Institutional Environment and Tourism: Moderating Role of Host Countries' Health System

Tourism has gradually emerged to become a significant factor for sustainable economic growth around the globe. Along with other variables, the institutional environment has a significant influence on the growth of the tourism industry. Consequently, there are two primary goals for this study: firstly, to improve the body of knowledge already available on the institution-tourism nexus;secondly, to investigate how the health systems of the host countries contribute to the said nexus. To analyze these relations, we collected data from 50 countries across 10 years (2009-2018). We apply multiple regression analysis to a balanced panel dataset of 500 observations. Furthermore, we also used an interactive variable in conjunction with the primary independent, dependent, and control variables of the study to determine the moderating effect of the host countries' health systems. The study's findings demonstrated the significance of an efficient institutional structure in boosting tourism. Additionally, the health systems of the host nations play a significant role in strengthening the connection between institutions and tourism, especially in the post-COVID-19 period. Through the creation of institutional frameworks and health infrastructure, the study's findings will assist policymakers in developing efficient tourism policies. All of the above strategies will eventually lead to a trustworthy, safe, and healthy environment for both locals and visitors.

Loop Residual Attention Network for Automatic Segmentation of COVID-19 Chest X-ray Images

As COVID-19 continues to put pressure on the global healthcare industry, using artificial intelligence to analyze chest X-rays (CXR) has become an effective way to diagnose the virus and treat patients. Despite that many studies have made significant progress in COVID-19 detection, accurately segmenting infected regions with variable locations and scales from COVID-19 CXR remains challenging. Therefore, this paper proposes a novel framework for COVID-19 CXR image segmentation. Specifically, we design a loop residual module to cyclically extract feature information in the process of encoding and decoding splicing, avoiding the loss of complex semantic information in network computing. At the same time, an absolute position information coding block is proposed to strengthen the position information of feature pixels. Moreover, a hybrid attention module is designed to establish semantic associations between channels and multi-scale spaces. Better feature representation is formed by the fusion of location and scale information to alleviate the impact of variable infection regions on segmentation performance. Extensive experiments are conducted on the public COVID-19 CXR dataset COVID-Qu-Ex, and the results show that our network is leading and robust compared to other networks in COVID-19 segmentation. Author