Polycystic ovary syndrome (PCOS) is a common infertility disorder affecting a significant proportion of the global population. It is the main cause of anovulatory infertility in women and is the most common endocrinopathy affecting reproductive-aged women, with a prevalence of 8–13% depending on the criteria used and population studied. The disease is multifactorial and complex and, therefore, often difficult to diagnose due to overlapping symptoms. Multiple etiological factors have been implicated in PCOS. Due to the complex pathophysiology involving multiple pathways and proteins, single genetic diagnostic tests cannot be determined. Progress has been achieved in the management and diagnosis of PCOS; however, not much is known about the molecular players and signaling pathways underlying it. Conclusively PCOS is a polygenic and multifactorial syndromic disorder. Many genes have been associated with PCOS, which affect fertility either directly or indirectly. However, studies conducted on PCOS patients from multiple families failed to find a fully penetrant variant(s). The present study was designed to review the current genetic understanding of the disease. In the present review, we have discussed the clinical spectrum, the genetics, and the variants identified as being associated with PCOS. The mechanisms by which variants in the genes confer risk to PCOS and the nature of the physical and genetic interaction between the genetic elements underlying PCOS remain to be determined. Elucidation of genetic players and cellular pathways underlying PCOS will certainly increase our understanding of the pathophysiology of this syndrome. The study also discusses the current status of the treatment modalities for PCOS, which is important to find new ways of treatment.

This study's aim is to investigate the role of e-governance in combating COVID-19 by integrating the implications of the China-Pakistan Economic Corridor (CPEC). We discuss and analyze the E-Government Development Index (EGDI) reports and rankings issued by the United Nations and big data implications during the COVID-19 pandemic. We used the Origin-pro 2018 application for the analysis and discussion. Overall, China's EGDI ranking has improved from 74 to 65 out of 193 countries, while Pakistan's ranking has gradually declined from 137 to 148. 5G and other big data technology and e-governance implications have helped to combat the COVID-19 pandemic. In this pandemic scenario, sustainable socioeconomic development in Pakistan needs significant improvement, similar to what has been done by China. We conclude that CPEC can help combat the COVID-19 pandemic because both countries are working together to mitigate social and economic problems. Pakistan should adapt and learn from the Government of China's experience of successful and proficient e-governance model of technological advancement. This effort will ensure successful CPEC regional extension and help combat the COVID-19 pandemic to ensure Pakistan's sustainable development.

Summary Due to an ever‐increasing number of Internet of Everything (IoE) devices, massive amounts of data are produced daily. Cloud computing offers storage, processing, and analysis services for handling of such large quantities of data. The increased latency and bandwidth consumption is not acceptable to real‐time applications like online gaming, smart health, video surveillance, etc. Fog computing has emerged to overcome the increase in latency and bandwidth consumption in Cloud computing. Fog Computing provides storage, processing, networking, and analytical services at the edge of a network. As Fog Computing is still in its infancy, its significant challenges include resource‐allocation and job‐scheduling. The Fog devices at the edge of the network are resource‐constrained. Therefore, it is important to decide the assignment and scheduling of a job on a Fog node. An efficient job scheduling algorithm can reduce energy consumption and response time of an application request. In this paper, we propose a novel Fog computing scheduler that supports service‐provisioning for Internet of Everything, which optimizes delay and network usage. We present a case study to optimally schedule the requests of Internet of Everything devices on Fog devices and efficiently address their demands on available resources on every Fog device. We consider delay and energy consumption as performance metrics and evaluate the proposed scheduling algorithm using iFogSim in comparison with existing approaches. The results show that the delay and network usage of the proposed scheduler improve by 32% and 16%, respectively, in comparison with FCFS approach.

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The study aimed to analyse the role of the capital structure in the financial performance of 90 textile firms listed in Pakistan Stock Exchange (PSX) during the period 2008–2017. The dependent variable was return on equity as a proxy for financial performance. The independent variables were the debt to equity, total debt to total assets, asset turnover ratios, sales growth, taxation, and export growth, while the firm size was taken as a control variable. The panel regression estimation technique was employed for analysis purposes, and both cross-sectional and time-series data were collected for this study. This study used the random-effect regression estimation model based on the Hausman diagnostic test statistics. The results indicate that the capital structure debt to equity variable has a negative and significant relationship with financial performance while the asset turnover ratio and firm performance showed a negative and statistically insignificant relationship. Export growth and sales growth have a considerable positive connection with financial performance; however, firm size has a negative and significant impact on firm performance, in favour of our alternative research hypothesis. The remaining variables include tax payable and the total debt to total assets ratio, which have an insignificant connection with financial performance (ROE) and validate the agency theory. With better corporate governance by putting more pressure on managers or increasing managerial ownership, institutional investors can reduce the capital, leverage risk and the overall firm capital cost that help to improve the firm's financial performance and economic stability.

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This study is reporting the biofuel synthesis and characterization from the novel nonedible feedstock cocklebur seeds oil. The Cocklebur crop seeds oil was studied as a potential source for biofuel production based on the chemical, structural and fuel properties analysis. The oil expression and FFAs content in cocklebur crop was reported 37.2% and 0.47 gram KOH/g, using soxhlet apparatus and acid base titration method, respectively. The maximum conversion and yield of the cocklebur crop seeds non-edible oil to biofuel was pursued 93.33%, using transesterification process. The optimum protocol for maximum conversion yield was adjusted: 1:7 oil-methanol molar ratios, ZnO nano-particle concentration 0.2 gm (w/w), reaction temperature 60 • C, and reaction time 45 min, respectively. ZnO nano-particle was prepared by a modified sol-gel method, using gelatin and the particle was XRD, TEM, XPS, and UV-vis spectroscopies. Qualitatively, the cocklebur crop synthesized biofuel was quantified and structurally characterized by GC/MS, FT-IR, NMR, and AAS spectroscopies. Quantitatively, the fuel properties of cocklebur crop biofuel was analyzed and compared with the international ASTM and EN standards.

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