METHODS: We examined e-cigarette market data from the Euromonitor Global Market Information Database (GMID) Passport database, searched in the academic literature, grey literature and news archives for any reports or studies of e-cigarette related diseases or injuries, e-cigarette marketing, and e-cigarette policy responses in Southeast Asian countries, and browsed the websites of online e-cigarette retailers catering to the region's active e-cigarette markets.
RESULTS: In 2019, e-cigarettes were sold in six Southeast Asian markets with a total market value of $595 million, projected to grow to $766 million by 2023. E-commerce is a significant and growing sales channel in the region, with most of the popular or featured brands in online shops originating from China. Southeast Asian youth are targeted with a wide variety of flavours, trendy designs and point of sale promotions, and several e-cigarette related injuries and diseases have been reported in the region. Policy responses vary considerably between countries, ranging from strict bans to no or partial regulations.
CONCLUSION: Although Southeast Asia's e-cigarette market is relatively nascent, this is likely to change if transnationals invest more heavily in the region. Populous countries with weak e-cigarette regulations, notably Indonesia, Malaysia, Vietnam and the Philippines, are desirable targets for the transnationals. Regulatory action is needed to prevent e-cigarette use from becoming entrenched into these societies, especially among young people.
OBJECTIVES: In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace.
RESULTS: The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk.
CONCLUSION: Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.