For years, several countries have been concerned about how to dispose of unused pharmaceuticals that can endanger human health and the environment. Moreover, some people are in desperate need of medical attention and medications, but they lack the financial resources to obtain them. In Saudi Arabia, there are no take-back medicine programs, and there is no published research on how medications properly are disposed. The aim of this research is to use the power of artificial intelligence to assist in the proper management and disposal of expired and unused medications and to develop a prototype device for collecting medication by automatically classifying medications for proper disposal and donation. In this research, artificial intelligence technologies such as web-based expert systems, image recognition and classification algorithms, chatbots, and the internet of things are used to assist in a take-back medications program. In conclusion, the prototype design of a web-based expert system and the device reduced improper disposal risks by providing significant advice on the safe disposal of unwanted pharmaceuticals. By using an organized method of collecting expired medications, the benefits were made possible.

Designing a health promotion campaign is never an easy task, especially during a pandemic of a highly infectious disease such as COVID-19. In Saudi Arabia, many attempts have been made to raise public awareness about COVID-19 infection and precautionary health measures. However, most of the health information delivered through the national dashboard and the COVID-19 awareness campaigns are generic and do not necessarily make the impact needed to be seen on individuals’ behavior. Health messages need to be applicable and reverent to the individual in the audience. In light of Fogg-Behavior model, this research aims to build and validate a behavior-change-based messaging campaign to promote precautionary health behavior in individuals during the COVID-19 pandemic. Intervention messages can then be targeted appropriately during the pandemic. An initial library of 32 text-based and video-based messages were developed and validated based on Fogg behavior model for behavior change. Based on this model, three groups of messages were created to reflect the model’s three theoretical concepts of motivation, ability and triggers. Each group of messages is designed to target different segment of the audience. The content of the messages was developed based on resources from the World Health Organization and the Ministry of Health in Saudi Arabia. The validity of this content was evaluated by domain experts through the content validity index. Fogg-Behavior Model was used to segment the audience into three different groups based on their perceived ability and motivation. The three groups of messages designed for those groups were found relevant to Fogg theoretical concepts. Thirteen professional health care workers (n = 13) evaluated the content of the message libraries in Arabic and English. Thirty-two messages were found to have acceptable content validity (I-CVI = 0.87). This research introduced Fogg Behavior Model as a behavior change model to develop targeted messages for three groups of the audience based on their motivation and ability level toward maintaining precautionary behavior during the pandemic. This targeted awareness messaging campaign can be utilized by health authorities to raise individuals’ awareness about the precautionary measures that should be taken, maintain these measures and hence help in reducing the number of positive cases in the city of Jeddah.

Ensuring hygiene compliance in regulated environments—such as food processing facilities, hospitals, and public indoor spaces—requires reliable detection of personal protective equipment (PPE) usage, including gloves, face masks, and hairnets. Manual inspection is labor-intensive and unsuitable for continuous, real-time enforcement. This study benchmarks three lightweight object detection models—YOLOv8n, YOLOv10n, and YOLOv12n—for automated PPE compliance monitoring using a large curated dataset of over 31,000 annotated images. The dataset spans seven classes representing both compliant and non-compliant conditions: glove, no_glove, mask, no_mask, incorrect_mask, hairnet, and no_hairnet. All evaluations were conducted using both detection accuracy metrics (mAP@50, mAP@50–95, precision, recall) and deployment-relevant efficiency metrics (inference speed, model size, GFLOPs). Among the three models, YOLOv10n achieved the highest mAP@50 (85.7%) while maintaining competitive efficiency, indicating strong suitability for resource-constrained IoT-integrated deployments. YOLOv8n provided the highest localization accuracy at stricter thresholds (mAP@50–95), while YOLOv12n favored ultra-lightweight operation at the cost of reduced accuracy. The results provide practical guidance for selecting nano-scale detection models in real-time hygiene compliance systems and contribute a reproducible, deployment-aware evaluation framework for computer vision in hygiene-critical settings.

Background and Objectives: In this study, we assessed the potential impact of employee empowerment on health care workers' performance during the novel coronavirus SARS-CoV-2 (COVID-19) pandemic. In particular, we aimed to determine the empowerment practices that would have the greatest positive effect on employee performance. Understanding the relationship between performance and empowerment can help health care providers better manage worker stress during any global crisis. This understanding is crucial in guiding policies and interventions aimed at maintaining health care workers' psychological well-being and their overall performance. Methods: This cross-sectional study evaluated the relationship between employee empowerment and performance, determining the best empowerment practices for health care leaders to utilize. Frontline health care workers (n = 100) selected using convenience and snowball sampling completed the survey between March 15 and 31, 2020. This is the period when the pandemic just started to accelerate in Saudi Arabia. We conducted Pearson's correlation analysis to assess whether there was a relationship between performance and health care workers' empowerment practice, and stepwise linear regression analysis to investigate the impact each of these empowerment practices on health care workers' performance. Results: Our results indicate that health care workers' performance can be expected to increase the most through 2 empowerment practices: giving employees the discretion to change work processes and offering performance-based rewards (R ² = 0.301, P < .05). Conclusion: Our findings suggest that health care leaders must invest in these 2 practices to better equip frontline health care workers. During a global crisis, additional discretion granted to employees helps reduce their anxiety and burnout and hence empowers them with the flexibility to adapt to unforeseen circumstances and improve the quality of their interactions with health service recipients.

The visualization of objects of an abstract nature has always been a challenge for chemistry learners. Thus, augmented reality (AR) and virtual reality (VR) have been heavily invested in as immersive learning methods for these concepts. This study targets the segment of the chemistry curriculum involving the chemical elements of the periodic table. For this purpose, we developed the AR educational tool called MicroWorld. This Arabic educational AR app was developed in unity with Vuforia SDK. Using MicroWorld, students can visualize chemical elements microstructures in 3D, see 3D models of the elements in their substantial forms, and combine two chemical elements to see how certain chemical compounds can be formed. In this work, MicroWorld’s usability was evaluated by junior high school students and chemistry teachers using the Arabic system usability scale (A-SUS). The A-SUS average score was 71.5 for junior high school students, while the scale for teachers reached 76. This research aims to design, develop, and evaluate the AR app, MicroWorld. This app was built and evaluated through the lens of the design science research paradigm.