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Buckling analysis of laminated multiphase composite plates under in-plane loading using data-driven soft computing

engineering Analysis with Boundary Elements 2025年 176卷

作者： Topal, Umut Ly, Duy-Khuong Vu, Ho-Nam Nguyen-Thoi, T. Department of Civil Engineering Faculty of Technology Karadeniz Technical University Trabzon Turkey Laboratory for Computational Mechanics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Civil Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam

Laminated multiphase composite plates reinforced with carbon nanotubes (CNTs) and carbon fibers in an epoxy matrix offer excellent mechanical performance for lightweight, high-strength applications. This study focuses on their buckling behavior under in-plane loads by developing a soft computing framework that couples isogeometric analysis (IGA) with Murakami's zigzag theory for layerwise displacement representation and a deep feedforward neural network for data-driven prediction. A set of numerical simulations is conducted to generate training and validation data across various laminate thicknesses, fiber orientations, CNT/carbon fiber volume fractions, and boundary conditions. These simulation results are split into training and validation subsets, ensuring the evaluation of the machine learning model's accuracy in predicting critical buckling loads. The trained predictive models are then evaluated against reference solutions to confirm their accuracy. After verifying the models’ reliability, the framework is employed to carry out a detailed parametric investigation, assessing key parameters that affect the buckling response of laminated multiphase composites. © 2025 Elsevier Ltd

关键词： Buckling loads

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FastER: Fast On-Demand Entity Resolution in Property Graphs

arXiv

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arXiv 2025年

作者： Wang, Shujing Kwashie, Selasi Bewong, Michael Hu, Junwei Nofong, Vincent M. Miao, Shiqi Feng, Zaiwen Huazhong Agricultural University Wuhan China AI & Cyber Futures Institute Charles Sturt University Australia School of Computing Mathematics and Engineering Charles Sturt University Australia Department of Computer Science and Engineering University of Mines and Technology Ghana

Entity resolution (ER) is the problem of identifying and linking database records that refer to the same real-world entity. Traditional ER methods use batch processing, which becomes impractical with growing data volumes due to high computational costs and lack of real-time capabilities. In many applications, users need to resolve entities for only a small portion of their data, making full data processing unnecessary—a scenario known as "ER-on-demand". This paper proposes FastER, an efficient ER-on-demand framework for property graphs. Our approach uses graph differential dependencies (GDDs) as a knowledge encoding language to design effective filtering mechanisms that leverage both structural and attribute semantics of graphs. We construct a blocking graph from filtered subgraphs to reduce the number of candidate entity pairs requiring comparison. Additionally, FastER incorporates Progressive Profile Scheduling (PPS), allowing the system to incrementally produce results throughout the resolution process. Extensive evaluations on multiple benchmark datasets demonstrate that FastER significantly outperforms state-of-the-art ER methods in computational efficiency and real-time processing for on-demand tasks while ensuring reliability. We make FastER publicly available at the Github link here. © 2025, CC BY-NC-SA.

关键词： Batch data processing

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A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty

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IEEE Transactions on Network science and engineering 2025年

作者： Hou, Yu Tran, Cong Li, Ming Shin, Won-Yong Yonsei University School of Mathematics and Computing Computational Science and Engineering Seoul03722 Korea Republic of Posts and Telecommunications Institute of Technology Faculty of Information Technology Hanoi100000 Viet Nam Zhejiang Institute of Optoelectronics Jinhua321004 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Graduate School of Artificial Intelligence Pohang37673 Korea Republic of

In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is often uncertain, thereby rendering established community detection approaches ineffective without costly network topology acquisition. To tackle this challenge, we present META-CODE, a unified framework for detecting overlapping communities via exploratory learning aided by easy-to-collect node metadata when networks are topologically unknown (or only partially known). Specifically, META-CODE consists of three iterative steps in addition to the initial network inference step: 1) node-level community-affiliation embeddings based on graph neural networks (GNNs) trained by our new reconstruction loss, 2) network exploration via community-affiliation-based node queries, and 3) network inference using an edge connectivity-based Siamese neural network model from the explored network. Through extensive experiments on three real-world datasets including two large networks, we demonstrate: (a) the superiority of META-CODE over benchmark community detection methods, achieving remarkable gains up to 65.55% on the Facebook dataset over the best competitor among our selected competitive methods in terms of normalized mutual information (NMI), (b) the impact of each module in META-CODE, (c) the effectiveness of node queries in META-CODE based on empirical evaluations and theoretical findings, and (d) the convergence of the inferred network. © 2013 IEEE.

关键词： Structured Query Language

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Multiphysics simulations of microstructure influence on hysteresis and eddy current losses of electrical steel

arXiv

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arXiv 2025年

作者： Kühn, Patrick Yang, Yangyiwei Chen, Guanyu Foster, Shanelle N. Egger, Herbert Xu, Bai-Xiang Mechanics of Functional Materials Division Institute of Materials Science Technische Universität Darmstadt Darmstadt64287 Germany Department of Electrical and Computer Engineering Michigan State University MI48824 United States Johann Radon Institute for Computational and Applied Mathematics Linz Austria Institute for Numerical Mathematics Johannes Kepler University Linz Austria

Improving efficiency of electrical machines requires fundamental knowledge on the mechanisms behind magnetic and eddy current losses of the magnetic core materials, with Fe-Si alloy as a prototype. These losses are intrinsically influenced by the microstructure of the materials. This necessitates physics-based, microstructure-informed multiscale simulations. In the present paper, we utilised micromagnetic simulations and computational homogenization methods to calculate the effective hysteresis and effective conductivities of Fe-Si electrical steels. To demonstrate the methodology, binder-jet printed electrical steel material samples with different microstructure were investigated. The microstructure samples were digitized based on both the descriptor-based synthetic reconstruction and SEM-image-based digitization. More samples were generated with varying microstructure features such as grain size and grain boundary phases. The micromagnetic simulations were then performed to investigate the magnetic hysteresis and hysteresis loss. The eddy current loss was also evaluated by using the effective conductivity through computational homogenization. By performing parameter research on a series of synthetic microstructures, effects of average grain size and grain boundary (GB) phase thickness on the hysteresis loss and eddy current loss were unveiled. An average grain size around 120 µm has the lowest hysteresis loss, although the eddy current loss increases with the grain size. Increasing GB-phase thickness helps reduce both losses. Results indicate the potential to decrease loss of magnetic core materials by microstructure optimization. © 2025, CC BY.

关键词： Silicon alloys

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High-performance computing at a crossroads

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science (New York, N.Y.) 2025年第6736期387卷 829-831页

作者： Deelman, Ewa Dongarra, Jack Hendrickson, Bruce Randles, Amanda Reed, Daniel Seidel, Edward Yelick, Katherine Information Sciences Institute University of Southern California Marina del Rey CA United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN United States Oak Ridge National Laboratory Oak Ridge TN United States Department of Mathematics University of Manchester Manchester United Kingdom Computing Directorate Lawrence Livermore National Laboratory Livermore CA United States Department of Biomedical Engineering Duke University Durham NC United States Scientific Computing and Imaging Institute University of Utah Salt Lake City UT United States Office of the President University of Wyoming Laramie WY United States Department of Electical Engineering and Computer Sciences University of California Berkeley Berkeley CA United States

Long-term plans and comprehensive vision are needed.

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Anisotropic high-Tc superconductivity above 90 K in hole-doped boron-nitrogen clathrates at ambient pressure

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Physical Review B 2025年第9期111卷 094520-094520页

作者： Yu-Lin Han Kai-Yue Jiang Bao-Tian Wang Ping Zhang Hong-Yan Lu School of Physics and Physical Engineering Qufu Normal University Qufu 273165 China Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China Spallation Neutron Source Science Center Dongguan 523803 China Institute of Applied Physics and Computational Mathematics Beijing 100088 China

Breakthroughs in near-room-temperature superconductivity within clathrate hydrides underscore the effectiveness of theory-driven approaches to designing high-Tc superconductors. However, superconductivity in these clathrate hydrides has not been realized at low or ambient pressures. To explore phonon-mediated superconductors with Tc exceeding the liquid-nitrogen temperature under ambient pressure, this study investigates the crystal structure, metallization, electron-phonon coupling, and anisotropic superconducting behavior in hole-doped (BN)6 and (BN)5 clathrates, with configurations akin to CaH6 and LaH10, utilizing first-principles calculations in junction with Wannier interpolation technique. By solving the fully anisotropic Migdal-Eliashberg equations, this work reveals a positive correlation between Tc and hole-doping levels, with the highest Tc approximately 32 K and 96 K in hole-doped (BN)6 and (BN)5, respectively, and both with single anisotropic energy gap nature. The high-Tc superconductivity in B-N clathrates arises from two primary factors: the enhanced density of states at the Fermi level and the presence of strong B-N sp3-hybridized covalent σ-bonding bands. These insights provide a new perspective for revealing the superconductivity of B-N clathrates from an anisotropic perspective and offer valuable avenues for inducing high-Tc superconductors at ambient conditions.

关键词： Energy gap

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A SPECTRAL LEVENBERG-MARQUARDT-DEFLATION METHOD FOR MULTIPLE SOLUTIONS OF SEMILINEAR ELLIPTIC SYSTEMS

arXiv

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arXiv 2025年

作者： Li, Lin Zhou, Yuheng Xie, Pengcheng Li, Huiyuan School of Mathematics and Physics University of South China Hengyang China Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States State Key Laboratory of Computer Science Laboratory of Parallel Computing Institute of Software Chinese Academy of Sciences Beijing100190 China

Many nonlinear differential equations arising from practical problems may permit nontrivial multiple solutions relevant to applications, and these multiple solutions are helpful to deeply understand these practical problems and to improve some applications. Developing an efficient numerical method for finding multiple solutions is very necessary due to the nonlinearity and multiple solutions of these equations. Moreover, providing an efficient iteration plays an important role in successfully obtaining multiple solutions with fast and stable convergence. In the current paper, an efficient algorithm for finding multiple solutions of semilinear elliptic systems is proposed, where the trust region Levenberg-Marquardt method is firstly used to iterate the resulted nonlinear algebraic system. When the nonlinear term in these equations has only the first derivative, our algorithm can efficiently find multiple solutions as well. Several numerical experiments are tested to show the efficiency of our algorithm, and some solutions which have not been shown in the literature are also found and *** Codes 65N35, 65N22, 65F05, 65L10 Copyright © 2025, The Authors. All rights reserved.

关键词： Nonlinear equations

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

引用

Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： S Priyadharshini G. Naveen Sundar D. Narmadha Senthil Kumar Thangavel Adeyemi Abel Ajibesin Selvanayaki Kolandapalayam Shanmugam Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa Department of Mathematics and Computer Science Ashland University USA

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future.

关键词： Training Emotion recognition Accuracy computational modeling Speech recognition Speech enhancement Predictive models Feature extraction Data models Convolutional neural networks

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Multi-Agent Systems for Autonomous IoT Network Management Using Distributed Reinforcement Learning

Multi-Agent Systems for Autonomous IoT Network Management Us...

引用

International Symposium on Advanced computing and Communication (ISACC)

作者： Neelamegam G Rajaram Venkatesan Ramya SR R.S. Ramya Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Builders Engineering College Tirupur Tamil Nadu India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems.

关键词： Smart cities Reinforcement learning Medical services Throughput Dynamic scheduling Energy efficiency Real-time systems Resource management Optimization Multi-agent systems

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Autoregressive Diffusion Networks for Trustworthy Node Identification and Optimized Routing in Wireless Sensor Networks

Autoregressive Diffusion Networks for Trustworthy Node Ident...

引用

Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： M. Satheesh Kumar P. Ganesh Kumar S. Amudha T. Kavitha P. Ravikumar M. Raghini Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of IT K.L.N. College of Engineering Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Instrumentation K.L.N. College of Engineering Department of Artificial Intelligence and Data Science Velammal College of Engineering and Technology

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Finding and eliminating unwanted nodes is a foremost challenge in wireless sensor networks (WSNs). This paper proposes an Autoregressive Diffusion Network (ADN) to find and categorize the nodes in the network as affected or trustworthy. Here, the proposed ADN provides a strong foundation for finding uncommon or insecure actions by effectively recording the characteristics of the sensor over a period of time. Thus, using the capability to capture successive data. In addition, an improved routing mechanism is developed using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocols. Then, the Pelican Optimisation Algorithm (POA) is used to optimize the cluster heads optimally. To further improve the routing mechanism using fitness proportional selection technique to transfer the data safely and effectively. Simulation results show that the proposed technique enhances the performance by reducing energy consumption and increasing the detection accuracy of affected nodes. The proposed LEACH + POA + ADN model shows the improved detection accuracy of 77%, false positive rate of 7.50%, average energy consumption of 8.02 J, network lifetime of 92.94 rounds, and packet delivery ratio of 92.97%, which is high compared to other benchmark models.

关键词： Wireless sensor networks Energy consumption Accuracy Protocols Simulation Clustering algorithms Routing Energy efficiency Security Reliability

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