In a natural disaster, when there is a sudden rush in vehicle movement from dense residential areas of a city towards some designated evacuation centers, traffic needs to be distributed along the road network in an optimal way so as to minimize the travel time for reaching those evacuation centers. We address this traffic distribution problem in a k x k Manhattan road network with multiple traffic generating sources and a single fixed destination (sink) at a corner point. We first consider queuing delay at each grid point along with a fixed link delay. We then extend our idea to consider a rectangular grid and also real-life road networks where some of the links may be absent. We present a traffic distribution algorithm that would minimize the average time of all vehicles to reach the given destination point. Simulation of our algorithm using SUMO on the grid-structured road map of Manhattan borough in New York city and also on a part of non-grid road map of city of Carbondale, IL USA shows a much shorter average travel time compared to the existing popular algorithms based on dynamic traffic assignments (DTA). The proposed technique is also robust in the sense that even with 20% deviation from optimal assignment of traffic distribution at cross-points, we get significantly better results compared to the existing traffic distribution policies. We then propose a congestion control technique based on detouring of vehicles and show how variable link delay as a function of the volume of traffic flow through a link can be incorporated in our traffic distribution policy. Finally, we show how to extend our technique to consider a scenario with the destination point being anywhere on the grid, instead of being at the corner point.

Inefficient patient transport in hospitals often leads to delays, overworked staff, and suboptimal resource utilization, ultimately impacting patient care. Existing dispatch management algorithms are often evaluated in simulation environments, raising concerns about their real-world applicability. This study presents a real-world experiment that bridges the gap between theoretical dispatch algorithms and real-world implementation. It applies process capability analysis at Taichung Veterans General Hospital in Taichung, Taiwan, and utilizes IoT for real-time tracking of staff and medical devices to address challenges associated with manual dispatch processes. Experimental data collected from the hospital underwent statistical evaluation between January 2021 and December 2021. The results of our experiment, which compared the use of traditional dispatch methods with the Beacon dispatch method, found that traditional dispatch had an overtime delay of 41.0%; in comparison, the Beacon dispatch method had an overtime delay of 26.5%. These findings demonstrate the transformative potential of this solution for not only hospital operations but also for improving service quality across the healthcare industry in the context of smart hospitals.

JAIT is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.

Automated surface defect detection often faces a “cold-start” problem due to limited annotated data for new anomalies. Traditional object detectors struggle to converge in such few-shot settings. To address this, we adapt Vision–Language Models (VLMs), specifically YOLO-World. We use semantic pre-training to mitigate data scarcity. We evaluate this approach on the MVTec AD dataset in bounding-box format. We use a strict 1:9 train-validation split, resulting in an average of 11.8 defect instances per category. YOLO-World surpasses traditional baselines, like YOLOv11s and YOLOv26s, in 12 of 15 categories. The optimized VLM pipeline achieves up to 64.9% mAP@50 on texture-heavy categories, such as Tile, with only nine training instances. Ablation studies show standard optimization techniques are limited under 10-shot constraints. We find a critical augmentation divide. Disabling spatial distortions (Mosaic) is vital to preserving rigid-object geometry. The Normalized Wasserstein Distance (NWD) improves the localization of microscopic anomalies. Varifocal Loss (VFL) often causes model collapse. Ultimately, VLMs offer a superior foundation for cold-start inspection but require carefully tailored pipelines for robustness.

JAIT is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.

JAIT is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.