Technology acceptance and COVID-19: a perspective for emerging opportunities from crisis

Ingredients for leadership development include strategic intent, sensibility, knowledge, analytic acuity, and the confidence to make tough calls. Emergencies highlight the importance of technical progress and the opportunity to enhance design and productivity. Significant technological and organisational barriers hinder acceptance of these technologies;hence it is necessary to employ strategy. Research should be at the system's heart through planned and unplanned transitions. The vision of building and maintaining business resilience and organising swift changes includes using perceptual methods. With this view, leaders can make deliberate decisions. It also tends to disrupt traditional approaches to dexterity. At the same time, strategic flexibility necessitates an evident connection between the business and the institution's mission. A leader's positive strategic intent is core that better enables investment in crisis response competencies. It includes timely use of knowledge management, organisational learning frameworks, business strategy, and system agility. Many nations in the developing world have simultaneously experienced the rise of digitisation and the spread of the covid-19 epidemic. Thus, academics should focus on the difficulties and prospects associated with people, groups, and management.

Do Online Challenge-Based Learning Help Professors Identify and Solve Community Problems During the COVID-19 Pandemic?

This research aims to identify online challenge-based learning (CBL) that builds on the problem-based learning practice to support professors working in two Mexican institutions with solutions to six social challenges defined by the institutions. Thirty-five professors from Tecnologico de Monterrey participated in developing the solutions related to given challenges by taking a social approach. For this activity, an online training session of one week was organized by the Faculty Development and Educational Innovation Center (CEDDIE) of Tecnologico de Monterrey in Mexico City, Mexico. The data was collected through an online survey based on quantitative and qualitative questionnaires. We received fifteen complete responses out of thirty-five. Analyzing the results of this study affirmed that online CBL activities served professors to promote social interactions, develop pedagogical competencies, and share knowledge based on their learning experience through active collaboration with peers in the same institutions but from different disciplines and campuses to identify and solve existing societal issues. © 2023 IEEE.

Tele-Psychiatry during COVID-19 at HMC Peshawar

OBJECTIVE: The main objectives of the current study were to find out the frequencies of Psychiatric disorders in the general population during COVID-19 and to compare the gender-based association between newly diagnosed patients during COVID-19 with already existing psychiatric patients in Peshawar to provide patient care on priority bases. METHODOLOGY: This Cross-sectional design study was carried out in the Department of Psychiatry and Behavioral Sciences, HMC/MTI, from May to August 2020. Those patients who approached psychiatry OPD through video/audio online calls and could understand and respond to suggestions were included. The bio-data was collected, and DSM-5 criteria were used for diagnosis. Descriptive statistics were used for statistical significance, and the statistical package of social sciences (SPSS-21) was used for analysis and results. RESULTS: The results findings of the current study revealed that 59.3% of the patients approached for telepsychiatry consultation were from the district of Peshawar. Among them, 54% were female, and most patients were young married females (50.7%) with no job outside the home. The finding further revealed that most of the sample affected by psychiatric illness were uneducated (31.3%) and unemployed (28%). Furthermore, in the present findings, 46% of patients were diagnosed with depression, and 12% had Dissociative disorders. CONCLUSION: It is concluded from the present study that in the Covid-19 Pandemic, primarily females who were married with no job description are more vulnerable to psychiatric illness. Furthermore, during Covid-19 mostly cases were reported with depression and dissociative disorders. © 2023, Liaquat University of Medical and Health Sciences.

Dramatic Increase in Fear-Related Discussion on Twitter during COVID-19: Analysis, Topic Modeling and Tweets Classification

COVID-19 is one the most lethal virus, causing millions of death to date. It was initially detected in Wuhan, China. It then spread rapidly around the globe, which resultantly created major setbacks in the public health sector. The reason of millions of deaths is not only due to the virus itself but it is also linked to peoples' mental state, and sentiments triggered by the fear of the virus. These sentiments are predominantly available on posts/tweets on social media. This paper presents a novel approach for exploratory data analysis of twitter to understand the emotions of general public;country wise, and user wise. Firstly K-Means clustering is employed for topic modeling to categorize the emotions in each tweet. Further supervised machine learning techniques are used to categorize the multi-label tweets. This research concluded that Fear was the most common emotion in twitter discussion. Furthermore, we classified the dataset by performing decision tree (DT), logistic regression (LR), and support vector machine (SVM), finally this paper concluded the results of classification, which shows that SVM can attain better classification accuracy (99%) for COVID-19 text classification. © 2022 IEEE.

Evaluation of stool and urine parameters correlation to CRP and D-dimer in COVID-19 infected adults and their contact children

Aim: Aim is to depict suggestive urine and stool parameters in asymptomatic suspected contact children living with COVID-19 infected adults. These parameters will facilitate identifying children who deserve the confirmatory diagnosis of COVID-19 by PCR test. Methods: Study was conducted in the National Hepatology and Tropical Medicine Research institute (NHTMRI) Cairo, Egypt. It included 66 mild COVID-19 adult patients (group1) and their 82 asymptomatic contact children (group 2). Results: In group 1, both C reactive protein (CRP) and D-dimer levels were significantly high. C reactive protein was significantly positively correlated with urinary microalbumin> 30, albumin/ creatinine ratio and urine pus >10 cells / HPF and significantly negatively correlated with vitamin C. D-dimer was significantly negatively correlated with vitamin C. In group 2, CRP and D-dimer were significantly negatively correlated with urine specific gravity (SG), urinary vitamin C. CRP was significantly negatively correlated with stool pus > 10 cells/ HPF, while D-dimer was significantly positively correlated with stool occult blood. Receiver Operating Curve (ROC) analysis revealed that urine SG showed the highest area under the curve (AUC);0.859, 0.96, sensitivity of 100%, 100% and specificity of 71.8%, 77.8% with reference to D-dimer and CRP;respectively. Conclusions: In contact children of adult COVID-19 proved infection, urine SG, stool occult blood and stool pus > 10 cells/ HPF can be feasible tool for suspected COVID-19 infection, based on its results COVID-19 PCR request can be an imperative option to confirm the diagnosis;particularly in developing countries where detection of COVID-19 by PCR is not readily feasible. © 2020 The author (s). Published by Zagazig University.

Does intrinsic motivation mediate perceived artificial intelligence (AI) learning and computational thinking of students during the COVID-19 pandemic?

The concept of Artificial Intelligence (AI), born as the possibility of simulating the human brain's learning capabilities, quickly evolves into one of the educational technology concepts that provide tools for students to better themselves in a plethora of areas. Unlike the previous educational technology iterations, which are limited to instrumental use for providing platforms to build learning applications, AI has proposed a unique education laboratory by enabling students to explore an instrument that functions as a dynamic system of computational concepts. However, the extent of the implications of AI adaptation in modern education is yet to be explored. Motivated to fill the literature gap and to consider the emerging significance of AI in education, this paper aims to analyze the possible intertwined relationship between students' intrinsic motivation for learning Artificial Intelligence during the COVID-19 pandemic;the relationship between students' computational thinking and understanding of AI concepts;and the underlying dynamic relation, if existing, between AI and computational thinking building efforts. To investigate the mentioned relationships, the present empirical study employs mediation analysis based upon collected 137 survey data from Universidad Politécnica de Madrid students in the Institute for Educational Science and the School of Naval Architecture and Marine Engineering during the first quarter of 2022. Findings show that intrinsic motivation mediates the relationship between perceived Artificial Intelligence learning and computational thinking. Also, the research indicates that intrinsic motivation has a significant relationship with computational thinking and perceived Artificial Intelligence learning. © 2023

AI-Enabled Health 4.0: An IoT-Based COVID-19 Diagnosis Use-Case

The Internet of Things (IoT) has revamped service-oriented architectures by enabling edge-based devices to collect and share information that is vital for the service provisioning process. IoT devices have evolved from simple data acquirers and have become part of the service provisioning process. These devices are now able to sense, acquire, communicate, and process data in an intelligent manner. With the support of Artificial Intelligence (AI), IoT devices can now support users with minimal reliance on centralized entities, such as the Cloud. IoT devices are now able to share raw and processed information securely, without or with minimal reliance on centralized devices. This paper proposes a general framework for Health 4.0 to provide edge-based health services with the support of AI. IoT devices collect and share patient information in a secure manner to enable user-side disease diagnosis. The solution enables both federated and centralized learning to coexist under one framework. As a proof-of-concept, the solution considers a COVID-19 diagnosis use-case. A Machine Learning (ML) web-based user application is developed to analyze frontal chest X-ray (CXR) images and make predictions on whether patients' lungs are damaged. The solution provides an experimental study on mechanisms and approaches needed to increase learning accuracy with reduced dataset sizes and image quality through Federated Learning (FL). © 2022 IEEE.

A study on the Impacts of COVID-19 on health, Economy, Employment and Social Life of People in Indonesia

Background: The aftershocks of COVID-19 pandemic are still emanating in different regions of the world in term of increasing number of cases and deaths due to mutation in the virulence and pathogenicity of the virus. The pandemic affected almost every part of our lives including health, economy, employment, and social interactions. This study surveyed the Indonesian public to better understand their health, employment, and economic deterioration during the early stages of the COVID-19 outbreak. Methods: An online cross-sectional survey of 200 participants was conducted from eight different regions (Jawa Timur, DKI Jakarta, Kalimantan Tengah, Yogyakarta, Bali, Sulawesi Selatan, Jawa Tengah) of Indonesia who speak Bahasa. A standardized questionnaire was used to obtain information about COVID-19 impacts on health, employment, the economy, and social life from the respondents. Descriptive statistics and Chi-square tests were conducted to analyze the data. Results: According to the findings, out of 200 participants, 40% stated that the impact of COVID-19 did not affect their salary. People under the age of 20 with an intermediate education who worked in government sectors were more likely to lose their jobs (p-value 0.05), which would result in a loss in salary that would have an impact on the education of their children. Only the "use of hand sanitizers" indicated a statistically significant difference between the practices of male and female respondents (p-value = 0.038), which is one of the activities that helps to prevent fever and respiratory difficulties during the present pandemic. Conclusion: The finding of the study depicted that COVID-19 has no immediate collateral effects on the economy of the study participants. However, the pandemic has a negative impact on the employment, health, and social life of the people. To mitigate the negative effects of this pandemic on health, employment, economy, and social life, a complete evaluation of COVID-19 impacts, as well as public health interventions, should be conducted. © 2022, The Running Line. All rights reserved.

Use of Eucalyptus Oil in Mobile and Migrant Populations As A Positive Deviance to Prevent Malaria on Buru Island, Indonesia: An Efforts to Accelerate and Maintain Malaria Elimination in Specific Populations

Malaria is still a public health problem worldwide, which is increasingly difficult to handle due to the Covid-19 pandemic. Indonesia is a country targeted by WHO to become a Malaria-free country by 2030. Mobile and migrant populations (MMPs) on Buru Island, as a Malaria-vulnerable group, pose a particular challenge in efforts to accelerate and maintain elimination. The use of eucalyptus oil as a positive deviation (PD) in this group is an innovative strategy in Malaria control programs in this population. Method(s): This research is an analytic observational study with a cross-sectional design to see the relationship between PD and Malaria in MMPs. Multivariate analysis with logistic regression was performed to determine the most associated PD with Malaria in the MMPs group. Primary data was collected through interviews with a structured questionnaire and observation of 72 people from the MMPs group who met the criteria. Results and Discussion: From 19 PD identified, only seven related to Malaria in MMPs (p-value <alpha 0.05) in preventing Malaria: cleaning the environment (p=0.032), burning garbage (p=0.005), burning dry leaves (p=0.013), using the eucalyptus oil (0.001), consuming herbal medicine (p=0.013), "Baupu"/" Baukuf" (p=0.028) and utilizing hot steam from a "Kettle" (p=0.043). The logistic regression analysis showed that eucalyptus oil was the variable most related to Malaria prevention in MMPs (p=0.027;95% CI for EXP(B): 1.227 - 30.799). Conclusions and suggestions: Identification of PD and applying them in everyday life is essential in preventing Malaria in MMPs. Utilizing the potential of eucalyptus oil as a natural way to prevent Malaria in the era of elimination is an innovative and promising specific local-based approach, considering that Buru Island is a eucalyptus granary area in Maluku, Indonesia. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

The Coronavirus Disease (COVID-19) pandemic has exposed the vulnerabilities of medical services across the globe, especially in underdeveloped nations. In the aftermath of the COVID-19 outbreak, a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional methods. Medical imaging has become a crucial component in the disease diagnosis process, whereas X-rays and Computed Tomography (CT) scan imaging are employed in a deep network to diagnose the diseases. In general, four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks, such as network training, feature extraction, model performance testing and optimal feature selection. The current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion (CFPADLDF) approach for detecting and classifying COVID-19. The presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical images. Initially, the proposed CFPA-DLDF technique employs the Gabor Filtering (GF) approach to pre-process the input images. In addition, a weighted voting-based ensemble model is employed for feature extraction, in which both VGG-19 and the MixNet models are included. Finally, the CFPA with Recurrent Neural Network (RNN) model is utilized for classification, showing the work's novelty. A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model, and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches. © 2023 Tech Science Press. All rights reserved.

Our community matters: An analysis of the students' and parents' emotions in the midst of COVID-19 pandemic

Changes in everyday activities, such as adapting to the new online format due to lockdowns during the COVID-19 pandemic and being far away from family and friends, greatly influenced the emotions and feelings of students and their parents. Assessing the emotions of students' parents at the higher education level is necessary since their emotional well-being has a direct impact on the emotional well-being of students throughout their distant learning experience. In this article, we held a quantitative study over 8 subsequent weeks from the onset of the COVID-19 pandemic in students and parents from the Mexican institution Tecnologico de Monterrey. Using a questionnaire from Inteligencia Audiencias (Intelligence Hearings), students and their parents could register their feelings and their valence from April 13th to July 20th, 2020. The results indicate that the most predominant emotions in both groups were very unpleasant and unpleasant in nature, being "worried"and "tired"the most common ones. The current study also provides some approaches for addressing the negative repercussions of the COVID-19 pandemic. © 2022 IEEE.

Role of CSR in achieving customers' satisfaction and retention during COVID-19 and post-pandemic period: Empirical evidence from emerging nations

BACKGROUND: COVID-19 is an ongoing virus disease also recognized as a coronavirus pandemic that propelled the world to rethink organizational strategies during this unprecedented challenge. Although research on CSR has broadly been done over the past decades;nonetheless, how CSR can contribute a leading role in engaging the stakeholders such as customers during this pandemic period and post-pandemic is an important research gap that ought to be uncovered. OBJECTIVES: This study explores the impact of CSR on external stakeholders like customers and how organizations can dramatically sustain the relationships during the COVID-19 period. First, this study investigates the relationships between CSR and customer satisfaction (CS). Second, this study explores the relationships between CSR and customer retention (CR). Finally, the moderating impact of gender and education were examined among the proposed relationships. METHODS: Using the survey of 500 respondents, this study prospected the linkages among CSR, CS, and CR from China using a convenience sampling approach. The questionnaires were disseminated to Chinese online shoppers between Jan 2020 and March 2020 and explored using SEM model. RESULTS: It found that customers are more attached and satisfied with those organizations that are socially responsible and value their stakeholders, especially during uncertain situations like COVID-19 since presently revealed a positive relationship between CSR and CS. Second, it is found that there is a positive influence of CSR on CR as well. Finally, the study affirmed the positive nexus of gender and education as the moderators among CSR, CR, and CS. CONCLUSION: CSR is always on the front line blending social and environmental goals into business operations, especially during uncertain times and challenges. Undeniably, the COVID-19 pandemic is not only a global health emergency but is also leading to a major global challenge that drives organizations to revisit policies to sustain the relationships with their stakeholders. This study concluded the positive nexus of CSR and affirmed the positive role in sustaining relationships with customers during distinct uncertainties like COVID-19.

A Blockchain-enabled and AI-Supported COVID-19 Detection Method

Given that COVID-19 symptoms might be similar to other viral infectious diseases, it becomes difficult to accurately diagnose for COVID-19 without traditional testing strategies like polymerase chain reaction (PCR) testing. As the quarantine and testing requirements have been lifted from most countries, easier and innovative testing strategies are being adopted to maintain high awareness levels in regards to the spread of the disease for both authorities and the public. This paper presents a COVID19 detection strategy that uses Machine Learning (ML) models to accurately diagnose for the disease in patients. The Artificial Intelligence (AI)-enabled solution not only serves the purpose of detecting whether patients are diagnosed with COVID, but also to track their daily symptoms and accurately classify the type of viral disease. Different ML models are trained and tested for accuracy and prediction timings. A decentralized approach is taken for the disease prediction, and hence, blockchain is adapted within the solution to ensure the authenticity of the user data. The solution has been implemented to allow users to receive real-time disease diagnosis using a web-based interface.

Covid-19 Contact Tracing and Vaccine Validation Using Blockchain Technology

The fourth industrial revolution (Industry 4.0) has prompted new and innovative solutions that are reliant on Artificial Intelligence (AI) and contemporary technological advancements. Secure, intelligent, and on-demand healthcare services for patients is one of the core pillars of Industry 4.0. Patient medical data security and privacy is a crucial part of electronic healthcare systems. Disease diagnosis and treatment are highly dependent on the authenticity and security of patient data, both when stored and communicated. Blockchain technology plays a vital role in transaction authentication and secure decentralized immutable data storage. With that said, in this paper, we present an interactive healthcare information system that enables COVID-19 contact tracing and vaccine certificate validation for users. The solution uses a blockchain technique to validate the certificates. The implementation and evaluation details of the system are presented together with result findings.

Skin Manifestations of Personal Protection Devices and Sanitizers Among COVID-19 Healthcare Providers: A Survey at Southern Region Hospitals, Saudi Arabia

Frontline worker's prevalence of occupational skin disease has increased as a result of staff infection control measures such increased personal protective equipment (PPE) and stricter hand hygiene procedures. During the COVID-19 pandemic, assess the frequency of occupational skin disorders among healthcare workers at the general hospitals in the Najran region of southern Saudi Arabia is our main aim. Our objective comprises a cross-sectional study that will be carried out at the general hospitals in the Najran region of southern Saudi Arabia between March 1 and April 31, 2022, in order to achieve the goal. All medical professionals received a self-administered online survey (physicians, nurses and paramedics). The questionnaire asked about the severity of skin damage and the frequency and length of time that various infection prevention strategies were used. It was found that 68.2% had new onset of obvious skin damage and 31.8 % did not. 21.6% of the new onset of obvious skin damage was on the fingertips, 46.6% was on the hands, 22.4% was in paws, 3.4% was on the face and 6.0% was under the eye. 29.8% of the symptoms of the damaged site was itching, 44.7% dryness, 12.8% burning/pain, and 12.8% tenderness. 16.5 % of the type of skin lesions was peeling, 28.2% fissure, 18.8% erosion/ulcer, 24.7% redness, 4.7% papule (pimples) and 7.1% others. In our study, 68.2% of our participants suffered obvious skin damage during the pandemic: 90.6% of this occurred on the hands and 9.4% on the face. Contact dermatitis in the form of itching, dryness, burning, pain and tenderness were the most common adverse effects noted. Therefore, it is important to organize training on the prevention and management of possible skin lesions due to PPE use according to guidelines.

Is Team-Based Online Learning Activities Enhances Critical Thinking Skills of Engineering Students or Not? An Exploratory Study During the COVID-19 Pandemic

This study was conducted during the COVID-19 pandemic to explore whether team-based, online learning activities play a role in enhancing undergraduate engineering students' critical thinking skills. To conduct the study, we distributed a Google Form-based online survey among undergraduate engineering students through Tecnolo & PRIME;gico de Monterrey learning management system platform during the fall semester of 2020. In total, we received 50 complete responses through a convenient sampling approach. To analyze the quantitative data, we applied a hierarchical regression technique using the IBM SPSS 26.0 statistical software program. The findings of this study affirm that participation in team-based online learning activities meant to improve (1) the quality of learning and (2) reasoning ability have a significant positive correlation with critical thinking skills of undergraduate students in engineering programs. We also conclude that quality of learning has higher significant association with critical thinking skills as compared to reasoning ability

Evaluation of Circulating Gelsolin in Iraqi Patients with COVID-19 as Early Predictor Marker of Disease Severity

Background: Coronavirus disease 2019 (COVID-19) has rapidly become a global health issue. The current coronavirus disease (COVID-19) epidemic necessitates the rapid development of therapies that enhance the outcomes of persons suffering from severe illness. To enhance the treatment of COVID-19 patients, early and effective indicators of disease severity are required. Gelsolin (GSN) is a circulating protein that is promptly consumed by extreme tissue injury and causes actin filament depolymerization, blocking downstream inflammatory processes. Objective: The aim of the presented work is to study if serum gelsolin levels had any relationship with Covid-19 infection and severity indicator in order to revealed if serum gelsolin could be utilized as a disease predictor marker severity. Materials and Methods: A case-control study was conducted with 90 Covid-19 patients and 90 healthy volunteers as the control group (with age ranged between 45-60 years) The patients were obtained from Al-Amal hospitals in Najaf city, Iraq, between Nov., 2020 and June, 2021. COVID-19 patients were separated into two groups based on the degree of their condition, which are mild/moderate disease and severe disease. Blood samples were taken and all demographic and clinical data of the sick and healthy groups were recorded. GSN levels in the blood were determined using enzyme-linked immunosorbent assays (ELISA). Colorimetric techniques were used to determine the activity of alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase and albumin levels. ichroma assessed serum ferritin and D-dimer, and CBC by spincell3. Ran a statistical analysis to noticed if they were linked to illness severity. Results: GSN levels were considerably lower in some patient groups. However, as compared to the mild/moderate instance of patients, the level of GSN was considerably lower in the mild and severe COVID-19 groups. Patients (95.45 ± 35.36) had considerably lower serum (GSN) levels than mild/moderate patients (172.32 ± 44.76) while. Healthy group (289.52 ± 71.33) (P 0.001). suggesting that it is an independent predictor of coronavirus infection Serum (GSN) levels were significantly and adversely connected with Age (year), SBP mmHg ferritin, (AST, ALT, ALP activity levels), and D-dimer levels, whereas GSN levels were significantly and positively correlated with Lymph percent levels. Conclusion: In conclusion, serum GSN concentration was lower in COVID-19 patients compared to the mild/moderate case group and healthy controls. Extensive tissue injury depletes and quickly consumes serum gelsolin (GSN), a naturally occurring, abundant circulating protein. The finding that considerable depletion is linked to eventual bad outcomes in a variety of clinical situations in severe inflammatory diseases holds hope for preventing lung harm and other injury organs. © 2022, ResearchTrentz Academy Publishing Education Services. All rights reserved.