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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Spatiotemporal Changes of China's Carbon Emissions by Bofeng Cai, Xiuquan Wang, Guohe Huang, Jinnan Wang, Dong Cao, Brian W. Baetz, Lei Liu, Hua Zhang, Adam Fenech, Zhu Liu

“…This suggests a clear transition to a more intensive economic growth model in South China as a result of the energy conservation and emission reduction policies, while the expanded carbon hot spots in North China are mainly dominated by the Grand Western Development Program. …”
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Impact of a national primary care pay-for-performance scheme on ambulatory care sensitive hospital admissions: a small-area analysis in England by Tim Doran, Christos Grigoroglou, Luke Munford, Evangelos Kontopantelis, Navneet Kapur, Darren Ashcroft, Roger Webb

“…Objective We aimed to spatially describe hospital admissions for ambulatory care sensitive conditions (ACSC) in England at small-area geographical level and assess whether recorded practice performance under one of the world’s largest primary care pay-for-performance schemes led to reductions in these potentially avoidable hospitalisations for chronic conditions incentivised in the scheme.Setting We obtained numbers of ACSC hospital admissions from the Hospital Episode Statistics database and information on recorded practice performance from the Quality and Outcomes Framework (QOF) administrative dataset for 2015/2016. …”
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Anthropogenic and natural influence on vegetation ecosystems from 1982 to 2023 by Teligeer Bao, Huaqiang Li, Yonghong Hao, Matthew Tom Harrison, Ke Liu, Gulnazar Ali

“…Here, we used 12 machine learning algorithms to perform pixel correction on 42 years of moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) and GIMMS NDVI data. The models exhibited high accuracy (93%–97%), yielding a robust ensemble R ^2 of 0.88 at the spatial scale. …”
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Plasma-wall interaction impact of the ITER re-baseline by R.A. Pitts, A. Loarte, T. Wauters, M. Dubrov, Y. Gribov, F. Köchl, A. Pshenov, Y. Zhang, J. Artola, X. Bonnin, L. Chen, M. Lehnen, K. Schmid, R. Ding, H. Frerichs, R. Futtersack, X. Gong, G. Hagelaar, E. Hodille, J. Hobirk, S. Krat, D. Matveev, K. Paschalidis, J. Qian, S. Ratynskaia, T. Rizzi, V. Rozhansky, P. Tamain, P. Tolias, L. Zhang, W. Zhang

“…Conservative assessments of the W wall source, coupled with integrated modelling of W pedestal and core transport, demonstrate that the elimination of Be presents only a low risk to the achievement of the principal ITER Q = 10 DT burning plasma target. …”
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Biomass distribution law of winter wheat in mining-affected area based on UAV remote sensing by Jing WANG, Wenbing GUO, Zhichao CHEN, Erhu BAI

“…The results show that: ① The selected vegetation indices and texture features were significantly correlated with biomass, and the combination of vegetation indices and texture features as input variables achieved the highest estimation accuracy. The SVR model had the highest prediction accuracy. ② Biomass in regions III (414–661 g/m2) and IV (662–822 g/m2) accounted for 66.4% of the total, indicating that most samples concentrated in the middle and high biomass range. …”
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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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Global patterns and drivers of soil dissolved organic carbon concentrations by T. Ren, T. Ren, A. Cai

“…Machine learning techniques were employed, including 10-fold cross-validation and evaluating model performance by <span class="inline-formula"><i>R</i>²</span> and root mean square error values, to predict the relative importance of various predictors and the global distribution of soil DOC concentrations. …”
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Time-Distributed Vision Transformer Stacked With Transformer for Heart Failure Detection Based on Echocardiography Video by Mgs M. Luthfi Ramadhan, Adyatma W. A. Nugraha Yudha, Muhammad Febrian Rachmadi, Kevin Moses Hanky Jr Tandayu, Lies Dina Liastuti, Wisnu Jatmiko

“…The time-distributed vision transformer learns the spatial feature and then feeds the result to the transformer to learn the temporal feature and make the final prediction afterward. …”
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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

“…WoE was employed to select negative samples, which were compared with those obtained using traditional random sampling methods. The optimal model was then utilized to generate seasonal spatial distribution maps of forest fire risk throughout Jiangxi Province. …”
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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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