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Power generation from fossil fuels is the biggest challenge in the next half of the century. Alternative power generation techniques such as solar photovoltaic (PV) show potential to act as a future fuel with a challenge to efficiently convert the harvested solar energy into electrical power. This investigation conclusively focused on setting a 2.160-kW solar PV system capable of working at a higher efficiency by developing a mechanical structure that optimizes power production and minimizes energy losses. In addition to that, solar PV system efficiencies at various tracking positions, performance coefficients during rainy and sunny days, and system degradation rates have also been investigated. The PVsyst v6.8 simulation tool was used to obtain the simulated results, which were compared with the actual experimental results. The parameters considered for the investigations include ambient temperature, irradiance, solar PV module surface temperature, solar PV voltage and current, wind velocity, and atmospheric turbidity. The solar PV system was evaluated based on two modes, namely, M1 (no tracking/fixed type) and M2 (manual tracking by changing the position of the solar PV system every hour). The predictive results obtained using PVsyst v6.8 concluded that total energy production from the installed system was 3,242 kWh/yr and 3,984 kWh/yr for M1 and M2, respectively. The performance ratio (PR), obtained from simulation, was 72% and 78% for M1 and M2, respectively, which was consistent with the experimental results, i.e., 70% and 72% for M1 and M2, respectively. Similarly, the power conversion efficiencies under standard temperature and conditions for both modes, simulated and experimental, were found to be 16.50% and 12.75%, respectively. The estimated degradation rate was observed in the range of -0.6% to -5.0%. [ABSTRACT FROM AUTHOR]

The evaluation of scientific journals poses challenges owing to the existence of various impact measures. This is because journal ranking is a multidimensional construct that may not be assessed effectively using a single metric such as an impact factor. A few studies have proposed an ensemble of metrics to prevent the bias induced by an individual metric. In this study, a multi-metric journal ranking method based on the standardized average index (SA index) was adopted to develop an extended standardized average index (ESA index). The ESA index utilizes six metrics: the CiteScore, Source Normalized Impact per Paper (SNIP), SCImago Journal Rank (SJR), Hirsh index (H-index), Eigenfactor Score, and Journal Impact Factor from three well-known databases (Scopus, SCImago Journal & Country Rank, and Web of Science). Experiments were conducted in two computer science subject areas: (1) artificial intelligence and (2) computer vision and pattern recognition. Comparing the results of the multi-metric-based journal ranking system with the SA index, it was demonstrated that the multi-metric ESA index exhibited high correlation with all other indicators and significantly outperformed the SA index. To further evaluate the performance of the model and determine the aggregate impact of bibliometric indices with the ESA index, we employed unsupervised machine learning techniques such as clustering coupled with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). These techniques were utilized to measure the clustering impact of various bibliometric indicators on both the complete set of bibliometric features and the reduced set of features. Furthermore, the results of the ESA index were compared with those of other ranking systems, including the internationally recognized Scopus, SJR, and HEC Journal Recognition System (HJRS) used in Pakistan. These comparisons demonstrated that the multi-metric-based ESA index can serve as a valuable reference for publishers, journal editors, researchers, policymakers, librarians, and practitioners in journal selection, decision making, and professional assessment. [ABSTRACT FROM AUTHOR]

This research investigated the best economically viable power source with the least environmental impact and socially acceptable for the maize crop. Maize is one of the key economic crops in Pakistan. Solar-, electric-, and diesel-powered drip irrigation systems (DIS) were considered for comparative study. We selected 45 sites of maize crop to collect the data, with an area of 1–3 ha, from three divisions. For economic viability, the benefit:cost ratio, life cycle cost, and payback period were calculated, and CO2 emissions were calculated to assess the environmental impact. The SPSS model was used for one-way ANOVA followed by post hoc and chi-squared tests to check the significance level between all power sources. It was found that the B-C of electric power, solar, and diesel drip irrigation systems was 1.65, 1.52, and 1.44, respectively. Solar, diesel, and electricity power DIS have CO2 emissions of 0.02, 0.730, and 1.106 tons/ha, respectively. The research concludes that solar power and electric power are the best sources for the environment and economically, respectively. It is recommended that solar power DIS be subsidized, which will help to lower CO2 emissions and reduce the electricity shortfall in Pakistan. [ABSTRACT FROM AUTHOR]

Background: The involvement of IgE in type 1 hypersensitivity reactions, including asthma and allergic rhinitis, is widely recognized, and established. However, within this region, there exists a dearth of research exploring the relationship between serum IgE levels and the severity of asthma within the pediatric population. Consequently, the primary objective of this study was to compare the IgE status among children and adolescents diagnosed with asthma, and to examine its association with asthma exacerbations and hospitalizations. By investigating these parameters, we aimed to shed light on the potential role of IgE in influencing disease outcomes in this specific population., Methods: A retrospective study was conducted at The Aga Khan University Hospital from January 2015 to December 2020. Children and adolescents aged 6-18 years who were diagnosed and admitted with asthma, and who had measurement of IgE levels during asthma exacerbation were included in the study. The participants were stratified into two groups based on their IgE levels: normal IgE levels and high IgE levels. Various parameters, including the number of asthma exacerbations, emergency room (ER) visits, hospitalizations, average length of stay, as well as other clinical characteristics such as the presence of allergic rhinitis in the past and a family history of asthma were assessed., Results: A total of 122 patients were included in the study. Of them 57 patients (46.72%) were found to have high IgE levels, and 65 patients (53.28%) had normal IgE levels based on their age group. The average number of exacerbations per year was significantly greater the in high IgE group (3.6±1.09), in comparison to the normal IgE group (2.49±1.22)., Conclusion: Higher serum IgE levels correlated with increased asthma exacerbations, emergency room visits, hospitalizations, and a positive family history of asthma, suggesting a potential link between IgE and asthma severity in the pediatric population of Pakistan.