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Applications of Multi-Robotic Arms to Assist Agricultural Production: A Review
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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“…Up to one year after randomization, the main outcomes plus data on complications, resource use, and school absence will be collected. …”
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Performance of invariants of gravity gradient tensor in matching navigation: A case study in South China Sea
Published 2025-05-01“…However, using gravity gradient invariants in existing research is seldom a concern. The gravity gradient tensor has three invariants, named as I1, I2 and I3. …”
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A study on life assessment of metro cowcatcher based on multi-axis vibration environment reconstruction
Published 2023-11-01“…It was found that the resonance fatigue of the structure was mainly caused by the rail corrugation with the frequency of 93 Hz on the line. …”
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Optimal Allocation of Hydrogen Storage Capacity Based on Improved Cat Swarm Optimization
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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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Machine-Learning-Based Integrated Mining Big Data and Multi-Dimensional Ore-Forming Prediction: A Case Study of Yanshan Iron Mine, Hebei, China
Published 2025-04-01“…Combined with spectral and elemental analysis, the universality of alteration features such as chloritization and carbonation, which are closely related to the mineralization process, was further verified. (3) Based on the spectral and elemental grade data of rock and mineral samples, a training model for ore grade–spectrum correlation was constructed using Random Forests, Support Vector Machines, and other algorithms, with the SMOTE algorithm applied to balance positive and negative samples. …”
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Evaluating regional sustainable energy potential through hierarchical clustering and machine learning
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