Optimal Scheduling Strategy of Newly-Built Microgrid in Small Sample Data-Driven Mode by CHEN Shi, YANG Linsen, LIU Yihong, LUO Huan, ZANG Tianlei, ZHOU Buxiang

“…Additionally, the optimal scheduling model is transformed into a Markov decision process and solved using double-delay deep deterministic policy gradient algorithm. Finally, the effectiveness of the proposed method is validated through a case study involving an improved CIGRE 14-node microgrid.…”
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A Simulation Based Metaheuristics for Capacitated Vehicle Routing Problem by Tamara Latifah Jasmine, Heru Purboyo Hidayat Putro, Niken Prilandita, Gatot Yudoko

“…The methodology utilizes a simheuristics approach, integrating a Genetic Algorithm (GA) to determine optimal routing solutions, while employing Discrete Event Simulation (DES) to incorporate key economic, environmental, and social variables. …”
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THE ROLE OF LEGAL TECHNIQUE IN CONDUCTING ANTI-CORRUPTION EXAMINATION OF THE LEGISLATION OF THE RUSSIAN FEDERATION by Irina Klyukovskaya, Ruslan Melekayev

“…Strengthening of anti-corruption policy is one of the main tasks of the development of a jural and democratic state in modern Russia. …”
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Synergistic Effects of Energy Storage Systems and Demand-Side Management in Optimizing Zero-Carbon Smart Grid Systems by Zeyad A. Almutairi, Ali M. Eltamaly

“…This work explores the challenges and opportunities of this integrated approach, offering policy recommendations and future research directions for truly optimized ZCSG implementation.…”
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A Big Data Approach for the Regional-Scale Spatial Pattern Analysis of Amazonian Palm Locations by Matthew J. Drouillard, Anthony R. Cummings

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Forest cover restoration analysis using remote sensing and machine learning in central Malawi by Jabulani Nyengere, Precious Masuku, Sylvester Chikabvumbwa, Weston Mwase, Msaiwale Kathewera, Allena Laura Njala, Wilson Tchongwe, Isaac Tchuwa, Tiwonge I Mzumara, Chikondi Chisenga, Wilfred Kadewa, Emmanuel Chinkaka, Harineck Tholo

“…These findings emphasize the effectiveness of participatory governance and targeted policy enforcement in fostering forest restoration. …”
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Reinforcement Q-Learning-Based Adaptive Encryption Model for Cyberthreat Mitigation in Wireless Sensor Networks by Sreeja Balachandran Nair Premakumari, Gopikrishnan Sundaram, Marco Rivera, Patrick Wheeler, Ricardo E. Pérez Guzmán

“…A Hybrid Policy Derivation Algorithm is introduced to balance encryption complexity and computational overhead by dynamically switching between these learning models. …”
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Solving Collaborative Scheduling of Production and Logistics via Deep Reinforcement Learning: Considering Limited Transportation Resources and Charging Constraints by Xianping Huang, Yong Chen, Wenchao Yi, Zhi Pei, Ziwen Cheng

“…To solve this problem, an improved PPO algorithm—CRGPPO-TKL—has been developed, which integrates candidate probability ratio calculations and a dynamic clipping mechanism based on target KL divergence to enhance the exploration capability and stability during policy updates. …”
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A Logarithmic Compression Method for Magnitude-Rich Data: The LPPIE Approach by Vasileios Alevizos, Zongliang Yue, Sabrina Edralin, Clark Xu, Nikitas Gerolimos, George A. Papakostas

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RON‐based cross‐chain routing optimization strategy in metaverse by Junjie Huang, Liang Tan, Jianmei Xiao

“…To optimize direct‐connect routing, this paper proposed a cross‐chain routing optimization strategy based on RON (Resilient Overlay Network), that is, Cross‐Chain_RON, which firstly applies RON to reconstruct the direct‐connect routing model, and then selects the optimal link through the shortest‐path algorithm and policy routing, and combines with the RON performance database to improve the data transmission efficiency. …”
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Evolution and Knowledge Structure of Wearable Technologies for Vulnerable Road User Safety: A CiteSpace-Based Bibliometric Analysis (2000–2025) by Gang Ren, Zhihuang Huang, Tianyang Huang, Gang Wang, Jee Hang Lee

“…Contemporary research emphasizes context-adaptive interfaces, seamless V2X integration, and user-centered design, and future work should focus on lightweight communication protocols, adaptive sensory algorithms, and personalized safety profiles. …”
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A Wide-Range TCSC Based ADN in Mountainous Areas Considering Hydropower-Photovoltaic-ESS Complementarity by Yao Guo, Shaorong Wang, Dezhi Chen

“…The optimal operation issue is formulated as a Markov decision process (MDP) with continuous action space and solved using the twin delayed deep deterministic policy gradient (TD3) algorithm. The optimal objective is to maximize the consumption of renewable energy sources (RESs) and minimize line losses by coordinating the charging/discharging of ESSs with the operation mode of the TCSC. …”
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A Reinforcement Learning-based Intelligent Learning Method for Anti-active Jamming in Frequency Agility Radar by J. Wei, L. Yu, Y. Wei, R. Xu

“…This model includes five submodules: signal processing, anti-jamming evaluation, target detection, optimization constraint design, and optimization algorithm design. We apply a proximal policy optimization combined with a generative pre-trained transformer (PPO-GPT) to solve this model, allowing FAR to adaptively learn jamming strategies and optimize IPCFH-IPSFC waveform parameters for effective anti-jamming. …”
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Data-driven bottleneck detection on Tehran highways by Hamid Mirzahossein, Pedram Nobakht, Iman Gholampour

“…As urban mobility challenges escalate globally, the integration of diverse data sources and cutting-edge techniques will be instrumental in guiding intelligent transportation planning and policy decisions.…”
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Reinforcement learning-based assimilation of the WOFOST crop model by Haochong Chen, Xiangning Yuan, Jian Kang, Danni Yang, Tianyi Yang, Xiang Ao, Sien Li

“…Based on the WOFOST crop model, two RL environments were constructed: a Daily-Data Driven approach and a Time-Series Driven approach. The Proximal Policy Optimization (PPO) algorithm was used to train these environments for 100,000 iterations. …”
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Accurate Extraction of Rural Residential Buildings in Alpine Mountainous Areas by Combining Shadow Processing with FF-SwinT by Guize Luan, Jinxuan Luo, Zuyu Gao, Fei Zhao

“…The results show that the FF-SwinT has improved in many indicators compared with the traditional Swin Transformer, and the recognition results have clear edges and strong integrity. These results suggest that the FF-SwinT establishes a novel framework for rural settlement extraction in alpine mountain regions, which is of great significance for regional spatial optimization and development policy formulation.…”
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A Reliability Assessment Method for Distribution Networks Based on Conditional Generative Adversarial Network and Multi-agent Reinforcement Learning by Huihui XU, Yunfei TIAN, Yuyang ZHAO, Jing PENG, Qingxin SHI, Rui CHENG

“…Secondly, a multi-agent reinforcement learning (MARL) model is established, and a training algorithm integrating imitation learning and exploratory learning is proposed, enabling the agents to acquire optimal policies through interactive learning with an expert experience model. …”
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A Hybrid Mathematical Framework for Dynamic Incident Prioritization Using Fuzzy Q-Learning and Text Analytics by Arturo Peralta, José A. Olivas, Pedro Navarro-Illana, Juan Alvarado

“…A mathematical formulation of the fuzzy Q-learning algorithm is developed, including fuzzy state definition, reward function design, and convergence analysis. …”
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Medical laboratory data-based models: opportunities, obstacles, and solutions by Jiaojiao Meng, Moxin Wu, Fangmin Shi, Ying Xie, Hui Wang, You Guo

“…The use of large-scale MLD improves diagnostic accuracy and allows for real-time disease monitoring. Additionally, integrating social and environmental data enables the analysis of disease mechanisms and trends. …”
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GEM-CRAP: a fusion architecture for focal seizure detection by Jianwei Shi, Yuanyuan Zhang, Ziang Song, Hang Xu, Yanfeng Yang, Lei Jin, Hengxin Dong, Zhaoying Li, Penghu Wei, Yongzhi Shan, Guoguang Zhao

“…Methods Our study proposes a supervised multilayer hybrid model called GEM-CRAP (gradient-enhanced modulation with CNN-RES, attention-like, and pre-policy networks), with three parallel feature extraction channels: a CNN-RES module, an amplitude-aware channel with attention-like mechanisms, and an LSTM-based pre-policy layer integrated into the recurrent neural network. …”
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