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Efficient Generative-Adversarial U-Net for Multi-Organ Medical Image Segmentation.
J Imaging
January 2025
School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213000, China.
Manual labeling of lesions in medical image analysis presents a significant challenge due to its labor-intensive and inefficient nature, which ultimately strains essential medical resources and impedes the advancement of computer-aided diagnosis. This paper introduces a novel medical image-segmentation framework named Efficient Generative-Adversarial U-Net (EGAUNet), designed to facilitate rapid and accurate multi-organ labeling. To enhance the model's capability to comprehend spatial information, we propose the Global Spatial-Channel Attention Mechanism (GSCA).
View Article and Find Full Text PDFIs Membrane Filtration Applicable for the Recovery of Biologically Active Substances from Spent Lavender?
Membranes (Basel)
January 2025
Department of Chemical Engineering, University of Chemical Technology and Metallurgy, 1576 Sofia, Bulgaria.
This study explored the batch membrane filtration of 40% ethanol extracts from spent lavender, containing valuable compounds like rosmarinic acid, caffeic acid, and luteolin, using a polyamide-urea thin film composite X201 membrane. Conducted at room temperature and 20 bar transmembrane pressure, the process demonstrated high efficiency, with rejection rates exceeding 98% for global antioxidant activity and 93-100% for absolute concentrations of the target components. During concentration, the permeate flux declined from 2.
View Article and Find Full Text PDFAdvancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring.
Front Plant Sci
January 2025
College of Information Technology, Jilin Agricultural University, Changchun, China.
Introduction: Potatoes and tomatoes are important Solanaceae crops that require effective disease monitoring for optimal agricultural production. Traditional disease monitoring methods rely on manual visual inspection, which is inefficient and prone to subjective bias. The application of deep learning in image recognition has led to object detection models such as YOLO (You Only Look Once), which have shown high efficiency in disease identification.
View Article and Find Full Text PDF[Research advance of solid-phase microextraction based on covalent organic framework materials].
Se Pu
February 2025
School of Chemistry and Chemical Engineering, University of Jinan, Jinan 250022, China.
Solid-phase microextraction (SPME) is a fast and simple sample preparation technique that enables the enrichment of analytes, and it is used in combination with other detection techniques to provide accurate and sensitive analytical methods. SPME is widely used in environmental monitoring, food safety, life analysis, biomedicine, and other applications. The extractive coating is the core of the SPME technique, and the properties of the extractive coating greatly influence extraction selectivity and efficiency, as well as the enrichment effect.
View Article and Find Full Text PDFStabilizing Single-Atom Catalysts on Metastable Phases of Transition Metal Dichalcogenides.
J Phys Chem Lett
January 2025
Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Single-atom catalysts have attracted a significant amount of attention due to their exceptional atomic utilization and high efficiency in a range of catalytic reactions. However, these systems often face thermodynamic instability, leading to agglomeration under the operational conditions. In this study, we investigate the interactions of 12 types of catalytic atoms (Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag, Ir, Pt, Au, and Bi) on three crystalline phases (1T, 1T', and 2H) of six transition metal dichalcogenide layers (MoS, MoSe, MoTe, WS, WSe, and WTe) using first-principles calculations.
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