Production, Transport, and Destruction of Dust in the Kuiper Belt: The Effects of Refractory and Volatile Grain Compositions by Thomas Corbett, Alex Doner, Mihály Horányi, Pontus Brandt, Will Grundy, Carey M. Lisse, Joel Parker, Lowell Peltier, Andrew R. Poppe, Kelsi N. Singer, S. Alan Stern, Anne J. Verbiscer

“…Models based on early SDC measurements predicted a peak dust number density at a heliocentric distance of ∼40 au, followed by a rapid decline. …”
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Leveraging ResNet50 With Swin Attention for Accurate Detection of OCT Biomarkers Using Fundus Images by S. Tamilselvi, M. Suchetha, Rajiv Raman

“…Therefore, regular screening of diabetes patients for the early detection of retinal abnormalities is essential to prevent the development and progression of DR and DME. The proposed model predicts DME-associated biomarkers, typically identified in Optical Coherence Tomography (OCT), using 2D fundus images. …”
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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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Evaluation of 3D‐Var and 4D‐Var data assimilation on simulation of heavy rainfall events over the Indian region by Shivaji S. Patel, Ashish Routray, Vivek Singh, R. Bhatla, Rohan Kumar, Elena Surovyatkina

“…Similarly, it is found that satellite wind assimilation improved the statistical skill scores in predicting heavy precipitation in all three cases. Overall, the study suggested that the performance of the NCUM‐R model integrated with the 4D‐Var technique improved the model's forecast skill in the simulation of HREs.…”
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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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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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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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Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability by Danish Raza, Hong Shu, Muhsan Ehsan, Hong Fan, Kamal Abdelrahman, Hasnat Aslam, Abdul Quddoos, Rana Waqar Aslam, Majid Nazeer, Mohammed S. Fnais, Azeem Sardar

“…Third, population dynamics were examined by applying an exponential growth model to forecast population growth and predict food demand. …”
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AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…Subsequently, the center pixel coordinates of the bounding boxes predicted by the detection model were integrated with the yaw angle data recorded in the UAV's flight logs to construct an Euler angle rotation matrix. …”
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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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Control Performance and Preliminary Application Investigation of Novel Rotational Impact Dampers by SHEN Junjie, WANG Jingjing

“…Following the verification of the effectiveness of the numerical model and the correctness of the theoretical model, the numerical model of the RID was used for subsequent numerical simulation research. …”
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Hypervelocity Stars Trace a Supermassive Black Hole in the Large Magellanic Cloud by Jiwon Jesse Han, Kareem El-Badry, Scott Lucchini, Lars Hernquist, Warren Brown, Nico Garavito-Camargo, Charlie Conroy, Re’em Sari

“…The predicted spatial and kinematic distributions of the simulated HVSs are remarkably similar to the observed distributions. …”
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Environmental factors influencing the odds of livestock predations by wolves in North–Eastern Italy across 10 years: a network analysis approach by Marcello Franchini, Salvatore Raniolo, Mirco Corazzin, Maurizio Ramanzin, Giulia Della Longa, Paolo Zanghellini, Natalia Bragalanti, Stefano Bovolenta

“…Large–scale data accessible to public institutions can help predict the temporal and spatial evolution of livestock predations by wolves. …”
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Analysing LULC transformations using remote sensing data: insights from a multilayer perceptron neural network approach by Khadim Hussain, Kaleem Mehmood, Sun Yujun, Tariq Badshah, Shoaib Ahmad Anees, Fahad Shahzad, Nooruddin, Jamshid Ali, Muhammad Bilal

“…The study highlights the effectiveness of the MLP-NN model in accurately predicting changes in LULC. The model’s exceptional accuracy and proficiency make it a powerful tool for future LULC forecasts. …”
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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

“…Extracted building images were used to train a deep learning neural network to predict whether a building was elevated or nonelevated. …”
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