A Hybrid Use of Soft Systems Methodology for Developing a Framework of Evidence-Based Teaching for Hospitality and Tourism Instructors in Vietnam.

This paper adopts the hybrid use of soft systems methodology (SSM) as a process of inquiry into understanding the lack of a framework for evidence-based teaching (EBT) in hospitality and tourism education in Vietnam. By combining SSM techniques with interview data, we also develop an EBT framework for the hospitality and tourism profession. The proposed framework addresses three essential sources of evidence for teaching: (1) research-based professional and pedagogical methods, (2) industry-based materials to ensure education-industry linkage, and (3) instructors' knowledge, experience and assumptions about their teaching roles in the classroom. This conceptual framework can be used as a guideline for conducting relevant curriculum renewal and pedagogical reforms in hospitality and tourism institutions in Vietnam.

Evidence-based recommender system for high-entropy alloys.

Existing data-driven approaches for exploring high-entropy alloys (HEAs) face three challenges: numerous element-combination candidates, designing appropriate descriptors, and limited and biased existing data. To overcome these issues, here we show the development of an evidence-based material recommender system (ERS) that adopts Dempster-Shafer theory, a general framework for reasoning with uncertainty. Herein, without using material descriptors, we model, collect and combine pieces of evidence from data about the HEA phase existence of alloys. To evaluate the ERS, we compared its HEA-recommendation capability with those of matrix-factorization- and supervised-learning-based recommender systems on four widely known datasets of up-to-five-component alloys. The k-fold cross-validation on the datasets suggests that the ERS outperforms all competitors. Furthermore, the ERS shows good extrapolation capabilities in recommending quaternary and quinary HEAs. We experimentally validated the most strongly recommended Fe-Co-based magnetic HEA (namely, FeCoMnNi) and confirmed that its thin film shows a body-centered cubic structure.

A satisfactory-oriented approach to multiexpert decision-making with linguistic assessments.

This paper proposes a multiexpert decision-making (MEDM) method with linguistic assessments, making use of the notion of random preferences and a so-called satisfactory principle. It is well known that decision-making problems that manage preferences from different experts follow a common resolution scheme composed of two phases: an aggregation phase that combines the individual preferences to obtain a collective preference value for each alternative; and an exploitation phase that orders the collective preferences according to a given criterion, to select the best alternative/s. For our method, instead of using an aggregation operator to obtain a collective preference value, a random preference is defined for each alternative in the aggregation phase. Then, based on a satisfactory principle defined in this paper, that says that it is perfectly satisfactory to select an alternative as the best if its performance is as at least "good" as all the others under the same evaluation scheme, we propose a linguistic choice function to establish a rank ordering among the alternatives. Moreover, we also discuss how this linguistic decision rule can be applied to the MEDM problem in multigranular linguistic contexts. Two application examples taken from the literature are used to illuminate the proposed techniques.