RESUMO
Background and Aims: Due to the availability of more sophisticated cell phones with top-notch gaming functions, the present generation is more active. The available literature indicates that adolescents experience a variety of psychological issues, like low self-control brought on by an addiction to mobile games. Because of this, the aim of this study is to control the prevalence of, and factors that contribute to, online gaming addiction and its effects on academic performance in Bangladeshi university students. Methods: Convenient sampling was adopted to collect primary data from 399 Bangladeshi university students utilizing a prestructured questionnaire. Descriptive statistics, the χ 2 test, binary logistic regression, and multinomial logistic regression were also used to accomplish the study's objective. Results: According to this study, 62.7% of students play online games over 30 h every week. The findings also show that male students are more inclined than female students to show signs of addiction. Also, regular online gaming can result in long-term problems, and that factor including age, internet access, educational background, and frequency of play can influence the likelihood of these problems. The findings shows that a lower cumulative grade point average (CGPA), less physical activity, and less study time are associated with playing online games for at least 30 h per week. Moreover, the study found that playing online games, playing for long time, and skipping class can all have an adverse effect on a student's academic performance. Conclusions: The authors recommend that the authorities set up a good entertainment environment and take into account the findings of this article to discourage students from playing online games. Furthermore, encouragement of extracurricular activities such as sports or other pursuits is also essential in assisting Bangladeshi students in overcoming their addiction to mobile games.
RESUMO
Due to the growing demand, assessing performance has become obligatory for photovoltaic (PV) energy harvesting systems. Performance assessment involves estimating different PV system parameters. Traditional ways, such as calculating solar radiation using satellite data and the IV characteristics approach as assessment methods, are no longer reliable enough to provide a reasonable projection of PV system parameters. Estimating system parameters using machine learning (ML) approaches has become a reliable and popular method because of its speed and accuracy. This paper systematically reviewed ML-based PV parameter estimation studies published in the last three years (2020 - 2022). Studies were analyzed using several criteria, including ML algorithm, outcome, experimental setup, sample data size, and error metric. The analysis revealed several interesting factors. The neural network was the most popular ML method (32.55%), followed by random vector functional link (13.95%) and support vector machine (9.30%). Dataset was sourced from hardware tests and computer-based simulations: 66% of the studies used data from only computer simulation, 18% used data from only hardware setup, and the 16% experiments used data from both hardware and simulations to evaluate different system parameters. The top three most commonly used error metrics were root mean square error (29.1%), mean absolute error (17.5%), and coefficient of determination (15.9%). Our systematic review will help researchers assess ML algorithms' projection in PV system parameters estimation. Consequently, scopes shall be created to establish more robust governmental frameworks, expand private financing in the PV industry, and optimize PV system parameters.
