Preliminary analysis of salivary microbiota in catathrenia (nocturnal groaning) using machine learning algorithms by Min Yu, Yujia Lu, Wanxin Zhang, Xu Gong, Zeliang Hao, Liyue Xu, Yongfei Wen, Xiaosong Dong, Fang Han, Xuemei Gao

“…Beta-diversity based on the Bray-Curtis dissimilarities revealed a significant inter-group separation (p = 0.001). …”
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Deep learning for air pollution detection: Analyzing scots pine needles with SEM/EDS by Mirosław Szwed, Witold Żukowski, Dariusz Pasieka

“…The use of artificial intelligence, using the analysis of surface images of selected air pollution indicators to build a machine learning algorithm, allowed to develop an inexpensive and effective method of identifying hazardous substances. …”
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Personalized decision making for coronary artery disease treatment using offline reinforcement learning by Peyman Ghasemi, Matthew Greenberg, Danielle A. Southern, Bing Li, James A. White, Joon Lee

“…We applied off-policy reinforcement learning (RL) to a composite data model from 41,328 unique patients with angiography-confirmed obstructive CAD. …”
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Cross-cultural adaptation and validation of the clinical learning evaluation questionnaire with Chinese clinical interns by Luhua Yang, Jiangang Sun, Ruirui Wang, Shaochen Tao, Shanshan Wei, Liang Dong, Yansheng Gu, Jiayue Wang

“…Validity testing showed that all dimensions, except “Learning Motivation,” had AVE values above 0.5 and CR values above 0.7,indicating good convergent validity and composite reliability. …”
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A New Composite Spiral Scanning Approach for Beaconless Spatial Acquisition and Experimental Investigation of Robust Tracking Control for Laser Communication System with Disturbanc... by Min Zhang, Bo Li, Shoufeng Tong

“…In this paper, we proposed a novel beaconless acquisition approach to achieve the coverage of a large uncertainty cone with a narrow communication beam, using a composite spiral scanning method based on fast steering mirror and servomechanism system. …”
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The Role of Muscle Density in Predicting the Amputation Risk in Peripheral Arterial Disease: A Tissue Composition Study Using Lower Extremity CT Angiography by Yueh-Hung Lin, Pei-Shan Tsai, Chung-Lieh Hung, Mirza Faisal Beg, Hung-I Yeh, Chun-Ho Yun, Ming-Ting Wu

“…These findings highlight the prognostic value of muscle quality and support the integration of advanced imaging techniques, such as automated CTA-based body composition analysis, for risk stratification in PAD patients.…”
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Application of machine learning in soil heavy metals pollution assessment in the southeastern Tibetan plateau by Yan Li, Yilong Yu, Shiyuan Ding, Wenjing Dai, Rongguang Shi, Gaoyang Cui, Xiaodong Li

“…By combining PMF with Mantel analysis, pollution was attributed to geological background, agricultural activities, traffic emissions, and atmospheric deposition. The BiLISA method revealed significant spatial interactions among HMs, with the composite pollution of As and Cd occupying the largest proportion in High (As)-High (Cd) aggregation zones, underscoring the need for integrated management strategies. …”
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Utilizing metagenomic profiling and machine learning model to identify bacterial biomarkers for major depressive disorder by Xuan Wang, Di Cao, Hanlin Zhang, Wei Chen, Jiaxin Sun, Huimin Hu

“…The objective of this study was to explore the potential of microbial biomarkers in the diagnosis of major depressive disorder.MethodsIn this study, we used a metagenomic approach to analyze the composition and differences of gut bacterial communities in 36 patients with major depressive disorder and 36 healthy individuals. …”
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Non-Invasive Multiclass Diabetes Classification Using Breath Biomarkers and Machine Learning with Explainable AI by Alberto Gudiño-Ochoa, Julio Alberto García-Rodríguez, Raquel Ochoa-Ornelas, Eduardo Ruiz-Velazquez, Sofia Uribe-Toscano, Jorge Ivan Cuevas-Chávez, Daniel Alejandro Sánchez-Arias

“…This study presents a breath-based multiclass diabetes classification system leveraging only three gas sensors (CO, alcohol, and acetone) to analyze exhaled breath composition. …”
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Predicting soil organic carbon with ensemble learning techniques by using satellite images for precision farming by Shyamal Mundada, Pooja Jain

“…The goal of the research is to create a system for evaluating soil organic carbon based on topographic features and soil properties incorporating machine learning algorithms. …”
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Transcriptomic analysis and machine learning modeling identifies novel biomarkers and genetic characteristics of hypertrophic cardiomyopathy by Feng Zhang, Chunrui Li, Lulu Zhang

“…Immune infiltration analysis revealed distinct patterns of immune cell composition. Based on the top differentially expressed genes, a robust 12-gene diagnostic signature (COMP, SFRP4, RASD1, IL1RL1, S100A8, S100A9, ESM1, CA3, MYL1, VGLL2, MCEMP1, and MT1A) was constructed, demonstrating superior performance in both training and testing cohorts.ConclusionThis study utilized bioinformatics approaches to analyze RNA-sequencing datasets, identifying DEGs and distinct immune infiltration patterns in HCM. …”
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Machine learning-driven design of rare metal doped niobium alloys with enhanced strength and ductility by Zhenqiang Xiong, Zhaokun Song, Jianwei Li, Heran Wang, Xiaoxin Zhang, Bin Liang, Dong Wang

“…In this study, a machine learning-based framework was developed to optimize the mechanical properties of niobium alloys. …”
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Artificial Intelligence-Based Approach for Damage Localization in Ultrasonic Guided Wave-Based Structural Health Monitoring by Anastasiia Volovikova, Steffen Freitag, Oliver Schackmann, Vittorio Memmolo, Ahmed Bayoumi, Inka Mueller, Jochen Moll

“…The accuracy is then analyzed with the probability of localization method and compared to existing non-artificial intelligence-based approaches. …”
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A Multi-Strategy Active Learning Framework for Enhanced Peripheral Blood Cell Image Detection by Yuheng Feng, Jiangtao He, Linjin Wang, Wuchen Yang, Sihan Deng, Lanlin Li, Xinwei Li

“…To address these challenges, we propose a novel composite multi-strategy active learning framework using YOLOv5 for enhanced peripheral blood cell detection. …”
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PBX micro defect characterization by using deep learning and image processing of micro CT images by Liang-liang Lv, Wei-bin Zhang, Xiao-dong Pan, Gong-ping Li, Cui Zhang

“…Using the same test dataset, the proposed method was compared and evaluated against four mainstream segmentation methods based on deep learning. …”
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Weaning performance prediction in lactating sows using machine learning, for precision nutrition and intelligent feeding by Jiayi Su, Xiangfeng Kong, Wenliang Wang, Qian Xie, Chengming Wang, Bie Tan, Jing Wang

“…Eleven statistical and machine learning (ML) regression algorithms were employed, incorporating stratified sampling and the recursive feature elimination method for feature selection. …”
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Improving thermal state preparation of Sachdev–Ye–Kitaev model with reinforcement learning on quantum hardware by Akash Kundu

“…The refinement process is guided by a composite reward signal derived from entropy and the expectation values of the SYK Hamiltonian. …”
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Developing a multivariate model for the prediction of concussion recovery in sportspeople: a machine learning approach by Antonio Belli, Vijay Sawlani, David Davies, Kamal Yakoub, Louise C Yates, Elliot Yates, Xuanxuan Li, Yiping Lu

“…The area under the curve was 96.3%.Discussion In this large single-centre cohort study, a composite RF machine learning algorithm demonstrated high performance in predicting sporting games missed post-mTBI injury. …”
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Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning by Xinpeng Zhao, Haiyou Huang, Yanjing Su, Lijie Qiao, Yu Yan

“…In this work, we propose a data-driven method that integrates global and focused learning (GFL) strategies. …”
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Enhanced Rolling Bearing Fault Diagnosis Using Multimodal Deep Learning and Singular Spectrum Analysis by Yunhang Wang, Hongwei Wang, Ruoyang Bai, Yuxin Shi, Xicong Chen, Qingang Xu

“…A decision-level multimodal fusion deep learning strategy is proposed for the effective fault detection of rolling bearings based on long-term fault signals collected from multiple sensors. …”
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