Search results

There are several methods developed in the recent past to predict the spread of COVID-19 in different countries. However, due to changing scenarios in terms of interaction among people, none could predict the case close to the actual figures. An attempt to simulate people's interaction due to economic reopening concerning the confirmed cases at various places as per changing situation has been made. The scenario development method's base lies in the hypothesis that if there were no inter-state transportation during India's lockdown after May 24th, the number of infection cases would have started lowering down in a normalized progression.

This study has developed three scenarios from the worst to the business-as-usual to the best in order to project the COVID-19 infections in India concerning infections observed from January 30th till May 24th, 2020, since the domestic flights became operational from May 25th, 2020, in India.

Based on the observed cases till May 24th, the rise of cases is projected further in a random progression and superimposed to the normal progression. The results obtained in the three scenarios present that worst case needs complete lockdown, business-as-usual case needs regulatory lockdown and best case assures complete lockdown release by the second week of September 2020. This study suggests the preparedness and mitigation strategy for a threefold lockdown management scheme in all-inclusive.

The work has been done on a hypothesis which is solely original.

As COVID-19 outbreak has created a global crisis, treating patients with minimum resources and traditional methods has become a hectic task. In this technological era, the rapid growth of coronavirus has affected the countries in lightspeed manner. Therefore, the present study proposes a model to analyse the resource allocation for the COVID-19 pandemic from a pluralistic perspective.

The present study has combined data analytics with the K-mean clustering and probability queueing theory (PQT) and analysed the evolution of COVID-19 all over the world from the data obtained from public repositories. By using K-mean clustering, partitioning of patients’ records along with their status of hospitalization can be mapped and clustered. After K-mean analysis, cluster functions are trained and modelled along with eigen vectors and eigen functions.

After successful iterative training, the model is programmed using R functions and given as input to Bayesian filter for predictive model analysis. Through the proposed model, disposal rate; PPE (personal protective equipment) utilization and recycle rate for different countries were calculated.

Using probabilistic queueing theory and clustering, the study was able to predict the resource allocation for patients. Also, the study has tried to model the failure quotient ratio upon unsuccessful delivery rate in crisis condition.

The study has gathered epidemiological and clinical data from various government websites and research laboratories. Using these data, the study has identified the COVID-19 impact in various countries. Further, effective decision-making for resource allocation in pluralistic setting has being evaluated for the practitioner's reference.

Further, the proposed model is a two-stage approach for vulnerability mapping in a pandemic situation in a healthcare setting for resource allocation and utilization.

The purpose of this paper is to identify the impact of governance structures in defining the relationship between profits and leverage.

The paper uses the standard design used by Fama and French (2002) and employs it under different governance structures. It is the first to identify the endogenous nature of the relationship between profits and leverage, compounded by the endogeneity of governance. The paper uses the instrumental variable (IV) technique to control for endogeneity and recommends a novel approach to control for multiple endogenous regressors.

The results demonstrate that firms operating under good governance verify the predictions of the trade-off theory of capital structure and that the evidence of negative relation in the literature is a subset of management inefficiency. The results are consistent after controlling for endogeneity and are robust to alternative iteration of governance. The activity in debt issuance and retirement supports the conclusion that firms with good governance structures actively seek an optimal capital structure corresponding to profits.

This study adds value to existing literature. It is the first to identify the importance of governance in defining the relationship between profits and leverage. It recognizes unaccounted endogeneity concerns and employs an inspired IV approach to control for feedback from multiple endogenous regressors. Evidence for capital structure adjustment by firms with good governance is also substantiated. Lastly, the first unqualified evidence for the trade-off model is provided.

For a long time, women have not been judged by the same standards as men when trying to enter a trade or profession reserved traditionally for men or even after becoming part of it, while progressing. This paper explores the concept of career progression of female staff in an academic institute. It seeks to answer the following research questions: What barriers are faced by women in career progression? What are the differences in perceptions among female sub‐groups regarding these barriers? What policies and improvement programs can be provided to promote women's career progression? What is the relationship between women's career progression and justice?

According to the research objectives, the best way to collect the required data was a questionnaire. In this research, three different questionnaires were used. Also, to rank women's career progression barriers, the TOPSIS technique was used.

The authors found that there is a significant relationship between career progression barriers with interactional and distributive justice. Our ranking results showed that lack of organisational support and job restrictions are the main barriers to women's career progression. Also, flexible working time options is the main support program for eliminating of these barriers.

The major research limitations of this study were the failure to collect and analyse data that would yield a quantitative assessment of organisational justice, and the distribution of questionnaires among women at work.

This is the first study of its nature conducted among female Iranian university staff to focus on barriers to women's career progression. The paper also offers practical guidance that can be used by management and women employees to facilitate career progression.

The purpose of this paper is to show how the social categories of gender, age and class influence networking practices and career progression in the 4–5-star hotel sector in Australia and New Zealand. It argues that in this type of workplace the practice of networking is so normalized that it is assumed an inclusive, gender-neutral activity.

This paper draws on 18 semi-structured interviews. Inductive analysis was used uncover themes, sub-themes and emergent patterns. An intersectionally sensitive approach was followed.

The significance of networking processes for career progression in the 4–5-star hotel sectors was a recurring theme. Networking reflects historically embedded gendering practices that heighten existing class-based structural privilege for groups of men.

The focus is on hotel employees in Australia and New Zealand with the findings are not implicitly generalizable.

Networks are important for women as their “merit” may not be immediately visible. Well-structured mentoring schemes need to be adopted as part of the affirmative action required to tilt the “skewed playing field”.

Studies that indicate how the gendering of networking practices reinforce career privilege and penalty in specific organizations have been lacking, as have studies favouring an intersectional approach. This study seeks to redress these omissions.

Digital literacy is not the mere ability to use a digital device or know to use various software. It is a domain of exploration for sociology, psychology, education and, of course, technology. This study aims to present a quantitative analysis of the literature on digital literacy using a bibliometric approach.

Using data from the Web of Science database, the importance of the research is evaluated by reviewing 2307 publications and examining the yearly publication, field category productivity, citation structure, most cited resources, documents, most-cited authors, most productive authors, and country in the field of digital literacy. Further, a cluster analysis is conducted to see the most recurrent keywords and emerging trends in this field. At last, the authors analyzed the thematic progression of keywords over these five years based on the normalized citations. Additionally, a graphical representation of the bibliometric data using VOSviewer is presented in the paper.

The results suggest a steady rate of publication in this field, with most of the research published in education and library fields and the USA leading the country in this realm. The emerging themes in this field are ‘Fake News’, ‘Competence’, ‘Educational Technology’, ‘Health Literacy’, ‘Self-Efficacy’ and, interestingly, ‘COVID-19’. The results also revealed that COVID-19 has been examined and associated with fake news, higher education, social media and information literacy.

This paper provides an overall summary of the most recent research work published from 2017 to 2021 on digital literacy in the backdrop of COVID-19. The study presents the thematic progression over the years and particularly the new keywords that emerged in the limelight of the pandemic. It contributes by updating the existing body of knowledge in the field of digital literacy and presents preliminary results related to COVID-19.

As the contradiction between economic development, resource and environment has become increasingly prominent, low-carbon competitiveness has received worldwide focus. This study aims to examine low-carbon competitiveness in 31 provinces (cities and regions) of China.

An evaluation index system for low-carbon competitiveness in China has been constructed, which is composed of 25 economic, social, environmental and policy indicators. To study the state of low-carbon competitiveness and resistance to China’ development of low-carbon competitiveness, this study uses a combination of the catastrophe progression model, the spatial autocorrelation model and the barrier method.

China’ low-carbon competitiveness gradually decreases from coastal to inland areas: the Tibet and Ningxia Hui autonomous regions are the least competitive regions, while the Shandong and Jiangsu provinces are the most competitive areas. The spatial correlation of the 31 provinces’ low-carbon competitiveness is very low and lacks regional cooperation. This study finds that the proportion of a region’ wetland area, the proportion of tertiary industries represented in its GDP and afforestation areas are the main factors in the development of low-carbon competitiveness. China should become the leader of carbon competitiveness by playing the leading role in the Eastern Region, optimizing the industrial structure, improving government supervision and strengthening environmental protection.

The paper provides a quantitative reference for evaluating China’ low-carbon competitiveness, which is beneficial for environmental policymaking. In addition, the evaluation and analysis methods offer relevant implications for developing countries.

After many years of equal opportunities legislation, motherhood still limits womens' career progress even in a feminized occupation such as nursing. While the effect of motherhood, working hours, career breaks and school aged children upon career progression has been discussed widely, its actual scale and magnitude has received less research attention. The purpose of this paper is to examine the impact of these factors individually and cumulatively.

This paper considers the impact of the above through a longitudinal analysis of a demographically unique national database, comprising the 46,565 registered nursing workforces in NHS Scotland from 2000‐2008. The variables examined include gender, employment grades, number and length of career breaks, lengths of service, age, working patterns, the number and age of dependent children.

The results indicate: motherhood has a regressively detrimental effect on women's career progression. However, this is a simplistic term which covers a more complex process related to the age of dependent children, working hours and career breaks. The degree of women's restricted career progression is directly related to the school age of the dependent children: the younger the child the greater the detrimental impact. Women who take a career break of greater than two years see their careers depressed and restricted. The results confirm that whilst gender has a relatively positive effect on male career progression; a women's career progression is reduced incrementally as she has more children, and part‐time workers have reduced career progression regardless of maternal or paternal circumstances.

This paper is the only example internationally, of a national workforce being examined on this scale and therefore its findings are significant. For the first time the impact of motherhood upon a women's career progression and the related factors – dependent children, career breaks and part‐time working are quantified. These findings are relevant across many areas of employment and they are significant in relation to broadening the debate around equal opportunities for women.

The purpose of this paper is to develop a quantitative risk assessment method for agricultural products cold chain logistics to assess the condition of the fresh agricultural products cold chain process objectively and accurately.

A risk assessment index system of agricultural products cold chain logistics is designed on the basis of the risk identification for the process of agricultural products cold chain logistics. This paper first uses catastrophe progression method and a new maximum deviation method to build an improved catastrophe progression assessment model for agricultural products cold chain logistics. In order to verify the reliability and validity of the model, two representative enterprises are selected as the case in the study.

The results in the empirical research indicate strong support for the assessment model and coincide with the reality. The risk assessment index system can also reflect the key risk factors from agricultural products cold chain logistics scientifically. In addition, the improved catastrophe progression assessment method proposed in this paper can be scientific and reasonable to predict risk.

This paper contributes to provide a new risk assessment model for agricultural products cold chain logistics. The new model overcomes the limitation of subjective empowerment and it increases the objectivity and scientificity in the process of cold chain logistics risk assessment. This paper also shows that practitioners involved in the field of products cold chain logistics can manage the potential risk by a set of scientific methods for assessing the risk before the accident.

The paper provides a practical guideline to practitioners, especially for cold chain logistics managers, relevant management departments, and cold chain logistics management consultants. It is proved that the new risk assessment method and the risk assessment index system of agricultural products cold chain logistics can help them assess the risk scientifically and reasonably.

Although the calculation is simple, the new model can overcome the limitation of subjective empowerment scientifically and reasonably, and thus has important practical value.

This paper aims to establish a standardized, quick, reliable and cost-efficient method of quality control (QC) in metal powder bed fusion (PBFM) based on process monitoring data.

Based on destructive testing results that emerged from a statistical investigation on powder bed fusion process exceeding reproducibility of mechanical properties, it was investigated if the generated monitoring data from a concept laser machine allows reliable deductions on resulting mechanical properties of the manufactured specimens.

The application of machine learning on generated melt pool images, under-recognition of destructive testing results, enables enhanced pattern recognition. The generated computational model successfully classified 9,280 unseen layer images by 98.9 per cent accuracy. This finding offers an automated approach to quality control within PBFM.

To the authors knowledge, it is the first time that machine learning has been applied for the purpose of QC in additive manufacturing. The ability of deep convolutional neural networks to recognize patterns, which are imperceptible to the human eye, shows high potential to facilitate activities of QC and to minimize QC-related costs and throughput times. The achieved processing speed for image analyses also points a way for future developments of self-corrective PBFM systems.