An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff.

The mental and physical well-being of healthcare workers is being affected by global COVID-19. The pandemic has impacted the mental health of medical staff in numerous ways. However, most studies have examined sleep disorders, depression, anxiety, and post-traumatic problems in healthcare workers during and after the outbreak. The study's objective is to evaluate COVID-19's psychological effects on healthcare professionals of Saudi Arabia. Healthcare professionals from tertiary teaching hospitals were invited to participate in the survey. Almost 610 people participated in the survey, of whom 74.3% were female, and 25.7% were male. The survey included the ratio of Saudi and non-Saudi participants. The study has utilized multiple machine learning algorithms and techniques such as Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (KNN), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The machine learning models offer 99% accuracy for the credentials added to the dataset. The dataset covers several aspects of medical workers, such as profession, working area, years of experience, nationalities, and sleeping patterns. The study concluded that most of the participants who belonged to the medical department faced varying degrees of anxiety and depression. The results reveal considerable rates of anxiety and depression in Saudi frontline workers.

Economic development, social media awareness, and technological innovation in biogas sector under climate change in the post-COVID-19 pandemic conditions.

After COVID-19, financing for emerging nation reserves in renewable energy bases was deemed a crucial aspect of sustainable development. Investing in biogas energy plants can be highly beneficial for lowering the use of fossil fuels. Using a survey of shareholders, investors, biogas energy professionals, and active social media participants in Pakistan, this study evaluates the intentions of individual investors to invest in biogas energy plants. The primary purpose of this study is to increase investment intent for biogas energy projects following COVID-19. This study focuses on financing biogas energy plants in the post-COVID-19 era and evaluates the research's assumptions using partial least squares structural equation modeling (PLS-SEM). The study employed the technique of purposive sampling to acquire data for this investigation. The results indicate that attitudes, perceived biogas energy benefits, perceived investment attitudes, and supervisory structure evaluations inspire one's propensity to finance biogas vitality plant efforts. The study found a link between eco-friendly responsiveness, monetary benefits, and investors' actions. The aspiration of investors to mark such reserves was set up to be unpretentious by their risk aversion. Conferring to the facts, evaluating the monitoring structure is the critical factor. The previous studies on investment behavior and other forms of pro-environmental intent and action yielded contradictory results. In addition, the regulatory environment was evaluated to see how the theory of planned behavior (TPB) affects financiers' objectives to participate in biogas power plants. The consequences of the study indicate that feelings of pride and discernment of energy expansively affect people's desire to invest in biogas plants. Biogas energy efficacy has little effect on investors' decisions to invest in biogas energy plants. This study offers policymakers practical ideas on enhancing investments in biogas energy plants.

Exploring the co-movements between COVID-19 pandemic and international air traffic: A global perspective based on wavelet analysis.

The travel and tourism industry was one of the fastest-growing industries before the onset of the COVID-19 pandemic. However, to avoid COVID-19 spread, the government authorities imposed strict lockdown and international border restrictions except for some emergency international flights that badly hit the travel and tourism industry. The study explores the nexus between international air departures and the COVID-19 pandemic in this strain. We use a novel wavelet coherence approach to dissect the lead and lag relationships between international flight departures and COVID-19 deaths from January 2020 to September 2020 (COVID-19 first wave period). The results reveal that international flights cause the spread of COVID-19 spread during May 2020 to June 2020 worldwide. The overall findings suggest asymmetries between daily international flight departures and COVID-19 deaths globally at different time-frequency periods due to uncertainty surrounding the COVID-19 pandemic. The study will be conducive for the policymakers to control the upsurge of COVID-19 spread worldwide.

Post-COVID-19 Pneumocystis pneumonia cases from Pakistan: an observational study

Background. Concurrent coronavirus disease 2019 (COVID-19) and Pneumocystis jirovecii pneumonia (PJP) has been described in various reports, with a recent study describing a 9.3 % P. jirovecii detection rate in critically ill COVID-19 patients. Methods. Patients with PCR-confirmed PJP following COVID-19 infection who were admitted to Aga Khan University Hospital, Karachi, Pakistan from March 2020–June 2021 were identified through a laboratory database. Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus was performed by RT-PCR Cobas SARS-CoV-2 qualitative assay. P. jirovecii PCR was performed using the RealStar Pneumocystis jirovecii PCR kit. Clinical, radiological and laboratory data for PJP patients were recorded. Results. During the study period, 3707 patients were admitted with COVID-19 at our hospital. P. jirovecii PCR was requested for 90 patients and was positive in 10 (11 %). Five out of 10 patients were discharged from the hospital and later developed cough and dyspnoea. Five patients remained hospitalized with severe COVID-19 and developed PJP. Eight patients in our study received systemic steroids. The trends of lymphocyte counts of all patients showed a lymphocyte count of <1000 mm−3 (<1.0×106 cells µl−1) in the week of PJP diagnosis. Four patients did not survive;one of these patients did not receive co-trimoxazole due to late diagnosis, one patient had concomitant nosocomial pneumonia and bacteraemia with multidrug-resistant Acinetobacter species, and two patients had concomitant aspergillosis. Conclusion. In summary, invasive fungal infections such as PJP should be considered as a complication in COVID-19 patients, with prompt evaluation and management.

Employee associations with R&D investment, firm performance, disruption risk, and supply chain performance during the COVID-19 pandemic: A multiple mediational model

Purpose: Scholars have concentrated their efforts on COVID-19's impact on industries worldwide in order to manage timely supply chain disruptions. Epidemic outbursts are a unique supply chain risk that is distinguished by prolonged disruption propagation, disruption existence, and high uncertainty. The purpose of this study was to investigate the role of R&D investment and firm performance in mediating the relationship between disruption risk and supply chain performance in Pakistani manufacturing industries and supply chain employees during the recovery phase of the COVID-19 pandemic via application of dynamic capability theory. Methodology: From July 21 to August 23, 2020, three hundred and eighteen employees from supply chains of manufacturing industries in Rawalpindi and Islamabad, Pakistan, participated in this cross-sectional online web-based survey. The four standard research scales were used to examine the research and development, disruption risk, firm, and supply chain performance. The response link was distributed to respondents via Facebook, WhatsApp, and email. The data was analyzed using structural equation modelling and a partial least squares technique in the study. Results: The study's findings suggest that disruption risk, research and development investment, and firm performance all improve supply chain performance, but the mediation effect is unsupported by the data. These measures help to plan a better supply chain in the face of disruption risk, and they provide one of the timely empirical conclusions on the role of R&D investment in mitigating risk disruptions and improving supply chain performance

Identifying How E-Service Quality Affects Perceived Usefulness of Online Reviews in Post-COVID-19 Context: A Sustainable Food Consumption Behavior Paradigm

In this study, the concept of online food purchasing is explored where consumers are not required to visit markets to purchase their foods, especially during the COVID-19 pandemic. Thus, the purpose of this study is to investigate the relationship between perceived e-service quality and related customer service outcomes in online shopping context. The influence of sustainable marketing practices in terms of perceived e-service quality (ESQ), perceived usefulness of online reviews (PUO), brand self-connection (BSC), personal innovativeness (PRI), and willingness to pay for online food services (WPO) has largely been neglected in the previous studies. The present study proposes a conceptual model to fill this gap and empirically examines how ESQ affects PUO, BSC, WPO, and e-word-of-mouth (e-WOM) of food delivery service brands. An online questionnaire survey was conducted with 423 customers utilizing the PLS-SEM-based approach to determine product indicators. Empirical results reveal that ESQ significantly influence BSC and PUO. In the same vein, PUO significantly influence BSC, while BSC significantly influence e-WOM and WPO. The results further indicate that PRI moderates the relationship between ESQ and BSC. In a post-pandemic context, our analysis indicates enormous implications for food service delivery brands in emerging economies. Online food service providers should consider reviewers' opinions about the products and services they offer and encourage their customers to write positive reviews of the products and services they offer.

Factors predicting mortality among patients with COVID-19 associated hospital acquired pneumonia: Insights from a tertiary care center.

Hospital acquired pneumonia (HAP) is a severe and dangerous complication in patients admitted with COVID-19, causing significant morbidity and mortality globally. However, the early detection and subsequent management of high-risk cases may prevent disease progression and improve clinical outcomes. This study was undertaken in order to identify predictors of mortality in COVID-19 associated HAP. A retrospective study was performed on all patients who were admitted to a tertiary care center with COVID-19 associated HAP from July 2020 till November 2020. Data was collected on relevant demographic, clinical and laboratory parameters to determine their association with in-hospital mortality. 1574 files were reviewed, out of which 162 were included in the final study. The mean age of subjects was 59.4 ± 13.8 and a majority were male (78.4%). There were 71 (48.3%) mortalities in the study sample. Klebsiella pneumoniae (31.5%) and Pseudomonas aeruginosa (30.2%) were the most common organisms overall. Clinically significant growth of Aspergillus sp. was observed in 41 (29.0%) of patients. On univariate analysis, several factors were found to be associated with mortality, including male gender (p=0.04), D-dimers >1.3mg/L (p<0.001), ferritin >1000ng/mL (p<0.001), LDH >500I.U/mL (p<0.001) and procalcitonin >2.0ng/mL (p<0.001). On multivariate analysis, ferritin >1000ng/mL, initial site of care in Special Care Units or Intensive Care Units, developing respiratory failure and developing acute kidney injury were factors independently associated with mortality in our patient sample. These results indicate that serum ferritin levels may be a potentially useful biomarker in the management of COVID-19 associated HAP.

Health Impacts of Surface Ozone in Outdoor and Indoor Environments of Hattar Industrial Units, KPK, Pakistan

This research was carried out to analyze variations in indoor and outdoor ozone concentrations and their health impact on local communities of megacities in Pakistan. For indoor ozone measurements, industrial units of an economic zone, Hattar Industrial Estate, Haripur, KPK, Pakistan, were selected. For outdoor ozone measurements, maximum and minimum peaks from different selected stations of three megacities (Islamabad, Abbottabad, and Haripur Hattar) in Pakistan were analyzed for paired comparisons. The tropospheric ozone levels were measured with the help of a portable SKY 2000-WH-O3 meter from December 2018 to November 2019. According to the findings of this investigation, the indoor ozone concentrations at Hattar Industrial Estate exceeded the permissible limit devised by the WHO. The highest concentration (0.37 ppm) was recorded in the month of May in the food industry, while the lowest concentration (0.00 ppm) was recorded in the cooling area of the steel industry in the month of December. For outdoor ozone concentrations, the maximum concentration (0.23 ppm) was detected in Islamabad in the month of March 2019, whereas the rest of year showed comparatively lower concentrations. In Haripur, the maximum concentration (0.22 ppm) was detected in the month of February 2019 and a minimum concentration (0.11 ppm) was found in the month of November 2019. In Abbottabad, the maximum concentration (0.21 ppm) was detected in the month of March 2019 and the minimum concentration was 0.082 ppm. Increasing tropospheric ozone levels might be harmful for local communities and industrial laborers in the winter season because of the foggy weather. In the Abbottabad and Hattar regions, since COVID infection is indirectly related to low temperature and high emission of gases may compromise the respiratory systems of humans. The results of the present study were shared with industrialists to set precautions for ambient air quality and support the adoption of low emission techniques in industries for the safety of labour and nearby residents.

The effect of COVID-19 vaccination on the menstrual pattern and mental health of the medical students: A mixed-methods study from a low and middle-income country.

OBJECTIVE: To assess the effect of COVID-19 vaccination on menstrual patterns and mental health of medical students and to explore the students' perspective regarding this effect. MATERIALS AND METHODS: This mixed-method study was conducted on the medical and dental students of the private and public sector institutions of Peshawar from September 2021 to March 2022. A Menstrual symptom questionnaire (MSQ) and hospital anxiety and depression scale (HADS) were used. This was followed by qualitative interviews with the students who faced problems in their menstruation after the COVID-19 vaccination. RESULTS: A total of 953 students were included, with a mean age of 20.67±1. 56 years. More than half (n = 512, 53.7%) experienced menstrual cycle abnormalities post-vaccination. The majority having disturbances in their menstrual cycle had significantly higher levels of anxiety (p = 0.000). Results on the menstrual symptom questionnaire, anxiety, and depression subtype of HADS showed a negative and statistically significant relationship with changes after COVID-19 vaccination (p<0.05). In the qualitative interviews, 10 (58.8%) students each had problems with frequency and flow, followed by 7 (41.2%) students, who had dysmenorrhea. Seven (41.2%) consulted a gynecologist for management. The majority (n = 14, 82.4%) stated that these issues had an adverse impact on their mental health and almost half (n = 8, 47.1%) suggested consulting a gynecologist while facing such situations. CONCLUSION: This study showed the impact of the COVID-19 vaccine on women`s menstrual patterns and subsequent mental health status. Although the majority of the students experienced menstrual cycle abnormalities and subsequent mental health adversities post COVID-19 vaccination but these were temporary and self-limiting and were attributed to the psychological impact of the vaccination. Therefore, it is imperative to alert health care professionals about possible side effects and prior counseling is expected to play an important role in this context.

Invasive pulmonary aspergillosis in critically ill patients with pneumonia due to COVID-19, influenza, and community-acquired pneumonia: A prospective observational study.

Background and Purpose: Influenza A and SARS-CoV-2 are risk factors for invasive pulmonary aspergillosis. Both influenza-associated pulmonary aspergillosis and COVID-19-associated pulmonary aspergillosis result in high mortality and poor clinical outcomes. No prospective study has so far compared the features, treatment, and outcomes of influenza-associated pulmonary aspergillosis and COVID-19-associated pulmonary aspergillosis within a similar time frame. Therefore, this study aimed to determine the frequency, risk factors, and outcomes of invasive pulmonary aspergillosis in critically ill patients with influenza, COVID-19, and community-acquired pneumonia. Materials and Methods: This prospective study included adult patients with pneumonia and was conducted at The Aga Khan University Hospital in Karachi, Pakistan. Patients were divided into three groups, including community-acquired pneumonia, influenza pneumonia, and COVID-19 pneumonia. The data collected included information on demographic characteristics, comorbidities, clinical features, laboratory results, treatment, and outcomes. Results: A total of 140 patients were included in this study. These included 35 (25%), 70 (50%), and 35 (25%) patients with community-acquired pneumonia, influenza pneumonia, and COVID-19 pneumonia, respectively. In addition, 20 (14.2%) patients were found to have invasive aspergillosis, of whom 10/35 (28.5%), 9/70 (12.8%), and 1/35 (2.8%) patients were in the COVID-19, influenza, and community-acquired pneumonia groups, respectively. Moreover, nine (90%) COVID-19-associated pulmonary aspergillosis patients required vasopressors, compared to three (33%) patients with influenza-associated pulmonary aspergillosis (P=0.020). In total, seven (70%) COVID-19-associated pulmonary aspergillosis patients required invasive mechanical ventilation compared to four (44%) influenza-associated pulmonary aspergillosis patients (P=0.37). The mean±SD length of hospital stay was highest in the COVID-19-associated pulmonary aspergillosis patients (18.3±7.28 days) compared to influenza-associated pulmonary aspergillosis patients (11.7±5.34 days) (P=0.036). The number of deaths in influenza-associated pulmonary aspergillosis and COVID-19-associated pulmonary aspergillosis patients was three (33.3%) and five (50%), respectively (P=0.526). Conclusion: A higher proportion of patients with COVID-19 developed invasive aspergillosis compared to those with influenza. Although the mortality rate in COVID-19-associated pulmonary aspergillosis was comparable to that in influenza-associated pulmonary aspergillosis patients, COVID-19-associated pulmonary aspergillosis patients had a significantly longer stay in the hospital.

Genome-Based Multi-Antigenic Epitopes Vaccine Construct Designing against Staphylococcus hominis Using Reverse Vaccinology and Biophysical Approaches.

Staphylococcus hominis is a Gram-positive bacterium from the staphylococcus genus; it is also a member of coagulase-negative staphylococci because of its opportunistic nature and ability to cause life-threatening bloodstream infections in immunocompromised patients. Gram-positive and opportunistic bacteria have become a major concern for the medical community. It has also drawn the attention of scientists due to the evaluation of immune evasion tactics and the development of multidrug-resistant strains. This prompted the need to explore novel therapeutic approaches as an alternative to antibiotics. The current study aimed to develop a broad-spectrum, multi-epitope vaccine to control bacterial infections and reduce the burden on healthcare systems. A computational framework was designed to filter the immunogenic potent vaccine candidate. This framework consists of pan-genomics, subtractive proteomics, and immunoinformatics approaches to prioritize vaccine candidates. A total of 12,285 core proteins were obtained using a pan-genome analysis of all strains. The screening of the core proteins resulted in the selection of only two proteins for the next epitope prediction phase. Eleven B-cell derived T-cell epitopes were selected that met the criteria of different immunoinformatics approaches such as allergenicity, antigenicity, immunogenicity, and toxicity. A vaccine construct was formulated using EAAAK and GPGPG linkers and a cholera toxin B subunit. This formulated vaccine construct was further used for downward analysis. The vaccine was loop refined and improved for structure stability through disulfide engineering. For an efficient expression, the codons were optimized as per the usage pattern of the E coli (K12) expression system. The top three refined docked complexes of the vaccine that docked with the MHC-I, MHC-II, and TLR-4 receptors were selected, which proved the best binding potential of the vaccine with immune receptors; this was followed by molecular dynamic simulations. The results indicate the best intermolecular bonding between immune receptors and vaccine epitopes and that they are exposed to the host's immune system. Finally, the binding energies were calculated to confirm the binding stability of the docked complexes. This work aimed to provide a manageable list of immunogenic and antigenic epitopes that could be used as potent vaccine candidates for experimental in vivo and in vitro studies.

Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images.

Early detection of abnormalities in chest X-rays is essential for COVID-19 diagnosis and analysis. It can be effective for controlling pandemic spread by contact tracing, as well as for effective treatment of COVID-19 infection. In the proposed work, we presented a deep hybrid learning-based framework for the detection of COVID-19 using chest X-ray images. We developed a novel computationally light and optimized deep Convolutional Neural Networks (CNNs) based framework for chest X-ray analysis. We proposed a new COV-Net to learn COVID-specific patterns from chest X-rays and employed several machine learning classifiers to enhance the discrimination power of the presented framework. Systematic exploitation of max-pooling operations facilitates the proposed COV-Net in learning the boundaries of infected patterns in chest X-rays and helps for multi-class classification of two diverse infection types along with normal images. The proposed framework has been evaluated on a publicly available benchmark dataset containing X-ray images of coronavirus-infected, pneumonia-infected, and normal patients. The empirical performance of the proposed method with developed COV-Net and support vector machine is compared with the state-of-the-art deep models which show that the proposed deep hybrid learning-based method achieves 96.69% recall, 96.72% precision, 96.73% accuracy, and 96.71% F-score. For multi-class classification and binary classification of COVID-19 and pneumonia, the proposed model achieved 99.21% recall, 99.22% precision, 99.21% F-score, and 99.23% accuracy.

Impact of Overlapping Disaster in Turkey: COVID-19 Pandemic and Wildfires

The current pandemic has highlighted the vulnerability of healthcare systems all over the world. Turkey experienced more than 130 brutal episodes of wildfires of history. According to a report, eight people died, more than 50 were hospitalized, and thousands of residents and tourists were evacuated. The wildfire impacted the healthcare facilities of Turkey directly as well as indirectly, exerting extra strain on an already overburdened system. In this article, we made some recommendations to combat the catastrophe aiming to unburden the healthcare system of Turkey.

Post COVID-19 sequelae of the respiratory system. A single center experience reporting the compromise of the airway, alveolar and vascular components.

The long-term sequelae of COVID-19 have now become more common and appreciable. The SARS-CoV-2 virus can cause a variety of infectious and non-infectious pulmonary complications. The purpose of this study is to raise awareness about post-COVID-19 pulmonary sequelae, both infectious and non-infectious, in this geographical area. A retrospective study was conducted from July 1st 2020 to December 20th 2020. A total of 1200 patients were evaluated, with 83 suffering from post-COVID-19 pulmonary complications. The patients' mean age was 62 years (IQR 55-69), with 63 (75.9%) being male. The most common co-morbid illnesses were hypertension (49, 59%) and diabetes (45, 54.2%). The majority of them (37, 44.6%) had severe COVID-19, followed by critical COVID-19 (33, 39.8%). There was no statistically significant difference in recurrence of respiratory symptoms or duration of current illness between non-severe, severe, and critical COVID-19 patients. Non-infectious complications were observed in the majority of patients (n=76, 91.5%), including organizing pneumonia/ground glass opacities in 71 (88%) patients, fibrosis in 44 (55%), pulmonary embolism in 10 (12.5%), pneumomediastinum in 6 (7.4%) and pneumothorax in 7 (8.6%). Infective complications (25, 30.1%) included aspergillus infection in 10 (12.0%) and bacterial infection in 5 (8.47%), with more gram-negative infections and one patient developing Mycobacterium tuberculosis. Post COVID-19 mortality was 11 (13.3%). The long-term pulmonary sequelae of COVID-19 are not rare. Cryptogenic organizing pneumonia, ground glass opacities, and fibrosis were common post-COVID-19 sequelae in our patients. This necessitates frequent close monitoring of these patients in order to initiate early appropriate management and prevent further morbidity and eventual mortality.

mRNA vaccines for COVID-19 and diverse diseases.

Since its outbreak in late 2019, the novel coronavirus disease 2019 (COVID-19) has spread to every continent on the planet. The global pandemic has affected human health and socioeconomic status around the world. At first, the global response to the pandemic was to isolate afflicted individuals to prevent the virus from spreading, while vaccine development was ongoing. The genome sequence was first presented in early January 2020, and the phase I clinical trial of the vaccine started in March 2020 in the United States using novel lipid-based nanoparticle (LNP), encapsulated with mRNA termed as mRNA-1273. Till now, various mRNA-based vaccines are in development, while one mRNA-based vaccine got market approval from US-FDA for the prevention of COVID-19. Previously, mRNA-based vaccines were thought to be difficult to develop, but the current development is a significant accomplishment. However, widespread production and global availability of mRNA-based vaccinations to combat the COVID-19 pandemic remains a major challenge, especially when the mutations continually occur on the virus (e.g., the recent outbreaks of Omicron variant). This review elaborately discusses the COVID-19 pandemic, the biology of SARS-CoV-2 and the progress of mRNA-based vaccines. Moreover, the review also highlighted a detailed description of mRNA delivery technologies and the application potential in controlling other life-threatening diseases. Therefore, it provides a comprehensive view and multidisciplinary insights into mRNA therapy for broader audiences.

IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults.

Approximately 30% of the global population is suffering from obesity and being overweight, which is approximately 2.1 billion people worldwide. The ratio is expected to surpass 40% by 2030 if the current balance continues to grow. The global pandemic due to COVID-19 will also impact the predicted obesity rates. It will cause a significant increase in morbidity and mortality worldwide. Multiple chronic diseases are associated with obesity and several threat elements are associated with obesity. Various challenges are involved in the understanding of risk factors and the ratio of obesity. Therefore, diagnosing obesity in its initial stages might significantly increase the patient's chances of effective treatment. The Internet of Things (IoT) has attained an evolving stage in the development of the contemporary environment of healthcare thanks to advancements in information and communication technologies. Therefore, in this paper, we thoroughly investigated machine learning techniques for making an IoT-enabled system. In the first phase, the proposed system analyzed the performances of random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and naïve Bayes (NB) algorithms on the obesity dataset. The second phase, on the other hand, introduced an IoT-based framework that adopts a multi-user request system by uploading the data to the cloud for the early diagnosis of obesity. The IoT framework makes the system available to anyone (and everywhere) for precise obesity categorization. This research will help the reader understand the relationships among risk factors with weight changes and their visualizations. Furthermore, it also focuses on how existing datasets can help one study the obesity nature and which classification and regression models perform well in correspondence to others.

A Turf-Based Feature Selection Technique for Predicting Factors Affecting Human Health during Pandemic.

Worldwide, COVID-19 is a highly contagious epidemic that has affected various fields. Using Artificial Intelligence (AI) and particular feature selection approaches, this study evaluates the aspects affecting the health of students throughout the COVID-19 lockdown time. The research presented in this paper plays a vital role in indicating the factor affecting the health of students during the lockdown in the COVID-19 pandemic. The research presented in this article investigates COVID-19's impact on student health using feature selections. The Filter feature selection technique is used in the presented work to statistically analyze all the features in the dataset, and for better accuracy. ReliefF (TuRF) filter feature selection is tuned and utilized in such a way that it helps to identify the factors affecting students' health from a benchmark dataset of students studying during COVID-19. Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machine (SVM), and 2- layer Neural Network (NN), helps in identifying the most critical indicators for rapid intervention. Results of the approach presented in the paper identified that the students who maintained their weight and kept themselves busy in health activities in the pandemic, such student's remained healthy through this pandemic and study from home in a positive manner. The results suggest that the 2- layer NN machine-learning algorithm showed better accuracy (90%) to predict the factors affecting on health issues of students during COVID-19 lockdown time.

Evaluating natural resources volatility in an emerging economy: The influence of solar energy development barriers

Utilizing natural resources aids in China's efforts to overcome its current energy issues. However, the country's ability to fulfill the rising energy demand is hindered by the instability of its natural resources. Multiple barriers to this failure have been identified by researchers. However, little is known about how these barriers affect the volatility of natural resources. In order to solve this research gap, the current work has used a systematic approach for ranking barriers according to their significance. Initially, we identified these barriers based on a comprehensive literature review and the modified Delphi method. Twenty-nine barriers were identified, which were subsequently grouped into six major dimensions, including technological, transparency & accountability, COVID-19, financial, policy & regulatory, and infrastructure. Secondly, using the Analytical Hierarchical Process (AHP), the relative weights and ranks of major barriers and sub-barriers were calculated. The results demonstrate that the “technological” barrier is the highest-ranked barrier, while “unreliable local technology” rates highest among all sub-barriers. Finally, we took an additional step by utilizing the Grey Preference by Similarity to Ideal Solution (G-TOPSIS) method to rank the alternatives. The results indicate that “R&D″ and “corruption-free system” are the most viable alternatives for overcoming these barriers. Research findings emphasize ensuring the availability of a skilled workforce, reforming policy structure, developing infrastructure, improving R&D activities, and highlighting the paybacks that solar energy offers to preserve natural resources by integrative and consistent efforts.

Modeling COVID-19 Impact on Consumption and Mobility in Europe: A Legacy Toward Sustainable Business Performance.

This article has explored the impact of coronavirus disease 2019 (COVID-19)-induced decline in consumer durables and mobility on nitrogen dioxide (NO2) emission in Europe by providing empirical and graphical justifications based on consumer price index (CPI) and gross domestic product (GDP) deflator indexes. The empirical estimations show that carbon dioxide (CO2) and NOx emission along with other greenhouse gases drastically decreased in the wake of COVID-19-induced lockdowns and decrease in the demand of consumer goods in Europe. This means that COVID-19 improved environment in the European region. However, high cost (e.g., unemployment, loss of life, and social segregation) makes COVID-19 an unstable solution to environmental woes where positive impact of COVID-19 on environment achieved in short run cannot be guaranteed in the long run. Besides environment, COVID-19 drastically curtailed economic activities and exposed them to the risk of economic crisis particularly in case of Europe.

Genome sequencing and analysis of genomic diversity in the locally transmitted SARS-CoV-2 in Pakistan.

Surveillance of genetic diversity of the SARS-CoV-2 is extremely important to detect the emergence of more infectious and deadly strains of the virus. In this study, we evaluated mutational events in the SARS-CoV-2 genomes through whole genome sequencing. The samples were collected from COVID-19 patients in different major cities of Pakistan during the four waves of the pandemic (May 2020 to July 2021) and subjected to whole genome sequencing. Using in silico and machine learning tools, the viral mutational events were analyzed, and variants of concern and of interest were identified during each of the four waves. The overall mutation frequency (mutations per genome) increased during the course of the pandemic from 12.19 to 23.63, 31.03, and 41.22 in the first, second, third, and fourth waves, respectively. We determined that the viral strains rose to higher frequencies in local transmission. The first wave had three most common strains B.1.36, B.1.160, and B.1.255, the second wave comprised B.1.36 and B.1.247 strains, the third wave had B.1.1.7 (Alpha variant) and B.1.36 strains, and the fourth waves comprised B.1.617.2 (Delta). Intriguingly, the B.1.36 variants were found in all the waves of the infection indicating their survival fitness. Through phylogenetic analysis, the probable routes of transmission of various strains in the country were determined. Collectively, our study provided an insight into the evolution of SARS-CoV-2 lineages in the spatiotemporal local transmission during different waves of the pandemic, which aided the state institutions in implementing adequate preventive measures.