Search Results (687)

Cryogenic industries handling liquid hydrogen and helium require rigorous safety verification. However, current standards (ASTM, ASME, ISO) are optimized for LNG at −163 °C and remain inadequate for extreme cryogenic conditions such as −253 °C. As the temperature decreases, materials experience ductile-to-brittle transition, raising the risk of sudden fracture in testing equipment. This study presents a fuzzy-integrated reliability framework that combines fault tree analysis (FTA) and Failure Modes, Effects, and Criticality Analysis (FMECA). The method converts qualitative expert judgments into quantitative risk indices for use in data-scarce conditions. When applied to a cryogenic impact testing apparatus, the framework produced a total failure probability of 1.52 × 10⁻³, about 7.5% lower than the deterministic FTA result (1.64 × 10⁻³). These results confirm the framework’s robustness and its potential use in cryogenic testing and hydrogen systems. Full article

This study introduces SHAP-Rule, a novel explainable artificial intelligence method that integrates Shapley additive explanations with fuzzy logic to automatically generate interpretable linguistic IF-THEN rules for diagnostic tasks. Unlike purely numeric SHAP vectors, which are difficult for decision-makers to interpret, SHAP-Rule translates feature attributions into concise explanations that humans can understand. The method was rigorously evaluated and compared with baseline SHAP and AnchorTabular explanations across three distinct and representative datasets: the CWRU Bearing dataset for industrial predictive maintenance, a dataset for failure analysis in power transformers, and the medical Pima Indians Diabetes dataset. Experimental results demonstrated that SHAP-Rule consistently provided clearer and more easily comprehensible explanations, achieving high expert ratings for simplicity and understanding. Additionally, SHAP-Rule exhibited superior computational efficiency and robust consistency compared to alternative methods, making it particularly suitable for real-time diagnostic applications. Although SHAP-Rule showed minor trade-offs in coverage, it maintained high global fidelity, often approaching 100%. These findings highlight the significant practical advantages of linguistic fuzzy explanations generated by SHAP-Rule, emphasizing its strong potential for enhancing interpretability, efficiency, and reliability in diagnostic decision-support systems. Full article

Under the dual pressures of global climate change and energy structure transition, the offshore wind–solar–current energy coupling hydrogen production (OCWPHP) system has emerged as a promising integrated energy solution. However, its complex multi-energy structure and harsh marine environment introduce systemic risks that are challenging to assess comprehensively using traditional methods. To address this, we develop a novel risk assessment framework based on hesitant fuzzy sets (HFS), establishing a multidimensional risk criteria system covering economic, technical, social, political, and environmental aspects. A hybrid weighting method integrating AHP, entropy weighting, and consensus adjustment is proposed to determine expert weights while minimizing risk information loss. Two aggregation operators—AHFOWA and AHFOWG—are applied to enhance uncertainty modeling. A case study of an OCWPHP project in the East China Sea is conducted, with the overall risk level assessed as “Medium.” Comparative analysis with the classical Cumulative Prospect Theory (CPT) method shows that our approach yields a risk value of 0.4764, closely aligning with the CPT result of 0.4745, thereby confirming the feasibility and credibility of the proposed framework. This study provides both theoretical support and practical guidance for early-stage risk assessment of OCWPHP projects. Full article

With the designation of the first cohort of national parks and the continued operation of remaining pilots, China’s national park reform has entered a critical stage requiring consolidation and adaptive improvement. A key challenge lies in the ambiguous status of five pilot zones, which lack a standardized evaluation mechanism to guide decisions on future inclusion or exit. This study develops a comprehensive indicator system specifically tailored to assess the construction and development of national park pilots, thereby supporting evidence-based governance beyond initial entry criteria. Drawing on relevant theories and China’s institutional context, the framework employs Analytic Hierarchy Process, expert consultation, and fuzzy scoring to determine indicator weights and evaluation standards. The resulting system integrates three dimensions—ecological protection system, management system, and public service system. Nanshan National Park was selected as a case study, scoring 87.77 in 2024 (Class II, “Proficient”), with strong overall performance but notable weaknesses in landscape connectivity, recreational product diversity, and regional integration. These findings suggest the need for targeted improvements in ecological corridors, service enrichment, and community benefit-sharing. Overall, the proposed framework provides a replicable tool for evaluating pilot zones, offering practical insights for refining China’s national park development and enhancing governance effectiveness. Full article

This study develops a comprehensive framework for assessing the sustainability performance of wind power systems integrated with hydrogen storage (WPCHS). Unlike previous works that mainly emphasized economic or environmental indicators, our approach incorporates a balanced set of economic, environmental, and social criteria, supported by expert evaluation. To address the uncertainty in human judgment, we introduce an interval-valued fuzzy TOPSIS model that provides a more realistic representation of expert assessments. A case study in Manjil, Iran, demonstrates the application of the model, highlighting that project A₄ outperforms other alternatives. The findings show that both economic factors (e.g., levelized cost of energy) and social aspects (e.g., poverty alleviation) strongly influence project rankings. Compared with earlier studies in Europe and the Middle East, this work contributes by extending the evaluation scope beyond financial and environmental metrics to include social sustainability, thereby enhancing decision-making relevance for policymakers and investors. Full article

The quality risks of land trusteeship services are increasingly prominent, leading to reduced crop yields for farmers and land degradation; however, relevant research remains insufficient. This paper aims to identify and evaluate the quality risk level of land trusteeship services. It comprehensively adopts a field survey, web crawler technology, and expert consultation methods to identify quality risk types, and then uses the fuzzy comprehensive evaluation method to assess the risk level based on survey data from Chinese farmers. The main conclusions are as follows: (1) Overall, the quality risk level of land trusteeship services is at a relatively high risk level. In terms of spatio-temporal patterns, the quality risk level shows an upward trend, and the quality risk level of mid-production services is increasing at the fastest rate. There are significant variations in service quality risk across prefecture-level cities in the Shandong Province of China. (2) In terms of risk heterogeneity, the quality risk level of small-scale pure farmers is higher than that of part-time farmers and large professional farmers, in that order. The quality risk level of the “farmer + service organization” model is higher than that of the “farmer + intermediary + service organization” model. According to the order of the quality risk level of different crops, the ranking (from highest to lowest) is cash crops, wheat, and corn. (3) The high quality risks of land trusteeship services will impact the multifunctionality of land systems. It exacerbates the land pollution and fertility degradation because of excessive application of chemical inputs like pesticides, fertilizers, and mulch by service organizations. It consequently destroys ecological systems, hinders sustainable agricultural development, and impacts farmers’ income and national food security by reducing yields. The research findings contribute to controlling the quality risks of land trusteeship services and protecting land. Full article

For centuries, artisans have resolved intricate engineering conundrums with intuitive ingenuity, bequeathing a legacy of design wisdom that remains largely untapped in contemporary biomimetics. This “anthro-creative” form of biomimicry, deeply embedded within traditional crafts such as Chaozhou woodcarving, is predominantly tacit and qualitative, which has traditionally eluded systematic interpretation. To address this, we propose the Hierarchical Biomimetics-Based Evaluation System (HBBES), a transdisciplinary framework that couples expert-defined hierarchies through the Analytic Hierarchy Process (AHP) with perceptual assessments from one hundred public evaluators via Fuzzy Comprehensive Evaluation (FCE). Applied to canonical works—including the Lobster and Crab Basket (overall score: 4.36/5.00)—the HBBES revealed a striking finding: both expert and public valuations are anchored not in structural hierarchy, but in aesthetic resonance, particularly the craft’s lifelike morphological analogy and nuanced modulation of light. Beyond offering a replicable pathway for translating artisanal intuition into operative design principles, this study proposes a culture-driven paradigm for biomimetics, bridging intangible heritage with technological innovation. Full article

Smaller data environments with expert systems are generally driven by the need for interpretable reasoning frameworks, such as fuzzy rule-based systems (FRBS), which cannot often quantify epistemic uncertainty during decision-making. This study proposes a novel Layered Entropy Model (LEM) comprising three semantic layers: Membership Function Entropy (MFE), Rule Activation Entropy (RAE), and System Output Entropy (SOE). Shannon entropy is applied at each layer to enable granular diagnostic transparency throughout the inference process. The approach was evaluated using both synthetic simulations and a real-world case study on the PIMA Indian Diabetes dataset. In the real data experiment, the system produced sharp, fully confident decisions with zero entropy at all layers, yielding an Epistemic Confidence Index (ECI) of 1.0. The proposed framework maintains full compatibility with conventional Type-1 FRBS design while introducing a computationally efficient and fully interpretable uncertainty quantification capability. The results demonstrate that LEM can serve as a powerful tool for validating expert knowledge, auditing system transparency, and deployment in high-stakes, small-data decision domains, such as healthcare, safety, and finance. The model contributes directly to the goals of explainable artificial intelligence (XAI) by embedding uncertainty traceability within the reasoning process itself. Full article

Industry 4.0 (I4.0) promises that technological advances are happening at an accelerating rate, which is pushing all industries to undergo digital transformation to boost competitiveness, productivity, and business efficiency. As industrial companies transition to Industry 4.0, one of the maturity models that helps them identify opportunities is the Smart Industry Readiness Index (SIRI). SIRI is in line with other international manufacturing initiatives and has the potential to become a global standard for the manufacturing sector’s future. To achieve market competitiveness, smart manufacturing requires the end-to-end integration of Industry 4.0 technologies and SIRI. The successful implementation of such a comprehensive integration depends on carefully selecting the I4.0 technologies to conform to industry requirements. The Influences of I4.0 technologies on SIRI are not clearly outlined in any of the earlier research. Thus, employing a dependable Multi-Criteria Decision Making (MCDM) methodology using fuzzy TOPSIS, this article aims to analyze the influence of Industry 4.0 technologies on SIRI from the perspectives of both academic and industry experts. Expert opinions were gathered on the relationship between SIRI and I4.0 technologies. TOPSIS utilizes fuzzy theory to address the ambiguity and uncertainty inherent in human judgment. The findings showed that the best I4.0 technology for SIRI is the cyber-physical system (CPS). Full article

A balance between support and independence in guardianship systems is of high concern, especially with those who need help in making decisions. The research presents a novel approach to evaluating voluntary models of guardianship, focusing on the preservation of individual autonomy and examining the underlying decision symmetry in assessing diverse guardianship options. The ultimate solution to the inherent uncertainty and lack of objectivity in expert evaluations is to apply the circular q-rung orthopair fuzzy (Cq-ROF) combined compromise for ideal solution (CoCoFISo) approach, an effective multi-criteria decision-making (MCDM) model that integrates ranking and sorting views using a Cq-ROF framework within a symmetry-oriented analytical perspective. These are five major assessment factors: how well autonomy is preserved, legal and ethical adherence, psychological health, social integration aid, and risk prevention. It explores ten alternative approaches to guardianship, ranging from complete legal guardianship to community-based self-management solutions, and the use of technology as an element of support. The suggested approach can facilitate more sophisticated modelling of expert opinions, rather than relying on simplistic and straightforward distinctions and diverse evaluations. The case study results indicate that the hybrid and supported forms of decision-making could offer opportunities to preserve a high degree of personal autonomy while ensuring safety and compliance. The research gives a coherent, adaptable, and explainable approach to managing ethical and policy-level judgment concerning voluntary guardianship systems. Full article

Smart grid operators face escalating cyber threats and tight resource constraints, demanding the transparent, defensible prioritization of security controls. This paper asks how to select cybersecurity controls for smart grids while retaining picture fuzzy evidence throughout and supporting policy-sensitive “what-if” analyses. We propose a hybrid Picture Fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) and Combinative Distance-based Assessment (CODAS) framework that carries picture fuzzy evidence end-to-end over a domain-specific cost/benefit criteria system and a relative-assessment matrix, complemented by multi-scenario sensitivity analysis. Applied to ten prominent solutions across twenty-nine sub-criteria in four dimensions, the model highlights Performance as the most influential main criterion; at the sub-criterion level, the decisive factors are updating against new threats, threat-detection capability, and policy-customization flexibility; and Zero Trust Architecture emerges as the best overall alternative, with rankings stable under varied weighting scenarios. A managerial takeaway is that foundation controls (e.g., OT-integrated monitoring and ICS-aware detection) consistently remain near the top, while purely deceptive or access-centric options rank lower in this context. The framework contributes an end-to-end picture fuzzy risk-assessment model for smart grid cybersecurity and suggests future work on larger expert panels, cross-utility datasets, and dynamic, periodically refreshed assessments. Full article

Terminal forensics in large mobile networks is a vital activity for identifying compromised devices and analyzing malicious actions. In contrast, the study described here begins with the domain of terminal forensics as the primary focus, rather than the threat itself. This paper proposes a new multi-criteria decision-making (MCDM) model that integrates complex picture fuzzy sets (CPFS) with the combinative distance-based assessment (CODAS), referred to throughout as complex picture fuzzy CODAS (CPF-CODAS). The aim is to assist in forensic analysis for detecting mobile botnet command and control (C&C) systems. The CPF-CODAS model accounts for the uncertainty, hesitation, and complex numerical values involved in expert decision-making, using degrees of membership as positive, neutral, and negative values. An illustrative forensic case study is constructed where three mobile devices are evaluated by three cybersecurity professionals based on six key parameters related to botnet activity. The results demonstrate that the model can effectively distinguish suspicious devices and support the use of the CPF-CODAS approach in terminal forensics of mobile networks. The robustness, symmetry, and advantages of this model over existing MCDM methods are confirmed through sensitivity and comparison analyses. In conclusion, this paper introduces a novel probabilistic decision-support tool that digital forensic specialists can incorporate into their workflow to proactively identify and prevent actions of mobile botnet C&C servers. Full article

Professional certification of engineers plays a crucial role in verifying competencies and ensuring the safety and quality of engineering outputs. However, most existing certification systems assign fixed validity periods (e.g., 3–5 years) without considering individual engineer characteristics or the intensity of technological progress in specific fields. This study examines the key factors influencing the optimal validity period of engineering certifications and proposes it as a measurable indicator to support safety in engineering practice. A new model is introduced that integrates expert judgment, fuzzy set theory, and bibliometric analysis of Q1/Q2 Scopus-indexed publications. The model incorporates three main factors: competence level, professional experience, and the technological intensity of the discipline. A case study from the engineering certification system of Kazakhstan demonstrates the model’s practical applicability. Certification bodies, policymakers, and engineering organizations can use these findings to establish more flexible certification validity periods, thereby ensuring timely reassessment of competencies and reducing safety risks. For example, for mechanical engineers, the optimal validity period is 3 years rather than the statutory 5 years; in other words, the model recommends a 40% reduction in certification validity. This reduction reflects the combined effects of competency level, professional experience, and technology intensity on certification renewal schedules. Overall, the proposed factorial approach supports a more personalized and safety-oriented certification process and offers insights into improving national qualification systems. Full article

Fuzzy logic has been applied to a wide range of problems, including process control, object recognition, image and signal processing, prediction, classification, decision-making, optimization, and time series analysis. These apply to solar energy systems. Though experts in renewable energy prefer fuzzy logic techniques, their contribution to the decision-making process of solar energy systems lies in the possibility of illustrating risk factors and introducing the concepts of linguistic variables of data from solar energy applications. In solar energy systems, the primary beneficiaries and audience of the fuzzy logic techniques are solar energy policy makers, as it concerns decision-making models, ranking of criteria or weights, and assessment of the potential location of the installation of solar energy plants, depending on the case. In a real-world scenario, fuzzy logic allows easy and efficient controller configuration in a non-linear control system, such as a solar panel. This study attempts to review the role and contribution of fuzzy logic in solar energy based on its applications. The findings from the review revealed that the fuzzy logic application identifies and detects faults in solar energy systems as well as in the optimization of energy output and the location of solar energy plants. In addition, fuzzy model (predicting), hybrid model (simulating performance), and multi-criteria decision-making (MCDM) are components of fuzzy logic techniques. As the review indicated, these are useful as a solution to the challenges of solar energy systems. Importantly, the integration and incorporation of fuzzy logic and neural networks should be recommended for the efficient and effective performance of solar energy systems. Full article

This article proposes a scientifically grounded approach to risk assessment for infrastructural and functional systems that underpin the development of digitally transformed regional territories under conditions of high threat dynamics and sociotechnical instability. The core methodology is based on modeling of multifactorial threats through the application of fuzzy set theory and logic–linguistic analysis, enabling consideration of parameter uncertainty, fragmented expert input, and the lack of a unified risk landscape within complex infrastructure environments. A special emphasis is placed on components of technogenic, informational, and mobile infrastructure that ensure regional viability across planning, response, and recovery phases. The results confirm the relevance of the approach for assessing infrastructure resilience risks in regional spatial–functional systems, which demonstrates the potential integration into sustainable development strategies at the level of regional governance, cross-sectoral planning, and cultural reevaluation of the role of analytics as an ethically grounded practice for cultivating trust, transparency, and professional maturity. Full article