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Free vibration analysis of a skew sandwich plate with bamboo biocomposite and polylactic acid core under temperature and moisture conditions

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Noise and Vibration Worldwide 2023年第9期54卷 434-459页

作者： Gawande, Atul S Kattimani, Subhaschandra Murigendrappa, S.M. Nguyen-Thoi, T. Kamyab, Hesam Althamer, Saeed Department of Mechanical Engineering National Institute of Technology Karnataka Mangalore Surathkal India Institute of Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering Van Lang University Ho Chi Minh City Viet Nam Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Mechanical Engineering King Khalida University Abha Saudi Arabia

This article investigates the impact of moisture and temperature on vibration characteristics of bio-composite skew-laminated composite sandwich (SLCS) plates. The bio-composite SLCS plates with bamboo face sheets and polylactic acid (PLA) cores are biodegradable, radiolucent, lightweight, high strength, and withstand vibrations. The coupled thermo-elastic and hygro-elastic finite element (FE) model of the SLCS plate is derived using the higher-order shear deformation theory (HSDT). An initial stress stiffness matrix is developed using the nonlinear strain-displacement relations to incorporate the effect of temperature and moisture in FE modeling. Temperature and moisture-dependent material properties of bamboo fiber-reinforced biocomposite (BFB) and PLA core are employed for the analysis. A comprehensive investigation has been carried out to study the impact of moisture and temperature situations with different geometrical parameters and various skew angles on the frequency of the SLCS plate. The results indicate that the biocomposite sandwich material has excellent potential for structural applications under different environmental conditions in various applications like food processing, and biomedical applications, including MRI and CT scan beds. © The Author(s) 2023.

关键词： Laminated composites

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MLRegTest: a benchmark for the machine learning of regular languages

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 13641-13685页

作者： Sam Van der Poel Dakotah Lambert Kalina Kostyszyn Tiantian Gao Rahul Verma Derek Andersen Joanne Chau Emily Peterson Cody St. Clair Paul Fodor Chihiro Shibata Jeffrey Heinz School of Mathematics Georgia Institute of Technology Department of Computer Science Haverford College Department of Linguistics & Institute of Advanced Computational Science Stony Brook University Department of Computer Science Stony Brook University Department of Computer Science Stony Brook University Department of Linguistics Stony Brook University Department of Advanced Sciences Graduate School of Science and Engineering Hosei University

Synthetic datasets constructed from formal languages allow fine-grained examination of the learning and generalization capabilities of machine learning systems for sequence classification. This article presents a new benchmark for machine learning systems on sequence classification called MLRegTest, which contains training, development, and test sets from 1,800 regular *** kinds of formal languages represent different kinds of long-distance dependencies, and correctly identifying long-distance dependencies in sequences is a known challenge for ML systems to generalize successfully. MLRegTest organizes its languages according to their logical complexity (monadic second order, first order, propositional, or restricted propositional) and the kind of logical literals (string, tier-string, subsequence, or combinations thereof). The logical complexity and choice of literal provides a systematic way to understand different kinds of long-distance dependencies in regular languages, and therefore to understand the capacities of different ML systems to learn such long-distance ***, the performance of different neural networks (simple RNN, LSTM, GRU, transformer) on MLRegTest is examined. The main conclusion is that performance depends significantly on the kind of test set, the class of language, and the neural network architecture.

关键词： formal languages regular languages subregular languages sequence classification neural networks long-distance dependencies

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Effect of radiofrequency coil and primary magnetic field on radiolucent composite plates

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Noise and Vibration Worldwide 2023年第1期54卷 44-58页

作者： Gawande, Atul S Mukka Ramachanra, Shreyas Kamyab, Hesam Trung, Nguyen-Thoi Kattimani, Subhaschandra Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal India Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Laboratory for Applied and Industrial Mathematics Institute of Computational Science and Artificial Intelligence Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering Van Lang University Ho Chi Minh City Viet Nam

Magnetic Resonance Imaging (MRI) systems need a material compatible with the imaging technique with lesser attenuation and provide accurate images without distortion. Carbon fibers are the best-suited materials for x-ray applications because of their radiolucent properties and reduced attenuation characteristics. However, carbon fiber produces images with distortion in the MRI system by absorbing electromagnetic energy because of its conductive nature. In the present study, five different fiber-reinforced radiolucent composite plates are analyzed to predict their suitability for a radio frequency coil and the effect of a primary magnetic field of 1.5 T and 3.0 T on mechanical responses. Simulation models are built to explore the impact of electromagnetic waves and birdcage coil configurations on composite material and quantify the temperature changes caused due to energy absorption. A multiphysics coupling simulation is being used to understand the effect of stacking sequence, ply orientation, and boundary conditions on the response of composite plates under an electro-magneto-mechanical environment. © The Author(s) 2022.

关键词： Magnetic resonance imaging

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DISCRETE ENERGY ANALYSIS OF THE THIRD-ORDER VARIABLE-STEP BDF TIME-STEPPING FOR DIFFUSION EQUATIONS

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Journal of computational Mathematics 2023年第2期41卷 325-344页

作者： Hong-lin Liao Tao Tang Tao Zhou Department of Mathematics Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Mathematical Modelling and High Performance Computing of Air Vehicles(NUAA) MIITNanjing 211106China Division of Science and Technology BNU-HKBU United International CollegeZhuhai 519087China Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China

This is one of our series works on discrete energy analysis of the variable-step BDF *** this part,we present stability and convergence analysis of the third-order BDF(BDF3)schemes with variable steps for linear diffusion equations,see,e.g.,[SIAM ***.,58:2294-2314]and[***.,90:1207-1226]for our previous works on the BDF2 *** this aim,we first build up a discrete gradient structure of the variable-step BDF3 formula under the condition that the adjacent step ratios are less than 1.4877,by which we can establish a discrete energy dissipation ***-robust stability and convergence analysis in the L^(2) norm are then *** the mesh robustness means that the solution errors are well controlled by the maximum time-step size but independent of the adjacent time-step *** also present numerical tests to support our theoretical results.

关键词： Diffusion equations Variable-step third-order BDF scheme Discrete gradient structure Discrete orthogonal convolution kernels Stability and convergence

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Exploring Data Management Challenges and Solutions in Agile Software Development: A Literature Review and Practitioner Survey

arXiv

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arXiv 2024年

作者： Fawzy, Ahmed Tahir, Amjed Galster, Matthias Liang, Peng School of Mathematical and Computational Sciences Massey University New Zealand Department of Computer Science and Software Engineering University of Canterbury New Zealand School of Computer Science Wuhan University China

Context: Managing data related to a software product and its development poses significant challenges for software projects and agile development teams. These include integrating data from diverse sources and ensuring data quality amidst continuous change and adaptation. Objective: The paper systematically explores data management challenges and potential solutions in agile projects, aiming to provide insights into data management challenges and solutions for both researchers and practitioners. Method: We employed a mixed-methods approach, including a systematic literature review (SLR) to understand the state-of-research followed by a survey with practitioners to reflect on the state-of-practice. The SLR reviewed 45 studies, identifying and categorizing data management aspects along with their associated challenges and solutions. The practitioner survey captured practical experiences and solutions from 32 industry practitioners who were significantly involved in data management to complement the findings from the SLR. Results: Our findings identified major data management challenges in practice, such as managing data integration processes, capturing diverse data, automating data collection, and meeting real-time analysis requirements. To address these challenges, solutions such as automation tools, decentralized data management practices, and ontology-based approaches have been identified. These solutions enhance data integration, improve data quality, and enable real-time decision-making by providing flexible frameworks tailored to agile project needs. Conclusion: The study pinpointed significant challenges and actionable solutions in data management for agile software development. Our findings provide practical implications for practitioners and researchers, emphasizing the development of effective data management practices and tools to address those challenges and improve project success. © 2024, CC BY.

关键词： Data quality

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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A Novel LSTM and Graph Neural Networks Approach for Cardiovascular Simulations

A Novel LSTM and Graph Neural Networks Approach for Cardiova...

引用

2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Iacovelli, Angelica Pegolotti, Luca Salvador, Matteo Stoppa, Edoardo Santambrogio, Marco D. Marsden, Alison Stanford University Department of Pediatrics StanfordCA94305 United States Institute for Computational and Mathematical Engineering Stanford University StanfordCA94305 United States Purdue University Department of Computer Science West LafayetteIN47907 United States Politecnico di Milano Department of Electronics Information and Bioengineering MI Milano20133 Italy Stanford University Department of Bioengineering StanfordCA94305 United States

ISBN: (纸本)9798350351552

We propose a novel method that integrates Long Short-Term Memory (LSTM) networks with Graph Neural Networks (GNNs) to build reduced-order models of cardiovascular simulations. Reduced-order models are often used as an alternative to full three-dimensional cardiovascular simulations, providing a way to simplify the computational demands associated with fully detailed 3D simulations. The proposed method encodes blood fluid dynamics within a MeshGraphNet-based framework, which is particularly effective in modeling complex physical systems by leveraging graph structures to represent the state of the system. Our method extends the capabilities of the original framework by incorporating LSTMs to capture long-term dependencies, thereby improving predictive accuracy and significantly reducing the computational resources required for the training process. This method achieves errors below 2% for blood pressure and flow rate predictions, showcasing a 65% improvement in average error rates compared to the MeshGraphNet-based framework and a notable increase in computational efficiency, reducing training time by at least 57%. Our method also introduces the ability to adapt the simulation to different cardiac cycles depending on the patient, providing a robust and efficient tool for patient-specific cardiovascular modeling. © 2024 IEEE.

关键词： Graph neural networks

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Survey on Finger Vein Recognition using Convolutional Neural Network 2

Survey on Finger Vein Recognition using Convolutional Neural...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Pandey, Surya Prabhu, Nagendra Kumar, Adithya Suresh New Horizon College of Engineering Computer Science and Engineering Bangalore India Srm Institute of Science Technology Kattankulathur School of Computing Department of Computational Intelligence Chennai603203 India

ISBN: (纸本)9781665400527

Vein identification, also known as vascular technology, it is a biometric identification approach that analyses the blood patterns visible on the skin's surface. Despite the fact that it is used by FBI and CIA for community security and criminal investigations, such as forensic investigation and law enforcement, this recognition method is still in development. Vein recognition technology offers great solutions to challenges. Some challenges are ID or password leakage and then reusing it, lost smart cards or keys. Finger Vein Recognition can become most promising for high security applications. Our paper explains about the different cutting-edge machine learning algorithms that are applied so far. At the end, a comparative analysis is provided. © 2022 IEEE.

关键词： Machine learning

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An Algorithm for the Decomposition of Complete Graph into Minimum Number of Edge-disjoint Trees

arXiv

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arXiv 2024年

作者： Sinha, Antika Saha, Sanjoy Kumar Basuchowdhuri, Partha Department of Computer Science Asutosh College Kolkata700026 India Department of Computer Science & Engineering Jadavpur University Kolkata700032 India School of Mathematical & Computational Sciences IACS Kolkata700032 India

In this work, we study methodical decomposition of an undirected, unweighted complete graph (Kn of order n, size m) into minimum number of edge-disjoint trees. We find that x, a positive integer, is minimum and x = ⌈n2 ⌉ as the edge set of Kn is decomposed into edge-disjoint trees of size sequence M = {m1, m2, ..., mx} where mi ≤ (n - 1) and Σxi=1mi = n(n-1) . For decomposing the edge set 2 of Kn into minimum number of edge-disjoint trees, our proposed algorithm takes total O(m) time. © 2024, CC BY.

关键词： Trees (mathematics)

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OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models

arXiv

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arXiv 2024年

作者： AhmadiTeshnizi, Ali Gao, Wenzhi Udell, Madeleine Department of Management Science and Engineering Stanford University CA United States Institute for Computational and Mathematical Engineering Stanford University CA United States

Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand rather than optimally by state-of-the-art solvers because the expertise required to formulate and solve these problems limits the widespread adoption of optimization tools and techniques. This paper introduces OptiMUS, a Large Language Model (LLM)-based agent designed to formulate and solve (mixed integer) linear programming problems from their natural language descriptions. OptiMUS can develop mathematical models, write and debug solver code, evaluate the generated solutions, and improve its model and code based on these evaluations. OptiMUS utilizes a modular structure to process problems, allowing it to handle problems with long descriptions and complex data without long prompts. Experiments demonstrate that OptiMUS outperforms existing state-of-the-art methods on easy datasets by more than 20% and on hard datasets (including a new dataset, NLP4LP, released with this paper that features long and complex problems) by more than 30%. © 2024, CC BY-NC-ND.

关键词： Integer programming

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