A Comprehensive Framework for Mitigating Digital Divide Factors in Higher Education: A Case Study of Kabul University

The issue of Digital Divide is a prevalent concern across various educational and non-educational domains in Afghanistan. The situation is particularly challenging at Kabul University, where numerous factors hinder the effective utilization of available digital technologies by students, teachers, and staff. Drawing on the existing factors, our research proposes a comprehensive solution framework to address this problem. Our framework aims to mitigate most of the Digital Divide factors that are present at Kabul University, thereby enabling its stakeholders to leverage digital technologies effectively. Compared to similar frameworks proposed in other developing countries, our approach is specifically tailored to the cultural and educational environments of Kabul University and Afghanistan. As such, it takes into account the unique challenges and opportunities presented by these contexts. Our research is a significant contribution to the discourse on Digital Divide in Afghanistan, and we believe that our proposed framework has the potential to transform the digital landscape of Kabul University. We hope that our findings and recommendations will inform further research and policy interventions in this area, ultimately contributing to the development of a more digitally inclusive society. In our research, we utilized a case study and design science methodology to address the issue of the Digital Divide at Kabul University in Afghanistan

Optimizing the Planting Date for Onion Production: Correlation Between Weather Conditions and Plant Growth, Yield, and Bulb Quality

oai:ojs2.kujnsr.com:article/3This investigation was carried out during 2018 and 2019 at the research farm of the Agriculture Faculty of Kabul University, Afghanistan to study the correlation between weather conditions (maximum, minimum and mean temperature, relative humidity, cumulative day light and cumulative rainfall) and onion plant growth, yield and bulbs quality, as well as to find the optimal planting date for onion variety Safid e Paisaye. The data was analyzed with STAR software. The results of this study reveal that, increasing temperature and decreasing relative humidity and rainfall during growing season, caused faster maturity of bulbs, produced small sized bulbs, and finally reduced onion bulbs yield. It was observed that, due to relatively lower temperature and higher relative humidity during early growth stages the onion grown early in spring (seed sown in early March and transplants planted early in May) took longer period for maturation, produced larger bulbs and higher yield. This was also noted that due to longer maturation period the onion plants grown in early spring received a higher cumulative heat and sun light which contributed to improve bulbs quality and yield of onion. Based on this study results it is concluded that, early planting date of onion variety Safid e Paisaye not only increased significantly yield but also improved bulbs quality

Genetic Architecture of Body Fat Composition in Mice

Body fat composition is a quantitative measure of obesity, a major health concern in humans. Laboratory mice are considered an excellent model for dissecting the genetic basis of obesity due to the genetic variation present in inbred strains for body size and fat composition, as well as their historical use as a model organism for human disease studies. To identify the loci controlling fat pad weights and body weight in mice, we performed a quantitative trait loci (QTL) analysis of 513 (SM/J x NZB/BINJ) F2 individuals fed a high-fat diet for 14 weeks. Our analysis separated fat composition genetic effects from those affecting overall body sizes in mouse.  Composite interval mapping (CIM) results showed that body weight was conditioned by three major additive QTLs, explaining 3 to 30% of the phenotypic variation. One significant QTL on chromosome 19 conditioned all fat pads with the exception of the inguinal fat weight, which was controlled by a different QTL also on chromosome 19. Significant QTLs associated with fat compositions were detected on chromosomes 17 and 19 and differed from those of body weight. The fat pad QTLs also showed mainly additive gene effects and they explained 2 to 7% of variation in fat composition. Joint analysis of correlated traits detected five additional large effect QTLs on five different linkage groups. These findings have indicated that fat composition and body weight in mouse are conditioned by one to three major additive genes and can therefore be potentially manipulated in controlling obesity