STUDY DESIGN: The analytic framework for this scoping review was performed using the methodology outlined by Arksey and O'Malley, which includes identification of the research question, study selection, charting the data, collating, summarizing and reporting the results that were primarily guided by the research question; 'what is known about the initiation of shisha smoking among youth?'
METHODS: Electronic databases such as Cochrane, MEDLINE and PsycINFO were used to search for relevant articles. Articles included were all in English and published within the year of 2006 to 2015. Inclusion criteria; i) age range of 10 to 29 years; ii) examined the reasons why youth started or tried WTS; iii) in full text. Therefore, 26 articles were included in this scoping review.
RESULTS: This review has identified and classified the initiating factors of WTS among youth in four subtopics: individual factors, interpersonal influences, cigarettes and alcohol use, and media influences. Individual factors and interpersonal influences played an important role in initiation factors of WTS among youth.
CONCLUSION: This study concludes that public health professionals within the Southeast Asia region need to promote innovative preventive measures through peer-to-peer led interventions that are also easily assessable on social media platforms. The public health messages need to address the misconceptions of risk associated to WTS use.
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.