Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels by Chengce Yuan, Yimin Shi, Zhichen Ba, Daxin Liang, Jing Wang, Xiaorui Liu, Yabei Xu, Junreng Liu, Hongbo Xu

“…A comparative analysis of various machine learning algorithms revealed that an optimized XGBoost model demonstrated superior predictive performance, achieving an R² value of 0.943 and an RMSE of 1.423 for the test dataset. …”
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Bioinformatics analysis of ferroptosis-related biomarkers and potential drug predictions in doxorubicin-induced cardiotoxicity by Jian Yu, Jian Yu, Jiangtao Wang, Xinya Liu, Xinya Liu, Cancan Wang, Cancan Wang, Li Wu, Yuanming Zhang, Yuanming Zhang

“…We constructed miRNA-lncRNA networks by identifying target miRNAs for KLHDC3 (hsa-miR-24-3p, hsa-miR-486-3p, hsa-miR-214-3p) and NDRG1 (hsa-miR-4510, hsa-miR-182-5p, hsa-miR-96-5p). …”
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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

“…Among these, the Gradient Boosting model demonstrated superior predictive capability, overcoming both traditional regression methods and other machine learning algorithms as validated through the Taylor diagram and ranking system (i.e., r = 0.998, R² = 997, Root mean square error = 0.075, Variance Accounted For = 99.50%, Mean Absolute Percentage Error = 0.385%, Alpha 20 Index = 100, and performance index = 0.975). …”
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Development of a three-species gut microbiome diagnostic model for acute pancreatitis and its association with systemic inflammation: a prospective cross-sectional study by Yuanyuan Gou, Long Yao, Wenli Yang, Qian Chen, Yuetao Wen, Jie Cao

“…While gut microbiome dysbiosis has been implicated in AP pathogenesis, prior studies have predominantly focused on descriptive compositional changes rather than linking specific microbial signatures to clinical inflammatory markers. …”
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Microbial biomarker discovery in Parkinson’s disease through a network-based approach by Zhe Zhao, Jing Chen, Danhua Zhao, Baoyu Chen, Qi Wang, Yuan Li, Junyi Chen, Chaobo Bai, Xintong Guo, Nan Hu, Bingwei Zhang, Rongsheng Zhao, Junliang Yuan

“…Abstract Associations between the gut microbiota and Parkinson’s disease (PD) have been widely investigated. However, the replicable biomarkers for PD diagnosis across multiple populations remain elusive. …”
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Predicting mechanical properties of low-alloy steels using features extracted from Electron Backscatter Diffraction characterization by Yu Li, Jingxiao Zhao, Xiucheng Li, Zhao Xing, Qiqiang Duan, Xiaojun Liang, Xuemin Wang

“…Machine learning (ML) approaches have recently been increasingly employed to establish quantitative relationships between material composition, processing, microstructure, and properties. …”
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Machine learning-driven benchmarking of China's wastewater treatment plant electricity consumption by Minjian Li, Chongqiao Tang, Junhan Gu, Nianchu Li, Ahemaide Zhou, Kunlin Wu, Zhibo Zhang, Hui Huang, Hongqiang Ren

“…Due to the challenge of characterizing influent water quality using traditional methods, satisfactory benchmarks have long been elusive. To overcome the complexity of wastewater compositions, an unsupervised machine learning algorithm, spectral clustering, is introduced to analyze 2,576 WWTPs across China, effectively characterizing influent quality as a single variable and contributing to robust benchmarks with 75 % of the fittings achieving coefficients of determination (R2) >0.85. …”
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Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations by Claudio Ronchetti, Sara Marchio, Francesco Buonocore, Simone Giusepponi, Sergio Ferlito, Massimo Celino

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Machine learning-based prediction of scale formation in produced water as a tool for environmental monitoring by Arash Tayyebi, Ali Alshami, Erfan Tayyebi, Ademola Owoade, MusabbirJahan Talukder, Nadhem Ismail, Zeinab Rabiei, Xue Yu, Glavic Tikeri

“…We used a database comprised of 2313 quality data points from different locations in the Bakken Shale Play, including values such as ionic compositions, pH, and the saturation index of the potential mineral scales in PW at 60°F and 60 psi to train the ML algorithms and identify what scale will likely form in the PW. …”
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Identification and verification of biomarkers associated with neutrophils in acute myocardial infarction: integrated analysis of bulk RNA-seq, expression quantitative trait loci, a... by Guoqing Liu, Xiangwen Lv, Jiahui Qin, Xingqing Long, Miaomiao Zhu, Chuwen Fu, Jian Xie, Peichun He

“…CIBERSORT was utilized to measure 22 degrees of immune cell composition. The causal link between neutrophils and AMI was determined by Mendelian randomization (MR) analysis. …”
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Deep learning-based sow posture classifier using colour and depth images by Verônica Madeira Pacheco, Tami M. Brown-Brandl, Rafael Vieira de Sousa, Gary A. Rohrer, Sudhendu Raj Sharma, Luciane Silva Martello

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Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools by Fu Limei, Xu Feng

“…Rubberized cementitious composites have emerged as a sustainable alternative in the construction sector by promoting circular economy principles. …”
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InvarNet: Molecular property prediction via rotation invariant graph neural networks by Danyan Chen, Gaoxiang Duan, Dengbao Miao, Xiaoying Zheng, Yongxin Zhu

“…Recently, deep learning methods, particularly Graph Neural Networks (GNNs), have significantly improved efficiency by capturing molecular structures’ invariance under translation, rotation, and permutation. …”
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