Convective-scale forecast errors over Taiwan: an ensemble analysis of error covariances and their influences by Kao-Shen Chung, Chih-Chien Tsai, Li-Hsin Chen, Shu-Chih Yang

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The Origin of the Cluster of Local Interstellar Clouds by Catherine Zucker, Seth Redfield, Sara Starecheski, Ralf Konietzka, Jeffrey L. Linsky

“…Our model predicts that the formation of the individual CLIC clouds occurred progressively over the past 1 Myr and offers a natural explanation for the observed distribution, column density, temperature, and magnetic field structure of the complex.…”
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Dynamics of street environmental features and emotional responses in urban areas: implications for public health and sustainable development by Yangfei Huang, Yangfei Huang, Chenjian Zhong, Chenjian Zhong, Tangtao He, Yuyang Jiang

“…By integrating an emotion dataset assessed by 40 experts, a random forest model was constructed to predict emotional responses to different street spaces. …”
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Landscapes, habitat, and migratory behaviour: what drives the summer movements of a Northern viper? by Chloe R. Howarth, Christine A. Bishop, Karl W. Larsen

“…Migratory distance was best predicted by two top models: terrain and combined effects (including terrain, physiology, and vegetation factors). …”
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Exploring Transfer Learning for Anthropogenic Geomorphic Feature Extraction from Land Surface Parameters Using UNet by Aaron E. Maxwell, Sarah Farhadpour, Muhammad Ali

“…Semantic segmentation algorithms, such as UNet, that rely on convolutional neural network (CNN)-based architectures, due to their ability to capture local textures and spatial context, have shown promise for anthropogenic geomorphic feature extraction when using land surface parameters (LSPs) derived from digital terrain models (DTMs) as input predictor variables. …”
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Descriptive epidemiology of Lassa fever, its trend, seasonality, and mortality predictors in Ebonyi State, South- East, Nigeria, 2018—2022 by Adanna Ezenwa-Ahanene, Adetokunbo T. Salawu, Ayo S. Adebowale

“…Lassa fever showed a seasonal trend across the years. The quadratic model provided the best fit for predicting Lassa fever cumulative cases (R2 = 98.4%, P-value < 0.05). …”
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Detection transformer-based approach for mapping trees outside forests on high resolution satellite imagery by Tao Jiang, Maximilian Freudenberg, Christoph Kleinn, Timo Lüddecke, Alexander Ecker, Nils Nölke

“…In addition, we adopted a two-level tiling scheme and developed an R-tree-based Box Merging method to adapt to large images and remove redundant predictions more efficiently. Comparative analyses underscore the superior detection performance of DINO with a SWIN transformer as backbone, exhibiting an F1 score of 74% and an AP of 76%, surpassing other models such as Faster RCNN, YOLO, RetinaNet, DETR, Deformable-DETR, and DINO-Res50. …”
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Detecting Sensitive Spectral Bands and Vegetation Indices for Potato Yield Using Handheld Spectroradiometer Data by Diego Gomez, Pablo Salvador, Juan Fernando Rodrigo, Jorge Gil

“…Remote sensing is a valuable tool in precision agriculture due to its spatial and temporal coverage, non-destructive method of data collection, and cost-effectiveness. …”
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A hybrid deep learning framework for skin disease localization and classification using wearable sensors by Xiaoling Zhao, Huixin Zhang, Qian Zheng, Caihong Jing

“…This CNN-based multimodal fusion approach improves the model’s ability to capture spatial relationships and enhances classification performance. …”
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Evaluating the Quality of Light Emitted by Smartphone Displays by Nina Piechota, Krzysztof Skarżyński, Kamil Kubiak

“…It covered the color gamut, channel linearity response, refresh rate, flickering, spatial radiation distribution, luminance, uniformity, and static contrast. …”
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Monsoonal influence on particulate organic carbon variability through satellite data analysis by C.K. Tito, D.G. Bengen, T. Prartono, A. Damar, A.J. Wahyudi

“…This study aims to examine the spatial and temporal distribution of particulate organic carbon and to model its variance within Jakarta Bay, contributing to a deeper understanding of organic carbon dynamics in coastal ecosystems.METHODS: Monthly moderate-resolution imaging spectroradiometer satellite data for surface particulate organic carbon and chlorophyll-a during 2011 to 2023 periods were collected. …”
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Neddylation status determines the therapeutic sensitivity of tyrosine kinase inhibitors in chronic myeloid leukemia by Congyi Zhang, Yikai Yao, Qiuting Qian, Xiongyu Han, Yunkun Lu, Xinyi Jiang, Hongqiang Cheng, Xue Zhang, Ying Chi, Yuehai Ke, Peng Xiao

“…Furthermore, an artificial intelligence (AI) 3-Dimensional spatial structure binding technology was employed to predict the impact of neddylation on the structure of ABL1 kinase domain. …”
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DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts by Chaonan Ji, Tonio Fincke, Vitus Benson, Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Fabian Gans, Guido Kraemer, Francesco Martinuzzi, David Montero, Karin Mora, Oscar J. Pellicer-Valero, Claire Robin, Maximilian Söchting, Mélanie Weynants, Miguel D. Mahecha

“…Abstract With climate extremes’ rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. …”
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Retinotopic biases in contextual feedback signals to V1 for object and scene processing by Matthew A. Bennett, Lucy S. Petro, Clement Abbatecola, Lars F. Muckli

“…This feedback architecture could reflect the internal mapping in V1 of the brain's endogenous models of the visual environment that are used to predict perceptual inputs.…”
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A study on forest fire risk assessment in jiangxi province based on machine learning and geostatistics by Jinping Lu, Mangen Li, Yaozu Qin, Niannan Chen, Lili Wang, Wanzhen Yang, Yuke Song, Yisu Zheng

“…This study integrated multiple factors, including topography, climate, vegetation, and human activities, and employed machine learning models, specifically random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN), to predict forest fire occurrence in Jiangxi. …”
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Characterization of 2D precision and accuracy for combined visual-haptic localization by Madeline Fischer, Umberto Saetti, Martine Godfroy-Cooper, Douglas Fischer

“…Overall, the lack of improvement in precision for bimodal cueing relative to the best unimodal cueing modality, vision, is in favor of sensory combination rather than optimal integration predicted by the Maximum Likelihood Estimation (MLE) model. …”
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Structure-guided deep learning for back acupoint localization via bone-measuring constraints by Yulong Wang, Tian Lan, Wenjian Dou, Zhi Chen, Song Zhang, Gong Chen, Gong Chen

“…The method employs an HRFormer backbone network combined with a Structure-Guided Keypoint Estimation Module (SG-KEM) and a structure-constrained loss function, ensuring anatomically consistent predictions within a standardized spatial coordinate system to improve accuracy across diverse body types. …”
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Rapid Disaster Data Dissemination and Vulnerability Assessment through Synthesis of a Web-Based Extreme Event Viewer and Deep Learning by P. Shane Crawford, Mohammad A. Al-Zarrad, Andrew J. Graettinger, Alexander M. Hainen, Edward Back, Lawrence Powell

“…A variety of frameworks, models, and tools exist for advancing infrastructure vulnerability research. …”
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Quantitative Vulnerability Assessment of Buildings Exposed to Landslides Under Extreme Rainfall Scenarios by Guangming Li, Dong Liu, Mengjiao Ruan, Yuhua Zhang, Jun He, Zizheng Guo, Haojie Wang, Mengchen Cheng

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Advanced leukocyte classification using attention mechanisms and dual channel U-Net architecture by Gauri Kalnoor, Vijayalaxmi Kadrolli

“…This ADCU-Net model is particularly selected to effectively capture various features and preserve spatial data, achieving98.4% accuracy. …”
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