A cross-chain model for warehouse receipts in port supply chain based on notary mechanism and ShangMi cryptographic algorithms by Yangbo Chen, Wei Ou, Mengxue Pang, Jianqiang Ma, Qiuling Yue, Wenbao Han

“…These features enable flexible data sharing while ensuring the protection of sensitive information. …”
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Developing an Equitable Machine Learning–Based Music Intervention for Older Adults At Risk for Alzheimer Disease: Protocol for Algorithm Development and Validation by Chelsea S Brown, Luna Dziewietin, Virginia Partridge, Jennifer Rae Myers

“…Given the increased risk for AD and health care barriers among rural-residing older adults, tailoring interventions to align with rural culture and distinct needs is important to improve accessibility and adherence. ObjectiveThis protocol aims to develop an intelligent recommendation system capable of identifying the optimal therapeutic music components to elicit engagement and resonate with diverse rural-residing older adults at risk for AD. …”
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Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future Directions by Norizal Abdullah, Masri Ayob, Meng Chun Lam, Nasser R. Sabar, Graham Kendall, Mohamad Khairulamirin Md Razali

“…Future research directions are outlined, emphasizing the need for more scalable algorithms, real-time scheduling capabilities, improved user interfaces, and comprehensive validation studies. …”
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Transient Time Reduction in Time-Varying Digital Filters via Second-Order Section Optimization by Piotr Okoniewski, Jacek Piskorowski

“…This study focuses on reducing transient time in time-varying filters through second-order section (SOS) optimization. By employing a numerical optimization approach, we selectively adjust the coefficients of a single SOS within a higher-order filter to minimize the transient period while maintaining overall stability. …”
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A Novel RMS-Driven Deep Reinforcement Learning for Optimized Portfolio Management in Stock Trading by Asma Sattar, Amna Sarwar, Saira Gillani, Maryam Bukhari, Seungmin Rho, Muhammad Faseeh

“…Algorithmic stock trading has improved tremendously, with Reinforcement Learning (RL) algorithms being more adaptable than classic approaches like mean reversion and momentum. …”
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Application of CycleGAN-based low-light image enhancement algorithm in foreign object detection on belt conveyors in underground mines by Anxin Zhao, Qiuhong Zheng, Liang Li

“…To address this issue, an improved CycleGAN-based low-illumination image enhancement algorithm is proposed, which employs a cycle generative adversarial network for unsupervised learning. …”
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Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids by Sungmin Lim, Jaekyu Lee, Sangyub Lee

“…While the isolated operation of individual MGs limits system-wide optimization, the proposed approach enhances both stability and efficiency through integrated control. …”
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Ecological and Statistical Evaluation of Genetic Algorithm (GARP), Maximum Entropy Method, and Logistic Regression in Predicting Spatial Distribution of Astragalus sp. by Amir Ghahremanian, Abbas Ahmadi, Hamid Toranjzar, Javad Varvani, Nourollah Abdi

“…This study aims to evaluate the potential habitat of Astragalus sp. using three different species distribution modeling methods: the maximum entropy (MaxEnt) model, the Genetic Algorithm for Rule-Set Production (GARP), and logistic regression. …”
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Model order reduction of boiler system using nature-inspired metaheuristic optimization of PID controller by Anurag Singh, Shekhar Yadav, Nitesh Tiwari, Dinesh Kumar Nishad, Saifullah Khalid

“…The HHO-based approach improves overall performance by 15%, while SSO significantly reduces computational complexity by 65% while maintaining 98% system accuracy. …”
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Process Optimization for Complex Product Assembly Workshops with AGV Integration via Discrete Event Simulation by Hailong Song, Shengluo Yang, Shuoxin Yin, Zhigang Xu

“…This model simulates the coordination between AGVs and workstations while optimizing workstation layout and material flow. …”
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Multi-Agent Deep Reinforcement Learning for Scheduling of Energy Storage System in Microgrids by Sang-Woo Jung, Yoon-Young An, BeomKyu Suh, YongBeom Park, Jian Kim, Ki-Il Kim

“…To defeat the above issues, in this paper, we propose a new DRL-based scheduling algorithm using a multi-agent proximal policy optimization (MAPPO) framework that is combined with Pareto optimization. …”
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An Intelligent Path Planning System for Urban Airspace Monitoring: From Infrastructure Assessment to Strategic Optimization by Qianyu Liu, Wei Dai, Zichun Yan, Claudio J. Tessone

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Algorithms for the treatment of patients with chronic cerebral ischemia of stages I and II with use of Picamilon: results of an open clinical comparative study by Andrei B. Danilov, Natalia N. Shindryaeva, Irina V. Borodulina, Timothy D. Lunegov

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Incremental GA-Based 3D Trajectory Optimization for Powered Parachute Aerial Delivery Systems by Hanafy M. Omar, Ayman Hamdy Kassem

“…A six-degrees-of-freedom (6-DOF) dynamic model of the PPC is developed, complemented by a novel optimization technique called Incremented Genetic Algorithms (IGA). …”
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Adaptive RFID Data Scheduling Using Proximal Policy Optimization for Reducing Data Processing Latency by Guowei Guo, Xinsen Yang, Ziwei Liang, Zeli Xi, Ximei Zhan, Peisong Li

“…This paper presents a novel approach for dynamically offloading data using deep reinforcement learning, specifically employing the Proximal Policy Optimization (PPO) algorithm. The proposed method utilizes a central controller equipped with the PPO model to make intelligent, real-time reader selection decisions based on environmental factors such as reader load, tag mobility, and network conditions. …”
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Energy Distribution Optimization in Heterogeneous Networks with Min–Max and Local Constraints as Support of Ambient Intelligence by Alessandro Aloisio, Domenico D. Bloisi, Marco Romano, Cosimo Vinci

“…Since many devices are battery-powered, choosing the right communication interface is critical for optimizing energy efficiency. Our primary objective is to improve network performance while extending its operational lifespan by identifying an optimal set of interfaces that balance power consumption and efficiency. …”
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An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining by Charan Kumar Ala, Zefree Lazarus Mayaluri, Aman Kaushik, Nikhat Parveen, Surabhi Saxena, Abu Taha Zamani, Debendra Muduli

“…Unlike empirical equations that lack generalizability or black box ML models with limited transparency, the proposed approach embeds domain specific physical laws while leveraging data driven learning to improve both predictive accuracy and interpretability. …”
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Optimizing a Double Stage Heat Transformer Performance by Levenberg–Marquardt Artificial Neural Network by Suset Vázquez-Aveledo, Rosenberg J. Romero, Lorena Díaz-González, Moisés Montiel-González, Jesús Cerezo

“…This study aims to simultaneously optimize both user-defined parameters. Levenberg–Marquardt and scaled conjugated gradient algorithms were compared from five to twenty-five neurons to determine the optimal operating conditions while the coefficient of performance and the gross temperature lift were simultaneously maximized. …”
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A Hybrid Model Integrating Variational Mode Decomposition and Intelligent Optimization for Vegetable Price Prediction by Gao Wang, Shuang Xu, Zixu Chen, Youzhu Li

“…This study proposes a hybrid forecasting model integrating variational mode decomposition (VMD), the Fruit Fly Optimization Algorithm (FOA), and a gated recurrent unit (GRU). …”
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Optimal Allocation Method of Integrated Energy System Considering Joint Operation of Multiple Flexible Resources by DENG Qianwen, LI Qi, QIU Yibin, LI Doumeng, HUO Shasha, CHEN Weirong

“…Then, aimed at the uncertainty of renewable energy output, the optimal clustering number is determined by Elbow method, and typical wind speed scenarios are obtained by K-means clustering algorithm. …”
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