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http://dx.doi.org/10.1111/anae.16765 | DOI Listing |
Med Biol Eng Comput
September 2025
Department of Computer Science, Università degli Studi di Bari Aldo Moro, Bari, Italy.
Fetal standard plane detection is essential in prenatal care, enabling accurate assessment of fetal development and early identification of potential anomalies. Despite significant advancements in machine learning (ML) in this domain, its integration into clinical workflows remains limited-primarily due to the lack of standardized, end-to-end operational frameworks. To address this gap, we introduce FetalMLOps, the first comprehensive MLOps framework specifically designed for fetal ultrasound imaging.
View Article and Find Full Text PDFInt J Vitam Nutr Res
August 2025
Department of Plastic and Cosmetic Center, The First Affiliated Hospital, Zhejiang University, 310003 Hangzhou, Zhejiang, China.
The vitamin B complex, a group of water-soluble vitamins, is essential for various metabolic and cellular processes and critical for achieving optimal surgical outcomes in plastic and cosmetic procedures. This review examines the mechanistic contributions of this complex at the cellular level, including any roles in mitochondrial bioenergetics, redox balance, gene regulation, and cellular repair mechanisms. Niacinamide, as a precursor to NAD⁺, enhances mitochondrial efficiency and facilitates energy production, supporting tissue regeneration.
View Article and Find Full Text PDFFront Plant Sci
August 2025
Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, China.
Introduction: Rice is an important food crop but is susceptible to diseases. However, currently available spot segmentation models have high computational overhead and are difficult to deploy in field environments.
Methods: To address these limitations, a lightweight rice leaf spot segmentation model (MV3L-MSDE-PGFF-CA-DeepLabv3+, MMPC-DeepLabv3+) was developed for three common rice leaf diseases: rice blast, brown spot and bacterial leaf blight.
Front Plant Sci
August 2025
Engineering Research Center of Edibleand Medicinal Fungi, Ministry of Education, Jilin Agricultural University Changchun, Changchun, China.
Traditional path planning algorithms often face problems such as local optimum traps and low monitoring efficiency in agricultural UAV operations, making it difficult to meet the operational requirements of complex environments in modern precision agriculture. Therefore, there is an urgent need to develop an intelligent path planning algorithm. To address this issue, this study proposes an improved Informed-RRT* path planning algorithm guided by domain-partitioned A* algorithm.
View Article and Find Full Text PDFFront Neurorobot
August 2025
College of Air Traffic Management, Civil Aviation Flight University of China, Chengdu, China.
Introduction: To address the challenges of current 4D trajectory prediction-specifically, limited multi-factor feature extraction and excessive computational cost-this study develops a lightweight prediction framework tailored for real-time air-traffic management.
Methods: We propose a hybrid RCBAM-TCN-LSTM architecture enhanced with a teacher-student knowledge distillation mechanism. The Residual Convolutional Block Attention Module (RCBAM) serves as the teacher network to extract high-dimensional spatial features via residual structures and channel-spatial attention.