Assessing Uneven Regional Development Using Nighttime Light Satellite Data and Machine Learning Methods: Evidence from County-Level Improved HDI in China by Xiping Zhang, Jianbin Xu, Saiying Zhong, Ziheng Wang

“…At the provincial and national levels, the improved HDI shows significant clustering, characterized by a multi-center pattern with declining diffusion. The spatial distribution of the improved Human Development Index remains closely associated with the natural geographic background and socio-economic development levels of the county regions. …”
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Identifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China: Machine Learning Approach to a Retrospective Population Analysis by Lizhong Liang, Tianci Liu, William Ollier, Yonghong Peng, Yao Lu, Chao Che

“… Abstract BackgroundThe mechanisms underlying the mutual relationships between chronic physical illnesses and mental health disorders, which potentially explain their association, remain unclear. Furthermore, how patterns of this comorbidity evolve over time are significantly underinvestigated. …”
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Machine Learning-Enhanced Model-Based Optical Proximity Correction Framework With Convolutional Neural Network-Based Variable Threshold Method Near the Diffraction Limit by Jinhao Zhu, Liwan Yue, Ying Li, Xianhe Liu, Qiang Wu, Qi Wang, Yanli Li

“…In CD simulations for typical patterns, the hybrid model reduces error medians and confines the statistical upper and lower limits of the distribution ranges to ±5 nm. …”
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Enhancement of Chest X-Ray Images to Improve Screening Accuracy Rate Using Iterated Function System and Multilayer Fractional-Order Machine Learning Classifier by Chia-Hung Lin, Jian-Xing Wu, Chien-Ming Li, Pi-Yun Chen, Neng-Sheng Pai, Ying-Che Kuo

“…The IFS with nonlinear interpolation functions is then used to reconstruct the 2D feature patterns. These reconstructed patterns are self-affine in the same class and thus help distinguish normal subjects from those with lung diseases. …”
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Synergistic Non-Intrusive Load Monitoring: Dual-Model Training and Inference for Improved Load Disaggregation Prediction by Mazen Bouchur, Andreas Reinhardt

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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

“…Our machine learning approach established an activity threshold, above which divergent wavefront patterns—termed “significant sources”—predicted AF recurrence. …”
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The Comprehensive Analysis of Weighted Gene Co-Expression Network Analysis and Machine Learning Revealed Diagnostic Biomarkers for Breast Implant Illness Complicated with Breast Ca... by Huang Z, Wang H, Pang H, Zeng M, Zhang G, Liu F

“…After constructing the PPI network, 17 key genes were selected, and three potential hub genes include KRT14, KIT, ALB were chosen for nomogram creation and diagnostic assessment through machine learning. The validation of these results was conducted by examining gene expression patterns in the validation dataset, breast cancer cell lines, and BII-BC patients. …”
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E-scooter crash severity in the United Kingdom: A comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variances by Ali Agheli, Kayvan Aghabayk, Matin Sadeghi, Subasish Das

“…We employed a random parameters logit model and investigated several machine learning algorithms, with XGBoost performing best. …”
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Identifying Health Care Services Offered in the HIV Care Continuum via a Machine Learning–Based Topic Modeling Approach: Exploratory Literature Review by SangA Lee, Layoung Kim, Mi-So Shim, Gwang Suk Kim

“…Notably, the distribution of topics exhibited a distinct pattern: while health care service diversity was the highest in the earlier stages of the HIV care continuum, it became increasingly limited in the later stages. …”
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The Influence of Running Technique Modifications on Vertical Tibial Load Estimates: A Combined Experimental and Machine Learning Approach in the Context of Medial Tibial Stress Syn... by Taylor Miners, Jeremy Witchalls, Jaquelin A. Bousie, Ceridwen R. Radcliffe, Phillip Newman

“…This study investigated whether changes to speed, cadence, stride length, and foot-strike pattern influence vGRF and TA. Additionally, machine-learning models were evaluated for their ability to estimate vGRF metrics. …”
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Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach by Seyyed Hossein Mousavie Anijdan, Daryush Moslemi, Reza Reiazi, Hamid Fallah Tafti, Ali Akbar Moghadamnia, Reza Paydar

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Integrating Machine Learning, SHAP Interpretability, and Deep Learning Approaches in the Study of Environmental and Economic Factors: A Case Study of Residential Segregation in Las... by Jingyi Liu, Yuxuan Cai, Xiwei Shen

“…The findings reveal that housing prices and poor environmental quality disproportionately affect minority populations, with distinct patterns across different ethnic groups, which may reinforce these groups’ spatial and economic marginalization. …”
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Identification of Leaf Rust-Related Gene Signature in Wheat (Triticum Aestivum L.) Using High-Throughput Sequencing, Network Analysis, and Machine Learning Algorithms by Muhammad Farhan, Muhammad Ikram, Jing-E Sun, San-Wei Yang, Yong Wang

“…Among these, 124 resistance (R) genes (~ 85.48% upregulated) were expressed differentially, and ~ 80% belonged to plant pattern recognition receptors (PPRs) that triggered immunity. …”
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Accuracy Assessment of Land Use Land Cover Classification Using Machine Learning Classifiers in Google Earth Engine; A Case Study of Jammu District by S. Khan, A. Bhardwaj, M. Sakthivel

“…This highlights the effectiveness of machine learning classifiers, especially RF and SVM, in accurately mapping LULC patterns in Jammu district, suggesting RF's potential as a reliable tool for remote sensing-based LULC mapping.…”
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What factors influence the willingness and intensity of regular mobile physical activity?— A machine learning analysis based on a sample of 290 cities in China by Hao Shen, Bo Shu, Jian Zhang, Yaoqian Liu, Ali Li

“…Interaction effects and non-linear patterns were also assessed.ResultsThe study identified three key findings: (1) A significant difference exists between the influencing factors of activity willingness and activity intensity. …”
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The OpenMindat v1.0.0 R package: a machine interface to Mindat open data to facilitate data-intensive geoscience discoveries by X. Que, J. Zhang, W. Chen, J. Ralph, X. Ma

“…<p>Technologies such as machine learning and deep learning are powering the discovery of meaningful patterns in Earth science big data. …”
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Quantifying Ecological Dynamics and Anthropogenic Dominance in Drylands: A Hybrid Modeling Framework Integrating MRSEI and SHAP-Based Explainable Machine Learning in Northwest Chin... by Beilei Zhang, Xin Yang, Mingqun Wang, Liangkai Cheng, Lina Hao

“…The study revealed the spatiotemporal evolution patterns through the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test, and adopted the Light Gradient Boosting Machine (LightGBM) combined with the Shapley Additive Explanation (SHAP) to quantify the contributions of ten natural and anthropogenic driving factors. …”
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Risk factors and predictive models for post-operative moderate-to-severe mitral regurgitation following transcatheter aortic valve replacement: a machine learning approach by Zhenzhen Li, Jianing Fan, Jiajun Fan, Jiaxin Miao, Dawei Lin, Jingyan Zhao, Xiaochun Zhang, Wenzhi Pan, Daxin Zhou, Junbo Ge

“…Shapley Additive Explanation (SHAP) values were used to interpret predictive patterns and develop a clinically relevant model. Results Among the evaluated models, the random forest model exhibited the highest predictive performance for post-operative moderate-to-severe MR after TAVR. …”
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Analysis of Ground Subsidence Evolution Characteristics and Attribution Along the Beijing–Xiong’an Intercity Railway with Time-Series InSAR and Explainable Machine-Learning Techniq... by Xin Liu, Huili Gong, Chaofan Zhou, Beibei Chen, Yanmin Su, Jiajun Zhu, Wei Lu

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Association Between Comorbidity Clusters and Mortality in Patients With Cancer: Predictive Modeling Using Machine Learning Approaches of Data From the United States and Hong Kong by Chun Sing Lam, Rong Hua, Herbert Ho-Fung Loong, Chun-Kit Ngan, Yin Ting Cheung

“…The same number of clusters was replicated based on the distinctive patterns and distribution of comorbidities observed within each cluster. …”
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