Collegiate basketball is characterized by high-impact movements such as jump landings, making athletes more susceptible to injuries. Critical biomechanical factors like knee flexion, lateral trunk flexion, and foot landing asymmetry are strongly associated with injury risk. This study aims to predict six biomechanical risk factors in the landing error scoring system (LESS). The dataset comprises 8600 video frames of counter-movement jumps (CMJs) from 17 NCAA Division I female basketball athletes, recorded from frontal and lateral perspectives and annotated using a customized error annotation algorithm. The study uses the You Only Look Once (YOLOv5nu) model to analyze the basketball athletes’ CMJ videos. It demonstrates high reliability in predicting risk factors with an average Box Precision (Box P) of 0.800, recall (R) of 0.877, and mean Average Precision at IoU threshold 0.5 ([email protected]) of 0.879.

Using Data Analytics is a vital part of sport performance enhancement. We collect data from the Division 1 'Women's basketball athletes and coaches at our university, for use in analysis and prediction. Several data sources are used daily and weekly: WHOOP straps, weekly surveys, polar straps, jump analysis, and training session information. In this paper, we present an online dashboard to visually present the data to the athletes and coaches. R shiny was used to develop the platform, with the data stored on the cloud for instant updates of the dashboard as the data becomes available. The performance of athletes can be compared to the group averages, while coaches have access to all athletes and can compare them to each other and the team averages for all parameters. A simple color-coded design was utilized to convey the coaches which of the measured parameters is in an acceptable range and which is deficient. The dashboard was reviewed by the athletes, coaches, and exercise scientists and was useful for their needs.

Competitive sports require rapid and intense movements, such as jump landings, making athletes susceptible to injuries due to altered neuromuscular control and joint mechanics. Biomechanical features during landings are associated with injury risk, emphasizing proper movement and postural stability. Computer vision techniques offer a time-efficient, noninvasive, and unbiased method to assess jump-landings and identify injury risks. This study proposes a video analysis framework to evaluate jump landing biomechanics in athletes to determine irregular movements and incorrect postures. It provides advice and recommendations to coaches for injury prediction and training improvements. The proposed framework is tested using countermovement jump videos of 17 NCAA Division I female basketball athletes. The results indicated a low Mean Absolute Error (0.97), high correlation (0.89), high average accuracy (98.31%) and F1 score (0.98), signifying the framework’s reliability in identifying injury risk.

Restraining orders issued by the family court are among primary methods for fighting the ever-growing problem of domestic violence against women in Turkey. However, even before the cancellation of an inclusive law issued to protect women, the system failed to provide effective protection for the victims of violence. One of the main reasons for this failure is that there is no standardized risk assessment method used during and after the orders are issued, making it impossible to specialize them according to the changing needs of the victims. To solve this problem, the study aims to provide a framework for a decision support system that runs on previous criminal history and the offence records of the suspect. For indicating the system's feasibility, the public records on the inmates published by the Florida Department of Corrections are used where the results from the most effective model yielded a 64.4% ROC accuracy rate when classifying the type of the following crime that the offender will commit.

This paper studies and quantifies the impact of active learning experienced through multinational projects. The hypothesis was engineering education delivered through Active Learning in multicultural environment improves student competencies. The investigation captures the impact of international exposure program in developing global competencies of the modern engineer. The paper shows positive trends in the development of domain and life skills of engineering students. Post-survey after six months of completion of the program revealed that the program was valuable to students and their motivation increased.

The paper report on the design and test of a low-voltage temperature sensor designed for MEMS power-harvesting systems. The core of the sensor is a bandgap voltage reference circuit operating with a supply voltage in the range of 1-1.5V. The prototype was fabricated on a conventional 0.5μm Silicon-on-Sapphire (SOS) process. The sensor design consumes 15μA of current at 1V. The internal reference voltage is 550mV. The temperature sensor has a digital square wave output whose frequency is proportional to temperature. A linear model of the dependency of output frequency with temperature has a conversion factor of 1.6kHz/°C. The output is also independent of supply voltage in the range of 1-1.5V. Measured results and targeted applications for the proposed circuit were reported.