Resilience Index Framework for the Construction Industry in Developing Countries

The construction industry in many developing countries is considered the main engine for economic growth. Quantification of the resilience of the construction industry in developing economies is essential for stakeholders and decision makers. Many researchers have attempted to quantify the construction industry's resilience in the context of developed economies;however, there is lack of established measures of such quantification in developing countries. This paper proposes a framework for the composition of an index that quantifies the resilience of the construction industry in developing countries. The proposed framework is demonstrated in the context of three developing countries: Rwanda, Egypt, and Turkey. The index is composed of measures such as the construction value added to a country's gross domestic product (GDP) and employment in construction. Principal Component Analysis (PCA) is utilized for weighting and aggregation of the individual variables. Studying the causal relationship between construction growth and economic development from 1971 to 2022, results show that construction growth leads to economic development in each of the three countries. Results of the proposed index values indicate that the construction industry in each of the three countries demonstrated increased resilience by sustaining both its outputs and its employment generation aspect in the two years following the coronavirus pandemic in 2019. Quantification of the construction industry's resilience in countries where the construction growth leads to the economic growth would provide a crucial insight for stakeholders and decision makers. © 2023 American Society of Civil Engineers.

Causal Relationships of the Stock Market with other Asset Classes: An Indian Perspective

The recent decade has seen a rapid rise in risk assets. Stocks, commodities, and cryptocurrencies have exploded to the upside. Global central banks have maintained interest rates at record low levels following the COVID-19 crisis. This has further acted as tailwinds for risky assets. With asset classes being increasingly interlinked with each other, useful information can be gained by studying these inter-relationships. This paper looks at the interrelationships between the Indian stock market Nifty index and some key asset classes such as Gold, Crude oil, short-term and long-term Indian government bond yields, the USD/INR exchange rate, and the cryptocurrency Bitcoin for the period January 2011 to December 2020. Co-integration analysis suggests the absence of long-run relationships between the Nifty and the asset classes studied. Granger causality analysis reveals bi-directional causality between Nifty and USD/INR and Crude oil returns. Gold returns, Bitcoin returns, and changes in short and long-term government bond yields uni-directionally granger-caused Nifty returns. Impulse response analysis reveals that shocks in each of the independent variables caused a shock in the Nifty that persisted for 1 to 3 weeks. Traders in the Nifty can monitor these shocks and accordingly fine-tune their strategies for possible moves in the Nifty. © 2022 ACM.

Burnout among Nursing Workers Who Provide Nursing Care for People Infected with COVID-19

Background: The unpredictable nature of the new COVID-19 pandemic and what is already troubling incidents of affecting nursing workers can have a significant impact on their psychological well-being. Objective: To describe the prevalence of burnout among nursing personnel caring for patients with COVID-19 and associated factors. Study Design: cross-sectional study. Setting: Alhossien Teaching Hospitals designated to isolate patients with COVID-19 in Thi Qar Governorate. Participants: A sample of 50 nurses practitioners in the study sites who were caring for COVID- 19 patients. Measurements: age, gender, marital status, job title, certificate, job category, number of years of service, working period, hospitalization, and work load, as well as burnout level in each subscale consist (12)items. Results: Nurses working in isolation hospitals suffer from high levels of burnout, emotional exhaustion, depersonalization, and personal underachievement. Limitations: There was no control group and therefore we cannot claim a causal relationship between COVID-19 and the level of fatigue observed. Not all confounders may have been accounted for. Conclusions: Burnout is prevalent among nurses caring for COVID-19 patients. Age, gender, job category, and location of practice contribute to the level of burnout experienced by nurses. Recommendations: Psychologically rehabilitate nursing workers under the supervision of specialists and give them financial and moral rewards to compensate for the harm they have suffered. © 2022 IEEE.

The Effects of COVID-19 and Workplace Mobility to Stock Price and Exchange Rate in Indonesia: An Econometric Approach

The COVID-19 pandemic has reached its 20th month in Indonesia and still damaged various sectors, particularly economy. The policies imposed by the government impacted mainly the stock price. exchange rate, and people mobility in Indonesia. However, there are limited studies that incorporate these variables in Indonesia context. Thus, this study investigates the relationship between the COVID-19 pandemic, stock price, exchange rate, and workplace mobility simultaneously. This study employs Vector Autoregressive (VAR) as the analysis considering its advantages in finding the causal relationship between variables and periodic interpretation using Impulse Response Function (IRF). The VAR results show that from the Granger Causality Test, it turns out that the shocks from COVID-19 positivity rate and mobility in workplaces caused the changes in stock price and exchange rate. On the other hand, the IRF results exhibit the depreciating responses of stock price and exchange rate due to the shocks of COVID-19 positivity rate and mobility are enormous in the short term. In the longer term, the stock price response needs a longer time to return to the initial condition than the exchange rate. Therefore, further policy evaluation and formulation become essential to maintain the stock price and exchange rate, mainly due to the effect of COVID-19 and workplace mobility. © 2022 IEEE.

Modelling of Air Pollution Dispersion in Thiruvananthapuram Corporation

Air is a crucial element of the earth’s ecosystem, and even minor changes in its composition can have a wide range of effects on the survival of creatures on earth. Deterioration of air quality is an important issue faced by many cities in India. Modelling of air pollution is a numerical method for describing the causal relationship between emissions, meteorology, atmospheric concentrations and deposition. The current study prepared annual and monthly air pollution dispersion maps at sensitive areas of Thiruvananthapuram Municipal Corporation, which is the administrative spot in the city of Thiruvananthapuram, the capital of Kerala. ADMS-Urban model was used in conjunction with GIS to produce the dispersion maps. The study has demonstrated a methodology for the development of emission inventory, dispersion modelling and mapping. Dispersion modelling and trend analysis were used to investigate the concentration of the pollutants and their intensity of dispersion in relation to meteorological conditions in the study area such as wind speed, wind direction, temperature and humidity. The present study calculates emission concentration of nitrogen dioxide (NO2), sulphur dioxide (SO2), suspended particulate matter (SPM) and respirable suspended particulate matter (RSPM), from various monitoring stations and industries within the study area from the year 2016–2020. It was found that concentration of pollutants lie within the Central Pollution Control Board limits. Also, trend analysis of pollutant concentration was done separately for the year 2020 and there was a significant reduction (>50%) in pollution concentration due to the lockdown scenario created by COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Causal Graph and Social Network Analysis for the Spread of COVID-19 from Self-reported Indicator Data

Dynamic Bayesian Network (DBN) is an useful tool to learn the causal inference and social network of random variables. In this article, we analyze the correlations between the spread of coronavirus (COVID-19) and certain self-reported COVID-19 indicators in the United States, and then adopt DBN model with search and score-based approach to analyze and interpret the causal relationships and social network between these variables by learning the structure of the Directed Acyclic Graph from the model. We explore the change of causality among fifty states during the pandemic of COVID-19 in the year of 2020 and interpret the root cause for changes and trends. We concentrate on five worst states with COVID-19 and then extended our studies to all states by comparing the causal relationships and analyzing the patterns of DAG. © 2021 IEEE.

Using Bayesian Belief Network to Solve Abnormal Symptoms after Vaccination under Modern Information Technology

Rare side effects are weakening confidence in the vaccine. The question is how we interpret the data. Within 15 months after the discovery of the new coronavirus, a variety of effective and safe vaccines against the new coronavirus were available. After receiving the new coronavirus vaccine, some people developed facial paralysis, thigh pain, and even cerebral venous thrombosis. Although these side effects are very rare, and there is a lack of clarity whether there is a causal relationship with the vaccine or not, such news may undermine the confidence of the global vaccine. In order to maintain the confidence of the public, adverse events after vaccination are called ordinary events, and deaths occurring within a few days after vaccination are also interpreted as being caused by their latent diseases. From the following research, the issue of causality divides the vaccinated population into healthy groups and long-term patient groups, and use Bayesian belief network to analyze whether there are symptoms or abnormal events after vaccination as well as the probability distribution of rare illness, death, etc., in order to understand the relationship among each other. Therefore, suspending the administration of COVID vaccine is not a zero-risk option. The reality is that nothing is without risk. Measures to mitigate a risk must be balanced with competitive hazards. Risk seems to be an and vague concept. Risk can be reduced, but it can never be eliminated. The advantage of the Bayesian model is that it is easy to bring the data of various variables into the graph and calculate the posterior data from the known data to strengthen the persuasiveness of vaccination. By using Bayesian Network with PGM Module of Pytorch, the death probability of these two groups can be calculated under abnormal symptoms or without them. The simulation result of death after inoculation is lower than that of normal state without Covid-19 pandemic. © 2021 ACM.

Attitudes towards the use of social media, the use of video games and cell phones in primary school students in public schools for vulnerable groups

The purpose of the research was to establish the causal relationship between attitudes towards social media, the use of video games, in the attitude towards the use of cell phones, likewise, the relationship of influence of the attitude towards video games in the use of social media. The study was carried out in Peru, with students of the primary level of economic condition of extreme poverty, where the cell phone is the most recurrent device of communication and social interaction in times of pandemic due to the presence of Covid-19. The study aims to validate a relationship model between the independent and dependent variables, for this purpose the investigation was designed in two phases, the first comprises an exploratory factor analysis using the IBM-SPSS, the second, was the realization of a confirmatory analysis, for this the PLS-SEM methodology was used, which is a multivariate method called, Modeling of Structural Equations with Partial Least Squares. The model was validated with a sample of 71 students, whose ages range from 11 to 13 years, from the Moquegua region. The study would reveal causal relationships, especially between the attitude towards the use of video games and its influence on the use of social media, in the same way, the level of influence that independent variables would exert, such as the attitude towards the use of networks. social and the use of video games in the attitude towards the use of cell phones. © 2021 IEEE.

Relationship between loneliness, thought and performance in art at the time of the Covid-19

The purpose of this study was to test the relationship between thought and performance in art in plastic artists, family loneliness and romantic loneliness and their influence on social loneliness, this because there is a well-known myth in the world of the arts as in painting, where exceptional artists reach their full development in contexts of social loneliness, the study aims to validate a model of causal relationship, taking into account the context, characterized by social isolation. The study comprises two phases, firstly, an exploratory factor analysis using the IBM-SPSS, secondly, for the confirmatory analysis, the PLS-SEM methodology was used, which is a multivariate method called, Modeling of Structural Equations with Least Partial Squares. The model was validated with a sample of 157 artists in visual arts from the Arequipa, Cusco, Tacna, Moquegua and Lima regions of Peru. The study would reveal causal relationships between independent variables such as thought and performance in art, family loneliness and romantic loneliness in social loneliness as a dependent variable, experienced by the artists studied. © 2021 IEEE.