Digital Transformation of Education Toward Successful Internships: A Methodology Developed Over Seven Years and Tested During COVID-19

Contribution: This article provides a teaching methodology which combines project-based learning, self-regulated learning (SRL), and design projects (DPs) to improve the preparedness of students for computing science-related internships. The methodology is supported by the implementation of the educational technology that transforms the way teaching and learning is transformed to benefit on-campus and off-campus students equitably, during the COVID-19 pandemic. Background: Success in the workspace is governed by the ability of an individual to learn on-the-job and independently. Online learning has led to a shift from instructor-led learning to SRL. This requires individuals to discipline themselves, and be in control of their learning and education. Outcome: The success of internships is improved with skills learnt in class through hands-on real-world projects. Both on/off-campus students gain equitable relevant experience. The teaching methodology developed over several years combines project-based learning, SRL, and DPs. Application Design: The methodology was applied using a flipped classroom approach. The educational technology was used to enhance SRL before in-class learning. This way, in-class rote learning was replaced with hands-on projects. Exam assessments were replaced with DPs where soft skills and technical skills are applied. Findings: The effectiveness of the developed methodology is measured through quantitative and qualitative evaluation tools. The evaluation demonstrates that combining well-designed education technology for SRL, with in-class project-based learning and DPs, can improve students' chances in getting high-impact internships.

The implementation of green supply chain management (GSCM) and environmental management system (EMS) practices and its impact on market competitiveness during COVID-19.

Despite great academic interest in global green supply chain management (GSCM) practices, its effectiveness for environmental management systems (EMS) and market competitiveness during COVID-19 remains untapped. Existing literature suggests that a fundamental link between GSCM, EMS, and market competitiveness is missing, as supply management is critical to maintain market competitiveness. To fill this gap in the literature, this study examines whether environmental management systems influence the link between GSCM practice and market competitiveness in China. We also propose the articulating role of big data analytics and artificial intelligence (BDA-AI) and environmental visibility toward these associations in the context of the COVID-19 pandemic. We evaluated the proposed model using regression-based structural equation modeling (SEM) with primary data (n = 330). This result provides empirical evidence of the impact of GSCM on EMS and market competitiveness. Moreover, the results show that the BDA-AI and the environmental visibility enhanced the positive relationship between GSCM-EMS and EMS and market competitiveness in China. Recent research shows that supply chain professionals, policymakers, managers, and researchers are turning to formal EMS, BDA-AI, and environmental visibility to help their organizations achieve the competitiveness that the market indicates they need.

Do entrepreneurship ecosystem and managerial skills contribute to startup development?

PurposeThe purpose of this study is to analyze the factors affecting startup development and the entrepreneurship ecosystem's contribution to it.Design/methodology/approachA quantitative methodology is used for data collection from different startup owners working across Pakistan. It is a cross-sectional descriptive study, which investigates the causal effect of variables at a definite point in time. Non-probability convenient sampling was used for selecting available startups from the incubation centers. The sampling framework consists of the founders of the startups that have been previously incubated at any of the selected incubation centers.FindingsRegression analysis results from 165 responses of entrepreneurs and incubation centers demonstrate that the most important factors affecting startup development were financial access, government support, marketing challenges, education, technology and managerial skills in order of occurrence. Entrepreneurship ecosystem also proved to have a very positive impact on the relationship of these factors with startup development.Practical implicationsIn this paper, the factors that affect the development of startup are analyzed and recommendations are provided.Originality/valueThis research is comprehensive, as we have collected data from actual entrepreneurs and incubation centers to explain how entrepreneurs initiate their startup business by considering their managerial skills. As such, this study is unique in that the data comes from newly developed incubations centers in one of South Asia's fastest-growing economies.

Modern vaccine strategies for emerging zoonotic viruses.

INTRODUCTION: The significant increase in the emergence of notable zoonotic viruses in the previous decades has become a serious concern to global public health. Ninety-nine percent of infectious diseases have originated from zoonotic viruses with immense potential for dissemination, infecting the susceptible population completely lacking herd immunity. AREAS COVERED: Zoonotic viruses appear in the last two decades as a major health threat either newly evolved or previously present with elevated prevalence in the last few years are selected to explain their current prophylactic measures. In this review, modern generation vaccines including viral vector vaccines, mRNA vaccines, DNA vaccines, synthetic vaccines, virus-like particles, and plant-based vaccines are discussed with their benefits and challenges. Moreover, the traditional vaccines and their efficacy are also compared with the latest vaccines. EXPERT OPINION: The emergence and reemergence of viruses that constantly mutate themselves have greatly increased the chance of transmission and immune escape mechanisms in humans. Therefore, the only possible solution to prevent viral infection is the use of vaccines with improved safety profile and efficacy, which becomes the basis of modern generation vaccines.

Evaluating the effect of the COVID-19 virus on the wind energy sectors (preprint)

The purpose of this study is to determine during the COVID-19 epidemic effects on wind and green energy and control the raising the cost of utilizing wind energy to power for country energy plants using the Levelized Cost of Energy methods. Objective 1) The COVID-19 pandemic can be provided through green financial policies such as coal pricing, transferable green certificates, and loans for wind energy markets. Objective 2) examined the cost of wind energy in china before and after the COVID-19 outbreak, using data from 100 wind energy projects constructed between 2014 and 2020. Based on results, wind energy's fixed average cost of electricity fell from 0.98 Chinese yuan in 2014 to 0.79 Chinese Yuan in March 2019, and subsequently to 0.75 Chinese Yuan in 2020, a 13.99 percent increase. Other results average electricity generation price down to 0.79 Yuan, 0.99 Yuan, and 0.79 Yuan and average carbon oxide emissions was 50 Yuan/ton increase. The green fiscal policies will be required during the COVID-19 epidemic to promote wind energy generation investment.

The implementation of Green Supply Chain Management (GSCM) and Environmental Management System (EMS) practices and its impact on Market Competitiveness during Covid-19 (preprint)

Despite great academic interest in global green supply chain management (GSCM) practices, its effectiveness for environmental management systems (EMS) and market competitiveness during COVID-19 remains untapped. Existing literature suggests that a fundamental link between GSCM, EMS and market competitiveness is missing, as supply management is critical to maintain market competitiveness. To fill this gap in the literature, this study examines whether environmental management systems influence the link between GSCM practice and market competitiveness. We also propose the articulating role of big data analytics and artificial intelligence (BDA-AI) and environmental visibility towards these associations in the context of the Covid-19 pandemic. We evaluated the proposed model using regression-based structural equation modeling (SEM) with primary data (n = 330). This result provides empirical evidence of the impact of GSCM on EMS and market competitiveness. Moreover, the results show that the BDA-AI and the environmental visibility enhanced the positive relationship between GSCM-EMS and EMS and market competitiveness. Recent research shows that supply chain professionals, policy makers, managers and researchers are turning to formal EMS, BDA-AI and environmental visibility to help their organizations achieve the competitiveness that the market indicates they need.

Role of knowledge, behavior, norms, and e-guidelines in controlling the spread of COVID-19: evidence from Pakistan.

The COVID-19 pandemic is straining public health systems and the global economy, triggering unprecedented measures by governments around the globe. The adoption of a preventive measure is required to control the spread. This research explores the impact of influencing factors like COVID-19 knowledge, behavioral control, moral and subject norms, preventive e-guidelines by the government, and environmental factors on the intention to prevent COVID-19 and risk aversion. A cross-sectional study was performed of 310 respondents about different COVID-19 related influencing factors in Pakistan. The partial least square-structural equation modeling was applied to estimate the path coefficient. Moral and subject norms (0.359) had a comparatively higher path coefficient. Other influencing factors/drivers were preventive e-guideline by the government (0.215) followed by COVID-19 knowledge (0.197), and behavioral control (0.121). The intention to prevent COVID-19 showed a positive and significant impact (0.705) on risk aversion. The indirect analysis also confirmed that the positive influence of moral and subject norms, COVID-19 knowledge, preventive e-guideline by the government, and behavioral control on risk aversion. However, the path coefficient of environmental factors was negative but insignificant, which implies than environmental factors do not influence the intention to prevent COVID-19. It is suggested to provide clear guidelines using print, social, electronic media. It is also suggested to provide e-guidelines in local languages. The COVID-19 knowledge about its transmission, symptoms, and precautions is also useful. It is suggested to include the causes, symptoms, and precaution of viral diseases in the educational syllabus. The government should ensure the availability of preventive medical items like surgical masks and sanitizers to meet the demand of the public.

Association between meteorological indicators and COVID-19 pandemic in Pakistan.

This study was designed to investigate the impact of meteorological indicators (temperature, rainfall, and humidity) on total COVID-19 cases in Pakistan, its provinces, and administrative units from March 10, 2020, to August 25, 2020. The correlation analysis showed that COVID-19 cases and temperature showed a positive correlation. It implies that the increase in COVID-19 cases was reported due to an increase in the temperature in Pakistan, its provinces, and administrative units. The generalized Poisson regression showed that the rise in the expected log count of COVID-19 cases was 0.024 times for a 1 °C rise in the average temperature in Pakistan. Second, the correlation between rainfall and COVID-19 cases was negative in Pakistan. However, the regression coefficient between the expected log count of COVID-19 cases and rainfall was insignificant in Pakistan. Third, the correlation between humidity and the total COVID-19 cases was negative, which implies that the increase in humidity is beneficial to stop the transmission of COVID-19 in Pakistan, its provinces, and administrative units. The reduction in the expected log count of COVID-19 cases was 0.008 times for a 1% increase in the humidity per day in Pakistan. However, humidity and COVID-19 cases were positively correlated in Sindh province. It is required to create awareness among the general population, and the government should include the causes, symptoms, and precautions in the educational syllabus. Moreover, people should adopt the habit of hand wash, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to control the COVID-19.

Deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics.

Artificial intelligence (AI) development imitates the workings of the human brain to comprehend modern problems. The traditional approaches such as high throughput screening (HTS) and combinatorial chemistry are lengthy and expensive to the pharmaceutical industry as they can only handle a smaller dataset. Deep learning (DL) is a sophisticated AI method that uses a thorough comprehension of particular systems. The pharmaceutical industry is now adopting DL techniques to enhance the research and development process. Multi-oriented algorithms play a crucial role in the processing of QSAR analysis, de novo drug design, ADME evaluation, physicochemical analysis, preclinical development, followed by clinical trial data precision. In this study, we investigated the performance of several algorithms, including deep neural networks (DNN), convolutional neural networks (CNN) and multi-task learning (MTL), with the aim of generating high-quality, interpretable big and diverse databases for drug design and development. Studies have demonstrated that CNN, recurrent neural network and deep belief network are compatible, accurate and effective for the molecular description of pharmacodynamic properties. In Covid-19, existing pharmacological compounds has also been repurposed using DL models. In the absence of the Covid-19 vaccine, remdesivir and oseltamivir have been widely employed to treat severe SARS-CoV-2 infections. In conclusion, the results indicate the potential benefits of employing the DL strategies in the drug discovery process.Communicated by Ramaswamy H. Sarma.

The blessing or curse of workplace friendship: Mediating role of organizational identification and moderating role of political skills

Workplace friendship is becoming a complex phenomenon in a working environment that signifies a need for a better understanding of when and how workplace friendship has positive and negative outcomes, specifically during the COVID-19 pandemic. Based on the social exchange theory, this study aims to determine the impact of workplace friendship on organizational identification and its subsequent influence on job embeddedness, job performance, and employee wellbeing, along with the moderating role of political skills in all relationships. Using a self-administered questionnaire, the final data from 206 hotel employees were collected during three waves of COVID-19. The results highlighted that workplace friendship negatively influences organizational identification in the first wave, while organizational identification negatively relates to job performance in the first- and second waves. Moreover, organizational identification mediates the relationships of workplace friendship with employee outcomes, whereas political skills also moderate the relationships in all three waves.

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.

Bio-Inspired Modelling of Disease Through Delayed Strategies

In 2020, the reported cases were 0.12 million in the six regions to the official report of the World Health Organization (WHO). For most children infected with leprosy, 0.008629 million cases were detected under fifteen. The total infected ratio of the children population is approximately 4.4 million. Due to the COVID-19 pandemic, the awareness programs implementation has been disturbed. Leprosy disease still has a threat and puts people in danger. Nonlinear delayed modeling is critical in various allied sciences, including computational biology, computational chemistry, computational physics, and computational economics, to name a few. The time delay effect in treating leprosy delayed epidemic model is investigated. The whole population is divided into four groups: those who are susceptible, those who have been exposed, those who have been infected, and those who have been vaccinated. The local and global stability of well-known conclusions like the Routh Hurwitz criterion and the Lyapunov function has been proven. The parameters’ sensitivity is also examined. The analytical analysis is supported by computer results that are presented in a variety of ways. The proposed approach in this paper preserves equilibrium points and their stabilities, the existence and uniqueness of solutions, and the computational ease of implementation.

Predicting Divorce Prospect Using Ensemble Learning: Support Vector Machine, Linear Model, and Neural Network.

A divorce is a legal step taken by married people to end their marriage. It occurs after a couple decides to no longer live together as husband and wife. Globally, the divorce rate has more than doubled from 1970 until 2008, with divorces per 1,000 married people rising from 2.6 to 5.5. Divorce occurs at a rate of 16.9 per 1,000 married women. According to the experts, over half of all marriages ends in divorce or separation in the United States. A novel ensemble learning technique based on advanced machine learning algorithms is proposed in this study. The support vector machine (SVM), passive aggressive classifier, and neural network (MLP) are applied in the context of divorce prediction. A question-based dataset is created by the field specialist. The responses to the questions provide important information about whether a marriage is likely to turn into divorce in the future. The cross-validation is applied in 5 folds, and the performance results of the evaluation metrics are examined. The accuracy score is 100%, and Receiver Operating Characteristic (ROC) curve accuracy score, recall score, the precision score, and the F1 accuracy score are close to 97% confidently. Our findings examined the key indicators for divorce and the factors that are most significant when predicting the divorce.

The Impact of COVID-19 Outbreak (2nd Wave) on Mental Health of the Healthcare Community in the NHS: A Web-Based Questionnaire Study

AimsTo determine the mental impact the second wave of COVID-19 has had on health care professionals working in the National Health Services (NHS), United Kingdom.MethodsA cross-sectional descriptive web-based survey was conducted among the staff of National Health Services (NHS) in Poole, United Kingdom. Two tertiary care hospitals staff were part of this study. The study was spanned over a duration of 6 months, October 2020 to April 2021. A standard GAD-7 and PHQ-9 questionnaire along with demographic information was uploaded on google docs for data collection. All healthcare staff working in the hospitals were included. Any person that did not fill the questionnaire completely was excluded. Data collected were analysed using SPSS for descriptive statistics and the chi-squared test was done keeping p < 0.05 as significant.ResultsA total of 160 health care professionals took part in the survey, with a mean age of 37.36 (SD = 11.51) years, predominantly females (58.8%). The majority of participants were not depressed (78.1%, p = 0.004) nor were they anxious (85%, p = 0.008). A significant difference (p = 0.050) was seen in participant's anxiousness regarding the source of information. All other demographic parameters were not significant for differences in depression or anxiety (p > 0.05). 33.6% of the respondents agreed and 9.6% totally agreed to being terrified of contracting the coronavirus. 40.4% disagreed while 16% did not have an opinion. A similar trend was seen for the other statements. More than half (56.3% and 56.9%) of the participants answered in the affirmative that they were worried about contracting the disease and getting their living place contaminated, a staggering 91.3% were anxious about affecting their families.ConclusionThe second wave of COVID-19 has had minimal effect on the mental health of health care workers in the NHS.

Real-time artificial intelligence based health monitoring, diagnosing and environmental control system for COVID-19 patients.

By upgrading medical facilities with internet of things (IoT), early researchers have produced positive results. Isolated COVID-19 patients in remote areas, where patients are not able to approach a doctor for the detection of routine parameters, are now getting feasible. The doctors and families will be able to track the patient's health outside of the hospital utilizing sensors, cloud storage, data transmission, and IoT mobile applications. The main purpose of the proposed research-based project is to develop a remote health surveillance system utilizing local sensors. The proposed system also provides GSM messages, live location, and send email to the doctor during emergency conditions. Based on artificial intelligence (AI), a feedback action is taken in case of the absence of a doctor, where an automatic injection system injects the dose into the patient's body during an emergency. The significant parameters catering to our project are limited to ECG monitoring, SpO2 level detection, body temperature, and pulse rate measurement. Some parameters will be remotely shown to the doctor via the Blynk application in case of any abrupt change in the parameters. If the doctor is not available, the IoT system will send the location to the emergency team and relatives. In severe conditions, an AI-based system will analyze the parameters and injects the dose.

Dynamical analysis of coronavirus disease with crowding effect, and vaccination: a study of third strain.

Countries affected by the coronavirus epidemic have reported many infected cases and deaths based on world health statistics. The crowding factor, which we named "crowding effects," plays a significant role in spreading the diseases. However, the introduction of vaccines marks a turning point in the rate of spread of coronavirus infections. Modeling both effects is vastly essential as it directly impacts the overall population of the studied region. To determine the peak of the infection curve by considering the third strain, we develop a mathematical model (susceptible-infected-vaccinated-recovered) with reported cases from August 01, 2021, till August 29, 2021. The nonlinear incidence rate with the inclusion of both effects is the best approach to analyze the dynamics. The model's positivity, boundedness, existence, uniqueness, and stability (local and global) are addressed with the help of a reproduction number. In addition, the strength number and second derivative Lyapunov analysis are examined, and the model was found to be asymptotically stable. The suggested parameters efficiently control the active cases of the third strain in Pakistan. It was shown that a systematic vaccination program regulates the infection rate. However, the crowding effect reduces the impact of vaccination. The present results show that the model can be applied to other countries' data to predict the infection rate.

Bio-Inspired Numerical Analysis of COVID-19 with Fuzzy Parameters

Fuzziness or uncertainties arise due to insufficient knowledge, experimental errors, operating conditions and parameters that provide inaccurate information. The concepts of susceptible, infectious and recovered are uncertain due to the different degrees in susceptibility, infectivity and recovery among the individuals of the population. The differences can arise, when the population groups under the consideration having distinct habits, customs and different age groups have different degrees of resistance, etc. More realistic models are needed which consider these different degrees of susceptibility infectivity and recovery of the individuals. In this paper, a Susceptible, Infected and Recovered (SIR) epidemic model with fuzzy parameters is discussed. The infection, recovery and death rates due to the disease are considered as fuzzy numbers. Fuzzy basic reproduction number and fuzzy equilibrium points have been derived for the studied model. The model is then solved numerically with three different techniques, forward Euler, Runge-Kutta fourth order method RK-4) and the nonstandard finite difference (NSFD) methods respectively. The NSFD technique becomes more efficient and reliable among the others and preserves all the essential features of a continuous dynamical system.

Nanobionics in Crop Production: An Emerging Approach to Modulate Plant Functionalities.

The "Zero Hunger" goal is one of the key Sustainable Development Goals (SDGs) of the United Nations. Therefore, improvements in crop production have always been a prime objective to meet the demands of an ever-growing population. In the last decade, studies have acknowledged the role of photosynthesis augmentation and enhancing nutrient use efficiency (NUE) in improving crop production. Recently, the applications of nanobionics in crop production have given hope with their lucrative properties to interact with the biological system. Nanobionics have significantly been effective in modulating the photosynthesis capacity of plants. It is documented that nanobionics could assist plants by acting as an artificial photosynthetic system to improve photosynthetic capacity, electron transfer in the photosystems, and pigment content, and enhance the absorption of light across the UV-visible spectrum. Smart nanocarriers, such as nanobionics, are capable of delivering the active ingredient nanocarrier upon receiving external stimuli. This can markedly improve NUE, reduce wastage, and improve cost effectiveness. Thus, this review emphasizes the application of nanobionics for improving crop yield by the two above-mentioned approaches. Major concerns and future prospects associated with the use of nanobionics are also deliberated concisely.

Dynamical analysis of coronavirus disease with crowding effect, and vaccination: a study of third strain

Countries affected by the coronavirus epidemic have reported many infected cases and deaths based on world health statistics. The crowding factor, which we named "crowding effects," plays a significant role in spreading the diseases. However, the introduction of vaccines marks a turning point in the rate of spread of coronavirus infections. Modeling both effects is vastly essential as it directly impacts the overall population of the studied region. To determine the peak of the infection curve by considering the third strain, we develop a mathematical model (susceptible–infected–vaccinated–recovered) with reported cases from August 01, 2021, till August 29, 2021. The nonlinear incidence rate with the inclusion of both effects is the best approach to analyze the dynamics. The model's positivity, boundedness, existence, uniqueness, and stability (local and global) are addressed with the help of a reproduction number. In addition, the strength number and second derivative Lyapunov analysis are examined, and the model was found to be asymptotically stable. The suggested parameters efficiently control the active cases of the third strain in Pakistan. It was shown that a systematic vaccination program regulates the infection rate. However, the crowding effect reduces the impact of vaccination. The present results show that the model can be applied to other countries' data to predict the infection rate.