Only a few researchers have addressed the issue of lifetime exposure related to air pollutant concentration. This study aims to develop a methodology to obtain the most reliable estimates of historical concentrations of air pollutants, which would be further applied to the long-term exposure evaluation. In particular, PM₁₀, PM_2.5, NO₂, SO₂, benzene, and B(a)P concentrations have been obtained. Data of monitored concentrations, model calculations, and subsequent implementation of several corrections based on previous work on temporal and spatial correlations of these substances in the air have been deployed. This work makes an original contribution to the field of meteorology and epidemiology because of this innovative technique to estimate the most reliable historical concentrations of air pollutants. The novelty of our work lies in the additional implications of this study because historical concentration data serve as input data for the construction of epidemiological associations. The approach is based primarily on the availability of monitoring results of air pollutants.

By applying the methodological framework of transition modeling and econometric convergence tests introduced by Phillips and Sul, we reveal the existence of convergence clubs and transition convergence paths of international visitor arrivals for Australia. Specifically, by using monthly data of international arrivals over the period of January 1991 to September 2017, we provide evidence that tourism markets can integrate. The analysis suggests the identification of five distinct convergence clubs. This in turn signifies an integration phenomenon of Australia’s tourism market, which is revealed through the different convergence patterns of international visitor arrivals. Finally, it is evident that the revealed integration behavior of Australia’s international tourism market will enable policy makers to target better tourism needs through customized policies.

Purpose Transcranial sonography (TCS) magnetic resonance (MR) fusion imaging and digital image analysis are useful tools for the evaluation of various brain pathologies. This study aimed to compare the echogenicity of predefined brain structures in Huntington’s disease (HD) patients and healthy controls by TCS-MR fusion imaging using Virtual Navigator and digitized image analysis. Materials and Methods The echogenicity of the caudate nucleus (CN), substantia nigra (SN), lentiform nucleus (LN), insula, and brainstem raphe (BR) evaluated by TCS-MR fusion imaging using digitized image analysis was compared between 21 HD patients and 23 healthy controls. The cutoff values of echogenicity indices for the CN, LN, insula, and BR with optimal sensitivity and specificity were calculated using receiver operating characteristic analysis. Results The mean echogenicity indices for the CN (67.0±22.6 vs. 37.9±7.6, p<0.0001), LN (110.7±23.6 vs. 59.7±11.1, p<0.0001), and insula (121.7±39.1 vs. 70.8±23.0, p<0.0001) were significantly higher in HD patients than in healthy controls. In contrast, BR echogenicity (24.8±5.3 vs. 30.1±5.3, p<0.001) was lower in HD patients than in healthy controls. The area under the curve was 90.9%, 95.5%, 84.1%, and 81.8% for the CN, LN, insula, and BR, respectively. The sensitivity and specificity were 86% and 96%, respectively, for the CN and 90% and 100%, respectively, for the LN. Conclusion Increased CN, LN, and insula echogenicity and decreased BR echogenicity are typical findings in HD patients. The high sensitivity and specificity of the CN and LN hyperechogenicity in TCS-MR fusion imaging make them promising diagnostic markers for HD.

The automotive industry is widely considered to be crucial for the economy, as it reflects economic development in general. Despite interest in financial performance, few studies have considered paying attention to the ownership structure among stockholders. Hence, the study aims to find out how the degree of ownership concentration, measured through the independence indicator of the Bureau van Dijk, is reflected in the financial management of companies in the automotive industry among selected European countries. The generalized method of moments (GMM) technique is widely used while investigating panel data with a short estimating period, i.e. nine years annually in this case. However, this study reveals that, without deploying techniques, subsequently introduced a modified version of GMM estimators with panel data by providing an implementation using Stata statistical software. Otherwise, these particular econometric tools to analyze a dynamic panel can often give false significant estimates. Overall, liquidity seems to be significant in the case of firms with less concentrated ownership, whereas companies with a major owner are affected more by selected macroeconomic variables.