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A Large-Scale Agricultural Land Classification Method Based on Synergistic Integration of Time Series Red-Edge Vegetation Index and Phenological Features.
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National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, China.
Agricultural land classification plays a pivotal role in food security and ecological sustainability, yet achieving accurate large-scale mapping remains challenging. This study presents methodological innovations through a multi-level feature enhancement framework that transcends traditional time series analysis. Using Shandong Province, northern China's agricultural heartland, as a case study, we first established a foundation with time series red-edge vegetation indices (REVI) from Sentinel-2 imagery, uniquely combining the normalized difference red edge index (NDRE) and plant senescence reflectance index (PSRI).
View Article and Find Full Text PDFA Precessing-Coin-like Rotary Actuator for Distal Endoscope Scanners: Proof-of-Concept Study.
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Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
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State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 611731, China.
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Background: Recombinant growth hormone (rhGH) has been used since 1985 to treat growth hormone (GH)-induced short stature, typically associated with transient adverse events. However, lipoatrophy, characterized by irreversible damage to subcutaneous fat, was first reported in 1999 and linked to antibody formation. In 2021, localized lipoatrophy was observed in 14.
View Article and Find Full Text PDFTransRAUNet: A Deep Neural Network with Reverse Attention Module Using HU Windowing Augmentation for Robust Liver Vessel Segmentation in Full Resolution of CT Images.
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Department of Medical Device and Healthcare, Dongguk University, Seoul 04620, Republic of Korea.
Liver cancer has a high mortality rate worldwide, and clinicians segment liver vessels in CT images before surgical procedures. However, liver vessels have a complex structure, and the segmentation process is conducted manually, so it is time-consuming and labor-intensive. Consequently, it would be extremely useful to develop a deep learning-based automatic liver vessel segmentation method.
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