Investigation of Vorticity during Prevalent Winter Precipitation in Iran by Iman Rousta, Farshad Javadizadeh, Fatemeh Dargahian, Haraldur Ólafsson, Amin Shiri-Karimvandi, Sayed Hossein Vahedinejad, Mehdi Doostkamian, Edgar Ricardo Monroy Vargas, Anayat Asadolahi

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The sow vaginal and gut microbiota associated with longevity and reproductive performance by Ziyu Liu, Tsungcheng Tsai, Bin Zuo, Samantha Howe, Jason E. Farrar, Christopher E. Randolph, Charles V. Maxwell, Jiangchao Zhao

“…., Lactobacillus) of the U4P group using RandomForest in the early breeding stage of the first parity. …”
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Machine Learning Algorithm for Estimating Surface PM2.5 in Thailand by Pawan Gupta, Shanshan Zhan, Vikalp Mishra, Aekkapol Aekakkararungroj, Amanda Markert, Sarawut Paibong, Farrukh Chishtie

“…The integrated data then used to train and validate a supervised MLA’ random forest’ to estimate hourly and daily PM2.5 concentrations. …”
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Inversion Methods of Soil Hydraulic Parameters Based on Hyperspectral Characteristics by WU Yanmei, ZHU Han, LI Ji, LI Zixin, HE Jianliang, LIN Kairong, DONG Chunyu, ZHUANG Luwen

“…The results are as follows. ① The spectral curves exhibit clear linearity in three bands: 700–750 nm, 830–1 100 nm, and 1 520–1 620 nm, with the mean values of coefficient of determination for the linear fitting <italic>R²</italic>all over 0.94. ② Among gradient boosting regression (GBR), partial least squares regression (PLSR), and random forest (RF), GBR performs the best and shows high sensitivity to the linear fitting parameter lg<italic>a</italic>₂ (the logarithm of the slope) in the 830-1100 nm band. …”
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Using decision analysis to develop a framework for nest protection for threatened birds by Joy. S. Tripovich, Terry Walshe, Andrew Elphinstone, Dean Ingwersen, Mick Roderick, Benjamin J. Pitcher

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Distribution of humic substances in sieved aggregates of soil under contrasting land use by Bassey Udom, Achimota Dickson, Gogo Arthur, Miebaka Ikiriko, Babatunde Nuga

“…Humified carbon (HC), humified acid carbon (HAC), and aggregate-associated fulvic acid carbon (FAC) in forest soils, cocoa plantations, five-year fallow, and five-year continuous cultivated soils were studied. …”
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Impact assessment of planned and unplanned urbanization on land surface temperature in Afghanistan using machine learning algorithms: a path toward sustainability by Sajid Ullah, Xiuchen Qiao, Aqil Tariq

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Efficient surface crack segmentation for industrial and civil applications based on an enhanced YOLOv8 model by Zeinab F. Elsharkawy, H. Kasban, Mohammed Y. Abbass

“…To evaluate the proposed approach and test its generalization ability, nine public datasets comprising images of civil and industrial structures were collected, including CracK500, Crack3238, Crack Forest Dataset, Deepcrack, Rissbilder, Volker, Sylvie, Magnetic Tile, and Pipeline Gamma Radiography Images. …”
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Total Organic Carbon Content Prediction in Lacustrine Shale Using Extreme Gradient Boosting Machine Learning Based on Bayesian Optimization by Xingzhou Liu, Zhi Tian, Chang Chen

“…In addition, five commonly used methods, namely, ΔlogR method, random forest, support vector machine, K-nearest neighbors, and multiple linear regression, were used to predict the TOC content to confirm that the XGBoost model has higher prediction accuracy and better robustness. …”
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Human-Gorilla and Gorilla-Human: Dynamics of Human-animal boundaries and interethnic relationships in the central African rainforest by Takanori Oishi

“…This paper (1) describes the perceptions of the western lowland gorilla (Gorilla gorilla gorilla) by forest dwellers of southeastern Cameroon and (2) investigates the sociocultural dimension of human–gorilla relationships focusing on folk theories of human–animal hybrids in which the gorilla is deeply embedded, enabling us to deal with the symbolic and social aspects of hunter-gatherer–farmer relations. …”
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A data driven machine learning approach for predicting and optimizing sulfur compound adsorption on metal organic frameworks by Mohsen Shayanmehr, Sepehr Aarabi, Ahad Ghaemi, Alireza Hemmati

“…Among the ML approaches, MLP model achieved the best performance with a low mean squared error (MSE) of 0.0032 on the test set and 0.0021 on the training set and mean relative error (MRE) of 15.26% on the test set. Also, Random Forest model yielded a higher test MSE of 0.0045 and MRE of 17.83%. …”
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Depression Recognition Using Daily Wearable-Derived Physiological Data by Xinyu Shui, Hao Xu, Shuping Tan, Dan Zhang

“…We extracted static features such as the mean, variance, skewness, and kurtosis of physiological indicators like heart rate, skin conductance, and acceleration, as well as autoregressive coefficients of these signals reflecting the temporal dynamics. Utilizing a Random Forest algorithm, we distinguished depressive and non-depressive individuals with varying classification accuracies on data aggregated over 6 h, 2 h, 30 min, and 5 min segments, as 90.0%, 84.7%, 80.1%, and 76.0%, respectively. …”
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Evaluation of Antioxidant and Antimicrobial Activity of Saponin Extracts from Different Parts of Argania spinosa L. Skeels by Yousra El Idrissi, Youssef Elouafy, Hamza El Moudden, Najoua Mghazli, Chakir El Guezzane, Adil El Yadini, Hicham Harhar, Abdelkader Zarrouk, Khang Wen Goh, Long Chiau Ming, Abdelhakim Bouyahya, Mohammed Tabyaoui

“…The argan tree is a versatile forest tree (silviculture-fruit-forestry) of great importance for the country both in biological, phytogenetic and ecological biodiversity as well as in economic and social aspects. …”
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Géographie et hydrologie de la ville de Reims/Durocortorum et de ses environs by Alain Devos, Claire Pichard, Gilles Fronteau, Sébastien Laratte

“…These valleys provide the plateaus with significantly diversified environments, between the crops on the loess layers, forests generally along the clay covered interfluves, wetland meadows at the foot of the valleys, as well as multiple slopes with suitable exposure for vine cultivation. …”
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Piezoresistive Cantilever Microprobe with Integrated Actuator for Contact Resonance Imaging by Tianran Ma, Michael Fahrbach, Erwin Peiner

“…A novel piezoresistive cantilever microprobe (PCM) with an integrated electrothermal or piezoelectric actuator has been designed to replace current commercial PCMs, which require external actuators to perform contact-resonance imaging (CRI) of workpieces and avoid unwanted “forest of peaks” observed at large travel speed in the millimeter-per-second range. …”
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Structural and Optothermal Properties of Iron Ditelluride Layered Structures in the Framework of the Lattice Compatibility Theory by K. Ben Messaoud, A. Gantassi, H. Essaidi, J. Ouerfelli, A. Colantoni, K. Boubaker, M. Amlouk

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Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNA by Sanying Mao, Xiyao Zhao, Lei Wang, Yilong Man, Kaiyuan Li

“…This study applied WGCNA along with three machine learning algorithms—random forest, LASSO regression, and SVM–RFE—to further select key PRGs (KPRGs). …”
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Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation by Ling Hao, Yang Chen, Xuejiao Su, Buyun Ma

“…Model performance was further evaluated using ROC curves and calibration curves, while clinical utility was assessed via decision curve analysis (DCA). Results: The random forest model exhibited superior performance in differentiating benign from malignant nodules in the validation set, achieving sensitivity of 0.765, specificity of 0.838, and an AUC of 0.787 (95% CI: 0.561–0.960). …”
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Exploring happiness factors with explainable ensemble learning in a global pandemic. by Md Amir Hamja, Mahmudul Hasan, Md Abdur Rashid, Md Tanvir Hasan Shourov

“…We design two ensemble ML and DL models using blending and stacking ensemble techniques, namely, Blending RGMLL, which combines Ridge Regression (RR), Gradient Boosting (GB), Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Linear Regression (LR), and Stacking LRGR, which combines LR, Random Forest (RF), GB, and RR. Among the trained models, Blending RGMLL demonstrates the highest predictive accuracy with R2 of 85%, MSE of 0.15, and RMSE of 0.38. …”
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Evaluation of laboratory findings indicating pancreatitis in healthy lean, obese, and diabetic cats by Freja K. Jørgensen, Charlotte R. Bjornvad, Birgit Krabbe, Stinna Nybroe, Ida N. Kieler

“…Fisher's exact test assessed the proportions of cats with fPLI and fTLI indicative of pancreatitis, and hypocobalaminemia. A random forest algorithm identified explanatory variables for cats having fPLI levels indicative of pancreatitis. …”
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