This is a forward-looking approach that uses a multiple-criteria decision analysis (MCDA) model as an assessment tool for risk identification. This study proposes an indifference threshold-based attribute ratio analysis and technique for order preference by similarity to an ideal solution (ITARA-TOPSIS)-based assessment model to identify critical failure modes in products and systems. The improved indifference threshold-based attribute ratio analysis (ITARA) method can generate more reliable weights for risk factors. In addition, the modified technique for order preference by similarity to an ideal solution (TOPSIS) is used to obtain the risk levels of the failure modes. The gray correlation coefficient is applied to replace the conventional Euclidean distance, and a new index is used to determine the priority of failure modes. The determination of risk factors is based on the failure mode and effect analysis (FMEA) theory, including severity, occurrence, and detection. An important indicator, the expected cost, is also included in the framework. The case of a steam turbine for a nuclear power plant is used to demonstrate the approach, and the analysis results show that the proposed model is practical and effective. Moreover, the advantages of our integrated model are illustrated through model comparisons and sensitivity analysis. This paper can help decision-makers, risk engineers, and related researchers to better understand how a systematic risk assessment can be conducted.

Improving people’s willingness to ride bicycles has become the main green transportation policy of the government in the world. Bikeability is an important factor affecting the willingness to ride. Since the urban riding environment is more complex than the suburbs, it is necessary to establish a complete urban bikeability evaluation framework. This study applies Bayesian BWM (Best Worst Method) and modified VIKOR to develop an urban bikeability evaluation framework. First, this study collects criteria affecting urban bikeability through literature review and experts’ surveys to develop a novel evaluation framework. Second, the Bayesian BWM was used to evaluate the relative weights of criteria and dimensions. Finally, the modified VIKOR was used to evaluate the riding environment of urban bicycle systems. The modified VIKOR replaces the relatively good concept as the aspiration level, which can effectively reflect the real situation. This study used two cities of Taiwan as case studies to demonstrate the usefulness and effectiveness of the proposed model. The results show that “completeness of facilities” is the most important dimension and “maintenance of bicycle pavements”, “width of bicycle lanes”, and “separation of bicycle lanes and car lanes” are the critical criteria. Based on the findings, some management implications and improving strategies are provided.

The impact of COVID-19 measures on airport performance is obvious, and there have been numerous studies on this topic. However, most of these studies discuss prevention measures, the effects on airport operations, forecasts of economic impacts, changes in service quality, etc. There is a lack of research on the effects of various prevention measures on airport operations and the interrelationships between these measures. This study focuses on addressing this gap. In this study, an integrated approach is devised that combines the decision-making trial and evaluation laboratory (DEMATEL) method and interpretive structural modeling (ISM). This integrated method is useful for exploring the relationship between pandemic measures and airport performance as well as the complex relationship between them, and the combination of methods improves upon the shortcomings of the original models. This study reveals that mandating vaccination certificates for entry into a country is the most significant measure affecting airport performance. Additionally, aircraft movement at the airport has the greatest overall impact and can be considered the most crucial factor influencing airport performance from an operational standpoint. The findings show that both factors directly influence financial performance, as reflected in the net income. Some management implications are provided to mitigate the consequences of the measures taken to counter the pandemic crisis. This integrated approach should also assist authorities and policy-makers in planning cautious action for future crises.

Construction accidents are predominantly rooted in human factors that cut across organizational, supervisory, and operational levels, yet traditional risk-assessment tools struggle to capture the multi-level causation and cognitive uncertainty involved. This study integrates the Human Factors Analysis and Classification System (HFACS) with an improved Failure Mode and Effects Analysis (FMEA) that adds Expected Cost as a fourth risk factor alongside Severity, Occurrence, and Detection. Interval-Valued Fermatean Fuzzy Sets (IVFFS) is used to capture the hesitancy of expert judgment; the Best–Worst Method (IVFFN-BWM) determines factor weights, and the Measurement of Alternatives and Ranking according to Compromise Solution (IVFFN-MARCOS) ranks fourteen failure modes. Severity (0.575) dominates the weighting structure, followed by Occurrence (0.218), Detection (0.141), and Expected Cost (0.066). Organizational Culture and Safety Climate, Personnel Physical and Mental Condition, and Environmental Factors emerge as the three most critical failure modes, while sensitivity analysis confirms the robustness of this ranking across a wide range of severity-weight scenarios. The proposed framework offers a theoretically grounded and practically actionable tool for prioritizing safety interventions under conditions of incomplete information.