The COVID-19 (coronavirus disease-2019) has been associated with psychological distress during its rapid rise period in Pakistan. The present study aimed to assess the mental health of healthcare workers (HCWs) in the three metropolitan cities of Pakistan. Methods: A cross-sectional, web-based study was conducted in 276 HCWs from April 10, 2020, to June 5, 2020. Depression, anxiety, and stress scale (DASS-21) were used for the mental health assessment of the HCWs. Multivariable logistic regression analysis (MLRA) was performed to measure the association between the demographics and the occurrence of depression, anxiety, and stress (DAS). Results: The frequency of DAS in the HCWs was 10.1%, 25.4%, and 7.3%, respectively. The MLRA showed that the depression in HCWs was significantly associated with the profession (P<0.001). The anxiety in HCWs was significantly associated with their age (P=0.005), profession (P<0.05), and residence (P<0.05). The stress in HCWs was significantly associated with their age (P<0.05). Limitation: This study was conducted in the early phase of the COVID-19 pandemic, when the number of COVID-19 cases was on the rise in Pakistan and it only represents a definite period (April to June 2020). Conclusion: The symptoms of DAS are present in the HCWs of Pakistan and to manage the psychological health of HCWs, there is a need for the initiation of psychological well-being programs.

The concept of smart grid was introduced a decade ago. Demand side management (DSM) is one of the crucial aspects of smart grid that provides users with the opportunity to optimize their load usage pattern to fill the gap between energy supply and demand and reduce the peak to average ratio (PAR), thus resulting in energy and economic efficiency ultimately. The application of DSM programs is lucrative for both utility and consumers. Utilities can implement DSM programs to improve the system power quality, power reliability, system efficiency, and energy efficiency, while consumers can experience energy savings, reduction in peak demand, and improvement of system load profile, and they can also maximize usage of renewable energy resources (RERs). In this paper, some of the strategies of DSM including peak shaving and load scheduling are highlighted. Furthermore, the implementation of numerous optimization techniques on DSM is reviewed.

With the emergence of computation-intensive and delay-sensitive applications, such as face recognition, virtual reality, augmented reality, and Internet of Things (IoT) devices ; Mobile Edge Computing (MEC) allows the IoT devices to offload their heavy computation tasks to nearby edge cloud network rather than to compute the tasks locally. Therefore, it helps to reduce the energy consumption and execution delay in the ground mobile users. Flying Unmanned Aerial Vehicles (UAVs) integrated with the MEC server play a key role in 5G and future wireless communication networks to provide spatial coverage and further computational services to the small, battery-powered and energy-constrained devices. The UAVenabled MEC (U-MEC) system has flexible mobility and more computational capability compared to the terrestrial MEC network. They support line-of-sight (LoS) links with the users offloading their tasks to the UAVs. Hence, users can transmit more data without interference by mitigating small-scale fading and shadowing effects. UAVs resources and flight time are very limited due to size, weight, and power (SWaP) constraints. Therefore, energy-aware communication and computation resources are allocated in order to minimize energy consumption.In this paper, a brief survey on U-MEC networks is presented. It includes the brief introduction regarding UAVs and MEC technology. The basic terminologies and architectures used in U-MEC networks are also defined. Moreover, mobile edge computation offloading working, different access schemes used during computation offloading technique are explained. Resources that are needed to be optimized in U-MEC systems are depicted with different optimization problem, and solution types. Furthermore, to guide future work in this area of research, future research directions are outlined. At the end, challenges and open issues in this domain are also summarized.

Background: Hyperlipidemia is the elevation of low density lipoprotein levels resulting in fat deposites in arteries and their hardening and blockage.  It is the leading cause of several life threatening pathological conditions like hypertension, cardiovascular diseases, diabetes etc. Purpose: The objective of this study was to prepare and optimize nontoxic, biocompatible β-CD-g-MAA/Na + -MMT nanocomposite hydrogels with varying content of polymer, monomer and montmorillonite. Moreover, lipid lowering potentials were determined and compared with other approaches. Methods: β-CD-g-MAA/Na + -MMT nanocomposite hydrogels (BM-1 to BM9) were prepared through free radical polymerization by using  β-CD  as polymer, MAA as monomer, MBA as crosslinker and montmorillonite as clay. Developed networks were evaluated for FTIR, DSC, TGA, PXRD, SEM, sol-gel fraction (%), swelling studies, antihyperlipidemic studies and toxicity studies. Results: Optimum swelling (94.24%) and release (93.16%) were obtained at higher pH values. Based on R 2 and “n” value LVT release followed zero order kinetics with Super Case II transport release mechanism, respectively. Tensile strength and elongation at break were found to be 0.0283MPa and 94.68%, respectively. Gel fraction was between 80.55 – 98.16%. Antihyperlipidemic studies revealed that LDL levels were markedly reduced from 522.24 ± 21.88mg/dl to 147.63 ± 31.5mg/dl. Toxicity studies assured the safety of developed network. Conclusion: A novel pH responsive crosslinked network containing β-CD – g – poly (methacrylic acid) polymer and MMT was developed and optimized with excellent mechanical, swelling and release properties and lipid lowering potentials.

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