Search results for: extended labelled dependency graph

Authors: Farzad Khajavi, Farzaneh Jalilfar, Faranak Jafari, Leila Shokrani

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Formulation of Ranolazine in the form of extended-release tablet in 500 mg dosage form was performed using Eudragit L100-55 as a retarding agent. Drug-release profiles were investigated in comparison with the reference Ranexa extended-release 500 mg tablet. F₂ and f₁ were calculated as 64.16 and 8.53, respectively. According to Peppas equation, the release of drug is controlled by diffusion (n=0.5). The tablets were put into accelerated stability conditions (40 °C, 75% humidity) for 3 and 6 months. The dissolution release profiles and other physical and chemical characteristics of the tablets confirmed the robustness and stability of formulation in this condition.

Keywords: drug release, extended-release tablet, ranolazine, stability

Procedia PDF Downloads 119

Authors: Junyou Shi, Weiwei Cui

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Testability modeling is a commonly used method in testability design and analysis of system. A dependency matrix will be obtained from testability modeling, and we will give a quantitative evaluation about fault detection and isolation. Based on the dependency matrix, we can obtain the diagnosis tree. The tree provides the procedures of the fault detection and isolation. But the dependency matrix usually includes built-in test (BIT) and manual test in fact. BIT runs the test automatically and is not limited by the procedures. The method above cannot give a more efficient diagnosis and use the advantages of the BIT. A Comprehensive method of fault detection and isolation is proposed. This method combines the advantages of the BIT and Manual test by splitting the matrix. The result of the case study shows that the method is effective.

Keywords: fault detection, fault isolation, testability modeling, BIT

Procedia PDF Downloads 296

Authors: Yihao Kuang, Bowen Ding

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With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graphs and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improved strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain a better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference

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Authors: Hoda Ghavamipoor, S. Alireza Hashemi Golpayegani

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An approach to model the customer interaction with e-commerce websites is presented. Considering the service quality level as a predictive feature, we offer an improved method based on the Customer Behavior Model Graph (CBMG), a state-transition graph model. To derive the Quality of Service sensitive-CBMG (QoS-CBMG) model, process-mining techniques is applied to pre-processed website server logs which are categorized as ‘buy’ or ‘visit’. Experimental results on an e-commerce website data confirmed that the proposed method outperforms CBMG based method.

Keywords: customer behavior model, electronic commerce, quality of service, customer behavior model graph, process mining

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Authors: S. Ghasemi, K. Khorasani

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In this paper, the problem of fault detection and isolation in the attitude control subsystem of spacecraft formation flying is considered. In order to design the fault detection method, an extended Kalman filter is utilized which is a nonlinear stochastic state estimation method. Three fault detection architectures, namely, centralized, decentralized, and semi-decentralized are designed based on the extended Kalman filters. Moreover, the residual generation and threshold selection techniques are proposed for these architectures.

Keywords: component, formation flight of satellites, extended Kalman filter, fault detection and isolation, actuator fault

Procedia PDF Downloads 408

Authors: Benny Benyamin Nasution, Rahmat Widia Sembiring, Abdul Rahman Dalimunthe, Nursiah Mustari, Nisfan Bahri, Berta br Ginting, Riadil Akhir Lubis, Rita Tavip Megawati, Indri Dithisari

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The earth coordinate system is an important part of an attempt for earthquake forecasting, such as the one using Single Layer Hierarchical Graph Neuron (SLHGN). However, there are a number of problems that need to be worked out before the coordinate system can be utilized for the forecaster. One example of those is that SLHGN requires that the focused area of an earthquake must be constructed in a grid-like form. In fact, within the current earth coordinate system, the same longitude-difference would produce different distances. This can be observed at the distance on the Equator compared to distance at both poles. To deal with such a problem, a coordinate system has been developed, so that it can be used to support the ongoing earthquake forecasting using SLHGN. Two important issues have been developed in this system: 1) each location is not represented through two-value (longitude and latitude), but only a single value, 2) the conversion of the earth coordinate system to the x-y cartesian system requires no angular formulas, which is therefore fast. The accuracy and the performance have not been measured yet, since earthquake data is difficult to obtain. However, the characteristics of the SLHGN results show a very promising answer.

Keywords: hierarchical graph neuron, multidimensional hierarchical graph neuron, single layer hierarchical graph neuron, natural disaster forecasting, earthquake forecasting, earth coordinate system

Procedia PDF Downloads 186

Authors: Lin Cheng, Zijiang Yang

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Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specification. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and node are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.

Keywords: program synthesis, flow chart, specification, graph recognition, CNN

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Authors: B. Roopa Sri, Y Lakshmi Naidu

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Chemical Graph Theory (CGT) is the branch of mathematical chemistry in which molecules are modeled to study their physicochemical properties using molecular descriptors. Amongst these descriptors, topological indices play a vital role in predicting the properties by defining the graph topology of the molecule. Recently, the bond-additive topological index known as the Mostar index has been proposed. In this paper, we compute the Mostar-type indices of octane isomers and use the data obtained to perform QSPR analysis. Furthermore, we show the correlation between the Mostar type indices and the properties.

Keywords: chemical graph theory, mostar type indices, octane isomers, qspr analysis, topological index

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Authors: Zhichao Jiao, Craig Farnham, Jihui Yuan, Kazuo Emura

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It is common to use the outdoor design weather data to select the air-conditioning capacity in the building design stage. The outdoor design weather data are usually comprised of multiple meteorological elements for a 24-hour period separately, but the dependency between the elements is not well considered, which may cause an overestimation of selecting air-conditioning capacity. Considering the dependency between the air temperature and global solar radiation, we used the copula approach to model the joint distributions of those two weather elements and suggest a new method of selecting more credible outdoor design conditions based on the specific simultaneous occurrence probability of air temperature and global solar radiation. In this paper, the 10-year period hourly weather data from 2001 to 2010 in Osaka, Japan, was used to analyze the dependency structure and joint distribution, the result shows that the Joe-Frank copula fit for almost all hourly data. According to calculating the simultaneous occurrence probability and the common exceeding probability of air temperature and global solar radiation, the results have shown that the maximum difference in design air temperature and global solar radiation of the day is about 2 degrees Celsius and 30W/m2, respectively.

Keywords: energy conservation, design weather database, HVAC, copula approach

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Masoumeh Ali Pouri, Hamid Haj Seyyed Javadi

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Nowadays, cloud processing is one of the important issues in information technology. Since scheduling of tasks graph is an NP-hard problem, considering approaches based on undeterminisitic methods such as evolutionary processing, mostly genetic and cuckoo algorithms, will be effective. Therefore, an efficient algorithm has been proposed for scheduling of tasks graph to obtain an appropriate scheduling with minimum time. In this algorithm, the new approach is based on making the length of the critical path shorter and reducing the cost of communication. Finally, the results obtained from the implementation of the presented method show that this algorithm acts the same as other algorithms when it faces graphs without communication cost. It performs quicker and better than some algorithms like DSC and MCP algorithms when it faces the graphs involving communication cost.

Keywords: cloud computing, scheduling, tasks graph, chakoos algorithm

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Authors: Mahmoudi Noureddine, Bouregba Rachid

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In this paper the stress intensity factors of a slant-cracked plate of AISI 304 stainless steel, have been calculated using extended finite element method and finite element method (FEM) in ABAQUS software, the results were compared with theoretical values.

Keywords: stress intensity factors, extended finite element method, stainless steel, abaqus

Procedia PDF Downloads 580

Authors: Fahmi Hassan, Noorizan Abdul Aziz, Yahaya Hassan, Hazlinda Abu Hassan

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Sepsis incidence in critical care settings is a major problem in health care. Extended antibiotic infusion is thought to be superior to traditional dosing especially when treating critically ill patients with sepsis. We compared clinical outcomes of critically ill patients with sepsis receiving 30-minute meropenem infusion and three-hour meropenem infusion. A retrospective case-control study was conducted among septic patients treated with meropenem infusion in ICUs of three hospitals. Patients included in the study received either extended or standard meropenem infusion as per the practice of individual settings. Outcomes and clinical data were retrospectively collected from the electronic databases and patients’ files. A total of 108 patients received extended meropenem infusion while another 117 patients received standard meropenem infusion. Patients receiving the extended meropenem infusion were found to have a significantly lower shorter length of hospital and ICU stay. It was also found that among those receiving extended meropenem infusion, 54.7% (64/117) had a reduction of SAPS II score, while only 44% (48/108) of patients receiving standard meropenem infusion had reduced scores. This study will strengthen the evidence in using extended meropenem infusion as a standard practice in critical care settings. As this is the first study of its kind done in Malaysia, it proves that prolonged meropenem infusion may be beneficial to critically ill patients with sepsis. However, randomized clinical trials with large sample size should be carried out in local settings in order to minimize other confounders that may influence with the result of the study.

Keywords: antibiotics, beta lactams, critical care, extended infusion, meropenem

Procedia PDF Downloads 371

Authors: Benniche Omar

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We are concerned with abstract fully nonlinear differential equations having the form y’(t)=Ay(t)+f(t,y(t)) where A is an m—dissipative operator (possibly multi—valued) defined on a subset D(A) of a Banach space X with values in X and f is a given function defined on I×X with values in X. We consider a graph K in I×X. We recall that K is said to be viable with respect to the above abstract differential equation if for each initial data in K there exists at least one trajectory starting from that initial data and remaining in K at least for a short time. The viability problem has been studied by many authors by using various techniques and frames. If K is closed, it is shown that a tangency condition, which is mainly linked to the dynamic, is crucial for viability. In the case when X is infinite dimensional, compactness and convexity assumptions are needed. In this paper, we are concerned with the notion of near viability for a given graph K with respect to y’(t)=Ay(t)+f(t,y(t)). Roughly speaking, the graph K is said to be near viable with respect to y’(t)=Ay(t)+f(t,y(t)), if for each initial data in K there exists at least one trajectory remaining arbitrary close to K at least for short time. It is interesting to note that the near viability is equivalent to an appropriate tangency condition under mild assumptions on the dynamic. Adding natural convexity and compactness assumptions on the dynamic, we may recover the (exact) viability. Here we investigate near viability for a graph K in I×X with respect to y’(t)=Ay(t)+f(t,y(t)) where A and f are as above. We emphasis that the t—dependence on the perturbation f leads us to introduce a new tangency concept. In the base of a tangency conditions expressed in terms of that tangency concept, we formulate criteria for K to be near viable with respect to y’(t)=Ay(t)+f(t,y(t)). As application, an abstract null—controllability theorem is given.

Keywords: abstract differential equation, graph, tangency condition, viability

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Authors: Ufuk Aydın

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The Coda attenuation and frequency dependency of seismic wave are strongly dependent on the effective stresses structures within the upper crust. In this study, the data of three different stations were used to examine the lateral variation of stress. The tectonic structures of these three areas have been examined comparatively using lateral coda tomography. In the study using the single scatter method, the window length selected to be 20 second. Coda values 80 with 94 and frequency dependency values obtained between 0.69 and 1.21. The 1/QC values for the three regions ranged from 0.0012 to 0.017, highlighting the regional differences in the seismotectonic activity of the crust. The lowest absorption values obtained from Erzurum station when the highest absorption values obtained at the Kemaliye station. The low Qc and high frequency dependency values obtained Kemaliye, which indicates that it has highest tectonic activity than other two regions. The seismo-dynamics data obtained from the study found to be in agreement with the tectonic structure of the region.

Keywords: regional coda attenuation, tectonic stress, crustal deformation

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Authors: M. Ghasemi, F. Eskandari, B. Hamzehei, A. R. Arshi

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Bioelectric activity of nervous cells might be changed causing by various factors. This alteration can lead to unforeseen circumstances in other organs of the body. Therefore, the purpose of this study was to model a single neuron and its behavior under an initial stimulation. This study was developed based on cable theory by means of the Bond Graph method. The numerical values of the parameters were derived from empirical studies of cellular electrophysiology experiments. Initial excitation was applied through square current functions, and the resulted action potential was estimated along the neuron. The results revealed that the model was developed in this research adapted with the results of experimental studies and demonstrated the electrical behavior of nervous cells properly.

Keywords: bond graph, stimulation, nervous cells, modeling

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Authors: Benazzouz Djamel, Termeche Adel, Touati Youcef, Alem Said, Ouziala Mahdi

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This paper presents an approach which detect and isolate efficiently a fault in a system. This approach avoids false alarms, non-detections and delays in detecting faults. A study case have been proposed to show the importance of taking into consideration the uncertainties in the decision-making procedure and their effect on the degradation diagnostic performance and advantage of using Bond Graph (BG) for such degradation. The use of BG in the Linear Fractional Transformation (LFT) form allows generating robust Analytical Redundancy Relations (ARR’s), where the uncertain part of ARR’s is used to generate the residuals adaptive thresholds. The study case concerns an electromechanical system composed of a motor, a reducer and an external load. The aim of this application is to show the effectiveness of the BG-LFT approach to robust fault detection.

Keywords: bond graph, LFT, uncertainties, detection and faults isolation, ARR

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Authors: Fuziyah Ishak, Siti Norazura Ahmad

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Volterra integro-differential equations appear in many models for real life phenomena. Since analytical solutions for this type of differential equations are hard and at times impossible to attain, engineers and scientists resort to numerical solutions that can be made as accurately as possible. Conventionally, numerical methods for ordinary differential equations are adapted to solve Volterra integro-differential equations. In this paper, numerical solution for solving Volterra integro-differential equation using extended trapezoidal method is described. Formulae for the integral and differential parts of the equation are presented. Numerical results show that the extended method is suitable for solving first order Volterra integro-differential equations.

Keywords: accuracy, extended trapezoidal method, numerical solution, Volterra integro-differential equations

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Authors: Weiran An, Junyou Shi

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Health management has become one of the design goals in the research and development of new generation avionics systems, and is an important complement and development for the testability and fault diagnosis technology. Currently, the research and application for avionics system health dividing and diagnosis technology is still at the starting stage, lack of related technologies and methods reserve. In this paper, based on the health three-state dividing of avionics products, state lateral transfer coupling modeling and diagnosis reasoning method considering sub-health are researched. With the study of typical case application, the feasibility and correctness of the method and the software are verified.

Keywords: sub-health, diagnosis reasoning, three-valued coupled logic, extended dependency model, avionics

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Authors: Jake Gonzalez, Tommy Dang

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This paper proposes a temporal graph approach to visualize and analyze the evolution of gene relationships and nomenclature over time. An interactive web-based tool implements this temporal graph, enabling researchers to traverse a timeline and observe coupled dynamics in network topology and naming conventions. Analysis of a real human genomic dataset reveals the emergence of densely interconnected functional modules over time, representing groups of genes involved in key biological processes. For example, the antimicrobial peptide DEFA1A3 shows increased connections to related alpha-defensins involved in infection response. Tracking degree and betweenness centrality shifts over timeline iterations also quantitatively highlight the reprioritization of certain genes’ topological importance as knowledge advances. Examination of the CNR1 gene encoding the cannabinoid receptor CB1 demonstrates changing synonymous relationships and consolidating naming patterns over time, reflecting its unique functional role discovery. The integrated framework interconnecting these topological and nomenclature dynamics provides richer contextual insights compared to isolated analysis methods. Overall, this temporal graph approach enables a more holistic study of knowledge evolution to elucidate complex biology.

Keywords: temporal graph, gene relationships, nomenclature evolution, interactive visualization, biological insights

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Authors: Royia Soliman, Salma ElAnsary, Akram Amin Abdellatif, Florian Holzapfel

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Model-Based Safety Analysis (MBSA) is an approach in which the system and safety engineers share a common system model created using a model-based development process. The model can also be extended by the failure modes of the system components. There are two famous approaches for the addition of fault behaviors to system models. The first one is to enclose the failure into the system design directly. The second approach is to develop a fault model separately from the system model, thus combining both independent models for safety analysis. This paper introduces a hybrid approach of MBSA. The approach tries to use informal abstracted models to investigate failure behaviors. The approach will combine various concepts such as directed graph traversal, event lists and Constraint Satisfaction Problems (CSP). The approach is implemented using an Object Oriented programming language. The components are abstracted to its failure logic and relationships of connected components. The implemented approach is tested on various flight control systems, including electrical and multi-domain examples. The various tests are analyzed, and a comparison to different approaches is represented.

Keywords: flight control systems, model based safety analysis, safety assessment analysis, system modelling

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Authors: Mohamed Barbary, Mohamed H. Abd El-Azeem

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Maneuvering extended object tracking (M-EOT) using high-resolution inverse synthetic aperture radar (ISAR) observations has been gaining momentum recently. This work presents a new robust implementation of the multiple models (MM) multi-Bernoulli (MB) filter for M-EOT, where the M-EOT’s ISAR observations are characterized using a skewed (SK) non-symmetrically normal distribution. To cope with the possible abrupt change of kinematic state, extension, and observation distribution over an extended object when a target maneuvers, a multiple model technique is represented based on MB-track-before-detect (TBD) filter supported by SK-sub-random matrix model (RMM) or sub-ellipses framework. Simulation results demonstrate this remarkable impact.

Keywords: maneuvering extended objects, ISAR, skewed normal distribution, sub-RMM, MM-MB-TBD filter

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Authors: Zuzana Fišerová, Marie Paseková

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Debt relief (also labelled personal bankruptcy) is a bankruptcy settlement method which was implemented into Czech legislation by the Insolvency Act (Act No. 182/2006 Coll. on Insolvency and its Resolution) on 1 January 2008. The need to implement the institute of personal bankruptcy arose from the excessive over-indebtedness of many inhabitants of the Czech Republic after the crisis that arose around 2008 and 2009. The contribution analyses the development in the manner in which households approach personal bankruptcy and assesses and surveys the differences between indebtedness among men and women. The first section analyses the development in numbers of filed personal bankruptcy petitions and the successfulness thereof; it likewise analyses the impact of other economic influences (regional differences, unemployment etc.). The differences between debtors in dependency to gender are also surveyed. A survey of insolvency proceedings for 664 persons whose insolvency proceedings were commenced in 2008 was conducted, whilst the data were acquired from the publicly accessible insolvency register. The hypothesis on the equality of the average debt level of men and women was tested when comparing indebtedness in dependency to debtor gender. At a significance level of 0.05, the test confirmed that the mean value of debt level for women is lower than the mean value of debt level for men. Through analysis of further results, it was found that the average level of debt among women was CZK 537 thousand, while the average level of creditor satisfaction reached 46.2%. Men in the monitored sample had an average level of reported receivables of CZK 652 thousand, satisfaction of their creditors reached 58.8%. The main changes in the institute of personal bankruptcy are then evaluated in the closing discussion, and the impacts of these changes for households are assessed. The development of legislation in the Czech Republic and practice are shifting towards broader usage of personal bankruptcy, especially insofar as it can now also be used by entrepreneurs. Furthermore, the amendment of the Insolvency Act has enabled married couples to apply for joint debt relief, which has improved the position of the marriage partner with lower income and who would not get permission for debt relief on his/her own (mostly women are at issue). In current practice, the condition of adequate income is also solved by the fact that another person (usually a family member) undertakes to donate a certain monthly sum throughout the duration of the debt relief. Personal bankruptcy can thus be completed also by individuals to whom it would previously have been denied by the court.

Keywords: debtor, households, insolvency act, over-indebtedness, personal bankruptcy

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Authors: Yaw-Ling Lin

Abstract:

A de Bruijn graph of order k is a graph whose vertices representing all length-k sequences with edges joining pairs of vertices whose sequences have maximum possible overlap (length k−1). Every Hamiltonian cycle of this graph defines a distinct, minimum length de Bruijn sequence containing all k-mers exactly once. A Kautz sequence is the minimal generating sequence so as the sequence of minimal length that produces all possible length-k sequences with the restriction that every two consecutive alphabets in the sequences must be different. A collection of de Bruijn/Kautz sequences are orthogonal if any two sequences are of maximally differ in sequence composition; that is, the maximum length of their common substring is k. In this paper, we discuss how such a collection of (maximal) orthogonal de Bruijn/Kautz sequences can be made and use the algorithm to build up a web application service for the synthesized DNA and other related biomolecular sequences.

Keywords: biomolecular sequence synthesis, de Bruijn sequences, Eulerian cycle, Hamiltonian cycle, Kautz sequences, orthogonal sequences

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Authors: Edward J. Kansa, Pavel Holoborodko

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Continuously differentiable radial basis functions (RBFs) are meshfree, converge faster as the dimensionality increases, and is theoretically spectrally convergent. When implemented on current single and double precision computers, such RBFs can suffer from ill-conditioning because the systems of equations needed to be solved to find the expansion coefficients are full. However, the Advanpix extended precision software package allows computer mathematics to resemble asymptotically ideal Platonic mathematics. Additionally, full systems with extended precision execute faster graphical processors units and field-programmable gate arrays because no branching is needed. Sparse equation systems are fast for iterative solvers in a very limited number of cases.

Keywords: partial differential equations, Meshfree radial basis functions, , no restrictions on spatial dimensions, Extended arithmetic precision.

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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: Ould Bouamama, M. Bressel, D. Hissel, M. Hilairet

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Fuel cell (FC) is one of the best alternatives of fossil energy. Recently, the research community of fuel cell has shown a considerable interest for diagnosis in view to ensure safety, security, and availability when faults occur in the process. The problematic for model based FC diagnosis consists in that the model is complex because of coupling of several kind of energies and the numerical values of parameters are not always known or are uncertain. The present paper deals with use of one tool: the Linear Fractional Transformation bond graph tool not only for uncertain modelling but also for monitorability (ability to detect and isolate faults) analysis and formal generation of robust fault indicators with respect to parameter uncertainties.The developed theory applied to a nonlinear FC system has proved its efficiency.

Keywords: bond graph, fuel cell, fault detection and isolation (FDI), robust diagnosis, structural analysis

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Authors: Yasemin Kaya, Ahmet N. Eraslan

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In this study, the thermoelastic behavior of a long tube is studied by taking into account the temperature dependency of all mechanical and thermal properties. As the tube is heated slowly, an uncoupled solution procedure is adopted under free and radially constrained boundary conditions. The nonlinear heat conduction equation is solved by a finite element collocation procedure and the corresponding distributions of stress and strain are computed by shooting iterations. The computational model is verified in comparison to the analytical solution by shutting down the temperature dependency of physical properties. In the analysis, experimental data available in the literature is used to describe the coefficient of thermal expansion $\alpha$, the thermal conductivity $k$, the modulus of rigidity $G$, the yield strength $\sigma_{0}$, and the Poisson's ratio $\nu$ of Nickel. Results of the analysis are presented in comparison to those having constant physical properties. As a result of the calculations, the temperature dependency of the material properties should be taken into account at higher temperature ranges.

Keywords: thermoelasticity, long tube, temperature-dependent properties, internal heating

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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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