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The personnel selection is a vital activity for companies, and multi-valued neutrosophic sets (MVNSs) can denote the fuzziness and hesitancy in the processes of the personnel selection. The extant fuzzy TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) methods take advantage of distance to denote the difference between two fuzzy sets (FSs). Nevertheless, the distance measurement, which ignores the included angle between two FSs, cannot comprehensively reflect the difference between two FSs. To cover this defect, a projection-based TODIM method with MVNSs for personnel selection is established to consider the risk preference of decision-makers and overcome the defect of the extant fuzzy TODIM methods. The proposed TODIM method makes use of an improved comparison method which overcomes the deficiency of extant comparison method. Furthermore, a projection-based difference measurement is defined and utilized in the projection-based TODIM method. We conduct a numerical example of the personnel selection to explain the application of the projection-based TODIM method and discuss the influence of the parameter. Finally, the proposed method is compared with several extant methods to verify its feasibility.

Background:The effect of interdomain interactions on the thermostability of microbial hormone-sensitive lipases (HSLs) remains unclear. Results: The absence of interdomain hydrophobic interactions between loop 1 and ␣7 leads to the thermolability of E40, a thermolabile HSL esterase. Conclusion: Interdomain hydrophobic interactions are a key element for the thermostability of microbial HSLs. Significance: Our study is helpful for protein engineering of thermolabile HSLs.

h i g h l i g h t sA novel restaurant decision support model for TripAdvisor.com is established. Fuzzy sets are introduced to describe tourists' review information. A similarity method is developed to deal with sparse data. Bonferroni mean are introduced to consider interdependence among criteria. The model is tested through a case study of TripAdvisor.com. a b s t r a c tDecision support models for satisfactory restaurants have attracted numerous researchers' attention. Many extant models do not consider the active, neutral and passive information in online reviews all at once. Moreover, they ignore the effect of interdependence among criteria on tourists' decision-making. To cover these defects, this study proposes a restaurant decision support model using social information for tourists on TripAdvisor.com. The model introduces fuzzy sets to denote online reviews and utilizes Bonferroni mean to consider interdependence among criteria. Furthermore, it uses a novel similarity measurement which can handle sparse data in fuzzy environments. To validate the model, we conduct a case study of TripAdvisor.com which compares the proposed model with four other models. The performance of each model is evaluated by the metric called the mean absolute error. The study shows that the proposed model can effectively support tourists' decision-making and it performs better than the other four models.

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