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Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load Uncertainty
Published 2025-04-01Get full text
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The protocol for a multicentre prospective randomized noninferiority trial of surgical reduction versus non-surgical casting for displaced distal radius fractures in children: Chil...
Published 2025-05-01“…The primary outcome is the PROMIS Upper Extremity Score at three months post-randomization. All data will be obtained through electronic questionnaires completed by the participants and/or parents/guardians. …”
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Missing values imputation using Fuzzy K-Top Matching Value
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GATCGGenerator: New Software for Generation of Quasirandom Nucleotide Sequences
Published 2023-09-01Get full text
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Robotic Hand–Eye Calibration Method Using Arbitrary Targets Based on Refined Two-Step Registration
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Chaos-enhanced metaheuristics: classification, comparison, and convergence analysis
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Enhancing Bee Mite Detection with YOLO: The Role of Data Augmentation and Stratified Sampling
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Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning
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Multi-objective Windy Postman Problem in a Fuzzy Transportation Network
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Forest canopy closure estimation in mountainous southwest China using multi-source remote sensing data
Published 2025-08-01“…Then, the multi-source remote sensing image Sentinel-1/2 and terrain factors were combined to perform regional-scale FCC remote sensing estimation based on the geographically weighted regression (GWR) model. The research results showed that (1) among the 50 extracted ATLAS LiDAR feature indices, the best footprint-scale modeling factors are Landsat_perc, h_dif_canopy, asr, h_min_canopy, toc_roughness, and n_touc_photons after random forest (RF) feature variable optimization; (2) among the BO-RFR, BO-KNN, and BO-GBRT models developed at the footprint scale, the FCC results estimated by the BO-GBRT model were the best (R2 = 0.65, RMSE = 0.10, RS = 0.079, and P = 79.2%), which was used as the FCC estimation model for 74,808 footprints in the study area; (3) taking the FCC value of ATLAS footprint scale in forest land as the training sample data of the regional-scale GWR model, the model accuracy was R2 = 0.70, RMSE = 0.06, and P = 88.27%; and (4) the R² between the FCC estimates from regional-scale remote sensing and the measured values is 0.70, with a correlation coefficient of 0.784, indicating strong agreement. …”
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Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques
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Enhanced Viral Genome Classification Using Large Language Models
Published 2025-05-01Get full text
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