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Insight on the reduction of copper content in slags produced from the Ausmelt Converting Process
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Load recognition method based on convolutional neural network and attention mechanism
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Identification of Two Types of Metallogenic Fluids in the Ultra-Large Huize Pb–Zn Deposit, SW China
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Gallium: A Universal Promoter Switching CO2 Methanation Catalysts to Produce Methanol
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Performance improvement of transcritical vapor generator by a novel double-heated annular configuration
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Experimental Evaluation of the Accuracy of Biped Model to Predict the Kinematics during Walking
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Image Super-Resolution Using Lightweight Multiscale Residual Dense Network
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Impact of Temperature on the Strength Development of the Tailing-Waste Rock Backfill of a Gold Mine
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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 Case Study of Combined Drainage Consolidation-Preloading Methods for a Highway Subgrade on Peat Soils
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Bio-Inspired Learning and Adaptation for Optimization and Control of Complex Systems
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Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights
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