Considering the growing global problem and the lack of obesity data in Bosnia and Herzegovina (BH) the main aim of this paper was to determine BH school-age children body composition and determine the differences in body composition between girls and boys classified in underweight, normal weight and overweight according to the body mass index (BMI) score. 2524 participants 1763 girls and 761 boys (aged 10-13 yrs.) from 32 elementary schools were randomly selected and divided to 3 BMI groups by WHO cut-off points. InBody 370 Body Composition Analyzer (BioSpace, Seoul, Korea), a segmental bioelectrical impedance analysis (BIA) device was used to gather data. The results showed ~38% of the sample were classified as overweight whilst gender differences showed higher body fat mass and fat percentages in arms, trunk and legs in underweight and normal weight girls and higher skeletal muscle mass in normal weight and overweight boys.

This study aimed to examine the relation between agility, sprint ability, and vertical jump performance of young basketball players. Fifty (n=50) young basketball players (mean±SD: age = 12.63±0.95; height = 160.84±6.31 cm; body mass = 50.82±6.88 kg) participated in the study. The agility T-test and 505 test were assessed to determine agility, 10m and 20m sprint was measured to determine sprint ability and countermovement jump (CMJ) for jumping performance. The results of Pearson’s Product Moment Correlation analysis indicated large to very large relation between agility tests and sprint performance (r = 0.61 to 0.85); agility and jump performance (r = - 0.64 to - 0.67); sprint and jumping performance (r = -0.59 to -0.77). The results of the study suggest that agility, sprint, and jumping performance share common physical demands, therefore it is necessary to develop them during the training.

This study examines student engagement in an online environment concerning the perception regarding the course and the technology used. A research model was developed from the principal tenets of the expectancy-value theory to which values and expectations are assumed to influence how students build engagement. The model conjoins student perception related to course factors (content and rigor), technology factor (technology convenience), and student engagement (psychological, cognitive, emotional, and behavioral). The model was tested using a sample composed of 328 business undergraduate students taking the courses online using the BigBlueButton e-learning system due to the global emergency caused by the COVID-19 pandemic. Hence, respondents did not voluntarily choose the online teaching delivery method. The results imply that both course content and perceived technology convenience predict overall student engagement, while course rigor influences student cognitive, emotional, and behavioral commitment, but not psychological engagement.

Nowadays, companies face numerous challenges to operate successfully and remain competitive in the market. Globalization is increasing competition in the market, allowing many companies to operate in foreign markets. Increasing competition has made companies constantly trying to increase their productivity while reducing costs. To meet all the requirements, and at the same time function in the best possible way, companies must be organized adequately, taking into account the whole set of processes from the company's internal organization to the company's appearance on the market. It means, if the end-user is to be satisfied, the company must implement quality in all phases of business, ie business ethics and company culture, through the quality of technology, personnel, etc. In other words, it must be satisfied the system quality of the company, known in the literature as QMS (Quality Management System). The main purpose of this paper is to review the multiple understanding of the concept of QMS and its different underpinning theories in a business to customer context. The design of this research is based on mere documentary analysis and some observations.

High penetration of renewable energy sources brings both opportunities and challenges for Smart Grid operation. Due to their high contribution to energy consumption, aggregated load flexibility of small residential and service sector consumers has a potential to address the intermittency challenge of distributed generation. Predicting aggregated load flexibility of this consumer sector involves access to sensitive smart meter data, raising data collection and sharing concerns. Federated Learning, a decentralized machine learning technique that uses data distributed on user devices to construct an aggregated, global model, offers potential solutions to tackling this challenge. This paper explores the potential of using Federated Learning for flexibility prediction in Smart Grids through an analysis of its opportunities and implications for different stakeholders involved, as well as the challenges faced. The analysis shows that Federated Learning is a promising approach for building privacy-preserving energy portfolios of aggregated demand data.