Search Results (108)

Multi-stakeholder supply chains require decision mechanisms capable of simultaneously interpreting dynamic system states, coordinating conflicting stakeholder interests, and managing uncertainty across interconnected operational decisions. This study proposes an integrated Human–AI Collaborative Neuromorphic Digital Twin framework in which synchronized supply-chain data are transformed into cognitive representations, enriched through human–AI collaborative intelligence, strategically coordinated through adaptive game-theoretic interactions, and subsequently mapped into a unified decision-knowledge representation for fuzzy multi-objective optimization. The principal innovation lies in this closed and interconnected decision architecture, where the outputs of cognitive, collaborative, and strategic intelligence layers are explicitly fused and transferred to the optimization space rather than being applied as independent analytical modules. The framework jointly optimizes economic, environmental, service, resilience, energy, and operational-risk objectives under fuzzy uncertainty. Evaluation was conducted using combined real and statistically consistent simulated data across six operational scenarios ranging from baseline conditions to a critical scenario involving simultaneous demand growth, capacity restrictions, cost escalation, uncertainty, and stakeholder conflicts. Results demonstrate progressive improvements in decision quality under increasingly complex conditions; in the critical scenario, Human–AI collaboration achieved a 19.6% cost improvement, while the service level reached 99.4%. The findings demonstrate that integrating cognitive representation, collaborative intelligence, strategic adaptation, and fuzzy optimization provides a unified mechanism for adaptive multi-stakeholder supply-chain decision-making. Full article

This study focuses on the Northern New District of Liaoyuan City, Jilin Province, and develops an indicator-based relative spatial assessment framework for geological environmental carrying capacity. Fourteen indicators were selected from the geological, ecological, and socio-economic dimensions to characterize spatial differences in regional geological environmental conditions. The weights of the indicators were determined by integrating subjective and objective methods, where the Fuzzy Analytic Hierarchy Process (FAHP) and the CRITIC method were applied respectively, and the final composite weights were obtained through a game theory-based combination weighting approach. Based on the weighted results, ArcGIS was used to perform spatial analysis, and the geological environmental carrying capacity was classified into four levels: excellent, good, moderate, and poor. The results indicate significant spatial heterogeneity in geological environmental carrying capacity. Moderate-capacity areas dominate the study area, with poor-capacity areas mainly distributed in the central and southeastern regions. The proposed framework provides spatial information for identifying areas with different geological environmental conditions and supports differentiated environmental management and planning in mining areas. Full article

To achieve the accurate prediction of the dynamic risk of water and mud inrush during underwater tunnel construction, a two-stage risk assessment theory and methodology of water and mud inrush based on game theory combination weighting and interval extension theory is proposed, comprising preliminary and secondary assessments. Due to the uncertainty of geological and hydrological conditions and the uncontrollability of construction factors, a ternary fuzzy interval number rather than a fixed value is used to quantify the evaluation index of water inrush. The correlation functions in extension theory are improved. A game-theory-based combination weighting model is constructed by considering the subjective weight of the improved AHP and the objective weight of the FCM. The proposed method is applied to evaluate the water inrush risk of the river-crossing section in the Yuelongmen Tunnel from the Chengdu–Lanzhou Railway. The results show that the risk level of water inrush at the river-crossing section is high, and the evaluation results of the proposed method are consistent with the actual situation. It has been proven that the method is effective and scientific, and it is more suitable for identifying the risk of water inrush in complex geological conditions. Full article

The Guanting Gorge section of the Yongding River features complex terrain and densely distributed hydraulic projects. Under extreme rainstorm conditions, flood disasters are highly likely to occur, triggering secondary disasters such as landslides, barrier lakes, and dam breaks, resulting in prominent chain disaster risks and difficult prevention and control. To accurately evaluate the potential flood disaster chain risks in this region, 12 representative evaluation indicators were selected to establish a flood disaster chain risk evaluation system. The analytic hierarchy process (AHP) and entropy weight method (EW) were adopted to calculate subjective and objective indicator weights, respectively. Game theory was applied to achieve the optimal weight fusion and determine the final indicator weights. On this basis, a variable fuzzy model was constructed to identify flood disaster chain risks, and the Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) was used to verify the accuracy and reliability of the model. The results show that cumulative precipitation, terrain slope, and annual maximum precipitation are the core driving factors inducing regional flood disaster chains, with corresponding weights of 0.2058, 0.1293, and 0.0980, respectively. High- and extremely high-risk areas are mainly concentrated in the western and southern river valleys, among which the Zhaitang–Luopoling river reach and the river section from Luopoling Reservoir to Sanjiadian Hub present the most prominent risks. The spatial distribution of risk zones is highly consistent with the actual disaster sites of the July 2023 Haihe River extreme rainstorm event, and the model achieves an AUC value of 0.901, indicating high accuracy and reliable simulation results. This study accurately identifies the core inducing factors and high-risk sections of flood disaster chains in the Guanting Gorge section of the Yongding River, which can provide scientific references and technical support for regional flood disaster chain prevention and control, hydraulic project operation and management, and disaster prevention and mitigation planning. Full article

The geological environment evaluation of abandoned open-pit mines frequently encounters challenges, including the reliance of subjective weighting on judgment matrices, the complexity of weight adjustment, and the inadequate interpretation of systematic evaluation results. Addressing these limitations in existing AHP/FAHP and their combinatory weighting models, this study proposes the IRBMO-G1-EWM-FCE framework. This framework embeds the Improved Red Billed Blue Magpie Optimizer (IRBMO) into the improved G1 method to optimize indicator contribution rates, and subsequently integrates EWM, game theory combinatory weighting, and Fuzzy Comprehensive Evaluation (FCE) to evaluate three abandoned quarries in the Yellow River Basin of Shaanxi Province. The results demonstrate that across 20 independent runs, IRBMO yields a mean fitness value of 1.6496, lower than the 1.7732 of RBMO, with a 63.8% reduction in standard deviation, thereby indicating superior convergence accuracy and stability. The comprehensive membership degrees of the three quarries are A = [0.405,0.143,0.452], B = [0.405,0.450,0.145], and C = [0.742,0.077,0.181], corresponding to evaluation grades of Grade III, Grade II, and Grade I, respectively. While circumventing the construction of complete judgment matrices and consistency modifications, this method incorporates both expert experience and data discreteness into the evaluation, thereby providing an interpretable quantitative tool for geological environment classification, governance priority identification, and restoration decision-making for abandoned open-pit mines. Full article

Existing Kansei-oriented product design studies often rely on simplified styling–color associations or weighted matching of perceptual imagery, making it difficult to capture users’ multi-affective requirements. To address this limitation, this study proposes a product form innovation design method based on a multi-domain coupling network model and establishes a mapping framework from target Kansei images to design solutions. Taking electric mopeds as a case study, the KJ method, k-means clustering, fuzzy Kano analysis, probabilistic hesitant fuzzy entropy weighting, and game-theoretic weighting were employed to identify the target Kansei image. Product form was then decomposed into styling, color, and material domains, and a multi-domain coupling network was constructed based on a form-element co-occurrence matrix. Through topological analysis and the LH-index, 14 key form elements associated with the target image were identified. Quantification Theory Type I was subsequently applied to establish the mapping relationship between form elements and Kansei images, while the Bee Evolutionary Genetic Algorithm (BEGA) was used to generate optimal design solutions. The results indicate that “elegance” is the dominant Kansei image for electric mopeds. The proposed model achieved strong predictive performance (R² = 0.923), and the optimized design obtained an 85% user recognition rate, showing high consistency across multiple validation methods. The proposed framework effectively reveals the coupling effects of styling, color, and material and provides a systematic approach for data-driven product form innovation. Full article

Against the backdrop of rapid urbanization, urban sewer networks face increasing challenges, including infrastructure deterioration and imbalanced resource allocation. Conventional single-dimensional risk assessment methods fail to capture the coordinated development of such complex systems. This study proposes a comprehensive HFM framework integrating Health (H), Failure (F), and Management (M), coupled with a Coupling Coordination Degree (CCD) model and an obstacle degree model to evaluate system interactions and identify key constraints. A game theory-based weighting approach combining AHP and CRITIC is applied to integrate subjective and objective weights, while fuzzy mathematics is used for multidimensional evaluation. CCD spatial analysis is conducted at the drainage unit scale. Results show that: (1) The system is in a transitional stage from disorder to coordination, with CCD values mainly ranging from 0.4 to 0.8 and exhibiting significant spatial heterogeneity. (2) High-risk areas tend to have better health conditions and stronger management inputs, whereas low-risk areas may still face latent risks due to insufficient management. (3) Key obstacles are concentrated in Failure and Management systems, particularly pipeline functionality and management capacity. Overall, system risk arises from mismatches between risk sources and management allocation rather than purely structural deficiencies. The proposed framework effectively identifies imbalance areas and priority interventions, supporting the transition toward proactive risk regulation. Full article

As climate governance increasingly shifts toward consumption-side intervention, digital platforms such as the Carbon Generalized System of Preferences have become important tools for promoting low-carbon behavior. However, existing studies have mainly focused on participation and engagement, paying limited attention to users’ advocacy intentions. Drawing on the perceived value perspective and Social Exchange Theory, this study examines how perceived rewards and gameful experiences influence advocacy intentions through perceived benefits and low perceived costs. A three-wave survey of Chinese respondents was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA). The results show that perceived rewards enhance perceived benefits, while gameful experiences increase perceived benefits and reduce users’ actual perceived burden. In turn, perceived benefits and lower perceived costs both promote advocacy intentions. The mediation analysis confirms the important roles of perceived benefits and low perceived costs, while the fsQCA results identify three distinct configurations leading to high advocacy intentions. This study extends CGSP research from participation to advocacy and offers practical implications for designing digital low-carbon platforms. Full article

Renovating the grid structure by converting existing transmission lines into VSC-HVDC transmission lines can address issues such as limited transmission capacity and excessive short-circuit current in load-concentrated areas. To effectively evaluate the effectiveness of grid structure renovation and provide a reference for selecting suitable renovation sites, this paper proposes a comprehensive evaluation method for assessing the effectiveness of grid structure renovation. Firstly, an evaluation indicator system is constructed from four aspects. Then, the Fuzzy Analytic Hierarchy Process (FAHP) and Entropy Weight Method (EWM) are used to determine the subjective weight and objective weight of each indicator, and a game theory-based combined weighting method is applied to obtain the combined weight, which is then used to calculate the comprehensive evaluation value before and after renovation to reflect the effectiveness of the renovation. Subsequently, the TOPSIS method is employed for comparative verification of the evaluation method’s validity, and a sensitivity analysis is conducted on the subjective weight to confirm the method’s robustness to subjective preference. Finally, based on the indicator data obtained from the PSD-BPA simulation, the effectiveness of renovating eight scenarios in a provincial power grid is evaluated. The results show that grid structure renovation can enhance power grid performance in load centers. Full article

The safe operation of power battery systems relies on the dynamic symmetric equilibrium of electrochemical distribution and thermal management states, whereas fault occurrence is often accompanied by symmetry breaking. To achieve accurate fault diagnosis and symmetry restoration, this study proposes a symmetrical closed-loop framework (DBP-KG-FBN) that integrates digital battery passport (DBP) text mining, knowledge graph (KG), and fuzzy Bayesian network (FBN). Power battery fault diagnosis is critical to new energy vehicle (NEV) safety; however, conventional methods face two key limitations: (1) they inadequately exploit multi-source heterogeneous textual data in DBPs; and (2) they fail to handle uncertainty in fault propagation. The methodology proceeds as follows. First, a BERT-BiLSTM-CRF model extracts fault-related entities and relations from unstructured DBP text, which are structured into a Neo4j-based knowledge graph. Second, via rule-based topological mapping, the KG topology is transformed into a Bayesian network through structurally symmetric transformation between the semantic and probabilistic layers, with cyclic dependencies resolved by introducing latent variables. Third, network parameters are determined by integrating fuzzy set theory with game theory-based weighting to quantify uncertainty and subjectivity in expert evaluations, thereby achieving symmetric utilization of subjective and objective information. This enables bidirectional symmetric reasoning for forward fault prediction and backward fault traceability. Experimental results demonstrate that while maintaining symmetric stability of the diagnostic knowledge topology, the proposed DBP-KG-FBN method achieves a diagnostic accuracy of 0.92 (Top-3). This symmetrical closed-loop framework significantly outperforms fault tree analysis (FTA) and event tree analysis (ETA) in diagnostic accuracy and reasoning efficiency. It transforms unstructured DBP data into computable knowledge for intelligent battery diagnosis. Future work will expand the corpus via transfer learning and optimize adaptive weighting algorithms for expert evaluations. Full article

Refined decision-making of the design intent is a key factor affecting the iterative design of complex equipment products. While current research on design intent decision-making generally emphasizes methodological innovation, it often neglects the individualized and fuzzy expressive characteristics of cognitive agents, as well as the actual status of the research object. This oversight leads to uncertainty in both design intent and design outcomes. To address these issues, in this paper, a refined decision-making method for the multimodal fuzzy design intent of complex products based on noncooperative–cooperative game serialization is proposed. First, through scenario analysis, the fuzzy design intent evaluation process of different cognitive agents is transformed into a cooperative game model based on a fuzzy network, achieving a preliminary assessment of design intent. On this basis, a noncooperative game-based refined matching and decision-making model for design intent across different dimensions is constructed, thereby completing the final design intent decision-making for a specific product model. Finally, the proposed method is applied to the design intent decision-making process of a CKA6180 CNC machine tool, yielding the conclusion that the two design intents of “good protective performance” and “grand appearance” should be prioritized, thereby verifying the practicality and effectiveness of the method. The analysis of the results reveals the following: ① The application of scenario analysis theory enables a more comprehensive and precise characterization of the design intents of different cognitive agents; ② The construction of a model combining a fuzzy network with a cooperative game facilitates a more complete representation and evaluation of multimodal fuzzy design intent data; ③ The integration of a refined design concept with a noncooperative game model leads to more definitive design intent decision outcomes, thereby reducing the “disturbance” of experience dependence in the early design phase and consequently enhancing subsequent design satisfaction. Full article

Traditional evaluations of revetment projects primarily focus on structural safety and economic analysis, which cannot comprehensively reflect the overall effectiveness of such projects. To address this issue, this paper establishes a comprehensive evaluation index system for ecological revetments based on ecosystem theory and sustainable development principles. The system is tailored for the Pinglu Canal Ecological Revetment Demonstration Project. It assesses three key aspects: structural stability, ecological health, and socioeconomic benefits. Subjective weights were calculated using the Fuzzy Analytic Hierarchy Process (FAHP). Objective weights were determined by optimizing the Projection Pursuit (PP) model with the Tent-improved Crocodile Ambush Optimization Algorithm (TCAOA). Game theory was employed to compute the combined weights. The evaluation grade of the ecological revetment project was subsequently determined using a cloud model. The results show that the cloud eigenvalues of the project’s comprehensive evaluation are (1.096, 0.209, 0.047), and the application effectiveness is rated as “Excellent”. The cloud expected values for structural stability, ecological health, and socioeconomic benefits are 1.02, 1.18, and 1.15, respectively. All of these values are at the “Excellent” level. Compared with GA-PP and PSO-PP, TCAOA-PP converges faster and more stably. It requires only 347 iterations, achieves a coefficient variation of 3.8%, and reduces computation time by 23%. By revealing the nonlinear coupling relationships among indicators, the model presented in this paper provides a methodological foundation for establishing an evaluation framework that is ecologically interpretable for bank protection. This study has important practical significance for promoting the high-quality development of inland waterways and the construction of ecological revetments. Full article

The Internet of things (IoT) is rapidly pervading daily life and linking everything. Although higher connectivity offers many benefits, including higher productivity, robotic processes, and decision-making guided by data, it also poses a number of security dangers. Modern risks to data authenticity and confidence are getting harder to handle through typical central safety solutions. In this paper, we present a detailed investigation of the latest innovations and approaches for assessing reputation and confidence in the blockchain-empowered Internet of Things (BIoT) area. A comprehensive literature search was conducted across major electronic databases, including IEEE, Springer, Elsevier, Wiley, MDPI, and top indexed conference proceedings. The publication year was restricted to the period from 2018 to 2025. The methodological quality of a total of 122 studies met the inclusion criteria assessed using predefined quality measures. We figure out existing flaws at each layer of IoT architecture, illustrating how autonomous, transparent, and impenetrable blockchain ledgers address these flaws. Plus, we analytically compare public, private, consortium, and hybrid blockchain networking architectures to emphasize the underlying compromises among security, reliability, and decentralization. We also assess how reputation evaluation techniques evolved over time, moving from classical fuzzy logic and weighted average models to modern mature game theory and machine learning (ML) models, addressing their limitations in terms of computational overhead, scalability, adaptability, and deployment feasibility in IoT systems. Additionally, we outline future directions for BIoT system trust assessment and identify research limitations and potential solutions. Our research indicates that although ML-driven models offer more accurate predictions for identifying illicit node activities, they are still constrained by limited unbalanced data and high processing overhead. Full article

To address high uncertainty, dynamic evolution, and limited information in emergency decision-making for major sudden disasters, this paper proposes a sliding-window game-theoretic method with four reference points for emergency response selection. Firstly, interval-valued T-spherical fuzzy sets are adopted to capture decision-makers’ uncertain and hesitant evaluations in interval form. Subsequently, a four-reference-point framework, including the external, internal, average development speed, and ideal proximity reference points, is established to reflect stage-dependent psychological baselines. Furthermore, criterion weights are updated by a sliding-window game-theoretic combination weighting scheme that integrates entropy, anti-entropy, criteria importance through intercriteria correlation, and the coefficient of variation, and performs rolling updates across stages. Prospect values are then computed relative to the four reference points and aggregated to rank alternatives at each stage. Finally, a case study of the 2024 Huludao extreme rainfall event applies the proposed method to evaluate four candidate schemes across six criteria over three decision stages. Results show that rescue cost has the highest weight in all stages, while the importance of rescue speed decreases and social impact increases as the response progresses. The proposed method identifies a comprehensive flood relief scheme led by the People’s Liberation Army and the People’s Armed Police Force as the best option in all stages, because it achieves the highest comprehensive prospect values among all alternatives. Comparative analyses indicate more consistent identification of the optimal scheme than existing approaches, supporting sustainable and resource-efficient disaster management. Full article

To achieve a scientific evaluation of land ecological resilience in mining areas and promote the green transformation and sustainable development of the mining industry, this study is based on the core concept of Nature-based Solutions (NbS), coupling the “Driving force–Pressure–State–Impact–Response” (DPSIR) framework, and constructs an evaluation system for mine land ecological resilience (MLER) focusing on sustainability. This system covers multiple aspects, including natural ecology, socio-economics, and policy management, comprising 21 secondary indicators that comprehensively respond to NbS’ fundamental principles of “nature-guided, multi-party collaboration, and long-term adaptation.” In terms of evaluation methodology, this study proposes a combined weighting model that integrates AHP-CRITIC game theory with Vague sets. First, subjective expert experience and objective data variance are balanced through combined weighting. Based on game theory, the optimal combination coefficients were determined (α₁ = 0.624, α₂ = 0.376) to reconcile subjective and objective preferences. Subsequently, the three-dimensional interval structure of Vague sets is utilized to effectively accommodate fuzzy information and data gaps. By characterizing the restoration process through interval membership, the model enhances the representational capacity of the evaluation results regarding complex ecological information. Empirical research conducted in the mining areas of Gan Xian, Xing Guo, Yu Du, and Xun Wu in Jiangxi Province effectively identified differences in resilience levels: the resilience of the Xing Guo mining area was classified as I, Gan Xian and Yu Du as II, and Xun Wu as IV. These results are fundamentally consistent with the AHP-Fuzzy Comprehensive Evaluation method, verifying the robustness and reliability of the model. The NbS-guided evaluation system and model constructed in this study provide scientific tools for identifying differences in the sustainability of MLER and key constraints, promoting the transformation of restoration models from “engineering-driven” to “nature-driven, long-term adaptation” in the context of NbS in China. Full article