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1.
Ieee Access ; 10:91828-91839, 2022.
Article in English | Web of Science | ID: covidwho-2032232

ABSTRACT

Fruit disease recognition is quickly becoming a hot topic in the field of computer vision. The presence of plant diseases not only reduces fruit production but also causes a significant loss to the national economy. Citrus fruits help to strengthen the immune system, allowing it to fight off diseases such as COVID-19. Manual inspection of fruit diseases with the naked eye takes time and is difficult;therefore, a computer based method is always required for accurate recognition of plant diseases. Several deep learning techniques for recognizing citrus fruit diseases have been introduced in the literature. Existing techniques had several issues, including redundant features, convolutional neural network (CNN) model selection, low contrast images, and long computational times. In this paper, single stream convolutional neural network architecture is proposed for recognizing citrus fruit diseases. In the first step, data augmentation is performed using four contrast enhancement operations: shadow removal, adjusting pixel intensity, improving brightness, and improving local contrast. The MobileNet-V2 CNN model is selected and fine-tuned in the second step. Using the transfer learning process, the fine-tuned model is trained on the augmented citrus dataset. The newly trained model is used for deep feature extraction;however, analysis shows that the extracted deep features contain little redundant information. As a result, an improved Whale Optimization Algorithm (IWOA) is used in the third step. The best features are then classified using machine learning classifiers in the final step. The augmented citrus fruits, leaves, and hybrid dataset were used in the experimental process and achieved an accuracy of 99.4, 99.5, and 99.7%. When compared to existing techniques, the proposed architecture outperformed them in terms of accuracy and time.

2.
Ieee/acm Transactions on Computational Biology & Bioinformatics. PP ; 14:14, 2022.
Article in English | MEDLINE | ID: covidwho-2029248

ABSTRACT

Machine learning (ML) models, such as SVM, for tasks like classification and clustering of sequences, require a definition of distance/similarity between pairs of sequences. Several methods have been proposed to compute the similarity between sequences, such as the exact approach that counts the number of matches between k-mers (sub-sequences of length k) and an approximate approach that estimates pairwise similarity scores. Although exact methods yield better classification performance, they pose high computational costs, limiting their applicability to a small number of sequences. The approximate algorithms are proven to be more scalable and perform comparably to (sometimes better than) the exact methods - they are designed in a "general" way to deal with different types of sequences (e.g., music, protein, etc.). Although general applicability is a desired property of an algorithm, it is not the case in all scenarios. For example, in the current COVID-19 (coronavirus) pandemic, there is a need for an approach that can deal specifically with the coronavirus. To this end, we propose a series of ways to improve the performance of the approximate kernel (using minimizers and information gain) in order to enhance its predictive performance pm coronavirus sequences. More specifically, we improve the quality of the approximate kernel using domain knowledge (computed using information gain) and efficient preprocessing (using minimizers computation) to classify coronavirus spike protein sequences corresponding to different variants (e.g., Alpha, Beta, Gamma). We report results using different classification and clustering algorithms and evaluate their performance using multiple evaluation metrics. Using two datasets, we show that our proposed method helps improve the kernel's performance compared to the baseline and state-of-the-art approaches in the healthcare domain.

3.
Frontiers in Immunology ; 13, 2022.
Article in English | Web of Science | ID: covidwho-2022707

ABSTRACT

The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has created an urgent global situation. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability and disease comorbidities. The pandemic continues to spread worldwide, despite intense efforts to develop multiple vaccines and therapeutic options against COVID-19. However, the precise role of SARS-CoV-2 in the pathophysiology of the nasopharyngeal tract (NT) is still unfathomable. This study utilized machine learning approaches to analyze 22 RNA-seq data from COVID-19 patients (n = 8), recovered individuals (n = 7), and healthy individuals (n = 7) to find disease-related differentially expressed genes (DEGs). We compared dysregulated DEGs to detect critical pathways and gene ontology (GO) connected to COVID-19 comorbidities. We found 1960 and 153 DEG signatures in COVID-19 patients and recovered individuals compared to healthy controls. In COVID-19 patients, the DEG-miRNA, and DEG-transcription factors (TFs) interactions network analysis revealed that E2F1, MAX, EGR1, YY1, and SRF were the highly expressed TFs, whereas hsa-miR-19b, hsa-miR-495, hsa-miR-340, hsa-miR-101, and hsa-miR-19a were the overexpressed miRNAs. Three chemical agents (Valproic Acid, Alfatoxin B1, and Cyclosporine) were abundant in COVID-19 patients and recovered individuals. Mental retardation, mental deficit, intellectual disability, muscle hypotonia, micrognathism, and cleft palate were the significant diseases associated with COVID-19 by sharing DEGs. Finally, the detected DEGs mediated by TFs and miRNA expression indicated that SARS-CoV-2 infection might contribute to various comorbidities. Our results provide the common DEGs between COVID-19 patients and recovered humans, which suggests some crucial insights into the complex interplay between COVID-19 progression and the recovery stage, and offer some suggestions on therapeutic target identification in COVID-19 caused by the SARS-CoV-2.

4.
Advances in Medical Education and Practice ; 13:913-926, 2022.
Article in English | Web of Science | ID: covidwho-2022199

ABSTRACT

Purpose: This study aimed to assess the burnout among faculty members of King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Jeddah, during the COVID-19 pandemic and investigate their adaptations to online teaching.Patients and Methods: The study utilized a survey research design, and a validated questionnaire was e-mailed to faculty members. The Maslach Burnout Inventory - Educators Survey was used to assess burnout in three domains (emotional exhaustion, depersona-lization, and personal accomplishment), in addition to their adaptations to online teaching.Results: A total of 112 faculty members completed the survey with a response rate of 25%. Females comprised 50.9% of the sample. Burnout assessment among faculty showed moderate emotional exhaustion and personal accomplishment. In contrast, the level of depersonalization was low. When assessing the impact of the shift to online education during the pandemic, 87.5% of the respondents reported increased confidence in online teaching and learning effectiveness.Conclusion: Faculty members at KSAU-HS reported moderate emotional exhaustion. Fortunately, this had a moderate impact on students' intellectual development and well-being. Most of the faculty feedback supported online teaching during the pandemic.

5.
BioMed Research International ; 2022:9932483, 2022.
Article in English | MEDLINE | ID: covidwho-2020563

ABSTRACT

The aim of this study is to predict the COVID-19 infection fifth wave in South Africa using the Gaussian mixture model for the available data of the early four waves for March 18, 2020-April 13, 2022. The quantification data is considered, and the time unit is used in days. We give the modeling of COVID-19 in South Africa and predict the future fifth wave in the country. Initially, we use the Gaussian mixture model to characterize the coronavirus infection to fit the early reported cases of four waves and then to predict the future wave. Actual data and the statistical analysis using the Gaussian mixture model are performed which give close agreement with each other, and one can able to predict the future wave. After that, we fit and predict the fifth wave in the country and it is predicted to be started in the last week of May 2022 and end in the last week of September 2022. It is predicted that the peak may occur on the third week of July 2022 with a high number of 19383 cases. The prediction of the fifth wave can be useful for the health authorities in order to prepare themselves for medical setup and other necessary measures. Further, we use the result obtained from the Gaussian mixture model in the new model formulated in terms of differential equations. The differential equations model is simulated for various values of the model parameters in order to determine the disease's possible eliminations.

6.
Nonlinear Dyn ; : 1-20, 2022.
Article in English | Web of Science | ID: covidwho-2014315

ABSTRACT

The coronavirus disease 2019 (COVID-19) is a recent outbreak of respiratory infections that have affected millions of humans all around the world. Initially, the major intervention strategies used to combat the infection were the basic public health measure, nevertheless, vaccination is an effective strategy and has been used to control the incidence of many infectious diseases. Currently, few safe and effective vaccines have been approved to control the inadvertent transmission of COVID-19. In this paper, the modeling approach is adopted to investigate the impact of currently available anti-COVID vaccines on the dynamics of COVID-19. A new fractional-order epidemic model by incorporating the vaccination class is presented. The fractional derivative is considered in the well-known Caputo sense. Initially, the proposed vaccine model for the dynamics of COVID-19 is developed via integer-order differential equations and then the Caputo-type derivative is applied to extend the model to a fractional case. By applying the least square method, the model is fitted to the reported cases in Pakistan and some of the parameters involved in the models are estimated from the actual data. The threshold quantity ( R 0 ) is computed by the Next-generation method. A detailed analysis of the fractional model, such as positivity of model solution, equilibrium points, and stabilities on both disease-free and endemic states are discussed comprehensively. An efficient iterative method is utilized for the numerical solution of the proposed model and the model is then simulated in the light of vaccination. The impact of important influential parameters on the pandemic dynamics is shown graphically. Moreover, the impact of different intervention scenarios on the disease incidence is depicted and it is found that the reduction in the effective contact rate (up to 30%) and enhancement in vaccination rate (up to 50%) to the current baseline values significantly reduced the disease new infected cases.

7.
International Journal of Environmental Science and Technology ; : 1-12, 2022.
Article in English | PMC | ID: covidwho-2007297

ABSTRACT

The study examines the role of technology transfer in preventing communicable diseases, including COVID-19, in a heterogeneous panel of selected 65 countries. The study employed robust least square regression and innovation accounting matrixes to get robust inferences. The results found that overall technological innovation, including innovative capability, absorptive capacity, and healthcare competency, helps reduce infectious diseases, including the COVID-19 pandemic. Patent applications, scientific and technical journal articles, trade openness, hospital beds, and physicians are the main factors supporting the reduction of infectious diseases, including the COVID-19 pandemic. Due to inadequate research and development, healthcare infrastructure expenditures have caused many communicable diseases. The increasing number of mobile phone subscribers and healthcare expenditures cannot minimize the coronavirus pandemic globally. The impulse response function shows an increasing number of patent applications, mobile penetration, and hospital beds that will likely decrease infectious diseases, including COVID-19. In contrast, insufficient resource spending would likely increase death rates from contagious diseases over a time horizon. It is high time to digitalize healthcare policies to control coronavirus worldwide.

8.
Letters in Drug Design and Discovery ; 19(8):741-756, 2022.
Article in English | EMBASE | ID: covidwho-1957133

ABSTRACT

Background: Coronavirus disease-2019 (COVID-19) has recently emerged as a pandemic respiratory disease with mild to severe pneumonia symptoms. No clinical antiviral agent is available so far. However, several repurposing drugs and vaccines are being given to individuals or in clinical trials against SARS-CoV-2 Objective: The aim of this study is to uncover the potential effects of Luteolin (Lut) as an inhibitor of SARS-CoV2 encoded proteins via utilizing computational tools. Methods: Molecular modelling to unfold the anti-SARS-CoV2 potential of Lut along with reference drugs namely remdesivir and nafamostat was performed by the use of molecular docking, molecular dynamic (MD) simulation, absorption, distribution, metabolism, excretion, toxicity (ADMET) and density functional theory (DFT) methods against the five different SARS-CoV-2 encoded key proteins and one human receptor protein. The chemical reactivity of Luteolin is done through prediction of HOMO-LUMO gap energy and other chemical descriptors analysis. Results: In the present study, Lut binds effectively in the binding pockets of spike glycoprotein (6VSB), ADP phosphatase of NSP3 (6W02), and RNA dependent RNA polymerase (7AAP) protein receptors with significant values of docking scores-7.00,-7.25, and-6.46 respectively as compared to reference drugs remdesivir and nafamostat. Conclusion: Thus, Lut can act as a therapeutic agent and is orally safe for human consumption as predicted by molecular modelling against SARS-CoV-2 in the treatment of COVID-19.

9.
Arab Gulf Journal of Scientific Research ; 39(Special Issue (2):48-59, 2021.
Article in English | GIM | ID: covidwho-1929360

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19) associated inflammatory cytokine storm that worsens COVID-19, relies heavily on the inflammatory response. IL-6, a TH1 cytokine, PCT and CRP have been linked to serious illness and a higher mortality rate. We further tried to evaluate the role of these indicators and their association with clinical severity in COVID-19 patients. Material and Methods: Eighty-three consecutive patients with age 18 years with RT-PCR test positive for SARS-CoV-2 were included in the study. Demographic characteristics (age and sex), underlying co-morbidities, symptoms, physical findings, and laboratory tests of the patients were recorded. All patients were categorized as having mild, moderate, and severe COVID-19 disease, according to the Indian Council of Medical Research (ICMR). The levels of IL-6 and PCT were estimated by electrochemiluminescence immunoassay (ECLIA) using Cobas-e411 Immunoassay System, and Quantitative CRP was done by Unicorn-230 automated biochemistry analyzer to find out their correlation with disease severity and outcome. Multiple Regression was performed to find out factors associated with the adverse outcome of the disease. Result: Mean age of patients was 51 years. IL-6, CRP, and PCT levels increased in 73%, 68.0%, and 8.2% patients on admission, respectively. The most common co-morbidity associated with the disease was hypertension (25%), followed by diabetes (24%) and respiratory disease (15%). Increased IL-6, CRP, and PCT levels were found in 77 percent, 79 percent, and 20 percent of patients, respectively. We found that IL-6 (P0.05), CRP (P0.05), and PCT (P0.05) were significantly raised in COVID-19 patients with increasing severity of the disease. The Area under the receiver operating characteristic (AUROC) of these parameters ranged between 0.65 and 0.8 (IL-6, 0.828;CRP, 0.809;and PCT, 0.658), indicating a reliable biomarker to assess clinical severity.

10.
Eur Rev Med Pharmacol Sci ; 26(12): 4431-4439, 2022 06.
Article in English | MEDLINE | ID: covidwho-1924913

ABSTRACT

OBJECTIVE: Our aim was to assess sexual activity, partner relationships among males who had been infected with COVID-19, to study the impact of COVID-19 infection on partner relationship and to find out the association between partner and sexual relationship during lockdown. MATERIALS AND METHODS: A cross sectional study was conducted in Saudi Arabia through social media platforms via online questionnaire between December 1, 2020 and January 31, 2021 among 871 participants after a pilot study among 20 participants of which 497 were included in the study. Statistical analysis was conducted using SPSS version 20.0 (IBM Inc., Armonk, NY, USA). Responses were presented as frequencies and percentages and the association was studied using Chi squared test/Fisher's exact test. The value of p ≤ .05 was considered significant. RESULTS: Out of the total study participants, nearly 85% of them belonged to the age range of 18 to 39 years, more than half of the participants were married. In the six months prior to the study being conducted, 268 respondents (53.9%) did not have sexual relationships. Respondents with positive COVID-19 infection reported that their partner lived with them in the same house during home isolation and was also found to be significantly associated with having intact sexual relationships in the last six months of the lockdown period (p-value < .001). Moreover, respondents who reported having good relationships with their partners during the pandemic were found to be significantly associated with having intact sexual relationships during the pandemic lockdown (p-value < .001). CONCLUSIONS: Among the COVID-19-positive respondents, sexual activity and partner relationships were largely found to be intact during the pandemic lockdown period.


Subject(s)
COVID-19 , Sexual Partners , Adolescent , Adult , COVID-19/epidemiology , Communicable Disease Control , Cross-Sectional Studies , Humans , Male , Pandemics , Pilot Projects , Sexual Behavior , Young Adult
11.
Mymensingh Med J ; 31(3): 887-889, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1918701

ABSTRACT

Mass testing for COVID-19 infection is one of the core measures in tackling the global spread of the disease. Testing is vital to diagnose and estimate cases, attack rates and case fatality rates- critical data for policy-making. As COVID-19 continues to spread globally, the demand for more extensive laboratory testing and innovative technology increases. However, countries around the world have been struggling to keep up pace with the worldwide demand to expand testing strategy. The pandemic evolves, so does our knowledge and understanding of diagnostic tests of COVID-19. Here we aim to review major challenges related to COVID-19 diagnostic tests and future development. So, the ongoing urgency and demand for tests would certainly steer the rapid uptake of novel techniques, which in turn would boost our understanding of diagnostic tests for COVID-19.


Subject(s)
COVID-19 , COVID-19/diagnosis , COVID-19 Testing , Humans , Pandemics
12.
European Journal of Tourism Hospitality and Recreation ; 11(2):267-279, 2021.
Article in English | Web of Science | ID: covidwho-1917157

ABSTRACT

The current study attempts to identify and measure the role of technology induction during the COVID-19 pandemic as either recreation or curse in students' learning. By integrating the technology acceptance model (TAM) and innovation diffusion theory (IDT) the study tries to measure the student's online learning experience as recreation or curse. Data was collected from 387 students through purposive sampling. The findings of the study confirm that introduction of technology in online learning plays a recreational and significant role in student's online learning. However, computer self-efficacy and relative advantage were found to be a curse in online learning during the pandemic. The overall findings of the study imply that the shift of student's learning from traditional to online learning has been through introduction of new technology and innovations, although the diffusion of innovation and technology among Pakistani students has been challenging because of a comparatively lower computer literacy level. The usefulness and ease of online learning have been the strongest predictive and recreational aspects in students' online learning. Institutes and higher education commissions should further invest in enhancing the quality and effectiveness of these factors to improve the overall learning outcome of students through recreational technological induction in education.

13.
EURASIAN JOURNAL OF EDUCATIONAL RESEARCH ; - (98):58-69, 2022.
Article in English | Web of Science | ID: covidwho-1912260

ABSTRACT

Purpose: The objective of this study was to assess the predictive association between distorted thinking patterns and psychological distress (depression, stress, anxiety) in university e-learners during COVID-19 outbreak. Methodology: In this correlational study, 643 participants between age18 to 29 years (M= 21.27, SD+4.06) participated online through convenient sampling technique. They were sent an online google questionnaire, including the informed consent form, the depression, anxiety, stress scale (DASS-21), and cognitive distortions scale in Urdu, which assessed the distorted thinking patterns of adults. Findings: Analysis through Pearson product moment correlation revealed that the distorted thinking patterns of predictive thinking, rigid thinking and stress-creating thinking pattern had a strong positive association with depression, stress, and anxiety. The distorted thinking pattern of self-criticism/selfblame also had a strong positive association with depression and stress, and a moderate positive association with anxiety. Multiple stepwise regression was performed to calculate the predictive association between distorted thinking patterns and psychological distress of university students seeking digital education during the COVID-19 outbreak. Analysis revealed that distorted thinking patterns of stress-creating thinking, self-criticism/self-blame, and predictive thinking are predictors of depression. However, stress-creating thinking was the strongest predictor of depression. Stress-creating thinking, predictive thinking, and rigid thinking were predictors of anxiety in university students during online education and stress-creating thinking is the strongest predictor of anxiety as well. Moreover, the distorted thinking patterns of stress creating thinking, self-criticism/self-blame, and rigid thinking strongly predicted stress in university students engaged in distant education during the COVID-19 outbreak. Implications to Research and Practice: The study's findings emphasize the role of distorted thinking patterns in the stress experience of students during COVID and encourage teachers and universities to consider the findings while developing an online education system for the students. (C) 2022 Ani Publishing Ltd. All rights reserved.

14.
15th IEEE International Conference on Nano/Molecular Medicine and Engineering, NANOMED 2021 ; 2021-November:34-37, 2021.
Article in English | Scopus | ID: covidwho-1874333

ABSTRACT

Viral diagnostic is essential to the fields of medicine and bio-nanotechnology, but such analyses can present some complex analytical challenges. While molecular methods that are mostly used in clinical laboratories, for instance, reverse transcription-polymerase chain reaction (RT-PCR) and antigens tests require long acquisition times, and often provides unreliable results for COVID-19 virus detection, the piezo-based sensors coupled with MEMS have demonstrated a significant role in robust viral detection. In this work, we have designed and simulated a piezoelectric MEMS-based biosensor integrated into a wearable face mask for early detection of the SARS-CoV-2 virus droplets. We systematically investigated the influence of virus droplets in changing the applied stress on the cantilever receptor pit with change in mass when viruses (pathogens) from airborne coughing droplets-nuclei binds with coated antibodies on the sensor's cantilever layer with receptor pit thereby generating electric potential. Additionally, Bio-MEMS sensor results have manifested that it has the ability to detect a single size particle of 1 virion with a diameter ≥100 nm and mass of 1fg in a single cough containing droplet nuclei of radius 0.05μm in a less amount of time. Additionally, we empirically set electrical potential as thresholds parameter for our wearable biosensor embedded in the face mask for public monitoring to detect contagious virus particle droplets. Furthermore, this study presented the prospective use of MEMS-based sensing method to identify and detect other biological (bacteria and toxins) analytes. © 2021 IEEE.

15.
15th IEEE International Conference on Nano/Molecular Medicine and Engineering, NANOMED 2021 ; 2021-November:23-27, 2021.
Article in English | Scopus | ID: covidwho-1874332

ABSTRACT

The current impact of COVID-19 on global health and the economy is enormous. Considering pandemic severity, there is an urgent need to develop a smart biosensor that can provide early detection of SARS-CoV-2 viruses with robust and reliable results. In this work, we have systematically developed a plasmonic-based biosensor chip for the early detection of the COVID-19 virus by providing fast and reliable results. The label-free plasmonic sensor utilizes light and detects the resonance oscillation of surface-bound free conduction electrons in the presence of the target analyte biomarker (virus), resulting in binding and affinity incidents at the surface of plasmonic gold (Au) material, causing a shift in the resonance wavelength. The results show the ability of biosensor to exhibit an increased shift in the resonance wavelength upon binding of the COVID-19 virus because of the change in the optical property, i.e., the refractive index of the medium in the vicinity of the Au film. This study further demonstrated the fabrication and performance optimization of the plasmonic biosensor for the potential point-of-care testing device. © 2021 IEEE.

16.
Management Systems in Production Engineering ; 30(2):156-162, 2022.
Article in English | Web of Science | ID: covidwho-1869321

ABSTRACT

COVID-19, mobility, socio-social changes have transferred to the world of social media communication, purchasing activities, the use of services. Corporate social media has been created to support clients in using various services, give them the possibility of easy communication without time and local barriers. Unfortunately, they still very rarely take into account the security and privacy of customers. Considering that the purpose of this article is to investigate the impact of social media on the company's image, it should be remembered that this image also works for the security and privacy of customer data. Data leaks or their sale are not welcomed by customers. The results of empirical research show that the safety, simplicity and variety of services offered on social media have a significant impact on the perceived quality, which in turn positively affects the reputation. The authors proposed a methodology based on the Kano model and customer satisfaction in order to examine the declared needs and undefined desires and divide them into different groups with different impacts on consumer satisfaction. The interview participants were employees of 10 randomly selected companies using social media to conduct sales or service activities. 5,000 people from Poland, Portugal and Germany participated in the study. 4,894 correctly completed questionnaires were received.

17.
IEEE Region 10 Symposium (TENSYMP) - Good Technologies for Creating Future ; 2021.
Article in English | Web of Science | ID: covidwho-1853496

ABSTRACT

The world has experienced the very first pandemic of 21st century, called the COVID-19 which is caused by a deadly virus named Coronavirus. In this regard, one of the very first strategy to minimize the number of affected patients and reduce casualties is to diagnose COVID-19 at an early stage. Currently, PCR test is primarily utilized for the diagnosis of COVID-19. However, PCR test requires a huge number of expensive test kits as well as trained experts. Therefore, chest X-Ray imaging technique (including Machine Learning) has been considered as an alternative for COVID-19 diagnosis among the researchers. This particular method is faster, less expensive and will allow the authorities to manage the COVID-19 diagnosis system in a cost-effective way. Machine learning techniques have been proven to be significantly efficient and accurate for image classification problems. On the other hand, One of the most utilized techniques in machine learning is supervised learning which is highly convenient and helping the experts to diagnose and make informed decisions about COVID-19. Supervised learning in image classification requires vast amount of radiography images with notable accuracy which can be a peculiar issue in medical domain. In order to address the problem, we have investigated a distinct approach for COVID-19 Diagnosis with a nominal dataset. In this work, We have studied the effectiveness of Semi-Supervised Learning (SSL) for COVID-19 diagnosis from chest X-ray images. We have investigated a prepossessing technique by extracting and combining local phase image feature into multi-feature image to train our SSL model in teacher/student archetype. Our study have shown that by using 17.0% of the total dataset for training, the SSL model achieve 93.45% accuracy. We also provide comparative metrics of SSL approach against other fully supervised techniques.

18.
Journal of Rural and Community Development ; 17(1):49-68, 2022.
Article in English | Web of Science | ID: covidwho-1848833

ABSTRACT

Smallholder farmers in Pakistan are at the frontlines of the COVID-19 crisis as their livelihoods have been disrupted due to a countrywide lockdown. This cross-sectional study was conducted over the duration of two months, April and May 2020, with the aim to assess awareness of smallholder farmers regarding COVID-19, their challenges, and attitude towards governments' lockdown strategies in Pakistan. The sample was composed of 384 cotton-wheat smallholder farmers from 1,403 villages of Bahawalnagar, Layyah, and Toba Tek Singh districts of Punjab province. Due to travel restrictions, a telephonic survey was conducted, and data were collected through a semi-structured interview schedule. The instrument contained both open and closed-ended questions and Likert scale items. Results revealed that the vast majority of the smallholder farmers was highly aware of the coronavirus disease, and they had positive attitudes towards the government lockdown strategies. However, some farmers were also facing great challenges in access to farm inputs, unavailability of farm laborers, high prices, and selling their farm produce in the market due to lockdown, which resulted in a drop of their crop incomes and lower food consumption. There remains a dire need to support them in the current crisis and address their challenges.

19.
SAGE Open ; 12(2), 2022.
Article in English | Scopus | ID: covidwho-1840923

ABSTRACT

In this modern world with the growing trend of globalization, cultural and environmental changes, and the increased pace of technological advancements, new challenges have appeared in a tremendous manner. In the wake of these recent trends and complexities alongside the worldwide pandemic (COVID-19), organizations are working to provide a self-motivated and highly engaging environment for employees. The study intends to explore the relationship between Transformational Leadership (TFL) and Work Engagement (WE) with the mediating role of Structural Empowerment (SE) and Process Innovation (PI) during COVID-19. The quantitative research methodology was utilized and a simple random sampling technique was used for collection of data from 360 supervisors of banks in Pakistan using questionnaires. Hypotheses are tested with the help of Structural Equation Modeling (SEM) by using AMOS. By using SEM, confirmatory factor analysis revealed a statistically significant model. Outcomes showed a significant connection among TFL, WE, and PI. However, SE only significantly relates to TFL. Partial mediation through PI was discovered with the presence of a significant indirect path between TFL and WE. In contrast, the mediation of SE is not ascertained due to insignificant indirect effects. The digitally evolving banking industry in Pakistan requires engagement at workplace. The organization must be very much aware of the crucial job of leaders for WE as well as for PI just as the SE of employees. The findings of the study also highlight the importance of best HR practices that should work for creating intangible motivators for boosting WE level. © The Author(s) 2022.

20.
Frontiers in Environmental Science ; 10, 2022.
Article in English | Scopus | ID: covidwho-1834385

ABSTRACT

This study aims to find the nexuses among energy efficiency, renewable energy consumption, foreign direct investment, logistics industry, manufacturing industry and global trade during the COVID-19 pandemic and their impact on global supply chains in exporting nations of the world. The data for this study has been extracted from the World Development Indicators and Statista 2021 for 13 years ranging from 2007-to 2020 for nine top exporting countries. The fixed effect panel estimation technique was implied to examine and analyze the data. The results of our study revealed that highly risky diseases significantly impact supply chain operations globally. Global supply chains, logistics and manufacturing industries significantly influence global trade operations. Our results implicate that the overall international trade and logistics can be enhanced by improving the manufacturing and logistics industries by coping with the risk of pandemic diseases. Moreover, by utilizing cost-effective, renewable and efficient energy resources companies address sustainability issues of global trade and operations. By exerting further attention to the proficiency of the levies approval process, competence and quality of logistics services, and ease of assembling competitively priced shipments, the governments can significantly enhance the export from the logistics industry. Also, increasing manufacturing and agricultural value-added healthier consequences might be acquired in global supply chain operations from the manufacturing industry. Copyright © 2022 Rehman Khan, Hassan, Khan, Khan, Godil and Tanveer.

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