Search Results (261)

The frequent occurrence of fires has prompted China to accelerate the development of community fire prevention and emergency management systems. Language, serving both communicative and affective functions by facilitating the flow of information and fostering mutual understanding, runs through the entire process of community fire emergency management. In response to the early-stage nature of this field and the lack of a systematic framework, this study constructs a dynamic capability evaluation system for urban community fire-related emergency language services (FELS) by integrating multi-source and heterogeneous data. First, by adopting a hybrid approach combining dynamic capability theory and text mining, a three-level indicator system is established. Second, based on domain knowledge, quantitative methods and scoring rules are designed for the third-level qualitative indicators to provide standardized input for the model. Third, a weighting and integration framework is developed that simultaneously considers the internal mechanism characteristics and statistical properties of indicators. Specifically, a knowledge-driven weighting approach combining FAHP and fuzzy DEMATEL is employed to characterize indicator importance and interrelationships, while the CRITIC method is used to extract Data-Driven weights based on data dispersion and information content. These knowledge-driven and Data-Driven weights are then integrated through a multi-feature fusion weighting approach. Finally, a linear weighting model is applied to combine the normalized indicator values with the integrated weights, enabling a systematic evaluation of the dynamic capabilities of community FELS. To validate the proposed framework,, application tests were conducted in four representative types of urban communities, including internationally developed, aging and vulnerable, newly developed, and economically diverse communities, using fire emergency scenarios as the entry point. The external validity and internal robustness of the proposed model were verified through these tests. The results indicate that the evaluation system provides accurate, objective, and adaptive assessments of dynamic capabilities in FELS across different community contexts, offering a governance-oriented quantitative tool to support grassroots fire prevention and to enhance community resilience. Full article

Against the backdrop of artificial intelligence (AI) empowering the medical industry, achieving symmetric coordination between patients and medical intelligent systems has emerged as a key factor in enhancing the efficacy of medical human–computer collaborative diagnosis. This study systematically identified the factors influencing the effectiveness of human–machine collaborative diagnosis in healthcare by combining literature analysis with expert interviews, based on the Socio-technical Systems Theory. It constructed a symmetric evaluation framework consisting of 19 indicators across four dimensions: user, technology, task, and environment. An integrated DEMATEL method incorporating symmetric logic was employed to quantitatively analyze the interdependent relationships among factors and identify 18 key factors. Subsequently, ISM was applied to analyze the dependency relationships between these key factors, thereby constructing a clear multi-level hierarchical structure model. Through hierarchical construction of a multi-level hierarchical structure model, four core paths driving diagnostic effectiveness were revealed. The research shows that optimizing user behavior mechanisms and technology adaptability and strengthening dynamic coordination strategies between tasks and the environment can effectively achieve the two-way symmetric mapping of the medical human–machine system from fuzzy decision-making to precise output. This has not only improved the efficacy of medical human–computer collaborative diagnosis, but also provided a theoretical basis and practical guidance for optimizing the practical application of medical human–computer collaborative diagnosis. Full article

Construction robots (CRs) are regarded as a promising solution to improve productivity, safety, and labor efficiency in the construction industry, yet their adoption remains limited. Although several studies have attempted to identify promotion strategies for CR adoption, few have explored the dynamic interdependencies among them. Using China as a case study, this research develops a hybrid framework integrating DEMATEL, Interpretive Structural Modeling (ISM), and a Fuzzy Bayesian Network (FBN) to examine the causal mechanisms, structural hierarchies, and dynamic sensitivities of strategies for promoting CR adoption. The results identify two strategies as the most influential, which exert broad effects on other strategies. ISM results indicate that three strategies, which are all financial related, serve as underlying causes that fundamentally support the overall CR adoption pathway. Furthermore, both DEMATEL and FBN analyses highlight that establishing standardized systems for CRs covering functionality, performance, and safety and improving CR compatibility with other intelligent construction technologies are the most critical strategies, as they achieved the highest integrated scores across sensitivity and importance dimensions, indicating their pivotal role in driving system-wide improvements. The findings provide valuable insights for policymakers and industry practitioners to better understand and implement multidimensional strategies to improve robot adoption in the construction industry. Full article

In the modern business environment, where uncertainty and complexity make decision-making difficult, the need for robust, transparent and adaptable support tools is highlighted. The proposed method, named Flexible Decision Navigator for Evaluating Trends and Strategies (F-DeNETS), offers a complementary perspective to classic Artificial Intelligence (AI), Big Data and Multi-Criteria Decision-Making (MCDM) tools. Despite their broad use, these methods frequently suffer from critical sensitivities in the weighting of criteria and the handling of uncertainty, leading to compromised reliability and limited practical utility in environments with limited data availability. To bridge this gap, F-DeNETS integrates intuition and uncertainty into a transparent and statistically grounded process. It introduces a balanced approach that combines statistical evidence with human judgment, extending the boundaries of classic AI, Big Data and MCDM methods. Classic MCDM methods, although useful, are sometimes limited by subjectivity, staticity and dependence on large volumes of data. To fill this gap, F-DeNETS, a hybrid framework combining Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Non-Asymptotic Fuzzy Estimators (NAFEs) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), transforms expert judgments into statistically sound fuzzy quantifications, incorporates dynamic adaptation to new data, reduces bias and enhances reliability. A numerical application from the shipping industry demonstrates that F-DeNETS offers a flexible and interpretable methodology for optimal decisions in environments of high uncertainty. Full article

Nervousness results from variance and changes in the verdicts of supply and logistics networks and activities. Nervousness is considered a source of confusion in supply chain (SC) systems because it is associated with frequent decision changes. New SC techniques are necessary to handle the growing supply chain nervousness (SCN) from globalization. Although they can be challenging to create, SCN metrics are crucial for assessing and optimizing the operations of a SC. The evaluation of SCN and future improvements in SC performance depend on correctly identifying SCN metrics. In this study, a method for measuring SCN was proposed, and a model was developed. The SCN measurement model seeks to quantify SCN for inclusion in the SC structure to support decision making. To assist organizations in determining the effect of nervousness on SCs and enhancing their general performance and competitiveness, this study quantified SCN, defined SCN metrics, and modeled and assessed SCN indicators. The model includes key SCN measurements, simulating, and evaluation, which can enhance future SC performance and resilience by enabling more precise SCN quantification. The importance of the designated SCN metrics was then determined using a fuzzy decision-making trial and evaluation-laboratory method (FDEMATEL). This method was used to evaluate and resolve complicated, interrelated scenarios, as it can demonstrate how metrics are interdependent and form a map that illustrates their relative relationships. The findings distinguish between cause and effect measurements as well as their interactions. Additionally, the results show the importance of the rankings of the SCN measurements. These outcomes can be used to establish a solid foundation for developing effective decision-making tools for SCN. Full article

The urgent global challenge of environmental sustainability has intensified interest in integrating Lean Management practices with environmental objectives, particularly within the automotive industry, a sector known for both innovation and high environmental impact. This study investigates the systemic relationships between 16 lean practices and three environmental performance metrics: energy consumption, CO₂ emissions, and waste generation. Using the Fuzzy Decision-Making Trial And Evaluation Laboratory (DEMATEL) methodology, data were collected from seven lean experts in the Italian automotive industry to model the cause–effect dynamics among the selected practices. The analysis revealed that certain practices, such as Total Productive Maintenance (TPM), just-in-time (JIT), and one-piece-flow, consistently act as influential drivers across all environmental objectives. Conversely, practices like Statistical Process Control (SPC) and Total Quality Management (TQM) were identified as highly dependent, delivering full benefits only when preceded by foundational practices. The results suggest a strategic three-step implementation roadmap tailored to each environmental goal, providing decision-makers with actionable guidance for sustainable transformation. This study contributes to the literature by offering a structured perspective on lean and environmental sustainability in the context of the automotive sector in Italy. The research is supported by a data-driven method to prioritize practices based on their systemic influence and contextual effectiveness. Full article

As a common coal mine disaster, a coal and gas outburst in coal mining seriously threatens the safety production of coal mines with its sudden and destructive nature. In order to accurately identify the main influencing factors of a coal and gas outburst in coal mines and assess the risk level of a coal and gas outburst, 12 indicators are established from three aspects: coal seam gas factors, coal seam physical and mechanical properties, and in situ stress state. This study introduces the fuzzy set theory on the basis of the DEMATEL and combines it with the TOPSIS to establish a fuzzy DEMATEL-TOPSIS risk assessment model. The model was applied to conduct a comprehensive evaluation of the coal and gas outburst in the 3908 working face of a coal mine in Jiangxi Province so as to determine the risk level of coal and gas outburst. The results show that, sorted by weight in descending order, the main influencing factors are gas pressure (0.105), in situ stress (0.101), gas content (0.098), burial depth (0.090), and geological structure type (0.087). The hazard grade identification of coal and gas outburst at the working face is Level II (with a relative approximation degree of 0.270), which is consistent with the actual situation. It can provide a reference for the prevention and control of coal and gas outbursts. Full article

With the rapid increase in global trade in recent years, the demand for maritime transportation has significantly intensified vessel activity, leading to a considerable rise in carbon emissions originating from the maritime sector. As a result, in line with the 2050 decarbonization targets set by the International Maritime Organization (IMO) and the European Union (EU), legal regulations addressing carbon emissions have been dynamically tightened and gradually enacted. This study aims to determine the significance levels of the factors affecting the maritime sector in response to carbon emission regulations and to reveal the interrelationships among these factors. In this context, the criteria regarding the impacts of climate-related carbon emission regulations were identified based on expert opinions using the Fuzzy Delphi method. The interaction strengths and significance levels among the factors were analyzed using the Fuzzy DEMATEL method, and the relationships were modeled through Interpretive Structural Modeling (ISM). According to the findings, “Fuel Preferences and Alternative Fuel Usage” (C2) emerged as the most critical factor under recent international regulations. “Adaptation to International and National Regulations” (C8) and “Port Infrastructure” (C3) were also identified as the key factors impacting shipping industry efficiency. The analysis revealed that “Logistics Costs” (C5) and “Environmental Protection and Sustainability” (C7) are the most significantly affected outcome factors within the system. The hierarchical structural modeling revealed that “Port Infrastructure” (C3) serves as a defining starting point within the system. This study contributes to the literature by uncovering the causal relationships among the factors determining the effectiveness of ever-evolving carbon emission regulations. It offers a valuable decision-support tool for maritime companies and policymakers. Accordingly, it provides an alternative roadmap and a structural model indicating which strategic areas should be prioritized to achieve the targeted low-carbon emission goals in maritime transportation. Full article

Gamified recommender systems, which mix game design with recommendation frameworks, are a new way to increase user involvement and satisfaction. Even though they have a lot of potential, there has not been any systematic research on how their design affects how people use them. This study introduces a fuzzy DEMATEL-based framework for the assessment and enhancement of gamified recommender systems. Four theoretically grounded gamified recommender system prototypes were developed as a novel contribution, as no readily available systems exist for these designs. The assessment utilized nine user-centric criteria—Effectiveness, Transparency, Persuasiveness, Satisfaction, Trust, Usefulness, Ease of Use, Efficiency, and Education—systematically derived from a PRISMA-guided literature review. This study integrates gamification theory, systematic review, and fuzzy decision-making to formulate a comprehensive framework for identifying the key factors influencing adoption. The fuzzy DEMATEL was applied to evaluate feedback from 25 end-users, and it was found that usefulness and ease of use were the most essential factors for satisfaction and system effectiveness. Analysis of design showed that competition in Points, Badges, and Leaderboards (PBL) design boosts short-term motivation, Acknowledgments, Objectives, and Progression (AOP) boosts progress and openness, Acknowledgments, Competition, and Time Pressure (ACT) boosts efficiency in competitive situations but might lower satisfaction, and Acknowledgments, Objectives, and Social Pressure (AOS) depends on social influence and accountability. Full article

Traditional multi-criteria decision-making (MCDM) methods face problems in sustainable product design evaluation, including weak semantic expression, single weight modeling, and insufficient ranking robustness. To address these issues, this paper proposes an evaluation framework based on Trapezoidal Intuitionistic Fuzzy (TrIF), named TrIF-DEC, which integrates Decision-Making Trial and Evaluation Laboratory (DEMATEL), Entropy, and Combined Compromise Solution (CoCoSo). Firstly, design criteria across four dimensions—social, economic, technological, and environmental—are identified based on sustainability considerations. Then, TrIF is used to capture the fuzziness and hesitation in expert judgments. The DEMATEL and Entropy methods are combined to extract causal relationships between criteria and quantify data variation, enabling the collaborative weighting of subjective and objective factors. Finally, multi-strategy integrated ranking is performed through TrIF-CoCoSo to enhance decision stability. An empirical case study on nursing bed design demonstrates the effectiveness of the proposed framework. Results demonstrate that TrIF-DEC can systematically integrate uncertainty information with multidimensional sustainability goals, providing reliable support for complex product design evaluation. Full article

In the digital context, how to achieve symmetrical integration between subjective evaluation and structural stability becomes the key to improving the design effect of iterative product optimization. In this paper, we propose an iterative design method for CIHFS-DEMATEL products that incorporates structural symmetry analysis. The method is based on online review mining and constructs a credibility-based interval hesitant fuzzy set (CIHFS) to symmetrically express the ambiguity and credibility differences in the decision-maker’s subjective evaluation. In turn, a novel exact score function called credibility interval hesitant fuzzy score function (CHFSF), incorporating information symmetric weights, is proposed to realize the bidirectional symmetric mapping between subjective fuzzy inputs and objective exact outputs. Subsequently, the CIHFS-DEMATEL model is introduced to identify the causal paths and a symmetric interaction structure between potential users’ demands. Finally, the demand module mapping matrix is constructed to realize the symmetric decision-making closure loop from demand to solution. Taking the “Intelligent Classified Trash Can” as a case study, we verify the superiority of the method in terms of recognition accuracy, rationality of weight allocation, and structural stability. This study emphasizes the structural symmetry between “input–evaluation–output”, which provides a theoretical foundation and practical framework for the optimal design of products with complex multi-source information. Full article

As climate variability intensifies, its impacts are increasingly visible through disrupted agricultural systems and rising food insecurity, especially in climate-sensitive regions. This study explores the complex relationships between environmental stressors, such as rising temperatures, erratic rainfall, and soil degradation, with food insecurity outcomes in selected districts of Uttarakhand, India. Using the Fuzzy DEMATEL method, this study analyzes 19 stressors affecting the food supply chain and identifies the nine most influential factors. An Environmental Stressor Index (ESI) is constructed, integrating climatic, hydrological, and land-use dimensions. The ESI is applied to three districts—Rudraprayag, Udham Singh Nagar, and Almora—to assess their vulnerability. The results suggest that Rudraprayag faces high exposure to climate extremes (heatwaves, floods, and droughts) but benefits from a relatively stronger infrastructure. Udham Singh Nagar exhibits the highest overall vulnerability, driven by water stress, air pollution, and salinity, whereas Almora remains relatively less exposed, apart from moderate drought and connectivity stress. Simulations based on RCP 4.5 and RCP 8.5 scenarios indicate increasing stress across all regions, with Udham Singh Nagar consistently identified as the most vulnerable. Rudraprayag experiences increased stress under the RCP 8.5 scenario, while Almora is the least vulnerable, though still at risk from drought and pest outbreaks. By incorporating crop yield models into the ESI framework, this study advances a systems-level tool for assessing agricultural vulnerability to climate change. This research holds global relevance, as food supply chains in climate-sensitive regions such as Africa, Southeast Asia, and Latin America face similar compound stressors. Its novelty lies in integrating a Fuzzy DEMATEL-based Environmental Stressor Index with crop yield modeling. The findings highlight the urgent need for climate-informed food system planning and policies that integrate environmental and social vulnerabilities. Full article

Distance learning has become essential to higher education, yet its application in military officer training presents unique academic, operational, and security challenges. For Lithuania’s future officers, remote education must foster not only knowledge acquisition but also decision-making, leadership, and operational readiness—competencies traditionally developed in immersive, in-person environments. This study addresses these challenges by integrating System Dynamics Modelling, Contemporary Risk Management Standards (ISO 31000:2022; Dynamic Risk Management Framework), and Learning Analytics to evaluate the interdependencies among twelve critical factors influencing the system resilience and effectiveness of distance military education. Data were collected from fifteen domain experts through structured pairwise influence assessments, applying the fuzzy DEMATEL method to map causal relationships between criteria. Results identified key causal drivers such as Feedback Loop Effectiveness, Scenario Simulation Capability, and Predictive Intervention Effectiveness, which most strongly influence downstream outcomes like learner engagement, risk identification, and instructional adaptability. These findings emphasize the strategic importance of upstream feedback, proactive risk planning, and advanced analytics in enhancing operational readiness. By bridging theoretical modelling, contemporary risk governance, and advanced learning analytics, this study offers a scalable framework for decision-making in complex, high-stakes education systems. The causal relationships revealed here provide a blueprint not only for optimizing military distance education but also for enhancing overall system resilience and adaptability in other critical domains. Full article

The complexities of megaprojects, particularly major transportation infrastructure projects (MTIs), require technological innovation that advances economic, social, and ecological objectives. Traditional engineering innovation emphasizes economic gains while neglecting sustainability. Therefore, implementing green innovation (GI) in MTIs is essential. This research examines key factors and correlations influencing MTI-GI to strengthen theoretical understanding and guide effective implementation. First, literature and interviews are used to identify MTI-GI influencing factors through the technology–organization–environment (TOE) framework. Second, an intuitive fuzzy number approach reduces subjectivity in expert scoring and, combined with the DEMATEL method, constructs a fuzzy DEMATEL model to quantify factor importance and identify critical drivers. Critical factors are then analyzed to formulate GI promotion strategies. Results reveal that MTI-GI influencing factors span technology, organization, and environment dimensions. Prioritizing green technological innovation and feedback mechanisms, optimizing organizational structures, and aligning with regional environmental characteristics are crucial for successful MTI-GI implementation. These findings support GI expansion in MTIs and offer targeted strategies for managing complex systems. Full article

To enhance the safety risk management and control capabilities for TBM (Tunnel Boring Machine) construction in hydraulic tunnels, this study conducts a correlation analysis and dynamic evolution study of safety risks. Data were collected through multiple channels, including a literature review, on-site records, and expert interviews. Grounded theory was employed for three-level coding to initially identify risk factors, and gray relational analysis was used for indicator optimization, ultimately establishing a safety risk system comprising 5 categories and 21 indicators. A multi-level hierarchical structure of risk correlation was established using fuzzy DEMATEL and ISM, which was then mapped into a Bayesian network (BN). The degree of correlation was quantified based on probabilistic information, leading to the construction of a risk correlation analysis model based on fuzzy DEMATEL–ISM–BN. Furthermore, considering the risk correlations, a safety risk evolution model for TBM construction in hydraulic tunnels was developed based on system dynamics. The validity of the model was verified using the AY project as a case study. The results indicate that the safety risk correlation structure for TBM construction in hydraulic tunnels consists of 7 levels, with the closest correlation found between “inadequate management systems” and “failure to implement safety training and technical disclosure”. As the number of interacting risk factors increases, the trend of risk level evolution also rises, with the interrelations within the management subsystem being the key targets for prevention and control. The most sensitive factors within each subsystem were further identified as adverse geological conditions, improper construction parameter settings, inappropriate equipment selection and configuration, weak safety awareness, and inadequate management systems. The control measures proposed based on these findings can provide a basis for project risk prevention and control. The main limitations of this study are that some probability parameters rely on expert experience, which could be optimized in the future by incorporating more actual monitoring data. Additionally, the applicability of the established model under extreme geological conditions requires further verification. Full article