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2026

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Articles 91 - 120 of 8952

Full-Text Articles in Physical Sciences and Mathematics

Semantic-Structural Decoupling: Disentangling Semantic Attention From Structural Bias In The Attention Manifold, Pengkun Jiao, Bin Zhu, Jingjing Chen, Yu-Gang Jiang Nov 2026

Semantic-Structural Decoupling: Disentangling Semantic Attention From Structural Bias In The Attention Manifold, Pengkun Jiao, Bin Zhu, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

The empirical success of attention mechanism in Multimodal Large Language Models (MLLMs) often obscures its inherent, subtle flaws. Specifically, MLLMs consistently exhibit disproportionate attention toward certain semantically uninformative visual tokens, a phenomenon termed "register" or "Visual Attention Sinks." While existing inference intervention methods attempt to identify these sink tokens and redistribute their attention weights, such approaches typically treat these tokens in isolation and suffer from computational inefficiency. Instead, we reframe this phenomenon as a generalized textual bias exerted over visual features that extends beyond isolated sink tokens. From this perspective, a pervasive structural bias leads to the dilution of the …

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Mgrre_Thinsections_Mgrre_11_2, Mgrre Nov 2026

Mgrre_Thinsections_Mgrre_11_2, Mgrre

Thin Sections

No abstract provided.

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Analyzing Developer Discussions On Eu And Us Privacy Legislation Compliance In Github Repositories, Georgia M. Kapitsaki, Maria Papoutsoglou, Christoph Treude, Ioanna Theophilou Nov 2026

Analyzing Developer Discussions On Eu And Us Privacy Legislation Compliance In Github Repositories, Georgia M. Kapitsaki, Maria Papoutsoglou, Christoph Treude, Ioanna Theophilou

Research Collection School Of Computing and Information Systems

Context: Privacy legislation has impacted the way software systems are developed, prompting practitioners to update their implementations. Specifically, the EU General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have forced the community to focus on users’ data privacy. Objectives: Relying on the vast amount of data on developer issues available in GitHub repositories, our aim is to gather empirical evidence on the issues developers of Open Source Software discuss to comply with privacy legislation. Method: We examined such discussions by mining and analyzing 32,820 issues from GitHub repositories. We partially analyzed the dataset automatically to identify …

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Spatialimaginer: Towards Adaptive Visual Imagination For Spatial Reasoning, Yian Li, Yang Jiao, Bin Zhu, Tianwen Qian, Shaoxiang Chen, Jingjing Chen, Yu-Gang Jiang Nov 2026

Spatialimaginer: Towards Adaptive Visual Imagination For Spatial Reasoning, Yian Li, Yang Jiao, Bin Zhu, Tianwen Qian, Shaoxiang Chen, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Spatial intelligence, which refers to the ability to reason about geometric and physical structure from visual observations, remains a core challenge for multimodal large language models. Despite promising performance, recent multimodal large language models (MLLMs) often exhibit fragile reasoning traces in spatial intelligence tasks that involve consistent spatial state recognition. We argue that these failures stem from a mismatch between the spatial recognition mechanism and the text-only reasoning behavior of these MLLMs. Effective spatial reasoning requires low-level geometric structure to be faithfully preserved and updated throughout the reasoning process, whereas textual representations tend to abstract away precisely these critical details. …

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A Finite Element Model For Thermomechanical Stress-Strain Fields In Transversely Isotropic Strain-Limiting Materials, Saugata Ghosh, Dambaru Bhatta, S. M. Mallikarjunaiah Nov 2026

A Finite Element Model For Thermomechanical Stress-Strain Fields In Transversely Isotropic Strain-Limiting Materials, Saugata Ghosh, Dambaru Bhatta, S. M. Mallikarjunaiah

School of Mathematical & Statistical Sciences Faculty Publications

This paper presents a comprehensive computational framework for investigating thermo-elastic fracture in transversely isotropic materials, where classical linear elasticity fails to predict physically realistic behavior near stress concentrations. We address the challenge of unphysical strain singularities at crack tips by employing a strain-limiting theory of elasticity. This theory is characterized by an algebraically nonlinear constitutive relationship between stress and strain, which intrinsically enforces a limit on the norm of the strain tensor. This approach allows the development of very large stresses, as expected near a crack tip, while ensuring that the corresponding strains remain physically bounded. A loosely coupled system …

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Managing Collaboration Under Conservation Crisis: Trust, Risk, And Control In The Gulf Of Maine Fishery Management Network, Evelyn Roozee, Owen Temby, Dongkyu Kim, Antonia Sohns, Anthony Rocha Lima, Jasper R. De Vries, Gordon M. Hickey Nov 2026

Managing Collaboration Under Conservation Crisis: Trust, Risk, And Control In The Gulf Of Maine Fishery Management Network, Evelyn Roozee, Owen Temby, Dongkyu Kim, Antonia Sohns, Anthony Rocha Lima, Jasper R. De Vries, Gordon M. Hickey

School of Earth, Environmental, & Marine Sciences Faculty Publications

Transboundary fishery management and the intertwined conservation of critically endangered species represent significant governance challenges that require ongoing inter-organizational communication, collaboration, and collective action to achieve shared objectives. Previous research suggests that inter-organizational collaborative performance depends heavily on different dimensions of trust and complementary control mechanisms to mitigate the perceived risks of collaborating and enable the reciprocal sharing of resources and the coordination of activities. However, it is unclear how proposed relationships between trust, risk, and control are shaped by disruptions to established transboundary management networks. This study presents the results of survey research conducted in the Gulf of Maine …

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Effects Of Demineralization Protocols On Hydrogen And Oxygen Isotope Ratios In Fossil Bone Collagen, Linda M. Reynard Nov 2026

Effects Of Demineralization Protocols On Hydrogen And Oxygen Isotope Ratios In Fossil Bone Collagen, Linda M. Reynard

Boise State University Publications and Presentations

The effect of three commonly used preparation protocols on bone collagen δ2H and δ18O values was investigated, using six generally well-preserved faunal bones (cattle, bison, horse) ranging in age from the Holocene to Late Pleistocene. The treatments tested were ethylene diamine tetraacetic acid demineralization without gelatinization and hydrochloric acid demineralization with and without gelatinization. There are significant shifts in δ18O values among preparation methods. No systematic shifts in bone collagen δ2H values were noted among the three treatments, and most offsets were low (< 2–3 ‰) and smaller than typical measurement uncertainties. A few sample/treatment combinations yielded slightly higher inter-treatment δ2H differences (∼ 5–7 ‰). Lower mass fraction of hydrogen in …

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A Review Of Gallium And Germanium Recovery From Industrial Solid Residues, Ernest V. Oteng, Marthias Silwamba, Lana Alagha, Alex Luyima Nov 2026

A Review Of Gallium And Germanium Recovery From Industrial Solid Residues, Ernest V. Oteng, Marthias Silwamba, Lana Alagha, Alex Luyima

Mining Engineering Faculty Research & Creative Works

The global demand for critical elements such as gallium (Ga) and germanium (Ge), and the shift toward circular mining has accelerated the development of new extraction and recovery processing these metals from industrial solid residues. This transition is driven by a substantial surge in demand for advanced technologies and the depletion of primary ore. Consequently, industrial solid residues, particularly zinc processing residues, smelter slags, flue dusts, and coal fly ash, are emerging as viable alternative sources. These residues incorporate Ga and Ge through isomorphic substitution in phases such as ferrites, silicates, and glassy aluminosilicates, and are enriched by industrial processes, …

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Keyframe Selection From Motion Capture Data With Dual-Agent Reinforcement Learning, Kun Hu, Wang, Clinton Mo, Mingyang Ma, Shaohui Mei, Zebin Chen, Zhiyong Wang Nov 2026

Keyframe Selection From Motion Capture Data With Dual-Agent Reinforcement Learning, Kun Hu, Wang, Clinton Mo, Mingyang Ma, Shaohui Mei, Zebin Chen, Zhiyong Wang

Research outputs 2022 to 2026

Animation production workflows centered around motion capture techniques require animators to edit motions based on a set of keyframes. However, most existing keyframe selection methods are optimization-based, which suffer from the issues of flexibility and efficiency. In this paper, a novel deep reinforcement learning method with dual agents are proposed for unsupervised keyframe selection. First, an S-Agent and an R-Agent evaluate the actions of selection and refinement, respectively. A deep spatio-temporal network, namely graph keyframe evaluation network (GKEN), is proposed for the agents. Then, an animation specified reward is devised based on reconstruction, which fulfills three important properties of the …

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Codee Expects That Every Mathematician Will Do Their Odes: From The Battle Of Trafalgar To Calculus (Or Nelson To Newton), Steven Miller Nov 2026

Codee Expects That Every Mathematician Will Do Their Odes: From The Battle Of Trafalgar To Calculus (Or Nelson To Newton), Steven Miller

CODEE Journal

Using the historical Battle of Trafalgar as a case study, we introduce students to mathematical modeling to describe and explore real-world systems. Starting with the question—how do force sizes and combat effectiveness determine which side wins?—we show how to use a blend of mathematics and computational tools to investigate the answer. Beginning by defining a simple differential equation model for combat dynamics, we analyze its behavior using differential equations and accessible algebraic techniques. We show how computational methods and simulations can support systematic, visual exploration and suggest conjectures that mathematical analysis can then establish. This material could serve as the …

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Climate Transition Stress Testing For Corporate Credit Risk: A Replicable Data-Mining Pipeline Using Esg Risk Scores, Huynh Long Hung Nguyen, Alan @ Ali Madjelisi Megargel, Rajesh Krishna Balan, Paul Robert Griffin Nov 2026

Climate Transition Stress Testing For Corporate Credit Risk: A Replicable Data-Mining Pipeline Using Esg Risk Scores, Huynh Long Hung Nguyen, Alan @ Ali Madjelisi Megargel, Rajesh Krishna Balan, Paul Robert Griffin

Research Collection School Of Computing and Information Systems

We present a replicable data-mining pipeline for illustrative, scenario-conditioned corporate distress analysis under feature decoupling. The pipeline links Compustat financial statements with Sustainalytics ESG Risk Rating records for 2,144 U.S. non-financial public firms, constructs a standardized Transition Risk Index (TRI), estimates a contemporaneous accounting-distress classifier, and applies transparent transition-cost sensitivities. Distress is defined as annual operating income insufficient to cover annual interest expense; interest coverage itself is excluded from the predictors. The resulting model is not a forward default forecast or a regulatory probability-of-default model. On a held-out cross-sectional test set, the logistic specification obtains AUC 0.909, PR-AUC 0.822, and …

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Tiny Large Language Models For Iot Networks: Potentials And Challenges, Muhammed Golec, Suhib Bani Melhem, Yaser Khamayseh, Abdulmalik Alwarafy, Naofal Al-Dhahir Nov 2026

Tiny Large Language Models For Iot Networks: Potentials And Challenges, Muhammed Golec, Suhib Bani Melhem, Yaser Khamayseh, Abdulmalik Alwarafy, Naofal Al-Dhahir

All Works

Large Language Models (LLM), which have gained great momentum in recent years, have revolutionized the field of Artificial Intelligence (AI); while their applicability for hardware-constrained Internet of Things (IoT) environments has begun to be questioned. This has led to the emergence of compact architecture and resource-efficient Tiny LLM models. This survey paper systematically examines Tiny LLMs for IoT networks and classifies existing approaches in five basic dimensions: model architectures, optimization strategies, transfer learning methods, deployment paradigms, and explainability-security integration. By applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, 139 related studies published between 2020 and 2025 …

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Semiconductor Supply Chain Risk Monitoring And Forecasting: A Hybrid Big Data Architecture And Empirical Validation, Ming Gong, Sangwhan Cha Nov 2026

Semiconductor Supply Chain Risk Monitoring And Forecasting: A Hybrid Big Data Architecture And Empirical Validation, Ming Gong, Sangwhan Cha

Harrisburg University Other Works

Global semiconductor manufacturing networks are characterized by deep geographical concentration, high capital intensity, and acute vulnerability to regional supply disruptions. While public merchandise trade statistics provide transparent, reproducible empirical records, transforming these raw macroeconomic streams into actionable supply chain risk intelligence requires both rigorous analytical framing and scalable computational architecture. In this paper, we propose an enterprise-grade cloud data architecture designed for semiconductor risk monitoring, combining scheduled distributed batch processing on Google Cloud (Dataproc/PySpark, BigQuery) with an event-driven streaming ingestion lane (Pub/Sub, Dataflow/Apache Beam). To rigorously evaluate the underlying scoring and forecasting logic without premature distributed deployment costs, we implement …

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Pattern Formation In Quantum Hierarchical Cellular Neural Networks, Wilson A. Zuniga-Galindo, B. A. Zambrano-Luna, Chayapuntika Indoung Nov 2026

Pattern Formation In Quantum Hierarchical Cellular Neural Networks, Wilson A. Zuniga-Galindo, B. A. Zambrano-Luna, Chayapuntika Indoung

School of Mathematical & Statistical Sciences Faculty Publications

We present a new class of quantum neural networks (QNNs) whose states are solutions of p -adic Schrödinger equations with a non-local potential that controls the interaction between the neurons. These equations are obtained as Wick rotations of the state equations of p -adic cellular neural networks (CNNs). The p -adic CNNs arise as continuous limits of large discrete hierarchical neural networks (NNs). The CNNs are bio-inspired by the Wilson–Cowan model, which describes the macroscopic dynamics of large populations of neurons. We provide a detailed study of the discretization of the new p -adic Schrödinger equations, which allows the construction …

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Glomalin-Related Soil Protein In Colluvial Soils As A Proxy For Reconstructing Millenary Changes In Landscape And Soil Health, Axel Werner, Lourdes López-Merino, Joeri Kaal, Cruz Ferro-Vázquez, Oscar Serrano Nov 2026

Glomalin-Related Soil Protein In Colluvial Soils As A Proxy For Reconstructing Millenary Changes In Landscape And Soil Health, Axel Werner, Lourdes López-Merino, Joeri Kaal, Cruz Ferro-Vázquez, Oscar Serrano

Research outputs 2022 to 2026

Glomalin-related soil protein (GRSP) is an operationally defined soil organic matter pool that includes a heat shock protein linked to arbuscular mycorrhizal fungi (AMF) living in symbiosis with most vascular plants and plays a key role in soil health. However, the potential of GRSP as a palaeoecological proxy remains unexplored. We analysed the GRSP content in a North-western Iberian colluvial soil profile encompassing the last 14,000 years. We aimed to 1) identify whether GRSP is preserved, and 2) investigate whether GRSP could be used as a palaeoecological proxy after comparing its record with palynological and pedoanthracological records that account for …

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Mgrre_Thinsections_Mgrre_11_1, Mgrre Oct 2026

Mgrre_Thinsections_Mgrre_11_1, Mgrre

Thin Sections

No abstract provided.

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Llm-Driven Simulation Method For Mobilization Decision-Making In Weapons And Equipment Supply Chain, Qichang Guo, Jihai Zhang, Zichu Qin Oct 2026

Llm-Driven Simulation Method For Mobilization Decision-Making In Weapons And Equipment Supply Chain, Qichang Guo, Jihai Zhang, Zichu Qin

Journal of System Simulation

Abstract: A LLM-driven multi-agent simulation model for production mobilization in the weapons and equipment supply chain is developed to address the practical challenge of transitioning the supply chain from peacetime to wartime production in response to surging wartime demand. The model consists of a military demand layer, a supply chain execution layer, and a mobilization decision-making layer. An LLM is incorporated as a mobilization decision generation module to represent the dynamic regulation of production conversion and expansion under war-readiness budget and production safety constraints. The simulation results show that compared with the no-mobilization and rule-based mobilization strategies, the LLM-based strategy …

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Chemical Plant Control Based On Dynamic Simulation And Reinforcement Learning, Yuxin Li, Haijie Li, Shangbo Han, Kai Gao, Qingyuan Zhang, Yanwen Xiao, Zhengcai Liu Oct 2026

Chemical Plant Control Based On Dynamic Simulation And Reinforcement Learning, Yuxin Li, Haijie Li, Shangbo Han, Kai Gao, Qingyuan Zhang, Yanwen Xiao, Zhengcai Liu

Journal of System Simulation

Abstract: An intelligent control strategy framework combining offline training and on-site deployment for regenerative thermal oxidizer (RTO) operation control based on RL is proposed to solve the problem that fluctuations in exhaust gas concentration and flow, as well as changes in working conditions, make it difficult for RTO to maintain the system operating parameters within the economically optimal range in practical applications. In the offline phase, RL is employed to enable autonomous policy learning and global optimization. In the online phase, an adaptive policy correction mechanism based on real-time data is introduced. RL control strategy is integrated with the operation …

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Five-Dimensional Digital Twin System For Closed-Loop Gasoline Blending Optimization, Yaning Fang, Renchu He Oct 2026

Five-Dimensional Digital Twin System For Closed-Loop Gasoline Blending Optimization, Yaning Fang, Renchu He

Journal of System Simulation

Abstract: An adaptive blending optimization system based on five-dimensional digital twin theory is designed and developed to address the challenges of strict nonlinear constraints and frequent raw material fluctuations. A five-dimensional model architecture is constructed to achieve virtual-real mapping and synergy throughout the blending process. A closed-loop control strategy of "online model correction and dynamic recipe optimization" is proposed. The constrained recursive least squares (RLS) algorithm is adopted to update the octane blending effect model parameters, resolving the model mismatch caused by raw material fluctuations. The linear decreasing weight particle swarm optimization algorithm (LDW-PSO) is combined to determine the optimal …

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Research On Digital Shipyard Design-Procurement Coupling Based On Multi-Agent Systems, Kaitai Peng, Dingyi Feng, Wenbo Zhao, Feier Chen Oct 2026

Research On Digital Shipyard Design-Procurement Coupling Based On Multi-Agent Systems, Kaitai Peng, Dingyi Feng, Wenbo Zhao, Feier Chen

Journal of System Simulation

Abstract: To address the issues of low supply chain collaboration efficiency and inadequate dynamic scheduling capability caused by the disconnection between design and procurement in digital shipbuilding, a method integrating multi-agent simulation with the Q-learning reinforcement learning algorithm is proposed, and a four-tier dynamic supply chain model coupling design and procurement is developed, covering mines, steel mills, shipyards, and shipowners. Based on the depth of collaboration, steel mills are classified into three levels, including full collaboration, partial collaboration, and noncollaboration, with differentiated state spaces and reward functions designed to simulate intelligent pricing and game behaviors under information asymmetry. Simulation results …

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Mission-Oriented Combat Effectiveness Evaluation Model For Intelligent Equipment System-Of-Systems, Long Zhang, Wenbo Huang, Xuanming Feng, Zhen Lei, Ying Wang Oct 2026

Mission-Oriented Combat Effectiveness Evaluation Model For Intelligent Equipment System-Of-Systems, Long Zhang, Wenbo Huang, Xuanming Feng, Zhen Lei, Ying Wang

Journal of System Simulation

Abstract: A hierarchical evolutionary adaptive learning (HEAL) framework is proposed to address the problems in the operational simulation of intelligent equipment systems under complex adversarial environments, including fragmented hierarchical decision-making, unclear mapping between algorithmic indicators and operational effectiveness, and the lack of adaptive evolution capability of simulation models. A multi-layer learning architecture that integrates robust autonomous decision-making for single units, emergent collaboration of system networks, and game-theoretic evolution of system-of-systems strategies is constructed by following the “micro-meso-macro” system-oriented hierarchical modeling paradigm. A cross-layer closed-loop coupling among learned policies, cooperative structures, and operational effectiveness is achieved by combining bottom-up behavioral abstraction …

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Multimodal Perception-Based Trajectory Planning Method For Uavs In Forest Environments, Dequan Li, Jiahao Jiang Oct 2026

Multimodal Perception-Based Trajectory Planning Method For Uavs In Forest Environments, Dequan Li, Jiahao Jiang

Journal of System Simulation

Abstract: To address the requirements for low-latency local planning and safe obstacle avoidance in UAV forest-fire patrol under dense tree-trunk occlusion and locally invalid depth observations, this paper proposes a learning-based local trajectory planning method that fuses depth images, local occupancy distance maps, candidate-level collision-risk assessment, and pre-execution safety reranking. The method uses depth images, local occupancy distance maps, motion states, and target direction as inputs and generates 15 quintic-polynomial candidate trajectories in parallel using 3 × 5 angular motion primitives. During training, candidate safety costs, ranking supervision, and near-collision risk labels are constructed. During inference, online LiDAR point clouds …

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Manifold Alignment And Dual Prediction-Based Adaptive Dynamic Multi-Objective Evolutionary Algorithm, Xuezhi Yue, Chao Xiong, Yanshen Jiang, Zhaolu Guo, Hu Peng Oct 2026

Manifold Alignment And Dual Prediction-Based Adaptive Dynamic Multi-Objective Evolutionary Algorithm, Xuezhi Yue, Chao Xiong, Yanshen Jiang, Zhaolu Guo, Hu Peng

Journal of System Simulation

Abstract: To address prediction failure, manifold topology distortion, and insufficient adaptability in environmental response and operator configuration in existing prediction-based algorithms for dynamic multi-objective optimization under complex nonlinear environments and small-sample settings, this paper proposes a manifold alignment and dual prediction-based adaptive dynamic multi-objective evolutionary algorithm, termed MADP-DMOEA. When the environment changes, the algorithm decomposes the decision-variable manifold into center and shape components, effectively aligns manifolds at adjacent time instants via bipartite optimal matching, and identifies environmental change severity using geometric displacement. A dual prediction model is developed, in which a GRNN performs center extrapolation under severe changes, while an …

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Cooperative Hunting Method For Multiple Usvs Based On Improved Maddpg, Yonghao Zhang, Changyou Xue, Xueman Fan, Zhuo Cheng Oct 2026

Cooperative Hunting Method For Multiple Usvs Based On Improved Maddpg, Yonghao Zhang, Changyou Xue, Xueman Fan, Zhuo Cheng

Journal of System Simulation

Abstract: A cooperative hunting method based on improved multi-agent deep deterministic policy gradient (MADDPG) is proposed to address the challenges of non-stationary environments, sparse rewards, and partial observations in the cooperative hunting of targets with unknown motion patterns by multiple unmanned surface vessels (USVs). A Markov game model for hunting is built, and a multidimensional guided reward shaping function incorporating collision avoidance, hunting success, and energy consumption penalties is designed to mitigate the sparse reward problem. ResNet residual networks are introduced to construct the Actor and Critic networks and thus enhance the representation capability of deep networks for complex state-action …

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Five-Axis Multi-Station Process Planning Based On Multi-Task Graph Reinforcement Learning, Junjie Liu, Jianlei Xu, Litao Ju, Yanhai Hu, Qinghao Zhang, Qiang Liu Oct 2026

Five-Axis Multi-Station Process Planning Based On Multi-Task Graph Reinforcement Learning, Junjie Liu, Jianlei Xu, Litao Ju, Yanhai Hu, Qinghao Zhang, Qiang Liu

Journal of System Simulation

Abstract: A hierarchical intelligent planning architecture integrating multi-task graph and masked proximal policy optimization (MTG-MPPO) is proposed to address the challenges of factorial expansion of the state space and multi-objective interference in five-axis multi-feature machining. An attributed adjacency graph integrating boundary representation and machining direction vectors is constructed. Probabilistic soft labels are output through a multi-task network to quantify process flexibility and thus achieve prior topological pruning of solution space. An MDP model integrating directed acyclic graph masks and dense reward shaping is built. A Monte Carlo (MC) optimization mechanism based on policy distribution is introduced to coordinate cutting, tool-changing, …

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Dynamic Multi-Objective Optimization Algorithm Based On Residual-Driven Two-Stage Prediction And Its Application, Xin Zhang, Han Wang, Pengjiang Qian, Wei Fang Oct 2026

Dynamic Multi-Objective Optimization Algorithm Based On Residual-Driven Two-Stage Prediction And Its Application, Xin Zhang, Han Wang, Pengjiang Qian, Wei Fang

Journal of System Simulation

Abstract: A dynamic multi-objective optimization algorithm based on residual-driven two-stage prediction (RDTP) is proposed to address the dilemma of prediction strategies in dynamic multi-objective optimization struggling with insufficient samples in the early stage and low accuracy in the later stage. In the initial stage, if an environmental change is detected, grey prediction, which is fast and suitable for small samples, is employed to generate individuals for the new environment. A residual analysis is performed to determine whether to switch to a LSSVM to improve prediction accuracy as more samples become available. The Pareto front is clustered to select detection objects. …

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Optimization Of Mobile Power Resource Allocation And Scheduling For Multi-Point Emergency Response, Ding Ma, Tao Zhang, Bo Jiang, Yajie Liu, Shuijia Li Oct 2026

Optimization Of Mobile Power Resource Allocation And Scheduling For Multi-Point Emergency Response, Ding Ma, Tao Zhang, Bo Jiang, Yajie Liu, Shuijia Li

Journal of System Simulation

Abstract: In extreme event scenarios, urban mobile power resources (MPRs) suffer from uneven spatial distribution and capacity mismatch during emergency scheduling. To achieve globally rational allocation and improve resource configuration and power supply guarantee, a two-stage decision-making method for MPR allocation and scheduling is proposed for multi-point emergency power demands. First, the characteristics of MPRs and travel energy consumption are modeled. In the first stage, an allocation model considering demand satisfaction, capacity matching, and dispatch time is established to solve the macro-level matching problem between MPRs and demand points. In the second stage, an interface scheduling model is constructed based …

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Coordinated Optimization Of Heterogeneous Fleet Configuration And Multimodal Routing For Industrial Clusters, Renxin Xiao, Yifan Xu, Guisheng Chen, Nan Pan Oct 2026

Coordinated Optimization Of Heterogeneous Fleet Configuration And Multimodal Routing For Industrial Clusters, Renxin Xiao, Yifan Xu, Guisheng Chen, Nan Pan

Journal of System Simulation

Abstract: A two-level optimization model integrating heterogeneous electric vehicle fleet configuration with multimodal path coordination is built to address the challenges in industrial cluster freight transportation involving electric heterogeneous fleet scheduling, such as diverse tasks and stringent constraints on energy consumption and delivery timeliness. Considering the performance coupling characteristics of heterogeneous electric fleets in terms of load-energy consumption-endurance, based on the coordination of multimodal paths and fleet configuration coding, the upper level employs the GWO to optimize task splitting and fleet configuration. The lower level adopts a multi-neighborhood SA algorithm to achieve path coordination scheduling. Simulation results show that the …

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Simulation Study On Circular-Track Rgv Scheduling Based On Railway Operating Rules, Hualan Wang, Li Wang, Zhongning Fu Oct 2026

Simulation Study On Circular-Track Rgv Scheduling Based On Railway Operating Rules, Hualan Wang, Li Wang, Zhongning Fu

Journal of System Simulation

Abstract: To address the limitations of circular RGV systems under high-density operating conditions, including a simple operational structure and limited parallel operation capability, this study investigates track structures and dispatching mechanisms for complex operational requirements. Based on the agent-based modeling method implemented on the AnyLogic platform, a simulation model of an RGV system with a nested circular track layout was developed, in which an embedded sub-loop equipped with track-switching devices is introduced into the traditional circular track structure. Operating and dispatching rules suitable for the nested circular structure are designed by incorporating the concepts of section operation and overtaking in …

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Air Defense Resource Allocation And Reconstruction Method Based On Kggom, Jiaoxiong Chen, Chenglong Zhang, Yuqiang Zhao, Xianglin Li, Qiang Zhao Oct 2026

Air Defense Resource Allocation And Reconstruction Method Based On Kggom, Jiaoxiong Chen, Chenglong Zhang, Yuqiang Zhao, Xianglin Li, Qiang Zhao

Journal of System Simulation

Abstract: To address the challenge that traditional solving methods cannot meet real-time requirements in the intelligent allocation and reconstruction of air defense operation resources, this paper focuses on an intelligent solving method for the dynamic allocation and reconstruction of intercept timing sensor-weapon-target. A multi-constraint model is established with damage effectiveness and operation cost as optimization objectives, and a knowledge-guided genetic optimization method (KGGOM) is proposed. Through knowledge-guided population generation and evolution, constraint-aware operators with correction mechanisms, and multi-path search combined with simulated annealing for gene correction, it improves its optimization capability and speed. Historical superior solution retention and dynamic elite …

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