Open source software (OSS) underpins modern software infrastructure, yet many projects struggle with long-term sustainability. We introduce OSSPREY, an AI-powered platform that can predict the sustainability of any GitHub-hosted project. OSSPREY collects longitudinal socio-technical data, such as: commits, issues, and contributor interactions, and uses a transformer-based model to generate month-by-month sustainability forecasts. When project downturns are detected, it recommends evidence-based interventions drawn from published software engineering studies. OSSPREY integrates scraping, forecasting, and actionable guidance into an interactive dashboard, enabling maintainers to monitor project health, anticipate decline, and respond with targeted strategies. By connecting real-time project data with research-backed insights, OSSPREY offers a practical tool for sustaining OSS projects at scale.The codebase is linked to the project website at: https://oss-prey.github.io/OSSPREY-Website/The screencast is available at: https://www.youtube.com/watch?v=N7a0v4hPylU

The worldwide sports industry is booming through the usage of information and communication technologies. The collection, analysis, and presentation of athlete data is common practice for professional individual and team sports to assess individuals (athletes)/teams capability, fatigue, and subsequent adaptation responses; examine potential improvement areas, and minimize the risk of injury. Nutrition and exercise plans are also blended to meet specific training requirements and build strategic programs to maximize athletes’ ability to perform. An effective and efficient system is required for the athletes and their mentors to monitor and manage the athlete’s physical exercise. Therefore, the purpose of this article is to reveal the user requirements for creating an athlete monitoring system and to propose a wearable system based on the revealed requirements. To achieve these objectives, a Design Science Research (DSR) approach was adopted. As such, an empirical study (through semi-structured interviews) was conducted with 41 participants to reveal the system requirements; then a wearable athlete monitoring application was developed considering the revealed requirements. Finally, the proposed system was evaluated with 21 participants through the System Usability Scale (SUS) method. The study found that the proposed system is reliable, user-friendly, and useful for monitoring and managing physical exercise for the athletes and their mentors. The study also showed that the proposed system is useful and usable regardless of the athlete’s age or gender.

Cesarean section around the world is increasing at an alarming rate. Cesarean section, on one hand, may introduce different short-term and long-term complications for mother; on another hand it may be a life-saving procedure for both mother and child, depending on childbirth complications. The purpose of this research is to predict whether or not the cesarean section is necessary with the help of data mining and consequently, increasing the safety of the mother and newborn during and after childbirth by avoiding unnecessary cesarean section. To attain the objective three different ensemble prediction models based on- XGBoost, AdaBoost and Catboost were developed. As an outcome XGBoost showed the highest accuracy-88.91% while AdaBoost showed 88.69% accuracy and Catboost showed 87.66% accuracy. This research also revealed that amniotic liquid, medical indication, fetal intrapartum ph, number of previous cesareans, pre-induction are the most influential features for predicting the target outcome accurately.