(2004). Anatolian Tigers or Islamic Capital: Prospects and Challenges. Middle Eastern Studies: Vol. 40, No. 6, pp. 166-188.

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.

In recent years there has been a great deal of research into environmental pollution using a variety of techniques in response to growing environmental concerns. Convergence analysis, one of these techniques, helps determine whether the developing countries will catch up with the rich countries in pollution using unit root tests. However, the vast majority of the research in the field has generally used conventional unit root tests. Since many economic series contain structural breaks, using unit root tests that account for structural breaks is essential for accurate prediction. More specifically, if the series has a fractional process, conventional unit root tests may erroneously conclude that the departure from linearity is permanent. Moreover, the existing literature mainly uses gas emissions, such as carbon dioxide, which represent pollution weakly. Therefore, we use per capita ecological footprint (EF hereafter) as a more comprehensive pollution indicator of environmental degradation. In this direction, the study aims to determine whether BRICS-T countries' EF converges to the average of the BRICS-T for the 1992–2017 period. Besides the ADF unit root test, we employed the Fourier ADF unit root test, which considers the structural breaks, and the Fractional Frequency Fourier ADF unit root test, which accounts for structural breaks by considering fractional values. Our results showed that EF converges in Russia and Turkey according to the conventional ADF test, in China and Russia according to the Fourier ADF test, and in Brazil and China according to the Fractional Fourier Frequency test.

Alzheimer’s disease will affect more people with increases in the elderly population, as the elderly population of countries everywhere generally rises significantly. However, other factors such as regional climates, environmental conditions and even eating and drinking habits may trigger Alzheimer’s disease or affect the life quality of individuals already suffering from this disease. Today, the subject of biomedical engineering is being studied intensively by many researchers considering that it has the potential to produce solutions to various diseases such as Alzheimer’s caused by problems in molecule or cell communication. In this study, firstly, a molecular communication model with the potential to be used in the treatment and/or diagnosis of Alzheimer’s disease was proposed, and its results were analyzed with an artificial neural network model. Secondly, the ratio of people suffering from Alzheimer’s disease to the total population, along with data of educational status, income inequality, poverty threshold, and the number of the poor in Turkey were subjected to detailed distribution analysis by using the random forest model statistically. As a result of the study, it was determined that a higher income level was causally associated with a lower risk of Alzheimer’s disease.