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A diffuse-interface lattice Boltzmann method for the dissolution through nonlinear heterogeneous reaction

Physics of Fluids 2025年第5期37卷

作者： Yang, Xiaofeng Zhan, Chengjie Liu, Xi Chai, Zhenhua School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China The State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

In this work, a diffuse-interface lattice Boltzmann method (DI-LBM) is proposed for the dissolution through nonlinear heterogeneous reaction. In this method, the sharp boundary between the fluid and solid phases is replaced by a diffuse interface with a nonzero thickness. To describe the changes of physical variables from the fluid region to the solid region, a smooth function is introduced, and simultaneously, the nonlinear Robin boundary condition imposed on the fluid-solid interface is reformulated as a source term into the modified convection-diffusion equation. We then test the DI-LBM through several benchmark problems, and find that the numerical results are in agreement with the analytical solutions and available data. Finally, the present DI-LBM is adopted to study the dissolution in a two-dimensional porous medium, and the results show that with the decrease in reaction order, the reactant in the central region with a large fluid velocity reacts faster and forms a conical pathway. © 2025 Author(s).

关键词： Kinetic theory of gases

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Federated Learning and Blockchain-Enabled Framework for Traffic Rerouting and Task Offloading in the Internet of Vehicles (IoV)

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IEEE Transactions on Consumer Electronics 2025年

作者： Devarajan, Ganesh Gopal Thangam, S. Alenazi, Mohammed J. F. Kumaran, U. Chandran, Gopalakrishnan Bashir, Ali Kashif SRM Institute of Science and Technology Department of Computer Science and Engineering Faculty of Engineering and Technology Delhi NCR Campus Uttar Pradesh Ghaziabad201204 India Amrita Vishwa Vidhyapeetham Amrita School of Computing Department of CSE Bangalore India Amrita Vishwa Vidhyapeetham Amrita School of Computing Bangalore India Apollo University Department of Computer Science and Engineering Andhra Pradesh Chittoor517127 India Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Woxsen University Woxsen School of Business India Lebanese American University Department of Computer Science and Mathematics Beirut Lebanon Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11451 Saudi Arabia

The Internet of Vehicles (IoV) presents significant opportunities for enhancing traffic management and vehicle coordination, but it also faces challenges related to traffic congestion, data privacy, and efficient computational resource allocation. Traffic congestion remains a critical problem, impacting travel time, fuel consumption, and emissions. Additionally, task offloading in the edge-cloud environment demands efficient strategies to balance latency, resource usage, and computational load. Our proposed system, Joint Federated Learning and Blockchain-Enabled Traffic Rerouting with Efficient Task Offloading of Consumer IoV in the Edge-Cloud Environment, addresses these issues by integrating federated learning and blockchain technologies. Federated learning allows vehicles to collaboratively train a global model without sharing raw data, preserving privacy and reducing bandwidth usage. Blockchain ensures the security and integrity of the model updates, fostering trust among participants. Efficient task offloading strategies optimize the use of edge and cloud resources, minimizing latency and energy consumption. Our approach is validated using a comprehensive dataset, and the results demonstrate significant improvements in traffic prediction accuracy, security, and overall system performance, highlighting the effectiveness of the integrated solution in addressing the challenges of Consumer Internet of Vehicles (CIoV). © 1975-2011 IEEE.

关键词： Traffic congestion

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Nonlinear bubble behaviours of compressible Rayleigh–Taylor instability with isothermal stratification in cylindrical geometry

arXiv

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arXiv 2025年

作者： Yuan, Ming Zhao, Zhiye Liu, Luoqin Wang, Pei Liu, Nan-Sheng Lu, Xi-Yun Department of Modern Mechanics University of Science and Technology of China Anhui Hefei230026 China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong Institute of Applied Physics and Computational Mathematics Beijing100094 China

Nonlinear evolutions of two-dimensional single-mode compressible Rayleigh–Taylor instability (RTI) with isothermal stratification are investigated in cylindrical geometry via direct numerical simulation for different Atwood numbers (AT = 0.1 − 0.9) and Mach numbers (Ma = 0.1 − 0.9). It is found that the nonlinear bubble growth involves the effects of density stratification, vorticity accumulation and flow compressibility and shows considerable differences between convergent (acceleration acting radially inward) and divergent (acceleration acting radially outward) cases. Specifically, the density stratification leads to non-acceleration at low AT and high Ma. The accelerations in convergent cases are dominated by vorticity accumulation at low AT and low Ma and by flow compressibility at high AT and high Ma whereas the accelerations in divergent cases are purely induced by flow compressibility at high AT and high Ma. Based on the nonlinear theory of incompressible cylindrical RTI with uniform-density background (Zhao et al., J. Fluid Mech., vol. 900, 2020, A24), an improved model is proposed by taking the density variation, vorticity accumulation and flow compressibility into consideration. This model is verified by numerical results and well reproduces the bubble evolution for different AT and Ma from linear to highly nonlinear regimes. © 2025, CC BY.

关键词： Vorticity

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Evaluation of Trade-Off Between Compression Ratio and Hardware Cost for Adaptive Bandwidth Compression Hardware Platform

Evaluation of Trade-Off Between Compression Ratio and Hardwa...

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IEEE Cool Chips

作者： Tomohiro Ueno Kaito Kitazume Masato Kiyama Kazutomo Yoshii Kento Sato Norihisa Fujita Ryohei Kobayashi Taisuke Boku Kentaro Sano Center for Computational Science Riken Kobe Japan Degree Programs in Systems and Information Engineering University of Tsukuba Tsukuba Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan Mathematics and Computer Science Argonne National Laboratory Lemont IL USA Center for Computational Sciences University of Tsukuba Tsukuba Japan Supercomputing Research Center Institute of Integrated Research Institute of Science Tokyo Kanagawa Japan

ISBN: (数字)9798331534844

ISBN: (纸本)9798331534851

Hardware-based, high-throughput data compression is a promising approach to reduce data movement costs in largescale systems and networks, thereby improving overall performance and power efficiency. However, since a data compression algorithm is only effective if it is suitable for the characteristics of the target data, data compression hardware with a fixed algorithm is unrealistic for use in general-purpose environments. To address this challenge, we have been researching adaptive bandwidth compression (ABC) hardware that flexibly provides an effective algorithm depending on the input data. This paper presents the design and the Chisel-based implementation of the ABC hardware platform for encoding compressed data and generating output blocks, as well as introducing a quantization parameter to reduce the circuit area. Our evaluation shows that the proposed quantization parameter can not only reduce the hardware cost, but also control the trade-off between the effective compression ratio and the hardware cost. In addition, based on the evaluation results, we discuss the design optimization of the ABC hardware platform.

关键词： Performance evaluation Costs Quantization (signal) Data compression Bandwidth Hardware Space exploration Resource management Compression algorithms Field programmable gate arrays

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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： B Shoba T Raghunathan S.Sam Peter Karthick K Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tirunelveli Department of Artificial Intelligence and Data Science Dr.NG.p.Institute of Technology Coimbatore Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore Department of Information Technology Sona College of Technology Salem Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles.

关键词： Adaptation models Pedestrians Heuristic algorithms Feature extraction Real-time systems Vehicle dynamics Collision avoidance Autonomous vehicles Optimization Logistics

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters

Cloud Based Automated Control System Workshops and Rooms for...

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Advances in Computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Dinesh Kumar Ravinder Singh Kuntal Pragya Deep Anup Shekhar Chamoli Pinki Singh Rishabh Mandal Department of Computer Science and Engineering SRM Institute of Science & Technology Ghaziabad UP Department of Mathematics Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of AI & DS IIMT College of Engineering Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun India Department of Computer Science and Engineering Accurate College of Engineering & Management Greator Noida India Department of Computer Science and Engineering Tula's Institute Dehradun UK India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing.

关键词： Knowledge engineering Machine control Clouds Control systems Software Machine tools Security Time factors Personnel Standards

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Crow Search Algorithm for Energy-Efficient UAV Path Planning in Large-Scale Aerial Surveys

Crow Search Algorithm for Energy-Efficient UAV Path Planning...

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Device Intelligence, computing and Communication Technologies, (DICCT), International Conference on

作者： S. Amutha S.V. Ramanan Selvendran Ganesan B. Leena Akshya J Mani Deepak Choudhry M. Sundarrajan Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering PPG Institute of Technology Coimbatore Tamil Nadu India Department of Biomedical Engineering Mahendra Institute of Technology Namakkal Tamil Nadu India Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies 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

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

UAVs have been extremely influential for applications such as remote/sensing, disaster response, surveillance, and infrastructure monitoring. They are valuable in modern-day operations because they can conduct high-precision aerial surveys autonomously. Most of the existing trajectory planning approaches are based on metaheuristic algorithms of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO), which are capable of providing satisfactory results. Still, they are computationally expensive with slow convergence and sensitive to local optima. In this paper, the challenges raised above are addressed by an Optimized Trajectory Planning Framework based on the Crow Search Algorithm (CSA). It is based on a nature-inspired optimization technique that mimics the intelligent foraging behavior of crows. The proposed approach dynamically adjusts the UAV flight path by balancing the exploration and exploitation, to compute the optimal trajectory quickly and meanwhile to save energy and avoid the obstacle. Unlike other metaheuristic methods, the Crow Search Algorithm does not confront the premature convergence problem and can guarantee adaptive path optimization even under uncertain environmental conditions. Results show that the energy efficiency performance of the Crow Search Algorithm is 15 percent better, the performance in terms of coverage is 20 percent improved, and the speed of convergence is 30 percent faster compared to various state-of-the-art methods. These experimental findings demonstrate that integrating CSA into UAV trajectory planning offers high computational efficiency, obstacle avoidance, and mission endurance, rendering it a superior UAV trajectory planning algorithm for real-time aerial navigation and autonomous surveillance applications.

关键词： Surveys Trajectory planning Surveillance Heuristic algorithms Metaheuristics Autonomous aerial vehicles Energy efficiency Real-time systems Collision avoidance Convergence

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Time-reversal-symmetry bounds on electromagnetic fields

arXiv

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arXiv 2025年

作者： Ma, Wenchao Pestourie, Raphaël Johnson, Steven G. Department of Chemistry Massachusetts Institute of Technology CambridgeMA02139 United States School of Computational Science and Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Mathematics Massachusetts Institute of Technology CambridgeMA02139 United States

For linear electromagnetic systems possessing time-reversal symmetry, we present an approach to bound ratios of internal fields excited from different ports, using only the scattering matrix (S matrix), improving upon previous related bounds by Sounas and Alù (2017). By reciprocity, emitted-wave amplitudes from internal dipole sources are bounded in a similar way. When applied to coupled-resonant systems, our method constrains ratios of resonant coupling/decay coefficients. We also obtain a relation for the relative phase of fields excited from the two ports and the ratio of field intensities in a two-port system. In addition, although lossy systems do not have time-reversal symmetry, we can still approximately bound loss-induced non-unitarity of the S matrix using only the lossless S matrix. We show numerical validations of the near-tightness of our bounds in various scattering systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Matrix algebra

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A Unified Evaluation Framework for Epistemic Predictions

arXiv

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arXiv 2025年

作者： Manchingal, Shireen Kudukkil Mubashar, Muhammad Wang, Kaizheng Cuzzolin, Fabio Institute School of Engineering Computing and Mathematics Oxford Brookes University United Kingdom M-Group and DistriNet Division Department of Computer Science KU Leuven Belgium Flanders Make@KU Leuven Belgium

Predictions of uncertainty-aware models are diverse, ranging from single point estimates (often averaged over prediction samples) to predictive distributions, to set-valued or credal-set representations. We propose a novel unified evaluation framework for uncertainty-aware classifiers, applicable to a wide range of model classes, which allows users to tailor the trade-off between accuracy and precision of predictions via a suitably designed performance metric. This makes possible the selection of the most suitable model for a particular real-world application as a function of the desired trade-off. Our experiments, concerning Bayesian, ensemble, evidential, deterministic, credal and belief function classifiers on the CIFAR-10, MNIST and CIFAR-100 datasets, show that the metric behaves as desired. © 2025, CC BY.

关键词： Prediction models

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Ion kinetic effects in the overtaking and coalescence of plasma shocks

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Plasma Physics and Controlled Fusion 2025年第5期67卷

作者： Zhang, E.H. Lin, Z.H. Zhang, W.S. Liu, Q.K. Meng, F.Q. Zhang, X. Chen, M.J. Luo, H. Cai, H.B. Zhu, G.H. Luo, M.X. Zhu, S.P. Beijing Computational Science Research Center Beijing100193 China Graduate School China Academy of Engineering Physics Beijing100193 China Institute of Applied Physics and Computational Mathematics Beijing100094 China Center for Applied Physics and Technology HEDPS School of Physics College of Engineering Peking University Beijing100871 China Center for Applied Physics and Technology HEDPS College of Engineering Peking University Beijing100871 China

The overtaking and coalescence of plasma shocks are important scientific topics in inertial confinement fusion design. We present the comparative study of the impact of ion kinetic effects on the overtaking and coalescence of two shocks in deuterium (D) plasmas of various densities with both hybrid fluid-particle-in-cell simulations and hydrodynamic simulations. It is found that the energetic ions escaping from 2 nd shock can prevent the compression of the fuel layer, by accelerating and heating upstream plasmas. In the strong-collision case ( ρ D > ρ th = 30 mg cc − 1 ), the coalescent density is reduced by about 8% due to the decreased shock Mach number resulting from energetic ion deposition in the upstream region. In the weak-collision case ( ρ D © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

关键词： Inertial confinement fusion

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