Barriers to micro/small/medium enterprises solar energy adoption: An integrated hyperbolic fuzzy decision system for prioritization

BackgroundCivil aviation cargo operations have expanded rapidly, but the occupational health and safety risks faced by cargo workers are still rarely examined through an integrated causal framework that captures chemical, ergonomic, psychosocial, and operational exposures together.ObjectiveThis study aims to identify, prioritize, and interpret the causal relationships among occupational health and safety risks in civil aviation cargo operations from a worker-centered perspective.MethodsThe study employs a comprehensive dataset drawn from industry professionals and applies the Fermatean Fuzzy Decision-Making Trial and Evaluation Laboratory (FF-DEMATEL) method. This approach enables the analysis of complex interrelationships among risk factors, offering a systematic framework for understanding the dynamics of aviation cargo hazards. FF-DEMATEL was applied to 16 cargo-related risk factors evaluated by three occupational safety experts. Expert weights were derived through a machine-learning-based dimensionality reduction procedure using age, occupational safety experience, and firm tenure, enabling the model to reflect both interdependence and expert heterogeneity.ResultsThe analysis reveals a network of critical risks, including improper cargo loading, closed storage conditions, hazardous substances, and unpredictable customer demands. The FF-DEMATEL method identifies both cause and effect relationships among these factors, highlighting which risks exert the greatest influence on overall safety outcomes. The model provides a clear hierarchy of risk sources that require targeted intervention. The leading weighted risks were sabotage, time pressure, incorrect loading of cargo, customer-related uncertainty, and third stakeholder effects. Prominence values showed that sabotage and time pressure were the dominant drivers of the system, while incorrect loading of cargo emerged mainly as an effect factor. A robustness check based on row sums of the normalized and total relation matrices preserved the same upper-tier risk set, supporting the consistency of the prioritization.ConclusionsThe findings indicate that security management, workload and schedule control, loading discipline, and stakeholder coordination should be prioritized together rather than addressed separately. By translating causal risk interactions into concrete priorities, the study offers practical guidance for improving worker protection and operational resilience in civil aviation cargo systems. The findings underscore the necessity of implementing proactive risk management strategies in air cargo operations. Emphasizing the role of advanced analytical methods and a strong safety culture, the study offers actionable recommendations to industry stakeholders.

In contemporary work environments, employees' psychological capacity to adapt to continuous economic, technological, and cultural changes have become a central concern within organizational and applied psychology. While constructs such as adaptability, learning orientation, and cognitive flexibility have been widely discussed, there remains limited consensus on which psychological components of learnability most strongly influence employee adaptation and how adaptation-oriented strategies can be systematically evaluated under uncertainty. Moreover, existing research often lacks integrative analytical frameworks that link individual cognitive-behavioral characteristics with strategic organizational learning outcomes. To address this gap, the present study conceptualizes Learnability Quotient as a multidimensional psychological construct encompassing cognitive flexibility, questioning behavior, openness to experience, and reflective learning tendencies. Building on expert-based psychological evaluations, a novel fuzzy multi-criteria decision-making framework is proposed by integrating spherical fuzzy sets, a newly developed comparative weighting evaluation based on the best criteria (COWEB) weighting technique, and the multiple normalization rating analysis (MUNRA) ranking approach. This integrated model enables the simultaneous assessment of the relative importance of learnability-related psychological indicators and the prioritization of employee adaptation strategies. Empirical results demonstrate that questioning and inquiry as well as mental flexibility emerge as the most influential psychological dimensions of learnability, highlighting the critical role of critical thinking, cognitive openness, and adaptive mental processes in effective organizational learning. Furthermore, the stability of strategy rankings across multiple scenarios supports the psychological robustness and methodological reliability of the proposed framework. By bridging cognitive-behavioral psychology and decision science, this study offers both theoretical contributions to organizational learning psychology and practical insights for designing psychologically informed adaptation strategies in contemporary organizations.

The agri-food supply chain (AFSC) needs resilience beyond organizational and dyadic levels due to its complex adaptive nature and increasing number of disruptions. Based on the investigation of expert perceptions, this study aims to explore the influence structure of social-ecological resilience capabilities to aid in understanding the complexity of resilience and informing decision making. Moreover, the prioritization of big data analytics (BDA) practices can support agri-food entities in adopting best practices for resilience improvement by using a fuzzy hybrid multiple-criteria decision analysis approach. To contribute to the two aims, first, responses from 26 distinguished supply chain resilience scientists were analyzed using a fuzzy DANP approach to uncover the influence relationships and priority weights of 19 organization, supply chain, and industry level social-ecological resilience capabilities. Second, 14 BDA practices categorized into three groups of sensing, seizing, and transforming―based on the dynamic capabilities perspective―were prioritized as judged by a total of 19 managers in three large food retailers using a fuzzy TOPSIS model, considering their assessed contribution to strengthening resilience capabilities. These capabilities have also been triangulated with secondary data to contextualize and corroborate case descriptions. The findings suggest the high prominence and net influence of adaptability and agility, alongside the centrality of collaboration, supply flexibility, and risk-aware culture within the elicited influence structure in AFSCs. Production and supply chain managers and policymakers in AFSCs can use the results to assess organizational, supply chain, and industry resilience, guiding strategic planning based on identified capability interdependencies and priority weights. In addition, retail managers can use the evaluation method to reach a consensus in their organization to better understand and implement the critical BDA practices that are prioritized for resilience enhancement in their specific context. • Adopting the theoretical angles of social-ecological resilience and dynamic capabilities • Analyzing data from 26 supply chain resilience scientists and 19 managers in three large food retailers • Uncovering the influence structures between social-ecological resilience capabilities • Developing an empirical ranking model of Big Data Analytics practices to support resilience at different levels in agri-food supply chains • Guiding future theory testing in supply chain resilience and Big Data Analytics

We propose Probabilistic Inclusion Depth (PID) for the ensemble visualization of scalar fields. By introducing a probabilistic inclusion operator $\subset_{p}$, our method is a general data depth model supporting ensembles of fuzzy contours, such as soft masks from modern segmentation methods, and conventional ensembles of binary contours. We also advocate for extending contour extraction in scalar field ensembles to become a fuzzy decision by considering the probabilistic distribution of an isovalue to encode the sensitivity information. To reduce the complexity of the data depth computation, an efficient approximation using the mean probabilistic contour is devised. Furthermore, an order-of-magnitude reduction in computational time is achieved with an efficient parallel algorithm on the GPU. Our new method enables the computation of contour boxplots for ensembles of probabilistic masks, ensembles defined on various types of grids, and large 3D ensembles not studied by existing methods. The effectiveness of our method is evaluated through numerical comparisons with existing techniques on synthetic datasets, examples of real-world ensemble datasets, and expert feedback.

A deck of cards-based co-constructive approach for modeling higher-order uncertainty in fuzzy decision-making

Leveraging a novel intuitionistic fuzzy three-way decision model for label correlation in multi-label classification

AI based intuitionistic dense fuzzy entropy decision- making system and its application in firefighting robot selection

Nature-based solutions for coastal protection based on intuitionistic fuzzy group decision making

Reinvestigating the off-grid project priorities of small-scale nuclear reactors using an enhanced integrated fuzzy decision support system

Establishing an inclusive climate in organizations has become a strategic priority for increasing employee engagement, effectively managing diversity, and achieving sustainable organizational success. Much of the existing research either focuses on a single leadership type or fails to consider the relationship between leadership and inclusive climate with a holistic approach. The aim of this study is to identify the most appropriate leadership types for establishing an inclusive climate. To this end, a new and holistic multi-criteria decision-making model, the Sierpinski Triangle fuzzy sets (STFSs), is integrated into the model, enabling a more flexible and robust modeling of uncertainties. The assessment relied on the judgments of five experts drawn from academia and industry in Türkiye, representing diverse disciplinary and sectoral backgrounds. The model's robustness was verified through sensitivity analysis, which confirmed the stability of rankings across multiple weighting scenarios. The findings reveal that the most important criteria for establishing an inclusive climate are psychological safety (.184) and ethical organizational climate (.158). Furthermore, the most suitable leadership types are determined to be ethical leadership (.948) and responsible leadership (.942).

A fuzzy decision support process for railway sleeper maintenance and renewal management

Vertical industrial IoT framework powered by transformer models and two-layer fuzzy decision-making

Understanding how gamification elements interact to influence student engagement and learning outcomes in higher education remains a significant challenge due to the complexity and uncertainty of these relationships. This research aims to analyze the causal interdependencies among key gamification elements and their combined effects on educational outcomes. To address this, the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to capture both direct and indirect relationships while accounting for the ambiguity in human judgment. Data were collected from 50 undergraduate and postgraduate students with experience in gamified learning environments. The findings indicate that feedback is a dominant driving factor, exerting a strong causal relationship on competition (0.45) and rewards (0.50). Competition further strengthens rewards (0.55) and leaderboards (0.60), while rewards promote engagement with challenges (0.50). The novelty of this research lies in applying Fuzzy DEMATEL to systematically model the interdependencies among core elements of gamification in higher education. The results offer practical implications for educators, providing a structured basis for designing effective gamified learning strategies that enhance student engagement and improve learning outcomes.

ABSTRACT The demand for renewable energy is growing, and offshore renewable energy plays a crucial role among the available alternatives. In this regard, the identification of suitable Offshore Renewable Energy Resource Areas (ORERA) and the implementation of effective marine spatial planning are essential for efficient and safe resource utilization. This study focuses on ORERA from the perspective of navigational safety. Factors affecting navigational safety for ORERA were identified through a literature review using academic studies and publications of the International Maritime Organisation (IMO) and the Maritime and Coast Guard Agency (MCA). The cause-and-effect relationships between these factors were analyzed using the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. The research aims to clarify factors affecting navigational safety within or near ORERA and explore their interrelationships. The analysis highlights weather conditions, water depth, tidal streams and local currents as the most influential factors. This research provides insights ORERA planning and nearby navigation.

This paper evaluates five sensor network architectures for coastal marine pollution monitoring using a fuzzy multi-criteria decision-making framework. Thirty domain experts assessed the alternatives against five criteria (detection performance, spatial coverage, life-cycle cost, energy demand, and robustness) using linguistic ratings, which were converted into triangular fuzzy numbers. A fuzzy decision matrix and the corresponding criterion weights were derived and analysed using Fuzzy TOPSIS. Spatial coverage (w = 0.26) and detection performance (w = 0.24) emerged as the most influential criteria. The glider-satellite architecture achieved the highest closeness coefficient (CC = 0.76), followed by a combined HF radar-innovative mooring system (CC = 0.71); the IoT node grid, fixed buoy network, and vessel-based system obtained coefficients of 0.63, 0.58, and 0.49, respectively. Sensitivity tests across multiple weight scenarios and a robustness check confirm that the top-ranked option is stable under plausible shifts in decision priorities. Methodologically, this study does not propose a new mathematical decision algorithm. Instead, it contributes a rigorously structured decision-support workflow that integrates established techniques, structured expert elicitation with consistency screening, fuzzy aggregation, and multi-scenario weight-sensitivity into a single, replicable pipeline tailored to the selection of coastal pollution monitoring architectures. The methodological contribution, therefore, lies in the systematic integration, operationalisation, and application of these components, rather than in modifying the underlying Fuzzy TOPSIS formulation. For decision-makers, the study clarifies which criteria drive architecture choice, when lower-cost opportunistic or IoT-grid options become competitive, and how to assemble hybrid monitoring designs that combine wide-area coverage with reliable detection.

Medical decision support requires models that remain interpretable under uncertainty while still adapting to evolving data and expert knowledge. Fuzzy cognitive maps (FCMs) are attractive in this setting because they encode concept-level relations in a transparent graphical form. However, static expert-defined FCMs are often too rigid, whereas purely data-driven updates may weaken semantic consistency and drift away from clinically meaningful relations. This paper proposes a human-centric adaptive framework for fuzzy cognitive map-based medical decision support. The proposed Human-Centric Fuzzy Decision Support System (HCFDSS) combines data-driven weight adjustment with an explicit expert-correction operator, allowing the causal structure of the map to be refined without discarding domain knowledge. The method is formulated through a two-stage procedure: an inner reasoning loop updates concept activations for fixed weights, while an outer learning loop updates the weight matrix using a regularized objective together with bounded expert intervention. Under standard smoothness and boundedness assumptions, we establish sufficient conditions for stability of the inner reasoning dynamics and convergence of the outer weight-update operator. A numerical illustration shows how expert feedback can revise clinically important edges and alter the final diagnostic activation in an interpretable way. In addition, the paper reports a reproducible empirical evaluation involving synthetic medical scenarios and three public medical benchmark datasets from the UCI Machine Learning Repository, together with sensitivity analysis for expert intervention, preprocessing validation, robustness checks under missingness and imbalance, and expanded comparisons with mainstream machine-learning baselines. The proposed HCFDSS is positioned not as a replacement for high-capacity black-box predictors, but as a transparent and editable decision-support framework in which clinicians can inspect, question, and refine the learned relations. The main contribution of the present work is therefore methodological and theoretical, while the empirical evaluation clarifies the predictive-interpretability trade-off of the proposed framework under reproducible benchmark settings.

Model selection in data science involves evaluating multiple alternatives across conflicting criteria under uncertainty, where existing fuzzy multi criteria group decision making (MCGDM) approaches often fail to capture asymmetric uncertainty and nonlinear interactions in expert judgments. To address this limitation, this study proposes a novel MCGDM framework based on fractional orthopair fuzzy sets (FOFS). The FOFS structure enables flexible and precise modeling of uncertainty by allowing independent fractional control of membership degree (MD) and non-membership degree (NMD). Furthermore, sine trigonometric aggregation operators are introduced to capture nonlinear relationships and fluctuations in expert evaluations. An integrated FOFS-TOPSIS method is then developed to rank candidate models based on their distances from positive ideal solution (PIS) and negative ideal solution (NIS). The applicability of the framework is demonstrated through a numerical study involving fifteen predictive models, fifteen evaluation criteria, and three experts. The results indicate that Alternative [Formula: see text] achieved the highest overall ranking, followed by [Formula: see text], while mid and lower ranked models revealed trade-offs in accuracy, computational efficiency, and robustness. Comparative and sensitivity analyses confirm the framework's robustness, stability, and superior ranking performance.

This paper investigates the fundamental algebraic laws and properties that hold in the framework of Picture Fuzzy Sets (PFS). Picture fuzzy sets extend classical fuzzy and intuitionistic fuzzy sets by incorporating an additional degree of neutrality, providing a more refined representation of uncertainty. We examine the validity of standard algebraic laws such as commutativity, associativity, distributivity, and idempotency under picture fuzzy operations, and identify the conditions under which these laws are preserved. The study contributes to a deeper understanding of the algebraic behavior of PFS and forms a theoretical basis for their further applications in fuzzy decision-making and information processing.

Construction disputes in metro-rail infrastructure projects have traditionally been linked to unclear contractual provisions, technical shortcomings, and inadequate coordination among stakeholders. This study aims to evaluate 138 construction dispute drivers in a mega infrastructure project like metro-rail construction through application of a fuzzy Multi-Criteria-Decision-Making (MCDM) framework like Fuzzy VIKOR to identify, prioritize, and evaluate the most critical, legal and operational dispute drivers. Out of 138 dispute drivers depending upon the lowest values of S ˜ i ​(group-utility) and R ˜ i (individual-regret), 30 dispute divers have been considered in order of priority with the lowest value factor getting the first rank. Furthermore, top 10 dispute drivers were identified based on Q ˜ i (VIKOR-compromise ranking-index-value). The principal research contribution and novelty of the study lies in establishing a quantitatively validated and sensitivity-tested prioritization model that explicitly captures the legal, regulatory, environmental, and governance dimensions of dispute formation. The findings reveal that the top dispute driver is climate-related compliance failures, particularly inability to ensure climate resilience. Sensitivity analysis confirms the structural stability of these rankings, with stability indices exceeding 0.91, establishing the robustness and legal reliability of the prioritization outcomes. The practical applications of this study are substantial and directly relevant to owners, contractors, policymakers, and legal professionals.