Long‐term data reveals increase in vehicle collisions of endangered birds in Hokkaido, Japan by Kazuya Kobayashi, Annegret Moto Naito‐Liederbach, Toshio Sadakuni, Yuta Morii

“…These results suggest that long‐term data accumulation over large spatial scales allows us to understand the dynamics of accidents and predict potential factors underlying collision risks.…”
Get full text

Indian summer monsoon’s role in shaping variability in Arctic sea ice by Jiawei Zhu, Zhiwei Wu

“…Additionally, from a dynamic standpoint, low-level wind-driven sea ice drift helps shape the spatial distribution and extent of sea ice cover. …”
Get full text

Beacon2Science: Enhancing STEREO/HI Beacon Data With Machine Learning for Efficient CME Tracking by J. Le Louëdec, M. Bauer, T. Amerstorfer, J. A. Davies

“…We maximize information coherence between consecutive frames with adapted model architecture and loss functions through the different steps. …”
Get full text

Comparative analysis of injection rate and spray characteristics of ammonia and diesel from multi-hole diesel injector by Seonho Park, Gyuhan Bae, Seoksu Moon

“…It was further found that the spray penetration of ammonia and diesel can be scaled and predicted based on a conventional momentum-conservation-based spray penetration model.…”
Get full text

FAMHE-Net: Multi-Scale Feature Augmentation and Mixture of Heterogeneous Experts for Oriented Object Detection by Yixin Chen, Weilai Jiang, Yaonan Wang

“…Current detectors struggle with integrating spatial and semantic information effectively across scales and often omit necessary refinement modules to focus on salient features. …”
Get full text

Environmental drivers of Culicoides phenology: how important is species-specific variation when determining disease policy? by Kate R Searle, James Barber, Francesca Stubbins, Karien Labuschagne, Simon Carpenter, Adam Butler, Eric Denison, Christopher Sanders, Philip S Mellor, Anthony Wilson, Noel Nelson, Simon Gubbins, Bethan V Purse

“…We conclude that the current treatment of Avaritia Culicoides as a single group inhibits understanding of environmentally-driven spatial variation in species phenology and hinders the development of models for predicting the SVFP from environmental factors. …”
Get full text

Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution by Bochao Jiang, Michael Dorosan, Justin Wen Hao Leong, Marcus Eng Hock Ong, Sean Shao Wei Lam, Tiing Leong Ang

“…We used convolutional neural network-based models pretrained on large-scale open-domain data to extract spatial features of CE images that were then used in a dense feed-forward neural network classifier. …”
Get full text

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method by Fa Li, Qing Zhu, Kunxiaojia Yuan, Fujiang Ji, Arindam Paul, Peng Lee, Volker C. Radeloff, Min Chen

“…Abstract More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. …”
Get full text

Geospatial SHAP interpretability for urban road collapse susceptibility assessment: a case study in Hangzhou, China by Bofan Yu, Hui Li, Huaixue Xing, Weiya Ge, Liling Zhou, Jinrui Zhang, Meijun Xu, Cheng Yu

“…In addition to interpreting the contributions of evaluation factors through traditional SHAP summaries and bar plots, we displayed the SHAP values for each evaluation factor using map visualizations, and discussed the model’s sensitivity to different values. To validate the alignment between model predictions and physical collapse mechanisms, our study selected typical collapse cases, interpreted these cases combining map visualizations, SHAP force plots at collapse points, and the physical mechanisms of collapse. …”
Get full text

Early Warning of Nerve Agent Release in Large Indoor Environments Based on Encoder-Decoder Coupling Physics-Informed Neural Network by Shuobei Sun, Yang Peng, An Wang, Yiwen Xie, Yang Hu, Zhongyu Hou

“…Therefore, in this work, a new model called Encoder-Decoder Coupling Physics-Informed Neural Network is proposed, which learns from concentration data generated by experimentally validated CFD simulations and the spatio-temporal information to solve high-dimensional partial differential equations and provide predictions of nerve agents distribution that more closely align with objective physical laws. …”
Get full text

Evaluating Modified Soil Erodibility Factors with the Aid of Pedotransfer Functions and Dynamic Remote-Sensing Data for Soil Health Management by Pooja Preetha, Naveen Joseph

“…The results highlighted that the Kmlr model provided more accurate sediment yield (SY) predictions, particularly in agricultural areas, where traditional models overestimated erosion by upto 59.23 ton/ha. …”
Get full text

APPLICATION OF THE GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) METHOD IN FORECASTING THE CONSUMER PRICE INDEX IN FIVE CITIES OF SOUTH SULAWESI PROVINCE by Ahmad Zaki, Lutfiah Shafruddin, Irwan Thaha

“…The study focuses on five cities within South Sulawesi, where direct relationships between cities are possible, allowing the spatial model to be limited to the first-order. The data used in this study consists of monthly CPI data from January 2014 to March 2023. …”
Get full text

Development of an Augmented Reality Surgical Trainer for Minimally Invasive Pancreatic Surgery by Doina Pisla, Nadim Al Hajjar, Gabriela Rus, Bogdan Gherman, Andra Ciocan, Corina Radu, Calin Vaida, Damien Chablat

“…A convolutional neural network (CNN) model predicts forces without physical sensors, achieving a mean absolute error of 0.0244 N. …”
Get full text

A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning by Shahab Aldin Shojaeezadeh, Abdelrazek Elnashar, Tobias Karl David Weber

“…The spatio-temporal analysis of the model predictions demonstrates its transferability across different spatial and temporal context of Germany. …”
Get full text

Optimizing county-level infectious respiratory disease forecasts: a pandemic case study integrating social media-based physical and social connectivity networks by Fengrui Jing, Zhenlong Li, Shan Qiao, M. Naser Lessani, Huan Ning, Wenjun Ma, Jinjing Hu, Pan Yang, Xiaoming Li

“…However, existing time series forecasting models that incorporate human mobility data have faced challenges in making localized predictions on a large scale across the country due to data costs and constraints. …”
Get full text

The radial spreading of volcanic umbrella clouds deduced from satellite measurements by Fred Prata, Andrew T. Prata, Rebecca Tanner, Roy G. Grainger, Michael Borgas, Thomas J. Aubry

“…This model also predicts the observed radial velocities. …”
Get full text

An Ensemble of Convolutional Neural Networks for Sound Event Detection by Abdinabi Mukhamadiyev, Ilyos Khujayarov, Dilorom Nabieva, Jinsoo Cho

“…An ensemble approach combines predictions from three models, achieving F1 scores of 71.5% for segment-based metrics and 46% for event-based metrics. …”
Get full text

Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition by Pawel Powroznik, Maria Skublewska-Paszkowska, Krzysztof Dziedzic, Marcin Barszcz

“…The proposed model demonstrated excellent tennis movement prediction ability.…”
Get full text

Early detection of Zymoseptoria tritici infection on wheat leaves using hyperspectral imaging dataData INRAE by Lorraine Latchoumane, Martin Ecarnot, Ryad Bendoula, Jean-Michel Roger, Silvia Mas-Garcia, Heloïse Villesseche, Flora Tavernier, Maxime Ryckewaert, Nathalie Gorretta, Pierre Roumet, Elsa Ballini

“…These data are valuable since they can be used as a basis to monitor disease's development over time, to build leaf classification models according to their infection status per genotype per day, to develop prediction models related to symptoms' appearance, or to test imaging and spectral analysis methods.…”
Get full text

Novel transfer learning based bone fracture detection using radiographic images by Aneeza Alam, Ahmad Sami Al-Shamayleh, Nisrean Thalji, Ali Raza, Edgar Anibal Morales Barajas, Ernesto Bautista Thompson, Isabel de la Torre Diez, Imran Ashraf

“…In this study, we propose a novel transfer learning-based approach called MobLG-Net for feature engineering purposes. Initially, the spatial features are extracted from bone X-ray images using a transfer model, MobileNet, and then input into a tree-based light gradient boosting machine (LGBM) model for the generation of class probability features. …”
Get full text