Influencing factors and prevention optimization of shallow shale gas inter-well frac-hits by Ming JIANG, Qingteng ZOU, Zhuang XIAO, Yong WANG, Jingnan GE, Zhao CHEN

“…The main controlling factors were identified as well spacing, construction intensity, and the production time of the parent well. Numerical simulations suggested an optimal parent-child well spacing of 450 m. …”
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Spatial-temporal Patterns and Factors of Soil Moisture in the Middle Reaches of the Yellow River under Changing Environments by LIU Bo, HAN Qinggong, ZHANG Jielin, PENG Shouzhang

“…The main driving factors were analyzed by combining the GeoDetector, Random Forest, and SHAP, and the contribution of land cover and climate change to the changes of SSM and RZSM was analyzed by using scenario-setting method. …”
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Geospatial Robust Wheat Yield Prediction Using Machine Learning and Integrated Crop Growth Model and Time-Series Satellite Data by Rana Ahmad Faraz Ishaq, Guanhua Zhou, Guifei Jing, Syed Roshaan Ali Shah, Aamir Ali, Muhammad Imran, Hongzhi Jiang, Obaid-ur-Rehman

“…The Agricultural Production Systems sIMulator was calibrated to simulate multiple traits across the growth season based on geo-tagged wheat field ground information. …”
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Cepharanthine Inhibits <i>Fusarium solani</i> via Oxidative Stress and CFEM Domain-Containing Protein Targeting by Yuqing Wang, Zenghui Yang, Jingwen Xue, Yitong Wang, Haibo Li, Zhihong Wu, Yizhou Gao

“…In this work, we used machine learning-based virtual screening with Random Forest, Neural Network, and Support Vector Machine models to identify potential inhibitors of <i>Fusarium solani</i>. …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and k-Nearest Neighbors (KNN). …”
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Observation and modeling of atmospheric OH and HO₂^∗ radicals at a subtropical rural site and implications for secondary pollutants by Z. Zou, T. Chen, Q. Chen, W. Sun, S. Han, Z. Ren, X. Li, W. Song, A. Ge, Q. Wang, X. Tian, C. Pei, X. Wang, Y. Zhang, T. Wang

“…Sensitivity tests suggest that adding <span class="inline-formula">HO_<i>x</i></span> sinks or an <span class="inline-formula">HO₂</span> recycle process to the model could improve the model performance. Over-simulation of <span class="inline-formula">HO_<i>x</i></span> in the model resulted in overestimations of midday (10:00–15:00 UTC) production rates by more than 79 % for ozone and a factor of 1.88 for nitric acid. …”
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Spatially and Seasonally Differentiated Response of Soil Moisture Droughts to Climate Change in Germany by Friedrich Boeing, Sabine Attinger, Thorsten Wagener, Oldrich Rakovec, Luis Samaniego, Stephan Thober, Julian Schlaak, Sebastian Müller, Claas Teichmann, Rohini Kumar, Andreas Marx

“…The recent extreme drought years in Germany, which resulted in multi‐sectoral impacts accounting to combined drought and heat damages of 35 billion Euros and large scale forest losses, underline the relevance of studying future changes in SM droughts. …”
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Data-Driven Fault Detection and Diagnosis in Cooling Units Using Sensor-Based Machine Learning Classification by Amilcar Quispe-Astorga, Roger Jesus Coaquira-Castillo, L. Walter Utrilla Mego, Julio Cesar Herrera-Levano, Yesenia Concha-Ramos, Erwin J. Sacoto-Cabrera, Edison Moreno-Cardenas

“…Finally, a validation test was performed with the best-selected model in real time, simulating a real environment for the PAC system, achieving an accuracy rate of 93.49%.…”
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New categorized machine learning models for daily solar irradiation estimation in southern Morocco's, Zagora city by Zineb Bounoua, Laila Ouazzani Chahidi, Abdellah Mechaqrane

“…Accurately estimating daily solar irradiation is essential for effectively sizing and simulating solar energy systems. Inaccuracies or discontinuities in solar data can lead to errors in system assessments, potentially resulting in misguided conclusions about their economic feasibility. …”
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A Synergistic Framework for Coupling Crop Growth, Radiative Transfer, and Machine Learning to Estimate Wheat Crop Traits in Pakistan by Rana Ahmad Faraz Ishaq, Guanhua Zhou, Aamir Ali, Syed Roshaan Ali Shah, Cheng Jiang, Zhongqi Ma, Kang Sun, Hongzhi Jiang

“…Optimized machine learning models, namely Extreme Gradient Boost (XGBoost) for LAI, Support Vector Machine (SVM) for Cab, and Random Forest (RF) for Cm and Cw, were deployed for temporal mapping of traits to be used for wheat productivity enhancement.…”
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Two-dimensional QSAR-driven virtual screening for potential therapeutics against Trypanosoma cruzi by Naseer Maliyakkal, Sunil Kumar, Ratul Bhowmik, Harish Chandra Vishwakarma, Prabha Yadav, Bijo Mathew

“…Following the calculation of molecular descriptors and feature selection approaches, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) models were developed and optimized to elucidate and predict the inhibition mechanism of novel inhibitors. …”
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Associations between ambient particulate matter exposure and the prevalence of arthritis: Findings from the China Health and Retirement Longitudinal Study. by Yuntian Ye, Kuizhi Ma, Aifeng Liu

“…The levels of air pollution exposure were estimated using a spatial-temporal extreme random forest model, integrating ground monitoring, remote sensing data, and model simulations, encompassing PM1, PM2.5, PM10, NH4, NO3, O3, and SO4 concentrations. …”
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Predicting Nitrous Oxide Emissions from China’s Upland Fields Under Climate Change Scenarios with Machine Learning by Tong Li, Yunpeng Li, Wenxin Cheng, Jufeng Zheng, Lianqing Li, Kun Cheng

“…This study employed four classical modeling approaches—the Stepwise Regression Model, Decision Tree Regression, Support Vector Machine, and Random Forest (RF)—to simulate soil N₂O emissions from Chinese upland fields. …”
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Potential Distribution and Cultivation Areas of <i>Argentina anserina</i> (Rosaceae) in the Upper Reaches of the Dadu River and Minjiang River Basin Under Climate Change: Applicati... by Yi Huang, Jian Yang, Guanghua Zhao, Yang Yang

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Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC by Yang Song, Xiaoming Li, Ying Zheng, Gui Zhang

“…To achieve this, annual multi-period remote sensing images from Landsat-5, Landsat-8 or Sentinel-2 satellites spanning from 1992 to 2022 were analyzed. Random Forest (achieving a Kappa coefficient of 0.9468) were employed to classify land use within the watershed. …”
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On the Role of Early Case Detection and Treatment Failure in Controlling Tuberculosis Transmission: A Mathematical Modeling Study by Dipo Aldila, Derio A. Ramadhan, Chidozie W. Chukwu, Bevina D. Handari, Muhammad Shahzad, Putri Zahra Kamalia

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Carbon additives to improve polymer performance in energy applications using machine learning by Juan Chen, Khidhair Jasim Mohammed, Elimam Ali, Riadh Marzouki

“…To guide composite optimization, a hybrid Machine Learning (ML) framework combining Random Forest Regression (RFR) and Support Vector Regression (SVR) was developed. …”
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MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma by Hong-Jian Luo, Jia-Liang Ren, Li mei Guo, Jin liang Niu, Xiao-Li Song

“…Logistic regression, random forest (RF), and support vector machine were conducted to establish radiomics models. …”
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Comparing the effects of hydromulching and application of biodegradable plastics on surface runoff and soil erosion in deforested and burned lands by Parhizkar Misagh, Lucas-Borja Manuel Esteban, Denisi Pietro, Tanaka Nobuaki, Zema Demetrio Antonio

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Machine Learning-Driven Identification of Exosome-Related Genes in Head and Neck Squamous Cell Carcinoma for Prognostic Evaluation and Drug Response Prediction by Hua Cai, Liuqing Zhou, Yao Hu, Tao Zhou

“…A predictive model was produced by using machine learning algorithms (LASSO regression, SVM, and random forest) to find disease-specific feature genes. Receiver operating characteristic (ROC) curve analysis was used to assess the model’s effectiveness. …”
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