The outcome prediction method of football matches by the quantum neural network based on deep learning by Yang Sun, Hongyang Chu

“…During the model training phase, gradient descent is used to optimize weight parameters, and quantum algorithms are integrated to continuously adjust network weights to minimize prediction errors. …”
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Real‐Time Parameter Identification and State of Charge Estimation of Electric Vehicle Batteries by A. Maheshwari, S. Nageswari, R. Palanisamy, B. Karthikeyan, Mohamed Metwally Mahmoud, Daniel Eutyche Mbadjoun Wapet, Ali M. El‐Rifaie, Ezzeddine Touti, Ahmed I. Omar

“…The fixed forgetting factor RLS (FFRLS) algorithm is implemented with two different forgetting factor (FF) values, while the VFFRLS method utilizes different initial FF values. …”
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Training multi-layer binary neural networks with random local binary error signals by Luca Colombo, Fabrizio Pittorino, Manuel Roveri

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Sum Rate Maximization of Active RIS-Assisted FD-NOMA Systems by Yi Peng, Yang Xiao, Qingqing Yang

“…Additionally, the optimal transmit powers for all uplink users are obtained through an iterative algorithm. …”
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Energy consumption reduction for an ultra-low-cost artificial pancreas using an event-trigger MPC strategy by Jhon E. Goez-Mora, Pablo S. Rivadeneira

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A Day-Ahead Economic Dispatch Method for Renewable Energy Systems Considering Flexibility Supply and Demand Balancing Capabilities by Zheng Yang, Wei Xiong, Pengyu Wang, Nuoqing Shen, Siyang Liao

“…This approach establishes a dual-layer optimized scheduling model. The upper-layer model focuses on the economic efficiency of unit start-up and shut-down, utilizing a particle swarm algorithm to identify unit combinations that comply with minimum start-up and shut-down time constraints. …”
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Bidding strategies for virtual power plants under the electricity-carbon joint market taking into account wind and solar uncertainties by Shuangchi ZHAO, Hong LIN, Haiyun WANG

“…The upper level maximizes the VPP's profit, while the lower level maximizes social welfare in the joint market. …”
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Multi-Objective Vibration Control of a Vehicle-Track-Bridge Coupled System Using Tuned Inerter Dampers Based on the FE-SEA Hybrid Method by Xingxing Hu, Qingsong Feng, Min Yang, Jian Liu

“…Using the vibration acceleration amplitudes of both the rail and track slab as dual control objectives, a multi-objective optimization model is established, and the TID’s optimal parameters are determined using a multi-objective genetic algorithm. …”
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Low-Resolution Quantized Precoding for Multiple-Input Multiple-Output Dual-Functional Radar–Communication Systems Used for Target Sensing by Xiang Feng, Zhongqing Zhao, Jiongshi Wang, Jian Wang, Zhanfeng Zhao, Zhiquan Zhou

“…To tackle this issue, we develop a weighted optimization framework that minimizes the mean squared error between the transmitted symbols and their estimates while satisfying specific radar performance requirements. …”
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Multi-Skilled Project Scheduling for High-End Equipment Development Considering Newcomer Cultivation and Duration Uncertainty by Yaohui Liu, Ronggui Ding, Shanshan Liu, Lei Wang

“…The model aims to minimize expected makespan and maximize newcomers’ skill efficiency by optimizing workforce assignment that enables experienced workers to mentor newcomers while simultaneously optimizing task scheduling. …”
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Quasi-preview control: A novel method for active structural vibration control subject to seismic disturbances using adaptive filters and multiple remote seismic observations by Shinya FUJIMURA, Tomohiro WATANABE, Kazuhiko HIRAMOTO

“…All design parameters in the quasi-preview control are optimized with the particle swarm optimization (PSO) algorithm. …”
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The development of an intelligent comprehensive detection instrument for circuit breakers in power systems and its key technologies by Weimin Guan, Han Hu, Chao Sun, Jie Ji

“…Additionally, this study optimizes the fault diagnosis algorithm, enhancing detection stability and robustness. …”
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A Semi-Trailer Path Planning Method Considering the Surrounding Traffic Conditions and Vehicle Roll Stability by Haochuan Zhang, Zhigen Nie, Yufeng Lian

“…Subsequently, we apply a particle swarm optimization algorithm to enhance the B-spline curve. …”
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An Adaptive Control Strategy for a Virtual Synchronous Generator Based on Exponential Inertia and Nonlinear Damping by Huiguang Pian, Keqilao Meng, Hua Li, Yongjiang Liu, Zhi Li, Ligang Jiang

“…Control parameters are optimized using an enhanced particle swarm optimization (PSO) algorithm based on a composite performance index that accounts for frequency deviation, overshoot, settling time, and power tracking error. …”
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Adaptive Temporal Reinforcement Learning for Mapping Complex Maritime Environmental State Spaces in Autonomous Ship Navigation by Ruolan Zhang, Xinyu Qin, Mingyang Pan, Shaoxi Li, Helong Shen

“…The model integrates an enhanced Proximal Policy Optimization (PPO) algorithm for efficient policy iteration optimization. …”
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Durability prediction of sustainable marine concrete under freeze-thaw cycles using multi-objective machine learning models by Aïssa Rezzoug, Ali H. AlAteah, Sadiq Alinsaif, Sahar A. Mostafa

“…This study introduces machine learning (ML)-based algorithms for forecasting FTC-induced durability of concrete while simultaneously predicting sustainability metrics such as CO₂ emissions and production cost. …”
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Sensorless real-time solar irradiance prediction in grid-connected PV systems using PSO-MPPT and IoT-enabled monitoring by Ali Zaki Mohammed Nafa, Adel A. Obed, Ahmed J. Abid, Salam J. Yaqoob, Mohit Bajaj, Mohammad Shabaz

“…The approach leverages the maximum power point current ( $$\:{\text{I}}_{\text{mpp}}$$ ) and voltage ( $$\:{\text{V}}_{\text{mpp}}$$ ) measured directly from a PV module to predict irradiance, utilizing a Particle Swarm Optimization (PSO)-based Maximum Power Point Tracking (MPPT) algorithm to ensure accurate tracking of power output across varying irradiance levels. …”
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Green Video Transcoding in Cloud Environments Using Kubernetes: A Framework With Dynamic Renewable Energy Allocation and Priority Scheduling by B. M. Beena, Prashanth Cheluvasai Ranga, A. Vinitha Chowdary, Rohan Gamidi, M. Hemasri, Tejaswi Muppala

“…The research addresses these challenges by developing a green, energy-aware video transcoding system that predicts energy availability from renewable sources (solar and wind) using machine learning techniques and optimizes tasks allocation. The system utilizes a Kubernetes-managed backend to dynamically scale resources for FFmpeg-based transcoding while prioritizing renewable energy, minimizing grid usage utilizing the advanced machine learning models, including Random Forest, XGBoost, and CatBoost, predict energy production and guide task assignments. …”
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Interacting Large Language Model Agents. Bayesian Social Learning Based Interpretable Models by Adit Jain, Vikram Krishnamurthy

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How many grid-forming converters are needed? – A techno-economic perspective by Guoxuan Cui, Hongyang Jia, Ning Zhang, Fei Teng

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