Search Results (14,357)

This paper proposes the Fuzzy Difference Equation Matrix Model (FDEMM), a novel predictive control algorithm designed for nonlinear multivariable systems. Standard Dynamic Matrix Control (DMC) often struggles with computational load and nonlinearities. FDEMM addresses this by integrating the Difference Equation Matrix Model (DEMM) with a generalized Takagi-Sugeno (T-S) fuzzy framework, utilizing a parameter-weighting scheme to handle overlapping membership functions. The method is validated on two distinct nonlinear systems: a binary distillation column and a delayed thermal mixing tank. Results demonstrate FDEMM’s ability to control complex systems achieving the desired output even in the presence of disturbances and noise. The proposed strategy offers a computationally efficient alternative for real-time control of complex nonlinear processes. Full article

Under the rapid development of live commerce, impulse buying has become a core consumption phenomenon, yet its psychological triggering pathways across different consumer groups remain to be fully elucidated. Drawing on the S–O–R framework, this study conceptualizes live-stream interactivity, novelty, and streamer attractiveness as external “stimuli,” and positions immersive experience as the core “organism” mechanism, thereby constructing and testing an integrated “stimulus–experience–response (impulse buying intention)” model. Using a mixed-method approach that combines structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), the results show that all three live-stream features significantly enhance impulse buying intention, primarily by strengthening immersive experience, with immersion exerting a significant partial mediating effect. Moreover, consumers’ loneliness significantly amplifies the indirect effect of live-stream features on impulse buying via immersive experience. The fsQCA further uncovers multiple equivalent pathways leading to high impulse buying intention, including a strong-experience pattern centered on “streamer attractiveness + immersive experience,” as well as a social compensation pattern centered on “high interactivity + high loneliness.” This study provides a testable theoretical framework, actionable operational strategies, and sustainable ethical guidance for live commerce, offering a pathway for the industry to achieve a “high experience × high conversion × high well-being” triple-win outcome. Full article

Natural gas separation and purification are critical stages for ensuring product quality, operational safety, and economic efficiency in the energy sector. However, a significant research gap exists: conventional control systems, predominantly based on a proportional-integral-derivative (PID) controller, are often static and lack the adaptability required to handle fluctuations in raw gas composition and operating conditions. This review aims to systematically analyze modern control strategies to identify the most influential parameters and effective methodologies for enhancing process efficiency. The methods involve a comparative assessment of classical PID control against advanced intelligent approaches, including adaptive control, fuzzy logic, and machine learning (ML) models, based on a synthesis of the recent literature and industrial case studies. The key finding is that data-driven and intelligent methods (e.g., neural networks, adaptive fuzzy controllers) demonstrate superior performance in achieving precise parameter adjustment, improving responsiveness, and optimizing energy consumption compared to traditional static systems. Such an integrated strategy transforms decision-making into a multivariable optimization framework with objectives encompassing minimizing pollutants, lowering energy usage, and enhancing end-product specifications. The present work argues for employing methodologies like systemic analyses and advanced computational techniques—particularly artificial neural networks—to forecast gas stream attributes. Full article

Multi-criteria decision-making (MCDM) problems in complex evaluation systems are often characterized by high uncertainty in expert judgments and dynamic variations in indicator importance. Traditional analytic hierarchy process (AHP) and entropy-based weighting methods typically suffer from two inherent limitations: the inability to explicitly quantify expert hesitation and the rigidity of static weight assignment under evolving data distributions. To address these challenges, this paper proposes a dynamic hybrid weighting framework that integrates an interval-valued intuitionistic fuzzy analytic hierarchy process (IVIF-AHP) with an entropy-triggered correction mechanism. First, interval-valued intuitionistic fuzzy numbers are employed to simultaneously model membership, non-membership, and hesitation degrees in pairwise comparisons, enabling a more comprehensive representation of expert uncertainty. Second, an entropy-triggered dynamic fusion strategy is developed by jointly incorporating information entropy and coefficient of variation, allowing adaptive adjustment between subjective expert weights and objective data-driven weights. This mechanism effectively enhances sensitivity to high-dispersion criteria while preserving expert knowledge in low-variability indicators. The proposed framework is formulated in a hierarchical fuzzy decision structure and implemented through a fuzzy comprehensive evaluation process. Its feasibility and robustness are validated through a concrete case study on teaching effectiveness evaluation for a university engineering course, leveraging multi-source data. Comparative analysis demonstrates that the proposed approach effectively mitigates the weight rigidity and evaluation inflation observed in conventional methods. Furthermore, it improves diagnostic resolution and decision stability across different evaluation periods. The results indicate that the proposed entropy-triggered IVIF-AHP framework provides a mathematically sound and practically applicable solution for dynamic MCDM problems under uncertainty, with strong potential for extension to other complex evaluation and decision-support systems. Full article

Health Internet of Things (Health IoT) systems continuously stream sensitive physiological data, making data access governance safety-critical under conflicting objectives such as privacy risk, latency, energy/resource cost, and robustness, especially when conditions change during emergencies. This paper proposes FiB-MOBA-EAFG, a hybrid blockchain–AI framework that separates on-chain accountability from off-chain decision intelligence. Off-chain, fuzzy context inference parameterizes scenario priorities, Pareto-based multi-objective search generates candidate governance policies, an emergency-aware feasibility guard filters unsafe trade-offs, and a bargaining-based selector chooses a single deployable policy. On chain, the blockchain layer records consent commitments, access events, and hashes of the selected policy and decision trace, serving as an immutable audit and accountability substrate rather than an online decision or optimization engine, while raw health data remain off-chain. Using simulation studies of home remote monitoring, clinic telehealth, and emergency triage under stochastic network variation and adversarial device behavior, FiB-MOBA-EAFG improves robustness and yields more repeatable policy selection than rule-based control and scalarized baselines within the evaluated simulation scenarios, while maintaining latency within ranges compatible with modeled edge deployment constraints through explicit emergency-aware feasibility constraints. A budget-matched random-search ablation further indicates that structured Pareto exploration is needed to reliably obtain robust, low-risk governance policies. Full article

Accurate segmentation of the intercondylar notch of the femur is of great significance for the diagnosis of knee joint diseases, surgical planning, and anterior cruciate ligament (ACL) reconstruction. Among them, the obvious anatomical heterogeneity, the interference of structurally similar tissues, and the blurred boundaries in MRI images make the segmentation of the intercondylar notch challenging. The segmentation of the intercondylar notch is often regarded as a standard semantic segmentation problem, but doing so leaves the inherent high-order internal variation and low-contrast features of its anatomical structure unresolved. We proposed a new Wavelet Multi-scale Contextual Attention Network (WMCA-Net). We have coordinated the Shallow High-frequency Feature Dense Extraction Block (SHFDEB) and Wavelet Split and Fusion Block (WSFB) modules with each other. The SHFDEB intensively extracts high-frequency detailed features at the shallowest layer of the network, while the WSFB effectively splits and fuses features at various resolutions, suppressing noise while better preserving the high-frequency detailed structural information we need. The Multi-scale Depth-wise Convolution Block (MDCB) captures cross-scale features from the narrow intercondylar notch (5–8mm wide) to the surrounding femoral structure (approximately 50 mm diameter), dynamically adapting to different morphologies, including pathological changes caused by osteophyte formation. The Contextual-Weighted Attention Module (CWAM) establishes long-term semantic associations between fuzzy regions and clear anatomical landmarks by precisely locating uncertain regions through foreground and background decomposition. The Dice Similarity Coefficient of WMCA-Net on the intercondylar notch dataset is 93.16%, and the 95% Hausdorff Distance is 1.42 mm, demonstrating its advanced segmentation performance and good anatomical adaptability. Full article

To address the challenges of real-time control in power systems with high renewable penetration, identifying historical transmission sections similar to future scenarios enables efficient reuse of mature control strategies. However, existing data-driven identification methods exhibit two primary limitations: they typically rely on static Total Transfer Capacity (TTC), ignoring the rapid regulation capability of Energy Storage Systems (ESS) in alleviating congestion; and they employ fixed weights for similarity measurement, failing to distinguish the varying importance of different features (e.g., critical line flows vs. ordinary voltages). To overcome these issues, this paper proposes a similarity identification method for transmission sections considering ESS regulation capabilities and adaptive feature weights. First, a hierarchical decision model is utilized to screen basic grid features. An optimization model incorporating ESS charge/discharge constraints and emergency power support potential is established to calculate the Dynamic TTC, constructing a multi-scale feature set that reflects the real-time safety margin of the grid. Second, a Dispersion-Weighted Fuzzy C-Means (DW-FCM) clustering algorithm is proposed. By introducing a dispersion-weighting mechanism, the algorithm utilizes data distribution characteristics to automatically learn and assign higher weights to key features with high distinguishability during the iteration process, overcoming the subjectivity of manual weighting. Furthermore, fuzzy validity indices (XB, PC, FS) are introduced to adaptively determine the optimal number of clusters. Finally, case studies on the IEEE 39-bus system verify that the proposed method significantly improves identification accuracy compared to traditional methods and provides more reliable references for dispatching decisions. Full article

Against the backdrop of global climate change, frequent public health crises, and escalating geopolitical conflicts, the stable operation of emergency logistics supply chains faces severe challenges. Building a resilient system that combines disturbance resistance and adaptability has become an urgent necessity. This paper, grounded in the evolution of resilience theory, clearly defines the meaning of emergency logistics supply chain resilience. It systematically identifies and constructs an indicator system comprising 17 influencing factors across four dimensions: Resistance, Responsiveness, Adaptability, and Development Capacity. Employing a hybrid fuzzy DEMATEL-ISM-MICMAC approach, the study quantifies causal relationships and hierarchical structures among factors while analyzing their driving forces and dependency attributes. Findings reveal that infrastructure development, emergency plan integrity, talent cultivation, financial safeguards, and regulatory support constitute core critical factors influencing emergency logistics supply chain resilience. Among these, regulatory support and financial safeguards form the fundamental pillars underpinning the system’s operation. The multidimensional influence factor framework and hybrid analytical method developed in this study not only enrich the theoretical research system on emergency logistics supply chain resilience but also provide scientific decision-making references and practical guidance for policymakers and industry practitioners to formulate targeted resilience enhancement strategies. Full article

In the field of modern power systems, power supply reliability has become a core indicator for measuring distribution network performance. It serves not only as a fundamental criterion for judging the continuous power supply capacity of distribution networks but also as a key benchmark for evaluating their power quality. Considering the current status of reliability assessment for distribution network power supply, this study conducts an in-depth analysis of a series of key indicators, namely outage duration, outage frequency, the number of affected customers, power supply reliability rate, and the proportion of affected customers. Through a detailed deconstruction of these indicators, an evaluation model for distribution network power supply reliability is established. In the process of model construction, this study innovatively combines the hierarchical recursive weighting method with the entropy measurement weight determination method to accurately define the weights of each evaluation dimension. On this basis, a fuzzy membership quantification strategy is introduced to precisely determine the classification level of distribution networks, and Monte Carlo simulation combined with triangular fuzzy number is used to carry out uncertainty modeling on the reliability score, realizing the transformation from deterministic evaluation to probabilistic evaluation. This strategy is developed to transform qualitative issues into quantitative analysis, effectively clarify the fuzzy and complex interrelationships among multiple influencing factors, and thereby realize a comprehensive evaluation of power supply reliability for distribution networks. To verify the effectiveness and practicality of the proposed method, a distribution network in a specific region is selected as the research object. The aforementioned model and method are applied to assess its power supply reliability, and the precise classification of distribution network levels in this region is successfully realized. This combined model significantly improves the accuracy of evaluation while ensuring the scientific rigor and fairness of the evaluation process. It provides an innovative and practical method for the field of distribution network power supply reliability assessment, and offers substantive reference and support for relevant decision-making and practical operations. Full article

User experience constitutes an essential element of effective interaction design. To enhance the user experience, it is necessary to identify the primary root causes related to human errors and then initiate actionable interventions for the high-priority root causes. Risk assessment of human errors in interaction design is characterized by subjective uncertainties and a lack of precise numerical values. However, very little attention has been given to these issues when assessing human errors. In this paper, a fuzzy fault tree analysis approach is proposed to execute risk assessment of human errors in interaction design. First, the system is analyzed, and a fault tree diagram is constructed. Subsequently, probabilities for each basic event are derived through the combination of fuzzy set theory and the similarity aggregation method. Then, the minimal cut sets (MCSs) are identified. Following this, the failure probability of the top event is determined, and the MCSs are ranked in accordance with their respective importance values. Finally, the results are evaluated, and the recommendations for corrective actions are provided for the high-priority MCSs. A practical case study of the in-vehicle information system in heavy trucks was conducted to demonstrate the feasibility and effectiveness of the proposed approach. The findings suggest that this approach can effectively address the subjective and even subconscious aspects of risk assessment related to human errors in interaction design, providing accurate results for risk evaluation. Furthermore, it can effectively address the situations where the probabilities related to human errors are imprecise, inadequate, and ambiguous. The proposed approach can be universally applied to enhance the user experience in interaction design. Full article

Much of the patient data recorded in electronic health records is stored as unstructured free text. Extracting medication information from such data is essential, particularly for antidiabetic drugs such as sodium–glucose cotransporter-2 (SGLT2) inhibitors, but remains challenging due to spelling variability, abbreviations, and non-standard documentation practices. This study compared four text-mining approaches, simple keyword search, regular expression–based matching, fuzzy string matching, and a transformer-based token classification baseline, for detecting SGLT2 inhibitor mentions in Hungarian clinical narratives. Clinical documents were obtained from the University of Debrecen Clinical Centre and covered patients with type 2 diabetes mellitus (ICD-10: E11) from 2018 and 2019. Searches targeted both generic and brand names and SGLT-related abbreviations. In the 2019 dataset (n = 5383), simple keyword search identified 1.49% of documents as containing an SGLT2 inhibitor mention, compared with 7.21% using regular expressions, 8.55% using fuzzy matching, and 0.71% using the transformer-based baseline. Mean execution times were 0.07 s, 1.64 s, 5.13 s, and 34.71 s, respectively. Method performance was further evaluated against a manually annotated reference set from 2018 using confusion matrices and standard classification metrics. Fuzzy string matching achieved the highest recall and F1-score, while regular expression-based matching provided a strong balance between precision and recall. The transformer-based baseline showed high precision but substantially lower recall in the absence of domain-specific fine-tuning. Overall, similarity-based fuzzy matching offered the most favorable balance between detection performance and computational efficiency for identifying SGLT2 inhibitor mentions in unstructured Hungarian clinical text. Full article

Electrochemical energy storage is pivotal in constructing new-type power systems. However, the large-scale deployment of energy storage stations poses severe safety challenges, particularly the risk of deflagration. The coupling of combustible accumulation within battery systems and the confined structure of storage units can trigger cascading thermal runaway and deflagration accidents. Existing research still falls short in systematically analyzing the deflagration risks and process evolution mechanisms in energy storage stations. To address this gap, this study develops a probabilistic risk assessment model that enables analysis of risk propagation through the integration of fault tree analysis (FTA) with a static fuzzy Bayesian network (BN). The proposed approach delineates the complete risk evolution pathway from battery thermal runaway to deflagration in a confined space. Diagnostic reasoning identifies a dominant risk escalation path initiated by internal short circuits, leading to thermal runaway, flammable gas release, and pressure accumulation due to inadequate pressure relief. Sensitivity analysis highlights gases ejected during thermal runaway (C22) and lack of pressure relief devices or insufficient venting area (C31) as the most influential risk drivers. This study thus offers a practical, model-based framework for enhancing targeted risk prevention and safety resilience in electrochemical energy storage station infrastructure. Full article

A study on the sources, bioconcentration, and translocation of heavy metals in Haloxylon ammodendron in the Eastern Junggar Coalfield, Xinjiang, China, was conducted and evaluated. The quantities of Pb, Cd, and Cr were 1.2, 22.5, and 1.9 times higher than the baseline values of Xinjiang soils, respectively. The mean concentrations of these heavy metals in the rhizosphere soil of Haloxylon ammodendron were 48.81, 17.74, 93.25, 3.32, 29.05, and 26.95 mg/kg. The exceedance rates for Cd, Cr, and Pb in bare soil were 100%, 99.03%, and 75.73%, respectively, indicating significant accumulation of heavy metals, with Cd demonstrating the highest enrichment degree. Most sampling sites showed moderate pollution according to the Pollution Load Index (PLI). Meanwhile, the Pollution Index (PN) indicated elevated pollution levels at all the sampling sites, with Cr identified as the first contaminant. The absolute principal component score–multiple linear regression (APCS-MLR) model revealed three principal sources of heavy metal pollutants in soil: 44.2% from natural processes and mining activities, 22.7% from industrial coal combustion and sewage, and 33.1% of undetermined origins. The bioconcentration factors (BCFs) and translocation factors (TFs) revealed Haloxylon ammodendron to have clear accumulation and translocation abilities with respect to these heavy metals. The fuzzy membership function showed that the overall assessment score for Haloxylon ammodendron was 9.1325, indicating the substantial remediation potential of Haloxylon ammodendron for heavy metal pollutants, especially for Cd. Furthermore, Haloxylon ammodendron demonstrated substantial Pb and Cr accumulation and remediation ability. Haloxylon ammodendron exhibited remarkable heavy metal accumulation and translocation abilities, making it a suitable tool for phytoremediation in the study area. The findings of this study will prove useful in promoting and implementing sustainable mining practices and safeguarding regional ecological security and may contribute to advancing local ecological conservation and social economic development. Full article

Against the backdrop of decarbonization in global maritime transport and logistics systems, port enterprises play a role in enhancing sustainable transport efficiency and system optimization through investments in carbon-reduction technologies. Situated within the institutional context of China’s “Dual-Carbon” targets, this study integrates Conservation of Resources theory and Behavioral Decision Theory to develop a dual-path analytical framework explaining carbon-reduction technology investment in port enterprises. A three-stage mixed-methods design is employed. First, grounded theory identifies four key resource categories: individual, conditional, material, and energy resources. Second, based on structural equation modeling and conditional process analysis of survey data from 372 port enterprise managers, the results show that individual and conditional resources significantly promote technology investment by enhancing perceived utility, while material resources exert a positive effect by increasing risk preference; the effect of energy resources is not significant. Environmental strategic orientation strengthens these relationships, whereas short-term performance pressure weakens them. Third, fuzzy-set qualitative comparative analysis reveals that multiple resource configurations can equivalently drive high levels of technology investment. Overall, this study uncovers the resource foundations and psychological mechanisms underlying carbon-reduction technology investment in port enterprises, offering empirical evidence for green technology investment in sustainable maritime transport and logistics. Full article

In this study, we present an extension of the Takagi–Sugeno fuzzy inference system (TS-FIS) framework based on prototypical fuzzy similarity (PFS) for defect detection in concrete. The key novelty lies in integrating the PFS mechanism into the TS-FIS+ANFIS architecture, thus enabling a hybrid rule–activation mechanism, bringing together fuzzy interpretability with data-driven similarity learning. To describe the ultrasonic concrete defect scenario, a high-fidelity finite element method (FEM) model that combines solid mechanics with fluid acoustics has been developed. From this numerical model, a synthetic dataset of about 36.8 million samples has been generated. The performance of the proposed TS-FIS+ANFIS+PFS classification system has been compared with that of a conventional FIS+ANFIS model, its particle-swarm-optimized (PSO) version and a Decision Tree (DT) classifier. The proposed model achieved the best performance, with a classification accuracy of 85.4% and an inference time of approximately 0.2 ms per sample. In contrast, the conventional, the PSO and the DT classifiers yielded accuracies of 60.5%, 62.0%, and 76.0%, respectively. These results confirm that PFS improves sensitivity and alleviates the computational effort, representing a potential candidate toward the realization of a defect abacus for concrete, an atlas conceived as a systematic collection of defect configurations associated with specific ultrasonic responses. Full article