Design of path planning robot simulator by applying sampling based method by Heru Suwoyo, Julpri Andika, Andi Adriansyah

“…The simulator uses sampling-based path planning algorithms such as RRT*, Fast RRT*, RRT*-Smart, informed-RRT*, and Honey Bee Mating Optimization-based Fast-RRT*. …”
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Application of GA in Furniture Modeling Style Design by Heng Yu, Chunjing Liu

“…In order to improve the production efficiency of furniture, it is necessary to optimize it. …”
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Development and Practice of Cloud Collaborative Platform for Downhole Measurement Tools by Che Yang, Yuan Guangjie, Qian Hongyu, Du Weiqiang, Wang Chenlong, Ding Jiping

“…The measurement tools are mainly single machine version， which is difficult to meet the current requirements for improving drilling quality and efficiency. In the paper， based on the remote operation requirements of coreless magnetic steering tool， a platform architecture of five modules， including simulation rehearsal， virtual training， remote operation， smart tool and intelligent decision， was designed in detail， achieving a whole process digitization from predrilling risk assessment and in-drilling acquisition and processing to post-drilling feedback and optimization， and a visual interface was developed. …”
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A Fault Diagnosis Method for Planetary Gearboxes Based on IFMD by Fengfeng Bie, Xueping Ding, Qianqian Li, Yuting Zhang, Xinyue Huang

“…Initially, the critical parameters (modal number n and filter length L) of FMD are optimized using an improved genetic algorithm (IGA), and the refined FMD is employed to decompose the vibration signals from the planetary gearbox. …”
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Fault diagnosis model of rolling bearings based on the M-YOLO network by NING Shaohui, ZHANG Shaopeng, WU Yukun, DU Yue, FAN Xiaoning

“…ObjectiveThe algorithms developed for the combination of deep learning and bearing fault diagnosis have achieved initial results, but most of them are processed by processing one-dimensional vibration data and input into the network structure for diagnosis, while the research on fault diagnosis technology using two-dimensional signals as input is still on the surface, and the analysis of such methods is rarely reported. …”
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Development of a machine learning-based surrogate model for friction prediction in textured journal bearings by Yujun Wang, Georg Jacobs, Shuo Zhang, Benjamin Klinghart, Florian König

“…This enhancement is achieved through an architecture design based on cross-validation and further optimization utilizing the genetic algorithm. Eventually, the average prediction accuracy is improved to 98.81% from 95.89%, with the maximum error reduced to 3.25% from 13.17%. …”
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ARIMA-Based Forecasting of Wastewater Flow Across Short to Long Time Horizons by Jiawen Ye, Xulai Meng, Haiying Wang, Qingdao Zhou, Siwei An, Tong An, Pooria Ghorbani Bam, Diego Rosso

“…Improving urban wastewater treatment efficiency and quality is urgent for most cities. …”
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A New Hybrid Model for Underwater Acoustic Signal Prediction by Guohui Li, Wanni Chang, Hong Yang

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Rethinking Exploration and Experience Exploitation in Value-Based Multi-Agent Reinforcement Learning by Anatolii Borzilov, Alexey Skrynnik, Aleksandr Panov

“…We aim to optimize a deep MARL algorithm with minimal modifications to the well-known QMIX approach. …”
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A Deep Learning Framework for Chronic Kidney Disease stage classification by Gayathri Hegde M, P Deepa Shenoy, Venugopal KR, Arvind Canchi

“…These findings demonstrate that the proposed MHMXAI method effectively integrates metaheuristic algorithms and XAI tools, improving CKD stage prediction accuracy and clinical interpretability.…”
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Adaptive DBP System with Long-Term Memory for Low-Complexity and High-Robustness Fiber Nonlinearity Mitigation by Mingqing Zuo, Huitong Yang, Yi Liu, Zhengyang Xie, Dong Wang, Shan Cao, Zheng Zheng, Han Li

“…In this paper, an improved A-DBP algorithm with long-term memory (LTM) is proposed, employing root mean square propagation (RMSProp) to achieve low-complexity and high-robustness compensation performances. …”
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Research on Control System for Material Transport Vehicle Based on Stacking Model by LIU Yuanming, TANG Lingsi, Zen Shuhua

“…Finally, the control system was reinforced using an improved proportional-integral-differential (PID) control algorithm to optimize the control performance. …”
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Predictive modeling of hydrogen production and methane conversion from biomass-derived methane using machine learning and optimisation techniques by Adegboyega Bolu Ehinmowo, Bright Ikechukwu Nwaneri, Joseph Oluwatobi Olaide

“…However, there is the need to optimise this process for better efficiency and improved hydrogen production from biomass sources. …”
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Machine learning analysis of molecular dynamics properties influencing drug solubility by Zeinab Sodaei, Saeid Ekrami, Seyed Majid Hashemianzadeh

“…Through rigorous analysis, the properties with the most significant influence on solubility were identified and subsequently used as input features for four ensemble machine learning algorithms: Random Forest, Extra Trees, XGBoost, and Gradient Boosting. …”
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Low power IoT device communication through hybrid AES-RSA encryption in MRA mode by Qiang Chang, Tianqi Ma, Wenzhong Yang

“…The improved AES algorithm transitions from a single-threaded encryption and decryption process to a three-threaded design, reducing the ten-round operation to seven rounds to enhance both speed and energy efficiency. …”
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Cost-effectiveness analysis of best management practices for non-point source pollution in watersheds: A review by CHANG Jian, YU Jie, WANG Fei’er, ZHENG Siyuan

“…According to the accounting results, two optimization criteria, namely cost minimization and benefit maximization, were employed to screen for the most cost effective measures. …”
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Two-stage resilience enhancement method for integrated electricity-gas systems through linepack and mobile compressors by Chao Qin, Yongxue Wang, Shuaihu Ye

“…The progressive hedging algorithm is employed to further improve the solution efficiency. …”
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Shoulder–Elbow Joint Angle Prediction Using COANN with Multi-Source Information Integration by Siyu Zong, Wei Li, Dawen Sun, Zhuoda Jia, Zhengwei Yue

“…To address the precision challenges in upper-limb joint motion prediction, this study proposes a novel artificial neural network (COANN) enhanced by the Cheetah Optimization Algorithm (COA). The model integrates surface electromyography (sEMG) signals with joint angle data through multi-source information fusion, effectively resolving the local optima issue in neural network training and improving the accuracy limitations of single sEMG predictions. …”
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Statistics and behavior of clinically significant extra-pulmonary vein atrial fibrillation sources: machine-learning-enhanced electrographic flow mapping in persistent atrial fibri... by Peter Ruppersberg, Steven Castellano, Philip Haeusser, Kostiantyn Ahapov, Melissa H. Kong, Stefan G. Spitzer, Stefan G. Spitzer, Georg Nölker, Andreas Rillig, Tamas Szili-Torok

“…These findings, supported by the FLOW-AF trial, underscore the usefulness of clinical outcome-based machine learning to improve the efficacy of algorithm based medical diagnostics.…”
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Bayesian Uncertainty Quantification of Reflooding Model With PSO–Kriging and PCA Approach by Ziyue Zhang, Dong Li, Nianfeng Wang, Meng Lei

“…As reflooding is a vital stage to cool the core and prevent serious accidents and uncertainties exist in the important results of the program because of the complexity of the phenomena, IUQ is performed for reflooding models in this study based on Bayesian theory and Markov chain Monte Carlo (MCMC) algorithm. In order to solve the problem of large time costs in sampling and inefficient use of transient sample points, particle swarm optimization (PSO)–Kriging model and principal component analysis (PCA) are adopted in this paper. …”
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