ABSTRACT
Emergency events are happening with increasing frequency, inflicting serious damage on the economic development and human life. A reliable and effective emergency decision making method is great for reducing various potential losses. Hence, group emergency decision making (GEDM) has drawn great attention in past few years because of its advantages dealing with the emergencies. Due to the timeliness and complexity of GEDM, vagueness and regret aversion are common among decision makers (DMs), and decision information usually needs to be expressed by various mathematical forms. To this end, this paper proposes a novel GEDM method based on heterogeneous probabilistic hesitant information sets (PHISs) and regret theory (RT). Firstly, the PHISs with real numbers, interval numbers and linguistic terms are developed to depict the situation that decision group sways precariously between several projects and best retain the original assessment. In addition, the score functions, the divergence functions and some operations of the three types of PHISs are defined. Secondly, the normalization model of PHISs is presented to remove the influence of different dimensions on information aggregation. Thirdly, group satisfaction degree (GSD) based on the score functions and the divergence functions is combined with RT for completely portraying the regret perception of decision group. Then, we introduce Dempster-Shafer (DS) theory to determine the probabilities of future possible states for emergency events. Finally, an example of coronavirus disease 2019 (COVID-19) situation is given as an application for the proposed GEDM method, whose superiority, stability and validity are demonstrated by employing the comparative analysis and sensitivity analysis. © 2022 - IOS Press. All rights reserved.
ABSTRACT
OBJECTIVE: To compare three sterilizing methods for reusable medical goggles. METHODS: A total of 180 medical goggles of the same brand and same model were randomly divided into three groups. In group A the goggles were first soaked with 2000 mg/L chlorine-containing disinfectant and then cleaned manually; goggles in other two groups were sterilized using pre-programmed automatic spray cleaning and disinfection machine, the disinfection program was set to 90 â for 5 min in group B and 70 â for 30 min in group C. The quality of the sterilization was monitored by visual inspection with luminous magnifying glass and residual protein detection assay. User satisfaction on cleanliness of medical goggles, clarity of mirror surface and suitability of elastic bands was investigated with questionnaire survey. RESULTS: The qualification rates verified by visual inspection were 82.4%, 84.6%and 98.3%in group A, B and C, respectively, the qualification rate in group C was significantly higher than those in group B and group C (all P<0.05). The qualification rates verified by residual protein detection assay were 96.7%, 100.0%and 100.0%in group A, B and C, respectively (P>0.05). A total of 54 questionnaires were submitted for the survey. The satisfaction rates were 100.0%, 90.7%and 94.4% for cleanliness of medical goggles, clarity of mirror surface and suitability of elastic bands, respectively. CONCLUSIONS: Machinery sterilization set 70 â for 30 min has better cleaning and sterilizing effects for reusable medical goggles.