Flow Structures of Gaseous Jet Injected into Liquid for Underwater Propulsion

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83627/1/AIAA-2010-6911-166.pd

Flow Structures of Gaseous Jets Injected into Water for Underwater Propulsion

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90659/1/AIAA-2011-185-740.pd

Positive outcome expectancy mediates the relationship between social influence and Internet addiction among senior high-school students

Background and aims Based on the foundations of Bandura’s social cognitive theory and theory of triadic influence (TTI) theoretical framework, this study was designed to examine the mediating role of positive outcome expectancy of Internet use in the relationship between social influence and Internet addiction (IA) in a large representative sample of senior high-school students in Taiwan. Methods Using a cross-sectional design, 1,922 participants were recruited from senior high schools throughout Taiwan using both stratified and cluster sampling, and a comprehensive survey was administered. Results Structural equation modeling and bootstrap analyses results showed that IA severity was significantly and positively predicted by social influence, and fully mediated through positive outcome expectancy of Internet use. Discussion and conclusions The results not only support Bandura’s social cognitive theory and TTI framework, but can also serve as a reference to help educational agencies and mental health organizations design programs and create policies that will help in the prevention of IA among adolescents

Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation

In this paper, we examine the recent Segment Anything Model (SAM) on medical images, and report both quantitative and qualitative zero-shot segmentation results on nine medical image segmentation benchmarks, covering various imaging modalities, such as optical coherence tomography (OCT), magnetic resonance imaging (MRI), and computed tomography (CT), as well as different applications including dermatology, ophthalmology, and radiology. Those benchmarks are representative and commonly used in model development. Our experimental results indicate that while SAM presents remarkable segmentation performance on images from the general domain, its zero-shot segmentation ability remains restricted for out-of-distribution images, e.g., medical images. In addition, SAM exhibits inconsistent zero-shot segmentation performance across different unseen medical domains. For certain structured targets, e.g., blood vessels, the zero-shot segmentation of SAM completely failed. In contrast, a simple fine-tuning of it with a small amount of data could lead to remarkable improvement of the segmentation quality, showing the great potential and feasibility of using fine-tuned SAM to achieve accurate medical image segmentation for a precision diagnostics. Our study indicates the versatility of generalist vision foundation models on medical imaging, and their great potential to achieve desired performance through fine-turning and eventually address the challenges associated with accessing large and diverse medical datasets in support of clinical diagnostics.Comment: Published in Diagnostic