Search results for: bond graph (BG)

Authors: Raghavi C. Janaswamy

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In the healthcare sector, heterogenous data elements such as patients, diagnosis, symptoms, conditions, observation text from physician notes, and prescriptions form the essentials of the Electronic Health Record (EHR). The data in the form of clear text and images are stored or processed in a relational format in most systems. However, the intrinsic structure restrictions and complex joins of relational databases limit the widespread utility. In this regard, the design and development of realistic mapping and deep connections as real-time objects offer unparallel advantages. Herein, a graph neural network-based classification of EHR data has been developed. The patient conditions have been predicted as a node classification task using a graph-based open source EHR data, Synthea Database, stored in Tigergraph. The Synthea DB dataset is leveraged due to its closer representation of the real-time data and being voluminous. The graph model is built from the EHR heterogeneous data using python modules, namely, pyTigerGraph to get nodes and edges from the Tigergraph database, PyTorch to tensorize the nodes and edges, PyTorch-Geometric (PyG) to train the Graph Neural Network (GNN) and adopt the self-supervised learning techniques with the AutoEncoders to generate the node embeddings and eventually perform the node classifications using the node embeddings. The model predicts patient conditions ranging from common to rare situations. The outcome is deemed to open up opportunities for data querying toward better predictions and accuracy.

Keywords: electronic health record, graph neural network, heterogeneous data, prediction

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Authors: Jiajun Wang, Xiaoge Li

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The purpose of the aspect-level sentiment analysis task is to identify the sentiment polarity of aspects in a sentence. Currently, most methods mainly focus on using neural networks and attention mechanisms to model the relationship between aspects and context, but they ignore the dependence of words in different ranges in the sentence, resulting in deviation when assigning relationship weight to other words other than aspect words. To solve these problems, we propose a new aspect-level sentiment analysis model that combines a multi-channel convolutional network and graph convolutional network (GCN). Firstly, the context and the degree of association between words are characterized by Long Short-Term Memory (LSTM) and self-attention mechanism. Besides, a multi-channel convolutional network is used to extract the features of words in different ranges. Finally, a convolutional graph network is used to associate the node information of the dependency tree structure. We conduct experiments on four benchmark datasets. The experimental results are compared with those of other models, which shows that our model is better and more effective.

Keywords: aspect-level sentiment analysis, attention, multi-channel convolution network, graph convolution network, dependency tree

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Authors: Pavel Boška, Jan Bomba, Tomáš Beránek, Jiří Procházka

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When using wood products bonded by polyvinyl acetate, glues such as windows are the most limiting element of degradation of the glued joint due to weather changes. In addition to moisture and high temperatures, the joint may damage the low temperature below freezing point, where dimensional changes in the material and distortion of the adhesive film occur. During the experiments, the joints were exposed to several degrees of sub-zero temperatures from 0 °C to -40 °C and then to compare how the decreasing temperature affects the strength of the joint. The experiment was performed on wood beech samples (Fagus sylvatica), bonded with PVAc with D3 resistance and the shear strength of bond was measured. The glued and treated samples were tested on a laboratory testing machine, recording the strength of the joint. The statistical results have given us information that the strength of the joint gradually decreases with decreasing temperature, but a noticeable and statistically significant change is achieved only at very low temperatures.

Keywords: adhesives, bond strength, low temperatures, polyvinyl acetate

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Authors: Jordy Padilla

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The New Jersey Department of Transportation (NJDOT) initiated a research project to install and evaluate Ultra-High-Performance Concrete (UHPC) as an overlay on existing bridges. The project aims to implement UHPC overlays in NJDOT bridge deck strategies for preservation and repair. During design, four bridges were selected for construction. The construction involved the removal of the existing bridge asphalt overlays, partially removing the existing concrete deck surface, and resurfacing the deck with a UHPC overlay. In some cases, a new asphalt riding surface was placed. Additionally, existing headers were replaced with full-depth UHPC. The UHPC overlay is monitored through coring and Non-destructive testing (NDT) to ensure that the interfacial bond is intact and that the desired conditions are maintained. The NDT results show no evidence that the bond between the new UHPC overlay and the existing concrete deck is compromised. Bond strength test data demonstrates that, in general, the desired bond was achieved between UHPC and the substrate concrete, although the results were lower than anticipated. Chloride content is also within expectations except for one anomaly. The baseline testing was successful, and no significant defects were encountered.

Keywords: ultra-high performance concrete, rehabilitation, non-destructive testing

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Authors: A. Zanj, F. He

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In the current paper, a domain independent conduction model compatible for multi-physical system dynamic investigations is suggested. By means of a port-based approach, a classical nonlinear conduction model containing physical states is first represented. A compatible discrete configuration of the thermal domain in line with the elastic domain is then generated through the enhancement of the configuration of the conventional thermal element. The presented simulation results of a sample structure indicate that the suggested conductive model can cover a wide range of dynamic behavior of the thermal domain.

Keywords: multi-physical domain, conduction model, port based modeling, dynamic interaction, physical modeling

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Authors: Prat Yotyawilai, Taratip Suwannasart

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Business Process Model and Notation (BPMN) is more important in the business process and creating functional models, and is a standard for OMG, which becomes popular in various organizations and in education. Researches related to software testing based on models are prominent. Although most researches use the UML model in software testing, not many researches use the BPMN Model in creating test cases. Therefore, this research proposes a design of a tool for generating test cases from the BPMN. The model is analyzed and the details of the various components are extracted before creating a flow graph. Both details of components and the flow graph are used in generating test cases.

Keywords: software testing, test case, BPMN, flow graph

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Ralph Gruber, Martina Hafner, Theresia Greunz, Christian Reisecker, David Stifter

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The application of dry lubricant on aluminium for automotive industry is indispensable for a high-quality forming behaviour. To provide a short production time those forming aids will not be removed during the joining step. The aim of this study was the characterization of the influence of dry lubricants on the bond strength and the corrosion resistance of an 6xxx aluminium alloy for automotive applications. For this purpose, samples with a well-defined surface were lubricated with 1 g/m² dry lubricant and joined with a commercial thermosetting 1K-epoxy structural adhesive. The bond strength was characterized by means of lap shear test. To evaluate the corrosion resistance of the adhered aluminium samples an immersion test in 5 w% NaCl-solution was used. Based on fracture pattern analysis, the corrosion behaviour could be described. Dissolved corrosion products were examined using ICP-MS and NMR. By means of SEM/EDX the elementary composition of precipitated solids was determined. The results showed a dry lubricant independent bond strength for standard testing conditions. However, a significant effect of the forming aid, regarding the corrosion resistance of adhered aluminium samples against corrosive infiltration of the metal-adhesive-interface, was observed

Keywords: aluminium alloys, dry film lubricants, automotive industry, adhesive bonding, corrosion

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Authors: Dilip K. Paul

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The most popular mesoporous molecular sieves, Mobil Composition of Matter (MCM) are keenly studied by researchers because of these materials possess amorphous silica wall and have a long range of ordered framework with uniform mesopores. These materials also possess large surface area, which can be up to more than 1000 m2g−1. Herein the investigation is focused upon the synthesis and characterization of chromium and aluminum doped MCM-41 using XRD and FTIR. Acid-base properties of Cr-Al-MCM 41 was investigated by molecularly sensitive transmission FT-IR spectroscopy by adsorbing pyridine. In addition, these MCM nanomaterial was used to catalyze C-C bond formation from acetaldehyde adsorption. The assignment of all infrared peaks during adsorption of pyridine provided detail information on the presence of acid-base sites which in turn helped us to explain the roles of these in the condensation reaction of aldehyde. Reaction mechanisms of C-C bond formation is therefore explored to shed some light on this elusive reaction detail.

Keywords: mesoporous nanomaterial, MCM 41, FTIR studies, acid-base studies

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Authors: Petra Palic, Maruska Vizek

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We provide an in-depth analysis of interdependence of asset returns and volatilities in developed and developing countries. The analysis is split into three parts. In the first part, we use multivariate GARCH model in order to provide stylized facts on cross-market volatility spillovers. In the second part, we use a generalized vector autoregressive methodology developed by Diebold and Yilmaz (2009) in order to estimate separate measures of return spillovers and volatility spillovers among sovereign bond, equity, foreign exchange and commodity markets. In particular, our analysis is focused on cross-market return, and volatility spillovers in 19 developed and developing countries. In order to estimate named spillovers, we use daily data from 2008 to 2017. In the third part of the analysis, we use a generalized vector autoregressive framework in order to estimate total and directional volatility spillovers. We use the same daily data span for one developed and one developing country in order to characterize daily volatility spillovers across stock, bond, foreign exchange and commodities markets.

Keywords: cross-market spillovers, sovereign bond markets, equity markets, value at risk (VAR)

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Authors: Mahdi Jalili

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Functional connectivity in the human brain can be represented as a network using electroencephalography (EEG) signals. Network representation of EEG time series can be an efficient vehicle to understand the underlying mechanisms of brain function. Brain functional networks – whose nodes are brain regions and edges correspond to functional links between them – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which graph theory metrics are sex dependent. To this end, EEGs from 24 healthy female subjects and 21 healthy male subjects were recorded in eyes-closed resting state conditions. The connectivity matrices were extracted using correlation analysis and were further binarized to obtain binary functional networks. Global and local efficiency measures – as graph theory metrics– were computed for the extracted networks. We found that male brains have a significantly greater global efficiency (i.e., global communicability of the network) across all frequency bands for a wide range of cost values in both hemispheres. Furthermore, for a range of cost values, female brains showed significantly greater right-hemispheric local efficiency (i.e., local connectivity) than male brains.

Keywords: EEG, brain, functional networks, network science, graph theory

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Authors: Hae-Yeoun Lee

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Quantification of cardiac function is performed by calculating blood volume and ejection fraction in routine clinical practice. However, these works have been performed by manual contouring,which requires computational costs and varies on the observer. In this paper, an automatic left ventricle segmentation algorithm on cardiac magnetic resonance images (MRI) is presented. Using knowledge on cardiac MRI, a K-mean clustering technique is applied to segment blood region on a coil-sensitivity corrected image. Then, a graph searching technique is used to correct segmentation errors from coil distortion and noises. Finally, blood volume and ejection fraction are calculated. Using cardiac MRI from 15 subjects, the presented algorithm is tested and compared with manual contouring by experts to show outstanding performance.

Keywords: cardiac MRI, graph searching, left ventricle segmentation, K-means clustering

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Authors: Subhash Chandra Sharma, Mohammad Soleimani

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Selection of the correct lubricant for the top of rail application is a complex process. In this paper, the selection of the proper lubricant for a Top-Of-Rail (TOR) lubrication system based on graph theory and matrix approach has been developed. Attributes influencing the selection process and their influence on each other has been represented through a digraph and an equivalent matrix. A matrix function which is called the Permanent Function is derived. By substituting the level of inherent contribution of the influencing parameters and their influence on each other qualitatively, a criterion called Suitability Index is derived. Based on these indices, lubricants can be ranked for their suitability. The proposed model can be useful for maintenance engineers in selecting the best lubricant for a TOR application. The proposed methodology is illustrated step–by-step through an example.

Keywords: lubricant selection, top of rail lubrication, graph-theory, Ranking of lubricants

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Authors: E. R. Swart, S. J. Gismondi, N. R. Swart, C. E. Bell

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We present an heuristic algorithm that decides graph non-Hamiltonicity. All graphs are directed, each undirected edge regarded as a pair of counter directed arcs. Each of the n! Hamilton cycles in a complete graph on n+1 vertices is mapped to an n-permutation matrix P where p(u,i)=1 if and only if the ith arc in a cycle enters vertex u, starting and ending at vertex n+1. We first create exclusion set E by noting all arcs (u, v) not in G, sufficient to code precisely all cycles excluded from G i.e. cycles not in G use at least one arc not in G. Members are pairs of components of P, {p(u,i),p(v,i+1)}, i=1, n-1. A doubly stochastic-like relaxed LP formulation of the Hamilton cycle decision problem is constructed. Each {p(u,i),p(v,i+1)} in E is coded as variable q(u,i,v,i+1)=0 i.e. shrinks the feasible region. We then implement the Weak Closure Algorithm (WCA) that tests necessary conditions of a matching, together with Boolean closure to decide 0/1 variable assignments. Each {p(u,i),p(v,j)} not in E is tested for membership in E, and if possible, added to E (q(u,i,v,j)=0) to iteratively maximize |E|. If the WCA constructs E to be maximal, the set of all {p(u,i),p(v,j)}, then G is decided non-Hamiltonian. Only non-Hamiltonian G share this maximal property. Ten non-Hamiltonian graphs (10 through 104 vertices) and 2000 randomized 31 vertex non-Hamiltonian graphs are tested and correctly decided non-Hamiltonian. For Hamiltonian G, the complement of E covers a matching, perhaps useful in searching for cycles. We also present an example where the WCA fails.

Keywords: Hamilton cycle decision problem, computational complexity theory, graph theory, theoretical computer science

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Authors: Niharika Keot, Manabendra Sarma

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A thorough investigation of Ln3+ complexes with more than one inner-sphere water molecule is crucial for designing high relaxivity contrast agents (CAs) used in magnetic resonance imaging (MRI). This study accomplished a comparative stability analysis of two hexadentate (H3cbda and H3dpaa) and two heptadentate (H4peada and H3tpaa) ligands with Ln3+ ions. The higher stability of the hexadentate H3cbda and heptadentate H4peada ligands has been confirmed by the binding affinity and Gibbs free energy analysis in aqueous solution. In addition, energy decomposition analysis (EDA) reveals the higher binding affinity of the peada4− ligand than the cbda3− ligand towards Ln3+ ions due to the higher charge density of the peada4− ligand. Moreover, a mechanistic overview of water exchange kinetics has been carried out based on the strength of the metal–water bond. The strength of the metal–water bond follows the trend Gd–O47 (w) > Gd–O39 (w) > Gd–O36 (w) in the case of the tris-aquated [Gd(cbda)(H2O)3] and Gd–O43 (w) > Gd–O40 (w) for the bis-aquated [Gd(peada)(H2O)2]− complex, which was confirmed by bond length, electron density (ρ), and electron localization function (ELF) at the corresponding bond critical points. Our analysis also predicts that the activation energy barrier decreases with the decrease in bond strength; hence kex increases. The 17O and 1H hyperfine coupling constant values of all the coordinated water molecules were different, calculated by using the second-order Douglas–Kroll–Hess (DKH2) approach. Furthermore, the ionic nature of the bonding in the metal–ligand (M–L) bond was confirmed by the Quantum Theory of Atoms-In-Molecules (QTAIM) and ELF along with energy decomposition analysis (EDA). We hope that the results can be used as a basis for the design of highly efficient Gd(III)-based high relaxivity MRI contrast agents for medical applications.

Keywords: MRI contrast agents, lanthanide chemistry, thermodynamic stability, water exchange kinetics

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Authors: Ayşe Dilek Maden

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For a given a simple connected graph, we present some new bounds via a new approach for a special topological index given by the sum of the real number power of the non-zero normalized Laplacian eigenvalues. To use this approach presents an advantage not only to derive old and new bounds on this topic but also gives an idea how some previous results in similar area can be developed.

Keywords: degree Kirchhoff index, normalized Laplacian eigenvalue, spanning tree, simple connected graph

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Authors: Günevi Uslu Evren

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Propaganda is one of the most effective methods for changing individual and mass opinions. Propaganda tries to get the message across to people or masses to effect rather than to provide objective information. There are many types of propaganda. Especially, political propaganda is a very powerful method that is used by states during in both war and peace. The aim of this method is to create a reaction against them by showing within the framework of internal and external enemies. Propaganda can be practiced by many different methods. Especially during the Cold War Era, the US and USSR have tried to create an ideological effect by using the mass media intensively. Cinema, which is located at the beginning of these methods, is the most powerful weapon to influence the masses. In this study, the historical process of the Cold War is examined. Especially, these propagandas that had been used by United States and The Soviet Union were investigated. The purposes of propaganda and construction methods were presented. Cold War events and relations between the US and the USSR during the Cold War will be discussed. Outlooks of two countries to each other during the Cold War, propaganda techniques used defectively during Cold War and how to use the cinema as a propaganda tool will be examined. The film "From Russia with Love, James Bond 007" that was filmed in Cold War were examined to explain how cinema was used as a propaganda tool in this context.

Keywords: cinema, cold war, James Bond, propaganda

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Authors: Camille A. Issa, Omar Masri

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In the study, the commercial finite element software Abaqus was used to develop a three-dimensional nonlinear finite element model capable of simulating the pull-out test of reinforcing bars from underwater concrete. The results of thirty-two pull-out tests that have different parameters were implemented in the software to study the effect of the concrete cover, the bar size, the use of stirrups, and the compressive strength of concrete. The interaction properties used in the model provided accurate results in comparison with the experimental bond-slip results, thus the model has successfully simulated the pull-out test. The results of the finite element model are used to better understand and visualize the distribution of stresses in each component of the model, and to study the effect of the various parameters used in this study including the role of the stirrups in preventing the stress from reaching to the sides of the specimens.

Keywords: pull-out test, bond strength, underwater concrete, nonlinear finite element analysis, abaqus

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Authors: Boumesbah Asma, Chergui Mohamed El-amine

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Since it has been shown that the multi-objective minimum spanning tree problem (MOST) is NP-hard even with two criteria, we propose in this study a hybrid NSGA-II algorithm with an exact mutation operator, which is only used with low probability, to find an approximation to the Pareto front of the problem. In a connected graph G, a spanning tree T of G being a connected and cycle-free graph, if k edges of G\T are added to T, we obtain a partial graph H of G inducing a reduced size multi-objective spanning tree problem compared to the initial one. With a weak probability for the mutation operator, an exact method for solving the reduced MOST problem considering the graph H is then used to give birth to several mutated solutions from a spanning tree T. Then, the selection operator of NSGA-II is activated to obtain the Pareto front approximation. Finally, an adaptation of the VNS metaheuristic is called for further improvements on this front. It allows finding good individuals to counterbalance the diversification and the intensification during the optimization search process. Experimental comparison studies with an exact method show promising results and indicate that the proposed algorithm is efficient.

Keywords: minimum spanning tree, multiple objective linear optimization, combinatorial optimization, non-sorting genetic algorithm, variable neighborhood search

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Authors: Itumeleng Phage

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Students’ comprehension of graphs may be affected by the characteristics of the discipline in which the graph is used, the type of the task as well as the background of the students who are the readers or interpreters of the graph. This research study investigated these aspects of the graph comprehension of 152 first-year undergraduate physics students by comparing their responses to corresponding tasks in the mathematics and physics disciplines. The discipline characteristics were analysed for four task-related constructs namely coordinates, representations, area and slope. Students’ responses to corresponding visual decoding and judgement tasks set in mathematics and kinematics contexts were statistically compared. The effects of the participants’ gender, year of school completion and study course were determined as reader characteristics. The results of the empirical study indicated that participants generally transferred their mathematics knowledge on coordinates and representation of straight line graphs to the physics contexts, but not in the cases of parabolic and hyperbolic functions or area under graphs. Insufficient understanding of the slope concept contributed to weak performances on this construct in both mathematics and physics contexts. Discipline characteristics seem to play a vital role in students’ understanding, while reader characteristics had insignificant to medium effects on their responses.

Keywords: kinematics graph, discipline characteristics, constructs, coordinates, representations, area and slope

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Authors: Zhengrong Wu, Haibo Yang

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In the context of escalating global climate change and environmental degradation, the complexity and frequency of natural disasters are continually increasing. Confronted with an abundance of information regarding natural disasters, traditional knowledge graph construction methods, which heavily rely on grammatical rules and prior knowledge, demonstrate suboptimal performance in processing complex, multi-source disaster information. This study, drawing upon past natural disaster reports, disaster-related literature in both English and Chinese, and data from various disaster monitoring stations, constructs question-answer templates based on large language models. Utilizing the P-Tune method, the ChatGLM2-6B model is fine-tuned, leading to the development of a disaster knowledge graph based on large language models. This serves as a knowledge database support for disaster emergency response.

Keywords: large language model, knowledge graph, disaster, deep learning

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Authors: Woo-tai Jung, Jong-sup Park, Jae-yoon Kang, Moon-seoung Keum

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CFRP (Carbon Fiber Reinforced Polymer) is mainly used as reinforcing material for degraded structures owing to its advantages including its non-corrodibility, high strength, and lightweight properties. Recently, dedicated studies focused not only on its simple bonding but also on its tensioning. The tension necessary for prestressing requires the anchoring of multi-CFRP tendons with high capacity and the surface treatment of the CFRP tendons may also constitute an important issue according to the type of anchor. The wedge type, swage type or bonded type anchor can be used to anchor the CFRP tendon. The bonded type anchor presents the disadvantage to lengthen the length of the anchor due to the low bond strength of the CFRP tendon without surface treatment. This study intends to overcome this drawback through the application of a method enlarging the bond area at the end of the CFRP tendon. This method enlarges the bond area by splitting the end of the CFRP tendon along its length and can be applied when CFRP is produced by pultrusion. The application of this method shows that the mono-CFRP tendon and 3-multi CFRP tendon secured the anchor performance corresponding to the tensile performance of the CFRP tendon and that the 7-multi tendon secured anchor performance corresponding to 90% of the tensile strength due to the occurrence of buckling in the steel tube anchorage.

Keywords: carbon fiber reinforced polymer (CFRP), tendon, anchor, tensile property, bond strength

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Authors: Somyung Oh, Jeonghyeon Ha, Kyungwon Lee, Sejong Oh

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Microarray is a general scheme to find differentially expressed genes for target concept. The output is expressed by heat map, and biologists analyze related terms of gene ontology to find some characteristics of differentially expressed genes. In this paper, we propose integrated visualization tool for heat map and gene ontology graph. Previous two methods are used by static manner and separated way. Proposed visualization tool integrates them and users can interactively manage it. Users may easily find and confirm related terms of gene ontology for given differentially expressed genes. Proposed tool also visualize connections between genes on heat map and gene ontology graph. We expect biologists to find new meaningful topics by proposed tool.

Keywords: heat map, gene ontology, microarray, differentially expressed gene

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Authors: P. van Tonder, U. Bagdadi, B. M. D. Lario, Z. Masina, T. R. Motshwari

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This paper analyses how concrete reinforcing steel bars corrode and how it can be minimised through the use of various protection methods against corrosion, such as metal-based paint, alloying, cathodic protection and electroplating. Samples of carbon steel bars were protected, using these four methods. Tests performed on the samples included durability, electrical resistivity and bond strength. Durability results indicated relatively low corrosion rates for alloying, cathodic protection, electroplating and metal-based paint. The resistivity results indicate all samples experienced a downward trend, despite erratic fluctuations in the data, indicating an inverse relationship between electrical resistivity and corrosion rate. The results indicated lowered bond strengths when the reinforced concrete was cured in seawater compared to being cured in normal water. It also showed that higher design compressive strengths lead to higher bond strengths which can be used to compensate for the loss of bond strength due to corrosion in a real-world application. In terms of implications, all protection methods have the potential to be effective at resisting corrosion in real-world applications, especially the alloying, cathodic protection and electroplating methods. The metal-based paint underperformed by comparison, most likely due to the nature of paint in general which can fade and chip away, revealing the steel samples and exposing them to corrosion. For alloying, stainless steel is the suggested material of choice, where Y-bars are highly recommended as smooth bars have a much-lowered bond strength. Cathodic protection performed the best of all in protecting the sample from corrosion, however, its real-world application would require significant evaluation into the feasibility of such a method.

Keywords: protection methods, corrosion, concrete, reinforcing steel bars

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Authors: Nasser Ghassembaglou, Abdullah Bolek, Oktay Yilmaz, Ertan Oznergiz, Hikmet Kocabas, Safak Yilmaz

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High quality air filters production using nanofibers, as a functional material, has frequently been investigated. As it is more environmentally friendly, melting method has been selected to produce nanofibers. Spun-bond production systems consist of extruder, spin-pump, nozzle package and attenuators. Spin-pump makes molten polymer steady, which flows through extruder. Fibers are formed by regular melts passing through nuzzle holes under high pressure. Attenuator prolongs fibers to micron size to be collected on a conveyor. Different designs of attenuator systems have been studied in this research; new analysis have been done on existed designs considering fibers effect on air flow; it was comprehended that, at fibers presence, there is an air flow which agglomerates fibers as a negative effect. So some new representations have been designed and CFD analysis have been done on them. Afterwards, one of these representations selected as the most optimum and effective design which is brought in this paper.

Keywords: attenuator, CFD, nanofiber, spun-bond

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Authors: Manel Brichni, Abdelhak-Djamel Seriai

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Systems are increasingly complex. To reduce their complexity, an abstract view of the system can simplify its development. To overcome this problem, we propose a method to decompose systems into subsystems while reducing their coupling. These subsystems represent components. Consisting of an existing object-oriented systems, the main idea of our approach is based on modelling as graphs all entities of an oriented object source code. Such modelling is easy to handle, so we can apply restructuring algorithms based on graph metrics. The particularity of our approach consists in integrating in addition to standard metrics, such as coupling and cohesion, some graph metrics giving more precision during the components identication. To treat this problem, we relied on the ROMANTIC approach that proposed a component-based software architecture recovery from an object oriented system.

Keywords: software reengineering, software component and interfaces, metrics, graphs

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Authors: Nicolas D. Norboge

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Purchasers of municipal bonds in primary and secondary markets are increasingly expecting issuers to disclose environmental, social, and governance factors (ESG) inissuance and continuing disclosure documents. U.S. tolling authorities are slowly catching up with other transportation sectors, such as public transit, in integrating ESG factors into their bond disclosure documents. A systematic mixed-methods evaluation of publicly available bond disclosure documents from 2010-2022 suggest that only a small number of U.S. tolling authorities disclosedall ESG factors; however, the pace has accelerated significantly from 2020-2022. Because many tolling authorities have a direct financial stake in the growth of passenger vehicle miles traveled on their toll facilities, and in turn the burning of more climate-warming fossil fuels, one crucial questionthat remains is how bond purchasers will view increasedESG transparency. Recent moves by large institutional investors, credit rating agencies, and regulators suggestan expectation of ESG disclosure is a trend likely to endure. This researchsuggests tolling authorities will need to proactively consider these emerging trends and carefully adapt their disclosure practiceswhere possible. Building on these findings, this research also provides a basic sketch framework for how issuers can responsibly position themselves within the changing global municipal debt marketplace.

Keywords: debt policy, ESG, municipal bonds, public-private partnerships, public tolling authorities, transportation finance, and policy

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Authors: Jerry Hu

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Let G = (V, E) with V = {1, 2, ..., k} be a graph, the k positive integers a₁, a₂, ..., ak are G-wise relatively prime if (aᵢ, aⱼ ) = 1 for {i, j} ∈ E. We use an inductive approach to give an asymptotic formula for the number of k-tuples of integers that are G-wise relatively prime. An exact formula is obtained for the probability that k positive integers are G-wise relatively prime. As a corollary, we also provide an exact formula for the probability that k positive integers have exactly r relatively prime pairs.

Keywords: graph, independent set, G-wise relatively prime, probability

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Authors: YingWei Tan, XueFeng Ding

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Attention-based voice activity detection models have gained significant attention in recent years due to their fast training speed and ability to capture a wide contextual range. The inclusion of multi-head style and position embedding in the attention architecture are crucial. Having multiple attention heads allows for differential focus on different parts of the sequence, while position embedding provides guidance for modeling dependencies between elements at various positions in the input sequence. In this work, we propose an approach by considering each head as a node, enabling the application of graph neural networks (GNN) to identify correlations among the different nodes. In addition, we adopt an implementation named rotary position embedding (RoPE), which encodes absolute positional information into the input sequence by a rotation matrix, and naturally incorporates explicit relative position information into a self-attention module. We evaluate the effectiveness of our method on a synthetic dataset, and the results demonstrate its superiority over the baseline CRNN in scenarios with low signal-to-noise ratio and noise, while also exhibiting robustness across different noise types. In summary, our proposed framework effectively combines the strengths of CNN and RNN (LSTM), and further enhances detection performance through the integration of graph neural networks and rotary position embedding.

Keywords: voice activity detection, CRNN, graph neural networks, rotary position embedding

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Authors: Choukri Lekbir, Mira Mokhtari

Abstract:

Hydrogen-Materials interaction and mechanisms can be modeled at nano scale by quantum methods. In this work, the effect of hydrogen on the electronic properties of a cluster material model «nickel» has been studied by using of density functional theoretical (DFT) method. Two types of clusters are optimized: Nickel and hydrogen-nickel system. In the case of nickel clusters (n = 1-6) without presence of hydrogen, three types of electronic structures (neutral, cationic and anionic), have been optimized according to three basis sets calculations (B3LYP/LANL2DZ, PW91PW91/DGDZVP2, PBE/DGDZVP2). The comparison of binding energies and bond lengths of the three structures of nickel clusters (neutral, cationic and anionic) obtained by those basis sets, shows that the results of neutral and anionic nickel clusters are in good agreement with the experimental results. In the case of neutral and anionic nickel clusters, comparing energies and bond lengths obtained by the three bases, shows that the basis set PBE/DGDZVP2 is most suitable to experimental results. In the case of anionic nickel clusters (n = 1-6) with presence of hydrogen, the optimization of the hydrogen-nickel (anionic) structures by using of the basis set PBE/DGDZVP2, shows that the binding energies and bond lengths increase compared to those obtained in the case of anionic nickel clusters without the presence of hydrogen, that reveals the armor effect exerted by hydrogen on the electronic structure of nickel, which due to the storing of hydrogen energy within nickel clusters structures. The comparison between the bond lengths for both clusters shows the expansion effect of clusters geometry which due to hydrogen presence.

Keywords: binding energies, bond lengths, density functional theoretical, geometry optimization, hydrogen energy, nickel cluster

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