A machine learning approach to assess the climate change impacts on single and dual-axis tracking photovoltaic systems by Udit Mamodiya, Indra Kishor, Priyam Ganguly, Isha Mukherjee, Nithesh Naik

“…COMLAT’s reinforcement learning controller optimized tracking angles with sub-second latency while minimizing mechanical movement. …”
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Recent Advances in Resistive Gas Sensors: Fundamentals, Material and Device Design, and Intelligent Applications by Peiqingfeng Wang, Shusheng Xu, Xuerong Shi, Jiaqing Zhu, Haichao Xiong, Huimin Wen

“…Machine learning (ML) algorithms have enabled intelligent design of novel sensing materials, optimized multi-gas identification, and enhanced data reliability in complex environments. …”
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Estimation and Reduction of CO₂ Emissions From Fossil Fuel Power Plants in Bangladesh by Deepak Kumar Chowdhury, Nur Mohammad, Tanjim Mahmud, Md. Khaliluzzaman, Karl Andersson, Mohammad Shahadat Hossain

“…The paper presents Matpower Interior Point Solver (MIPS), and Dynamic Non-Linear Particle Swarm Optimization (DNPSO) algorithms to solve combined economic emission dispatch (CEED) to optimize generation dispatch and minimize emissions. …”
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Advancement in public health through machine learning: a narrative review of opportunities and ethical considerations by Sumit Singh Dhanda, Deepak Panwar, Chia-Chen Lin, Tarun Kumar Sharma, Deependra Rastogi, Shantanu Bindewari, Anand Singh, Yung-Hui Li, Neha Agarwal, Saurabh Agarwal

“…Ethically, this review surfaces critical issues, including algorithmic bias, data privacy concerns in mental health analytics, and the interpretability of black-box models used in outbreak surveillance. …”
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Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

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Design and Performance Characterization of the E-Core Outer-Rotor Hybrid-Excitation Flux Switching Machine by Zhiyuan Xu, Ming Cheng

“…Based on the optimization result, a prototype was manufactured and tested to verify the whole investigation of this paper.…”
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Bond behavior of galvanized iron fiber reinforced concrete with recycled coarse aggregate and model prediction using machine learning by Md. Jahidul Islam, Ummul Wara Labiba, Tasfiah Faisal Chowdhury, Md. Shahjalal, Tanvir Mustafy, Tanvir Hassan Tusher

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A Dual-Variable Selection Framework for Enhancing Forest Aboveground Biomass Estimation via Multi-Source Remote Sensing by Dapeng Chen, Hongbin Luo, Zhi Liu, Jie Pan, Yong Wu, Er Wang, Chi Lu, Lei Wang, Weibin Wang, Guanglong Ou

“…<i>langbianensis</i> forests, while GA-optimized machine learning models demonstrate excellent performance, providing strong support for regional-scale forest resource monitoring and carbon stock assessment.…”
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Metaheuristic-Based Cepstral Sensing Technique for Prolonging Network Lifetime and Mitigating Primary User Emulation Attacks in Cognitive Radio Sensor Network by V. Abilasha, A. Karthikeyan

“…Whale optimisation algorithm maintains network performance and energy efficiency while refining sensing parameters and enhancing decision-making. …”
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Multi-Objective Robustness of Integrated Energy System Considering Source-Load Uncertainty by LI Jianlin, ZHANG Zedong, LIANG Ce, ZENG Fei

“…A grid-connected electric-thermal-hydrogen operation model including fuel cells and electrolytic cells is developed, and the source-load uncertainty is added by using the hierarchical Latin hypercube sampling and Euclidean distance scenario reduction methods, and solved by a two-stage robust optimization algorithm. The results show that the proposed method can effectively mitigate the impact of source-load uncertainty on the configuration and operation planning of the integrated energy system, which is expected to provide new ideas for the construction and operation of integrated energy systems in the future.…”
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Evolutionary Game-Based Battery Scheduling: A Comparative Study for Prosumers in Smart Grids by Anas Karaki, Khaled Abedrabboh, Luluwah Al-Fagih

“…A novel decentralized evolutionary game theory (EGT) algorithm is introduced to enhance energy management while preserving privacy and scalability. …”
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Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis by Fernanda Pereira Leite Aguiar, Irenilza de Alencar Nääs, Marcelo Tsuguio Okano

“…Future work will explore dynamic parameter optimization and algorithm adaptability across diverse datasets to further refine its capabilities. …”
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Multi video stream collaborative adaptive offloading scheme based on equilibrium game theory by Shijun Yuan

“…Secondly, an adaptive task offloading algorithm based on game theory and Nash equilibrium is designed to achieve optimal node selection through offloading algorithm and weight allocation, minimizing the processing cost of the entire multi video stream system and balancing video stream computation latency and energy consumption; Finally, the simulation results show that the proposed scheme satisfies long-term MEC energy constraints while significantly outperforming existing benchmark research schemes in terms of cost performance.…”
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Fault Location and Route Selection Strategy of Distribution Network Based on Distributed Sensing Configuration and Fuzzy C-Means by Bo Li, Guochao Qian, Lijun Tang, Peng Sun, Zhensheng Wu

“…Firstly, the DBPSO algorithm is used to optimize the configuration scheme of the distributed voltage and current sensing device, which reduces the number of measuring devices and system cost on the premise of ensuring the global observability of the distribution network. …”
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A Frequency Hybrid Multi-mode PWM Modulation Method for SiC Traction Inverter by HU Liang, FU Xiangyu, LYU Yongcan, FENG Ling, HU Dong

“…In order to improve the efficiency of the traction system, this paper researches the PWM control strategy of high-frequency SiC traction inverter system from multiple dimensions such as inverter switching frequency optimization, modulation strategy selection and motor control algorithm adaptation. …”
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RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning by Yang Jing, Li Weiya

“…The QPSO module is responsible for global path optimization, using quantum mechanics to avoid local optima, while the DRL module adjusts strategies in real-time based on environmental feedback, thus enhancing decision-making capabilities in complex high-dimensional scenarios.Results and discussionExperiments were conducted on multiple datasets, including Cityscapes, NYU Depth V2, Mapillary Vistas, and ApolloScape, and the results showed that RL-QPSO Net outperforms traditional methods in terms of accuracy, computational efficiency, and model complexity. …”
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Evaluation method for gas pre-extraction status in coal seam boreholes based on semi-supervised learning by YAN Li, WEN Hu, WANG Zhenping, JIN Yongfei

“…Current evaluation methods for single-borehole gas extraction status typically rely on gas concentration, while overlooking the diversity of coal seam gas occurrence. …”
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Two-Timescale Cross-Layer Design for URLLC Over Parallel Fading Channels With Imperfect CSI by Hongsen Peng, Meixia Tao

“…Based on the small timescale optimization method, we adopt the ternary search algorithm to optimize the pilot length and pilot power through Monte Carlo evaluations in the large timescale. …”
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An Innovative Online Adaptive High-Efficiency Controller for Micro Gas Turbine: Design and Simulation Validation by Rui Yang, Yongbao Liu, Xing He, Zhimeng Liu

“…When the DL_ELM model detects a gas turbine’s performance change, a particle swarm optimization (PSO) algorithm is employed to iteratively calculate the DFF_DL_OSELM model, determining the optimal speed control scheme to ensure the gas turbine operates at maximum efficiency. …”
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DRL-Leveraged and RIS-Assisted Hybrid Network Slicing for eMBB and URLLC Co-Existence in 6G Systems by Bhagawat Adhikari, Ahmed Shaharyar Khwaja, Muhammad Jaseemuddin, Alagan Anpalagan

“…The URLLC allocation is achieved using a Deep Reinforcement Learning (DRL)-based Proximal Policy Optimization (PPO) algorithm. The performance of the proposed technique with hybrid network slicing is compared with the NOMA-based and puncturing-based slicing, and other state-of-the-art (SOTA) techniques for URLLC allocation. …”
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