DCOOL: Dynamic computation offloading and resource allocation for LEO satellite-assisted edge computing in a ground-space integrated framework by Jeonghwan Kim, Jeongho Kwak

“…To this end, this paper introduces a dynamic computation offloading and resource allocation framework in the LEO satellite MEC architecture. Using Lyapunov optimization, we propose an efficient DCOOL algorithm to minimize average power consumption and propagation delay constrained by queue stability. …”
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Construction of a risk prediction model for pulmonary infection in patients with spontaneous intracerebral hemorrhage during the recovery phase based on machine learning by Jixiang Xu, Yuan Li, Fumin Zhu, Fumin Zhu, Xiaoxiao Han, Liang Chen, Yinliang Qi, Yinliang Qi, Xiaomei Zhou, Xiaomei Zhou

“…Seven ML algorithms were employed to build predictive models, with performance evaluated based on the area under the receiver operating characteristic (AUC) curve, sensitivity, specificity, and accuracy. …”
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Rock image classification based on improved EfficientNet by Kai Bai, Zhaoshuo Zhang, Siyi Jin, Shengsheng Dai

“…Second, the shuffle attention mechanism is integrated into the backbone network to enhance feature extraction while reducing model parameters. Finally, the Lion optimizer is employed to optimize the training process, improving both the accuracy and stability of the model. …”
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An Evolutionary Toolchain for Morphological Filter Mapping on Many-Core Architectures by Emerson C. Pedrino, Denis P. Lima, Igor F. Gallon, Valentin O. Roda, Naijia Liu, Gianluca Tempesti

“…The need to find a balance between performance and communication is a representative example of the general problem of mapping optimizations. The approach presented in this paper explores a two-phase design-time optimization toolchain based on evolutionary algorithms: a front-end single-objective algorithm decomposes a MM filter using smaller operators, while a back-end multi-objective (fault-tolerance, energy, and communication) algorithm searches for optimal mappings of the filter on a specific many-core system, taking into account the architectural parameters of the hardware. …”
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Predominance Preferential Selection for Minimizing Surplus Parts in the Selective Assembly of a Flow Production System by Kanghyeon Shin, Hyobin Son, Kyohong Jin

“…This paper presents the Predominance Preferential Selection (PPS) algorithm, an advanced approach to optimizing selective assembly in flow production systems. …”
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Conceptual Design of a Batch Process for the Production of Biodiesel from High Free Fatty Acid Feedstocks by A. Mohammadi, B. Ghobadian

“…The batch process was found to be suitable for a capacity of less than 260 tons/yr, while the continuous process was suitable for a capacity of greater production rates. …”
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Improving the area-preserving parameterization of rational Bézier surfaces by rational bilinear transformation by Xiaowei Li, Yingjie Wu, Yaohui Sun, Xin Chen, Yanru Chen, Yi-jun Yang

“…To improve the area-preserving parameterization quality of rational Bézier surfaces, an optimization algorithm using bilinear reparameterization is proposed. …”
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Reduction of Energy Consumption in Mobile Cloud Computing by ‎Classification of Demands and Executing in Different Data Centers by H. Yeganeh, A. Salahi, M. A Pourmina

“…We derived a penalty function for cost and then by using Lyapunov optimization theorem; we designed an algorithm to minimize the average cost of energy consumption based on the online information in MCC. …”
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Adaptive control for vehicle formation movement with multiple sensors by TANG Mingzhu, XIANG Guoliang, SHI Hongwei, GUO Shenghui

“…The adaptive motion algorithm based on predetermined motion trajectories is studied in this paper, addressing the problem of vehicle formation with multiple sensors. …”
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A Tri-Level Transaction Method for Microgrid Clusters Considering Uncertainties and Dynamic Hydrogen Prices by Hui Xiang, Xiao Liao, Yanjie Wang, Hui Cao, Xianjing Zhong, Qingshu Guan, Weiyun Ru

“…A polygonal uncertainty set method quantifies wind and solar energy uncertainties, while an enhanced interval optimization technique refines the model. …”
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Enhancing drought prediction through machine learning: advanced techniques combining phenotypic and agrometeorological data by Efrem Yohannes Obsie, Yongguo Liu

“…To enhance efficiency, mutual information (MI) was utilized for feature selection, and the grasshopper optimization algorithm (GOA) was employed for hyperparameter tuning. …”
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Analysis of Techno–Economic and Social Impacts of Electric Vehicle Charging Ecosystem in the Distribution Network Integrated with Solar DG and DSTATCOM by Ramesh Bonela, Sriparna Roy Ghatak, Sarat Chandra Swain, Fernando Lopes, Sharmistha Nandi, Surajit Sannigrahi, Parimal Acharjee

“…In this work, a comprehensive planning framework for an electric vehicle charging ecosystem (EVCE) is developed, incorporating solar distributed generation (DG) and a distribution static compensator (DSTATCOM), to assess their long-term techno–economic and environmental impacts. The optimal locations and capacities of the EVCE, solar DG, and DSTATCOM are determined using an improved particle swarm optimization algorithm based on the success rate technique. …”
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Improved Electrochemical–Mechanical Parameter Estimation Technique for Lithium-Ion Battery Models by Salvatore Scalzo, Davide Clerici, Francesca Pistorio, Aurelio Somà

“…Accurate and predictive models of lithium-ion batteries are essential for optimizing performance, extending lifespan, and ensuring safety. …”
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Comparative analysis of lumped and semi-distributed hydrological models for an upland watershed in Ethiopia by Gebiaw T. Ayele, Bofu Yu

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Coordinated Interaction Strategy of User-Side EV Charging Piles for Distribution Network Power Stability by Juan Zhan, Mei Huang, Xiaojia Sun, Zuowei Chen, Zhihan Zhang, Yang Li, Yubo Zhang, Qian Ai

“…The results show that the optimization strategy proposed by NSGA-II can increase the operating revenue of charging stations by 33.43% while reducing user energy costs and grid voltage deviations by 18.9% and 68.89%, respectively.…”
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Bayesian Time-Domain Ringing Suppression Approach in Impulse Ultrawideband Synthetic Aperture Radar by Xinhao Xu, Wenjie Li, Haibo Tang, Longyong Chen, Chengwei Zhang, Tao Jiang, Jie Liu, Xingdong Liang

“…Building on this analysis, we propose a Bayesian ringing suppression algorithm based on sparse optimization. The method effectively enhances imaging quality while balancing the trade-off between ringing suppression and image fidelity. …”
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Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR by Yuren Huo, Zhaoguang Li, Yan Wang

“…This study develops an integrated optimization framework combining Response Surface Methodology (RSM), Non-dominated Sorting Genetic Algorithm III (NSGA-III), Criteria Importance Through Intercriteria Correlation (CRITIC) weighting, and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) decision-making to optimize the mix proportions water–cement ratio (W/C), sand ratio, and an air-entraining agent (AEA) for sustainable pavement concrete. …”
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AI-Driven Predictive Maintenance in Mining: A Systematic Literature Review on Fault Detection, Digital Twins, and Intelligent Asset Management by Luis Rojas, Álvaro Peña, José Garcia

“…The findings highlight the increasing adoption of deep learning, reinforcement learning, and digital twins for anomaly detection and process optimization. Additionally, AI-driven methods are improving sensor-based data acquisition and asset management, extending equipment lifecycles while reducing failures. …”
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Benchmarking Diffusion Annealing-Based Bayesian Inverse Problem Solvers by Evan Scope Crafts, Umberto Villa

“…The results provide insight into the strengths and limitations of existing diffusion-model based posterior samplers, while the benchmark problems provide a testing ground for future algorithmic developments.…”
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A Review of Systematic Methodologies for Shipyard Facility Layout Design by Ghulam Tulus Pambudi, Gunawan Gunawan, Dimas Angga Fakhri Muzhoffar, Wanda Rulita Sari

“…Heuristic algorithms, including genetic algorithms and simulated annealing, offer flexible and rapid solutions but may not always achieve global optimization. …”
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