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Algorithm for Cloud Particle Phase Identification Based on Bayesian Random Forest Method
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Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm
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Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Plantar Pressure
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Structural Modal Analysis and Optimization of SUV Door Based on Response Surface Method
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Virtual Reconstruction-Based Robust Adaptive Beamforming for Distributed Digital Subarray Antennas
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A Novel Influence Maximization Algorithm for a Competitive Environment Based on Social Media Data Analytics
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Using the TOPSIS method to select the best low-toxicity organic cosolvent for rice-based toxicity tests
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Numerical Simulation Study on Directional Solidification of DD5 Nickel-Based Single-Crystal Turbine Blades
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A 10 GHz Dual-Loop PLL With Active Cycle-Jitter Correction Achieving 12dB Spur and 29% Jitter Reduction
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CECT-Based Radiomic Nomogram of Different Machine Learning Models for Differentiating Malignant and Benign Solid-Containing Renal Masses
Published 2025-01-01“…Lu Qian,1,* BinHai Fu,2,* Hong He,2 Shan Liu,1 RenCai Lu2 1Department of Pathology, the First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, 650032, People’s Republic of China; 2Department of Nuclear Medicine, The First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, 650032, People’s Republic of China*These authors contributed equally to this workCorrespondence: RenCai Lu, Department of Nuclear Medicine, The First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, No. 157 Jinbi Road, Kunming, Yunnan, 650032, People’s Republic of China, Tel +86 087163614503, Email lrc_09@126.comObjective: This study aimed to explore the value of a radiomic nomogram based on contrast-enhanced computed tomography (CECT) for differentiating benign and malignant solid-containing renal masses.Materials and Methods: A total of 122 patients with pathologically confirmed benign (n=47) or malignant (n=75) solid-containing renal masses were enrolled in this study. …”
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A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors
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Advances in plant-based raw materials for food 3D printing
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