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Research on injection molded parts defect detection algorithm based on multiplicative feature fusion and improved attention mechanism.
Sci Rep
December 2024
School of Construction Machinery, Shandong Jiaotong University, Jinan, 250023, China.
Injection molded parts are increasingly utilized across various industries due to their cost-effectiveness, lightweight nature, and durability. However, traditional defect detection methods for these parts often rely on manual visual inspection, which is inefficient, expensive, and prone to errors. To enhance the accuracy of defect detection in injection molded parts, a new method called MRB-YOLO, based on the YOLOv8 model, has been proposed.
View Article and Find Full Text PDFNanocomposite based slow release atrazine effectively controlled Striga asiatica incidence, and enhanced sugarcane yield.
Sci Rep
December 2024
ICAR-Indian Grassland and Fodder Research Institute, Jhansi, 284 003, India.
Sugarcane is a major industrial crop highly susceptible to parasitic weed (Striga spp.), causing a 38% reduction in cane yield due to a longer lag phase of 20-40 days, and wider spacing. Herbicides with a longer retention and slow-release nature could allow Striga seeds to germinate and be killed before attaching to the host.
View Article and Find Full Text PDFAutomated and reference methods for the calculation of left ventricular outflow tract velocity time integral or ejection fraction by non-cardiologists: a systematic review on the agreement of the two methods.
J Clin Monit Comput
December 2024
Department of Anesthesia and Intensive Care, "Policlinico San Marco" University Hospital, Catania, Italy.
Echocardiography is crucial for evaluating patients at risk of clinical deterioration. Left ventricular ejection fraction (LVEF) and velocity time integral (VTI) aid in diagnosing shock, but bedside calculations can be time-consuming and prone to variability. Artificial intelligence technology shows promise in providing assistance to clinicians performing point-of-care echocardiography.
View Article and Find Full Text PDFAutomated Distal Radius and Ulna Skeletal Maturity Grading from Hand Radiographs with an Attention Multi-Task Learning Method.
Tomography
November 2024
Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong 999077.
Assessment of skeletal maturity is a common clinical practice to investigate adolescent growth and endocrine disorders. The distal radius and ulna (DRU) maturity classification is a practical and easy-to-use scheme that was designed for adolescent idiopathic scoliosis clinical management and presents high sensitivity in predicting the growth peak and cessation among adolescents. However, time-consuming and error-prone manual assessment limits DRU in clinical application.
View Article and Find Full Text PDFTransformer Dil-DenseUnet: An Advanced Architecture for Stroke Segmentation.
J Imaging
November 2024
Research Laboratory: Networked Objects, Control and Communication Systems, NOCCS-ENISo, National Engineering School of Sousse, University of Sousse, Soussse 4023, Tunisia.
We propose a novel architecture, Transformer Dil-DenseUNet, designed to address the challenges of accurately segmenting stroke lesions in MRI images. Precise segmentation is essential for diagnosing and treating stroke patients, as it provides critical spatial insights into the affected brain regions and the extent of damage. Traditional manual segmentation is labor-intensive and error-prone, highlighting the need for automated solutions.
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