Imola: A decentralised learning-driven protocol for multi-hop White-Fi

In this paper we tackle the digital exclusion problem in developing and remote locations by proposing Imola, an inexpensive learning-driven access mechanism for multi-hop wireless networks that operate across TV white-spaces (TVWS). Stations running Imola only rely on passively acquired neighbourhood information to achieve scheduled-like operation in a decentralised way, without explicit synchronisation. Our design overcomes pathological circumstances such as hidden and exposed terminals that arise due to carrier sensing and are exceptionally problematic in low frequency bands. We present a prototype implementation of our proposal and conduct experiments in a real test bed, which confirms the practical feasibility of deploying our solution in mesh networks that build upon the IEEE 802.11af standard. Finally, the extensive system level simulations we perform demonstrate that Imola achieves up to 4x\u97 more throughput than the channel access protocol defined by the standard and reduces frame loss rate by up to 100%

Prediction of Injuries in CrossFit Training: A Machine Learning Perspective

CrossFit has gained recognition and interest among physically active populations being one of the most popular and rapidly growing exercise regimens worldwide. Due to the intense and repetitive nature of CrossFit, concerns have been raised over the potential injury risks that are associated with its training including rhabdomyolysis and musculoskeletal injuries. However, identification of risk factors for predicting injuries in CrossFit athletes has been limited by the absence of relevant big epidemiological studies. The main purpose of this paper is the identification of risk factors and the development of machine learning-based models using ensemble learning that can predict CrossFit injuries. To accomplish the aforementioned targets, a survey-based epidemiological study was conducted in Greece to collect data on musculoskeletal injuries in CrossFit practitioners. A Machine Learning (ML) pipeline was then implemented that involved data pre-processing, feature selection and well-known ML models. The performance of the proposed ML models was assessed using a comprehensive cross validation mechanism whereas a discussion on the nature of the selected features is also provided. An area under the curve (AUC) of 77.93% was achieved by the best ML model using ensemble learning (Adaboost) on the group of six selected risk factors. The effectiveness of the proposed approach was evaluated in a comparative analysis with respect to numerous performance metrics including accuracy, sensitivity, specificity, AUC and confusion matrices to confirm its clinical relevance. The results are the basis for the development of reliable tools for the prediction of injuries in CrossFit. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Association Between Isokinetic Knee Strength Characteristics and Single-Leg Hop Performance In Healthy Young Participants

Objective: The purpose of this study was to determine the extent to which the mean peak moment (MPM) of knee flexors and extensors could predict performance in a group of healthy individuals. Methods: Eighty-four healthy individuals—32 men and 52 women (mean age, 22.1 ± 3 years; range, 18-35 years)—participated in this study. Isokinetic unilateral concentric knee flexor and extensor MPM was assessed isokinetically at angular velocities of 60°/s and 180°/s. Functional performance was assessed using the single hop of distance (SHD). Results: Positive moderate to good statistically significant correlations (r = .636 to r = .673) were found between knee flexor and extensor MPM at 60°/s and 180°/s for the SHD test. Knee flexor and extensor MPMs are strong predictors for the SHD test at 60°/s and 180°/s (R2 = .40 to R2 = .45). Conclusion: Knee flexor and extensor strength was substantially correlated with SHD. © 202