysis of the confusion matrix determines how well a machine learning system performs in classifcation.Te confusion matrix measures the diference between actual and expected values.Figures 7-9 show that the proposed method can recognize human activity at three diferent levels of video quality, i.e., 70, 40, and 20% of the original frames, with over 90% accuracy.It has even been observed that 100% accuracy has been achieved in some instances.Tere are separate sections for each assessment.
UCF50.
As stated before, the flms collected from this database are classifed into 50 distinct categories.Each category's videos are broken into subcategories that share characteristics such as baseball, basketball shooting, bench press, and motorbike riding.Bicycling, shooting pool, diving, drumming, and numerous other activities are incorporated into sports.Some of them are quite similar to other human acts and movements.Figure 7 shows the algorithm results for three distinct video quality levels with falling rosettes.While the frame size has not changed, the output accuracy varies slightly from the original resolution.However, despite the drop in-frame resolution, the diference between the results is relatively small.Te standard deviation is slight between them.Although the CAM-AE structure has a large number of classes, it has developed discriminative features and representation learning through changes in the set of frames.
As a result, the accuracy of more than 50 categories exceeded 96% and fve of them exceeded 97% in the various action categories.Figure 10 shows the learning, training, and convergence process of the proposed method based on the model's accuracy and loss criterion.Tis is for the set of video frames obtained from UCF50 video data for all three types of frame quality.In comparison with other deep structures, the method identifes human actions with less computational complexity.Te hybrid structure, however, will be more efective with more repetition.Moreover, there are also many layers of other CNN family structures with similar challenges, such as generalizability, uninterpretability, and computational complexity.
UCF101
. Te UCF101 dataset is complex and difcult to use since there are numerous action classes represented by humans who perform various activities with a variety of items, such as playing musical instruments, using sports equipment, or interacting with a procedure with diferent body parts.Figure 8 shows that when the size and resolution are reduced from 70% to 20% of the original frame, the classifcation error rate stays the same with low International Journal of Intelligent Systems variances.Even when video frames are poor, processing has not been challenged and accuracy is higher than 96% in some cases.
For a set of UCF101 video frames with three diferent quality levels, Figure 11 illustrates the learning, training, and convergence processes of the proposed method.Tis is International Journal of Intelligent Systems based on accuracy and loss functions.Moreover, it is evident that in addition to completing the claim of the previous section, the proposed method is more accurate and requires less computational complexity than other similar deep structures and algorithms for recognizing human actions.
HMDB51
. Te HMDB51 video frame set is one of the most complex sets of human activities ever studied.HMDB51 video frame set includes categories related to human exercise, body movements, and body contact with objects.In total, there are 6849 YouTube actions divided into 51 categories.Tere are approximately 100 videos in each category.Participants' varying brightness and perspectives have made the dataset more complex.State-of-the-art methods have 60% precision in this dataset.Interest in this form of data collection has grown dramatically in recent years, with some studies reporting a 70 percent interest rate.Te suggested technique estimates a 78 percent increase in output despite video quality loss.
It is true that the proposed method for identifying human actions in the HMDB51 dataset is less accurate than that in the other two datasets; however, compared to other similar methods, the results are satisfactory.Te obtained results are inaccurate due to the high complexity of the videos.Tere is little variation between reported outputs despite a signifcant quality drop.Figure 9 shows the results for three diferent video quality levels.Figure 12 depicts the training and convergence procedures of the proposed technique for a collection of HMDB51 video frames of three diferent quality levels.
Discussion
Tis research aims to reduce the number and size of video frames received from human actions while maintaining accuracy.Classifcation accuracy, however, will decrease as the video quality decreases.Trough CAM and creating a deep hybrid structure with AE, the proposed method has overcome the challenge of low video quality in terms of frame number and size.
Recognition and Video
Frame Quality.In Table 1, the performance of the proposed method is examined by reducing the dimensions of video frames as well as the number of frames.Labeling frames are determined by random sampling based on the original labels.To make the analysis less computationally complex, we randomly selected one of the three frames.When we analyzed what the fnal accuracy would be if a random frame were chosen from 2, 3, 4, . .., and 10 frames, we also considered other scenarios.Table 2 shows that the highest accuracy was obtained when one of the three frames was selected.Table 2 shows one frame at a time from 2, 3, 4, . .., and 10 frames.
In addition, frames with reduced dimensions were evaluated in terms of frames per second (FPS) to estimate the computational complexity of the frames.As the number of deleted frames increases, the correlation between the extracted features and the video sequence decreases.Methods may be used to dynamically fnd the most appropriate video frames.By using diferent strategies, preparing the video and fnding the proper frames can, however, take a long time.12 International Journal of Intelligent Systems
Comparison.
Comparative methods detect actions more accurately and with less computing expense than the suggested method.Using our method instead of handcrafted methods, we extract features more accurately.Many new approaches to action recognition have appeared in recent years, including deep learning methods [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37].Despite International Journal of Intelligent Systems Besides extracting features, discriminating features that can be generalized under diverse acquisition conditions are essential.Feature extraction is sometimes achieved by creating a skeleton from the video; however, the information gained from the skeleton is sometimes discarded, making the method less robust and accurate [37,45,89,90].Several of the methods in [25, 32-36, 41, 45, 91-99] obstruct the operation of features by adding unnecessary parts.Tus, the addition will lower the accuracy of actions.Based on the attention mechanism, the autoencoder network, and the convolutional structure, the approach suggested in this paper has created a robust method that lowers video frame numbers and dimensions.Te results are compared with those of similar approaches used in recent years as shown in Table 3. Te method can also compete with deep learningbased methods that have emerged in recent years for action recognition [101][102][103].
For UCF50, UCF101, and HMDB51, the model learning training duration was 3, 5, and 4 hours, respectively.By using the benchmark dataset, the suggested classifcation approach is validated for its ability to achieve superior or comparable classifcation precision.We fnd that our suggested technique correctly detects human actions in videos in the majority of cases.Video information overlaps with human actions.Current approaches may incorrectly classify similar actions, such as drinking, eating, chewing, and talking.
Limitations.
To date, considerable eforts have been dedicated to the recognition of human actions; however, only a limited subset of these eforts has adequately addressed the diverse range of limitations associated with this feld.Video recording protocols for people's movements are one of the fundamental challenges encountered in this domain.Tere are a variety of limitations involved, including time considerations, camera positioning, diverse weather conditions, video interference, and the inherent ambiguity surrounding movement classifcation.Human position and speed infuence video images and recognition performance.As a result of excessive illumination and fuctuating weather conditions, human action recognition precision was occasionally compromised.A variety of camera angles make it difcult to accurately evaluate performance based on captured frames.Multiple instances of the proposed model's performance have been deemed satisfactory.However, it is still necessary to train it using videos.Complexity, duration, and poor quality of video frames are signifcant challenges in this task.It may be possible to conduct simultaneous activities over video.In contrast, humans engaging in multiple activities at the same time interfere with decision-making.It is necessary to consider distinct videos that can adequately train the model to address this concern.Human actions are intrinsically complex and challenging to comprehend.Additionally, most action recognition models on standard video datasets focus on videos captured under optimal conditions, ignoring videos captured under abnormal conditions.Moreover, implementation and constraint challenges may lead to pixel occlusion.Limitations such as camera movements and perspective distortion may infuence individual actions.Recognition performance problems can be particularly aggravated when the camera moves.Variations in a system's operational classifcation afect its performance.Tere is a marginal diference between walking and running, for example.Understanding human behavior requires discernment between diferent categories.In scenarios involving changes in style, perspective, behavior patterns, and attire, recognizing human actions becomes increasingly challenging.Human-object communication and analogous activities remain active scholarly topics.In addition to monitoring and tracing multiple actions, recognizing irregularities, such as fraud detection and anomalous behavior, within a limited set of training data is challenging.International Journal of Intelligent Systems
Conclusion
Our method utilizes CNN-based channel attention mechanisms and autoencoders (AE) to recognize human actions in low volume and low number of frames dynamic video.Even low-quality videos transmitted over the Internet or from social media can be handled by our system.Additionally, CNN's model takes channel attention into account when choosing frame-level presentation.Te designed AE can reliably identify multiple actions from poor-quality video frames.Before constructing a low-dimensional feature map, AE converts high-dimensional data into a lowdimensional feature map.Our experiments demonstrate that the proposed system is capable of processing a large number of frames per second (i.e., higher than 25 FPS) and can be employed in real time even when the resolution is poor.Using UCF50, UCF101, and HMDB51 benchmark datasets, this method identifes monitoring performance under nonstationary conditions.By using video frames with appropriate dependability ratings, the action recognition model can be fne-tuned to accommodate changes in nonstationary environments.With an improved version of our current system's architecture, our long-term strategy attempts to set and track specifc goals.Te video dataset does not include multiple actions performed by one individual.Actions that overlap, such as eating, drinking, and speaking, reduce video sample precision.As a multiview surveillance video architecture, we will develop a hybrid action recognition model.In addition, we will design a training architecture to overcome challenges such as noise, similar actions, actions under diferent weather conditions, and multiple actions at once.
FrameFigure 1 :
Figure 1: Te recognition of actions in this fgure is negatively afected by a decrease in resolution and dimensions.
Figure 3 :Figure 2 :
Figure 3: Te random sampling for a baseball action.With the help of the downsizing strategy, a new and low-volume video is reconstructed by randomly choosing frames for each segment.
Figure 4 :
Figure 4: Te framework of the proposed architecture is based on AE and CNN architecture.Network architecture has two branches.On the left side of the plot, a CNN is used with a channel attention block to recognize actions.Meanwhile, the right-side branch involves implementing an AE module to assess frame sequence characteristics.
One of the most challenging datasets is the YouTube Action database.Te action video images of people in this dataset are associated with low resolution, changing camera angles, changing scales, and bright and variable backgrounds.Te dataset contains 11 sports classes with videos from 25 disciplines with four examples per action, as well as YouTube videos.
Figure 5 :
Figure 5: Te channel-based attention mechanism by means of processing feature channels.
Figure 7 :Figure 8 :Figure 9 :
Figure 7: Te confusion matrices of UCF50 action recognition datasets based on three diferent video quality levels.
Figure 10 :
Figure 10: Te training and convergence process of the proposed method is based on (a-c) accuracy and the (d-f ) loss criterion of the model for the UCF50 video data for all three types of frame quality.
Figure 12 :
Figure12: Te proposed method is evaluated through accuracy evaluation (a-c) and loss analysis (d-f ) in the HMDB51 dataset for each of the three types of frame quality.
Figure 11 :
Figure11: Te proposed method is evaluated through accuracy evaluation (a-c) and loss analysis (d-f ) in the UCF101 dataset for each of the three types of frame quality.
Table 1 :
By reducing the dimensions of the video frames as well as the number of frames, the accuracy of the proposed method is revealed in this table.
Table 2 :
From several frame sequences, this table shows the accuracy, frame per second (FPS), and dimensions of choosing a frame.
Table 3 :
Analyzing the proposed method against other comparable methods based on accuracy and computational complexity metrics.
Te best values are in bold. | 9,693.6 | 2024-01-04T00:00:00.000 | [
"Computer Science"
] |
Preparation of an Aminated Lignin/Fe(III)/Polyvinyl Alcohol Film: A Packaging Material with UV Resistance and Slow-Release Function
To reduce the usage of petroleum-based plastic products, a lignin-based film material named aminated lignin/Fe(III)/PVA was developed. The mixture of 8 g lignin, 12 mL diethylenetriamine, 200 mL NaOH solution (0.4 mol·L−1), and 8 mL formaldehyde was heated at 85 °C for 4 h; after the aminated lignin was impregnated in the Fe(NO3)3 solution, a mixture of 3 g aminated lignin/Fe(III), 7 g PVA, and 200 mL NaOH solution (pH 8) was heated at 85 °C for 60 min; after 2 mL of glycerin was added, the mixture was spread on a glass plate to obtain the aminated lignin/Fe(III)/PVA film. This film demonstrated hydrophobicity, an UV-blocking function, and a good slow-release performance. Due to the formation of hydrogen bonds between the hydroxyl groups of lignin and PVA, the tensile strength, the elongation at break, and the fracture resistance of the film were 9.1%, 107.8%, and 21.9% higher than that of pure PVA film, respectively. The iron content of aminated lignin/Fe(III)/PVA was 1.06 wt%, which mainly existed in a trivalent form. The aminated lignin/Fe(III)/PVA film has the potential to be used as a food packaging material with anti-ultraviolet light function and can also be developed as other packaging materials, such as seedling bowls, pots for transplanting, and coating films during transport.
Introduction
Lignin is the second most abundant organic matter in plants. It possesses the advantages of being an abundant source, and is non-toxic, biodegradable, and inexpensive [1,2]. Lignin is mainly composed of phenylpropane structural units connected by carbon-carbon bonds and ether bonds [3]. The rich functional groups along with the large number of chemical reaction active sites present in lignin are conducive to further expanding the functions of lignin through chemical modification approaches, such as hydrogen alkylation, amination, nitration, sulfation, sulfonation, alkylation/dealkylation, esterification, and pharmacology [4,5]. In recent years, lignin, as a biomass material, along with its inherent biodegradability and biocompatibility, have attracted widespread levels of attention and research in the fields of green sustainable agriculture and new packaging materials [6,7].
Current commercially available seedling bowls and plant transplanting pots are still dominated by petroleum-based materials, such as plastics, which are difficult to biodegrade and are environmentally unfriendly [8,9]. The development and preparation of lignin-based films or flake packaging materials will effectively solve the above problems. However, pure lignin does not have film-forming properties, meaning it usually needs to
Preparation Method of the AL/Fe(III)/PVA Film
The preparation method of aminated lignin was referred to the study published by the authors of [23]. Briefly, a mixture of 8.0 g lignin, 200 mL NaOH solution (0.4 mol·L −1 ), and 12.0 mL DETA was heated at 85 • C with magnetic stirring. Then, 8.0 mL of formaldehyde solution was added dropwise into the mixture and then was continued to be heated at 85 • C and stirred for 4 h. The mixture was adjusted to pH 4.5 with 1.0 mol·L −1 of HCl solution, following which it was vacuum filtered. The solid residue was washed to neutral with distilled water and vacuum dried at 55 • C for 24 h with a vacuum of 0.08 MPa to obtain the aminated lignin. Following this, 5 mL of Fe(NO 3 ) 3 solution was added dropwise into a mixture of 4.0 g aminated lignin and 200 mL deionized water. After the mixture was magnetically stirred at room temperature for 6 h, it was centrifuged at 4000 rpm for 5 min. Then, 50 mL of deionized water was mixed with the precipitate and centrifuged again. This step was performed three times in total. Finally, the precipitate was vacuum dried at 55 • C for 24 h to obtain the AL/Fe(III). The effect of the Fe(NO 3 ) 3 solution weight concentration was investigated at 3%, 4%, 5%, 6%, and 7%, respectively.
PVA and 200 mL of solution (adjusting with 0.1 mol·L −1 hydrochloric acid or sodium hydroxide solution and distilled water) were added to a round-bottomed flask. The mixture was then magnetically stirred and heated for 1 h. The effects of the pH value (at 4, 6, 8, 9, and 11, respectively) of the solution and the reaction temperature (at 65, 75, 85, 95, and 105 • C, respectively) were assessed. The AL/Fe(III) was added into the mixture, and the mixture was then continuously heated with magnetic stirring at 300 rpm. The total addition mass of AL/Fe(III) and PVA was 10 g. The effect of the mass ratio of the two materials was assessed at the ratios of 0:10, 1:9, 1:4, 3:7, 2:3, and 1:1, respectively. The effect of the reaction time was examined at 30, 45, 60, 75, and 90 min, respectively. After that, 2 mL of glycerin was poured into the mixture and stirred for another 30 min. Finally, about 10 mL of the mixture was spread on a glass plate and dried at room temperature for 24 h to obtain the AL/Fe(III)/PVA film. The tensile performance of the different film samples was conducted using an IMT-202A universal material tensile testing machine (Dongguan Yingte Naisen Precision Instrument Co., Ltd., Dongguan, China). The film was cut into strips of 100 mm × 10 mm and then clamped on the test machine. The loading speed was set at a constant speed of 50 mm per minute. The tensile strength and elongation at break were calculated according to the following equations: where S refers to the tensile strength, kN·m −1 ; F refers to the maximum tensile resistance, N; b refers to the width of the sample, mm; ε refers to the elongation at break; ∆l refers to the elongation at fracture, mm; and l refers to the initial length of the sample, mm. The breaking strength was determined using a YT-NPY5600Q cardboard rupture tester (Hangzhou Yante Technology Co., Ltd., Hangzhou, China). Prior to testing, the film was cut into a 70 mm × 70 mm square and fixed on the testing machine.
UV Light-Shielding Performance
The film was cut into strips of 40 mm × 10 mm, following which it was assessed using a UV-2600 dual-beam UV-visible spectrophotometer (Shanghai Leiyun Test Instrument Manufacturing Co., Ltd., Shanghai, China). The scan wavelength was in a range of 200-800 nm and the resolution was 1 nm. Moreover, the UV light at wavelength 365 nm was used to further evaluate the resistance of the film to UV light. The ultraviolet lamp was used to shine the 100 Yuan RMB to observe for the presence of any anti-counterfeiting signs.
Slow-Release Performance Analysis
The soil column leaching test was used to detect the release behavior of the iron in the AL/Fe(III)/PVA film. The test was performed in a polymethyl methacrylate tube with an inner diameter of 4.72 cm and a height of 15 cm. The method was referred to the methodology outlined by the authors of [24] with some modifications. The film was cut into squares of 10 mm × 10 mm. Approximately 0.2 g of film fragments and 50 g of quartz sand were filled into the tube, in which the filling depth of the film fragments was about 1 cm from the surface. An initial volume of 220 mL of distilled water was added to the tube, and 25 mL of distilled water was added to the tube on the 1st, 3rd, 5th, 7th, 10th, 15th, 20th, 25th, and 30th day, respectively. After each addition of distilled water, the valve at the bottom of the tube was opened and 25 mL of solution was taken out to measure the iron content using an atomic absorption spectrophotometer (iCE3500, Thermo Fisher Scientific Inc., Waltham, MA, USA). Different kinetic models were used to fit the slow-release data of the nutrient element iron, and their slow-release pattern was analyzed. The correlation coefficient R 2 obtained was used to evaluate its fitting degree. The closer the R 2 value was to 1, the better the model fitted.
Characterization Method of the AL/Fe(III)/PVA Film
The contact angle of the films was measured with 5 µL of deionized water on an OCA20 video optical contact angle meter (DataPhysics Instruments, Stuttgart, Germany). The morphology of the film was analyzed using an S-3400N tungsten filament scanning electron microscope (Hitachi Corporation, Tokyo, Japan). The magnification was set as 5000 times. The functional groups of the film samples were assessed using an Agilent Cary 630 FT-IR spectrometer (Agilent Technologies Inc., Santa Clara, CA, USA). The scanning range was in a range of 400-4000 cm −1 and the resolution was 2 cm −1 . An Agilent 7800 inductively coupled plasma mass spectrometer (Agilent Technologies Inc., Santa Clara, CA, USA) was used for the determination of iron in film samples. The crystallinity of the samples was measured using a Rigaku Ultima IV X-ray diffractometer (Rigaku Corporation, Saitama, Japan) with Cu Kα radiation. The range of the scanning angles were from 5 • to 80 • , respectively, and the scanning speed was 4 • ·min −1 . XPS of the samples were tested using an AXIS Supra+TM X-ray photoelectron spectrometer (Shimadzu Corporation, Tokyo, Japan) with a monochromatic Al Kα source. The photon energy (hv) was 1486.6 eV.
Statistical Analysis
The tests of the mechanical properties of the film samples were performed in triplicates. The data were analyzed statistically through conducting the analysis of variance (ANOVA) statistical test using the software SPSS 17.0 (IBM (China) Investment Co., Ltd., Shanghai, China), and were presented as mean ± standard deviation. Figure 1A shows the effect of the weight concentration of the ferric nitrate solution on the iron content of AL/Fe(III). When the weight concentration of the Fe(NO 3 ) 3 solution was 3% and 4%, respectively, the iron content of AL/Fe(III) was 1.76 wt% and 2.29 wt%, respectively. When the weight concentration of the Fe(NO 3 ) 3 solution increased to 5%, the iron content of AL/Fe(III) reached 3.58 wt%. However, as the weight concentration of Fe(NO 3 ) 3 solution continued to increase, the iron content of the prepared AL/Fe(III) did not increase, indicating that the maximum loading capacity of the aminated lignin had been reached. Therefore, the optimum Fe(NO 3 ) 3 solution weight concentration for the preparation of AL/Fe(III) was 5%. Under this condition, the iron content of the further prepared AL/Fe(III)/PVA film was 1.06 wt%. Figure 1D,E display the effects of the reaction temperature on the tensile strength, elongation at break, and breakage resistance of the film materials under the reaction conditions of 60 min, pH 8, and a mass ratio of 3:7. When the reaction temperature was 65 °C, the values of the above mechanical properties of the composite film were 0.96 MPa, 88.13%, and 101.0 kPa, respectively. With the increase in the reaction temperature, the mechanical properties of the composite films also showed an increasing trend. When the reaction temperature was 85 °C, the above three mechanical properties reached 3.6 MPa, 372.7%, and 317 kPa, respectively. After that, increasing the reaction temperature had no significant effect on the mechanical properties of the composite films. The analysis results showed that when the reaction temperature was lower than 85 °C, the reaction did not occur completely, resulting in poor mechanical properties of the composite film. The optimum reaction temperature was thus determined to be 85 °C. Figure 1F,G show the effects of the reaction time on the mechanical properties of the films under the reaction conditions of 85 °C, pH 8, and a mass ratio of 3:7. In the time range from 30 to 60 min, respectively, the tensile strength, elongation at break, and Figure 1B shows the effect of the mass ratio of AL/Fe(III) to PVA on the tensile strength and the elongation at break of the film materials under the reaction conditions of 85 • C, 60 min, and pH 8. As the mass ratio of AL/Fe(III) to PVA increased, the tensile strength and elongation at break of the film initially showed an increasing trend followed by a decreasing trend. When a small amount of AL/Fe(III) was added, such as a mass ratio of 1:9, the tensile strength of the film was 2.55 MPa, indicating a decrease of 29.4% compared to the pure PVA film (3.3 Mpa). This observed phenomenon was deemed to be due to the uneven distribution of a small amount of AL/Fe(III) in the PVA film, which subsequently damaged the intermolecular structure of the pure PVA film. When the mass ratio of AL/Fe(III) to PVA was 3:7, the tensile strength and elongation at break of the film reached a maximum of 3.6 MPa and 372.7%, respectively, which indicated an increase of 9.1% and 107.8% compared to the pure PVA film, respectively. This phenomenon may have been observed due to PVA being a rigid ion, the lignin fraction containing reactive groups (e.g., phenolic hydroxyl and alcohol hydroxyl), along with the co-blending of the lignin hydroxyl group hydrogen bonds formed between the hydroxyl groups of lignin and PVA to enhance the tensile strength of the films [25]. Figure 1C shows the effect of the mass ratio on the breakage resistance of the film materials. With the increase in the mass ratio of AL/Fe(III) to PVA, the breaking resistance of these films first increased and then decreased. When the mass ratio was 1:9, the breakage resistance of the AL/Fe(III)/PVA film was 34.9 kPa lower than that of the pure PVA film (260 kPa), indicating that the mechanical properties of the composite film was worse than that of the pure PVA film. The optimal value of the breakage resistance of the film was 317.0 kPa at a mass ratio of 3:7, which was 21.9% higher than that of the pure PVA film. Therefore, the mechanical properties of this composite film can be improved by adding an appropriate amount of AL/Fe(III). The mass ratio of AL/Fe(III) to PVA was determined to be 3:7. Figure 1D,E display the effects of the reaction temperature on the tensile strength, elongation at break, and breakage resistance of the film materials under the reaction conditions of 60 min, pH 8, and a mass ratio of 3:7. When the reaction temperature was 65 • C, the values of the above mechanical properties of the composite film were 0.96 MPa, 88.13%, and 101.0 kPa, respectively. With the increase in the reaction temperature, the mechanical properties of the composite films also showed an increasing trend. When the reaction temperature was 85 • C, the above three mechanical properties reached 3.6 MPa, 372.7%, and 317 kPa, respectively. After that, increasing the reaction temperature had no significant effect on the mechanical properties of the composite films. The analysis results showed that when the reaction temperature was lower than 85 • C, the reaction did not occur completely, resulting in poor mechanical properties of the composite film. The optimum reaction temperature was thus determined to be 85 • C. Figure 1F,G show the effects of the reaction time on the mechanical properties of the films under the reaction conditions of 85 • C, pH 8, and a mass ratio of 3:7. In the time range from 30 to 60 min, respectively, the tensile strength, elongation at break, and fracture resistance of the film materials increased with the increase in the reaction time. When the reaction time was 60 min, the above three mechanical property indexes reached their maximum values of 3.6 MPa, 372.7%, and 317 kPa, respectively. With the increase in the reaction time, the tensile strength and fracture resistance of the film material did not increase, but the elongation at break decreased slightly. This phenomenon was determined to be due to the short reaction time and the crosslinking process between PVA and the chelated iron amine lignin being insufficient. In contrast, having too long of a reaction time might destroy the complete crosslinking process between PVA and AL/Fe(III), resulting in a slight decrease in the mechanical properties of the film. Therefore, the reaction time was set as 60 min.
Effect of Preparation Conditions on the Properties of the Films
The effect of the pH value on the mechanical properties of the films is shown in Figure 1H,I. The results showed that the mechanical properties of the composite films were the best at the value of pH 8, especially in terms of the tensile strength. Under acidic conditions, AL/Fe(III) would precipitate to some extent, which would therefore not be conducive to the solution cross-linking reaction, and result in an uneven distribution of the cross-linked film. If the solution was too basic, the cross-linking reaction might be somewhat restricted, and the mechanical properties of the film would be deteriorated as a result.
Zhou et al. [26] found that the mechanical properties of the PVA-chitin nanofiber composite film were improved by adding a 1 wt% of lignin nanoparticles. Li et al. [27] found that when the weight content of alkaline lignin was 5%, the tensile strength and fracture strain of the PVA-alkaline lignin composite film were significantly enhanced. In the process of stretching the composites, lignin function as intermolecular sacrificial hydrogen bonds, limiting the movement of different molecules, thereby improving the mechanical properties of the composites [28]. However, the addition of excessive lignin in the composite film will cause a certain degree of microphase separation, resulting in a decrease in the tensile strain [13]. Even so, the rigid aromatic structure of lignin enhances the mechanical properties of the AL/Fe(III)/PVA film, which is beneficial for the development of these AL/Fe(III)/PVA films as packaging materials.
Characterization of the AL/Fe(III)/PVA Film
The FT-IR spectra of different materials are displayed in Figure 2. A few typical lignin absorption peaks can be clearly observed from the spectra of lignin, aminated lignin, and AL/Fe(III). The wide peak at 3393 cm −1 was assigned to the hydroxyl groups present in the aliphatic and phenolic structures [29]. The peaks observed at 1603 cm −1 and 1506 cm −1 were attributed to the aromatic skeletal stretching vibrations. The peaks at 1457 cm −1 and 855 cm −1 were determined to be derived from the C-H single bond bending and out-of-plane deform vibration processes, respectively [30]. In the aromatic structures, the peaks observed at 1269 cm −1 and 1213 cm −1 were assigned to the guaiacyl and syringyl structures, respectively, and the peak at 1032 cm −1 originated from an ether bond in the lignin structure [31][32][33], suggesting that the skeleton structure of lignin was not destroyed during the Mannich reaction. As the Mannich reaction occurred in the aromatic region of lignin, the intensity of the peaks of the C-H single bond vibrations in the aromatic skeleton decreased significantly in the spectra of aminated lignin and AL/Fe(III), such as the peaks observed at 1593 cm −1 , 1508 cm −1 , 1457 cm −1 , and 855 cm −1 , respectively. In addition, in the spectra of aminated lignin and AL/Fe(III), a new peak at 1082 cm −1 was observed, which was attributed to the C-N stretching and bending vibrations [31], implying that the amino groups were grafted into the lignin structure. In the spectra of AL/Fe(III), the intensity of the broad peak observed at 3393 cm −1 was significantly weakened due to the attachment of iron [34]. Compared with the spectra of pure PVA, the peak of the typical hydroxyl (O-H) stretching vibration in the spectra of AL/Fe(III)/PVA was blue-shifted from 3264 cm −1 to 3270 cm −1 , respectively. This was determined to be due to the strong hydrogen bonds formed by the presence of a large number of hydroxyl groups between the PVA and AL/Fe(III) complexes [35]. Based on the above results, the addition of the AL/Fe(III) complex did not affect the PVA structure, indicating that the reaction between these two materials was mainly a physically bonded blending reaction rather than a chemical reaction. complex did not affect the PVA structure, indicating that the reaction between these two materials was mainly a physically bonded blending reaction rather than a chemical reaction. Figure 3A shows the XRD spectra of PVA and AL/Fe(Ⅲ)/PVA. In the spectra of PVA, the sharp peak at 19.5° corresponded to the (101) crystalline plane [36], and non-significant broad diffraction peaks were also observed at 11° and 40°, respectively [37]. These findings indicate that PVA is a semi-crystalline substance encompassing crystalline and amorphous regions. Compared with the spectra of PVA, the diffraction peak intensity observed at 19.4° in the spectra of AL/Fe(Ⅲ)/PVA was reduced. This phenomenon was determined to have been caused by the cross-linking of aminated lignin/Fe(III) with the Figure 3A shows the XRD spectra of PVA and AL/Fe(III)/PVA. In the spectra of PVA, the sharp peak at 19.5 • corresponded to the (101) crystalline plane [36], and non-significant broad diffraction peaks were also observed at 11 • and 40 • , respectively [37]. These findings indicate that PVA is a semi-crystalline substance encompassing crystalline and amorphous regions. Compared with the spectra of PVA, the diffraction peak intensity observed at 19.4 • in the spectra of AL/Fe(III)/PVA was reduced. This phenomenon was determined to have been caused by the cross-linking of aminated lignin/Fe(III) with the hydroxyl groups of PVA, which destroys the crystalline region of the PVA molecule. Furthermore, in the spectra of AL/Fe(III)/PVA, a few new peaks were observed at 29 • , 36 • , 38 • , 44 • , and 67 • , respectively. The above enhanced and emergent peaks match the standard card for Fe 3 O 4 (PDF#28-0419), indicating that iron was successfully loaded onto the AL/Fe(III)/PVA films. corresponded to Fe 3+ [34], indicating that the iron element present was mainly trivalent iron. Figure 3C shows the XPS survey of PVA, AL/Fe(Ⅲ), and AL/Fe(Ⅲ)/PVA. Compared with the spectra of PVA, the binding energies for N 1s and Fe 2p were observed in the spectra of the other two materials, which was caused by the AL/Fe(Ⅲ) in the composite film. In Figure 3D, the binding energies for the Fe 2p of AL/Fe(Ⅲ)/PVA observed at 725.6 eV and 711.8 eV showed a slight blue-shift. In the spectra of AL/Fe(Ⅲ)/PVA, the weakened binding energies at 710.8 eV and 723.2 eV may have been caused by the presence of strong hydrogen bonds between AL/Fe(III) and PVA in the film material [23]. Meanwhile, in Figure 3E, compared with the spectra of AL/Fe(Ⅲ), the binding energies for the N 1s of the AL/Fe(Ⅲ)/PVA at 399.6 eV and 406.4 eV showed a slight blue-shift.
UV Light-Shielding Properties of the Films
The UV-visible transmittance curves for the PVA and AL/Fe(III)/PVA films are shown in Figure 4A. The AL/Fe(III)/PVA film can shield almost 100% of the UVB (325-275 nm) and UVC (275-200 nm) and most of the UVA (400-325 nm) spectrum, indicating that the AL/Fe(III)/PVA film exhibits strong UV-shielding properties. Moreover, the shielding effect of the AL/Fe(III)/PVA film on ultraviolet light was evaluated using the method entailing the illumination of the RMB with the ultraviolet light, as shown in Figure 4B. Under ultraviolet light, the RMB will display fluorescent anti-counterfeit markings. The results showed that the PVA film had no shielding effect against UV light, with the anticounterfeiting mark on 100 Yuan RMB appearing under ultraviolet light. When the AL/Fe(III) was added to the PVA, the composite film had a strong ultraviolet shielding performance, and no anti-counterfeiting mark was observed on the RMB under UV Figure 3B shows the XPS survey of lignin, aminated lignin, and AL/Fe(III). Compared with the spectra of lignin, the characteristic peak of N 1s was observed in the spectra of aminated lignin and AL/Fe(III), indicating that the lignin was successfully aminated. Moreover, the binding energies for Fe 2p were observed in the spectra of AL/Fe(III), which indicated that the iron was successfully complexed to the aminating lignin. More specifically, the N 1s spectrum of AL displayed signals at 401.9 eV and 407.1 eV, corresponding to the substituted amine and amine group binding energies, respectively [38]. After the aminated lignin was complexed with iron, the binding energy at 401.9 eV was shifted to the lower binding energy at 401.5 eV, which could be attributed to the formation of Fe-N due to the higher electronegativity of iron compared to nitrogen. This result is consistent with the FT-IR analysis, confirming that due to the presence of the amino groups, the aminated lignin had a strong ability to complex the transition metal ions. In addition, the binding energies of 725.6 eV (for Fe 2p 1/2 ) and 711.8 eV (for Fe 2p 3/2 ) corresponded to Fe 3+ [34], indicating that the iron element present was mainly trivalent iron. Figure 3C shows the XPS survey of PVA, AL/Fe(III), and AL/Fe(III)/PVA. Compared with the spectra of PVA, the binding energies for N 1s and Fe 2p were observed in the spectra of the other two materials, which was caused by the AL/Fe(III) in the composite film. In Figure 3D, the binding energies for the Fe 2p of AL/Fe(III)/PVA observed at 725.6 eV and 711.8 eV showed a slight blue-shift. In the spectra of AL/Fe(III)/PVA, the weakened binding energies at 710.8 eV and 723.2 eV may have been caused by the presence of strong hydrogen bonds between AL/Fe(III) and PVA in the film material [23]. Meanwhile, in Figure 3E, compared with the spectra of AL/Fe(III), the binding energies for the N 1s of the AL/Fe(III)/PVA at 399.6 eV and 406.4 eV showed a slight blue-shift.
UV Light-Shielding Properties of the Films
The UV-visible transmittance curves for the PVA and AL/Fe(III)/PVA films are shown in Figure 4A. The AL/Fe(III)/PVA film can shield almost 100% of the UVB (325-275 nm) and UVC (275-200 nm) and most of the UVA (400-325 nm) spectrum, indicating that the AL/Fe(III)/PVA film exhibits strong UV-shielding properties. Moreover, the shielding effect of the AL/Fe(III)/PVA film on ultraviolet light was evaluated using the method entailing the illumination of the RMB with the ultraviolet light, as shown in Figure 4B. Under ultraviolet light, the RMB will display fluorescent anti-counterfeit markings. The results showed that the PVA film had no shielding effect against UV light, with the anti-counterfeiting mark on 100 Yuan RMB appearing under ultraviolet light. When the AL/Fe(III) was added to the PVA, the composite film had a strong ultraviolet shielding performance, and no anti-counterfeiting mark was observed on the RMB under UV irradiation. Therefore, this film material can be developed as a food packaging material with an anti-ultraviolet light function, slowing the oxidation rate of the contents. irradiation. Therefore, this film material can be developed as a food packaging material with an anti-ultraviolet light function, slowing the oxidation rate of the contents. Figure 5A,B show the water contact angles of different films. The results showed that the water contact angles of the PVA film and AL/Fe(III)/PVA film were 35.3° and 100.5°, respectively. PVA contains a large number of hydroxyl groups and is a hydrophilic material [35]. Therefore, according to the principle of surface chemistry, the water contact angle of the PVA film is small. Lignin is insoluble in water, and even though it was modified by ammonia, the hydrophobicity of the composite film was further improved by adding it to PVA. Therefore, the water contact angle of the composite film was larger than that of the PVA film. Additionally, the AL/Fe(III)/PVA film demonstrated a certain level of water resistance. Figure 5C,D show the microscopic surface morphology of the PVA film and the AL/Fe(III)/PVA film. The results showed that the surface of the pure PVA film was smooth, and there were no large visible particles present even after being magnified 5000 times. The surface of the AL/Fe(III)/PVA film exhibited visible lignin particles and uneven concave-convex states. Figure 5E,F show the cross-sectional morphology of the PVA film and the AL/Fe (III)/PVA film. The results indicated that the cross-section of the pure PVA Figure 5A,B show the water contact angles of different films. The results showed that the water contact angles of the PVA film and AL/Fe(III)/PVA film were 35.3 • and 100.5 • , respectively. PVA contains a large number of hydroxyl groups and is a hydrophilic material [35]. Therefore, according to the principle of surface chemistry, the water contact angle of the PVA film is small. Lignin is insoluble in water, and even though it was modified by ammonia, the hydrophobicity of the composite film was further improved by adding it to PVA. Therefore, the water contact angle of the composite film was larger than that of the PVA film. Additionally, the AL/Fe(III)/PVA film demonstrated a certain level of water resistance. Figure 6A shows the cumulative release rate of the iron element from the AL/Fe(III)/PVA film into the soil. The release rate of iron displayed an S-shaped trend. Within 3 days, the release rate of iron was relatively slow. This was deemed to be due to the film material having a certain level of hydrophobicity, which can inhibit the dissolution of the iron elements to a certain extent. From 3 days to 10 days, respectively, the release rate of iron increased significantly, which was determined to be due to the water molecules entering the membrane material and dissolving the iron; meanwhile, the film material exhibited a certain degree of degradation, resulting in the rapid release of iron. The release rate leveled off between 10 days and 30 days, respectively, probably because the aminated lignin contained functional groups with some chelating ability, including the phenolic, hydroxyl, carboxyl, and ammonium groups, which formed a reticulation structure inside the film [39], thus slowing down the cumulative release rate of the iron element. If the nutrient release rate of the material is less than 15% and 75% in 1 day and 30 days, respectively, this material meets the requirements as a slow-release fertilizer [40,41]. In this study, the 1 day, 3 days, and 30 days release rates of iron from the AL/Fe(III)/PVA film reached 1.23%, 10.88%, and 64.67%, respectively. This suggests that the AL/Fe(III)/PVA film exhibits the slow-release effect of the iron element and can be used as a packaging material with a slow-release performance. Figure 5E,F show the cross-sectional morphology of the PVA film and the AL/Fe (III)/PVA film. The results indicated that the cross-section of the pure PVA film exhibited a uniform and smooth state. When AL/Fe(III) was added to the mixture matrix, this resulted in the formation of a rough cross-section of the film. However, the mechanical analysis results showed that this small phase transition does not lead to the deterioration of the mechanical properties. Figure 6A shows the cumulative release rate of the iron element from the AL/Fe(III)/PVA film into the soil. The release rate of iron displayed an S-shaped trend. Within 3 days, the release rate of iron was relatively slow. This was deemed to be due to the film material having a certain level of hydrophobicity, which can inhibit the dissolution of the iron elements to a certain extent. From 3 days to 10 days, respectively, the release rate of iron increased significantly, which was determined to be due to the water molecules entering the membrane material and dissolving the iron; meanwhile, the film material exhibited a certain degree of degradation, resulting in the rapid release of iron. The release rate leveled off between 10 days and 30 days, respectively, probably because the aminated lignin contained functional groups with some chelating ability, including the phenolic, hydroxyl, carboxyl, and ammonium groups, which formed a reticulation structure inside the film [39], thus slowing down the cumulative release rate of the iron element. If the nutrient release rate of the material is less than 15% and 75% in 1 day and 30 days, respectively, this material meets the requirements as a slow-release fertilizer [40,41]. In this study, the In addition, according to the experimental data of iron release from the composite film, the slow-release curves of iron were fitted using different dynamic models. The fitting results are shown in Table 1 and Figure 6. The determination coefficient R 2 was used to evaluate the fitting degree. From the fitting equations and determination coefficient R 2 , the validity sequence of the fitted slow-release model was as follows: Weibull model > First-order kinetic model > Polynomial fitting model > Second-order dynamic model > Higuchi model > Ritger-Peppas model > Zero-level dynamics model > Hixson-Crowell model. In addition, according to the experimental data of iron release from the composite film, the slow-release curves of iron were fitted using different dynamic models. The fitting results are shown in Table 1 and Figure 6. The determination coefficient R 2 was used to evaluate the fitting degree. From the fitting equations and determination coefficient R 2 , the validity sequence of the fitted slow-release model was as follows: Weibull model > First-order kinetic model > Polynomial fitting model > Second-order dynamic model > Higuchi model > Ritger-Peppas model > Zero-level dynamics model > Hixson-Crowell model.
Slow-Release Performance of the AL/Fe(III)/PVA Film
The Weibull model indicated that the release law of the iron elements in the film conformed to the dissolution law of solid drug formulations in sustained-release media [42]. The AL/Fe(III) complex was mixed in the PVA matrix as with small solid particles. When the film material was washed by water, the small particles of the AL/Fe(III) complex on the surface of the material entered the soil with the water flow, and the iron ions were released, resulting in a slow and uneven release of the iron element in the PVA matrix [43]. The results of the polynomial model fit showed that the second-order polynomial equation can better characterize the release kinetics of the iron nutrients and is convenient for practical calculation and application. The Higuchi model showed that these fertilizers were released through dissolution and diffusion [44]. When the film fertilizer was immersed in water, several complex iron ions were flushed into the soil by the water. At the same time, the polymer chains of PVA expanded. Since the film structure was highly cross-linked, van der Waals forces between the polymer chains further limited the migration of the polymer chains. As a result, the dissolution of the iron ions from the film was slowed down. The N value of the Ritger-Peppas model was in the range from 0.45 to 0.89, respectively, indicating that the drug release behavior observed was the result of a combination of drug diffusion and skeletal dissolution [45]. The fitting of the Ritger-Peppas model (n = 0.49818) showed that the release of iron from the film was regulated through the water diffusion and polymer relaxation mechanisms. Therefore, the iron release mechanism of the AL/Fe(III)/PVA film may be that the AL/Fe(III) complex on the surface of the film was dissolved first, and then the iron elements inside the material dissolved and diffused with the dissolution of the AL/Fe(III)/PVA matrix. Future work could consider a further analysis of the relationship between the degradation and release performance of these film materials in the soil.
Conclusions
In this study, a lignin-based film packaging material with an iron content of 1.06 wt% was prepared using the flow casting method. The AL/Fe(III) complex was prepared by impregnating the aminated lignin in the Fe(NO 3 ) 3 solution with a concentration of 5% by weight. The AL/Fe(III)/PVA film was prepared under the following conditions: the mass ratio of AL/Fe(III) to PVA was 3:7, the pH value of the reaction solution was 8, the reaction temperature was 85 • C, and the reaction time was 60 min. The water contact angle of the AL/Fe(III)/PVA film was 100.5 • . Almost 100% of the UVB (325-275 nm) and UVC (275-200 nm) spectrum, as well as most of the UVA (400-325 nm) spectrum, was shielded by the film. The Weibull model was suitable for describing the release rate of the film in the soil. The film can be used for processing into planting bowls, transplanting pots, and food packaging material with an anti-ultraviolet light function. In addition, the molten AL/Fe(III)/PVA material can be used for root treatment during seedling transport. The wet soil located close to the seedling roots can be covered and fixed with the molten AL/Fe(III)/PVA material using the proper spraying processes. The slow release of the nutrient iron is beneficial in promoting chloroplast synthesis in seedlings, thus shortening the slow seedling stage. | 8,397.2 | 2023-07-01T00:00:00.000 | [
"Materials Science"
] |
Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm
The quality of cocoa beans is crucial in influencing the taste, aroma, and texture of chocolate and consumer satisfaction. High-quality cocoa beans are valued on the international market, benefiting Ivorian producers. Our study uses advanced techniques to evaluate and classify cocoa beans by analyzing spectral measurements, integrating machine learning algorithms, and optimizing parameters through genetic algorithms. The results highlight the critical importance of parameter optimization for optimal performance. Logistic regression, support vector machines (SVM), and random forest algorithms demonstrate a consistent performance. XGBoost shows improvements in the second generation, followed by a slight decrease in the fifth. On the other hand, the performance of AdaBoost is not satisfactory in generations two and five. The results are presented on three levels: first, using all parameters reveals that logistic regression obtains the best performance with a precision of 83.78%. Then, the results of the parameters selected in the second generation still show the logistic regression with the best precision of 84.71%. Finally, the results of the parameters chosen in the second generation place random forest in the lead with a score of 74.12%.
Introduction
Ivory Coast is the world's leading cocoa producer, with an annual production of around 2 million tons.This sector represents 15% of Ivorian GDP and 40% of export revenues.The quality of cocoa beans is a determining factor for the sector's success.It influences chocolate's taste, aroma, texture, and consumer satisfaction.Higher-quality cocoa beans are more valued internationally, allowing Ivorian producers to benefit from higher prices.The cocoa industry is a pillar of the Ivory Coast economy [1].With this in mind, the Ivorian government and stakeholders in the cocoa industry are actively committed to improving the quality of cocoa beans.They invest in the research and development of innovative technologies aimed at increasing yield while providing producers with a deeper understanding of the quality of their products.These efforts help them make informed decisions to improve their agricultural practices [2].In recent years, scientific research and technological innovation have converged to give rise to sophisticated systems capable of rapidly, accurately, and non-invasively analyzing colorimetric and spectral properties.This significant advancement has paved the way for creating artificial intelligence models that can assess the quality of cocoa beans in real time based on their unique visual and spectral characteristics [3,4].
As a result, using cutting-edge technologies, such as Spectral Analysis combined with Machine Learning and Optimization through the genetic algorithm, leads to a radical transformation of how we evaluate and classify cocoa beans [5].
The integration of artificial intelligence in this sector has marked a significant advancement, providing players in the cocoa industry, including producers, processors, and traders, with substantial benefits in terms of improved efficiency, precision, and profitability.Using a genetic algorithm as an optimization tool is a powerful method to refine and optimize the parameters that machine learning models use to achieve even better and more precise cocoa bean classification [6,7].
This study will explore the analysis of spectral measurements, machine learning, and the genetic algorithm to classify cocoa beans.This research represents a significant step in the search for advanced technological solutions to improve the quality and competitiveness of the cocoa industry while paving the way for innovation opportunities in agriculture and food processing.
Our contributions are as follows: • Implementation of spectral measurements of cocoa beans: we have implemented a method to measure the spectral properties of three categories of cocoa beans.These properties are measured using a spectrometer, a device that measures the amount of light reflected by an object based on its wavelength.
•
Classification of spectral measurements using a set of algorithms: we used a set of algorithms to classify the spectral measurements of the three categories of cocoa beans.These algorithms are based on machine learning, a discipline of artificial intelligence that allows computers to learn from data without being explicitly programmed.
•
Selection of spectra using the best algorithm coupled with the genetic algorithm: we used the genetic algorithm to select the most relevant spectra for classification.The genetic algorithm is an optimization algorithm inspired by natural selection.• Analysis and comparison of the different classifications: We analyzed and compared them to evaluate their performance.
Related Work
Extensive research has been undertaken in cocoa cultivation, as well as in the utilization of spectral measurements.Wei et al. developed a broad-spectrum detection method for OTA, OTB, and OTC to improve the accurate detection of ochratoxins in cocoa using colorimetric and spectral analysis [8].However, gaps remain and deserve improvement.By expanding the sample size of cocoa beans, providing more detailed information about feature extraction methods, and evaluating the method's performance on various varieties of cocoa beans, researchers could increase the accuracy and reliability of this approach.Lin et al. developed a new method to detect volatile markers in wheat infected with Aspergillus glaucus.The method combines a colorimetric sensor (CS) and visible/near-infrared spectroscopy (VNIR).The optimized Si-PLS model based on the HBDP sensor gave the best detection performance.The correlation coefficient of the prediction ensemble was 0.9387, indicating a strong correlation between the model predictions and the actual values [9].However, the study used a dataset of 100 wheat samples, of which 50 were infected with Aspergillus glaucus and 50 were healthy.A larger sample size would be needed to confirm the study results.The study was conducted in a controlled culture chamber.The method's performance could vary in different environments, such as agricultural fields.Hernández-Hernández et al. focused their study on the application of near-infrared (NIR) spectroscopy to efficiently identify promising cocoa genotypes based on their chemical composition, including levels of bioactive compounds, in more than 80 cocoa bean samples from the Mexican Germplasm Bank, collected over three years.Notably, different genotypes observed significant differences in chemical composition in fermented and dried cocoa samples.The research focused on determining the content of fat, protein, total sugars, total phenols, phenolic compounds, and theobromine in samples of whole cocoa beans and ground cotyledons.Calibration models were developed using the spectra of intact beans, raw cocoa beans, and ground cotyledons [10].The study by Santika et al. looked into cocoa cultivation at Kebun Kalisepanjang Cocoa Center to assess the quality of cocoa beans.To achieve this objective, the study implemented the ANFIS method within an expert system, thus making it possible to consider the various external factors influencing quality.At the same time, to determine the quality of the cocoa beans, they used the genetic algorithm (GA), which played a key role in the evaluation process.The results obtained in this research are significant, with a root mean square error (RMSE) of 4.3, which testifies to the suitability of the algorithms used as an expert system for bean quality selection.This approach shows promise for improving the quality and management of cocoa production in this region [4].Karada g et al. undertook a study to identify healthy peppers from those infected with Fusarium wilt (Capsicum annuum).They used a spectroradiometer to analyze reflections coming from pepper leaves.These reflections were obtained from four groups of pepper leaves grown in a closed environment, including healthy peppers, peppers with fusarium wilt, peppers with the presence of mycorrhizal fungi, and peppers with fusarium wilt with mycorrhizal fungi.The measurements were carried out over a wavelength range from 350 nm to 2500 nm.The pepper disease detection process occurs in two distinct phases.In the first step, a feature vector is created using the coefficients from the wavelet decomposition and the statistical values of these coefficients.The second step is to classify the feature vectors of the input data.Three methods were used for this classification, namely artificial neural networks (ANN), Naive Bayes (NB), and the k-nearest neighbors (KNN) method.The results obtained are satisfactory [11].Chen et al. carried out a study to identify sensitive spectral bands and develop a hyperspectral vegetation index specific to detecting leaf spots.The canopy hyperspectral reflectance spectra of leaf-spot-susceptible peanut varieties were measured at two experimental sites in 2017.The normalized difference spectral index (NDSI) was derived based on their correlation with the Disease index (DI) in the leaf spectrum between 325 nm and 1075 nm.The results showed that the spectral reflectance of the vegetation cover significantly decreased in the near-infrared (NIR) regions as the disease index increased (r < −0.90).The spectral index for detecting peanut leaf spots was LSI: (NDSI (R938, R761)), with R 2 values reaching 0.68 for the regression model [12].
Materials and Methods
The experiments used Python programming at the ImViA laboratory, utilizing a DELL desktop computer.This computer has an Intel ® Core i7-10700 CPU (Dell, Montpellier, France) running at 2.90 GHz, 32 GB of RAM, and an NVIDIA Quadro P400 GPU (NVIDIA, Biot, France).The models were configured within Python version 3.8.8,utilizing Keras API version 2.4.3,TensorFlow version 2.3 as the backend, and CUDA/CuDNN dependencies for GPU acceleration.
The Konica-Minolta CS-2000 (Konica Minolta, Bourges, France) is an advanced optical spectroradiometer designed to accurately measure the visible light spectrum, recording the different wavelengths and their respective intensity levels.It can dissect light into its spectral components, thereby providing an in-depth analysis of the spectral composition of light [13].This device was used as part of our study to create our dataset.This includes spectral and colorimetric measurements of three distinct categories of cocoa beans: Category 1-fermented beans, Category 2-low-grade fermented beans, and Category 3-unfermented beans.The CS-2000 has allowed us to obtain detailed information on the spectral composition of the light emanating from these different categories of cocoa beans, which is essential for our analysis and research work.
Spectral Measurement of Cocoa Beans
Spectral measurements were performed over a wavelength range from 380 nm to 780 nm, with a resolution of 10 nm for each measurement.
In our study, we have three categories of cocoa beans in this case: • Category 1: these are superior quality fermented and dried cocoa beans; • Category 2: these are fermented and dried cocoa beans of intermediate quality; • Category 3: these are unfermented and dried cocoa beans.
The cocoa beans used in our study come from several plantations selected with the assistance of agronomists in Côte d'Ivoire.Preparing the beans begins with harvesting the pods, carefully separating the beans and the pods, followed by the fermentation phase for 5 to 7 days in the shade, away from light, and then the drying process.The beans are sorted into three distinct categories and classified according to quality.Category 1 includes premium-quality fermented and dried cocoa beans, while Category 2 includes lower-quality fermented and dried cocoa beans after sorting.Category 3 concerns unfermented and dried cocoa beans.Currently, this sorting is carried out manually by growers, potentially leading to variations in quality.Figure 1 shows an extract of the data.
Spectral measurements were performed over a wavelength range from 380 nm to 780 nm, with a resolution of 10 nm for each measurement.
In our study, we have three categories of cocoa beans in this case: • Category 1: these are superior quality fermented and dried cocoa beans; • Category 2: these are fermented and dried cocoa beans of intermediate quality; • Category 3: these are unfermented and dried cocoa beans.
The cocoa beans used in our study come from several plantations selected with the assistance of agronomists in Côte d'Ivoire.Preparing the beans begins with harvesting the pods, carefully separating the beans and the pods, followed by the fermentation phase for 5 to 7 days in the shade, away from light, and then the drying process.The beans are sorted into three distinct categories and classified according to quality.Category 1 includes premium-quality fermented and dried cocoa beans, while Category 2 includes lower-quality fermented and dried cocoa beans after sorting.Category 3 concerns unfermented and dried cocoa beans.Currently, this sorting is carried out manually by growers, potentially leading to variations in quality.Figure 1 shows an extract of the data.The measurements of the cocoa beans were carried out separately.The Minolta CS-2000 spectroradiometer was used for the measurements.This spectroradiometer is a precise machine that provides all the necessary information on the light produced by the source in the visible range (380-780 nm).The first step in our process is to measure the source response.We use a halogen source directed towards a Lambertian white for the total energy (Etotal).The second step involves measuring the reflectance of the cocoa bean on a non-reflective plate for total reflectance (ER).
The Reflectance (R) value is determined by calculating the ratio between the ER/Etotal, and the measurement is automatically provided by the device, thus establishing the relationship between the source's response and the cocoa bean's reflectance.The measurement setup is shown in Figure 6.The measurements of the cocoa beans were carried out separately.The Minolta CS-2000 spectroradiometer was used for the measurements.This spectroradiometer is a precise machine that provides all the necessary information on the light produced by the source in the visible range (380-780 nm).The first step in our process is to measure the source response.We use a halogen source directed towards a Lambertian white for the total energy (Etotal).The second step involves measuring the reflectance of the cocoa bean on a non-reflective plate for total reflectance (ER).
The Reflectance (R) value is determined by calculating the ratio between the ER/Etotal, and the measurement is automatically provided by the device, thus establishing the relationship between the source's response and the cocoa bean's reflectance.The measurement setup is shown in Figure 6.The measurements of the cocoa beans were carried out separately.The Minolta CS-2000 spectroradiometer was used for the measurements.This spectroradiometer is a precise machine that provides all the necessary information on the light produced by the source in the visible range (380-780 nm).The first step in our process is to measure the source response.We use a halogen source directed towards a Lambertian white for the total energy (Etotal).The second step involves measuring the reflectance of the cocoa bean on a non-reflective plate for total reflectance (ER).
The Reflectance (R) value is determined by calculating the ratio between the ER/Etotal, and the measurement is automatically provided by the device, thus establishing the relationship between the source's response and the cocoa bean's reflectance.The measurement setup is shown in Figure 6.We separated our data, allocating 80% to the training and validation set while the remaining 20% were reserved for testing.We deliberately selected 30 cocoa beans from each category for our data collection.To carry out the spectral measurement of each bean, we used the CS2000 spectrometer.At the end of this procedure, we obtained a data set comprising 90 measures, each corresponding to a single bean.It is important to emphasize that, in our case, the possibility of increasing the data is limited since these are unique measurements for each bean.This constraint influences our analysis and modeling approach, requiring careful management of the available data to guarantee a faithful representation of the diversity of the spectral characteristics of the different categories of beans.In our measurement acquisition protocol, we take three measurements of a bean, and then we recover the average of these measurements, constituting the bean's final measurement.
Machine Learning
Machine learning is a subfield of artificial intelligence that focuses on creating models and algorithms to learn from data and make predictions or classifications without explicit programming.In our work [14], we employed the following algorithms: • SVM (Support Vector Machine): SVMs are used for classification and regression.They work by finding a hyperplane that maximizes the margin between classes in a multidimensional data space.They are effective for binary classification but can be extended to multi-class classification [15].The choice of these models is motivated by the limited availability of spectral measurement data and their relevance for the analysis of tabular data.This selection is based on problem-specific considerations.SVMs are favored for their effectiveness in highdimensionality nonlinear classification.Logistic regression is chosen for its simplicity and interpretability in linear tasks.Random forests are known for their robustness against overfitting and performance in complex datasets.AdaBoost improves weak models, while decision trees provide a graphical representation of decisions.KNN suits complex data structures, and XGBoost excels in exact nonlinear tasks.
Genetic Algorithm
Genetic algorithms (GAs) are a class of optimization algorithms inspired by the theory of Darwinian evolution.They are used to solve optimization and research problems in various fields, from system design to parameter optimization.Genetic algorithms are based on biological concepts such as natural selection, reproduction, and mutation.
Genetic algorithms are used in various fields, including parameter optimization, neural network design, planning, game strategy evolution, electronic circuit design, and many others.They are instrumental when the search space is ample, discontinuous, or complex to explore with traditional optimization methods [22][23][24].
The main components and stages of a genetic algorithm are as follows: • Initial Population: An initial group of potential solutions is randomly generated.Each solution is represented as chromosomes or genotypes, which encode the parameters or characteristics of the solution.
•
Evaluation: Each solution in the population is evaluated using an objective or fitness function.This function measures the quality of each solution to the optimization objective.
•
Selection: Solutions are selected for reproduction based on their fitness.Higher-quality solutions are more likely to be chosen, thus simulating natural selection.
•
Reproduction: Selected solutions are combined to create a new generation of solutions.This can include operations such as recombination (crossover) and mutation.
Crossbreeding involves merging two parental solutions to produce offspring, while mutation slightly modifies one solution.
•
Replacement: The new generation replaces the old generation, often using a fitnessbased replacement model.This ensures that the best quality solutions are preserved.
•
Stopping Criterion: The genetic algorithm runs for a certain number of iterations or until a predefined stopping criterion is reached or setting a maximum number of iterations.
•
Result: The best solution found during the execution of the algorithm is returned as the optimization result.
The genetic algorithm plays a fundamental role in identifying the optimal combinations of parameters linked to spectral measurements in the visible range, covering from 380 nm to 780 nm.Thanks to evolution through generations, the genetic algorithm manages to develop effective combinations of different spectra, reducing the number of parameters.This simplification significantly contributes to facilitating the classification process.By tuning the parameters optimally, the genetic algorithm promotes a better understanding of spectral characteristics and thus improves the accuracy of data classification.
The General Architecture of the Methodology
Our comprehensive methodology includes the following steps: 1.
Step 1: Spectral and Colorimetric Measurements In this first step, we perform spectral and colorimetric measurements using the CS-2000 Optical Spectroradiometer.This spectroradiometer is a measuring device that quantifies the light reflected from a sample of cocoa beans.It generates a reflection spectrum representing the distribution of light intensities as a function of wavelength, generally covering the entire visible spectrum, from blue to red.
Spectral measurements, for their part, make it possible to characterize the spectral properties of cocoa beans by measuring the transmission of light at different wavelengths.This spectral information is essential for detecting subtle variations in the beans' chemical composition, maturation, or quality.
2.
Step 2: Data Export Once the measurements are made, we export these data to standard format files, such as CSV (Comma-Separated Values).This step prepares the data for subsequent analysis.
3.
Step 3: Data Division We split the extracted data, creating three sets: training, validation, and testing data.This division is essential for the application of our models and algorithms.We separated our data, allocating 80% to the training and validation set while the remaining 20% were reserved for testing.
4.
Step 4: Classification In spectral measurements, we employ classification algorithms to categorize the obtained data.The algorithm that demonstrates the highest precision is selected for further analysis.Step 5: Selection of the Best Spectral Parameters In this last step, we refine our results using the algorithm selected in step 4, combined with genetic algorithms.Genetic algorithms help us identify and choose the best parameters for spectral measurements, thus allowing the realization of a new classification with all classification algorithms.The principle of the genetic algorithm is as follows: a.
First, we run a function to initialize a random population.b.The randomized population is then subjected to the fitness function, which returns the best parents (with the highest accuracy).c.
The best parents will be selected according to the n-parents parameter.d.After performing the same operation, the population will be subjected to the crossover and mutation functions, respectively.e.
The cross is created by combining the genes of the two most suitable parents by randomly selecting the first parent and part of the second parent.f.
The mutation is obtained by randomly inverting the bits selected for the child resulting from the crossover.g.A new generation is created by selecting the most suitable parents from the previous generation and applying crossing over and mutation.h.This process is repeated for five generations.
Figure 7 summarizes the structure of our methodological approach.
our data, allocating 80% to the training and validation set while the remaining 20% were reserved for testing.
Step 4: Classification
In spectral measurements, we employ classification algorithms to categorize the obtained data.The algorithm that demonstrates the highest precision is selected for further analysis.
Step 5: Selection of the Best Spectral Parameters
In this last step, we refine our results using the algorithm selected in step 4, combined with genetic algorithms.Genetic algorithms help us identify and choose the best parameters for spectral measurements, thus allowing the realization of a new classification with all classification algorithms.The principle of the genetic algorithm is as follows: a. First, we run a function to initialize a random population.b.The randomized population is then subjected to the fitness function, which returns the best parents (with the highest accuracy).c.The best parents will be selected according to the n-parents parameter.d.After performing the same operation, the population will be subjected to the crossover and mutation functions, respectively.e.The cross is created by combining the genes of the two most suitable parents by randomly selecting the first parent and part of the second parent.f.The mutation is obtained by randomly inverting the bits selected for the child resulting from the crossover.g.A new generation is created by selecting the most suitable parents from the previous generation and applying crossing over and mutation.h.This process is repeated for five generations.
Figure 7 summarizes the structure of our methodological approach.Based on the results obtained for the different wavelengths, we undertook classification operations according to the specific optimal generations.This approach allowed us to analyze the performances of the different generations with the wavelengths in more depth, thus contributing to a better understanding of the results.
Table 4 presents the classification metrics for different algorithms with the parameters of the lengths obtained during the second generation of the genetic algorithm.The key metrics included are accuracy, precision, mean square error (MSE), F-score, recall, and the Matthews correlation coefficient (MCC).The SVM shows an accuracy of 78.26%, which indicates that it correctly predicts about 78.26% of the samples.Its accuracy is also high at 79.42%.The F-score and recall are also solid, while the MCC is 68.10%.Overall, SVM gives good performance.Decision Tree has an accuracy of 65.21%, which is lower than most other algorithms.The precision and recall are also 65.21%.The F-score is 65.21%, reflecting a balance between precision and recall.However, the MCC is relatively low at 47.72%.Random forest achieves an accuracy of 69.56%, with a precision of 69.15%.F-score and recall are similar at 69.19%.The MCC is 54.41%.Random forest shows a reasonable performance.XGBoost has an accuracy of 78.26%, similar to SVM, with a precision of 79.08%.The F-score is slightly higher at 78.46%, as is the recall.The MCC is 67.52%, indicating good performance.KNeighbors performs similarly to Decision Tree, with an accuracy of 65.21%, a precision of 65.21%, an F-score of 65.21%, and an MCC of 47.72%.Logistic regression achieves the best accuracy at 82.60%, with an exceptional precision of 84.71%.The F-score is 82.22%, recall is 82.60%, and MCC is the highest at 75.31%.Logistics is the leading algorithm during this generation.AdaBoost performs worse than other algorithms, with an accuracy of 60.86%, a precision of 43.47%, an F-score of 49.27%, a recall of 60.86%, and an MCC of 51.63%.racy sees a slight improvement at generation five.Logistic accuracy is high at generation two (84.71%) but drops significantly to 71.49% at generation five.AdaBoost's accuracy remains low, showing no significant improvement between generations.
These results highlight the variability in algorithm performance over different generations.Performance can fluctuate, increase, and sometimes decrease, underscoring the importance of monitoring model performance over time and adjusting algorithms accordingly to maintain quality results.Figure 8 shows the histogram of the synthesis.generation five, which suggests an improvement over the previous generation.The accuracy sees a slight improvement at generation five.Logistic accuracy is high at generation two (84.71%) but drops significantly to 71.49% at generation five.AdaBoost's accuracy remains low, showing no significant improvement between generations.
These results highlight the variability in algorithm performance over different generations.Performance can fluctuate, increase, and sometimes decrease, underscoring the importance of monitoring model performance over time and adjusting algorithms accordingly to maintain quality results.Figure 8 shows the histogram of the synthesis.These results show that the choice of generation and parameters plays an essential role in the performance of the algorithms.The improvement in accuracy between generations can be attributed to the better adaptation of parameters or features to a specific generation.On the other hand, variations in accuracy suggest that parameters optimized at one generation are only sometimes best for different generations.Users will need to consider these results to choose the algorithm and generation best suited to their classification task.
Conclusions
Our study examined the performance of different classification algorithms on spectral measurement data using a genetic algorithm for parameter optimization.The results reveal significant variations in algorithm accuracy between different generations, highlighting the importance of parameter optimization to achieve the best performance.The Logistic Regression, SVM, and random forest algorithms demonstrated performance stability, while XGBoost showed significant improvement in generation two but lost accuracy in generation five.AdaBoost was the algorithm that presented the least satisfactory performance across all generations.The use of deep learning in the context of spectral measurements could be possible, but certain considerations specific to this field could influence the choice of other, more traditional models.Deep learning often requires massive data sets to achieve optimal performance.If the spectral measurement dataset is relatively small, more data are needed to train a deep learning model effectively.
The quality improvement discussed in this study concerns the ability of our method to achieve a more precise classification of beans based on their spectral properties.This approach allows for a finer and more objective classification of beans, thereby reducing variations associated with manual sorting.Our method offers a technological alternative for agricultural research centers, offering a more objective, efficient, and precise solution These results show that the choice of generation and parameters plays an essential role in the performance of the algorithms.The improvement in accuracy between generations can be attributed to the better adaptation of parameters or features to a specific generation.On the other hand, variations in accuracy suggest that parameters optimized at one generation are only sometimes best for different generations.Users will need to consider these results to choose the algorithm and generation best suited to their classification task.
Conclusions
Our study examined the performance of different classification algorithms on spectral measurement data using a genetic algorithm for parameter optimization.The results reveal significant variations in algorithm accuracy between different generations, highlighting the importance of parameter optimization to achieve the best performance.The Logistic Regression, SVM, and random forest algorithms demonstrated performance stability, while XGBoost showed significant improvement in generation two but lost accuracy in generation five.AdaBoost was the algorithm that presented the least satisfactory performance across all generations.The use of deep learning in the context of spectral measurements could be possible, but certain considerations specific to this field could influence the choice of other, more traditional models.Deep learning often requires massive data sets to achieve optimal performance.If the spectral measurement dataset is relatively small, more data are needed to train a deep learning model effectively.
The quality improvement discussed in this study concerns the ability of our method to achieve a more precise classification of beans based on their spectral properties.This approach allows for a finer and more objective classification of beans, thereby reducing variations associated with manual sorting.Our method offers a technological alternative for agricultural research centers, offering a more objective, efficient, and precise solution for sorting cocoa beans.This could contribute to a significant improvement in the quality of the final products.
Figure 1 .
Figure 1.Extract of spectral measurement data from beans.
Figures 2 -
Figures 2-5 illustrate the respective curves of each category of beans.
Figure 1 .
Figure 1.Extract of spectral measurement data from beans.
Figures 2 - 19 Figure 2 .
Figures 2-5 illustrate the respective curves of each category of beans.J. Imaging 2024, 10, x FOR PEER REVIEW 5 of 19
Figure 2 .
Figure 2. Curve of spectral reflectance data from cocoa beans-category 1. (each sample is represented by a different pseudo-color).Figure 2. Curve of spectral reflectance data from cocoa beans-category 1. (each sample is represented by a different pseudo-color).
Figure 2 .
Figure 2. Curve of spectral reflectance data from cocoa beans-category 1. (each sample is represented by a different pseudo-color).
Figure 3 .
Figure 3. Curve of spectral reflectance data from cocoa beans-category 2. (each sample is represented by a different pseudo-color).
Figure 3 .
Figure 3. Curve of spectral reflectance data from cocoa beans-category 2. (each sample is represented by a different pseudo-color).
Figure 2 .
Figure 2. Curve of spectral reflectance data from cocoa beans-category 1. (each sample is represented by a different pseudo-color).
Figure 3 .
Figure 3. Curve of spectral reflectance data from cocoa beans-category 2. (each sample is represented by a different pseudo-color).
Figure 4 .
Figure 4. Curve of spectral reflectance data from cocoa beans-category 3. (each sample is represented by a different pseudo-color).
Figure 5
Figure 5 illustrates the curves representing the means of each category.The measurements of the cocoa beans were carried out separately.The Minolta CS-2000 spectroradiometer was used for the measurements.This spectroradiometer is a precise machine that provides all the necessary information on the light produced by the source in the visible range (380-780 nm).The first step in our process is to measure the source response.We use a halogen source directed towards a Lambertian white for the total energy (Etotal).The second step involves measuring the reflectance of the cocoa bean on a non-reflective plate for total reflectance (ER).The Reflectance (R) value is determined by calculating the ratio between the ER/Etotal, and the measurement is automatically provided by the device, thus establishing the relationship between the source's response and the cocoa bean's reflectance.The measurement setup is shown in Figure6.
Figure 4 .
Figure 4. Curve of spectral reflectance data from cocoa beans-category 3. (each sample is represented by a different pseudo-color).
Figure 5
Figure 5 illustrates the curves representing the means of each category.
Figure 5 .
Figure 5.The average curve of spectral reflectance data for each category.
Figure 5 .
Figure 5.The average curve of spectral reflectance data for each category.
Figure 5 .
Figure 5.The average curve of spectral reflectance data for each category.
Figure 6 .
Figure 6.Protocol for spectral measurement of cocoa beans using the spectroradiometer.
Figure 7 .
Figure 7. Schematic representation of our approach.Figure 7. Schematic representation of our approach.
Figure 7 .Algorithm 2
Figure 7. Schematic representation of our approach.Figure 7. Schematic representation of our approach.This comprehensive methodology allows us to deeply analyze the characteristics of cocoa beans, optimize our classification models, and obtain accurate and meaningful results for the classification of cocoa beans based on their spectral and colorimetric properties.The functions and general algorithm present the essential operation and principle of the implemented model.The purpose of the Train_test_evaluate Algorithm 1 is to carry out the training and evaluation of the model, and is presented as follows:
Figure 8 .
Figure 8. Histogram of the summary of spectral measurements.
5 Figure 8 .
Figure 8. Histogram of the summary of spectral measurements.
•
[18]stic Regression: Logistic regression is mainly used for binary classification and can be extended to multi-class classification.It models the probability that an observation belongs to a particular class.It uses a logistic function to estimate the probability based on the input features[16].•RandomForest:Randomforest is an ensemble algorithm that combines multiple decision trees.Each tree is trained on a random sample of the data, and the predictions are aggregated to obtain a final prediction.It is robust, less susceptible to overfitting, and effective for classification and regression[17].•AdaBoost(Adaptive Boosting): AdaBoost is another ensemble algorithm that adjusts based on errors from previous models.It assigns different weights to observations based on their previous performance.It combines several weak models to create a robust model[18].
[19]cision Tree: Decision trees are used for classification and regression.They divide the dataset into subgroups based on characteristics, using criteria such as entropy or Gini coefficient.They are easy to interpret but can suffer from overfitting[19].• K-Nearest Neighbors (KNN): KNN is a similarity-based classification and regression algorithm.It assigns a class to an observation based on the classes of the k nearest neighbors in the feature space.It is simple to understand but can be sensitive to the distance used [20].• XGBoost (Extreme Gradient Boosting): XGBoost is an improved implementation of gradient-boosted learning.It is efficient and effective, suitable for classification and regression.It uses regularization and advanced tree management to improve accuracy [21].
Table 3 .
The best wavelengths at the second and fifth generations. | 7,658 | 2024-01-01T00:00:00.000 | [
"Agricultural and Food Sciences",
"Computer Science"
] |
DYNAMICS OF A MODEL OF TUMOR-IMMUNE INTERACTION WITH TIME DELAY AND NOISE
. We propose a model of tumor-immune interaction with time delay in immune reaction and noise in tumor cell reproduction. Immune response is modeled as a non-monotonic function of tumor burden, for which the tumor is immunogenic at nascent stage but starts inhibiting immune system as it grows large. Without time delay and noise, this system demonstrates bistability. The effects of response time of the immune system and uncertainty in the tumor innate proliferation rate are studied by including delay and noise in the appropriate model terms. Stability, persistence and extinction of the tumor are analyzed. We find that delay and noise can both induce the transition from low tumor burden equilibrium to high tumor equilibrium. Moreover, our result suggests that the elimination of cancer depends on the basal level of the immune system rather than on its response speed to tumor growth.
1.
Introduction. The immune system is a host defense system that distinguishes pathogens from one's own healthy cells and destroy them. Pathogens include bacteria and viruses as well as abnormal cells. There are evidence that immune systems can detect and eliminate cancer [27]. Cancer immunology has seen renewed interests due to recent achievements in immunotherapy [22]. Unlike common pathogens, cancer cells are not as distinguishable. Moreover, cancer can evade the control of immune systems by developing ways to achieve immune suppression [30]. Recent progress in immunotherapy has focused on removing immune suppression so that cancer cells will be under attack of immune systems. Further development of immunotherapy hinges on a better understanding of tumor-immune system interaction.
The immune system consists of immune cells of several types and complex signaling network. For example, immune cells include cytotoxic T cells, helper cells, natural killer cells, and signaling networks involve cytokines, e.g., interlukin-2. The immune response falls into two categories: innate and adaptive. Though the adaptive response enables fast reaction to certain antigens, immune responses oftentimes involve recruiting immune cells from bone marrows and further training or activation. So it is inherently a delayed process. On the other hand, cancer is well-known for its heterogeneity and characterized by fast mutation, which can render it the ability to suppress immune response [6,30]. In this process, stochasticity plays an important role.
Given the complexity of tumor-immune system interaction, mathematical modeling naturally offers some insights by capturing the key mechanisms. Literature on modeling non-spatial tumor growth under immune surveillance is abundant (see [5] for a review). In [17], the authors derived a system of five equations from a kinetic scheme and further reduced it to two equations tracking only effector cells and tumor cells. In their model, immune suppression is represented as annihilation by mass action of tumor cells and effector cells. Their paper inspired a number of later work. In [3], the author summarized models of this type into a family as follows where x is the size or density of tumor population, y is the size or density of immnue effector population and θ(t) represents treatment . With biological-relevant assumptions on f (x), φ(x) and q(x), some general conclusions of were drew in [3].
In [13], the authors took into account cytokines in their model in order to gain further insight into immunotherapy. In most cases, introducing more equations does not introduce new dynamics but is necessary to shed light on cancer treatments [5]. Signaling is arguably a fast process, so sometimes a quasi-steady state approximation can reduce more complicated models into the family of models studied by [3]. For example, in a recent study on immune check point inhibitor [25], a variation of (1) is derived from a more complicated model consisting of a system of 14 partial differential equations [18].
Building on aforementioned earlier work, the effect of time delay in tumorimmune interactions are also extensively studied [1,4,8,28]. On the other hand, stochasticity in tumor-immune interaction is less often studied, and mostly focused on single-equation models of tumor population growth ignoring explicit interaction with immune system [20,2,21]. Models that take into account both time delay and stochasticity are even rarer. To the best of our knowledge, the work of [11] is the only exception. In [11], a single equation was considered and noise was introduced in an ad-hoc way. To fill this gap, we will introduce time delay and noise one at a time to a variation of (1) with the novelty in its immune response term being non-monotonic.
The paper is organized as follows. In section 2, we introduce our model formulation and state general properties of the model in absence of noise and time delay. In section 3, we study the model with time delay where persistence, local stability and global stability results are presented. Section 4 is devoted to the stochastic version of the model where tumor extinction and persistence conditions are presented. In section 5, the paper comes to the culmination where both noise and time delay are present in the model, for which we study the effects of time delay on the stationary distribution. We conclude the paper with a discussion of future work in section 6.
2. ODE model formulation and properties. We consider the following equations where x denotes the tumor burden and y denotes the level of immune response. This is a special case of (1) for which we specify σq(x) =β +α x κ+x 2 to represent the immune response to tumor burden in a non-monotonic fashion. In spite of being phenomenological, it accounts for the fact that the cancer cells acquire mutations that can down-regulate immune response as the tumor grows bigger while the tumor is immuogenic at its nascent stage. Death term is assumed to be independent of tumor burden and thus we have Ψ(x)y = −μy. Note that without tumor, immune system activity is maintained at the basal level β/µ. We assume logistic growth of tumor and that killing of tumor cells by the immune system is represented as a mass action term so that we have f (x) = 1− x K and φ(x) =γx. Both are common choices in modeling literature [8,25]. Even though Gompertz growth is also commonly used in modeling tumor growth and backed up by a lot of experimental data, it predicts unbound growth rate as tumor burden approaches zero [5]. Hence it is not suitable to our purpose.
Note that we represent the two populations generically as tumor burden and level of immune response, and intentionally avoid using specific units of cell density or volume. In some sense, it justifies our choice of Ψ(x)y = −μy since we are not tracking explicitly effector cells. The rationale behind our generic representation is that our objective is to study interesting dynamics that can arise from a simple model with time delay and noise. It is our intention to bring attention to the possible key mechanisms underlying cancer-immune system dynamics without making the wrong impression that it is capable of clinical prediction.
By nondimensionlization, we can reduce the number of parameters. Let x = √ κu, y = α µ √ κ v and t = 1 µt . We get the dimensionless system where ρ =ρ µ , K =K √κ , γ =αγ µ 2 √κ and β =β √κ α are dimensionless (positive) parameters. It can be shown that there is always a tumor free equilibrium E 0 = {0, β} which is stable if and only if ρ < γβ by linear stability analysis. If ρ > γβ, depending on the parameters, there is additional either one or three interior equilibria. In this paper, we focus on the case where there are three interior equilibria, namely low tumor equilibrium E 1 , intermediate tumor equilibrium E 2 and high tumor equilibrium E 3 (see Figure 1). Since the intersections of nullclines are roots of a cubic polynomial, any results involving analytical expressions of parameters for stability conditions, if possible, would be unwieldy. Nevertheless, qualitative argument similar to that of in ( [24] Page 226-230) can be made regarding to the local stability . By doing so, we obtain the theorem below.
Theorem 2.1. Suppose that ρ > γβ and there are three interior equilibria E 1 , E 2 and E 3 of (4), then E 1 and E 3 are stable, and E 2 is unstable.
Proof. Consider the Jacobian matrix at the equilibrium where f u , f v , g u and g v are the partial derivatives of f (u, v) and g(u, v) evaluated at the equilibrium. The signs of f u , f v , g u and g v can be determined by geometric arguments. For example, to see the sign of g u at E 1 , we first note that g(u, v) < 0 in the region above g(u, v) = 0 and g(u, v) < 0 in the region below. Thus as it moves across g(u, v) = 0 at E 1 along the direction of u-axis from left to right, g(u, v) increases from being negative to being positive. Therefore, g u > 0 at E 1 . By the same argument, we find out the signs of the entries of Jacobian matrices J 1 , J 2 and J 3 at E 1 , E 2 and E 3 as follows To determine stability, we need to find signs of their determinants too. This can be done by noting the sign of the slopes of the nullclines. For example, at . Similarly, we have det(J 2 ) < 0 and det ( J 3 ) > 0. Therefore, we proved the claimed stability. 3. With delay. It is known that it takes time for the immune system to respond and take effect. So we introduce time delay into the term representing immune response. The model now reads as follows where τ ≥ 0 is the time delay of immune response. We are curious about any possible new dynamics due to the time delay in contrast to the ODE model, especially if the time delay in immune response can lead to oscillatory solutions that may represent cancer growth episodes.
3.1. Analysis. First we want to establish positivity and boundedness of the system (6) given appropriate initial values. For initial values, we assume v(0) ≥ 0 and The boundedness is straightforward.
The above proposition ensures that the solution of (6) is biological meaningful and will also be useful prerequisite for our later analysis. Next we present a theorem of the local stability of (6) as a counterpart to Theorem 2.1. In particular, we show that as τ increases there is a possible stability switching for low tumor equilibrium E 1 . Recalling the same definition for f u , f v , g u and g v as before, we have the following theorem.
Theorem 3.1. Suppose that ρ > γβ. Then the tumor free equilibrium E 0 is unstable for for all τ ≥ 0. In addition, suppose that there are three interior equilibria E 1 , E 2 and E 3 of (6). Then E 3 is stable, E 2 is unstable for all τ ≥ 0 and E 1 change its stability as τ increases if f u g v + f v g u < 0.
Proof. Linearizing (6) at the equilibrium gives Substituting u = Ae λt , v = Be λt into the above equations yields a linear system of A and B for which the existence of a solution entails the characteristic equation At E 0 , (7) becomes λ 2 − (ρ − γβ)λ − ρ + γβ = 0, which always has a positive root and thus E 0 remains unstable for all τ ≥ 0. Theorem 2.1 gives the stability results of E 1 , E 2 and E 3 at τ = 0. We want to study if their stability changes as time delay τ increases. If the equilibrium changes its stability as τ increases, there must exists ω > 0 such that λ = iω is a solution of (7) [14]. Substituting λ = iω into (7) gives Collecting real and imaginary parts gives Equating the real and imaginary parts to zeros respectively gives Squaring and adding the above equations gives From (5), we see that (9) is not satisfied for E 3 and hence it has no stability switching. Stability switching is possible for E 1 and E 2 . For E 1 , (9) entails an additional requirement which is stated in the theorem. The critical value of τ can be found by substituting ω = ω 0 to (8), which leads to To ensure stability switching, We need further confirm that the imaginary root crosses the imaginary axis as τ increases beyond τ 0 . To do so, we think λ as a function of τ and differentiating (7) gives It follows that So for E 1 which is stable at τ = 0, the imaginary root indeed crosses the imaginary axis and stability switching is ensured. But for E 2 which is unstable at τ = 0, the imaginary root does not cross the imaginary axis and there is no change in its stability.
The above theorem indicates that the time delay in immune response can lead to tumor escaping the control of the immune system as a consequence of loss of stability of the low tumor equilibrium. The condition (10) for this stability switching is not explicit in terms of parameters. Nevertheless, it has a geometric interpretation that the slope of the v-nullcine is larger than the magnitude of the slope of u-nullcline at their interception E 1 . In a biological sense, it means that there would be instability if the objective of immune response level is set to increase steeply with respect to tumor burden while immune response time is not fast enough to catch up.
Next we study the global stability of the tumor free equilibrium. Proof. We first shift v to make its equilibrium value to be zero by letting w = v − β.
If the stability condition of tumor-free equilibrium is not satisfied, we can prove that the tumor population is persistent. Proof. Suppose not. Then lim t→∞) u(t) = 0. From this, it can be shown that lim sup t→∞ v(t) ≤ β. There are two cases we need to consider. The first case is that u is eventually monotonically decreasing to zero. Then lim t→∞ v(t) = β. It follows that there is a t * > 0 such that u(t * ) is sufficiently small and v(t * ) is sufficiently close to β so that u (t * ) = u(ρ − ρu K − γv) > 0. So we have a contradiction. The second case is that u approaches zero in an oscillatory fashion. That is, there is a sequence of {t i } such that u(t i ) → 0 and u(t i ) is local minimum, i.e., u (t i ) = u(ρ − ρu K − γv) = 0. Then for any δ > 0 there is an N (δ) such that u(t i ) < δ for i > N (δ). Since ρ > γβ, there is a δ such that ρ − ρδ K − γ(β + δ) > 0. We see that for this δ, there is a T 1 such taht t > T 1 implies that v(t) < β + δ and an N such that for i > N , t i > T 1 . Hence for i > N , u (t i ) > 0 which is a contradiction to u (t i ) = 0.
Putting the conditions of the above two theorems in original units, we find an important dimensionless value R 0 =ρμ γβ . It is the ratio of the proliferation ability of the tumor verse the strength of the immune system, which can be viewed as the tumor reproduction number: if the tumor can be totally cleaned out by immune system. The biological meaning of (11) is more clear by recognizing that it is equivalent to saying that at the onset of tumor growth, the killing rate of tumor by the immune system (γβ/μ) is larger than the tumor growth rate (ρ). Theorem 3.3 tells us that otherwise (R 0 > 1) the tumor will always exist.
3.2. Numerical simulation. We studied (6) numerically using dde23 in Matlab. We confirmed the criteria for tumor persistence and extinction in our numerical experiment. The existence of a stability switching for low tumor equilibrium as τ increases is also demonstrated ( Figure 2). Moreover, we discovered that as τ is further increased, there is a transition to high tumor equilibrium (bottom right of Figure 2). Interestingly, the jerky transition to high tumor equilibrium is reminiscent of saltatory growth which is a commonly observed pattern in tumor growth [15]. This suggests that a weakened immune system response may be indicated by its slow action which takes longer time delay, which may result in increased tumor growth.
4. With noise. Tumor cell growth is known to lack of certain regulations compare to normal cells, we thus assume that the innate proliferation rate of tumor cells is subject to uncertainty. Adding white noise to it results in the following stochastic differential equations where B(t) is a scalar Brownian motion defined on the complete probability space (Ω, F, {F t } t≥0 , P ) with filtration {F t } t≥0 . We use a ∧ b to denote min(a, b), a ∨ b to denote max(a, b) and a.s. to denote almost surely.
4.1.
Analysis. We first show existence and uniqueness of a global solution remains in D = (0, K)×(β, β +u m ) where u m = max u∈(0,K) { u 1+u 2 }, whenever it starts in D. Because the drift term and noise term in (12) do not satisfy linear growth condition, the general existence and uniqueness theorem (see [23] chapter 2) does not apply. The techniques employed here are standard and follow the similar lines as [29,9]. Let D n = ( 1 n , K − 1 n ) × (β + 1 n , β + u m − 1 n ) where n ∈ N + . Define the stopping time τ n = inf{t ∈ (0, τ e ) : (u(t), v(t)) / ∈ D n }. Clearly τ n increases as n. Let τ ∞ = lim n→∞ t n and we have τ e ≥ τ ∞ . If we can show that τ ∞ = ∞ a.s., then τ e = ∞ a.s. and we have the unique solution (u(t), v(t)) ∈ D for all t. Suppose on contrary that there is a pair of constants T > 0 and ∈ (0, 1) such that P (τ ∞ ≤ T ) > . Then there is a n 1 such that P (τ n ≤ T ) ≥ for n > n 1 .
where we note it is nonnegative for u, v ∈ D.
In general, given an Ito process dX(t) = b(X(t)dt + σ(X t )dB(t) and if f ∈ C 2 0 (R n ), then its generator A acting on f gives (see Theorem 7.3.3 on Page 126 of [26]).
Consider the generator A of (12) and let A act on V , i.e., AV : D → R. Some calculation shows that where C = ρ k ∨γ(β +u m )∨(γ(β +u m )+1) a positive constant. By Dynkin's formula (see, e.g., [26] Page 127), we have By Gronwall's inequality, On the other hand, consider the set Ω n = {τ n ≤ T } for n > n 1 for which we know P (Ω n ) ≥ . For ω ∈ Ω n , we know that V ∼ O(n). Thus if we let n → ∞. Hence we have a contradiction.
Proof. By Ito's formula, we have where f (y) = − 1 2 σ 2 y 2 + ρy − γβ. Note that f (y) is a concave-down quadratic function with axis of symmetry y = ρ σ 2 and y(0) < 0. There are two ways to ensure max y∈(0,1) f (y) < 0, i.e., which corresponds to the stated conditions i) or ii). Thus, lim sup where the term with integral is zero a.s. by law of large numbers of martingale (see, e.g., [23]).
To complement with the condition for tumor extinction, we present a theorem for tumor persistence in the following. The proof involves construction of a contradiction to show that lim sup t→∞ u(t) is bounded from below, and again Ito's formula is the workhorse behind this type of proof.
Proof. Assume ρ > σ 2 /2 + γ(β + u m ). We note that g(K) < 0 and g(0) = − 1 2 σ 2 + ρ − γ(β + u m ) > 0. Thus ξ exists and g (u) < 0 for u in a small neighborhood of ξ. Suppose on contrary that there is a small ∈ (0, 1) such that P (Ω 1 ) > where Ω 1 = {ω : lim sup t→∞ u(t, ω) < ξ − 2 }. Then there is T (ω) > 0 such that We choose small enough so that g(u(t, ω) > g(ξ − ) for t > T (ω). Moreover, by law of large numbers of martingale there is Ω 2 ⊂ Ω with P (Ω 2 ) = 1 such that for any ω ∈ Ω 2 , lim t→∞ Thus we have lim inf which implies that u(t, ω) → ∞, which contradicts (14). Table 1. Parameter values used in Figure 3 We compare the conditions in the previous two theorems to their counterparts in section 3. In the original units, we find two noise magnitude regimes withρ µ as the threshold (we call σ 2 <ρ µ small noise regime and σ 2 >ρ µ big noise regime). In the small noise regime, the tumor extinction condition is weaker than the one for the deterministic system. The biological interpretation is that the small noise can possibly help eliminate the tumor. The condition for tumor persistence is stronger in the stochastic system. However, it is only a sufficient condition and there is a gap between between the persistence condition and the extinction condition.
4.2.
Numerical simulation. (12) is simulated using Milstein's method [12]. As in Figure 3, the simulation confirmed our extinction criteria. Also shown is that the tumor persists in the monostability where the solution simply fluctuates about the equilibrium and bistablity where there is a noise-induced transition to high tumor equilibrium from low tumor equilibrium. The parameter values used in the simulations are summarized in Table 1. 5. With delay and noise. In this section, we study the system when both delay and noise are present as follows 5.1. Analysis. The analysis in section 4 can be extended to (15). Because of the time delay τ , here we need to supply suitable initial data on [−τ, 0]. The extension of Theorem 4.1 is stated as follows there is a unique solution defined for all t ≥ −τ and remains in D a.s.
Proof. The proof follows similar lines as the one for Theorem 4.1 with an application of method of steps. We keep the same definition of τ n , τ ∞ , τ e and V (u, v). Same as before we want to show that τ e = ∞ by proving that τ ∞ = ∞. First we want to show that τ ∞ > τ . For any n ∈ N + and t ∈ [0, τ ∞ ), it can be shown by Ito formula that Table 1 where where K 1 is a positive constant. For t 1 ∈ [0, τ ], integrating (16) from 0 to t 1 ∧ τ n and taking expectation gives , v(0))e τ < ∞. In particular, Suppose on contrary there is > 0 such that P (τ ∞ < τ ) > . Then there is n 1 such that for n > n 1 , P (Ω 1 ) ≥ where Ω 1 = {ω : τ n (ω) < τ }. Thus ≥ n → ∞ as n → ∞, which is a contradiction to 17. Thus for τ ∞ ≥ τ . By the same argument on t ∈ [τ, 2τ ],we have τ ∞ ≥ 2τ . Repeating this procedure gives τ ∞ = ∞.
It is easy to see that v(t) ∈ (β, β + 1 2 ) for all t > 0. This enables Theorem 4.2 and Theorem 4.3 be extended to the system (15) with similar arguments.
Numerical simulation.
From previous analysis, we notice that the system (15) bears a lot of similarity to the system (12). In this subsection, we instead focus on the effect of time delay on stationary distributions by numerical simulations. As seen in Figure 4, the stationary distribution is bimodal which is not a surprise since the underlying deterministic system is bistable. As the delay increases (larger τ ), the more density shifts to the high tumor stable state and the bimodality becomes indistinguishable at τ = 2. We also observe that the mean first passage time from the low tumor stable state to the high tumor stable state is reduced by increasing τ .
6. Discussion and conclusion. In this paper, we presented a simple model of tumor-immune system interactions, in which the immune response to tumor is modeled as a non-monotonic function of tumor burden. We studied the effects of time delay in the immune response and the uncertainty in the innate proliferating rate of cancer cells on the tumor growth dynamics. The conditions of tumor extinction and persistence were proved in presence of noise or time delay. We also performed numerical experiments to confirm analytical results and to guide future analytical work.
We showed that the magnitude of the tumor reproduction number R 0 =μρ γβ relative to 1 dictates the stability of tumor-free equilibrium. This condition does not depend on time delay. It suggests that the elimination of cancer depends on the basal level of the immune system rather than on its response speed to tumor growth. However, it maybe possible to have delay-induced stability switching for the low-tumor steady state. We also established the global stability of tumorfree equilibrium, but that of interior equilibria is challenging and remains an open problem.
For stochastic version of the model, we showed that the noise can help eliminate cancer in either case of big or small noise. Similar results in an epidemic model were obtained by [9]. We note that the criteria for persistence in Theorem 4.3 is not a necessary condition and in fact far from being a sharp result. Indeed, parameter sets (c) (d) do not satisfy the persistence condition but appear to be persistent in the numerical simulation. The improvement of the current result will be deferred to future work. Also, as seen in the simulation, there is a stationary distribution of (12). However, it is challenging to show this analytically because the diffusion matrix is degenerate, which makes the standard techniques employed in [29,9] not applicable. There have been recent studies on the stationary distribution resulting from a degenerate diffusion matrix [19]. It will also be the focus of our future work.
When including time delay with noise, we showed that the results we obtained for noise-only system (12) can be easily extended to (15). Moreover, we found numerically that the noise favors transition to high tumor stable state, which was also observed for an ecological model studied in [31]. The biological implication is that the less responsive the immune system is, the easier for the tumor to escape to high burden. Same as for the noise-only version of the model, the existence of a stationary distribution of (15) will be the focus of future work. There are some perturbation techniques applied to delayed Focker-Planck equation to study the effects of time delay on mean first passage time [16,10,7]. Those techniques are limited to a scalar equation. Possible extension to study a system of equations will be carried out in the future.
Time delay and stochasticity are two hallmarks of cancer dynamics. Serious modeling work shall not shy away from them. The model we studied was kept minimal but nevertheless exhibited interesting dynamics. It is hoped that this paper will stimulate interests in stochastic delayed differential equations in modeling cancer dynamics. | 6,686.4 | 2020-01-01T00:00:00.000 | [
"Physics"
] |
Fock-Space Coupled Cluster Theory: Systematic Study of Partial Fourth Order Triples Schemes for Ionization Potential and Comparison with Bondonic Formalism
In this paper, we have made a systematic study of partial fourth order perturbative schemes due to triples to compute the ionization potential within Fock-space multi-reference coupled-cluster theory. In particular, we have obtained computationally less expensive correlation schemes due to fourth order triples. Prototype examples have been considered to explore the efficacy of the approximate methods mentioned, while the bondonic formalism supporting the bonding phenomenology is also respectively for the first time here advanced.
Introduction
Photo-ionization of electrons is a very important step to transfer the energy of radiation to matter and thus plays a crucial role in physics and chemistry [1]. Ionization of main peaks is associated with ejection of electrons from the molecular orbitals and simple Koopman's approximation is often inadequate to describe the process of ionization. While the satellite ionization peaks cannot be explained without electron correlation, as pointed out by Cederbaum and co-workers [2], the role of electron correlation and thus the correlated theories in the calculations of main peaks has been noted in several works [3]. For the main peaks, such correlated theories improve the main peaks significantly and very often without such correlation, even the qualitative ordering of ionization peaks does not come out correctly. Several correlated theories, configuration interaction, perturbation-based, Green's function, equation-of-motion based theories have been used for vertical ionization potential (VIP) calculation. The ones, which can produce direct calculation of VIPs by cancellation of the common ground state energies, have been preferred [4,5]. Several quantum mechanical theories are able to describe these energies as well as microscopic interactions of the systems. At the same time, theories based on bondons provide the link to the extended systems [6,7]. The bondons describe chemical particles, which are associated with electrons implicated in the chemical bond as single, delocalized, or the above analysis, the triply excited cluster amplitudes were included in the FS (0.1) sector at the third order and later by Pal and co-workers at the fourth order [38]. Importance of triples in the context of EOM-CC was also noted in several studies for IP, EA, and EE [43,48].
The objective of this paper is to relook the fourth order perturbative triples inclusion. We present in this paper new partial fourth order schemes to FS-CCSD, which is also called MRCCSD, which may be useful for IP calculation. The main purpose is to show that one can design computationally less expensive partial fourth order triples schemes, which provide results in close agreement with the full fourth order method. Thus, essentially, a computationally efficient scheme in the context of fourth order triples to FS-CCSD will be presented.
The paper is presented as follows. In Section 2, we will introduce briefly the FS-CC, in particular, the (0.1) sector FS, which is the main context of this paper. Subsequently, we present the different fourth order schemes, from partial to the full, originating due to the triples; the corresponding bondonic-diagrammatic general formalism that phenomenological support the present partial fourth order triples schemes and further inspires the forthcoming perturbative many-body higher order chemical bonding dynamics is then innovatively advanced. Results and discussion section (Section 3) will highlight the main point of this paper, computationally the least expensive partial fourth order scheme, called the MRCCSD+T * − a(4) scheme, provides results of IP in agreement with full fourth order calculations in most cases. As one expects, the results of IPs are not very sensitive and the experimental numbers can only be reproduced with vibrational corrections. In our calculations presented here, the vibrational corrections are not included. In this section, third order triples results, full (T * (4)) and another partial fourth order scheme (T * − b(4)) will also presented, such that a more detailed analysis can be made. We present results of outer-valence and inner-valence IPs of some test systems, N 2 , CO, BeO, and CH + .
FS-CC Theory
The common vacuum defines the set of holes and particles, which can be further sub-divided into "active" and "inactive" subsets. The reference in FS theory consists of determinants containing active particles and holes. In case determinants with all possible active holes and particles included in the model space, it is called a complete model space. If the reference consists of one active hole or one active particle only, the choice of active subsets can be always such that it is complete. A general m-active particle and n-active hole model space may be termed (m,n) model space. Such a reference Ψ (0)[m,n] µ may be written as: where ϕ [m,n] i are k-number of determinants with m active particles and n active holes in the reference.
The wave-operator, Ω, transforming Ψ (0) µ to Ψ µ may be written as: where: Here, { } denotes the normal-ordering of the operators within the curly bracket. s (k,l) represents an operator destroying exactly k active particles and l active holes. Thus, S [m,n] consists of operators which can destroy up to m active particles and n active holes. The s (k,l) operators do not contract among themselves, since the Ω is normal ordered. In addition to the destruction of k-active particles and l-active holes, the cluster operators also create holes and particles involving inactive orbitals. This defines the total rank of the S operators. In singles and doubles approximations, one and two body cluster operators are used. Thus, the Bloch equations are partially decoupled and are solved from the (0.0) sector progressively upwards. The cluster operators are obtained through the Bloch equations at every FS sector, starting from (0.0) to (m,n). It can be shown that since the wave-operator is normal ordered, the equations for the lower sector are decoupled from the equations of the higher sector. This is called sub-system embedding condition (SEC) [49]. For the specific (0.1) sector, which is a complete model space, intermediate normalization (IN) is valid and the Bloch equation remains connected with IN [30,31]. For general model space, however, the connectivity of the Bloch equation is not consistence with the IN [28] and in such case, the IN condition is sacrificed. The IN is given as PΩ ≡ P. The Bloch equations for the specific (0,1) sector are: and: The first set of equations for (0.0) sector are, just the SRCC (single reference coupled cluster) equations.
H N is the normal ordered Hamiltonian. Using the IN condition, the (0,1) effective Hamiltonian H (0,1) e f f can be defined as: The Schrodinger equation can be written for all roots corresponding to the number of determinants k in the reference space, as the eigen-value equation for H (0,1) e f f in P (0,1) space: Here, { } denotes the normal-ordering of the operators within the curly bracket. ( , ) represents an operator destroying exactly k active particles and l active holes. Thus, [ , ] consists of operators which can destroy up to m active particles and n active holes. The ( , ) operators do not contract among themselves, since the is normal ordered. In addition to the destruction of k-active particles and l-active holes, the cluster operators also create holes and particles involving inactive orbitals. This defines the total rank of the S operators. In singles and doubles approximations, one and two body cluster operators are used. Thus, the Bloch equations are partially decoupled and are solved from the (0.0) sector progressively upwards. The cluster operators are obtained through the Bloch equations at every FS sector, starting from (0.0) to (m,n). It can be shown that since the wave-operator is normal ordered, the equations for the lower sector are decoupled from the equations of the higher sector. This is called sub-system embedding condition (SEC) [49]. For the specific (0.1) sector, which is a complete model space, intermediate normalization (IN) is valid and the Bloch equation remains connected with IN [30,31]. For general model space, however, the connectivity of the Bloch equation is not consistence with the IN [28] and in such case, the IN condition is sacrificed. The IN is given as ≡ . The Bloch equations for the specific (0,1) sector are: and: The first set of equations for (0.0) sector are, just the SRCC (single reference coupled cluster) equations.
is the normal ordered Hamiltonian. Using the IN condition, the (0,1) effective Hamiltonian ( , ) can be defined as: The Schrodinger equation can be written for all roots corresponding to the number of determinants k in the reference space, as the eigen-value equation for ( , ) in ( , ) space: Due to the used of normal ordered Hamiltonian, is the correlation contributed to the state, computing with respect to the RHF of the N-electron. Further, if we compute ≡ ( , ) and drop ( ) in the construction of ( , ) , the common correlation energy of the ground state is cancelled and we can obtain the direct difference energies. The earlier applications of FS-CC were done using singles and doubles approximations i.e.: This approximation is known as FS-CCSD approximation. The (0.0) sector operators are standard hole-particle creation operators, while (0,1) operators have one active hole destruction operator.
( , ) operator must scatter from an inactive hole to an active hole and will thus be absent for a case where all holes are active: Due to the used of normal ordered Hamiltonian, E µ is the correlation contributed to the µ th e f f , the common correlation energy of the ground state is cancelled and we can obtain the direct difference energies. The earlier applications of FS-CC were done using singles and doubles approximations i.e.: This approximation is known as FS-CCSD approximation. The (0.0) sector operators are standard hole-particle creation operators, while (0,1) operators have one active hole destruction operator. S (0,1) 1 operator must scatter from an inactive hole to an active hole and will thus be absent for a case where all holes are active: where a h refers to the subset of active holes.
Perturbative Triples
The full inclusion of triples is very expensive and may be unnecessary. Therefore, approximations have been proposed, motivated by perturbation. Perturbative triples were first proposed by Pal et al. [29]. In order to consider a balanced correlation for an entire wave function, the inclusion of both theT 3 (0,0) andT 3 (0,1) were considered. The corrections of the effective Hamiltonian at both third and fourth order due to triples were taken into account. To analyze the effect of perturbative triples tô e f f , let us first consider the expression ofĤ (0,1) e f f , including triples. First, let us write H N as the sum of one-body, two-body, and three-body operators as: The equation forŜ can be written up to second order as: We recall that correlation required for perturbation corrections, H N will now containŜ is what is w amplitudes, at the second order, when inserted in Equation (14), provide what is called MRCCSD+T * (3). This also implies that whileŜ (0,1) 3 is corrected up-to the second order, partial higher order corrections are also taken care due to higher order effects onŜ (0,1) 2 from the MRCCSD equation.
To explain the various fourth order schemes, first, we include the effects ofŜ Clearly, this will affect the effective Hamiltonian at the fourth order and this scheme has been called MRCCSD+T * − a(4), which is a partial fourth order correction due to the triples. Hence, this scheme only includes changes inŜ Subsequently, we use w N to at least the third order with the term vŜ amplitudes to the third order. The resulting equation is as follows: In contrast to Equation (15), w N is used up to the third order. This partially correctsŜ (0,1) 3 up-to the third order. It is important to emphasize that up-to this stage, the corrections in terms of triples are essentially non-iterative. Effective Hamiltonian, generated at this level, is still only partially correct up to the fourth order. This, we call MRCCSD+T * − b(4). To highlight the difference between a(4) and b(4) schemes due to the triples, a(4) includes changes inŜ values, which are correct up to third order (Ŝ (0,1) 3 [3] ): The consequentĤ e f f is correct at least up to the fourth order. This final approximation is known in the construction of H N have been taken to obtain the desired order correction.
Comparison with the Bondonic Diagrammatic Formalism of Many-Body Perturbation Theory
Since ionization potential issue is closely related with molecular stability, through correlation, so with chemical bonding too, the natural additional matter may address how the quantum chemical bonding ultimate theory may accommodate the present perturbative many-body high-order schemes, in a general framework that may be eventually further developed. Fortunately, the bondonic theory of chemical bonding [6,7] In contrast to Equation (15), w is used up to the third order. This partially corrects S ( , ) up-to the third order. It is important to emphasize that up-to this stage, the corrections in terms of triples are essentially non-iterative. Effective Hamiltonian, generated at this level, is still only partially correct up to the fourth order. This, we call MRCCSD+ * − b (4). To highlight the difference between a(4) and b(4) schemes due to the triples, a(4) includes changes in S ( , ) amplitudes at the third order, keeping S ( , ) amplitudes at the second order, while in b(4) scheme, additional corrections are made to have S ( , ) partially corrected via Equation (17).
Finally, the term v S ( , ) with the second order S ( , ) has been included (one iteration) in The consequent H is correct at least up to the fourth order. This final approximation is known as MRCCSD+ * (4). Effects of S ( , ) in the construction of have been taken to obtain the desired order correction.
Comparison with the Bondonic Diagrammatic Formalism of Many-Body Perturbation Theory
Since ionization potential issue is closely related with molecular stability, through correlation, so with chemical bonding too, the natural additional matter may address how the quantum chemical bonding ultimate theory may accommodate the present perturbative many-body high-order schemes, in a general framework that may be eventually further developed. Fortunately, the bondonic theory of chemical bonding [6,7] may address this matter in a phenomenological way. To unfold this venture, one may start with the quantum electro-dynamically (Feynman) diagrams of chemical bonding, having the bondons as "gluing bosons" of electrons in bonding (Figure 1a), in analogy with the photons driving the free inter-electronic repulsion (Figure 1b). Starting from it, one may proceed with the next phenomenological step towards proposing the chemical bonding diagram of Figure 2a, while recognizing it is a superimposed of two interacting loops-in the decomposed version of Figure 2b. Note that in Figure 2, due to the bosonic character of bondons in chemical bonding modeling, the interaction lines are mixed with the interaction centers, that is the single particle can self-interact in the first order and interact at distance in the second order, respectively for the mixed states 1 and 2 . Accordingly, we may advance the bondonic graph (de)composition for electronic pairing in chemical bonding as the following: where we identified (from Figure 2b) the individual perturbative first and second order graphs, namely the active-hole and the active hole-particle pair, respectively. The prefactors of Equation (19) we better interpret when we provide its generalized form, i.e., for the N-body k-order of interaction: This way: k-stay for the perturbation order; l-accounts for the total number of loops over all [51] for special realization of such diagrams up to the fourth order, for instance; N-is the total number of electrons in the bonding state in matter (it can be either ground state or valence state, or other involved in chemical reactivity therefore). Of course Equation (20) is not "a derivation", yet it has a phenomenological consistency, since: i) it carries the "effective formalism" feature by involving the summation (superimposing) of diagrams "each centered" on mate-/pairing-contributing active holes, as it is Ñ , along the higher interaction with active particles ( 1 > k ), as the effective formalisms usually prescribe; ii) it features the "superposition" multiplied with the 2 N recalling the undiscernible particle statistics; iii) it is a particle size dependent as 2 N so "recuperating" in a bosonic way (viz. the bondons as "gluing" Note that in Figure 2, due to the bosonic character of bondons in chemical bonding modeling, the interaction lines are mixed with the interaction centers, that is the single particle can self-interact in the first order and interact at distance in the second order, respectively for the mixed states |1 and |2 . Accordingly, we may advance the bondonic graph (de)composition for electronic pairing in chemical bonding as the following: where we identified (from Figure 2b) the individual perturbative first and second order graphs, namely the active-hole and the active hole-particle pair, respectively. The prefactors of Equation (19) we better interpret when we provide its generalized form, i.e., for the N-body k-order of interaction: This way: kstay for the perturbation order; laccounts for the total number of loops over all diagrams involved k 1 , k + 1 1 -see Ref. [50] for special realization of such diagrams up to the fourth order, for instance; Nis the total number of electrons in the bonding state in matter (it can be either ground state or valence state, or other involved in chemical reactivity therefore). Of course Equation (20) is not "a derivation", yet it has a phenomenological consistency, since: i) it carries the "effective formalism" feature by involving the summation (superimposing) of diagrams "each centered" on mate-/pairing-contributing active holes, as it is ote that in Figure 2, due to the bosonic character of bondons in chemical bonding modeling, teraction lines are mixed with the interaction centers, that is the single particle can self-interact first order and interact at distance in the second order, respectively for the mixed states 1 2 . Accordingly, we may advance the bondonic graph (de)composition for electronic pairing mical bonding as the following: we identified (from Figure 2b) the individual perturbative first and second order graphs, ly the active-hole and the active hole-particle pair, respectively. The prefactors of Equation (19) tter interpret when we provide its generalized form, i.e., for the N-body k-order of interaction: ay: k-stay for the perturbation order; l-accounts for the total number of loops over all [51] for special realization of such diagrams up to the order, for instance; N-is the total number of electrons in the bonding state in matter (it can be ground state or valence state, or other involved in chemical reactivity therefore). Of course ion (20) is not "a derivation", yet it has a phenomenological consistency, since: i) it carries the tive formalism" feature by involving the summation (superimposing) of diagrams "each ed" on mate-/pairing-contributing active holes, as it is Ñ , along the higher ction with active particles ( 1 > k ), as the effective formalisms usually prescribe; ii) it features uperposition" multiplied with the 2 N recalling the undiscernible particle statistics; iii) it is a le size dependent as 2 N so "recuperating" in a bosonic way (viz. the bondons as "gluing" , along the higher interaction with active particles (k > 1), as the effective formalisms usually prescribe; ii) it features the "superposition" multiplied with the ∼ N 2 recalling the undiscernible particle statistics; iii) it is a particle size dependent as ∼ N 2 so "recuperating" in a bosonic way (viz. the bondons as "gluing" the fermions in chemical bonding) the "condensation ordering parameter", and being in the same time suitable for chemical bonding dynamics -since chemical reactivity usually conveys with ∼ N 2 energy variation (e.g. by the chemical hardness dependency, etc.), see Ref. [51]. Moreover, worth mentioning that earlier study of applying bondonic theory to elemental chemical bonding in hydrogen molecule revealed that, indeed, the presence of ∼ N 2 order parameter in the master quantum equation (of Hartree-Fock-Bogoliubov type) development; while, when combining with fermionic superposition in a Heitler-London formalism leaves with the shifts of both nominator and denominators of resulted variational energies of bonding, see Ref. [52]; this is in phenomenological agreement with the many-body perturbation theory which, through infinite summation of interacting orders yields with geometrical series solved with such energetic corrections in both effective potential and in referential energies alike. Of course, much work should be done in order to establish one-to-one correspondence between the present bondonic formalism as diagrammatic results and the standard perturbative theory of N-states. Yet, the present endeavors like to open such a way. All-in-all, as a corollary for the theoretical purpose of the actual paper, the bondonic diagrammatic framework of the present partial fourth order triples scheme looks like the composed diagram: Expression (21) fulfils the current approximation scaling approach, while allowing specific realization (viz. the orders α, β, γ) as depending on the implemented scheme; particularly, the presently implemented schemes are represented by the bondonic composed diagrams, respectively: One remarks the elegancy in expressing each of the above schemes in coding of the one-body, two-body, and three-body "bondonic operators", in various orders of interactions, and with the reference to the active-hole creation.
Computational Details
In this section, we present prototype results of VIP using the formulation of partial fourth order schemes for inclusion of triples in FS-CCSD. We chose four molecules, N 2 , CO, BeO, and CH + in different basis-sets. For simplicity, in this and the subsequent section, we simply write IP in place of VIP. We present MRCCSD, MRCCSD+T * (3), MRCCSD+T * − a(4), MRCCSD+T * − b(4), as well as full MRCCSD+T * (4) results for each of the molecules. We present the details of geometry, active orbitals, and the basis sets used for each molecule below.
For N 2 , the two basis sets used are a [5s4p2d1f] (basis-A) and aug-cc-pVDZ basis (basis-B). Experimental geometry of 2.07 a.u. has been used for the calculations. N 2 is a well-tested molecule for which earlier FS-CCSD results are available. Basis-A has been generated by contracting 11 primitive s-type and 6 primitive p-type Gaussians to 5s and 40. Uncontracted 2d and 1f functions are added. The entire basis has been included in the Supplementary Information. Two active holes, 3σ g and 1π u (in actual calculation, three, sin ce π is a doubly degenerate orbital), are used as active.
For CO, the results have been computed with three different basis sets, cc-pVDZ, cc-pVTZ, and augmented cc-pvDZ basis sets. Experimental bond distance of 2.132 a.u. has been used for the calculations. We chose four active holes, 5σ, 1π (double degenerate), 4σ for the calculations.
For BeO, we took π and σ orbitals, which are the two highest occupied molecular orbitals. cc-pVDZ, cc-pVTZ, and augmented cc-pvDZ basis sets were used. Experimental geometry of 2.515 a.u has been used to present vertical IPs using the above methods.
Finally, a small molecule, CH + , has been taken for study where two highest molecular orbitals have been taken as active. In this case, however, we presented the lowest IP only. The calculation has been done using cc-pVDZ and cc-pVTZ basis at a bond distance of 1.8 a.u., 1.9 a.u., and 2.0 a.u. The IP calculations for CH + was earlier used for discussing binding of CH 2+ [53,54].
The molecules and basis sets have been chosen such that the conclusion can be drawn on a verity of things. A few cases have been chosen with augmentation of basis to explore the effects of diffuse functions on vertical IPs.
Results and Discussions
First, we look at the results of IPs of N 2 in the two bases (Table 1). We compare the results using perturbative triples at third and fourth order. We find that in the basis-A, the results for both 3σ g and 1π u decrease with triples at third order and then oscillate. In this basis, we see not so significant effect of fourth order correlation due to the triples. However, what is more important is that T * − a(4), which is computationally the least expensive method and T * − b(4) can be regarded to be satisfactory. Due to the oscillating nature, it is difficult to judge the quality of the approximations. This is true for both 3σ g and 1π u IPs. When we examine the results of IP in other basis, called basis-B, we find that 3σ g and 1π u IPS have differing trends. 3σ g IP increases with the triples and then typically at different partial fourth order schemes oscillates. On the other hand, 1π u IP decreases with the triples at third order before having oscillatory trends at partial fourth order schemes. One of the differences between the two bases is the presence of diffuse functions in basis-B. It is likely that the diffuse functions affect the 3σ g and 1π u differently as we add the triples and can be the cause for varying trends. The results in both the basis agree well with the experiments. As a next example, we consider CO molecule. Three different basis sets have been used. The results are presented in Table 2. We find in all the three bases, the lowest IP i.e., one which is ionized from the 5σ orbital, decreases slightly at T * (3) level from MRCCSD results and then oscillates at different partial fourth order levels. On the other hand, the other two inner valence IPs 1π and 4σ, have different trends at the third order level. It increases and then as in the earlier cases of triples at the fourth order levels, oscillates. A comparison with basis-A indicates that diffuse functions do not play a significant role in this case and neither does it change the trends of the three IPs. For BeO, the highest MO is of π symmetry. The IPs of the highest 1π and the nest highest 4σ orbital are given in the Table 3.
We see in this case the larger effect of triples at third order. The IPs increase compared to the SD results and then the partial fourth order triples bring them back towards the FS-CCSD calculations. This is seen for all three cases. Comparing with the full results, we clearly see in this case, MRCCSD+T * − a(4) results are quite sufficient in providing results close to the full. T * − b(4) makes marginal changes. The trend is seen for all three basis sets, cc-pVDZ, cc-pVTZ, and aug-cc-pVDZ basis. Comparison of cc-pVDZ and aug-cc-pVDZ, we also find the negligible contribution of diffuse functions.
As a final example, we discuss IPs of CH + . There were earlier studies on the stability of CH-dication, many of which showed the repulsive nature of the bonding. Calculations by Wetmore et al. [53] using a multi-reference configuration interaction model with dzp basis showed a shallow potential well of a very small depth (0.01 eV) trapped behind a slight potential barrier in the ground state curve of CH 2 +. The dip was caused by a strong interaction with the second excited state (C 2+ + H) of the same symmetry. It was, however, too shallow to explain and support the existence of a metastable dication and, in fact, disappeared with a slightly larger basis set. We recomputed with somewhat larger basis using the FS-CC method and observed a very small dip [5Total energy of CH-dication was computed by adding the IP value to the SRCC ground sate energy of CH + . In that sense, the calculation of IP was used to explain the binding of a radical. The calculation of potential energy surface (PES), however, needs theories, which are of Hilbert-space type, but FS theory can be used to throw some light. In this example, we do not wish to generate PES, but calculate the lowest IP of CH + at three bond distances to see how they change with the triples using cc-pVTZ and cc-pVDZ basis. The results are presented in Tables 4 and 5, respectively. We see from Table 4 that the IP for each of the distances decreases with the triples at the third order as well as various partial fourth order schemes, compared with the MRCCSD. The oscillating character of the partial fourth order schemes is not seen in this case. This indicates that energy of the CH-dication will be less as triples are added to the MRCCSD. This, however, does not indicate stronger binding, since we have not computed the full PES, computation of which is outside the scope of the present paper. Further, the results of IP decrease with stretching from 1.8 a.u. to 2.0 a.u. For the cc-pVDZ basis reported in Table 5, we find similar trends, except that there is a slight oscillation between MRCCSD +T* − a(4) and T* − b(4) results. For this small basis, for comparison, we have presented full CI results. The general agreement is observed. What is of significance, however, is that T * − a(4), which is computationally far less expensive, is sufficient for this case. Table 3. Vertical IPs of BeO using various basis sets. We now look at the computational cost of different fourth order schemes. We note that the schemes of third order and fourth order triples are calculated sequentially in the order MRCCSD+T*(3), MRCCSD+T* − a(4), MRCCSD+T* − b(4), and MRCCSD+T*(4). Naturally computing times progressively go up. All of these scale as N 7 . However, third order triples are calculated first and thus run fast. These also have very few diagrams. Essentially, it means that the prefactor is quite small. The next scheme computed is T * − a (4), followed by T * − b(4) and full fourth order. Clearly, the computational time required goes in the same order. However, it is important to note that in terms of diagrams, the two latter schemes T * − b(4) and full fourth order have more in number. Typical computing time to calculate T*(4) takes at least 2 to 3 times the time that is taken for T* − a(4) for the molecules that we presented. This will scale even worse as we go for larger molecules. Exact computing times are not relevant yet, since the code is unoptimized. The prefactor for coding the diagrams of T* − a(4) is much smaller compared to the prefactor for additional diagrams of T* − b(4). Although T* − a(4) results, by themselves, are not sufficient, these will still turn out to be computationally optimum. Hence, we conclude that this itself is a promising candidate for approximate inclusion of fourth order triples from the efficacy of computational time. However, the approximations MRCCSD+T* − a(4) and MRCCSD+T* − b(4) have the limitations in reproducing the full fourth order values, as is seen in the cases of N 2 and CO.
Conclusions and Perspectives
Analysis of the results has pointed out that the MRCCSD+T * − a(4) scheme, has come out as a promising candidate a search of computationally least expensive partial fourth order scheme. With some limitations, it provides results in agreement with the full fourth order and experiments. Moreover, the present partial fourth order triples schemes stimulate the advancing of the diagrammatic bondonic formalism featuring the compact representation of the chemical bond dynamics, here applied on vertical ionization schemes, while opening further challenging in treating exotic or bigger molecules with the aid of diagrammatic perturbation theory for many-states.
Conflicts of Interest:
The authors declare no conflict of interest. | 7,369.6 | 2020-08-27T00:00:00.000 | [
"Physics"
] |
DEVELOPING A DESKTOP APPLICATION FOR INFORMATION MANAGEMENT AND DATA RETRIEVAL SYSTEM FOR A SMALL SCALE INDUSTRY
A small-scale industry having various departments deals with various kinds of data. The management and the flow control of data becomes a tedious job if the work is manual. So for the ease of the above purpose, an application is created on .net framework using C# language and Server Management Studio as a back-end for database management purposes. Various modules including friendly user interfaces are created for powerful data management, data analysis, and data retrieval system. The data visualization tools help in better data analysis.
Introduction
Every company generates various types of data which is difficult to manage. The storage, retrieval, and manipulation of the data require lots of effort and are very time-consuming. The real problem arises when we have to use the data for extracting information and generate reports accordingly. Thus, these problems require a need to develop a system that caters to all these needs without requiring any human efforts.
Problem Definition
In the organization of BBNL, excel sheets were the primary source of data storage and retrieval. Thus the all work related to the data requires a lot of effort and time.
Methodology 2.1 Software Development Life Cycle
According to the project requirement, we have used iterative software development methodology as we have an iterative cycle for various modules we have.
Planning and Gathering of Requirements
The requirements and raw data gathered in this phase was used to plan the basic project approach and to conduct a software feasibility study in the operational and technical areas.
Defining Requirements
After the requirement analysis is done the next step was to clearly define and document the product or software requirements in the form of SRS (Software Requirement Specification).
Designing
A design document defines all the architectural models of the product along with its communication among the modules and data flow representation. Based on the requirements specified in SRS a design approach for the product architecture was proposed and documented in DDS -Design Document Specification.
Development
The programming code for the modules are generated as per DDS during this stage. Developing was followed according to the coding guidelines proposed by the organization.
Testing
The stage involves testing the product where product defects are reported, tracked, fixed, and retested until the product reaches the quality standards defined in the SRS.
Deployment
Deployment is the final stage of the development cycle. The product is released in the market and further according to the feedback gathered the product is enhanced with maintenance followed.
Data Source
The database provided by the BBNL Company is used. It mainly consists of 10000 records with 36 attributes. LG Code is taken as the primary key attribute. Figure 1 shows the snapshot of some of the attributes and figure 2 lists the attributes of the database. JREAS, Vol. 06, Issue 3, July 2021
Software Requirement
The Visual Studio IDE was used to develop the application using C# language. The database was developed on the SQL server studio platform. Other than these the PCs in which the application needs to be installed must be in LAN connection so that they share a common centralized database while working with different modules.
Modules
The application starts with Login Page for any existing user and the New User page will open for registration of any new user. With the domain specified during login, the respective module will be opened. The admin login will be given access to all the modules irrespective of the domain. The specified modules that were prepared for the application were:
Database Management Module
The storage, retrieval, and modification of individual entries in the database can be done through this module. Various forms for individual tables are designed to reduce manual efforts and time.
Report Generation Module
The complete database is analyzed according to the usergenerated criteria. The module is divided into 4 sub-parts i.e. complete report, general report, often report, and extra report. Data Visualization is an add-on feature that was added to the module to represent the information of ongoing projects in the form of pie charts.
Systems and Management module
This module caters to the need for current application form the customers regarding bandwidth requirements and dark fiber requirements. According to the requirements, a demand note is generated and issued to the customer and after the payment, an advisory note will be generated for the suppliers. The real work lies in the background where on a single click the amount needs to be calculated and a demand note or advisory note must be generated instantly without human intervention.
Operations and Management
This module deals with the inconsistency in the connections, if any. The penalty is charged if the leased cable is damaged or is unable to give the desired results. The background code calculates the cost that must be deducted as a penalty from the original bill generated.
Back End
We have used SQL Server Management Studio as our backend software development tool.
Fig.3 ER Diagram
An entire ER Diagram is designed before the actual creation of the database. Various tables are created which are inter-connected with each other using a primary key, 'LG_Code'. A master table comprising of all other tables, used while generation of various reports, is connected to all other child tables. This generalization of Master Table helps in easy retrieval, storage, and manipulation of data.
Architecture
The architecture of the project is a basic 2-tier architecture that is similar to the client-server model. Here the client-side is deployed on the PCs of employees and the server (local) is on one centrally connected server through LAN cables to other node PCs to get access to the database.
Literature Survey
In this segment, we reassess the existing literature and research works done on the system automation and database management system in various fields such as college institutes, companies, inventory management, etc.
In the year 2019, V. P. Jadhav and V. M. Nandedkar performed a work, "Development of Database Management System for Small Scale Manufacturing Industry". In this paper, the author proposed a data management system to enhance information storage control. The application replaces manual paperwork and stores all information in a local MS SQL database server which can be remotely managed by company management to track the inventory information then eventually it facilitates to take a brief decision. The application can prove to be very useful in small-scale companies for saving precious time and reduces paperwork.
In the year 2017, Aishwarya Tamboli, Priti Shinde, Pravin Pariskar, Manisha Sonawane, and Chinmay Anaokar performed a work," Institute Administration Automation and Student Database Management System". In this paper, the authors here developed a method to use a personal computer to automatize the administration procedures of an educational institute and efficiently manage student's database using a Web-based application. For flexible use and lower cost, the proposed system uses a normal personal computer and a local server than a much more expensive dedicated server.
In the year 2016, Fazal Mithani, Sahista Machchhar, and Fernaz Jasdanwala performed a work," A Novel Approach for SQL Query Optimization". In this paper, the author put forth a schema that represents how some queries are going to be converted into an optimized query in a very accurate way with a minimum amount of time and eventually it will reduce the cost of execution and increase the speed of retrieval.
In the year 2014, Haroon Shakirat Oluwatosin performed a work, "Client-Server Model". In this paper, the author explained the client-server architecture at 2-tier and 3-tier with an application and database server, and also PC as the main elements of the system. The client-server system has minimized application development time to a good extent by dividing functions into small modules by sharing information into both the client and server.
Future Scope
The proposed application is a desktop-oriented local server application. The application can be hosted on a remote server for employees that needs to work from different locations. The architecture of the application can also be converted from 2-tier to 3-tier architecture. The three-layer architecture will provide numerous benefits over client-server architecture.
Conclusion
In this paper, we have put forward a data management system application that provides an efficient data storage, retrieval, and manipulation system. A centralized data helps in better analysis and reduces time and human efforts also reduce the | 1,948.8 | 2021-01-01T00:00:00.000 | [
"Computer Science"
] |
Playful Metaphors for Narrative-Driven E-Learning
Featured Application: The presented narrative-driven technique of storyfication can be used in online education to transform e-learning courses into engaging, motivating stories. Abstract: A team of e-learning specialists was assigned the mission to design and develop seven short e-learning modules for young learners on popular topics with a prime focus on social-emotional learning. However, these courses had to be produced on a limited budget, hosted in a Moodle platform, and be delivered for free in asynchronous only mode. Hence, a playful method of meaningful storyfication was applied in e-learning to captivate attention and spark interest. In each module, a fictional story or metaphorical challenge with playful elements was introduced where learners were invited to assist the story’s protagonists or become the heroes themselves by engaging with content. As the role of teachers is vital for the adoption of technology-based solutions in education, a mixed method evaluation was undertaken to assess the effectiveness of the method. Forty-two educators reviewed the courses, answered a questionnaire and participated in interviews. Results indicated that participants highly appreciated the narrative aspects, visual, and auditory elements rendering the e-learning courses effective for the target audience. This article can assist educators, distance education designers and developers to adopt a playful mindset and learn how to integrate practically storytelling elements into their classroom-based and online teaching. Earth defender gear (A4); the dismissal of divergent persons (A5); Interstellar communication with the galaxy Europe-21 (A6); Invisible Internet threats visualized as obscure hacker Dr Megavirus machinations (A7).
Introduction
Computer-based activities such as e-learning courses can sometimes be perceived by children as uninteresting or boring tasks when they are disconnected from their interests [1]. This issue was further amplified in the current coronavirus (COVID-19) pandemic period where all educational activities in physical spaces were canceled. Due to the social distancing measures, remote teaching became the norm globally in all levels of education and for informal professional development and life-long learning projects [2,3]. However, as emergency remote teaching has distinct differences from distance education, the effectiveness of the former was not always desired, especially in the early stages of the pandemic [4]. However, this unprecedented situation had an impact on pupils' emotional and psychological state [5] increasing, for instance, anxiety levels among students [6]. Children of all ages need more than ever social emotional competencies to analyze nascent challenges and apply empathy skills in various social contexts [7]. Social-emotional learning (SEL) competencies have been identified as important for school readiness and predictors of academic performance [8]. SEL includes intrapersonal emotional skills, e.g., self-regulation social awareness, as well as interpersonal skills, e.g., social relationships management and decision making both in the physical realm and in cyberspace [8]. Consequently, the integration of social-emotional learning into all distance education programs in formal education has been suggested as a new imperative priority [9]. This study aims to contribute 2 of 13 to the elimination of tedious, monotonous content production in e-learning course design through techniques that can captivate young learners' attention and interest towards SEL competence. The main hypothesis is that playful design can be acceptable by educators and deemed as an enhancing factor of e-learning. Its main contribution is the suggestion of a narrative-driven procedure of storyfication to transform online learning into an engaging, motivating story in all levels of education.
The current study is structured as follows: Section 2 delineates the theoretical background on playful learning, narrative-based learning, and the use of metaphors in education. Next, the application context and involved course activities and materials are described in depth. The research goal and the data collection methods follow in Section 4, while the results are reported next. Section 6 contains the discussion along with recommendations and pedagogical, managerial, and social implications for practice. Limitations and directions for future research are described in the concluding segment.
Theoretical Background: Playful and Narrative-Based Learning
Previous studies have shown that educators accept technological solutions that supplement their role respectfully and open new learning horizons to their students. Indeed, not everything in education needs to be a game. As demonstrated in various educational models such as the taxonomy of technology-supported instructional methods for Science Technology Engineering Mathematics (STEM) Education [10] and the liquid curriculum [11], students' role in education can vary from passive recipients to active actors, even curriculum co-creators [12]. Frameworks on gameful design such as RECIPE (Reflection, Exposition, Choice, Information, Play, Engagement) [13] and playful design such as TANC (Theme, Activities, Narrative, Components) [14] emphasize the meaningfulness of fun and memorable experiences that win the intrinsic interest of students. This comes in sharp contrast to gimmicky regarded applications of game-based learning where games serve as an extrinsic reward, a facade to disguise learning that would otherwise be seen as undesirable, a phenomenon coined as chocolate-covered broccoli.
Playful learning is the application of the concept of playfulness in education [15]. Playfulness is a lusory disposition and lighthearted attitude demonstrated in various situations [16]. It contains the elements of humor, joy, and spontaneity. Playful learning is one form of game-based learning, next to gameful learning (gamification) and serious games [17][18][19]. Frameworks for playful learning emphasize the importance of openness, curiosity, imagination, risk-taking, experimentation, and safe failure in learning [16]. Having fun through meaningful play is beneficial to children's achievement, motivation, imagination, and creativity [15]. Having fun in education goes beyond games and is associated with a passionate instructor who applies active, experiential pedagogies and creates anxiety-free, safe experimentation spaces where learners can share views and experiences [20]. Gamebased learning is an effective pedagogical method for teaching SEL to children [21]. Fun in learning depends also on autobiographical and contextual factors [22]. A framework of fun in education includes exploration, positive emotions and sensations, social interactions, challenge, failure, and finally, naughty elements [22].
The TANC model for playful learning suggests applying narrative-driven actions within a theme [14]. A theme is a common semiotic domain of reference and relevance for both teachers and students. A narrative is a sequential account of events within a story. A story has characters and one or more protagonists, heroes who respond to a challenge and undertake an adventure, the hero's journey. Adding story or mythic elements to a learning experience or platform is called narrative-based learning [23]. Narrative-based learning in educational technology systems and educational material has been applied predominantly in STEM but also Humanities Arts and Social Sciences (HASS) [23,24]. Story-based learning in narrative-centered learning environments has produced significant affective outcomes such as interest and self-efficacy [25]. Storyfication is linked with emergent narrative storytelling in filmmaking [26]. In this study, the term storyfication underlies the transformation of learning into an interactive digital story that the learner can Appl. Sci. 2021, 11, 11682 3 of 13 experience in an emotionally engaging way. Stories improve fact recall in comparison to a dry, unstructured presentation form [27]. Storytelling has been an effective method for elearning development in STEM subjects [28,29]. Stories facilitate the achievement of deeper, enduring student knowledge [28,30]. Storytelling can be achieved through visualizations with animated characters or cartoons (animated videos) and 3D virtual worlds [31,32]. Animated videos can supplement or replace other forms of knowledge generation [33]. According to the cognitive load theory, students learn better through animation with spoken text than with written text (modality effect) [34]. One powerful element of stories is the use of metaphors. Metaphors are ubiquitous in everyday communication and cognition. A metaphor is a linguistic or artistic expression that connects different concepts in a descriptive, meaningful way [35]. Metaphoric meanings can be used in education to reconceptualize situations and phenomena facilitating empathetic and critical reflection. Metaphors can be a powerful medium for elementary or primary education and teachers' education [36].
Materials
In our study, the used materials were designed, developed, and provided by the Municipality of Thessaloniki. More specifically, the Public Benefit Enterprise of the Municipality of Thessaloniki (KEDITH) in Thessaloniki, Macedonia, Greece has the mission to organize activities, projects, and actions for children and young people [37]. Therefore, it operates and supervises five Centers for Creative Employment of Children. In the aftermath of the COVID-19 pandemic, KEDITH designed in 2020, a new, free, distance, creative, and socially protected platform for children entitled to the e-kedith experience [37]. The e-kedith experience is a safe and fun electronic edugaming environment for young learners. The platform, based on Moodle, contained modules on SEL, sustainability, and citizenship with the following titles next to other gameful and entertaining resources: A1: I have the right to be a child A2: Stop Bullying A3: Recycling as a way of life A4: Climate Change: Be part of the solution A5: Diversity in the country of Solfeggio A6: Journey to the galaxy of Europe A7: Are you surfing online? Stay safe The main intended learning outcomes of each module are illustrated in Table 1. Each module had an estimated total study time between 1 and 2 h and contained three to five levels or sub-modules as well as a section with additional activities, material, and links for further exploration and discovery. Each submodule included dialogs, narration, and interactive elements such as quizzes and branching scenarios. All modules were storyfied; they were constructed around a narrative with fictional elements that resulted in a tangible challenge that learners were called upon to address progressively, either in the first or third person. The narrative of each module is described in Table 2. Then, each module was divided into three to five units of specific focus. In each unit, learners engage with multimedia content and a series of learning activities. Activities are of various nature: prompting knowledge questions, exercises for learners to demonstrate the retention and comprehension of information. An elite detective is recruited in the melody land of Solfège to restore harmony and find two missing musical notes, Dodo and Sisi who were persecuted due to their divergent features.
Become a detective assistant to locate the missing notes
A6 European Union
Penelope is contacted through radio by Galateia, a traveler from the future in a remote galaxy who is seeking missing information about Europe's history and culture.
Help Penelope provide the missing information of their identity A7 Internet safety Stathis is returning to his native island for summer vacations but he discovers that his friends have been influenced by an evil Internet villain.
Help Stathis rescue his three friends All narrative components were implemented with animated characters. The animated videos were designed taking into account implications from research findings such as eliminating unnecessary visual elements [34]. They were developed primarily with the Vyond and Articulate Storyline platforms along with audio, image, and video editing software.
The story of each module contained playful and gameful elements as well as metaphors that are presented in Table 3. Metaphors are the central vehicles for meaning-making and sophisticated reflection and critical thinking within the module's narratives. In module A2 on bullying, friendship is conceived as a cooperative sport where users achieve milestones visualized in the friendship progress bar, leading to the trophy of the most valuable friend (MVF). This is a direct reference to the, most valuable player (MVP) award. The term became viral among youth, as Giannis Antetokounmpo, was the first Greek basketball player in history to win the MVP title of the National Basketball Association in 2019 [38]. In this way, the story conveys the moral message that social relationships and friendship is a desirable competence that is being acquired through mindful action and consistent, empathetic effort. In module A4 on global warming and climate crisis, saving the planet from environmental catastrophe is a deed worthy to superheroes. These superheroes are, in fact, scientists: Super Smaragda, botanologist, Fantastic Filippos, oceanologist, and Tetraperatos (canny in Greek) Timos, volcano geologist. But even a squad of superheroes cannot achieve it, mass participation is needed. Hence, an army of earth defenders is called upon, where everyone can join by acquiring its gear consisting of four items: the cape of knowledge, the shield of nature, mixed reality glasses, flying e-scooter. Each of these badges corresponds precisely to the content of the module's units, e.g., on sustainable mobility. However, the real task of earth defenders against global warming and climate change is actually local, in everyday life, so the completion of the module in a first-person perspective marks the beginning of behavior and habit change; the story continues in the physical world, facilitating the transfer in the real-world.
Playful and gameful elements were introduced in multiple levels ( Figure 1). In some modules, the plot unfolds around well-known themes and genres such as science fiction, time travel, mystery, superheroes, escape rooms. Module A1 takes place in a haunted castle, a metaphor for any uncomfortable situation, where an international group of children explores its rights and seeks the keys to open the unlocked doors and escape. In module A6 on European Union and its member states culture, players help a time traveler, an interstellar historian from the remote galaxy Europe-21 who needs to collect information about Europe as it relates to the re-discovery of their lost identity and origins. module A6 on European Union and its member states culture, players help a time traveler, an interstellar historian from the remote galaxy Europe-21 who needs to collect information about Europe as it relates to the re-discovery of their lost identity and origins. On the linguistic layer of world-building, funny meaningful character and location names were introduced. For example, module A5 on plurality takes place in the melody land of Solfège where musical notes live as citizens. The story starts with the news anchorwoman Mirela (from the notes mi, re, la) Diatympanidou (from the Greek word tympanon, a type of tambourine instrument) reporting about the employment of Jacques Crousteau (homonymous with the word 'krousto', a percussion instrument in Greek) to restore harmony. Music in the land turned into cacophony when two divergent notes, disabled Sisi (female) and the overweight Dodo (male) went missing. In module A7 on internet safety, the story takes place on the island of Istopalaia, a fictional name derived from the word 'istos' (web in Greek), and Astipalaia, a real island in the Aegean Sea. Children fell victims to machinations of the devious Dr. Megavirus and are held virtually On the linguistic layer of world-building, funny meaningful character and location names were introduced. For example, module A5 on plurality takes place in the melody land of Solfège where musical notes live as citizens. The story starts with the news anchorwoman Mirela (from the notes mi, re, la) Diatympanidou (from the Greek word tympanon, a type of tambourine instrument) reporting about the employment of Jacques Crousteau (homonymous with the word 'krousto', a percussion instrument in Greek) to restore harmony. Music in the land turned into cacophony when two divergent notes, disabled Sisi (female) and the overweight Dodo (male) went missing. In module A7 on internet safety, the story takes place on the island of Istopalaia, a fictional name derived from the word 'istos' (web in Greek), and Astipalaia, a real island in the Aegean Sea. Children fell victims to machinations of the devious Dr. Megavirus and are held virtually captive in the dark Diktyonisi (another composite name in Greek meaning network-island) instead of playing and enjoying the sea and their companionship.
In other modules, acts of kindness and social responsibility are used as story cornerstones modeling desirable real-life behaviors. Indicatively, in module A3 on recycling, players adopt a stray dog and have the challenge to build its shed from recycled materials while learning about recycling. This conscious design decision was made to a continuum of awareness-Interest-Reflection-Decision-Action leading to the holistic, communal construction of SEL and sustainability competencies.
Methods
The adoption of various media in education depends heavily on the perceptions of adult educators who will assess its quality and appropriateness for their pupils. Educators tend to resist using a technological or pedagogical medium that is not suited to their needs or not aligned with the learning objectives and outcomes [39]. As quantitative and qualitative research methods exhibit different advantages and limitations, a mixed approach can combine their strengths to corroborate results and of deeper interpretation of data analysis. This study employs a mixed-method evaluation research design combining quantitative and qualitative data collection procedures [40]. The guiding research questions were to assess expert educators' views towards (a) instructional and affective aspects of playful e-learning course design and (b) the alignment of instructional and technological elements with content and learning outcomes.
The study was conducted between June-September 2021 and included two data sources. The primary data were educators' answers in a survey instrument, designed by the first author that combined twenty-four qualitative and quantitative data in the form of open and closed questions, respectively. The survey was divided into four sections: (i) demographics, (ii) instructional and affective elements, (iii) aesthetical visual and auditory elements, and (iv) open questions. The closed questions were structured using a 5-level Likert scale from 1 (strongly disagree) to 5 (strongly agree). The open questions were the following: how would you summarize your experience; what did you like the most or the least-What will you remember most vividly; what would you change in the narratives? The survey was validated by three university faculty members who reviewed the questionnaire and suggested improvements that were implemented. The secondary data collection source were follow-up semi-structured interviews that addressed the validation of respondents' comments. Seventeen interviews took place online with voluntary participation. The duration of the meetings was 10 to 15 min. Interview notes were processed applying thematic content analysis. The questionnaire, as well as the interviews, utilized the Greek language. The translation into English was carried out by one of the authors. Researchers had the opportunity to verify questionnaire data, make correlations and identify possible contradictions. Moreover, the potential use in formal educational settings was explored. By using a dual collection approach, data triangulation was addressed through the honesty, depth, richness, and scope of the data [41].
Participants were forty-two expert educators in primary (elementary) and lowersecondary education. Educators with expertise in the subject areas were identified through their teaching subjects and invited electronically to participate in the study. At the beginning of the survey, educators declared their expertise in the specific subject area. Participants were predominantly female (64%) and holders of a postgraduate degree (58%). They were distributed across four age groups as follows: 41-50 (36%), 31-40 (36%), 50+ (24%), and 18-30 (4%).
Results
Participants evaluated favorably all instructional and affective aspects of the playful storyfied modules, as illustrated in Table 4. The implemented narratives were deemed as successful and attractive. More importantly, educators evaluated the narratives as very appropriate and helpful for the achievement of the intended learning outcomes. Looking at individual courses, the highest regarded narratives were A2 (Bullying), A4 (Climate change), and A5 (Diversity). During interviews, teachers were asked explicitly about the alignment of the narratives with learning outcomes. Synthesizing thematically their expressed views, it was deduced that the alignment was indeed achieved through the following three mechanisms of raising complexity:
Agency activation The simplest method was turning an abstract concept into a tangible situation. For instance, building a dog shed from recycled materials visualizes the result of the recycling process. Moreover, it introduces the element of progress feedback to drive the task's completion. Raising stakes was a second, dramatizing technique to emphasize the importance of learning objectives. Learning about Europe becomes of vital assistance for a time traveler in need. The perils of the Internet are no longer theoretical when your friends are victims, and you have to find ways to help them. Finally, the most powerful storyfication mechanism is agency activation; players become the story's focal persons and are invited to take action so as to address an existing problem. The courses are just the beginning, and the adventure transverses from the digital to the physical world. For instance, learners are not accessing content about global warming, they are joining forces with superheroes to save the earth through their everyday behavior and habits.
Qualitative data from open questions and interviews revealed that narratives positively surprised educators who are accustomed to a more direct exposition of facts and theory. Some of the most memorable aspects were story characters (P5), artificial constructs such as the most valuable friend-MVF (P8), the plot twists, and the generated emotions (P33). The purposeful inclusion of characters from minorities such as disabled children and persons of different color, race, and religion was also noticed and commented positively (P14).
The technical development of a playful narrative is equally important so as to express aesthetically the story's vision, translating words into multimedia with animated characters. The quantitative evaluation of the technical aspects of the presented playful modules are presented in Table 5. All technical aspects received high scores regarding fitness for purpose, despite the fact that they were not of the highest fidelity in comparison to commercial games. The auditory elements improve and make the game experience more enjoyable 4.60 0.73 5 1 5 The auditory elements match the module's mood and style 4.43 0.83 4 1 5 Critical voices recommended a less theatrical audio narration as it comes across as staged or hyperbolic (P15). One interviewee (P2) suggested the addition of a short story in video format at the end of the module so as to improve memory retention. Another technical suggestion was mixing up background music instead of keeping the same track throughout the whole module (P23). Nevertheless, graphics, music, and sound effects were among the most notable elements according to three educators (P9, P20, P31).
Overall, participants characterized the overall experience as "interesting and rich with opportunities for active learning" (P1). One teacher (P6) noted that "The experience was very interesting and pleasant. It offers an attractive and original way the development of cognitive, social and emotional skills that children retain through playing and creative, entertaining engagement". Another educator (P26) detected "a different approach to issues we experience, expressed in a pleasing and creative manner". The following participant quotes were indicative: "I liked the fact that my interest was maintained during the whole duration (of the module) thanks to the plot and its interactive character by which children play while simultaneously being taught how to express and think about their feelings, experiences and how to enter into another person's shoes." (P4) "I will remember vividly the way the story was illustrated (graphics, narrative, music, games, effects) and its meanings and also its characters who offer extra incentives through their goals for children to progress in the action." (P6) P17 summed up that "the module was pleasant, interesting, without sterile didacticisms and boring lectures". In conclusion, the majority of participants were very satisfied with the end result and are willing to incorporate the e-learning courses into their teaching. More specifically, all e-learning courses have been recognized as highly relevant for the 21st-century skills labs, an innovative educational policy initiative, newly introduced in the compulsory curriculum of all Greek kindergartens, primary (elementary), and lowersecondary (middle) schools since September 2021. Skills labs link school education with a global outlook, active citizenship, democratic values, innovative mindset, and United Nations' Sustainable Learning Goals. They include four thematic modules for the construction of learning skills, digital literacy skills, social-emotional skills, life skills, soft skills, and STEM skills. These modules are entitled I live better-Well Being, I take care of the environment, I am interested and act-Social Empathy and Accountability, I create and innovate-Creative Thinking and Innovation. The alignment of the playful courses with skills labs is presented in Table 6.
Discussion
This study focused on the teacher perceptions on the storyfication, the playful transformation through the storytelling of short online courses for school children on SEL skills, and contemporary topics related to environmental protection and citizenship. These are issues of highest priority and importance both for their everyday lives as well as for their future as citizens. Educators often reject gameful playful practices as frivolous or inappropriate for serious teaching [20]. In this instance, participating educators approved the approach and evaluated highly the instructional, affective, and technical implementation aspects.
Previous studies have documented positive influences on cognitive and affective outcomes from the integration of storytelling-driven multimedia in education [23,24,31]. A systematic review on narrative-based learning calls for a closer alignment of pedagogy and story, a focal point in the current study. [23]. It also highlights that fictional elements ignite subject-matter interest. Dubovi et al. [31] employed similar animated videos to promote students' reasoning. In their conclusions, they emphasize the importance of enabling students to experience stories and simulations from a first-person perspective, another common observation in this study. A mixed-method study in primary and secondary education with a blended story-powered experience in 3D virtual worlds had also recorded both high teacher praise and tangible student fascination that resulted in creative collaborative digital artifacts [32]. It confirmed the research finding that fun and student enjoyment is more influential for learning than technical visual fidelity of graphics. A qualitative study on a narrative-driven game on science ethics deployed in elementary and middle schools enabled an empathetic perspective and yielded powerful affective effects [24]. Their results corroborated our finding that effective educational narratives trigger emotional engagement are built around characters, dilemmas, decisions, and meaningful actions that model incentives and behaviors in authentic contexts. Additionally, they point out that narrative-based games are not stand-alone learning experiences; they should be part of lesson plans, supplemented by reflective and argumentative discussions to consolidate learning insights.
Consequently, this study leads to the following implications for practice. From a pedagogical perspective, educators and instructional designers can use meaningful storyfication, stories, and metaphors to place learners in the center of a problem or a challenge to activate their agency and drive their cognitive and emotional involvement. However, narratives or other game elements should not be used as a vehicle to disguise tedious tasks and push content to students. On the contrary, learning activities and content should be incorporated meaningfully in a patient way that serves and promotes the story. In this direction, open-ended scenarios can enhance the replayability of playful courses. Once the students' curiosity has been energized, a story with several branches can be replayed to experience different game resolutions. Playful e-learning courses with high replay value can facilitate spaced task repetition leading to enhanced retention.
From a technological-managerial angle, e-learning designers and developers should consider the thoughtful inclusion of multimedia elements in manners that keep cognitive load to acceptable levels. In other words, graphics' quality is not the most decisive factor but rather the fitness for their playful purpose. In this sense, developers can enhance the e-learning quality by building mental bridges using visual and auditory fictional metaphors between the content and the story. From an educational policy point of view, playful materials can accommodate especially interdisciplinary, transversal, and horizontal 21st-century skills. Hence, their systematic incorporation in holistic and blended self-and group-reflective experiences should be explored.
Limitations and Directions for Future Research
The current work has several notable limitations that provide suggestions for future research and development. Teachers' approval and appreciation of e-learning materials do not necessarily translate to students' acceptance and adoption. As student-centered learning approaches dictate, students should be involved in the evaluation process. This is indeed the next step in the current project. Future work will focus on the evaluation of students' attitudes towards playful learning so as compare them with teachers' observations. Another limitation pertains to the study's sample. The sample of teacher participants cannot be regarded as representative of the entire population. The underrepresentation of younger teachers in the 18-30 demographic category was particularly noticeable. Moreover, additional research directions include the estimation of cognitive effects such as knowledge retention and the impact on values and behavior (transfer). Another future research avenue is the implementation of cross-cultural comparative studies to examine the role of humor and playfulness in online learning across different continents and cultures.
In conclusion, this study aims to contribute to the elimination of boring, fact-based learning by presenting a simple, practical method that is easy to implement: the introduction of playful elements and stories with multiple, inherent, and metaphorical connections with learning outcomes and content. This playful disposition is associated with a creative and innovative attitude and can be applied both in online and classroom-based contexts to bring excitement and passion back to both teachers and learners in all levels and modes of education. | 6,584.2 | 2021-12-09T00:00:00.000 | [
"Education",
"Computer Science"
] |
Direct observation of degassing during decompression of basaltic magma
Transitions in eruptive style during volcanic eruptions strongly depend on how easily gas and magma decouple during ascent. Stronger gas-melt coupling favors highly explosive eruptions, whereas weaker coupling promotes lava fountaining and lava flows. The mechanisms producing these transitions are still poorly understood because of a lack of direct observations of bubble dynamics under natural magmatic conditions. Here, we combine x-ray radiography with a novel high-pressure/high-temperature apparatus to observe and quantify in real-time bubble growth and coalescence in basaltic magmas from 100 megapascals to surface. For low-viscosity magmas, bubbles coalesce and recover a spherical shape within 3 seconds, implying that, for lava fountaining activity, gas and melt remain coupled during the ascent up to the last hundred meters of the conduit. For higher-viscosity magmas, recovery times become longer, promoting connected bubble pathways. This apparatus opens frontiers in unraveling magmatic/volcanic processes, leading to improved hazard assessment and risk mitigation.
INTRODUCTION
Volatile exsolution, expansion, and outgassing during magma ascent play a key control on the intensity and style of eruptions (1).The vesiculation process is a consequence of the supersaturation of dissolved volatiles within magma (caused, for example, by decreasing pressure during magma ascent or by magma crystallization), producing volatile exsolution (2)(3)(4).Bubbles of initially supercritical fluid, mostly containing water and lower amounts of carbon dioxide, halogens, sulfur, and other volatiles, nucleate and grow during magma ascent (5).During ascent, multiple processes occur: decompressioninduced nucleation and growth of bubbles, coalescence of these bubbles with one another, and potentially outgassing, through channels formed by a network of coalescing bubbles (6)(7)(8).Ultimately, all of these processes play major roles in determining the eruptive style by influencing whether magma explosively fragments, produces lava fountains, or erupts effusively in lava flows (7,9,10).Understanding bubble coalescence is therefore crucial, given its role in controlling permeability (11,12), the amount and rate of outgassing (13), and hence eruption dynamics (7).
As bubbles nucleate and grow, whether during eruptive magma ascent or experiments in the laboratory, interbubble melt films are thinned to the point of rupture, and bubble coalescence starts to occur (7,(14)(15)(16)(17)(18)(19)(20).Bubble coalescence in volcanology has been investigated by several studies, mainly focusing on the mechanisms that lead toward coalescence (7,15,(21)(22)(23)(24)(25)(26)(27)(28).In general, coalescence is a function of the viscosity of the surrounding melt, and it is an important process affecting bubble dynamics and, hence, magma ascent and transport to the surface (13).Bubble coalescence rates (i.e., how many times coalescence events occur per unit time) depend on the timescale of approach between two bubbles (29), thinning of the melt film separating two bubbles to a critical value, film rupture, and relaxation (21,22,27,28,30).Several studies focused on estimating the value of the critical film thickness in silicate melts (21,31), as well as the timescales of film rupture once the critical thickness is reached (21,22,(25)(26)(27)(28)(30)(31)(32).The estimated rupture timescales range from 1 to 10 4 s, as a function of melt viscosity, bubble size, and the critical film thickness (21,30).Bubble deformation plays also a key role on coalescence rate and on bubble population (23,24,33), and as a result, they can affect magma ascent dynamics.Other mathematical and numerical studies investigated more closely the dynamics of two merging bubbles in a viscous fluid, showing that the Ohnesorge number (a dimensionless number that relates the viscous forces to inertial and surface tension forces) plays a critical role on how quickly two merged bubbles recover the spherical shape (34)(35)(36)(37).These studies, however, are not calibrated for magmatic fluids; thus, more studies are necessary to constrain these dynamics for volcanic systems.
Following the pioneering study of Sparks (1), degassing and bubble dynamics in silicic melts have been widely investigated by means of experiments (38)(39)(40)(41)(42) and numerical models (20,(43)(44)(45)(46)(47).Many studies have focused on the effect of decompression rate on bubble nucleation in silicic melts (48)(49)(50)(51)(52)(53)(54), while others on the parameters controlling bubble growth (1,16,38,43,45,55,56).Accurate insights into the exsolution process were obtained with experiments performed to study vesiculation in silicic melts at both 1 atm (42,55,57,58) and high pressure (39,41,59).Recently, further developments concerned the use of in situ four-dimensional (4D) x-ray tomographic microscopy (where sequences of 3D tomographic scans are collected rapidly and continuously, creating a time series of 3D scans) to study vesiculation of silicate melts at high temperature, but at atmospheric pressure (60,61).However, none of these studies were able to investigate the vesiculation process in basaltic melts in real time at pressures and temperatures comparable to those of an ascending basaltic magma from depth.Here, we combined x-ray synchrotron radiography with a novel x-ray transparent Internally Heated Pressure Vessel (IHPV) apparatus to simulate magma storage and ascent within the crust at pressures ≤100 MPa and temperatures ≤1180°C.With this apparatus, we performed in situ vesiculation experiments to study bubble growth and coalescence in a basaltic magma in real time at magmatic pressures and temperatures under water-saturated conditions.
Our experiments provide visualization and quantification of timescales of bubble formation (i.e., bubble growth, expansion, and coalescence) in real time, confirming and empirically validating theoretical and modeling results for bubble growth and expansion.The experimental results offer an improved understanding of coupling and decoupling between magma and volatiles during ascent in the conduit, providing insights into processes leading to eruptive style transitions and, ultimately, having fundamental implications for hazard assessment and risk mitigation in areas of active basaltic volcanism.Although basaltic volcanoes are usually characterized by effusive and mildly explosive Strombolian and lava fountaining activity (8), some basaltic volcanoes also produce highly explosive Plinian eruptions (62)(63)(64)(65)(66)(67)(68), with a much higher risk for population safety and critical infrastructure, as well as larger environmental impacts.
High-pressure, high-temperature x-ray radiography experiments
Experiments were performed in situ at beamline I12-JEEP, Diamond Light Source, Harwell, UK, combining a novel x-ray transparent IHPV apparatus (fig.S1) with fast synchrotron x-ray radiography.We used a hydrous basaltic glass (table S1) from the 2001 Mt.Etna eruption as the starting material (see Materials and Methods).
We performed decompression experiments at superliquidus and subliquidus conditions to study the vesiculation process at different viscosities and crystallinities.To investigate vesiculation kinetics in pure basaltic melts with 1 wt % of H 2 O dissolved, we performed three decompression experiments at superliquidus (Superliq_Dec) conditions (i.e., no crystals) with decompression rates of 0.05 and 0.08 MPa s −1 representative of basaltic magma ascent rates during fountaining activity (69).The temperature was kept constant at 1180°C during decompression, above the bulk liquidus that is represented by clinopyroxene (table S2).To investigate vesiculation kinetics in crystal-bearing basaltic melts with 0.5, 1, and 2 wt % dissolved H 2 O, we performed five decompression experiments at subliquidus conditions (Subliq_Dec).This set of experiments is characterized by an initial cooling, with a cooling rate of 0.75°C s −1 , at isobaric conditions (50 and 75 MPa) from 1180°C to different target temperatures (1050° to 1080°C) to promote crystallization before decompression.After subliquidus conditions were reached, the system was isothermally decompressed to 0.1 MPa with a decompression rate of 0.08 MPa s −1 to simulate different magma ascent rates during basaltic explosive or fountaining activities (69).
An additional decompression experiment at superliquidus condition was performed to investigate vesiculation kinetics in a hydrous rhyolitic melt (with 0.2 wt % of H 2 O); this experiment gives us the opportunity to compare vesiculation kinetics of basaltic and rhyolitic melts and to extrapolate the role of viscosity and crystals on bubble kinetics and dynamics.In particular, this allows us to compare experiments with a high-viscosity crystal-free magma (rhyolite at superliquidus temperature) with those with a less viscous melt but with similar bulk viscosity, due to the presence of crystals (Subliq_ Dec experiments).Once all the experiments performed reached 0.1 MPa, the temperature was dropped to the atmospheric one with a continuous cooling of 0.75°C s −1 (fig.S2).
Radiographic images show that the vesiculation process of hydrous basaltic melts is quite different in the Superliq_Dec and the Sub-liq_Dec experiments (table S2).In the first group, we observe the growth of single bubbles that during the decompression paths tend to coalesce forming an individual bubble moving upwards to the top of the system setup (movies S1A and S2).A different behavior is observed in the rhyolite sample (Rhyo) in which bubbles remain confined in the melt, reflecting the higher viscosity compared with basalt.In this case, bubbles expand but do not separate from the melt as individual bubbles because of the higher viscosity (movie S1B).The Sub-liq_Dec experiments behave like the Rhyo as a consequence of the initial cooling (0.75°C s −1 ) that favors crystallization of microlites and a consequent increase in viscosity.In these runs, bubble expansion remains confined in the melt (movies S1C and S3), and outgassing is clearly observed through channels of interconnected bubbles, with a "breathing" pattern where groups of bubbles expand and then release gas through a pathway.While in the Superliq_Dec experiments it is possible to observe the nucleation of spherical bubbles that can easily grow and coalesce, in the Rhyo and Subliq_Dec samples, instead, bubbles are always deformed (e.g., bubbles repulsing each other to accommodate their volume increase), and coalescence occurs but to a lesser extent.In the Subliq_Dec samples, the presence of microlites seems to have a relevant impact on bubble behavior, because bubble expansion occurs following a less regular pattern than that observed in the Rhyo sample (movie S1), in which crystals are absent (fig.S6) because of its superliquidus conditions.The irregular bubble expansion seems dictated by the presence of microlites that physically impede bubble growth confining expansion in the less crystalline portion of the melt.The differences observed in the radiographic images are also visible in the textures of the recovered samples (see Supplementary Text and table S3).
High-temporal and high-spatial resolution bubble kinetics in basaltic magma
Our x-ray radiography experiments provided high-temporal and high-spatial resolution bubble kinetics (growth, expansion, and coalescence) in basaltic magma during decompression, simulating magma ascent in volcanic conduits.Once nucleated, bubbles grow by the combined effect of H 2 O diffusion from melt to bubble and decompression-induced gas expansion (16).Incremental bubble growth rate (ΔG R = Δr/Δt, μm s −1 ) was calculated in the basaltic runs of the Superliq_Dec group as the incremental increase in bubble radius (Δr) over time (Δt) (table S4).We measured the growth rate of bubbles that do not coalesce.In Fig. 1A, we plotted the evolution of ΔG R , as function of pressure for each of the Superliq_Dec experiments.Each dot represents ΔG R calculated for a given bubble, and the dashed lines indicate how ΔG R evolves as function of pressure for that specific bubble.Different dashed lines indicate the evolution of ΔG R for different bubbles.We observed that, at a given decompression rate, bubble growth rate increases with decreasing pressure (megapascals) following a power-law relation ΔG R = 1.428 ⋅ P (−0.69) for a decompression rate of 0.08 Mpa s −1 ΔG R = 2.183 ⋅ P (−0.685) for a decompression rate of 0.05 Mpa s −1 As shown in Fig. 1A, it is possible to see that the ΔG R vary from ~10 −1 to ~10 μm s −1 passing from ~40 to 0.1 MPa, with a major rapid increase starting from ~10 MPa.Similarly to Fig. 1A, in Fig. 1B, we plotted the evolution of bubble radius as function of pressure for each of the Superliq_Dec experiments.Also in this case, each dot represents a bubble radius calculated for a given bubble, and the dashed lines indicate how the radius of a specific bubble evolves as function of pressure.Different dashed lines indicate the evolution of radii for different bubbles.Looking at the results, the bubble radius displays a similar trend as that observed for the bubble growth rate (Fig. 1B).In particular, for bubbles grown at different times during the decompression path, we noted that both bubble radius and growth rate increase more gently for bubbles nucleated at the beginning of the decompression (P = 30 to 50 MPa; ΔG R from ~0.1 to ~0.2 μm s −1 in 10 MPa), while they increase faster with a high slope for those nucleated at the end of the decompression path (P = 0.1 MPa; ΔG R from ~2 to ~8 μm s −1 in 0.1 MPa).This can be related to the effect of both the overpressure of bubbles compared to the pressure of the melt and the oversaturation of volatiles.The effect of both bubble overpressure and volatile oversaturation on bubble growth can be noted also comparing our data with those obtained by Masotta et al. (58) for bubbles grown in a basaltic melt at constant ambient pressure.We noticed that the values they obtained (G R = ~10 −1 to 10 −3 μm s −1 ) are approximately one to three orders of magnitude lower than those calculated in our experiments once bubbles reached ambient pressure (ΔG R ~ 10 μm s −1 ).Masotta et al. (58) observed fast bubble growth (G R = ~10 −1 to 10 μm s −1 ), triggered by melt degassing, shortly after nucleation (t < 20 s), followed by a nearly linear growth (G R = ~10 −3 to 10 −1 μm s −1 ) for the rest of the experiment (t > 20 s).According to Masotta et al. (58), the fast initial growth, which they did not see directly during their in situ experiments because of the opacity of the sample, is consistent with the classical formulation of bubble growth with the bubble radius proportional to the square root of time (1,45,47) or the logarithmic growth law (39,41).Thus, the G R calculated in this study once bubbles reached ambient pressure (ΔG R ~ 10 μm s −1 ), which we measured for time intervals <20 s (table S4), are representative of the G R at the very beginning of the exsolution process as reported by Masotta et al. (58).Another interesting comparison can be done with the bubble growth rates obtained by Bai et al. (70) for a basaltic melt through 1-atm in situ degassing experiments in which bubble growth is controlled by diffusion of the volatiles from the supersaturated melt to the bubble at constant pressure.The bubble growth rates, which we extrapolated from Bubble Size Distribution data reported in Bai et al. (70), show values between 10 −2 and 10 0 μm s −1 .
We also compared our experimentally derived growth rates with those calculated using the experimentally validated numerical model of bubble growth by Coumans et al. (44).This bubble growth model is based on the mathematical formulation of Blower et al. (11), Proussevitch et al. (31), and Proussevitch and Sahagian (20), and describes bubble expansion in a viscous magma due to decompression and diffusion.This model requires information on the bubble number density (N b ), which is not accessible from our radiography experiments.We performed some numerical simulations using Coumans' bubble growth model, implementing it with the water diffusion model for basaltic melts (equation 22) of Zhang and Ni (71) and assuming common bubble number densities for basaltic magmas (N b = 10 10 to 10 13 m −3 ) (54).We considered two decompression rates (0.05 and 0.08 MPa s −1 ) and two different water Plots showing (A) incremental bubble growth rate (ΔG R ) and (B) bubble radius versus pressure for the Superliq_dec experiments.in (A), the decompression rate (megapascal per second) for each run is reported in parentheses.insets show magnification of ΔG R (A) and bubble radius (B) at low pressure (P < 3 MPa).Both (A) and (B) show the comparison between the observed bubble growth rates and radius measured from the decompression experiments with those calculated (table S5) using the numerical bubble growth model of coumans et al. (44).numerical results are obtained assuming different decompression rates (0.05 and 0.08 MPa s −1 ) and different volatile contents (1 and 2 wt % of h 2 O).Plotted numerical simulations have been computed using the etna composition (table S1), a magma density of 2700 kg m −3 , and assuming N b = 10 12 m −3 .
content (1 and 2 wt % of H 2 O).The results of these numerical simulations for N b = 10 12 m −3 are plotted in Fig. 1 (blue and green, dashed and solid lines), and they show a good agreement with our observed growth rates except at low pressure (<1 MPa), where modeled growth rates exceed 10 μm s −1 (Fig. 1A, fig.S3, and table S5).However, Coumans et al. (44) reported that the numerical model overestimates bubble growth rates at high gas volume fraction (>0.4), which would explain the discrepancy with the observed values at low pressures.In addition, our growth rates at very low pressures (<1 MPa) might be underestimated because of the effect of the walls of the crucible, which exert a resistance on the melt to deform and flow as bubbles expand, resulting in a reduced expansion of bubbles.
In the Subliq_Dec runs and in the Rhyo sample, bubble growth and coalescence are hampered by the higher viscosity of the melt and not easily resolvable in the radiographic images.Consequently, we could not extrapolate any quantitative data on these processes.However, because the radiographic images show an expansion of the trapped bubbles, it was possible to obtain the incremental bubble expansion rate (ΔE R = ΔA/Δt; square micrometer per second) as the incremental increase of the bubble area (ΔA) with time (Δt).For comparison, we calculated bubble ΔE R also in the Superliq_Dec runs that show values approximately one to three orders of magnitude higher than those of the Subliq_Dec ones as a consequence of their different viscosity before the decompression path (Fig. 2 and table S6; see also Supplementary Text for more details).
In the Superliq_Dec runs, it was possible to observe in real time and identify several steps and related times leading to bubble coalescence (table S7 and Fig. 3) including the following: (1) time of contact, when two separate bubbles enter in contact; (2) time of interconnection, when two bubbles are interconnected and their films start to thin; (3) time of coalescence, when there is an open connection between the bubbles as a result of the rupture of the thinned films; and (4) time of recovery, when the coalesced bubbles recover to a spherical shape.All of these steps can be observed in detail in Fig. 3.A similar sequence for bubble coalescence has been also observed by Masotta et al. (58) during in situ high-temperature observations of bubble growth in a rhyodacitic melt and by Ohashi et al. (27) during in situ decompression experiments of viscous Newtonian analogues in a mini-desiccator box.Some textural features of bubble coalescence [i.e., bubble-melt wall thinning by bending, stretching, and dimpling (7), corresponding to steps 1 and 2 of this study] have been observed in previous ex situ experimental decompression/ vesiculation studies on both basaltic (49,50) and rhyolitic ( 16) compositions.As shown in this study, once two bubbles enter in contact (step 1) there is an interconnection (step 2) during which bubble walls thin until the film ruptures.During interconnection bubbles assume an "eight" shape with cusps on their walls.Because the film thickness is too thin to be resolved by radiography, we cannot see the rupture of the films in real time, but we can ascertain that it has occurred when we observe the replacement of the cusps in the "eight" shape by smooth bumps on the walls of the new coalesced bubble, and so bubbles coalescence (step 3).Once coalesced, bubbles assume an oval shape and lastly recover to a spherical one (step 4).The recovery time (i.e., the time required by coalesced bubbles to recover a spherical shape) is in the order of 1 to 3 s and results to be affected by pressure.We noted, indeed, an increase of the recovery time with decreasing pressure (Fig. 4).The recovery time (τ) for a coalesced pair of bubbles to return to a spherical shape can be compared with theoretical estimations (7,21,32,(72)(73)(74).These allow us to predict τ from the rheological properties of the liquid, because retraction of the common wall between two interconnected bubbles is fostered by surface tension (σ) and resisted by the effective viscosity of the liquid (μ) ([τ = (Rμ)/σ]).Theoretical results (table S7) were obtained assuming σ = 0.1 N m −1 (7, 73), μ = 148 Pa•s [table S3; calculated using the model of Giordano et al. (75)], and R as the equivalent bubble radius (i.e., the radius of an undeformed sphere of equal volume; table S7).From the comparison, we noticed that the theoretical recovery times are faster than the experimental ones with a difference up to one order of magnitude at the lowest pressures (P < 3 MPa).However, fully understanding this discrepancy requires targeted and in-depth studies that are beyond the aim of this study.
Coalescence and degassing were also observed in the Subliq_Dec runs (movies S1C and S3).Evidence of coalescence events can be also found in the recovered samples.We can presume that large bubbles in backscattered electron (BSE) images (fig.S4, D to G) are the product of coalescence between two or more smaller bubbles.The presence of these coalesced bubbles implies that, at the end of the decompression in the Subliq_Dec runs, the formation of single large bubbles moving toward the top of the apparatus did not occur as in the Superliq_Dec experiments.This suggests that the formation of permeable pathways in the Superliq_Dec experiments allowed gas to escape, which can be related to the presence of microlite crystals.The role of microlite crystals on coalescence and formation of permeable pathways is still a debated topic.On the one hand, the presence of microlite crystals could lead to the following: (i) an increase in melt viscosity, as their presence indicates an increased silica content of the melt; (ii) an increase in bulk viscosity, which reduces film drainage ability and consequently the rate of coalescence (13); and (iii) a physical block or impediment of bubble expansion, movement, or coalescence.On the other hand, however, some studies (76-78) observed higher permeabilities and thus a more efficient degassing in crystal-bearing than in crystal-free basaltic magmas.This would suggest that the presence of crystals, forcing the bubbles concentration in some regions of the melt rather than in others, would facilitate the contact between the bubbles and therefore the formation of permeable pathways.The presence of crystals, slowing down the recovery time because of a higher viscosity, would favor a longer opening of permeable pathways and therefore a more efficient degassing.
Influence of bubble coalescence on degassing
Volcanoes such as Etna, Stromboli, and Kilauea are generally characterized by the ascent of basaltic magma that involves different degrees of decoupled, open-system degassing, in which volatiles are able to flow faster than their originating, slowly ascending melt (69,(79)(80)(81).In the presence of more viscous melts (such as rhyolitic melts), bubbles are relatively immobile with respect to the melt (i.e., the slip velocity of bubbles is negligible compared to magma ascent velocity), and this is commonly referred to as coupled or closedsystem degassing (82,83), although in rhyolites open pathways may form, which allow degassing to take place (84,85).The style of basaltic explosive behavior is strongly related to the ability of gas to decouple from the melt, which depends on the relative rates of ascent of melt and bubbles, establishment of percolation bubble frameworks, and the extent of bubble coalescence (80,82,86,87).
In this study, we measured bubble recovery time in the Superliq_ Dec runs and the ascent time of Kilauea magma to investigate the role of bubble connections on degassing [i.e., both closed-system degassing (volatile exsolution and vesiculation) and open-system degassing (outgassing); ( 95)] of low-viscosity crystal-poor/crystalfree basaltic magmas.The quicker this process, the more likely a permeable pathway closes before connecting with the vent of the conduit, preventing gas from decoupling efficiently from the melt.For basaltic lava fountaining activity on Kilauea, La Spina et al. (69) show a maximum ascent velocity of ~60 m s −1 [average velocity ~ 15 m s −1 consistent with Ferguson et al. (96)], which means that magma would need more than 3 s to reach the surface from a depth greater than 200 m.Our obtained recovery timescales are on the order of ~3 s at low magmatic pressures, but they are even shorter at higher pressures, implying that the recovery of spherical shape at depth may occur too quickly to allow permeable pathways (2) bubble interconnection during which bubble walls thin (bubbles assume an "eight" shape with cusps on their walls); (3) bubble coalescence after the rupture of the thinned films (replacement of the cusps in the "eight" shape by smooth bumps on the walls of the new coalesced bubble); (4) bubbles assume an oval shape and lastly recover to a spherical one.
consisting of chains of interconnected bubbles to reach the surface and connect with the vent during fountaining.To better show this, we used results from the lava fountaining simulations performed by La Spina et al. (69) for Kilauea, and we used their model to extrapolate the time required by magma to reach the surface for each pressure.From Fig. 4 and table S8, we note that the time to reach the surface becomes greater than 3 s when the pressure is greater than ~4 MPa (~90-m depth).This implies that, for pressures higher than ~4 MPa, the formation of permeable pathways is mostly inhibited, and thus, the gas-melt system remains coupled at least up to the last 100 m of the conduit (Fig. 4).In general, our results show that lava fountaining eruptions at low-viscosity basaltic volcanoes (such as at Kilauea) are associated with rapid bubble coalescence and recovery time and a high magma ascent rate, resulting in a coupled behavior until the last ~100 m of the conduit (69,79,80).We speculate that some degree of decoupling may occur in the shallowest part of the conduit; otherwise, it is likely that intense fragmentation would be produced by closed-system degassing.This has been observed, for instance, in basaltic pumices from the paroxysmal events of Stromboli where bubble populations are consistent with closedsystem degassing.Close to the surface, where the gas volume fraction is high, the occurrence of multiple events of coalescence at the same time may generate permeable pathways allowing some degree of open-system degassing.Such a process may contribute to near-surface open-system degassing in slow-ascending magmas, such as within a lava lake.
Another possible mechanism for gas-magma decoupling that might happen during ascent is the formation of a slug flow due to the coalescence of numerous smaller bubbles (97,98).Our Superliq_ Dec experiments show that, at low pressures, the increase in coalescence events produce ultimately a very large bubble, which indicates that the formation of a slug flow in the shallowest part of the conduit is possible.A high gas volume fraction (>0.6) ( 99) is required to reach the size of the conduit and develop a slug flow.For a lowvolatile content magma, such as that of Kilauea, this high gas volume fraction is obtained in the shallowest part of the conduit, where most of the exsolution occurs.The formation of slug flow may occur also at depth due to accumulation of gas entrapped at some geometrical discontinuities within the plumbing system (97), but this would generate Strombolian, rather than lava fountain, activity (81,97,98).
Although in the Subliq_Dec runs we cannot visualize in detail the steps that lead to bubble coalescence through radiography, we can, however, obtain some insights on magma-gas coupling/decoupling.We observe that, in the presence of a more viscous magma (η ≥ 10 3 Pa•s), in case of a rupture of the film between bubbles, the recovery time is much longer than that obtained for the less viscous Super-liq_Dec runs.This longer recovery time promotes the formation of degassing pathways that allow gas to escape.An increase in magma viscosity, indeed, can affect the eruptive behavior in several ways such as increasing the fragmentation capability due to bubble overpressure (100,101), suppressing large bubble floatation, and increasing the capillary number and the role of shear deformation (102,103).Our novel experiments with an in situ view show a slow bubble expansion rate and consequently a long recovery timescale at subliquidus conditions (movies S1C and S3), which supports assumptions of previous studies (72,77,104,105).This result suggests that high viscosity produced by microlite crystallization restricts bubble growth and expansion and extends bubble coalescence time, promoting connected pathways between bubbles (as those visible in movie S3 from minute 15 onwards) and thus increased connectivity, which, in turn, favor outgassing (72,77,95,106,107).
Our work represents a substantial step forward in the understanding of magma and gas dynamics, even though it has limitations that result from the use of 2D radiography and inability to apply a shear stress (which are likely to affect bubble growth and coalescence).Thanks to the new IHPV apparatus presented here, we were able to capture and study the vesiculation kinetics in basaltic magmas in real time, in situ, and at pressures and temperatures compatible with those of basaltic volcanoes.Our novel x-ray transparent apparatus has proved to be an invaluable tool to capture and quantify kinetic of bubble formation (i.e., bubble growth, expansion, and coalescence) and magma dynamics (i.e., degassing and gas-magma coupling/decoupling) at syneruptive conditions.In this regard, the growth rates derived from our experiments represent a noteworthy contribution, as they confirm estimations calculated using numerical and theoretical models.
Future developments of the x-ray transparent IHPV will be dedicated to allowing fast synchrotron x-ray tomography of magmatic samples at high-pressure and high-temperature conditions, to visualize and quantify the vesiculation process (nucleation, growth, and coalescence) directly in 4D (3D space plus time) both at superliquidus and subliquidus conditions.This will allow us to further improve first the current numerical model by integrating previously unknown constraints, and then our understanding of magma behavior at pre-and syneruptive conditions and the related volcanic hazard.
Starting material
The starting material used for our vesiculation experiments is a trachybasalt from the lower vents of the 2001 Mt.Etna eruption (62,(108)(109)(110)(111).We used hydrous, crystal-free basaltic samples from Etna with different water contents (0.5 to 2 wt %) and a rhyolitic sample with 0.2 wt % of water.The anhydrous glassy starting material (table S1) was synthesized by melting a crushed rock sample in a Pt crucible.Melting was performed in a Nabertherm MoSi 2 box furnace at 1400°C and at atmospheric pressure.The melt was left in the furnace for 4 hours to fully degas and dissolve any crystals present.The melt was then quenched in air to glass, and this procedure was repeated twice to enhance homogenization.Hydrous starting glasses with 0.5, 1, and 2 wt % H 2 O were obtained by melting the starting material and homogenizing it with H 2 O in Au 80 Pd 20 capsules at 100 MPa and 1200°C using a Titanium Zirconium Molybdenum (TZM) cold-seal pressure vessel apparatus at the School of Earth Sciences, University of Bristol, UK.The water content of the starting materials was confirmed to be present in the glasses by Fourier Transform Infrared spectroscopy.The FTIR measurements were performed in transmission mode by a PerkinElmer Spotlight 400 spectrometer equipped with a Mercury Cadmium Telluride (MCT; or HgCdTe) array detector cooled with liquid N 2 at the Department of Earth and Environmental Sciences at the University of Manchester, UK.Spectra were collected by accumulating 64 scans using a square aperture of 100 μm across with a spectrum resolution of 4 cm −1 .Spectra were analyzed using Spectragryph (112).Using the density trend and Etna basalt extinction coefficient of Testemale et al. (113), the molecular H 2 O peak at 3550 cm −1 gives 2.05 ± 0.01 wt % for Etna 4, 1.32 ± 0.01 wt % for Etna 12, 1.48 ± 0.01 wt % for Etna 13 and 1.13 ± 0.01 wt % for Etna 15 starting materials.
In situ high-pressure, high-temperature synchrotron x-ray radiography experimental apparatus
In situ high-pressure, high-temperature experiments were performed at the x-ray tomography/radiography beamline I12-JEEP, Diamond Light Source, Harwell, UK.We used a dedicated x-ray transparent IHPV apparatus developed at Neel Institute and based on a previous one (113) combined with x-ray radiography to perform in situ vesiculation experiments under water-saturated conditions at crustal pressures.The IHPV apparatus was pressurized with He, which allowed us to precisely control the decompression rate during in situ experiments and to quantify disequilibrium in basaltic magmas after pressure perturbations.The pressurization was controlled by a pressure regulator (114).The vessel is characterized by the placement of the furnace inside the vessel (internally heated).The vessel is a thick-walled steel cylinder having both ends open.The open ends are closed by heads through which pressure, electrical lead, and thermocouple lead enter.The vessel has two sapphire windows at 180°, which allow the x-ray beam to enter the vessel, passing through the sample and reaching the camera for radiography acquisitions.Temperature was measured with a K-type thermocouple positioned close to the sample in the middle of the furnace hotspot.The K-type thermocouple measures the sample temperature with an uncertainty of ±0.5°C.The sample holder was an alumina, which is suitable for the temperature range investigated and has a low x-ray attenuation coefficient.The hydrous glass (~1.5 mm by 3 mm by 5 mm; ~22 mm 3 once melted) was placed in the customized alumina crucible whose sizes are reported in fig.S1.
Experimental strategy
We combined fast x-ray synchrotron radiography with our novel IHPV apparatus to quantify bubble growth and coalescence in basaltic magmas during decompression.The experiments focused on bubble kinetics as a function of initial pressure, decompression rate, and H 2 O content.In all the experiments, we placed an Etna basalt with approximately 0.5 to 2 wt % of water in the sample crucible, except for the one in which we used a rhyolitic sample.We pressurized the system at first with gas (He), and then we heated up to 1180°C with a heating rate of 0.75°C s −1 .At this point, we continued the experiments by keeping isothermal conditions (1180°C; Superliq_ Dec experiments; table S2) or dropping the temperature to different target isothermal conditions (1050° to 1080°C; Subliq_Dec experiments; table S2) with a cooling rate of 0.75°C s −1 .After that, we dropped the pressure to 0.1 MPa with a decompression rate between 0.03 and 0.08 MPa s −1 to simulate different ascent rates during basaltic eruptions, starting decompression at different initial pressures (75, 50, 30, and 20 MPa; table S2).Once reached 0.1 MPa, the temperature was dropped to the ambient one with a cooling rate of 0.75°C s −1 (fig.S2).
In situ synchrotron x-ray radiography acquisition
The x-ray radiography beamline I12-JEEP (Diamond Light Source, Harwell, UK) allowed us to perform experiments using monochromatic 53-keV x-rays, a pixel size of 6.642 μm, and a scanning time of 40 ms per frame to achieve 25 frames per second, at a sample to detector distance of 35 cm, an exposure time for a single projection of 15 ms, and an acquisition time of 988.28 s, for a total of 24,707 images.The acquisition of radiographic projections began shortly before the start of decompression and covered the entire decompression path until ambient pressure was reached.
Image processing and analysis
The radiographic images were processed and stacked using ImageJ software (115) to obtain movies (movie S1).Movies were made by importing in ImageJ the radiographic images in TIFF format as image sequences and then saved as an AVI file.The movies reported in movies S1 to S3 were edited by using DaVinci Resolve (version 18.0.4)video editing software.ImageJ was also used to measure bubble diameter and area.First of all, for all the runs, we used "set scale" to convert pixel in micrometers (1 pixel = 6.642 μm); then frames were converted in 8-bit and then adjusted by brightness/ contrast to better highlight bubbles from melts.Because of the low contrast between bubbles and melt and to bubble overlays, it was not possible to use the "threshold" tool and the "tracking plugin." For the Superliq_Dec runs bubbles diameter and area were measured by manually tracking bubbles using "oval selections" and then the "measure" tool.To better highlight edges of the bubbles the "Find edges" tool was also applied.For the Subliq_Dec runs, instead, because of the absence of spherical bubbles, bubble area was measured manually by contouring bubble edges using the "polygon selections" and then the measure tool.
Scanning electron microscope and electron microprobe analysis BSE images were collected using a FEI Quanta 650 FEG-SEM electron microscope in the Department of Earth and Environmental Sciences, University of Manchester, UK, to analyze vesicles shapes and crystals morphologies.We used an acceleration voltage of 15 kV and a working distance of 10 mm.The starting material (glass) and the samples obtained during in situ vesiculation experiments were analyzed with a JEOL JXA-8530F field-emission electron microprobe at the Photon Science Institute, University of Manchester, UK.The operating conditions were as follows: 15-kV accelerating voltage, 10-nA beam current, and a beam diameter of 10 or 5 μm.Na and K were measured first to minimize loss by volatilization.Calibration standards were albite for Na, periclase for Mg, corundum for Al, fayalite for Fe, tephroite for Mn, apatite for P, sanidine for K, wollastonite for Ca and Si, and rutile for Ti.
Fig. 1 .
Fig.1.Variation of incremental bubble growth rate and bubble radius with pressure.Plots showing (A) incremental bubble growth rate (ΔG R ) and (B) bubble radius versus pressure for the Superliq_dec experiments.in (A), the decompression rate (megapascal per second) for each run is reported in parentheses.insets show magnification of ΔG R (A) and bubble radius (B) at low pressure (P < 3 MPa).Both (A) and (B) show the comparison between the observed bubble growth rates and radius measured from the decompression experiments with those calculated (tableS5) using the numerical bubble growth model of coumans et al.(44).numerical results are obtained assuming different decompression rates (0.05 and 0.08 MPa s −1 ) and different volatile contents (1 and 2 wt % of h 2 O).Plotted numerical simulations have been computed using the etna composition (tableS1), a magma density of 2700 kg m −3 , and assuming N b = 10 12 m −3 .
Fig. 2 .
Fig. 2. Variation of incremental expansion rate with pressure.Plot showing incremental expansion rate (ΔE R ) versus pressure for (A) Subliq_dec and (B) Superliq_dec experiments.in (A), the liquid viscosity (log Pa•s) for each run is reported in parentheses.
Fig. 3 .
Fig. 3. Radiographic images of Superliq_Dec runs showing coalescence steps and timescales.(1)contact between two bubbles; (2) bubble interconnection during which bubble walls thin (bubbles assume an "eight" shape with cusps on their walls); (3) bubble coalescence after the rupture of the thinned films (replacement of the cusps in the "eight" shape by smooth bumps on the walls of the new coalesced bubble); (4) bubbles assume an oval shape and lastly recover to a spherical one.
Fig. 4 .
Fig. 4. Sketch reporting bubble growth and coalescence within the conduit and mechanisms of coupling (up to the last ~100 m) and decoupling between volatiles and magma.Plots show bubble recovery time and magma time to surface versus pressure.in particular, the time required by magma to reach the surface for each pressure within the conduit using the lava fountaining simulations performed by la Spina et al. (69) for Kilauea is reported. | 9,011.8 | 2024-08-16T00:00:00.000 | [
"Geology",
"Physics"
] |
Simple Wireless Nurse Call on Distance Measurement
On this era, many patients go to the hospital and the clinic because the service is better than yesterday. If there are patient suffer a sickness and on the patient bed so the patient will call the nurse using a bell to call the nurse on the treatment room on the hospital. The previous research uses cable to connect the nurse room with the patient room. In this research we proposed a new scheme that is wireless nurse call. The wireless nurse call will minimize the cable. This research module using Arduino. There are consist of transmitter and receiver module. The system consists of the software and the hardware. There are one master transmitter module and four receiver module. The receiver module is tested on several place and several barriers. The visualization can be displayed on the PC (Personal Computer) or Laptop using Microsoft Visual C# software. The visualization is included the four room notification with display and sound. From the research, this module can work well. From the research, we conclude that the module can work and can over the barrier and several far away from the master module. Keywords—Arduino, nurse call, visual C#, wireless
INTRODUCTION
The hospital can be filled by much patient with many kind of sickness. The patient is on several condition start from the few to hard suffer of the sickness level. The doctor and the nurse can coordinate to cover the patient illness [1][2] [3]. The patient if in the accute condition usually call the nurse from the bed by clicking the bell [4][5] [6] [7].
The previous module uses cable to connect the master (the nurse control room) and the small module (on the patient bed side) on each treatment room [8][9][10] [11] [12]. From the research by Taufik Alfianur Wibowo that is the research using cable to transmit the data and the display using seven segment [13] [14][15] [16]. This research is improved by Sultan Al Badrul Munir, the data transmission using Bluetooth wirelessly with range up to fifteen meter [17][18] [19]. But the transmitter is branched by four items so it will so close [20] [31]. The improved system by Abrory Lutfi is using cable to connect the transmitter and receiver but this system can allow voice to communicate between the nurse and the patient [32].
From the previous research, this research proposed the new scheme to cover this previous module. This module using one master and four receivers. This module using the Arduino Nano and the Visual C# to implement this system. This paper is consisted of four section. The first section is Introduction. The second section is System Design. The third section is the result and discussion and the last section is conclusion.
II. SYSTEM DESIGN
The system design contains hardware design and software design. The hardware system contains Arduino Nano, NRF24L01, and push button. The nurse call room can be displayed on the PC or Laptop with Visual C#. The workflow of the system can be seen on the diagram in Figure 1. The Fig. 1 shows that the module is connected to the 220 V AC source. The adaptor converts the 220 V AC to the 5 V DC to be connected to the microcontroller. When the push button is pressed so the analog signal will be transferred to the minimum system. This analog signal is changed to the digital signal. The digital signal will be sent by the NRF24L01 transmitter to the NRF24L01 receiver via radio frequency. This information (digital signal) will be displayed on the software designed using Visual C#.
The software interface can be seen on the Fig. 2 shows the four room notification. There are four room. If the module on the room one is clicked so this notification will be displayed on the room one on the software shown in Fig. 2.
Fig. 2. The Display When Room 1 is Clicked
The research module can be seen on the Fig. 3. The device is transmitter and the receiver. The transmitter is in the patient room and the receiver is in the nurse office that connect to the computer. The opening interface in computer can be seen on the Fig. 4. The OK button is used to connect to the receiver device. The module is tested by using several various of obstacle and distance. The distance will be shown on the Table I. Based on the results in Table I, it can be analyzed that testing the module with a variable distance without an obstacle as much as 11 times the data collection, it is found that the farthest distance that the module can sending the data reach as far as 53 meters.
IV. CONCLUSION
Based on the results of the research, it can be concluded that the appliance cannot work using a battery voltage source, for example using a power bank. During the test, several factors can be found that can affect the distance the device can reach that is wall material, wall thickness and networks or signals such as Wi-Fi signals that are around the module. | 1,192.8 | 2021-05-05T00:00:00.000 | [
"Computer Science",
"Engineering"
] |
Gaining confidence in inferred networks
Network inference is a notoriously challenging problem. Inferred networks are associated with high uncertainty and likely riddled with false positive and false negative interactions. Especially for biological networks we do not have good ways of judging the performance of inference methods against real networks, and instead we often rely solely on the performance against simulated data. Gaining confidence in networks inferred from real data nevertheless thus requires establishing reliable validation methods. Here, we argue that the expectation of mixing patterns in biological networks such as gene regulatory networks offers a reasonable starting point: interactions are more likely to occur between nodes with similar biological functions. We can quantify this behaviour using the assortativity coefficient, and here we show that the resulting heuristic, functional assortativity, offers a reliable and informative route for comparing different inference algorithms.
www.nature.com/scientificreports/ explicit; (iii) apart from sanitised simulated data there is typically very little to go on for a meaningful evaluation of an algorithm's performance.
Here we introduce and discuss a heuristic that allows us to quantify relatively the confidence we should have in proposed biological networks, such as those emerging from network inference. Heuristics of this type-and we shall revisit and stress this point below-offer primarily a sanity check: if the inferred network scores very poorly, we should probably resist from analysing it further. The heuristics are not meant to replace experimental or statistical (in)validation 8,9 rather they aim to put on a quantitative basis what is frequently done by visual inspection.
Below we first outline network inference and the plausibility of inferred networks; we then illustrate how network assortativity 10,11 allows us to compare and rank different network inference algorithms; we then outline how this approach can be employed in practice, before concluding with a discussion on difficulties in the process of network inference.
Assessing the plausibility of inferred networks
A network is represented by the ordered pair where V denotes the set of nodes or vertices V = {v 1 , v 2 , . . . , v N } , and E = {e 1 , e 2 , . . . , e M } , the set of links or edges. While V is typically known, E only is in a few instances, and, arguably, exceedingly rarely in biology; instead we rely on statistical methods to infer the presence or absence of edges between pairs of nodes v i , v j ∈ V , i, j = 1, . . . , N . We will not distinguish between directed and undirected networks as our discussion is applicable to both with only minor modification.
Network inference algorithms typically score edges 1,2,12 , and this score, here denoted by ξ ij , represents the relative weight in favour of an edge existing between nodes v i and v j . We shall often write ξ(q) , to denote the q-th highest score (we ignore possible ties, which can be straightforwardly resolved by ordering such sets of edges randomly), and understand that this refers to the score of the corresponding edge. Network inference is thus based on a process by which a pair of nodes is assigned a real value, In fact, in network inference, we generally consider a function φ that takes states, η i and η j , associated with nodes, i and j, to determine the scores, ξ, Thus we use a property of the nodes, such as expression levels, to determine if there is an edge present between them. For a set of l network inference methods, which will result in inferred sets of edges, E 1 , E 2 , . . . , E l , we want to assess the relative merit of these candidate inferred networks, which are, within the constraints of the methodology, the best available representation of the real network of interest.
Properties of biological networks. Any real biological network (we note that there are limitations to networks as representations of real-world biological systems) is expected to have certain properties, which include 1. Specificity: interactions will be more likely between nodes that have certain functionality (e.g. belong to the same functional class; or belong to different functional classes that have a high probability of interacting-here Gene Ontology annotations can serve as a proxy for, or best guess of, functionality). 2. Modularity: groups of nodes will form tightly interacting modules with pronounced clique structure to fulfil their biological function; modules are expected to be enriched for nodes that have similar or related functions. 3. Connectedness: the true network will connect all nodes (this is not necessarily the case for incomplete data 13 ). 4. Robustness: gross structural features, and thus the function of the network, should be robust against the removal of individual nodes. 5. Hierarchy: some nodes will have more prominent network positions (degree, centrality) and may orchestrate module and modular dynamics. 6. Balance: a real network should have a structure that reflects function and functional importance 14,15 . For similar importance we can expect similar levels of network organisation, robustness, and modularity across the whole network 16,17 .
None of these points should be contentious if we accept (with the usual caveats) the functional relevance of biological networks. These points may contradict some simplistic network models 18 , but, as has been argued, and indeed demonstrated, elsewhere, the structure of real biological networks is much more nuanced and "scale-rich" than simple models might have suggested 14,17,19 . Point 1, in particular (and to a lesser extent also point 2), allows us to develop quantitative criteria against which proposed networks (here we are predominantly concerned with inferred networks) can be evaluated. Points 3 and 4 reflect on network properties that go beyond local interactions, which may nevertheless help to www.nature.com/scientificreports/ compare the performance of different network inference methods 3,13 . For points 5 and 6 we may also be able to develop testing procedures, but these would have to start more explicitly from the top-down: coarse-graining and renormalisation methods may offer some potential routes 25 . One important distinction needs to be made regarding the types of node properties we may want to compare in points 1 and 2. They can be categorical or structural: among the former we include biological annotations 26 ; among the latter network properties of nodes 10,11 . For the former we can assume a null-model of independence. For the latter we can only assume conditional independence (conditional on aspects of network structure) which makes testing more complicated 26 . Quantifying aspects of network organisation through assortativity. Mixing patterns refer to the overall network organisation arising through attachment of nodes to other nodes with similar properties, and for pairwise comparisons we can use the assortativity coefficient 10,11 to quantify this behaviour. This assumes that we can assign each node to a set of q properties, K = {κ 1 , κ 2 , . . . , κ q } ; here κ q may represent "unknown". Crucially, the properties κ i , i = 1, . . . , q must be different from the measurements or states, η j , j = 1, . . . , u , that were used for inferring the network 26 .
The number of nodes with annotation κ i is denoted by ν i . We then define a matrix, A, where the entries, a ij , are the number of edges connecting nodes with annotation i with those with annotation j. The assortativity coefficient 11 , r, then is given by where the second equality results straightforwardly from conventional properties of matrix representations of networks.
The assortativity coefficient quantifies mixing patterns: confined to the range −1 ≤ r ≤ 1 , a network is said to be assortative when r > 0 (where nodes tend to be connected to nodes with similar properties), and disassortative otherwise 10 . The assortativity coefficient was originally calculated using node degree as a basis to compare node similarity, yielding degree assortativity 10 . However, in addition to node degree, any other node annotation may be used.
Functional network modules play a crucial part in cellular processes [27][28][29][30] , and inferred networks should reflect this organisation. Quantifying network assortativity with respect to functional annotations of nodes then allows us to draw from both points 1 and 2 in "Properties of biological networks" section, (functional) specificity and modularity: assortativity can be used as a heuristic to quantify the explicit assumption of mixing patterns by biological function.
Experimental evidence supporting the importance of functional modules in biological networks includes: observations in Saccharomyces cerevisiae of preferential interaction between functionally related genes 26,31,32 that cluster at the level of cellular process 20 into functional modules with more connections within, as opposed to between, modules than expected to be the case in random networks 33 ; and the identification of groups of gene ("dynamical modules") coherently implementing biological functions in the Drosophila melanogaster gap gene network 30 . In general, the clustering of genes within biological process supports the assumption of functional modules, i.e. mixing patterns with respect to biological function.
As we have argued, this behaviour is quantified by the assortativity coefficient: under this assumption, we expect biological networks to exhibit assortative mixing with respect to biological function; a higher coefficient indicates more support in favour of a given network. We refer to this heuristic as functional assortativity, which is a function of node annotations corresponding to biological function. This proxy measure for quantifying the plausibility of inferred networks presents the advantage to hold regardless of the inference methodology and thus allows us to compare inference algorithms.
Measuring confidence in inferred networks
Below we outline the inference methods used, before discussing their respective candidate networks in light of the assortativity coefficients.
Inference algorithms considered. We compare the performance of seven inference algorithms and use these to illustrate the behaviour of the assortativity coefficient. We use two correlation-based approaches-linear correlation (LC) and rank correlation (RC) coefficients-and an information-theoretic approach-based on the mutual information (MI)-as baseline predictions because of their popularity and ease of use (e.g. 34 ); to these we add three other information-theoretic approaches-context likelihood of relatedness (CLR) 23 , proportional unique contribution (PUC) 3 , and partial information decomposition and context (PIDC) 3,35 -and a regressionbased algorithm-GENIE3 24 , ran here with default settings-see Table 1 for more detailed descriptions of each. The focus on information-theoretic approaches stems from the ability of mutual information to capture nonlinear relationships in a largely unbiased fashion 22,36 , which is of obvious importance in a biological context.
We choose to focus on undirected networks; that way, assumptions about putative regulatory relationships are kept minimal and each edge can be treated as a falsifiable hypothesis. GENIE3 24 produces directed networks, and we turn the edges into undirected edges in order to allow comparison; we do this by retaining only the first occurrence of each edge in either direction (meaning that each edge in the undirected network is ranked according to the position of the most likely interaction in the directed network).
We illustrate the methods by applying these inference algorithms to a single cell dataset of mouse embryonic stem cells, where gene expression is measured over seven days as cells differentiate into neurons 37 . Each gene www.nature.com/scientificreports/ is manually annotated with one 12 classes of biological functions (mesoderm, primitive endoderm, endoderm, neuroectoderm, trophoectoderm, naive pluripotency, primed pluripotency, core pluripotency, loading control, cell cycle, chromatin modulator, and signalling), which allows us to measure functional assortativity as described above.
Functional assortativity coefficient. We plot the functional assortativity coefficient (FAC) as a function of the number of candidate edges included in the networks resulting from the different methods in Fig. 1. By definition this is either 1 or −1 depending on whether the first edge is between nodes with the same or with different annotations. Both can be biologically reasonable: diverging annotations can, for example, result when one node is annotated as "primed pluripotency" and the other node as "signalling", as is the case for the toprated edge resulting from PIDC (which connects CLDN6 and IGF2); this is a biologically plausible, and in line with known relationships in several organisms. The same annotation of both nodes is indicative of functional relationship as outlined above; "core pluripotency", for example, is shared by FGF4 and POU5F1/OCT4, the topranked edge for CLR, PUC, MI, and RC, and the 8th highest ranked edge for PIDC; this is a well-documented interaction playing a central role in stem cell differentiation [38][39][40] . It is, of course, possible to work through the whole list of interactions and seek explicit confirmation for each scored interaction. If this is not automated this could be subject to investigator bias. The rationale for using the assortativity coefficient is to make this process automated and, conditional on the available network and annotation data, unbiased. So while a realistic network will have-even for high-quality and nuanced annotations-a proportion of cross-category edges, a majority of within-category edges is expected.
The three more advanced information-theoretic inference methods, PIDC, CLR and PUC, display the highest FAC values for each fixed network size considered (Fig. 1). For all inference methods the FAC eventually decreases into the background noise as the networks become completely connected graphs. For each inference method we observe a maximum in the FAC for low to moderate values of the number of edges included in Table 1. Description of inference algorithms compared.
Algorithm Description References
Linear correlation Measures the linear correlation between a pair of random variables 20 Rank correlation Measures the rank correlation between a pair of random variables 21
MI
Measures dependency between variables using the mutual information, that is the sum of the entropy of the variables minus their joint entropy; it represents the amount of information about one variable when another variable is known 22
CLR
Based on the value of the MI between pairs of variables in the context of MI scores for each possible combination of variable pairs. This approach is referred to as network context and amounts to calculating the likelihood of each MI score conditional on the overall score distribution 23
PUC
Based on the mean unique information between variable pairs that accounts for their MI, as calculated via the partial information for each possible variables triplet for a given pair
Discrepancies in inference algorithms predictions.
The different inference algorithms, l, yield different sets of inferred edges, E l , as is obvious in the overlap patterns of the Venn diagrams shown in Fig. 2: while a substantial number of edges are shared across inference algorithms, each method infers a set of interactions that no other methods pick up. This is already known, and is consistent with observations of discrepancies in widely used between inference methods for single-cell data 5,6 . It further highlights the need for developing better ways to assess our confidence in inferred networks, especially in the absence of ground truths 12 .
Other noteworthy trends are the large overlap between PIDC, CLR and PUC; more surprising perhaps is the apparent similarity of the signal picked up by the two correlation methods and MI (Fig. 2). Furthermore, GENIE3 appears to be an outlier and routinely scores a relatively sizeable set of candidate edges that are not picked up by any other method. In the absence of a ground truth it is hard to make too much of these Venn diagrams, except perhaps at the extremes: groups of strong methods are expected to result in high concordance (reflected in large overlap), whereas very small overlap may indicate a set of three particularly poor inference methods.
Behaviour under artificial noise. In order to investigate how sensitive functional assortativity is to the assumption of mixing patterns, we show in Fig. 3 its behaviour as the inferred networks are perturbed in different ways.
We find that the FAC tends to 0 as biological functions are randomised among the nodes (Fig. 3, left column), showing that the signal it picks up is not merely an artefact of a particular network topology. Instead this suggests www.nature.com/scientificreports/ that the inferred networks pick up a real signal from the nodes, which is a non random function of the particular topology of inferred networks and the associated group labels. This is supported by the signal disappearing into noise with increasing levels of randomness in network structure (Fig. 3, middle column) and a sanity check of random values as expected in random networks (Fig. 3, right column).
From this, we conclude that functional assortativity is informative and reliable. Informative, because it is different than random: it measures the extent of mixing patterns by function, and the values it takes are not the result of chance alone. Reliable, because it is robust to low levels of noise-it can still pick up a signal under reasonable perturbations-but that signal vanishes for higher levels of noise, thus apparently avoiding false positives.
Discussion
The lack of comprehensive, experimentally-derived networks that can be used as a reference makes rigorous assessment of network inference algorithms challenging. Most methods have their specific assumptions and this will lead to discrepancies in their predictions.
In the context of analysing real biological networks, such discrepancies are a clear indication that rankings of network inference algorithms should be taken with caution: they are only a reflection of their performance in the specific context they were tested in (and indeed, for the same inference method, we have seen discrepancies in performance-e.g. excellent predictions in some contexts, but only slightly better than random in others 24 ). This goes to show that there is no definitive "best" method and performance is context-dependent.
We argue that this motivates the need for ways to compare inferred networks that are not biased towards our necessarily limited current knowledge 41 . We believe that the assumption of mixing patterns by function achieves this: it uses expectations as a basis for comparison, and these expectations are backed by both theoretical arguments and empirical results. This frees us of the potentially misleading circularity that is inherent to in silico approaches, and has the advantage of making our assumptions explicit and thus falsifiable.
We find that the behaviour of mixing patterns by function is reliably measured by the FAC. This makes it conceptually related to network modularity, where instead of quantifying aspects of network structure based purely on topological properties, it does so based on biological function. This balances the limited mechanistic assumptions of many network inference methods (although GENIE3 and other methods allow inclusion of prior knowledge)-only quantifying statistical dependency at its core-by grounding the process in realistic biological assumptions.
While clearly not all interactions are between genes performing the same biological function, this type of interaction will dominate (compared to the case of purely random connections). Thus functional assortativity allows us to quantify confidence in inferred networks as we would thus put more trust in networks that are functionally assortative than those that are not. As such, it is a heuristic that can guide the decision-making part of the inference process when it is understood as an inverse problem 42 . It effectively displaces the notion of confidence from the ability to reproduce previous observations to ability to produce expected results. We believe this approach, and others based on a similar perspective, to be useful in contexts where our knowledge is limited.
Conclusion
Networks remain a useful starting point for mechanistic analysis and assessing confidence in in silico inferred networks is important for the further use of such networks. Two limiting factors in our approach are (i) it only provides a heuristic way of ranking different inferred networks; and (ii) it requires that genes be annotated with a biological function 43,44 -this data may not be readily available; it may be incomplete; and it may be subject to uncertainty and or errors. We believe that there is an urgent need for an approach such as the one described here. In the absence of rigorous statistical assessments of inferred networks, the simple heuristic provided by the functional assortativity coefficient can provide criteria by which to gauge the reliability of inferred networks.
The present approach relies on the annotation of nodes, and increasing the quality of such annotations will clearly benefit this proxy measure. Additional improvements could come from considering functional assortativity locally, that is in specific areas of the overall network. Currently, however, as a rule of thumb, functional assortativity allows us to rank different candidate networks or network inference methods. knowing which inferred networks are worth further consideration, and which ones are best ignored will have a profound impact on our ability to make use of networks. Quickly being able to reject some network inferences does allow for more streamlined analysis, but is also essential 45 if we want to base predictions on ensembles of network inference methods: ensembles of inference methods can be severely affected by poorly performing algorithms and filtering out those methods with poor performance-as assessed, for example, via the FAC-can boost the reliability of networks inferred from ensemble approaches. | 4,911.2 | 2020-09-20T00:00:00.000 | [
"Computer Science",
"Biology"
] |
A robot vision navigation method using deep learning in edge computing environment
In the development of modern agriculture, the intelligent use of mechanical equipment is one of the main signs for agricultural modernization. Navigation technology is the key technology for agricultural machinery to control autonomously in the operating environment, and it is a hotspot in the field of intelligent research on agricultural machinery. Facing the accuracy requirements of autonomous navigation for intelligent agricultural robots, this paper proposes a visual navigation algorithm for agricultural robots based on deep learning image understanding. The method first uses a cascaded deep convolutional network and hybrid dilated convolution fusion method to process images collected by a vision system. Then, it extracts the route of processed images based on the improved Hough transform algorithm. At the same time, the posture of agricultural robots is adjusted to realize autonomous navigation. Finally, our proposed method is verified by using non-interference experimental scenes and noisy experimental scenes. Experimental results show that the method can perform autonomous navigation in complex and noisy environments and has good practicability and applicability.
Introduction
The development direction of world agricultural production in the twenty-first century is shifting from traditional agriculture to modern agriculture [1,2]. Agriculture is an important basic industry to protect the national economy. The maximum utilization of agricultural resources, maximum production, and maximum development are the keys to measuring the level of modern agriculture [3,4]. For China, one of the checks and balances in the level of modern agricultural production is the independence and intelligence for production machinery and equipment. The development of high-level intelligent agricultural machinery is an important direction for current agricultural development [5,6].
With the rapid development of electronic technology and intelligent algorithms, intelligent robots have been widely used in many fields. Its autonomy and intelligence are becoming more and more perfect. Facing the demand for efficient production in modern agriculture, intelligent robots have also attracted many attentions of agricultural researchers. As a new concept of agricultural machinery [7,8], agricultural robots have huge economic benefits in the field of agricultural production and have broad market prospects. The timely development and development of a new generation for agricultural machinery represented by agricultural robots are of great significance for my country's transition to modern agriculture [9,10].
At present, the existing image semantic segmentation algorithm, the network model is very complex, the parameter calculation is large, and the requirement of hardware equipment is also high. How to optimize the algorithm structure and reduce the dependence on hardware equipment is the current research focus, so as to better apply the technology in real life.
Related work
A visual navigation system is the core device of agricultural robots. An excellent visual navigation system can help agricultural robots to process and analyze collected images with the help of advanced intelligent algorithms or artificial intelligence algorithms. This helps robots to observe and understand the outside world and realize the intelligence and autonomy of mechanical equipment. The robot vision system first captures a two-dimensional image of a threedimensional external environment by an image acquisition device such as a camera. The obtained two-dimensional images are processed by intelligent algorithms to realize image segmentation, feature extraction, and other image understanding processes [11]. Finally, the symbolic description of the image itself is obtained to support agricultural robots to make decisions on the next action. The workflow is shown in Fig. 1.
Many scholars have conducted research on the visual navigation of agricultural robots. They confirmed the importance of vision systems for agricultural robots and the feasibility of practical applications [12][13][14]. Researchers at Kyoto University in Japan confirmed the feasibility of machine vision in agricultural mobile robot applications and extracted HIS space of images. In HIS, images were scanned with horizontal lines, and the least square method was used to identify crop spacing [15]. Han et al. used the K-means clustering algorithm to obtain crop row spacing information. And they through image comparison and evaluation judged the accuracy of image processing, in order to achieve agricultural tractor navigation [16]. Akane team discussed an image processing method that classifies collected images based on grayscale histograms. In addition, different methods were used to distinguish between traversable and nontraversable areas in the farmland to realize the navigation of agricultural vehicles [17]. Researchers such as David relied on the global positioning system and inertial navigation system. They combined a robot vision system to solve the problem of autonomous navigation for agricultural robots and realized the sustainable intensification of largescale agriculture [18].
Due to its high reliability and detection accuracy, Hough has been used in intelligent agricultural equipment [19][20][21]. Chen et al. solved the problem of machine vision on the effect of multiple environmental variables on crop row recognition during the entire growth period of lettuce and green cabbage and at the same time improves the effectiveness of the machine vision crop row recognition algorithm. This paper proposed a multi-crop row extraction algorithm based on automatic Hough transform accumulation threshold [22]; Li and other studies analyzed the principle of Hough transform proposed in image processing. They proposed to use this transformation for the processing of gravity and magnetic data. Based on the linear features contained in this identification data, it corresponded to information such as the geological body boundary and plane distribution characteristics of fault structure. The calculation and analysis of the theoretical model and actual data show that this method can extract the boundary information of gravity and magnetic data more accurately, and it had good noise robustness [23]. Olsen and Sogaard proposed a method that uses machine vision to obtain RGB three-channel images and used 2G-R-B operators to convert color images into single-channel grayscale images. Calculate the position of the center for crop gravity in the horizontal direction by analyzing images, and use the least square method to fit the spacing of crop position [24]. Qun et al. designed a greenhouse robot based on machine vision, using a watershed algorithm to segment images and convert them into a binary image. The establishment of a navigation path by Hough transform can significantly reduce the effect of natural light and greenhouse plastic film on image segmentation in a greenhouse environment. The correct rate of road information extraction was 95.7% [25]. Agricultural robot research is an interdisciplinary subject, which is a comprehensive product of many fields and disciplines. The vision navigation system is just like human eyes, which is the premise of the normal and stable operation of an intelligent robot. The actual agricultural production environment is complex and diverse, so compared with other industrial robots, the accuracy of agricultural robot navigation is much higher. Therefore, it is of great significance for precision vision navigation of agricultural robots. Drawing on the existing research on autonomous navigation of crops, this paper proposes a visual navigation algorithm for agricultural robots based on deep learning image understanding. The main contributions are as follows: 1) Improve the Hough transform method based on subdivision algorithm, improve the calculation efficiency of the traditional Hough algorithm, and realize the effective extraction of robot path. 2) And the correspondence relationship between the image coordinate system and the actual scene coordinate system and the state equation are established to achieve robots' autonomous navigation posture adjustment.
The rest of this paper is organized as follows. The third section introduces vision system image processing technology, including image segmentation and edge detection technology. Section 4 introduces the technology of path extraction and pose adjustment for agricultural robots. Section 5 uses actual scenarios to verify our proposed method. Section 6 is the conclusion of this paper.
Image processing of farmland scenery
Efficient and good image processing is the prerequisite for agricultural robots to autonomously navigate. The main flow of image processing technology is shown in Fig. 2, which mainly includes steps such as image preprocessing, image segmentation, and feature extraction.
Image acquisition
Image acquisition is the first step in image processing. Generally, the vision system cannot directly process simulated images because collected images are simulated images. This paper uses a CCD image sensor in the robot vision system to convert analog images collected by an image acquisition device into a digital image and transmit it to the vision system computing center to ensure the goodness of acquired image attributes. That is, the position and gray scale are helpful for further research on subsequent image processing.
Image preprocessing
In order to provide better quality images to the vision system computing center, images are preprocessed to solve the problems of distortion and deformation caused by hardware equipment and digital-analog conversion during image acquisition and transmission.
Grayscale image
Image graying is an important method for image enhancement. Make targeted corrections to the pixels in images to enhance the obvious features of images. At the same time, expand the image dynamic adjustment range and contrast to make the image effect more clear and uniform. The piecewise linear grayscale transformation is used to realize the grayscale processing of images, enhance the target grayscale interval, and suppress the non-target grayscale interval. And set the image grayscale range to [0, X]; the linear relationship is shown in Fig. 3.
By changing the coordinates of each inflection point and the slope of the line segment by a piecewise linear transformation, the grayscale interval can be expanded or compressed. The mathematical expression is
Grayscale histogram
Grayscale histogram is the simplest and most effective tool for describing grayscale values of images. It reflects the frequency of occurrence of uniform gray values, and it is the basis of image processing.
If the gray value of the gray image h(x, y) is within the range of [0, X − 1], the gray histogram equalization expression of image h(x, y) is: where η(g i ) is the probability of gray level i, g i is the gray level of level i, n is the total number of pixels, and n i is the number of pixels of gray level g i .
Image segmentation
In actual farming scenes, the environment is complex and crops are diverse. It is difficult to obtain the ideal image segmentation results only by underlying feature information. It has been confirmed that deep learning technology can collect global feature information in images to obtain better segmentation results. Based on the hybrid dilated convolution, the cascaded deep residual network is improved to complete image segmentation processing in the agricultural robot vision system. A one-to-one mapping relationship between image pixels and semantic categories is established.
The more the number of network layers in the deep convolutional neural network, the richer the level of information extraction for global feature items of images [26][27][28]. However, it should also be noted that with the deepening of the network layer, the gradient disappearance and network degradation caused by chain derivative in the back propagation of the network will cause the image segmentation speed and accuracy to decrease. In order to solve this problem, we add a residual structure to a deep convolutional network to increase the shortcut constant connection, which avoids the harm of segmentation processing caused by the disappearance of gradients and network degradation in deep networks. Figure 4 shows the residual structure added to the deep network.
Set the input parameter of the shallow network of the deep convolutional network to x, and the expected output value is E(x). If the deep network is not improved, the input parameter x is passed to output as the initial result. The mapping function required for network learning is F(x) = E(x) − x, and the feature mapping is also E(x) = F(x) + x. After adding the residual unit and maintaining the dimension of the input and output parameter elements unchanged, the residual unit adds the parameter input elements and output elements of multiple parameter layers cascaded. Ensure that input parameters and output parameters are within a reliable range. And we, through the ReLu activation function to get the final output, reduce the impact of network gradient disappearance and mesh degradation.
We use ResNet101 as the reference network for deep networks, because of its deeper network layer core and more elaborate network structure design. The deep residual network ResNet is divided into 5 network layers. Each network layer is configured with 5 convolution modules, an average pooling layer, and a classification layer, as shown in Fig. 5. The convolution modules are convl, conv2_x, conv3_x, conv4_x, and conv5_x. For the parameters in each convolution module, 7×7 is the size of the convolution kernel, and 64 is the number of channels in the convolution kernel. The brackets are a residual unit and X3 indicates that there are 3 residual units in the convolution module. Gradient disappearance and network degradation are very serious for image segmentation results. To this end, we cascade a new convolution module conv6_x behind ResNet101 network to form a cascaded deep residual network. The network structure and network parameter settings of its convolution module are the same as conv5_x. To further extract the image features globally, consider adding the conv7_x module. However, it was found by experiments that the semantic segmentation accuracy has not been improved compared to the cascaded conv6_x module. Therefore, as shown in Fig. 6, the cascaded deep residual network is finally composed of 6 convolution modules, convl, conv2_x, conv3_x, conv4_x, conv5_x, and conv6_x.
At the same time, using hollow convolution can increase the receptive field of the agricultural robot vision system, so as to better control image resolution [29] and fusion convolution of conv5_x and conv6_x convolution modules in ResNet network. To avoid the influence of the "grid" phenomenon in the convolutional network on segmentation results, set different void rates in the convolution module so that the receptive field can completely cover the input feature map. Taking conv5_x as an example, the module contains 3 consecutive residual units. The conv5_1 residual unit void rate is set to 1, the conv5_2 void rate is set to 2, and the conv5_3 void rate is set to 3. The conv5_x and conv5_x network structure and parameter settings are consistent. Thus, the void parameter of the residual unit in the conv6_x convolution module is set the same as conv5_x. Figure 7 is a schematic diagram of a convolution structure of a mixed cavity. The proposed model improves the cascaded deep convolution network based on the hybrid hole convolution method to solve the problem of network degradation caused by too many layers of deep network, and uses B-spline wavelet transform to detect the image edge to realize the image processing steps in the vision system, so as to provide the optimal image data support for the follow-up aircraft autonomous navigation.
Multi-resolution edge detection
The B-spline wavelet transform is used to detect the outline of the large-scale area after the above processing, and the image signal can be multi-resolution analyzed.
After processing the two-dimensional image signal, a low-pass smoothing function ω(x, y) is used to perform wavelet transformation along the x and y directions, that is, the two-dimensional image wavelet transform can be expressed as where R 1 g and R 2 g are the two variables after the image changes, which are the gradients of the two-dimensional image along x and y directions. The time-domain two-scale equation of scale function and wavelet function is The two-scale equation in the frequency domain is where the wavelet function is the scale function Fourier transform P 0 and P 1 are filters corresponding to the scale function and wavelet function, respectively, according to the conservation of energy of space division. where In this paper, the impulse response coefficients of the third-order B-spline wavelets (n = 4), P 0 (z), and P 1 (z) are shown in Table 1.
Due to the spatial separability of a two-dimensional image signal, the rows and columns can be separately subjected to wavelet transform according to the above algorithm to achieve multi-resolution edge detection.
Path extraction for visual navigation
The farming environment is a multi-variable time-varying and nonlinear complex system, which brings great difficulty to the intelligent robot autonomous navigation. Based on the image processing results in Section 3, improved Hough transform is used to extract the navigation path of the crop row, so that robots' posture can be adjusted in time.
Improved Hough transform
Hough transform is based on the global characteristics of images, forming a local peak at a point in the parameter space where straight line points in images are concentrated. Find and link line segments in the images.
Hough transform has the advantages of strong robustness and strong anti-noise ability. But at the same time, there is also a problem of a large amount of calculation, which will affect the real-time nature of autonomous navigation. Therefore, this paper uses the following steps to improve Hough transform: Determine the parameter value range after changing polar coordinates. The image after image processing is U × V, and the polar coordinate parameter space is (ρ,θ), It is worth noting that we use every 2°to calculate, and the amount of calculation is 1/2 of traditional transformation. This is because when digitally quantizing polar coordinate parameters, if the quantization precision is too small, the effect of parameter space cohesion is not obvious. The accuracy is too large, the calculation process is cumbersome, and the calculation amount is large. Store the sine and cosine values as an array. Store the sine and cosine values from 0 to 180°as values. When the query is needed during the calculation process, directly call the calculation, which is simple and quick.
Use refinement algorithm to improve the Hough algorithm. The refinement of the algorithm can effectively reduce the amount of data after image segmentation, thereby reducing the calculation process and shortening the calculation time.
Effectively determine the corresponding peak of parameter space and the straight line in images. First, the median filter is used to remove noises in parameter space. And a few larger peak points are detected according to the phase angle and deviation characteristics of navigation. Finally, the peak point of the navigation path is determined by statistical analysis.
Obtaining navigation parameters
where L is the length of the top and bottom edges for view field in the actual scene, U is the distance from agricultural intelligent device camera to the top and bottom edges of view field, and V is the width of the processed image. After vertices A ′ and B ′ of the coordinate system are obtained, the two equations of a straight line A ′ B ′ can be obtained, and then, the distance and yaw angle from camera point to a straight line A ′ B ′ can be obtained.
Path extraction
The steps of extracting the navigation path of the agricultural robot by the improved Hough transform algorithm are as follows: 1) A thinning algorithm is used to refine segmented images in the third section; 2) Discretize parameter space ρ and allocate memory for each; 3) Calculate θ step by step every 2°, and calculate ρ corresponding to (x, y) in the image to achieve one-to-one correspondence; 4) Use median filtering method to remove the noise points of detected images in the parameter space; 5) According to the phase angle and deviation characteristics of navigation, a few larger peak points are detected. Finally, the peak point of the navigation path is determined by statistical analysis.
Pose determination of robots
When agricultural robots perform normal command operation, its own posture determination is the prerequisite for navigation and agricultural operations. The values of offset angle α and offset distance γ can determine the posture of agricultural robots relative to the center line of the crop row.
Existing studies have shown that the pose adjustment of intelligent robots can be determined according to the correlation between actual coordinates and image coordinates [30]. Figure 8a is a schematic diagram of the coordinates of the actual scene for robots. X r axis refers to the left side of the car body in the actual scene, and Z r is the upper side of the car body center line (car body navigation line). L r is the center line between rows and crops; γ is the robot offset distance, it is the vertical distance from camera coordinate point to L r ; α is the angle between robot center line and navigation line. Figure 8b is a schematic diagram of image coordinates, the u-v coordinate system is the image coordinate system in pixels, and the x-y coordinate system is the image coordinate system in millimeters. Based on homogeneous coordinates and matrix form, the mathematical expressions corresponding to pixels and sizes are: Based further on the Hough transform, the straight line in Fig. 8b can be expressed as According to the camera perspective principle, the actual scene coordinates in image coordinates correspond to: where k is any real number, and the angle formed by the horizontal line of camera β and the smallest observation point to the ground. Then, offset angle α and offset distance γ of the agricultural robot are respectively: where h is the distance between the camera and ground. σ x and σ y are the scale factors
Results
The experimental equipment of this paper is a Tesla K80 GPU host, and the experimental environment is Ubuntu16.04. The code is written based on Tensorflow, a deep learning framework. The camera equipment is Bumbelee2, a stereo vision product produced by Point Grey Research (PGR). The software environment is operating system Chinese Windows 10, English version software Microsoft Visual Studio 2012. The main programming language is C#. This section tests the visual navigation system and analyzes the data in the test. This test is divided into posture measurement error test, non-interference navigation test, and weed background navigation test.
Measurement and analysis for pose errors
The actual working environment is more complicated, and it is difficult to measure the pose of robots. Therefore, the accuracy of robot pose calculation directly affects subsequent control actions. Choose to test the simulated rice seedlings in the laboratory. The experimental design is as follows: (1) The deviation of fixed phase angle is 0, that is, keep the robot's median line parallel to the actual direction of advance, and choose to move the robot perpendicular to the direction of the seedling row. Keep the displacement deviation range as [−40, 40], the recorded data displacement interval is 10mm, and the recorded data is shown in Table 2. The calculated standard deviation is 0.312mm. is 5°, and the recorded data is shown in Table 3. The calculated standard deviation is 0.121°.
From the above test data, it can be seen that the results obtained by pose calculation are consistent with the measured results. And the standard deviation is small, which satisfies the measurement requirements.
Non-interference navigation test
The experiment uses plastic to simulate seedling rows and simulates the farming environment under ideal conditions indoors for testing. Its purpose is to verify whether the navigation first extraction is correct in image processing, so as to confirm whether the visual navigation system is effective.
Take the initial angle deviation and position deviation as (−5°, 0mm), (5°, −5mm) two initial states for analysis, draw the curve of its movement process, and analyze the test results.
Scenario 1: The robot motion curve of initial position angle deviation −5°and displacement deviation 0mm is shown in Fig. 9. The phase angle returns to 0°in about 1.53s, and the displacement deviation is 2.11 to reach 0mm deviation. And in the later movement, due to the vibration of robots, the jitter alternates between positive and negative, so it does not affect the overall effect. Scenario 2: The motion curve of initial phase angle deviation 5°and displacement deviation −5mm is shown in Fig. 10. The angle deviation is large at the beginning of the movement, and the angle deviation quickly attenuates after the movement starts. The displacement deviation reaches the peak value at about 2.61s, and the angle deviation reaches the minimum value at about 2.34s. After several fluctuations, the angle deviation and position deviation both decay to 0. The non-interference navigation test result proves that the method proposed in this paper can effectively set the navigation line of the seedling row and keep the robot posture and timely and effective adjustment, which can meet the accuracy of autonomous navigation of agricultural robots.
Weed background navigation test
In order to verify the feasibility of this method in this paper in actual farming scenarios, the actual environment is simulated in the laboratory, and the layout scenario is shown in Fig. 11. Artificial turf is used to simulate the most complex paddy field environment in the farming environment including duckweed and waterweed. Due to the inability to accurately measure the displacement deviation and phase angle deviation in the manual layout scenario, this paper selected two random combinations for experimental analysis. Scenario 3: The motion curve of initial phase angle deviation −6°and displacement deviation −2.3mm is shown in Fig. 12. The phase angle deviation converges to 0 and continues to increase in the opposite direction 5 s after the movement starts. The displacement deviation converges to 0°in 2.6s, the standard deviation of phase angle deviation is 4.21°, and the displacement deviation is 5.31mm. Because the background color is similar to the seedling color, there is still noise after image processing. This causes feature point extraction and clustering errors, resulting in unstable navigation line parameters. But in general, it can still travel along the seedling column and does not step on the seedling. Scenario 4: The motion curve of initial phase angle deviation 2.3°and displacement deviation 8.12mm is shown in Fig. 13. As shown in Fig. 13, the displacement deviation converges to 0 at 3.1s due to the large displacement deviation relative to phase angle deviation and reaches the extreme value when phase angle deviation is 3.2s. At 11s, the displacement deviation increases in the positive direction. In order to correct the displacement deviation, the phase angle deviation is corrected. The phase angle deviation also increases to correct displacement deviation, and eventually, the displacement deviation converges to 0. The navigation test is carried out in the presence of background noise, and the proposed method can still accurately extract the navigation line when the background noise is large. By setting the coefficients in time and walking along the set route, the feasibility and practicability of the proposed method for autonomous navigation in complex farming environments are confirmed. The proposed model improves the cascaded deep convolution network based on the hybrid hole convolution method to solve the problem of network degradation caused by too many layers of deep network.
Results and discussion
Facing the accuracy requirements of autonomous navigation of intelligent agricultural robots, this paper proposes an agricultural robot visual navigation algorithm based on deep learning image understanding. The algorithm mainly includes two aspects of image processing and visual navigation path extraction. In the processing of collected images, collected images are processed based on a cascaded deep convolutional network and hybrid dilated convolution method, which provides optimal image data support for the subsequent autonomous navigation of robots. Moreover, the Hough transform method is improved based on the subdivision algorithm in visual navigation path extraction. And the correspondence relationship between the image coordinate system and the actual scene coordinate system and state equation are established to achieve robots' autonomous navigation posture adjustment. The experimental results show that our proposed method embodies rapid response characteristics at the same time in the non-interference scene and complex noise scene to ensure normal and stable operation of agricultural robots. The focus of future research will be to explore the adaptability of the proposed algorithm and agricultural robots in the market to improve algorithm scalability. However, limited by the author's level, the proposed algorithm still cannot get very accurate segmentation results for object boundary and small object segmentation. To solve this problem, we can consider using deeper network structure in the future, such as Resnet152, Densenet169, Densenet201, etc.; we can also consider fusing other deep learning technologies to complete image semantic segmentation tasks, such as a new variant of recurrent neural network RNN, counter network GAN, etc. | 6,525.4 | 2021-02-24T00:00:00.000 | [
"Computer Science"
] |
High-Temperature-Resistant Diverting Acid for Carbonate Formation Fracturing in Sichuan Basin: A Property Evaluation and Field Study
The targeted carbonate formation in Gao-Mo Block located in the middle of Sichuan Basin becomes deeper with the progress of development, and it mainly exhibits high temperature and strong heterogeneity. Higher requirements for temperature resistance performance and acidizing e ff ect of diverting acid are put forward. In this work, the high-temperature-resistant diverting acid (HTRDA) was employed to evaluate the compatibility, temperature resistance, friction reduction, and acidizing e ff ectiveness based on formation characteristics, and it was applied to demonstrate its reliability and adaptability in fi eld. The following study results are obtained. The HTRDA has good compatibility and its peak viscosity could reach 31mPa · s at the temperature of 170 ° C. Compared with the friction of water, the friction reduction rate of HTRDA can reach 67.8%. And the permeability change of core with low permeability could be increased by 53.5-61% after acidi fi cation. Furthermore, during the acid fracturing of Well M, the maximum diverting pressure of HTRDA gets to 10MPa and the friction reduction rate is about 66.5% that is consistent with the experimental data. The test production of Well M is 35 : 64 × 10 4 m 3 /d and the acid fracturing obtain a great result.
Introduction
Sichuan Basin is rich in natural gas resources, and the carbonate formation in Gao-Mo Block as one of significant development targets exhibits huge potential. Previously, diverting acid is a main mean to stimulate carbonate formation and has achieved great results in Gao-Mo Block, in which the test production of many wells reaches to 30-50 × 10 4 m 3 /d [1]. With the progress of exploration, the burial depth and temperature of targeted carbonate formation become deeper and higher, and the heterogeneity of formation is strong in Gao-Mo Block. Therefore, higher requirements for temperature resistance performance and acidizing effect of diverting acid are put forward.
Diverting acid for carbonate formation fracturing has been studied by many scholars so far. Li et al. introduced the diverting acid with peak viscosity 100 mPa·s at temperature 90°C to the Mishrif reservoir in Iraq. It has excellent performance in corrosion resistance and iron stability, and the permeability of cores could be increased by 59.2% after acidification [2]. Taylor et al. synthesized the diverting agent and developed the diverting acid system based on formation characteristics in Kuwait area. It has the limitation of the thermal stability at about 130°C, and the peak viscosity is about 20 mPa·s [3]. The diverting acid was obtained by erucamide hydroxyl sulfonate amphoteric surfactant viscoelastic diverting agent, corrosion inhibitor with high temperature resistance, and cationic oligomer temperature inhibitor by Ma. Its thermal stability is at 150~160°C, and the peak viscosity is about 40 mPa·s, which exhibits good self-diverting performance [4]. Zhang et al. synthesized a viscoelastic surfactant by erucamide propyl dimethyl tertiary amine and epoxy chloropropane and formed the diverting acid system that has peak viscosity about 50 mPa·s at 120°C. It can increase the permeability of carbonate cores by 2.35 times and has been widely applied in field [5,6].
Alleman et al. developed the diverting acid system that its temperature resistance is up to 170°C and the peak viscosity is about 20 mPa·s. This diverting acid system has been applied more than 15 wells in the Gulf of Mexico [7]. Schlumberger developed and optimized the diverting acid that is suitable for reservoirs with the highest temperature of 160°C. More than 70 wells have been stimulated to verify its property and effect for acidizing fracturing [8][9][10].
The researches above indicated that the temperature resistance for diverting acid is 165°C. And the peak viscosity is not large enough for higher temperature reservoir to realize effective stimulation. At present, the burial depth of carbonate formation gets to 7500 m, and the temperature reaches 170°C in Sichuan Basin. Besides, the carbonate formation exhibits strong heterogeneity that the porosity is 2-8% and the permeability difference can reach 53 times, so it is difficult to achieve acid uniform distribution. Therefore, in order to improve acid fracturing effectiveness, the compatibility, temperature resistance, friction reduction, and acidizing effectiveness of high-temperature-resistant diverting acid (HTRDA) were evaluated based on formation characteristics. And the HTRDA was applied to demonstrate its reliability and adaptability in field.
Property Evaluation of HTRDA
2.1. Experiment Material and Equipment. The diverting agent used in the experiment is mainly synthesized through the erucic acid group, amido propyl group, and hydroxyl sulfonate group as the main functional group in the laboratory, and the effective content is 40-60%. 20% hydrochloric acid and 3% corrosion inhibitor are used, and 1% iron stabilizer is added based on formation characteristics in Sichuan Basin [11]. To reduce friction, 0.1% high-molecular polymer is introduced. The specific experiment materials and equipment are listed in Table 1.
Compatibility Evaluation.
The compatibility is one of the basic properties for diverting acid evaluation. In this section, hydrochloric acid, additives, and mud are used to mix with diverting agent, respectively, leaving mixed solutions for 24 h at the temperature of 170°C to discuss the compatibility. The experiment results are shown in Table 2.
According to the above experimental results, the diverting agent has good compatibility with hydrochloric acid and additives and there is no precipitation and stratification.
When HTRDA was mixed with mud, they could react with each other due to the existence of drilling cuttings in mud, which could promote the removal of mud damage.
Temperature Resistance Evaluation.
As the temperature of carbonate reservoir becomes higher in Sichuan Basin, the temperature resistance of diverting acid is required to be improved. The HTRDA is reacted with different amounts of calcium carbonate, and the maximum viscosity value is obtained. Then, the peak viscosity can be tested at a shear rate of 170S -1 at a temperature of 30-175°C. Figure 1 shows the peak viscosity of the HTRDA at different temperatures. The peak viscosity of conventional diverting acid was conducted at the temperature of 170°C at the same way.
It can be seen from the figure that when the temperature reaches 60°C, the peak viscosity of the HTRDA decreases gradually with the increase of temperature. And the peak viscosity can still reach 31 mPa·s at the temperature of 170°C, which is higher than that of conventional diverting acid with about 10 mPa·s. So the HTRDA exhibits a good temperature resistance.
Friction Reduction Evaluation.
Wellbore friction is a problem that cannot be ignored in acidizing fracturing, especially in that of deep wells. The GLM-200 acidizing friction instrument was employed to compare and analyze the friction reduction of the HTRDA, conventional diverting acid, and water at room temperature. And the inner diameter of the pipeline is 8 mm. The friction of the three kinds of liquids is shown in Figure 2.
It is obtain in the figure that with the growth of linear velocity, the friction of the three liquids increases. The friction of HTRDA is just 209 KPa at linear velocity 10 m/s that is lower than that of conventional diverting acid. And compared with the friction of water, the friction reduction rate of HTRDA can reach 67.8%, which meets requirements in field greatly.
Acidizing Effectiveness Evaluation.
Acidizing effectiveness directly affect well production in carbonate formation stimulation. In this study, two cores with different permeability were selected to simulate reservoirs with different permeability, and two groups of experiments were conducted at the temperature of 170°C with the confining pressure of 20 MPa. The HTRDA acidified the high permeability core first, and with the progress of reaction, the viscosity of HTRDA increased and temporary plug was formed, which made the HTRDA enter the low permeability core. The Table 3.
According to the results of Group 1 experiment at 170°C, after breakthrough of high permeability core (Figure 3(a)), the permeability of low permeability core increased from 0.1523 mD to 0.2337 mD (Figure 3(b)), and the acidizing effectiveness was increased by 60.9%. In Group 2, the acidiz-ing effectiveness was improved by 53.5% as well. So the HTRDA has a remarkable self-diverting performance under high temperature.
2.
3. Discussion. The compatibility, temperature resistance, friction reduction, and acidizing effectiveness of HTRDA were evaluated in the experiment above. For its
Geofluids
compatibility, the precipitation and stratification are not produced and HTRDA can react with cuttings in mud, which means that there is no damage to the formation and it could promote the removal of mud damage in bottom hole. It has been known that the temperature resistance of diverting acid has reached 170°C. But in this work, the peak viscosity of HTRDA can get to 31 mPa·s at the temperature of 170°C, which is higher than that of other diverting acid [12,13]. In heterogeneous carbonate reservoirs, the diverting acid with higher peak viscosity is easier to block the formation with high permeability and then turns to the formation with low permeability in favor of uniform acid distribution.
For the development of carbonate formation in Sichuan Basin, the length of horizontal well gets to 6500 m or more, so reducing friction exhibits a great importance. Compared with the friction of water, the friction reduction rate of HTRDA is 67.8% which is much higher than that of conventional diverting acid of just 53%. The high friction reduction rate could decrease fracturing pressure and provide conditions for large flow rate, which is beneficial to acid fracturing efficiently.
At present, the diverting ability and acidizing effectiveness of diverting acid are mainly determined by viscosity [14], and the most commonly used method is to characterize the pressure change in the process of injection by acid and brine in the cores. The large change of pressure indicates that the viscosity of acid increases to form temporary block in the acidizing process, but it cannot represent and quantitative the degree of acidizing effectiveness [15]. So permeability change of cores with different permeability is used 6 Geofluids to evaluate the acidizing effectiveness of HTRDA in this work. At the condition of temperature 170°C, the permeability change of core with low permeability could be increased by 53.5-61%, which could provide more passages for gas to flow. So the HTRDA has a great acidizing effectiveness.
Field Study
Well M is a high temperature horizontal well with vertical deep 5230 m in Gao-Mo Block. The carbonate formation of Well M performs strong heterogeneity that the maximum permeability is 2.223 mD and the minimum is 0.0443 mD, and the permeability difference is 51.7 times. The temperature of bottom hole reaches 165°C. The HTRDA was used for the acid fracturing. The acid fracturing curve of Well M is shown in Figure 4. It is indicated that when the flow rate is 7.0m 3 / min, the pump pressure rises several times and the maximum diverting pressure is 10 MPa, which means the diverting effectiveness of HTRDA is remarkable. At the later stage of acid fracturing, when the reaction between acid and the carbonate is basically finished, pump pressure reaches 61.85 MPa at a certain flow rate and stop pump pressure is 27.58 MPa. The calculated friction is 34.27 MPa (about 6.55 MPa/1000 m). Compared with water friction, the friction reduction rate of HTRDA is about 66.5%. The test production of Well M is 35:64 × 10 4 m 3 /d that achieves a great result.
Conclusions
(1) In order to meet the requirements of acidizing fracturing in carbonate formation with high temperature, the properties of developed high temperature resistant diverting acid (HTRDA) were evaluated. The HTRDA has good compatibility and its peak viscosity could reach 31 mPa·s at the temperature of 170°C. Compared with the friction of water, the friction reduction rate of HTRDA can reach 67.8%. Besides, the permeability change of core with low permeability could be increased by 53.5-61% after acidification. So the HTRDA has a great acidizing effectiveness (2) The HTRDA has been applied in Well M. During the acid fracturing, the maximum diverting pressure gets to 10 MPa and the friction reduction rate is about 66.5% that is consistent with the experimental data. The test production of Well M is 35:64 × 10 4 m 3 /d and the acid fracturing used by HTRDA obtains a great result
Data Availability
The data used to support the findings of this study are included within the supplementary information file.
Conflicts of Interest
The authors declare no conflict of interest. | 2,871.4 | 2022-06-06T00:00:00.000 | [
"Materials Science"
] |
Influence of Casein kinase II inhibitor CX-4945 on BCL6-mediated apoptotic signaling in B-ALL in vitro and in vivo
Background Casein kinase II (CK2) is involved in multiple tumor-relevant signaling pathways affecting proliferation and apoptosis. CK2 is frequently upregulated in acute B-lymphoblastic leukemia (B-ALL) and can be targeted by the ATP-competitive CK2 inhibitor CX-4945. While reduced proliferation of tumor entities including B-ALL after CX-4945 incubation has been shown in vitro and in vivo, the detailed way of action is unknown. Here, we investigated the influence on the PI3K/AKT and apoptosis cascades in vivo and in vitro for further clarification. Methods A B-ALL xenograft model in NSG mice was used to perform in vivo longitudinal bioluminescence imaging during six day CX-4945 treatment. CX-4945 serum levels were determined at various time points. Flow cytometry of bone marrow and spleen cells was performed to analyze CX-4945-induced effects on tumor cell proliferation and distribution in B-ALL engrafted mice. ALL cells were enriched and characterized by targeted RNA sequencing. In vitro, B-ALL cell lines SEM, RS4;11 and NALM-6 were incubated with CX-4945 and gene expression of apoptosis regulators BCL6 and BACH2 was determined. Results In B-ALL-engrafted mice, overall tumor cell proliferation and distribution was not significantly influenced by CK2 inhibition. CX-4945 was detectable in serum during therapy and serum levels declined rapidly after cessation of CX-4945. While overall proliferation was not affected, early bone marrow and spleen blast frequencies seemed reduced after CK2 inhibition. Gene expression analyses revealed reduced expression of anti-apoptotic oncogene BCL6 in bone marrow blasts of CX-4945-treated animals. Further, BCL6 protein expression decreased in B-ALL cell lines exposed to CX-4945 in vitro. Surprisingly, levels of BCL6 opponent and tumor suppressor BACH2 also declined after prolonged incubation. Simultaneously, increased phosphorylation of direct CK2 target and tumor initiator AKT was detected at respective time points, even in initially pAKT-negative cell line NALM-6. Conclusions The CK2 inhibitor CX-4945 has limited clinical effects in an in vivo B-ALL xenograft model when applied as a single drug over a six day period. However, gene expression in B-ALL cells was altered and suggested effects on apoptosis via downregulation of BCL6. Unexpectedly, the BCL6 opponent BACH2 was also reduced. Interactions and regulation loops have to be further evaluated.
Background
Casein kinase II (CK2) is a constitutively active, ubiquitously expressed serine/threonine kinase aberrantly activated in numerous solid and hematological tumors including acute B-lymphoblastic leukemia (B-ALL) [1]. CK2 phosphorylates a variety of target proteins with numerous functions involved in cell cycle regulation, cell growth, proliferation, transcription, translation and apoptosis. It thus influences pathways involved in tumorigenesis like PI3K/AKT, JAK/STAT and NFkB [2,3]. CK2 acts via the inactivation of tumor suppressor genes PTEN and IKZF1 (Ikaros) as well as stimulating proliferation and cellular growth in lymphoid malignancies including B-ALL [4]. Further, the CK2-mediated induction of apoptotic pathways as an additional mode of action has been studied in solid tumors and acute myeloid leukemia (AML) [5][6][7][8] but remains largely uninvestigated in B-ALL so far.
CX-4945 is a selective, ATP competitive CK2 inhibitor and currently under investigation in clinical studies for renal tumors, cholangiocarcinoma, basal cell carcinoma and medulloblastoma. Our group as well as others have previously shown that CX-4945 inhibits tumor cell proliferation and metabolic activity in vitro and in vivo for numerous neoplastic entities including B-ALL [1,9,10]. Several mechanisms have been discussed to identify the inhibitor's anti-proliferative mode of action. These include intervention or modification of signaling pathways like PI3K/AKT, DNA repair response, angiogenesis, splicing regulation, stress-induced cell death or epigenetic modulation [10][11][12][13][14][15][16]. Nevertheless, it is still unknown how anti-leukemic effects are evoked in B-ALL. Induction of CK2-mediated apoptotic cascades or inhibition of anti-apoptotic pathways by CX-4945 might be mechanisms involved.
It has been demonstrated that incubation of B-ALL cells with CX-4945 induces apoptosis, possibly via increased cellular stress or inhibition of NFkB signaling [10,11,17]. So far it has not been investigated if in vitro effects are also present if CX-4945 is applied in vivo in B-ALL models. Further, little is known about which signaling molecules are involved in CX-4945-induced pro-apoptotic mechanisms and how those cascades are regulated during therapeutic approaches. We recently reported CX-4945induced effects in B-ALL xenografts [10]. In this follow-up study, we investigate the early molecular mechanisms of CK2 inhibitor CX-4945 on B-ALL. We aim to explore whether and how apoptotic processes play a role for the anti-leukemic properties of CX-4945.
Methods
Animal studies NOD scid gamma mice (NOD.Cg-Prkdc scid Il2rg tm1Wjl / SzJ, NSG, Charles River Laboratories, Sulzfeld, Germany) were bred and housed under specific pathogen-free conditions with access to water and standard chow ad libitum. All experiments were carried out in a laboratory setting and no intervention was performed within the animal housing and breeding rooms. Only healthy female animals aged 8 to 14 weeks and 19.1-27.7 g weight were included in the experiments. Study group sizes were four animals per time point and intervention group. Experiments were approved by the review board of the federal state Mecklenburg-Vorpommern, Germany (reference number: LALLF MV/7221.3-1.1-002/15).
Study endpoints for all mice used are listed in Additional File 1: Table S1. Female mice were i.v. injected with 2.5 × 10 6 SEM cells stably transduced with GFP and enhanced firefly luciferase (ffLuc). Transfection was performed using the pCDH-EF1-MCS-T2A-copGFP vector (System Biosciences, Mountain View, CA, USA) using EcoRI and BamHI as previously described [18]. SEM-GFP-ffluc cells were kindly provided by Prof. Irmela Jeremias (Helmholtz Center Munich, Germany). Lentivirus production and cell transduction were carried out as described before [19]. Tumor cell engraftment was evaluated 7 days after injection via bioluminescence imaging (BLI) using the NightOWL LB 983 in vivo Imaging System (Berthold Technologies, Bad Wildbach, Germany) and Indigo software (Berthold Technologies, version 1.04). For detection, animals were intraperitoneally injected with 4.5 mg D-Luciferin (GOLDbiotechnology, St. Louis, MO, USA), anesthetized with ketamine (75 mg/kg) and xylazine (5 mg/kg) and imaged in dorsal and ventral position (60 s exposure, 560 nm emission). Mice were randomized based on weight, age and tumor cell engraftment on d7. CX-4945 was dissolved in 0.9% saline containing 5% DMSO. Animals were simultaneously treated with either vehicle (isotonic saline supplemented with 5% DMSO) or 50 mg/kg i.p. CX-4945 twice daily d7-12 based on previous dose finding studies. BLI was performed at d7, d10, d13 and d15. At d10, d13 or d15 mice were anesthetized and euthanized by cervical dislocation. Leukemic blast frequency was analyzed in peripheral blood (PB), bone marrow (BM) and spleen by flow cytometry (GFP + ) using FACSCalibur and CellQuest™ Pro software (BD, Heidelberg, Germany). Means and standard deviation of BLI and blast frequency values of all mice of a study group were calculated. Student's t-test was performed to compare study groups and values < 0.05 were considered significant. Spleens, tibiae and femora were stored on ice in PBS (Biochrom, Berlin, Germany) containing 2% FCS (Biochrom) until tumor cell isolation.
ALL cell enrichment from spleen and bone marrow
Briefly, spleens were passed through 100 μm cell strainers and incubated with erythrocyte lysis buffer (155 mM NH 4 Cl, 10 mM KHCO 3 , 0.1 mM EDTA). BM cells were isolated from tibiae and femora using PBS containing 2% FCS. Leukemic blast frequency was determined in BM and spleen cell populations by flow cytometry (GFP + ) using FACSCalibur and CellQuest™ Pro software (BD). For enrichment of human ALL cells, mouse cells were depleted using the Mouse Cell Depletion Kit (Miltenyi Biotec, Bergisch Gladbach, Germany) for use with AutoMACS® (Miltenyi Biotec) according to the manufacturer's guidelines.
Mass spectrometric analysis of CX-4945 in mice sera CX-4945 was determined by liquid coupled tandem mass spectrometry (LC-MS/MS) in the sera of treated mice to which the internal standard acridin orange was added prior to extraction. For this purpose, 10-250 μl serum was extracted by triple liquid-liquid extraction with ethyl acetate and n-hexane (1:3 V/V). The first extraction was only ethyl acetate and the solvent was evaporated under nitrogen. The residue was then reconstituted in 100 μl of a mixture of mobile phases A and B (65% A) and 80 μl were mass spectrometrically analyzed. Mass spectrometric analysis was performed on a Micromass Quattro Micro™ API mass spectrometer. A HPLC Shimadzu LC-20 AD was used to separate the samples. The separation was performed using a Multospher 120 C18 AQ column 125 × 2 mm, 5 μm particle size (CS-Chromatographie Service GmbH, Langerwehe, Germany) coupled with a guard column 20 × 3 mm, 5 μm particle size at a flow rate of 0. . For targeted RNA sequencing, an in-house custom panel was designed using Ion AmpliSeq Designer (Thermo Fisher Scientific), containing 179 genes involved in B-cell-receptor-and PI3K/AKT pathway signaling. Ion AmpliSeq RNA Libraries were prepared according to the manufacturer's protocol (MAN0006735). In brief, RNA was quantified with Qubit RNA HS Assay Kit (Thermo Fisher Scientific) and Qubit 2.0 Fluorometer (Life Technologies, CA, USA) and transcribed into cDNA by SuperScript VILO cDNA Synthesis Kit (Thermo Fisher Scientific). cDNA targets were amplified, amplicons partially digested, ligated to the adapters and purified with the Ion AmpliSeq™ Library Kit 2.0 (Thermo Fisher Scientific) in the ProFlex PCR System (Thermo Fisher Scientific). The final librabries were quantified by Ion Library TaqMan Quantification Kit (Thermo Fisher Scientific) using the ViiA 7 Real-Time PCR System (Thermo Fisher Scientific). Following this, template preparation was carried out with the Ion PGM Hi-Q View OT2 Kit -200 using the Ion One Touch 2 Instrument (Thermo Fisher Scientific) and enrichment of template-positive Ion Sphere Particles (ISP) with the Ion OneTouch ES (Thermo Fisher Scientific). The sequencing reaction run was performed with the PGM System and 400 flows. The evaluation of data sets was performed using Transcriptome Analysis Console (TAC) Software 4.0.0.25 (Thermo Fisher Scientific). The following filter criteria were set to identify relevantly regulated genes: Avg (log2) > 5, fold change > 2 or < − 2, p-value < 0.05.
BCL6 and BACH2 gene expression analysis RNA isolation of BM tumor cell fractions and cell culture samples was performed using the AllPrep DNA/RNA/ Protein Mini Kit (Qiagen, Hilden, Germany) and miR-Neasy® Mini Kit (Qiagen), respectively, and followed by cDNA synthesis using the PrimeScript™ RT Reagent Kit (Takara Bio Europe, Saint-Germain-en-Laye, France) according to the manufacturers' protocols. Gene expression analysis was carried out in technical triplicates using TB Green™ Premix Ex Taq™ II mastermix (Takara Bio Europe) with 0.3 μM primers (BCL6 forward CTGTGATGGC CACGGCTAT; BCL6 reverse CGGCAAGTGTCCACAA; BACH2 forward GCGGCCCCAAATTAAATGT; BACH2 reverse AACGATCCGGATTCGTCACT; GAPDH forward CTGCACCACCAACTGCTTAG; GAPDH reverse GTCTTCTGGGTGGCAGTGAT) and 10 ng cDNA in a ViiA7 Real Time PCR system (Applied Biosystems, Foster City, CA, USA) using the following protocol: 15 min initial denaturation at 95°C followed by 45 cycles of 15 s denaturation at 94°C, 30 s annealing at 54°C and 30 s elongation at 72°C. Relative gene expression was normalized to GAPDH and calculated using the 2 -ΔΔCt formula. Cell culture experiments were carried out in biological triplicates. Four mice per group and time point were analyzed. Results are described as mean ± standard deviation. Significance (p < 0.05) was estimated by 2-tailed student's t test.
Early effects of targeted CK2 inhibition on B-ALL xenograft mice
Previous studies involving CX-4945 anti-tumor regimens are mainly based on observations made at time points several days after the last therapeutic dose. We herein focused on CX-4945-mediated effects during and shortly after therapy. To evaluate the immediate effect of CK2 inhibition (twice daily from d7 to d13) on the proliferation of SEM cells in NSG mice, animals were sacrificed during (d10), immediately after (d13) or 48 h after the last therapeutic dose (d15). To examine tumor cell proliferation and distribution, longitudinal bioluminescence imaging (BLI) of all animals was performed on d7, d10, d13 and d15. No differences in blast distribution (Fig. 1a) and proliferation (Fig. 1b) were observed between treated and untreated animals.
Pharmacokinetic experiments were conducted to elucidate in which concentrations CX-4945 was present in serum of treated mice (Fig. 1c). During therapy CX-4945 concentrations of 190 ± 150 μM (d10) and 155 ± 88 μM (d13) were measured. CX-4945 levels then declined rapidly: two days after the final CX-4945 application (d15) traces were detectable only in one out of four mice. No traces of CX-4945 were detected in untreated animals.
Further, BLI was performed for several organs to identify minor tumor cell infiltration (Additional File 2: Fig. S1). Blasts were mostly localized in tibiae, femora and skull while infiltration of sternum, spleen, liver and lung was rarely observed. Tumor cell distribution was comparable in controls and CX-4945-treated animals.
Downregulation of BCL6 induces apoptosis in BM-derived ALL blasts
To identify molecular pathways involved in the decreased BM and spleen blast infiltration, we next performed gene expression profiling. We analyzed 177 genes involved in B cell receptor (BCR), PI3K/AKT and CK2 downstream signaling in selected bone marrow blast samples (Additional File 1: Table S2). Samples taken at the distinct observation time points (d10, d13, d15) were analyzed separately, revealing significant upregulation of CDC42, CD19 and JAK1 after CX-4945 therapy at d10 (Fold changes 2.22, 2.71 and 2.29 compared to controls, respectively). However, these changes were not found at d13 and d15. In fact, no relevant upor downregulation was present at the latter time points. Comparing all controls and treated animals, no relevant difference was observed and groups did not form distinct clusters (Fig. 2a, b).
Throughout all time points, gene expression of antiapoptotic transcription factor BCL6 was slightly reduced after CX-4945 therapy (Fold changes − 1.17, − 1.15 and − 1.11 compared to controls at d10, d13 and d15, respectively). In contrast, expression of BCL2L11, an inducer of apoptosis, was increased (Fold changes 1.76, 1.04, 1.40; Additional File 3: Table S2), possibly encouraging pro-apoptotic signaling (Fig. 2c). Interestingly, apoptosis-inducing BAD, IKBKB and RELA expression was decreased, suggesting the previously observed induction of cell death [10] was probably not mediated through NFkB. According to wikipathways, 15 genes included in our panel are involved in apoptotic signaling [20]. None of these was significantly regulated by CX-4945 therapy at any time point. Most genes showed ambiguous gene expression profiles during observation time points. Raw data, fold changes and p-values of all genes for respective time points are summarized in Additional File 4: Table S3.
CX-4945-induced apoptosis is evoked by BCL6 deregulation
As BCL6 was found consistently downregulated in RNA sequencing analyses and can be regulated by CX-4945 target Ikaros, we evaluated this pathway in more detail.
We also investigated BCL6 counterpart and tumor suppressor BACH2.
For characterization of BCL6 and BACH2 involvement in anti-proliferative effects after CK2 inhibition, MLLrearranged pro-B-ALL cell lines SEM and RS4;11 as well as BCR + pre-B-ALL cell line NALM-6 were incubated with CX-4945 for up to 96 h. All cell lines initially expressed different amounts of BACH2 and BCL6 proteins (Additional File 5: Fig. S2). High levels of BCL6 correlated with low BACH2 expression (SEM, RS4;11) and vice versa (NALM-6).
In SEM cells, BACH2 gene expression (Fig. 3a) and protein expression (Fig. 3b, c) remained constant during short term-incubation (< 24 h) while incubation for extended periods induced decreased gene and protein Longitudinal bioluminescence imaging was conducted at d7 to verify tumor cell engraftment and subsequently repeated at all study days. Increasing luminescence is proportional to proliferation of luciferase-expressing blasts. Quantification of full body bioluminescence (ph/s) after treatment was performed by adding total luminescence signals of dorsal and ventral imaging. Four animals per time point and study group; mean ± standard deviation. c Pharmacokinetic analyses were conducted at d10, d13 and d15 to investigate CX-4945 serum concentrations. Serum levels were determined by liquid coupled tandem mass spectrometry. Four animals per time point and study group; mean ± standard deviation. d,e Influence of CX-4945 on blast frequency in bone marrow d and spleen e. Tumor cell frequency was evaluated by flow cytometry of GFP + leukemic blasts. Four animals per time point and study group; mean ± standard deviation Phosphorylation of AKT residue Ser473 was evaluated to further characterize the effect of CX-4945 on PI3K signaling. AKT phosphorylation decreased after short term incubation with CX-4945 (Fig. 3b). Conversely, pAKT levels were elevated after extended incubation periods (Fig. 3c). Total AKT protein expression remained constant throughout all time points observed.
To further validate the observed effects, the influence of CX-4945 on B-ALL cell lines of distinct molecular backgrounds (RS4;11, NALM-6) was determined on protein level. Both cell lines exhibited reduced BACH2 and BCL6 expression detectable from as early as 6 h incubation (Additional File 6: Fig. S3). In RS4;11 cells, pAKT levels decreased during short term incubation (Additional File 6: Fig. S3a) before increasing after 48 h, 72 h and 96 h CX-4945 incubation compared to DMSOtreated control cells (Additional File 6: Fig. S3b). Even though no basal AKT phosphorylation was detected in NALM-6 cells, pAKT levels increased after extended incubation with CX-4945 (Additional File 6: Fig. S3d).
For comparison with BCL6 and in vitro data, gene expression analysis of BM-derived tumor cell populations was subsequently performed for BACH2 (Additional File 7: Fig. S4). BACH2 gene expression was slightly increased at d10 and d15 (1.19-fold and 1.26-fold compared to control animals, respectively). Though, changes were not significant (p = 0.234 and 0.333, respectively), matching our findings that changes in BCL6 rather than BACH2 gene expression contribute to altered apoptotic signaling.
Discussion
CK2 is frequently upregulated in many neoplasms including B-ALL [4]. The selective CK2 inhibitor CX-4945 has previously been shown to exhibit anti-proliferative effects in vitro and heterogeneously in vivo [10,11,15,17,[21][22][23]. However, molecular mechanisms and the exact mode of action of CX-4945 in B-ALL remain widely unknown. We have previously demonstrated induction of apoptosis in B-ALL cell lines including SEM [10]. Other groups also described CX-4945-mediated apoptosis in various tumor cells including B-ALL [8,11,13,15,17,22,[24][25][26][27]. Thus, the aim of the current study was to evaluate whether CX-4945 treatment of B-ALL xenograft mice results in early reduced proliferation. Further, we investigated CX-4945-induced apoptotic and molecular processes during and after CK2 inhibition using in vitro and in vivo approaches. Our results suggest that downregulation of the oncogenic transcription factor BCL6 might contribute to antiproliferative signaling.
While previous in vitro experiments using CX-4945 demonstrated significant anti-proliferative effects in B-ALL cell lines, our results could not demonstrate this effect in a B-ALL xenograft model. In contrast, Song et al. detected prolonged survival and reduced tumor cell proliferation in B-ALL engrafted mice [28]. This discrepancy might be due to different cell lines used or varying therapeutic regimens. Our study used lower CX-4945 doses and shorter application periods than Song et al. Also, we evaluated tumor cell proliferation during or immediately after our short therapeutic period, explaining the lack of anti-proliferative action. In line with our observations, Prins et al. reported only minimal effects of CX-4945 on de novo B-ALL xenograft mice [22]. To ensure that the lack of anti-proliferative effects was not due to limited bioavailability, we performed pharmacokinetic studies and demonstrated that CX-4945 was present in blood serum samples of treated animals. Achieved CX-4945 concentrations are similar to otherwise reported effective concentrations. Serum levels declined rapidly when the drug was removed, which is in line with the observations of Siddiqui-Jain et al. who also reported a quick reduction of plasma concentrations from as early as 15 min after CX-4945 removal [13]. Other groups calculated a CX-4945 half-life of~5 h in mice [29]. This rapid decline in CX-4945 bioavailability might explain the continued proliferation of B-ALL tumor cells after therapy we observed in our previous study. This finding suggests that prolonged or continuous CX-4945 exposure might overcome the limited therapeutic potential [10].
Still, in our present cohort CX-4945 treatment of mice engrafted with B-ALL cell line SEM resulted in decreased blast frequencies in BM and spleen. This indicates that even short application periods and a moderate dosis of CX-4945 are sufficient to induce signaling-modulating changes in tumor cells. Focusing on BCR-and PI3K/ AKT-related genes, we performed targeted RNA sequencing to firstly evaluate if PI3K downstream signaling modifications could be responsible for the observed effects. Analysis of BM-derived blasts revealed a significant upregulation of CDC42, CD19 and JAK1 after CX-4945 treatment. JAK1 activation and CD19 overexpression are generally not associated with anti-leukemic signaling but with leukemogenesis [30,31]. In contrast, the small GTPase CDC42 plays a key role in cell cycle regulation [32] and might be involved in apoptotic signaling via activation of JNK and FasL in HL-60 and Jurkat cells [33,34].
We then evaluated the gene expression of apoptosisrelated genes after CX-4945 incubation in more detail. We found that the anti-apoptotic transcription factor BCL6 was constantly, yet not significantly, downregulated throughout all observation time points.
Most other genes involved in apoptosis showed ambiguous regulation patterns. In line with this finding, Ge et al. recently reported that in vitro BCL6 gene expression is regulated by direct CX-4945 target Ikaros, and that increased BCL6 expression in adult B-ALL patients is associated with inferior outcomes [35]. We subsequently evaluated protein expression of BCL6 and its opponent, tumor-suppressing transcription factor BACH2 in B-ALL cell lines after CX-4945 incubation and found reduced BCL6 levels. Unexpectedly and in contrast to Ge et al., BACH2 gene and protein levels were also decreased. This could be explained by CX-4945-mediated regulatory mechanisms other than Ikaros binding, or by upstream signaling modulation in the cell lines used. BACH2 gene expression can be controlled by hypoxiainduced factor 1α, a target protein of CK2 [36][37][38]. Also, Tamahara et al. further demonstrated AKT-and mTORmediated inhibition of gene and protein expression of BACH2 [39]. This matches our findings of both decreased BACH2 levels as well as AKT activation occurring after extended CX-4945 incubation periods. This observation suggests the possibility of a shared AKT and BACH2 regulatory mechanism.
In vitro western blot analyses in SEM and RS4;11 cells demonstrated that the direct CK2 target protein AKT was initially dephosphorylated by CK2 inhibition. This results in decreased PI3K/AKT pathway activity and potential induction of apoptotic signaling via downstream mechanisms. Unexpectedly, further CX-4945 incubation evoked strong phosphorylation of AKT even in the initially phospho-AKT-negative cell line NALM-6, suggesting an induction of cellular escape mechanisms to evade apoptosis. The analyzed AKT residue Ser473 is phosphorylated by PI3K/AKT pathway member mTOR and necessary for AKT kinase activity, underlining the involvement of this signaling cascade in CX-4945mediated anti-leukemic effects [40]. Recently Chen et al. as well as Baumgarten et al. both reported feedback loops between AKT and MEK signaling, suggesting increased AKT phosphorylation might be evoked by escape strategies involving upregulation of the MEK pathway [41,42]. This also fits in well with the increased gene expression of CDC42 observed after CX-4945 treatment, with CDC42 being involved in JNK and p38 signaling cascades [43]. However, whole transcriptome and whole methylome sequencing approaches as well as further pharmacokinetic experiments are necessary to elucidate in detail how CX-4945 influences distinct signaling cascades. In addition, combinatory approaches using mTOR, AKT and MEK inhibitors can shed light on so far unexplored pathway regulation and feedback loops. Also, these results should be validated in human primary B-ALL cells.
Conclusions
In conclusion, we herein identify a potential mechanism for the regulation of apoptotic processes in B-ALL in vitro and in vivo. Apoptosis is probably evoked by Ikaros-mediated downregulation of BCL6 and tightly regulated by AKT. Limitations in anti-proliferative and pro-apoptotic CX-4945-induced effects could possibly be overcome by additional application of AKT or MEK inhibitors.
Additional File 1: Table S1. List of all mice recruited in the study, respective end points and analyses conducted.
Additional File 2: Figure S1. Evaluation of CX-4945 application on organ infiltration in SEM-engrafted NSG mice. NSG mice were i.v.-injected with 2.5 × 10 6 GFP-and luciferase-transduced SEM cells and treated with vehicle (control) or 50 mg/kg CX-4945 i.p. twice daily from d7-13. Mice were sacrificed on d10, d13 or d15 for subsequent analyses. Bioluminescence imaging of brain, skull, lung, heart, liver, sternum, spleen, kidney, femur, tibia and fat tissue was performed directly after mice were sacrificed. Four animals per time point and study group.
Additional File 3: Table S2. Gene expression fold changes of genes involved in apoptotic processes.
Additional File 4: Table S3. Gene expression fold changes and pvalues of all genes included in the BCR/PI3K/CK2 panel.
Additional File 5: Figure S2. Basal characterization of protein expression in B-ALL cell lines SEM, RS4;11 and NALM-6. Analysis of BACH2 and BCL6 protein expression was carried out by western blot with GAPDH as housekeeping gene. Representative images of three independent biological experiments. Blots were processed and cropped using Image Studio Lite 5.2 software and MS PowerPoint (2011) to improve clarity and conciseness. Full size blots are uploaded in Additional File 8: Fig. S5.
Additional File 6: Figure S3. Evaluation of AKT phosphorylation, BACH2 and BCL6 protein expression in RS4;11 and NALM-6 cells. RS4;11 (a, b) and NALM-6 cells (c, d) were cultured and incubated with 5 μM CX-4945 or DMSO (control) for up to 96 h. Analysis of BACH2 and BCL6 protein expression as well as AKT phosphorylation was carried out by western blot with GAPDH as housekeeping gene. Representative images of three independent biological experiments. Short term effects of CX-4945 incubation were determined after 0.5 h, 2 h and 6 h (a, c). Long term effects of CX-4945 incubation were determined after 24Blots were processed and cropped using Image Studio Lite 5.2 software and MS PowerPoint (2011) to improve clarity and conciseness. Full size blots are uploaded in Additional File 8: Fig. S5. Additional File 7: Figure S4. Gene expression analysis of BACH2. Bone marrow-derived leukemic blast populations of controls and treated mice sacrificed at d10, d13 and d15 were analyzed for changes in gene expression of BACH2 using qPCR. Mean values of ΔCT values from controls were calculated and set to 1 for each time point. ΔΔCT values were calculated for CX-4945-treated samples and compared to the respective timematched control. Analyses were carried out in three technical replicates. Four animals per time point and study group; mean ± standard deviation. | 5,999 | 2020-03-04T00:00:00.000 | [
"Biology",
"Medicine",
"Chemistry"
] |
Unusual Water Oxidation Mechanism via a Redox-Active Copper Polypyridyl Complex
To improve Cu-based water oxidation (WO) catalysts, a proper mechanistic understanding of these systems is required. In contrast to other metals, high-oxidation-state metal–oxo species are unlikely intermediates in Cu-catalyzed WO because π donation from the oxo ligand to the Cu center is difficult due to the high number of d electrons of CuII and CuIII. As a consequence, an alternative WO mechanism must take place instead of the typical water nucleophilic attack and the inter- or intramolecular radical–oxo coupling pathways, which were previously proposed for Ru-based catalysts. [CuII(HL)(OTf)2] [HL = Hbbpya = N,N-bis(2,2′-bipyrid-6-yl)amine)] was investigated as a WO catalyst bearing the redox-active HL ligand. The Cu catalyst was found to be active as a WO catalyst at pH 11.5, at which the deprotonated complex [CuII(L–)(H2O)]+ is the predominant species in solution. The overall WO mechanism was found to be initiated by two proton-coupled electron-transfer steps. Kinetically, a first-order dependence in the catalyst, a zeroth-order dependence in the phosphate buffer, a kinetic isotope effect of 1.0, a ΔH⧧ value of 4.49 kcal·mol–1, a ΔS⧧ value of −42.6 cal·mol–1·K–1, and a ΔG⧧ value of 17.2 kcal·mol–1 were found. A computational study supported the formation of a Cu–oxyl intermediate, [CuII(L•)(O•)(H2O)]+. From this intermediate onward, formation of the O–O bond proceeds via a single-electron transfer from an approaching hydroxide ion to the ligand. Throughout the mechanism, the CuII center is proposed to be redox-inactive.
■ INTRODUCTION
The global energy crisis requires the utilization of sustainable energy to replace fossil fuels and stop global warming. 1−7 One promising sustainable energy carrier is dihydrogen, which can be produced by water splitting using renewable energy sources such as solar energy. However, the activation of water, a rather inert molecule, is a great challenge and still remains one of the most important tasks of modern chemistry. Water oxidation (WO) forming dioxygen, in which four protons and four electrons (2 H 2 O → O 2 + 4H + + 4e − ) are produced, is the bottleneck reaction in the water-splitting process. The utilization of an efficient and cheap water oxidation catalyst (WOC) is required to enable the production of dihydrogen as an energy carrier on a large scale. Molecular Ru-and Ir-based electrocatalysts have been reported as WOCs with low overpotentials and high turnover numbers. 8−11 However, in the past decade considerable progress has been made in the utilization of first-row transition metals Mn, Fe, Co, Ni, and Cu as cheaper and earth-abundant alternatives for the expensive Ru-and Ir-based WOCs. 12−18 Since the first reported homogeneous Cu-based catalyst in 2012, 19 Cu complexes have attracted increasing attention as catalysts for the oxidation of water. 20,21 Mononuclear Cu-based WOCs are reported with bipyridine-type, 19,22−25 alkylamine-type, 26−29 pyridine/aminetype, 30−46 peptide-type 47−50 and porhyrin-type 51 ligands. In addition, dinuclear, 44,52−55 trinuclear, 56,57 and tetranuclear 58−60 Cu-based WOCs have been reported. Despite all of these publications, reports on Cu-catalyzed WO often lack detailed mechanistic information, especially compared to the mechanistically well-studied Ru-based systems. For the latter systems, it has been well-established that O−O bond formation occurs via water nucleophilic attack (WNA) or the inter-or intramolecular coupling between two metal−oxo or metal−oxyl units (I2M) (Figure 1). 61−63 An important element herein is the formation of an electrophilic oxo group through π donation from the oxo ligand to the empty d orbitals of a high-valent Ru species.
Whereas Mn-and Fe-based WOCs are likely to follow reaction paths similar to that of Ru, 64,65 for Cu-based WOCs, these mechanisms are quite unlikely. The formation of a highvalence Cu−oxo species is in disagreement with the oxo wall principle. 66−72 The oxo wall is an imaginary border between the group 8 and 9 transition metals in the periodic table. The oxo wall principle describes that transition metal−oxo complexes in C 4v symmetry on the left side of the oxo wall can form metal−oxo species with double-bond character, M� O. On the right side of the oxo wall, for high-oxidation-state complexes (d n , where n ≥ 5) with the same C 4v symmetry, a metal−oxo double bond cannot be formed due to occupation of the π* orbitals of the metal center. Because Cu lies far beyond the oxo wall, the formation of a Cu−oxyl radical species with single-bond character in the form of M−O • is expected. It is therefore doubtful that the oxyl radical is sufficiently electrophilic to allow a WNA or I2M mechanism to occur. The formation of a Cu IV intermediate is rather unlikely. On the other hand, Cu III complexes have been reported multiple times. 73−80 However, the existence of d 8 Cu III complexes is questionable. In a thorough study, the Lancaster group has spectroscopically and computationally investigated 17 Cu complexes with formal oxidation states ranging from Cu I to Cu III without finding any diagnostic evidence for the presence of Cu III , suggesting that most of these species should probably be reformulated as Cu II species. 81 Therefore, the formation of Cu n+ �O (n = III or IV) is rather unlikely, and the true active species for WO is expected to have Cu n+ −O • (n = II or III) character. 72,82,83 Two protons and one electron need to be removed from an initial Cu II −OH 2 species to produce a Cu II −O • intermediate. The utilization of redoxactive ligands allows for the accumulation of sufficient redox equivalents while avoiding the buildup of a high oxidation state on the metal center. Examples of redox-active ligands used in WO catalysis have been reported for Ru-, 84−86 Co-, 87,88 Ni-, 89,90 and Cu-based 23,35,[41][42][43]91 catalysts. The utilization of redox-active ligands in combination with Cu sites has led to the formulation of a variety of alternative mechanistic pathways via which WO is expected to occur. 92 In all of these pathways, single-electron transfer (SET) from an incoming hydroxide ion to the oxidized catalytic intermediate takes a central role. In the literature, this reaction step is often indicated as SET-WNA but thus far has predominantly been shown to occur upon attack of a hydroxide ion; hence, we prefer a SET-HA (hydroxide attack) terminology. 93 In this mechanism, O−O bond formation proceeds via two consecutive SET steps. After the first SET from the hydroxide ion to the oxidized Cu complex, an intermediate is formed with a two-center threeelectron (2c3e, symbolized as ∴) bond between the two O atoms with a formal oxidation state of 1.5− for each O atom. 93−96 The formation of 2c3e bonds is unusual in WO chemistry; therefore, a brief description of this bond is given. The 2c3e bond is based on the valence bond theory by Pauling, which describes that stability arises due to resonance between the two Lewis structures by charge transfer. 96−99 Recent studies based on the Pauling valence bond theory lead to formulation of the charge-shift bond, a new type of bonding besides the covalent and ionic bonds. 96,100−104 The total bond energy of the 2c3e charge-shift bond is obtained from the resonance of the charge shift between the valence bond structures. Here none of the valence bond structures themselves have any bonding, and in each valence bond, the three electrons maintain Pauli repulsion. The molecular orbital (MO) scheme of a species with a 2c3e bond contains two electrons in the bonding MO and one in the antibonding MO, leading to a bond order of 0.5. 95 Four variations on the SET-HA mechanism have been postulated in the literature, which we have classified as type 1, 2, 3, or 4 ( Figure 2). 46 A SET-HA type 1 mechanism has been proposed for WO catalyzed by [Cu II (N1,N1′-(1,2-phenylene)bis(N2-methyloxalamide))] 2− (Figure 2). 41,93,105,106 A SET from a hydroxide ion to the oxidized ligand of a L ox(+) −Cu III − OH intermediate is proposed. The ligand is reduced, and a 41 Computational research by these groups on two previously reported catalysts shows that WO mediated by these species occurs via the SET-HA type 2 and 3 pathways. A SET-HA type 2 is proposed for WO catalyzed by [Cu(2,2′bipyridine-6,6′-bis(olate))(OH 2 ) 2 ]. 23 In this mechanism, a SET from a hydroxide ion to the Cu III center of the L ox(+) − Cu III −OH intermediate is proposed. The Cu center is reduced to a II+ oxidation state, and a (HO∴OH) − bond is formed ( Figure 2). 93 19 Because the 2,2′-bipyridine ligand is considered to be redox-inactive, L− Cu III −O • is proposed as the active intermediate ( Figure 2). In this mechanism, a SET from a hydroxide ion to the oxyl ligand is proposed to form a (O∴OH) 2− bond. 93 A second SET from this 2c3e bond to the Cu III ion reduces the Cu center to a II+ oxidation state and results in the formation of a Cu II −(O− OH) − intermediate. Although the computational study suggests that no redox-active ligand is required for a SET-HA mechanism, this catalyst requires a +750 mV overpotential to form the active species. 19 A SET-HA type 4 mechanism was proposed for WO catalyzed by a Cu-based catalyst with a πextended tetraamidate macrocyclic ligand 42 and [2,2′-bipyridine]-6,6′-dicarboxamide ligands substituted with phenyl or naphthyl groups. 43 In this proposed mechanism, the ligand is 46 However, the mechanistic study for this catalyst was performed in a water (2.0 M)/ acetonitrile (MeCN) solution, which makes a thorough mechanistic comparison problematic. For Cu-based catalysts that contain redox-active ligands, the SET-HA mechanisms appear to be a more realistic pathway than the classical WNA-and I2M-type mechanisms. The ligand N,N-bis(2,2′-bipyrid-6-yl)amine (HL) seems to be an ideal candidate for applications in WO chemistry because HL contains a conjugated π system and therefore can be easily oxidized. Moreover, in the case of the Cu-based complex [Cu II (HL)(OTf) 2 ], deprotonation of the amine function occurs at a relatively mild pH of 9.5. Both properties are beneficial for a SET-HA mechanism. On top of that, HL has already been successfully utilized in a WOC in combination with Co and Fe (i.e., [(MeOH)Fe(HL)-μ-O-(HL)Fe-(MeOH)](OTf) 4 ) (MeOH = methanol). 107,108 In this paper, [Cu II (HL)](OTf) 2 is investigated mechanistically as a WOC in a combined experimental and theoretical study.
Synthesis and Characterization.
[Cu(HL)(OTf) 2 ] was synthesized according to modified synthetic protocols (see the experimental section), 109−112 while the synthesis of the analogous [Zn(HL)(OTf) 2 ] was reported previously. 108 An elemental analysis was obtained and shows that the composition of the crystalline material is in good agreement with the chemical composition and thus assignment of [Cu(HL)(OTf) 2 ]. Crystal structures were obtained for [Cu(HL)(OTf) 2 ], as well as for the compound [Cu(L)-(MeOH)](OTf), which was obtained via deprotonation of [Cu(HL)(OTf) 2 ] with NaH ( Figures 3 and S1 and Tables S1 and S2). The removal of the proton on the amine moiety does not lead to any major structural changes because only minor differences in the bond lengths are obtained (Table S3). However, a significant change is observed in the bond angle around the amine moiety (C10−N3−C11), which is 131.19 (14) Figure S2). Thereby, a weak/distant π−π-stacking interaction of approximately 3.320 Å between two pyridine planes is observed within the dimer.
For solutions of [Cu(HL)(OTf) 2 ], a square-pyramidal geometry is expected in which the two axial triflate ions are substituted for a solvent molecule (e.g., H 2 O, MeOH, or MeCN). In situations where the coordinated solvent ligand is not specifically known, the nomenclature Cu(HL) will be used. Cu(HL) was found to be stable in Milli-Q water for at least 6 days because no changes were observed in the UV−vis spectra ( Figure S3). The color of the solution changed visibly from green to yellow upon the addition of a base (NaOH) to an aqueous solution of Cu(HL), resulting in Cu(L) ( Figure S4). In the UV−vis spectrum, the absorbance band at 346 nm disappears and two new bands are formed at 331 and 403 nm upon deprotonation of the ligand amine ( Figure S6). By a UV−vis-monitored titration with NaOH, a pK a of 9.5 was determined for the secondary amine in Cu(HL) ( Figure S7).
Electron paramagnetic resonance (EPR) spectra of Cu(HL) in MeOH were recorded at room temperature. The structurally related complex [Cu II (N,N′-di(pyrid-2-yl)-2,2′-bipyridine-6,6′diamine)(H 2 O)] 2+ has been reported under these conditions to have an isotropic g value of 2.11. 113 For Cu(HL) in MeOH at room temperature, we found an EPR spectrum that we could simulate with g iso = 2.11 ( Figure S9; for simulation data, see Table S4). EPR spectra of Cu(HL) recorded in water at 130 K show an isotropic EPR signal with a g value of 2.06 Figure S10). However, in a MeOH solution at 130 K, the EPR is rhombic with three g values of 2.200, 2.055, and 2.030 ( Figure S9). No significant changes in the g values were found upon deprotonation with NaOMe. Cyclic Voltammetry in an Organic Solvent. A cyclic voltammogram (CV) of Cu(HL) was recorded in a MeCN solution under noncatalytic conditions (Figure 4). A reversible redox event assigned to the Cu I/II redox couple was found at −0.64 V vs ferrocene/ferrocenium (Fc/Fc + ). Furthermore, an irreversible oxidative wave at 0.68 V and another reversible redox event at 0.91 V vs Fc/Fc + were found. The peak currents of the Cu I/II redox couple and the irreversible oxidative wave were linearly dependent on the square root of the scan rate, which is in good agreement with a freely diffusive species (Figures S11 and S12). The chemistry of the intermediate formed upon irreversible oxidation was evaluated by measuring three CVs in different potential windows ( Figure 4). The first CV was recorded in the potential range between −1.0 and 0.35 V vs Fc/Fc + , and only the Cu I /Cu II redox couple is found (gray line, fully overlapped by the red line). In the second cycle, the potential window is increased up to 1.2 V vs Fc/Fc + , the irreversible oxidative wave and the second reversible redox couple are observed (black solid line). In this cycle, an enhanced current is found for the reductive wave of Cu I/II at −0.64 V vs Fc/Fc + . In the third cycle, the potential window of −1.0 to +0.35 V vs Fc/Fc + was again applied, and the current of the Cu II reduction was similar to the current obtained in cycle 1 (red dotted line). The reductive current in the second scan is thus enhanced, indicating that the species that is obtained by irreversible oxidation of the Cu II compound, is stable under these conditions, and is immediately reduced to the Cu I species.
Cyclic Voltammetry under Catalytic Conditions. A CV of Cu(L) was recorded in an aqueous phosphate solution of pH 11.5. A quasi-reversible wave with a relatively broad reductive peak and a sharper oxidative peak (ΔE = 100 mV) is found at −0.29 V vs normal hydrogen electrode (NHE) and associated with the Cu I /Cu II redox couple with E pc at −0.34 V and E pa at −0.24 V (Figure 5a). These potentials are in good agreement with the reversible redox event observed in an organic solvent. 114 A linear correlation on the square root of the scan rate was found for both the oxidation of Cu I and the reduction of Cu II , indicative of a freely diffusive process ( Figure S13). Furthermore, a catalytic wave arises from 1.0 V vs NHE onward. Additional studies of the catalytic wave with differential-pulse voltammetry revealed two oxidative waves underneath the catalytic waves at 1.08 and 1.22 V vs NHE ( Figure 5b). The catalytic current was also found to linearly correlate on the square root of the scan rate, again indicative for a free diffusive process ( Figure S14).
Cyclic voltammetry experiments with the analogous Zn complex [Zn(HL)(OTf) 2 ] were performed, showing a single irreversible oxidative wave at 1.0 V vs NHE ( Figure S15). Because oxidation of Zn II to Zn III is very unlikely, this irreversible oxidative wave is assigned to oxidation of the ligand, illustrating that L is a redox-active ligand. 115 Because the electrochemical oxidation is irreversible, a chemical process to a more stable intermediate via an EC (EC = electrontransfer step followed by chemical reaction) mechanism is expected. 116,117 It must be noted that the pK a of Zn(HL) is around 11.5, which is two pH units higher than that of Cu(HL) ( Figure S8). We can therefore conclude that the irreversible oxidation waves observed in both MeCN ( Figure 4) and aqueous solutions ( Figure 5) should be assigned to a ligand-centered oxidation reaction.
To show that Cu(L) is indeed able to catalyze the oxidation of water to produce dioxygen, online electrochemistry mass spectrometry (OLEMS) was applied to detect the formation of oxygen. 118 With OLEMS, gases formed at the electrode surface can be detected. For Cu(HL), the mass signal (m/z = 32) for
Inorganic Chemistry pubs.acs.org/IC
Featured Article dioxygen increased simultaneously with the increasing catalytic wave in the CV from the onset potential of 1.1 V vs NHE onward ( Figure S16). Pourbaix Diagram. The potentials of the redox events of Cu(HL) were determined as a function of the pH by cyclic and differential-pulse voltammetry ( Figure 6). From the pH dependence of a given redox couple, the type of electron transfer (ET) can be determined following the Nernst equation. 116 Slopes of 0 and −59 mV/pH units correspond to an ET and a proton-coupled electron transfer (PCET) step, respectively. [Cu II (L)(H 2 O)] + can be found on the right side of the Pourbaix diagram, between pH 9.5 and 13. A pH dependence of −60 mV/pH is found for its reduction to [Cu I (HL)] + , indicating that this proceeds via PCET. This step is expected to occur with dissociation of a H 2 O ligand, which would lead to a stable 18-electron complex for [Cu I (HL)] + . Two subsequent oxidation reactions of [Cu II (L)(H 2 O)] + with pH dependences of −60 and −66 mV/pH are found, which are assigned to two PCET events, respectively. These two PCET steps would lead to a formal "Cu IV " intermediate, which is a very unlikely species. 72,82 Given that L − is a redox-active ligand, the first oxidation step is assigned to oxidation of the ligand. Given that also the Cu III oxidation state is questionable, the second oxidation reaction is assigned to the oxidation of Cu II −OH to Cu II −O • , leading to formation of the key oxidative species [Cu II (L • )(O • )] + . 81 We anticipate that the same species is formed in an organic solvent (Figure 4), albeit in small concentrations due to the low concentration of water in MeCN, which is illustrated by the small reversible redox couple at high potential.
[Cu II (HL)(H 2 O)] 2+ is found on the left side of the Pourbaix diagram, between pH 0 and 9.5. In this window, other pH dependences are found for the different redox events. This shift in ET types between acidic and alkaline parts of the Pourbaix diagram is correlated to the pK a of Cu(HL) at pH 9.5. The reduction of [Cu II (HL)(H 2 O)] 2+ to [Cu I (HL)] + proceeds via an ET reaction, given that a dependence of 0 mV/pH is found. In contrast to the high pH window, only a single oxidative event was found with a pH dependence of −60 mV/pH. This single line in the Pourbaix diagram is expected to be the result of two redox events that at more alkaline conditions become separated and can be observed in the differential-pulse voltammogram (Figure 5b) Because the highest catalytic activity in cyclic voltammetry experiments can be observed between pH 10 and 13, [Cu II (L • )(O • )] + is expected to be the intermediate species that is involved in O−O bond formation. Therefore, the WO mechanism of Cu(L) was studied in more detail in this pH window.
Homogeneity Study. Several experiments were performed to investigate whether Cu(L) is a molecular catalyst. 119 A dipping test was employed, to rule out the formation of catalytically active heterogeneous species on the electrode surface. After scanning 20 cycles between −0.68 and 1.31 V vs NHE in a 0.3 mM Cu(L) pH 11.3 phosphate solution, the electrode was rinsed to remove any remaining droplets containing Cu(L). Subsequently, a CV was recorded in a blank pH 11.3 phosphate buffer solution ( Figure S17). In the first scan of the postcatalysis blank, a slightly higher current of 2.0 μA is observed around 1.3 V vs NHE than the current of 0.8 μA that was recorded in the initial blank before catalysis. However, this increased current of the blank is significantly lower than the catalytic current of 13.4 μA in the presence of complex Cu(L) in solution. The increased background current may be ascribed to roughening and oxidation of the carbon electrode surface rather than adsorption of the Cu(L) complex to the electrode.
To investigate the homogeneity of Cu(L) in more detail, electrochemical quartz crystal microbalance (EQCM) experiments were performed. EQCM is an in situ technique that enables the detection of mass changes on the electrode surface by changes in the oscillation frequency (Δf). A negative Δf corresponds to mass deposition on the electrode surface. 120 The Cu I/II redox couple showed a negative Δf upon the reduction of Cu II (L) to Cu I (HL) ( Figure S18). This indicates that Cu I (HL) precipitates from the solution and deposits on the electrode. Upon reoxidation to Cu II (L), a positive frequency change suggests that the deposit is redissolved. This may be linked to the expected H 2 O dissociation upon reduction of [Cu II (L)(H 2 O)] + to [Cu I (HL)] + . Overall, there is a net frequency change of zero, pointing to all of the deposited Cu I (HL) being redissolved in the solution upon oxidation. This reversible deposition process may explain the broad reductive and sharp oxidative wave of the Cu I/II redox couple in cyclic voltammetry experiments.
Subsequently, EQCM experiments were recorded of Cu(L) under catalytic conditions, which were compared with those of the blank phosphate buffer solution (pH 11.5). At first, cyclic voltammetry measurements were performed by scanning 50 cycles between 0.82 and 1.32 V vs NHE ( Figure S19). In both cases, the same order in Δf was observed, indicating a mass increase on the electrode surface. A significant change of Δf is also found in the blank, which can be assigned to the interaction between gold β-oxide (formed on the electrode upon oxidation) and phosphate ions. 121 Because Δf appears to be on the same order of magnitude for the blank and Cu(L), deposition of Cu(L) seems to be limited.
The homogeneity of Cu(L) was further evaluated by performing EQCM measurements combined with chronoamperometry at 1.22 V vs NHE. Cu(L) was again compared to the blank phosphate buffer (Figure 7). A change in Δf was The postcatalysis Au EQCM electrode surface was investigated with scanning electron microscopy in combination with energy-dispersive X-ray spectroscopy (SEM-EDX) after 20 min of constant potential electrolysis (CPE) at 1.22 V vs NHE in the presence or absence of Cu(L). After the electrochemical experiment, the electrodes were carefully rinsed to remove the remaining catalyst and buffer solution. The electrodes were dried at 40°C under reduced pressure for 1−3 h to remove the remaining traces of water. After chronoamperometry, no particles were found on the electrode surface in both the absence and presence Cu(L) (Figures S21 and S22). The EDX spectra of the postcatalysis Au electrodes show mainly the signals of Au and Si, corresponding to the electrode material and the quartz glass of the EQCM electrode, respectively (Figures S23 and S24). The absence of any Cu peaks in the EDX spectrum and no observation of particle formation on the electrode surface with SEM point toward Cu(L) likely being a molecular catalyst.
A bulk electrolysis experiment in the presence of Cu(L) was carried out in a two-compartment cell in which the WE and CE were separated by a membrane to prevent the possible cross-mixing of (by)products and reduction of oxidized byproducts at the CE. A large surface GC electrode of 0.79 cm 2 was used to increase the conversion. Both sides of the cell were equipped with a magnetic stir bar to facilitate mass transport. Chronoamperometry at 1.20 V vs NHE was performed for 5 h ( Figure S25). During the chronoamperometry, gas bubbles were formed on the electrode surface, causing signal spikes and noise in the current response. Apart from bubble-related issues, the chronoamperogram showed no depletion or increase of the overall current, indicating no major changes in the catalytic activity over time. After chronoamperometry for 5 h, only minor changes in the UV− vis and mass spectrometry spectra were observed, indicating that Cu(L) is still the major species present in solution ( Figures S25 and S26).
Kinetic Analysis. Kinetic experiments were performed to elucidate the mechanism of the Cu(L)-catalyzed WO reaction. The rate order in the catalyst was determined by measuring CVs at different concentrations of Cu(L) ( Figure S27). A plot of the logarithm of the (baseline-corrected) current, obtained at 1.22 V vs NHE, against the logarithm of the concentration of the Cu complex results a linear regression, with a slope of 1.0 indicating a first-order dependence ( Figure S27). This makes it unlikely that the WO occurs via an I2M mechanism for which a second-order dependence in the catalyst is expected.
The rate law of the reaction with respect to phosphate ions was determined in a similar fashion. CVs were recorded with In these experiments, Na 2 SO 4 was added in order to keep the ionic strength constant. An additional experiment was performed in 0.1 M Na 2 SO 4 and NaOH at pH 11.6 in the absence of phosphate ions. A plot of the concentration of phosphate ions versus the measured k obs in the CV resulted in a horizontal line, indicating zeroth-order dependence in phosphate ( Figure S29). This zeroth-order dependence indicates that specific acid−base catalysis is involved in the mechanism and that the reaction rate depends on the concentration of hydroxide ions and not the concentration of phosphate ions. 125 In the case of a WNA mechanism (Figure 1), a base (phosphate ion) is expected to activate and eventually subtract a proton from the attacking water molecule. As a consequence, the rate constant of the WO reaction via a WNA mechanism is typically dependent on the concentration of buffer. 126 In our case, the involvement of hydroxide ions in the rate-determining step (RDS) is therefore more likely because, under the experimental conditions of pH 11.5, the concentration of hydroxide ions is an order of magnitude higher than the catalyst concentration. In a WNA mechanism, a proton is removed from the nucleophilic water molecule (Figure 1). The rate of this proton subtraction by a base is lower in D 2 O, due to the higher bond energy of the O−D bond than that of the O−H bond. 127 When an O−H bond is broken during the RDS via WNA, a KIE of 2 or higher is expected. 126 Because Cu(L) operates at a relatively high pH, the RDS could potentially involve a nucleophilic attack of an OH − ion instead of water. If this were to be true, no O−H bond may need to be broken during the RDS, which would result in a KIE of 1.0. However, KIEs between 2 and 20 are regularly observed for Cu-based WOCs operating at pH 11.5 or higher. 34,44,45,55 The CVs of Cu(L) were recorded in H 2 O and D 2 O (Figure 8). A positive potential shift in the Cu I/II redox couple and onset potential is observed for Cu(L) in D 2 O. This potential shift is assigned to a shift in the RHE reference potential where H 2 was bubbled through a saturated D 2 O blank solution. 128 However, both CVs show identical current profiles, suggesting that no significant change in the WO activity upon H 2 O replacement with D 2 O takes place. Logically a KIE of 1.0 was found for Cu(L). The absence of a KIE is fully in line with the observed zeroth-order in phosphate ions. Based on these kinetic results, both the I2M and WNA mechanisms can be ruled out for the WO reaction mediated by Cu(L).
The WO reaction mediated by Cu(L) was further investigated by obtaining the activation enthalpy and entropy of the catalytic reaction via temperature-dependent electro-chemistry in the range of 10−40°C ( Figure S30). 129 For the WO reaction mediated by Cu(L), ΔH ⧧ of 4.49 kcal· mol −1 and ΔS ⧧ of −42.6 cal·mol −1 ·K −1 were found. From the enthalpy and entropy, the Gibbs free energy of the system was calculated to be 17.2 kcal·mol −1 at 298 K. To better understand the reaction mechanisms, the contribution by enthalpy and entropy is of importance. 130 So far, ΔH ⧧ and ΔS ⧧ have only been obtained for WO catalyzed by Fe and Ir complexes in the presence of sacrificial oxidants. 131−134 In these examples, ΔH ⧧ and ΔS ⧧ were found to vary from 10.5 to 17 kcal·mol −1 and from −41 to −1 cal·mol −1 ·K −1 , respectively. Comparing the electrocatalytic WO by Cu(L) with chemically driven WO at Fe and Ir complexes, the enthalpy found is significantly lower and the entropy is in the high range. Mechanistically, these relatively low enthalpy and high entropy values are in agreement with a complex transition state in which two (or more) molecules need to be arranged close to each other.
Proposed Mechanistic Cycle. The mechanistic cycle of the Cu(L)-catalyzed WO reaction can partly be elucidated using the obtained experimental data. Based upon these data, a WO mechanism is proposed that is initiated by two PCET steps: The first oxidation is assigned to oxidation of the ligand, based on the observation that a similar irreversible oxidation was observed for the analogous Zn complex. Based upon the Figure 9). According to density functional theory calculations, one of these three electrons is located on the d x 2 −y 2 orbital of the Cu center, one is delocalized throughout the π system of the two pyridine rings and the N − in the ligand, and one is present on a p orbital of the oxyl group. The redox-active ligand delocalizes the remaining radical over the ligand, retaining the Cu ion in a II+ oxidation state, even after two consecutive PCET steps. The formation of [Cu II (L • )(O • )] + is in line with the oxo wall theory because a Cu II −O • intermediate is formed rather than a Cu III �O species. 72,82 Formation of the O−O bond was computed based on the combination of [Cu II (L • )(O • )] + in the quadruplet state with an OH − ion. For this combination, a SET-HA mechanism is proposed, starting with a SET from the approaching hydroxide ion to the ligand ( Figure 10). This returns the negative charge to the ligand and results in the formation of a 2c3e ( • O∴OH) − bond when the oxyl radical and the incoming hydroxyl radical combine ( Figure S31). Next, intersystem crossing occurs wherein the species returns from the quadruplet state to a doublet state and a Cu II −(O−OH) − intermediate is formed. The description of a charge-shift bond for the ( • O∴OH) − bond is suitable for this mechanism because a covalent bond would be repulsive. 96 ■ CONCLUSION Cu(L) was synthesized, characterized, and established as a molecular WOC. The ligand L − was found to be redox-active and most likely directly participates in the reaction mechanism. Experimental observations and supporting theoretical calculations point to a mechanism that is different from WNA and I2M, which are usually proposed for Ru-based WOCs. In line with other proposed SET-HA mechanisms, we suggest a reaction path where O−O bond formation proceeds via SET from a hydroxide ion to the key L • −Cu II −O • intermediate, resulting in a 2c3e bond between both O atoms, ( • O∴OH) − . The utilization of redox-active ligands facilitates the ability to delocalize an electron in the π system of the ligand, which
Inorganic Chemistry pubs.acs.org/IC
Featured Article circumvents increasing the oxidation state of the Cu II center to higher oxidation states. 81 Overall, the Cu II center is redoxinnocent throughout the catalytic cycle. This type of SET-HA seems to be a reasonable mechanism for metal ions that are unlikely to reach high oxidation states, such as Cu.
■ ASSOCIATED CONTENT
* sı Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.inorgchem.3c00477. Experimental, synthetic protocols, and additional figures and tables as noted in the text (PDF) | 7,687.4 | 2023-03-29T00:00:00.000 | [
"Chemistry"
] |
Omnisolver: an extensible interface to Ising spin–glass and QUBO solvers
We introduce a new framework for implementing Binary Quadratic Model (BQM) solvers called Omnisolver. The framework provides an out-of-the-box dynamically built command-line interface as well as an input/output system, thus heavily reducing the effort required for implementing new algorithms for solving BQMs. The proposed software should be of benefit for researchers focusing on quantum annealers or discrete optimization algorithms as well as groups utilizing discrete optimization as a part of their daily work. We demonstrate the ease of use of the proposed software by presenting a step-by-step, concise implementation of an example plugin.
Motivation and significance
The rapidly developing field of quantum information brings us ever closer to developing practical quantum computers.Currently, we are living in an era marked by the so-called Noisy Intermediate-Scale Quantum (NISQ) [1] devices.It comes as no surprise that these machines have attracted attention from both the scientific and business communities.This attention results in a myriad of proposed potential applications for NISQ devices.Implementing these applications requires developing appropriate software, which comes with its own set of challenges.One of such potential obstacles, which this work aims to remedy, is the cost of testing on actual NISQ devices.
The main way to cut quantum infrastructure access costs is to utilize frameworks that simulate or approximate their behavior.Nonetheless, if we try this approach, we quickly run into another problem: there are multiple frameworks available, each of which can be based on different numerical algorithms.Here, we are concerned with one particular NISQ architecturethe D-Wave annealer.The behavior of this machine can be simulated using tensor networks [2,3], approximated using dynamical systems approach [4], neural networks [5] and, for sufficiently small problem sizes, through a bruteforce approach [6].
The downside of this multitude of options is that the implementations of each of these approaches have incompatible input/output APIs.In this work, we introduce the Omnisolver package which provides an extensible, unified API for Ising spin-glass solvers and the actual D-Wave annealer.
The Omnisolver's core motivation lies in addressing the fragmentation and compatibility issues that can often stymie the development of quantum software.By providing a unified API for Ising spin-glass solvers and the D-Wave annealer, our solution presents a uniform interface to the users, allowing the development of algorithms to be performed in a more simplified, streamlined manner.
One of the significant advantages of this approach is that it eliminates the need for developers to learn and adapt to the varied input/output APIs of different solvers or to become entwined in the technical complexities of switching between them.Instead, they can devote their full attention and resources to designing, implementing, and fine-tuning their algorithms, hence accelerating the overall development process.The ease of integration further encourages experimentation with different approaches, fostering innovation within the field.
Omnisolver is builds upon the D-Wave's dimod framework.Our software extends dimod's capabilities in two ways.Firstly, Omnisolver's plugin library provides additional, high-performance algorithms readily usable from Python scripts or command line.Secondly, and more importantly, the Omnisolver's core library provides an automatic construction of a command line interface and a unified input/output system automatically wired to the implemented solvers.Having a framework that not only consolidates existing methods but also allows for the easy addition and integration of new ones is in our opinion a valuable asset.
In this context, the claim about a reduction in the effort required to write new algorithms is not meant to indicate that Omnisolver directly simplifies the algorithm-writing process, but rather that it facilitates and expedites the process by providing a more consistent, comprehensive, and accommodating environment for developers.By simplifying the integration with various solvers and enabling easy swapping of them for testing and comparison, Omnisolver significantly reduces the peripheral tasks associated with quantum software development, leaving developers more time and energy to focus on their core task: creating and optimizing algorithms.
Compared to other similarly looking libraries like pyQUBO or QUBOTools.jlour package provides a significant new feature.pyQUBO [7,8] provides a platform to formulate and solve QUBO and Ising problems.It does this by converting combinatorial optimization problems to QUBO or Ising problems, which can then be solved by annealing machines.While pyQUBO is a valu-able tool, its scope is limited to problem formulation and conversion; it does not provide an integrated approach to using various solving methods.
In contrast, Omnisolver's unified API allows it to handle a broader set of tasks.By providing a consistent interface to multiple solvers, including both Ising spin-glass solvers and D-Wave annealer, it significantly simplifies the process of experimenting with different approaches and offers greater flexibility to the user.
QUBO.jl [9], a Julia library, shares a similar purpose with pyQUBO.It assists in the formulation of QUBO problems from a combinatorial optimization standpoint, offering various utility functions for working with QUBO matrices.The use of Julia allows for high-performance computations, particularly beneficial when dealing with large-scale problems.Compared to Omnisolver, QUBO.jl provides similar problem formulation capabilities and offers a similar, extensible platform for integrating and testing various QUBO and Ising solvers.The main difference between QUBO.jl and Omnisolver is the underlying technology.Omnisolver, beeing written in pure Python, allows for seamless integration with external solvers accessed via public APIs, like for example D-Wave's quantum annealer.Additionally, we provide a specialized plugin for brute-force solving of QUBO problems, which guarantees finding the optimal solution and has a reasonable execution time for instances of up to 50 variables which allows for certification of results obtained from other methods.These features make Omnisolver a more comprehensive tool, catering to a broader spectrum of the QUBO and Ising problem-solving workflows.
Furthermore, it is important to acknowledge the vast landscape of Ising machines, as reviewed in [10].These machines, both software and hardwarebased, have made significant strides in solving complex combinatorial problems.However, many of these implementations operate in isolation and come with their own specific input/output requirements, adding to the complexity of algorithm development and testing.
Software Architecture
Omnisolver is built in a modular fashion and comprises the following elements: • core Omnisolver package, • plugins providing samplers built on top of dimod library [11].
The architecture of the Omnisolver, as well as the typical execution flow, is presented in Fig. 1.The core package handles input/output operations, including parsing the command line arguments, reading an input problem file, and outputting solutions computed by samplers.It can be extended via a plugin system built using the pluggy [12] library.The plugins are responsible for implementing algorithms solving instances of Ising spin-glass or QUBO models (collectively known as Binary Quadratic Models) and providing descriptions of available parameters.At the start of the program the core package auto-discovers the plugins, and collects the description of the samplers they implement.Based on those descriptions, a Command Line Interface (CLI) is constructed and exposed to the user.Using the CLI, the user selects which sampler, and with what parameters, should be used.The core package then asks the plugin to instantiate the sampler, uses it to solve the provided problem instance, and then serializes the output.
The plugin definition comprises: • name of the plugin, • the create sampler function used to construct new sampler instances, • the populate parser callback used for defining command line arguments used for creating sampler and running its sample method.
• init args and sample args determining which command line arguments should be passed respectively to sampler's initializer and sample method.
Omnisolver extensions should provide one or more callables decorated with omnisolver.plugin.pluginimpl returning an instance of Plugin class.
Software Functionalities
The major functionality of the Omnisolver project is providing a framework for implementing arbitrary solvers of classical Ising spin-glasses.To this end, the core omnisolver package provides: • plugin system for registering solvers inheriting from dimod.Sampler, • helper functions for implementing most typical plugins which remove the burden of writing plugin boilerplate from the end-user, • input/output system automatically wired to the plugins.Currently, this allows reading spin-glass instances from coordinate format and writing solutions as a comma-separated value (CSV) file, • dynamically generated command line interface (CLI).The CLI takes into account information provided by the registered plugins, and hence is able to correctly recognize parameters that need to be passed to each solver.
Sample code snippets analysis
Code snippets in this section demonstrate an example implementation of an Omnisolver plugin, providing a dummy solver returning random solutions.While this example is clearly artificial, it should be easy for the reader to extend it to an implementation of an arbitrary nontrivial solver.The plugin is implemented as a Python package called dummysolver.The layout of the package is shown in the Listing 1, and the Fig. 2 summarizes the relationship between the package's essential components.The dummysolver/solver.py file, presented in Listing 2, contains the implementation of the solver.The DummySolver class inherits from dimod.Sampler and implements its abstract sample method.This method accepts an instance of dimod.BinaryQuadraticModel (required by the base class' sample method) and an additional keyword parameter num solutions indicating how many solutions should be returned.To inform the Omnisolver's plugin system about DummySolver, one needs to provide information about the solver's class, displayed name, and parameters accepted by the solver and their command line counterparts.The easiest way to achieve this is by providing the information in the YAML file, as exemplified by Listing 3. schema_version : 1 name : " dummy " sampler_class : " dummysolver .solver .DummySolver " description : " Uniformly random dummy sampler " init_args : [] sample_args : -name : " num_solutions " help : " number of sampled solutions " type : int default : 1 Listing 3: dummysolver/dummy.yml The init args field, specified as an empty list in the example, defines parameters for the solver's init method.The sample args list describes the parameters of the solver's sample method.In principle, one could always design the plugin in such a way that all parameters are passed to the sample method of the sampler, and thus it might seem redundant to include init args as well.However, to support samplers with a parametrized initializer that already exist on the market, we decided to include the initializer's args as well.An example of such a sampler is the D-Wave's DWaveSampler, which can accept several parameters during its initialization [13].
One also needs to define the plugin's entrypoint, which in this case is located in the package's initialization file (Listing 4).Here, we used Omnisolver's convenience function plugin from specification for building the plugin from the definition read from the YAML file.Finally, the entrypoint has to be defined in the package's setup file (Listing 5) to be picked up by the plugin system.from omnisolver .plugin import ( plugin_from_specification , plugin_impl , ) from pkg_resources import resource_stream from yaml import safe_load @plugin_impl def get_plugin () : """ Construct plugin from given yaml file .""" specification = safe_load ( resource_stream ( " dummysolver " , " dummy .yml " ) ) return p l u g i n _ f r o m _ s p e c i f i c a t i o n ( specification ) Listing 4: dummysolver/ init .py
Illustrative Examples
Examples in this section assume that omnisolver, omnisolver-pt and dummysolver (presented in Subsection 2.3) are installed in the current Python environment.
The available solvers can be displayed by running omnisolver -h, as exemplified in Listing 6 Help for the specific solver, automatically constructed using Python's standard library argparse module, can be obtained by running omnisolver <solver-name> -h.Listing 7 shows an example output of running command omnisolver pt -h.Finally, let us demonstrate solving an instance of the Ising spin-glass using a chosen solver.To this end, let us assume that the instance file instance.txtas presented in Listing 8 is present in the current directory.The input file comprises rows of the form "i j J ij".Here, i and j are indices of variables and J ij is the coupling coefficient between them.In the case of i = j, the J ii coefficient is treated as linear bias (magnetic field) acting on the i-th spin.For the QUBO formulation, the last element in the row is naturally interpreted as either the quadratic or linear term.
Running omnisolver pt instance.txt--vartype=SPIN yields result similar to the one presented in Listing 9.
Impact
This software has two main contributions.Firstly, it provides a unified framework for implementing Ising or QUBO solvers.As such, it should accelerate the process of implementing new algorithms, saving the developers the time needed for writing code handling CLI and input/output system.For the researchers, common CLI and input/output interface should make it easier to interchangeably use a variety of solvers, thus decreasing time needed for preparing and running experiments.In particular, this will mainly benefit entities that face combinatorial optimization problems in their daily routine, such as railway companies.As an example of such an application, we refer an interested reader to the works [14,15].
Secondly, Omnisolver provides several readily available samplers implemented as its plugins, including the parallel-tempering sampler and the brute-force sampler capable of finding low-energy spectra of instances with sizes similar to the largest cliques embeddable on present-day D-Wave hardware.To emphasize the usefulness of the currently implemented solver, in Fig. 3 we present initial performance benchmarks for Omnisolver's bruteforce sampler.
Conclusions
We provide the community interested in quantum computing with an extensible framework allowing for communication with various computational backend for solving Ising spin-glasses or QUBOs.The plugins for the framework may implement numerical algorithms as well as serve as interfaces to actual computing devices.
We plan further development of plugins for Omnisolver, which already contains implementations of such algorithms as exhaustive search or parallel tempering.The community is also encouraged to provide their own implementations when coming up with interesting approaches to solving Ising instances.
Conflict of Interest
No conflict of interest exists: We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.
Figure 3 :
Figure3: Performance of the GPU-accelerated Omnisolver-bruteforce sampler using a variable number of GPUs.The x-axis shows the problem size, and the y-axis shows the time needed to solve it using 1,2,4 and 8 NVIDIA A100 GPUs.
Table 1 :
Code metadata | 3,257.8 | 2021-12-21T00:00:00.000 | [
"Computer Science",
"Physics"
] |
Gravity Gradient Tensor of Arbitrary 3D Polyhedral Bodies with up to Third-Order Polynomial Horizontal and Vertical Mass Contrasts
During the last 20 years, geophysicists have developed great interest in using gravity gradient tensor signals to study bodies of anomalous density in the Earth. Deriving exact solutions of the gravity gradient tensor signals has become a dominating task in exploration geophysics or geodetic fields. In this study, we developed a compact and simple framework to derive exact solutions of gravity gradient tensor measurements for polyhedral bodies, in which the density contrast is represented by a general polynomial function. The polynomial mass contrast can continuously vary in both horizontal and vertical directions. In our framework, the original three-dimensional volume integral of gravity gradient tensor signals is transformed into a set of one-dimensional line integrals along edges of the polyhedral body by sequentially invoking the volume and surface gradient (divergence) theorems. In terms of an orthogonal local coordinate system defined on these edges, exact solutions are derived for these line integrals. We successfully derived a set of unified exact solutions of gravity gradient tensors for constant, linear, quadratic and cubic polynomial orders. The exact solutions for constant and linear cases cover all previously published vertex-type exact solutions of the gravity gradient tensor for a polygonal body, though the associated algorithms may differ in numerical stability. In addition, to our best knowledge, it is the first time that exact solutions of gravity gradient tensor signals are derived for a polyhedral body with a polynomial mass contrast of order higher than one (that is quadratic and cubic orders). Three synthetic models (a prismatic body with depth-dependent density contrasts, an irregular polyhedron with linear density contrast and a tetrahedral body with horizontally and vertically varying density contrasts) are used to verify the correctness and the efficiency of our newly developed closed-form solutions. Excellent agreements are obtained between our solutions and other published exact solutions. In addition, stability tests are performed to demonstrate that our exact solutions can safely be used to detect shallow subsurface targets.
Introduction
Gravity exploration methods try to identify the anomalous mass bodies in the Earth (Blakely 1996). Gravity signals such as gravity fields and gravity gradient tensors are measured using gravimeters and gravity gradiometers, which can be located on the air-Earth interface, in boreholes, or be carried by marine ships and aircraft, and even by satellites (Nabighian et al. 2005). The amplitudes of the gravity field are inversely proportional to the square of the distance (R) between the observation site and the causative body, that is O(R −2 ) . As for the gravity gradient tensor, its amplitude is inversely proportional to the third power of distance, that is O(R −3 ) . Therefore, compared to the gravity field, gravity gradient tensor signals are more sensitive to the shallow anomalous structures in the Earth (Droujinine et al. 2007;Beiki and Pedersen 2010;Martinez et al. 2013;Beiki et al. 2014;Gutknecht et al. 2014;Li 2015;Ramillien 2017). Mathematically, both gravity field and gravity gradient tensor signals can be formulated as volume integrals over the causative body. When the observation site is located inside the mass body, a local spherical coordinate system can be introduced at the observation site, so that a factor of distance squared ( R 2 ) would be introduced (such as dv = R 2 sin drd d , Blakely 1996), which can further weaken the singularity appearing in the integrands of the gravity signals (Jin 2002). Therefore, from the mathematical point of view, the gravity field can be evaluated without mathematical singularities, as O(R −2 × R 2 ) = O(1) , but the mathematical singularities always remain in the gravity gradient tensor formulation, when observation sites approach the causative body, that is O(R −3 × R 2 ) = O(R −1 ).
Designing gravity gradiometers for measuring gravity gradient tensor signals is a difficult task. The gravity gradient tensor is a 3 × 3 symmetrical tensor with each entry being the second derivatives of the gravitational potential (or the first derivatives of the gravity field or the gravity acceleration). In terms of differential measurements, accelerations of at least two spatially separated masses were generally used to estimate the components of the gravity gradient tensor. Depending on the accuracies of the available gradiometers, gravity gradient tensor signals can be applied in geodesy (0.01 Eötvös to 0.1 Eötvös), autonomous navigation (0.1 Eötvös to 1 Eötvös) and oilfield and mineral exploration geophysics (1 Eötvös to 10 Eötvös) (Evstifeev 2017). In exploration geophysics, the cause of the increased interest in gravity gradiometers is that, compared to gravity anomalies measured by gravimeters, gravity gradient tensor signals contain more detailed information about the subsurface structures (Chapin 1998;Nabighian et al. 2005). Furthermore, compared to gravity field data, gravity gradient tensor anomaly maps generally provide more contrasting 1 3 and clearer edge delineations, such as those arising from salt domes in oil and gas prospecting (Pedersen and Rasmussen 1990). Up to now, several gravity gradiometers have been adopted in realistic exploration geophysical problems, such as the 3D FTG (full tensor gradiometer) system by Bell Geospace (Bell and Hansen 1998;Bell et al. 1997;Brewster 2016;Abtahi et al. 2016) and the Falcon AGG airborne gravity gradiometer by BHP Billiton (Australia) (Lee 2001).
To invert measured gravity gradient tensor (GGT) signals, we need an accurate forward solver. In general, GGT forward modelling can be performed either numerically or analytically. Using numerical methods such as Gaussian quadrature approaches (Talwani and Ewing 1960), Fourier domain methods (Parker 1973;Wu and Chen 2016), finite-element methods and finite-difference methods (Cai and Wang 2005;Farquharson and Mosher 2009;Jahandari and Farquharson 2013), the GGT signals caused by a mass body can be straightforwardly evaluated. However, the accuracies of numerical GGT signals can be seriously reduced by these numerical methods. For instance, using finite-element and finite-difference methods, improper translation of the computed gravitational potential to its second-order derivatives can lead to serious numerical errors. These undesired precision loss issues can be completely avoided by analytic approaches, which offer highly accurate GGT signals in terms of exact solutions.
Due to early limitations in the considered model geometries, the simple rectangular prismatic element has been widely accepted as a basic element in gravity forward modelling. Using these prismatic elements, the mass distributions in the Earth were simply approximated conceding a certain loss of geometrical accuracy. As for this basic element, several exact solutions of the gravity gradient tensor signals were developed (Forsberg 1984;Li and Chouteau 1998;Montana et al. 1992;Nagy and Papp 2000;Holstein et al. 2013;De Stefano and Panepinto 2016). However, it is difficult to accurately approximate a complex body using prisms. Polyhedral elements are endowed with great flexibility in presentation of 3D mass sources with complex geometries. Compared to prismatic elements, discretisations in terms of polyhedrons need lower numbers of elements to represent complicated mass bodies (Petrović 1996). Several analytical solutions have been successfully derived for the gravity gradient tensor signals of a homogeneous polyhedral body (Okabe 1979;Götze and Lahmeyer 1988;Kwok 1991;Petrović 1996;Werner and Scheeres 1996;Tsoulis and Petrović 2001;Holstein 2002;Tsoulis 2012;Holstein et al. 2013;D'Urso 2014a). Closed-form solutions of the gravity gradient tensor were also derived for other homogeneous simple geometries, such as pyramids (Sastry and Gokula 2016) and cylinders (Rim and Li 2016).
Seeking simplicity, geophysicists often assume that the Earth is composed of 3D anomalies in a layered medium or a succession of strata with horizontally undulating interfaces (e.g., sedimentary basins and underlying bedrock). In each layer, the rock mass density predominantly exhibits depth-dependent variations. To approximate the density variations in sedimentary basins, several closed-form solutions of gravity signals were derived for depth-dependent mass contrast functions, such as exponential functions (Cordell 1973;Chai and Hinze 1988;Chappell and Kusznir 2008), hyperbolic functions (Litinsky 1989;Rao et al. 1995), parabolic functions (Chakravarthi et al. 2002), quadratic polynomials (Rao 1985(Rao , 1990Gallardo-Delgado et al. 2003) and cubic polynomials (García-Abdeslem 2005). Compared to these depthdependent functions, polynomials offer a more flexible way to approximate arbitrarily variable density distributions. Very recently, Jiang et al. (2017) have given a detailed performance comparison between polynomial mass contrast functions and depth-dependent mass contrast functions for the capability of approximating complicated density distributions in the Earth. The result clearly demonstrates the superiority of polynomial mass contrast functions over the 1 3 depth-dependent mass contrast functions. As the real Earth has complicated mass density distributions, it is important to deal with a general polynomial mass contrast function, which not only varies in depth (vertical direction), but also varies in the horizontal direction.
For general polynomial functions, Pohánka (1998), Holstein (2003, Hansen (1999), D'Urso (2014b) and Ren et al. (2017a) have successfully derived closed-form solutions for the gravity field of a polyhedral body, in which the mass contrast linearly varies in both horizontal and vertical directions. Quite recently, analytical expressions for the gravity field of a polyhedral body with cubic polynomial density contrast in both horizontal and vertical directions were derived by D'Urso and Trotta (2017) and Ren et al. (2018). Nevertheless, only Holstein (2003) and D'Urso (2014b) have successfully derived closed-form solutions for gravity gradient tensor of a polyhedral body, in which the mass contrast varies linearly in both horizontal and vertical directions. To the best of our knowledge, closed-form solutions for gravity gradient tensor signals of a polygonal body with high-order polynomial mass contrasts (such as quadratic and cubic orders) varying in both horizontal and vertical directions have not been previously reported.
To overcome this both theoretically and practically important issue, we have successfully derived closed-form solutions for modelling the gravity gradient tensor of the above cases in this study. Inside the polyhedral body, its density contrast is approximated by a general polynomial function (currently, a maximum order up to and including three is considered). The polynomial mass contrast function can vary in both horizontal and vertical directions. Therefore, it has the capability to approximate complicated mass contrasts in realistic Earth models. To begin with, we apply the Gaussian gradient theorem to transform the volume integrals of GGT signals into surface integrals over the faces of polyhedra. Then, we apply the surface divergence theorem to further transform these surface integrals into a sequence of line integrals along the edges of the polyhedra. In the process of reducing the integral dimension, several vector or dyadic identities are employed to reduce the orders of the density polynomials and of the singularities in R in the integrands. The line integrals along the edges of the polyhedra are evaluated in terms of known analytical expressions (e.g., Gradshteyn and Ryzhik 2007).
To verify the accuracies of our new analytical solutions, a right rectangular prism with depth-dependent density contrasts, an irregular polyhedron with linear density contrast and a tetrahedral element with horizontally and vertically varying density contrast are tested. For the prism model, its reference solution for the gravity gradient tensor is computed from the derivatives of García-Abdeslem's (2005) analytical solution for the gravity field. The analytical solution derived by Holstein (2003) is used as reference for the irregular polyhedral model. Results from high-order Gaussian quadrature are used as reference solutions for the tetrahedron model.
Theory
For an arbitrary interior point of the polyhedral body (see Fig. 1, Appendix A and Table 5), let ( ) denote the density contrast in the polyhedral mass target at that point. The difference between two points denotes a vector, for example, the vector from a source point to the observation point ′ is denoted by − � . The gravitational acceleration field at the observation point ′ is (Ren et al. 2018) (1) where G = 6.673 × 10 −11 m 3 kg −1 s −2 is Newton's gravitational constant, ∇ is the gradient operator on point , R = | − � | is the distance from the observation site to the running integral point in the source polyhedral body H, and the gradient theorem (Tai 1997) is applied. Using Eq. (1) for the gravity field, the gravity gradient tensor can be derived as follows: where ∇ � denotes the gradient operator on point ′ and the relation ∇ 1 R = −∇ � 1 R is used. Using the following vector identity (Tai 1997): where can be a scalar or a vector, setting = 1 R and = ∇ and using the gradient theorem (Tai 1997), the volume integral term ∭ H (∇ 1 R )(∇ )dv in Eq.
(2) can be further transformed as Table 5 r (2) can be transformed as where the surface gradient theorem (Tai 1997) is used. The symbol ∇ s denotes the surface gradient operator, where the subscript s means it is an operator on a 2D surface. In the above, the definition (A.3) of the number h i , constant over facet plane i, as the projection onto the facet normal i of the position vector of a planar point relative to the observation point ′ and the following vector identity were used: where f is an arbitrary scalar function and ̂ is the outward pointing normal unit vector of the considered surface. In Eq. (5), we set f = R . Substituting Eqs. (4) and (5) into Eq. (2), we obtain the following expression for the gravity gradient tensor of a polyhedral body H with arbitrary mass contrast function ( ): The density function is defined as a general polynomial function. It allows for the density variations in both horizontal and vertical directions, which is defined as: where P is the maximum polynomial order, and d are the d-th order density terms. For instance, the zero (constant), first (linear), quadratic and cubic order density terms are given as: where a klm are known coefficients which can be estimated by fitting measured gravity field data (Blakely 1996). The total gravity signal is the sum of the individual contributions from different mass densities, such as 0 , 1 , 2 and 3 . Substituting the density contrast in Eqs. (8) into (7), we get the final gravity gradient tensor: where d denotes the individual contribution from the d-th order density contrast. In this study, we only consider the cases 0 ≤ P ≤ 3 . For simplicity, we denote x, y and z by x p (p = 1, 2, 3) in the following sections, that is
Constant Density Contrast
For the constant term, substituting 0 = a 000 and ∇ 0 = 0 into Eq. (7), we get 0 as follows: Detailed derivations of expressions for the line integral ∫ C ij 1 R dl and the surface integral ∬ H i 1 R 3 ds are given in Appendix B.
Linear Density Contrast
As for the linear terms in the polynomial density contrast, we set 1 = ⋅ , where = (a 100 , a 010 , a 001 ) and = (x, y, z) . Substituting = 1 = ⋅ and ∇ = ∇ 1 = and ∇ ∇ 1 = into Eq. (7), we get The above equation consists of one kind of line integral, i.e., ∫ C ij R dl and two kinds of surface integrals, i.e., ∬ H i R 3 ds and ∬ H i 1 R ds , with their detailed derivations given in Appendix C.
Quadratic Density Contrast
As for the quadratic term in the polynomial density contrast in Eq. (11), its gradient is The second derivative of 2 is Substituting 2 and its derivatives ∇ 2 and ∇ ∇ 2 into the general expression of the gravity gradient tensor in Eq. (7), four kinds of integrals need to be considered (their detailed derivations are given in Appendix D), which are the line integral term ∫ C ij where, using p, q = 1, 2, 3 , the notations in Eq. (14) are adopted, and the product x p x q represents an arbitrary quadratic monomial.
Cubic Density Contrast
Finally, we discuss the gravity gradient tensor due to the cubic density terms given in Eq. (12). For the first-and second-order derivatives in Eq. (7), we have and Substituting 3 , ∇ 3 and ∇ ∇ 3 into the expression of the gravity gradient tensor in Eq. (7), we find that we only need to deal with four types of integrals (their detailed derivations are given in Appendix E) to get the final closed-form solutions of 3 in Eq. (13). These four types of integrals are the line integral ∫ C ij Here, the notations in Eq. (14) are used, such that x p x q and x p x q x t represent arbitrary quadratic and cubic monomials, respectively.
Comparison with Other Solutions
The previously published analytical solutions of gravity gradient tensors for different geometries and mass density contrasts are listed in Table 1. Most of these solutions were designed for rectangular prisms with constant density contrasts (Forsberg 1984;Montana et al. 1992;Li and Chouteau 1998;Nagy and Papp 2000;Rim and Li 2016) (19) ∇ 3 ( ) = a 102 z 2 + a 111 yz + a 120 y 2 + 2a 201 xz + 2a 210 xy + 3a 300 x 2 ̂ + a 012 z 2 + 2a 021 yz + 3a 030 y 2 + a 111 xz + 2a 120 xy + a 210 x 2 ̂ + 3a 003 z 2 + 2a 012 yz + a 021 y 2 + 2a 102 xz + a 111 xy + a 201 x 2 ̂ (20) (Okabe 1979;Götze and Lahmeyer 1988;Kwok 1991;Werner and Scheeres 1996;Holstein 2002;Holstein et al. 2007a, b;Tsoulis and Petrović 2001;Tsoulis 2012;D'Urso 2014a). Only a few studies have been carried out for linear mass density contrasts (Holstein 2003; D'Urso 2014b; Sastry and Gokula 2016). Using our new findings, we successfully derive a set of analytical expressions of the gravity gradient tensor signals for a general polyhedron with constant, linear, quadratic and cubic polynomial mass functions. Our closed-form solutions for constant and linear polynomial mass functions may have different forms from the previously published solutions, but they should produce the same results. In addition, we were not only the first to find the exact solutions for the quadratic and cubic cases, but also our solutions allow for mass contrasts varying simultaneously in both horizontal and vertical directions. However, it is difficult to derive closed-form solutions of the GGT for higher-order polynomial density contrasts, simply because analytical expressions cannot be found for 1D edge integrals in terms of the existing integral tables (Gradshteyn and Ryzhik 2007).
Verification
Three models were used to validate our closed-form solutions. The first one is a rectangular prism model (Fig. 2), for which the derivatives of closed-form solutions for the gravity field in García-Abdeslem (2005) were taken as references. The second model is an irregular polyhedron with linear density contrast, taken from Holstein (2003), for which a comparison with Holstein's (2003) solution is given. The third model is a relatively complicated tetrahedral body (Fig. 5). As there are no reference solutions for the GGT of polyhedral mass bodies with quadratic and cubic density contrasts, we have used high-order Gaussian quadrature rules (such as 512 × 512 × 512 = 124,217,728 quadrature points) to calculate GGT reference solutions. Additionally, we allowed the density in the tetrahedral body to vary in both horizontal and vertical directions. We should mention that because our formulae require the observation site to be located at the origin of the Cartesian coordinate system, for each observation site, a coordinate translation must be performed to move the observation site to the origin.
A Prismatic Body with Depth-Dependent Density Contrast
The prism is located in a Cartesian coordinate system with the z-axis downward. In Fig. 2a, the coordinate ranges of the prismatic body are x = [10, 20] km, y = [10, 20] km and z = [0, 8] km. The density function is a depth-dependent cubic polynomial which is taken from García-Abdeslem (2005): where the density contrast is in kg∕m 3 and z is in km.
In the García-Abdeslem's (2005) work, an analytic formula for the vertical gravity field caused by a rectangular prism with density contrast varying as a depth-dependent cubic polynomial was derived. Therefore, we took the derivatives of García-Abdeslem's solution (i.e., T xz , T yz , T zz ) as references to verify our solution for the gravity gradient tensor. Furthermore, to verify our solution for an arbitrary polyhedral body, we decomposed the original rectangular prism into two polyhedra (as shown in Fig. 2b). These two polyhedra share the plane with vertices 1, 3, 8 and 9. Note, this test requires points 1, 3, 8 and 9 to be co-planar. If they were not co-planar, the surface (1, 3, 8, 9) would need to be divided into two triangles (1, 3, 8) and (3,8,9). The values of T xz , T yz and T zz were computed at two observation sites. One is located at point (12, 12, − 0.001) km with a 1-m offset right above the top face of the prismatic body, whereas the other one is located at point (20, 10, − 0.001) km, that is near a corner of the rectangular prism.
The results computed by our closed-form solution and by derivatives of García-Abdeslem's (2005) gravity field solution are compared in Table 2. We compare GGT (21) ( ) = − 747.7 + 203.435z − 26.764z 2 + 1.4247z 3 , Table 2 Comparison of gravity gradient tensors calculated by our new closed-form solution and derivatives of García-Abdeslem's (2005) gravity field solution for the prismatic body given in Fig. 2 The relative errors of the tensor trace are calculated as |T xx + T yy + T zz |∕(|T xx | + |T yy | + |T zz |) . Symbol (−) indicates no solution available
Observation sites (km) Component
Our solution (s −2 ) Derivatives of García-Abdeslem's solution (s −2 ) Above the top face (12, 12, -0.001) Above a corner(20, 10, − 0.001) 3.77463390064588E-13 -elements T xz , T yz , T zz , because only g z is given in García-Abdeslem (2005). Excellent agreement is obtained between these two approaches, with relative errors on the order of 10 −13 % at site (12, 12, − 0.001) km and relative errors 10 −6 -10 −7 % at site (20, 10, − 0.001) km. The tensor trace T xx + T yy + T zz = ∇ 2 � U is a useful tool for indicating numerical error. Using the Poisson equation ∇ 2 � U = −4 G for the gravitational potential U, the tensor trace vanishes outside the source body and is −4 G inside the source body. Since our observation points are outside the source body, we have calculated the relative errors shown in Table 2 as |T xx + T yy + T zz |∕(|T xx | + |T yy | + |T zz |) . The relative error of the tensor trace is 2.74 × 10 −13 % at the location above the top face and 3.77 × 10 −11 % above the corner. Both our solution and García-Abdeslem's (2005) solution for the GGT are singular, when observation sites are located on edges and corners. When the observation site gets close to the corner, the mathematical singularity becomes stronger, and this is, why the relative error is larger at site (20, 10, − 0.001) km.
In summary, the excellent agreement of the different solutions has successfully verified the accuracy of our new closed-form solutions for a polyhedral body with depth-dependent polynomials up to and including cubic order.
An Irregular Polyhedron with Linear Density Contrast
An irregular polyhedron is tested to compare our solution with Holstein's (2003) solution for linear media. The target model is composed of 8 faces and 10 points (Fig. 3), originally designed by Holstein et al. (1999, Appendix A). The linear density contrast inside the target body is taken from Holstein (2003, Appendix C), that is where the density is in kg∕m 3 , coordinates are given in km. First, we compute the gravity gradient tensor at two test points, an outer point with coordinates of (0, 0, 0) km and an interior point at the centroid ̄ = 1 88 (40, 250, −1541) km. The results are shown in Table 3. An excellent agreement is obtained between our closed-form solution and Holstein (2003)'s solution.
Second, the gravity gradient anomalies are evaluated on a vertical profile passing through the target body, in order to validate our formulas by the well-known Poisson equation ∇ 2 � U = −4 G , where U is the gravitational potential. The vertical profile is from a point with coordinates of (0, 0, − 34) km to a point with coordinates of (0, 0, 0) km, with observation points placed at a uniform vertical (z) spacing of 1 km. Due to the discontinuity of the gradient tensor signals on the boundary, we only perform tests at two points which are very close to the boundary (with a small distance of ± 10 −10 km). The value of Fig. 4 a Comparison of the Laplacian term T xx + T yy + T zz calculated by our new closed-form solution to the reference value of −4 G for the causative body in Fig. 3. b Relative errors calculated as −4 G is used as the reference. The relative errors are calculated using the formula (T xx +T yy +T zz )+4 G |T xx |+|T yy |+|T zz | and shown in Fig. 4. The maximum absolute relative error is 3.08389 × 10 −13 %.
A Tetrahedral Mass Body with Horizontal and Vertical Density Contrasts
In practical gravity exploration, the underground mass bodies can have complicated shapes. To calculate their GGT signals, we generally need to discretise the underground mass bodies into sets of disjoint elements with different shapes, such as structured hexahedral or prismatic elements and unstructured tetrahedral elements. Compared to regular prismatic elements, unstructured tetrahedral elements can well approximate arbitrarily complicated anomalies. Using recent Delaunay triangulation techniques (e.g., Si 2015), geophysicists can easily set up discretised triangulated grids to represent complicated anomalous targets. Therefore, unstructured grid techniques have been widely used in the geophysical community, not only in gravity exploration (Jahandari and Farquharson 2013;Ren et al. 2017c), but also in the electromagnetic induction community (Li and Key 2007;Schwarzbach et al. 2011;Ren et al. 2013) and the seismic imaging field (Lelièvre et al. 2011). Testing a single tetrahedral mass body not only aims to test the performance of our new closed-from solutions for complicated density contrasts (horizontal and vertical density contrasts), but also demonstrates its compatibility with other codes developed for unstructured grids. This verification is an important step towards joint inversion for multiple parameters, such as magnetisation vector, conductivity, velocity and mass density on the same unstructured gird. The geometry of the tetrahedron is shown in Fig. 5. We used the density contrast as given in D'Urso and Trotta (2017, equation 166), which includes horizontal variation of the density contrast where the unit of the density is kg /m 3 and the units of the coordinates are km. A measuring plane is located above the tetrahedron in a range of x = [− 0.16, 0.16] km, y = [− 0.16, 0.16] km and z = 0km. On the measuring plane, the gravity gradient tensors were computed using our closed-form solution and a Gaussian quadrature rule with 512 × 512 × 512 points. The high-order Gaussian quadrature rule was built by projecting the tetrahedral element into a hexahedral element (Rathod et al. 2006) and then applying the standard Gauss Legendre Quadrature rule (Golub and Welsch 1969). Since there are no published results of GGTs for high-order polynomial density contrasts, we compare to results computed by high-order Gaussian quadrature as the reference solutions. The six components of the gravity gradient tensor are shown in Fig. 6 for our analytical solutions and in Fig. 7 for Gaussian quadrature solutions. The relative errors with regard to the Gaussian quadrature solution are shown in Fig. 8. Clearly, the results from our analytical solution agree quite well with those computed by the Gaussian quadrature rule, with absolute relative errors less than 2 × 10 −7 % . The computation time for the analytical solution is about 0.9 s, and the computation time for 512 × 512 × 512 points Gaussian quadrature is about 3.7194 h.
Furthermore, the sums of the diagonal entries T xx + T yy + T zz were computed using the analytical solution. When the observation site is outside the source region, as in our example, these three diagonal entries satisfy the Laplace equation, that is ∇ 2 U = T xx + T yy + T zz = 0 , thus providing an independent means of verification of our closed-form solutions. As shown in Fig. 9, the relative error |T xx + T yy + T zz |∕(|T xx | + |T yy | + |T zz |) has a maximum value of 3.26 × 10 −12 % . Therefore, the condition of ∇ 2 U = T xx + T yy + T zz = 0 is satisfied by our solutions. Next, with the aim of testing the case of cross-varying mass contrasts, the following density contrast and the same tetrahedral body as shown in Fig. 5 were used: where the unit of density is kg /m 3 and the units of the coordinates are km. This density contrast function simultaneously varies in both horizontal and vertical directions, ranging from about −300 to 400 kg/m 3 . A measurement profile is positioned at x = [−0.2, 0.2] km, y = 0 km and z = 0 km. The six components of the gravity gradient tensor were calculated by our analytical formulae and Gaussian quadrature with 512 × 512 × 512 points as shown in Fig. 10. The relative errors of our solutions with respect to the Gaussian quadrature solutions are shown in Fig. 11. Note, that the maximum absolute relative errors are less than 5 × 10 −8 %. Therefore, the results computed by our analytical solution have excellent agreement with those computed by high-order Gaussian quadrature, for this case where the density contrast simultaneously varies in both horizontal and vertical directions. Furthermore, a test was conducted with observations sites located on a path at x = [−0.05, 0.05] km, y = 0 km, z = 0.04 km crossing the interior. For this setup, the tensor elements and traces T xx + T yy + T zz were calculated. An excellent agreement between the computed traces of the gravity gradient tensor and the reference value of −4 G (see Fig. 12) is obtained, which clearly verifies our closed-form solution.
Numerical Stability
Theoretically, closed-form solutions should be accurate. However, due to the limitations of floating point arithmetics, these solutions contain inevitable rounding errors, when the amplitudes of the gravity signals approach zero. This phenomenon has been observed in previous studies (Holstein and Ketteridge 1996;Holstein 2003;Holstein et al. 2007a), which shows that the relative error of the gravity field will grow, when the distance between the mass target and the observation site approaches infinity. We use relative error here to avoid discussion of anomaly magnitude and instrument-dependent measurement accuracy. Beyond a certain distance, gravity anomalies can even be totally corrupted by floating point errors. Assuming that the mass body has a size of and the site-to-target distance is denoted by , the growth of the relative error satisfies the following formula (Holstein and Ketteridge 1996;Holstein 2003;Holstein et al. 2007a): where is defined as = ∕ , −1 is referred to as the dimensionless target distance, the exponent indicates the speed of error growth, and is the relative error when 1 = 1 , identified in these papers as the floating point machine precision constant. On a log-log scale plot, Eq. (25) is a linear error growth curve lg( ) = lg 1 + lg( ). We present an experiment, in which the tetrahedron shown in Fig. 5 was used to recover the above linear error growth curve. The size of the tetrahedron was set to the diameter of its circumscribed sphere, that is = 0.134 km. The starting point for the profile is located at the centre of the circumscribed sphere of the tetrahedral model. The measuring profile is formulated as x = y, z = 0 km.
Following Holstein et al. (2007a), the equivalent mass point approach was adopted as the reference solution, by which the tetrahedral body is approximated by a point source located at the centre of the circumscribed sphere. The anomalies calculated by our new closed-form expressions and by the equivalent point-mass approach are denoted by T and T point , respectively. In addition, the relative errors were computed. Four polynomial density contrasts of different orders were used for the stability test: where the unit of the density contrast is kg/m 3 and the units of the coordinates are km. The ranges of density contrasts are about 200-1800 kg/m 3 for the linear case, about 40-2200 kg/m 3 for the quadratic case and about 10-2250 kg/m 3 for the cubic case.
(26) 0 = 1000, (27) 1 = 10,000(x + y + z), (28) 2 = 100,000(x 2 + y 2 + z 2 + xz + yz + xy), (29) 3 = 1,000,000(z 3 + yz 2 + y 2 z + y 3 + xz 2 + xyz + xy 2 + x 2 z + x 2 y + x 3 ), The relative error curves are shown in Fig. 13 for the above four density contrasts. In Fig. 13, at low dimensionless target distances −1 , the relative errors decrease with increasing −1 , because the tetrahedron is better approximated as a mass point with increasing distance. However, the gravity anomalies are progressively corrupted with increasing target distance, and when the dimensionless target distance ( −1 ) is beyond a critical value, the rounding error in the unstable calculations exceeds the solution difference, resulting in a rising trend of the relative errors (cf. Holstein 2003). We reconstructed the entire error growth curves by extrapolating the ascending parts back to ( −1 = 1, = ) . In Fig. 13, the reconstructed curves are denoted by blue dashed lines. We observe that there are two linear error growth trends for each polynomial density contrast, that is T zx and T zy share the same error growth behaviour, and T xx , T yy , T zz and T yx share the same error growth behaviour. The estimated values of are shown in Table 4. Using the estimated , the critical dimensionless target distances were calculated from that value of −1 where the rising part of the error curve indicates a relative error of 1 (Holstein and Ketteridge 1996;Holstein 2003;Zhou 2010) in Eq. (25), which are also shown in Table 4.
As shown in Table 4, for the linear density case, is about 4.31 for T xx , T yy , T zz and T xy and about 5.28 for T xz and T yz , which agrees with previously estimated values of 4 and 5 given by Holstein (2003), respectively. As indicates the error growth speed, Table 4 and Fig. 13 also show that the higher the order of the density contrast polynomial is, the faster the errors accumulate. At the critical dimensionless target distance, the accumulated rounding error has the same amplitude as the true anomaly. Therefore, to safely use our exact solution, we should require our observation sites to be located in a range, which is less than the critical value 1 crit . Finally, interested readers are referred to Table 4 for the specific dimensionless site-target distances for the constant, linear, quadratic and cubic density cases.
Conclusions
In the presented work, we have derived a set of closed-form solutions for the gravity gradient tensor of an arbitrary polyhedral body. The density contrast of the polyhedral body is represented as a polynomial function of up to and including third order. The polynomial function allows density contrasts to simultaneously vary in both horizontal and vertical directions. To our best knowledge, this is the first time that analytical solutions of the gravity gradient tensor are derived for an arbitrary polyhedral body with polynomial orders up to three. Three synthetic models (a prismatic body, an irregular polyhedron and a tetrahedral body) were used to test the correctness and the efficiency of our newly developed closed-form solutions. By comparing to published closed-form solutions and high-order Gaussian quadrature solutions, the high accuracies of our solutions with deviations of less than 2 × 10 −7 % from the Gaussian quadrature solutions were demonstrated. The computation time used by our analytical solution is significantly less than that of the high-order Gaussian quadrature. The numerical stability tests show that, when dealing with cubic density contrast, our closed-form solutions would generate inaccurate solutions, if the dimensionless target distance, which is defined as the ratio of the distance between the observation site and the causative body to the dimension of the causative body, is larger than about 282 for T xx , T yy , T zz , T yx components, and 137 for T zx , T zy components, on a profile with x = y and z = 0 m . This inaccuracy problem is caused by the limited precision of floating point operations, when the amplitudes of the gravity gradient tensors are very close to zero. However, since most applications of gravity gradient tensors aim at detecting anomalous bodies in the very shallow subsurface of the Earth, this problem of precision loss should not cause a serious problem in practical situations, and our closed-form solutions are very safe to be applied in exploration geophysics.
For the cases of constant, linear, second and cubic polynomial order, there are systematic recurrences of previously evaluated integrals as well as occurrences of new types of integrals, when going to higher polynomial order. We assume that this is a trend even for higher polynomial orders ( P > 3 ). However, extending our work to higher polynomial orders will be a nontrivial task as more and more complicated integrals need to be considered. By contrast, using the analytical expressions presented in this study, closed-form solutions for the magnetic field can be derived easily.
Furthermore, the outward pointing unit vector ij of edge C ij and face H i is calculated as: Let us establish a local Cartesian coordinate system, the origin of which is located at the observation point ′ , that is � = (0, 0, 0) . Symbol = (x, y, z) represents an arbitrary source point in the polyhedron, and R = | − � | is the distance from a source point to the observation point. The three unit vectors ̂ i , ̂ ij and ̂ ij form a natural orthonormal basis on edge C ij so that projections of vector ( − � ) along these three unit vectors yield a set of local coordinates (m ij , s ij , h i ) on edge C ij , which are calculated as: In Fig. 1, point ij is the projection of ′ onto edge C ij . Vector ij = ij − � is pointing from the observation site ′ to point ij with its magnitude being denoted by L 0ij . Since − � = ij + ( − ij ) and ij ⋅̂ ij = 0 , the 1D parametrised local coordinate s ij along edge Using the fact that � = (0, 0, 0) , the above equation becomes: Meanwhile, the distance R = | − � | , from any source point on edge C ij to the observation site, is parametrised as The distances from ′ to the vertices 0ij and 1ij are defined as where s 0ij and s 1ij are the parametrised coordinates of the vertices 0ij and 1ij , respectively. In addition, in Fig. 1, i is the projection of the observation site ′ on the i-th face H i , and ̂⊥ ij = ij − i | ij − i | is the unit vector which points from point i to point ij . The direction of ̂⊥ ij can be either identical or opposite to the direction of the outward normal vector ij on edge C ij . i = | − i | is the distance between the projection centre i and a source point ∈ H i .
Appendix B: Gravity Gradient Tensor due to Constant Density Contrast
Using the local coordinate s ij = ( − � ) ⋅̂ ij along edge C ij and using Eq. (A.6), the line integral in (15) can be converted into a definite integral: where ds ij is a differential of variable s ij . The values of s ij at the vertices 0ij and 1ij are s 0ij and s 1ij , respectively. The analytical solution of the above definite integral can be looked up in integral tables (equation 2.261 in Gradshteyn and Ryzhik 2007): When the observation site lies on the extension of edge C ij ( L 0ij = 0 and s 1ij ⋅ s 0ij > 0 ), the integrand function is reduced to 1 |s ij | which leads to the second formula of Eq. (B.2). We should note that when the observation site ′ is located within the edge C ij ( L 0ij = 0 and s 1ij ⋅ s 0ij ≤ 0 ), the linear integral term ∫ C ij 1 R dl has a weak logarithmic singularity. Therefore, we cannot compute gravity gradient tensors, when the observation site is located on an edge of the polyhedral body.
The analytic expression for the surface integral term ∬ H i 1 R 3 ds in Eq. (15) can be derived using the result from Ylä-Oijala and Taskinen (2003) and Ren et al. (2017b) where where the variables m ij , s 1ij , s 0ij , L 0ij , h i , R 1ij and R 0ij are shown in Fig. 1 and all can be calculated in terms of the coordinates of the vertices of the polyhedral body. In Eq. (B.3), the observation site cannot be located on the surface H i of the polyhedral body. In the case that the observation site is located on the surface of the polyhedral body, � ∈ H i , the gravity gradient tensor is singular, which is an inherent feature of gravity gradient tensor (Li and Chouteau 1998;Holstein 2003).
Appendix C: Gravity Gradient Tensor due to Linear Density Contrast
First, we consider the line integral term ∫ C ij R dl in Eq. (16). Using the parametrisation in Second, we deal with the surface integral term ∬ H i R 3 ds . Using the identity ∇ 1 R = � − R 3 , our previous assumption � = (0, 0, 0) , and Eq. (6) (setting f = 1 R ), we have where ∇ s denotes the surface gradient operator, and the surface gradient theorem is used (Tai 1997 where as shown in Fig. 1, i is the projection point of the observation site onto the plane containing the face H i , i = | | − i | | , is a running integral point on edge C ij , i.e., ∈ C ij . Using the geometrical variables given in Fig. 1, we have (Ren et al. 2017a): 14 Illustration of the angular extent ij ( i ) subtended by edge C ij of polygon H i and the total angular extent ( i ) = 2 , when i ∈ H i When m ij → 0 , the limit of Eq. (C.5) exists and is equal to zero. Therefore, there is no singularity in Eq. (C.5). As demonstrated in Fig. 14, ( i ) is the angular extent of the arc region lying within a plane containing surface H i and centred at i . Note that the projection point i can be located inside, outside or on edges of the surface H i . The value of the extent angle ( i ) depends on the geometrical relation between i and the polygon H i . When point i is located inside H i , ( i ) = 2 ; when point i is located on an edge of the polygon H i but not at a vertex, ( i ) = ; when point i is located at a vertex, ( i ) is the angle enclosed by its two adjacent edges. To avoid judgement of the geometrical relation between point i and the polygon H i and facilitate programming, the angle ( i ) can be expressed as a sum of the angles subtended by each edge of the polygon H i (Wilton et al. 1984, page 278): where the contribution of each edge is calculated as to the angular extent ( i ) vanishes, when point i is on the edge C ij or on its extension, and otherwise the sign of ij depends on whether unit vector ̂⊥ ij is identical or opposite to unit normal vector ̂ ij . Finally, the closed-form solution for gravity gradient tensor 1 in Eq. (13) caused by a linear density contrast can be obtained by substituting Eqs. (C.4), (C.3) and (C.1) into Eq. (16).
Appendix D: Gravity Gradient Tensor due to Quadratic Density Contrast
First, using Eqs. (A.5) and (14), the line integral ∫ C ij Using the integral x p x q R 3 ds, (p, q = 1, 2, 3 ). In view of the assumption that � = (0, 0, 0) , we have Using the above equation and the following divergence vector identity: x p x q R 3 can be transformed as (setting = x p̂ q and = 1 R ): Integrating the above equation over the face H i and applying the surface divergence theorem (Tai 1997 where ∫ C ij x p R dl =̂ p ⋅ ∫ C ij R dl and ∬ H i x p R 3 ds =̂ p ⋅ ∬ H i R 3 ds . The analytic solutions for the linear integral ∫ C ij R dl and for the surface integral ∬ H i R 3 ds have already been given in Eqs. (C.1) and in (C.3), respectively. Furthermore, the surface integral term ∬ H i 1 R ds has been derived in Eq. (C.4).
Third, the surface integrals ∬ H i x p R ds for (p = 1, 2, 3) are the three components of the vector surface integral ∬ H i R ds , that is ∬ H i x p R ds =̂ p ⋅ ∬ H i R ds . Using our assumption � = (0, 0, 0) , Eq. (6) (setting f = R ) and the surface gradient theorem (Tai 1997), the integral ∬ H i R ds can be calculated as (Ren et al. (2017a) where the analytic solution for the integral term ∬ H i 1 R ds has been given in Eq. (C.4).
Appendix E: Gravity Gradient Tensor due to Cubic Density Contrast
First, we deal with the line integral ∫ C ij x p x q x t R dl . In terms of the definition of the local coordinate s ij , which is given in Eq. (A.5), we obtain Integrating the above equation over the face H i and applying the surface divergence theorem (Tai 1997) to the first term on the right-hand side, we get where the line integral ∫ C ij x p x q R dl can be analytically evaluated using Eq. (D. x p x q x t The surface integral ∬ H i Rds in Eq. (E.5E.6) can be calculated using the result of Ren et al. (2017a, equation 33). This means where the calculation of the angular extent ( i ) has been presented in Eq. (C.6), and When m ij approaches zero, the last two terms in Eq. (E.9) approach zero. Fourth, volume integral ∭ H x p R dv can be calculated from Ren et al. (2018, equation 24) where the surface integral ∬ H i Rds is given in Eq. (E.8). | 11,233.6 | 2018-09-01T00:00:00.000 | [
"Geology",
"Physics"
] |
Modelling of Tilting and Steering Control System for a Tadpole Three-Wheeled Vehicle
Three-Wheeled Vehicle with tadpole configuration is a vehicle which has one wheel at the back and two wheels at the front. This vehicle promotes and ensures safety as it does in a four-wheeled vehicle. However, the vehicle’s dynamics is much different compared to a four-wheeled vehicle. The objective of this research is to find the relationship between turning, steering, and tilting of this vehicle to ensure and determine the safety limit of it. Physics modelling and iteration is done to see how each of the three components (turning, steering, and tilting) could affect each other.
Introduction
Three-wheeled vehicle is that operates on three wheels. Three-wheeled vehicle has two configurations which are the tadpole configuration and the delta configuration. A tadpole configuration is a threewheeled vehicle which has two wheels upfront and one wheel at the back while the delta configuration has two wheels at the back and one wheel at the front. The dynamics system of a tadpole configuration three-wheeled vehicle could be very complicated. Few matters to be considered are the turning and tilting system, components such as: turning radius, center of gravity, wheel track, etc. This components are then can be measured and determined to ensure the ride safety of the vehicle. For a three-wheeled vehicle to maintain its stability, a tilting system is developed. Tilting was meant to lower the CG so the maximum lateral acceleration that could be taken by the vehicle is higher which results in better stability, and so is the safety. The modelling aims to represent the physics that is happening with the vehicle for future research in active steering and tilting assist for a three-wheeled vehicle.
Design and constraint
The design that was made to be the test subject for this research is a three-wheeled vehicle with a one rear wheel and two front wheels, a tadpole configuration, with a wheelbase of 1050 mm and a trackwidth of 625 mm. The center of gravity point is located at the center of its y-axis, measured to be 441 mm from the front and 609 mm from the back with respect to the x-axis, and has a height of 825 mm from the ground. The vehicle has a total weight of 74 kg without any driver on it, and test was done with a driver with weight of 52 kg.
Geometric 'bicycle' modelling
The basic vehicle model is by using the 'Bicycle' model which refers to the model made by Milliken and Milliken. This 'Bicycle' model represents the whole vehicle as a bicycle, where the front system is represented as a single-wheel model and so is the rear. The definition of this model includes no load transfer either on longitudinal or lateral axis, no roll, pitch, and yaw motion, constant velocity, no aerodynamic effects, no vehicle chassis and suspension compliance effects, and full position control. These assumptions are justified since the purpose of this model is to study the basic motion of the vehicle.
Figure 3. Bicycle geometric model by milliken and milliken
This model does not include roll as it's degree of freedom but we could calculate the lateral force that is undergoing when the vehicle is turning to be used later for the rollover equation. The lateral force (2) In the lateral force equation, we replace the "m" with the total weight of the vehicle (W T ) divided by gravity (g) and "a" with velocity (v) divided by the turning radius of the vehicle (R) in equation (1), respectively, with: Consider for the moment only the steady-state condition of constant angular velocity; it is in this steady-state condition (or as close to it as can be reasonably approximated on a skidpad) that the maximum lateral acceleration level is to be obtained. Therefore, in this limited case, the matter reduces to just a consideration of how the lateral forces are influenced by the weight and center of gravity ("Iα = 0"). We get: Now solve for "F f ", and "F r ", then substitute "W f " for "Wt L b /L", "Wr" for "Wt L a /L", and "a y " for "v 2 /gR":
Two-dimensional weight transfer
In the static case the normal loads would be equal, force at inner tire equals to outer tire (Ni = No). However, it is not the static case, but that of dynamic equilibrium in a steady-state turning situation, in which we are interested. In such a case, a "weight transfer moment" occurs which alters the lateral force generation potential by decreasing the normal load on the tire closest to the turn center ("inner tire") and increasing, by an equivalent amount, the normal load on the tire furthest from the turn center ("outer tire"). When the vehicle is turning right, the outer tyre has the most weight transferred to it. The difference of normal load that is occuring on both tires could be represented by:
Rollover condition
A rollover condition is where the vehicle is turning over if it exceeds some speed. By calculating from formula (9), the speed could be used to determine the height needed in order to turn safely where the height is later translated to the tilt angle. The rollover equation is as follows: (10) where if the undergoing acceleration of the vehicle in equation (4) exceeds the maximum rollover acceleration in equation (10), the vehicle would roll over and safety is compromised. Figure 4 shows the control diagram for the dynamic using the steering and tilting input of the system. The steering input is used to determine the turning radius that the vehicle is having while the tilting input is extracted from the linear potentiometer sensor which goes in the suspension system. Where in the iteration, the rollover acceleration should be less than the lateral acceleration or a rollover condition would happen. Therefore, everytime the lateral acceleration is increasing due to a rise in speed or a reduction in the turning radius (see equation (4)), a new tilting angle has to be set in order to reduce the center of gravity in order to also reduce the height difference (h cg ) in the vehicle.
Results and discussion
The turning radius that is used for this iteration is varied between 0 meter to 4 meter maximum, while the speed is also varied at 30 kph maximum. The iteration is done by using the diagram in figure 4 above.
Lateral acceleration
The results of the lateral acceleration occurred on the vehicle where the speed is varied between 2 -30 kph and the turning radius is varied between 1 -4 meter is on the figure 5. below, Figure 6. Lateral acceleration results to undergone speed with varied radius The resulted lateral acceleration keeps going down in terms of increasing turning radius. Where if the speed is increasing, the lateral acceleration would also increase.
Rollover condition and maximum center of gravity
The results of the maximum lateral acceleration which is calculated based on the test subject compared to the varied turning radius and speed on the vehicle results in a table on figure 6. below. The maximum turning speed on a normal road which has an approximate turning radius of 4 meter is 22 kph and on the smallest road possible which has 1 meter turning radius is below 10 kph. If the condition goes onto the red zone, beyond the speed limit, the vehicle would result in a roll over condition.
The diagram shows where the CoG should be located within the vehicle (from the ground). The color shown by purple is unsafe for the vehicle to turn as it is much safer for a vehicle to turn at a low speed in a long turning radius. The brighter the color, the safer the vehicle will turn.
Conclusions
Based on the modelling and iteration that has been done, the following conclusion could be drawn: The higher the speed that a three-wheeled vehicle with a tadpole configuration is in when turning, the higher the lateral acceleration on the vehicle it would be. The smaller the turning radius on a normal road, if it is going with the same speed respectively, the higher the lateral acceleration. Higher Center of Gravity will result in smaller maximum lateral acceleration where if the lateral acceleration exceeds the maximum, a roll over accident would happen On a normal road condition with a turning radius of 1 -4 meter, a vehicle speed when turning is maximum compromised at 22 kph. | 1,986.6 | 2020-04-01T00:00:00.000 | [
"Engineering"
] |
Spatial Linear Mixed Effects Modelling for OCT Images: SLME Model
Much recent research focuses on how to make disease detection more accurate as well as “slimmer”, i.e., allowing analysis with smaller datasets. Explanatory models are a hot research topic because they explain how the data are generated. We propose a spatial explanatory modelling approach that combines Optical Coherence Tomography (OCT) retinal imaging data with clinical information. Our model consists of a spatial linear mixed effects inference framework, which innovatively models the spatial topography of key information via mixed effects and spatial error structures, thus effectively modelling the shape of the thickness map. We show that our spatial linear mixed effects (SLME) model outperforms traditional analysis-of-variance approaches in the analysis of Heidelberg OCT retinal thickness data from a prospective observational study, involving 300 participants with diabetes and 50 age-matched controls. Our SLME model has a higher power for detecting the difference between disease groups, and it shows where the shape of retinal thickness profiles differs between the eyes of participants with diabetes and the eyes of healthy controls. In simulated data, the SLME model demonstrates how incorporating spatial correlations can increase the accuracy of the statistical inferences. This model is crucial in the understanding of the progression of retinal thickness changes in diabetic maculopathy to aid clinicians for early planning of effective treatment. It can be extended to disease monitoring and prognosis in other diseases and with other imaging technologies.
Introduction
Diabetes is a major global health challenge. It affected approximately 463 million people (9.3% of the world's population) in 2019, and this figure is estimated to rise to 700 million (10.9% of the world's population) in 2045 [1]. Diabetic retinopathy (DR) is a common complication of diabetes, affecting approximately one-third of people with diabetes [2]. DR is the leading cause of visual loss in working age adults with visual loss caused by proliferative DR or Diabetic Macular Oedema (DMO). Where disease affects the central macula, a loss of central vision occurs with potentially severe quality of life impacts. In England in 2010, 7.12% (166325) of people with diabetes had DMO in one or both of their eyes, and 40% of DMO patients had clinically significant DMO with visual acuity poorer than 6/6 (Snellen) in at least one eye [3]. DMO is caused by an accumulation of fluid (oedema) in the macula thought to be secondary to vascular leakage. It involves retinal thickness changes in the macula. It has been identified that macular thickness is associated with visual loss [4,5]. OCT is now widely used for the diagnosis and monitoring of DMO as it is able to produce high-resolution cross-sectional images of the retina from which retinal thickness "maps" can be constructed [6]. A data-efficient method for analysis of spatial imaging data and the association between imaging data and clinical data are needed for more effective management of the disease.
Medical images are often divided into several clinically meaningful sectors to facilitate clinical investigations. The macula can be divided into nine subfields as initially described by the Early Treatment of Diabetic Retinopathy Study (ETDRS) research group [7]. These subfields comprise of three concentric circles with radii of 500, 1500 and 3000 µm subdivided into four regions (superior, temporal, inferior and nasal; Figure 1). These subfields are named by their location as the central subfield (CS), superior inner (SI), temporal inner (TI), nasal inner (NI), superior outer (SO), temporal outer (TO), inferior outer (IO) and nasal outer (NO). OCT measurements provide retinal thickness measurements for each of these nine subfields. Such spatial data measured in nine sectors are an example of lattice data in spatial statistics [8]. A key barrier to properly analyse such retinal imaging data is the limited understanding of the relationship between spatially collected data, i.e., spatial correlations. In some analyses only measurements of the central subfield (i.e., CS in Figure 1) are used, and the other measurements are disregarded. If the measurements of all sectors (i.e., all nine sectors in Figure 1) are considered in the analyses, there are two main statistical approaches used to analyse such imaging data. One is to ignore the spatial correlations (i.e., non-spatial approach), and the other is to consider the spatial correlations (i.e., spatial approach). For example, one non-spatial approach is to analyse data separately for each sector, which leads to multiple comparison problems. If the spatial dependency between the measurements of different sectors is not fully analysed, it will affect the precision of estimates, which may produce inaccurate results in statistical tests.
A spatial image analysis approach accounts for the spatial correlations when analysing the data by using spatial statistical models [8,9]. Therefore, a model is required which incorporates spatial information from measurements in all the subfields into the analysis. Such a model could provide valuable information for detecting retinal disease and discriminating between disease severity states [10]. Spatial statistical models have already been applied in other medical imaging contexts, such as functional neuroimaging and cardiac imaging, where spatial correlations are captured. For example, Bowman et al., constructed a spatial statistical model for cardiac imaging from single photon emission computed tomography [11]. They utilised a 20-sector model which considered the correlations among multiple perfusion measurements, in order to detect perfusion change in an individual's left ventricle. Bernal-Rusiel et al. explored the spatial structures in Magnetic Resonance Image data in patients with Alzheimer's disease [12]. Images were segmented into relatively small homogeneous regions, and a region-wise spatial model was developed. However, the application of spatial statistics to ophthalmic images has not yet been extensively studied. Moreover, the advantages of considering the spatial correlations are not fully understood. Hence the development of methods and further studies analysing spatial retinal imaging data are needed.
Another barrier relevant to the analysis of ophthalmic images is the issue of the unit of analysis. Often, the correlation between the two eyes from the same individual is ignored. Treating the two eyes as associated with each other can introduce spuriously small standard errors. Although there is continuing debate regarding this issue and methods are available for adjusting the correlation between the two eyes [13,14], the majority of studies do not take this problem into account when data from both eyes are available. This methodological barrier has not advanced much over the past two decades [15].
In this paper, we present a new statistical spatial inference framework for retinal images and study the effect of the spatial correlations on the analysis of spatial data. This framework is based on a linear nested mixed effects model with a spatial error structure (Gaussian, autoregressive-1, exponential and spherical) for the analysis of OCT imaging data, where correlations between eyes from the same patient and their individual clinical data are adjusted within the model. The model is estimated using restricted maximum likelihood estimation, which provides an unbiased estimation for both the fixed effects and the variance component for the mixed effects model. We compared the performance of our model with multivariate analyses of variance (MANOVA), which is one of the extensions of linear regression models called multivariate linear regression. In addition, we conducted a simulation study to validate our model and study the benefits of using a spatial modelling framework when different levels of spatial correlations exist. This paper is a substantial extension of previously published analysis [16]. In this paper, we refine the parameter estimation method, further validate the approach in a substantially larger clinical dataset, and extend comparisons to a three-group scenario.
The organization of the rest of the paper is as follows. The image dataset and the statistical modelling framework are presented in Section 2. In Section 3, we present results from the real data sets. Simulation setting and simulation results are presented in section 4. Discussion of our work and the conclusions are presented in Sections 5 and 6.
Dataset
The retinal imaging data used in this study are from a prospective observational clinical study (Early Detection of Diabetic Macular Oedema; EDDMO). All participants gave written, informed consent for inclusion before they participated in the EDDMO study, which was conducted in accordance with the principles laid down in the Declaration of Helsinki. Ethical approval was obtained from the UK's Health Research Authority (North West -Preston Research Ethics Committee; REC reference 16/NW/0163; date of approval 31/3/2016). An interim, smaller dataset of 150 participants with diabetes who had been referred from the National Diabetic Eye Screening programme (NDESP) as screen positive to the Royal Liverpool University Hospital recruited at their first hospital visit were included in our analyses [17]. This dataset was used in our previous analysis [16] and will be used here for comparison with the full EDDMO dataset. Approximately 90% of these participants were Caucasians. Participants with diabetes who had co-existing pathologies (1 participant with intracranial lesions, and 4 participants with ocular pathologies) were excluded from the analysis). All participants were examined by an ophthalmologist with slit lamp biomicroscopy and had a dilated fundoscopy examination. All eyes were graded by an ophthalmologist for DR severity based on feature specific grading from the NDESP. Based on NDESP grading criteria, each eye of participants with diabetes was graded as having no evidence of maculopathy (M0) or having evidence of maculopathy (M1) [18]. Overall retinal thickness measurements for both the left and right eyes were obtained by Heidelberg Spectralis OCT. We excluded a small number of eyes that did not have OCT thickness data collected. A summary of the dataset stratified by clinical diagnosis based on slit lamp biomicroscopy is shown in Table 1. Although the measurements of both foveal centre point thickness and central subfield mean thickness are available using OCT, central subfield mean thickness is more commonly used in clinical research when tracking centre-involved DMO [19]. Therefore in the statistical analyses in this paper, we used central subfield mean thickness (CS) instead of foveal centre point thickness. The full EDDMO dataset included data from 50 age-matched healthy controls and an additional 150 participants with diabetes. Self-reported ethnic background revealed that 96% of the healthy participants were Caucasian, and the characteristics of the additional 150 participants with diabetes was similar to the interim sample. Therefore in total, data from 300 participants with diabetes and 50 age-matched controls were available for inclusion. A summary of the full sample from EDDMO study is described in Table 2. We excluded the eyes that did not have OCT thickness data collected. As for the clinical covariates, we included both patient-level demographic and clinical data (including age, gender, duration of diabetes, and smoking history), and eye-level clinical data (including axial length and best corrected distance visual acuity) in our model for three groups comparisons in this paper.
Statistical Model
We propose a spatial linear mixed effects (SLME) model for the spatially collected imaging data. It has the general form described in Equation (1), which is based on a linear mixed effects model with two levels of nested random effects.
In the SLME model, Y ij is the response vector for ith individual in the nested level j of grouping, X ij is the p-dimensional fixed effects vector (e.g., clinical information) associated with β, b i is the first level of random effects (e.g., individual level random effects) associated with Z i , and u ij is the second level of random effects (e.g., eye level random effects nested within each individual) associated with D ij .
where the first level random effect b i is independent of the second level random effect u ij , and ij are within group error representing spatial correlations in the images which are assumed to be independent of random effects. The random effects b i and u ij are assumed to follow Gaussian distributions with variances G 1 and G 2 respectively. In the following sections, we explain the details of the SLME model and the parameter estimation.
Spatial Correlations in the SLME Model
The spatial correlations are used to describe the associations between the sectors of an image. These correlations are organised into a covariance matrix. The covariance matrix Σ s for ij can be decomposed to Σ s = σ 2 s Ψ ij where Ψ ij is a positive-definite matrix which can be decomposed to Ψ ij = Λ ij C ij Λ ij , and σ s is the parameter for residuals. Λ ij is a diagonal matrix and C ij is correlation matrix with parameter γ. In our model, Λ ij is a identity matrix and it is easy to write that cor( ijk , ijk ) = There are a large number of correlations to be estimated if we consider the k, and k as associated, for example, 9 × 8/2 correlations for imaging data of 9 sectors. Such a large matrix can lead to a computationally unstable estimation and can cause matrix inversion problems.
A large number of parameters of the covariance matrix can be reduced via imposing a model of restriction. The spatial correlation cor( ijk , ijk ) is modelled as either lag autoregressive model, Gaussian model, exponential model and spherical model where γ can take the value of γ a , γ g , γ e , γ s respectively. For simplicity, let s k,k denotes the spatial correlations between locations k and k , and let s k,k+1 denotes the spatial correlations between two neighbouring locations k and k + 1 in one single image.
For the lag autoregressive model, the correlation function decreases in absolute value exponentially with lag δ (δ = 1, 2, . . .) model has the form of For spatial structured correlation, let d k,k denotes the Euclidean distance between locations k and k . The Gaussian correlation has the form of, the exponential model has the form of and the spherical model has the form of
Statistical Inference from the SLME Model
The idea is to derive the parameter estimates from the SLME model and then use the parameter estimates and their standard errors to make the inference. In this section, we explain the estimation of the parameters via a frequentist approach. We assume a multivariate normal distribution for Y ij , (1), we aim to estimate the fixed effects (including β), make predictions for random effects (including b i and u ij ) and estimate the variance component (including θ). One of the most common methods is to use maximum likelihood (ML) estimation by maximizing the log-likelihood function, and it can be written as, where C is a constant. By maximizing (6), we can obtain the ML estimates for β [20], and it can be shown that given θ,β However, ML estimation for the variance component θ will be biased downwards because the loss of degree of freedom in estimation for β. In contrast, we consider a restricted maximum likelihood (REML) estimation procedure to obtain less biased estimators for the variance components.
If we estimate the variance component θ via REML, then we can maximise the restricted log-likelihood function with respect to θ as follows, apart from a constant, wherê The REML estimator for θ can be obtained by numerical optimisation algorithms such as Newton-Raphson algorithm [21]. Once we haveθ, we can insert it into (7) and the estimatorβ can be obtained. And the random effects b i and u ij can be predicted using their conditional expectations.
Parametrisation of the SLME for OCT Data
The SLME model can be flexibly parametrised to suit many applications. In our application to analyse the OCT retinal thickness data from EDDMO study, we used a nested linear random intercept model with spatial correlations, which is described as follows, where β is a parameter vector for the fixed effects, b i denotes the random effects for participant i, u ij denote the random effects for j eye in participant i, m is the number of participant and max n i = 2.
Let x ijk denotes the covariate for ith participant from j eye in sector k(k = 1, . . . , 9), and can be further partitioned as where x i represent patient-level demographic or clinical data vector for ith participant; x ij is eye-level clinical data vector for j eye from ith participant, including clinical outcomes such as healthy eye without diabetes, diabetic eye without maculopahty and diabetic eye with maculopathy; sector ijk is a categorical variable from 1 to 9 which represent the 9 sectors in ETDRS grid with central subfield (CS) as a baseline; sector ijk × x i and sector ijk × x ij represent all possible interaction term between sector and patient-level clinical variables, and interaction term between sector and eye-level clinical variables, respectively. The model (10) was fitted using the nlme-R package [22] and the spatial dependency Ψ ij was fitted with structures as described in section 2.3. Missing observations were tested whether they were missing at random and then handled using multiple imputation method in mice-R package [23].
Finding the Correct Parametrisation of the SLME
It is crucial to find a suitable parametrisation of the SLME model, i.e., model selection. There are two commonly used approaches for model selection, namely the Akaike information criterion (AIC) [24] and the Bayesian information criterion (BIC) [25], which are defined as, whereξ is either ML or REML estimates of the parameter vector ξ from the model, p is the dimension of parameter vector ξ, n is the number of observations (but equals n − p when REML estimates are used), and l(·) is the log-likelihood function. The smaller the AIC or BIC is, the better the model is.
As for selecting the best mixed effects model in this paper, we used a top-down method to choose not only the optimal fixed effects but also the optimal random effects [26]. Firstly, we fit a saturated model with a simple covariance structure (e.g., a working independence structure), where all possible covariates and interaction terms are chosen as the fixed effects. Secondly, we investigate the optimal variance structure using AIC, BIC and liklihood ratio tests based on REML estimators. After selecting the optimal random effects structure, we then choose the optimal spatial structure. Finally, we refit the saturated model with the chosen covariance, and simplify the model by comparing the models with nested fixed effects using AIC, BIC and F-statistics based on ML estimation.
Results
We aimed to compare two models for spatial imaging profiles: a MANOVA model and our spatial model. MANOVA models do not utilise spatial correlations because they do not consider the relative spatial location of sectors. Our spatial model is built to explain the spatial imaging profiles, but it also utilised the spatial correlations.
To illustrate the proposed concepts, we used data from the EDDMO study. We conducted analysis on the interim dataset (150 participants with diabetes [16]), as well as the full dataset (300 participants with diabetes and 50 healthy age-matched controls). The demographics and clinical data of the participants included in the analysis are summarised as below (Table 3).
Statistical Spatial Modelling to Explain Whole Thickness Profiles: for Two Patients Groups
Firstly, we illustrate the model concepts using the interim dataset with two patients groups. There are 150 participants with diabetes, and the data included in the analyses are summarised in Table 1. In total, data from 143 participants (i.e., 257 eyes) were used for the model selection, the parameter estimation and the inference. We made pairwise visualisations for mean profiles of retinal overall thickness over nine sectors at the participants' baseline visit ( Figure 2). This shows a large within group variability and it suggest a pattern for the mean profiles of retinal thickness over the nine sectors. We can see that the mean retinal thickness profile of the participants with diabetes with maculopathy (M1) is consistently higher than that of the participants with diabetes without maculopathy (M0), but this difference is quite subtle. MANOVA was applied to study the two disease groups with respect to the OCT thickness data from nine locations of the ETDRS grid. The nine dependent variables were the OCT thickness from the nine locations, and the independent variable was the group, where the group is dichotomous with two levels: with maculopathy or without. The MANOVA did not find a statistical difference between the two disease groups in terms of retinal thickness (p = 0.11 > 0.05). We also considered a Welch's test with winsorized variances for retinal thickness between groups in CS (central subfield), which is one of the most important locations that a clinician will focus on in diagnosis. However we did not find any difference between retinal thickness in the group with maculopathy compared with the group without maculopathy in the CS (p = 0.38 > 0.05). Then we considered the correlations between the two eyes and the spatial correlations between the nine sectors in statistical analyses using our model described in Section 2, which also allows heteroscedasticity between the groups. We investigated different spatial dependency structures described in Section 2.3; an exponential correlation structure was the most informative with the lowest AIC and BIC.
Our SLME model was applied to study the difference between two disease groups. With two levels of random effects model and an exponential correlation structure, we detected the difference in the main effect of diagnosis between the group with maculopathy and the group without maculopathy (p = 0.02 < 0.05). The REML estimates of β 2 (i.e., the effect size between the groups) was 4.50 with standard error equal to 1.96. However, we did not detect a shape effect, which is measured as the interaction term between diagnosis and sector mathematically, between the group with maculopathy and the group without maculopathy (p = 0.97). We also found a negative correlation between age and the mean retinal thickness profile (p < 0.01). We further used a likelihood ratio test to confirm the significance of the eye within the patient random effects (u ij ) in the model (p < 0.01). A detailed description of the model parameters can be found in the Supplementary Materials (Table S1).
In summary, we utilised spatial correlations and the whole imaging profile in the presented example above via the SLME model. It showed that the two disease groups are different (p = 0.02). Although the MANOVA approach also explains the imaging profiles, it does not utilise the spatial correlations, and it did not find the difference between the two groups (p = 0.11).
Statistical Spatial Modelling to Explain Whole Thickness Profiles: Full EDDMO Dataset with Three Participants Groups
Next, we compare the analysis approaches to imaging data on the full EDDMO dataset (300 diabetic participants and 50 healthy participants). The data used in this section are summarised in Table 2. In total, data from 340 participants (i.e., 624 eyes) were used for the model selection, the parameter estimation and the statistical inference. Figure 3 shows the profiles of retinal overall thickness for healthy eyes, eyes with maculopthy and eyes without maculopathy in the nine ETDRS subfields. In this figure, we can see that the spatial profiles of the healthy eyes are similar, and it has a relative smaller variability in each of the nine locations. By contrast, the participants with diabetes have much larger within group variabilities. Moreover, some eyes in the maculopathy group have very high retinal thickness measurements in some specific sectors. These high retinal thickness measurements are a part of the usual range of retinal thickness in this patient cohort. Therefore, data from these participants are not treated as statistical outliers and is included for modelling purposes.
We plotted the mean retinal thickness profiles over nine sectors among the three groups in one figure (Figure 4) with an enlarged y-axis scale. The mean retinal thickness profile of the group with maculopathy is consistently higher than that of the group without maculopathy. In contrast, the difference between the healthy controls and all participants with diabetes (both with and without maculopathy) is very small. Interestingly, the profile of the healthy participants is in between of the profiles of the participants with diabetes without maculopathy and participants with diabetes with maculopathy. Pairwise group comparisons using MANOVA was performed with respect to the OCT thickness data from nine locations of the ETDRS grid. It returned statistically significant results (all three p values are smaller than 0.01) between the three groups of participants (healthy, eyes with maculopathy, eyes without maculopathy) over the nine sectors. Using the fitted MANOVA model, we further assessed which of the nine locations showed significant differences across disease groups in terms of OCT measurements -in the pairwise group comparisons as a follow-up analysis of MANOVA. It shows that, for all sectors, the thickness was significantly different (p < 0.01) between the eyes with maculopathy and the eyes without maculopathy. In contrast, eyes with maculopathy have thinner retinal thickness than healthy eyes only in SI and II sectors (both in imputed full data and in the original full data, p < 0.05). Pairwise comparison of eyes with maculopathy and healthy eyes also showed that the retinal thickness in the central subfield (p = 0.017 < 0.05) and temporal outer subfield (p = 0.016 < 0.05) is significantly thicker in the eyes with maculopathy. Regarding our SLME model, we also found statistically significant difference among the three groups (p = 0.0057 < 0.05). Our model confirms that there is a correlation between two eyes from the same patient (p < 0.01), and the variability among the three groups are different (p < 0.01). Moreover, an exponential correlation structure gives the most informative model, and is able to detect a shape effect, which is measured as the interaction term between diagnosis and sector mathematically. Using the model selection strategy for fixed effects described in Section 2.6, we also found that age is negatively correlated with the mean retinal thickness profiles (p < 0.01). The REML estimates for age, variance component REML estimates for random effects and residuals, and heteroscedasticity range in the final selected model are summarised in Table 4. A more detailed description of all the model parameters can be found in the Supplementary Materials (Table S2). We also used the SLME model to make pairwise comparisons among the three groups. As shown in Table 5, there is significant difference between eyes with maculopathy and eyes without maculopathy group in terms of the main effect of diagnosis, but there was no difference between participants with diabetes and healthy participants. However, there is a shape effect detected, which is measured as the interaction term between diagnosis and sector mathematically, between the eyes with maculopathy and the healthy participants (p < 0.01), as well as between the eyes without maculopathy and healthy participants (p < 0.01) ( Table 6).
Simulation
In previous sections, we illustrated differences between MANOVA and our SMLE model in a real-world dataset. To give a further understanding of the performance of our spatial model, we carried out a simulation study to investigate the importance of incorporating spatial correlation in the statistical imaging analyses. We simplified the nested random intercept model (11) into a one-level random effects model with spatial exponential correlation. Covariates were chosen based on statistical analyses results from the EDDMO study, including nine locations from the ETDRS grid and a negatively continuous correlated risk factor (e.g., age). Only two disease groups (e.g., maculopathy group versus no-maculopathy group) were considered in the simulation as we are interested the effect of the spatial correlation and the power of our model rather than the clinical outcomes. The aim of our simulation study was to establish how well the spatial approach is able to estimate the risk factor and to test the difference between the diagnosis group in terms of the main effect and the shape effect.
In order to investigate how the spatial correlation can change the statistical inferences, we set three simulation scenarios in this section, one without correlation, one with a moderate (γ e = 0.5) and the other with a high correlation (γ e = 0.1) structure between different locations. Sample size were chosen as n = 200 participants with one eye per individual where 70% of the eyes do not have maculopathy and 30% of the eyes have maculopathy. All the simulation results in this section are based on 1000 Monte Carlo replications. The simulation results including the true parameter values, sample size, Monte Carlo standard deviation, the mean of standard error estimates, the coverage probabilities for the estimates and the power to detect the shape effect are reported in Tables 7-10. We compare results of our spatial model and a non-spatial model, which is a general linear model.
Firstly, we simulated a Scenario 1 with no correlation between the spatial thickness values. As expected, our SMLE model performed the same as the non-spatial model (Table 7) because there is no correlation between the spatial data. The parameter estimates were both practically unbiased, the Monte Carlo standard deviation agreed with the mean of standard error estimates, and the coverage probability was around 95%, which is reasonable.
Next, we simulated Scenarios 2 and 3, with moderate and high correlations between the spatial data, respectively. In Scenario 2, we used moderate exponential correlation with γ e = 0.5, and in Scenario 3, we used a high exponential correlation with γ e = 0.1 (Tables 8 and 9 respectively). As shown in Table 8, there is a lower coverage probability in the non-spatial approach compared to our spatial approach. When higher spatial correlations exist, the coverage probability for the estimates of β 1 is much worse (Table 9). Using our spatial approach, the estimates of the parameters were practically unbiased with a reasonable coverage probability both in the moderate correlation setting and the high correlation setting. As expected, when there is no correlation between the simulated data as reported in Table 10, the spatial approach and the non-spatial approach were the same in detecting the shape effect (i.e., the interaction term). However, our spatial approach performed much better in the other two correlation settings. Table 7. Simulation studies: Scenario 1: no correlation between simulated spatial data (n = 200).
Non-Spatial Approach Using Linear Regression
Spatial Approach Using SLME Model
Discussion
We used our SLME model to analyse the retinal thickness data in all nine subfields of the ETDRS grid from the EDDMO study. This approach is capable of incorporating spatial correlations in images, and investigating important information from subfields outwith CS in contrast to using CST only. It was also able to detect the association between disease and spatial topology that was undetected by a non-spatial approach. An exponential spatial correlation provides the best model with the lowest AIC and BIC. Using our spatial model, we found differences in mean retinal thickness between no-maculopathy versus maculopathy groups both in the interim dataset (150 participants) from our previous paper and the complete dataset (300 participants). However, MANOVA fails to detect the difference in the smaller dataset (150 participants). Moreover, no shape effect was detected between the retinal thickness of eyes with maculopathy and eyes without maculopathy. We also found that age is negatively correlated with the mean retinal thickness profile.
There are several advantages of our SLME model. From the clinical perspective, the principle advantage of our SLME model is the ability to pool the data from other sectors to effectively estimate the whole retinal thickness profile, thus increasing the power of between-group comparisons. Our method modelled the locations of the variables of interest in the images explicitly. It considers how far the location of the variable is from the centre, and the distance between the neighbouring variables of interest. Another advantage is that it works in the absence of some data, and does not require data imputation, but assumes that missing data are missing at random. Our model also incorporates clinical covariates into the image analyses, thus providing interpretable explanations of the results, which is important when studying disease etiology. Other advantages of our method include that it allows for heteroscedasticity between groups, and the correlation between two eyes from the same individual.
From the computational view, we provided further insight into the advantages of our SLME model using simulated data under different levels of spatial correlations in the images. Simulations demonstrated that our spatial approach is able to provide more accurate inference on the risk factor and has higher power to detect the main effect and the shape effect between diagnosis groups. It would be interesting to investigate the performance of our spatial approach over a wider range of sample sizes. Our spatial approach may perform better than a non-spatial approach with smaller sample sizes. However, when the sample size is large enough, the non-spatial approach may perform as well as our spatial approach in terms of detecting the main effect and the shape effect between groups.
In the clinical study (the EDDMO study), our SLME model did not find a statistically significant difference in the mean retinal thickness profile between healthy eyes and eyes with and without maculopathy. Nevertheless, Figure 4 shows that the spatial profile of healthy eyes is lower than the profile of maculopathy but slightly higher than that of the no-maculopathy group. Some researchers have reported a statistically significant decrease in retinal thickness when comparing diabetic patients without DMO to healthy participants, which may be explained by a loss of certain cell types in the retina in the early stages of preclinical DR [27][28][29]. In addition, we detected a shape difference between the retinal thickness profile of participants with diabetes and healthy participants. This suggests thickening of specific locations rather than general thickening across all locations in the early stages of DR and emphasises the potential importance of spatial relationships in the retina. This finding may be useful for the early detection of the DR and might help clinicians in planning early intervention.
The SMLE model is flexible and could be applied to investigate the spatial context or other features [30] of images from patients with other retinal diseases such as age related macular degeneration or retinal vascular occlusion. A further aim would be to develop flexible anisotropic spatial dependency structures adaptable to other medical images. It would also be useful to investigate the prediction of disease onset by extending our spatial modelling to spatio-temporal modelling by incorporating longitudinal datasets [31].
Conclusions
We have extended the standard analytic approach into spatial methods that adjust for spatial correlations and correlations between eyes from the same patients. In a clinical dataset, the SMLE model outperformed the non-spatial MANOVA by demonstrating higher power to detect differences. In simulated data, the SMLE model showed a higher power than non-spatial models. The increase was from 88.1% to 95.3%, 88.9% to 100% for moderate and high spatial correlations; hence the highest power increase was for circumstances where high correlations are present. In the future, the spatial approach could be extended into prediction or prognosis (i.e., predictive modelling) and the development of personal clinical management and monitoring tools.
Supplementary Materials: The following are available online at http://www.mdpi.com/2313-433X/6/6/44/s1, which includes two supplementary tables, i.e., Table S1: Detailed summary of all the estimated parameters in the final model in Section 3.1, and Table S2 | 8,177.4 | 2020-06-01T00:00:00.000 | [
"Computer Science"
] |
Modeling material transport regulation and traffic jam in neurons using PDE-constrained optimization
The intracellular transport process plays an important role in delivering essential materials throughout branched geometries of neurons for their survival and function. Many neurodegenerative diseases have been associated with the disruption of transport. Therefore, it is essential to study how neurons control the transport process to localize materials to necessary locations. Here, we develop a novel optimization model to simulate the traffic regulation mechanism of material transport in complex geometries of neurons. The transport is controlled to avoid traffic jam of materials by minimizing a pre-defined objective function. The optimization subjects to a set of partial differential equation (PDE) constraints that describe the material transport process based on a macroscopic molecular-motor-assisted transport model of intracellular particles. The proposed PDE-constrained optimization model is solved in complex tree structures by using isogeometric analysis (IGA). Different simulation parameters are used to introduce traffic jams and study how neurons handle the transport issue. Specifically, we successfully model and explain the traffic jam caused by reduced number of microtubules (MTs) and MT swirls. In summary, our model effectively simulates the material transport process in healthy neurons and also explains the formation of a traffic jam in abnormal neurons. Our results demonstrate that both geometry and MT structure play important roles in achieving an optimal transport process in neuron.
Introduction
The neuron exhibits a highly polarized structure that typically consists of a single long axon and multiple dendrites which are both extended from its cell body.Since most of the materials necessary for the neuron are synthesized in the cell body, they need to experience long-distance transport in axons or dendrites to reach their effective location [1,2].The intracellular material transport is therefore especially crucial to ensure necessary materials are delivered to the right locations for the development, function, and survival of neuron cells.The disruption of intracellular transport can lead to the abnormal accumulations of certain cellular material and extreme swelling of the axon, which have been observed in many neurological and neurodegenerative diseases such as Huntington's, Parkinson's, and Alzheimer's disease [3,4,5,6,7].Therefore, it is essential to study and understand mechanisms of the transport function and dysfunction. of neuron diseases.For instance, the partial differential equations (PDEs) of linear reaction-hyperbolic form have been used to approximate the traveling waves of a single moving species [16].This model was further extended to account for multiple moving species [17] and their diffusion [18,19].Based on PDE-based transport, stochastic models have also been developed for both axonal transport [20,21] and dendritic transport [22,23].In addition, several mathematical models were developed to simulate material transport in unhealthy neurons.Xue et al. presented a stochastic model to explain the segregation of MTs and neurofilaments in neurological diseases [24].Bertsch et al. proposed to couple Smoluchowski equations and kinetic-type transport equations to study the onset and progression of Alzheimer's disease [25].
Though the aforementioned PDE and stochastic models can successfully simulate and explain certain phenomena during transport, most of these models were solved only in simple one-dimensional (1D) or 2D domains without considering the complex neuron geometry.Recent developments in numerical methods allow us to obtain accurate solution of PDEs in complex geometries.Specifically, isogeometric analysis (IGA) [26] directly integrates geometric modeling with numerical simulation and achieves better accuracy and robustness compared to the conventional finite element method (FEM), making it a perfect tool to tackle the highly branched neuron geometry.In particular, IGA performs simulation with different types of splines as basis functions instead of Lagrange polynomials used in conventional FEM.The same smooth spline basis functions [27] used for both geometrical modeling and numerical simulation lead to accurate geometry representation with high-order continuity and superior numerical accuracy in simulation.Therefore, IGA has been extensively used in shell analysis [28,29,30,31], cardiovascular modeling [32,33,34,35,36,37], neuroscience simulation [38,39], fluid-structure interaction [40,41,42,43], as well as industrial application [44,45].Truncated T-splines [46,47] were developed to support local refinement over unstructured quadrilateral and hexahedral meshes.Blended B-splines [48] and Catmull-Clark subdivision basis functions [49] were investigated to enable improved or even optimal convergence rates for IGA.With the advances in IGA, we developed an IGA-based simulation platform to accurately reconstruct complex neuron geometries and solved a 3D motor-assisted transport model within them [38].We also developed a deep learning framework based on the IGA simulation platform to predict the material transport process in complex neurite networks [50].The results from our IGA solver showed how the complex neuron geometry affects the spatiotemporal material distribution at neurite junctions and within different branches.However, the motor-assisted model only provides a simplified model of the actual transport process but ignores the active regulation from neuron itself.
To model the active regulation from neurons to control the transport process, we propose to use PDE-constrained optimization (PDE-CO).PDEs are commonly used in science and engineering to mathematically represent biological and physical phenomena.Recent advances in numerical methods and high-performance computing equip the development of large-scale PDE solvers.As a result, PDE-CO problems arise in a variety of applications including optimal design [51,52,53], optimal control [54,55,56], and inverse problem [57,58].In particular, PDE-CO has important biomedical applications in exploiting valuable information from real medical data.For instance, Hogea et al. presented a PDE-CO framework for modeling gliomas growth and their mass-effect on the surrounding brain tissue [59].Kim et al. proposed a transport-theory-based PDE-constrained multispectral imaging algorithm to reconstruct the spatial distribution of chromophores in tissue [60].Melani utilized the blood flow data and solved a PDE-CO problem based on fluid-structure interaction to estimate the compliance of arterial walls in vascular networks [61].PDE-CO problems was also used to model tumor growth model by fitting the numerical solution with real experiment data and estimating unknown parameters in the model [62,63].
In this study, we develop a novel IGA-based PDE-CO framework to simulate the material transport regulation and investigate the formation of traffic jams and swirl during the transport process in complex neurite structures.Specifically, we design a new objective function in the PDE-CO model to simulate the control mechanism to (1) mediate the transport velocity field; and (2) avoid the traffic jam caused by local material accumulation.The control strength can be adjusted through two penalty parameters in the objective function.We can also modify the governing PDEs to study the formation of traffic jam.In particular, our model can simulate the traffic jam caused by the reduction of MTs and MT swirls during transport.To further study the influence of geometry on transport, we simulate material transport in two neuron tree structures with swelling geometry.In summary, our simulation reveals that the molecular motors and MT structure play fundamental roles in controlling the delivery of material by mediating the transport velocity on MTs.The defective transport on MTs can cause material accumulation in a local region which may further lead to the degeneration of neuron cells.Combined with geometry of the neurite network, the motor-assisted transport on MTs controls the routing of material transport at junctions of neurite branches and effectively distributes transported materials throughout the networks.Therefore, our study provides key insights into how material transport in neurite networks is mediated by MTs and their complex geometry.Our IGA optimization framework is also transformative and can be extended to solve other PDE-CO models of cellular processes in complex neurite networks.
IGA-based material transport optimization in neurons
Our interest lies in the transport of particles along an axon or dendrite in neuron cell.In our previous work, we simulated the material transport process using a macroscopic molecular-motor-assisted transport model without any transport control [38].Built upon this transport model, we propose a novel transport optimization model to further study the transport control mechanism of neuron and predict the formation of a traffic jam in abnormal neurons.The proposed optimization problem is described as subject to where the open set Ω ⊂ R d (d = 2 or 3) represents the d-dimensional internal space of the neuron, V ± is a predefined velocity field inside neuron; n 0 , n + and n − are the spatial concentrations of free, incoming (relative to the cell body; retrograde), and outgoing (anterograde) particles, respectively; D ± is the diffusion coefficient of incoming and outgoing materials; v + and v − are velocities of incoming and outgoing particles, respectively; k ± and k ± are rates of MT attachment and detachment of incoming and outgoing materials, respectively; l ± represents the density of MTs used for motor-assisted transport; f ± represents the control forces that mediate the material transport; µ is viscosity of traffic flow; λ i , λ o represent the degree of loading at inlet and outlet ends, respectively [18]; and n i , n o represent the boundary value of n 0 at inlet and outlet ends, respectively.Note that in this PDE-CO model, n 0 , n ± and v ± are referred as the "state variables" while f ± are referred as the "control variables".In this study, we assume the MT system is unipolar that leads to a unidirectional material transport process and ignore n − , l − , v − , k − , k − terms in Eq. 1b-1f.The default values of simulation parameters are summarized in Table 1.
Herein, we account for active regulation from neuron in the objective function (Eq.1a), and we assume the optimal material transportation within neuron can be achieved by solving the proposed optimization model.The first term in Eq. 1a measures the difference between v ± and the predefined optimal velocity field V ± .It serves as a velocity control mechanism that neuron expects to achieve the predefined velocity field V ± during transport.The second term measures the cost from concentration gradient ∇n ± within the entire neuron cell.It serves as a traffic jam control mechanism that the neuron can improve local traffic jam by detecting and avoiding high concentration gradient in the entire geometry.The value of parameter α represents to what extent we want to optimize the transport process and avoid traffic jams.The third term is a regularization that measures the control forces applied by neuron to mediate the transport.The value of parameter β represents how much the neuron can affect the transport velocity.To introduce traffic jams in neurons, we modify the simulation parameters in the governing equations.In this study, we modify the spatial distribution of l ± to model the traffic jam caused by abnormal MTs such as the reduction of MTs and MT swirls during transport.
We employ the "all-at-once" method [64,65] and IGA to formulate and solve the optimization model (Eq.1a) with PDE constraints (Eq.1b-1f) simultaneously.We first discretize the objective function to obtain its approximation We also discretize PDE constraints (Eq.1b-1f) to obtain their weak form where p T is the Lagrange multiplier and is also referred to as the "adjoint variable".By taking derivatives of the discrete Lagrangian with respect to state, control, and adjoint variables and setting the resulting expressions to zero, we obtain the first-order conditions, or Karush-Kuhn-Tucker (KKT) conditions.The resulting KKT system is then solved using the GMRES [66] solver implemented in PETSc [67].In this study, we focus on solving the proposed optimization model in 2D neuron geometries.
As shown in Fig. 1, we use a bifurcation example to illustrate the pipeline of our simulation.We first generate a control mesh and reconstruct the neuron geometry with Truncated Hierarchical B-splines (THB-spline) by utilizing the geometry information stored in a SWC file.The SWC file is widely used to store neuron morphologies including vertices and the associated diameters on the skeleton of the neuron.We can obtain the SWC files for various real neuron geometries from the NeuroMorpho database [68].The raw SWC file needs to be pre-processed to ensure no duplicated vertices or overlapping skeleton exist in the geometry.During the geometric modeling of our workflow, we take the cleaned-up neuron skeleton as input and use the skeleton-based sweeping method [32] to generate quadrilateral control mesh of the neuron geometry.Then, we build THB-spline on the quadrilateral mesh [30,31] for the final representation of the neuron geometry.Once the spline information for the geometry is obtained, we run a steady-state Navier-Stokes solver to generate the pre-defined velocity for the optimization.We then use the default simulation parameters in Table 1 and modify the spatial distribution of l + in the red circle regions to introduce traffic jam.Finally, we run the optimization solver and obtain the velocity field and concentration distribution.In this paper, we apply the pipeline to various neural structures with material transport regulation, traffic jam and MT swirl.All simulations are conducted on the XSEDE (Extreme Science and Engineering Discovery Environment) supercomputer Bridges at the Pittsburgh Supercomputer Center [69,70].
Simulation of material transport regulation and traffic jam
We first simulate the normal material transport and the abnormal transport with traffic jam in a single pipe geometry (Fig. 2).The predefined velocity field for both cases is computed by solving a steady-state Navier-Stokes equation and the result is shown in Fig. 2A.The other simulation parameter settings are summarized in Table 1.As shown in Fig. 2G-I and Fig. S1, we also perform parameter analysis using the single pipe geometry to study the influence of simulation parameters on the material distribution results.In particular, we focus on three parameters that may have significant effect when dealing with traffic jam caused by the reduction of MTs.The values selected for these parameters are displayed in Table 2.We assume the active regulation from neuron is less dominant than natural transport via diffusion or MTs, and thus select two smaller values for α and β compared to the default values in Table 1.Regarding the value selection of k/k , we refer to the values utilized in [71] and ensure the selected values stay within a biologically realistic range.Fig. 2G shows the effect of the penalty parameter of the concentration gradient cost, α, on the concentration distribution.One can see that the decrease of α leads to a severer material accumulation around the region with reduced MTs in the single pipe geometry.We also find that the concentration gradient becomes larger around the traffic jam region, which indicates that there is less control over the concentration gradient due to the decrease of α.Fig. 2H is similar to Fig. 2G but shows the effect of the penalty parameter of the control force, β, on the concentration distribution.We find similar phenomena that the traffic jam gets worse when β decreases.By comparing Fig. 2G with 2H and Fig. S1A with S1B, we find β has a greater influence on the concentration than α when decreasing both parameters by the same amount.Since β affects the control force in Eq. 1d while α affects the concentration in Eq. 1b&1c, the result indicates that the regulation of transport velocity on MTs is vital to achieve the optimal material transport process in neuron.
Fig. 2I shows the effect of the ratio between the attachment rate and detachment rate, k/k , on the material concentration.We find that when k/k increases, the location of maximum concentration moves toward right, which indicates the decrease of detachment rate k causes more material get attached to MTs and transport faster as expected in [18].However, the reduction of MTs slows down the motor-assisted transport on MTs and results in worse traffic jam.Interestingly, when k/k decreases from 10 to 1, we also observe a similar traffic jam phenomena.The possible reason is that the increase of k causes more material transported via free diffusion.Although free diffusion helps to transport the material farther along the branch, the slow diffusion speed limits its ability to mitigate the traffic jam caused by the reduction of MTs.
To account for morphological effect on the transport process, we simulate the normal material transport and the abnormal transport with traffic jam in two neuron tree structures as shown in Figs.3&4.The predefined velocity fields for both geometries are shown in Figs.3A&4A.To quantitatively study the influence of traffic jam on the material concentration among tree structures, we also plot the concentration distribution curves along the centerline from the inlet to each outlet of these two neurons.In each curve plot, we compare the distribution between the normal transport and the abnormal transport with traffic jam, as shown in Figs.3E&4E.For both cases, we model traffic jam by reducing the number of MTs (l + ) used for transport in the red dashed circle regions.As a result, a sudden decrease of velocity (Figs.3C&4C) and material accumulation (Figs.3E&4E) can be observed in these regions.By observing the distribution curve of the outlets downstream the traffic jam region (curve plots 1-4 of Fig. 3E and 3-8 of Fig. 4E we find that the reduced number of MTs not only causes high concentration in the local region, but also decreases the material concentration along the downstream of traffic jam region.The distribution curves of the other outlets (curve plots 5 of Fig. 3E and 1, 2, 9, 10 of Fig. 4E) demonstrate that more materials are transported to these outlets to minimize the hazard of traffic jam.The result also shows that materials rely on motor-assisted transport in longer branches of neurons and the directional transport on MTs contributes significantly to the entire transport process.
As shown in Fig. 3F-H and Fig. S2, we also perform parameter analysis on the concentration distribution in the neuron tree structure.Similar to the parameter analysis in single pipe geometry, we study the influence of three parameters on the concentration distribution and the selected values are listed in Table .2. To quantitatively study the influence, we also plot and compare the concentration curves on the centerline from inlet to outlet 2 of the neuron tree.We obtain similar results as in single pipe geometry that the decrease of α or β leads to a severer material accumulation around the region with reduced MTs, and β shows greater effect than α on the concentration distribution.In addition, we observe in Figs.3E, 3F and S2 that when α or β increases, more material is transported to the bottom long branch to mitigate the traffic jam in other branches.In Fig. 3H, we also find that the maximum concentration location moves downstream slightly when k/k increases, and either increasing or decreasing k/k intensifies the traffic jam.
Simulation of traffic jam with MT swirls and local swelling
Recent studies have shown that the formation of MT swirls can lead to accumulation of transported material and cause local swelling of neuron geometries [72].In our model, we modify the spatial distribution of l ± and enlarge the radius of neuron in a local region to simulate the effect of MT swirls and local swelling on the transport process.We explain the simulation setting by using a straight pipe geometry with MT swirls and swelling in the middle L 2 region, as shown in Fig. 5.We assume the normal transport is unidirectional from left to right (+ direction, red arrow in Fig. 5A).Due to the MT swirls in the middle region, the transport path is extended by two segments: one segment reverses to transport the material from right to left (− direction, blue arrow in Fig. 5A) and the other segment transports in the normal direction from left to right.Therefore, we increase the values of l + and l − along the longitudinal direction in the swelling region to describe the transport path change caused by swirling.We also assume that the swirl direction is counter-clockwise and assign an asymmetric distribution of l ± on the cross-section in the swelling region.In particular, l + is higher on the bottom of cross-section while l − is higher on the top of cross-section, as shown in Fig. 5A.We perform simulation with the new parameter setting and compare with the results of normal transport in the same geometry.The velocity field and concentration distribution of normal and abnormal transport are compared in Fig. 5B&C, respectively.The decrease of velocity and material accumulation can be observed in the swollen region.We also find that the velocity magnitude is not symmetric anymore due to the MT swirls in abnormal transport.In Fig. 5D, we plot the velocity streamline with concentration distribution in the zoomed-in swollen region for both normal and abnormal transport.Compared to the uniform velocity streamline in normal transport, the velocity displays vortex pattern in the abnormal transport, which reflects a longer transport distance due to MT swirls.We also find that the swirl of velocity streamline usually happens in the high concentration region, which implies that the material accumulation is caused by the vortex-shape velocity field.
As shown in Fig. 6, we then apply the same approach to simulate the normal and abnormal transport with MT swirls in two neuron tree structures with local swelling.The swelling is introduced by increasing the skeleton radius in the red dashed circle regions.We also assume a counter-clockwise MT swirl in these swollen regions and modify the distribution of l ± accordingly.For each model, we simulate the abnormal transport process due to MT swirls to obtain velocity field and concentration distribution results and compare with the result of normal transport in the same geometry.By comparing Fig. 6A&C with Fig. 6B&D, we find the velocity magnitude decreases and material accumulates in the swollen region.In other branches that are not downstream the swollen region, the material concentration also increases to mitigate the traffic jam in the swollen region.In addition, similar to the results in straight pipe with swelling geometry (Fig. 5D), we also observe that the velocity streamline with vortex pattern matches with the high concentration region (Fig. 6B&D).These results illustrate that the MT swirls lead to the circular transport velocity field in a local region which not only extends the transport distance but also traps the material and causes traffic jam.
Discussion
In this paper, we develop a PDE-constrained optimization model to simulate material transport control in neurons.Using our simulation, we examine both normal and abnormal transport processes in different geometries and discover several spatial patterns of the transport process.Our results show the formation of traffic jams due to the reduction of MTs and MT swirls in the local region.We also observe how the traffic jam affects the spatial patterns of transport velocities that in turn drives the transported materials distributed distinctly in different regions of neurite networks to mitigate traffic jam.By solving the proposed new optimization problem, we build a more realistic transport model for neurons by including active traffic regulation.
The model is successfully applied to complex 2D neuron geometries and provides key insights into how neuron mediates the material transport inside its complex geometry.Our study shows that MTs have a major impact on the material transport velocity and further affect the material concentration distribution.As shown in Fig. 2, the reduction of MTs in the middle of the single pipe slows down the transport velocity downstream and leads to traffic jam in the middle region.When the neuron has more branches in its geometry (Figs.3&4), the reduction of MTs in one branch has a similar influence on the transport downstream the branch.However, we observe an increase in transport velocity and material concentration in other branches, indicating that the active regulation from neuron takes effect to avoid traffic jams.In addition, we perform parameter analysis to study the influence of different simulation parameters on the material concentration distribution.The ratio between the attachment rate k and detachment rate k affects the amount of material transported via MTs or free diffusion.This will affect the overall transport speed and material distribution due to the different transport behaviour between motor-assisted transport and free diffusion.The penalty parameters α and β affect the ability of neuron to handle traffic jams.β has a greater influence on the traffic regulation compared to α since it directly affects the transport velocity on MTs, this again verifies the vital role of MTs during the intracellular transport process.
Our model can also model the influence of diverse neuron topologies on material distribution.For the transport in healthy neurons (Figs.3-4B&D), the magnitude of transport velocity is different among branches due to the asymmetric geometry.The different velocity magnitude further contributes to the distinct material concentration in different branches.In particular, we find that shorter branches tend to have faster transport speed and higher material concentration, which may result from the high demand of materials for their growth.
Our study also successfully simulates and provides reasonable explanation on the traffic jam caused by MT swirls.We assume the counter-clockwise MT swirls exist in a local region of neuron geometry which cause traffic jam and geometry swelling.The spatial distribution of MT density (l ± ) and neuron geometry are modified accordingly to model this phenomena.We compare the simulation result of abnormal transport on swirly MTs with normal transport and find that MT swirls have severe impact on the transport velocity field.Compared to the uniform velocity streamline in normal transport, the abnormal transport exhibits a streamline with counter-clockwise vortex pattern (Figs.5D, 6B&D), which is caused by the counter-clockwise MT swirls.This circular streamline not only extends the transport distance but also traps the material in the local region, and therefore explains why high concentration region matches with the circular streamline pattern.
Our study develops an IGA solver (available at https://github.com/truthlive/NeuronTransportOptimization)for solving the PDE-CO problem in complex neuron geometries.Specifically, we adopt the skeleton-based sweeping method [32,38] for mesh generation to represent the tree structures of neuron geometry.Given the geometry information of neurons, our method automatically reconstructs 2D network geometry with high accuracy and high order of continuity for IGA computation.
Our automatic IGA optimization solver provides an efficient computation tool for studies of material transport regulation in complex neurite networks.The current 2D solver can be easily generalized to 3D and it is also extensible to solve other PDE-CO models of cellular processes in complex neurite network geometry.
Our study has its limitations, which we are addressing in the ongoing work.In the current model, we only consider the influence of traffic jams on the material concentration but neglect its effect on the deformation of neuron geometries.In addition, although IGA offers great advantages in accurately simulating material transport control in complex neuron geometries, the computational cost of simulating transport in large-scale neurite networks remains very expensive, which limits its biomedical application.To improve the computational efficiency of our model, we will adopt deep learning techniques to build fast and accurate surrogate models [73,50].Despite these limitations, our simulation directly shows how the traffic jam is formed in neurons and how neurons could control material traffic to avoid traffic jams.The simulation results provide references to further answer the question of how neurons deliver the right material to the right destination in a balanced manner in their complex neurite networks and how the transport may be affected by disease conditions.
Figure 1 :
Figure 1: An overview of the material transport control simulation in a bifurcation geometry.The traffic jam is introduced by reducing MTs in the red dashed circle region.Color bars unit for velocity field: µm/s and concentration: mol/µm 3 .
The computed velocity field and the distribution of concentration in the normal transport are shown in Fig. 2B&E.To model traffic jam caused by the reduction of MTs, the distribution of l + along the pipe is defined as shown in Fig. 2D.The velocity field and material distribution results in the abnormal transport are shown in Fig. 2C&F.The comparison between normal and abnormal transport shows that the velocity magnitude decreases in the red dashed circle region due to the reduced number of MTs, and this further leads to accumulation of the material in this area.
Figure 2 :
Figure 2: Simulation of material transport and parameter analysis in a single pipe geometry.(A) The predefined velocity field V + .Black arrow points to the inlet of the pipe.The computed velocity field in (B) a healthy neuron and (C) an abnormal neuron with reduced MTs in the red dashed circle region.(D) Distribution of l + to model the traffic jam caused by the reduction of MTs.Distribution of concentration in (E) a healthy neuron and (F) an abnormal neuron with reduced MTs in the red dashed circle region.(G-I) The concentration curve on the centerline of the single pipe affected by different settings of (G) α; (H) β; and (I) k/k .Unit for color bars: (A-C) µm/s and (E, F) mol/µm 3 .
Figure 3 :
Figure3: Simulation of material transport and parameter analysis in a neuron tree extracted from NMO_54504.(A) The predefined velocity field V + .Black arrow points to the inlet of the neuron tree.The computed velocity field in (B) a healthy neuron and (C) an abnormal neuron with reduced MTs in the red dashed circle region.Distribution of concentration in (D) a healthy neuron and (E) an abnormal neuron.We also compare the concentration curve on the centerline from the inlet to every outlet between normal and abnormal transport in (E).The red dashed curve shows the centerline from the inlet to one of the outlets and each outlet is indexed by a unique number.(F-H) The concentration curve on the centerline from inlet to outlet 2 affected by different settings of (F) α; (G) β; and (H) k/k .Unit for color bars: (A-C) µm/s and (D, E) mol/µm 3 .
Figure 4 :
Figure 4: Simulation of material transport in a neuron tree extracted from NMO_54499.(A) The predefined velocity field V + .Black arrow points to the inlet of the material.The computed velocity field in (B) a healthy neuron and (C) an abnormal neuron with reduced MTs in the red dashed circle region.Distribution of concentration and the concentration curve on the centerline of the circled region in (D) a healthy neuron and (E) an abnormal neuron.We also compare the concentration curve on the centerline from the inlet to every outlet between normal and abnormal transport in (E).The red dashed curve shows the centerline from the inlet to one of the outlets and each outlet is indexed by a unique number.Unit for color bars: (A-C) µm/s and (D, E) mol/µm 3 .
Figure 5 :
Figure 5: Simulation of material transport in a straight pipe with swelling in the middle region.(A) The simulation setting for modeling MT swirls.The red and blue arrows show the transport path along swirly MTs.Due to the MT swirls in the L 2 region, both l + and l − are increased along centerline and their distributions on cross-section are also modified.(B, C) The computed velocity field and concentration distribution in the swollen geometry.(D) The velocity streamline and concentration distribution in the swollen region.Different color maps are used to distinguish between velocity and concentration.Unit for color bars: Concentration: mol/µm 3 ; Velocity: µm/s.
Table 1 :
Simulation parameters utilized in computations
Table 2 :
Value selection for parameter study | 7,044.8 | 2021-08-30T00:00:00.000 | [
"Engineering",
"Computer Science",
"Materials Science"
] |
Zero mode solutions of quark Dirac equations in QCD as the sources of chirality violating condensates
It is demonstrated, that chirality violating condensates in massless QCD arise from zero mode solutions of Dirac equations in arbitrary gluon fields. Basing of this idea, the model is suggested, which allows one to calculate quark condensate magnetic susceptibilities in the external constant electromagnetic field.
where G n µν is gluon field tensor, the sum is over quark flavours.
and A n µ is the gluon field. Pay attention, that in Euclidean formulation of QCD ψ is replaced by ψ + . (The review of Euclidean formulation of QCD and instantons is given in [1], see especially [2].) The Dirac equation for massless quark in Euclidean space time has the form: − iγ µ ∇ µ ψ n (x) = λ n ψ n (x) where ψ n (x) and λ n are the eigenfunctions and eigenvalues of the Dirac operator −∇ = −iγ µ ∇ µ . Expand the quark fields operators into the left and right ones where Then for nonzero λ n the Lagrangian and the action reduces to the sum of two terms completely symmetric under interchange L ←→ R. Therefore the solutions of the equations for left and right quark fields are also the same -the states, constracted from left and right quarks are completely symmetrical. This conclusion was obtained for fixed gluon field. It is evident, that the averaging over the gluon fields does not change it. Quite different situation arises in case λ 0 = 0. The contribution of this term to the Lagrangian: is equal to zero and no conclusion can be done about the symmetry of states build from left and right quark fields.
One of the consequences from the said above is that all chirality violating vacuum condensates in QCD arise from zero mode solutions of Dirac equations (3).
These general arguments are supported by the well known facts: 1. The general representation of the trace of quark propagator S(x) is expressed through the spectral function ρ(λ) as a function of eigenvalues λ (Källen-Lehmann representation): At x 2 = 0 ∆(x 2 , λ) reduces to δ(λ) and we have (in Minkowski space-time): (The Banks-Casher relation [3]).
Basing on the statements, presented above, let us formulate the model for calculation of chirality violating vacuum condensates in QCD. Suppose, that vacuum expectation value (v.e.v.) of the chirality violating operator O c.v. is proportional to matrix element ψ + 0 O c.v. ψ 0 , where ψ 0 is the zero-mode solution of Eq.(3) in Euclidean space-time: ψ 0 depends on x, on the position of the center of the solution x c , as well as on its size ρ: ψ 0 = ψ 0 (x − x c , ρ). Eq. (10) must be integrated over x c , what is equivalent to integration over x − x c . (In what follows the notation x will be used for x − x c .) We assume, that ρ =Const and find its value from comparison with the known v.e.v.'s. Finally, introduce in (10) the coefficient of proportionality n. So, our assumption has the form: Our model is similar to delute instanton gas model [6], where x c is the position of instanton center. Unlike the latter, where the instanton density has dimension 4, n has dimension 3 and may be interpreted as the density of zero-modes centers in 3-dimension space. Note, that the left-hand side of (11) is written in the Minkowski space-time, while the right-hand side in Euclidean ones. (The sign minus is put in order to have n positive.) For x and ρ-dependens of ψ 0 (x, ρ) we take the form of the zero-mode solution in the field of instanton in SU (2) colour group: where χ 0 is the spin-colour isospin (| T |= 1/2) wave function, corresponding to the total spin I + T = J equal to zero, J = 0. ψ 0 (x, ρ) is normalized to 1: Consider first the quark condensate 0 |qq | 0 , the most important chirality violating v.e.v., determining the values of baryon masses [7]- [9]. (Here q = u, d are the fields of u, d-quarks). In this case O c.v. = 1 and in accord with (The integration over SU (2) subgroup position in SU (3) colour group as well as anti-instanton contribution are included in the definition of n.) The anomous dimension of quark condensate is equal to 4/9.) According to (14) n has the same anomalous dimension. The size ρ of the zero-mode wave function can be found by calculation in the framework of our model of the v.e.v.
Working in the SU (2) colour group, substitute λ n by τ a (a = 1, 2, 3) and take for G a µν the instanton field where the parameter η aµν were defined by ′ t Hooft [12] (see also [2]). The substitution of (12) and (16) into (11) gives after simple algebra Therefore, We are now in a position to calculate less well known quantities -the magnetic susceptibilities of quark condensate, induced by external constant electromagnetic field.
The dimension 3 quark condensate magnetic susceptibility is defined by [13]: where quarks are considered as moving in external constant weak electromagnetic field F µν and e q is the charge of quark q in units of proton charge (the proton charge e is included in the definition of F µν ). The left-hand side of (19) violates chirality, so it is convenient to separate explicitly the factor 0 |qq | 0 in the right-hand side. It was demonstrated in [13] that 0 |qσ µν q | 0 F is proportional to the charge e q of the quark q. A universal constant χ is called the quark condensate magnetic susceptibility.
Let us determine the value of χ in our approach. For this goal it is necessary to consider Eq.3 in the presence of external constant electromagnetic field F µν and to find the first order in F µν correction to zero mode solution (12).
This can be easily done by representing ψ as where ψ 0 is given by (12) and ψ 1 represents the proportional to F µν correction. Substitute (20) in Eq.3 added by the term of interaction with electromagnetic field, neglect ψ 1 in this term and solve the remaining equation for ψ 1 (x, ρ)).
The result is: where σ a are Pauli matrices. The matrix element ψ + σ µν ψ appears to be equal: (The properties of η aµν symbols [12], [2] were exploited.) The v.e.v. (19) in the Minkowski space-time is given by: (The normalization condition (13) for ψ 0 (x, ρ) was used.) It is convenient to express n through quark condensate by (14), use the notation x 2 = r 2 , where r is the radius-vector in 4-dimensional space. Then according to (19) we have: The integral (24) is quadratically divergent at large r. So, the cut-off R is introduced. Its value can be estimated in following way. The volume occupied by one zero-mode in 3-dimensional space is approximately equal to 1/n (the volume of the Wigner-Seitz cell). So, for cut-off radius square R 2 in four-dimensions we put where the factor 4/3 corresponds to transition from 3 to 4 dimensions. The calculation of the integral (24) at the values of parameters ρ (18) and R 2 (25) gives The quark condensate magnetic susceptibility was previously calculated by QCD sum rule method [14]- [17] and expressed through the masses and coupling constants of mesonic resonances. The recent results are: [16]; χ(1 GeV) = −2.85 ± 0.5GeV −2 [17] (27) (The earlier results, obtained by the same method, were: χ(0.5 GeV) =−5.7 GeV −2 [14] and χ(1 GeV) = -4.4±0.4 GeV −2 [15]. The anomalous dimension of χ is equal to -16/27. It was accounted in [14]- [17], but not in the presented above calculation. (In some of these papers, the α s -corrections and continuum contribution, were also accounted.) One can believe, that the value (26) refer to 1 GeV, because the value of quark condensate (14) refer to this scale and also because the scale 1 GeV is a typical scale, where, on the one hand, the zero-modes and quark condensates are quite important (see, e.g. [10]) and, on the other, the instanton gas model is valid [6]. Since the integral is quadratically divergent it is hard to estimate the accuracy of (26). I guess, that it is not worse, than 30-50%. In the limit of this error the result (26) is in an agreement with those found in phenomenological approaches.
Turn now to quark condensate magnetic susceptibilities of dimension 5, κ and ξ defined in Ref. [13] g 0 |q Perform first the calculation of κ. In this case the expression of ψ 1 (x, ρ) (21) must be multiplyed by the additional factor: 1 2 τ b G b µν where G b µν is given by (16) and the indices µ, ν in (21) are changed to λ, σ. In the further calculation it will be taken into account, that χ 0 in (12) corresponds to total spin-colour isospin J = 0 and consequently In the relation the last term drops out after summation of zero-modes from instanton and anti-instanton configuration. The final result for κ is: where z = R 2 /ρ 2 = 12.7. Numerically, we have: The calculation of ξ is very similar to those of κ and the result is The values of κ and ξ only logarithmically depend on the cut-off. But unfortunately the logarithm in (32) is not very large and its main part is compensated by the term −13/6, appearing in (32). So, the accuracy of (33),(34) can be estimated as about 30%. The phenomenological determination of 5-dimensional quark condensate magnetic susceptibilities was performed by Kogan and Wyler [18] along the same lines, as it was done in [14], [15]. No anomalous dimensions were accounted. The results of [18] are: As can be seen, they are in a good agreement with (33),(34). The 5-dimensional quark condensate magnetic susceptibilities play a remarkable role in determination of Λ-hyperon magnetic moment [19]. | 2,384.2 | 2009-06-01T00:00:00.000 | [
"Physics"
] |
The impact of surgically induced ischaemia on protein levels in patients undergoing rectal cancer surgery
The goal of targeted therapy has driven a search for markers of prognosis and response to adjuvant therapy. The surgical resection of a solid tumour induces tissue ischaemia and acidosis, both potent mediators of gene expression. This study investigated the impact of colorectal cancer (CRC) surgery on prognostic and predictive marker levels. Tumour expression of thymidylate synthase, thymidine phosphorylase, cyclin A, vascular endothelial growth factor (VEGF), carbonic anhydrase-9, hypoxia inducible factor-1α, and glucose transporter-1 (GLUT-1) proteins was determined before and after rectal cancer surgery. Spectral imaging of tissue sections stained by immunohistochemistry provided quantitative data. Surgery altered thymidylate synthase protein expression (P=0.02), and this correlated with the change in the proliferation marker cyclin A. The expression of hypoxia inducible factor-1α, VEGF, and GLUT-1 proteins was also different following surgery. Colorectal cancer surgery significantly impacts on intratumoral gene expression, suggesting archival specimens may not accurately reflect in situ marker levels. Although rectal cancer was the studied model, the results may be applicable to any solid tumour undergoing extirpation in which molecular markers have been proposed to guide patient therapy.
There has been considerable recent interest in markers of tumour prognosis and response to adjuvant therapy in a range of tumour types, including colorectal cancer (CRC). It is hoped these markers will allow targeted tumour therapy while minimising the toxicity of inefficacious agents. Thymidylate synthase (TS) is a key enzyme in DNA synthesis and is the main site of action of the chemotherapeutic agent 5-fluorouracil (5-FU) (van der Wilt and Peters, 1994). It is the most widely studied prognostic and predictive marker in CRC, with low TS levels predicting a better outcome to 5-FU based chemotherapy (Aschele et al, 2002), and high TS expression being associated with a poor prognosis (Allegra et al, 2003). However, previous reports of TS display methodological heterogeneity, in that TS expression within preoperative biopsies (Okonkwo et al, 2001) as well as postoperative archival tumour sections (Edler et al, 2000), has been correlated with treatment outcome.
The resection of a solid tumour, such as a CRC, often involves early clamping of the vascular pedicle to facilitate the surgical dissection and aid tumour extirpation. Interrupting the vascular inflow leads to tumour hypoxia and acidosis (Parkins et al, 1997), both of which are potent mediators of gene expression (Helfman and Falanga, 1993). In addition, delays in tissue fixation following extirpation reduce the efficacy of subsequent protein and mRNA analysis (Almeida et al, 2004).
We have previously shown in an experimental CRC model that tumour vascular clamping significantly alters gene expression levels (Atkin et al, 2006). There are no formal clinical guidelines regarding the rapidity of tumour fixation following extirpation, and if CRC surgery and tissue processing methods do alter gene expression levels, it may be that marker expression in postoperative tumour samples does not reflect true in situ levels. Therefore, the aim of this study was to determine the effect of surgery on marker expression in patients with CRC, looking in particular at changes in thymidylate synthase. The relationships between TS expression and the levels of cyclin A, a cell proliferation marker, and the hypoxia-related protein hypoxia inducible factor-1a (HIF-1a) are also presented.
Study patients
Patients undergoing surgical resection of histologically proven rectal cancer were recruited over a 2-year period. There were no exclusion criteria. Standard curative resections and anaesthetic techniques were performed in all cases. Local ethics committee approval was obtained and all patients gave informed consent.
Five preoperative tumour samples were obtained on the day of surgery, immediately prior to the surgical resection. A further five biopsies were obtained immediately following tumour extirpation.
In addition, for each patient a control sample of rectal mucosa was obtained before and after surgery, at the same time as tumour biopsies. Tumour and mucosal sampling was confirmed by examination of haematoxylin and eosin stained sections by an independent, experienced histopathologist.
Each tumour and control mucosal biopsy was stored in 10% neutral buffered formalin solution (Sigma, Poole, UK) for subsequent protein analysis by immunohistochemistry. For all patients, the duration of tumour ischaemia (defined as time of arterial pedicle clamping until postoperative biopsy) was noted, as well as the time between extirpation and postoperative tumour sampling. In all cases, the true clinical duration of tumour ischaemia was longer than that noted in this study, as the postoperative biopsies were obtained before actual fixation of the surgical specimen by the operating theatre staff. Table 1 shows the proteins and the conditions used for immunohistochemistry. Sections (4 mm) were dewaxed in xylene for 5 min and rehydrated through graded alcohol (100, 90, and 70%) to water. Heat mediated antigen retrieval was performed using 250 ml 10 mM citric acid pH 6 for all markers, apart from vascular endothelial growth factor (VEGF) (0.1 M Tris-HCL pH 10) and , by boiling the sections in an 800 W microwave oven (Panasonic NN-6453BBPQ, 2450 MHz).
Immunohistochemistry
For all markers apart from CA-9 and HIF-1a, sections were transferred to the DAKO Autostaining machine (DAKO, UK) containing peroxidase block (DAKO, S2023), the detection reagents (ChemMate HRP, DAKO K5001), and anti-human primary antibody diluted in antibody diluent. The Autostainer programme included 5 min in peroxidase block, 1 h incubation in primary antibody, 30 min incubation in ChemMate secondary and tertiary reagents and 5 min in diaminobenzidine (DAB) substrate.
Sections were stained for HIF-1a using the DakoCytomation CSA II signal amplification system (DAKO Corporation, Carpinteria, USA). In summary, the sections were first incubated with 3% hydrogen peroxide for 5 min to quench endogenous peroxidase activity, following which incubation with a protein block for 5 min was performed to inhibit nonspecific binding. Diluted primary antibody was added and sections were incubated for 15 min. Sequential 15-minute incubations were performed with anti-mouse Ig-HRP, fluorescyl-tyramide hydrogen peroxide, and antifluorescein-HRP. Finally, the slides were incubated for 5 min with DAB/ hydrogen peroxide. For CA-9, endogenous peroxidase activity was blocked using DAKO peroxidase block (Envision kit) for 5 min. Then a DAKO Protein Block (X0909) was added for a further 5 min, following which incubation with the CA-9 primary antibody diluted 1/50 in Tris-buffered saline for 30 mins was performed. A further incubation was then performed with DAKO Envision HRP Mouse polymer (K4006) for 30 min, followed by 5 min with DAB solution.
When the programme was complete, stained slides were removed from the machine and counterstained in Gills Haematoxylin (Surgipath Europe Ltd, 01500E) for 5 s. Slides were then washed in tap water, dehydrated in graded alcohols (70, 90, and 100%), cleared in xylene and mounted in DPX (Surgipath Europe Ltd, 08600E). Each staining run incorporated a control slide that had previously demonstrated positive for the antibody of interest. A negative control was also incorporated and involved the substitution of the anti-human primary antibody for an isotypic control antibody at the same protein concentration.
Quantification of marker protein expression
Immunohistochemical staining of marker protein expression was quantified using a spectral imager developed and constructed in our Institute, as reported previously (Barber et al, 2003). This allowed accurate immunostain quantification, with stain intensity being expressed as optical density (OD) normalised to reference spectra. Most markers demonstrated greater expression in tumour compared with stroma, so nonspecific background staining could be minimised by applying arbitrary thresholds to the OD data. For markers exhibiting similar tumour and stromal staining, a threshold was chosen that included the staining of both tissue compartments. In addition, the number of pixels with stain intensity above the threshold was determined and represented the area of the captured image demonstrating marker expression. A standard image capture protocol was used to ensure the maximum tumour/stroma ratio was obtained for each image, which allowed comparison of stained area between captured images.
Two images were captured for each of the five biopsies taken before and after surgery, and for each captured image the stain intensity and area were determined by spectral imaging. The change in marker stain intensity was given by the difference between mean stain intensity after surgery (Ma) and mean before (Mb), whereas the change in the number of pixels demonstrating marker expression was given by Pa -Pb. Hence, a positive difference implies greater marker expression after surgery. Cyclin A is a nuclear antigen and HIF-1a showed mixed nuclear and cytoplasmic staining. Therefore, a labelling index (ratio of positive to negative nuclei) was calculated by counting stained cells within each captured image for HIF-1a and cyclin A, and the change in nuclear score for these markers was given by the difference between mean labelling index before and after surgery.
Statistics
The Wilcoxon signed ranks test was used to determine the magnitude of overall deviation from zero of the difference between pre-and postoperative marker levels, where a significant deviation from zero would suggest an effect of surgery on marker expression. The sign test was used to calculate the significance of the deviation with respect to direction. A P-value of o0.05 was taken to represent significance. Spearman's rank correlation coefficient, r s , was used to determine the nonparametric correlation between variables. The effect of CRC surgery on tumour protein expression The overall changes in marker stain intensity and area are given in Figures 1A and B. Thymidylate synthase stain intensity in postoperative biopsies was significantly different than biopsies taken before surgery (P ¼ 0.02), suggesting an effect of surgery on the level of TS expression. For most patients there was a reduction in stain intensity after surgery, but this direction of change was not significant (16 patients with reduced expression postoperatively vs 8 patients with increased expression; P ¼ 0.15). Thymidylate synthase stain area also differed following surgery (P ¼ 0.04), with most patients showing a reduction in area (17 vs 7 patients; P ¼ 0.06). Surgery also reduced cyclin A expression (P ¼ 0.01), but the direction of change was not significant (18 vs 6 patients; P ¼ 0.2). The only other marker to demonstrate a change in stain intensity following surgery was the HIF-1a nuclear count, which increased postoperatively (magnitude: P ¼ 0.002; direction: P ¼ 0.007). GLUT-1 and VEGF stain area were altered by surgery (P ¼ 0.004 and 0.03 respectively), with most patients showing increased expression of GLUT-1 postoperatively (19 vs 5; P ¼ 0.007).
The effect of CRC surgery on control protein expression
The only marker to show altered protein expression within samples of normal rectal mucosa was CA-9, which demonstrated a reduction in stain area following surgery (magnitude: P ¼ 0.009; direction: P ¼ 0.02).
Correlations with durations of ischaemia and extirpation, and with the tumour level
The median duration of tumour ischaemia was 70 min (range 26 -140), while the median time between extirpation and postoperative tumour sampling was 30 min (range 3 -125). There was no correlation between the change in expression and the durations of ischaemia or extirpation for any marker studied. Correlations for TS are given in Figures 2A and B. There was also no correlation between the changes in stain intensity and area and the tumour level above the anal verge. The poor correlation between changes in hypoxic marker expression and tumour level would suggest there was no direct relationship between ischaemic insult and the degree of residual vascular inflow from the middle and inferior rectal arteries following clamping of the main vascular pedicle.
Correlations between TS expression and the markers of proliferation and hypoxia
Thymidylate synthase stain area correlated with cyclin A expression (r s ¼ 0.61, P ¼ 0.002), suggesting changes in TS mirrored changes in cell proliferation. Thymidylate synthase stain area also correlated with cytoplasmic HIF-1a and GLUT-1 stain area (r s ¼ 0.73, Po0.0001; r s ¼ 0.49, P ¼ 0.02 respectively), whereas the correlation between TS stain intensity and nuclear HIF-1a expression approached significance (r s ¼ 0.39, P ¼ 0.06).
Impact of surgery on immunohistochemical visual grading scores
Spectral imaging is a novel method of immunostain quantification, and as such is not widely available. The most common method of quantifying immunohistochemical staining patterns is by visual estimation of marker stain intensity and area. Figures 3A and B show the magnitude and direction of the changes in TS expression after surgery in relation to these manual visual grades. The limits for the visual grades are given in Table 3 and were derived by analysis of a subset of patients and reported in a previous study (Atkin et al, 2005). Although these limits are somewhat arbitrary, it is evident the visual intensity score is different in five patients after surgery compared with the preoperative score, whereas in six patients the visual area grades are different postoperatively. These visual scores are widely used to grade the expression of markers such as TS, and in future may be involved in guiding management decisions based on marker expression. These results suggest that surgery may alter these visual scores, and hence there may be discrepancies in marker expression in archival tumour samples compared with in situ levels.
DISCUSSION
This study investigated the change in gene expression occurring during the surgical resection of primary rectal cancer. Warm Table 3 Spectral stain intensity and area limits for each visual grade (visual intensity score represents overall TS stain intensity, visual area score denotes the percentage of the section staining positively for TS) Visual intensity score Negative Weak Moderate Strong Spectral intensity range (a.u.) 0 0.53 -0.66 0.75 -0.88 0.91 -1.11 Visual area score o20% 20 -50% 50 -75% 475% Spectral area range (pixels  10 5 ) 0.02 -1.1 1.7 -2.4 2.5 -3.1 3.7 -4.0 ischaemia has been shown to alter the gene expression profile of a range of tissue types (Miyatake et al, 2004), and transcriptional changes following extirpation have been demonstrated for multiple genes within a microarray of colorectal mucosal samples beginning 20 min after excision (Huang et al, 2001). Extirpation was not shown to have an affect on DPD enzyme activity in CRC samples (Sadahiro et al, 2003); however, delays in fixation following excision have been shown to impair the analysis of the oestrogen receptor (ER) status in breast cancer patients (Von Wasielewski et al, 1998). We found CRC surgery significantly altered intratumoural biomarker levels, with TS showing the greatest response. There was a trend towards a reduced expression of TS following surgery, and it was interesting to note the correlation between TS and the proliferation marker cyclin A, a finding also seen previously in an experimental model of surgically induced CRC ischaemia (Atkin et al, 2006). In fact, TS has been suggested to be a marker of cell proliferation, as it is induced during the G1 phase of the cell cycle and levels increase 20-fold as cells enter the S phase (Santi et al, 1974). Hypoxia slows the metabolic rate and is associated with lower levels of transcription and translation (Dachs and Tozer, 2000); hence, the reduction in TS levels seen after surgery may be related to the effect of tumour ischaemia on the proliferation status.
Thymidine phosphorylase (TP) is an enzyme involved in thymidine homeostasis and is involved in the activation and metabolism of the fluoropyrimidines, including 5-FU. It is also known as platelet derived endothelial cell growth factor, and has been shown to promote angiogenesis (Miyazono et al, 1987). High tumour TP levels have been shown to correlate with poor prognosis (van Triest et al, 2000), and response to 5-FU (Metzger et al, 1998) in CRC patients. In our study, CRC surgery did not appear to have an effect on TP expression. Previous work has demonstrated TP protein induction by hypoxia in a breast carcinoma cell line (Griffiths et al, 1997), but only after 16 h and there was no induction with oxygen concentrations greater than 0.3%. The same study also detected an increased TP expression after 2 h of vascular clamping in a breast tumour xenograft model. Our data, however, suggest that the degree and duration of tumour ischaemia is insufficient to alter TP levels in rectal cancer patients undergoing surgery, which is an important finding and suggests archival specimens are appropriate for TP analysis.
The absolute intratumoral oxygen tensions were not measured in this study, and so it is not possible to document the degree of ischaemia for each tumour following arterial clamping. This will vary depending on tumour level and anatomical differences in collateral blood supply. Hypoxia-related markers were measured in an attempt to determine the ischaemic insult. Hypoxia-inducible factor-1 is a transcription factor involved in oxygen homeostasis. It was noted there were changes in HIF-1a expression, with a significant upregulation in HIF-1a nuclear protein expression postoperatively. The cytoplasmic protein expression was unaltered. However, hypoxia results in translocation of the HIF-1 complex to the nucleus to allow DNA binding (Bos et al, 2004), and so one would expect an increased differential nuclear expression. Carbonic anhydrase-9 (CA-9) is a downstream mediator of HIF-1 activation induced by hypoxia (Wykoff et al, 2000). There were no changes in CA-9 expression noted in this study. However, cell line data (Lal et al, 2001) suggest the duration of ischaemia noted in our patients was not long enough to cause CA-9 induction. Vascular endothelial growth factor and glucose transporter-1 (GLUT-1) are also products of HIF-1 activation, and CRC surgery did have an effect on the expression of both these markers. Taken together, these data indicate a considerable tumour hypoxic insult during vascular clamping.
In an experimental model of surgically induced CRC ischaemia, TS expression correlated directly with cyclin A and inversely with CA-9 (Atkin et al, 2006), suggesting greater TS downregulation occurred at lower oxygen tensions secondary to a decreased cell proliferation. The direct correlation between TS and HIF-1a expression noted in this study therefore appears paradoxical. It may be that other microenvironmental factors, such as acidosis or alterations in the availability of glucose and other enzymatic substrates, are more active in human tumours, thereby affecting the level of TS or HIF-1a expression. Alternatively, it may relate to the role of HIF-1 in hypoxiainduced growth arrest (Goda et al, 2003), or the regulation of TS by other factors, such as p53 or E2F-1, which are induced by hypoxia and are involved in TS regulation (Ash et al, 1995). It was interesting to note there was no correlation for any marker between the change in expression and the durations of ischaemia and extirpation, suggesting a complex interplay between gene expression and the tumour microenvironment following vascular clamping.
The findings of this study have important implications on the timing of biomarker measurement in relation to the surgical procedure. They suggest the need for guidelines on specimen fixation following excision, and the importance of methodological standardisation of studies investigating prognostic and predictive markers, particularly if they are to be used to dictate therapeutic strategy. The results are relevant as standard surgical techniques were used and the tumour processing methods were those encountered in everyday practice. In fact, the duration of tumour ischaemia was shorter in all cases than that occurring in actual clinical practice.
Several potential sources of error were encountered. The tumour/stroma ratio varied between captured images, making it difficult to compare directly the stained area between tumour sections. However, a standard protocol for image capture was followed, ensuring each captured image contained the maximum available tumour tissue. Solid tumours show architectural heterogeneity, with varying levels of hypoxia and acidosis throughout the tissue. This introduces sampling error when measuring gene expression levels in biopsy specimens. This was minimised as far as possible by multiple sampling before and after surgery, and determining the mean marker level at each time point. Gene expression profiles of tumour biopsies are representative of the whole tumour (Perou et al, 2000); hence, the changes in expression seen should be applicable to the whole lesion.
The effects of tissue processing must be considered when using immunohistochemistry to measure marker levels. Tumour fixation occurs at unpredictable rates, and may take days depending on the fixative and the size of the specimen (Heyderman et al, 1989). Similarly, protein crosslinking during tissue fixation may alter the antigenic determinants, whereas antigen retrieval techniques may 'unmask' unrelated antigens (Heyderman et al, 1989). To reduce protein degradation during processing, large specimens should be incised to expedite fixation, or a representative tumour sample should fixed immediately and used for subsequent protein quantification. Alternatively, where delays in fixation are unavoidable, refrigeration of the specimen may minimise the loss of immunoreactivity.
CONCLUSIONS
In patients undergoing rectal cancer surgery, significant changes in gene expression were noted between pre-and postoperative tumour biopsies. These findings have important considerations when investigating prognostic markers and markers predicting the response to adjuvant therapy. These data suggest the duration of tumour ischaemia should be minimised prior to fixation, and that subsequent studies need to standardise prognostic and predictive marker measurement in relation to the surgical procedure. | 4,827.2 | 2006-10-01T00:00:00.000 | [
"Medicine",
"Biology"
] |
Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis
This research article is aimed at improving the efficiency of a computer vision system that uses image processing for detecting cracks. Images are prone to noise when captured using drones or under various lighting conditions. To analyze this, the images were gathered under various conditions. To address the noise issue and to classify the cracks based on the severity level, a novel technique is proposed using a pixel-intensity resemblance measurement (PIRM) rule. Using PIRM, the noisy images and noiseless images were classified. Then, the noise was filtered using a median filter. The cracks were detected using VGG-16, ResNet-50 and InceptionResNet-V2 models. Once the crack was detected, the images were then segregated using a crack risk-analysis algorithm. Based on the severity level of the crack, an alert can be given to the authorized person to take the necessary action to avoid major accidents. The proposed technique achieved a 6% improvement without PIRM and a 10% improvement with the PIRM rule for the VGG-16 model. Similarly, it showed 3 and 10% for ResNet-50, 2 and 3% for Inception ResNet and a 9 and 10% increment for the Xception model. When the images were corrupted from a single noise alone, 95.6% accuracy was achieved using the ResNet-50 model for Gaussian noise, 99.65% accuracy was achieved through Inception ResNet-v2 for Poisson noise, and 99.95% accuracy was achieved by the Xception model for speckle noise.
Introduction
The identification of cracks on different types of structures has always been tedious and time consuming work. Regular checks have to be made in order to prevent any serious damage to infrastructure. Traditional inspections would require the use of specialized personnel to manually check for any cracks. This process is greatly complicated when it has to be done in areas such as roads, bridges and highways. It can cause disturbance to regular work or create traffic due to the need to employ additional platforms or machinery for aiding in the inspection process. Furthermore, after the examination, the reports are usually checked manually to identify the underlying issues. This procedure is time consuming and costly to implement. In order to reduce the cost, time and labor involved in such scenarios, the use of unmanned aerial vehicles (UAV) and transfer learning methods can be used to identify cracks. Once a crack is identified, it can be separated, based on the severity level. This tedious process would be simplified by automation. The main goal of this research article is to improve this nondestructive method of investigation which can be employed at a considerably lower cost, while maintaining good accuracy. The convolutional neural network (CNN) method is one of the most efficient network methods which can be used
1.
A deep learning model for crack detection using image processing for computer vision is proposed; 2.
In order to detect whether the image has been affected by noise, a unique technique which uses pixel-intensity resemblance is implemented; 3.
A binarization-skeletonization-edge detection (BSE) algorithm is proposed for estimating the width of cracks. Based on the width, the images are segregated into high-risk, medium-risk and low-risk cracks using preset thresholds.
Section 2 elaborates the available literature on crack detection. Section 3 explains the proposed work. Sections 4 and 5 discuss the results and conclusions.
Background
Cracks can be detected using basic machine learning algorithms. As opting for deep learning is more fruitful in terms of accuracy and speed, many state-of-the-art techniques have concentrated in these techniques. The research article by Raza Ali et al. [6] surveyed different CNN-based algorithms and stated that Unet was the best performer when compared to Pixelnet, Alexnet, Googlenet and a few other algorithms. V Mandal et al. [3] was able to detect cracks in real time by mounting a camera on the dashboard of a moving car. Y Zhang et al. [7] used the YOLO v3 algorithm as a base and was able to detect the cracks efficiently by using MobileNet for transfer learning and the convolutional block attention model. The authors of [8] carried out research on various CNN-based algorithms, and found that MobileNet yielded the best accuracy for a masonry dataset. J. K. Chow et al. [9] carried out crack detection on concrete images using a convolutional autoencoder and decoders. Zhong qu et al. [10] and Cheng Wang et al. [11] discussed improving accuracy by using only two convolutional layers and the Inception model. SY Wang et al. [12] compared R-CNN-based ResNet, visual geometry group (VGG) and feature pyramid network (FPN). It was concluded that VGG16 took less time and memory to detect cracks, but yielding the lowest accuracy. ResNet-50 gave the highest accuracy but took more time and some extra memory.
The addition of certain pre-processing steps can be of great value; they can make or break an algorithm. A very good example would be the research of Thendral et al. [13], where cracks on railway tracks were collected using a camera on a self-moving vehicle and various pre-processing procedurees were carried out to classify the cracks appropriately. Similarly the research by Zhong Qu et al. [10] proved that, using a simple technique of dividing an image into smaller patches, considerable improvement can be achieved, compared to most of the state-of-the-art deep learning models. CV Dung et al. [14] used a fully convolutional network (FCN) and scanned the dataset for common features on crack images and classified the images. Using the FSM module, UH Billah et al. [15] found the weak features of the dataset and eliminated them. They concatenated the encoder-decoder modules and upscaled the remaining features. This method is particularly useful when a dataset has different types of images. Although it improves the accuracy, this method is highly sensitive to the input data.
The research of Zhang et al. [16] relies heavily on the concept of feature fusion. The crack images are very susceptible to noise. It cannot be guaranteed that all the images can be taken in well-lit conditions. In similar research, the researcher used a multiscale-fusion generative adversarial network (GAN) to improve the quality of the output images while preserving the features of the original images. However, they assumed the noise type to be Gaussian and the variance to be between 0.05 and 0.2 [17,18]. Junmei Zhong et al. [19] took a different approach for reducing the noise; by using orthogonal wavelet transform (OWT), the higher scale levels are preserved, and noise in the lower level is filtered by using minimum mean squared error. Although this noise reduction method was yielding better results, they only reported it for images with Gaussian noise. Ehsan Akbari Sekehravani et al. [20], utilized the Canny algorithm for edge detection. Traditionally Canny is implemented with Gaussian filter, but to counteract any type of noise, the authors utilized a filtering approach. Another unique method is denoising the images using the Wiener filter and detecting cracks by the Otsu method [21]. Kittipat Sriwong et al. [22] and discussed various CNN-based algorithms for efficient crack detection. Even though the technique implemented in [23] was not able to carry out a proper categorization of the crack image, it could detect cracks even on road markings using ResNet-v2 algorithm. By adding feature fusion and network in network (NIN) modules, the edges were highlighted and also prevented the loss of model features, in the meanwhile reducing the time complexity. In [24], to inspect the severity of the crack, the authors used crack magnifier. The deep learning models such as VGG-16, ResNet50 and Inception ResNet-V2 are discussed in [25][26][27]. Paramanandham et al. [28] discussed about concrete crack detection using various deep learnbing models. Qi Chen et al. [29] used the guided filter approach for the removal of noise and analyzed the characterization of the crack structure using Hessian structures followed by refinement process. The authors achieved around 90% in precision, recall and F1 measurements through the implemented approach. Dawei Li et al. [30] developed a defect detection system for metro tunnel surfaces. Junjie Chen and Donghai Liu [31] proposed a model for detecting damage in the water channel based on super pixel segmentation and classification and achieved an accuracy around 91%. Miguel Carrasco et al. [32] discussed a methodology for measuring the width of cracks using smoothing, filtering, segmentation and estimation. The authors of [33][34][35][36][37][38][39][40][41] proposed several techniques based on CNN, pyramidal residual network for concrete crack detection, binocular vision system for pipe crack and deformation detection and also analyzed the performance of the techniques. From the literature, it can be identified that the existing techniques for detecting cracks can be classified into two broad domains. One is based on the combination of several networks or concentrating on segmentation of cracks. Hence, the proposed technique concentrated on overcoming the limitations in the detection of cracks even though the images are corrupted or have dissimilar structures. Figure 1 shows the general block diagram for crack detection. Once the images are acquired, the database is created. Before classifying the images into crack and non-crack, pre-processing procedures such as removal of noise, contrast enhancement, change in resolution, etc., can be performed to obtain enhanced results. Once the crack has been detected, it can be assessed through evaluation parameters.
Proposed Method
The cracks are detected for both noisy and noiseless environment images captured from various surfaces. To accomplish this, the proposed technique consists of three processes, namely, pixel-intensity resemblance measurement, crack detection using a deep learning model and classification based on the width of the crack. As shown in Figure 2, in the pre-processing stage, the filtering process and the following step (i.e.,) pixel-intensity resemblance algorithm were used for measuring similar pixels. In this pixel-matching technique, the images to be tested are passed through a common filter. The filtered image pixels are compared with the original image. The number of mismatched pixels is calculated and, according to that calculation, the type of noise is determined. Once the type of noise is identified, the proper denoising filters are used for the removal of noise. The filtered images are then segregated properly on the basis of whether the images are inclusive of noise or not. Once the separation filtering of possible noisy images is completed, the images are then passed through a crack-detection model. The images in which cracks have been detected are then passed through the last stage of the algorithm where the width of the crack is determined; by doing so, it segregates the various cracked images into three different categories based on the severity level, namely, high, medium and low so that the appropriate actions can be taken without any delay.
To examine the efficiency of the implemented technique under several noise conditions, images were generated with different noises with Gaussian, salt and pepper, and speckle with various mean and variance levels. The Gaussian noise model [37] is expressed in Equation (1). where σ denotes the standard deviation, g indicates the gray value and µ represents the mean value.
The binary noise is also called impulse noise and salt and pepper, as its value is either 0 or 255. Speckle noise is also termed as multiplicative noise [37]. It occurs in the same way in an image as Gaussian noise. It is expressed in Equation (2).
The proposed crack detection model was developed in view of the following parameters:
Proposed Method
The cracks are detected for both noisy and noiseless environment images captured from various surfaces. To accomplish this, the proposed technique consists of three processes, namely, pixel-intensity resemblance measurement, crack detection using a deep learning model and classification based on the width of the crack. As shown in Figure 2, in the pre-processing stage, the filtering process and the following step (i.e.,) pixel-intensity resemblance algorithm were used for measuring similar pixels. In this pixel-matching technique, the images to be tested are passed through a common filter. The filtered image pixels are compared with the original image. The number of mismatched pixels is calculated and, according to that calculation, the type of noise is determined. Once the type of noise is identified, the proper denoising filters are used for the removal of noise. The filtered images are then segregated properly on the basis of whether the images are inclusive of noise or not. Once the separation filtering of possible noisy images is completed, the images are then passed through a crack-detection model. The images in which cracks have been detected are then passed through the last stage of the algorithm where the width of the crack is determined; by doing so, it segregates the various cracked images into three different categories based on the severity level, namely, high, medium and low so that the appropriate actions can be taken without any delay.
To examine the efficiency of the implemented technique under several noise conditions, images were generated with different noises with Gaussian, salt and pepper, and speckle with various mean and variance levels. The Gaussian noise model [37] is expressed in Equation (1).
where σ denotes the standard deviation, g indicates the gray value and µ represents the mean value. The binary noise is also called impulse noise and salt and pepper, as its value is either 0 or 255. Speckle noise is also termed as multiplicative noise [37]. It occurs in the same way in an image as Gaussian noise. It is expressed in Equation (2).
The proposed crack detection model was developed in view of the following parameters: 1.
Cracks should be detected on any surfaces captured from any device under any environment; 2.
Time complexity is considered; 3.
Once a crack is identified, it should be categorized and an immediate alert will be given to the authority in order to avoid major accidents.
1. Cracks should be detected on any surfaces captured from any device under any environment; 2. Time complexity is considered; 3. Once a crack is identified, it should be categorized and an immediate alert will be given to the authority in order to avoid major accidents.
Filtering and Pixel-Intensity Resemblance Measurement for Noise Classification
The images of any structure or surface taken from a drone or some other device are classified into noisy and noiseless (very little noise) images, based on the measurement of pixel-intensity resemblance and a filter-based approach. To classify the images accurately, the images are initially passed through a common filter. The filtered image pixels are compared to the database images for finding similarities between the pixels. After extensive study of filters and from Table 1, it was found that the median filter yielded better results when compared to all other filters for the proposed technique. Hence, all the images were passed through the median filter and the filtered image was then given to the next stage. A median filter is a non-linear digital filtering technique that is often used in the pre-processing of images as it helps remove the noise efficiently but preserves the edge details. It is very useful for edge detection and other image-based detection methods.
Filtering and Pixel-Intensity Resemblance Measurement for Noise Classification
The images of any structure or surface taken from a drone or some other device are classified into noisy and noiseless (very little noise) images, based on the measurement of pixel-intensity resemblance and a filter-based approach. To classify the images accurately, the images are initially passed through a common filter. The filtered image pixels are compared to the database images for finding similarities between the pixels. After extensive study of filters and from Table 1, it was found that the median filter yielded better results when compared to all other filters for the proposed technique. Hence, all the images were passed through the median filter and the filtered image was then given to the next stage. A median filter is a non-linear digital filtering technique that is often used in the pre-processing of images as it helps remove the noise efficiently but preserves the edge details. It is very useful for edge detection and other image-based detection methods. The filtered images are passed through the pixel-matching algorithm where the pixels of the original image and the filtered image are compared and the number of matched and mismatched pixels are computed. The code works by extracting the intensity of pixels that have the same coordinates within the image. If the pixel intensity of both images matches, then it is marked as a common pixel, otherwise it is denoted as a mismatched pixel. In Figure 3, the yellow output is the mismatched pixels while the purple output is when the pixels match. The filtered images are passed through the pixel-matching algorithm where the pixels of the original image and the filtered image are compared and the number of matched and mismatched pixels are computed. The code works by extracting the intensity of pixels that have the same coordinates within the image. If the pixel intensity of both images matches, then it is marked as a common pixel, otherwise it is denoted as a mismatched pixel. In Figure 3, the yellow output is the mismatched pixels while the purple output is when the pixels match. For each image, the mismatches are compared against certain thresholds and the decision is made whether to use the original image or the filtered image. If the number of mismatched pixels is between 15 and 100 then the original image is passed through to the deep learning model, as the filtering of images with supposedly very little or no noise ends in needless loss of the image. The images whose pixel mismatch range is over 100 are deemed to be noisy, the original image is first filtered and then passed through the detection algorithm.
Noise Estimation
In order to decide the filter that should be used for denoising, the level of noise is estimated and the flow for the estimation is explained using the Equations (3)- (8). Noise is estimated for various types of noises with different mean and variance levels. Let us consider an image I with the patch size p, row R and column C and dataset D that is specified in Equations (3) and (4) I ϵ A RXCX3 , and where D contains s = (R − d + 1) (C − d + 1) patches with size q = 3p 2 For each image, the mismatches are compared against certain thresholds and the decision is made whether to use the original image or the filtered image. If the number of mismatched pixels is between 15 and 100 then the original image is passed through to the deep learning model, as the filtering of images with supposedly very little or no noise ends in needless loss of the image. The images whose pixel mismatch range is over 100 are deemed to be noisy, the original image is first filtered and then passed through the detection algorithm.
Noise Estimation
In order to decide the filter that should be used for denoising, the level of noise is estimated and the flow for the estimation is explained using the Equations (3)- (8). Noise is estimated for various types of noises with different mean and variance levels. Let us consider an image I with the patch size p, row R and column C and dataset D that is specified in Equations (3) and (4) I A RXCX3 , and where D contains s = (R − d + 1) (C − d + 1) patches with size q = 3p 2 Computing the Eigen values {λ i } q j=1 of the covariance matrix Σ with q = p 2 and order λ 1 ≥ λ 2 ≥ . . . ≥ λ r For j = 1: q, median τ is calculated If τ is the median of the set {λ k } q k=1 then, where σ represents the estimated noise level. If the estimated range is within ±5, it is lying under Gaussian noise and the Wiener filter is chosen for denoising these types of images as it is more appropriate. Similarly, an image with the estimated range 15 to 25 specifies speckle noise, the mean filter is used for denoising and if it is greater than 30, it shows the image is corrupted due to salt and pepper. If the images are corrupted by salt and pepper noise, the median filter is used for the removal of noise. Figure 4 shows the noise estimation of the proposed technique.
where σ represents the estimated noise level. If the estimated range is within ±5, it is lying under Gaussian noise and the Wiener filter is chosen for denoising these types of images as it is more appropriate. Similarly, an image with the estimated range 15 to 25 specifies speckle noise, the mean filter is used for denoising and if it is greater than 30, it shows the image is corrupted due to salt and pepper. If the images are corrupted by salt and pepper noise, the median filter is used for the removal of noise. Figure 4 shows the noise estimation of the proposed technique.
VGG-16 Architecture
The VGG-16 consists of 13 convolutional layers and three fully connected layers as shown in Figure 5. A set of filters comprises a convolutional layer which is an essential block in any convolutional neural network. VGG-16 [25] has 13 of them. The parameters of the filters have to be learned. The size of the filter must be relatively less than the input. The features of the training set are extracted only using convolutional layers. The next layer in VGG-16 is the pooling layer. Generally, pooling layers are added between two convolutional layers. Pooling layers reduce the number of parameters between successive layers. There are two pooling functions, namely average and max pooling. Max pooling is generally preferred as it functions more efficiently. The flattened layer in VGG-16 converts feature maps into 1D tensors. The last layer is the fully connected layer which gives the output of the model.
VGG-16 Architecture
The VGG-16 consists of 13 convolutional layers and three fully connected layers as shown in Figure 5. A set of filters comprises a convolutional layer which is an essential block in any convolutional neural network. VGG-16 [25] has 13 of them. The parameters of the filters have to be learned. The size of the filter must be relatively less than the input. The features of the training set are extracted only using convolutional layers. The next layer in VGG-16 is the pooling layer. Generally, pooling layers are added between two convolutional layers. Pooling layers reduce the number of parameters between successive layers. There are two pooling functions, namely average and max pooling. Max pooling is generally preferred as it functions more efficiently. The flattened layer in VGG-16 converts feature maps into 1D tensors. The last layer is the fully connected layer which gives the output of the model.
VGG-16 Architecture
The VGG-16 consists of 13 convolutional layers and three fully connected layers as shown in Figure 5. A set of filters comprises a convolutional layer which is an essential block in any convolutional neural network. VGG-16 [25] has 13 of them. The parameters of the filters have to be learned. The size of the filter must be relatively less than the input. The features of the training set are extracted only using convolutional layers. The next layer in VGG-16 is the pooling layer. Generally, pooling layers are added between two convolutional layers. Pooling layers reduce the number of parameters between successive layers. There are two pooling functions, namely average and max pooling. Max pooling is generally preferred as it functions more efficiently. The flattened layer in VGG-16 converts feature maps into 1D tensors. The last layer is the fully connected layer which gives the output of the model.
ResNet-50 Architecture
ResNet-50 is a variant of ResNet with 50 neural network layers [26] as shown in Figure 6, redrawn from [39]. Over the years, the higher accuracy and efficiency of neural network models have been achieved by deepening the neural network model, i.e., adding more layers and blocks or changing the filter size. This, however, is not always the case. Adding more and more layers can also cause performance degradation in deep learning. In order to overcome this, residual networks which are made up of residual blocks have been invented. The concept of skip connection is being introduced in residual models. While training a model, the skip connections skip some of the layers in the model (layers that are skipped vary from model to model). The output of one layer is fed as the input to another layer. This basically solves the problem of vanishing gradients in deep neural networks. The skip connections also ensure that the higher layers and lower layers of a model perform efficiently. The residual blocks in the model help to increase efficiency as learning becomes much easier.
ResNet-50 Architecture
ResNet-50 is a variant of ResNet with 50 neural network layers [26] as shown in Figure 6, redrawn from [39]. Over the years, the higher accuracy and efficiency of neural network models have been achieved by deepening the neural network model, i.e., adding more layers and blocks or changing the filter size. This, however, is not always the case. Adding more and more layers can also cause performance degradation in deep learning. In order to overcome this, residual networks which are made up of residual blocks have been invented. The concept of skip connection is being introduced in residual models. While training a model, the skip connections skip some of the layers in the model (layers that are skipped vary from model to model). The output of one layer is fed as the input to another layer. This basically solves the problem of vanishing gradients in deep neural networks. The skip connections also ensure that the higher layers and lower layers of a model perform efficiently. The residual blocks in the model help to increase efficiency as learning becomes much easier.
Inception ResNet-V2
Inception ResNet-v2 basically uses the inception architecture combined with the residual connections from the ResNet network. The major improvement from the traditional model is the addition of a filter expansion layer to scale up the dimensionality of the filter bank before the addition to match the depth of input. The network has a total of 164 layers, as shown in Figure 7, redrawn from [39], and can classify the images in up to 1000 different categories, in the same way as the VGG-16 and ResNet-50. The input size of this network is 299 × 299 and the output is a list of estimated class probabilities.
Inception ResNet-V2
Inception ResNet-v2 basically uses the inception architecture combined with the residual connections from the ResNet network. The major improvement from the traditional model is the addition of a filter expansion layer to scale up the dimensionality of the filter bank before the addition to match the depth of input. The network has a total of 164 layers, as shown in Figure 7, redrawn from [39], and can classify the images in up to 1000 different categories, in the same way as the VGG-16 and ResNet-50. The input size of this network is 299 × 299 and the output is a list of estimated class probabilities.
Inception ResNet-v2 basically uses the inception architecture combined with the residual connections from the ResNet network. The major improvement from the traditional model is the addition of a filter expansion layer to scale up the dimensionality of the filter bank before the addition to match the depth of input. The network has a total of 164 layers, as shown in Figure 7, redrawn from [39], and can classify the images in up to 1000 different categories, in the same way as the VGG-16 and ResNet-50. The input size of this network is 299 × 299 and the output is a list of estimated class probabilities.
Xception Model
The Xception model uses depth-wise separable convolutions and works as shown in Figure 8a-c, redrawn from [27]. A general convolution step makes the spatial-wise and channel-wise computation in one single step. However, on the other hand, the depth-wise separable convolution divides the process of computation into two different
Xception Model
The Xception model uses depth-wise separable convolutions and works as shown in Figure 8a-c, redrawn from [27]. A general convolution step makes the spatial-wise and channel-wise computation in one single step. However, on the other hand, the depth-wise separable convolution divides the process of computation into two different steps. The depth-wise convolution initially adds a single convolutional filter to each input channel. It is then followed by point-wise convolution which creates a linear combination of the output from the depth-wise convolution. This method improves the efficiency of the model.
The word "Xception" literally translates as "extreme inception". It basically means that the properties of the inception model are extremized to give better results. In the traditional inception neural network model, the original input image was compressed using a one-by-one convolution. After this, different types of filters were used on each depth space. However, in the Xception model, this step is reversed. Here, the filter is applied in the first step of the depth map and then the compression of the input takes place. This technique is called depth-wise convolution. The Xception model also does not introduce non-linearity which was the case in the inception model. This is also yet another difference between the models.
Crack Segregation Based on BSE Algorithm
The images that are identified with cracks are passed to the proposed crack risk analysis algorithm (binarization-skeletonization-edge detection-BSE) where the width of the crack is estimated. Based on the width, the images are segregated into high-risk, medium-risk and low-risk cracks by the preset threshold.
Crack risk analysis using BSE algorithm: 1. Image binarization: this is the operation of dividing the image into black/white pixels in order to separate the cracks and non-cracks within the image; 2.
Skeletonization: extracts the central skeleton of the crack which helps to identify the progression of the crack. Hence, it is possible to find the crack width by drawing a line perpendicular to the crack propagation direction at the pixel on the skeleton; 3.
Edge detection: extracts the outline of the crack. From the skeleton, the line perpendicular to the crack propagation direction and the crack outline are used together to find the crack width.
Results and Discussion
Once the type of noise is estimated, the appropriate filters are applied. These images are converted into gray scale images and the width is calculated. The crack estimation accuracy is drastically increased when the images are denoised based on the proposed technique. For calculating performance evaluation parameters, confusion matrix is used and it is represented in Table 2. Tables 3 and 4 show the accuracy of various deep learning models before and after denoising. From Tables 3 and 4, it is proved that the proposed technique is efficient in denoising and detecting the cracks. Figure 9 shows the visual representation of crack width prediction. Once the crack was identified, it was classified into high risk, medium risk and low risk and these are shown in Figures 10-14. The high-risk cracks would be immediately alerted to the authority to ensure avoidance of major disasters or accidents and to prevent a calamity. The work was implemented using Python software in Google Colab. The proposed system will be very helpful to many industries and public transport authorities, including bridges on the pathway. To assess the effectiveness of the proposed technique, it was compared with state-of-the-art techniques such as Auto-CAE [9], ResNet-50 [26], Crack Hessian [29] and Seg + SVM [30] and the results are tabulated in Table 5. The high-risk cracks would be immediately alerted to the authority to ensure avoidance of major disasters or accidents and to prevent a calamity. The work was implemented using Python software in Google Colab. The proposed system will be very helpful to many industries and public transport authorities, including bridges on the pathway. To assess the effectiveness of the proposed technique, it was compared with state-of-the-art techniques such as Auto-CAE [9], ResNet-50 [26], Crack Hessian [29] and Seg + SVM [30] and the results are tabulated in Table 5. By searching the skeleton of the image through breadth-first search (BFS), the direction of the crack was estimated. Then the distance was calculated when the line of perpendicular met the edge of the crack, as shown in Figure 9. This was repeated multiple times until various widths were covered and the average of the distances obtained was used as the estimated width value of the crack. Figure 10 shows some sample images collected from industry. With multiple hand-selected images of various degrees of severity in the crack, accurate thresholds for high, medium, and low risk were identified by the proposed model. Figure 11a,b shows the sample of predicted output of the model "No Crack", even though the images have many irregularities, grainy surface and complicated structure. Figure 12a,b shows the predicted output of the model "Low-Risk Crack", Figure 13a,b shows the classified output of "Medium-Risk Crack". Once a Figure 14. (a,b) Sample output images from the model classified as "High-Risk Crack". By searching the skeleton of the image through breadth-first search (BFS), the direction of the crack was estimated. Then the distance was calculated when the line of perpendicular met the edge of the crack, as shown in Figure 9. This was repeated multiple times until various widths were covered and the average of the distances obtained was used as the estimated width value of the crack. Figure 10 shows some sample images collected from industry. With multiple hand-selected images of various degrees of severity in the crack, accurate thresholds for high, medium, and low risk were identified by the proposed model. Figure 11a,b shows the sample of predicted output of the model "No Crack", even though the images have many irregularities, grainy surface and complicated structure. Figure 12a,b shows the predicted output of the model "Low-Risk Crack", Figure 13a,b shows the classified output of "Medium-Risk Crack". Once a high-risk crack is detected, an immediate alert will be given to the authority and the necessary action will be taken to avoid accidents. The high-risk-classified crack images are shown in Figure 14a,b. Even though the images have different surface properties, the proposed model can effectively classify according to category. A confusion matrix was generated for all the models to assess the efficiency of the models and this is given in Table 2.
This table indicates the predictions made by the model and how right/wrong those predictions were. The parameters such as accuracy, precision, recall and F1 score were calculated using Equations (9)-(12), respectively.
Discussion and Conclusions
The adverse effects of noise on image-based detection methodologies, especially in the detection of cracks, are successfully identified in this article by exploring various deep learning algorithms. The discussed deep learning models showed around 30-50% decrease in accuracy when the test images were noisy. To counteract this, the noise was estimated and the appropriate filters were used for denoising using the developed technique. From the results, it was identified that the implemented technique had different effects on the various models. When the dataset contained all the types of images excluding the images corrupted from Gaussian noise, speckle noise, Poisson noise, salt and pepper noise, the proposed technique achieved 6% improvement without PIRM and 10% improvement with the PIRM rule for the VGG-16 model. Similarly, it showed a 3 and 10% improvement for ResNet-50, a 2 and 3% improvement for Inception ResNet and a 9 and 10% improvement for the Xception model. When the images were corrupted from single noise, 95.6% accuracy was achieved using the ResNet-50 model for Gaussian noise, 99.65% accuracy was achieved through Inception ResNet-v2 for Poisson noise, and 99.95% accuracy was achieved by the Xception model for speckle noise.
From these results, it was concluded that the ResNet-50 model was the most suitable both when the test images contained no noise, as well as for all types of noisy images, achieving 95.78% with the proposed technique. To evaluate the performance of the developed technique, it was compared with state-of-the-art techniques and the obtained results depicted that the proposed technique outperformed the existing techniques. Thus, it can be concluded that the pixel-intensity resemblance measurement, noise estimation and crack classification-based technique proposed here is most suitable for all types of real-time images taken from any environment.
In future, the authors plan to work on the limitations of the proposed work, i.e., detecting cracks and uneven surfaces occurring in various materials such as steel, iron, and compound cylindrical structures due to strain or some other external environmental factors.
Institutional Review Board Statement:
The study did not require ethical approval.
Informed Consent Statement: Not Applicable.
Data Availability Statement: Publicly available data is used and cited appropriately. | 8,575 | 2023-03-01T00:00:00.000 | [
"Computer Science"
] |
Seasonal dependence of the longitudinal variations of nighttime ionospheric electron density and equivalent winds at southern midlatitudes
It has been indicated that the observed Weddell Sea anomaly (WSA) appeared to be an extreme manifestation of the longitudinal variations in the Southern Hemisphere, since the WSA is characterized by greater evening electron density than the daytime density in the region near the Weddell Sea. In the present study, the longitudinal variations of the nighttime F2-layer peak electron density at southern midlatitudes are analyzed using the observations of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) satellites between 2006 and 2008. It is found that significant longitudinal difference (> 150 %) relative to the minimum density at each local time prevails in all seasons, although the WSA phenomenon is only evident in summer under this solar minimum condition. Another interesting feature is that in summer, the maximum longitudinal differences occur around midnight ( ∼ 23:00– 00:00 LT) rather than in the evening (19:00–21:00 LT) in the evening, when the most prominent electron density enhancement occurs for the WSA phenomenon. Thus the seasonal– local time patterns of the electron density longitudinal variations during nighttime at southern midlatitudes cannot be simply explained in terms of the WSA. Meanwhile, the variations of the geomagnetic configuration and the equivalent magnetic meridional winds/upward plasma drifts are analyzed to explore their contributions to the longitudinal variations of the nighttime electron density. The maximum longitudinal differences are associated with the strongest windinduced vertical plasma drifts after 21:00 LT in the Western Hemisphere. Besides the magnetic declination–zonal wind effects, the geographic meridional winds and the magnetic inclination also have significant effects on the upward plasma drifts and the resultant electron density.
Introduction
In the Southern Hemisphere, significant longitudinal variations of the ionospheric electron density occurred at midlatitudes in evening due to the Weddell Sea anomaly (WSA).The intensively reported Weddell Sea anomaly is a diurnal cycle anomaly that is characterized by greater nighttime density than daytime density in the ionosphere instead of a typical diurnal cycle with a midday maximum and a midnight minimum (e.g., Liu et al., 2010).The WSA phenomenon has recently attracted wide attention in the community.Similar but much weaker summer evening enhancements also occur in the North American sector and East Asian sector (Lin et al., 2009(Lin et al., , 2010)), known as midlatitude summer nighttime anomaly (MSNA).Several mechanisms are applied to explain these interesting phenomena, including the equatorward neutral wind, electric field, photoionization and the downward plasma diffusion from the plasmasphere (Burns et al., 2008(Burns et al., , 2011;;Jee et al., 2009;He et al., 2009;Chen et al., 2011Chen et al., , 2012;;Liu et al., 2010;Lin et al., 2009;Zhang et al., 2011Zhang et al., , 2012a, b;, b;Zhao et al., 2013).Among those explanations, the magnetic configuration and the neutral winds are considered as important and even major contributors.The magnetic meridional winds can drag the charged particles X. Luan and X. Dou: Seasonal dependence of the longitudinal variations moving along the magnetic field line, which will push the plasma up (down) when the winds are equatorward (poleward).The resultant height change of the plasma has a further impact on plasma density through changing the electron recombination, which decays exponentially with the increase of altitude.
The observed WSA has been indicated as an extreme manifestation of the longitudinal variations (Jee et al., 2009).It can occur at wide latitudinal locations (∼ 30-70 • S) in summer in the Southern Hemisphere, and the strongest summer evening enhancement of F2-layer peak density occurs centered around 50-60 • S, 90 • W at midlatitudes (He et al., 2009;Lin et al., 2010;Liu et al., 2011;Burns et al., 2011).In recent years, longitudinal differences of electron density, or the density longitudinal asymmetries, have been studied over east-west regions of the continental US and the Far East area at northern midlatitudes (Zhang et al., 2011(Zhang et al., , 2012a, b;, b;Zhao et al., 2013).In both the northeast of East Asia and the continental US, the electron density diurnal variations show some similarity to the WSA in summer (Lin et al., 2009;Lin et al., 2010).Interestingly, Zhang et al. (2011Zhang et al. ( , 2012a, b) , b) showed that in the North American sector, the most significant longitudinal difference occurs during nighttime in winter and the solar minimum, which is possibly associated with the strongest nighttime zonal winds.Their results present a vital contribution of the zonal wind to electron density longitudinal variations under special magnetic configuration, i.e., a magnetic declination change between westward (negative) and eastward (positive) at midlatitudes.These magnetic declination-zonal wind effects favor more equatorward magnetic meridional wind and thus the resultant upward plasma drift in eastern and northern US coast, when the zonal wind is eastward at night.Zhang et al. (2012b) found good correlation between the longitudinal variation of the ionospheric electron density (Ne) from ISR and the zonal wind from FPI at Millstone Hill (42.5 • N, 288.6 • E).Zhao et al. (2013) examined the climatology of peak electron density (NmF2) in the Far East regions, and provided evidence of the longitudinal density change further supporting the thermospheric zonal wind mechanism.
For a long time there have been too few wind observations to examine the wind effects on ionospheric density.Except for the study from Zhang et al. (2012b), the neutral winds analyzed in previous studies were either given by an artificial value or were from an empirical model (e.g., Jee et al., 2009;Liu et al., 2010;Chen et al., 2012;Zhao et al., 2013).Also, the effects of magnetic inclination and geographic meridional winds are seldom discussed.Although the magnetic meridional winds result from both the geographic meridional and zonal winds, Zhang et al. (2012b) assumed insignificant effects of the geographic meridional winds for the North American sector.In addition, the windinduced vertical drifts of plasma are modulated by the magnetic inclinations.He et al. (2009) compared the variations of the F2-layer peak electron density and peak height with the magnetic declination and inclination angles, indicating contributions from the magnetic inclination in the Southern Hemispheres as well.It is expected that a similar positive magnetic declination-zonal wind effect on electron density will occur in the Southern Hemisphere at locations where the magnetic declination is eastward (positive).In the southern midlatitudes, the geomagnetic configurations are more longitudinally asymmetric than in the northern latitudes, and thus it is interesting to further examine the electron density longitudinal variations and the roles of neutral winds.Further, at southern midlatitudes, most studies have focused on local time evolution of the electron density in regions where the WSA phenomenon occurs, whereas little attention has been given to the quantitative comparison of the electron density longitudinal patterns for given local times.
This study will carry out a quantitative study on the longitudinal variations of the F2-layer peak electron density in the southern midlatitudes in order to examine the linkage of these variations with the magnetic configuration (both the declination and inclination) and the simultaneous magnetic meridional winds and wind-induced vertical plasma drifts.The global ionospheric F2-layer peak density retrieved from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) satellites' measurements will be used in this study.The corresponding magnetic meridional winds will be derived from a servo model on the basis of the observed F2-layer peak density and peak height (e.g., Luan and Solomon, 2008).
Database and analysis method
The electron density profiles retrieved from all COSMIC satellites (Lei et al., 2007;Schreiner et al., 2007) were collected over a period of about two years (April 2006-February 2008), a period that corresponded to solar minimum conditions.Then the F2-layer peak height (hmF2) and peak density (NmF2) were calculated from each profile.These data were divided into three seasons, i.e. equinox, summer and winter.For each season, data within ±45 day from either solstice or equinoctial day were collected.Then the data were binned in the grid within 5 • in latitude, 20 • in longitude in the geographic coordinate, and within 1 h in local time.The median data in each season and each grid were used for final analysis.At each instance of local time, the longitudinal difference ratio of the electron density is calculated by the format of r = (Ne − Ne min )/Ne min • 100 % along geographic longitudes, where Ne min represents the minimum NmF2 among all longitudes.
Using the median F2-layer peak height and peak density, the simultaneous meridional winds along the magnetic meridian are then derived using a servo model (Rishbeth, 1967;Rishbeth et al., 1978) in each location of data binned.The servo method and similar techniques are based on a nearly proportional relationship between the winds and the variation of the F2-layer peak height (Rishbeth, 1967;Rishbeth et al., 1978;Buonsanto et al., 1989Buonsanto et al., , 1997;;Miller et al., 1997;Liu et al., 2003).For the servo model, a balance height is assumed to be established in the F2-layer due to chemical loss and plasma diffusion in the absence of any applied drifts induced by neutral winds and electric fields.The true F2 peak height results from the applied drifts on the balance height.In our calculations, ion and electron temperatures are obtained from the empirical IRI-2001 model (Bilitza, 2001), and the neutral density and temperature from MSISE00 model (Picone et al., 2002).Among all the input parameters to calculate the meridional winds, the hmF2 and ion-neutral collision frequency are the most important ones.A detailed description of the calculation method is given by Luan and Solomon (2008).
This kind of method has been found to present a good climatology of the magnetic meridional winds (e.g., Liu et al., 2003;Luan and Solomon, 2008) as compared to ISR and FPI observations, especially during nighttime.The servo wind includes both the contribution from neutral winds and that from electric field drifts.The pure neutral wind effects can be estimated at magnetic midlatitudes, where the effect of north-perpendicular E × B drift (V ⊥N ) can be ignored under conditions without large geomagnetic disturbances (Luan and Solomon, 2008, and references therein).
In this study, we consider the effects from both the magnetic declination and inclination on neutral winds and the electron density.The magnetic meridional winds (U M , referred to as meridional winds in the following) are contributed by the geographic meridional winds (V GM , equatorward positive), the geographic zonal winds (U GZ , eastward positive), and the magnetic declination angle (D), as expressed by Eq. ( 1): where the signs "−" and "+" correspond to winds in the Northern and Southern Hemisphere, respectively.The eastward declination is positive and the westward declination is negative.
The projection of the magnetic meridional winds on the magnetic field line is the one that works to pull down or lift up the plasma.Thus the vertical drifts (W ) of the plasma, which are produced by neutral winds, are modulated by the absolute magnetic inclination angle (I ), which can be expressed as follows: where we define f meri = cos(D) • cos(I ) • sin(I ) and f zon = ± sin(D)•cos(I )•sin(I ), which represent the modulation coefficients of the geographic meridional and zonal winds on the vertical plasma drifts, respectively.In the Southern Hemisphere, f zon = sin(D) • cos(I ) • sin(I ).These two factors are determined by the geomagnetic declination and inclination.
To some extent, these two factors represent the contribution rates of the geographic meridional and zonal winds to the vertical drifts of plasma.The International Geomagnetic Reference Field (IGRF) model is used to calculate the geomagnetic field (Maus et al., 2005).
Results
Figure 1 shows longitudinal variations of the F2-layer peak electron density between 18:00 and 03:00 LT during local equinox, summer and winter at 40 • S. The percentage difference of the electron density relative to the minimum one along longitude for each local time is shown in Fig. 1b. Figure 1 also presents the corresponding magnetic declination and the contribution rates of the geographic meridional (f meri ) and zonal (f zon ) winds to vertical plasma drifts.These rates are functions of the magnetic declination and inclination angles.As shown in Fig. 1a, electron density is generally higher in western longitudes than in eastern longitudes.The higher electron density occurs roughly in the longitudinal sectors where the magnetic declination is positive (eastward).In summer the WSA phenomenon occurs in the longitudinal sectors west of about 90 • W and east of about 110 • E. At these sectors, daily maximum electron density occurs in the evening after 18:00 LT rather than during the noon-toafternoon hours for a diurnal circle, and the strongest enhancement of NmF2 occurs between 18:00 and 19:00 LT.After 19:00 LT the NmF2 decays continuously.No such evening enhancement is found during equinox and winter.During equinox a nighttime density enhancement occurs before midnight around 110 • W.
As shown in Fig. 1b, a significant longitudinal difference ratio (> ∼ 100 %) due to electron density enhancements occurs in all seasons in broad longitudes of Western Hemisphere, which is mostly under positive geomagnetic declination.The maximum longitudinal enhancement ratio is more than 250 % in summer and more than 150 % in winter.The peak equinoctial ratio is between the magnitudes in summer and winter.It is interesting that the maximum longitudinal enhancement ratio in summer occurs around midnight, i.e., between 23:00 and 02:00 LT.In the region where the WSA occurs, the maximum ratio occurs at around 23:00 LT, which is a few hours later than the occurrence of maximum density enhancement of the WSA phenomenon.During equinox, the peak longitudinal enhancement ratio occurs centered at around 23:00 LT.No evident relative longitudinal enhancement peak is present in winter.
Figure 2 presents results of the absolute (NmF2) and relative (r) electron density variations for 60 • S in the same format as Fig. 1.The longitudinal-seasonal pattern of the highest density in the Western Hemisphere and in summer is generally similar to that at 40 • S. At 60 • S, the WSA phenomenon in summer is located at longitudes west of about 20 • W and east of about 170 • E. Its evening peaks are formed between 18:00 and 23:00 LT.The strongest enhancements and f meri (positive) for vertical plasma drifts from the geographic eastward zonal wind and equatorward meridional wind, respectively.The maximum and minimum locations of these factors are also marked.White dots (left) represent the occurrence time of the peak electron density of the WSA, and white dots (right) mark the maximum r value when it is larger than 50 %.In the bottom panels, blue dashed lines represent the edges of the longitudes where the WSA occurs at this particular latitude.Note that the electron density scales are different in different seasons.See the text for more detail.
occur around 21:00 LT within the 120 to 80 At both 40 and 60 • S, the longitudinal variations of the two rates f zon and f meri are different.Thus the maximum zonal and meridional wind contribution rates occur in different longitudinal locations, which could affect the longitudinal patterns of the electron density.At 40 • S, the zonal and meridional winds will be major contributors to the vertical plasma drifts at ∼ 110 • W and 60 • W, respectively, if they do not vary much along longitude.At 60 • S, these two peaks are separated further away in longitudes, i.e., 110 • W and 50 • W for f zon and f meri , respectively.Further, at 60 • S the peak of f meri is sharper than that at 40 • S. At both latitudes, a separation of the minimum f zon and maximum western declination angle (DEC) occurs due to the geomagnetic inclination.This separation is also larger at 60 • S than at 40 • S.These differences reveal stronger geomagnetic inclination effects at 60 • S than at 40 • S on the longitudinal patterns of the vertical drifts.
Figures 3 and 4 show the derived equivalent magnetic meridional winds (U M , left-hand panels) from the servo method and their resultant vertical plasma drifts (W , righthand panels) for all seasons at 40 • S and 60 • S, respectively.At 60 • S only winds in the western sectors are available, since in other longitudinal sectors, the magnetic latitudes (> 60 • ) fall beyond the reliability of the servo method.The seasonal-longitudinal patterns are generally similar between the winds and drifts at each latitude, except for some small differences.At 40 • S in summer and during equinox, stronger equatorward winds generally occur in the positive DEC sectors before midnight and in the negative DEC sectors after midnight.As a result, the maximum magnetic equatorward winds/upward drifts expand eastward from the evening to early morning hours in these two seasons.This may be due to the direction change of the geographic zonal wind from eastward to westward after midnight in the Southern Hemisphere, as predicted by the NCAR-TIEGCM model (Luan and Solomon, 2008).The magnitudes of the peak equatorward winds along longitudes are comparable during the evening and early morning hours.In winter the longitudinal-local time pattern presents the highest meridional winds between 21:00 and 03:00 LT and at around 180 • W. For both latitudes, the strongest upward drifts (> ∼ 40 m s −1 ) occur during equinox, while longer duration of the relatively stronger upward drifts (> ∼ 30 m s −1 ) occurs in winter in the western sectors.In summer and during equinox, relatively stronger upward drifts start at around 21:00-22:00 LT and last a few hours in the western longitudes.These starting times of upward drifts are later than the occurrence times of the absolute maximum density, but are consistent with the occurrence of a larger relative density ratio along longitudes.
The correlation between the variations of winds and electron density
At midlatitudes, the wind-induced plasma drifts have significant effects on the formation of the ionospheric F2 layer.Figures 1 and 2 show that larger absolute and relative electron density mostly occurs in the Western Hemisphere, where the DEC angles are positive (eastward).These patterns could be associated with the magnetic meridional wind and windinduced plasma drift variations, which respectively exhibit more equatorward and upward components for positive DEC angles during the evening hours.These wind and density patterns are generally consistent with the zonal wind-magnetic declination effects, as studied in the North American and Far East regions (Zhang et al., 2012b;Zhao et al., 2013).However, the observed longitudinal patterns of the electron density should not be attributed to the winds and the windinduced vertical plasma drifts alone.It is shown in summer and during equinox that the maximum-minimum locations of the equatorward winds or upward drifts along longitudes reverse at about 01:00 LT (Fig. 3), but no corresponding changes of the density patterns along longitudes are present after this time (Fig. 1).The ionospheric response between 03:00 and 06:00 LT remains the same as that before 03:00 LT (not shown).This disagreement might be related to the low background electron density in the evening and the lack of the ionization source in the negative DEC region after midnight.Disagreement between winds and density also occurs in their seasonal variation, since maximum upward drifts occur during equinox, while the strongest density enhancements occur in summer.At 40 • S and 60 • S, the maximum absolute and relative densities are corresponding to the equatorward magnetic meridional winds.However, the longitudinal asymmetry in electron density is more severe around midnight than in the early evening in summer.This feature could be related to the local time variations of the magnetic equatorward winds, which are much stronger around midnight than in the early evening (Figs. 3 and 4).Also the longitudinal asymmetry of the magnetic meridional winds in the evening would contribute to the relative density peak during later hours, when a time delay between the winds and electron density is considered (Zhang et al., 2012b).Zhang et al. (2012b) showed that the density is most affected by winds occurring three hours previously, although significant correlation occurs between the simultaneous winds and density.Note that uncertainties in the derived winds/drifts from the servo method might occur, since we use the ion and neutral temperature and neutral density from the empirical models in the Southern Hemisphere for wind calculation.It was revealed that the magnetic meridional winds have shown good consistency with the NCAR-TIEGCM winds for their longitudinal-local time dependence at southern midlatitudes, especially during nighttime (Luan and Solomon, 2008).Thus the possible uncertainties should have no significant effects on the longitudinal patterns of winds/drifts.
The contribution of the geographic meridional winds
The geographic meridional winds were assumed to have little effect on the longitudinal patterns of the electron density in the North American sectors in a previous study (Zhang et al., 2012b).However, in the Southern Hemisphere, their contribution rate (f meri ) varies between 0.2 and 0.5 at 40 • S, and between 0 and 0.5 at 60 • S within 180 • W-180 • E. These amplitudes are comparable to those of the contribution rates of geographic zonal winds (f zon ), especially at 60 • S. Therefore, the maximum NmF2 and the percentage difference ratio r tend to occur at the location between the maximum f meri and f zon .This kind of phenomenon occurs obviously in summer and during equinox at 40 • S and in all seasons at 60 • S. At 60 • S the minimum absolute and relative densities are mostly associated with minimum f zon , although the longitudinal variation amplitudes of f meri and f zon are similar (Fig. 2).This is due to a negative effect of the eastward zonal winds at the negative declination locations.At those locations, the effects from the geographic equatorward meridional winds and eastward zonal winds cancel each other out.At 40 • S, the minimum f meri and minimum f zon locations are separated by 30 • in longitude.This separation is much smaller than that at 60 • S. At 40 • S, the combination of the two minimum results in a low relative electron density ratio (r < 50 %) occurring in wider longitudinal sectors than at 60 • S.
X. Luan and X. Dou: Seasonal dependence of the longitudinal variations
The contribution of the geomagnetic declination and inclination
As introduced in Sect.2, the wind-induced upward plasma drifts are contributed by the geographic meridional and zonal winds, whose effects are modulated by the magnetic inclination and declination.The longitudinal patterns of the absolute and relative density are generally associated with the magnetic declination variations at both 40 and 60 • S.However, the magnetic inclination also contributes to the electron density longitudinal patterns by modulating both the contribution rates of the geographic zonal winds and meridional winds.
The magnetic meridional winds result from the combined effects of the geographic zonal and meridional winds through the modulation of magnetic declination (Eq.1).They can drag the plasma moving along the geomagnetic field line, and thus the plasma's velocity has a component in the vertical direction at midlatitudes, i.e., the vertical plasma drifts (see Eq. 2).These drifts are modulated by a factor of cos I sin I .Under this modulation, from Figs. 3 and 4, the drifts are enhanced within 120 • W-0 • E (Figs. 3 and 4), and weakened within 90 • E-120 • E (Fig. 3), as compared with the patterns in the horizontal winds.These two sectors are in the locations of the larger and smaller geomagnetic latitudes/inclinations, respectively.The above changes of drift pattern relative to the horizontal winds contribute to the relative electron enhancement to the east of ∼ 120 • W in summer and during equinox at 40 • S and in all seasons at 60 • S (Figs. 1 and 2).These changes are also responsible for electron density depletion around 90 • E after midnight for all seasons at 40 • S. At 60 • S, after the modulation of the geomagnetic inclination, a significant peak of the vertical drifts along longitude is present at ∼ 90 • W (winter and equinox) before midnight and at ∼ 50 • W (in all seasons) after midnight.
The geomagnetic inclination separates the minimum f zon from the minimum DEC angle locations at both latitudes (Figs. 1 and 2).This separation is as large as ∼ 50 • in longitude at 60 • S. As a result, the minimum density is located west of the minimum DEC (Fig. 2a).At both 40 and 60 • S, the locations of minimum f zon are well correlated with those of minimum absolute and relative density.This is due to a negative contribution to density by eastward zonal winds under negative DEC conditions.
The WSA and the electron density longitudinal difference
In summer at both 40 and 60 • S, an obvious time delay occurs between maximum absolute and relative density.Further, the seasonal variation of the WSA at solar minimum is different from that of the longitudinal difference ratio of the electron density.Figures 1 and 2 show that at solar minimum, a western enhancement occurs for all seasons for the relative density, while the results from the present and previous studies (e.g., Jee et al., 2009;He et al., 2009) showed that the WSA occurs only in summer.In the evening, the equatorward magnetic meridional winds and the additional plasma source are considered as either the major cause of or important contributions to the WSA (Burns et al., 2008(Burns et al., , 2011;;Jee et al., 2009;He et al., 2010;Lin et al., 2010;Liu et al., 2010;Chen et al., 2011Chen et al., , 2012)).For the WSA, the phase reversal of the density in a diurnal circle is also related to concurrent noontime depletion (Liu et al., 2010).Thus it is indicated that the WSA and the corresponding daytime longitudinal changes are also associated with the midlatitude phenomenon involving the neutral composition changes, such as the annual and semiannual variations of the midday ionospheric F layer (Liu et al., 2010;Zhao et al., 2013).These phenomena are longitudinally dependent due to the circulation of the O-rich air (e.g., Rishbeth, 1998).However, there is a lack of information for the longitudinal variations of the neutral density of N2 during nighttime.A few studies revealed that the annual and semiannual variations of the nighttime electron density are absent at midlatitudes in both hemispheres (e.g., Ma et al., 2003;Natali and Meza, 2011), which suggests the neutral composition may have a lesser effect on the ionospheric electron density longitudinal pattern during nighttime than it has during daytime.
In the present study, the maximum relative longitudinal difference occurs around midnight in summer, which is associated with the occurrence time of maximum upward plasma drifts.This consistency suggests an important contribution of magnetic meridional winds to the longitudinal-local time pattern of the electron density.Additional contribution can be made by the persistent ionization through the entire night in some western longitudes, since the center of maximum relative density is on the western side of the peak upward plasma drifts.Local photoionization has been reported to last all day in December at the southern station of Argentine Islands (Argentine IS) (−65.2 • N, 64.3 • W) (Chen et al., 2012).These combined effects of winds and photoionization may be responsible for a larger relative longitudinal difference in summer than during equinox, although peak vertical plasma drifts are larger during equinox than in summer.
For the WSA and WSA-like phenomena, their evening enhancements of the electron density are also suggested to be contributed by the downward plasma flux.During summer evenings, it is suggested that the downward flux might originate from the poleward edge of the equatorial anomaly region (Lin et al., 2010).It could be also induced by stronger cooling effects or thermal contraction in the Southern Hemisphere in local summer (Burns et al., 2008;Liu et al., 2010).During later hours close to midnight, the equatorial anomaly disappears and the cooling effects slow down or even stop due to a decreasing temperature gradient with time (Balan et al., 1996;Liu et al., 2010), and thus the above suggested flux from the plasmasphere could be much weaker in those regions where the WSA occurs.However, it is not clear whether the expected weak downward flux around midnight in the Western Hemisphere is still stronger than that of the Eastern Hemisphere at southern midlatitudes.Overall, a full understanding of the relative contribution to electron density variations by the neutral winds, photoionization and topside downward flux should be further studied by coupled magnetosphere-ionosphere models.
Summary
The ionospheric F2-layer peak density retrieved from observations by COSMIC was used to analyze its longitudinal variations during nighttime at southern midlatitudes, where the prominent WSA occurs in Western Hemisphere in summer.The longitudinal difference is calculated by a relative ratio r, which is defined by the percentage difference relative to the minimum density along longitudes at a fixed local time.The simultaneous magnetic meridional winds and their induced vertical plasma drifts are also presented, which are derived from the F2-layer peak height and peak density from a servo model.Thus we discussed the effects of winds and drifts on the ionospheric longitudinal pattern, as well as the contribution rates of the neutral horizontal winds to the vertical plasma drifts.The contribution rates of both the geographic zonal and meridional winds are determined by the geomagnetic inclination and declination.We find that the seasonal-local time patterns of the electron density longitudinal variations during nighttime at southern midlatitudes cannot be simply explained in terms of the WSA.Our major findings are as follows: 1.The greatest longitudinal difference (r > 250 %) of the ionospheric peak electron density occurs around midnight in summer with larger density in the Western Hemisphere, where the WSA is located, which usually occurs in the evening.Significant longitudinal difference (r > 150 %) also occurs during equinox and winter.This feature is different from the seasonal variation of the WSA at solar minimum.
2. The wind-induced vertical plasma drifts generally show a stronger upward component in the Western Hemisphere for most of the night.At a fixed local time, the stronger electron density in the WSA region is generally consistent with the larger upward plasma drifts in each season.Thus, the longitudinal patterns of winds/drifts and density are generally consistent with the magnetic declination-zonal wind effects.
3. The locations of the maximum and minimum upward drifts tend to reverse at ∼ 01:00-02:00 LT from their pre-midnight patterns, whereas no corresponding longitudinal pattern of the electron density occurs after this reversal.This suggests that the wind could largely contribute to but not determine the longitudinal pattern of the electron density.
4. There is significant contribution from the geographic meridional wind to the location of the maximum and minimum electron density besides the well-known magnetic declination-zonal wind effects.The longitudinal patterns of the upward plasma drifts and the consequent electron density are also modulated by the magnetic inclination.These features are unlike the role of the winds in the northern US and Far East regions, where the magnetic declination-zonal wind effects dominate.
Figure 1 .Fig. 1 .
Figure 1.The longitudinal variations of the absolute (NmF2, left) and relative (r, right) peak electron density (top three panels) during 18:00-03:00 LT at 40°S.The percentage difference r=(Ne-Ne min )/Ne min *100% is calculated for each local time hour.The bottom panels present the
Figure 2 .
Figure 2. Similar with Figure 1, but for the longitudinal variations of the electron density at 60°S.
Fig. 2 .
Fig. 2. Similar to Fig. 1 but for the longitudinal variations of the electron density at 60 • S.
Fig. 3 .
Fig. 3.The derived magnetic meridional winds (U M , left) and the wind-induced vertical plasma drifts (W , right) for local equinox, summer and winter at 40 • S. The magnetic declination angle is also shown.In the bottom panels, blue dashed lines represent the edges of the longitudes where the WSA occurs.The meridional winds are equatorward positive and the vertical drifts are upward positive.
Figure 4 .Fig. 4 .
Figure 4. Similar with Figure 3, but at 60°S.Winds are absent at geomagnetic latitudes higher than 478 • W sector. The electron density decays after its peak enhancements.The electron density during equinox shows obvious nighttime enhancement in western longitudes, which is similar to that at 40 • S. At 60 • S the major equinoctial enhancement area expands from about 130 to 90 • W in longitudes as time passes.This enhancement lasts from 22:00 to 02:00 LT.In winter, a nighttime electron density enhancement occurs at 60 • S around 100 • W, while no such enhancement occurs at 40 • S. Stronger maximum longitudinal enhancement ratio occurs at 60 • S than at 40 • S in each season.At 60 • S, this ratio is more than 400 % in summer and more than 200 % in winter.The maximum longitudinal enhancement ratio in summer mostly occurs at around midnight, which is about 3 h later than the occurrence time of the maximum NmF2 enhancement of the WSA.In winter, a clear peak enhancement ratio occurs at ∼ 22:00 LT at 60 • S.During equinox, the peak longitudinal enhancement ratio is comparable to that in summer at 60 • S. | 7,606.2 | 2013-10-15T00:00:00.000 | [
"Physics"
] |
A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions
In most crops, genetic and environmental factors interact in complex ways giving rise to substantial genotype-by-environment interactions (G×E). We propose that computer simulations leveraging field trial data, DNA sequences, and historical weather records can be used to tackle the longstanding problem of predicting cultivars’ future performances under largely uncertain weather conditions. We present a computer simulation platform that uses Monte Carlo methods to integrate uncertainty about future weather conditions and model parameters. We use extensive experimental wheat yield data (n = 25,841) to learn G×E patterns and validate, using left-trial-out cross-validation, the predictive performance of the model. Subsequently, we use the fitted model to generate circa 143 million grain yield data points for 28 wheat genotypes in 16 locations in France, over 16 years of historical weather records. The phenotypes generated by the simulation platform have multiple downstream uses; we illustrate this by predicting the distribution of expected yield at 448 cultivar-location combinations and performing means-stability analyses.
A ccording to a recent report by the World Bank, overall food demand will increase by more than 50% by 2050 1 . This remarkable increase in food demand places enormous pressures on crop production. The same World Bank report concludes that crop yields will need to grow faster than historically to meet the anticipated food demand. The genetic improvement of crops is one of the main ways in which modern agriculture can maintain and increase production levels while reducing its environmental impacts.
In plants, genetic and environmental factors can interact in complex ways giving rise to substantial genetic-by-environment (G×E) interactions 2 . This source of variation can be used to select genotypes adapted to specific environments 3 . However, making selection decisions and agronomic recommendations is exceptionally challenging because future environmental conditions are mainly uncertain. Indeed, accurate predictions of future performances in target environments require considering the possible weather conditions that may occur within a region and how individual genotypes are expected to react to those conditions. Extensive field testing, including evaluating genotypes over many years and across multiple locations, is required to make such predictions. However, efficient trial networks can only test genotypes over a limited number of years and testing sites. Thus, in the early stages of their breeding cycle, genotypes are often advanced without being tested under weather conditions that may critically affect their performance (e.g., cold, heat, or drought stress).
We propose that computer simulations that integrate field trial data, DNA sequences, and historical weather records can be used to address the difficult task of predicting genotype performance and stability using limited years of field testing per genotype. Figure 1 summarizes the proposed simulation framework. Our approach builds on modern genomic models that integrate DNA sequences (e.g., single nucleotide polymorphisms-SNPs) and environmental covariates (ECs 4,5 ). The use of ECs as a means to characterize the environmental conditions that occurred during a growing season enables us to link past field trial data with historical (or simulated) weather records that describe environmental conditions that are likely to occur in a location or region.
Our approach is largely data-driven; we use a G×E model incorporating SNPs and ECs to learn how each cultivar reacted to the environmental conditions. We then use these patterns, together with DNA polymorphisms and historical weather records, to simulate the expected performance of specific genotypes at specific locations. The Monte Carlo (MC) method used to simulate phenotypes integrates uncertainty about future weather conditions and model parameters (e.g., SNP or EC effects and their interactions).
We apply the proposed simulation platform to wheat data from an extensive trial network generated by Arvalis-Institut-duvégétal (Arvalis). The data set comprises (n = 25,841) wheat grain yield records from French-registered cultivars, linked to DNA sequences (167,440 SNPs) and (106) ECs describing temperature, radiation, and water availability in different phases of the crop cycle. We use this extensive data set to fit the models proposed by Jarquín et al. 4 with various genetic and environmental specifications. We validate the predictive performance of the models using a "leave-trial-out" cross-validation. Subsequently, we use samples from the posterior distribution of the model parameters and ECs derived from historical weather records to simulate the performance of 28 wheat lines across possible occurrences of environmental conditions at 16 target locations representative of major French wheat-producing regions. Finally, we use the simulated data for two downstream analyses: prediction of expected grain yield (and its distribution) at target cultivar-location combinations, and mean and stability analyses, based on Finlay-Wilkinson (FW) regression 6 . Our results show that wheat yield forecasts of individual varieties at target locations derived from simulations integrating many years of historical weather data are more precise than forecasts that can be derived from trial data alone.
Results
An extensive, highly-connected trial network. Field trial data were generated by Arvalis (https://www.arvalisinstitutduvegetal. fr) and included 25,841 grain yield records collected from 1998 to 2014 at 242 locations (Fig. 2). There were 752 year-location combinations with one (un-replicated) trial per year-location. In total, 481 French-registered cultivars were tested. All trials were connected through a common tester and by many other cultivars that generated partial connections between trials. On average, each trial was connected with at least 340 other experiments through at least five genotypes (see "Methods" and Supplementary Fig. 1 Fig. 1 Computer-simulated performance of candidate genotypes in target locations. The proposed computer-simulation platform uses phenotype, genotype (e.g., SNPs), and environmental covariate data collected in an existing network of trials and historical weather records at target locations ("virtual network of trials") to simulate the performances of selected genotypes in target locations, under possible weather conditions. data were retrieved. All trials received fungicide and seed treatments. The average grain yield (standardized at 15% moisture) was 9.49 (±1.50) tons per hectare. The distribution of grain yield was reasonably symmetric (Supplementary Fig. 2).
Each variety was genotyped with an Axiom high-density genotypic platform (Affymetrix Inc., Santa Clara, CA) containing 420K SNPs 7 ; these genotypes were generated within the Breedwheat project (ANR-10-BTR-03). After standard quality control, we had a total of 167,440 SNPs available for analyses. Principal components derived from SNP genotypes revealed a weak substructure among genotypes ( Supplementary Fig. 3).
Environmental data consisted of 106 environmental covariates (EC) generated using a crop model developed by Arvalis 8 . The crop model computes EC values based on predicted growth stages and weather records (temperature, radiation, rainfall). The output of the model consists of ECs describing critical temperatures, radiation, and water availability for eight distinct phases of crop phenology. A complete list of the ECs used is presented in Supplementary Table 1. Further details about how the ECs were derived are provided in the "Methods" section and in Soenen et al. 8 .
The ECs used in this study were used before to characterize French wheat-growing environments 9 and to predict regional wheat yield in France 10 . A principal component analysis of the 106 ECs revealed almost no structure ( Supplementary Fig. 3). To validate the ECs, we regressed the trial means on the 106 ECs and quantified the ability of the ECs to predict trial means in crossvalidation (see "Methods" for more details). Over 100 trainingtesting partitions, the average testing correlation between the ECpredicted means and the (BLUE of the) trial means was 0.600 (+/−0.04).
EC captured 50% the environmental variance. We used a sequence of models to evaluate the proportion of grain yield variance explained by genetic and environmental factors (Table 1), and to reveal the fraction of those variance components that could be captured using SNPs and ECs.
The baseline model (TL) included an intercept plus the random effects of year (Y i ), location (L j ), year-location (YL ij ), and cultivar (V k ); thus ; this model does not incorporate any SNP or EC data.
Estimates indicate that environmental differences between trials (i.e., those due to year, location and year-by-location effects) explained 83% of the grain yield variance [(0.289 + 0.780 + 0.944)/(0.289 + 0.780 + 0.944 + 0.191 + 0.215), Table 1]. Approximately one-half of the between-trial variance corresponds to year-by-location interactions [0.944/(0.289 + 0.780 + 0.944)], thus highlighting the importance of accounting for year-to-year variations in environmental conditions. In the baseline model, the amount of variance explained by the main effect of the genotypes was~8% of the total variance and about 50% of the within-trial variance.
We expanded the baseline model by adding the cultivar-byyear (V × Y) and cultivar-by-location interactions (V×L) (we did not include cultivar-by-year location because in the trials, there was only one plot per genotype-year-location). The interactions captured a very small amount of variance (0.0323 +/− 0.0019 and 0.0415 +/− 0.0023, for V×L and V×Y, respectively). Of the two terms, only the cultivar-by-location term could be learned from past data. However, the amount of variance captured by this term is small, and accurate prediction of V×L would require evaluating cultivars over many years at the same location, which is both costly and inefficient. Therefore, instead of leveraging V×L and V×Y for prediction, we focused on modeling those effects using SNPs, ECs, and their interactions.
To assess the proportion of genetic variance that can be captured by SNPs, we modeled the cultivar effect (V k ) using assumptions of the GBLUP model 11 . Thus, where G is an (SNP-derived) additive relationship matrix, and σ 2 V is a genomic variance. Likewise, we used an "EBLUP" model to introduce the effects of the ECs; thus Supplementary Fig. 4).
cultivar effects and Ω is an environmental similarity matrix derived from the ECs (see "Methods" for more details). Therefore, the GW model specifies In the GW model, the genomic term (V k ) captured~83% of the variance of the genotypes (0.158/0.191); however, the ECs captured~44% of the between-trial variance [0.892/(0.289 + 0.780 + 0.944)]. We conclude that there was almost no "missing heritability" 12 and that there was a sizable (~56%) "missing environmentability". The latest could be attributed to deficiencies in the ECs (e.g., lack of data on soil fertility) and to limitations in the model used to link ECs with grain yield (e.g., absence of nonlinear effects or interactions between ECs).
Next, we added interactions between SNPs and ECs to the GW model (GW-G×W). With the number of SNPs and ECs involved in this study, modeling all possible pairwise SNP-by-EC interactions is computationally very challenging. However, SNP-by-EC interactions can be modeled (implicitly) using a Gaussian random effect with a covariance structure which is the Hadamard product (i.e., cell by cell) of the genomic (G) and environmental (Ω) covariance structures 4 . Thus, to introduce SNP-by-EC interactions we used the following model where μ, V k , and w ijk are as in Eq. (2), and VW ijk is a Gaussian random effect representing SNP-by-EC interactions that has a zero mean and (co)variance function proportional to the product of the genomic (G ii′ ) and environmental ðΩ ijk;i0j0k0 Þ similarity between entries, Cov VW ijk ; VW i0j0k0 / G ii0 Ω ijk;i0j0k0 (see "Methods" for further details).
In the GW-G×W model, the regression on SNPs captured 83% of the variance associated with genotypes (0.16/0.191), while the ECs captured~42% of trial variance [0.863/(0.289 + 0.780 + 0.944)]. Relative to the additive effects of model GW, adding the interactions between SNPs and ECs led to a reduction in the error variance of about~5% (0.074/1.300). However, the error variance of the GW-G×W model was substantially larger than the error variance of our baseline model, reflecting that even after including SNP-by-EC interactions, the model did not fully capture environmental effects.
Therefore, we then combined the trial information (year, location, and year-by-location) with SNPs and ECs. We did this without including SNP-by-EC interactions (model TGW = trials, genotypes and ECs, Eq. (4) in "Methods") and including those interactions (TGW-G×W, see Eq. (5) in "Methods"). The full model (TGW-G×W) captured the same amount of variance as the baseline model, with (almost) all of the genetic variance being captured by SNPs and a sizable fraction of the environmental variance captured by ECs.
Models achieved moderately high prediction accuracy. We used 10-fold cross-validation (CV), with trials (i.e., entire year-location combinations) assigned to folds, to assess each of the models' ability to predict grain yield in year-locations not used to train the model. For each fold, we fitted models using data from the remaining ninefold and used the fitted model to predict yield in the trials assigned to the left-out fold. This was repeated 10 times, and each time onefold was left aside for testing. The assignment of year-locations to folds poses a prediction problem similar to the one faced when one uses data from some year-locations to predict the performance of genotypes in other year-locations. We chose this approach because it represents the prediction problem that one faces when simulating genotypes' performance in future years at locations present in the network of trials. Figure 3 shows the average within-trial correlation between realized yield and CV predictions (letters indicate groups that are significantly different according to a paired t-test). The full model (TGW-G×W) achieved the highest within-trial correlation between predicted and realized yield (0.58, Fig. 3). The CVcorrelations we obtained with our full model were slightly higher than the ones previously reported for wheat using a similar CV scheme 4,13,14 .
We also conducted a validation with cultivars assigned to folds; this mimics the problem of predicting the performances of a cultivars that were tested in any past trial 13 . In this new CV, the prediction performance was worse than when year-locations were assigned to folds. The baseline model yielded a slightly negative (−0.12, Supplementary Fig. 5) within year-location correlation. This happens because, within-trial, only genotype, genotype-bylocation, and genotype-by-year-location can contribute to prediction accuracy. But these effects cannot be learned by the baseline model in a CV1 scheme. On the other hand, the models that included SNPs and ECs achieved a positive (albeit moderate) prediction correlation, and the relative ranking of the models was the same as the one observed in the leave-trial-out CV. Models GW-G×W and TGW-G×W achieved an average correlation of 0.25 ( Supplementary Fig. 5). Previous studies 13 have also reported low prediction accuracy when predicting phenotypes of untested cultivars. However, the reduction in prediction correlation was particularly marked in this data set because the cultivars included in it are not 'close relatives', and genomic prediction relies heavily on genetic relatedness 15 . Considering the low accuracy achieved in CV1 by the best performing model, we conclude that accurate prediction of within-year location performance requires, for the type of data that we considered, at least 1 year of testing per cultivar included in the simulation.
Computer-simulated performances. The results presented thus far used only past trial data. In this section, we describe a computer-simulation platform that integrates those data with historical weather records.
A conceptual description of the simulation process is presented in Fig. 4. We used the full model (TGW-G×W) to predict yield Grain yield (tons/ha) Predictive analytics. We used the simulated phenotypes to study the fitness of varieties to specific locations. Figure 5 shows a heatmap (left) and a biplot of the predicted average yields for the selected varieties at the target locations. Importantly, simulations are not restricted to the years in which each genotype was tested at each site. The heatmap in the margins of Fig. 5a shows dendrograms describing the clustering of locations and varieties. Celllule, Rubisko, Barok, and Pakito appeared as a cluster of highyielding cultivars. On the other hand, historical varieties such as Soissons had relatively low-predicted yield across locations. The first two PCs of the matrix containing the predicted means by variety and location explained~95% of the total variance of the cultivar means in the locations. Both the heatmap and the biplot can be used to identify varieties with high expected yield at each location.
The results in Fig. 5 are based on the average predicted yield at each of the 448 (16 × 28) cultivar-location combinations. The simulation also predicts the distribution of expected yield across possible weather conditions. This is displayed in Fig. 6 for four contrasting locations, including low (Crenay), intermediate (Thizay and Montans), and medium-high (Estrées-Mons) yield. In addition to differences in means (which can also be assessed in the heatmap of Fig. 5), the boxplots describe the variability in expected yields that can be attributed to differences in weather conditions, genotype-by-year-location, and uncertainty about model parameters. The results in Fig. 6 suggest that, compared to Crenay and Estrees-Mons, there is more uncertainty about average grain yield in Thizay and Montans. Likewise, the simulated performance can be used to quantify uncertainty about the yield performance of different varieties within each of the locations.
The red circles and blue diamonds in Fig. 6 represent the BLUEs (derived from the baseline model) and the raw means of grain yield of each cultivar-location combination, respectively.
Since not all varieties were tested at all the locations, some location-cultivar combinations do not have BLUEs and raw means. There are important differences between the raw means, the BLUEs, and the median yield estimated from the simulation (represented by the central horizontal line in each of the boxes). The medians (and means) from the simulation are smoother than the raw means and the BLUEs because the simulation means are computed by averaging over 16 years of weather conditions and multiple configurations of model parameters. This is not the case of the raw means and the BLUEs, which are point-predictions derived using data from the years in which each cultivar was tested. The raw means, the BLUEs, and the simulated means produced distinct rankings of cultivars within each of the locations, which in turn can lead to different breeding decisions and variety recommendations.
Finally, we conducted a mean-stability analysis using a FW regression 6 where the mean of the kth genotype on the jth location (M jk ) was modeled as the sum of an environmental mean (E j ), plus a cultivar-specific intercept (b 0k ), plus a regression on the environmental means, that is, is a genotype-specific slope. We conducted these analyses using as data: (A) the raw genotype-by-trial means, (B) best linear unbiased estimates (BLUEs) of the same means derived from the baseline model, and (C) the variety-by-location means obtained by averaging the simulated results over 16 years of historical weather data. FW analyses based on the raw means (Fig. 7a) suggest substantial G × E, with estimated slopes ranging from 0.8 to 1.4. However, the same FW analyses based on BLUEs of the expected performance of a line in a location (Fig. 7b) showed much less variability in the slopes of the FW regressions. Results based on the simulated means (Fig. 7c) exhibited even smaller variability in slopes and much more precise estimates. Thus, the FW analyses based on the simulated means (which averaged predicted performances over 16 years of weather data for each of the locations) suggest small genotype-by-location variance at the level of the genotype's mean in a location. This is consistent with the fact that, within this trial network, a sizable proportion of the environmental variance corresponds to year and year-location variance. The FW analysis in Fig. 7c identifies high-yielding varieties that are expected to perform well across many locations in French wheat-growing regions, including Cellule (intercept = 10.40, slope = 1.03) and Rubisco (intercept = 10.40, slope = 1.02).
To assess the stability of the FW results, we conducted 100 twofold cross-validations. Briefly, we split the observed and the simulated data of each cultivar into two folds. We then performed the FW regression analyses presented above within each of the folds. The averages (SEs) of the CV correlation between the predicted slopes of folds 1 and 2 were 0.342 (+/−0.015), 0.447 (+/−0.012), and 0.502 (+/−0.016), for the raw means, BLUEs, and the simulated means, respectively. In a paired t-test, the three correlations were significantly different (all the Holm's adjusted p-values were smaller than 0.001).
Discussion
Genetic-by-environment interactions (G×E) are a significant source of variance in crop phenotypes. The importance of G×E was recognized almost a century ago 17 . Since then, several statistical methods for the study of G×E were developed, including fixed-effects 18-21 and mixed-effects 22,23 models, reaction norms 24 , factor analytic methods 25 , and other reduced-rank methods 26 . More recently, G×E models that integrate DNA sequences, alone 13,14 or in combination with ECs 4,27,28 were developed and tested in multiple crops. However, a sizable fraction of the environmental and G×E variance is often due to year and year-location effects. This has limited the use of G×E models in breeding and research because predictions from such models require knowledge about future weather conditions which are mainly uncertain.
The concept of "target breeding environments" has been proposed as a way to incorporate G×E in breeding decisions 3 . However, predicting the performance of a cultivar in a target environment is challenging because, as noted, a sizable fraction of the environmental variance (50% in the data set analyzed in this study) corresponds to within-location year-to-year variation in weather conditions. Therefore, accurate prediction of the performance of genotypes at target environments would require testing genotypes over many years. This is both costly and inefficient because it delays breeding decisions, ultimately reducing genetic progress. On the other hand, when breeding decisions are based on the predicted performance derived from 1 or 2 years of testing, many genotypes are advanced without evaluating them under weather conditions that may greatly impact their performance (e.g., drought, heat, and cold stress).
We propose that computer simulations that integrate field trial data with DNA sequences and historical weather records can be used to tackle the longstanding problem of predicting genotypes' future performance under largely uncertain weather conditionsa similar idea was used before by Chenu et al. 27 . However, the study by Chenu et al. simulated phenotypes assuming a crop model with known parameter values, simulated genotypes and selected environmental scenarios. Our approach builds upon Chenu's work by proposing a simulation strategy that is heavily data-driven. We use real genotypes, historical weather data, and G×E patterns learned from field trial data without assuming an underlying crop model. Furthermore, rather than fixing parameter values (e.g., QTL effects) to point estimates and weather conditions to scenarios, our simulation strategy fully accounts for uncertainty about model parameters and uses historical weather data to account for likely weather conditions. The continuous development of computing power and algorithms has led to increased interest in using computer simulations to assist breeding decisions and agronomic recommendations. However, the adoption of computer simulations in plant breeding remains limited because it is difficult to develop realistic simulations that can fully account for the complexity of how genomes respond to environmental conditions. There are often too many options as to how to simulate genotypes and phenotypes (e.g., how to model additive and non-additive effects, and how to simulate G×E) that make fully in-silico simulators mostly unrealistic. The approach used in this study overcomes these limitations by developing a computer-simulation platform that learns G×E patterns from past trial data and uses DNA sequences and historical weather records to simulate performances over possible environmental conditions. The simulation platform predicts not only the expected performance of a cultivar in a location but also the expected distribution of a trait over likely weather conditions. Importantly, the predictive distribution also accounts for uncertainty about model parameters. Implementing the simulation approach proposed in this study requires (i) a model that integrates genetic and EC data, (ii) extensive multi-environment field testing data linked to genotypes and environmental covariates, and (iii) historical weather data.
For the model, we used a reaction norm for SNPs and ECs 4 ; however, we highlight that the platform can be implemented with any (Bayesian) model that integrates DNA sequences and ECs, including crop models 5 .
The implementation of the proposed simulation platform requires extensive field trial data covering a diverse set of genotypes, many years, and locations. Access to such data is essential because those data are the source from where G×E patterns are learned. Ideally, the data should be dense enough to guarantee that simulations "interpolate" and do not "extrapolate". Many public and private breeding organizations have generated large volumes of genetic and phenotypic data from field trials. The proposed simulation platform offers these organizations the opportunity to leverage their data with historical weather records to produce predictions of cultivars' performances that are more robust than those that can be derived when varieties are tested for 1 or 2 years. However, we emphasize that the simulation platform presented here should not be conceived as a tool to predict performances outside of the genotype/environment space represented in the training data. Moreover, we do not recommend simulating phenotypes for untested cultivars, unless such varieties are closely related to the ones used to train the models.
Historical weather data, the third input in the simulation platform, should be readily accessible for locations with (or with nearby) weather stations. In this study, we applied the framework using historical weather data from individual locations. However, there are multiple potentially useful variants of this approach that are worth mentioning. First, if the goal is to breed or to produce agronomic recommendations for large growing regions, one could simulate at various locations and aggregate the simulated data at the regional level. Second, in this study, we used a long series of historical weather data. However, the same framework could be used as a tool for sensitivity analyses. For example, one could evaluate the effects of climate change by over-sampling years with adverse events (e.g., drought, heat stress) that may increase in frequency under a climate change model.
We used the proposed simulation platform to generate~143 M grain yield values resulting from the combination of 28 genotypes at 16 locations, 16 years of historical weather data for each of those locations, and 20,000 possible configurations of model parameters. The simulated data were used to approximate the expected distribution of yield for 448 (28 × 16) genotype-location combinations. The mean-stability analysis conducted using the simulated phenotypes averages over likely weather conditions and suggests lower G×E levels in the cultivar-location means, than analyses based on raw-trial means or trial BLUEs. Therefore, the simulation platform can aid in identifying cultivars with consistently high yield across locations.
In conclusion, we presented a data-driven simulation platform that uses experimental data and historical weather records to predict cultivars' future performances, accounting for uncertainty about future weather and model parameters. The resulting forecasts smooth-out variability attributable to year-location and genotype-by-year-location; therefore, they are more precise than the ones derived using a few years of testing.
Methods
Experimental data. Genotypes for each of 481 French-registered varieties were obtained using the Axiom high-density genotyping platform (Affymetrix Inc., Santa Clara, CA) containing 420K (K = 1,000) SNPs 7 and generated within the Breedwheat project (ANR-10-BTR-03). Only SNPs with minor allele frequency >0.05, with a calling rate >0.8, and with <10% of heterozygous loci, were used in the analyses. A total of 167,440 SNPs fulfilled this criterion. The remaining missing genotypes were imputed to the mean (i.e., two times the allele frequency at the SNP).
Phenotype data originated in post-registration evaluation trials carried out each year by Arvalis for newly registered varieties. The trial network included 242 locations and a total of 752 year-locations. Each year-location had one (unreplicated) yield trial in which cultivars were arranged in a randomized complete block design with a plot size of 12 m². Sowing dates and densities were adjusted at each location to represent the usual practices at each location. Trials were managed to reduce any biotic or abiotic factor that may reduce grain yield potential (optimal nitrogen fertilization, weed, insect, and disease control). Plots were harvested at maturity using a combine harvester, and the grain yield of each plot was adjusted to 15% moisture.
A single check was used across all trials; however, since cultivars were tested at multiple year-locations, many genotypes provided strong connections between trials. To quantify this, we created a connectivity index (T ij(r) ) that counted the number of trials connected to trial ij through at least r genotypes (r = 1,…,5). The average values of these indices in the data set were T 2 ð Þ ¼ 573:3, T 3 ð Þ ¼ 473:3, T 4 ð Þ ¼ 402:3, and T 5 ð Þ ¼ 339:8. Histograms of the distribution of these indices are presented in Supplementary Fig. 1.
Environmental covariates were chosen based on physiological knowledge of bread wheat response to environmental factors (water, radiation, temperature) at different periods of the crop cycle (see Supplementary Table 1 for a list of ECs).
Weather data were gathered from weather stations located within 10 km of the trial. The crop cycle was divided into phases between sowing, emergence, the beginning of stem elongation, meiosis, heading, anthesis, milky, and maturity corresponding to growth stages (GS) 00, 10, 30, 39, 55, 65, 75, 92 29 . The GS dates were simulated using an ecophysiological model based on the daily accumulation of thermal time, possibly modified by vernalization and photoperiod factors [30][31][32] .
Temperature accumulation was calculated daily using a piece-wise linear function with three knots or cardinal temperatures (0, 24, and 35°C). Accumulation of temperature was nil below 0°C or above 35°C; it increased linearly between 0 and 24°C, and decreased linearly between 24 and 35°C. Emergence reached after 150°C-days from sowing with no effect of photoperiod or vernalization.
From emergence to GS30, temperature accumulation was reduced by vernalization and photoperiod factors both varying between 0 and 1. The photoperiod factor was calculated as PF ¼ P H À P base ð Þ = P opt À P base where P H is the daily photoperiod (in hours) and P base and P opt are parameters equal to 6.3 and 20 h, respectively. The vernalization factor was calculated as where VDD is the number of accumulated "vernalizing days" and V base and V sat are parameters equal to 0 and 45, respectively. VDD was calculated daily using a piece-wise linear function of daily mean temperature defined by three cardinal temperatures of −1, 6, and 17°C. Accumulation of vernalizing days was nil when daily mean temperature was ≤−1°C or ≥17°C; it increased linearly between −1 and 6°C, was equal to 1 at 6°C, and decreased linearly between 6 and 17°C. From GS30 to GS55, the base temperature was set at 3.5°C, P base was set at 7.7 h, and the accumulation of thermal time was limited by the photoperiod factor only. Two cultivar-dependent parameters (GDD pv = growing degree days reduced by photoperiod and vernalization factors, and GDD p = growing degree days reduced by the photoperiod factor) determined the accumulation of modified thermal time required from emergence to GS30 and from GS30 to GS55, respectively. GS39 was determined using backward calculation from GS55 as GS39 = GS55-1.2 × Phyll, with GS55 representing the heading date in degree days since sowing, and Phyll representing the phyllotherm parameter, which was calculated as follows: Above, P GS10 and P GS00 are the photoperiods at GS10 and GS00, respectively; GDD GS00-GS10 represent growing degree days between GS00 and GS10; Rg GS00-GS10 is the average global radiation (joules.cm −2 ) between GS00 and GS10. Finally, d GS10 represents the plant density at GS10. The phyllotherm parameter was bound to the 66-120 range.
Statistical models. The baseline model (TL) included an intercept, plus the normal, independent and identically distributed (NIID) random effects of the year, Y i $ NIIDð0; σ 2 Y Þ, location, L j $ NIIDð0; σ 2 L Þ, year-location, YL ij $ NIIDð0; σ 2 YL Þ, and cultivar, V k $ NIIDð0; σ 2 V Þ; therefore, where ε ijk $ NIID 0; σ 2 ε À Á : The GW model was obtained by substituting the cultivar, year, location, and year-location effects with genetic and environmental random effects linked to SNPs and ECs, respectively. Specifically, we replaced the cultivar effect with a multivariate normal random effect, Here X is a matrix of (centered and scaled) SNP genotypes (p is the number of SNPs). Likewise, we replaced the year, location, and year-location effects in Eq. (1) with a multivariate normal random effect that had a covariance structure derived from the ECs: Here, W n×q is a matrix of (centered and scaled) environmental covariates, and q is the number of environmental covariates. Therefore, the equation for the GW model was Subsequently, we expanded the GW model by adding interactions between SNPs and ECs. Specifically, we introduced a random effect Here, Z g is a design matrix connecting phenotypes with cultivars, and G and Ω are genomic and environmental relationship matrices, respectively. Above, "#" represents the Hadamard (or cell-by-cell) product between two matrices, and σ 2 V EC is a variance parameter associated with the interaction term. Thus, Variance component estimates revealed that the GW and GW-G×W models did not fully capture the environmental variance captured by the TL model. Therefore, to fully capture between-trial differences, we added back year and location effects, without (TWG) and with (TGW-G×W) interactions between SNPs and ECs, that is: and TGW À G W: The distributional assumptions for each of the terms in Eqs. (4) and (5) were as described before (see models TL, GW, and GW-G×W).
Assessment of prediction accuracy. We used a 10-fold CV with trials (i.e., yearlocation IDs) assigned to folds to assess prediction accuracy. We chose the withinyear-location correlation between predictions and observed yield as a metric to assess prediction accuracy. Thus, from a 10-fold CV we had as many correlations (r) per model as year-locations were represented in the data (767). To assess statistical differences between models, we employed a paired t-test applied to Fisher's z-transform, that is, z ¼ ffiffiffiffiffiffiffiffi n ij À3 p 2 logðð1 þ rÞ=ð1 À rÞÞ; with n ij being the number of records in the particular year-location. P-values were derived using the t-test function of R. These p-values were used to group the models according to their predictive power using the orderPValue function in the agricolae R-package 33 with α = 0.05. Fig. 4. Using the TGW-G×W Eq. (4), we simulated performances for 28 genotypes that are well-represented in Arvalis' trial network and 16 locations representative of French wheat-producing regions (Supplementary Fig. 4). For each location, we retrieved 16 years of historical weather data (from 2000 to 2015) and used those data to derive ECs for each for the 6720 (16 × 16 × 28) year-location-cultivar combinations represented in the simulation. Subsequently, we used 20,000 samples from the posterior distribution of the parameters of the TGW-G×W model to evaluate, for each of the sample-genotype-year-location combinations, the prediction function: f i; j; k; θ s ð Þ¼μ ðsÞ þ L jðsÞ þ V kðsÞ þ w ijkðsÞ þ VW ijkðsÞ
Simulation. A conceptual description of the simulation algorithm is presented in
Above, i indicates years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), j location, k cultivar, and s sample from the posterior distribution of the model parameters (θ s ). Note that the prediction Eq. (6) uses (from the regression model TGW-G×W) only the terms that can be learned from past data (μ (s) + L j(s) ) or predicted from knowledge of model parameters, SNPs, and ECs (V k(s) + w ijk(s) + VW ijk(s) ). Thus, predictions from the simulation did not include year and year-location effects that cannot be predicted from knowledge of ECs. Equation (6) was evaluated for each genotype-location-year-sample combination, thus producing~143.4 million simulated data points.
Biplots. We analyzed the simulated data using the site regression (SREG) model 2,23 . As the response variable, we used the mean of simulated grain yield for variety i, location j 34,35 .
Finlay-Wilkinson (FW) regressions. We conducted means-stability analyses using an FW regression 6 of the form M jk ¼ b 0k þ E j þ E j b 1k þ δ jk , where M jk are the means of genotype k in location j, E j $ NIID 0; σ 2 are the location means and cultivar-specific intercepts and slopes, respectively. We implemented the FW regressions in two steps: (i) in the first one we estimated environmental means using a random-effects additive model of the form M jk ¼Ṽ k þẼ j þδ jk , and (ii) subsequently, we inferred intercepts and slopes using the FW model with E j replaced byẼ j . Both steps were implemented using the BGLR R-package (see Supplementary Software).
The results in Fig. 7 are based on an analysis of the entire data set. The dots are the estimated means for the intercepts and slopes, and the vertical and horizontal lines are the estimated posterior standard deviations. We also conducted 100 twofold cross-validations in which we divided the observed (in the case of the raw means and BLUP method) or the simulated data of each cultivar into two halves. Subsequently we applied FW regressions to each of the halves and with this we estimated the correlation between the slopes inferred in each of the halves. This was repeated 100 times, and statistical differences were assessed using paired t-tests.
Software. Data analyses were performed using the R Statistical package 36 . Models were fitted using the BGLR package 16 . All the models were fitted with the default hyper-parameter values chosen by BGLR. The SREG model and the biplots were generated using custom R-scripts. The scripts used to fit the models and perform the biplot analyses and FW regressions are provided in the Supplementary Software.
Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data availability
The data are not publicly available due to them containing proprietary information. However, the data that support the findings of this study are available upon request. Specific data transfer agreements may be required for each individual request.
Code availability
The scripts used to fit the models and perform the biplot analyses are provided, together with a sample data set, in the Supplementary Software. | 8,859.2 | 2020-09-25T00:00:00.000 | [
"Agricultural and Food Sciences",
"Computer Science",
"Environmental Science"
] |
iCN718, an Updated and Improved Genome-Scale Metabolic Network Reconstruction of Acinetobacter baumannii AYE
Acinetobacter baumannii has become an urgent clinical threat due to the recent emergence of multi-drug resistant strains. There is thus a significant need to discover new therapeutic targets in this organism. One means for doing so is through the use of high-quality genome-scale reconstructions. Well-curated and accurate genome-scale models (GEMs) of A. baumannii would be useful for improving treatment options. We present an updated and improved genome-scale reconstruction of A. baumannii AYE, named iCN718, that improves and standardizes previous A. baumannii AYE reconstructions. iCN718 has 80% accuracy for predicting gene essentiality data and additionally can predict large-scale phenotypic data with as much as 89% accuracy, a new capability for an A. baumannii reconstruction. We further demonstrate that iCN718 can be used to analyze conserved metabolic functions in the A. baumannii core genome and to build strain-specific GEMs of 74 other A. baumannii strains from genome sequence alone. iCN718 will serve as a resource to integrate and synthesize new experimental data being generated for this urgent threat pathogen.
INTRODUCTION
Acinetobacter baumannii has recently emerged as a deadly nosocomial threat with rising rates of both infection and antibiotic resistance. Reports using data from hospital-based surveillance studies as well as from the Infectious Diseases Society of America have begun to refer to a dangerous group of nosocomial pathogens, including A. baumannii, as "ESKAPE pathogens" (Rice, 2008). A. baumannii in particular is known for its highly persistent and opportunistic nature, most often resulting in hospital-acquired pneumonia while also having the ability to infect various other tissues (Weber et al., 2015). Organisms of the genus Acinetobacter inhabit a wide variety of environments, ranging from humans to water and soil . These diverse environmental niches are reflected in the genomic content of the organisms as well as their metabolic capabilities. Acinetobacter are Gram-negative, aerobic, and non-motile. Pathogenic A. baumannii antibiotic resistance has risen from a susceptible level in the 1960s to extended and pan-drug resistant today (Peleg et al., 2008). As such, the need for new treatment targets and strategies is dire.
Genome-scale models (GEMs) of metabolism have been used to discover new drug targets (Kim et al., 2011) and pursue novel treatment options. Genome-scale metabolic reconstructions offer an established framework for systems-level analyses of an organism's metabolism . GEMs provide a formal way to link genotype to phenotype and mechanistically analyze the metabolic capabilities of organisms. A previous reconstruction of the metabolic network of A. baumannii AYE was undertaken and produced: AbyMBEL891 (Kim et al., 2010). This reconstruction provided a valuable starting point for the progress and use of GEMs to study the pathogenic nature of A. baumannii. However, one issue that has limited the use of this and other reconstructions is the lack of standardization in identifiers for metabolites and reactions . Since the publication of AbyMBEL891 in 2010, numerous studies have produced new data (Farrugia et al., 2013;Gallagher et al., 2015;Presta et al., 2017) that provide an opportunity to update this A. baumannii reconstruction, allowing for more accurate representations of its physiology. One such study was a highquality reconstruction of A. baumannii ATCC 19606, iLP844, that served as a valuable resource for model improvements (Presta et al., 2017). Furthermore, given that Acinetobacter is known to populate a diverse array of environments, particularly hospitals, it is likely that diverse metabolic capabilities may be present throughout the different strains in this species.
We present iCN718, a new and updated GEM of A. baumannii AYE (Supplementary Datasheets S1-S3). This reconstruction utilizes AbyMBEL891 as a foundation. We validated our model by comparing phenotypic predictions made by iCN718 to those made by AbyMBEL891. We extended our analysis to additional datasets published after AbyMBEL891. We assessed iCN718 on its ability to predict both gene essentiality and to recapitulate experimental growth capabilities. We then utilize this reconstruction to create draft models of 74 other A. baumannii strains from their sequence data alone. We leverage the reconstruction to produce draft models to gain insight into these other strains and the species as a whole. Thus, iCN718 offers a framework for sequence-to-model comparisons. Our updated model of A. baumannii will provide new opportunities to advance the understanding of pathogenic microbes and their interactions with human hosts.
Workflow for Network Reconstruction
We began the metabolic network reconstruction process by updating AbyMBEL891. We found that the AbyMBEL891 reconstruction could be updated and improved in three main areas: (1) standardization of reaction and metabolite identifiers to increase the tractability of the network, (2) mass and charge balance metabolic reactions, and (3) transport processes. Before updating and improving the reconstruction, we recognized that it was necessary to translate AbyMBEL891 into a format that could be more readily analyzed. We obtained a draft reconstruction of A. baumannii AYE using the ModelSeed database (Henry et al., 2010). We then cross-referenced draft reconstruction reactions against AbyMBEL891 and utilized additional databases to map all reactions and metabolites to the standardized BiGG format (King et al., 2016). Additionally, we added the curated gene product rules (GPRs) from AbyMBEL891 into iCN718 to improve ease of simulation (Presta et al., 2017). The resulting model was then continually and iteratively improved through manual curation of new organism knowledge in the literature published since the release of AbyMBEL891 (See section "Materials and Methods" and Figure 1).
iCN718 comprises 718 genes, 1016 reactions, and 890 metabolites compared to the 650 genes, 891 reactions, and 770 metabolites in AbyMBEL891. The majority of the difference in reactions included arises from the inclusion of exchange reactions in iCN718 as well as revamping the transport reactions. The reversibility of reactions within iCN718 was referenced against the reversibility of corresponding reactions in a recently published model of A. baumannii ATCC 19606, iLP844 (Presta et al., 2017). In some cases, reaction reversibility was changed to reflect the state in iLP844. Reversibility was corroborated with iLP844 for a set of about 50 reactions and edited accordingly. iLP844 was also used to identify GPRs for transport reactions present in both models, leading to the inclusion of 66 new genes in iCN718. Further, new reactions that were missing in the original reconstruction were added in peptidoglycan biosynthesis, propanoate metabolism, and glycolate catabolism. The end product of iCN718 is a reconstruction of A. baumannii AYE that rectifies issues with AbyMBEL891 regarding identifiers, reversibility of reactions, transport/exchange reactions, and mass/charge balancing. Well-curated identifiers were added for every reaction in the network. Thus, iCN718 provides an improved knowledge-base for the study of A. baumannii.
After completing the reconstruction of iCN718, we calculated the metabolite connectivity to evaluate the network structure for both iCN718 and AbyMBEL891 (Becker et al., 2006). Metabolite connectivity refers to the number of reactions in which a metabolite participates. Given that metabolites are the nodes of the network connected by reactions, this metric reveals the connectivity of a metabolic network. We compared the metabolite connectivities of iCN718 and AbyMBEL891 (Supplementary Figure S1) and found that overall, the networks were comparable, but these plots do not visualize dead-end metabolites (i.e., metabolites with a connectivity of one). iCN718 has four dead-end metabolites whereas AbyMBEL891 has 145 dead-end metabolites, demonstrating that iCN718 is more highly connected overall. The increase in connectivity is a result of converting to BiGG standard identifiers which improves the regularity of the network.
Functional Evaluation of iCN718
Our first functional evaluation of iCN718 consisted of analyzing its accuracy in predicting gene essentiality for three datasets (Figures 2A,B). The most comprehensive essentiality dataset available was used (Gallagher et al., 2015). This complete TNseq essentiality dataset was conducted with A. baumannii AB5075 and is particularly valuable because it is of genome scale and every gene in iCN718 has an ortholog. iCN718 was able to achieve 80.22% accuracy ( Figure 2B). Unfortunately, given the FIGURE 1 | Workflow of the reconstruction process. The starting reconstruction, AbyMBEL891, was cross referenced against a draft model generated utilizing ModelSEED (Henry et al., 2010). Next, the reconstruction was standardized using various databases mapped to standard BIGGs IDs. This process was followed by manual curation based on current literature on the organism, aided by the use of ESCHER to visualize pathways throughout the process. Finally, the model was evaluated against experimental datasets and compared to iLP844 a model of Acinetobacter baumannii ATCC 19606 to further improve the reconstruction. The model was iteratively evaluated against gene essentiality and phenotypic datasets to improve the reconstruction accuracy. It is worth noting that the Berardinis dataset was of Acinetobacter baylyi ADP1 and therefore not every gene in iCN718 had an orthologous gene in the essentiality dataset. Green represents correct predictions, red represents incorrect predictions. The Gallagher dataset is from Acinetobacter baumannii strain AB5075 of which there is an ortholog for every gene within iCN718. Model-predicted ability to catabolize various sole carbon (C) and sole nitrogen (D) sources compared to the Farrugia et al. (2013) Biolog Phenotypic Array data for Acinetobacter baumannii AYE exhibited 89% and 84% accuracy, respectively. Blue represents correct predictions, orange represents incorrect predictions. Only compounds readily mapped to model metabolites were included from the Biolog data.
lack of GPRs in AbyMBEL891, we were unable to analyze its performance on this dataset. We also evaluated iCN718's performance on the two datasets originally used to validate AbyMBEL891. The first was an insertional mutagenesis dataset with A. baumannii ATCC 19606 by Dorsey et al. (2002) on a set of 14 mutants. We repeated the same knockouts in silico as done in the original experiment and found that iCN718 was able to correctly predict 100% (14/14) of the mutant cases as did AbyMBEL891. The obvious limitation of this dataset is that it is on such a small scale. The second dataset used to validate AbyMBEL891, by de Berardinis et al. (2008), was a complete, genome-scale set of single-gene deletions in Acinetobacter baylyi ADP1. iCN718 fell short in predictive ability on this dataset compared to AbyMBEL891 (Figure 2A), with 68% and 72% accuracy, respectively.
The higher predictive accuracy on the Gallagher dataset compared to the de Beradinis dataset is encouraging because strain AB5075 is a clinical isolate like AYE whereas A. baylyi ADP1 is a soil strain. The disparity in genomic content between A. baumannii AYE and A. baylyi ADP1 is evident in the limited number of genes in iCN718 that have an ortholog. Despite the limitations of the original two datasets, whether it be scale or lack of similarity, it was important to test iCN718's ability to recapitulate the capabilities of AbyMBEL891. Overall, iCN718 performed the same as AbyMBEL891 on the datasets originally used for validation. Further, there is more agreement of genes with a dataset on a strain that is closer to the target of the reconstruction. It is reasonable to conclude from these geneessentiality results that at a minimum, iCN718 performs in line with AbyMBEL891 in regard to gene essentiality and more likely is superior in predictive capability. An obvious avenue for further improvement of the reconstruction would be to develop a gene essentiality dataset for strain AYE.
We further extended our assessment of iCN718 to large-scale phenotypic data. By utilizing the Biolog Phenotype Microarray data published by Farrugia et al. (2013), we were able to iteratively improve iCN718 through manual curation for discrepancies. The model had encouraging agreement at the end of this process for sole carbon and nitrogen sources readily tractable to the model (116 total; Figures 2C,D). Growth rates were calculated in Simmons' Minimal Medium and iteratively investigated for each carbon or nitrogen source in the microarray wet lab experiment. The model result of growth or no growth determined by optimizing for the biomass function was compared to the data from the microarray (Supplementary Tables S1, S2). For the carbon sources tested on the microarray plate, 73 metabolites were analyzed and showed that iCN718 has 89.1% agreement with the experimental data. Likewise, for nitrogen sources, 43 metabolites were screened with 83.7% agreement. Importantly, out of all the datasets used for validation of the reconstruction, this microarray data was the only set executed with the strain of interest, A. baumannii AYE. Therefore, this dataset was particularly valuable for insight into the capabilities of this specific strain.
We have demonstrated that iCN718 performs as well as AbyMBEL891 on datasets originally used to validate AbyMBEL891. We note that these datasets suffer from limitations in that they are either not genome scale or are not of an ideally similar species to the strain of interest. To expand the validation of iCN718 and address these limitations, we analyzed a genomescale set of gene essentiality data of another A. baumannii clinical strain and found a reasonably high level of agreement. Further we analyzed iCN718's agreement with phenotypic microarray experiments conducted with strain AYE. iCN718's ability to capture this growth behavior is a major improvement over AbyMBEL891, which fails to simulate on the minimal media conditions corresponding to these experiments. Overall, we showed that iCN718 maintains comparable performance on the original datasets used for validation, has a higher agreement with gene essentiality data for a more closely related strain, and is able to correctly predict phenotypic growth experiments (Figure 3). We used the model to perform synthetic lethals analysis to generate new predictions. Briefly this resulted in 49 synthetic lethal gene pairs that include 62 unique genes. These genes correspond to reactions involved in fatty acid metabolism, purine metabolism, glycine/serine/threonine metabolism, phenylalanine/tyrosine/tryptophan biosynthesis, TCA cycle, lysine degradation, glycerophospholipid metabolism, glycolysis, pyrimidine metabolism, nicotinate/nicotinamide metabolism, riboflavin metabolism, pentose phosphate pathway, cysteine metabolism, and methionine metabolism. Full double-gene deletion results and synthetic lethal gene pairs are reported in Supplementary Tables S4 and S5, respectively.
Pan-Genome Analysis of A. baumannii Using iCN718
A GEM can be used to investigate the capabilities of organisms across multiple strains. We applied these principles using iCN718 to explore the different genotypes and phenotypes within the A. baumannii species. There are 75 full complete sequences of A. baumannii available on the PATRIC database (Wattam et al., 2014); these range from a wide variety of isolation countries and are largely isolates from a clinical/human setting (See Supplementary Table S6). We collected the annotated open reading frames (ORFs) from each of these genomes and used CD-HIT (Fu et al., 2012) to assign their coding sequences into clusters of at least 80% similarity. Clusters that were found in at least 74 of the 75 strains were determined to be core genes, while those found in only some of the strains were designated as accessory genes. In total, 24% (2448/10200) of the genes were found across all strains (core genome) while 76% (7752/10200) were part of the accessory genome (Figure 4). We further classified the core genome by clusters of orthologous groups (COGs) and found that while a large group (21%) had unknown functions, the remaining 79% of the core genome had a widely varied classification spanning 19 other COG categories. Overall the core genome had ∼33% COGs pertaining to metabolic functions. Particularly interesting was that 8.9% of the core genome was composed of functions in amino acid transport and metabolism (category E), suggesting that this area of metabolism might be particularly conserved over these strains of A. baumannii. We also classified the pan genome and note that roughly half could not be COG classified and almost half of that classified portion was classified as having unknown function (Supplementary Figure S2). This suggests that more robust study and classification of these strains is necessary.
After analyzing the full set of annotated ORFs across the 75 strains, we were particularly interested in applying the iCN718 reconstruction to construct draft strain-specific models of them. To accomplish building these draft models, we determined presence or absence of the 718 genes in the reconstruction iCN718 FIGURE 3 | Summary of AbyMBEL891 and iCN718 Performance. Overall performance of iCN718 compared to a previous Acinetobacter baumannii AYE reconstruction (AbyMBEL891). Both models perform similarly on the datasets originally used to validate AbyMBEL891; however, the ability to simulate sole carbon and nitrogen sources in minimal media is exclusive to iCN718. AbyMBEL891 could not be simulated with the Gallagher dataset and was incapable of growth in the conditions of the Farrugia dataset.
FIGURE 4 | Pan and Core Genome of Acinetobacter baumannii. The total number of gene clusters in 75 Acinetobacter baumannii strains (pan-genome) compared to those that are shared among all strains (core-genome). In total, 76% of the clusters are classified as accessory and 24% as core. The core genome was functionally classified into COG categories. COG categories are as follows: Cellular processes and signaling: D is cell cycle control, cell division, and chromosome partitioning; M is cell wall/membrane/envelope biogenesis; N is cell motility; O is posttranslational modification, protein turnover, and chaperones; T is signal transduction mechanisms; U is intracellular trafficking, secretion, and vesicular transport; V is defense mechanisms; W is extracellular structures; Y is nuclear structure; and Z is cytoskeleton. Information storage and processing: A is RNA processing and modification; B is chromatin structure and dynamics; J is translation, ribosomal structure, and biogenesis; K is transcription; and L is replication, recombination, and repair. Metabolism: C is energy production and conversion; E is amino acid transport and metabolism; F is nucleotide transport and metabolism; G is carbohydrate transport and metabolism; H is coenzyme transport and metabolism; I is lipid transport and metabolism; P is inorganic ion transport and metabolism; and Q is secondary metabolite biosynthesis, transport, and catabolism.
of AYE and deleted genes accordingly for the other 74 strains (See Supplementary Table S7). After this process, we had a measure of the "metabolic pan-genome" as it relates to the genes contained within iCN718. Utilizing the same thresholds, we found that 86% of the genes in iCN718 were considered to be core to all 74 additional strains. Therefore, much of the metabolism represented in iCN718 is maintained in these strains. Three genes were unique to strain AYE within the iCN718 reconstruction: p3ABAYE0029, p2ABYAYE0004, and ABAYE3614. Noting that most of the iCN718 reconstruction was determined to be part of the core metabolic function for all 75 of these strains, we decided to investigate each strainspecific model's metabolic capabilities. We were additionally interested in analyzing which genes from iCN718 were lost most Originally, only three of the 74 strain-specific models could simulate growth and the predominantly determining factor of this was the inability to produce lipopolysaccharide (LPS). This result is unsurprising given that LPS is known to vary from strain to strain (Pantophlet et al., 2001). The strains that could still synthesize LPS were A1, AB0057, and AB307-0294, suggesting that these strains may have similar LPS compositions to strain AYE. After recognizing LPS as the main limitation to growth for the majority of the strains, we removed LPS from the biomass function for the remaining strains to investigate other properties. With LPS removed, all but four strains could grow. The four strains unable to grow were, as expected, the four strains with the most deletions from the original AYE model. Interestingly, the one strain that was not isolated from a human, SDF, was instead isolated from lice and required 71 more deletions than the next highest dissimilar strain. This suggests that Acinetobacter are indeed highly adaptable to varying environments in their metabolic capabilities and that an expanded pan-genome analysis with a higher number of varied strain environments would yield interesting insights.
We then looked at every strain's ability to grow in the same minimal media conditions with sole carbon and nitrogen sources on which iCN718 was originally tested. All of the strains that could grow without LPS in the biomass function maintained the carbon and nitrogen catabolic capabilities exhibited by AYE in iCN718. This analysis is limited in that we are dealing with draft strain specific models, which are all derived from the content common to iCN718. To account for additional capabilities of each strain requires more data and deeper study of these strains. However, this approach demonstrates that with one high-quality reconstruction, insight can be gleaned into a large number of strains from their sequences alone.
CONCLUSION
Acinetobacter baumannii is an urgent clinical threat for which treatment is becoming increasingly difficult. High-quality GEMs of strains of A. baumannii can be an important tool to accelerate the advancement of new treatments. We updated and improved a previous reconstruction, AbyMBEL891, to produce a new reconstruction, iCN718. We tested iCN718 on multiple gene essentiality datasets as well as phenotypic microarray data. We demonstrated the utility of iCN718 and GEMs to gain further insight into related strains through their sequences alone. iCN718 is in a standardized and curated format that lends itself to further use by the community studying Acinetobacter, as well as in future multi-strain reconstructions of diverse A. baumannii strains. We demonstrated that iCN718 represents a significant improvement on AbyMBEL891 and a critical step in the progress toward a truly comprehensive knowledge-base for A. baumannii. As the knowledge of this organism continues to grow, iCN718 will provide a platform for the integration of further knowledge and data as well as a tool for future investigations.
Reconstructing iCN718
We first obtained a draft metabolic reconstruction of A. baumannii AYE utilizing the ModelSeed (Henry et al., 2010). AbyMBEL891 was then referenced against this draft reconstruction to compare for the content of each reconstruction. Additional databases (ExPASy, KEGG, MetaNetX, BiGG) were used to refine the reconstruction and obtain a reconstruction utilizing standardized BiGG identifiers (Kanehisa and Goto, 2000;Gasteiger et al., 2003;Ganter et al., 2013;King et al., 2016). The result was a draft reconstruction in BiGG format built upon AbyMBEL891, the draft reconstruction via ModelSeed, and information from the aforementioned databases. To obtain the most accurate final model, this draft reconstruction was then extensively manually curated. This process involved investigating the current literature and rectifying inconsistencies present in the reconstruction. We determined and subsequently filled gaps identified through topological gap analysis and flux-based functional tests. The pathway visualization tool, ESCHER, was instrumental in this gap analysis (King et al., 2015). We also utilized the GrowMatch algorithm to obtain potential reactions to fill identified gaps (Kumar and Maranas, 2009). Additionally, the recently published model of A. baumannii ATCC 19606, iLP844, was used as an additional resource for cases of conflicting information amongst the aforementioned sources (Presta et al., 2017). iLP844 was particularly used to check reaction reversibility. The model content was further improved by comparing it to numerous experimental datasets. In particular, iLP844 was used to confirm reaction reversibility. The model content was further improved by comparing it to numerous experimental datasets and making iterative improvements to increase agreement with experimental data. The manual curation was an iterative process and as such was continuously repeated to yield the highest quality reconstruction possible.
Constraint-Based Modeling
The network reconstruction was converted to a mathematical representation formed from the stoichiometric coefficients of the biochemical reactions. This stoichiometric matrix, S, encapsulates in its columns each mass-and charge-balanced reaction of the network, while each row represents a specific metabolite. The model is assumed to be at homeostatic state (Equation 1).
Thermodynamic constraints for network fluxes are incorporated in the form of bounds that incorporate directionality of reactions. The reconstructed model was analyzed with CoBRApy-0.6.1 (COnstraints-Based Reconstruction and Analysis for Python; Ebrahim et al., 2013) and GLPK 4.32 solver. Flux balance analysis (FBA) is a well-established optimization technique and was used in this study. For a primer on FBA, refer to Orth et al. (2010).
Gene Essentiality
Gene essentiality predictions were determined by simulating single gene deletions of each applicable gene in the model depending on the dataset in question. Growth of the single gene deletion mutants was predicted using FBA and if, following a gene deletion, there was no growth, this gene was determined to be essential. For all gene-essentiality datasets, the corresponding set of orthologous genes, since no available single gene deletion datasets exist for A. baumannii AYE, was obtained via NCBI Bidirectional BLAST (Sayers et al., 2012).
Growth Conditions
For comparison to experimental data, there were two growth media needed for in silico simulations. For any experimental set executed in rich media all exchange reactions were set to −10 mmol.g −1 .h −1 to mimic non-limiting conditions and access to multiple carbon and nitrogen sources. The second media condition utilized was Simmons Minimal Media (Simmons, 1926). The validation of the de Berardinis et al. (2008) gene essentiality dataset and recapitulation of Biolog Phenotypic Array data by Farrugia et al. (2013) Table S3). Lower bounds for the exchange reactions of nutrients present in Simmons' Minimal Media were set to −10 mmol.g −1 .h −1 and the carbon source of interest was also then set to −10 mmol.g −1 .h −1 . As described in the experimental protocol by Farrugia et al. (2013) for testing of nitrogen sources, the minimal media was supplemented with xylose.
Metabolite Connectivity
The stoichiometric matrices of iCN718 and AbyMBEL891 were used to calculate the metabolite connectivities of every species in each network. The metabolite connectivity is a sum of the number of each reaction a metabolite participates in. Metabolite connectivities were then ranked from greatest to least connected to form a discrete distribution (Supplementary Figure S1).
Pan-Genome Analysis
The pan-genome of all 75 completely sequenced strains was constructed by clustering protein sequences based on their sequence homology using the CD-hit package (v4.6). CD-hit clusters protein sequences based on their sequence identity (Li and Godzik, 2006). CD-hit clustering was performed with 0.8 threshold for sequence identity and a word length of 5. A cluster formed by CD-hit is hereon referred to as a gene family. The pangenome was subdivided into core and accessory genomes. We defined the core genome as gene families that were found in at least 74/75 strains. The subdivided pan-genome was subsequently utilized to identify genes that were part of the core or accessory genome.
AUTHOR CONTRIBUTIONS
JM and BP conceived and designed the study. JM, BP, and CN prepared the first draft of the manuscript. CN performed the metabolic network reconstruction and model simulations. All the authors discussed the results and participated in the writing process.
ACKNOWLEDGMENTS
We thank J. T. Yurkovich for critical comments on the manuscript.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene. 2018.00121/full#supplementary-material FIGURE S1 | Calculated metabolite connectivity for (A) iCN718 and (B) AbyMBEL891. The dashed orange line serves as a reference and points above the line indicate strong connectivity.
FIGURE S2 | COG classifications for the pan-genome content of the 75 strains. Note that this only represents those genes that could be COG classified which is only half of the entire set designated in the pan-genome. DATA SHEET S1 | iCN718.xls: Reconstruction in spreadsheet format. | 6,181 | 2018-04-10T00:00:00.000 | [
"Medicine",
"Biology",
"Computer Science"
] |
Research on the development of global digital tax and countermeasures of Chinese digital tax
. International taxation faces challenges brought by the development of the digital economy. Countries around the world and relevant international organizations, based on different positions, advocate or oppose the establishment of digital tax collection policies. This paper aims to summarize the development status of digital tax collection supervision policies of major countries and international organizations, to analyze the impact of the digital tax in combination with the development of China's digital economy, and try to put forward policy suggestions for the development of China's digital tax.
Policy dispute over digital tax development
The digital economy is based on modern information networks, takes digital knowledge and information as production elements, takes digital technology innovation as a driving force, integrates digital technology and the real economy, and makes traditional industries more digital and intelligent.It is a new economic form (China Academy of Information and Communication Technology 2017).
Due to the imbalance of economic development, asset-light strategy, transnational, rich sources of income, and fuzzy boundaries, the current tax system is designed based on the traditional industry.It isn't easy to make fair and reasonable taxation to the digital economy enterprise.Due to tax imbalance, the digital economy and the enterprise tax burden are unfair, resulting in a digital tax dispute.
The legal basis of taxation: First, not taxing digital products violates the principle of tax fairness for traditional goods trade (Mao 2020).Secondly, in the current tariff system, the positioning of digital products is not precise, and the taxation of digital products lacks appropriate legal provisions, which violates the principle of legal taxation (Dai 2015).Third, as the most active market players in the international trade of digital products, small and micro-businesses and individual business operators have small operating quotas and significant uncertainties.Therefore, taxing them violates the principle of tax efficiency (Zhang 2021).Fourth, if a company maintains a permanent establishment in its own country, it does not follow international tax principles to levy digital taxes.Fifth, digital tax is a separate tax levied based on the existing tax of enterprises, which violates the principle of "no heavy tax."(Hou and Bai 2020; Cao 2021) The definition of taxation jurisdiction: According to whether establishing agencies and offices in the country, the traditional tax classifies enterprises into resident and non-resident taxpayer taxpayers, who need to take the international income tax obligations, and nonresident taxpayers only need to afford their income tax liability, whether the source of income is related to its in the country established institutions.However, digital enterprises do not need to set up relevant institutions but operate virtually based on the modern information network.Moreover, digital enterprises that have set up institutions are also finding it tricky to judge whether their income sources are related to those institutions, which affects the definition of jurisdiction of digital tax collection (Bai and Yue 2021).
The scope of taxation and the determination of tax rates: Due to the different development stages of the digital economy in different countries and national interests, the range and tax rate of digital tax formulated by different countries and regions may cause unfair taxation or tax discrimination among enterprises in different countries.
Digital tax position major countries 2.1 Countries opposed to digital taxes
The United States has long argued for a permanent exemption from tariffs on digital goods to promote international digital trade.In 1997, the United States formulated the Global Ecommerce Policy Framework, holding that the government should not add new taxes and fees to digital products traded through the Internet.And it suggests that the Internet space should build into duty-free zones to promote the development of digital trade.In 2003, the United States amended the Uniform Commercial Code of the United States to exclude information from the definition of goods.Its relevant judicial practice considers that mixed products of information with the physical carrier are not goods to maximize the promotion of digital trade liberalization.As the largest exporter of digital products, the United States owns more than two-thirds of the global digital trade network companies.If the digital product trade is taxed, the United States will be the biggest beneficiary, opposing the digital tax.In addition, some countries, such as Ireland and Luxembourg, oppose the digital tax because they believe that it will reduce the attractiveness of the original low tax rate to foreign investment.Sweden, Finland, Denmark, and other countries also oppose the digital tax, arguing that it may cause "anti-globalization" and hinder innovation development.
Countries in favour of digital taxation
According to the legislation and implementation progress of digital taxation, this paper divides the countries in favor of digital tax into three categories.
Here are countries that have enacted digital taxes and implemented them.So far, France, the United Kingdom, India, Spain, Hungary, Slovakia, Zimbabwe, Angola, Italy, Austria, Tunisia, Malaysia, Kenya, Poland, Turkey, and other countries have adopted relevant legislation on digital tax and put it into effect, as shown in Table 1.
Countries that tax digital services under other tax headings without separate legislation.Including Singapore, Russia, Myanmar, and others.
3 Position of international organizations on digital taxation
The world trade organization (WTO)
WTO has not formed a formal and permanent resolution on the issue of tariff exemption for digital products.In 1998, the Second Geneva Conference of WTO formulated the Outline of Global E-commerce, which clarified the attitude of digital product trade liberalization.WTO recommended a zero-tariff policy for digital products and issued the WTO Memorandum on Tariff Exemption for electronic Transmission.It needs to be discussed again at the ministerial meeting held once every two years whether to agree to the extension of "suspension of tariff on digital products."The WTO is committed to designing digital trade rules, and significant member states have been actively submitting digital trade proposals.As the digitalization of international business continues to develop, economic and trade links among economies rely more and more on the Internet.More member states participate in international digital trade.WTO will invite more members to formulate global digital trade rules to promote digital commerce's liberalization, facilitation, and transparency.
Organization for economic cooperation and development (OECD)
In 2013, OECD and G20 launched the BEPS project.In 2015, OECD released 15 BEPS action plans and believed that the digital economy has three characteristics: dependence on intangible assets, participation of data and users, and disembodied cross-border operations.In 2019, BEPS129 members agreed on a policy framework based on "double pillars": Pillar one focuses on redistributing taxation rights.It considers "user participation," "intangible market assets," and "significant economic presence" comprehensively.It identifies two types of taxable profits of global digital platforms and reviews and revises profit distribution and connection rules.Pillar two addresses the problem of uncoordinated crossborder tax regulation.For example, it sets a minimum tax rate, ensuring that multinational enterprises pay a minimum level of tax.And it solves the problem of base erosion and profit shifting caused by unclear tax jurisdiction.
On July 1, 2021, the BEPS Inclusive Framework made the latest progress, with the twopillar approach supported by 130 countries and jurisdictions, accounting for more than 90% of the global economy.Pillar one ensures that large multinationals, including the digital industry, pay fair taxes in the markets where they make their profits.Pillar two controls fiscal and tax competition among countries by setting a global minimum tax rate.On October 8, 2021, 136 members issued "The Statement on Responding to the Challenges of Digitalized Taxation of the Economy, " which agrees on crucial core technical details of the "Dual Pillar" plan.It is a substantial step forward in the reform of the international tax system.After the Biden-Harris administration took office, it changed the previous U.S. administration's attitude towards the two-pillar negotiation and actively promoted the conclusion of the two-pillar plan.
European Union (E.U.)
The E.U. believes that an international digital tax is essential.First, it ensures the integrity and proper functioning of the single market.Second, it helps to avoid base erosion and promote fiscal sustainability.Third, it levels the playing field for the E.U.Fourth.It effectively cracks down on tax planning to avoid evading tax from international tax rules.The main features of the E.U. digital tax scheme: First, the clarification of taxation scope: enterprises with more than 100,000 users in member states, annual revenue of more than 7 million euros in one of the member states, or enterprises with more than 3,000 digital service business contract established during the tax year should be ensured the same tax base as traditional enterprises and digital enterprises.Secondly, provisional tax is levied on some digital economic activities to identify the data value and user value in the digital economy to further ensure the rationality of tax jurisdiction.
United nations
Following the current international taxation framework, the United Nations has incorporated automated digital services into the existing agreements by revising the model agreement.Based on the royalties in Article 12 of the original Agreement, the revised model includes digital services in withholding tax by providing that income derived from and paid to a Contracting State for digital services may be taxed in the other Contracting State.The tax base shall be the gross payment or net income, and the tax rate follows the withholding tax rate agreed by the Contracting States.
Strengthen tax reform and improve the tax system
Digital enterprises are rapidly expanding their markets.China is a big consumer of digital economy products and services.However, the tax factors of the digital economy, such as taxpayers, tax objects, tax items, tax rates, and tax locations, are not clear.Traditional enterprises pay higher taxes, which is unfair.It is necessary to strengthen tax reform and improve the tax system.First, fully demonstrate and comprehensively weigh the impact of digital tax and its advantages and disadvantages.China is home to large Internet companies such as Alibaba and Tencent.As a significant digital economy, China enjoys strong international competitiveness.In tax reform, it is necessary to comprehensively analyze the influence of digital tax, thoroughly balance the interests of users, enterprises, and the state, and establish the basic framework of digital tax scientifically and reasonably.
Second, promote the construction of China's digital tax legal system.Improve relevant systems and legal guarantee systems to help enterprises adapt to the increasingly strengthened international digital tax rules as soon as possible.In promoting tax reform legislation, the first step is to establish the legal basis of digital tax and create a fair tax system environment.
Third, do an excellent job in digital tax technology preparation.Establishing a tax approach that matches the digital enterprises' innovation model is essential.The collection and supervision of digital tax need corresponding information technology support.Therefore, it is necessary to strengthen the research on digital enterprises' business models, strengthen tax systems' information construction, and establish an efficient and safe digital tax supervision system.
Strengthen international cooperation and promote tax consultation in regional cooperation
The digital tax has an important impact on the development of the digital economy, and China must actively participate in and promote the formulation of international tax laws and regulations.
First, we will actively form international rules on digital taxation to safeguard China's tax rights and interests.The collection scheme of digital tax is related to national sovereignty and tax interests and directly affects the development of the global digital economy and tax fairness.The imbalance of international digital economy development, the righteousness of tax interests, and the complexity of digital tax make it challenging to reach an agreement on the worldwide digital tax scheme.The European Union, OECD, G20, and other international organizations are speeding up establishing global digital tax rule systems.In order to safeguard China's tax rights and interests and promote the development of the digital economy, China should strengthen communication and cooperation with various international organizations, enhance its voice in international tax affairs and safeguard the country's tax rights and interests.
Second, actively participate in the consultation work of international organizations and promote the establishment of a multilateral tax mechanism.China is second only to the United States regarding the digital economy.To compete internationally, Chinese digital enterprises need a fair international business environment.Therefore, it is necessary to make full use of the right to speak in international organizations and actively carry out multilateral and bilateral consultations within the framework of relevant international organizations to create a better tax environment for Chinese enterprises going abroad.
Third, pay attention to international digital tax trends and build a rapid response mechanism for taxation.Before forming a unified global digital tax rule, different countries take different policies and measures.It is challenging to create an excellent digital tax collection regulatory order.Although the Statement on Addressing the Tax Challenges of digitization of the economy has achieved a relatively broad global consensus, several approvals and political decisions still need to be completed.China should pay close attention to the international development of digital tax, assess the impact of the digital tax plan on China, form a rapid response mechanism, and earnestly safeguard China's interests.
In light of the development trend of the digital economy, launch the China plan of digital tax
First, formulate a timetable and roadmap for the implementation of digital tax.Analyze the development status of the digital economy at home and abroad, learn and draw lessons from the international experience of digital tax policy, deeply analyze the "double pillar" policy framework and its development trend, and define the development goals of digital tax.In principle, it should conform to the development trend of the international digital economy and balance tax interests at home and abroad and closely combine China's national conditions and be conducive to the development of China's digital economy.It should accelerate China's digital tax plan into the tax reform agenda and formulate a timetable and roadmap for implementing a digital tax that suits China's national conditions.
Second, should carry out pilot projects in stages.Digital tax is not the same.The function of various regions and industries' comprehensive digital tax may cause adverse
Table 1 .
Digital tax legislation and implementation of countries and digital tax policies.
Table 2 .
National digital tax policies that have enacted or are in the process of legislating but have not yet imposed digital taxes.
euros worldwide and 100 million kroner in the Czech Republic with more than 200,000 users, for a time to be determined
Table 3 .
Countries that levy digital taxes under other tax headings without separate legislation and their policies. | 3,285.8 | 2022-01-01T00:00:00.000 | [
"Economics",
"Law",
"Computer Science"
] |
Adaptive Sample-Size Unscented Particle Filter with Partitioned Sampling for Three-Dimensional High-Maneuvering Target Tracking
: High-maneuvering target tracking is a focused application area in radar positioning and military defense systems, especially in three-dimensional space. However, using a traditional motion model and techniques expanded from general two-dimensional maneuvering target tracking may be inaccurate and impractical in some mission-critical systems. This paper proposes an adaptive sample-size unscented particle filter with partitioned sampling (PS-AUPF), which is used to track a three-dimensional, high-maneuvering target, combined with the CS-jerk model. In PS-AUPF, the partitioned sampling is introduced to improve the resampling and predicting process by decomposing motion space. At the same time, the adaptive sample size strategy is used to adjust the sample size adaptively in the tracking process, according to the initial parameters and the estimated state variance of each time step. Finally, the e ff ectiveness of this method is validated by simulations, in which the sample size of each algorithm is set to the minimum required for the optimal accuracy, thus ensuring the reliability of the tracking results. The results have shown that the proposed PS-AUPF, with higher accuracy and lower computational complexity, performs better than other existing tracking methods in three-dimensional high-maneuvering target tracking scenarios.
Introduction
Maneuvering target tracking is a fundamental and critical task in many practical applications with a wide range of military and civil backgrounds [1,2]. Due to the inconsistency of maneuverability in all directions in three-dimensional space, the target motion cannot be accurately described by the expansion of a general two-dimensional model and method, especially for the high-speed and high-maneuvering targets. Therefore, three-dimensional high-maneuvering target tracking has now become one of the difficulties in this kind of problem [3][4][5][6].
For high-maneuvering target tracking, the establishment of a motion model is the primary task. So far, the multiple-model (MM) methods [7][8][9][10] are usually adopted because of their fine modeling. However, such methods need to add as many motion models as possible to ensure the completeness of the model set, which will lead to a significant increase in computational complexity and unnecessary competition among similar models.
Besides, the MM methods rely heavily on the priori assumptions, such as the Markov transition matrix. Another method is to assume that the motion model is fixed, and that the maneuver is a disturbance caused by an injection of maneuver noises into our model [11]. Through different assumptions about the statistical characteristics of noises, researchers proposed many different models, among which the Singer model [12], the semi-Markov model [13] and the "current" statistical model [14] are widely used. However, these models only consider the acceleration maneuver of a target. For high-order maneuvers, Kishore proposed the Jerk model [15,16]. This model achieves high accuracy when tracking high-speed and high-maneuvering targets, but it has some steady-state deterministic error in tracking the step acceleration change rate signal. Considering the limitations of the Jerk model, Qiao et al. drew on the idea of the "current" statistical model and proposed a CS-Jerk model that adapts to high-maneuvering targets [17,18]. Compared with the Jerk model, this CS-jerk model has a better tracking performance for high-speed and high-maneuvering targets. Therefore, in our study, the CS-Jerk model is selected as the motion model to solve the three-dimensional high-maneuvering target tracking problem.
In target tracking, the filtering algorithm is the most critical step, which directly affects tracking performance. Owing to the adaptability in strong nonlinear non-Gaussian systems, Particle Filter (PF) is often used [19][20][21]. However, the PF scheme, of which the complexity depends largely on its sample size, would suffer particle degeneracy. Another new solution is based upon artificial intelligence methods, such as combining a general regression neural network (GRNN) as our tracking algorithm with a multiple-model method [22][23][24]. This method can deal with non-linear problems well, but its real-time performance is poor [25]. When the target motion changes a lot, the network needs to be retrained in order to adapt to the new motion mode. Therefore, the current mainstream tracking algorithm research is still based on the PF scheme, and on this basis to make improvements. There are usually three ways to improve: Choosing proper proposal distribution, resampling to obtain a high-quality sample set and introducing an effective sample size. Based on the first point, Merwe proposed the unscented particle filter (UPF) [26], which uses the mean and variance of the particle state obtained by an unscented Kalman filter (UKF) to approximate the importance density function. Compared with general PF and UKF, UPF achieves higher accuracy while bringing about higher computational complexity, and it will also be troubled by particle degeneracy when the proposal distribution deviates too far from the real state [27][28][29].
For further improvement, most studies are focused on improving particle resampling, usually based on traditional resampling mechanisms, such as hierarchical resampling [30], adaptive resampling [31][32][33], deterministic resampling [34][35][36], etc. Another way is to increase the diversity of samples by introducing intelligence optimization ideas, such as a genetic algorithm [37,38], firefly algorithm [39], bat algorithm [40] and so on. The above methods could relieve the particle degeneracy to some extent, whereas if they are applied to three-dimensional maneuvering target tracking, the particle distribution would be sparse and hard to cover uniformly, due to an inconsistent maneuvering mode and intensity in different directions, which would intensify particle degeneracy and reduce the diversity of samples. Noting that the target motion could be decomposed into three mutually perpendicular directions in three-dimensional space, the maneuvering form and noises of the target in each direction do not interfere with each other. If these three directions are regarded as orthogonal independent subspaces, the reduction in dimensionality of the maneuver could be considered. The partitioned sampling (PS) method proposed by MacCormick [41] increases the diversity of samples by some decomposition of system dynamics to reduce the sample size required for target tracking when the state space dimension is large [42]. It is mainly used for multi-feature visual target tracking, and is rarely used in other fields. Besides, the premise of using partitioned sampling is that the state space can be decomposed into several independent subspaces, but when there are correlative noises that are difficult to decompose in the system, this method cannot be used directly. Some decoupling methods are needed to remove the correlations in advance [3].
Another improved way that could effectively reduce the redundancy of particles is to adaptively adjust our sample size to avoid computational burden and poor real-time performance caused by fixed sample size.
The adaptive particle filter with KL-sampling proposed by Dieter [43] limits the estimation error through adaptive changes of sample size, but it may bring high computational cost [44,45]. Torma et al. [46] proposed a particle filter based on an adaptive adjustment of likelihood distribution, whereas the weight variance of particles has a great influence on the sample size [47]. In Section 3, we will introduce an adaptive strategy based on the relationship between state variances and sample size, which is derived by introducing theories in Probability & Statistics.
Based on the CS-jerk model, this paper proposes a three-dimensional high-maneuvering target tracking method, by combining the partitioned sampling and the adaptive strategy with UPF. During the tracking process, the target change motion is matched by an adaptive modification of the CS-jerk model, which involves the estimation of the motion state and the correction of the statistical characteristics of maneuver noises, and the UPF is chosen as the basic filtering algorithm to implement this modification. Then, by using adaptive strategy, particles in the UPF are adaptively adjusted to the appropriate amount according to the state variance of target motion for each time step, thereby maintaining an effective sample size and real-time tracking. Furthermore, according to the dynamic decomposability of the target motion, partitioned sampling is used to decompose the motion space so that particles could be independently sampled in each subspace, which reduces the computational complexity and increases the sample diversity, further mitigating the particle degeneracy. Finally, the proposed algorithm is compared to other classic tracking algorithms by experiments. The results verify that the proposed algorithm outperforms other algorithms, in terms of accuracy, and running time, with significant advantages in tracking performance.
Model Establishment
This section describes the motion model and measurement models of high-maneuvering target tracking in three-dimensional space. Considering high maneuverability, the CS-jerk model is chosen as the motion model, and the measurement information preprocessing is added in our measurement model to facilitate partitioned sampling. The above two parts will be introduced next separately.
Target Motion Model
For the motion model, we assume that it is fixed in structure and that maneuver is an interference caused by noise injection into this motion model, so the CS-jerk model can be used to match the target motion by adaptive modification. In this section, we start with a brief introduction of the CS-jerk model.
Suppose the change rate of acceleration of the target is a time-dependent stochastic process with a non-zero mean, which can be expressed as follows [18], where ... x (t) is the time-dependent rate of change in maneuvering acceleration with non-zero mean, j is the mean of ... x (t), j(t) is the exponentially-related rate of change of random acceleration with zero mean. Using the Wiener-Kolmogorov whitening algorithm, the random acceleration rate is expressed as result of driving by Gaussian white noises, and the discretized state model is obtained as follows: ..
x k ] T is the target state vector at time k which includes the position x k , velocity .
x k and the rate of change of acceleration ... x k ; F is the state transition matrix and U is the input control matrix. F and U are expressed as follows: where T is the sampling interval, ω is the random Jerk maneuver frequency, w k is the white noise sequence with zero mean, and its covariance matrix can be expressed as follows: where σ 2 j is the variance of random jerk. For details of the remaining parameter q, see [14]. During the tracking process, by using a filtering algorithm, the statistical characteristics of maneuver noises are corrected according to the estimated target motion state, and further, the noise covariance matrix Q k is updated, so that the CS-jerk model can be adjusted to match the target-changing motion. For brevity, only the basic form of this CS-jerk model is given in this section; details of the CS-jerk model can be found in [17,18].
The above analysis is based on one-dimensional space. In three-dimensional motion space, the motion model needs to be extended. The target state vector will be expanded to The state transition matrix F and the process noise covariance matrix Q k are both expanded into a 12 × 12-dimensional matrix.
Measurement Model
In practical applications, the measurement information of a maneuvering target is obtained by sensors such as radar, which is mostly based on spherical coordinates, including distance r, azimuth b, and elevation e [48].
where v r , v b , and v e are measurement noises of distance, azimuth and elevation, respectively; h r , h b and h e are noisy measurement information.
Considering the strong nonlinearity in the spherical coordinate system, in order to facilitate partitioned sampling, the Cartesian coordinate system is selected as the reference system to process the measurement information, and the coordinate conversion for data measured by sensors is required. In this paper, we assume that the measurement model in the Cartesian coordinate system is as follows, Appl. Sci. 2019, 9, 4278 5 of 22 where Z k = [x k , y k , z k ] T is the measurement vector at time k, H k is the measurement matrix, v k is the measurement noise.
Assuming that there is a coordinate transformation between the two coordinate systems ϕ = h −1 , we can get measurements in the transformed Cartesian coordinate system.
The actual measurements in the Cartesian coordinate system after conversion can be expressed as follows: where v x , v y , and v z are the measurement noises after conversion with the variance matrix R = J(Z p ×R p × J(Z p , R p is the original noises variance matrix and J(Z p is the Jacobian determinant of measurements. After conversion process, the noise variances of various parties are coupled to each other, so the decoupling process needs to be performed later. The detail of variance decoupling of measurement noises is shown in [3].
Unscented Particle Filter with Adaptive Sample Size
In this section, an adaptive sample-size UPF (AUPF) is proposed to reduce the computational cost of invalid and redundant samples. We will start with a brief review of the unscented Kalman filter for establishing the importance sampling density. Then, an adaptive sample-size strategy based on probability will be introduced. Finally, the iteration step of AUPF will be given at the end of this section.
Unscented Kalman Filter
In the UPF, the important density function is generated by the nonlinear filtering algorithm UKF, so that its overlap with the true posterior probability density is larger. The idea of UT transformation is mainly used to approximate a Gaussian distribution by a fixed number of parameter branches. Assuming that the posterior probability distribution of a system state approximates a Gaussian distribution, UKF directly estimates the probability density function of this system state according to the model of a nonlinear system. At time k, the state distribution N(X k−1 ,P k−1 ) at time k − 1 is approximated by a set of samples, whereX k−1 andP k−1 are estimates of the mean and variance at time k − 1, respectively. These samples are then propagated to obtain the predicted mean and variance of state through the nonlinear state model of the system. Next, the weights of particles are given by the measurement model to update the estimated state. Finally, estimatedX k andP k at time k are obtained by a particles weighted summation. The detail of the UKF is shown in [49].
Adaptive Sample-Size Strategy
For the algorithms based on PF with fixed sample size, the invariant sample size directly affects the computational complexity, making the real-time and accuracy of these algorithms worse [5]. To find out the relationship between accuracy and the number of samples, χ is defined as the state space, and the target motion model shown in Equation (2) is rewritten as the general form of the nonlinear system state equation [50], where f (·) ∈ χ is the state transition function, which includes the current and previous state. It is assumed that N k , the number of samples at time k, is known, the probability density function of samples in state space χ is expressed as p(X), and the expectation of X k can be expressed as follows, The integral in Equation (11), which is hard to solve in reality, is usually expressed by sum of ∼ p be the sample set randomly sampled from the probability density function p, then the empirical sample mean of state X k is as follows, whereX is the updated sample propagated by the state transition function. Assuming the variance of state X k is defined as P k , i.e., the propagated samples come from the state population N(X k , P k , then we have a Gaussian density with mean X k and variance P k . Once the mean and variance of the samples are obtained, a t-distribution could be obtained according to the definition of t-distribution [51]. Actually, the sample meanX k is obtained by Equation (12), and the sample variance P k can be calculated as follows, However, due to the presence of process noises and measurement noises, the sample variance directly calculated by (13) is inaccurate and needs to be corrected by the amendment algorithm, such as a Kalman filter [52]. Then, with corrected sample mean and variance, there iŝ where t(N k − 1) is the t-distribution with N k − 1 degrees of freedom. Based on the confidence interval estimate of the t-distribution [53], given the sample size N k and the probability value 1 − α, with the quantile t α/2 (N k − 1), we can get the following results, i.e., where t α/2 (N k − 1) is the bilateral α quantile of t(N k − 1). The confidence interval of X k with a confidence level of 1 − α is as follows, For brevity, it is recommended to reorganize the interval shown in (17) by using L to represent the length of interval, where The interval in Equation (17), which is expressed as (X k − 0.5L, X k + 0.5L) now, is a random interval with the probability of 1 − α containing the empirical sample meanX k .
As the degree of freedom increases, t-distribution will infinitely approach the standard normal distribution [53]. In this case, t α/2 (N k − 1) can be replaced by z α/2 , where z α/2 is the bilateral α quantile of a standard normal distribution, and the length of interval L shown in (18) can be expressed as With certain varianceP k and confidence coefficient α, as sample size N k increases, the length of confidence interval L decreases. Once the confidence coefficient, variance and interval length are determined, the lower bound of the sample size N k is required to meet the following equation, where N k is proportional to the state varianceP k at time k and inversely proportional to (L/2z α/2 ) 2 , i.e., given the parameters L and α, and the state variance of each time step, the lower bound of the sample size can be determined.
To verify the effectiveness of adaptive sample-size strategy, it is combined with the PF, and a simple experiment is carried out by the adaptive sample-size particle filter (APF). The detail of general PF is shown in [5].
is the sample set after importance sampling and weight update at time k, N k−1 is the sample size estimated at time k−1, P k is the uncorrected estimated variance of the system state at time k. Next, P k is corrected by introducing the Kalman gain which is calculated as follows, where K k is the Kalman gain at time k, P XZ is the cross-covariance between the sample measurements and the sample states, P ZZ is the auto-covariance of the sample measurements. The detail of the calculation of P XZ and P ZZ is shown in [26]. Based on K k , the estimated variance is corrected as follows, With the corrected estimated varianceP k , the new sample size N k is obtained by (20), and further the sample set can be resampled to . Based upon the above discussion, the structural diagram of the adaptive sample-size strategy is shown in Figure 1.
Appl. Sci. 2019, 9, x FOR PEER REVIEW 7 of 21 quantile of a standard normal distribution, and the length of interval L shown in (18) can be expressed as With certain variance ˆk P and confidence coefficient α , as sample size k N increases, the length of confidence interval L decreases. Once the confidence coefficient, variance and interval length are determined, the lower bound of the sample size k N is required to meet the following where k N is proportional to the state variance ˆk P at time k and inversely proportional to L z α , i.e., given the parameters L and α , and the state variance of each time step, the lower bound of the sample size can be determined.
To verify the effectiveness of adaptive sample-size strategy, it is combined with the PF, and a simple experiment is carried out by the adaptive sample-size particle filter (APF). The detail of general PF is shown in [5].
It is assumed that is the sample set after importance sampling and weight update at time k, 1 k N − is the sample size estimated at time k−1, k P is the uncorrected estimated variance of the system state at time k. Next, k P is corrected by introducing the Kalman gain which is calculated as follows, where k K is the Kalman gain at time k, XZ P is the cross-covariance between the sample measurements and the sample states, ZZ P is the auto-covariance of the sample measurements. The detail of the calculation of XZ P and ZZ P is shown in [26]. Based on k K , the estimated variance is corrected as follows, With the corrected estimated variance ˆk P , the new sample size k N is obtained by (20), and further the sample set can be resampled to . Based upon the above discussion, the structural diagram of the adaptive sample-size strategy is shown in Figure 1. To compare the performance of APF and general PF, the univariate non-static growth (UNGM) model [5], a common verification model for filtering algorithms, is chosen as the experimental model, where the state transition model is: and the measurement model is: where x k is a one-dimensional state variable with no practical physical meaning, which replaces the target state vector X k to preliminarily verify the effectiveness of the adaptive sample-size strategy. The variance of process noises w k and measurement noises v k are set to 20 and 1, respectively, and the simulation duration is 100 s. Both of these two algorithms repeat 100 Monte Carlo simulations, and the algorithm accuracy is defined by the root mean square error (RMSE) [35], which reflects the deviation between the estimated statex and the real value x.
The experimental results are shown in Table 1. For further verification, the experimental results of the KL-sampling particle filter (KL-PF), known as a classical adaptive algorithm, is added for comparison. It is found that the estimation accuracy of the PF could be improved by increasing the sample size, but this improvement is limited. When sample size is increased from 500 to 1000, there is only a small improvement in accuracy, and the running time is positively correlated with sample size. For KL-PF and APF, their estimation accuracy and running time are both stable with different initial sample sizes. In terms of accuracy, both of them are close to the PF with 1000 samples. However, in terms of running time, under the same accuracy, APF is about 76% of PF, while KL-PF is higher than PF, and is about 1.5 times of APF. Since KL-PF requires us to calculate the information distance between samples, the computational complexity is increased to some extent. On the contrary, APF adaptively adjusts the sample size according to the state variance at each time step, therefore as initial sample size changes, the estimation accuracy could be maintained, while the running time is reduced. Figure 2 shows changes of particle number caused by adaptive sample size strategy.
Adaptive Sample-Size Unscented Particle Filter
The adaptive sample-size UPF aims to obtain a proposal distribution through UKF for importance sampling, and before particle resampling of each iteration, use the current estimated variance of samples to calculate the lower bound of the sample size of the next time step by introducing the adaptive method, so that the sample size can be adaptively adjusted as the estimated variance of the samples changes. In order to prevent particle shortage caused by too-few samples at a certain moment, we will set a lower limit of sample size min N at the time of initialization. When the estimated sample size is lower than the lower limit, min N is taken as the required number of particles of the next iteration. Based on the above ideas, the detail of the AUPF is as shown in Appendix A.
Partitioned Sampling
In this section, we concentrate on the combination of partitioned sampling and highmaneuvering target tracking algorithm in three-dimensional motion space. In simple terms, partitioned sampling divides the system state space into several independent subspaces, and for each one, to apply the transition function and to perform a weighted resampling operation. The particles are independently sampled in each subspace with their respective best strategies, then we can further predict the optimal sub-states.
Adaptive Sample-Size Unscented Particle Filter
The adaptive sample-size UPF aims to obtain a proposal distribution through UKF for importance sampling, and before particle resampling of each iteration, use the current estimated variance of samples to calculate the lower bound of the sample size of the next time step by introducing the adaptive method, so that the sample size can be adaptively adjusted as the estimated variance of the samples changes. In order to prevent particle shortage caused by too-few samples at a certain moment, we will set a lower limit of sample size N min at the time of initialization. When the estimated sample size is lower than the lower limit, N min is taken as the required number of particles of the next iteration. Based on the above ideas, the detail of the AUPF is as shown in Appendix A.
Partitioned Sampling
In this section, we concentrate on the combination of partitioned sampling and high-maneuvering target tracking algorithm in three-dimensional motion space. In simple terms, partitioned sampling divides the system state space into several independent subspaces, and for each one, to apply the transition function and to perform a weighted resampling operation. The particles are independently sampled in each subspace with their respective best strategies, then we can further predict the optimal sub-states.
The final output is the weighted sum of all predicted sub-states. It is assumed that the state space χ can be partitioned as χ = χ 1 × · · · × χ M , while g(·) : χ → R is a weighting function used to weight the sub-states in weighted resampling, and the particles are propagated by the proposal function f k (·) : χ → χ which can be decomposed as follows [41], where X k is the state vector at time k, n X k variable correlated with state X and time, • is the usual function composition operator. By definition, f i k can propagate particles over subspace χ 1 × · · · × χ M and modify the sub-states of particles defined on χ i in practice. Exploiting the features of weighted resampling, partitioned sampling achieves the same result by substituting the f k propagation by a sequence of applications of the f i k followed by weighted resampling, as shown in Figure 3. In this figure, operations " * f i k " refer to propagations of particles using proposition function f i k as defined above, and operations "∼ g i k " refer to weighted resampling w.r.t. the importance function g i k .
Appl. Sci. 2019, 9, x FOR PEER REVIEW 10 of 21 The final output is the weighted sum of all predicted sub-states. It is assumed that the state space χ can be partitioned as which can be decomposed as follows [41], where k X is the state vector at time k, It is noted that in the Cartesian coordinate system, the sub-states of the target in each direction of motion space are independent of each other, and are not affected by maneuvering motion in other directions, i.e., the motion space is three-dimensionally orthogonal. Based on the idea of dynamic decomposition and synthesis, the overall motion state of the target can be expressed as a superposition of motions in all directions. Figure 4 shows the structural diagram of three-dimensional space partitioned sampling. By using partitioned sampling, the three-dimensional motion space could be decomposed into three independent subspaces. Suppose that particles are sampled in each one-dimensional subspace, If particles are sampled directly in three-dimensional space, the number of particle species is It is noted that in the Cartesian coordinate system, the sub-states of the target in each direction of motion space are independent of each other, and are not affected by maneuvering motion in other directions, i.e., the motion space is three-dimensionally orthogonal. Based on the idea of dynamic decomposition and synthesis, the overall motion state of the target can be expressed as a superposition of motions in all directions. Figure 4 shows the structural diagram of three-dimensional space partitioned sampling. which can be decomposed as follows [41], where k X is the state vector at time k, It is noted that in the Cartesian coordinate system, the sub-states of the target in each direction of motion space are independent of each other, and are not affected by maneuvering motion in other directions, i.e., the motion space is three-dimensionally orthogonal. Based on the idea of dynamic decomposition and synthesis, the overall motion state of the target can be expressed as a superposition of motions in all directions. Figure 4 shows the structural diagram of three-dimensional space partitioned sampling. By using partitioned sampling, the three-dimensional motion space could be decomposed into three independent subspaces. Suppose that particles are sampled in each one-dimensional subspace, If particles are sampled directly in three-dimensional space, the number of particle species is By using partitioned sampling, the three-dimensional motion space could be decomposed into three independent subspaces. Suppose that particles are sampled in each one-dimensional subspace, N x , N y and N z indicate the particles number of each direction respectively, then it is easily find the total number of particles is N = N x + N y + N z , and the number of particle species is N x × N y × N z . If particles are sampled directly in three-dimensional space, the number of particle species is N x + N y + N z , with the same sample size as the former, and obviously, the spatial coverage of particles is lower than the former. Next, the algorithm complexity is compared in both cases. According to the analysis in Section 2 of this paper, the state vector dimension is 12, and the measurement vector dimension is 3. It is assumed that the state one-step prediction time in filtering is T f , and the measurement one-step prediction time is Th. Using partitioned sampling, the state space is decomposed into three independent subspaces, the state one-time prediction time is reduced to T f /9, and the measurement one-step prediction time is reduced to T h /3. With the same model, the complexity of the traditional method is N × (T f + T h ), whereas the partitioned sampling one is N × (T f + 3T h )/9, which has the same order of complexity but less computational time than the former. When T f T h , the algorithm complexity is dominated by the state one-step prediction time, as the complexity of the state model increas, and the operation time of the algorithm with partitioned sampling can be reduced to a minimum of 12% of the original method.
In fact, partitioned sampling can also be used for general two-dimensional target tracking. Similar to the above analysis for three-dimensional high-maneuvering targets, the state vector of a general two-dimensional maneuvering target is 6-dimensional, and the two-dimensional motion space could be decomposed into two independent subspaces. At this time, the number of particle species is N x × N y rather than N x + N y , and when T f T h , as the complexity of state model increases, the operation time of the algorithm with partitioned sampling can be reduced to a minimum of 25% of the original method. Obviously, this method still maintains its advantage for the general two-dimensional target, but with the decrease of state dimension, this advantage will not be as significant as in three-dimensional high-maneuvering target tracking. Therefore, partitioned sampling can be interpreted as a method applicable for any high-dimensional problems with a high dimensional decomposable space model; as the space dimension is higher and the decomposable subspace is smaller, its advantage will be more significant.
AUPF with Partitioned Sampling
Combining partitioned sampling with the AUPF introduced in Section 3, the adaptive sample-size UPF with partitioned sampling (PS-AUPF) is proposed in this section. In PS-AUPF, the dimension of target maneuver is reduced by decomposing the three-dimensional motion space into three independent one-dimensional subspaces according to the Cartesian coordinate system, so that particles could be sampled in each subspace by using the optimal sampling strategy respectively, and further be propagated by the AUPF, thus reducing the computational complexity and increasing the sample diversity. Then, the sub-states corresponding to these subspaces are predicted according to the measurements, and finally are synthesized into the output results. Based on the above ideas, the summary of PS-AUPF is presented in Appendix B.
Results
In this section, we evaluate the proposed PS-AUPF in the high-maneuvering target tracking problem through simulations and compare the performance of PS-AUPF to that of PF, PF with partitioned sampling (PS-PF), UKF, UPF and UPF with partitioned sampling (PS-UPF). In addition, a modern tracking method of artificial intelligence, the multiple-model neural filter (MMNF) [24], is also selected for comparison. All of the algorithms are implemented using MATLAB R2015a on computer with the following specification: CPU, Intel Core i5-3230M 2.6 GHz; Memory, 8 GB. The target motion model and measurement model given in Section 2 of this paper are used in our experiments.
Initial Setting
To validate the effectiveness of the algorithm, a high-maneuvering target motion process with variable acceleration in three-dimensional state space is simulated for experiments. The maneuvering parameters are set to the maneuvering frequency f = 1/6, and the maximum acceleration rate of change j max = 5 m/s 3 . The initial position of the given target in the Cartesian coordinate system is assumed as Figure 5 shows the real target trajectory.
Sample Size Selection
For algorithms based on the PF scheme with fixed sample size, expanding the sample size will improve tracking accuracy. However, once the sample size reaches a certain degree, due to the limitations in algorithms and application scenario, the tracking performance will gradually stabilize, i.e., the expansion of sample size will not lead to significant improvement in accuracy as before, and the tracking error will approach the minimum error threshold. In this section, assuming that both the process noises and measurement noises obey the Gaussian distribution with the mean of 0, the standard deviation of measurement noises is set to 100 m for experiments to find the required sample size of PF, PS-PF, UPF and PS-UPF with the optimal accuracy. Furthermore, the simulation of PS-AUPF is also carried out in this section. By changing the initial sample size, the adaptive strategy in Section 3 is further validated. Figure 6 shows the relationship between the sample size and tracking performance of algorithms, where the tracking performance is defined by the root mean square error (RMSE) of the target position, reflecting the deviation between the estimated position and the real state.
For general PF, when the sample size is less than 1,000, increasing particles can effectively reduce the tracking error. After 1,000, the trend of improvement slowed down, and after 3,000, it approached the minimum error threshold. For PS-PF, the tracking error can be effectively reduced by increasing particles when the sample size is not more than 500, and the minimum error threshold can be
Sample Size Selection
For algorithms based on the PF scheme with fixed sample size, expanding the sample size will improve tracking accuracy. However, once the sample size reaches a certain degree, due to the limitations in algorithms and application scenario, the tracking performance will gradually stabilize, i.e., the expansion of sample size will not lead to significant improvement in accuracy as before, and the tracking error will approach the minimum error threshold. In this section, assuming that both the process noises and measurement noises obey the Gaussian distribution with the mean of 0, the standard deviation of measurement noises is set to 100 m for experiments to find the required sample size of PF, PS-PF, UPF and PS-UPF with the optimal accuracy. Furthermore, the simulation of PS-AUPF is also carried out in this section. By changing the initial sample size, the adaptive strategy in Section 3 is further validated. Figure 6 shows the relationship between the sample size and tracking performance of algorithms, where the tracking performance is defined by the root mean square error (RMSE) of the target position, reflecting the deviation between the estimated position and the real state.
For general PF, when the sample size is less than 1000, increasing particles can effectively reduce the tracking error. After 1000, the trend of improvement slowed down, and after 3000, it approached the minimum error threshold. For PS-PF, the tracking error can be effectively reduced by increasing particles when the sample size is not more than 500, and the minimum error threshold can be approached as long as the number of particles exceeds 1000. Clearly, the accuracy of PS-PF is improved faster by increasing samples compared to PF, and only needing fewer samples will it achieve the optimal accuracy. Besides, the minimum error threshold of PS-PF is lower than PF. Both UPF and PS-UPF can obtain more accurate results than PF, because the importance sampling density established by UKF is closer to the real posterior probability distribution, which can reduce the required sample size to some extent. Therefore, expanding sample size could not significantly improve the performance of UPF and PS-UPF. Among these five algorithms, the tracking error of PS-AUPF is the lowest and most stable. Since its sample size is adaptively adjusted according to the estimated variance at each time step, the initial sample size has little effect on the accuracy of PS-AUPF, so the optimal accuracy can be obtained under a different initial sample size. In subsequent experiments, the sample size of PF, PS-PF, UPF and PS-UPF are set to 3000, 1000, 30, and 30, respectively, where all of these algorithms could achieve their optimal accuracy, ensuring the reliability of experimental results. The initial sample size of PS-AUPF is also set to 30 to hold the same initial condition as UPF.
Appl. Sci. 2019, 9, x FOR PEER REVIEW 13 of 21 approached as long as the number of particles exceeds 1,000. Clearly, the accuracy of PS-PF is improved faster by increasing samples compared to PF, and only needing fewer samples will it achieve the optimal accuracy. Besides, the minimum error threshold of PS-PF is lower than PF. Both UPF and PS-UPF can obtain more accurate results than PF, because the importance sampling density established by UKF is closer to the real posterior probability distribution, which can reduce the required sample size to some extent. Therefore, expanding sample size could not significantly improve the performance of UPF and PS-UPF. Among these five algorithms, the tracking error of PS-AUPF is the lowest and most stable. Since its sample size is adaptively adjusted according to the estimated variance at each time step, the initial sample size has little effect on the accuracy of PS-AUPF, so the optimal accuracy can be obtained under a different initial sample size. In subsequent experiments, the sample size of PF, PS-PF, UPF and PS-UPF are set to 3,000, 1,000, 30, and 30, respectively, where all of these algorithms could achieve their optimal accuracy, ensuring the reliability of experimental results. The initial sample size of PS-AUPF is also set to 30 to hold the same initial condition as UPF.
Comparison of Tracking MAE Distribution
Assuming that both the process noises and measurement noises obey the Gaussian distribution with mean of 0, Figure 7 shows the simulation results of target tracking from one Monte Carlo run when the standard deviation of measurement noises is 100 m, and where the tracking mean absolute error (MAE) distribution over time can be found. Since the non-determinism of the CS-jerk model requires constant modification of the maneuvering noises to match real the motion state, whether in the initial uniform motion phase or the later variable acceleration phase, there is an equal magnitude of tracking errors.
Comparison of Tracking MAE Distribution
Assuming that both the process noises and measurement noises obey the Gaussian distribution with mean of 0, Figure 7 shows the simulation results of target tracking from one Monte Carlo run when the standard deviation of measurement noises is 100 m, and where the tracking mean absolute error (MAE) distribution over time can be found. Since the non-determinism of the CS-jerk model requires constant modification of the maneuvering noises to match real the motion state, whether in the initial uniform motion phase or the later variable acceleration phase, there is an equal magnitude of tracking errors.
Among these algorithms, PF has the largest randomness of errors, while the error distribution of UPF is approximately close to that of MMNF, which shows the effectiveness of using UKF to establish the proposal distribution of PF. PS-PF, both in the error randomness and in the occurrences of error spikes, is lower than PF and UKF, and is close to UPF and MMNF. Relatively speaking, the MAE distributions of PS-UPF and PS-AUPF are more concentrated than that of others, with lower fluctuation range, randomness of error and occurrences of large error spikes, and the tracking accuracy of these two are also close. For further verification, Figure 8 shows the average distribution of tracking MAE in each numerical interval during 100 Monte Carlo runs. From PF to PS-AUPF, the MAE distribution appears to be concentrated to the left, and its randomness tends to decrease. For PS-UPF and PS-AUPF, the medians of MAE are reduced by about one interval compared with general PF, and the overall MAE distributions are more concentrated than UPF and MMNF. Especially for PS-AUPF, its 80% MAEs are less than 200, reflecting the improvement of accuracy. Among these algorithms, PF has the largest randomness of errors, while the error distribution of UPF is approximately close to that of MMNF, which shows the effectiveness of using UKF to establish the proposal distribution of PF. PS-PF, both in the error randomness and in the occurrences of error spikes, is lower than PF and UKF, and is close to UPF and MMNF. Relatively speaking, the MAE distributions of PS-UPF and PS-AUPF are more concentrated than that of others, with lower fluctuation range, randomness of error and occurrences of large error spikes, and the tracking accuracy of these two are also close. For further verification, Figure 8 shows the average distribution of tracking MAE in each numerical interval during 100 Monte Carlo runs. From PF to PS-AUPF, the MAE distribution appears to be concentrated to the left, and its randomness tends to decrease. For PS-UPF and PS-AUPF, the medians of MAE are reduced by about one interval compared with general PF, and the overall MAE distributions are more concentrated than UPF and MMNF. Especially for PS-AUPF, its 80% MAEs are less than 200, reflecting the improvement of accuracy. Among these algorithms, PF has the largest randomness of errors, while the error distribution of UPF is approximately close to that of MMNF, which shows the effectiveness of using UKF to establish the proposal distribution of PF. PS-PF, both in the error randomness and in the occurrences of error spikes, is lower than PF and UKF, and is close to UPF and MMNF. Relatively speaking, the MAE distributions of PS-UPF and PS-AUPF are more concentrated than that of others, with lower fluctuation range, randomness of error and occurrences of large error spikes, and the tracking accuracy of these two are also close. For further verification, Figure 8 shows the average distribution of tracking MAE in each numerical interval during 100 Monte Carlo runs. From PF to PS-AUPF, the MAE distribution appears to be concentrated to the left, and its randomness tends to decrease. For PS-UPF and PS-AUPF, the medians of MAE are reduced by about one interval compared with general PF, and the overall MAE distributions are more concentrated than UPF and MMNF. Especially for PS-AUPF, its 80% MAEs are less than 200, reflecting the improvement of accuracy.
Discussion on Tracking Accuracy
Change the distribution of noises, we perform over 100 Monte Carlo simulations for each algorithm. The simulation results are shown in Tables 2-4, where σ G is the standard deviation of Gaussian noise, σ R is the scale parameter of Rayleigh noise, and the tracking performance is defined by the root mean square error (RMSE) of the target position, reflecting the deviation between the estimated position and the real state. It should be noted that those results are close to the optimal accuracy of these algorithms. Nevertheless, the process noises and the measurement noises are randomly generated during the tracking process, so the results are still somewhat random even if simulations are repeated over 100 times.
For the noises with Gaussian distribution, when the standard deviation of measurement noises σ G is 50 or 100, the RMSE of PF are the largest. However, as σ G is increased to 150, the RMSE of UKF is over PF. Obviously, UKF is more susceptible to noise than PF. The tracking accuracy of PS-PF and PS-UPF are higher than that of PF and UPF, respectively. This shows that the introduction of partitioned sampling improves the performance of different algorithms to varying degrees, which is related to the limitations of the algorithm and its corresponding system. MMNF combines GRNN with a multiple-model method, while its tracking accuracy is significantly higher than that of traditional methods such as PF and UKF, and even slightly higher than that of UPF. By introducing partitioned sampling, the tracking accuracy of PS-UPF is significantly improved. For the proposed PS-AUPF, its tracking error is reduced by 6.06%, 6.43% and 6.60%, compared to UPF, when σ G is 50, 100 and 150, respectively. It performs better than the traditional algorithms and MMNF, and could achieve closer accuracy of PS-UPF with a small extent of improvement. As we can see, the accuracy of the improved algorithm with adaptive sample-size strategy is close to and even higher than that of the general algorithm, but this type of improvement in accuracy is not significant. Changing the noise distribution as a Uniform distribution or Rayleigh distribution, we can find that the performance change of the algorithm is similar to that with Gaussian noise. With the increase of noise randomness, the accuracy improvement of partitioned sampling and adaptive strategy becomes more and more significant. Overall, regardless of the noise distribution, the tracking accuracy of PS-AUPF is higher than that of traditional algorithms and MMNF, and it can be well applied to non-linear, non-Gaussian systems. Table 5 shows the average running time obtained after 100 Monte Carlo experiments of each algorithm. Due to the absence of sampling and propagation of particles, UKF took the shortest running time. The MMNF also has no particle propagation, but unlike UKF, as a multiple-model method based on GRNN, MMNF needs to train multiple models simultaneously to adapt to different motion patterns of the target. Although those models can be implemented in parallel, it still takes a lot of time in the process of parameter training. For the traditional PF, it needs to generate 3000 particles during operation, which means that it spent the longest time. The sample size of PS-PF is reduced to 1000 by partitioned sampling, and its running time is reduced to 11.35% of PF with higher accuracy. For UPF, its accuracy is already greatly improved by using UKF to establish the importance density. Therefore, only 30 particles needed could allow UPF to achieve lower tracking errors with less running time than PF, even though the extra computation of UKF increased the computational complexity. After introducing partitioned sampling, the running time of PS-UPF is reduced to 24.66% of UPF with the same sample size. In addition, by adaptively adjusting our sample size, with the same initial sample size, the time complexity of PS-AUPF is further reduced by nearly half of PS-UPF. Figure 9 shows changes of the total number of particles in PS-AUPF during a tracking process with N min = 9. running time. The MMNF also has no particle propagation, but unlike UKF, as a multiple-model method based on GRNN, MMNF needs to train multiple models simultaneously to adapt to different motion patterns of the target. Although those models can be implemented in parallel, it still takes a lot of time in the process of parameter training. For the traditional PF, it needs to generate 3,000 particles during operation, which means that it spent the longest time. The sample size of PS-PF is reduced to 1,000 by partitioned sampling, and its running time is reduced to 11.35% of PF with higher accuracy. For UPF, its accuracy is already greatly improved by using UKF to establish the importance density. Therefore, only 30 particles needed could allow UPF to achieve lower tracking errors with less running time than PF, even though the extra computation of UKF increased the computational complexity. After introducing partitioned sampling, the running time of PS-UPF is reduced to 24.66% of UPF with the same sample size. In addition, by adaptively adjusting our sample size, with the same initial sample size, the time complexity of PS-AUPF is further reduced by nearly half of PS-UPF. Figure 9 shows changes of the total number of particles in PS-AUPF during a tracking process with min 9 N = . Figure 9. Changes of sample size in adaptive sample-size unscented particle filter with partitioned sampling (PS-AUPF) during a tracking process. Figure 9. Changes of sample size in adaptive sample-size unscented particle filter with partitioned sampling (PS-AUPF) during a tracking process.
Summary of Overall Performance
In general, as for traditional PF, the high complexity of the motion model in high-maneuvering target tracking requires a large number of particles to achieve the optimal accuracy, i.e., the accuracy and complexity of the algorithm, both of which must sacrifice one party to satisfy the other. UPF improves the accuracy to reduce the required sample size by establishing the importance sampling density through UKF. However, the extra computation of UKF increases the iteration time of each sample. The MMNF chooses GRNN as the basic tracking algorithm, and uses a set of models to match different motion patterns of the target. Its accuracy is slightly better than UPF, but the real-time performance is poor. The proposed PS-AUPF combines the adaptive sample-size strategy to adaptively adjust sample size according to the state variance at each time step, together with the partitioned sampling to reduce the dimension of the maneuver. Finally, with only about 13.6% of the running time of UPF, PS-AUPF improved accuracy by about 6% over UPF, bringing about better real-time and effectiveness of tracking performance. In summary, the PS-AUPF proposed in this paper has better overall performance.
Conclusions
In this paper, we have proposed a novel adaptive sample-size unscented particle filter with partitioned sampling to track a high-maneuvering target in three-dimensional motion space. Firstly, aiming at the high maneuver of a target, the CS-jerk model was introduced as the motion model to match the target-changing motion by its self-adaptive modification, and to facilitate subsequent processing, the measurement model with coordinate transformation was further introduced to convert the coordinate of measurements obtained by sensors. Next, aiming at the tracking algorithm, a novel adaptive sample-size UPF was proposed, in which the number of samples could be self-adaptively adjusted in real time according to the estimated state variance at each time step, thus effectively satisfying the accuracy and avoiding computational redundancy. Then, the partitioned sampling method was combined with the AUPF presented in Section 3 to reduce the dimension of the maneuver by decomposing the motion space and to take the optimal sample strategy in each subspace, further reducing the computational complexity and increasing the sample diversity. Finally, the simulations in Section 5 verified that the proposed PS-AUPF improves tracking performance in high-maneuvering target tracking scenarios, with at least 6% reduction in terms of RMSE of tracking errors and 86% reduction in terms of running time compared to UPF. In our future work, the target motion model will be further improved to describe the target motion more accurately, which involves attempts to combine multiple-model methods.
Also, based on an improved motion model, the proposed algorithm would be compared with other new artificial intelligence methods. We also consider to apply the PS-AUPF to the multi-target tracking problem and some other computer vision applications.
Conflicts of Interest:
The authors declare no conflict of interest.
Appendix B Algorithm A2. AUPF with Partitioned Sampling
Input: Initial sample size N 0 ∈ N + , lower limit of sample size N min , the prior probability p 0 of X, the confidence interval L, the confidence level α, the system model given in Section 2, Output: State estimation resultsX k , at time step k. Initialize: N = N 0 , for i = 1, 2, . . . , N, draw particle X (i) 0 from the prior p 0 . decompose state space into 3 one-dimensional subspace according to orthogonal independence, allot particles number N x , N y , N z , respectively for each subspace, where N x + N y + N z = N 0 ; For subspace d = x, y, z do for i = 1, 2, . . . , N, draw particles from the prior p 0 to obtain particle set X (20), when N k < N min , set N k = N min ; Remove the particles with small weight and multiply the particles with large weight from sample Composite output:X k =X x k ⊗X y k ⊗X z k , where ⊗ is the state composition operator. End | 12,476.8 | 2019-10-12T00:00:00.000 | [
"Engineering"
] |
Evaluating Scholarly Impact: Towards Content-Aware Bibliometrics
Quantitatively measuring the impact-related aspects of scientific, engineering, and technological (SET) innovations is a fundamental problem with broad applications. Traditional citation-based measures for assessing the impact of innovations and related entities do not take into account the content of the publications. This limits their ability to provide rigorous quality-related metrics because they cannot account for the reasons that led to a citation. We present approaches to estimate content-aware bibliometrics to quantitatively measure the scholarly impact of a publication. Our approaches assess the impact of a cited publication by the extent to which the cited publication informs the citing publication. We introduce a new metric, called “Content Informed Index” (CII), that uses the content of the paper as a source of distant-supervision, to quantify how much the cited-node informs the citing-node. We evaluate the weights estimated by our approach on three manually annotated datasets, where the annotations quantify the extent of information in the citation. Particularly, we evaluate how well the ranking imposed by our approach associates with the ranking imposed by the manual annotations. CII achieves up to 103% improvement in performance as compared to the second-best performing approach.
Introduction
Scientific, engineering, and technological (SET) innovations have been the drivers behind many of the significant positive advances in our modern economy, society, and life. To measure various impact-related aspects of these innovations various quantitative metrics have been developed and deployed. These metrics play an important role as they are used to influence how resources are allocated, assess the performance of personnel, identify intellectual property (IP)-related takeover targets, value a company's intangible assets, and identify strategic and/or emerging competitors.
Citation networks of peered-reviewed scholarly publications (e.g., journal/conference articles and patents) have widely been used and studied in order to derive such metrics for the various entities involved (e.g., articles, researchers, institutions, companies, journals, conferences, countries, etc. (Aguinis et al., 2012)). However, most of these traditional metrics, such as citation counts and h-index, treat all citations and publications equally and do not take into account the content of the publications and the context in which a prior scholarly work was cited. Another related line of work, such as PageRank (Page et al., 1999) and HITS (Kleinberg, 1999) considers the node centrality (as a proxy for influence) but still operate in a content-agnostic manner.
Content-agnostic metrics fail to precisely size up the scholarly impact of an article as they do not differentiate between the possible reasons that a scholarly work is being cited. In addition, they can be easily manipulated by the presence of malicious entities, such as publication venues indulging in self-citations, which leads to high impact factor, or a group of scholars citing each others' work. For example, Journal Citation Reports (JCR) 1 routinely suppresses many journals that indulge in citation stacking, a practice where the reviewers and journal editors pressure authors to cite papers that either they wrote or that are published in their journal. Thus, there is a need to establish content-aware metrics to accurately measure various innovationrelated aspects such as their significance, novelty, impact, and market value. Such metrics are essential for ensuring that SET-driven innovations will play an ever more significant role in the future.
A straightforward solution to develop contentaware metrics is to manually annotate the citations, where the annotations describe the reasons for the citations. These annotations can then be used to train a machine-learning system that takes the content of the publications as input and predicts the reasons for the citation. Along this direction, there has been considerable effort to identify important citations (Valenzuela et al., 2015;Jurgens et al., 2018;Cohan et al., 2019). However, generating labeled data for such supervised approaches is difficult and time-consuming, especially when the meaning of the labels is user-defined.
In this work, we present approaches to estimate content-aware bibliometrics to quantitatively measure the scholarly impact of a publication. Our approaches are distant supervised, that require no manual annotation. The proposed approaches leverage the readily available content of the papers as a source of distant supervision. Our approaches assess the impact of a cited publication by the extent to which it informs the citing publication. They automatically estimate the weights of the edges in the citation network, such that higher-weighted edges correspond to higher-impact citations. We use these weights to introduce a new metric, called Content Informed Index (CII). We evaluate CII on three manually annotated datasets, where the annotations tell us the citation importance, thus, quantify the extent of information in the citation. Particularly, we evaluate how well the ranking imposed by CII associates with the ranking imposed by the manual annotations. The proposed approach achieves up to 103% improvement in performance as compared to the second-best performing approach.
Related Work
The research areas relevant to the work present in this paper belong to citation indexing, citation recommendation, link prediction approaches, and distant-supervised credit attribution approaches, and citation-intent classification approaches. We briefly discuss these below:
Citation indexing
A citation index indexes the links between publications that authors make when they cite other publications. Citation indexes aim to improve the dissemination and retrieval of scientific literature. CiteSeer (Giles et al., 1998;Li et al., 2006) is the first automated citation indexing system that works by downloading publications from the Web and converting them to text. It then parses the papers to extract the citations and the context in which the citations are made in the body of the paper, storing this information in a database. Other examples of popular citation indices include Google Scholar 2 , Web of Science 3 by Clarivate Analytics, Scopus 4 by Elsevier and Semantic Scholar 5 . Some examples of subject-specific citation indices include INSPIRE-HEP 6 which covers high energy physics, PubMed 7 , which covers life sciences and biomedical topics and Astrophysics Data System 8 which covers astronomy and physics.
Citation recommendation
Citation recommendation describes the task of recommending citations for a given text. It is an essential task, as all claims written by the authors need to be backed up to ensure reliability and truthfulness. The approaches developed for citation recommendation can be grouped into 4 groups as follows (Färber and Jatowt, 2020): hand-crafted feature-based, topic-modeling-based, machine-translation-based, and neural-networkbased approaches. Hand-crafted feature-based approaches are based on features are manually engineered by the developers. For example, text similarity between the citation context and the candidate papers can be used as one of the textbased features. Examples of hand-crafted featurebased approaches include (Färber and Jatowt, 2020;He et al., 2011;LIU et al., 2016;Livne et al., 2014;Rokach et al., 1978). Topic modeling based approaches represent the candidate papers' text and the citation contexts using abstract topics and thereby exploiting the latent semantic structure of texts. Examples of topic modeling-based approaches include (He et al., 2010;Kataria et al., 2010). The machine-translation-based approaches apply the idea of translating the citation context into the cited document to find the candidate papers worth citing. Examples in this category include (He et al., 2012;Huang et al., 2012). Finally, the popular examples of neural-network-based models include (Ebesu and Fang, 2017;Han et al., 2018;Huang et al., 2015;Kobayashi et al., 2018;Tang et al., 2014;Yin and Li, 2017).
Link-prediction
Link-prediction is the problem of predicting the existence of a link (connection) between two nodes in a network. A good link-prediction model predicts the likelihood of a link between two nodes, thus, link-prediction can be a useful tool to find likely citations in a citation network. The citation recommendation task described previously can be thought of as a special case of link-prediction. Following the taxonomy described in (Martínez et al., 2016), link-prediction approaches can be broadly categorized into three categories: similarity-based approaches, probabilistic and statistical approaches, and algorithmic approaches. The similarity-based approaches assume that nodes tend to form links with other similar nodes and that two nodes are similar if they are connected to similar nodes or are near in the network according to a given similarity function. Examples of popular similarity functions include number of common neighbors (Liben-Nowell and Kleinberg, 2007), Adamic-Adar index (Adamic and Adar, 2003), etc. The probabilistic and statistical approaches assume that the network has a known structure. These approaches estimate the model parameters of the network structure using statistical methods and use these parameters to calculate the likelihood of the presence of a link between two nodes. Examples of probabilistic and statistical approaches include (Guimerà and Sales-Pardo, 2009;Huang, 2010;Wang et al., 2007). Algorithmic approaches directly use the link-prediction as supervision to build the model. For example, link-prediction task can be formulated as a binary classification task where the positive instances are the pair of nodes that are connected in the network, and negative instances are the unconnected nodes. Examples include (Menon and Elkan, 2011;Bliss et al., 2014). Unsupervised or self-supervised node embedding (such as Deep-Walk (Perozzi et al., 2014), node2vec (Grover and Leskovec, 2016)), followed by training a binary classifier and Graph Neural network approaches such as GraphSage (Hamilton et al., 2017) belong to this category.
Distant-supervised credit-attribution
Various distant-supervised approaches have been developed for credit-attribution on text documents. A document may be associated with multiple labels but all the labels do not apply with equal specificity to the individual parts of the docu-ments. Credit attribution problem refers to identifying the specificity of labels to different parts of the document. Various probabilistic and neuralnetwork-based approaches have been developed for this problem, such as Labeled Latent Dirichlet Allocation (LLDA) (Ramage et al., 2009), Partially Labeled Dirichlet Allocation (PLDA) (Ramage et al., 2011), Multi-Label Topic Model (MLTM) (Soleimani and Miller, 2017), Segmentation with Refinement (SEG-REFINE) (Manchanda and Karypis, 2018), and Credit Attribution with Attention (CAWA) .
Another line of work uses distant-supervised credit-attribution for query-understanding in product search. Examples include, (i) using the reformulation logs as a source of distant-supervision to estimate a weight for each term in the query that indicates the importance of the term towards expressing the query's product intent (Manchanda et al., 2019a,b); and (ii) annotating individual terms in a query with the corresponding intended product characteristics, using the characteristics of the engaged products as a source of distantsupervision .
Citation-intent classification
In general, these approaches treat citation-intent classification as a text classification problem and require the availability of training data with ground truth annotations. Representative examples include rule-based approaches (Pham and Hoffmann, 2003;Garzone and Mercer, 2000) as well as machinelearning driven approaches (Valenzuela et al., 2015;Jurgens et al., 2018;Cohan et al., 2019). Generating labeled data for these supervised approaches is difficult and time-consuming, especially when the meaning of the labels is user-defined. In contrast, our approaches require no manual annotation.
Content-Informed Index (CII)
To address the disadvantages of content-agnostic bibliometrics, we present approaches that use machine-learning to estimate content-aware bibliometrics to measure the scholarly impact of a publication. Our approaches are distant supervised, requiring no manual annotation. They automatically estimate the weights of the edges in the citation network, such that edges with higher weights correspond to higher-impact citations. We use these weights to come up with a new metric, called Content Informed Index (CII). Next, we discuss the assumptions behind CII and provide deeper details.
Assumptions and problem definition
In the absence of labels that define the impact, we assume that the extent to which a cited paper informs (contributes or is used by) the citing paper is an indication of the citation's impact. We assume that each paper P i can be represented as a set of concepts C i , a subset of which are the historical concepts that were already known prior to P i . These historical set of concepts of the paper P i are borrowed from the papers that P i cites, and are denoted by H i .
The contribution of a cited paper P j towards the citing paper P i is the set of concepts that P i borrows from P j , i.e., the set of concepts C j ∩ H i . The task at hand is to quantitatively approximate the extent to which C j contributes towards H i , and hence contributes towards C i . Next, we describe the framework that we employed to achieve this.
Representing the set of concepts associated with a paper
Figuring out the explicit-human-interpretable concepts associated with a paper is not trivial, and can be interpreted differently by different audiences. However, in our case, we are interested in getting a representation of the semantic meaning associated with the concepts, rather than the concepts themselves. One of the simple approaches to get the representation of the semantic meaning associated with the concepts is to use the pre-trained representation (embedding) of the text associated with the concepts themselves. Being trained on language-modeling tasks, such pre-trained representations easily capture semantic meanings of words/sequence of words. For simplicity, we use the representations pre-trained on scientific documents provided by ScispaCy (Neumann et al., 2019). In addition, we only use the representation of the abstract to get the representation of the concepts of a paper. The representation of C i is denoted by r(C i ). Note that we can use more sophisticated representation techniques for this part, but limit ourselves to abstract representations provided by Scis-paCy 9 for simplicity (further discussed in Section 7). Other potential improvements include: (i) using better pre-trained representations such as BERT (Devlin et al., 2019), ELMo (Peters et al., 2018), etc., and (ii) representation for a more representative summary of the paper than the abstract. Further, CII is not suitable for the class of papers for which our assumptions do not hold. A particular case is of the review papers, which tend to have a lot of content, and a limited-word abstract may not be a representative summary of the complete paper. Thus, the CII estimates that depend on these papers would not be reliable.
Representing the set of historical concepts H i
As the set of historical concepts H i is a union of the borrowed concepts from the cited papers (C j ), we simply represent the set of historical concepts as a weighted linear combination of the representation of the concepts of the cited papers, i.e., (1) We have the constrained norm condition ( P i cites P jw 2 ji = 1) to make the representation of r(H i ) agnostic to the number of cited-papers (a paper can cite multiple papers to reference the same borrowed concepts) 10 . We model the weightsw ji as a function of the concepts of the cited paper, and the concepts in the citation context. The approach to estimating these weights is described next.
Supervision task
Since CII does not depend upon the availability of explicit manual annotations, we need to address the challenge of finding an alternative task, with similar underlying principles as the task at hand. Recall that, CII assumes the extent to which a cited paper informs (or explains) the citing paper is an indication of the citation's impact. In this direction, we propose to minimize the explanation loss, where the explanation tries to explain the concepts C i of the paper P i using the historical concepts H i i.e., the concepts of the cited papers (C j ). Thus, we formulate our problem as a distant-supervised Figure 1: Overview of Content-Informed Index. Paper P 1 cites papers P 2 , P 3 and P 4 . The weights w 21 , w 31 , and w 41 quantifies the extent to which P 2 , P 3 and P 4 informs P 1 , respectively. The function f is implemented as a Multilayer Perceptron.
problem, and the content of the papers acts as a source of distant-supervision. Combining it with the discussion in Sections 3.2 and 3.3, we formally describe our formulation as follows: We model the weightsw ji in Equation 1 as the normalized similarity measure between the concepts of the cited paper, and the concepts in the citation context. Thus, to estimatew ji , we first estimate unnormalizedw ji , denoted by w ji , and then normalize w ji so as to have unit norm. The unnormalized weight w ji is precisely the extent to which C j contributes towards H i (and hence C i ), i.e., the weight that we wish to estimate in this paper. Specifically, the above discussion leads to the following mathematical formulation: We estimate w ji as a multilayer perceptron, that takes as input the representations of the concepts in the cited paper and the concepts in the citation context. Similar to r(C j ), we use the ScispaCy vector representation for the citation context as the representation of the context and denote it by r(C ji ). To take care non-negativity constraint for the w ji , the function f (·) can be implemented as a multilayer perceptron, with a single output node, and a non-negative mapping at the output node. Note that, if the set of weights w ji minimize Equation (2), then so will any scalar multiplication of the weights w ji . This can potentially lead to the estimated weights being incomparable across different citing papers. Empirically, we found that having an additional max-bound constraint on the estimated weights (w ji ≤ b) helps to avoid this pitfall 11 , as it essentially limits the projection space of the weights w ji . We do not need to explicitly set the max-bound b, but it is implicitly set by the L2 regularization of the weights of the function f . The L2 regularization parameter is treated as a hyperparameter. Figure 1 shows an overview of Content-Informed Index (CII).
Experimental methodology 4.1 Evaluation methodology and metrics
We need to evaluate how well the weights estimated by our proposed approach quantifies the extent to which a cited paper informs the citing paper. To this end, we leverage various manually annotated datasets (explained later in Section 4.3), where the annotations quantify the extent of information in the citation. The task inherently becomes an ordinal association, and we need to evaluate how well the ranking imposed by our proposed method associates with the ranking imposed by the manual annotations. As a measure of rank correlation, we use the non-parametric Somers' Delta (Somers, 1962) (denoted by ∆). Values of ∆ range from −1(100% negative association, or perfect inversion) to +1(100% positive association, or perfect agreement).
Baselines
We choose representative baselines from diverse categories as discussed below:
Link-prediction approaches
The citation weights that we estimate in this paper can also be looked at from the link-prediction perspective, i.e., assigning a score to every citation (link) in the citation graph, that encodes the likelihood of the existence of a link. We compare against two link-prediction methods, one based on the classic network embedding approach, and the other belonging to Graph Neural Network approaches.
• DeepWalk (Perozzi et al., 2014) is a popular method to learn node embeddings. Once we have node embeddings as the output of Deep-Walk, we train a binary classifier, with the positive instances as the pairs of nodes which are connected in the network, and negative instances are the unconnected nodes (generated using negative sampling). We provide results using two different classifiers: Logistic Regression (denoted by DeepWalk+LR) and Multilayer Perceptron (denoted by DeepWalk+MLP). Note that Deepwalk is a transductive model, and does not use the content of the papers to estimate the model.
• GraphSage (Hamilton et al., 2017) is a Graph Convolutional Network (GCN) based framework for inductive representation learning on graphs.
GraphSage uses the link-prediction loss for training, so does not use a second step (as in Deep-Walk) to train the classifier. Note that, GraphSage is an inductive model, so considers the content of the papers in addition to the network topology.
Text-similarity based baselines
We can think of the function f as a similarity measure between the cited paper and the citation context. Thus, we consider the following similarity measures as our baselines: We use the same pretrained representations as we used as an input to CII, and cosine similarity as the similarity measure, which is a popular similarity measure for text data.
• Similarity-Abstract-Context: Similarity between the cited abstract and the citation context.
• Similarity-Context-Abstract: Similarity between the citing abstract and the citation context.
• Similarity-Abstract-Abstract: Similarity between the cited abstract and citing abstract.
To calculate each of the above similarity measures, we use the same pre-trained representations as we used as an input to CII, and cosine similarity as the similarity measure. The baselines belonging to this category can also be thought of as similarity-based link prediction approaches.
Reference Frequency based baselines
We also consider another simple baseline, referred to as Reference Frequency, where we assume that the more frequently the cited paper is referenced in the citing paper, the higher the chances of the cited paper informing the citing paper. This assumption has also been used as a feature in prior supervised approaches (Valenzuela et al., 2015). The absolute frequency of referencing a cited-paper may provide a good signal regarding the information borrowed from the cited paper when comparing with other papers being cited by the same citing paper. However, as the citation behavior differs between papers, the absolute frequency may not be comparable across different citing papers. Thus, we also provide results after doing normalization of the absolute frequency of the citation references for each citing paper. We provide results for mean, max, and min normalization. Specifically, given a citation and the corresponding citing paper, the information weight for a citation is calculated by dividing the number of references of that citation, by the mean, max, and min of references of all the citations in that citing paper, respectively.
Datasets
The Semantic Scholar Open Research Corpus (S2ORC): The S2ORC (Lo et al., 2020) dataset is a citation graph of 81.1 million academic publications and 380.5 million citation edges. We only consider the publications for which full-text is available and abstract contains at least 50 words. This leaves us with a total of 5, 653, 297 papers, and 30, 533, 111 edges (citations).
ACL-2015:
The ACL-2015 (Valenzuela et al., 2015) dataset contains 465 citations gathered from the ACL anthology 12 , represented as tuples of (cited paper, citing paper), with ordinal labels ranging from 0 to 3, in increasing order of importance. The citations were annotated by one expert, followed by annotation by another expert on a subset of the dataset, to verify the inter-annotator agreement. We only use the citations for which we have the inter-annotator agreement, and the citations are present in the S2ORC dataset we described before. The selected dataset contains 300 citations among 316 unique publications. The total number of unique citing publications are 283 and the total number of unique cited publications are 38. ACL-ARC: The ACL-ARC (Jurgens et al., 2018) is a dataset of citation intents based on a sample of papers from the ACL Anthology Reference Corpus (Bird et al., 2008) and includes 1,941 citation instances from 186 papers and is annotated by domain experts. The dataset provides ACL IDs for the papers in the ACL corpus, but does not provide an identifier to the papers outside the ACL corpus, making it difficult to map many citations to the S2ORC corpus. However, it provided the titles of those papers, and we used these titles to map these papers to the papers in the S2ORC dataset, if matching titles were found. The annotations in ACL-ARC are provided at individual citationcontext level, leading to multiple annotations for some of the (cited paper, citing paper) pair. In such cases, we chose the highest-informing annotation for such (cited paper, citing paper) pairs. The selected dataset contains 460 citations among 547 unique publications. The total number of unique citing publications are 145 and the total number of unique cited publications are 413. SciCite: SciCite (Cohan et al., 2019) is a dataset of citation intents based on a sample of papers from the Semantic Scholar corpus 13 , consisting of papers in general computer science and medicine domains. Citation intent was labeled using crowdsourcing. The annotators were asked to identify the intent of a citation, and were directed to select among three citation intent options: Method, Result/Comparison and Background. This resulted in a total 9, 159 crowdsourced instances. We use the citations that are present in the S2ORC dataset we described before. Similar to ACL-ARC, the annotations are provided at individual citation-context level, leading to multiple annotations for some of the (cited paper, citing paper) pair. For such cases, we chose the highest-informing annotation for the (cited paper, citing paper) pairs. The selected dataset contains 352 citations among 704 unique publications. There is no repeated citing or cited publication in 13 https://www.semanticscholar.org/ this dataset, thus, the total number of unique citing as well as unique cited publications are 352 each.
Parameter selection
We treat one of the evaluation datasets (ACL-ARC) as the validation set and chose the hyperparameters of our approaches and baselines concerning best performance on this dataset. For DeepWalk, we use the implementation provided here 14 , with the default parameters, except the dimensionality of the estimated representations, which is set to 200 (for the sake of fairness, as the used 200 dimensional text representations for CII). For the models that require learning, i.e., the logistic regression part of Deepwalk, MLP part of Deepwalk, GraphSage, and CII, we used the ADAM (Kingma and Ba, 2015) optimizer, with an initial learning rate of 0.0001, and further use step learning rate scheduler, by exponentially decaying the learning rate by a factor of 0.2 every epoch. We use L2 regularization of 0.0001. The function f in CII was implemented as a multilayer perceptron, with three hidden layers, with 256, 64, and 8 neurons, respectively. We use the same network architecture for the MLP that we train on top of DeepWalk representations. We train the logistic regression and MLP parts of Deepwalk, GraphSage, and CII for a maximum of 50 epochs, and do early-stopping if the validation performance does not improve for 5 epochs. For GraphSage, we use the implementation provided by DGL 15 . We used a mini-batch size of 1024 for training. Table 1 shows the performance of the various approaches on the Somers' Delta (∆) for each of the three evaluation datasets. For ACL-2015 and Sci-Cite, CII outperforms the competing approaches; while for the ACL-ARC dataset, CII performs on par with the best performing approach. The improvement of CII over the second-best performing approach is 22% and 103%, on the ACL-2015 and SciCite datasets, respectively. Interestingly, the simplest baseline, Referencefrequency, and its normalized forms are the secondbest performing approaches. While Referencefrequency performs at par with the CII on the ACL-ARC dataset, it does not perform as well on the other two datasets. This can be attributed to the fact that the number of unique citing papers in the ACL-ARC dataset is relatively small. Thus, many citations in ACL-ARC are shared by the same citing paper, which is not the case with the other two datasets. Thus, as mentioned in Section 4.2, the absolute frequency of referencing a cited-paper may provide a good signal regarding the information borrowed from the cited paper, when comparing with other papers being cited by the same citing paper. Further, even the normalized forms of the Reference-frequency lead to only a marginal increase in performance for the ACL-2015 and Sci-Cite datasets. Thus, the simple normalizations (such as mean, max, and min normalization used in this paper), are not sufficient to address the difference in citation behavior between different papers.
Quantitative analysis
Furthermore, we observe that simple similaritybased approaches, such as cosine-similarity between pairs of various entities (each combination of citing abstract, citing abstract, and citation-context) perform close to random scoring (∆ value of close to zero). This validates that the simple similarity measures, like cosine similarity, are not sufficient to manifest the information that a cited-paper lends to the citing-paper; thus, showing the necessity of more expressive approaches, like CII.
In addition, the other learning-based linkprediction-based approaches perform considerably worse than the simple baseline reference-frequency. While on ACL-2015 and SciCite datasets, they perform close to random scoring, the performance on ACL-ARC dataset is better than the random baseline. For the link-prediction approaches to perform well, the basic assumption is that the majority of the edges (links) in the training set are indeed the informing citations. If such assumption holds, the link-prediction approaches can pick the majority signal (informing citations) and ignore the noise (non-informing citations) owing to the lowdimensional projections of the nodes (or edges). However, such assumption does not hold in the citation graphs, with only a fraction of citation being the informing citations. For example, it has been estimated that authors read only 20% of the works they cite (Simkin and Roychowdhury, 2002).
Qualitative analysis
To understand the patterns that the proposed approach CII learns, we look into the data instances with the highest and lowest predicted weights. As the function f takes as input both the abstract of the cited paper and the citation context, the learned patterns can be a complex function of the cited paper abstract and the citation context. Thus, for simplicity, we limit the discussion here to understand the linguistic patterns in the citation context, and their association with the predicted weights.
We repeat the same exercise for the citationcontexts with the lowest predicted weights. Figures 2 and 3 shows the wordclouds for the highest weighted citations and lowest weighted citations, respectively. These figures show clear discriminatory patterns between the highest-weighted and lowest-weighted citations, that relate well with the information carried by a citation. For example, the words such as 'used' and 'using' are very frequent in the citation contexts of the highest weighted citations. This is expected, as such verbs provide a strong signal that the cited work was indeed employed by the citing paper. Another interesting pattern in the highest weighted citations is the presence of words like ' fig', 'figure', and 'table'. Such words are usually present when the authors describe important concepts, such as methods and results. As such, citations in these important sections indicate that the cited work is used/extended in the citing paper, which signals importance.
On the other hand, the wordcloud for the least weighted citations (Figure 3) is dominated by weasel words such as 'may', 'many', 'however', etc. The words such as 'many' commonly occur in the related work section of the paper, where the paper presents some examples of other related works to emphasize the problem that the citing paper is solving. The words like 'may', 'however', 'but' etc
1.55e−5
Figure 2: Word-cloud for the words that appear in the citation context of the citations with the highest predict importance weights. are commonly used to describe some limitations of the cited work. Such citations are expected to be incidental and carry less information.
We also look at some examples of individual citation contexts and the predicted weights for them. Table 2 shows two citing papers, with an example of a high weighted citation and an example of a low weighted citation for each of those papers. For these examples, we see that the high predicted weight corresponds to cited work indeed being employed by the citing paper. For example, the high weight citations for the papers titled 'AAVmediated gene therapy for retinal disorders: from mouse to man' and 'Hippocampal Memory Traces are Differentially Modulated by Experience, Time, and Adult Neurogenesis' in Table 2 correspond to formulas employed by these papers, that were developed in the cited papers. Similarly, the lowest weighted citations correspond to cited papers that are not informative. For example, for the paper titled 'AAV-mediated gene therapy for retinal disorders: from mouse to man', the lower-weighted citation describes the limitation of the cited paper. Similarly, for the paper titled 'Hippocampal Memory Traces are Differentially Modulated by Experience, Time, and Adult Neurogenesis', the lower-weighted citation corresponds to background work, which is not an informing citation.
Discussion and Conclusions
In this paper, we presented approaches to estimate content-aware bibliometrics to accurately quantitatively measure the scholarly impact of a publication. Our distant-supervised approaches use the content of the publications to weight the edges of a citation network, where the weights quantify the extent to which the cited-publication informs the citing-publication. Experiments on the three manually annotated datasets show the advantage of using the proposed method on the competing approaches. The code is available on GitHub 16 .
Our work makes a step towards developing content-aware bibliometrics, and envision that the proposed method will serve as a motivation to develop other rigorous quality-related metrics.
Broader impact and ethics discussion
Quantitative metrics to measure the impact-related aspects of scientific, engineering, and technological (SET) innovations play an important role in the modern society. These metrics are used to influence how resources are allocated, assess the performance of personnel, identify intellectual property (IP)-related takeover targets, value a company's intangible assets (IP is such an asset), and identify strategic and/or emerging competitors. Thus, metrics that accurately and quantitatively the innovation-related aspects, are essential for ensuring that SET-driven innovations will play an ever more significant role in the future. This paper is a step in this direction.
While our discussion and evaluation focused on identifying informing citations, our approach is not restricted to this domain, and can be used to derive impact metrics for the various involved entities. For example, the content-aware weights estimated by the CII convert the original unweighted citation network to a weighted one. Consequently, this weighted network can be used to derive impact metrics for the various involved entities, like the publications, authors etc. For example, to find the impact of a publication, the sum of weights outgoing from its corresponding node can be used to quantify the impact of the publication, instead of using vanilla citation count. Further, the impact can be propagated through generations of citations (similar to CiteRank (Walker et al., 2005)), by simply doing a weighted pagerank on this weighted graph.
However, as there are benefits, there are also risks and concerns. Like other bibliometrics, CII is also prone to be manipulated by the bad actors. For example, the citation contexts can be constructed in a way (using particular keywords as shown in Figures 2 and 3) so as to fool CII. A way of mitigating these risks is to use more advanced information extraction approaches for the accurate assessment of the citation context. In this direction, we can leverage the extensive literature on concept and context extraction in NLP: from the highly specific ('does this cited paper really discuss the entity our approach found in the citing sentence?') to much more general ('is this mention positive or negative?') and much in between. Having said that, it is also important for an impact metric needs to be simple to be widely adopted, and added complexity can lead to issues of trust and acceptance by the user community. Thus, we encourage the research community and policy makers to come together to understand and evaluate the specific impacts and risks of using more expressive and relatively complex metrics. We envision that this paper will serve as a motivation to continue the discussion in the aforementioned directions. | 8,181.6 | 2021-01-01T00:00:00.000 | [
"Computer Science"
] |
Optical detection for magnetic field using Ni-subwavelength grating on SiO2/thin-film Ag/glass structure
An optical sensor for magnetic field detection using Ni-subwavelength grating (SWG) on SiO2/Ag-thin-film/glass substrates was experimentally developed on the basis of the re-radiation condition of surface-plasmon-polaritons (SPPs) at Ag surfaces. The fabricated sample showed two dips in the reflection spectra associated with SPP excitation, and the optical response exhibited good agreement with that simulated by the finite-difference time-domain method. The reflectivity at one of the dip wavelengths varied minimally with the application of the magnetic field, whereas that at the other dip wavelength significantly decreased owing to the large electric field overlap of SPP with the magnetized Ni-SWG. As a result, a magnetic field on the order of a few mT could be detected with a simple normal-incidence optical system.
Results
Operating principle. The structural parameters of the Ni-SWG and SiO 2 /Ag/glass structure were designed for magnetic field sensing. Figure 1 illustrates the geometry of our sensor. SiO 2 and Ag films were deposited on the glass substrate. Ni was selected as the ferromagnetic-SWG material because of its large saturation magnetization 39 . We arranged the Ni-SWG on the top of the SiO 2 /Ag/glass multi-layers. A 5 nm Ti film was also inserted between the Ni-SWG and the SiO 2 film to connect both firmly. The symbols Λ, w, t SWG , t SiO2 , t Ag , represent the grating period, grating finger width, grating height, and thickness of SiO 2 film and Ag film, respectively. SP, the surface modes composed of collective electron oscillation, exists at the interfaces of SiO 2 /Ag and Ag/ glass substrates. For the structure, a p-polarized incident light entered vertically. The electric field of p-polarized light was vertical to the periodic fingers of the SWG, as shown in Fig. 1. The SWG modulated the lateral wavenumber (namely, x-direction in Fig. 1) of the incident light, and several order diffractions occurred. In particular, all higher-order diffractions except for 0th-orders (namely, transmission and reflection) had an evanescent form owing to the shorter SWG period than that of the incident wavelength. When the lateral wavenumber of the diffraction of the higher-order coincided with that of SP, the diffraction coupled with the SP and formed surface-plasmon-polariton (SPP). The excitation of SPP led to decreasing reflectivity of the proposed structure because the incident light energy transformed into SPPs. As a result, a dip in the reflection spectrum of the structure appeared.
The excited SPP propagated along the surface of the Ag film. The SPP was re-radiated by the SWG during the propagation along the Ag surface. The interference between 0th diffraction (reflection) and re-radiated waves exhibited a considerable influence on the reflection spectrum of the structure. The excitation and re-radiation conditions of the SPP are sensitive for the polarization state of the light. As we applied the external magnetic field to the structure, the polarized direction of the light rotated because of the non-diagonal dielectric tensor of the magnetized Ni-SWG. Thus, the reflected intensity at the reflection dip originated from the SPP excitation significantly varies for the applied magnetic field.
In particular, the SPs at SiO 2 /Ag and the Ag/glass interacted and coupled, considering that the Ag film was thinner than the penetration depth of each mode. As a result, LRSP and SRSP were excited 40,41 . Moreover, the electric field of LRSP mostly seeped toward the dielectric material side (SiO 2 and glass substrate in this study), whereas the dominant field of the SRSP was concentrated in the Ag layer. Thus, we expected that the LRSP mode was particularly influenced by the magnetization of Ni because its electric field largely overlapped with that of the Ni-SWG.
Design of SiO 2 /thin-film-Ag/glass structure for magnetic field sensing. To excite the LRSP in the visible-wavelength region, we determined the geometrical parameters of our structure using the ω-k dispersion relation of SP at the SiO 2 /Ag/grass substrate. The detail of the dispersion relations was discussed in previous publication 40,41 . The visible light was employed as incident light owing to its ease of treatment. The information on the dielectric functions of Ni, Ag and SiO 2 were found from literatures 42,43 . The glass was assumed to commercialized glass (D263 T eco Thin Glass: SCHOTT). The structural parameters were set to grating period Λ = 300 nm, height t SWG = 100 nm, and width w = 150 nm, and thicknesses of t Ag = 30 nm and t SiO2 , = 70 nm, respectively. Figure 2 shows the calculated reflection spectrum of our designed structure using FDTD method. In the calculation, the propagation direction of the p-polarized plane wave was the + z-direction and entered the designed structure normally. The calculation detail is described in method section. In Fig. 2, we found two deep reflection dips at the wavelength of 500 nm and 590 nm, respectively. The reflected intensities vanished at these wavelengths. We also illustrate the z-component electric field distributions at the wavelengths of 500 nm (LRSP) and 590 nm (SRSP), as shown in Fig. 3a,b, respectively.
The z-components of the electric field appeared at Ni-SWG and Ag surfaces despite the incident light propagating along the z-direction. The electric field colored by black and white mean the saturated field. The field patterns indicated that the dips found at the wavelength of 500 nm and 590 nm resulted from LRSP and SRSP, respectively 40,41 . We also found that the electric field concentrated considerably on the SiO 2 /Ag and Ag/glass surfaces. The distribution indicated that the diffracted lights coupled with the SPPs, which propagated along the www.nature.com/scientificreports/ surface of Ag. In particular, the most of electric field of the LRSP largely concentrated into the Ni-SWG structure, while that of SRSP spread to air-gap region of the SWG (See the field in the SiO 2 layer in Fig. 3a,b). This considerable overlap of the electric field implied that the re-radiation conditions of LRSP were significantly affected by the magnetization of Ni-SWG.
Moreover, we calculated the effect of the Ni magnetization on the electromagnetic field distributions around our sample at two dip wavelengths (500 nm and 590 nm). When magnetic field was applied to our sample (magnetic field and light propagation direction are z-direction, as shown in method section), Ni dielectric tensor ε Ni is given as following equation.
where the ε xx , ε yy , and ε zz are diagonal components and the ε xy and ε yx are non-diagonal components. The relations between these components of dielectric tensor are ε xx = ε yy = ε zz and ε xy = − ε yx , respectively. The non-diagonal components ε xy and ε yx are originated via the magnetization of Ni, and these components induces the polarization rotation of the reflected light (Kerr MO effect). As a result, the y-component of the electric field E y is generated when magnetic field was applied to our sample (the incident light originally has only an electric field of x-component, as described in method section). The diagonal and non-diagonal component values are taken from the experimental results in these literatures 42,44 , and ε xy are set to 0.016-0.03i at both wavelengths of 500 nm and 590 nm. Although these values of the Ni dielectric tensor are that of bulk Ni and deviate from that of nano sized Ni, it is enough for the qualitative investigation of the tendency of the light behavior for the Ni magnetization. Figure 4a,b show the normalized E y distributions our sample at the reflection dips, respectively. As shown in Fig. 4a,b, the E y components appear around our structure at both wavelengths. The distributions indicate that the polarization of the light is rotated by the non-diagonal components of the magnetized Ni-SWG. Especially, Figure 2. Normal reflection spectrum of the designed sensor using numerical calculation based on FDTD method. Poynting vector was used to evaluate the reflectivity. www.nature.com/scientificreports/ the larger amplitude of E y appears at the wavelength of 500 nm (LSPS dip wavelength), and the reflected power of E y component is about 2 times greater than that at the wavelength of 590 nm (SRSP dip wavelength). This result is evidence that the large overlap of the LRSP electric field with Ni-SWG enhances the interaction between magnetization and light, and the re-radiation condition of LRSP is more sensitive than that of SRSP. Thus, we can assume that the reflected intensity at dip resulting from LRSP excitation significantly varies with the applied magnetic field.
Optical characteristics. We employed traditional lithography techniques with EB for the fabrication of the designed Ni-SWG/SiO 2 /Ag structure. Figure 5 shows the scanned EB microscope (SEM) image of the surface view of the fabricated sample; it also illustrates a 300 nm period and 150 nm line width, respectively. The measured reflection spectrum is shown in Fig. 6. The reflectivity was measured utilizing very simple system with normal incidence. The detail of system is described in method section. The reflectivity value was determined on the basis of the Al mirror (TFA-50C08-4: Sigma). As shown in Fig. 6, the reflectivity of the sample decreased at wavelengths of 502 and 570 nm, and two reflection dips appeared. These results agree well with FDTD calculation results, and the dips at the wavelengths of 502 and 570 nm correspond to LRSP and SRSP excitation, respectively.
Magnetic field sensing.
To clarify the magnetic response of the fabricated device, we applied a magnetic field perpendicular to the sample. Figure 7a Figure 7a indicates that the reflectivity at the dip resulting from the excitation of LRSP in our structure decreased with an increase of the value of the applied magnetic field. On the other hand, the reflectivity at the excitation of SRSP depends minimally on the magnetic field up to 39.5 mT, as shown in Fig. 7b. According to the experimental results, we find that LRSP is more sensitive to the magnetic field rather than SRSP and that the designed sensor with a very simple optical setup can distinguish magnetic fields of several mT. This sensitivity performance of our sensor is almost equality high to other optical sensors despite its simple and compact measurement system [45][46][47] .
Discussion
The high sensitivity of our sensor can be qualitatively explained by considering the enhanced Lorentz force because of the electric field of LRSP. The polarization direction of the electrons was influenced by the Lorentz force caused by the magnetization of Ni-SWG, and non-diagonal tensor of the dielectric constant were generated. The significant overlap of LRSP electric field with Ni-SWG significantly contributed to the enhancement of the non-diagonal dielectric tensor, because the Lorentz force increased with an increase in the electric field. As a result, the excitation and re-radiation condition of LRSP sensitively varied by the applied magnetic field for Ni-SWG. The high sensitivity and simplicity of our sensor are suitable for the practical use of the magnetic field sensor, and our sensor open new integration device concepts for magnetic field detection.
Conclusion
In conclusion, we experimentally developed a highly sensitive magnetic field sensor incorporating Ni-SWG/ SiO 2 /Ag structure. The sensor was designed based on the ω-k relation of SPP modes at Ag surfaces to excite the modes in visible-wavelength regions. The numerically calculated reflection spectrum of the designed structure indicated the two reflectivity dips caused by LRSP and SRSP; in addition, the electric field distribution of LRSP largely overlapped with that of the Ni-SWG. The calculated electric field distribution also predicted the larger MO response of LRSP than that of SRSP. We fabricated the designed Ni-SWG on the SiO 2 /Ag/glass substrate using the EB lithography technique and obtained experimental reflectivity dip values at wavelengths of 502 (LRSP) nm and 570 nm (SRSP), respectively. The reflectivity at the LRSP dip dramatically decreased as the value of applied magnetic field for the sample increased, and several mT of the magnetic field were detected using simple optical setups. Moreover, these results indicate that the magnetically modulation depth of the reflection spectrum can be improved by adjusting the SiO 2 thickness between Ag film and Ni-SWG because the overlap of LRSP electric field with the SWG strongly depend on the thickness of SiO 2 spacer. In the further work, we will optimize the SiO 2 thickness for higher sensitivity of magnetic field sensing and will report the optimization elsewhere.
FDTD calculation.
We investigated the reflection characteristics of the designed structure using FDTD numerical simulation (Fullwave: R-Soft and Poyinting for Optics: FUJITSU) for electromagnetic field distribution and the interaction between magnetization of the Ni-SWG and the light. Figure 8 shows the model for the FDTD simulations. The area surrounded by green dashed lines represents the calculation region, and the dashed lines mean boundaries in the simulations. We postulated that the structural length is infinite for y-direction and the structure repeats for x-direction in the simulation. Hence, we employed periodic boundary conditions (PBC), in which the electromagnetic fields infinitely repeat, as x-and y-boundaries. The thickness of the glass substrate was also assumed infinite thick, and perfect matched layer (PML) boundary conditions, in which the electromagnetic field was perfectly absorbed, were used for z-boundaries. These assumptions were justified because the actual lengths of structure for x-and y-directions were much larger than the incident wavelength. The incident plane wave was polarized along x-direction. The propagation direction of the incident wave was the + z-direction and entered the designed structure normally. The Poynting vector was utilizing for the estimation of the reflected light intensity.
Fabrication.
We fabricated the Ni-SWG and SiO 2 /Ag/glass structures. First, Ag film with 30 nm-thickness was thermally evaporated on the glass substrate (D263 T eco Thin Glass: SCHOTT). The 70 nm-thickness SiO 2 film was deposited on the Ag film using EB evaporation technique. Second, the EB lithography resist film (ZEP520A: Zeon) was spin-coated on the SiO 2 film at 3000 rpm for 90 s. The SWG pattern was drawn by the resist film EB lithography techniques with an acceleration voltage of 50 kV. The area size of the SWG pattern was a square region of 300 µm × 300 µm. Subsequently, we formed the SWG resist pattern using a developer (ZED-N50: Zeon) with 20 °C. Finally, Ti and Ni films with thicknesses of 5 and 100 nm were evaporated on the patterned resist film, respectively, and the resist film was removed using an N-methyl-pyrrolidone solution.
Optical and magnetic characterization. We investigated the reflection spectrum and magnetic response of the fabricated sample for normal incident light. The optical irradiation system is shown in Fig. 9. To apply the magnetic field, we set the sample at top of the electromagnet with the iron core. The insertion of blacked Al film between the sample and the electromagnet prevented reflection at the surface of the magnet. A halogen lamp was employed as the visible light source. The light from the lamp passes through the shutter in order to irradiate the light into only SWG region. The light was p-polarized by polarizer, and the | 3,491.6 | 2020-11-09T00:00:00.000 | [
"Physics"
] |
Productivity Growth in Different Firm Sizes in the Malaysian Manufacturing Sector: An Empirical Investigation
Based on Cuesta (2000), this paper develops a stochastic frontier production model that allows for different groups of firms to have different patterns of technical efficiency over time. The authors apply the model to the Malaysian manufacturing sector to decompose total factor productivity growth into technical efficiency change and technical progress for different firm sizes - e.g., large and small - in seven industries during 2000-2004. The empirical results indicate that technical efficiency has worsened across all industries and firm sizes. In contrast, evidence of substantial technical progress was found in all industries. In fact, technical progress has been larger than technical efficiency deterioration in most industries and firm sizes, leading to total factor productivity growth. The analysis identifies the industries and firm sizes that lag the most in productivity, and thus have the greatest scope for policies that facilitate productivity growth.
I. Introduction
Malaysia has been hit hard by the global financial and economic crisis, with its gross domestic product (GDP) growth slowing down sharply from an average of 6% in 2003-2007 to 4.6% in 2008, and an outright contraction of 3.1% is projected for 2009.
A successful small open economy with exceptionally high levels of openness and integration into the world economy, Malaysia has borne the full brunt of the recession in the industrialized countries, in particular the United States (US). Although Malaysia's financial system was largely unscathed by the global financial crisis, the collapse of demand for imports in the US had a pronounced negative impact on Malaysia's exports and growth. Trade rather than financial contagion has been the primary mechanism that transmitted the crisis from the industrialized countries to Malaysia. The economy is expected to recover in 2010, with a projected GDP growth of 4.2%. Nevertheless, the global crisis has been a sobering experience for Malaysian policy makers, highlighting the vulnerability of their economy to the global business cycle.
Malaysia is thus currently grappling with the short-term task of achieving a secure recovery from the slowdown. However, well before the onset of the global crisis, the country was already confronted with a number of structural issues that threatened to slow down its long-run trend growth. For one, the investment rate, or the ratio of aggregate investment to GDP, has declined noticeably since the Asian financial crisis of 1997/1998. Partly as a result, GDP growth has also fallen since the Asian crisis. The balance of evidence suggests that the investment drop-off largely reflects a return to more optimal investment rates rather than suboptimal underinvestment. That is, it is more likely that Malaysia suffered from over-investment in the pre-crisis period than under-investment in the post-crisis period. At a broader level, as a high-flying second-generation newly industrialized economy (NIE), Malaysia has reached income levels where output growth would have to rely more on productivity growth and less on accumulation of capital and labor. While the high-savings, high-investment paradigm has propelled Malaysia's rapid growth in the past, future growth will have to be driven by higher total factor productivity.
Although from a macroeconomic perspective an economy can use capital and labor more efficiently to boost economic growth, productivity is more accurately a microeconomic concept that refers to how firms use their factors of production efficiently. Intuitively, total factor productivity is likely to differ for different groups of firms even within the same industry. For example, there are big structural differences between large multinational companies (MNCs) employing hundreds of employees and domestic small-and-medium enterprises (SMEs) with only a dozen employees. Even if it is assumed that both groups of firms have access to the same production technology-i.e., same potential production frontier-they may differ a lot with respect to their technical efficiency-i.e., the gap between potential output and actual output. There is also no obvious a priori reason why productivity growth should be identical for different groups of firms over time. For example, in response to the gradual introduction of restrictive labor market regulations, larger firms may suffer greater productivity losses than smaller firms that typically rely more on part-time workers. Or, a chronic shortage of skilled workers may have a bigger effect on the productivity of smaller firms since larger firms tend to be better at attracting and retaining skilled workers.
Productivity differences across different groups of firms can affect the productivity of an industry and the economy as a whole. In particular, in some countries there are concerns that the productivity of domestic SMEs may lag substantially behind the productivity of larger companies, many of which are foreign-owned. If the productivity of the SMEs is in fact significantly lower than that of other firms in the same industry, this can drag down the productivity of the industry. Replicated on an economy-wide basis, low SME productivity can drag down the productivity of the entire economy. These kinds of concerns are highly relevant for the Malaysian manufacturing sector where SMEs account for about 90% of total firms, 30.7% of output, and 31.6% of employment. In recognition of the significance of SMEs in the economy, the Government of Malaysia has recently started to look to SMEs as a potential source of growth. The rebalancing of growth toward domestic sources in the aftermath of the global crisis will give further impetus to this renewed interest in the SMEs, which are typically geared more toward domestic demand than larger firms. In the Ninth Malaysian Plan, for 2006-2010, the government has identified as a key strategic priority the development of competitive and resilient SMEs that are equipped with strong technical and innovation capacity as well as managerial and business skills. The plan recommended that SMEs in the manufacturing sector upgrade themselves into higher value added activities.
The difference in productivity and productivity growth across different groups of firms is of more than passing interest for policy makers. In particular, information about the productivity of each group of firms in an industry is more useful for policy makers than information about the productivity for the industry as a whole. For example, if an industry's poor productivity performance is due to the low and stagnant productivity of a particular group of firms-e.g., SMEs-enhancing the group's productivity will be the key to enhancing the industry's productivity. Different groups of firms are subject to different types of structural impediments and policy distortions that impede their productivity growth. SMEs typically face higher cost of and more difficult access to bank credit and other external financing than large firms. For example, although developing Asia did not suffer the severe credit crunch that gripped the US and the European Union during the global financial crisis, the flow of credit to SMEs was disrupted to some extent. Another example of a production constraint that is more binding for SMEs than larger firms is shortage of skilled workers. Although a chronic shortage of skilled and professional workers is an economy-wide problem that is hindering Malaysia's transition to higher value added, more knowledge-based industries and activities, SMEs are suffering disproportionately from the skills crunch.
The central objective of this paper is to empirically examine recent trends in total factor productivity and its two components-technical efficiency change and technical progress-for different groups of firms in several Malaysian manufacturing industries during 2000-2004. The firms are grouped by size, which, in turn, is determined by the number of employees. To pursue the objective, the authors develop a stochastic frontier production model that allows for group-specific temporal variation in technical inefficiency. The model is based on Cuesta (2000), which specifies a production model with firm-specific temporal variation in technical efficiency. The production model is useful when different groups of firms have different productivity trends. The model occupies an intermediate position between the model in Battese and Coelli (1992), which imposes a common temporal pattern in technical efficiency on all sample firms, and the model in Cuesta (2000), which assumes a unique temporal pattern for every firm. In addition to allowing for different temporal patterns of productivity growth across different groups of firms, the model also solves the "incidental parameters problem" in Cuesta's model, which results from the number of parameters increasing with sample size. To perform the empirical analysis, the authors apply the model to the Malaysian manufacturing sector.
This paper is organized as follows. Section 2 presents a stochastic frontier production model with group-specific temporal variation in technical inefficiency and gives the functional form of the estimation model. Section 3 discusses the data and reports the main empirical results, and Section 4 presents some concluding observations.
II. A Model with Group-Specific Time-Varying Technical Inefficiency
A stochastic frontier production function is defined by where y it is the output of the i th firm (i = 1, .., N) in the t th time period (t = 1, ..., T), f(·) is the production frontier, x is an input vector, β is a k x 1 vector of parameters to be estimated. The efficiency error, u, represents production loss due to companyspecific technical inefficiency; thus, it is always greater than or equal to zero ( u ≥ 0 ), and it is assumed to be independent of the statistical error, v, which is assumed to be independently and identically distributed as N v 0 2 ,σ ( ) . Note that technical inefficiency in (1) varies over time. Cornwell, Schmidt, and Sickles (1990) introduced firm-specific time-varying technical inefficiency in the stochastic frontier approach by modeling technical inefficiency through the intercept of the production frontier in panel data model. 1 In this model, stochastic frontier can be rewritten as: Thus, every firm has its own temporal pattern of technical inefficiency specified by a quadratic function. This model requires only three parameters to capture the time path of individual efficiency change and is suitable for a short cross-section and long time-series panel dataset. Lee and Schmidt (1993) suggested an alternative time-varying generalization by specifying that technical inefficiency is time-varying and subject to an arbitrary temporal pattern of technical efficiency. In this model, the technical inefficiency effects are defined as the product of individual firm effect and arbitrary time effects: where θ t is a parameter to be estimated. Therefore, this model is flexible in estimating the temporal pattern of technical inefficiency because it does not restrict the time path to a specific functional form. Recently, Kim and Lee (2006) generalized the Lee and Schmidt (1993) model to allow for different temporal patterns across different groups of firms by relaxing the unrealistic restriction that the temporal pattern be the same for all firms. Kim and Lee (2006) modified technical inefficiency (3) as: where the subscript g represents the group. Kim and Lee (2006) showed that the model is very useful in identifying and estimating the unique temporal patterns of productivity changes in East Asian countries, which is distinct from those of the other group of countries.
The above models utilized panel data model in specifying the time-varying technical inefficiency captured by the intercept of fixed effects model. Panel data are also utilized in random effect models in which technical inefficiency is identified through an error component. In this approach, Kumbhakar (1990) proposed technical efficiency as a function of time as: The panel data model in the stochastic frontier approach was developed by Pitt and Lee (98), Schmidt and Sickles (984), Kumbhakar (987), and Battese and Coelli (988).
where b and c are parameters to be estimated and u 1 one-sided frontier error with truncated normal distribution. Battese and Coelli (1992) adjusted the model to deal with unbalanced panel data by using a different function of time for each firm. This model specifies time-varying technical inefficiency as: where the distribution of u 1 is taken to be the non-negative truncation of the normal distribution, N u µ σ , 2 ( ) , and η is a parameter that represents the rate of change in technical inefficiency. A positive value (η > 0) is associated with an improvement in the technical efficiency of a firm over time. Under this specification, the temporal pattern of technical inefficiency is monotonous and common to all firms, as every firm shares the same η that determines the time path of technical inefficiency. Cuesta (2000) generalized (6) by allowing for firm-specific pattern of temporal change of the technical inefficiency term-i.e., every firm has its own unique time path of technical inefficiency. In this case, technical inefficiency can be rewritten as: where η i is firm-specific parameters that capture the different patterns of temporal variation among firms. Cuesta (2000) suggested this model as a stochastic frontier counterpart of the Cornwell, Schmidt, and Sickles (1990) and Lee and Schmidt (1993) model that proposed a time varying pattern of temporal change in the fixed-effect panel model. Thus, in principle, Cuesta's model is desirable because it can use the information that technical inefficiency is one-sided. At the same time, the model has the advantage of not imposing a common pattern of inefficiency change to all sample firms, unlike earlier models. However, the model has to assume independence between inputs and technical efficiency. Moreover, the model suffers from "incidental parameters problem" as the number of parameters increases with the sample size. This means that the maximum likelihood estimator could be inconsistent. 2 To address the incidental parameters problem, the authors propose to modify Cuesta's model so that group-specific parameters for groups of firms are estimated instead of firmspecific parameters. Thus, the model modifies technical inefficiency (5) as: where the subscript g represents the group of firms (g=1,…,G). By modifying Cuesta (2000) in a straightforward manner, the log-likelihood function of the production frontier model (1) and (6) becomes: where Ω * ' ' , , , , ,f(.) and F(.) represent the probability density function and cumulative probability density function, respectively and: Maximum-likelihood estimates can be applied for the parameters of the stochastic frontier model, defined by (1) and (8), in which the variance parameters are expressed in terms of γ σ σ = u s 2 2 and σ σ σ The model is a counterpart of Kim and Lee (2006) in the sense that it allows for different temporal patterns across different groups of firms. While the Cuesta (2000) model is useful for estimating firm-specific technical inefficiency, measuring such efficiency often becomes impossible if the sample size becomes large, to more than several hundred, due to lack of convergence. In this case, the model could provide a practical alternative if grouping firms makes economic sense. Now the model will be applied to Malaysian manufacturing industries to consider the impact of firm size on technical efficiency and productivity growth.
For estimation purposes, the production frontier can be specified in translog form as where y it is the observed output, t is the time variable and the x variables are inputs. Subscripts j and l indicate inputs (j, l = L, K), and the efficiency error, u, is specified by (8).
The technical efficiency level of firm i at time t (TE it ) is defined as the ratio of the actual output to the potential output as follows: The rate of technical progress (TP) is defined by the following: The growth rate of total factor productivity (TFP), which is the sum of technical progress (TP) and technical efficiency change (TEC), can be derived from equations (12) and (13) as follows: TFP depends not only on technical progress but also changes in technical inefficiency. TP is positive (negative) if exogenous technical changes shift the production frontier outward (inward). If du/dt is negative (positive), then technical efficiency improves (deteriorates) over time, and -du/dt can be interpreted as the rate at which an inefficient producer inside the production frontier moves toward the production frontier, or, equivalently, reduces the gap between potential and actual output.
III. Data and Empirical Results
In this section, the data and variables used in the empirical analysis are discussed, and main findings reported.
A. Data and Variables
The data used in this paper are from a balanced panel consisting of annual time-series observations for 1,965 Malaysian manufacturing firms during 2000-2004, yielding a total of 9,825 observations. The sample covers all the companies within the seven main manufacturing industries listed in the Annual Survey of Manufacturing Industries published by the Department of Statistics, Malaysia. The survey provides a unique firm identification number for every participating firm and this number was used to transform the annual survey into a panel data set. Since the data set is a balanced panel, every firm in the sample has data for all five sampling years. Therefore, no firms have been dropped from the data set, even though the survey questionnaire does not identify mergers and acquisitions. The seven sample industries are electrical and electronics (E&E), textiles and apparel (textiles), transport equipment, chemical, rubber, machinery and food industries. According to the 2004 survey, these industries account for about 64.8% of total Malaysian manufacturing output, 55.6% of total employment, and 63.1% of total capital stock. Of the seven industries, the E&E industry is the largest in terms of output and capital, followed by the chemical, and transport equipment industries.
Capital stock (K) is defined as the actual quantity of tangible fixed assets, and the survey provides the market value of a firm's net fixed asset. 3 Labor input (L) was represented by the total number of workers engaged in production, including paid part-time and full-time employees and working proprietors and unpaid family members. Real value-added (VA) is measured as total revenue minus bought-in materials, and services represented output.
For purposes of estimation, value-added (capital stock) figures were deflated into 2000 constant prices by using the GDP deflator (the gross domestic fixed capital formation deflator) obtained from the National Accounts compiled by the Department of Statistics, Malaysia. Labor compensation was deflated by the consumer price index published by the Bank of Malaysia. All other nominal variables were also deflated into constant prices. Table 1 presents sample means and standard deviations. To apply the group-specific technical inefficiency model, sample firms are classified into ultra-small-sized firms (5-15 employees), small-sized firms (16-50 employees), medium-sized firms (51-150 employees), large-sized firms (151-300 employees), and ultra-large-sized firms (300+ employees). Table 2 presents the maximum-likelihood estimates of the parameters in the translog stochastic frontier production function with group-specific technical inefficiency effects, as defined by equations (7) and (11). The estimates of γ are statistically significant at the 1% level for every industry. All of the coefficient estimates of η are negative except for the transport equipment industry, and those are statistically significant for all size groups in the rubber, textiles, and food industries, for three small-to large-size groups in the chemical and machinery industries, and for two larger-size groups in the E&E and transport industries. A significant γ, along with a negative and significant η, implies that technical inefficiency exists and increases over time. 4 If the null hypothesis is true, then λ has approximately a Chi-square distribution with degrees of freedom equal to the number of restrictions. The null hypothesis of group-specific technical inefficiency is supported in every industry, except the E&E and chemical industries. Thus, the group-specific technical inefficiency model is an appropriate specification for the Malaysian manufacturing sector, except the two industries. 5 , , , , represents group η of ultra-small sized firms (5-5), small sized firms (6-50), medium sized firms (5-50), large sized firms (5-300), and ultra-large sized firms (300+), respectively. Source: Authors' calculations. Table 4 represents average TEC by industry and firm size groups. TEC is estimated as logdifference of TE (TEC t = lnTE t -lnTE t-1 ≅(TE t -TE t-i )/TE t-1 ). 6 Average TEC was -0.119 for the entire sample period, implying an 11.9% decrease in output due to technical inefficiency. Average TEC was -0.097 in 2001-2002, and -0.140 in 2003-2004. 5 The authors used the group-specific technical inefficiency model for every industry in order to estimate group-specific TEC and TFP estimates. However, coefficient estimates of Battese and Coelli (992) in which η is the same for all firm size groups are available from the authors upon request. 6 Notice that lnTE t -lnTE t-1 = -(u t -u t-1 ) = -du/dt. E & E = electrical and electronics industry. Notes: Firm size group of US, S, M, L, and UL represent ultra-small sized firms (5−5 employees), small-sized firms (6−50), medium-sized firms (5−50), large-sized firms (5−300), and ultra-large-sized firms (300+), respectively. Source: Authors' calculations.
B. Decomposition of Total Factor Productivity
TEC is negative, which implies that TE is deteriorating for every industry and every firm size, except the smallest two firm sizes in the transport industry. The deterioration of TE is fastest in the rubber industry at -0.237, followed by the food, machinery, and chemical industries. The deterioration of TE is slowest in the E&E industry at -0.050, followed by the transport and textiles industries. TEC varies across firm sizes within each industry. The deterioration of TE is slowest in the ultra-small firm-size group at -0.083 and the fastest in large firm-size group at -0.143. 7 The other groups have estimates that range from -0.134 to -0.102. The TEC difference between each firm-size group is especially conspicuous in the transport industry. The deterioration of TE has gathered speed from the sub-period of 2001-2002 to the sub-period of 2003-2004. The distribution of TEC among the firm-size groups within industry is more or less stable between the two sub-periods, as is the overall TEC rankings of the industries. The one noticeable exception is the decline of TEC in ultralarge firms in the machinery industry.
Overall, the TEC estimates indicate that technical inefficiency is a big obstacle to higher productivity in the Malaysian manufacturing sector. Technical efficiency has worsened for almost every firm-size group in every industry. This implies that firms are moving further away from, rather than closer toward the production frontier. According to the results, the TE of larger firms deteriorated more sharply than the TE of smaller firms. This suggests 7 However, TE increases as firm size increases, since TE is highest for the largest firm size at 0.458 and lowest for the smallest firm size at 0.288. Estimates of TE are available from the authors upon request. that the TE slowdown in the Malaysian manufacturing sector during the sample period was driven by the growing inefficiencies of larger firms. One possible explanation is that larger firms were less flexible and adaptable than smaller firms to the economic downturn of 2003-2004. For example, smaller firms typically rely more on part-time and informal workers, and this gives them more leeway to adjust their workforce during recessions. Specific groups of firms that lag in terms of TE include ultra-large firms in the transport, E&E, and rubber industries and large firms in the textile and chemical industries. Table 5 reports the average TP for each firm size in each industry. TP is estimated for every observation according to equation (13). Average TP was 0.124 for the entire sample period, implying a 12.4% gain in output due to technical progress. Average TP was 0.112 in 2001-2002 and 0.137 in 2003-2004. TP is positive for every industry for the sample period as a whole and both sub-periods. Therefore, in contrast to technical efficiency change, which was a source of lower productivity and output growth, TP is a source of higher productivity and growth. However, TP dropped greatly in the rubber and food industries between these two periods. TP was led by the chemical industry in the first sub-period, and by the machinery industry in the second sub-period. In terms of TP by firm size, TP is the fastest in the largest firm-size group at 0.136 and the slowest in the smallest firm-size group at 0.116. TP for the other size groups ranges from 0.119 to 0.128. However, TP varies widely across the firm sizes within each industry-TP increases as firm size increases in the chemical, machinery, and transport industries, but decreases in the other industries. This implies that the shifting-up of the production frontier of the manufacturing sector is not always led by larger firms, contrary to the popular perception that TP is driven by larger firms that typically invest more in research and development activities.
In fact, in the Malaysian manufacturing sector, TP was the fastest among the smallest firms in the food, rubber, textiles, and E&E industries. Other than the E&E industry, these industries are mature and declining, and unlikely to have much room for major breakthrough in production technology. Instead, TP is often brought about by incremental improvements in production techniques or adoption of existing technology by smaller firms. Furthermore, larger firms are slow to invest in mature and declining industries. In the E&E industry, both large and small firms have avenues for TP. In this fast-growing industry, technological innovation is often led by small venture firms and labs, but large foreign multinational companies that have operations in Malaysia tend to be early adopters of new technologies. Table 6 reports the average total factor productivity growth (TFPG) for each firm size and industry. TFPG is the sum of TP and TEC as in equation (14). For the entire sample period, average TFPG was 0.005, implying a 0.5% increase in output due to total factor productivity growth. Average TFPG was 0.015 in 2001-2002, and -0.003 in 2003-2004. Therefore, TFP improved during the sample period as a whole and the first sub-period but deteriorated during the second sub-period. TFP growth is the slowest in the rubber industry at -0.152 for the entire sample period, followed by the food industry at -0.115. For these two industries, TFP deteriorated throughout the sample period whereas TFP grew in all the other industries, with growth ranging from 0.012 to 0.092. 8 TFPG was led by the largest firms in the chemical and machinery industries but by ultra-small firms in all the other industries, except that in the transport industry. TFP grew the fastest among small firms (Figure 1). For the entire sample of firms, the estimated TFPG was highest among the smallest firms at 0.034 and lowest among large firms at -0.015. The estimates for the other firm sizes range from -0.008 to 0.017. (Overall, the estimated TFPG was positive in every industry during 2001-2004 except the in the rubber and food industries. This indicates that the substantial negative impact of technical inefficiencies on the productivity of the Malaysian manufacturing sector is generally more than offset by robust technical progress. As a result, the Malaysian manufacturing sector as a whole has become more productive even though productivity improvement significantly varies across industries. Of particular concern are the food and
IV. Concluding Observations
Malaysia is an upper middle-income country that is now reaching a development stage where productivity growth will be more important to economic growth than accumulation of capital and labor. TFP growth refers to the increase in output that cannot be accounted for by an increase in inputs. A more accurate measurement of productivity and productivity growth calls for using firm-level or industry-level data. Such analysis is also more useful for policy makers since it enables them to identify industries and groups of firms that are lagging in productivity, and thus to effectively target productivity-enhancing policies. Dividing Malaysian firms on the basis of size is especially meaningful because there are big structural differences between SMEs and larger firms in the Malaysian manufacturing sector. Many larger manufacturers are foreign multinational companies that use state-of-the-art technology to produce for the global markets while SMEs tend to use older technology and are more geared toward domestic demand.
To measure TFP growth and its two main components-TEC and TP-for five different firm sizes in seven Malaysian manufacturing industries during 2000-2004, the authors develop a stochastic frontier production model that allows for TEC, TP and TFP growth to vary across the different groups. The empirical results indicate that technical inefficiency is a serious impediment to higher productivity and hence output growth in the Malaysian manufacturing sector. Technical inefficiency grew in almost every firm size in every industry although it is more pronounced for larger firms. On the other hand, the authors find that technical progress has boosted productivity and output growth in every industry. The authors also find that larger firms generally experienced stronger TP but in some industries, smaller firms also experienced substantial TP. TP was larger than the loss of technical efficiency and hence led to positive TFP growth in five out of seven industries. Therefore, by and large the Malaysian manufacturing sector has become more productive during the sample period.
In terms of policy implications, the findings imply that technical efficiency change may be at least as important as technical progress in lifting up the TFP of the Malaysian manufacturing sector. As such, policies that enable firms to reduce the gap between their actual and potential output-e.g., more flexible markets that allow firms to use labor more efficiently-may do as much to improve productivity as policies that shift out their production frontier-e.g., subsidies for research and development. Evidence thus reconfirms the often overlooked fact that productivity is often the result of mundane incremental improvements in using inputs more efficiently rather than quantum leaps in production technology or techniques. Interestingly, evidence indicates that the need for improving technical efficiency is greater for larger firms than smaller firms. More generally, the findings identify the industries and firm sizes that lag the most in productivity growth and thus require the most attention from policy makers. However, it is important to note that promoting productivity often calls for governments to do less rather than more.
More specifically, policy distortions that provide explicit or implicit subsidies to particular industries or groups of firms are often a serious impediment to productivity growth. Removing such distortions will not only raise productivity at the firm-and industry-level but, at a broader level, facilitate the economy-wide reallocation of resources toward highproductivity industries and activities.
The stochastic frontier production model and its application to the Malaysian manufacturing sector suggest a number of useful topics for future research. Most immediately, the model can be applied to other countries and the services sector. East Asian countries are in various stages of transiting from growth driven by factor accumulation to growth that relies more on productivity growth, and empirical analysis that yields productivity estimates for different industries and groups of firms should help policy makers facilitate the transition. More generally, the model allows productivity growth of different groups of firms to be estimated and compared-there is no reason why structurally different groups of firms should experience the same pattern of productivity growth. It is possible to group firms on the basis of other firm characteristics such as foreign versus domestic ownership. Finally, the production model can also be applied to different groups of countries to compare their productivity growth patterns.
About the Paper
Using a stochastic frontier production model, Sangho Kim, Donghyun Park and Jong-Ho Park examine the recent evolution of technical efficiency change, technical progress, and total factor productivity growth across five firm sizes and seven industries in the Malaysian manufacturing sector during 2000-2004. They find that while technical efficiency has generally worsened, substantial technical progress has more than offset it, resulting in positive total factor productivity growth. The study identifies the industries and firm sizes that lag the most in productivity, and thus have the greatest scope for productivityenhancing policies.
About the Asian Development Bank
ADB's vision is an Asia and Pacific region free of poverty. Its mission is to help its developing member countries substantially reduce poverty and improve the quality of life of their people. Despite the region's many successes, it remains home to two-thirds of the world's poor: 1.8 billion people who live on less than $2 a day, with 903 million struggling on less than $1.25 a day. ADB is committed to reducing poverty through inclusive economic growth, environmentally sustainable growth, and regional integration. Based in Manila, ADB is owned by 67 members, including 48 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. < 0 0 9 1 1 9 6 2 > | 7,473.2 | 2009-11-01T00:00:00.000 | [
"Economics"
] |
RadioGalaxyNET: Dataset and Novel Computer Vision Algorithms for the Detection of Extended Radio Galaxies and Infrared Hosts
Creating radio galaxy catalogues from next-generation deep surveys requires automated identification of associated components of extended sources and their corresponding infrared hosts. In this paper, we introduce RadioGalaxyNET, a multimodal dataset, and a suite of novel computer vision algorithms designed to automate the detection and localization of multi-component extended radio galaxies and their corresponding infrared hosts. The dataset comprises 4,155 instances of galaxies in 2,800 images with both radio and infrared channels. Each instance provides information about the extended radio galaxy class, its corresponding bounding box encompassing all components, the pixel-level segmentation mask, and the keypoint position of its corresponding infrared host galaxy. RadioGalaxyNET is the first dataset to include images from the highly sensitive Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope, corresponding infrared images, and instance-level annotations for galaxy detection. We benchmark several object detection algorithms on the dataset and propose a novel multimodal approach to simultaneously detect radio galaxies and the positions of infrared hosts.
Introduction
Recent advancements in radio astronomy have enabled us to scan large areas of the sky in a short timescale while generating incredibly sensitive continuum images of the Universe.This has created new possibilities for detecting millions of galaxies at radio wavelengths.For example, the ongoing Evolutionary Map of the Universe (EMU; Norris et al., 2021) survey, conducted using the Australian Square Kilometre Array Pathfinder (ASKAP; Hotan et al., 2021) telescope, is projected to discover more than 40 million compact and extended galaxies in the next five years (Norris et al., 2021;Hotan et al., 2021).Similarly, the Low-Frequency Array (LOFAR; van Haarlem et al., 2013) survey of the entire northern sky is also expected to detect more than 10 million galaxies.With the advent of the Square Kilometre Array (SKA 2 ) radio telescope, which is expected to become operational in the coming years, the number of galaxy detections is expected to increase further, potentially reaching hundreds of millions.Such an enormous dataset will significantly impact our understanding of the physics of galaxy evolution.It will allow us to constrain the theoretical models of the Universe (e.g. the Big Bang model) at unprecedented levels.To capture the full potential of these radio surveys comes the need to redesign the galaxy detection techniques.
Raw Radio
Processed Radio ).The processed radio images highlight the categories of extended radio galaxies, and the bounding boxes denote their total radio extent encompassing all of its components.The infrared images show host galaxies inside the circles.
Radio galaxies are characterized by giant radio emission regions that extend well beyond their structure at visible and infrared wavelengths.While most radio galaxies typically appear as simple, compact circular sources, increasing the sensitivity of radio telescopes result in the detection of more radio galaxies with complex extended structures.These structures typically consist of multiple components with distinct peak radio emissions.Figure 1 displays examples of these extended radio galaxies in the first (raw noisy data) and second (processed data) columns, along with their compact infrared host galaxies in the third column.To construct scientifically useful catalogues of radio galaxies, it is crucial to group the associated components of extended radio galaxies accurately.Currently, visual inspections are used to cross-identify associated radio source components and their infrared host galaxies.This limitation highlights the critical need for developing automated methods, such as machine learning algorithms, to accurately and efficiently cross-identify and group associated components.However, to train and test such algorithms, a large and diverse dataset of labelled radio galaxy images is necessary.Unfortunately, such a dataset is not currently available to train models for the next generation of radio surveys, which poses a significant challenge to developing automated methods for detecting and grouping components of radio galaxies.This paper introduces a novel dataset aimed at addressing the problem of radio galaxy component association.The dataset has been • We introduce the first publicly available dataset curated by professional astronomers that includes state-of-the-art images from a highly sensitive radio telescope and instance-level annotations for extended radio galaxies.
• As a novel addition, our dataset also includes corresponding images of the infrared sky, along with the positional information of the host galaxies.
• We benchmark the object detection algorithms on our dataset to demonstrate the challenge of detecting and associating components of radio galaxies.Additionally, we propose a novel method to detect the positions of infrared host galaxies simultaneously.
2 The Dataset
Radio and Infrared Images
Our dataset contains radio images derived from observations with the ASKAP telescope.We use the Evolutionary Map of Universe pilot survey (EMU-PS; Norris et al., 2021) that covers a sky area of 270 deg 2 , achieving an RMS sensitivity of 25 − 35 µJy/beam at a frequency range of 800 to 1088 MHz, centred at 944 MHz (wavelength of 0.37 to 0.28m, centred at 0.32m).The extended radio galaxies were visually identified by the experts in the 270 deg 2 EMU-PS image.At the same sky locations of radio images, we obtain AllWISE (Cutri et al., 2021) infrared images from the Wide-field Infrared Survey Explorer's (WISE; Wright et al., 2010) W1 band that correspond to 3.4 µm wavelength.We create 3-channel RGB images by combining the processed radio and infrared images.To achieve this, we fill the B and G channels with 8-16 bit and 0-8 bit radio information, respectively.In contrast, the 8-16 bit infrared information is inserted into the R channel.
Annotations
Our dataset comprises four types of annotations: the classification labels for extended radio galaxies, bounding boxes encompassing all components of each radio galaxy, segmentation masks for radio galaxies, and the positions of infrared host galaxies.The comprehensive methodology for source identification will be presented in detail by Yew et al. in prep. (2024).Here, we provide a brief overview of the process.We visually inspected infrared images to determine the infrared host galaxy associated with each radio source.Following the criteria of Fanaroff and Riley (1974), we classified the galaxies as FR-I and FR-II.The unreliable classifications, which can either be FR-I or FR-II in reality, are labelled as FR-x sources.In some cases, barely resolved sources have only one peak outside the central component, we classify them as R (for "resolved") sources.The radio annotations for each galaxy are stored as 'categories', 'bbox', and 'segmentation'.The positions of the infrared hosts are stored as 'keypoints'.The statistics for the train, validation, and test data splits, including the number of objects in one frame, categories of extended radio galaxies, and the occupied area of labeled objects, are depicted in Figure 2. Additionally, Table 1 provides a comparison with the existing MiraBest (Miraghaei and Best, 2017) and Citizen Science RGZ (Wu et al., 2019) The process of obtaining annotations for our dataset took nearly 1.5 years, involving multiple discussions over each source, marking the first such dataset in radio astronomy that utilizes such extensive scientific resources.
Experiments
We propose a novel multimodal modelling approach to simultaneously detect radio galaxies and their corresponding infrared hosts by incorporating keypoint detection in existing object detection algorithms.Note that the multimodal methods are tailored to specific tasks.Here we have radio images where galaxies appear larger due to extended emission, while in infrared images, the same galaxies look like point objects (as depicted in columns 2 and 3 of Figure 1).To the best of our knowledge, there are no specific models that deal with objects that look completely different in two image modalities.As a result, we introduce our own approach to multimodal modelling.
We implemented keypoint detection for Gal-DETR (based on DETR; Carion et al., 2020), Gal-Deformable DETR (based on Deformable DETR Zhu et al., 2021), and Gal-DINO (based on DINO; Zhang et al., 2022).Specifically, we implemented keypoint detection to the model, augmentations, and Hungarian matcher and added additional random rotation augmentations during training.We reduced the learning rate to 5 × 10 −5 and the number of queries to 10. Similar changes were made for Gal-Deformable DETR model, where keypoint detection was also implemented in the deformable attention mechanism.For Gal-DINO model, we made the same changes as for Gal-DETR and additionally implemented keypoint detection in the de-noising anchor box mechanism.All networks are trained and evaluated on an Nvidia Tesla P100.Table 2 presents the results of Gal-DETR, Gal-Deformable DETR, and Gal-DINO for bounding box detection of extended radio galaxies and keypoint detection for the positions of infrared host galaxies, evaluated using the COCO evaluation metric.Figure 3
Conclusions
We present a multimodal dataset comprising 2,800 images capturing both radio and infrared sky data, with annotations curated by professional astronomers.The dataset features 4,155 instances of annotations, including class information of extended radio galaxies, bounding boxes encompassing all associated components of each radio galaxy, segmentation masks for radio galaxies, and positions of host galaxies in infrared images.We benchmark various object detection strategies on the dataset and propose a novel method for simultaneously detecting the extended radio galaxies and the positions of infrared host galaxies.The availability of our dataset will facilitate the development of machinelearning methods to detect radio galaxies and infrared hosts in the next generation of radio sky surveys, enabling the creation of efficient multimodal algorithms with a focus on small objects and partial annotations.
Figure 1 :
Figure1: Raw radio (left), processed radio (middle) and processed infrared (right) images with the frame size of 450 × 450 pixels (0.25 • × 0.25 • ).The processed radio images highlight the categories of extended radio galaxies, and the bounding boxes denote their total radio extent encompassing all of its components.The infrared images show host galaxies inside the circles.
Figure 2 :
Figure2: The dataset split distributions.Shown are the distributions of extended radio galaxies in one frame (left), their categories (middle) and the occupied area per galaxy.
displays RGB images and ground truth annotations (first column), ground truth and predicted keypoints as circles and triangles over infrared images (second column) and Gal-DINO Radio Detection (SIOD-DM FR-II: 0.56 FR-II: 0
Figure 3 :
Figure 3: Object detection results: Shown are the processed radio-radio-infrared images and ground truth annotations (first column), ground truth and Gal-DINO keypoint detections as circles and triangles over infrared images (second column), Gal-DINO (third column) class and bounding box predictions over radio images.
Table 1 :
(Lin et al., 2014)for radio galaxy classification.The annotations C, B, S, and K are categories, bounding boxes, segmentation and keypoint labels, respectively.structured in the COCO dataset format(Lin et al., 2014), allowing for straightforward comparison studies of various object detection strategies for the machine learning community.It features 2,800 3-channel images, each containing two radio sky channels, one corresponding infrared sky channel, and 4,155 annotations.To summarize, our work contributes to the following aspects: datasets.
Table 2 :
Bounding box and keypoint detection results on the test set.Model Params Epochs AP AP 50 AP 75 AP S AP M AP L | 2,483.6 | 2023-12-01T00:00:00.000 | [
"Computer Science",
"Physics"
] |
Inverse silica opal photonic crystals for optical sensing applications
This work reports fabrication of inverse silica opal photonic crystal structures from direct polystyrene micro sphere opals using lowtemperature sol-gel infiltration of silica, and examines performance of these photonic crystals as environmental refractive index sensors. Sensitivity of the spectral position and optical attenuation of photonic stop gaps is found to allow detection of the index changes by the amount of ~10. The high value of sensitivity, which is comparable with those of other optical sensing techniques, along with simplicity of the optical detection setup required for sensing, and the low-temperature, energy-efficient fabrication process make inverse silica opals attractive systems for optical sensing applications. ©2007 Optical Society of America OCIS codes: (230.5298) Photonic crystals; (350.4238) Nanophotonics and photonic crystals References and links 1. S. John, “Strong localization of photons in certain disordered dielectric superlattices,” Phys. Rev. Lett. 58, 2486–2489 (1987). 2. E. Yablonovitch, “Inhibited spontaneous emission in Solid-State Physics and Electronics,” Phys. Rev. Lett. 58, 2059–2062 (1987). 3. A. Blanco, E. Chomski, S. Grabtchak, M. Ibisate, S. Jhon, S. W. Leonard, C. Lopez, F. Meseguer, H. Miguez, J. P. Mondia, G. A. Ozin, O. Toader and H. M. van Driel, “Large-scale synthesis of a silicon photonic crystal with a complete three-dimensional bandgap near 1.5 micrometres,” Nature 405, 437-440 (2000) 4. H. Fudouzi and Y. Xia, “Photonic papers and inks: color writing with colorless materials,” Adv. Mater. 15, 892–896 (2003). 5. J. H. Holtz and S. A. Asher, “Polymerized colloidal crystal hydrogel films as intelligent chemical sensing materials,” Nature 389, 829 (2003). 6. H. Altug and J. Vučkovič, “Polarization control and optical sensing with two-dimensional coupled photonic crystal microcavity arrays,” Opt. Lett. 30, 982–984 (2005). 7. E. Chow, L. Mirkarimi, M. Sigalas, and G. Girolami, “Ultracompact biochemical sensor built with twodimensional photonic crystal microcavity,” Opt. Lett. 29, 1093–1095 (2004). 8. T. Prasad, D. M. Mittleman, and V. L. Colvin, “A photonic crystal sensor based on the superprism effect,” Opt. Mater. 29, 5659 (2006). 9. M. C. Phan Huy, G. Laffort, Y. Frignac, V. Dewynter-Marty, P. Ferdinand, P. Roy, J-M. Blondy, D. Pagnoux, W. Blanc and B. Dussardier “ Fibre Bragg grating photowriting in microstructured optical fibres for refractive index measurement.” Meas. Sci. Technol. 17, 992-997 (2006). 10. T. Ritari, J. Tuominen, H. Ludvigsen, J. Petersen, T. Sørensen, T. Hansen, and H. Simonsen, “Gas sensing using air-guiding photonic bandgap fibers,” Opt. Express 12, 4080–4087 (2004). 11. A. Baryshev, R. Fujikawa, A. Khanikaev, A. Granovsky, K. Shin, P. Lim, and M. Inoue, “Mesoporous photonic crystals for sensor applications,” in Proceedings of the SPIE, Photonic Crystals and Photonic Crystal Fibers for Sensing Applications II. H. H. Du, R. Bise, eds., (2006), pp. 63690B. 12. S. Matsuo, T. Fujine, K. Fukuda, S. Juodkazis, and H. Misawa, “Formation of free-standing micro-pyramid colloidal crystals grown on silicon substrate,” Appl. Phys. Lett. 82, 4283–4285 (2003). 13. V. Mizeikis, S. Juodkazis, A. Marcinkevicius, S. Matsuo, and H. Misawa, “Tailoring and Characterization of Photonic Crystals,” J. Photochem. Photobiol. C 2, 35–69 (2001). 14. S. Juodkazis, E. Bernstein, J.-C. Plenet, C. Bovier, J. D. J. Mugnier, and J. V. Vaitkus, “Waveguiding properties of CdS-doped (Si0.2Ti0.8)O2 films prepared by sol-gel method,” Thin Solid Films 322, 238–244 (1998). #86262 $15.00 USD Received 9 Aug 2007; revised 18 Sep 2007; accepted 18 Sep 2007; published 25 Sep 2007 (C) 2007 OSA 1 October 2007 / Vol. 15, No. 20 / OPTICS EXPRESS 12979 15. S. Juodkazis, E. Bernstein, J.-C. Plenet, C. Bovier, J. D. J. Mugnier, and J. V. Vaitkus, “Optical Properties of CdS Nanocrystallites Embedded in (Si0.2Ti0.8)O2 Sol-Gel Waveguide,” Opt. Commun. 148, 242–248 (1998). 16. R. C. Schroden, M. Al-Daous, C. F. Blanford, and A. Stein, “Optical properties of inverse opal photonic crystals,” Chem. Mat. 14, 3305–3315 (2002). 17. D. L.Wood, E. M. Rabinovich, J. D.W. Johnson, J. B. MacChesney, and E. M. Vogel, “Preparation of highsilica glasses from colloidal gels: III Infrared spectrophotometric studies,” J. Am. Ceram. Soc. 66, 693 – 699 (1983). 18. S. Sakka and J. D. Mackenzie, “Relation between apparent glass transition temperature and liquids temperature for inorganic glasses,” J. Non-Cryst. Solids 6, 145 – 162 (1971). 19. Y. Nishijima, et al., to be published (2007). 20. J. D. Joannopoulos, R. D. Meade, and J. N. Winn, Photonic Crystals: Molding the Flow of Light (Princeton University Press, Princeton, New Jersey, 1995). 21. J. Ye, R. Zentel, S. Arpiainen, J. Ahopelto, F. Jonsson, S. G. Romanov, and C. M. S. Torres, “Integration of self assembled three-dimensional photonic crystals onto structured silicon wafers,” Langmuir 22, 7378– 7383 (2006). URL http://dx.doi.org/10.1021/la0607611. 22. K. Yoshino, S. Satoh, T. Shimoda, H. Kajii, T. Tamura, Y. Kawagishi, T. Matsui, R. Hidayat, A. Fujii, and M. Ozaki “Tunable optical properties of conducting polymers infiltrated in synthetic opal as photonic crystal” Synthetic Met. 121, 1459-1462 (2001).
Introduction
Photonic crystals (PhC) [1][2][3] are promising materials for applications in optoelectronics and photonics as well as in the field of optical sensing. Photonic crystal sensors exploit sensitivity of PhC dispersion bands to the modification of their refractive index and periodicity modulation by gases or fluids [4,5]. PhCs are dielectric or metalodielectric materials whose periodic spatial variation of dielectric function is achieved using advanced micro-and nanostructuring techniques and leads to formation of spectral photonic band gaps (PBG) or stop gaps (PSG). Within these intervals propagation of electromagnetic waves is forbidden along all (for PBG) or along specific directions (for PSG). Most PhCs consist of permeable dielectric (or metalodielectric) networks open to gaseous or fluidic flows. Optical sensing can be realized in the simplest form by detecting modification of the PBG or PSG spectral signatures in the PhC reflectivity or transmission spectra due to the infiltration of PhC voids by various materials. More sophisticated sensing techniques exploiting resonant PhC microcavity states [6,7], superprism effects [8], and optical interactions in photonic crystal fibers [9,10] have been recently also developed.
Although three-dimensional (3D) PhC structures are not required for the optical sensing, which is possible with simpler one-dimensional (1D) or two-dimensional (2D) PhCs, artificial 3D opal PhC structures can be attractive as optical sensors [11]. First, their fabrication from colloidal suspensions of commercially available silica or polystyrene microspheres was perfected during the recent decades [12,13]. Second, optical sensing is perhaps the only field of application for as-fabricated opal structures, which have no PBG due to the low index contrast between the closely-packed silica (n = 1.47) or polystyrene (n = 1.57) spheres and surrounding air (n = 1.0). Nevertheless they still retain one or several PSGs that can be utilized for optical sensing. Third, existence of multiple PSG regions along different directions in the 3D opal might add an interesting capability of performing simultaneous optical sensing at several wavelengths along different directions.
This work addresses the fabrication and application of silica inverse opal structures as simple optical fluidic sensors. Although use of various direct and inverse opal photonic crystals for optical sensing has been addressed before, suitability of inverse silica opals for this purpose has not yet been examined. The results of the present study clearly demonstrate that sensors based on silica inverse opals have several important advantages compared to other opal-based or photonic crystal-based sensors. Below we will briefly outline these advantages. First of them is that opal inversion is performed by infiltrating the initial direct template composed of polystyrene microspheres, with silica glass using a low-temperature sol-gel process, and by subsequently removing the template. The low-temperature silica infiltration is an energy-efficient process, which nevertheless produces high-quality periodic networks of silica. High thermal and chemical stability of silica glass allows performance of optical sensing in a wide temperature range in a variety of fluidic environments. The next potential advantage stems from general properties of inverse opals. The inverse silica opal consists of closely packed spherical air voids arranged in a face-centered cubic (fcc) point lattice, and embedded in a framework of solid silica. Since inverse silica opal would have the same or even lower index contrast than the initial polystyrene template, use of inverted opals at a first glance promises no significant improvement. To clarify this issue it is helpful to consider analytical expression for the spectral position of the fundamental (lowest frequency) PSG of 3D opal structures. For example, along the direction perpendicular to the (111) crystal planes, the PSG central wavelength λ c can be deduced from Bragg condition: (1) Where d is diameter of the spheres, f sph is the sphere volume filling fraction, n sph is the refractive index of spheres, and n bg is the "background" refractive index of the empty regions, or voids between the spheres. Closely packed opals have sphere filling fraction f sph = 0.74, while the voids fill the remaining 1 − f sph = 0.26 fraction of the volume. In direct opal n bg can be altered by the infiltration of other materials into the voids, whereas n sph is the constant refractive index of spheres. In inverse opal n sph represents voids whose refractive index can be altered by infiltration, whereas n bg is the constant refractive index of solid dielectric framework. According to Eq. (1) the PBG wavelength is more sensitive to the variations of n sph than of n bg due to the larger sphere volume-filling fraction. Although this circumstance is quite obvious, so far it has not been exploited for improving accuracy of opal PhC sensors. The refractive index sensing limit obtainable with PhCs fabricated and investigated in this work is estimated to be about 10 -3 . This value is quite high and can be regarded as another potential advantage of inverse silica opal structures, since it is close to that reported for twodimensional PhC microcavities [7] prepared using advanced semiconductor nanofabrication techniques. Recently, a novel optical sensing scheme using opal PhC and based on superprism effect was reported to provide sensitivity of 10 -5 [8]. Although performance of such sensor is superior to ours, it also requires a more complex optical setup. Sensitivity of about 10 -3 , i.e., similar to ours, was also reported recently for optically-structured photonic crystal fibers [9] (It is difficult to compare sensitivity of our opal structures with that of some other 2D photonic crystal fibers [10], since they are used for gas sensing exploiting absorption of the optical radiation, rather than its refraction).
Here we report fabrication of synthetic opal PhC structures from polystyrene microspheres having various diameters, and subsequent inversion of these templates with silica glass using low-temperature sol-gel process and removal of the template. By comparing optical properties of the direct and inverse opal structures we demonstrate that in accordance to the expectations, inverse silica opals exhibit higher environmental sensitivity. These findings may help improve optical sensors based on opal PhC structures.
Experimental details
Preparation of the initial polystyrene templates was done by centrifuged sedimentation (6900, KUBOTA, Co.) of polystyrene spheres (Sekisui, Inc.) at 5000 rpm (4200 G) for 10 min on cover glass substrates. The centrifuged sedimentation process is shown schematically and explained in Fig. 1. Spheres with different diameters of d = 220, 320, 400, 520, and 600 nm were used. After the centrifugation the structures were dried and annealed at 90ºC for 3 min. , which are attached to the axle of the centrifuge by swivel-mounts, and are oriented vertically by the gravity force when the centrifuge is at standstill (b), when the centrifuge is turned on, centrifugal forces overcome the gravity, aligning the holders horizontally, and govern sedimentation from the colloidal suspension in the sample holders (c), after the centrifugation the cylindrical holders realign vertically, a film of synthetic opal having uniform thickness is obtained on the glass substrate (d).
The polystyrene template was then removed by immersion in ethyl acetate. No further high temperature sintering of gel was employed. The resulting inverse opal structure is shown in Fig. 2(b). The SEM images allow clear identification of the (111) fcc lattice plane.
Optical reflection spectra of the samples were measured using micro-spectroscopic setup consisting of an optical microscope (BX-51, Olympus Co.) equipped with an objective lens with x40 magnification and numerical aperture of NA = 0.75, using halogen lamp (Usio, Inc.) broadband illumination, and a multi-channel photodetector (PMA-11, Hamamatsu Photonics, Inc.). Infrared (IR) reflection spectra were measured using a Fourier-Transform Infrared (FTIR) spectrometer equipped with an IR microscope attachment (FT-IR, IRT-3000, Jasco, Inc.), employing a Cassegrainian objective lens with maximum NA = 0.5. With both measurement setups the samples were oriented with (111) crystallographic planes normal to the optical axis. In reciprocal space this orientation corresponds to the Γ-L direction. To mimic the environments of different refractive index, samples were immersed into a range of organic solutions: fluorinert FC72 (n = 1.24), fluorinert FC77 (n = 1.26), methanol (n = 1.33), ethanol (n = 1.36), 2-propanol (n = 1.37), and propylene carbonate (n = 1.42).
Structural properties of PhC structures and uniform silica glass obtained via sol-gel route
Good periodic ordering of the direct and inverse opal PhCs prepared for this work were already illustrated by SEM images in Fig. 2. In addition to periodic opal structures, we have briefly studied properties of uniform silica films prepared via the same low-temperature solgel route as used for the opal inversion. Films with thickness of about 100 μm exhibit transmission T = 0.8 (limited mostly by Rayleigh scattering) in the wavelength range from 350 to 1700 nm. This proves the possibility to obtain films of high optical quality by energy-efficient method, which does not require high-temperature annealing of gel (annealing at high temperature is usually required in order to sinter gel for obtaining optical quality uniform silica-titania films [14,15] and inverted opals in zirconia [16]). Additional treatment at elevated temperature can improve optical quality of glass even further: annealing at 520ºC for 2 hr resulted in considerable reduction of Rayleigh scattering in the 200-400 nm wavelength range and an increase of transmission to T > 90% in the wavelength range from 250 nm to 1.7 μm. The network of water vibrational bands hydrogen-bound to SiOH near the 1.4 and 1.9 μm wavelengths become weakened by sintering resulting from the annealing [17]. It is noteworthy that the highly-transmissive silica was obtained using gel processing temperatures three times lower than the glass transition temperature, T g . Glasses usually need annealing at T g for the release of stress and removal of defects (T g = 2T m /3, where T m is the melting temperature. For silica T m = 1723°C [18]). Hence, sol-gel synthesis performed in this work can be categorized as a low-temperature process (even if elevated temperatures were used) and may help provide silica glass for demanding optical applications, such as inverted opal PhC structures. It must be noted that high optical damage threshold of silica makes silica-inverted opals suitable for applications that require optical pumping (such as lasing of dyes infiltrated into opal PhC). For example, zirconia and titania inverted opals can be easily damaged at fluences above 0.1 GW/cm 2 , whereas silica structures survive under the same conditions [19]. Figure 3 summarizes the optical properties of direct and inverse opal structures in air. Detailed examination of optical reflectivities of the samples has revealed presence of a single major reflectivity peak in each of them. The measured peak wavelengths are close (albeit not exactly coincident) to the central wavelengths of the fundamental PSG inferred from Eq. (1). The relative mismatch between the experimentally measured and calculated values of λ c was about 1% for direct and 4-7% for inverse structures; in the latter case shrinkage of silica sol during drying and the corresponding reduction of opal lattice period is the most likely origin of mismatch. As can be seen from Fig. 3, in both kinds of samples the peak central wavelength λ c scales nearly linearly with the sphere diameter or PhC lattice period. Such scaling is a characteristic feature of PBG and PSG materials, known as Maxwell's scaling property [19]. By varying the sphere diameter, the PSG wavelength can be tuned across the visible and near-infrared (NIR) spectral ranges. Since in inverse opal structures only 26% of volume is high-index material and the rest is air, their average refractive index is somewhat lower than that of direct polystyrene templates. Consequently, their PSGs are somewhat blueshifted, which is another manifestation of the Maxwell's scaling. For the spheres of smallest diameter, d = 220 nm, the PSG of inverse opal structure is tuned beyond the shortest observation wavelength of 530 nm and therefore is absent in Fig. 3(b). Next, we turn our attention to spectral variations in the reflectivities of opal structures due to infiltration of voids by different solutions. Figure 4 gives comparison between the reflectivities of direct and inverse opals with the same sphere diameter (d = 520 nm) in air and in various liquid solutions. Both samples exhibit red shift of the wavelength λ c with refractive index. However, this trend is noticeably more pronounced for the inverse opal, as can be seen by comparing the spectra for n = 1.24 and 1.26 in both samples. It will be examined in more detail later (see Fig. 7 and the accompanying discussion). Spectral shifts deduced from Fig. 4, and also from spectra of other samples with different sphere diameters, are summarized in Fig. 5. The figure clearly illustrates linearity of the λ c (n) dependencies. Inverse opal samples exhibit somewhat steeper slopes than the direct opals, in qualitative agreement with Eq. (1). However, the above equation cannot provide quantitative fits to the experimental data. Although this circumstance is not really a disadvantage for sensing applications, which would rely on empirical calibration of the λ c (n) dependency (and its linearity) rather than on theoretical predictions, it is helpful to point out the most likely reason for the discrepancy between Eq. (1) and the experiments. Reflectivity measurements along the (111) direction should ideally use a collimated parallel beam propagating along that direction. In reality, both micro-spectroscopic setup and FTIR spectrometer with IR microscope attachment use focusing by objective lenses having NA = 0.75 and 0.5, respectively. Hence, incidence angles are distributed within the angular ranges of up to 50º and 32º with respect to the normal to (111) plane. In these circumstances full 3D photonic band structure of opal should be taken into account instead of the single direction in order to correctly determine the spectral positions of reflection peaks. In addition to the red shift, the reflection peaks become suppressed with increasing refractive index. This behavior is seen in Fig. 4, and is summarized in Fig. 6. The amplitudes of reflection peaks decrease nearly linearly with refractive index of the infiltrating material. One can extrapolate that the peaks will disappear completely at the refractive index values of ≈ 1.60 for direct and ≈ 1.46 for inverse opal, i.e., at vanishing index contrast.
Proposed utilization of inverse silica opal for optical sensing
To illustrate utilization of the spectral modifications described above for optical sensing, it is helpful to compare reflectivities of PhC structures infiltrated by materials with very close values of refractive index. As an example, in Fig. 7 we compare the pairs of spectra from Fig. 4 for inverse and direct opal PhCs infiltrated by solutions of FC72 (n = 1.24) and FC77 (n = 1.26). Despite the small index difference of |Δn| = 0.02, the inverse structure [ Fig. 7 (a)] generates strong differential reflectivity with symmetrical negative and positive wings having amplitudes of about 0.15. Notice, that these amplitudes constitute about 20-30% of the samples' reflectivity (≈ 0.5, see insets to Fig. 7) and hence can be easily detected in practice. One can expect that inverse opal will enable detection of index changes as small as 10 -3 . In comparison, the direct opal structure [ Fig. 7 (b)] has about twice lower sensitivity: its differential reflectivity has asymmetric shape with amplitude of the dominant negative wing of about 0.08. The lower overall sensitivity of direct opal can be expected, having in mind the dependencies shown in Fig. 4. It is helpful to notice that this kind of optical sensing can be realized by simple reflection (or transmission) measurements that register only the power flows of reflected and transmitted radiation. Transmission signal can be easily measured even in the near field, for example by sandwiching an inverse opal PhC film between a planar broadband source and a planar detector (with optional narrowband spectral filter). Hence, smaller sensors requiring simpler optical detection scheme would be achieved in comparison to sensors exploiting superprism effect [8].
Conclusions
Opal PhC structures from polystyrene microspheres of various sizes were prepared, and subsequently inverted using low-temperature silica sol-gel process. High quality of silica glass obtained using this process was confirmed. Comparison of optical properties of direct and inverse opal structures was conducted with aim to reveal potential benefits of inverse silica opal PhC structures for sensor applications. It was found that photonic stop gaps in inverse opals have about twice-higher sensitivity to the variations in the refractive index of environment, compared to direct opals. The inverse opal structure enables easy detection of index variations by Δn = 0.02 and has a tentative sensitivity limit of Δn ~ 10 -3 , or of the same order of magnitude as that of optical sensors based on 2D photonic crystal microcavities [7] and photonic crystal fibers [9]. Although this result lags behind the ~ 10 -5 sensitivity inferred for opal sensors exploiting superprism effect [8] and colloidal tunable photonic crystals [22], these approaches require somewhat more complicated optical measurement schemes or sample preparation procedures. Silica inverse opal PhCs described in the present study does not require complicated fabrication procedures or optical measurement schemes, and yet can provide a substantial sensitivity. The simplicity of their preparation and application may be helpful for the integration of direct and inverse opal sensor elements into photonic or microfluidic chips [21]. Besides the improved sensitivity, the use of silica glass material allows application of PhC sensors in thermally and chemically harsh environments. These findings may help improve characteristics and widen the range of possible applications of optical sensors based on opal PhC structures. | 5,147.8 | 2007-10-01T00:00:00.000 | [
"Materials Science",
"Physics"
] |
Engineering Hydroponic Systems for Sustainable Wastewater Treatment and Plant Growth
: This study aimed to optimize hydroponic systems for simultaneous wastewater treat-ment/nutrient recovery and plant growth. Various hydroponic systems (geyser pump, full flow, ebb and flow, nutrient film techniques, aeroponics, misting) were constructed using 160 mm PVC waste pipes supported on a 200 L reservoir. Secondary wastewater was used to cultivate rice ( Oryza sativa ), ivy ( Hedera helix ), tomatoes ( Solanum lycopersicum ), and wheatgrass ( Triticum aestivum ). Parameters such as plant height, biomass, retention time, temperature, conductivity, pH, dissolved oxygen, ammonia, nitrite, nitrate, total phosphorus, COD, BOD, TDS, TSS, and TS were monitored. Results indicated minor variations in pH, EC, and TDS over time in systems with and without plants, with no significant differences. Turbidity decreased significantly ( p ≤ 0.001) in all systems, while TOC levels reduced significantly ( p ≤ 0.05) only in the presence of plants. BOD and COD levels exhibited similar reductions with and without plants. Ammonium levels decreased in plant systems, while nitrite levels remained unchanged. Nitrate levels increased significantly in plant systems, and phosphate levels showed no significant difference. Additionally, significant ( p ≤ 0.001) plant length (12.84–46.75%) and biomass (31.90–57.86%) increases were observed in all hydroponic systems, accompanied by higher levels of dissolved oxygen (36.26–53.65%), compared to the control (4.59%). The hydroponic system that created a moist atmosphere, either through misting or aeroponics, thus allowing maximum access to oxygen, showed the greatest growth. This study confirmed the importance of oxygen availability to the rhizosphere for plant growth and wastewater treatment. It also identified limitations and investigated the impact of dissolved oxygen concentration on plant–microorganism interactions. Optimal oxygen availability was achieved when plant roots were exposed to a moist atmosphere created by the hydroponic system through aeroponics or misting. The findings have practical implications for hydroponic system design in urban vertical farms, benefiting wastewater treatment, mitigating eutrophication, and reducing food miles.
Introduction
The urbanization trend has caused a spatial shift in human habitation, impacting the capacity of cities to match the historical ecological support once provided by rural areas through ecological services (ESs) [1] (which are products of nature, directly enjoyed, consumed, or used to yield human well-being [2].Urban areas face ongoing challenges such as water scarcity, nutrient-depleted soils, food miles, and pollution, which are expected to intensify with population growth and climate change [3].
To address these challenges and meet the growing needs of urban populations, innovative approaches are essential, as cities heavily depend on ESs for vital benefits such as water and air purification [4].Sewage mining offers an ecologically and cost-effective decentralized approach for effluent treatment, mitigation of eutrophication, and local cultivation of valuable crops.Wetlands, known as "nature's kidneys," serve as ESs for solar-driven, decentralized phytoremediation and nutrient extraction from waste streams [5].However, constructed wetlands face limitations in urban settings, such as the requirement for large horizontal space, mainly relying on anaerobic processes [6], and being limited in plant choices to those with roots adapted to low-oxygen environments [7].
To overcome these limitations, a vertical wetland engineered on the facades of buildings, which increases oxygen to the root zone, is proposed.To do this, we need to ecologically engineer the wetland ES in a systematic approach.Hydroponics, a soilless method of agriculture, have traditionally been used to maximize crop yields by optimizing plant root access to nutrients and oxygen [8].As a significant part of wastewater treatment in constructed wetlands occurs in the root zone [5], a hydroponic system offers a potential answer to wastewater that can be effectively treated to meet legislative standards using different hydroponic systems (Figure S1 in Supplementary Materials) [9][10][11].Additionally, this approach enables the cultivation of food crops and oxygen-demanding plants, establishing an urban vertical farm.It promotes at-source treatment, reduces food transportation distances, facilitates nutrient recycling, and fosters circularity.
Gebeyehu [12] reported a 48% nitrogen removal efficiency with a gravel and ebb-flow system from brewery wastewater.Rababah et al. [13] demonstrated the effectiveness of the nutrient film technique (NFT) system for domestic sewage treatment through biofilm establishment [14].However, Vaillant et al. [15] found that the hydroponic system failed to meet the required phosphorus (P) and nitrogen (N) removal levels due to insufficient dissolved oxygen (DO).High biochemical oxygen demand (BOD) hindered the nitrification process by creating oxygen competition between nitrifying bacteria and other microorganisms.Significant nitrification occurred only when BOD was below 45 mg L −1 [15].Limited research exists on the application of aeroponics for wastewater treatment and the effects of different hydroponic methods on DO levels, plant growth, and microorganisms [9,10,15].Oxygen and nutrient access in the rhizosphere are the key property for maximizing plant growth and wastewater treatment efficiency in these systems [16][17][18][19].Therefore, this research aimed to investigate the limiting criteria for such a hydroponic system, specifically access to oxygen.The experiment discussed in this paper aims to investigate how different methods of hydroponics influenced the accessibility of oxygen to the rhizosphere.To achieve this, the specific objectives of the experiment were to (1) confirm that oxygen is the key property for the growth of the plants and treatment of secondary wastewater, and (2) analyze how different hydroponic systems treat and affect growth in plants.
Preparation of the Plants for All Experiments
Rice (Oryza sativa, macrophyte and one of the most grown food crops), wheatgrass (Triticum aestivum, also a popular food crop) ivy (Hedera helix, a known phytoremediator), and tomatoes (Solanum lycopersicum, nitrophilic and commonly grown food crop) were propagated (rice and wheatgrass) or cloned (tomatoes and wheatgrass) from an original mother plant according to the method outlined by Alexander [20].This was started 4-6 weeks prior to each experiment, and a specific hybrid line was exclusively employed to maintain genetic uniformity and control diversity.After substantial growth, 60 plants of similar root length within the range of ±2 cm were selected from the propagators.The mass and length of the individual plants were measured and recorded before and at the end of each experiment.
Hydroponic Systems
Six hybrids were used in the following systems: geyser pump (GP), full flow (FF), ebb and flow (EF), NFT, aeroponics (AP), and misting (MT) (the fogponic unit), along with a control (C) that was just a reservoir (Figure S2).Each system was constructed using PVC waste pipes with a diameter of 0.16 m and a length of 1 m, capable of accommodating 10 plants per pipe, and a reservoir tank with a volume of 200 L (Figure S3).The secondary wastewater used in the experiment was obtained from Bellozanne Wastewater Treatment Works in Jersey, and the reservoirs were directly filled from nearby secondary clarifiers.The wastewater was recirculated for a week, with flow rates varying based on the hydroponic method.The systems were arranged in pairs, utilizing the reservoir as support.A pump at the bottom of the reservoir circulated the wastewater through the system and back into the reservoir.Figure 1 illustrates the different system arrangements.
Works in Jersey, and the reservoirs were directly filled from nearby secondary clarifier The wastewater was recirculated for a week, with flow rates varying based on the hydr ponic method.The systems were arranged in pairs, utilizing the reservoir as support.pump at the bottom of the reservoir circulated the wastewater through the system an back into the reservoir.Figure 1 illustrates the different system arrangements.
All drawings were made using the software Sketchup (version 2021) and Microso Office 365, all being licensed under University College London.
Parameters for the Experiments
Table 1 outlines the variables that were analyzed and/or controlled in this study.All drawings were made using the software Sketchup (version 2021) and Microsoft Office 365, all being licensed under University College London.
Parameters for the Experiments
Table 1 outlines the variables that were analyzed and/or controlled in this study.The experiment consisted of 5 runs with tomatoes, rice, ivy, wheat, and no plants.Each run was conducted twice over 4 weeks.Analysis was performed at intervals of 0 h, 24 h, 48 h, 72 h, 120 h, and 168 h.Samples of 1 L were taken at 0 h and 168 h for COD and BOD analyses in the first two experiments.Sewage was replaced at the end of 168 h and again at the start of the second run.After filtration through a 45 µm membrane filter, samples were stored in 50 mL sterile bottles and refrigerated until analysis.On-site analysis included pH (Hanna HI 9813-5, Smithfield, RI, USA), TDS, TSS, TS, and DO (Extech DO 2 Meter 407510, Nashua, NH, USA).COD (HACH DR2000 Photometer, Loveland, CO, USA) and BOD analyses were performed at the States Jersey Laboratory, while Ion and IC (Dionex ICS 1100, Sunnyvale, CA, USA) and TOC (Shimadzu TOC-L, Kyoto, Japan) samples were transported to the UK for analysis at UCL within 4.5 h.Note: chemical oxygen demand (COD); total organic carbon (TOC); total dissolved solids (TDS); total suspended solids (TSS); total solids (TS).
Statistical Analysis
All data were first evaluated for homogeneity of variance and normality using Bartlett's and Levene's tests.Statistical significance between parameters and plants in each experiment was examined using the paired t-test and Spearman regression in SPSS 22. Significance was determined at a p-value of ≤0.05.[24].
Results
Experiments on rice, ivy, tomatoes, and wheat lasted 7 days each run, and a total of 2 runs were undertaken.However, the second run with tomatoes was excluded due to their unexpected death within 24 h, and as a result, only the data from the first run were utilized for the tomato runs.This was possibly caused by high sodium chloride levels or nearby vegetation spraying (Figures S4 and S5).The cause remains unclear, despite no significant recorded environmental conditions.
The Plants
All plants created dense roots, particular the AP and GP systems, with GP roots vacating the effluent pipe (Figure S6).The dense mat of roots serves as an anchor for plants and facilitates biofilm growth.However, it posed challenges for accurate plant measurements when removing them due to tangled roots, requiring cutting for removal (Figure S7).The difference between the mass plants before and at the end of each experiment is shown in Figure 2. Overall, ivy showed the highest plant mass, ranging from 34.60 to 86.20 g, followed by wheat (3.20-43.00g) and rice (6.60-23.90g).It can be seen from Figure 2 that there was a significant mass increase for all plants (p ≤ 0.001).
The length of the plants at the start and end of the experiment is shown in Figure 3. EF (10-25 cm) and FF (10-5 cm) systems showed the lowest growth, while MT (28-60 cm) and AP (25-50 cm) presented the highest growth.
In relation to nutrient analysis, ammonium (Table S8) levels decreased (GP: 96.16%, NFT: 96.16%, MT: 100%, EF: 100%, FF: 100%, and AP: 100%) significantly (p ≤ 0.05 over time in all systems with plants, except for the control system (C), which remained relatively stable.However, removing plants from the systems did not result in a significant change in ammonium levels.Nitrite levels showed no significant changes (Table S9), and nitrate levels increased significantly (p ≤ 0.05; Table S10) over time in all systems with plants compared to the control.Phosphate levels showed no significant (Table S11) difference.
Dissolved Oxygen
Initially, all hydroponic systems showed low DO levels (GP: 1.28 mg L −1 , NFT: 1.87 mg L −1 , MT: 2.01 mg L −1 , EF: 1.5 mg L −1 , FF: 1.80 mg L −1 , and AP: 2.27 mg L −1 ), some below the recommended minimum (DO = 5 mg L −1 ) [25].To account for temperature variations, the theoretical saturation point corresponding to the recorded temperature was utilized due to the temperature-dependent nature of DO concentration.Figure 5a shows the percentage of the DO compared to the theoretical saturation point for all experiments, and Figure 5b shows the average DO for each hydroponic.In the beginning of the experiment, the theoretical saturation averaged between 10% and 20%.After the initial 24 h, the saturation levels increased in all systems as follows: GP (61.39%),NFT (59.44%),MT (60.43%),EF (40.37%),FF (48.70%), and AP (96.1%,), except for the control group (C) which remained at 11.88%, 1.61 mg L −1 .It was found that the rice runs showed noticeably lower average saturated oxygen.All the hydroponic systems started at a low level, some below the recommended minimum level of the DO, but quickly increased.In contrast, the C tended to have a level below the recommended level and at some points dropped close to the level of inhibition.The relationship between DO levels at the start, after 1 day, and at the end of the experiment was examined using a t-test, with significance set at p ≤ 0.05 (Table 2).All the systems started at a low level, some below the recommended minimum level of the DO, but quickly increased.In contrast, the C tended to have a level below the recommended level and at some points dropped close to the level of inhibition.The relationship between DO levels at the start, after 1 day, and at the end of the experiment was examined using a t-test, with significance set at p ≤ 0.05 (Table 2).Table 2 highlights that all hydroponic systems experienced a significant (p ≤ 0.05) increase in DO, with the GP system exhibiting the highest increase, followed by AP.FF and EF, characterized by their slow flow, showed a comparatively lower DO level and displayed the lowest growth.Surprisingly, the MT system demonstrated the greatest growth, despite not having the highest DO level, while the AP system, with the second-highest DO level, showed the next highest growth.
Discussion
After the first week of the tomato and at the end of the ivy experiments, a dense mat of roots created a strong anchor and a large surface area for biofilm growth [26].
The complex root system posed challenges during plant removal, often necessitating cutting, while measured data offer a comprehensive representation of substantial systemwide growth rather than specific plant growth.
Initial sewage samples exhibited high TSS and turbidity, but both decreased significantly within 48 h.After 72 h, levels were negligible.ANOVA confirmed a significant reduction (p ≤ 0.001) in TSS and turbidity across all systems, including the control, which agrees with the literature [11,13,15].Systems with plants showed significant (p ≤ 0.05) reductions in TOC, as well as a decrease in ammonia levels (Tables S7 and S8).Ammonia oxidation to nitrite was faster in systems with plants.Adequate DO levels supported nitrification, resulting in decreased ammonium and increased nitrite and nitrate concentrations.It is expected that biofilms on roots aided in the nitrification process.
Water plays a critical role in plant survival, but it can hinder gas exchange and result in oxygen deficiency in the rhizosphere [27].Optimal water and oxygen access is vital for plant growth, giving an advantage to MT and AP systems that allow access to gaseous oxygen form, which is preferred, as saturated soil has limited access [28].Despite an overall increase in theoretical saturated oxygen, the rice experiment showed lower levels, suggesting potential influences from wastewater flow, root structure, biofilm, and temperature [29].No significant correlation was found between plant species and DO.Further research is necessary to elucidate these factors and address the existing knowledge gap.
All systems started with a low DO; however, this increased rapidly over time, confirming the results for NFT found by Rababah [12].Conversely, the C system consistently maintained lower DO levels, occasionally approaching inhibitory levels.Average DO across all systems was highest in the "no plants" experiment.No strong correlation was found between plant mass and DO levels.(Spearman correlation coefficient of 0.52, p ≤ 0.05).Although the NFT system was anticipated to have the lowest DO levels, results indicated near saturation in the absence of plants.Contrary to expectations, the NFT system exhibited higher-than-anticipated DO levels, possibly due to turbulent water flow during return to the reservoir.the rice experiment displayed distinct patterns, with an initial decline in DO levels followed by a gradual increase throughout the entire duration compared to other runs.
Roots require oxygen for respiration unless they are flood-tolerant [30].Initially, wastewater with low DO levels enters due to high BOD.However, hydroponic systems create turbulence and agitation, increasing the water's surface area exposed to air and facilitating oxygenation, leading to a significant increase in DO levels.The growth of oxidizing bacteria and decreased ammonium oxidation rates contribute to higher DO levels, observed in most systems except for the rice experiment, where continuous DO increase is attributed to oxygen transfer from aerial parts to roots, causing a leaching effect in the rhizosphere [31][32][33].
The DO concentration increases through surface interaction with the atmosphere [34].In hydroponic systems, the water movement increases DO compared to stagnant water, such as in wetlands [35].This increased availability of DO improved efficiency of biofilm oxidation of organic matter and increased survival chances for non-flood-tolerant plants.Successful plant growth of all systems affirms the stated advantages, while the reduced frequency of sewage-air interaction in the EF system led to lower DO.
The AP and MT systems, utilizing mist generation, exhibited enhanced root development.The observed rapid growth in the MT and AP systems resulted in the blockage and malfunction of the mister (Figure S12) and spray nozzle, respectively.In the MT system, root growth obstructed the outlet (Figure S6), leading to flow impeding and initial tomato run flooding.Extensive root growth in these systems was facilitated by the mist zone and the presence of air, especially oxygen (Figure 6).The AP and MT systems create a mist zone that provides oxygen and nutrients to t roots and biofilm through microdroplets, ensuring proper moisture in the rhizosphere [3 However, inadequate root length in our experiment hindered sewage access [28].Co versely, the FF system showed excess moisture but limited oxygen, smothering the rhiz The AP MT systems create a mist zone that provides oxygen and nutrients to the roots and biofilm through microdroplets, ensuring proper moisture in the rhizosphere [36].However, inadequate root length in our experiment hindered sewage access [28].Conversely, the FF system showed excess moisture but limited oxygen, smothering the rhizosphere (Figure 6d).Rhizosphere narcosis (Figure S13) occurred in plant samples with minimal development, while the EF system differed from FF, allowing adjustment of oxygen-moisture ratio through drainage and flood cycles.
Ensuring adequate root volume is essential in hydroponic systems to facilitate maximize operation and promote biofilm development.Factors such as root architecture, dimension, surface area, and presence of root hairs influence biofilm formation and nutrient absorption [28,37].Oxygen availability is critical for ideal root growth, with environments with abundance of both oxygen and moisture supporting the best results.AP and MT systems demonstrated uniform root development, while NFT systems concentrated roots at flow surfaces.In water-limited environments, oxygen availability plays a vital role by providing increased access to oxygen and nutrients for root uptake.The rhizosphere of the root system facilitates the breakdown of organic matter through the interaction of water droplets, leading to the production of ammonium and nitrate.These compounds, along with water and oxygen, are assimilated by the root system.In the design of a vertical urban farm, a vertical AP system is employed, incorporating an internal waterfall and fine droplets (Figure 7) to optimize this process.
Appl.Sci.2023, 13, x FOR PEER REVIEW 10 of Figure 7. Utilizing a schematic inspired by the concept of plant growth on a waterfall, the AP h dronic system is transformed into a vertical AP system for vertical farming.
Our findings highlight the significance of oxygen availability in the degradation organics and root health, thereby influencing plant growth and symbiotic biofilm fo mation.In constructed wetland systems, oxygen is scarce, but plants survive due to ox gen transport via aerenchyma, supporting some biofilm in the rhizosphere [5].In contras hydroponic systems provide ample oxygen and DO, leading to accelerated aerobic oxid tion of organics compared to anaerobic degradation [38].
Conclusions
In pursuit of these objectives, the experiments successfully demonstrated that oxyge availability is indeed a crucial factor for both plant growth and wastewater treatment hydroponic systems.Results revealed that different hydroponic systems exhibit varyin abilities to facilitate oxygen accessibility to the rhizosphere.The experiments conducted u ing different hydroponic systems, including GP, NFT, MT, EF, FF, and AP, demonstrate significant growth in plant mass and length.Results indicated minor variations in pH, E and TDS over time, while turbidity and TOC levels decreased significantly in the presen of plants.BOD and COD levels were reduced similarly with and without plants, while am monium levels decreased, and nitrate levels increased significantly in plant systems.Mor over, notable increases in plant height and biomass were observed across all hydropon Our findings highlight the significance of oxygen availability in the degradation of organics and root health, thereby influencing plant growth and symbiotic biofilm formation.In constructed wetland systems, oxygen is scarce, but plants survive due to oxygen transport via aerenchyma, supporting some biofilm in the rhizosphere [5].In contrast, hydroponic systems provide ample oxygen and DO, leading to accelerated aerobic oxidation of organics compared to anaerobic degradation [38].
Conclusions
In pursuit of these objectives, the experiments successfully demonstrated that oxygen availability is indeed a crucial factor for both plant growth and wastewater treatment in hydroponic systems.Results revealed that different hydroponic systems exhibit varying abilities to facilitate oxygen accessibility to the rhizosphere.The experiments conducted using different hydroponic systems, including GP, NFT, MT, EF, FF, and AP, demonstrated significant growth in plant mass and length.Results indicated minor variations in pH, EC, and TDS over time, while turbidity and TOC levels decreased significantly in the presence of plants.BOD and COD levels were reduced similarly with and without plants, while ammonium levels decreased, and nitrate levels increased significantly in plant sys-tems.Moreover, notable increases plant height and biomass were observed across all hydroponic systems, accompanied by higher levels of DO compared to the control.The hydroponic systems that provided a moist atmosphere through misting or aeroponics exhibited the greatest growth, confirming the importance of oxygen availability in the rhizosphere for plant growth and wastewater treatment.The MT and AP systems showed the greatest root growth due to their saturated environments.However, all systems exhibited overall growth and demonstrated the potential for plant cultivation in hydroponics.These findings underscore the importance of considering oxygenation strategies when designing hydroponic systems for optimal plant growth and wastewater treatment.
DO levels in the wastewater were a critical parameter for the success of the hydroponic systems.While initially, all systems showed low DO levels, they quickly increased over time.The NFT system, contrary to expectations, showed higher DO levels compared to other systems during experiments without plants.The control system consistently showed lower DO levels, sometimes dropping close to the inhibitory level.The rice experiment showed different patterns with a temporary dip in DO levels before gradually increasing.
Our results contribute to the development of sustainable urban vertical farming strategies by emphasizing the integration of ecological services and hydroponic systems for efficient wastewater treatment, resource recycling, and enhanced food production.Findings highlight the importance of optimizing access to both oxygen and nutrients in the rhizosphere to achieve maximum plant growth and wastewater treatment efficiency.
Further research is crucial to comprehend the impact of hydroponic methods on DO and plant-microorganism interactions.This will enhance engineering criteria, enabling more efficient and sustainable wastewater treatment and urban farming solutions, ultimately contributing to circular cities and environmental sustainability.
Supplementary Materials:
The following supporting information can be downloaded at https: //www.mdpi.com/article/10.3390/app13148032/s1. Figure S1.Diagrams of different hydroponic systems, NFT, DFT, aeroponic and fogponics.Figure S2.An illustration of the complete setup and the location of the different systems.Figure S3.An illustration of the proposed setup.The system that is gray is removed for illustrative purposes so that the system in color can be seen.The pipe is supported by its reservoir (colored) and that of the opposite system (gray).The pump pushed the water to the far end of the pipe.It flowed through the system via its specific method (illustrated is the MT system) and returned the other end to the reservoir by the return pipe.Figure S4.The six systems with tomato plants.Clearly, the fogponic system (MT) and the aeroponic system (AP) show the greatest growth.Figure S5.A photo of the experiment at the point when all tomato plants died.It was suspected that this could have been caused by high amounts of salts in the sewage seeping in from the sea. Figure S6.Photo of the outlet of the fogponic system, showing the extent of root growth, restricting the exit flow from the system.Figure S7.A photograph of the roots of an ivy plant.It can be seen that the roots have been cut, creating a log of roots.Table S1.Average lengths and mass over all the experiments, at the start and end, the percentage change in lengths and the p-value of the t-test where p < 0.001 were considered extremely significant.Table S2.Descriptive results of the pH for all the experiments and all the systems with plants and no plants.Table S3.Descriptive results of the conductivity for all the experiments and all the systems with plants and no plants.Table S4.Descriptive statistics and t-tests (p ≤ 0.05 significant, p ≤ 0.01 very significant, p ≤ 0.001 extremely significant) of the TDS for all the experiments and all the systems with plants and without plants.Table S5.Descriptive statistics and t-tests (p ≤ 0.05 significant, p ≤ 0.01 very significant, p ≤ 0.001 extremely significant) of the turbidity for all the experiments and all the systems with plants.Table S6.Descriptive statistics and t-tests (p ≤ 0.05 significant, p ≤ 0.01 very significant, p ≤ 0.001 extremely significant) of the TSS for all the experiments and all the systems with plants and without plants.Table S7.Descriptive statistics and t-tests (p ≤ 0.05 significant, p ≤ 0.01 very significant, p ≤ 0.001 extremely significant) of the TOC for all the experiments and all the systems with plants.Figure S8.Average reduction in TOC as a percentage change from start to finish in overall runs.Figure S9.Average reduction in TOC as a percentage change from start to finish of all systems per run.Figure S10.BOD from the beginning of each experiment in red and at the end of the experiment in blue.The percentage change is shown by the gray line.Figure S11.COD from the beginning of each experiment in
Figure 1 .
Figure 1.Cross-section drawing of the different hydroponic setups and flow direction of sewage.
Figure 1 .
Figure 1.Cross-section drawing of the different hydroponic setups and flow direction of sewage.
Figure 2 .
Figure 2. Change in mass (g) for all experiments for the rice, ivy and wheat grass.The tomato experiment is not shown as the plant growth failed.Geyser pump (GP), nutrient film technique (NFT), misting (MT), ebb and flow (EF), full flow (FF), aeroponics (AP).
Figure 2 .
Figure 2. Change in mass (g) for all experiments for the rice, ivy and wheat grass.The tomato experiment is not shown as the plant growth failed.Geyser pump (GP), nutrient film technique (NFT), misting (MT), ebb and flow (EF), full flow (FF), aeroponics (AP).
Figure 2 .
Figure 2. Change in mass (g) for all experiments for the rice, ivy and wheat grass.The tomato experiment is not shown as the plant growth failed.Geyser pump (GP), nutrient film technique (NFT), misting (MT), ebb and flow (EF), full flow (FF), aeroponics (AP).
Figure 2 .
Figure 2. Change in mass (g) for all experiments for the rice, ivy and wheat grass.The tomato experiment is not shown as the plant growth failed.Geyser pump (GP), nutrient film technique (NFT), misting (MT), ebb and flow (EF), full flow (FF), aeroponics (AP).
Figure 4 .
Figure 4. Average growth as a measurement of length (a) and mass (b) as a percentage for each system across all experiments.Geyser pump (GP), nutrient film technique (NFT), misting (MT), ebb and flow (EF), full flow (FF), aeroponics (AP).
Figure 5 .
Figure 5. (a) Average DO throughout the experiment for all the systems averaged to the experiment run.(b) Average DO throughout the experiment for all the systems.Geyser pump (GP), nutrient film technique (NFT), misting (MT), ebb and flow (EF), full flow (FF), aeroponics (AP), control (C).
Figure 5 .
Figure 5. (a) Average DO throughout the experiment for all the systems averaged to the experiment run.(b) Average DO throughout the experiment for all the systems.Geyser pump (GP), nutrient film technique (NFT), misting (MT), ebb and flow (EF), full flow (FF), aeroponics (AP), control (C).
Figure 6 .
Figure 6.Schematics of the AP (a) and MT (b) systems showing the zone of moisture that encoura the growth of the rhizosphere.NFT (c) system showing the stream of sewage above the rhizosph zone.A schematic of an FF (d) system showing that the rhizosphere was smothered in sewage stricting its access to oxygen.
Figure 6 .
Figure 6.Schematics of the AP (a) and MT (b) systems showing the zone of moisture that encourage the growth of the rhizosphere.NFT (c) system showing the stream of sewage above the rhizosphere zone.A schematic of an FF (d) system showing that the rhizosphere was smothered in sewage restricting its access to oxygen.
Figure 7 .
Figure 7. Utilizing a schematic inspired by the concept of plant growth on a waterfall, the AP hydronic system is transformed into a vertical AP system for vertical farming.
Table 1 .
The variables within the study.
Table 1 .
The variables within the study.
Table 2 .
Average change of the DO for the first 24 h and the last 168 h of operation, and the p-values
Table 2 .
Average change of the DO for the first 24 h and the last 168 h of operation, and the p-values of the t-tests where p ≤ 0.05 was considered significant. | 6,771.8 | 2023-07-10T00:00:00.000 | [
"Environmental Science",
"Engineering"
] |
Intersection of Epigenetic and Immune Alterations: Implications for Fetal Alcohol Spectrum Disorder and Mental Health
Prenatal alcohol exposure can impact virtually all body systems, resulting in a host of structural, neurocognitive, and behavioral abnormalities. Among the adverse impacts associated with prenatal alcohol exposure are alterations in immune function, including an increased incidence of infections and alterations in immune/neuroimmune parameters that last throughout the life-course. Epigenetic patterns are also highly sensitive to prenatal alcohol exposure, with widespread alcohol-related alterations to epigenetic profiles, including changes in DNA methylation, histone modifications, and miRNA expression. Importantly, epigenetic programs are crucial for immune system development, impacting key processes such as immune cell fate, differentiation, and activation. In addition to their role in development, epigenetic mechanisms are emerging as attractive candidates for the biological embedding of environmental factors on immune function and as mediators between early-life exposures and long-term health. Here, following an overview of the impact of prenatal alcohol exposure on immune function and epigenetic patterns, we discuss the potential role for epigenetic mechanisms in reprogramming of immune function and the consequences for health and development. We highlight a range of both clinical and animal studies to provide insights into the array of immune genes impacted by alcohol-related epigenetic reprogramming. Finally, we discuss potential consequences of alcohol-related reprogramming of immune/neuroimmune functions and their effects on the increased susceptibility to mental health disorders. Overall, the collective findings from animal models and clinical studies highlight a compelling relationship between the immune system and epigenetic pathways. These findings have important implications for our understanding of the biological mechanisms underlying the long-term and multisystem effects of prenatal alcohol exposure, laying the groundwork for possible novel interventions and therapeutic strategies to treat individuals prenatally exposed to alcohol.
REPROGRAMMING OF PHYSIOLOGICAL SYSTEMS BY PRENATAL ALCOHOL EXPOSURE
Alcohol (ethanol) exposure in utero can have numerous adverse effects on a developing fetus. In humans, prenatal alcohol exposure (PAE) can result in Fetal Alcohol Spectrum Disorder (FASD), which refers to the broad spectrum of structural, neurocognitive, and behavioral abnormalities or deficits that can occur following PAE (Astley and Clarren, 2000). The magnitude of these effects is variable and depends on factors such as timing and level of maternal alcohol use, physiological and genetic background, and a host of environmental factors including overall maternal health and nutrition (Pollard, 2007). Despite the innate variability of these moderating factors, children across the entire spectrum of FASD display long-term cognitive and neurobehavioral alterations, including neurocognitive impairment (i.e., cognition, learning, memory, and executive function), impaired selfregulation (i.e., attention, impulsivity, behavioral regulation, stress responsiveness, mood/affect, and sleep), and deficits in adaptive functioning (i.e., communication, social behavior, and activities of daily living) (Streissguth and O'Malley, 2000;Astley et al., 2009;Doyle and Mattson, 2015;Lynch et al., 2015;Carter et al., 2016;Panczakiewicz et al., 2016). Taken together, these findings highlight the complex reprogramming effects of PAE on neurobehavioral, neurobiological, and physiological systems. However, the mechanisms underlying the pervasive and multisystem impacts of alcohol are not yet fully understood.
The Developmental Origins of Health and Disease (DOHaD) hypothesis provides an important framework to interpret both the transient and long-term effects of PAE (Hellemans et al., 2010). This hypothesis developed through mounting evidence indicating that early-life exposures or events can have a longlasting impact on adult health outcomes (Barker and Osmond, 1986;Barker et al., 1989Barker et al., , 1993Wadhwa et al., 2009). Importantly, the DOHaD hypothesis suggests that early environments can exert their influence on long-term health through the mechanism of fetal programming, by which early life experiences shape development of neurobiological and physiological systems, altering their function over the lifespan. However, additional research is needed to fully uncover the mechanisms that drive the reprogramming of physiological systems.
Disruption of immune development has been recognized as a "pathway to pathology", impacting risk for both childhood and adult diseases (Dietert, 2014). Specifically, the field of developmental immunotoxicity has identified a wide range of agents capable of immune disruption or programming of the immune system with long-term health consequences, which include diet, environmental, chemical, and physical factors such as UV radiation, and notably, alcohol/drug exposure, as well as psychological factors (Dietert, 2014). While the molecular mechanisms underlying these effects have yet to be firmly established, epigenetic mechanisms are emerging as attractive candidates for the biological embedding of environmental factors on immune function, as they may link external stimuli and physiological systems to influence health and behavior into later life (Kobor and Weinberg, 2011;Yuen et al., 2011;Shulha et al., 2013;Fujii et al., 2021). In the present review, we address the impact of PAE on immune function and epigenetic patterns, highlighting a potential role for epigenetic mechanisms in the reprogramming of immune function and risk for mental health disorders. These links are conceptually summarized in Figure 1.
IMPACT OF PRENATAL ALCOHOL EXPOSURE ON IMMUNE AND NEUROIMMUNE FUNCTION
Clinical data examining alcohol-induced alterations in immune competence in children and adults with FASD remain somewhat limited [reviewed in Bodnar and Weinberg (2013), Reid et al. (2019)]. However, one of the earliest and most consistently clinically demonstrated effects of PAE is increased infection rates. Specifically, early investigations identified that children with Fetal Alcohol Syndrome (FAS), at the more severe end of the FASD spectrum, have a higher incidence of major and minor infections, including recurrent otitis media, upper respiratory tract infections, urinary tract infections, sepsis, pneumonia, and acute gastroenteritis (Johnson et al., 1981;Ammann et al., 1982;Church and Gerkin, 1988). In addition, decreased eosinophil and neutrophil cell counts in alcohol-exposed compared to unexposed children, and decreased leukocyte response to mitogens [(Johnson et al., 1981); reviewed in Gottesfeld and Abel (1991)] were observed. Maternal alcohol consumption has also been shown to result in a 5-7-fold increase in the rates of chorioamnionitis (inflammation of the chorion, amnion, and placenta) (Rickert et al., 1998;Aly et al., 2008), which is a common cause of preterm birth (Galinsky et al., 2013), and associated with higher rates of poor neurological outcomes due to inflammation and placental perfusion defects (Pappas et al., 2014). Recently, Gauthier et al. (2004) also reported that very low birth weight newborns exposed to alcohol in utero have a 15-fold higher incidence of early-onset sepsis compared to matched controls. Furthermore, high levels of maternal drinking (binge drinking), specifically during the second trimester, has also been shown to increase the risk of infection by approximately 4fold, compared to that of unexposed newborns (Gauthier et al., 2005). Similarly, Libster et al. (2015) have reported increased rates of adverse infection outcomes in young children (<2 years of age) with PAE who were hospitalized for acute respiratory infections.
Several studies have also explored the link between PAE and atopic disorders (mainly dermatitis, eczema and asthma). Whereas PAE is associated with an increased risk of dermatitis (Linneberg et al., 2004;Carson et al., 2012), the risk of eczema is less clear, as there are conflicting reports of PAE increasing risk of eczema (Wada et al., 2016) and having no associations with eczema (Apfelbacher et al., 2011;Shaheen et al., 2014). By contrast, there are consistent reports of a lack of association between PAE and asthma, with studies including a range of developmental time points and alcohol exposure levels (Oleson et al., 1998;Yuan et al., 2004;Magnus et al., 2014;Shaheen et al., 2014;Wada et al., 2016). (1) Prenatal alcohol exposure (PAE) can result in (2) maternal immune system activation, altering the fine cytokine balance during pregnancy, which in turn impacts the developing immune system of the fetus. In turn, both direct effects of PAE and alcohol-induced maternal immune activation may result changes in (3) epigenetic mechanisms, which include alterations to DNA methylation levels, histone modification patterns, and miRNAs expression levels. These epigenetic changes are likely important mechanistic drivers of (4) life-long impairments in offspring immune function and (5) neuroimmune system alterations, including microglial activation and central cytokine changes. Together, offspring central and peripheral immune system activation, by way of epigenetic changes, are hypothesized as driving, at least in part, the increased risk of mental health conditions, such as depression and anxiety, in alcohol-exposed offspring. Created with BioRender.com.
Although the mechanisms underlying the immuneteratogenic effects of PAE remain unclear, alcohol consumption in adulthood has been shown to increase circulating cytokine levels (Crews et al., 2006;He and Crews, 2008), with chronic alcohol consumption during pregnancy increasing levels of key cytokines in both the fetus and mother (Ahluwalia et al., 2000). Importantly, as maternal cytokine induction can have a considerable impact on fetal development (Deverman and Patterson, 2009), alcohol-induced maternal immune system activation may drive some of the adverse developmental outcomes that occur following PAE. Indeed, our work has shown that alcohol consumption during pregnancy alters the maternal cytokine milieu through activation and/or inhibition of key cytokine networks (Bodnar et al., 2018). Importantly, these distinct maternal immune profiles predict neurodevelopmental status, distinguishing children with high risk or resilience to alcohol-induced neurodevelopmental delay 1 from typically developing children (Bodnar et al., 2018). We have also shown that child cytokine networks are themselves disrupted following 1 Neurodevelopment was assessed using the Bayley Scales of Infant Development, Second Edition (BSID-II) administered at 6 and 12 months. Children were considered to have neurodevelopmental delay if they received a score of <85 on either the Mental Development Index (MDI) or the Psychomotor Development Index (PDI) at 6 and/or 12 months. PAE and that network activity patterns again differ based on neurodevelopmental status of the child (i.e., typical development vs. neurodevelopmental delay) (Bodnar et al., 2020), further linking cytokine disruptions to altered developmental outcomes. Finally, while studies investigating immune outcomes of PAE in older individuals is limited, there is evidence for increased rates of atopic conditions and elevated lymphocyte counts in adolescents with PAE (Oleson et al., 1998). However, despite clear evidence of immune alterations and associated developmental alterations following PAE, further studies are needed to elucidate the mechanism(s) underlying the impact of alcohol exposure on immune function. Importantly, in utero alcohol exposure appears to induce long-lasting changes in immune function; however, most immune cells themselves are not long-lived, and as such, mechanistic investigations will be required to fill this gap.
Animal model experiments have allowed for more in-depth explorations of the immune disturbances associated with PAE, and in particular, have allowed for investigations into the impact of PAE on the neuroimmune system. Work from a range of animal models has shown that alcohol exposure generally increases cytokine production within the brain, a marker of neuroimmune activation (reviewed in Table 1). In third trimester equivalent exposure models, alcohol increased cytokine levels in the cerebellum, cortex, and hippocampus (Drew et al., 2015; Topper et al., 2015). Our laboratory has identified alterations in cytokine levels in the brain following alcohol exposure throughout gestation (i.e., first and second trimester equivalent), with increased cytokine levels observed in the hippocampus, and prefrontal cortex but decreased cytokine levels observed in the hypothalamus (Bodnar et al., 2016). Despite inherent differences among the various models, such as method and timing of alcohol administration, species, and cytokine detection method, the overall concordance of these findings highlights that neuroinflammation may be a cross-cutting feature in both FASD and animal models of PAE. In addition to alterations in cytokine levels, alcohol exposure can alter microglial levels and/or activational status (Fernandez-Lizarbe et al., 2009;Kane et al., 2011;Drew et al., 2015;Topper et al., 2015;Boschen et al., 2016;Ruggiero et al., 2018;Chastain et al., 2019;Gursky et al., 2020) [reviewed in Wilhelm and Guizzetti (2015), Mahnke et al. (2019), Kane and Drew (2021)]. During early development, microglia, resident macrophages of the CNS, exist in an activated state. In this state, microglia produce cytokines (Fujita et al., 1981;Ling and Wong, 1993) and contribute to brain development through their important roles in neurobiological processes, including phagocytosis of newborn neurons (Marin-Teva et al., 2004), synaptic pruning and maturation (Paolicelli et al., 2011), remodeling of synaptic circuits (Tremblay et al., 2011), and synaptic plasticity (Xavier et al., 2014). As a result, alterations in microglial populations and activational status may be an important mechanism through which alcohol exposure impacts early brain development. By weaning, microglia transition to a quiescent state and remain relatively inactive throughout adulthood, unless activated by injury or immune challenge (Ling and Wong, 1993). However, alcohol exposure may impair/delay the transition of microglia to a quiescent state (Drew et al., 2015) and as such, may result in heightened responses to challenges such as infection, with potential consequences for behavior and cognition [reviewed in Bilbo and Schwarz (2012)]. Thus, microglia are uniquely poised to retain an immunological memory of early-life insults, such as exposure to alcohol, due the long-lived nature of these cells amid a more ephemeral immune cell background. Nevertheless, the mechanisms underlying the impact of alcohol exposure on cytokine levels and microglial activation are not fully known. Based on evidence of fetal programming by PAE and from other models where epigenetic alterations of microglia are associated with neuroinflammation (Jovicic et al., 2013;Chauhan et al., 2015), we propose that epigenetic influences may be a critical link, tying together alcohol exposure, long-term impacts on immune function and subsequent health outcomes.
EPIGENETIC MECHANISMS BRIDGE EARLY-LIFE ENVIRONMENTS AND LONG-TERM HEALTH
Epigenetics refers to modifications of DNA and/or its regulatory factors that mediate the accessibility of DNA, which can, in turn, modulate gene expression and cellular functions without changes to underlying genomic sequences (Bird, 2007). These regulatory factors include histone modifications, noncoding RNA (ncRNA), and direct DNA modifications, such as methylation and hydroxymethylation. In general, epigenetic patterns are closely associated with cellular specification and differentiation, highlighting their role in the regulation of cellular functions (Ziller et al., 2013). As each cellular subtype is closely associated with a characteristic epigenomic landscape that provides long-term stability to its identity, cell type is the main driver of stable epigenetic patterns. However, environmental stimuli can also influence epigenetic patterns throughout the genome, albeit with subtler effects than ontogenic profiles. These mechanisms rely on an apparent paradox between the stability of cell-specific profiles and plasticity in response to external cues to modulate both short-and long-term epigenetic regulation (Boyce and Kobor, 2015;Aristizabal et al., 2020). Overall, epigenetic patterns act in concert to fine-tune the cellular response to external stimuli and regulate cellular functions [reviewed in Allis and Jenuwein (2016)]. Importantly, emerging evidence suggests that epigenetic patterns, such as DNA methylation, may mediate the relationship between environmental insults and chronic disease, highlighting a potentially crucial role in our understanding of the biological embedding of early-life exposures (Fujii et al., 2021).
PRENATAL ALCOHOL EXPOSURE ALTERS EPIGENETIC PROGRAMS
The initial evidence that epigenetic mechanisms might be involved in programming of physiological function by PAE originated from studies of gene expression. Genome-wide alterations to gene expression patterns occur in the brains of fetal, neonatal, and adult animals following PAE, highlighting the possibility that the effects of alcohol may shift early developmental trajectories and lead to persistent alterations in adulthood (Hard et al., 2005;Green et al., 2007;Zhou et al., 2011b). One recent example comes from a study identifying large-scale alterations to neuroimmune gene networks of the olfactory system (Gano et al., 2020). Our work has shown that PAE animals show changes in the brain's transcriptome both under basal conditions and in response to an immune challenge, supporting the reprogramming of neuroendocrine and neuroimmune function by alcohol (Lussier et al., 2015). Studies investigating possible epigenetic mechanisms of developmental alcohol exposure followed closely behind the initial gene expression studies, identifying widespread alterations to epigenetic profiles in both central nervous system and peripheral tissues, including DNA methylation, histone modifications, and miRNA expression [reviewed in Lussier et al. (2017)]. These findings have highlighted a potential role for epigenetic factors in the reprogramming of neurobiological functions by PAE in both animal models of PAE and clinical cohorts of individuals with FASD. Although relatively few epigenome-wide studies have been performed to date, several have identified alcohol-induced alterations to genes involved in immune function (Liu et al., 2009;Zhou et al., 2011a;Khalid et al., 2014;Laufer et al., 2015;Marjonen et al., 2015;Chater-Diehl et al., 2016;Portales-Casamar et al., 2016;Frey et al., 2018;Lussier et al., 2018a,b;Sharp et al., 2018;Cobben et al., 2019; Table 2). These findings provide further evidence that the immune deficits observed following PAE may be linked to changes in epigenetic patterns.
EPIGENETIC PROGRAMS PLAY A KEY ROLE IN IMMUNE SYSTEM DEVELOPMENT AND FUNCTION
Epigenetic programs are crucial to the broader development and function of the immune system, playing important roles in the regulation of immune cell development and identity as well as neuroinflammatory processes (Garden, 2013). Given the vital role of epigenetic mechanisms in the regulation of cell fate, it is perhaps not surprising that epigenetic mechanisms play an important part in the developmental cascades associated with immune cell differentiation (Obata et al., 2015). In particular, epigenetic processes regulate stem-cell properties of progenitor cells and become increasingly specialized as immune cells progress through lineage commitment. The vital importance of these patterns is exemplified by the deficits in immune development and function in animals lacking components of the epigenetic machinery, including DNA methyltransferase (DNMTs) and histone deacetylases (HDAC) (Lee et al., 2001;Akimova et al., 2012;Dovey et al., 2013;Obata et al., 2015). miRNAs also play a key role in immune system development, displaying unique expression signatures in different cellular subtypes, including microglia, granulocytes, and monocytes, which likely help modulate their specific functions and developmental trajectories (Taganov et al., 2006;Johnnidis et al., 2008;Hashimi et al., 2009;Schmidl et al., 2018). Moreover, the activation state of immune effectors relies on epigenetic mechanisms, particularly histone modifications, to induce the phenotypic alterations necessary for their rapid response to pathogens (Aung et al., 2006;Nicodeme et al., 2010;Satoh et al., 2010;Obata et al., 2015;Zhang and Cao, 2019). Of particular relevance to the current review, the epigenetic profiles of microglia are closely linked to neuroinflammatory processes, reflective of their role in the immune response of neural tissues (Jovicic et al., 2013;Chauhan et al., 2015;Kaminska et al., 2016). Importantly, the epigenetic responsivity of microglia to both external and internal signals may play a crucial role in *Data were obtained from immport.org. Total genes represents the total number of genes associated with alcohol exposure. Bolded genes were present across multiple studies. The cytokines and chemokines category includes chemokines, cytokines, interleukins, TGFb family members, TNF family members, and interferons (none found). The receptors category includes receptors for chemokines, cytokines, interleukins, TGFb family members, TNF family members, and interferons (none found). The B or T cell signaling category includes BCR signaling pathways and TCR signaling pathway.
Frontiers in Neuroscience | www.frontiersin.org modulating the inflammatory status of the brain, which has important ramifications for neurobiological functions (Cheray and Joseph, 2018). Overall, immune system development occurs in parallel with epigenetic changes in immune cells, which are responsive to both environmental and biological cues. Several lines of evidence also suggest that developmental exposures can influence epigenetic patterns within the developing organism to potentially alter immune function and susceptibility to neurobiological deficits later in life (Hinz et al., 2012). For instance, animal model studies suggest that increased maternal care can alter IL-10 expression and DNA methylation in microglial cells to diminish morphine-induced addictive behavior (Schwarz et al., 2011;Wang et al., 2018). Such findings highlight the role of early life experiences in shaping developmental trajectories within the immune system and suggest that epigenetic mechanisms could play an integral role in the reprogramming of immune functions by PAE.
EPIGENETIC MECHANISMS MAY INFLUENCE THE IMMUNE ALTERATIONS ASSOCIATED WITH PRENATAL ALCOHOL EXPOSURE
Studies investigating epigenetic mechanisms involved in PAE effects have also identified alterations to cytokines, chemokines, and signaling pathways involved in the cellular response to immune molecules. Table 2 outlines the findings from genomewide studies of PAE, highlighting epigenetic alterations to genes involved in immune response and regulation (immune gene annotations obtained from immport.org, May 2021) (Bhattacharya et al., 2018). Of particular note, results from work on a cohort of children with FASD reported that DNA methylation levels in buccal epithelial cells showed alterations in HLA-DPB1, a component of the major histocompatibility complex previously associated with rheumatoid arthritis (Raychaudhuri et al., 2012;Liu et al., 2013;Portales-Casamar et al., 2016;Lussier et al., 2018b). Importantly, both evidence from animal models (Zhang X. et al., 2012) and reports from a recent informal health survey in adults with FASD (Himmelreich et al., 2020) indicate that the incidence of rheumatoid arthritis is higher following PAE. While the findings of altered HLA-DPB1 were identified in a peripheral tissue not involved in immune modulation, they may provide insight into changes in global epigenetic patterns associated with altered immune profiles in children with FASD. Members of the complement system, a key immune pathway that promotes inflammatory responses to combat infection (Sarma and Ward, 2011), also appear across multiple studies, from animal models to clinical cohorts of individuals with FASD. For instance, CFP displays alterations in both mouse and human embryonic cells exposed to alcohol, while C1R and C1S show alterations in the peripheral tissue of individuals with FASD (Khalid et al., 2014;Portales-Casamar et al., 2016).
Beyond genes directly involved in immune system functioning, several immune-related transcription factors also show differential epigenetic profiles following PAE and could play a role in the altered genomic response to immune signals. Of note, PPARG (PPAR-γ), a transcription factor that promotes anti-inflammatory processes (Le Menn and Neels, 2018), shows differential DNA methylation and expression in the brain following PAE and has been previously implicated in the prevention of alcohol-induced cell death (Kane et al., 2011;Khalid et al., 2014). The polycomb group proteins are also altered by PAE and have been implicated in the deficits caused by PAE, as they play a key role in modulating the properties of neural stem cells and immune cell progenitors (Aloia et al., 2013;Veazey et al., 2013). Taken together, epigenetic alterations to immune genes provides a potential mechanistic link between PAE and long-lasting immune system dysfunction.
In addition to DNA and protein-based epigenetic alterations, several differentially expressed miRNAs known to be critical regulators of neuroimmune function have also been identified in PAE models, including miR-9, -21, -153, -155, and -335 (Sathyan et al., 2007;Wang et al., 2009;Balaraman et al., 2012;Jovicic et al., 2013;Ignacio et al., 2014;Qi et al., 2014). For example, alcohol exposure increases miR-155 expression, which typically promotes the secretion of proinflammatory cytokines by microglial cells following Toll-like receptor activation (Cardoso et al., 2012;Ignacio et al., 2014). By contrast, alcohol decreases miR-21 expression, a neuroprotectant that suppresses Fas ligand levels, which may lead to greater vulnerability to microglial-induced cell death (Sathyan et al., 2007;Balaraman et al., 2012;. These data suggest that developmental alcohol exposure may shift the balance of different neuroimmune cell types in the brain, as well as the cytokines they produce, setting the stage for more robust neuroinflammatory responses. This possibility represents an important consideration for epigenetic studies, as cell type proportions are the major drivers of epigenetic patterns and must be taken into consideration when analyzing these types of data. To this point, a recent single-cell RNAsequencing study of GD14.5 mice exposed to binge-levels of alcohol showed that PAE altered the cell cycle status of microglia in the ventricular zone, suggesting that alcohol may shift the developmental trajectories of neuroimmune pathways and mechanisms (Salem et al., 2021). These results also highlight the importance of timing in the study of PAE, as identifying the developmental trajectories of key neurobiological pathways may provide profound insight into the mechanisms that drive the effects of PAE on neurodevelopmental and physiological outcomes. Taken together, these findings suggest a complex interplay between the immune system and epigenomic profiles, which may, at least partially, influence the neurobiological and neuroinflammatory profiles observed following PAE, and, in the future, may enable us to describe unique immune and neuroinflammatory signatures in FASD.
EPIGENETIC DYSREGULATION OF IMMUNE FUNCTION -THE MISSING LINK BETWEEN PRENATAL ALCOHOL EXPOSURE AND MENTAL HEALTH DISORDERS?
Individuals with FASD experience high rates of mental health problems. In the general population, approximately 20% of individuals experience a mental health disorder (Nestler et al., 2002), whereas 90% of individuals with FASD have a mental health disorder, with anxiety and depression among the most common (Famy et al., 1998;O'Connor et al., 2002;Pei et al., 2011). Although the molecular mechanisms underlying this increased vulnerability in alcohol-exposed individuals remains unclear, alterations in the epigenetic regulation of immune genes resulting in abnormal immune/neuroimmune functioning has been implicated in the pathophysiology of a number of mental health disorders (Alam et al., 2017). For instance, individuals diagnosed with major depressive disorder (MDD) show increases in circulating leukocytes and proinflammatory cytokine production [reviewed in Hodes et al. (2015)], with higher childhood levels of IL-6 and C-reactive protein (CRP) potentially predating the onset of depression (Khandaker et al., 2014). Importantly, these differences are linked to epigenetic alterations, as blood cells from individuals with a lifetime history of depression show alterations to DNA methylation in IL-6 and CRP (Uddin et al., 2011). Beyond these gene-specific epigenetic alterations, recent evidence from human studies shows that epigenetic risk scores for higher inflammatory status, measured through CRP levels, are associated with increased internalizing and externalizing behaviors in children (Barker et al., 2018).
Taken together, these findings suggest a correlation between alterations in immune function and increased risk of mental health disorders, which may be mediated, at least in part, through epigenetic alterations. While this connection has yet to be specifically evaluated following PAE, the high prevalence of mental disorders in individuals with FASD concomitant with lasting alterations to immune function and epigenetic programs highlight a need for future mechanistic studies that explore this complex bidirectional relationship.
CONCLUSION AND FUTURE DIRECTIONS
As a whole, it is becoming increasingly apparent that a multisystem approach is needed to gain a better understand of mechanisms underlying the teratogenic effects of alcohol.
To that end, we propose that immune disturbances arising as a result of in utero alcohol exposure may have long-term consequences extending beyond direct immune functions (e.g., protection from pathogens) to include an impact on mental health and that this may be occurring through the mechanism of epigenetics. However, the findings from epigenetic studies must be interpreted with caution, as the vast majority are correlative rather than causative in nature. As such, they do not provide a direct link between molecular mechanisms and disease and further studies are needed prior to making inferences as to causality. Nevertheless, the findings from epigenetic studies have provided important insights into potential regulatory mechanisms of immune reprogramming and may represent future targets to investigate the molecular underpinnings of alcohol-induced deficits.
Interactions between the gut microbiome, immune system, and brain are now emerging as potential moderators of neural function and potentially disease, although their connection to neuroepigenetics and neuroinflammation remain mostly unknown (Alenghat et al., 2013;Stilling et al., 2014;Carabotti et al., 2015;Petra et al., 2015;Mohajeri et al., 2018;Morais et al., 2021). Moving forward, and with this multisystem approach in mind, it will be important that future research also consider the impact of PAE on the gut-brain-immune axis (Louwies et al., 2019), as to date, there is no research in this area. It is, however, known that chronic alcohol consumption results in compromised gut-barrier function and increased rates of dysbiosis (Keshavarzian et al., 2001;Bull-Otterson et al., 2013) and as a result, in utero alcohol exposure would be expected to have an impact on the immature, developing gut. Moreover, dysbiosis during early life is linked to a proinflammatory state and an increased incidence of inflammatory-related diseases in adulthood (Yan et al., 2011;Hooper et al., 2012;Tamburini et al., 2016). Alterations in the microbiome may also confer increased risk of disease by altering immune system development and potentially inducing long-term epigenetic changes in immune regulators (Tamburini et al., 2016;Alam et al., 2017). Importantly, the establishment of the gut microbiome appears to rely partially on epigenetic mechanisms to establish microbe -T-cell mutualism, suggesting a complex interplay between physiological systems to dynamically regulate interactions between the microbiome and immune system (Obata et al., 2015). Thus, in the context of the present overview, future work to investigate the impact of in utero alcohol exposure on the gut microbiome and the gut-brain-immune axis will complement the growing body of work on immune and epigenetic alterations in preclinical PAE models and clinical studies of individuals with FASD.
Finally, a better understanding of mechanisms underlying the teratogenic effects of PAE will also pave the way for the development of more informed, targeted intervention strategies for individuals with FASD. Unlike other neurodevelopmental disorders where the underlying cause(s) are still under investigation, such as autism spectrum disorder (Won et al., 2013) or schizophrenia (Fatemi and Folsom, 2009), alcohol is a known teratogen and intervention is the key to better long-term outcomes. Due to the pervasiveness of immune disturbances across PAE models (Drew et al., 2015;Topper et al., 2015), and the link between immune function and overall physical and mental health (Raison et al., 2006), the immune system may be an ideal pharmacological target for individuals with FASD. Moreover, immune activation/cytokines play a key role in brain development, and increasing evidence demonstrates that altered immune activation may underlie altered cognition, attention, behavior, self-regulation, and adaptive functioning. Thus research on immune-based interventions will have broad implications for improving overall function of individuals with FASD [reviewed in Drew and Kane (2014)]. As such, future investigations examining the safety and utility of anti-inflammatory agents applied during early postnatal life will be important. This is particularly urgent in that currently, with the exception of ongoing work to evaluate the therapeutic potential of choline 2 supplementation (Thomas et al., 2000(Thomas et al., , 2007Wozniak et al., 2015Wozniak et al., , 2020Nguyen et al., 2016) and evaluation of pioglitazone 3 in animal models 2 Choline is an essential nutrient that, in pre-clinical models, has been shown to decrease the learning and memory deficits associated with PAE. In clinical studies, choline supplementation has been shown to result in higher non-verbal intelligence, visual-spatial skills, and working and verbal memory, as well as fewer attention deficit hyperactivity disorder symptoms. 3 Pioglitazone is a PPAR-γ agonist and PPAR-γ agonists have been shown to prevent ethanol-induced neuronal and microglial loss, as well as dampen microglial activation. (Kane et al., 2011;Drew et al., 2015) there are relatively few available drugs specifically shown to significantly improve the outcomes of PAE.
As a whole, the collective findings from animal models and clinical studies of FASD point to a compelling relationship between the immune system and epigenetic pathways, which may have important causal links to the long-term and multisystem effects of PAE. Ultimately, additional research in this area will not only provide deeper insight into the molecular mechanisms that influence mental health processes, but also help identify novel interventions and therapeutic strategies that may alleviate the adverse health consequences arising from alcohol exposure. Table 2 were obtained from https://www.immport.org/ shared/genelists.
AUTHOR CONTRIBUTIONS
AL and TB conceptualized and wrote the manuscript. All authors contributed to manuscript revision and read and approved the submitted version.
FUNDING
This research was supported by grants from the Collaborative Initiative on Fetal Alcohol Spectrum Disorders (CIFASD) (NIH/NIAAA U01 AA026101), NIH/NIAAA R37 AA007789, and a Kids Brain Health Network grant to JW, as well as NIH/NIAAA R01 AA022460 to JW and TB. AL was supported by a Developmental Neurosciences Research Training Award from Brain Canada and NeuroDevNet. | 7,222.6 | 2021-12-03T00:00:00.000 | [
"Medicine",
"Biology"
] |
Computer simulation in production process of wiper blades for cars
The article presents the most important information concerning computer modelling and simulation, system dynamics modelling, discrete event modelling and agent modelling. An exemplary model of the production process of windscreen wipers, which was created on the basis of assumptions, discussed the individual components of the model. An example of using JAVA code in AnyLogic program is presented and examples of indicators that can be calculated and presented in the form of a graph in the program are shown. Computer simulations enable tracking and analysis of the production process. They help to verify assumptions and detect irregularities in the modeled process. Simulation programs have a wide range of possibilities, allow you to create reports, charts, comparisons, allow you to optimize processes.
INTRODUCTION
Computer modelling is a way to solve real problems, but most often you can't afford to experiment on real objects. One of the public and simple modelling tools is Microsoft Excel, which has several advantages: it is easy to use, widely available, and allows you to add scripts to formulas. Modeling, which is based on spreadsheets allows for quick results -values are entered into cells and output data are displayed in others. Very complex models allow for data optimization. Spreadsheets are used for scheduling, loading or unloading, as well as determining delivery times [2].
When it is difficult to obtain a solution with analytical methods on a real model, simulations and modelling are used. They make it possible to track and analyse the production process. They are also used to verify certain assumptions, which allows for the detection of irregularities. Production process modelling is based on the creation of a virtual computer model, which shows the real production system. During the simulation, various types of reports are obtained, which allow the development of further actions or improvement of the existing process. The computer model can be improved by adding more machines, workstations, warehouses, determining their capacity, which makes it possible to carry out further simulations and verify them [1].
Computer simulation models contain logic in their structure, presenting the behavior and mutual relations of individual elements of the system. The functioning of such a system can be presented graphically using animation [8].
Optimization of production structures applies to every company that wants to gain an advantage on the market. Already at the planning stage it is necessary to make decisions that will allow to maximize profits. Creating a good production process takes time, but it allows to shorten the time of operations and increase profits [4].
Combining optimization with modelling and simulation can form the basis for improvement of production systems, the difference between them is shown in Figure 1. The task in modelling is to find a simulation model, in the simulation -determination of results, and in the optimization -determination of input data [10].
TYPES OF SIMULATION MODEL
There are three types of simulation models that differ in the description of the variables. There are continuous modelling, discrete modelling and agent modelling.
Modelling the dynamics of system
The dynamics of the systems is a continuous simulation and is distinguished from discrete processes by the fact that it takes place in fixed conditions in which raw materials are supplied and received at the same time in an uninterrupted manner [5].
The elements that make up the dynamics of the systems are: the resource and the stream. There are also auxiliary variables, constants, functions and relationships. A resource is a size and defines the quantitative state. If it is a variable size, the term stream is also used. The resource is related to input and output streams [8].
An example of continuous simulation can be the simulation of physical phenomena, e.g. change of weather or gas flow around a rigid mass. The first continuous simulations were carried out using analogue machines [7].
Modelling of discrete events
Discrete simulation is a simulation in which system state variables change discretely. The passage of time in this system takes place through the sequence of events. Modelling in a discrete simulation is objectoriented modelling or modelling by means of a network of activities. Network models are Petri networks as well as evaluation networks. During the experiment, it is necessary to introduce uncertainty and randomness of the system. Then random values are introduced into the model according to a certain probability distribution by means of the Monte Carlo method. Application of the method consists of selection of random variables, selection of input variables, calculation of several observations (generation of random variables, determination of output variables, repetition of observations in the sample, determination of parameters of output statistics), and at the end of the calculation. There are several modelling tools in discrete simulation and these are [8]: 1. GPSS modelling languagethe programming language of simulation, which has a process orientation. This language has elements such as object, commands, programming blocks and predefined variables SNA, 2. ExtendSim modeling systemwith the help of this system it is possible to build models that are oriented towards processes, events or processes and events, 3. Arena modeling systemit is an integrated software for modeling and simulation, this system has universal elements used to describe any systems. Arena helps to model and simulate processes with different levels of complexity. Its basic functions include modeling, simulation, visualization and analysis.
Agent modelling
Agent modelling is currently used in many areas, including economy, management, technical and social fields. An agent can be a person, device, machine or software that performs a specific activity [8].
Agent modelling was created because of technological progress, which allowed to apply new methods of object-oriented modelling thanks to computer science. Faster processors and larger memory have been introduced to the market, and agent-based models are more demanding than system dynamics or discrete event modelling. Agents in agent modeling can take the form of e.g. Vehicles, equipment, products, organization, investments, ideas, as well as people in different roles [2].
An agent in information technology is a computer system that is placed in an environment is capable of responding in the environment to perform specific actions. Agent technology is used in anthropomorphic systems e.g. as virtual teachers, salesmen, actors in computer games. In order to build an agent system, an appropriate platform is needed, which should provide an agent management system. Artificial intelligence systems are used to support agent systems, which are based on inference rules, neural networks and genetic algorithms [8].
EXAMPLE OF A SIMULATION MODEL
Preparing a model and conducting a simulation involves costs. While preparing the model, one should strive to simplify it as much as possible, the model becomes more comprehensible and easier to analyze [9].
When creating a production process model, the process boundaries and the connection of the process with the environment should be defined (e.g. material deliveries, output of finished products), changeover of machines, programmed of possible downtimes, probable failures. It is also necessary to model workstations and their mutual relations by defining the flow of materials and information. The functioning of the process is presented graphically through e.g. animation, and after the simulation the results in the form of statistics are obtained [6].
The presented example of a simulation model was created in AnyLogic, which uses three modelling methods: system dynamics, discrete events and agent modelling. The software has a graphical programming interface and also uses a graphical modelling language, which allows to create a model using graphical elements, which can be further expanded through the Java programming language.
The production process concerns the production of windscreen wipers. Table 1 presents the assumptions for the simulation model. The time described in brackets is the time interval, which in the simulation is described by a triangular distribution. The first value is the minimum time, the next one is the intermediate time and the last one is the maximum time.
Production of wipers consists of several stages. The first one is to make a pen and spoiler on an extruder for rubber. Synthetic rubber is used in the production process. After leaving the extruder, the material is cut to the desired length. The second stage is profiling and cutting springs, which are located in the wiper arm. The third and final stage is the production of tips, clamping covers and central fixing using a plastic injection moulding machine. A doubleseat injection moulding machine is used, which allows for the production of a larger number of elements. Assumptions to the production process allow to determine the holding time of elements in individual blocks in the simulation program.
The production process of car wipers is shown in Figure 2. Two machines have been modelled, which can produce different elements when they are rearmed. All manufactured components are also stored in separate blocks, which allows for picking appropriate for assembly. In the process modelling, Java code was used, which allowed to model the changeover of the machines.
In the AnyLogic simulation program you can find a number of elements that allow you to create a process. The elements used to create the process are shown in Figure 2 and described below.
The elements 'elastomer', 'spring' and 'material' are the input elements of the model Agents are responsible for the generation of the raw material, which is then processed. 1. The elements 'changeElastomer', 'separateTypeElastomer', 'changeMateralPET', 'separateType' and 'splitFixture' are those elements which allow the separation of two activities and have two outputs. Very often they are used to sort two elements according to established criteria, for example as separation of a well and incorrectly made element or dividing elements according to the number of outputs to be output by one and the other output. In the presented model, the second solution was applied, because the machine changeover takes place after a certain number of pieces of detail is produced, so the AnyLogic program needs information about the change of the produced item. 2. Elements such as 'changeoverElastomer' or 'cutting' are elements that can hold the workpieces for a defined, fixed period of time. Very often they are used as machines to process workpieces when modelling production processes. 3. The "stopElastomer" and "stopMaterial" elements block material flow. In the presented model they are used because two elements are produced on one machine at a different time, so they allow to produce one element for another. 4. Elements such as "BufferSpoiler" or "BufferFixture" are known as "Queue", allowing you to store items. In production process modeling they serve as warehouses for semi-finished or finished products. 5. The "assembling" and "assemblingMaterial" elements are used as assemblies to allow several parts to be assembled into one. The output element is the end point of the model. With a ready-made simulation you can define many different indicators and present them on charts. During the simulation it is also possible to collect data, which can then be transferred to Microsoft Excel and used for further research. Figure 3 shows a part of the Java code mentioned above, which makes it possible to simulate a machine changeover. The code was written in the "cutting" dropdown window. The presented code allows to stop the production of feathers and start the production of spoilers, and vice versa. The first indicator is the utilisation rate of the machine. Figure 5 shows the use of the machine to cut parts that are produced by an extruder for rubber. Initially, the use of the machine is 100%, but in 30 seconds it drops to about 66%, and then the use of the machine changes from 74 to 78%. The average value is 76%.
The second indicator is the indicator that allows you to count how many wipers have been assembled ( fig. 6). Figure 6 shows that the first wiper is only assembled in 170 seconds of simulation, due to the wait for the injection moulding machine to produce the components, as it produces two components and has to be rearmed. Within 15 minutes, 41 wipers are assembled. fig. 7 is the utilisation rate of the assembly station. This is very low and is due to the production of the central clamping on the plastic injection machine. A small number of elements in the warehouse causes downtime for assembly.
The figure 8 shows the use of the machine for cutting springs. Initially it is 0%, because the elements enter the buffer and are then cut on the machine. The utilization of the machine increases to the total utilization, the machine runs continuously.
In the model, an agent was used, who counted how many individual elements were produced, and then the cost of production for each element was calculated. Table 2 shows a summary of the produced elements in the time that was adopted (15 minutes).
Within 15 minutes 1119 details were produced, whose cost of materials was 261.76 PLN and the weight of the plastic from which they were made was 27.84 kg.
CONCLUSIONS
The article presents the most important information about computer simulations, system dynamics modelling, discrete events and agent modelling, which was used in the design of simulation of the wiper manufacturing process. Computer simulations are becoming one of the most important tools for planning production processes, simulation techniques are increasingly used in the design of new production systems, and also allow for the analysis of existing systems, allow for the detection of errors, or help in process optimization. The main advantages of computer simulations are the flexibility of the model (it is possible to introduce changes in a simple way), low cost and time of preparation and execution of the simulation, reliability of the results, recognition of process limitations. This process needs to be optimised because the machines are not used to their full capacity due to changeovers. The spring cutting machine is used entirely, which means that you should think of another machine that is the same, which will allow you to produce more parts. | 3,229.8 | 2019-07-15T00:00:00.000 | [
"Computer Science"
] |
Supervised Treebank Conversion: Data and Approaches
Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing performance. However, previous work mainly focuses on unsupervised treebank conversion and has made little progress due to the lack of manually labeled data where each sentence has two syntactic trees complying with two different guidelines at the same time, referred as bi-tree aligned data. In this work, we for the first time propose the task of supervised treebank conversion. First, we manually construct a bi-tree aligned dataset containing over ten thousand sentences. Then, we propose two simple yet effective conversion approaches (pattern embedding and treeLSTM) based on the state-of-the-art deep biaffine parser. Experimental results show that 1) the two conversion approaches achieve comparable conversion accuracy, and 2) treebank conversion is superior to the widely used multi-task learning framework in multi-treebank exploitation and leads to significantly higher parsing accuracy.
Abstract
Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing accuracy. However, previous work mainly focuses on unsupervised treebank conversion and makes little progress due to the lack of manually labeled data where each sentence has two syntactic trees complying with two different guidelines at the same time, referred as bi-tree aligned data.
In this work, we for the first time propose the task of supervised treebank conversion. First, we manually construct a bi-tree aligned dataset containing over ten thousand sentences. Then, we propose two simple yet effective treebank conversion approaches (pattern embedding and treeLSTM) based on the state-of-the-art deep biaffine parser. Experimental results show that 1) the two approaches achieve comparable conversion accuracy, and 2) treebank conversion is superior to the widely used multi-task learning framework in multiple treebank exploitation and leads to significantly higher parsing accuracy.
Introduction
During the past few years, neural network based dependency parsing has achieved significant progress and outperformed the traditional discrete-feature based parsing (Chen and Manning, 2014;Dyer et al., 2015; Zhou * The first two (student) authors make equal contributions to this work. Zhenghua is the correspondence author.
Meanwhile, motivated by different syntactic theories and practices, major languages in the world often possess multiple large-scale heterogeneous treebanks, e.g., Tiger (Brants et al., 2002) and TüBa-D/Z (Telljohann et al., 2004) treebanks for German, Talbanken (Einarsson, 1976) and Syntag (Järborg, 1986) treebanks for Swedish, ISST (Montemagni et al., 2003) and TUT 1 treebanks for Italian, etc. Table 1 lists several large-scale Chinese treebanks. In this work, we take HIT-CDT as a case study. Our next-step plan is to annotate bi-tree aligned data for PKU-CDT and then convert PKU-CDT to our guideline. For non-dependency treebanks, the straight-forward choice is to convert such treebanks to dependency treebanks based on heuristic head-finding rules. The second choice is to directly extend our proposed approaches by adapting the patterns and treeLSTMs for non-dependency structures, which should be straightforward as well.
Considering the high cost of treebank construction, it has always been an interesting and attractive research direction to exploit various heterogeneous treebanks for boosting parsing performance. Though under different linguistic theories or annotation guidelines, the treebanks are painstakingly developed to capture the syntactic structures of the same language, thereby having a great deal of common grounds.
Previous researchers have proposed two approaches for multi-treebank exploitation. On the one hand, the guiding-feature method projects the knowledge of the source-side treebank into the target-side treebank, and utilizes extra pattern-based features as guidance for the target-side parsing, mainly for the traditional discrete-feature based parsing . On the other hand, the multi-task learning method simultaneously trains two parsers on two treebanks and uses shared neural network parameters for representing common-ground syntactic knowledge (Guo et al., 2016). 2 Regardless of their effectiveness, while the guiding-feature method fails to directly use the source-side treebank as extra training data, the multi-task learning method is incapable of explicitly capturing the structural correspondences between two guidelines. In this sense, we consider both of them as indirect exploitation approaches.
Compared with the indirect approaches, treebank conversion aims to directly convert a source-side treebank into the target-side guideline, and uses the converted treebank as extra labeled data for training the targetside model. Taking the example in Figure 1, the goal of this work is to convert the under tree that follows the HIT-CDT guideline into the upper one that follows our new guideline. However, due to the lack of bi-tree aligned data, in which each sentence has two syntactic trees following the sourceside and target-side guidelines respectively, most previous studies are based on unsupervised treebank conversion (Niu et al., 2009) or pseudo bi-tree aligned data (Zhu et al., 2011;Li et al., 2013), making very limited progress.
In this work, we for the first time propose the task of supervised treebank conversion.
The key motivation is to better utilize a largescale source-side treebank by constructing a small-scale bi-tree aligned data. In summary, we make the following contributions.
(1) We have manually annotated a highquality bi-tree aligned data containing over ten thousand sentences, by reannotating the HIT-CDT treebank according to a new guideline.
(2) We propose a pattern embedding conversion approach by retrofitting the indirect guiding-feature method of to the direct conversion scenario, with several substantial extensions.
(3) We propose a treeLSTM conversion approach that encodes the source-side tree at a deeper level than the shallow pattern embedding approach.
Experimental results show that 1) the two conversion approaches achieve nearly the same conversion accuracy, and 2) direct treebank conversion is superior to indirect multi-task learning in exploiting multiple treebanks in methodology simplicity and performance, yet with the cost of manual annotation. We release the annotation guideline and the newly annotated data in http://hlt.suda.edu.cn/ index.php/SUCDT.
Annotation of Bi-tree Aligned Data
The key issue for treebank conversion is that sentences in the source-side and target-side treebanks are non-overlapping.
In other words, there lacks a bi-tree aligned data in which each sentence has two syntactic trees complying with two guidelines as shown in Figure 1. Consequently, we cannot train a supervised conversion model to directly learn the structural correspondences between the two guidelines. To overcome this obstacle, we construct a bi-tree aligned data of over ten thousand sentences by re-annotating the publicly available dependency-structure HIT-CDT treebank according to a new annotation guideline.
Data Annotation
Annotation guideline.
Unlike phrasestructure treebank construction with very detailed and systematic guidelines (Xue et al., 2005;Zhou, 2004), previous works on Chinese dependency-structure annotation only briefly describe each relation label with a few concrete examples. For example, the HIT-CDT guideline contains 14 relation labels and illustrates them in a 14-page document.
The UD (universal dependencies) project 3 releases a more detailed language-generic guideline to facilitate cross-linguistically consistent annotation, containing 37 relation labels.
However, after in-depth study, we find that the UD guideline is very useful and comprehensive, but may not be completely compact for realistic annotation of Chinese-specific syntax. After many months' investigation and trial, we have developed a systematic and detailed annotation guideline for Chinese dependency treebank construction. Our 60-page guideline employs 20 relation labels and gives detailed illustrations for annotation, in order to improve consistency and quality.
Please refer to Guo et al. (2018) for the details of our guideline, including detailed discussions on the correspondences and differences between the UD guideline and ours.
3 http://universaldependencies.org Partial annotation. To save annotation effort, we adopt the idea of Li et al. (2016) and only annotate the most uncertain (difficult) words in a sentence. For simplicity, we directly use their released parser and produce the uncertainty results of all HLT-CDT sentences via two-fold jack-knifing. First, we select 2, 000 most difficult sentences of lengths [5, 10] for full annotation 4 . Then, we select 3, 000 most difficult sentences of lengths [10, 20] from the remaining data for 50% annotation. Finally, we select 6, 000 most difficult sentences of lengths [5,25] for 20% annotation from the remaining data. The difficulty of a sentence is computed as the averaged difficulty of its selected words.
Annotation platform. To guarantee annotation consistency and data quality, we build an online annotation platform to support strict double annotation and subsequent inconsistency handling. Each sentence is distributed to two random annotators. If the two submissions are not the same (inconsistent dependency or relation label), a third expert annotator will compare them and decide a single answer.
Annotation process. We employ about 20 students in our university as part-time annotators. Before real annotation, we first give a detailed talk on the guideline for about two hours. Then, the annotators spend several days on systematically studying our guideline. Finally, they are required to annotate 50 testing sentences on the platform. If the submission is different from the correct answer, the annotator receives an instant feedback for selfimprovement. Based on their performance, about 10 capable annotators are chosen as experts to deal with inconsistent submissions.
Statistics and Analysis
Consistency statistics. Compared with the final answers, the overall accuracy of all annotators is 87.6%. Although the overall inter-annotator dependency-wise consistency rate is 76.5%, the sentence-wise consistency rate is only 43.7%. In other words, 56.3% (100 − 43.7) sentences are further checked by a third expert annotator. This shows how difficult it is to annotate syntactic structures and how important it is to employ strict double annotation to guarantee data quality. Table 2, the averaged sentence length is 15.4 words in our annotated data, among which 4.7 words (30%) are partially annotated with their heads. According to the records of our annotation platform, each sentence requires about 3 minutes in average, including the annotation time spent by two annotators and a possible expert. The total cost of our data annotation is about 550 person-hours, which can be completed by 20 full-time annotators within 4 days. The most cost is spent on quality control via two-independent annotation and inconsistency handling by experts. This is in order to obtain very high-quality data. The cost is reduced to about 150 personhours without such strict quality control.
Annotation time analysis. As shown in
Heterogeneity analysis. In order to understand the heterogeneity between our guideline and the HIT-CDT guideline, we analyze the 36, 348 words with both-side heads in the train data, as shown in Table 2. The consistency ratio of the two guidelines is 81.69% (UAS), without considering relation labels. By mapping each relation label in HIT-CDT (14 in total) to a single label of our guideline (20 in total), the maximum consistency ratio is 73.79% (LAS). The statistics are similar for the dev/test data.
Indirect Multi-task Learning
Basic parser. In this work, we build all the approaches over the state-of-the-art deep biaffine parser proposed by Dozat and Manning (2017). As a graph-based dependency parser, it employs a deep biaffine neural network to compute the scores of all dependencies, and uses viterbi decoding to find the highestscoring tree. Figure 2 shows how to score a dependency i ← j. 5 First, the biaffine parser applies multi-layer bidirectional sequential LSTMs (biSeqLSTM) to encode the input sentence. The word/tag embeddings e w k and e t k are concatenated as the input vector at w k .
where r H k is the representation vector of w k as a head word, and r D k as a dependent. Finally, the score of the dependency i ← j is computed via a biaffine operation.
During training, the original biaffine parser uses the local softmax loss. For each w i and its head w j , its loss is defined as − log e score(i←j) ∑ k e score(i←k) . Since our training data is partially annotated, we follow Li et al. (2016) and employ the global CRF loss (Ma and Hovy, 2017) for better utilization of the data, leading to consistent accuracy gain.
Multi-task learning aims to incorporate labeled data of multiple related tasks for improving performance (Collobert and Weston, 2008). Guo et al. (2016) apply multi-task learning to multi-treebank exploitation based on the neural transition-based parser of Dyer et al. (2015), and achieve higher improvement than the guiding-feature approach of .
Based on the state-of-the-art biaffine parser, this work makes a straightforward extension to realize multi-task learning. We treat the source-side and target-side parsing as two individual tasks. The two tasks use shared parameters for word/tag embeddings and multilayer biSeqLSTMs to learn common-ground syntactic knowledge, use separate parameters for the MLP and biaffine layers to learn taskspecific information.
Direct Treebank Conversion
Task definition. As shown in Figure 1, given an input sentence x, treebank conversion aims to convert the under source-side tree d src to the upper target-side tree d tgt . Therefore, the main challenge is how to make full use of the given d src to guide the construction of d tgt . Specifically, under the biaffine parser framework, the key is to utilize d src as guidance for better scoring an arbitrary target-side dependency i ← − j.
In this paper, we try to encode the structural information of i and j in d src as a dense vector from two representation levels, thus leading to two approaches, i.e., the shallow pattern embedding approach and the deep treeLSTM approach. The dense vectors are then used as extra inputs of the MLP layer to obtain better word representations, as shown in Figure 2.
The Pattern Embedding Approach
In this subsection, we propose the pattern embedding conversion approach by retrofitting the indirect guiding-feature method of to the direct conversion scenario, with several substantial extensions.
The basic idea of is to use extra guiding features produced by the sourceside parser. First, they train the source parser P arser src on the source-side treebank. Then, they use P arser src to parse the target-side treebank, leading to pseudo bi-tree aligned data. Finally, they use the predictions of P arser src as extra pattern-based guiding features and build a better target-side parser P arser tgt .
The original method of is proposed for traditional discrete-feature based parsing, and does not consider the relation labels in d src . In this work, we make a few useful extensions for more effective utilization of d src .
• We further subdivide their "else" pattern into four cases according to the length of the path from w i to w j in d src . The left part of Figure 2 shows all 9 patterns.
• We use the labels of w i and w j in d src , denoted as l i and l j .
• Inspired by the treeLSTM approach, we also consider the label of w a , the lowest common ancestor (LCA) of w i and w j , denoted as l a .
Our pattern embedding approach works as follows. Given i ← j, we first decide its pattern type according to the structural relationship between w i and w j in d src , denoted as p i←j . For example, if w i and w j are both the children of a third word w k in d src , then p i←j = "sibling". Figure 2 shows all 9 patterns.
Then, we embed p i←j into a dense vector e p i←j through a lookup operation in order to fit into the biaffine parser. Similarly, the three labels are also embedded into three dense vectors, i.e., e l i , e l j , e la .
The four embeddings are combined as r pat i←j to represent the structural information of w i and w j in d src .
Finally, the representation vector r pat i←j and the top-layer biSeqLSTM outputs are concatenated as the inputs of the MLP layer.
Through r pat i←j , the extended word representations, i.e., r D i,i←j and r H j,i←j , now contain the structural information of w i and w j in d src .
The remaining parts of the biaffine parser is unchanged. The extended r D i,i←j and r H j,i←j are fed into the biaffine layer to compute a more reliable score of the dependency i ← j, with the help of the guidance of d src .
The TreeLSTM Approach
Compared with the pattern embedding approach, our second conversion approach employs treeLSTM to obtain a deeper representation of i ← j in the source-side tree d src . Tai et al. (2015) first propose treeLSTM as a generalization of seqLSTM for encoding treestructured inputs, and show that treeLSTM is more effective than seqLSTM on the semantic relatedness and sentiment classification tasks. Miwa and Bansal (2016) compare three treeL-STM variants on the relation extraction task and show that the SP-tree (shortest path) treeLSTM is superior to the full-tree and subtree treeLSTMs.
In this work, we employ the SP-tree treeL-STM of Miwa and Bansal (2016) for our treebank conversion task. Our preliminary experiments also show the SP-tree treeLSTM outperforms the full-tree treeLSTM, which is consistent with Miwa and Bansal. We did not implement the in-between subtree treeLSTM. Figure 2: Computation of score(i ← j) in our proposed conversion approaches. Without the source-side tree d src , the baseline uses the basic r D i and r H j (instead of r D i,i←j and r H j,i←j ).
Given w i and w j and their LCA w a , the SPtree is composed of two paths, i.e., the path from w a to w i and the path from w a to w j , as shown in the right part of Figure 2.
Different from the shallow pattern embedding approach, the treeLSTM approach runs a bidirectional treeLSTM through the SP-tree, in order to encode the structural information of w i and w j in d src at a deeper level. The topdown treeLSTM starts from w a and accumulates information until w i and w j , whereas the bottom-up treeLSTM propagates information in the opposite direction.
Following Miwa and Bansal (2016), we stack our treeLSTM on top of the biSeqLSTM layer of the basic biaffine parser, instead of directly using word/tag embeddings as inputs. For example, the input vector for w k in the treeL-STM is x k = h seq k ⊕ e l k , where h seq k is the toplevel biSeqLSTM output vector at w k , and l k is the label between w k and its head word in d src , and e l k is the label embedding.
In the bottom-up treeLSTM, an LSTM node computes a hidden vector based on the combination of the input vector and the hidden vectors of its children in the SP-tree. The right part of Figure 2 and Eq. (5) illustrate the computation at w a .
where C(a) means the children of w a in the SP-tree, and f a,k is the forget vector for w a 's child w k . The top-down treeLSTM sends information from the root w a to the leaves w i and w j . An LSTM node computes a hidden vector based on the combination of its input vector and the hidden vector of its single preceding (father) node in the SP-tree.
After performing the biTreeLSTM, we follow Miwa and Bansal (2016) and use the combination of three output vectors to represent the structural information of w i and w j in d src , i.e., the output vectors of w i and w j in the topdown treeLSTM, and the output vector of w a #Sent #Tok (HIT) #Tok (our) in the bottom-up treeLSTM.
Similar to Eq. (4) for the pattern embedding approach, we concatenate r tree i←j with the output vectors of the top-layer biSeqLSTM, and feed them into MLP H/D .
Experiment Settings
Data. We randomly select 1, 000/2, 000 sentences from our newly annotated data as the dev/test datasets, and the remaining as train. Table 2 shows the data statistics after removing some broken sentences (ungrammatical or wrongly segmented) discovered during annotation. The "#tok (our)" column shows the number of tokens annotated according to our guideline. Train-HIT contains all sentences in HIT-CDT except those in dev/test, among which most sentences only have the HIT-CDT annotations.
Evaluation. We use the standard labeled attachment score (LAS, UAS for unlabeled) to measure the parsing and conversion accuracy.
Implementation. In order to more flexibly realize our ideas, we re-implement the baseline biaffine parser in C++ based on the lightweight neural network library of . On the Chinese CoNLL-2009 data, our parser achieves 85.80% in LAS, whereas the original tensorflow-based parser 6 achieves 85.54% (85.38% reported in their paper) under the same parameter settings and external word embedding.
Hyper-parameters. We follow most parameter settings of Dozat and Manning (2017). The external word embedding dictionary is trained on Chinese Gigaword (LDC2003T09) with GloVe (Pennington et al., 2014). For 6 https://github.com/tdozat/Parser-v1 efficiency, we use two biSeqLSTM layers instead of three, and reduce the biSeqLSTM output dimension (300) and the MLP output dimension (200). For the conversion approaches, the sourceside pattern/label embedding dimensions are 50 (thus |r pat i←j | = 200), and the treeLSTM output dimension is 100 (thus |r tree i←j | = 300). During training, we use 200 sentences as a data batch, and evaluate the model on the dev data every 50 batches (as an epoch). Training stops after the peak LAS on dev does not increase in 50 consecutive epochs.
For the multi-task learning approach, we randomly sample 100 train sentences and 100 train-HIT sentences to compose a data batch, for the purpose of corpus weighting.
To fully utilize train-HIT for the conversion task, the conversion models are built upon multi-task learning, and directly reuse the embeddings and biSeqLSTMs of the multitask trained model without fine-tuning. Table 3 shows the conversion accuracy on the test data. As a strong baseline for the conversion task, the multi-task trained target-side parser ("multi-task") does not use d src during both training and evaluation. In contrast, the conversion approaches use both the sentence x and d src as inputs.
Results: Treebank Conversion
Compared with "multi-task", the two proposed conversion approaches achieve nearly the same accuracy, and are able to dramatically improve the accuracy with the extra guidance of d src . The gain is 7.58 (82.09 − 74.51) in LAS for the treeLSTM approach.
It is straightforward to combine the two conversion approaches. We simply concatenate h seq i/j with both r pat i←j and r tree i←j before feeding into MLP H/D . However, the "combined" model leads to no further improvement. This indicates that although the two approaches try to encode the structural information of w i and w j in d src from different perspectives, the resulted representations are actually overlapping instead of complementary, which is contrary to our intuition that the treeLSTM approach should give better and deeper representations than the shallow pattern embedding approach.
We have also tried several straightforward modifications to the standard treeLSTM in Eq. (5), but found no further improvement. We leave further exploration of better treeL-STMs and model combination approaches as future work. Feature ablation results are presented in Table 4 to gain more insights on the two proposed conversion approaches.
In each experiment, we remove a single component from the full model to learn its individual contribution.
For the pattern embedding approach, all proposed extensions to the basic pattern-based approach of are useful. Among the three labels, the embedding of l i is the most useful and its removal leads to the highest LAS drop of 0.88 (82.03 − 81.15). This is reasonable considering that 81.69% dependencies are consistent in the two guidelines, as discussed in the heterogeneity analysis of Section 2.2. Removing all three labels decreases UAS by 0.73 (86.66−85.93) and LAS by 1.95 (82.03 − 80.08), demonstrating that the source-side labels are highly correlative with the target-side labels, and therefore very helpful for improving LAS.
For the treeLSTM approach, the source-side labels in d src are also very useful, improving UAS by 0.49 (86.69 − 86.20) and LAS by 1.53 (82.09 − 80.56).
Results: Utilizing Converted Data
Another important question to be answered is whether treebank conversion can lead to higher parsing accuracy than multi-task learning. In terms of model simplicity, treebank conversion is better because eventually the target-side parser is trained directly on an enlarged homogeneous treebank unlike the multi-task learning approach that needs to simultaneously train two parsers on two heterogeneous treebanks. Table 5 shows the empirical results. Please kindly note that the parsing accuracy looks very low, because the test data is partially annotated and only about 30% most uncertain (difficult) words are manually labeled with their heads according to our guideline, as discussed in Section 2.1.
The first-row, "single" is the baseline targetside parser trained on the train data.
The second-row "single (hetero)" refers to the source-side heterogeneous parser trained on train-HIT and evaluated on the target-side test data. Since the similarity between the two guidelines is high, as discussed in Section 2.2, the source-side parser achieves even higher UAS by 0.21 (76.20 − 75.99) than the baseline target-side parser trained on the small-scale train data. The LAS is obtained by mapping the HIT-CDT labels to ours (Section 2.2).
In the third row, "multi-task" is the targetside parser trained on train & train-HIT with the multi-task learning approach. It significantly outperforms the baseline parser by 4.30 (74.51 − 70.21) in LAS. This shows that the multi-task learning approach can effectively utilize the large-scale train-HIT to help the target-side parsing.
In the fourth row, "single (large)" is the basic parser trained on the large-scale converted train-HIT (homogeneous). We employ the treeLSTM approach to convert all sentences in train-HIT into our guideline. 7 We can see that Table 5: Parsing accuracy on test data. LAS difference between any two systems is statistically significant (p < 0.005) according to Dan Bikel s randomized parsing evaluation comparer for significance test Noreen (1989). the single parser trained on the converted data significantly outperforms the parser in the multi-task learning approach by 1.32 (75.83 − 74.51) in LAS.
In summary, we can conclude that treebank conversion is superior to multi-task learning in multi-treebank exploitation for its simplicity and better performance.
Results on fully annotated data
We randomly divided the newly annotated data into train/dev/test, so the test set has a mix of 100%, 50% and 20% annotated sentences. To gain a rough estimation of the performance of different approaches on fully annotated data, we give the results in Table 6. We can see that all the models achieve much higher accuracy on the portion of fully annotated data than on the whole test data as shown in Table 3 and 5, since the dependencies to be evaluated are the most difficult ones in a sentence for the portion of partially annotated data. Moreover, the conversion model can achieve over 90% LAS thanks to the guidance of the source-side HIT-CDT tree. Please also note that there would still be a slight bias, because those fully annotated sentences are chosen as the most difficult ones according to the parsing model but are also very short ([5, 10]).
Conclusions and Future Work
In this work, we for the first time propose the task of supervised treebank conversion by constructing a bi-tree aligned data of over ten thousand sentences. We design two simple yet effective conversion approaches based on the state-of-the-art deep biaffine parser. Results show that 1) the two approaches achieves nearly the same conversion accuracy; 2) relation labels in the source-side tree are very helpful for both approaches; 3) treebank conversion is more effective in multi-treebank exploitation than multi-task learning, and achieves significantly higher parsing accuracy.
In future, we would like to advance this work in two directions: 1) proposing more effective conversion approaches, especially by exploring the potential of treeLSTMs; 2) constructing bi-tree aligned data for other treebanks and exploiting all available single-tree and bi-tree labeled data for better conversion. | 6,462.4 | 2018-07-01T00:00:00.000 | [
"Computer Science"
] |
The Normalized Reduced Form and Cell Mathematical Tools for Lattice Analysis—Symmetry and Similarity
To intelligently and effectively use crystallographic databases, mathematical and computer tools are required that can elucidate diverse types of intra- and interlattice relationships. Two such tools are the normalized reduced form and normalized reduced cell. Practical experience has revealed that the first tool—the normalized reduced form—is very helpful in establishing lattice metric symmetry as it enables one to readily deduce significant relationships between the elements of the reduced form. Likewise research with crystallographic databases has demonstrated that the second tool—the normalized reduced cell—plays a vital role in determining metrically similar lattices. Knowledge of similar lattices has practical value in solving structures, in assignment of structure types, in materials design, and in nano-technology. In addition to using the reduced cell, it is recommended that lattice-matching strategies based on the normalized reduced cell be routinely carried out in database searching, in data evaluation, and in experimental work.
Introduction
The various crystallographic databases [1] now available constitute a large, comprehensive, and rapidly growing scientific resource, serving as an invaluable source of data for the intelligent design of materials, for crystal engineering, and for nanotechnology. To evaluate data entering these databases and to intelligently and effectively use this resource, diverse mathematical tools are required that can establish intralattice relationships or elucidate various types of interlattice relationships.
Two such tools are the normalized reduced form and the normalized reduced cell-tools that are ideal for elucidating certain types of intra-and interlattice relationships. For example, with the normalized reduced form, one can determine lattice-metric symmetry and deduce other types of intralattice relationships. With the normalized reduced cell, one can determine metrically similar lattices 1 via lattice matching techniques against the lattices in the crystallographic databases. Practical experience has revealed that these tools are very useful for routine and complex lattice analyses. Before proceeding with applications of these tools, it is necessary to define the normalized reduced cell and form.
Definitions
The reduced cell is a unique primitive cell of the lattice, which is based on the three shortest lattice translations. For the precise mathematical definition of the reduced cell and form and for procedures to calculate this cell, see [2] and NBS Technical Note 1290 [3].
The normalized reduced cell of a lattice is determined simply by dividing the cell edges of the reduced cell by the a-cell edge. The normalized reduced form is calculated from the normalized reduced cell and is defined by the vector dot products of the normalized reduced cell edge vectors: As an example, consider the reduced cell for a typical triclinic crystal structure reported in the recent literature [4]: The corresponding normalized reduced cell and form are: The fact that there is no specialization 2 in the normalized reduced form shows that the metric lattice is triclinic.
Discussion and Applications
The reduced form and cell have long been used in lattice metric symmetry determination and identification, respectively. Although the reduced form can be used in the symmetry checks discussed below, the normalized reduced form has the advantage in that it makes the interrelationships-and specialization-of the elements of the reduced form more transparent. Recognition of matrix-element specialization is a basis of symmetry determination as well as for investigations of many other lattice-related phenomena. Likewise, although Crystal Data Determinative Ratios [5] may be used to locate similar lattices within a given crystal system, the normalized reduced cell provides the logical basis for a far more powerful and comprehensive lattice-matching technique which is crystal system independent and conceptually parallel to techniques based on matching reduced cells. Details of the application of the normalized reduced form and cell for symmetry determination and for the determination lattice-metric similarity are outlined below.
Symmetry Determination via the Normalized Reduced Form (NRF)
The normalized reduced form (NRF) is a practical tool, which can be used-in conjunction with the matrix method [6]-for metric symmetry determination. With the NRF one can readily determine the metric symmetry of the lattice by matching it against a table of the 44 reduced forms [7].
To illustrate, Table 1 herein presents the 13 reduced forms corresponding to the centered monoclinic lattices. Typical examples of the NRFs are given that have been derived from cell constants published in recent issues of Acta Crystallographica Section E. Once normalized, the pattern of the relationships of dot products in the NRF is easy to ascertain. From the examples, one can see that it is especially easy to determine the reduced form number (first column) by matching a given NRF against the characteristic reduced form matrices presented in the second through fourth columns in Table 1. Once the reduced form number is known, one can consult the reference table of the 44 reduced forms [7] to obtain the appropriate transformation matrix to determine the conventional cell. In the last column, the frequency of occurrence (for the first 2.4 years that Acta Crystallographica Section E has been in existence) of each reduced form is given. The frequencies reveal that reduced form numbers 39, 27, 10, 37, and 14 are the most common for the centered monoclinic lattices.
448
Volume 108, Number 6, November-December 2003 Journal of Research of the National Institute of Standards and Technology 2 A reduced form is said to be specialized if there is a simple mathematical relationship between two or more of the matrix elements (e.g. a·a = b·b; b·c = 1/2 b·b; b·c = 1/2 a·c). Table 1 gives the types of specialization found in the reduced forms corresponding to the centered monoclinic lattices. -|a·b| -1.14 -0.40 -0.20 a For each example, the first symbol "M" stands for monoclinic, and the second symbol "C or I" represents the centering of the conventional cell of the lattice. b Created for illustrative purposes; an actual example was not found in Acta Crystallogr., Sect. E.
The crystal symmetry can never exceed the metric symmetry, but it can be less. However, by analyzing the crystallographic databases, it has been observed that the metric and crystal symmetry are almost always the same [20,21]. This coincidence of crystal and metric symmetry continues to hold true in recently published structures. For example, a detailed analysis of the NRFs, for 205 centered monoclinic cells published in Acta Crystallographica Section E, revealed that in every case the crystal and metric symmetry are identical. This fact provides a basis for a reliable method for evaluation of the symmetry of crystalline compounds [20,21]. For example, cases in which metric symmetry exceeds the crystal symmetry represent either misidentified symmetry [22] or something unusual in the crystal structure [23]. Furthermore, from inspection of the NRF, one may ascertain extra relationships (not required by one of the 44 reduced forms) among the dot products. The experimentalist (or user of cell data in the crystallographic databases) should be aware that any extra specialization in the NRF may signify an important fact: for example, that one has inadvertently determined a derivative cell of a lattice of higher symmetry. Finally, as an integral part of routine practice, it is suggested that the normalized reduced form be determined and checked against a table of reduced forms [7] to ascertain the highest possible metric symmetry, to check for extra specialization, and to determine the transformation matrix to a conventional cell.
Lattice Similarity Determination via Normalized Reduced Cells
The reduced cell has long played a practical role in lattice-matching strategies [24, 25, and 26]. Likewise, the normalized reduced cell can play a useful role in lattice-matching techniques. Lattice-matching methods based on the reduced cell are used to locate lattices or derivative lattices that have the same metric parameters. Lattice-matching techniques based on the normalized reduced cell are designed to find lattices that have similar metric parameters. The two strategies are conceptually analogous. To understand how the normalized reduced-cell strategy works, first we summarize the reduced cell strategy, which is in common use.
Lattice-Matching Procedure Based on Reduced Cells
The basic identification strategy is to check the lattice of the unknown (or an existing lattice) against all lattices in a database for a match and then to exclude unwanted matches on the basis of chemical information. In this scheme, for example, an unknown crystal is selected and mounted on a single-crystal diffractometer and a unit cell is determined and reduced. The reduced cell is then checked against the file of known materials. If desired, one calculates derivative lattices, which are also reduced and checked against the file of known lattices.
Experience has shown that identification based on matching reduced cells is very straightforward and reliable. In fact research with the crystallographic databases has shown that the reduced cell coupled with the element types uniquely defines a material. Currently this identification strategy is used in association with several crystallographic databases that are distributed to the scientific community. It has also been integrated into automated single-crystal x-ray diffractometers [27]. Similarly, a registration-identification procedure based on reduced cells is used in the addition of new compounds to the Cambridge Crystallographic Database [28]. Further details on lattice matching, on a computer program for lattice matching, and on the calculation of derivative lattices have been published as an NBS Technical Note [25] and in Acta Crystallographica [26].
Lattice-Matching Technique Based on Normalized Reduced Cells
In a manner strictly parallel to the above, the normalized reduced cell can be used instead of the reduced cell in lattice-matching techniques. This is illustrated in Fig. 1 in which the normalized reduced cell has replaced the reduced cell. Here the basic search strategy is the same as above except that the normalized reduced cell is checked against the file (database) of normalized reduced cells for known materials. If desired, one calculates derivative lattices, which are also reduced, normalized and checked against the file of known lattices represented by their respective normalized reduced cells. The set of matches can be further restricted using chemical or other types of data. As most materials crystallize in the low symmetry crystal systems (e.g., over 90 % of organic and organometallic compounds crystallize in the triclinic, monoclinic, and orthorhombic systems), this type of lattice matching generally produces a limited and meaningful set of matches.
With this technique, the experimentalist can find metrically similar lattices. Knowledge of similar lattices has practical value in solving structures, Volume 108, Number 6, November-December 2003 Journal of Research of the National Institute of Standards and Technology in relating structures, in assignment of structure types, in materials design, and in nanotechnology. For example, information gained from a similarity search is valuable in the development of materials having a desired physical property.
If a given compound has the desired property, one can find all compounds with similar lattices, some of which may exhibit the specified property to a greater extent.
Conclusion
The normalized reduced form and cell represent practical mathematical tools for the analysis of intraand interlattice relationships. With respect to intralattice relationships, experience with thousands of lattices has revealed that the normalized reduced form is a very useful tool for the evaluation of lattice-metric symmetry as well as for the determination of other signif-icant relationships between the elements of the reduced form. Likewise, with respect to interlattice relationships, experience has shown that the normalized reduced cell is an excellent tool to determine metrically similar lattices.
In deducing significant interlattice relationships, one can systematically run the normalized reduced cell and reduced cell search in parallel with each other. It is suggested that such dual searching be routinely carried out-in data evaluation, in searching crystallographic databases, and in determining crystal structures-to ascertain the manner in which an extant or new lattice is related to the field of existing lattices. First, to find lattices that are metrically the same, the reduced cell can be matched against a file of reduced cells of known materials, and second, to find lattices that are metrically similar, the normalized reduced cell can be checked against a file of normalized reduced cells. | 2,770.6 | 2003-11-01T00:00:00.000 | [
"Mathematics"
] |
Fucoidan modulates SIRT1 and NLRP3 to alleviate hypertensive retinopathy: in vivo and in vitro insights
Background Hypertension influences the inflammatory pathological changes in the retina. The function of the inflammasomes is significant. To see if Sirtuin 1 (SIRT1) regulates angiotensin II (Ang II)-induced hypertensive retinopathy and inflammation by modulating NOD-like receptor thermal protein domain associated protein 3 (NLRP3) inflammasome activation and the potential protective effects of fucoidan (FO) in mouse retinal vascular endothelial cells (mRECs) and mice retina. Methods The diagnosis of hypertensive retinopathy was made after three weeks of Ang II infusion (3000 ng/kg/min). One day prior to the commencement of Ang II infusion, the mice were treatment with NLRP3 inhibitor MCC950 (10 mg/kg/day, intraperitoneal injections) or FO (300 mg/kg/day, oral gavage). A blood pressure was recorded. Hematoxylin and eosin (H&E) staining was used to conduct pathological alterations, dihydroethidium bromide (DHE) was utilized to assess oxidative stress damage in the retina, and fluorescence angiography was used to identify vascular disorders in the eye. Using immunohistochemical labeling, NLRP3 expression was found. Reactive protein and mRNA expression levels in mouse retina and cells were assessed using Western blot and real-time quantitative polymerase chain reaction (RT-qPCR). Results NLRP3 inflammasome activation and SIRT1 decrease were brought about by Ang II infusion. Retinopathy and dysfunction were lessened by MCC950 target-induced NLRP3 inflammasome activation, while overexpression of SIRT1 had the opposite impact on NLRP3 inflammasome activation, indicating that SIRT1 functions as an upstream regulator of NLRP3 activity. FO may improve SIRT1 expression and decrease NLRP3 activation in retinopathy and dysfunction brought on by Ang II, and the effects were consistent across both in vivo and in vitro models. Conclusions SIRT1 adversely regulates the NLRP3 inflammasome pathway, which in turn increases Ang II-induced inflammation and hypertensive retinopathy. FO may mitigate Ang II-induced retinopathy and dysfunction via modulating the expression of SIRT1/NLRP3. This implies practical approaches to the management of hypertensive retinopathy.
Background
Many vision-threatening eye disorders, such as non-arteritic anterior ischemic optic neuropathy, retinal vascular occlusion, retinal macroaneurysm, and hypertensive retinopathy (HR), are associated with an increased risk of hypertension [1].Furthermore, hypertension has been linked to the etiology of age-related macular degeneration and may worsen the vision-threatening consequences of diabetic retinopathy.Hypertensive retinopathy and choroidopathy, which are direct manifestations of chronic hypertension in the eye, are indicative of a systemic pathology that affects the whole body [2].
There is growing evidence that inflammation plays a role in the etiology of hypertension.[3].Inflammasomes are involved in the pathophysiology of several inflammatory disorders.A sequence of protein oligomerization events that trigger the identification of certain molecular patterns from infections, cellular injury, or changed homeostatic circumstances is required for inflammasomes formation [4].The NOD-like receptor thermal protein domain associated proteins (NLRPs), which include NLRP1, NLRP3, NLRC4, and missing in melanoma 2 (AIM2), are the four primary constituents of the inflammasomes.The most researched of which are NLRP3 inflammasomes [5,6].In the host's immunological response to infections and sterile injuries, the NLRP3 inflammasome is essential [7].One important stage in the activation of inflammasomes is the overexpression of NLRP3 [8].Two signals are needed for the NLRP3 inflammasome to activate.Nuclear factor kappa B (NF-κB) signaling is triggered in one way [9], while certain chemicals, such as mitochondrial reactive oxygen species, may activate a second signal [10].The activation process of cysteine protease CASP1 is carried out by the NLRP3 inflammasome, which provides a molecular platform for the release of mature CASP1 (CASP1 p20) and the production of proinflammatory cytokines, namely IL-18 and Interleukin-1β (IL-1β) [11,12].Even though the NLRP3 inflammasome has been studied in great detail, little is known about the endogenous mechanisms that control the NLRP3 inflammasome negatively.
Reactive oxygen species (ROS) is considered as one of the triggers of NLRP3 inflammasome activation.ROS are unstable and highly reactive molecules produced by reduction of oxygen mainly during mitochondrial oxidative phosphorylation.Excessive ROS production and/or failure of anti-oxidant defense systems result in oxidative stress leading to damage of cellular macromolecules including nucleic acids, proteins, and lipids, and has been implicated in pathogenesis of several diseases [13].A member of the NAD + -dependent deacetylase enzyme family, sirtuin (SIRT) controls a number of cellular targets and activities.The most extensively investigated is SIRT1.Both in vitro and in vivo, SIRT1 regulates the generation and build-up of ROS [14][15][16].Excessive inflammation brought on by ROS buildup results in mitochondrial malfunction and cell death [17].According to recent research, SIRT1 may inhibit inflammatory reactions that are mediated by the NF-κB signaling pathway.Conversely, SIRT1 also activates AMP-activated protein kinase alpha 1 (AMPK), peroxisome proliferator activated receptor alpha (PPARα), and peroxisome proliferative activated receptor, gamma, coactivator 1 alpha (PGC-1α), which collectively function as inhibitors of NF-κB signaling.These actions subsequently stimulate the production of oxidative energy and mitigate inflammation [18].However, it is uncertain whether SIRT1 could downregulate NLRP3 expression.
Due to its numerous beneficial properties, including its anti-inflammatory properties, fucoidan (FO), a fucoseenriched sulfated polysaccharide, has been widely utilized as a dietary supplement and health food [19][20][21].Research indicates that the administration of FO may mitigate renal fibrosis caused by diabetes by upregulating SIRT1 protein levels via overexpression [22].
In order to highlight a unique targeted method to treat HR, we investigated the effects of FO as a protective agent on SIRT1/NLRP3 in Ang II-induced retinopathy.
Animals
Wukong Biotechnology (Jiangsu, China) provided 40 8-week-old male C57BL/6 mice, which we used in our investigation as wild type (WT) animals.A week of adaptive feeding is required for all animals prior to experimentation.As previously mentioned, Ang II infusion (3000 ng/kg/min, aladdin, 4474-91-3) or saline infusion utilizing osmotic mini-pumps (ALZET MODEL1004, 28 days, DURECT, Cupertino, CA) for 3 weeks were used to produce hypertensive retinopathy model [23].All of the animals were sedated when we withdrew the ocular tissues after the Ang II and saline infusion.The Institutional Animal Care and Use Committee (IACUC) of Dalian Medical University authorized all animal experiments, and the research followed the NIH's (No. 85-23; Berthesda, MD, USA) Guide for the Care and Use of Laboratory Animals.
Inhibition of NLRP3 inflammasome in mice
The mice were administered with NLRP3 inhibitor MCC950 (10 mg/kg/day, intraperitoneal injections; HY-12815A, MedChem Express, Shanghai, PRC) in 200 μl of normal saline once daily from one day before Ang II infusion to the day of euthanasia (HR model) [24].
Fucoidan (FO) treatment in mice
The mice were fed with FO (300 mg/kg/day, HY-132179, MedChem Express, Shanghai, PRC) in 200 μl of normal saline once daily from one day before Ang II infusion to the day of euthanasia (HR model) [25].
Blood pressure monitoring method
The tail-cuff device monitored blood pressure.The mice were put on a fixator and allowed to adapt to a heating pad for 10 min before to the measurement.When the waveform was stable, the tail was completely exposed, and blood pressure readings were taken.At least five measurements were made of each mouse.
Fluorescence angiography
We used an intraperitoneal injection of 2.5% tribromoethanol (0.020 mL/g; Sigma-Aldrich, Dorset, UK) to anesthetize the mice.One compound-tropicamide eye drop was used to dilate each pupil, and then the eye was treated with ophthalmic gel (hypromellose 2.5% ophthalmic-demulcent solution; Gonak; Akorn, Lake Forest, IL, USA).The mice were subsequently given a tail vein injection of fluorescein sodium (13 mL/kg in saline; Alcon, TX, USA).After that, for five minutes, we used a retinal imaging equipment (OPTO-RIS; Optoprobe Science, Burnaby, BC, Canada) to take pictures of the retinal arteries every thirty seconds.The branch architecture and pulsatile activity of arteries were used to identify them.In order to determine the arteriovenous ratio for each mouse, we selected an identifiable anatomical site that was two optic-disc diameters from the optic disc.ImageJ (Rasband; NIH) software was used to compare measurements [25].
Histological analyses
The eye tissues were implanted in a paraffin block or OCT after being preserved with 4% paraformaldehyde for several days.The tissues from the eyes were cut into 8 μm fresh frozen sections and 4 μm paraffin sections.Dihydroethidium (DHE) staining was used to assess oxidative stress damage in the retina, while hematoxylin and eosin (H&E) staining was used to evaluate pathological alterations.Details on the DHE and H&E staining process were according the kit instructions.
Immunohistochemical staining
Briefly, the eye sections were incubated for 10 min with hydrogen peroxide, followed by an hour at room temperature with 5% BSA closure.Next, they were incubated at 4 °C overnight with a specific primary antibody, anti-NLRP3 (1:200, ET1610-93, HUABIO).The following day, the sections were washed with PBS and incubated for one hour at room temperature with horseradish peroxidase.Subsequently, the slices were examined under a microscope after being stained with DAB solution and then again with hematoxylin.
Cell culture and treatment
Procell (ml096624, mlbio, Shanghai, China) provided the mouse retinal vascular endothelial cells (mRECs), which were then cultivated in 89% high glucose-dulbecco's modified eagle medium (H-DMEM) + 10% fetal bovine serum + 1% penicillin/streptomycin.The cells were then incubated at 37℃ in a humidified environment with 5% CO 2 .Six-well plates were used to cultivate one million cells per well.In the tests, mRMECs at passages 3-6 were used.
Real-time quantitative polymerase chain reaction (RT-qPCR).
Using Trizol reagent, total RNA was isolated from both fresh and frozen retinal tissues.Next, cDNA is created by reversing the mRNA.Next, RT-qPCR was carried out using a particular primer set and SYBR Green mix.For the Gapdh gene, relative gene expression levels were adjusted.Table 1 had a list of the primer.
Statistics analysis
The mean ± SD is used to show the data.For RT-qPCR and Western Blot analysis, we calculated the data with the mean of housekeeping gene/protein to get the relative expression results.Then, Software called Graph Pad Prism was used to carry out statistical analysis.Dunnett's multiple comparison test and the control group were used after one-way ANOVA for statistical comparison.To compare the two groups, the student's unpaired t test was used.P values less than 0.05 were regarded as statistically significant.
Ang II infusion induces SIRT1 reduction and NLRP3 inflammasome activation
After Ang II (3000 ng/kg/min) or saline infusion for 3 weeks, we evaluated the level of Sirt1, Nlrp3 and Il1b mRNA.As shown in Fig. 1a, Ang II infusion decreased the mRNA level of Sirt1, while those of Nlrp3 and Il1b were increased significantly.The western blot results showed in Fig. 1b, c, the expression of SIRT1 was also decreased, the expression of NLRP3 and IL-1β and its bioactive form, IL-1β p17 were increased.Those results prompted that SIRT1 and NLRP3 inflammasome might involve in Ang II-induced HR.
Targeted inhibition of NLRP3 alleviates retinopathy and dysfunction in Ang II-infused mice
To investigate the role of NLRP3 inflammasome in Ang II-induced HR, we treated the mice with NLRP3 inhibitor MCC950 (10 mg/kg/day, intraperitoneal injections) one day before Ang II infusion (Fig. 2a), we found that after treatment with MCC950, the systolic blood pressure (SBP) was not decreased in Ang II-infused mice (Fig. 2b).H&E staining showed that the inhibition of NLRP3 reduced Ang II-induced central retinal thickening (Fig. 2c).We next detected the oxidative stress in each group, the results showed that in Fig. 2d, DHE Table 1 The details of primers used in RT-qPCR
Gene Forward primer (5′-3′)
Reverse primer (5′-3′) staining showed the inhibition of NLRP3 had significantly decreased Ang II-induced ROS production.Moreover, Ang II-induced impairment of the retinal arteriolar structure, as indicated by arteriolar narrowing (decreased artery-to-vein (A/V) ratio), tortuosity and exudation, was markedly better in the retinas of MCC950-treated mice than in those of PBS-treated controls (Fig. 2e).RT-qPCR results in Fig. 2f, g showed that NLRP3 inhibition reduced the levels of mRNA expression of NADPH oxidases (Nox1 and Nox4) and inflammatory (Il6 and Tnf) in Ang II-infused mice.
In addition, we tested the level of NLRP3 and IL-1β mRNA, the results showed in Fig. 3a, treatment with MCC950 inhibited the level of Nlrp3 and Il1b mRNA in Ang II-infused mice.Similarly, MCC950 significantly suppressed NLRP3 protein levels and IL-1β p17 secretion under Ang II infusion (Fig. 3b, c).The immunohistochemical staining of NLRP3 in Fig. 3d revealed that the retina's ganglia cell layer (GCL), inner plexiform layer (IPL), and inner nuclear layer (INL) were the areas where NLRP3 was most highly expressed.While levels in the outer layers increased following Ang II treatment, NLRP3 expression was significantly suppressed following MCC950 treatment.Above all, the findings indicated that the best option for blocking Ang II-induced HR and NLRP3 inflammsome activation is the NLRP3 protein inhibitor MCC950.
SIRT1 modulates Ang II-induced injury and NLRP3 inflammasome activation in mRECs
To assess whether SIRT1 plays a regulatory role in NLRP3 inflammasome activation, we treated the mRECs with SRT1720 (0.5 μM) for an additional 1 h and then treated with Ang II (100 nM) for 24 h.After treatment with SRT1720, the mRNA and protein levels of Nlrp3 and mRNA of Il1b and IL-1β p17 protein expression were both decreased in Ang II-treated cells (Fig. 4a-c).DCFH-DA staining showed that after treatment with SRT1720, the ROS production was reduced in Ang II-treated cells (Fig. 4d).Thus, the upregulation of SIRT1 plays an inhibitory role of Ang II-induced NLRP3 inflammasome activation and ROS production.
FO enhances SIRT1 expression and reduces NLRP3 activation in Ang II-treated mRECs
Fucoidans, which are extracted from various species of brown seaweeds, and have shown a wide spectrum of activities, such as anti-oxidation, anti-aggregation and anti-inflammation [29].To evaluate the therapeutic effects of FO, we continued to treat the cells with FO (60 μg/ml) for an additional 4 h, and then treated with Ang II (100 nM) for 24 h.The results showed in Fig. 5a, the production of ROS was decreased in Ang II-treated cells after FO treatment.Next, we tested the level of Sirt1 mRNA, the mRNA level of Sirt1 was increased by treatment with FO.Similarly, the expression of SIRT1 protein was upregulated after treated with FO (Fig. 5b-d).
In addition, we texted the mRNA and protein levels of Nlrp3, the expression level of Nlrp3 mRNA and protein were both decreased in Ang II-treated cells (Fig. 5b-d).
Those results suggested that FO could upregulate SIRT1 expression and reduce NLRP3 activation.
Activation of SIRT1 by FO reduces Ang II-induced retinopathy and NLRP3 inflammasome
We gave the mice with FO (300 mg/kg/day) one day before Ang II infusion (Fig. 6a).We found that after treatment with FO, the SBP was not decreased in Ang II-infused mice (Fig. 6b).H&E staining showed that FO treatment decreased Ang II-induced central retinal thickening (Fig. 6c).We next detected the oxidative stress in each group, the results showed that in Fig. 6d, DHE staining showed treatment with FO had significantly inhibited Ang II-induced ROS production.RT-qPCR results in Fig. 6e, f showed that FO reduced the levels of mRNA expression of Nox1, Nox4, Il6 and Tnf in Ang II-infused mice.
Next, we tested the level of Sirt1, Nlrp3 and Il1b mRNA, the results showed in Fig. 7a, treatment with FO increased the level of Sirt1 mRNA, and inhibited the level of Nlrp3 and Il1b mRNA in Ang II-infused mice.Similarly, FO significantly upregulated SIRT1 and suppressed NLRP3 protein levels and IL-1β p17 secretion under Ang II infusion (Fig. 7b, c).
(See figure on next page.)Fig. 2 Application of MCC950 decreased Ang II-induced hypertensive retinopathy, ROS production and inflammation.A The mice were treated with NLRP3 inhibitor MCC950 (10 mg/kg/day, intraperitoneal injections) one day before Ang II infusion, and then once/day for 3 weeks.B SBP of each group was recorded (n = 6).C Images of hematoxylin and eosin (H&E) staining of central retinal sections (left), the thickness was quantified (n = 6).D Dihydroethidium (DHE) staining of retina in each group (left), the quantification of DHE intensity (n = 6).E Typical retinal angiograms and fundus photos (left), these white arrows indicate vascular fluorescein leakage and the corresponding area in the fundus photo.The ratio of retinal arteriovenous and fluorescence intensity was quantified (right; n = 6).F qPCR analyses of inflammatory mRNA of Il6 and Tnf (n = 6).G qPCR analyses of oxidative stress mRNA of Nox1 and Nox4 (n = 6).***P < 0.0001 vs control; ns P > 0.05, # P < 0.05, ## P < 0.01, ### P < 0.0001 vs Ang II group
Discussion
It is well recognized that hypertension increases the risk of a number of illnesses, including heart failure, renal failure, stroke, disability, and early death [30].A number of pathophysiological changes brought on by hypertension may harm the retinal, choroidal, and optic nerve circulations, resulting in retinopathy, choroidopathy, and optic neuropathy, in that order, in the eyes [2,31,32].During the first phase, the retinal arterioles undergo vasoconstriction and localized vasospasm in response to high blood pressure.The local autoregulatory systems responsible for optimizing blood flow are the cause of the vasospasm.The clinical manifestation of these occurrences is a reduction in the normal arteries to vein ratio, which indicates either localized or global constriction of the retinal arteries.Over time, high blood pressure ).**P < 0.01, ***P < 0.0001 vs control; # P < 0.05, ## P < 0.01, ### P < 0.0001 vs Ang II group causes structural alterations in the arterial wall, including hyaline degeneration, mediawall hyperplasia, endothelial damage, and intimal thickening.This phase causes the vessel walls' focused or diffuse light response to be emphasized, as well as a shift in arteriovenous crossing or nicking [33][34][35].Numerous investigations have shown that inflammation is a significant factor in hypertensive retinal vascular damage and the retinopathy that follows [33].Our findings show that Ang II-induced retinal lesions in mRECs and mice were caused by inflammation and pyroptosis linked to the NLRP3 inflammasome, and that SIRT1 is an upstream negative regulator that blocks the NLRP3 inflammasome pathway.These results may have therapeutic implications since they point to possible mechanism-based medication approaches for the treatment of HR (Fig. 8).
In many disorders, the NLRP3 inflammasome is important in controlling the immune system's inflammatory reactions [5].According to recent research, individuals with hypertension consistently have elevated plasma levels of NLRP3 [36].The pro-inflammatory kind of cell death known as pyroptosis, which is brought about by NLRP3 activation, exacerbates the inflammatory response by causing the release of IL-1β and other proinflammatory intracellular components [37].Vascular dysfunction and pro-inflammatory cytokine levels may be correlated [38].Moreover, cytokine levels, including as Tnf, Il6, and Il1b, are downregulated when the NLRP3 inflammasome is inhibited [39].It is advantageous to inhibit the NLRP3 inflammasome in order to lessen inflammation and the pathological alterations that follow from inflammation [7].
Our research revealed that Ang II infusion may increase the expression of NLRP3 and reduce the level of SIRT1 (Fig. 1); these findings showed that Ang IIinduced hypertensive retinopathy and dysfunction may involve both SIRT1 and NLRP3.Next, we administered MCC950, an NLRP3 inhibitor, to the mice.Following MCC950 therapy, we observed that NLRP3 and IL-1β expression were suppressed, and that in Ang II-infused animals, NLRP3 suppression reduced retinopathy and dysfunction (Figs. 2, 3).In order to examine SIRT1's function in the Ang II-induced NLRP3 inflammasome and HR, we administered SIRT1 agonist, SRT1720, to the mRECs.The data in Fig. 4 demonstrated how Ang II-induced NLRP3 inflammasome activation and ROS generation are inhibited by SIRT1 overexpression.As a sensor and defender of the redox environment, SIRT1 is involved in the control of cell survival, apoptosis, and inflammation [40].It is a NAD-dependent deacetylase that controls how proteins function through lysine residue deacetylation.According to a publication, SIRT1 prevents NLRP3 inflammasome-induced IL-1β production, therefore shielding mesenchymal stem cells from radiation damage [41].SIRT1 may also deacetylate NF-κB to promote the suppression of NLRP3 inflammasome activation [7].Inflammation and cell pyroptosis linked to the NLRP3 inflammasome are negatively regulated by SIRT1, and this has an impact on avoiding Ang II-induced HR and malfunction.Furthermore, p53, another transcription factor that targets apoptosis-associated speck-like protein containing a CARD (ASC), which is necessary for NLRP3 inflammasome assembly, was affected by SIRT1's diverse deacetylase activity [42].Thus, it is plausible that SIRT1 acted as an upstream regulator of the activation of the NLRP3 inflammasome produced by Ang II in conjunction with the current investigation.SIRT1 overexpression could significantly decrease the inflammasome activation.In our present study, we found FO could inhibit apoptosis and improve cardiac remodeling by inhibiting tumor suppressor protein (p53) transcriptional activation through ubiquitin-specific protease (USP22)-SIRT1 [24].FO mainly extracted from brown algae is a fucose-enriched sulfated polysaccharide, and it has been widely used as a dietary supplement and health food due to its numerous beneficial effects, including anti-inflammatory, anticancer, and antidiabetic activities [18].Recent studies have found that FO reduced secretion and expression of vascular endothelial growth factor in the retinal pigment epithelium and reduced angiogenesis in vitro [43].Fucoidan is currently considered a functional food, but is also investigated in clinical trials [44].Its effects have been studied not only in vitro, but also in animal and human studies, were it exhibits an excellent toxic profile.While its oral availability is under debate, recent studies indicate a possible absorption of fucoidan by the gastrointestinal tract, which would render an oral application an attractive alternative to intravitreal injections [29].In our study, we found FO enhanced SIRT1 expression and reduced NLRP3 activation and retinopathy and dysfunction in Ang II-treated mice and mRECs (Fig. 5-7).This approach provides a potential targeted strategy to treat HR and dysfunction.But we have some limitations, one potential drawback is the study's applicability to human populations, which could be addressed by discussing any known similarities and differences in the SIRT1 and NLRP3 inflammasome pathways between mice and humans.Future research could focus on the exploration of the SIRT1/NLRP3 pathway in other models of hypertensive organ damage, and translation into clinical research.The clinical significance would be more compelling if it included functional endpoints that mirror human disease, such as vision acuity or electrophysiological assessments of retinal function.
Conclusion
This study has shown that Ang II-infusion caused HR and dysfunction through altering SIRT1 decrease and NLRP3 inflammmasome activation overexpression.
Here, we discovered that FO therapy decreased NLRP3 activation, retinopathy, and dysfunction while increasing SIRT1 expression.This method offers a possible focused treatment plan for dysfunction in HR.
1 NLRP3PSRT1720FFig. 8 A
Fig. 8 A working model of the mechanism by SIRT1/NLRP3 in Ang II-induced hypertensive retinopathy.Ang II infusion induced hypertensive retinopathy, ROS production, SIRT1 reduction and NLRP3 inflammasome activation.FO could rescue those reactions.SIRT1/NLRP3 might by a new sight of hypertensive retinopathy and dysfunction and FO might be used as an agent to protect against Ang II-induced hypertensive retinopathy | 5,107.2 | 2024-02-15T00:00:00.000 | [
"Medicine",
"Environmental Science",
"Biology"
] |
Literary Text as a Unit of Culture in the Classes of Russian as a Second Language
This article discusses some features of using literary text as a unit of culture as didactic material in the classes of Russian as a second language. The theoretical premise of our study is the postulates of modern linguistics, functional stylistics, cultural linguistics, and linguodidactics. This scientific paper presents the interrelation of the literary text and culture, the criteria for the selection of the texts, and describes some aspects of the work with literary text in teaching Russian as a second language The author proposes a methodological description of the use of literary texts in teaching Russian as a foreign language. The article concludes that the literary text is important cultural information that should be considered while studying the Russian language.
Introduction
Modern linguistic education emphasizes the interrelated study of language and culture, due to the communicative and cultural language teaching conception (Vereschagin, 2005;Shakirova, 2008;Bystrova, 2007;Soureshjani, 2013;Watson, 2002;Karabulatova, 2013;Ter-Minasova, 2008;Zamaletdinov, 2012;Fattakhova, 2014 etc.).In this regard, studies on intercultural and interlingual communication, cultural linguistics and ethnolinguistics, ethnopsycholinguistics, cognitive linguistics, comparative typology of languages and cultures, and the problem of text and ethnopedagogics and etnomethodic, in particular, are currently of interest.It should be noted that the results of linguistic research are of practical importance for the teaching of language methods.In the process of teaching Russian as a second language, the interrelation between language and culture is transmitted by literary texts, which are the main didactic material.It is well known that the text is a unified communicative, cultural, informative and semantic, compositional and linguistic entity.This article will examine some features of the literary text's use as a unit of culture, which is a didactic material in the classes of Russian as a second language.
Method
The study of the text as a unit of culture has attracted and continues to attract the attention of many researchers (Bakhtin, 1997;Deykina, 1998;Bolotnova, 2009 etc.).
Scientists concerned with such questions as what is the text of which characteristics of text are most important, which definition of the text is the most comprehensive, how through the text can be translated the culture, how to select texts in teaching Russian language and others.According to scientists, the greatest cultural information has artistic text.In this regard, we analyzed the scientific and methodological literature in terms of lingua methodic lighting it, the specifics of work with literary texts, but also studied the textbooks (textbooks and manuals) to determine the place assigned to it on cultural (artistic) texts.
In the modern methods of teaching Russian as a foreign language, literary text has a special place as a unit of culture, a form of communication and a didactic material, which have a great educational and aesthetic value.Some possibilities of the literary text are used for training, upbringing and initiation to Russian culture are considered in the studies of (Sayakhova, 2010;Hodyakova, 2011;Maslova, 2012;Zakirova, 2013;Mukhametshina, 2014 etc.).
It is well known that national and cultural peculiarities are brightly shown in literary texts.Literary texts are a source of culture and contain not only lexical and grammatical material, but also rich cultural information.In this regard, in modern linguistics and linguomethodic the approach to the text has become relevant as a cultural phenomenon, and is included in the scientific use of the term "cultural texts." A. Hodyakova defines cultural texts as "culturally-relevant texts, reflecting historical and cultural values of the people, their spirituality, aesthetic content, form, structure and lexicology.These texts often describe cultural objects (interpretive and commentary), artifacts, language phenomena, traditions of the people, religious rituals, social rituals, holidays, biographies of cultural personalities, historical and significant events or natural phenomena, providing an emotional and moral impact on the reader (listener), evoking a certain sense of goodness, justice, or indignation " (Hodyakova, 2011, p. 79).As a rule, cultural texts represent phraseological units, aphorisms, proverbs and sayings as a generalization of the historical experience of human life.The researchers L. G. Sayakhova call units of culture linguoculturems.The words, idioms, proverbs, sayings, folk omens, texts, and grammatical categories are related to them.The selection of the training content (hence, for linguistic and cultural analysis) is important for the formation of the students' linguistic and cultural competence in the study of the text.It should be done in accordance with the following criteria, which have been developed in linguistics: descriptive, evaluative, normative, spiritual, dialogic, informational, symbolic, and typological.Each of them is important in the formation of the students' linguistic and cultural competence.These criteria for the selection of educational material can determine the nature of didactic material -special texts (linguoculturem), reflecting the history of language and culture.A cultural approach to the study of literary text allows researchers to reveal the relation of the text as a communicative unit of a higher level with culture: 1) the text is generated by a certain social and cultural situation; 2) the text is generated in the context of time and in a particular cultural space, functions as a collective cultural memory, and reflects trends of the society development; 3) any text represents the author, the specific linguistic personality, who is a native speaker and of his culture's time; 4) the units of all language levels are presented in any text with their particular national and cultural components.These components include national and specific vocabulary (nonequivalent, background, connotative), word -symbols, word -concepts, idioms, etc." (Sayakhova, 2010, pp. 158-159).
Results
In the process of teaching Russian as a second language the use of literary text raises a number of questions related to its selection and adaptation.The selection of the text is connected with the age characteristics of students, the saturation of grammar material, the presence of educational potential, and relevance for contemporary students.Adaptation should not touch on the transformation of the literary text and be possibly minimal.Possible reduction of the text's separate components, and their exemption is shown by ellipsis.Textual material is not often extracted from literary works.It is often extracted from the existing educational literature (text books, collections of dictations, didactic material, etc.), where there are mistakes.
Following N. S. Bolotnova, we believe, that the selected texts must meet these criteria: 1) "the text should educate, i.e. meet the moral, ethical and aesthetic requirements; 2) convey new knowledge, i.e. perform an informative function; 3) impact the student's mind and heart, i.e. carry a pragmatic charge; 4) arouse interest to the subject, and enrich the student's specific knowledge, i.e. respond to general didactic requirements (continuity, consistency, clarity, accessibility, etc.)" (Bolotnova, 2009, p. 126).
The literary text as a unique speech unit allows to solve a range of problems, that are set in the teaching process.Besides learning the language system and getting skills of the construction of coherent speech text contributes to solve the students' cognitive problems, moral and aesthetic development, to enhance their speech and thinking activity and the formation of evaluations (how to properly evaluate).In accordance with the theme of the lesson and those concepts which should be learned, the selection of the text should take into account the pupils' age.
Artistic text links such important concepts as language, culture and people.As is well known, being accessory art style, artistic text is different: 1) the unity of aesthetic and communicative functions; 2) a lot of styles; 3) extensive use of figurative-expressive language means; 4) the manifestation of the creative personality of the author.In a literary text can be elements of other styles, such as dialect words, jargon, vernacular, vocabulary book, scientific terms, professionalism, archaisms, etc.They are used by the author of the text to create the voice characteristics of actors, local color.In literary texts much attention are drawn to the imagery and expression, expressive and emotive words.It is known that the word in the context of art is both nominative-communicative units and means of creating artistic expression, imagery.
In contemporary textbooks on Russian as a foreign language texts primarily transmit culture.The authors of modern textbooks focus on highly literary texts, saturated, on the one hand, with grammar material, and on the other hand, reflecting the cultural values of the Russian and other peoples.Literary texts as a source of culture hold a special place, because the reflection of reality in the linguistic picture of the world, connected with its literary display, is clearly shown in them.Through the text we can form language, communication, cultural knowledge, develop skills, and the text also helps organize the aesthetic, moral and patriotic education of students.Nowadays systematic standardized representation of literary texts with a national and cultural component has been received as the optimal solution by the authors of teaching materials for Russian as a second language under the guidance of E. A. Bystrova, L. Z. Shakirova, and L. G. Sayakhova.By using cultural texts, L. Z. Shakirova recommends that "we should consider the relation between language and culture in two ways: firstly, how students learn culture and traditions in the process of mastering the communicative function of the Russian language, and secondly, they deepen their knowledge of other cultures by means of the Russian language."(Shakirova, 2008, p. 55).In the Russian textbook of 6th form (Akhmadullina, 2006) the introduction to Russian culture and Russian worldview is provided primarily by texts and extracts of the best examples from literary works of the Russian writers and poets A. S. Pushkin, N. Nekrasova, L. Tolstoy, A. Kuprin, M. Prishvin, S. Yesenin, K. Paustovsky, G. Skrebitskogo etc. Key concepts are presented in the extracts: Motherland, friendship, labour, home, family, health, nature, book, knowledge, birch etc.They are close and understandable by students.Texts acquaint students with the life of the Russian great personalities of native and world culture: for example, the texts about P. I. Tchaikovsky, V. A. Mozart, I. I. Shishkin, I. E. Repin, A. S. Pushkin, L. N. Tolstoy, A. P. Chekhov, G. Tukae, D. S. Lihachev, etc. Exercises, which we do before and after reading texts, focus on the study of grammatical topics, and also organize discussions about the text.
The teaching material was selected in accordance with the lexical topics in the textbook: "Language is a means of communication and mirror of society's development", "Travelling all over the country, using the map", "The Forest in a person's life," "The Earth is our home", "The World of Music", etc. Thematically selected material allows us to more efficiently organize the work on students' vocabulary enrichment in the Russian lessons.
The genres of folklore have a worthy place in the textbooks of Russian language: riddles, proverbs and sayings.It is known that the knowledge of proverbs and sayings acquaints students with folk wisdom, enriches their vocabulary, helps them learn linguistic imagery, develops memory, logical thinking, and creative imagination.The ways of the realization of linguistic and cultural approaches in the teaching literature are represented in the scientific works of Z. F. Yusupova's (see, e.g., Yusupova, 2012).
Discussion
After the analysis of scientific and methodological literature, textbooks and manuals on Russian language suggests that today linguists and methodologists recognized the importance and value of the literary text as a unit of culture in the process of learning the Russian language.We believe that cultural studies insufficiently realized the cultural studies potential of literary texts in the process of learning the Russian language.Teachers have difficulty in lingo cultural studies text analysis.We propose to consider two passages from the artistic text, which can be discussed with the students in the study of the Russian language.
Cultural texts help expand the cultural horizons of students, enrich the students' vocabulary with the cultural words, and the terms and vocabulary used in the socio-cultural sphere.For example, the text about the Russian oven: …As I took a closer look, the Russian oven is inconvenient for cooking: cooking is hidden from the cook, and the heat comes to the pot from the different sides unevenly.But because it might have come to our ancestors from the Stone Age, it was heated till the dawn, and kept the food warm all day and slop for cattle, food and water for humans.And it is warm to sleep on.(Solzhenitsyn, 2001, pp. 153-154) In this extract, the small cultural information about the Russian oven is presented, which performs a symbolic role in the Russian izba (peasant's house).The Russian oven is arranged by taking into consideration the average cold climate, especially in northern Russia.So as it was heated in the morning, the oven kept warm until late night, and let us cooks food for people and feed for cattle.The image of the Russian oven is often found in fairy tales, proverbs and sayings, folk songs, and literary works.All this allows us to formulate a conversation with students about the peculiarities of the Russian oven.For example, the text about the Russian dances: .....Grandma wasn't dancing, but she was saying something.Here she is going quietly, thinking, rocking, looking around out of hand, and all her big body is swaying hesitantly, legs are groping the road carefully.Suddenly she stopped frightened of something, face quivered.She frowned and immediately lit up with a good friendly smile.She shrank away, giving way to someone, taking someone by her hand; put her head down, froze, listening, smiling merrier -and suddenly she jumped up like a whirling vortex, becoming slimmer, taller, and so it was impossible to look away from her eyes -she was becoming so wildly beautiful and nice in these moments of the wonderful change to youth! (Gorkiy, 1979, pp. 231-232) In this passage, the author shows the mood of the Russian dance, and reveals the soul of Russian people.Russian dance can be slow and fast, solo, pair, and mass.Dance includes round-lays, improvisation, the elements of the quadrille dances and others.We can talk with students about Russian dance as a folk dance, and other dances of the peoples in the world.
In a literary text we find information about national holidays, such as Mardi Gras, Easter, Christmas and others.For example, students can learn about the features of this holiday as Mardi Gras through a passage from a work of art: "Mardi Gras <...> And evennow feel that word, as it felt when I was a child: bright spots, call causes it in me; blazing furnace, bluish waves in happy hum of the assembled people, bumpy snowy road already oily in the sun, with diving on her merry sleigh with cheerful horses in rosanna, in campanula and bells, with playful harmony busting <...>Mardi Gras, walk!In this broad word and now even for me alive bright joy, beforesadness -before Easter?" (Shmeliov, 2001, p. 223) By analyzing this text, students find out the value of holiday Mardi Grasfor the Slavic people.Mardi Gras -an ancient national festival farewell of winter and meeting ofspring.It was customary for a week pancakes, each day of Mardi Gras had own value.Besides, in this passage should clarify the meaning of suchwords as people, bumpy road, the horses in rosanna, harmony, Easter.
Conclusions
The study of some aspects of the literary text's use as a unit of culture in the lessons of Russian as a second language leads to several conclusions.Nowadays in the language teaching (native, Russian and foreign languages) intercultural and cross-language peculiarities are taken into account (for example, Sadykova, 2009;Yusupova, 2014;Shakurova, 2014).It is an undisputed fact that literary text has a great cultural potential.By using the "textual" fund, we can acquaint students with the holidays and traditions of Russian and other peoples, objects of decorative art, folk songs, dances, musical instruments, etc.All these arouse a lively interest in the students to the teaching material, and to the language as a translator of culture.Despite the availability of the texts with cultural content in modern textbooks of Russian language, we believe that their opportunities are not fully realized, therefore cultural texts have more opportunities to be used in Russian language lessons with the aim of forming the language, communication, and cultural competencies, providing the content of the student's speech development, his/her intellectual, moral and spiritual spheres as a way of the creative person's self-realization.Thus, co-learning of languages and cultures by literary texts generates communicatively developed, culturally educated person, and develops and improves a student's language and communication skills in a contemporary multi-ethnic world.
However, in one article the author has failed to show all the potential uses of artistic text in learning the Russian language.In the future, could explore this topic, show samples of linguistic-cultural analysis of literary texts in the study of such sections of the Russian language like, phonetics, orthoepy, vocabulary and phraseology, morphology and syntax. | 3,801 | 2015-02-25T00:00:00.000 | [
"Linguistics",
"Art",
"Education"
] |
Camouflage Generative Adversarial Network: Coverless Full-image-to-image Hiding
Image hiding, one of the most important data hiding techniques, is widely used to enhance cybersecurity when transmitting multimedia data. In recent years, deep learning-based image hiding algorithms have been designed to improve the embedding capacity whilst maintaining sufficient imperceptibility to malicious eavesdroppers. These methods can hide a full-size secret image into a cover image, thus allowing full-image-to-image hiding. However, these methods suffer from a trade-off challenge to balance the possibility of detection from the container image against the recovery quality of secret image. In this paper, we propose Camouflage Generative Adversarial Network (Cam-GAN), a novel two-stage coverless full-image-to-image hiding method named, to tackle this problem. Our method offers a hiding solution through image synthesis to avoid using a modified cover image as the image hiding container and thus enhancing both image hiding imperceptibility and recovery quality of secret images. Our experimental results demonstrate that Cam-GAN outperforms state-of-the-art full-image-to-image hiding algorithms on both aspects.
I. INTRODUCTION
Image hiding [1] is an important data hiding technology that allows to conceal a secret message into images when using public communication channels in order to transmit sensitive information, with many image hiding algorithms have been developed to enhance data security [2], [3] . The design of these algorithms focusses mainly on optimising a trade-off between increasing the hidden capacity to embed secret images and preserving their imperceptibility to a third party.
Conventional image hiding techniques, such as LSB [4], HUGO [5], WOW [6], and S-UNIWARD [7], exploit the less significant components from either the image spatial space or frequency space to embed a secret message to achieve imperceptibility. These conventional image hiding methods unavoidably leave traces of the modification which might be detected by some advanced steganalysis tools.
Coverless image hiding via texture synthesis has become one of the key mechanisms to solve this problem [8]- [11]. These methods design mapping functions based on convolutional neural networks to transfer secret images into another synthesised texture form. Consequently, the embedding stage in the classical image hiding pipeline is not required, making hidden messages much harder to be detected from container images [8].
However, insufficient hidden capacity is one of the major issues of both conventional image hiding techniques and stateof-the-art coverless methods. None of these methods is capable of hiding a full-size secret image. This problem hinders further development of image hiding techniques to satisfy the increasing demand of protecting privacy and security of largesize multimedia data.
Recently, several advanced deep learning-based methods have reached the capacity of a full-size secret image, thus supporting full-image-to-image hiding [12]- [15]. These approaches minimise both the error between cover and container image and the reconstruction error of the secret image using convolutional neural networks. However, an optimal balance between imperceptibility of the container image and reconstruction quality of the secret image is difficult to achieve in these end-to-end deep learning image hiding methods.
In this paper, we propose a two-stage coverless fullimage-to-image hiding algorithm named Camouflage Generative Adversarial Network (Cam-GAN) by exploiting both the advantage of coverless image hiding and deep learningbased techniques to achieve full-image-to-image hiding with insignificant reconstruction error of the secret image. The key contributions of Cam-GAN are as follows: (i) A coverless image hiding structure based on image synthesis is deployed to address the error trade-off for better imperceptibility and higher reconstruction quality of the secret image. (ii) A cycle consistent GAN is designed to ensure full-image size hidden capacity by synthesising realistic container images. To our knowledge, it is the first coverless image hiding algorithm that can achieve full-image size hidden capacity. iii) Another refining GAN is introduced together with the cycle consistent GAN as the image hiding decoder to further improve the reconstruction quality of the secret image. The remainder of the paper is organised as follows. In Section II, we explain the details of our proposed Cam-GAN method. Section III presents experimental results that demonstrate the superiority of our approach. Finally, conclusions are drawn in Section IV.
II. CAM-GAN NETWORK
As illustrated in Fig. 1, we propose a two-stage generative adversarial network (GAN) model for image hiding. The idea is mainly inspired by recent advancements of GANs [16], [17]. Our proposed framework comprises two components: an image hiding encoder and an image hiding decoder. The image encoder is composed of a generator G 1 in Cam-GAN Stage I which synthesises a container image to hide secret images via a texture synthesis function. The image decoder is composed of the other generator, G 2 , in the Cam-GAN Stage I network, and the generator G 3 in the Cam-GAN Stage II network to recover the secret image. In the following, we explain the details of our proposed architecture and its loss functions.
A. Cam-GAN Architecture
In Cam-GAN Stage I, an asymmetric cycle consistent GAN is trained to hide a secret image into a container image via an encoder generator G 1 , and then recover the secret image with insignificant quality loss via a decoder generator G 2 . Here, G 2 is an approximate inverse function of the generator G 1 . The Arnold transformation [18] is applied to scramble the secret image before feeding it into G 1 to conceal the global structure of the secret image. The network architectures of G 1 and G 2 are illustrated in Fig. 2. They comprise four convolutional layers, nine residual network (ResNet) blocks and four deconvolutional layers. This architecture has been successfully deployed in various image transfer tasks [17]. Furthermore, a discriminator D 1 is used to make the container image hard to be discriminated from the gallery of images. This discriminator is a CNN with four convolutional layers and one fully connected layer as illustrated in Fig. 3. The output of the discriminator is a possibility value indicating whether the input image belongs to the image gallery or a synthesised image. The training stage performs adversarial learning as the generators learn a mapping function to synthesise more realistic images so that these images can fool the discriminator.
In Cam-GAN Stage II, a refining GAN is trained to improve the quality of the recovered secret image. The generator G 3 learns the mapping between the recovered and original secret images to improve the recovery quality, while a discriminator D 2 is employed to further enhance the reconstruction quality.
ReflectionPad (3,3) +Conv2d 7x7/1 +Tanh ReflectionPad (3,3) As illustrated in Fig. 4, G 3 uses a U-Net architecture which contains nine convolutional layers and nine deconvolutional layers and shortcut connections between the convolutional and deconvolutional layers to reuse the feature representations generated from different layers via various convolutional kernels for better recovery quality. The discriminator D 2 shares a similar architecture with the discriminator D 1 (Fig. 3) although the first layer has a different input size.
B. Cam-GAN Loss Function
In Cam-GAN Stage I, there are two terms in our loss function: a reconstruction loss term L 1 defined as and a discriminator loss term L 2 defined as leading to (3) Here, x represents the secret image, y one of the gallery images, G 1 and G 2 denote the two generators to encode and decode the secret image, D 1 is the discriminator to make the synthesised container image more realistic, and λ 1 is a weight to balance the reconstruction and discriminator terms. With the iterative update of D 1 , G 1 and G 2 , the encoder and decoder generators can be converged to hide secret images with texture synthesis.
In Cam-GAN Stage II, we also have a loss function with two terms, a reconstruction term L 3 defined as and a discriminator term L 4 defined as giving where x represents the secret image, x the recovered secret image from the first stage, G 3 is the generator to refine the quality of the secret image, D 2 the discriminator to enhance the recovered secret image with higher quality, and λ 2 is a weight to balance the reconstruction and discriminator terms.
C. Cam-GAN Implementation
For the implementation, the weight parameters in the loss functions, λ 1 and λ 2 , are set to 10 and 100, respectively, while as training parameters we employ a batch size of 1. The number of epochs of the asymmetric cycle consistent GAN is 300 to 400, while the number of epochs of the refining GAN is fixed to 300. Both learning rates are set to 0.0002 with a linear decay after 150 epochs. For different secret images, we use individual keys to randomly choose the asymmetric cycle consistent GAN models trained after different epochs (randomly selected from 300 to 400) to further enhance the security.
A. Experimental Setup
In our experiments, we first evaluate the image quality of the encoder and decoder of Cam-GAN both subjectively and objectively. We then test the performances of Cam-GAN against steganalysis. To ensure a fair comparison, we use a several image hiding algorithms which have full-image-to-image hidden capacity, including [12] and [13] as benchmark methods, because both image quality and hiding undetectability are directly affected by the size of the hidden message. Further, we compare the hidden capacity of Cam-GAN with [8], [10], [11] to demonstrate superiority compared with state-of-the-art coverless image hiding algorithms.
Our dataset includes 400 gallery images of paintings [17] and 1200 secret images to hide. The image size of both gallery and secret images is 256 × 256 pixels. The secret images contain 400 facades [19], 400 faces [20] and 400 aerial images [17]; 800 of them are used for Cam-GAN training, while the other 400 are used for testing.
B. Image Quality Evaluation
We first evaluate the performance of the Cam-GAN image hiding encoder and decoder visually, and then provide quantitative comparison results of the reconstruction performance against the benchmark methods in Table I. Note that the proposed method is a coverless image hiding technique, and thus there is no cover image needed in the hiding process. Fig. 5 and Fig. 6 illustrate that our proposed Cam-GAN can generate realistic container images. In addition, it yields the best reconstruction quality of secret images. As shown in Fig. 5, the residual cover images of both [12] and [13] clearly show patterns from the secret images that can be easily detected by visual analysis. In contrast, the synthesised painting images from Cam-GAN are visually similar to real paintings. The significant advantage of our proposed method is that the container image can better fool both human perception and machine steganalysis without modifying cover images as in the other methods. As illustrated in Fig. 6, for Cam-GAN, the residuals between recovered and original secret images are much more insignificant compared to the other algorithms, demonstrates Cam-GAN to outperform them in terms of recovery quality for the secret image.
To further demonstrate the effectiveness of our proposed Cam-GAN, an objective image quality comparison is performed based on pixel errors, peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).
PSNR is defined as where I is the original image, I the recovered image, H and W are the image dimensions, and (i, j) indicate pixel coordinates. SSIM is calculated as where µ x , µ y are the averages of x and y, σ 2 x , σ 2 y are the variances and σ xy are covariance for x and y respectively. C 1 and C 2 are balancing constants.
Lower pixel errors, a higher PSNR and a higher SSIM on the secret image (cover image) indicate better recovery quality (image hiding imperceptibility).
The obtained results are shown in Table I from where we can see that the average pixel errors of each channel of the cover images are 9.83 and 8.92 for [12] and [13], respectively. Our proposed Cam-GAN completely avoids using a modified cover images by synthesising new container images, which ensures higher security. Moreover, the average pixel error of the recovered secret image is 2.85 for Cam-GAN, which is significantly lower than for the other two methods, while we also obtain better results in terms of both PSNR and SSIM, clearly demonstrating superior recovery quality.
C. Steganalysis
Avoiding detection from automatic steganalysis tools is another important performance measure of image hiding methods. Following [12], we use a publicly available steganalysis tool, StegExpose [21], for this purpose. We perform detection on a dataset of 400 clean images from [17] and 400 container images from Cam-GAN, [12] and [13] (i.e., 800 images for each method).
A hypothesis test is used to evaluate the efficacy of the proposed method due to the following two reasons: (1) visual comparison based on the receiver operating characteristic (ROC) curve is subjective; it is hard to judge which algorithm performs better if the ROC curves from two methods have many overlapping regions; (2) the curves become unstable when the test sample size is relatively small since the area under the curve (AUC) can change significantly when adding or removing samples. A significance level p from the hypothesis test can provide an objective comparison by calculating a reliable statistical indicator. We assume that the null hypothesis H 0 is that the ROC curve generated from an algorithm has no significant difference to that obtained from random guessing. Once a p-value is higher than 0.05, this indicates there is no statistically significant difference to the null hypothesis, while a higher p-value indicates better imperceptibility. The obtained p-values are 0.098 for Cam-GAN, 0.0056 for [12], and 0.033 for [13]. Thus, for the two benchmark methods, the p-values are below 0.05 and thus indicate strong evidence against the null hypothesis. Cam-GAN achieves the best result with a p-value larger than 0.05 thus obeying the null hypothesis that there is no statistical difference between our algorithm and random guessing, confirming the hiding imperceptibility capability of our proposed algorithm,
D. Hidden Capacity
Finally, we further compare our Cam-GAN and three coverless image hiding algorithms via image synthesis [8], [10], [11] in terms of hidden capacity as shown in Table II. From there, we can find that Cam-GAN enlarges the hidden capacity significantly compared with these state-of-the-art coverless image hiding algorithms and only our approach yields a capacity 24 bits per pixel (bpp) to enable full-image-to-image hiding.
IV. CONCLUSIONS
In this paper, we have proposed Cam-GAN, a novel coverless full-image-to-image hiding algorithm. To our best knowledge, it is the first coverless image hiding algorithm that can ensure full-size image hiding capacity. Extensive experimental results have demonstrated that our proposed method also outperforms state-of-the-art full-image-to-image hiding algorithms. On one hand, Cam-GAN can generate realistic container images that are difficult to distinguish from gallery images in order to hide the secret image. In this manner, image hiding imperceptibility is significantly enhanced. On the other hand, recovery of the secret image is accompanied by only insignificant image quality loss via a two-stage adversarial learning network. In future work, we will investigate the use of the proposed method in real-world applications. original secrect images recovered secrect images of [12] residual secrect images (×5 ) of [12] recovered secrect images of [13] residual secrect images (×5 ) of [13] recovered secrect images of Cam-GAN residual secrect images (×5 ) of Cam-GAN original secrect images original cover images modified cover (container) images of [12] residual cover images (×5 ) of [12] modified cover (container) images of [13] residual cover images (×5 ) of [13] synthesed container images of Cam-GAN | 3,578.4 | 2020-08-21T00:00:00.000 | [
"Computer Science",
"Engineering"
] |
Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems
Multiple response optimization (MRO) problems are usually solved in three phases that include experiment design, modeling, and optimization. Committee machine (CM) as a set of some experts such as some artificial neural networks (ANNs) is used for modeling phase. Also, the optimization phase is done with different optimization techniques such as genetic algorithm (GA). The current paper is a development of recent authors’ work on application of CM in MRO problem solving. In the modeling phase, the CM weights are determined with GA in which its fitness function is minimizing the RMSE. Then, in the optimization phase, the GA specifies the final response with the object to maximize the global desirability. Due to the fact that GA has a stochastic nature, it usually finds the response points near to optimum. Therefore, the performance the algorithm for several times will yield different responses with different GD values. This study includes a committee machine with four different ANNs. The algorithm was implemented on five case studies and the results represent for selected cases, when number of performances is equal to five, increasing in maximum GD with respect to average value of GD will be eleven percent. Increasing repeat number from five to forty-five will raise the maximumGD by only about three percent more. Consequently, the economic run number of the algorithm is five.
Introduction
Multiple response optimization (MRO) problems need to find a set of input variable values (x's) which get a desired set of outputs (y's).The current study develops a proposed algorithm in recent authors' work to solve MRO problems [1].MRO solution methodologies usually include three phases: experiments design, modeling, and optimization.
There are some techniques for experiments design.Some methodologies in this phase are as follows: design of experiments (DOEs) knowledge such as factorial design and fraction factorial design, response surface methodology (RSM) such as central composite design (CCD), and Box Behnken [2,3].Furthermore, Taguchi orthogonal arrays [4][5][6][7] are derived from the Taguchi method.
Modeling as the second phase is done using different mathematical or statistical models such as multiple linear and nonlinear regressions in the form of polynomials [2,8,9] and artificial neural networks (ANNs).Due to the existence of complicated relationship between inputs and outputs, usually ANNs are mostly used for modeling rather than for polynomials.One famous artificial neural network (ANN) is back propagation neural network (BPNN) that is used in many engineering problems [10,11].Cheng et al. [12] utilized MANFIS (multiadaptive neuro fuzzy inference system) for modeling and showed that the results are superior to RSM polynomial models.
The last phase is optimization, which is usually done on a performance metric such as global desirability function.In this process, each predicted response is converted to a value between 0 and 1.Finally, a composite function is defined which converts all desirability functions to a unique number by global desirability function (GDF).Also, Chatsirirungruang and Miyakawa [13] proposed a combination of Taguchi and GA to get more accurate responses by using the benefits of both techniques together.
Neural Networks and Committee Machine
Different kinds of neural networks are used to model in complicated prediction problems.Four neural networks are used in this study that include feed forward neural networks (FF) [14], radial basis function networks (RBFNs) [15], generalized regression neural network (GRNN) [16], and adaptive neural fuzzy inference system (ANFIS) [17,18].
A committee machine (CM) is a collection of some intelligent systems named experts and a combiner which combines the outputs of each expert (Figure 1).The advantage of CM is that it reaps the benefits of all work with only little additional computation.Independent variables are entered for experts, and all experts' outputs are transferred to a combiner to get the final response.
One of the most popular methods to combine the experts' outputs is the simple ensemble averaging method according to (1) [19].Furthermore, a combiner could be an intelligent system such as a neural network.Consider where is the weight coefficient of th expert, is the estimated response from th expert, and is the total number of the experts [20].
Genetic algorithm could be used to yield the experts' contribution (weights) in a committee machine.Equation (2) represents that the committee machine gives smaller errors than the average of all the experts [20,21]: where = ANN− real is the error of predicted and real response of each expert and 2 is the squared error for the th expert.Error ave is the average error for all experts and Error CM is the error of CM.
Global Desirability and Genetic Algorithm
Overall, desirability or global desirability function is used to transmit multiple responses to a single response case.Desirability function converts each estimated response into a dimensionless desirability value .It gets values according to the kind of objects in the problem.These conditions are shown in (3), (4), and (5) [31,32].
Desirability Functions Formula with Different Objects.
The desirability for goal of "Target:" The desirability for goal of "Maximum:" The desirability for goal of "Minimum:" [22] 2008 RSM RSM Graphical Chang [5] 2008 Taguchi ANN SA Chatsirirungruang and Miyakawa [13] 2009 Taguchi Taguchi GA Cheng et al. [12] 2 0 0 2 R S M A N N G A Cojocaru et al. [23] 2009 Full factorial MLR Graphically Martinez Delfa et al. [24] 2009 RSM RSM, ANN Mathematically Mukherjee and Ray [10] 2008 N/A ANN Modified TS Nagesh and Datta [25] 2010 Fractional factorial design MLR, ANN GA Noorossana et al. [11] 2008 RSM ANN, FS GA Pasandideh and Niaki [9] 2 0 0 6 R S M R S M G A Patnaik and Biswas [26] 2007 Taguchi Taguchi (S/N) Weighting Pizarro et al. [27] 2006 Taguchi RSM Graphically 4 Advances in Artificial Neural Systems where the parameters and in the formulas are convexity coefficients and specify how strictly the target value will be desired.In the current study, and are equal to one.Global desirability (GD) function is according to (6): Equations from (3) to ( 5), yield the single desirabilities for different objects and ( 6) calculates the global desirability (GD).Both 's and GD values range vary from zero to one.In the MRO problems, it is important that all responses optimize simultaneously, and GD is a suitable performance metric to achieve this target.Genetic algorithm (GA) is a population-based search technique, which can quickly and reliably solve problems that are difficult to tackle by traditional methods.One advantage of GA is that it is extensible and can interface with existing models and hybridize with them and optimizes the fitness function [33,34].
Also Brie and Morignot [35] state that genetic algorithm has stochastic nature, and consequently, the results may highly vary from test to test, even for the same problem and parameter set.
Different methods have been proposed in the literature for the optimization of multiple response problems.Table 1 shows corresponding techniques.In this table, some include only investigation for analysis and comparison not optimization.
As a consequence, by reviewing the above works and other works in the literature, since the genetic algorithm has been widely implemented by the researchers for optimization phase of MRO problems with respect to other techniques, this metaheuristic algorithm was selected as the optimization technique.
Methodology
First of all, an important matter is the selection of data for training and testing of model.Dixit and Chandra [36] have suggested a selection method for ANNs.According to their suggestions, for n inputs, the minimum number of training set should be such that it includes the corners of n-dimensional space with respect to more contribution to input variables with more influence on output.In the current investigation, this suggestion was applied for corners of lower and upper limits for all independent variables.Also, training and testing dataset numbers were 80 and 20 percent, respectively.
Different criteria are used to assess forecasting models performance.Two criteria were selected in the current work, which compare models' results with the observed or real data.They are root mean square error (RMSE) [37] and correlation coefficient () [38].
where ŷ is th predicted value or model output, is the th actual value, and is the number of data used for prediction.Furthermore, and ŷ are the means of actual and predicted values [39].The current work includes two conditions to build ANNs model: first is that RMSE for all data is the minimum and the second condition is that the correlation coefficient of testing data is positive.Usually, MRO solution includes three phases.Phase one is experiments design, in which in the current work, all data are selected from the literatures.The second phase is modeling which is done by building four different neural networks and a committee machine.ANNs include feed forward, RBF, GRNN, and ANFIS models.All neural networks have the same inputs and one output, and so the number of ANNs in each model is equal to the number of responses (Figure 2) [1].
A committee machine (CM) was made by a combination of all four ANN models (Figure 3). inputs are entered for each expert of CM simultaneously, and responses are multiplied to their weights and then are added together to get the final response.Committee machine combiner is an ensemble averaging.Genetic algorithm (GA) computes CM weights with the object to minimize RMSE of CM response.So the weight matrix is an * matrix.
The object of the current study is to find the economic performance number of the committee machine and genetic algorithm to get the best responses in MRO problems solving.Therefore, firstly, four ANNs and one committee machine were created separately.Committee machine weights were calculated by means of GA with the object of minimizing overall RMSE.Then in the optimization phase, GA yields the best responses with the object of maximizing global desirability.The result is * and * with the highest possible GD.These calculations of finding CM weights and * were repeated 45 times.The schematic of the methodology is shown in Figure 4 and corresponding algorithm (Algorithm 1).
Results and Discussion
Genetic algorithm is applied in two steps.The first step is to find CM weights with the object of minimizing the overall RMSE of CM, and the second step is to find the 's by GA and ANNs with the object of maximizing global desirability.In both steps, GA specifications are listed in Table 2.
The current algorithm is implemented on five MRO problems.These problems include different numbers of inputs and outputs and different numbers of experiments.Table 3 represents their properties.
Case 1.The first problem is based on the wire-bonding process in the semiconductor industry.Table 4 represents the process inputs and outputs.Different neural networks Case 2. The problem is to optimize the yield of recombinant Oryza sativa nonsymbiotic hemoglobin 1 in a medium containing byproduct glycerol.Table 7 represents the input and output variables of this case.
Case 3. The problem is multiple response optimization of styrene-butadiene rubber (SBR) emulsion batch polymerization.Table 8 lists the input and output variables.
Case 4. The object of this case is to optimize process variables, electrolysis voltage, and treatment time for the electrocoagulation removal of hexavalent chromium (Cr(VI)).Table 9 represents the input and output variables.
Case 5.The problem is to optimize multiple characteristics in CNC turning of AISI P-20 tool steel using liquid nitrogen as a coolant.Table 10 lists the input and output variables.
In all five cases, the CM responses that include GD and RMSE were calculated 45 times.The results of GD are listed in Tables 11 and 12, representing the statistical results.Case 1 was eliminated in the calculations and the reason is due to the existence of zero values in GD; the increasing of maximum GD to average GD is very high and this can mislead us to unmoral results.So only cases from two to five are considered and this will yield smaller increase, but more reliable.
The GD ratio is defined in formula (8) and represents ratio of increasing maximum GD to average GD: GD ratio (for runs) = Max GD − Ave.GD Ave.GD × 100.(8) Advances in Artificial Neural Systems Also, to investigate for ANNs behavior, the results of five runs are listed in Table 13.For abstract only Case 2 is listed.Table 14 represents statistical results of this case.It is obvious that in both CM and ANNs, RMSE is constant for all run numbers.Table 13 shows this reality for Case 2 with ANNs models.Table 13 shows there is no significant difference between GD values with respect to run numbers for different ANNs runs.Table 13 represents, for all case, that there is an increase in the mean of GD ratio (or mean of increasing the maximum GD to average GD) with respect to increasing the run number.
Figure 5 shows the corresponding results graphically and it illustrates that for committee machine, if the program performs, for example, 3 times, the maximum to average will increase to 7.8%.In addition, it shows that if the program runs 5 times, the maximum value of GD can increase to 11.7% with respect to average.From run numbers 5 to 8, there is a slight rise about 1.6%.From run numbers 8 to 10, there is a relatively fall in GD ratio.Then from run numbers ten to forty-five, there is no significant rise in GD ration and it is only 2.3 percent (from 12.8% to 15,1%).Consequently, the economical run number for the algorithm is five times.Because by consuming time from 5 to 45 times will increase GD ratio about 3.2% (15.1%-11.7%)whereas run number equal five times has 11.7% and more than 3 times.
Table 13 shows for different ANNs run numbers, there is only about 1% increasing in GD ratio for run numbers more than one and this rise is not noticeable, because increasing 1% is due to nature of GA.So to run more than 3 times for neural networks models has no noticeable effect to increase GD ratio.
Conclusion
Multiple response optimization (MRO) problem solving is usually done in three phases that include experiments design, modeling, and optimization.Committee machine (CM) as a collection of some experts such as some artificial neural networks (ANNs) can be used in the modeling phase of MRO.Genetic algorithm is used to find CM weights in the modeling phase and also as main optimization techniques in the optimization phase.
The current study modifies a proposed algorithm from recent works of authors that had used CM and GA to solve MRO problems.Due to stochastic nature of GA, the final solutions vary together and different performances will yield different responses with related global desirability (GD).So since object of MRO is to find responses with highest GD, to know economic run number will be useful to obtain best responses in minimum possible time.According to this investigation and for the selected MRO problems, the results represent that the economic run number of the algorithm is five.With five run numbers, maximum global desirability of final solution can increase about 11 percent in concern with average of GD.Whereas, to run the algorithm from five to forty-five numbers, the maximum of global desirability can increase only about 3 percent more.
Figure 1 :
Figure 1: A typical architecture of a committee machine based on static structure.
Figure 2 :
Figure 2: Inputs and outputs of every model.
Figure 5 :
Figure 5: GD ratio increasing with respect to number of runs.
Table 1 :
Classification of some works in MRO subject in the literature.
Table 4 :
, * and GD( * ) using GA for goal of maximizing in Global desirability Input and response variables and optimization criteria for every response (output) in Case 1. Target 4 : maximum temperature at position B ( ∘ C) Target 5 : beginning bond temperature at position B ( ∘ C) Target 6 : finish bond temperature at position B ( ∘ C) Target * 1 : flow rate (SCFM) 1 : maximum temperature at position A ( ∘ C) Target 2 : flow temp ( ∘ C) 2 : beginning bond temperature at position A ( ∘ C) Target 3 : block temp ( ∘ C) 3 : finish bond temperature at position A ( ∘ C)
Table 7 :
Input and response variables and optimization criteria for every response (output) (Case 2).
Table 8 :
Input and response variables and optimization criteria for every response (output) (Case 3).
Table 10 :
Input and response variables and optimization criteria for every response (output) (Case 5).
Table 11 :
GD values according to run number of CM.
Table 12 :
Statistical results of GD values according to run number of CM (Cases 2-5).
Table 13 :
Results of five runs for Case 2.
Table 6 .
Case 3 has deferent specifications to get acceptable results.Four neural networks that include feedforward (FF), radial base function (RBF), GRNN, and ANFIS were consisted in each response for each problem data.So every problem finds (4 * no. of responses) models.A committee machine was set with the object to minimize the overall RMSE. | 3,904.6 | 2013-01-01T00:00:00.000 | [
"Computer Science"
] |
Tuning magnetoelectricity in a mixed-anisotropy antiferromagnet
Control of magnetization and electric polarization is attractive in relation to tailoring materials for data storage and devices such as sensors or antennae. In magnetoelectric materials, these degrees of freedom are closely coupled, allowing polarization to be controlled by a magnetic field, and magnetization by an electric field, but the magnitude of the effect remains a challenge in the case of single-phase magnetoelectrics for applications. We demonstrate that the magnetoelectric properties of the mixed-anisotropy antiferromagnet LiNi1−xFexPO4 are profoundly affected by partial substitution of Ni2+ ions with Fe2+ on the transition metal site. This introduces random site-dependent single-ion anisotropy energies and causes a lowering of the magnetic symmetry of the system. In turn, magnetoelectric couplings that are symmetry-forbidden in the parent compounds, LiNiPO4 and LiFePO4, are unlocked and the dominant coupling is enhanced by almost two orders of magnitude. Our results demonstrate the potential of mixed-anisotropy magnets for tuning magnetoelectric properties.
weak ME couplings are lingering barriers for applicability of singlephase magnetoelectrics.
The ME properties of a given single-phase material are a consequence of the magnetic point group symmetry inherent to its magnetically ordered state 13,14 . More specifically, the absolute and relative orientation of the ordered moments dictate the non-zero elements of the ME tensor describing the coupling between electric and magnetic degrees of freedom 14,15 . Mixing magnetic ions with incompatible, or mismatched, single-ion anisotropies gives rise to what can be thought of as a composite on the atomic level. This random site-dependent anisotropy in combination with the inter-species exchange interaction creates frustration in the system and may result in what is known as an oblique antiferromagnetic phase. Here, the ordered moments are oriented away from any of the easy axes observed in the stoichiometric compounds [16][17][18] .
A well-known family of isostructual magnetoelectric has chemical formula LiMPO 4 (M = Mn, Fe, Co, Ni) and space group Pnma (No. 62) 19 with the crystallographic unit cell illustrated in Fig. 1a. The compounds, LiNiPO 4 (S = 1) [20][21][22] and LiFePO 4 (S = 2) 23,24 order antiferromagnetically at 20.8 K and 50 K, respectively. Below their Néel temperatures, they display similar commensurate spin structures except for the orientation of the magnetic moments, which are predominantly along the 4 . a Crystallographic unit cell of LiMPO 4 with four magnetic ions (red numbered spheres) and the two most important exchange paths, J bc and J ab , shown. The MO 6 octahedra and PO 4 tetrahedra are illustrated with red and blue shading, respectively. b (x, T) phase diagram constructed from experimental data and simulation. The open circles correspond to phase transitions observed in the simulated specific heat. Filled stars represent phase transitions detected in magnetic susceptibility and neutron diffraction experiments for samples with x = 0, 0.06, 0.20 and 1. Both simulations and experiments reveal three phases: Commensurate phases with S||c (grey shading) and S||b (blue shading) are seen at small and large x, respectively, while an oblique phase is present in the range 0.1 < x < 0.6 (red shading). For each phase, the observed form of the magnetoelectric tensor at low temperature is indicated. The gradient of the blue shading illustrates that the ordered moment along b, 〈S||b〉, decreases when decreasing x while 〈S||a〉 ≈ 〈S||c〉 = 0. For small x there exists an incommensurate (IC) phase in a narrow temperature interval above the commensurately ordered phase (cyan shading) 21,22 . crystallographic b and c axes for respectively LiFePO 4 and LiNiPO 4 . In LiNiPO 4 there exists in addition an incommensurate phase in a narrow temperature interval just above the Néel temperature 21,22 . The static and dynamic properties of LiMPO 4 are well-described by the spin HamiltonianĤ where the first sum accounts for the exchange interactions of magnitude J ij between spins on sites i and j. The second sum over all sites i and three crystallographic directions, α = a,b,c È É , reflects single-ion anisotropy energies, parameterized by the vector D = (D a , D b , D c ). This term is responsible for the distinct ordered moment direction selected upon ordering in stoichiometric LiNiPO 4 25 and LiFePO 4 26 . Here, we explore chemical tuning of mixed-anisotropy antiferromagnets as a novel route for tailoring the properties of single-phase magnetoelectrics. We have employed magnetic susceptibility and pyrocurrent measurements, neutron diffraction and Monte Carlo simulations to investigate the (x, T) phase diagram of LiNi 1−x Fe x PO 4 ( Fig. 1b). We observe three commensurate magnetic phases with propagation vector k = 0. At low temperature and for x < 0.2, the spins order along c like in LiNiPO 4 . For x > 0.6, the spins order along b like in LiFePO 4 . For x = 0.2, two magnetic phases appear upon cooling 27 . Neutron diffraction reveals ordered moments predominantly along the crystallographic b-axis below T 2 = 25 K, while below T 1 = 21 K, the moments partially reorient towards the a-axis in a low-temperature oblique phase. Our investigations of the field-induced polarization in these phases have uncovered a complex ME coupling scheme. The lowered magnetic symmetry of the oblique phase combined with the broken discrete translational symmetry, unlocks ME tensor elements that are otherwise forbidden in the parent compounds. Simulations show that the key factors responsible for the observed oblique phase are mismatched anisotropies combined with an inter-species exchange coupling creating competing exchange and single-ion anisotropy energy terms. This unusual mechanism is of general applicability and represents a promising approach to search for oblique ME phases in other families of compounds where the ME properties can be chemically tuned.
Magnetic susceptibility
Figure 1c-e illustrate distinct differences in magnetic susceptibility between LiNi 0.8 Fe 0.2 PO 4 and its parent compounds, LiNiPO 4 and LiFePO 4 . The susceptibility curves, χ a , χ b and χ c , of both LiFePO 4 and LiNiPO 4 for fields along a, b and c display textbook behavior for antiferromagnets with easy axes along b and c, respectively. The component of χ parallel to the easy axis drops towards zero below the transition temperature while the two perpendicular components remain nearly constant. By contrast, the susceptibility of LiNi 0.8 Fe 0.2 PO 4 shows clear evidence of two magnetic phase transitions. Below T 2 = 25 K, χ b decreases while χ a and χ c remain constant. At a slightly lower temperature, T 1 = 21 K, χ a begins to drop precipitously and the decrease of χ b is interrupted, while χ c remains approximately constant. These observations are indicative of a negligible c-axis component of the ordered moment at all temperatures, and of a rotation of the ordered moments from the b axis towards the a axis for temperatures lower than T 1 . These two transitions were previously reported and we compare our findings with those of the authors of ref. 27 later in the Results section. Note that overall the susceptibility of the mixed system is higher than for the parent compounds. This, together with the overall different temperature dependence of the susceptibility as compared to the parent compounds, is evidence that LiNi 0.8 Fe 0.2 PO 4 is indeed a solid solution and we can exclude phase separation in the system.
Magnetic structures
To determine the magnetic structures in LiNi 0.8 Fe 0.2 PO 4 we turn to neutron diffraction. At all temperatures below T 2 , the commensurate magnetic Bragg peaks were found to be resolution limited, implying long-range order ( Supplementary Fig. 2d-f in the Supplementary Information). A representative selection of temperature-dependent integrated intensities as obtained at the diffractometer, E5, is shown in Fig. 2a. The intensity of each magnetic Bragg peak reflects different combinations of symmetry components of the magnetic order. In addition, it carries information about the spin orientation in the ordered states, because neutrons couple exclusively to components of the magnetic moment perpendicular to the scattering vector Q (see Supplementary Table I). Our analysis indicates that the main magnetic structure component at all temperatures below T 2 is (↑↑↓↓) with the numbering of spins defined in Fig. 1a. Rietveld refinement of the magnetic Bragg peak intensities at base temperature yields magnetic moments predominantly in the (a, b)-plane with major component along a. For T 1 ≤ T ≤ T 2 , our data suggests moments aligned along b.
The two transitions observed in our susceptibility measurements have clear signatures in the diffraction data: The (0, 0, −1) and (3, 0, −1) reflections grow linearly with decreasing temperature below T 2 ≈ 25 K. By contrast, the (0, 1, 0) peak appears only below T 1 ≈ 21 K where in addition, there is a kink in the temperature profile of the (3, 0, −1) intensity. The temperature dependencies of all recorded peaks are well described by a combination of a linear function and a power law, reflecting the existence of two order parameters, below T 2 and T 1 , respectively (solid lines in Fig. 2a and in Supplementary Fig. 1). Simultaneous fits to all data sets yield transition temperatures T 2 = 25.7(2) K and T 1 = 20.8(1) K respectively, in good agreement with refs. 27 and 28. We note that the critical exponents for the two order parameters are clearly different. Below T 2 , the neutron intensity increases linearly with decreasing temperature which means a critical exponent of 1 2 as assumed fixed in the fit. This corresponds to the critical exponent resulting from long-range interactions or from a secondary order parameter. At T 1 , (0, 1, 0) displays a power law behavior with β = 0.32 (3) which is comparable to the critical exponent of a 3D Heisenberg, XY or Ising system.
To unambiguously determine the spin orientations, we performed a polarized neutron diffraction experiment using the triple axis spectrometer 4F1 and with scattering vector Q = (0, K, L) in the horizontal scattering plane. Uniaxial polarization analysis allows the two spin components perpendicular to Q to be individually addressed. This is done by measuring spin-flip (SF) and non spin-flip (NSF) intensities for the neutron beam polarization along the scattering vector (P||x), perpendicular to Q in the horizontal scattering plane (P|| y), and along the direction perpendicular to the scattering plane (P||z). The temperature-dependencies of the resulting six cross sections were collected for the (0, 1, 0), (0, 0, 1) and (0, 1, 2) reflections. The SF cross sections carry information on spin components perpendicular to both Q and the neutron beam polarization P. The NSF cross sections reveal spin components perpendicular to Q but parallel to P in addition to any finite nuclear Bragg peak intensity.
Noting that the (0, 1, 0) magnetic peak exclusively reflects (↑↑↓↓) symmetry components (Supplementary Table I), Fig. 2b, c show that the magnetic structure below T 1 involves sizeable spin components along a, but only negligible c-axis components. Spin components parallel to b do not contribute to magnetic scattering at Q = (0, 1, 0), but can be probed at Q = (0, 0, 1) or (0, 1, 2). Figure 2d, e confirm the involvement of an a-axis spin components below T 1 , and show that the scattering is dominated by spins oriented along b in the range T 1 ≤ T ≤ T 2 . Note that here we plot only data for (0, 1, 0) and (0, 0, 1) as their interpretation is straightforward. The data for (0, 1, 2) is shown in Supplementary Fig. 2a, b. A comparison of the observed intensities to the structure factors for the magnetic symmetry components contributing to the (0, 1, 0),(0, 0, 1) and (0, 1, 2) peaks makes it clear that the dominant symmetry component for T 1 ≤ T ≤ T 2 is also (↑↑↓↓). The scattering from b-axis spin components, reflected by the NSF, P||y and SF, P||z cross sections in Fig. 2d, e increases monotonically for temperatures in the range T 1 ≤ T ≤ T 2 and levels off to a finite value at our experimental base temperature. The rotation angle, φ, in the (a, b)-plane may be calculated from the ratio of P||y and P||z data in Fig. 2d, e leading to the conclusion that the angle between the moments and the b axis approaches φ = 60 ∘ at low temperatures (Fig. 2f).
The small but finite nuclear intensity for P||x in Fig. 2b and Supplementary Fig. 2a may be due to a change of the lattice symmetry which could be caused by magnetostriction. Magnetostriction is common in magnetoelectrics and for LiFePO 4 this effect has been observed when applying magnetic fields 29 . Future synchrotron X-ray studies will uncover the evolution of the crystal lattice and symmetry as a function of temperature.
The solid grey lines in Fig. 2d, e represent the intensity of the second harmonic generation (SHG) susceptibility tensor element, χ zxx , from ref. 27. Here the first subscript signifies the component of the nonlinear polarization induced by an electric field with components denoted by the last two subscripts. The similarity of the SHG signal with the NSF, P|| y and SF, P||z cross sections is clear evidence that these two observations are intimately related. The SHG data was interpreted by the authors of ref. 27 as a signature of spin rotation from the easy b axis of stoichiometric LiFePO 4 towards the easy c axis of stoichiometric LiNiPO 4 , upon cooling below T 1 . Our polarized neutron diffraction results only allow for a small spin component along c and show instead a sizeable component along a. This picture is consistent with the susceptibility data in Fig. 1c. The physical mechanism for this surprising reorientation away from the easy axes of the two parent compounds is explored in our Monte Carlo simulations to be presented further on, but first we look into its profound consequences for the ME coupling.
Magnetoelectric effect
The linear ME effect is described by the relation P E = αH between the components of the induced electrical polarization, P E , and those of the applied magnetic field, H. A related equation, μ 0 M = α T E, connects the components of the induced magnetization, M, to those of the applied electric field, E. For systems invariant to integer lattice vector translations, the allowed elements of the ME tensor α are imposed by the point group symmetry of the magnetically ordered state 14,15 . Specifically, for the stoichiometric parent compounds LiNiPO 4 and LiFePO 4 , the reported magnetic structures imply that the elements which may be non-zero are α ac , α ca and α ab , α ba , respectively.
The ME response of LiNi 0.8 Fe 0.2 PO 4 was probed with measurements of the pyrocurrent produced by a temperature change (see Methods and Supplementary Information for details). Our results for LiNi 0.8 Fe 0.2 PO 4 are shown in Fig. 3 and are compared to the ME response of the parent compounds, LiNiPO 4 19 and LiFePO 4 30 . Note that in the following analysis we assume space group Pnma although it was recently shown that LiFePO 4 may display a lower symmetry 30 . The pyrocurrent for LiNi 0.8 Fe 0.2 PO 4 for two orthogonal orientations of the electric poling field, E, and three directions of the magnetic field shows clear signatures of two ME phase transitions slightly below T 2 and T 1 , see Fig. 3a-c. The evidence is in the form of spikes in the pyrocurrent, which following a geometrical correction can be integrated to obtain the temperature dependent polarization components, P E i . A signal is thus observed for all probed couplings except α bc .
The electric polarization corresponding to the tensor elements α ab and α ba together with that corresponding to α ac are shown in Fig. 3d. As mentioned above, these components are known to be nonzero for stoichiometric LiFePO 4 and LiNiPO 4 23,31 . When comparing the ME response of LiNi 0.8 Fe 0.2 PO 4 to that of LiFePO 4 and LiNiPO 4 measured under identical conditions (blue and grey dashed lines in Fig. 3d), it is apparent that the polarizations induced along a and b are significantly larger in LiNi 0.8 Fe 0.2 PO 4 at all temperatures below the transition temperature. Most strikingly, in the limit T → 0, the polarization due to the dominant tensor component, α ab is increased by almost two orders of magnitude compared to LiFePO 4 . A second remarkable observation is that the onset temperatures of α ab and α ba are different. α ba vanishes in the range T 1 ≤ T ≤ T 2 whereas α ab is finite already below T 2 and displays a kink at T 1 .
Finally, in Fig. 3e we probe tensor components that are by symmetry not allowed for LiNiPO 4 and very small for LiFePO 4 30 . Similarly, we observe α bb below T 2 while α aa is finite only below T 1 . For the last tensor element measured, α bc , there is no spike to be seen in the pyrocurrent and we conclude that this element is either very weak or zero.
By measuring the pyrocurrent at different magnetic field strengths, we obtain the electric polarization as a function of field as shown in Fig. 3f. The values of the polarization shown here are the mean values for temperatures below 5 K. For the corresponding polarization curves at the different field strengths, see Supplementary Fig. 4. The measured polarization is linear with field for most couplings, except notably for α ba . Interestingly, α ba is exactly the component with a different onset temperature compared to α ab , underlining that the behavior of the ME as a function of temperature for magnetic fields applied along a (red and orange curves), b (dark and light blue curves) and c (grey and black curves), respectively. The insets indicate which elements of the ME tensor, α, were probed. The error on the measured current is of the order of 5 fA. The colour codes given in a-c are followed in the remaining panels of this figure. d Electric polarization as a function the reduced temperature with transition temperatures 21, 25 and 50 K at zero field for LiNiPO 4 , LiNi 0.8 Fe 0.2 PO 4 and LiFePO 4 , respectively. Note the two y-axes: the left one for the data for the mixed system (solid lines) and the right one for the parent compounds (dashed lines). The curve shown for LiNiPO 4 is from ref. 19 and that for LiFePO 4 is reprinted with permission from ref. 30. Copyright (2023) by the American Physical Society. e Temperature dependency of the electric polarization originating from tensor elements not present in the parent compounds. For T < T 1 and T 1 < T < T 2 , all observed non-zero ME tensor elements in LiNi 0.8 Fe 0.2 PO 4 are indicated. The measurements shown in a-e were carried out with an applied magnetic field strength of 2 T where the ME effect is still linear. The errors on the polarization are of the order of 1 μCm −2 . f Field dependency of the average of the induced electric polarization for T < 5 K for non-zero couplings. The error bars are estimated from the variations observed in the temperature profiles of the polarization (see Supplementary Fig. 4). Note that strong and weak ME components are plotted on two different y-axes as illustrated with encircled symbols and arrows. The dashed lines are linear fits, P E i = α ij H j , to the data with the obtained ME coefficients, α ij , listed in the legend. The inset shows the corresponding data for LiFePO 4 with H||b, E||a. effect in LiNi 1−x Fe x PO 4 is complex. Nevertheless, it is clear from our measurements that the effect is much stronger in the mixed system compared to the parent compounds and that at low field strengths, the system is in the linear regime.
Monte Carlo simulations. We now show that classical Monte Carlo simulations reproduce the salient features of the susceptibility and diffraction results for x = 0.20. In the calculations, we chose J bc = 1 meV and J ab = 0.3 meV for all corresponding pairs of sites (see Fig. 1a In Fig. 4a, we plot the calculated magnetic susceptibility and specific heat for x = 0.20 as a function of temperature. Two phase transitions are observed near 25 and 20 K. The transition temperatures as well as the temperature-dependencies of the three components of χ are in excellent agreement with the experimental results shown in Fig. 1c. The accuracy of the simulations is further illustrated by comparing the calculated and measured susceptibilities for x = 0.06 with the corresponding experimental data (Supplementary Fig. 5).
Next, we study the simulated C-type order parameter C = | S 1 + S 2 À S 3 À S 4 | for spin components along a, b and c, respectively. Figure 4b-d show the temperature dependencies of C for the full range of Fe concentrations, x. Focusing on x = 0.20, the resemblance with the polarized neutron diffraction data in Fig. 2 is striking. Note that the neutron diffraction intensity is proportional to the moment squared. The first phase transition at 25 K corresponds to spins ordering along b. The growth of the corresponding order parameters is interrupted at 20 K where a rotation towards a starts and the oblique low-temperature phase is entered. The c-axis component remains zero at all temperatures. From the a and b components of the simulated order parameter we arrive at a rotation angle of 76°at low temperature, which compares reasonably well with the value of ≈60°o btained from the experimental data (Fig. 2f). We use the transition temperatures derived from the simulated specific heat and order parameters data to construct the (x, T) phase diagram shown in Fig. 1b. The simulations underestimate the transition temperatures for small x compared to the measured values (star symbols for x = 0, 0.06 and 0.20), but the ratios of simulated to observed transition temperatures are relatively constant with x in this range.
The oblique antiferromagnetic state is relatively robust. The simulations show that the only requirements are an inter-species exchange interaction as well as competing single-ion anisotropies with opposite easy and hard axes for the parent compounds but a common intermediate axis off any of these easy and hard axes. It is this frustration between exchange and single-ion anisotropy energies that generates the oblique state. In the analysis of the neutron diffraction data we assumed a collective behavior of all spins regardless of species. The simulations show that indeed the ensemble average of the moments give a collectively ordered picture. However, we also find local fluctuations between Ni and Fe sites (Supplementary Fig. 6). The ordered moment for the oblique phase is therefore lower when calculating the average over the entire system than when considering individual sites, not only due to thermal fluctuations but also due to site specific differences in the moment orientation. This effect and the general consequences of violation of discrete translational symmetry for the ME effect are an interesting topic of future theoretical investigations.
Discussion
The effects on the magnetic ground state of a quenched random distribution of ions in mixed anisotropy antiferromagnets have been extensively studied by renormalization group theory and in various mean field models [16][17][18][33][34][35][36] . The resulting phase diagrams generically contain one or more oblique phases at intermediate compositions, in which the ordered moments are oriented away from the easy axes of the parent compounds. Depending on the details of the exchange and anisotropy terms in the spin Hamiltonian, the oblique ground state may involve ordered moments in a plane spanned by the easy axes of the two parent compounds, or perpendicular to both in the particular case where the easy and hard axis of one species coincide with the hard and easy axes of the other species 17 . These predictions of a chemically tunable magnetic ground state are broadly consistent with our experimental observations and Monte Carlo simulations for LiNi 1−x Fe x PO 4 , and have in the past been found to agree well with experimental studies of other mixed-anisotropy antiferromagnets [37][38][39][40][41][42] .
In the lithium orthophosphates, the magnetic C-type structure is the dominant structure component for all stoichiometric family members. The single-ion anisotropy plays a crucial role for the ME coupling, as the allowed tensor elements derive from the magnetic point group, ipso facto the direction of the ordered moments. Since S||b in LiFePO 4 , α ab and α ba are allowed, while as S||c in LiNiPO 4 , α ac and α ca are allowed. When S||a, the diagonal elements are allowed (α aa , α bb , α cc ≠ 0). It follows from our results, however, that such simple rules do not apply in the mixed systems, where discrete translational symmetry is broken and the local spin anisotropy is site dependent. Between 21 and 25 K, the predominant spin orientation in LiNi 0.8 Fe 0.2 PO 4 is S||b. Nevertheless, the observed diagonal tensor element α bb is almost as strong as the expected α ab -component, while α ba = 0. Below 21 K, the ME-tensor is more complex and most tensor elements are observed. This is due to the off-axis direction of the ordered moments. However, the fact that some expected tensor elements are extinct between 21 and 25 K, while some unexpected elements are not, is a strong hint that while discrete translational symmetry is broken, the existence of the ME coupling is still governed by magnetic point group symmetry, yet not in the same manner as in the stochiometric systems. This is an interesting point in itself and should be subject to further theoretical study.
The most intriguing observation is the almost hundredfold increase in the strength of the ME coupling for LiNi 0.8 Fe 0.2 PO 4 observed in the pyrocurrent measurements. In LiNiPO 4 and LiFePO 4 , the effect is believed to arise from exchange striction 22,26,43,44 . Specifically, a microscopic model connects the electric polarization caused by a displacement, x i , of PO 4 tetrahedra along i, to the component of the applied magnetic field along j as follows: Here 〈S〉 is the order parameter, χ j the magnetic susceptibility for fields along j, ϵ i the elastic energy constant associated with tetrahedron displacement (E i = ϵ i x 2 i ) and λ i reflects the strength of the exchange striction (J H≠0 → J H=0 + λ i x i ). In addition to the general increase in magnetic suceptibility in LiNi 0.8 Fe 0.2 PO 4 as compared to the parents, both a reduction of the elastic displacement energy and an increase in exchange striction could cause stronger ME couplings. A lowering of crystal symmetry may indeed result in lower energy cost for displacing the PO 4 tetrahedra as well as more allowed options for displacement directions, i.e. a softer lattice. Moreover, the local number of nearest neighbors of a given species, and variations thereof, could bring exchange striction terms into play which would otherwise cancel out (in the parent compounds only interactions between ion pairs (1,2) and (3,4) contribute to the ME effect 22 ).
Tuning of the magnetic symmetry was also recently achieved in the olivine series of compounds, Li 1−x Fe x Mn 1−x PO 4 45 , as well as in Mn and Co doped LiNiPO 4 46 . These studies do not report on the corresponding consequences for the ME effect but they further illustrate that the lithium orthophosphate family harbour many possibilities for tailoring the magnetic and consequently also ME material properties. In general, the existence of ME and multiferroic oblique antiferromagnets is unlikely to be limited to this family. Notably, our Monte Carlo simulations show that competing single-ion anisotropies with a common intermediate axis combined with a significant inter-species exchange coupling are the decisive ingredients to prodcue an oblique magnetoelectric state, whereas details of the exchange coupling scheme play almost no role. Generally, transition metal ions exhibit complex single-ion anisotropies in octahedral environments, and will likely share an intermediate anisotropy axis in many families of compounds. An oblique ME phase may therefore also exist in other classes of materials. In future studies, Monte Carlo simulations similar to those performed in this work, possibly combined with DFT calculations to determine exchange constants, could precede time-demanding material synthesis in order to predict the viability of the candidate family to produce oblique ME phases. An interesting family of compounds for future studies of this type is for example Mn 1−x M x WO 4 (M = Fe, Co, Cu, Zn) [47][48][49][50] .
In summary, we studied LiNi 0.8 Fe 0.2 PO 4 experimentally using magnetometry, neutron diffraction and pyrocurrent measurements and theoretically through Monte Carlo simulations. We have identified an oblique low-temperature phase over an extended range of compositions. In this phase the spins rotate away from the distinct easy axes of the parent compounds, LiNiPO 4 and LiFePO 4 , towards the direction of the common intermediate axis. The magnetoelectric properties correlate with the observed magnetic phase transitions, but the form of the magnetoelectric tensor departs from theoretical expectations based on discrete translational invariance and magnetic point group symmetries. Most dramatically, we observe a strong enhancement of almost two orders of magnitude for the dominant magnetoelectric tensor element compared to the parent compounds. These data in combination with our Monte Carlo simulation results suggest that the observations have broader implications and that chemical tuning of oblique magnetoelectric phases represents a promising path for tailoring magnetoelectric material properties.
Magnetic susceptibility
Magnetization measurements were performed using a Cryogenic Ltd. cryogen free vibrating sample magnetometer. A magnetic field of 0.5 T was applied along a and b for temperatures in the range 2-300 K. For measurements with field along c we used a Physical Property Measurement System (PPMS) from Quantum Design. The sample for all measurements of LiNi 0.8 Fe 0.2 PO 4 was a 25 mg box-shaped single crystal.
Neutron diffraction
Unpolarized neutron diffraction data on LiNi 0.8 Fe 0.2 PO 4 were collected at the E5 diffractometer at the Helmholtz-Zentrum Berlin using an 4-circle cradle, a neutron wavelength of λ = 2.38 Å selected by a pyrolytic graphite (PG) monochromator and a 2D position sensitive neutron detector of size 9 × 9 cm 2 . A PG filter before the sample position was used for suppression of second order neutrons (λ/2) from the monochromator.
The polarized neutron diffraction data were obtained on the 4F1 triple-axis spectrometer at the Laboratoire Léon Brillouin, using a wavelength of λ = 2.44 Å. The monochromatic incident beam was polarized using a bending mirror and a PG filter after the bender reduced second order contamination of the incident beam. A spin flipper was placed before the sample position. In combination with a Heusler analyzer this allowed for probing both spin flip and non spinflip scattering. A set of Helmholtz-like coils around the sample position enabled polarization of the neutron beam along x, y or z in the coordinate system decribed in the Supplementary Information. The same high-quality 250 mg LiNi 0.8 Fe 0.2 PO 4 single crystal was used in both polarized and unpolarized neutron diffraction experiments. At 4F1, it was oriented with (0, K, L) in the horizontal scattering plane. Flipping ratios F = 40 and 25 were deduced from measurements on the structural (020) and (002) reflections, and used to correct for non-ideal beam polarization.
Preliminary studies of the magnetic structures of LiNi 1−x Fe x PO 4 were carried out at the triple-axis spectrometer RITA-II and diffractometer TriCS at the Paul Scherrer Institute.
Pyrocurrent measurements
The quasi-static method 51 was used to perform pyrocurrent measurements with a Quantum Design PPMS at the Helmholtz-Zentrum Berlin. The custom-build insert allows for sample rotations with respect to the vertical magnetic field and thus enables probing off-diagonal ME tensor elements 30,52 . Plate-shaped LiNi 0.8 Fe 0.2 PO 4 single crystals with gold sputtered faces perpendicular to a and b, and sample thickness 0.5 and 0.9 mm, respectively, were used. Single crystals of LiNiPO 4 and LiFePO 4 were similarly prepared with faces perpendicular to a and thickness 0.6 and 0.5 mm, respectively. The precision of the cuts were within 0.5°w hich determines the uncertainty of the field direction with the respect to the axes perpendicular to the surface. For the other field directions, the crystals were aligned by eye to obtain a sample alignment within 2°. A potential of 100 V was applied as well as a magnetic field while cooling to obtain a single ferroelectric domain. The potential was switched off at the experimental base temperature. The measurement was then performed upon heating at a constant temperature rate. Magnetic fields were applied along the a, b and c directions.
Monte Carlo simulations
Classical Monte Carlo simulations were carried out using the spin Hamiltonian [Equation (1)] and employing the Metropolis algorithm 53 . The system was of size 10 × 10 × 10 crystallographic unit cells (i.e. 4000 magnetic sites) and with Fe and Ni ions randomly distributed. The simulation was run for 10 5 Monte Carlo steps for each temperature in the range 1-100 K with step size 0.35 K and decreasing temperature. For each temperature we use the final configuration from the previous temperature step as a starting point. This procedure mimics the process of slowly lowering the temperature in the physical experiment. All simulations were run at zero field.
For each value of the Fe concentration, x, we simulated 5 distinct configurations from which we calculated the magnetic susceptibility, specific heat and order parameter. The susceptibility is calculated as χ = βðhM 2 i À hMi 2 Þ, where M denotes the total magnetization of the system, β = 1 k B T and k B is the Boltzmann constant. The brackets, 〈〉, denote the ensemble average over system configurations. In Fig. 4a and in Supplementary Fig. 5b we show χ normalized per magnetic site. The specific heat is calculated from the energy dissipation theorem, C V = k B β 2 ðhE 2 i À hEi 2 Þ, where E is the total energy of the system. The order parameter is calculated as an average over all unit cells, each containing four magnetic sites, i.e. C = | S 1 + S 2 À S 3 À S 4 |. The curves shown in Fig. 4b-d are then the average quantities over the 5 configurations.
Data availability
The data that support the findings of this study are available at https:// doi.org/10.5281/zenodo.7515107 and from E.F. upon request. Source data are provided with this paper. | 8,167.6 | 2022-09-14T00:00:00.000 | [
"Physics",
"Materials Science"
] |
Long-term stabilization of carrier envelope phases of mid-infrared pulses for the precise detection of phase-sensitive responses to electromagnetic waves
We report a newly designed mid-infrared-pump visible-probe measurement system, which can measure phase-sensitive responses to a mid-infrared pulse along the oscillating electromagnetic field. In this system, the pump light is a phase-locked mid-infrared pulse with temporal width of 100 fs, which is produced via difference frequency generation (DFG) from two idler pulses of two optical parametric amplifiers (OPAs) that are excited by the same Ti:sapphire regenerative amplifier. The probe pulse is a visible pulse with temporal width of 9 fs, and it is generated from a custom-built non-collinear OPA. By measuring the electric-field waveforms of mid-infrared pump pulses with electro-optic sampling and evaluating their carrier envelope phase (CEP) and the temporal positions of their envelopes relative to ultrashort visible probe pulses, we are able to perform double feedback corrections that eliminate both the following sources of drift. The CEP drift in mid-infrared pulses originating from fluctuations in the difference of optical-path lengths of the two idler pulses before the DFG is corrected by inserting a wedge plate in one idler path, and the drift in pump-probe delay times due to fluctuations in the difference of the overall optical-path lengths of the pump and probe pulses is corrected with mechanical delay lines. In this double-feedback system, the absolute carrier phase of mid-infrared pulses can be fixed within 200 mrad and errors in the measurement of phase-sensitive responses can be reduced to within 1 fs over a few tens of hours.
temporal width of 9 fs, and it is generated from a custom-built non-collinear OPA. By measuring the electric-field waveforms of mid-infrared pump pulses with electro-optic sampling and evaluating their carrier envelope phase (CEP) and the temporal positions of their envelopes relative to ultrashort visible probe pulses, we are able to perform double feedback corrections that eliminate both the following sources of drift. The CEP drift in mid-infrared pulses originating from fluctuations in the difference of optical-path lengths of the two idler pulses before the DFG is corrected by inserting a wedge plate in one idler path, and the drift in pump-probe delay times due to fluctuations in the difference of the overall optical-path lengths of the pump and probe pulses is corrected with mechanical delay lines. In this double-feedback system, the absolute carrier phase of mid-infrared pulses can be fixed within 200 mrad and errors in the measurement of phase-sensitive responses can be reduced to within 1 fs over a few tens of hours.
Recent developments in techniques for generating a strong terahertz (THz) pulse have opened up new ways using electromagnetic waves to control physical quantities like polarization and magnetization in solids 1,2 . The control of electronic phases using a THz pulse is also attractive, however only a few examples of using electromagnetic fields to induce phase transitions have been reported so far 3,4 . It is mainly because the amplitude of a THz pulse is generally difficult to enhance larger than 1 MV/cm, although several techniques have been proposed 5,6 . In a mid-infrared (MIR) region, it is easier to enhance electromagnetic-field amplitudes than in a THz region. Strong MIR pulses have indeed been used to induce a metal-superconductor transition in cuprates 7 and an insulator-metal transition in vanadium dioxide 8 . A strong MIR pulse can also induce novel nonlinear phenomena in solids. Studies have reported observations of non-perturbative responses in semiconductors 9-12 , ultrafast polarization reversal in ferroelectrics 13 , modulation of the on-site Coulomb interaction in Mott insulators 14 , and sum-frequency excitation of Raman-active phonons in diamonds 15 .
To reveal ultrafast electron and lattice dynamics in phase transitions and other nonlinear phenomena in solids with an MIR pulse, one must detect any changes to optical indexes induced by an oscillatory electric field. An MIR pump pulse with a stable carrier envelope phase (CEP) and a probe pulse with duration shorter than the period of the oscillatory fields are indispensable for monitoring these changes. CEP is defined by the phase of the carrier wave in a light pulse with respect to its envelope. In general, the CEP of the output of femtosecond laser sources such as a Ti:sapphire regenerative amplifier (RA) fluctuates unless a feedback device is introduced in the cavity 16 . To this end, several methods for passive stabilization of CEP during frequency down-conversion processes of the output of laser sources to an MIR pulse have been proposed: (1) inter-pulse differential frequency generation (DFG) between two frequency-detuned near-IR pulses generated separately by a common light source [17][18][19] , (2) intra-pulse DFG of a spectrally broadened visible/near-IR pulse 20,21 or a two-color pulse generated in a dual-wavelength optical parametric amplifier (OPA) 22 , and (3) four-wave-mixing with two-color filamentation 23 .
Among those methods, the most general is the use of inter-pulse DFG using outputs from two OPAs 17 , since it uses a relatively simple setup and offers wide tunability of the MIR-pulse frequencies. In this scheme, two signal (or idler) pulses with a constant phase difference ∆ are generated separately from two OPAs, in which both the excitation pulses and seed white-light pulses come from the same laser source. In an MIR pulse obtained via DFG processing of two signal (or idler) pulses, the original CEP fluctuations are cancelled. In an actual experimental setup, however, the optical-path-length difference between the two OPAs fluctuates due to jitter that originates from instabilities in temperature and air flow. This gives rise to fluctuations in the phase difference between the two OPA outputs, which result in the instability of CEP of the MIR pulse. For example, in the case that the photon energies of two OPA outputs are 0.7 eV and 0.6 eV, a change of 300 nm in the path-length difference (corresponding to a time difference of 1 fs) induces a change in the CEP of the MIR pulse (0.1 eV) of 0.3π rad or 7 fs in time. Note that the temporal change is magnified during the DFG process. The optical-path length of each OPA is typically 2 m, so that fluctuation of around 1.5 × 10 −7 of that length (= 300 nm/2 m) gives rise to a temporal error of 7 fs. Moreover, the path-length difference of an MIR pump pulse and a visible probe pulse in the pump-probe system will also fluctuate, leading to drift in the pump-probe delay time d . The overall optical-path length in a pump-probe system is typically 8 m, so that the same degree of fluctuation in length (1.5 × 10 −7 ) produces an error of 4 fs, which is comparable to the above-mentioned error due to fluctuations in the OPA path length. Therefore, active stabilization is indispensable for long-term experiments. For the stabilization of CEP in an MIR pulse, C. Manzoni et al. introduced a feedback correction device to an inter-pulse DFG system, in which phase stability of 110 mrad over 2 hours was achieved 24 , although any fluctuations in the pump-probe delay were not eliminated. In case that measurements over a few tens of hours are necessary to detect CEP-sensitive phenomena, it is important to introduce feedback devices that stabilize both the CEP of the MIR pulse and the pumpprobe delay.
In this letter, we report a newly designed feedback-control system for MIR-pump visible-probe measurements. This system measures the electric-field waveform of the MIR pump pulses with electro-optic sampling (EOS) and detects fluctuations in both CEP of the MIR pulse and the temporal position of its envelope peak relative to an ultrashort visible pulse. Hereafter, the latter is called the envelope peak position (EPP). Feedback devices are included that control the difference between the optical-path lengths of two OPAs and also the difference between the optical-path lengths of the pump and probe pulses. In the constructed system, error in the detection of a phase-sensitive response is reduced to as little as 1.0 fs over a few tens of hours.
Our setup is illustrated in Fig. 1(a). The light source is a Ti:sapphire RA, which generates a pulse with central wavelength (photon energy) of 800 nm (1.55 eV), duration of 35 fs, repetition rate of 1 kHz, and fluence of 7.5 mJ. The output from the RA is split into two. One is frequency-doubled in a β-BaB2O4 crystal and is used as the excitation pulse for a type-I non-collinear OPA (NOPA) with a β-BaB2O4 crystal 25 . In Fig. 1 show the intensity profile of the NOPA output, obtained with a frequency-resolved optical-gate (FROG) using the retrieval algorithm 26 . The full width at half maximum (FWHM) of the pulse is 8.9 fs. Figure 1(c) shows the spectrum of the pulse, which ranges from 520 nm to 710 nm. The details of the NOPA were reported in ref. 25. The other output from the RA is introduced to a dual OPA that includes OPA1 and OPA2, in which a small part of the RA output is used to create a common white-light seed pulse and the residual is used to amplify the seed pulse in each OPA. By introducing two idler pulses from two OPAs to a 250-μm-thick GaSe crystal, an MIR pulse is generated via type-I DFG 27 . Although the CEP of the original RA output is unstable, the resulting MIR pulse is phase-stabilized 17 . In the experiments reported below, the wavelength and power of the idler pulse from OPA1 were fixed at 1640 nm and 390 μJ, respectively. The idler pulse from OPA2 was changed from 1900 nm to 2080 nm and its power was typically 120 μJ.
Under these conditions, MIR pulses with the central frequency of 28-40 THz can be obtained.
The diameter of the MIR pulse thus obtained is expanded to 3 cm by two off-axis parabolic mirrors, OAP1 and OAP2 [ Fig. 1(a)]. A beam splitter (BS) (a 500-μm-thick Si plate) is introduced to split the MIR pulse into two. The reflected MIR pulse with 60% the original intensity is used as the pump pulse for pump-probe measurements. Using OAP3, this pulse is focused to a spot 50 μm in diameter (FWHM), the position of which is hereafter referred to as the sample position. The transmitted MIR pulse is used in the EOS to measure its electric-field waveform. Using OAP4, the pulse is focused on a LiGaS2 crystal, the position of which is hereafter referred to as the control position. The spot diameter at the control position is adjusted to be as large as that at the sample position.
The visible probe pulse from the NOPA is also divided into two. Each pulse is focused through a hole drilled in the OAP onto the center of the sample or the control position with diameter of 20 m. The former is used as the probe pulse in the pump-probe measurement and the latter as the sampling pulse for EOS.
A schematic of the EOS is shown in the lower-right part of Fig. 1(a). When an electric field is applied to a nonlinear optical crystal, the crystal's birefringence for the sampling pulse changes in proportion to the electric field through the Pockels effect, which can be measured as the difference in the signal of balanced photodiodes detecting the sampling pulse after passing through a quarter-wave plate and a polarizing BS. Thus, by changing the delay time of the sampling pulse relative to the MIR pulse, one can measure the electric-field waveform. We used a 20-μm-thick LiGaS2 crystal, which can be used to detect electric fields with a wide range of frequencies. The electric-field waveform and The procedures for stabilizing the CEP and the pump-probe delay time d are illustrated in Fig. 2(a). In the first step, the electric-field waveform of the MIR pulse at the control position is measured, which is then used as a reference and it is therefore called the reference waveform. In the second step, feedback is used to keep the electric-field waveform of the MIR pulse at the control position identical to the reference waveform in terms of both CEP and EPP. The stabilization of the EPP enables the stabilization of d .
In the loop shown in step 2 in Fig. 2(a), the EOS and pump-probe measurements are performed simultaneously by changing the delay of the sampling pulse and that of the probe pulse to 300 fs, which takes about 30 seconds. This temporal range of measurements was chosen to strike a compromise between a short feedback interval and coverage of nearly the whole duration of the MIR pulse [see Fig. 1(d)].
To evaluate the drifts in CEP and EPP of an electric-field waveform at the control position relative to those of the reference waveform, we use the cross correlation of the two electric-field waveforms of the MIR pulses and its Hilbert transform. The drifts in CEP and EPP are illustrated in Fig. 2(b). CEP is expressed as the temporal difference between the carrier phase and the EPP multiplied by the carrier's angular frequency. The Fig. 3(d).
Because of the wide temporal width (FWHM100 fs) of MIR pulses, the EPP data vary widely. Therefore, we next use a linear regression of EPP data from 300 consecutive cross-correlation profiles, which include information about waveforms during the last 2.5 hours of the experiment. The result is shown with the blue line in Fig. 3 Fig. 1(a)]. The WP has a refractive index of 1.43 in the near-IR region and its wedge angle is 4°. One of the WPs is mounted on a motorized stage. By varying its insertion length, we can eliminate the path-length differences between the outputs of two OPAs due to fluctuations. The pump-probe delay time is stabilized by controlling two delay lines DL2 and DL3 [ Fig. 1(a)] using EPP data.
To evaluate the performance of our system with the two kinds of feedback applied, we performed continuous EOS measurements at the sample position with and without feedback. The results are highlighted in Fig. 4(a) Step 1 Step 2 EPP drift CEP drift<EMAIL_ADDRESS>Contents S1. Detectable frequency range of electro-optic sampling S2. Determination of envelope peak position of MIR pulses using cross-correlation profile S1. Detectable frequency range of electro-optic sampling In this section, we discuss the frequency range in which the electro-optic sampling (EOS) can be used. In EOS, the detection sensitivity of electric-field waveforms is dominated by the temporal width of a probe pulse, p . In the case that the period of an oscillatory electric field is much longer than p , the electric-field waveform is precisely measured. As approaches p , the sensitivity of the detection of the electricfield waveform decreases. However, by adding appropriate corrections of the detection sensitivity depending on p / to the data obtained by the EOS, we can evaluate a wide wavelength range of electric-field waveforms for ≳ p . In our system in which the probe pulse with p = 9 fs is used, the shortest wavelength detectable is estimated to be 3.3 μm corresponding to = 11 fs. This value is determined by the wavelength where the detection sensitivity is about 10% of that for a DC electric field. The longer wavelength bound is 12 µm, above which finite absorptions of a nonlinear optical crystal, LiGaS2, exist. The other necessary condition for EOS is a phase matching between an MIR pulse and a sampling pulse. Since the LiGaS2 crystal we used is very thin (20 μm thick), the phase-matching condition is fulfilled for 3.3 μm to 12 µm. In the region of 5.0-7.7 µm, the optics must be purged with dried air in order to avoid absorption by water vapor. Here, ( ) and ( − 0 ) are the envelopes of ( ) and ( ), respectively, and 0 is the envelope peak position (EPP) of ( ) relative to that of ( ). 1 which is a product of an oscillation with the phase (− 0 − 1 + 2 ) and the crosscorrelation function of the envelopes of the two pulses. Here, the phase of the oscillation is equivalent to the carrier phase difference between ( ) and ( ). Next, we calculate the envelope of ( ) using the Hilbert transform, from which we can evaluate EPP ( 0 ) of ( ) relative to that of ( ). Thus, we obtain the CEP difference of the two waveforms, ( 1 − 2 ). | 3,816.4 | 2019-09-04T00:00:00.000 | [
"Physics"
] |
Preliminary Network Centric Therapy for Machine Learning Classification of Deep Brain Stimulation Status for the Treatment of Parkinson’s Disease with a Conformal Wearable and Wireless Inertial Sensor
The concept of Network Centric Therapy represents an
amalgamation of wearable and wireless inertial sensor systems and machine
learning with access to a Cloud computing environment. The advent of Network
Centric Therapy is highly relevant to the treatment of Parkinson’s disease
through deep brain stimulation. Originally wearable and wireless systems for
quantifying Parkinson’s disease involved the use a smartphone to quantify hand
tremor. Although originally novel, the smartphone has notable issues as a
wearable application for quantifying movement disorder tremor. The smartphone
has evolved in a pathway that has made the smartphone progressively more
cumbersome to mount about the dorsum of the hand. Furthermore, the smartphone
utilizes an inertial sensor package that is not certified for medical analysis,
and the trial data access a provisional Cloud computing environment through an
email account. These concerns are resolved with the recent development of a
conformal wearable and wireless inertial sensor system. This conformal wearable
and wireless system mounts to the hand with the profile of a bandage by
adhesive and accesses a secure Cloud computing environment through a segmented
wireless connectivity strategy involving a smartphone and tablet. Additionally,
the conformal wearable and wireless system is certified by the FDA of the United
States of America for ascertaining medical grade inertial sensor data. These
characteristics make the conformal wearable and wireless system uniquely suited
for the quantification of Parkinson’s disease treatment through deep brain
stimulation. Preliminary evaluation of the conformal wearable and wireless
system is demonstrated through the differentiation of deep brain stimulation
set to “On” and “Off” status. Based on the robustness of the acceleration
signal, this signal was selected to quantify hand tremor for the prescribed
deep brain stimulation settings. Machine learning classification using the
Waikato Environment for Knowledge Analysis (WEKA) was applied using the
multilayer perceptron neural network. The multilayer perceptron neural network
achieved considerable classification accuracy for distinguishing between the
deep brain stimulation system set to “On” and “Off” status through the
quantified acceleration signal data obtained by this recently developed
conformal wearable and wireless system. The research achievement
establishes a progressive pathway to the future objective of achieving deep
brain stimulation capabilities that promote closed-loop acquisition of
configuration parameters that are uniquely optimized to the individual through
extrinsic means of a highly conformal wearable and wireless inertial sensor
system and machine learning with access to Cloud computing resources.
Introduction
The advent of Network Centric Therapy offers the potential for a quantum leap respective of the treatment of neurodegenerative movement disorders, such as Parkinson's disease. The nascent origins of Network Centric Therapy for providing objective quantification of Parkinson's disease hand tremor derive from the application of wearable and wireless systems, such as the smartphone. The smartphone is equipped with an inertial sensor package that enables the wireless transmission of inertial sensor signal data as an email attachment to the Internet.
The email resource serves as a functional Cloud computing resource [1]- [9].
The visionary concept of Network Centric Therapy in the context of neurodegenerative movement disorders, such as Parkinson's disease, pertains to the application of highly wearable and wireless inertial sensor systems that utilize local wireless connectivity to devices with broader wireless accessibility to the Internet, such as a smartphone or tablet. The inertial sensor data package would be conveyed to Cloud computing resources using this segmented wireless strategy. Post-processing resources could be applied from anywhere in the world, which could enable optimal clinical intervention. The neurodegenerative move- Advances in Parkinson's Disease inherent nature of the neurodegenerative movement disorder. In particular, the robust process of optimizing the parameter configuration for therapy by deep brain stimulation system can be achieved [7] [8] [9].
Recent developments have manifested the opportunities of Network Centric
Therapy. The BioStamp nPoint consists of a flexible inertial sensor system that has a profile on the order of a bandage. The BioStamp nPoint may be mounted at a predetermined position on the body for the quantification of movement.
The inertial signal data are transmitted by connectivity through connectivity to a device, such as a smartphone. The smartphone with its inherently stronger wireless transmission capability conveys the inertial signal data to a Cloud computing resource for subsequent post-processing [10]. The BioStamp nPoint is the recent evolution of conformal wearable and wireless systems that have been advocated and successfully demonstrated for the capability to quantify characteristics of movement disorder, such as Parkinson's disease [10]- [20].
The research objective is to conduct preliminary Network Centric Therapy for the treatment of Parkinson's disease regarding deep brain stimulation therapy efficacy with the application of machine learning. The BioStamp nPoint mounted about the dorsum of the hand objectively quantifies tremor for Parkinson's disease respective of deep brain stimulation "On" and "Off" status. Based on the quantified inertial sensor signal data the feature set is established through software automation. The multilayer perceptron neural network is selected as the machine learning algorithm to distinguish between the deep brain stimulation "On" and "Off" status. Preliminary Network Centric Therapy for the treatment of Parkinson's disease through deep brain stimulation enables a plausible pathway for the future goal of achieving real-time parameter configuration optimization.
General Perspective of Parkinson's Disease and Conventional Intervention Techniques
Respective of the United States of America for Parkinson's disease approximately one million people have been diagnosed with this particular movement disorder [21]. The symptoms of Parkinson's disease are notably endemic for people greater than age 55-years-old [22]. A predominant symptom of Parkinson's disease is the presence of a resting tremor [21]. In general, the tremor frequency is on the order of four to five per second [21] [23]. Furthermore, this resting tremor may attenuate or even cease during voluntary movement [23]. The neurological basis for Parkinson's disease is associated with the degeneration of dopaminergic neuron of the substantia nigra [21]. Parkinson's disease symptoms manifest with the progressive decrement of dopamine production for structures of the basal ganglia, such as the caudate and putamen [24].
Traditional therapies for Parkinson's disease involve medication, such as pre-
Deep Brain Stimulation for the Treatment of Parkinson's Disease and Optimization Challenges
On the order of three decades ago during the later 1980's Dr. Alim-Louis Benabid successfully demonstrated the concept of deep brain stimulation for the treatment of Parkinson's disease [30] [31] [32]. The deep brain stimulation system consists of electrode leads that are connected to an implantable pulse generator powered by a battery to generate an electrical signal to a targeted structure of the deep brain [33]. Respective of Parkinson's disease, a primary target is the ventral intermediate nucleus (VIM) for candidates for deep brain stimulation [34]. The deep brain stimulation system has four available parameters: stimulation amplitude, stimulation frequency, pulse width, and polarity [33]. The determination of an optimal parameter configuration is an inherent aspect of providing therapy through deep brain stimulation [35] [36]. Using current approaches, the optimization process can take on the order of several months [37].
Network Centric Therapy has been proposed as a viable strategy for optimizing the deep brain stimulation system parameters with the future goal of attaining real-time optimization. Central to this concept is the use of wearable and wireless systems for objectively quantifying tremor feedback with machine learning to distinguish between various parameter configuration scenarios. With wireless connectivity to Cloud computing resources a patient could be treated by expert clinicians effectively anywhere in the world [7].
Quantification Techniques for Parkinson's Disease Tremor with Transition to Network Centric Therapy
The ability to quantify the status of a neurodegenerative movement disorder, such as Parkinson's disease, is an inherent aspect for the ability to establish diagnosis and treatment strategy [ [43]. Furthermore, the translation between multiple established ordinal scale strategies has not been definitively established [40]. The use of wearable inertial sensors, such as accelerometers, has been proposed as an advancement beyond the inherently resource constrained ordinal scale technique [7] [39] [44]. Preliminary attempts to apply wearable accelerometer systems for evaluating intervention strategies for neurodegenerative movement disorders, such as Parkinson's disease, successfully demonstrated the potential of the inertial sensor. With these preliminary inertial sensor systems, the efficacy of various therapy [50]. The original data transfer methods have become effectively outmoded with the progressive evolution of the wearable and now wireless inertial sensor systems [7] [44] [51].
Preliminary wireless systems incorporated accelerometer systems secured by a glove or strap mechanism to the dorsum of the hand. The recorded acceleration dataset would be conveyed by local wireless connectivity to a laptop computer for subsequent post-processing [52] [53] [54]. Other similar evolutions involved wired connectivity of the inertial sensor system to a wrist mounted command module for wireless data transfer [55]. The nascent origins of Network Centric Therapy were demonstrated by Le-Moyne et al. during 2010 with the application of a smartphone as a wearable and wireless accelerometer platform. The smartphone was mounted about the dorsum of the hand through a glove. A software application enabled the recording of the accelerometer signal, which could then be conveyed wirelessly as an email attachment to the Internet. During this preliminary demonstration the experimental and post-processing resources were situated on other sides of the continental United States of America [1].
Using the smartphone as a wearable and wireless inertial sensor system the efficacy of deep brain stimulation for the treatment of neurodegenerative movement disorders, such as Parkinson's disease and Essential tremor, has been successfully determined. The inertial sensor signal data were consolidated into a feature set through software automation for machine learning classification of deep brain stimulation system set to "On" and "Off" status. The machine learning algorithms achieved considerable classification accuracy to distinguish between deep brain stimulation system set to "On" and "Off" status for their respective movement disorder scenarios [ There are many opportunities for improvement beyond the strategy of using the smartphone to quantify movement disorder tremor. The generational development of the smartphone has led to a more cumbersome device for mounting by glove at the dorsum of the hand. The smartphone is not intended for the acquisition of medical grade data. Also, instead of an actual Cloud computing environment, a provision email account was utilized [ [59].
The next evolutionary phase of Network Centric Therapy is represented by the development of the BioStamp nPoint, which constitutes a highly wearable and wireless system with a volumetric profile on the scale of a bandage that is more than ten times lighter than the standard smartphone. Furthermore, BioStamp nPoint is an FDA 510 (k) cleared medical device for the acquisition of medical grade data. The BioStamp nPoint presents a segmented wireless strategy with local connectivity from the BioStamp nPoint equipped with inertial sensors, such as an accelerometer, to a more powerful wireless device, such as a smartphone. The more powerful wireless device conveys the BioStamp nPoint inertial sensor signal data to a Cloud computing resource for post-processing [10]. This strategy has been demonstrated and proposed as a subsequent evolution to the use of a smartphone as a wearable and wireless system for quantifying human movement [7] [8] [60] [61] [62]. The BioStamp nPoint is the recent evolution of conformal wearable and wireless systems that have been advocated and successfully demonstrated for acquiring quantified characteristics of movement disorders, such as Parkinson's disease [10]- [20].
The objective of the research endeavor is to demonstrate the preliminary Network Centric Therapy for the treatment of Parkinson's disease through deep brain stimulation. The BioStamp nPoint provides a highly wearable and wireless inertial sensor system mounted to the dorsum of the hand for quantifying deep brain stimulation set to "On" and "Off" status. The inertial sensor signal is consolidated into a feature set through software automation. Machine learning classification using the multilayer perceptron neural network attains classification accuracy to distinguish between the deep brain stimulation set to "On" and "Off" status for a subject with Parkinson's disease.
Material and Methods
Preliminary demonstration of Network Centric Therapy was achieved from the perspective of engineering proof of concept for the treatment of Parkinson's disease through a deep brain stimulation system, for which the efficacy of the treatment was established based on machine learning classification to distinguish between "On" and "Off" status of deep brain stimulation. The BioStamp nPoint provided the inherent aspects of Network Centric Therapy, as it constitutes a highly wearable and wireless inertial sensor system capable of providing an accelerometer signal with a volumetric profile on the scale of a bandage. The Bi-oStamp nPoint acquires local connectivity to a device with broader wireless capability, such as a smartphone. The smartphone subsequently transmits the inertial sensor signal data wirelessly to a Cloud computing resource for post-processing anywhere in the world with sufficient Internet connectivity [10].
One subject (66-year-old female) diagnosed with Parkinson's disease in 2011 was selected while being administered bilateral subthalamic nucleus deep brain stimulation. Informed consent was established, and the experimental research was conducted at Allegheny General Hospital in conjunction with ethical clearance. The BioStamp nPoint was mounted about the dorsum of the hand through an adhesive medium, such that the BioStamp nPoint was symmetrically aligned in a longitudinal manner relatively to the third metacarpal (the aspect of the dorsum of the hand that is a collinear extension of the middle finger). This mounting strategy emulates the successful applications of wearable and wireless systems for objectively quantifying Parkinson's disease hand tremor [ [58]. Figure 1 illustrates a representative mounting strategy of the Bi-oStamp nPoint. Figure 2 presents BioStamp nPoint with the associated supporting apparatus for operation. Figure 3 provides the deep brain stimulation system clinician programmer, which is capable of controlling the "On" and "Off" mode settings. . The deep brain stimulation system clinician programmer for commanding "On" and "Off" modes. [59]. In particular the multilayer perceptron neural network was considered to be the optimal machine learning algorithm, and ten-fold cross validation was incorporated into the machine learning classification endeavor. The application of WEKA requires the development of an Attribute-Relation File Format (ARFF) based on the numeric attributes that compose the feature set [63] [64] [65].
The inertial signal data from the BioStamp nPoint was consolidated to develop the ARFF, which was based on the magnitude of the accelerometer signal.
The ARFF was established through software automation enabled by Python.
Based on previous successful machine learning applications involving wearable and wireless systems, the feature set was composed of the following attributes for the magnitude of the accelerometer signal maximum, minimum, mean, standard deviation, and coefficient of variation [6] [62] [66] [67] [68].
The experimental protocol involved five trials each spanning two seconds for the deep brain stimulation system set to "On" status and five trials each spanning two seconds with the deep brain stimulation system set to "Off" status. The subject's wrist was suspended beyond a support for the forearm for each respective trial. The BioStamp nPoint was set to 250 Hz for the sampling rate of the accelerometer signal with the recording set to a sufficient duration to provide two second intervals for each of the five trials. The experiment was conducted based on the following protocol: 1) Mount the BioStamp nPoint through an adhesive medium to the dorsum of the hand, such that the BioStamp nPoint is symmetrically and longitudinally oriented about the third metatarsal.
2) Orient the forearm of the subject relative to the support, such that the wrist is suspended beyond the support, ensuring that hand tremor is unimpaired.
3) Activate the recording process of the BioStamp nPoint for acquisition of the BioStamp nPoint accelerometer signal with pending upload to the Cloud computing resource.
4)
Continue recording duration such that five trials spanning two seconds can be acquired with the deep brain stimulation system set to "On" status.
5)
Repeat the same protocol for the deep brain stimulation system set to "Off" status.
Results
The hand tremor for the subject with Parkinson's disease is perceptivity disparate from an observational perspective regarding deep brain stimulation set to "On" status and "Off" status. The BioStamp nPoint enables quantification of the hand tremor for each respective scenario through inertial sensors, such as an accelerometer. The visualized quantification of the acceleration magnitude is readily determined. Figure 4 represents hand tremor acceleration magnitude for the subject with Parkinson's disease while the deep brain stimulation system is set to "On" status. Figure 5 illustrates the hand tremor acceleration magnitude with the deep brain stimulation system set to "Off" status.
Based on the observation of the inertial sensor signal data a feature set for the five instances of deep brain stimulation system set to "On" status and five instances of deep brain stimulation system set to "Off" status is consolidated through five attributes: maximum; minimum; mean; standard deviation; coefficient of variation.
These attributes back been successfully applied to an assortment of machine learning classification endeavors pertaining to the application of wearable and wireless inertial sensor systems [6] [62] [66] [67] [68].
WEKA consists of a considerable array of machine learning classification algorithms. The multilayer perceptron neural network was selected as the most suitable machine learning algorithm to distinguish between deep brain stimulation system set to "On" status and deep brain stimulation system set to "Off" status based on quantification of the inertial sensor signals derived from a wearable and wireless system. The acquired multilayer perceptron neural network is presented in Figure 6. A classification accuracy of 100% was achieved by the multilayer perceptron neural network for differentiating between deep brain stimulation set to "On" and "Off" status for a subject with Parkinson's disease. Figure 4. The acceleration magnitude of hand tremor for the Parkinson's disease subject with the deep brain stimulation system set to "On" status. Figure 5. The acceleration magnitude of hand tremor for the Parkinson's disease subject with the deep brain stimulation system set to "Off" status. Figure 6. The multilayer perceptron neural network for distinguishing hand tremor for a subject with Parkinson's disease with respect to deep brain stimulation set to "On" and "Off" status based on a wearable and wireless inertial sensor system. The input layer consists of five feature set attributes (Amag_max: maximum of the acceleration magnitude, Amag_min: minimum of the acceleration magnitude, Amag_mean: mean of the acceleration magnitude, Amag_stdev: standard deviation of the acceleration magnitude, and Amag_CV: coefficient of variation of the acceleration magnitude).
Discussion
The BioStamp nPoint serves as a preliminary demonstration of the opportunity and potential of Network Centric Therapy, which represents a highly wearable With the inertial sensor signal acquired the data can be consolidated into a feature set for machine learning classification. The role of machine learning is envisioned as instrumental for augmenting the clinician's ability to rapidly converge upon an optimal series of parameter configuration for efficacious deep brain stimulation therapy. In particular, the Cloud computing storage of the inertial sensor signal data through wireless connectivity enables a skilled clinician to proactively optimize treatment strategy for a subject with a movement disorder, such as Parkinson's disease.
The achievements of the research objective elucidate perspective regarding future goals, such as automated real-time optimization of deep brain stimulation system parameter configurations. Machine learning and highly wearable and wireless systems that are effectively conformal and envisioned serve as inherent aspects of achieving the real-time automation of parameter configurations capability for deep brain stimulation systems for the treatment of movement disorders, such as Parkinson's disease. Another design requirement to consider is whether to conduct the machine learning classification at the Cloud computing resource level or intrinsic to the wearable and wireless systems, for which respective Network Centric Therapy architectures have been proposed [7] [69].
The selection of the appropriate machine learning algorithm is imperative depending on the selected Network Centric Therapy architecture. A wearable and wireless system would likely be equipped with substantially less processing capability than a Cloud computing resource, however the wireless transmission of an optimized parameter configuration would be essentially instantaneous. Previous research has addressed and contrasted the processing time to attain classification accuracy for an assortment of machine learning algorithms regarding the application of wearable and wireless systems for determining the efficacy of deep brain stimulation to ameliorate movement disorder tremor symptoms [9] [58] [59]. In summary, Network Centric Therapy with the integration of wearable and wireless systems with Cloud computing access, such as the Bi-oStamp nPoint, deep brain stimulation, and machine learning are envisioned to provide a quantum leap for the treatment of movement disorders, such as Parkinson's disease.
Conclusions
Network Centric Therapy has been successfully demonstrated through the implementation of wearable and wireless systems and machine learning that utilizes the synergistic capabilities of Cloud computing for the treatment of Parkinson's disease through deep brain stimulation. The Biostamp nPoint represents a highly conformal wearable and wireless system with a profile on the order of a bandage that offers a considerable reduction in mass compared to other devices, such as a smartphone. This device achieves wireless access to a Cloud computing environment. The inherent characteristics of this conformal wearable and wireless system enable mounting to the dorsum of the hand by adhesive as opposed to a specialized glove used in previous experiments to secure a device, such as a smartphone. The BioStamp nPoint acquired accelerometer signal data to quantify the efficacy of a deep brain stimulation system for treating a subject with Parkinson's disease. The magnitude of the accelerometer signal was consolidated to a feature set through software automation using Python. A multilayer perceptron neural network machine learning algorithm achieved considerable classification accuracy to distinguish between deep brain stimulation set to "On" and "Off" status for the treatment of a subject with Parkinson's disease using the Bi-oStamp nPoint, which is FDA cleared for the acquisition of medical grade data.
The association of these achievements infers the pathway to the progressive research, development, testing, and evaluation of Network Centric Therapy for the treatment of people with movement disorders. Network Centric Therapy represents a combination of the expansive opportunities enabled with conformal wearable and wireless systems with segmented access to a Cloud computing environment with machine learning to augment clinical situational awareness respective of the efficacy of the prescribed intervention strategy. Respective of deep brain stimulation, these capabilities are envisioned to provide a pathway for the optimization of deep brain stimulation parameters in a closed-loop context for people with Parkinson's disease. | 5,187 | 2019-10-31T00:00:00.000 | [
"Medicine",
"Engineering",
"Computer Science"
] |
MULTI SENSOR AND SMART GIMBAL FOR ENHANCED POWERLINE MONITORING ON HELICOPTER AND UAVS
: The inspection of overhead high voltage power lines is important because power supply has to be seen as a critical infrastructure. Monitoring power lines using helicopters and UAVs requires a setup of various sensors. The combination of high-resolution aerial cameras for capturing better than 2 mm GSD, LiDAR collecting 200 point/m², thermal cameras detecting hot spots in cm accuracy of anomalies and UV-cameras checking for corona discharges are important elements in the newest monitoring setups. The combination of online analytics with the Corona Camera (defined as a combination of UV detector and RGB monitoring camera) and the Smart Gimbal technology enhances the quality and speed of the inspection. Besides that, it reduces the need of too many sensors. We show different integrations and their pros and cons and as an outlook where the technology with respect of entering AI technologies will go. A basic task is the handling of the enormous data volume, at this point online analytics may help to reduce the data-volume beside a rapid response.
BACKGROUND
High and ultra-high voltage overhead power transmission lines are part of a sensitive and very important infrastructure. The international power line network helps to bridge gaps but especially due to new technologies like windfarms and solar-power plants, the distribution and storage becomes more important than in the past. Thus, the need of maintenance and detection of issues/findings became an obligation and remote sensing technologies help to automate or at least document this process. Still today observers visually inspect infrastructure of cables and poles during the helicopter missions. Insulators, wireclamps, bumpers and many other features must be monitored and maintenance frequently. During the mission observers typically use handheld cameras for the documentation but are meanwhile supported by more complex technologies e.g., LiDAR to detect vegetation that grow too close to the power line. In the last 7 years the integration of multi sensor systems with higher resolution entered the inspection workflow. Thermal, Corona and high-resolution visual cameras became the dominant part but also LiDAR and other electronic devices help to detect anomalies on the overhead lines. While some of the sensors provide visual data or measurements (RGB and LiDAR) others, e.g., thermal and Corona Cameras, directly show anomalies that may indicate a finding. The combination of both enables the automated detection if issues.
SENSOR SETUP IN HELICOPTERS
The most complex sensor setup is used by Siemens in the SIEAERO program. Flying with a side and height-offset * Corresponding author of 40 and 15 m from the power line this distance typically fulfills the core of security regulations. During the flight the data are captured with forward and sideward oblique angles that give the best view into the pole structure. Using 4 high resolution cameras take oblique images in order to detect the findings. The cameras are rotated in forward and backward direction and tilted in a certain angle downward to capture the pole with 2 cameras entirely. 2x100MP forward and 2x100 MP backward cameras, one nadir orthophoto camera, an array of 4 thermal sensors, LiDAR and Corona discharge detection system make this setup unique. Nevertheless, each flight hour generates about 2.5 Terabyte of RAW data that has to be processed.
All data need to be preprocessed using GNSS/INS. Processing of LiDAR data need a perfect GNSS/INS trajectory but also the small Thermal camera data need to be directly referenced. The RGB images may be processed by tie point matching, but due to the huge number of captured images the direct referencing is a much more useful approach. Procedures for the internal, Sensor to IMU, bore sight and sensor to sensor calibration are an intensive process to get the entire sensor output prepared for a fast processing and analytic pipeline. More difficult is the integration of the video stream generated by the Corona camera. This camera uses a narrow band daylight blind and UV amplified Sensor that only detects discharges and in parallel to a coregistered RGB Video camera that is used to overlay the discharges to the video stream. However, this setup with a specific focus needs a dedicated mission plan that takes all poles and infrastructure into account. Missing information or imprecise mission navigation can cause gaps in the images or lower data quality. This indeed blocks the defined processing workflow and may result in a re-flight of the power line. For planning the mission, a GIS based tool was developed that takes the pole structure into account as well as the footprint of the images related to a certain trigger point. It is important to have the exact coordinate of the pole, the height and length of the traverses and the layout of the structure e.g., the places of the Insulators and cables. In order to be effective during the mission, a flight path as a curved line was calculated that entered a Flight Management System with specific instruments to show the curves to the pilot in advance. All sensors were triggered and controlled automatic without the need to interfere by an operator.
The basic idea of such a complex sensor setup is to combine sensors that directly detect anomalies like the corona discharges or thermal effects. To reduce the number of sensors as well as the number of data to be captured, 2 strategies help to get the system smaller and smarter.
ONLINE ANALYTICS ON THE CORONA-CAMERA
A Corona Camera is defined by the combination of an ultraviolet sensitive camera capable to capture images of Corona-Discharges and a RGB Camera of the same view. The developed Corona Camera System is a combination of a daylight blind UV amplified camera and a parallel mounted RGB camera. Both cameras have the same sensor installed and can make use of the same interfaces with control libraries. The image capture rate should be close to but not exactly the same as the power grid A/C frequency of 50 Hz. With 48 Hz both cameras are capturing images simultaneously. Using PTP and GNSS interface, both cameras are perfectly synchronized and thus enable real-time image pre-processing. Both cameras are pre-calibrated to each other, that way the images can be overlaid to each other, displayed, analyzed and saved in real-time. The output data of the UV Camera contains 8bit grey scale pixel values and can be analyzed online by detecting UV radiation as pixel areas exceeding a certain threshold in size and intensity. Detected UV radiation patterns above the thresholds are then highlighted in red on the RGB image. As a part of the online -analytics, during detection of corona discharges the data are stored with the full frame rate of 48 Hz. For time spans without UV events only for documentation purposes the storage frame rate is reduced to 1 fps. Thus, the data volume for the corona discharge images is reduced dramatically, depending on the number of findings, to 0.1-5%. Analyzing the intensity of the discharges over a certain period is a task that has to be managed in further developments. Also online scanning the detection area on the RGB image was planned, so the discharge area can be marked with specific tags (e.g., detected discharge near isolator). For this task an information data interface and fast image data access was provided during software development. But with regard to the high utilization of the computing unit in case of computer vision applications not only strong hardware, but also continues code optimizations of the scanning and detecting process have to be considered.
SMART GIMBAL
To provide the same capturing quality as with the before mentioned setup, a smart gimbal development was initiated. A standard gimbal typically has two functions, on one side to stabilize the flight conditions (roll, pitch and heading), it can on the other side can direct the camera to a specific target, for example to inspect different parts of a transmission pole. For optimal response time, high angular accelerations, high precision and low power consumption (enhanced battery life on UAVs) there are two options for the motor decision: Stepper motors or brushless DC (BLDC) motors with a decent amount of pole pairs. Stepper motors will result in a higher holding torque while BLDC motors achieve slightly better accelerations and higher angular rates. In both cases encoders need to be installed to track the position of the rotor and to enable closed loop operation. Most common setups use BLDC motor controllers with field orientated control capability (foc). This ensures low power consumption (<100mA per motor) when the gimbal operates in a good balanced state. On the other hand, the overall low motor temperature creates a high temperature buffer enabling high current peaks for the motors, so it handles fast acceleration and high torque without overheating. This is especially important to ensure fast turning rates from pole to pole and to resist gusts of wind during operation.
In contrast to the standard setup with stabilization only, the smart gimbal is a fine-tuned all-in-one system with an onboard high-end single-board computer with several interfaces to manage more specific functionalities and interactions between external devices and the gimbal controller unit. Thus, the smart gimbal is able to calculate and execute new operations on the fly and also recalculate them with high refresh rates while moving. While the compensation of the flight parameters typically is automated using GNSS/INS information and the planned track, the controller can make use of pre-planned POIs to follow specific 3D points along the flight path. The approach of using a database for managing point of interests increases the efficiency of the inspection process while decreasing the workload of the operator. The gimbal controlling unit is able to access the nearest PoI with its own coordinates with just one simple database query. Also, it has the ability to log important information to the database entries. Those could be if a corona discharge was detected or the angles/angular rates of the gimbal at the time images were captured. Besides that, it also is able to interface with the cameras, e.g., to refocus the lens by the distance between the actual and the target position in real-time.
The main advantage is that a smart gimbal enables the forward and backward view of the sensor-head, compensates flight conditions, acts to compensate inaccurate flight paths and also compensates uneven terrain in the perspective view. This is shown in the Figure 4 as a nadir view and in figure 5 to demonstrate the influence of the terrain in the side view. A smart gimbal solution needs beside an IMU and GPS a dedicated mission plan that accounts for the optimal flight path and the 3D information of the Poles as POIs. The 3D terrain information is important as well. A definition of rotation speed from POI 1 to POI 2, correcting the focus and the capability to get information of the wires even in-between the poles, are challenging.
Our designed smart gimbal carries three 100 MPix RGB cameras and two thermal or alternatively one corona detection system. With this combination, movements of the aircraft are compensated, motion blurs minimized, and the transmission tower is always kept in the field of view. Furthermore, the online evaluation of the gimbal parameters (angles, current offset angles to POI, current angular rates etc.) can be used to enhance the image quality. This is possible because of the computing unit is monitoring those values while communicating with the camera. The focal distance can be calculated and set in advance before the trigger is released at the point of minimum motion of the gimbal. This feature is especially important for longer focal lengths where small angular rates have a huge impact on motion blur.
Backward, forward and sideward views prevent objects of interest from being hidden behind other parts of the installation. Besides that, contour flights in hilly terrain are also possible. An additional challenged is to modify the system to a single camera with a longer focal length that captures data with a higher resolution of the infrastructure but cannot capture the entire pole in a single image. To solve this issue several POIs on the same pole may be defined and an intelligent algorithm applied that makes use of a sequential operation of the gimbal and the camera. There are several aspects to consider here. The most critical part is the time the gimbal needs to move from POI to POI. The closer the pole the greater the angle to travel and so the drive time increases. If this process of moving the gimbal, refocus the lens and capture one image e.g. takes about one second, the vehicle moves about 10 meters at a speed of 36km/h. That would be 30-40 meters covered distance until the entire pole is completely captured from one side. Minimizing this range with higher acceleration torque as well as fast image data processing and refocusing during gimbal motion period. Another task consists of getting the best order of captures, so that the gimbal's travel distance is minimized. This way in a continuous sweeping motion all parts of a transmission tower can be captured in sequence. Figure 6: Adjustment to the Terrain following while bypassing
ADJUSTMENT TO UAVS
Besides the regulations (BVLOS Missions) for using UAVs, a reduced sensor setup is needed in order to reduce payload weight. We testes integrations on 3 different models for power line inspections.
The AeroSpector Quadcopter from GGS, specially designed for such monitoring work, carries 6.5 kg payload and provides 45' flight-time. This is sufficient for a power line observation length of 10 km (+-5 km both sides) but needs a reduced sensor setup due to the payload compared with a helicopter. The before mentioned gimbal was adjusted and then used to capture thermal anomalies in forward and backward directions well as high resolution images with 2 mm GSD of the entire power line. In a second project the combination of aerial and thermal cameras in a SwissDrones UAV was used to perform a mission in France. The variable setup to exchange cameras and focal length was used to adapt to the regulations of the power-line providers. In that case the camera layout was designed in that way, that 2 cameras with different focal length capture the entire pole with similar resolution while a thirds camera captures in a single shot the entire pole with its environment. The Mission was performed in the Département Creuse at an overhead power line of the network provider RTE.
In a third project a multi sensor system was adjusted to a one axis smart gimbal using the CAMCOPTER S100 r UAV from Schiebel. This test was part of the Innspektor project with Siemens, Schiebel, GGS and Lufthansa. The CAMCOPTER S100 is a huge UAV that is able to carry up to 50 kg for several hours. A LiDAR was fix mounted at the UAVs nose while the other sensors with RGB, NIR and UV were adjusted to the main payload bay in a smart gimbal below the CAMCOPTER S100 Figure 9: Installation at the CAMCOPTER S100 before the mission in Austria Figure 10: Report generated by AI analytics out of a Mission with Siemens on a CAMCOPTER S100 The gimbal was specifically redesigned for this Innspektor application and has a high freedom to rotate around the z-axis. This was done according to the left-or righthand side mission who was needed to adapt the flight of the CAMCOPTER S100 to the wind conditions. A testmission in Austria was performed in summer 2021 successfully.
CONCLUSION
While UAVs are suitable for limited areas due to VLOS mission regulations, the technology of a smart gimbal and the online analytics can be part of the helicopter surveys as well. The interest in this technology is increasing and the possibilities, to run more online analytics opens new fields for the inspection. To do real-time analytics on the UV and the Thermal band (these sensors are used to find anomalies anyway) can setup the other sensors to capture more or less data. Using UAVs, the small corona images can be linked e.g., via 5G to the ground control for immediate validation and needed repairs. AI can assist to find decisions based on multi data analytics -in real time online.
The demand for such inspection work is growing and beside the overhead lines also the inspection of windpower plants becomes very important. Using thermal cameras, the use in the inspection of solar power plants also is in focus of the electric power supplier. The technologies demonstrated are just one step in the more and more automated inspection workflow. | 3,861.2 | 2022-05-30T00:00:00.000 | [
"Physics"
] |
Language assessment literacy: what do we need to learn, unlearn, and relearn?
Recently, we have witnessed a growing interest in developing teachers’ language assessment literacy. The ever increasing demand for and use of assessment products and data by a more varied group of stakeholders than ever before, such as newcomers with limited assessment knowledge in the field, and the knowledge assessors need to possess (Stiggins, Phi Delta Kappa 72:534-539, 1991) directs an ongoing discussion on assessment literacy. The 1990 Standards for Teacher Competence in Educational Assessment of Students (AFT, NCME, & NEA, Educational Measurement: Issues and Practice 9:30-32, 1990) made a considerable contribution to this field of study. Following these Standards, a substantial number of for and against studies have been published on the knowledge base and skills for assessment literacy, assessment goals, the stakeholders, formative assessment and accountability contexts, and measures examining teacher assessment literacy levels. This paper elaborates on the nature of the language assessment literacy, its conceptual framework, the related studies on assessment literacy, and various components of teacher assessment literacy and their interrelationships. The discussions, which focus on what language teachers and testers need to learn, unlearn, and relearn, should develop a deep understanding of the work of teachers, teacher trainers, professional developers, stakeholders, teacher educators, and educational policymakers. Further, the outcome of the present paper can provide more venues for further research.
Introduction
The traditional thought of literacy or illiteracy as the ability or inability respectively to read and write has now begun to take on a new functional aspect. This aspect is conceptualized within different domains as possessing knowledge, skills, and competence for specific purposes and in particular fields. An individual is expected to be able to understand the content related to a given area and be able to engage with it appropriately. As with this growing number of domains and rapid advances in this era, it is imperative to acquire multiple literacies to keep up with this contemporary trend, such as computer literacy, media literacy, academic literacy, and many others. Given this evident growth of new literacies, it should not come as no surprise that assessment literacy began to appear as an early contribution in the general education literature (Inbar-Lourie, 2008;Popham, 2008;Stiggins, 1999Stiggins, , 2001Taylor, 2009) and in language testing (Brindley, 2001;Davies, 2008) focusing on identifying the characteristics of testing knowledge and skills of teachers. The 1990 Standards for Teacher Competence in Educational Assessment of Students (AFT, NCME,, & NEA, 1990) made a considerable contribution to this field. Following these Standards, a substantial amount of for and against research has been published on the knowledge base and skills for assessment literacy, assessment goals, the stakeholders, formative assessment and accountability contexts, and measures examining teacher assessment literacy levels. This paper inquires into the philosophy behind language assessment literacy, its theoretical and conceptual framework, the related studies on assessment literacy, and various components of teacher assessment literacy and their interrelationships.
Language assessment literacy: the road we have gone Language assessment literacy is generally viewed as a repertoire of competences, knowledge of using assessment methods, and applying suitable tools in an appropriate time that enables an individual to understand, assess, construct language tests, and analyze test data (Inbar-Lourie, 2008;Pill & Harding, 2013;Stiggins, 1999). Davies (2008) suggested a "skills + knowledge" approach to assessment literacy. "Skills" describe the practical know-how in assessment and construction, and "knowledge" to the "relevant background in measurement and language description" (p. 328). As it is evident in the literature, there has been a shift in developing language assessment literacy from a more componential view (e.g., Brindley, 2001;Davies, 2008;Inbar-Lourie, 2008) to a developmental one. For example, Fulcher (2012) believed that language assessment literacy should fall into a classification of (a) practical knowledge, (b) theoretical and procedural knowledge, and (c) socio-historical understanding. Fulcher argued that practical knowledge is the base and more important than all other aspects of language assessment literacy. Focusing on mathematics and science literacy, Pill and Harding (2013) classified language assessment literacy from "illiteracy," through "nominal literacy," "functional literacy" and "procedural and conceptual literacy," to an expert level of knowledge: "multidimensional language assessment literacy" (p.383).
In her review paper, Taylor (2013), having considered these notions, suggested that language assessment literacy requires specific levels of knowledge and thus proposed eight levels (1) knowledge of theory, (2) technical skills, (3) principles and concepts, (4) language pedagogy, (5) sociocultural values, (6) local practices, (7) personal beliefs/attitudes, and (8) scores and decision making. However, Taylor was cautious about calling this a model, but her suggestion offered a useful starting point and paved the way for further research on more conceptualization of language assessment literacy.
For example, Baker and Riches (2018), in response to Taylor's call for more research, investigated language assessment literacy characterization of 120 Haitian language teachers. They suggested an alternative language assessment literacy aspect required for language teachers and assessors. They elaborated on how language assessment literacy is different for these two groups, but their knowledge could be considered complementary in accomplishing collaborative task. Yan, Zhang, and Fan (2018) investigated the factors, namely experiential and contextual, mediate language assessment literacy development for three secondary-level Chinese teachers. The semi-structured retrospective interviews revealed that teachers had a distinct language assessment literacy profile and more robust training needs in assessment practice than in theories of assessment. However, the need to study language assessment literacy with larger groups shows a gap in this field of research.
More recently, Kremmel and Harding (2020), through empirical research, investigated language assessment literacy needs of various groups, 1086 persons. They analyzed the responses of language teachers, language testing developers, and language testing researchers to provide research support to their survey's application and findings.
Language assessment and language learning
Language learning is viewed as a transdisciplinary process within multilingual multicultural realities in this current globalization, and this process is largely affected by new genres as a result of technological innovations and affordances of the current era (Leung & Scarino, 2016;Shohamy & Or, 2017). As Kern and Liddicoat (2010) asserted, language learners are perceived as a "social speaker/actor," for he "acts and speaks in multiple communities (scholarly, social, virtual, etc.), and he experiences the intercultural through his affiliations with various communities that often straddle different languages and cultures" (p.22). In these multi-contextually bound environments and discipline-specific assessment literacies, research on assessment literacy in different subject domains seems to be more focused on the combination of disciplinary knowledge and assessment. In language teaching, language teachers need to combine and use their disciplinary pedagogical knowledge or teaching with assessment knowledge in current language-learning constructs (Inbar-Lourie, 2008;Zolfaghari & Ahmadi, 2016). As Farhady (2018) has stated, the current understanding of language learning and use should be matched with the related assessment theory and practice to meet the challenge raised by the realities of different languages and cultures.
Since language learning possesses multifaceted modes and constructs, this requires corresponding assessment practices. Through an examination of the language testing literature, six assessment themes which reflect current language-learning constructs have been identified: 1) Assessment to promote language learning: This type of assessment has led to approaches that improve learning in the language learning context such as Learning Oriented Assessment (LOA, Turner & Purpura, 2016) and Dynamic Assessment (Poehner, 2008). 2) Classroom assessment: It helps process-oriented learning via appropriate testing methods and by focusing on assessing task performance, which requires competency in the related language construct, the educational settings in which language learning and teaching takes place, and task design (Wigglesworth & Frost, 2017). 3) Integrated language assessment: It has led to the integration rather than separation of language skills. According to Lee (2015), multiple competencies are required to complete integrated tasks in multi-mediated contexts. For example, integrated reading-writing tasks require different competencies such as extracting the source texts for ideas, selecting ideas, and organizing ideas. 4) Content assessment: In this type of assessment, content and language are supplementary to one another. Any assessment of content requires language and any assessment of an individual's ability to use language will involve content or topical knowledge. For example, in the Content Language Integrated Learning (CLIL) approach, the language of teaching used in conveying meaning is important in meaning-oriented language learning (Lopriore, 2018). 5) Multilingual assessment: It is reflective of translanguaging pedagogies where learners can use their whole language-learning repertoires and multilingual competence. This allows for the assessment of dynamic language use as a result of interactions occurred amongst speakers (Lopez, Turkan, & Guzman-Orth, 2017). 6) Multimodel assessment: This classification of assessment is proposed since texts in different languages are conceptualized in multifaceted modes, presented with several meanings, delivered on-screen, live or on paper, presented with various sensory modes, and presented through various channels and media (Chapelle & Voss, 2017).
Different areas of the related assessment research
Reviewing the related literature provides insights into assessment literacy and helps with our understanding of what has worked for developing assessment literacy by examining the links between previous studies. The 1990 Standards for Teacher Competence in Educational Assessment of Students (AFT, NCME,, & NEA, 1990) has made a considerable contribution to the field. The Standards prescribed that teachers need to attain competence in selecting suitable instructional assessment methods, developing suitable instructional assessment methods, administering, scoring, and interpreting the results assessment methods, applying assessment results in decision-making, developing valid student grading procedures, sharing assessment results to various stakeholders, and identifying unethical and in some cases illegal assessment methods and uses of the related information obtained from assessment tasks and tests. Following the Standards, a considerable amount of literature has been published on the knowledge base and skills for assessment literacy, assessment goals, and the stakeholders (Abell & Siegel, 2011;Inbar-Lourie, 2008;Taylor, 2013), formative assessment and accountability contexts (Brookhart, 2011;JCSEE, 2015;Stiggins, 2010), and assessment education (DeLuca, Klinger, Pyper, & Woods, 2015). Likewise, instruments were developed to examine teacher assessment literacy levels (Campbell & Collins, 2007;DeLuca, 2012;DeLuca, Klinger, et al., 2015;Fan, Wang, & Wang, 2011;Graham, 2005;Greenberg & Walsh, 2012;Hill, Ell, Grudnoff, & Limbrick, 2014;Koh, 2011;Lam, 2015;Leahy & Wiliam, 2012;Lukin, Bandalos, Eckhout, & Mickelson, 2004;Mertler, 2009;Sato, Wei, & Darling-Hammond, 2008;Schafer & Lizzitz, 1987;Schneider & Randel, 2010;Smith, 2011;Wise, Lukin, & Roos, 1991).
Assessment literacy measures: a complex world of measures
Developing assessment literacy measures is a major area of interest within the field of assessment literacy. Most of the related studies have involved quantitative measures. Eight instruments regarding assessment literacy or teacher competency in assessment for pre-service and in-service teachers published between 1993 and 2012 were identified: Assessment Literacy Inventory (Campbell, Murphy, & Holt, 2002); Assessment Practices Inventory (Zhang & Burry-stock, 1997); Assessment Self-Confidence Survey (Jarr, 2012); Assessment in Vocational Classroom Questionnaire (Kershaw IV, 1993), Part II; Classroom Assessment Literacy Inventory (Mertler, 2003); Measurement Literacy Questionnaire (Daniel & King, 1998); the revised Assessment Literacy Inventory (Mertler & Campbell, 2005); and the Teacher Assessment Literacy Questionnaire (Plake, Impara, & Fager, 1993) (see Table 1). DeLuca, Klinger, et al. (2015) represented these assessment instruments based on their item characteristics, the instrument's guiding framework, and the instrument's psychometric properties. Plake et al. (1993) studied the assessment proficiency of 555 teachers and 268 administrators across American states. The results underscored the significant gaps in teachers' pedagogical and technical knowledge of language assessment. The participants had basic problems in assessment results' interpretation and communication. O'Sullivan and Johnson (1993) employed Plake et al.'s (1993) questionnaire with 51 teachers during a measurement course offering performance-based tasks that were related to the standards (AFT, NCME,, & NEA, 1990). The results indicated that Classroom Assessment Task responses supported a strong match between performance tasks and the Standards, which further validated the questionnaire. Similarly, Campbell et al. (2002) investigated a revised version of Plake et al.'s (1993) questionnaire with 220 undergraduate students enrolled in a pre-service measurement course. They concluded that teacher participants' competency differed across the seven standards, and respondents were found to have lacked critical aspects of competency upon entering the teaching profession. Mertler (2003), in his study with 67 pre-service and 197 practicing teachers, found results that paralleled Plake et al.'s (1993) and Campbell et al.'s studies. Similarly, Mertler and Campbell (2004), with the aim of restructuring items into scenario-based questions, found low critical assessment competencies across teachers. Brown (2004) and his later co-authors (e.g., Brown & Harris, 2009;Brown & Hirschfeld, 2008;Brown, Hui, Flora, & Kennedy, 2011) employed the Teachers' Conceptions of Assessment (COA) questionnaire to specify New Zealand primary school teachers' and managers' priorities based on four purposes of assessment: (a) improvement of teaching and learning, (b) school accountability, (c) student accountability, and (d) treating assessment as irrelevant. On this instrument, teachers were asked if they agreed or disagreed with various assessment purposes related to these four conceptions. The results from these studies indicated that teachers' conceptions of assessment were different based on context and career stage and participants agreed with the improvement conceptions and the school accountability conception while rejecting the view that assessment was irrelevant. Subsequently, Brown and Remesal (2012) used COA. They examined teachers' conceptions based on three purposes of assessment: (a) assessment improves, (b) assessment is negative, and (c) assessment shows the quality of schools and students. They reported similar results.
Overall, most studies on teacher's assessment competency use instruments that aim to identify teachers' conceptions toward different assessment aims. Findings from these studies revealed that teachers' assessment competency was inconsistent with the recommended 1990 Standards (Galluzzo, 2005;Mertler, 2003Mertler, , 2009Zhang & Burry-Stock, 1997). As Brookhart (2011) argued, the 1990 Standards for Teacher Competence in Educational Assessment of Students (AFT, NCME,, & NEA, 1990) was no longer useful in supporting assessment practices, or the assessment knowledge teachers require within the current classroom context. 1990 Standards 393 in-service teachers α = 0.91; mean total score = 97.0 (out of 130 max. points), SD = 12.9 Classroom Assessment Literacy Inventory (CALI) (Mertler, 2003) 35 content-based items (5 items per standard) 1990 Standards 197 in-service teachers α = 0.57; mean total score = 22.0, SD = 3.4; α = 0.74; mean total score = 19.0, SD = 4.7 Measurement Literacy Questionnaire (Daniel & King, 1998) 30 true/false items Assessment literature (e.g., Gullickson 1984;Kubiszyn and Borich 1996;Popham 1995) 67 pre-service teachers, 95 in-service teachers α = 0.60; mean total score = 18.2, SD=3.3 Revised Assessment Literacy Inventory (ALI) (Mertler & Campbell, 2005) 35 scenario-based items (5 scenarios; 5 items per standard) 1990 Standards 250 pre-service teachers α = 0.74; mean total score = 23.9, SD=4.6 Teacher Assessment Literacy Questionnaire (TALQ) (Plake et al., 1993) 35 content-based items (5 items per standard) 1990 Standards 555 in-service teachers α = 0.54; mean total score = 23.2, SD = 3.3 The assessment-based teaching practices over the past 20 years (Volante & Fazio, 2007), and the recently revised Classroom Assessment Standards (JCSEE, 2015) set grounds for developing a new instrument for measuring teacher assessment literacy that directs the current demands on teachers. Gotch and French (2014) examined a recent systematic review of 36 literacy measures. They found that these measures do not support psychometric aspects and that existing instruments lack "representativeness and relevance of content in light of transformations in the assessment landscape (e.g., accountability systems, conceptions of formative assessment)" (p. 17). Gotch and French (2014) called for further research and developing an efficient and reliable instrument to measure teacher's assessment literacy reflecting contemporary demands. In response, DeLuca, LaPointe-McEwan, and Luhanga (2016) studied professional learning communities to support teachers' assessment practices and data literacy to reflect contemporary practices for classroom assessment. They developed the Approaches to Classroom Assessment Inventory containing 15 assessment standards from six countries, namely the USA, Canada, UK, Europe, Australia, and New Zealand. They identified eight themes indicating contemporary aspects of teacher assessment literacy (see Table 1).
The themes of assessment standards from 1990 to 1999 included Assessment Purposes, Assessment Processes, Communication of Assessment Results, and Assessment Fairness; from 2000 to 2009 involved Assessment Purposes, Assessment Processes, Communication of Assessment Results, and Assessment Fairness, and Assessment for Learning; 2010present focused on Assessment for Learning. Assessment Purposes, Assessment Processes, Communication of Assessment Results, and Assessment for Learning have become a more dominant theme in modern assessment standards.
Assessment Purposes refers to selecting the appropriate form of assessment according to instructional purposes. Assessment Processes involves constructing, administering, and scoring assessment and interpreting assessment results to facilitate instructional decision-making. Communication of Assessment Results includes communicating assessment purposes, processes, and results to stakeholders. Assessment Fairness entails providing fair assessment conditions for all learners by considering student diversity and exceptional learners. Assessment for Learning explains the use of formative assessment during instruction to guide teacher practice and student learning. Harding and Kremmel (2016) have claimed that language teachers, as the primary users of language assessment, need to be "conversant and competent in the principles and practice of language assessment" (p.415). Teacher assessment literacy involves teachers' mastery of knowledge and skills in designing and developing assessment tasks, analyzing the relevant assessment data, and utilizing them (Fulcher, 2012). Scarino (2013), focusing on assessment literacy for language teachers, argued that two aspects should be considered in developing language teacher assessment literacy, including the identification of relevant domains that contain the knowledge base and the relationship among these domains. He explained that the knowledge base composes some intersecting domains such as knowledge of language assessment, which entails not only various assessment paradigms, theories, purposes, and practices related to elicitation, judgment, and validation in diverse contexts, but also learning theories and practices and evolving theories of language and culture. Additionally, Scarino stated that assessment could not be separated from its relationship with the curriculum and processes of teaching and learning in schooling. Scarino (2013) further claimed that "… it is necessary to consider not only the knowledge base in its most contemporary representation but also the processes through which this literacy is developed" (p. 316).
Assessment training and other affective factors
Some researchers have emphasized assessment in training (Boyles, 2005), establishing a framework of core competencies of language assessment (Inbar-Lourie, 2008), developing language testing textbooks (Davies, 2008;Fulcher, 2012;Taylor, 2009), and developing online tutorial materials (Malone, 2013). In a study with 66 Hong Kong secondary school teachers, Lam (2019) examined knowledge, conceptions, and practices of classroom-based writing assessment. He found that most teachers had related assessment knowledge and positive notions about alternative writing assessments; some teachers had a partial understanding of the assessment of learning and assessment for learning, but not assessment as learning as they could only follow the procedures without internalizing them. In a study of EFL teachers in Colombia, Mendoza (2009) found that teachers frequently and inappropriately use summative rather than formative assessments; they used test scores not to facilitate the learning process; they lacked knowledge of different types of language assessments and what information each type provides; how to give more effective feedback to students; how to empower students to take charge of their learning; ethical issues related to test and assessment use and how results are used; the role of the language tester; and concepts such as validity, reliability, and fairness. The authors concluded that teachers lack adequate language assessment training.
In a skill-based study with 103 Iranian teachers, Nemati, Alavi, Mohebbi, and Masjedlou (2017) pointed to the inadequacy of teachers' assessment knowledge and training in writing skill. Crusan, Plakans, and Gebril (2016) also surveyed 702 second language writing instructors from tertiary institutions and studied teachers' writing assessment literacy (knowledge, beliefs, practices). Teachers reported training in writing assessment through graduate courses, workshops, and conference presentations; however, nearly 26% of teachers in this survey had little or no training. The results also showed the relative effects of linguistic background and teaching experience on teachers' writing assessment knowledge, beliefs, and practices.
With this training-supportive perspective, the first focus was on the quality of assessment courses (Greenberg & Walsh, 2012), course content (Brookhart, 1999;Popham, 2011;Schafer, 1991), assessment of course characteristic factors (e.g., instructors, content, students, and alignment with professional standards) (Brown & Bailey, 2008;Jeong, 2013;Jin, 2010), and pedagogies that reflect knowledge about assessment (DeLuca, Chavez, Bellara, & Cao, 2013). Brown's, 1995, andreplicated in 2008) study was a starting point in the investigation of language assessment courses. They examined the teachers' backgrounds, the topics they taught, and their students' perspectives toward those courses. Although their study offered useful findings, the issue of non-language teachers who teach language assessment courses was a missing link in their research. Kleinsasser (2005) also explored language assessment courses from the teachers' attitude. Kleinsasser argued that the major problem with teaching a language assessment course the failure of bridging between theory and practice. For example, the connection between the class discussions and the final assessment product is not well constructed. Qian (2014) found that English teachers did not have marking skills when assessing learners speaking in a school-based assessment in Hong Kong. DeLuca and Klinger (2010) found that Canadian teachers (288 candidates) knew how to conduct a summative assessment, but they were not familiar with formative assessment. They stressed the importance of direct instruction in developing teacher assessment literacy.
The usefulness of assessment education in both pre-and in-service programs is the other focus. The related studies stressed that assessment education should take different forms and integrate different stakeholders' views (DeLuca, 2012;Hill et al., 2014;Mertler, 2009), assessment literacy should be part of teacher certification and qualification (Sato et al., 2008;Schafer & Lizzitz, 1987), mentors should attend to student teachers' prior beliefs about assessment (Graham, 2005), and the instruction of content should be localized, subject-area specific that allow for teachers' free choice (Lam, 2015;Leahy & Wiliam, 2012). In a European study, Vogt and Tsagari (2014) reported that most teacher respondents lacked adequate assessment training, and they had only on-the-job experiences. For those who are unable to attend formal instruction may learn from on-line learning resources (Fan et al., 2011), workplace (Lukin et al., 2004), and daily classroom practices (Smith, 2011), instructional rounds (DeLuca, Klinger, et al., 2015, and design of assessment tasks and rubrics (Koh, 2011). For example, in a recent study, Koh, Burke, Luke, Gong, and Tan (2018) investigated the development of the task design aspect of assessment literacy in 12 Chinese language teachers. They found that although teachers quickly perceived many aspects of task design, they found it difficult to incorporate specific knowledge manipulation criteria into their assessments.
Although there has been much attention devoted to assessment-related training, most teachers are not well-equipped to perform classroom-based assessment confidently and professionally (DeLuca & Johnson, 2017). Thus, a large and growing body of literature has investigated how to improve teacher assessment knowledge via course work, professional development events, on-the-job training and self-study (Harding & Kremmel, 2016), assessment textbooks (Brown & Bailey, 2008), university-based coursework , and curriculum-related assessment (Brindley, 2001). Despite a large body of research on training, the assessment knowledge remains a challenge as teachers believe that it is theoretical and pedagogically irrelevant to everyday classroom assessment practices (Popham, 2009;Yan et al., 2018); the knowledge is not contextualized, and they usually learn about related assessment knowledge with a cookie-cutter approach (Leung, 2014); most training programs only include a generic assessment course which provides insufficient detail for developing an adequate assessment knowledge base.
The literature also shows that some research has been carried out on teachers' conceptions about assessment. The conception of assessment is believed to diagnose and improve learners' performance and the quality of teaching (Crooks, 1988), account for quality instruction offered by schools and teachers (Hershberg, 2002), make students individually responsible for their learning through assessment (Guthrie, 2002), and show that teachers do not use assessment as a formal, organized process of evaluating student performance (Airasian, 1997). Cizek, Fitzgerald, and Rachor (1995) examined elementary school teachers and argued that many teachers have their assessment policies based on their conceptions of teaching. Kahn (2000) studied high school English classes and argued that teachers employed different assessment types because they eclectically held and practiced transmission-oriented and constructivist models of teaching and learning. And yet, conceptions may be individualistic because they are socially and culturally shared cognitive phenomena (van den Berg, 2002). In their study, Looney, Cumming, van Der Kleij, and Harris (2018) worked on a conceptualization of Teacher Assessment Identity. They argued that language teacher's professional identity, their beliefs about language assessment, their practice and performance in language assessment related tasks, and their cognition of their perceived role as language assessors play a vital role in evaluation of their effectiveness in the field of language assessment. Some researchers also suggested that perceptions might be resistant to training (Brown, 2008), while others claimed a positive relationship between assessment training and teacher assessment literacy (Levy-Vered & Alhija, 2015;Quilter & Gallini, 2000). Interestingly, a study by investigated the assessment beliefs of preservice teachers and found that the teachers' assessment beliefs were framed by their past experiences rather than by what they had been taught about assessment theories or policy requirements.
Some studies have also suggested that teachers' conceptions and assessment practices are dependent on specific contexts (Forsberg & Wermke, 2012;Frey & Fisher, 2009;Gu, 2014;Lomax, 1996;Wyatt-Smith, Klenowski, & Gunn, 2010;Xu & Liu, 2009). Willis, Adie, and Klenowski (2013) viewed assessment literacy as "a dynamic contextdependent social practice" (p. 242). According to this contextualized view, teacher assessment literacy is considered to be a joint property that needs input and support from many stakeholders, such as students, school administrators, and policymakers (Allal, 2013;Engelsen & Smith, 2014;Fleer, 2015). Assessment literacy has also been investigated by considering different stakeholders in various educational contexts. For example, Jeong (2013) studied the difference between language assessment courses for language testers and non-language testers. The results revealed significant differences in the content of the courses based on the teachers' background in test specifications, test theory, basic statistics, classroom assessment, rubric development, and test accommodation. Additionally, the results indicated that non-language testers were less confident in teaching technical assessment skills than language testers and were willing to focus more on classroom assessment issues. Malone (2013) examined assessment literacy among language testing experts and language teachers through an online tutorial. The results reported from both language testing experts and language teachers revealed that testing experts stressed the need to develop an increasing knowledge of the theories, and teachers emphasized the need to increase their knowledge of the "how to" components in the tutorial. Pill and Harding (2013) explored policymakers' understanding of language testing. They observed that how a lack of understanding of both language and assessment issues and lack of familiarity with the tools used and with their intentions can result in meaningful misconceptions which can undermine the quality of education. Evidently, such misconceptions may lead to misinformed and misguided decisions by the policy makers on crucial issues.
Web-Based Testing (WBT), another area of interest within the field of assessment, has been employed in different educational settings (He & Tymms, 2005;Sheader, Gouldsborough, & Grady, 2006;Wang, 2007;Wang, Wang, Wang, Huang, & Chen Sherry, 2004). WBT is used to administer tests, correct test papers, and record scores on-line. WBT can be presented in the form of online presentation, application-or software-product representation, hypermedia, audio and video representation, and so on. For example, Wang, Wang, and Huang (2008) investigated "Practicing, Reflecting and Revising with Web-based Assessment and Test Analysis system (P2R-WATA) Assessment Literacy Development Model" for improving pre-service teacher assessment literacy. They reported improvement in teachers' assessment knowledge and assessment perspectives.
In a different focal area, O'Loughlin (2013) studied the IELTS (International English Language Testing System) using an online survey. Fifty staff completed the survey. The study examined how well the test goals were met and how they might be best addressed in the future. The results showed that the participants mainly considered the minimum test scores required for entry. They needed information about the ways IELTS scores can be interpreted and used validly, reliably, and responsibly in decisionmaking in higher education contexts. O'Loughlin suggested information sessions and online tutorials for learning about the IELTS test.
Conclusion
To sum up, the studies reviewed on assessment literacy clarify the fact that language assessment literacy is a multi-faceted concept and that defining it presents a major challenge. Clearly, it is related to educational measurement and influenced by current paradigms in this field. It is not answered what is the relative or general balance between what agreed-upon issues, and themes represent knowledge in this field and make it distinct. This uncertainty reflects the lack of unanimity within the professional assessment community as to what shapes the assessment knowledge that will be passed on to future experts in the field.
The studies reviewed on assessment literacy also indicate that teachers need assessment knowledge. Assessment courses programs should be part of teachers' qualifications and requirements. Additionally, the content of the assessment knowledge base needs to be kept up with what is most recent, based on research and policy innovations. Teacher assessment training needs to become long and sustainable enough to engage teachers in profound learning about assessment, which will possibly help them improve and expand conceptions and practices about assessment. Further, assessment training needs to take the knowledge base and the context of practice into account and make connections between them. In other words, assessment literacy should be developed by considering various educational contexts and necessities of times and contexts. Assessment literacy also needs support from different stakeholders. Teachers as individuals and professionals need to be considered because teachers' conceptions, emotions, needs, and prior experiences about assessment may help to improve the efficacy of training, assessment knowledge, and skills of teachers. Teacher assessment literacy development does not only mean an increased assessment knowledge, but it also needs to expand and broaden contextual-related knowledge and inter-related competencies. In line with teacher professionalization in assessment, it requires a consideration of many inter-related factors such as teacher independence, identity as assessor, and critical perspectives. Teachers need to engage in learning networks where they can understand each other through a common language, communicate, and decide about their assessment practices.
Last but not the least, this review about assessment literacy challenges provides researchers with both general predictions and needs for further related investigations in developing assessment literacy and workable solutions to cope with such challenges. Besides, the present information may help teachers, policymakers, stakeholders, and researchers know where they are, where they need to be, and how best to proceed with their developmental work and research.
Taken together, there are still many unanswered questions about assessment literacy, and further studies are required give us a more comprehensive insight into language assessment literacy and enrich this ongoing discussion. More research can also be used to verify and validate, as well as the question, the current issues in assessment literacy. Further investigations are needed to guide policymakers in conducting standards that address both the contemporary development of assessment research and the cultural aspects of assessment. Also, more research can identify specific problems in pre-or inservice assessment education in specific contexts and provide supplementary methods to achieve better implementation of professional standards or policies. Further work can enrich teachers' assessment knowledge with insights from the latest assessment research findings since the assessment knowledge base is dynamic. Also, due to the importance of teacher conceptions in shaping teacher assessment literacy, further studies can provide greater insight into their conceptions and practice of assessment. We do need to learn, unlearn, and relearn about language assessment literacy which is of primary importance in enhancing the quality of language education. | 7,261.8 | 2020-05-26T00:00:00.000 | [
"Education",
"Linguistics"
] |
Study of Adaptive Clothing in Hong Kong: Demands, Analysis and Future Direction
Aging population is substantively increased over last decade and they have specific clothing needs especially for the elderly with disabilities. Their clothing needs to cover functional and aesthetic requirements in order to improve their quality of life. Adaptive clothing is specially designed for the elderly and the disabled. However, there is no public policy to support such the elderly with disabilities in their clothing needs. In this paper, we aim to study the adaptive clothing and its significance, the problems encountered by the elderly with disabilities in adaptive clothing, analysis of public policy in Hong Kong for the elderly with disabilities in adaptive clothing over last decade, and implications and future directions for adaptive clothing in Hong Kong. In our findings, the demand of adaptive clothing in Hong Kong was substan-tially increased over last decade and the predicted demand will be twice of current demand after 50 years. However, the Government policy in Hong Kong has not yet fully supported their clothing needs, and the non-profit clothing services centre is set up to provide tailoring services to meet their needs. As the capacity of the centre is very limited, it is necessary to expand its capacity through assistive technology and to encourage non-government organizations (NGOs) to establish more social enterprises with Government’s support. Such findings would be beneficial to the Government for streng-thening such services for the elderly and the disabled as well as public awareness.
What Is Adaptive Clothing and Its Significance?
Adaptive clothing consists of garments and footwear designed for the elderly and people with disabilities (PWD), and is designed to eliminate, or at least reduce, the impact of functional limitations of its wearer's body [1], enabling them to go about their everyday lives with nonrestrictive comfort. In addition, it also allows caregivers and nurses to more easily provide assistance to those in need.
That adaptive clothing increases the wearer's sense of autonomy and control over their body means that they can more meaningfully participate in their community [2], increasing their engagement in occupations, as well as prospects for education and employment-all of which have a great impact on their quality of life [3]. As a means of lowering the barriers to social participation that the elderly and PWD encounter, adaptive clothing enables them to be more socially involved; thus, it plays a role in reducing the negative consequences of physical and mental problems, such as higher rates of coronary heart disease, stroke, depression, and cognitive decline [4], that arise from social isolation.
Clothing is fundamental to identity and self-image; it influences how one thinks, feels, and is perceived [5]. Therefore, aesthetic considerations figure just as much as functional considerations when it comes to adaptive clothing. In a society that privileges the needs and interests of the able-bodied, it comes as no surprise that the clothing choices of people with physical impairments are based on a desire to meet the dominant socio-cultural standards of appearance, so as to increase social acceptance and minimize differences from non-disabled peers [6] [7]. As such, fashionable adaptive clothing that fulfills its wearers' pursuit for style and image can positively influence wearers' self-concept, self-esteem, and body image, which in turn bolster cognitive functioning, mental health, and physical health [6].
Demands of Adaptive Clothing in Hong Kong from the Elderly and PWD
The need and demand for adaptive clothing will undoubtedly increase in the fu- [9]. Given the projected increase in people aged 65, which also entails an increase in people with restricted body movement, the significance and need for accessible adaptive clothing ought to be considered just as much as other much-welcomed initiatives to improve the well-being of PWD and the elderly.
Current Government Policy for Supporting the Elderly and PWD in Hong Kong
Over the years, the Hong Kong government has demonstrated an awareness of the ways in which disabilities are very much social problems created and intensified by environmental barriers in society. In terms of policy, it has addressed the need to lower these environmental barriers, through measures such as the expansion of barrier-free and assistive transport and facilities [10].
Problems Encountered by the Elderly and PWD in Adaptive Clothing
Curiously, while there are measures to make the built environment more bar-
Setup of Non-Profit Servicing Centre and Social Enterprise for Adaptive Clothing
In the absence of policies to expand the provision of and access to adaptive The significance of adaptive clothing for the elderly and PWD should not be understated. It is just like assistive devices, such as wheelchairs and catheter bags, in that it fulfills the elderly and PWD's utilitarian need to better meet the demands of everyday life. Additionally, adaptive clothing fulfills the elderly and PWD's hedonic need for self-expression [7], should it be designed to be aesthetically pleasing, which is something that assistive devices cannot fulfill. Accordingly, any attempt by the government and the RAC to formulate a comprehensive plan for the rehabilitation and habilitation of the elderly and PWD needs to take into account the role of adaptive clothing in diminishing disablement and improving mental and physical well-being.
Facts Findings of Adaptive Clothing in Hong Kong over Last Decade
This section examines the data that the CAC has collected from 2007 to 2017 with regards to the adaptive clothing it has sold to its customers (see Table 1). Figure 1 illustrates the number of adaptive clothing items purchased from Table 2), and the latter consisting of purchases made by individuals.
It can be seen that the individual purchases increase in a roughly linear man- There is no pattern to the institutional purchases throughout the decade, because these purchases are contingent upon whether institutions decide to purchase adaptive clothing items through Care Apparel Center. The purchase records show that all of the items that are usually purchased in bulk by these institutions are mass-produced ones that CAC sources from adaptive clothing suppliers. As such, institutions do not necessarily have to make their purchases through CAC, because there exist many suppliers in Hong Kong that offer mass-produced adaptive clothing items, such as non-slip socks and disability aid aprons, the top two most ordered items (see Table 3). This explains why no trend can be discerned from the bulk purchases throughout the decade. The nature of the most purchased items from individual purchases generally differs from the items purchased by various institutions (see Table 3). All of the adaptive clothing items that CAC sells to various institutions are mass-produced items that the center sources from various suppliers. Some of the items that individuals purchase are also mass-produced, such as non-slip socks, constraint mittens, and disability aid aprons. Many more are custom-made adaptive clothing items that are modified, tailored and manufactured to fit the specifications that individual customers require. For instance, the most ordered item from non-bulk purchases is a protective shirt, and customers who order a protective shirt from CAC experience a variety of physical impairments, many of which a pre-made protective shirt cannot address. A customer may require a special pocket to conceal the urine bag they carry with them, an attached arm sling on which to rest their arm, an enlarged zipper tab for those with finger dexterity impairments, and so on. CAC's two full-time tailors would have to take the measurements of these customers in order to manufacture a customized protective shirt for them. It also has to be noted that Table 3 only shows the most ordered
Implications and Further Actions for Adaptive Clothing in Hong Kong
Data from the CAC's records show that there is a high demand among the PWD and elderly for both mass-produced and customized adaptive clothing. The CAC does its best to meet this demand, but due to its small size, its ability to procure, manufacture, modify, and repair adaptive clothing items is limited. . Such an equipment center can feature a much larger center than the existing CAC. It could train and hire PWD for tailoring services, and it could offer its PWD and elderly clients customized adaptive clothing at a subsidized price, or even for free, to honor the notion that PWD is entitled to habilitation services as a basic human right.
In contrast, establishing more social enterprises that provide tailoring services for customers who want to buy customized adaptive clothing is less preferable, because of their need to earn a profit in order to be sustainable. The Fullness of Social Enterprises Society Report published in 2015 paints a bleak picture of the social enterprises' ability to sustain themselves-23% of SEs closed within 5 years, and the non-survival rate increased 55% within 10 years [17]. Given the low socio-economic background that PWD disproportionately belongs to, it does not seem appropriate for PWD customers to purchase customized clothing at a social enterprise. After all, the lack of competitive markets for adaptive clothing entails that the tailoring services offered for PWD customers, as well as the production of customized adaptive clothing, will be quite costly. Moreover, social enterprises need to maximize their profits in order to maintain and expand their operations-to profit off a marginalized group's need for adaptive clothing is not ideal. That said, elderly and PWD customers may not have to worry about the potentially high costs of customized adaptive clothing, given that many are fully subsidized by the government initiatives that grant them disability allowances and vouchers for these purchases. Even so, the fundamental problem remains: social enterprises like Home Care Apparel and non-profits like Care Apparel Center are too small to fulfill the burgeoning demand for customized adaptive clothing.
Lastly, to craft policies that are responsive to existing and rising demand for adaptive clothing, it may do well for the RAC and the Hong Kong Government to expand their conception of assistive technology and devices, so as to bring adaptive clothing within its fold. Browsing the information pages of programmes such as the Innovation and Technology Fund for Application in Elderly and Rehabilitation Care [18], as well as RPP's proposals of a Funding Scheme on Life-supporting and Assistive Devices, and Community Care Service Voucher for the Elderly [19], one gets the impression that assistive technology is mainly conceived as electronic devices and equipment such as wheelchairs and urine drainage bags. If assistive technology was defined as devices and equipment that maintain, increase, or improve the abilities of individuals with disabilities [1], then adaptive clothing belongs to this category, and should be perceived | 2,433.2 | 2020-01-02T00:00:00.000 | [
"Materials Science"
] |
Operator thermalisation in $d>2$: Huygens or resurgence
Correlation functions of most composite operators decay exponentially with time at non-zero temperature, even in free field theories. This insight was recently codified in an OTH (operator thermalisation hypothesis). We reconsider an early example, with large $N$ free fields subjected to a singlet constraint. This study in dimensions $d>2$ motivates technical modifications of the original OTH to allow for generalised free fields. Furthermore, Huygens' principle, valid for wave equations only in even dimensions, leads to differences in thermalisation. It works straightforwardly when Huygens' principle applies, but thermalisation is more elusive if it does not apply. Instead, in odd dimensions we find a link to resurgence theory by noting that exponential relaxation is analogous to non-perturbative corrections to an asymptotic perturbation expansion. Without applying the power of resurgence technology we still find support for thermalisation in odd dimensions, although these arguments are incomplete.
Introduction
Free field theories are the simplest and most prominent examples of (super-)integrable quantum field theories (QFTs), rendered exactly solvable by the existence of an infinite set of conserved charges. A direct consequence of the presence of such charges is a severely constrained time evolution even in thermal backgrounds. In particular, simple operators in free QFTs fail to satisfy the requirements of the eigenstate thermalisation hypothesis [1,2] and their late time behaviour is therefore unlikely to approach ensemble averages, tantamount to the absence of thermalisation.
Nonetheless, it is known that nontrivial interference effects can effectively mimic equilibration. For example, after quantum quenches [3][4][5][6], correlation functions in free QFTs approach those of a generalised Gibbs ensemble [4,[7][8][9], characterised by chemical potentials for all conserved charges which in free QFTs is equivalent to a momentum-dependent temperature. Similarly, nontrivial time dependence arises when considering composite operators. Such operators can in fact interact with the thermal bath and as such exhibit a range of phenomena that are usually attributed to their interacting counterparts. For instance, their correlation functions can exhibit exponential decay at late times [10] and their spectral densities have support in the deeply off-shell regime [10,11], reminiscent of collision-less Landau damping [12].
Clearly, the composite nature of an operator is a necessary condition for its effective thermalisation, since only then does it couple to a thermal bath, indicated by a temperature dependence of its response functions. On the other hand, to which extent it is also a sufficient condition is less understood. In recent work [13,14], a simple criterion has been formulated that guarantees the absence of thermalisation of a given operator, characterised by a lack of exponentially decaying contributions to its linear response function. In addition, it was conjectured that a converse statement can be made and any operator that fails this non-thermalisation condition in fact thermalises. This conjecture was introduced as the Operator Thermalisation Hypothesis (OTH).
This note aims to shed light on several remaining puzzles. First, in singlet models [10], the calculated correlation functions were observed to display exponential decay in even dimensions d > 2. This decay is directly related to the thermalisation later extracted in [13] by arguments which however do not resolve a difference between even and odd dimensions. Since the odd-dimensional singlet model correlation functions do not decay exponentially, the results appear to be in tension with each other. There is something to learn about thermalisation or singlet models, in fact both, from a closer study. The requisite developments of concepts indeed leads to a more precise formulation of the OTH. Second, the singlet model study demonstrated how phases below or above a critical temperature exhibit different relaxation properties, most plainly for auto-correlators. Since response functions diagnose thermalisation, previous singlet model studies should be extended with results on response functions in different phases.
Thus, we will study OTH in a particular class of free field theories, namely those with a large-N singlet constraint. These theories have received widespread attention in the context of gauge/gravity duality as the holographic duals of gravitational theories with an infinite tower of massless fields of higher spin. They exhibit an interesting thermal structure on compact spaces with a large N confinement/deconfinement phase transition. In ordinary AdS/CFT, this transition is also present and can be mapped to the Hawking-Page transition from thermal AdS to the large AdS black hole in the bulk. In the deconfined phase, thermalisation in holographic gauge theories is in direct correspondence with black hole formation and equilibration in the bulk. Understanding thermal properties of free singlet models thus provides insight on putative black holes in higher spin gravity. More generally, however, they allow one to disentangle generic properties of composite operators from those particular to strong coupling, thereby teaching valuable lessons on the inner workings of gauge/gravity duality.
In the low temperature phase we observe the absence of thermalisation to leading order in 1/N , in complete accordance with the OTH. Below the phase transition, a composite operator tr(Φ(x)Φ(x)), built of N adjoint scalars, plays the role of a generalised free field with interaction strength of order 1/N . To leading order, it obeys our generalisation of the non-thermalisation condition, confirmed by the absence of temperature dependent contributions to its response functions and in particular the lack of exponential damping. This is generic to all QFTs that admit a description in terms of generalised free fields and the thermal version of this concept will be presented below. At high temperatures, the time dependence becomes significantly richer. Response functions become temperature dependent and are characterised by non-analyticities off the real axis in the complex frequency plane. They describe a damped response to sources, with a power law tail and sub-leading exponentially decaying contributions. The latter contribution is the exponential damping predicted by the OTH. The presence of the power law tail implies that information about the source is retained to a larger degree than in standard thermalisation, although parts are effectively lost in exponentially damped terms.
The general lessons from our study concern details of the formulation of OTH, and the difference between even and odd dimensions. Indeed, it is well known that the interior of the light cone plays a fundamentally different role in wave propagation in even and odd dimensions (cf Huygen's principle and Hadamard's problem [15]). By explicitly focusing on evaluating correlators close to the light cone we reduce the difference between odd and even dimensions, and identify the damped quantities that continue analytically between different dimensions, to put d > 2 OTH on a firmer footing. This light cone limit notwithstanding, crucial differences between even and odd dimensions remain. While OTH can be confirmed straightforwardly in even dimensions, we observe that subtleties involved in isolating exponentially decaying terms in the response functions become critical in odd dimensions. We describe the difficulties and find some support for thermalisation, but also indications that the resolution requires more powerful tools from the theory of resurgence [16][17][18][19]. That cautionary observation aside, our scrutiny of OTH permits us to give a more precise formulation of both the hypothesis and the converse non-thermalisation condition in all d > 2.
Our paper is organised as follows. In section 2.1, we introduce the concept of operator thermalisation, non-thermalisation and the role stable thermal quasi-particles and generalised free fields. Before going into basics of singlet models in 2.2 we also introduce the potential relation of thermalisation to resurgence. In section 3.1, we then deduce and discuss absence of exponential relaxation in singlet model response functions in the low temperature phase. The high temperature phase, which displays relaxation in even dimensions and appears to allow for it in odd dimensions, is analysed in section 3.2 and a discussion in section 4 leads up to our conclusions 5.
Operator thermalisation
Sabella-Garnier et al formulated the operator thermalisation hypothesis in [13] and considered the thermalisation properties of operator correlation functions in a fixed background rather than operator expectation values in the presence of of assumptions on the energy spectrum, as done by the eigenstate thermalisation hypothesis, ETH [1,2]. They discuss thermalisation in terms of an exponentially fast return to equilibrium of operator expectation values in response to a perturbation by the operator in question. More precisely, in [13] the retarded Green's function of the operator in question is taken to define thermalisation of a perturbation, when it decays exponentially, in line with the retarded Green's function encoding the linearised response of the operator O induced by a perturbation by the same operator O. The requirement of exponential decay for a perturbation to thermalise corresponds to the intuition that a thermalising perturbation is "forgotten" by the system at late times. The latter means that exponential precision would be required in order to fully reconstruct the source from the response of the medium.
A motivation behind the operator thermalisation hypothesis, and one of its strengths, is that it can be used to study surprising similarities between ordinary interacting systems and free or integrable systems [6,10,11,13]. While pure exponential decay occurs in free systems in contrast to naive expectations, it is generally masked by leading power law decay for d > 2, as well as multiplied by inverse powers of time. We will provide such examples below. In reviewing the operator thermalisation hypothesis, we will therefore introduce new terminology which precisely captures these features. In effect, we demonstrate an operator non-thermalisation condition which excludes this kind of partial thermalisation, and state a converse partial operator thermalisation hypothesis. Our arguments are essentially copied from [13], and the "partial" qualifier only indicates a slight shift of definitions. The new definitions are important for consistency with the examples we discuss, but the idea is approximately the same.
Partial operator thermalisation
We define partial thermalisation of an operator O to mean that: The retarded Green's function of O contains terms with exponentially damped factors at late times. This definition allows for leading power-law decay, and exponential terms which are only sub-leading 1 . In such cases, time evolution still "forgets" part of the initial perturbation, but not all of it. Clearly, partial thermalisation includes the thermalisation notion discussed in [13] and the more conventional notion of approach to a thermal ensemble, but it is a broader concept 2 .
Crucially, partial operator thermalisation captures the observation that conservation laws prevent some operators in free or integrable theories to thermalise, but that almost all other operators thermalise partially. A special class of non-thermalising operators was characterised by Sabella-Garnier et al [13]. We will see that these operators do not even thermalise partially. In essence, these non-thermalising operators are generalisations of free fields which satisfy a sharp dispersion relation relating energy to momentum. Formally, the conditions on the operators are given by the mathematical descriptions below. Physically, they correspond to stable thermal quasi-particle fields having clear-cut dispersion relations, which are permitted to differ from those of free relativistic particles. One may invoke the Narnhofer-Requardt-Thirring theorem [21] to argue that they describe a sector of the thermal system which is completely free from interactions, except for modified dispersion relations. The theorem permits other sectors, but they are completely decoupled from the quasi-particles.
We interpret the operator thermalisation hypothesis proposed in [13] to state that any other local operator, not representing a stable quasi-particle field, will thermalise. This is the converse of the above non-thermalisation condition. For it to hold, the notion of thermalisation has to be weakened to partial thermalisation. Thus, we propose a more precise partial operator thermalisation hypothesis: Any local operator not representing what we call a thermal generalised free field 3 , or a generalised quasi-particle field, thermalises partially. Note that we still have not proven this hypothesis, though we find it reasonable. All the plausibility arguments in [13] still apply.
Non-thermalisation and the thermalisation hypothesis
We now proceed to essentially repeat the arguments of [13], expressed in our terminology.
Consider a stable thermal quasi-particle operator, which we denote Q(t, x) to distinguish it from more general local operators O(t, x). By definition it has a definite dispersion relation. In finite volume and in a basis which simultaneously diagonalises energy and momentum, this means that the transitions Q can mediate between momentum states determine the simultaneous transitions between energy eigenvalues. We do not need to know if there is a single functional relation between momentum and energy for the operator Q or if there are several branches of solutions to the dispersion relations coupling to Q. To reproduce branch cuts which can be found, for example, in singlet models, it will turn out to be important to allow for a growth of the number of solutions to dispersion relations with volume.
The retarded thermal Green's function is which can be expanded in a sum of expectation values 2) where s is the spin of the operator Q. Making use of translations and inserting a complete set of states In Fourier space where ǫ > 0 is infinitesimal. Now, the special properties of the quasi-particle operator Q lead to a proof of nonthermalisation. Denoting by M the number of different branches of solutions labeled j = 1, . . . , M to the dispersion relations for Q and defining the residue functions we find (2.8) The frequencies and wave numbers above are related to the matrix elements m| Q |n by signifying that all contributions from the operator Q are due to transitions between states whose energies and momenta differ by amounts related by the allowed dispersion relations in eq. (2.6). For a more detailed analysis of thermodynamic and large N limits, it may become useful to allow an effective temperature dependence in the dispersion relations contributing to eq. (2.8). Noting that Ω Q j (k) has to be real by definition, the retarded Green's function only has singularities on the real axis, which is tantamount to non-thermalisation of the operator Q. For finite M , the singularities are manifestly poles. If M grows without bound in the thermodynamic or large N limit, branch cuts may also arise, but they will be on the real axis. There will still not even be partial thermalisation, since only singularities off the real axis can produce exponentially decaying terms.
The converse of the original non-thermalisation result would be that only stable quasiparticle operators are non-thermalising. Allowing for partial thermalisation, which includes power law fall-offs related to branch cuts on the real axis, it seems judicious to consider branch cuts also in the non-thermalisation results. Thus we are led to allow unbounded M . This generalisation replaces quasi-particles with generalised quasi-particles or thermal generalised free fields.
The OTH in our version becomes: All local operators which are not generalised quasiparticles thermalise partially. The original plausibility arguments of [13] remain, and this adjusted version survives all tests we have considered.
Thermalisation and resurgence
Below we will introduce examples of retarded Green's functions with asymptotic late time expansions containing both inverse powers and damped exponentials of time. In free systems, they force us to consider the partial, and more general, version of operator thermalisation, which allows for the possibility that exponential damping terms are sub-leading. Unfortunately, the price for the generalisation is another level of mathematical sophistication. It is required for a physical reason: Only under very special circumstances, e.g. when an asymptotic series of inverse powers terminates, is it possible to operationally separate sub-leading exponentials from more important inverse powers. Only under these special circumstances can we have a chance to resolve and observe the damped exponentials, even in principle.
This discussion is parallel to the potentially more familiar discussion about prescription dependence of non-perturbative terms in quantum mechanics and in quantum field theory. There, one encounters non-perturbative exponentials e −1/g 2 complementing power series in a coupling g. Substituting where t is time and β is inverse temperature, we are alerted to the possibility that thermalisation, signalled by exponential damping at late times, can be analogous to nonperturbative effects. The analogy indeed holds for standard Green's functions: Their late time expansion in inverse powers of t/β is typically asymptotic rather than convergent, and exponential terms can sometimes be extracted from integral representations of the Green's functions. Cases with terminating or at least a convergent (inverse) power series would be useful in practice, and would allow unambiguous identification of exponentials, but are exceptional.
The beautiful idea that there is a relation between the form of non-perturbative terms and the divergence of asymptotic series [22] can be systematised in non-perturbative techniques like Borel resummation, but does not always yield a unique answer for the series. To be clear, for thermal Green's functions in free field theory, the integral representations are unambiguous. A series representation does not improve the already complete encoding of a response function. However, a well-defined representation of the result of the integral in a double series expansion with inverse powers and exponentials as above, a trans-series in the framework of resurgence theory [17][18][19], would lend itself nicely to an extended definition of partial thermalisation. The response function would be said to thermalise partially if the series contained exponentials 4 .
Thermal singlet models
In order to distinguish low and high temperatures, we consider free field theories on R×S d−1 leading to a characteristic temperature scaling as 1/R, the inverse of the radius R of the sphere S d−1 . To make the distinction sharper we consider a large number N of fields. A large N will then allow for qualitatively different limits for physics below and above the characteristic temperature. We consider a scalar field transforming in a representation, usually fundamental or adjoint, of some large N symmetry group, for example U(N ) or O(N ). Projection onto the singlet sector is achieved by weakly gauging the symmetry, i.e. introducing a gauge field A µ in the limit of vanishing gauge coupling, where only the zero mode α ∼ S d−1 A 0 that imposes the Gauss' law constraint remains. We will focus attention on correlation functions on scales much smaller than R, corresponding to times and distances t ≪ R, |x| ≈ Rθ ≪ R, where θ is the polar angle on the sphere. The entire difference between low and high temperature physics in effectively flat space can then be encoded completely in functions ρ(λ), which appear as eigenvalue densities in the more detailed description in the next two paragraphs.
At finite temperature, the integral over the gauge field can be recast into a unitary matrix model, where the projection onto singlets results from the integral over the gauge group over unitary matrices [24] corresponding to the Polyakov loop operator, P ∼ e i S 1 dτ α , or gauge holonomy around the thermal circle [25]. The distribution of the large N number of matrix eigenvalues then controls the thermal behaviour.
At large N , the model can be solved in a saddle point approximation [24]. This is readily achieved by introducing the eigenvalue density ρ(λ). At low temperatures, T < O(1), the dominant saddle corresponds to a constant eigenvalue distribution, ρ(λ) = 1 2π . This is the confined phase, with a free energy of order N 0 . At intermediate temperatures whose N scaling depends on the representation under consideration, there is a transition to a deconfined phase, characterised by a free energy that is extensive in N . At very high temperatures, the eigenvalue distribution becomes a delta-function 5 , ρ(λ) → δ(λ).
Correlation functions of singlet operators can be constructed through finite temperature Wick contractions. For simplicity, we focus here on the scalar singlet primary, x)) for scalars in the adjoint representation, whose time ordered twopoint function is given by [10] (2.10) where we have used rotational and time translational invariance to set one of the insertion points to zero. The pre-factor has been chosen to simplify the expression, while the operator is normalised such that its two-point function is of order N 0 . Eq.(2.10) can formally be derived using the aforementioned Wick contraction, as well as the fact that the unitary matrix is represented in the scalar kinetic term like a temporal gauge field. The retarded Green's function can be extracted using its definition, G R (t, x) = Θ(t)Im G(t, x). It is simple to see that the purely thermal contributions to eq. (2.10) are real. An imaginary part can thus arise only from the vacuum piece, and the mixed thermal-vacuum term.
Explicitly, one finds [10] G R (t, x) = Θ(t) Im 1 (cos t − cos θ) d−2 where in the last line we have introduced the k-th Fourier cosine coefficient of the eigenvalue distribution, ρ k = dλ ρ(λ) cos(kλ). We note that the infinite series in the second term captures all temperature dependence, and in fact is precisely that of the thermal Feynman propagator of the fundamental scalar field, when all ρ m become equal, which is the case in the high temperature limit.
Thermalisation in singlet models
A number of challenges to the OTH may be tested in thermal singlet modes in d > 2.
In this section, we describe our technical results, which support the hypothesis in even dimensions, given the adjustments we have introduced in section 2.1. In odd dimensions the interpretation of results is intricate, and will be deferred to the discussion 4. The low and high temperature phases of the singlet models are qualitatively different and are discussed separately below, with equations specialised to scalars in the adjoint representation. In both cases, the concrete operator under study is the lowest dimension singlet operator O(t, x) = 1 N tr(Φ 2 (t, x)).
Low temperatures: T < T H
As noted in the thermal singlet model section 2.2, the eigenvalue distribution at low temperatures is constant, ρ(λ) = 1 2π , and thus ρ 0 = 1 and ρ k =0 = 0. For the retarded Green's function (2.11), this implies in the large N limit. From the explicit lack of exponentials we see that O(t, x) fails to thermalise at low temperatures. For completeness, let us take the "thermodynamic limit" of large R corresponding to t, θ ≪ 1, and Fourier transform, thus for example obtaining where µ is a renormalisation scale. In this Lorentz invariant expression, there is only one branch cut located at ω 2 > k 2 on the real line, representing a continuum of physical excitations on top of the vacuum state. That (3.2) is analytic everywhere off the real line corresponds one-to-one with the fact that the corresponding expression in configuration space lacks exponentially decaying contributions. We see explicitly that it is useful to extend the notions of the non-thermalisation condition beyond poles in the frequency plane to cuts, as long as they are on the real axis. In position space, the corresponding thermodynamic limit of (3.1) involves power-law fall-off, and we will find similar fall-offs to be general consequences of free field dynamics in d > 2 below, even for response functions of operators that display relaxation after long time in exponentially decaying terms. The physical origin of non-thermalisation is clear from large N considerations. At low T , one finds for the connected components of n-point functions Here, the conservation of individual momentum modes is explicit to zeroth order in 1/N . In consequence, quasi-particles remain intact to this order. Of course, this argument is rather superficial, but can be made more precise by properly constructing the effective action, for example using collective field theory [27]. By the above argument, taking into account 1/N corrections will reveal nontrivial features in the response functions even below the phase transition. While this requires a finite N analysis, and is therefore beyond the scope of this work, even the leading order behaviour can change drastically once occupation numbers in the thermal background are of order of the inverse coupling. Indeed, as we will show now, this is what happens in the high temperature phase.
High temperatures: T ≫ T H
At very high temperature, the eigenvalue distribution can be approximated by a deltafunction. One thus obtains for the Fourier cosine coefficients Note that one should only really expect effective thermalisation in the "thermodynamic limit" of large R, here corresponding to t ≪ 1, θ ≪ 1 and β ≪ 1. In this regime the retarded Green's function (2.11) becomes upon insertion of (3.5) Clearly, the operator now responds to the thermal bath, which may induce thermalisation. In fact, the second term represents the cross term between vacuum and thermal propagation contributing to the response function of the quadratic composite operator O(t, x).
d = 4
To get a better understanding of the precise dynamics, we will confine ourselves to d = 4, since generalisation to higher even dimensions is simple once the basic ingredients are understood. There, which can be simplified to Evidently, the Green's function falls off as a power law, with a power that is smaller than in vacuum. This is in fact a manifestation of the effective dimensional reduction that is prevalent in generic thermal systems in the high temperature limit (see e.g. [28]). However, judging by the coth term there are sub-leading exponentially decaying contributions. This may be further illuminated by Fourier transforming (3.8), yielding [10], This expression allows us to map the late-time dominant behaviour of (3.8) to the branch cut in the ω plane located between −k and k on the real line and the subdominant exponential decay to the branch cuts located off the real line. Similar analytic structures are discussed in [29]. It can be contrasted with that of eq. (3.2). Let us now return to how thermalisation could be consistent with the effective action arguments presented in the low temperature discussion 3.1. Only large N counting, which is the same at high temperature, seemed to be important. The large N suppression of interactions is indeed the same as at low temperature, but the action (3.4) assumes the vanishing of thermal one-point functions O β . Above the phase transition, the equilibrium background expectation value is non-zero and of order N , which invalidates the argument that individual O momentum modes are conserved in the large N limit, due to order N 0 interactions with the background. Generalised quasi-particles are then not intact in the large N expansion, although their response functions are well-defined. The thermalisation of O ensures that O does not represent a generalised quasi-particle, by the arguments of subsection 2.1.2. As explained above, this is consistent with large N counting, thanks to the thermal condensate of O above the critical temperature, which eq. (3.6) thus probes indirectly.
General d > 2.
The above retarded Green's function of an operator quadratic in free fields clearly separates into a vacuum-vacuum term and a mixed vacuum-thermal term. Higher powers of free fields also decompose analogously. (Purely thermal terms will not contribute to the retarded propagator.) Now, the vacuum factors differ significantly in behaviour between odd and even dimensions. The imaginary part of (x 2 − t 2 ) −(n+1) for integer n ≥ 0 is given by which demonstrates that the support of the retarded Green's function is confined to the light cone for even d, while square root branch cuts ensures support also inside the light cone for odd d. This is a known property of the wave equation, which evidently is inherited by thermal systems probed by composite operators built of powers of free fields. While the behaviour in the interior of the light cone is interesting, both for other correlation functions than the retarded Green's function, and for odd d, the temperature dependent term of the response function is entirely determined by the factor which multiplies a simple light cone divergence. We thus factor out its singular light cone behaviour and study the behaviour of what might be called the position space "residue" of the singularity, by abuse of terminology. The light-cone factor isolated from eq. (3.6) is then This expression lends itself to a comparatively uniform treatment independently of dimension, and it measures the effect of the heat bath on the light cone in position space. The functionsS d (t, x) and its light cone limit S d (t) are discussed in the appendix A.
In even dimensions the calculation confirms thermalisation on the light cone, essentially by expressing S d (t) as an expansion in modified Bessel functions each of which equals a decaying exponential times a terminating sum of inverse powers for even dimensions (i.e. half-integer orders of the Bessel function). Details concerning finiteness of the expressions are also given in the appendix A. For all even dimensions the positive m terms in the series above explicitly yield exponentially decaying terms, which are of a form that cannot cancel with other exponentials. The negative m terms produce power law fall-off. Thus, the response functions signal partial thermalisation on the light cone in even dimensions. The odd-dimensional case is significantly more subtle. A similar treatment of the Bessel function terms in series (3.12) now leads to an asymptotic expansion in inverse powers for each m, which does not terminate. Hence, it is far from clear what significance to attach to exponentially small terms. If the asymptotic series is truncated, the error terms will be larger than the exponentials we have extracted, even if exponentially improved expansions are used [30].
Discussion
Our description of an important class of non-thermalising operators as generalised free field operators in section 3 connects to the intuition that such operators should not thermalise. Generally, however, naive intuition is treacherous, and our study is founded on the observation that free field equations of motion do not generally guarantee absence of relaxation for operators which are non-linear in free fields. Composite operators are regularised operators belonging to this class, and as described in the introduction 1, they have in many instances been shown to display the decay which we take to define thermalisation. The idea of the OTH is that the implication could go in the other direction: Operators that do not thermalise partially would have to be generalised quasi-particle operators. Or equivalently, any other operators thermalise partially.
Known thermal behaviour of singlet models motivated a closer study of response functions in order to compare with the OTH in dimensions d > 2. The d > 2 treatment of [13] is somewhat less detailed than the d = 2 discussion, and we were able to resolve new even/odd dimension differences in eqs. (3.11-3.12) from the high temperature phase of the singlet model response functions. To get expressions which depend analytically on d, it proved important to focus on the light cone. Thereby, the qualitative difference between the support for the Green's functions in the light cone, related to the absence or not of Huygens' principle were factored out.
In the large N limit, where there is a phase transition, and below the critical temperature, the response function (3.1) lacks exponentially decaying terms and the individual momentum modes are independently conserved. This means non-thermalisation and also the presence of generalised quasi-particles of definite momenta, as expected from the general non-thermalisation results. Clearly, we can only expect a precise match to the general operator thermalisation theory, described in section 2.1.2, to be valid at leading order in small 1/N . To the extent that the generalised quasi-particle picture holds, we can rely on non-thermalisation. Indeed, the idea that the general theory applies parametrically close to ideal cases makes the results much more powerful. In this example, we see how it works.
Above the critical temperature, the response functions develop exponentially decaying terms, as for example in the d = 4 expressions (3.8) and (A.3). These examples clearly show how power law tails and damped exponentials combine non-trivially. Indeed, such terms which generally appear in d > 2 motivate us to consider partial operator thermalisation. This partial thermalisation concept also simplifies the non-thermalisation results for stable thermal quasi-particles, by allowing branch cuts in the thermodynamic limit as in the paragraph after eq. (2.9).
To conclude the match of the OTH and singlet model response functions we should now argue that the operators we consider fail to be generalised quasi-particle operators above the critical temperature. Without going deeply into the physics of singlet models, we have found a suitable mechanism, namely that the background condensate in the high temperature phase modifies the propagation of perturbations at order N 0 which is too much for a generalised free field, unless there is extreme fine-tuning.
Partial thermalisation diffuses the dichotomy between thermalising and non-thermalising operators to some degree, but in even dimensions calculations like (A.9) and (A.12) demonstrate the general structure from modified Bessel functions of order d−3 2 . At half integer order the resulting functions are simple polynomials of exponentials exp (−4πt/β) and powers of β/t. The further sums in the thermal response functions primarily gives rise to an infinite series of higher order terms exp (−4πnt/β), where n are integers, but the expansion in β/t terminates and the damped exponential terms can be distinguished from the resulting polynomial. Thermalisation can be confirmed although with a bit more work than if power law tails had not been present. This comparatively simple procedure works in even dimensions, when the whole effect of the induced thermalisation is confined to the light cone by Huygens' principle.
In odd dimensions Huygens' principle does not apply and some of the induced thermalisation diffuses into the interior of the light cone. The series encoding the thermalisation on the light cone, which corresponds to the polynomial in β/t, now fails to terminate. Instead it produces an infinite asymptotic expansion controlled by the asymptotic expansion of modified Bessel functions of integer order. The resulting series in β/t is divergent and the task to identify sub-leading exponentials becomes quite subtle. The potential meaning of exponential terms can only be ascertained within a larger framework, such as the study of resurgence of asymptotic series. In such a framework one should be able to assign a meaning to partial thermalisation of composite operators in odd-dimensional free field theories, but a firm conclusion is beyond the scope of the present work.
Tentatively, the Borel summability of modified Bessel asymptotic expansion indicates that there are no exponential correction terms in its asymptotic expansions, which would suggest that the exponential terms we actually find in the appendix are not masked, but on the other hand error terms of even doubly improved asymptotic series are of the same order as the sub-leading exponential terms.
Conclusions
We have refined the operator thermalisation concept and the OTH, and related it to generalised free fields. Except in the special case d = 2, operator thermalisation is generally incomplete and partial, since there are power law tails that dominate exponentially decaying terms at late times. This finding establishes the intermediate nature of thermalisation in free field theories: while exponential relaxation is ubiquitous, it typically coexists with the more unyielding time dependence expected from the presence of conservation laws. In our model system, large N singlet models, we have found both non-thermalising and thermalising behaviour of the same operator: generalised quasi-particle behaviour without exponential relaxation below the critical temperature, and thermalising exponential behaviour above the critical temperature. Importantly, the operator thermalisation concepts turn out to be applicable to operators which only satisfy the theoretical conditions in a limit, in this case when 1/N vanishes. This enhances the scope of our analysis.
The analysis is comparatively straightforward in even dimensions, where Huygens' principle holds and ensures that the thermalised responses induced by a heat bath are localised to the light cone. In contrast, the thermalised responses in odd dimensions are quite intricate due to their distribution over the forward light cone and the whole of its interior. We refrain from formulating a definite conclusion in odd dimensions, since we believe in a deeper conceptual analysis. The importance of simultaneous infinite expansions in inverse powers, and decaying exponentials, of time, suggests resurgent analysis. A connection between thermalisation of integrable systems and resurgence may find further applications.
Some properties of singlet models that are highlighted by our study generate further questions. For example, an efficient description of the high temperature phase remains elusive. We expect that all standard composite operators will thermalise and no longer represent generalised quasi-particles. The fundamental free fields Φ describe the thermodynamics of the high temperature "deconfined" limit well, but they do not represent physical singlet states. Do they provide the best description, or are there better alternatives? There are also holographic gravity duals to these questions, since singlet models are limits of large N gauge theories, some of which are conformal.
Finally, we find it inspiring to contemplate other conformal or integrable systems, in particular in odd dimensions, where resurgence appears to be fundamental.
Since in even d the retarded Green's function only has support on the light cone we will only evaluate the sum there. We have The sum (A.1) can be rewritten using the formula 1 y α = 1 Γ(α) where ϑ(z, q) is the third Jacobi theta function andt = t/β. Using the modular transformation property of ϑ, we obtain ϑ(rt, e −r ) = π r e −rt 2 ϑ(iπt, e − π 2 r ) = π r e −rt 2 The last term is divergent but will be canceled by a divergence stemming from the sum. In order to allow for such a cancellation we regularise the integral by introducing an exponential suppression e −r/R with R taken to infinity after performing the integral. We have which is valid for d > 3. As we are interested in large t-behaviour we employ the asymptotic expansion As a check we set d = 4 to compare with (A.3). We obtain which agrees with (A.3).
For odd dimensions we investigate (A.11) for d = 3 even though the expression is technically only valid for d > 3 because of the logarithmic divergence in the first term. As can be seen, the asymptotic power series does not terminate and the sub-leading exponentials are masked, suggesting that more powerful asymptotic or resurgence method should be employed. | 8,843.8 | 2020-07-01T00:00:00.000 | [
"Physics"
] |
Development of equation of motion deciphering locomotion including omega turns of Caenorhabditis elegans
Locomotion is a fundamental behavior of Caenorhabditis elegans ( C. elegans ). Previous works on kinetic simulations of animals helped researchers understand the physical mechanisms of locomotion and the muscle-controlling principles of neuronal circuits as an actuator part. It has yet to be understood how C. elegans utilizes the frictional forces caused by the tension of its muscles to perform sequenced locomotive behaviors. Here, we present a two-dimensional rigid body chain model for the locomotion of C. elegans by developing Newtonian equations of motion for each body segment of C. elegans . Having accounted for friction-coefficients of the surrounding environment, elastic constants of C. elegans , and its kymogram from experiments, our kinetic model (ElegansBot) reproduced various locomotion of C. elegans such as, but not limited to, forward- backward-(omega turn)- forward locomotion constituting escaping behavior and delta-turn navigation. Additionally, ElegansBot precisely quantified the forces acting on each body segment of C. elegans to allow investigation of the force distribution. This model will facilitate our understanding of the detailed mechanism of various locomotive behaviors at any given friction-coefficients of the surrounding environment. Furthermore, as the model ensures the performance of realistic behavior, it can be used to research actuator-controller interaction between muscles and neuronal circuits.
Introduction
With only a few hundred neurons, Caenorhabditis Elegans (C.elegans) perform various behaviors such as locomotion, sleeping, reproduction, and hunting (Hall and Altun, 2008).The connectome structure among 302 neurons and 165 somatic cells of C. elegans was discovered by pioneering works (Cook et al., 2019;White et al., 1986).C. elegans is a cost-efficient and widely used model in neuronal research.Its small body size and minimal nutritional requirements contribute to its cost efficiency.The organism matures in a shorter period, about three days, compared to other model animals such as fruit flies or mice.Its transparent body allows for easy microscopic observation of its internal structures or artificially expressed green fluorescent proteins.Moreover, due to the hermaphroditic nature of C. elegans, offspring mostly share the same genotype as the parent, which simplifies the multiplication of the worm population for research purposes (Hall and Altun, 2008).
C. elegans bends its body with a sinusoidal wave pattern when moving forward or backward.The driving force for this movement comes from the difference between perpendicular and parallel frictional forces, which it experienced from a surrounding environment.This thrust force pushes the worm along the ground surface with which the worm contacts (Berri et al., 2009;Boyle et al., 2012;Hu et al., 2009;Niebur and Erdös, 1991).Even if a worm has a sinusoidal modulation generated inside it, it has difficulties in forward and backward locomotion if it does not feel the difference in frictional forces from its surroundings.
Mechanical simulators of rod-shaped animals such as C. elegans (Boyle et al., 2012;Niebur and Erdös, 1991), fish (Ekeberg, 1993), and snakes Hu et al., 2009 have been used in various studies.These simulators demonstrate how the activities of muscle cells are represented as behavioral phenotypes, which are determined by signals from a neuronal circuit simulator (Boyle et al., 2012;Ekeberg, 1993;Niebur and Erdös, 1991).They also show how muscle cells return proprioceptive signals back to the neuronal circuit simulator and how animals intentionally distribute body weight for locomotion patterns (Hu et al., 2009).Similarly, the kinematic simulator of fish (Ekeberg, 1993), which has a locomotion pattern in that the animal mostly undulates in a particular direction, was used with a neuronal network simulator to model the undulation of swimming behavior.This combination of kinematic simulator and neuronal network simulator was also used to model how the locomotion pattern changes due to a surrounding environment (Boyle et al., 2012) and how the central pattern generator arises from a few cells (Boyle et al., 2012;Izquierdo and Beer, 2018).
Even though there were studies on kinematic simulation of rod-shaped animals (Boyle et al., 2012;Ekeberg, 1993;Hu et al., 2009), to our best knowledge, there was no kinetic model that reproduces complex locomotion behavior of C. elegans, which includes all of the various modes of locomotion of C. elegans such as forward locomotion, backward locomotion, and turn from experimental observations.Instead, muscle cell activities from Ansatz (Hu et al., 2009), a hypothesis of the solution, or signals from a neuronal circuit simulator (Boyle et al., 2012;Ekeberg, 1993) were applied to the kinematic simulators.A simulator should have an operational structure that imitates physical quantities from an experiment to reproduce the motion of C. elegans in the experiment.However, until now, no kinetic simulation has such a structure.If there is a simulator that reproduces the motion of individual experiments, analysis of the kinetics of motion of specific experiments, which provides information on the individual force that exerts on each body part of the animal, will be enabled.Also, as the kinetic simulation reproduces the motion of C. elegans, the behavioral phenotype that emerged from the muscle activity of neuronal circuit simulation will be more credible.
We built a Newtonian-mechanics two-dimensional rigid body chain model of C. elegans to reproduce its locomotion.We incorporated its body angle, related to the contraction of the body wall muscle of C. elegans, into the primary operating principle of our kinetic model so that the model simulates measurable physical quantities of C. elegans from its experimental video.The model includes a chain of multiple rod rigid bodies, a damped torsional spring between the rigid bodies, and a control angle, which is the dynamic baseline angle from the value of the kymogram of a physical experiment.We formulated Newtonian equations of translational and rotational motion of the rigid body model and computed the numerical solution of the equations by numerical integration using the semi-implicit Euler method.As a result, we were able to demonstrate trajectories and kinetics of the general locomotion of C. elegans, such as crawling, swimming (Vidal-Gadea et al., 2011), omegaturn, and delta-turn (Broekmans et al., 2016).
Results
Newton's equation of motion for locomotion of Caenorhabditis elegans: How does ElegansBot work?
We introduce the simple chain model of C. elegans' body.C. elegans has an elongated body along the head-to-tail axis.Thus, the worm's body can be approximated as a midline extended along the anterial-posterial axis in the xy-coordinate plane (Figure 1A).Let M (=2 µg, details in 'Worm's mass, actuator elasticity coefficient, and damping coefficient' of Appendix) be the mass and L (=1 mm) be the length of the worm.Midline was approximated as n (=25) straight rods, whose ends are connected to the ends of neighboring rods (Figure 1A).The mass, length, and moment of inertia of each rod is m = M/n , 2r = L/n , and I = mr 2 /3 , respectively.When numbering the rods in order, with the rod at the end of the head being labeled as '1-rod' and the rod at the end of the tail being labeled as 'n-rod,' let us designate the i-th rod as 'i-rod.'The point where i-rod and (i+1)-rod meets is 'i-joint.'.
The motion of the worm corresponds to the motion of all the rods.To describe the motion of each rod (i-rod), we need to determine the displacement vector ( d i ), velocity vector ( v i ), the angle measured counterclockwise from the positive x-axis to the tangential direction of the rod ( s i ) (Figure 1B), and angular velocity ( ω i ) of i-rod at a given time t .However, the minimum information required to describe the motion of all rods includes the displacement vector ( dc ) and velocity vector ( vc ) of the worm's center of mass, s i and ω i for each rod (Details in 'Minimum information required to describe the motion of each rod' of Appendix).
Value of time-dependent variables such as dc , vc , s i , and ω i at a given time, t will be expressed as * (t) .When initial values, d (0) c , v (0) c , s (0) i , and ω (0) i are given, the Newtonian equation of motion for acceleration, ac and angular acceleration, {α i } i∈1,••• ,n must be acquired and numerically integrated twice to find d (t) c , v (t) c , s (t) i , and ω (t) i at a given time, t .To obtain the Newtonian equations of motion, we must find every force and torque acting on each rod.There are frictional force, muscle force, and joint force among types of forces acting on the rod, and there are frictional torque, muscle torque, and joint torque among types of torques whose descriptions are as follows.
The only external force acting on the worm is a frictional force from a ground surface such as an agar plate or water.The frictional force is an anisotropic Stokes frictional force, with a magnitude proportional to the speed and assumed different friction coefficients in perpendicular and parallel directions (Boyle et al., 2012), which guarantees that linearity in velocity is preserved in frictional force as well (Details in 'Preservation of linearity in friction' of Appendix).Because of this preservation of linearity, the frictional forces of translational motion (Figure 1D) and rotational motion (Figure 1E) can be calculated separately and added together to find total frictional force and torque.Previously known values of the friction coefficients in perpendicular and parallel directions are used (Boyle et al., 2012).
Let the perpendicular and parallel friction coefficients be b ⊥ and b ∥ for a straightened worm, respectively.Each rod experiences 1/n of the frictional force the worm gets.Thus, the perpendicular and parallel friction coefficients of each rod are b ⊥ /n , b ∥ /n , respectively.The ratio of perpendicular friction coefficient to parallel friction coefficient ( b ⊥ /b ∥ ) is 40 in agar plate and 1.5 in water (Berri et al., 2009;Boyle et al., 2012).This ratio is an important determining factor in whether the locomotion would be crawling or swimming (Boyle et al., 2012).The total frictional force that i-rod receives is ] T : unit vector perpendicular to i-rod), and the total frictional n r 2 ω i (positive or negative values are for torque pointing away from or into the paper plane, respectively.)(The proof is in 'Frictional torque by rotational motion' of Appendix).
Mature hermaphrodite C. elegans has four muscle strands at the left dorsal, right dorsal, left ventral, and right ventral part of the body, and each muscle strand has 24, 24, 23, and 24 muscle cells, respectively (White et al., 1986).Muscle cells at similar positions on the anterior-posterior axis have an activity pattern in that muscles on one side (either dorsal or ventral) cooperate, and those on the opposite side have alternative activities.(Hall and Altun, 2008).
Therefore, we modeled a group of about four muscle cells, which are left dorsal, right dorsal, left ventral, and right ventral, at the same position on the anterior-posterior axis as one actuator (Figure 1C) so that there is a total of 24 ( ≃ ( 24 + 24 + 23 + 24 ) /4 ) actuators in the worm.On i-joint of the chain, there is an actuator labeled as i-actuator.As the number of actuators is 24, we set the number of rod( n ) as 25, which is one more than the number of actuators.The actuator was modeled as a damped torsional spring due to the viscoelastic characteristics of muscle (Boyle et al., 2012;Hill, 1938).If the dorsal muscles of i-actuator contract more than the ventral muscles, i-actuator will bend to the dorsal direction and vice versa.To express this phenomenon by an equation, we defined the torque that i-actuator exerts on i-rod as where θ ctrl,i is control angle, θ i = s i+1 − s i , and κ and c are the elasticity and damping coefficients of an actuator, respectively.
Control angle ( θ ctrl,i ) is a variable inside the elastic part of the muscle torque ( τ κ,i ), to which τ κ,i drives θ i close.Also, the control angle ( θ ctrl,i ), which can be expressed by a heatmap (Figures 2A, C The online version of this article includes the following video(s) for figure 3: Reproduced escaping behavior of experimental video (Broekmans et al., 2016).https://elifesciences.org/articles/92562/figures#fig3video13A and B), is an input value based on experimental data, a numerical model, or a neuronal network model.τ c,i represents the damping effect of muscle cells and somatic cells near i-actuator.The elasticity coefficient ( κ ) and damping coefficient ( c ) of an actuator were induced from previously known values (Boyle et al., 2012) (Details in 'Worm's mass, actuator elasticity coefficient, and damping coefficient' of Appendix).
By assuming that i-rod receives torque ( τ i ) from i-actuator, and (i+1)-rod receives torque ( −τ i ), we can depict the bending that arises from the differential contraction of the dorsal and ventral muscles in i-actuator.The total muscle torque that i-rod receives from damped torsional springs on both ends is τ cκ,i = τ i − τ i−1 .The total muscle force ( F cκ,i ) that i-rod receives from both of its ends is as follows (Details in 'Proof of muscle force' of Appendix).
Two neighboring rods (i-rod and (i+1)-rod) are connected at i-joint.Therefore, when a force is applied to i-rod, (i+1)-rod also receives distributed force (Figure 1F) which we name as 'joint force.'The joint force that (i+1)-rod exerts on i-rod is symbolized as i .By Newton's third law of motion about action and reaction, the joint force that i-rod exerts on (i+1)-rod is F i ( i+1 1F).Joint force ( F i ) can be calculated from the previously introduced given values ( s i , F cκ,i , F b,i , τ cκ,i , τ b,i ) (Details in 'Joint force calculation method' of Appendix).When F 0 = Fn = 0 , then the total joint force that i-rod receives is F joint,i = F i − F i−1 and the total torque caused by joint force is where '×' between two vectors means cross-product.As all forces and torques are found, d (t) c , v (t) c , s (t) i , ω (t) i can be calculated by solving translational and rotational Newtonian equations of motion with numerical integration.The time-step ( ∆t ) used in this work is 10 −5 s unless otherwise noted.Because the only external force exerts on the worm is the frictional force, the equation of translational motion is ac = ∑ i Fb,i M . If friction coefficients ( b ⊥ , b ∥ ) are significantly greater than M ∆t , numerical integration using the explicit Euler method ( v (t+∆t) c = v (t) c + a (t) c ∆t = v (t) c + ∑ i Fb,i M ∆t ) becomes unstable (Butcher, 2003).So, we tackled this instability of numerical integration by developing semi-implicit Euler Method b,i M ∆t ), which makes numerical integration stable when any frictional coefficients greater than or equal to 0 is given (Details in 'Proof of numerical integration for the translational motion of a worm using semi-implicit Euler method' of Appendix).
The equation of rotational motion of i-rod is Iα i = τ total,i = τ b,i + τ cκ,i + τ joint,i .When the frictionrelated value ( b ∥ r 2 ), elasticity-related value ( κ∆t ), or damping coefficient( c ) is significantly larger than I ∆t , numerical integration using explicit Euler method ( ω (t+∆t total,i I ∆t ) becomes unstable (Butcher, 2003).To solve this instability, we constructed a semi-implicit Euler method for rotational motion and an error-corrected equation for angular momentum (Details in 'Numerical integration of the rotational motion of i-rod using semi-implicit Euler method' and 'Correction formula for the rotational inertia of the entire worm' of Appendix).By using these semi-implicit Euler methods, solutions for i of a worm at a given time can be available for the ground surface of agar whose b ⊥ , b ∥ are significantly larger than M ∆t , water which has smaller friction coefficients than agar, or frictionless ground surface.
Can C. elegans in ElegansBot crawl or swim?
A kymogram is a heatmap that shows body angle, ) at a given time, t .By fitting a sine function to the kymogram of previous work (Vidal-Gadea et al., 2011), we obtained linearwavenumber (after this referred to as wavenumber) and period of C. elegans crawling on the agar plate and swimming in water.The wavenumber ( ν ) and the period ( T ) are, respectively, 1.832 and 1.6 (s) on (Broekmans et al., 2016).https://elifesciences.org/articles/92562/figures#fig3video2 Figure 3 continued the agar plate and 0.667 and 0.4 (s) in water.For both crawling and swimming, amplitude ( A ) was set to 0.6 (rad) arbitrarily to match the trajectory shown in the experimental video (Vidal-Gadea et al., 2011).Each kymogram of crawling (Figure 2A) and swimming (Figure 2C) was calculated by substituting amplitude ( A ), wavenumber ( ν ), and period ( T ) into into θ (t) ctrl,i = A cos . Crawling trajectory, which performs sinusoidal locomotion in the positive x-axis direction, was obtained by inputting a crawling kymogram as θ (t) ctrl,i input to ElegansBot (Figure 2B).Regarding crawling, the head track and the tail track have similar shapes.However, the tail track is more toward the negative x-axis direction than the head track.The difference between the head and tail tracks indicates that the worm pushes the ground surface by the distance between the head track and tail track to obtain thrust (Figure 2B).Indeed, we found that the body part placed diagonally with respect to the direction of the worm's locomotion is pushing along the ground surface (Figure 2-figure supplement 1A).The thrust force of the worm cancels out most of the drag force, which enables the worm to move at nearly constant velocity.The average velocity of the worm is 0.208 (mm/s), which is consistent with the known values (Cohen et al., 2012;Jung et al., 2016;Omura et al., 2012;Shen et al., 2012).
In the previous work, the worm showed swimming behavior in a water droplet on an agar plate (Vidal-Gadea et al., 2011).As the friction coefficient of water is smaller than that of agar, even though the area that the worm swept was wider during swimming than crawling, the worm did not move forward much in comparison to the area it swept (Figure 2D).The worm gained significant momentum in the forward direction of locomotion when the body bent in the c-shape (Figure 2-figure supplement 1B).In contrast to crawling, during swimming, the worm did not receive constant thrust force over time.Thus, the speed of the worm exhibited significant oscillations over time (Figure 2-figure supplement 1B), and the average velocity was 0.223 (mm/s).
ElegansBot exhibits more complex behavior including the turn motion
Unlike previous C. elegans body kinematic simulation studies, our simulation can replicate the worm's behavior using a kymogram (Figure 3A and B) derived from experimental videos.We utilized opensource software, Tierpsy Tracker (Javer et al., 2018) and WormPose (Hebert et al., 2021), to obtain the kymogram input ( θ (t) ctrl,i ) for the ElegansBot.Through simulation, we aimed to reproduce the omega-turn and delta-turn behaviors observed in the experimental videos (Broekmans et al., 2016).When we used the vertical and horizontal friction coefficients b ⊥ and b ∥ on agar, as proposed in the previous work (Boyle et al., 2012), the trajectory was not accurately replicated.Given that the friction coefficients could vary depending on the concentration of the agar gel, we used b ⊥ /100 and b ∥ /100 for the vertical and horizontal friction coefficients, respectively, which resulted in a better trajectory replication (Details in 'Proper selection of friction coefficients' in Appendix).
The trajectory (Figure 3C and D, Figure 3-videos 1 and 2) obtained from ElegansBot accurately reproduces the experimental video (Broekmans et al., 2016).The changes in the direction of movement caused by turns are well replicated.Additionally, during the omega-turn or delta-turn, the body briefly performs a deep bend, and we newly discovered the mechanism that gains significant propulsion from the deep bend region to change direction using ElegansBot (Figure 3C and D).Moreover, the ElegansBot accurately reproduces not only the turns but also complex behaviors like the sequence of forward-backward-turn-forward, also known as escaping behavior.
Additionally, we calculated the mechanical power of the worm as a quantitative indicator to explain its locomotion during sequenced locomotive behavior, based on behavior classification (forward, backward locomotion, or turn, as defined in Methods).During escaping behavior, the worm produced an average power of 2094 fW in the initial forward locomotion, followed by an average of 16,437 fW (7.85 times that of the initial forward locomotion) in backward locomotion, and an average of 11,118 fW (5.31 times that of the initial forward locomotion) during turning (Figure 4A).After turning and resuming forward locomotion, it produced an average power of 5480 fW (2.62 times that of the initial forward locomotion).This indicates that the worm produced more power than that of initial forward locomotion to escape sudden threats.Let's denote the average of a quantity for all given i as ⟨ * ⟩ i .At the moment the worm formed a deep bend (t=11.2s), the average magnitude of frictional force of the body part forming the deep bend ( where i=4 to 15) was 3536 pN, compared to the average magnitude of the remaining parts ( = 1737 pN where i=1 to 3 or i=16 to 25), which was 2.04 times greater (Figure 3C, Figure 4A).We analyzed delta-turn in the same manner.The worm produced an average power of 3514 fW in the initial forward locomotion, followed by an average of 11,176 fW (3.18 times that of the initial forward locomotion) in subsequent backward locomotion.In the relatively short duration of forward locomotion following the backward locomotion, the worm produced an average power of 17,544 fW (4.99 times that of the initial forward locomotion), and an average of 13,046 fW (3.71 times that of the initial forward locomotion) during turns (Figure 4B).After the turn, when resuming forward locomotion, the worm produced an average power of 6429 fW (1.83 times that of the initial forward locomotion).At the moment the worm formed a deep bend (t=6.1 s), the average magnitude of frictional force of the body part ( where i=16 to 25) was 10,497 pN, compared to the average magnitude of the remaining parts ( ,677 pN where i=1 to 15), which was 3.92 times greater (Figure 3D, Figure 4B).In both escaping behavior and delta-turn, the worm consistently produced more power in the subsequent backward locomotion and turn than in the initial forward locomotion.
ElegansBot presents body shape ensembles of C. elegans from a shape in water en route to agar While there have been studies on how locomotion patterns change in agar and water by merging neural and kinematic simulations (Boyle et al., 2012), there have been none that solely used kinetic simulation to analyze how speed manifests depending on the frequency and period of locomotion.
We demonstrate this aspect.We studied the locomotion speed of the worm under different friction coefficients, which represent the influence of water, agar, and intermediate frictional environment, using ElegansBot.The vertical and horizontal friction coefficients in water are b water,⊥ = 5.2 × 10 3 (μg/ sec) and b water,∥ = b water,⊥ /1.5 , respectively, while in agar, these values are b agar,⊥ = 1.28 × 10 8 (μg/sec) and b agar,∥ = b agar,⊥ /40 (Boyle et al., 2012).For environmental index σ ∈ [ 0, 1 ] , we have defined the vertical and horizontal friction coefficients in the environment between water ( σ = 0 ) and agar ( σ = 1 ) as b σ,⊥ = b 1−σ water,⊥ b σ agar,⊥ and b σ,∥ = b 1−σ water,∥ b σ agar,∥ , respectively.Under an environmental index σ , for various pairs of frequency-period ( ν , T ) when the control angle is θ (t) ctrl,i = A cos ( 2π )) (with A = 0.6 (rad)), we have found the ( ν , T ) that maximizes the worm's average velocity(optimal ( ν , T )) (Figure 5A).The optimal ( ν , T ) exhibits a nearly linear distribution (Figure 5B).We noticed a transition from swimming body shape to crawling body shape as σ varies (Figure 5, Figure 5-figure supplement 1).The optimal ( ν , T ) for σ =0(water) is (0.65, 0.4 s), matching the actual ( ν , T ) value of swimming behavior (Vidal-Gadea et al., 2011).The optimal ( ν , T ) for σ =1(agar) is (1.9, 0.8 s), and the optimal ν (1.9) matches the actual ν value (1.832) for crawling behavior (Vidal-Gadea et al., 2011), with the optimal T (0.8 s) being half the actual T value (1.6 s).We wanted to understand the impact of the environmental index σ not only on forward locomotion but also on sequenced locomotive behavior.First, we analyzed the effect of the environmental index σ on escaping behavior as follows.Let's denote the set of a quantity for all pairs of index i and time t as { * } i,t .When the escaping behavior kymogram input was same as Figure 3A, we explored the effect of vertical and horizontal friction coefficients on the worm's motion.Where σ ranged from 1.0 to 0, the trajectory varied with σ (Figure 5-figure supplement 2A), and decreased as σ decreased (Figure 5-figure supplement 2B).From σ = 1.0 to σ = 0.1, the total absolute angular change ( where T is the total time of the experimental video.)increased as σ decreased.However, from σ = 0.7 to σ = 0, S remained constant within the error of 0.33 rad, and the total traveled distance( ∑ Where σ ranged from 1.0 to 0, the trajectory varied with σ (Figure 5-figure supplement 3A), and E θ also decreased as σ decreased (Figure 5-figure supplement 3B).From σ = 1 to σ = 0.6, S increased as σ decreased.From σ = 0.6 to σ = 0, S decreased as σ decreased.The maximum total traveled distance was at σ = 0.9.From σ = 0.9 to σ = 0, the total traveled distance decreased as σ decreased.
Discussion
ElegansBot is an advanced kinetic simulator that reproduces C. elegans' various locomotion The known crawling speed range of C. elegans (Cohen et al., 2012;Jung et al., 2016;Omura et al., 2012;Shen et al., 2012) matches the speed in our simulation.The force dispersion pattern of the forward movement of a snake (Hu et al., 2009) is similar to the force dispersion pattern of crawling in our model, where the body part placed diagonally in the direction of movement generates thrust.
The head and tail tracks of our simulation resemble the trace left on the agar plate by C. elegans during locomotion (Yeon et al., 2018), providing evidence of the mechanism where C. elegans moves forward by pushing along the ground surface.Given that friction and elasticity coefficients can vary between experiments, the appropriate selection of these values allows the trajectories of omegaturns and delta-turns in our simulations to match the experimental videos (Broekmans et al., 2016).
Previous work (Berri et al., 2009;Boyle et al., 2012) eliminated inertia from the equations of motion, but our simulation includes it, allowing calculation even in cases where inertia is significant due to low and period ( T ) for a given friction coefficient.The star symbol indicates the pair of ( ν , T ) that maximizes the worm's average velocity. (B) For each environmental index σ , the pair of ( ν , T ) that maximizes the worm's average velocity.The worm figures inside the small rectangles pointed to by the arrows represent the body shape corresponding to the respective ( ν , T ) pair.
The online version of this article includes the following figure supplement(s) for figure 5: friction coefficients.Using the crawling and swimming wavenumbers and periods from the experiments (Vidal-Gadea et al., 2011), we computed sine functions to create trajectories for crawling and swimming.We also analyzed how friction forces act on the worm during crawling and swimming, studying how the worm gains propulsion.We demonstrated that we could reproduce various locomotion observed in experimental videos, such as forward-backward-(omega turn)-forward constituting escaping behavior and delta-turn navigation, by providing the kymogram obtained from representative physical values from the experimental videos, as well as the kymogram obtained from a program (Hebert et al., 2021;Javer et al., 2018) extracting the body angles from actual experimental videos into ElegansBot.Our established Newtonian equations of motion are accurate and robust, suggesting that not only does our simulation replicate the experimental videos, but it also provides credible estimates for detailed forces.
ElegansBot will serve as a strong bridge for enhancing the knowledge in 'from-synapse-to-behavior' research Our method could be used for kinetic analysis of behaviors not covered in this paper.It could also be used when analyzing behavior changes caused by mutation or ablation experiments.Given that our simulation allows for kinetic analysis, it could be used to calculate the energy expended by the worm during locomotion, serving as an activity index.Our simulation only requires the body angles as input data, so even if the video angle shifts and trajectory information is lost, the trajectory can be recovered from the kymogram.Our simulation could also be used when studying neural circuit models of C. elegans.It could be used to check how signals from neural network models manifest as behaviors which is a needed function from previous work (Sakamoto et al., 2021), and it could be used when studying compound models of neural circuits and bodies.For example, when creating models that receive proprioception input based on body shape (Boyle et al., 2012;Ekeberg, 1993;Izquierdo and Beer, 2018;Niebur and Erdös, 1991), our method could be used.Finally, our method could be used in general for the broad utility to analyze the motion of rod-shaped animals like snakes or eels and to simulate the motion of rod-shaped robots.
Frequency and wavelength of C. elegans locomotion
Sine function fitting was applied to the crawling and swimming kymograms (Vidal-Gadea et al., 2011) to determine the frequency and wavelength of C. elegans locomotion on agar and water.
C. elegans locomotion videos
Videos of C. elegans' escaping behavior and foraging behavior were obtained from previous work (Broekmans et al., 2016).A single representative video out of a total of one hundred escaping behavior videos was used as data in this paper.Additionally, only the delta-turning portion of the foraging behavior videos was cut out and used as data in this paper.
Obtaining kymograms from video
The following method was used to extract the kymogram from the video of C. elegans: The body angles and midline were extracted using Tierpsy Tracker (Javer et al., 2018) from the original video where the worm is locomoting.Tierpsy Tracker failed to extract the midline of the worm when the body parts meet or the worm is coiled.The midline information of the frames successfully predicted by Tierpsy Tracker and the original video information were used as ground truth training data for a program called WormPose (Hebert et al., 2021).WormPose trained an artificial neural network to extract the body angles and midline of a coiled worm using a generative method based on the input data.The body angles were extracted from the original video using the trained WormPose program.
For the frames where Tierpsy Tracker failed to extract the body angles, it was replaced with the body angles extracted by WormPose.Nonetheless, there were frames where the body angle extraction failed.If the period of failed body angle prediction was continuously less than three frames (about 0.01 s), the body angles for that period was predicted using linear interpolation.
Program code and programming libraries
Equations for the chain model, friction model, muscle model, and numerical integration that constitute the ElegansBot were designed from the body angle information and kymogram that change every moment of time.Python (van Aken et al., 1995) version 3.8 was used to implement the equations constituting ElegansBot as a program.NumPy (Harris et al., 2020) version 1.19 was used for numerical calculations, and Numba (Lam et al., 2015) version 0.54 was used for CPU calculation acceleration.SciPy (Virtanen et al., 2020) version 1.5 was used for curve fitting and Savitzky-Golay filter (Savitzky and Golay, 1964) to classify the worm's behavioral categories.The Matplotlib (Hunter, 2007)
Physical constants of the ground surface
The friction coefficient values for the ground surface where C. elegans crawled and swam and the elastic and damping coefficients of C. elegans muscles were obtained from previous work (Boyle et al., 2012).The muscle elasticity and damping coefficients were converted into coefficients for the damped torsional spring to be used in our model (Details in 'Worm's mass, actuator elasticity coefficient, and damping coefficient' of Appendix).
Preservation of linearity in friction
The velocity v of an arbitrary point particle which is included by i-rod can be decomposed into two velocity components v = vα + v β .In this case, if the object is subject to an anisotropic Stokes friction, there is linearity between the friction F b,α , F b,β obtained from each velocity component vα , v β and the friction b obtained from velocity v .
Frictional torque by rotational motion
Let us denote variable ρ as the distance from the center of i-rod measured along the direction of vector r i .It is to be noted that ρ is within the range [−r, r] .For an infinitesimal dρ where 0 < dρ ≪ 1 , the moment arm vector for the infinitesimal interval [ ρ − dρ/2, ρ + dρ/2 ] (hereafter referred to as the infinitesimal interval ρ ) from the center of i-rod is ρ r i .The coefficient of friction for the infinitesimal interval ρ is: The velocity component due to the rotational motion of the infinitesimal interval ρ is ρω i N i .Therefore, the frictional force received by the infinitesimal interval ρ due to rotational motion is: The torque received by the infinitesimal interval ρ due to rotational motion is: The total frictional torque received by i-rod due to rotational motion is
Proof of muscle force
The muscle force, which makes the torque τ i received from i-actuator to i-rod, was designed as follows.The damped torsion spring is connected at the center of each rod and gives a force in a direction perpendicular to the rod.The support is connected to i-actuator's midpoint and i-joint (Appendix 1-figure 2A).Let us say that the mass of the support and i-actuator are both 0. The support gives forces ( F sup 1 ,i , F sup 2 ,i ) of the same size to i-rod and (i+1)-rod in a direction parallel to the support (Appendix 1-figure 2B).Let us assume that the resultant force received by i-actuator is 0 (Appendix 1-figure 2C).Thus, the forces from the actuator generate no torque at the connection point in the middle of the rod but at the connection point at the end (Appendix 1-figure 2D).
The force that i-rod receives from i-actuator is F p 1 ,i + F sup 1 ,i and the magnitude of the force and the direction of the force is . These equations come down to the following equations.
Therefore, the total force that i-rod receives from i-actuator and (i-1)-actuator is
Joint force calculation method
Let us calculate the joint force F i from the given values ( s i , F cκ,i , F b,i , τ cκ,i , τ b,i ).In this part, the superscript * T means the transpose of a vector or a matrix.i-rod and (i+1)-rod always meet at i-joint can be expressed as a following vector equation.
Differentiating this equation twice for time, we can see that the accelerations of the ends of i-rod and (i+1)-rod at i-joint are the same.
Multiplying both sides by m gives: If F res,i is the total force applied to i-rod other than F i and −F i−1 , which is F res,i = F cκ,i + F b,i , then: If τ res,i is the total torque applied to i-rod excluding r i × ( , which is τ res,i = τ cκ,i + τ b,i , then: Dividing both sides by 1 3 r 2 gives: If h res,i ≡ 3r −2 τ res,i × r i , then: Following the same method, To organize the terms of the equations into known values and unknown values, Therefore, if we know all F res,i and τ res,i for each i, we can find , and if we expand If we set A i ≡ P i − I and B i ≡ P i + P i+1 + 2I , then: If we set 0 = 0 0 0 0 and express the above equation in a multidimensional tensor form, Let us set: As A i and B i can be calculated from P i , P i from N i , and N i from s i , we can find D from s i .
Therefore, if we know F cκ,i , F b,i , τ cκ,i , τ b,i , we can find Q .
Let us set: Since A i , B i are symmetric matrices, D is a heptadiagonal symmetric matrix.Since D is a symmetric matrix, the solution to the matrix equation can be found with the Cholesky decomposition.Therefore, we can find the x-axis and y-axis components of F i .
As a result, we can find the joint force F i from the known values ( Proof of numerical integration for the translational motion of a worm using semi-implicit Euler method When the friction coefficients b ⊥ , b ∥ are sufficiently large compared to M/∆t , numerical integration via the explicit Euler method ( b,i M ∆t ) becomes unstable (Butcher, 2003).Therefore, for all friction coefficients greater than or equal to 0, the semi-implicit Euler method ( v (t+∆t) c = v (t) c + a (t+∆t) c b,i M ∆t ) was used to ensure numerical integration remains stable, and its proof is as follows.
Newton's equation for the translational motion of each i-rod is as follows.
The integration formula for a i using the implicit Euler method is as follows.(where ) If both sides are divided by M , This approximation ensures computational stability regardless of the size of b ⊥ , b ∥ .That is, this approximation solves the problem of the decrease in computational stability of numerical integration through the explicit Euler method when b , b ∥ are sufficiently large compared to M/∆t .Numerical integration of the rotational motion of i-rod using semiimplicit Euler method First, the numerical integration formulas for ω i and α i using the implicit Euler method are as follows.
n r 2 , the equation describing the rotation of i-rod is as follows.
If the above formula is expanded for time t + ∆t , The above formula is impossible to integrate because θ (t+∆t) ctrl,i , θ (t+∆t) ctrl,i+1 , τ (t+∆t) joint,i are unknown at time t.
If the above formula is assumed to be true, the following approximation can be used. ) The above formula can be expressed as a matrix formula as follows.
In the above vector matrix formula, let us represent the vectors and matrix by the following symbols.Correction formula for the rotational inertia of the entire worm For a floor surface with low friction like water, when numerically integrating the rotational motion of the worm, if ∆t > 1 × 10 −6 sec , the calculation error accumulated for the rotational inertia of the whole worm significantly influenced the calculation result ω i and s i .To prevent this, the error is corrected as follows.If xi ≡ x i − xc , ȳi ≡ y i − yc , the moment of inertia of the entire worm at time t is as follows by the parallel axis theorem.
If i-rod is approximated as a point particle, the torque applied to the entire worm at time t is as follows where subscription x, y indicates x, y components of the vector.(See 'Numerical integration for translational motion' in Appendix) τ (t+∆t) body If i-rod is approximated as a point particle, the rotational inertia of the whole worm at time t is as follows.
L (t) body ≃ m ) The predicted value of ω i at t + ∆t , ω p i , is calculated by the semi-implicit Euler method (See 'Numerical integration of the rotational motion' in Appendix).
The predicted value of s i at t + ∆t is as follows.) ω (t+∆t) i is calculated as follows.s (t+∆t) i = s (t) i + ω (t+∆t) i ∆t By correcting the rotational inertia for the whole worm, numerical integration of the rotational motion of the worm was well calculated even for cases when ∆t > 1 × 10 −6 sec , as if ∆t ≤ 1 × 10 −6 sec .Appendix 1-figure 5 continued
Figure 1 .
Figure 1.Components of ElegansBot.(A) Chain model for C. elegans body.(B) Rods in chain model.(C) i-actuator, which is a damped torsional spring.(D) Frictional force (black arrow) due to the translation motion of a rod.(E) Frictional force (black arrow) due to the rotational motion of a rod.(F) Joint force (black arrows) acting on i-rod and (i+1)-rod. ,
Figure 2 .
Figure 2. Simulated locomotion from a sine kymogram.(A) Crawling kymogram.Kymogram indicates the angle of i-joint which is located between i-rod and (i+1)-rod.Red and blue color mean i-joint bend in the dorsal and ventral directions, respectively.(B) Crawling trajectory.The yellow circle indicates the position of the worm's head.The Orange and sky-blue lines show the worm's head and tail trajectories, respectively.(C) Swimming kymogram.(D) Swimming trajectory.The online version of this article includes the following figure supplement(s) for figure 2:
Figure 3 .
Figure 3. Simulated locomotion from a kymogram of a real worm locomotion video.The length and direction of a black arrow indicate the magnitude and direction of the frictional force ( −F (t) b,i ) that the corresponding body part, which is the starting point of the arrow, exerts on the surface.(A) Escaping behavior kymogram.Triangles over the heatmap indicate the corresponding time of snapshots shown in Figure (C).(B) Delta-turn kymogram.Triangles over the heatmap indicate the corresponding time of snapshots shown in Figure (D).(C) Escaping behavior trajectory.(D) Delta-turn trajectory.The arrow length scale is different from Figure (C) to clearly show the arrows' directions and head and tail tracks.
Figure 3
Figure 3 continued on next page
Figure 4 .Figure 4
Figure 4. Frictional force on each rod.(A) Escaping behavior.The top panel represents the frictional force F (t) b,i experienced by i-rod.As indicated on the color wheel to the right, the hue of this heatmap represents the direction of the force, and the saturation represents the magnitude of the force.The second panel from the top shows the magnitude of the frictional force F (t) b,i .The third panel from the top represents the average ⟨� � �F (t) b,i � � � ⟩ i (black Figure supplement 1. Process of defining behavioral categories.
Figure 4 continued
Figure 5 .
Figure5.Body shape transition from the shape in water to the shape in agar.(A) Average velocity of the worm as a function of wavenumber ( ν ) and period ( T ) for a given friction coefficient.The star symbol indicates the pair of ( ν , T ) that maximizes the worm's average velocity. (B) For each environmental index σ , the pair of ( ν , T ) that maximizes the worm's average velocity.The worm figures inside the small rectangles pointed to by the arrows represent the body shape corresponding to the respective ( ν , T ) pair.
Figure supplement 2 .
Figure supplement 2. The effect of the environmental index σ on escaping behavior.
Figure supplement 3 .
Figure supplement 3. The effect of the environmental index σ on the delta-turn.
figure 1. Method of compressing motion state information.(A) Method of calculating the relative position of the rod.(B) Method of calculating the absolute position of the rod.(C) Method of calculating the relative velocity of the rod.
..
figure 2. The i-actuator.(A) Composition of i-actuator.(B) Forces that i-actuator gives to i-rod and (i+1)-rod.(C) Resultant force given by i-actuator.(D) Force component of i-actuator that applies torque to i-rod.The trajectory of the worm for each scaling factor η. (B) Characteristics of the trajectory.The top graph represents The middle graph shows the total traveled distance of the worm.The bottom graph represents the total absolute angle change ( trajectory of the worm for each scaling factor η. (B) Characteristics of the trajectory.The top graph represents The middle graph shows the total traveled distance of the worm.The bottom graph represents the total absolute angle change ( library was used to represent C. elegans' body pose and trajectory in figures and videos.The program code used in the research can be obtained from the open database GitHub ( Let us denote the average of a quantity for all time t as ⟨ * ⟩ t .We calculated ξ(t) 0 0 dt ∆t .
Then, the matrix formula is expressed as follows. | 10,432.8 | 2024-04-29T00:00:00.000 | [
"Biology",
"Physics",
"Engineering"
] |
Magnetosensibility and Magnetic Properties of Ectatomma brunneun Smith , F . 1858 Ants
Several experiments have shown that animals can use the geomagnetic field (GMF) for orientation and homing using a magnetoreception process (Wiltschko & Wiltschko, 2005). Social insects, such as bees, ants and wasps, can use magnetoreception for magnetic orientation in guidance and navigation tasks (Wajnberg et al., 2010; Pereira-Bomfim et al., 2015). Unlike magnetic orientation, there are some reports of sensibility to magnetic fields (MF) by insects, where variations in MFs can change common behaviors. Vowles (1954) presented a pioneer study with the ant Myrmica laevinodis (Nylander, 1846). He studied the orientation of ants to gravity and used tiny particles of soft iron cemented to Abstract The present paper aims to study magnetosensibility and seek magnetic nanoparticles in ants. By living in colonies, the social insects developed very efficient methods of nestmate recognition, being less tolerant towards individuals from other colonies. Therefore, any strange behavior between nestmates and/or conspecifics, besides those present in their behavioral repertoire, is not expected. The present paper analyzes whether changes in the intensity of applied magnetic fields on Ectatomma brunneun (Smith) ants can cause changes in the typical pattern of interaction between conspecifics. We used a pair of coils generating a non-homogeneous magnetic field to change the whole local geomagnetic field. Magnetometry studies were done on abdomens and head + antennae using a SQUID magnetometer. The results show that changes in the geomagnetic field affect the usual pattern of interactions between workers from different colonies. The magnetometry results show that abdomens present superparamagnetic nanoparticles and heads present single-domain magnetic nanoparticles. Behavior experiments show for the first time that Ectatomma brunneun ants are magnetosensible. The change in nestmate recognition of Ectatomma ants observed while a magnetic field is applied can be associated with disturbance in a magnetosensor presented in the body based on magnetic nanoparticles. Sociobiology An international journal on social insects
Introduction
Several experiments have shown that animals can use the geomagnetic field (GMF) for orientation and homing using a magnetoreception process (Wiltschko & Wiltschko, 2005). Social insects, such as bees, ants and wasps, can use magnetoreception for magnetic orientation in guidance and navigation tasks Pereira-Bomfim et al., 2015). Unlike magnetic orientation, there are some reports of sensibility to magnetic fields (MF) by insects, where variations in MFs can change common behaviors. Vowles (1954) presented a pioneer study with the ant Myrmica laevinodis (Nylander, 1846). He studied the orientation of ants to gravity and used tiny particles of soft iron cemented to
Abstract
The present paper aims to study magnetosensibility and seek magnetic nanoparticles in ants. By living in colonies, the social insects developed very efficient methods of nestmate recognition, being less tolerant towards individuals from other colonies. Therefore, any strange behavior between nestmates and/or conspecifics, besides those present in their behavioral repertoire, is not expected. The present paper analyzes whether changes in the intensity of applied magnetic fields on Ectatomma brunneun (Smith) ants can cause changes in the typical pattern of interaction between conspecifics. We used a pair of coils generating a non-homogeneous magnetic field to change the whole local geomagnetic field. Magnetometry studies were done on abdomens and head + antennae using a SQUID magnetometer. The results show that changes in the geomagnetic field affect the usual pattern of interactions between workers from different colonies. The magnetometry results show that abdomens present superparamagnetic nanoparticles and heads present single-domain magnetic nanoparticles. Behavior experiments show for the first time that Ectatomma brunneun ants are magnetosensible. The change in nestmate recognition of Ectatomma ants observed while a magnetic field is applied can be associated with disturbance in a magnetosensor presented in the body based on magnetic nanoparticles. different parts of the ant body to generate a magnetic couple, in the presence of a magnetic field of about 300 μT, to be added to the gravitational couple. He aimed to identify where the gravitation sensor is located in the ant body. During the experiments, Vowles observed that the magnetic couple does not alter the orientation to the gravitational force. Still, he observed that ants changed their behavior when the magnetic field was on: they stopped their movement or often cleaning their antennae when the iron particles were located in the funiculi or the scape of both antennae (Vowles, 1954). The study of Kermarrec (1981) showed that Acromyrmex octospinosus (Reich, 1793) ants are sensitive to strong static MFs provided by magnets (of about 19100 μT and 27300 μT), by avoidance reactions including the repeatable movement of brood by workers. In the same study, no reaction was observed for magnetic fields of about 200 μT, and 500 μT. Anderson and VanderMeer (1993) showed that Solenopsis invicta (Buren, 1972) ants are sensitive to changes in the GMF direction analyzing the time of trail formation in an experimental arena. In those studies, with M. laevinodis, A. octospinosus, and S. invicta, the observed responses to MFs are examples of magnetosensibility, not necessarily related to orientation and homing. A magnetoreception mechanism essentially assumed in ants is the ferromagnetic hypothesis (Johnsen & Lohmann, 2005). It claims that the magnetic field transduction occurs through magnetic nanoparticles located inside some specialized cells associated with the nervous system. In Pachycondyla marginata (Roger, 1861) ants, the search for magnetic nanoparticles involved studies on their isolation (Acosta-Avalos et al., 1999), SQUID magnetometry (Wajnberg et al., 2004), ferromagnetic resonance (Wajnberg et al., 2000), and electron microscopy . Such studies allowed the identification of the antennae as the location of the magnetoreceptor. For other insects, such as Atta colombica (Guérin-Méneville, 1844) and Schwarziana quadripunctata (Lepeletier, 1836) (Lucano et al., 2006;Alves et al., 2014), similar studies identify the antennae as the body part with a higher magnetic signal.
Like other social insects, Ants show a high degree of cooperation among the individuals that interact in the colony (Zinck et al., 2008). For instance, workers play an active role in nest construction, colony protection against predators, foraging, and brood care (Ratnieks et al., 2006). The colony's integrity depends on social interactions and communication among nestmates (Crozier & Pamilo, 1996). Those abilities are well developed in ants, and individuals from a foreign nest are usually attacked and rejected from foreign colonies (Crozier & Pamilo, 1996;Sturgis & Gordon, 2012). However, the level of aggression towards conspecifics non-nestmates, i.e., during nestmate recognition, can vary among species (d 'Ettorre & Lenoir, 2010). Ants, in general, control and defend their territory, where they extract resources (Newey et al., 2010). Encounters among foragers from different colonies can generate conflicts that can turn into clashes, resulting even in individuals' death (Matthews & Matthews, 2010). In some cases, ants can be tolerable to neighbors, implying that nestmate recognition and aggression can be associated with colony-recognition odors from environmentally derived cues, not only to the genetically derived ones (Chen & Nonacs, 2000;Frizzi et al., 2015). As far as we know, magnetosensibility has not been applied to analyzing the aggressive behavior during nestmate recognition in the presence of different magnetic fields.
The present study aimed to investigate magnetosensibility in Ectatomma brunneum (Smith). We hypothesized that the usual pattern of interaction among conspecifics could be affected by applied magnetic fields. We also looked for the presence of magnetic material in the ant's body using SQUID magnetometry techniques.
Materials and Methods Behavior experiments
We used a pair of coils connected to a digital power supply (Skill Tech, model SKFA-05D) to test the effect of magnetic fields on ants' intraspecific interactions, as described by Pereira-Bomfim et al. (2015). The coils had a 30 cm diameter with a space of 15 cm between them. They were built with 58 spirals of Cu wire 14 AWG (Figs 1A and 1C), generating a non-uniform static magnetic field (Fig 1B) applied to a plastic container called "arena of encounters" (Fig 1C). The coils' axis was kept in the South/North direction, oriented with a compass. When the power supply was switched on (15 V, 1.16 A), the horizontal component of the magnetic field (open circles in Fig 1B) inside the coils presented its polarity inverted in the region from 4 cm to 12 cm (Figs 1A and 1B). The intensity of the GMF in the lab was -16 μT horizontal, 21 μT vertical, and 16 μT perpendicular, meaning a total intensity of 31 μT. To measure the intensity of the MF, we used a sheet of graph paper. In this paper, we measured the intensity value in the vertical, horizontal and perpendicular directions at every centimeter. We took all measurements using a gaussmeter (GlobalMag, Model TLMP-HALL 050). The total magnetic field generated is shown in Fig. 1B. The essential characteristics in the generated magnetic field, relative to the standard geomagnetic field, are the inversion in the vertical component (open squares in Fig 1B), changing the sign of the inclination, the increasing of the magnetic field intensity from the center to the periphery, and the U-form of the field intensity. From 5 cm to 9 cm, the magnetic field gets its lower value of about 100 μT (about three times the local value in the lab, see the open triangles in Fig 1B). The magnetic field generated by the pair of coils corresponds with a quadrupolar field generated by an anti-Helmholtz configuration. The electrical current circulates in the opposite direction in each coil (Youk 2005). In contrast, in a Helmholtz configuration, the current circulates in the same direction in each coil generating a dipolar field. This magnetic field configuration is interesting because it changes non-uniformly all the parameters of the local GMF (intensity, inclination and declination) and the increase in MF intensity is very low compared with that generated by magnets, as was done by Vowles (1954) and Kermarrec (1981).
We collected a total of 56 foragers from six colonies of E. brunneum from their nests' entrance. The colonies were in the campus of Universidade Estadual de Mato Grosso do Sul, Dourados, MS, Brazil. After transfer to the laboratory, we kept the individuals in a 250 ml plastic container with holes in the sides and lid to allow air to circulate, with water and molasses ad libitum, wrapped in red cellophane to minimize the stress caused by collection and luminosity. For acclimatization to laboratory conditions (24ºC and 70% of humidity), a period of 24 hours was considered. Then, we performed induced encounters among intraspecific workers. To this end, the ants were transferred to the "arena of encounters" (Fig 1C), a container of 16cm x 10cm x 10cm, inside which there was a smaller container (6cm x 4cm x 3cm) in the central inner part, where we placed one ant. Then, after one minute, a second worker from another colony was placed in the arena, outside the smaller container. This smaller container was only removed after 1 minute, thus enabling the encounter among the two workers.
We induced 24 encounters, peer to peer, among 48 workers of six colonies of the species E. brunneum whose nests were at varying distances from each other (Fig 2). As control of experimental conditions, we induced four nestmate encounters. We used each ant only once in the experiment to avoid pseudoreplication, and at each encounter, we used a new arena.
We observed each pair interacting for 45 minutes in sessions of 15 minutes. In the first 15 minutes, the interactions occurred with the coils switched off, i.e., before the change in MF; for the next 15 minutes, the coils were switched on to generate a different MF in them; and in the last 15 minutes, the coils were switched off again. We kept the coils always in the same position to avoid their influence on the behavioral responses.
To assess the level of aggression during the encounters, we scored behaviors from 0 to 2 as follows: 0 for ignoring (Thomas et al., 2004), touching and avoiding (Suarez et al., 1999); 1 for attempted seizure, seizure, antennal boxing, body lifting, gaster curling or aggression from one worker (Monnin & Peeters, 1999); and 2 for a fight, identified when both workers execute the aggressive behaviors (modified from Suarez et al., 1999). We observed the evolution in the aggressive behavior during each encounter and recorded scores when the behavior changed. For each encounter, we calculated the arithmetic mean of the score regarding the observed levels of aggression. Representation of the orientation of the MF components measured among the coils. A* represents the arena of encounters. B. Intensity of the magnetic field components as a function of the distance among the coils. The first coil is positioned at x = 1cm and the second coil is at x = 15cm. Also is represented the total intensity of the magnetic field, whose variation corresponds with a quadrupolar field. C. Photography showing the pair of coils, made of 58 spirals of Cu wire 14 AWG. In the middle is shown a plastic box that corresponds to the "arena of encounters".
While functioning, the coils can increase the temperature. To assess whether the increase in temperature could lead to behavioral response changes, we photographed all encounters with a thermal camera (Testo® 870). We took thirty pictures at each step of the experiment from random encounters. With the aid of the software Testo IRSOFT, we selected ten random points of thermal photos from both the arena and ants' bodies to establish the mean temperature of the ants' body during the encounters and assess whether the ant temperature varies when coils are switched on/off.
SQUID magnetometry
To assess whether there are magnetic nanoparticles on the ant's bodies, we collected individuals of E. brunneum (Eb). Afterward, we separated the heads + antennae (denominated only as head) and abdomens, kept these body parts in the fridge, and preserved them in a solution of 70% alcohol. We used a SQUID magnetometer (MPMS Quantum Design) to search for magnetic material in these body parts. Five abdomens and five heads of Eb were dried and placed inside a gelatine capsule, which we placed in a plastic straw fixed on the magnetometer's sample holder for magnetization measurements. We took two magnetization measurements as a function of temperature: Zero Field Cooling (ZFC), where the sample is cooled from room temperature in the presence of a null magnetic field; and Field Cooling (FC), where the sample is cooled in the presence of a magnetic field, which was 10000 µT (100 Oe) in our measurements. After cooling the sample, a magnetic field was applied to it, and the magnetization was measured as the temperature increased. The temperature range was between 10 K and 330 K, under a magnetic field of 10000 µT (100 Oe). We took hysteresis measurements at 300 K or 150 K, on a magnetic field range between -1T and +1T (1T = 10 6 μT). Based on the hysteresis curves, we determined the saturation magnetization (M S ), remanent magnetization (M R ) and coercive field (H C ). M S results from all contributions to magnetization, while M R is solely composed of the ferromagnetic component. As heads and abdomens have different sizes and masses, we calculated M S and M R, dividing the original values by analyzing the parts' total mass.
Statistics
We evaluated the difference in aggression levels among the three experimental conditions (coils off, on, and off again) using a Kruskall-Wallis test. To assess whether there were significant differences between the thermal photos' mean temperature during the same encounters, we applied a Kruskall-Wallis test. We performed all statistical tests using the free software R 3.2.1 version.
Aggressive behavior
We recorded a total of 1571 behaviors during the encounters between workers of E. brunneum. From this total, 43.7% occurred before the change in MF, 27.6% during the change in MF, and 28.7% after the change in MF. The main behaviors performed by workers of E. brunneum were seizure (10.6%), attempted seizure (9.73%) and antennal boxing (9.35%). During controls, we did not observe aggressive behavior (touch and antennation represented 100% of behaviors).
The mean level of aggression between workers from different colonies of E. brunneum was 1.5 ± 0.3 before the change in MF, 1.1 ± 0.1 during the change in MF, and 1.3 ± 0.2 after the change in MF (Fig 3). The Kruskal-Wallis test showed significant differences between the groups (χ 2 = 17.8 p < 0.05). The Kruskal-Wallis test shows that before, during, and after the coils switching on/off, there were no significant differences in the mean body temperature of E. brunneum workers (χ 2 = 1.3 p > 0.052) (Fig 4).
Magnetometry
Fig 5 shows the ZFC and FC curves for the head and abdomen. All the curves are compatible with the presence of magnetic nanoparticles. For the abdomen, the nanoparticles show a strong dipolar interaction and a smaller size than the head.
As for the head, in low temperatures, we observed a sudden increase in magnetization. This phenomenon is characteristic of paramagnetic contributions due to spins not compensated on the surface of the nanoparticles (Peck et al., 2011). From Fig 5 is observed that the blocking temperature for the abdomen is 282 K that can be associated with nanoparticles with an average diameter of about 24 nm (considering magnetite as the magnetic material). For heads, the blocking temperature must be higher than 340 K associated with the presence of magnetic nanoparticles whose sizes are bigger than 26 nm (considering magnetite as the magnetic material). Fig 6 shows an example of magnetization curves for abdomens and heads after removing the diamagnetic + paramagnetic contributions. Table 1 shows the results at 300 K for the coercive field H C , the remanent magnetization M R and the saturation magnetization M S . Fig 6. Example of hysteresis curve obtained for heads and abdomens of Ectatomma brunneum. The graph shows the magnetization M as a function of the magnetic field B. The magnetic field increases from -1T to +1T (1 T = 10000 Oe). The insert shows an amplification in the region from -200 Oe to +200 Oe, where can be observed the remanent magnetization M R and the coercive field H C . The magnetization is relative to the total mass. state, producing higher MS values. On the other hand, those values for the head are compatible with single domain or blocked superparamagnetic nanoparticles, producing higher coercivities. The superparamagnetic fraction (fsp) was calculated as: The values obtained for fsp are compatible with the values for M R /M S : the abdomen shows a higher amount of superparamagnetic particles than the head. The crude curves M(B) showed diamagnetic + paramagnetic contributions as a function of temperature. From these outcomes, it is possible to calculate the paramagnetic susceptibility χ PM showed in Table 1. The head shows a higher value of χ PM compared to the value for the abdomen. That correlates with the low temperature behavior observed for heads in the ZFC-FC curves (Fig 5). PM's higher value could mean that the nanoparticles in the head are single domains with defects in the surface producing an extra paramagnetic contribution (Peck et al., 2011). nests. This recognition ability and consequent intolerance among themselves are related to competition for resources (Temeless, 1994;Sanada-Morimura et al., 2003). This type of dispute has been described in the ants Pogonomyrmex barbatus (Smith, 1858) (Gordon, 1989), Pristomyrmex punctatus (Smith, 1860) (Sanada-Morimura et al., 2003), Linepithema humile (Mayr, 1868) (Thomas et al., 2004), and the termite Nasutitermes corniger (Motschulsky, 1855) (Dunn & Messier, 1999).
We can explain the observed results through the change in the local temperature generated by the coils when they were turned on because of resistive losses. Some studies have reported that temperature changes can affect the behavior of various animal groups (Walther et al., 2002;Deutsch et al., 2008;Dell et al., 2011Dell et al., , 2014Huey et al., 2012;Gilbert et al., 2014;Sunday et al., 2014;Vasseur et al., 2014;Woods et al., 2015). However, our results demonstrate that the temperature variation in the surface of the ants' body is not statistically significant (Fig 4), so the temperature is not a stressing factor to explain the ant behavior observed.
When the coils were turned on, the change in MF decreased the aggression score (see scores for During in Fig 3A). For E. brunneum, a significant decrease is observed only for further nests. When the coils were turned off, the aggression score increased, but it did not recover the same level as it had at the beginning of the encounter. Therefore, when the coils were turned on, the change in MF seems to have caused disorientation on the ants, consequently changing the pattern of interaction displayed when the coils were turned off. A piece of evidence that supports the previous comments is that during the encounters with the MF turned on, ants showed "immobility", an atypical behavior of them, remaining still without even moving the antennae and, after a period, they started to clean their antennae more often than usual. Our results show for the first time that E. brunneum is magnetosensible, being perturbed by local changes in the MF. We cannot claim that MF changes affect the aggressive ant behavior but that changes in MF change the ant behavior.
The magnetometry measurements show the presence of magnetic nanoparticles in the head and the abdomen of E. brunneum ants. In each case, the nanoparticles show different magnetic properties at room temperature: in the abdomen,
Discussion
In normal conditions, under the influence of the geomagnetic field, we found higher levels of aggression among E. brunneum ants (scores for before in Fig 3A) whose nests were further apart in more than 100 m (Fig 2). This finding means that these ants are tolerant to individuals from nests in a radius of 100 m. Pereira et al. (2019) showed that E. brunneum colonies are more tolerant to ants from nearest the nanoparticles are mainly in the superparamagnetic state, and in the head, the nanoparticles are mainly single domains and superparamagnetic nanoparticles in the blocked state. Magnetometry studies done in other ants have shown that all parts of the ant body are magnetic , but the abdomen and the head show different magnetic properties. Those studies led to the proposal that the magnetosensor must be in the antennae for Pachycondyla marginata, Atta colombica, and Schwarziana quadripunctata (Wajnberg et al., 2004;Lucano et al., 2006;Alves et al., 2014). Our results show that the abdomen and the head have different magnetic properties, and both can host the magnetosensor responsible for the observed magnetosensibility. In both cases, the type of magnetosensor must be different. On the one hand, the abdomen has an arrangement of superparamagnetic nanoparticles sensitive to the magnetic field intensity. On the other hand, the head has an arrangement of magnetic single domains sensitive to the magnetic field vector's intensity and direction (Johnsen & Lohmann, 2005).
Our results and other studies show that ants can detect MF intensity changes. proposed that P. marginata ants can perceive the magnetic field through magnetic nanoparticles in the antennae. Since the orientation of those magnetic nanoparticles and their magnetic moment can be rapidly altered when ants walk inside the "arena of encounters", because the MF is displaced from the center of the arena (Fig 1B), the increase in cleaning behavior suggests an attempt to adjust the magnetic sensor to the new MF configuration. Another evidence that the MF is perceived by ants and causes changes in behavior is that soon after the coils were turned off, the ants stopped performing the abovelisted behaviors, and aggression levels increased again, but not to the same level of aggression shown before the coils were on.
Conclusion
Our results show that E. brunneum ants tolerate ants from other nests at distances lower than 100 m. For the first time, our results show that E. brunneum ants are magnetosensible, changing their behavior under the effect of applied MF. Their body presents magnetic nanoparticles with different properties in the abdomen and the head. As the MF changes are about three times the local magnetic field, the change in behavior can be associated with disturbance and/or disorientation in some magnetosensor located in the ant's body. Our results do not discard the presence of a magnetoreceptor based on the radical pair mechanism. Future experiments must address the impact of MF on several ants' physiological and ethological traits, to establish the origin of the reported magnetosensiblity and understand its biophysical basis. | 5,653.2 | 2021-03-31T00:00:00.000 | [
"Physics"
] |
GUT Relations from String Theory Compactifications
Wilson line on a non-simply connected manifold is a nice way to break SU(5) unified symmetry, and to solve the doublet--triplet splitting problem. This mechanism also requires, however, that the two Higgs doublets are strictly vector-like under all underlying gauge symmetries, and consequently there is a limit in a class of modes and their phenomenology for which the Wilson line can be used. An alternative is to turn on a non-flat line bundle in the U(1)_Y direction on an internal manifold, which does not have to be non-simply connected. The U(1)_Y gauge field has to remain in the massless spectrum, and its coupling has to satisfy the GUT relation. In string theory compactifications, however, it is not that easy to satisfy these conditions in a natural way; we call it U(1)_Y problem. In this article, we explain how the problem is solved in some parts of moduli space of string theory compactifications. Two major ingredients are an extra strongly coupled U(1) gauge field and parametrically large volume for compactification that is also essential in accounting for the hierarchy between the Planck scale and the GUT scale. Heterotic-M theory vacua and F-theory vacua are discussed. This article also shows that the toroidal orbifold GUT approach using discrete Wilson lines corresponds to the non-flat line-bundle breaking above when orbifold singularities are blown up. Thus, the orbifold GUT approach also suffers from the U(1)_Y problem, and this article shows how to fix it.
Introduction
The gauge coupling unification of the minimal supersymmetric standard model (MSSM) is the biggest (phenomenological) motivation to study supersymmetric unified theories. The SU(5) GUT unified symmetry is broken down to the standard-model gauge group SU(3) C × SU(2) L × U (1) Y without reducing the rank of the gauge group, when an expectation value is turned on for a scalar field in the SU(5) GUT adjoint representation.
For higher-dimensional supersymmetric theories such as geometric compactification of the superstring theory, there always exists SU(5) GUT gauge field with polarization pointing to the directions of internal manifold, and a Wilson line in the U(1) Y direction can play the role of the D = 4 scalar field in the adjoint representation. The Wilson lines can be introduced only in a manifold Z with a non-trivial homotopy group π 1 (Z) = {1} [1,2,3]. The Wilson lines in the U(1) Y direction, or equivalently the flat bundles, break the SU(5) GUT symmetry, get rid of gauge bosons in the off-diagonal blocks from the massless spectrum and allow the spectrum of coloured Higgs multiplets to be different from that of Higgs doublets.
Since those goals can be achieved also by line bundles that are not flat, one could think of compactification on a simply connected manifold with a line bundle turned on in the U(1) Y direction, instead. Many models fall into this category, including toroidal orbifold compactification [4,5,6,7,8] 1 and SU(5) × U (1) Y bundle compactification of Heterotic E 8 × E ′ 8 string theory [11,12] and Calabi-Yau orientifold compactification models of Type IIB string theory [13,14,15,16].
The problem of this approach with non-flat line bundles is that U(1) Y gauge field in the SU(5) GUT symmetry (and hence U(1) QED ) generically does not remain massless. This problem can be avoided by starting from a gauge group larger than SU(5) GUT , such as U(6) in a model of Type IIB compactification [13], or E 8 × E 8 in Heterotic compactification [12]. The massless U(1) Y gauge field below the Kaluza-Klein scale is a linear combination of the ordinary U(1) Y gauge field in the SU(5) GUT gauge group and an additional U(1) symmetry contained in the larger gauge group. The gauge coupling constant of the low-energy U(1) Y gauge field is, however, weakened due to the mixture of the additional U(1) gauge field, and the successful prediction of the gauge coupling unification is lost. The primary goal of this note is to show that the gauge coupling unification is restored in certain region (limit) of moduli space.
We are not only trying to explore just another class of string vacua with successful gauge coupling unification. Note that Wilson lines can be a solution to the doublet-triplet splitting problem only when a pair of Higgs doublets H u and H d is completely vector like under the underlying gauge symmetry such as E 8 (and in fact, E 8 is the only candidate of the underlying gauge symmetry if we assume SU(5) GUT unification and the vector-like nature of H u and H d ; see [17]). In the Heterotic E 8 × E 8 string theory, for instance, the Higgs multiplets H (5) and H(5) may originate from H 1 (Z; ∧ 2 V 5 ) ≃ H 2 (Z; ∧ 2 V × 5 ) and H 1 (Z; ∧ 2 V 5 ), respectively, where Z is a Calabi-Yau 3-fold, V 5 is a rank-5 vector bundle in one of E 8 and V 5 = V × 5 its dual bundle. In this case, H(5) ⊃ H u andH(5) ⊃ H d are vector-like not only under SU(5) GUT but also under the structure group SU (5). A flat bundle L Y can be turned on in the U(1) Y direction, when (Z, V 5 ) has an isometry group Γ that acts freely on Z. The index theorem says that and hence coloured Higgs multiplets can be absent in the low-energy spectrum (that is, #H c = 0 and #H c = 0), while we have a pair of massless Higgs doublets, #H u = #H d = 1.
If H u and H d originate from bundles that are not dual, on the other hand, the index theorem has to be applied separately for the bundle of H u and that of H d . Suppose, say, that they are identified with cohomology groups H 1 (Z/Γ; U Hu ⊗ L ). If the symmetry breaking of SU(5) GUT were due to a flat bundle L Y in the U (1) Y direction, then the Euler characteristic of the bundle in the doublet parts and the triplets part cannot be different, because flat bundles do not contribute to the Euler characteristics. Thus, if there is a pair of Higgs doublets H u and H d in low energy spectrum, and there is only a pair, then there is also a pair of Higgs triplets at low energies. Gauge coupling unification is no longer expected in the presence of this additional triplets in ths spectrum. If the SU(5) GUT symmetry is broken by a non-flat line bundle in the U (1) Y direction, however, the chirality in the doublet part and the triplet part can be different, and there can be no triplets at low energies; such compactifications are consistent with the gauge coupling unification. (hereafter, whenever we say a line bundle in this article, it is meant to be non-flat unless specifically mentioned as a flat bundle.) Models with non-vector-like two Higgs doublets has a natural mechanism to bring dimension-5 proton decay operators under control [17,18]. A pair of Higgs multiplets being completely vector-like is the essence of the dimension-5 proton decay problem, and hence this problem is always an issue for the SU(5) GUT symmetry breaking using the Wilson line. Although the dimension-5 operators can be eliminated by imposing an extra discrete symmetry for this special purpose, probability of finding such a symmetry in a landscape of vacua is very small. 2 Thus, there exists a phenomenological motivation to study the SU(5) GUT symmetry breaking due to a line bundle in the U (1) Y direction.
This article is organized as follows. Section 2.1 explains why it is difficult in wide class of string compactification to get a massless U (1) Y gauge field while maintaining the gauge-coupling unification. We see in section 2.2, however, that this generic problem can be solved by assuming an extra strongly coupled U(1) gauge theory; the disparity between the strongly coupled U(1) sector and the visible perturbative SU(5) GUT sector can be attributed to a parametrically large volume of compactification, which also accounts for the hierarchy between the unification scale and the Planck scale [20,21]. 3 This observation is elaborated in sections 3 and 4, by using the compactifications of Heterotic string and F-theory, respectively. Along the way, we will also see that the idea of containing U (1) Y flux in a local region in the internal space [22] is useful in bringing threshold corrections under control. Presentation of [22] (and orbifold-GUT papers that followed) is based exclusively on toroidal orbifold compactification (of the Heterotic E 8 × E 8 string theory), but we find a way to implement the idea in general string theory compactifications.
The appendix, which constitutes a big part of this paper, is somewhat independent from the main text of this article. It explains how the toroidal orbifold compactification is understood as certain limits of Calabi-Yau compactification. Heterotic orbifold-GUT approach in the last several years often make use of "discrete Wilson lines" in breaking the SU(5) GUT symmetry, and the primary purpose of the appendix is to clarify the meaning of discrete Wilson lines of toroidal orbifold compactification in terms of Calabi-Yau compactification. The discrete Wilson lines in toroidal orbifolds are totally different from the Wilson lines associated with finite discrete homotopy group π 1 (Z) of non-simply connected Calabi-Yau Z. They should be understood as special cases (and special corners of moduli space) of non-flat line bundles in the U (1) Y direction on Calabi-Yau compactifications.
Thus, orbifold GUT models also suffer from the U (1) Y problem in section 2.1, and this problem is solved as we explain in this article. Because the idea of orbifold GUT has received attention for the last several years from much wider community, the appendix is pedagogically presented. The appendix A.2 shows that the "continuous Wilson lines" in toroidal orbifold compactifications corresponds to vector-bundle mouli of smooth Calabi-Yau compactifications, and has nothing to do with Wilson lines associated with π 1 (Z) ∼ Z. 4 As we were finishing this work, an article [23] was posted on the web, which also discusses SU(5) GUT breaking due to a line bundle in the U (1) Y direction. There, an idea of [15] in perturbative Type IIB string theory is generalized to F-theory compactifications, and explicit examples of geometry are given. Thus, a solution to the U (1) Y problem in this article (and in [20,21]) is different from those in [15,23]. We have also learnt that Donagi and Wijnholt have been working on a related subject ( [65]). Let us first consider the Heterotic E 8 × E 8 theory compactified on a Calabi-Yau 3-fold Z with vector bundles V 5 and L Y turned on in one of E 8 . The structure group of V 5 is SU(5) bdl , whose commutant in the E 8 symmetry is the SU(5) GUT symmetry. The line bundle L Y is in the U (1) Y ⊂ SU(5) GUT direction. The SU(3) C × SU(2) L × U (1) Y symmetry of the standard model is the commutant of the bundle structure group SU(5) × U (1) Y . The gauge fields of the non-Abelian part of the unbroken symmetry, SU(3) C × SU(2) L , remain massless below the Kaluza-Klein scale.
The U(1) Y gauge field, however, does not remain massless [24,11,25]. The D = 10 action of the Heterotic string theory contains the kinetic term of the B-field where ω grav is the Chern-Simons 3-form of gravity. Fluctuations of the B-field of the form b k ω k are massless in the Kaluza-Klein reduction, where b k (k = 1, · · · , h 1,1 ) are D = 4 scalar fields and ω k form a basis of H 1,1 (Z) of a compact Calabi-Yau 3-fold Z. Their kinetic terms in the D = 4 effective theory are of the form 5 G kl is a metric on the Kähler moduli space [32,33], and A is the U(1) Y gauge field. Thus, a linear combination of these B-field fluctuations is absorbed to be the longitudinal mode of the U(1) Y gauge field. The kinetic term above also contains the mass term of the U(1) Y gauge field. Thus, whether the bundle L Y is flat (c 1 (L Y ) ∝ dA = 0) or not leads to a big difference in phenomenology. The same problem exists in Type IIB Calabi-Yau orientifold compactification. Let us consider the Type IIB string theory compactified on a Calabi-Yau 3-fold X with a holomorphic involution I; the Calabi-Yau 3-fold is modded by an orientifold projection associated with I; D7-branes are wrapped on holomorphic 4-cycles, so that N = 1 supersymmetry is preserved in D = 4 effective theory. If 5 D7-branes are wrapped on a holomorphic 4-cycle Σ of X, the SU(5) GUT gauge field propagates on Σ. Suppose that a line bundle L Y is turned on on Σ in the U(1) Y direction in SU(5) GUT symmetry. Then the SU(5) GUT symmetry is broken to SU(3) C × SU(2) L × U (1) Y symmetry of the standard model. Although the SU(3) C × SU(2) L part of the gauge field remains massless in this Type IIB compactification as well, the U(1) Y gauge field does not. The Wess-Zumino action on Σ contains where D = 4 2-form fields c m describe massless fluctuations of the Ramond-Ramond 4-form field C (4) ∼ c m ω m . A is the U(1) Y gauge field. Thus, a linear combination of the D = 4 Hodge dual of the 2-forms c m is absorbed to be the longitudinal mode of the U(1) Y gauge field. The U(1) Y gauge field becomes massive, and so does the QED gauge field. This is a problem in the context of large volume compactification, e.g. [34] in toroidal orbifolds and e.g. [35] in orientifolded Calabi-Yau 3-folds in general. These phenomena in the Heterotic theory and Type IIB theory are related by the string duality. It is the B-field fluctuation of the form b k ω k ∝ Q k ω k in the Heterotic theory that is 5 In addition to this generalized Green-Schwarz couplings of Kähler moduli chiral multiplets at tree level, there is also a 1-loop coupling for the dilation chiral multiplet [29,30,31,26]. Coefficients of the tree-level generalized Green-Schwarz couplings are worked out in [27,12] in the Heterotic E 8 × E ′ 8 string theory. (For SO (32) Heterotic string theory, see [28].) mixed with the U(1) Y gauge field. Roughly speaking, it corresponds to a fluctuations of the Ramond-Ramond 2-form field C (2) ∼ĉ k ω k ∝ Q k ω k in the Type I string theory, whereĉ k are D = 4 scalar fields, and then to C (4) ∼ c k ( * ω k ) ∝ Q k ( * ω k ) in Type IIB string theory, where the Hodge dual * is taken in a complex 2-fold B that the Heterotic and Type IIB string theory share in the duality.
The above argument, however, does not mean that it is impossible to obtain a massless U(1) gauge field in the low-energy spectrum. Each line bundle in a compactification leaves a U(1) gauge field, and each massless fluctuation of the B-field or Ramond-Ramond field couples to a linear combination of those U(1) gauge field through the generalized Green-Schwarz mechanism [29]. If there is an abundant supply of U(1) gauge fields compared with the number of the bulk moduli fields, the U(1) gauge fields with no moduli-field counterpart remain massless. 6 Reference [12] considered an SU(5)× U (1) Y × U (1) 2 -bundle compactification of the Heterotic E 8 × E ′ 8 string theory. The SU(5) × U (1) Y bundle is in one of E 8 , and another line bundle has a structure group U(1) 2 in E ′ 8 . The first Chern classes of the two line bundles are chosen to be parallel in H 1,1 (Z), so that the gauge fields of both U(1) Y and U(1) 2 couple to the one and the same linear combination of the B-field fluctuations: . This B-field fluctuation absorbs only a linear combination of the two massless U(1) gauge fields, and the other combination remains massless. This gauge field, which is a linear combination of gauge fields in the visible E 8 and the hidden E ′ 8 , can be identified with the massless hypercharge gauge field. The ratio of the hypercharges of the fields in the visible sector is determined by the charges of the original U (1) Y ⊂ SU(5) GUT gauge field; hence the standard explanation of the hypercharge quantization in SU(5) unified theories-the original motivation of unified theories-is maintained.
The C 3 /Z 3 model in Type IIB string theory in [13] breaks an SU(6) symmetry by turning 6 In Type IIB compactification on an orientifold of a Calabi-Yau 3-fold X, there are h 1,1 (X) chiral multiplets containing fluctuations of Ramond-Ramond fields. In Heterotic compactification on a Calabi-Yau 3-fold, there are h 1,1 (Z) Kähler moduli chiral multiplets and one dilaton chiral multiplet. Under the Heterotic-F-theory duality, an elliptic-fibred Z on a base 2-fold B is mapped to a K3-fibred Calabi-Yau 4-fold X ′ on B. Heterotic compactification has an F-theory dual only when line bundles are trivial in the elliptic fibre direction (if they had non-trivial first Chern classes in the fibre direction, vector bundles would not be stable in the small fibre limit). Thus, the Kähler moduli multiplet associated with the size of the elliptic fibre does not participate in the generalized Green-Schwarz mechanism. So, (h 1,1 (Z) − 1) = h 1,1 (B) Kähler moduli chiral multiplets and the dilaton chiral multiplet can absorb massless U(1) gauge fields in the Heterotic compactification. On the other hand, the Type IIB compactification has h 1,1 (X) = h 1,1 (B) + 1 chiral multiplets containing fluctuations of the Ramond-Ramond 4-form or 2-form. Thus, the same number of massless gauge fields are absorbed in both descriptions; otherwise those two descriptions were not dual! on a line bundle. 7 The SU(6) symmetry is broken down to SU(3) × SU(2) × U (1) × U (1), the non-Abelian part of which is identified with those of the standard model gauge group. The chiral multiplet that describes the blow-up of the C 3 /Z 3 singularity (and hence the size of the CP 2 cycle) absorbs a linear combination of the two U(1) gauge fields, and the other linear combination remains massless. This massless gauge field can be identified with that of the hypercharge. Models in [14,15,16] adopt essentially the same strategy in maintaining a massless U(1) gauge field in the low-energy spectrum. One should keep in mind that how many massless U(1) gauge field remains massless is a global issue.
Normalization of the Hypercharges
The overall normalization of hypercharges-not just the quantized ratio among them-is also an important prediction of supersymmetric unified theories. The SU(5) GUT GUT's predict that which is called the GUT relation. The factor (5/3) in the denominator comes from In this article we imply tr = T −1 R tr R for any representations R and, in particular, tr = 2 tr F for fundamental representations of SU(N) symmetries, tr = tr vect. for vector representations of SO(2N) symmetries and tr = (1/30) tr adj. for adjoint representations of E 8 and SO (32). Now, when considering the idea of section 2.1.1 to maintain a massless U(1) gauge field at low energies, the low-energy U(1) gauge symmetry is not exactly the same as the U(1) hypercharge of SU(5) unified theories. Let us first pick up an example in the Heterotic string compactification that we mentioned above. The linear combination of U(1) gauge fields that becomes massive is 7 The fractional D3-branes at the C 3 /Z 3 singularity are not just D7-branes wrapped on the vanishing 4-cycle isomorphic to CP 2 . One of the three fractional D3-branes at this singularity should be interpreted as a two anti-D7-branes wrapped on the vanishing cycle with a rank-2 vector bundle turned on [36]. Thus, this model does not immediately fit to the discussion so far that is based on large-volume compactification. However, we only discuss symmetry breaking pattern and counting of massless U(1) gauge fields, and in that context, the difference between anti-D7 branes and D7-branes does not make an essential difference. The same is true for other models such as those in [14,15,16].
The assumption that c 1 (L Y ) ∝ c 1 (L 2 ) in H 1,1 (Z) allows us to express the first Chern classes by using the same set of linear combination coefficients Q k Y . Gauge fields A Y and A 2 have kinetic terms in the effective Lagrangian in D = 4, where α and α ′ are effective fine structure constants in the visible and hidden sectors, and hence the canonically normalized gauge fields A Y and A 2 are obtained from A Y and A 2 by rescaling them by 4πα/ tr(q 2 Y ) and 4πα ′ / tr(q 2 2 ) , respectively. Thus, the canonically normalized massive vector field A massive and its orthogonal complement AỸ are given in terms of A Y and A 2 by It is AỸ that remains massless in low-energy effective theory. Fields in the visible sector are coupled to the massless gauge field AỸ through the original hypercharge gauge field A Y : AỸ .
Thus, the gauge coupling constant of this massless hypercharge gauge field is given by The above discussion is essentially the same as calculating the QED coupling constant in the Weinberg-Salam model. In the weakly coupled Heterotic E 8 × E ′ 8 string theory, the gauge coupling constants of the visible and hidden sector E 8 , namely, α = α GUT = α E 8 and α ′ = α E ′ 8 are the same at the tree level, and hence the second term in (15) makes the hypercharge coupling constant weaker by of order 100% [12]. The GUT relation (8) is not satisfied at all.
Let us now take an example of [13] in Type IIB string local singularity. There, U (1) Y massless gauge field comes essentially from a subgroup of U(6) generated by When all the six fractional D3-branes are assumed to have the same gauge coupling constant, the massless gauge field has a coupling constant given by This is much smaller than those of SU(3) C × SU(2) L , and this is because of the extra 8 last entry of (16).
In summary, when the SU(5) GUT symmetry is broken by a line bundle in the U(1) Y direction, the U(1) Y gauge field tends to be massive by absorbing the Kähler moduli along the direction of the first Chern class of the line bundle. By considering compactification with multiple line bundles, however, it is possible to keep a massless U(1) gauge field, under which the ratio of the charges of the standard-model particles is that of the hypercharges. The overall normalization of the new hypercharges, or equivalently the gauge coupling constant of the new massless hypercharge gauge field, is different from the standard prediction of SU(5) GUT unified theories. We call it the U(1) Y problem.
Solving the U (1) Y Problem with a Strongly Coupled U(1) Gauge Field
Gauge coupling constants are functions of moduli fields in string theory, and hence the GUT relation may be satisfied somewhere in the moduli space. Since we know that the first term in (15) satisfies the GUT relation, it is clear that the GUT relation is satisfied approximately, if the contribution from the second term in (15) is negligible compared with the first term. In other words, as long as the extra U(1) gauge symmetry that mixes into the hypercharge is strongly coupled at the compactification scale, the effective gauge coupling constant of hypercharge at 8 It is still possible to maintain the GUT relation (8) in Type IIB compactification, if we give up Georgi-Glashow SU(5) unification. For example, one can take One could imagine that the gauge coupling constants of all of SU(4) C × SU(2) L × SU(2) R are the same, if 4 + 2 + 2 D7-branes are wrapped on one and the same holomorphic 4-cycle with parametrically large volume. If this is the case, then the GUT relation is satisfied because tr((q B−L /2 − q R ) 2 ) = 5/3. (This fact was exploited in a Type IIA model [37].) In order to obtain appropriate spectrum, however, some of the 7-branes forming SU(4) C × SU(2) L × SU(2) R have to be anti-7-branes, because there are sum rules in the net chirality of various representation if they are all D7-branes [17]. Thus, the volume of the 4-cycle has to be comparable to the string length, and there, values of B-fields integrated over various 2-cycles also have significant contributions to gauge couplings of (subgroups of) SU(4) C × SU(2) L × SU(2) R . Thus, it is not obvious whether such quiver standard model in Type IIB string theory naturally predicts the GUT relation. [39], shows renormalization-group evolution of the three gauge coupling constants of the MSSM. Supersymmetry partners of the Standard-Model particles are assumed to be around 100 GeV-1 TeV, and 2-loop renormalization group equation was used for calculation. ±2σ error bar associated with the measurements of the QCD coupling is shown as the three parallel trajectories for 1/α 3 . (See [39] for more details.) low-energy is not very much different from the ordinary prediction of SU(5) GUT unified theories. There are such field-theory models in the literature (eg. [38]).
As one can see in Figure 1, the three gauge coupling constants of the minimal supersymmetric standard model do not unify exactly at any energy scale around the GUT scale; at the energy scale M 2−3 in the figure, where α C and α L are equal, (5/3) × α Y is different from the others by 2-4%. Thus, the contribution from the second term in (3/5)/α Y is phenomenologically acceptable. Furthermore, the extra contribution is supposed to be positive in 1/α, which is really the case if the deviation from the GUT relation is due to the mixing with an extra strongly coupled U(1) gauge field. We will see in the following sections that the extra U(1) is strongly coupled and hence the extra contribution to 1/α Y is small enough for some classes of string vacua in certain region of its moduli space. Now one might wonder what is the point of maintaining the SU(5) unification. This is certainly a legitimate question. Unified theories can predict one of the three gauge coupling constants of SU(3) C × SU(2) L × U (1) Y in terms of the other two, because there are only 2 parameters-the GUT scale and the unified gauge coupling constant. What is the point of considering a unified framework if one allows oneself to introduce an extra (moduli) parameter that change the U(1) Y gauge coupling? Predictability on the gauge coupling constants seems to be lost. As we will see in the following sections, this is actually not the case. In the Heterotic-M-theoy compactification, the hidden sector gauge coupling is strong, due to the warping in the 11-th direction. In F-theory compactifications, which is motivated (as opposed to the perturbative Type IIB Calabi-Yau orientifold compactification) by the up-type Yukawa couplings [17], the dilaton vev cannot be small everywhere in the internal manifold. Thus, having an extra strongly coupled U(1) gauge theory is extremely natural. Parametrically large volume for compactification is required in order to account for the little hierarchy between the GUT scale and the Planck scale, and a parametrically large volume to string length ratio can render the visible sector SU(5) GUT weakly coupled, in contrast to other strongly coupled sectors SU(5) GUT [20,21]. From a perspective of phenomenology, the framework with a unified SU(5) and a strongly coupled extra U(1) symmetries says more than just having SU(3) C × SU(2) L × U (1) Y massless gauge field at low energy with the GUT relation. The GUT gauge bosons exist around the energy scale of the gauge coupling unification, leading to dimension-6 proton decay. Since the rate of dimension-6 decay is proportional to the fourth power of the unification scale, the rate, and the proton lifetime is very sensitive to where the unification scale really is. If we take a closer look at where the "unification scale" is, it is important to note that the extra contribution to (3/5)/α Y is always positive. Thus, "the unification scale" is more likely to be around M 2−3 in Figure 1 than M 1−2 ≃ 2 × 10 16 GeV conventionally referred to as the GUT scale. Although one has to take account of threshold corrections and non-perturbative corrections in order to determine the GUT gauge boson mass (or the Kaluza-Klein scale) precisely, it is unlikely that the scale is as high as M 1−2 without an accidental cancellation between the threshold/nonperturbative corrections and the tree-level deviation from the GUT relation. This implies that the proton decay may be faster considerably than estimation based on M 1−2 as the GUT scale. All the statements above on proton decay is valid whether the framework is implemented in the Heterotic-M-theory or in F-theory compactifications. See also related comments in the following sections.
Heterotic-M Theory Vacua
The Heterotic E 8 × E ′ 8 string theory is compactified on a Calabi-Yau 3-fold Z to yield a D = 4 effective theory with N = 1 supersymmetry. Vector bundles V 1 and V 2 have to be turned on in both visible and hidden E 8 symmetries, so that Apart from special cases, does not vanish for a Kähler form J of the Calabi-Yau 3-fold Z. When (19) is not zero, it is known (as we review later) that the gauge coupling of one of the two E 8 gauge groups is stronger than that of the other E 8 . For a large string coupling, g s , the difference becomes significant, and in the limit of the largest possible g s , one of the gauge couplings of D = 4 effective theory is really strongly coupled [40,41]. Thus, if the E 8 gauge group with the weaker gauge coupling is identified the visible sector, α E 8 = α GUT , and the other E ′ 8 symmetry is strongly coupled, 9 and 1/α E ′ 8 in (15) is small; the GUT relation is maintained approximately. The purpose of this section is to check if this idea really works.
In Language of the Weak Coupling Heterotic String Theory
A vector bundle V 5 whose structure group is SU(5) bdl ⊂ E 8 breaks the E 8 symmetry down to the commutant of the SU(5) bdl , SU(5) GUT . The SU(5) GUT symmetry is further broken down to SU(3) C × SU(2) L × U (1) Y by turning on a line bundle L Y in the hypercharge direction. The E 8 super Yang-Mills fields of D = 10 Heterotic string theory yield all the gauge and matter multiplets except just one, U(1) Y vector multiplet. The U(1) Y symmetry may remain unbroken as a global symmetry, but the gauge field absorbs a fluctuation of the B-field, and becomes massive. Whether the SU(5) GUT symmetry is broken by a flat bundle or by a line bundle makes a big difference [11]. 9 An unbroken subgroup of this E 8 symmetry may lead to dynamical supersymmetry breaking. The energy scale of the supersymmetry breaking Λ DSB is, however, determined by a combination (2π/b 0 α E ′ 8 ) where b 0 is the 1-loop beta function of the gauge coupling of the unbroken symmetry; the coupling α E ′ 8 alone does not determine the scale. Thus, the supersymmetry breaking scale can be much lower than the Kaluza-Klein scale when this hidden sector is nearly conformal, b 0 ≈ 0. In model-building in F-theory, there is no such tight relation between the supersymmetry breaking scale and the deviation from the GUT relation. This may be regarded as a motivation for model building in F-theory. Table 1: Vector bundles of chiral multiplets in supersymmetric standard models. For a realistic model, the vector bundle V 5 cannot be generic; otherwise, there is a problem of dimension-4 proton decay. For example, a Z 2 symmetry (matter parity or R-parity) or an extension structure removes virtually all the dimension-4 proton decay operators [17,42,18]. We do not go into details because such extra structures of the bundle V 5 are not essential to the gauge coupling unification, the main theme of this article. We used a notation L for L Y in the text to save space in this table.
References [11,12] proposed a solution to this problem. Here, we briefly review the construction of [12] in order to set the notation in this article.
The (weakly coupled) E 8 × E ′ 8 Heterotic string theory is compactified on a Calabi-Yau 3-fold Z, whose π 1 (Z) does not have to be non-trivial. A vector bundle V 1 is turned on in the visible sector E 8 , which consists of a rank-5 vector bundle V 5 and a line bundle L. The D = 10 E 8 super Yang-Mills multiplet yields all the chiral multiplets necessary in supersymmetric standard model; see Table 1. SU(3) C × SU(2) L gauge fields remain massless. A vector bundle V 2 in the "hidden sector" E ′ 8 should contain a line bundle L 2 (and possibly another bundle V ′ whose structure group commutes with the U(1) 2 structure group of L 2 ) which satisfies We set the normalization of the generator q 2 for L 2 as in (11), using c 1 (L Y ). The second Chern classes are given by and they have to satisfy the consistency condition (18). An explicit example of a Calabi-Yau 3-fold Z and vector bundles on it is found in [12]. In order to obtain the spectrum of supersymmetric standard model, bundles introduced so far have to satisfy Dimensional reduction of a Calabi-Yau compactification leaves a dilaton chiral multiplet S and h 1,1 (Z) Kähler moduli chiral multiplets T k (k = 1, · · · , h 1,1 (Z)): whereφ and a are dilaton fluctuation and model-independent axion of the Heterotic string theory; M G ≃ 2.4 × 10 18 GeV is given by (26) α k and b k parametrize the metric and B-field on Z by where ω k (k = 1, · · · , h 1,1 (Z)) are basis of H 1,1 (Z), and J is a Kähler form 10 The kinetic term of the B-field contains where A Y and A 2 are gauge fields associated with the generators q Y and q 2 , respectively. A linear combination of vector multiplets, V massiv ≡ tr(q 2 Y )V Y + tr(q 2 2 )V 2 , enters the Kähler potential as in and becomes massive. On the other hand, these vector multiplets do not have a similar coupling with the dilaton in the Kähler potential; although they could enter the Kähler potential as in Q S is proportional to U(1) 1 -[non-Abelian] 2 mixed anomalies with SU(3) C (and SU(2) L ) as the non-Abelian gauge group (see also footnote 14), and hence vanishes in vacua with spectra of supersymmetric standard models. Since only one linear combination, V massive becomes massive, another linear combination of the gauge fields A Y and A 2 remains massless. All the particles in Table 1 are charged under this massless U(1) gauge symmetry through its A Y component, and hence the ratio of the U(1) charges remains the same. This massless U(1)Ỹ vector field is regarded as the hypercharge gauge field of the Standard Model [11,12]. The only problem of this solution is that the gauge coupling constants of SU(3) C × SU(2) L × U (1) Y -given as functions of moduli S and T k -do not satisfy (generically) the GUT relation. To see this, note that the gauge kinetic term of the two U(1) gauge fields A Y and A 2 is in the large volume limit, where We only consider ReA ∝ Z J ∧ c 1 (L Y ) 2 = 0 for simplicity for the moment. 11 Following the process described in section 2, one can see that the massless linear combination is and the gauge coupling constant is given by Note that 1/g 2 C and 1/g 2 L in the visible sector are given by 1 11 Later, we will see that it is an important assumption necessary for the gauge coupling unification.
in the large volume limit. When the hidden sector has an unbroken non-Abelian symmetry group, its gauge coupling constant is given by The U(1)Ỹ gauge coupling in (37) is given just as the discussion in section 2.1. In the weakly coupled Heterotic string theory, the tree-level coupling ReS dominates, with 1-loop corrections ∝ ReT being subleading. Thus, ignoring ReT in ReT, the GUT relation is badly violated; the factor in the parenthesis on the right-hand side is different from 1 by of order unity for the model in [12]. If the 1-loop threshold correction, the second term in (41), were to partially cancel the tree level gauge coupling so that the gauge coupling constants of the MSSM apparently satisfy the GUT relation, it sounds very artificial. This is the Heterotic-string version of the U(1) Y problem. Reference [11] points out that the GUT relation is maintained approximately if tr(q 2 2 ) is chosem much larger than tr(q 2 Y ). While this is true, we will see in the following, that the approximate GUT relation is actually maintained even if tr(q 2 2 ) and tr(q 2 Y ) are comparable.
Strongly Coupled Hidden Sector
When the gauge coupling constant in the hidden sector is way stronger than that of the visible sector for some reason, the second term of (15) and (37) is negligible, and the GUT relation is approximately satisfied; that was the idea of section 2, phrased in the context of the Heterotic string theory. Such a disparity between the gauge coupling constants naturally happen in strongly coupled Heterotic E 8 × E ′ 8 string theory. The Bianchi identity of the NS-NS 2-form field requires that the total sum of the second Chern classes vanish, but they are not necessarily distributed equally to the visible and hidden sector. In general, (19) does not vanish, and the asymmetric distribution of the second Chern classes provide sources for the configuration of the Ramond-Ramond 3-form field in the bulk of the Heterotic-M theory.
Coefficients are taken from [43]. The non-zero 4-form field strength of the Ramond-Ramond field in the bulk, in turn, becomes the source of metric. The metric of D = 11 gravity is expanded as and at the linear order in κ 2/3 , first order deformation b(x 11 ) and h(x 11 , z,z) follow the equations Here, Θ αβ := 2igδ γ G αβγδ and α = 2igβ α Θ αβ as in [40]. If the last one above becomes ∂ 11 h αβ = − √ 2/24αg αβ , and hence the (g αβ + h αβ part is of the form e f g αβ with f satisfying ∂ 11 f = − √ 2/24α [41]. k(z,z) should have the same (z,z) dependence as f , and its x 11 can be chosen so that k = f [40,41]. Thus, the metric has the warped structure [41]: where The volume of Calabi-Yau 3-fold varies over x 11 , and in particular, decreases monotonically. It and the gauge coupling constants of the visible and hidden sectors in D = 4 effective theory are given by Larger volume at x 11 = 0 makes the visible sector coupling weaker, while the hidden sector coupling remains strong [40,44,41]. The expression for the two gauge coupling constants in the weakly coupled Heterotic theory, (38) and (39) captures the warped factor effect. Indeed, in Heterotic-M theory language agrees with the result of weakly coupled Heterotic string theory, Re(S + T ) − Re(S − T ) = 2ReT (up to a proportionality factor). 12 Here, higher order O(κ Although the perturbative expansion of the Heterotic string theory is not reliable for g s > 1, the gauge kinetic function is protected by holomorphicity. Only the tree and 1-loop level contributions exist, apart from non-perturbative corrections. They are given by S ± T at this level, and the holomorphicity of f and f ′ guarantees that their expressions are right as the perturbative part even in the strong coupling regime. It is true that the physical gauge coupling constants receive higher loop corrections despite the holomorphicity of N = 1 supersymmetry. However, such corrections arise only through the rescaling of the vector supermultiplets (U(1)Ỹ and SU(3) C × SU(2) L in this case) and super-Weyl transformation in rewriting Lagrangian in the Einstein frame. The former only involve ln(gỸ ) and ln(g C ) = ln(g L ) and are always small, while the latter is universal to all the gauge coupling constants. Thus, these corrections, which correspond to higher loops, are not the concern for us.
It appeared in language of weakly coupled Heterotic string theory that a fine-tuning between the tree-level contribution to the gauge coupling ReS and 1-loop ReT is necessary for the approximate GUT relation. We have seen, however, that the 1-loop ReT to the visible sector and −ReT to the hidden sector corresponds to the warped factor in the 11-th direction in language of Heterotic M-theory. The warped metric is a consequence of asymmetric distribution of the second Chern class (instanton numbers). Once we have such a geometric meaning, and a hierarchy is easily generated between the two gauge coupling constants, unless the "instanton numbers" are distributed precisely the same in the visible and hidden sectors. Thus, actually the approximate GUT relation does not require a fine-tuning; we can understand it as a natural consequence of dynamics of Ramond-Ramond field and metric in the 11th direction. This is still a predictive framework of GUT. Conventional unified theories use two continuous parameters, M GUT and α GUT , to fit two gauge coupling constants, e.g., α C and α Y , and predict the last one, e.g., α L . Now, in this framework, three continuous parameters are involved, namely, κ 2 , ρ and the compactification scale vol(Z)| x 11 =0 , but there are four observable data that are given by those parameters, namely the three gauge coupling constants α C,L , and αỸ , and the Planck scale. When the three parameters are use to fit α C,L and the Planck scale, this framework predicts that αỸ is quite close to α C,L at the unification scale, and is a little smaller. We know that this prediction is consistent with the precise measurement of the Standard Model gauge couplings at LEP. See Figure 1.
Note that it is not necessary to assume (46) for the disparity between the gauge coupling constants of unbroken non-Abelian symmetries in the visible and hidden sectors; the running of vol(Z) along the x 11 direction is always given by (46) is satisfied or not. However, we keep this assumption because we need another phenomenological requirement, namely A ∝ Z c 1 (L Y ) 2 ∧ J = 0. As one can see from (34)(35), A can potentially be of order of T . Even if warped metric in the x 11 direction accounts for why Re(S − T ) ≪ Re(S + T ), nonvanishing A ≈ O(S, T ) in the kinetic mixing matrix (33) invalidates the scenario in this section. The Kähler form is expanded as in (27), and the coefficients α k (x 11 ) would run differently in the x 11 direction, if (46) were not satisfied. If α k 's change their ratio among them over the interval x 11 ∈ [0, πρ], then A will not vanish even if it does somewhere in the interval. Thus, in order to impose that A = 0, we assume (46). This may not be a problem because A is of order κ 2 3 to begin with, and the running effect of A in x 11 comes only in another κ 2 3 order, hence in the next-to-next-to-leading order, O(κ 4 3 ). But, for making an error in safe side, 13 as well as for simplicity, we maintain the assumption (46) in what follows. 13 A = 0 when the volume of certain cycle vanishes, as we discuss later. In this sufficient condition for A = 0, some Kähler moduli are chosen to be zero. If the running of α k is totally arbitrary, as oppose to the case (46) when ∂ 11 α k ∝ α k , some of α k , already chosen to be zero may run into negative value. The Heterotic M theory compactification in this case is geometric in part of the interval of x 11 ∈ [0, πρ], while possibly non-geometric for the rest of the interval. Such a situation is avoided when (46) is satisfied.
Generalized Green-Schwarz Coupling in the Heterotic-M Theory
Just like α k , the coefficients of the Kähler form, run in x 11 when (19) does not vanish, the zero modes from the Ramond-Ramond 3-form field C (3) , i.e., b k in (27), also have non-trivial wavefunction along the x 11 direction [45]. Thus, one has to check whether the generalized Green-Schwarz coupling (10) of D = 4 effective theory is modified or not; the discussion so far on the gauge coupling unification is based on an assumption that only the gauge coupling constants 1/g 2 and 1/g ′ 2 are affected by the warping geometry, but the linear combination coefficients of the generalized Green-Schwarz coupling (10) are not.
It is sufficient to see the coefficients of the the cross terms of (10), now in the warped compactification of the Heterotic M theory. The cross term originates from the interaction where (3) . The interaction above yields the source term to the Bianchi identities The wavefunction of the zero modes from C (3) have the form [45] Here, we maintained only the modes in the chiral multiplets T k , dropping the one in S, because Q S = 0 and we are interested in the generalized Green-Schwarz interaction involving the Kähler moduli chiral multiplets. Now, we take the Hodge dual of this zero-mode wavefunctions. They where * 6 is the Hodge dual on a Calabi-Yau 3-fold Z with the unwarped Kähler metric g αβ .
The warped factor e f (x 11 )/2 in (56) is cancelled and disappears in C (6) after taking the Hodge dual. Thus, the coefficients of the cross term in (10), which arises from (53), are not suppressed or enhanced by the warped factor e f (x 11 ) . Therefore, the discussion until section 3.2.1 does not have to be altered.
Fayet-Iliopoulos Parameters and a Global U(1) Symmetry
Let us take a brief look at Fayet-Iliopoulos parameters of those U(1) symmetries. They are given by where e −2φ 4 = e −2φ vol(Z)/ vol(Z) , and they enter in the D = 4 effective theory as The auxiliary fields D Y and D 2 are rotated just as the vector fields A Y and A 2 are, and the Fayet-Iliopoulos parameters are also re-organized accordingly. Thus, Fayet-Iliopoulos parameters of the U(1) massive and U (1)Ỹ vector multiplets are given by linear combination of ξ Y and ξ 2 . Zero modes from the visible sector-denoted by φ above-carry charges under the massless U (1)Ỹ and massive U (1), and if there are zero modes from the hidden sector charged under the U (1) 2 symmetry, then they are also charged under the both. If the Fayet-Iliopoulos parameter of the massive U (1) does not vanish, and if it is absorbed by vev's of chiral multiplets, then their vev's break the U (1)Ỹ symmetry as well. Thus, the Fayet-Iliopoulos parameters of both U (1) massive and U (1)Ỹ have to vanish, and so do ξ Y and ξ 2 (at the supersymmetric limit). Geometry of Calabi-Yau 3-fold and vector bundles on it has to be arranged so that just the matter spectrum of the supersymmetric standard model arise from the visible sector. Thus, the It is known that the coefficient of the one-loop Fayet-Iliopoulos parameters Q S of (possibly anomalous) U(1) symmetries are proportional to the U(1)-[non-Abelian] 2 mixed anomaly in low-energy effective theories of the Heterotic E 8 × E ′ 8 string theory, 14 and hence Q S vanishes for ξ Y . Without the 1-loop term, the 14 Reference [46] argues based on field theory that 1-loop Fayet-Iliopoulos parameters are proportional to U(1)-[gravity] 2 anomalies of low-energy spectrum, but this argument implicitly assumes that quadratically divergent contributions from any one of massless chiral multiplets are regularized exactly in the same way. It is very subtle, however, to discuss cancellation among divergent quantities, and it is more appropriate to study this issue (Fayet-Iliopoulos parameter) in a UV finite framework such as string theory. In a compactification of Heterotic SO(32) string theory with an SU(3) vector bundle, Fayet-Iliopoulos parameter of a U(1) vector multiplet was tree-level term should also vanish in order for ξ Y to vanish. Thus, It also follows from this condition that Q S 2 = 0 by requiring ξ 2 = 0. All of this argument ignores all the non-perturbative (and stringy) corrections to the Fayet-Iliopoulos parameters.
Orbifold GUT and Beyond
Localized U (1) Y Breaking Two assumptions that are essential in maintaining the gauge coupling unification are The first one comes from the stability condition of the vector bundle V 1 (also from requiring the vanishing Fayet-Iliopoulos parameters ξ 2,Y ), and the second one was introduced right after (35) in order to bring the 1-loop threshold corrections under control. These conditions are derived in the supersymmetric and large-volume limit. Suppose that c 1 (L Y ) is given by where D I are divisors of a Calabi-Yau 3-fold Z, and n I coefficients. The first equation of (62) becomes calculated explicitly, and it turned out to be proportional to U(1)-[gravity] 2 indeed [30,31]. Reference [28] further showed that this is true for Calabi-Yau 3-fold compactifications of Heterotic SO(32) string theory with generic (supersymmetry preserving) vector bundles. In compactifications of Heterotic E 8 × E ′ 8 string theory, however, [27] showed that the 1-loop Fayet-Iliopulos parameters Q S are proportional to U(1)-[non-Abelian] 2 mixed anomalies. Q S does not have to be proprotional to U(1)-[gravity] 2 anomalies, because various massless multiplets originate from cohomology groups of vector bundles in various representations, and UV divergent contriubtions to Fayet-Iliopoulos parameters from those multiplets are not regularized (cut-off and made UVfinite) exactly in the same way. Section 3 of [30] argues, however, that the 1-loop Fayet-Iliopoulos parameters (i.e. Q S ) are proportional to U(1)-[gravity] 2 anomalies in compactifications of Heterotic E 8 × E ′ 8 string theory as well. We have not yet clarified how the two apparently contradicting statements from [30] and [27] are related. In this article, we adopted the statement in [27].
This condition is satisfied, if all the D I 's that appear in (63) have vanishing sizes, for example.
The second equation of (62) becomes If all the curves D I · D J = φ have vanishing volumes, then the second condition is also satisfied.
For an example, T 6 /Z 3 orbifold has 27 isolated vanishing exceptional divisors, each of which is isomorphic to CP 2 . Another example is W P 1,1,1,3,3 ⊃ (9), which also contains 3 isolated C 3 /Z 3 singularities, and hence 3 such divisors each of which is isomorphic to CP 2 . Reference [22] argued that containing a source of SU(5) GUT symmetry breaking into an orbifold singularity brings the threshold correction under control. Indeed, we found that the 1-loop threshold corrections to the U(1) Y gauge coupling is proportional to A, and hence this correction is made small when A = 0 [stringy correction would remain, but it will not have a large-volume enhancement]. Thus, we largely confirm their claim that the 1-loop threshold correction can be made small when the symmetry breaking is confined to orbifold singularities. By now, we see that (62) is the generalized version of the idea of [22], and it is obvious that the global geometry does not have to be a toroidal orbifold, as long as (62) are satisfied. This generalization should allow much more variety in the choice of geometry.
Naive Dimensional Analysis There are a couple of different sources that give rise to a small deviation from the GUT relation. As we have seen, one of such sources was the mixing with an extra massless strongly coupled gauge field. The extra contribution to the gauge coupling (3/5)/g 2 Y is suppressed relatively to the leading contribution ≃ 1/g 2 C ≃ 1/g 2 L by a factor of order Since the observed values of the Planck scale, GUT scale and the unified gauge coupling constant suggest that the vol(Z)| x 11 =πρ is almost close to α ′ 3 [40,41,47], the inequality above is almost saturated in the reality, and it can be quite small. Only supergravity approximation (large-volume limit) was used in (33) in the expression for the threshold corrections to the gauge coupling constants. There will be extra stringy contributions, which cannot be captured by supergravity approximation. Since there are literatures on the threshold corrections to the gauge kinetic functions, results in such references can be used to obtain a precise estimate of how large they are (to the level of whether some power of π is involved or not). This article does not cover such calculation, however. Instead, we just assume in this article that they are of order unity, because there is no characteristic scales other than the string scale for such contributions. Since we consider a situation where ReS ∼ ReT ∼ R 2 /α ′ , the order-one stringy and possibly SU(5) GUT -breaking corrections to the gauge coupling are relatively compared with the leading term Re(S + T ). Therefore, this correction is more important than (66), which may be of order O((α ′ /R 2 ) 3 ). Orbifold calculations may be useful, as we mentioned above, in obtaining more precise estimate of the stringy corrections to the GUT relation.
Dimension-6 Proton Decay
Here is a remark on dimension-6 proton decay. In toroidal orbifold compactification, (fermion) zero-mode from untwisted sector (bulk) has absolutely flat wavefunctions, while that of the Kaluza-Klein gauge bosons are Fourier modes on the torus. The overlap integration involving two untwisted-sector fermions and one Kaluza-Klein gauge boson vanishes, and the Kaluza-Klein gauge bosons do not induce a transition between zero-mode fermions from the untwisted sectors. Although such predictions appear in the literature from phenomenology community, they should hold only for toroidal orbifold compactifications. In general Calabi-Yau 3-fold compactification of the Heterotic string theory, wavefunctions of zero-modes of chiral multiplets are identified with elements of bundle-valued cohomology groups on a Calabi-Yau 3-fold, and they are not absolutely flat on a curved manifold. Products of two cohomology group elements multiplied by a higher harmonic function do not vanish generically, after being integrated over a Calabi-Yau manifold. Branching fractions of various decay modes of a proton can be generation dependent, but more detailed geometric data is necessary in order to calculate branching fractions for individual models of Heterotic string compactification.
Digression: Landscape of Unified Theories
Our presentation has consisted in considering the Georgi-Glashow SU(5) GUT unified theories and study how to break the SU(5) GUT symmetry down to the Standard-Model SU(3) C × SU(2) L × U (1) Y . There are other types of unified theories, among which flipped SU(5) model and Patti-Salam model will be the most famous. We could have studied how to construct such unified theories, and then consider how to break those unified symmetries.
Our choice of Georgi-Glashow SU(5) GUT is not without a reason. The electroweak mixing angles in the quark sector are all small, but those in the lepton sector are large (apart from the last one yet to be measured). In Pati-Salam type unified theories, the quark doublets and lepton doublets are contained in a common irreducible representation of the unified gauge group. In order to obtain the qualitative pattern of the electroweak mixing stated above, one generically needs to have Yukawa couplings that heavily involve the source of symmetry breaking of the Pati-Salam gauge group. In the flipped SU(5) model with Froggatt-Nielsen (or Abelian flavour symmetry) type Yukawa matrices, 15 not all the Yukawa eigenvalues and mixing angles come out right either, meaning presumably that the Yukawa couplings heavily involve symmetry breaking of the flipped SU(5) symmetry. The Georgi-Glashow SU(5) GUT symmetry does not have this problem, and it can be a fairly well approximate symmetry (to some extent) 16 in Yukawa couplings of quarks and leptons.
In field-theory model building, different types of unified theories are just different models. It is a matter of which model provides better approximation to the reality. From the perspective of (landscape of) string theory, however, things begin to look a little different. If the moduli space of various Calabi-Yau manifolds and vector bundles are interconnected, 17 there may 15 It is known that Yukawa couplings follow such a pattern in certain region of the moduli space; examples include small torus fibered compactification [48] and near-orbifold-limit region. 16 In most generic compactification of Heterotic string theory, it may not be an important issue whether or not SU(5) GUT (or some other unified-theory gauge group) is a good approximate symmetry with respect to Yukawa couplings. Gauge field background is introduced in the U(1)Y direction, and zero modes (cohomology group elements) in the same irreducible representation of SU(5) GUT but with different U(1)Y charges have different zero-mode wavefunctions, and hence the Yukawa couplings are not expected to satisfy relations that would have followed from SU(5) GUT symmetry. In Heterotic vacua with F-theory dual and also in F-theory vacua, however, zero modes from a common SU(5) GUT representation are localized in a common matter curve, and flavour properties associated with fields in representations such as 10 or5 may be attributed to some properties of matter curves of corresponding representations. Thus, it is not a meaningless question which unified symmetry is a good approximation in Yukawa matrices. A relevant discussion is found in [48]. 17 Note, however, that we are not interested in dynamical (or cosmological) transitions between vacua in this article. Thus, we are not concerned about whether there is a topological barriers within the moduli space. Note also that the connectedness of landscape of vacua depends on the "sea level"-how much symmetry breaking one allows when one goes from one vacuum to another. not actually be a definite distinction between various types of unified theories. From one vacuum in one type of unified theory to another in a different type of unified theory, it may be possible to deform continuously over the moduli space (before introducing fluxes). Lowenergy observables such as Yukawa eigenvalues and mixing angles are functions of moduli, and they change continuously until they look phenomenologically qualitatively different. Thus, any types of unified theories in landscape of string vacua cannot be absolutely "wrong"; it is just a matter of how far those vacua are from ours. String landscape accommodates hundreds of models of unified theories, and may set a stage to discuss dynamical selection of models of unified theories. String landscape works as a unified theory of unified theories.
In what follows, we study the relation between Georgi-Glashow SU(5) and flipped SU(5) unified theories in string landscape. We will be very crude in that we do not restrict ourselves to a partial moduli space where matter parity is preserved, or to a moduli space where vector bundles have appropriate extension structure.
Both the Georgi-Glashow SU(5) gauge group and the flipped SU(5) ′ gauge group can be embedded in a common SO(10) model. Thus, it is easiest to see how those theories are obtained by breaking SO(10) symmetry. Georgi-Glashow SU(5) GUT symmetry is the commutant of a U (1) χ in a maximal torus, specified by a charge vector q χ in the Cartan subalgebra. The gauge group of the flipped SU(5), SU(5) ′ × U (1) χ ′ is the commutant of U (1) χ ′ generated by q ′ χ . Those two theories share a rank-5 Cartan subalgebra of SO (10), and the charge vectors are related by q χ = diag(2, 2, 2, 2, 2), Those charge vectors satisfy In order to obtain Georgi-Glashow SU(5) unified theories in compactification of the Heterotic E 8 ×E 8 string theory, we can begin with an SU(4) vector bundle V 4 and a line bundle L χ . By further turning on vev's in zero modes H 1 (Z; V 4 ⊗ L −5 χ ) and H 1 (Z; V × 4 ⊗ L 5 χ ), one obtains an SU(5) bundle, leaving unbroken Georgi-Glashow SU(5) GUT symmetry. Vev's in the zero modes are regarded as deformation of the vector bundle, since those cohomology groups describe the deformation of the bundles. The Georgi-Glashow SU(5) GUT symmetry can be broken down to SU(3) C × SU(2) L (and U (1) Y ) when a line bundle L Y is turned on in the direction specified by q Y .
The flipped SU(5) theories are obtained in Heterotic string compactification 18 by turning on the same SU(4) bundle V 4 and a line bundle L χ ′ in the direction specified by q ′ χ . Furthermore, vev's are turned on within zero modes 10 ′ 's = H 1 (Z; V 4 ⊗ L −1 χ ′ ⊗ L +1 Y ′ ) and its conjugate, so that the SU(5) ′ × U (1) χ ′ symmetry is broken down to SU(3) C × SU(2) L × U (1) Y . (L Y ′ is a trivial bundle in the flipped SU(5) models; we included L Y ′ in the expression above to clarify where the chiral multiplets in the 10 ′ representation vev's should develop.) Vev's in these chiral multiplets correspond to deformation of the vector bundle. The structure group is enlarged.
Because of the relation among the charge vectors above, we find a translation Thus, the deformation of the bundle in the flipped SU(5) unified theories H 1 (Z; V 4 ⊗ L −1 χ ′ ⊗ L Y ′ ) is actually the same deformation as the one in Georgi-Glashow SU(5) unified theories, . When one talks of flipped SU(5) unified theories, one usually assumes that the Kaluza-Klein scale is higher than the unification scale, where the vev in 10 ′ breaks the symmetry. As the vev increases, and it becomes comparable to the Kaluza-Klein scale, however, it is more appropriate to treat the vev as a part of vector bundle moduli. In the large vev limit of the flipped SU(5) unified theories, a rank-5 vector bundle breaks SU(5) ⊂ E 8 containing SU(4) and U (1) χ , and a line bundle still remains, with the structure group of the U(1) bundle set in the direction Thus, this is nothing but the Georgi-Glashow SU(5) unified theories with a line bundle in the U (1) Y direction.
F-theory Vacua
The SU(5) GUT symmetry can be broken by turning on a line bundle in the U (1) Y direction. The line bundle is given by a 2-form field strength tensor of a gauge field on the D7-brane world 18 In the flipped SU(5) unified theories, one needs to assume that the gauge coupling constant of U (1) χ ′ is the same as that of SU(5) ′ in order to obtain the GUT relation after the symmetry breaking due to the vev. This assumption seems to be satisfied when they are obtained through compactification of string theory containing SO(10) gauge group, because the U(1)χ ′ symmetry originates from the same SO(10) gauge group. However, a line bundle in the U(1)χ ′ direction removes the massless U(1)χ ′ gauge field from the spectrum, just like in the case of U(1)Y gauge field. Thus, an extra gauge field has to be obtained through a line bundle sharing the same first Chern class with L χ ′ . In order to maintain the approximate GUT relation, the gauge coupling of the combined massless U(1) gauge field should be almost the same as that of U(1)χ ′ . This is achieved when the extra U(1) gauge field has a large coupling constant, just like in sections 2 and 3. The same idea works for the flipped SU(5) unified theories as well.
volume in the perturbative Type IIB string theory, and in F-theory vacua in general, essentially the same thing is expressed by a four-form field strength borrowing language of M-theory.
The U (1) Y problem exists for such models, just like we already explained in section 2.1 in Type IIB models, and the Green-Schwarz coupling that makes the U (1) Y gauge field a mass term is rephrased from the Chern-Simons interaction on the D7-brane worldvolume in the Type IIB description to the Chern-Simons term in the eleven-dimensional supergravity.
The U (1) Y problem can be, in principle, solved by allowing an extra U(1) gauge symmetry to mix with the U(1) Y gauge field contained in the SU(5) GUT symmetry; the extra U(1) gauge field has to be strongly coupled so that the deviation from the GUT relation is not too large. Note that the unification between the SU(2) L and SU(3) C gauge coupling constants is already achieved, by wrapping two and three D7-branes (or by just having a locus of A 4 singularity) on a common holomorphic 4-cycle.
The extra U(1) gauge field may arise from an extra D7-brane (or from an extra 7-brane locus in F-theory in general). In order to obtain a little hierarchy between the GUT scale (Kaluza-Klein scale) and the Planck scale of the D = 4 effective theory, the volume of A 4 singularity is chosen to be parametrically large in string scale units. Since the gauge kinetic function 1/g 2 is roughly proportional to the volume of the 4-cycle a D7-brane is wrapped in the Type IIB string theory, the unified gauge coupling constant 1/g 2 C ∼ 1/g 2 L is small. An effective theory below the Kaluza-Klein scale becomes perturbative, just like we expect the MSSM to be. On the other hand, if the extra 7-brane is wrapped on a 4-cycle whose volume is of order one in string scale units, its gauge kinetic function remains small, and the gauge theory on the 7-brane is strongly coupled. Thus, the deviation of the U (1)Ỹ coupling from the GUT relation is (positive, in ∆(1/g 2 ), and) small as long as the extra U(1) gauge theory is strongly coupled. This picture dates back to [21] (and further to [20]), where fractional D3-branes were used as the extra 7-brane; fractional D3-braes are known to be wrapped (possibly anti-) D7-branes or D5-branes depending on a nature of singularity. Why some 4-cycle has a parametrically large volume, and some do not is a question associated with stabilization of Kähler moduli. Thus, the U (1) Y problem is translated into a problem of moduli stabilization. Since the doublet and triplet part of the Higgs multiplets are regarded as global holomorphic sections of different line bundles (they differ by L ⊗5 Y ), the massless spectrum of doublets and triplets can be different, giving a solution to the doublet-triplet splitting problem.
There may be threshold corrections to the gauge kinetic functions of order The first term should vanish because it is the stability condition (or the Fayet-Iliopoulos D-term parameter of the U (1) Y symmetry). There may be a threshold corrections of the order of the second term above to the gauge coupling 1/g 2 Y , but it is small by a factor of α ′ 2 /vol(Σ) ∼ α ′ 2 /R 4 compared with the leading order term. Thus, the threshold correction does not affect the GUT relation seriously.
As we discussed at the end of section 2.2, dimension-6 proton decay is expected to be fast. Furthermore, in F-theory vacua, there may be an extra enhancement in the decay rate, because the amplitude receives an UV-divergent enhancement factor when matter multiplets are localized in the extra dimensions [49]. The enhancement factor depends on the number of codimensions in which matter multiplets are localized relatively to gauge fields, and if there is an UV-divergent factor indeed, then string theory calculation has to be involved in making an estimate of the form factor, just like in [50]. It is an interesting open problem what the enhancement factor will be in F-theory models.
Note added in version 3: This enhancement factor was studied in [65] after the first version of this article. The prediction of the enhancement factor is indeed one of the most important results of F-theory phenomenology; it is not mere a hindsight explanation of known parameters of the Standard Model, but it does change the prediction of observables in experiments in the future (i.e., that is physics!). Furthermore, the enhancement factor directly originates from localization of quarks and leptons in internal dimensions relatively to gauge fields, and hence is clearly an effect that is absent in field theory models purely in 3+1 dimensions (i.e., that is string theory!). Note also that the enhancement factor is very robust, in that it does not depend on details of how SU(5) GUT symmetry is broken. It is (and the following result is) applied to the SU(5) GUT breaking scenario discussed in this article, as well as to the scenario in case of non-surjective pull-back of 2-forms, i * : H 2 (B 3 ) → H 2 (S), in [66, 23,65]. Unfortunately the paper [65] considered that dominant contribution to (86) and (91) comes from a subset where the Green function G int diverges, but this subset is measure zero. In this version of our article we present our study on the enhacement factor by evaluating (86) and (91) carefully, and we obtain predictions on dimension-6 proton decay that are qualitatively different from those of [65].
Before we begin to study the enhancement factor in F-theory compactifications, let us briefly review the essence of [49] in M-theory compactifications on manifolds with G 2 holonomy. In G 2 holonomy compactifications with SU(5) unification, SU(5) GUT gauge fields propage on a 3-cycle Q, and charged matter fields such as those in 10 and5 representations are localized at isolated points y i in Q; here, we use y as coordinates of Q, and x µ for the Minkowski space R 3,1 . Charged matter fields are coupled to the gauge field as and the gauge field has a kinetic term The gauge field A M and current J µ are given 1 and 3 mass dimensions, respectively, and the gauge coupling g 2 7 have −3 mass dimensions. The proton decay amplitude through the gaugeboson exchange is given by Here, G(x − x ′ ; y i , y j ) is a Green function on R 3,1 × Q, and is approximately q labels eigenmodes of Laplace operator on Q, and their eigenvalues (Kaluza-Klein massessquare) and eigenfunctions are denoted by | q| 2 and e i q· y / vol(Q), respectively. The momentum transfer p µ in R 3,1 direction is only of order 1 GeV in proton decay process. Since p 2 in the propagator is much smaller than the Kaluza-Klein masses | q| 2 , p 2 can be ignored and dropped from the propagator. Carrying out d 4 p integration, one obtains dimension-6 operators in an effective theory: G int is the Green function on the internal space Q. The coefficient in the square bracket has mass dimension −2.
For two different currents i = j, the effective (mass) −2 parameter is of order where | y i − y j | is set to a typical Kaluza-Klein radius of Q, R, and we assumed that the SU(5) GUT symmetry is broken by topological gauge field configuration on Q (such as Wilson line), and hence M GUT ∼ M KK ∼ 1/R. Thus, there is no enhancement compared with typical proton decay amplitude through gauge-boson exchange in 4D field theory models. For the same current, i = j, however, 1/| y i − y i | diverges. The amplitude diverges (linearly), because all the Kaluza-Klein modes with arbitrary large momentum q equally contribute without cancellation in (77) in case y i = y j . In reality, however, localized charged matter fields have certain form factor, or put differently, intersecting D6-branes effectively have certain "thickness". The Kaluza-Klein momentum sum in (77) is effectively cut-off at around | q| ∼ M * , where M * is the string scale, and the effective (mass) −2 parameter becomes The effective dimension-6 operator for the same current, 10 † 1010 † 10 in the effective Kähler potential, has a coefficient enhanced by (M * /M KK ) [49].
Let us now study the enhancement factor in F-theory compactifications. We begin with the 10 † 1010 † 10 operator. In supersymmetric F-theory compactifications, chiral multiplets in the SU(5) GUT -10 representation correspond to holomorphic sections f i of a line bundle on the matter curvec (10) in a complex surface S of A 4 singularity [67, 68]. Here, i = 1, 2, 3 is now the generation index of chiral multiplets in the SU(5) GUT -10 representation. We take the coordinates on S as y 1,2 and w 1,2 , where y 1,2 correspond to normal directions of the matter curvec (10) , and w 1,2 to coordinates on the curve. The gauge fields on S and the chiral zero modes in the 10 representation couple as where J µ (x) ji is a dimension-3 currentλ jσ µ λ i on R 3,1 , where λ i (x) and λ j (x) are fermions in the effective theory corresponding to the zero modes f i (w) and f j (w). A µ (x, y, w) is the gauge field on S, and is assigned a mass-dimension 1. Its kinetic term is χ i,j (y, w) are the zero-mode wavefunctions on S, corresponding to f i,j (see [69]). Their approximate form, as well as their normalization, are .
Thus, the proton decay amplitude becomes and the approximate form of the Green function is p 2 in the propagator is negligible (just like in the case of M-theory compactifications), and the d 4 p integration can be carried out. Thus, we obtain a dimension-6 operator To evaluate the effective (mass) −2 parameter in the square bracket, we proceed as follows. Because of the fact that the 10-representation fields are localized along the curve, or equivalently because of the exponentially falling off wavefunctions χ i,j,k,l in (83), dominant contribution to the amplitude comes from a region where y and y ′ are close to each other (and also to the matter curve where y ∼ y ′ ∼ 0), very large q can contribute in (85). On the other hand, all the zero modes in 10-representation are characterized by holomorphic (and hence smooth) sections f i,j,k,l (w) of a line bundle (without a torsion component), and only low-lying Kaluza-Klein momenta k can contribute after d 2 wd 2 w ′ integration. 19 Thus, we ignore | k| 2 and keep only | q| 2 in the denominator of (85). Now, summation in k can be carried out, and G int becomes proportional to δ 2 ( w− w ′ ). The Kaluza-Klein momentum sum in q yields a logarithmic divergence, which is cut-off at | q| ∼ M * because of the thickness of the Gaussian wavefunction in (83). In the end, the amplitude looks Since the factor g 2 8 vol(c (10) ) ∼ g 2 19 This is where our analysis is different from that of [65].
is the usual effective (mass) −2 scale of the dimension-6 proton decay operator, the enhancement factor in F-theory compactifications is ln(M * /M GUT ) 2 . This logarithmic enhancement factor 20 originates from the fact that chiral multiplets in the SU(5) GUT -10 representation are localized relatively to the SU(5) GUT gauge fields in real two dimensions. Finally, we study the enhancement factor associated with the effective dimension-6 proton decay operator 10 † j 10 i5 † b5 a . Here, a, b are generation indices. Chiral multiplets in the SU(5) GUT -5 representation are also described by holomorphic sections h a,b of a line bundle on a curvec (5) , which is obtained by resolving all the double point singularities of the matter curvec (5) at the codimension-3 loci of enhanced D 6 singularity [70] (called type (d) points there). In light of Heterotic-F theory duality,5's may well be described only as sections of a sheaf onc (5) , and the sheaf may not be torsion free or locally free, in principle. Whether such a localized component exists in the sheaf on the curvec (5) , and hence in the zero modes, is crucial for the analysis of the enhancement factor in proton decay amplitude. Reference [70] concluded, however, that there is not a localized component at all; all the zero modes are described by smooth sections h a,b on the covering matter curvec (5) .
The current of zero modes of5's couple to the SU(5) GUT gauge field through Here, J µ ba is a current on R 3,1 that consists of fermions λ a,b corresponding to the zero modes h a,b , and χ a,b are their zero-mode wavefunctions (see [69]). The dominant contribution to the 10 † 105 †5 decay amplitude most likely comes from a region around intersection points of the two matter curves,c (10) andc (5) . Although there are two different types of intersection points (type (a) and type (d) points in the classfication of [70]), the difference will not matter for the proton decay that takes place within SU(5) GUT . Thus, here, we assume that the matter curvec (10) is along y = 0 (locally), andc (5) along w = 0. Then, the wavefunction χ a,b becomes approximately Repeating the same process as before, one finds that the effective dimension-6 operator is 20 If one ignores the difference between M * , 1/l s or 1/ √ α ′ , and sets g s ∼ 1, then (M * /M GUT ) 4 ∼ 1/α GUT ∼ 24. Thus, the enhancement factor in the amplitude is of order ln(1/ √ α GUT ) ∼ ln 5. This result differes from the (1/α GUT ) 1/2 enhancement (which corresponds to quadratic divergence) in [65]. Quantitatively, ln 1/α GUT ∼ ln 5 is about a factor 4 smaller than 1/ √ α GUT ∼ 5 in the decay amplitude, and the decay rate based on logarithmic enhancement is about an order of magnitude smaller than that based on quardratic enhancement.
given by Since h a,b (y ′ ) and f i,j (w) are zero modes, and are smooth everywhere along the matter curves, only Kaluza-Klein gauge bosons with low-lying Kaluza-Klein momenta q AND k couple to both5's and 10's.
[Remember that we have d 2 y ′ d 2 w integration in the expression above.] 21 Thus, the infinite sum in q and k is effectively dropped, q and k replaced by 1/R, and we find that the effective coefficient of the dimension-6 operator is of order Therefore, the prediction of the 10 † j 10 i5 † b5 a dimension-6 proton decay in F-theory compactifications is just as the same as that of the ordinary GUT dimension-6 proton decay. There is no particular enhancement factor for this mode; 22 this is essentially because only low-lying Kaluza-Klein modes can couple to both zero modes in the 10 representation and those in5.
To conclude, ∆K = 10 † j 10 i 10 † l 10 k dimension-6 proton decay amplitude has a logarithmic enhancement. The enhancement factor is ln(M * /M KK ) 2 , which is roughly ln 5 ∼ 1.6, where we used 1/α GUT ∼ 25, and ignored a difference among M * , 1/l s and 1/ √ α ′ . On the other hand, ∆K = 10 † j 10 i5 † b5 a amplitude is dominated by low-lying Kaluza-Klein gauge bosons, and is not enhanced. Thus, the decay rates to left-handed positively charged leptons (ℓ + ) L in 10 are enhanced typically by a factor of 1.6 2 ∼ (2 ∼ 3), relatively to rates of decay to right-handed positively charged leptons, (ℓ + ) R , or to right-handed anti-neutrinos,ν R in5 † (c.f. [49]). It should be noted, however, that the decay amplitudes have generation-dependent factors 23 1 vol(c (10) ) 21 This is where our study differs from that in [65]. 22 This conclusion differs from the result, 1/ √ α GUT enhancement, in [65]. 23 These expressions should not be taken literally. It should be reminded that f k and f l for the current J ν lk correspond to zero modes in different irreducible representations of the Standard Model gauge group, although they are in the same irreducible representation 10 under SU(5) GUT . Thus, f k is not necessarily the same as f l even when k = l. The same is true for the zero modes h a and h * b in the current J ν ba for fermions in the SU(5) GUT -5 representation. It should also be clear from the discussion in the main text that e i k· w and e i q· y ′ with low-lying Kaluza-Klein momenta k and q are omitted from the second factor. for the 10 † j 10 i 10 † l 10 k and 10 † j 10 i5 † b5 a processes, respectively, and these factors may well be more important for individual decay modes than the logarithmic enhancement factor. Thus, the logarithmic enhancement of decay rates to charged leptons should be regarded only as a tendency predicted among all the decay modes.
As for the total decay rate of proton through the gauge-boson exchange, the enhancement remains only logarithmic, and is of order a factor of 2-3. It is not even clear whether this enhancement is more important than the yet to be (and hard to be) calculated factors in (93), which may result in suppression. More important is a fact that the total decay rate is proportional to M 4 GUT , and that the value of M GUT still has a large uncertainty, ranging from, say, 10 15.7 GeV to 10 16.5 GeV (see Figure 1). The decay rate for M GUT = 10 15.75 GeV is three orders of magnitude larger than that for M GUT = 10 16.5 GeV ≃ 3 ×10 16 GeV. In the scenario of SU(5) GUT symmetry breaking discussed in this article, M GUT tends to be small, because of the tree-level correction to the gauge couplings, and hence the decay rate tends to be large. Such model-dependence is more important in the total decay rate than the logarithmic enhancement that is applied to all the F-theory models of SU(5) GUT . Thus, the total decay rate can be used in discriminating various models, and the ratio of rates of decays to charged leptons to rates of decays to anti-neutrinos can be used to see whether charged matter fields are localized in internal space dimensions or not. and O7-planes are little more involved in its interpretation as limits of smooth Calabi-Yau orientifold, 24 yet some works have already been done.
In the appendix of this article, we clarify how one should interpret "Wilson lines" in toroidal orbifold compactifications of the Heterotic string theory in terminology of smooth Calabi-Yau compactifications. Toroidal orbifold models using discrete Wilson lines gained a renewed attention triggered by an activity that followed papers on S 1 /Z 2 ×Z ′ 2 orbifold GUT [55,22,56]. We will see in Heterotic theory compactification that the toroidal orbifolds with "discrete Wilson lines" are also understood as some limits of compactifications described by smooth Calabi-Yau manifold and a vector bundle on it. The discrete Wilson lines in toroidal orbifolds are not Wilson lines (or flat bundles) on smooth Calabi-Yau Z associated with a discrete homotopy group π 1 (Z), but rather they correspond to turning on line bundles on a Calabi-Yau with the U(1) structure group of the line bundles chosen differently at different vanishing cycles buried at orbifold singularities. 25 Once one adopts the interpretation above, then the SU(5) GUT symmetry breaking in Heterotic toroidal orbifold compactifications (with or without discrete Wilson lines) are regarded as special cases of the material discussed in the main text. Thus, as we discuss in the appendix A.1.3 (and as one can understand as special cases of the discussion in section 2.1), so-called the toroidal orbifold GUT's in Heterotic string theory also suffer from the U (1) Y problem.
In the literature of toroidal orbifolds, another terminology "continuous Wilson line" is also found. Although the continuous Wilson lines have nothing to do with the main theme of this article, we take this opportunity (in the appendix A.2) to clarify that the "continuous Wilson lines" in Heterotic toroidal orbifold correspond to a part of vector bundle moduli in smooth Calabi-Yau compactification.
A.1 Discrete Wilson Lines
Since our motivation is to understand what the "discrete Wilson lines" really are, we do not have to work on a very realistic model. Simple examples that illustrate the point will be better suited for our purpose. Thus, we use T 4 /Z k orbifolds instead of T 6 /Z N orbifolds, and provide interpretations of discrete Wilson lines in terms of compactification on K3 surfaces with vector bundles on them. K3 compactification [T 4 /Z k in orbifold limits] has an advantage over CY 3 [resp. T 6 /Z N ] compactification in that index theorem can calculate the massless spectrum of vector bundle moduli in addition to that of charged multiplets, so that we can compare the number of vector bundle moduli of smooth manifolds with that of orbifolds. We also use the Heterotic SO(32) string theory, instead of E 8 × E ′ 8 , because we are not trying to analyse geometry of specific toroidal orbifolds to be used for semi-realistic models, but we try to understand what the discrete Wilson lines are. For that purpose, difference in the choice of gauge group is not a big deal. We calculate the massless spectrum both in K3+bundle compactification and in toroidal orbifolds and confirm that the results do agree. The agreement shows that the K3+bundle interpretation is correct for the toroidal orbifolds of the Heterotic theory, and at the same time tells us the geometric meaning of twisted sector fields.
A.1.1 Spectrum of Smooth-Manifold Compactification
Let us consider a Heterotic SO(32) string theory compactified on a K3 manifold Z, with a vector bundle V turned on. The D = 10 supergravity multiplet reduces to • D = 6 supergravity multiplet and a D = 6 tensor multiplet, containing D = 6 metric, one 2-form field and one scalar.
Case with r = 2, however, needs a separate treatment, because the structure group of a rank-4 vector bundle SO(4) ≃ SU(2) × SU(2) is not a simple group. The rank-4 bundle is a tensor product V ≃ V 1 ⊗ V 2 , and the instanton number is given by One can see that the numbers of SO(28)-vector and singlet hypermultiplets given above are correct also for the r = 2 cases, if I V 1 and I V 2 are both non-zero. If the instanton number is only in either one of SU(2), say, I V 2 = 0, however, the unbroken symmetry group is SU(2) × SO (28), and there are [T V 1 I V 1 − dim.V 1 ] = (24 − 4)/2 = 10 hypermultiplets in the (2, 28) representation and 2 × 24 − 3 = 45 vector bundle moduli.
A.1.2 Spectrum of Orbifold Compactification
Let us now calculate massless spectra of some of T 4 /Z k orbifolds, and compare them with what we have got from the field-theory calculation. The Heterotic SO(32) string theory is described by bosons on the worldsheet, X µ (µ = 0, 1, 2, 3), Z A , ZĀ (A = 1, 2), right-moving fermions, ψ µ , ψ A , ψĀ, and left-moving fermions, λ I , λ I (I = 1, · · · , 16). Toroidal orbifolds T 4 /Z k (k = 2, 3, 4, 6) are quotients C 2 /(Z k σ ⋉ Λ), where Λ is a rank-4 lattice in C 2 whose basis consists of 4 vectors e A a (a = 1, 2, 3, 4) and σ is an SU(2) ⊂ SO(4) rotation on C 2 , satisfying σ k = id.. The worldsheet fields Z A and ZĀ transform under the generators of the space group Z k ⋉ Λ as where τ a (a = 1, 2, 3, 4) are translation along the vectors e a , and eĀ a are complex conjugates of e A a . σ is a generator of rotation on the complex coordinates, and v A = (1/k, −1/k). Other fields on the worldsheet transform under the translation and rotation as all of β a ≡ diag(e 2π iW I a , e −2π iW I a ) (a = 1, 2, 3, 4) and γ σ ≡ diag(e 2π iV I , e −2π iV I ) in SO(32) acting on (λ I , λ I ) commute each other; although they do not have to commute as long as those matrices satisfy the algebra of τ a and σ in the space group, we only consider the simplest cases here. 27 When diag(W I a , −W I a ) = 0, W I a are called discrete Wilson lines. In toroidal compactification, 2πW I a = A I A e A a + A IĀ eĀ a are the Wilson lines along the four independent topological 1-cycles of T 4 . But, in (the blow up of) toroidal orbifolds T 4 /Z k (k = 2, 3, 4, 6), there are no topological 1-cycles. 28 Thus, there is no way the "Wilson lines" have anything to do with a flat bundle associated with a non-trivial homotopy group. The "Wilson lines" W I a in toroidal orbifolds are allowed to take only discrete values, because of the algebraic relation between σ and τ a (and of the relation bewteen γ σ and β a ), and hence they are called "discrete Wilson lines" in the literature of toroidal orbifold compactifications, but they are not Wilson lines associated 27 Slightly more complicated examples-non-diagonal γ σ -will be discussed in the appendix A.2. 28 The Euler number of a simply connected K3 manifold is 24, and the Euler number of the resolved T 4 /Z k should be 24/#π 1 (T 4 /Z k ), where the resolution of T 4 /Z k were to have a non-trivial homotopy group. The Euler number of the blow up of T 4 /Z k can be calculated (See e.g., [62] for how to calculate the Euler number of toroidal orbifolds.) and is known to be 24 for all of k = 2, 3, 4, 6. Thus, all the T 4 /Z k 's have trivial homotopy groups. It is also possible to confirm that they are simply connected, by explicitly looking at the geometry of A k−1 -type ALE space expressed as S 1 -fibration over a real three-dimensional space.
with a non-trivial homotopy group π 1 (Z) that is allowed to take discrete values because of the discreteness of ω 1 (Z).
Cases Without Discrete Wilson Lines Now that the notation is set, let us compute the massless spectrum of toroidal orbifolds. We discuss only T 4 /Z 2 and T 4 /Z 3 orbifolds for simplicity. As a warming up, we start with cases without discrete Wilson lines. toroidal orbifolds with the discrete Wilson linse are discussed later.
In the r = 2 case, the symmetry is broken down to SU(2) × SO (28) Thus, the number of moduli and the multiplicity of SO(32 − 2r)-vect. hypermultiplets are calculated both by field theory and by orbifold, and they agree. In the two examples of T 4 /Z 2 30 V I r=2 corresponds to the embedding of the spin connection, and hence the instanton number is in only one of SU (2), not distributed in both SU(2)'s. 31 k − 1 2-cycles are burried in a C 2 /Z k singularity. For each 2-cycle, there are three degrees of freedom of deforming metric, and value of B-field integrated over the 2-cycle is another freedom. Those four scalar degrees of freedom in D = 6 effective theory form one hypermultiplet for such a 2-cycle. (See e.g., [63].) Thus, there is one hypermultiplet corresonding to a C 2 /Z 2 singularity. orbifolds we studied, the geometry T 4 /Z 2 is regarded as a particular limit of a K3 manifold, where 16 2-cycles are collapsed. An example with V I r=6 is obtained by taking a limit further in the moduli space of SO(2r = 12) vector bundle on the K3-manifold, a limit where the structure group is reduced from SO(12) until the SO(12) symmetry is enhaced. Likewise, the toroidal orbifold compactification with V I r=2 can be approached from a field theory compactification, by taking a limit in the moduli space of SU(2) ⊂ SO(4) vector bundle. It is a limit where the structure group is reduced from SU(2) until the SU(2) symmetry is enhanced and restored.
Let us also see examples of T 4 /Z 3 orbifolds. For the T 4 /Z 3 orbifold (k = 3), solutions to the consistency condition (103) are The massless spectra of those models are: • Untwisted sector of V I r=2,5,8 models -D = 6 sugra and tensor multiplets, ) representation], in agreement with the field-theory calculation. The number of singlet moduli are also equal to the sum of the vector bundle moduli and h 1,1 = 20 K3 moduli. Since the two singlet hypermultiplets in the untwisted sector are genuine K3 moduli, remaining 18 are from the 9 twisted sectors. Thus, roughly speaking, each twisted sector at C 2 /Z 3 has two hypermultiplets for the K3 moduli and all the other singlet hypermultiplets in each twisted sector correspond to the vector bundle moduli. This is in good agreement because two 2-cycles emerge from the blow up of a C 2 /Z 3 singularity (see footnote 31). The geometry T 4 /Z 3 orbifold is a limit of a K3-manifold, where 2 × 9 2-cycles are collapsed. The toroidal orbifold copmactifications with V I r=2,5,8 in (106-108) are obtained on top of such a singular "manifold", by taking a limit in the moduli space of SU(2), SO(10) and SO(16)-bundle compactification. For the cases with r = 5 and 8, this is a limit where the structure groups are reduced from SO(10) (r = 5) and SO(16) (r = 8) to U (1), so that SU(5) and SU(8) symmetries are restored. Thus, the vector bundles have become line bundles at the orbifold limit. The U(1) structure group of the line bundles is also restored as a global symmetry there, but we will argue in the appendix A.1.3 that a massless gauge field of the U (1) symmetry does not remain in the spectrum.
Cases With Discrete Wilson Lines
Let us now look at toroidal orbifold compactifications with discrete Wilson lines W I a = 0. Only a couple of examples are examined in the following, and we think that it is enough to see that such compactifications are also nothing more than special limits of geometric smoothmanifold compactification.
Suppose that an orbifold T 4 /Z k is a quotient of C 2 by a space group generated by a rotation σ (σ k = id) and translations τ a (a = 1, 2, 3, 4). Associated with an each element of the space group, say, τ ma a • σ n , is a (τ ma a • σ n )-twisted sector, quantized states of worldsheet fields satisfying a boundary condition Ψ(σ + 2π) = (τ ma a • σ n )(Ψ)(σ), where Ψ denotes worldsheet fields, Z, Z, ψ, ψ, λ and λ. In the presence of non-trivial discrete Wilson lines, 32 left-moving fermions are twisted by a matrix γ n σ · β m a a = diag e 2π i(nV I +m a W I a ) , e −2π i(nV I +m a W I a ) .
A consistency condition corresponding to (103) should be satisfied for each twisted sector, where V I in (103) is replaced by nV I + m a W I a mod Z, chosen in an interval [0 : 1] for the (τ m a a • σ n )-twisted sector. The generators of the space group satisfy algebraic relations such as • σ)-twisted sector is localized at a fixed point x satisfying σ n x + m a e a = x. Because of +Λ ambiguity in the fixed points x, twisted sectors are grouped into Λ/(σ − id)Λ. Consistency conditions like (109) have to be satisfied for nV I + m a W I a for each one of Λ/(σ n id)Λ. Example A: The following choice of the discrete Wilson line is consistent with T 4 /Z 2 orbifold with V I r=2 in (104): Eight twisted sectors have a twist vector V I , while eight others have (V + W 1 ); they are given (mod Z) by The unbroken symmetry is SO(4) × SO(4) × SO(24) at the orbifold limit, 32 but it can be broken down to SO(24) by turning on vev's in some of hypermultiplets. Each fixed point has one massless hypermultiplet in the SO(24)-vect. representation, while such multiplet is absent in the untwisted sector. Thus, there are overall 16 hypermultiplets in the vector representation, which agrees with the multiplicity (24 − 2r) in the case of r = 4 of the smooth-manifold calculation, with an SO(8) bundle and SO(24) unbroken symmetry. The massless spectrum calculated through the orbifold technique have 136 SO(24) singlets, after the SO(4) × SO(4) symmetry breaking absorbs 12 hypermultiplets. This agrees with the sum of the number of vector bundle moduli, 24(2r − 2) − r(2r − 1) = 116, and of the K3 moduli, 20. Thus, this orbifold compactification can be regarded as a limit of smooth K3 manifold compactification with a rank-4 bundle. Even a toroidal orbifold with non-trivial discrete Wilson line is regarded as a limit of a smooth-manifold compactification with a vector bundle. Not only the moduli spaces of rank-2, 5, 6, 8 bundles but also that of rank-4 bundle contains an orbifold point. Example B: The T 4 /Z 2 orbifold with the twist V I r=2 in (104) is also consistent with the following discrete ilson lines: 0, · · · , 0), W I 3,4 = 0, (114) 32 The unbroken symmetries at fixed points (in twisted sectors) are determined by the twist vectors associated with the fixed points. The symmetry group at fixed points with the twist V I and those with (V + W 1 ) I are different subgroups of SO(32), though they are both SO(4) × SO (28).
The sixteen fixed points of T 4 /Z 2 are classified into 4 groups of four fixed points, and each group has its own twist vector given by Thus, the T 4 /Z 2 orbifold with V I r=6 and W I = 0 and with V I r=2 and W I in (114) are both regarded as special limits in the moduli space of SO(12) vector bundle, limits where the structure group is reduced and the unbroken symmetry is enhanced. In the case with V I r=6 and W I = 0, instantons are squeezed in the U(1) generated by a charge vector q = diag(V I r=6 , −V I r=6 ) at all the 16 collapsed 2-cycles. In the case with V I r=2 and the discrete Wilson lines in (114), however, they are squeezed in a U(1) subgroup generated by q = diag((V r=2 +m a W a ) I , −(V r=2 +m a W a ) I ) at the collapsed 2-cycles at (m a e a )/2; the charge vector q can be different at different collapsed 2-cycles.
The unbroken symmetry at the orbifold limit is SU (2) q = diag ((V r=2 + W 1 ), −(V r=2 + W 1 )) at 1 3 (2e 1 + e 2 + m(e 3 + e 4 )), (122) q = diag ((V r=2 + W 1 + W 2 ), −(V r=2 + W 1 + W 2 )) at 1 3 (e 1 + 2e 2 + m(e 3 + e 4 )). (123) The moduli space of rank-5 bundle compactification contains more orbifold points than the two explicitly described above; W I 1 = W I 2 can be multiplied by a factor of 2, and W I 3 = W I 4 can also be non-zero. At the toroidal orbifold limits with non-trivial discrete Wilson lines, the U(1) subgroups in which the instantons are squeezed (that is, the structure group of line bundles) can be different from one singularity to another. Variety of the choice of W I a correspond to the variety of finding such U(1) subgroups in which the instantons are squeezed. Apart from that, there is no essential difference between toroidal orbifolds with or without discrete Wilson lines. They are all special limits of a simply-connected K3-manifold compactification with a vector bundle on it.
A.1.3 Discussion
We have seen that the toroidal orbifold compactifications of the Heterotic theory corresponds to special points in the moduli space of compactifications with Calabi-Yau and a vector bundle on it. At the orbifold points, the structure group of the vector bundle is reduced and an unbroken symmetry is enhanced. This interpretation holds true regardless of "discrete Wilson lines" are used or not. If the twist vectors V I 's and W I a 's are arranged so that U(1) symmetries are left, then the structure group of the bundle contains the U(1) symmetries. That is, the bundle contains line bundles at such orbifold limits.
It is important to note that U(1) symmetries in effective theory below the Kaluza-Klein scale does not imply that the low-energy spectrum has corresponding massless vector field. This is why we put all the U(1) factors in brackets in the examples of toroidal orbifolds in the appendix A.1.2. If we label multiple U(1) factors of the structure group at various fixed points by a, b, then the effective lagrangian contains where the second term comes from (6). At the orbifold limits, U(1) symmetries in the directions spanned by q's may be preserved as global symmetries, but the gauge fields acquire mass terms from the second term in the effective action above. The B-field fluctuations b k ω k in (6) will be played by twisted sector fields in orbifold language. 34 Multiple U(1) gauge fields acquire large masses from the Stuckelberg form interactions in Heterotic compactifications, and toroidal orbifolds with or without "discrete Wilson lines" are not exceptions. If orbifold projection conditions are to be used in breaking the SU(5) GUT symmetry down to SU(3) C × SU(2) L × U (1) Y symmetry in Heterotic theory, then such toroidal orbifold compactifications are regarded as limits where vector bundle has a structure group U (1)× U (1)×· · · ⊂ E 8 . Whether the discrete Wilson lines are used or not does not make an essential difference in this argument. The charge vector for the hypercharge q Y is not orthogonal to all the charge vectors of the structure groups of the line bundles above. Thus, the U (1) Y vector field also has a large mass term through (6,124) in such toroidal orbifolds.
The Stuckelberg coupling with the dilaton chiral multiplet vanishes in compactifications of the Heterotic E 8 ×E ′ if the vev's of vector bundle moduli (twisted/untwisted sector fields) are chose appropriately. Yet, there may not be massless U (1) Y gauge field in the low-energy spectrum, because of the Stuckelberg coupling with the Kähler chiral multiplets (twisted sector fields).
The main text of this article also proposes an idea of how to get out of this U (1) Y problem. If one can find a U (1) symmetry in the hidden sector E ′ 8 that is a structure group of a line bundle of compactification, and if the first Chern class of the U (1) line bundle is the same as that of U (1) Y , then the their linear combination U (1)Ỹ remains massless. If the hidden sector is strongly coupled, then the gauge coupling constant of U (1)Ỹ still satisfies the GUT relation approximately.
Those matrices for the orbifold twists are chosen so that they satisfy algebraic relations σ • τ a=2 • σ −1 = (τ a=1 + τ a=2 ) −1 → γ −1 σ · β a=2 · γ σ = (β a=2 · β a=1 ) −1 . The spectrum can also be calculated using the standard techniques in toroidal orbifolds. Each one of the twisted sectors localized at 9 C 2 /Z 3 singularity contribute to the spectrum of hypermultiplets by 2 in the vector representation, 2 in the U(1) charged ones, and 9 in the U(1) neutral ones. Thus, all of the hypermultiplets in the 4th and 5th items in the above list are accounted for in the orbifold calculation. The 9 × 9 neutral hypermultiplets account for 9 × 2 of the K3 moduli hypermultiplets and 9 × 7 of the vector bundle moduli; each C 2 /Z 3 singularity has two hypermultiplet worth of resolution/deformation degrees of freedom. Among the twisted-sector spectrum of neutral hypermultiplets, two are still missing in the moduli of K3, and one in the bundle moduli.
Gravitational part of the untwisted sector gives rise to two neutral hypermuliplets, and hence all the 20 hypermultiplets for the K3 moduli are recovered from toroidal orbifold calculation. The SU(3)-adjoint part of the untwisted sector leaves one massless hypermultiplets, and this is identified with the remaining one vector bundle moduli. This hypermultiplet takes values in the diagonal entries of 3 × 3 matrix in the basis that diagonalisesβ as in (125-127).
One can further see from the orbifold calculation that two more hypermultiplets become massless if α = β = 0, and the unbroken symmetry is enhanced to SO(26)× U (1) × U (1) × U (1). This phenomenon is better understood in a frame that diagonalizes the twisting matrixγ σ rather thanβ a=1,2 . Generators ofβ a=1,2 and the hypermultiplets from the untwisted sector take their values now in off-diagonal entries of the 3 × 3 matrix of adjoint SU(3), and the symmetry breaking U (1) × U (1) × U (1) → U (1) is understood as the Higgs mechanism due to the vev in the untwisted-sector hypermultiplet. Put another way, vev's in the untwisted sector hypermultiplet correspond to deformation of vector bundle that enlarges the structure group from U (1) × U (1) to SU (3). That is, the continuous Wilson line (and the vev's of the untwisted sector hypermultiplets) studied in this example corresponds a part of vector bundle moduli explained above.
Continuous Wilson lines exist in cases where the twisting matrix γ σ acts as permutation. When a basis is chosen so that γ σ is diagonal, generators of the continuous Wilson lines β becomes off-diagonal, and the off-diagonal vev's enlarge the structure group of vector bundle. | 25,418.4 | 2008-06-04T00:00:00.000 | [
"Physics"
] |
Introducing programmability and automation in the synthesis of virtual firewall rules
The rise of new forms of cyber-threats is mostly due to the extensive use of virtualization paradigms and the increasing adoption of automation in the software life-cycle. To address these challenges we propose an innovative framework that leverages the intrinsic programmability of the cloud and software-defined infrastructures to improve the effectiveness and efficiency of reaction mechanisms. In this paper, we present our contributions with a demonstrative use case in the context of Kubernetes. By means of this framework, developers of cybersecurity appliances will not have any more to care about how to react to events or to struggle to define any possible security tasks at design time. In addition, automatic firewall ruleset generation provided by our framework will mostly avoid human intervention, hence decreasing the time to carry out them and the likelihood of errors. We focus our discussions on technical challenges: definition of common actions at the policy level and their translation into configurations for the heterogeneous set of security functions by means of a use case.
I. INTRODUCTION
The networking field is currently facing a deep revolution based on virtualization. In the decade which has just ended, innovative paradigms shook the traditional vision of networks as a mesh of heterogeneous functions providing different services. The first time when networking embraced virtualization is represented by the born of Software-Defined Networking (SDN) [1], [2]. The main pillars of this technology are the decoupling between data plane and control plane, centralization of all the control plane functions in a software module that is referred to as SDN controller, abstraction between the specificity of user applications and the generality of controller interfaces. More recently, Network Functions Virtualization (NFV) [3], [4] introduced the possibility to create network functions as software programs and to make them run as traditional virtual machines or containerized applications, supervised by a software MANagement and Orchestration (MANO) module [5]. Physical middleboxes have been thus progressively replaced by general-purpose servers where the programs implementing the network functions can run.
Among the contributions brought over, automatic (re)programmability of the network functions are nowadays becoming feasible, with respect to the traditional troubles coming from a manual function configuration [6]. On one side, a fundamental novelty provided by SDN has been the reactive generation and deployment of forwarding rules by the controller onto the data plane switches. Whenever a packet which does not match any switch rule is received, it is forwarded to the controller, so that it can take the best decision according to the internal logic and consequently generates rules for all the network switches which would have to manage packets with the same characteristics in the future. On the other side, if the network functions are implemented in the NFV fashion, MANO can automatically manage their life-cycle and deployment, so as to optimize either resource consumption for the underlying physical infrastructure or availability of the provided services.
Although many organizations are migrating virtual machine (VM)-based applications to containers, virtualization is still present in data centers and public clouds. We are also seeing new ways of integrating virtualization with containers and Kubernetes (K8s) to provide innovative solutions to new problems. In other words, virtual machines are also becoming part of the cloud-native architecture -this concept is called container-native virtualization. Kubernetes is an example of the fulfillment of SDN and NFV paradigms, which is an opensource system for automating deployment, scaling, and management of virtualized applications. It significantly simplifies the works of network and service administrators.
However, in an environment like Kubernetes, where multiple software processes run in parallel, the correct global management becomes more difficult than what traditionally was with single hardware devices. The increase of complexity, unfortunately, contributed to raising the number of cyberattacks, which became more variable by having the possibility to exploit new kinds of breaches. In particular, misconfiguration of network functions has become more critical, because more variable factors must be considered when enforcing a correct security defence against both external and internal attacks. This statement is confirmed by recent surveys, such as Verizon's most recent study [7]. In this report, the misconfigurations have been identified as the third most critical threat in cloud environments, that can lead to catastrophic breaches.
In light of these observations, the challenge we propose to face is to effectively exploit the benefits provided by the virtual networking paradigms, minimizing the impact of their beforehand illustrated drawbacks. With this aim, we designed a framework based on the innovative methodology presented in [8], based on Maximum Satisfiability Modulo Theories (MaxSMT), and we integrated it in the context of Kubernetes. The proposed approach automatically configures virtual firewalls, where a consistent number of configuration errors are traditionally performed. Moreover, we will particularly describe how this methodology is effectively introduced in the framework architecture of ASTRID (AddreSsing ThReats for virtualIseD services), which is an EU H2020 Project [9].
The remainder of this paper is structured as follows. In Section II, the most related works are described, so that the main differences with respect to the methodology proposed in this paper are illustrated. In Section III, first, the general architecture of the ASTRID framework is presented. Then the focus will be shifted on the methodology for the automatic firewall configuration, present inside the Security Controller, the central component of the ASTRID framework which enforces security in cloud-based networks. In Section IV, additional details about the implementation will be provided, alongside with a validation based on the framework's application in a realistic scenario. Finally, Section V briefly concludes the paper and describes the planned future works.
II. RELATED WORK
The focus of this paper is centered on the automatic configuration of firewalling functions in Kubernetes framework. Therefore, we briefly introduce the main characteristics of the Kubernetes framework and then we report the main works related to the automatic firewalls configuration.
As shown in Fig. 1, a Kubernetes cluster is composed of multiple nodes, which can be virtual or physical. A Pod is a minimal management unit and can accommodate one or more containers. Each Pod is protected by a packet filter (i.e., FW in Fig 1). Pods are assigned with network addresses and are allocated to nodes. Containers inside a Pod share resources, such as volumes where they can write and read data. Clients contact the cluster through another firewall, which distributes requests to nodes according to load balancing rules. The proxy receives requests from this firewall and forwards them to Pods. Each node has a proxy installed. If a Pod is replicated, the proxy distributes the load among the replicas. The kubelet is a component that manages Pods, containers, images and other elements in the node. The kubelet forwards data on the monitoring of containers to the main node, which acts when necessary. In this framework, one of the main key points concerns the correct and consistent configuration of this graph of firewalls that protect the access to each container.
In literature, an automatic configuration of firewalls is a challenge where research has been partially carried out. However, most of the works describe either technique which can be only applied to traditional networks (e.g., with hardware firewalls), or mathematical models that do not have a correspondent implementation proving their feasibility and effectiveness. Moreover, only a limited subset of them enrich the computed configurations with optimality or formal correctness assurance [10].
The three papers which gave birth to this research trend have been [11], [12] and [13]. In particular, Firmato [11] represents a vital milestone, because it is the first approach based on policy refinement that is able to automatically synthesize Nevertheless, its limitations are evident:the most critical is that it has been validated on a single centralized firewall, instead of a distributed security architecture. The other two works ( [12], [13]) added the possibility to configure a distributed firewall as the main novelty. However, all these three works exclusively target traditional networks, and do not offer either optimality or formal verification. Formal mathematical models have been, instead, presented in [14] and [15], where formal methodologies are used to automatically compute firewall configuration. However, in both cases these techniques work only in specific cases, not related to virtualized networks: [14] follows the syntax of IPChains and Cisco's PIX, whereas [15]'s technique has been validated only with SCADA-firewall configuration. Besides, optimization is overlooked in both these two works.
A recent work which, with respect to all the others, specifically targets NFV-based networks is [16], [17]. The proposed approach is the first step toward a security policy aware NFV management, with the introduction of a specific module, called Security Awareness Manager (SAM), into frameworks which provide NFV MANO, such as OpenMANO. This module performed a complete refinement of high-level, user-specified network security policies into the low-level configuration of virtual network functions, using optimization models defined for each function type. There are limitations in this work, though: the achieved results are not formally verified and little information is provided about how firewall policies are managed, since this paper provides a comprehensive approach for multiple security function types. Anyhow, it shows how, despite its drawbacks, virtualization is altogether characterized by features which can be positively and efficiently exploited in the automatic programmability of next-generation computer networks Finally, the proposed work integrates the automatic configu-ration approach, presented in [8], into Kubernetes. Specifically, the solution in [8] adopts a formal approach based on the MaxSMT problem, which provides formal assurance about the correctness of the solution. More details will be provided in the next sections.
III. APPROACH
This section presents the design of the ASTRID framework and presents a generic workflow to illustrate the main functionalities. Next, our proposed approach is presented as a Security Controller component that resides in the ASTRID framework.
A. ASTRID framework
The term orchestration is commonly being used in the IT field. In the NFV and microservice system, there is Service Orchestration for Service, and in the Cloud system, there is Cloud Orchestration for cloud resource description. With the development and maturity of container technology, more and more enterprises and individuals choose to containerize traditional applications or directly develop container-based cloud-native applications and then run applications on the container platform. Faced with a complex container operating environment, the needs for container orchestration have raised. In general, container orchestration is responsible for the lifecycle scheduling of containers, and it improves container usage by managing container clusters. There are currently three major industry giants such as Kubernetes, Docker Swarm, and Apache Mesos. They belong to the category of DevOps infrastructure management tools and are called "container orchestration engines".
But when developers enter the world of orchestration, one thing that needs special attention is security. Various blogs, videos, books, and tutorials teach developers how to use these solutions, but only a few mention the need to add security controls to protect applications in the cluster. Moreover, if the underlying infrastructure of the cloud is unreliable (or configured in a vulnerable manner), for instance, there is no way to guarantee the security of a Kubernetes cluster built on this foundation.
The main goal of the AddreSsing ThReats for virtualIseD services (ASTRID) project is to address these technological gaps in the scope of cloud infrastructures. The project proposes a novel cyber-security framework to provide situational awareness for cloud applications and NFV services. The overall workflow of the framework is presented in Fig. 2. According to the workflow, the ASTRID framework allows software and service developers to provide a description of the service request, which is enriched with security policies by security provider entity. The Security Orchestrator component of the framework is in charge of reaction, creation, delivering endto-end services.
The scope and the contribution of this work are associated with the Initialization and Reaction phase provided in Fig. 2. We develop the Security Controller that is in charge of this phase in the workflow. It is one of the most valuable parts of the run-time subsystem, conceived to automate as much as Fig. 2: Overall workflow of the ASTRID framework [18] possible the behaviour of the security functions, in the control plane. In the next subsection, we describe the component in detail.
B. Security Controller
The Security Controller has been developed on the basis of the methodology presented in [8]. It incorporates programmability and automation in the synthesis of virtual firewall rules from a user-provided security policy. With this respect, the Security Controller works in close coordination along with the service orchestrator. The service orchestrator is in charge of providing a description of the service graph as well as the infrastructure information. The infrastructure information includes the actual number of launched virtual network functions and parameters assigned after the enforcement process such as IP and port addresses. After receiving the required data from the orchestrator, the controller performs an automatic translation from the high-level policy to lowlevel configuration parameters for firewall network functions. This process of automatic configuration is formally proven to meet these security policies as a part of this analysis. The security controller formulates the problem of the automatic configuration of firewall rule tables as the Maximum Satisfiability Modulo Theories (MaxSMT) problem. It is a basic constraint optimization problem that we use to provide two main features: i) high assurance in the correctness of the computed solutions, thanks to the intrinsic formal correctnessby-construction paradigm; ii) optimality of the solution, by minimizing the number of automatically generated firewall rules, with the purpose to improve the filtering operations.
To this day, optimization problems are modeled by Integer Programming (IP) languages. At the same time, most of them are NP-hard classes, and large-scale integer problems are difficult to solve. Moreover, none of the variations of the IP formulation are able to model the problem of automatic firewall configuration having in mind the verification of endto-end reachability. This is due to the less expressive power of the approaches compared to the Constraint Satisfaction Problem (CSP) representations. An instance encoding of CSP, MaxSMT in our case, is defined by a set of variables, a set of possible values (or domains) for each variable, and a set of soft and hard constraints, each constraint involving one or more variables. A MaxSMT solver determines for a given instance whether it is possible to assign a value to each variable from its respective domain in order to satisfy all hard constraints and an optimal number of soft constraints simultaneously.
The Security Controller translates the input service graph into a MaxSMT instance by means of a set of First-Order Logic formulas. In a nutshell, these formulas will be converted to boolean variables in Conjunctive Normal Form, eventually. In addition to the topological definition of the service graph, each network function of the service graph will be translated into the abstract model according to the guidelines given by Verigraph [19]. This allows us to provide a higher level of assurance that the automatically generated configuration parameters of the firewall will satisfy the security policies in the presence of complex network functions. The level of abstraction of these models covers all the forwarding behavior of the network and their configuration parameters that are already defined. Instead, we model the firewall network function by introducing soft constraints over variables, which then will be decided to satisfy or not by the MaxSMT solver. These variables represent the IP and port addresses to be autoconfigured in order to satisfy the end-to-end policies. Initially, these variables are set to false, which means that a firewall does not contain any rule. If the policy requires that the firewall must block the traffic, it must falsify the soft constraint in favor of satisfying the policy requirement. Hence, the policy requirement is modeled as a hard constraint, which means it must be always satisfied. In this way, the solver tries to minimize the falsifying constraints in the formula and satisfy the hard constraints. This is the definition of the optimization problem we pursue to solve.
In order to represent the reachability policies by means of hard constraints, we introduce the concept of packet flows between endpoints. The first constraint we assert is that the network function model defined in the service graph must forward a packet flow if it receives a packet flow. This constraint must be true under the functional behavior of the network device. For instance, this is true if a firewall network function does not contain any rule that drops packets. The second constraint states that the packet flow sent from a source node must be received by the destination node. Other constraints include the forwarding path definitions and static configuration parameters of network functions.
This concludes the fact that IP formulation of the same problem would be limited to a set of constraints over binary, integer, or real variables. Instead, the approach presented in this paper allows us to model the problem and using very expressive constraints. These constraints include configuration parameters of network functions, forwarding behavior of the service graph, and complex security policies, in addition to the automatic configuration constraints of the problem. Therefore, existing IP algorithms are not comparable to our algorithm for that class of problems. In the next section, we demonstrate our approach by means of a representative scenario.
IV. USE CASE SCENARIO
This section presents our framework in greater detail with a practical use case and motivates our design decisions. For the sake of simplicity, we focus our attention on a specific component of the ASTRID framework, the Security Controller, and emphasize the fact that the interaction between other components is performed by means of a REST API. We expose a number of resource endpoints to the Security Orchestrator, which will use to deliver the service graph and infrastructure information and to retrieve the automatically generated firewall rules. We underline the fact this methodology can be extended to more general scenarios than the ASTRID framework. In fact, the Security Controller is a standalone web service application, which makes it possible to be easily incorporated into existing cloud platforms and orchestrators.
We consider the scenario where an administrator predefines the logical service graph presented in Fig. 3a and feeds it to the dashboard of the ASTRID framework. This service graph represents a realistic scenario where the nginx web server is made public to the Internet and functions as a reverse proxy to fetch dynamic data from multiple instances of nodejs and apache servers. In this case, both servers can acquire data from a mysql database. As we can see from the figure, reachability policies required by the use case are rather obvious (i.e., highlighted with arrows). Instead, the isolation property required by the service graph is not evident. For instance, all the communications, which are not highlighted with arrows must be isolated. Considering the fact that each service in the graph is associated with a firewall, firewalls are preconfigured with deny-all rules, in order to satisfy this policy. This ensures that all other interactions within the service graph must be isolated, except the ones predefined by the user (i.e., arrows).
A Service Orchestrator of the ASTRID framework is in charge of deploying the service graph onto the infrastructure and generating the enriched service graph shown in Fig. 3b. During this enforcement phase, all the services are assigned with corresponding IP addresses and ports where these services can be reached. It is important to highlight that the multiple instances of the services are deployed in separate Pods and each will have its own IP addresses. In this scenario, Fig. 3: a) a logical service graph b) enriched service graph after the deployment the user specified to have two instances of the nodejs server to handle the load. To illustrate the complexity introduced by this simple use case, we included all the links connecting each service in the infrastructure in Fig. 3b. Taking into account the deny-all rules of each firewall of the service, we can assure that there is no reachability between the Pods in this phase. Although, we have specified the user policy that needs to be satisfied by means of the arrows in the figure. As an example, apache server needs to be configured to allow traffic from itself to a mysql database and allow communication from nodejs. However, it needs to be isolated from each instance of the nodejs servers.
Without the Security Controller, an administrator of the infrastructure must manually configure each firewall. This process of manual configuration of each firewall is errorprone and time-consuming. This scenario motivates the use of the Security Controller presented in this paper, in order to automatically generate firewall configurations for each service and provide formal assurance that the network policy defined by the user is satisfied. To obtain the low-level configuration of each firewall component, the Security Controller accepts as an input the infrastructure information and logical service graph as described in Section III. Infrastructure information contains the IP and port addresses of each service that is shown in Fig. 3b. This information is required to define the firewall rules, which allows to block specific packet flows involving specific Pods. In the next step, the Security Controller automatically generates an output with a low-level configuration of each firewall component. As an example, we present the partial output format and the actual configuration parameters generated by the Security Controller in Listing 1. In this prototype evaluation experiment, we use a machine with 3.40 GHz Intel i7-6700 CPU and 32GB of RAM. The average time needed for the overall procedure is less than a second. We need to emphasize the fact that for most service requests, the time required to schedule VMs to be several orders of magnitude larger than the this computation time.
Listing 1 shows the configuration parameters generated for the firewall component of the mysql service. It includes all the neighbors of the firewall in the infrastructure network and firewall rule entries. According to the output, we need to configure the firewall with 3 entries. The first rule states that the packets arriving from the Pod with an IP address 172.20.1.13 need to be allowed. The rest of the rules are associated with the two instances of the nodejs server of the service graph. Due to the default action set by the firewall in line 8, Listing 1, all the other packets arriving from the network is dropped. For instance, intruders from the Internet are not able to access the mysql database in accordance with these rules. This, in fact, ensures the satisfiability of the initial service graph policy defined by the user. Eventually, the output file generated by the Security Controller is sent back to the Context Broker, which is in charge of translating the low-level configuration of each firewall into a vendor-specific format of the firewall.
An important feature of the Security Controller is in the possibility to have firewalls without any configuration as in the use case or with partial configuration, giving to the tool itself the task of providing the missing configurations as an output. The tool generates the configuration with the objective of satisfying all the requested policies while minimizing the number of generated rules in order to achieve it. In the case of partial configuration, a firewall may include static rule entries that will not be changed in the output. This is useful when the service graph is updated according to an event when a Pod is terminated or a new Pod has been created to handle the overhead to the service. In this scenario, in order not to recompute the configuration parameters of all the other services, we can provide their rules in a static manner, meaning that they can be left unchanged. This process not only generates a set of configuration parameters but also provides an optimal set of rules to satisfy the user policy. Optimality is achieved by minimizing the number of rules inside each firewall to improve the performance of the virtual network functions.
V. CONCLUSION AND FUTURE WORKS
In this paper, we illustrated the benefits which the introduction of automatic programmability would bring for the synthesis of firewall rule sets in virtual networks, in the respect of NFV and cloud infrastructures with special emphasis on Kubernetes. In particular, the role of the presented automated methodology in the ASTRID framework architecture has been described, with an emphasis on the contributions provided to the Security Controller. We formulate the problem of automatic firewall configuration as a MaxSMT instance and solve it to provide reachability assurance between endpoints.
As possible future works, we are currently planning to introduce programmability for other kinds of network security functions, such as intrusion detection systems and security devices for channel protection (e.g., VPN gateways). Moreover, we plan to provide automatic configuration settings in the presence of minor changes in the initial service graph without solving the problem from scratch. As the initial results show promises in smaller instances, we plan to evaluate the model in larger scale scenarios. | 5,798.8 | 2020-06-01T00:00:00.000 | [
"Computer Science"
] |
Solid argon as a possible substrate for quasi-freestanding silicene
We study the structural and electronic properties of silicene on solid Ar(111) substrate using ab-initio calculations. We demonstrate that due to weak interaction quasi-freestanding silicene is realized in this system. The small binding energy of only $-32$ meV per Si atom also indicates the possibility to separate silicene from the solid Ar(111) substrate. In addition, a band gap of $11$ meV and a significant splitting of the energy levels due to spin-orbit coupling are observed.
I. INTRODUCTION
Silicene is based on a two-dimensional honeycomb Si lattice, similar to graphene, attracting interest by the predicted linear dispersion near the Dirac point and various potential applications in electronic devices. The structure is not perfectly two-dimensional, but a finite buckling mixes some sp 3 hybridization into the sp 2 states. The weaker Si-Si bonding, as compared to the C-C bonding in graphene [1], strongly complicates the synthesis. Still, silicene has been grown on thin film ZrB 2 , resulting in electronic properties that are different from the expectations for freestanding samples [2]. Both the buckling and the electronic properties can be modified by epitaxial strain, which hints at a strong interaction with the substrate. Silicene on Ir(111) has been investigated experimentally and theoretically in Ref. [3].
It has been demonstrated that Si nanoribbons can be grown on Ag(110) substratea [4,5] and the electronic structure has been investigated by angular resolved photoelectron spectroscopy [6]. Epitaxial growth with a highly ordered honeycomb structure on Ag(111) has been confirmed by scanning tunneling microscopy [7]. The Si nearest neighbor distance of 1.9 ± 0.1Å, obtained by line analysis of the microscopy data, also points to strong interaction with the substrate. A systematic study of Si superstructures on Ag(111) has been performed in Ref. 9 using low energy electron diffraction, scanning tunneling microscopy, and ab-initio calculations. Further results from scanning tunneling microscopy have been reported in Refs. [10][11][12], confirming that the quasiparticles in silicene behave as massless Dirac fermions. However, experiments indicate that the Dirac nature is perturbed by symmetry breaking due to the substrate [13]. This argumentation is supported by ab-initio results, which lack a Dirac dispersion for various stable and metastable structures of silicene on Ag(111) [14,15].
Graphene has been separated from SiC(0001) substrate, on which the binding energy experimentally amounts to 106 meV [16]. As Si bonds are usually weaker *<EMAIL_ADDRESS>+966(0)544700080 than C bonds, to separate silicene from a substrate probably a significantly smaller energy will be necessary. Hexagonal boron nitride [17] and SiC(0001) [18,19] are known substrates for graphene and therefore have been studied also for silicene by ab-initio calculations, finding that the Dirac cone is preserved, though slightly doped in the case of hydrogenated SiC(0001) [20]. For a superlattice of silicene and hexagonal boron nitride a binding energy of 57 meV per Si atom has been predicted theoretically [21]. The electronic properties of silicene on II-VI and III-V semiconducting (111) substrates, including AlAs, AlP, GaAs, GaP, ZnS, and ZnSe have been investigated in some detail [22][23][24], finding n-doping on metal terminated and p-doping on non-metal terminated surfaces.
Ar exists as solid at low temperature, with short range, weak, and attractive London dispersion forces responsible for the molecular bonding [25]. The temperaturepressure phase diagram has been studied in Refs. [26,27], demonstrating that a face centered cubic structure is stable below a temperature of 84 K. Despite various attempts, so far no suitable substrate could be identified such that the characteristic electronic structure of silicene would not be perturbed dramatically on it [28]. This is probably the consequence of too high binding energies on the tested substrates. For example, values of 89 and 76/84 meV per atom have been reported for hexagonal boron nitride and Si/C-terminated SiC(0001), respectively [20]. In this context, we study the possibility of utilizing solid Ar(111) as a substrate and analyze the consequences on the electronic properties of silicene. We will argue that silicene on solid Ar(111) is quasi-freestanding. In addition, in Refs. [29,30] a buffer layer of solid noble gas has been used to deposit metal clusters by soft landing and subsequent evaporation of the noble gas. A similar approach with solid Ar(111) on top of the desired substrate can provide access to growth of silicene on essentially any substrate.
II. COMPUTATIONAL METHOD
All calculations are performed using density functional theory in the generalized gradient approximation (Perdew-Burke-Ernzerhof parametrization) and ultrasoft pseudopotentials, as implemented in the Quantum-ESPRESSO package [31]. The plane wave cutoff energy for pure Ar is set to 476 eV and for all other systems to 544 eV. In the self-consistent calculation of pure Ar a Monkhorst-Pack 32 × 32 × 32 k-mesh is employed, whereas for all other systems a 32 × 32 × 1 k-mesh is used. To achieve a high resolution, dense 64 × 64 × 64 and 64 × 64 × 1 k-meshes are used for calculating the density of states (DOS). An energy convergence of 10 −5 Ry and a force convergence of 10 −4 Ry/Bohr are achieved. Calculations are performed with and without spin-orbit coupling (SOC) and with and without van der Waals (vdW) interaction [32]. In the zoomed band structures shown in the following we use δK = (0.002; 0.002; 0) with K = (1/3; 1/3; 0). We consider a slab geometry with silicene on one side of an Ar(111) slab, which inherits hexagonal symmetry from the two subsystems.
III. RESULTS AND DISCUSSION
The optimized lattice parameter of solid Ar is 5.36Å, which leads to an Ar-Ar distance of 5.36/ √ 2Å= 3.79Å. On the other hand, for freestanding silicene we obtain 3.86Å in agreement with Ref. [33]. For the combined argon-silicene (ArSi) system we set the lattice parameter to 3.79Å, i.e., silicene is subject to a lattice mismatch of 1.9%. Figure 1 shows for the ArSi system a buckling of 0.53Å (distance between the bottom and top atomic layers), which is slightly higher than predicted for freestanding silicene (0.46Å) in Ref. [34]. We note that an artificially planarized structure with the same Si-Si bond length as the ground state buckled structure is only 32 meV per Si atom higher in energy. The Ar(111) substrate consists of six Ar layers. The top layer is arranged such that two thirds of the atoms are located below Si atoms and the last third is located below the center of a Si hexagon, which turns out to minimize the energy. After structural optimization without vdW interaction, the Si-Si bond length is 2.27Å with a bond angle of 115 • , in agreement with Refs. [34][35][36]. We obtain a distance of 4.3Å between the top Ar layer and the silicene, whereas the interlayer spacing in the substrate amounts to 3.6Å. Taking into account the vdW interaction, we obtain the same buckling but reduced interlayer spacings of 3.4Å and 3.1Å, respectively. The band structure and DOS of solid Ar are shown in Fig. 2 and those of freestanding silicene in Fig. 3. Without SOC we obtain for silicene the characteristic linear dispersion of the π and π * bands around the K point [1], reflecting massless Dirac fermions. The inclusion of SOC opens a band gap of 2 meV, which is small, but much larger than in the case of graphene (due to the stronger SOC) and agrees with Refs. [21,36]. When we turn on the vdW interaction we obtain virtually identical results For the combined ArSi system the band structure and DOS are presented in Fig. 4. Without vdW interaction and without SOC we obtain again a band gap of 2 meV, indicating minor influence of the substrate despite the fact that the interlayer spacing between Ar and Si is large. SOC splits the bands near the K point, one forming a perfect Dirac cone and one showing a band gap of 2 meV. We note that the band gap of silicene can be tuned by applying an external electric field, because the insulating Ar does not screen electric fields. The binding energy (E ArSi −E Ar −E Si )/2 per Si atom between silicene and the substrate amounts to −3 meV, both without and with SOC.
Switching on the vdW interaction enhances the band gap to 11 meV without and 10 meV with SOC. This is much larger than in graphene but for many potential device applications still too small. For example, for metal-oxide-semiconductor field-effect transistors a sizeable band gap is required for a good on-off ratio and low power dissipation [37]. If SOC is included, both split bands show energy gaps, where the splitting at the K point is of similar magnitude as seen in the top part of Fig. 4. The larger band gap reflects the relevance of the vdW interaction in the hybrid system, in contrast to freestanding silicene, while SOC splits the bands near the Fermi level. The binding energy per Si atom accordingly is enhanced to −32 meV, which, however, is still very small. In particular, it is much less than reported for silicene on inert hexagonal boron nitride [21]. Due to the weak interaction, we conclude that solid Ar(111) will support quasi-freestanding silicene.
IV. CONCLUSION
In conclusion, we have discussed the structure and electronic properties of silicene on solid Ar(111). It turns out to be critical to take into account the vdW interaction to obtain realistic results. We have shown that the Dirac cone of freestanding silicene remains intact on Ar(111), which points to a weak interaction with the substrate. In fact, we obtain for the binding energy a small value of −32 meV per Si atom, indicating a quasi-freestanding nature of silicene on Ar(111). Any other substrate employed so far has resulted in fundamental perturbations of the Dirac states, which is not the case on solid Ar according to our simulations. It is likely that separation of silicene from this substrate is possible. | 2,287.6 | 2014-06-03T00:00:00.000 | [
"Physics"
] |
Neurocomputing Device for Image Processing and Classification
This article is concerned with problems of construction of specialized computing device for image processing and classification. The device may be used for classification of objects images in conditions of various image scales of the same object. The device may be realized by neurochips technology.
INTRODUCTION
The problem of classification of the object images be the area of the k th object, knowing that objects images don't intersect.
A set of background images S (x,y) and separate different objects 0 ( , ), 1, k S x y k m = forms the image: (3) Satisfying the conditions 1-3 means that: On solving the problem of classification performed on the image S(x,y) of multi m 0 objects, into unknown in advance number of various classes. First stage of processing is procedure of segmentation of the image S(x,y), mathematically segmentation procedure may be described by the evaluation of the following predicate: The result of predicate (4), the source image in equation (3) = ; we get the following: We see that the ratio (5) is invariant to the change of square scale. Now let us show that the invariant of the ration (5) to the scale changes for any shape. In Fig. 1 is shown that any figure may be shown as a sum of squares the area of which aims to zero. For shape A(x,y), the ratio (5) may be given in the following equation: where 1 , q a a -are the length of the first and last squares; ( 1, ) -are the width of squares. On changing the scale of the image A(x,y) in k times, we have Thus, the ratio (5) is invariant for any shape presented in different scale.
To calculate the area of k-object image, which is in the area of vision D(x,y) using predicate (4) it is enough to calculate the number N k of pixels forming the image and multiply it by the area S p of one pixel: .
To calculate the perimeter of any k image, we consider that it consists of the length of separate pixels. As shown in Fig. 2 any pixel of the image may have one of the following numbers of adjacent pixels.
Pixels marked on Fig. 2 by the digit 1, have 4 neighboring pixels (up, down, right, left), the contribution of theses pixels to the length of perimeter is equal zero.
Pixels marked on Fig. 2 by the digit 2, each have three neighboring pixels; their contribution to the perimeter length is equal to the length of one pixel 1d.
Pixels marked on Fig. 2 by the digit 3, they have two neighboring pixels; their contribution to the perimeter length is equal to 2d.
Pixels marked on Fig. 2 by the digit 4 have only one neighboring pixel; their contribution to the perimeter length is equal to 3d.
Pixels marked on Fig. 2 by the digit 5; their contribution to the perimeter length is equal to 4d.
By analyzing Fig. 2 and the given reasoning, we can calculate the length of perimeter of any image by the following: Where , 1,3 r N r = is the number of excited neurons which have r neighboring excited pixels in the image; N 0 is the number of neurons which don't have neighboring pixels in their space.
Images classification using ratio (5), it is rational to be carried out with the help of technology of neural networks. One of the possible architecture is given in Fig. 3. The device has the area of vision D(x,y) from matrix n 1 ×n 2 of binary x neurons, where n 1 -number of rows, n 2 -number of columns. Every x-neuron corresponds to one pixel of the image D(x,y) .
The function of X-neurons may be described by the following formula: Where U in ; U out -are correspondingly input and output signals of X-neurons; n θ -Threshold value of xneurons. ( 1 1), ( 1 ), ( 1 1), The value of output signals from neurons of field Z k may be determined by the following formula: where k 1 -is a positive constant; Thus, the output signal will be signal proportional to the general number of the excited x-neurons and the area of given image. When output signal of a neuron ( 1, 4) q q = is proportional to the number of elements of the image which have from q to four excited conditions. Using the equation (9), it is easy to receive the signals proportional to number N 0 of neurons which don't have neighboring elements on the image and signals proportional to numbers N r ( 1,3) r = of neurons which has correspondingly from one to three excited elements in the image Having signals proportional to the area and perimeter of the image, it is easy to receive signal as well with the help of ALU block, which will be proportional to the first part of equation (5).the resultant value of signal may be used to classify input image.
RESULTS
Experiments conducted on image like given in Fig. 4 prove the ability to work with the offered algorithm. The developed device is suggested to apply for speeding up image process in different fields of applications. | 1,230.6 | 2005-02-01T00:00:00.000 | [
"Computer Science"
] |
Endogenous β-glucocerebrosidase activity in Abca12⁻/⁻epidermis elevates ceramide levels after topical lipid application but does not restore barrier function.
ABCA12 mutations disrupt the skin barrier and cause harlequin ichthyosis. We previously showed Abca12−/− skin has increased glucosylceramide (GlcCer) and correspondingly lower amounts of ceramide (Cer). To examine why loss of ABCA12 leads to accumulation of GlcCer, de novo sphingolipid synthesis was assayed using [14C]serine labeling in ex vivo skin cultures. A defect was found in β-glucocerebrosidase (GCase) processing of newly synthesized GlcCer species. This was not due to a decline in GCase function. Abca12−/− epidermis had 5-fold more GCase protein (n = 4, P < 0.01), and a 5-fold increase in GCase activity (n = 3, P < 0.05). As with Abca12+/+ epidermis, immunostaining in null skin showed a typical interstitial distribution of the GCase protein in the Abca12−/− stratum corneum. Hence, we tested whether the block in GlcCer conversion could be circumvented by topically providing GlcCer. This approach restored up to 15% of the lost Cer products of GCase activity in the Abca12−/− epidermis. However, this level of barrier ceramide replacement did not significantly reduce trans-epidermal water loss function. Our results indicate loss of ABCA12 function results in a failure of precursor GlcCer substrate to productively interact with an intact GCase enzyme, and they support a model of ABCA12 function that is critical for transporting GlcCer into lamellar bodies.
dogenous GlcCer species, despite the presence of high levels of active GCase enzyme. Moreover, we show that topical application of GlcCer can partially circumvent this blockage, enabling Abca12 Ϫ / Ϫ skin to enhance synthesis of ceramides critical for epidermal barrier function. However, exogenous delivery of GlcCer was only able to restore approximately 15% of the ceramides found in wild-type skin, and this change was insuffi cient to improve the water permeability barrier of the Abca12 Ϫ / Ϫ epidermis.
Abca12 ؊ / ؊ mice
Mice that were heterozygous for a null allele at the Abca12 locus have been previously described ( 13 ). Procedures were approved by the Massachusetts General Hospital Committee on Research Animal Care and conducted in accordance with the USDA Animal Welfare Act and the PHS Policy for the Humane Care and Use of Laboratory Animals.
Skin, epidermis, and lipid isolation
For all the described studies, day 18.5 mouse embryos (E18.5) were obtained using timed pregnancies and Caeserean section. After euthanasia, limbs and tails were removed, a dorsal cut was made at the neck down to the hypodermis and was extended to the tail stump, and then the skin was peeled off in one piece using dissecting forceps ( 17 ). This skin sample, which contains dermis and epidermis, is referred throughout this article as an "embryo whole-skin peel." The histological presentation of these late gestational skin samples is shown in Fig. 2D . To isolate epidermis, the embryonic whole-skin peels were fl oated dermis-side down in 0.5% trypsin (GIBCO) in Dulbecco's phosphate-buffered saline (PBS) at 4°C overnight, and then epidermis was separated from the dermis using tweezers. Total lipid was extracted from the samples by the method of Bligh and Dyer ( 18 ). Briefl y, the whole skin or epidermis derived from one embryo was fi nely minced with scissors in 3.75 ml of a single-phase solution of methanol/chloroform/PBS (2:1:0.75). After 30 min sonication at 40°C using a Branson water bath sonicator, 1 ml of chloroform and 1 ml of PBS were added to achieve phase separation. The organic phase separated upon centrifugation was collected and dried under a gentle nitrogen gas stream.
To model HI and defi ne how a loss of ABCA12 function disrupts the epidermal barrier, we and others have developed mice with inactivating mutations in the Abca12 locus (11)(12)(13). These animals recapitulate key features of the HI syndrome in that Abca12 Ϫ / Ϫ pups do not survive ex utero and display a marked hyperkeratosis of the epidermis. Phenotyping of the Abca12 Ϫ / Ϫ pups shows they lack a proper permeability barrier, which is associated with a profound reduction in skin linoleic esters of -hydroxyceramides (Cer-EOS) and a corresponding increase in their glucosylceramide precursors (GlcCer-EOS) ( 13 ). Consistent with these lipids playing a critical role in forming the SC interstitial lamellae, ultrastructural analysis of the Abca12 Ϫ / Ϫ epidermis showed no SC lamellae, and the stratum granulosum (SG) lacked intact lamellar bodies ( 12,13 ). Furthermore, ABCA12 has been shown to localize to the lamellar bodies ( 14 ). These observations are consistent with a model of ABCA12 function that posits the transporter resides on the limiting membrane of the lamellar body where it acts to specifi cally move GlcCer-EOS across the bilayer for eventual secretion at the SC/SG interface. Though it is well established that ABCA12 is essential for forming the epidermal lipid permeability barrier, little biochemical data exists as to how ABCA12 affects the metabolism of skin ceramides, including the enzymes involved in their synthesis.
Using a new N-terminal anti-ABCA12 antibody that shows no cross-reactivity to other ABCA proteins, we show that ABCA12 is highly expressed in the skin, with the stomach expressing detectable, but markedly less, ABCA12 protein. Given the restricted pattern of ABCA12 expression, we focused on developing a skin organ culture system to explore the metabolic function of ABCA12 in this tissue.
With this system, we show novel biochemical evidence that indicates that loss of ABCA12 blocks the hydrolysis of en- Fig. 1. Synthesis and metabolic conversion of ceramides and glucosylceramides in the epidermis. Nucleated keratinocytes synthesize the precursors GlcCer and SM and store these lipids in lamellar bodies. Upon release into the interstices of the stratum corneum, they are hydrolyzed to Cer by GCase or sphingomyelinase as part of the process that forms the interstitial lamellar lipid domains that surround the corneocytes of the stratum corneum. GlcCer synthase, glucosylceramide synthase.
Histology and immunohistochemistry
E18.5 embryo whole-skins peels were fi xed in Bouin's solution and paraffi n embedded. For immunohistochemistry, slides were deparaffi nized in xylene and ethanol and rehydrated in water. Slides were blocked with 10% normal horse serum and 1% BSA/ PBS, and then incubated with primary antibody (1:75 dilution) in 1% BSA/PBS overnight at 4°C. The slides were then washed, and a secondary biotinylated goat-anti-mouse IgG antibody was applied (1:250 dilution). The avidin-biotin peroxidase complex and DAB chromophore-staining method was used (ABC kit, DAB kit; Vector Laboratories), and the slides counterstained with hematoxylin. a horseradish peroxidase-conjugated goat anti-rabbit antibody and enhanced chemiluminescence (Amersham, Pittsburgh, PA).
TLC procedures
Lipids extracted from the epidermis of E18.5 mouse embryos were separated by TLC using a three-solvent system described previously ( 19 ), radiolabeled lipids were visualized and quantitated by phosphor imaging (Amersham Biosciences), and total lipids were subsequently quantifi ed by densitometry of the plates charred with cupric sulfate in aqueous phosphoric acid ( 19 ). For preparative extraction, bands were located with iodine vapor, then scraped and extracted in methanol/chloroform 1:2 (v/v).
Mass spectrometry lipid profi ling procedures
Lipid structure was determined using an ESI-MS/MS approach as reported previously ( 13 ). Scans were performed for precursors of m/z 264 in the positive mode (collision energy, 50 V) for GlcCers and Cers. Internal standards for quantifi cation were d18:1/14:0Cer and d18:1/12:0GalCer. The amounts of the analyte lipids are indicated in units of normalized mass spectral signal with one unit representing the amount of lipid producing the same amount of signal as 1 nmol of the internal standard. in parallel conditions using this GlcCer preparation spiked with a radioactive GlcCer tracer, and estimated 3.2 ± 0.5 µg and 3.7 ± 0.6 µg of Cer were formed during 24 h and 48 h incubation, respectively. This is equivalent to a replenishment of 13% and 15% of wild-type Cer levels.
Statistical analysis
All statistical analyses were performed using a Student t -test. P < 0.05 was considered statistically signifi cant.
ABCA12 tissue expression
We fi rst examined the tissue distribution of ABCA12. When assessed in the adult mouse, the expression of ABCA12 mRNA was detectable in a number of other tissues besides skin, including the heart, intestine, stomach, and kidney ( Fig. 2A ). To corroborate the mRNA fi ndings, protein expression was assayed using a novel antibody that we had generated against the fi rst putative N-terminal loop of ABCA12. This antibody detected ABCA12 in wild-type but not Abca12 Ϫ / Ϫ skin and showed no cross-reactivity against multiple ABCA transporters, including ABCA1, ABCA2, ABCA3, and ABCA7 expressed in HEK 293 cells ( Fig. 2B ). Given this lack of cross-reactivity, we probed a tissue blot derived from E18.5 mouse embryos with this antibody. Compared with ABCA12 expression in the epidermis, there was much reduced but detectable expression of ABCA12 in the stomach, whereas no signal was detected in the intestine or any of the other tissues analyzed, including the lung ( Fig. 2C ). When the blots were stripped and reprobed for ABCA1, ABCA2, ABCA3, and ABCA7, no expression of any of those transporters in the epidermis was evident, providing evidence that a compensatory upregulation of alternative ABCA transporters does not occur in the absence of ABCA12 ( Fig. 2B , fi rst two lanes). As reported previously (11)(12)(13), the loss of ABCA12 function results in a marked hyperkeratosis of the SC in E18.5 mouse embryos ( Fig. 2D ). Given our detection of ABCA12 protein in the stomach, we tested whether there were histological abnormalities in this organ of the Abca12 Ϫ / Ϫ mouse. Histological analysis of day 18.5 mouse embryo stomach revealed a hyperkeratosis in the gastric mucosa, which was confi ned to the forestomach region of Abca12 Ϫ / Ϫ animals ( Fig. 2E ). No other anatomical or histological differences were identifi ed in the stomach. In composite, these results demonstrate a very restricted distribution pattern of ABCA12 protein, with epidermal expression predominating.
De novo ceramide synthesis
Having established that ABCA12 expression is largely confi ned to the epidermis, we sought to clarify the function it plays in the skin. To this end, skin samples from E18.5 embryos were cultured in media containing [ 14 C] serine. Because serine palmitoyltransferase initiates de novo sphingolipid synthesis by condensing serine and palmitoyl-CoA ( Fig. 1 ), use of radiolabeled serine allows for the monitoring of de novo synthesized glucosylceramide  -Glucocerebrosidase assays SC samples were scraped from E18.5 embryo skins, and then tissue lysates and in vitro measurements were performed as described previously ( 20,21 ). For in situ activity, embryo whole-skin peels were preincubated with 0.54% sodium taurocholate in McIlvaine citrate-phosphate buffer pH 5.6 (taurocholate buffer) ( 21 ) in the presence or absence of 10 mM CBE for 30 min. Then 100 µl of a solution of 0.5 mM 4-methylumbelliferyl- -Dglucopyranoside (Sigma Catalog # M3633) in taurocholate buffer was added onto the epidermis, and samples were incubated in the dark. The reaction was stopped with 1 ml of 200 mM carbonate-bicarbonate buffer pH 10.5. Samples were vortexed and centrifuged at 14,000 g at 4°C, and fl uorescence (Ex/Em = 355/460 nm) was measured in the supernatant. In situ GCase activity represents the difference in activity obtained with or without CBE.
C]GlcCer conversion
For generation of labeled [ 14 C]GlcCer precursor, embryo whole skins were cultured as described above but with 20 µCi/ skin of [ 14 C]serine for 24 h and purifi ed as described in the TLC procedures. A solution of 40,000 dpm of [ 14 C]GlcCer/ml in taurocholate buffer was prepared by water bath sonication. Onehundred microliters of this solution was added to the epidermal surface of an E18.5 embryo whole-skin peel. After 1 h at room temperature and an overnight incubation at 4°C, lipids were extracted from the whole-skin peel as described in the lipid isolation section above. Generated [ 14 C]Cer products were separated from the input GlcCer substrate by silica gel column chromatography ( 22 ) with the following modifi cation: Ceramides were eluted with 7 ml chloroform/methanol 98:2 (v/v) followed by 1.5 ml chloroform/methanol 92:8 (v/v). Then unhydrolyzed GlcCer was subsequently eluted with an additional 6.5 ml chloroform/methanol 92:8 (v/v). An aliquot of these fractions was quantifi ed with liquid scintillation counting and the reminder analyzed by TLC as described above. To control for nonenzymatic GlcCer hydrolysis that may occur during the extraction process, radiolabeled GlcCer was added to a control skin sample immediately before lipid extraction during each experiment. These controls showed that observed ceramide formation was not due to the extraction process in either the wild-type or Abca12 Ϫ / Ϫ samples.
Acute GlcCer treatment
To isolate milligram GlcCer quantities that were needed to conduct the exogenous treatment assays, E18.5 Abca12 +/+ and Abca12 +/ Ϫ embryo total epidermal lipid, dissolved in chloroform, was applied to a column of hexane-equilibrated silica gel 60 (6 mg of lipid/g of silica). After washing the column with 18 ml of chloroform/acetic acid 1000:1 (v/v) per gram of silica, and 18 ml of chloroform/methanol 191:9 (v/v), GlcCers were eluted with 14 ml of chloroform/methanol 92:8 (v/v). This method yielded 0.125 mg of GlcCer /embryo epidermis of a purity that ranged 91-95% as assayed by TLC. The isolated fraction contained both GlcCer and GlcCer-EOS species. A suspension of 1 mg/ml these GlcCers in taurocholate buffer was prepared by water sonication. Freshly isolated E18.5 embryo whole-skin samples of 0.5 × 0.5 cm were placed dermis-side down onto a 1 cm diameter Millicell-PCF 0.4 µm membrane inserts (Millipore). A dose of 25 µl of the 1 mg/ml GlcCer suspension or 25 µl of vehicle (taurocholate buffer) was added onto the epidermal side of the skin samples. Inserts were incubated at 37°C in 60 mm dishes containing 3.6 ml of CnT-02-3DP1 Epidermial Keratinocyte 3D Prime medium (CellnTec) for 6, 12, 24, and 48 h. At the specifi ed treatment times, epidermal barrier function was assayed as previously described using the gravimetric method ( 11,13 ). Experiments run was principally composed of very long chain ceramide esters. Scans of Abca12 Ϫ / Ϫ samples showed there was a signifi cant reduction (81%) in these lipids compared with amounts isolated from the Abca12 +/+ samples, which we have previously shown, using collision induced product ion analysis, to be linoleic esters of -hydroxy very longchain ceramides (Cer-EOS, Fig. 3B ) ( 13 ). Profi ling lipids extracted from the region of the plates where lipid "b" migrated indicated that "b" was principally composed of 16 to 22 carbon acyl chain ceramides (40%, Cer-d18:1(16:0), 30% Cer-d18:1 (18:0), Fig. 3C ). These two shorter acyl chain ceramides were signifi cantly increased in the absence of ABCA12 (by 6.5-and 18.6-fold, respectively ). These lipids have been previously reported to be generated by sphingomyelinase ( 10 ). Scans of lipid "c" showed and its conversion into ceramide. The charred TLC plate in Fig. 3A depicts total lipid profi les of the epidermis isolated from these skin cultures. Lipids from Abca12 Ϫ / Ϫ epidermis showed a pattern that clearly differed from the lipids detected in Abca12 +/+ epidermis. The most dramatic changes in these samples were in the species marked "a" to "d" ( Fig. 3A ). Species denoted "a" and "b" comigrated in the region of ceramide standards and lipids denoted "c" and "d" in the region of glucosylceramide standards. To defi ne the molecular nature of these lipids, we ran preparative TLC plates to isolate microgram quantities of these lipids from an additional set of Abca12 +/+ and Abca12 Ϫ / Ϫ epidermal samples (n = 5). The isolated lipids were subjected to quantitative lipid profi ling using an ESItriple quadrupole tandem mass spectrometer. Lipid "a" Furthermore, the modest effect of the inhibitors on the amount of labeled Cer in the null epidermis ( Fig. 4 ) suggests that the ceramides that persist in the null skin are mainly processed through SM precursors and not from GlcCer. This is further supported by the behavior of serine labeled lipid "b." Though present in signifi cantly greater amounts in the Abca12 Ϫ / Ϫ epidermis, its levels were not signifi cantly altered by either CBE or PDMP treatment in either ABCA12 wild-type or null skin.
GCase expression and activity
One possible explanation for our results is that a loss of ABCA12 inhibits GCase expression or enzymatic function, leading to the observed GlcCer buildup. This possibility was tested by measuring GCase activity and protein in the Abca12 Ϫ / Ϫ epidermis and shown not to be the case. In fact, 5-fold higher levels of GCase were detected in the Abca12 Ϫ / Ϫ epidermis of E18.5 mouse embryos by immunoblot analysis of tissue lysates ( Fig. 5A , P < 0.01). Furthermore, immunostaining of Abca12 +/+ and Abca12 Ϫ / Ϫ whole-skin sections for GCase levels was used to assess the distribution of the enzyme. In Abca12 +/+ epidermis, staining was evident in the nucleated cellular layers in a cytoplasmic distribution, whereas in the stratum corneum, the staining localized to the interstitial spaces. Immunostaining of the Abca12 Ϫ / Ϫ embryo whole skins confi rmed that levels of GCase were increased. As with the Abca12 +/+ tissue, however, this abundant GCase staining in the hyperkeratotic Abca12 Ϫ / Ϫ stratum corneum maintained a largely interstitial distribution ( Fig. 5C ).
that the most signifi cant change was a 5-fold increase in the level of linoleic esters of -hydroxy very long chain glucosylceramide in the Abca12 Ϫ / Ϫ samples (GlcCer-EOS, Fig. 3D ). Analysis of lipid "d" showed that it was principally composed of GlcCer species containing acyl chains of 24-26 carbons which were also increased in the null epidermis compared with the Abca12 +/+ samples ( Fig. 3E ) as quantitated by mass spectrometry. Next, we explored the ceramide synthesis pathways in skin cultures using small molecule inhibitors of glucosylceramide synthase (PDMP) and GCase (CBE) to respectively block synthesis of glucosylceramides and their conversion into ceramides (see Fig. 1 ) ( 23 ). In Abca12 +/+ samples, treatment with CBE signifi cantly reduced the amount of serine-labeled Cer-EOS by 76%, whereas labeling of their glycosylated precursors, GlcCer-EOS "c" and GlcCer "d" were signifi cantly increased by 3.5-and 2.4-fold, respectively ( Fig. 4 ). This labeling pattern resembled that of the Abca12 Ϫ / Ϫ samples without inhibitor treatment, with the exception that lipid "b" did not accumulate. When PDMP, an inhibitor of glucosylceramide synthase ( 24,25 ), was used in the wild-type skin, the labeling of lipids "a," "c" and "d" was signifi cantly decreased ( Fig. 4 ). In contrast, in the Abca12 Ϫ / Ϫ epidermis, CBE inhibition of GCase did not produce a signifi cant change in the labeling of GlcCers (lipids "c" and "d"); however, PDMP treatment of the null skin signifi cantly decreased the level of labeled GlcCers, indicating that GlcCers are still being actively synthesized in the null epidermis but are not being hydrolyzed by GCase. Circumventing the Abca12 ؊ / ؊ metabolic defect 499 mice, even after normalization of the activity to total SC protein levels ( Fig. 5E ). Thus our data indicate that loss of ABCA12 function is associated with increased GCase protein and activity in the SC. Finally, the enhanced levels of GCase protein in Abca12 Ϫ / Ϫ epidermis appear to result from a posttranscriptional process, as loss of ABCA12 caused no signifi cant change in the amount of Gba1 mRNA, which encodes GCase ( Fig. 5B ).
Topical application of GlcCer precursors
The above data indicated that the enzyme activity required for the conversion of ceramides from glucosylceramide precursors was not impaired in the Abca12 Ϫ / Ϫ skin. This suggests that in the absence of ABCA12, endogenously synthesized GlcCer fails to localize to the intercellular lamellar region of the stratum corneum, which is rich in GCase activity. If this hypothesis were correct, we The observations of reduced GCase enzymatic products (i.e., ceramides) in the presence of abundant GCase protein in the epidermis of Abca12 Ϫ / Ϫ mice led us to examine the activity of the GCase present in the null skin. To accomplish this, we measured GCase activity in situ in skin samples from E18.5 mouse embryos based on a method developed by Takagi et al. ( 21 ). In accord with our observations of increased GCase protein levels, the null skin displayed 10-fold higher in situ GCase activity than was present in the Abca12 +/+ samples ( Fig. 5D ). To further determine whether this increase simply refl ected a greater abundance of hyperkeratotic skin rather than increased GCase activity, GCase activity was measured in vitro using stratum corneum lysates from samples obtained by physical abrasion of the skin of E18.5 mouse embryos. This assay showed signifi cantly higher levels of GCase activity in the null samples compared with those taken from wild-type Ϫ / Ϫ day 18.5 embryos are not signifi cantly different from those of Abca12 +/+ mice as determined by RT-QPCR; graphed is the GCase/  -actin ratio expressed relative to that of the Abca12 +/+ samples (n = 4, ± SD, P = 0.7). C: Immunostaining of GCase protein in day 18.5 embryo skin cross-sections. The brown GCase staining is markedly increased in the Abca12 Ϫ / Ϫ epidermis, particularly in the upper SC (bar = 30 µm; SB, stratum basale; SS, stratum spinosum). D: GCase activity measured in situ on whole-skin samples (n = 3, ± SD, P < 0.01). E: GCase-specifi c activity measured in vitro using SC lysates ( Abca12 +/+ n = 4, Abca12 Ϫ / Ϫ n = 3, ± SD, P < 0.05).
substrate. Thus, these controls show the ceramides produce during this incubation period were catalyzed by endogenous GCase. Remarkably, considering the near complete block in GCase processing of endogenously synthesized GlcCers, the Abca12 Ϫ / Ϫ skin synthesized all major ceramides from the topically applied precursors. Importantly, this included the generation of the missing ceramide "a" or Cer-EOS ( Fig. 6B ). Thus, circumvention of the block in endogenous GlcCer processing and restoration of ceramide production by topical administration of GlcCer precursors onto skin lacking ABCA12 activity are possible. Given endogenous GCase in Abca12 Ϫ / Ϫ stratum corneum could generate lost barrier ceramides when provided an exogenous source of GlcCer substrate, we tested whether this improved performance of the barrier. As above, a suspension of GlcCer in buffer or buffer alone was applied to the epidermal side of freshly excised E18.5 reasoned that topical application of GlcCer should be able to bypass this transport defect and would restore ceramide production in the Abca12 -/stratum corneum. To test this hypothesis, we isolated [ 14 C]GlcCer from [ 14 C] serine-radiolabeled mouse skin cultures by preparative TLC and prepared a solution of this radiolabeled precursor. The solution was applied topically to the stratum corneum of unlabeled E18.5 embryo whole-skin samples. After incubation for 1 h at room temperature and overnight incubation at 4°C, lipids were extracted from the whole-skin sample, and ceramides were separated from their nonhydrolyzed precursors using a Silica Gel column ( 22 ). Signifi cantly, the Abca12 Ϫ / Ϫ skins converted 21-25% of the applied GlcCer into ceramides, while the Abca12 +/+ skin converted 11-14% ( Fig. 6A ). Additional controls performed in parallel during these experiments showed GlcCer hydrolysis did not occur when the skin samples were extracted immediately after the addition of radiolabeled by inducing sphingomyelin hydrolysis ( 28 ), which results in the generation of shorter acyl chain ceramides of the class we have identifi ed to be contained in the band "b" ceramides that are elevated in the Abca12 Ϫ / Ϫ epidermis. Thus, as with genetic loss of GCase activity, we now demonstrate specifi c alterations in sphingolipid metabolism in the Abca12 Ϫ / Ϫ epidermis that are occurring despite intact GCase activity. A substantial amount of literature demonstrates that inhibiting cholesterol, fatty acid, ceramide, or glucosylceramide synthesis leads to abnormal lamellar bodies and impaired barrier function ( 7,(29)(30)(31)(32). Thus, stratum granulosum cells must generate all these lipids and then transport them into the LB where they can be assembled into a lamellar structure required for barrier function ( 7 ). In normal skin, ABCA12 localizes to LB ( 33 ), and keratinocytes demonstrate a widely distributed pattern of cellular GlcCer expression ( 1,12 ). In contrast, in HI skin, GlcCer fails to localize to the periphery of the keratinocyte cytoplasm ( 34 ), and there is a loss of lamellae structure in the stratum corneum and LB ( 1,(11)(12)(13). These data, together with our observations of the enzymatic processing of de novo-synthesized ceramides in the Abca12 Ϫ / Ϫ skin and the partial circumvention of the GlcCer processing defect via topical application of exogenous precursors, are consistent with a critical role for ABCA12 in transporting GlcCer to the extracellular space via its action at the lamellar body membrane ( 1, 7, 11-13, 35, 36 ). With loss of ABCA12, an accumulation of GlcCer species is observed that is due not to a block in GCase enzyme activity, as with Gaucher's patients, but from a defect in the traffi cking of GlcCer into the LB and hence to the extracellular domains of the stratum corneum. Without proper supply of this precursor to the interstices of the stratum corneum, there is a loss in the production of ceramides via the GCase pathway. Since some ceramide species can only be generated via the activity of GCase ( 28,37 ), the result is loss of specifi c epidermal ceramides that are critical for maintaining the permeability barrier function of the skin ( 7 ). ABCA12 tissue distribution in the neonate is largely epidermal, and in the absence of ABCA12 function, no compensatory upregulation of other ABCA-class transporters, including ABCA1, ABCA2, ABCA3, and ABCA7, was detected in the Abca12 Ϫ / Ϫ epidermis. Expression of ABCA12 was also observed in the stomach; as in skin, the Abca12 Ϫ / Ϫ gastric epithelia presented evidence of a marked hyperkeratosis. This fi nding suggests that ABCA12 has a role at the affected region of the stomach that results in a similar thickening of the keratinized layers as is found in the skin. Keratinized epithelia in certain regions of the stomach is characteristic of rodent gut but not of human stomach. Therefore, these fi nding cannot be translated to explain the gastrointestinal problems described in HI patients ( 38 ). Lastly, supplying GlcCer to the epidermis under the conditions assayed was not suffi cient to restore the permeability barrier. As our experiments were limited to a short treatment period and resulted in only up to 15% restoration of the wild-type ceramide levels, it is possible that
Abca12
Ϫ / Ϫ embryo whole-skin samples. These were grown dermis-side down in a millicell-PCF membrane insert at an air/liquid interface in CnT-02-3DP1 Epidermial Keratinocyte 3D Prime medium (CellnTec) for 6, 12, 24, and 48 h at 37°C in a humidifi ed tissue culture incubator. At the specifi ed times, the ability of the skin to retain water was measure using a trans-epidermal water loss (TEWL) assay. Application of GlcCer onto Abca12 Ϫ / Ϫ whole-skin samples under the assayed conditions did not show any statistical signifi cant improvement in permeability as compared with the vehicle treatment at any of the measured times ( Fig. 6C ). To confi rm that signifi cant amounts of ceramides are generated and persist during the longer time frame of the 37°C functional assays, we conducted parallel biochemical assays using a radioactive GlcCer tracer spiked into the GlcCer preparation. These control experiments confi rmed this approach was replenishing approximately 13% and 15% of the wild-type epidermal Cer levels in the Abca12 Ϫ / Ϫ epidermis at the 24 h and 48 h incubation points, respectively.
DISCUSSION
In these studies, skin organ cultures from Abca12 Ϫ / Ϫ late-term embryos were used to interrogate ABCA12 function within the epidermis, in which we show the transporter is most abundant. Ex vivo culture of embryonic whole skin, combined with metabolic labeling, small-molecule inhibition, and mass spectrometry lipid profi ling, demonstrates that the absence of ABCA12 induces a block in the hydrolysis of de novo synthesized GlcCer-EOS, as well as other nonester glucosylceramides species. Importantly, the GlcCer processing block is not explained by a decline in either the amount or activity of GCase enzyme. Indeed, Abca12 Ϫ / Ϫ skin contains signifi cantly enhanced levels of GCase protein and activity, particularly in the stratum corneum. Moreover, we demonstrate that Abca12 Ϫ / Ϫ stratum corneum, when presented with exogenous GlcCer precursors, is able to overcome the GCase-mediated GlcCer conversion defect and regenerate some of the lost ceramides that are known to be critical for formation of the skin permeability barrier. These results provide novel biochemical data into how glucosylceramides accumulate in the Abca12 Ϫ / Ϫ skin. By exploring the enzymatic processing of de novo-synthesized GlcCer, we show that fl ux of this lipid processed by GCase was signifi cantly diminished. These observations now identify a key processing step in the conversion of GlcCer to Cer that, in the absence of ABCA12, accounts for our and other observations of the enhanced steady-state levels of glucosylceramides in the Abca12 Ϫ / Ϫ epidermis (11)(12)(13). Gaucher's disease, caused by mutations in the GCase gene, is also characterized by accumulation of GlcCer, and a subset of Gaucher's patients present with an ichthyotic phenotype ( 26 ). Likewise, mice lacking GCase display ichthyotic skin and accumulate excess GlcCer ( 27 ). Moreover, in the absence of GCase, these animals maintain ceramide levels different treatment paradigms, such as using higher levels of topical exogenous lipid or applying GlcCer as part of a three-component mixture that includes cholesterol and fatty acids, could result in higher ceramide levels and perhaps greater restoration of function. Alternatively, exogenous GlcCer may need fi rst to be taken up into granular cells, and then secreted via the lamellar body secretory system to impact barrier function ( 39 ). Moreover, the loss of ABCA12, which is associated with a number of other pathological skin features, including hyperkeratotic stratum corneum and abnormal protein distribution in the epidermis involving involucrin, transglutaminase 1, and fi laggrin ( 11,13,34,40 ), may cause disruptions that no amount of topical lipid replacement can correct. These defi cits could arise as a consequence of cellular GlcCer accumulation in keratinocytes that may cause lipotoxic effects ( 41 ). Therefore, supplementation of ceramide production by itself may not suffi ce to correct skin barrier function, even if it were to more completely restore levels of extracellular Cer. In a recent report on congenital hemisdysplasia with ichthyosiform erythroderma and limb defect (CHILD) syndrome, an X-linked dominant disorder of distal cholesterol metabolism, treatments targeted to reduce the accumulation of precursors were required to restore skin function in patients that was not restored by the provision of topical cholesterol alone ( 42 ). Thus, as in the CHILD syndrome, dual therapies may be required to correct the cutaneous phenotype of ABCA12-null skin.
In summary, we present novel evidence that strengthens the view that ABCA12 is required to transport de novosynthesized GlcCer to the interstices of the stratum corneum. The results provide biochemical data that advance our understanding of the defect in epidermal sphingolipid metabolism due to loss of ABCA12 activity. These fi ndings suggest new strategies that may be required to ameliorate defects in skin function in individuals with harlequin ichthyosis. | 7,305.2 | 2014-03-01T00:00:00.000 | [
"Biology",
"Medicine"
] |
Reproductive Strategies of the Insidious Fish Ectoparasite, Neobenedenia sp. (Capsalidae: Monogenea)
Fish monogeneans are lethal parasites in aquaculture. We provide the first experimental evidence that a notorious fish monogenean, Neobenedenia sp., can produce viable eggs in isolation for three consecutive generations. We infected individual, isolated, farmed barramundi, Lates calcarifer (Bloch) with a single oncomiracidium (larva) of the hermaphroditic monogenean Neobenedenia sp. Isolated parasites reached sexual maturity at day 10 post-hatch (24°C, 35‰) and laid ∼3,300 embryonated eggs over 17 days. Egg production rapidly increased following sexually maturity on day 10 (58±15 eggs) and peaked on day 15 (496±68 eggs) before gradually decreasing. Neobenedenia sp. exhibited egg laying and egg hatching rhythms. Parasites laid eggs continuously, but egg production increased in periods of darkness (64.3%), while the majority of oncomiracidia (81%) emerged from eggs in the first three hours of light. Eggs laid by isolated ‘parent’ parasites hatched and individual emerging oncomiracidia were used to infect more individual, isolated fish, with three consecutive, isolated, parasite generations (F1, F2 and F3) raised in the laboratory. Infection success and egg hatching success did not differ between generations. Our data show that one parasite, in the absence of a mate, presents a severe threat to captive fish populations.
Introduction
Monogeneans exhibit sophisticated life history strategies in order to ensure their survival in contrasting and unpredictable environments. Evolutionary strategies include multiple reproductive mechanisms, predator avoidance and behavioural responses to host and environmental cues that favour enhanced infection success. In wild populations these strategies ensure some parasites survive to the next generation, whereas in captive populations, where host organisms are confined in high densities, it can lead to parasite epizootics.
Various reproductive mechanisms have been observed in monogeneans including oviparity [1], viviparity [2] and selffertilisation [3]. Most oviparous monogeneans deposit fewer than 100 eggs/parasite/day [4][5][6][7], although some species can produce more than 550 eggs/parasite/day [8]. In viviparous monogeneans, up to three consecutive generations can develop inside the mother parasite. For example, a single individual Gyrodactylus salaris bears the first daughter within 24 hours of the birth of the parent [9] and thus has the capacity to produce six million offspring in four weeks [1]. Self-fertilisation is known to occur in monogenean species that infect the bladder of amphibians [3,10] and ensures reproductive potential when a parasite finds itself alone on a host. Monogeneans also exhibit egg laying and egg hatching rhythms, which can reduce the risk of predation and coincide with host behaviours to ensure infection success. Monogeneans maximise their chances of finding a host by releasing eggs into the environment during certain times of the day or night [7], extending the hatching period [11], responding to hatching cues such as shadows [12], chemicals [11,13], mechanical disturbance [14][15][16] and osmotic changes [17], most of which are generated by the host.
Neobenedenia are marine capsalid monogeneans of critical concern to aquaculture because they exhibit several life history traits that aid their survival. Neobenedenia spp. have direct life cycles with short generation times [18,19] and low host specificity [20,21] which has resulted in major stock losses in several aquaculture fish species (see [18,[22][23][24]). Furthermore, attached parasite stages are transparent, which can reduce predation by cleaner organisms [21]. Eggs are encapsulated by a proteinaceous shell which confers protection to the developing embryo from digestion [25], most chemicals [26][27][28][29] and bacteria [1]. Capsalid monogeneans are hermaphrodites, displaying several types of reproduction including mutual cross-insemination [30], attach-ment of spermatophores to other individuals [31] and selfinsemination (as observed by the copulatory organ lodged in the parasite's own uterus; see [30,32]). However, no studies have experimentally examined whether fish monogeneans can successfully reproduce in isolation and produce viable eggs and larvae.
The aim of this research was to experimentally examine the reproductive strategies of Neobenedenia. Specifically we sought to determine: 1) whether Neobenedenia can reproduce in isolation; 2) Neobenedenia fecundity over time; 3) whether Neobenedenia exhibit egg laying rhythms and/or hatching rhythms. We used a barramundi, or Asian seabass, Lates calcarifer -Neobenedenia sp. model system for our experiments. Barramundi (Perciformes: Latidae) are distributed in estuaries and coastal seas from southwestern India to north-eastern Australia, with approximate latitudes of 625u [33]. This species is among the most important food fishes in tropical Australasia and is farmed throughout eastern and western Asia (China, India, Israel, Indonesia, Malaysia, Philippines, Singapore, Tahiti, Taiwan, Thailand) and Australia [34]. High intensities of Neobenedenia on farmed fish damage host epidermis through attachment and feeding [19,23,35] and increase secondary infections [36,37].
Ethics Statement
This work was conducted using a barramundi, Lates calcarifer -Neobenedenia sp. model system with all procedures approved by the James Cook University Animal Ethics Committee (A1579). Neobenedenia sp. used in experiments were collected from private land in north Queensland, Australia. Future permissions should be directed to Coral Coast Barramundi Pty Ltd.
Source of Animals
Hatchery reared freshwater L. calcarifer; mean size 125623 mm) were purchased from Good Fortune Bay Hatchery, Queensland, Australia for use in experiments 1-4. Fish were not previously exposed to Neobenedenia. Fish were transported to the laboratory in 50 L tanks with air supplied through battery powered aerators and held in fresh water in 100 L aquaria until required. Fish were acclimated to sea water 48 h prior to experiments by increasing salinity to 5, 10, 20, 30 and 35% over 6 h intervals. Sea water used in experiments was UV treated, 10 mm filtered, 35%, unless stated otherwise.
Neobenedenia sp. used in experiments were collected from a land-based marine L. calcarifer farm (Coral Coast Barramundi Pty Ltd) in north Queensland, Australia. An infection was maintained on L. calcarifer (size range 110-220 mm) held in 100 L marine aquaria to provide a continuous source of parasites. Neobenedenia sp. (hereafter as Neobenedenia) investigated in this study is presently unidentified given the absence of diagnostic criteria to differentiate between geographical/host isolates and species [20,21,38] [20,21,33]. Representative parasites were accessioned in the Australian Helminth Collection (AHC) at the South Australian Museum (SAMA AHC 35461; see [39]).
Experiment 1: Reproduction of Isolated Neobenedenia
To determine whether hermaphroditic Neobenedenia can reproduce in isolation, individual, isolated fish were infected with a single oncomiracidium (larva). Oncomiracidia were sourced from embryonated Neobenedenia eggs collected from the laboratory infection. Eggs were incubated in glass cavity blocks in sea water at 25uC in culture chambers on a 12:12 h LD cycle (Sanyo: ML-351 Versatile Environmental Incubation Chamber). A third of the solution (2 mL) was exchanged every 24 h and eggs were monitored daily under a stereomicroscope using both transmitted and incident light. When eye spots were observed in the eggs, monitoring was increased to every 2 h in order to obtain newly hatched oncomiracidia. Individual oncomiracidia ,4 h old were gently aspirated using a fine-tip glass pipette under a stereomicroscope and introduced to a 10 L aquarium containing an individual L. calcarifer in 6 L of sea water. Each oncomiracidium was released at the bottom of the aquarium to avoid the effects of surface tension and currents which can trap and kill oncomiracidia [1]. When the oncomiracidia were introduced, air supply to the fish was stopped for 1 h in order to reduce water currents and thereby increase infection success [19]. Thirty replicates were made at room temperature (24.360.2uC). Salinity in each aquarium was checked using a refractometer every 24 h and adjusted by adding distilled water to maintain 3561%.
To determine the onset of egg production, a piece of 5 cm 2 fine gauge (0.560.5 mm) netting was immersed in each aquarium and checked daily under a stereomicroscope. Eggs have filamentous strings which easily entangle on netting [40]. The day that eggs were observed on the netting was recorded as time to sexual maturity. Eggs always entangle on netting on the day of sexual maturity (AK Brazenor, unpublished data). Netting was renewed daily and any suspended eggs remaining in the sea water were collected daily by filtering the solution through a 60 mm mesh.
In order to confirm isolated fish were infected by a single, individual parasite, fish were immersed in fresh water at the end of the experiment, which kills Neobenedenia [see 35]. Fish were bathed in 1 L of fresh water containing a mild sedative (Aqui-S 1:1000) for 5 min. The fresh water solution and the body surface of the fish were examined twice under a stereomicroscope to collect detached and attached parasites, respectively.
We sought to determine the reproductive viability of consecutive generations of reproductively isolated parasites for a single parasite lineage. Eggs laid by a randomly selected isolated 'parent' parasite were incubated in culture chambers (25uC, 35%). A single oncomiracidium from these eggs was used to infect each of 15 replicate, isolated L. calcarifer. This process was repeated to infect 10 and 30 fish using oncomiracidia from generation F1 and F2, respectively (Table 1). Infection success was recorded as the number of oncomiracidia that attained sexual maturity from the number of fish challenged.
Experiment 2: Egg Hatching Success
In order to assess the viability of eggs laid by isolated parasites, egg hatching success was determined for a single parasite lineage for three consecutive, reproductively isolated, Neobenedenia generations. A total of 30 fish (ten for each generation) were infected as per the methods in Experiment 1. Infection success was recorded as the number of oncomiracidia that attained sexual maturity from the number of fish challenged. Neobenedenia eggs were collected by filtering the aquarium sea water through a 60 mm filter three days following sexual maturity. Following egg collection, individual fish were bathed in fresh water to confirm infection by an individual parasite (see Experiment 1). Clusters of eggs (containing 8-46 individual eggs) were incubated in sea water in cavity blocks in laboratory conditions (natural light, 24.360.1uC) with six replicate egg clusters made for each infected fish. Cavity blocks were filled with sea water to the brim and covered with a glass cover. Blocks were monitored for egg hatching every 24 h, when one third of the solution was changed with minimal disturbance to the eggs. When hatching was observed, oncomiracidia were removed with a pipette. Hatching experiments were continued until 48 h passed without hatching in any treatment. Hatching success of Neobenedenia eggs was measured as the number of oncomiracidia removed divided by the total number of eggs.
Experiment 3: Fecundity of Isolated and Cross-fertile Neobenedenia
To determine the fecundity of isolated parasites, egg production was monitored every 24 h. Twenty-five fish were infected with an individual oncomiracidium sourced from the laboratory infection (see Experiment 1). All aquaria (10 L with 6 L sea water) were maintained in laboratory conditions (24.360.1uC, 3561%). Ten fish (40%) were successfully infected. Daily egg production was determined by filtering the aquarium sea water through a 60 mm filter at 1800 each day. Immediately prior to filtering, fish were gently removed by hand and placed into a new aquarium containing fresh sea water. Eggs were counted under a stereomicroscope using a hand held counter to determine egg production/ parasite/day. The filtered sea water was also examined carefully following death and detachment of a parasite. The experiment was terminated when mean egg production was ,50 eggs/day for two consecutive days. At the termination of the experiment, individual fish were bathed in fresh water to confirm infection by an individual parasite (see Experiment 1).
To determine the fecundity of parasites given an opportunity to cross-inseminate, egg production was monitored every 12 h for fish infected with multiple oncomiracidia. Groups of ten oncomiracidia were used to challenge three fish, maintained in three separate aquaria in laboratory conditions. Egg production was monitored every 12 h (at 0600 and 1800). The experiment was terminated when mean egg production was ,50 eggs/day for two consecutive days. On the day the experiment was terminated, fish were bathed in fresh water and parasites were counted. Egg production in each replicate was divided by the total number of parasites recovered to determine mean number of eggs laid per parasite.
Experiment 4: Egg Laying Rhythm
To determine whether Neobenedenia exhibit an egg laying rhythm, egg production was monitored every 3 h for three days. Four fish infected with an individual, isolated Neobenedenia, were monitored between day 12 and 15 post-infection. Ten litre aquaria containing individual infected fish were placed in culture chambers on day 10 post-infection on a 12:12 h LD cycle at 25uC. Aquaria were aerated with battery operated aerators and salinity was maintained at 3561%. Egg production was determined every 3 h for three days (72 h) by filtering the aquarium sea water through a 60 mm filter, commencing at the first period following the onset of darkness (1800-2100) on day 12. Immediately prior to filtering, fish were gently removed by hand and placed into a new aquarium with fresh sea water. Eggs were counted under a stereomicroscope using a hand held counter to determine egg production/parasite/3 h. At the termination of the experiment, individual fish were bathed in fresh water to confirm infection by an individual Neobenedenia (see Experiment 1).
Experiment 5: Hatching Rhythm
To determine whether Neobenedenia exhibit an egg hatching rhythm, hatching was observed every 3 h throughout the day and night until all eggs had hatched. Eggs were sourced from parasites infecting fish held in the laboratory infection. Individual infected fish were removed from 100 L tanks to a 10 L aquarium with fresh sea water containing pieces of 5 cm 2 fine gauge netting for 2 h. Netting was removed and immediately placed in a Petri dish containing fresh sea water. Pieces of netting were randomly chosen and cut with dissecting scissors to obtain pieces with ,20 to 25 entangled eggs. A piece of netting was placed in each of 10 replicate glass cavity blocks and filled with fresh sea water to the brim and sealed with a glass lid.
Cavity blocks were exposed to natural light from a window in the laboratory. Temperature (24.160.1uC) was measured using a Hach Temperature Meter (HQ30d/LDO101). Approximate official times of sunrise and sunset during the experiment were 0645 and 1755, respectively. Eggs were monitored daily and a third of the sea water (2 mL) was exchanged daily. When eye spots were detected on developing embryos, monitoring was increased to every 3 h. When oncomiracidia began hatching, each piece of netting was transferred to a new cavity block containing fresh sea water at 3 h intervals, throughout the day and night. During the night, netting was transferred using dim light directed away from the cavity blocks. After each transfer, a few drops of formaldehyde were added to the cavity blocks from which the netting had been removed. Formaldehyde rapidly killed any oncomiracidia that had hatched in the block in the previous 3 h period [41]. Oncomiracidia were counted under a stereomicroscope using a hand held counter. The experiment was terminated when all embryonated eggs had hatched. Non-embryonated eggs (n = 7) were excluded from the analyses.
Data collected for each of the five experiments described was made publically available in the James Cook University research data repository: https://research.jcu.edu.au/researchdata/default/ detail/d9eda2a2d49ef0b1ab4de74a2c77c826/
Statistical Analysis
A chi-square distribution was used to examine proportional infection success between consecutive isolated generations. Egg hatching data were analysed by permutational analysis of variance in the PERMANOVA function of PRIMER 6.0. PERMANOVA compares the observed value of a test statistic (F-ratio) against a recalculated test statistic generated from random permutation of the data. PERMANOVAs with 999 permutations based on Euclidean distance were used to test the effect of generation on hatching success with fish as a nested factor in the design. Once egg production started in Experiment 3, fecundity increased rapidly to a peak approximately six days later, and then declined more slowly over subsequent days. This is a relatively common pattern of fecundity in invertebrate species (see [42]) and can be modelled in a variety of ways. We chose to use a model of the following form y = a x b e 2cx (where y is the number of eggs produced, x is the reproductive age in days). The model was fitted using the nls function in SPlus/R. An ANOVA was used to determine significant difference in average fecundity between model groups. Levene's test was used to compare model fit between groups. Day 25 and 26, where mean egg production was ,50 eggs/parasite/day, were excluded because less than three replicate fish remained in the isolated group. A chi-square test was used to examine the egg hatching rhythm using the proportion of eggs hatched during 3 h intervals. Significance was accepted at p, 0.05.
Multiple Generations from Isolated Neobenedenia
Single Neobenedenia infecting an individual, isolated fish, laid embryonated eggs that hatched into viable oncomiracidia. A further two consecutive, isolated, generations were reared in the laboratory (Table 1). There was no significant difference in infection success (p = 0.277) or hatching success (p = 0.723) between consecutive generations (Table 1), however, fish host had a significant effect on hatching success (p = 0.001). Following fresh water bathing, all experimental fish were confirmed to be infected with a single, individual Neobenedenia.
Fecundity
Individual, isolated Neobenedenia were fecund and produced 3,229637 eggs/parasite over the course of the experiment (17 days of egg-laying; or 862 eggs/parasite/hour (e/p/h) or 190611 eggs/parasite/day (e/p/d)). All parasites reached sexual maturity on day 10 post-infection (at 24.260.1uC). Egg production rapidly increased from the day of sexual maturity (58615 on day 10) and peaked on day 15 (496668) post-infection (Fig. 1). Following day 16, egg production gradually decreased, to almost negligible egg production on day 25 and 26. A total of eight fish mortalities occurred over the course of the experiment with 9, 8, 6, 3 and 2 fish remaining on days 12, 13, 20, 24 and 26, respectively. No dead, detached parasites were observed in filtered sea water for the duration of the experiment. Following fresh water bathing, all experimental fish (including mortalities) were confirmed to be infected with a single, individual Neobenedenia.
There was no significant difference between fecundity of isolated parasites and fish infected with multiple parasites (p = 0.44). Three fish challenged with multiple oncomiracidia (n = 0) were successfully infected with three, three and four parasites, respectively. Neobenedenia with the opportunity to crossinseminate produced 861 e/p/h or 191622 e/p/d or 2,865677 eggs/parasite over the course of the experiment (15 days), with 64.3% of eggs produced during periods of darkness (Fig. 2). Parasites reached sexual maturity on day 10 post-infection (Fig. 2). Egg production rapidly increased from 6665 on day 10 to 489643 on day 15. On day 16, egg production began to gradually decrease, prior to almost negligible egg production on day 23 and 24 (Fig. 2). No fish mortalities occurred during the experiment, and there were no dead, detached parasites observed in filtered sea water for the duration of the experiment. Parameter estimates for isolated and cross-fertile individuals modelled separately are shown in Table 2. Although there was no significant difference in average fecundity between groups, the model better described the crossfertile group (R 2 = 0.93) than the isolated group (R 2 = 0.585). Comparing the residuals of each model with a Levene's test indicated that the residual variance was significantly higher in the isolated group than the cross-fertile group (p = 0.0048).
Egg Laying Rhythm
Neobenedenia laid eggs continuously, but exhibited a distinct egg-laying rhythm with more eggs laid during periods of darkness. Egg production began gradually increasing in the 3 h period prior to darkness and during periods of darkness, peaking between midnight and 0300 (Fig. 3). Production decreased in the 3 h period prior to illumination and the lowest egg production was between midday and 1500 (Fig. 3). Each parasite laid an average of 2264 eggs/h in periods of darkness and 1262 eggs/h in periods of light between day 12 and 15 post-infection.
Egg Hatching Rhythm
Neobenedenia exhibited a distinct hatching rhythm (p,0.001) with the majority of oncomiracidia (81%) hatching in the first 3 h of light (Fig. 4). Eggs began hatching on day 5, with the majority of hatching occurring on days 7 and 8. Egg hatching ceased on day 8. A total of 216 embryonated eggs were incubated and all eggs hatched during periods of natural light (0600-1500) with no eggs hatching between 1500 and 0600 on any day/night (Fig. 4).
Discussion
This study provides unambiguous experimental evidence that the fish monogenean, Neobenedenia sp., can successfully reproduce in isolation. One isolated Neobenedenia has the capacity to produce more than three thousand eggs that hatch into infective larvae within two weeks (Fig. 1), revealing that low Neobenedenia burdens in host populations do not necessarily restrict reproductive potential. Furthermore, the progeny of isolated parasites are viable for at least two more consecutive isolated generations (Table 1). While inbreeding tends to decrease hatching and infection success and the genetic diversity of parasite populations [43], we found no significant difference in egg hatching or infection success in consecutive isolated generations (Table 1). Moreover, the nested factor of fish was significant, indicating that the relationship between parasites and their individual hosts is an important aspect in determining parasite reproductive success.
Self-fertilisation is a strategy commonly seen in parasitic platyhelminths where low parasite burdens occur in host populations or where there may be a high frequency of single parasite infection [6,44,45]. At least four species of monogeneans in the bladders of amphibians are capable of producing viable eggs in isolation [3,6,10]. It is possible that Neobenedenia examined in this study reproduced by natural parthenogenesis (a form of asexual production where growth of embryos can occur without fertilisation), however, this is unlikely considering that Neobenedenia have reproductive organs of both sexes. Kearn and Whittington [30] observed two capsalid monogenean species, Benedeniella macrocolpa and B. posterocolpa, self-inseminating in preserved specimens mounted on slides, but insemination via the vaginal route is not an option in Neobenedenia spp. as there is no vagina. Insemination in Neobenedenia spp. is most likely achieved by sperm being introduced via the common genital pore or the uterus. Indeed, Whittington and Horton [20] observed the penis of one Neobenedenia melleni lodged in its own uterus. Selfinsemination has been observed in live specimens of Neobenedenia girellae (see [46]) and Heterobothrium okamotoi (Monogenea: Diclidophoridae) (see [32]). Furthermore, Ogawa et al. [46] suggested that self-insemination in N. girellae may involve passage of sperm through the tegument from externally attached spermatophores. While the specific mechanism of self-insemination was not determined, our study provides the first experimental evidence that capsalid monogeneans of fish can produce viable eggs in isolation.
Parasites that exhibit high fecundity increase the likelihood of offspring successfully locating and infecting a new host. Isolated Neobenedenia were fecund, with egg production rapidly increasing following sexual maturity, before peaking and slowly decreasing over time (Fig. 1). This trend is typical of many invertebrate species [47,48]. Neobenedenia egg production was negligible by the time adult parasites were ,23 days old, indicating that fecundity was captured over the reproductive life span of the parasite ( Fig. 1 and 2). Egg production varied with parasite age and time of day, indicating that egg production measured on an hourly or daily rate may not accurately represent parasite fecundity ( [49]; Fig. 1; Fig. 3). Egg production can also be influenced by other environmental and host variables, including temperature [50][51][52]. Parasite fecundity can vary between self-fertile and cross-fertile individuals. Wedekind et al. [53] reported that self-fertile cestodes, Schistocephalus solidus, infecting stickleback, Gasterosteus aculeatus, exhibited higher fecundity compared to parasites placed in pairs. In contrast, Tinsley and Owen [3] found that multiple infections of the monogenean Protopolystoma xenopodis infecting the toad, Bufo regularis, sometimes resulted in greater output per individual than isolated parasites. In our study, there was no significant difference in fecundity for isolated and cross-fertile Neobenedenia. It is plausible that Neobenedenia may not have cross-fertilised, despite being infected with multiple individuals and their ability to 'crawl' along the external surfaces of fishes in order to locate another parasite [54]. Thus, molecular methods are warranted to quantify the frequency of cross-fertilisation in Neobenedenia.
Parasite egg laying rhythms could be a predator avoidance behaviour and could also align with temporal host behaviours [25]. Neobenedenia laid eggs continuously, but significantly more eggs (64.3%) were laid at night (Fig. 3 & 4). Egg-laying rhythms are common in many invertebrates, with most releasing their gametes during periods of darkness ( Fig. 3; [55,56,57]). The egg laying rhythm of Diplozoon homoion gracile (Monogenea: Diplozoidae), a gill parasite of southern barbel, Barbus meridionalis, is also nocturnal [7]. Similarly, Mooney et al. [51] found that Heteraxine heterocerca (Monogenea: Heteraxinidae) a gill parasite of Japanese yellowtail, Seriola quinqueradiata, laid eggs continuously, but more eggs (72.9%) were laid during periods of darkness, with the majority of eggs released during the first 3 h periods immediately after dark. Alternatively, some monogenean species store their eggs in utero to be released at a specific time of day [8,51].
In conclusion, Neobenedenia exhibits a variety of strategies to aid survival of subsequent generations. Parasites can reproduce in isolation to produce viable, infective oncomiracidia for three consecutive generations. High fecundity, egg laying and egg hatching rhythms ensure the success and persistence of this harmful parasite in wild and farmed fishes. | 5,831 | 2014-09-29T00:00:00.000 | [
"Biology"
] |
Effect of Electrohydraulic Discharge on Viscosity of Human Blood
Electrohydraulic plasma discharge is a novel technology with high efficiency and high speed and can generate chemically active species like free radicals, ions, atoms, and metastables, accompanied by ultraviolet light emission and shock pressure waves. The aim of this work is to examine the effect of electrohydraulic discharge (EHD) system on viscosity of the human blood after different exposure time. The voltage pulsation introduces electric field and temperature jump and at the same time leads to haemolysis of the blood cells. The ratio of blood viscosity under the influence of magnetic field ηmag to the viscosity in the absence of magnetic field η is directly proportional to the applied magnetic fieldH.
Introduction
Blood viscosity is one measurement currently obtained invasively via a blood sample and can be defined as the intrinsic resistance to blood flow due to internal friction arising between blood's molecular and particulate components.The viscosity of any fluid (measured in millipascals⋅seconds) is a function of its sheer stress (force per unit area applied to a fluid layer producing this layer's movement relative to an adjacent fluid layer) and its sheer rate (velocity gradient between two adjacent fluid layers), defined in (dynamic viscosity) (mPa ⋅ s) = shear stress shear rate .
Of the factors influencing blood viscosity, the major contributors include blood plasma, plasma proteins, and both leukocyte and erythrocyte volume (hematocrit), shape, and aggregation [1].Blood viscosity variations by erythrocytic factors are indicative of various human ailments.The two major components of RBCs resulting in abnormal viscosity measurements are individual RBC deformation and collective RBC aggregation.Compared to other components of whole blood, the RBC component is a strong magnetic material, whose orientation has been shown to be affected by external magnetic fields [2].The magnetic force felt by erythrocytes depending on their magnetic state has been approximated by [3] where 0 is the permeability of free space, is the difference in magnetic susceptibility between blood cells and buffer solution (in our case, mainly plasma and white blood cells), BC is the volume of the blood cell, and is the applied magnetic field.Additionally, the orientation of an erythrocyte in an external magnetic field is dependent on the oxygenation state of the RBC's haemoglobin, the iron-containing oxygen carrying component of RBCs [3,4].In its oxygenated state, haemoglobin acts as a diamagnetic particle, and in its deoxygenated state, haemoglobin acts as a paramagnetic particle.These differing erythrocytes magnetic susceptibilities suggest that blood oxygenation affects the orientation and aggregation of flowing erythrocytes under a magnetic field and thus affects blood viscosity [4,5].The arterial blood has oxygenated and deoxygenated states.The oxygenated state refers to oxygen bound to haemoglobin of erythrocytes (HbO 2 ) and deoxygenated state having haemoglobin lacking oxygen (Hb).
Electrohydraulic discharges (EHDs) are produced during the rapid release of stored electrical charge across electrodes submerged below the blood surface.The resulting formation of an electrical arc across the spark gap produces a localized plasma region (i.e., ionized gas) that emits UV radiation and generates pressure and thermal shocks [6].These phenomena can also produce radical species and ionic reactions.One acoustic mechanism that can cause haemolysis is cavitation, which is the expansion and compression of gas bubbles caused by the applied acoustic field.The violent implosion of a bubble can lead to the production of shock waves, high-velocity liquid jets, free radical species, and strong shear forces that can damage blood cells [7,8].
Materials and Methods
Before experiments were started, human blood was drawn from healthy donors and was anticoagulated with ethylene diamine tetra-acetic acid (EDTA).All experiments used whole blood.At least three 5 cm 3 samples of blood were used for each exposure condition.After exposure, plasma viscosity was measured using microviscometer.The total haemoglobin level in unexposed blood and the red cell, white cell, and platelet counts in exposed and unexposed samples were measured.Schematic diagram of the electrohydraulic discharge device is shown in Figure 1.
The point electrode was made from a sharpened copper rod, which was almost totally insulated from surrounding blood and ground copper electrode (reactor vessel) by an insulated tube.A pulsed high voltage applied to the point electrode was provided by a pulse power supply (HV).It consists of a variable voltage 0-40 kV DC source, a low inductance storage capacitor, and a rotating spark gap.The temporal evolution of the gap voltage and electrical current through the reactor were measured by a home-made resistive voltage divider and a Rogowski coil, respectively, and recorded simultaneously by the four-channel LeCroy digital oscilloscope with a sampling rate of up to 200 MSa s −1 .The blood sample was injected into the treatment chamber after cleaning and sterilizing it for the purpose of treatment.One of the electrodes was connected to the high voltage pulse generator and the other electrode was connected to the ground.The high voltage pulse generator parameters were adjusted as follows: output voltage variable from 20 kV up to 30 kV, discharging frequency 25 Hz, and action time variable from 60 seconds up to 210 seconds.
After treatment, the sample was taken out from the treatment chamber and put in a sterile tube after the voltage drops to zero.The blood samples were divided into two groups: one unexposed control group and test group which was exposed to a number of pulses.At the end of exposure time, the blood samples collected were analyzed in laboratory.
The circuit of the pulse voltage generator used in this study consisted of the pulse-forming capacitor (20 nF), charged through a 50-kilo-ohm resistor by a negative DC high voltage power supply and rotating spark gap switch.As the voltage on the capacitor reached the spark-over voltage of the spark gap electrode, the capacitor was discharged, producing narrow positive high voltage pulse.
Results and Discussion
The peak value of the discharge current was approximately 450 A during the pulse.Figure 2 shows the current and voltage waveforms that characterized the EHD device.Current and voltage were measured as a function of time at an input energy of 9 J (maximum applied voltage 30 kV) and air pressure of 1 bar.From these measurements the power transfer to the reactor vessel was obtained as a function of time as shown in Figure 3.
Fourier transformation shows the amplitude spectrum of the discharge current that is displayed at the different frequency components in the pulse (Figure 4).As observed, most of the energy in the discharge current is between 100 kHz and 50 MHz.The spectrum has several peaks at different frequencies, with the maximum component at 0.29 MHz (fundamental frequency) and the other components at 3.32 MHz, 16.0 MHz, and 35.0 MHz (harmonic frequencies).It is clear that the energy is spread over a very large frequency range, which is a characteristic feature of a short pulse.
Figure 5 indicates the measured whole blood viscosity at different exposure durations (to plasma products).Figure 6 shows the ratio of the blood viscosity under the influence of magnetic field mag to the viscosity in the absence of magnetic field at different values of applied magnetic field (the exposure time being about 60 seconds).
The energy at the reactor is generally dissipated from the arc channel by radiation, shockwaves, and thermal conduction to the surrounding blood.The shockwave is the most effective parameter on hemolysis in whole blood.The results of the arc discharge reveal information about the highly ionized, high pressure, and gaseous plasma, which tends to expand in volume.The surrounding blood resists the plasma expansion; therefore, it creates high transient pressure from each discharge, resulting in the production of a shockwave Figure 6: The ratio of blood viscosity under the influence of magnetic field mag to the viscosity in the absence of magnetic field as a function of applied magnetic field (exposure time about 60 seconds).[9].It is observed that shockwave intensity is mostly related to the pulse energy.
Shockwave intensity is also a function of pressure.When the arc discharge occurs, pressure can be expressed as a function of the dissipated pulse energy [10]; that is, where is pressure (atm), is dissipated energy of the capacitor (), and is distance from the discharge source (cm).The pressure is related to the shockwave velocity by where is density (1059 kg/m 3 ), is particle velocity (m/s), is sound velocity (1584 m/s in blood), and is shockwave velocity (m/s).The shockwave velocity is very high (over 10,000 m/s) at the beginning.As the shockwave propagates in blood, the shockwave velocity decreases eventually to sound velocity.Table 1 and Figure 7 show the theoretical values of pressure in terms of the discharge energy at different distances from the discharge.Both diamagnetic and paramagnetic particles develop a magnetic moment proportional to the applied field.The magnetic moment per unit volume called the magnetic polarization, or the magnetization, is represented by the symbol in the following equation.Magnetization is the measure of how much the magnetic field affects the magnetic fluid.The simplest is the linear equation for isothermal cases [11,12].Consider where is a constant called magnetic susceptibility (dimensionless number), negative for diamagnetic particles and positive for paramagnetic.The magnetic susceptibility of blood has already been measured to be −6.6 * 10 −7 for the oxygenated and 3.5 * 10 −6 for the deoxygenated blood, respectively [13].The oxygenated erythrocytes have diamagnetic properties because of the = 0 spin state of the oxyhaemoglobin.The ferrous deoxyhaemoglobin with its = 2 spin state is responsible for the paramagnetic behavior of the deoxygenated erythrocytes.All other erythrocytes are paramagnetic with different susceptibility depending on the spin state of the methemoglobin, high at = 5/2 or low at = 1/2.An external magnetic field consists of high voltage power supply, capacitor bank, air gap switch, and solenoid around EHD reactor as shown in Figure 8. Discharging the capacitor bank through the inductive coil, electric energy is transformed into magnetic energy and a sinus-shaped magnetic field pulse can be generated.The instantaneous field in the coil may be determined by measuring the current that circulates through the solenoid.
Typical values of the circuit parameters are = 1 F and = 10 KV, giving an estimated peak field of 2 k-Gauss.
When the blood is exposed to the magnetic field, the action of magnetization will introduce a rotational motion to orient the fluid particles with the magnetic field.The orientation of blood cells when subjected to a magnetic field is due to the magnetic torque .The magnetic torque is due to the difference in the orientation between the magnetization vector and the applied field.When the blood cells are suspended in the plasma without applied field, it has a viscosity .However, if the magnetic field is applied, then the blood cells and the surrounding plasma fluid will interact and, combined with the magnetic force, increase the viscosity of the blood.An increase of the viscosity due to the magnetic torques exerted on the blood cells, which in turn increase the frictional coupling between the plasma fluid and blood cells.
The voltage pulsation introduces electric field, and temperature jump at the same time leads to haemolysis of the blood cells.The haemolysis occurs because of the osmotic imbalance generated by the leakage of ions and small molecules.The magnitude of the temperature jump depends on the square of current and duration of the pulse.Also, the cavitation which is expansion and compression of gas bubbles caused by shock wave that can damage the blood cells.Ultraviolet radiation may cause ultrastructure changes in the platelet membrane.The ultraviolet light increases the ion permeability of human red blood cell membranes.Therefore, such ultraviolet radiation may initiate aggregation by direct effect on blood cells.The sonochemical effects are due to the electrohydraulic cavitation in the blood that causes the formation of cavitation bubbles which can grow and implode under the variations of the pressure field of the ultrasonic waves.In the case of blood structured with air, these conditions give rise to cleavage of blood and direct destruction of the suspended cells.
Figure 1 :
Figure 1: Schematic diagram of the electrohydraulic discharge device.
Figure 2 :
Figure 2: The current (red line) and voltage (black line) waveforms of the EHD device.
Figure 5 :
Figure 5: Relationship between whole blood viscosity and exposure duration.
Figure 7 :
Figure 7: The theoretical value of pressure as a function of discharge energy.B, D, and are the pressures (atm) and E, F, and G are the shock velocities (cm/s) at different energies 4 J, 6.5 J, and 9 J, respectively, at different distances (cm) from the discharge.
Figure 8 :
Figure 8: Schematic representation of pulsed magnetic field generator.
Table 1 :
The theoretical value of pressure by the discharge energy. | 2,896.6 | 2013-07-07T00:00:00.000 | [
"Physics"
] |
A QALY loss is a QALY loss is a QALY loss: a note on independence of loss aversion from health states
Evidence has accumulated documenting loss aversion for monetary and, recently, for health outcomes—meaning that, generally, losses carry more weight than equally sized gains. In the conventional Quality-Adjusted Life Year (QALY) models, which comprise utility for quality and length of life, loss aversion is not taken into account. When measuring elements of the QALY model, commonly, the (implicit) assumption is that utility for length and quality of life are independent. First attempts to quantify loss aversion for QALYs typically measured loss aversion in the context of life duration, keeping quality of life constant (or vice versa). However, given that QALYs are multi-attribute utilities, it may be possible that the degree of loss aversion is dependent on, or inseparable from, quality of life and non-constant. We test this assumption using non-parametric methodology to quantify loss aversion, under different levels of quality of life. We measure utility of life duration for four health states within subjects, and present the results of a robustness test of loss aversion within the QALY model. We find loss aversion coefficients to be stable at the aggregate level, albeit with considerable heterogeneity at the individual level. Implications for applied work on prospect theory within health economics are discussed.
Introduction
Like other decisions, medical decisions often involve tradeoffs between gains and losses in different domains. In health economics, an important trade-off concerns that between length and quality of life (QoL), also in the context of health state valuations. Research in behavioral economics and psychology has established that in such trade-off losses typically carry more weight than gains of the same size. This sensitivity to losses is referred to as loss aversion [1,3]. Recently, scholars demonstrated the importance of loss aversion within the health domain, both for life duration [4][5][6][7] and quality of life (QoL) [7][8][9]. In health economic analyses, utilities are often defined as a product of these two attributes, jointly comprising Quality-Adjusted Life Years (QALYs) [10]. Commonly, the utility function over these two outcomes is decomposed into separate utility functions over life duration and QoL. This separability of QALYs is, however, only possible under several assumptions, which have solely been tested under conditions in which no distinction is made between gains and losses [11].
Here, we use prospect theory (PT), which incorporates loss aversion and judges changes from the perspective of some relevant reference point (RP). Bleichrodt and colleagues [11] established that, when considering multiattribute outcomes, such as QALYs, gains and losses may be determined per attribute with separate attribute-specific RPs. This also makes it possible quantify loss aversion, to see how much more weight losses carry than gains. Earlier attempts at quantifying loss aversion under PT have typically focused on single attributes within the QALY framework, for example by obtaining loss aversion for life duration while maintaining QoL constant [4,5] or vice versa [8]. Although these studies produced similar median estimates of loss aversion, with health losses receiving between 1.5 and 2 times more weight than gains, they did not allude to the issue of 1 3 separability. In other words, these studies ignored the possibility that loss aversion for one attribute (e.g., length of life) depends on the level of the other attribute (which is typically held constant) and, hence, assumes loss aversion for health outcomes to be constant, independent of their QALY profile.
However, it could be the case that some QALY losses carry more weight relative to commensurate QALY gains than others, for example if loss aversion is more pronounced for more severe health states. In this article, we test this assumption using a non-parametric method [12] to quantify loss aversion over life duration, under varying levels of QoL. This non-parametric method was developed recently and allows the estimation of utility curvature and loss aversion without imposing parametric assumptions on either. Earlier work has argued that the choice of parametric family or functional form restricts interpretation of subjects' choice patterns, and may lead to considerable bias especially for extreme cases [12,13]. This method has been adapted to and used in the health domain before [5].
Theoretical framework
Consider a decision maker facing choices with regard to his health under uncertain conditions, operationalized by presenting decision makers with risky prospects representing different life durations and QoL. We assume completeness and monotonicity for both attributes. We consider lotteries involving chronic health profiles, described as ( , T) , where β represents QoL and T duration in years. According to the generalized QALY model [14], a decision maker's preferences for health profiles can be represented by the following: with V( , T) being a product of U(β), the utility of β, and L(T) denoting the utility of T life years.
Here, we assume PT under risk with a sign-dependent utility function for life duration, so that gains are evaluated differently than losses, relative to an attribute-specific RP. We assume that, through instruction, it is possible to set this attribute-specific RP to a specific health condition c and life duration T 0 . To elicit a continuous utility function for life duration, we elicit a standard sequence for life duration that runs through L(T 0 ) = 0 . Meanwhile, we keep QoL constant at c throughout the task. We repeat this process under different levels of c .
We elicit the utility function for life duration, relative to this RP, both for gains and losses for the different health states. Hence, we obtain L i (T) for each c , with i = + for gains and i = − for losses. L i (T) is a standard ratio scale utility function, which is strictly increasing and real-valued with L i (T 0 ) = 0 . We incorporate loss aversion by taking L − (T) = L(T) for T < T 0 , where λ denotes a loss aversion index, with λ > 1 [= 1, < 1] indicating loss aversion [loss neutrality, gain seeking]. Hence, by obtaining the utility around the RP, the degree of loss aversion can be derived.
Methods
A total of 111 students (average age 20.23, SD = 1.52) of Rotterdam School of Management (61 female) participated in this study for a course credit reward. Experimental sessions lasted for 25 min and were run with up to four subjects per session. One experimenter was presented in the room to answer questions. The experiment was computerized with Matlab.
To test the robustness of loss aversion, we used the nonparametric method [12] under four levels of QoL. In other words, each subject completed the non-parametric method four times, with a different c throughout each of these four phases. This process allows us to obtain estimates of utility curvature and loss aversion for each of the four levels of QoL, and compare them within subjects.
QoL was defined by means of EQ-5D-5L health state descriptions [15], which utilize five domains: mobility, selfcare, usual activities, pain/discomfort, and anxiety/depression. The 5L version of the EQ-5D distinguishes five levels of severity on each domain, ranging from 'no problems' to 'extreme problems/unable to'. Health states are typically denoted by 5 digit codes like 22113, with each number representing severity of the relevant domain level of QoL. In this study, we used four relatively mild-to-moderate health states as RP c in the non-parametric method: 11111, 21211, 31221, and 32341 (see "Appendix 1" for exact description). This was done to have variation in health states but avoid states worse than dead, for which no separate procedure was included.
The non-parametric method used here consisted of three stages which are described in detail in "Appendix 2". 1 The first stage connects the utility for gains and losses. The second and third stages employ the trade-off method developed by [16] to measure a standard sequence of outcomes in life years for gains (x + 1 , x + 2 , … , x + 5 ) , and for losses ( . This enables measuring loss aversion, without imposing parametric assumptions on utility curvature. 2 In addition, the standard sequences allow the testing of utility independence [11]. The three stages had slightly different instructions, providing context for the required trade-offs. The instructions were similar to those used by Lipman and colleagues [5]. During all the stages of the experiment, it was made clear to subjects that they should imagine living until 70 years in c , after which they would contract a disease, resulting in immediate death without any pain. Subjects completed a series of binary choices between two drugs which could change their situation (leading to gains and losses compared to living until 70). Employing a bi-section choice method, we obtained indifferences, set equal to the midpoint after the fifth binary choice. Some stimuli and constants relevant to the non-parametric method had to be set beforehand; these are listed in "Appendix 1".
Results
Seven subjects were excluded from further analyses for the following reasons: mechanical failure (n = 2), refusing to incur life year losses (n = 3), and observed misbehavior (e.g., rushing through the task, n = 2). The results are reported for the reduced sample (n = 104). 3 Throughout, we will first report aggregate analyses, where median parameters are compared for the whole sample, and refer to these as results at 'the aggregate level'. Second, we will investigate individual results more closely, by classifying each individual according to classification rules reported in "Box 1" and we explore within-subjects parameter instability. We refer to these analyses as 'individual-level analyses'. Table 1 demonstrates the results at the aggregate level, by comparing point-estimates for utility curvature and loss aversion for each health state. We compared differences between health states using omnibus tests (i.e., comparing all four health states simultaneously), more specifically Friedman's tests, which are robust against the violations of normality typically observed for parameters under the definitions reported in "Box 1". Next, we compared all health states in pairs with Wilcoxon signed-rank tests. For the omnibus tests, no significant differences were observed between health states, both for utility curvature and loss aversion (all p's > 0.06). When comparing parameter estimates in pairs of health states, some significant differences were observed. For loss aversion under both definitions, parameter estimates for β2 were significantly lower than for β3 (p's < 0.03). All other pairwise comparisons for loss aversion yielded no significant differences (all p's > 0.07). Using pairwise comparisons for utility curvature, we observe no significant differences for both parametric and non-parametric estimations (all p's > 0.05).
In general, we observe close to linear utility for all health states, both for gains and losses. 4 Furthermore, we observe considerable loss aversion at the aggregate level, with λ significantly greater than 1 for all c (Wilcoxon tests: p < 0.001 for all β's). Table 2 demonstrates how subjects classify under different estimations of utility curvature and loss aversion (see "Box 1"). For all individual classifications, we observed that the conventionally assumed loss neutrality and linear utility curvature are not present in our data. Although, at the aggregate level, linear utility was found, when classifying individually, considerable heterogeneity in utility curvature was observed, with proportions of concave/convexity varying between definitions and health states. This finding could be explained by the near equal division of concavity/ convexity in our sample, resulting in roughly linear utility at the aggregate level. For loss aversion, however, such an equal division was not visible, with the majority of subjects classifying as loss averse across definitions and health states. Our design allowed exploring point-estimate stability for utility curvature and loss aversion between different levels of c . To this end, we calculated the difference between the smallest and largest estimates within subjects (e.g., the lowest and highest ). Furthermore, to allude to within-subjects heterogeneity in classification, we calculated the proportion of subjects for whom classifications were dependent on health states (e.g., loss averse for β0-2 and gain seeking for β3). Both exploratory measures of within-subjects parameter and classification variance demonstrated considerable heterogeneity between health states (see Table 3). Finally, we investigated whether systematic patterns in utility curvature or loss aversion could be observed in our sample. To this end, we determined the extent to which subjects showed monotonically increasing (or decreasing) parameters (see Table 3). For loss aversion, this classification indicated that subjects became more (less) loss averse for increasing health state severity for c . These analyses indicate that these patterns did occur, but only for a small part of our sample, again suggesting non-systematic heterogeneity of parameter estimates.
Discussion
In this paper, we compared estimates for utility curvature and loss aversion for QALY outcomes under four levels of QoL, to test the robustness of these estimates. An extensive literature exists testing the validity of QALY models, which has documented mixed evidence with regard to the separability of life duration and QoL [e.g., [18][19][20][21]. In addition, many authors have investigated utility independence with regard to health state valuation (e.g., the relation between utilities and time horizon in the standard gambles), finding many descriptive violations of this independence [for a review, see : 20]. Ours was the first experimental test of this separability for QALY gains and losses separately, and we also tested the robustness of loss aversion. Our results, at the aggregate level, provided evidence that estimations of loss aversion and utility curvature are independent of QoL. However, loss aversion and utility curvature estimates were heterogeneous at the individual level, i.e., varied considerably between health states for the same individual.
Our findings are in many regards similar to earlier work that measured PT for QALY outcomes. We observed considerable loss aversion (defined over length of life), as was found in similar magnitude in earlier work applying similar methodology [5,22], or with different elicitation methods [4,8]. In contrast to what was observed in earlier applications of the non-parametric method for health outcomes [5,22], we found linear utility for both gains and losses at the aggregate level. Applying a parametric approach to our non-parametric measurements did not affect these conclusions. However, when estimating individual classifications, we found none for whom our data supported this linearity, as we observed a near equal spread in concave/convex utility (i.e., averaging out to linear).
We document considerable heterogeneity in parameter estimates between subjects, and also observed such heterogeneity within subjects for different health states. Our exploratory analyses did not uncover systematic or monotonic patterns in this within-subjects heterogeneity. An explanation related to our chosen chained utility elicitation method could be that these individual differences occurred as a result of preference imprecision [23]. Such 'noisy preferences' could result in error propagation, i.e., cascading of errors or imprecision in the early stages of our chained method into later stages, producing differences in parameters between health states when errors occur randomly. Although earlier work using similar methodology [5,24] observed no effects of error propagation, we cannot rule out it affected current results. Another factor contributing to possible error propagation in our study could be that we opted to obtain indifferences via bi-section only (to reduce complexity), whereas earlier work [5,12] using this method applied a slider to obtain indifferences, allowing subjects to correct errors adaptively. Future work could explore this further, for example by adding a slider to obtain indifference points, using non-chained methodology, or running an error propagation simulation. Some additional limitations of this study deserve noting. First, since this study involved a first test of independence of loss aversion in health, we used a convenience sample consisting of students. Of course, future extensions preferably should include representative samples to generalize our findings. Although power analyses suggested that our sample was adequately powered to detect small effects, using a larger sample could, perhaps, result in the detection of smaller effects, also given the large heterogeneity for parameter estimates reported here. Second, we assumed that it is possible to set the RP through instruction, while it may be the case that respondents took another RP in mind. Still, given the high loss aversion coefficients that we found, it seems plausible that our respondents, indeed, held the induced RP in mind. Finally, our study used four mildto-moderate health states, including perfect health, while the EQ-5D descriptive system enables many more possible health states, with more severe health problems than our selection. Given the aim of our study, this is a clear limitation, as, perhaps, these states where insufficiently spaced in terms of utility for us to observe systematic patterns in loss aversion or utility curvature parameters. However, our empirical approach required us to make a fundamental assumption: monotonicity. The non-parametric method breaks down if monotonicity is not satisfied, i.e., if subjects prefer to lose years of life instead of gaining them. For more severe health states, monotonicity need not always hold [25]. Obviously, many other mild health states were available for our purposes, but to reduce cognitive strain for our subjects that we decided on including just four. For reference, these four health profiles receive utility weights ranging from 1.00 to 0.46 in the Dutch tariff [26], which we considered to be sufficient for our purposes. Future work could replicate our findings with a different or larger selection of health states.
Our findings may have implications for policy makers and researchers aiming to apply PT measurements to healthrelated decision-making. Our results imply that median parameters in applications of PT may have merit, as these estimates appear to be robust across different scenarios (in terms of QoL). For example, our work warrants the conclusion that, at the aggregate level, life year losses are weighed twice as much as similarly sized gains, regardless of QoL level. However, as our exploratory analyses of within-subject heterogeneity demonstrated, individuals' loss aversion and utility curvature may depend on the health state used during elicitation. This heterogeneity at the individual level may be problematic for approaches using averages, like median-optimized parameters (e.g., [27]). When aiming to address PT biases for QALYs [28], such as loss aversion, at the individual level, our data would suggest that assuming such median loss aversion parameters may misrepresent individuals' actual preferences and trade-offs. When one aims to apply PT to allude to biases in individual cases (e.g., in health state valuation), an individual approach may be more suitable, given both the considerable between subjects and betweenhealth states' heterogeneity reported in this study. Such corrections with individually estimated parameters could be too time-consuming and labor-intensive when applied separately for each economic evaluation. However, in many countries, such as the UK, QALYs are not derived individually, but from indirect preference-based classification systems, such as EQ5D or SF6D via social tariff lists [29]. Recent developments in de-biasing QALY measurement [5] suggest that it may be suitable and possible to apply the correction for PT at the individual level to obtain value sets for these social tariffs [see 30]. 5 When considering such individual correction, however, it seems important to consider which health state is used to quantify PT parameters.
In conclusion, although we observed large heterogeneity of loss aversion and utility of life duration depending on QoL, we failed to observe systematic patterns in this dependence, and observed no differences on average. Future work should aim to address whether this heterogeneity is methoddependent or due to systematic differences between individuals or health states. For now, it appears that, on average, loss aversion is equal across health states, i.e., a QALY loss 1 3 is a QALY loss is a QALY loss, and it receives approximately twice as much weight as equally sized QALY gains.
Funding None.
Compliance with ethical standards
Conflict of interest The author declares that they have no conflict of interest.
Ethical approval This paper as approved by Erasmus Research Institute of Management (ERIM) Internal Review Board, Section Experiments.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Introduction and framing
Subjects were asked to imagine that they would live until 70 years in a health state denoted as health state C. This health state C would be varied for each repetition (4 in total) of the non-parametric method (i.e., c ). After becoming 70, they were instructed that they would contract a deadly disease, which would lead to a direct, painless death. Their task was to compare two drugs and indicate their preferences between treatments given their health state C and the treatment options, which could be risky, or involve possible side-effects (i.e., losses of life).
Stages of non-parametric method
The non-parametric method is chained, i.e., answers from the previous stage carry over to the next, meaning that differences in questions may exist between subjects. For a completely general description of the method, we refer to Abdellaoui and colleagues [12]. Throughout, as is common for applications of the trade-off method [16], any risky gamble had 50% chance (p = 0.5) of success. We denote such gambles as X p Y , meaning that X is obtained with probability p, and Y otherwise. In our adaptation of the nonparametric method, outcomes (i.e., X and Y) reflected life years. Importantly, it was emphasized throughout that any life years gained or lost were to be spent in health state C. All indifferences were obtained via bi-section. Whenever a variable was elicited, a starting level had to be set to start the bi-section method. We chose to set it, such that the expected value would be equal for both treatments that subjects could choose from. For example, when eliciting the indifference Z ∼ 10 p 0 , we would start at Z = 5. This experiment was completely counterbalanced, meaning that health state order and gain-loss order were randomized between subjects. All pre-specified stimuli and elicited indifferences can be found in Table 5.
Stage 1: Connecting gains and losses
Subjects first faced a mixed gamble, which could increase their length of life by G years with probability p, or otherwise decrease it by L years. They could also choose to take a drug that gave 0 years. The negative outcome Lwas elicited by obtaining the following indifference G p L ∼ T 0 , where T 0 indicates living until 70 in state C. As can be seen from Table 5, G was fixed at 5, while L was initially set at 2.5 and varied based on individual choices. Next, two certainty equivalents (CEs) were elicited, which would form the starting points of the standard sequences elicited in stages 2 and 3. The CE for gains, i.e., the starting point for stage 2 was elicited by offering subjects a choice between a certain gain x + 1 in life years (in state C), and a gamble offering G (i.e., 5 years) with probability p, and 0 years otherwise. The amount of life years gained by taking the certain drug (x + 1 ) was varied to obtain indifference x + 1 ∼ G p T 0 . For losses, this procedure was exactly the same, i.e., subjects were offered a choice between a certain drug resulting in a loss of x − 1 life years in state C, and a risky drug. To introduce the loss domain, we instructed them that they had contracted another fatal disease that should also be treated, and thus explained their likely loss compared to T 0 (i.e., 70 years in C). We thus elicited x − 1 ∼ L p T 0 , providing the starting point ( x − 1 ) for eliciting utility for losses in stage 3.
Stages 2 & 3: Trade-off method to elicit utility for gains and losses
The trade-off method consists of comparisons between two lotteries. Within our framing, this consisted of two risky drugs, which could increase subjects' life duration in state C to a different extent. In addition, both drugs could have risks of adverse effects, and thus decrease lifetime in state C. To introduce the loss domain, subjects were instructed that they had contracted another fatal disease for which treatment was required. Subjects were instructed that they would compare a series of drugs to each other. This series constituted the procedure to elicit the standard sequence, which consists of a sequence of outcomes equally spaced in terms of utility (see [16] for proof).
Stage 2, i.e., the trade-off method for gains, commenced by us setting , a small offset-loss of 1 year in state C. Subjects were offered a choice between two risky drugs: one would offer x + 1 p , where is a larger offset-loss which we aimed to elicit, while the other would offer p T 0 . We varied t o obtain the indifference x + 1 p ∼ p T 0 . Next, we elicited the standard sequence (x + 2 , … , x + 5 ) by eliciting indifferences in the form of x + j p ∼ x + (j−1) p . Stage 3, i.e., the trade-off method for losses, commenced by us setting ℊ , a small offset-gain of 1 year in state C. Subjects were offered a choice between two risky drugs: one would offer p x − 1 where is a larger offset-gain which we aimed to elicit, while the other would offer ℊ p T 0 . We varied t o obtain the indifference p x − 1 ∼ ℊ p T 0 . Next, we elicited the standard sequence (x − 1 , x − 2 , … , x − 5 ) by eliciting indifferences in the form of p x − j ∼ ℊ p x − (j−1) .
Repeating this procedure four times-for each health state (see Table 4)-resulted in four utility curves, and allowed us to obtain loss aversion parameters and both parametric and non-parametric estimates of utility curvature (see "Box 1").
Box 1: Analyses of utility curvature and loss aversion
We non-parametrically calculated the area under the curve for L i (T) , which was normalized to [0, 1] , for gains and [0, −1] for losses. If utility is linear, the area under this normalized curve equals one-half for both gains and losses. Utility for gains in life duration is convex (concave) if the area under the curve is smaller (larger) than one-half, while, for losses, the opposite direction holds (convex > ½, concave < ½). Second, we fitted a parametric utility curve to our data by employing the power family, with the utility of life duration defined as x with > 0 . As is well known, for gains [losses] > 1 corresponds to convex [concave] utility, = 1 corresponds to linear utility, and < 1 corresponds to concave [convex] utility.
Kahneman and Tversky [1] defined loss aversion (λ) as −U(−x) > U(x) for all x > 0 . To measure loss aversion coefficients according to this definition, we computed −U(−x + j )∕U(x + j ) and −U(−x − j )∕U(x − j ) for j = 1, … , 5 . As a result of the trade-off procedure, U(−x + j ) and U(−x − j ) could usually not be observed directly and thus were determined through linear interpolation. Subjects were classified as loss averse if −U(−x)∕U(x) > 1 for more than half of the observations, as loss neutral if −U(−x)∕U(x) = 1 for more than half of the observations, and as gain seeking if −U(−x)∕U(x) < 1 for more than half of the observations. Köbberling and Wakker [2] provided an easier method to determine loss aversion. They defined loss aversion (λ) as the kink of utility at the reference point. That is, they defined loss aversion as U | 6,497.4 | 2018-09-18T00:00:00.000 | [
"Economics"
] |
Designing Computational Substrates using Open-Ended Evolution
Evolutionary algorithms are powerful tools to discover novel and diverse solutions to complex problems. Here, we discuss how open-ended algorithms, such as novelty search, can be used to design and evaluate new unconventional computing systems, from the design of materials to the creation of new computational models.
Introduction
Computing with unconventional materials is a growing area of research across many disciplines (Adamatzky, 2016a,b). Unconventional computers have the potential to be faster and consume less power than conventional CMOS technology (Stepney et al., 2018). However, to achieve this requires innovations in material design and the discovery of new computational models.
Designing new materials creates significant technical challenges often requiring advanced computer modelling, new fabrication techniques, ingenuity and creativity. There are many approaches to material design, for example, bottom-up design where structures can grow and selforganise, or top-down design where 'basic' components are connected together in a well-defined manner. Design and validation of any material is typically expensive, labour intensive and requires considerable expertise.
The other challenge in unconventional computing is designing new computational models. Getting the right computational model is critical. The computational model should naturally fit the material's implementation; a poor model causes inefficiencies, requires extensive engineering and may ignore promising qualities of the material.
Computational models are typically abstracted from or inspired by the behaviours of specific physical or biological processes. One problem is that designing new models is difficult without some formal language to express them. Another problem is how to experimentally validate these models, how to assess their suitability for a specific material.
To reduce the need for expertise and to automate design, search algorithms inspired by natural evolution are often used. Artificial evolution requires minimal prior knowledge of the system; this makes them easy to implement and removes designer bias. In the field of Evolvable Hardware, evolution is used to create and optimise the design of hardware systems, from analogue circuits to robots (Tan et al., 2004;Doncieux et al., 2015). Recently, algorithms inspired by the qualities of open-ended evolution, such as novelty search and quality diversity algorithms (Lehman and Stanley, 2008;Pugh et al., 2016), have added new methods to design artefacts, producing solutions that direct optimisation often struggles to recreate.
We have developed a framework (Dale et al., 2019b) that exploits novelty search to assess the quality of material substrates. Here we discuss how it can be modified to improve material design and to evaluate new computational models.
Computing with Materials
To compute with materials we need to be able to determine when some desired abstract computation is performed, in contrast to the material undergoing other natural physical processes. To determine when a physical or biological system is computing, abstraction/representation theory (AR theory) has been developed (Horsman et al., 2014(Horsman et al., , 2017Stepney and Kendon, 2019). AR theory defines when a material substrate is computing with respect to a model, according to a representation. The theory defines the general compute cycle for a physical computer, starting with an initial abstract problem, the encoding step in terms of an abstract computational model, and its instantiation into a physical material. The theory defines the compute process and whether the system fulfils the computing definition, but it does not evaluate the efficiency of the material or the suitability of the model.
We have developed a framework to explore and compare the computational expressiveness and capability of physical materials with respect to one particular computational model, that of Reservoir Computing (Lukoševičius et al., 2012). This CHARC (CHAracterisation of Reservoir Computers) framework (Dale et al., 2019b) provides a means to characterise a material's quality for reservoir computing according to its range and diversity of physical behaviours.
Reservoir computing is a popular computational model to exploit a range of novel physical computing devices. Its structure and simplicity make it suitable to be implemented in many open non-linear dynamical systems. A recent review of physical reservoirs highlights the diversity of systems the model is applied to, including chemical, optical, electronic and mechanical systems (Tanaka et al., 2019).
There are typically two stages to program a physical reservoir. The first stage is to find a set of physical configuration parameters that induce desirable system dynamics. In this state, the reservoir acts as a dynamical filter on its inputs. The second stage is to train a separate task-specific readout layer, typically forming a linear combination of system states.
What tailors the CHARC framework to reservoir computing is the dynamical properties and metrics used to define a behaviour. In reservoir computing, some properties are described as being essential, such as non-linearity and a fading memory (Dambre et al., 2012).
CHARC Framework Outline
The CHARC framework measures the quality of a material, where quality is defined as the total capacity to realise distinct reservoirs in terms of different dynamical properties. To characterise a test material, two phases must be completed: quality assessment of a reference material (phase one), and characterisation of the test material (phase two). Phase one provides a baseline to compare to, and is typically carried out only once, provided a suitable reference is chosen. The basic process for each phase has three steps.
Step one, create an abstract space to explore, map, and measure. This space represents the dynamical properties of the material when configured. We refer to this space as the behaviour space, inspired by the same representation used in novelty search. To form each behaviour, n independent property measures are used. Increasing the number of measures leads a more detailed representation of the material, but also increases the size of search space -a suitable tradeoff is therefore needed. In previous work, three measures are used to define the behaviour space: Kernel Rank (nonlinearity), Generalisation Rank (stability), and Memory Capacity; for more information about these measures, see Dale et al. (2019b). These measures are somewhat generic and build a basic dynamical picture of the material. For example, low values in both rank measures signify a material configured in an ordered regime, and high values equate to chaotic regimes.
Step two, explore the material configurations. Here, the mapping between abstract reservoir and material configuration is explored. Exploration is carried out in the behaviour space using novelty search (Lehman and Stanley, 2008). Novelty search, an open-ended and objective-free genetic algorithm, navigates the behaviour space searching for novel solutions. In this implementation, every behaviour considered is stored in an external database for later use. This database forms the core resource used to analyse the relationship between parameters, tasks and behaviours after the search process.
Step three, measure the quality. To do this, the behaviour space is divided into voxels; the number and size of voxels depends on the spaces being compared. The total number of voxels occupied by discovered behaviours forms the measure of quality. The quality value therefore represents an approximation of the system's dynamical freedom, or, the material's capacity to instantiate different reservoirs.
Substrate Design
In Dale et al. (2019b), CHARC is used to manipulate a limited set of parameters referred to as configuration parameters. These do not change the physical material, only how to interact with it. In AR theory, this would cover how to encode the abstract problem and instantiate the material.
To explore material design, material properties can be added to the parameter search space, for example, parameters that define the physical structure of the material, or the natural unperturbed behaviour of the material. Properties such as these could be realised through fabrication techniques or specific layouts of components.
To demonstrate the concept, CHARC has been used to compare different simulated network topologies of varying complexities as an analogy for material design (Dale et al., 2019a). Using CHARC, the dynamical limitations and boundaries of different structures are characterised. It is shown that simple structures with greater network size can mimic the same behaviours of smaller complex network structures.
Computational Model Design
The CHARC framework is not fundamentally limited to the reservoir computing model. The measures defining the behaviour space are adapted according to the specific computational model. The same process is then repeated as before.
Given a suitable language for computational models, such as a general dynamical systems representation (Stepney, 2019), novelty search could explore the space of models whilst parameters of the material remain constant. A codesign approach is also possible (Stepney, 2019). Both material and model design are combined, to improve each individually and also the fit between them.
We believe that this framework could be exploited for codesigning materials and models for many forms of ALife, such as soft-bodied robots (Cheney et al., 2014) and other forms of embodied cognition. | 2,000.6 | 2020-07-14T00:00:00.000 | [
"Computer Science"
] |
Finding the Words: The Impact of VOCA on Language Acquisition
The current study researches the impact of voice output communication aids (VOCA) on the language acquisition of toddlers and school-aged children with developmental disabilities. There are a wide variety of augmentative and alternative communication (AAC) devices available to nonverbal individuals, making the decision of parents, teachers, and speech pathologists of which to use and implement a substantial task, one that needs the guidance of research based evidence. SPSS software was used to conduct a series of analyses with secondary data from Nancy Brady’s study Language Development of Non-verbal Children Age 3 Years through 7 Years, 2007 to 2012, looking specifically at the increase in total words rate of children using various AAC interventions over a year’s time. Total words rate scores were determined using the number of different words each child spoke, signed, or selected during observations and assessments completed by researchers. A multitude of T-tests and a multiple regression equation were run, comparing the outcomes of participants based on their use of a VOCA and presence of an autism diagnosis. Results found participants using other forms of augmentative communication to have a higher total words rate at time 2 than those using VOCA, though these findings were not significant. Gender and autism were not found to be significant predictors of language acquisition, though being male was positively correlated with total words rate scores. Analyses also concluded that participants with an autism diagnosis using VOCA had a slightly higher total words rate at time 2 than those with other developmental disabilities using VOCA, though these findings were also not significant. Future research should consider looking at a randomly selected sample with a wider quantitative range of expressive vocabulary, as well as obtaining the identification of the type and severity of a child’s diagnosis to further clarify the evidence-based benefits of VOCA with specific populations.
v student and professional, solidifying my career goals while also pushing me closer to achieving them. I am forever grateful for the roots I have planted in this field of work and will always remember the knowledge and support that was given to me here at the University of Rhode Island. I myself truly cannot find the words to express the impact this master's degree has made on my life. I promise to pay forward the education and life lessons that have been passed along to me. Thank you to everyone who allowed me to strengthen my voice and nurture my skill set. I will use these tools to fight for the support of diverse populations and nurture my community members who need inspiration and motivation of their own. vi
INTRODUCTION
Parents, teachers, and speech-language pathologists of nonverbal children utilize therapy strategies and scaffold surrounding interactions in an attempt to expand repertoires of communication skills, work on new vocabulary, build up the length of syntax, and/or implement alternative means of expression in cases that the learned language is not capable of being physically spoken (Mirenda, 2003). These supplemental means of expression, called augmentative and alternative communication (AAC), allow individuals with not only autism but all types of developmental disabilities to express their thoughts, needs, wants, and ideas (American Speech-Language-Hearing Association, no date). Often times, AAC can even encourage and nurture verbal language, utilized by children as a temporary means of communication rather than a permanent solution.
Delays in receptive and expressive language are common amongst toddlers and school-aged children diagnosed with developmental disabilities (Branson & Demchak, 2009). Individuals with developmental disabilities who remain nonverbal throughout their lifespan commonly develop subsequent behavioral problems as a result of difficulties with communication, resorting to maladaptive actions like aggression and self-injury to get their needs met (Morgan, Farkas, Hillemeier, & Hammer, 2015).
Being able to provide this population with a means by which to communicate and help their caretakers, teachers, and therapists better understand and address their challenges is imperative and beneficial to all involved. The purpose of the current research will be to investigate one specific means of AAC, voice output communication aids (VOCA), also know as speech generating devices (SGD), looking at their impact on the language acquisition of toddlers and school-aged children with developmental disabilities in comparison to other AAC interventions.
REVIEW OF LITERATURE
Toddlers and school-aged children diagnosed with developmental disabilities frequently encounter delays in receptive and expressive language that are either overcome or continually managed throughout their lifespans (Branson & Demchak, 2009). Often times, these language delays, or delays in areas like social-emotional, adaptive, and/or cognitive skills, are what initially signal to doctors, early intervention specialists, and/or parents the possibility of a future developmental disability diagnosis (Solomon-Rice, 2010 Prevention, 2018). A lack of ability to communicate is often the cause of frustration and consequential problematic behaviors for children with developmental disabilities (Morgan et al., 2015), making figuring out an efficient tactic to promote the language development of this population a necessary, preventative measure the field of human development and aligning professional disciplines should advocate for and invest in.
Individuals with ASD have been observed to acquire expressive language even after age 5, but very few children begin to speak after middle childhood (NIH, 2010).
The atypical developmental trajectory of this population coincides with delays in multiple domains as noted earlier, pushing milestone markers to later dates, asking professionals to readjust their age appropriate expectations and continue utilizing teaching methods associated with success in early childhood. In a study by Wodka and colleagues (2013), which focused on a sample of 535 children with ASD who were at least 8 years of age and had not acquired phrase speech before age 4, 70% of participants attained phrase and/or fluent speech by age 8, with almost half of the sample achieving fluent speech, showcasing the potential of delayed language learners.
Intellectual disability (ID) is characterized by significant limitations in intellectual functioning (e.g., reasoning, learning, and problem solving), significant limitations in adaptive behavior (i.e., conceptual, social, and practical skills in everyday life), and an onset in childhood (before 18 years of age) (American Speech-Language-Hearing Association, no date). ID is a subset of developmental disability (DD), which is defined as a severe, chronic disability in an individual 5 years of age or older, with an onset before 22 years of age, that results in substantial functional limitations in three or more areas of life activity (self-care, receptive and expressive language, learning, mobility, self-direction, capacity for independent learning, economic self-sufficiency) (American Speech-Language-Hearing Association, no date). ASD can co-occur with ID, though it is classified as a developmental disability (American Speech-Language-Hearing Association, no date).
Research evidences developmental differences between females and males, including neurological variations that can affect related functional and academic skills. Jensen (2015) discusses some of these variations in regard to language development, writing "girls' language development, specifically reading and writing, is generally about one to one and a half years ahead of boys. " Huttenlocher, Haight, Bryk, Seltzer, and Lyons (1991) found child gender to be associated with early differences in language capacities, suggesting the amount of parent speech modeled for children after 20 months to be a more important variable pertaining to later vocabulary growth.
These findings implicate the dissipation of gender differences in language acquisition around age 3. (Mirenda, 2003). Discrimination learning is the basis of VOCA, users needing to make connections between the motivation to communicate, graphic symbols and subsequent technology-based vocalizations, and message recipients.
Individuals with ASD typically maintain strong visual-spatial learning abilities, keeping associated skills, such as symbol recognition and recall memory, intact, making it more likely that they will find it easier to learn aided systems than manual sign, especially with the added bonus of the immediate speech production of VOCA devices upon picture selection (Mirenda, 2003). the use of VOCA to facilitate language acquisition, as these devices combine both evidence-based intervention methods. It is important to note that these findings originate from a study that only included three participants; however, this allowed researchers to monitor and record the progress of each child thoroughly, adding to the limited scope of evidence-based knowledge pertaining to the facilitation of vocabulary with toddlers using AAC. Mirenda (2003) compares speech and manual signing to aided communication across all age groups and participant populations, summarizing the findings of a multitude of literature focused on these alternative methods and their connection to successful language acquisition. Those supporting speech and sign argue associated discrimination is less complicated because it involves only a single stimulus and response (2003). For instance, a child can manually sign the word cup (stimulus) to which a parent will respond by bringing over the desired item (response) (Mirenda, 2003). Aided communication systems have multiple stimuli, including the need for a physically present symbol of a cup and the motivation to scan and select a field to find the appropriate symbol. As argued above, other researchers stand by the argument that speech and manual signs require the cognitive processes of recall memory and physical effort and coordination (Mirenda, 2003). Though aided communication systems still require some level of memorization and symbolic understanding, the pictures have a strong resemblance to their referents and are easier to learn and remember than speech and sign, which are more abstract language components. Moore and Calvert (2000) compared teacher and computer instruction to see which most benefitted the language acquisition of children with ASD ages 3 to 6.
Solomon-Rice and
Findings revealed the children were more attentive, recalled more nouns, and were more interested in continuing treatment with computer-based instruction (Moore & Calvert, 2000). Though this study does not address VOCA specifically, its results should be considered when looking at the field of alternative and augmentative communication, as VOCA is the only method of AAC that is technology-based.
Schepis, Reid, Behrmann, and Sutton (1998) looked at the use of VOCA with four children with ASD in a self-contained classroom ranging in age from 3 to 5. language tasks, the activated areas of females' brains were associated with abstract thinking through language, while accuracy of completion for the males depended on their senses of hearing and sight (Jensen, 2015). This implies the importance of symbolic understanding for females and physiological functioning for males. Though all individuals need healthy auditory and speech mechanisms for optimal communication abilities, there is science linking specific components of VOCA as more beneficial depending on gender. The pictures used on the VOCA, whether they are digital or Mayer-Johnson (downloaded using the Boardmaker Software), may be more influential in determining progress made in terms of language understanding and acquisition for females, while the speech generated, spoken aloud by the device after a picture has been selected, may be the component of more weight for males.
Results
The current study will utilize Vygotsky's social constructivist theory which roots all cognitive processes in social interactions as each function in the child's development appears initially at the social level and eventually is internalized (Vygotsky, 1978, & Solomon-Rice, 2010. Effective use and mastery of VOCA, the AAC device that will be investigated in the current study, relies on adult modeling and scaffolding, components of social constructivist theory. The "zone of proximal development" (ZPD) is the "distance" between what the child is able to do by him or herself and what he/she is able to do with the support of an adult. Specific to language development, adults supply communicative meaning to a child's actions and guide the child in negotiating meaning and expressing him/herself (Solomon-Rice, 2010). In terms of AAC, the supporting adult would need to scaffold a child's language using the chosen device to model how this alternative method of communication should be used. Adults need to be as sensitive and responsive to words produced using an AAC as they would be to spoken language in order to reinforce language acquisition and appropriate implementation so children make necessary connections.
Huttenlocher et al. goes on to explain that the acquisition of a large number of vocabulary items learned later on may depend more on the number of presentations of particular words (learning trials), touching on one of the advantages of VOCA usage, consistent word articulation and repetition, whether a parent or guardian is available for communication modeling or not (1991). In terms of gender differences regarding language acquisition, this could project a necessary overall shift in scaffolding methods for typically developing children once they near two years of age. As for children with developmental delays and disabilities, the combination of both symbolic and auditory reinforcement may present as more beneficial, allowing communication connections to be made on two different levels in two different ways. As parents, educators, and professionals working with children with language delays and disabilities, it is necessary to remain mindful of potential learning differences and differing developmental trajectories, like the "small but consistent female advantage" in early language development (Wallentin, 2009).
This study looks specifically at autism and gender as predicting variables of language acquisition from the use of VOCA. It should be noted that the male to female diagnostic ratio for ASD is approximately 3 to 1, lending itself to the assumption that more males will require augmentative communication services than females in terms of this population (Wallentin, 2009). As a distinguishing feature of ASD is language deficiency (e.g., muteness, language delay, echoing of speech, and idiosyncratic use of language), most individuals with a diagnosis will need some sort of speech therapy at one point or another and any neurological gender differences are important to note and keep in mind when developing strategies for functional communication.
Summary
In summary, there is a gap in the research when looking specifically at the impact of AAC devices on the language acquisition of children diagnosed with ASD This study will also compare the language acquisition of male and female children and those with an ASD diagnosis versus those without in relation to VOCA instruction to examine the effectiveness of these aided systems with different populations. Children with developmental disabilities can take longer to learn how to vocalize their thoughts, needs, wants, and ideas. Social-emotional, adaptive, and cognitive delays are often partnered with the language impairments of these populations (Thurm, Lord, Lee, & Newschaffer, 2007). Researching which AAC devices have been most effective in increasing the expressive language skills of children with developmental disabilities, along with tracking the time period of implementation and duration of therapy, are important next steps in this field. Giving children with developmental disabilities a better chance to access their words and use them in a communicative manner will reduce the likelihood of consequential problem behaviors that result from frustrations around an inability to convey messages.
Procedures
The data for this study is derived from Nancy Brady's study, Language would identify the change between time 1 and time 2 for the core sample with available data for both assessments, but had to be changed to an independent samples T-test upon further cleaning of the dataset.
Data Analysis
Data analyses were conducted using SPSS software. Descriptive statistics and frequencies were conducted in order to assess the gender, age in months at enrollment, and race of all participants. These initial analyses also look at the presence of an ASD diagnosis and usage of VOCA for each participant. A contingency table was conducted to determine if the demographic groupings of participants using VOCA and those using other forms of AAC were similar. In order to compare the impact of VOCA versus other AAC devices on the language acquisition of children with developmental disabilities, an independent samples T-test was conducted looking at the difference in total words rate at the time 2 assessment for both groups, which was determined by the recorded use of VOCA in the dataset. Next, a multiple regression equation was used to predict the strength of the relationship between VOCA and expressive language by assessing whether ASD diagnosis or gender mediates this relationship, controlling for significant demographic differences between the VOCA and non-VOCA groups.
RESULTS
The first analyses conducted examined participant demographics for those with available data at time 1 and time 2. Table 1 describes the final study sample consisting of eighty-one children with an average age of 46 months (3.8 years). Age and race were not found to be significant and therefore were not controlled for in the multiple regression equation, as the VOCA and non-VOCA groups were demographically similar. A large percentage of the included sample is male and white, however the presence of an ASD diagnosis or lack thereof was almost evenly split for both groups.
Just under half of the sample used a VOCA, with the rest of the sample using some other kind of augmentative communication. Participant demographics were all within the -3 to +3 range in regard to skew, supporting normal distribution of the tested population. A hierarchical linear regression analysis was conducted to predict the overall total words rate scores at time 2 from use of VOCA, presence of an ASD diagnosis, and gender. The results of this analysis indicated that none of these variables account for a significant amount of participants' total words rate scores at time 2. Finally, an exploratory analysis was conducted to compare the total words rate at time 2 of the participants using VOCA with an ASD diagnosis and those using VOCA with other diagnoses, comparing the benefits of the AAC intervention studied in this research for the language acquisition of specific populations. A second independent samples T-test indicated that both groups had similar means for total words rate at time 2, with participants with ASD scoring only slightly higher (m=35.67, SD=22.08) than those with alternate diagnoses (m=34, SD=29.16). It is important to note the sizable standard deviation of both groups. These findings were not significant, p=.856, t(32)=-.184.
DISCUSSION
Contrary to the hypothesis, the findings of the first independent samples T-test indicate a slightly higher total words rate at time 2 for those participants using forms of augmentative communication other than VOCA, though this difference was not Adults are responsible for keeping these aided communication systems in good condition and making them available to children at all times allowing for continuous opportunities for communication and interaction. Adults need to be as sensitive and responsive to words produced using an AAC as they would be to spoken language in order to reinforce language acquisition and appropriate implementation so children make necessary connections. The current body of literature pertaining to AAC methods used with toddlers and school-aged children does not focus on this potentially mediating or predicting variable of parent involvement, but rather the direct relationship between intervention strategies and language acquisition.
Considering the social constructivist theory, parents, teachers, and therapists must choose AAC intervention methods while simultaneously thinking about the "zone of proximal development" (ZPD): the "distance" between what the child is able to do by him or herself and what he/she is able to do with the support of an adult.
Successful implementation of AAC requires commitment and comprehension in terms of the adults who will procure associated therapy strategies and mediate related interactions.
Another possible explanation for why findings do not confirm the initial hypothesis could be that those using VOCA have more significant developmental disabilities than those using other communication methods. Considering the level of support provided by VOCA, across both visual and auditory modalities, it is likely teachers and speech-language pathologists would use this means of communication with children needing more intensive support. Children with more mild to moderate developmental disabilities are more probable to use unaided communication systems due to the likelihood of them being at a higher level of mastery in neighboring areas of development (e.g., the motor and cognitive domains). Though the VOCA and non-VOCA groups had minimal differences between them and a similar distribution of participants with ASD, the severity of developmental disabilities of both groups is unknown, which could explain the lack of significance of the current findings.
A hierarchical linear regression revealed no significance of use of VOCA, presence of an ASD diagnosis, or gender as predictors of language acquisition. Huttenlocher et al. (1991) found gender effects in acquisition of new words to already be declining at 20-24 months of age. Wallentin (2009) reiterates the presence of a small but consistent female advantage pertaining to early language development, noting that this seems to disappear during childhood and is not readily identifiable in adults. After the 20-month marker, parent speech becomes a heavier predictor of language acquisition, again emphasizing the importance of adult interactions with children working to expand their vocabulary and subsequent communication skills (Huttenlocher et al., 1991). Considering the older age and heavily weighted male sample of the current study, the lack of significance of gender as a predictor of total words rate correlates with available literature, any potential associated differences likely to dissipate by toddlerhood.
Wallentin (2009) discusses the skewed sex distribution of ASD, the male to female ratio being approximately 3 to 1, linking this statistic to the language function of diagnosed individuals. Many children with ASD who present with severe language delay can be expected to make notable gains in the development of language after age 4, which potentially explains the lack of significance of ASD as a predictor of language acquisition in the current study (Wodka, Mathy, & Kalb, 2012 lasted a year in duration, which could also explain the insignificant findings as progress with language with children with developmental disabilities can vary drastically during the early childhood years (Wodka et al., 2012).
An exploratory analysis indicated a higher total words rate at time 2 for those participants with an ASD diagnosis who use VOCA versus those with other developmental disabilities who use VOCA, though this difference was not significant.
There is considerable research suggesting that individuals with ASD typically do not experience deficits in discrimination learning, especially when the stimuli are concrete in nature (e.g., BoardMaker symbols and digital pictures) (Mirenda, 2003). This would support the use of aided communication systems, like VOCA, with the ASD population, as this form of AAC intervention is less abstract and more aligned with the learning style of these individuals, allowing a strengths-based approach to language acquisition. Mirenda (2003) reiterates that cognitive scientists would argue any discrimination that requires recognition (e.g., the graphic symbols on a communication display) rather than recall memory (e.g., manual signs) is easier to achieve because fewer cognitive resources are involved. Aided communication systems also allow ease of motor planning, a child simply needing to gesture to or point at a picture to express his/her thoughts, needs, wants, and ideas. It is unlike the complex motor planning needed for sign language (Branson & Demchak, 2009 (2009). It is possible that participants using AAC interventions other than VOCA in the current study may use them in an augmentative (supplemental) sense rather than as an alternative (primary) means of communication depending on the goals of therapy pertaining to language acquisition in terms of pace and expected progress. This could potentially explain the lack of difference of total words rate between the VOCA and non-VOCA groups.
A number of limitations must be considered regarding the current study. First, the small sample size, which consisted of mostly white, male participants, calls into question the ability to generalize findings across settings and populations. Secondly, as one of the criteria for participation was a vocabulary of fewer than 20 different words said, signed or selected, previous researchers classified all children as nonverbal, making their time 1 data a quantitative score of zero. The original data analysis plan involved a paired samples T-test, which would identify the change between time 1 and time 2 for the core sample with available data for both assessments, but had to be changed to an independent samples T-test upon further cleaning of the dataset. It is possible that differences in language abilities at time 1 still existed despite the nonverbal classification given by researchers determining participant qualification for entry into the study, which must also be considered when analyzing the current results.
The six language assessments used to determine each participant's total words rate scores, the dependent variable in the current study, were originally developed to evaluate the expressive and receptive language of typically developing children. These underlying measures may not be sensitive enough to the language skills of children with developmental disabilities, the population composing the sample used in this research. Though the total words rate score accounted for a range of means of expression (words spoken, signed, or selected), the six language assessments may not have accurately measured the progress and abilities of participants being observed.
It should also be noted that an outcome variable clarifying participants' method(s) of communication would be useful. This would help researchers identify the associated benefits of specific AAC interventions. For instance, using PECS may increase an individual's selected and signed language, while VOCA may increase an individual's selected and verbal language. Findings of the current study still lend themselves to useful information regarding the effects of VOCA on language acquisition, but future studies should consider looking at a randomly selected sample with a wider quantitative range of expressive vocabulary linked to identifiable communicative means. Additionally, participants' IQ scores were not obtained by previous researchers, which could potentially provide rationale for the progress of language acquisition and success of intervention methods in the current study, though it is important to note IQ scores are not always indicative of the presence of a disability or its severity.
Future researchers should also consider identifying the type and severity of a child's diagnosis during data collection so subsequent findings can be correlated with these factors, which are not measurable in the current study. Though the current study looked specifically at participants with an ASD diagnosis, the sample size and lack of data identifying the functioning level of each individual made results difficult to analyze in terms of a relational direction. Considering the broad spectrum of symptoms associated with ASD, the benefit of utilizing VOCA for each participant could drastically differ based on the severity of their social communication and interaction deficits. The current study also suggests the need for future research comparing the impact of aided and unaided communication systems on the language acquisition of specific populations, with a predetermined purpose of intervention methods as either augmentative (supplemental) or alternative (primary).
CONCLUSION
The findings of no significant difference between participants using VOCA and those using other forms of AAC indicate that AAC interventions should be chosen on an individual basis with consideration to the baseline skills associated with other developmental domains such as motor coordination and cognitive processes which | 6,172.6 | 2018-01-01T00:00:00.000 | [
"Education",
"Linguistics",
"Medicine"
] |
Applying Genetic Programming with Similar Bug Fix Information to Automatic Fault Repair
Owing to the high complexity of recent software products, developers cannot avoid major/minor mistakes, and software bugs are generated during the software development process. When developers manually modify a program source code using bug descriptions to fix bugs, their daily workloads and costs increase. Therefore, we need a way to reduce their workloads and costs. In this paper, we propose a novel automatic fault repair method by using similar bug fix information based on genetic programming (GP). First, we searched for similar buggy source codes related to the new given buggy code, and then we searched for a fixed the buggy code related to the most similar source code. Next, we transformed the fixed code into abstract syntax trees for applying GP and generated the candidate program patches. In this step, we verified the candidate patches by using a fitness function based on given test cases to determine whether the patch was valid or not. Finally, we produced program patches to fix the new given buggy code.
Introduction
Owing to the high complexity of recent software products, developers cannot avoid major/minor mistakes, and software bugs are generated during the software development process.In open source projects, a large number of bug reports containing 350 bugs a day are submitted to the bug repository for Eclipse and Mozilla [1].Because of the huge number of bug reports, developers spend more time fixing bugs, thus increasing their workloads and costs.To reduce their bug fixing efforts, automatic fault repair is necessary.
In the general bug-fixing process, developers try to fix software bugs according to the descriptions in bug reports by creating a patch solution.After that, quality assurance engineers may check the patch and then update the program with the patch solution.Because of the large number of daily bugs, developers spend more time tracing the bugs; hence, they may generate incorrect patches.
Our motivations are the following: • As developers' workloads increase owing to many daily bugs, they may spend more time debugging to fix these.If an automatic fault repair technique is provided to fix bugs, program debugging time and cost can be reduced.
•
Developers may make mistakes in debugging the program source code.As a result, they may generate incorrect program patches.Thus, if an automatic fault repair technique that generates the correct patch is provided, software quality will improve significantly.
•
If bug reporters give descriptions with helpful information to developers such as stack trace and scenario reproduction, the developers can trace and fix the bugs easily.However, if the bug reports contain insufficient information [2], the developers may have difficulty debugging.Thus, it is expected that the automatic fault repair with similar bug fix information can effectively fix the bugs despite lacking descriptions.
To address these problems, many researchers have proposed automatic fault repair approaches.To our best knowledge, GenProg [3] is well-known for automatic fault repair.They utilize Genetic Programming (GP) to generate the program patches.However, as GenProg conducts patch validation by passing given test cases, it might require too much time and generation cycles.PAR [4] captures fault patterns by analyzing human-written patches.However, if it encounters patterns that it did not capture, it cannot fix the bugs.RSRepair [5] utilizes a random search algorithm instead of the GP algorithm on GenProg.AE [6] uses a deterministic patch search algorithm and program equivalence relations to prune equivalent patches during testing.However, even if the patches pass all the test cases to verify whether the patches can be adopted or not, they may not be correct.To produce the correct patches, we utilized bug fix information in this paper.In web applications, PHPQuickFix [7] and PHPRepair [7] use a string constraint solving technique to automatically fixed HTML generation errors.FixMeUp [8] automatically fixes the missing access-control statement using program analysis.Prophet [9] is based on learning the correct code.The work extracts code interaction information from the fixed code and changes the nearby code.By applying a machine learning algorithm, it generates a new patch.SPR [10] generates the correct patches in large-scale applications.To generate the candidate program patches, the work utilizes a set of transformation schemas.Although it generates program patches, the performance should be improved.
To resolve these problems, we propose a novel approach for automatic fault repair by using similar bug fix information based on GP.First, we searched for similar buggy codes related to a new given buggy code and the fixed code of the similar buggy codes.Then, we converted the source code into abstract syntax trees (ASTs).We applied GP with the ASTs of the fixed codes.Finally, we generated the program patches for the new given buggy code from the ASTs.
Our contributions are the following:
•
The bug fixing time and effort can be reduced as we support automatic fault repair.
•
The quality of bug fixing can be improved as we utilize similar bug fix information with GP to generate program patches.
•
We perform a small case of study using our model in IntroClass [11].The result will likely generate a correct patch.
This paper is organized as follows.We describe the background information on fault repair in Section 2. In Section 3, we discuss related studies.We describe our approach in Section 4 and present a case study in Section 5.Then, we provide the discussion in Section 6.Finally, we conclude our study in Section 7.
Background Knowledge
Genetic programming (GP) is a programmatic approach based on evolutionary algorithm.In order to apply GP, the input buggy code should first be represented in AST.Next, we adopted GA [12] operators such as selection, mutation, and crossover to generate a new population.Then, we utilized a fitness function to evaluate the validity of each patch in the population.Finally, we transformed the valid patch into a program source code to patch the given buggy code.
Owing to the random generation characteristic of GP, the quality of program patches can decline.To ensure patch quality, we carried out program analysis [13] using a test suite.In this case, we are likely to improve the quality of candidate program patches, because the GP process and the analysis are independent.If we adopt white-box and black-box test cases, the overall quality of the patches is expected to improve.
Related Work
Many researchers have proposed automatic fault repair approaches.We provide a qualitative comparison of related works on automatic fault repair, as shown in Table 1.
Study Evolutionary Algorithm Metric
GenProg [3] O Basis of GP for Repairing PAR [4] X Bug Fix Pattern Templates AE [6] X Deterministic Algorithm Prophet [9] X Machine Learning Algorithm SemFix [14] X Component-based Synthesis Yokoyama [15] O Code Similarity (Line based) Our Approach O Bug Fix Information Le Goues et al. proposed GenProg [3], which is a popular approach in this fault repair domain.They utilized AST-based GP.First, they transformed an input buggy source code into AST (e.g., original AST).Then, they adopted GP operations like selection, crossover, and mutation to generate a new AST.Next, the new AST transform program source code was used to verify whether the buggy code was fixed using a test case.Finally, they generated a new program patch.However, during the generation, it occurred too many times and in too many generation cycles.Similar to GenProg, Qi et al. utilized GP operations, including selection and crossover as well as test case prioritization [16].Kim et al.,proposed PAR [4], an automatic fault repair method using a fixing template from human-written patches.They verified a buggy pattern and classified several pattern templates.In detail, they classified the target program code by fault localization into the related code in the pattern template.Then, they computed the fitness function from the target source code using a test case.Next, they selected the related code using tournament selection.Finally, they compared the new generated patch to the fixed source code from developers.However, they could fix a new bug if it does not exist in the proposed pattern.
Long et al. introduced SPR [10] and improved the performance of fault repair by more than five times compared with previous work using parameterized transformation schemas.However, they could not fix the bug correctly if they needed a new condition that did not exist in the program.
Qi et al., presented an automated fault repair technique called RSRepair [5] that uses random search.Then, they adopted test case prioritization to speed up the patch validation process.Finally, they showed results that outperformed the GenProg results.Weimer et al. proposed an approach called AE [6].They used a deterministic patch search algorithm and program equivalence relations to prune equivalent patches during testing.However, the results of RSRepair and AE did not generate the correct patches.
Long et al. proposed Prophet [9] based on learning the correct code.They extracted code interaction information from the fixed code and changed nearby code.Then, they adopted a machine learning algorithm to generate a new patch by learning.
Nguyen et al. proposed SemFix [14] to fix the buggy program code.They utilized semantic information via source code and the SemFix outperformed the GenProg.
Yokohama et al. proposed an automated repair approach [15] based on source code analysis.In detail, they analyzed changed source lines in before/after patches.
In web applications, PHPQuickFix [7] and PHPRepair [7] utilized string constraint solving techniques to automatically fix HTML generation errors.
In the data mining area, Guo et al. proposed [17] an approach for bug severity prediction.They analyzed descriptions of bug reports to predict bug severity in an Android project.Singh et al. proposed an approach [18] for sentiment analysis using machine learning.Souri et al. surveyed [19] malware detection approaches using data mining.
In terms of testing, the GA technique was proposed by Sabharwal [20] to generate test cases for pairwise testing.
The main differences are the following: • If we do not consider bug fix information, the buggy code cannot be fixed correctly by GP.In this paper, we first find the most similar buggy codes related to the new given buggy code in order to find the related fixed code.Thus, we can generate a program patch for the buggy program.
•
If a bug pattern and template are adopted, the various bugs that do not exist in the proposed pattern will not be fixed.Thus, we utilized a buggy code and a related fixed code in order to fix various buggy codes.
Applying Genetic Programming with Similar Bug Fix Information
In this section, we describe our approach for automatic fault repair by applying GP with similar bug fix information.First, we identified suspicious buggy code lines (a) using a fault localization technique.Then, we applied our approach to generate patches (b-1) by applying GP with similar bug fix information.Next, we verified the candidate patches to determine whether the patches could be adopted using test cases (b-2).If the patches could not be adopted (e.g., test case failure), we repeated our patch generation (b-1).Finally, we applied the patches to the buggy program for fixing (c).The overview of our approach is shown in Figure 1.
Fault Localization
In general, we first identified the program source code lines that are buggy.Identifying the buggy code lines is a prerequisite for repair.For example, to fix the buggy program, e.g., multiple buggy lines, the fault localization technique should be performed.Many researchers have proposed techniques to find buggy code lines using the information retrieval model [21] and Latent Dirichlet Allocation [22].However, the aims of these fault localization studies are different from those of fault localization for automatic fault repair, in the sense that all the correctly identified lines are used together for repair.First, we verified whether a program can pass all the black box and white box test cases.If the program cannot pass all the test cases, we modified the source code and ran the test cases again to ensure that the modified program was correct.After that, we identified the modified line in the program.This information was used in the application of our approach and makes the repair better.
Converting Code to AST
To apply the GP technique, we transformed the program source codes into AST.The AST conversion can be expressed by a relation between AST nodes and the source code as follows: • Target indicates the original buggy code and similar fixed buggy code.
•
CodeLines are source code lines.
After that, we changed the source codes by modifying AST nodes.Then, we generated source codes from the changed AST nodes.
Applying GP
In this section, we describe the application of GP with similar bug fix information.Creating the initial population, GP operation, fitness computation, etc., are shown in Figure 2. The GP operations consist of selection, crossover, and mutation.The initial patches were created from random generation and each patch was evaluated using a fitness function.While applying the GP operations, we generated the candidate patches to fix the buggy code.They were also evaluated using a fitness function to determine whether they were adoptable.For the fitness function, we utilized test cases in a test suite for each program.If the fitness value of a patch is 1.0, it means that the patch passes all test cases and is the correct patch for the buggy program.If not, our approach repeats the GP generations.
Making Initial Population
First, we searched for buggy codes similar to the given buggy code by utilizing a clone detection technique [23] and obtained the fixed code of the most similar code.We produced an initial population by utilizing the similar lines in the fixed code.To produce an initial population, we found lines in the fixed code that were similar to buggy lines in the buggy code identified by fault localization technique.To find similar lines, we transformed each line into token sequences and compared token sequences between those lines using the longest common subsequence (LCS) [24].We took the token sequence with the highest LCS value from the lines.We compared the token sequences between a buggy line and the similar fixed buggy code using LCS.Then, we took the token sequence with the highest LCS values from the lines.
We produced each solution by utilizing a random operator, except for keywords and names of variables and functions from the token sequence.A constant in a line can be adopted from both the buggy code and fixed code.Then, we produced a new line from the changed token sequence and swapped that line with the buggy line.This step is repeated until the number of the initial patches is equal to the size of the population.
GP Operation
To produce each patch, we first selected two candidate patches from the population using a selection operator, and executed crossover and mutation operators.It is expected that the nodes in the buggy line were changed by the GP operations.In the crossover operation, we start with ASTs of the two selected candidate patches.We describe the details of GP operations as follows:
•
Selection: Fitness normalizing process was applied to guarantee the probabilities for patches that have low fitness.To normalize fitness, we used the following process.
•
We first sorted the candidate patches in the population in descending order from the previous generation using the fitness values.Then, we computed the difference between the largest and smallest fitness values.We divided the difference by the normalizing factor "n" and added the difference between the fitness value and the smallest fitness value in the generation to obtain the normalized fitness value.The smaller the value of the normalizing factor "n", the greater the probability that a patch with a small fitness value will be selected.This normalized fitness value was used only for selection.The two ASTs from the current population were selected to construct two children using a roulette wheel selection operator with the normalized fitness value.
According to roulette wheel selection, the patch that has the greater normalized fitness value will be selected with greater probability.Then, we used the two parent patches to generate two new children patches.
•
Crossover: The crossover aims to exchange the sub-tree nodes in two ASTs.To do this, we first selected the changeable target nodes in each parent tree.We then constructed a set of target nodes from the buggy line to the nodes within "r" lines, including the lines above and below.The factor "r" refers to the range of the patch target code.After selecting the target nodes, we verified whether the two nodes can be exchanged with each other by tracing the parent's node.Then, we exchanged the changeable nodes.
•
Mutation: In this operation, the target node will be removed, added, or modified.The node was selected from the target set.The set is the same as the target set in the crossover, but the set can have different a code range, and the set is not dealt with as crossover target set.There are three sub-operations: deletion, addition, and modification.Deletion removes the target node from the AST.Addition inserts a copied node of its own tree.Modification changes the operator of the target node.When the modification is executed, an operator is selected from an operator list, except for the original operator.However, the mutation operator introduces variety into the population in a positive or negative way; thus, the operator is adjusted by a suitable probability parameter.
Fitness Function
Owing to the random generation characteristic of GP, the generated candidate patches should be evaluated by a fitness function to determine whether the patches are adoptable.The fitness function is expressed as follows: From Equation (3), we consider the generated patch as correct if the fitness value is 1.0.If not, the patch cannot be adopted to fix the given buggy program.The larger fitness values are better than the smaller fitness values.Our approach can be described in the following pseudocode as shown in Figure 3.To generate the initial population, we first set the target lines (lines 1-5).In this process, we assume that we already know where the fault is (line 1).Then, we set the target lines around the buggy line (lines 2-5).One of these lines will be modified to generate the initial population (line 7).After setting the target line, we collect the constants and the names of the variables in the line (lines 8 and 9).This set is used to construct a new line (line 21).To select a line from patched code, we compute the LCS value between the token type sequence of the target line and the token type sequence of lines from the correct patched code of the similar bug (line 13).In detail, variables are changed to "varname", operators are changed to "op", and constants are changed to "constant" (e.g., "sum = sum% 64 + 32" will be changed to [varname = varname, op, constant, op, constant]).Then, we obtain the LCS values of each line.The line that has the largest LCS value is selected to write the new line.If there are several lines with the same LCS value, we select a line randomly (line 20).We construct a new code line from the token type sequence that has the largest LCS value (line 21).The token type "varname" is changed to one of the names of the variables in the original code, and the token type "constant" is changed to one of the constants of the original buggy code line and the selected patched code line.The token type "op" is changed to a random operator.The selected line of buggy code is alternated by a new line (line 22).
Case Study
In this section, we present a case of study of our approach.First, we utilized a benchmark dataset from IntroClass [11].The dataset consists of checksum, digits, grade, median, smallest, and syllables programs.In this case study, we used the checksum program as shown in Figure 4.In Figure 4, an add variable in Line 6 can be an initial value problem in a runtime execution; thus, the variable cannot be affected by the test case.However, Line 16 can be affected by the test case, because a wrong number (e.g., −32) in the formula is subtracted from the sum of local variables.
Similar Buggy Detection
To detect a similar buggy code, we utilized a tool named CCFinder [23].With the CCFinder, we can analyze a token-based source code to find the similar fixed files from the new given buggy file.Thus, we adopted these similar files to retrieve the fixed buggy information for constructing the initial population.If we use more similar fix information, we can get a variety of information.However, this results in a rapid increase in overhead; hence, we adopted one of the most similar buggy files from the result.In this case, we get a similar value (0.246377) from the given buggy code (e.g., File ID: 98d873cde . . . ) and find the similar fixed buggy code (e.g., File ID: 36d8008b1 . . .), as shown in Figure 5.According to Figure 5, we can verify the similarity with Figure 4 by the following: (1) applying while loop statement; (2) the while loop statement contains a getchar function related to the scanf function and an accumulation equation; (3) in the sum formula, they utilize an add (e.g., "+") operator and a module (e.g., "%") operator.
AST Conversion
To apply GP, we transformed source codes into the ASTs as shown in Figure 6 (from Figure 4) and Figure 7 (from Figure 5).In Figure 6, the AST is related to the buggy line (e.g., Line 18) in Figure 4. We show the buggy node of the tree using a red box and a check mark in the figure.When we transformed the source codes into AST, we utilized a mapping concept (e.g., line numbering) between the buggy nodes of AST in Figure 6 and the source codes of related nodes in Figure 7 in order to verify the target node in the GP operation.Then, we show the AST result of the similar fixed buggy code in Figure 7.The nodes of tree are related to the line (e.g., Line 19) in Figure 5.
Applying GP
By applying GP to the previous tree, we can get a result as shown in Figure 8.Each operation was executed by the probability from the previous operation.We can verify that the target nodes were changed by GP in Figure 8.The buggy code does not contain an add (e.g.,"'+") operator and a module (e.g., "%") operator.If we do not use the similar fixed code, the program cannot be fixed.However, we have to consider the same type of variable in the similar fixed code and the original buggy code when we perform the crossover operation.Finally, we can get a result from Figure 8, as shown in Figure 9.
Fitness Function Computation
Owing to the random generation characteristic of GP, we have to verify whether the patch can be adopted by using a fitness function, because the GP operations perform tasks including random combination, insertion, deletion, and edition.We computed the fitness function using test cases.After patch generation, we executed the patch program by compiling the patch source code.Then, we inserted the related test case into the running program.Finally, we observed the results.By using the fitness function, we verified the total number of passed test cases.If the result of the fitness function is 1.0, we consider the patch as successfully generated.However, if the result of one generation is not 1.0, we removed the patch that holds the lowest fitness function value according to the parameters of the parent population.Then, we applied the GP operations to the remaining patches to generate a new patch.The new patch was also computed by the fitness function.In this step, we expect the fitness value of the patch population to increase during the iteration of these steps.The patch generation for the buggy program will not be executed (e.g., patch fail) if the patch generation is larger than the generation parameter, and if the generation time reaches the timeout parameter.
Adjusting Mutation Parameter
In this section, we performed experiments by adjusting mutation parameters (0.3, 0.5, and 0.7).This experiment was executed on a server machine with Xeon CPUs (10 cores, 2.40 GHz) and 256 GB RAM.Due to a time limit (e.g., a random generation characteristic of GP), we set the time limit for 8 h, which means the corrected patch generation should be made within 8 h.If not, the patch is fail.The results showed that the corrected patch was made within 8 h when we set the mutation parameter to 0.5.Also, we keep the sub-mutation parameters (e.g., insertion, deletion, swapping, and modifying) to default (each for 0.25) in all the cases.In addition, if we have a larger population size than 100, we can attain plentiful information.However, some of them might not be useful.Moreover, we cannot generate the corrected patches when we adjust the mutation parameters (0.3 and 0.7).In the future, we will investigate the correlation between the GP parameter and population size.
Experiment Analysis
In the case study, we presented an approach for program fault repair using similar bug fix information on GP.Then, we generated the patch for a buggy program in IntroClass.In Figure 4, we added a formula (e.g., sum = sum − 32) so that the program returns a value of " " (e.g., whitespace) when we take the #1 black box test case (e.g., 1234567890).However, our patch returned "-" (e.g., hyphen) with the same test case; hence, we generated the correct patch in Figure 9.In this case, we accept all the black box test cases.
Threats to Validity
Fault Localization: We investigated buggy code lines manually in IntroClass.The size of each project is small, so we can trace the buggy code manually using black box and white box test cases.In the future, we would like to adopt an automatic fault localization technique or tool to increase the accuracy of this study in large-scale projects.
Dataset: We utilized a benchmark dataset called IntroClass.However, the dataset was created by students in the class.Thus, the size and complexity of the code are small, and the code contains small keywords for programming.In the future, we will utilize an open source code to verify our study of effectiveness.
Adjusting Mutation Parameter: In this paper, we performed a case of study by adjusting GP parameters (mutation parameters = 3, 5, and 7).The result shows that the corrected patch was made when we set the mutation parameter to 0.5 (population size = 100).However, we cannot conclude that the parameters are always appropriate.In the future, we would like to investigate the correlation between GP parameters and others.
Conclusions
This study proposed a method to fix a buggy program automatically.First, we found suspicious buggy code lines by manual checking.Then, we generated candidate patches by applying GP with similar bug fix information.Next, we verified whether the candidate patches were adoptable.Finally, we generated the patch to fix the program fault.With our approach, we expect that the time and effort spent on fixing bugs can be reduced.In the future, we would like to utilize a test case prioritization algorithm [16] and large-scale bug fix information.Moreover, we plan to create a tool for automatic fault repair.
Figure 2 .
Figure 2. A detailed view of b-1 and b-2 in Figure 1.
Figure 3 .
Figure 3.A pseudocode of our approach.
Figure 4 .
Figure 4.A sample of buggy code.
Figure 5 .
Figure 5.A sample of similar fixed buggy code.
Figure 6 .
Figure 6.Result of AST Conversion from Figure 4.
Figure 7 .
Figure 7. Result of AST Conversion from Figure 5.
Figure 8 .
Figure 8.The AST Result from the GP.
Figure 9 .
Figure 9.A result of patched source code from Figure 8 (e.g., the fitness value is 1.0).
Table 1 .
A Qualitative Comparison. | 6,321 | 2018-04-02T00:00:00.000 | [
"Computer Science"
] |
CATHODIC ELECTRODEPOSITION AND CHARACTERIZATION OF Ni 3 Se 2 THIN FILMS
Nickel selenide thin films have been potentiostatically electrodeposited on titanium substrate at room temperature from aqueous solution containing Ni-EDTA and Na 2 SeO 3 . Various deposition potentials were attempted in order to determine the optimum electrodeposition potential. The films were characterised using x-ray diffraction analysis (XRD) and the photoactivity of the electrosynthesised films were studied using linear sweep voltammetry (LSV). The band-gap energy was determined using UV-visible spectroscopy. The XRD analysis indicated the formation of polycrystalline Ni 3 Se 2 . The film exhibited p-type semiconductor behaviour with good photosensitivity. The bandgap energy (E g ) was about 1.4eV.
INTRODUCTION
In order to sustain the present standard of life and development, a reliable and safe supply of electrical energy is vital. The electrical energy currently used mainly comes from fossil fuels and nuclear reactors but there is uncertainty for the future limited resources of fossil fuels and nuclear reactors also adds to the energy problem. Thus, renewable energy sources may play a significant role in sustainable energy supply 1 . Solar cells at present furnish the most important long duration power supply for satellites and space vehicles. Solar cells also had successfully employed in small scale terrestrial application 2 . The solar is considered a major candidate for obtaining energy from the sun, since it can convert sunlight directly to electricity with high conversion efficiency and can provide nearly permanent power at low operating cost and is virtually pollution free 3 . Considerable efforts have been made in recent years in the search for low cost materials for solar energy conversion. Among the materials of great interest are polycrystalline metal chalcogenides [4][5][6][7][8][9][10][11] . These compounds are also used as sensor and laser materials, thin films polarizers and thermoelectric cooling materials 12 . They possess certain criteria to make them potential candidates in the photoelectrochemical solar cells. The electrodeposition technique has been a promising deposition method to grow thin films. In particular, for absorber films in solar cell, this technique is a perspective competitor because of several advantages such as the possibility for large-scale production, minimum waste of components and easy monitoring of the deposition process 13 . This technique is generally less expensive than those prepared by the capital-intensive physical methods. Recently, the electrochemical synthesis has been indicated as a promising method for the preparation of semiconductor films. We report here the electrochemical deposition of nickel selenide thin film semiconductor for photoelectrochemical cells on titanium substrates from aqueous solution.
EXPERIMENTAL
An EG & G Princeton Applied Research potentiostat driven by a software model 270 Electrochemical Analysis System was used to control the electrodeposition process and to monitor the current and voltage profiles. A conventional three-electrode cell was employed, where the reference electrode is a Ag/AgCl to which all potential were quoted and the counter electrode is platinum (Pt). Titanium (99.99%) was used as the working electrode. The working electrode was polished prior to the deposition process and its surfaces not to be contacted by the electrolyte was sealed using polyethylene terephthalete (PTFE) tape before the insertion into the cell. Ethylenediaminetetraacetate (EDTA) was used to chelate with Ni 2+ to obtain Ni-EDTA solution.
The presence of EDTA in aqueous solution was found to improve the lifetime of the deposition bath as well as the adhesion of the deposited film on the Ti substrate 14 . The electrolyte bath comprised of Ni-EDTA and sodium selenite (Na 2 SeO 3 ) solution. Prior to the deposition, a cyclic voltammetry (CV) experiment was carried out between two potential limits (-0.40 to -1.00 V) to probe the effect of the applied potential and to determine the most likely suitable electrodeposition potential for the deposition of nickel selenide. The deposition was attempted at various potentials such as -0.40, -0.50, -0.60, -0.70 V to determine the optimum deposition potential. The experiment was performed at room temperature (27˚C) under N 2 blanket without stirring. The pH was maintained at 2.5 using HCl. HCl was added to prevent the formation of hydroxyl species and insoluble compound 14 . After the electrodeposition process, the films were washed with distilled water, dried and kept for analysis. The films were characterised by x-ray diffraction (XRD) using a Philips PM 1730 diffractometer for the 2θ range = 2˚ -60˚ with CuK α radiation. Photoelectrochemical experiments were performed by running linear sweep voltammetry (LSV) between two potentials limits (-0.40 to -1.00 V) in contact with sodium thiosulphate, Na 2 S 2 O 3 .5H 2 O (0.02 M) solution. The sequence of constant illumination, chopped illumination and dark period were performed on the PEC cell to study the effect on photoactivity behavior. A halogen lamp (300 W, 120 V) was used for illuminating the electrode. Optical absorption study was carried out using the UV/Vis Shimadzu Lambda 20 spectrophotometer. The filmcoated on indium doped tin oxide (ITO) glass was placed across the sample radiation pathway while the uncoated ITO glass was put across the reference path.
RESULTS AND DISCUSSION
The CV of the electrodeposition bath containing acidified Ni-EDTA (40 ml, 0.02M) and Na 2 SeO 3 (40 ml, 0.02M) is shown in Figure 1. The forward scan showed a very low value up to -0.40 V, whereby the small wave at -0.40 V was onset followed by a strong cathodic current starting at -0.50 V. This is in response with a reduction process associated with Ni(II) reduction on the Ti substrate. Sharper cathodic peak attributable to hydrogen evolution is evident as the sweep reaches -0.95 V and above. During the reverse scan, the current reduces as the sweep past -0.70 V. The reduction process appears to be irreversible as no anodic peak appears. Hence, the CV graph conclusively suggests that a deposition on the working electrode can be expected when the potentials above -0.40 V are applied and the deposit is stable from the dissolution. After the complete CV run, a grey, uniform and smooth film was clearly observed on the working electrode.
The following experiments were carried at different deposition potentials, -0.40, -0.50, -0.60 and -0.70 V. Figure 2 shows the XRD pattern of the samples prepared at these potentials. The intensity of the peaks increased as the potential was increased to a more negative value from -0.40 to -0.60 V. However, the film prepared at -0.70 V showed a decrease in the intensity. Four peaks could be observed for the film prepared at -0.60 V at 2θ = 28.7, 44.6, 53.2 and 58.9c orresponding to interplanar distances of 3.1, 2.0, 1.7, 1.5 Å (Table 1). These values are in good agreement with the JCPDS values of 3.0, 2.1, 1.7 and 1.5 Å (File No. 19-0841). The films were subjected to the photoactivity test, which could determine the suitable potential needed to prepare a film with good photoresponse. This is also an important test as the films are expected to be a semiconductors, thus should be sensitive to light, by showing a photocurrent in the region corresponding to their minority carriers current flow. Only the film prepared at -0.50 V showed
Figure 2 : XRD pattern of samples prepared at different deposition potentials
the best photoactivity ( Figure 3). Films prepared at other deposition potentials did not show good photoresponse as the currents produced wee much lower compared to the film prepared at -0.50 V. From the result (Figure 3), the film was found to be a good semiconductor with significant photocurrent output in the cathodic polarisation region. The upper value of the current correspond to the photocurrent when the sample employed as cathode was illuminated, while the lower value correspond to the dark current when the illumination was interrupted by chopping. The fact that photocurrent occur on the negative potentials region indicates that electrons are minority carriers of the film and their concentration was then enhanced by the illumination. Thus, the films prepared are p-type semiconductor and can be deployed as photocathode in the PECs application to facilitate a reduction process 3 .
Deposition was carried out on an ITO-coated glass substrate to study the optical behaviour of the Ni 3 Se 2 films. Figure 4 shows the optical absorbance data of the film vs. wavelength obtained from UV-visible spectrophotometer. An absorption could be observed between 300 to 600 nm. This indicates that the film is active in the visible portion of the spectrum. Band gap energy and transition type can be derived from mathematical treatment of the data obtained from the optical absorbance vs. wavelength with Stern relationship (1) of near-edge absorption 15,16 : where ν is the frequency, h is the Planck's constant, k equals a constant while n carries the value of either 1 or 4. The bandgap, E g , could be obtained from a straight line plot of (Ahν) 2/n as a function of hν. Extrapolation of the line to the base line, where the value of (Ahν) 2/n is zero, will give Eg. A linear trend is apparent where n in the Stern relationship equals 4 ( Figure 5). The line segments required to by bypass the energy of the gap lies at about 1.4 eV.
CONCLUSION
Ni 3 Se 2 films could be prepared by the method explained. The presence of EDTA was found to induce better films formation. Deposition at -0.50 V was found to be optimum for the preparation of Ni 3 Se 2 in this set up concluded from the XRD data and the photoresponse test. | 2,205 | 2017-10-27T00:00:00.000 | [
"Materials Science",
"Physics"
] |
Feedback control method to suppress stick-slip in drill-strings featuring delay and actuation constraints
In this work, performance of a modified-integral resonant controller with integral tracking is investigated numerically under the effects of actuator delay and actuation constraints. Actuation delay and constraints naturally limit controller performance, so much so that it can cause instabilities. A 2-DOF drill-string m with nonlinear bit–rock interactions is analysed. The aforementioned control scheme is implemented on this system and analysed under the effects of actuation delay and constraints and it is found to be highly effective at coping with these limitations. The scheme is then compared to sliding-mode control and shows to be superior in many regimes of operation. Lastly, the scheme is analysed in detail by varying its gains as well as varying system parameters, most notably that of actuation delay.
Introduction
In the Oil and Gas industry, drill-strings are critical engineering systems and structures for exploration and production drilling. Over the years there has been a significant interest in understanding the dynamics of drill-strings, namely real-time data gathering via MWD [1] (Measurement While Drilling) and the use of FE models [2,3]. Due to the complex dynamics inherent within drill-strings and their frictional interactions with the borehole, they are highly susceptible to unwanted oscillatory effects, which come in three primary forms, namely torsional [4][5][6][7], lateral [7,8] and axial [7,9] vibrations. These problems occur in all types of well configurations (vertical [10,11], directional [11] and horizontal [11][12][13]) and their accompanying drilling methodologies (rotational [14] and percussive [14]) (Fig. 1).
These unwanted oscillations present challenges with the drilling procedure and can and will continue to produce downtime, and incur significant financial losses, on rigs due to the damage they cause entire drill assemblies [15]. This paper focuses on a class of torsional vibrations known as stick-slip oscillations [16]. Stick-slip is one of the most commonly encountered vibration phenomenon in any type of well and is the most common reason for down-hole tool and tool joint failure [10]. Thus, stick-slip has garnered great interest in its cause as well as its necessary prevention. Stick-slip studies began with the majority of its studies focusing on sima e-mail<EMAIL_ADDRESS>(corresponding author) b e-mail<EMAIL_ADDRESS>c e-mail<EMAIL_ADDRESS>d e-mail<EMAIL_ADDRESS>plifying and isolating the stick-slip phenomenon to low DOF drill-string models [17,18] based on the torsional pendulum. The friction model developed for lumpedmass modelling, is a discontinuous switch case one by Navarro-Lopez [19]. This friction model elegantly captures the stick-slip dynamics while preserving essential bifurcation behaviour caused by changes in Weighton-Bit (WOB) and top-torque. By focusing solely on stick-slip, low-degree of freedom models have allowed for a greater understanding of this phenomenon without involving the other aforementioned axial and lateral vibrations. while simultaneously allowing for the development of friction models.
A drill-string, in general, can be modelled by an 'infinite' number of rotational spring-mass-dampers connected in series. Due to the overall complexity of drillstrings, FEA modelling of them was of the utmost importance to understand the overall behaviour of drillstrings [20] including their cutting ends [21]. This type of modelling is too complex to design control signals for and as a result, lumped-parameter models [17,18] became popular, as they isolate specific dynamics and allow for controllers to be designed for these problems. In recent years, 2-DOF models are still used for producing and benchmarking novel control methods for stickslip [22,23] as well being used for researching dynamics involving complex blade patterns on rock cutting [24]. In this paper, a 2-DOF vertical drill-string model is adopted and derived from first principles as the system of choice. The choice to use a 2-DOF model allows for a sufficiently complex system that demonstrates multiple rich dynamics of stick (no drilling), stick-slip and constant drilling to exist for a range of WOB and toptorque values and is still relevant in the benchmarking of novel control schemes. Table 1 presents the model parameters, their meanings and corresponding values adopted for this paper (from [37]) A number of strategies aimed at mitigating stickslip oscillations have been reported in the literature. The company Tomax patented their unique AST (Anti-Stall Tool) [25,26] which tackles the stick-slip problem from that of a mechanical design perspective. The control input to the drill-string itself, whether it be toptorque, WOB or top-drive rpm, acts as other potential control parameters for the prevention of stick-slip. In recent times, the μ-synthesis control method [27,28] has been proposed as a way with which to overcome stick-slip oscillations, however this methodology relies on linearisation methods which only possess expected performance in a very small range around the equilibria of interest. There has also been the suggestion that a linear quadratic regulator-based controller to suppress stick-slip using a discretised model of axial and torsional dynamics [29]. Another source suggested using WOB as a control parameter [30], but this requires knowing the exact WOB being applied at any given instant to a drill-string, which is a very challenging precondition. Consequently, this method lacks robustness in the face of uncertain values of WOB. PID and PD control has been proposed by [31][32][33][34] as a way with which to avoid stick-slip. Soft-Torque control (patented by Shell in [35]) and Z-torque control [36] are effectively PI controllers and have great sensitivity to actuator delays and measurement delays which also belong to the PID family. None of these control methods are particularly robust to system parameters or bit-rock changes. In order to mitigate the very real problem of system parameter changes while making sure constant drilling occurs, Sliding-mode Control (SMC) has been thoroughly investigated [37,38]. Due to its robustness to parameter uncertainties, the SMC has emerged as a benchmark against which all other stick-slip mitigating control schemes are compared. That being said, SMC has one sizeable downside, its inherent complexity in its design procedure.
The control of systems with delay is always a challenging area of research. Drill-strings are subject to three main types of delay, namely regenerative statebased cutting delay terms [23,24,39] due to PDC heads, actuation delays [40] and measurement delays [41]. Actuation delays even exist in experimental setups such as the in-house experiment [37]. There is a lack of detailed work with actuation delay as well as measurement delay. Some work in recent times has factored in studying the effects of state-based delay on PID performance on a lumped-mass model [42]. In addition to stick-slip and actuation delay, there is also a lack of literature on actuation constraints affecting the ability to reach a desired control outcome. In this paper, a combined control approach to tackling stick-slip featuring both actuation delay and constraints is considered by utilising the 'Modified Integral Resonant Control' (MIRC) with Integral Tracking [43]. This aforementioned scheme is a modified version of the IRC damping scheme first developed to mitigate linear system resonances [44], and is capable of imparting significant damping to nonlinear resonances as well [45]. This combined control scheme is a simple, combination of two first-order controllers that work by adding two extra state equations to the system in question and requires no complicated design as required by the SMC. It then includes the use of integral tracking to meet the desired criterion of constant drilling. Incidentally, the scheme only requires a selection of the control gain/s-easily achievable via a limited numerical search. Moreover, as it is of a similar complexity to PID control, this allows for easy implementation. It should also be noted that this controller does not rely on the linearisation of the drill-string model unlike the μsynthesis control, thus allowing for more realistic global performance.
The rest of the paper is structured as follows: Sect. 2 presents the open-loop model for the drill-string and classifies all the different parameters and demonstrates its well known open-loop behaviour; Sect. 3 introduces the controller structure and demonstrates how it is added to the drill-string model presented in Sect. 3 with a subsection devoted to demonstrating its results. Section 4 introduces the Sliding-Mode Control with its parameters and construction. Section 5 compares said scheme with the SMC directly via more simulations. Section 6 delves into the detail of the scheme's behaviour in terms of its gains and also further investigates the effects of varying actuation delay on the drillstring system response. Finally, Sect. 7 concludes the paper.
Experimental model with open-loop behaviour
In this section, the open-loop model for a 2-DOF underactuated drill-string is presented along with a table of parameters. The model is numerically simulated and its dynamical behaviour is explored via bifurcation diagrams, phase portraits and time histories.
To derive the system equations, the Euler-Lagrange equation can be used: where the generalised coordinates can be defined as Assuming that; φ t > φ b (as choosing a consistent convention for Lagrangian Mechanics is essential), the total kinetic energy T can be defined as follows: The total potential energy U can be defined as follows: Consequently, Rayleigh's Dissipation Function D can be defined as follows: Lastly, the generalised force vector Q k can be defined as follows: where u and T b are the input top-and friction torques, respectively. By substituting Eqs. (2, 3, 4 and 5) into (1) and evaluating for each coordinate q k , the set of differential equations which describe the model are The friction model for T b used is defined as follows [17]: The adopted friction model operates the drill-string in one of the three key phases. Phase 1 is the sticking phase in which the bit is not moving (φ b < ζ) due to the static friction torque τ s being equal to or more than the absolute value of the reaction torque τ r : τ r ≤ τ s . Phase 2 is the stick-to-slip shase in which the bit just about to move (φ b < ζ) as the static friction torque τ s is less than the absolute value of the reaction torque τ r : τ r > τ s . Phase 3 is the slip phase in which the bit begins to move (φ b ≥ ζ) and cuts into the rock. Table 2 presents the relevant mathematical expressions/ranges for all the system parameters.
For simulation purposes, Eqs. (6, 7) and the friction model (8), can be seen as producing three separate state-space models that the system can discontinuously switch between, based on initial conditions, WOB and u. To numerically simulate Eqs. (6 and 7), the following state-variables are defined: Equations (6 and 7) can be rearranged as a system of first-order differential equations as follows: Dry friction coeff.
To classify the precise behaviour of the drill-string, details about the equilibria of the uncontrolled system need to be extracted. Let Γ be a manifold where the bit velocity equals zero: Similarly, let Σ be a manifold where the bit velocity equals some constant angular velocity Ω c : There exists a subset of Γ that represent a unique attractive region depending upon the WOB, u and the initial conditions of the system. LetΓ s ⊆ Γ which relates to the Stick-phase, i.e.φ b = 0: LetΣ c ⊆ Σ which concerns the case of steady drilling, where Ω c represents a constant angular velocity: The creation of these subsets is caused by the friction model defined by Eq. (8) and can lead to three different behaviours. The bit remains stuck in the borehole: ∀t > t tr , q k ∈Γ s . Thus, after a finite transient time t tr , the system reaches the attractive region Γ s and remains there indefinitely. The bit goes into steady drilling mode and enters the attractive region Γ c : ∀t > t tr , q k ∈Γ c . Thus, after a finite transient time t tr , the system reaches the attractive regionΓ c and remains there indefinitely. The system enters and leaves the subsetΓ s in the form of stick-slip oscillations.
In this case, the system can never permanently stay in the attractive region of permanent sticking nor can it reach constant-drilling region.
To infer some important information about the drillstring model, the equilibria of the system should be considered. There exists two distinct equilibria, namely the constant drilling and the stick equilibria, respectively. Consider the case of stick in which the top-drive and bit-head velocities are zero, respectively, (x 1 = x 3 = 0) and the state variable derivative is also zero (Ẋ = 0 and) in Eq. (10). Then, the equilibrium for x e2 is This translates to an overall equilibrium vectorx s as follows: x Now, consider the case of constant drilling in which the top-drive and bit-head velocities are some positive constant Ω c , respectively, (x 1 = x 3 = Ω c ) and the state variable derivative is also zero (Ẋ = 0 and x 1 = x 3 = Ω c ) in Eq. (10). Then, the equilibrium x e2 can be shown to be This translates to an overall equilibrium vectorx c as follows: x With these system equilibria demonstrated, the behaviour of Eq. (10) and the aforementioned bit-rock model (8) is shown overleaf in the form of a labelled centre figure (see Fig. 2). Bifurcation diagrams (d) and (f) show that the WOB and top-torque both play an equally pivotal role for producing co-existing attractors. The region of u = [8,20] N m in (d) possesses an unstable constantdrilling branch not accessible practically (or via traditional numerical integration) except with the method of numerical continuation or via some stabilising control method. As can be seen in Fig. 2d, stick-slip and constant-drilling attractors coexist within the region bound by u = [21,56] N m while u = [57, 60] N m denotes region where the drill-string only operates in the constant-drilling mode. Another important point to note is that as shown in the basins of attraction plotted in Fig. 2e, the parameter-space defined by the range of initial conditions analysed herewith is dominated by stick-slip. With the adopted drill-string and bit-rock models validated via the simulation results shown in Fig. 2, this work proceeds to design and implement the Modified Integral Resonant Control with Integral Tracking aimed at eliminating the unwanted stick-slip oscillations featuring actuation constraints and actuation delay.
Modified integral resonant control-based damping with integral tracking featuring delay and constraints
The main control objective for any effective drill-string control strategy is to minimise (ideally eliminate) the damaging stick-slip oscillations and guide the system into a state of constant drilling where possible [46,47]. The MIRC-based damping scheme with Tracking Control is a combination of four gains with a desired reference variable viz: the Output gain λ, the Feed-Through gain κ and the Integrator gain η, which are all connected to an Integrator belonging to the MIRC; as depicted in Fig. 3f, [43] and then a single integrator gain k i with desired reference Ω c . These gains are easily selected via a simple numerical search over a range of parameter-space. Two new controller states ψ and ν are defined and then embedded into Eqs. (6 and 7) by producing extra state differential equations. Consider the following closed-loop Lagrangian, with an adapted set of generalised coordinates q cl k = [φ t , φ b , ψ, ν] ∈ R 4×1 accompanied by a new generalised forcing vector Q cl k : The updated generalised force vector Q k is then redefined as Thus, by applying the Euler-Lagrange equation to L cl the controller differential equation can be written asψ Consequently, the system dynamics can be described by To aid in simulation, a state-vectorX includes the controller variable: This redefinition allows the aforementioned equations (22) to be re-written as a system of first-order differential equations given bẏ A full discussion on the closed-loop stability as well as closed-loop equilibria is shown in the appendix. Both experimental and real-life drill-string structures are subject to system-induced delay and in the case of many experiments, actuator constraints as well. Equations (24) are modified to include control actuation delay and actuation according to Table 1: The following table details the exact scheme gains used for the following simulations seen in Fig. 3.
To verify the effectiveness of the scheme, the notion of 'Vibration Reduction Factor' (VRF) must be introduced. VRF is a relative means of comparing the amplitude of stick-slip oscillations in open-loop with the Fig. 3 a-c Represent the time-history, 2D and 3D phaseplane, respectively, for the case in which u = 40. The natural system response is given by (.-red) and scheme altered response (-blue), respectively. d is the central top-torque bifurcation diagram from which the results of (a-c), (e) and (g) are derived and is marked to show the stick-slip to constant-drilling transition. e shows the control input for this case. (f) shows the scheme's structure. g shows the scheme controlled basins of attraction amplitude of the closed-loop response. There exists four possible controller outcomes with which VRF can assist with classifying effectiveness, namely Case 1 (≥ 95%VRF) which is constant drilling, Case 2 (≤ 95%VRF) and no bit-sticking which is torsional vibration, Case 3 (+ve%VRF) with bit-sticking (reduced stick-slip amplitude) and lastly, Case 4 (−ve%VRF) with bit-sticking (in which the stick-slip is made worse). Successful results lie in Cases 1 and 2, respectively, wherein the drill-string is no longer undergoing stickslip. The formula used to classify VRF is as follows: whereā ol is the average open-loop stick-slip amplitude of vibration andā cl is the average closed-loop vibration amplitude. It should be noted that this formula does not indicate if a system is under stick-slip or not in cases 3 and 4. In these cases, extra care is required to label and classify the response along with the VRF result.
The following simulation shows preliminary results of the MIRC with tracking control under the effects of delay and constraints. Figure 3d shows the detailed bifurcation diagram. In this diagram, the top-torque is varied from u ∈ [0, 60] N m. In the experimental setup, as discussed in Table 1, there are minimum and maximum torque constraints. To reflect this, the unstable constant-drilling solutions that are not reachable are represented by grey pentagons. When within these control input limits, the scheme is capable of finding the blue circle constant-drilling attractors and eliminating the stickslip found within this region. Figure 3a, b and c shows the natural system response of stick-slip being driven to that of the other natural system response of constant drilling thanks to the scheme's resonance suppression and tracking effects. The system is first settled into a stick-slip regime up to t = 30 s and then the scheme is activated therewith. Under both actuation constraints and actuation delay, tracking a constant velocity of Ω d = 2.992rad s −1 and suppressing stick-slip is observed. Figure 3e depics the control input graph and demonstrates a brief saturation of the upper and low control input limits and settles once constant drilling is achieved. Figure 3g demonstrates that with the scheme enabled, the initial conditions do not affect the final response of the system thereby demonstrating invariance. The following simulations demonstrate the scheme's ability to track from different scenarios.
In these simulations, six test scenarios are chosen. (a), (b) and (c) are all started in stick-slip at u st = 40 and then lower, middle and higher desired velocities are chosen. Each case is successful as none of these desired velocities rely on a final u < u l . The next three cases consider the situations of starting from stick, attempting to track from stick-slip to the natural constantdrilling attractor and starting from constant drilling and going to a high desired velocity. The scheme is successful for cases Fig. 4e and g as the desired velocity requires a final u that is within the constraints given. Figure 4f is naturally unsuccessful as the scheme is attempting to track a velocity that requires a lower u than the minimum constraint. To examine the controller's robustness, a multi part bifurcation diagram which examines how the change in the drill-string stiffness k s changes affects the controller's ability to suppress stick-slip and track a desired velocity is examined overleaf.
The centre Fig. 5c acts as the primary bifurcation diagram for all other plots around it. In (d) the bifurcation variable is the torsional spring coefficient k s and for each k s , the controlled system is made to track Ω d = 2.552 rad s −1 . Figure 5a and e is sub-bifurcation diagrams derived from Fig. 5d at k s = 10 N m rad and k s = 20 N m rad, respectively, in which the bifurcation parameter is the desired velocity Ω d . Overall the scheme has great success up to k s = 15 upon which it fails to suppress stick-slip and guide the system to the desired velocity (100% V RF ) which is confirmed by Fig. 5a. In addition to this, Fig. 5a shows that when k s ≤ 15, any desired velocity that requires a final u within the constraints mentioned, it is successful. Overall, there exists some excellent robustness to the main changing system parameter k s and the scheme, when not working optimally for k s > 15, does not cause system instabilities as seen in Fig. 5e, but overall the scheme can not stabilise the majority of velocities for this k s range. In the following section, the SMC is defined and compared directly to the scheme in question (Tables 3, 4).
Sliding-mode control
In this section, the SMC is defined mathematically, investigated and compared directly to the scheme in question. Bifurcation plots and basins of attraction like plots are used to make these comparisons. The SMC adopted as a candidate for comparison starts by defining a sliding surface given by [37] (27) where Γ is a user chosen control variable and Ω d is the desired angular speed. The key benefit of this SMC is its capability of utilising estimated system parameters. But this is easily matched by the proposed MIRC-based damping+tracking scheme by its excellent robustness to parameter uncertainty. The estimated parameters are denoted as:ĉ r ,ĉ s ,k s andĴ t . The ideal controller equation can be derived by differentiating (27) and substituting it into equations (10) and rearranging for u. Upon doing this, by replacing all original variables with their estimated ones, the estimated controller equation is shown to be: In addition, the upper bounds of the estimated model are given as From this, the asymptotically convergent switching law is defined as where δ 1 and δ 2 are small controller parameters that are user chosen and are much smaller than 1 and ρ is another user chosen control parameter. The final SMC control input is defined as The SMC parameter table can be found in the appendix.
To compare the SMC to the scheme in question, Fig. 5a, c, and e is repeated for the SMC. In addition to this, a basins of attraction like analysis for k s and Ω d
Scheme vs sliding-mode control comparison
To compare the scheme in question with the SMC, similar analyses seen in Fig. 5 are carried out. To fully compare the two methodologies, additional basins of attraction like simulations are also carried out between the two to fully compare the regions of operation. When comparing Fig. 6b to Fig. 5c, it is clear that the SMC performs much more poorly than the scheme highlighted in this work. There exists only a single 100% V RF result whereas the scheme had constant 100% V RF results up to and including k s = 15. Comparing Fig. 6a to Fig. 5a reinforces the success of the scheme over the SMC as the SMC is incapable of generating any 100% V RF results for a range of desired velocities. Comparing Fig. 6e to Fig. 5d, a similar performance is observed between the two methodologies with the SMC coming out on top slightly by providing Figure 6c and e perfectly summarises the performance difference between the two methodologies. The scheme in question has a large range of constant-drilling responses whereas the SMC only has a large range of torsional vibration responses with a few constant-drilling ones. It is clear that the scheme has superior performance to the SMC. In the following result table, a constant-drilling response scores 2/2, torsional vibration response 1/2 and stick-slip response 0/2 and any positive result is scaled and added together appropriately.
Detailed scheme analysis
To understand the effects that the scheme's gains have on the system response, a gain study for; λ, η and κ is performed. In the following simulations top row of simulations, the desired outcome is to suppress stick-slip and reach the desired velocity of Ω d = 2.552 rad s −1 . In the second row of simulations, the desired velocity Ω d is varied along with the gain of interest. When investigating a single gain, others are kept constant. Table 6 used for this simulation can be found in the Appendix and details each simulation's parameters. The λ and κ gains are the two main gains, as seen in Fig. 7b and c, and e and f, that produce resonance suppressive effects or can even induce torsional vibrations in the drill-string as evidenced by the small regions of torsional vibrations in the basins-like plots. The η gain serves to engage the scheme and has only a small part to play in producing the ideal response of constant drilling. Conclusively, there exists very defined regions of optimal operation for each gain which aids in the robustness of the scheme to variations in system response as well the desired velocity. Further to this, a detailed analysis is carried out with three test cases of k s = 1, 10, 20 under the effects of varying actuator delay τ d and desired velocity Ω d . Table 7, seen in the appendix, details the simulation parameters for investigating varying delay and changing the λ gain.
In the first row of simulations, there are clear regions in which the scheme successfully produces the ideal response under varying delay and even under chang- Table 7 for the cases of ks = 1, 10, 20 and demonstrates clear regions of success and failure in each case. d-f show the effects of the row 2 gains chosen in Table 7 for the cases of ks = 1, 10, 20. For these cases, the change in λ creates a greater desired regime for Ω d ≥ 3 for τ d ∈ [0, 2.4]. This comes at the price of losing access to lower desired velocities across most delays ing k s . In general, the higher the k s , the harder it becomes to drive in the presence of increasing delay. In these simulations, it is shown that under the effects of varying delay and the same actuation constraints, extra robustness to delay can be produced by altering the λ gain, but this comes at the cost of the ability to track smaller target velocities even within the actuation constraints. The overall robustness to the incredibly detrimental effect of actuator delay is an impressive feature of this scheme especially when aggressive control is not possible due to the actuation limits thereby proving its worth over the SMC (Fig. 8).
Closing remarks
In this work, a modified integral resonant controller with integral tracking is investigated on a 2-DOF drillstring featuring actuator delays as well as actuation constraints. The combination of these systematic limitations, which are based directly on an in-house experiment, make for a unique investigative opportunity to test the aforementioned controller. First, the 2-DOF model with nonlinear bit-rock interaction is verified numerically in the form of torque and weight-on-bit bifurcation diagrams. Time histories, combined with 2D and 3D phase portraits are shown to demonstrate the three main drilling responses available to the aforementioned model, namely stick (no drilling), stick-slip (a limit cycle response) and constant drilling. Further to this, the bifurcation diagrams also reveal the coexistence of system solutions for a given torque and weight-on-bit, i.e. the co-existence of constant drilling and stick-slip. The ideal case of constant drilling is the desired response and is the main control focus in this work.
The scheme in question is subsequently introduced and it is able to successfully track to steady drilling when the drill-string is started in stick-slip. Due to the control input constraints caused by the motor's characteristics as well as actuation delay, saturation of the control input is observed, but despite this saturation and delay it does not affect the chosen case with the stabilisation of constant drilling. Subsequent to this, numerous cases from different starting points on the torque bifurcation diagram in to determine if the scheme runs into stick-slip on the way to any of the solutions. It is found that tracking is possible from almost any starting torque or initial condition except when it comes to trying to reach an unstable constant-drilling solution due to the lower control input constraint imposed. When verifying the scheme's robustness, it is tested under the most commonly vary- ing drill-string model parameter, namely the torsional spring coefficient and is the primary bifurcation variable of choice. Change in stiffness of this variable has the potential to greatly affect the ability for it to work properly especially while contesting with constrained inputs and actuation delay. It is found that stabilisation of the ideal constant-drilling response is possible for a large range spring coefficients is possible, but failure occurs for larger spring values when the gains are not changed. Two sub-bifurcation diagrams, which utilise the desired velocity as the bifurcation parameter, also confirm its success and failure in extreme cases. Using these bifurcation diagrams as the framework for control performance, the Sliding-Mode Controller is subsequently introduced as the previous state-of-the-art for comparing the scheme to. It is found that the SMC has much worse performance over the successful range that the scheme in question has and at best, produces a torsional vibration response only with many stick-slip responses. The scheme is found to have a very clear and defined region of constant drilling whereas the SMC has only a clear torsional vibration response with a small region of constant drilling for low spring values. These simulations confirmed that the scheme is superior in overall performance as well as for consistency in operation when compared to the SMC.
A detailed gain analysis on the MIRC portion of it is carried out as it is responsible for the majority of the stick-slip suppression. It is found that there is connection between reaching the desired response of constant drilling and each gain. In particular, the output feedback gain λ has the greatest effect on reaching the desired response as it has the greatest affect on resonant dynamics present within a system. The feedforward gain κ is also found to have an impact on the resonant dynamics of the system response but to a lesser degree. Lastly, the scheme is also investigated under varying actuator delay while still under the same actuation constraints. It is found that there exists small regions of successful constant drilling when its gains are not tuned to the system. When tuning the output feedback gain λ, it is found that greater consistent ranges of constant drilling can be produced as the cost of losing the ability to track lower desired velocities for smaller actuator delay values.
Conclusively, the scheme has promising robustness to varying system parameters, varying actuator delay and actuation constraints and proves itself to be superior to that of the previous state-of-the-art SMC. Further work would include an experimental verification of the controller with extensive gain testing to confirm the simulations in this work.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecomm ons.org/licenses/by/4.0/. | 7,557.2 | 2021-04-27T00:00:00.000 | [
"Engineering"
] |
Multiscale image denoising using goodness-of-fit test based on EDF statistics
Two novel image denoising algorithms are proposed which employ goodness of fit (GoF) test at multiple image scales. Proposed methods operate by employing the GoF tests locally on the wavelet coefficients of a noisy image obtained via discrete wavelet transform (DWT) and the dual tree complex wavelet transform (DT-CWT) respectively. We next formulate image denoising as a binary hypothesis testing problem with the null hypothesis indicating the presence of noise and the alternate hypothesis representing the presence of desired signal only. The decision that a given wavelet coefficient corresponds to the null hypothesis or the alternate hypothesis involves the GoF testing based on empirical distribution function (EDF), applied locally on the noisy wavelet coefficients. The performance of the proposed methods is validated by comparing them against the state of the art image denoising methods.
Introduction
The acquisition and transmission normally corrupt an image by introducing an additive noise. In this regard, image denoising algorithms are utilized to suppress noise while preserving the desired image features. Let x p,q denote a pixel of a noisy N × N sized image X at location (p, q), acquired from an acquisition device, a transmission medium or a reconstruction process as where s p,q denotes the pixels of the true image S while η p,q denotes noise at pixel location (p, q). In matrix form, the above equation can be written as The goal of denoising is to estimate the true signal S from its noisy observation X. Here, η is considered an independent Gaussian noise N ð0; s 2 Þ with zero mean and arbitrary variance σ 2 . Earlier, denoising was achieved by linear methods such as Weiner filtering in the Fourier domain [1]. However, the scope of such techniques is only limited to stationary data because a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 the Fourier transform is incapable of handling non-linear or non-stationary data. That resulted in multi-scale denoising methods employing non-linear operations such as thresholding in the transform domain [2]. For that purpose, discrete wavelet transform (DWT) was employed which decomposes a dataset into multiple scales that gives a sparse representation of the signal in transform domain [3]. The DWT based denoising algorithms exploit the sparsity of the wavelet coefficients [4][5][6] through simple yet powerful nonlinear thresholding operations [7,8] to obtain the denoised image. Similar principle is adopted while denoising with variants of the DWT like double density discrete wavelet transform (DDDWT), complex wavelet transform (CWT), dual tree complex wavelet transform (DT-CWT) etc.
Among the wavelet based denoising methods, VisuShrink [9] is one of the simplest techniques; it employs a universal threshold for all the scales depending largely on image size and noise level. The disadvantage of this method is that it tends to over smooth large sized images. This is due to the dependence of the estimated threshold on the input image size. Therefore, comparatively better performance is shown by the adaptive data driven techniques which estimate the threshold separately for each scale [10][11][12][13][14][15][16][17][18]. An example of such a method is the Sur-eShrink [10], which exploits the Stein's unbiased risk estimator (SURE) to get an unbiased estimate of the threshold to perform signal/image denoising. An extension of the SureShrink is the Surelet [12], which employs the principle of SURE along with the linear expansion techniques (LET) to cast the denoising problem as the one with linear system of equations. The BayesShrink [13], on the other hand, operates within the Bayesian framework with prior application of Generalized Gaussian Distribution (GGD) on wavelet coefficients. An empirical Bayes approach of denoising based on the Jeffrey's non-informative prior [14] exploits the sparsity and de-correlation properties of DWT for denoising purposes. Recently, empirical Bayes approach of denoising has been extended to 2D scale-mixing complex valued wavelet transform, namely cSM-EB [15].
Sparsity based signal recovery methods have also been explored as an avenue for image denoising. To that end, a compressive sensing based image denoising algorithm is proposed in [19] where L 1 -minimization has been used to recover the true signal. In [20], sparse and redundant signal representation over learned dictionaries is used for denoising images. Clustering based locally learned dictionaries are employed for image denoising in [21] whereby clusters of local patches are obtained based on likewise geometrical structures. Similarly, clustering based sparse representation (CSR) method for image denoising combines the dictionary learning with structured clustering to exploit enhanced sparsity in [22]. A hybrid image denoising algorithm is proposed in [23] based on wavelet transform in combination with the learned and redundant dictionaries. In this method, the wavelet transform is used to obtain multiscale feature and sparse prior for wavelet coefficients which leads to the sparse representation in wavelet domain. Subsequently, the K-SVD algorithm is used to build sparse overcomplete dictionaries of wavelet coefficients resulting in a state of the art image denoising algorithm. Patch based noisy image specific orthogonal dictionaries are learned using PCA in [24] to threshold the patch coefficients for image denoising, namely PaPCA.
A collaborative hard thresholding based filtering technique is used within BM3D [25] to exploit enhanced sparsity of transform domain. Here, a complex multistage process is adopted starting with the grouping of similar fragments of 2D transformed coefficients which are then arranged into 3D data arrays. Subsequently, attenuation of noise is achieved via spatial collaborative hard-thresholding followed by the collaborative Weiner filtering on the 3D arrays of the transformed coefficients. Despite its efficacy, the computational complexity of BM3D is considerably large owing to its complicated multi-step procedure [25].
Sparsity driven iterative algorithms are also used to solve total variation (TV) minimization for image denoising. For instance, several iterative algorithms have been designed for TV denoising including iterative soft thresholding algorithm (ISTA), fast ISTA (FISTA) and a monotone version of FISTA [26]. In addition, split Bregman algorithm has been used for efficient isotropic and anisotropic TV image denosing in [27]. Similarly, Beltrami regularization is considered in [28] for image denoising and has been shown to outperform TV based methods.
Spatial domain filtering techniques such as mean and median filtering are commonly used but are known to produce sub-optimal denoising. However, an efficient spatial domain non local mean (NLM) filtering technique for image denoising is proposed in [29], which happens to be a gold standard denoising method owing to its effective denoising performance. In this technique, image pixels having smallest euclidean distance from each other are grouped together leading to weighted mean of these pixels for noise smoothing. Hence, for each pixel, similar pixels are searched, grouped and averaged leading to very high computational complexity. Though, this technique yields visually pleasing denoising results but it is known to over-smooth details of an image.
Mostly, classical thresholding strategies exploit sparsity in transform domain by considering that coefficients corresponding to the signal have higher amplitudes compared to the noisy coefficients. Contrarily, Cai and Silverman [16] observed that wavelet coefficients corresponding to signal are distributed in the locality of each other while coefficients corresponding to noise are distributed uniformly. They used this fact to introduce neighbourhood based thresholding strategies for 1D signals [16] in which a coefficient is classified as signal if it is surrounded by likewise coefficients and vice versa. NeighShrink [17] introduces neighbourhood based thresholding to image denoising which operates by classifying a wavelet coefficient surrounded by higher amplitude coefficients as desired signal while a coefficient surrounded by the lower amplitude coefficients is classified as noise. Similarly, NeighSure [18] refines neighbourhood based thresholding via the SURE to achieve image denoising. A simple yet effective image denoising method exploiting the statistical neighbourhood dependencies of wavelet coefficients is proposed in [30]. A statistical model for neighbourhoods of oriented pyramid coefficients is developed in [31], which is based on Gaussian scale mixtures of empirical wavelet coefficients. The intra-scale dependencies within the wavelet coefficients have been modeled using fuzzy features in Fuzzy-Shrink [32], where a fuzzy feature distinguishes between the image discontinuities and noise.
Recently, statistical methods have emerged as a strong tool in the wavelet based image denoising. These methods exploit statistical dependencies within the wavelet coefficients for estimating the thresholds for denoising. BiShrink [33] models inter-scale dependencies in wavelet coefficients (obtained via the DWT as well as the DT-CWT) based on a new non-Gaussian bivariate distribution for threshold estimation. The method also includes a nonlinear bivariate shrinkage function driven through a maximum a posteriori (MAP) estimator. The ProbShrink [32] estimates a threshold based on the probability that a given coefficient contains significant information (signal of interest) by assuming a generalized Laplacian prior for noise free data.
A major issue in the conventional DWT is the lack of translation invariance in the traditional wavelet basis functions resulting in artifacts in the aftermath of denoising. These artifacts could be explained by the Gibbs phenomena in the neighbourhood of discontinuities. Stationary DWT, which is rotation invariant, can render partial translation invariance to the denoising results and can be implemented via cycle spinning approach [34]. In cycle spinning, noisy data is first shifted left or right, denoised via a wavelet based method and subsequently un-shifted. This process is repeated several times and all the results are averaged to produce a denoised signal/image with lesser artifacts. It has been shown in [34] that denoising results can be improved considerably by making the DWT partially translation invariant through cycle spinning.
In contrast to DWT, the DT-CWT enjoys near translation invariance and directional selectivity at the cost of a higher degree of redundancy [35]. The redundancy in DT-CWT is due to the fact that real and imaginary parts of the complex wavelet coefficients are dealt as independent wavelet coefficients which makes it twice redundant. However, in order to incorporate directional selectivity in the two dimensional DT-CWT, the complex wavelet coefficients are obtained at six directions compared to the three directions of the DWT (i.e. horizontal, vertical and diagonal), which further increases the redundancy by two. Hence, the two dimensional DT-CWT is 4:1 redundant as compared to the DWT [35]. In the two dimensional DT-CWT, dual tree of filters oriented at 6 directions are employed, yielding six bands of real parts and six bands of imaginary parts of the complex wavelet coefficients at each scale.
The directional selectivity in DT-CWT preserves orientation of the edges or discontinuities having a line or a curve shape, unlike DWT which only preserves the point discontinuities. In addition, the directional selectivity in DT-CWT helps avoid the checker-board artifacts during denoising process by differentiating between the edges oriented at 45˚and −45˚ [35].
The redundancy, in combination with the filter banks designed to achieve complex number representation, makes DT-CWT approximately translation invariant. The maximal decimation in DWT causes aliasing in the decomposed wavelet coefficients. In order to cancel the effect of aliasing and achieve perfect reconstruction, the synthesis filters for inverse DWT operation are designed to fulfill the aliasing-free condition. However, the aliasing can only be avoided if the wavelet coefficients are not perturbed, which is not the case in wavelet based denoising. Contrarily, in DT-CWT, the inherent redundancy (4:1) suppresses aliasing to a large extent, yielding better denoising results.
Several denoising methods have been reported in literature which utilize the above desirable properties of the DT-CWT: In [30], dependencies among three scales of DT-CWT coefficients are exploited. NeighSure [18] employs Stein's unbiased risk estimator (SURE) on complex wavelet coefficients of the DT-CWT to find an optimum threshold and a window size. Furthermore, image denoising methods reported in [36][37][38][39][40][41] are some of the recent methods which exploit near translation invariance and directional selectivity of the DT-CWT for improved denoising performance.
In this paper, two image denoising methods are proposed which employ statistical goodness of fit (GoF) tests on multi-scale wavelet coefficients obtained via DWT and DT-CWT. The decision process regarding the presence of noise at multiple scales is based on the statistical GoF tests, wherein Anderson Darling (AD) statistic is used as a measure of similarity between the local wavelet coefficients and reference Gaussian noise distribution. A coefficient is detected as corresponding to noise if its associated AD measure is less than a threshold, which is a function of probability of false alarm. Those coefficients are then eliminated (set to zero) while the remaining coefficients are retained. We demonstrate the effectiveness of the proposed methods by comparing them against the state-of-the-art in wavelet based image denoising on both natural and medical input images.
In our previous work [42][43][44][45], we had employed GoF test on multiple 1D signal scales, obtained via the 1D DWT, for signal denoising. Also, Poisson denoising in the context of CMOS/CCD images has also been proposed in [46]. In this work, we employ GoF test on multiple image scales for image denoising. To this end, a novel framework is developed for GoF testing on multiple scales of DWT as well as the DT-CWT, which offers better translation invariance and directional selectivity. The proposed methodology is significantly different from classic wavelet thresholding techniques in which the wavelet coefficients are directly compared against a threshold. In the proposed thresholding method, decision regarding the noisy image coefficients is made based on the statistical distance between the distribution or model of the local wavelet coefficients from the reference noise distribution. This paper is organized as follows: Section II gives the background of wavelet based image denoising along with an insight into the GoF testing and its operation. A detailed discussion on the proposed algorithms is presented in Section III. Section IV presents the experimental results and discussion, while Section V concludes the paper while also highlighting possible avenues for future work.
Wavelet transform based image denoising
Let W denote the wavelet transform operated over a noisy image X to decompose it into wavelet coefficients at multiple scales as where W denotes the matrix composed of wavelet coefficients w j i with j denoting the scale of decomposition, i denotes location of a coefficient at multiple scales. The operator W may refer to the DWT or the DT-CWT operation: when W refers to DWT, W is a two dimensional matrix of wavelet coefficients w j i and its formation is depicted in Fig 1 (left), where each scale of decomposition contains three bands of wavelet coefficients, each of which is associated to a direction namely horizontal, vertical and diagonal. The location index i first lists the horizontal coefficients (column wise) followed by the listing of vertical and diagonal wavelet coefficients.
On the other hand, when the operator W denotes the DT-CWT operation, W is a three dimensional matrix of wavelet coefficients as shown in Fig 1 (right), where each scale of decomposition contains twelve bands of wavelet coefficients. In order to achieve this representation we placed the redundant wavelet coefficients, yielded via DT-CWT, in four different two dimensional matrices in accordance with the formation shown in Fig 1 (left) and then those four matrices are placed above each other to make four layers of a three dimensional matrix as shown in Fig 1 (right). It must be noted that first two layers contain the real parts of the complex wavelet coefficients and last two layers contain the imaginary parts of the complex wavelet coefficients for each scale).
A threshold value T is next estimated to classify the coefficients as belonging to signal or noise i.e. a popular universal threshold T ¼ s ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi 2logðN � NÞ p [9] is based on image size N × N and noise standard deviation σ which is estimated as here i denotes the index of only the diagonal wavelet coefficients at the scale j = 1. A thresholding operator U is next applied individually on each wavelet coefficient as given beloŵ whereŵ j i are thresholded empirical wavelet coefficients, U could be soft or hard thresholding rule which exhibit near optimal properties in minimax sense and better convergence rates for approximating functions in Besov spaces [7,8]. In the soft thresholding operation, the signal elements less than threshold T are floored to zero and the amplitudes of the remaining signal elements are reduced (shrunk) by T. The hard thresholding operation keeps the signal elements whose values are greater than T and sets the remaining coefficients to zero.
After performing thresholding operation, inverse wavelet transform [3] is applied on the noise suppressed wavelet coefficients to get an estimateŜ of the true image S in the spatial domainŜ whereŴ are thresholded empirical wavelet coefficientsŵ j i (see Fig 1).
Statistical goodness-of-fit testing
The goodness-of-fit (GoF) test indicates how well a specified model or distribution fits a given set of observations. The GoF test performs hypothesis testing whereby the case with observations or data fitting the specified model/distribution is termed as null hypothesis H 0 and the case where observation reject the specified model/distribution is termed as alternate hypothesis H 1 . In order to quantify the difference between the observed values and the values expected under the specified distribution, different statistics/measures of GoF have been defined [47,48]. Several measures of GoF test are employed in practice [49][50][51][52], each having unique properties of their own but only the Anderson Darlington (AD) statistics [51] will be discussed here because of its relevance with our work. A detailed discussion on the topic is presented in [53]. Let F ðtÞ ¼ P t 1ðz > tÞ denote the empirical cumulative distribution function (ECDF) of input samples z with support t and F r ðtÞ ¼ R t pðz > tÞdz represent the hypothesized cumulative distribution function (reference CDF) corresponding to a probability density function p (z). The AD statistic τ is given as follows where cðF r ðtÞÞ is the weighting function responsible for giving more weight to the tail of the distribution function F r ðtÞ is given as In order to compute τ, numeric expression for the AD statistic relation in (7) is as follows where L denotes the size of the given observations x t or the size of window in case of local operation of GoF test and H is defined as The probability distribution of distance τ is specified asymptotically as window lengths L ! 1.
Within the framework of GoF test, a threshold T is computed for error probability of given observations falsely reject the reference distribution. In spectrum sensing related literature [54][55][56], the probability of falsely rejecting a candidate distribution is termed as the probability of false alarm P fa , defined as follows, where the range {z s.t. τ > λ} are the values yielding false alarm. P fa is generally kept very very low to estimate an appropriate threshold T [57]. Next, hypothesis testing defined in (15) is performed to validate the null hypothesis H 0 or reject it i.e. the alternate hypothesis H 1 .
GoF based multiscale image denoising
Two novel image denoising methods are proposed which employ GoF test on the wavelet coefficients of the noisy image obtained by using DWT and DTCWT respectively. The DT-CWT exhibits approximate translation invariance and directional selectivity which helps it to suppress the artifacts otherwise present in the DWT based denoising results. We denote the proposed denoising methods as the GoFShrink based on the DWT and the DT-CWT.
Conventionally, GoF tests have been applied to detection problems where they operate directly on input data to test the binary hypothesis of noise only and signal plus noise cases e.g. spectrum sensing [54][55][56], as follows Contrarily, in the denoising problem, the alternate hypothesis H 1 must correspond to the detection of signal only case. To achieve that, we propose to employ multiscale wavelet transforms on the input noisy data before applying the GoF test. The DWT and DT-CWT distribute the signal coefficients sparsely as compared to noise coefficients which are distributed uniformly across the scales, thus segregating signal and noise into separate coefficients at multiple scales. The modified binary hypothesis using the GoF test at multiple scales are given bellow where H 0 0 and H 0 1 denote modified null and alternate hypothesis at multiple scales respectively and w j i denotes multiscale wavelet coefficients obtained through DWT or the DT-CWT operation as specified in (3).
Given a scale dependent threshold T j , the proposed framework first computes a test statistic τ i for a sub-image centered around the coefficient w j i at scale j and then compares it with the threshold T j . The decision regarding the null hypothesis H 0 0 or alternate hypothesis H 0 1 , as defined in (14) is taken as follows Finally, the coefficients identified as noise (i.e. H Remark 1: For the GoF testing, the reference CDF F r ðtÞ (i.e. CDF describing noise in the signal) must be known a-priori. In our case, the reference distribution is white Gaussian noise which means specifying mean and variance completely specifies F r (t).
Remark 2: τ could be computed using any GoF based empirical distribution function (EDF) statistic e.g. Anderson Darling (AD), Cramer Von Mises (CVM) and Kolmogrov Smirnov (KS) statistics etc. AD and CVM have been found to be relatively robust as compared to other EDF statistics. An insight into how these statistics ensure detection of signal only and noise only cases, is shown in Fig 3. Let an input noisy image X be decomposed into wavelet coefficients W at multiple scales j = 1 ‥ J through the DWT operation W in (1). We next estimate the standard deviation of noise σ in the input image via (4) and subsequently normalize the wavelet coefficients by the σ to make the noise unit variance at multiple scales, as follows, whereW denotes the normalized DWT coefficients. Next, the level dependent threshold T j must be computed for a probability of false alarm P fa which requires the estimation of F r ðtÞ; the reference noise distribution at scale k. In this work, the reference distribution at multiple scales corresponds to zero mean white Gaussian noise i.e., N ð0; s 2 Þ since DWT and DT-CWT retain the Gaussianity of input noise at multiple scales and can be computed as follows, where z is a zero mean Gaussian random variable with arbitrary variance σ 2 which can be estimated using (4). The EDF F i ðtÞ of local wavelet coefficients around the coefficient w j i at scale j is computed as where l × l denote the window size. For empirically estimating T j at scale j, a large sized WGN η is decomposed using the DWT and the resulting multiscale WGN coefficients W η are divided into small windows of size l × l. Let L j be the total number of such windows at scale j. For each window centered at i, let τ i be the value of AD statistic computed via (7) by employing the F r ðtÞ and F i ðtÞ defined in (17) and (18) respectively. If T j be a chosen threshold then let M j be the number of false alarms where τ i � T j , then the P fa ðT j Þ ¼ M j L j . This way, the P fa versus threshold curve is estimated for a range of values of threshold T j as shown in Fig 4. Remark 3: Owing to the orthogonal and linear nature of the DWT, the T j versus P fa curves were found to be similar for all the scales as expected. The following mathematical model for threshold selection based on P fa was obtained using polynomial curve-fitting as Remark 4: Probability of false alarm (P fa ), in this case, denotes the probability that a noise coefficient is detected as a signal. That probability should be very small and is specified in the range of P fa = 10 −3 ! 10 −5 .
Letw j i be the wavelet coefficients which are part ofW, the GoF test is applied on eachw j i by taking a window of size l × l aroundw j i and then computing their EDF F i ðtÞ using (18). Subsequently, the AD distance τ i between the F i ðtÞ and the reference CDF F r ðtÞ at scale j is estimated via (7). For a given P fa , a threshold T j is selected and the following GoF based thresholding function is employed,ŵ
Remark 5:
The thresholding function (20) performs hard thresholding on the wavelet coefficients. This is in-line with the neighbourhood based thresholding rules reported in [16-18, 30, 31], whereby the central coefficient of a neighbourhood or a window is either retained as desired signal or removed as noise based on statistical or deterministic dependencies between the local wavelet coefficients.
Finally, the denoised empirical wavelet coefficients are reconstructed by inverse DWT operation to yield the estimateŜ p;q of the true image S p,q . However, before the reconstruction, the normalization process in step 2 is reversed by multiplying all the retrieved signal coefficients with the estimated variance of the noise.
Subsequently, cycle spinning operation defined in [34] is performed to obtained denoised image. We shall denote the proposed algorithm by GoFShrink-TI in the remainder of this paper.
The above method can be extended to DT-CWT by applying the GoF test has been employed on the complex wavelet coefficients obtained by applying the DT-CWT on the noisy image. The DT-CWT exhibits near translation invariance and directional selectivity, which enables it to suppress various artifacts otherwise present in the DWT based denoising results [58].
The DT-CWT yields complex wavelet coefficients by separately calculating their real and imaginary parts. We propose to apply GoF based denoising operation, namely GoFShrink, separately on both sets of real and imaginary parts. These steps include: (i) calculation of the scale dependent thresholds for the real and imaginary trees of noisy wavelet coefficients (a graphical depiction of this process is shown in Fig 6 (middle)); (ii) computation of the complex wavelet coefficients W of the noisy image by employing (1), where W denotes the DT-CWT operation; (iii) normalization of the DT-CWT coefficients of the noisy signal by employing (16); (iv) performing the GoF based thresholding in parallel, whereby AD statistics was employed independently on the real and imaginary DT-CWT coefficients locally, followed by the use of thresholding function in (20) for detecting and annihilating coefficients belonging to noise while the remaining coefficients are retained as desired signal (the shaded region in Fig 6 shows this process for imaginary parts while the unshaded region shows the same for real parts); (v) taking the inverse-DT-CWT operation, after the reverse normalization operation, to yield the denoised signal. For the rest of the paper, we will denote this method by GoFShrink-DT. Matlab code of both of the proposed methods is available online at https:// www.mathworks.com/matlabcentral/fileexchange/64531-gofshrink.
Algorithm 1 GoFShrink based on DWT WðXÞ ⊳ DWT operation on input X 3: P fa 0.005 ⊳ P fa selection based on the experiment given in Fig 5 4: T l k T ðW Z ; P ðk;lÞ fa Þ ⊳ Operation T implemented via the procedure given at Fig 2 (
Computational complexity
In this section we present the computational cost of the GoFShrink based on DWT. The computational cost of the GoFShrink based on DT-CWT will be four times to that of GoFShrink based on DWT, provided the length of filters used by both transforms is exactly the same. The DWT operation on an image (of size N × N) involves separate filtering of the rows and columns, where first rows are processed via 1D low and high pass filters followed by the decimation by 2, and then the same process is applied on the columns of the input matrix.
If M denotes the size of the 1D low and high pass filters then the computation of the DWT coefficients will take 2M multiplications and 2(M − 1) additions per sample point. Since at kth level, the coefficients in the rows will be down sampled by 2 k−1 , the total cost of implementing a filter at kth level will involve 2M(1 − 2 −k ) multiplications and 2(M − 1)(1 − 2 −k ) additions per sample point. The total number of coefficients processed by row filters will be N 2 as there are N rows in the image with each row having N number of pixels. Hence, the total complexity for implementing the row filters at all scales becomes 2N 2 M(1 − 2 −k ) multiplications and 2N 2 (M − 1)(1 − 2 −k ) additions. After including the computational cost on image columns, which is the same as that on the rows, the total computational cost of the 2D DWT operation on the noisy image will be 4N 2 M(1 − 2 −k ) multiplications and 4N 2 (M − 1)(1 − 2 −k ) additions. Next, these DWT coefficients will be normalized by the estimated noise standard deviation which required N 2 multiplications.
The computation of the empirical CDF F ðxÞ is an important part of GoF tests and will require the computations of the order of O(LlogL) where L denotes total number of coefficients in the ffi ffi ffi L p � ffi ffi ffi L p window which are to be used for the GoF test. From (10), we can see that the computation of the AD statistics measure will require 3N 2 L multiplication and 2L(L − 1)N 2 additions for the N 2 coefficients of the DWT.
At the end, the inverse DWT operation will be performed on the thresholded wavelet coefficients. The inverse DWT operation mirrors the operation of the forward DWT but with different filters having the same length M. Therefore, the computational complexity of the inverse DWT will be exactly the same as the forward DWT operation.
Experimental results
This section presents the performance comparison of the proposed algorithms against the state of the art in image denoising. The peak signal to noise ratio (PSNR) has been employed as the measure of quantitative performance, given as The mean squared error (MSE) is calculated as where s p,q denotes pixels of the true image S of size N × N andŝ p;q represents the pixels of the denoised imageŜ. Note that MSE of noisy image is equal to the variance of the noise σ 2 .
For qualitative analysis, we employ the structural similarity (SSIM) measure and feature similarity (FSIM) measure. While SSIM evaluates the quality of a recovered image based on the structure, the FSIM evaluates the subjective quality of the recovered image based on how the human visual system (HVS) perceives the quality of an image [59].
The set of input images used for experimentation consisted of standard test images including Lena, Barbara, Peppers, Aeroplane and Cameraman images coupled with images used in other practical applications such as medical Brain MRI image, a diffused Multi-focus image and a natural View image. The Brain MRI image was taken from the NIH IMAGE program ImageJ (https://imagej.nih.gov/nih-image/about.html), a public domain software package distributed freely by the National Institutes of Health. The Multi-focus image set was acquired during the study in [60]. The View image was selected due to higher amount of details in it and is captured by authors at COMSATS University Islamabad campus using a 13 mega-pixel digital camera. These test images were corrupted by Gaussian noise at multiple noise levels corresponding to σ = 10, 20, 30, 40 and 50, which produces noisy images with PSNRs = 28. [13], BiShrink (DT-CWT) [33], Surelet (DWT) [12], NeighSure (DT-CWT) [18], cSM-EB (CWT) [15]. In addition to the wavelet based methods, sparsity driven methods like PaPCA [24], iTVD [27], aTVD [27] and BeltDen [28] have also been considered for comparison. Computationally expensive technique non local mean (NLM) filtering method [29] has also been used as a comparative denoising method on practical images.
The DWT based denoising methods including the proposed GoFShrink-TI were implemented using Daubechies wavelet filters of eight taps, namely db8. The noisy images were decomposed into D = 5 wavelet levels. For the DT-CWT based image denoising methods, namely the NeighSure, BiShrink, and the proposed GoFShrink-DT, the dual tree of wavelet filters developed by Kingsbury in [61] for complex wavelets, were employed to decompose the noisy image into D = 5 levels. The parameters corresponding to the other comparative methods were used as specified by authors for best performance. The window size for performing the GoF test in the proposed methods was selected to be 5 × 5, though experiments with other window sizes including 3 × 3, 7 × 7 yielded similar results. Table 1 presents the PSNR values obtained by applying various denoising methods on the selected test images. These PSNR values represent the average values taken over twenty iterations. The highest PSNR value is highlighted in shaded bold, while the second highest PSNR value is highlighted in bold (without shade) to underline the two best performing denoising algorithm at each noise level. The results in Table 1 demonstrate the superior performance of the proposed GoFShrink-DT against the selected state of the art of image denoising at all the noise levels for all the test images. Note that the GoFShrink-TI showed competitive performance when with other comparative image denoising methods for natural as well as medical images. For the input image Barbara (of size 512 × 512), the GoFShrink-DT and the GoFShrink-TI outperformed other denoising methods at all noise levels. The best results were shown by the GoFShrink-DT which beat the rest of the denoising methods including the second best GoFShrink-TI method by a considerable margin. The GoFShrink-DT also demonstrated superior performance for Lena image (of size 512 × 512) at all noise levels while the second best results were shown by GoFShrink-TI at noise levels 10 � σ � 40 and iTVD at σ = 50, which outperformed GoFShrink-TI by a small margin.
For Aeroplane and Side MRI images, the proposed GoFShrink-DT outperformed all the comparative methods at all noise levels, while second best results were obtained by GoFShrink-TI and PaPCA alternatively at different noise levels. The second best performance was demon- For Peppers image (of size 512 × 512) at σ = 10 & 20, the BeltDen yielded best performance in terms of output PSNRs followed by the NeighSure at σ = 10 and the GoFShrink-DT at σ = 20. For noise levels σ � 30 GoFShrink-DT yielded best results.
For Cameraman image (of size 256 × 256), the PaPCA method demonstrated best performance against the rest of the denoising methods for 10 � σ � 40. However, at σ = 50, BeltDen yields the best results. The GoFShrink-DT shows the second best performance for Cameraman image at 20 � σ � 40. The NeighSure exhibited second best performance at the noise level σ = 10, while at noise level σ = 50, iTVD yielded second highest PSNR values. Even though, the GoFShrink-TI failed to be among top two performing methods for Cameraman image, it showed competitive performance against the best methods.
Similarly, the GoFShrink-DT outperformed the comparative state of the art methods for View and Multi-focus images (of size 512 × 512) at all noise levels. For Multi-focus image, the GoFShrink-TI yielded next best results at noise level σ � 20, while the iTVD showed second best performance at σ = 30 & 40. For the View image, the PaPCA yielded second best results at σ � 20, while the BeltDen, iTVD and aTVD were second best respectively for noise levels σ = 30, 40 & 50. Table 2 presents the qualitative analysis of the denoised images obtained from the comparative state of the art methods along with the proposed GoFShrink-DT method. For that purpose, we obtain results for input images 'Lena', 'Plane', 'Peppers' and 'MRI'. It can be observed that the denoised images obtained from the proposed method yields highest SSIM and FSIM values on most occasions. In cases where other methods yield better results, the proposed method still remains quite competitive. Among the state of the art, PaPCA and BeltDen yields the best results in terms of the SSIM and FSIM values.
The above results and discussion clearly demonstrate the efficiency of the GOF based methods against the state of the art denoising methods for a variety of practical input images. Similarly, the GoFShrink-TI also showed competitive performance against the state of the art in image denoising. From the state of the art methods, PaPCA and iTVD yielded good performance against the proposed methods while the NeighSure and the Surelet have also been competitive.
To show the visual quality of the recovered images by various denoising methods, we take a specific case of a Brain MRI image in Fig 8, corrupted with WGN at σ = 20. The Fig 8(a) shows noisy versions of the Brain MRI image while Fig 8(b)-8(h) show the corresponding denoised images obtained by employing BiShrink, PaPCA, Surelet, NeighSure, cSM-EB, GoFShrink-TI and GoFShrink-DT, respectively. It can be noticed that the GoFShrink-DT retained the image details and avoided artifacts thereby providing the best visual quality denoised image as compared to the other denoising methods. The GoFShrink-TI though contains some artifacts but it also manages to preserve important details as compared to NeighSure, Surelet and BiShrink which also yielded artifacts. The cSM-EB performed comparatively better but fails to capture the clarity as evident in GoFShrink-DT results. The PaPCA demonstrated visually pleasing results with lesser artifacts, however, the denoised image is over-smoothed and it is hard to differentiate between smoother regions and inherent image discontinuities. We also computed the difference images corresponding to all the denoised images and then estimated the power of the difference images. It was observed that least power of the difference image was yielded by proposed methods i.e. 38.7 & 50.9 while the comparative methods yielded higher power difference images. In Fig 9, the performance of the proposed GoFShrink-TI and the GoFShrink-DT is compared with the iTVD, Surelet and NeighSure for the Multi-focus image. It can be observed that Fig 9(c). Another evidence of the best visual performance by the proposed methods is the least power of difference images (obtained by subtracting denoised images from original) 38.18 and 43.82 respectively while the comparative methods Surelet and NeighSure yield 45.64 and 54.71 respectively. Even though the iTVD yields lower noise power compared to the GoFShrink-TI, the visual quality of its denoised image is not particularly impressive. In Fig 10, shows the actual and noisy view image along with the denoised images obtained from the BeltDen, aTVD and cSM-EB and the proposed for the input noise level σ = 40. Note that the denoised image obtained from GoFShrink-DT in Fig 10(g) yielded few artifacts with most details intact. The GoFShrink-TI also managed to recover important details when compared against the state of the art methods but it also yielded considerable amount of artifacts. The denoised images from other comparative methods including the BeltDen and the cSM-EB show significant artifacts. The aTVD yielded lesser artifacts as compared to BeltDen, cSM-E, albeit few line artifacts are still present while image details are missing.
In order to validate our work, the proposed GoFShrink-DT is also compared against the NLM method, which is a computationally intensive state of the art method known for its effective denoising performance. For this purpose, Brain MRI and Multi-focus images have been used. The denoised images obtained from the the NLM and the GoFShrink-DT, at input noise Auxiliary Fig 2, shows noisy images while the second and third columns show denoised images obtained from the NLM and the GoFShrink-DT respectively. It is evident that NLM method yielded higher PSNRs and also managed to smooth out noise very effectively. However, NLM smooths images discontinuities or edges thereby loosing important details of the MRI image. Contrarily, the GoFShrink yielded comparatively less PSNR but it recovered important signal details which might be useful in the clinical diagnosis.
Similar trends can be observed in the bottom two rows of the Auxiliary Fig 2 where the NLM over smooths the Multi-focus image at input noise level σ = 20 & 30 while yielding comparatively higher PSNR values than those of the proposed method. However, the proposed GoFShrink gives sharper denoised image with more signal details.
Conclusion
A class of multiscale image denoising algorithms have been proposed which employ the goodness of fit test on multiple image scales obtained from discrete wavelet transform (DWT) and dual tree complex wavelet transform (DT-CWT). The Anderson Darling (AD) statistics have been employed, within the framework of GoF test, on the wavelet coefficients of the noisy image to compute the distance between the empirical distribution function (EDF) of local coefficients and the CDF of reference Gaussian noise. A local thresholding function is then used to classify the wavelet coefficients as belonging to signal or noise depending on the given probability of false alarm (P fa ) and the estimated AD statistic. The signal coefficients are retained while the noise coefficients are discarded to yield the denoised image. While the current work only deals with the case of Gaussian noise, the proposed scheme has potential to remove any type of noise with prior knowledge of the noise distribution. The proposed methods have been shown to outperform the state-of-the-art image denoising methods on a variety of input images ranging from standard test datasets to medical and diffusion images. The results have revealed that from the two proposed methods, the GoFShrink-DT (based on DT-CWT) has outperformed the GoFShrink-TI (based on DWT) which was expected given directional selectivity and translation invariance of the DT-CWT transform. | 9,676.8 | 2019-05-10T00:00:00.000 | [
"Computer Science",
"Engineering"
] |
Enhanced monolithically integrated coherent 120 ° downconverter with high fabrication yield
Conventional monolithically integrated 90° downconverter suffers from hardware-induced non-linear constellation distortion, which gets worse far away from the central wavelength or when fabrication errors are taken into account. To overcome these problems, a 120° monolithically integrated downconverter with full compensation of hardware non-idealities has been proposed. It is numerically demonstrated that, in a realistic scenario exposed to the combined effects of fabrication tolerances and limited ADC resolution, this approach exhibits a significantly better signal dynamic range and a remarkable improvement of fabrication yield. ©2012 Optical Society of America OCIS codes: (000.4430) General: Numerical approximation and analysis; (060.1660) Coherent communications; (060.2330) Fiber optics communications; (250.5300) Photonic integrated circuits; (250.3140) Integrated optoelectronic circuits. References and links 1. Optical Internetworking Forum (OIF), “100G ultra long haul DWDM framework document,” document OIF-FD100G-DWDM-01.0 (June 2009), http://www.oiforum.com/public/impagreements.html. 2. M. Nakazawa, “Ultrafast and high-spectral-density optical communications systems,” in Conference on Lasers and Electro-Optics (CLEO) (2011), OSA Technical Digest (CD), paper CThGG3. 3. Mirthe Project, “Monolithic InP-based dual polarization QPSK integrated receiver and transmitter for coherent 100–400Gb Ethernet,” http://www.ist-mirthe.eu/. 4. R. Kunkel, H. G. Bach, D. Hoffmann, C. Weinert, I. Molina-Fernández, and R. Halir, “First monolithic InPbased 90 degrees-hybrid OEIC comprising balanced detectors for 100GE coherent frontends,” in International Conference on Indium Phosphide & Related Materials (IPRM) (2009), paper TuB2.2, pp. 167–170. 5. A. Moscoso-Mártir, I. Molina-Fernández, and A. Ortega-Monux, “Signal constellation distortion and BER degradation due to hardware impairments in six-port receivers with analog I/Q generation,” Prog. Electromagn. Res. 121, 225–247 (2011). 6. I. Fatadin, S. J. Savory, and D. Ives, “Compensation of quadrature imbalance in an optical QPSK coherent receiver,” IEEE Photon. Technol. Lett. 20(20), 1733–1735 (2008). 7. P. Pérez-Lara, I. Molina-Fernández, J. G. Wanguemert-Pérez, and A. Rueda-Pérez, “Broadband five-port direct receiver based on low-pass and high-pass phase shifters,” IEEE Trans. Microw. Theory Tech. 58(4), 849–853 (2010). 8. T. Pfau, S. Hoffmann, O. Adamczyk, R. Peveling, V. Herath, M. Porrmann, and R. Noé, “Coherent optical communication: towards realtime systems at 40 Gbit/s and beyond,” Opt. Express 16(2), 866–872 (2008). 9. C. Xie, P. J. Winzer, G. Raybon, A. H. Gnauck, B. Zhu, T. Geisler, and B. Edvold, “Colorless coherent receiver using 3x3 coupler hybrids and single-ended detection,” Opt. Express 20(2), 1164–1171 (2012). 10. P. J. Reyes-Iglesias, I. Molina-Fernández, A. Moscoso-Mártir, and A. Ortega-Moñux, “High-performance monolithically integrated 120° downconverter with relaxed hardware constraints,” Opt. Express 20(5), 5725– 5741 (2012). 11. V.E. Houtsma, N. G. Weimann, T. Hu, R. Kopf, A. Tate, J. Frackoviak, R. Reyes, Y. K. Chen, L. Zhang, C. R. Doerr, and D. T. Neilson, “Manufacturable monolithically integrated InP dual-port coherent receiver for 100G PDM-QPSK applications,” Tech. Digest Optical Fiber Comm. (OFC) (2011), paper OML2. 12. A. Besse, M. Bachmann, H. Melchior, L. B. Soldano, and M. K. Smit, “Optical bandwidth and fabrication tolerances of multimode interference couplers,” J. Lightwave Technol. 12(6), 1004–1009 (1994). 13. F. M. Ghannouchi and R. G. Bosisio, “An alternative explicit six-port matrix calibration formalism using five standards,” IEEE Trans. Microw. Theory Tech. 36(3), 494–498 (1988). 14. T. Pfau, S. Hoffmann, and R. Noé, “Hardware-efficient coherent digital receiver concept with feedforward carrier recovery for M-QAM constellations,” J. Lightwave Technol. 27(8), 989–999 (2009). #173935 $15.00 USD Received 6 Aug 2012; revised 5 Sep 2012; accepted 6 Sep 2012; published 24 Sep 2012 (C) 2012 OSA 8 October 2012 / Vol. 20, No. 21 / OPTICS EXPRESS 23013
Introduction
Once dual polarization quadrature phase-shift keying (DP-QPSK) modulation has been proposed by the Optical Internetworking Forum [1] to reach 100 Gbps per channel over existing infrastructure, higher-order quadrature amplitude modulation (e.g., 64-512 QAM) arises now as a promising alternative to further increase capacity while reducing bandwidth requirements [2,3].Polarization diversity coherent receiver combines two main parts: i) a polarization diversity circuitry (e.g.polarization beam splitters) and ii) two phase diversity receivers, one per polarization.In this paper we will focus on the second part, i.e. in the phase diversity receiver also called optical downconverter.
The most widely used solution for optical coherent reception is the 90° hybrid downconverter.A cost-efficient implementation, appropriate for commercial applications, is the monolithic integration on Indium Phosphide (InP) of 90° optical hybrid, based on a 2x4 multimode interference structure (MMI), with four photodiodes on the same photonic integrated circuit [3,4].Real hardware suffers from different kinds of imbalances which get worse far away from the central wavelength and increase with fabrication-induced process variations.Hardware imbalances introduce two types of distortion on the received in-phase and quadrature (IQ) signal components [5]: linear and nonlinear.A common linear algorithm, carried out in the digital signal processor (DSP), to mitigate non-ideal performance is the Gram-Schmidt orthogonalization procedure (GSOP) [6].However, GSOP cannot remove non-linear distortion.Therefore stringent fabrication requirements would be necessary to obtain a high performance coherent receiver for higher-order QAM modulations, which would lead to low fabrication yield and a relatively high cost per device.
A promising alternative to overcome these problems is the 120° phase diversity receiver, which has been recently used as a hardware-impairment tolerant wideband receiver at microwave frequencies [7].The 120° downconverter has been reported several times for optical communications by making use of 3x3 fiber couplers [8,9].Recently, authors have proposed [10] a monolithically integrated 120° downconverter based on a 2x3 MMI.This approach makes use of a simple linear calibration strategy to fully correct the receiver imbalances.When compared with the traditional 90° hybrid receiver, the main advantages of the 120° downconverter are higher dynamic range, wider operating bandwidth and relaxed fabrication tolerances.Although these conclusions were stated in [10], the last one was only deduced from the others but not proven, since only optimized nominal designs (i.e.without fabrication errors) were considered.Moreover, the effect of finite resolution of analog-todigital converters (ADC) placed before the DSP was not taken into account.
In this work the optical front-end modeling is significantly improved, including most of the non-idealities inherent to realistic 90° and 120° integrated downconverters.On one hand, we have studied the effect of realistic fabrication tolerances on InP platforms on the performance of both types of receivers.On the other hand, it has been also analyzed the effect of limited ADC resolution.Simulation results clearly show that fabrication errors, that substantially degrade the 90° receiver performance, can be easily overcome using the 120° downconverter.It is also found that quantization noise caused by ADC limited resolution affects both receivers in the same limited way.This is an important result, since it proves that linear calibration procedure for 120° downconverter is robust enough for practical implementations.In authors' opinion, the analysis performed in this work confirms the superior performance of 120° downconverter architecture and makes it an interesting alternative to traditional 90° hybrids in those applications in which the cost of the additional ADC is not critical (as it will happen in future optical access networks where lower sampling rates will be required as compared with optical backbone networks).
The paper is organized as follows: Section II describes the monolithically integrated optical downconverters and the effect of typical fabrication errors on the behavior of the passive network.Performance of linear DSP algorithms to overcome degrading hardware non-idealities is carried out numerically in Section III.The detrimental effect on each scheme of a limited ADC resolution will be assessed in Section IV.Finally, Section V provides the main conclusions.
Monolithic integration of 90° and 120° optical downconverters
Figure 1 shows the block diagrams of the two complex downconverters considered in this work.The typical approach, see Fig. 1(a), is based on a 2x4 MMI device monolithically integrated with four photodiodes followed by differential transimpendance amplifiers (TIA) [4].Output analog IQ components are then digitized from two ADCs and further corrected in the DSP to get the received symbols.On the other hand, from Fig. 1(b), the proposed 120° downconverter is based on a 2x3 MMI coupler monolithically integrated with three photodiodes followed by TIAs, ADCs and the DSP.The passive components of the receiver are based on InP/InGaAsP rib waveguides (n InP = 3.18, n InGaAsP = 3.27) with nominal core thickness H = 1 μm and nominal etch depth D = 0.5 μm.Input and output sections have been designed using monomode interconnection waveguides of width W g = 2 μm.Radii of curvature have been chosen to be higher than 650 μm to assure negligible bending losses.Waveguide crossings that can be seen at the output section of the 2x4 MMI, form an angle greater than 40° to reduce losses and crosstalk.The geometry of both MMI devices has been optimized to minimize amplitude and phase imbalances at 1550 nm.The nominal widths and lengths of designed MMIs are respectively W MMI,90° = 25.2 μm, L MMI,90° = 1379 μm, W MMI,120° = 18.8 μm, L MMI,120° = 1034 μm.Adiabatic tapers with a length L taper = 100 μm are used to adapt, with negligible losses, the interconnection monomode waveguides to the MMI access ports, of width W p = 4 μm.The tolerance analysis has been performed taking into account the combined effect of the two main deviations that usually occur during the fabrication of InP based photonic integrated circuits: waveguide width errors (δ W ) and etch depth errors (δ D ).Deviations from the nominal core thickness H has not been considered, since epitaxial growth of InGaAsP/InP layers is very accurate.For a properly adjusted fabrication process, width and etch depth errors are quite low (e.g.|δ W | < 250 nm and |δ D | < 100 nm) and they do not affect significantly the performance of interconnection waveguides, tapers or crossings.Unfortunately, it can be shown that these errors are high enough to deteriorate the behaviour of MMI devices [11].We have considered two different non-ideal situations: i) Low fabrication error (δ W = −150 nm, δ D = 45nm) and ii) High fabrication error (δ W = −250 nm, δ D = 100nm).It is worth to say that a smaller MMI width (δ W < 0) has the same effect than a greater etch depth (δ D > 0), because in both cases the confinement of the guided modes is reduced.Therefore, the combined effect of a negative δ W and a positive δ D leads to MMI devices with a noticeable reduced effective width.Since it is well known that effective width is the most critical parameter in these devices [12], the performance of both MMIs will be considerably degraded when compared with the nominal design.Accurate electromagnetic (EM) simulation of all the passive parts of the downconverters has been carried out by a combination of different commercial tools, such as FIMMWAVE & FIMMPROP (Photon Design) and BeamPROP & FullWAVE (Rsoft Inc.).
Maximum Amplitude Imbalance (MAI) and Maximum Phase Imbalance (MPI) are used as figures-of-merit to characterize the passive network.They are defined as the maximum deviation from the ideal value of any of the outputs of the downconverter within a frequency band.Table 1 shows MAI and MPI, calculated over the complete C-band, for three different cases: I. Nominal design, II.Low fabrication error and III.High fabrication error.As it is expected from basic MMI theory [12], the 2x3 MMI exhibits a slightly better performance and tolerance to fabrication errors than the 2x4 MMI, despite both are 'best effort' designs.For the system simulations, performed for both the 90° and the 120° cases, the receiver model comprises the passive part, characterized by the previously EM obtained scattering parameters, and the photodiodes which are modeled by an ideal square-law detector with a common-mode rejection ratio (CMRR) of −22 dB and additive shot-noise.Also, it has been considered an external local oscillator (LO) power of 5 dBm and a bit rate of 56 Gbps, enabling 112 Gbps under dual polarization (in this case LO would require an additional 3dB power if it were followed by a beam splitter).Incoming optical signal-to-noise ratio (OSNR) has been adjusted for a BER = 10 −4 in an ideal coherent receiver in absence of internal noise sources (24.3 dB for 64-QAM and 30.2 dB for 256-QAM).We will consider ideal behaviour of TIAs and DSP blocks.More details of the system simulation scenario can be found in [10].
Receiver performance evaluation with ideal ADC
In this section it will be shown that, in the absence of ADC quantization noise (ADC resolution has been set to 10-bit which corresponds to an almost quantization noise free situation), the 120° receiver gives a much greater tolerance to fabrication errors than the 90° receiver.
As it was already studied in [5], in a 90° downconverter receiver amplitude and phase imbalances will distort IQ signal constellation due to: i) DC offset ii) rotation and imbalance of the reference axes iii) non-linear error proportional to the signal-to-LO power ratio.The standard procedure to correct the hybrid induced-distortion is the GSOP algorithm [6], which cannot remove the non-linear constellation distortion, and this makes this solution limited for high signal-to-LO power ratios [10].However, the 120° downconverter was shown to be free from this limitation.Indeed, in this type of receiver, IQ signals can be obtained with negligible distortion by a linear combination of the readings of the three photodiodes (this algorithm is carried out in the DSP), after a simple calibration strategy [10,13].
In Fig. 2 we study the robustness of proposed architectures (90° hybrid plus GSOP algorithm, versus 120° coupler plus linear calibration) to correct the inevitable imbalances of the receivers due to fabrication tolerances (Cases I, II and III in Table 1).The OSNR penalty (for a BER = 10 −4 ) in the C-band versus signal power, under higher-order QAM modulation (64-QAM and 256-QAM), is compared for both receivers.It can be seen that the 90° receiver performance is limited by shot-noise and non-linear constellation distortion at low and high signal power levels respectively.These limitations increase for greater fabrication errors finally reducing its dynamic range as manufacturing tolerances are relaxed.In fact, OSNR penalty for 256-QAM reception will be out of the scope of the Fig. 2(b) when fabrication process variations follow Case III.On the contrary, 120° receiver, although also suffering from the same shot noise limitation as the 90° hybrid, is more resistant to non-linear constellation distortion occurring for high signal powers.As a consequence, it is much more resistant to hardware imbalances derived from fabrication tolerances, and it can deal with a worst case fabrication scenario (Case III) with low OSNR penalty.This behaviour can be used beneficially to relax the fabrication tolerances of monolithic receivers, increasing this way the fabrication yield and reducing the overall fabrication cost.
Degradation induced by ADC limited-resolution
Once that it was shown in previous section that the 120° downconverter, with ideal ADCs, can be advantageously used to relax the fabrication tolerances of monolithic coherent downconverters, it is time to study for the first time the effects of ADC resolution on this new type of receiver.
A limited ADC resolution will increase the quantization noise and this will impact on the algorithms to compensate for hardware imbalances in the 90° and the 120° downconverters.Figure 3 shows for both downconverters the OSNR penalty as a function of the ADC resolution when considering fabrication tolerances described by Case III under 64-QAM and Case II under 256-QAM.It can be seen for the 90° downconverter that, as previously reported [14], a low quantization noise penalty (<0.1 dB at BER = 10 −4 ) can be achieved with an ADC resolution of at least 7 and 8 bits, respectively, for 64-QAM and 256-QAM.This figure also shows that the same penalty is obtained for the 120° downconverter.Thus, despite using different calibration algorithms, this new type of receiver is not more sensible to quantization noise that the classical 90° approach.
Furthermore, attending to the OSNR penalty obtained for 64-QAM reception, the results of Fig. 3(a) show that, in a realistic scenario suffering from the combined effects of quantization noise and hardware imbalances encountered in practical monolithic receivers, the 120° downconverter can offer almost 1dB improvement of total OSNR penalty while reducing ADC resolution by one bit.This has been highlighted in Fig. 3(a), where it can be seen that a 6-bit ADC resolution 120° receiver outperforms the 7-bit ADC 90° receiver giving 0.9 dB of OSNR penalty improvement.Similar conclusions can be obtained from Fig. 3(b) for Case II under 256-QAM modulation.As a consequence, it is concluded that the superior correction procedure of the 120° receiver allows relaxing not only the fabrication tolerances but also the ADCs resolution requirements in a realistic scenario.
Finally, to demonstrate that the 120° downconverter continues to offer excellent hardware correction capabilities for high density modulations even under the presence of ADC quantization noise, Fig. 4 shows the results for a 120° receiver in a high fabrication error scenario (Case III) under 256-QAM.Compared with those in Fig. 3(b), it can be seen that OSNR penalty is not degraded and thus the proposed receiver offers a robust strategy to correct high fabrication errors with limited ADC resolution even for high density modulations as 256-QAM.Please notice that results of Fig. 4 cannot be compared with those of the 90° downconverter, because this type of receiver is not able to get the required BER for this scenario.
Conclusion
We have exhaustively compared the performance of two monolithically integrated optical downconverters.The first one is a conventional 90° hybrid, whereas the second one is the 120° downconverter recently proposed in [10].In both cases, passive structures have been designed in InP technology, taking into account typical fabrication errors.Higher-order QAM transmission schemes (e.g.64-QAM and 256-QAM) have been considered under limited ADC resolution.It has been shown that, in a realistic scenario suffering from the combined effects of fabrication errors and limited ADC resolution, very good results in terms of signal dynamic range and operating bandwidth can be obtained with the new type of receiver.Therefore, it can be concluded that this approach, not only improves the receiver performance, but also allows relaxing fabrication tolerances and ADC resolution requirements, thus improving fabrication yield and offering lower potential costs. | 4,125.8 | 2012-10-08T00:00:00.000 | [
"Engineering",
"Physics"
] |
Characterization of Soil and Sediment Parameters of Jisike-Izombe Upper Aquifer System for Assessment of the Potential of Groundwater Pollution
The JES field, an onshore field in the Jisike – Izombe area of southeastern Nigeria had a number of oil-producing wells before it was abandoned over a decade ago. By means of soil/sediment samples retrieved from three strategically located boreholes around the field, the subsurface units were delineated and the physical characteristics of the vadose zone were determined in order to predict the groundwater pollution potential of the upper aquifer system in the area. Ground conditions were found to be approximately 1metre (3.043ft) of loamy top soil overlying about 1.2metres (3.65ft) of clayey laterite which overlies 19.4 – 24.6metres i.e (60ft – 75ft) of reddish-brown silty sand, beneath which is medium – coarse grained white sand which constitutes the aquifer system with estimated hydraulic conductivity in the range 1 x 10 -1 to 169 x 10 -1 mm/sec. Flow analysis of groundwater indicates a southwesterly flow with the River Niger as possible discharge zone. The high leaching potentials and high transmissive properties of the sediments below the clayey laterite suggests a vulnerability of the aquifer to pollution through vertical infiltration. However, borehole water quality parameters for the area show that groundwater quality is not in anyway compromised when compared to the WHO limits. Rather, it is argued that the groundwater is naturally well protected by the upper part of the vadose zone where the clayey lateritic soil with iron oxide cementation of soil particles provides an appreciable degree of barrier to downward movement of contaminants. Because of the clayey behavour of the near-surface soils and their affinity for the retention of contaminants, it is concluded that the area is not a locus of groundwater pollution. © JASEM
Introduction
Petroleum activity (exploration and exploitation) has been on for many years over a vast area in the Niger Delta region of Nigeria covering some 6400km 2 on-shore and 76,000km -2 off-shore.The JES field located in the Jisike -Izombe area of southeastern Nigeria is one of the on-shore oil fields within an estimated total land area of about 3.95km 2 .It was discovered in 1975 and is adjacent to and east of Izombe field.The entire area lies between longitudes 0 5 6 0 and 3 5 6 0 East, and latitudes 6 3 5 0 and 8 3 5 0 North.
As a consequence of petroleum operations, different aspects of the geo-environment (e.g soil and groundwater) may be vulnerable to pollution and degradation.It is therefore imperative to carry out an Environmental Impact Assessment (EIA) as a sine-qua-non of a field development plan (FDP) prior to the commencement of the project.Where this had not been done, prediction of impacts of petroleum activity would then be based on a postimpact assessment (PIA) and environmental evaluation report (EER) relying essentially on control samples, since baseline data would not be available.Any of these studies would entail gathering of data that govern the transfer of contaminants and wastes from the site down to the groundwater.With regards to groundwater, it is often necessary to characterize the geomaterials above the aquifer (vadose zone) because of the need for groundwater protection against pollution.
Geological factors of soils and sediments must be identified so that their ability to transport pollutants can be ascertained, including lateral changes (facies changes) in the formation.For example, many non-aqueous phase liquids (NAPLs) which are volatile organic compounds of environmental concern (gasoline, chlorinated solvents etc) frequently enter groundwater systems after they have been spilled on the surface, and pass through the unsaturated zone (Palmer and Johnson, 1991).Impacts on soil quality from pollutants, which may be direct, could also be quite significant (Petts and Eduljee, 1994).Therefore, an understanding The ultimate goal of site characterization is to make informed decisions and one of the objectives is to predict/establish the impacts of certain facilities or natural phenomena on water quality under a variety of conditions (Mercer and Spalding 1991).Inevitably, an assessment of the groundwater flow system is necessary as is the assessment of the contamination in the groundwater.The latter involves groundwater quality monitoring over a period of time.
This contribution presents soil and sediment characteristics of the upper aquifer system of the Jisike -Izombe area as a tool for assessment of the potential of groundwater pollution.
METHODOLOGY
Three boreholes, strategically located such that they form a triangular pattern, were drilled near the oil field by the rotary method using a water-based drilling mud.The boreholes BH 1 , BH 2 and BH 3 (Fig. 1) terminated at depths about 2metres after groundwater was encountered.Soil and sediment samples were collected according to the dictates of changes in physical characteristics with depth.Borehole water samples were collected after the well had been pumped sufficiently to ensure a particulate free sample (purging), representing ground source.Coordinates of the three locations together with ground elevations and water table elevations are given in Table 2. Particle size analyses of the soils/sediments were conducted in accordance with BS1377 (1990 for each of the three boreholes.Soil classification was based on the uniformity coefficient of the size distribution.Selected physico-chemical parameters (available phosphorus, conductivity, chloride content, pH, organic matter content, total hydrocarbon etc) were determined for the top soil and control samples at depths of 15 -30cm, 30 -60cm and 60 -90cm while the borehole water samples were analysed for the following quality parameters: pH, total solids, total hydrocarbon content (THC), total hardness, chlorides, sulphates Cu, Fe, Mn, Zn, and Pb.The results were compared to the WHO limits for drinking water with a view to establishing negative impacts, if any, consequent on past petroleum operations.Geology of the Area: The specific site within the area of study lies at elevations of between 210feet (69 metres) and 230feet (75 metres) above sea level and slopes gently in a southwesterly direction.The area is underlain by the Benin Formation (Oligocene -Pliocene) which together with the Agbada Formation and the Akata Formation constitute the three chronostratigraphic units that have been recognized and described in the subsurface of the Niger Delta Basin (Short and Stauble, 1967;Hospers, 1971;Avbovbo, 1978).
FIG. 2: LITHOLOGIC LOG OF THE THREE BOREHOLES
Lithologically, the Benin Formation consists of over 90% sand with occasional clay/shale intercalations (Short and Stauble, 1967).The sands, which are medium to coarse grained are generally white in colour with a reddish brown lateritic top due to feruginization of surface materials.Texturally, they are subangular to well rounded consisting largely of quartz and minor amounts of feldspar.They are generally poorly sorted but well sorted in places.
Clays are known to be prominent in the formation westwards of Benin City.The clay layers and lenses are generally greyish-brown.Local textural variations are megascopically discernible in places.
Due to its predominantly sandy nature, and the fact that it is underlain by the relatively less permeable Ogwashi -Asaba Formation, the Benin Formation is known to have very good aquifer properties.
Most prolific boreholes around Benin City tap from this formation (Adetola et al, 1999).Table 1 is a summary of the stratigraphy of the Niger Delta.
The Agbada Formation is considered as a down-dip continuation of the Ogwashi-Asaba Formation.
RESULTS AND DISCUSSION
The granulometric data and other physical attributes of the soil/sediment samples from the three boreholes are listed in Tables 3A, 3B and 3C which also show the lithologic logs in minute details. Figure 2 illustrates, in a pictorial fashion, the lithologic correlation of the subsurface geological materials in the area.The microstratigraphy of the area as revealed by borehole drilling clearly shows a lithologic distribution comprising four main units: a top soil (approximately 1m thick) consisting essentially of light-brown, fine-grained loamy soil with a greyish to a grey-brown hue characterized by the presence of plant rootlets.This is underlain by approximately 1.2m of fairly indurated clayey lateritic soil which in turn overlies 60 to 75 feet of reddish-brown silty sand, gravelly in places.
Here, the proportion of silt-size particles is clearly subordinate (< 10%).However, the lateral continuity of this unit is broken in BH1 and BH2 by reddish brown medium to coarse-grained silty sand interspersed with gravel.Below the reddish brown silty sand is a coarse to medium-grained whitish sand which constitutes the main aquifer system in the area.Groundwater was encountered at relatively shallow depths ranging from 100 to 120 feet from existing ground level.
The properties of the sediments listed in Tables 3A, 3B and 3C show that below the clayey laterite, estimated hydraulic conductivity is in the range 1x10 -1 to 169x10 -1 mm/s (BH1) 2.10x10 -1 to 81x10 - 1 mm/s (BH2) and 1.23x10 -2 to 18.6x10 -1 mm/s (BH3).These values are typical of medium to coarse-grained sand.More importantly, hydraulic * 1 T.U.S ONYEOBI C.N AKUJIEZE conductivity generally increases with depth, excluding the top near-surface units that are characterized by considerably smaller transmissive properties.(hydraulic conductivity 3.66 x 10 -5 -5.7 x 10 -5 mm/s for all three boreholes).These values are typical of silts (Smith, 1978) or very fine sands, silts and clay-silt (Craig, 1994).2) followed by a mechanical contouring technique to establish the dominant flow direction, since groundwater flows in a direction orthogonal to the contours from a higher to a lower potentiometric surface.The result of the analysis indicates a southwesterly flow of groundwater with the River Niger as a possible discharge zone.Figure 1 shows the flow direction, an indication of potential paths for leachate and contaminant migration.It is noteworthy, that although flow direction takes site-specific geology into account, variability could occur over a period of time, considering recharge to or withdrawal from the aquifer.
Top Soil Leaching Potentials And Mobility Of Ions:
The top soil (0 -90cm) of the area was studied with a view to assessing their leaching potentials and mobility of ions.In general terms, based on sand fractional content, the soil leaching potentials vary from very low at the top to very high at depth (below top soil and clayey laterite).The lithologs of BH1, BH2 and BH3 show that the near surface soils consist, for the most part, of fines, (i.e silt and clay) ranging from 73% to 77%, with about 25% sand-size particles.Applying the scale of Brown et al (1997, Table 4) the leaching potentials of the top soil and clayey lateritic soil are rated as very low, a situation that is corroborated by the profile variations of such mobile ions as available phosphorus and chlorides with depth.However, there is a drastic change in leaching potential beyond 2.2 metres from low to very high.This a reflection of the change in the texture of the soil and aquifer formation to greater than 90% sand fraction with depth.Physico-Chemical Properties Of The Top Soil: The soils of the studied area are acidic with pH values ranging from 5.03 to 6.9 (Table 5).There is no significant difference between the pH values and those of the "control" samples and neither of the results depicts any discernible trend in terms of variation with depth.The conductivity values are relatively low with a range of 20 -48.08µs/cm but these values are slightly higher than those recorded for control samples.They reflect low electrolyte concentration in the soil and, to a large extent, the continental, non-marine nature of the Benin Formation.
The soils were found to have low organic matter content (0.65 -2.65) consistent with their lateritic nature.Expectedly, the highest values were associated with the top 0-30cm (1.68-2.65)and are generally higher than those of the control samples.
Available phosphorus was the most abundant macronutrient (79.30 -96.90).Their mean values were slightly higher than values recorded for the control samples.The total hydrocarbon content, a strong indicator of petroleum pollution, was not so high (15.07-68.14) and although higher than those of control samples, betrayed no strong inkling of hydrocarbon contamination in the soil.
There is, however, subtle evidence of contaminant retention in the near-surface zone (Moore and Newbry, 1976;Khalid et al., 1977) would, therefore, be expected to be minimal especially as the sands are composed mainly of quartz grains.Under normal circumstances, these are factors that would favour easy rainfall infiltration and concomitant entry of any transported contamination into the groundwater via the vadose zone.
However, the uppermost section of the vadose zone consists of clayey laterite with iron oxide coating and cementation of soil particles.The top soil is also loamy thus providing a natural barrier to groundwater pollution.Here, contaminants could be purified by processes such as anaerobic decomposition, filtration, ion exchange and adsorption (Petts and Eduljee, 1994).
With the presence of the top soil cover and clayey laterite, the upper aquifer would not be vulnerable to pollution.The groundwater quality parameters for the area (Table 6) attest to this inference as all the values fall within the WHO permissible limits for drinking water although microbiological data are not included.It is possible that any break in the soil at the surface leading to the removal of the top soil and clayey laterite could render the upper aquifer highly vulnerable to pollution.Where that happens, groundwater contaminants have the potential to move more rapidly in the aquifer unit with high hydraulic conductivity.Therefore, because of the high transmissive properties of the sediments, groundwater flow rates would be high and contaminant transport fast.Below the silty sand is a coarse-medium grained whitish sand unit which constitutes the main aquifer system in the area.Flow analysis of groundwater, which was encountered at depths below 110feet, indicates a southwesterly direction with the River Niger as possible discharge zone.Apart from the topsoil and near surface clayey laterite, the vadose zone, for the most part, consists of material indicative of properties, suggesting vulnerability of the aquifer to pollution.However measured groundwater quality parameters betray no obvious pollution, a situation that is attributable to the protection provided by the near surface clayey lateritic soil and/or good waste management practice during petroleum operations nearly two decades ago.
A risk assessment based on such other factors as local topography, vis-à-vis run off and the nature of the near-surface soil suggests that the area is not a locus of groundwater pollution.The chemical quality of the groundwater in the upper aquifer system corroborates this inference.
Fig. 1 :
Fig. 1: Groundwater Flow Direction in JES Oil Field and Sampling Points (Inset Study Area, Nigeria)
Table 1 :
Summary of Stratigraphy of the Niger Delta Continental sandsover 90% sandstone, with clayey and pebbly layers Eocene to Pleistocene Agbada Formation Upper predominantly sandy unit with minor clay intercalations, and lower clay unit thicker than upper sandy unit.Rich in micro fauna at the base.Eocene to Miocene Akata Formation (Oldest) Uniform shale development consisting of dark gray sandy silty shale.Rich in micro fauna.
Table 2 :
Coordinates of Borehole Locations and Static groundwater levels
Table 3B :
Physical Parameters and Classification of Borehole Soil Samples **Indeterminate
Table 3C :
Physical Parameters and Classification of Borehole Soil Samples ** Indeterminate Flow analysis of groundwater based on water table measurements was carried out.This involved the measurement of depths to water level in each of the boreholes by means of a water level indicator, while elevation of each of the boreholes was determined by means of a GPS.The water level depths so obtained, were converted to elevation data (Table * 1 T.U.S ONYEOBI C.N AKUJIEZE
Table 5 :
Physico-Chemical Properties of Composite and Control Soil Samples for the Area
Table 6 :
Range of Groundwater quality parameters for borehole water samples in the study area compared with WHO limits for drinking water.The Jisike -Izombe area is underlain by the Continental Benin Formation of Oligocene -Pliocene age.The micro-stratigraphic architecture around the JES field within the area comprises a loamy topsoil, a thin near surface clayey lateritic horizon which is underlain by a high hydraulic conductivity reddish brown silty sand, gravelly in places. Conclusion: | 3,630.2 | 2015-02-02T00:00:00.000 | [
"Environmental Science",
"Geology"
] |
Reproductive Biology and Endocrinology Open Access the Impact of Cocaine and Heroin on the Placental Transfer of Methadone
Background: Methadone is the therapeutic agent of choice for the treatment of opiate addiction in pregnancy. The co-consumption (heroin, cocaine) which may influence the effects of methadone is frequent. Therefore, the impact of cocaine and heroin on the placental transfer of methadone and the placental tissue was investigated under in vitro conditions.
Background
In developed countries an increase in substance abuse in pregnancy can be observed. Opiates cross the placenta easily and lead to intrauterine growth restriction (IUGR), preterm birth and spontaneous abortion [1][2][3][4]. Abstinence cannot be achieved in most of the patients and methadone is the recommended standard of care for pregnant opioid-dependent women [5]. The positive effects of methadone are an increase in birth weight and the prolongation of gestation [4,6]. In addition, women in a maintenance therapy program receive more prenatal care which improves the situation for both, mother and fetus. How-ever, the main disadvantage of this therapy is the neonatal abstinence syndrome (NAS) which occurs in 60-80% of the newborns from mothers who consumed methadone and is more intensive than in babies who were prenatally exposed to heroin [3,7]. NAS due to opiate withdrawal may result in disruption of the mother-infant relationship, sleep-wake abnormalities, feeding difficulties, weight loss and seizures. It's unclear whether the maternal methadone dose correlates with the intensity and duration of NAS [5,8]. Nevertheless, transfer rates of methadone are higher from the fetal to the maternal circuit than vice versa [9]. This phenomenon is based on the fact that methadone is a substrate of the ATP-dependent efflux transporter protein, the P-glycoprotein (P-gp) which is expressed in the brush-border membranes of the maternal derived syncytiotrophoblast and works against the concentration gradient [7,9,10]. Because co-consumption of methadone with other drugs such as cocaine and heroin is frequent, additional drugs may influence the placental transfer of methadone and other substances by different mechanisms. In case of inhibition of the P-gp function by other drugs, the placental barrier may disrupt, and P-gp substrates may increasingly transfer to fetal circulation [11,12]. However, there exist no information whether heroin or cocaine are substrates of P-gp. Another mechanism is that of apoptosis or necrosis of the syncytiotrophoblast caused by some drugs. The syncytiotrophoblast is relatively thick in early pregnancy. At first trimester there is less syncytiotrophoblast and mostly of the trophoblast is cytotrophoblast. With increasing gestational age there is a differentiation of cytotrophoblast to syncytiotrophoblast. The release of microparticles shed from the syncytiotrophoblast into the maternal blood is generated during apoptosis or necrosis. MPs are larger than 100 nm in diameter and originate from blebbing membranes of either activated cells or cells undergoing apoptosis and mainly consist of nuclear proteins as well as nucleic acids [13]. The efficiency of transfer may be reduced by thickening of the basement membranes of capillary endothelium, obliteration of maternal vessels and the increase of the fibrinoid deposits [14][15][16]. Both mechanisms may affect the incidence and intensity of NAS. Cocaine as opiates leads to spontaneous abortion, low birth weight, fetal growth restriction and in addition to impaired neurodevelopment [17,18]. Because of its vasoactivity, cocaine affects the fetal vasculature and impairs placental permeability [19]. Heroin may be involved in leptin metabolism. Leptin is produced in the placenta [20,21] and regulates fetal growth and angiogenesis. In pregnancies with heroin abuse [22] as well as with IUGR [23], umbilical cord blood levels of leptin are reduced. It can therefore be assumed that heroin abuse in pregnancy may reduce placental leptin synthesis and contributes to IUGR by reducing fetal growth. Similar effects of methadone or cocaine on leptin are still unknown.
The aim of this study was therefore to investigate the placental transfer of methadone without and with addition of cocaine or heroin in the ex-vivo placenta perfusion model. Thereby placental functions as well as the response of the placental tissue on methadone were key issues.
Placenta collection
Intact placentae were consecutively obtained from uncomplicated term pregnancies after caesarean section at our Department. All women provided written informed consent for the study which was approved by the local institutional review board (KEK-StV-Nr. 07/07).
Placenta perfusion
Placentae were used for perfusion within 20-30 minutes after delivery. For the dual ex-vivo perfusion (earlier so called as "in-vitro" perfusion) of an isolated cotyledon the method originally described 1972 by Schneider, Panigel and Dancis [24] with a closed perfusion system for both maternal and fetal circuits was used [25]. The perfusion medium was composed of NCTC-135 tissue culture medium (ICN Biomedicals, Inc., Irvine, California, USA) diluted with Earle's buffer (1:1) with the addition of glucose (1.3 g/l; close to a physiological level), dextran 40 (10 g/l), sodium bicarbonate (2.2 g/l) bovine serum albumin (4 per cent), heparin (2500 IU/l) and clamoxyl (250 mg/l). Initial volume used on both sides (maternal and fetal) was 120-130 ml. The flow rate on the maternal side and fetal side was 12 and 6 ml/min, respectively. Two oxygenators (LSI-OX) are used: 95% N 2 plus 5% CO 2 for the fetal circuit and 95% air plus 5% CO 2 for the maternal perfusate respectively. The oxygen level in the perfusion medium was 130-150 mg Hg (maternal side) and 40-60 mm Hg (fetal side), respectively ( Figure 1). Experiments using a crossover system were performed according to four different protocols (Table 1) including always a 20 minute pre-phase of open perfusion of both compartments to flush the blood out of the intervillous space and the villous vascular compartment. For each protocol six experiments, (each on one placenta) were carried out, so six placentae per group and a total of 24 placentae for all experiments were used. Each experiment included two phases (I and II) of 2 hours. The crossover system was used in relation to the fact that metabolic parameters measured under control group (protocol A) differ between phase I (2 hours) and phase II (2 hours) (0.26 vs. 0.18 μmol/g/min glucose consumption; 0.22 vs. 0.18 μmol/g/min lactate production; 60 vs. 37 mU/g/min hCG accumulation; 82 vs. 66 pg/g/min leptin accumulation). The same effect was already shown by Miller RK et al., 1985 [26] and Di Santo et al., [27]. Therefore, in the following experimental groups (protocol B, C, D), each experiment included a control phase (2 hours), which was alternately applied either in the phase I or in the phase II, while the test substances were added in the remaining other experimental phase. In protocol B methadone (Sigma Aldrich, Fluka, CH-Buchs) at a concentration of approximately 500 ng/ml corresponding to plasma levels following a dose of 120 mg per day was added to the medium on the maternal side at the beginning of either phase I or phase II. In protocol C, cocaine (Sigma Aldrich, Fluka, CH-Buchs) at a concentration of 3 mg/l together with methadone 500 ng/ml and in protocol D, heroin [28] 150 ng/ml together with methadone 500 ng/ml respectively was added. The exact methadone concentration of used initial maternal medium was verified by analytical determination simultaneously with the perfusion samples. The perfusion media were replaced on both maternal and fetal sides after phase I. In all experiments 45 nCi/ml of 14 measure placental permeability from the maternal to the fetal direction. Antipyrine is a common reference marker to measure passive diffusion-dependent transfer [29].
Sample collection
Perfusion media aliquots of 5 ml from both sides (fetal and maternal) were collected at each hour of the perfusion and stored at -20°C until analysis of placental methadone levels, placental metabolism, permeability and microparticles. Tissue specimens before and after perfusion were collected and snap-frozen in liquid nitrogen and stored at -80°C until analysis of methadone and Pglycoprotein.
Concentrations of methadone
Methadone and its primary metabolite (2-ethyliden-1, 5dimethyl-3, 3-diphenyl-pyrrolidine = EDDP) were analyzed using High Pressure Liquid Chromatography (HPLC) and Mass Spectrometry (MS) [30]. 0.5 ml H 2 O and 500 μl 100 mM ammonium carbonate buffer (pH 9.3) were added to 1 ml of the perfusate sample and the tissue homogenate respectively and then extracted with 5 ml n-hexane/dichlormethane (4:1 ratio v/v). The organic extract was dried completely with a rotavap (35°C) and aired additionally under nitrogen (N 2 ). The residue was dissolved in 100 μl acetonitrile buffer and 300 μl ammonium acetate buffer. Aliquots were analysed by HPLC and MS. The calibration curve ranged from 0.69 to 828 ng/ml.
Placental metabolism
Glucose and lactate determinations were performed using an enzymatic assay of hexokinase/glucose-6-phosphate dehydrogenase and lactate dehydrogenase/glutamate pyruvate transaminase respectively [31]. Glucose consumption was calculated from the differences between initial and final levels in the medium on both maternal and fetal sides and were normalized to wet tissue weight and length of perfusion period as previously described by Di Santo et al., 2003 [27]. For the measurement of hCG and leptin in the medium, standard enzyme-linkedimmunosorbent assays (ELISA) developed earlier by Malek et al. [21] were used whereas the rate of accumulation was calculated as previously described [32]. Thereby the respective protein in samples is bounded by-purified anti-hCG or anti-leptin antibodies (capture antibodies) and immobilized on plastic plates (NUNC Maxisorpy™ microplates, Nalge Nunc International, Denmark). The captured protein was detected by a second enzyme-conjugated antibody. Following the addition of a chromogenic substrate, the level of the generated colored product is measured spectrophotometrically and the protein concentration is determined from the respective standard curve.
Antipyrine transfer
The transfer rate of antipyrine was determined according to the 14 C-labeled antipyrine reference method of Challier, 1985 [29].
The protein content was determined using the BCA™ Protein Assay Kit (Sigma-Aldrich, Buchs, Switzerland) with bovine serum albumin as a standard. All steps were performed at 4°C [10,16]. Identification of MPs as syncytiotrophoblastic shedding was performed by flow cytometric analysis with mouse monoclonal anti-Placental Alkaline Phosphatase (PLAP) described by Di Santo et al. 2007 [31].
P-glycoprotein
Isolation of brush-border membranes: 3 g tissue (mixed from three different locations of the placenta collected before and after perfusion respectively) were dissected and washed four times with PBS (83 mM NaCl, 22 mM Na2HPO4, 5.5 mM KH2PO4, pH 7.4). The tissue was homogenized for 1 minute and the tissue suspension was centrifuged at 3'200 × g for 10 min and the supernatant was further centrifuged at 10'000 × g for 30 min. Thereafter, the supernatant (1.0 ml) was centrifuged again at 100'000 × g (Sorvall Ultraspeed Centrifuge Rotor Log, Thermo Fisher Scientific) and the pellet was resolved in 100 μl PBS and used for western blot. Its protein content was determined using the BCA™ Protein Assay Kit (Sigma-Aldrich, Buchs, Switzerland) with bovine serum albumin as a standard. All steps were performed at 4°C [9,20].
Western blot: The identification of P-glycoprotein expression was achieved using 10.0% SDS/polyacrylamide gel electrophoresis. The amount of sample protein loaded on each well was 40 μg. The procedure follows the method of the trouble shooting protocol of the R&D System [36]. The transfer of the protein on the nitrocellulose membrane (ECL Hybond, nitrocellulose membrane, Amersham Biosciences, Buckinghamshire) was performed using the tank blotting system overnight. Non-specific binding domains were blocked by incubation with 5% non-fat dry milk in TBS for 1 h. The blots were incubated with the primary murine monoclonal anti-P-glycoprotein antibody (diluted 1:200) (Sigma-Aldrich) and the anti-beta-actin antibody (diluted 1:500) (Sigma-Aldrich) as an internal standard for one hour. The membrane was washed and incubated with the goat antimouse horseradish peroxidase-conjugated antibodiy (diluted 1:1000) (Dako) as secondary antibody for one hour. The detection was carried out using chemiluminescence and the visualization was performed at chemiluminescence films (ECC hyper film, high performance chemiluminescence film, Amersham Biosciences, Buckinghamshire, UK) [9,20]. The density of the simultaneously obtained bands of P-gp and beta-actin was measured using a densiometer. Values of P-gp were normalized to the respective value of the beta-actin in the same run.
Calculations
The glucose consumption and lactate production were calculated from the differences between initial and final levels in the perfusate on both, the maternal and fetal side, and normalized to tissue weight and length of perfusion period.
The net production (NP) of hCG and leptin after 4 h was calculated with the formula NP = A tot + T E -T 0 , where A tot means the hormone accumulation in the perfusate, T E the tissue content determined after perfusion and T 0 the tissue content before initiation of the experiment. Results were normalized to tissue weight and to T 0 .
Statistics
Statistical analysis was performed using Prism version 3.0 (GraphPad Software, San Diego California, USA). Mean ± SD were calculated. The Mann-Whitney U-test was used to estimate the statistical significance of the differences between or in experimental groups respectively. Differences were considered significant if P < 0.05.
Methadone concentration
In the perfusion with methadone alone, a concentration of 389 ± 79 ng/ml was applied either in the first or in the second phase (Table 2). After 2 hours the methadone concentration in the perfusate from the maternal side as well as from the fetal side was below the detection limit of < 0.69 ng/ml. In perfusions with methadone plus cocaine 3 mg/l, the methadone uptake was slower, which was indicated by the detection of methadone (1.6 ± 1.1 ng/ml) on the maternal side, while on the fetal side the level remained again undetectable. When heroin was added, the methadone uptake was more reduced than with cocaine and the concentrations were measurable both, on the fetal and on the maternal side with a fetal/maternal ratio of 1:7. In all experiments methadone increased in the tissue (cotyledon), whereas the highest accumulation was seen in perfusions with methadone plus heroin (172% vs. methadone alone, P = 0.008). In cases where methadone was applied in the first phase of the perfusion, no methadone could be detected in the perfusates of the second phase indicating that there was no wash out of methadone from the tissue. Concentrations of the metabolite were under the detection limit in all samples.
Placental metabolism
There was no significant difference in the glucose consumption (μmol/g/min) and lactate production (μmol/g/ min) between phase I (0.16 ± 0.04 glucose; 0.19 ± 0.04 lactate) and phase II (0.14 ± 0.03 glucose; 0.17 ± 0.04 lactate) in any group of perfusion. There was also no significant difference between perfusions with and without drugs (controls).
The tissue content for hCG and leptin was significantly higher at the end than before the perfusion in all experiments excluding that with methadone/heroin. In case of methadone/heroin addition the tissue content for hCG before vs. after perfusion was unchanged whereas the leptin content decreased significantly after perfusion (P < 0.05). The net production of both hormones when related to the tissue initial content gave values over 100% in every group ( Table 3).
Transfer of antipyrine
Antipyrine transfer profiles from maternal to fetal side were similar in phase 1 and phase 2 (control experiments).
Methadone decreased the transfer of antipyrine. The mean fetal to maternal ratio was 80% (0.50 ± 0.06) of the control ratio (0.60 ± 0.07, P < 0.01). The perfusion with methadone plus cocaine increased the transfer of antipyrine; the fetal to maternal ratio was 120% (0.68 ± 0.13) of the control ratio (0.56 ± 0.08, P = 0.03). A non-significant The detection limit of methadone was 0.69 ng/ml. Values are mean ± SD a P < 0.01 vs. perfusion with methadone increase in the transfer was seen when methadone plus heroin was perfused indicating a fetal to maternal ratio of 0.71 ± 0.24, which was 127% of the control ratio (0.58 ± 0.21, P = 0.179) (Figure 2).
Microparticles
The concentration of the released microparticles (MPs) isolated from the maternal side after perfusion without addition of any drug (control) was 2190 ± 655 ng protein/ g/min. A non-significant reduction in the release of MPs (96 ± 7% of the the control) was observed when methadone was added. The accumulation of MPs increased after perfusion with methadone/cocaine 128 ± 8% of the control (P = 0.03). In case of the perfusion with methadone/ heroin the MP's percentage increased to 134 ± 14 of the control (P = 0.03). No significant difference to the control was seen after the perfusion of methadone alone ( Figure 3). The percentage of trophoblast-derived MPs as indicated by PLAP was unchanged in all experimental groups.
P-glycoprotein
The P-glycoprotein expression (percentage of actin expression) in the tissue increased significantly during the perfusion with methadone alone or methadone in combination with cocaine or heroin by 49 ± 41% (P = 0.03), 75 ± 63% (P = 0.01) and 59 ± 51% (P = 0.03) Values are mean ± SD a P < 0.05 after vs. before; b P < 0.01 after vs. before; c P < 0.001 after vs. before.
Table 3: Tissue content before perfusion (T 0 ) and after 4 hours of perfusion (T E ), accumulation (A tot ) and net production (NP) as well as percent NP of T 0 (% NP) of hCG and leptin
There is no statistical difference in NP of hCG or leptin between control perfusion and each of the other experimental groups.
Effect of methadone (+MTD), methadone plus cocaine (+MTD+COC) and methadone plus heroin (+MTD+HRO) on antipyrine transfer Effect of methadone (+MTD), methadone plus cocaine (+MTD+COC) and methadone plus heroin (+MTD+HRO) respectively on the release of microparticles into the mater-nal circuit quantified by total protein content respectively compared to the tissue before perfusion (control) ( Figure 4A, B).
Discussion
Transplacental transfer of drugs (or other substances) and the amount that enters the fetal circulation is determined by at least three processes: simple diffusion, efflux transporters, and biotransformation by metabolic enzymes. The syncytiotrophoblast layer with the brush border membrane contains several transporter proteins and is in direct contact with the maternal blood in the intervillous space. It's therefore extremely sensitive for changes induced by maternal uptake. In the present study we found differing mechanisms of placental function and tissue reaction for methadone perfused in a closed circulation: There was no measurable concentration of methadone in the fetal circulation when methadone was perfused alone or with the addition of cocaine or heroin, respectively. Of course this could be proven in an open system where the re-uptake of transferred substances (methadone and heroin) is not possible. However, these results are in contrast to that of other authors [7,10], who perfused methadone alone. Nanovskaya et al. demonstrated a transfer into the fetal circuit of 31% [10] and Nekhayeva et al. [7] detected methadone in the fetal circuit already within 5 minutes of its addition to the maternal reservoir even though they used a lower concentration of methadone than we did (100-400 ng/ml vs. 500 ng/ ml) used. One reason for this difference may be the use of different methods. While in the study of Nekhayeva IA et al. [7] the perfusion lasted for 4 hours and methadone was measured by the radioactivity of the 3 H-labelled methadone, we perfused methadone only for 2 hours and measured its concentration by HPLC and MS.
Our control experiments showed similar values for placental metabolism and function (glucose consumption, lactate production, production and accumulation of hCG and leptin) as found in previous studies with the same exvivo placenta perfusion model [21,[30][31][32]. These results demonstrate that the perfused tissue maintains under in vitro condition the ability for de-novo synthesis of hCG and leptin. The addition of cocaine or heroin to methadone showed no different results of glucose consumption, lactate production, hCG accumulation and production. There was only a nonsignificant difference in the leptin production in the experiments with heroin. As it was already observed under in vivo conditions where serum leptin levels decreased in patients with heroin addiction [22], the consequences of modified leptin production during heroin consumption in pregnancy should be verified in further experiments. Consequently, until now, it has to be postulated that methadone plus cocaine or methadone plus heroin do not affect placental viability.
In case of cocaine the present results do not agree with those reported in a previous study [19,37], where cocaine (without methadone) led to a significant decrease in the hCG release to the maternal circuit.
In contrast to Nekhayeva et al., we could show that methadone has a significant influence on placental permeability. In the perfusion with methadone alone, the antipyrine transfer decreased. This fact could lead to a dysfunction in the supply of the fetus with oxygen and nutrients from the maternal circulation. Intrauterine growth restriction observed under in vivo supplementation with methadone A. Western blot analysis of P-glycoprotein expression in the isolated brush border membrane before perfusion (T0) and after perfusion (TE) ** *** [1] could be based on this mechanism. In contrast to the experiments with methadone alone antipyrine transfer increased significantly in the phases where cocaine or heroin was added. Even though the supply of the fetus with oxygen and nutrients may be better, the amelioration of the placental permeability could affect the barrier function of the placenta adversely. Consequently, more toxic substances or bacteria and viruses may cross the placenta and harm the fetus. Previous studies reported increased prevalence of infectious diagnoses in cocaine-exposed infants [38].
Cocaine and heroin increased the MPs production into the maternal circuit. Previous reports have shown that under different in vitro conditions the production of MPs can differ [16,39,40]. Under low oxygen (2%) there was an increased proliferation of the cytotrophoblast and lack of the fusion with syncytiotrophoblast, so shedding of MPs was predominantly the result of necrosis [39]. In this study we have used the already validated method of the ex-vivo placenta perfusion using 95% air on the maternal circuit [33]. It was also shown that, this method is a good model to investigate the shedding of MPs from synyctiotrophoblast as a sign for oxidative stress seen in preeclampsia [33]. One possibility that we can postulate, is that methadone alone does not influence the tissue structure whereas the combination with cocaine and heroin may induce oxidative stress. Further work is required to confirm this and the mechanisms involved.
Methadone is a substrate of the ATP-dependent efflux transporter P-gp [9][10][11]41]. Our results showed increasing expression after perfusion with methadone but no additional increase if cocaine or heroin were added to methadone. As it was shown by other authors P-gp expression is 30% higher in preterm than in term placentas [10]. Increased activity of P-gp may therefore affect the incidence and intensity of NAS in babies of women who were treated with methadone during pregnancy and who delivered preterm in most cases.
Conclusion
As the consumption of illegal drugs, especially cocaine is increasing in many countries as demonstrated by the World Drug Report 2007 [42], our results concerning cocaine and heroin causing an increased antipyrine transfer and possibly inducing oxidative stress in placental tissue, although we have not measured it directly, may improve the practical management in monitoring pregnant women. It should clarify the efficiency of a consequent and exclusive maintenance therapy with methadone during pregnancy. | 5,332.6 | 0001-01-01T00:00:00.000 | [
"Biology",
"Medicine"
] |
Combined Ostracoda and Foraminiferal Biozonation with Environment of Fatha Formation (Middle Miocene) In Different Oilfields, Southern Iraq
Abstract
Introduction
The Fatha Formation is an important stratigraphic unit in the Middle East: it is the caprock to numerous oil reservoirs in Iraq and Iran (Tuker, 1999).Busk and Mayo (1918) are the first to describe the studied formation in Iran, exactly in the province of Fars with the name Lower Fars ( Van Bellen et al., 1959).According to Jassim et al. (1984) the Fatha Formation was proposed to replace the old name in Iraq, and select of Fatah area (Hemrin and Makhul anticlines) as a type locality for it.
The Middle Miocene of Fatha Formation is one of the most widespread formations in Iraq, extending from North to South of Iraq (Al-Juboury et al., 2001;Jassim and Goff, 2006).It is characterized by rhythmic nature (Al-Juboury and McCann, 2008).Each rhythm comprises two to five lithologies namely, greenish-grey marl, limestone, gypsum (and/or anhydrite), halite, and reddish-brown mudstone (with occasional sandstone) (Tamar-Agha et al., 2015).It has uncommercial criteria for petroleum and gas accumulation in some oilfields in central Iraq (Al-Jubury and McCann, 2008).Also, the current formation considers a cup-rocks for many important oilfields in Iraq.However, in northern Iraq, it includes a significant amount of Sulphur (Aqrawi et al., 1989).This study considered is the first detailed paleontological study in the South of Iraq for Fatha Formation, most of the previous studies focused on central and Northern Iraq, especially in the outcrops, but in Basrah oilfields, the studies are rare.The important previous studies are Abdol Rassul et al. (2001), Al-Asadi, (2002), Hawramy, (2013), Hawramy and Ali (2018), Al-Hadithi and Aziz, (2019) and Khalaf and Kharruffa,(2020).They are intensive Ostracoda studies in North of Iraq, while rare studies specifically related to Foraminifera.Mahdi, (2007) and Al-Abbasi et al., (2011) studied the bivalve in Northern Iraq.In addition to several essential stratigraphical studies such as Aqrawi et al. 1989, Al-Jubouri and McCann, 2008, Tamar-Agha et al., 2015and Sissakian et al., 2016., etc.This study mainly aims to identify the important fossils groups (Foraminifera and Ostracods) and then divided them into local biozone and correlated this biozone with other studies.Also, to determine the environment of the formation, this study is first at Southern Iraq that used of core samples, in addition to identifying new species in the formation.
Materials and Methods
The study area is located in selected wells at different oil fields in the south of Iraq.These are three wells in the Nahr Umr oilfield, one well in the Zubair oilfield, one well in the West Qurna I, two wells in the North Rumaila and one well in the South Rumaila oilfield (Table .1), all of them having heavy crude oil in the Fatha Formation.The current study is located between longitude lines 47°14'5" -47°81' 24" and latitude circles 30°15'8"-31° 12' 42" N) (Fig. 1).Preparation of 100 core samples (70 samples of Nahr Umr, 30 samples of West Qurna) and 100 cutting samples (20 of North Rumaila, 20 of South Rumaila and 20 of Zubair, 20 of Nahr Umr and 20 of West Qurna) for picking to identify the critical fossils.First, dried samples, weighing about 50 g.For the picking process, the best procedure was used to extract the fossils according to Karimina et al. (2003) method, but it was modified by Al-Shawi et al. (2019).For hard limestone, hydrogen peroxide (15%) was used for 24 hours, and then wet sieving was used by a 63-micron sieve.Then, the total fossils content of samples was handpicked and counted, using flat black trays, a sable brush and a stereomicroscope.After the extraction of the fossils, they were saved in a micropaleontological slide.Two groups were identified: Foraminifera and Ostracods.Foraminifera were classified depending on Loeblich and Tappan (1988), while Ostracoda was classified according to Moore and Pitart (1961), Morkhoven (1963), andHartman andPuri (1974).
Geological Setting
The Fatha Formation was deposited in the northwest-southeast oriented basin, which extended from Syria, Iraq into Iran.Fatha Formation was associated within the megasequence of Late Eocene-Recent AP11 (Sharland et al., 2001).Megasequence AP11 is associated with the collision of Neo-Tethys terrains along with the N and E sides of the Arabian Plate, also with the opening of the Gulf of Aden and the Red Sea on the S and W sides of the Arabian Plate.The opening of the Red Sea and the Gulf of Aden was associated with thermal uplift, flood basalt, and rifting during Early-Miocene (Makris and Henke, 1992).The N and NE drift of the Arabian Plate and the closure of the Neo-Tethys led to folding and thrusting along the NE margin of the Arabian Plate.The megasequence contains many formations, the Early-Mid Miocene sequence can be divided into two-second order sequences, varied with shallow water carbonates passing up into evaporites.These sequences are the Early Miocene and the Mid Miocene.The formations previously included in the Early-Mid Miocene Sequence include the Asmari, Euphrates, Serikagni, Dhiban, Kalhur Gypsum, Ghar, Jeribe, and Fatha formations (Bellen et al., 1959;Sharland et al., 2001).
The Miocene age has an important regional maximum flooding surface, the third MFS; Ng 30 is present in the middle of the Burdigalian Stage.It presents the contact between the Jeribe and Fatha formations.While the fourth MFS; Ng 40, exists in the middle of the Serravallian Stage and is marked by the contact between the Fatha Formation and the overlying Injana Formation, this stage witnessed a major change in the depositional environments from marine to continental (Sissakian et al., 2016).Tucker and Shawket (1980) stated that the absence of angular unconformities in Iraq's Tertiary sediments (only disconformities) is thought to support the hypothesis of vertical block movements and their participation in the formation of the Fatha depositional basin.The area is flat, which buried longitudinal structures of differing sizes below the Quaternary cover, separated by large synclines.The trending fold structures have an effect on the pre-tertiary layers, which are caused by the basements and faults (Al-Atabi, 2014).
The diapiric salt produced by the Infracambrian Hormoz salt series, which is thought to be underlying areas of Iraq, is most likely responsible for these structures.Negative gravity residuals' relationship with important Zubair and Nahr Umr oilfields confirms these salts (Ditmar, 1971).Structurally, the current study is located within the Zubair Subzone, it is Iraq's greatest oil-producing area and the southernmost subdivision of the Mesopotamian Zone (Al-Kaabi et al., 2023).According to Fouad (2015), the studied area is located in the Outer Platform, which is the main part of the Mesozoic Arabian plate's passive margin of the Foreland basin, exactly at Mesopotamia Foredeep and is greatly influenced by the Alpine orogenic deformation (Fig. 2).The conformable structural arrangement of the oilfields reflects the regional effect of these salts in Iran and the Arabian Gulf countries, and their comparison is very important in hydrocarbon prospecting (Christian, 1997).(Fouad, 2015)
Results
The studied fossils focused on two types of fossils, these are Foraminifera and Ostracoda (Tables 2 and 3).
Foraminifera
Many species of foraminifera have been classified in this study based on the Loeblich and Tappan classifications (1964).42 species belong to 21 genera of Foraminifera have been identified, among which 6 genera belong to 3 families (Hauerinida, Spiroloculinidae, Miliamminidae), 14 genera to 8 families (Rotaliidae, Elphidiidae, Discorboidea, Nonionidae, Rosalinidae, Cibicididae, Cymbaloporidae Gavelinellidae) and one genus to Family Globigerinidae, the detailed data for systematic study was summarized in Table .2, with their photos (Plates 1 to 6).Several of identified species has characterized environments, such as:
Elphidium craticulatum
It is a public species that occur in the low tidal, and also existed in shallow subtidal with depths not exceeding twenty meters, it prefers the pure sand in normal marine salinity (Hayward et al., 1997). .
Ammonia beccaria
It is the most frequent species in the study area after Elphidium, and it is distinguished by its occurrence in a variety of environments, which reflects its capability to live in a wide range of conditions.Ammonia species are found from the subtidal to the outer continental shelves (Schweizer and Nikulina, 2011).Ammonia baccarii (Plate 3-1, 3-1A, 3-2, and 3-2A), A.parkinsoniana (Plate 3-4, and 3-4A) and A. tepida (Plate 3-6, and 3-6A), these species are indicative of the brackish environment, living under conditions with temperatures between 15 -30° C and depths not exceeding 50 m (Murray, 1969).
Ostracoda
Forty species related to thirty genera of Ostracoda have been identified in this study, depending on the classification of Moore andPitart (1961), Morkhoven, (1963);and Hartman and Puri, (1974), two genera belong to family Cytheridea, three genera to family Leptocytheridae, two genera belong to family Cytherellidae, four genera belong to family Cytherideidae, one genus belong to family Cytherettidae, one genus belong to family Krithidae and finally one genus belong to family Trachyleberididae, the detailed information about classification illustrated in Table 3; Plates 7 to 11.On the basis of Ostracoda that recorded from the Fatha Formation in the study areas.Many genera were identified for Ostracoda in this study which are indicator environment such as:
Callistocythere
This genus indicates shallow water environment Khalaf, 1984.Species of this genus are predominantly surface dwellers on sandy mud, sand or algae from the littoral to eulittoral zones (Manh and Tsukagoshi, 2015) (Plate 7-9).
Leptocythere
Some species of this genus typically occur in estuarine (brackish water) environments, while others are mainly found in shallow marine (littoral) environments (Morkhoven, 1963).It is abundant and widely distributed across the shallow seawaters stretching from the tropics to the subarctic zone, including the water (Shurupova and Tesakovab, 2021) (Plate 7-8).
Biozonation
Biostratigraphic zones were conducted based on the results of the identified foraminifera and Ostracoda genera depending on thin sections and picking samples, which covered the Middle Miocene in the study area.In order to summarize the results of the current study, all the biozone trends of the identified fossils have been placed within one table for all the studied wells, because most of the extending fossils are located in each well somewhat similar, so making repeated tables for each well will increase the pages of the article.Therefore, the biozones were illustrated in the Figs. 3 and 4; Table 4.It is worth mentioning, that most of the vertical extensions of some genera are missed because of the deposition of evaporite and clastic beds.After plotting the extensions for each fossil, the study suggested these biozones for foraminifera and Ostracoda.
Geological Age
There is a great deal of controversy regarding the age of the Fatha Formation, and many studies have been presented in order to determine the most appropriate age for the studied formation.The process of determining ages using planktonic foraminifera is more effective and accurate than benthonic foraminifera.Inopportunely, the current study is devoid of any planktonic genera, except one genus, therefore, the benthonic fossils were relied upon Bellen (1957) determine the Borlis melo is index fossil to the Middle Miocene.Also, there are several important index fossils to the lower and middle Miocene, such as Austrotrillina howchini, Peneroplis thomasi, Nummulites fichteli, Rotalia viennoti, Miogypsina sp.
Discussion
The current study was conducted in the Fatha Formation in the south of Iraq.Generally, the paleontological studies in Basra oilfields are rare, especially in the Fatha Formation.Most of the studies are related to formations that have petroleum accumulation.Fortunately, exploration wells were made for the Nahr Umr and West Qurna oilfields, core samples were taken for the Fatha Formation.Therefore, this study is important because it relies on confirmed depths, and those results have been strengthened and compared with cutting samples from the other oil fields.The results of the current study are different from the other, most of the foraminifera fossils are not recorded in the middle or northern Iraq, it missed the index fossils and common existence of Elphidium craticulatum, while this species is not recorded in other basins, it reflects the tidal environment, while the rest of the foraminiferal fossils were indicated on the lagoonal environment but this lagoon not isolated on marine water, it could be partially separated lagoon, because of the abundance of ostracods genera that reflect shallow marine environments.The salinity of the Fatha basin in the southern is less than that of the northern basins.The types of lithology and fossils emphasize that conclusion.
Conclusions
Forty-two species related to twenty-one genera of Foraminifera, forty Ostracoda species belonging to thirty genera were described from the Fatha Formation in the studied wells.Based on the fossil taxa found in this study, it was determined that the Fatha Formation was deposited in the lagoonal environment as the main basin but this basin is affected by tidal, subtidal and shallow marine water at the same time, especially in the lower part of the Fatha Formation, where a thin seaway connection remained open and they were in direct contact with the open sea.All genera recorded in the study area belong to benthic Foraminifera except Globigerina quinquelob belongs to planktonic Foraminifera.
The age of the studied formation is Middle Miocene depending on index fossils of ostracods, therefore, the current study determines one biozone for Foraminifera which is Elphidium craticulatum Total Range Zone.Also, one biozone for Ostracoda which is Schneiderella unispinata Assemblage zone, these biozones with age Langhian-Serravallian.
Fig. 1 .
Fig. 1.The location map for the study area, the green circles represent the studied wells at different oilfields
Fig. 3 .
Fig. 3. Biostratigraphic occurrence of identified Foraminifera for Fatha Formation in the studied wells, Southern Iraq
Fig. 4 .
Fig. 4. Biostratigraphic occurrence of identified Ostracoda for Fatha Formation in the studied wells, Southern Iraq
Table 1 .
The tops, bottoms and thickness of the Fatha Formation in studied wells, Southern Iraq
Table 2 .
The important identified Foraminifera with systematic trends that recorded in studied wells
Table 3 .
The Family, genera and species of Ostracoda that recorded in the present study
Table 4 .
Comparing of different local biozones of foraminifera with current study
Table 5 .
Comparing of different local biozones of Ostracoda with current study | 3,196.2 | 2023-04-30T00:00:00.000 | [
"Geology",
"Environmental Science"
] |
Effects of Land Finance on Resource Misallocation in Chinese Cities during 2003–2017: A Dynamic Panel Econometric Analysis
Based on the review of previous studies and the analysis of the dynamic changes of misallocation of capital and labour in China during 2003–2017, this study has adopted the Dynamic Panel Econometric Model to investigate the effects of land finance on resource misallocation at national, regional, and urban tier levels. ,e results show that resource misallocation has a significant feature of temporal dependence, while the misallocations of capital and labour in China have experienced different dynamic changes during the investigation period. In addition, land finance has aggravated resource misallocation at the national level, while spatial heterogeneity is established at regional and urban tier levels, which deserves much more attention in further studies. ,e conclusions will not only provide a relatively scientific and accurate reference for the effective allocation of production factors but also be conducive to the coordinated and sustainable development of China’s economy in the long run.
Introduction
Since the reform and opening up in 1978, China's marketization has improved significantly, while there still exists the mechanism barrier of resource misallocation so far [1]. Due to the fiscal decentralization in 1994, there was sharp decline in local revenue, while the responsibilities of local expenditure basically remained unchanged, which led to the high fiscal pressure and local governments had to seek other revenue sources [2][3][4][5]. Since land leasing revenue does not belong to the budgetary revenues and should not be shared with the central government, it has become one of the most important solutions to promote urbanization, industrialization, and modernization in China [6,7]. As a result, local governments have heavily relied on land conveyance income in recent years, which has accounted for 53.5% of the local governments' total revenue during 2000-2017 [8,9]. Hence, land-centered development model has been the most remarkable feature in China [10,11]. erefore, considerable academic attention has been raised for land finance (tudi caizheng) to check the security of China's economic achievements, because it enables local governments to capture land value and spend it on urban and rural infrastructure [12]. Although plenty of literatures have evaluated the outcomes and impacts of land finance, few have seriously addressed its impacts on resource misallocation in China, which has aroused our interest and become the initiative aspiration of this study. Under the parallel system of fiscal decentralization and administrative centralization, Chinese central government has delegated the pricing power of production factors to local governments, which eventually leads to the distortion of factor prices and the existence of resource misallocation [13,14]. As a result, the factor market in China was still incomplete and the free flow of factors was restricted until now [15]. For instance, under the traditional extensive development model, the high-efficient enterprises cannot absorb more production factors, while the low-efficient enterprises still exist, which leads to the actual output being lower than the potential optimal output and the loss of total factor productivity and results in misallocation of capital and labour eventually [16][17][18].
At present, some scholars have paid attention to the measurement and causes of resource misallocation in China by using provincial-level data [15]. However, considering that most economic activities take place in cities, the utilization of city-level data is necessary to capture the spatial heterogeneity [19,20]. In addition, the resource misallocation among different regions and urban tiers has not only led to the low-efficiency of resource allocation but also brought about challenges to the coordinated and sustainable development of China's economy in the long term; thus it is important and necessary to investigate the spatial distribution pattern of misallocation of capital and labour, respectively [21,22]. Furthermore, most empirical studies in the field of econometric analysis have adopted the OLS method, while the omission of the dynamic (temporal lag) effects may lead to biased or inconsistent results [15]. Last but not least, considering the probability of spatial heterogeneity, it is essential to test the robustness by using different studying samples [20].
Compared with the existing literature, this study has four contributions, which may shed light on further studies. Firstly, different from the former researches by using provincial-level data, this study has locked the research objects into the city-level data, which can better reveal the effects of land finance on resource misallocation in China. Secondly, this study has illustrated the spatial distribution pattern of misallocation of capital and labour, respectively, which can further clarify the dynamic changes of resource misallocation during the investigation period. irdly, this study has adopted the Dynamic Panel Econometric Model to explore the effects of land finance on misallocation of capital and labour in China, which will not only help to reveal the consequence of land finance and the temporal dependence of resource misallocation but also enrich the theories of coordinated and sustainable development. Last but not least, this study has comprehensively investigated the effects of land finance on resource misallocation at national, regional, and urban tier levels, which can provide inspiration for scientific and accurate policymaking, and it is one of the practical contributions to promote effective allocation of production factors. e remainder of this study is organized as follows. e second section has provided a brief introduction of variables selection, data source, and model specification. e dynamic changes of resource misallocation in space are illustrated in the third section. It is then followed by the empirical analysis of financial development affecting resource misallocation at national, regional, and urban tier levels.
e final part provides the main conclusions, puts forward the policy implications, and informs the research prospect. To exhibit the studying steps of the work progress intuitively, this study has adopted a flowchart diagram in a graphical way ( Figure 1).
Variables Selection, Data Source, and
Model Specification 2.1. Variables Selection 2.1.1. Resource Misallocation. "Effective allocation" refers to an ideal state of the Pareto optimality, where resource can flow freely, while resource misallocation means a deviation from the optimal allocation state correspondingly [23]. Based on and improving the study of Bai and Liu [15], this study has calculated the resource misallocation of capital and labour, respectively. Firstly, this study has estimated the marginal output of capital by using the Cobb and Douglas production function.
where Y it denotes the local GDP in city i at year t, which is converted to constant price GDP with 2003 as the baseline year by using the provincial consumer price index; A denotes the Solow value; β Ki denotes the marginal output of capital; K it denotes the capital stock, which was calculated by using the perpetual inventory method in equation (2); L it denotes the number of employed people at year end in urban entities in each city. I it denotes the total investments in fixed assets on the current level; P it denotes the provincial price index (Due to the lack of urban data, this study has replaced it by the corresponding provincial price index for investments in fixed assets) for investments in fixed assets with 2003 as the baseline year; δ denotes the depreciation rate, which has been set as 9.6% by referring to the existing literature [24]. After the treatment of logarithm, equation (1) can be converted into equation (3) as follows: Due to the differences in economic and technological levels, the marginal output may not be consistent in each city. erefore, this study has estimated the cross section coefficients of β Ki by using the least-squares dummy variable (LSDV) method. e estimation results indicate that all those cross section coefficients of β Ki are significant in statistics; thus the utilization of the least-squares dummy variable (LSDV) method is proper. Finally, the resource misallocation of capital and labour was calculated as follows: 2 Discrete Dynamics in Nature and Society where β K denotes the contribution value of weighted capital output, β L denotes the contribution value of weighted labour output, s i denotes the share of local GDP in the whole GDP, c Ki denotes the absolute distortion factor of capital price, c Li denotes the absolute distortion factor of labour price, Kmis i denotes the capital misallocation in city i, and Lmis i denotes the labour misallocation in city i. If the value of capital misallocation or labour misallocation is greater than 0, it indicates the establishment of insufficient allocation; if the value of capital misallocation or labour misallocation is less than 0, it indicates the establishment of excessive allocation; and the greater absolute value means the graver resource misallocation. Specifically, all the absolute values of capital misallocation and labour misallocation have been introduced into the regression equations.
Land Finance.
is study adopts the shares of land leasing revenue to GDP to be the proxy indicator of land finance, because the land leasing revenue can accurately measure the scale of local government's financial income from land transfer and belongs to extrabudgetary revenue or government fund revenue, which has left greater discretionary space for local governments, while the taxable revenue related to land such as farmland occupation tax belongs to the general budget income with rigid expenditure.
Control Variables.
According to the literature review and data availability, the control variables that may affect resource misallocation include government intervention, foreign direct investment, industrial structure, infrastructure, and urbanization. (1) Government intervention (GI) is measured by the shares of fiscal expenditure to GDP. (2) Foreign direct investment (FDI) is measured by the shares of the actual foreign investment to GDP, and, with the use of the exchange rate between CNY and USD of the corresponding year, this study has transformed this variable to constant price with 2003 as the baseline year. (3) Industrial structure (IS) is measured by the shares of GDP for secondary industry. (4) Infrastructure (INF) is measured by the per capita road area. (5) Urbanization rate (UR) is measured by the urbanization rate of permanent resident population. Specifically, the data description of all logarithmic variables is reported in Table 1.
Data
Source. Samples in this study are panel data from 285 prefecture-level and above cities for the period of 2003-2017, and the data sources include the China City Statistical Yearbook, the China Urban Construction Statistical Yearbook, and the China Land and Resources Almanac.
Model Specification.
In order to investigate the impact of land finance on resource misallocation, this study has constructed the basic equation by adopting the OLS Model. Specifically, for processing potential heteroscedasticity of the Discrete Dynamics in Nature and Society regression model setting, all variables were used as their logarithmic term.
where RM it denotes the resource misallocation in city i at year t, a 0 denotes the constant term, LF it denotes the land finance in city i at year t, β denotes the coefficient of land finance, x ijt denotes a series of control variables in city i at year t, c j denotes the corresponding coefficient of control variables, μ i denotes the city-fixed effect, λ t denotes the yearfixed effect, and ε it denotes the error term.
Considering the impact of economic inertia, resource misallocation may have a temporal dependence; that is, previous resource misallocation may have a significant impact on that of current period. Hence, this study has constructed the Dynamic Panel Econometric Model, which can further control the endogeneity caused by missing variables and other issues and improve the accuracy of the estimation results. e functional formula can be expressed as follows: where RM i,t−1 denotes the first-phase lag term of resource misallocation and a 1 denotes the corresponding coefficient of it.
Generally, the first-differenced GMM (DIFF-GMM) and the system GMM (SYS-GMM) are the two most widely used methods of the Dynamic Panel Econometric Model, mainly because they can control the potential endogenous and heteroskedastic problems of variables. Compared with the DIFF-GMM, the SYS-GMM can improve the validity and consistency of the estimation results, because it contains the difference estimation and the level estimation in a system simultaneously and adds the lagging difference variable as an instrumental variable in the level equation [25]. Moreover, the GMM model (including both DIFF-GMM and SYS-GMM) can be calculated by a one-step or two-step method according to its weight matrix, and the latter is not easily interfered with by heteroscedasticity. Hence, this study has conducted the two-step method to estimate the Dynamic Panel Econometric Model.
In order to meet the preconditions of no second-order sequence correlation and the exogeneity of the tool variables, this study has conducted the Arellano-Bond test (including AR(1) and AR (2)) to investigate the potential serial correlation problem of residuals and the Sargan test to check the validity of instrumental variables [15]. e null hypothesis of AR(2) means there is no second-order sequence correlation, and the null hypothesis of the Sargan test means the instrumental variables with first-order lag are effective, with no overrecognition constraint. If the P values of AR (2) and Sargan are greater than 0.1, the above two preconditions of GMM are considered to be established, and the estimation results are consistent and reliable.
The Dynamic Changes of Resource Misallocation in Space
To illustrate the dynamic change of the spatial distribution pattern of capital misallocation in an intuitive way, the capital misallocations in 2003 and 2017 are presented in Figure 2. As Figure 2 illustrates, the spatial distribution pattern of market misallocation is relatively stable during the investigation period, and most of the insufficient allocation cities locate in the coastal areas, especially in the Bohai Sea Economic Circle, the Yangtze River Delta, and the Pearl River Delta. Due to the high level of marketization in the above-mentioned areas, the capital price is relatively higher than the national average level, so the actual input of capital is lower than the theoretical value of effective allocation, which indicates insufficient allocation of capital. However, most cities are in the state of excessive allocation of capital, which indicates the utilization efficiency of capital in those cities is relatively low, so it is necessary and important to improve the utilization efficiency of capital in those cities through deepening the reform of market mechanism. Correspondingly, to illustrate the dynamic change of the spatial distribution pattern of labour misallocation in an intuitive way, the labour misallocations in 2003 and 2017 are presented in Figure 3. As Figure 3 illustrates, the spatial distribution pattern of labour misallocation has changed remarkably during the investigation period; many cities in the central and western regions have changed from excessive allocation to insufficient allocation, while some cities in the Yangtze River Delta and the Pearl River Delta have changed from insufficient allocation to excessive allocation, which indicates that the migrants from west to east have not only to some extent optimized the labour allocation pattern but also caused the emerging issue of population congestion in the east and the rapid loss of population in the central and Discrete Dynamics in Nature and Society western regions. erefore, the improvement of the labour allocation efficiency still has a long way to go.
National Empirical Analysis.
is study has adopted Stata 15.0 to estimate the OLS Model in equation (1) and the Dynamic Panel Econometric Model in equation (2), and the results are reported in Table 2. As shown in Table 3, AR(1) is significant at 1% and AR(2) and the Sargan statistics are not significant at 10%, which indicates that the first-order sequence correlation exists, while the second-order sequence correlation does not exist; instrumental variables with firstorder lag are effective, with no overrecognition constraint. Moreover, the results based on the OLS Model are not very robust, and the significance of coefficients is relatively poor, which may be due to the endogenous problems and highlights the superiority of the Dynamic Panel Econometric Model [15]. As mentioned earlier, this study has selected the results based on the SYS-GMM model for discussion.
No matter the dependent variable is capital misallocation or labour misallocation, the effects of the first-phase lag of resource misallocation, land finance, government intervention, and industrial structure on resource misallocation are significantly positive, which indicates that the abovementioned variables have aggravated the degree of misallocation of capital and labour at the national level. Under the extensive development mode dominated by land finance and secondary industry, although the rapid economic growth has been achieved through government intervention, the resource misallocation has also intensified and solidified over time, which will eventually hinder the improvement of economic efficiency and the realization of sustainable development in the long run.
In addition, the effect of FDI on capital misallocation is negative but not significant, while the effect of FDI on labour misallocation is significantly negative, which indicates that FDI has to some extent hindered labour misallocation, while there is no clear evidence that FDI has impacted the capital misallocation at the national level. As previous literature notes, the inflow of FDI mainly focuses on traditional resource-intensive and labour-intensive industries, which has caused high-pollution and high-energy consumption problems, but it promotes labour allocation by providing plenty of employment opportunities. Moreover, the effects of infrastructure on capital misallocation and labour misallocation are negative and positive, respectively, but not significant, implying that there is no clear evidence that infrastructure has impacted the resource misallocation at the national level. Furthermore, the effect of urbanization rate on capital misallocation is significantly positive, while the effect of urbanization rate on capital misallocation is negative but not significant, which indicates that urbanization rate has aggravated capital misallocation but tends to hinder the labour misallocation at the national level. ere is no doubt that urbanization rate has promoted the urban-rural migrant, while the dual separation of household registration system between urban and rural has to some extent declined the efficiency of labour allocation. In addition, the increasing economic gap between urban and rural areas caused by urbanization rate has also declined the efficiency of capital allocation.
Heterogeneity Analysis of Region and Urban Tier.
Due to the comprehensive influence of geographical, economical, and institutional factors, the effects of land finance on resource misallocation may present a typical spatial differentiation pattern, and the spatial distribution patterns of misallocation of capital and labour in 2003 and 2017 also show a significant spatial heterogeneity (as indicated in Figures 2 and 3). erefore, this study will also examine the spatial heterogeneity of the effects of land finance on resource misallocation from the perspectives of regions (Based on the regional division criterion noted by the State Council, the research sample has been split into three groups: Eastern, Central, and Western, and each region includes 101, 109, and 75 cities, respectively) and urban tiers (Based on administrative and economic development levels noted by the National Bureau of Statistics of the People's Republic of China, the research sample has been classified into first-, second-, and third-tier cities. Specifically, the first-tier cities cover the 4 most developed metropolitan areas: Beijing, Shanghai, Shenzhen, and Guangzhou, the second-tier cities cover 31 cities, including most provincial capitals and a few highly developed prefecture-level cities, and the third-tier cities cover all the other 250 cities.), and the results based on the SYS-GMM Model are reported in Tables 2 and 4, respectively.
As shown in Table 4, whether the dependent variable is capital misallocation or labour misallocation, the effects of the first-phase lag of resource misallocation on that of current period are significantly positive among three regions, which indicates that previous resource misallocations have aggravated the degree of resource misallocation of current period at the regional level. In addition, the effect of land finance on capital misallocation is significantly negative in the central region, while the other five coefficients of land finance are significantly positive, which indicates that land finance has significantly aggravated resource misallocation in the eastern and western regions but presents a spatial differentiation in the central region. As mentioned earlier, land finance has been criticized for distorting resource allocation for a long time. Compared with the eastern and western regions, the central region has no superiority in geographical location and policy support, so land finance has made up for the gap and promotes the efficiency of capital allocation. However, the effect of partial improvement in capital allocation in the central region cannot offset its negative effect on resource allocation at the national level, so the distortion effect of land finance on resource allocation still needs our full attention in the long run. As shown in Table 2, whether the dependent variable is capital misallocation or labour misallocation, the effects of the first-phase lag of resource misallocation on that of current period are significantly positive, which indicates that previous resource misallocations have aggravated the degree of resource misallocation of current period at the urban tier level. In addition, the effects of land finance on capital misallocation in the first-and second-tier cities and labour misallocation in the third-tier cities are significantly positive, which indicates that land finance has aggravated the misallocation of capital or labour in the corresponding cities. However, the effects of land finance on labour misallocation in the first-and second-tier cities and capital misallocation in the third-tier cities are positive and negative, respectively, but both are not significant in statistics. In the process of urbanization rate driven by land finance, the rapid increase of capital price has solidified the insufficient allocation of capital in the first-and second-tier cities, while the excessive Notes: * * * , * * , and * denote a significance level of 1%, 5%, and 10%, respectively; standard errors are reported in parentheses; the results of AR(1), AR(2), and Sargan statistics are the corresponding P value; Kmis and Lmis denote capital misallocation and labour misallocation, respectively. Discrete Dynamics in Nature and Society 7 outflow of population has also aggravated the insufficient allocation of labour in the third-tier cities (as indicated in Figures 2 and 3).
Robustness Test.
Due to the distortion of factor markets in China, not only does the actual remuneration of production factors such as capital and labour deviate from the due value, but also the optimal effective resource allocation cannot be achieved with the market mechanism; thus the level of resource distortion can also be utilized to measure the degree of resource misallocation [15]. In order to further test the robustness of the above results, this study intends to replace capital misallocation (Kmis) and labour misallocation (Lmis) with capital distortion (Kdis) and labour distortion (Ldis), respectively.
where r denotes the interest rate, which has been set as 10% by referring to the study of [26]; w i denotes the average wage of urban workers, and it is converted into the price GDP index in 2003. If the value of capital distortion or labour distortion is greater than 0, it indicates the establishment of excessive allocation; if the value of capital distortion or labour distortion is less than 0, it indicates the establishment of insufficient allocation; and the greater absolute value means the graver resource distortion. Specifically, all the absolute values of capital distortion and labour distortion have been introduced into the regression equation. Based on the new dependent variables, the Dynamic Panel Econometric Model is estimated using the SYS-GMM method, and the robustness results are reported in Table 5. As shown in Table 5, after replacing resource misallocation with resource distortion, the robustness results are basically consistent with the former, so the conclusions of this study are robust and reliable.
Conclusions.
Based on the review of previous studies and the analysis of the dynamic changes of resource misallocation in China during 2003-2017, this study has adopted the Dynamic Panel Econometric Model to investigate the effects of land finance on resource misallocation at national, regional, and urban tier levels. e main conclusions of this study are as follows. Firstly, the misallocations of capital and labour in China have experienced different dynamic changes during the investigation period, because the spatial distribution pattern of capital misallocation is relatively stable, while the spatial distribution pattern of labour misallocation has changed remarkably between 2003 and 2017. In addition, the first-phase lag of resource misallocation has significantly aggravated the current period of resource misallocation at national, regional, and urban tier levels, which provides a robust evidence for the existence of temporal dependence for resource misallocation in China. Moreover, the significantly positive effect of land finance affecting resource misallocation has been proven at the national level, while spatial heterogeneity is established at regional and urban tier levels, because land finance has hindered capital misallocation in the central region, and the effects of land finance on labour misallocation in the firstand second-tier cities and capital misallocation in the thirdtier cities are not significant in statistics. Furthermore, government intervention and industrial structure have also aggravated resource misallocation at the national level; FDI tends to hinder resource misallocation at the national level (the coefficient of FDI on labour misallocation is significantly negative, while the coefficient of FDI on capital misallocation is negative but not significant in statistics), while the effects of infrastructure and urbanization rate on misallocation of capital and labour are not robust at the national level (the coefficient signs of infrastructure and urbanization rate on misallocation of capital and labour are completely different); thus the correction of resource misallocation requires a comprehensive consideration of the impacts of influencing factors.
Policy Implications.
Correspondingly, the policy implications of the above conclusions include several aspects. Firstly, in order to get rid of the temporal dependence of resource misallocation, the formulation of relevant policies should be suited to local conditions. For example, the Yangtze River Delta and the Pearl River Delta have faced dual constraints of insufficient capital allocation and excessive labour allocation at present. In order to improve the efficiency of resource allocation in the above-mentioned areas, not only should the capital price be declined to a reasonable range, but also the outflow of low-end labour should be properly guided in the process of industrial upgrading and new urbanization. Secondly, the dependence of local governments on land finance should be reduced through promoting the transformation from one-time land leasing revenue to real estate tax, so as to alleviate the vertical financial imbalance and the vicious competitions among local governments. irdly, the construction of marketoriented mechanism should be promoted, unreasonable government intervention should be reduced, and market segmentation and narrow protectionism between different regions and urban tiers should be broken down, so as to promote the free flow of production factors and improve the efficiency of resource misallocation in the long run. Last but not least, the economic growth pattern from extensive to intensive should be adjusted step by step, such as improving the threshold and quality of FDI, promoting industrial upgrading, and optimizing the rational spatial distribution of capital, labour, and other resources.
Research Prospects.
Although this study for the first time analyzes the effects of land finance on resource allocation in China at national, regional, and urban tier levels, two limitations still deserve being paid attention, and they may become the possible research directions for further studies. First, due to the limitations of data availability, we adopt the shares of land leasing revenue to GDP to act as the proxy of land finance, while the impact of land finance from different uses (i.e., industrial, commercial, and housing) on resource misallocation may be not consistent, which should be explored in further studies [5]. Moreover, this study has analyzed the temporal dependence of resource misallocation by introducing the Dynamic Panel Econometric Model, while the spatial dependence of resource misallocation has not been fully analyzed; thus the Dynamic Spatial Econometric Model, which can make up for this research gap, may be adopted in further studies [27]. erefore, when a wider range of indicators and highly advanced techniques are available, the effects of land finance on resource misallocation in China still deserve further in-depth studies.
Data Availability
e data used to support the findings of this study are available from the corresponding author upon request.
Disclosure
Any remaining errors in the paper are the responsibility of the authors.
Conflicts of Interest
e authors declare that they have no conflicts of interest. Notes: * * * denotes a significance level of 1%; standard errors are reported in parentheses; the results of AR(1), AR (2), and Sargan statistics are the corresponding P value; RD, Kdis, and Ldis denote resource distortion, capital distortion, and labour distortion, respectively.
Discrete Dynamics in Nature and Society 9 | 6,552 | 2020-03-19T00:00:00.000 | [
"Economics"
] |
The Distribution of Superconductivity Near a Magnetic Barrier
We consider the Ginzburg–Landau functional, defined on a two-dimensional simply connected domain with smooth boundary, in the situation when the applied magnetic field is piecewise constant with a jump discontinuity along a smooth curve. In the regime of large Ginzburg–Landau parameter and strong magnetic field, we study the concentration of the minimizing configurations along this discontinuity by computing the energy of the minimizers and their weak limit in the sense of distributions.
Introduction
1.1. Motivation. The Ginzburg-Landau theory, introduced in [LG50], is a phenomenological macroscopic model describing the response of a superconducting sample to an external magnetic field, when the sample is close to its critical temperature T c . The phenomenological quantities associated with a superconductor are the order parameter ψ and the magnetic potential A, where |ψ| 2 measures the density of the superconducting Cooper pairs and curl A represents the induced magnetic field in the sample.
In this paper, the superconducting sample is an infinite cylindrical domain subjected to a magnetic field with a direction parallel to the axis of the cylinder. For this specific geometry, it is enough to consider the horizontal cross section of the sample, ⊂ R 2 . The phenomenological configuration (ψ, A) is then defined on the domain .
The study of the Ginzburg-Landau model in the case of a uniform or a smooth nonuniform applied magnetic field has been the focus of much attention in the literature. We refer to the two monographs [FH10,SS07] for the uniform magnetic field case. Smooth magnetic fields are the subject of the papers [Att15a,Att15b,HK15, LP99,PK02]. Given the current interest in magnetic steps for various physical systems, we focus on the case where the applied magnetic field is a step function, which is not covered in the aforementioned papers.
Nonhomogeneous magnetic fields have been the focus of great amount of research. Current fabrication techniques allow the creation of such magnetic fields [FLBP94,STH+94,GGD+97], something that opens new paths in quantum physics and possible applications [RP98,JBY+97,MJR97]. Indeed, these magnetic fields appear in models involved in nanophysics such as in quantum transport in 2DEG (bidimensional electron gas) (see [PM93,RP00] and references therein) and in the Ginzburg-Landau model in superconductivity [SJST69]. More recently, piecewise constant magnetic fields are considered in the analysis of transport properties in graphene [GDMH+08,ORK+08]. Such magnetic fields are interesting because they induce snake states, carriers of edge currents flowing in the interface separating the distinct values of the magnetic fieldthe magnetic barrier (for instance see [HPRS16,HS15,DHS14,HS08,RP00,PM93]). While such edge currents have been discussed for linear problems in earlier works, the main contribution of this manuscript lies in establishing their existence in the context of the non-linear Ginzburg-Landau functional in superconductivity, by examining the presence of superconductivity along the magnetic barrier. Our configuration is illustrated in Fig. 1.
In an earlier contribution [AK16], we explored the influence of a step magnetic field on the distribution of bulk superconductivity, which highlighted the regime where an edge current might occur near the magnetic barrier. In this contribution, we will demonstrate the existence of such a current by providing examples where superconductivity concentrates at the interface separating the distinct values of the magnetic field.
The functional and the mathematical set-up.
We assume that the domain is open in R 2 , bounded, and simply connected. The Ginzburg-Landau (GL) free energy is given by the functional with ψ ∈ H 1 ( ; C) and A = (A 1 , A 2 ) ∈ H 1 ( ; R 2 ). Here, κ > 0 is a large GL parameter, the function B 0 : → [−1, 1] is the profile of the applied magnetic field, and H > 0 is the intensity of this applied magnetic field. The parameter κ depends on the temperature and the type of the material. It is a physical characteristic scale of the sample, the inverse of the penetration depth, and it measures the size of vortex cores (which is proportional to κ −1 , in some typical situations dependent on the strength of the applied magnetic field). Vortex cores are narrow regions in the sample, which corresponds to κ being a large parameter. That is the main reason behind our analysis of the asymptotic regime κ → +∞, following many early contributions addressing this asymptotic regime (see e.g. [SS07]). We work under the following assumptions on the domain and the magnetic field B 0 , which are quite generic as revealed from the illustration in Fig. 2. = ∂ 1 ∩ ∂ 2 is the union of a finite number of disjoint simple smooth curves { k } k∈K ; we will refer to as the magnetic barrier . (5) = ( 1 ∪ 2 ∪ ) • and ∂ is smooth.
∩ ∂ is either empty or finite . The ground state of the superconductor describes its behaviour at equilibrium. It is obtained by minimizing the GL functional in (1.1) with respect to (ψ, A). The corresponding energy is called the ground state energy, denoted by E g.st (κ, H ), where E g.st (κ, H ) = inf E κ,H (ψ, A) : (ψ, A) ∈ H 1 ( ; C) × H 1 ( ; R 2 ) .
One may restrict the minimization of the GL functional to the space H 1 ( ; C)× H 1 div ( ) where Indeed, the functional in (1.1) enjoys the property of gauge invariance. 1 Consequently, the ground state energy can be written as follows (see [ This restriction allows us to make profit from some well-known regularity properties of vector fields in H 1 div ( ) (see [AK16,Appendix B]).
1.3. Some earlier results for uniform magnetic fields. The value of the ground state energy E g.st (κ, H ) depends on κ and H in a non-trivial fashion. The physical explanation is that a superconductor undergoes phase transitions as the intensity of the applied magnetic field varies.
To illustrate the dependence on the intensity of the applied magnetic field, we assume that κ is large and H = bκ, for some fixed parameter b > 0. Such magnetic field strengths are considered in many papers (for instance see [AH07,LP99,Pan02,SS03]).
Assuming that the applied magnetic field is uniform, which corresponds to taking B 0 = 1 in (1.1), the following scenario takes place. If b > −1 0 , where 0 ≈ 0.59 is a universal constant defined in (2.5) below, the only minimizers of the GL functional are the trivial states (0,F), where curlF = 1 (see [GP99,LP99]). This corresponds in Physics to the destruction of superconductivity when the sample is submitted to a large external magnetic field, and occurs when the intensity H crosses a specific threshold value, the so-called third critical field, denoted by H C 3 .
Another well-known critical field is the second critical field H C 2 , which is much harder to define. When H < H C 2 , superconductivity is uniformly distributed in the interior of the sample (see [SS03]). This is the bulk superconductivity regime. When H C 2 < H < H C 3 , the surface superconductivity regime occurs: superconductivity disappears from the interior and is localized in a thin layer near the boundary of the sample (see [AH07,HFPS11,Pan02,CR14]). The transition from surface to bulk superconductivity takes place when H varies around the critical value κ, which we informally take as the definition of H C 2 (see [FK11]).
One more critical field left is H C 1 . It marks the transition from the pure superconducting phase to the phase with vortices. We refer to [SS07] for its definition.
Expected behaviour under magnetic steps.
Let us return to the case where the magnetic field is a step function as in Assumption 1.2. At some stage, the expected behaviour of the superconductor in question deviates from the one submitted to a uniform magnetic field. Recently, this case was considered in [AK16] and the following was obtained. Suppose that H = bκ and κ is large. If b < 1/|a| then bulk superconductivity persists ; if b > 1/|a| then superconductivity disappears in the bulk of 1 and 2 , and may nucleate in thin layers near ∪ ∂ (see Assumption 1.1 and Fig. 1). The present contribution affirms the presence of superconductivity in the vicinity of when b is greater than, but close to the value 1/|a|, for some negative values of a. The precise statements are given in Theorems 1.7 and 1.11 below.
The aforementioned behaviour of the superconductor in presence of magnetic steps is consistent with the existing literature about the electron motion near the magnetic barrier at which the strength and/or the sign of the magnetic fields change (for instance see [HPRS16,HS15,DHS14,RP00,Iwa85]). Particularly, the case where a ∈ [−1, 0) is called the trapping magnetic step (see [HPRS16]), where the discontinuous magnetic field may create supercurrents (snake orbits) flowing along the discontinuity edge. On the other hand, such supercurrents do not seem detectable in the case when a ∈ (0, 1), which is called the non-trapping magnetic step. However, the approach was generally spectral where some properties of relevant linear models were analysed (see [HPRS16,HS15,Iwa85,RP00]), and no estimates for the non-linear GL energy in (1.1) were established in these cases.
The contribution of this paper together with [AK16] provide such estimates. Particularly in the case when a ∈ [−1, 0) and b > 1/|a|, Theorems 1.7 and 1.11 below establish global and local asymptotic estimates for the ground state energy E g.st (κ, H ), and the L 4 -norm of the minimizing order parameter. These theorems assert the nucleation of superconductivity near the magnetic barrier (and the surface ∂ ) when b crosses the threshold value 1/|a|.
Remark 1.4. Our study does not cover the potentially interesting case a = 0, which deserves to be studied independently in a future work. This case, referred to as magnetic wall, was considered in [RP00,HPRS16].
Remark 1.5. Even though the case a ∈ (0, 1) is included in Assumption 1.3, it will not be central in our study (the reader may notice this in the majority of our theorems statements). The reason is that, our main concern is to analyse the interesting phenomenon happening when bulk superconductivity is only restricted to a narrow neighbourhood of the magnetic edge , and this only occurs when the values of the two magnetic fields interacting near are of opposite signs, that is when a ∈ [−1, 0). This can be seen through the trivial cases in Section 3.2, and is consistent with the aforementioned literature findings (non-trapping magnetic steps). Moreover, the case b < 1/|a| is treated previously in [AK16] and corresponds to the bulk regime.
The statements of the main theorems involve two non-decreasing continuous functions e a : |a| −1 , +∞) → (−∞, 0] and E surf : [1, +∞) → (−∞, 0] , respectively defined in (3.5) and (6.27) below. The energy E surf has been studied in many papers (for instance see [CR14,FKP13,FK11,HFPS11,AH07,Pan02]). We will refer to E surf as the surface energy. The function e a is constructed in this paper, and we will refer to it as the barrier energy. Remark 1.6. It is worthy of mention that e a (b) vanishes if and only if • a ∈ (0, 1) ; or • a ∈ [−1, 0) and b ≥ 1/β a , where β a is defined in (2.11) below and satisfies β a ∈ (0, |a| ) (see Theorem 2.6).
The surface energy E surf (b) vanishes if and only if b ≥ −1 0 , where 0 is the constant defined in (2.5).
The main contribution of this paper is summarized in Theorems 1.7 and 1.11 below.
Theorem 1.7 [Global asymptotics] For all a ∈ [−1, 1)\{0} and b > 1/|a|, the ground state energy E g.st (κ, H ) in (1.3) satisfies, when H = bκ, (1.7) Remark 1.8. In the asymptotics displayed in Theorem 1.7, the term | |b −1/2 e a (b) corresponds to the energy contribution of the magnetic barrier. The rest of the terms indicate the energy contributions of the surface of the sample. In light of Remark 1.6, the critical value b = β −1 a marks the transition between the superconducting and normal states along .
Remark 1.9. The edge creates vertices in the case where ∩ ∂ = ∅ (see Fig. 2) which may have non-trivial energy contributions hidden in the remainder term in (1.6). This case alters the breakdown of superconductivity too and shares some similarities with corner domains [BNF07,CG17,HK18,Ass].
Remark 1.10. Theorem 1.7 does not cover the case when the intensity of the magnetic field satisfies b = 1/|a|. However, we expect that some additional bulk terms will contribute to the estimate of the energy in this case, by analogy with [FK11].
Our next result, Theorem 1.11 below, describes the local behaviour of the minimizing order parameter ψ, thereby enhancing the statement in Theorem 1.7. We define the following distribution in R 2 , Here ds and ds denote the arc-length measures on and ∂ respectively.
Similarly as in [CR16b], we expect that the second correction term in the asymptotics in (1.8) will depend on the surface geometry of and ∂ , and will require a restrictive assumption on the way the support of the test function ϕ meets the edges and ∂ .
Discussion of the main results.
We will discuss the results in Theorems 1.7 and 1.11, in the interesting case where the magnetic barrier intersects the boundary of . Hence, we will assume that ∂ j ∩∂ = ∅ for j ∈ {1, 2}. When this condition is violated, the discussion below can be adjusted easily.
The following observations mainly rely on Remark 1.6 and the order of the values |a| 0 , 0 , β a , and |a|.
• For a = −1, we have β a = 0 < |a| [see (2.26)]. Consequently, in light of Remark 1.6: − If 1 < b < −1 0 , then the surface of the sample carries superconductivity and the entire bulk is in a normal state except for the region near the magnetic barrier (see Fig. 3). Moreover, the energy contributions of the magnetic barrier and the surface of the sample are of the same order and described by the surface energy, since in this case e a (b) = E surf (b), see Remark 3.12. This behaviour is remarkably distinct from the case of a uniform applied magnetic field. − If b ≥ −1 0 , then all the aforementioned energy contributions vanish, E L a (b) = 0. Fig. 4. Superconductivity distribution in the set subjected to a magnetic field B 0 , in the regime where a ∈ (− 0 , 0), H = bκ, and respectively |a| −1 < b < β −1 a and β −1 a ≤ b < |a| −1 −1 0 . The white regions are in a normal state, while the grey regions carry superconductivity • For a ∈ (−1, 0), comparing the values β a , 0 and |a| is more subtle. In (2.18), (2.23) and Theorem 2.6 below, we show that Moreover, numerical results about the variation of β a with respect to a show that β a is strictly decreasing for a ∈ [−1, 0) (see Fig. 5). 2 Having β −1 = 0 [see (2.26)], this suggests that β a < 0 for a ∈ (−1, 0). However, such a result is not rigorously established yet. With (1.9) in hand, Theorem 1.11 and Remark 1.6 indicate the following behaviour for a ∈ (− 0 , 0) and b > |a| −1 : − The part of the sample's surface near ∂ 1 ∩∂ does not carry superconductivity. − If |a| −1 < b < β −1 a , then surface superconductivity is confined to the part of the surface near ∂ 2 ∩ ∂ . At the same time, superconductivity is observed along the magnetic barrier (see Fig. 4). This behaviour is interesting for two reasons. Firstly, it demonstrates the existence of the edge current along the magnetic barrier, which is consistent with physics (see [HPRS16]). Secondly, it marks a distinct behaviour from the one known for uniform applied magnetic fields, in which case the whole surface carries superconductivity evenly (see for instance [HK17,FKP13,Pan02]). − If β −1 a ≤ b < |a| −1 −1 0 , then superconductivity only survives along ∂ 2 ∩ ∂ (see Fig. 4). Our results then display the strength of the applied magnetic field responsible for the breakdown of the edge current along the barrier. − If b ≥ |a| −1 −1 0 , then all energy contributions in Theorem 1.7 disappear. • For a ∈ (0, 1), β a = a [see (2.19)]. When b > a −1 , Theorem 1.11 reveals the absence of superconductivity along the magnetic barrier. As for the distribution of superconductivity along the surface of the sample, we distinguish between two regimes: Regime 1, a ∈ (0, 0 ] The part of the boundary, ∂ 1 ∩ ∂ , does not carry superconductivity. It remains to inspect the energy contribution of ∂ 2 ∩ ∂ . In that respect: 0 , then the entire surface of the sample is in a superconducting state, though the superconductivity distribution is not uniform.
0 , then all the energy contributions in Theorem 1.7 vanish.
• The letter C denotes a positive constant whose value may change from one formula to another. Unless otherwise stated, the constant C depends on the value of a and the domain , and is independent of κ and H . • Let a(κ) and b(κ) be two positive functions. We write a(κ) ≈ b(κ), if there exist constants κ 0 , C 1 and C 2 such that for all κ ≥ κ 0 , C 1 a(κ) ≤ b(κ) ≤ C 2 a(κ). • The quantity o(1) indicates a function of κ, defined by universal quantities, the domain , given functions, etc., and such that |o(1)| → 0 as κ → +∞. Any expression o(1) is independent of the minimizer (ψ, A) of (1.1). Similarly, O(1) indicates a function of κ, absolutely bounded by a constant independent of the minimizers of (1.1). • Let n ∈ N, p ∈ N, N ∈ N, α ∈ (0, 1), K ⊂ R N be an open set. We use the following Hölder space • Let n ∈ N, I ⊂ R be an open interval. We use the space (1.10) 1.7. Heuristics of the proofs. In this section, we present our approach in an informal way, not organized according to the order of appearance of various effective models in the paper, but following a scheme highlighting some important links between these models. We are mainly interested in examining the behaviour of the minimizer of the GL energy in (1.1) near the magnetic barrier . Working under Assumption 1.3, one can use the (Agmon) decay estimates established in [AK16] (see Theorem 2.4) to neglect the bulk energy contribution and restrict the study near the edge and the boundary ∂ .
As the applied magnetic field behaves uniformly near ∂ \ , the study of surface superconductivity is the same as that in the case of uniform fields, frequently encountered in the literature. Therefore in Sect. 6.2, the reader is referred to the existing literature.
The rest of the paper mainly focuses on the study of superconductivity in a tubular neighbourhood of . In Sect. 6, we decompose this neighbourhood into small cells, each of size O(κ −3/2 ), in order to establish the local asymptotics of the minimizer as well as the corresponding energy estimates as κ → +∞. This decomposition aims to reveal the existence of superconductivity in each of these small patches, in a certain regime of the applied magnetic field (i.e. for certain values of the parameter b, as in Assumption 1.3).
Using Frenet coordinates, cut-off functions, a suitable gauge transformation allowing to replace the induced magnetic field A by the applied magnetic field F (curl F = B 0 , see Lemma 2.2), together with a rescaling argument (Sects. 4-6), we may reduce the study of the GL energy in (1.1) into that of the 2D-effective energy G a,b,R defined on Here, x 1 and x 2 are respectively the tangential and the normal coordinates with respect to the magnetic edge. We also consider the ground state energy Hence, we launch an investigation of the new energy model, G a,b,R , with a step magnetic field. It is standard to begin by exploring the linear part of this energy, which leads us to the following linear magnetic Schrödinger operator defined in the plane (Sect. 2.4) The ground state energy corresponding to this operator is denoted by β a . One can easily see that the non-triviality of the energy G a,b,R minimizer (that is when g a (b, R) = 0) is equivalent to 1/|a| < b < 1/β a (under Assumption 1.3). Therefore, to ensure the non-emptiness of the interval (1/|a|, 1/β a ), thus the non-triviality of our study, we shall compare the values |a| and β a . In order to get the aforementioned comparison (of |a| and β a ), we use partial Fourier transform to perform a new reduction, this time of the 2D-operator L a to a 1D-effective operator in R, h a [ξ ], parametrized by ξ ∈ R (Sect. 2.4): and with a lowest eigenvalue denoted by μ a (ξ ). The ground state energy β a satisfies Next, we provide information about this infimum by collecting some spectral properties of the operator h a [ξ ]. This 1D-operator has already been considered in the literature, and some spectral information was established experimentally and rigorously in earlier works (for instance see [HPRS16,HS15,DHS14,RP00,Iwa85]). However, the approach in the aforementioned references was rather complicated, since all energy levels were examined. In addition, some of the spectral results we need in our study were not explicitly stated in these references. Therefore, for the sake of clarity and since we are only interested in the lowest eigenvalue, we opt to use a direct approach to provide such results (see Sect. 2.4). Moreover, our results slightly improve those of the aforementioned works (see Theorem 2.6). Our proofs call some spectral data of well-known effective models in the half-line (Sect. 2.3). From Sect. 2.4, we collect the following useful properties: • β a = a, for a ∈ (0, 1), Here, 0 is the value in (2.5). Now, the comparison of β a and a is in hand and a consequence of this is the following observation: We highlight the contribution of Theorem 2.6 in obtaining the latter property. This gives us the desired information about the values of a and b for which our study is non-trivial. Subsequently, we neglect the case a ∈ (0, 1) and proceed under the more restrictive assumption The main results about the reduced energy G a,b,R are stated in Theorem 3.1. In particular, this theorem introduces the limiting energy e a (b) appearing in our main theorems (Theorems 1.7 and 1.11): R .
In addition, the bounds in the last item of this theorem are important to control the error terms arising while establishing the energy and minimizer estimates in Sect. 6. The proof of Theorem 3.1 occupies Sect. 3. It relies on the approach in [Pan02,FKP13] in the case of uniform fields, with some additional technical difficulties caused by the discontinuity of our magnetic field. For instance, we step carefully while establishing some regularity properties needed in proving the existence of G a,b,R minimizer (see Lemmas B.3-B.6).
Finally, inspired by the recent work of Correggi-Rougerie [CR14] studying the surface superconductivity in the case of constant fields (more precisely by their energy lower bound proof), we interestingly prove that the 2D-limiting energy e a (b) is nothing but a one dimensional energy, E 1D a,b , defined in Sect. 3.6. This reduction serves in providing a more explicit definition of the enregy e a (b) and suggests that the profile of the minimizing order parameter ψ near the edge is as follows (up to a gauge transformation): where ( f 0 ,ξ 0 ) is a minimizing couple of the energy E 1D a,b,ξ defined in (3.16), s is the tangential distance along and t is the normal distance to . Such a profile suggests that the supercurrent along the edge , j = Im ψ(∇ − iκ H A)ψ , behaves to leading order as bκξ 0 f 0 (0) 2 τ , with τ being a unit tangent vector along the edge .
The rigorous derivation of (1.11) is not given in the present paper, but we expect that the analysis in this paper paves the way to a future investigation of the profile of ψ displayed in (1.11). In that respect, a special attention is required due to the nonhomogeneity of the order parameter ψ as revealed in Theorem 1.11; indeed ψ seems to have different profiles along and the parts of ∂ .
One remarkable aspect of our proofs is that we have not used the a priori elliptic L ∞ -estimate (∇ − iκ H A)ψ ∞ ≤ Cκ. Such estimate is not known to hold in our case of discontinuous magnetic field B 0 . Instead, we used the easy energy estimate (∇ − iκ H A)ψ 2 ≤ Cκ and the regularity of the curl-div system (see Theorem 2.3). This also spares us the complex derivation of the L ∞ -estimate (see [FH10,Chapter 11]).
1.8. Organization of the paper. Section 2 presents some preliminaries, particularly, a priori estimates, exponential decay results, and a linear 2D-operator with a step magnetic field. Theorem 2.6 is an improvement of a result in [HPRS16]. Section 3 introduces the 2D-reduced GL energy along with the barrier energy e a (·). In Sect. 4, we present the Frenet coordinates defined in a tubular neighbourhood of the curve . These coordinates are frequently used in the context of surface superconductivity (see [FH10,Appendix F]). In Sect. 5, we introduce a reference energy that describes the local behaviour of the full GL energy in (1.1). Section 6 is devoted for the analysis of the local behaviour of the minimizing order parameter near the edge . Also, we recall well-known results about the local behaviour of the order parameter near the surface ∂ . Finally, collecting all the estimates established in Sect. 6, we complete the proof of our main theorems (Theorems 1.7 and 1.11 above).
A priori estimates.
We present some celebrated estimates needed in the sequel to control the various errors arising while estimating the energy in (1.1). Recall the magnetic field B 0 introduced in Assumption 1.2. In the next lemma, we will fix the gauge for the magnetic potential generating B 0 (see [ We collect below some useful estimates whose proofs are given in [AK16, Theorem 4.2]. Theorem 2.3. Let α ∈ (0, 1) be a constant. Suppose that the conditions in Assumptions 1.1 and 1.2 hold. There exists a constant C > 0 (dependent on b) such that if (1.5) is satisfied and (ψ, 2.2. Exponential decay of the order parameter. The following theorem displays a regime for the intensity of the applied magnetic field where the order parameter and the GL energy are exponentially small in the bulk of the domains 1 and 2 .
A family of Sturm-Liouville operators on L
In this section, we will briefly present some spectral properties of the self-adjoint realization on L 2 (R + ) of the Sturm-Liouville operator: where B 2 (R + ) is the space introduced in (1.10), and ξ and γ are two real parameters. Denote by μ(γ , ξ ) the lowest eigenvalue of the operator The particular case where γ = 0 corresponds to the Neumann realization, and we use the following notation, and 2.4. An operator with a step magnetic field. Let a ∈ [−1, 1)\{0}. We consider the magnetic potential A 0 defined by which satisfies curl A 0 = 1. We define the step function σ as follows. For We introduce the self-adjoint magnetic Hamiltonian The ground state energy of the operator L a is denoted by (2.11) Since the Hamiltonian L a is invariant with respect to translations in the x 1 -direction then, by using the partial Fourier transform with respect to the x 1 -variable, we can reduce L a to a family of Shrödinger operators on L 2 (R), h a [ξ ], parametrized by ξ ∈ R and called fiber operators (see [HPRS16,HS15]). The operator h a [ξ ] is defined by . (2.17) Consequently, for all a ∈ [−1, 1)\{0}, we may express the ground state energy in (2.11) by Below, we collect some properties of the eigenvalue μ a (ξ ).
The case 0 < a < 1 This case is studied in [HS15,Iwa85]. The eigenvalue μ a (ξ ) is simple and is a decreasing function of ξ . The monotonicity of μ a (·) and its asymptotics in Proposition A.4 imply that and that β a , introduced in (2.11), satisfies The case a = −1 This case is studied in [HPRS16]. Using symmetry arguments, μ −1 (ξ ) is simple and satisfies where ξ −1 = −ξ(0) = − √ 0 , 0 and ξ(0) are respectively introduced in (2.5) and (2.7). The case −1 < a < 0 See also [HPRS16] for the study of this case. The eigenvalue μ a (ξ ) is simple, and there exists ξ a < 0 satisfying 3 Combining the foregoing discussion in the case a ∈ [−1, 0), we get that β a , introduced in (2.11), satisfies (2.25) In particular, In the next theorem, we will use a direct approach, different from the one in [HPRS16], to establish the existence of a global minimum ξ a in the case when a ∈ (−1, 0) and to prove that β a < |a|. Theorem 2.6 slightly improves the estimates in [HPRS16], since it provides an upper bound of β a stronger than |a|. This theorem is necessary to validate the hypothesis 1/|a| < 1/β a in (3.7), under which we work in Sect. 3. Indeed, it guarantees the existence of a non-empty b-parameter region where the minimizer of the reduced GL energy G a,b,R , introduced in Sect. 3, is non-trivial, which is key in the study of this energy.
Proof. The proof is inspired by [Kac07]. For all γ ∈ R, let (γ ) and ξ(γ ) be the quantities introduced in (2.4) and (2.6) respectively such that (γ ) = μ γ, ξ(γ ) . Denote by ϕ γ = ϕ γ,ξ(γ ) the positive L 2 -normalized eigenfunction of the operator in (2.2) with eigenvalue (γ ). Define the function where γ and m are two positive constants to be fixed later. One can check that u ∈ Dom q a [ξ ] , hence by the min-max principle, for all ξ ∈ R, . (2.28) Pick ξ ∈ R. We will choose ξ precisely later. The quadratic form q a [ξ ](u) defined in (2.14) can be decomposed as follows: A simple computation gives On the other hand, for t < 0, we do the change of variable y = − √ |a|t, which in turn . That way we get The definition of the function u in (2.27) yields Combining the results in (2.29)-(2.31) and using (2.7), we rewrite (2.28) as follows . Now we choose γ = √ 1/(2|a|(1 − |a|)) and m = √ |a|γ . Using again the fact that (γ ) < 1, we obtain The existence of the global minimum is now standard (it is a consequence of Proposition A.4 in the appendix).
Remark 2.7. Collecting the foregoing results in (2.19)-(2.23) and Theorem 2.6, we deduce the following facts regarding the bottom of the spectrum of the operator L a introduced in (2.10).
The functional and the main result.
Assume that a ∈ [−1, 1)\{0} is fixed, σ is the step function defined in (2.9) and A 0 is the magnetic potential defined in (2.8). For every R > 1, consider the strip We introduce the space Note that the boundary condition in the domain D R is meant in the trace sense. For b > 0, we define the following Ginzburg-Landau energy on D R by along with the ground state energy Our objective is to prove is the ground state energy in (3.4), and β a is defined in (2.11).
The following holds: The proof of Theorem 3.1, along with other properties of e a (b), will occupy the rest of this section.
3.2. The trivial case. We start by handling the trivial situation where the ground state energy vanishes: (1) Under the assumptions in Lemma 3.2, the function u = 0 ∈ D R is the unique minimizer of the functional in (3.3).
Proof of Lemma 3.2. We have the obvious upper bound
For the lower bound, pick an arbitrary function u ∈ D R and extend it by zero on R 2 . Using the min-max principle, we get 3.3. Existence of minimizers. Now we handle the following case (which is complementary to the one in Lemma 3.2) where β a is the lowest eigenvalue introduced in (2.11) . Under the hypothesis in (3.7), we can prove the existence of a non-trivial minimizer of the functional in (3.3) along with decay estimates at infinity.
The proof of Proposition 3.4 relies on the approach in [FKP13, Theorem 3.6] and [Pan02]. It can be described in a heuristic manner as follows. The unboundedness of the set S R makes the existence of the minimizer ϕ a,b,R in (3.8) non-trivial. To overcome this issue, we consider a reduced Ginzburg-Landau energy G a,b,R,m defined on the bounded set S R,m = (−R/2, R/2) × (−m, m), and we establish some decay estimates of its minimizer ϕ a,b,R,m . Later, using a limiting argument on G a,b,R,m and ϕ a,b,R,m for large values of m, we obtain the existence of the minimizer ϕ a,b,R together with the properties in Proposition 3.4. The details are given in Appendix B for the convenience of the reader.
The limit energy.
In this section, we will prove the existence of the limit energy e a (b), defined as the limit of g a (b, R)/R as R → +∞. After that, we will study, when the parameter a is fixed, some properties of the function b → e a (b).
In the sequel, we assume that a, b, R are constants such that R ≥ 1 and (3.7) holds. The next lemma displays some simple, yet very important, property of the energy. This property is mainly needed in Theorem 3.1 to establish an upper bound of the limit energy e a (b).
Lemma 3.5. Let n ∈ N. Consider the ground state energy g a (b, R) defined in (3.4), then Proof. Lemma 3.5 follows from the translation invariance of the integrand of G a,b,R with respect to the variable x 1 and the Dirichlet boundary conditions, where G a,b,R is defined in (3.3).
Our next result easily follows from the property of monotonicity with respect to the domain.
Lemma 3.6. The function R → g a (b, R) defined in (3.4) is monotone non-increasing.
The existence of the limit of g a (b, R)/R as R → +∞ will be derived from a wellknown abstract result (see [FK13, Lemma 2.2]). To apply this abstract result, we need some estimates on the energy g a (b, R), that we give in Lemmas 3.7 and 3.8 below.
Lemma 3.7. Let g a (b, R) be the ground state energy in (3.4). There exist positive constants C 1 , C 2 , and C 3 dependent only on a and b such that Multiplying this equation by ψ a θ 2 R and integrating by parts yield Taking the real part of each side of the equation above, we get Hence, using φ a L 2 (R) = 1 and the properties of θ R in (3.12), we obtain Consequently, for t = √ (1 − bβ a )/ν a and ν a = R |φ a (x 2 )| 4 dx 2 , we get where C 2 = (1/2)t 2 and C 3 = Cb/ν a . Lower bound. Let ϕ = ϕ a,b,R be the minimizer in Proposition 3.4. It follows from the min-max principle that By (3.10), S R |ϕ| 2 dx ≤ CbR, where C > 0 is some constant. Hence, choosing C 1 = C/β a establishes the desired lower bound.
Lemma 3.8. There exists a universal constant C such that, for all n ∈ N and α ∈ (0, 1), Proof. Let n ≥ 1 be a natural number, α ∈ (0, 1) and consider the family of strips Notice that the width of S j is 2(1 + α), and the overlapping occurs only between two adjacent strips (S j and S j−1 , for any j). There exists a universal constantC > 0 and a partition of unity (χ j ) j∈Z of R 2 such that and Since the overlapping is between a finite number of strips, one may further write where C is some universal constant. Define (3.14) where S R, j = {x R/2 : x ∈ S j }. The family of strips (S R, j ) j∈{1,2,...,n 2 } yields a covering of S n 2 R = −n 2 R/2, n 2 R/2 × R by n 2 strips, each of width (1 + α)R. Let ϕ a,b,n 2 R ∈ D n 2 R be the minimizer in Proposition 3.4. We decompose the energy associated to ϕ a,b,n 2 R as follows The first inequality above follows from the celebrated IMS localization formula (see [CFKS09, Theorem 3.2]), while the second comes from (3.10) and the properties of (χ R, j ) in (3.14). Notice that χ R, j ϕ a,b,n 2 R is supported in an infinite strip of width (1 + α)R. By energy translation invariance along the x 1 -direction, we have As a consequence, For R ≥ 1, dividing both sides by n 2 R and using the monotonicity of R → g a (b, R), we get 3.5. Proof of Theorem 3.1. Here we will verify all the statements appearing in Theorem 3.1. Noticing that G a,b,R (0) = 0, we get Item (1). The second item is already proven in Lemma 3.2.
Defining e a (b) = 0 for b ≥ 1/β a , the items (3) and (5) hold trivially since g a (b, R) = 0 in this case. We handle now the case where 1/|a| ≤ b < 1/β a . Define in R the two functions d a,b (l) = g a (b, l 2 ) and f a,b (l) = d a,b (l)/l 2 . Using Lemmas 3.6-3.8, we see that the functions d a,b (l) and f a,b (l) satisfy the following properties: • d a,b (·) is non-positive, monotone non-increasing, and f a,b (·) is bounded.
where C > 0 is a constant dependent on b and independent from l, n and α.
Then, by [FK13, Lemma 2.2], the following limit exists Moreover, for every integer n ≥ 1, Lemma 3.5 asserts that, Dividing both sides by n R and taking n → +∞ yields e a (b) ≤ g a (b, R)/R. By Lemma 3.7, e a (b) < 0 ; that the function e a (·) is monotone non-decreasing follows from the monotonicity of the function b → g a (b, R) ; the continuity of the function e a (·) follows from the estimates in (3.10) and the bounds in (3.6) (see [FKP13,Theorem 3.13]).
An effective one
We would like to find a relationship between the 2D-energy in (3.4) and the 1D-energy in (3.16) for some specific value of ξ . The existing results on the Ginzburg-Landau functional with a uniform magnetic field suggest that we should select ξ so as to minimize the function ξ → E 1D a,b (ξ ), see [AH07,CR14,Pan02]. In light of Remark 3.3, we will assume that a and b satisfy 1/μ a (ξ ). Furthermore, one can find a positive minimizer f a,b,ξ , dependent on a and b, such that any minimizer has the form c f a,b,ξ where c ∈ C and |c| = 1.
(2) Any minimizer f of E 1D a,b,ξ satisfies f ∞ ≤ 1 and the equation: (3) For 1/|a| < b < 1/β a , there existsξ 0 , dependent on a and b, such that Remark 3.10. Guided by the numerical computations of [HPRS16, Sect. 1.3], we expect that: • the global minimum β a , defined in (2.18), is attained at a unique point ξ a ; • ξ a is the unique critical point of the function ξ → μ a (ξ ) .
However, such results have not been analytically proven yet.
The proof of Proposition 3.9 may be derived as done in [FH10, Sect. 14.2] devoted to the analysis of the following 1D-functional defined over the space B 1 (R + ). We introduce the energies where the remainder term O(1) depends on the geometry and is explicitly computed in [CR16a,CR16b,CDR17]. That has been conjectured by Pan [Pan02], then proven by Almog-Helffer and Helffer-Fournais-Persson [AH07,HFPS11] under a restrictive assumption on b, using a spectral approach. In the whole regime b ∈ (1, −1 0 ), the upper bound part in (3.19) easily holds (see [FH10,Sect. 14.4.2]), while the matching lower bound is more difficult to obtain and has been finally proven by Correggi-Rougerie [CR14]. The proof of Correggi-Rougerie, based on the non-negativity of a certain cost function, was markedly different from the spectral approach of [AH07,HFPS11].
Going back to our step magnetic field problem and the one dimensional energy in (3.15), we prove the following theorem.
20)
and E 1D a,b (·) is defined in (3.16). Remark 3.12. By a symmetry argument, Theorem 3.11 trivially holds in the case a = −1, namely To prove Theorem 3.11, we will adopt the method of [CR14], which relies on remarkable identities, including an energy splitting [LM99], along with the non-positivity of a certain potential function and the non-negativity of another cost function.
We propose the potential and cost functions of our problem. These are defined as follows, and whereξ 0 and f 0 = f a,b,ξ 0 are introduced in Proposition 3.9. We recall the set S R in (3.1) and the energy G a,b,R in (3.3) defined over the space D R in (3.2). Let u ∈ C ∞ 0 (S R ) (note that this space is dense in D R with respect to the norm (∇ −iσ A 0 )u L 2 (S R ) + u L 2 (S R ) ). Since f 0 > 0 on R (see Proposition 3.9), we may introduce the function v via the relation (3.23) Lemma 3.13. It holds
Proof. Note that (3.25) We will compute each term of G a,b,R (u) apart. Starting with (3.26) An integration by parts yields since the functions f 0 and f 0 vanish at ±∞. Plugging (3.27) in (3.26) and using the second item of Proposition 3.9, we find (3.28) Next, we compute the second term of G a,b,R (u) Moreover, by Proposition 3.9 we have We put (3.28)-(3.30) in (3.25) to complete the proof.
Lemma 3.14. Let F 0 and K 0 be the functions defined respectively in (3.21) and (3.22). where Since F 0 (0) = 0 and F 0 (±∞) = 0, we can handle the next term through an integration by parts: Now we handle the integral involving the term in (3.24). An integration by parts yields since u = 0 (and consequently v) for x 1 = ±R/2. We plug (3.32) into (3.31) and we use Cauchy's inequality to get since F 0 ≤ 0. This completes the proof in light of Lemma 3.13 and the definition of the function K 0 .
Now, looking at the expression of E 1 (v) in Lemma 3.14, we obtain Thus, if F 0 ≤ 0, F 0 (±∞) = 0 and K 0 ≥ 0, then we get the lower bound (3.33) Our next task is to verify these conditions. We have the following Feynman-Hellmann equation (see Proposition 3.9): which can be expressed as follows Regarding the function K 0 , we get immediately from (3.22), If we manage to prove that F 0 (±∞) = 0, then by the same argument in [CR14, Lemma 3.2 and Proposition 3.4], we may prove that F 0 ≤ 0 and K 0 ≥ 0. Such information is known in the particular case a = −1, thanks to symmetry considerations and [CR14]; indeed In the asymmetric case when a ∈ (−1, 0), one needs to work a little bit more for obtaining (3.37). The next lemma will be useful for establishing that F 0 (±∞) = 0.
Lemma 3.16 (Alternative expression of F 0 ). It holds Proof. For t ≤ 0 and a < 0, we have Similarly, one proves for t > 0 that Now, we use the Feynman-Hellmann equation in (3.35) and the vanishing of f 0 and f 0 at ∞ to get Since R 1 = 1/|a|R 2 , we conclude that R 1 = R 2 = 0.
Proof. From the definition of F 0 , we have F 0 (0) = 0. In addition, the alternative expression of F 0 in Lemma 3.16 and the decay and vanishing of f 0 and f 0 at ∞ imply that and similarly that F 0 (+∞) = 0. Next, we will study the variations of F 0 . Recall the derivative of F 0 We know that f 0 > 0 on R. Hence, assuming thatξ 0 ≥ 0 yields that F 0 (t) > 0 for all t > 0, which contradicts the fact that F 0 (0) = F 0 (+∞) = 0. This proves thatξ 0 < 0. Consequently, we find that F 0 < 0 in a right-neighbourhood of 0, and F 0 > 0 in a left-neighbourhood of 0. Since F 0 (0) = 0, we find that F 0 ≤ 0 in a neighbourhood of 0.
Remark 3.18. Along the proof of Lemma 3.17, we proved that anyξ 0 minimizing E 1D a,b (·) satisfiesξ 0 < 0. Now, we are ready to prove the non-negativity of the cost function K 0 . Proof. Lemma 3.17 and (3.36) simply imply that K 0 (±∞) = 0. Consequently if K 0 becomes negative at some point t, this definitely means the existence of a global minimum at some point t 0 in R * , since K 0 (0) > 0. We have then K 0 (t 0 ) < 0 and K 0 (t 0 ) = 0, where Since K 0 (t 0 ) = 0 and f 0 (t 0 ) > 0, we get that On the other hand, we may use the alternative expression of F 0 in Lemma 3.16 to write the function K 0 in the following form (3.39) Plug (3.38) into (3.39) to get Since a ∈ [−1, 0), b > 1/|a| and f 0 > 0 everywhere in R, then obviously K 0 (t 0 ) > 0 which is the desired contradiction.
Collecting the aforementioned lemmas, we can now prove Theorem 3.11.
The lower bound e a (b) ≥ E 1D a,b is a consequence of (3.33) after passing to the limit R → +∞.
The Frenet Coordinates
In this section, we assume that the set consists of a single simple smooth curve that may intersect the boundary of transversely in two points. In the general case, consists of a finite number of such (disjoint) curves. By working on each component separately, we reduce to the simple case above.
To study the energy contribution along , we will use the Frenet coordinates which are valid in a tubular neighbourhood of . For more details regarding these coordinates, see e.g. [FH10, Appendix F]. We will list the basic properties of these coordinates here.
Let The curvature k r of is defined by
A Local Effective Energy
In this section, we will introduce a 'local version' of the Ginzburg-Landau functional in (1.1). For this local functional, we will be able to write precise estimates of the ground state energy, which in turn will prove useful in estimating the ground state energy of the full functional in (1.1). Select a positive number t 0 sufficiently small so that the Frenet coordinates of Sect. 4 are valid in the tubular neighbourhood (t 0 ) defined in (4.4). Let 0 < c 1 < c 2 be fixed constants and be a parameter that is allowed to vary in such a manner that We will refer to (5.1) by writing ≈ κ −3/4 . Let s 0 ∈ − | | 2 , | | 2 . After performing a linear change of variable, we may assume that s 0 = 0 (for simplicity). For large values of κ, consider the neighbourhood of s 0 LetF be the magnetic potential defined in V( ) bỹ where σ = σ (s, t) was defined in (2.9). Consider the domain For u ∈ D , we define the (local) energy a −2 (∂ s − iκ HF 1 )u 2 + |∂ t u| 2 − κ 2 |u| 2 + κ 2 2 |u| 4 a ds dt , where a(s, t) = 1 − tk r (s). Now we introduce the following ground state energy Using standard variational methods, one can prove the existence of a minimizer u 0 of G.
Lower bound of G 0 .
Lemma 5.1. Under Assumption (5.7), there exist two constants κ 0 > 1 and C > 0 dependent only on a and b such that, if κ ≥ κ 0 and is as in (5.1), then where G 0 and e a (b) are defined in (5.6) and (3.5) respectively.
Proof. Notice that a(s, t) is bounded in the set V( ) as follows We apply Cauchy's inequality and the uniform bound of u to get where We introduce the parameters R = √ κ H , γ = √ κ Hs, τ = √ κ Ht, and define the re-scaled function In the new scale, we may write where G a,b,R is the functional in (3.3), andȗ ∈ D R the domain in (3.2) (since u ∈ D ). Invoking Theorem 3.1, we conclude that We plug the estimates (5.12) and (5.13) in (5.10), then we use e a (b) ≤ 0 and the assumptions on κ and to complete the proof of Lemma 5.1.
Upper bound of G 0 .
Lemma 5.2. Under Assumption (5.7), there exist two constants κ 0 > 1 and C > 0 dependent only on a and b such that, if κ ≥ κ 0 and is as in (5.1), then where G 0 and e a (b) are defined in (5.6) and (3.5) respectively.
Next, we define the following function (with the re-scaled variables) with γ = √ κ Hs, τ = √ κ Ht. Using (5.9) and (3.10), we get where J (u) was defined in (5.11), |v| 4 dγ dτ , Let χ R (τ ) = χ τ/R = χ t/ . We will estimate now each term of K(v) apart, using mainly the estimates on the minimizer ϕ in (3.10) and the properties of the function χ R . We start with the following two estimates that result from Cauchy-Schwarz inequality, Next, we may select R 0 sufficiently large so that, for all R ≥ R 0 , The decay of ϕ in (3.9), and (5.18) yield Finally, we write the obvious inequality Gathering the foregoing estimates, we get 1.1 and 1.2). This will be displayed by the local estimates of the Ginzburg-Landau energy and the L 4 -norm of the Ginzburg-Landau parameter in Theorem 6.1. We will introduce the necessary notations and assumptions. Starting with the local energy of the configuration (ψ, A) ∈ H 1 ( ; C) × H 1 div ( ), in any open set D ⊂ as follows (6.1) Let ≈ κ −3/4 satisfy (5.1) (for some fixed choice of the constants c 1 and c 2 ). For κ sufficiently large (hence sufficiently small), let x 0 ∈ \∂ be chosen so that Consider the following neighbourhood of x 0 , Thanks to (6.2), we have N x 0 ( ) ⊂ . As a consequence of the assumption in (6.2), all the estimates that we will write will hold uniformly with respect to the point x 0 . We assume that a ∈ [−1, 0) and b > 0 are fixed and satisfy b > 1 |a| .
(6.4) When (6.4) holds, we are able to use the exponential decay of the Ginzburg-Landau parameter away from the set and the surface ∂ (see Theorem 2.4).
where N x 0 (·) is the set in (6.3), E 0 is the local energy in (6.1), and e a (b) is the limiting energy in (3.5). Furthermore, the function r is independent of the point x 0 ∈ .
The proof of Theorem 6.1 follows by combining the results of Proposition 6.3 and Proposition 6.4 below, which are derived along the lines of [HK17,Sect. 4] in the study of local surface superconductivity.
Part of the proof of Theorem 6.1 is based on the following remark. After performing a translation, we may assume that the Frenet coordinates of x 0 are (s = 0, t = 0) (see Sect. 4). Recall the local Ginzburg-Landau energy E 0 introduced in (6.1). Let F be the vector field introduced in Lemma 2.2. We have the following relation where G is defined in (5.5), u ∈ H 1 0 (N x 0 ( )),ṽ = e −iκ H ωũ ,ũ is the function associated to u by the transformation −1 [see (4.5)], and ω = ω − , is the gauge function defined in Lemma 4.1.
6.1.1. Lower bound of the local energy. We start by establishing a lower bound for the local energy E 0 u, A; N x 0 ( ) for an arbitrary function u ∈ H 1 0 (N x 0 ( )) satisfying |u| ≤ 1. We will work under the assumptions made in this section, notably, we assume that (6.4) holds, and ≈ κ −3/4 [see (5.1)], and in the regime where H = bκ. Proposition 6.2. There exist two constants κ 0 > 1 and C > 0 such that, for κ ≥ κ 0 and for all x 0 ∈ satisfying (6.2), the following is true. If where N x 0 (·) is the neighbourhood in (6.3), E 0 is the functional in (6.1), and e a (b) is the limiting energy in (3.5).
Proof. Let α ∈ (0, 1) and F be the vector field introduced in Lemma 2.2. We define the function φ x 0 (x) = A(x 0 ) − F(x 0 ) · x. As a consequence of the fourth item in Theorem 2.3, we get the following useful approximation of the vector potential A We choose α = 2/3 in (6.8). Let h = e −iκ H φ x 0 u. Using (6.8), Cauchy's inequality, and the uniform bound |h| ≤ 1, we may write Finally, the lower bound in Lemma 5.1, together with the inequality e a (b) ≤ 0, yield the claim of the inequality.
Sharp upper bound on the L 4 -norm.
We will derive a lower bound of the local energy E 0 ψ, A; N x 0 ( ) and an upper bound of the L 4 -norm of ψ, valid for any critical point (ψ, A) of the functional in (1.1). Again, we remind the reader that we assume that (6.4) holds, ≈ κ −3/4 [see (5.1)] and H = bκ. Proposition 6.3. There exist two constants κ 0 > 1 and C > 0 such that, for all x 0 ∈ satisfying (6.2), the following is true.
Sharp lower bound on the L 4 -norm.
Complementary to Proposition 6.3, we will prove Proposition 6.4 below, whose conclusion holds for minimizing configurations only. We continue working under the assumption that (6.4) holds, ≈ κ −3/4 [see (5.1)] and H = bκ.
Proof. The proof is divided into five steps.
Step 1. Construction of a test function and decomposition of the energy. The construction of the test function is inspired from that by Sandier and Serfaty, in their study of bulk superconductivity in [SS03]. For γ = κ −3/16 andˆ = (1 + γ ) , we define the function , φ x 0 is the gauge function introduced in (6.8), ω = ω s 0 ,s 1 is the function introduced in Lemma 4.1 for s 0 = −ˆ and s 1 =ˆ , is the coordinate transformation in (4.3), u 0 is a minimizer of the functional G ·, V(ˆ ) defined in (5.5), and η is a smooth function satisfying (6.21) Recall the energies defined in (1.1) and (6.1). Let us write the obvious decomposition Adding the magnetic energy term κ 2 H 2 curl A − B 0 2 L 2 ( ) on both sides, we obtain the following identity, since the same magnetic energy term is present in both energies E κ,H (·, A) and E ·, A; N x 0 (ˆ ) . We denote by 2γ ) . Hence, we get the following decomposition of the functional in (1.1), Step 2. Estimating E 1 (u, A). Using (6.8) for α = 2/3, |v 0 | ≤ 1 and the Cauchy-Schwarz inequality, we write But by (6.7), we have E 0 v 0 , F; N x 0 (ˆ ) = G u 0 , V(ˆ ) . Hence, Lemma 5.2 and (6.22) imply Step 3. Estimating E 2 (u, A).
Then, we do a straightforward computation, similar to the one done in the proof of (6.14), replacing f by η and N x 0 (ˆ ) by N x 0 (˜ )\N x 0 (ˆ ). This gives the following relation between E 2 (u, A) and E 2 (ψ, A) Step 4. Estimating E 1 (ψ, A).
Step 5. Lower bound of the L 4 -norm of ψ. Consider the function f defined in (6.11).
We use the properties of this function, mainly that f = 1 in N x 0 ( ) and 0 ≤ f ≤ 1 in , to obtain Following an argument similar to the one for (6.13), we divide the set γ }, and we use this time the exponential decay of |ψ| 4 , deduced from Theorem 2.4, to get Inserting (6.26) into (6.16) gives The previous inequality together with (6.14) and (6.25) establish the lower bound in (6.19).
Surface superconductivity.
In this section, we are concerned in the local behaviour of the sample near the boundary of , under the assumption The analysis of superconductivity near ∂ in our case of a step magnetic field (B 0 satisfying 1.2) is essentially the same as that in the uniform field case, since B 0 is constant in each of 1 and 2 . Thereby, the results presented in this section are well-known in the literature since the celebrated work of Saint-James and de Gennes [SJG63]. We refer to [CG17,CR16a,CR16b,CR14,FKP13,FK11,HFPS11,AH07,FH05,Pan02,LP99] for rigorous results in general 2D and 3D samples subjected to a constant magnetic field, and to [NSG+09] for recent experimental results. Particularly, local surface estimates were recently established in [HK17], when B 0 ∈ C 0,α ( ) for some α ∈ (0, 1). We will adapt these results to our discontinuous magnetic field (see Theorem 6.5 below).
The statement of Theorem 6.5 involves the surface energy E surf , that we introduce in the next section.
where (γ , τ ) ∈ R 2 , A 0 (γ , τ ) = (−τ, 0), U R = (−R/2, R/2) × (0, +∞), and The boundary condition in the domain W(U R ) is meant in the trace sense. Let d(b, R) be the ground state energy defined by Pan proved in [Pan02] the existence of a non-decreasing continuous function E surf : Later, it was proven that (see e.g. [CR14]) . One important property of the function E surf (·) is (see [FH05]) This property allows us to extend the function E surf (·) continuously to [1, +∞), by setting it to zero on [ −1 0 , +∞). This extension of the surface energy is still denoted by E surf for simplicity.
Local surface superconductivity.
Let t 0 > 0 and j ∈ {1, 2}. We define the following set Assume that t 0 is sufficiently small, then for any x ∈ j (t 0 ), there exists a unique point , p(x)) .
Let ≈ κ −3/4 be the parameter in (5.1). Assume that κ is sufficiently large and choose We introduce the following neighbourhood of x 0 The assumption on x 0 in (6.30) guarantees that N j x 0 ( ) ⊂ j . Consequently, the estimates in Theorem 6.5 below hold uniformly with respect to the point x 0 .
where N j x 0 (·) is the set in (6.31), and E 0 is the local energy in (6.1). Furthermore, the functionȓ is independent of the point x 0 .
The estimates in Theorem 6.5 are established in [HK17], when the function B 0 is smooth. Since B 0 is constant in the neighbourhood N j x 0 ( ), the proof in [HK17] still holds in our case.
Proof of main results.
In this section, we work under the conditions of Theorems 1.7 and 1.11. We will gather the results of the two previous sections to establish the two aforementioned theorems.
Proof of Theorem 1.11. We will decompose the sample into the sets * ( ), * 1 ( ), analyse the behaviour of the minimizer in each of these sets. We assume to be the parameter in (5.1) which satisfies ≈ κ −3/4 . In a neighbourhood of the magnetic barrier We start by introducing the set * = * ( ) which covers almost all of the set . Recall the assumption that consists of a finite collection of simple disjoint smooth curves that may intersect ∂ transversely. For the simplicity of the exposition, we will focus on the particular case of a single curve intersecting ∂ at two points. The construction below may be adjusted to cover the general case by considering every single component of separately. We may select two constants 0 ∈ (0, 1) and c > 2, and for all ∈ (0, 0 ), a collection of pairwise distinct where N x i ( ) is the set introduced in (6.3). The family N x i ( ) 1≤i≤N consists of pairwise disjoint sets. The number N depends on as follows In a neighbourhood of the boundary Now, we define the two sets * 1 = * 1 ( ) and * 2 = * 2 ( ) which cover almost all of the set ∂ . In a similar fashion to the definition of * ( ), we fix 0 ∈ (0, 1) and c > 2 and we select two collections of points for 1 ≤ j ≤ N 1 − 1 and 1 ≤ k ≤ N 2 − 1. Furthermore, where N 1 y j ( ) and N 2 z k ( ) are defined in (6.31). The numbers N 1 and N 2 depend on as follows The bulk set Next, we introduce the set bulk = bulk ( ) representing the bulk of the sample: In a neighbourhood of the T -zone We finally introduce the remaining set in the decomposition of , the neighbourhood T = T ( ) of ∩ ∂ The definition of the sets * , * 1 , * 2 and bulk in (6.32), (6.34), (6.35) and (6.36) ensures that |T | = O( 2 ) as → 0. Behaviour of the minimizer Now, we are ready to prove the convergence of |ψ| 4 in the sense of distributions, claimed in Theorem 1.11.
Let ϕ ∈ C ∞ c (R 2 ). We have We will estimate each of these right hand side integrals. Starting with C is a constant independent of κ.
Next, we have [see (6.32)] For i ∈ {1, . . . , N }, let p i and q i be two points of such that We may write We estimate |ϕ(x) − ϕ( p i )| in N x i ( ) by the mean value theorem. Using the size of N x i ( ) and the bound ψ L ∞ ( ) ≤ 1, we get for some C independent of κ. Hence, by (5.1) and (6.33) On the other hand, using the uniform bounds in (6.10) and (6.19), we get where C is a constant independent of κ. We use further that and N = O(1) by (6.33). We get that whereC is a new constant independent of κ. Combining (6.40)-(6.43) yields (6.45) We combine (6.44) and (6.45) to obtain But by (6.33) Hence, One can proceed similarly to prove that and κ * 2 Gathering pieces in (6.38), (6.39), (6.46) and (6.47), we establish Theorem 1.11.
Proof of Theorem 1.7 We apply Theorem 1.11 for ϕ ∈ C ∞ c (R 2 ) such that ϕ = 1 in a neighbourhood of to get (1.7).
Multiplying both sides of the first equation in (1.4) by ψ then integrating by parts give where E(ψ, A; ·) and E 0 (ψ, A; ·) are the energies in (6.1). Using (6.48) and (1.7), we get the lower bound of E g.st (κ, H ) in (1.6). The upper bound of E g.st (κ, H ) can be derived by the help of a suitable trial configuration. We are still considering the parameter as in (5.1). Let F be the magnetic potential introduced in Lemma 2.2. We define the function h ∈ H 1 ( ; where * ( ) and N x i ( ) are respectively the sets in (6.32) and (6.3), v i (x) = e iκ H ω u i • −1 (x), ω = ω − , is the gauge function in Lemma 4.1, is the coordinate transformation in (4.3), u i is defined by u i (s, t) = u 0 (s − s i , t) for (s i , t i ) = −1 (x i ), and u 0 is the minimizer of G(·, V( )) defined in (5.5). From the definition of v i , we derive the following [see (6.7)] The previous identity together with Lemma 5.2, (6.33) and ( ≈ κ −3/4 ) give (6.49) Similarly, for j ∈ {1, 2}, using the results of Theorem 6.5, one may define a function where * j ( ) is defined in (6.34) and (6.35). Now, we define the trial function Noticing that E g.st (κ, H ) ≤ E h, F; = E 0 h, F; [see (6.1)], we gather the results in (6.49) and (6.50) to derive the upper bound in (1.6). and lowest eigenvalue The perturbation theory [Kat66] ensures that the functions are analytic, where μ(γ , ξ ) and μ N (ξ ) are respectively defined in (2.3) and (2.5). We list the following well-known spectral properties (for instance see [DH93,RS72,Kac06]): Proposition A.2. The lowest eigenvalue μ a (ξ ) of h a [ξ ] is simple. Furthermore, there exists a positive eigenfunction g a,ξ normalized with respect to the norm · L 2 (R) . g a,ξ is the unique function satisfying such properties.
The bounds in Lemma A.3 are useful for establishing Proposition A.4, which is crucial in our study of the eigenvalue μ a (ξ ) (see Sect. 2.4).
• If a ∈ [−1, 0), then Proof. We will prove the lemma in the case a ∈ (−1, 0). The proof follows similarly in the case a ∈ (0, 1). We start by establishing the upper bound in (A.3). Let ξ ∈ R. Consider u = u D −ξ the normalized eigenfunction of the operator H D [−ξ ] defined in (A.1), corresponding to the lowest eigenvalue μ D (−ξ). Then Noticing that u ∈ H 1 0 (R + ), we extend it by zero on R − (the extension is still denoted by u for simplicity). Hence, we have q a [ξ ](u) = μ D (−ξ), where q a [ξ ] is the quadratic form in (2.14). Using the min-max principle, we get .
sponding to the lowest eigenvalue μ D (−ξ/ √ |a|), we can prove that by the min-max principle, after employing the change of variable x = −t/ √ |a| and extending the resulting function by 0 on R + .
Next, we establish the lower bound in (A.3). We consider g = g a,ξ the normalized eigenfunction of the operator h a [ξ ] corresponding to the lowest eigenvalue μ a (ξ ) (see Proposition A.2). We have Using the min-max principle, we write a lower bound for each integral appearing in (A.4). Indeed, where g a,ξ is the eigenfunction in Proposition A.2.
Proof. (Feynman-Hellmann) For simplicity, we write μ, g and h respectively for μ a (ξ ), g a,ξ , and h a [ξ ] . Differentiating with respect to ξ and integrating by parts in Hence using and recalling that g is normalized, we obtain Integrating by parts the right hand side of (A.9), and using (A.8) establish the result.
Proof. Let ξ a be such that β a = μ a (ξ a ) (see [HPRS16]). We use the lower bound proof of Lemma A.3, with g = g a,ξ a the positive normalized eigenfunction of the operator h a [ξ a ] corresponding to μ a (ξ a ) (see Proposition A.2). We get Since g is normalized and positive, and |a| 0 < 0 for a ∈ (−1, 0), the proof is completed.
Proof. Suppose that ξ a ≥ 0. Let g a,ξ a be the positive normalized eigenfunction of the operator h a [ξ a ] corresponding to the lowest eigenvalue μ a (ξ a ) (see Proposition A.2).
Appendix B. Decay Estimates for the 2D-Effective Model
The aim of this appendix is to prove Proposition 3.4. Recall that we work under (3.7), namely, where β a is the lowest eigenvalue introduced in (2.11) . For every m ∈ N and R > 1, we introduce the set S R,m = (−R/2, R/2) × (−m, m) and the functional defined over the space Here σ was defined in (2.9). Now we define the ground state energy (ϕ a,b,R,m ) = g a (b, R, m) .
Here G a,b,R,m is the functional introduced in (B.1) and g a (b, R, m) is the ground state energy introduced in (B.3).
Proof. The boundedness and the regularity of the domain S R,m guarantee the existence of a minimizer ϕ m := ϕ a,b,R,m of G a,b,R,m in D R,m , satisfying Multiply (B.8) by χ 2 ϕ m and integrate by parts, (B.10) It follows from (B.9) and (B.10) Using Hölder's inequality, (B.12) Now, using Cauchy-Schwarz inequality together with (B.11) and (B.12), we obtain Consequently, under the assumption 1 ≤ 1/|a| ≤ b < 1/β a , we get (B.6). Inserting (B.6) into (B.12), we get We still need to establish To that end, we select η ∈ C ∞ (R) such that η(x 2 ) = 0 if |x 2 | ≤ 1, and η(x 2 ) = √ |x 2 |/ ln |x 2 | if |x 2 | ≥ 4. Multiplying the equation in (B.8) by η 2 ϕ m and integrating It is easy to check by a straightforward computation, and using Cauchy's inequality, that Proof. For the sake of brevity, we will write ϕ m for ϕ a,b,R,m . Using (B.14) and the fact that |x 2 |/ ln |x 2 | 2 ≥ 1 for |x 2 | ≥ 4, we get On the other hand, using ϕ m ∞ ≤ 1 and b > 1 we get Next, since ϕ m satisfies a simple integration by parts over S R,m yields Now, we will investigate the regularity of the minimizer ϕ a,b,R,m in Lemma B.1. We have to be careful at this point since the magnetic field is a step function and therefore has singularities. As a byproduct, we will extract a convergent subsequence of (ϕ a,b,R,m ) m≥1 .
We will use the following terminology. 1 and α ∈ (0, 1) be fixed. The sequence ϕ a,b,R,m m≥1 defined by Lemma B.1 is bounded in H 3 loc (S R ) and consequently in C 1,α loc (S R ). Proof. For simplicity, we will write ϕ m = ϕ a,b,R,m . The proof is split into three steps.
Step 1. We first prove the boundedness of ϕ m in H 2 loc (S R ). Using (B.8) we may write Let K ⊂ S R be open and relatively compact. Choose an open and bounded set K such that K ⊂ K ⊂ S R . There exists m 0 ∈ N such that for all m ≥ m 0 , K ⊂ S R,m and by Cauchy's inequality, Using |ϕ m | ≤ 1, the decay estimate in (B.19) and the boundedness of σ and A 0 in K , we get a constant C = C( K , R) such that where C is a constant independent from m. This proves that (ϕ m ) m≥1 is bounded in H 2 loc (S R ).
Step 2. Here we will improve the result in Step 1 and prove that (ϕ m ) m≥1 is bounded in H 3 loc (S R ). It is enough to prove that the sequence ∇ϕ m m≥1 is bounded in H 2 loc (S R ). Let ς m = ∂ x 2 ϕ m . We will prove that ς m m≥1 is bounded in L 2 loc (S R ). Recall that, for all x = (x 1 , x 2 ) ∈ R 2 , A 0 (x) = (−x 2 , 0) and σ (x) = 1 R + (x 2 ) + a1 R − (x 2 ) , hence, in the sense of weak derivatives. By Step 1, the sequence (ϕ m ) is bounded in H 2 loc (S R ). Consequently, since |ϕ m | ≤ 1, it is clear that ( ς m ) m≥1 is bounded in L 2 loc (S R ). By the interior elliptic estimates, we get that (ς m = ∂ x 2 ϕ m ) m≥1 is bounded in H 2 loc (S R ). In a similar fashion, we prove that (∂ x 1 ϕ m ) m≥1 is bounded in H 2 loc (S R ).
Step 3. Finally, for every relatively compact open set K ⊂ , the space H 3 (K ) is embedded in C 1,α (K ). Consequently, ϕ m is bounded in C 1,α loc (S R ).
Lemma B.4. Assume that R > 1 and that (3.7) holds. Let ϕ a,b,R,m m≥1 be the sequence defined in Lemma B.1. There exist a function ϕ a,b,R ∈ H 3 loc (S R ) and a subsequence, denoted by ϕ a,b,R,m m≥1 , such that ϕ a,b,R,m −→ ϕ a,b,R in H 2 loc (S R ) and ϕ a,b,R,m −→ ϕ a,b,R in C 0,α loc (S R ) α ∈ (0, 1) .
Proof. We continue writing ϕ m for ϕ a,b,R,m . Let K ⊂ S R be open and relatively compact. By Lemma B.3, (ϕ m ) m≥1 is bounded in H 3 (K ), hence it has a weakly convergent subsequence by the Banach-Alaoglu theorem. By the compact embedding of H 3 (K ) in H 2 (K ), and of H 2 (K ) in C 0,α (K ), we may extract a subsequence, that we denote by (ϕ m ), such that it is strongly convergent in H 2 (K ) and C 0,α (K ). The subsequence in Lemma B.4 and its limit are then constructed via the standard Cantor's diagonal process.
Lemma B.5. Let R > 1 and ϕ a,b,R be the function defined by Lemma B.4. The following statements hold: where C > 0 is a universal constant, and D R is the space introduced in (3.2).
Proof. Let (ϕ a,b,R,m ) be the subsequence in Lemma B.4. Again, we will use (ϕ m ) and ϕ for (ϕ a,b,R,m ) and ϕ a,b,R respectively.
By Lemma B.1, the inequality |ϕ m | ≤ 1 holds for all m. The inequality |ϕ| ≤ 1 then follows from the uniform convergence of (ϕ m ) stated in Lemma B.4. By the convergence of ϕ m in H 2 loc (S R ) and C 0,α loc (S R ), we get (B.27) from −b(∇ − iσ A 0 ) 2 ϕ m = (1 − |ϕ m | 2 )ϕ m . Taking m 0 → +∞, we write by the monotone convergence theorem, This proves that ϕ ∈ L 2 (S R ). Next we will prove that (∇ − iσ A 0 )ϕ ∈ L 2 (S R ). In light of the convergence of (ϕ m ) in H 1 loc (S R ), we can refine the subsequence (ϕ m ) so that (∇ − iσ A 0 )ϕ m → (∇ − iσ A 0 )ϕ a.e. Furthermore, by Lemma B.3, ϕ m is bounded in C 1 loc (S R ), hence in C 1 (S R,m 0 ), for all m 0 ≥ 1. Using the dominated convergence theorem and the estimate in (B.19), we may write, for all m 0 ≥ 1, Sending m 0 to +∞ and using the monotone convergence theorem, we get Thus, we have proven that ϕ, (∇ −iσ A 0 )ϕ ∈ L 2 (S R ). It remains to prove that ϕ satisfies the boundary condition To see this, let x 2 ∈ R. There exists m 0 such that x 2 ∈ (−m 0 , m 0 ). By the convergence of (ϕ m ) to ϕ in C 0,α (S R,m 0 ), we get Finally, we may use similar limiting arguments to pass from the decay estimates of ϕ m in (B.5) and (B.6) to the decay estimates of ϕ in (B.28) and (B.29). Now, we are ready to establish the existence of a minimizer of the Ginzburg-Landau energy G(a, b, R) defined in the unbounded set S R . Lemma B.6. Let R > 1. The function ϕ a,b,R ∈ D R defined in Lemma B.4 is a minimizer of G a,b,R , that is G a,b,R (ϕ a,b,R ) = g a (b, R).
Here G a,b,R is the functional introduced in (3.3) and g a (b, R) is the ground state energy defined in (3.4).
Proof. The proof is divided into three steps.
Step 1. (Convergence of the ground state energy) . Let g a (b, R, m) and g a (b, R) be the energies defined in (B.3) and (3.4) respectively. In this step, we will prove that lim m→+∞ g a (b, R, m) = g a (b, R) . (B.31) Let u ∈ D R,m . We can extend u by 0 to a functionũ ∈ D R . As an immediate consequence, we get g a (b, R, m) ≥ g a (b, R), for all m ≥ 1. Thus, lim inf m→+∞ g a (b, R, m) ≥ g a (b, R). Next, we will prove that lim sup m→+∞ g a (b, R, m) ≤ g a (b, R) . (B.32) Consider (ϕ n ) ⊂ D R a minimizing sequence of G a,b,R , that is g a (b, R) = lim n→+∞ G a,b,R (ϕ n ).
Consider the re-scaled function ϑ m (x 2 ) = ϑ(x 2 /m). The function ϑ m (x 2 )ϕ n (x) restricted to S R,m belongs to D R,m and consequently Taking the successive limits → 0 + and n → +∞, we get (B.32).
Step 2. (The L 4 -norm of the limit function). Let (ϕ m = ϕ a,b,R,m ) be the sequence in Lemma B.4 which converges to the function ϕ = ϕ a,b,R . We would like to verify that the limit function ϕ is a minimizer of the functional G a,b,R . To that end, we will prove first that Taking the successive limits m → +∞ and → 0 + , we get (B.39).
Step Proof of Proposition 3.4. All the properties stated in Proposition 3.4 (except the nontriviality of the minimizer) are simply a convenient collection in one place of already proven facts in Lemmas B.5 and B.6. With these properties in hand, the non-triviality of ϕ a,b,R follows from Lemma 3.7. | 19,077.8 | 2019-02-06T00:00:00.000 | [
"Physics",
"Mathematics"
] |
Simulations of spin-driven AGN jets in gas-rich galaxy mergers
In this work, we use hydrodynamical simulations to explore the effects of kinetic AGN jet feedback on the progression and outcome of the major merger of two isolated, gas-rich galaxies. We present simulations that use the moving-mesh code AREPO to follow the progression of the merger through first passage and up to the final coalescence, modelling the black holes at the centres of both of the merging galaxies using our prescription for black hole accretion via an $\alpha$-disc and feedback in the form of a spin-driven jet. We find that the jets drive large-scale, multiphase outflows which launch large quantities of cold gas out to distances greater than 100 kpc and with velocities that reach $\sim 2500 \, {\rm km \, s^{-1}}$. Gas in the outflows that decelerates, cools and falls back on the galaxies can provide a rich source of fuel for the black hole, leading to intense episodes of jet activity in which the jet can become significantly misaligned. The presence of AGN jets affects the growth of the stellar component: star formation is moderately suppressed at all times during the merger and the peak of the star formation rate, attained during the final coalescence of the galaxies, is reduced by a factor of $\sim 2$. Analysis of simulations such as these will play a central role in making precise predictions for multimessenger investigations of dual radio-AGN, which next-generation observational facilities such as LISA, Athena and SKA will make possible.
INTRODUCTION
Galaxy mergers are a natural prediction of hierarchical models of structure formation (White & Rees 1978) and, therefore, play a crucial role in determining the properties of the observed galaxy population.The dynamics of galaxy mergers was first 1 investigated numerically by Toomre & Toomre (1972) who showed, using restricted N-body techniques, that stellar bridges and tails, as observed in interacting and peculiar galaxies, can form through the action of gravitational tidal torques.Subsequent work, using full N-body methods, confirmed these conclusions (Barnes 1988(Barnes , 1992;;Hernquist 1992Hernquist , 1993)), giving a fairly complete picture of the dynamics of the collisionless component in galaxy mergers.Toomre & Toomre (1972), despite focusing on the collisionless dynamics of mergers, also noted that the observed galaxies they were attempting to model displayed particularly high levels of star formation.They anticipated that mergers could 'bring deep into a galaxy a fairly sudden supply of fresh fuel in the form of interstellar material'.Indeed, the importance of the gaseous component in merger scenarios is now well established.Numerical simulations have shown that during mergers, extreme tidal torques experienced by gas that cools radiatively can drive significant quantities of gas towards the central regions of the galaxies (Barnes & Hernquist 1991, 1996).This abundance of cold gas can lead to highly elevated star formation ★ E-mail: rosie@mpa-garching.mpg.de(RYT) 1 Although, see also Holmberg (1940Holmberg ( , 1941) ) for an ingenious method of simulating galaxy interactions using light-bulbs.rates (SFRs) and can also provide the fuel required to power highly energetic outbursts from active galactic nuclei (AGN).
Idealised numerical simulations, including both gaseous and collisionless components, have played a fundamental role in galaxy merger studies over the past few decades.Such simulations have been used to explore topics such as the feeding of and feedback from supermassive black holes (SMBHs; see e.g.Di Matteo et al. 2005;Springel et al. 2005a;Johansson et al. 2009;Barai et al. 2014;Choi et al. 2014), the processes responsible for driving starbursts (see e.g.Mihos & Hernquist 1996;Di Matteo et al. 2007, 2008;Cox et al. 2008) and the properties of the merger remnant (see e.g.Naab & Burkert 2003;Cox et al. 2006b;Di Matteo et al. 2009;Wuyts et al. 2010;Perret et al. 2014), amongst many others.
In the past decade or so, significant improvements have been made to the numerical models used in such simulations, increasing their physical accuracy and allowing more complex processes to be included.For example, recent idealised merger simulations are now able to include explicit models of stellar feedback alongside detailed models of the interstellar medium (ISM) that capture its multiphase structure (Hopkins et al. 2013;Moreno et al. 2021;Li et al. 2022).
AGN feedback is believed to operate in, perhaps, two distinct modes, taking the form of either massive, wide-angle winds or highly-energetic, relativistic jets (see e.g., McNamara & Nulsen 2007;Fabian 2012;King & Pounds 2015;Harrison et al. 2018, for reviews of AGN feedback).Recent observations have also shown that ionised outflows may, in fact, be more prevalent and have a greater effect on the surrounding ISM in cases where the radio emission is compact (Harrison et al. 2015;Morganti et al. 2015).Such power-ful expulsions of energy during the merger process clearly have the potential to dramatically alter the properties of the galaxies before coalescence as well as those of the final remnant and its surroundings.Indeed, numerical simulations of idealised mergers have shown that black hole feedback in the form of AGN winds produces elliptical galaxies that are in much better agreement with observations (for seminal papers on this, see Di Matteo et al. 2005;Springel et al. 2005a,b).While there have been a number of simulations exploring the evolution of jets in isolated disc galaxies and their interactions with the ISM (Wagner & Bicknell 2011;Gaibler et al. 2012;Mukherjee et al. 2016;Bieri et al. 2016;Talbot et al. 2021Talbot et al. , 2022)), the effects of kinetic AGN jet feedback in merging galaxies remains largely unexplored.
Galaxy mergers are expected to lead to the formation of SMBH binaries.The low-frequency gravitational waves emitted during the inspiral, merger and ringdown of such binaries is one of the key targets of the LISA mission (Amaro-Seoane et al. 2017) which is sensitive to the coalescence of SMBHs in the mass range 10 4−7 M ⊙ all the way out to ≈ 20 (Colpi et al. 2019).Detecting merging SMBHs that host discernible radio jets is incredibly difficult and, to date, only a few dual radio AGN candidates have been observed (see e.g.Kharb et al. 2017;Bansal et al. 2017;Britzen et al. 2018;Dey et al. 2021;Gopal-Krishna et al. 2022).Large-scale surveys in the radio, carried out by upcoming observatories such as the Next Generation Very Large Array (ngVLA) and the Square Kilometre Array (SKA), however, will significantly increase the number of dual AGN observed at sub-kpc separations (Paragi et al. 2015).Data from these instruments will also provide new insights into the nature and properties of AGN jets, particularly at low frequencies.With X-ray missions such as Athena and Lynx on the horizon, as well as the newly operational JWST, the multimessenger study of merging SMBHs will become a reality.
Using numerical simulations of galaxy mergers to investigate the formation and evolution of dual, jetted AGN and their effect on their surroundings is, therefore, exceptionally timely.Such simulations will play a central role in providing firm theoretical predictions for, as well as accurately interpreting, the abundance of data that these next-generation observational facilities will provide.To this end, this paper presents work in which we use numerical simulations to explore kinetic AGN jet feedback in the context of isolated gas-rich major mergers.The properties of SMBH host galaxies have been selected such that they are good analogues of galaxies at ≈ 2, where galaxies are expected to be highly gas-rich (Förster Schreiber & Wuyts 2020), facilitating rapid AGN accretion and leading to elevated star formation rates.This likely results in powerful AGN feedback episodes and vigorous starbursts, meaning that outflows are expected to be prominent in such systems.We apply our spindriven AGN jet feedback model (see Talbot et al. 2021Talbot et al. , 2022)), to the black holes at the centres of both of the merging galaxies.Our simulations then follow the progression of the two galaxies and their AGN through the first passage and up to the final coalescence of their host galaxies.Using these simulations we explore the impact of AGN jet feedback on the stellar component and the gaseous haloes of the galaxies.Additionally, we explore how the AGN jets self-regulate when subject to the extreme environments present in such mergers.
The structure of the paper is as follows.In Section 2 we provide a brief overview of our black hole accretion and feedback model before explaining the changes made to the model that is used in this work, relative to that which was used in the simulations presented in Talbot et al. (2021) and Talbot et al. (2022).Then, in Section 3, we describe some of the additional physical processes modelled in these simulations.In Section 4 we describe how the different components of the galaxies are modelled and how we set up the initial conditions for the merger simulations.Our results are presented in Section 5 and in Section 6 we compare our results to those of previous works and discuss the limitations of our simulations.Finally, in Section 7, we end with our conclusions.
BLACK HOLE ACCRETION AND FEEDBACK
In this paper we use numerical simulations to explore the merger of two galaxies hosting active black holes.We adopt a novel sub-grid model for black hole accretion and feedback, as presented in Talbot et al. (2021Talbot et al. ( , 2022)).This model couples an -disc accretion prescription (Fiacconi et al. 2018) to a feedback prescription for high resolution kinetic jets (Bourne & Sijacki 2017) via the Blandford-Znajek mechanism.This model has been implemented into the moving-mesh code arepo (Springel 2010;Pakmor et al. 2016;Weinberger et al. 2020).
We model black holes as sink particles and assume that they are surrounded by a sub-grid, thin, (potentially warped) -disc (Shakura & Sunyaev 1973) which modulates the flux of mass and angular momentum across the black hole horizon.The mass and angular momenta of the black hole and -disc evolve due to inflows of gas from the surroundings, mutual Bardeen-Petterson torquing (Bardeen & Petterson 1975), the launching of the Blandford-Znajek jet itself and the accretion of material by the black hole from the inner edge of the -disc.Accounting for these processes allows us accurately follow the mass and spin evolution of the black hole and self-consistently calculate the power and direction of the Blandford-Znajek jet (Blandford & Znajek 1977) that it launches.
The power of a Blandford-Znajek jet depends on the magnetic flux that threads the black hole horizon.Due to the fact that our simulations do not include magneto-hydrodynamic effects and, more importantly, we are unable to resolve the black hole horizon, we instead make use of the predictions from general relativistic magnetohydrodynamic (GRMHD) simulations and analytic arguments to fully parameterise the model (Tchekhovskoy et al. 2012).
The fluxes of mass and angular momentum onto the -disc are estimated from the properties of inflowing gas cells in the vicinity of the black hole.The jet is then launched by injecting mass, energy and momentum into gas in the 'jet-cylinder', centred on the black hole, with axis determined by the black hole spin direction.
Gas circularisation condition for accretion
In our original implementation of the -disc model, inflowing gas could only flow onto the -disc if it was able to circularise and settle within the disc.We imposed this condition by allowing gas to accrete when the sector-averaged2 specific angular momentum of the gas was smaller than that of the outer edge of the disc.
The imposition of such a condition requires that, in the simulation, the -disc is consistently being resolved by at least one cell.This was the case in the simulations presented in Talbot et al. (2021) and Talbot et al. (2022), however, the simulations performed in this work have lower resolution (due to the significantly larger physical scales considered) and this condition is no longer satisfied.We, therefore, introduce a new circularisation condition for accretion which takes the lower resolution of our simulations into account.
Specifically, for each black hole in the simulation, at every timestep we calculate the smallest resolvable scale relevant to black hole accretion in each of the accretion sectors.When the jet is inactive, we take this to be the characteristic inflow radius in the relevant sector where the sum is over the inflowing cells in the relevant sector, is the volume of the gas cell and ( ) is a cubic spline kernel with compact support over the black hole smoothing length, ℎ BH (for further information, see section 3.2 of Talbot et al. 2021).
When the jet is active, however, the act of injecting mass, energy and momentum into the gas cells surrounding the black hole means that the hydrodynamics on scales smaller than this injection region are unresolved.We, therefore, choose the smallest resolvable scale to be the larger out of the radius of the jet cylinder and the characteristic inflow radius, given in Equation (1) above.
We then calculate the specific angular momentum of the inflowing gas in each sector.If it is less than that of the outer edge of the disc then the gas is allowed to flow onto the -disc, as per our original prescription.If, however, the specific angular momentum in the sector corresponds to a circularisation radius that is larger than the disc radius but smaller than this 'smallest resolvable scale', we assume that the gas will be able flow onto the -disc but that, due to unresolved physical processes, its specific angular momentum is reduced to that of the outer edge of the -disc.
In doing so, we have implicitly assumed that all of this gas will reach the -disc.This may not necessarily be the case, but the efficiency by which unresolved processes transfer mass and angular momentum is not well understood and so we choose to assume 'maximal efficiency' and highlight the fact that our inflow rate estimate may naïvely be taken to represent an upper limit to the accretion rate we would find, were we able to resolve the -disc.However, as the actual mechanism by which angular momentum is transferred in discs that are self-gravitating and star-forming gas is poorly understood, especially in the presence of supernova (SN) and AGN feedback, in reality our estimated mass flux on the -disc may be too low.Particularly as our simulations do not fully resolve the ISM structure and thus, we might expect the gas to be more clumpy and to reach much higher densities (see e.g.Springel & Hernquist 2003, and Section 3.1 of this paper).
Targeted refinement criteria
In section 3.7 of Talbot et al. (2021), we detail several additional targeted refinement criteria 3 which we use to ensure the accretion flow as well as the jet injection and lobe inflation are sufficiently well resolved.In the work presented here, we make use of all of these refinement schemes, some of which we make improvements to, which we now briefly discuss.
We adopt a 'black hole refinement scheme' which ensures that the spatial resolution within some chosen 'refinement radius' around the 3 These criteria are applied in addition to the standard refinement criterion in arepo which ensures that the mass of gas cells remains approximately constant.
black hole increases linearly towards the black hole (Curtis & Sijacki 2016).We also use the jet cylinder refinement scheme (Bourne & Sijacki 2017;Talbot et al. 2021) as well as an additional refinement scheme which increases the spatial resolution in gas cells that contain a sufficiently high fraction (greater than 10 −5 ) of a passive tracer that is injected with the jet.At later times in the simulations presented in this work, however, a significant volume of the simulation domain can be filled with jet material.In such scenarios, we found that using this jet lobe refinement scheme without modification ultimately leads to the formation of a numerically intractable number of cells.To ensure that we are able to follow the merger of these galaxies to completion, we instead base the jet lobe volume refinement on a decaying jet tracer with a fixed 'decay' timescale of 100 Myr.
Additionally, the original refinement schemes did not prohibit the mass of a cell from becoming arbitrarily small.While this was not a problem for the setups considered in Talbot et al. (2021) and Talbot et al. (2022), in the simulations presented here we found, however, that very low mass cells could lead to prohibitively small timesteps.These small cells were typically those into which jet energy had recently been injected and were often located close to a black hole.We therefore impose a mass floor, which acts on all cells in the black hole refinement region and all those subject to the jet volume refinement scheme (i.e.those with a decaying jet tracer fraction greater than 10 −5 ).In the simulations presented in this work, we choose a mass floor of 10 3 M ⊙ which is approximately 100 times smaller than the target gas mass of these simulations (see Section 4.1).
Energy injection in extreme environments
The radius of the cylinder into which we inject the jet feedback is calculated at each timestep and for each black hole, such that the mass contained within the cylinder is roughly constant and the cylinder is contained within the black hole smoothing length.In addition to this, we impose the condition that each half of the jet cylinder must be populated by at least 10 gas cells.The jet cylinder refinement is usually able to ensure this condition is satisfied and the jet cylinder largely stays at constant mass.
Occasionally, however, these refinement schemes are not able to respond fast enough and, in order for this minimum cell number criterion to be satisfied, the jet cylinder would have to be too large.Such an occurrence is usually associated with a particularly powerful jet outburst.When this happens, instead of injecting the jet into too few cells, we store the mass, energy and momentum that should have been injected in this timestep, and inject in the next timestep when the cell number criterion is satisfied.Having too few cells in the jet cylinder is relatively rare in these simulations and and when it does happen the jet energy, mass and momentum usually only need to be stored for, at most, a few timesteps.
ADDITIONAL PHYSICS IN THE SIMULATIONS
In addition to the growth of, and feedback from SMBHs, the simulations presented in this work include models for several other key astrophysical processes, including star formation and evolution, chemical enrichment, the galactic winds driven by stellar feedback and primordial and metal-line gas cooling.In this section we provide a brief description of these processes and how they are modelled in the simulations.
Star formation, stellar feedback and galactic winds
The simulations presented in this work do not have sufficient resolution to be able to capture small-scale physical processes, such as molecular cloud formation, their collapse and supersonic turbulence that are expected to lead to the development of a self-regulated ISM and the formation of stars.To model star formation and the pressurisation of the ISM due to unresolved SNe we, therefore, describe the star-forming gas using an effective equation of state (eEOS), largely following the prescription described in Springel & Hernquist (2003) (see also Vogelsberger et al. (2013); Pillepich et al. (2018)).To avoid overpressurising the ISM gas, which would lead to unrealistically 'smooth' ISM, we interpolate between the eEOS detailed in Springel & Hernquist (2003) and an isothermal equation of state with temperature 104 K, using an interpolation parameter EOS = 0.5.We assume a Chabrier initial mass function (IMF) (Chabrier 2003) and model mass and metal return from Type Ia and Type II SNe, and asymptotic giant branch (AGB) winds (for further details see Vogelsberger et al. 2013).
In these simulations, stars are prevented from forming in the refinement regions surrounding the black holes.The additional refinement (as described in Section 2.2) means that gas cells in this region can have masses that are significantly smaller than cells in the majority of the simulation domain.Were stars to be allowed to form in this region, then the resulting wide range of star particle masses could lead to spurious N-body heating effects, which could potentially affect the temperature of the gas cells close to the black hole, as well as lead to the 'ejection' of light stellar particles.
The simulations in this work use a threshold density for star formation of th = 0.03 cm −3 and * 0 = 12.6 Gyr, using the notation of Springel & Hernquist (2003).Our gas consumption timescale is longer than the fiducial value presented in Springel & Hernquist (2003), to better match that which is expected in gas-rich galaxies at ≈ 2. Additionally, we would expect these galaxies to have significantly higher levels of turbulence in their ISM than local galaxies.We chose to model this enhanced pressurisation by using a lower value of th than the value presented in Springel & Hernquist (2003).We additionally use a sub-grid model for galactic winds that are launched directly from the star-forming phase, again, largely following the formalism outlined in Springel & Hernquist (2003) (see also Vogelsberger et al. (2013); Pillepich et al. (2018)).Our simulations use a wind mass loading factor of = w / * = 1 where w is the mass flux into the wind and * is the star formation rate.The initial velocity of a wind cell is determined under the assumption that some fraction of the energy available from SNe (in this work, we assume a value of 10 per cent) drives the winds and its direction is randomly oriented along × ∇ (i.e. in a bipolar manner) where is the velocity of the gas cell and is the local gravitational potential (Springel & Hernquist 2003).Note that this differs from the wind launching model used in the Illustris simulations (Vogelsberger et al. 2013) and from that used in the subsequent Illustris TNG simulations (Pillepich et al. 2018).
Radiative cooling and heating
In these simulations, primordial species undergo radiative cooling according to the primordial atomic network described in Katz et al. (1996).Metal-line cooling is implemented in the simulations using pre-calculated look-up tables, generated by the photoionisation code cloudy (Ferland et al. 2017).These tables provide cooling rates down to temperatures of 10 K and are calculated for a solar compo-sition gas and then scaled linearly with the metallicity of the cell 4 .The ultraviolet background (UVB) is modelled as a time-dependent, spatially uniform radiation field which injects heat into the gas.These simulations use the UVB intensity from Faucher-Giguère et al. (2009) and include the self-shielding correction from Rahmati et al. (2013).Since the simulations presented in this work are intended to model galaxy mergers at cosmic noon, we ensure that the time-dependent cooling processes correspond to = 2 values.
When using the eEOS model, described in Section 3.1 above, typically metal-line cooling is usually only considered in gas above 10 4 K (Vogelsberger et al. 2013).There is no reason, however, why the gas at densities lower than the star formation threshold should not be able to cool down to these temperatures.We, therefore, considered two different cooling prescriptions: In the 'fiducial' case, metal-line cooling is only effective down to 10 4 K.In the 'additional cooling' case, metal-line cooling is effective down to 10 4 K for gas with density higher than the star formation threshold, while gas at lower densities is allowed to cool down to 10 K via this channel.
SIMULATION SET-UP
In this work we use simulations to explore the major merger of two gas-rich galaxies hosting AGN-driven jets.We consider two simulations which, throughout this work, we refer to as the 'jet' and 'no-jet' simulations.The black holes in the 'jet' simulation launch jets via our spin-driven jet model, whereas the black holes in the 'nojet' simulation accrete via sub-grid -discs but do not launch jets.Both of these simulations use the 'additional cooling' prescription, as described in Section 3.2, wherein gas with density lower than the eEOS threshold can cool down to 10 K via metal-line cooling.This low temperature cooling acts to somewhat increase the mass in cool and star-forming phases, particularly in the gas that is cooling out of the outflow.This facilitates somewhat higher black hole inflow rates and, ultimately, higher jet powers.On the other hand, the more powerful jets more readily heat and destroy the cool gas that forms.Hence, overall we find very modest qualitative difference with respect to the 'fiducial' cooling simulation, and we only provide analysis of the data from the 'additional cooling' simulations in this work.
In this section, we detail the properties of the merging galaxies and explain how the initial conditions for the simulations were set up.
Initial conditions
To create the initial conditions for the merger, we first set up a single isolated system of mass 200 = 10 12 M ⊙ using the procedure outlined in Springel et al. (2005a).The system consists of a dark matter halo, a disc of gas and stars, a stellar bulge, a hot, gaseous halo and a black hole.
Specifically, the stellar and gaseous disc have total masses of 1.64× 10 10 M ⊙ and 2.46 × 10 10 M ⊙ , respectively, giving a gas fraction of approximately 0.6.Both discs are modelled as having exponential surface density profile with a scale length of 2.6 kpc.The stellar disc has vertical structure corresponding to that of an isothermal sheet with scale-height of 0.26 kpc, while the gas disc is initialised with a temperature of 10 4 K and its vertical structure is set up to ensure hydrostatic equilibrium.The dark matter halo structure and that of the hot halo are modelled using a Hernquist profile (Hernquist 1990) whose structure closely follows that of an NFW profile (Navarro et al. 1997) with concentration parameter, = 9 and a spin parameter, = 0.033.The stellar bulge is assumed to be spherical and follow a Hernquist profile (Hernquist 1990), with = 0.26 kpc and with a total mass of 8 × 10 9 M ⊙ .At the centre, we place a black hole of mass 10 7 M ⊙ , which is consistent with the black hole mass-bulge mass relation.
The dark matter halo is sampled by collisionless particles of mass 3 × 106 M ⊙ , the star particles in the disc and bulge are of mass 8.2 × 10 4 M ⊙ and 1.6 × 105 M ⊙ , respectively, and the initial mass of the gas cells in the halo and disc is 1.23 × 10 5 M ⊙ .The dark matter particles have gravitational softening lengths of 2 kpc while that of stars in the disc and bulge is 60 pc.The black hole has a softening length of 150 pc and that of the gas is treated adaptively, but with a minimum of 60 pc.
In these simulations we do not allow the black hole particles to merge and, instead, they form a binary.We do so as the standard merger prescription in arepo (which instantaneously merges black holes within their own smoothing lengths) is too efficient and does not account for realistic stellar and gaseous hardening processes which will likely cause the black holes to merge on a significantly longer timescale than this 'instantaneous merger' prescription.We wish to highlight, however, that since the gravitational force law used in the simulations is softened, it will not be possible to accurately follow the hardening of the binary to separations smaller than twice the black hole softening length (∼ 300 pc).We did, however, perform analogous simulations with smaller black hole softening lengths and confirmed that the results presented in this paper are robust to such changes.
It should also be pointed out that we do not pre-enrich the gas disc, nor the hot halo of the galaxies since significant star formation and subsequent metal return rapidly enriches the disc.The galactic winds and jets then transport this enriched gas into the halo, such that the metallicity of the circumgalactic medium (CGM) is consistent with Suresh et al. (2015) within a few Myr.
Having set up initial conditions for an isolated galaxy, as described above, we then create initial conditions for the merger by combining two such galaxies.The initial positions and velocities of the galaxies are chosen such that they are on a prograde, parabolic orbit with the plane of both discs lying in the orbital plane.The initial displacement of the galaxies is set to 2 × 200 = 325 kpc and the expected pericentric distance is set to 2 kpc such that significant funnelling of gas towards the centres of the galaxies is expected during first passage, as a result of the powerful tidal torques in this configuration (see e.g.Lotz et al. 2008).This merger setup is then placed in a cubic simulation domain with a side-length of 1 Mpc.
Since the gas discs are not guaranteed to be in equilibrium and since we wish to focus on the behaviour of the system as the galaxies approach first passage and beyond, we run an initial simulation of ∼ 1 Gyr and then use the output of the simulation as initial conditions for relevant simulation with the -discs and jets.At the end of this simulation the separation between the galaxies is ∼ 25 kpc and the discs are already showing signs of significant tidal deformation.
In this initial simulation we do not use our black hole accretion and jet model.During the time in which the galaxies are coming into equilibrium, the properties of the jets that would form would likely be driven by our choice of initial conditions which could potentially bias our results (for example if the jets were to significantly perturb the galactic discs).Instead, during this time, we allow the black holes to accrete at a fraction of their Bondi-Hoyle-Lyttleton rate, where the fraction is chosen to ensure that the final black hole masses remain consistent with the black hole mass-bulge mass relation.The output of this simulation is then used as initial conditions for the 'production' simulations which use the full black hole spin-driven jet model (or just the -disc model for the simulation without jets).The rest of this paper will focus on the 'production' simulations and will not discuss these 'initial' simulations any further.
In the 'production' simulations, we choose the initial spins of both black holes to have a magnitude of 0.7 and direction parallel to the -axis of the simulation domain.As the simulations progress, these quantities are then free to evolve according to our model.The initial masses of the -discs are chosen to be 10 5 M ⊙ and their angular momenta are also initialised parallel to the -axis.
Since our choice of initial -disc mass does not guarantee that the disc will be in equilibrium with the accretion flow, we wait 10 Myr at the start of the simulation before allowing jets to be launched 5 .
Qualitative overview of the simulations
We begin our analysis by giving a qualitative overview of how the galaxy merger progresses when jets are present.We do so with the aid of Fig. 1 which shows projections of various gas properties for the simulation with jets.Each of the three rows shows the state of the system at a key moment in the merger process (as indicated by the row headings) and the corresponding time is indicated in the top left-hand corner of each of the main panels.Each projection is centred on the midpoint of the separation vector of the black holes and has axis that is perpendicular to the plane of the galaxies 6 .
In the first row of Fig. 1, the two galaxies are approaching first passage.The hot, diffuse cocoons, inflated by the jets, are clearly visible in the temperature map and there is a region of enhanced temperature where the cocoons overlap and shock.Due to slight differences in the masses of the black holes and their accretion flows, the initial powers of the jets are not the same (see Section 5.2) and one of the jets is slightly more powerful at first.This results in a slightly larger cocoon driven by this jet and hotter hotspots.
In addition to the hot gas in the cocoons, there is also gas with temperatures of 10 4 − 10 5 K associated with the galactic wind outflows.Inspection of the SFR projection shows that star forming gas is present throughout much of the galactic discs and, additionally, in some of the dense outflowing material associated with the galactic winds.From the metallicity and jet tracer maps, it is also clear that the jet material is highly metal enriched, which highlights the fact that jets can act to expel material that has been enriched by stellar evolution from the galactic discs and draw it up in the outflows.
In the second row of Fig. 1, more than half a Gyr has elapsed and the galaxies are now approaching their final coalescence.The strong tidal forces experienced by the galaxies after their first passage have led to the formation of extended tidal tails and a bridge of gas (and stars) between the two galaxies.Now the other jet is considerably more active (this will be quantified in Section 5.2).This more active jet is heating much of the gas in its vicinity and is significantly metal-enriching the halo, whilst the largely 'quiescent' jet is currently having very little effect on the gas beyond its immediate vicinity.
There is also significant amount of warm and cold gas that can be seen falling back towards the midplane, in the region between the two galaxies.This gas is associated with earlier jet activity where the jet-driven outflows have decelerated and cooled, and begun to fall back into the potential well of the system and it is this infall of cooling gas that is actually responsible for feeding the black hole and causing the jet power to increase.At this time, star formation is still occurring throughout the majority of the galactic discs and in the stellar winds.It is also clear that star forming gas is still present in the dense outflows as well as the inflows associated with the jet activity.
The fact that we find star forming gas out of the plane of the galactic discs highlights the possibility that stars may exist in these jet-driven outflows.Indeed, we do also find that stars are present and form in the outflows and inflows associated with jet activity in our simulations.The star formation rate in gas associated with the outflow 7 can reach ∼ 10 −3 M ⊙ yr −1 although outbursts from the jet can reduce this significantly.Additionally, the mass of stars in the outflow can reach ∼ 10 7 M ⊙ by the end of the simulation.While non-negligible, the mass of outflowing stars (and star-forming gas) is significantly smaller than the stellar mass of the disc (see Section 5.4).We do wish to emphasise, however, that the model we use for star formation and stellar feedback (see Section 3.1) is not necessarily applicable to the formation of stars in galaxy-scale outflows.The presence of stars and star forming gas in the outflows in our simulations should, therefore, be interpreted as indicative of the possibility of finding stars in these outflows.To quantify this further, a more accurate model for star formation and stellar feedback would be required.
In the final row of Fig. 1, the galaxies have reached coalescence and have formed a dense compact system that can be identified in the temperature, density and SFR projection and is surrounded by significantly metal enriched gas.The merger of the galaxies has triggered a powerful jet outburst extending up to 50 kpc away from the merger remnant.One other interesting feature to highlight is that the axis of the jet-driven bipolar outflow is no longer aligned with the vertical, as is clearly visible in the temperature map.This change in the orientation of the large-scale outflow arises due to the fact that the spin of the black hole associated with the most active jet has been torqued and is now somewhat inclined to the vertical as, during the course of the simulation, this black hole is fed by gas with misaligned angular momenta.This will be further discussed and quantified in Section 5.2.
Black hole and jet properties
Having qualitatively explored the key processes that influence the evolution of the merger in the presence of jet-driven outflows, we now turn to more quantitative analyses, first examining the properties of the black holes and jets themselves.
Fig. 2 shows the evolution of the jet powers, the mass inflow rates onto the sub-grid -discs, averaged in 10 Myr windows, and the inclinations of the jets (i.e.black hole spins) to the vertical.The initial jet outburst is more powerful for BH 2 than BH 1.This is consistent with the discussion in Section 5.1 where we highlighted the fact that, in the first row of Fig. 1, the cocoon associated with the jet on the right is slightly larger.For the rest of the simulation, however, BH 1 is the most active: in the period of time before the galaxies coalesce, the power of the jet associated with BH 2 is maintained at a few times 10 42 erg s −1 while that of the jet associated with BH 1 is able to reach powers as high as ∼ 10 45 erg s −1 during the final coalescence8 , resulting in the highly energetic outflow seen in the bottom row of Fig. 1.
To understand the general behaviour of these jet powers, consider the middle panel of Fig. 2, which shows the time-averaged mass inflow rates onto the sub-grid -discs.For the vast majority of the time before the final coalescence of the galaxies (which begins at ∼ 0.8 Gyr), gas is able to flow onto the sub-grid -disc of both black holes.From the shaded region around each line, which bounds the range of mass inflow rates, we can see that, while gas is being fairly consistently supplied to the -disc, there are short periods of time without inflow.BH 2 experiences fairly steady inflows that are able to maintain a reasonably constant jet power, while BH 1 experiences much more variable inflow rates that result in larger fluctuations in the jet power.These bursts of inflow are driven by the cool gas that is raining back onto the galaxy, as discussed in Section 5.1.The fact that there tends to be one black hole that is accreting more, thus launching a more powerful jet initially comes about due to the lack of perfect symmetry of gas properties around the two black holes.Additionally, the launching of a powerful jet leads to further accretion by inducing the 'rain' of gas which cools out of the hot jet-driven outflow and can then further enhance the jet power, leading to a situation reminiscent of a positive feedback loop.Throughout the final coalescence of the galaxies, bursty inflows are able to persist, primarily for BH 1, as is reflected in its high jet power.
The behaviour of the inflow rate during the coalescence of galaxies is markedly different from what would be found when using accretion rate estimates such as Bondi-Hoyle-based prescriptions, which would be approximately 1-2 orders of magnitude higher.Our requirements that the gas be flowing towards the black hole and have angular momentum such that it is able to circularise and settle on the -disc means that inflow is not always guaranteed to occur (even in simulations without jets during the final coalescence, where the inflow rates become sporadic), despite the fact that gas is always present in the vicinity of the black holes9 .These inflows lead to black hole growth rates that are typically ∼ 10 −4 M ⊙ yr −1 but can reach ∼ 10 −1 M ⊙ yr −1 when the inflow rates are highest.This rather modest black hole growth is primarily due to the fact that the black hole spin energy is used to power the jets (see e.g.Blandford & Znajek 1977;Tchekhovskoy et al. 2012;Talbot et al. 2022).Indeed, in our simulations without jets, the inflow rates are 2-3 orders of magnitude higher than those found in the simulations with jets (see the dashed lines in the middle panel of Fig. 2), reaching typically ∼ 10 −1 M ⊙ yr −1 .This highlights the considerable impact that jets can have on the feeding of the black holes, which is discussed further in Section 5.3.It also clearly indicates that our accretion model (and the circularisation condition) onto the -disc allows for high and sustained accretion rates onto the black holes prior to the coalescence as expected in merging gas-rich galaxies.
One additional point worth highlighting is that the motions of the black holes at late times in these simulations will be affected by the numerical softening of the gravitational force, as discussed in Section 4.1.To robustly measure the accretion rate onto the black holes after the formation of the binary would require a more accurate model for the black hole orbital dynamics.We now turn to the evolution of the jet direction (i.e. the black hole spin direction), which is shown in the bottom panel of Fig. 2. It is clear that the spin of BH 2 is not significantly torqued over the course of the simulation and remains approximately vertical throughout.The jet associated with BH 1, however, shows quite different behaviour.The direction of this jet is much more variable and, after ∼ 1 Gyr, it is inclined by ∼ 60 • (this is consistent with the misalignment of the outflow that can be seen in Fig. 1 and was discussed in Section 5.1).
The significant torquing of the jet associated with BH 1 arises due to mechanism by which this black hole is fed.This black hole primarily accretes gas that has cooled out of the outflow and fallen back onto the galaxy and this material does not necessarily have angular momentum perpendicular to the plane of the galactic discs.As discussed in Section 5.1, comparatively less cold and cooling gas falls back onto the galaxy that hosts BH 2 and so, during the course of the simulation, it is largely being fed by gas that has circularised in the disc, causing little change to its angular momentum direction.Furthermore, during coalescence, the discs of the galaxies are disrupted as gas is violently shocked and subject to extreme tidal torques meaning that the gas available for accretion does not necessarily have angular momentum perpendicular to the orbital plane.This is likely responsible for the rapid changes to the jet direction during this time.
Black hole feeding and local gas properties
In the previous section, we examined the behaviour of the gas inflow rate onto the black hole--disc system.Here, we explore some of the physical processes responsible for modulating the accretion rates.We do so using Fig. 3, which shows thin projections (1×1×0.4kpc) of the magnitude of the specific angular momentum of the gas, overlaid with arrows that show the streamlines of the velocity field (left column) and the temperature field (right column) for the jet simulation.In the first row, the projections are centred on BH 2 (the most powerful jet at this time, see Fig. 2) and then in the second and third rows, the projections are centred on BH 1.In each panel, the locations of the black hole(s) are marked with a coloured dot.The first row shows the state of the gas during the initial outbursts of the jets.The second row corresponds to the time when gas is raining back down onto the galaxy and feeding the black hole, but the jet power has yet to increase significantly.Finally, the third row shows the state of the system during the coalescence of the galaxies and the formation of the black hole binary.In Fig. 2, the jet power, mass inflow rate and jet inclination at these times are indicated with a filled black dot, for the relevant black hole.
In the the top row, we can clearly identify the hot, jet-driven outflow, which consists of comparatively low specific angular momentum gas.The jet-driven outflow is propagating away from the galactic disc which, as is visible in the temperature map, has not yet been significantly disrupted.The streamlines of the gas in the disc indicate the presence of turbulence and, additionally, that a significant amount of disc gas is moving radially towards the black hole.At this time, the inflow rate onto the sub-grid -disc is still fairly high (see Fig. 2), which we can, therefore, infer as being largely due to these 'secular' inflows of gas from the galactic disc.In the temperature map we can identify regions of cool gas that likely originated in the galactic disc within the hot outflow and inspection of the streamlines indicate that this gas has been entrained and is radially outflowing. .Thin projections (of dimension 1 × 1 × 0.4 kpc) for the simulation with jets which show, in the left-hand column, the magnitude of the specific angular momentum of the gas where the arrows correspond to the streamlines of the velocity field, and in the right-hand column, the temperature of the gas.In the first row, the projections are centred on BH 2 and then in the second and third rows, the projections are centred on BH 1.In each panel, the locations of the black hole(s) are marked with a coloured dot, using the same colours as those of the lines in Fig.In the middle row of Fig. 3, the projections are now centred on the other black hole (BH 1).The power of its jet is currently at a minimum (see Fig. 2) and this energy injection rate is not high enough to drive any significant outflow, but rather, the streamlines indicate that gas is flowing from the halo towards the galaxy and black hole.Interestingly, some of this inflowing halo gas has a comparatively low specific angular momentum and temperature.As discussed in Section 5.1, the jet-driven outflows act to seed thermal instabilities in the halo gas which, ultimately, results in cold and warm gas raining down on this galaxy.Having inspected specific angular momentum projections of the halo gas (not shown here), analogous to those shown in Fig. 3, we indeed find that this cool inflowing gas typically has low enough specific angular momentum such that it can circularise onto our sub-grid -disc and feed the black hole.This lends weight to our observation that, at this time, a non-negligible contribution to the gas that feeds the black hole comes from gas that condenses out of the halo and falls back down on the galaxy.
In the final row of Fig. 3, the galaxies have now reached coalescence.The projections are still centred on BH 1 (the green circle) but the other black hole is now also visible.We have already seen that BH 1 launches the more powerful jet during galaxy coalescence and is, thus, primarily responsible for driving the fast, hot outflow.The other jet is also active at this time, albeit with a jet power that is more than an order of magnitude lower (see Fig. 2).It is, therefore, the interaction of these two jets that results in the complex velocity field that can be seen in the velocity streamlines at this time.
In Talbot et al. (2022), we performed similar analysis of the properties of gas feeding black holes in the presence of jets, focusing on isolated, non-interacting galaxies.We showed that that black holes can be fed via gas inflows from the surrounding (circumnuclear) disc but that when the jets are active, they drive backflows which can also play a crucial role in funneling low specific angular momentum gas towards the black hole (see also Antonuccio-Delogu & Silk 2010;Cielo et al. 2014;Bourne & Sijacki 2017).Here we confirm that both of these processes contribute to black hole feeding: the streamlines indicate that gas in the galactic disc is being funneled towards the black hole and the presences of vortices, associated with 'small-scale' jet-driven backflows can also be seen in Fig. 3, drawing gas back towards the black holes.
We find, however, that when radiative cooling processes are included in the simulations, jet-induced condensation of gas that then falls back onto the galaxy can also responsible for providing fuel for the black hole (see also McCourt et al. 2012;Gaspari et al. 2013Gaspari et al. , 2015;;Prasad et al. 2015;Wang et al. 2019).Additionally, whilst not shown in Fig. 3, when the galaxies pass each other for the first time during the final coalescence (at about 0.85 Gyr), the extreme merger torques cause the gas in the galactic discs to lose angular momentum, thus forming compact, dense structures and this decrease in specific angular momentum, ultimately, leads to enhanced accretion rates, particularly in the case of BH 1.It is important to highlight that the interplay of all these processes is what, ultimately, determines black hole fuelling rate which is further modulated by the strength of the jet feedback.
Evolution of the stellar component
Having examined the evolution of the black holes and jets, we now explore that of the stellar component.The left-hand panel of Fig. 4 shows the time evolution of the SFR, averaged in 10 Myr windows, and the right-hand panel shows the time evolution of the mass of newly formed stars10 that have formed in the simulation.
Before exploring how the jets affect the stellar component, we first discuss the physical processes responsible for some of the general features in the evolution of the SFRs.Recall that all simulations begin as the galaxies approach first passage.The tidal torques during this encounter remove angular momentum from the gas, leading to rapid nuclear gas inflows and enhanced central densities.This, in turn, leads to the early peak in the SFR of ∼ 100 M ⊙ yr −1 and the subsequent decline that is seen in all of the simulations.A more significant peak in the SFR is seen during the final coalescence of the galaxies at ∼ 0.8 Gyr.These two peaks in the SFR at first passage and coalescence are typical of galaxy mergers (see e.g.Springel et al. 2005a), however the magnitude of the SFR and the relative heights of the peaks is highly dependent on the properties of the merging galaxies.
During coalescence, the star formation rates can reach ∼ 700 M ⊙ yr −1 in the simulation without jets.The simulation with jets, however, shows a lower peak SFR at coalescence, that only reaches ∼ 400 M ⊙ yr −1 .This reduction in SFR is also reflected in the mass of stars that form, with the jet simulation forming and maintaining a lower stellar mass.It is worth reiterating the fact that jet-driven suppression of the SFR by a factor of ∼ 2 is possible despite relatively little black hole growth (see the discussion in Section 5.2).Whilst the SFR does drop after the peak, during the coalescence, it is relatively gradual and star formation does continue during this time and the galaxies do not undergo 'instantaneous quenching' as a result of the merger.If the merger were to be responsible for quenching the galaxies, then this process must, therefore, occur on longer timescales than were captured in these simulations.Furthermore, this could perhaps also indicate that jets alone do not lead to quenching during a merger, and that we do additionally need to include the effects of AGN winds and/or radiation.Alternatively, it may be that the black hole accretion rates and jet powers in these simulations are too low.Future radio observations of jetted merging galaxies with e.g. the ngVLA and SKA will help us constrain jet energetics in these systems.
It is interesting to note that the presence of jets acts to suppress the SFR throughout the majority of the merger process and does not lead to the triggering of significant star formation.This is reflected in the total stellar mass that has ever formed which is a factor of up to 1.5 times higher in simulation without jets compared to that with jets.At this point it is worth mentioning that we do not allow star particles hosted by jet cells (i.e.those with a jet fraction greater than 10 −3 ) to return mass, nor metals to their surroundings as we found that this often results in artificial features in the jet lobes.Doing so does, however, mean that mass can remain locked up in stars for longer.Ultimately, this means that the mass of the (evolved) stellar component in the simulation with jets is typically larger than expected, rising to ∼ 6 × 10 10 M ⊙ just before the final coalescence of the galaxies and ∼ 7 × 10 10 M ⊙ at the end of the simulation, compared to the final stellar masses of ∼ 8 × 10 10 M ⊙ found in the simulation without jets.
Properties of the large-scale outflows
In this section we explore the properties and evolution of the largescale jet-driven outflows, and quantify their multiphase nature.As discussed in Section 5.1, SN-driven galactic winds are present in all our simulations.These winds in our setup are generally unable to propagate beyond ∼ 10 kpc from the midplane and so, to focus the discussion on the jet-driven outflows, our analysis in this section is restricted to gas that lies between 20 − 100 kpc above the midplane and within a cylindrical radius of 100 kpc relative to the centre of the simulation domain.
Mass inflow and outflow rates
In this section, we examine the mass outflow rates that these jets drive.Since non-negligible gas motions exist in the absence of jets, we must analyse the jet-driven outflows by identifying and quantifying the ways in which the outflows differ from those found in the simulations without jets.To this end, Figs 5 and 6 show the time evolution of the vertical mass outflow rate profiles for the simulations without and with jets, respectively.In these figures, each panel shows the vertical mass outflow rate profile at times which are indicated in the top left.To examine the phase-structure of the outflow, we split the mass outflow rate into contributions from 'hot' ( > 5 × 10 5 K), 'warm' (2 × 10 4 < < 5 × 10 5 K) and 'cold' ( < 2 × 10 4 K) gas.Note that solid/dotted curves correspond to a net outflow/inflow of gas.
In the simulation without jets the gas motions at these altitudes must arise due to the merger dynamics and cooling of the hot halo.In the first and second panels of the top row of Fig. 5, gas motions are dominated by inflows, with warm and hot gas flowing back towards the midplane as the gas cools and falls into the potential well of the system.In the third panel, there is very little net motion of warm gas and, beyond 40 kpc, all hot gas is outflowing.This behaviour comes about due to the propagation of the quasi-spherical shock that results from the first passage of the galaxies.This outflow is initially seen in the hot gas but is subsequently followed by a slower-moving warm outflow (as can be seen in the panel corresponding to 0.61 Gyr).After the passage of this shock the gas settles into a new equilibrium, begins to cool, and net inflows resume.In the final panel, the galaxies have coalesced but the shock associated with this merger is much weaker and has not had time to propagate far enough to affect these outflow rate profiles.
Turning now to the simulation with jets, the early stages of a hot, jet-driven outflow can be seen in the first panel of Fig. 6.The warm gas profile is largely identical to that shown in Fig. 5, indicating that, at these early times, the jets are largely having an effect on the hot gas.After 0.08 Gyr, the hot outflow has already propagated out to 100 kpc and, beyond this time, a net hot outflow of ∼ 10 M ⊙ yr −1 that is fairly constant in height above the midplane persists throughout the remainder of the simulation.In the final panel (bottom right), however, there is evidence of an enhancement in this hot outflow rate which is due to the powerful jet outburst associated with the final coalescence of the galaxies, as seen in the bottom row of Fig. 1.
Whilst, initially, there is a net inflow of warm gas, after 0.08 Gyr the warm gas in the inner regions becomes outflowing due to the action of the jets.By 0.49 Gyr however, the jets have become relatively quiescent and the inner regions have returned to a state of net inflow.The fact that the warm outflow takes longer to propagate than the hot component indicates that it is moving more slowly.Indeed, this is the case, and we will discuss the dynamics of the gas shortly.When the jet activity increases again, net outflows of warm gas can be supported throughout the entire domain considered (see the final two panels).
One of the most obvious differences between Figs 5 and 6 is the presence of cold gas in the outflows of the latter.In the simulation without jets, all non-negligible gas motions occur in the hot and warm component meaning that the cold component in Fig. 6 must be attributed to the action of the jets.Processes that are likely responsible for the development of a cold phase are the condensation of the warm phase associated with the jet-driven outflows, the entrainment of SNdriven wind material and the direct expulsion of gas from the galactic discs, as discussed in Section 5.1.
After 0.08 Gyr, a significant cold gas component has already formed which, at this time, has a net outflow rate at all heights above the galactic disc, out to ∼ 95 kpc.The mass of cold gas in this region can be as high as ∼ 10 8 M ⊙ , corresponding to ∼ 1 per cent of the initial mass of gas in the galactic discs.As in the case of the warm component, when the jets are less active (see the third panel in the top row of Fig. 6) they are not able to maintain a net outflow and the inner regions become inflowing.At later times, there is evidence of both inflowing and outflowing cold gas at a range of different heights above the midplane.This highlights the fact that the cold gas does not exist as a 'monolithic' entity, but rather in clumps, clouds and streams (see e.g.Fig. 1).
In this section we have shown that significant amounts of cold outflowing and inflowing gas can be produced due to the action of the jets, thus enhancing the multiphase nature of the surrounding CGM and halo gas.It is also worth mentioning that the typical mass outflow rates in the cold gas (and even more so for the hot gas) are comparable or even higher than the mass inflow rate onto the -disc (shown in Fig. 2), highlighting the efficiency by which our simulated jets are able to launch these large scale outflows.
Outflow dynamics
In this section we examine the dynamics of these jet-driven gas motions with the aid of Fig. 7, which shows mass-weighted, joint probability density functions (PDFs) in log 10 (), and vertical velocity, , for the same volume of gas used to calculate the outflow profiles in Figs 5 and 6.The time to which each PDF corresponds is indicated in the top-left of the panel and is the same as those shown in Figs 5 and 6.Additionally, the vertical blue and red dashed lines separate the temperature space into cold, warm and hot gas, defined in the same way as in Section 5.5.1.
The pink contour in each panel outlines the region of phase-space occupied in the simulation without jets.From this, it is clear that in the absence of jets, the majority of the gas is hot and relatively slow moving, with maximum velocities typically not exceeding 500 km s −1 .There is also a non-negligible inflowing component in both the warm and hot gas, consistent with the discussion in Section 5.5.1.
The first panel in the top row corresponds to just after the jets have been launched and the hot, outflowing component is clearly visible, reaching temperatures of up to 10 8 K and velocities approaching 4000 km s −1 .By the time 0.08 Gyr have elapsed, the velocities of the gas, while significantly enhanced above what would be found if the jets were not present, no longer reach such extreme speeds.At this time, the hot gas has begun to cool and has led to the formation of an outflow of warm gas and an increase in the mass of the cold phase.Additionally, some of this cold and warm gas is now inflowing, albeit at comparatively low velocities.
After 0.49 Gyr, the gas has slowed considerably and a nonnegligible fraction of the cold and warm phases are inflowing, facilitating the net warm and cold inflows seen in the inner regions in the third panel of Fig. 6.When the jets become active again, as is the case in the bottom row of Fig. 7, the hot, fast, outflowing phase is replenished, with gas able to reach even more extreme velocities.At 0.61 Gyr, the cold and warm inflowing components remain and the outflow velocities are somewhat higher, but still significantly slower than those observed in the hot gas.At 0.82 Gyr, the hot outflow has cooled somewhat and has begun to repopulate the warm phase with gas that has velocities reaching ∼ 2500 km s −1 .
In the final panel, the gas associated with the jet outburst that populated the hot, fast phase in the previous two panels has cooled and slowed and, now, largely consists of gas with temperatures in the range 10 5 − 10 7 K with velocities that reach 2500 km s −1 .The powerful jet outburst associated with the merger is also clearly visible as it replenishes the hot phase with gas of temperatures and velocities that can exceed 10 8 K and 5000 km s −1 , respectively.
After the jets launch, the total mass of cold gas that lies beyond 20 kpc from the midplane rapidly rises to ∼ 10 7 M ⊙ and can even exceed 10 8 M ⊙ at times.Interestingly, the cold outflows can also have very high vertical velocities that reach ∼ 2500 km s −1 .
Line-of-sight velocities and velocity dispersions
In the previous section we explored the velocity structure of the gas above the galactic discs.Here, we extend this analysis and exam- ine spatially resolved maps of the line-of-sight (LoS) velocity and velocity dispersion in the merging system.Even in the absence of jets, the kinematics of the gas in a merging system are highly complex.Non-negligible gas motions arise due to the orbital motion of the galaxies, the rotation of the gas discs and the fact that the gas in the halo is not static.To understand how the presence of jets affects the gas kinematics we must, therefore, analyse the velocity structure of the simulations with jets through comparison to that of the simulations without jets.Ultimately this allows us to isolate the features of the velocity field that can be attributed solely to the action of the jets.
Figs 8 and 9 show, respectively, emission-weighted LoS velocity and LoS velocity dispersion maps.The first two rows of Figs 8 and 9, show edge-on projections and the third and fourth rows show face-on projections.The projections in the first three columns have dimensions of 100×100×100 kpc, whilst the fourth column shows a zoomin of the third, with dimensions 20 × 20 × 10 kpc and 20 × 10 × 20 kpc for the face-on and edge-on projections, respectively.The first and third rows correspond to the simulation with jets while the second and fourth rows show the simulations without jets.The time at which the projection was made increases from left to right and is indicated in the column headings.Note that these times are the same as those shown in Fig. 1.
At early times, in both the jet and no-jet cases, Fig. 8 shows that the most prominent feature in the LoS velocity field is orbital motion of the galaxies.As well as this, the rotation of the galactic discs and the outflows and inflows associated with the galactic winds are clearly imprinted in the edge-on and face-on projections, respectively.In the edge-on projections, it is possible to discern the jets, although their impact on the structure of the velocity field is relatively moderate at this time.The effects of the jet on the velocity dispersion (Fig. 9), however, are much more obvious.The jets have inflated cocoons of turbulent gas that has velocity dispersions reaching several 100 km s −1 .These jet-driven outflows with high velocity dispersions are clearly distinct from the outflows associated with the galactic winds which, we can see, typically exhibit much lower velocity dispersions, and provide a good way for observations with spatially resolved kinematics to distinguish these two different types of outflows.
After 0.61 Gyr (the middle columns of Figs 8 and 9) the jets are now clearly having an impact on the gas velocity, with significant enhancements seen in both the vertical and the lateral components of the velocity.This is particularly evident when considering the jet launched from the galaxy on the right-hand side of these projections which, as has been discussed in previous sections, is more powerful at this time.As well as having a greater impact on the velocity field, this jet is also clearly causing significant enhancement of the velocity dispersions, indicative of highly turbulent flows.The jet on the left-hand side, however, is relatively quiescent at this time and the velocities and velocity dispersions of the gas in the vicinity of this galaxy are lower.Nevertheless, both jets are associated with velocities and velocity dispersions that are higher than those found in the simulation without jets, in which the highest velocities are largely confined to the galaxy discs and the winds, which typically do not extend further than 10 kpc from the orbital plane.
One other feature to highlight, is the fact that the velocity distribution of the gas in the jet-driven outflows has multiple components.This comes about due to the fact that, as discussed in Section 5.2, the power of the jets and, therefore, the velocity of the gas at the base of the jet, can be significantly variable.Additionally, as the jets propagate away from the midplane, they interact with the galactic winds.Altogether this acts to disrupt the jet-lobes and enhances the 1 ⊙ ) gas (orange curve) and the total metal mass (green curve) in the gas that lies between 20 and 100 kpc above the midplane and within 100 kpc of the centre of the simulation domain.The solid/dashed curves correspond to the simulations with/without jets.levels of turbulence in the resulting outflows, which is clearly seen in Fig. 9.
As has been discussed in previous sections, at 0.61 Gyr gas is condensing out of the jet-driven outflows (see Fig. 1) and falling back towards the orbital plane of the galaxies.From Fig. 9 it is clear that this cooling gas typically has lower velocity dispersions which are comparable to those found in the galactic winds than those of the newly launched jet material.
At later times (third column of Figs 8 and 9), as the galaxies coalesce, the fast, bipolar outflow associated with the powerful, postmerger jets is clearly visible in the LoS velocity map, with velocities exceeding 1000 km s −1 seen in both the edge-on and face-on maps.Due to the fact the more powerful jet is significantly inclined to the vertical at this time (see Fig. 2), both the redshifted and the blueshifted components of the outflow can be seen in the face-on map.The velocity dispersions also exhibit significant enhancement, and can exceed velocities of 1000 km s −1 .This is in contrast to the run without jets where the velocities and velocity dispersions remain moderate, despite the violent motions associated with the coalescence of the galaxies.These differences between the mergers with and without jets can also be seen in the inset projections, shown in the final columns of Figs 8 and 9, which highlight the considerable impact that jets can have on the properties of the merger remnant.
Metal enrichment
In this section, we investigate the extent to which the jets are able to influence the metallicity of the gas in the halo.Fig. 10, shows the mass of enriched ( > 0.1 Z ⊙ ) gas (orange curve) and the total metal mass (green curve) in the gas that lies between 20 and 100 kpc above the midplane and within 100 kpc of the centre of the simulation domain (this is the same region as considered in Sections 5.5.1 and 5.5.2).The solid/dashed curves corresponding to the simulations with/without jets.
It is clear that, after ∼ 0.1 Gyr there is a non-negligible mass of enriched gas in this region in the simulations with jets.A mass of at least 2 × 10 9 M ⊙ of enriched gas is then maintained in the halo throughout the rest of the simulation.This observation is consistent with our discussion in Section 5.1, where the projections in Fig. 1 indicated that the metallicity of the halo in the simulations with jets is enhanced relative to that in the simulation without jets.Indeed, Fig. 10 shows that the simulation without jets have very little enriched gas present in this region, above the midplane, with the mass of enriched gas only briefly exceeding 10 7 M ⊙ .Examining radial metallicity profiles (not shown in this paper) we find that, with jets, the average metallicity is above 0.1 Z ⊙ out to ∼ 75 kpc at first passage and then beyond 150 kpc post-merger.When jets are not present, however, the average metallicity remains above 0.1 Z ⊙ at all times only within a sphere of radius ∼ 20 kpc.
The mass of metals in this region is non-negligible at all times after ∼ 0.1 Gyr in the simulations with jets and largely stays above 10 7 M ⊙ .On the other hand, the metal mass for the simulations without jets is not shown in Fig. 10 as we found it to be insignificant at all times.Focusing on the simulations with jets and comparing the time evolution of the mass of enriched gas and metals to that of the jet power, shown in Fig. 2, one can tentatively associate times at which the metal content and the mass of enriched gas increases with peaks in the jet power (with a slight time-delay due to the time it takes to 'communicate' changes in jet power to the halo gas).
Altogether, this analysis highlights the fact that jets can be very efficient at drawing enriched material up out of the galaxy and into the halo.In fact, it is likely that the jets would be even more efficient at enriching the halo gas due to the fact that, as discussed in Section 5.4, mass and metal return from stars in the jet lobes is purposefully suppressed in our simulations.
DISCUSSION: BLACK HOLES IN GALAXY MERGERS
Previous works that have investigated galaxy mergers using simulations have shown that the inclusion (or lack thereof) of wide range of physical processes and components such as: galactic winds, black hole accretion and feedback and the presence of a hot, gaseous halo or a stellar bulge can have significant and varied impacts on the progression and outcome of the merger.In this section we first discuss the ways in which the effects of kinetic AGN jets on the mergers differs in comparison to other merger simulations and outline some of the limitations of our simulations.
The effects of black hole jets in galaxy mergers
Many idealised merger simulations that model black hole feedback processes do so by injecting thermal energy into the gas cells local to the black holes (Springel et al. 2005a;Di Matteo et al. 2005;Robertson et al. 2006;Johansson et al. 2009;Hayward et al. 2014;Gabor et al. 2016).Some also consider injecting a kinetic wind (Barai et al. 2014;Costa et al. 2020;Torrey et al. 2020).Investigations of AGN feedback from black holes in galaxy mergers have also been carried out within the context of large-volume cosmological simulations (see e.g.Rodríguez Montero et al. 2019;Hani et al. 2020;Quai et al. 2021) and also in cosmological zoom simulations (see e.g.Sparre & Springel 2016;Whittingham et al. 2021).The effects of kinetic AGN jet feedback in merging galaxies (as investigated in this work), however, remains largely unexplored.
When the galaxies in our simulations pass each other for the first time, they experience strong tidal forces which lead to the formation of extended tidal tails and bridges of gas and stars.As the galaxies approach for the second time during the final coalescence, the tidal torques are even more extreme, ultimately resulting in the formation of a compact dense merger remnant.This general picture of the progression of the merger and the qualitative morphology of the two galaxies in our simulations are consistent with the majority of existing literature (see e.g.Springel et al. 2005a;Cox et al. 2006a;Johansson et al. 2009;Teyssier et al. 2010;Moster et al. 2011;Hopkins et al. 2013;Hayward et al. 2014) and is, therefore, largely unchanged by the presence of the jets.
In general, the behaviour of the SFR (see Section 5.4) in our simulations is also qualitatively similar to those found in other merger studies (provided that the galaxies in question have stellar bulges; see e.g.Springel et al. 2005a;Johansson et al. 2009;Moster et al. 2011).The magnitude of the SFR in our simulations is only moderately suppressed when jets are present (see Section 5.4).We do not, however, see evidence for rapid quenching as a result of the final coalescence of the galaxies.This is in contrast to some works who find that extreme tidal torques during the final coalescence drive significant inflows, enhancing the black hole accretion rate and powering the AGN which results in star formation being shut off and black hole growth stalling (see e.g.seminal papers by Di Matteo et al. 2005;Springel et al. 2005a).
One of the reasons for this difference may be due to the fact that the AGN feedback models used in such simulations are typically intended to reproduce the effects of the 'quasar mode'.This is often implemented via an isotropic injection of energy, whereas the jets in our simulations are highly collimated, meaning that a greater fraction of the injected energy is likely deposited in the halo, rather than impacting the galactic disc.The jets, therefore, typically do not clear out significant quantities of gas from the centre and likely have a comparatively smaller effect on the outer disc when compared to an isotropic energy injection, ultimately making it harder for jets to quench star formation and suppress the growth of the black holes.Detailed numerical simulations of a variety of AGN feedback mechanisms (e.g.energy-, radiation-pressure-driven outflows and jets) within a spatially resolved ISM will be needed to understand which physical processes lead to galaxy quenching.
The reduction of the SFR that we find occurs when jets are present is in general agreement with studies that suggest that jets may prevent catastrophic cooling in clusters and regulate the gas accretion rate and, therefore, the star formation rate (see e.g.Gaspari et al. 2011;Yang & Reynolds 2016;Prasad et al. 2020).But other studies find that jets may have a positive feedback effect, resulting in enhanced star formation rates (Gaibler et al. 2012;Fragile et al. 2017;Mukherjee et al. 2018).It is likely, however, that jets are capable of having both a positive and a negative effect on the star formation rate (see e.g.Mandal et al. 2021), depending on the properties of the jet as well as the local-and wider-environment.To shed further light on the effects of jets on the SFR during a galaxy merger, analogous simulations would need to be carried out with a more accurate model for star formation and stellar feedback, including a resolved ISM, as well as probing a wider range of merging systems.
Additionally, simulations have shown that jets may act to change to location of star formation (Nayakshin & Zubovas 2012;Zubovas & Bourne 2017) with observations indicating that star formation in AGN-driven outflows may well be occurring (see e.g.Maiolino et al. 2017).So, while the total SFR may be reduced by AGN, the location of the star formation may be changed, with enhancements seen in some areas and reductions seen in others.
Another point worth noting is that our black hole accretion prescription differs from the commonly-used Bondi-Hoyle-like prescriptions.As discussed in Section 5.2, the inflow rates onto the sub-grid -disc that we measure in our simulations can be rather sporadic due to the fact that we only allow gas to reach the sub-grid -disc if it is radially inflowing and able to circularise at a radius smaller that the outer edge of the -disc.With this accretion prescription, we find that accretion rate due to the coalescence of galaxies is not as significant as those in Di Matteo et al. (2005); Springel et al. (2005a) and is comparatively short lived.This likely also contributes to the fact that the AGN outburst associated with coalescence that we observe is less effective at quenching the galaxies and suppressing black hole growth.We do, however, find that, before the coalescence, the magnitude of the inflow rates in our simulations are largely similar to those measured in analogous simulations (Springel et al. 2005a).This highlights a very important open question in the field, and more studies focusing on accurate and realistic modelling of black hole accretion are needed to understand how gas is delivered from large scales to the actual accretion discs.It is worth emphasising that Bondi-like accretion flows are radiatively inefficient.Therefore, to explain the quasar phenomena, ultimately, simulations modelling accretion discs, as attempted here, are required.
Whilst not shown explicitly in this paper, we find that the black hole mass growth throughout the simulation is rather moderate.This is even true during the final coalescence of the galaxies where we do find somewhat elevated accretion rates.This can be attributed to the fact that, in our jet feedback model, the black hole spin energy (and, therefore, its mass) is the source of energy powering the jet, along with the accretion flow (this was discussed at length in Talbot et al. (2022)).This is in contrast to many other works that focus on black holes in merger scenarios, where the black hole growth typically traces the accretion rate.
In Section 5.3 we saw that the launching of the jets can induce the condensation of hot gas out in the halo.This gas can then fall back onto the galaxies and provide additional fuel for the black hole.Such processes have been proposed and investigated in works such as Gaspari et al. (2013Gaspari et al. ( , 2015)), where this process is termed 'Chaotic Cold Accretion' (see also McCourt et al. 2012;Prasad et al. 2015;Wang et al. 2019).Such processes clearly have the potential to be an important source of fuel for black holes, both within merger scenarios and otherwise.But, as highlighted here, 'secular' processes, mergerinduced torques and jet-driven backflows (Antonuccio-Delogu & Silk 2010; Cielo et al. 2014;Bourne & Sijacki 2017) also lead to fresh gas inflows onto the -discs.
Limitations of the simulations
For an extensive discussion of the key assumptions and limitations of our black hole accretion and jet feedback model, we refer the reader to Talbot et al. (2021) and Talbot et al. (2022).It is, however, worth reiterating a few key points that are particularly relevant to the work presented in this paper.Firstly, we use GRMHD simulation data to parameterise the magnetic flux on the black hole horizon and the data we use corresponds to that which would be found when the accretion disc is in a magnetically arrested (MAD) state.This means that, for a given accretion rate, the powers of the jets are likely on the high end of what would be expected in reality.In addition, the power of the jet will depend on the choice of initial black hole spin, for which we only consider one initial, relatively high value ( 0 = 0.7) in this work.Our model also assumes that the sub-grid accretion disc is geometrically thin, following the Shakura-Sunyaev solution (Shakura & Sunyaev 1973).A more complete picture would require the incorporation of accretion prescriptions to describe the gas flow when the black hole is surrounded by a thick or possibly 'truncated' accretion disc.
Whilst the mass of stars particles that are present in the outflow in the runs with jets is similar to those without jets, we do find that in the jet simulations the mass of star forming gas in the outflows is higher, highlighting the potential for jet-driven outflows to entrain and draw up star forming gas from the disc, as well as induce the formation of stars in the outflows themselves.To properly assess the viability and effects of star formation in the jet-driven outflows, however, a better model of star formation and feedback would be required, which explicitly models the multiphase structure of the ISM.Such a model would also be required to properly assess the effects of the jets on the stars in the disc and remnant, as well as allowing for a more accurate estimate of the black hole accretion rate.
It is also worth highlighting, again, the highly idealised nature of the setup explored in this work.Future simulation work should include the of the wider cosmological environment, so as to properly capture the effects of cosmic gas inflows which, at this redshift, should significant.We have, however, attempted to somewhat mitigate some of these effects through our choice of eEOS parameters (see Section 3.1) and by modelling the hot halo gas.Future work that aims to assess the impact of jets on mergers should also probe a wider range of orbital configurations, mass ratios and black hole spin directions and magnitudes.
Finally, with regard to physical processes, these simulations do not include the effects of magnetic fields.The inclusion of MHD effects in future simulations will be crucial if we are to make accurate predictions for the radio emission and, indeed, ensure that the simulations accurately capture the gas dynamics and properties of the discs in the merger (see e.g.Whittingham et al. 2021).
CONCLUSIONS
In this paper, we presented simulations of AGN jets in the context of a major merger of two gas-rich galaxies, such as would be found at cosmic noon.These simulations follow the progression of the merger through first passage and up to the final coalescence, modelling the black holes at the centres of both of the merging galaxies using our prescription for black hole accretion via an -disc and feedback in the form of a spin-driven jet.The analysis of the simulations, presented in this work, focused on exploring the fuelling of black holes and self-regulation of AGN jets as they are subject to these extreme merger environments, as well as how the presence of AGN jets affects the SFR, outflows, galaxy kinematics and CGM metal enrichment.Analysis of simulations such as these will play a central role in making precise predictions for multimessenger investigations of dual radio-AGN, which next-generation observational facilities such as LISA, Athena and SKA will make possible.The work we have presented here is a first step in this direction.
Our main results are: • Jets launched by black holes at the centre of galaxies that are undergoing a major merger are capable of driving large-scale, multiphase outflows, whose kinematic is complex and characterised by large velocity dispersions.
• Specifically, the jets lead to generation of significant quantities of cold gas out to distances of ∼ 100 kpc by entraining and drawing-up cold gas from the galactic discs, and also by promoting the formation of thermal instabilities in the hot halo gas.
• The velocities of the hot gas in the outflow can exceed 5000 km s −1 while the warm and cold components are typically slower, but still reaching velocities of order ∼ 2500 km s −1 .
• The gas in the outflows can, eventually, decelerate, cool and fall back down towards the orbital plane of the galaxies.This inflowing, cool gas can provide a rich source of fuel for the black hole if it falls back onto the central regions of a galaxy, ultimately resulting in further episodes of intense jet activity.
• In fact, in these merger scenarios, the black hole feeding is mediated by the interplay between four distinct processes: (i) secular inflows of gas from the galactic discs, (ii) the funneling of low angular momentum gas towards the black hole by small-scale backflows, (iii) the infall of cold gas that has cooled out of the hot, jet-driven outflow and (iv) extreme merger torques driving inflows towards the centre.
• The jets associated with black holes that are primarily fed by the infalling gas (rather than by gas accreted from the galactic disc) can be torqued significantly due to the fact that the infalling gas that is accreted does not necessarily have angular momentum direction perpendicular to the plane of the galactic discs.
• AGN jets are able to moderately suppress star formation at all times during the merger and can lower the peak SFR attained during the final coalescence of the galaxies by a factor of ∼ 2, but they do not lead to rapid quenching of galaxies.It remains to be understood if alternative AGN fuelling or feedback are needed for rapid shutdown of star formation.
Figure 1 .
Figure1.A qualitative overview of the simulation with jets.In each of the three rows, the main panel shows a projection of the gas temperature.The four smaller panels show (clockwise, starting in the top left) the star formation rate, the gas density, a passive tracer injected with the jet and the gas metallicity.Each row shows the state of the system at an important moment in the merger process (as indicated by the row headings) and the time to which this corresponds is shown in the top left hand corner of each of the main panels.The position of the black holes are indicated by coloured markers in the temperature panel, using the same colours as those of the lines in Fig.2.Note that in the bottom row, the black hole separation is very small so the markers are largely overlapping.
7 i.e. in the region 20 − 100 kpc above and below the midplane and within a cylindrical radius of 100 kpc from the centre of the box (see Section 5.5).
Figure 2 .
Figure2.The evolution of the jet powers (top panel), the time-averaged mass inflow rates onto the sub-grid -discs (middle panel) and the evolution of the inclinations of the jets (i.e.black hole spins) to the vertical (bottom panel).In the middle panel, the shaded regions bound the range of observed inflow rates and the dotted lines indicates the inflow rates measured in the analogous simulation without jets.The properties of the two black holes in each simulation are indicated by the different colour curves (see the legend in the top panel).These colours match those of the markers in Fig.3that indicate the location of each black hole, and the black markers indicate the properties of the relevant black hole at each of the times shown in Fig.3.The grey, vertical, dotted line indicates the time at which the jets first become active.
Figure3.Thin projections (of dimension 1 × 1 × 0.4 kpc) for the simulation with jets which show, in the left-hand column, the magnitude of the specific angular momentum of the gas where the arrows correspond to the streamlines of the velocity field, and in the right-hand column, the temperature of the gas.In the first row, the projections are centred on BH 2 and then in the second and third rows, the projections are centred on BH 1.In each panel, the locations of the black hole(s) are marked with a coloured dot, using the same colours as those of the lines in Fig.2.
Figure 4 .
Figure3.Thin projections (of dimension 1 × 1 × 0.4 kpc) for the simulation with jets which show, in the left-hand column, the magnitude of the specific angular momentum of the gas where the arrows correspond to the streamlines of the velocity field, and in the right-hand column, the temperature of the gas.In the first row, the projections are centred on BH 2 and then in the second and third rows, the projections are centred on BH 1.In each panel, the locations of the black hole(s) are marked with a coloured dot, using the same colours as those of the lines in Fig.2.
Figure 5 .Figure 6 .
Figure 5.Time evolution of the vertical mass outflow rate profiles for the simulation without jets.Each panel shows gas between 20 − 100 kpc above the midplane and within a cylindrical radius of 100 kpc relative to the centre of the simulation domain.The time to which the profiles correspond is indicated in the top left of each panel.The mass outflow rate is split into contributions from hot ( > 5 × 10 5 K) and warm (2 × 10 4 < < 5 × 10 5 K) gas (see legend in the top left panel).Note that, in this simulation, there is no cold ( < 2 × 10 4 K) gas present at these vertical distances from the midplane.Solid/dotted curves indicate a net outflow/inflow of gas.
Figure 7 .
Figure7.Each panel shows, for the simulation with jets, mass-weighted, joint PDFs in log 10 ( ) and for gas within a cylinder of radius 100 kpc relative to the centre of the simulation domain and with vertical extent 20-100 kpc above the midplane.The time to which each PDF corresponds is indicated in the top left of the panel and is the same as those shown in Figs5 and 6.In each panel, the pink contours outline the region of phase-space occupied in the matching simulation without jets.The vertical blue and red dashed lines separate the temperature space into cold, warm and hot gas, using the same definitions as in Figs 5 and 6.
Figure 8 .Figure 9 .
Figure 8. Emission-weighted LoS velocity maps.The projections in the first two rows are edge-on and those in the third and fourth rows are face-on.The first and third rows correspond to the simulation with jets while the second and fourth rows show the simulation without jets.The first three columns correspond to projections of gas within a volume of 100 × 100 × 100 kpc and the time at which the projection was made increases from left to right and is indicated in the column headings.The fourth column shows a zoom-in of the third, with dimensions 20 × 20 × 10 kpc and 20 × 10 × 20 kpc for the face-on and edge-on projections, respectively.
Figure 10 .
Figure10.The mass of enriched ( > 0.1 ⊙ ) gas (orange curve) and the total metal mass (green curve) in the gas that lies between 20 and 100 kpc above the midplane and within 100 kpc of the centre of the simulation domain.The solid/dashed curves correspond to the simulations with/without jets. | 20,980.4 | 2023-06-12T00:00:00.000 | [
"Physics"
] |
“Crawling” on the self-assembly system: A molecular simulation of peptide position adjusting over self-assembly block
By combining non-equilibrium molecular dynamics(NEMD), umbrella sampling, and weighted histogram analysis method(WHAM), we calculated the potential of mean force of histidine peptide moving over a self-assembly structure. The reaction coordinate is along the main chain direction of the histidine peptide in the self-assembly structure. It is found that the energy needed for the histidine peptide with 3 and 5 residues while moving along the reaction coordinate is around -2.2 kCal/mol and -7.4 kCal/mol, respectively. And the histidine peptide crawls along the reaction coordinate, performing a snake-like movement. This result could illustrate how histidine peptide adjusts its position during self-assembly process.
Introduction
Self-assembly phenomenon is common in nature and many materials with excellent optical, electrical, catalytic properties such as nonlinear optics, chemical biosensors, information storage materials, and tissue growth scaffolding materials, are produced by self-assembly. Molecular self-assembly is the process by which molecules adopt a defined arrangement without guidance or management from an outside source. Noncovalent interactions (e.g., hydrogen bonding, metal coordination, hydrophobic forces, van der Waals forces, π-π stacking, and/or electrostatic) are crucial to the self-assembly process. [1] Histidine is an α-amino acid that is used in the biosynthesis of proteins. It contains an αamino group, a carboxylic acid group, and an imidazole side chain (which is partially protonated), classifying it as a positively charged amino acid at physiological pH. Histidine was first isolated by German physician Albrecht Kossel and Sven Hedin in 1896. The imidazole ring of histidine is aromatic at all pH values [2] and it can form π stacking interactions [3].
The self-assembly of histidine peptide is by hydrogen bonding between the backbone of the peptide, forming an antiparallel β-sheet second structure. What we are interested in is the self-assembly process. For the initial stage of the self-assembly process is always at random, and the final result is "neat" structure. To find out how histidine peptide adjust itself into neat structure is the target of this article. One guess is that histidine peptide is like Lego peptide [4] which can "slide" on the surface of the assembly only that instead of hydrophobic effect of the residues, there are other mechanisms for histidine peptides. Thus, we perform the following simulation, trying to find out the energy needed for the histidine peptide to adjust its position over the assembly block.
Constructing histidine peptide and histidine self-assembly block
We use full atom model to do our simulation so that a clearer picture can be obtained. First, we obtain the peptides with 15 histidine residues to build an antiparallel β-sheet second structure. Then the structure is equilibrated in water at 300K and 1.0 bar of pressure for 10 ns. Two layers in the middle of the structure are taken out, one as our "reaction" surface, the other being the source of histidine peptide which will be pulled over the reaction surface, showing in Fig 1-A Then 2 types of histidine peptides are "cut" (unnecessary residues removed and only the peptide in the middle of the three chains is kept) from the layer described above, producing 2 different lengths (with 3,5 residues) of histidine peptide. The initial model showing in
Umbrella pulling
To calculate the PMF of the peptide along the reaction coordinate, a non-equilibrium pulling method is used. [5].
When the distance between two groups is changed continuously, work is applied to the system, which means that the system is no longer in equilibrium. However, one can use the Jarzynski relation [6] to obtain the equilibrium free-energy difference ∆G between two distances from many non-equilibrium simulations: where WAB is the work performed to force the system along one path from state A to B, kB is the Boltzmann constant, T is temperature and the angular bracket denotes averaging over a canonical ensemble of the initial state A and β = 1/kBT The pull code applies forces or constraints between the centres of mass of one or more pairs of groups of atoms. In this article, an umbrella pulling is used, in which case a harmonic potential is applied between the centres of mass of two groups. Thus, the force is proportional to the displacement. And the peptide, in our case, is "dragged" by the force along the reaction coordinate. Fig.2 illustrates the umbrella pulling simulation. The alpha-carbon of the first residue is attached to a virtual spring with the force constant being 1500. The pulling is in the direction of the main chain and the other two directions' movements are restrained. The pulling simulation duration is 500 PS of MD, saving snapshots every 1 PS and the distance pulled is 5.0 nm. After the pulling simulation, a series of configurations are generated and selected so that configurations are of 0.2nm spacing. Fig. 2. The pulling simulation. The red arrow represents the force applied by the virtual spring.
Umbrella sampling
The binding energy (ΔGbind) is derived from the potential of mean force (PMF), extracted from a series of umbrella sampling simulations. A series of initial configurations is generated along the reaction coordinate ξ, as described above, each corresponding to a location wherein the ligand is harmonically restrained at increasing centre-of-mass (COM) distance from a reference molecule using an umbrella biasing potential. This restraint allows the ligand to sample the configurational space in a defined region along a reaction coordinate between it and its reference molecule.
In this article, we sample COM distances from 2.0 -5.0 nm (and 1.1 -3.4 nm for the 3residue model) along the x-axis using roughly 0.2-nm spacing. Start by running a brief NPT equilibration in each window. Then each window is sampled 10 ns.
PMF extraction using WHAM
The Weighted Histogram Analysis Method (WHAM) is one of the earliest methods that take into account information from all Intermediate States. The precursor to WHAM and first version of multiple histogram relighting techniques came from Ferrenberg and Swendsen; [7] WHAM was developed later for alchemical simulations [8]. WHAM is included in GROMACS as the wham utility, which this article uses to extract the potential of mean force.
Results of Molecular Dynamics Simulation
The potential of mean force profile is extracted by WHAM. We can thus calculate the energy needed for the histidine peptide to "crawl" on the reaction surface, which is -7.4kCal/mol for peptide with 5 residues of His and -2.2kCal/mol for peptide with 3 residues. From the PMF curve, we can also see that the process is periodic, which is reasonable as the peptide moving along the reaction coordinate, antiparallel β-sheet structure is periodically formed. The bottom of the curve means that such antiparallel β-sheet is formed and the peak means that nearly all of the hydrogen bonds between the main chain of histidine peptide and the assembly block are broken.
From the simulation result we can see that the peptide moves like snake to adjust its positions along the reaction coordinate. The simulation result shows in Fig.3-A,B. Fig. 3. A: The peptide with 5 residues of His. "crawling" along the reaction coordinate, with labels from "a" to "e" indicating different state on the PMF curve. B: Peptide with 3 residues, from one stable state to another, and PMF curve. C: Backbone molecules' hydrogen bonds amount change in one period, with black representing peptide with 5 His, red 3 His. The Position 1 to 5 are positions shown in A, from position "a" to position "e", respectively.
Conclusions
From the PMF curve obtained above, we can find that the energy needed for the histidine peptide to adjust its position along the reaction coordinate is not much (-7.4kCal/mol and -2.2kCal/mol for peptide with 5 His residues and 3 His residues, respectively). This means the peptide could easily move on the assembly surface and find its stable position by "crawling" and form a neat β-sheet structure. And the difference between the peptides of two different lengths (3 residues and 5 residues), we guess, probably suggests that the energy required to make position adjustment is in associate with the hydrogen bonds amount which is formed between the backbone atoms of histidine peptides. The hydrogen bonds (on backbone atoms) average amount's change in one period of the "crawling" process proves that what we guess is true.
Firstly, the hydrogen bonds of the residue which the virtual spring is attached are broken, causing the hydrogen bonds on the residue next to it broken too. The "free" residue moves to either side of the reaction layer due to the effect of the side chain interaction. From the Fig.3-C we can see the amount change of hydrogen bonds in one moving period, and position 1 to 5 are .
To sum up, the histidine peptide could adjust its position via interacting with other histidine peptide chains in the self-assembly surface, doing a snake-like "crawling". This could indicate that in the self-assembly process, the histidine peptides would form the assembly block in a similar pattern. | 2,076.2 | 2018-01-01T00:00:00.000 | [
"Chemistry"
] |
Full phase diagram of isolated skyrmions in a ferromagnet
Magnetic skyrmions are topological quasi particles of great interest for data storage applications because of their small size, high stability, and ease of manipulation via electric current. Theoretically, however, skyrmions are poorly understood since existing theories are not applicable to small skyrmion sizes and finite material thicknesses. Here, we present a complete theoretical framework to determine the energy of any skyrmion in any material, assuming only a circular symmetric 360$^\circ$ domain wall profile and a homogeneous magnetization profile in the out-of-plane direction. Our model precisely agrees with existing experimental data and micromagnetic simulations. Surprisingly, we can prove that there is no topological protection of skyrmions. We discover and confirm new phases, such as bi-stability, a phenomenon unknown in magnetism so far. The outstanding computational performance and precision of our model allow us to obtain the complete phase diagram of static skyrmions and to tackle the inverse problem of finding materials corresponding to given skyrmion properties, a milestone of skyrmion engineering.
While homochiral skyrmions were first observed in bulk materials with broken inversion symmetry, multilayers stacks, such as such as Pt/Co/Ir [17], Ta/CoFeB/TaO x [21,22], Pt/Co/Ta [19], and Pt/CoFeB/MgO [19,23] with arbitrary repetitions of these layers, have seen increasing popularity recently. In such structures, inversion symmetry breaking and spin-orbit coupling at the ferromagnet/heavy-metal interface can lead to a strong DMI that promotes magnetic skyrmions with well-defined chirality. Multilayers are particularly attractive since the relevant energy terms (e.g., anisotropy, DMI, magnetostatic) can be engineered by controlling the interfaces and the volume fraction of magnetic material in the stack. However, with this flexibility comes a considerable engineering challenge in that the parameter space for engineering is overwhelmingly large: It has six dimensions (five material parameters plus external magnetic field) and thanks to the interfacial origin of many magnetic properties and to the effective medium scaling of these properties with the thickness of non-magnetic spacer layers [19,24], all of these dimensions can be tuned individually. Blind experimental investigation of this parameter space is hence prohibitive. Existing theories are of limited help, since they either involve crude approximations that fail to reproduce the wealth of skyrmion states even qualitatively [12,[25][26][27][28] or contain unsolved complicated integrals and differential equations, which renders the evaluation of the theory extremely computationally expensive and slow (for instance, micromagnetic simulations and Refs. [29][30][31]). In particular, most theories ignore the non-local nature of stray field interactions [28,[30][31][32], which is responsible for many interesting features in the intermediate film thickness regime. A single coherent theory that quickly and accurately predicts the existence and the properties of isolated skyrmions for any given point in the six-dimensional parameter space remains elusive. Here, we provide a theoretical model for all energy terms of an isolated skyrmion in a given material with infinite in-plane extent. The model is fully analytical and accurate to 1 % in the entire parameter space. Thanks to the analytical nature, we can find energy minima extremely quickly by searching for roots of the partial derivatives. The resulting equilibrium states show excellent agreement with simulations and experiments. Using our theory, we find exotic new states, such as multi-stabilities (co-existence of skyrmions with different properties in the same sample under the same conditions), zero stiffness skyrmions, and zero field skyrmions. These new states can have many novel applications, some of which we suggest here. We obtained the minimum energy skyrmion states for millions of material parameter and field combinations in less than a week, yielding the full phase diagram of skyrmion states and demonstrating that our model is suitable to solve the inverse problem of engineering skyrmion properties through material selection.
Our theory takes as input the uniaxial anisotropy constant K u , saturation magnetization M s , exchange constant A, interface and bulk DMI strengths D i and D b , magnetic layer thickness d, and applied out-of-plane field H z . Given these parameters, we derive the energy function that determines the spin structure of skyrmions in any material. In general, stable skyrmions correspond to sufficiently deep minima of the energy functional E[m] of all possible spin structures m(r), where m is the unit magnetization vector and r is the position vector. In practice, minimizing the energy functional in its raw form, as done in micromagnetic simulations, is prohibitively slow for systematic skyrmion engineering. Our simple and efficient analytical model is enabled by the recent experimental confirmation [15,18] of an analytic and universal 360°DW model [33] for the spin structure m(r) of all skyrmions. The full analytic model for the total energy function is provided in the supplemental information, along with a detailed discussion of how to solve the integrals of the individual interactions. Here, we focus on its implications.
Effective energy contributions
The skyrmion spin structure (Fig. 1a) can be accurately described by four parameters: radius R, DW width ∆, DW angle ψ, and topological charge N. R and ∆ are independent parameters that determine the magnetization profile m z (x, y), whereas ψ specifies whether the in-plane component of the DW spins is radial (Néel, ψ = 0, π), azimuthal (Bloch, ψ = π/2, 3π/2), or intermediate (transient). For large ρ = R/∆, skyrmions consist of an extended out-of-plane magnetized domain bounded by a narrow circular DW, while for ρ ∼ 1 the inner domain is reduced to a point-like core resembling a magnetic vortex. We refer to these limiting cases as bubble skyrmions and vortex skyrmions, respectively, consistent with the literature, but note that many skyrmions observed recently [8,17,19,34] showed intermediate values of ρ and cannot be classified distinctly.
The case of bubble skyrmions is readily treated analytically through the so-called wall-energy model known for a long time [35]. In this limit, the skyrmion energy simplifies to with 2πdσ DW R being the DW energy (σ DW is the energy density of an isolated DW), aR + bR ln(R/d) the Zeeman-like surface stray field energy, and cR 2 the Zeeman energy. Here a, b, c are material-dependent parameters that include corrections to the original model [35] to account for DMI, volume charges, and finite ∆ (see supplemental information). The crucial assumption of Eq. (1) is that σ DW , ∆, and ψ do not depend on R and that hence ∆ and ψ can be obtained from minimizing σ DW while R is the minimum of E assuming a constant σ DW . The simplicity of this wall-energy model is reason for its popularity [28,36]. However, our accurate model shows that ∆ can change with R even up to unexpectedly large radii of R = 100∆. Indeed, we find that the wall-energy model fails quantitatively and qualitatively for almost all skyrmions that are of interest today. Our model fully accounts for the correlation between radius, DW width and DW angle, thereby providing a single theoretical framework that accurately describes any skyrmion. Importantly, our model reveals unexpected and qualitatively new exotic behaviors that are precluded by the approximations inherent in prior treatments.
Our analytic equations for the total energy function can be numerically minimized with respect to R, ∆, and ψ, for a given set of material parameters and external field H z , to obtain the equilibrium skyrmion configuration. The predictions of our model agree precisely with micromagnetic simulations and with the experimental data of Romming et al. [15], see Fig. 1b. Note that fields are negative in our convention, antiparallel to the skyrmion core. Heuristically, we find that the function fits the field dependence of the equilibrium radius for a wide range of parameters.
Our model yields the energy of a given skyrmion configuration in less than a millisecond on a regular personal computer, thereby providing dramatic improvement over micromagnetic simulations in terms of [15], and the solid grey line is a fit with the simple model of Eq. (2). The insets show the relaxed spin structures obtained from micromagnetic simulations corresponding to the large solid data points. c, Total energy E, normalized to Ad, as a function of skyrmion radius at a field of µ 0 H z = −2 T. At each point of the solid red line the energy has been minimized with respect to ∆ and ψ while keeping R fixed. R eq is the equilibrium radius as plotted in b. d-f, Decomposition of the total energy in c into individual components. d, DW energies: DMI energy (inverted), exchange energy, effective anisotropy energy, and volume stray field energy (multiplied by 10). e, Bulk energies: effective Zeeman energy and remaining surface stray field energies (multiplied by 10). f, Sum of all DW and sum of all bulk energies, together with the total energy. In this particular example, the sum of DW energies has a negative slope at the minimum of the total energy, which makes this skyrmion DMI stabilized according to our definition. The parameters for this data set are experimental values from Ref. [15] and our predictions are in excellent agreement with the experimental observations. computation speed. Moreover, it gives access to information that cannot be obtained by simulations. For example, by virtue of taking partial derivatives, the model allows to minimize ∆ and ψ for any non-equilibrium skyrmion radius and, therefore, to obtain the energy as a function of radius E(R). Micromagnetic simulations can only yield the equilibrium R, ∆, ψ. The E(R) curve directly relates to the skyrmion stability (by quantifying the energy barriers) and to its rigidity (by the curvature near the energy minimum). Figure 1c shows E(R) calculated for the skyrmion described in Fig. 1b at µ 0 H z =-2T, which exhibits a single minimum corresponding to an isolated skyrmion. The only other stable state is the ferromagnetic ground state at E = 0. Despite the different topology of the skyrmion and the ferromagnetic state, there is a continuous path from one to the other, which goes through the singular R = 0 state. The singluar R = 0 state does not have a topology, which is why topological quantization is lifted here. Remarkably, the energy along the path towards R = 0 remains finite, in contrast to earlier believes [28]. At R = 0, the skyrmion energy takes a universal value, The zero radius energy depends only on A and d and not on DMI. By finding a topologically valid and energetically possible path to annihilation we prove that skyrmions are not protected by their topology, even in continuum models and in the presence of strong DMI. Note that a finite value for the energy barrier has been found before [32,37], but the role of DMI and implications for the topological stability were not discussed.
The skyrmion of Fig. 1c exhibits a finite annihilation energy E a = E 0 − E(R) and a nucleation energy barrier E n = E 0 . Note that E n and E a overestimate the energy barriers because skyrmions can deform in a way that is not covered by the 360°DW model underlying our calculations and therefore reduce the nominal energy barrier [38]. However, previous studies [38] and our own micromagnetic simulations indicate that the reduction of the energy barrier due to deformations is smaller than 2Ad (even though sometimes extremely small cell sizes are required). This is in excellent agreement with the observation of Belavin and Polyakov [37] that the minimum energy of a skyrmion state in a model that includes only exchange energy is 8π Ad, which is approximately 2Ad smaller than the zero radius exchange energy of our 360°DW theory. Including thermal effects, we therefore consider minima of the total energy to be stable in our discussions below if All energy contributions to the total energy can be classified into two categories, DW and bulk energies, and inspection of the individual energy terms allows one to identify the mechanism responsible for skyrmion stability. At large radii, DW energies are linear in R, whereas all non-linear terms are bulk energies. Exchange, anisotropy, DMI, and volume stray field energies are DW energies. The Zeeman energy is a bulk energy.
Surface stray fields contribute to both categories: The DW contribution leads to an effective reduction of the anisotropy and the bulk contribution effectively reduces the external field.
The decomposition of the total energy into DW and bulk terms is depicted in Figs. 1d- can exist only if energy terms with positive slope are compensated by terms with negative slope, and the latter can only arise through DMI and surface stray fields. One can therefore classify skyrmions as DMI (stray field) stabilized if the sum of DW (bulk) energies has a negative slope at the equilibrium radius R eq . Our model hence provides the first mathematical basis for a terminology commonly used in the literature without rigorous justification [1].
New phases
We now apply our model to gain further insight into skyrmion properties and to analyze the phase diagram of static isolated skyrmions. Features of particular interest are highlighted in Fig. 2. First, Fig. 2a (Figs. 2b, c). We find this pocket in the stray-field stabilized part of the phase diagram where the phase boundary of the instability region has a cusp. The two types of skyrmions in the bi-stability region have very different properties (Fig 2d), confirmed by micromagnetic simulations: Their radii differ by more than one order of magnitude and their spin structure is Néel-like for the small skyrmion and transient for the large skyrmion. The transient value of ψ for the larger skyrmion originates in a wider domain wall, which increases the importance of the volume stray field energy that favors a Bloch-like spin orientation. The different size and domain wall angle can be used to move the skyrmions in non-collinear directions by spin orbit torques, as detailed in the supplemental information.
The unexpected emergence of multiple minima in E(R) is a consequence of introducing ∆ and ψ as free parameters, resulting in ∆(R) being nonlinear and sometimes nonmonotonic. The existence of degenerated isolated domain states is new in the entire field of magnetism. Decomposition of E(R) into DW and bulk energies (Fig. 2e) reveals that the origin of this phenomenon is that each term individually exhibits a minimum.
The minimum in E(R) at small radius derives from the minimum in the DW energy terms, shifted towards larger radii by the negative-sloped bulk energies and vice versa for the large radius minimum. This observation helps explain why the phase boundaries between stray field stabilized and DMI stabilized skyrmions in Fig. 2b are vertical and horizontal (see also supplemental figure S1). The horizontal line marks the critical DMI value above which σ DW is negative, i.e., where the DW energies have a negative slope everywhere and all minima are DMI stabilized. The vertical line indicates the critical field value above which the applied field fully compensates the Zeeman-like surface stray field, meaning that the bulk energies are always positive with a positive slope beyond that point and again all minima are DMI stabilized. In any of these cases, either the DW or the bulk energies cease to have a minimum, which finally also explains why we find the bi-stable phase pocket in the stray field stabilized phase.
The last peculiar phenomenon we uncover in our analysis is the existence of zero stiffness skyrmions. Figure 2f shows E(R) for a system that manifests three energy minima, where the maxima between these minima can easily be overcome by thermal energies at room temperature. In this particular example, the skyrmion radius can thermally fluctuate between 2 nm and 11 nm, such that it exhibits effectively zero stiffness with respect to variations of radius within this range. We expect that such skyrmions have a very low resonance frequency associated with their breathing mode, which could be exploited in skyrmion resonators [40] and should have impact on their inertia [8] and on skyrmion Hall angle [22,23]. a and E ∞ a minus the estimated internal energy of 2Ad that can be drawn from deformations, at given material parameters. The light grey area indicates that no minima exist at zero fields or that E eff a is smaller than 2Ad + k B T. The dark grey area illustrates where spontaneous domain nucleation is expected at zero field, which is excluded from being useful for isolated skyrmion applications. b, The inset shows a slice of panel a at Q = 1.8, plotting the annihilation energy barrier and the equilibrium skyrmion radius. The maximum energy barrier and the corresponding skyrmion radius are highlighted by a red and grey circle, respectively. This maximum energy barrier and corresponding radius are plotted in the main panel as a function of Q (effectively showing a slice of panel a when moving along the maxima of E eff a ). The solid lines correspond to the regular material parameters used throughout this paper, the dotted lines correspond to a reduced M s and the dashed lines represent a larger exchange constant. All lines end when the maximum E a is found at D i > 4 mJ/m 2 , which to the best of our knowledge is the maximum DMI that can be engineered. If larger DMI values were allowed, all energy lines would continue to go up and all radii lines would continue to go down.
Applications
We now consider the design of skyrmions suitable for applications, such as racetrack-type memory devices in which bit sequences are encoded by the presence and absence of skyrmions that can be shifted by electric current [9][10][11]41]. Three key attributes for such applications are (i) small bit sizes, (ii) long term thermal stability, and (iii) skyrmion stability in zero applied field. Indeed, we find a section in the phase diagram that meets all these requirements, as illustrated in Fig. 3. As sketched in the inset of Fig. 3a, zero field skyrmions are local energy minima bounded by two annihilation energy barriers E 0 a and E ∞ a that prevent shrinking to zero size and infinite growth, respectively. By subtracting the internal deformation energy 2Ad from the minimum of E 0 a and E ∞ a , we obtain the effective annihilation energy barrier E eff a that can be used to estimate long term thermal stability. For properly chosen material parameters, E eff a can exceed the threshold 40k B T ( Fig. 3b) required for commercial storage devices. The corresponding radius is < 20 nm. Note that all energy contributions generally become larger at larger radii. Therefore, fluctuations of any material parameter or of the field affect the energy barrier E ∞ a much stronger than E 0 a , see inset of Fig. 3a. This is why E eff a increases slowly for increasing DMI values before it drops very rapidly after passing the maximum of E eff a , see inset of Fig. 3b. It is therefore advantageous to have a DMI value slightly below the maximum E eff a to ensure that E eff a is robust against moderate external fields. Finally, all zero field skyrmions have E > 0, consistent with earlier assessments excluding ground state zero field skyrmions below the Curie temperature [16,42]. It is reasonably easy to obtain E eff a = 10Ad, which explains why the largest energy barriers can be obtained when increasing A (or d).
The full phase diagram
To demonstrate the power of our model and to understand the effect of material parameters on skyrmion properties, we derived and analyzed the properties of skyrmions as a function of more than one million different material parameters and magnetic fields, a task that is impossible with existing theoretical tools. Fig. 4 illustrates some of the most interesting features of the derived full phase diagram. Specifically, the figure analyzes a magnetic multilayer in which the non-magnetic spacer layers (e.g., Pt and Ta) are three times as thick as the magnetic layers. All room-temperature skyrmion systems today are based on such multilayers [8,17,19,[21][22][23]. We employ the effective medium approach [19,24] to treat these multilayers. The total magnetic material thickness in such films is between 1 nm and 100 nm, the interfacial DMI strength is below 4 mJ/m 2 and the anisotropy quality factor Q = 2K u µ 0 M 2 s is typically between 1 and 2. For this parameter range, we show in the first two rows of Fig. 4 the radius R and the DW angle ψ of the smallest possible skyrmions, i.e., under the maximum field just before collapse. Below, we present the means of stabilization (DMI or stray fields). Similar diagrams with variable A, M s , and non-magnetic spacer layer thickness are provided in the supplemental information.
The most striking common feature of all panels in Fig. 4 Phase diagram as a function of total magnetic layer thickness d, interfacial DMI strength D i , and anisotropy quality factor Q for a multilayer with three layers of non-magnetic material for every layer of magnetic material. The panel rows show, from top to bottom: the radius R and the DW angle ψ of the smallest skyrmion just before collapse, and the possible means of stabilization (where "DMI" indicates that all skyrmions in this material are DMI stabilized, "SF" indicates that all skyrmions are stray field stabilized and "both" means that both types of stabilizations can be found, depending on the external field). The solid line in the ψ panels depicts the minimum DMI strength D SW cψ required to obtain fully Néel skyrmions (ψ = 0) in all skyrmions, independent of their size. but quantitatively unrelated trend: Above a critical DMI value of D cψ , skyrmions are of Néel type when they collapse. The transition in ψ is not as sharp as for R and, importantly, D cψ is consistently smaller than D cr , implying that extremely small skyrmions are always of Néel type. Note also that small skyrmions are more likely to be of Néel type than straight walls in the same material. In other words, the critical DMI value for finding isolated Néel walls D SW cψ is much larger than D cψ . The region between D cψ and D SW cψ is where the dependence of ψ on the skyrmion size is most pronounced and bi-stable states are most likely to have different spin orientations.
Sub-10 µm skyrmions exist in almost the entire phase diagram. Materials with purely DMI stabilized skyrmions exist, but almost exclusively at very small values of Q. Already at Q = 1.4, which is a typical value for cobalt based multilayers [19,23], purely DMI stabilized skyrmions exist only for DMI values larger than 4 mJ/m 2 , well beyond experimentally-reported values. Hence we conclude that most skyrmions investigated experimentally so are best described as stray field stabilized.
Finally, comparing the additional phase diagrams in the supplemental information, we can qualitatively note that small skyrmions are favored by a low anisotropy, a low M s , a small exchange constant, a large DMI value, and sizable non-magnetic spacer layers. Low M s , low A and thick spacer layers also lead to more abundant bi-stability regions, but note that here larger Q values are beneficial.
Conclusions and outlook
In summary, we presented an analytical model that allows exploration of the entire static phase diagram of isolated magnetic skyrmions via rapid, systematic calculations. We expect many new applications to arise from the exotic states found here, beyond what we already suggested. In principle, our model assumes infinite films and the behaviour in finite sized elements can be different [28]. However, in most cases confinement increases the stability of skyrmions, as long as skyrmions still fit into the element. Therefore, our predictions can be considered conservative and applicable to most nanostructures as well. Also, skyrmions in antiferromagnets [43,44] are covered by our theory by setting M s to zero. Still, some open challenges remain.
For instance, the dynamics of skyrmions, and the effects of in-plane fields, are not yet covered by our model. But we believe that the concepts presented here to solve the integral equations pave the path to tackle those issues as well. | 5,600 | 2017-04-27T00:00:00.000 | [
"Physics"
] |
RELATIONSHIPS BETWEEN PRIMARY PRODUCTION AND CROP YIELDS IN SEMI-ARID AND ARID IRRIGATED AGRO-ECOSYSTEMS
In semi-arid areas within the MENA region, food security problems are the main problematic imposed. Remote sensing can be a promising too early diagnose food shortages and further prevent the population from famine risks. This study is aimed at examining the possibility of forecasting yield before harvest from remotely sensed MODIS-derived Enhanced Vegetation Index (EVI), Net photosynthesis (net PSN), and Gross Primary Production (GPP) in semi-arid and arid irrigated agro-ecosystems within the conflict affected country of Syria. Relationships between summer yield and remotely sensed indices were derived and analyzed. Simple regression spatially-based models were developed to predict summer crop production. The validation of these models was tested during conflict years. A significant correlation (p<0.05) was found between summer crop yield and EVI, GPP and net PSN. Results indicate the efficiency of remotely sensed-based models in predicting summer yield, mostly for cotton yields and vegetables. Cumulative summer EVI-based model can predict summer crop yield during crisis period, with deviation less than 20% where vegetables are the major yield. This approach prompts to an early assessment of food shortages and lead to a real time management and decision making, especially in periods of crisis such as wars and drought.
INTRODUCTION
In the last decade, remote sensing techniques were one of the main components that contributed to a shift toward increased precision in crop management (Jones & Vaughan, 2010).Such techniques provide spatially and temporally distributed information, leading to a real time management and decision making.Early diagnosis and estimation of yield is a must when early intervention is needed, mainly in the case when yield deficit threatens food security.The importance of these techniques lies in regions where yield data is either unreliable or non-existent, the case in war affected countries.Many image-based parameters and models that monitor agricultural performance exist in literature.However, verification and validation of such models remain a challenge.Primary production and vegetation indices were used to assess and predict crop yields (Running, et al., 2004;Reeves, Zhao, & Running, 2005).The Normalized Difference Vegetation Index (NDVI) was frequently used in crop forecasting and to detect crop areas (Domenikiotis, Spiliotopoulos, Tsiros, & Dalezios, 2004;Mkhabela, Mkhabela, & Mashinini, 2005).Quarmby et al. (1993) demonstrated that NDVI is an accurate early warning indicator for years with poor yield.NDVI was a good predictor of wheat, cotton and rice yields in northern Greece (Quarmby, Milnes, Hindle, & Silleos, 1993).NDVI was also used in crop discrimination in Northern China (Mingwei, et al., 2008).Maize and cotton fields were discriminated using MODIS derived NDVI and results were well correlated with statistical data at regional spatial scales.The Enhanced Vegetation Index (EVI), an improved index that accounts for soil reflectance, was rarely assessed in literature.EVI has similar potential as NDVI in estimating yield of many crops (corn, wheat, alfalfa, sorghum, soybeans) (Wardlow, Egbert, & Kastens, 2007).EVI was also used in the estimation of wheat area in China (Pan, et al., 2012).The Moderate Resolution Imaging Spectroradiometer (MODIS) produces both EVI and NDVI in addition to the primary production parameters such as the Gross Primary Production (GPP), Net Primary Production (NPP) and Net Photosynthesis (net PSN).Data acquired from MODIS allow an accurate monitoring of crop due to its frequent acquisitions of remote sensing data and the rapid availability of data over large regions (Running, et al., 2004;Zhao, Heinsch, Nemani, & Running, 2004).MODIS GPP is the result of combining MODIS data with meteorological inputs in a plant growth algorithm.Few studies had used this parameter in yield estimation. Reeves et al. (2005) converted GPP to biomass through a conversion equation of carbon to yield.This conversion was sufficiently accurate at state level but not at county level nor at climate district.
In this paper a methodology is introduced for summer cropyield prediction using MODIS vegetation and productivity indices.The remote sensing algorithm capitalizes on MODIS historical archive of these indices.The study is useful in conflict affected areas where reported data are unavailable or discrepant, or where access to agricultural areas is not possible due to security situations.Summer crop production in selected governorates of Syria was estimated from MODIS derived indices (GPP, net PSN and EVI).Regression models were built during pre-conflict years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) and simulated during years of conflict (2012-2013).
Study area
The conflict affected country of Syria is chosen as a case study.Syria produces 3.3 million tons of crops and vegetables in summer season.The vast majority of summer production relies on irrigation (from wells, rivers, and governmental irrigation projects).Most of irrigated land is located around Euphrates and Orontes River and their streams.Cotton is the most important summer crop.Syria is ranked by FAO as the 10 th in the world in cotton production.The annual average production of cotton in the last decade amounts to 740,000 tons equivalent to 3.75 tons/ha.The north-eastern region of Syria (Al-Hassake, Deir-Ezzor, Al-Raqqa and Aleppo) is reputed in cotton and maize culture in summer.The other governorates produce in summer mainly vegetables, in addition to cotton and tobacco.Winter and spring agricultural production is not within the scope of this study.
Data analysis
Three remotely sensed parameters were analyzed and compared to administrative statistics.The Gross Primary Production (GPP), Net Photosynthesis (net PSN) and Enhanced Vegetation Index (EVI) were extracted from MODIS datasets published by NASA and improved by the Numerical Terradynamic Simulation Group (NTSG) at the University of Montana.Those datasets are available from 2000 until present, on monthly and yearly basis, at 1-km spatial resolution (NASA LPDAAC, 2014;NTSG, 2014).The photo-synthetically active radiation (PAR), meteorological data, the estimated growth and maintenance respiration are the main parameters to obtain primary production.The EVI is an improved form of NDVI (Normalized Difference Vegetation Index) where vegetation conditions are compared in a spatio-temporal horizon.EVI is defined as per equation ( 1).
Where ρ are atmospherically corrected or partially atmosphere corrected surface reflectance, L is the canopy background adjustment that addresses nonlinear, differential NIR and red radiant transfer through a canopy, and C1, C2 are the coefficients of the aerosol resistance term, which uses the blue band to correct for aerosol influences in the red band (Huete, et al., 2002).Delineation of summer irrigated lands was relatively easy using the EVI.The EVI gives a good first approach to these lands.The mean (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) summer EVI raster was used to delineate summer irrigated lands.The resulting shape file was then compared with high resolution Google Earth imagery available for the summer seasons between 2000 and 2011, and fine-tuned where necessary.The delineated irrigated lands were comparable with the average of total irrigated lands stated by the MOAAR over the period 2000-2011 in each political unit.The statistical department in the Syrian Ministry of Agriculture and Agrarian Reform (MOAAR) published, since 2000, annual reports about summer crops and vegetables production and areas in all Syrian governorates (Ministry of Agriculture & Agrarian Reform in Syria, 2014).Two years of record are excluded from analysis (2005 and 2006) due to missing/discrepant data.
Monthly GPP, net PSN and EVI rasters were summed for the months of June, July and August to obtain summer indices between 2000 and 2011.Zonal statistics analysis was performed over delineated irrigated lands.Means of the cumulative sums for summer GPP, summer net PSN and summer EVI were obtained in the irrigated lands of each Syrian governorate.Linear regression was applied to relate summer crop production and remotely sensed indices in all governorates during the pre-conflict period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011).An F-test statistic was used to assess the significance of the linear regression parameters.The significance of analysis was evaluated using Fstatistics at p < 0.05.The derived relationships were simulated to estimate the summer crop production during conflict years (2012-2013).
To evaluate the spatial variation of production, the standardized summer EVI was calculated pixel-by-pixel as in equation ( 2).
𝑆𝐸𝑉𝐼 𝑖 = 𝐸𝑉𝐼 𝑖 −𝐸𝑉𝐼 ̅̅̅̅̅ 𝑆 𝑛
(2) Where is the standardized EVI in year i; EVI i is the sum of summer EVI in year i; ̅̅̅̅̅ is the mean of summer EVI in n years; S n is the standard deviation of summer EVI in n years Pixel-by-pixel statistics were derived from multiple rasters.The spatial mean and spatial standard deviation of EVI were calculated for the period of record 2001 and 2011.The spatial summer SEVI was derived by applying the Equation 1 on a cell-by-cell basis using a GIS-based raster calculator.
Summer EVI was regressed against summer crops (r 2 > 0. Following examination of significant relationships between summer crops and summer EVI, GPP and net PSN, regression models were derived for the period 2000-2011 and tested over the period 2012-2013.Table 1 show the deviation of predicted summer yield from reported during conflict years 2012-2013. The EVI based model performed well in 2012 in Hama, Ghab, Al-Hassake and Al-Raqqa.In 2013, the regression model did not conform well to the reported crop yields, with the exception of Hama governorate where the major crops are cotton and vegetables.Hama also is almost entirely under full government control.In governorates were maize was planted, no significant relationship between the tested indices and crop yield could be derived.It was noted that summer EVI was inversely correlated with netPSN is such areas (cotton and maize).It is well-worth noting that reported yield cannot be verified, as many of the areas are combat grounds, and hence the reliability of reported yields by the Syrian government in such areas is questionable.For example, heavy battles occurred in the Deir-Ezzor and Al-Raqqa governorates during 2012 and 2013.This may have prevented the government employees to access farmers' lands to conduct farm surveys and/or to collect crop yields.
Two main reasons could be arbitrated: 1) the unreliability of government reported data in conflict years (Endowment, 2014); 2) the sharp drop in production due to damages in irrigation systems and farmers displacement (Swiss Agency for Development and Cooperation SADC, 2014).
To compare different models, the work focuses on two governorates: Hama (vegetables main producer) and Al-Hassake (cotton main producer).Figure 1 and 2 illustrates the time series of reported and simulated summer crops and vegetables production in Hama and Al-Hassake.In both governorates, a rise in production is noticed between 2000 and 2004 where irrigation projects were enhanced (Hole & Smith, 2012).Since 2006, Syria had faced a severe drought that contributed to water shortages allowing for decreasing irrigated lands and a decrease in production (FAO, 2009).In 2011, the production slightly increased.The Syrian conflict started in 2011, and peaked during 2012 and 2013 where a decline in production is noticed both from reported yield and from cumulative summer EVI.The EVI predictor fits well the reported production in Hama most years, with less than 20% of error.In Al-Hassake, more fluctuations were observed.Models underestimated the high production in 2004 (-30%).The more suitable model during crisis years is the EVI model.
To evaluate the spatial variation of production, the SEVI was calculated pixel-by-pixel.Figure 3
CONCLUSION
The analysis of MODIS-derived EVI and primary production indices indicate a high correlation with reported summer yield in major irrigated agriculture in the pilot area within Syria.Simple regression spatially-based models were developed to predict summer crop production, found mostly effective for predicting cotton yields and vegetables.The regression models can be used as an indicator to predict summer crop yields during conflict years, and are able to show incidents were reported data could be questionable.Cumulative summer EVIbased model was the most effective among other parameters in predicting summer yields.The approach can contribute to an early diagnosis of food shortages and help decision makers' to focus relief efforts, especially in wars and periods of drought.
and 4 shows the summer SEVI of 2012 against the mean and the standard deviation of pre-conflict years(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011).Most regions in Hama and Al-Hassake faced a drop in EVI during 2012.Interior irrigated lands in Hama was not affected in 2012 nor the western regions.Ghab plain faced a significant drop of summer EVI in 2012.In the Kurdish area, northeastern Al-Hassake, summer EVI was similar to pre-conflict years.In other regions, summer SEVI was significantly less than before crisis.
Table 1 -
Deviation of predicted total summer yield from reported (%) for conflict years of 2012 and 2013 5)in major irrigated lands (Hama, Ghab, Homs, Al-Hassake, Deir-Ezzor and Al-Raqqa) with high significance.The regression was noticed to be significantly negative in Deir-Ezzor and Al-Raqqa.The agricultural lands of the latter governorates surround Euphrates River within a narrow strip in a hyper-arid landscape.The second major crop in these governorates is maize.Summer GPP was a significant estimator of summer crops in Hama, Al-Hassake, Deir-Ezzor, Ghab and Dar'a.Summer crops can be predicted from net PSN in four governorates (Hama, Ghab area within Hama, Al-Hassake and Dar'a) but with less significance than the other parameters. | 2,907.8 | 2015-04-28T00:00:00.000 | [
"Environmental Science",
"Mathematics"
] |
p57 Suppresses the Pluripotency and Proliferation of Mouse Embryonic Stem Cells by Positively Regulating p53 Activation
Embryonic stem cells (ESCs) are pluripotent stem cells that have indefinite self-renewal capacities under appropriate culture conditions in vitro. The pluripotency maintenance and proliferation of these cells are delicately governed by the concert effect of a complex transcriptional regulatory network. Herein, we discovered that p57Kip2 (p57), a cyclin-dependent kinase inhibitor canonically inhibiting cell proliferation, played a role in suppressing the pluripotency state of mouse ESCs (mESCs). p57 knockdown significantly stimulated the expressions of core pluripotency factors NANOG, OCT4, and SOX2, while p57 overexpression inhibited the expressions of these factors in mESCs. In addition, consistent with its function in somatic cells, p57 suppressed mESC proliferation. Further analysis showed that p57 could interact with and contribute to the activation of p53 in mESCs. In conclusion, the present study showed that p57 could antagonize the pluripotency state and the proliferation process of mESCs. This finding uncovers a novel function of p57 and provides new evidence for elucidating the complex regulatory of network of mESC fate.
Introduction
Embryonic stem cells (ESCs) are pluripotent cells derived from the inner cell mass of a preimplantation blastocyst [1]. These cells are characterized by an indefinite selfrenewal capacity and pluripotency, with the potential to differentiate into cells of all three germ layers [1]. The selfrenewal and pluripotency of ESCs are delicately modulated by a variety of internal and external signals and governed by a concert effect of the transcriptional regulatory network [2]. Among the transcriptional factors studied, NANOG, OCT4, and SOX2 are at the heart of the network to maintain the self-renewal and pluripotency of ESCs [3]. In addition, other proteins such as LIF, Klf4, Tbx3, Otx2, p53, and Foxo1/3a also critically regulate ESC fate [4][5][6][7]. In order to fully identify the clinical potential of ESCs, it is pivotal to understand how ESC fate is controlled by the intricate regulatory network and whether other unknown signaling proteins/pathways are involved in this network. p57 Kip2 (p57) belongs to the Cip/Kip family that could block cell proliferation by inhibiting the activities of cyclins and cyclin-dependent kinases (CDK) [8]. This canonical function of the cyclin/CDK inhibitor p57 is wellestablished and has been extensively reported. p57 can bind to all cyclins and CDKs and functions as an ATP mimic, thereby preventing the binding of ATP with these cell cycle regulation proteins [9,10]. In addition, p57 has a proliferating cell nuclear antigen-(PCNA-) binding domain through which its interaction with PCNA prevents PCNAdependent DNA replication [11]. By doing so, p57 blocks cells in the G1 phase and inhibits cell cycle progression. Indeed, downregulation of p57 expression usually accelerates cell proliferation and this is frequently observed during the development of many cancers, making p57 an important tumor suppressor [8,10,12].
More recently, emerging evidence has identified and characterized a variety of novel functions for p57 in addition to its role in cell cycle regulation. For example, p57 plays an important role in determining the differentiation process of several cell types. p57-null mice exhibited numerous and severe abnormalities in cell proliferation and differentiation, characterized by cleft palate, abdominal muscle formation defects, endochondral bone ossification delay and bone shortening, adrenal hyperplasia, renal dysplasia, and lens cell hyperproliferation and apoptosis [13,14]. A reduction in p57 expression was observed in parallel with delayed chondrocyte differentiation [15]. During skeletal muscle differentiation, the suppression of p57 expression resulted in abortive myoblast differentiation, while induction of p57 efficiently restored this differentiation process [16]. Other studies also showed that the expression profile of p57 determined neurogenesis via cell differentiation regulation of the central and peripheral nervous systems [17,18].
In comparison, the role of p57 in stem cell modulation is relatively unclear. Several studies showed that p57 was required for maintaining the quiescence state of hematopoietic stem cells (HSCs), hair follicle stem cells, and neural stem cells [18][19][20]. In addition, proper p57 expression is necessary for the self-renewal of bronchioalveolar stem cells [21]. p57 expression was also observed in a subset of intestinal stem cells, although its function was not explored in this study [22]. Studies regarding the role of p57 in ESCs are far less. There is limited evidence indicating that p57 is involved in affecting ESC proliferation as a downstream signaling protein, while direct evidence is lacking [23,24]. In addition, whether p57 plays a role in pluripotency maintenance of ESCs remains undetermined.
In this study, we provide evidence showing that p57 acted to suppress the pluripotency and proliferation of mESCs, and this effect was mediated through a positive modulation of p53 activation. Our findings uncover a novel connection between p57 and the self-renewal of mESCs.
2.2. Retinoic Acid (RA) Treatment. 1 × 10 5 mESCs were plated into each well of 12-well plates and cultured in the above mESC medium. For RA treatment, each well of the cultured mESCs was treated by 2 μM of RA (Sigma-Aldrich) in DMSO or DMSO alone of the same volume for 48 h.
2.3. Embryoid Body (EB) Formation and Differentiation. 2 × 10 6 mESCs were suspended in a 35 mm nonadherent culture dish (Axygen Biotechnology, Hangzhou, China) in mESC medium described above. Two days later, EBs were formed. The EBs were further transferred into 12-well plates in mESC medium without LIF for another 3 days to allow their spontaneous differentiation.
Real-Time Quantitative PCR.
Total RNA was extracted with TRIzol Reagent (Takara, Kusatsu, Japan), and reverse transcription was performed using a RevertAid RT Reverse Transcription Kit (Thermo Fisher Scientific, Waltham, Massachusetts, USA). Real-time PCR analysis was conducted using a SYBR Premix Ex Taq II Kit (Takara). Data were collected using a Bio-Rad CFX96 system (Bio-Rad, Hercules, California, USA). The primers used in real-time PCR are listed in Table S1. The reference gene used for real-time PCR data analysis was GAPDH in this article. 2.6. EdU Staining. EdU staining was conducted using a Cell-Light EdU Apollo 567 In Vitro Kit (Ribobio, Guangzhou, China) as previously described [26]. For the counting of EdU-positive cells, at least 3 fields of cells were randomly chosen and the percentage of EdU-positive cells of each field was counted. The mean value of the fields was calculated as the final percentage of EdU-positive cells.
2.7. Alkaline Phosphatase (AP) Staining. AP staining was performed using AST Fast Red TR and α-Naphthol AS-MX Phosphate (Sigma-Aldrich) according to the manufacturer's instructions.
Stem Cells International
For p57 overexpression, p57 primer (Table S2) was designed using primer 5. p57 gene was obtained by PCR amplification according to the instructions of PrimeStar Max Premix (Takara) and analyzed by agarose gel electrophoresis. The target band was harvested using a TIANgel Midi Purification Kit (Tiangen, Beijing, China). The collected p57 gene and PCDH-EF1-3×FLAG-T2A-Puro plasmid were treated using QuickCut restriction enzyme (Takara) according to the manufacturer's instructions and further linked together using T4 DNA Ligase (Takara) according to the manufacturer's instructions.
Lentivirus packaging and cell infection were performed as previously described [28]. In brief, pSIH1-H1-shp57-CoGFP plasmid or PCDH-EF1-3×FLAG-p57-T2A-Puro (or the corresponding empty plasmid pSIH1-H1-CoGFP or PCDH-EF1-3×FLAG-T2A-Puro) was transfected with PAX and VSV-G into 293 T cells using TurboFect Transfection Reagent (Thermo Fisher Scientific) according to the manufacturer's instructions. The cell culture medium was replaced with fresh medium after 12 h of culture. The cell culture supernatant was harvested after another 48 h and then mixed with mESC culture medium at 1 : 1 containing 10 μg/mL of polybrene (Sigma-Aldrich) to infect mESCs. 12 h later, the transfection medium was replaced with fresh mESC culture medium and the mESCs were harvested after another 48 h for further analyses.
2.11. Cell Apoptosis Assay via Flow Cytometry. Cell apoptosis was analyzed by an Annexin V-FITC/PI apoptosis kit (Multi Sciences, Hangzhou, China) as previously described [29]. The samples were tested by a flow cytometer (BD Biosciences, USA) and analyzed using FlowJo software.
2.12. Statistical Analysis. All assays were replicated for at least 3 times in the present study. Data are presented as mean ± standard deviation. Statistical significance was determined using two-tailed Student's t-test. Difference was considered significantly if the calculated P value was less than 0.05 ( * P < 0:05, * * P < 0:01, and * * * P < 0:001).
Increased p57 Expression during mESC Differentiation.
To investigate the role of p57 in mESCs, its expression level was analyzed in undifferentiated and differentiated mESCs. During the spontaneous differentiation of mESC-derived embryoid bodies (EBs), the expression level of p57 increased significantly (Figures 1(a)-1(d), Figure S7A). RA is one of the most effective inducers of mESC differentiation. During RA-induced differentiation of mESCs, higher p57 expression was also observed (Figures 1(e)-1(h), Figure S7B). The increased expression of p57 during mESC differentiation suggested that p57 may potentially affect the pluripotency maintenance of mESCs. This idea prompted us to further examine the effects of p57 knockdown and overexpression in mESCs. The efficiencies for p57 knockdown and overexpression were confirmed at both mRNA and protein levels ( Figures S1 and S2, Figure S11A-B). In response to p57 interference, mESCs expressed significantly higher levels of NANOG, OCT4, and SOX2, all essential transcription factors to maintain the pluripotent state of mESCs (Figures 2(a)-2(c), Figure S8A). In line with these results, Figure S8B). The suppressing effect of p57 on the pluripotency state of mESCs seems to be long-lasting, as this suppression was also evident after 2, 4, and 6 days of culture ( Figure S6).
p57 Suppressed the Proliferation of mESCs.
Interestingly, in addition to affecting the pluripotent state of mESCs, we observed that p57 expression profile also influenced the areas of mESC clones. The result showed that p57 interference resulted in larger mESC clones while its overexpression produced much smaller mESC clones (Figures 3(a)-3(d)). This observation implicated that p57 could also restrain the proliferation of mESCs. To validate this assumption, we performed proliferation-associated analyses in mESCs. As expected, mESCs with p57 interference showed more vigorous proliferation rate, as reflected by higher cell numbers and greater EdU-incorporation abilities (Figures 4(a)-4(c)). In line with these observations, PCNA, Cyclin A, and Cyclin E, factors that are crucial for cell cycle progression, were also significantly upregulated in mESCs with p57 interference (Figures 4(d)-4(f), Figure S9A). Consistent with these findings, mESCs with p57 overexpression exhibited much lower proliferation ability and expressed reduced levels of proliferation-associated markers (Figures 4(g)-4(l), Figure S9B). The suppressing effect of p57 on the proliferation of mESCs seems to be long-lasting, as this suppression was also evident after 2, 4, and 6 days of culture ( Figure S6). However, p57 has little effects on the apoptosis of mESCs ( Figure S3A-B).
p57
Interacted with and Contributed to the Activations of p53 in mESCs. The above evidence suggested that p57 played Stem Cells International an important role in controlling the pluripotency and proliferation of mESCs. To further explore the underlying mechanism, we screened proteins interacting with p57 in mESCs. BiFC assays and coimmunoprecipitation assays both showed an active interaction between p57 and p53 (Figures 5(a) and 5(b), Figure S4, Figure S5). Further evidence revealed that although p57 did not affect the total expression level of p53, it could positively regulate the
10
Stem Cells International activation of p53, as p57 knockdown restrained while its overexpression promoted the phosphorylation of p53 at Ser 315, a phosphorylation site closely associated with p53 transcriptional activity [30] (Figures 5(c)-5(f), Figure S10A-B). As expected, under the action of p53 inhibitor pifithrin-α hydrobromide, the pluripotency-and proliferationassociated genes and proteins showed no significant changes between the p57 group and control group (Figures 5(g)-5(i), Figure S10C). Thus, the evidence presented here strongly suggested that the inhibition of mESC self-renewal by p57 is mediated, at least in part, by positive regulation of p53 activation.
Discussion
ESCs have the potential to differentiate into any type of terminal-differentiated somatic cells such as hepatocytes, cardiomyocytes, skeletal muscle cells, epithelial and vascular smooth muscle cells, neurons, and germ cells upon proper in vitro induction [25,31,32]. This distinguishing characteristic of ESCs has encouraged many attempts to employ human ESCs for the treatment of corresponding clinical problems such as end-stage liver diseases, heart failure, severe skin burns, stroke, Parkinson's disease, and infertility [33][34][35][36][37][38]. In order to fulfill the clinical application of human ESCs in regenerative medicine, basic researches that could provide a better understanding of the regulatory network governing the self-renewal and pluripotency of ESCs are necessary. In the present study, we identified p57 as a novel regulator of mESC pluripotency and proliferation, and we also demonstrated that the regulation of mESC by p57 was partly mediated via p53 signaling. These findings provided new evidence for elucidating the complex regulatory network of the fate of ESCs. p57 is a pleiotropic protein that is involved in many important processes of various cell types. Its canonical role in blocking cell cycle progression as a cyclin/CDK repressor has been widely reported. The involvement of this protein in promoting neural precursor migration [39], stimulating neurogenesis, and promoting the differentiation process of chondrocytes and myoblasts has also been reported [15,18,32]. In stem cells, p57 mainly functions to maintain the quiescence state of these cells in several tissues. p57 are highly expressed in quiescent adult hematopoietic stem cells (HSC) and profoundly control the quiescence and stemness these cells [20,40]. In mice with p57 deletion specifically in the hematopoietic system, decreased HSC pool with profoundly reduced self-renewal capacity was observed, which was caused by a failure of quiescence maintenance and increased apoptosis rate of these cells [20,40]. Similarly, quiescent neural stem cells expressed high levels of p57 while proliferative progenitors exhibited very weak or completely undetectable p57 signals [18]. Further evidence suggested that p57 also regulated the quiescence state of neural stem cells and control the pace of lifelong neurogenesis [18]. In bronchioalveolar stem cells, either knockdown or overexpression of p57 caused defective self-renewal, which ultimately resulted in compromised lung regeneration after injury [21]. Unlike these adult stem cells that usually reside in the G0/G1 phase of the cell cycle, ESCs are characterized by fast proliferation and a short G1 phase, and this is closely associated with the pluripotency of ESCs [41]. However, studies regarding the role of p57 in embryonic stem cells are very limited. The elegant work of Li et al. showed that p57 was posttranscriptionally inhibited by microRNA miR-221 in mouse mESCs, making miR-221 critically required for mESC proliferation [42]. Similarly, p57 was a predicted target of an ESC-enriched miR-92b, which affected the G1 to S phase transition in human ESCs [23]. In addition, p57 was presumably involved in the regulation of human ESC proliferation by protein arginine methyltransferase 5 (PRMT5) [24]. Nevertheless, whether this protein plays a role in ESC pluripotency regulation remains unclear. The present study provides direct evidence showing that p57 antagonizes mESC pluripotency and this protein also functions to restrain mESC proliferation. Although it is unknown whether the effect of p57 on mESC pluripotency was a result of inhibited proliferation of mESC or a direct effect of p57, the evidence provided here unraveled a novel function of p57 and the underlying mechanisms deserve further in-depth investigations in the future.
The critical role of p53 in maintaining genomic stability as a tumor suppressor has been extensively reported and well-established in multiple somatic cells [43]. However, its role in ESCs remains much more elusive and has received great interest in the past few years. It has been shown that p53 activation stimulated the differentiation of ESCs by directly suppressing the genes required for ESC pluripotency. For example, p53 could bind to the promoter of Nanog and suppress its expression in response to DNA damage [30]. In line with this finding, Lee et al. found that a majority of p53-targeted genes in mESCs are involved in developmental processes, especially genes associated with mesodermal and ectodermal development [7]. Interestingly, p53 is not only able to induce ESC differentiation but also antagonizes the pluripotency and self-renewal of ESCs, a process involved with the activation of miR-34a and miR-145, two microRNAs potently repressing the expressions of Klf4, Oct4, Lin28a, and Sox2 [44]. Thus, it is not surprising that p53 activity is stringently regulated to guarantee ESC identity and fate. One regulator for p53 activity is Oct4, which prevents p53 activation via Sirt1-mediated deacetylation of p53 [45]. Findings of the present study again underscore the importance of p53 regulation in ESCs. To our knowledge, this is the first report describing the control of p53 by p57 in mESCs. However, further investigations are still needed to identify the detailed mechanism on how this regulation process works.
Conclusions
In the present study, we found that p57 knockdown promoted the expressions of core factors associated with mESC pluripotency, while its overexpression inhibited the expressions of these factors. In addition, p57 also suppressed the proliferation of mESCs. Further evidence showed that the function of p57 in mESCs was mediated by p53. Thus, p57 11 Stem Cells International could negatively regulate the pluripotency state and the proliferation of mESCs through p53 activation.
Data Availability
All data is available from the corresponding author Jinlian Hua<EMAIL_ADDRESS>upon request.
Conflicts of Interest
The authors have no conflicts of interest to declare. Table S1: primer sequences used for real-time quantitative PCR. Table S2: primer sequences used for PCR amplification in BiFC assay. Figure S1: construction of shp57-and p57overexpressing vectors. Figure S2: p57 knockdown or overexpression efficiency in ESCs. Figure S3: p57 have no effects on apoptosis of mESCs. Figure S4: construction of the vectors for BiFC assay. Figure S5: visualization of the interactions between p57 and candidate proteins (PCNA, p21, p27, p16, WNT6, and WNT2) in vivo by BiFC assay. Figure S6: the effect of p57 on mESCs at day 2, day 4, and day 6. Figure S7: increased p57 protein expression during mESC differentiation. Figure S8: p57 suppressed the pluripotency state of mESCs. Figure S9: p57 suppressed the proliferation of mESCs. Figure S10: p57 interacted with and contributed to the activations of p53 in mESCs. Figure S11: p57 knockdown or overexpression efficiency in mESCs. (Supplementary Materials) | 4,153.8 | 2021-12-24T00:00:00.000 | [
"Biology"
] |
A Tensor-Based Method for Completion of Missing Electromyography Data
This paper discusses the recovery of missing data in surface electromyography (sEMG) signals that arise during the acquisition process. Missing values in the EMG signals occur due to either the disconnection of electrodes, artifacts and muscle fatigue or the incapability of instruments to collect very low-amplitude signals. In many real-world EMG-related applications, algorithms need complete data to make accurate and correct predictions, or otherwise, the performance of prediction reduces sharply. We employ tensor factorization methods to recover unstructured and structured missing data from the EMG signals. In this paper, we use the first-order weighted optimization (WOPT) of the parallel factor analysis (PARAFAC) decomposition model to recover missing data. We tested our proposed framework against non-negative matrix factorization (NMF) and PARAFAC on simulated as well as on off-line EMG signals having unstructured missing values (randomly missing data ranging from 60% to 95%) and structured missing values. In the case of structured missing data having different channels, the percentage of missing data of a channel goes up to 50% for different movements. It has been observed empirically that our proposed framework recovers the missing data with relatively much-improved accuracy in terms of relative mean error (up to 50% and 30% for unstructured and structured missing data, respectively) compared with the matrix factorization methods even when the portion of unstructured and structured missing data reaches up to 95% and 50%, respectively.
I. INTRODUCTION
ELECTROMYOGRAPHY (EMG) is a diagnostic technique which records the electrical activity produced by contraction of muscles.The electric activity or potential is generated by the muscle cells when these cells are electrically activated.Generally, two types of EMG exist surface EMG (sEMG) and intramuscular EMG (iEMG).sEMG is the recording of electrical activity from the muscle surface (non-invasive) whereas iEMG is recorded directly within the muscle tissue.EMG signals have many applications such as upperlimb prostheses [1]- [3], electric wheelchairs control [4] and muscle-computer interaction [5].In these applications, complete EMG signals without missing data are required The associate editor coordinating the review of this manuscript and approving it for publication was Huaqing Li.
for efficient and successful implementation.However, practically, EMG data acquisition is not lossless.During Signal acquisition, data is lost due to many reasons such as artifacts or disconnection of electrodes with the body [6].These missing values in the EMG signal can cause degradation in the overall performance of healthcare applications such as myoelectric pattern recognition to predict motor intention from sEMG signals [6].Moreover, missing values also reduce the accuracy of the classification of movements for prostheses control [7].If data is incomplete and the percentage of missing data is large, then the classification performance and statistical power of those classification methods highly degrade, which makes it important to have complete data set.To effectively estimate the missing data, proper imputation methods must be utilized.Generally, in EMG applications, missing data had either not been recovered or estimated by simply replacing it with mean values of the neighboring data values, which proved to be highly sub-optimal [8].In this work, we have focused on estimating missing values using multidimensional data structure [9], [10] based upon multilinear algebra (tensors).
In this paper, we aim to recover missing values in surface EMG signals by estimating the latent structure of the data.In order to estimate latent structure, we employ tensor factorization methods which produce factor matrices which are used to produce the reconstructed tensor.We further formulate a weighted version of an error function that ignores the missing values and model only the known values which improve the estimation accuracy of recovering missing data significantly.
A. RELATED WORK
Matrix and Tensor decomposition of EMG signals have been widely studied in the literature.In [11]- [15], non-negative matrix factorization (NMF) has been applied on EMG signals for various applications, e.g.recognition of gestures, to obtain information for neural control and identification of various surface EMG signals.In [16], various matrix factorization algorithms such as Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), and Non-negative Matrix Factorization (NMF) were evaluated on EMG recording.In [17], surface EMG signals are decomposed using non-negative Tensor factorization to find the features for classification purpose.In [18], NMF was employed to identify EMG finger movements to evaluate the functional status of hand so that it can assist in hand gesture recognition, prosthetics and rehabilitation applications.In [19], FastICA method is implemented for EMG signals decomposition.In [20], NMF along with different initialization techniques was applied to acquire muscle synergies which are important for generating biomechanical tasks.In [31], higher order tensor decompositions are employed on EMG signals to estimate muscle synergies.
However, so far in the literature, missing data in EMG signals has been recovered by using ensemble classifier system [8], nonlinearities interpolation approach [21], mean data imputing [6], Empirical Decomposition Mode (EMD) [22] and marginalization and conditional-mean imputation [23].In [8], imputation and reduced-feature models were employed to perform classification in presence of missing data but the results were not promising.In [21], missing data of up to 80% was recovered.However they tested algorithm on single subject and it is also unclear whether they recovered unstructured or structured missing data.In [6], imputation was carried out using mean of data which works poorly on non-stationary EMG data.In [22], EMD fails to recover structured missing data.In [23], the main focus was on developing classification model.However they also employed a simple mean imputation method to recover missing data.In [9] and [24], tensor factorization techniques are applied on EEG signals.However, so far, EMG signals have not been explored that way.For the first time, in this work, missing data is recovered in EMG signals with a detailed analysis in which matrix, as well as tensor factorization methods, are employed.We apply NMF for matrix factorization and, PARAFAC and CANDECOMP/PARAFAC -Weighted OPTimization (CP-WOPT) for tensor factorization.As normalized EMG data contains non-negative values; hence, for the case of matrix factorization we apply NMF, which is the unsupervised learning algorithm used for dimensionality reduction and construction of low-dimensional approximation of observed data.NMF is more suitable because other methods such as Principal Component Analysis (PCA) produce the factors which can be positive or negative.To our knowledge, tensor factorization for recovering missing data in EMG signals has not been studied yet.In this work, for the first time, we employ the tensor factorization method to recover unstructured and structured missing data in EMG signals.We apply PARAFAC and weighted optimization (WOPT) of PARAFAC model to EMG signals and recover missing data efficiently as compared to matrix factorization techniques.
The novelty of this work is found in the follows: a) For the first time, missing data in EMG signals are recovered using the tensor factorization-based method.b) We compare both matrix factorization, and tensor factorization-based approaches to recover missing data in noisy simulated data and real-world EMG data to show that the tensor-based approach outperforms matrix factorization based approach.c) We address the problem of missing data in extreme cases when up to half consecutive EMG samples of a particular channel are missing.Our proposed framework successfully recovers the missing data even in such an extreme case.
B. NOTATIONS AND PRELIMINARIES
Tensor X (i,j,k...) is a multi-dimensional array which has different modes for data representation.A tensor with one mode is a one-dimensional array referred to as a vector and with two modes is known as the matrix.A tensor of third order is shown in Fig.The Hadamard product of two tensors X , Y is defined as: The Frobenius norm of a tensor X is given by: . . .
The Weighted norm of X for two tensors X and W is defined as follows: The Khatri-Rao product is defined as follows: where size of matrices X and Y is I ×K and J ×K respectively.The symbol ⊗ is the Kronecker product.
The Kronecker product ⊗ is defined as follows: where X is an m × n matrix and Y is a p × q matrix, and the Kronecker product X ⊗ Y is the mp × nq block matrix.
The Outer product • between two vectors x and y is given by: where x and y are column vectors and their outer product gives rank-1 matrix.
Tensor mode-n unfolding, which is also called tensor matricization, is analogous to vectorizing a matrix.Mode-n unfolding of X ∈ R I 1 ×I 2 ×...×I N re-arranges the elements of X to form a matrix X (n) ∈ R I n ×I 1 I 2 ...I n−1 I n+1 I N , where The notation A (1) , A (2) , . . ., A (N ) defines a tensor of size R I 1 ×I 2 ×...×I N whose elements are given by: ( A (1) , A (2) , . . ., A (N )
Remaining sections of the paper are organized as follows:
In Section II, we explain methods which include signal processing technique and problem formulation, subjects' details, experimental setup and details of data used for evaluation.In Section III, we show results of tensor and matrix factorization methods applied on simulated and EMG data to recover both unstructured and structured missing data.Discussion on the results is given in Section IV.Section V concludes the work.
A. SIGNAL PROCESSING 1) PROBLEM FORMULATION a: NMF
The objective function for recovering missing values of the EMG data in the form of Matrix is given as: where X ∈ R m×n is the input matrix which contains EMG data with missing values and X is reconstructed matrix obtained by minimizing the objective function in (1).In order to solve (1) using NMF, the objective function in (1) becomes: where P and Q are R m×k and R k×n matrices, respectively.'' In order to apply NMF to multidimensional input data, we metricize it as a matrix X with dimensions time × channels.NMF decomposes the data of matrix X into two matrices P and Q, as mentioned above.Our objective is to find factor matrices P and Q that minimize the objective function in (2).
b: PARAFAC
The objective function for recovering missing values of the EMG data in the form of tensors is given as: where X ∈ R I 1 ×I 2 ×...×I N is an order-N input tensor and assume its rank is R. X contains EMG data with missing values and X is the reconstructed tensor obtained by minimizing the objective function.To solve (3), a standard tensor factorization is CANDECOMP/PARAFAC (CP), which can be used to find the reconstructed tensor, then the objective function in (3) becomes: f A (1) A (2) , . . ., A (N ) = min 1 2 X − A (1) , A (2) , . . ., where A (n) is factor matrix corresponding to n-th dimension, A (1) , A (2) , . . ., A (N ) makes an order-N tensor equivalent to: X ≈ A (1) , A (2) , . . ., where a (n) r is r-th column vector of A (n) factor matrix, and n = 1, 2, . . ., N .The sum of the outer products of vectors a (n) r in (5) shows the CP decomposition as a sum of R rank-1 tensors to estimate a tensor.We use this CP decomposition [9] to find the factor matrices of the input tensor.The tensor in ( 5) is an approximation proposed by CP/PARAFAC method which is one of the standard methods for tensor factorization.In (5), a particular constraint is the value of R which is determined heuristically.We further modify this CP tensor factorization method to a weighted CP model which caters for the missing data recovery.Elementwise, (5) can be written as: In mode-n unfolded (matrix) form, ( 5) is represented as: where 1) . . .A (1) In unfolded form, our objective function to find mode-n factor matrices becomes: f A (1) A (2) , . . ., A (N ) = min 1 2 There exist many methods to compute CP decomposition to find a good approximation of original data such as alternating least squares (ALS) [25], [30], gradient descent (GD) [30] and enhanced line search [30] etc.Our experiments show that conventional method such as CP decomposition only give comparable results to that of matrix factorization methods that even worsens when large amount of data is missing.To overcome this problem, we model CP factor matrices only from non-zero values of the input data.For this purpose, we multiply the input data with a weighting tensor W with size equal to the size of input data tensor X in such a way that The weighted CP factorization of the EMG tensor yield factor matrices, which reconstruct the tensor using (7) to estimate the missing values.
2) CP-WOPT CP-WOPT solves the problem of fitting the CP model to missing data by solving the following weighted least-squares objective function: where W is tensor of the same size as X , and its samples are defined as: for all i = 1, . . ., I , J = 1, . . ., J and k = 1, . . ., K .
For the sake of simplicity ( 8) is redefined as: where The gradient equation for the weighted case would be: for n = 1, . . ., N .Our main objective is to find factor matrices A (n) R I n ×R for n = 1, . . ., N that minimize the weighted objective function in (8).Once gradients in (12) are known, then any gradient-based optimization method can be used to solve the optimization problem.We use CP-WOPT [25] and the nonlinear conjugate gradient (NCG) as the optimization method with Hestenes-Stiefel updates [26].The stopping conditions of both tensor based algorithms were based on the relative change in the function value f W in (8) (set to 10 −8 ).The maximum number of iteration is set to 10 3 and the maximum number of function evaluations is set to 10 4 .These choices are based on the values used in [9].The brief methodology of CP-WOPT is summarized below:
Algorithm 1 Methodology of CP-WOPT
Task: To find gradient matrices G (n) that minimize the weighted objective function in (6).Input: X (Input tensor with missing values) Output: G (n) Steps to compute G (n) : A (2) , . . ., A (N ) 3. Compute value of functions: Assume Y = W * X is pre-computed as both W and X remain the same in the algorithm.The gradient is computed VOLUME 7, 2019 as a series of matrices G (n) ≡ ∂f W ∂A (n) for n = 1, . . ., N .While T (n) is the unfolding of the tensor T in mode n.Once gradients G (n) are computed, then any gradient-based optimization method can be used to solve the optimization problem.
B. SUBJECTS DETAIL
For this research, we have used sEMG data used by M. Zia ur Rehman et al. in [1] and A Waris et al. in [27].Ten subjects (all male) were recruited for EMG data acquisition.Ages of all subjects ranged from 18 to 38 years old (mean ± standard deviation (SD), 24.5 ± 2.3y).All subjects were healthy with no neuromuscular disorders.The procedures were in accordance with the Declaration of Helsinki and approved by the local ethical committee of Riphah International University (approval no: ref# Riphah/RCRS/REC/000121/20012016).Subjects provided written informed consent prior to the experimental procedures.
C. THEORETICAL FRAMEWORK
Missing data in EMG has been categorized into two types: 1) unstructured missing data 2) structured missing data.If the observed data in the original structure is missing randomly, then such a pattern of missing data is categorized as unstructured missing data.For example, samples of EMG data missing at random entries.However, if the data is missing in some consistent and structured way, it is termed as structured missing data.For example, 25% consecutive values of an EMG channel are missing either at the start, middle or end of data acquisition process/session.This block of missing values is repeated randomly in other channels of EMG data.
D. EXPERIMENTAL SETUP
Surface EMG signals were acquired using six surface EMG electrodes.Three electrodes were placed on flexor and three electrodes on extensor muscles.The sampling frequency of surface EMG signals was 8 kHz, whereas we filtered it using bandpass filter of thi rd order with bandwidths 20-500 Hz.Total of four-hand motions was performed by each subject: (1) hand open (2) hand close (3) pronation and (4) extend a hand.For each session, each hand motion was repeated four times with a contraction and relaxation time of five seconds, and hence a single session took a time of 400 seconds.
E. DATA ANALYSIS
We applied NMF, PARAFAC and CP-WOPT on simulated and EMG data to recover both types of missing data.In order to carry out the comparison, we assess the performance of methods based on validation metric termed as Relative Mean Error (RME) mentioned in (14).
1) SYNTHETIC DATA
We generated tensor of size R I ×J ×K and kept a number of true factors R = 5.In order to test the performance of different methods to recover unstructured missing data from synthetic data, we produced synthetic data of different size such as 60 × 50 × 40, 120 × 100 × 80, 180 × 150 × 120.For the case of structured missing data, we test the methods on a dataset of size 120 × 100 × 80. Factor matrices A, B and C were generated with sizes: R I ×R , R J ×R and R K ×R respectively.All the factor matrices were randomly chosen from N (0, 1) and then normalized every column to unit length.e then create the data tensor as: Here N is a noise tensor (of the same size as X ) in which all samples were drawn from Gaussian i.i.d.distribution with mean zero and variance one.The term [[A, B, C]] is a tensor being constructed from factor matrices A, B and C where η is noise parameter which has value 0.1.
In order to implement matrix and tensor-based factorization methods, we set some samples of a tensor to zero to model missing data.In the case of weighted tensor factorization, the tensor W indicates the binary values zero or one where zero and one represent missing and known values, respectively.In particular, we have considered two cases of missing data: (1) Unstructured missing data and (2) Structured missing data.In case of unstructured missing data, we randomly set some percentage of data (from 60% to 95% of total data) to zero in the tensor W whereas in case of structured missing data we set, within multiple channels, large consecutive-samples (up to 50%) to zero which is usually the case in practical situations.It is tantamount to a situation where half EMG data of multiple channels is missing for a particular movement.
2) EMG DATA
For EMG data acquisition, six electrodes were used to collect EMG signals on a single day.The movement-wise size of data was 320000 × 6 × 4, which was down-sampled to 80000 × 6 × 4. 80000 is the number of samples, 6 represents total number of electrodes/channels and 4 is total number of movements for which EMG data was collected.After downsampling EMG data, we normalized it between 0 and 1. Surface EMG data in the form of a tensor X can be viewed as X R 80000×6×4 for each of four movements.If we relate it with Fig. 1, then I = 80000, J = 8 and K = 4 where I, J and K represent samples of EMG data, total number of channels and total number of movements respectively.Fig. 2 shows lateral slices of a tensor X :j: which in our case represents EMG data of channels.As we have total of six channels, hence X :1: would be a slice representing EMG data of first channel and so on.The black slices in Fig. 2 are the ones with missing EMG data.
We removed samples in the following two ways: 1) unstructured and 2) structured missing data.amplitudes represent intervals of rest.Fig. 6(b) shows structured missing data in which first half part (chunk of consecutive samples) is removed whereas Fig. 6(d) shows structured missing data in which second half part is removed.
In our experiments, we recover these missing intervals in extreme cases where half consecutive EMG samples of a particular channel are missing.However in [9] and [32], it is known that missing data cannot be recovered by low rank tensor completion if entire slice is completely missing.
Matrix and tensor factorization methods are applicable to a wide range of real world signals and do not depend on statistical and mathematical properties of the signals.However initialization of matrix and tensor factorization models does have effect on estimation of the signals in question.For example, in our case, EMG is stochastic in nature hence initialization of our matrix and tensor factorization models with random values help to recover the missing values efficiently.Moreover our proposed framework can be employed on other biomedical signals as well e.g.EEG signals.
F. EVALUATION METRIC
Let X be the original data and let X be the estimated data produced by the matrix or tensor factorization methods.Then the Relative Mean Error (RME) is: The best possible score is zero which shows the recovered data matches with original data completely.
G. SIMULATION ENVIRONMENT
We used Matlab 2017a on Windows 8 operating system with a core i3 processor and 6 GB RAM.CP-WOPT is implemented using Tensor Toolbox.
H. STATISTICS
A three-way ANOVA was used to assess which method had the least amount of RME.Three factors: methods (NMF, PARAFAC and CP-WOPT), Movement type (hand open, hand close, pronation and extend hand) and missing data percentage (10%, 20%, 30%, 40% and 50%) were used, post hoc pairwise comparisons were made using Tukey's HSD tests if required.Statistical significance was set at P < 0.05 for all comparisons.
III. RESULTS
In this paper, the proposed framework is tested on both synthetic and EMG data set to recover both unstructured and structured missing data.For both cases, we assess the performance of our proposed framework CP-WOPT against matrixbased method NMF and tensor-based method PARAFAC to recover both types of missing data.Our results show that missing values can be efficiently recovered with CP-WOPT as compared to NMF and PARAFAC.
A. ESTIMATION PERFORMANCE ON SYNTHETIC DATA
In Fig. 3, we compare the estimation performance of matrix and tensor-based factorization methods to recover unstructured missing data in the synthetic dataset for different proportions, e.g.60%, 70%, 80%, 90% and 95%.In It can be seen in Fig. 5(c) that all the missing values that were replaced by zeroes were successfully recovered with amplitudes around 0.48.Fig. 6 illustrates a segment of EMG data with no missing values having same four movements where each movement exists at higher amplitudes from which first half (with two movements) and second half (with two movements) is removed and then recovered.In Fig. 6(a), EMG signal with no missing values is shown that has been obtained from a particular channel.The four epochs of higher amplitudes indicate execution of movement however it can be seen that there is a very small difference between amplitudes of movement and no-movement (at rest) epochs.In Fig. 6 (b & d), first and second half (the worst case of removing 50% of data) of channel values is removed to model the structured missing data.It is tantamount to the scenario where data of two movements is missed completely.In Fig. 6(c & e), recovered signal by CP-WOPT is shown in which it can be seen that the difference between amplitudes of movement and no-movement epochs have increased which clearly differentiate epochs.Fig. 7 shows a comparison of three methods to recover unstructured missing data.There was a significant decrease (P < 0.05) in the RME value with CP-WOPT as compared to PARAFAC and NMF across all four movements and different percentage of missing data.From each of four movements, we remove 60%, 70%, 80%, 90% and 95% data randomly in an unstructured manner.
In Fig. 8, results are shown when NMF, PARAFAC and CP-WOPT are applied, respectively, to recover structured missing data.Results clearly show that CP-WOPT outperformed PARAFAC and NMF in recovering structured even for the extreme case when half of the channel data is missing.In structured missing data, we gradually increased the proportion of missing data from 10% to 50%.Removing 10% data from first half means data removal of first 10% samples from all six channels of particular movement whereas removing 50% data means data removal of first 50% samples (as shown in Fig. 6(b)) from all channels.Likewise, removing 10% data from second half means data removal of last 10% of samples from all six channels of particular movement whereas removing 50% means data removal of last 50% of samples (as shown in Fig. 6(d)).
In Fig. 9, we show computational complexity of NMF, PARAFAC and CP-WOPT.It can be seen that CP-WOPT takes slightly more time than NMF and PARAFAC to estimate 10%, 20%, 30%, 40% and 50% structured missing values to produce the reconstructed EMG data.
IV. DISCUSSION
We assessed matrix and tensor factorization techniques to evaluate their performance to recover missing data for synthetic and real EMG data.For matrix and tensor factorization we applied NMF, and PARAFAC and CP-WOPT respectively.One of the reason for tensor factorization to outperform NMF is the arrangement of EMG data in a multidimensional way.This multidimensional arrangement of the data to constitute a tensor captures the global structure of observed data and models it efficiently by covering entire spatial and temporal dimension with an additional feature of multi-mode correlations.Moreover CP-WOPT outperforms PARAFAC as well because it is a weighted version of PARAFAC and models only the known values of EMG data.The key finding is that tensor factorization technique CP-WOPT in which only known samples are modeled outperformed both NMF and PARAFAC.The performance of NMF and PARAFAC to recover missing data was almost the same as both the methods model, both known and unknown values.
Although PARAFAC is a tensor-based technique with the benefit of preserving the multi-way nature of data, yet its performance is comparable with NMF.The results reveal that CP-WOPT outperformed both NMF and PARAFAC to recover both unstructured and structured missing data.Usually, factorization methods find latent factors and then exploits those latent factors to predict the missing values.However, Matrix factorization based latent factors only capture two-dimensional linear relationships for estimating missing values, which can be improved if multi-linear relations are used.The main advantage of working through latent factors is that they let us take into account the information of the tensor explicitly by exploiting the multilinear interactions between obtained latent factors.For example, in our case, EMG data has dimensions: samples × channels × movements.
Once we obtain latent factors, the inter-relation between factors of EMG data in each mode can be analyzed, such that columns of the first factor explicitly describes EMG signal, columns of seconds factor describes channels and columns of the third factor depicts movement-wise data.The main advantage of employing tensor factorization is that solution provided by it is unique [28].Moreover, tensor factorization offers better computational capabilities and storage [29].
We divided the missing data into two categories: 1) unstructured missing data and 2) structured missing data.CP-WOPT gave promising results in recovering unstructured and structured (which is a more realistic assumption in Muscle-Computer Interface) missing data.This study is a preliminary step in the feasibility of improving the accuracy of classification methods to efficiently classify hand movements using surface EMG signals.Performance of classification methods will improve because firstly missing data is replaced with efficiently calculated estimated data and secondly; it increases the total size of data.However, the study presented here is an offline analysis and based on a small number of able-bodied subjects, which limits the possibility of generalizing the results.Furthermore, the relation between RME and classification performance needs to be developed so that it can be claimed that improved RME improves the classification performance for myoelectric control application.
V. CONCLUSION
In this paper, we addressed the problem of recovering two types of missing data in surface EMG signals: unstructured and structured missing data, using NMF which is a matrixfactorization method, and PARAFAC and CP-WOPT which are tensor-factorization methods.In NMF, EMG data is matricized unlike in PARAFAC and CP-WOPT.CP-WOPT outperformed both NMF and PARAFAC in terms of RME because CP-WOPT has the ability to recover missing data efficiently such that it models only the known samples from EMG signals, which make it very useful for improving the performance of classification methods.However, this study is limited to offline analysis of sEMG signals.His research interests focus on rehabilitation engineering with a patientcentered approach.He is interested in studying and understanding the altered mechanism of motor control and learning in neurological disorder to develop various technologies that can enhance the QOL of these patients.
Fig. 5(b) shows unstructured missing data in which individual samples of EMG data of Fig. 5(a) are randomly missing.EMG data for four movements is shown in Fig. 6(a) where higher amplitudes show intervals of hand movements and lower 104714 VOLUME 7, 2019
FIGURE 2 .
FIGURE 2. Missing lateral slices of a tensor X :j : (in black).
Fig. 4 ,
Fig.5shows a segment of original EMG data with no missing values, the same EMG segment with unstructured missing values, and lastly the recovered EMG signal.A segment of the original EMG signal is shown in Fig.5(a) with no missing values and it contains information of movement of muscle from a single channel.Fig.5(b)shows the same EMG signal with unstructured missing values, which are the input signal to factorization methods.It can be seen in Fig.5(b) that a lot of values with different amplitudes are replaced by zeroes to model unstructured missing data.Fig.5(c) shows a recovered EMG signal when CP-WOPT is applied on the EMG signal of Fig.5(b).It can be seen in Fig.5(c) that all the missing values that were replaced by zeroes were successfully recovered with amplitudes around 0.48.Fig.6illustrates a segment of EMG data with no missing values having same four movements where each movement exists at higher amplitudes from which first half (with two movements) and second half (with two movements) is removed and then recovered.In Fig.6(a), EMG signal with no missing values is shown that has been obtained from a particular channel.The four epochs of higher amplitudes indicate execution of movement however it can be seen that there is a very small difference between amplitudes of movement and no-movement (at rest) epochs.In Fig.6 (b & d), first and second half (the worst case of removing 50% of data) of channel values is removed to model the structured missing data.It is tantamount to the scenario where data of two movements is missed completely.In Fig.6(c & e), recovered signal by CP-WOPT is shown in which it can be seen that the difference
FIGURE 6 .
FIGURE 6.(a) Original EMG data (b & d) first and second half of a channel missing (c) & (e) recovered missing channel by CP-WOPT.
FIGURE 8 .
FIGURE 8. RME for recovering structured missing samples from first and second half of real EMG data by NMF, PARAFAC and CP-WOPT.
M.Sc.degrees in biomedical engineering from Aalborg University, Denmark, in 2010 and 2012, respectively, and the Ph.D. degree from the Faculty of Medicine, Aalborg University, in 2015, where he was with the Department of Health Science and Technology and is currently an Assistant Professor with the department.His research interests include brain-computer interfacing, signal processing of electrophysiological signals, and neurorehabilitation.ERNEST NLANDU KAMAVUAKO received the master's and Ph.D. degrees in biomedical engineering from Aalborg University, Denmark, in 2006 and 2010, respectively, where he was an Assistant Professor, from 2010 to 2014, and an Associate Professor, from 2014 to 2017, with excellent teaching and supervision skills.From 2012 to 2013, he was a Visiting Postdoctoral Fellow with the Institute of Biomedical Engineering, and since January 2017, he has been an Adjunct Professor with the Department of Electrical and Computer Engineering, University of New Brunswick, Canada.In 2015, he was named the Teacher of the year by the students of study board for health technology and sport science.Between February and September 2017, he was an Academic Visitor with the Department of Bioengineering, Imperial College London, U.K.He has also been a Senior Lecturer with the Department of Informatics, King's College London, since October 2017.He has a good publication record with main research interests related to the use of EMG recordings in the control of upper limb prostheses.He is an Associate Editor of the IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING.IMRAN KHAN NIAZI received the B.Sc. degree in electrical engineering (specialization in biomedical engineering) from Riphah International University, Islamabad, Pakistan, in 2005, the master's degree in biomedical engineering from University & FH Luebeck, Luebeck, Germany, in 2009, and the Ph.D. degree from the Center of Sensory-Motor Interaction, Health Science Technology Department, Aalborg University, Aalborg, Denmark, in 2012.He held a postdoctoral position for a year, and then, he moved to New Zealand, in 2013, where he is currently a Senior Research Fellow with the New Zealand College of Chiropractic.
× I 2 × . ..×I N ) samples then its n-th element is denoted by x i 1 i 2 ,...,i N .The Scalar product of two tensors X , Y with size I 1 × I 2 × . . .× I N is defined as: | 7,700 | 2019-07-26T00:00:00.000 | [
"Computer Science"
] |
Homotopy Analysis Solution for Magnetohydrodynamic Squeezing Flow in Porous Medium
The aim of the present work is to analyze the magnetohydrodynamic (MHD) squeezing flow through porous medium using homotopy analysis method (HAM). Fourth-order boundary value problem is modeled through stream function ψ(r, z) and transformation ψ(r, z) = rf(z). Absolute residuals are used to check the efficiency and consistency of HAM. Other analytical techniques are compared with the present work. It is shown that results of good agreement can be obtained by choosing a suitable value of convergence control parameter h in the valid region R h . The influence of different parameters on the flow is argued theoretically as well as graphically.
Introduction
The squeezing movement normal to two plates is observed in many hydromechanical devices such as motors, engines, and hydraulic lifters, where compression/injection processes using pistons and clutches are found.Due to the utility of these devices, significant research effort is being made for their improvement.Other industrial applications include polymer processing, while medical applications include the modeling of synthetics transportation inside living bodies.As such, the study of squeezing effect, in addition to other properties such as magnetohydrodynamics (MHD) and porosity, has become one of the most active topics in fluid mechanics.The study of porosity effects after introduction of the modified Darcy Law [1] specifically contributed to oil and gas production industry, detection of ground water pollution due to leakage of chemicals from tanks and oil pipelines, ground water hydrology, and recovery of crude oil from pores of reservoir rocks [2][3][4][5].These contributions and others [6,7] are generally found in reservoir, chemical, civil, environmental, agricultural, and biomedical engineering.
These studies are typically modeled using small parameters in nonlinear differential equations which are expressed as series expansions.The exact solution using perturbation methods is therefore not always possible and this poses a considerable challenge to researchers.A recently developed analytic method known as homotopy analysis method (HAM) by Liao in 1992, however, has given promising results as it does not require modeling of the small parameter [8].In fact, in quite contrast, HAM provides a way to accelerate the series solution conversion in the form of auxiliary parameter.The equations are reduced by HAM to a set of linear ordinary differential equations based on the homotopy of topology.These sets of equations can then be computed by mathematical software like Mathematica, Maple, MATLAB, Octave, SageMath, or Maxima.Applications are found in various problems of science and engineering such as expression of skin friction coefficient and reduced Nusselt and Sherwood numbers [9][10][11][12].Analytical tools like homotopy perturbation method (HPM), -expansion method, and Adomian decomposition method (ADM) are special cases of HAM [13].
Mabood and Khan [14,15] successfully applied homotopy analysis method for the study of heat transfer on MHD stagnation point flow in porous medium and boundary layer flow and heat transfer over a permeable flat plate in a Darcian porous medium.Analytic solutions for unsteady twodimensional and axisymmetric flows were presented by Rashidi et al. [16].The study of heat and mass transfer in the context of squeezing flow was performed by Mustafa et al. [17].Kirubhashankar and Ganesh [18] analyzed electrically conducting MHD viscous flows.A two-dimensional MHD problem was approximated using homotopy perturbation method (HPM) by Siddiqui et al. [19,20].Unsteady flow of viscous fluid between porous plates is studied by Ganesh and Krishnambal [21] and Mohamed Ismail et al. [22].Shevanian et al. [23,24] successfully used HAM to study singular linear vibrational BVPs and MHD squeezing flow between two parallel disks.The same authors [25,26] used predictor homotopy analysis method (PHAM) to investigate nonlinear reactive transport model and nanoboundary layer flows with nonlinear Navier boundary condition.
This work is an effort to investigate MHD squeezing flow of Newtonian fluid between two parallel plates passing through porous medium by homotopy analysis method.Using similarity transforms, the governing partial differential equations are converted to equivalent nonlinear ordinary differential equation and then solved using the mentioned scheme.Velocity profile of fluid is argued by varying various parameters involved.
Application of HAM to Squeezing Flow
HAM logically contains some analytic techniques such as Adomian's decomposition method, Lyapunov's artificial small parameter method, and -expansion method.Thus, this technique can be regarded as a unified or generalized theory of these analytical techniques.Unlike other analytic techniques, the homotopy analysis method provides a simple way to control and adjust the convergence region and rate of solution series of nonlinear problems.Thus, this method is valid for nonlinear problems with strong nonlinearity.
Homotopy analysis method provides great freedom to use base functions to express solutions of a nonlinear problem so that one can approximate a nonlinear problem more efficiently by means of better base functions [13].
In the present section, this technique is applied on (9) using boundary conditions (10).For solution expression polynomial base function { 2+1 | = 0, 1, 2, 3, . ..} is used to determine () as follows: where are constants.By rule of solution expression and according to conditions in (10), the initial guess of the problem is The auxiliary linear operator is chosen as with the property Here, ( = 1, 2, 3, 4) are integration constants whose obtained values are By rule of solution expression in (2.51) and by (2.39) in [13], the auxiliary function () is chosen to be () = 1.
Zeroth-Order Deformation Equation.
Using the homotopy introduced in [13], zeroth-order deformation equation is given by where is a nonlinear operator defined by From here, the zeroth-order problem obtained is 2 (0; ) 2 = 0, (0; ) = 0, Here, is an embedding parameter and ℎ is a nonzero auxiliary parameter.It is observed that, Hence, when varies from 0 to 1, then (; ) varies from 0 () to ().By Maclaurin's expansion, (; ) can be expressed as The value of the auxiliary parameter ℎ is chosen in such a way that the series in (18) converges at = 1; that is, where 3.2.th-Order Deformation Equation.Differentiate ( 18) and ( 19) -times with respect to and put = 0 to get th-order deformation equation as follows: Equation ( 24) reduces to where
Exact Solution in Case of Zero Reynold Number
In this section, a special case is studied when the Reynold number is zero and hence (9) becomes a linear differential equation.The exact solution obtained using boundary conditions in (10) is given by Here, = ℎ + .Homotopy analysis solution is also derived in this case.The operator in (17) becomes and for th-order deformation equation in (28) becomes In Table 4, comparison of exact solution with fifth-and tenthorder HAM solutions is made with the help of absolute error.
Convergence of HAM Solution
Solution obtained by homotopy analysis method in (23) contains auxiliary parameter ℎ which adjusts and controls the convergence.There is great freedom to choose the auxiliary parameter.For influence of ℎ on the solution, the convergence of () (0), where is odd, is considered.The valid region ℎ of ℎ for which (0) converges is shown for different order solutions in Figure 1.The curve (0) versus ℎ is said to be ℎ-curve.From Figure 1, it is observed that ℎ increases with the increase of approximation order.For fifth-order solution, the valid region for ℎ is −1.6 ≤ ℎ ≤ −0.3.It is obvious from Figure 2 that when ℎ , increase further, the valid region moves towards the right.Figures 3 and 4 are constructed to examine ℎ for increasing ℎ , , and .
Results and Discussion
Analytic solution, using homotopy analysis method, of magnetohydrodynamics squeezing flow through porous medium is studied.Four figures (Figures 1-4) are constructed, for various values of Reynold and Hartmann numbers, to examine the valid region ℎ which has a vital role in convergence of analytic solution.Table 1 shows different order absolute residuals for HAM solutions and it is clear to see that as the order of approximation increases further, the solution converges to exact solution.Fifth-order absolute residuals for various values of and for fixed values of ℎ , are displayed in Table 2. Table 3 is constructed to display tenth-order absolute residuals for different values of ℎ , , and ℎ while keeping Reynold number fixed.Exact solution is obtained in case of zero Reynold number when the differential equation in ( 9) becomes linear.Fifth-and tenth-order HAM solutions are compared with this exact solution in Table 4 using the concept of absolute error.Keeping ℎ and Reynold and Hartmann numbers fixed, Table 5 displays different order HAM solutions.Table 6 shows important information about the consistency and efficiency of HAM by means of average absolute residuals for fifth-order approximation.Convergence of the present technique is given in Table 7 for different order approximations by means of (0), | (0)|, V (0), and | V (0)|.As (0) becomes zero for even , odd order derivatives are taken to study the convergence of the technique used.Comparison of different analytical techniques and one numerical Mathematica command NDSolve with the present work is displayed in Table 8 which shows that results obtained by HAM are in high agreement.One can refine these results by selecting suitable ℎ in the valid region ℎ .The rapid convergence of HAM can also be seen in Figure 5 which shows the residuals of various analytical schemes.
Conclusion
All tables (Tables 1-8) show the efficiency and consistency of the mentioned scheme.The valid region ℎ can be more refined to choose such value of ℎ for which the obtained solution converges rapidly which is the beauty of the present analytic technique.It is also observed that while increasing the value of Hartmann numbers (Figures 2 and 4), the valid region moves towards the right.that is, increasing Reynold number (when density of fluid increases) results in the decrease in velocity of fluid while keeping Hartmann number fixed as shown in Figure 6.
(2) Figure 7 shows that while increasing ℎ (i.e., increasing the imposed magnetic effect) and keeping , fixed, the velocity of fluid increases.
(3) The same effect is studied in Figure 8 by increasing (i.e., when the permeability decreases) and keeping ℎ and fixed.
(4) Increasing Hartmann and Reynold numbers together, it is observed from Figure 9 that the velocity of fluid increases.It is concluded that Hartmann number is more influential as compared to Reynold number.
Table 3
also tells the same story.The influences of different parameters on the velocity profile are displayed in Figures6-9and the following observations are made:(1) Velocity of fluid in porous medium and the Reynold number are inversely proportional to each other;
Table 2 :
Absolute residuals for different keeping ℎ and fixed.
Table 4 :
Absolute errors of fifth-and tenth-order HAM solutions when = 0.
Table 7 :
Convergence of HAM for different order approximations.
Table 8 :
Comparison of HAM with numerical and other analytical techniques. | 2,468.2 | 2016-06-22T00:00:00.000 | [
"Mathematics"
] |
Personalizing Course Design, Build and Delivery Using PLErify
The Course-Building technique called PLErify was developed by the author in response to the emerging roles of university faculty in the technology-driven teaching with the rising popularity of AI and deep learning. Topics that support personalized teaching and learning using technology to make it more efficient, more effective and more pragmatic. Early attempts at pedagogy and trends that pushed the personalization movement are explained. The progress of the project in a Web App format is detailed focusing on a faculty building a sample hybrid course planned for a course offering of a framework of digital resources within the app in a technology-rich smart classroom. The PLErify course-building Template is explained with methodologies to add content to it in various ways with sugges-tions to insert multimodal techniques, e.g., Augmented Reality, Virtual Reality and Simulation, however applicable, alongside numerical data-science-supported technologies that will comprise the most part of course presentation technique. A portion of a full course will be demonstrated using PLErify with an accompanying Course Evaluation for Professors to mull to prepare for course redesign current to improve next year’s offering of same course.
Introduction
In a digital society, every aspect of our daily lives is interconnected and each person has an identity that is solely one's own that is encrypted and authenticable by a system. That identity allows you to interactively access multiple parts of any platform to perform actions and obtain something as a result. In an ideal version of a digital society, we humans are interconnected as citizens (e-government) and members of various groups (private) and afforded rights and privileges accordingly. We see micro versions of the workings of a digital society in Big Tech such as Facebook, LinkedIn, Twitter, Instagram, and Google with each connectivity model functioning according to predetermined business model.
Our ever-evolving digital society intertwines the roles of humans and robots in institutions and industries. These roles keep changing as technology advances to near capability of humans through artificial intelligence and deep learning permeating the deepest trenches of every industry, not excluding higher education. In higher education, it is hardly noticeable that the roles of main players (faculty and instructors) as primary owners, designers, and deliverers of their own courses for live instruction need to step up and adapt more aggressively more so than any other group. Not to be An American philosopher and educator, John Dewey (1859-1952) gave a very powerful quote with a whole new meaning that is truer now than in his time. Truer now because the educational methods we now deal with goes beyond the chalkboard, goes beyond talking in front of students, and goes beyond doing projects in isolation using pen and paper. Not completely discounting the power and value of note-taking using pen and paper and would not advise against the method, it is important to recognize the presence of computational tools being used as part of current teaching methods he would have never imagined would exist today.
Undeniably, educational technology tools ushered the transition from passive (sit, listen, take notes) to active (interactive "constructivist" learning) with students defining their knowledge accumulation, construction, and learning pathways. Deep learning, Machine Learning, Big Data and Internet of Things (IoT), and artificial intelligence disrupting education in more pervasive ways have no specific timeframe. While innovation and adaptation slowly chip away traditional education system, the idea of faculty being put aside with little to no role in designing, offering, syndicating, and delivering courses (aptly described as course massification) is in fact a repelling thought. In universities however, few scenarios must play out to avoid a scenario where the machine decides and controls. The professor must play the central role but in an enhanced strategic [1] and impactful way. They (professors) must assume leadership roles to formulate actionable changes but be cognizant and fully prepared to face added responsibilities from programming robot consciousness to designing robotized courses through aggregating and updating content, that is, build virtual (academic versions of Alexa-like) robot assistants [2], automated pulling of content from a variety of resources. These new robot-driven challenges in higher education are succinctly described as follows.
Robots and professors for efficient teaching
Widely practiced in Japan, Korea, Taiwan, Singapore, and China, are robots (built in the likeness of a professor/researcher), robot applications, and robots programmed to co-teach/co-research juggling the myriad roles of the human instructor. Other foreseen creative uses of these robots involve individualizing attention to each student, thereby ensuring progress, remediation, and success (knowledgebase-driven virtual assistants). Missing in those possible roles are robots that build online courses for professors based on didactic teaching styles and student learning styles all utilizing high integrity knowledge bases with optimum performance. Past attempts at course development using course sequencing [3], adaptive learning paths [4], computational teaching, and participatory teaching [5] can inspire new innovations in this area. One deep-learn course building technique is an AI-based course aggregator (software-based) which pulls different curriculum (using Big Data) of the same course from many places/universities and then gets stored to a central location where students get to pick and choose a course program of study. These new courses would be up-to-date with new data that include recent Personalizing Course Design, Build and Delivery Using PLErify DOI: http://dx.doi.org /10.5772/intechopen.85414 developments in the discipline. Since my focus on PLErify is to assist the instructor, the adaptive learning concept for learners based on any learner model (cognitive, behaviorist, and mental models) has been intentionally skipped.
The MOOC-as-course augmentation for faculty and as resource for PLErify teaching learning
In AI age, faculty must face their new roles as programmer and owner/builder of learning environments of their courses that they must update per semester. Professors who remain indifferent in the new reality of a virtualized higher education vis-a-vis their expanded roles and new responsibilities will face major challenges as industry-driven automation-driven AI persistently seek dormant or stagnant unchanging areas to automate and simplify. A faculty [6] from Scotland recalls his very productive sabbatical spent at Google (Big Tech and AI-driven) in Silicon Valley. In that sabbatical, this academically trained faculty learned and eventually re-adapted his purely academic mindset (coding) to meld with that of a practitioner's mindset and started building coding projects that work in real life. Faculty (particularly those in the sciences and engineering) can follow Barker's example and come out technologically empowered fusing the academic with the practical real world and impart the same mentality to its students, that is, college to career.
Regardless, MOOC courses will continue to be made available online to anybody for free, or at a minimal cost. Boosting acceleration of acceptance by universities globally, MOOC continues an upward evolution toward a better practical higher education option for both career (skills training or mastery certifications system) and advanced degrees (bachelors, masters, and doctorate). Also, with student advancement (and their interest in mind) and ease of teaching for instructors as prime motivators for MOOC development, there is truly so much about the MOOC system to be appreciated that will make higher education leaner, more efficient, very current, and very affordable.
Another very encouragingly useful aspect of MOOC that is only now being realized is, they add to the personalization of learning both as a teaching resource for instructors and as an inexpensive way for students to obtain advanced degrees and lastly but more importantly to upgrade skills of work professionals. MOOC business model is being revamped and, evolving toward a more profitable version, thereby offering fee-based enrollments where certification of completion is a student's objective. The free aspect of the MOOC model however can be used by all faculty as another teaching tool. For example, professors can require students to take the MOOC version of their course offered by other universities (now estimated at 900) shown in Figure 1, before students take the real course. In a PLErify state, courses already in MOOC database can be treated as a required mastery before registering and enrolling in the equivalent actual university traditional course or program unloading faculty of heavy teaching. Some tedious portions of the course can be bypassed having mastered it beforehand through MOOC.
While debates and experimentation continue to grow in artificial intelligence, PLErify App (2007) remains a precursor to the above scenarios. Even though the core of educational technology research centers on academic applications, academe, ironically remains the most resistant and the slowest to adapt to a scenario of AI, Big Data and IoT which when taken as a group suddenly changes the course delivery game. Groups in the tech industry persistently hint at a future without a human teacher and professor, as computer scientists now and then flirt with the idea of adding consciousness to a computer. At this time though, a robot cannot actually augment human cognitive and emotional capabilities through what they claim as smarter machines currently experimented in other industries (automobile industry). I would simply and safely assume that use of virtual robot assistant is an easy spillover for use in higher education [2].
New Innovations in Engineering Education and Naval Engineering
It is best to speculate that whatever happens in the corporate industry will, in some form happen in the education industry. The digital society interconnects everything, from machines and app to the software/hardware; from knowledgebase to users; from different variety and degrees of transactional computing; from the teachers to the students; from the businesses to the consumers; from the students to the universities; from the faculty to the students to the universities; and finally, from the ordinary users to everything which can occur via our desktops and our handhelds. Apocalyptic ideas have been flouted at global corporate e-learning events that hint at the idea of massification to replace traditional creative teaching without a human teacher, which may appeal to select academicians who fall into the trappings of "easy teaching," that is, less classroom presence and letting the students watch video lectures and digitized .pdf files of the syllabus. Given that these handheld tools are now a normal part of everyday life blending the here, the now, and the future, a DIY culture for course building becomes inevitable. Embodied by the PLErify application (2007), the DIY mindset provides a solid training ground for ubiquitous computing vis-a-vis course building as it involves an interplay of a variety of cognitive skills combined with digital conversion of ideas into a viewable medium.
Today, 24/7 we carry our smartphones, iPad, and other handhelds also known as mini/microcomputers, more powerful than any computers built in the 1980s and the 1990s, with us and with these technologies we socialize, network, listen to music, share photos, financially transact, chat on live video, and much more, thereby doing tasks never before possible at the very same period of time educators were theorizing on learner styles, cognitive styles, etc. My own observation over this past decade is that while educators spent so much time researching learner styles and cognitive styles, they believed impacted learning, Big Tech simply went ahead and produced a plethora of handhelds and smartphones that rapidly jumpstarted user acquiring tech skills in turn accelerating mastery that are, fortunately, usable in both daily life and university learning but unfortunately left out those who could not keep up with the constant roll-out of new versions and models. What that phase did to each of us was it made us tech-savvy and I would argue, smarter. Now, certain tech user interactions have become ingrained for majority of us smartphone and multiple device owner and users. Majority of learner tasks to: make choices, complete learner tasks, solve problems, think about thinking (metacognition), compute, analyze have become second nature.
Indeed, technology has a very democratizing effect on its dedicated users from acquiring uniformity of skills to performing actions to obtain something back as a result; skills, which by the way, are also transferable to other domains from personal, to business, to higher education with specific attention to learners. All users get it. We can turn on the device, charge the device, download and use apps, transact, collaborate, blog, share documents, and so many other things that it is now second nature to have (as opposed to not have) our smart devices even while we sleep. Technology has intercepted our lives in unimaginable and remarkable ways psychologically but best of all, educationally. I must conclude that though technology tools are not advisable for use by children, technology for mature adults is an additive rather than a subtractive experience.
Didactic models for creative computational teaching
The timely re-entry of computational [7] tools to teach creatively befits this era of our technology-driven education. Less intervention on how students create their learning paths as they meld new learning with what they already know in working memory gives students a better grasp at how to manage their interactions and the accumulation of those interactions in a self-directed way exemplified by the constructivist didactic model used in the Virtual Mentor Project notably learning by asking LBA Project [8].
The actors on stage in the world of tech-based teaching and learning and their functions in the teaching learning equation are summarized with one infrastructure in common: connection to the Internet (Figure 2).
Software applications accessible on the web allow both instructors (course makers) and students (users) to manipulate course content to create, present, and store. Learning Management Systems (LMS) are prepackaged applications that act as an administrative tool to manage the online course, the students who enroll, and the professor who offers the courses. Artificial intelligence (AI) is the byproduct of deep learning, huge swathes of databases within a database that when meshed together gives it intelligence though within a limit, that is, you can program a robot to do certain things that will be limited to the amount of intelligence you put in it. A human is still in command and an ill-programmed robot like a biased robot gives New Innovations in Engineering Education and Naval Engineering 6 disastrous results. Data science is the highest application of computational ability that can detect patterns of naturally occurring events in nature, analyze it, and provide very accurate predictions and analysis in context. Interactive rooms/walls that began in the mid to late 1990s, but was not very successful such as the project at Stanford University and though unsuccessful at its early attempts to merge it in the teaching and learning game, has re-emerged (Germany Applications) with more sophisticated screens that cover the entire walls making possible the projection of visualized data comprehensible to all learners.
Augmented reality when applied has the capability to freeze, slow down or speed up, and look for patterns among piles and piles of data and of events that are intended to be analyzed and deduce from. It is very useful in the science and engineering course making in particular, though also usable in other fields when observation of actions is sought. Virtual reality or virtual environments are computing environments otherwise called virtual immersions where "users are immersed in low latency high-quality visual stimuli and spatial audio amplified by motion platforms, dispersed systems, and active/passive haptic device that allow users to fill objects with more sophisticated systems equipped with gesture recognition and voice input. Success of VE systems depend on system performance matching user expectations" [9]. Ubiquitous computing is computing at its finest you hardly notice it. The Clouds are on demand delivery model that enables the synchronized delivery of computing resources such as applications, storage, servers, networks, and services (liberating software providers of low-level IT on premise infrastructure) that allows instant scale up of various types of web services [10]. Web services are on-demand applications, storage, and servers and sometimes called SaaS, PaaS, and IaaS enabled mostly through the cloud. DevOps and Containerization [11] is a systems management technique for on-demand services performed in a bundled way as a completely packaged infrastructure (operating system and software applications) updateable on the fly with the least hassle.
Bonk [12] aptly describes a changed e-learning ecosystem in the past two decades and summarizes it based on three themes namely Learner Engagement, Pervasiveness, and Customizability using the same computing technologies for document-sharing, collaborating, software, hardware, communicating, and digital resource use. Based on learner engagement, he cites, 1-mobility, 2-visual, 3-touch sensory, 4-game-based, 5-immersive, 6-collaborative, 7-social, 8-digital and resource rich, 9-adventurous, 10-hands-on and in its pervasiveness, 11-mostly online, 12-video-based, 13-global, 14-immediate, 15-access to experts, and 16-synchronous in being customizable, e-Learning is more 17-open, 18-more blended, 19-more competency-based, and 20-ubiquitous. With its customizable quality, e-learning is 21-more blended, 22-more self-directed, 23-more competency-based, 24-more on demand, 25-more massive, 26-more modular, 27-more communal, 28-more modifiable, 29-more flipped, and 30-more personal. The "more personal" thus sets the stage for personalization (PLErify) in both teaching and learning, which lends itself appropriately to the different didactic models [13] of teaching for different types of courses whether it is intended for knowledge-building, theorizing, knowledge or skills acquisition, model-building, simulation, and argumentation.
General instructional design with less focus on user learning style
Instructional design for high-performance computing [5,7,14,15] focuses on the principles governing working memory vis-a-vis cognitive load [16] extracting memories associated with completion of task (primary and secondary memories). Primary memory are those cognitive schemas a person acquires as a result of interacting with the environment stored in long-term memory which the secondary memory (the cognitive schemas stored in short-term memory) uses to solve problems though not without limits. Knowledge creation for working memory follows the principles of (a) "narrow limit of change," that is, within a small span of time, new information should be very limited in order for it to be stored in long-term memory. "Unlimited": it may be in the amount of information it can process, working memory follows the principle of being capable of spewing (b) "unlimited amount of information" for information retrieval. When that knowledge creation does not occur, the mind adjusts by following the principle of (c) "randomness as genesis" methods (the borrowing and reorganizing principle, that is, imitation, listening, reading, and social interaction) to compensate in the knowledge creation. Finally, when cognitive overload must be overcome to commit new knowledge in long-term working memory, the mind does what is called the principle of environmental organizing and linking, retrieving, and cycles through information already stored in long-term memory.
In non-scientific, non-engineering subject matter, focus on cognitive load combined with learning theories has not been exhaustively studied. Learning styles has been linked to the effective design of course materials as it affects comprehension and overall performance [17]. A person's style of learning is determined by environmental factors manifested through behavioral patterns. In my 2001 Doctoral Dissertation [15] experimental study based on learning style effect on user performance, I found out that in a matched condition, i.e., matching concrete icons with concrete learners and matching abstract icons with abstract learners resulted in better performance on recall and memory and task completion. Concrete learners performed better overall in a matched condition. There are other learning theories besides that was used (Kolb's) in my experiment and most are in the style of thinking and therefore behaving, extent of proficiency or lack of and style of responding to environmental triggers. Knowing fully well that style of learning in the AI-driven teaching will override the learning style consideration, platforms will be built mainly based on learner independence during the learning process. That is, they will determine the route, path, and speed at accumulation of knowledge and skills as they see fit. In the past, there was "adaptive learning" where the computer adjusts to the learner based on the speed of knowledge acquisition of the user and then readjusts the next set of materials based on that performance. If the previous task proved hard, the computer generates an easier task to complete and vice versa.
Though the idea that learner pathways must still be considered in designing personal learning environments, it is safe not to overly worry about learners and skip the time-consuming practice of hand-holding knowing that users have full control of their digital strategies and techniques to learn. It is both consoling and problematic at the same time: consoling because instructors would not need to look over learner's shoulders during the process of mastery, yet problematic because it now forces the instructors to be on the top of every technology used by the learner. Instructors need to possess tech skills better than students. Casual everyday users (including the dark forces of the web) of tech will possess mastery of technology for every intended purpose.
State of purely online learning
MOOCs, such as EdX, Coursera, Open CourseWare (OCW), and hybrid designs, are designed to offer free courses for poor countries (MOOC), to corporations (Coursera and Udacity), EdX (universities), however of late modified it to a paid model adding some validation features to address legitimacy of courses, such as granting of certificates, shortening of Master's program as the case in Georgia Tech. Student attrition remains a problem compared to regular traditional classroom model proving that regardless of convenience, students still prefer a human teacher in the classroom indicative of what history has revealed, that is, the most valuable teacher is a human. Another acute finding is that MOOC courses are not suitable for advanced courses that can only be handled by a human.
Alternatively, MOOC courses, based on a very interesting observation of Cooper and Mehran [18], have the potential utility in personalized learning in the same manner as YouTube online video courses do, that is, a place to find highly reputable learning resources for students to pre-familiarize themselves of courses they will take before they turn up at actual class lectures. This utility when applied as a "before-you-attend-a-class" feature skirts the nagging issues attached in MOOC such as validation, plagiarism, certification, and lack of richer evaluation. MOOC, in that capacity, is indeed a welcome addition to personalized learning. One monetization [19] possibility explored by MOOC concerns that of providing added validation about the student for employment which, to my mind is very interesting and closes the loop of education to career. In an interview [13] with John Hennessey by his longtime colleague Davis Patterson, John was very enthusiastic about MOOC and thought of it is a compelling solution for continuing education (skills upgrading for working professionals) with his continuing belief that Masters and PhD program will be part of MOOC and non-MOOC.
In that vein, professors in higher education institutions need to skill themselves sufficiently to be able to create a digital course only once but updated for every semester's offering. Faculty load of work is, in truth, lightened while students carry most of the load of a course, that is, reading materials, accessing mixed modal multimedia, collaborating, project work, homework, assignments, critiquing, mid-term exams, and final exams. In the PLErify platform, AI tools, in the research (Research Resources and Libraries Semantic Knowledgebase), analytics (Quantifying/Analytical engines), authoring (Multimedia and Multimodal), and learning management systems (Course Delivery Student Access Point) are placed in a private server for the instructor where he is given the freedom to extract all sorts of information for his course and capture and store those information in an organized cataloged file directory (on both desktop and the Private server) purely for his own access Figure 6.
PLErify course design and future AI prospects
PLErify components that are visually depicted in Figure 3 are as follows.
Research is a set of live databases to extract content through digital libraries for download of scholarly materials, quantitative and qualitative data. Analytics is a set of measurement tools to analyze datasets and extract visualization files for inclusion in the instructor's course curriculum. Authoring tools are a set of applications to convert text to viewable mixed media and mix it up for immediate playback as one full course. Learning management system (LMS) is described in my paper [1] "Virtualized Higher Education" as the course's administrative tool performing the tedious tasks as the container that holds a course or courses. An LMS is an application that typically contains the following: administrative features for managing content: content uploading/downloading; calendar and scheduling instructor timings; student administration; faculty/student communication tool; conferencing; and homework/project submission and grading. These processes and activities as shown in Figure 4 are time-consuming and repetitive some of which can be made efficient through automation. Decisions to automate parts of the PLErify platform depends on the instructor who (after updating his skills in AI, deep learning, virtual assistants, and robots) can select which activities to augment as shown in Figure 7.
Personalizing Course Design, Build and Delivery Using PLErify DOI: http://dx.doi.org/10.5772/intechopen.85414 Identify simulation videos or games to illustrate LS application. Include the URL's of videos (simulation and VR) within the course before packaging it for export to the LMS. Package the three modules as one course and export it to the LMS enroll students taking the course.
Add a Course Evaluation ( Table 1) showing a generic form freely available online at https://www.jotform.com/form-templates/course-evaluation-form-3.
Student participation
The amount of effort you put into this course was: Excellent Very Good Good Fair Poor Very Poor. On average, how many hours a week did you spend on this course (in and out of class)?
0 Every e-learning course is organized into modules shown in Figure 5. To populate content, the method is quite straightforward starting with Module 1 but not necessarily following a linear process, that is an instructor can jump from Module 1 to other modules in no particular order depending on how they interconnect topics and ideas.
Click Module 1. Module 1 will load chapters. In the edit mode, you can replace the content with your content. Chapter 1's format is repeated for Chapters 2-4. You can replace the content as your syllabus progresses. In each chapter, you can include datasets (from the research toolset analyzed with results presented). These analyses of presented data, or sample data can be saved in database readable format backed up in instructor's private server and desktop for inclusion in the digital course. The content of the modules is managed as shown in Figure 6. For example, in a digital course on Learning Theories, an instructor will find the timeline data to present the history of the early to modern learning theorists. This timeline tool in the PLErify App can be dramatized through an augmented reality historical film on the significance of each era and how it influenced education at different times in the history of the modern world.
Personal learning environments or expert systems as it is sometimes called is disruptive enough to education due to its "lean to use automation." Any AI application is still limited in capability where human skills of negotiation, detection, mobilization, and understanding of power and trust (much like the gut instinct humans have to sense danger and change) is required. In other words, regardless of whether it is continually built to be smarter and smarter and contradicted by Krakovsky [20] who has a more optimistic view, AI as predicted may not develop a true "sense of self." Her research to a certain extent can be useful for building the intelligent robot as instructor assistants that will be tasked to meet students, answer course-related concerns, substitute the human instructor who is on research travel, and track student progress as described in Section 2.
Assigning automation features shown in Figure 7 in PLErify in the next 5-10 years will center on course preparation, in converting a simple text to something more graphic or visual, combining the visuals into a more powerful single visual based on context, capturing real live data from a source known only to the instructor, citing the link of that source in the course materials, mastery in the use of sophisticated tech-enhanced classroom, synching course presentation of materials with the tools in the smart tech-enhanced classroom, and automating tasks in use of the LMS.
Reflections on the profession vis-a-vis digital society
We can entrust the ability to recognize learner styles, learner abilities, comprehension and understanding in Artificial Intelligence (AI) as it continues its ascent towards enhanced intelligence in almost all facets of our digital life in this case Higher Education and Course building. In that token, Instructor (as Designers) and Technologists can look into Chris Stary's [13] research on the Scholion Project as one guide looking into the design and implementation of his constructivist-based project that aids learners taking into account learners' mental models, cognition and metacognition.
In this second half of this decade, AI's recognition capability has gone far beyond its early beginnings that it is now termed the Age of the Machine. Much similar to Elon Musk's Tesla, the machine can now build other machines. Thinking about this new reality in education also means the teaching and learning can now rid of a lot of the mundane tasks in: course creation (by instructors), course management (by LMS's), course participation (students), grading and course evaluation methodology, and issuing valid certification. The machine age that will make our transition to a fully digital society brings with it a whole slew of new realities as well for the professional. The expectations from each of us has become compounded to an extent that a degree certificate attached to an individual that requires skills are also updated be it technical, socialization, community, or connectivity [21].
Professional degree certificates (undergraduate and graduate) now also comes with it the responsibilities of skills upgrading on a continuing basis; of being in communities attached to that profession; of being in the know in those communities; of being socialized in the platforms of Facebook, Twitter, Instagram, and LinkedIn to not just being there but being a part of the larger whole comprised of billions of people in the world. To think about it in terms of how AI responds to that changed perspective is to assume that all these interactions are captured by the AI machine and adds it in terms of quantifiable data, making new assumptions about the added data in effect, understanding ourselves more from that newly captured information producing an updated profile of a person's learners style based on captured cognitive functions and user action decisions as opposed to self-reported learner style [22].
Schneider [21] points out that through data analytics, these learner characteristics can be extracted automatically from user's ongoing interaction to perform a variety of transactions (from finding a route using GPS (geographical positioning system) or learning online to online banking transaction) in day-to-day lives. When these patterns of user interaction are validated, a method called "user nudging" could be used much like adaptive computing that "adjusts to serving user tasks based on prior action," that is, nudging would guide user actions by giving choices through prompts.
Denning [23] summarizes it brilliantly that to truly survive in the age of machines where the knowledge worker conducts work on highly intelligent machines, new expectations come to the fore that requires pragmatism in belongingness, ever adapting skillsets that changes as the system changes, community building based on chosen areas of belongingness (professional, leisure, or recreational), and last but not least, willingness to mentor, to display your skills to the person that needs it so that the next learner improves the knowledge to the next and so on and so forth.
Security concerns with respect to PLErify, MOOC, and tech tools
Security breaches from China, North/South Korea, and Russia are a threat to our tech-enabled life. These countries' very advanced cyber-surveillance and intrusion system have penetrated US cyber defense system potentially undoing major education technology advancements. The industry needs to come up with a very strong authentication system as well as cyber-blocking mechanisms beyond the obvious firewalls.
Without a strong cyber security strategy attached to all these tech innovations, any attempts at technologizing higher education would face enormous challenges. China's breaches covered the entire hardware/software and telecommunication ecosystem (home routers included) baffling Europe, the US, and Australia. A solution that has been proposed is virtualization and containerization. If virtualization and containerization provides a guarantee for the safety and security of cumulative progress and strides made in the education sector and if we are willing to adapt to rapid changes demanded of us as educators, faculty, and students, then the future will certainly be bright.
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | 7,532 | 2019-03-29T00:00:00.000 | [
"Computer Science",
"Education"
] |
DETERMINING SOUTH AFRICA ’ S EXPORT POTENTIAL TO AUSTRALIA : A PANEL DATA APPROACH
This study explores South Africa’s exports to Australia from 2000 to 2012, using both a static and a dynamic augmented gravity model. Sectors with export potential are identified, whether these are reliable and stable is considered. The largest export potential includes the apparel sectors as well as the basic metals, communication, furniture, glass, iron, leather, motor, paper and printing sectors. The most stable and reliable export sectors are the motor, machinery, iron, basic chemicals and food sectors. Although these sectors could target the promotion of South African exports, South Africa could also serve as an important source country for Australia in strengthening ties with the African continent.
Introduction
South Africa and Australia have a long history of cooperation, as diplomatic relations were established as far back as 1947.A Joint Ministerial Commission was established between the two countries in 1997, which serves as the main forum for discussion and strengthening trade and economic ties.The two countries have a firm bilateral economic relationship, with their shared interest in international matters.This includes cooperation on issues such as the Commonwealth, the World Trade Organisation (WTO), the Cairns Group, the New World Wine Producers Group, climate change, protection of fisheries, human rights, human trafficking, law enforcement, defence relations and the customs regulations.South Africa and Australia are both members of the Indian Ocean Rim Association for Regional Cooperation (IOR-ARC), which facilitates trade and investment in the region (DFAT, 2013a).
Australia has a number of additional advantages in terms of international trade with South Africa.The business culture, accounting practices and the legal system are rather similar, and English is the official language in both countries.Recently, there has also been a strong business migration from South Africa to Australia.
According to Australia's trade and investment history with southern Africa, it seems that Australia was notably cautious in its exploration of foreign markets (Parliament of Australia, 1995).However, this is apparently changing, because, according to the Foreign Minister, Stephen Smith (Mercer, 2010), it makes sound economic and strategic sense to engage more with Africa.He added that increased trade could lead to greater prosperity in Africa."Australia needs to look west to Africa.For too long, Australia had not given Africa the priority that it deserved."He concluded by stating that Australia's minerals and resources companies are conducting more projects in Africa than in any other region of the world.
Given this, it would be interesting to evaluate South Africa's exports to Australia to determine the trade prospects between the two countries.Australia ostensibly views South Africa as the gateway into the Southern African Development Community (SADC) region, as it is already the regional powerhouse.South Africa has a first-world transport, banking and telecommunications infrastructure and its gross domestic product (GDP) is seven times higher than the average GDP per capita for the sub-Saharan region (DFAT, 2013a).
Furthermore, South Africa is the 28 th largest merchandise trading partner, with the 23 rd most significant merchandise export market and the 36 th most important import market for Australia.
Abstract
South Africa is also the 17 th largest foreign investor in Australia.Investment in Australia was valued at $A2.310 billion in 2012.Australian investment in South Africa during the same time period was valued at $A3.024 billion, with the mining sector, agriculture, infrastructure and services being the main sectors attracting investment in South Africa (DFAT, 2013b).Bilateral trade between the two countries in 2012 was valued at $A2.233 billion (DFAT, 2013b).
The merger of the Australian BHP and South African Billiton in 2001 created BHP Billiton, which is the largest mining company in the world.In 2012, the main Australian exports to South Africa were coal, wheat, civil engineering equipment and parts, with specialised machinery and parts valued at $A1.788 billion.South African exports to Australia comprised passenger motor vehicles (mainly BMW Series 3), goods vehicles, specialised machinery and parts; and pig iron, valued at $A1.281 billion (DFAT, 2013b).
Australia was South Africa's 20 th most important export destination and its 18 th most important import source worldwide during 2009 (DTI, 2010).South African exports to Australia increased by 251 per cent from 2000 to 2008.Between 2000 and 2009, the increase in exports measured 72 per cent, owing mainly to the international financial crisis.Imports from Australia to South Africa increased by 191 per cent from 2000 to 2008 and by 98 per cent between 2000 and 2009, also because of the international financial crisis.The trade balance between South Africa and Australia in 2009 showed a deficit for South Africa of approximately R3 billion ($A 428 million) (DTI, 2010).
In 2012, South Africa's three principal export destinations were China (11.6 per cent), the United States (8.7 per cent) and Japan (6.2 per cent), while Australia (1.0 per cent) was at number 22. South Africa's three principal import sources in 2012 were China (14.4 per cent), Germany (10.1 per cent) and Saudi Arabia (7.7 per cent), while Australia (1.4 per cent) was at number 17 (DFAT, 2013b).
The top export destinations for Australia during the same period were China (31.6 per cent), Japan (18.8 per cent), South Korea (7.7 per cent), India (4.6 per cent) and the United States of America (3.6 per cent).The main sources for Australia's imports were China (18.8 per cent), the United States (11.8 per cent), Japan (7.7 per cent), Singapore (6.1 per cent) and Thailand (4.7 per cent) (DFAT, 2013b).This shows that there is the potential for enhancing trade with an African country, and South Africa could well be that partner.
Given this, the paper aims to provide some insight into South Africa's exports to Australia, seeing that the country is Australia's largest trading partner on the African continent.In addition, it would be intriguing to determine South Africa's export potential to Australia and to establish which sectors could be enhanced further to exploit this potential.The objective of this paper is thus to analyse South Africa's exports to Australia by applying a gravity-model approach.The paper also investigates the existence of any unexploited trade potential between South Africa and Australia.The rest of the paper is organised as follows.A gravity model will be discussed in Section 2, followed by the estimation methodology in Section 3. Section 4 presents the estimation results and Section 5 elaborates on the trade potential.Section 6 will conclude the paper.
A gravity model
A gravity model is an important instrument for determining the export potential and is used to analyse the relationship between the volume and direction of bilateral international trade.Tinbergen (1962) and Pöyhönen (1963) pioneered the idea of explaining bilateral trade flows using Newton's law of gravity.The economic mass of a country, generally measured by the gross domestic product (GDP), acts as the attraction factor between two countries.However, the attraction would be partially offset by the distance between the countries, which serves as a resistance factor.In theory, one would thus expect that countries with a stronger GDP, which are in close proximity to one another, would experience higher volumes of bilateral trade.Conversely, the smaller the GDP and the further away the countries are from one another, the less trade would occur.Anderson (1979) and Bergstrand (1985Bergstrand ( , 1989) ) both emphasised that the gravity model is a good representation, irrespective of the structure of the product markets.
Being a proxy for transportation costs, distance is normally expected to be negatively related to the flow of exports i.e., the greater the distance, the higher the costs involved in trading.There is consequently a negative effect on the trade flows.However, as shown by Marimoutou, Peguin and Peguin-Feissolle (2009) and Brun, Carrère, Guillaumont and de Melo (2005), distance can play a different role in a gravity model of bilateral trade.Marimoutou et al. (2009) show in particular that the larger the GDP of the trading partner country, the less the effect of distance on trade flows.
The basic gravity model is augmented by a number of variables, such as infrastructure and differences in per capita income, to enhance the explanatory power of trade between countries (Martinnez-Zarzoso & Nowak-Lehmann, 2003).Bergstrand (1985Bergstrand ( , 1989) ) included the population size, while Oguledo and MacPhee (1994) included a measure of the price variable.Several studies, such as those by Mátyás (1997) andTri Do (2006), extended the gravity equation by including the exchange rate.
The basic gravity equation explains the extent of the exports between country i and country j in terms of three factors.These are the total supply of the exporting country (i), the potential demand of the importing country (j) and the various factors which represent either a resistance to or an enhancement of the trade flow between countries.This study follows a generalised gravity model specified as (Martinez-Zarzoso & Nowak-Lehmann, 2003;Jakab, Kovacs & Oszlay, 2001;Breusch & Egger, 1999;Oguledo & MacPhee, 1994): where X ij is South Africa's (country i) sectoral exports of goods to Australia (country j) during time t, ER jt is the nominal exchange rate (rand/$A) between South Africa and Australia, GDP jt is the domestic product of Australia and GDPSA it is the GDP of South Africa.The population of Australia is represented by POP jt and POPSA it the population of South Africa, with Z ij representing any other factor enhancing or restricting trade between the two countries, and ε ijt is the normal error term.
A higher rate of exchange (depreciation of the South African rand) generally leads to an increase in exports, while a lower rate of exchange (appreciation) leads to a decrease in exports.It is therefore expected that the coefficient β 1 should be positive when the exchange rate depreciates and negative when the real exchange rate appreciates.
A higher level of GDP in the importing country indicates a higher level of the potential demand for imports.Similarly, a higher exporter's GDP represents increased production potential and implies increased volumes of export ability.The coefficients β 2 and β 3 are therefore expected to indicate positive signs.
According to Martinez-Zarzoso and Nowak-Lehmann (2003) and Armstrong (2007), there is no clear a priori relationship between exports and the populations of either the exporting or importing country.The estimated coefficient of the exporter's population could be positive or negative.A large local population indicates a large domestic market with high levels of consumption (absorption effect) and thus lower quantities to export (Nilsson, 2000).Large populations may also encourage the division of labour (economies of scale), which means higher production levels and thus opportunities for exporting more.In the same vein, the estimated coefficient of the trading partner's population could be positive or negative.Thus, the effects of population on both the exporting and importing country cannot be assigned a priori.It is thus expected that β 4 and β 5 will have ambiguous signs (Oguledo & MacPhee, 1994).
This paper makes a further effort to introduce a dynamic effect.In Equation 2, it is assumed that, in any period of time, exporters exchange the products and an exact zero trade balance between two countries exists.However, because equilibrium exports are not necessarily achieved in time period t, a trade deficit or surplus normally occurs.Exporters in South Africa have to bear sunk costs, such as distribution and service networks, bringing about inertia effects in bilateral trade flows.In addition, previous investment in export-assisting infrastructure could also affect the trade relationship between countries (Sichei, Erero & Gebreselasie, 2008).This means that, if South Africa exported products to certain destinations at time t -1, it would generally still do so at time t.Although still taking account of this inertia effect, certain panel studies using gravity estimations introduced dynamism (De Grauwe & Skudelny, 2000).By relaxing the zero trade balance and incorporating the persistence effect, a partial adjustment mechanism changes exports to have the form: Incorporating this into Equation 1 and rearranging it generates; Equation 3 can now be rewritten as; This represents a partial adjustment gravity model, where variables with asterisks in Equation 4represent the short-term effects and β 1 to β 6 in Equation 3 represents the long-term effects.The speed of adjustment is indicated by the coefficient δ (0<|δ|<1), which is equal to one if adjustment occurs in a one-time period.
In addition, it was decided to include the standard dynamic panel GMM, as it leads to more efficient and robust estimates, especially in the presence of possible heteroscedasticity and serial correlation.The Generalised Method of Moments (GMM) was introduced by Hansen in his famous 1982 paper (Hansen, 1982).The starting point for GMM estimation is a theoretical relation that the parameters should satisfy.The idea is to choose the parameter estimates so that the theoretical relation is satisfied as closely as possible.The theoretical relation is replaced by its sample counterpart and the estimates are chosen to minimize the weighted distance between the theoretical and actual values.GMM is a robust estimator, in that it does not need to be informed of the exact distribution of an error term (Wojcik & Rosiak-Lada, 2007).The theoretical relation that the parameters should satisfy is usually called orthogonality conditions between some function of the parameters f(θ) and a set of instrumental variables zt: where θ is the parameters to be estimated.The GMM estimator selects parameter estimates so that the sample correlations between the instruments and the function f are as close to zero as possible.For the GMM estimator to be identified, there have to be at least as many instruments as there are parameters to estimate.
An important contribution by Hansen's (1982) original work was to alter the requirements for instrument exogeneity.In GMM, instruments are exogenous if they satisfy a conditional mean restriction, so careful consideration of instrument exogeneity is a standard procedure in GMM empirical analysis.Instrument exogeneity, together with instrument relevance, is an important criterion when it comes to an instrument's validity.When testing instruments' relevance, an instrumental variable must satisfy the requirements of correlation with the included endogenous variable(s) and orthogonal to the error process (Baum, Schaffer & Stillman, 2003).
Estimation methodology
A panel data approach would be used to estimate the gravity model of bilateral trade.This has many advantages, such as the role of the business cycle and the fact that the interactions between variables over a long period of time can be captured (Egger, 2000;Egger & Pfaffermayr, 2003;Martinez-Zarzoso & Nowak-Lehmann, 2003).Panel data involves different models that can be estimated, such as pooled, fixed and random effects.The pooled model assumes that sectors are homogeneous, while fixed and random effects introduce heterogeneity in the estimation.The pooled model is restricted and assumes a single intercept and the same parameters over time and across sectors.Sector-specific effects are not estimated.However, the unrestricted models (fixed or random effects models) allow the intercept and other parameters to differ across sectors.Because sectors differ from one another, there should be a decision on whether to use a random or a fixed-effect model, as the regressions include individual sectoral effects.When estimating the trade flows between a randomly-drawn sample of sectors from a large population, a randomeffects model is more appropriate.However, the fixed-effects model is preferred when estimating the flows of trade between an ex-ante pre-determined selection of sectors (Egger, 2000;Martinez-Zarzoso & Nowak-Lehmann, 2003).This paper analyses sectoral trade between South Africa and Australia, so the fixed-effects model will be employed.It covers the period between 2000 and 2012.
Univariate characteristics of variables
Before the estimation, the univariate characteristics of the variables are analysed, using panel unit root tests.This is done to establish whether there is a potentially cointegrated relationship between the variables.If all the variables are stationary, the traditional ordinary least square (OLS) estimation can be used to estimate the relationship between the variables.If the variables contain a unit root or are non-stationary, a cointegration test should be performed.This study applies two different types of panel unit root tests.The first test is that of Levin, Lin and Chu (2002), which assumes that the autoregressive parameters are common across cross-sections.Levin, Lin and Chu (LLC) (2002) use the null hypothesis of a unit root.The second panel unit root test allows for the autoregressive parameters to vary across cross-sections as well as for individual unit root processes.The test was developed by Im, Pesaran and Shin (IPS) ( 2003) and combines individual sectors' unit root tests.In the IPS test, the null hypothesis assumes that all series contain a unit root, while the alternative hypothesis is that at least one series in the panel contains a unit root.IPS is a one-tailed or lower-tailed test based on N(0,1) distribution.The results of the panel unit root tests are presented in Table 1.Table 1 shows that the LLC reject the null of a unit root for all the variables.A rejection of unit roots by at least one test assumes a verdict of stationarity.That implies that a cointegration test is not required and Equations (3 and 4) can be estimated using the OLS method.The detailed data source and description are provided in the Appendix.
Estimation results
Table 2 presents the results for the fixed effects model, which estimates sector-specific effects and introduces heterogeneity.To check the poolability of the data, the F-test is performed and the results show that the null hypothesis of equality of the individual effects or homogeneity for all sectors is rejected.This confirms that a model with individual sector effects (fixed effects) is the preferred model.The Hausman test is also executed within the random effects model in order to detect misspecification or to ensure that the X-regressors and the individual effects are not correlated.The results show that the Hausman specification test [0.000(1.000)] accepts the null hypothesis of no misspecification.This result therefore indicates the exogeneity of the Xregressors and there is thus no correlation between the individual effects and the X-regressors.
The results of the static fixed effects model in Table 2 show that the coefficient of the exchange rate is positive, which indicates an increase in exports from South Africa.As the exchange rate depreciated over the sample period, it could be expected that exports would increase and the coefficient is thus consistent with theory.It therefore shows that a weaker South African exchange rate relative to the Australian dollar will enhance exports from South Africa to Australia.
An increase in Australia's GDP causes a decrease in exports from South Africa to Australia, which is in contrast with the theoretical expectations.This means that, as the Australian economy improves, fewer products are imported from South Africa and there is possibly a demand shift to more developed countries.The coefficient of South Africa's GDP is positive, indicating that exports to Australia increase as South Africa's GDP rises, in accordance with the theoretical expectations.The coefficient of the Australian population is negative, which means that a growing Australian population will cause a decline in imports from South Africa.An increase in the importer's population therefore implies that a possible division of labour (economies of scale) will be encouraged, resulting in higher local production and fewer imports from South Africa.It may also show that Australia is not particularly reliant on South African exports.South Africa's population had a positive coefficient, but was statistically insignificant and was therefore omitted from the estimation.The effect of import tariffs was also tested but was found to have the incorrect sign, although it was statistically significant.The dataset comprises 377 observations, including 13 annual observations for 29 sectors.All the variables were statistically significant and the adjusted R-square is 0.955.However, this model experiences persistence effects, as is evident from the relatively low Durbin-Watson statistic.
The second model estimated was the dynamic fixed effect using the 2 Stage Least Square (2SLS) approach.This model allows for heterogeneity among sectors, including the inertia effect by exporters in response to export opportunities.This model is more appropriate when it comes to the persistence effects indicated by the higher Durbin-Watson statistic.Again, the Hausman test was executed and the results show that the Hausman specification test [0.000(1.000)] accepts the null hypothesis of no misspecification.This result therefore indicates exogeneity of the Xregressors and thus no correlation between the individual effects and the X-regressors.
Two parameter estimates were determined for the 2SLS dynamic model, producing a short-and long-run coefficient.The speed of adjustment (δ in Equation 2) was 0.46, meaning that, if an export opportunity exists in any sector, exporters from South Africa would adjust on average to meet 46 per cent of the export contract in one year and the remaining 54 per cent in the following period.On average, exporters will take advantage of export opportunities to Australia within approximately 26 months. 1 The long-run coefficients are in general slightly higher than the shortrun coefficients, as there is more time available to adjust.This may indicate that there is reason to believe that increased trade between the two countries tends to be more long-run.
The sector-specific effects estimates from the static estimation are presented in the Appendix.The sectoral or cross-section specific effects show the effect of factors that are unique to each sector but are not included in the estimation of the model.It shows that trade between South Africa and Australia differs from sector to sector, given the unique features of each sector.Table A1 shows that there are features in some sectors that promote exports from South Africa to Australia in the motor industry, machinery, iron, basic chemicals, food, metals, other chemicals, mining, electrical items, paper, base metals, communications and textiles.However, it is also shown that there are imperceptible sectoral characteristics that discourage South Africa's exports of printing, footwear, leather, glass, beverages, apparel, agriculture, rubber, minerals, transport, furniture, plastic, equipment, wood, petroleum, and other industrial products.It is important from the policy perspective to analyse these export-inhibiting factors which discourage exports from South Africa to Australia.
The results of the Generalised Method of Moments (GMM), testing the robustness of the relationships, were also favourable.The appropriate test statistic can be constructed as the J-value multiplied by the number of observations.Under the null hypothesis that the over-identifying restrictions are satisfied, the J-statistic multiplied by the number of observations is asymptotically χ2 with degrees of freedom equal to the number of over-identifying restrictions (Stock, Wright & Motohiro Yogo, 2002).The statistic for the validity of instruments has a χ2 distribution, with 6 degrees of freedom, and takes the value of 2.714. 2 The null of validity of instruments and overall specification of the model therefore cannot be rejected (χ2 0.05(6) = 12.59).The hypothesis that the full set of orthogonality conditions is valid can therefore be accepted.The results of the GMM model are shown in Table 3.
Trade potential
To determine the within potential exports from South Africa to Australia, the estimated fixed effects of Equation ( 4) are simulated.The estimated export potential is then compared with actual exports to find out whether there is any unexploited trade potential in the respective sectors.The results show that the sectors indicating the greatest export potential are presented in Figure 1.They include the apparel, basic metals, communication, furniture, glass, iron, leather, motor, paper and printing sectors.Other sectors indicating a relatively small potential include footwear, metal products, minerals, products from other industries and transport, where potential exports still exceed actual exports.From the South African perspective, it is important to promote exports from these sectors to gain the benefits from this unexploited trade potential.However, a further analysis of all these sectors is important in order to determine and identify possible factors that may inhibit the promotion of actual exports, given the unexploited potential.Another point of interest is that the actual exports exceed the potential exports in some sectors, including the agriculture, basic chemicals, beverages, electric, equipment, food, machinery, mining, other chemicals, petroleum, plastic, rubber and the textile and wood sectors.A further analysis of these sectors would provide important information on the reasons why the actual exports exceed the potential exports.
Variability of potential trade
Although the sectors with unexploited trade potential have now been identified, it is also important to determine which of them are actually stable, and reliable export sectors.Stability of export flows in a specific sector are of the utmost importance, as job security and revenue generation depend on them.From the estimation, the paper also determines this stability by using the coefficient of variation (CV) computed from the stochastically-solved model.This can be computed in a percentage as follows: %CV = (Standard deviation/Mean) x 100 The coefficient of variation provides an indication of the South African export sectors in Australia and whether they can be classified as stable or not.The lower the CV, the more stable and thus the more reliable the export sector; and the higher the CV, the less stable and reliable the sector.With reference to the group of sectors included in the study, Figure 2 shows South Africa's 12 most stable and reliable sectors exporting to Australia.The most stable and reliable sector is the motor industry (1.42) and the 12 th most stable is the communication sector (1.71).However, the motor industry currently receives the highest level of government incentives, previously under the Motor Industry Development Plan (MIDP), which was adjusted in 2012 and then replaced by the Automotive Production and Development Programme (APDP) in 2013.From the perspective of policy, export promotion policies should be directed to the more stable export sectors not receiving any government assistance.Policies directed at improving the exports from the less stable sectors with a high CV should also be pursued.
Sectors which have unexploited trade potential and are among the most stable and reliable export sectors to Australia include the motor, iron, paper, basic metal, communication and metal product sectors.Policy-makers should pursue the correct policy mix with these sectors, as both unexploited trade potential is available and seems to score reasonably well in terms of reliability and stability.This could ensure a consistent flow of foreign currency revenue to South Africa and increase job creation possibilities.
Conclusion
This study identifies the South African exports to Australia that had unexploited potential for the period 2000 to 2012 using a gravity model approach.The coefficient of South Africa's exchange rate was positive and was thus aligned with theory.Exports from South Africa decreased as Australia's GDP increased and were in contrast with theory.The coefficient of South Africa's GDP was positive and in line with theoretical expectations.Australia's population had a negative coefficient, indicating a decreased demand for South African products as the Australian population increased Large populations could encourage the division of labour (economies of scale), which would mean higher production levels and a smaller import demand for South African products.
Export opportunities for South African exporters were utilised, as 46 per cent of the export contract was met within the first year, while the remaining 54 per cent was met during the following period.It seemed that, on average, South African exporters would take advantage of export opportunities within 26 months.As the coefficients were generally larger over the long run, it appears that the trade relationship between the two countries could be enhanced over the longer term.The paper identifies unexploited trade potential in the apparel, basic metals, communication, furniture, glass, iron, leather, motor, paper and printing sectors.Of these, the most reliable and stable export sectors included the motor, iron, paper, basic metals and communication sectors.
The results of this study could provide important information to guide policy-makers in developing tailor-made policies to ensure that the export potential is exploited in order to accelerate growth and job creation in South Africa.The success rate could be further enhanced by focussing on reliable and stable export sectors as indicated by the coefficient of variation.The allocation of resources and support could be directed to the sectors with the highest level of unexploited potential.The paper provides important avenues to be considered for enhancing exports to Australia.Australia may also re-evaluate its trade links with South Africa in particular and Africa in general.In addition, Australia could use South Africa as an important source country in the African context to give the continent the "priority it deserves".Australia could therefore contribute to Africa's increased productivity levels, enhanced export flows and improved growth.Promoting bilateral trade between South Africa and Australia could establish these countries as the future pillars of strength between the two continents.
Figure 1 Export Potential Per Sector
Figure 2
Figure 2 Coefficient of variation
Table 3
Panel GMM | 6,477.8 | 2015-08-25T00:00:00.000 | [
"Economics"
] |
Evolution of Freshwater Runoff in the Western Adriatic Sea over the Last Century
: The evaluation of the hydrography and biogeochemistry of the Adriatic Sea over the last century was summarized in this review to point out any changes in river runoff and provide an overview of the cause and effect of these trends on marine ecosystems. Although several rivers flow into the Adriatic, the most affected area is the northern Adriatic, where the Po River loads into the basin half of the total freshwater input, carrying river runoff and causing algal blooms and hypoxia phenomena. These fresh waters of the northern Adriatic flow predominantly along the entire western side, reaching the southernmost part of the basin up to the Mediterranean Sea. Here, and in the whole basin, variations in river runoff and nutrient concentration have been observed through the years. Starting from 1960 until the end of the century, an increase in nutrient discharge and phytoplankton activity was reported, with negative repercussions on local fisheries, species richness, and recreational activities within the basin. However, a recent decrease in river inflow has been observed along the coastal belt, which can trigger negative consequences for the food web of the marine ecosystem. These trends, more broadly, corroborate the vulnerability of the Adriatic Sea and stress the importance of implementing strategies for the defense of the relevant ecosystems within its confines.
Introduction
The Adriatic Sea (Figure 1) is a continental basin of the Mediterranean Sea, located between the Italian peninsula (western coast) and the coasts of Slovenia, Croatia, Bosnia-Herzegovina, Montenegro and Albania (eastern coast) [1].Around the world, coastal marine areas represent approximately 4% of the Earth's total land surface, yet they host about half of the world's human population and provide a key contribution to global economic profit and ecosystem services.The Adriatic basin presents these characteristics as well [2][3][4][5].The entire western coast of the Adriatic Sea is part of Italian territory which, with 8 out of the total 20 regions overlooking this basin, marks a total length of coastal line of 1472.66 km.The morphology of the Adriatic coast, characterized by low and sandy beaches and a flat hinterland, have contributed over time to the construction of infrastructure and residential settlements, making the Italian eastern coast the most densely urbanized one in the entire Mediterranean basin.Between the 1950s and 2001, the population in coastal municipalities increased by almost 720,000 inhabitants, reaching 3,372,138 citizens in 2001.Economic interests, especially in the northern Adriatic, are tied to fishing, trade, intensive use of farming and, since the beginning of the 20th century in the northern and since the 1960s in the southern region, to tourist seaside development [6].
Environments 2024, 11,22 use of farming and, since the beginning of the 20th century in the northern and sin 1960s in the southern region, to tourist seaside development [6].The Adriatic Sea extends for 800 km, oriented in the northwestern-southeaste rection.The basin has been divided into northern, central, and southern sub-basins based on the bathymetry.The central Adriatic is deeper, reaching a maximum de 270 m (Jabuka Pit) and is divided from the southern Adriatic by the Palagruža Si m).In the southern basin, the maximum depth is 1250 m [9].The central-southern Ad is characterized by lower primary productivity in comparison with the northern su sin, receiving lower continental influx of nutrients and presenting benthonic-pelag teractions of lesser importance when compared to the northern area [10][11][12][13].Conve the northern Adriatic is characterized by shallow waters (average depth ~35 m), a bathymetric gradient from northwest to southeast, and a significant influx of fluvi ters, mainly from Italian rivers [9,14,15].The Po River, in particular, has an annual av flow rate of 1500 m 3 s −1 , representing approximately half of the total freshwater inpu the Adriatic Sea.The Adriatic collects, in total, one-third of the freshwater flowing in Mediterranean Sea [16][17][18][19].Due to the predominant cyclonic circulation of the Ad Sea, the Po River and other alpine rivers located in the northernmost part of the discharge nutrients into its waters along the western side.These rivers are responsib significant eutrophication processes that have been periodically observed along the I coasts [20][21][22][23][24], at least since the 1960s, causing hypoxia phenomena and dinoflag blooms [25,26].Since the 1950s, the ever-increasing use of fertilizers, prompted by sive farming along the Po River basin, has had a significant impact on the inflow of gen (N) and phosphorus (P) into the Northern Adriatic Sea [27,28].The release of nutrients in the sea creates the perfect storm for the eutrophication process, where cessive growth of phytoplankton and macroalgae creates a biomass cover that pre sunlight and oxygen from penetrating the water column, potentially depleting o levels and ceasing primary production within the ecosystem [23,29].
In the last decades, the Adriatic Sea, especially the Northern Adriatic Sea (NAS been the subject of several studies regarding algal or phytoplankton bloom [30-3 trophication [35-37] and discharge as well as enrichment of nutrients from Italian The Adriatic Sea extends for 800 km, oriented in the northwestern-southeastern direction.The basin has been divided into northern, central, and southern sub-basins [7,8], based on the bathymetry.The central Adriatic is deeper, reaching a maximum depth of 270 m (Jabuka Pit) and is divided from the southern Adriatic by the Palagruža Sill (170 m).In the southern basin, the maximum depth is 1250 m [9].The central-southern Adriatic is characterized by lower primary productivity in comparison with the northern sub-basin, receiving lower continental influx of nutrients and presenting benthonic-pelagic interactions of lesser importance when compared to the northern area [10][11][12][13].Conversely, the northern Adriatic is characterized by shallow waters (average depth ~35 m), a weak bathymetric gradient from northwest to southeast, and a significant influx of fluvial waters, mainly from Italian rivers [9,14,15].The Po River, in particular, has an annual average flow rate of 1500 m 3 s −1 , representing approximately half of the total freshwater input into the Adriatic Sea.The Adriatic collects, in total, one-third of the freshwater flowing into the Mediterranean Sea [16][17][18][19].Due to the predominant cyclonic circulation of the Adriatic Sea, the Po River and other alpine rivers located in the northernmost part of the basin discharge nutrients into its waters along the western side.These rivers are responsible for significant eutrophication processes that have been periodically observed along the Italian coasts [20][21][22][23][24], at least since the 1960s, causing hypoxia phenomena and dinoflagellate blooms [25,26].Since the 1950s, the ever-increasing use of fertilizers, prompted by intensive farming along the Po River basin, has had a significant impact on the inflow of nitrogen (N) and phosphorus (P) into the Northern Adriatic Sea [27,28].The release of these nutrients in the sea creates the perfect storm for the eutrophication process, where an excessive growth of phytoplankton and macroalgae creates a biomass cover that prevents sunlight and oxygen from penetrating the water column, potentially depleting oxygen levels and ceasing primary production within the ecosystem [23,29].
However, there is still a knowledge gap regarding the variability of river runoff throughout the Adriatic Sea and the consequent changes, both in physical parameters and nutrient concentrations, over the last 150 years.
We performed a review of the evolution of river runoff in the Adriatic Sea in the literature.Hence, we used the Scopus database to conduct the query.Here, we report the specifics of our query: 'Search Within' > Keywords: "Adriatic Sea" AND "nutrient"; 'Publication Dates' > Range: From 1990 to 2022.This query resulted in 232 items.Among these, 83 were selected based on the relevance of the title and the abstract.Other articles were then added starting from the bibliography of these 83.Hence, the bibliography resulted in 134 scientific papers, published from 1873 to 2023, characterizing changes in oceanographic parameters and in nutrient discharge in seawater, highlighting their impact on this marine ecosystem.
River Runoff and Oceanographic Evolution of the Adriatic Sea
The Adriatic Sea is a continental sea and is therefore strongly influenced by its own geomorphological and meteorological conditions.These conditions trigger physical and chemical changes over the years.Freshwater inflow from rivers, as well as water exchange with the Ionian Sea and the influence of Levantine Intermediate Water (LIW), significantly impact the oceanographic properties of the Adriatic Sea.The Adriatic and Ionian Seas are connected through the Strait of Otranto, allowing for water exchange and influencing the oceanographic properties of both seas.LIW, originating in the eastern Mediterranean, plays a role in the water masses present in the Adriatic, contributing to the overall complexity of its hydrological characteristics [42,43].The relatively shallow depth of the sea and the local river inflows contribute to variations in the hydrological characteristics of the water column.The coastal waters, with reduced salinity caused by river inflow, can impact the circulation of the basin in two ways.Firstly, they can act directly as a mechanism forcing circulation through buoyancy-related processes.Secondly, they can have an indirect impact by modifying the stratification conditions crucial for controlling specific features of the Adriatic, such as the exchange of water masses through the Otranto Strait and the generation of deep water along the northern continental margin [44].Thanks to the influence of the freshwater inflow, mainly from the Po River, the northern Adriatic acts as a heat sink and a buoyancy factor.River runoff mainly depends on precipitation melting of glaciers, impacting water circulation through buoyancy input and affecting the ecosystem by introducing significant amounts of organic matter, nutrients, salts, and sediments [11,45].The discharges into the Adriatic Sea are primarily attributed to the Dinaric Alps and the Julian Alps, with rivers such as the River Isonzo contributing to the inflow into the eastern part of the basin [19,46,47].Rivers flowing into the northern Adriatic can be categorized by location, distinguishing between those located above and below the Po delta.Among the main rivers in the northern group are the Isonzo, Tagliamento, Livenza, Piave, Brenta, and Adige.The southern group consists of rivers of lesser importance, with lower flow rates, including the Po di Volano, Reno, Destra Reno, Lamone, Savio, Rubicone, Marecchia, Conca, Foglia, Metauro, Esino, Musone, Chienti, Tenna Tronto, Vomano, Pescara, Sango, Trigno, Biferno, and Fortone [38] (Figure 2).These Adriatic rivers showed an annual cycle characterized by two high-discharge periods, in late autumn and spring, alternating with two low-discharge periods, in winter and summer [48].Unlike the Northern Adriatic Sea, in the south-western part of the basin, particularly the Gulf of Manfredonia, river discharges are relatively low.Here, among minor rivers that dry up during the summer, the Ofanto is the main river flowing into the basin, with an average flow of 13.9 m 3 s −1 [49,50].The Eastern Central Southern Adriatic Sea (ECSAS), in addition to the flow from the Neretva River, represents one important source of freshwater in this part of the basin, with cold waters with an average discharge of 300 m 3 s −1 [51][52][53][54].ECSAS receives waters from the Buna/Bojana River, which is the Southeastern Adriatic's most important river, with a discharge of about 700 m 3 s −1 , second only to the Po River.The Southern Adriatic Sea is influenced by the freshwater inflow of several other Albanian rivers, among which are the Drini, Mati, Ishimi, Erzeni, Shkumbini, Semani, and Vjosa Rivers, discharging a total of about 1250 m 3 s −1 into the basin [43,55] (Figure 2).which are the Drini, Mati, Ishimi, Erzeni, Shkumbini, Semani, and Vjosa Rivers, discharging a total of about 1250 m 3 s −1 into the basin [43,55] (Figure 2).The most affected areas by river runoff in the Adriatic Sea during all seasons are the northern and western regions, where a strong lateral temperature and nutrient gradient is experienced in spring and autumn [19,41].Indeed, the annual hydrological regime observed for the Po River shows a low flow during winter (January and February) and an even lower flow in August, a period of maximum flow in May-June due to glaciers thawing, and another high flow period in October-November due to the intensive rainfall [38].This trend is observed also for the Adige River, where two yearly periods of low water flow (winter and summer) and two of high flow (spring and autumn) occur.Although these two rivers are mainly responsible for the runoff flow in the Adriatic Sea, the Po River is the major source of freshwater in the northern Adriatic [40], with a mean flow rate of 1470 m 3 s −1 , which is higher than that of the Adige River (220 m 3 s −1 ; [38]).
An analysis of long-term changes in river runoff pointed out three distinct periods in the historical series of the Po River, with a variable runoff from 1917 to 1941, followed by an increasing load from 1942 to 1975, and subsequently a decrease from 1976 to 2008, mainly due to the drought that characterized the most recent years.Overall, in the period from 1917 to 2008, the runoff of the Po ranged from 20.54 to 82.49 km 3 yr −1 , with an average value of 47.17 km 3 yr −1 [40].This decreasing trend continued until October 2023 (Figure 3), as also reported in Marini and Grilli, 2023 [23].However, regarding the Adige, Brenta, The most affected areas by river runoff in the Adriatic Sea during all seasons are the northern and western regions, where a strong lateral temperature and nutrient gradient is experienced in spring and autumn [19,41].Indeed, the annual hydrological regime observed for the Po River shows a low flow during winter (January and February) and an even lower flow in August, a period of maximum flow in May-June due to glaciers thawing, and another high flow period in October-November due to the intensive rainfall [38].This trend is observed also for the Adige River, where two yearly periods of low water flow (winter and summer) and two of high flow (spring and autumn) occur.Although these two rivers are mainly responsible for the runoff flow in the Adriatic Sea, the Po River is the major source of freshwater in the northern Adriatic [40], with a mean flow rate of 1470 m 3 s −1 , which is higher than that of the Adige River (220 m 3 s −1 ; [38]).
An analysis of long-term changes in river runoff pointed out three distinct periods in the historical series of the Po River, with a variable runoff from 1917 to 1941, followed by an increasing load from 1942 to 1975, and subsequently a decrease from 1976 to 2008, mainly due to the drought that characterized the most recent years.Overall, in the period from 1917 to 2008, the runoff of the Po ranged from 20.54 to 82.49 km 3 yr −1 , with an average value of 47.17 km 3 yr −1 [40].This decreasing trend continued until October 2023 (Figure 3), as also reported in Marini and Grilli, 2023 [23].However, regarding the Adige, Brenta, Piave, Isonzo, and the rivers along the Slovenian Coast, including Rizâna and Dragonja, throughout the whole period, a substantial decline in runoff was observed [40].
Environments 2024, 11,22 Piave, Isonzo, and the rivers along the Slovenian Coast, including Rizâna and Dra throughout the whole period, a substantial decline in runoff was observed [40].The main winds affecting the Adriatic Sea are the bora winds, dry and cold blowing offshore from the east, and scirocco winds, humid and warm winds blowing the southeast [56].In particular, during autumn and winter, bora winds are respo for the formation of very dense deep waters, known as Northern Adriatic Deep (NADW), characterized by low temperatures (less than 10 °C), and relatively low s (~38.3 psu) [57,58].These waters flow southwards across the Palagruža Sill, near th tom of the Italian shelf [59].The major portion of these waters flow southwards reaching the southern part of the shelf, which due to the presence of a canyon, be deeper [60,61].Here, mixing processes occur with the inflowing warmer and saltier ified Levantine Intermediate Water (MLIW) from the Mediterranean, resulting in th mation of the Adriatic Bottom Water (ABW).The ABW then outflows into the Ionia at the bottom of the Otranto Strait.
Importance of River Runoff for the Food Web and Influence on Plankton
The rising frequency of short-term phenomena due to climate change, especia increase in intense rainfall events and the resulting terrigenous loads entering coast ters from rivers, has been the focus of several studies conducted around the [22,59,[62][63][64][65][66][67].Eutrophication is the process resulting from the decomposition of quantities of dead algal biomass following an algal bloom event, leading to the form of hypoxic or anoxic sea areas [68].Since the 1800s, the Po River in the northern Ad has carried a very high nutrient load (N range: 1-300 µM; P range: 0.01-4 µM [41]) le to occurrences of algal, diatom, and dinoflagellate blooms [69][70][71][72][73].However, it wa in the mid-1970s that eutrophication episodes developed a chronic yearly pattern, va only in intensity [29,38,74].Since the 1970s, eutrophication, in conjunction with ad The main winds affecting the Adriatic Sea are the bora winds, dry and cold winds blowing offshore from the east, and scirocco winds, humid and warm winds blowing from the southeast [56].In particular, during autumn and winter, bora winds are responsible for the formation of very dense deep waters, known as Northern Adriatic Deep Water (NADW), characterized by low temperatures (less than 10 • C), and relatively low salinity (~38.3 psu) [57,58].These waters flow southwards across the Palagruža Sill, near the bottom of the Italian shelf [59].The major portion of these waters flow southwards until reaching the southern part of the shelf, which due to the presence of a canyon, becomes deeper [60,61].Here, mixing processes occur with the inflowing warmer and saltier Modified Levantine Intermediate Water (MLIW) from the Mediterranean, resulting in the formation of the Adriatic Bottom Water (ABW).The ABW then outflows into the Ionian Sea at the bottom of the Otranto Strait.
Importance of River Runoff for the Food Web and Influence on Plankton
The rising frequency of short-term phenomena due to climate change, especially the increase in intense rainfall events and the resulting terrigenous loads entering coastal waters from rivers, has been the focus of several studies conducted around the world [22,59,[62][63][64][65][66][67].Eutrophication is the process resulting from the decomposition of large quantities of dead algal biomass following an algal bloom event, leading to the formation of hypoxic or anoxic sea areas [68].Since the 1800s, the Po River in the northern Adriatic has carried a very high nutrient load (N range: 1-300 µM; P range: 0.01-4 µM [41]) leading to occurrences of algal, diatom, and dinoflagellate blooms [69][70][71][72][73].However, it was only in the mid-1970s that eutrophication episodes developed a chronic yearly pattern, varying only in intensity [29,38,74].Since the 1970s, eutrophication, in conjunction with adverse meteorological and hydrographic conditions, has led to the presence of oxygen-depleted bottom waters in the coastal areas, leading to lethal effects such as the impoverishment or mass mortality of benthic fauna [74], hence confirming the hypothesis that riverine influence is exacerbating hypoxia/anoxia [75].It is well known that the frequency of eutrophic events increased exponentially in coastal areas of the Adriatic Sea until the 1990s, particularly due to the intensive release of inorganic nutrients from anthropic agricultural practices [76], continental emissions, and river runoff, resulting in a significant increase in nutrient concentration.Data available for the Po River, which can be considered a significant proxy of the total runoff in the north Adriatic, indicate that the transport of total nitrogen has increased about two times since the 1980s, reaching the highest estimate of 173 × 10 3 t N yr −1 in the years 1996-2000 due to a constant increase of anthropogenic activities.At the same time, the transport of total phosphorous has decreased from 15.6 to 8.1 × 10 3 t P yr −1 since 1978 thanks to Italian regulations that in 1986 required the reduction of phosphate in detergents [28,29,40].Regarding the transport of nutrients of the Adige River, at the end of the 1980s, they reached values of 12.7 × 10 3 t N yr −1 for total nitrogen and 1.2 × 10 3 t P yr −1 for total phosphorous.However, due to the high correlation between runoff and nutrient transport, these changes could be related to pronounced oscillations rather than a progressive trend [40].The mean Po River flow rate in the last two decades was significantly lower than in the years 1972-1999, producing a significant decreasing trend for P and Chlorophyll a concentration in North Adriatic Sea, but not for N [77].The influence of river runoff on the Western Adriatic's plankton communities is notable [77,78].Seasonal variations in river discharge, characterized by increased flows in spring and autumn and reduced flows in winter and summer, directly influence the composition and abundance of plankton species [14].Moreover, the increase in nutrient discharge can interfere with the natural annual nutrient cycles, leading to an increase in primary producers' activity.This has significant ecological implications for both pelagic and benthic organisms, including an increase in phytoplankton activity [79].Indeed, macroalgal blooms resulting from eutrophication processes can lead, in addition to hypoxia events and the production of toxic hydrogen sulfide, to negative effects on the abundance and diversity of benthic fauna living in coastal waters [80].
Although beneficial for an increase in fishery availability [81][82][83], higher primary production rates have been observed to result in phytoplankton blooms, with negative repercussions on local fisheries and recreational activities [84][85][86][87][88][89].Phytoplankton blooms, often induced by increased nutrient input, provide a growing environment for specific zooplankton species [90].This, in turn, influences the distribution and abundance of higher trophic levels, including fish and other marine organisms.Changes in plankton composition and abundance, in relation also to coastal oceanographic conditions, not only affect the survival and growth of fish larvae but also have repercussions on the distribution and availability of commercially valuable species, as well as on the diffusion of benthic species more tolerant to hypoxia and high nutrient concentrations [91,92].Nowadays, the rise in sea temperatures is causing a reduction in primary production and, subsequently, a decrease in zooplankton populations [77,93,94].Warmer waters, combined with more frequent extreme weather events, have the potential to intensify the competition among pelagic predators for food resources.As a result, we may see more significant fluctuations in the stocks of species, with dramatic impacts on the overall ecosystem [95][96][97][98].
The long-term alterations in nutrient concentrations within the northern Adriatic region are intricately linked to climatic fluctuations and, in particular, to the decrease in rainfall during the winter in northern Italy, and the Alps region [40,99].Intensity of precipitation and increased frequency of rainfall events have the potential to influence several key factors.These can modify the hydrological cycles, such as the flow rate of the Po River (Figure 2), which is a significant source of freshwater input into the northern Adriatic Sea [41,48].Variations in the Po River's flowrate can impact the quantity and composition of nutrients entering the sea, consequently affecting the overall nutrient balance in the ecosystem (Figure 1) [23,45,77,100,101].The geostrophic circulation of the northern Adriatic comprises multiple gyres that can last for extended periods within the region [102][103][104].Gyres have the capacity to transport freshened, nutrient-rich waters, originating from the region near the Po River delta, across extensive portions of the northern Adriatic, and potentially as far as the opposing eastern coast, including the Istrian Peninsula [45, 105,106].
Furthermore, climatic fluctuations can also have a profound impact on the water dynamics within the Adriatic Sea.This includes alterations in the vertical mixing of the water column, which can be heavier under conditions of low stratification and lighter when the water column is highly stratified, including temperature and salinity [107][108][109].These alterations in vertical mixing can have cascading effects on the distribution of nutrients throughout the water column [18,101,110], thus influencing the availability of these essential elements to marine organisms.Alterations in the water exchange rate between the northern and central Adriatic [10,105] can impact the overall nutrient content and ecological conditions in these areas, as the rate can determine the extent to which they are connected and influence the exchange of organisms and materials.A recent study comparing physical and chemical data of the 2002-2007 and 2007-2016 periods showed that the latter decade was characterized by an increase in phytoplankton abundance, as well as biomass and inorganic nutrient concentrations [14,111].This indicates a reversal trend compared to the tendency of oligotrophication observed in the previous period, due to a combination of factors that includes nutrient load mitigation strategies, different use of the land of the watersheds, and reduced runoff due to prolonged drought periods [10,93,100].Moreover, a marked change occurred in the community structure and seasonality of phytoplankton [14,100].
Nutrient distribution and concentration in the Adriatic Sea are strongly affected by the physical characteristics of the basin.The Western Adriatic basin is characterized by a decrease in surface nutrient concentration, from north to south and from inshore to offshore, due to different freshwater inputs; this is particularly significant in the northern Adriatic [112].Here, a dominant cyclonic circulation determines a southward nutrient flow along the western coast [13,14,41,100,101,105,[113][114][115].The Adriatic circulation is also strongly influenced by winds, due to the seasonality difference in wind regimes [116], and meteorological conditions, among which hydrological balance as well as densification related to river inflows and perturbations, with the resulting cooling and mixing, affect the thermohaline structure [8].Among the winds affecting the Adriatic biogeochemical characteristics and its marine ecosystems are the bora winds.Previous studies showed that bora events, which occur more frequently in winter [9], could generate a relevant increase of nutrient loads exported from the northern Adriatic through the accentuation of the Western Adriatic Current (WAC), which, forced by the Po River runoff, enfolds the Western Adriatic coast, flowing south-eastward to finally leave the Adriatic basin via the Otranto Strait [117], and therefore playing a relevant role in the nutrient balance of the basin [118][119][120].Due to the WAC, during bora events, sediments with nutrients and other waterborne materials are resuspended and driven southward along the Italian coast [16,22,45,115].
Future Directions
The Adriatic Sea is a segment of the Mediterranean Sea enclosed by the mountain chains of the Alps, the Apennines, and the Balkans.It is rich in resources such as subsoil, supports fishing and tourism industries, and is marked by intense maritime transport and cultural exchanges among the people along its shores.The western side, encompassing the entire Italian state, is densely populated and heavily influenced by human activities [6].The current trend of ocean warming, coupled with an increase in the frequency of extreme events, may intensify water stratification.This, combined with reduced water exchange, could extend the turnover time of Adriatic waters.Under these conditions, the Adriatic Sea ecosystem's susceptibility to eutrophication and acidification processes could increase, despite the ongoing trend of lower eutrophication [77].Additionally, increased rates of primary production due to the rising temperatures in shallow water have been noted to give rise to phytoplankton blooms, causing adverse impacts on local fisheries and recre-ational activities [89].Nevertheless, alterations in the structure of the fish community have taken place, potentially reducing the efficiency and resilience of the ecosystem [77].In recent decades, climate change, with a specific emphasis on the increase in sea temperature and salinity, has manifested itself not only in the Mediterranean Sea but also in the Adriatic Sea.This environmental shift has triggered the proliferation and spread of alien species of marine organisms [121].Mariculture and shipping represent potent mechanisms for introducing native warm-water species into the northwestern Mediterranean and the Adriatic Sea [122][123][124][125][126][127][128][129][130][131][132].The majority of the introduced species are adapted to warm temperatures, exhibiting thermophilic characteristics and forming self-sustaining populations, like the ornate wrasse Thalassoma pavo (Linnaeus, 1758) and the parrotfish Spariosoma cretense (Linnaeus, 1758) [133].These species impact the survival of native species and engage in resource competition, such as the predation of small pelagics by tunas [134] and the voracious feeding behavior of the blue crab, Callinectes sapidus (Rathbun, 1896), on benthic organisms [125].The Adriatic Sea, a hotspot of biodiversity and economic activities, faces significant challenges due to climate change and the introduction of invasive species.Proactive measures, informed by scientific research and community engagement, are essential to preserve the ecological integrity and economic sustainability of this vital marine environment.Observing the evolution of coastal oceanographic conditions on the western side of the Adriatic, we believe they can be of particular interest in shaping the prospects for research development.An interdisciplinary approach, integrating marine biology, oceanography, and socio-economic factors, will provide a comprehensive understanding of ongoing changes.Research in this direction will provide valuable insights that can prove beneficial for a diverse array of stakeholders engaged in economic activities throughout the Adriatic region.
Conclusions
The analysis of the freshwater runoff evolution in the Western Adriatic Sea over the past century has provided valuable insights into the dynamic interactions between climatic factors and hydrological processes.A comprehensive examination of historical data and present-day observations has facilitated a nuanced comprehension of the diverse factors influencing freshwater discharge into the Adriatic.Changes in precipitation patterns, snowmelt dynamics, and land-use practices have contributed to fluctuations in freshwater runoff.Notably, climate change emerges as a significant driver, with observable impacts on the timing and intensity of runoff events.Eutrophication, driven by increased nutrient concentrations since the 1970s, adversely affects marine life and ecosystems, favoring hypoxic areas and causing benthic fauna to suffer.Hydrodynamic variations within seasons influence river flow and impact plankton communities, with altered nutrient cycles impacting primary production.Increasing sea temperatures pose further challenges, resulting in reduced primary production and subsequently decreasing zooplankton populations.Indeed, the long-term changes in nutrient concentration could lead to wider diffusion of benthic species tolerant to hypoxia and high nutrient concentrations.The findings of this review highlight the importance of considering long-term trends in freshwater runoff to comprehend the broader environmental implications.Shifts in runoff patterns have direct consequences on coastal ecosystems, influencing nutrient dynamics and overall water quality.These changes, in turn, have significant implications for the ecological balance of the Adriatic Sea and its marine biodiversity.
Observed changes in the Western Adriatic Sea show that different patterns have occurred over time in the offshore and coastal waters.Indeed, in coastal waters, a decrease in the river runoff corresponds to a decrease in the concentration of nitrogen and phosphorous, while at basin level, they continue to represent an important food source for the trophic chain.
Figure 1 .
Figure 1.Location of the Adriatic Sea within the Mediterranean Sea.The map was downloade d-maps.comhttp://www.d-maps.com(accessed on 3 January 2024) (a).Map of the Chlorop concentration (mg.m −3 ) in the Adriatic Sea surface during the (b) winter (20 January 2023), summer (2 July 2023).Image provided by My Ocean, Copernicus Marine Service (accessed on 2023).
Figure 1 .
Figure 1.Location of the Adriatic Sea within the Mediterranean Sea.The map was downloaded from d-maps.comhttp://www.d-maps.com(accessed on 3 January 2024) (a).Map of the Chlorophyll a concentration (mg.m −3 ) in the Adriatic Sea surface during the (b) winter (20 January 2023), and (c) summer (2 July 2023).Image provided by My Ocean, Copernicus Marine Service (accessed on 2 July 2023).
Figure 2 .
Figure 2. Map of the Adriatic coast showing the locations of the main rivers and streams that flow into the Adriatic Sea.The map was downloaded from the d-maps.comwebsite http://www.dmaps.com(accessed on 3 January 2024) with subsequent modifications applied.
Figure 2 .
Figure 2. Map of the Adriatic coast showing the locations of the main rivers and streams that flow into the Adriatic Sea.The map was downloaded from the d-maps.comwebsite http://www.d-maps.com(accessed on 3 January 2024) with subsequent modifications applied.
Figure 3 .
Figure 3.Long-term trends in daily discharge of the Po River (m³s −1 ) from January 1970 to O 2023, with a significant linear regression.Data sourced from the Pontelagoscuro station an vided by the Hydro-Meteorological Service of the Regional Agency for Environmental Protec Emilia-Romagna Region (ARPAE-ER).Modified from Marini and Grilli., 2023, Applied Scienc
Figure 3 .
Figure 3.Long-term trends in daily discharge of the Po River (m³s −1 ) from January 1970 to October 2023, with a significant linear regression.Data sourced from the Pontelagoscuro station and provided by the Hydro-Meteorological Service of the Regional Agency for Environmental Protection of Emilia-Romagna Region (ARPAE-ER).Modified from Marini and Grilli., 2023, Applied Sciences [23]. | 7,375.2 | 2024-01-20T00:00:00.000 | [
"Environmental Science",
"Geology"
] |
RESEARCH ON COMBUSTION MODE OF METHANOL MICRO-RECIPROCAING PISTON INTERNAL COMBUSTION ENGINE
Constrained by the micro-space structure, it is proposed to use platinum wire incandescent ignition combustion mode to achieve the operation of internal combustion engine. However, the combustion test of the platinum wire incandescent ignition in miniature piston internal combustion engine shows: the combustion mode of micro-space platinum wire incandescent ignition has a poor combustion characteristic, low heat release rate, long combustion duration, and low combustion pressure. Therefore, a homogenous charge compression ignition mode is proposed to realize the operation of miniature internal combustion engine. However, it is found that the compression combustion cannot be come true in the cold start-up state of the micro engine. And the compression combustion in the first cycle was realized by the way of increasing the temperature of the cylinder block and platinum wire appropriately. The results show that: The maximum heat release rate is obviously improved and the combustion duration shortened by 28.6 oCA, and pmi increased by 76%. So, a novel hybrid combustion mode of in-cylinder compression combustion supported by the platinum wire incandescent ignition is put forward, through the way of adjusting the temperature of platinum wire, and this combustion mode is regarded as the ideal combustion mode of micro reciprocating piston internal combustion engine.
INTRODUCTION
Micro-energy power systems can achieve ultra-high energy density power output at micro / intermediate scale [1]. Its representative research includes micro-turbine engines, micro-triangular rotor Wankel engines, miniature free piston engines, micro-steam turbines and micro Stirling engines as well as micro fuel cell systems, micro-thermoelectric systems and micro-thermal photovoltaic systems [2][3][4][5].
From the perspectives of energy density and conversion efficiency, the gas power cycle micro heat engine of the liquid hydrocarbon fuel has the potential to compete with the LiSO2 battery system [6][7][8][9] (the lower calorific value of hydrocarbon fuel can reach 105,kJ/kg level, while the LiSO2 battery's energy density was only 102kJ/kg magnitude). Among them, the micro-reciprocating piston internal combustion engine may become a practical micro-energy power system because of its advantages such as simple structure [10][11].
However, the study on micro space combustion shows that the micro size effects such as high surface-volume ratio and short residence time lead to the difficulty of combustion in micro space and deteriorate the incomplete combustion, miss-fire and other abnormal combustion phenomena [12][13]. Constrained by the micro-space structure, the traditional spark ignition cannot be used in micro reciprocating piston type internal combustion engine. It is found that the fuel utilization rate is very low in the thermal wire ignition mode because of the limitation of the size of micro-combustion engine. In order to realize the homogenous charge compression ignition in micro-combustion engines, it is necessary to afford conditions which are difficult to control, such as sufficiently temperatures and active elements' catalytic action. Therefore, further analysis on different combustion modes of micro-combustion engines to develop new efficient and fast micro-space transient combustion modes is meaningful in the development of micro reciprocating piston internal combustion engine [14].
In view of this, a combustion test platform is established for the miniature reciprocating piston internal combustion engine to test the basic combustion characteristics of micro combustion engine. Furthermore, a novel combustion mode that is suitable for rapid and efficient combustion in micro-space is explored. This study contributes to the development of the ultra-high energy density power system of the miniature piston internal combustion engine.
DIAGNOSIS OF PLATINUM WIRE INCANDESCENT IGNITION COMBUSTION MODE
A combustion test platform for a miniature reciprocating piston internal combustion engine was constructed, as shown in figure 1. The test system was driven by the motor, and the speed of the motor was adjusted by the transducer. The hysteresis brake was used as an adjustable load device to absorb the output torque of Tang GZ, Wang SB, Li Z, Shang HC.: Research on combustion mode of methanol micro-reciprocating ... the engine and the motor. Kistler6052B quartz pressure sensor was adopted to collect the combustion pressure signal in the micro-piston internal combustion engine. The charge signal output by the piezoelectric sensor was converted into voltage signal by 5011B charge amplifier. The data was analyzed by DEWE2010 combustion analyzer. At the same time, the AVL365X angle instrument collects the crank angle signal, the sampling resolution was set to be 0.2℃A. The test condition was full load at 6000r/min.
The basic parameters of the micro-piston internal combustion engine are as follows: the cylinder bore was 11.25mm, the stroke was10mm, the compression ratio was 8. The fuels are mixture of methanol, nitromethane (with 15% volume fraction) and castor oil. Restrained by the micro-space structure, traditional spark ignition mode cannot be applied.
In order to evaluate the combustion characteristic of the platinum wire incandescent ignition combustion mode in a miniature piston internal combustion engine, it is compared with the combustion characteristics of a variety of conventional-size engines. Three different conventional-size engines are selected for the combustion diagnosis, which are labeled as 154FMI, 157FMI and 165FMI respectively. At the same time, the micro piston type internal combustion engine is labeled as ME. The basic parameters of the engine are shown in Table 1. The test speed is 6000 r/min. The combustion diagnosis of the conventional size engine adopts the Kistler 6052B pressure sensor to collect the pressure signal in the cylinder and convert the charge signal output by the piezoelectric sensor into a voltage signal through the 5011B charge amplifier. The DEWE-2010 combustion analyzer performs data processing. At the same time, the crank angle signal is collected by the Kistler 2613B crankshaft angle signal generator, sampling resolution is set to be 0.2 ºCA. Figure 2 shows the comparison of the cylinder pressure and the instantaneous heat release rate between the micro piston internal combustion engine and the conventional size engine. Table 2 is the combustion characteristic parameters of different engines. It can be seen from the figure 2 that the in-cylinder combustion characteristics of the micro piston internal combustion engine are poor, the combustion pressure and the heat release rate are very low. The maximum combustion pressure is only 1.17 MPa, as shown in Table 2, which is much lower than the pressure (6 to 7 MPa) of the conventional size engine. The crank angle corresponding to the maximum pressure is 17.4ºCA after the top dead center, which is 5~9ºCA later than the conventional engines. The lower combustion pressure and the delay of the crank angle corresponding to the maximum pressure reduce the expansion ratio of the engine and the thermal efficiency of the cycle. It is tested that the mean indicated pressure pmi is much lower than that of the conventional engines, with only 0.27 MPa, about 20% of the latter. According to the test, the comparison of crank angles corresponding to the cumulative combustion heat release 5%, 10%, 50%, 90% (CA05, CA10, CA50, CA90) is shown in Table 2. It can be seen that the starting point of combustion (CA05) of the micro internal combustion engine is relatively late, 5~8ºCA later than the conventional size models. Due to the lower combustion heat release rate, the combustion duration was obviously prolonged, the rapid burning period (CA10 to CA90) extends about 20 ºCA compared with the conventional models. Among them, the early stage of rapid combustion (CA10 to CA50) extends by 8~10 ºCA; the late stage of rapid combustion (CA50 to CA90) extends by 10~13 ºCA.
The main reason for the low rate of combustion heat release of the micro internal combustion engine is that the combustion is not enough. Because of the small scale of the micro internal combustion engine, its fuel residence time is short. The fuel is exhausted without complete combustion at high speed. In addition, the large surface-volume ratio makes it large ratio of heat dissipation loss relative to combustion heat release, and it is also not conducive to fuel combustion in the cylinder.
Usually, compared with the platinum wire incandescent ignition combustion mode, the homogeneous charge compression ignition mode has the characteristics of longer ignition delay period, faster combustion rate in the main combustion period, shorter combustion duration and higher pressure rise rate [14][15]. Therefore, if we can shorten the ignition delay period to achieve the homogeneous charge compression ignition mode in the micro piston internal combustion engine, the above problems of the platinum wire incandescent ignition combustion mode can be solved.
Realization of the Homogeneous Charge Compression Ignition Mode
For the reason of it is difficult to achieve compression combustion in the cold start-up state of the micro-combustion engine, we first put forward the idea: to achieve the homogenous compression combustion of the micro-piston internal combustion engine by increasing the compression ratio [16]. Four cylinders heads with different compression ratios are designed. And the compression ratio figure 3. The compression ratio of the micro piston type internal combustion engine is changed by adjusting the depth of the electric plug. All the combustion chamber is a flat-top structure. However, it is found that under normal temperature, even if the cylinder head with a compression ratio of 20 is used, the compression ignition cannot be realized.
An important reason why it is difficult to achieve combustion under cold start state is that the fuel has a high ignition temperature, and the compression of the micro-combustion engine does not provide sufficient temperature conditions. In view of this, we put forward the idea: make the homogenous compression combustion come true by raising the temperature of the cylinder and the platinum wire appropriately. The test is designed as follows: the micro piston internal combustion engine runs in the platinum wire incandescent ignition combustion mode for a period of time firstly, with a speed of 6000 r/min. When the cylinder body reaches a certain temperature, shut off the throttle and make the engine flameout. During this process, the motor speed is kept at 6000 r/min, that is, the micro piston internal combustion engine is under motored condition. After the temperature of platinum wire is reduced, the throttle is reopened, and the cylinder pressure is collected synchronously to test whether it can achieve the homogeneous charge compression ignition. Figure 4 is the change curve of the mean indicated pressure (pmi) of the micro piston internal combustion engine. It can be seen that in the thirty-fifth cycle, that is, when the throttle is opened, its pmi value is changed from 0 of the previous cycle to 0.48 MPa, indicating that the engine realized combustion in this cycle.
In order to judge the combustion mode of the first combustion cycle in figure 4, the combustion pressure of a single cycle is diagnosed and compared with that of the ignition mode. Figure 5 is the in-cylinder pressure of the ignition process after the platinum wire is electrified. It is shown in the figure that from the fifty-fourth cycle, the pmi value increases gradually from 0, that is, the mixed gas in cylinder is ignited by the hot platinum wire. Figure 6a is a comparison of the combustion pressure of the first combustion cycle (thirty-fifth cycle) and the previous cycle (thirty-fourth cycle). Figure 6b is a combustion pressure comparison of 3 consecutive ignition cycles under the platinum wire incandescent ignition combustion mode, in which, the combustion pressure of the fifty-eighth, fifty-ninth, sixtieth cycle is selected for comparison. It can be seen from the figure that the maximum combustion pressure pmax is significantly higher than that of the platinum wire ignition mode in the first combustion cycle. It can be seen that the combustion of the cycle conforms to the homogenous compression combustion characteristics.
Compression ratio12 Compression ratio8
Compression ratio20 Compression ratio16 Therefore, this cycle is considered to be the homogeneous charge compression ignition.
In figure 4, the first combustion cycle (thirty-fifth cycle) has the highest pmi value in the 300 test cycles. It is considered that the subsequent cycle is the platinum wire ignition mode, which does not belong to the homogeneous charge compression ignition mode. This is because under the high temperature of the platinum wire, the mixed gas will be directly ignited by the hot platinum wire.
The reason why the homogeneous charge compression ignition mode of the first cycle can be achieved is that the higher temperature of the cylinder block helps to combustion. In addition, although the temperature of the platinum wire is lower, it is not enough to ignite the mixed gas, it still has a certain temperature, which has a combustion-supporting effect.
The Combustion Characteristics of the Homogeneous Charge Compression Ignition Mode
In order to research the combustion characteristic of homogeneous charge compression ignition mode in micro piston type internal combustion engine, the combustion parameters of homogeneous charge compression ignition mode are tested under different temperatures. The selected temperatures are 90℃、 140℃ and 150℃, and the test speed is 6000 r/min. Figure 7 shows the mean indicated pressure pmi change curve of the micro piston internal combustion engine under different temperature. As can be seen from the figure 7, the pmi value of the first combustion cycle is the maximum in the 300 cycles measured. It is suggested that the first combustion cycle in figure 7 is the homogeneous charge compression ignition. It can be seen from the figure that compared with the platinum wire incandescent ignition mode, when the mean compression ignition mode is adopted, the combustion heat release rate is greatly increased, the maximum combustion heat release rate is significantly increased, and the corresponding combustion duration is significantly shortened. The maximum heat release rate in the ignition mode is only about 2 %/°CA, while in the homogeneous charge compression ignition mode, the maximum combustion heat release rate reaches 4.4 %/°CA at 90 ℃, and the improvement is more than doubled. So, the homogeneous charge compression ignition is an ideal combustion mode for micro piston type internal combustion engine, which contributes to improve its combustion characteristics. In addition, the heat release time of the homogeneous charge compression ignition cycle is relatively late, but as the temperature rises, there is an advance trend in the heat release time of the homogeneous charge compression ignition. Fig. 9. Comparison of combustion parameter at different combustion modes and gradually advance as the temperature increases. After the homogeneous charge compression ignition mode is adopted, the combustion duration is substantially shortened, which facilitates the improvement of thermal efficiency.
For example, when the test temperature is 90℃, the combustion duration is shortened from 53.1℃ to 24.5℃ and shortened by 28.6℃. It can also be seen from the figure that the pmi is greatly increased when the homogeneous charge compression ignition mode is adopted and the dynamic performance is significantly improved. When the test temperature is 90°C, the pmi in homogeneous charge compression ignition mode reaches 0.48 MPa, which is a 76% increase compared to 0.27 MPa in the platinum wire ignition mode. When the test temperatures are 140 °C and 150 °C, there are also such obvious results, the increase rate is more than 80%.
With the above analysis, it can be found that the micro-combustion engine can achieve homogenous compression combustion under a certain condition, and the combustion characteristics of the micro-internal combustion engine in compressed combustion mode is greatly improved.
So, a novel hybrid combustion mode, in-cylinder compression combustion supported by the platinum wire incandescent ignition, is put forward through the way of adjusting the temperature of platinum wire, and this novel combustion mode is considered as an ideal combustion mode of a miniature reciprocating piston internal combustion engine. In addition, it is also a potential approach to explore a fuel with the low ignition point and fast combustion flame speed to realize the homogeneous charge compression ignition mode in micro space.
CONCLUSION
1) The combustion characteristics of the platinum wire incandescent ignition combustion mod in the micro piston internal combustion engine are poor, the heat release rate is low, the combustion duration is long, and the combustion pressure is very low. The combustion duration is prolonged by about 20 ºCA compared with conventional-size engines, the mean indicated pressure pmi is about 20% compared with the conventional-size engines.
2) The compression combustion cannot be achieved in the cold start-up state of the micro-combustion engine, and the compression combustion in the first cycle was realized by the way of increasing the temperature of the cylinder block and platinum wire appropriately 3) The combustion characteristics of the homogeneous charge compression ignition mode of the micro piston internal combustion engine are obviously better than the platinum wire incandescent ignition mode. The maximum heat release rate is obviously improved and the combustion duration is shortened by 28.6℃A, and pmi is increased by 76%. So, a novel hybrid combustion mode, in-cylinder compression combustion supported by the platinum wire incandescent ignition, is proposed and regarded as an ideal combustion mode of a miniature reciprocating piston internal combustion engine. and Technology Research Project of Chongqing City, China (Grant No. KJ1705112) for support of this work. | 3,920 | 2020-03-10T00:00:00.000 | [
"Engineering",
"Physics"
] |
Recent advances in selective photothermal therapy of tumor
Photothermal therapy (PTT), which converts light energy to heat energy, has become a new research hotspot in cancer treatment. Although researchers have investigated various ways to improve the efficiency of tumor heat ablation to treat cancer, PTT may cause severe damage to normal tissue due to the systemic distribution of photothermal agents (PTAs) in the body and inaccurate laser exposure during treatment. To further improve the survival rate of cancer patients and reduce possible side effects on other parts of the body, it is still necessary to explore PTAs with high selectivity and precise treatment. In this review, we summarized strategies to improve the treatment selectivity of PTT, such as increasing the accumulation of PTAs at tumor sites and endowing PTAs with a self-regulating photothermal conversion function. The views and challenges of selective PTT were discussed, especially the prospects and challenges of their clinical applications.
Introduction
Cancer therapy is one of the most significant challenges facing the health care industry today [1]. According to a recent survey, in 2020, the number of new cancer patients globally is approximately 19.29 million, and the number of deaths has reached 9.6 million. Cancer's high incidence and mortality have led researchers worldwide to work hard to develop more accurate and rapid diagnostic strategies and effective anticancer methods [2,3]. As an effective treatment, traditional treatments (chemotherapy, radiotherapy, and surgery) are the most commonly employed clinical treatment methods. However, patients may have a high risk of treatment failure or posttreatment side effects during or after traditional treatment [4,5]. Among the emerging cancer therapies, photothermal therapy (PTT) utilizes the photothermal effect of photothermal agents (PTAs), which converts absorbed light energy to heat to cause thermal burns on the tumor. PTT has high research value because of its simple operation, short treatment time, and rapid recovery [6,7]. More importantly, PTT is a highly effective and noninvasive therapy that can eliminate various types of cancer. It is well known that the ultimate goal of cancer treatment is to kill cancer cells without damaging normal cells [8][9][10]. The greatest problem of PTT is the systemic distribution of PTAs in the body and non-precision exposure of lasers, which can cause serious side effects on normal tissues around tumors when using existing PTAs for PTT [1].
Increasingly, researchers have recognized the advantages of selective killing of tumor cells in PTT and developed a variety of strategies to achieve selective killing by PTT (Scheme 1), such as increasing the concentration of PTAs at the tumor site and giving PTAs a self-regulating photothermal conversion capability [1,[11][12][13][14]. The simplest and most universal solution is to increase the enrichment amount of PTAs at the tumor site. The concentration difference between normal tissue and tumor tissue can be generated, and the temperature of the tumor site can be selectively increased [15]. The ideal solution is to give PTAs a self-regulating photothermal conversion capability, which means that PTAs have a weak photothermal conversion ability in normal tissue but a strong photothermal conversion ability at the tumor site [13,14,16]. Theoretically, the temperature of the tumor site can be selectively increased, with minimal or no damage to normal cells. In this review, we summarized strategies for improving the selective efficiency of PTT and discussed the views and challenges of PTT in the fight against cancer.
Improve the enrichment of photothermal agents at tumor sites
The process of PTT is the delivery of PTAs to tumor tissue, which is then radiated to raise the local temperature [13,16]. Therefore, the most straightforward strategy is to increase the concentration of PTAs at the tumor site so that normal tissue and tumor tissue produce a concentration difference, which selectively raises the tumor site temperature [13]. Presently, the most convenient and commonly employed solution is intratumor injection. A large amount of PTAs can be enriched in the tumor tissue, and the temperature of the tumor site can be selectively increased [17,18]. However, this method is not directly applied for metastatic and deep tissue tumors in the body compared to intravenous injections [19]. Scheme 1 Schematic of strategies for improving selective photothermal therapy Zhao et al. J Nanobiotechnol (2021) 19:335 To achieve a high abundance of intravenous drugs at tumor sites, nanodelivery systems have been developed from nanoparticles to targeted nanoparticles, biomimetic targeting systems, and programmed targeting systems. These strategies have made significant progress in enhancing the stability of drug circulation and tumor cell uptake [20].
Intratumor injection
According to a previous study, the amount of PTAs that can reach the tumor site for cancer treatment is much smaller than the amount of intravenous injection due to the devouring effect of the RES system after administration of the whole body and does not have a satisfactory role in the efficacy of the drug [21]. The most striking quality of intratumor injection is its effectiveness in regard to avoiding PTAs loss, which is more conducive to heat ablation of the tumor. Given that light absorbed in the near-infrared second window with a range of 1000-1400 nm has excellent potential to penetrate deep tissue [22]. Haijun Yu et al. prepared (NH 4 ) x WO 3 nanocubes, which indicated through in vivo and in vitro studies that (NH 4 ) x WO 3 nanocubes have an excellent ability to suppress breast cancer under the second near-infrared window (Fig. 1a, b). After injecting nanocubes and irradiating with a 1064 nm laser in the NIR-II window, they eliminated tumors and inhibited lung metastasis of tumors in mouse models [23]. Polydopamine (PDA) as a mimic of the adhesive proteins found in mussels, shows excellent biocompatibility and biodegradability and has been recently utilized as an effective PTA agent in PTT research. The polydopamine coated Fe 3 O 4 magnetic composite particles prepared by Shen Shun et al. have a better effect of avoiding interference from the endothelial reticulum system (Fig. 1c, d) [24]. Fe 3 O 4 @PDA particles were injected into the tumors of tumor-bearing mice and then irradiated with a laser. The temperature of the tumor surface rapidly increased to 59.7°C, which demonstrated its excellent photothermal conversion capability. Via intratumoral injection, nanoparticles enter the tumor. These nanoparticles usually remain at the injection site and have poor permeability in the tumor, leading to incomplete ablation and recurrence [25]. Thus, cellmediated delivery has great potential in cancer therapy. Fig. 1 a Infrared thermal images and b temperature rise curves of 4T1 tumors injected with PBS or (NH 4 ) X WO 3 Nanocubes (100 μL of 5.0 mg/mL) after 100 s of 1064 nm laser irradiation (Reprinted from Ref. [23] with permission. Copyright 2015, Elsevier Ltd.). c Schematic diagram of intra-tumoral injection of polydopamine coated magnetic composite particles into mice to enhance the photothermal therapy; d The infrared thermal image of Fe 3 O 4 , PDA, and Fe 3 O 4 @PDA under NIR laser irradiation (λ = 808 nm; 6.6 W/cm 2 ) (Reprinted from Ref. [24] with permission. Copyright 2015, American Chemical Society) Nanoparticles can cross nearly impermeable biological barriers to reach target sites that are generally inaccessible to common drugs or nanoparticles [26,27]. Xue-Feng Yu and colleagues constructed a cell-mediated delivery system using macrophage vehicles to transport BSAcoated Au nanorods (sAuNRs) [28]. Due to their small size, BSA-coated sAuNRs carried by macrophage vehicles exhibited superior anti-phagocytosis because of the better biocompatibility of BSA. After intratumoral injection, macrophages transported, BSA-coated sAuNRs showed greater photothermal conversion efficiency in tumors, and the tumor recurrence rate was the lowest compared with free BSA-coated sAuNRs. However, intratumor injection has some limitations. PTT through intratumor injections can easily damage the external tissue of the tumor and has the risk of spreading cancer cells to other parts of the body. In addition, intratumor injections cannot be used for metastatic tumors and deep tumors [29].
Tumor targeted enrichment
Systemic administration is utilized more widely than intratumoral injection, especially for metastatic and deep tumors [30]. However, due to the phagocytosis of the reticuloendothelial system (RES) in general systemic administration, the amount of drugs that can reach the tumor site for cancer treatment is substantially less than the injection amount, and the efficacy of PTAs is hindered [31]. To achieve a high concentration of PTAs at the tumor site, nanodelivery systems have evolved from ordinary passive targeting systems to active targeting systems. As a common strategy in recent years, various targeting methods have been explored [32,33]. Nle4-d-Phe7-α-melanocyte-stimulating hormone (NDP-MSH) is an effective receptor agonist of melanocortintype-1, which is overexpressed in many melanoma cells and combines with the melanocortintype-1 receptor with high affinity [34]. Chun Li and colleagues developed melanoma-targeted hollow gold nanospheres, which stabilized with polyethylene glycol (PEG) coating and combined with NDP-MSH (NDP-MSH-PEG-HAuNS) [35] (Fig. 2a). NDP-MSH-PEG-HAuNS and their aggregates were detected in coated pits by cell uptake experiments, and many NDP-MSH-PEG-HAuNS were detected in the cytoplasm. Moreover, the NIR laser power was 30 J/ cm 2 which is lower than the clinical data (~ 255 J/cm 2 ) and can avoid unnecessary damage to surrounding normal tissues. These results indicate that NDP-MSH-PEG-HAuNS can be well phagocytized into cells to prolong the treatment time in tumors and enhance the efficacy of PTT.
Yu Hu et al. designed a self-amplified drug delivery system for tumor PTT using multiwalled carbon nanotubes (MWNTs) as a carrier and modifying CREKA (Cys-Arg-Glu-Lys-Ala) peptides with a particular affinity to fibrin as the targeting moiety (CMWNTs-PEG) [36] (Fig. 2b). This system amplified tumor targeting by a positive feedback mechanism of the coagulation response which means that fibrin is a byproduct of the coagulation reaction and can be specifically and substantially located at the site of vascular damage due to the strong signal amplification of the clotting reaction. The accumulation of CMWNTS-PEG in tumor sites was significantly higher than that of other groups, which showed an excellent tumor homing effect and realized selective killing of tumors. In addition to passive and active targeting, the exogenous magnetic field can also enhance the controlled killing of PTAs on tumors. Magnetic nanoparticles (MPSs) carrying PTAs in the blood (for example, based on superparamagnetic Fe 3 O 4 ) can be redirected and accumulate in the tumor tissue under the application of an external magnetic field, selectively destroying the tumor tissue while preserving normal tissue, thus improving the selectivity and efficiency of PTT. Magnetic field-guided PTT has been successfully applied in preclinical models, truly showing its excellent clinical application prospects [37,38].
Although active nanodelivery systems that show significant efficacy in treating tumors have been developed, most of them have been affected by unanticipated high uptake of reticuloendothelial systems (RES) (e.g., liver and spleen) [39]. An endogenous, alkaline phosphatasetriggered co-assembly strategy was proposed by Peng Huang et al. for the preparation of tumor-specific indocyanine green (ICG) nanofibers [21] (Fig. 2c). Tumorspecific supramolecular self-assemblies can be achieved through the regulation of specific enzymes. The nature of these noncovalent forces allows in situ formed nanostructures to readily incorporate drugs via the same kind of intermolecular interactions. This supramolecular system can easily avoid the undesired uptake of RES while sustaining advantages, including high tumor accumulation rate and long tumor retention time. Phosphatasedirected co-assembly processes and their diagnostic capabilities were carried out successfully at various levels, from in vitro experiments, cell experiments, and tissue simulations to in vivo experiments. The researchers observed that the tumor uptake of ICG significantly increased to 15.05 ± 3.78% ID/g after intravenous injection for 4 h, which is 25 times higher than that of free ICG (0.59 ± 0.24% ID/g). The resulting high signal-tonoise ratio (> 15) clearly distinguished the tumor from the surrounding normal tissue. Complete tumor elimination with high therapeutic accuracy was successfully achieved by laser irradiation (0.8 W/cm 2 , 5 min). In this way, this strategy successfully avoided the high uptake of RES. Nanofibers used for PTT therapy, including tumor-specific ICG-doped nanofibers, have great potential to be transformed into personalized nanomedicine treatment mediums and for clinical use in cancer treatment.
Biomimetic targeting strategies
Although nanocarrier technology has made significant progress in cancer treatment research, the actual effect substantially differs from what people expect. In vivo, experimental data show that drugs collected into tumor cells are usually less than 5% of the injection amount. Most drugs are filtered from the body before entering tumor cells [40]. Therefore, the prolonged blood circulation time of nanoparticles in the blood is a prerequisite for targeted delivery. It is well known that specific cells can be used as bionic targeting ligands to help target or home drugs to tumors or other lesion sites, to increase blood circulation time and to improve the pharmacokinetics of drugs [41]. Some stem cells, for example, are being applied for tumor-targeted treatment. Stem cells also have a crucial role in tumor metastasis. Daxiang Cui et al. fabricated Au nanorods@SiO 2 @CXCR4 nanoparticles and loaded the prepared nanoparticles into human induced pluripotent stem cells (AuNRs-iPS) to obtain PTT nanoplatforms [42] (Fig. 3a). Due to the excellent tumor target migration capabilities of iPS cells, the researchers discovered that the Au nanorods mediated by the nanoplatform AuNRs-iPS had longer retention times and even spatial distribution. Most importantly, the discovery has demonstrated that iPS can target tumor sites and improve the efficacy of PTT to inhibit tumor growth in tumor-bearing mice.
Macrophages can realize drug homing at the tumor site through their excellent ability to target tumor migration [43,44]. Macrophages containing therapeutic nanoparticles (including some magnetotactic bacteria) can act as Trojan horses, transporting the nanoparticles to the tumor site and destroying areas of low oxygen within the tumor to prevent malignant progression [45]. Jong-Oh Park et al. designed a macrophage-based nanotherapeutic drug delivery system to treat solid tumors by utilizing PTT, an anticancer drug, and the tumor-infiltrating properties of macrophages [46] (Fig. 3b). Compared with using nanoparticles alone, when macrophages were participating, the tumor penetration of nanoparticles was significantly improved. In addition, in vivo experiments involving local and systemic administration in tumorbearing mice have shown that the drug delivery system of macrophage-based nanotherapeutics can effectively target and kill tumors. In addition, studies have shown that the use of red blood cell (RBC) membranes as a bionic strategy can also extend the internal blood circulation time of nanoplatforms [47]. Sheng Wang et al. prepared RBC-coated, superparamagnetic nanoclusters (MNCs); after loading with NIR cypate molecules, their NIR absorbance was dramatically improved, and efficient photothermal conversion efficiency was achieved [18] (Fig. 3c). Cyp-MNC@RBCs had a significant tumor homing ability after intravenous administration. Moreover, the tumor-bearing mice showed higher temperatures at the tumor site under laser irradiation.
In other cases, the two membranes were merged to improve the targeting capability, to increase circulation time and to reduce macrophage phagocytosis. To further enhance the therapeutic efficacy of PTT, Zhiqing Pang and colleagues fabricated an erythrocyte-cancer (RBC-M) hybrid membrane-camouflaged melanin nanoparticle (Melanin@RBC-M) platform by fusing the RBC membrane with the MCF-7 cell membrane [48]. These hybrid membrane vesicles retained both RBC cell membraned proteins and MCF-7 cell membrane proteins; the MCF-7 membrane component can significantly enhance the homotypic targeting function of Melanin@ RBC-M; and the RBC membrane component can effectively reduce the cellular uptake of macrophages by Mela-nin@RBC-M and improve their circulation time, which greatly increases the photothermal therapeutic effect of nanomaterials.
While treating cancer with nanodelivery systems, it is often observed that most necrosis in the center of solid tumors is caused by long-term anoxicity: the availability of oxygen and glucose is insufficient for meeting the metabolic needs of malignant cells, and the destruction of the tumor in anoxic areas, especially tumor-associated macrophages (TAMs) in these areas, can effectively prevent the proliferation, growth, invasion, migration, and metastasis of malignant cells, directly affecting the mortality rate of patients [26,47]. However, delivering therapeutic agents to the oxygen-deprived areas of the tumor is a significant challenge [25]. To solve this problem, Susan E. Clare's team hypothesized that autonomous recruitment of tumor monocytes could be used for nanoparticle-based drug delivery and tumor therapy [44]. Because monocytes have a natural phagocytosis capacity, they can easily carry therapeutic nanoparticles to otherwise inaccessible tumor areas. After entering the tumor, the monocytes differentiate into macrophages, and then nanoparticle-laden macrophages migrate/converge to the hypoxic region of the tumor. Once in place, nanoparticle-based therapeutic functions can be activated by NIR irradiation of the tumor to destroy TAMs. Depending on the irradiation protocol, this therapeutic response can also include the destruction of adjacent tumor cells and can be combined and coordinated with other chemicals and molecular or nanoparticle-based therapies to facilitate the destruction and remission of the tumor while significantly reducing the risk of tumor regrowth and metastasis.
Programmed targeting systems
The targeted ligand on the surface of nanoparticles can increase the affinity between nanoparticles and target cells, thus improving the uptake efficiency of cells [49]. Nevertheless, the presence of a targeted ligand can trigger an immune response, leading to the removal of nanoparticles by the mononuclear phagocyte system [50]. Most of the target ligands are hydrophobic, which can easily cause aggregation of nanoparticles in vivo. As a result, the blood circulation time of the nanoparticles is shortened [51]. However, the contradiction for aggregation between tumors and blood cannot be solved by ordinary nanoparticles. Programmed targeting strategies can confer on-demand properties on target ligands to "shield" in the bloodstream and "deshield" at tumor sites, enabling them to become suitable candidates to avoid immune system recognition and to prolong the blood circulation time [52,53]. The use of shields or blocking groups protects the ligand from being recognized by the immune system and prolongs blood circulation. Once the shielding layer is removed at the tumor site by endogenous or exogenous stimulation, the intake of tumor cells will increase [49,54].
By utilizing the reversible protonation of weak electrolytic groups to pH changes, Guangming Lu et al. designed long-chain amine/carboxyl-terminated PEG decorated gold nanostars (GNSs) for PTT [41] (Fig. 4a). When incubated with HeLa cells, the degree of cellular uptake of GNS-N/C at pH 6.4 was significantly higher than that at pH 7.4 (Fig. 4b). Taking advantage of shielding nanoparticles from nonspecific interactions with normal cells/ tissues before they reach tumors and after they leave tumors is crucial for the selective delivery of GNS-N/C into tumor cells, which provides a novel effective means of tumor-selective therapy. They irradiated tumors with an 808 nm laser at 1 W/cm 2 for 5 min 24 h postinjection. Mice treated with GNS-N/C 4 (one PEGylated mixedcharge GNS with a certain surface composition) experienced an average increase of 23°C temperature, reaching an average temperature of 56°C after 5 min of treatment, which proved the excellent PTT effect of GNS-N/C 4.
Moreover, not all PTAs that arrive at the tumor would be retained in the tumor tissue. Therefore, a nanosystem that could re-shield ligands is needed to enhance the treatment selectivity of PTT. Zhi Yuan et al. researched the reversible ligand shielding strategy by a reversible ligand shielding system based on a temperatureresponsive polymer [49] (Fig. 4c). The ligand biotin, cisplatin-loaded chain poly (acrylic acid)-Pt, and shielding segment thermosensitive poly(N-isopropylacrylamide-co-acrylamide) (P(NIPAAm-co-AAm)) were co-modified onto the surface of gold nanostars (Au@ Pt/Re-Biotin). Among them, the lower critical solution temperature of P(NIPAAm-co-AAm) is approximately 39 °C, which helps to shield the ligand by the extension of P(NIPAAm-co-AAm) in the blood circulation (37°C). The ligand would be deshielded through P(NIPAAmco-AAm) contraction utilizing the heat generated from gold nanostars upon NIR irradiation when the nanoparticles arrived at the tumor site. The results indicated that the system could extend blood circulation (1.6-fold at 24 h), reduce immune system clearance (28% lower), and enhance tumor accumulation (37% higher) effectiveness compared with the irreversible ligand shielding system (Au@Pt/irRe-Biotin) by analysis of platinum (Fig. 4d). Photothermal imaging of tumors in vivo was conducted to evaluate the photothermal conversion ability. Upon NIR (808 nm, 1 W/cm 2 , 6 min) irradiation, both Au@Pt/Re-Biotin and Au@Pt/irRe-Biotin showed apparent temperature increases, thus indicating the accumulation of gold nanostars, which possessed satisfactory photothermal conversion ability at the tumor site. In addition, this strategy showed tumor inhibition (11% higher) that was significantly superior to the irreversible system.
Self-regulating photothermal conversion system
Selective killing of tumor cells means killing tumor cells with minimal or no damage to normal cells. Although improving the enrichment of photothermal materials in tumor sites can solve the problem to some extent, low concentrations of PTAs in normal tissue also have the ability of photothermal conversion, so how to achieve PTT without damaging normal tissue remains a challenge [55]. Therefore, is it possible to selectively increase tumor site temperature by intravenous injection with the same concentration and laser radiation in tumor and normal tissue? Assume that PTAs have a weak photothermal conversion ability in normal tissues and a robust photothermal conversion ability in tumor sites. In this case, this PTAs can selectively increase the temperature of tumor sites under inaccurate irradiation in theory, with minimal or no damage to normal cells [56]. Therefore, PTT's other strategy for selective killing is to give PTAs a self-regulating photothermal conversion capability [57,58].
Self-regulating of metal nanoparticles
To date, many PTAs have been discovered, including photosensitizer polymers, metal nanoparticles, carbon nanomaterials, black phosphorus (BP) based nanomaterials, and metal sulfides [59]. However, these PTAs cannot achieve a controllable photothermal conversion ability. Gold nanoparticles have become one of the most promising drug delivery materials due to their excellent biocompatibility, surface modification, and excellent photothermal conversion efficiency. The aggregation and self-assembly of spherical gold nanoparticles give them the photothermal conversion ability that they do not possess [60], making them the best candidate for materials that can regulate photothermal conversion. In the disassembled state, the NIR absorption of spherical gold nanoparticles is very weak and almost does not have a photothermal conversion capability. The absorption peak re-shifted to approximately 808 nm and generated a photothermal conversion ability in the assembled state, achieving the photothermal treatment function [61]. To meet the functional requirements of PTAs in different environments in vivo and enable them to possess unique characteristics of assembled and unassembled states, researchers have explored how to achieve responsive aggregation of small particle-sized gold nanoparticles in tumor sites.
It has been reported that many pH-sensitive, spherical gold nanoparticles can achieve responsive self-assembly at the tumor site under the influence of the tumor microenvironment, showing strong near-infrared absorption and thermal ablation of tumors [62]. Zhi Yuan et al. [63] used a one-pot reaction to modify lipoic acid-PEG (LA-PEG), LA-PEG-Biotin, 4-mercaptobenzoic acid, and p-hydroxythiophenol on gold nanoparticles (Au@T), which can increase the hydrophobicity of the system under acidic conditions of pH = 6.0 to agglomerate the nanoparticles (Fig. 5a). By using dynamic light scattering to measure the size of Au@T, they discovered that after being added to pH = 6.0 PBS solution for 2 min, the size of Au@T rapidly increased from 32.8 nm (PDI = 0.229) to 249.3 nm (PDI = 0.187). Moreover, in vivo studies showed that after 8 h of exocytosis, the content of acid-responsive Au@T in HepG2 cells changed by less than 5%, while the content of non-responsive gold nanoparticles decreased by more than 10%. This finding proves that the aggregation of Au@T can prolong the residence time of nanoparticles in cells. Importantly, the temperature changes caused by different power laser irradiation were analyzed at Au@T ([Au] 150 μg/mL) in PBS buffer (pH = 6.0), and the temperature was observed to reach 50°C at 1.0 W/ cm 2 laser irradiation after 3 min. The photothermal conversion efficiency of aggregated Au@T is 30.48%, higher than the photothermal conversion efficiency of the commonly reported gold nanoparticles [64][65][66], which proves that aggregated Au@T has an excellent photothermal conversion ability under acidic conditions. Haibin Shi and Mingyuan Gao developed novel lighttriggered gold nanoparticles (dAuNPs) that can selfassemble in vivo by covalently cross-linking the end groups of the diazirine (DA) of the PEG 5000 ligand on the surface of gold nanoparticles (20.5 nm) with the help of 405 nm laser irradiation [67] (Fig. 5b). After continuous irradiation for about 15 min, a second maximum absorption appeared at the shoulder is approximately 700 nm and gradually extended to the NIR of 700-900 nm. The strong surface plasmon resonance of the dAuNPs appearing in NIR renders it potentially useful for PTT. Under 808 nm laser irradiation, the cross-linked nanoparticles showed greatly enhanced photothermal effects compared with non-cross-linked nanoparticles (Fig. 5c). As a proof of concept study, the penetration depth of a 405 nm laser remains limited, but the current strategy for manipulating Au particles in vivo can be extended to other types of light-triggered assembly nanoparticles, which can be triggered by lights with wavelengths that are more suitable for clinical applications. Qiwei Tian and Shiping Yang et al. [68] proposed a novel synergistic triggering mechanism to realize the self-assembly of gold nanospheres. Au@ZIF-8 does not produce photoacoustic signal and photothermal conversion capability in normal tissue. In contrast, in the presence of overexpressed glutathione and hydrogen ions in the tumor, gold nanospheres were released from Au@ZIF-8 to form aggregates and showed solid signals for imaging and effective PTT. This work provides a new strategy for designing therapeutic agents with sequential response steps to avoid interfering with diagnostic signals from normal tissue and to reduce damage to normal tissue during treatment.
However, the excitation window of existing PTT is mainly located in the visible or NIR region, with insufficient penetration depth and relatively low interaction with tissues, limiting its thermal sensitivity effect. Therefore, Professor Zhang Dong et al. [69] developed an activatable NIR-II plasmonic theranostics system based on silica-encapsulated, self-assembled, gold nanochains (AuNCs@SiO 2 ). In this study, the optical properties were precisely controlled by the structural changes of plasmonic nanoparticles in response to the tumor microenvironment, leading to accurate diagnosis and effective treatment of tumors. In normal tissue, the self-assembled gold nanochain does not change its structure and shows photoacoustic and photothermal "OFF" states in the NIR-II region. When the gold nanochain enters malignant tumor tissue, it will obtain electron conductivity through the fusion of its chain structure, and the electric field intensity is significantly enhanced, so that the surface plasmonic resonance extinction peak has a redshift, presenting an NIR-II region of photoacoustic and photothermal "ON" states. Because of the existence of "hot spots" between the gold nanoparticles and the electronic conductivity effect of the chain structure, the photoacoustic enhancement effect is significant, and the photoacoustic signal at the malignant breast tumor is greatly enhanced to realize the specific diagnosis and PTT of breast cancer. This activated strategy can realize in situ and sensitive tumor detection while effectively killing tumors, which may prominently improve the survival rate of cancer patients and introduce a new way for optical nanoengineering to become intelligent, accurate, and non-invasive in the NIR-II window. Although all these studies reported the PTT of tumors by stimuli-responsive selfassembling AuNPs, an evaluation methodology for damage to normal tissues and skin is still lacking. To validate the possibility of specifically killing tumor cells, we established an in vitro selective photothermal transformation model (Fig. 6a), a "one facula" experiment ( Fig. 6b, c), and an in vivo skin-damaging assessment model (Fig. 6d) [61]. This study is the first attempt to construct an evaluation methodology for precise PTT using in vitro and in vivo models.
In addition to self-assembling gold nanoparticles, other materials can be used to self-assemble nanomaterials at tumor sites to achieve better photothermal conversion and selective thermal burn of tumors. As a rising star in the family of two-dimensional materials, BP has attracted much attention from researchers. BP has unique optical properties, and relevant reports have proven that BP twodimensional material can serve as an efficient photothermal preparation, and phosphorus is an essential element in organisms, making its application in the biomedical field an unparalleled advantage [70][71][72]. Recently, Han Zhang et al. [73] conducted acid-activatable smart selfassembly of BP and polyoxometalates (POM) to produce POM@BP, which can self-assemble into large nanoparticles in the acidic tumor microenvironment, which prolongs the retention period in the tumor site, significantly improves the light absorption ability of BP nanosheets, and enhances the photothermal transformation in tumor tissue. This kind of rational design and effective customization method is promising for future scientific breakthroughs in nanomedicine.
Self-regulating of molecule-based PTAs
To date, PTAs based on small organic molecules, such as anthocyanin dyes and porphyrins, have often been employed for PTT of tumors. Anthocyanin dyes have been shown to have excellent biophysical compatibility, improved photophysical properties, and superior nearinfrared absorption, making them effective molecular PTAs, including imaging and therapy. Therefore, anthocyanin molecules with suitable photophysical ability, such as ICG, IR825, IR780, and CYPATE, have become potential candidates for PTT.
Based on a previous project, Professor Gaolin Liang and his colleagues designed and synthesized an organic small-molecule dye, biotin-Cystamine-Cys-Lys(Cypate)-CBT, which can specifically recognize the high expression of biotin receptors in cancer cells (Fig. 7a) [13]. After being reduced by intracellular GSH, CBT-Cys forms dimers by a click condensation reaction, and the fluorescence of the two dye molecules is quenched by fluorescence resonance energy transfer (FRET). Subsequently, the dimers self-assemble in situ to form nanoparticles; intermolecular charge transfer occurs between dye molecules; and fluorescence quenching is further enhanced. Under laser irradiation, this fluorescence quenching enhanced the non-radiative excitation process of the dye, thus increasing the thermal conversion efficiency of the dye and its photothermal therapeutic effect on tumors (Fig. 7b, c). This "smart" strategy was successfully verified by enhancing the effect of PTT on living tumors. More importantly, by replacing biotin in dyes with other targeted ligands, this "smart" strategy holds promise for the PTT of other diseases.
By the same stimuli-responsive principle, Zhiyong Qian et al. prepared a nanosystem (NLG919/IR780 micelle) with the characteristics of both photothermal conversion and regulation of the tryptophan metabolism pathway to inhibit tumor growth [74]. NLG919/IR780 micelles can accumulate effectively in tumors and migrate to lymph nodes and lymphatic systems to achieve excellent tumor thermal ablation. Moreover, NLG919/IR780 inhibited the growth of the tumor margin after PTT of the primary tumor. In addition, Peng Huang et al. reported endogenous alkaline phosphatase-triggered co-assembly of indocyanine green (ICG) nanofibers [21]. In addition to increase tumor uptake, the assembled nanofibers significantly enhance the ICG NIR absorbance based on intermolecular interactions, improving the photothermal properties.
However, metallic nanoparticles and organic dyes have disadvantages, such as poor water solubility, limited tumor accumulation, and bioavailability. In addition, although the stimulus-response system has realized the self-regulating photothermal conversion ability of PTAs in vitro and animal experiments, the complexity of the in vivo environments of the body is still a major challenge for the precisely controlled photothermal conversion capability of PTAs. Therefore, researchers must conduct further studies to prove the practicality of the materials in clinical practice.
Conclusions and perspectives
Since the concept of "precision medicine" was proposed, tumor treatment has gradually developed toward the direction of individualization and precision. The multisubject crossing, combination, and arrival of the nanotechnology revolution have extensively promoted the application of PTT in tumor therapy [75,76]. Due to the systemic distribution of PTAs in the body and inexact laser irradiation during treatment, PTT may cause serious damage to normal tissue. Improving the irradiation accuracy of the device and developing interventional treatment equipment can promote the development of selective PTT to a certain extent. For an ordinary NIR instrument, the selective effect of PTT depends on the difference in photothermal conversion ability between the tumor site and the normal tissue site. According to the two idea, researchers' exploration directions can be roughly divided into two categories: (1) increasing the concentration difference between tumor sites and normal tissues and (2) endowing PTAs with self-regulating photothermal conversion capability.
Increase the concentration difference between the tumor site and normal tissue
The photothermal conversion ability of most PTAs is in direct proportion to the concentration. Hence, increasing the concentration of photothermal nanomaterials at tumor sites is an effective method for improving the PTT accuracy. Intratumoral injection, targeting systems, biomimetic systems, and programmed targeting systems aim to increase the PTAs concentration in tumors. Although intratumoral injection can effectively cause the concentration difference between normal tissue and tumor tissue to selectively increase the temperature of the tumor site, this method cannot be directly employed for metastatic and deep tissue tumors in vivo. Additionally, many times intratumoral injections may cause to tumor metastasis. Intelligent transportation systems (targeting systems, biomimetic systems, and programmed targeting systems) enhance the uptake of tumor cells. Nevertheless, most PTAs are filtered from the body before entering tumor cells, which makes the amount of enrichment in tumor sites is far from expected. Therefore, although the method increases the concentration difference between the tumor site and normal tissue, which is feasible in theory, actual results will not materialize unless the nano-drug delivery system achieves rapid progress.
Endowing PTAs self-regulating photothermal conversion capability
Assume that PTAs have weak photothermal conversion ability in normal tissue and strong photothermal conversion ability in the tumor site. In this case, the temperature of the tumor site will selectively increase after the same enrichment amount and infrared laser irradiation, with either minimal or no damage to normal cells. Therefore, endowing PTAs with a self-regulating photothermal conversion capability through the responsive activation of PTAs at the tumor site can achieve a better precision killing effect on tumors. Although the self-regulating photothermal conversion ability of PTAs has been realized by stimulus-response systems (such Fig. 7 a Schematic illustration of reduction-controlled condensation of 1 to yield 1-Dimer, which self-assembles into 1-NPs to enhance the photothermal efficacy of the fluorophore Cypate. b Cell viability of HeLa cells or biotin-blocked HeLa cells after 1 h incubation with 1, 1-Fmoc, or Cypate at different concentrations, washed to remove the free compounds followed by laser irradiation and another 24 h incubation. All experiments were performed in triplicate. c Calcein-AM/PI live/dead staining of HeLa cells after 1 h incubation with the compounds, washed to remove the free compounds followed by another 24 h incubation, respectively (Reprinted from Ref. [13] with permission. Copyright 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) as the self-assembly of gold nanoparticles or dye molecules) in vitro and animal experiments, the complexity of the in vivo environments of the body is still a significant challenge for the precisely controlled.
Prospects and challenges of clinical applications
As a non-invasive and spatiotemporally controllable tumor treatment method, PTT is still in the preliminary clinical research stage and has proven excellent anticancer efficacy in the laboratory and clinic [30,77]. All the above mentioned methods are theoretically selective, and relevant experiments have not verified the safety of normal skin and body organs. For effective clinical translation of selective PTT, the following aspects should be taken into account: (1) provide in vitro and in vivo new evaluation methods to evaluate the ability of selective PTT; (2) improve the sensitivity of self-regulating PTAs to achieve selective PTT; and (3) investigate the longterm prognosis of selective PTT.
Although the complete clinical application of PTT has not fully emerged, it offers new hope for the clinical treatment of cancer. Highly selective PTT will render the construction of clinical PTT more refined and intelligent and become a new opportunity to develop clinical cancer therapy. With rational technological innovation and strategic improvements, there is a considerable scope for clinical expansion of the new PTT platform. Simultaneously, we hope that this review will provide valuable information and insights for future research into selective PTT. | 8,201.2 | 2021-10-24T00:00:00.000 | [
"Medicine",
"Engineering"
] |
d-matrix – database exploration, visualization and analysis
Background Motivated by a biomedical database set up by our group, we aimed to develop a generic database front-end with embedded knowledge discovery and analysis features. A major focus was the human-oriented representation of the data and the enabling of a closed circle of data query, exploration, visualization and analysis. Results We introduce a non-task-specific database front-end with a new visualization strategy and built-in analysis features, so called d-matrix. d-matrix is web-based and compatible with a broad range of database management systems. The graphical outcome consists of boxes whose colors show the quality of the underlying information and, as the name suggests, they are arranged in matrices. The granularity of the data display allows consequent drill-down. Furthermore, d-matrix offers context-sensitive categorization, hierarchical sorting and statistical analysis. Conclusions d-matrix enables data mining, with a high level of interactivity between humans and computer as a primary factor. We believe that the presented strategy can be very effective in general and especially useful for the integration of distinct data types such as phenotypical and molecular data.
Background d-matrix, originally designed with cardiovascular clinical and molecular genetic data in mind, is a generic database front-end that can be used to explore, visualize and analyze different typologies of datasets.
Both the generation and the analysis of genome, transcriptome and proteome data are becoming increasingly widespread, and these data must be merged to generate a molecular phenotype. Moreover, the correlation between molecular and phenotypical data requires acquiring both with comparable profoundness leading to the develop-ment of large and small scale databases holding both information [1][2][3]. In the same line, we developed a Car-dioVascular Genetic database (CVGdb), storing the detailed clinical phenotype of patients with congenital heart diseases as well as molecular data such as gene expression analysis results [4] and genotypes. However, querying and analyzing the stored data to uncover the valuable information hidden in the databases are difficult tasks. With some exceptions, these are approached by a two-step procedure, in which a database specific front-end serves the query and extraction of data, which are subsequently imported in stand-alone analysis tools for visualization, mining and statistics [5][6][7][8][9][10][11]. Moreover, the visualization and mining tools frequently focus on presenting overall views of data sets for a specific task and seldom permit single-case addressability or have drill-down capability. In today's systems, the perceptual abilities of human users are only used to a limited extend. We believe that it is essential to make users part of the overall process through computer support of their intelligence, creativity and perceptual abilities. Hence, a major research challenge is to find human-oriented forms of representing information and enabling rapid interaction between humans and computers in the query, visualization and analysis process [12].
It is not the purpose of this paper to survey the various solutions available to query, visualize and mine data, but rather to illustrate how such concepts could be combined usefully within one software tool. Here, the layout should not only preserve the structure of the information, it should also convey the quality of the distribution of the values contained in the database. The features of the display should then be designed to highlight those regularities, patterns or dependencies that are not easily detectable with an ordinary front-end.
One visual representation, which motivated the graphical display of the tool we describe here, is the data matrices handled in microarray studies, in which rows in the matrices typically represent genes and columns individual samples [13]. Rather than showing a numerical 'spreadsheet', it is convenient to display microarray data in such matrices, which indicate varying expression levels in a grid of varying colors.
With d-matrix we propose a generic front-end solution capable of extracting, exploring, visualizing and analyzing complex data. The software can be interfaced with the most common relational database management systems without any intervention on the schema or pre-processing phase. As the name suggests, the visual model proposed has the form of a matrix. Its elements are boxes whose colors show the quality of the underlying information. The granularity of the data display allows consequent drill-down, i.e. the user is able to focus the observation on a single data point. In addition, value frequency bars are available to present compact overviews. It also offers the possibility to define categories using context-sensitive rules and to assign colors to classes. The direct implementation of a broad range of descriptive and advanced statistics together with a hierarchical sorting feature permits user-defined exploration of the data.
Data Model
The process of developing a uniform web interface for disparate data sources is a complex task because of the variability in the data models that underlie each source. To enable an effective two-dimensional display, the d-matrix model consists of a three-level tree. For the representation of a large database schema requiring a higher number of levels, several d-matrix instances can be built on the same database.
Within the proposed model, the main table addressing the objects of a study is considered as the root, the first level of the tree. The second level consists of tables that are joined with the root by means of its primary key and the third level consists of tables that are further joined with the ones at level two. In particular, the dependency of the root table with the second level tables can be either oneto-many or one-to-one, while the dependency of the second level table with the third level ones can be either many-to-one or one-to-one. To apply different query and visualization rules each branch of this tree is defined as a data group characterized by the same storing strategy. In cases where Entity-Attribute-Value (EAV) tables are interfaced, the Entity must correspond to the main ID.
As an example, we can refer to the CardioVascular Genetics database (CVGdb) schema set up by our group ( Figure 1). Here, we selected the table Patients as the root of the tree, so that the CVGdb instance main ID is the Patients primary key. This selection is arbitrary and one could also choose Clones or Hybridizations, thereby focusing on different aspects of the overall dataset. In Figure 2, data groups and tree levels are represented. The data groups 2 to 4 address the EAV tables Invasive_Treatments, Medications and Samples; the groups 5 to 6 contain the same table Clones joined with different tables containing gene expression analysis results [4]; whereas the last group is built by two tables describing sequence variations (SV).
Data selection and query
The schema is presented to the user in a structure recalling a file system selector ( Figure 3A). Nodes represent attributes or value attributes that can be optionally divided further into more folders without any depth limitation. Collecting nodes in visually distinct entities becomes a necessity when coping with a large number of attributes. To obtain a quantitative measure of the information that is contained within groups of nodes, a summary node can be included in the query form. For each value of the x-axis, the values of the summary nodes are computed by counting the nonempty nodes in the respective folders.
The query process consists of two steps. First, the users select all nodes they want to be included in the query (Figure 3A); second, these are listed in a query form where conditions and analysis features can be specified ( Figure 3B). To visually distinguish between nodes referring to data belonging to single -table data group and two-table data groups, single-table group nodes are represented as sheet-like-icons, whereas two-tables data group nodes are represented by double-arrow-like icons: diagonally oriented for the nodes that belong to the second level tables, and vertically oriented for the nodes that belong to the third level tables ( Figure 3A). The attribute on which the query display shall be focused can be selected by means of the three-banded icons placed on the right side of the nodes. For each of the nodes, the query form permits the definition of sorting order and direction (ascendant/ descendent), values and operators for query conditions, display order and parameters for statistical evaluations. The value cell is not shown if the node itself is an attribute value. Alternatively to the matrix view of the query result, the user can optionally export the resulting dataset in form of text or XML ( Figure 3B).
Data visualization
The graphical output of d-matrix consists of two-dimensional matrices, whose colored boxes code the meaning of the underlying information, the description of the chosen nodes and a prospect of statistical evaluations ( Figure 4). The display of the data is determined by the data dimensionality. The main ID corresponds always to the x-axis of the matrix. To permit the display of single and multiple dependencies with the main ID, the y-axis shows either node descriptions or node values.
In cases of single dependency each data point is represented by one box of the matrix. If there is a multiple dependency (two-table data groups), subsequently more rows for each value of a single node are displayed. EAV data groups can lead to both single or multiple dependency; in the second case the entries are aggregated in one matrix box. In Figure 4 the tuples of the data group "PHE-NOTYPES" addressing the table Patients are displayed in the first matrix. Each tuple corresponds to a column whereas row headers are node descriptions. The tuples of the data group "SEQUENCE VARIATIONS" addressing the tables SV_Genotypes and SV_Loci are aggregated column-wise and grouped by the main ID. Here, there is more than one tuple for each column whereas row headers are values of the node Locus ID. Hence, each column of boxes on the matrix display represents an aggregation of more than one tuple of the query result. Following data mining terminology, we can say that in d-matrix cases (and aggregations of them) are represented column-wise.
Relational database schema of the CardioVascular Genetics database (CVGdb) Figure 1 Relational database schema of the CardioVascular Genetics database (CVGdb) Tables are represented as boxes and foreign keys constraints as arrows. Grey boxes mark the schema subset interfaced in d-matrix. SV -Sequence Variations; RV_versus_LV, A_versus_V, RVH_in_RV, VSD_in_RA and TOF_in_RV are tables containing gene expression results [4]. Figure 2 Data Model Shown is an excerpt of the three-level tree for interfacing d-matrix with CVGdb. The table "Patients" defines the root of the tree. Each branch refers to a defined data group consisting of one or two tables, respectively.
Data Model
When the matrix oversize the available space, the use of two distinct scrollbars lets the user move the data matrix horizontally and vertically. The general overview is given together with the advantage of single-case addressability, Data selection and query Figure 3 Data selection and query Within the data selection schema (A) users can choose all nodes they want to be included in the query. If a data group consists of two tables, the nodes are represented by vertical arrows for the first table and diagonal arrows for the second. The attribute on which the query display is focused can be selected by the three-banded icons, which switch from black-white to color and vice versa upon selection. Furthermore, trees can be saved and reloaded for subsequent analysis. Upon selection all nodes are listed in a secondary form (B), where query conditions, display and sorting order as well as the implementation of descriptive and advanced statistic can be specified. In addition to the graphical output, the query can be exported as a text of XML file.
i.e. each case (tuple) representation is entirely visible and its components clearly distinguishable.
The display is obtained as a group of images (generated using the Perl GD module and stored as temporary files), each in a separate HTML DIV container, which can be moved independently.
Drill-down
The matrix display represents a summarized view of the query. Each box holds three levels of detail: first, the coordinates that uniquely identify the box position and represent two units of information; second, the color that corresponds to either a single value or a category; third, the hidden content of the box obtained by drill-down, which gives all remaining information for that box.
In the d-matrix display the drill-down can be obtained for each box in form of a pop-up window (Figure 4). The content structure of this new window varies according to the data group to which the box belongs, although it always contains the value that is substituted by its color code together with the underlying node description. Further supplementary data can be included from attributes of the same data group.
It is possible to add further detail by the mean of hyperlinks to grant access to remote databases, external analysis results and multimedia documents (Figure 4), or even to trigger further analysis processes.
Schema interface and configuration
The software requires four configuration files: the data definitions file that is needed to connect d-matrix with the relational schema, a database settings file storing the information to access the database, a color file for the definition of the colors used in the matrix and a general server settings file. Every configuration file is maintained as plain text to permit easy access and modification.
The structure of the data definitions file must reflect the hierarchy in which the metadata (relational schema definition) have to be organized on the screen, while its textual content depicts a level of abstraction (definitional abstraction) [14] between the database physical representation and the human-comprehensible view of the data. Therefore, the data definitions file reflects the subdivision of the database schema in data groups. For each group the table attributes, information about identifiers, joining conditions as well as aggregation (where needed), display settings and the content of the pop-up window have to be defined. User-defined human-intelligible terms can be assigned for any term used in the database. Besides the attributes' names, types and descriptions, it is possible to define categories, orderings and associations with colors. It is important to notice that the rules that define categories can even involve other attributes of the same data group. This context-sensitive categorization, intended as a qualitative abstraction [14], allows the concurrent representation of two layers of information.
For each attribute value, value range or defined category, rules can be given to assign its respective color. This leads to a common method to visualize both discrete and continuous variables. In addition, categorized numeric values can be treated as categorical in specific contexts like sorting and statistics. Furthermore, colored boxes can be composed by combining the values of two nodes, which enables, for example, the visualization of both Alleles within horizontally split boxes for sequence variations ( Figure 4).
Several data definitions files (each defining a separate dmatrix instance) can independently coexist on the same server for the same or different database systems and schemata.
Visual data mining and statistical analysis d-matrix permits consecutive data-filtering operations that -as a whole -can be seen as a single user-driven data mining session. A compact and information-dense Graphical output of d-matrix Figure 4 Graphical output of d-matrix The graphical output consists of the matrices itself, the description of the nodes displayed, a prospect of statistical evaluations and hyperlinks to external resources. Each matrix corresponds to a single data group (Phenotypes; Sequence variations). The x-axis of the matrix is defined by the main ID (Record) and the y-axis by the nodes displayed. The terms like "Gender", "Age (Years)" and "IVS Shunt" are descriptive names for the respective column names GENDER, AGE_YEARS and IVS_SHUNT of table PATIENTS; terms like "Ichd0001" and "Ichd0002" refer to locus names, values of the column LOCUS_ID of table SEQ_VAR_LOCI. The matrix is built by colored boxes coding for the meaning of the information itself, which is further described in the pop-up window (as shown for Record 366 and Ichd0009). Frequency bars and boxes for descriptive statistics are displayed. Numbers are reflecting the sorting order, whereas blue boxes at the left border hold the hyperlinks.
graphical outcome, context-sensitive categorization, hierarchical sorting and drill-down enable this mining process. Frequency bars give an overview of the overall queried dataset whereas box plots improve the visual perception of the data distribution. A key feature within the mining process is the opportunity to obtain different views of a single data set rapidly in parallel using different browser windows. Here, the interactivity becomes a primary factor and is supported by the human-oriented representation.
A wide range of descriptive statistics and statistical tests is directly accessible. This permits statistical evaluation of the correlation between attributes and determination whether it is reasonable or not to assume that a sample fits to a specific distribution. For numerical values it is possible to perform up to ten different statistical tests, while for non-numerical entities (Boolean and categorical data) the Chi-square and Fisher exact tests are available. The user interface automatically performs a selection of attributes and tests according to their respective compatibility.
In addition to directly implemented tests, external data analysis environments like R [15] or user defined routines can be easily interfaced. The results of the tests, together with the descriptive statistics, are displayed at the side of the matrix and colors of the boxes reflect the results (e.g. significance) of the tests.
CardioVascular Genetics database (CVGdb)
For interfacing d-matrix with the CVGdb, we assigned categories if appropriate and colors to more than 700 nodes. Figure 5 shows an example of a single user-driven data mining session, which was initiated with the aim to discover cardiac phenotype features associated with shunts abroad the interventricular septum (IVS shunt). Therefore, the only query condition specified is that "IVS shunt" is not "NULL". This condition is fulfilled by 211 out of 560 IDs stored to date. In addition, a subset of nodes referring to phenotype descriptions physically surrounding the interventricular septum has been chosen to be displayed. To structure the display, hierarchical sorting has been applied to the 'IVS shunt' and an arbitrary selection of other nodes. Viewing the entrance matrix ( Figure 5A), one could easily recognize data clusters such as the relation of the category 'bidirectional' of the 'IVS shunt' (blue boxes) to categories of interatrial septum shunts (IAS shunt) and right ventricular systolic pressure 'RV sys pressure'. Almost all patients with a bidirectional 'IAS shunt' are also characterized by a bidirectional 'IVS shunt'. Furthermore, the majority of bidirectional 'IVS shunt' is associated with severe 'RV sys pressure', whereas the nonsorted nodes pulmonary valve morphology (PV morphology), pulmonary valve systolic pressure gradient (PV Psys gradient) and right ventricular anatomy (RV anatomy) are distributed in a questionable co-occurrence to each other in this first matrix. For further evaluation, we focus on the 'RV anatomy' or the 'PV morphology' chosen as the first sorted nodes in the second and third matrix ( Figure 5B,5C), respectively. By using the tree save/reload option to retrieve these new matrices, only the sorting criteria needed to be modified to obtain different views on the same data set in parallel using three browser windows. Hence, the frequency bars remain the same in all visualization sessions. Now it becomes clear that more than half of the patients with infundibular stenosis (RV anatomy) show a stenotic 'PV morphology', which by itself is highly associated with an extreme 'PV Psys gradient'. Applying the correlation analysis implemented in d-matrix, the significance of the correlation of the 'PV Psys gradient' with the 'RV sys pressure' could be verified ( Figure 5D). The described data are available for the exploration using dmatrix at the web supplement.
Finally, the session explained is just one out of several examples in which d-matrix proved to be highly effective for the visualization of regularities and dependencies within the CVGdb data. Moreover, based on the general visualization concept, d-matrix provides an integration between clinical and genetic information that is crucial for the correlation of phenotypical and molecular data (Figure 4).
Other applications
With respect to an ongoing project on gene regulation, we found it very convenient to visualize potential transcription factor binding sites (TFBS) in promotor sequences by interfacing d-matrix [16]. Here, the nucleotides are used as the main ID (x-axis) and the TFBS are consequently displayed at the y-axis. This allows a much higher level of interactivity than a usual figure output. One could easily have different views of the data set by sorting or parallel display of different information, like color coded core or matrix match similarities.
To demonstrate the versatility of the software, we further interfaced d-matrix with a database that represents the periodic table of the elements [16]. Although we did not expect unusual or unexpected regularities in such a simple case, it was easy to obtain a matrix that shows the wellknown dependency between Atomic Number, Atomic Mass and Energy Levels and the obvious lack of available information about elements with seven energy levels, which are the most unstable and rare.
The interfacing with both dataset required only one working day for each.
Results and discussion
We have presented d-matrix, a non-task-specific database front-end with a new visualization strategy with embedded analysis features.
The graphical outcome of d-matrix consists of colored boxes arranged in matrices; it permits single-case addressability with further drill-down capability. Together with the hierarchical sorting and statistical feedbacks, d-matrix enables consecutive data-filtering operations that -as a whole -can be considered as a single data mining session. Also, the result of such a session can be exported for further study. For a qualitative evaluation of d-matrix, one should not only focus the attention on the final display, which only represents the end product of a sequence of user-driven data exploration sessions. The high level of interactivity that our approach offers is indeed a primary factor; with d-matrix, the communication between human and computer is a rapid interaction.
The future development of d-matrix will focus on the implementation of clustering algorithms to be executed before display. Furthermore, we envisage the design of instruments to inquire metadata to maximize the quantity of information that will be eventually displayed and analyzed [17]. In addition, a user-friendly way to interact with configuration files will be granted by specific CGI scripts leading to a further reduction of the time to interface dmatrix with relational schemata.
An inquiry of the solutions reported to date for data exploration, visualization and analysis resulted in an approximate distinction between reports about efforts for database development with their task specific front-end solutions and stand-alone data analysis, visualization and mining tools. In our view, d-matrix stands in between those two groups and aims to combine features of both efforts, which we believe can be very effective and useful in general and especially for the association of distinct Example of a data exploration session for CVGdb Figure 5 Example of a data exploration session for CVGdb Shown are the first 61 of 211 records that meet the query condition "IVS shunt" is not "Null" focusing on different views of the data given by different sorting options (A, B, C). To provide information about the color code as well as the overall query output, pop-up windows for frequency bars of sorted nodes are shown (D). Further, the pop-up window for the correlation analysis between 'RV sys pressure' and PV Psys gradient' is displayed (D). See text for detailed description of the observed cluster. data types such as phenotypical and molecular data. As a front-end, it does not require complex installation processes or maintenance, and it is suitable for multi-user remote access. As a visual data mining tool, it gives an effective display that allows the detection of exceptions, trends, regularities, clusters and dependencies, as well as incomplete or erroneous data.
Operating system(s): Platform independent
Programming language: Perl Other requirements: d-matrix was successfully interfaced to Oracle 8i, MySQL, Microsoft Access and text-based databases and is compatible with recent JavaScript-enabled browsers.
License: d-matrix is available on request from the author. To academic institutions d-matrix is available for a fee of 250 Euro that is intended to cover our costs of distribution and maintenance. | 5,576.2 | 2004-10-28T00:00:00.000 | [
"Computer Science",
"Biology"
] |
PR1-Specific T Cells Are Associated with Unmaintained Cytogenetic Remission of Chronic Myelogenous Leukemia After Interferon Withdrawal
Background Interferon-α (IFN) induces complete cytogenetic remission (CCR) in 20–25% CML patients and in a small minority of patients; CCR persists after IFN is stopped. IFN induces CCR in part by increasing cytotoxic T lymphocytes (CTL) specific for PR1, the HLA-A2-restricted 9-mer peptide from proteinase 3 and neutrophil elastase, but it is unknown how CCR persists after IFN is stopped. Principal Findings We reasoned that PR1-CTL persist and mediate CML-specific immunity in patients that maintain CCR after IFN withdrawal. We found that PR1-CTL were increased in peripheral blood of 7/7 HLA-A2+ patients during unmaintained CCR from 3 to 88 months after IFN withdrawal, as compared to no detectable PR1-CTL in 2/2 IFN-treated CML patients not in CCR. Unprimed PR1-CTL secreted IFNγ and were predominantly CD45RA±CD28+CCR7+CD57-, consistent with functional naïve and central memory (CM) T cells. Similarly, following stimulation, proliferation occurred predominantly in CM PR1-CTL, consistent with long-term immunity sustained by self-renewing CM T cells. PR1-CTL were functionally anergic in one patient 6 months prior to cytogenetic relapse at 26 months after IFN withdrawal, and in three relapsed patients PR1-CTL were undetectable but re-emerged 3–6 months after starting imatinib. Conclusion These data support the hypothesis that IFN elicits CML-specific CM CTL that may contribute to continuous CCR after IFN withdrawal and suggest a role for T cell immune therapy with or without tyrosine kinase inhibitors as a strategy to prolong CR in CML.
Introduction
Since the introduction of interferon-a (IFN) as a treatment for CML [1,2], randomized trials have shown that it is able to induce hematological remission in 70-80% cases and cytogenetic remission in 35-55% cases [3]. IFN also induces complete cytogenetic remission (CCR) in 13% of patients, of which over approximately 50% are durable for 2-8 years [4]. Studies have shown that IFNtreated chronic phase patients in complete or sustained cytogenetic remission (CR) still have significant (1-12%) numbers of BCR-ABL positive cells [5,6,7], identified by fluorescence in situ hybridization (FISH) or by quantitative real time-PCR (QR-PCR) of the BCR-ABL fusion gene [6,8,9]. In patients with blast crisis CML, CFU-BM may be the reservoir of leukemia stem cells (LSCs) [10]. These cells are not likely to be eliminated by traditional chemotherapy or by tyrosine kinase inhibitors [11]. Conversely, after IFN is discontinued, a small number of patients remain in cytogenetic or molecular remission for months to years without treatment [12], suggesting possible elimination of CML, or the persistence of residual undetectable LSC, which may be intrinsically constrained, controlled by indirect mechanisms that suppress growth, or both. Previous studies showed that IFN directly inhibits CML cells [13,14], although this mechanism cannot explain persistence of cytogenetic or molecular remission many years after stopping IFN. As an alternative mechanism, IFN induces specific immunity against CML and T cells specific for leukemia-associated antigens (LAA) can target leukemia progenitors and contribute to CR [15,16]. These observations suggest that long-term leukemiaspecific immunity may prevent future outgrowth of CML or may even eliminate leukemia. It is not possible to predict which patients will continue in CR after stopping IFN, and previous studies have not examined such patients to determine whether immunity to LAA persists after IFN withdrawal.
One such LAA is the HLA-A2-restricted PR1 peptide (VLQELNVTV), which is derived from proteinase 3 (P3) and neutrophil elastase (NE), differentiation stage-specific serine proteinases stored in azurophil granules of polymorphonuclear leukocytes [17]. P3 is over-expressed in a variety of myeloid leukemias, including 75% of CML patients, [18] and may be involved in the process of leukemia transformation or maintenance of the leukemia phenotype [19] via the proteolytic regulation of the cyclin dependent kinase inhibitor p21 waf1 [20]. P3 is expressed in CML progenitors and PR1-specific cytotoxic T lymphocytes (CTL) kill leukemia cells [21] and inhibit HLA-A2 + CML colony forming units in proportion to over-expression of P3 in leukemia cells as compared to normal bone marrow cells [15]. Interestingly, the expression of P3 and NE in CD34+ CML cells correlated with improved clinical outcomes after treatment with allogeneic stem cell transplantation or IFN therapy, potentially due to improved PR1-specific anti-leukemia effects [22,23]. Importantly, PR1-CTL are increased and contribute to CCR in CML patients receiving IFN, but they are not detected in patients at relapse despite continuous treatment with IFN [16]. In addition, PR1-CTL expressing either high or low avidity T cell receptors can be expanded from healthy donor peripheral blood and TCR avidity correlates with CTL effector function [24], similar to T cell immunity to foreign antigens [25,26,27,28]. Importantly, CML cells that overexpress P3 can shape host immunity by inducing apoptotic deletion of high avidity PR1-CTL, which results in loss of functional immunity to CML [24]. Moreover, both P3 target antigen and PR1-CTL were found to be increased in CML patients that continued in CCR while receiving IFN as a single agent maintenance therapy after first achieving CCR during upfront treatment with IFN plus the bcr-abl tyrosine kinase inhibitor imatinib mesylate [29]. This suggests that continuous IFN treatment may maintain CCR by increasing PR1-CTL immunity, although the effect of further IFN withdrawal on persistent PR1-CTL immunity and CCR status is not known.
Therefore, we hypothesized that functional memory PR1-CTL may persist in some patients via long-term self-renewal mechanisms that work to maintain CCR after IFN withdrawal. Here we report that PR1-specific CTL are increased and persist in the peripheral blood of CML patients during unmaintained CCR after stopping IFN and that PR1-CTL secrete IFNc. Although surprisingly enriched in phenotypically naïve cells, the central memory (CM) PR1-CTL subset showed robust antigen-induced proliferation. Our data suggest that IFN may induce the expansion of memory PR1-CTL with self-renewing capacity in a subset of patients in unmaintained CCR, which may be critical for effective long-term immunity to CML, thereby sustaining CCR in CML patients after IFN withdrawal.
Ethics Statement
All donors and patients were enrolled on a study protocol that was reviewed and approved by the University of Texas M. D. Anderson Cancer Center (MDACC) Institutional Review Board and provided written informed consent to participate.
Patients
Seven patients that had an established diagnosis of chronic phase CML based on presence of Philadelphia chromosome, were HLA-A2 + and in complete cytogenetic remission (CCR) that was induced by IFN treatment, and two chronic phase CML patients that did not respond to IFN were studied ( Table 1). Three patients (patients 3, 4, and 6) had a molecular CR documented by absence of BCR-ABL transcripts by quantitative real timepolymerase-chain-reaction (QR-PCR), standardized in the Department of Hematopathology at M. D. Anderson Cancer Center. RNA was analyzed by QR-PCR to quantitate BCR-ABL and ABL transcripts and transcript levels reported on the International Scale (IS). Cytogenetic relapse occurred in 3 patients (patients 2, 6, and 7) after IFN withdrawal, which was treated with imatinib monotherapy, 400 mg daily. Cytogenetic response was assessed by G-banding and counting Ph-positive metaphases in at least 20 cells in metaphase per bone marrow biopsy sample. Median disease duration was 5 years (range of 1 to 7 years). Patients who had previously discontinued IFN treatment, most often due to constitutional side effects, and who remained in CCR, were selected for this study. Prior to study, patients had not received any treatment for CML for 6-88 months.
After informed consent, peripheral blood mononuclear cells (PBMC) from patients were obtained at various time points during their routine clinic follow up, approximately every six months. The cells were separated using Ficoll-Histopaque gradient-density (Organon Teknika Corp., Durham, North Carolina, USA) and subsequently cryopreserved in RPMI-1640 complete medium (CM) (25 mM HEPES buffer, 2 mM L-glutamine, 100 U/ml penicillin, 100 mg/ml streptomycin; Life Technologies Inc.,
HLA Testing
HLA-A2 expression was confirmed with the BB7.2 antibody, which is specific for the HLA-A*0201 allele. One million PBMC were washed 3 times in PBS and stained with the BB7.2 antibody for 25 minutes on ice, washed again and stained with FITClabeled goat-anti-mouse secondary antibody (Becton Dickinson Immunocytometry Systems, San Jose, California, USA) for 30 minutes at 4uC in the dark. Cells were washed twice in PBS and re-suspended in PBS +1% paraformaldehyde (PFA). All samples were acquired within 24 hours of staining, using a FACScan flow cytometer and CellQuest software. At least 25000 events were collected.
Peptide Synthesis
PR1 (aa 169-177) peptide (VLQELNVTV) is derived from the azurophilic granule protein proteinase 3. The cytomegalovirus (CMV) peptide (NLVPMVATV) is derived from pp65, the immunodominant antigen from CMV. Both peptides were synthesized by the MD Anderson Cancer Center Synthetic Antigen Laboratory (Houston, Texas) to a minimum of 95% purity as measured by high performance liquid chromatography.
Tetramer Synthesis
A 15-amino acid substrate peptide (BSP) for BirA-dependent biotinylation has been engineered onto the C-terminus of HLA-A2. The A2-BSP fusion protein and human b 2 -microglobulin (b 2 M) were expressed in Escherichia coli, and were folded in vitro with the specific peptide ligand. The properly folded MHCpeptide complexes were extensively purified using FPLC and anion exchange, and biotinylated on a single lysine within the BSP using the BirA enzyme (Avidity, Denver, Colorado). Tetramers were produced by mixing the biotinylated MHC-peptide complexes with phycoerythrin (PE)-conjugated Neutravidin (Molecular Probes, Eugene, Oregon) at a molar ratio of 4:1. PR1 tetramers were validated by staining PBMC from patients immunized with the PR1 peptide. CMV tetramers were validated by staining PBMC from a CMV-immune subject. The concentration of each tetramer used in this study was individually determined, and was generally 40-50 mg/ml.
Cytokine Flow Cytometry
PBMC were washed 2 times in serum free Ex-Vivo (BioWhittaker, Walkersville, MD) re-suspended at 10610 6 cells/ml in CFC media (Ex-Vivo +5 mg/ml CD28 +2.5 mg/ml CD49d (BD Biosciences, Immunocytometry Systems, San Jose, CA)). PBMC were plated at 1610 6 cells/well and incubated with PR1 (0.2 mM, 2 mM, 20 mM and 200 mM) or pp65-A2 (20 mM) in a total volume of 200 mL/well. Un-stimulated cells were used to set background and Staphylococcal Enterotoxin B (SEB, Sigma-Aldrich, St. Louis, MO), a non-specific T cell activator, was used at 5 mg/ml as a positive control. Plates were incubated at 37uC and 5% CO 2 for 1 hour, Brefeldin A was added to a final concentration of 10 mg/ ml and incubation continued for an additional 5 hours. Cells were then washed once in PBS and incubated in PBS +0.02% EDTA (Sigma-Aldrich, St. Louis, MO) at 37uC and 5% CO 2 for 15 minutes. Cells were washed once with cold PBS and treated with 1x FACS Lysing Solution (BD Biosciences, Immunocytometry Systems, San Jose, CA) for 10 min at room temperature. Cells were centrifuged and treated with 1x FACS Permeabilizing Solution (BD Biosciences, Immunocytometry Systems, San Jose, CA) for 10 min at room temperature. Cells were then washed 2 times in wash buffer (PBS +1% BSA and 0.02% NaN 3 ). Cells were incubated with anti-CD8, anti-IFNc and anti-CD69 (both from BD Biosciences, Immunocytometry Systems, San Jose, CA) for 30 min at 4uC in the dark, washed 2 times in wash buffer and resuspended in PBS +1% PFA. All samples were acquired within 24 hours of staining, using a FACScan flow cytometer and CellQuest software. At least 50,000 events were acquired and gating was done on small lymphocytes and CD8+ cells.
T cell Phenotyping and Sorting
Titrated PR1/HLA-A2 tetramer was used to identify and distinguish between high and low avidity PR1-CTL, defined by a 1-log separation in fluorescence intensity [24]. 1610 6 PBMC were washed 3 times in PBS and surface stained with CD8 as well as CD14, CD16 and CD19 (all from Caltag Labs, Burlingame, CA) at 4uC for 30 minutes. CD14, CD16 and CD19 were used to exclude monocytes, NK cells and B cells from analyses. After washing, the cells were then incubated with 40-50 mg/ml of PElabeled PR1/HLA-A2 tetramer at 4uC for 40 minutes, washed twice in PBS and fixed in 2% PFA. Cells were analyzed on a BD FACScan using CellQuest software. At least 80,000 events were collected.
For higher order multi-parameter phenotyping and cell sorting, the PBMC were thawed at 37uC, washed twice in PBS and resuspended at 10610 6
CFSE Cell Proliferation Assay
Cell proliferation was measured using carboxyfluorescein diacetate succinimidyl ester (CFSE, Molecular Probes, Eugene, OR) labeling. A 10 mg/ml stock solution of CFSE was prepared in dry DMSO (Sigma-Aldrich, St. Louis, MO) and stored at 220uC in 10 mL single-use aliquots. A working solution was prepared by diluting the stock solution with 990 mL dry DMSO. Up to 2.5610 6 PBMC or sorted cells were washed with PBS and re-suspended in 1 ml PBS at room temperature. 15 mL of 10 mg/ml CFSE was added and cells were incubated at 37uC for 8 minutes. Labeling was quenched with FBS. Cells were washed in PBS and incubated in media (Ex-Vivo+10%AB) for 5 minutes at 37uC. Cells were then washed with media, re-suspended in 1 ml media and stimulated with 20 mM PR1, or 50 ng/ml purified anti-CD3 (OKT3, Ortho-Biotech, Bridgewater, NJ) and 1 mg/ml anti-CD28 (BD Biosciences). IL-2 (R&D Systems) was added at 100 IU/ml to each well and un-stimulated cells were used to define CFSE intensity in undivided cells. Cells were incubated at 37uC and 5% CO 2 for 80 hours and analyzed for proliferation by FACS. For analysis of surface phenotype after proliferation, cells were washed once with PBS and incubated with previously titrated amounts of antibodies to CD4, CD14, CD16, CD19, CD8, CD45RA, CCR7 and CD25 for 30 min at 4uC. After washing twice in PBS, cells were stained with 40-50 mg/ml PE-conjugated PR1/HLA-A2tetramer for 40 minutes at 4uC in the dark, washed twice in PBS and fixed in PBS +1% PFA. Cells were acquired on a FACSAria cytometer or a Cyan cytometer. Single stained controls were used to set compensation and the data was analyzed using FlowJo. Cell divisions are measured by successive 2-fold dilution in CFSE intensity and represent a 2-fold increase in frequency. To estimate the frequency of cells that proliferated, the proportion of cells in each CFSE dilution peak was divided by 2 n (where n is the number of the cell division) and summed. To calculate proliferation index or average number of divisions per cell, the frequency of cells that proliferated in each division was normalized to 1, multiplied by the division number and summed.
Statistical Analysis
A paired, two-tailed Student's t-test (95% confidence interval) was used to determine statistical significance of differences between samples. All data are represented as mean 6 standard error.
PR1-CTL persist in IFN-sensitive CML patients after IFN withdrawal
Previously, we have shown that PR1-CTL are found in most patients that achieve remission while being treated with IFN and that these PR1-CTL contribute to remission by killing CML cells [16]. Since 1-3% of patients that achieve remission do not relapse after IFN has been withdrawn, we hypothesized that PR1-CTL may contribute to the maintenance of remission in patients who remain in cytogenetic remission after IFN withdrawal. In order to determine whether PR1-CTL persist in the peripheral blood of Seven patients achieved CCR, then IFN was withdrawn and patients received no maintenance therapy. Three patients subsequently developed cytogenetic relapse (patients 2, 6, and 7) and were treated with imatinib, as summarized in Table 2. Two patients (patients 8 and 9) did not achieve CCR while receiving IFN. Complete molecular remission was noted in patients 3 and 4, and increased circulating PR1-CTL persisted during the study period (12-15 months). Figure 1 shows the clinical status of the seven HLA-A2+ patients over time relative to the date of IFN withdrawal. From available archived samples, PBMC were stained with CD8, CD14, CD16, and CD19 antibodies, followed by staining with the PR1/ HLA-A2 or pp65/HLA-A2 tetramers. All patients in cytogenetic remission (patients 1, 2, 5, and 7) and all patients in molecular remission (patients 3, 4 and 6) had detectable PR1-CTL in their peripheral blood ( Table 2). The frequency of PR1-CTL as a percentage of CD8+ cells relative to time after IFN withdrawal is shown in Figure 2 for each of the seven patients studied. At the time of cytogenetic relapse (patients 2, 6, and 7), the previously detectable PR1-CTL were no longer detectable.
PR1-CTL are functional and secrete IFNc
In order to assess whether the PR1-CTL were functional, when sufficient cells were available for analysis we incubated PBMC in media overnight prior to stimulation with PR1 peptide and measured IFNc production by CD8+ T cells using cytokine flow cytometry in a 6 hour assay. Corresponding to time points after IFN withdrawal for each patient, Table 2 shows the disease burden by BCR-ABL transcripts and the frequencies of PR1-CTL and of CD8+ cells that produce IFNc when stimulated with either PR1 or SEB. Cells were stimulated with 4 different PR1 concentrations and the table shows the highest frequency of cells producing IFNc. All patients that maintained remission (patients 3, 4, and 5) maintained a measurable functional response to PR1 at all times sampled after IFN withdrawal.
PR1-CTL express all T cell differentiation phenotypes
The pattern of lineage differentiation phenotype of antigenspecific T cells has been shown to proceed from CD8 + CD45RA + CCR7 + -. CD8 + CD45RA 2 CCR7 + -. CD8 + CD45RA 2 CCR7 2 -. CD8 + CD45RA + CCR7 2 , though this pattern may in part depend upon the antigen specificity of the CTL. This final stage of maturation has been associated with a non-proliferating terminally differentiated population [30]. However, antigenspecific CD8 + CD45RA 2 CCR7 2 T cells can re-express both CD45RA and CCR7, acquiring either the CD8 + CD45RA + CCR7 + (naïve) or the CD8 + CD45RA 2 CCR7 + (central memory) phenotype upon antigen stimulation, suggesting that there is a memory T cell population that can undergo expansion and ensure continuous replenishment of the effector cell pool. Furthermore, although the phenotype of the population of CD8+ T cells with replication senescence is not well described [31,32], CD57 expression without CD28 or CCR7 expression has been shown to define terminally differentiated T cells [33].
To determine whether all lymphocyte differentiation phenotypes were present, we assessed the surface phenotype of PR1-CTL from all of the patients. Figure 3A shows a representative PR1-CTL phenotype from patient 3, 60 months after IFN treatment was stopped. Shown in Figure 3B and 3C are the percentage of PR1/HLA-A2 tetramer-negative CD8 + lymphocytes (CD8+) and the PR1/HLA-A2 tetramer-positive CD8 + (PR1-CTL) that express central memory (CD45RA-CCR7+CD28+) or naïve (CD45RA+ CCR7+CD28+) phenotypes, averaged from all of the patients. Although there was no significant difference in the percentage of CD8+ lymphocytes and PR1-CTL that expressed the central memory phenotype (p = 0.2981), the PR1-CTL population was enriched for cells expressing the naïve phenotype as compared with the overall CD8+ lymphocyte population (p = 0.0217). Furthermore, none of the PR1-CTL expressed CD57 in any of the patients studied (data not shown). Since PR1-CTL did not express CD57 and were capable of producing IFNc when stimulated with the PR1 peptide, together these data show that PR1-CTL from these patients are not anergic.
PR1-CTL with central memory phenotype retain selfrenewal capacity
A discrete population of PR1-CTL from all patients express a phenotype consistent with memory T cells. We hypothesized that PR1-CTL from IFN-sensitive CML patients consists of a self-renewing memory T cell population. To determine whether PR1-CTL could proliferate, we labeled PBMC from patient 3 with CFSE, added an equal number of cells per well and stimulated them with OKT3, anti-CD28 and IL-2. To assess the surface phenotype of proliferating PR1-CTL, the cells were labeled with the PR1/HLA-A2 tetramer and antibodies to CD8, CD45RA, CCR7 and CD25 after stimulation and analyzed by flow cytometry. Figure 4 shows that in the absence of stimulation, PR1-CTL underwent cell death and could not be identified. However, with CD3/CD28/IL-2 stimulation, PR1-CTL proliferate robustly and the largest fraction of proliferating PR1-CTL express a phenotype consistent with central memory T cells. We compared the phenotype of PR1-CTL before and after stimulation and determined that although prior to stimulation a majority of PR1-CTL expressed a naïve T cell phenotype, after proliferation the PR1-CTL were predominantly central memory T cells (Figure 4).
While the results in Figure 4 support our hypothesis that memory PR1-CTL proliferate, labeling to identify phenotypes after proliferation may have biased the results toward cells that undergo multiple cell divisions or that re-express CCR7. Therefore, to determine whether the central memory PR1-CTL could proliferate, we sorted CD8+ T cells from patient 4 into 4 distinct differentiation phenotypes and measured proliferation of each. CD8+ T cells were sorted into the following populations: CD45RO+CD45RA-CCR7+ (central memory), CD45RO+C-D45RA-CCR7-(effector), CD45RO-CD45RA+CCR7+ (naïve) and CD45RO-CD45RA+CCR7-(terminally differentiated). After sorting, cells were labeled with CFSE and stimulated for 80 hours. Cells were then stained with tetramer and analyzed for proliferation. For each cell population, the fraction of cells that diluted CFSE was analyzed and the frequency of cells that proliferated was calculated. We found that that the frequency of effector and central memory PR1-CTL that proliferated was similar to the frequency of the overall CD8+ that proliferated ( Figure 5A). We also found that a higher frequency of naïve PR1-CTL proliferated as compared with the overall CD8+ population. We next compared the proliferation index or average number of divisions per cell of PR1-CTL with the overall CD8+ cells in each sub-population. Figure 5B also shows that the proliferation indices for the naïve, effector and central memory PR1-CTL were approximately 2.5 whereas the proliferation index for the terminally differentiated PR1-CTL was 4.4. Senescent T cells have been shown to express CD57 [33] and PR1-CTL from IFNsensitive CML patients off all therapy do not express CD57 (data not shown), indicating that PR1-CTL with a terminally differen-tiated phenotype are not senescent. These data also suggest that there is a self-renewing PR1-CTL population that may contribute to maintaining remission in this subset of patients.
Loss of functional PR1-CTL immunity occurs prior to cytogenetic relapse Patient 2 relapsed 26 months after IFN withdrawal. Following relapse, the patient was treated with imatinib ( Table 2). We hypothesized that functional PR1-CTL would be observed prior to relapse that would be selectively lost at relapse. We assessed the persistence of PR1-CTL after IFN withdrawal by staining with PR1/HLA-A2 tetramer and determined whether the PR1-CTL were functional by assessing intra-cellular IFNc production in response to stimulation with the PR1 peptide at varying concentrations. At 15 months after IFN withdrawal, patient 2 remained in CCR and had functional high and low avidity PR1-CTL ( Figure 6) that exhibited a memory phenotype (as shown in Figure 3). Of the CD8 + cells, 0.18% showed IFNc production in response to stimulation with 0.2 mg/ml PR1 peptide. At 21 months after IFN withdrawal, the patient remained in CCR and had 0.19% PR1-CTL. However, PR1-CTL did not produce IFNc in response to stimulation with the PR1 peptide at concentrations varying from 0.2 mg/ml to 200 mg/ml of PR1 (Figure 6), although CD8+ T cell responses to SEB were maintained. This shows a selective loss of functional PR1-CTL. Five months later, at 26 months after IFN withdrawal, patient 2 had a cytogenetic relapse with 35% Ph + cells and PR1-CTL were no longer detectable. Furthermore, PR1-CTL that were increased after stopping IFN were either not detectable or at the lower limit of detection in patients 6 and 7 at time of cytogenetic relapse ( Table 2). Similarly, PR1-CTL were also not functional at the time of cytogenetic relapse ( Table 2). Interestingly, after the three patients were started on imatinib, PR1-CTL once again increased and secreted IFNc. In contrast, PR1-CTL were not detectable in patients 8 and 9 that did not achieve CCR during IFN therapy. Thus, patients who relapse after IFN withdrawal may preferentially loose functional PR1-CTL immunity prior to the decrease in total PR1-CTL.
Discussion
Although direct effects of IFN on CML cells have been described as a mechanism of inducing remission, how this remission is maintained in the absence of continuous IFN treatment in patients with persistent molecular disease remains unknown. We hypothesized that IFN induces a self-renewing population of leukemia-specific CTL that contribute to remission in the absence of continuous therapy. We have previously shown that although the frequency of PR1-CTL in the peripheral blood of healthy HLA-A2+ donors are below the limits of detection by tetramer analysis, they can be expanded in vitro [34]. We have also shown that PR1-CTL are not detectable in newly diagnosed CML patients or in patients that do not respond to treatment with IFN and that PR1-CTL contribute to T cell mediated anti-CML immunity [16]. Furthermore, a recent study by Buchert et al demonstrated that PR1-CTL persist after withdrawal of imatinib in patients that continued to receive maintenance IFN and was associated with long-term CR [29]. In that study, PR1-CTL immunity was not assessed in two patients that achieved complete molecular remission in IFN maintenance. Here, we extend these observations and present three findings that provide evidence that functional memory PR1-CTL contribute to antileukemia immunity after IFN withdrawal. First, we show that PR1-CTL are maintained in IFN-sensitive CML patients for 6 months to 7 years after stopping all treatment. Second, we show that while all lymphocyte differentiation phenotypes are present in PR1-CTL, the CD45RA-CD28+CCR76CD57-PR1-CTL expand preferentially after antigen stimulation, which is consistent with a self-renewing central memory T cell population. Finally, we show that PR1-CTL produce IFNc in response to PR1 peptide stimulation in a recall response. In 3 patients that relapsed, however, PR1-CTL were unable to produce IFNc prior to or concomitant with relapse of CML. Combined with previous studies showing PR1-CTL kill CML and contribute to CR in IFN-sensitive CML patients [16], our current study suggests that PR1-CTL expand during IFN therapy and that IFN may facilitate long-term persistence of PR1-CTL in a subset of CML patients that remain disease-free after all treatment has stopped.
Our data showing the persistence of functional PR1-CTL in patients 1 to 7 after IFN withdrawal extend the results of a previous study that showed measurable PR1-CTL by tetramer staining in 4/4 patients 3 years after cessation of IFN treatment [35]. Memory T cells are characterized by expression of the CD45RO isoform [36], but a fraction of memory cells express CD45RA [37,38]. Previous studies with antigen-specific T cells for the CMV-derived pp65-A2 peptide have shown that upon stimulation, cell division is predominantly restricted to the CD8+CCR7+ population and that CD8+CCR7+ cells are also capable of secreting IFNc upon antigen-specific stimulation, consistent with our findings. Furthermore, CD45RA-CCR7-CD8+ T cells that are at a more advanced stage of differentiation, though not senescent, can re-acquire CCR7 and/or CD45RA upon antigen-specific stimulation [30]. In our study, all patients that remained in remission had PR1-CTL that were enriched for CD8+CD45RA+CCR7+, with a smaller fraction that were CD8+CD45RA-CCR7+. In order to determine whether PR1-CTL were capable of dividing, we sorted the cells to .95% purity and stimulated them in a proliferation assay. Consistent with the characteristics of memory T cells, we found that the effector and central memory PR1-CTL proliferated robustly, supporting our hypothesis that IFN treatment induces a self-renewing population of leukemia-specific CTL that may help to maintain anti-leukemia immunity and remission in the absence of ongoing treatment. Interestingly, we found that a higher frequency of PR1-CTL with a naïve T cell phenotype proliferated as compared with the overall CD8+ cells while the average number of divisions undergone was similar to that of memory PR1-CTL ( Figure 5). Although CD45RA+CCR7+ CTL includes naïve T cells, the high frequency of PR1-CTL in the peripheral blood of IFN-sensitive patients, the production of IFNc in a recall response and the ability to proliferate strongly suggests that this may be a re-expanded population rather than an antigen-inexperienced one. Future studies should discern whether these phenotypically naïve cells are recent thymic emigrants or whether they come from self-renewing central memory stem cells [39] that re-express CD45RA. Since PR1 is a self-antigen derived from P3 and NE, proteins that are expressed in myeloid cells, the antigen required for maintaining this population may come from normal myeloid cells. Alternatively, PR1 might derive from residual small fraction of leukemia cells that over-express P3 or NE, or it is possible that the level of PR1 expression on non-leukemia cells is sufficient to maintain a PR1-CTL population.
The loss of function of PR1-CTL prior to relapse ( Figure 6) suggests that broader anti-CML immunity might be lost prior to disease relapse. We were unable to address whether loss of function is a broad-based phenomenon that applies to other LAAs or whether this is restricted to the PR1 peptide antigen due to limited availability of patient material. However, we observed that at each time-point studied, CD8+ T cells produced IFNc in response to stimulation with SEB, suggesting that loss of function is antigen-specific and might therefore apply to immunity against other LAAs. Following relapse, patients 2, 6, and 7 were treated with imatinib, achieved CCR, and PR1-CTL again increased. There are contradictory reports on the effect of imatinib on T cell function and although many investigators have described the immunosuppressive effects of imatinib on both T cells and dendritic cells [40,41,42], other reports indicate that imatinib does not induce apoptosis of T cells and that its inhibitory effects on T cell proliferation and function are reversible after removal of the drug [43,44]. While enhanced IFNc production by T cells has also been reported in IFN-refractory CML patients treated with imatinib [45], a more recent study showed distinct immunological status of patients treated with imatinib and IFN [46]. In our study, we observed that following treatment with imatinib, both PR1-CTL and pp65-specific CTL from patients 2, 6, and 7 produced IFNc when stimulated. Since the cells were studied in vitro, we cannot rule out inhibitory effects of imatinib in vivo. However, our data are consistent with studies showing that imatinib does not induce T cell apoptosis [43] and that treatment with IFN after cessation of imatinib is associated with an induction of PR1-CTL [29].
Although the data from this study shows that functional immunity to PR1 persists during unmaintained remission of CML, it does not demonstrate that PR1-CTL immunity is the sole driver of sustained molecular remission. Because of limited availability of patient material, we did not study immunity to other known CML antigens such as WT1 or the BCR-ABL fusion region peptides, but we would expect that CTL immunity against these and perhaps other leukemia antigens should be similarly increased. Furthermore, because we did not demonstrate that PR1-CTL were directly cytotoxic against CML cells or against LSC in particular, it is possible that the observed changes in the number of PR1-CTL may merely reflect changes in the leukemia burden. For instance, we have shown that CML shapes host immunity by preferential elimination of high avidity PR1-CTL, Figure 6. Loss of functional PR1-CTL precedes cytogenetic relapse in patient 2. Staining with the PR1/HLA-A2 tetramer was done to determine the percentage of high-and low-avidity PR1-CTL as described in the methods. In the top panel, PR1-CTL are defined as CD8+ Lin-PR1/ HLA-A2-tetramer+. The center panel shows the percentage of CD8+ lymphocytes that produce IFNc in response to stimulation with 0.2 mM PR1 peptide after subtracting the percentage of CD8+ cells that produced IFNc in the absence of stimulation. Non-stimulated cells were used to adjust background. Stimulation with SEB superantigen was used to confirm that the lymphocytes were not anergic. The lower panel shows the frequency of CD8+ cells that produce IFNc in response to SEB stimulation. doi:10.1371/journal.pone.0011770.g006 which are more cytolytic against CML than low avidity PR1-CTL, and the data in Figure 6 support these previous observations [24].
In summary, our data provide evidence that PR1 leukemia antigen-specific CTL-mediated anti-tumor immunity after IFN withdrawal may contribute to continued CR in the absence of subsequent treatment, including withdrawal of IFN. This study extends the observations of Buchert et al and suggests that longterm persistence of tumor-reactive CTL may be necessary to control the outgrowth of residual leukemia cells and prevent relapse. Subsequent prospective studies should examine whether loss of functional PR1-CTL immunity predicts relapse. Strategies to boost immunity by vaccination with known leukemia-antigens in combination with IFN or strategies for IFN maintenance therapy after imatinib treatment, as suggested by the results of a prospective clinical trial in patients treated with imatinib and IFN [29], might improve responses in patients. | 7,504 | 2010-07-26T00:00:00.000 | [
"Biology",
"Medicine"
] |
CONCEPTUAL FRAMEWORK FOR INTEGRATING BIM AND AUGMENTED REALITY IN CONSTRUCTION MANAGEMENT
. The need for systematic data collection and processing to generate real-time building site progress information is critical. Building Information Modelling (BIM) provides the benefit of aggregating information about the building site on a single platform. Augmented reality (AR) emerges to enhance BIM concerning visualization of the building site, through processing and automatic absorption of information. This work aims to analyse the potential of AR association to BIM, by adopting an approach based on literature review. Trends in contemporary research are checked by categorizing applied research methods, areas of expertise, and AR technologies. Publications produced between 2008 and 2018 from journals of architecture, engineering, and construction areas in databases Web of Science, SciSearch, SCOPUS, INSPEC, Google Scholar, Academic OneFile, EBSCO, OCLC, VINITI, SCImago, and ProQuest were investigated. As main results, it was found that the case study approach was adopted in 41% of the publications analysed. The building site inspection was the research object in 48% of papers. Fiducial markers, GIS/GPS, laser scanners, and photogrammetry emerged as main options for automatic data capture on the progress of the building site. Integration between AR and BIM has the potential to solve information processing problems and improving construction management
Introduction
Current construction industry's projects are increasingly complex and challenging to control. Solutions have been developed based on the use of information and communication technologies. BIM was created by aiming to produce technology to absorb the information within a 3D visualization environment (Grazina, 2013). Through BIM, project's stakeholders can work collaboratively and simultaneously, minimizing the risk of incompatibilities. At any stage of the project, BIM systems insert, extract, update, or modify the information contained in models (Martins, 2014).
BIM has proven to support various life-cycle activities and assist managers in decision making. Despite the increasing use of BIM in recent years, most existing buildings do not have complete information within the model, due to changes in the construction phase. Incomplete or even incorrect information in the records are the main reasons for the low level of efficiency in construction management. A high precision model should be created, inserting all information being carried out in the construction stage, to generate a representation of the reality about what was executed in the construction site (Hamledari, Azar, & Mccabe, 2018). Opposing this need, BIM has been restricted to a representation and simulation tool, presenting obstacles to interact with the vast amount of parameterized data and the gap between the plan and the executed project (Wang, Kim, Love, & Kang, 2013a;Hamledari et al., 2018).
Integration between BIM and augmented reality (AR) can work as a reliable tool for coordination and communication. Visualization of the construction, associated with the planned model, can improve the identification, processing, and communication of progress discrepancies in the construction site (Golparvar-Fard, Peña-Mora, & Savarese, 2009b;Shin & Duston, 2010;Park, Lee, Kwon, & Wang, 2013;Kwon, Park, & Lim, 2014). Although this visualization may theoretically be the primary means of communicating BIM content in-place in 3D, there is cur-rently no complete understanding of how this can impact the building and performance of industry professionals (Chalhoub & Ayer, 2018).
Fast development of information technology provided an opportunity to adopt AR in various industrial sectors. AR concept was well described by Azuma (1997), who defined it as the linkage between real and virtual elements, in any degree of complexity, increasing both individual and collective visual perception of graphics information. According to Grazina (2013), AR is a technology that allows overlapping information and computer-generated graphics to real-world images. AR makes possible to combine a real environment with computer-generated information, developing a space in which generated computational elements are superimposed on user's real field of vision (C. Kirner, Zorzal, & T. G. Kirner, 2006;Wang et al., 2013a).
According to Fernandes (2012), a virtual window such as a computer screen, mobile phone or a head-mounted display (HMD) can produce an AR experiment. It is necessary to select the type of tracking that will allow associating images with digital information to integrate the models. The process comprises identifying the user's point of view, position, and orientation, about the elements in the space that have associated digital information. Position and orientation sensors such as laser scanners, GPS, gyroscopes or accelerometers (Golparvar-Fard, Bohn, Teizer, Savarese, & Peña-Mora, 2011a;Behzad & Kamat, 2007;Irizarry, Gheisari, Williams, & Roper, 2014;Williams, Gheisari, Chen, & Irizarry, 2015) and visual markers, also known as fiducial markers (Kiziltas, Akinci, Ergen, & Tang, 2008), which are identified by a combination of digital cameras and computer vision algorithms can achieve the tracking process. Each technique has advantages, regarding costs, practicality, and maintainability. On the other hand, the limitations are related to low resolution, precision, and computational cost (Fernandes, Cunha, Lopes, & Mota, 2011). Peres, Scheer, Faria, and Vian (2015) emphasize the need for some devices to use AR as a tracker, virtual information (text, image, and video), and video camera. Moreover, they suggest the use of a Global Positioning System (GPS) as an alternative to the visual markers. AR system was initially used in scientific visualization applications, as described by Caudell and Mizell (1992). Furthermore, it was applied in aircraft parts assembling, power plants inspection (Klinker, Stricker, & Reiners, 2001), medicine (Vogt, Khamene, & Frank, 2006), entertainment games (Fan & Liu, 2011;Juan, Llop, Abad, & Lluch, 2010), and construction of building pipes (Hou, Wang, & Truijens, 2015).
Keen interest has emerged to introduce AR in architectural, engineering, and construction sectors (AEC) by improving conventional methods of project visualization, monitoring, and control of activities (Wang & Love, 2012;Chi, Kang, & Wang, 2013;Wang et al., 2013a). To reinforce this idea, Rankohi and Waugh (2013) argue that the complex nature of AEC provides a significant demand for information to evaluate, communicate, and monitor the progress of construction site operations. Chi, Chen, Kang, and Hsieh (2012) point out main shortcomings identified in AEC can be detected in the lack of information regarding work on construction site, gaps between planning and execution, and interaction among project's actors. The authors argue that AR can contribute to solving these difficulties, becoming a vital ally to the development of the construction industry.
AR was initially studied by Sutherland (1968), who proposed a display using a device connected to a computer put on observer's head, denominated HMD. After, a joint effort conducted by the United States Air Force, the Massachusetts Institute of Technology (MIT), and NASA Research Centre has taken over development of AR. Subsequently, Boeing Corporations' scientists Caudell and Mizell (1992) developed an experimental AR system to aid assembly work. Loomis, Golledge, and Klatzky (1993) created a GPS-based AR system with navigation assistance to visually impaired people with spatial audio overlay. Mobile devices and the advancement of computing technologies have leveraged development and popularity of AR (Starner et al., 1997;Kopsida & Brilakis, 2016). In this sense, Feiner, Macintyre, Höllerer, and Webster (1997), Azuma (1997), and Azuma et al. (2001) developed prototypes for the mobile AR system, reporting advances and constraints. Klinker et al. (2001) developed an AR system for power plants inspection. Friedrich and Wohlgemuth (2002) presented another application of AR to assist in solving electrical problems in Ford vehicles assembling process. Wang and Dunston (2007) have used AR in a training platform for equipment's operators of the heavy construction industry. Golparvar-Fard et al. (2009b) applied the model using a localization system with overlapping photographic images of the environment, generating information from the context of the construction site, helping to monitor its progress. Behzadan, Timm, and Kamat (2008) and Hakkarainen, Woodward, and Rainio (2010) applied the GPS to automatically capture the data regarding the operations performed on the construction site. As a result, they presented a framework that could be reused by any AR application. Golparvar-Fard, Peña-Mora, and Savarese (2011b) produced a conceptual platform to promote integration between BIM 4D and AR for monitoring and to plan the activities of a building. Yeh, Tsai, and Kang (2012) applied a location-based information technology called iHelmet for services performed at the construction site in order to improve the efficiency of the transfer of information on the progress of the work through real-time visualization. Wang et al. (2013a) used AR to control the progress of a gas installation project. Jiao, Zhang, Li, Wang, and Yang (2013) developed an application that integrated AR and BIM, showing the use of construction 3D objects information modelling embedded in an augmented reality online environment. The contribution was to use an open platform, called web3D, where on-premises images are processed and registered with virtual objects. Grazina (2013) described a theoretical concept of an integration platform for the BIM and AR technologies using GPS as the tracking method. They found as results a database generated by the information extracted from the integration of BIM and AR, feeding back the planning. Irizarry et al. (2014) studied the application of AR as a tool to support the activities of building pipe installations, through the position and orientation of the observer. The authors developed a system called In-foSpot (Information Surveyed Point for Observation and Tracking), which uses tracking sensors such as gyroscope, accelerometer, and GPS embedded in a mobile data collection device. They concluded the solution is affordable and helps facility managers in their routine tasks, since the display space may be accompanied by information on projects in a single interface. H. S. Kim, S. K. Kim, Borrmann, and Kang (2017) proposed an AR-based 4D CAD system developed to reflect real-time construction site information and provide a practical simulation of 4D and 5D models. The system manages the schedule of activities through the images extracted in real-time, using web cameras installed in the construction site. Li, Yi, Chi, Wang, and Chan (2018) investigated the state-of-the-art application of virtual reality and augmented reality technologies considering safety in the productive processes carried out in the construction industry.
Considering the previous scenario, answers to the following research question must be sought: how can BIM be integrated with AR technology? The ideal solution to this problem would involve an experimental investigation considering the development of a system integrating BIM and AR technologies. However, before this critical step of the study, appropriating the results of the research carried out should be done, in order to verify what already a conceptual basis on the subject has, which, in the view of the authors of this work, represents a complex research problem. As formerly discussed, various proposals integrating AR technologies to BIM have been developed, but there is not a comprehensive study associating technological options to specific situations. It is in this research opportunity that this work has been inserted. Therefore, to fill the knowledge gap in the area, this work aims to carry out a systematic literature review to identify the potential of integrating AR to BIM, especially concerning the adopted AR technology and the purpose of its application. The work contributes to state of the art by getting involved with the improvement of the visualization of the activities carried out in the construction site in real time.
The paper is organized as follows. Section 1 presents the methodology used to collect and analyze publications for the review. Section 2 offers an analysis of the potentialities of integration between augmented reality and BIM, through the discussion of the research carried out in the area. In Section 3, trends related to traceability technologies are discussed. Finally, a conclusion about the study is presented.
Research methodology
In this work, a systematic literature review produced between 2008 and 2018 concerning AR applications in the construction industry related to construction management was carried out. The research approach adopted was based on the proposal presented by Rankohi and Waugh (2013), as shown in Figure 1.
Initially, indexed journals were selected in the major databases of research in the AEC area, such as Web of Science, SciSearch, SCOPUS, INSPEC, Google Scholar, Academic OneFile, EBSCO, OCLC, VINITI, SCImago, and ProQuest. Articles were selected considering the existence in their titles and summaries of the keywords combinations "Building Information Modelling" and "Augmented Reality" and "BIM" and "Augmented Reality".
Thirteen journals were selected in the initial stage of the research, as presented in Table 1.
The definition of categories was extracted from the 64 articles found in the search within the selected journals in the databases. Publications were considered once when they appeared in more than one database. Each article was classified according to the periodical it was published, the publisher, the first author of the work and his / her country of residence.
Considering the aspects related to integration between BIM and AR, four criteria were selected to review the literature: the research approaches, areas of activities, technologies used to integrate BIM to AR, and trends in the application of AR technologies. The reviewed articles were analyzed according to these characteristics in order to build a framework to provide future directions in the development of integration between BIM and AR.
A quantitative and qualitative analysis of the articles was carried out, classifying them in relation to the quantity of annual publications, publications by journals, publications by country of residence of the first author, publications by the first author, publications by research methodology employed, publications by area of activity, and publications by technologies used in AR tracking. The annual publication history was analysed considering each one of the 13 journals selected in the study. Next, the trend of production of articles considering the theme associating BIM with AR in construction management was verified. The following analysis involved the identification of countries where there was significant interest in the subject studied in this article, considering the first author's country of residence. After, an analysis was made in search of the author who had appeared most in publications associating BIM and AR. In the analysis of the work concerning the research methodology, based on Rankohi and Waugh (2013), four approaches were considered: case study, experimental/empirical study, proof of concept (proof of principle study), and literature review. A case study is a research method in which the researcher passively analyses (without making interventions) a contemporary phenomenon over a period. Experimentation is an empirical scientific method in which the researcher arbitrates between competing models or hypotheses. A proof of concept has a research approach in which an assumption method or model is put to demonstrate its feasibility or to check whether a concept, theory or prototype has the potential to be used. The literature review is based on research using a method that considers the critical points in the knowledge chain, including substantive results, as well as methodological contributions to a given topic (Cohen, Manion, & Morrison, 2007;Rankohi & Waugh, 2013).
Another analysis involved the investigation of the technologies, purposes, and areas related to the association between BIM and AR in the construction industry. AEC industry has many areas of research. In this research, inspection of activities in the construction site, verification of the execution of the operations in the construction site in comparison to project (Chi et al., 2013), and building maintenance were considered (Jiao et al., 2013;Olbrich et al., 2013;Nagy, 2013). Also, it was investigated technological development in the process of data capture in virtual environments of the model integrating BIM and AR. For this analysis, the main existing and developing technologies in the area were analysed.
Preliminary analyses
The first analysis carried out the history of publications in the 13 journals previously presented in Table 1. Results obtained are shown in Table 2.
Among the journals selected in this research, we highlight Automation in Construction, with 25 publications, corresponding to 34.4% of the articles found. The second journal with the highest production was the Journal of Computing in Civil Engineering, with nine publications, corresponding to 14.7% of the total. The Journal of Infor- mation Technology in Construction -ITcon appeared in third place, with seven publications, accounting for 11.4% of the total. The first three journals that appear in Table 2, present 64% of the articles. These journals have a policy of publication of topics that aggregate research applying information technology within the AEC sector, becoming a reference to the researchers. When analyzing data in Table 2, it is interesting to note that, considering the publications of all journals together, there is an exceptional volume of scientific productions (16 articles) in 2013, the year in which journal Automation in Construction focused on the study of AR in architecture, engineering, and construction, through a special edition in the issue. In 2014, the nine publications appeared to have been influenced by the 2013 call. In the remaining years, excluding 2017 and 2018 (which will be discussed below), there were not a substantial number of publications, ranging from a minimum of 2 to a maximum of 5 (in 2011), indicating little repercussion of actions in the construction sites related to AR. In 2017 (with eight publications) and 2018 (with nine publications), however, there seems to be a clear trend towards improvement efforts within the construction industry for the use of AR.
The subsequent preliminary analysis involved the identification of countries where there was an interest in the subject AR. This study was based on the consideration of the first author's country of residence. USA researchers ranked first in 34% of journals, followed by authors from Australia with 25% and South Korea with 10%. China and Canada have lead authors with the same number of publications, representing 5% of the total.
The following analysis sought to determine the author who had most published on the topic BIM associated with AR. Xiangyu Wang appeared in first place, with 11% of articles published. It was found that Wang's publications were distributed from 2008 to 2015. Among his various works, X. Wang prospectively investigated the applica-tion of AR in the architecture and design sectors in 2009 (Wang, 2009). In this research, after analyzing the various existing technological options, the author argued that an adequate system of AR should work on any industrial environment without the need to study it before. Also, the AR system should function properly in open or closed environments. When analyzing the evolution of the AR, it is verified that the challenges launched a decade ago by Wang were overcome and became a reality. In 2013, X. Wang published a review on AR in the built environment, classifying and demonstrating implications for future research (Wang et al., 2013a). In the same year, X. Wang and other researchers presented a proposal for a conceptual framework to integrate BIM with AR (J. Wang, X. Wang, Shou, & Xu, 2013b). Wang and his research group have suggested that AR should operate in conjunction with tracking and detection technologies such as RFID, sensors, and motion tracking. In 2014, X. Wang and his team created an integration of BIM with AR, presenting a model that solved real problems of the oil and gas industry, such as low productivity in information retrieval, the tendency to make mistakes in building system and low communication efficiency (Wang et al., 2014a). In the same year, X. Wang and his research team developed a collaborative project, integrating AR technologies and telepresence (X.
Researches approaches
Among the 64 articles analysed, 42% adopted a proof-ofconcept approach, 32% carried out bibliographic reviews, 14% performed case studies, and 13% conducted experimental work, as shown in Figure 2. A proof of concept approach is used to indicate a practical model that can prove the concept established by a research. It may also be considered a generally summarized or incomplete implementation of a method or an idea carried out to verify that the concept or theory in question is capable of being exploited in a useful way. As highlights of the use of the proof of concept approach, Wang et al. (2014b) and Park et al. (2013) obtained a visualization of parts of the project using fiducial markers. Grazina (2013) and Martins (2014) produced a conceptual platform getting updates on progress information. In research using case studies, Clemente and Cachadinha (2012) addressed the visualization of AR in updates of information regarding the progress of the building site. Kim et al. (2017) examined the construction of a bridge over the AR performing simulations. In the research using the experimental approach, Chu, Matthews, and Love (2018), Shin, Park, Woo, and Jang (2013) and Shin and Dunston (2010) demonstrated detection of defects using AR was significant, improving the perception about occurrences of this nature. In studies employing literature review, Rankohi and Waugh (2013) found in 133 articles a strong tendency of application of AR in the construction industry. Irizarry et al. (2014), Wang et al. (2013a), and Leite et al. (2016) found visualization for the construction helps planning, operation, and maintenance of work. Proof-of-concept method contributes to the consolidation of innovative aspects of AR that still need to be evolved, explaining the high percentage of publications in the area using this scientific approach. The reduced pres-ence of case studies in the analyzed publications can be explained by the reduced amount of study objects in the maturity stage that allows the application of this research approach. Although it represents small participation in the sample of articles analyzed in this work, the experimental approach seems to be the most adequate at a time when the creation of an integrating model between BIM and AR is still in development. Figure 2 supports some considerations regarding investigations on research theme adopted in this work. It was verified that 42% of the researches took proof of concept approach. These investigations involved studies with prototypes, allowing to test the potential for integration between BIM and AR technologies in the construction industry. Experimental approach represented only 12% of the investigations conducted, indicating there is a need for further practical researches, which coexists with intense technological innovation requiring field discoveries and validations.
Areas of activities
Another analysis was directed to the identification of the areas of activities in which AR applications were applied in construction management. Table 3 presents the result of this study. Behzadan and Kamat (2007), Golparvar-Fard et al. Fazel and Izadi (2018), and Chalhoub and Ayer (2018) conducted a large number of applications of AR to assist in the execution and detection of information on the progress of activities in the building site. Nagy (2013) researched the implementation of AR for a building maintenance system. Hou et al. (2015) applied AR to the assembly of a piping system, achieving an improvement in productivity and professional performance and reducing the cognitive workload. For the infrastructure area, Behzadan, Dong, and Kamat (2015) worked in trench excavations for infrastructure facilities, providing, through the application of AR, higher reliability of the location for the operation. Figure 3 shows the percentage of shares of the identified research areas.
As presented in Figure 3, the inspection area is the most significant occurrence (with 64% of the research done). Maintenance appears with 11% of the researches, installation of building systems appears with 11%, and infrastructure area represents 12%.
Technologies used to integrate BIM with AR
In addition to previous studies, a particularly important aspect in the study of AR interaction to BIM involves understanding the technological development in the area for automatic data capture in a virtual environment. These technologies provide user's interaction, producing experiences in immersive environments, desktops, mobile devices, stationary environmental scanning equipment, HMD devices, and glasses (Rankohi & Waugh, 2013). Technologies studied in the analysed articles are presented in Table 4. In general, when assessing a tracking technology for the tasks that users can perform, consideration should be given to the workload, the desired range of motion, accuracy, and precision required, and the likelihood of crawler occlusion (Dunston & Wang, 2011). For Meža et al. (2014), the primary technical challenge of AR systems not using landmarks as visual markers is to determine the user's position in space accurately.
Photogrammetry is one of the most popular tools for acquiring three-dimensional data (3D) and providing a digital surface model (Liarokapis, 2007). This technology can provide simultaneous real-time positioning information over multiple entities, as well as self-calibrate and minimize positioning errors when multiple cameras are installed (Zhou, Duh, & Billinghurst, 2008). Development of this technology has become popular due to the practicality of its acquisition, low cost, and easy manipulation (El-Omari & Moselhi, 2008;Golparvar-Fard et al., 2009b;Bae et al., 2013;Barazzetti et al., 2015). However, for Golparvar-Fard et al. (2011a), automating the detection of the progress of a productive operation from the images of a site is a challenge, as there are limitations on its use at the construction site. These limitations occur due to climatic changes, affecting lighting and making it difficult for the camera to capture the image and automatically overlay the projected with the actual.
Lasers scanners are promising to automate data collection. However, they are still expensive, and implementation of this technology is a challenge at the construction sites. Limitations such as movements in the field of view of the scanner impair the continuity of the capture of spatial information. Also, the level of detail within the captured components is reduced, requiring regular calibrations as well as a warm-up time to start capturing the cloud of points. Other factors are associated with the difficulty of transporting the equipment within the construction site, the impossibility of using it in closed environments, delay in data processing and difficulty of generating semantic information that can associate scanned points with the respective structural components. Therefore, the laser scanner is still an error-prone system (Golparvar-Fard et al., 2009b).
Fiducial markers can be easily implemented in construction sites (Le et al., 2015;Zhou et al., 2017). However, the use of markers within the construction site becomes difficult because the recognition rate is reduced under sunlight. In this sense, improving the recognition of markers through image pre-processing should be considered (Kwon et al., 2014). Also, the calibration of the camera must be precise, to obtain the alignment in the images (Fazel & Izadi, 2018). Figure 4 presents a comparative analysis of the most used tracking technologies: Table 4 and Figure 4 present the technologies applied in the investigations conducted between 2008 and 2018. GPS / GIS mapping represents the highest percentage of use, with 46% of applications, followed by fiducial markers, with 40% photogrammetry, with 10% and laser scanner, with 5%. Significant amounts in the utilization of GIS / GPS and fiducial markers derive from their low costs, as well as the functionality and practical process of implementation compared to the other technologies.
Considerations of some trends related to AR technologies
With the improvement of positioning systems, AR devices are not required to rely on markers to know where to locate the virtual element. Through the real environment elements analysis, the new systems can identify relationships between the camera and the real-world.
Advancement of tracking and detection technology relies on research and development efforts from industrial and academic areas. In addition to accurate and longrange tracking, it is essential for AR systems to have high quality and real-time rendering (Wang et al., 2014b).
Google's online services and software company introduced its Google Glass project in 2013 in the form of a head-mounted display device (HMD), which allows the user to interact with the real world. Continuing this evolution, in 2016, DAQRI and Autodesk developed an HMD associated with virtual reality, with the goal of transforming the productive processes in the construction industry. In 2017, the Microsoft organization launched the Hololens (updated in 2019 to Hololens 2), which is an AR glasses, expanding its operations to the construction industry, by having an integrated system and sensors more potent than the existing mobile devices. HMD based technology moves beyond simple viewing displays, including more sophisticated environmental inputs, capturing spatial sounds, and location elements. Improvement of human and environmental understanding, functionality, ergonomics, and connectivity with information processing technologies means new HMD technologies surpass alternatives discussed in this paper emerged in the last decade.
There is a trend directing the technological development aimed at modelling of AR based on the advance of sensors embedded in mobile devices and software development packages, called SDKs, that allow the structuring of 3D models and position them in environments without the need for markers. This technology identifies critical points in the environment and tracks their movements by combining this information with additional information from the equipment's sensors to determine the position and orientation of the device as it moves through the environment.
AR technology is suffering a fast evolution, and there is not a consensual understanding of what device will be the most prominent. Challenge for future research lies in the development of accessible and practical devices in the implantation process within the construction site, generating a higher precision and occlusion activity inspection at the construction site.
Conclusions
This research found a high potential for applicability of integrated AR to BIM modelling to assist in operations inspection, building maintenance, infrastructure, and instal- 4% % lation assemblies. This integration allows interacting with the user intuitively, generating immersion within the BIM models, to reduce the response time concerning possible solutions for re-fitting activities at the construction site. The most intuitive visualization platform seeks to quickly update the planning of the construction through information generated in the building site (as-built). The potential use of the integration between BIM and AR increases, due to the evolution in the performance of portable computers, mobile devices, and other solutions of visualization devices in a virtual environment. In this sense, it is verified that academic investigations are focused on the development of structures to perform the inspection in the constructions for data capture.
Techniques of tracking images such as visual (fiducial) markers and use of positioning sensors for information and guidance, such as GPS has stood out. Much of this dissemination is due to low cost and ease of use and deployment of these technologies. However, there are still difficulties in obtaining more significant results using AR, associated with limitations of precision, calibration, and occlusion. Investments to popularize AR have been improving the performance of smartphones, mobile computers, and devices using external sensors and embedded accelerometers and gyroscopes.
As a recommendation for future work, it is essential to investigate how AR impacts the performance of work, in terms of its quality, speed of execution, reduction of losses and increase of productivity of the workforce. Also, an integrative model between BIM and augmented reality must be developed in order to validate the promising implementation of the integration. | 7,056 | 2020-01-22T00:00:00.000 | [
"Engineering",
"Computer Science"
] |
Metastable Markov chains: from the convergence of the trace to the convergence of the finite-dimensional distributions
We consider continuous-time Markov chains which display a family of wells at the same depth. We provide sufficient conditions which entail the convergence of the finite-dimensional distributions of the order parameter to the ones of a finite state Markov chain. We also show that the state of the process can be represented as a time-dependent convex combination of metastable states, each of which is supported on one well.
INTRODUCTION
Several different methods to prove the metastable behavior of Markov chains have been proposed in the last years [37,10,15,16,19,9,18]. Among them, the martingale approach [2,5,6] provides tools, based on the uniqueness of solutions of martingale problems, to derive the convergence of the trace of a reduced model. The two main results of this article state that with slightly stronger assumptions one can prove the convergence of the finite-dimensional distributions of the reduced model and express the state of the process as a time-dependent convex combination of metastable states.
Consider a sequence of finite sets (E N : N ≥ 1). The elements of E N are called configurations and are denoted by the Greek letters η, ξ, ζ. Let {η N (t) : t ≥ 0} be a continuous-time, E N -valued, irreducible Markov chain.
Trace process. Let η(t) be a process on a state space X. Fix a proper subset A of X. The trace of the process η(t) on the set A, denoted by η A (t), is the process obtained from η(t) by stopping its evolution when it leaves the set A and by restarting it when it returns to the set A. More precisely, denote by T A (t) the total time spent at A before time t: where 1{B} represents the indicator of the set B. Note that the function T A is piecewise differentiable and that its derivative takes only the values 1 and 0. It is equal to 1 when the process is in A and it is equal to 0 when it is not. Let S A (t) be the generalized inverse of T A (t): S A (t) = sup{s ≥ 0 : T A (s) ≤ t} .
The trace process is defined as η A (t) = η(S A (t)). It is shown in [2, Proposition 6.1] that if η(t) is a continuous-time, irreducible Markov chain, then η A (t) is a continuous-time, A-valued, irreducible Markov chain whose jump rates can be expressed in terms of the probabilities of hitting times of the original chain.
Slow variables. Consider a partition E 1 N , . . . , E n N , ∆ N , n ≥ 2, of the set E N , and let E N = ∪ 1≤x≤n E x N ,Ȇ x N = ∪ y =x E y N , so that (1.1) Here and below we use the notation A⊔B to represent the union of two disjoint sets A, B: A ⊔ B = A ∪ B, and A ∩ B = ∅. The set ∆ N has to be understood as a set which separates the sets E x N that we will refer to as wells. Let φ N : E N → {0, 1, . . . , n}, Ψ N : E N → {1, . . . , n} be the projections defined by Note that φ N (η) = 0 for η ∈ ∆ N , while Ψ N is not defined on the set ∆ N . In general, φ N (η N (t)) is not a Markov chain, but only a hidden Markov chain. We say that φ N is a slow variable if there exists a time-scale (θ N : N ≥ 1) for which the dynamics of φ N (η N (tθ N )) is asymptotically Markovian. This property is made precise in (H1), (H2) below.
Martingale approach. Let (θ N : N ≥ 1) be a time-scale, and denote by ξ N (t) the process η N (t) speeded-up by θ N : ξ N (t) = η N (tθ N ). Let D(R + , E N ) be the space of right-continuous functions ω : R + → E N with left-limits endowed with the Skorohod topology. Let P η = P N η , η ∈ E N , be the probability measure on the path space D(R + , E N ) induced by the Markov chain ξ N (t) starting from η. Expectation with respect to P η is represented by E η .
Let ξ EN (t) be the trace of the process ξ N (t) on E N , and recall that ξ EN (t) is a continuous-time, E N -valued Markov chain. Denote by X N (t), X T N (t) the hidden Markov chains given by X N (t) = φ N (ξ N (t)), X T N (t) = Ψ N (ξ EN (t)), respectively. Note that X N (t) takes values in {0, 1, . . . , n}, while X T N (t) takes values on the set S := {1, . . . , n}. Moreover, X T N (t) is the trace on the set S of the process X N (t).
A theory has been developped recently in [2,5,6] to prove the convergence of the process X N (t) = φ N (ξ N (t)) to a Markov chain. In the terminology introduced above, this corresponds to the identification of the slow variables of the process η N (t) in the time-scale θ N . Under some general assumptions, the approach yields that (H1) The dynamics X T N (t) = Ψ N (ξ EN (t)) is asymptotically Markovian: For all x ∈ S, and sequences η N ∈ E x N , under the measure P ηN , the process X T N (t) converges in the Skorohod topology to a Markov chain denoted by X(t); The first condition asserts that the trace on S of the process X N (t) converges to a Markov chain, while the second one states that the amount of time the process X N (t) spends outside S vanishes as N ↑ ∞, uniformly over initial configurations in E N .
The second condition can be restated as (1.2) Soft topology. It is clear that the convergence of the process X N (t) to X(t) in the Skorohod topology does not follow from conditions (H1) and (H2). Consider, for example, a continuous-time, S-valued Markov chain Y (t), and a sequence δ N > 0, δ N ↓ 0. Fix t 0 > 0, and define the process The sequence of processes Y N (t) fulfills properties (H1) and (H2), but Y N (t) does not converge to Y (t) in the Skorohod topology. Actually, not even the 1-dimensional distributions converge. This example is artificial, but in almost all models in which a metastable behavior has been observed (cf. the examples of Section 4), due to the very short sojourns of X N (t) in 0, the process X N (t) can not converge, in any of the Skorohod topologies, to X(t). To overcome this obstacle a weaker topology has been proposed in [29], called the soft topology, in which the convergence takes place.
Convergence of the finite-dimensional distributions
We propose here an alternative. Instead of searching for a weak topology in which the convergence takes place if conditions (H1) and (H2) are fulfilled, we show that conditions (H1), (H2) together with some further properties entail the convergence of the finite-dimensional distributions of X N (t) to those of X(t). The first main result of the article reads as follows: Proposition 1.1. Beyond (H1) and (H2), suppose that for all x ∈ S, Then, for all x ∈ S, and all sequences {η N : N ≥ 1}, η N ∈ E x N , under P ηN the finite-dimensional distributions of X N (t) converge to the finite-dimensional distributions of the chain X(t).
The proof of this result is presented in Section 2, together with several sufficient conditions for (1.3) to hold.
Metastability.
We have coined properties (H1) and (H2) as the metastable behavior of the Markov chain η N (t) in the time-scale θ N . However, it has been pointed out that in mathematical-physics metastability means the convergence of the state of the process. The second result of this note fills the gap between these two concepts by establishing that properties (H1), (H2) together with conditions (M1), (M2) below lead to the convergence of the state of the process to a convex combination of states supported on the wells E x N . The precise statement of this result requires some notation.
Denote by µ N the unique stationary state of the chain ξ N (t), and by µ y N , y ∈ S, the probability measure µ N conditioned to E y N : Note that µ y N is defined on E N and it is supported on E y N .
Reflected process. For x ∈ S, denote by {ξ N R,x (t) : t ≥ 0} the Markov chain ξ N (t) reflected at E x N . This is the Markov chain obtained from ξ N (t) by forbiding jumps from E x N to its complement (E x N ) c . This mechanism produces a new Markov chain whose state space is E x N , which might be reducible. We assume that for each x ∈ S the reflected chain ξ N R,x (t) is irreducible and that µ x N is its unique stationary state. In the reversible case this latter assumption follows from the irreducibility. In the non-reversible case, if the Markov chain η N (t) is a cycle chain (cf. [20,34]) it is easy to define the sets E x N for the reflected chain on E x N to be irreducible. Let (S R,x N (t) : t ≥ 0), be the semigroup of the Markov chain ξ N R,x (t). Trace process. Similarly, we denote by ξ N T,x (t) the trace on E x N of the process ξ N (t), and by (S T,x N (t) : t ≥ 0) the semigroup of the Markov chain ξ N T,x (t). Mixing times. Denote by µ − ν TV the total variation distance between two probability measures defined on the same denumerable set Ω: where x + = max{x, 0} denotes the positive part of x ∈ R. Hereafter, the set Ω will be either the set E N or one of the wells E )-mixing time of the reflected, trace processes, respectively: where δ η stands for the Dirac measure concentrated on the configuration η.
Hitting times. For a subset A of E N , denote by H A , H + A the hitting time and the time of the first return to A: where τ 1 represents the time of the first jump of the chain ξ N (t): Let (α N : N ≥ 1), (β N : N ≥ 1) be two sequences of positive numbers. The relation α N ≪ β N means that lim N →∞ α N /β N = 0. In the next result, we assume that for each x ∈ S there exists a set B x N ⊂ E x N fulfiling the following conditions: (M1) For every δ > 0 we have (1.10) Condition (M1) requires that, once in E x N , the process reaches the set B x N quickly. Additionally, condition (M2) imposes that it takes longer to leave the set E x N , when starting from B x N , than it takes to mix in E x N . Slightly more precisely, condition (M2) requests the existence of a time scale ε N , longer than the mixing time of the reflected process and shorter than the exit time from the set E x N . Note, however, that in condition (1.10) the initial configuration belongs to the set B x N , while in the definition of the mixing time the initial configuration may be any element of the set E x N . In any case, condition (1.10) is in force if ε N ≫ T N,R,y mix . Assume that the chain is reversible. Fix y ∈ S, denote by p R,y t (ζ, ξ) the transition probabilities of the reflected process ξ N R,y (t), and fix η ∈ B y N . By definition, where f t (ζ) = p R,y t (η, ζ)/µ y N (ζ) and t = ε N . By Schwarz inequality and a decomposition of f t along the eigenfunctions of the generator of the reflected process (cf. equation (12.5) in [35]), the square of the previous expression is bounded by exp{−2λ R,y t} f 0 2 µ y N , where λ R,y represents the spectral gap of ξ N R,y (t) and f 0 µ y N the norm of f 0 in L 2 (µ y N ). Since f 0 2 µ y N = 1/µ y N (η), as t = ε N , we conclude that δ η S R,y N (ε N ) − µ y N TV ≤ 1 µ y N (η) 1/2 e −λR,yεN . Therefore, in the reversible case, condition (1.10) of (M2) is fulfilled provided 12) and that either of the following three conditions (a), There exists a constant 0 < C 0 < ∞, such that for all y, z ∈ S, N ≥ 1, (1.14) Then, for every t > 0, x ∈ S and every sequence {η N : where (S N (t) : t ≥ 0) represents the semigroup of the Markov chain ξ N (t).
The article is organized as follows. Propositions 1.1 and 1.2 are proved in Sections 2, 3, respectively. In Section 4 we show that the assumptions of these propositions are in force for four different classes of dynamics. In the last section, we present a general bound for the probability that a hitting time of some set is smaller than a value in terms of capacities (which can be evaluated by the Dirichlet and the Thomson principles). Throughout this article, c 0 and C 0 are finite positive constants, independent of N , whose values may change from line to line.
CONVERGENCE OF THE FINITE-DIMENSIONAL DISTRIBUTIONS
In this section, we prove Proposition 1.1, and we present some sufficient conditions for (1.3). We will use the shorthand T N (t) for the time T EN (t) spent by the process ξ N (t) in E N before time t. Likewise, we will denote the generalized inverse of T N (t) by S N (t). Note that condition (H2) can be stated as The proof of Proposition 1.1 is based on the next technical result, which provides an estimate for the distribution of the trace process X T N in terms of the distribution of the process X N . Lemma 2.1. Assume conditions (H1) and (H2). Then, for all N ≥ 1, δ > 0, y ∈ S, η ∈ E N , and r > 3δ, for all r > 0, y ∈ S and Proof. Fix N ≥ 1, δ > 0, y ∈ S, η ∈ E N and r > 3δ. By definition of X T N , and since S N (r − 3δ) ≥ r − 3δ, A N (r, δ, y) = X N (s) = y for some r − 3δ ≤ s ≤ r − 2δ , and J (1) On the other hand, Denote by H the first time the process X N (s) hits the point y after r − 3δ: By the strong Markov property, the second term on the right hand side of the penultimate equation is equal to Recall from (1.1) the definition ofȆ y N . The previous probability is bounded by By condition (H1), J N (y, δ) vanishes as N → ∞ and then δ → 0. To complete the proof of the lemma, it remains to set R Denote by P x , x ∈ S, the probability measure on D(R + , S) induced by the Markov chain X(t) starting from x. Since P x [X(t) = X(t−)] = 0 for all t ≥ 0, the finite-dimensional distributions of X T N converge to the ones of X(t). Proof of Proposition 1.1. We prove the result for one-dimensional distributions. The extension to higher order is immediate. Fix x, y ∈ S, r > 0, and a sequence {η N : N ≥ 1}, η N ∈ E x N . By assumption (H1), by Lemma 2.1, and by (1.3), the inequality in the penultimate formula must be an identity for each y ∈ S, which completes the proof of the proposition.
In many examples, however, it is not true that the right hand side vanishes, uniformly over configurations in E N , as N → ∞ and then δ → 0. In condensing zero ranges processes or in random walks in a potential field, starting from certain configuration in a valley E x N , in a time interval [0, δ], the chain ξ N (s) visits many times the set ∆ N and the right hand side of the previous inequality, for such configurations η, is close to 1.
Proof. Fix x ∈ S, η ∈ E x N and s > 0. Multiplying and dividing the probability In particular, condition (1.3) follows from the assumption of the lemma.
The next condition is satisfied by random walks in a potential field [11,31,34,33], illustrated by Example 4.3. It is instructive to think of the sets B x N ⊂ E x N below, as the deep part of the well E x N . The first condition requires the process to reach the set B x N quickly, while the second one imposes that it will not attain the set ∆ N in a short time interval when starting from B x N .
. By the strong Markov property and since s belongs to the interval [2δ, 3δ], the first term on the right hand side is bounded by which completes the proof of the lemma.
In Lemma 2.7 below we present some conditions which imply that, for all Recall from (1.4), (1.5) that µ x N represents the stationary measure µ N conditioned to E x N , and S R,x N (t) the semigroup of the reflected process on E x N . The third set of conditions which yield (1.3) relies on the next estimate. Lemma 2.5. For every 0 < T < δ < s, x ∈ S, and configuration η ∈ E x N , up to time T , we may couple the chain ξ N (s) with the chain reflected at the boundary of E x N , which has been denoted by ξ N R,x (s). By the Markov property at time T and replacing ξ N (s) by ξ N R,x (s), the second term of the previous equation becomes By definition of the total variation distance, and since, by assumption, the stationary measure of the reflected process is given by µ , this sum is less than or equal to The second term is equal to , which completes the proof of the lemma.
Corollary 2.6. Assume that for each
Then, condition (1.3) is in force.
Proof. The assertion of the corollary is a straightforward consequence of the assumptions and Lemma 2.4, Lemma 2.5 with η ∈ B x N .
Denote by λ N (η), η ∈ E N , the holding rates of the Markov chain ξ N (t). For two disjoint subsets A, B of E N , denote by cap N (A, B) the capacity between A and B: Similarly, for two disjoint subsets A, B of E x N we represent by cap N,x (A, B) the capacity between A and B for the trace process ξ N T,x (t): where λ T,x N (η) stands for the holding rates of the trace process ξ N T,x (t).
The following lemma offers sufficient conditions for having for any δ > 0, in terms of mixing time or capacity estimates.
The first term on the right hand side of the preceding equation vanishes ,as N → ∞, by (2.1). The second term is bounded by Up to this point, we proved that To prove the assertion of the lemma under the assumption (2.6), note that by Proposition A.2 in [5], where the last equality follows from identity (A.10) in [5].
Assume, now, that (2.7) is in force. The following argument is inspired by Theorem 6 in [1] and Theorem 1.1 in [38]. We include it here for completeness.
Recall from (1.5) that we denote by S T,x N (t) the semigroup of the trace process We may choose, for example, Since this estimate is uniform in η, we may iterate it, using the Markov property, to get (2.10) This expression vanishes, as N → ∞, if (2.7) is satisfied and if we choose ϑ N according to (2.9). Finally, if the process is reversible, by Theorem 5 in [1], there exists a finite universal constant C 0 such that Hence, (2.5) follows from (2.8) by Markov's inequality.
The previous lemma evidences the importance of an upper bound for the mixing time of the trace process. This is the content of Remark 2.8 below.
Denote by R N (η, ξ), η, ξ ∈ E N , the jump rates of the Markov chain ξ N (t), and by R T,x N (η ′ , ξ ′ ), η ′ , ξ ′ ∈ E x N , the jump rates of the trace process ξ N T,x (t). Assume that the Markov chain ξ N (t) is reversible and denote by D N , D N,T,x the Dirichlet form of the processes ξ N (t), ξ N T,x (t), respectively: By replacing, in the first line of the previous formula, the measure µ N by the conditioned measure µ x N , and by restricting the sum to configurations η, ξ ∈ E x N , we obtain the Dirichlet form of the reflected process, denoted by D N,R,x (f ).
Denote by T N,T,x rel , T N,R,x rel the relaxation times of the trace process ξ N T,x (t), the reflected process ξ N R,x (t), respectively: where the supremum is carried over all functions g : E x N → R with mean zero with respect to µ x N , and g µ x N represents the L 2 (µ x N ) norm of g: g 2 Hence, the Dirichlet form corresponding to the trace on E x N dominates the Dirichlet form corresponding to the reflected process on E x N and, consequently, the relaxation time T N,T,x rel of the former is smaller than the relaxation time T R,x rel of the latter. Then, by the proof of [35,Theorem 12.3], The right hand side of the preceding inequality, which is often used as an upper bound for the mixing time T N,R,x mix of the chain ξ N (·) restricted in the well E x N , is also a bound for the mixing time of the trace process. Remark 2.9. In many interesting cases, e.g. random walks on a potential field [11,31,34,33] or condensing zero-range processes [4,28], the set B x N may be taken as a singleton.
CONVERGENCE OF THE STATE
In this section, we prove Proposition 1.2. From now on, we assume that the number of valleys is fixed and that the sequence of Markov chains fulfills conditions (H1), (H2), and (M1), (M2). Proof. It is enough to show that all assumptions of Corollary 2.6 are fulfilled. The first one follows from (2.8) and assumption (M1). The second one is assumption (1.12). Finally, the third and fourth follow from condition (M2).
The next result is a straightforward consequence of the previous lemma and Proposition 1.1. Proof of Proposition 1.2. The proof is divided in several steps. At each stage we write the main expression as the sum of a simpler one and a negligible remainder.
By the strong Markov property, using the notation ξ N B = ξ N (H B y N ), the first term in (3.2) is equal to
Let us now define
and recall the definition of the time-scale ε N introduced in condition (M2). Rewrite the previous sum as where By (1.9), this latter expression vanishes as N → ∞. By the Markov property, the sum appearing in (3.3) is equal to On the set {H ∆N > ε N }, we may replace the chain ξ N (t) by the reflected chain at E y N , denoted by ξ N R,y (t). The previous expression is thus equal to This sum can be rewritten as where, by (1.9) and a similar argument to the one following (3.3) Since, for every η ∈ B y N , ξ ∈ E N , the first term of (3.4) is equal to where the remainder R Therefore, The first term in (3.5) can be written as where R The probability inside the expectation is less than or equal to whereȆ y N has been introduced in (1.1). Since µ y . On the other hand, the second term is less than or equal to and, by assumption (H1) and (1.13), Lemma 3.3 below shows that the first term in (3.6) is equal to where lim δ→0 lim sup We may rewrite the sum in (3.7) as where R (8) By (2.8) and condition (M1), for every 0 < δ < t, In view of the definition of p(η), the first term in (3.8) can be written as Clearly, ξ∈EN |R N (t, δ, ξ)| is less than or equal to where the supremum is carried over real numbers r, s in [0, t]. By assumption (H1) and Corollary 3.2, Up to this point we proved that where lim δ→0 lim sup Therefore, in view of (3.9), which completes the proof of the proposition, in view of (3.10) and Corollary 3.2. Proof. For all ξ ∈ E y N , (3.11) By (1.12), the first term of this sum vanishes, as N → ∞. It remains to show that the second term also vanishes under assumption (a), (b) or (c).
Assume first that (a) holds. Then, by reversibility, the last term in (3.11) is equal to This expression vanishes, as N → ∞, by assumption (H1). This completes the proof of the lemma under the hypothesis (a). Assume now that condition (b) is in force. In this case, the last term in (3.11) is bounded by Here again, by assumption (H1), this expression vanishes, as N → ∞. This completes the proof of the lemma under the hypothesis (b). Assume, finally, that condition (c) is fulfilled. Note that , by Markov's inequality. The last expression vanishes as N → ∞ by (1.12). Define the stopping time σ N as By repeating the arguments that led to (2.8) and (2.10) we obtain that This concludes the argument.
EXAMPLES
We present in this section four examples to evaluate the conditions introduced in the previous sections. The first example belongs to the class of models in which the metastable sets are singletons. In the second and third examples the metastable sets are not singletons, but the process visits all configurations of a metastable set before hitting a new metastable set. These processes are said to visit points. In the second example the assumptions of Lemma 2.3 are in force, but not in the third. For this latter class, we show that the conditions of Corollary 2.6 are fulfilled for an apropriate singleton set B x N . In the last example, the process does not visit all configurations of a metastable set before reaching a new metastable set. In these models the entropy plays an important role in the metastable behavior of the system. For this last model, we prove that the hypotheses of Lemma 2.4 hold.
The purpose of this section is not to show that the conditions of Lemmata 2.3, 2.4 or Corollary 2.6 are in force in great generality. Actually, in some cases, this requires lengthy arguments and a detailed analysis of the dynamics. We just want to convince the reader that this is possible. In other words, that one can deduce from conditions (H1), (H2) and some additional reasonable conditions the convergence of the finite-dimensional distributions and the convergence of the state of the process.
In the arguments below we use the Dirichlet and the Thomson principles for the capacities between two disjoint sets of E N . We do not recall these results here and we refer to [10, Section 7.3] Example 4.1 (Inclusion process [23,8]). Denote by T L , L ≥ 1, the discrete, onedimensional torus with L points. The inclusion process, and the condensing zero-range process presented below, describes the evolution of particles on T L . Denote by η x , x ∈ T L , the total number of particles at x, and by E N the set of configurations on T L with N particles: Let σ x,y η be the configuration obtained from η by moving a particle from x to y: where r(−1) = r(1) = 1, r(x) = 0, otherwise.
The inclusion process is clearly irreducible and it is reversible with respect to the probability measure µ N given by In this model the metastable sets E x N are singletons. This phenomenon occurs in many other models. For instance, in spin systems evolving in large, but fixed, volumes as the temperature vanishes (cf. the Ising model with an external field under the Glauber dynamics [36,40,3] and the Blume-Capel model with zero chemical potential and a small magnetic field [16,30,17]). It also occurs for random walks evolving among random traps [25,24].
We claim that all hypotheses of Propositions 1.1, 1.2 are in force. Actually, with the exception of (H1) and (H2), all assumptions trivially hold because the metastable sets are singletons. Set B x N = E x N = {ξ x,N }. A. Conditions (H1) and (H2). We already mentioned that assumptions (H1) and (H2) have been proved in [8] Since the process has been speeded-up by θ N = 1/d N , τ 1 is an exponential random variable of rate 2N . It is thus enough to choose a sequence ε N such that ε N ≪ 1/N . E. Condition (1.10) of (M2). This condition is empty because E x N = {ξ x,N }. It holds for any sequence ε N > 0. F. Condition (1.12) of (M2). This is a consequence of [8, Proposition 2.1] which asserts that µ N (ξ x,N ) → 1/L. G. Conditions (a), (b) or (c). Assumption (a) of Proposition 1.2 is in force as the process is reversible. [4,28]). Let E N , N ≥ 1, be the set given by (4.1). Fix α > 1, and define g : N → R + as g(0) = 0 , g(1) = 1 and g(n) = a(n) a(n − 1)
Fix 1/2 ≤ p ≤ 1, and denote by p(x) the finite-range transition probability given by p(1) = p, p(−1) = 1 − p, p(x) = 0, otherwise. Recall from (4.2) the definition of the configuration σ x,y η. The nearest-neighbor, zero-range process associated to the jump rates {g(k) : k ≥ 0} and the transition probability p(x) is the continuous-time, E N -valued Markov process {η N (t) : t ≥ 0} whose generator L N acts on functions f : E N → R as The Markov process corresponding to the generator L N is irreducible. The invariant measure, denoted by µ N , is given by where Z N is the normalizing constant.
Fix a sequence {ℓ N : N ≥ 1} such that 1 ≪ ℓ N ≪ N , and let According to equation (3.2) in [4], for each The condensing zero-range process is an example of a process which visits points in the sense that, starting from a well E N x , the dynamics visits all configurations of E N x before reaching another well. This property reads as follows.
Other examples of metastable dynamics which visit points are random walks in a potential field [14,11,31,34].
We show below that all hypotheses of Propositions 1.1, 1.2 are in force. In certain cases we impose further assumptions on the dynamics, e.g., that it is reversible or that |S| = 2, to avoid lengthy arguments. The main tool to prove this assertion is the fact that the process visit points. Recall from (2.4) that we denote by cap N (A, B) the capacity between two disjoint subsets A and B of E N . Since ξ N (t) is the process η N (t) speeded-up by θ N , by [4, Theorem 2.2], for any disjoint subsets A, B of S, where C(A, B) is the capacity between A and B for the random walk on S with transition probabilities p(y −x), for x, y ∈ S.
A. Conditions (H1) and (H2). Assumptions (H1) and (H2) have been proved in [4] in the reversible case and in [28] in the totally asymmetric case, p = 1. We assume from now on that the process is reversible: p(1) = p(−1) = 1/2. B. Condition (1.3). We prove that the assumptions of Lemma 2.4 are in force for B x N = {ξ x,N }, where ξ x,N represents the configurations in which all particles are placed at site x.
Fix x ∈ S and η ∈ E x N . By the Markov inequality and [2, Proposition 6.10], By (H1), page 806 in [4], Therefore, by (4.3), for every δ > 0, On the other hand, for every s > 0, (1.9) of (M2). Since the exterior boundary of E x N is contained in for some finite constant C 0 . In particular, condition (1.9) of (M2) is fulfilled provided we choose ε N θ N ≪ ℓ α N . Indeed, by Corollary 5.4 we have On the other hand, by monotonicity of capacities Since the holding rates λ N (η) are uniformly bounded by C 0 θ N , if we denote by ∂E x N the interior boundary of the set E x N , the previous sum is bounded by ). An explicit computation shows that the measure of ∂E x N is bounded by ℓ −α N . The proof of this assertion is similar to the one of [4, Lemma 3.1] and is omitted.
Together with (4.6) and [4, Proposition 2.1], this gives (4.5). (Remark: In the case |S| = 2, it is possible to compute exactly cap N (ξ x,N , ∆ N ) and one gets that it is of order θ N ℓ −(1+α) N . We lost a factor 1/ℓ N at the first estimate in the preceding display.) E. Condition (1.10) of (M2). The proof relies on an estimate of the spectral gap. We prove this condition in the case of two sites, the general case can be handled using the martingale approach developped by Lu and Yau [26,Appendix 2].
Assume that |S| = 2, and denote by λ R,1 the spectral gap of the process ξ N (t) reflected at E 1 N = {0, . . . , ℓ N }. We claim that On two sites, the zero-range process is a birth and death process, and the reflected process on E 1 N is the continuous-time Markov chain whose generator is given by for all ζ = N − ℓ N , and g R,N (N − ℓ N ) = 0, due to the reflection at E 1 N . Denote by µ 1 N the stationary measure µ N conditioned to E 1 N . In order to prove (4.7), we have to show that there exists a finite constant C 0 such that for all N ≥ 1 and all functions f : {0, . . . , ℓ N } → R, where f, g µ 1 N represents the scalar product in L 2 (µ 1 N ).
Fix a function f : {0, . . . , ℓ N } → R. By Schwarz inequality, The sum over ξ ′ is bounded by C 0 η 1+α . Hence, since µ 1 N (η) ≤ C 0 η −α , changing the order of summations the previous expression is seen to be less than or equal to
This expression is bounded by
N because g is bounded below by a positive constant and the process is speeded-up by θ N . This proves claim (4.8), and therefore (4.7).
G. Conditions (a), (b) or (c). Assumption (b) of Proposition 1.2 is in force since
for all x, y ∈ S. Example 4.3 (Random walk in a potential field). In this example, the sets B x N are still reduced to singletons, B x N = {ξ x,N }, but µ N (ξ x,N ) → 0. To simplify the discussion as much as possible, we assume that the process is reversible and that the potential has two wells of the same height, but the arguments apply to the more general situations considered in [11,31,34].
Let Ξ be an open, bounded and connected subset of R d with a smooth boundary ∂ Ξ. Fix a smooth function F : Ξ ∪ ∂ Ξ → R, with three critical points, satisfying the following assumptions: Denote by Ξ N the discretization of Ξ: Ξ N = Ξ ∩ (N −1 Z) d , N ≥ 1. Let µ N be the probability measure on Ξ N defined by where Z N is the partition function Z N = η∈ΞN exp{−N F (η)}. By equation (2.3) in [31], where Hess F (x) represents the Hessian of F calculated at x and det Hess F (x) its determinant. Let {η N (t) : t ≥ 0} be the continuous-time Markov chain on Ξ N whose generator L N is given by
11)
where · represents the Euclidean norm of R d .
Recall that m i , i = 1, 2, represent the two local minima of F in Ξ, and σ the saddle point.
κ > 0, two balls of radius κ centered at the local minima. Assume that κ is small enough for sup x∈Vi F (x) < H. Denote by E i N the discretization of the sets It has been proved in [31,34] that the process X T N (t) fulfill conditions (H1) and (H2). We claim that the assumptions of Propositions 1.1 and 1.2 are in force.
We prove condition (1.3) through Corollary 2.6 with B i N = {ξ i,N }, where ξ i,N is a point in Ξ N which approximates the local minima m i . A. First condition of Corollary 2.6. Fix η ∈ E i N . By the Markov inequality, it is enough to prove that lim By [2, Proposition 6.10], the expectation is bounded by 1/cap N (η, B i N ). Consider a path (η 0 = η, η 1 , . . . , The factor θ N appeared as the process has been speeded-up. This expression vanishes as N → ∞ in view of (4.10), the definition of θ N , and because F ( Since it is clear that µ N (∆ N )/µ N (E i N ) → 0, we turn to the last two conditions of Corollary 2.6, which correspond to conditions (1.9) and (1.10) of (M2). B. Condition (1.9). Let h i = inf x∈∂Vi F (x). We claim that this condition is in force provided Since, under P ξ i,N , H (E i N ) c = H ∆N , we need to estimate P ξ i,N [H ∆N ≤ 2ε N ]. By Corollary 5.4, where ∂ − E i N stands for the inner boundary of E i N : By definition of E i N , the right-hand side of the penultimate formula is bounded above by C 0 ε N θ N N d exp{−N [h i − h]}, which proves the claim.
C. Condition (1.10). We claim that this condition is fulfilled provided for some b > 0.
We first estimate the spectral gap of the reflected process ξ N R,i (t), denoted by λ R,i . We claim that λ R,i ≥ c 0 θ N N −(d+1) . To prove this assertion, we have to show that for all N ≥ 1 and all functions f : E i N → R, where f, g µ i N represents the scalar product in L 2 (µ i N ). For each η ∈ E i N , denote by γ(η) = (η 0 = η, . . . , η M = ξ i,N ) a discrete version of the path from η to ξ i,N given byẋ(t) = −(∇F )(x(t)). This means that η j+1 − η j = 1 N , M ≤ C 0 N , and η j is the closest point of the lattice Ξ N to x(t j ) for some increasing sequence of times {t j } 0≤j≤M . Cleary, In particular, Since M ≤ C 0 N , by Schwarz inequality, where the last inequality follows from (4.15). Fix an edge (ζ, ζ ′ ) and consider all configurations η ∈ E i N whose path γ(η) contains this pair (that is (ζ, ζ ′ ) = (η j , η j+1 ) for some 0 ≤ j < M ). Of course, there are at most |E i N | ≤ C 0 N d such configurations. Hence, changing the order of summation, the previous sum is seen to be bounded above by This proves claim (4.14) since the double sum is equal to (2/θ N ) f, (−L R,i N )f µ i N . We turn to the proof of condition (1.10). Fix a sequence ε N satisfying (4.13) for some b > 0. By (4.10), µ N (ξ i,N ) ≥ c 0 N −d/2 . Hence, by (4.14), [39,6] In this example, the metastable behavior is not due to an energy landscape but to the presence of bottlenecks. After attaining a well, the system remains there a time long enough to relax inside the well before it hits a point from which it can jump to another well. In this example, to fulfill condition (M1) the set B x N can not be taken as a singleton.
In many other models the entropy plays an important role in the mestastable behavior. In the majority of them, the time-scale in which the mestastable behavior is observed can not be computed explicitly and is given in terms of the spectral gap or the expectation of hitting times. This is the case of polymers in the depinned phase [13,12,27], or the evolution of a droplet in the Ising model with the Kawasaki dynamics [7,22].
We consider below a random walk on a graph E N which is illustrated in Figure 1 in the two-dimensional case. For N ≥ 1, d ≥ 2, let I N = {0, . . . , N }, Let µ N be the probability measure on E N given by where Z N is the normalizing factor. The measure µ N is the unique stationary (actually, reversible) state. Denote by θ N the inverse of the spectral gap of this chain. By [39,Example 3.2.5], there exist constants 0 < c(d) < C(d) < ∞ such that for all N ≥ 1, Recall that we denote by C the four corners of E N . Let ∆ N be the points at graph distance less than ℓ N from one of the corners: where d(η, ξ) stands for the graph distance from η to ξ.
We refer to Figure 1 for an illustration of these sets. Assumptions (H1) and (H2) for this model follow from the arguments presented in [ .7) is an easy consequence of (2.12). The following argument also works for d = 2.
Fix δ > 0, η ∈ E 0 N , and recall that we denote by C the set of corners. Let ε N ≪ 1 be a sequence such that N 2 ≪ ε N θ N . By equation (6.18) in [25], (4.17) We may therefore assume that the process ξ N (t) does not hit C before ε N . On this event, we may couple ξ N (t) with a speeded-up random walk ξ N (t) on I d N , and ξ N (t) hits B x N when ξ N (t) hits J d N . By Theorem 5 in [1] applied to ξ N (t), and in particular the first condition of Lemma 2.4. B. Second condition of Lemma 2.4. The argument is based on the fact that the process relaxes to equilibrium inside each cube much before it hits the corners. Fix δ > 0, δ < s < 3δ, η ∈ E 0 N , and let ε N be as in A, i.e. N 2 ≪ ε N θ N ≪ θ N . By (4.17), we may insert the event {H C > ε N } inside the probability appearing in the second displayed equation in Lemma 2.4. After this operation, applying the Markov property, the probability becomes On the set {H C > ε N }, we may couple the process ξ N (t) with the speeded-up, random walk reflected at Q 0 N . Denote by P 0 N the distribution with respect to this dynamics and by E 0 N the expectation. Up to this point we proved that Since the mixing time of the (speeded-up) random walk on Q 0 N is of order N 2 /θ N ≪ ε N , the previous expression is bounded by where µ 0 N is the stationary state of the reflected random walk. As µ 0 N (η) ≤ C 0 µ N (η), and since µ N is the stationary state, the previous expression is bounded by which completes the proof of the second condition of Lemma 2.4.
The convergence of the finite-dimensional distributions has been addressed in [6]. We show below that conditions (M1) and (M2) are in force in dimension d ≥ 3. C. Condition (1.8). It is enough to prove that for all δ > 0, This condition has been proved above in A.
D. Condition (1.9). Recall from (4.16) N . Up to the hitting time of the set ∆ N the process ξ N (t) behaves as the chain ξ N (t) introduced below (4.17). It is therefore enough to prove condition (1.9) for this latter process. Let ∆ N be the simplexes given by We have to show that for i = 1, 2, where P η stands for the distribution of ξ N (t) starting from η. By symmetry, it suffices to do so for i = 1. Set γ N = ε −1 N , and denote by ζ ⋆ N (t) the γ N -enlargement of the process ξ N (t). We refer to Section 5 for the definition of the enlargement and the statement of some properties. Denote by P ⋆ η the distribution of the process ζ ⋆ N (t) starting from η, and by V ⋆ the equilibrium potential between ∆ (1) To bound the equilibrium potential V ⋆ , we follow a strategy proposed in [6]. We first claim that Fix L N = 2ℓ N , and let f : N → R + the function given by where D ⋆ N represents the Dirichlet form of the enlarged process ζ ⋆ N (t). There are two contributions to the Dirichlet form D ⋆ N (F ⋆ ). The first one corresponds to edges whose vertices belong to the set The other contribution, is due to the edges between the sets Λ N and Λ ⋆ N . Since F ⋆ is bounded by 1, this contribution is bounded by 1 4 This completes the proof of (4.20).
We turn to (4.19). Let ≺ be the partial order on J d N defined by η ≺ ξ if η i ≤ ξ i for 1 ≤ i ≤ d. We may couple two copies of the process ξ N (t), denoted by ζ η N (t), ζ ξ N (t), starting from η ≺ ξ, respectively, in such a way that ζ η Suppose that (4.19) does not hold. There exists, therefore, δ > 0, a subsequence N j , still denoted by N , and a configuration η N ∈ J d N such that V ⋆ (η N ) ≥ δ. By the previous inequality and by definition of J d N , V ⋆ (ξ) ≥ δ for all ξ such that max i ξ i ≤ M N . In particular, Comparing this bound with (4.20) we deduce that δ 2 γ N M d N ≤ C 0 ℓ d−2 N θ N , which is a contradiction since γ N = ε −1 N and ε N θ N ≪ M d N /ℓ d−2 N . E. Condition (1.10). It is well known that the mixing time of a random walk on a d-dimensional cube of length N is of order N 2 , which proves that condition (1.10) is fulfilled since ε N θ N ≫ N 2 .
F. Last conditions of Proposition 1.2. Condition (1.12) is clearly in force by definition of ∆ N . On the other hand the chain is reversible.
APPENDIX
We present in this section a general estimate for the hitting time of a set in Markovian dynamics. Fix a finite set E and let {η(t) : t ≥ 0} be a continuoustime, irreducible, E-valued Markov chain. Denote by π the unique stationary state of the process, by R(η, ξ), η, ξ ∈ E its jump rates, and by P η its distribution starting from η.
We start with an elementary lemma.
Lemma 5.1. Let X, T γ be two independent random variables defined on some probability space (Ω, F , P ). Assume that T γ has an exponential distribution of parameter γ > 0. Then, for all b > 0, P X ≤ b ≤ e γb P X ≤ T γ .
Proof. Since X and T γ are independent, for every b > 0, The last term is equal to e −γb P X ≤ b , which completes the proof of the lemma.
Note that if X is an exponential random variable of parameter θ, the inequality reduces to 1 − e −θb ≤ e γb θ θ + γ · Hence, choosing γ = 1/b, if θb is small, the inequality is sharp in the sense that the left-hand side is equal to θ b + O([θ b] 2 ), while the right-hand side is equal to e θ b + O([θ b] 2 ).
Taking into account that for every ξ ∈ E we have P ⋆ ξ H A ⋆ > H A = 1 because points η ⋆ ∈ A ⋆ are only accessible from η ∈ A, the preceding computation gives Denote by ν ⋆ A,B the equilibrium measure between A, B for the chain ξ γ (t), which is concentrated on the set A and is given by If A is a set with small measure with respect to the stationary measure, it is expected that, for most configurations η ∈ E, H A is approximately exponentially distributed under P η . Let λ −1 be its expectation, so that P η H A ≤ b ≈ 1 − exp{−bλ} ≈ bλ, provided bλ ≪ 1. On the one hand, by [5,Proposition A.2], where V * η,A is the equilibrium potential between η and A for the time-reversed dynamics, and cap(η, A) the capacity between η and A. If V * η,A π ≈ 1 (for instance, because π(η) ≈ 1), we conclude that λ ≈ cap(η, A). On the other hand, choosing γ = b −1 as the parameter for the enlarged process, for every η ∈ E, Once more, if V ⋆, * η,E ⋆ π ⋆ ≈ 1, we conclude that b −1 ≈ cap ⋆ (η, E ⋆ ), so that The next lemma establishes this estimate.
Lemma 5.3. Fix a proper subset
A of E. For every b > 0 and η ∈ E \ A, and Proof. Fix a proper subset A of E, b > 0 and η ∈ E \ A. Fix γ > 0, and consider the γ-enlarged process. Denote by H E ⋆ the hitting time of the set E ⋆ . By definition of the enlargement, under P ⋆ η , H E ⋆ has an exponential distribution of parameter γ and is independent of H A . Hence, by Lemma 5.1, The previous probability is the value of the equilibrium potential between A and E ⋆ computed at the configuration η, denoted hereafter by V ⋆ A,E ⋆ . By equation (3.3) in [30] and by (5.2), the previous expression is bounded by This proves the first assertion of the lemma. We may also rewrite the right-hand side of (5.5) as where 1{η} represents the indicator of the set {η}. By [5,Proposition A.2], the previous sum is equal to where P ⋆, * represents the distribution of the process ξ γ (t) reversed in time, and ν A,E ⋆ the equilibrium measure given by (5.4). By definition of the enlarged process, for every initial condition η ∈ E, H E ⋆ has an exponential distribution of parameter γ. The penultimate displayed equation is thus bounded by γ −1 cap ⋆ (A, E ⋆ ), which completes the proof of the lemma. | 12,700.8 | 2017-03-28T00:00:00.000 | [
"Mathematics",
"Physics"
] |
PolyU CBS-Comp at SemEval-2021 Task 1: Lexical Complexity Prediction (LCP)
In this contribution, we describe the system presented by the PolyU CBS-Comp Team at the Task 1 of SemEval 2021, where the goal was the estimation of the complexity of words in a given sentence context. Our top system, based on a combination of lexical, syntactic, word embeddings and Transformers-derived features and on a Gradient Boosting Regressor, achieves a top correlation score of 0.754 on the subtask 1 for single words and 0.659 on the subtask 2 for multiword expressions.
Introduction
The notion of complexity has often been debated in linguistics and, depending from the disciplines, it might have different meanings.
In linguistic typology, for example, complexity is generally studied as a property of the language system as a whole, it is conceived as the number of (morphological, syntactic, semantic etc.) distinctions that a speaker has to master, and it is assessed by comparing different languages (McWhorter, 2001;Parkvall, 2008). On the other hand, in the perspective of psycholinguistics and cognitive science, the notion of complexity can be described as the difficulty encountered by language users while processing concrete linguistic realizations (sentences, utterances etc.) (Blache, 2011;Chersoni et al., 2016Chersoni et al., , 2017Chersoni et al., , 2021Iavarone et al., 2021;Sarti et al., 2021). Finally, in the Computational Linguistics community, the assessment of complexity at the lexical level is often related to readability applications (Shardlow et al., 2020), with the goal of determining if a word in a given text will be difficult to understand for the language users. Such applications are extremely useful for second language learners, for speakers with relatively low literacy and for people with reading disabilities, helping to tailor the difficult level of the texts to the needs of the target users.
Task 1 of SemEval 2021 (Shardlow et al., 2021) aims at the development of systems for the estimation of lexical complexity in context, both for single words and for multiword expressions. The organizers provided two datasets with the target words in a sentence context, with annotations consisting of a mean of the complexity ratings assigned by humans. In our paper, we present the system developed by the PolyU CBS-Comp team for the competition. Our top system achieves a Pearson correlation of, respectively, 0.754 on the single words dataset and 0.659 on the multiword expressions one.
Related Work
In the earliest shared task on the lexical complexity problem, organized in 2016 (Paetzold and Specia, 2016), complexity was defined as a binary variable: given a word in context, the word will be judged as complex or not. Of course, this was a simplifying assumption, since there might be many situations where the boundary is not a clear-cut one, and annotators would rather indicate a value in a continuous scale. Moreover, the "complex" words in the data only needed to be categorized as such by just one of the annotators. A further study by Zampieri et al. (2017) analyzed the output of the participating systems, showing that modeling complexity as binary actually hindered their performance.
A second iteration of the shared task was organized in 2018 (Yimam et al., 2018), this time features two separate subtasks: the traditional binary classification task, where systems had to predict whether one word was complex or not, and a regression task, where systems had to estimate the probability that an annotator would have considered a given word as complex.
Recently, Shardlow et al. (2020) have introduced CompLex, a new gold standard for the estimation of lexical complexity in context for English: the corpus, including sentences from different textual genres, is annotated with the mean complexity ratings for the target words. As a preliminary evaluation, the authors presented the results of a linear regression model trained on sets of features including word and sentence embeddings and some handcrafted features that are traditionally associated to complexity, such as frequency, word length and syllable count. The best scores, in terms of mean absolute error, were obtained when using only the latter set of features, while models based on the dimensions of the embeddings were lagging behind.
Datasets
The datasets for the shared task are part of the CompLex corpus, which has been published and described by Shardlow et al. (2020). The annotated sentences were collected using three different corpora: the Europarl corpus (Koehn, 2005), which includes the proceedings of the European Parliament; the CRAFT biomedical corpus (Bada et al., 2012); and the Bible, in the modern version of the World English Bible translation (Christodouloupoulos and Steedman, 2015).
The organizers selected targets as either single words (Sub-Task 1) or multiword expressions (Sub-Task 2), and the datasets include also multiple examples with the same target, as different contexts can determine different complexity values. As for the multiword expressions, they were identified via syntactic patterns, being either adjective-noun or noun-noun phrases. 20 annotations per data instance were collected, with annotators coming from different Englishspeaking countries (US, UK and Australia): the possible ratings ranged from 1 → Very Easy to 5 → Very Difficult. Mean scores were then normalized in the 0-1 range.
In a first phase, the organizers released a training data of 7661 samples for the single words track and 1517 samples for the multiword expressions track, together with a trial/validation dataset of 420 and 99 samples, respectively. Later, they released a test set of 917 samples for the single words track and 184 samples for the multiword expressions track.
Examples of the instances are shown in Table 1.
Evaluation
For both the single words and the multiword expressions track, we used the same set of features as input for a regression algorithm. In the multiword expressions track, we computed the value of the features for each of the two words in the target expression and then we took the average.
Features
As hand-crafted features, we adopted the same ones used by Shardlow et al. (2020) in the original evaluation of their dataset: Logarithmic Frequency, Word Length and Syllable Length. The latter two have been extracted using the Python textstat for each target word. As for the frequency feature, we extracted a general, out-of-domain frequency for each target word using the SUBTLEX database (Brysbaert and New, 2009) and the wordfreq Python package (Speer et al., 2018), and then we extracted the frequency of the word in each one of the three corpora composing CompLex. In total, we obtained 6 features (4 frequency + 2 length features) for each instance. We also added two Boolean features for Capitalization: the first was equal to 1 if the first letter of the target word was upper case and 0 otherwise; the second one was equal to 1 if all the letters of the target word were upper case and 0 otherwise. The latter feature was added because we noticed that some of the target words in the dataset are acronyms. Apart from the lexical information, Syntactic Features were explored for both single words and multiword expressions. The StanfordNLP tools (Manning et al., 2014) were first used to acquire both the part-of-speech (POS) tags and dependency trees. POS tags of target words were manipulated using one-hot encoding, for a total of 20 POS-based features. On the other hand, directed and path from the target word to the root were extracted as dependency features. We concatenated all dependency tags to the root, using one-hot encoding once again to encode every distinct path as a single feature. In total, we generated 267 dependency paths features with this mechanism.
Another feature was based on Word Embedding similarity: first, we computed the sum of the embeddings for all the words preceding the target, as a sort of general representation of the sentence context 1 , and then we measured the cosine similarity with the embedding of the target word. If the target was a multiword expression, we summed the embeddings of the words composing it. As word embeddings, we used the publicly available Fast-Text vectors, pre-trained on the Wikipedia corpus (Bojanowski et al., 2017). 2 We added one feature based on the BERT Transformer Model (Devlin et al., 2019) 3 by masking the target word in the original sentence and taking the probability value provided in output by the Softmax. For multiword expressions, we sequentially masked the words composing the target and took the average value.
Similarly, we used the GPT-2 Transformer Model (Radford et al., 2019) 4 to obtain a probability score for the full sentence, computed as the product of the probabilities of the single tokens.
The total number of extracted features is 300. Finally, we decided to generate polynomial features from our set, in order to exploit potential interactions. We used the PolynomialFeatures functionality of the scikit-learn Python package to generate interaction features of order 2, so that the final number of features that was fed to the regressors was 45151. 1 The use of vector sum as a compositional function has been used in Distributional Semantics since Mitchell and Lapata (2010).
Regressors
We tested several regression algorithms, using the implementations in the scikit-learn Python package. The adopted scikit-learn API and the main hyper-parameters are listed below: • RR Ridge: Ridge Regression solves a regression model where the loss function is the linear least squares function and regularization is given by the l2-norm. alpha=1.0, normalize=True.
• PLSR PLSRegression: PLS Regression implements the PLS2 blocks regression in case of one dimensional response. components=5.
• LR LinearRegression: Linear Regression is trained based on ordinary least squares function. normalize=True.
• RF RandomForestRegressor: a Random Forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. min samples split=2, min samples leaf=1.
• GBR GradientBoostingRegressor: Gradient Boosting builds an additive model in a forward stage-wise fashion which allows for the optimization of arbitrary differentiable loss functions. learning rate=0.1,min samples split=2, min samples leaf=1.
Metrics
The performance of the participating systems was evaluated in terms of Pearson correlation (r) between the outputs and the human mean ratings.
In the Results section, we also report the scores for Spearman correlation (ρ), Mean Absolute Error (M AE), Mean Squared Error (M SE) and R-Square (R2).
Results
We evaluated our system for two subtasks based on given trial datasets. For each regressor, we tuned hyper-parameters according to each subtask. Performance evaluation has been carried out in two aspects: the assessment of the overall correlation with human ratings and the analysis of the contribution of the features.
Complexity Prediction
Evaluation metrics are reported ranking by Pearson correlation in Table 2 and 3 for single words and multiword expressions, respectively. It can be observed that predicting the complexity of single words is naturally less difficult than multiword expression. Concerning the regression algorithm, gradient boosting regression outperforms other investigated methods by a large gap, while PLS regression, Bayesian ridge regression, linear regression and random forest regression perform very similarly. Though PLSR has a worse Pearson correlation than BRR, its R2 and Spearman correlation are slightly better. Further studies about regressors brought some unexpected results for our feature based approaches: based on the features we selected, Ridge Regression performs worse than linear regression, suggesting that some features are not suitable for applying L2-norm.
Feature Study
As our proposed method heavily relies on feature selection, the acquired features are investigated in four groups: Hand-crafted (including Logarithmic Frequency, Word and Syllable Length and Capitalization), Syntactic (including the POS-and the Dependency-based features), Embedding and Transformer features. We adopted the features of the Hand-crafted group as baseline, and present a comparison between the performance of systems using the other features as add-on components. The scores in Table 4 refer to the performance on the single words dataset, by using GBR as a regressor.
According to Table 4, syntactic, embedding and transformer based features can all contribute to improve the prediction results. As expected, the combination of all feature type groups can achieve the best predicting capability.
Comparing with the baseline of hand-crafted features, syntactic and embedding features have very marginal contribution. Yet, it should not be neglected supplementing only transformer based features cannot achieve the maximum performance gain. This indicates that the interaction of the individual features can bring latent useful information to model, further revealing the complexity values of the target words.
Conclusion
In this paper, we presented the PolyU CBS-Comp system for lexical complexity prediction, which took part in the SemEval shared task 1. Our method, based on a combination of lexical, syntactic, embeddings and Transformers features, achieved a 0.754 correlation on single words and 0.659 on multiword expressions, when using Gradient Boosting as a regression algorithm.
Traditional hand-crafted features, followed by Transformer-based ones, seem to give the strongest contribution to the classification performance, which is further improved by adding feature in-teractions to the input for the regressor.
For future studies on lexical complexity, we plan to further exploit the text genre information, for example by adding domain-adapted language model features (Van Schijndel and Linzen, 2018) to the information available to our models. | 3,031 | 2021-08-01T00:00:00.000 | [
"Computer Science"
] |
Estimating present and future profits within the Namibian hake industry: a bio-economic analysis
Namibia's fishing industry is managed using a system of fishing rights and individual fishing quotas. This property rights system was intended to encourage the local fishing industry to exploit the resource responsibly. Unfortunately, unintended perverse incentives have promoted induced overcapacity and inefficient use of vessels. In combination with inconsistent quota allocations, the result has been persistent pressure on the already depleted biological resource. This paper uses a bio-economic model to estimate actual and potential profits in Namibia's hake fishery. N$300 million annual profit was not realised due to the depressed state of the resource. Mean annual profits for the years 2007–2009 were N$80 million, which provides the fishing industry, as a whole, only about 36% of the potential normal profit. Theoretically this implies that the fishing industry would probably receive better returns with less risk if they invested their money elsewhere. This study demonstrates that by rationalising quotas and improving management, better efficiency and higher profits for the fishers and government could be obtained.
which the total allowable catch (TAC) is not landed (14 of 21 years since independence) (Kirchner and Leiman 2014), the most common request is for increased quota, a shortterm solution that exerts additional pressure on an already depleted resource. Other relief measures sought are the reduction of quota fees or extension of their payment schedule, and the conversion from wetfish quota to freezer quota -the industry believing freezer quota to be more profitable. Industry representatives point to the excess capacity of the fleet and factories relative to TAC, and the threat of worker retrenchment if the TAC is not increased. The Minister of Fisheries is the ultimate decision-maker, and the industry has on various occasions been invited to approach the Minister in the event of economic hardship (CHK pers. obs.). Such an ad hoc dimension to management makes analysis difficult, a problem worsened by the paucity of realistic data. Although economic information is provided by rights-holders as part of their quota applications, the functioning of the hake industry is complex and secretive, and interrelationships between various stakeholders are opaque (CHK pers. obs.).
The hake industry is a restricted access fishery with 38 rights-holders, 3 each having long-term rights allocated for 7 or 15 years. Each rights-holder is entitled to a predetermined share of the annually announced TAC. At the time of this study, the smallest of these shares was 0.6% of the TAC and the largest was 13.4%. However, those percentages may change from year to year and the resulting insecurity prevents rights-holders from planning effectively, reducing the advantages that would flow from a wholly secure property right. In addition, some quota is reserved for distribution in one-off rewards for investments made by rights-holders during any year, a policy that encourages overcapacity. Although rights are officially non-tradable, some rights-holders lease their TAC shares to others, 3 The number of rights-holders was increased in 2011 by 12 new group rights each consisting of fi ve subsidiary companies (Paterson et and some merge into joint ventures (Kirchner and Leiman 2014). As the TAC is shared between many, and the percentage shares are not totally secure, the quota system does not induce the rational, collective, rent-maximising behaviours one would hope for from individual quota systems, so it is understandable that rights-holders lobby for larger TACs. There is certainly little incentive for individual firms to invest in the resource by curtailing their present efforts. Until recently, TAC recommendations have been submitted to the Minister of Fisheries and the Marine Advisory Council from biological and economic interest groups separately (Kirchner and Leiman 2014). However, in a management system whose objective is to maximise rents by avoiding the dissipation of abnormal profits, an objective bio-economic modelling approach would be an appropriate way of assessing the value of the different recommendations.
This paper presents economic information collected from the fishing industry and uses a bio-economic model to estimate the impacts of various management strategies on the hake fishery's present value.
Material and methods
Biologists normally estimate fish stocks in units of mass, whereas bio-economics treats fish as financial assets; the hake fishery would therefore be valued as the net present value of the stream of earnings it is expected to generate (Lange 2000). This study generally follows the residual approach to rent estimation described by Clark (2006), but it allows catches to fluctuate with the estimated condition of the resource. Although exogenous factors such as fuel prices (Figure 2b), exchange rate and fish prices are kept constant, costs still fluctuate with stock status, because in this approach the abundance of hake determines the catch per unit effort (CPUE).
The biological part of the bio-economic model is described in detail in Kirchner et al. (2012). The hake resource is estimated to be well below the level required for the maximum sustainable economic yield (Kirchner et al. 2012), meaning that rebuilding the resource would increase the present value of the industry's future earnings. Stock rebuilding takes place when only part of the replacement yield (RY) is harvested, the remainder being left to support stock-size increase. The TAC calculated using the bio logical model then becomes where is the proportion harvested of the 5-year average RY. Under Namibia's management strategy, the TAC cannot be changed by >10% from one year to the next except under extraordinary circumstances (Kirchner et al. 2012). This TAC is then apportioned between freezer (F) and wetfish (ice, W) vessels following a policy (Japp and Steenkamp 2004;Kirchner and Leiman 2014) that currently restricts freezer vessels to 30% of the TAC. This policy aims to increase onshore processing and thereby increase employment in the hake wetfish sector.
and
(2) As the resource recovers, future CPUE levels increase and average costs decline: where Bexp y is the exploitable biomass per year in the biological assessment model and q the proportionality constant estimated by the model (Kirchner et al. 2012). The predicted number of hours (h i y ) needed to catch the calculated annual TAC for the two fishing sectors (i) is therefore: where m W and m F (raising factor) were set as 1 and 1.4 respectively. This should roughly represent the hours that are needed by the two fishing sectors to catch the TAC. Personal observations, supported by local landings data, suggest the wetfish vessel CPUE is typically 40% less than that of freezer vessels. The number of vessels of each type needed per year V i y to catch their portion of the TAC is: where T i is the average fishing time per day per vessel type and D i the average number of annual trawling days per vessel type (Table 1). The predicted hourly costs per vessel type (C i ) can be calculated from: where AC i refers to the average annual cost per vessel type ( Table 1). The hake industry's total costs (TotC y ) have two components, fishing and processing costs: where h i y is the time taken to catch the TAC, and FC i refers to the factory costs per quota tonne for the different fishing types.
Total revenue R y is estimated from: where p i is the price per quota tonne. Profit follows as: which is increased compounded by pc ('profit-creep'), set currently at 5%. The Namibian hake fishery is still developing by employing innovative business strategies that lower costs; this issue is not included in the current total cost, so the 'profit-creep' has been allowed for in the base case. Net present value (NPV) is then calculated from: where d is the discount rate and n the number of years in the future, with 2010 being n 1 and 2030 being n 20. Namibian hake rights-holders have exclusive 15-year fishing rights (Kirchner and Leiman 2014), with the remaining life of a right, together with the probability that it will be renewed, influencing the value its holder attaches to future stocks and catches. Identifying the appropriate discount rate in this evaluation is a challenge; the lower the discount rate the more weight that is attached to future yields. High discount rates imply a short time horizon (and raise the incentive to overfish). The risk caused by natural processes such as environmental variation is not considered in this process. Expected cash flows and discount rates change over time and affect NPVs (Damodaran 2002), but in this case the decision-making process is independent of both, because the NPVs of the different management scenarios (Table 2) are being compared over the same time-period. According to Ulibarri and Wellman (1997), a real discount rate of 7% is the highest that would be used in the USA. However, the Asian and African development banks routinely use real rates of 10% and 12% (COMPAS 2008). Given the risks associated with the industry and its consequent high hurdle rate, and recognising Namibia's 'developing country' status, a 10% real discount rate would seem to be reasonable for the next 20 years.
Employment is estimated to be (11) where E i is the number of employees per vessel type and FE i the number of employees per 1 000 quota tonnes for each fishing type.
The short, simple questionnaire used to obtain additional economic data, some of which are listed in Table 1, is available online as Supplementary Appendix S1. It was completed by fish processing plant managers and freezer boat owners, the intention being to cover the complete fishing industry population.
Results
Revenues of the industry depend largely on the stock of natural capital (Kirchner et al. 2012), the size distribution of the catch (Table 3), international prices and the prevailing exchange rate (Figure 2a), whereas fishing costs are driven by catch rates, fuel prices ( Figure 2b) and wage rates (Leiman and Harris 2009). In 2009, 30% by weight of the hake caught in Namibian waters were <35 cm ( Table 3). As smaller fish fetch lower prices per unit weight, it also TAC 100 000 t for 4 years then further at 0.8 5 TAC 100 000 t for 4 years then further at 0.85 6 TAC 100 000 t for 4 years then further at 0.9 7 Zero catch for 4 years then further at 0.8 8 Zero catch for 3 years then further at 0.8 9 Zero catch for 2 years then further at 0.8 10 Zero catch for 1 year then further at 0.8 11 Constant catch of 140 000 t for 20 years indicates how excessive effort can simultaneously raise the costs per unit and lower average revenues. Namibia is a price-taker (i.e. the industry has no control to dictate prices; their selling transactions have no effect) in the world market for hake, making exchange rates and production costs particularly important drivers of profitability. Historically, Spain has been Namibia's main market for hake (Rey and Grobler 2011), so as the N$ strengthens against the €, so revenues fall. This effect was especially severe in the years 2003-2005 and again post-2009 ( Figure 2a). Further, Spain's recent economic downturn has caused demand for the relatively expensive Namibian hake to decrease (Steenkamp 2012), further damaging Namibian revenues.
Demersal trawling is fuel-intensive. It is therefore unsurprising that the oil price is a source of rent volatility. For the wetfish segment, and at 2007 prices, wages constituted about 30% of the total expenses, followed by fuel at 20%. At that time, the annual average fuel price was N$6.4 per litre (€0.6/US$0.76) (MFMR unpublished data, released in 2007). Wetfish vessels stay at sea no longer than seven days, so a large proportion of their fuel is used in travelling to and from the fishing grounds. Although freezer trawlers stay out longer (up to three months), they are even more fuel-intensive (typically 35-40% of their operating costs) because they use fuel to run the processing plant on the vessel. When the N$ was strong it partly compensated for high fuel costs, but this benefit was outweighed by the negative impact on export revenues. Since then, fuel's share of fishing costs has been driven up by sharp rises in the price of oil and the rapid weakening of the N$ (Figure 2a, b).
Nature, politics and economics all make fishing risky (Figure 2), and vessel/factory owners need to perceive a potential benefit that will compensate for the level of risk they are taking. The opportunity cost of capital ('normal' profit) 4 is the cost of borrowing capital plus an appropriate risk premium. In her work on fisheries satellite accounts for Namibia, Lange (2000) set the opportunity cost of capital at 30%. Although it would be reasonable to follow Lange's lead, 10-12% is the standard elsewhere in the developing world (IPCC 2001), but that level does not recognise the special risks implicit in fishing. Unfortunately, data on capital costs in the Namibian hake sector are not available, so a conventional 'normal' profit cannot be calculated. Instead, to represent opportunity costs, a 10% addition has been added here to operating costs, and the remaining 'abnormal' profit, if any, has been regarded as additional resource rent (rent above that which the industry pays for quota fees, corporate tax and contributions to the research fund [Kirchner and Leiman 2014]).
Information on the total revenue and costs was collected from the hake industry. The response rate of 50% could be a source of inaccuracy in the interpretation of the state of the hake industry, especially in segments whose population sizes are very small, e.g. freezer trawlers and fish processing plants. In addition, some of the provided information was variable in quality (see for example 'cost per tonne' in Figure 3). It is likely that the data provided by vessels 5, 19, 21 and 23 are incorrect, as they indicate considerably higher costs than the others. Similar variations were found throughout the dataset. 4 A normal profi t (opportunity cost) is the potential profi t, if time and money would be invested elsewhere (Carbaugh 2006) Year Total catch ( 10 3 t)
Weight (%)
Number (%) 1998 107 5 20 1999 158 12 34 2000 171 9 24 2001 174 17 43 2002 156 28 64 2003 189 25 52 2004 174 24 58 2005 148 29 62 2006 137 34 69 2007 126 29 63 2008 126 16 38 2009 145 30 71 Source: Ministry of Fisheries and Marine Resources (MFMR) TAC reports The data indicate that many of the wetfish vessels are either underutilised or did not report the total quota caught. Figure 4 indicates how the cost per tonne decreases substantially as the annual quota caught by individual vessels increases. Some of the outliers are obvious in Figures 3 and 4, and by visual inspection alone it appears that a cost per tonne between N$6 000 and N$10 000 for wetfish vessels is probably a reasonable estimate. Wetfish vessel costs of more than N$15 000 per tonne do not seem plausible. For the 2007-2009 quota years, an average of N$10 994 (CV 0.10) was estimated if all the data were included, which declined to N$9328 (CV 0.08) when only vessels that caught more than 1 000 t were averaged.
Two of the five processing plants reported higher costs per tonne than the other three ( Figure 5a); the costs averaged N$5 806 (CV 0.19) per quota tonne. The average cost per tonne for a wetfish vessel (N$10 994 [CV 0.10]) has to be added to the average cost of the processing plant per quota tonne to obtain the total cost of catching and processing a wetfish quota tonne, which totalled N$16 800; 10% of this value was considered as opportunity costs and added to the total costs (Table 4). Product price per tonne ranged between N$11 000 and N$25 000 per quota tonne (Figure 5b), with an average of N$17 331 (CV 0.10) ( Table 4); this variability in price could be due to the plants producing different products. Once the opportunity costs per tonne (10% of costs) are added to the operating costs, it is shown that the wetfish fishery is short by N$1 149 per tonne for achieving its normal profit (Table 4).
Only six of the 12 freezer vessels submitted data ( Figure 6a) and some of the reported costs appear to be unreasonably high. All freezer vessels reported catching 3 000 t or more of hake per year and Figure 6 suggests that costs decrease substantially if >4 000 t are caught (i.e. when fixed overheads are spread more widely). The average cost per freezer quota tonne was calculated as N$13 270 (CV 0.08) per tonne whereas the average price of hake product landed from freezer trawlers was reported to be N$13 940 (CV 0.14) per tonne (Table 5). However, the reported price was highly variable (Figure 6b), with one enterprise reporting prices >N$20 000 per tonne, well exceeding the prices for product from the wetfish processing plants.
The results obtained from the six freezer vessels (Table 5) also showed an annual deficit of normal profit of N$657 per quota tonne from 2007 to 2009. Given a TAC that year (2010) of 140 000 t, of which 30% was awarded to the freezer fleet (i.e. 42 000 t), the resulting annual accounting profit (revenue-operating costs) would have been N$28.14 million. For the wetfish fleet (Table 4), an accounting profit of N$531 per quota tonne is estimated, providing N$52.03 million per year at that time. Combining those two estimates (N$80.18 million) yields the estimated annual profit accrued by the hake fishery between 2007 and 2009. However, in order for the fishing industry to be compensated for their opportunity costs, they should have made in the order of N$220 million.
The potential for efficiency gains is shown by excluding wetfish vessels that land <1 000 t (Figure 3). Average vessel costs then fall to N$9328 (CV 0.08) per tonne, which means that after ʻnormalʼ profit has been deducted (10% of the total cost), the economic rent/additional resource rent for the wetfish sector would have averaged ~N$67 million per year between 2007 and 2009. Setting the input parameters of the bio-economic model required a number of assumptions to be made (Table 1). The cost of a freezer vessel is about 4 that of a wetfish vessel and the price received for freezer vessel product is typically less than that of wetfish product. The annual costs of running a freezer vessel were set at N$50 million and wetfish vessels at N$15 million. The price received per quota tonne of hake by freezer vessels was externally set at N$15 000 (approximate price for 2009), and for wetfish vessels N$17 500. The net present value profit stream for the hake industry (with and without 'profit-creep') was calculated for different management scenarios (Table 2) over 20 years and is compared in Figure 7.
The TAC in 2010 was 140 000 t. Maintaining it at that level for the following 20 years (Scenario 11) yields the lowest NPV of all the scenarios. The profit would decrease after only three years and become negative in 20 years (Figure 8). The highest NPV would be provided by closing the fishery for three years and then fishing it at 80% of mean RY computed over the preceding five years (Scenario 8), but the closure of the fishery is obviously not feasible. Scenario 1, which politically seems the most acceptable, suggests fishing the resource at 80% of RY on an annual basis ( Figure 9); the discounted profit stream only declines slightly before starting to rise again. From the analysis it seems unlikely that the TAC will exceed 150 000 t at the end of 20 years under any of these management scenarios. Discounted resource rent to be expected is some N$550 million or N$200 million in 20 years (Scenario 1) with and without the inclusion of 'profit-creep', respectively, with ~N$300 million lost on an annual basis by the resource being depressed.
As the resource increases, CPUE will increase, which will result in a drop in the number of vessels and consequently employees needed by the fishery. If Scenario 1 (20% of RY used to rebuild the resource) is followed, the number of freezer vessels would decrease from 16 to 7, wetfish vessels from 71 to 30, and the total number of employees by ~1 000. Overall, though, the CPUE would increase by 2.6 times over the 20-year period.
Using the information collected in this study from the fishing industry indicates that the current total accounting profit would be some N$80 million. Taking the model results, the present value for 2010 for Scenario 1 would be estimated to be N$240 million. Marsden and Sumaila (2008) estimated the resource rent to be N$222 million,
Parameter
All data included Vessels that caught more than 1 000 t Average price per quota tonne ( similar to the estimate of N$227.5 million obtained using the de facto selling price quota (Kirchner and Leiman 2014), although Namibia's market for quota is inefficient, and such a valuation exceeded expectations. Net present value (for the base case) for the different management scenarios ranged from N$4.4 billion (Scenario 11) to N$15.6 billion (Scenario 7). NPV for Scenario 1 is estimated to be some N$10 billion over 20 years, a value also estimated by Sumaila ( , 2001. However, this could only be achieved if the wetfish fishery aligns itself into a more profitable arrangement over the next 20 years. For the current situation, the NPV for Scenario 1 would be only ~N$6 billion (Figure 7).
Discussion
The most influential factor driving the hake industry's revenue is the exchange rate (ZAR [South African Rand] to the €), 5 because most of Namibia's hake resource is exported to Europe, in particular to Spain (Rey and Grobler 2011). For example if Namibia exports 50 000 t of hake valued at €2 kg -1 at an exchange rate of N$10 €1, and the N$ strengthens by 50 c, Namibia would lose about N$50 million. Indeed, in 2010, the N$ was strong against the €, so the hake industry was foregoing considerable revenue. In weakening by >25% against the € recently, the industry has benefitted. The labour intensiveness of the Namibian operation means that the wetfish sector's greatest expense is its wage bill. For that reason, rights-holders are often successful when they approach the Ministry of Fisheries for additional quota and threaten that 'workers will otherwise have to be laid off'. Such a short-term approach by industry has worked in the past, but it is detrimental to the biological resource and hence to future rents and employment.
Fuel also contributes extensively to the cost, so it would be of great value if rights-holders could work more closely together in order to rationalise such costs. Fishing vessels, however, especially those in the wetfish sector, are not used particularly efficiently (see also Kirchner and Leiman 2014), keeping the cost per tonne of hake high. Figure 4 shows that vessels that catch more than 1 000 t annually are able to keep their costs below N$10 000 per tonne. Another confounding factor is that some fish processing plants have much higher costs than others; it is possible for plants to operate at about N$5 000 per tonne, allowing a break-even price of N$15 000 per quota tonne for the best-performing wetfish vessels and plants. Unfortunately, the data provided for this study reveal that some operators are struggling to survive and that many are actually making a loss at the current time (2007)(2008)(2009).
Only six of the 12 freezer vessel owners responded to the survey, but contrary to popular belief, those six freezer vessels are struggling to make a ʻnormalʼ profit (N$28.14 5 The N$ is tied to the ZAR which has oscillated between strong and weak for many years in the past (Figure 2a). Despite this, the fi shing industry has survived 2 0 1 2 2 0 1 0 2 0 1 4 2 0 1 6 2 0 1 8 2 0 2 0 2 0 2 2 2 0 2 4 2 0 2 6 2 0 2 8 2 0 3 0 YEAR Figure 9: With a TAC (t) set as 80% of replacement yield (RY), the annual discounted profit stream in the Namibian hake fishery for the next 20 years million), which represents only 5% of the total costs, half of their opportunity costs. One reason is that few vessel owners have sufficient quota to utilise their vessels optimally. As a result they are forced to lease quota, the cost of which can make up 20% of their total expenses. Most rights-holders have a small amount of freezer quota, which is sold informally to the 'highest bidder'. Freezer operators made an average accounting profit of N$670 per quota tonne over the years 2007-2009. The non-operating quota-holders, on the other hand, sell their freezer quota at a price of N$2 000-N$2 500, and other than a low quota fee (wetfish quota, N$600; freezer quota, N$850 per quota tonne) and corporate tax, they face no expenses and avoid the risks involved in the fishing operation. The wetfish fishery made only one third of its expected normal profit during the sampled time period. This means that if it invested its money in the bank it would get a better return with less risk on its investment. In order to run the bio-economic model, average values, such as the cost per fishing hour, are needed to evaluate the viability of the fishing methods. Some of the data provided for this analysis by industry may well be of doubtful accuracy, but the function of the model is to compare alternative management scenarios, so the relative magnitude of the figures is more important than their absolute values. If one is willing to accept the industry's stated estimates of productivity and assumes additional innovative streamlining in the wetfish fishery, then approximately N$300 million annually in sustainable harvests is being lost due to the stock currently being depressed. The model does make it clear that increasing the pressure on the stock can maintain jobs and foreign exchange flow in the short term, but it will result in much lower overall benefit in the long term. Responsible management, i.e. mainly in the form of rebuilding the stock, would be in the best interests of all.
In the past, TAC recommendations were considered only in biological terms and only current returns were taken into account. The trade-offs between present and future jobs and income have never been evaluated rigorously. Incorporating such economic information, although there are reservations about the accuracy of some of the commercial data and model assumptions, does reveal the likely costs of mismanagement. The stock externality (unintended cost incurred by the industry) caused by there being excess effort for the TAC may be more easily recognised by industry members and fishery managers when presented as the impact of current catches on the discounted stream of future profits (Parslow 2010). | 6,286 | 2014-07-03T00:00:00.000 | [
"Economics",
"Environmental Science"
] |
Characterization of a GHF45 cellulase, AkEG21, from the common sea hare Aplysia kurodai
The common sea hare Aplysia kurodai is known to be a good source for the enzymes degrading seaweed polysaccharides. Recently four cellulases, i.e., 95, 66, 45, and 21 kDa enzymes, were isolated from A. kurodai (Tsuji et al., 2013). The former three cellulases were regarded as glycosyl-hydrolase-family 9 (GHF9) enzymes, while the 21 kDa cellulase was suggested to be a GHF45 enzyme. The 21 kDa cellulase was significantly heat stable, and appeared to be advantageous in performing heterogeneous expression and protein-engineering study. In the present study, we determined some enzymatic properties of the 21 kDa cellulase and cloned its cDNA to provide the basis for the protein engineering study of this cellulase. The purified 21 kDa enzyme, termed AkEG21 in the present study, hydrolyzed carboxymethyl cellulose with an optimal pH and temperature at 4.5 and 40°C, respectively. AkEG21 was considerably heat-stable, i.e., it was not inactivated by the incubation at 55°C for 30 min. AkEG21 degraded phosphoric-acid-swollen cellulose producing cellotriose and cellobiose as major end products but hardly degraded oligosaccharides smaller than tetrasaccharide. This indicated that AkEG21 is an endolytic β-1,4-glucanase (EC 3.2.1.4). A cDNA of 1013 bp encoding AkEG21 was amplified by PCR and the amino-acid sequence of 197 residues was deduced. The sequence comprised the initiation Met, the putative signal peptide of 16 residues for secretion and the catalytic domain of 180 residues, which lined from the N-terminus in this order. The sequence of the catalytic domain showed 47–62% amino-acid identities to those of GHF45 cellulases reported in other mollusks. Both the catalytic residues and the N-glycosylation residues known in other GHF45 cellulases were conserved in AkEG21. Phylogenetic analysis for the amino-acid sequences suggested the close relation between AkEG21 and fungal GHF45 cellulases.
INTRODUCTION
Cellulose, a structural polysaccharide comprising 1,4-linked β-D-glucopyranose residues, exists mainly in plant cell wall as crystalline microfibrils (Jagtap and Rao, 2005). Since plant cellulose accounts for almost a half of total carbohydrate biomass on the Earth, intensive uses of the cellulose are expected to solve various problems that we are facing in ecological, environmental and energy fields (Agbor et al., 2011;Yang et al., 2011). In this respect, degradation of cellulosic materials by cellulose-degrading enzymes will be a fundamentally important technique because the cellulose-degrading enzyme can convert insoluble cellulose to soluble oligosaccharides and glucose without consuming high energy and producing hazardous byproducts (Michel and Czjzek, 2013;Ojima, 2013;Tsuji et al., 2013). The resulted sugars are applicable for foods, feeds, pharmaceutics, fermentation substrates, etc.
To date, GHF45 cellulase genes have been identified only in a few mollusks (Xu et al., 2001;Guo et al., 2008;Sakamoto and Toyohara, 2009) and enzyme proteins have been isolated only from M. edulis (Xu et al., 2000), A. crossean (Li et al., 2005), and A. kurodai (Tsuji et al., 2013). Molluscan GHF45 cellulases were suggested to be acquired by horizontal gene transfer from fungi by phylogenetic analyses (Scholl et al., 2003;Kikuchi et al., 2004;Sakamoto and Toyohara, 2009); however, accumulation of primary structure data seems to be still insufficient for detailed discussion about the origin and molecular evolution of molluscan GHF45 cellulases.
The common sea hare A. kurodai is a good source for polysaccharide-degrading enzymes since it harbor much digestive fluid in gastric lumen (Kumagai and Ojima, 2009;Rahman et al., 2010;Zahura et al., 2010;Tsuji et al., 2013;Kumagai et al., 2014). Recently, four cellulase isozymes, i.e., 21, 45, 66, and 95 K cellulases, were isolated from the digestive fluid of A. kurodai (Tsuji et al., 2013). Among these enzymes, the 21K enzyme was suggested to be GHF45 cellulase. We also had noticed that the digestive fluid of A. kurodai contained plural cellulases and the smallest enzyme was of ∼21 kDa. This enzyme was considered to correspond to the 21K cellulase reported by Tsuji et al. (2013). Although partial amino-acid sequences of the 21K cellulase were reported, no entire primary structure has been determined yet.
In the present study, we isolated the ∼21 kDa enzyme from the digestive fluid of A. kurodai and investigated its general properties. Further, we cloned the cDNA encoding this enzyme and confirmed that this enzyme is a member of GHF45. This cDNA will provide the basis for protein-engineering studies on Aplysia GHF45 cellulase.
MATERIALS
Sea hares identified as A. kurodai (average body length and weight; ∼15 cm and ∼400 g, respectively) were collected in the shore of Hakodate, Hokkaido Prefecture of Japan in July 2012. Approximately 112 mL of digestive fluid was obtained from the gastric lumen of 14 animals after dissection. The digestive fluid was dialyzed against 2 mM sodium phosphate buffer (pH 7.0) for 2 h and centrifuged at 10,000 × g for 10 min to remove insoluble materials. The supernatant (crude enzyme) was used for purification of cellulase. Carboxymethyl cellulose (CMC, medium viscosity) was purchased from ICN Bio medicals, Inc. (OH, USA). TOYOPEARL CM-650M was purchased from Toyo Soda Mfg, Co. (Tokyo, Japan) and Superdex 200 10/300 GL from GE Healthcare UK Ltd. (Little Chalfont, Buckingham shire, England). Cellooligosaccharides (disaccharide -hexasaccharide, G2 -G6) were prepared by limited acid hydrolysis. Briefly, 1 g of cellulose powder (Wako Pure Chemical Industries Co. Ltd. Osaka, Japan) was hydrolyzed with 100 mL of 0.2 N HCl at 100 • C for 1 h, and the supernatant containing cellulose fragments was neutralized with 1 N NaOH. Approximately 50 mg of cellulose fragments were subjected to gel-filtration through a column of BioGel-P2 (2 × 100 cm) and cellooligosaccharides were separately eluted with 10 mM sodium phosphate buffer (pH 7.0) and stored at −20 • C until use. RNAiso Plus and Oligotex dT30 were purchased from TaKaRa (Tokyo, Japan). cDNA synthesis kit and 5 -and 3 -Full RACE kits were purchased from TaKaRa and TA-PCR cloning kit comprising pTAC-1 and E. coli DH5α was from Biodynamics (Tokyo, Japan). Restriction endonucleases, T4 DNA ligase, agarose, E. coli strain DH5α were purchased from TaKaRa. AmpliTaq Gold PCR Master Mix and BigDye-Terminator Cycle Sequencing kit were from Applied Biosystems (Foster city, CA, USA). Bacto-tryptone, Bacto-yeast extract and other reagents were from Wako Pure Chemicals Industries Ltd. (Osaka, Japan).
PURIFICATION OF A. KURODAI CELLULASE
Crude enzyme from A. kurodai (∼100 mL) was first subjected to ammonium sulfate fractionation. Cellulase activity was detected in the fraction precipitated between 40 and 60% saturation of ammonium sulfate. This fraction was collected by centrifugation at 10,000 × g for 20 min, dissolved in and dialyzed against 10 mM sodium phosphate buffer (pH 7.0) for 24 h. The dialysis bag was changed every 2 h to avoid puncturing by cellulase action. The dialysate was then applied to a TOYOPEARL CM-650M column (1.5 × 20 cm) pre-equilibrated with the same buffer. Proteins adsorbed to the column were developed by linear gradient of NaCl from 0 to 0.3 M. Fractions showing cellulase activity were pooled and dialyzed against 10 mM sodium phosphate buffer (pH 7.0) and lyophilized. The dried material was dissolved in 0.05 M NaCl-10 mM sodium phosphate buffer (pH 6.0) and subjected to AKTA-FPLC (GE Healthcare) equipped by Superdex 200 10/300 GL column. Cellulase was eluted with 0.05 M NaCl-10 mM sodium phosphate buffer (pH 6.0) at a flow rate of 1 mL/min.
ASSAY FOR CELLULASE ACTIVITY
Standard assay for cellulase activity was carried out with a reaction mixture containing 0.5% CMC, 10 mM sodium phosphate (pH 6.0), and 0.01-0.1 mg/mL of enzyme at 30 • C. Reducing sugar released by the reaction was determined by the method of Park and Johnson (1949). One unit of cellulase activity was defined as the amount of enzyme that produces reducing sugar equivalent to 1 μmol of glucose per 1 min. Temperature dependence of the cellulase was determined at 10-70 • C and pH 6.0. pH dependence was determined at 30 • C in reaction mixtures adjusted to pH 4.0-10.0 with 50 mM sodium phosphate. Thermal stability was assessed by measuring the residual activity in the standard assay condition after heating at 10-70 • C for 30 min. The average values of triplicate measurements were shown with standard deviations.
THIN-LAYER CHROMATOGRAPHY
Thin-layer chromatography (TLC) for degradation products of cellulose and cellooligosaccharides was carried out with Silica gel-60 TLC plates (Merck KGaA, Darmstadt, Germany). Two μL of the degradation products (∼5 mg/mL) were applied to the TLC plate and developed with 1-butanol/acetic acid/water (2:1:1, v/v/v). The sugars separated on the plate were detected by heating at 120 • C for 15 min after spraying 10% (v/v) sulfuric acid in ethanol.
SDS-PAGE
SDS-PAGE was carried out by the method of Porzio and Pearson (1977) using 10% (w/v) polyacrylamide gel containing 0.1% (w/v) SDS. After the electrophoresis, the gel was stained with 0.1% (w/v) Coomassie Brilliant Blue R-250-50% (v/v) methanol-10% (v/v) acetic acid, and the background of the gel was destained with 5% (v/v) methanol-7% (v/v) acetic acid. Molecular masses of proteins were estimated with a Protein Marker, Broad Range (New England Biolabs, Inc. MA, USA).
PROTEIN CONCENTRATION
Protein concentration was determined by either the biuret method (Gornall et al., 1949) or the method of Lowry et al. (1951) using bovine serum albumin fraction V as a standard protein.
DETERMINATION OF PARTIAL AMINO-ACID SEQUENCES
The N-terminal amino-acid sequence of cellulase was determined with specimens electro-blotted to polyvinylidene difluoride membrane and ABI 492 protein sequencer (Applied Biosystems). The internal amino-acid sequences of cellulase were determined by mass spectrometry with tryptic and lysylendopeptidyl fragments prepared by the digestion with 1/200 (w/w) enzymes at 37 • C for 12 h. The fragments were subjected to matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) using Proteomics Analyzer 4700 (Applied Biosystems) and the amino-acid sequences of the fragments were analyzed by MS/MS mode with DeNovo Explorer software (Applied Biosystems). Homology searches for the amino-acid sequences to the public databases were performed with the BLAST program (http://blast.ddbj.nig.ac.jp/top-j.html) provided by DNA Data Bank of Japan.
cDNA CLONING
Total RNA was extracted from ∼0.1 g of hepatopancreas of A. kurodai using RNAiso Plus and mRNA was selected from the total RNA with Oligotex-dt(30) (TaKaRa). Hepatopancreas cDNA was synthesized from the mRNA with a cDNA synthesis kit (TaKaRa) using random hexanucleotide primers. Cellulase cDNAs were amplified by the PCR using the hepatopancreas cDNA and degenerated primers synthesized on the basis of partial amino-acid sequences. PCR was performed in 20 μL of reaction mixture containing 50 mM KCl, 15 mM Tris-HCl (pH 8.0), 0.2 mM each of dATP, dTTP, dGTP, and dCTP, 2.5 mM MgCl 2 , 5 pmol/μL primers, 1 ng/μL template DNA, and 0.5 units AmpliTaq Gold DNA polymerase (Applied Biosystems). A successive reaction consisting of 94 • C for 30 s, 55 • C for 30 s, and 72 • C for 60 s was repeated 40 cycles with Thermal Cycler Dice mini (TaKaRa). Sizes of amplified cDNAs were estimated by 1.2% agarose-gel electrophoresis and the target cDNAs were excised from the gel and cloned with TA-PCR cloning kit (Invitrogen).
The transformed E. coli was grown in 2 × YT medium supplemented by 50 μg/mL ampicillin at 37 • C for 14 h with shaking at 150 rpm/min. The plasmids extracted from the transformants were subjected to sequence analysis with BigDye-Terminator Cycle Sequencing kit and ABI 3130xl Genetic Analyzer (Applied Biosystems). The 3 -RACE and 5 -RACE PCRs were carried out with specific primers synthesized on the basis of nucleotide sequences of above amplified cDNAs with a successive reaction at 94 • C for 30 s, 57 • C for 30 s, and 72 • C for 1.0 min, which was repeated 30 cycles. The amplified DNAs were cloned and sequenced as described above.
PHYLOGENETIC ANALYSIS FOR GHF45 CELLULASES
Phylogenetic analysis was carried out with amino-acid sequence data of A. kurodai cellulase and other GHF45 cellulases which are enrolled in GenBank (http://www.ncbi.nlm.nih.gov/) and CAZy database (http://www.cazy.org/fam/acc_GH.html). The aminoacid sequences of GHF45 cellulases were first aligned with ClustalW2 program (Larkin et al., 2007). The alignment was then corrected by trimming the sequences with Gblocks (Castresana, 2000;Talavera and Castresana, 2007). The maximum-likelihood algorithm implemented in MEGA6 software (Tamura et al., 2013) was used to generate phylogenetic tree. The bootstrap values were calculated from 1,000 replicates.
ISOLATION AND CHARACTERIZATION OF APLYSIA 21 kDa CELLULASE
Cellulase activity was detected in four peak fractions (P-1-P-4) in TOYOPEARL CM-650M chromatography performed for the proteins obtained by the ammonium sulfate fractionation (Figure 1). The N-terminal amino-acid sequences of major proteins in P-1-P-4 fractions were analyzed with the samples blotted to PVDF membranes after SDS-PAGE. According to BLAST search analyses, the 40 kDa protein in P-1 fraction (N-terminal sequence: RLHIQNGHFVLNGQRVFLSG) was identified as A. kurodai β-mannanase AkMan (Zahura et al., 2010). The 21 kDa protein in P-2 fraction (N-terminal sequence: EQKCQPNSHGVRVYQGKKCA) was considered to be a GHF45 cellulase corresponding to 21K cellulase previously reported by Tsuji et al. (2013). The 45 kDa protein in P-4 fraction (N-terminal sequence: AKNYGQALGLSIKFYEAQ) was regarded as a GHF9 cellulase similar to H. discus hannai HdEG66 (Suzuki et al., 2003) and 45K cellulase reported by Tsuji et al. (2013). While 38 kDa protein (N-terminal sequence: RLTVSGKTFRLNNQQVFLSG) was regarded as the β-mannanase-like protein that had been annotated in A. california genome (GenBank accession number, XP_005100017). The 21 kDa cellulase in P-2 fraction was recovered in high yield, while the GHF9-type cellulase in P-4 fraction poorly recovered. The GHF9-type cellulase exhibited similar properties as abalone cellulase HdEG66 (Suzuki et al., 2003) and Aplysia 66K cellulase (Tsuji et al., 2013). Therefore, in the present study, we focused on the 21 kDa cellulase in P-2 fraction to characterize it as a GHF45 cellulase.
The 21 kDa cellulase in P-2 fraction was purified by gelfiltration through Superdex 200 (Figure 2). In this chromatography, the 21 kDa cellulase eluted as a single peak showing a single band on SDS-PAGE. Thus, we named this enzyme AkEG21 (Aplysia kurodai endo-β-1,4-glucanase with 21 kDa). By the above purification procedure, AkEG21 was purified at a yield of 3.3% with the specific activity 67.3 U/mg ( Table 1). Optimal pH of AkEG21 was 4.5 and more than 80% of maximal activity was retained in a pH range from 4.3 to 5.6 ( Figure 3A). AkEG21 showed an optimal temperature at around 40 • C and it was resistant to the incubation at 55 • C for 30 min. The temperature that caused a half inactivation of AkEG21 during 30 min incubation was ∼65 • C (Figures 3B,C). These results indicated that AkEG21 was relatively heat-stable among the molluscan cellulases reported so far. Degradation products of cellulose and cellooligosaccharides produced by AkEG21 were analyzed by TLC. As shown in Figure 4A, AkEG21 degraded amorphous cellulose producing cellobiose and cellotriose. Cellooligosaccharides larger than cellotriose were not detected in the reaction products even after 16 h incubation. On the other hand, AkEG21 showed high activity toward hexaose (G6) and pentaose (G5) and weak activity toward tetraose (G4), but no activity toward triose (G3) (Figure 4B). AkEG21 readily degraded G5 to G2 and G3 (plus trace amount of G4 and glucose), and degraded G6 to G2 and G4 along with small amount of G3. These degradation profiles were practically unchanged even in the longer reaction time and higher enzyme concentrations although the amounts of the products were increased (data not shown). These results indicate that AkEG21 is an endo-β-1,4-D-glucanase (EC 3.2.1.4). However, readily production of cellobiose and small amount of cellotriose from amorphous cellulose without larger intermediate oligosaccharides may indicate that this enzyme can act as cellobiohydrolase-like enzyme as suggested by Tsuji et al. (2013).
CLONING OF AkEG21 cDNA
The N-terminal amino-acid sequence of AkEG21 was determined as EQKCQPNSHGVRVYQGKKCA-by the protein sequencer ( Table 2). This sequence shared ∼40, 40, 55, and 60% amino-acid Figure 6) are also shown in the parentheses.
To determine the entire amino-acid sequence of AkEG21, we amplified AkEG21 cDNA by the PCR using degenerated forward and reverse primers, cDNA-1(Fw) and cDNA-1(Rv), respectively ( Table 3). The amplified cDNA comprised 405 bp and encoded an amino-acid sequence of 135 residues. Then, 3RACE-cDNA of 519 bp covering the 3 -terminal region was amplified by 3 -RACE PCR with specific primers designed on the basis of the nucleotide sequence of the first amplified cDNA. Finally, 5RACE-cDNA of 363 bp covering 5 -terminal region was amplified by 5 -RACE PCR with a series of specific primers synthesized on the basis of the sequence of the amplified cDNA. By overlapping the nucleotide sequences of 5RACE-cDNA, first amplified cDNA and 3RACE-cDNA in this order, the nucleotide sequence of total 1013 bp including the complete translational region of AkEG21 was determined (Figure 5). The transcription-initiation codon (ATG) was found in the nucleotide positions from 162 to 164, while the termination codon (TGA) was in 753-755. Accordingly, the coding region of AkEG21 cDNA was found to locate in the nucleotide positions from 162 to 752 and encode 197 aminoacids. All the partial amino-acid sequences determined with peptide fragments, L-1, L-2, and T-1-T-3 (Table 2), were found in the deduced sequence (Figure 5). A putative polyadenylationsignal sequence (AATAAA) located at 22 nucleotides upstream from the poly (A) + tail. This suggested that the origin of AkEG21cDNA was not intestinal prokaryotes but eukaryote, i.e., Aplysia itself. The N-terminus of mature AkEG21 protein was identified as Glu18 in the deduced sequence indicating that the N-terminal region of 16 residues except for initiation Met was the signal peptide for secretion. Indeed, KTFAAILAALIACALA located in the N-terminus of the deduced sequence was predicted as the signal peptide by SignalP 4.1 server (http://www. cbs.dtu.dk/services/SignalP/). Accordingly, the mature AkEG21 was concluded to comprise 180 amino-acid residues with the calculated molecular mass of 19,854.0 Da (Figure 5). The nucleotide and the deduced amino-acid sequences are available from the DNA Data Bank of Japan with the accession number AB920344.
The amino-acid sequence of AkEG21 was aligned with those of other molluscan GHF45 cellulases (Figure 6) and 47, 49, 54, and 62% identities were calculated between AkEG21 and Eg from M. edulis (GenBank accession number, CAC59695), CjCel45A from C. japonica (GenBank accession number, BAH23793), endo-β-1,4-glucanase 1 from H. discus discus (GenBank accession number, ABO26608), and EG27I from A. crossean (GenBank accession number, ABR92637), respectively. The consensus amino-acid sequence T-T-R-Y-X-D that has been shown to take part in the catalytic site of GHF45 enzymes (Girard and Jouanin, 1999;Guo et al., 2008) was conserved in AkEG21 as Thr39-Asp44. The N-glycosylation site (Asn-X-Thr/Ser) was also conserved as Asn64-Ser66 where Ans64 was the N-glycosylation residue according to the analyses with NetNGlyc 1.0 server (http://www.cbs.dtu.dk/services/ NetNGlyc/). Twelve Cys residues that form six disulfide bonds stabilizing the catalytic domain were also conserved in AkEG21. Two Asp residues that function as catalytic nucleophile and proton donor in GHF45 enzymes were conserved as Asp44 and Asp154, respectively, in AkEG21. These features in the aminoacid sequence of AkEG21 indicate that this enzyme belongs to GHF45.
PHYLOGENETIC ANALYSIS
To reveal the structural relationship between AkEG21 and other GHF45 cellulases, phylogenetic analysis was performed using amino-acid sequence data of GHF45 cellulases from mollusk, fungi, insects, nematode, protists and bacteria. The tree topology drawn by the maximum likelihood analysis revealed that molluscan GHF45 cellulases are assembled as a large clade (bootstrap values above 50%) with some fungal enzymes (Figure 7). Whereas, enzymes from insects, nematode, protists, bacteria and some other fungi formed another paraphyletic group. These clustering results suggest that molluscan GHF45 cellulases have been deviated from other animal cellulases but closely related to some fungal cellulases.
DISCUSSION
The ocean that covers 70% of surface on the Earth is rich in biodiversity, e.g., organisms from 34 of 38 animal phyla are living in the ocean. Through the adaptation to diverse physical and chemical conditions of marine environments, marine organisms are believed to have deviated along with acquiring specific phenotypes. Thus, the marine organisms have become capable of producing variety of characteristic chemical compounds relating to lipids, proteins and carbohydrates, as a result of adaptation to marine environments. Such marine bio-products are promising materials for functional food additives, pharmaceutics, cosmetics, industrial materials, energy sources, etc. Among the marine bio-products, polysaccharides produced by marine algae, e.g., agar, carrageenan and alginate, which have already been used as gelling agents, viscosifiers and dietary fibers in food, are also important materials for producing functional oligosaccharides and fermentable sugars (Nishida et al., 2007;Kumagai and Ojima, 2009;Rahman et al., 2010;Zahura et al., 2010;Takeda et al., 2011;Wargacki et al., 2012;Tsuji et al., 2013;Yanagisawa et al., 2013;Enquist-Newman et al., 2014;Kumagai et al., 2014). Actually, enzymatically degraded seaweed polysaccharides were shown to exhibit beneficial activities to human (Deville et al., 2007;Wang et al., 2012;Thomas and Kim, 2013). Further, monosaccharides produced by the degradation of alginate were found to be available as a source material for ethanol fermentation (Takeda et al., 2011;Wargacki et al., 2012;Enquist-Newman et al., 2014). While, sea lettuce Ulva pertusa was also used as feedstock for acetone and ethanol (Yanagisawa et al., 2011;van der Wal et al., 2012). These trends have stimulated the exploration of new enzymes that convert seaweeds' polysaccharides to value-added materials.
AkEG21 showed an optimum pH at around 4.5 and more than 80% of maximal activity retained at pH range from 4.3 to 5.6. This pH range is consistent with the pH range of the digestive fluid of A. kurodai, i.e., pH 4-6 (Zahura et al., 2011;Tsuji et al., 2013). Although optimal pH of microbial cellulases is known to vary from acidic to alkaline range (Hurst et al., 1977;Ito et al., 1989;Park et al., 2002), those of animal GHF45 cellulases are usually in acidic pH range. For example, optimal pHs of EG27 from A. crossean (Li et al., 2005) and Cel45A from M. edulis (Xu et al., 2000) were shown at around 4.4-4.8. While, heat stability of AkEG21 was found to be considerably high, i.e., it retained more than 80% of maximal activity after the pre-incubation at 55 • C for 30 min and was not completely inactivated even at 70 • C (Figure 3). Such high heat stability was also reported in EG27 from A. crossean (Li et al., 2005), Cel45A from M. edulis (Xu et al., 2000) and 21K cellulase from A. kurodai (Tsuji et al., 2013). EG27 retained ∼85% of maximal activity after the incubation at 60 • C for 24 h (Li et al., 2005) and Cel45A retained more than 70% of the activity after the incubation in boiling water bath for 10 min (Xu et al., 2000). Such stabilities of GHF45 cellulases in acidic and high temperature conditions may be due to the formation of plural disulfide bonds in the catalytic domain. Such stability of GHF45 cellulase will be advantageous in performing both basic researches and biotechnological applications. On the other hand, M. edulis cellulase Cel45A was reported to show an unusual psychrophilic feature, i.e., it retains 55-60% of its maximum activity even at 0 • C (Xu et al., 2000). AkEG21 also showed relatively high activity in low temperature conditions, e.g., it retained ∼40% of the maximal activity at 10 • C (Figure 3). In this respect, molluscan GHF45 cellulases may be applicable for cellulose degradation in acidic and broad temperature conditions. AkEG21 produced cellotriose and cellobiose as major products from amorphous cellulose and efficiently hydrolyzed cellohexaose and cellopentaose, and moderately cellotetraose, but not cellotriose and cellobiose. These indicated that AkEG21 recognized cellotetraose unit in cellulose chain and split the central glycosyl linkage of tetraose. Such substrate-recognition profiles of AkEG21 were essentially the same as those of from Cel45A from M. edulis (Xu et al., 2000) and 21K cellulase from Aplysia (Tsuji et al., 2013).
By the cDNA method, an entire amino-acid sequence of AkEG21 comprising 197 residues was predicted. The sequence of catalytic domain comprising 180 residues shared 47-62% aminoacid identities with the other molluscan GHF45 cellulases and conserved T-T-R-Y-X-D motif and two Asp residues which were identified as catalytic site and residues of GHF45 enzymes (Girard and Jouanin, 1999;Bourne and Henrissat, 2001;Guo et al., 2008) (Figure 6). AkEG21 possessed a typical N-glycosylation motif (Asn-X-Thr/Ser) at amino-acid positions of 64-66, and the Ans64 was predicted to be the N-glycosylation residue (Figure 6). The 21K cellulase from Aplysia was shown to be glycosylated (Tsuji et al., 2013). This indicated that AkEG21 was a glycosylated enzyme. Cel45A from M. edulis (Xu et al., 2000) and CjCel45A from C. japonica (Sakamoto and Toyohara, 2009) were also suggested to be glysocylated at the N-glycosylation sites, while no N-glycosylation site was found in GHF45 cellulase from A. crossean (Guo et al., 2008) and H. discus discus (GenBank accession number, ABO26608) (Figure 6). On the other hand, coleopteran GHF45 cellulases, e.g., Ag-EGase I (contain 2 N-glycosylation sites) and Ag-EGase II (contain 3 N-glycosylation sites) from Apriona germari, and Oa-EGase II (contain 2 Nglycosylation sites) from Oncideresalbomarginata chamela were found to be N-glycosylated and the N-glycosylations were important for secretion and enzyme activity (Wei et al., 2006;Calderon-Cortes et al., 2010). Previous report showed that 90% of proteins possessing the sequence Asn-X-Ser/Thr were glycosylated (Gavel and von Heijne, 1990). The roles of glycosylation are known to vary from protein to protein (Bisaria and Mishra, 1989;Wang and Gao, 2000). It is necessary to examine the physiological significance of the N-glycosylation in AkEG21 using recombinant enzymes expressed in prokaryote cells where no glycosylation takes place.
AkEG21 contained 12 Cys residues. This suggested that occurrence of six disulfide bonds in AkEG21, which may structurally stabilize the catalytic domain. All the molluscan GHF45 possess 12 Cys residues in common positions, suggesting that the stabilization by 6 disulfide bridges is a common feature among the GHF45 cellulases. Extremely high thermal stability of Cel45A from M. edulis, which withstands the heat-treatment at 100 • C for 10 min (Xu et al., 2000), may be derived from such disulfide bonds. AkEG21 was also considerably heat stable probably due to the multiple disulfide formations.
In most organisms, cellulases are produced as modular enzymes made up of a catalytic domain and cellulose-binding domain(s) (CBD) that facilitates adsorption of the catalytic domain to insoluble cellulose (Gilkes et al., 1991;Henrissat and Davies, 2000). However, AkEG21 lacked cellulose-binding domain (CBD). Lack of CBD was also the cases of Cel45A from M. edulis (Xu et al., 2000), EG27 from A. crossean (Guo et al., 2008) and CjCel45 from C. japonica (Sakamoto and Toyohara, 2009). Physiological meaning of the lack of CBD in molluscan GHF45 cellulases is currently obscure; however, low affinity of enzyme to cellulose substrate may rather suitable for the turnover of enzyme in the digestive fluid to digest amorphous seaweed cellulose.
Kinds of animal digestive enzymes appeared to be closely related to the staple foods of animals (Baldwin, 1949). However, distribution of cellulase in animal kingdom was found to be more closely related to their phylogenetic relationships than their feeding habits (Yokoe and Yasumasu, 1964). GHF45 cellulases have been found in fungi, bacteria, protists, and some invertebrate animals (Henrissat and Bairoch, 1993; http://www.cazy. org/Glycoside-Hydrolases.html). Phylogenetic analysis revealed that molluscan GHF45 cellulases and some fungal enzymes were clustered as a distinct group (Figure 7). Such clustering of molluscan GHF45 cellulases suggested that they have evolved from the same origin. Relatively close relation between molluscan cellulases and fungal cellulases suggests that molluscan enzymes were acquired by horizontal gene transfer from fungi as suggested by Sakamoto and Toyohara (2009). On the other hand, presence of potential N-glycosylation sites in all molluscan GHF45 cellulases is in common with some coleopteran cellulases may suggest that the molluscan cellulases share the common ancestor with insect GHF45 cellulases and have diverged from them during the evolutionary process (Davison and Blaxter, 2005;Watanabe and Tokuda, 2010). Rigorous investigation is necessary before concluding that the animal cellulases are acquired by horizontal gene transfer from fungi (Ochman et al., 2000;Genereux and Logsdon, 2003).
Besides AkEG21, a GHF9 cellulase of 45 kDa was also found in the digestive fluid of A. kurodai (see Figure 1). Occurrence of multiple cellulase genes belonging to different GHFs in mollusks has already been reported (Zhang et al., 1999;Wang et al., 2003;Li et al., 2005;Sakamoto et al., 2007;Guo et al., 2008;Sakamoto and Toyohara, 2009) and synergistic action of multiple cellulases was recently reported (Tsuji et al., 2013). It may be reasonable to consider that herbivorous mollusks rely on plural cellulases to degrade cellulose to obtain carbohydrate nutrient from seaweeds. Indeed, the GHF9 cellulase of A. kurodai exhibited relatively higher specific activity compared with the GHF45 cellulase AkEG21 upon degradation of amorphous cellulose (Tsuji et al., 2013). Such differences in enzymatic properties were attributed to the differences in enzymatic parameters (Tsuji et al., 2013). The protein-engineering study on AkEG21 for application of this enzyme as biocatalyst for degradation of cellulosic materials from seaweeds is now under the investigation. | 6,633.2 | 2014-06-28T00:00:00.000 | [
"Biology",
"Chemistry",
"Environmental Science"
] |
Nanomedicine‐boosting icaritin-based immunotherapy of advanced hepatocellular carcinoma
Traditional treatments for advanced hepatocellular carcinoma (HCC), such as surgical resection, transplantation, radiofrequency ablation, and chemotherapy are unsatisfactory, and therefore the exploration of powerful therapeutic strategies is urgently needed. Immunotherapy has emerged as a promising strategy for advanced HCC treatment due to its minimal side effects and long-lasting therapeutic memory effects. Recent studies have demonstrated that icaritin could serve as an immunomodulator for effective immunotherapy of advanced HCC. Encouragingly, in 2022, icaritin soft capsules were approved by the National Medical Products Administration (NMPA) of China for the immunotherapy of advanced HCC. However, the therapeutic efficacy of icaritin in clinical practice is impaired by its poor bioavailability and unfavorable in vivo delivery efficiency. Recently, functionalized drug delivery systems including stimuli-responsive nanocarriers, cell membrane-coated nanocarriers, and living cell-nanocarrier systems have been designed to overcome the shortcomings of drugs, including the low bioavailability and limited delivery efficiency as well as side effects. Taken together, the development of icaritin-based nanomedicines is expected to further improve the immunotherapy of advanced HCC. Herein, we compared the different preparation methods for icaritin, interpreted the HCC immune microenvironment and the mechanisms underlying icaritin for treatment of advanced HCC, and discussed both the design of icaritin-based nanomedicines with high icaritin loading and the latest progress in icaritin-based nanomedicines for advanced HCC immunotherapy. Finally, the prospects to promote further clinical translation of icaritin-based nanomedicines for the immunotherapy of advanced HCC were proposed.
clinical practice. Yet, chemotherapy offers only a modest overall survival rate due to the serious side effects of long-term medication [5,6]. Moreover, advanced HCC patients suffering from liver functions impairments are often more vulnerable to drug-associated toxicity. In addition, advanced HCC increases the expression of proteins that can generate drugs chemoresistance [7]. Hence, there is an urgent need to develop novel drug delivery and therapeutic approaches according to the properties of advanced HCC.
In the recent decade, immunotherapy has emerged as a powerful method against many types of cancers due to its minimal side effects and long-lasting therapeutic memory effects [8]. Along with acceleration in clinical approvals, several modalities of cancer immunotherapy have achieved significant progress [9]. Recently, scientists explored the feasibility of immunotherapy for advanced HCC, such as immune checkpoint blockade (ICB) [e.g., the monoclonal antibodies (mAbs) tremelimumab and nivolumab], which have enhanced survival rates in advanced HCC patients [10,11]. However, chronic inflammation and antigenic stimulation during the progression of advanced HCC lead to an immunosuppressive microenvironment with the functionally impaired effector T cells, as well as a high level of infiltration and large accumulation of suppressive myeloid cells [12,13]. Previous research reported that ICB treatment for advanced HCC using a programmed death-ligand 1 (PD-L1) antibody had a low remission rate of less than 20% [14]. It is important to reduce the recruitment of immunosuppressive cells in the HCC microenvironment. Thus, overcoming the immunosuppression of advanced HCC with newly-explored immunomodulators would be one of the key ways to improve the therapeutic outcomes.
The prenylated flavonoid icaritin, a traditional Chinese medicine, is an active natural compound derived from Epimedii Folium (Yinyanghuo in Chinese) (Fig. 1). Icaritin has been reported to display enormous potential to treat various diseases. In particular, icaritin and its derivatives play an anticancer role by triggering cell apoptosis, modulating the cell cycle and hormone signaling, inhibiting cancer angiogenesis and metastasis, suppressing the growth of cancer stem cells, and immunomodulation [15]. In the last decade, it was found that icaritin displays the excellent therapeutic efficacy on advanced HCC [16,17]. Notably, recent study indicated that icaritin represents a potential immunomodulator with favorable biosafety, prolonged survival rate in advanced HCC patients [18]. In January 2022, icaritin soft capsules were approved as an immunomodulatory agent by Fig. 1 Icaritin soft capsule for immunotherapy of advanced HCC. Icaritin can be extracted from Epimedii Folium or produced by hydrolyzing the other major flavonoids in Epimedii Folium. The icaritin soft capsule formulations can be applied for immunotherapy of advanced HCC by interacting with the MyD88/IкB kinase α protein complex, suppressing IL-6/JAK/STAT3 signaling pathway, and reducing the generation of cytokines (e.g., TNF-α and IL-6) and the expression of immune checkpoints (e.g., PD-L1). Moreover, icaritin can inhibit the bioactivity of MDSCs by down-regulating the tumor-associated splenic extramedullary hematopoiesis. Finally, effector T cell function is enhanced and the immune tolerance in advanced HCC is alleviated, which ultimately improve the efficacy of icaritin-based immunotherapy in advanced HCC. HCC hepatocellular carcinoma, MyD88 myeloid differentiation factor 88, IL-6 interleukin-6, JAK Janus kinase, STAT3 signal transducer and activator of transcription 3, TNF-α tumor necrosis factor-α, PD-L1 programmed death-ligand 1, MDSCs myeloid-derived suppressor cells the NMPA of China for the treatment of advanced HCC based on the positive results from clinical phase III trials (NCT03236636, NCT03236649) [18,19].
Despite considerable progress, the immunotherapeutic effect of icaritin in vivo for advanced HCC is still impaired by its low bioavailability and delivery efficiency. Pharmacokinetic studies have shown that only 4.33% of icaritin is rapidly absorbed into the blood of rats, and the overall bioavailability was as low as 2% due to its limited aqueous solubility and low permeability [20,21]. In recent years, the development of icaritin-based nanomedicines has provided a solution for enhancing therapeutic efficacy with low side effects. Various nanomedicines have been developed to improve the bioavailability, prolong blood circulation, augment targeted accumulation, and elevate tumor penetration of icaritin. In addition, stimuli-responsive nanocarriers allow the tailored release of icaritin in advanced HCC with excellent spatial, temporal and dosage control. Therefore, these functionalized nanomedicines exhibit favorable biodistribution of icaritin and reduced toxicity to healthy tissues [22][23][24][25]. Moreover, these nanomedicines may help to raise the antigenicity of advanced HCC for a sustained cancer-immunity cycle. Remarkably, several polymeric nanocarriers including polymer conjugates, liposomes and micelles have been approved for clinical practice for the treatment of cancers worldwide. The combination of the clinically approved nanocarriers with icaritin would be one of the most promising ways to create the icaritinbased nanomedicines with clinical translation potential for the treatment of advanced HCC.
In this perspective, we summarize the latest progress of icaritin and icaritin-based nanomedicines for enhanced immunotherapy of advanced HCC. First, several icaritin preparation methods are compared. Second, the HCC immune microenvironment and the mechanisms of icaritin against advanced HCC are interpreted. Third, the design of icaritin-based nanomedicines with high icaritin loading efficiency is described. Fourth, icaritinbased nanomedicines for improved immunotherapy of advanced HCC are discussed. Finally, the prospects for facilitating the further clinical translation of icaritinbased nanomedicines are proposed.
Preparation of active icaritin
Icaritin and some major flavonoids (e.g., icariin, epimedin A, epimedin B, epimedin C, and baohuoside I) in Epimedii Folium share the same fundamental skeleton but have different glycosyl substitutions at the C-3 and C-7 positions [26]. The content of icaritin, which is an aglycone of the epimedium flavonoids without any sugar moieties, is lower than 0.1%, while icariin is the most abundant flavonoid in Epimedii Folium [27,28].
For the production of icaritin, several methods including column chromatography, chemical synthesis, and acid and enzymatic hydrolysis have been developed. However, column chromatography, the commonly used method for icariin preparation, is not feasible for largescale production due to the low amount of icaritin found in natural plants [29]. The industrial application of chemical synthesis remains difficult due to the tedious procedures (more than 8 steps), harsh reaction conditions (high temperature of 110 ℃), low yield (less than 23%), and adverse effects on product activity [30,31]. In addition, acid hydrolysis of icariin tends to generate various byproducts, such as baohuoside I, icariside I, maohuoside A, and anhydroicaritin 3-O-rhamnoside [32]. Recently, enzymatic hydrolysis has been utilized to obtain icaritin, as it has the advantages of remarkable selectivity, mild reaction conditions, high efficiency and environmental friendliness. In enzymatic reactions, icaritin is produced by removing the sugar moieties at both the C-3 and C-7 positions of other flavonoids, such as icariin. For instance, icariin has been hydrolyzed into icaritin with the high productivity of 86.2% and a corresponding molar conversion rate of 91.2% using GH78 α-L-rhamnosidase and recombinant β-glucosidase through a two-stage transformation [33].
Although enzymatic catalysis is superior to other preparation methods, the productivity of icaritin decreases when enzyme activity and stability are impaired after a long reaction time. To further improve enzymatic catalytic efficiency, a directed evolution campaign can be used to generate enzyme variants that tolerate high reaction temperatures, product inhibition, and organic solvents. This strategy has been previously verified to improve the thermostability, activity, and glucose tolerance of β-glucosidase and α-L-rhamnosidase [34,35]. Likewise, enzyme immobilization technology is another option to improve the stability and reusability of enzymes [36,37]. Dong et al. [36] reported that two thermostable glycosidases (β-glucosidase DthBgl3 and α-Lrhamnosidase DthRha) were successfully immobilized on 1000NH amino resin. The immobilized DthBgl3 and DthRha transformed the total flavonoids extract from epimedium completely into icaritin with a molar conversion rate of 87.21% and productivity of 141 mg/(L·h) after 15 cycles of repeated use. Moreover, noninvasive green solvents, such as poly ethylene glycol (PEG), polyacrylate, ionic liquids, and deep eutectic solvents, have been successfully applied to improve the enzymatic hydrolysis of other flavonoids, such as rutin, and can be employed to dissolve poorly water-soluble icariin and maintain enzyme activity by virtue of their biocompatibility and biosecurity [38,39]. The combination of the aforementioned strategies, such as conducting deglycosylation in a hydrolysis system containing immobilized β-glucosidase and α-L-rhamnosidase variants with high performance and deep eutectic solvent-dissolved icariin, would significantly improve hydrolysis efficiency and thus increase the productivity of icaritin.
Additionally, biosynthetic methods with engineered microbial strains offer alternative ways to generate icaritin from glucose, which could facilitate the industrialscale production of icaritin and other prenylflavonoids [40]. The productivity of icaritin can be further increased by engineering microbial strains and optimizing the fermentation conditions.
HCC immune microenvironment
During HCC progression, HCC cells reprogram their metabolism and interact with stromal cells and the complex ECM to shape an immunosuppressive microenvironment for immune privilege. First, compared to tumors with high tumor mutation burden (TMB), such as melanoma, HCC shows a relatively low TMB [41]. Correspondingly, the antigenicity of HCC is generally unfavorable for a sustained cancer-immunity cycle that requires enough tumor neoantigen to stimulate antigen presenting cells (APCs) and T cells. Additionally, local liver cells are actively involved in tumor immune tolerance. For example, Kupffer cells in the liver produce the inhibitory cytokine interleukin-10 (IL-10) and indoleamine 2,3-dioxygenase (IDO) to activate immunosuppressive regulatory T cells (Tregs), and hepatic stellate cells (HSCs) and liver sinusoidal endothelial cells (LSECs) also drive the accumulation of myeloid-derived suppressor cells (MDSCs) and Tregs [42]. The densities of these immunosuppressive types of immune cells within the immune microenvironment correlate with the poor prognosis of advanced HCC. For example, the increased infiltration of Tregs in the liver is associated with the dysfunction of T cell-mediated tumor surveillance. MDSCs support the progression of HCC by promoting the production of vascular endothelial growth factor (VEGF), which further facilitates the vascularization and angiogenesis of tumors. As a result, multiple immunosuppressive factors are constantly observed in the immune microenvironment. Tumor infiltrating lymphocytes (TILs) in the advanced HCC immune microenvironment tend to be dysfunctional with the enhanced expression of co-inhibitory molecules, including programmed death-1 (PD-1), cytotoxic T lymphocyte associated antigen 4 (CTLA-4), lymphocyte-activation gene 3 (LAG-3), and T cell immunoglobulin and mucin domain containing-3 (TIM-3) [43]. Additionally, a decreased CD8 + /CD3 + T cell ratio and CD56 + natural killer (NK)/natural killer T (NKT) cell infiltration can be found in the HCC differentiation.
Anti-HCC mechanisms of icaritin
Icaritin exerts therapeutic efficacy on advanced HCC through both chemotherapy and immunomodulation, and acts both on cancer cells and immune cells, especially MDSCs. As a chemotherapeutic compound, icaritin can induce cell apoptosis via a caspase-dependent pathway [44]. In recent work, icaritin was found to promote apoptosis of HCC cells by down-regulating alpha-fetoprotein gene expression [35]. In addition, Wang et al. [17] found that icaritin could trigger cellular senescence by inducing the production of reactive oxygen species (ROS) and DNA damage. Importantly, a lower amount of icaritin was needed to trigger cellular senescence than to induce cell death, which can avoid the severe side effects of drugs. Likewise, icaritin was demonstrated to potentiate doxorubicin (DOX)induced immunogenic cell death (ICD) in advanced HCC and thus improve the immune response by inducing mitophagy and apoptosis [45,46].
Recently, a growing amount of evidence has suggested that icaritin can be applied for advanced HCC immunotherapy by modulating the immune system. As shown in Fig. 1, the immunomodulatory activities of icaritin are mainly associated with the interleukin-6 (IL-6)/Janus kinase (JAK)/signal transducer and activator of transcription 3 (STAT3) signaling pathway. Icaritin interacts with the myeloid differentiation factor 88 (MyD88)/IкB kinase α protein complex, which further suppresses the downstream IL-6/JAK/STAT3 signaling pathways and influences the secretion of cytokines and the expression of immune checkpoint molecules as well as the differentiation of immune cells. Specifically, the involvement of icaritin in advanced HCC treatment can relieve immunosuppression by reducing the generation of the inflammatory cytokines tumor necrosis factor-α (TNF-α) and IL-6, down-regulating the expression of the PD-L1 checkpoint in MDSCs and neutrophils, and restoring the function of interferon-γ (IFN-γ) + CD8 + T cells [2,47,48]. In addition, icaritin can directly downregulate the tumor-associated splenic extramedullary hematopoiesis (EMH), and thereby reduce the generation, activation and accumulation of MDSCs in tumor sites as well as recover the functions of effector T cells. This can then coordinate with PD-1 antibody-based ICB for enhanced antitumor responses in mouse tumor models [49]. Encouragingly, in a clinical phase I trial, icaritin was found to improve the survival rate by inducing the changes in immune biomarkers and immunosuppressive myeloid cells in patients with advanced HCC [2]. These findings not only help to speed up the approval of icaritin as an immunomodulator for advanced HCC, but also provoke thoughts for the development of other anti-IL-6/JAK/STAT3 drugs for cancers. Despite the abovementioned progress, the underlying mechanism of icaritin in advanced HCC treatment is still elusive.
Design of icaritin-based nanomedicines
Nanocarriers have been considered to enhance the immunotherapy of icaritin for advanced HCC. The design of such nanocarriers is closely related to the physicochemical properties of icaritin. Icaritin possesses hydrophobic properties and contains active groups, such as phenolic hydroxyl moieties and benzene rings, which can form covalent and/or noncovalent interactions with nanocarriers. In addition, icaritin exhibits a negative charge under a physiological environment [50]. To achieve high loading efficiency, several kinds of suitable nanocarriers have been designed.
Micelles or liposomes with hydrophobic regions have been utilized to load icaritin through hydrophobic interactions. For instance, icaritin-loaded micelles were produced by encapsulating icaritin in the hydrophobic core of poly lactic-co-glycolic acid (PLGA) [45]. Additionally, nanocarriers with hydrogen bond acceptors or donors and aromatic planes can form hydrogen bonds and π-π interactions with icaritin [51,52]. For example, the hydrogen bonding interaction between icaritin and a polysaccharide carrier was adopted to form icaritin-loaded pectin micelles [52]. Several cationic nanocarriers can also encapsulate icaritin through electrostatic interactions [53,54]. In addition, icaritin can also be covalently conjugated with nanocarriers. A functionalized hyaluronic acid/collagen hydrogel was prepared by the formation of a disulfide bond between thiolated icariin and hyaluronic acid, which can be cleaved by the reductant glutathione that is secreted from cells [55]. Moreover, icaritin-based nanomedicines can be designed to release icaritin under certain conditions. Notably, a near infrared (NIR) light-responsive nanocarrier was designed for the controlled release of icariin [56]. In this study, icariin was loaded into mesoporous silica, and the formed nanomedicines were further covered with β-cyclodextrin (β-CD) through the linker 4-(hydroxymethyl)-3-nitrobenzoic acid (ONA). Upon 980 nm NIR light irradiation, ONA was photocleaved and the removal of β-CD was triggered, resulting in the release of icariin.
Icaritin-based nanomedicines for improved immunotherapy of advanced HCC
Although icaritin soft capsules have been approved for advanced HCC treatment via oral administration, there are some limitations that have arisen from the properties of icaritin and administration route. First, oral administration imposes high demands on the solubility and permeability of drugs, and is thus not suitable for icaritin. In addition, the oral administration of icaritin faces some issues, including instability in the luminal fluid, insolubility in the intestinal tract, poor absorption in the mucous and cell membrane, and loss of bioactivity after first pass drug elimination process [57]. Taken together, all these factors contribute to the low oral bioavailability of icaritin (2%) [21]. Consequently, compared with other anti-HCC agents [250 mg/(person‧d) for gefitinib and 800 mg/(person‧d) for sorafenib], the larger dose of icaritin required [1200 mg/(person‧d)] may lead to severe side effects. Intravenous injection bypasses absorption barriers and avoids first pass drug elimination process. Moreover, intravenous injection is suitable for the drugs that are poorly absorbed by the gastrointestinal tract. Intravenous injection can be applied to overcome the problems from oral administration and thus obtain satisfactory therapeutic efficacy with a lower dose. A representative study on the intravenous injection of the anti-HCC agent anlotinib was performed by Luo et al. [58], which showed that a greatly reduced dose (1/10 of the oral dose) produced a significantly improved anti-HCC effect.
Recent studies have shown that nanocarriers have emerged as a novel toolbox for further improving the bioavailability and delivery efficiency of drugs as well as alleviating their toxicity of drugs during intravenous injection [59][60][61][62]. A variety of functionalized nanocarriers have been designed to improve drug solubility and stability, prolong blood circulation, augment targeted accumulation, elevate tumor penetration, and control drug release (Fig. 2). For example, PEG, peptides, zwitterions, and various ligands can be used to modify nanocarriers to render nanomedicines with desired functionality to avoid the clearance by the reticuloendothelial system (RES), and to cross the biological barriers in the body. However, the capabilities to cross multiple biological barriers simultaneously might be achieved by further endowing functionalized nanocarriers with sizeor charge-reversible properties [63,64]. For advanced HCC, several nanomedicines containing DOX [2], paclitaxel [65], simvastatin [66], sorafenib [67] and arsenic trioxide [68] have been developed to improve the chemotherapeutic effects. Recently, nanomedicines loaded with siRNA [69] and CRISPR/Cas system [70] were fabricated to increase the chemotherapeutic sensitivity in advanced HCC. Moreover, several drugs have been combined into a single nanoplatform to further improve the efficacy and reduce the toxicity of drugs during the treatment of advanced HCC [71].
Nanomedicines are expected to solve several existing issues that icaritin faces during the treatment of advanced HCC, including low bioavailability, limited delivery efficiency, and a large required dose. Recent preclinical studies have proven that the applications of icaritin-based nanomedicines could boost the therapeutic efficacy against advanced HCC [45,72]. The area under the curve (AUC 0-24 ) and C max of the icaritin-based nanomedicine were significantly higher than those of free icaritin, indicating that the bioavailability of the icaritin-based nanomedicines was greatly enhanced [21,73]. In 2021, Guo et al. [72] reported that icaritin and coix seed oil co-loaded lipid complexes (IC-ML) displayed elevated penetration and growth inhibition of advanced HCC. Moreover, Yu et al. [45] developed (poly lactic-co-glycolic acid)-(poly ethylene glycol)-aminoethyl anisamide nanoparticles (PLGA-PEG-AEAA NPs) for highly efficient immunotherapy of advanced HCC by co-delivery of icaritin and DOX (Fig. 3). The designed nanomedicines extended the halflife times (t 1/2 ) values of icaritin and DOX significantly from 0.41 and 0.14 h (free drugs) to 1.65 and 1.96 h, respectively. The AUC of the nanomedicines was threefold higher than those of their free forms. These results indicated that the icaritin and DOX-loaded nanomedicine displayed improved blood circulation and bioavailability. In addition, the accumulation of PLGA-PEG-AEAA NPs in the tumor region was accelerated via enhanced permeability and retention (EPR) effect due to the suitable size of approximately 100 nm. At the same time, AEAA, which can bind the σ − 1 receptor expressed in tumor tissue, rendered the delivery system with the property to actively target the tumor region. Such active targeting together with the EPR effect reduced the off-target risks. Moreover, PLGA NPs can be degraded in the acidic tumor region, enabling controlled drug release. In animal experiments, augmented tumor growth inhibition and an increased survival rate were observed in tumor-bearing mice. Additionally, this work revealed the collaborative mechanism of icaritin with DOX for enhancing the antitumor immune response. Clearly, on one hand, icaritin induces ICD Fig. 2 Icaritin-based nanomedicines for improved immunotherapy of advanced HCC. Smart drug delivery systems including stimuli-responsive nanocarriers, cell membrane-coated nanocarriers, and living cell-nanocarrier systems, can be designed to load icaritin. The formed icaritin-based nanomedicines show improved bioavailability and delivery efficiency as well as alleviated immunogenicity. Icaritin-based nanomedicines could enhance the immunotherapy of advanced HCC by improving drug solubility and stability, prolonging blood circulation, augmenting targeted accumulation, elevating tumor penetration and controlling drug release. Part of this figure was created partially utilizing the templates on https:// smart. servi er. com/ as a reference. HCC hepatocellular carcinoma, RES reticuloendothelial system markers by promoting cell mitophagy, and on the other hand, it synergizes with the DOX-based ICD effects, together remodeling the immunosuppressive microenvironment of advanced HCC. Additionally, only a low-dose of each drugs was needed in this co-delivery strategy so that the vulnerable liver could be protected.
Despite the significant progress made, icaritin-based nanomedicines suffer from systematic immunogenicity Fig. 3 Icaritin-based nanomedicines for inducing ICD in advanced HCC. a PLGA-PEG-AEAA NPs were prepared by a solvent displacement technique to load icaritin and DOX. b The produced NPs displayed targeted delivery of icaritin and DOX, and efficiently improved the anti-HCC effect by remodeling the immunosuppressive tumor microenvironment and triggering a robust immune memory response. Reprinted with the permission from Ref. [45] Copyright © 2020, American Chemical Society. ICD immunogenic cell death, HCC hepatocellular carcinoma, PLGA poly lactic-co-glycolic acid, PEG poly ethylene glycol, mPEG monomethoxy poly ethylene glycol, AEAA aminoethyl anisamide, DOX doxorubicin, HMGB1 high mobility group box 1, ER estrogen receptor, CRT calreticulin, LC3-II microtubule-associated protein light chain 3 II, DC dendritic cell, IFN-γ interferon-γ leading to the inevitable clearance by RES and subsequently limited delivery efficiency and tumor accumulation. To address these limitations, the application of natural exosomes extracted from fetal bovine serum to delivery icariin was explored by Dong et al. [74]. The icariin-loaded exosomes displayed enhanced bioavailability and promoted osteoblast proliferation due to their low immunogenicity and reduced sensitivity to RES stress. In addition, cell membrane-coated nanomedicines combine the versatility of synthetic nanocarriers and intrinsic functionalities of natural cell membranes, contributing to RES clearance evasion and targeted delivery to specific sites. For example, a nanomedicines coated with both 4T1 tumor cell and dendritic cell membranes exhibited both blood circulation ability and immunotherapeutic effects [75]. This biomimetic complex can serve as a nanovaccine to effectively accumulate in both tumors and lymph nodes for intrinsic immune activation, eventually resulting in the inhibition of tumor growth, prevention of metastasis, and generation of immunological memory. In recent years, living cell-nanomedicine systems have been established for cancer treatment [76]. For instance, macrophage cell-nanomedicine systems with highly-efficient tumor homing abilities were utilized to deliver DOX-silica nanocomplex (DSN) [77]. Once the DSN was transported to tumors via chemotactic migration, DOX was released from DSN to kill cancer cells. These aforementioned novel biomimetic drug delivery strategies would be an ideal solution to further increase the tumor accumulation of icaritin and minimize toxicity to normal tissues.
In addition, the application of nanomedicines would benefit cancer immunotherapy through optimal design principles [78][79][80]. For example, composite carriers with photodynamic, chemodynamic, sonodynamic and other dynamic properties to trigger ICD could be further constructed to deliver icaritin to increase the antigenicity of HCC, maximize the immunoactivities of icaritin, and reduce the immunosuppression in advanced HCC [81,82].
With constant efforts and preliminary outcomes toward the development of functionalized nanomedicines, 14 systemically administered nanomedicines based on polymeric conjugates, micelles and liposomes have been approved for anti-cancer use in clinical practice worldwide [83]. This has inspired the utility of these nanocarriers to deliver icartitin for advanced HCC treatment (Fig. 4) [84]. Despite this, the interplay between the HCC microenvironment and nanomedicines should be taken into consideration in clinical practice. The nanomedicines used for the advanced HCC treatment should be carefully assessed in terms of their own burden on liver tissue, and the fate of icaritin as well as the nanomedicine within the complex HCC microenvironment should be studied, especially how they affect the infiltration of immunosuppressive cells and the expression of co-inhibitory molecules. Only when the clinical demands are considered can the success of icaritin-based nanomedicines for clinical translation in the treatment of advanced HCC be realized.
Conclusions
In summary, icaritin-based nanomedicines display enormous potential for immunotherapy of advanced HCC. Herein, we first compared the preparation methods of icaritin, and enzymatic hydrolysis and biosynthesis represent two promising methods to produce icaritin on a large-scale with great potential in industrial applications. Then, the HCC immune microenvironment and the treatment mechanisms of icaritin for advanced HCC were interpreted. The immunomodulatory mechanism related to the IL-6/JAK/STAT3 signaling pathway in advanced HCC immunotherapy was highlighted. Additionally, the design of icaritin-based nanomedicines for high icaritin loading efficiency was discussed. The development of icaritin-based nanomedicines for improved immunotherapy of advanced HCC was also presented. Moreover, further directions for the clinical translation of icaritin-based nanomedicines in advanced HCC treatment were proposed.
With the development of functionalized nanocarriers, icaritin-based nanomedicines could, in principle, be constructed to improve therapeutic efficacy against advanced HCC. Several potential directions are put forward for challenges that exist in the clinical applications. First, a deep investigation should be carried out regarding the immunomodulatory mechanisms of icaritin for advanced HCC treatment as well as the crosstalk between cancer cell death pathways. Second, functionalized nanomedicines with photodynamic, chemodynamic, sonodynamic or other dynamic properties could be used to increase the antigenicity of HCC to synergize with the immune activities of icaritin. Third, the in vivo biocompatibility, delivery efficiency, and therapeutic effects of icaritin-based nanomedicines should be comprehensively investigated. Fourth, to relieve the nanotoxicity of nanomedicines, a feasible industrialization route and safety profile of icaritin-based nanomedicines should be explored. Fifth, clinical trials of icaritin-based nanomedicines should be preferentially performed with clinically approved nanocarriers. Sixth, prodrug nanosystems with the integrated advantages of both nanocarriers and prodrugs might be exploited for efficient immunotherapy with decreased side effects. Several prodrug nanomedicines are currently in clinical trials, such as the NK012 for the treatment of colorectal cancer (NCT00542958) [85]. Thus, it is anticipated that the construction of icaritin prodrug nanosystems could potentially enhance the immunotherapy of advanced HCC in clinical practice [86].
Last but not least, the complicated designs of nanomedicines do not necessarily mean more barriers in vivo can be overcome. There is a possibility that the complicated nanomedicines could have negative effects on icaritin therapeutic efficacy. Thus, additional efforts should be made to balance the functionality and complexity of nanomedicine design [87]. It is also urgent to better understand the interaction between the nanomedicine and the complicated physiological environment. In the case of advanced HCC, it is especially important to clarify the own burdens of nanomedicines on the vulnerable liver and their interplay in the HCC microenvironment. Along with desirable functionality and improved physicochemical properties, frameworks or databases for nanotoxicity and nano-bio interactions should be further established to serve as guidelines for the general applications of nanomedicines and the development of icaritin-based nanomedicines. We believed that, with rational design of icaritinbased nanomedicines considering the abovementioned aspects, the therapeutic effects for advanced HCC or other human diseases can be significantly improved and the barriers of icaritin in terms of both the systemic circulation in the body and on the way toward clinical translation can be overcome. Fig. 4 Immunotherapy of advanced HCC using icaritin (a) or icaritin-based nanomedicines (b). Clinically approved nanocarriers (e.g., polymeric conjugates, micelles and liposomes) can be used to deliver icaritin in clinical practice. Compared with free icaritin, icaritin-based nanomedicines display augmented targeted delivery in vivo, resulting in improved therapeutic efficacy for immunotherapy of advanced HCC. Part of this figure was created partially utilizing the templates on https:// smart. servi er. com/ as a reference. HCC hepatocellular carcinoma | 6,473.6 | 2022-12-01T00:00:00.000 | [
"Biology",
"Medicine",
"Chemistry"
] |
Robust genome-wide ancestry inference for heterogeneous datasets: illustrated using the 1,000 genome project with 3D facial images
Estimates of individual-level genomic ancestry are routinely used in human genetics, and related fields. The analysis of population structure and genomic ancestry can yield insights in terms of modern and ancient populations, allowing us to address questions regarding admixture, and the numbers and identities of the parental source populations. Unrecognized population structure is also an important confounder to correct for in genome-wide association studies. However, it remains challenging to work with heterogeneous datasets from multiple studies collected by different laboratories with diverse genotyping and imputation protocols. This work presents a new approach and an accompanying open-source toolbox that facilitates a robust integrative analysis for population structure and genomic ancestry estimates for heterogeneous datasets. We show robustness against individual outliers and different protocols for the projection of new samples into a reference ancestry space, and the ability to reveal and adjust for population structure in a simulated case–control admixed population. Given that visually evident and easily recognizable patterns of human facial characteristics co-vary with genomic ancestry, and based on the integration of three different sources of genome data, we generate average 3D faces to illustrate genomic ancestry variations within the 1,000 Genome project and for eight ancient-DNA profiles, respectively.
Supplementary
Adjusting population structure Simulated GWAS
Supplementary Note S1: Determination of the number of relevant SUGIBS components
A key question for any lower dimensional embedding of data into a latent-space is the determination of the number of relevant latent components. In previous work 11 , we used PCA to obtain lower dimensional facial shape presentations in combination with a technique referred to as Parallel Analysis 12,13 . A Parallel Analysis determines the amount of eigenvalues (and thus the number of principal components (PCs)) from the observed data that are significantly different from eigenvalues computed from permuted versions of the original data. By running multiple permutations, a null distribution of noisy eigenvalues is obtained, against which significance of the original eigenvalues can be tested (whilst taking the properties of the data itself into account). Similar to a Parallel Analysis in PCA 12 , our preliminary method or suggestion to select the number of components for SUGIBS is by comparing the spectrum of eigenvalues from an observed potentially heterogeneous dataset (HED) with that of simulated homogeneous datasets (HOD). This is done using the same number of SNPs and samples as in the observed dataset.
For the HODs, we generate the genotypes of each SNP independently according to the allele frequency calculated from the observed data. This implies that each SNP is in HWE but is not in LD with any other SNP. For each simulated HOD and the HED, we calculate the eigenvalues of − 1 2 − 1 2 , where the unnormalized genomic relationship matrix is defined as and is a similarity degree matrix defined by the IBS similarity. By comparing the eigenvalues of the HEDs with the eigenvalues from the simulated HODs, an indication whether the observed dataset deviates from a single homogeneous population is provided. However, we observed that the LD between the SNPs in a sample does affect the sloop of the eigenvalue spectrum. To illustrate this, we simulated three datasets each with 10,000 SNPs and 1,000 samples assuming homogeneity, but with different levels of LD between SNPs (no LD, 2 ≤ 0.2 and 2 ≤ 0.8). The results in Figure S3 show that the higher the LD level, the steeper the eigenvalue spectrum becomes. In other words, the first eigenvalues explain more of the total variance due to correlation in the data, which is expected given the increased levels of LD.
Figure S3: Spectrum in descending order in function of LD level. Y-axis represents the values of eigenvalues.
In order to adjust for the different slopes of the eigenvalue spectrum caused by different levels of LD, we fit a robust regression (robustfit in MATLAB) between the observed eigenvalue spectrum and the simulated eigenvalue spectrum. A robust regression, was chosen since it is not influenced by the first few large eigenvalues, which are expected for highly heterogeneous population samples. In practice, we run the simulation 100 times and robustly fit the observed eigenvalue spectrum with the median of the 100 simulated eigenvalue spectrums. Subsequently, we plot the observed eigenvalue spectrum against the adjusted simulated eigenvalue spectrums.
Results for simulated heterogeneous population samples with an admixture from three, six and nine different ancestries with different levels of Fst (0.1, 0.01 and 0.001) are shown in Figure S4. It is observed that the simulated HOD eigenvalue spectrum is consistently lower than the observed HED eigenvalue spectrum, and this for all 30 eigenvalues plotted. Therefore, in contrast to Parallel Analysis, the simulated HOD eigenvalue spectrum could not be used as a direct indicator for the number of significant components, since all the observed eigenvalues remain larger (and thus significant) compared to the simulated ones. However, an indication of the amount of relevant (instead of significant) components that represent admixture is still observed. For larger values of Fst (0.1, 0.01), the correct number of relevant components (2 for three ancestries, 5 for six ancestries and 8 for nine ancestries), are visually distinct in magnitude in comparison to the simulated HOD eigenvalue spectrum, and this distinction is larger than the subsequent (non-relevant) components. For lower values of Fst (0.001), and an admixture from more than 3 ancestries, this visual distinction is lost. Ancestry facial predictions have good value in a range of applications. In archeology, ancestry faces reconstructed from ancient DNA profiles, as done in this work, is of strong interest. Generally, for ancient DNA profiles, missing data is abundantly present, making SUGIBS a valuable technique to use. Note that, the ancestry faces are limited to modern facial constructs, due to the contemporary facial data used. However, they can help to bring ancient DNA profiles into the context of present-day populations for which facial images (e.g. open-source facial databases, Google images, etc.) are available but DNA is not. Furthermore, there is a good relationship between the face and the skull 14,15 , such that ancestry faces can be used to compare against skeletal remains. In the future, it is of interest to deploy our work on datasets of 3D skeletal craniofacial surfaces extracted from Computer Tomography (CT) or Magnetic Resonance Imaging (MRI). In medicine, and more particularly in oral and maxillofacial surgery, the surgical reconstruction of a patient's face benefits from a proper notion of normal facial shape 16 . In the next five to 20 years, whole genome sequencing will become the standard of care in clinics and a patient-specific ancestry face provides a personalized norm of facial shape towards precision medicine in surgical planning.
In forensics, an ancestry facial prediction circumvents the often legally debated reporting of ancestry proportions of a probe DNA profile in a criminal investigation. In France, for example, DNA phenotyping of externally visible traits is legally allowed, since such traits are considered to be public. However, and in contrast, genomic ancestry proportions, as typically reported in forensic DNA testing, is considered to be private information and cannot be used during criminal investigations. We agree that ancestry proportions are not an externally visible characteristic of an individual. The construction of ancestry proportions is also inherently flawed by labelling the individual into so-called parental populations. Furthermore, such numeric information is hard to interpret and use by a forensic investigator. The reconstruction of an ancestry face on the other hand, avoids needing to explicitly label a DNA profile in function of parental populations and provides a visual feedback to an investigator that is perceptually useful, even in admixed cases. However, a strong limitation is that the ancestry projection and face creation is only as good as the data used to create it. If your background face data doesn't match the ancestry of your test data, then your estimation of the face will remain poor. The challenge in forensics also involves the ability to reconstruct ancestry faces using often limited and contaminated DNA material. Another strong limitation is of ethical concerns that warrants us of the misuse of DNA and facial recognition technology in general beyond the positive implications of solving crime 17 . | 1,916.6 | 2020-07-16T00:00:00.000 | [
"Biology"
] |
Discharge Coefficients of a Heavy Suspension Nozzle
: The suspensions used in heavy vehicles often consist of several oil and two gas chambers. In order to perform an analytical study of the mass flow transferred between two gas chambers separated by a nozzle, and when considering the gas as compressible and real, it is usually needed to determine the discharge coefficient of the nozzle. The nozzle configuration analyzed in the present study consists of a T shape, and it is used to separate two nitrogen chambers employed in heavy vehicle suspensions. In the present study, under compressible dynamic real flow conditions and at operating pressures, discharge coefficients were determined based on experimental data. A test rig was constructed for this purpose, and air was used as working fluid. The study clarifies that discharge coefficients for the T shape nozzle studied not only depend on the pressure gradient between chambers but also on the flow direction. Computational Fluid Dynamic (CFD) simulations, using air as working fluid and when flowing in both nozzle directions, were undertaken, as well, and the fluid was considered as compressible and ideal. The CFD results deeply helped in understanding why the dynamic discharge coefficients were dependent on both the pressure ratio and flow direction, clarifying at which nozzle location, and for how long, chocked flow was to be expected. Experimentally-based results were compared with the CFD ones, validating both the experimental procedure and numerical methodologies presented. The information gathered in the present study is aimed to be used to mathematically characterize the dynamic performance of a real suspension.
Introduction
Hydro-pneumatic suspensions consist on two or more oil chambers and a couple of gas ones. During the alternative displacement of the suspension, oil and gas flows back and forth between two consecutive chambers often separated by one or several nozzles or valves, therefore generating the smooth suspension displacement characteristic of such devices. Figure 1 introduces a typical heavy vehicle suspension, where several oil and gas chambers can be observed. To mathematically evaluate the dynamic fluid variations associated with the compression and extension of a given suspension, it is required to obtain the dynamic discharge coefficient of the different nozzle shapes separating the gas chambers. The discharge coefficient is defined as c d =ṁ m t , whereṁ characterizes the real mass flow flowing through the nozzle, whileṁ t is the mass flow obtained using a theoretical equation. For the suspension configuration presented in Figure 1, the gas chambers consist of a constant volume chamber and a variable volume one. Compressible gas, generally nitrogen, flows between two chambers through a narrow passage of constant cross-section, a nozzle. In the suspension of the present study, the pressure in both nitrogen chambers is time-dependent, which is the reason why it is important to determine the discharge coefficient variation for a real compressible flow under transient conditions. The main advantage of knowing the discharge coefficient of a given nozzle is that it allows to determine the real mass flow through the nozzle via employing theoretical equations. From the existing literature, several traditional experimental procedures [1,2] are described to experimentally determine the discharge coefficients on nozzles under real flow compressible conditions. The accuracy of these methods is good for a limited range of pressures, but it is jeopardized in applications involving high pressure metering. One of the most relevant early papers on non-ideal gas flow through orifices is the work undertaken by Johnson [3], where an expression for one-dimensional real flow through nozzles and based on the Beattie-Bridgeman equation was developed. The specific heat ratio was considered to be constant. Air at room temperature and for 10 MPa pressure differential was used as working fluid, and he observed a deviation of 3.5 percent for critical flow in nozzles when employing real versus ideal gas equations. Bober and Chow [4], using Methane as working fluid and for a pressure differential of 23 MPa between chambers, compared the ideal and real gas flow performance through a venturi-shaped nozzle using the Redlich-Kwong equation. Under choked flow conditions, the difference between ideal and real gas models was about 20%. Based on these early papers, it became clear that real gas effects had to be considered if precision metering was required.
Kouremenos et al. [5] and Kouremenos and Antonopoulos [6], based on the Lee-Kesler and Redlich-Kwong equations of state, described a constant entropy process via using three isentropic exponents. A set of simulations and experimental measurements of real compressible flow though convergent divergent nozzles, at very high pressure differentials, were recently undertaken by Kim et al. [7,8] and Nagao et al. [9,10]. They observed that, for a given range of Reynolds numbers, the discharge coefficient exceeded unity. This fact was previously reported by Nakao [11] experimentally. They realized that the molecules' vibrational energy had to be considered in the non-equilibrium thermodynamic process. Working with hydrogen, Ding et al. [12] observed the discharge coefficient was not just dependent on the Reynolds number but also on the throat diameter, stagnation pressure and stagnation temperature. They realized that the compressibility factor (Z) was changing in opposite direction than the discharge coefficient and concluded that the compressibility factor was likely the most important parameter when studying the discharge coefficient. They also noticed that, due to real gas effects, the fluid density at the nozzle throat became smaller than the theoretical one. In most of the research undertaken previously, the theoretical work was not supported by an experimental method, which could allow working directly with the experimental data. These aspects were covered in Reference [13], where, for constant section short nozzles, an expression defining the discharge coefficient was developed using an experimental method and a gas flow model based on the Lee-Kesler equation of state [14]. Experimental results were compared with the ones obtained from the new developed equation, observing that, for tests performed using nitrogen up to 7.6 MPa pressure differential, a good correlation was obtained.
Computational Fluid Dynamic (CFD) applications are gaining reliability every day. The consideration of the gas as real and compressible, under sonic or supersonic flow conditions, is still not fully extended. In reality, turbulent models quite often have some difficulties in dealing with such kind of flows. Nevertheless, there are many industrial applications where sonic, and even supersonic, flow is used. CFD simulations on compressible flow through valves, whether purging or relief ones, have recently been performed in Reference [15,16]. The modeling of flow ejectors under sonic flow conditions was considered in Reference [17][18][19][20][21]. Some of the recent papers simulating compressible flow conditions at high Mach numbers inside nozzles are Reference [22][23][24][25].
From all these studies, it is particularly relevant to highlight the work done by Farzaneh-Gord et al. [15], where they numerically evaluated the exit flow of natural gas through a purging valve, during its opening time. They considered the gas as real and compressible, being the maximum pressure differential of 3.5 MPa. As a turbulent model, the standard k − was selected. They concluded supersonic flow was to be expected at the pipe outlet. García-Todolí et al. [16] performed CFD simulations on air valves under compressible flow condition. They showed how CFD models are efficient to represent the behavior of air entering and leaving the valve. The maximum pressure differential studied was 0.1 MPa. In their analysis, they used the realizable k − turbulent model and their results matched very well with the experimental data. Mazzelli et al. [17] performed numerical and experimental analyses in order to check the effectiveness of the commonlyused computational techniques when predicting ejector flow characteristics at supersonic flow conditions. For the numerical part, they considered the working fluid as an ideal gas. They tested different Reynolds-averaged Navier-Stokes (RANS) turbulent models, among them, the k − ω SST and k − realizable ones, and observed that, in general, all turbulent models generated very similar results, although epsilon-based models were more accurate at low pressure differentials (around 0.2 MPa). The different pressure differentials they evaluated were of 0.2, 0.35, and 0.5 MPa. On the other hand, they stated that the main differences between the numerical and experimental results appeared when comparing 2D and 3D models. Lakzian et al. [18] performed a compressible 2D RANS simulation on an air ejector pump. In their analysis, they assumed the working fluid as ideal gas and the walls were treated as adiabatic. Pressure differentials of 0.5, 0.6, 0.7, and 0.8 MPa were considered. They used a k − realizable turbulent model along with a wall function and a very good agreement with experimental data was obtained. They concluded that the main sources of entropy are the mixing and normal shock occurred in the mixing chamber and diffuser, respectively. Arias and Shedd [21] used CFD to develop a 3D model of compressible flow across a venturi in which obstacles were located inside. Air was considered as compressible and was treated as ideal gas. The turbulent model they used was RNG k − and the maximum pressure differential was about 0.1 MPa. The results showed that the obstacles located at the converging nozzle of the venturi causes negligible pressure losses, while other obstacles that generate wakes in the flow are responsible for the largest pressure drop. Discharge coefficients of critical nozzles used for flow measurement under compressible flow conditions were evaluated by Ding et al. [22]. Fluid was considered as real, the standard k − turbulent model with a wall function was employed in all simulations. Nozzle roughness was considered, being the maximum pressure differential between nozzle inlet-outlet of 120 MPa. They observed that, when the nozzle roughness was very small, and for pressure differentials until 1 MPa, the effect on the discharge coefficient was negligible. Sonic and supersonic flow inside micro/nanoscale nozzles was studied by Darbandi and Roohi [23]. They used a density-based solver (rhoCentralFoam) employed in OpenFOAM. Second order spatial discretization scheme along with a first order Euler-scheme for time integration were implemented. They observed supersonic flow was impossible to set in nanoscales once Knudsen number exceeded a given value. Zhao et al. [24] numerically studied the fuel flow in a nozzle considering the fuel compressibility. They used the RANS method with a Realizable k − turbulent model and they investigated the effect of injection pressure on the fuel flow under fuel compressibility conditions. They concluded that the nozzle discharge coefficient for compressible flow was larger than when fluid was considered as incompressible.
According to the authors knowledge, the nozzle configuration studied in the present paper, which has a T shape, has not been previously studied under real gas compressible flow conditions, and just the work done by Farzaneh-Gord et al. [15] presents some similarities. In fact, under incompressible flow conditions, a similar shape was studied by Reference [26,27], where it was stated the discharge coefficient was highly dependent on the flow direction. The present study consists of the following parts: initially, the test rig employed to do all experimental tests is introduced, and then the mathematical equations used to analytically determine the flow parameters are presented. In a third stage, the Computational Fluid Dynamics (CFD) methodology employed to numerically evaluate the compressible flow between the two tanks is introduced. Next, the experimentally-based and numerical results are presented and compared. Finally, the discharge coefficients as a function of the Reynolds number, and for both flow directions, are presented and discussed, and the paper ends with the conclusions.
Experimental Test Rig
Since the primary idea in the present paper was to experimentally determine the directional dynamic discharge coefficients for a real gas, air, the test rig introduced in Figure 2 was created. Figure 2a,b, respectively, show a general view of the test rig and the two reservoirs. Figure 2c presents a schematic view of the two reservoirs central section with the different transducers employed. Apart of the two reservoirs, the test rig consisted of a stopper cylinder, which was employed to displace the shutter valve located inside the large reservoir; see Figure 2c. When this valve was closed, it prevented the fluid from flowing between the two reservoirs, allowing to pressurize each of them independently. The volume of each reservoir was of 2288.48 and 700.18 cm 3 . Notice that the volume of the large reservoir was slightly increasing as the shutter valve was opening. Therefore, in order to know at each instant which was the real volume, a position transducer was attached to the stopper cylinder. The pressure multiplier in conjunction with the pressurizedepressurize valve were connected to the large reservoir and allowed to set the required pressure in the reservoir chambers. A static and a dynamic pressure transducer were connected to each reservoir; this was done due to the uncertainty of the static transducer in properly measuring the dynamic pressure variations. The fluid temperature was aimed to be measured by several dynamic thermocouples placed on the internal reservoirs walls; one of them was located in the large reservoir, and three were placed on the smaller one, and then larger temperature variations were expected in there.
It is important to notice that the dynamic thermocouples were welded to the internal walls of the reservoirs; therefore, the measured temperature was, in reality, the internal wall temperature, which may not be exactly the same as the fluid temperature, specially under dynamic conditions. Considering known the dynamic temperature at the upstream reservoir, Kagawa et al. [2] identified the susceptibility of the pressure response to temperature changes in a reservoir at natural conditions, and suggested the use of an upstream isothermal chamber to guarantee no temperature variation. This idea appears to be a good solution since it reduces the number of variables, and assuming pressure is a known variable, it would be possible to mathematically determine the temperature, heat transfer, and the mass flow downstream by means of integration. The main difficulty lies in achieving no upstream temperature variation during the discharge, Kagawa et al. [2] suggested stuffing extremely thin steel wool or copper wire in the upstream reservoir to obtain isothermal conditions. For the present experimental test rig, and in order to tend to achieve isothermal conditions during experimentation, both reservoirs walls were constructed with a thickness of 35 mm.
The main characteristics of the different transducers were: the static pressure sensors were from Keller series model 21/21PRO, capable of measuring pressures of 10 MPa and having a resolution of 100 Pa. To properly evaluate the dynamic pressure, Kistler transducers model 601A were used; their resolution was of 100 Pa, and the time response was of 1 µs. Dynamic temperature was measured using low inertia Nammac thermocouples model E6-20, and their resolution was of 0.01 • C. The transducer used to measure the position of the shutter valve was a LVDT type, model CGA-2000 from TE connectivity, and its resolution was of 0.001 mm. The dynamic variables were recorded, thanks to an in-house LabVIEW program specifically developed for this application. Table 1 introduces the initial absolute pressure on both reservoirs employed in each experimental test, as well as in each CFD case. The main dimensions of the T shape nozzle are defined in Figure 2c. The constant section nozzle diameter of both the horizontal and vertical nozzle branches was d = 1.5 mm, the horizontal branch length (L 1 ) was L 1 = 29.5 mm, and the length of the vertical branch (L 2 ) was L 2 = 10 mm. The process followed to perform the simulations was a function of the flow direction, and it was depending on which of the two reservoirs was initially pressurized. In other words, it depends on if the discharge was from the large to the small reservoir (L-to-S) or from the small to the large (S-to-L) one. It is important to notice that the T shape nozzle was always kept in the same position, regardless of the flow direction.
CFD
The measurements done when the large reservoir was pressurized started with the shutter valve open and both reservoirs at the atmospheric pressure, 0.1 MPa absolute pressure. Under these conditions, the shutter valve was closed, and the large reservoir was filled with air, passing through the pressurize-depressurize valve, until reaching the required pressure. Then, the pressurize-depressurize valve was closed. The last step consisted of opening the shutter valve, which was accomplished by pressurizing the stopper cylinder, and the flow was allowed to go from the large to the small reservoir. The stopper cylinder position, the static, and dynamic pressure, as well as the internal walls temperature, on both reservoirs, were recorded using an external computer, and thanks to a labVIEW program. For tests at which the fluid was going from the small reservoir to the large one, the same procedure was used but initially both reservoirs were pressurized at the pressure required for the small reservoir. After closing the shutter valve and using the pressurize-depressurize valve, the pressure at the large reservoir was decreased until obtaining the one needed. It is important to highlight at this point that each test was done ten times, and the resulting curves presented in the results section are the average value of the ten measurements done for each variable.
Mathematical Equations and Analytical Process Followed to Determine the Physical Variables
From the experimental tests, it was soon realized that, as the temperature transducers were welded to the reservoirs internal walls, in reality, they were measuring the temperature of the wall and not the fluid temperature at each reservoir. The measured temperature was almost constant in both reservoirs, and it was used to instantaneously estimate the heat transferred through the walls. Therefore, providing that the only trustworthy dynamic information in both reservoirs was the temporal pressure evolution, the methodology employed to determine the mass flow between reservoirs was based on the following equations, developed by the authors in a former paper [13]. Equation (1) was obtained from the application of the energy equation in the upstream reservoir, it characterizes the temporal mass variation in the upstream reservoir, dm u dt , as a function of the tank's temperature T u , the heat transferred to the fluid dQ u dt , the upstream pressure temporal variation dp u dt , the variation of the compressibility factor versus the temperature and specific volume ∂Z u ∂T ; ∂Z u ∂ν , the mass of the fluid in the upstream reservoir m u , the enthalpy h u , and the internal energy u u associated with the upstream fluid. The heat transferred across the walls of the reservoir was estimated based on the Fourier equation Q = −λ dT dx x=0 , being the value of the thermal conductivity λ = 54( W mK ). Equations (2) and (3) arise from the differentiation of the real gas equations applied to the upstream and downstream reservoirs, p u ∀ u = Z u m u R u T u ; p d ∀ d = Z d m d R d T d , and they link the pressure, volume, temperature, mass flow, and compressibility factor temporal variations existing in the respective upstream and downstream reservoirs. Equation (4) simply characterizes the mass transfer balance. In all these equations, sub-indices u and d stand for upstream and downstream, respectively.
An in-house computer program was created to solve the preceding Equations (1)-(4) with a Runge-Kutta method based on DVERK from the International Mathematics and statistics library, (IMSL). The air density was determined every time step using the Lee-Kesler equation iteratively as performed by Plocker and Knapp [28]. According to this equation, the compressibility factor as a function of the reduced parameters, can be expressed as: The parameters B, C, D, c 4 , β, and γ 1 , for various gases, can be determined from Reference [5]. P r , T r , and ν r stand for reduced pressure, reduced temperature, and reduced specific volume, respectively.
According to Lee and Kesler [14], the compressibility factor can be defined as: where Z (R) and Z (0) are the compressibility factors for a reference fluid and simple fluid, respectively, while ω (R) and ω stand for the acentric factors of the reference and working fluids. Considering known the pressure and temperature in a given location and time, the following steps were used to calculate the fluid compressibility factor. Initially, the reduced upstream pressure and temperature (P r and T r ) were obtained based on the working fluid critical properties (P c ; T c ) and the values of the pressure and temperature. When introducing the values of P r and T r in Equation (5), introducing, as well, the values of the parameters given for a simple fluid and obtained from reference [5], the value of ν r could be determined. Substituting the value of ν r in the same Equation (5), the corresponding compressibility factor for a simple fluid Z (0) was obtained. Following the same procedure just described, but using the values of the parameters characterizing the reference fluid, which, for the present study, was n-octane, and values obtained from Reference [5], the value of Z (R) was determined. Substituting the compressibility factors Z (0) and Z (R) in Equation (6) and considering the acentric factor values ω (R) = 0.3978 and ω = 0.039, the compressibility factor for the working fluid could finally be obtained. This procedure allows to determine the compressibility factor and the fluid density at any position and time, and just the values of the pressure and temperature are required at the generic location where the information is needed.
To be able to determine the instantaneous mass at each reservoir, the pressure evolution was measured in both reservoirs at any time; the volume of both reservoirs was also known, and the fluid temperature, as well as the compressibility factor, were estimated based on the previous equations. The variation of the fluid mass between two consecutive time steps allowed to calculate the instantaneous mass flow leaving one reservoir and entering the other one. The only problem associated with this methodology was that the fluid temperature had to be estimated. As previously defined by Kagawa et al. [2] and Comas et al. [13], if the reservoirs were large enough, the fluid temperature was likely to remain constant. Yet, which were the required dimensions to fulfill this condition, for each particular case, was not clearly stated.
Based on the previous information, the instantaneous space averaged fluid velocity at the nozzle minimum section S = πd 2 4 was determined as presented in Equation (7). The fluid velocity at the critical section was determined based on the experimentally-based mass flowṁ, the nozzle section S, and the fluid downstream density ρ d .
To determine the Mach number, the sound speed was initially obtained from Equation (8), when substituting Equations (7) and (8) in Equation (9), the Mach number at the nozzle critical section was obtained.
On the other hand, and due to the fact that the pressure differential between both reservoirs was relatively small, as in Table 1, the following equation was employed to calculate the theoretical mass flow.
The instantaneous discharge coefficient was determined at each time step by comparing the real and theoretical mass flows. Actually, the discharge coefficient at each time step was obtained according to Equation (11).
whereṁ is the real mass flow obtained based on the temporal variation of the mass in the upstream reservoir, which was determined from the experimental upstream and downstream pressure evolution, the initial fluid temperature, and after calculating the compressibility factor, as well as the fluid temperature evolution, at each time step.ṁ t is the mass flow obtained via using Equation (10). At each instant, the Reynolds number was determined using the following equation.
where µ is the fluid dynamic viscosity.
Dynamic Computational Fluid Dynamic Simulations
In order to be able to analyze the dynamic flow evolution between the two reservoirs, several 3D Computational Fluid Dynamic (CFD) simulations were undertaken. The working fluid was air, and it was considered as ideal and compressible. Some recent papers in which the fluid was considered as ideal and compressible and working under similar pressure differentials are Reference [17,18]. In the present paper, the simulations were performed under dynamic conditions, therefore matching the experimental test conditions. Figure 3 shows the two reservoirs separated by the T shape nozzle, and the dimensions of both reservoirs and the T shape nozzle were the same as the ones used in the experimental tests. The only difference was the shutter valve needed in the experimental test rig, as in Figure 2c, which was not required in the CFD simulations. The mesh employed was generated using GMSH, and it was unstructured and consisted of 126,633 cells. The OpenFOAM software was used for all 3D simulations, and finite volumes is the approach OpenFOAM uses to discretize Navier-Stokes equations. The solver rhoCentralFoam was used for all tests, and the spatial discretization was set to second order being the first order Euler scheme the one used for time discretization. The maximum Courant number was kept below 0.8, being the time step around 5 × 10 −8 s.Turbulence intensity was set to 0.05% in all cases. The realizable k − turbulent model, along with a wall function, as previously used by Lakzian et al. [18], were employed in all the simulations. The maximum y + on the wall of the nozzle was about 90. Volumetric Dirichlet pressure and temperature boundary conditions were initially set in both reservoirs, Newman boundary conditions for pressure and temperature were set in all walls, and Dirichlet boundary conditions for velocity were established in all walls. Regarding the heat transfer, all walls were set as adiabatic.
To compare with the experimental results, four simulations were performed; in two of them, the flow was going from the large to the small reservoir, and the respective L-to-S reservoirs pressures were 0.4-0.1 MPa and 1.1-0.1 MPa. In the other two simulations, the fluid was flowing from the small to the large reservoir, being the S-to-L reservoirs pressures, respectively, of 0.6-0.1 MPa and 1.2-0.4 MPa; see Table 1.
Results and Discussion
In the present section, initially the measured temporal pressure evolution inside the reservoirs is compared with the ones obtained from the CFD simulations. The same is later being done with the temporal temperature at the upstream reservoirs. Next, the timedependent mass at each reservoir is also compared between CFD and experimentally-based results, which is followed by the temporal mass flow comparison. The time-dependent Mach numbers at the respective critical sections and the discharge coefficients versus the Reynolds number are presented next. At the end of this section, a figure showing the flow inside the nozzle and for both flow directions is introduced. In this figure, the critical sections where the flow becomes sonic and the locations where supersonic flow is to be expected are clearly stated. Figure 4 presents the temporal pressure variation measured in both reservoirs for the different initial pressure differentials introduced in Table 1. Each curve is, in reality, the average one obtained after performing each test ten times. Although not presented in Figure 4, the standard deviation of each point was smaller than 1% for all tests performed. From Figure 4a, it is observed the discharge lasts about two seconds, regardless of the initial pressure differential existing between the two reservoirs. In fact, the time needed to complete the discharge suffers an increase of about 22.5% when comparing the discharge from 0.4 to 0.1 MPa with the 1.1 to 0.1 MPa one. This phenomenon is clearly understandable then the higher the upstream pressure the higher is the mass to be transferred from one reservoir to the other. Notice that the initial mass of fluid in the downstream reservoir is the same for all cases presented in Figure 4a, except the case at which the initial downstream pressure is of 0.4 MPa, being the upstream pressure of 1.2MPa. This case shows clear differences versus the rest of the discharges; then, the time required to complete the discharge is about 10% shorter than the one needed to complete the discharge when the upstream/downstream reservoir pressures were 0.4 and 0.1 MPa, respectively. In fact, the discharge time is directly related to the initial fluid density ratio ρ upstream /ρ downstream between reservoirs, given the rest of the parameters, reservoirs volumes, and initial fluid temperature as constant, the smaller the initial upstream/downstream density ratio ρ upstream /ρ downstream , and the shorter the discharge time.
From the observation of the temporal pressure decay when the flow goes from the small reservoir to the large one, as in Figure 4b, it is realized that the discharge time obeys to the same upstream/downstream density rule just presented. It is interesting, as well, to observe that, when comparing discharge times for the same pressure differential and opposite flow directions, the discharge time is larger when the fluid goes from the small to the large reservoir. This is likely linked to the resistance the T shape nozzle is presenting when the fluid flows in such direction. The time the flow remains under chocked conditions it is expected to depend on such resistance. This point is to be clarified in the remaining part of the paper. Figure 4 also compares the pressure decay and increase measured experimentally with the ones obtained from the CFD simulations, two cases are compared for each flow direction. The comparison shows a very good agreement, generating the same discharge times and final pressures as the ones measured experimentally. Small discrepancies are observed in the final pressure values when the flow goes from the small to the large reservoir, a discrepancy of 4.4% is observed for a discharge from 1.2 to 0.4 MPa, and the variation reaches 11.3% for a discharge from 0.6 to 0.1 MPa. Such relatively small discrepancies are understandable when considering that, in the CFD simulations, the process is considered as adiabatic, the fluid is considered as ideal, and the large volume remains constant. Figure 5 presents the fluid temperature evolution in both reservoirs and for the four cases numerically evaluated. When the discharge is from the large to the small reservoir, as in Figure 5a, the fluid temperature on the large reservoir suffers a decrease of less than 20 • , and the increase of the fluid temperature in the small reservoir lies between 55 • and 85 • ; such a large increase is perfectly understandable when considering the reduced volume of this particular reservoir and that the walls are considered adiabatic. Notice, as well, that the temperature decrease and increase are directly dependent on the pressure ratio between reservoirs. When the flow goes from the small to the large reservoir, as in Figure 5b, the temperature decrease in the small tank oscillates between 50 • and 70 • , and a maximum temperature rise of around 30 • is observed in the large reservoir. A point which needs to be considered, and which could help to explain why the discharge from the small to the large reservoir takes longer than the one in the opposite direction, is that, regardless of the flow direction, the temperature variation at the small reservoir is several times larger than the one observed at the large one. Another possible explanation needs to be found in the possible existence of a flow restriction under these conditions, therefore reducing the effective flow section. In fact, the most plausible explanation is likely to be the different nozzle resistance the fluid is facing when flowing in opposite directions. These hypotheses will be analyzed in the remaining part of the paper. Figure 6 introduces the experimentally-based fluid temperature temporal variation on both reservoirs for the two flow directions and for all pressure differentials evaluated; see Table 1. Figure 6a,b characterize the temperature decrease in the large and small reservoirs when the flow goes from the large to the small and from the small to the large reservoirs, respectively. The fluid temperature evolution in both reservoirs obtained from the CFD simulations is also presented for comparison. The first thing to be observed is that the fluid temperature decay is proportional to the initial pressure ratio between reservoirs, and the higher the pressure ratio, the higher the fluid temperature decay in the upstream reservoir. As previously observed, the temperature drop is particularly high in the small reservoir. Temperature decreases of over 50 • are observed in the small reservoir, and such decrease is of less than 20 • in the large one. When comparing the temperature evolution experimentally-based with the CFD one, a particularly good agreement is observed in the large reservoir, and a maximum difference between experimental and numerical results of about 3.3% is observed in the small reservoir when the discharge is from 0.6 to 0.1 MPa. The experimentally-based results generate final temperatures slightly lower than the ones obtained via CFD simulations. As the walls were assumed adiabatic in the CFD simulations, Figure 6 confirms this assumption; then, the heat transferred through the walls appears to be negligible.
Based on the experimental pressure temporal evolution and the calculated temperature, the temporal mass variation at each reservoir for both flow directions and for all different pressures studied is presented in Figure 7. The same figure presents, as well, the mass decay/increase obtained via CFD. Figure 7a,b introduce the reservoirs temporal mass variation when the air flows from the L-to-S and S-to-L reservoirs, respectively. As previously observed, when the discharge is from 1.2 to 0.4 MPa, the discharge time is minimum, and this is due to the small density ratio associated to the fluid. Regardless of the flow direction, the curves representing the temporal mass variation on both reservoirs are, for the discharge initial first second, having a constant pendent, but, during the next 1.5 discharge seconds, the curves are rounded. The constant pendent is likely to indicate chocked flow conditions. The curves of the Mach number versus time should clarify this hypothesis.
The instantaneous mass flow flowing between the two reservoirs for all pressures studied and for both flow directions, is presented in Figure 8. Notice that the information presented in this figure was directly extracted from Figure 7. For each pressure ratio, Figure 7 presents two curves, representing the mass decrease in one reservoir and the mass increase in the other; therefore, each of the mass flow curves could be obtained twice, considering the mass decrease and increase in the respective reservoirs. Since both mass flow curves were almost identical, in Figure 8, just the curves representing the mass flow decrease in the upstream reservoir are presented. Figure 8a characterizes the mass flow between reservoirs when the fluid is going from the large to the small reservoir. Notice that, as the pressure ratio increases, the mass flow also increases. In reality, this mass flow increase associated with the pressure ratio increase is due to the upstream fluid density increase. It is as interesting to see that, as the pressure ratio increases, the overall discharge time, and the time at which the flow remains under sonic conditions, also increases; Figure 9 shall further clarify this point. Figure 8b presents the mass flow for the fluid going from the small to the large reservoir. It is interesting to realize that, regardless of the pressure ratio evaluated, the discharge time lasts almost a second longer than when the flow goes in the opposite direction. As already observed in Figure 8a, for a discharge from 1.2 to 0.4 MPa, the pendent of the mass flow curve is much higher than for the rest of the cases evaluated, clearly showing that the initial downstream density plays an important role when considering the discharge temporal evolution and final time, and such time decreases with the density ratio decrease. Figure 8 also compares the mass flow obtained experimentally with the numerical one, and the agreement appears to be very good for all cases studied; just when the discharge is from 1.2 to 0.4 MPa, and the flow goes from the small to the large reservoir, the pendent of the mass flow during the initial 0.5 s shows some discrepancy. In fact, already, in Figure 7b, clear differences in the temporal mass evolution are observed for this particular discharge. When comparing Figure 8a,b, for any given pressure differential, it is observed that, at time zero, the mass flow, when the fluid goes from L-to-S, is slightly larger than when the fluid goes from the S-to-L, indicating the flow is seeing a higher restriction when the fluid is going from S-to-L reservoir. Figure 9a,b introduce the Mach number temporal evolution at the nozzle minimum section as a function of the pressure differential and for the two flow directions, L-to-S and S-to-L reservoirs, respectively. The information presented obeys the cases where numerical and experimentally-based results can be compared. When the fluid goes from the large to the small reservoir, the flow is initially sonic, and the time during which the flow remains under sonic conditions increases with the pressure ratio increase, as in Figure 9a. When the fluid is flowing from the small to the large reservoir, and for initial respective pressures of 1.2 MPa and 0.4 MPa, the discharge is sonic during a very small time. But, when the initial reservoirs pressure is of 0.6 MPa and 0.1 MPa, respectively, the time at which the flow remains sonic is of nearly 1 s, which is almost the same time observed when the fluid goes from the large to the small reservoir, and, for a respective pressure of 1. The reason why the fluid remains sonic during a longer time, when the flow goes from S-to-L reservoirs, is likely to be caused by the sudden flow restriction the fluid is suffering when the flow enters the horizontal section of the nozzle and coming from the two T shape branches. The two T shape branches promote the existence of a flow restricted section at the horizontal nozzle inlet, restricting, as well, the entrance of the fluid from a vertical plane, the fluid can only move vertically in the two vertical branches of the T nozzle. In reality, this effect is creating a smaller effective section of the flow in this case than when flow goes from large to small reservoir. In other words, the nozzle resistance to the fluid is larger when the flow goes from the small to the large reservoir; therefore, the mass flow is also smaller. In fact, when comparing the mass flow curves for the same pressure drop presented in Figure 8a,b, it can be clearly seen that the mass flow is higher during the initial times when the fluid goes from the L-to-S reservoir. At this point, it must be highlighted that the location where the Mach number values are computed is always where the spatially averaged Mach number is maximum. Such location is at the end of the horizontal nozzle, beginning of the T junction, when the flow goes from L-to-S reservoir, and at the end of the horizontal nozzle and beginning of the large tank, when the flow goes from the S-to-L reservoir. Such different locations were expected; then, regardless of the flow direction, the fluid at the entrance of the horizontal nozzle has to be subsonic and accelerates along it.
The temporal discharge coefficients as a function of the Reynolds number, and for the four cases at which CFD and experimentally-based results are generated, is presented in Figure 10. The variations of the discharge coefficient when the flow goes from the large to the small tank, and vice versa, is given in Figure 10a,b, respectively. For both flow directions, the numerical and experimentally-based results are presented for the pressure differentials studied using both methodologies. The first thing to notice is that, for a given flow direction, the discharge coefficient shows a very similar temporal trend, regardless of the pressure ratio evaluated. In fact, it is to be expected that the discharge coefficient depends on the Reynolds number but not on the pressure ratio between reservoirs. Some differences are observed between the discharge coefficients obtained numerically and the ones obtained based on experimental data, particularly at low Reynolds numbers. Authors believe such differences are due to the differences in fluid temperature between experimental and CFD results appearing at the end of the discharge. However, for a given flow direction, the asymptotic values of the discharge coefficients are almost the same regardless of the methodology employed to calculate them. The discharge coefficients obtained when the flow is going from the large to the small reservoir are slightly higher than the ones obtained when the flow is going in the opposite direction. This supports what has been presented until the moment, which is the time required to discharge from the small to the large reservoir is larger than the one needed when the discharge is from the large to the small tank. In other words, the fluid is finding more resistance to flow from the small to the large reservoir than in opposite direction. As explained before, this must be due to the restriction the fluid is observing when flowing from the two T branches and entering the horizontal one.
In order to obtain a single curve representing the evolution of the discharge coefficient as a function of the Reynolds number, and for each flow direction, at each Reynolds number, the average discharge coefficient was determined. The mathematical equation of the resulting curves is presented in Equation (13), which represents the generic equation for the discharge coefficient as a function of the Reynolds number and for both flow directions. The parameters a 0 . . .. . .a 10 characterizing the discharge coefficient curve for each flow direction are defined in Table 2. Notice that, as in Figure 10b, the maximum Reynolds number is 50,000, and the parameters given in the first two rows of Table 2 are valid for this Reynolds number range. Nevertheless, based on the results from the CFD simulations, a second set of parameters valid for a Reynolds number range 1000 ≤ Re ≤ 130, 000 are also presented in the last two rows of Table 2.
(13) One of the advantages of performing 3D-CFD simulations is that it allows to carefully analyze the flow evolution inside the nozzle. The flow field dynamics given as instantaneous velocity contours at both ends of the horizontal pipe, for both flow directions, reservoir pressures and at three different time instants, 0.02 s, 0.5 s, and 1 s, is introduced in Figure 11. Each column characterizes the time at which the velocity field is presented. The initial two rows of Figure 11 show the flow field at both horizontal nozzle ends when the fluid goes from the L-to-S reservoir, and the initial upstream-downstream pressure on each tank is 0.4 MPa-0.1 MPa and 1.1 MPa-0.1 MPa, respectively. The final two rows show the velocity field that, when the flow goes from the S-to-L reservoir, the upstreamdownstream initial pressures are 0.6 MPa-0.1 MPa and 1.2 MPa-0.4 MPa, respectively.
When the fluid goes from the large to the small reservoirs, and for the two pressure ratios studied, during the initial milliseconds, t = 0.02 s, the fluid reaches sonic conditions at the horizontal nozzle outlet just before the T junction, and supersonic flow conditions are observed as the fluid expands to the two lateral vertical branches; see the first and second rows of Figure 11. At t = 0.5 s, and for upstream-downstream initial pressures of 0.4 MPa-0.1 MPa, the fluid has become subsonic at all points, the maximum spatial averaged velocity is 239 m/s, which corresponds to M = 0.74, and the fluid still remains detached when entering the T junction. After 1 s of the origin of the discharge, the maximum fluid velocity has decreased to 120 m/s, but the flow keeps being detached at the T junction entrance. But, when the L-to-S reservoirs initial pressure is of 1.1 PMa and 0.1 MPa, respectively, after 0.5 s, the flow is still under sonic conditions, and the Mach number and the associated spatial averaged velocities at the inlet and outlet of the horizontal pipe are of M = 0.53 (176 m/s) and M = 0.99 (310 m/s), respectively. The respective values of the Mach number in these two pipe locations are of 0.45 and 0.56, after the initial second of the discharge.
The discharge when the fluid flows from the small to the large reservoir at initial pressures of 0.6 MPa and 0.1 MPa, respectively, is presented in the third row of Figure 11. Now, the maximum Mach number appears at the entrance of the large reservoir, and horizontal pipe outlet, as expected according to the theory [29,30]. At time t = 0.02 s, the respective Mach numbers and spatial averaged fluid velocities at the horizontal pipe inlet and outlet are M = 0.6 (198 m/s) and M = 1 (353 m/s). For these particular initial pressures, and after 0.5 s, the flow still remains under sonic conditions, and the inlet and outlet Mach numbers and fluid velocities are M = 0.59 (190 m/s) and M = 1 (317 m/s), respectively. At this point, it is important to realize that under sonic conditions, the spatial averaged fluid velocity depends on the instantaneous fluid temperature. When comparing these figures with the ones characterizing the initial pressure drop of 1.2 MPa to 0.4 MPa, presented as the bottom row of Figure 11, it is realized that now just at the initial instants, t = 0.02 s, the flow is sonic, but the pressure drop is not large enough to generate a supersonic expansion as the fluid enters the large reservoir. After 0.5 s, the discharge is completely subsonic, being the horizontal pipe inlet and outlet Mach numbers and associated spatial averaged fluid velocities of M = 0.57 (182 m/s) and M = 0.73 (230 m/s), respectively. At this point, it is interesting to observe the agreement between the CFD results presented in Figures 9 and 11, noticing that the time during which flow is sonic has a perfect match for all pressures studied. The observations made in Figure 11 are clarifying why, during the experimental tests and CFD simulations, the discharge time was larger when the flow was flowing from the S-to-L reservoir than when flowing in the opposite direction; see all figures between Figures 4 and 8. Notice, as well, from Figure 8, that the maximum mass flow at time zero is always larger when the flow is going from the L-to-S reservoir than when going from the S-to-L one, clearly indicating the added difficulty for the fluid to flow from the small to the large reservoir. This difficulty can be understood when analyzing the inlet section under both flow conditions. When the flow goes from the large to small reservoir, the flow enters the horizontal nozzle from any direction, 360 degrees, but, when the flow goes from the small to the large reservoir, initially, the fluid needs to enter from the two ends of a T branch and then the fluid needs to enter the horizontal nozzle from the two sides of the T branch, therefore facing a particularly narrow inlet when compared to the opposite fluid direction. The effects of this higher flow restriction, when the fluid is going from the small to the large reservoir, can also be observed when analyzing the discharge coefficients in both flow directions. Notice that the discharge coefficient, when the fluid is going from the S-to-L reservoir, is asymptotically smaller than when the fluid flows from the L-to-S reservoir; see Equation (13) and Table 2.
The work presented in the present manuscript consisted of evaluating the discharge coefficient of a T shape nozzle under compressible flow conditions. Experimental and numerical analyses were performed. Numerical simulations clarified where the sonic conditions are to be expected. Discharge coefficients were dependent on the flow direction and the Reynolds number, and they agree well with the ones obtained by Comas et al. [13] and Nagao et al. [9], specially when considering the different nozzle length to diameter ratio. In the CFD simulations, the fluid was considered as ideal, and similar CFD simulations were performed by Lakzian et al. [18] and Mazzelli et al. [17], where the fluid was considered as ideal, as well, and studied under similar pressure differentials and the same turbulence model. From the comparison of the present study with Reference [9,13,17,18], it can be concluded that the error generated by the CFD simulations is small and acceptable under the engineering applications point of view.
Conclusions
The discharge time is proved to be directly related to the upstream/downstream density ratio. The discharge coefficients on both flow directions of a T shape nozzle, and considering the fluid as compressible and real, were obtained in the present manuscript based on experimental data. The same information was obtained from CFD simulations. The CFD simulations performed showed a good match with the experimental results and allowed understanding of the differences of the temporal flow evolution inside the nozzle at different flow directions. The exact locations where the flow was sonic, and even supersonic, were detected, allowing further modification of the T shape nozzle design in future applications. The theoretical methodology presented was based on the experimental data and proved to be very accurate and reliable, particularly when temporal pressure and temperature were known. The final equations characterizing the discharge coefficient as a function of the Reynolds number and for both flow directions are provided.
Conflicts of Interest:
The authors declare no conflict of interest. | 11,526.2 | 2021-03-15T00:00:00.000 | [
"Engineering",
"Physics"
] |
Hydrodynamic analysis of particle collection efficiency : comparing downflow and upflow filtration
Models of the filtration phenomenon describe the mass balance in bed filtration in terms of particle removal mechanisms, and allow for the determination of global particle removal efficiencies. These models are defined in terms of the geometry and characteristic elements of granule collectors, particles and fluid, and also the composition of the balance of forces that act in the particle collector system. This work analyzes particles collection efficiency comparing downflow and upflow direct filtration, taking into account the contribution of the gravitational factor of the settling removal efficiency in future proposal of initial collection efficiency models for upflow filtration. A qualitative analysis is also made of the proposal for the collection efficiency models for particle removal in direct downflow and upflow filtration using a Computational Fluid Dynamics (CFD) tool. This analysis showed a strong influence of gravitational factor in initial collection efficiency (t = 0) of particles, as well as the reasons of their values to be smaller for upflow filtration in comparison with the downflow filtration.
Introduction
The mathematical model allows the prediction of the control and operation conditions which lead to an improvement of the filtration process when producing drinking water.The mathematical models describe filtration process through particles removal mechanisms which take place in the granular bed filtration.This work analyzes the conditions of initial efficiency collector for upflow direct filtration in comparison with initial efficiency collection downflow filtration in saturated porous media.
The trajectory particles analysis through mathematical correlation by the dimensionless numbers representatives of fluid and particles characteristics are considered to be the main approach for mathematical modeling of initial efficiency collector of particles removal in water filtration context (TUFENKJI;ELIMELECH, 2004).
The filtration phenomenon is based on mass balance in granular bed filtration and permits the determination of global particles efficiency removal.The filtration phenomenon is defined in terms of geometry and characteristic elements of grain collectors (bed components), particles and fluid and also the forces composition balance which act in the particlecollector system.This type of resolution is well known as the trajectory analysis theory (TUFENKJI; ELIMELECH, 2004).This work makes a qualitative analysis to the initial efficiency collector particles removal comparing downflow and upflow direct filtration trough the use of Computational Fluid Dynamics (CFD) tool.And also, shows the influence of the flow direction in pilot-plant filtration experiments, comparing upflow and downflow direct filtration runs.
The filtration medium can be considered a set of collectors in a given control volume.It is therefore possible to determine the removal efficiency of a single collector and then, assuming a geometric cell structure, add the contribution of the other collectors to complete the filtration medium.
The conception of the collector removal model required the definition of the following elements (TIEN, 1989): -A geometric model of the collector and of the cellular arrangement (or set) of collectors and the respective conditions of the surrounding fluid; -Forces acting in the removal of particles; -Conditions for the solution of the trajectory or convective-diffusive equation.
For the non-Brownian particles, the convectivediffusive equation can be written as equation ( 1) (TIEN, 1989): where: m o represents particle mobility (s kg -1 ); Φ is the interaction colloidal energy (J); D is the diffusion constant (m 2 s -1 ); C is the particle concentration in the liquid phase (kg m -3 ); and U is the fluid's superficial velocity (m s -1 ).
The resolution of equation ( 1) requires extensive calculations and powerful computational tools, but a more practical approach is based on the correlation of dimensionless numbers (TUFENKJI; ELIMELECH, 2004).This approach simplifies the trajectory analysis by correlating the power functions by dimensionless numbers that represent fluid and particle characteristics of the mass balance in the control volume and the removal efficiency.
Representative flow equations and particle-tracking equations
In this section it is presented a Computational Fluid Dynamics (CFD) modeling for a qualitative analysis intending to show the main influence of the gravity vector in quantifying initial efficiency collection by settling mechanism.Further, is presented the experimental data of efficiency collection for upflow and downflow direct filtration.
Considering the continuity hypothesis for a Newtonian fluid, there are equations associated to conservation principles (CFX, 2004).
Continuity Equation where: F is force, m is mass, and , which is acceleration.
Considering the incompressible flow and constant physical properties hypothesis, one can obtain: (5) where: T is temperature; α is thermal conductivity; and S E is the energy source.
For a discrete particle in a continuous flow, the forces acting upon this particle and affecting its acceleration are due to the differences of velocity between fluid and particle and the mass fluid displaced by the particle's path.According CFX (2004), the particle-tracking equation was described by Basset, Boussinesq and Oseen for rotational references: where: m p : particle mass, d: particle diameter, v: velocity, ρ: specific mass, μ: fluid dynamic viscosity, g is the gravity acceleration (m s -2 ), C d : drag coefficient, ω: rotational velocity, R: rotation axis vector; and F U : external force (set by the user).The variable t o is used for the initial time, while the subscript "f" refers to the fluid and "p" to the particle.
The qualitative analysis of the hydrodynamic behavior of a single particle in an upflow or downflow was conceived for the laminar regime and is as known as the Forchheimer flow regime (N Re < 50 -grain Reynolds number), according to the type of flow that takes place in granular bed filtration.
Experimental data -filtration runs for upflow and downflow
The experimental work, which was conducted in pilot-plant facilities, aimed to compare the initial particle collection efficiency (η 0 α 0 -initial efficiency collector) of direct downflow and upflow filtration ccording to the conditions listed in Table 1, the influent characteristics is presented in Table 2 and the schemes illustrated in Figures 1 and 2. The examples results of filtration runs for upflow and downflow are presented in Figures 3 and 4.
As can be observe in hydrophobic or hydrophilic.The electrical superficial charge, hidrophobicity or not, and other superficial contact forces can be modify the adhesion conditions to the grain collector and, therefore, conditioning the attachment or detachment of the particle in the grain collector (BERGENDAHL; GRASSO, 1999).The particle suspension were placed in two 20liter plastic reservoirs where they were maintained in suspension by a rotating shaft mixer.A peristaltic pump was used to pump the particle in suspension at a fixed flow rate through a pulsation dampener and a rotameter.The Calcium Chloride and Aluminum Sulfate solutions were prepared in a 4 L beaker and pumped into a main line through a Tconnection.A series of expansions and contractions were provided to allow mixing of the two streams before the influent (Particles + CaCl 2 or alum) enters the filtration column.The column is made of 3.81 cm inner diameter plexiglass tube and is 35 cm high.
Glass microspheres ranging in size from 430 to 600 μm, with a specific mass of 2.5 g cm -3 , were used as the filtration medium.Two types of particles were added to the water: hydrophobic particles of polystyrene latex microspheres with the sulfate group (PGS) and hydrophilic particles of polystyrene latex microspheres with the carboxylate modify group (CML).The particles in both groups had an average diameter of 2.9 μm and a specific mass of 1.055 g cm -3 .
The main idea in varying the types of particles (hydrophobic and hydrophilic) was to allowed the large range to particles (primary particles) interaction in the filtration such real conditions and therefore to obtain the generalized model of initial efficiency collector in this aspect and considerer the gravitational settling influence in fluid flow direction.
The effluent turbidity and the total particle concentrations were consistently higher for upflow experiments, confirming the importance of the gravity effect on the filtration efficiency.According the Figures 3 and 4, for the two types of particles added to the water: hydrophobic particles of polystyrene latex microspheres with the sulfate group (PGS) and hydrophilic particles of polystyrene latex microspheres with the carboxylate modify group (CML), were obtained the similarity results for filtration with both coagulants: Calcium Chloride or aluminum sulfate.Nevertheless, in micro scale, the colloidal surfaces interaction point of view, is important to considerer such influence in the particle-collector interactions.To the other hand, the scale of gravity effect force and your influence in determination of the global initial efficiency collection is much higher to any kind of colloidal surfaces interaction forces (TUFENKJI; ELIMELECH, 2004).
Experimental filtration runs results
The Computational Fluid Dynamics tool used here was developed with ANSYS CFX ® 10.0 -AEA Technology -Engineering Software.This tool consists of three modules: CFX ® -Pre, CFX ® -Solver and CFX ® -Post.Each module is responsible for one stage of the mathematical modeling.
Table 3 summarizes the general characteristics of the domain and the simulations performed to analyze the particle's trajectory in an upflow and a downflow.Figure 5 presents the grid of the domain and a detail of the grid surrounding the granule (collector) used in the simulations to analyze the particle's trajectory using the CFD tool.As can be observed in Table 3 and Figure 5, the dimensions of filter bed is different to the pilot plant experimental facilities, because the collector (Sphere) adopted in this simulation indented to show particle tracking and the trajectory surround, therefore its size as well as diameter of the cylinder was increased to minimize the "wall effect" and maximize the view of particles trajectories.Figure 6 shows CFD simulations for 2.1 μm diameter particles in downflow (Figure 6a) and upflow (Figure 6c).This figure also shows a 25-fold magnified view of the particle's path through the streamlines around the collector granules in downflow (Figure 6b) and upflow (Figure 6d) directions, respectively.Figure 7 shows CFD simulations for 21.0 μm diameter particles in downflow (Figure 7a) and upflow (Figure 7c).A 25-fold magnification of the particle's path through the streamlines around the collector granules in downflow is depicted in Figure 7b, and a 2.5-fold magnification of the upflow is shown in Figure 7d.
The detail in Figure 6b shows that in the downflow, the particle's path is tangential to the streamline, but does not cross it.In contrast, the magnified view of the simulated upflow in Figure 6d reveals that the distance between particle and collector is also greater than in downflow and that the particle's path crosses the streamline.
This same behavior is illustrated clearly in Figure 7a and b, which show the downflow path, with the particle tracking across the streamline towards the collector.In the upflow, the particle trajectory crosses two consecutive streamline flows, moving away from the collector, according to the detail magnified in Figure 7c and Figure 7d, for a 21 μm diameter particle.However, the hydrodynamic characteristics of fluid and particle mass are not the only parameters that characterize the equations of initial collection efficiency models.Evidently, the hydrodynamic characteristics of the fluid and the particle mass are important aspects in the determination of efficiency.However, these aspects cause other physicochemical characteristics to diverge, especially the surface interaction forces.These characteristics are components of the efficiency collector equation and are related to the gravitational settling term in the total initial collection efficiency equation for particle removal.
Particle mass is obviously the main factor responsible for the augmented influence of the trajectory.However, it is the direction of the flow, i.e., upflow or downflow, which determines whether the trajectory is towards the collector or away from it.The pressure and velocity vectors for the opposite directions do not present any difference between upflow or downflow in terms of intensity.Figures 8a and b
Conclusion
In this work, a fundamental approach for incorporating the effect of flow direction in the clean-bed filtration model has been described.Specific conclusions are: the gravity effect was demonstrated by the observation that effluent turbidity and total particle concentration depended on flow direction through the bed; the initial efficiency collector values are lower for upflow than for downflow; further work should be done in order to develop a filtration model incorporating the flow direction and the practical aspects regarding upflow direct filtration.
Figure 3 .
Figure 3. Examples of Filtration runs indicated in Table 1.Comparison between Upflow and Downflow-Turbidity.
Figure 4 .
Figure 4. Examples of Filtration runs indicated in Table 1.Comparison between Upflow and Downflow-Particles number.CFD studies results.
Figure 5 .
Figure 5. Mash domains and a detail of the grain contour mesh used in the simulations of particle tracking for a trajectory analysis by CFD tool.
Figure 6 .
Figure 6.(a) Particle hydrodynamic behavior related to the stream lines in CFD modeling.Particle diameter 2.1 μm.(b) Detail amplified 25-fold magnification -Downflow; (c) Particle hydrodynamic behavior related to the stream lines.Particle diameter 2.1 μm; (d) Detail amplified 25-fold magnification -Upflow.The CFD simulations corroborate the observations of Gebhart et al. (1973), Paretsky et al. (1971) and Thomas et al. (1971) about the initial collection efficiency equations for upflow filtration, in which the collection efficiency appears to be smaller.However, the hydrodynamic characteristics of fluid and particle mass are not the only parameters that characterize the equations of initial collection efficiency models.Evidently, the hydrodynamic characteristics of the fluid and the particle mass are important aspects in the determination of efficiency.However, these aspects cause other physicochemical characteristics to diverge, especially the surface interaction forces.These characteristics are illustrate the symmetry between velocity vectors, while Figures8c and dalso show the symmetry of pressure for up and downflow, respectively.
Table 1 .
Characteristics of the experimental runs used to obtain initial efficiency collection data to conceived equation models for upflow direct filtration.
Table 2 .
Influent Characteristics of the filtration experimental runs summarized in Table1.
Table 3 .
Summary of the general characteristics of the domain and the simulations of CFD tool for particle tracking and trajectory analysis in upflow and downflow. | 3,208.8 | 2012-03-07T00:00:00.000 | [
"Environmental Science",
"Engineering"
] |