Efficient resource allocation for D2D-enabled social IoT networks: A tripartite and time-scale optimization approach by Saurabh Chandra, Rajeev Arya, Maheshwari Prasad Singh

“…The problem is solved in two stages: a tripartite graph-based resource allocation stage and a time-scale optimization stage. The proposed approach prioritizes maintaining Quality of Service (QoS) and resource allocation to minimize power consumption while maximizing sum throughput. …”
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Optimizing UAV Location for Deployment in Cell-Free Massive MIMO Networks Using a Soft Actor-Critic Reinforcement Learning by Okello Kenneth, Elijah Mwangi, Dominic Bernard Onyango Konditi

“…The uncrewed aerial vehicles (UAVs) communication network has recently garnered significant attention and motivation in academic and industrial research due to its flexible mobility concept in providing dynamic coverage in hotspot or congested areas with frequent user demand. While UAVs offer reliable connectivity in such challenging scenarios, integrating them with Cell-Free Massive MIMO (CF-mMIMO) networks presents severe issues in optimal deployment and coverage efficiency. …”
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Optimizing the train timetable in a high-speed rail corridor: The implications on departure time, fare cost and seat preference of passengers. by Zhipeng Huang, Limin Yang, Jinlian Li, Tao Zhang, Zixian Qu, Yusen Miao

“…A bi-level programming model is formulated: the upper level optimizes train operations and fare structures, while the lower level employs user equilibrium (UE) theory to distribute OD passenger demands across trains. …”
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Blockchain-based heterogeneous resource configuration scheme in computing power network by Qiang Gao, Chunyu Liu, Lei Wang, Yiqing Liu, Yueqiang Xu

“…Given the complexity of the original problem, we decompose it into two subproblems and solve each using the Lagrange multiplier iterative algorithm. To validate the effectiveness of our proposed solution, we compare it with several baseline approaches, including those based on Particle Swarm Optimization (PSO) and reinforcement learning methods such as Proximal Policy Optimization (PPO). …”
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Fluid flow characteristics estimation of a new integrated bifluid/airbased photovoltaic thermal system utilizing a hybrid optimization method by Ghassan A. Bilal, Abdullateef A. Jadallah, Omayma M. Abdulmajeed, Müslüm Arıcı

“…For the reliability of the proposed algorithm, the mean absolute percentage error was used for error estimation detection. …”
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STUDY ON CALCULATION METHOD OF STRESS SEPARATION BY OBLIQUE INCIDENCE TECHNIQUE by LIU XiaoMeng, DAI ShuGuang

“…Conventional method for solving nonlinear overdetermined equations is difficult,new particle swarm optimization algorithms are proposed for oblique incidence stress separation. …”
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Post-extrasystolic variation of ST segment and T wave as a mortality risk predictor after myocardial infarction by Ralf J. Dirschinger, Ralf J. Dirschinger, Alexander Müller, Petra Barthel, Alexander Steger, Michael Dommasch, Axel Bauer, Karl-Ludwig Laugwitz, Georg Schmidt, Daniel Sinnecker, Daniel Sinnecker

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High-Performance Thin PTFE-Ni Metamaterial Absorber for Ultra-Broadband Applications by Camilla C. Moro Carmo, Ursula C. Resende, Silvia C. Albuquerque, Mauricio D. Almeida

“…A systematic parametric analysis was conducted to optimize the structural parameters, maximizing absorption efficiency while maintaining minimal thickness. …”
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Optimizing space heating efficiency in sustainable building design a multi criteria decision making approach with model predictive control by Zheng Qi, Nan Zhou, Xianwei Feng, Sama Abdolhosseinzadeh

“…Abstract Efficient space heating is vital for sustainable building design, offering opportunities to reduce energy consumption and costs while maintaining thermal comfort. This study examines the optimization of space heating in a nearly-zero energy building (nZEB) located in Oslo, Norway, under cold climatic conditions. …”
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Performance evaluation and multi-objective optimization of EDM parameters for Ti6Al4V using different tool electrodes by Manoj Jagdale, Masuk Abdullah, Nitin Ambhore, Atul Kulkarni, Rakesh Chaudhari, Jay Vora, József Menyhárt

“…Output responses—material removal rate (MRR), tool wear rate (TWR), surface roughness (SR), and dimensional deviation (DD)—were measured and optimized using the Teaching–Learning-Based Optimization (TLBO) algorithm. …”
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A Rapid Sand Gradation Detection Method Based on Dual-Camera Fusion by Shihao Zhang, Yang Zhang, Song Sun, Xinghai Yuan, Haoxuan Sun, Heng Wang, Yi Yuan, Dan Luo, Chuanyun Xu

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Carbon Policies and Liner Speed Optimization: Comparisons of Carbon Trading and Carbon Tax Combined with the European Union Emissions Trading Scheme by Ming Sun, Midakpe P. Vortia, Guangnian Xiao, Jing Yang

“…With the use of advanced optimization methods, such as the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS), this research explores the balance between adjusting vessel speeds and minimizing emissions. …”
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Reduced-Complexity and Robust Surface Water Path Delineation for Large Real-World Terrain Using Boundary Element Method by Ajalawit Chantaveerod, Kampol Woradit, Andrew Seagar, Thunyawat Limpiti

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SNet: A novel convolutional neural network architecture for advanced endoscopic image classification of gastrointestinal disorders by Samra Siddiqui, Junaid A. Khan, Tallha Akram, Meshal Alharbi, Jaehyuk Cha, Dina A. AlHammadi

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A secure and energy-efficient routing using coupled ensemble selection approach and optimal type-2 fuzzy logic in WSN by S. Ambareesh, Pundalik Chavan, S. Supreeth, Rajesh Nandalike, P. Dayananda, S. Rohith

“…To deal with those challenges, we advocate a novel routing framework that is both steady and power-efficient, leveraging an Improved Type-2 Fuzzy Logic System (IT2FLS) optimized by means of the Reptile Search Algorithm (RSA). …”
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Reinforcement Learning With Deep Features: A Dynamic Approach for Intrusion Detection in IoT Networks by Mohamad Khayat, Ezedin Barka, Mohamed Adel Serhani, Farag Sallabi, Khaled Shuaib, Heba M. Khater

“…The extracted feature sets were concatenated within a feature fusion layer that enabled efficient dimensionality reduction via principal component analysis. A hybrid optimization approach was also introduced for feature selection using green anaconda optimization and the chaotic learning osprey optimization algorithm. …”
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Intelligent Network-Level Energy Saving Strategy With STGNN-Driven Traffic Prediction and Path Optimization in Transport Networks and Field Trial by Xinyu Chen, Liuyan Han, Minxue Wang, Jiang Sun, Yong Gao, Xuegang Ou, Dechao Zhang, Han Li

“…Combined with K-shortest-path optimization algorithm, the system dynamically reallocates traffic to minimize active switch fabrics while maintaining strict service quality guarantees. …”
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Optimizing Geospatial Data for ML/CV Applications: A Python-Based Approach to Streamlining Map Processing by Removing Irrelevant Areas by David Kasperek, Michal Podpora

“…The research results of the paper reveal substantial file size reduction, and improved processing efficiency, thus making the optimized geospatial graphical data more practical for ML/CV applications, while still maintaining the original data quality and relevance of the analyzed parcels or infrastructure.…”
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Two-Stage Optimization of Mobile Energy Storage Sizing, Pre-Positioning, and Re-Allocation for Resilient Networked Microgrids with Dynamic Boundaries by Hongtao Lei, Bo Jiang, Yajie Liu, Cheng Zhu, Tao Zhang

“…Networked microgrids (NMGs) enhance the resilience of power systems by enabling mutual support among microgrids via dynamic boundaries. While previous research has optimized the locations of mobile energy storage (MES) devices, the critical aspect of MES capacity sizing has been largely neglected, despite its direct impact on costs. …”
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Double layers optimal scheduling of distribution networks and photovoltaic charging and storage station cluster based on leader follower game theory by Zixuan Liu, Jun Wu, Ruijin Zhu, Dewen Kong, Hao Guo

“…The upper layer takes the high-penetration distribution network as the decision-making entity and aims to maximize its own revenue while considering the energy trading of PCSSIS. The lower layer takes PCSSIS as the decision-making entity, and PCSSIS adjusts energy flow and optimizes revenue based on the internal electricity price provided by the upper-layer distribution network. …”
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