A preliminary simulator study on exploring responses of drivers to driving system reminders on four stimuli in vehicles.

In the realm of autonomous vehicles, society is undergoing a transition from conventional human-driven vehicles to autonomous driving systems. Therefore, there is an increasing demand for vehicles integrated with assistive driving systems. This pilot study designed to explore which type of driving system reminders, namely Text display, Image display, alarm notification, or humanoid voice command, provokes stronger preferences and higher rates of cooperation from drivers.

Evaluating segment anything model (SAM) on MRI scans of brain tumors.

Addressing the challenge of automatically segmenting anatomical structures from brain images has been a long-standing problem, attributed to subject- and image-based variations and constraints in available data annotations. The Segment Anything Model (SAM), developed by Meta, is a foundational model trained to provide zero-shot segmentation outputs with or without interactive user inputs, demonstrating notable performance on various objects and image domains without explicit prior training. This study evaluated SAM's performance in brain tumor segmentation using two publicly available Magnetic Resonance Imaging (MRI) datasets.