Dynamic Modeling of Multi-category Jointing for the Autonomous-railRapid Tram and Real Vehicle Validation by HUANG Ruipeng, YUAN Xiwen, HU Yunqing, ZHANG Xinrui, ZHANG Sha, LI Xiaoguang

“…In order to iteratively optimize and reduce the lateral deviation of the precision lane keeping control system, a 35 m train dynamic model was built in this paper to meet the characteristics of autonomous-rail rapid tram such as tire-ground coupling and multi-group flexible articulating. …”
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Optical fiber eavesdropping detection method based on machine learning by Xiaolian CHEN, Yi QIN, Jie ZHANG, Yajie LI, Haokun SONG, Huibin ZHANG

“…Optical fiber eavesdropping is one of the major hidden dangers of power grid information security,but detection is difficult due to its high concealment.Aiming at the eavesdropping problems faced by communication networks,an optical fiber eavesdropping detection method based on machine learning was proposed.Firstly,seven-dimensions feature vector extraction method was designed based on the influence of eavesdropping on the physical layer of transmission.Then eavesdropping was simulated and experimental feature vectors were collected.Finally,two machine learning algorithms were used for classification detection and model optimization.Experiments show that the performance of the neural network classification is better than the K-nearest neighbor classification,and it can achieve 98.1% eavesdropping recognition rate in 10% splitting ratio eavesdropping.…”
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Scrub-and-Learn: Category-Aware Weight Modification for Machine Unlearning by Jiali Wang, Hongxia Bie, Zhao Jing, Yichen Zhi

“…(1) Background: Machine unlearning plays a crucial role in privacy protection and model optimization, particularly in forgetting entire categories of data in classification tasks. …”
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Censoring Sensitivity Analysis for Benchmarking Survival Machine Learning Methods by János Báskay, Tamás Mezei, Péter Banczerowski, Anna Horváth, Tamás Joó, Péter Pollner

“…While established methodologies exist for validating standard machine learning models, current benchmarking approaches rarely assess model robustness under varying censoring conditions. …”
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Advancements in machine learning for estimating parameters of wastewater treatment plants by Kolyeva Natalya, Rastyagaev Alexander, Kortenko Lyudmila, Rozhkov Sergey, Sbitneva Mariia, Kuznetsov Aleksandr

“…As a result of the study, a model of the XGBoost algorithm was developed, which successfully coped with the task of optimization of calculations, providing high accuracy, and this, in turn, opens up new opportunities for improving the efficiency of design of wastewater treatment plants.…”
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Design and Characteristic Research of the Spindle Box of Vertical Machining Center by Gao Dongqiang, Yang Lei, Sun Qian, Zhang Xifeng

“…Headstock is the key components of the high speed vertical machining center,its static stiffness,dynamic characteristics has great effect on machining precision and stability of machining center.Taking DVG850 high speed vertical machining center spindle box as the research object,the design scheme of geometrical model of four kinds of different structure of spindle box is established,by using the finite element simulation software,the simulating calculation of the statics,modal and harmonic response of the spindle box are carried out,the resonance region of spindle box under static stiffness,natural frequency,vibration mode and the excitation frequency are got.Through the comparison analysis of finite element simulation of cloud and data,known that scheme two rib structure is the best layout,it is indicated that the research method can be to improve the spindle box structure natural frequency and increase the structure stiffness.provides the basis for avoiding resonance,for other machine tool spindle box the theory basis for optimization of structural design and improvement is provided,the design cycle can be shorten.…”
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Analysis and Optimization of Flapping-Wing Mechanism Based on Genetic Algorithm by Yunyu Ling, Benyou Liu, Hongxin Zhang, Lan Bo, Mingjie Liu

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Predicting Insomnia Response to Acupuncture With the Development of Innovative Machine Learning by Qingyun Wan, Kai Liu, Yuyang Bo, Xiya Yuan, Mufeng Li, Xiaoqiu Wang, Chuang Chen, Lanying Liu, Wenzhong Wu

“…Data from 51 patients, considering 19 key factors such as age, sleep quality, anxiety level, and insomnia severity, were analyzed to identify the most influential predictors of treatment outcomes. The proposed model combines the Relief algorithm for feature selection, a weighted support vector machine (WSVM) to map these factors to treatment efficacy, and the NDPGWO optimization method, which incorporates a nonlinear convergence factor, dynamic weight, and probability perturbation. …”
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A PSO-based approach for parameter estimation in synchronous machines by Leguebedj Farid, Benmahamed Youcef, Boukhetala Djamel, Boughrara Kamel

“…This study employs the particle swarm optimization (PSO) approach using Stand Still Frequency Responses Testing (SSFR) to identify the time constants (poles and zeros) of the operational inductances along the d and q axes, as well as the parameters of the equivalent circuits for the SSFR1, SSFR2, and SSFR3 synchronous machine models. …”
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Research Progress in the Screening of Antimicrobial Substances Based on Machine Learning by HOU Jiangxia, JIANG Jinhui, WANG Chenxin, WANG Lan, SHI Liu, WU Wenjin, GUO Xiaojia, CHEN Sheng, CHEN Lang, CAO Feng, SUN Li, ZHOU Zhi

“…As a branch of artificial intelligence, machine learning algorithms have demonstrated exceptional capabilities in processing large-scale data, feature extraction, and model optimization, leading to their increasing application in the screening of antimicrobial substances. …”
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Intelligent System for Student Performance Prediction Using Machine Learning by Mustafa S. Ibrahim Alsumaidaie, Ahmed Adil Nafea, Abdulrahman Abbas Mukhlif, Ruqaiya D. Jalal, Mohammed M AL-Ani

“…This study aims to develop an intelligent solution for predicting student performance using supervised machine learning algorithms. This proposed focus on addressing the limitations of existing prediction models and enhancing prediction accuracy. …”
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Optimization Design for Reliability of the Oscillating Follower Disk Cam Mechanism Based on Improved Hunter-Prey Optimization Algorithm by Chao Wang, Wei Peng

“…To maximize the motion reliability of the mechanism, an improved Hunter Prey Optimization (IHPO) algorithm was proposed to identify the optimal dimensional parameters of the oscillating follower disk cam mechanism. …”
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Machine learning-powered, high-affinity modification strategies for aptamers by Gubu Amu, Xin Yang, Hang Luo, Sifan Yu, Huarui Zhang, Yuan Tian, Yuanyuan Yu, Shijian Ding, Yufei Pan, Zefeng Chen, Yixin He, Yuan Ma, Baoting Zhang, Ge Zhang, YANG XIN

“…This approach harnessed the power of machine learning to predict the most promising high-affinity modification strategy for aptamers.…”
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Precision Healthcare for UTIs: Leveraging Machine Learning to Reduce Readmissions by Odai Mohammad Al-Jbour, Mohammad Alshraideh, Bahaaldeen Alshraideh

“…This is executed through a retrospective analysis of electronic health records from January 2020 to June 2023. By leveraging machine learning techniques, the study identifies high-risk patients by evaluating demographic, clinical, and outcome characteristics, ensuring model reliability through thorough optimization, validation, and performance assessment. …”
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Enhancing augmented reality with machine learning for hands-on origami training by Mikołaj Łysakowski, Jakub Gapsa, Chenxu Lyu, Thomas Bohné, Sławomir Konrad Tadeja, Piotr Skrzypczyński, Piotr Skrzypczyński

“…This research explores integrating augmented reality (AR) with machine learning (ML) to enhance hands-on skill acquisition through origami folding. …”
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Enhancing phase change thermal energy storage material properties prediction with digital technologies by Minghao Yu, Jing Liu, Cheng Chen, Mingyue Li

“…To address these limitations, the integration of digital technologies, such as computational modeling and machine learning (ML), has become increasingly important.MethodsThis paper proposes a hybrid multiscale modeling framework that integrates molecular dynamics (MD) simulations, finite element methods (FEM) from continuum mechanics, and supervised ML algorithms—including deep neural networks and gradient boosting regressors—to enable accurate and efficient prediction of material properties across scales. …”
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Outlier Traffic Flow Detection and Pattern Analysis Under Unplanned Disruptions: A Low-Rank Robust Decomposition Model by Zhipeng Duan

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Advances in the application of machine learning technology in the field of environmental health by ZHENG Yu, LI Cheng, HU Guiping, JIA Guang

“…Among them, advanced machine learning (ML) algorithms can reveal laws that are difficult for humans to detect, showing important potential in biomarker identification, disease prevention, and environmental engineering optimization. …”
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AI-based Assessment of Risk Factors for Coronary Heart Disease in Patients With Diabetes Mellitus and Construction of a Prediction Model for a Treatment Regimen by Zhen Gao, Qiyuan Bai, Mingyu Wei, Hao Chen, Yan Yan, Jiahao Mao, Xiangzhi Kong, Yang Yu

“…Background: This study aimed to construct a prediction model for a treatment plan for patients with coronary artery disease combined with diabetes mellitus using machine learning to efficiently formulate the treatment plan for special patients and improve the prognosis of patients, provide an explanation of the model based on SHapley Additive exPlanation (SHAP), explore the related risk factors, provide a reference for the clinic, and concurrently, to lay the foundation for the establishment of a multicenter prediction model for future treatment plans. …”
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Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning by Liu Liu, Rongyu Sun, He Zhao

“…Here an automatic identification pipeline based on machine learning model ShuffleNet V2 is developed, after trained with large amount of raw data, high efficiency is achieved. …”
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