We aimed to investigate whether the addition of whole-body vibration (WBV) to resistance training (RT) will be more beneficial in improving lower limbs muscle strength, power and sprinting performance than RT alone in young basketball players. We recruited 30 young basketball players to participate in four weeks of training and assessments. They were randomized into the WBV resistance training group (VRTG, n=15) and a conventional resistance training group (RTG, n=15), performed 3 times per week. At the beginning and end of the four weeks a back squat one-repetition maximum (1RMBS), Countermovement jump (CMJ), Squat jump (SJ), 10 meters (10m) and 20 meters sprint (20m) were performed. We found that: a) VRTG when added to RT can induce greater improvements in 1RMBS (percentage difference [PD], 8.4%, p < 0.001), CMJ (PD = 4.7%, p = 0.001) and SJ (PD = 1.6, p = 0.02) than RT alone. In contrary, significant time*group interactions were found for sprint times at 10m (p=0.08, F=3.2) and 20m (p=0.17, F=1.93). An additional 4-week WBV resistance training program proved effective in improving lower limb power and strength in young basketball players. When performed on a vibration platform (with accurate and constant vibration stimulus parameters), the resistance exercises were superior to their conventional forms and resulted in additional gains on measures of muscle power and strength, while sprint performance remained unchanged.

Introduction: Machine learning (ML) plays a significant role in the fight against the COVID-19 (officially known as SARS-CoV-2) pandemic. ML techniques enable the rapid detection of patterns and trends in large datasets. Therefore, ML provides efficient methods to generate knowledge from structured and unstructured data. This potential is particularly significant when the pandemic affects all aspects of human life. It is necessary to collect a large amount of data to identify methods to prevent the spread of infection, early detection, reduction of consequences, and finding appropriate medicine. Modern information and communication technologies (ICT) such as the Internet of Things (IoT) allow the collection of large amounts of data from various sources. Thus, we can create predictive ML-based models for assessments, predictions, and decisions. Methods: This is a review article based on previous studies and scientifically proven knowledge. In this paper, bibliometric data from authoritative databases of research publications (Web of Science, Scopus, PubMed) are combined for bibliometric analyses in the context of ML applications for COVID-19. Aim: This paper reviews some ML-based applications used for mitigating COVID-19. We aimed to identify and review ML potentials and solutions for mitigating the COVID-19 pandemic as well as to present some of the most commonly used ML techniques, algorithms, and datasets applied in the context of COVID-19. Also, we provided some insights into specific emerging ideas and open issues to facilitate future research. Conclusion: ML is an effective tool for diagnosing and early detection of symptoms, predicting the spread of a pandemic, developing medicines and vaccines, etc.

The 2030 Agenda for Sustainable Development builds upon the Millennium Development Goals while at the same time reaffirming the conclusions of the leading instruments in the field of human rights and international law. The 17 integrated and indivisible sustainable development goals (SDGs) require innovation through digitalization and legal activities. Digitalization and new technologies are crucial for SDG 8, 9, and 16. SDG 16: Peace, justice, and strong institutions directly focus on law. While SDG 16 does not directly mention it, digitalization is essential in achieving its specific targets. Examples include concepts of e-government (including data protection and public access to information), e-commerce, equal access to dispute resolution mechanisms in cyberspace, and enforcement of non-discriminatory laws for sustainable development. The right to a healthy and sustainable environment encompasses economic, social, and environmental aspects that SDGs capture. To achieve these goals, the 2030 Agenda relies on international law instruments. The right to a healthy and sustainable environment is developing towards an internationally recognized human right. As environmental goals do not recognize national borders, international law plays a key role. International environmental law should facilitate a broader application of existing clean technologies through the transfer of technology and examine the development of new technologies as to its compatibility with a sustainable environment. Moreover, the human right to share in scientific advancement and enjoy its benefits embodies equal access to technology. The legal enforcement of sustainable goals in the private and governmental sectors remains one of the main concerns of climate change.

The question of a general part for EU Private International Law has attracted research and debate among PIL scholars long before the EU started to codify this legal field after the Amsterdam Treaty. However, the EU has made no attempt to make use of the existing research to adopt a comprehensive regulation of the general part of EU PIL and has adopted sector-specific regulation with inconsistent regulation of several general institutes. For the occasion of the anniversary of the Zeitschrift für Europarechtliche Studien this paper revisits the topic to answer the question which institutes of PIL should be codified in a general part of EU PIL. The conclusions are made based on four criteria: which general institutes of PIL are already regulated in the EU; are they generally applicable to all special parts of EU PIL; what is their practical relevance; and is there a need for their uniform regulation. The paper analyzes the discrepancies between existing general institutes of EU PIL and offers brief discussions on the possible formulation of some of the institutes for the general part.

The aim of this paper is to determine the relationship between the quality of information from the Enterprise Resource Planning system (ERP system) and business performance. The quality of information from the ERP system is assessed using a survey questionnaire examining the end users of the ERP system, namely middle and top management because they use information from the ERP system to make business decisions. Business performance is monitored from a controlling point of view, using selected indicators from the DuPont system. Empirical research was conducted on medium and large enterprises in Bosnia and Herzegovina. The existence of a positive correlation between information quality and business performance was examined using regression analysis and correlation analysis. Regression analysis and correlation analysis indicate that return on sale (ROS) and return on assets (ROA) have a medium significant correlation with the quality of information, and total asset turnover ratio (TR) does not correlate with the quality of information. Based on the obtained results, a positive relationship between the quality of information from the ERP system and business performance was confirmed. This can be interpreted that information is becoming an increasingly important resource in supporting organizational activities, and information quality has been identified as one of the main determinants influencing the decision-making process.