In diverse biological flight systems, the leading edge vortex has been implicated as a flow feature of key importance in the generation of flight forces. Unlike fixed wings, flapping wings can translate at higher angles of attack without stalling because their leading edge vorticity is more stable than the corresponding fixed wing case. Hence, the leading edge vorticity has often been suggested as the primary determinant of the high forces generated by flapping wings. To test this hypothesis, it is necessary to modulate the size and strength of the leading edge vorticity independently of the gross kinematics while simultaneously monitoring the forces generated by the wing. In a recent study, we observed that forces generated by wings with flexible trailing margins showed a direct dependence on the flexural stiffness of the wing. Based on that study, we hypothesized that trailing edge flexion directly influences leading edge vorticity, and thereby the magnitude of aerodynamic forces on the flexible flapping wings. To test this hypothesis, we visualized the flows on wings of varying flexural stiffness using a custom 2D digital particle image velocimetry system, while simultaneously monitoring the magnitude of the aerodynamic forces. Our data show that as flexion decreases, the magnitude of the leading edge vorticity increases and enhances aerodynamic forces, thus confirming that the leading edge vortex is indeed a key feature for aerodynamic force generation in flapping flight. The data shown here thus support the hypothesis that camber influences instantaneous aerodynamic forces through modulation of the leading edge vorticity.
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http://dx.doi.org/10.1088/1748-3182/6/3/036007 | DOI Listing |
Sci Rep
January 2025
College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211800, China.
Graph data is essential for modeling complex relationships among entities. Graph Neural Networks (GNNs) have demonstrated effectiveness in processing low-order undirected graph data; however, in complex directed graphs, relationships between nodes extend beyond first-order connections and encompass higher-order relationships. Additionally, the asymmetry introduced by edge directionality further complicates node interactions, presenting greater challenges for extracting node information.
View Article and Find Full Text PDFJ Prosthet Dent
January 2025
Professor and Chairman, Department of Prosthodontics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States. Electronic address:
Statement Of Problem: Information on predicting the measurements of the nose from selected facial landmarks to assist in maxillofacial prosthodontics is lacking.
Purpose: The objective of this study was to identify the efficiency of machine learning models in predicting the length and width of the nose from selected facial landmarks.
Material And Methods: Two-dimensional frontal and lateral photographs were made of 100 men and 100 women.
PLoS One
January 2025
School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
Optical Coherence Tomography (OCT) offers high-resolution images of the eye's fundus. This enables thorough analysis of retinal health by doctors, providing a solid basis for diagnosis and treatment. With the development of deep learning, deep learning-based methods are becoming more popular for fundus OCT image segmentation.
View Article and Find Full Text PDFBackground: Alzheimer's disease (AD), the leading cause of dementia accounting for 70% of cases, involves complex pathogenesis with amyloid, tau, and cerebrovascular dysfunction contributing significantly. Vascular changes and impairment are strongly associated with AD pathogenesis in new knock-in models from the MODEL-AD consortium - early-onset AD (EOAD) and late-onset AD (LOAD) mice - which can be influenced by spatial transcriptomic genetic factors.
Method: We will thoroughly characterize vascular dysfunction in these models over time: 3, 6, 9 months for EOAD mice; 4, 8, 12 months for LOAD mice.
Sci Rep
January 2025
Physics Department, Science College, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Semantic segmentation of high-resolution images from remote sensing is crucial across various sectors. However, due to limitations in computational resources and the complexity of network architectures, many sophisticated semantic segmentation models struggle with efficiency in real-world applications, leading to an interest in developing lightweight model like borders. These models often employ a dual-branch structure, which balances processing speed and performance effectively.
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