Interconnected components of holistic development, such as being thankful, addressing basic psychological needs, and acting effectively toward others, should be a priority for college athletes. Athletes at the College level need all-encompassing support systems to ensure their health, happiness, and success because of the special difficulties they have juggling their academic, athletic, and personal schedules. Problems with work-life balance, stress, and performance expectations all impede College Student Athletes’ holistic development. A thorough plan that considers all of the social, emotional, and psychological aspects impacting athlete development is necessary to overcome these obstacles. An Integrated Holistic Development Program for College Athletes (IHDP-CA) is suggested in this paper as a method that incorporates various aspects of positive psychology, mindfulness, resilience training, and the enhancement of interpersonal skills. Athletes at the College level can benefit from this all-encompassing program’s emphasis on helping others, developing an attitude of gratitude, and meeting basic psychological requirements. Sports counseling services, schools, and College athletic teams can all benefit from the IHDP-CA. A more positive and supportive sporting environment can be achieved when the program takes a more holistic approach to athletes’ needs, improving their mental health, social connections, and overall performance. The possible effect of the IHDP-CA on the holistic development outcomes of College Student-Athletes will be predicted through simulation analysis. To gain a better understanding of the program’s long-term viability, efficacy, and scalability, this analysis will run simulations of different situations and tweak program settings.

This paper presents a practical approach to empowering software entrepreneurship in Saudi Arabia through a unique course offered by the Software Engineering department at Prince Sultan University. The course, SE495 Emergent Topics in Software Engineering: Software Entrepreneurship, combines software engineering and entrepreneurship to equip students with the necessary skills to develop innovative software solutions that solve real-world problems. The course covers a range of topics, including platform development, market research, and pitching to investors, and features guest speakers from the industry. By the end of the course, students will have gained a deep understanding of the software development process and its intersection with entrepreneurship and will be able to develop a working prototype of a software solution that solves a real-world problem. The course’s practical approach ensures that students are well-prepared to navigate the complexities of the digital and software sectors and succeed in an ever-changing business landscape.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

This study analyzes the perception of university students regarding the use of virtual reality (VR) in higher education, focusing on their level of knowledge, usage, perceived advantages and disadvantages, as well as their willingness to use this technology in the future. Using a mixed-methods approach that combines questionnaires and semi-structured interviews, both quantitative and qualitative data were collected to provide a comprehensive view of the subject. The results indicate that while students have a basic understanding of VR, its use in the educational context is limited. A considerable number of students recognize VR’s potential to enhance the learning experience, particularly in terms of immersion and engagement. However, significant barriers to adoption were identified, such as technical issues, the high cost of equipment, and inadequate access to technological infrastructure. Additionally, there is a need for broader training for both students and faculty to ensure the effective use of this technology in academic environments. The semi-structured interviews confirmed that perceptions of VR vary depending on prior exposure to the technology and access to resources. Despite the challenges, most students appreciate VR’s potential to enrich learning, although its effective adoption will depend on overcoming the identified barriers. The study concludes that strategies must be implemented to facilitate the integration of VR into higher education, thus optimizing its impact on the teaching-learning process.

This research explores the factors influencing consumers’ intentions and behaviors toward purchasing green products in two culturally and economically distinct countries, Saudi Arabia and Pakistan. Drawing on Ajzen’s Theory of Planned Behavior (TPB), the study examines the roles of altruistic and egoistic motivations, alongside environmental knowledge, in shaping green consumer behavior. Altruistic motivation, driven by concern for societal well-being and environmental sustainability, is found to have a stronger impact on green purchase intention and behavior in both countries, particularly in Pakistan. Egoistic motivation, which focuses on personal benefits like health and cost savings, also contributes but with a lesser influence. The research employs a cross-sectional survey design, collecting data from 1000 respondents (500 from each country) using a stratified random sampling technique. The collected data were analyzed using structural equation modeling (SEM) to examine the relationships between variables and test the moderating effects of environmental knowledge. The results reveal that environmental knowledge significantly moderates the effect of both altruistic and egoistic motivations on green purchase intention, enhancing the likelihood of eco-friendly consumption. These findings underscore the importance of environmental education in promoting sustainable consumer behavior. The originality of this study lies in its comparative analysis of green consumerism in two distinct contexts and its exploration of motivational factors through the TPB framework. Practical implications suggest that policymakers and marketers can develop strategies that appeal to both altruistic and egoistic drivers while enhancing consumer knowledge of environmental issues. The study contributes to the literature by expanding TPB to include the moderating role of environmental knowledge in understanding green consumption behavior across diverse cultures.

Electrical energy is known as an essential part of our day-to-day lives. Renewable energy resources can be regenerated through the natural method within a reasonably short time and can be used to bridge the gap in extended power outages. Achieving more renewable energy (RE) than the low levels typically found in today’s energy supply network will entail continuous additional integration efforts into the future. This study examined the impacts of integrating renewable energy on the power quality of transmission networks. This work considered majorly two prominent renewable technologies (solar photovoltaic and wind energy). To examine the effects, IEEE 9-bus (a transmission network) was used. The transmission network and renewable sources (solar photovoltaic and wind energy technologies) were modelled with MATLAB/SIMULINK^®. The Newton-Raphson iteration method of solution was employed for the solution of the load flow owing to its fast convergence and simplicity. The effects of its integration on the quality of the power supply, especially the voltage profile and harmonic content, were determined. It was discovered that the optimal location, where the voltage profile is improved and harmonic distortion is minimal, was at Bus 8 for the wind energy and then Bus 5 for the solar photovoltaic source.

This article aims to measure and identify the factors influencing the decision to use Chatbot in e-banking services for GenZ customers in Vietnam through 292 customers. Testing methods: Cronbach’s Alpha trust factor, EFA discovery factor analysis, and regression analysis have shown that 07 factors directly affect GenZ’s decision to use Chatbot. Those factors include (1) Customer attitude; (2) Useful perception; (3) Perception of ease of use; (4) Behavioral control perception; (5) Risk perception; (6) Subjective norms and (7) Trust. On that basis, the article has set out management implications for Vietnamese commercial banks to approach and increase the decision of customers aged 18–24 years in Vietnam.

This article addresses the complex challenge of defining the concept and principles of juvenile justice within the realm of legal science: juvenile justice is a specialized legal framework that focuses on addressing legal issues involving minors, emphasizing rehabilitation over punishment. The article explores the evolution of juvenile justice, examining its theoretical foundations, legislative developments, and practical applications across different legal systems. By dissecting various definitions and principles proposed by scholars and practitioners, this article aims to clarify the core components of juvenile justice and propose a coherent conceptual framework. This article seeks to analyze and elucidate the concept and principles of juvenile justice by examining its historical development, theoretical underpinnings, and current practices. Through a comprehensive review of existing literature and comparative analysis of various legal systems, the article seeks to provide a robust framework for understanding juvenile justice, to offer clarity on “juvenile justice” definition and principles, thereby enhancing the effectiveness of juvenile justice systems and contributing to more informed policy-making and legal reform. The analysis underscores the importance of protecting minors’ rights while balancing the interests of society, thereby contributing to a more nuanced understanding of juvenile justice in contemporary legal discourse. Based on the research, it is suggested to define juvenile justice as a comprehensive system of legal norms and institutions, state and other bodies that protect the rights of minors, as well as a complex of preventive and other measures in this area.