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This paper aims to investigate prosperity–international tourism expenditure nexus to discover the prosperity sub-indices which affect tourism expenditure.

Using annual panel data for the sample period between 2009-2013 on 98 countries, this study implements a two-stage least squares estimation method with fixed effects specification in a panel regression analysis to find the relationships between international tourism expenditure and prosperity sub-indices.

The estimation results reveal a statistically significant relationship between the tourism expenditures of the citizens and prosperity, when prosperity is measured using its sub-indices, including Entrepreneurship and Opportunities, Government Efficiency, Education, Health, Safety and Security, Personal Freedom, Social Capital and Economy of the country. Education, Safety and Security and Health are the most significant factors which affect tourism expenditures of the country of origin.

To decrease the money outflow, policymakers may have plans to improve health infrastructure and, at the same time, increase quality of education and access to education in the country. Tourism policies which do not consider these prosperity sub-indices as explanatory variables may make mistakes in controlling actual tourism expenditures.

The paper’s originality lies in using new independent variables (prosperity sub-indices) in estimating tourism expenditure by using an appropriate panel data approach that deals with endogeneity problems.

In this study, to maximize returns and minimize investment risk, an attempt was made to form an optimal portfolio under conditions where the capital market has a price bubble. According to the purpose, the research was of the applied type, in terms of data, quantitative and postevent, and in terms of the type of analysis, it was of the descriptive-correlation type. Sequence, skewness and kurtosis tests were used to identify the months with bubbles from 2015 to 2021 in the Tehran Stock Exchange. After identifying the bubble courses, artificial bee colony meta-heuristic and invasive weed algorithms were used to optimize the portfolio. The purpose of this paper is to address these issues.

The existence of bubbles in the market, especially in the capital market, can prevent the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country. However, due to the goal of investors to achieve a portfolio of high returns with the least amount of risk, there is need to pay attention to these markets increases.

The results identify 14 periods of price bubbles during the study period. Additionally, stock portfolios with maximum returns and minimum risk were selected for portfolio optimization. According to the results of using meta-heuristic algorithms to optimize the portfolio, in relation to the obtained returns and risk, no significant difference was observed between the returns and risk of periods with price bubbles in each of the two meta-heuristic algorithms. This study can guide investors in identifying bubble courses and forming an optimal portfolio under these conditions.

One of the limitations of this research is the non-generalizability of the findings to stock exchanges of other countries and other time periods due to the condition of the price bubble, as well as other companies in the stock market due to the restrictions considered for selecting the statistical sample.

This study intends to form an optimal stock portfolio in a situation where the capital market suffers from a price bubble. This study provides an effective and practical solution for investors in the field of stock portfolio optimization.

This paper aims to present the most influential factors on classroom indoor PM_2.5 (Particulate Matter < 2.5 µ), determining the level of PM_2.5 concentration in five pre-schools located in the most densely populated district of the Tehran metropolitan area (district 6) as a case study to consider the children's exposure to air pollutants and introducing a suitable model, for the first time, to predict PM_2.5 concentration changes, inside pre-schools.

Indoor and outdoor classes PM_2.5 concentrations were measured using two DUSTTRAK direct-reading instruments. Additional class status information was also recorded; concurrently, urban PM_2.5 concentrations and meteorological data were obtained from the fixed monitoring stations and Meteorological Organization. Then, the predicted concentrations of the indoor PM_2.5, from introduced multiple linear regression model via SPSS, compared with the nearest urban air pollution monitoring stations data.

The average outdoor PM_2.5 concentration (43 ± 0.32 µg m⁻³) was higher than the mean indoor (32 ± 0. 21 µg m⁻³), and both were significantly (p < 0.001) surpassing the 24-h EPA standard level. The indoor PM_2.5 concentrations had the highest level in the autumn (48.7 µg m⁻³) and significantly correlated with the outdoor PM_2.5 (r = 0.94, p < 0.001), the number of pupils, ambient temperature, wind speed, wind direction and open area of the doors and windows (p < 0.001). These parameters, as the main determinants, have led to present a 7-variable regression model, with R² = 0.705, which can predict PM_2.5 concentrations in the pre-school classes with more than 80% accuracy. It can be presumed that the penetration of outdoor PM_2.5 was the main source of indoor PM_2.5 concentrations.

This study faced several limitations, such as accessibility to classrooms, and limitations in technicians' numbers, leading to researchers monitoring indoor and outdoor PM concentrations in schools once a week. Additionally, regarding logistical limitations to using monitoring instruments in pre-schools simultaneously, correction factors by running the instruments were applied to obtain comparable measurements.

The author hereby declares that this submission is his own work and to the best of its knowledge it contains no materials previously published or written by another person.