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Enhancing Building Safety: A Brief Review of Agent-Based Modeling for Fire Evacuation Simulation

Procedia computer science 2025年 257卷 668-675页

作者： Selain K. Kasereka Ortega M. Kabwe Maria W.K. Kinyanta Audrey L. Kasongo Godwill W.K. Ilunga Katya Muhambya Tasho Tashev Kyandoghere Kyamakya Mathematics Statistics and Computer Science Department University of Kinshasa Kinshasa DR. Congo Faculty of English Engineering Studies Technical University of Sofia Sofia Bulgaria ABIL Research Center Kinshasa DR. Congo Faculty of Computer Science Université Nouveaux Horizons Lubumbashi DR Congo Ganpat University Faculty of Computer Application Gujarat India Université Libre des Pays des Grands Lacs Faculty of Sciences and Technology Goma DR Congo Institute of Smart Systems Technologies University of Klagenfurt Klagenfurt Austria Polytechnic Faculty University of Kinshasa Kinshasa Democratic Republic of the Congo

The issue of building evacuation in the event of a fire is a significant concern in urban planning and architecture. In the absence of appropriate measures, an emergency situation can potentially result in disastrous outcomes. In this review article, we explore the application of agent-based modeling (ABM) in the simulation of building evacuations in the event of a free breakout. To address this issue, we present a synthesis of findings from several studies, highlighting the advantages of ABM in modeling complex evacuation dynamics such as crowd behavior, consideration of multiple parameters, and decision-making processes. Additionally, we provide insight into the simulation tools and techniques used in studies that demonstrate the practical applications of ABM in enhancing evacuation strategies. Furthermore, we identify current challenges, research gaps, and propose future directions to enhance the accuracy and effectiveness of fire evacuation modeling and simulations. This study aims to contribute to the improvement of proposed solutions and models for the development of successful, effective, and reliable evacuation plans to enhance the safety of occupants in buildings.

关键词： Agent-based Modeling Evacuation Fire Breakout Building Safety Disaster Agent-Based Simulation

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XNet-Enhanced Deep BSDE Method and Numerical Analysis

arXiv

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arXiv 2025年

作者： Zheng, Xiaotao Xia, Zhihong Li, Xin Yue, Xingye Center for Financial Engineering Soochow University Jiangsu Suzhou215008 China School of Natural Science Great Bay University Guangdong Dongguan523808 China Department of Mathematics Northwestern University EvanstonIL60208 United States Department of Computer Science Northwestern University EvanstonIL60208 United States Mathematical Modelling and Data Analytics Center Oxford Suzhou Centre for Advanced Research Jiangsu Suzhou215123 China

Solving high-dimensional semilinear parabolic partial differential equations (PDEs) challenges traditional numerical methods due to the"curse of dimensionality." Deep learning, particularly through the Deep BSDE method, offers a promising alternative by leveraging neural networks’ capability to approximate high-dimensional functions. This paper introduces a novel network architecture, XNet, which significantly enhances the computational efficiency and accuracy of the Deep BSDE method. XNet demonstrates superior approximation capabilities with fewer parameters, addressing the trade-off between approximation and optimization errors found in existing methods. We detail the implementation of XNet within the Deep BSDE framework and present results that show marked improvements in solving high-dimensional PDEs, potentially setting a new standard for such computations. © 2025, CC BY.

关键词： Numerical methods

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Anisotropic Exchange Spin Model to Investigate the Curie Temperature Dispersion of Finite-Size L10-FePt Magnetic Nanoparticles

arXiv

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arXiv 2025年

作者： Ochiai, Kohei Tsuyama, Tomoyuki Shimizu, Sumera Zhang, Lei Watanabe, Jin Kudo, Fumito Zhu, Jian-Gang Okuno, Yoshishige Resonac Corporation Research Center for Computational Science and Informatics 8 Ebisu-cho Kanagawa-ku Kanagawa. Yokohama221-8517 Japan Resonac Hard Disk Corporation Research & Development Center 5-1 Yawatakaigan-dori Chiba Ichihara290-0067 Japan Data Storage Systems Center Carnegie Mellon University PittsburghPA15213 United States Electrical and Computer Engineering Department Carnegie Mellon University PittsburghPA15213 United States Materials Science and Engineering Department Carnegie Mellon University PittsburghPA15213 United States

We developed an anisotropic spin model that accounts for magnetic anisotropy and evaluated the Curie temperature (Tc) dispersion due to finite size effects in L10-FePt nanoparticles. In heat-assisted magnetic recording (HAMR) media, a next-generation magnetic recording technology, high-density recording is achieved by locally heating L10-FePt nanoparticles near their Tc and rapidly cooling them. However, variations in Tc caused by differences in particle size and shape can compromise recording stability and areal density capacity, making the control of Tc dispersion critical. In this study, we constructed atomistic LLG models to explicitly incorporate the spin exchange anisotropy of L10-FePt, based on parameters determined by first-principles calculations. Using this model, we evaluated the impact of particle size on Tc dispersion. As a result, (1) the Tc dispersion critical to the performance of HAMR can be reproduced, whereas it was previously underestimated by isotropic models and (2) approximately 70% of the experimentally observed Tc dispersion can be attributed to particle size effects. This research highlights the role of exchange anisotropy in amplifying finite-size effects and underscores the importance of size control in HAMR media. Copyright © 2025, The Authors. All rights reserved.

关键词： Curie temperature

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An efficient binary salp swarm algorithm for user selection in multiuser MIMO antenna systems

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Scientific Reports 2025年第1期15卷 1-17页

作者： Sasikumar, A. Ravi, Logesh Devarajan, Malathi Almazyad, Abdulaziz S. De, Shuvodeep Xiong, Guojiang Mousavirad, Seyed Jalaleddin Mohamed, Ali Wagdy Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Centre for Advanced Data Science Tamil Nadu Chennai 600127 India School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai 600127 India School of Computer Science and Engineering Vellore Institute of Technology Chennai 600127 India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia Virginia Tech Blacksburg United States Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical Engineering Guizhou University Guiyang 550025 China Department of Computer and Electrical Engineering Mid Sweden University Sundsvall Sweden Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza 12613 Egypt Applied Science Research Center Applied Science Private University Amman 11931 Jordan Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The past ten years have seen notable research activity and significant advancements in multiuser multiple-input multiple-output (MU-MIMO) antennas. An MU-MIMO antenna system must accommodate many subscribers without additional bandwidth or energy. User scheduling becomes a critical strategy to take advantage of multiuser heterogeneity and acquire maximum gain in systems where the total number of recipients exceeds the number of transmitting antennas. Due to their high computational cost, many user selection methods currently in use, such as greedy algorithms and exhaustive search are unsuitable for MU-MIMO systems. A suitable scheduling mechanism is essential for the various users in an MU-MIMO system to utilise bandwidth and enhance the system’s total rate effectively. In this article, we proposed a user and antenna scheduling with a population-based meta-heuristic approach, namely the binary salp swarm algorithm (binary SSA), to increase the system sum rate with low computing complexity. We specifically used a population-based meta-heuristics optimisation technique to simulate the user scheduling problem in MU-MIMO systems, characterising complicated issues with binary decisions. Additionally, binary SSA significantly outperforms existing population-based models, such as the binary bat algorithm (binary BA), PSO, SSA, FPA and binary flower pollination algorithm (binary FPA), regarding system throughput/sum rate. The proposed binary SSA technique also effectively achieves a system sum rate compared to a random search scheme and other existing suboptimal scheduling methods. Compared to binary BA and binary FPA approaches, the binary SSA has a higher convergence rate and superior searching capabilities. The simulation outcomes show the proposed binary SSA-based scheduling scheme delivers noticeable performance benefits. © The Author(s) 2025.

关键词： Antenna Binary salp swarm algorithm Metaheuristics optimization Multiuser MIMO User scheduling

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How Deep is Love in LLMs’ Hearts? Exploring Semantic Size in Human-like Cognition

arXiv

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arXiv 2025年

作者： Yao, Yao Yang, Yifei Ma, Xinbei Yang, Dongjie Zhang, Zhuosheng Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan University Wuhan430072 China

How human cognitive abilities are formed has long captivated researchers. However, a significant challenge lies in developing meaningful methods to measure these complex processes. With the advent of large language models (LLMs), which now rival human capabilities in various domains, we are presented with a unique testbed to investigate human cognition through a new lens. Among the many facets of cognition, one particularly crucial aspect is the concept of semantic size—the perceived magnitude of both abstract and concrete words or concepts. This study seeks to investigate whether LLMs exhibit similar tendencies in understanding semantic size, thereby providing insights into the underlying mechanisms of human cognition. We begin by exploring metaphorical reasoning, comparing how LLMs and humans associate abstract words with concrete objects of varying sizes. Next, we examine LLMs’ internal representations to evaluate their alignment with human cognitive processes. Our findings reveal that multi-modal training is crucial for LLMs to achieve more human-like understanding, suggesting that real-world, multi-modal experiences are similarly vital for human cognitive development. Lastly, we examine whether LLMs are influenced by attention-grabbing headlines with larger semantic sizes in a real-world web shopping scenario. The results show that multi-modal LLMs are more emotionally engaged in decision-making, but this also introduces potential biases, such as the risk of manipulation through clickbait headlines. Ultimately, this study offers a novel perspective on how LLMs interpret and internalize language, from the smallest concrete objects to the most profound abstract concepts like ‘love’. The insights gained not only improve our understanding of LLMs but also provide new avenues for exploring the cognitive abilities that define human intelligence. Our code and dataset is publicly available at https://***/Zoeyyao27/LLM-Semantic-Size-Understanding. © 2025, CC BY.

关键词： Semantics

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Linguistic Knowledge Transfer Learning for Speech Enhancement

arXiv

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arXiv 2025年

作者： Hung, Kuo-Hsuan Lu, Xugang Fu, Szu-Wei Tseng, Huan-Hsin Lin, Hsin-Yi Lin, Chii-Wann Tsao, Yu Department of Biomedical Engineering National Taiwan University Taipei Taiwan National Institute of Information and Communications Technology Japan NVIDIA Taipei Taiwan AI & ML Department Brookhaven National Laboratory UptonNY United States Department of Mathematics and Computer Science Seton Hall University South OrangeNJ07079 United States Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan

Linguistic knowledge plays a crucial role in spoken language comprehension. It provides essential semantic and syntactic context for speech perception in noisy environments. However, most speech enhancement (SE) methods predominantly rely on acoustic features to learn the mapping relationship between noisy and clean speech, with limited exploration of linguistic integration. While text-informed SE approaches have been investigated, they often require explicit speech-text alignment or externally provided textual data, constraining their practicality in real-world scenarios. Additionally, using text as input poses challenges in aligning linguistic and acoustic representations due to their inherent differences. In this study, we propose the Cross-Modality Knowledge Transfer (CMKT) learning framework, which leverages pre-trained large language models (LLMs) to infuse linguistic knowledge into SE models without requiring text input or LLMs during inference. Furthermore, we introduce a misalignment strategy to improve knowledge transfer. This strategy applies controlled temporal shifts, encouraging the model to learn more robust representations. Experimental evaluations demonstrate that CMKT consistently outperforms baseline models across various SE architectures and LLM embeddings, highlighting its adaptability to different configurations. Additionally, results on Mandarin and English datasets confirm its effectiveness across diverse linguistic conditions, further validating its robustness. Moreover, CMKT remains effective even in scenarios without textual data, underscoring its practicality for real-world applications. By bridging the gap between linguistic and acoustic modalities, CMKT offers a scalable and innovative solution for integrating linguistic knowledge into SE models, leading to substantial improvements in both intelligibility and enhancement performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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AgriField3D: A Curated 3D Point Cloud and Procedural Model dataset of Field-Grown Maize from a Diversity Panel

arXiv

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arXiv 2025年

作者： Kimara, Elvis Hadadi, Mozhgan Godbersen, Jackson Balu, Aditya Jubery, Talukder Li, Yawei Krishnamurthy, Adarsh Schnable, Patrick S. Ganapathysubramanian, Baskar Department of Computer Science Iowa State University Ames United States Translational AI Research and Education Center Ames United States Department of Mechanical Engineering Iowa State University Ames United States Plant Science Institute Iowa State University Ames United States Interdepartmental Genetics and Genomics Graduate Program Iowa State University Ames United States Department of Agronomy Iowa State University Ames United States

The application of artificial intelligence (ai) in three-dimensional (3D) agricultural research, particularly for maize, has been limited by the scarcity of large-scale, diverse datasets. While 2D image datasets are abundant, they fail to capture essential structural details such as leaf architecture, plant volume, and spatial arrangements that 3D data provide. To address this limitation, we present AgriField3D (website), a curated dataset of 3D point clouds of field-grown maize plants from a diverse genetic panel, designed to be ai-ready for advancing agricultural research. Our dataset comprises over 1,000 high-quality point clouds collected using a Terrestrial Laser Scanner, complemented by procedural models that provide structured, parametric representations of maize plants. These procedural models, generated using Non-Uniform Rational B-Splines (NURBS) and optimized via a two-step process combining Particle Swarm Optimization (PSO) and differentiable programming, enable precise, scalable reconstructions of leaf surfaces and plant architectures. To enhance usability, we performed graph-based segmentation to isolate individual leaves and stalks, ensuring consistent labeling across all samples. We also conducted rigorous manual quality control on all datasets, correcting errors in segmentation, ensuring accurate leaf ordering, and validating metadata annotations. The dataset further includes metadata detailing plant morphology and quality, alongside multi-resolution subsampled versions (100k, 50k, 10k points) optimized for various computational needs. By integrating point cloud data of field grown plants with high-fidelity procedural models and ensuring meticulous manual validation, AgriField3D provides a comprehensive foundation for ai-driven phenotyping, plant structural analysis, and 3D applications in agricultural research. © 2025, CC BY-NC-SA.

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

引用

Computational Intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS), IEEE Symposium on

作者： Matthew Andres Moreno Anika Ranjan Emily Dolson Luis Zaman Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and AI in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this “hereditary stratigraphy” approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation “surface”-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation “column”-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions.

关键词： Surface reconstruction Accuracy Annotations Heuristic algorithms Computational modeling Biological system modeling Stratigraphy Phylogeny History Bars

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

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2025 IEEE Symposium on Computational Intelligence in Artificial Life and Cooperative Intelligent Systems, ALIFE-CIS 2025

作者： Moreno, Matthew Andres Ranjan, Anika Dolson, Emily Zaman, Luis University of Michigan Department of Ecology and Evolutionary Biology Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and Ai in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Michigan State University Department of Computer Science and Engineering East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (纸本)9798331508371

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this 'hereditary stratigraphy' approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation 'surface'-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation 'column'-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions. © 2025 IEEE.

关键词： Biostratigraphy

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Digital Twin-Enabled Real-Time Optimization System for Traffic and Power Grid Management in 6G-Driven Smart Cities

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IEEE Internet of Things Journal 2025年

作者： Prathiba, Sahaya Beni Krishnamoorthy, Sri Ram Kannan, Karuna Soundari Selvaraj, Arikumar K. Ranganayakulu, Dhanalakshmi Fang, Kai Gadekallu, Thippa Reddy Vellore Institute of Technology Centre for Cyber Physical Systems School of Computer Science and Engineering Chennai600127 India Vellore Institute of Technology School of Electronics Engineering Chennai600127 India College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur603 203 India Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Zhejiang A and F University Division of Research and Development Lovely Professional University College of Mathematics and Computer Science Hangzhou Phagwara311300 India Chitkara University Center of Research Impact and Outcome Punjab Rajpura140401 India

The advent of 6G-enabled Internet of Everything(IoE) technologies is set to revolutionize urban infrastructures by providing fast, consistent, and low-delay capabilities for communication. 6G connectivity will integrate traffic and power grids for adaptive urban management. However, current traffic networks and power grids face critical challenges such as fragmented data processing, delayed responses, and outdated resource management leading to inefficiencies like traffic congestion and power outages. In 6G-enabled smart grid cities, system complexity and interdependence demand dynamic, real-time solutions, further exacerbating inefficiencies. To address these issues, this study introduces Digital Twin-enabled Real-time Optimization System (DT-ROS), a dynamic framework designed to optimize urban traffic and power grid systems. DT-ROS integrates a dual-tier Digital Twin (DT) and an advanced scheduling framework based on Priority Age of Information Deep Q Scheduler (PAoI-QS). The dual-tier framework builds an elementary and Integrated Digital Twin (IDT) with Auto-Regressive Integrated Moving Average (ARIMA)-based forecasting for accurate real-time traffic and energy demand predictions. The advanced scheduling framework minimizes the Age of Information (AoI), ensuring decision-making relies on the most current and relevant data. By continuously monitoring and processing real-time data, DT-ROS creates virtual models to simulate system behavior and dynamically allocate resources. Simulation results demonstrate the effectiveness of DT-ROS, achieving a 30% reduction in traffic congestion and a 25% improvement in power grid stability compared to existing methods. To create effective, robust, and sustainable urban systems for future smart cities, DT-ROS addresses traffic and electricity problems. © 2014 IEEE.

关键词： Smart power grids

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