Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis

The identification and prediction of athletic talent are pivotal in the development of successful sporting careers. Traditional subjective assessment methods have proven unreliable due to their inherent subjectivity, prompting the rise of data-driven techniques favoured for their objectivity. This evolution in statistical analysis facilitates the extraction of pertinent athlete information, enabling the recognition of their potential for excellence in their respective sporting careers. In the current study, we applied a logistic regression-based machine learning pipeline (LR) to identify potential skateboarding athletes from a combination of fitness and motor skills performance variables. Forty-five skateboarders recruited from a variety of skateboarding parks were evaluated on various skateboarding tricks while their fitness and motor skills abilities that consist of stork stance test, dynamic balance, sit ups, plank test, standing broad jump, as well as vertical jump, were evaluated. The performances of the skateboarders were clustered and the LR model was developed to classify the classes of the skateboarders. The cluster analysis identified two groups of skateboarders: high and low potential skateboarders. The LR model achieved 90% of mean accuracy specifying excellent prediction of the skateboarder classes. Further sensitivity analysis revealed that static and dynamic balance, lower body strength, and endurance were the most important factors that contributed to the model’s performance. These factors are therefore essential for successful performance in skateboarding. The application of machine learning in talent prediction can greatly assist coaches and other relevant stakeholders in making informed decisions regarding athlete performance

Prevalence and risk factors of Japanese encephalitis virus (JEV) in livestock and companion animal in high-risk areas in Malaysia

Japanese encephalitis (JE) is vector-borne zoonotic disease which causes encephalitis in humans and horses. Clinical signs for Japanese encephalitis virus (JEV) infection are not clearly evident in the majority of affected animals. In Malaysia, information on the prevalence of JEV infection has not been established. Thus, a cross-sectional study was conducted during two periods, December 2015 to January 2016 and March to August in 2016, to determine the prevalence and risk factors in JEV infections among animals and birds in Peninsular Malaysia. Serum samples were harvested from the 416 samples which were collected from the dogs, cats, water birds, village chicken, jungle fowls, long-tailed macaques, domestic pigs, and cattle in the states of Selangor, Perak, Perlis, Kelantan, and Pahang. The serum samples were screened for JEV antibodies by commercial IgG ELISA kits. A questionnaire was also distributed to obtain information on the animals, birds, and the environmental factors of sampling areas. The results showed that dogs had the highest seropositive rate of 80% (95% CI: ± 11.69) followed by pigs at 44.4% (95% CI: ± 1.715), cattle at 32.2% (95% CI: ± 1.058), birds at 28.9% (95% CI: ± 5.757), cats at 15.6% (95% CI: ± 7.38), and monkeys at 14.3% (95% CI: ± 1.882). The study also showed that JEV seropositivity was high in young animals and in areas where mosquito vectors and migrating birds were prevalent

A cluster analysis of identifying team and individual sports athlete based on anthropometric, health and skill related components

The purpose of this study is to identify the essential fitness attributes in an individual sport and team sport. A total number of 218 male competitive youth athletes aged 13 to 21 years old were examined anthropometric, health and skill-related performance parameters (PP). Anthropometric parameters namely, weight, height, sitting height, and arm span were tested while the health-related parameters consisting of sit and reach, 1 min sit up, push up, handgrip, predicted VO2max, and medicine ball throw were also collected. The 20 m speed, vertical jump, standing wide jump, stork stand test, and t-test represented the skill-related component tests. Based on the three components, hierarchical agglomerative cluster analysis (HACA) was utilized to group the athlete concerning their similarities in the PP examined. The athletes’ performance within the clusters differs in three areas: age and arm span are significant in individual sports while height, weight, and sitting height are vital for team sports. Muscle endurance is shown to be essential in individual sports. Meanwhile, vertical jump and 20 m speed are more attributed to individual sports while standing broad jump, stork stand test, and t-test are more inclined to team sports. Some fundamental traits could determine the type of sport suitable for athletes. These traits should be prioritized for identifying team or individual sports athletes. The findings could aid coaches in making decisions about the types of sports that could best suit an athlete for better performance delivery

A cluster analysis and artificial neural network of identifying skateboarding talents based on bio-fitness indicators

This research aims to identify talented skateboarding athletes with reference to their bio-fitness indicators. A total of 45 skateboarders (23.09 ± 5.41 years) who were playing for recreational purposes were recruited for the study. Standard assessment of their bio-fitness as well as their skateboarding performances was performed. The bio-fitness investigated consisted of stork balance, star excursion balance test, vertical jump, standing broad jump, single-leg wall sits, plank and sit-up while the related-skill performances consisted of the observation on skateboarding tricks execution, namely Ollie, Nollie, Frontside 180, Pop-Shuvit and Kickflip. To achieve the objective of the study, a hierarchical agglomerative cluster analysis (HACA) was performed to cluster the athletes into groups in reference to the level of their bio-fitness markers. The clusters identified two groups of performance named High-Potential Skaters (HPS) and Low-Potential Skaters (LPS) following their skateboarding performance scores. An Artificial Neural Network (ANN) was conducted to ascertain the classified athletes into the clusters (HPS and LPS) based on the bio-fitness indicators evaluated along with the skateboarding tricks performance scores. The result demonstrated that ANN accomplished a high classification accuracy of 91.7% indicating excellent performance from the classifier in classifying the skateboarding athletes. Similarly, the area under the curve of the classifier was found to be 0.988 signifying further the validity of the model developed. Overall, these results suggest that the proposed technique was able to classify the skateboarding athletes reasonably well which will in turn possibly assist coaches to identify talents in this sport through the bio-fitness indicators examined

Infection-mediated priming of phagocytes protects against lethal secondary Aspergillus fumigatus challenge

Phagocytes restrict the germination of Aspergillus fumigatus conidia and prevent the establishment of invasive pulmonary aspergillosis in immunecompetent mice. Here we report that immunecompetent mice recovering from a primary A. fumigatus challenge are protected against a secondary lethal challenge. Using RAGγc knock-out mice we show that this protection is independent of T, B and NK cells. In protected mice, lung phagocytes are recruited more rapidly and are more efficient in conidial phagocytosis and killing. Protection was also associated with an enhanced expression of CXCR2 and Dectin-1 on bone marrow phagocytes. We also show that protective lung cytokine and chemokine responses are induced more rapidly and with enhanced dynamics in protected mice. Our findings support the hypothesis that following a first encounter with a non-lethal dose of A. fumigatus conidia, the innate immune system is primed and can mediate protection against a secondary lethal infection

Community building and knowledge sharing by individuals with disabilities using social media

The use of social media to share information, enhance learning, and connect with an online community has grown rapidly over the past 10 years. As social media becomes a more common tool in both formal and informal education, it is imperative to under- stand how it is used by individuals with disabilities. Through a systematic study of the literature, 215 articles on social media used by individuals with disabilities were selected and 29 selected for in‐depth thematic analysis. Six major themes were iden- tified: community, cyberbullying, self‐esteem, self‐determination, access to technology, and accessibility. To confirm these six categories, we expanded our search, yielding an additional 30 articles, for a total 59 articles reviewed in‐depth. Interactions between individuals with disabilities within online communities often had the goal of acquiring knowledge or learning new information. A communities of practice theo- retical framework is used to discuss interactions among the elements of social media design, learning, and the building of community by individuals with disabilities

Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.Mohamed Elgendi, Björn Eskofier, Socrates Dokos, Derek Abbot

Socioeconomic vulnerability and adaptation to environmental risk: A case study of climate change and flooding in Bangladesh

In this article we investigate the complex relationship between environmental risk, poverty, and vulnerability in a case study carried out in one of the poorest and most flood-prone countries in the world, focusing on household and community vulnerability and adaptive coping mechanisms. Based upon the steadily growing amount of literature in this field we develop and test our own analytical model. In a large-scale household survey carried out in southeast Bangladesh, we ask almost 700 floodplain residents living without any flood protection along the River Meghna about their flood risk exposure, flood problems, flood damage, and coping mechanisms. Novel in our study is the explicit testing of the effectiveness of adaptive coping strategies to reduce flood damage costs. We show that, households with lower income and less access to productive natural assets face higher exposure to risk of flooding. Disparity in income and asset distribution at community level furthermore tends to be higher at higher risk exposure levels, implying that individually vulnerable households are also collectively more vulnerable. Regarding the identification of coping mechanisms to deal with flood events, we look at both the ex ante household level preparedness for flood events and the ex post availability of community-level support and disaster relief. We find somewhat paradoxically that the people that face the highest risk of flooding are the least well prepared, both in terms of household-level ex ante preparedness and community-level ex post flood relief. © 2007 Society for Risk Analysis

Lanthanide &Actinide Complexes of Embelin

966-96

The impacts of Malaysian managed care on doctor-patient relationships, ethical practice of medicine, and quality of care: a study on primary care physicians’ perception

Managed care is becoming a critical factor of healthcare delivery which assures a lower cost without compromising the quality of care. However, this practice has increased the physicians’ responsibilities in terms of cost containment and patient care. This study analyzed the perception of physicians on the impact of managed care on doctor-patient relationships, the ethical practice of medicine, and the quality of care. Data were gathered from a survey of physicians in Malaysia. Variables representing doctor-patient relationships, ethical practice of medicine, and quality of care were calculated using median. The findings showed that physicians have a positive perception of managed care on doctor-patient relationships, the ethical practice of medicine, and the quality of care in clinical practice