TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance by Xuanbai Yu, Olivier Caspary

“…These findings offer new insights to support reliable predictive maintenance in industrial settings and provide a new perspective for future research into active vibration control, where vibration signal analysis, feature extraction, and mathematical modeling play key roles in optimizing control algorithms and enhancing the efficiency of adaptive control systems.…”
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Identifying the risk of Kawasaki disease based solely on routine blood test features through novel construction of machine learning models by Tzu-Hsien Yang, Ying-Hsien Huang, Yuan-Han Lee, Jie-Nan Lai, Kuang-Den Chen, Mindy Ming-Huey Guo, Yan Pan, Chun-Yu Chen, Wei-Sheng Wu, Ho-Chang Kuo

“…Trained using the light gradient boosting machine algorithm on clinical data from 1,927 KD cases and 45,274 febrile controls, KDpredictor achieved strong performance metrics (auROC: 95.7%, auPRC: 72.4%, recall: 0.89) on a reserved test set, outperforming previous models by at least 3% in auROC and 39.3% in auPRC. …”
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Bioinformatic analysis, clinical implications and experimental validation of ferroptosis-related feature gene in IgA nephropathy: focus on DUSP1 by Tingting Liu, Tingting Pan, Mingxin Chang, Shaojie Fu, Hongzhao Xu, Hao Wu, Zhonggao Xu, Yanli Cheng

“…The findings were validated in an IgAN mouse model using immunohistochemistry and western blotting. Gene set enrichment analysis (GSEA) was conducted to explore the underlying mechanism of FFG in IgAN. …”
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Current and Future Trends of the Automotive Industry by Dieter Hermann Schramm

“…More activities are required for robust algorithms and strategies to hand over control back to the driver in complex situations and even vice versa from the driver to the car in cases where the driver is unable to further control his car, e.g. in case of a heart attack.…”
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Livestock production losses attributable to brucellosis in northern and central Tanzania: Application of an epidemiological-economic modelling framework. by Ângelo J F Mendes, Daniel T Haydon, William A de Glanville, Rebecca F Bodenham, AbdulHamid S Lukambagire, Paul C D Johnson, Gabriel M Shirima, Sarah Cleaveland, Emma McIntosh, Nick Hanley, Jo E B Halliday

“…Livestock brucellosis is an endemic disease in many low-resource settings. Despite its widespread distribution, little is known about the scale of economic impacts caused by the disease. …”
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Predicting Endpoint Temperature of Molten Steel in VD Furnace Refining Process Using Metallurgical Mechanism and Bayesian Optimization XGBoost by Ji XU, Zicheng XIN, Mo LAN, Wenhui LIN, Bo ZHANG, Qing LIU

“…The dataset is randomly shuffled, with 80% selected as the training set and the remaining 20% as the testing set. Following this, based on metallurgical mechanism (MM) analysis, the initial feature importance in the input variables of the extreme gradient boosting (XGBoost) model is partially amplified. …”
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Machine learning‐based model for worsening heart failure risk in Chinese chronic heart failure patients by Ziyi Sun, Zihan Wang, Zhangjun Yun, Xiaoning Sun, Jianguo Lin, Xiaoxiao Zhang, Qingqing Wang, Jinlong Duan, Li Huang, Lin Li, Kuiwu Yao

“…Multiple machine learning (ML) algorithms were compared, and the results were interpreted using SHapley Additive exPlanations (SHAP). …”
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Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review. by Kaitlin Wurtz, Irene Camerlink, Richard B D'Eath, Alberto Peña Fernández, Tomas Norton, Juan Steibel, Janice Siegford

“…Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. …”
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The key genes ME1 and ALDH3A1 involved in glycolysis regulate the lactate pathway and mediate COPD related immune dysregulation by Wenli XIE, Guang ZHOU, Zhitao JING, Shilin QIU

“…Subsequently, key genes involved in glycolysis were identified using the LASSO and SVM-RFE algorithms. After subtyping, GSEA analysis was conducted. …”
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The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy by Szandra László, Ádám Nagy, József Dombi, Emőke Adrienn Hompoth, Emese Rudics, Zoltán Szabó, András Dér, András Búzás, Zsolt János Viharos, Anh Tuan Hoang, Vilmos Bilicki, István Szendi

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A Review on Machine Learning Models in Injection Molding Machines by Senthil Kumaran Selvaraj, Aditya Raj, R. Rishikesh Mahadevan, Utkarsh Chadha, Velmurugan Paramasivam

“…Some problems include data division, collection, and preprocessing steps, such as considering the inputs, networks, and outputs, algorithms used, models utilized for testing and training, and performance criteria set during validation and verification. …”
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Design, development and manufacture of a small electric motor dynamometer by Hong Thong Duc, Truong Phat Tan, Pham Minh Quang, Ho Hoa Nam, Le Hoang Minh Do, Vu Thang Viet

“…Especially in this study, the chain drive has three stages with transmission ratios of 1/4, 1/3, and 1/2.5. Besides, a controller that can automatically adjust the electric load level to determine various motor characteristics under constant load and throttle settings is also designed to control the dynamometer and collect essential data. …”
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Web service and dynamic pricing competition by Ehram Safari, Masoud Babakhani

“…Moreover, we conduct numerical analyses to examine the impacts of some parameters on control and state variables.…”
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Datasets of Faults in Variable Air Volume Terminal Units in a Multi-Zone Commercial Building by Sungkyun Jung, Yeobeom Yoon, Piljae Im

“…We identified two common faults in HVAC variable air volume systems: a stuck damper fault in the variable air volume terminal unit and a discharge airflow sensor fault. We conducted three sets of damper stuck tests and two sets of airflow sensor tests, each including a fault-free scenario and scenarios with varying levels of faults, over one day. …”
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Comparison of the effectiveness of various conservative treatment options for trophic ulcers and varicose eczema by E. V. Ivanov, E. P. Burleva

“…Based on the obtained data, therapeutic algorithms were proposed for the management of patients with venous TU and VE in outpatient settings.Conclusions. …”
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Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data by DongHyeon Yoon, Ha-Eun Yu, Euiho Hwang, Ki-mook Kang, Gibeom Nam, Jin-Gyeom Kim

“…Regression analysis was conducted between the extracted water surface area and observed water levels to create a predictive model, yielding a highly accurate equation with an R2 core of 0.981 on the test set. …”
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The pre- and post-COVID-19 pandemic dengue fever patterns in southeastern coastal China in 2019 and 2024: molecular evolution and strain replacement by Tianran Zhang, Yaqi Shi, Leyi Zhang, Jian Wang, Chengchao Yu, Binbin Lv, Sisi Wu, Yiru Huang, Xuewei Liu, Hupiao Dai, Mingshi Zhou, Yanjuan Liao, Wei Wang, Guankai Lin

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TSTBench: A Comprehensive Benchmark for Text Style Transfer by Yifei Xie, Jiaping Gui, Zhengping Che, Leqian Zhu, Yahao Hu, Zhisong Pan

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Exploration of Epigenetic Mechanisms and Biomarkers Among Patients with Very-Late-Onset Schizophrenia-Like Psychosis by Gan Y, Yue W, Sun J, Yang D, Fang C, Zhou Z, Yin J, Zhou H

“…We prioritized key methylation sites through integrated analysis of methylation quantitative trait loci (meQTL), linkage disequilibrium (LD) patterns, and blood-brain methylation correlations. Machine learning algorithms generated diagnostic models, with classification performance evaluated using Area Under the Curve (AUC) metrics.Results: Analysis revealed distinct DNA methylation signatures in VLOSLP patients compared to controls. …”
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Data-driven modeling approaches for pressure drop prediction in a multi-phase flow system by Nezar M. Alyazidi, Aiman F. Bawazir, Ala S. AL-Dogai

“…These findings contribute to enhanced pressure management and optimization strategies, setting a foundation for future studies on real-time flow control and broader industrial applications.…”
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