Snow Distribution Patterns Revisited: A Physics‐Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub‐Arctic by R. L. Crumley, C. L. Bachand, K. E. Bennett

“…We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). …”
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Can vegetation breakpoints in Eastern Mongolia rangeland be detected using Sentinel-1 coherence time series data? by Shuxin Ji, Ganzorig Gonchigsumlaa, Sugar Damdindorj, Tserendavaa Tseren, Densmaa Sharavjamts, Amartuvshin Otgondemberel, Enkh-Amgalan Gurjav, Munguntsetseg Puntsagsuren, Batnaran Tsabatshir, Tumendemberel Gungaa, Narantsetseg Batbold, Lukas Drees, Bayarchimeg Ganbayar, Dulamragchaa Orosoo, Bayartsetseg Lkhamsuren, Badamtsetseg Ganbat, Myagmarsuren Damdinsuren, Gantogoo Gombosuren, Batnyambuu Dashpurev, Thanh Noi Phan, Nandintsetseg Dejid, Thomas Müller, Lukas Lehnert

“…In drier and less intensively used areas, the predicted pattern agrees less with the known movements. …”
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Objective dairy cow mobility analysis and scoring system using computer vision–based keypoint detection technique from top-view 2-dimensional videos by Shogo Higaki, Guilherme L. Menezes, Rafael E.P. Ferreira, Ariana Negreiro, Victor E. Cabrera, João R.R. Dórea

“…In addition, the study determined the potential of a machine learning classification model to predict mobility scores based on the newly extracted mobility variables. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. …”
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Shoe configuration effects on equine forelimb gait kinetics at a walk by Rita Aoun, Zaneta Ogunmola, Anaïs Musso, Takashi Taguchi, Catherine Takawira, Mandi J. Lopez

“…A random forest classifier algorithm was used to predict shoeing condition from kinetic outcome measures. …”
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In silico assessment of biocompatibility and toxicity: molecular docking and dynamics simulation of PMMA-based dental materials for interim prosthetic restorations by Ravinder S. Saini, Rayan Ibrahim H. Binduhayyim, Vishwanath Gurumurthy, Abdulkhaliq Ali F. Alshadidi, Shashit Shetty Bavabeedu, Rajesh Vyas, Doni Dermawan, Punnoth Poonkuzhi Naseef, Seyed Ali Mosaddad, Artak Heboyan

“…The HADDOCK standalone version was utilized for docking calculations, employing a Lamarckian genetic algorithm to explore the conformational space of ligand-receptor interactions. …”
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Molecular profiles of tumor contrast enhancement: A radiogenomic analysis in anaplastic gliomas by Xing Liu, Yiming Li, Zhiyan Sun, Shaowu Li, Kai Wang, Xing Fan, Yuqing Liu, Lei Wang, Yinyan Wang, Tao Jiang

“…Gene set enrichment analysis (GSEA), significant analysis of microarray, generalized linear models, and Least absolute shrinkage and selection operator algorithm were used to explore radiogenomic and prognostic signatures of AG patients. …”
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Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography by Jiao Wu, YongXin Li, Wanqing Gong, Qian Li, Xue Han, Tingting Zhang

“…Conclusions The multi-modality radiomics model based on MRI, ultrasound, and mammography can predict benign and malignant breast lesions.…”
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YOLOv8n-WSE-Pest: A Lightweight Deep Learning Model Based on YOLOv8n for Pest Identification in Tea Gardens by Hongxu Li, Wenxia Yuan, Yuxin Xia, Zejun Wang, Junjie He, Qiaomei Wang, Shihao Zhang, Limei Li, Fang Yang, Baijuan Wang

“…To enable the intelligent monitoring of pests within tea plantations, this study introduces a novel image recognition algorithm, designated as YOLOv8n-WSE-pest. Taking into account the pest image data collected from organic tea gardens in Yunnan, this study utilizes the YOLOv8n network as a foundation and optimizes the original loss function using WIoU-v3 to achieve dynamic gradient allocation and improve the prediction accuracy. …”
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A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis by Fatima Hasan Al-bakri, Wan Mohd Yaakob Wan Bejuri, Mohamed Nasser Al-Andoli, Raja Rina Raja Ikram, Hui Min Khor, Zulkifli Tahir, The Alzheimer’s Disease Neuroimaging Initiative

“…The findings demonstrated significant progress in diagnostic transparency, as the algorithm consistently linked predictions to observed structural changes in the dilated lateral ventricles of the brain, which serve as a clinically reliable biomarker for AD and can be easily verified by medical professionals. …”
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Neural Network Constraints on the Cosmic-Ray Ionization Rate and Other Physical Conditions in NGC 253 with ALCHEMI Measurements of HCN and HNC by Erica Behrens, Jeffrey G. Mangum, Serena Viti, Jonathan Holdship, Ko-Yun Huang, Mathilde Bouvier, Joshua Butterworth, Cosima Eibensteiner, Nanase Harada, Sergio Martín, Kazushi Sakamoto, Sebastien Muller, Kunihiko Tanaka, Laura Colzi, Christian Henkel, David S. Meier, Víctor M. Rivilla, Paul P. van der Werf, ALMA Comprehensive High-resolution Extragalactic Molecular Inventory (ALCHEMI) collaboration

“…The neural network model successfully recovers UCLCHEM molecular abundances with ∼3% error and, when used with our Bayesian inference algorithm, increases the parameter-inference speed tenfold. …”
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Hot deformation physical mechanisms and a unified constitutive model of a solid solution Ti55511 alloy deformed in the two-phase region by Huijie Zhang, Y.C. Lin, Gang Su, Yongfu Xie, Wei Qiu, Ningfu Zeng, Song Zhang, Guicheng Wu

“…Material constants are determined using a genetic algorithm (GA), and the experimental data align well with the predicted data. …”
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Wood Species Identification Based on Gray Level Co-Occurrence Matrix (GLCM) Features on Macroscopic Images by Muhammad Ghiffaari Ilham Ramadhan, Bambang Sugiarto, Okta Dwi Mulya, Defti Septian Chairulsyah, Adyanto Syahrizal, Gunawan Gunawan, Riffa Haviani Laluma, Rini Nuraini Sukmana, Teguh Wiharko

“…For the classifier, the Random Forest algorithm is proposed for the identification of the machine learning model. …”
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Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow by Marius Huguet, Canan Pehlivan, François Ballereau, Antoine Dodane-Loyenet, Franck Fontanili, Thierry Garaix, Youri Yordanov, Vincent Augusto, Karim Tazarourte, Abdesslam Redjaline

“…Additionally, we developed a user recognition algorithm (i.e., random forest classifier) capable of detecting the job category of the participant wearing the sensor. …”
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The Role of Immunohistochemistry as a Surrogate Marker in Molecular Subtyping and Classification of Bladder Cancer by Tatiana Cano Barbadilla, Martina Álvarez Pérez, Juan Daniel Prieto Cuadra, Mª Teresa Dawid de Vera, Fernando Alberca-del Arco, Isabel García Muñoz, Rocío Santos-Pérez de la Blanca, Bernardo Herrera-Imbroda, Elisa Matas-Rico, Mª Isabel Hierro Martín

“…Background/Objectives: Bladder cancer (BC) is a highly heterogeneous disease, presenting clinical challenges, particularly in predicting patient outcomes and selecting effective treatments. …”
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Covariate Model Selection Approaches for Population Pharmacokinetics: A Systematic Review of Existing Methods, From SCM to AI by Mélanie Karlsen, Sonia Khier, David Fabre, David Marchionni, Jérôme Azé, Sandra Bringay, Pascal Poncelet, Elisa Calvier

“…AALASSO, a hybrid genetic algorithm, FREM with a clinical significance criterion and SCM+ with stagewise filtering were the best covariate model selection techniques—AALASSO being the very best one. …”
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Study on the factors influencing the impaired abilities of daily living in middle-aged and older adult arthritis patients based on binary logistic regression and categorical decisi... by Bao-xuan Zhang, Bao-xuan Zhang, Bao-xuan Zhang, Jin-ping Luo, Jin-ping Luo, Jin-ping Luo, Jia-ying Sun, Jia-ying Sun, Jia-ying Sun, Ming-hui Geng, Ming-hui Geng, Ming-hui Geng, Yi-fan Mou, Yi-fan Mou, Yi-fan Mou, Nan-nan Cheng, Nan-nan Cheng, Nan-nan Cheng, Zhao-xuan Wang, Zhao-xuan Wang, Zhao-xuan Wang, Wen-qiang Yin, Wen-qiang Yin, Wen-qiang Yin, Zhong-ming Chen, Zhong-ming Chen, Zhong-ming Chen, Dong-ping Ma, Dong-ping Ma, Dong-ping Ma

“…The decision tree results showed that pain was the most important variable predicting impaired ADL in middle-aged and older adult arthritis patients. …”
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Developing A Digital Twin of a TRUEX Extraction Process that Enables Non-proliferation and Safeguards Monitoring through Optical Spectroscopy and Machine Learning [version 1; peer... by Justin T. Cooper, Ramedy Flores, Addyson Barnes, Eduardo Trevino, Kathrine Jesse, Kolton Heaps, Adam J. Pluth, Ashley Shields, Jaren Brownlee

“…Furthermore, the digital twin’s ML anomaly detection algorithm was shown to be successful in identifying several non-ideal process upset conditions.…”
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Reaction Behavior and Kinetic Model of Hydroisomerization and Hydroaromatization of Fluid Catalytic Cracking Gasoline by Haijun Zhong, Xiwen Song, Shuai He, Xuerui Zhang, Qingxun Li, Haicheng Xiao, Xiaowei Hu, Yue Wang, Boyan Chen, Wangliang Li

“…Kinetic parameters were estimated using the Levenberg–Marquardt algorithm and validated against experimental data. …”
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Skin microbiome-biophysical association: a first integrative approach to classifying Korean skin types and aging groups by Seyoung Mun, Seyoung Mun, Seyoung Mun, HyungWoo Jo, HyungWoo Jo, Young Mok Heo, Chaeyun Baek, Hye-Been Kim, Haeun Lee, Kyeongeui Yun, Kyeongeui Yun, Jinuk Jeong, Wooseok Lee, Dasom Jeon, Dasom Jeon, So Min Kang, So Min Kang, Seunghyun Kang, Young-Bong Choi, Young-Bong Choi, Sangjin Han, Gabriel Kim, Kung Ahn, Dong Hun Lee, Yong Ju Ahn, Dong-Geol Lee, Dong-Geol Lee, Kyudong Han, Kyudong Han, Kyudong Han, Kyudong Han

“…To further amplify our findings, we harnessed the potent capabilities of the CatBoost boosting algorithm and achieved a reliable framework for predicting skin types based on microbial composition with an impressive average accuracy of 0.96 AUC value. …”
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