A two-stage deep learning architecture for detection global coastal and offshore submesoscale ocean eddy using SDGSAT-1 multispectral imagery by Linghui Xia, Baoxiang Huang, Ruijiao Li, Ge Chen

“…A generalized and efficient deep learning architecture that combines developments in deep learning with Sustainable Development Goals Science Satellite 1 (SDGSAT-1) multispectral data from earth observations offers a potential pathway for more fine detection of ocean eddies. …”
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Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Armin Sorooshian, Lingbo Liu, Shuming Bao, Soo-Mi Choi

“…The ensemble machine learning algorithm was selected as the base model, specifically the Random Forest (RF). …”
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Fire Intensity and spRead forecAst (FIRA): A Machine Learning Based Fire Spread Prediction Model for Air Quality Forecasting Application by Wei‐Ting Hung, Barry Baker, Patrick C. Campbell, Youhua Tang, Ravan Ahmadov, Johana Romero‐Alvarez, Haiqin Li, Jordan Schnell

“…Hence, a novel machine learning (ML) based fire spread forecast model, the Fire Intensity and spRead forecAst (FIRA), is developed for AQF model applications. …”
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Development and validation of machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic features by Hongwei Zhang, Kexin Wang, Shurong Liu, Guowei Chen, Yong Jiang, Yingchao Wu, Xiaocong Pang, Xiaoying Wang, Junling Zhang, Xin Wang

“…This study aimed to develop a machine learning model for predicting metastasis in No. 253 LN. …”
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Enhancing Urban Flood Susceptibility Assessment by Capturing the Features of the Urban Environment by Juwei Tian, Yinyin Chen, Linhan Yang, Dandan Li, Luo Liu, Jiufeng Li, Xianzhe Tang

“…Unlike those studies that focus primarily on topographic and surface characteristics, our approach integrates urban-specific factors that capture the distinctive attributes of the urban environment, including Urban Heat Island Intensity, Urban Rain Island Intensity, Urban Resilience Index, and Impervious Surface Percentage. …”
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Predictive Modeling of the Softness of Facial Tissue Products: A Spectral Analysis Approach by Yong Ju Lee, Ji Eun Cha, Geon-Woo Kim, Tai-Ju Lee, Hyoung Jin Kim

“…In this study, softness values were obtained from the authors’ previous research using the Interval Scale Value (ISV) method, involving panelists’ round-robin pairwise comparisons. A machine-learning approach was developed to predict softness from one-dimensional power spectral density (1D-PSD) spectra of surface roughness profiles. …”
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SERS-based technique for accessible and rapid diagnosis of multiple myeloma in blood serum analysis by Lyudmila A. Bratchenko, Yulia A. Khristoforova, Irina A. Pimenova, Elena N. Tupikova, Maria A. Skuratova, Georgy A. Dvoynikov-Sechnoy, Shuang Wang, Petr A. Lebedev, Ivan A. Bratchenko

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The Music of Rivers: The Mathematics of Waves Reveals Global Structure and Drivers of Streamflow Regime by Brian C. Brown, Aimee H. Fullerton, Darin Kopp, Flavia Tromboni, Arial J. Shogren, J. Angus Webb, Claire Ruffing, Matthew Heaton, Lenka Kuglerová, Daniel C. Allen, Lillian McGill, Jay P. Zarnetske, Matt R. Whiles, Jeremy B. Jones Jr., Benjamin W. Abbott

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Coupling Artificial Intelligence with Proper Mathematical Algorithms to Gain Deeper Insights into the Biology of Birds’ Eggs by Valeriy G. Narushin, Natalia A. Volkova, Alan Yu. Dzhagaev, Darren K. Griffin, Michael N. Romanov, Natalia A. Zinovieva

“…Examining egg weight, volume, surface area and air cell calculations, we consider how DL might be applied, e.g., for egg storage. …”
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Object classification based on impulse response analysis using vibration propagation by Tomohiro Nozawa, Hideyuki Sawada

“…The proposed system detects not only the characteristics of the object’s surface but also its internal properties. …”
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An XGBoost-SHAP framework for identifying key drivers of urban flooding and developing targeted mitigation strategies by Xiaoping Fu, Mo Wang, Dongqing Zhang, Furong Chen, Xiaotao Peng, Lie Wang, Soon Keat Tan

“…In terms of identifying driving factors, impervious surface percentage (ISP) and fractional vegetation cover (FVC) are the primary driving factors of urban flooding. …”
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Computational Techniques for Analysis of Thermal Images of Pigs and Characterization of Heat Stress in the Rearing Environment by Maria de Fátima Araújo Alves, Héliton Pandorfi, Rodrigo Gabriel Ferreira Soares, Gledson Luiz Pontes de Almeida, Taize Calvacante Santana, Marcos Vinícius da Silva

“…This research aims to develop a sequential methodology for the extraction of automatic characteristics from thermal images and the classification of heat stress in pigs by means of machine learning. …”
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Selection of features for modeling the risk of fatal outcomes in patients after myocardial infarction or unstable angina by D. A. Shvets, S. V. Povetkin

“…The filter feature selection identified significant quantitative characteristics, including age, left ventricular (LV) ejection fraction (EF), glomerular filtration rate, creatinine, body mass index, height, weight, body surface area (BSA), red blood cells, hemoglobin, glucose, total cholesterol (TC), lowdensity lipoprotein cholesterol, high-density lipoprotein cholesterol, heart rate, LV end-diastolic volume index, LV end-systolic volume index, pulmonary artery systolic pressure. …”
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Exploring the Habitat Distribution of <i>Decapterus macarellus</i> in the South China Sea Under Varying Spatial Resolutions: A Combined Approach Using Multiple Machine Learning and... by Qikun Shen, Peng Zhang, Xue Feng, Zuozhi Chen, Jiangtao Fan

“…The selection of environmental variables with different spatial resolutions is a critical factor affecting the accuracy of machine learning-based fishery forecasting. In this study, spring-season survey data of <i>Decapterus macarellus</i> in the South China Sea from 2016 to 2024 were used to construct six machine learning models—decision tree (DT), extra trees (ETs), K-Nearest Neighbors (KNN), light gradient boosting machine (LGBM), random forest (RF), and extreme gradient boosting (XGB)—based on seven environmental variables (e.g., sea surface temperature (SST), chlorophyll-a concentration (CHL)) at four spatial resolutions (0.083°, 0.25°, 0.5°, and 1°), filtered using Pearson correlation analysis. …”
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SFANet: A Ground Object Spectral Feature Awareness Network for Multimodal Remote Sensing Image Semantic Segmentation by Yizhou Lan, Daoyuan Zheng, Yingjun Zheng, Feizhou Zhang, Zhuodong Xu, Ke Shang, Zeyu Wan

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Capturing Subgrid Cold Pool Dynamics With U‐Net: Insights From Large‐Eddy Simulation for Storm‐Resolving Modeling by Yi‐Chang Chen, Chien‐Ming Wu

“…The high‐resolution data is coarsened to 0.8, 1.6, 3.2, and 6.4 km to mimic the horizontal resolutions of GSRMs. U‐Net deep learning models are developed to predict the high‐resolution distribution of cold pools using coarsened near‐surface (at height of 100 m) physical variables, including horizontal winds, potential temperature, and relative humidity. …”
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SA‐RelayGANs: A Novel Framework for the Characterization of Complex Hydrological Structures Based on GANs and Self‐Attention Mechanism by Zhesi Cui, Qiyu Chen, Gang Liu, Lei Xun

“…Abstract Increased availability and quality of surface and subsurface observations provide the chance to improve the perception of hydrological phenomena. …”
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Integration of Sentinel-1 and Sentinel-2 for temporal identification of aquacultural ponds by Vaishnavi Joshi, Dipanwita Haldar, Subhadip Dey

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Identification and evaluation of bioactive compounds from Azadirachta indica as potential inhibitors of DENV-2 capsid protein: An integrative study utilizing network pharmacology,... by Md. Ahad Ali Khan, Md. Nazmul Hasan Zilani, Mahedi Hasan, Nahid Hasan

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Soil Moisture Prediction Using the VIC Model Coupled with LSTMseq2seq by Xiuping Zhang, Xiufeng He, Rencai Lin, Xiaohua Xu, Yanping Shi, Zhenning Hu

“…Compared with classical machine learning (ML) models, traditional LSTM models, and advanced transformer models, the LSTMseq2seq model achieved R² values of 0.949, 0.9322, 0.8839, 0.8042, and 0.7451 for the prediction of surface SM over 3 days, 7 days, 30 days, 60 days, and 90 days, respectively. …”
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