Pandemics, Income Inequality, and Refugees: The Case of COVID-19.

Refugees are more vulnerable to COVID-19 due to factors such as low standard of living, accommodation in crowded households, difficulty in receiving health care due to high treatment costs in some countries, and inability to access public health and social services. The increasing income inequalities, anxiety about providing minimum living conditions, and fear of being unemployed compel refugees to continue their jobs, and this affects the number of cases and case-related deaths. The aim of the study is to analyze the impact of refugees and income inequality on COVID-19 cases and deaths in 95 countries for the year 2021 using Poisson regression, Negative Binomial Regression, and Machine Learning methods. According to the estimation results, refugees and income inequalities increase both COVID-19 cases and deaths. On the other hand, the impact of income inequality on COVID-19 cases and deaths is stronger than on refugees.

The Relationship between Health Expenditures and Economic Growth in EU Countries: Empirical Evidence Using Panel Fourier Toda-Yamamoto Causality Test and Regression Models.

The aim of this study is to investigate the effect of health expenditures on economic growth in the period 2000-2019 in 27 European Union (EU) countries. First, the causality relationship between the variables was analyzed using the panel Fourier Toda-Yamamoto Causality test. The findings demonstrate a bidirectional causality relationship between health expenditures and economic growth on a panel basis. Secondly, the effects of health expenditures on economic growth were examined using the Random Forest Method for the panel and then for each country. According to the Random Forest Method, health expenditures positively affected economic growth, but on the country basis, the effect was different. Then, government health expenditures, private health expenditures, and out-of-pocket expenditures were used, and these three variables were ranked in order of importance in terms of their effects on growth using the Random Forest Method. Accordingly, government health expenditures were the most important variable for economic growth. Finally, Support Vector Regression, Gaussian Process Regression, and Decision Tree Regression models were designed for the simulation of the data used in this study, and the performances of the designed models were analyzed.

Thermoluminescence characteristics of calcite with a Gaussian process regression model of machine learning.

Thermoluminescence (TL) is defined as a luminescence phenomenon that can be detected when an insulator or semiconductor is thermally stimulated. Defective crystals store radiation until they are stimulated. Thermoluminescence is a method of monitoring the absorbed dose of dosimeters. The irradiation crystal is heated to 500°C to display the absorbed dose as a luminescent light. The TL dosimetric properties of calcite obtained from nature were investigated in this study. Machine learning was also examined using Gaussian process regression (GPR) for stimulated TL characteristics. According to the experimental output, the TL glow curve had two main peaks located at 90°C and 240°C with good dosimetric properties. In the four regression models of GPR, the data for the heating rate of 3°C s-1 have the lowest residual.

Socio-Economic, Demographic and Health Determinants of the COVID-19 Outbreak.

OBJECTIVE: In this study, the effects of social and health indicators affecting the number of cases and deaths of the COVID-19 pandemic were examined. For the determinants of the number of cases and deaths, four models consisting of social and health indicators were created. METHODS: In this quantitative research, 93 countries in the model were used to obtain determinants of the confirmed cases and determinants of the COVID-19 fatalities. RESULTS: The results obtained from Model I, in which the number of cases was examined with social indicators, showed that the number of tourists, the population between the ages of 15 and 64, and institutionalization had a positive effect on the number of cases. The results obtained from the health indicators of the number of cases show that cigarette consumption affects the number of cases positively in the 50th quantile, the death rate under the age of five affects the number of cases negatively in all quantiles, and vaccination positively affects the number of cases in 25th and 75th quantile values. Findings from social indicators of the number of COVID-19 deaths show that life expectancy negatively affects the number of deaths in the 25th and 50th quantiles. The population over the age of 65 and CO2 positively affect the number of deaths at the 25th, 50th, and 75th quantiles. There is a non-linear relationship between the number of cases and the number of deaths at the 50th and 75th quantile values. An increase in the number of cases increases the number of deaths to the turning point; after the turning point, an increase in the number of cases decreases the death rate. Herd immunity has an important role in obtaining this finding. As a health indicator, it was seen that the number of cases positively affected the number of deaths in the 50th and 75th quantile values and the vaccination rate in the 25th and 75th quantile values. Diabetes affects the number of deaths positively in the 75th quantile. CONCLUSION: The population aged 15-64 has a strong impact on COVID-19 cases, but in COVID-19 deaths, life expectancy is a strong variable. On the other hand, it has been found that vaccination and the number of cases interaction term has an effect on the mortality rate. The number of cases has a non-linear effect on the number of deaths.

Classification of thermoluminescence features of CaCO3 with long short-term memory model.

Calcium carbonate (CaCO3 ), a mineral commonly found in the Earth's crust, is mainly in the forms of calcite and aragonite. Calcite has the most stable type of thermodynamics at room temperature and ambient pressure. It has wide band gap structure and is of great interest for a wide-range technical and industrial applications due to its physical properties and suitability. In this study, a new method based on the long short-term memory (LSTM) model of deep learning is proposed to classify the thermoluminescence properties such as fading, cycle of measurement, heating rate, and dose-response of CaCO3 . Therefore the thermoluminescence properties of calcite was investigated as a suitable band structure and its coherent data were used to classify the features using a deep learning LSTM model. Experiments were carried out on a dataset consisting of four classes. The accuracy, precision, and sensitivity values of the proposed model obtained were 98.34, 97.90, and 98.56%, respectively. The classification process of the results obtained from the designed model showed good performance.

Role of the Health System in Combating Covid-19: Cross-Section Analysis and Artificial Neural Network Simulation for 124 Country Cases.

In the fight against Covid-19, developed countries and developing countries diverge in success. This drew attention to the discussion of how different health systems and different levels of health spending are effective in combating Covid-19. In this study, the role of the health system in the fight against Covid-19 is discussed. In this context, the number of hospital beds, the number of doctors, life expectancy at 60, universal health service and the share of health expenditures in GDP were used as health indicators. In the study, firstly 2020 data was estimated by using the Artificial Neural Networks simulation method and this year was used in the analysis. The model, with the data of 124 countries, was estimated using the cross-sectional OLS regression method. The estimation results show that the number of hospital beds, number of doctors and life expectancy at the age of 60 have statistically significant and positive effects on the ratio of Covid-19 recovered/cases. Universal health service and share of health expenditures in GDP are not significant statistically on the cases and recovered. Hospital bed capacity is the most effective variable on the recovered/case ratio.

Analysis of thermoluminescence characteristics of a lithium disilicate glass ceramic using a nonlinear autoregressive with exogenous input model.

Dental ceramics because of their translucency exemplify the most biologically realistic restorative materials for aesthetic rehabilitation and can be used to estimate dose accumulated as a result of a nuclear accident or attack. In this study, lithium disilicate ceramic obtained from Vivadent Ivoclar, Turkey was studied for its thermoluminescence (TL) properties. The lithium disilicate glass ceramic was irradiated with a 90 Sr-90 Y ß-source from 10 Gy to 6.9 kGy and the results read on a Harshaw 3500 reader. The TL peak of lithium disilicate ceramic showed sublinearity in the range 12 Gy to 6 kGy. The area under the TL glow curve increased by about 25% by the end of 10th measurement cycle. Fading values were also considered after irradiation. Lithium disilicate ceramic samples underwent 37% fading after 1 h and 59% fading after 1 week. In addition to the experimental study, a software-based simulation study was also undertaken using a MATLAB system identification tool. Experimental studies are generally time consuming and some materials used for experiments are very expensive. In this study, experimental, and simulation results were compared and produced almost the same outcome with a similarity of more than 98%.

TLD characteristic of glass, feldspathic and lithium disilicate ceramics.

Thermoluminescence (TL) emission of dental ceramics could be potentially used for retrospective dosimetry purposes as this allows a quick and reliable dose assessment in case of nuclear accident or bad use of a nuclear attack. This paper reports on the chemical and luminescence characterization of glass, feldspathic and lithium disilicate glass ceramic (LS2 ). Swedish and Turkish dental ceramics supplied by Vivadent Ivoclar considering: (i) the dose response in the range 10 Gy to 6.9 kGy which displays a linear dose-response at low dose values up to 36 Gy (glass and feldspathic ceramics) and shows sublinear behavior from 12 Gy to 6 kGy (lithium disilicate glass ceramics), (ii) a reproducibility of the TL signal in which the area under the glow curve increased about 25% after 10 cycles for glass and lithium disilicate ceramics and increased about 30% after seven cycles for feldspathic ceramics, (iii) stability of the luminescence emission with the elapsed time and (iv) effect of the heating rate. Glass, lithium disilicate and feldspathic ceramics display a complex UV-blue glow emission that can be respectively fitted to five and four groups of components assuming first-order kinetics behavior.