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

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

Deep Learning Models for Health and Safety Risk Prediction in Power Infrastructure Projects

Inappropriate management of health and safety (H&S) risk in power infrastructure projects can result in occupational accidents and equipment damage. Accidents at work have detrimental effects on workers, company, and the general public. Despite the availability of H&S incident data, utilizing them to mitigate accident occurrence effectively is challenging due to inherent limitations of existing data logging methods. In this study, we used a text-mining approach for retrieving meaningful terms from data and develop six deep learning (DL) models for H&S risks management in power infrastructure. The DL models include DNN_classify (risk or no risk), DNN_reg1 (loss time), DNN_reg2 (body injury), DNN_reg3 (plant and fleet), DNN_reg4 (equipment), and DNN_reg5 (environment). An H&S risk database obtained from a leading UK power infrastructure construction company was used in developing the models using the H2O framework of the R language. Performances of DL models were assessed and benchmarked with existing models using test data and appropriate performance metrics. The overall accuracy of the classification model was 0.93. The average R² value for the five regression models was 0.92, with mean absolute error between 0.91 and 0.94. The presented results, in addition to the developed user-interface module, will help practitioners obtain a better understanding of H&S challenges, minimize project costs (such as third-party insurance and equipment repairs), and offer effective strategies to mitigate H&S risk.     

International Academic Mobility in Chinese Academia: Opportunities and Challenges

International academic mobility is an important dimension of the internationalization of higher education institutions, which aims to enact practical changes in economies and societies. Although many studies have investigated the mobility of international students, the mobility of international academics has been less investigated, particularly in the context of mainland China. This qualitative study explores the experience of international academics in Chinese academia. The study is based on semi-structured interviews with twenty-six international academics from different countries, working at different universities in Beijing. Through a four-stage systematic analysis, the study shows that international academics’ engagement with mobility in Chinese academia can be characterized by a combination of gains and losses. This article explores how international academics view Chinese academia as either resourceful or restrictive for their academic career. It then discusses the hidden narratives concerning the challenges that international academics face in relation to mobility.     

An effective approach to linear calibration estimation with its applications

Abstract

The availability of some extra information, along with the actual variable of interest, may be easily accessible in different practical situations. A sensible use of the additional source may help to improve the properties of statistical techniques. In this study, we focus on the estimators for calibration and intend to propose a setup where we reply only on first two moments instead of modeling the whole distributional shape. We have proposed an estimator for linear calibration problems and investigated it under normal and skewed environments. We have partitioned its mean squared error into intrinsic and estimation components. We have observed that the bias and mean squared error of the proposed estimator are function of four dimensionless quantities. It is to be noticed that both the classical and the inverse estimators become the special cases of the proposed estimator. Moreover, the mean squared error of the proposed estimator and the exact mean squared error of the inverse estimator coincide. We have also observed that the proposed estimator performs quite well for skewed errors as well. The real data applications are also included in the study for practical considerations.     

Performance of some new Liu parameters for the linear regression model

Abstract

This article introduces some Liu parameters in the linear regression model based on the work of Shukur, Månsson, and Sjölander. These methods of estimating the Liu parameter d increase the efficiency of Liu estimator. The comparison of proposed Liu parameters and available methods has done using Monte Carlo simulation and a real data set where the mean squared error, mean absolute error and interval estimation are considered as performance criterions. The simulation study shows that under certain conditions the proposed Liu parameters perform quite well as compared to the ordinary least squares estimator and other existing Liu parameters.     

Construction of repeated measurements designs strongly balanced for residual effects

Abstract

Repeated Measurements Designs have been widely used in agriculture, animal husbandry, education, biology, botany and engineering. Balanced or strongly balanced repeated measurements designs are useful to balance out the residual effects. In this article, some new generators and construction procedures are proposed to obtain circular strongly balanced repeated measurements designs in periods of (a) equal sizes, (b) two different sizes, and (c) three different sizes.     

An Assessment of Vulnerability to Poverty in Punjab,Pakistan: Subjective Choices of Poverty Indicators

In general, the happiness literature has paid little attention to the relationship between physical appearance and well-being. In this paper, we examine the link between weight, height and well-being for three distinct samples in China given that attractiveness effects likely vary greatly across sociocultural contexts. As China has recently undergone rapid economic transformation in the urban areas, this empirical exercise is particularly interesting because it can highlight how changing social norms have affected the relationship between physical appearance and subjective well-being. For the rural and migrant samples, we find that for both men and women, big and tall individuals have higher levels of well-being. This is consistent with the notion that the strong are better off when more labor intensive work is the norm. For the urban sample and for urban males in particular, no well-being penalty is found for being obese, unlike previous results based on Western samples. It is very likely that the unique Chinese cultural practice of network building banquets and feasting is behind this finding.     

Fertility and Financial Development in South Asia

The paper empirically examines old-age security hypothesis to explain fertility rates in South Asia. Panel data is used for the period 1972–2013 for seven South Asian countries which include Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka. The estimated results reveal that in South Asia fertility rate decreases with the increase in financial development. Thus, the findings support old-age security hypothesis that parents use children as financial instruments to secure their old age. This paper validates the theory that the availability of alternative financial tools reduces the incentives of households to have large offspring. Infant mortality is also shown an important factor for high fertility rate in South Asia. This implies that households cover their risk from losing children by producing more children. The results also reveal that fertility rate decreases with the increase in per capita income, which implies that households treat children as inferior good in this region. In other words, households prefer quality of children over quantity of children when their income level increases. The results have also shown that fertility decreases with the increase in education, urbanization, agriculture productivity and industrialization. The study has some important policy implications.     

Human Capital Outflow and Economic Misery: Fresh Evidence for Pakistan

This paper visits the impact of economic misery on human capital outflow using time series data over the period of 1975–2012. We have applied the combined cointegration tests and innovation accounting approach to examine long run and causal relationship between the variables. Our results affirm the presence of cointegration between the variables. We find that economic misery increases human capital outflow. Foreign remittances add in human capital outflow from Pakistan. The migration from Pakistan to rest of world is boosted by depreciation in local currency. Income inequality is also a major contributor to human capital outflow. The present study is comprehensive effort and may provide new insights to policy makers for handling the issue of human capital outflow by controlling economic misery in Pakistan.