Limited Performance of Machine Learning Models Developed Based on Demographic and Laboratory Data Obtained Before Primary Treatment to Predict Coronary Aneurysms by Mi-Jin Kim, Gi-Beom Kim, Dongha Yang, Yeon-Jin Jang, Jeong-Jin Yu

“…Future studies should focus on enhancing predictive models by incorporating additional clinical data, such as acute-phase coronary artery diameter measurements, to improve accuracy and clinical utility.…”
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Generating 1 km Seamless Land Surface Temperature from China FY3C Satellite Data Using Machine Learning by Xinhan Liu, Weiwei Zhu, Qifeng Zhuang, Tao Sun, Ziliang Chen

“…This method successfully reconstructed the FY-3C satellite’s 1 km level all-weather LST time series, providing reliable technical support for the use of domestic satellite data in remote sensing applications such as ecological drought monitoring and urban heat island tracking.…”
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Effects of azithromycin in young adults with cystic fibrosis: a protocol for emulating a published randomised controlled trial using registry data by Bin Huang, Rhonda Szczesniak, Lutz Naehrlich, Freddy Frost, Gwyneth Davies, Sanja Stanojevic, Susan Charman, Nicole Mayer-Hamblett, Emily Granger, Alex Gifford, Josh Ostrenga, Jonathan Todd, Susan Christine Charman, Elizabeth Cromwell, Nicole Mayer Hamblett, Ruth Keogh, Josh Osttrenga

“…This protocol describes a study which aims to assess the applicability of target trial emulation in CF. We aim to emulate an existing trial in CF and assess to what extent the results from the trial can be replicated using registry data.Methods and analysis The target trial is a published randomised controlled trial which found evidence for beneficial effects of azithromycin use on lung function in young adults with CF. …”
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Predicting errors in accident hotspots and investigating satiotemporal, weather, and behavioral factors using interpretable machine learning: An analysis of telematics big data. by Ali Golestani, Nazila Rezaei, Mohammad-Reza Malekpour, Naser Ahmadi, Seyed Mohammad-Navid Ataei, Sepehr Khosravi, Ayyoob Jafari, Saeid Shahraz, Farshad Farzadfar

“…While machine learning (ML) has been increasingly used to predict RTAs, the lack of interpretability limits its applicability in policymaking. This study aimed to utilize interpretable ML models to predict the occurrence of errors in road accident hotspots using telematics data in Iran and interpret the most influential predictors.…”
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Effectiveness and patient-reported impact of on-flare retreatment in patients with rheumatoid arthritis: insights from retrospective long-term real-world data by Delphine Bertrand, Elias De Meyst, Michaël Doumen, Diederik De Cock, Johan Joly, Barbara Neerinckx, Sofia Pazmino, Nele Pype, Tom Conings, René Westhovens, Patrick Verschueren

“…Trial registration Not applicable.…”
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Real-world data on clinical outcomes and validation of prognostic models for angioimmunoblastic T-cell lymphoma: a multicentric retrospective study in Southern China by Qiu-Yuan Xiang, Jing-Song Wu, Ling Huang, Zong-Kai Zou, Ting-Bo Liu, Zhi-Gang Peng, Li-E. Lin, Xiao-Lei Wei, Hong-Yu Zhang, Yun Lin, Guo-Wei Li, Yi-Rong Jiang, Hua Wang, Ke-Qian Shi, Rui-Ji Zheng, Zhi-Jun Han, Xiao Qiu, Wen-Yu Li, Ji-Hao Zhou

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Toward an Operational System for Automatically Detecting <i>Xylella fastidiosa</i> in Olive Groves Based on Hyperspectral and Thermal Remote Sensing Data by Annarita D’Addabbo, Raffaella Matarrese, Francesco Lovergine, Alberto Refice, Antonella Belmonte, Fabio Bovenga, Antonia Gallo, Serafina Serena Amoia, Raied Abou Kubaa, Giovanni Mita, Donato Boscia

“…First, we assess whether the size of a data set, composed of a limited number of labelled examples, is sufficient to train accurate classifiers. …”
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A Hybrid Brain Tumor Classification Using FL With FedAvg and FedProx for Privacy and Robustness Across Heterogeneous Data Sources by N. Sivakumar, Ahmad Raza Khan, Syed Umar, R. N. Ravikumar, I. Bremnavas, Munindra Lunagaria, Krunal Vaghela, Ghanshyam G. Tejani, Sunil Kumar Sharma

“…This method is especially suitable for medical applications with strict privacy regulations, as it provides an approach to linking heterogeneous data from multiple healthcare institutions.…”
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Sea Surface Height Inversion Model Based on Multimodal Deep Learning for the Fusion of Heterogeneous FY-3E GNSS-R Data by Yun Zhang, Ganyao Qin, Shuhu Yang, Yanling Han, Zhonghua Hong

“…The current deep-learning-based SSH inversion techniques primarily relying on single-modal data are unable to fully leverage the rich feature information from global navigation satellite system reflectometry (GNSS-R) remote sensing data, therefore limiting the potential accuracy improvement. …”
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Space-Frequency Fusion Dual-Branch Convolutional Neural Networks for Significant Wave Height Retrieval From GF-3 SAR Data by Xuan Jin, Yawei Zhao, Xin Zhang, Yanlei Du, Jinsong Chong

“…Most retrieval models based on GF-3 data primarily focus on wave mode data, with limited utilization of data from other imaging modes. …”
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Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning by Steve Agius, Vincent Cassar, Caroline Magri, Wasiq Khan, Dhiya Al-Jumeily Obe, Godwin Caruana, Luke Topham

“…In Stage 1, predictions were made at the triage level using basic demographic and presenting symptom data. Stage 2 incorporated critical blood test results (e.g., Haemoglobin, C-Reactive Protein, Troponin T, and White Blood Cell Count) alongside the demographic and symptom data from Stage 1 to refine and enhance predictions. …”
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Interpretable Machine Learning for Multi-Crop Yield Prediction in Semi-Arid Regions: A Hierarchical Approach to Handle Climate Data Sparsity by Rachid Ed-daoudi, M’barek El Haloui

“… This study develops a hierarchical machine learning framework to address the challenges of multi-crop yield prediction in semi-arid regions, focusing on sparse climate data, model interpretability, and heterogeneous climate-crop interactions. …”
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A Novel Approach to Generate Large-Scale InSAR-Derived Velocity Fields: Enhanced Mosaicking of Overlapping InSAR Data by Xupeng Liu, Guangyu Xu, Yaning Yi, Tengxu Zhang, Yuanping Xia

“…For instance, the LiCSAR processing platform provides interferometric images covering approximately 250 km × 250 km, facilitating scientific applications of InSAR data. However, the transition from individual interferograms to large-scale, three-dimensional deformation fields often requires additional processing steps, including ramp correction within the images, mosaicking between adjacent images, and the joint inversion of InSAR observations from different viewing angles. …”
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Multimodal Data-Driven Visual Sensitivity Assessment and Planning Response Strategies for Streetscapes in Historic Districts: A Case Study of Anshandao, Tianjin by Ya-Nan Fang, Aihemaiti Namaiti, Shaoqiang Zhang, Tianjia Feng

“…Subjective weights were obtained via the analytic hierarchy process (AHP), incorporating expert and public judgments, while objective weights were derived through the entropy weight method (EWM) based on data information entropy. The integration of both approaches enhances the methodological rigor and scientific validity of SVS weight determination. …”
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Spatio-Temporal Fusion of Landsat and MODIS Data for Monitoring of High-Intensity Fire Traces in Karst Landscapes: A Case Study in China by Xiaodong Zhang, Jingyi Zhao, Guanzhou Chen, Tong Wang, Qing Wang, Kui Wang, Tingxuan Miao

“…The surface fragmentation of karst landscapes leads to a high degree of coupling between fire scar site boundaries and topographic relief. However, the applicability of spatio-temporal data fusion methods for fire scar extraction in such geomorphological areas remains systematically unevaluated. …”
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Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in Coal Mining Areas and Their Responses to Mining Activities Using Sentinel-2A Data by Pengfei Wang, Shidong Wang, Hebing Zhang, Yiheng Jiao, Menglin Liu

“…Based on the Sentinel-2A remote sensing data during 2016&#x2013;2024, this article assessed the applicability of NDVI-f_PV and DFI-f_NPV in low hilly areas by establishing two sets of quantitative models; innovatively adopted GF-2 imagery and utilized supervised classification to remove pixel noise in Sentinel-2A to quantitatively estimate the photosynthetic and NPV cover in the coal mine area. …”
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Expert identification blitz: A rapid high value approach for assessing and improving iNaturalist identification accuracy and data precision and confidence by Thomas Mesaglio, Kelly A. Shepherd, Juliet A. Wege, Russell L. Barrett, Hervé Sauquet, Will K. Cornwell

“…Societal Impact Statement Citizen science data are increasingly used in research and conservation, so assessing and improving data accuracy is important. …”
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Fine-grained building function recognition with street-view images and GIS map data via geometry-aware semi-supervised learning by Weijia Li, Jinhua Yu, Dairong Chen, Yi Lin, Runmin Dong, Xiang Zhang, Conghui He, Haohuan Fu

“…This study offers a new solution for large-scale and multi-city applications by efficiently utilizing multi-source geoinformation data, enhancing urban information acquisition efficiency, and assisting in rational resource allocation.…”
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Super-Resolution of Landsat-8 Land Surface Temperature Using Kolmogorov–Arnold Networks with PlanetScope Imagery and UAV Thermal Data by Mahdiyeh Fathi, Hossein Arefi, Reza Shah-Hosseini, Armin Moghimi

“…Despite this, the proposed approach is effective in areas with limited UAV data availability.…”
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The Capabilities of Optical and C-Band Radar Satellite Data to Detect and Understand Faba Bean Phenology over a 6-Year Period by Frédéric Baup, Rémy Fieuzal, Clément Battista, Herivanona Ramiakatrarivony, Louis Tournier, Serigne-Fallou Diarra, Serge Riazanoff, Frédéric Frappart

“…This study analyzes the potential of optical and radar satellite data to monitor faba bean (<i>Vicia faba</i> L.) phenology over six years (2016–2021) in southwestern France. …”
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