Search Results (115)

Large-scale water network projects are a crucial approach for the rational allocation of water resources and addressing water resource crises. Reliable sustainability evaluation is essential to ensure the sustainable operation of large-scale water network projects. This study develops an improved Fuzzy Comprehensive Evaluation (FCE) method based on Game Theory weight fusion (GWF) for the quantitative evaluation of the sustainability of water network projects. By combining the Analytic Hierarchy Process (AHP), Entropy Weight Method (EWM), and Game Theory approach, the study integrates the advantages of both subjective and objective weighting methods to achieve the allocation of indicator weights; the sustainability of the Jiaodong Water Network Project was quantitatively evaluated by employing the improved FCE method. The results indicate that the resource and management dimensions are the two most critical factors affecting the sustainability of large-scale water network projects. Indicators with high weight such as per capita water resources, the rationality of the management system, and level of management intelligence are the primary risk factors affecting the sustainable operation of large-scale water network projects. The sustainability evaluation value of the Jiaodong Water Network Project is 82.83 points, which is classified as “high” sustainability. This validates the reliability of the evaluation indicator system and the method used. Full article

The acceleration of global urbanization has caused severe damage to, and even the disappearance of, traditional villages, significantly reducing the diversity of cultural landscapes. To effectively preserve and transmit the cultural landscape characteristics of traditional villages, this study adopts the “landscape gene” theory and proposes a traditional village landscape gene perception evaluation method combining game theory-based weight assignment and the cloud model. Using Huangtutang Village in Wuxi, China, as a case study, the study follows the framework and paradigm of “identification-translation-perception evaluation-preservation inheritance” to identify, translate, map, and comprehensively evaluate its landscape genes. Finally, targeted strategies for the preservation and development of Huangtutang Village are proposed based on the evaluation results. The results indicate that residents and tourists generally perceive the landscape genes of Huangtutang Village as “Satisfied,” with perception levels ranking from high to low as follows: environmental pattern, cultural characteristics, architectural character, and spatial layout characteristics. Perceptions of traffic location, street texture, building form, roof form, facade features, folk tales, and historical and cultural context were relatively low, showing lower “expectation values.” The findings provide valuable references for the preservation and development of Huangtutang Village and other traditional villages. The proposed traditional village landscape gene perception evaluation model advances the development of landscape gene theory, effectively supplements existing methods for traditional village preservation and sustainable development, and demonstrates broad applicability. Full article

At present, although there are a variety of assessment systems to rate the financing ability of enterprises, these systems suffer from the problems of outdated indicators and subjective weighting methods. In this paper, the impact of ESG performance on financing ability is taken as an evaluation index and combined with 13 other indexes to construct a new TOPSIS assessment system. Cooperative game theory in the form of the entropy weight method and a BP neural network is used to avoid the subjectivity of weighting. After establishing the evaluation model, we selected cross-sectional data from 4590 listed companies on the Shanghai and Shenzhen stock exchanges in 2023 to train the evaluation model and explore the impact of various indicators on financing capabilities. The results show the following: (1) Total revenue and total assets of main board companies are the main factors affecting financing ability. (2) Total revenue growth rate, total revenue, and R&D costs of Science and Technology Innovation Board Market (STAR Market) companies are the main factors affecting the financing ability. (3) Growth Enterprise Market (GEM) companies’ total revenue and R&D costs are the main factors affecting financing ability. This study uses data from 2023. In practical applications, it is recommended to use the latest data for evaluation and analysis, and to update the weights every six months. Full article

The increasing frequency and severity of forest fires, driven by climate change and intensified human activities, pose substantial threats to ecological security and sustainable development. However, most assessments remain centered on occurrence risk, lack a resilience-oriented perspective and comprehensive indicator systems, and therefore offer limited guidance for building system resilience. This study developed a forest fire resilience (FFR) assessment framework with 25 indicators in three levels and six domains across four resilience dimensions. Balancing expert judgment and data, we obtained indicator weights by integrating the Analytic Hierarchy Process (AHP) and the Criteria Importance Through Intercriteria Correlation (CRITIC) via a game-theoretic scheme. The analysis revealed that, among the level-2 indicators, climate factors, infrastructure, and vegetation characteristics exert the greatest influence on FFR. At the level-3 indicator scale, monthly minimum relative humidity, fine fuel load per unit area, and the deployment of smart monitoring systems were critical. Among the four resilience dimensions, absorption capacity plays the predominant role in shaping disaster response. Building on these findings, the study proposes targeted strategies to enhance FFR and applies the assessment framework to twelve administrative divisions of Baise City, China, highlighting marked spatial variability in resilience levels. The results offer valuable theoretical insights and practical guidance for strengthening FFR. Full article

With the widespread application of artificial intelligence in real estate price prediction, model explainability has become a critical factor influencing its acceptability and trustworthiness. The Shapley value, as a classic cooperative game theory method, quantifies the average marginal contribution of each feature, ensuring global fairness in the explanation allocation. However, its focus on average fairness lacks robustness under data perturbations, model changes, and adversarial attacks. To address this limitation, this paper proposes a hybrid explainability framework that integrates the Shapley value and Least Core attribution. The framework leverages the Least Core theory by formulating a linear programming problem to minimize the maximum dissatisfaction of feature subsets, providing bottom-line fairness. Furthermore, the attributions from the Shapley value and Least Core are combined through a weighted fusion approach, where the weight acts as a tunable hyperparameter to balance the global fairness and worst-case robustness. The proposed framework is seamlessly integrated into mainstream machine learning models such as XGBoost. Empirical evaluations on real-world real estate rental data demonstrate that this hybrid attribution method not only preserves the global fairness of the Shapley value but also significantly enhances the explanation consistency and trustworthiness under various data perturbations. This study provides a new perspective for robust explainable AI in high-risk decision-making scenarios and holds promising potential for practical applications. Full article

The increasing frequency of extreme weather events and rapid urbanization has exacerbated pluvial flood risks, underscoring the urgent need to strengthen the assessment of pluvial flood resilience in China’s southwestern mountainous regions. Kunming—a plateau basin city—was selected as a case study, and an urban pluvial flood resilience assessment system was developed based on the DPSIR model. The analytic hierarchy process (AHP), entropy method, and game theory-informed combination weighting were applied to determine indicator weights, while the extension cloud model was utilized to quantitatively assess resilience evolution from 2013 to 2022. The results reveal that: (1) Kunming’s pluvial flood resilience experienced a clear three-stage evolution—initial construction (Level II), resilience enhancement (Level III), and resilience reinforcement (Level IV)—reflecting a transition from rudimentary resilience to advanced adaptive capacity; (2) the ranking of primary indicator weights is as follows: Driving Forces > Pressure > State > Response > Impact, with Flood Disaster Risk (P6), Flood Disaster Early Warning Capability (R1), and Topographic and Geomorphological Characteristics (P7) identified as key influencing factors; (3) marked disparities exist across the five dimensions: the Driving Forces dimension demonstrates increasing economic support; the Pressure dimension reflects structural vulnerabilities and climate variability; the State and Impact dimensions advance incrementally through policy implementation; and the Response dimension has substantially improved due to smart city technologies, although persistent gaps in inter-agency emergency coordination remain. This research offers a scientific basis for enhancing pluvial flood resilience in southwestern mountainous cities. Full article

Mine roof water inrush represents a prevalent hazard in mining operations, characterized by its concealed onset, abrupt occurrence, and high destructiveness. Since mine water inrush is controlled by multiple factors, rigorous risk assessment in hydrogeologically complex coal mines is critically important for operational safety. This study focuses on the roof water inrush hazard in coal seams of the Banji coal mine, China. The conventional water-conducting fracture zone height estimation formula was calibrated through comparative analysis of empirical models and analogous field measurements. Eight principal controlling factors were systematically selected, with subjective and objective weights assigned using AHP and EWM, respectively. Game theory was subsequently implemented to compute optimal combined weights. Based on this, the vulnerability index model and fuzzy comprehensive evaluation model were constructed to assess the roof water inrush risk in the coal seams. The risk in the study area was classified into five levels: safe zone, relatively safe zone, transition zone, relatively hazardous zone, and hazardous zone. A zoning map of water inrush risk was generated using Geographic Information System (GIS) technology. The results show that the safe zone is located in the western part of the study area, while the hazardous and relatively hazardous zones are situated in the eastern part. Among the two models, the fuzzy comprehensive evaluation model aligns more closely with actual engineering practices and demonstrates better predictive performance. It provides a reliable evaluation and prediction model for addressing roof water hazards in the Banji coal seam. Full article

The complex interdependencies among numerous safety risk factors influencing oil tanker loading/unloading operations constitute a focal point in academic research. To enhance safety management in oil port operations, this study conducts a risk analysis of oil tanker berthing and cargo transfer safety. Initially, safety risk factors are identified based on the Wu-li Shi-li Ren-li (WSR) systems methodology. Subsequently, a hybrid weighting approach integrating the Fuzzy Analytic Hierarchy Process (FAHP), G2 method, and modified CRITIC technique is employed to calculate indicator weights. These weights are then synthesised into a combined weight (GVW) using cooperative game theory and variable weight theory. Further, by integrating grey theory with the cloud model (GCM), a risk assessment is performed using Tianjin Port as a case study. Results indicate that the higher-risk indicators for Tianjin Port include vessel traffic density, safety of berthing/unberthing operations, safety of cargo transfer operations, safety of pipeline transfer operations, psychological resilience, proficiency of pilots and captains, and emergency management capability. The overall comprehensive risk evaluation value for Tianjin Port is 0.403, corresponding to a “Moderate Risk” level. Comparative experiments demonstrate that the results generated by this model align with those obtained through Fuzzy Comprehensive Evaluation Methods. However, the proposed GVW-GCM framework provides a more objective and accurate reflection of safety risks during tanker operations. Based on the computational outcomes, targeted recommendations for risk mitigation are presented. The integrated weighting model—incorporating game theory and variable weight concepts—coupled with the grey cloud methodology, establishes an interpretable and reusable analytical framework for the safety assessment of oil port operations under diverse port conditions. This approach provides critical decision support for constructing comprehensive management systems governing oil tanker loading/unloading operations. Full article

With the continuous increase in urban population, the subway is the main way to alleviate traffic congestion. However, the construction environment of subway stations is complex, and the safety risks are extremely high. Therefore, it is of great practical significance to scientifically and systematically evaluate the safety vulnerability of subway station construction. This paper takes the Chengdu subway project as an example, and establishes a metro station construction safety vulnerability evaluation index system based on the driving forces–pressures–state–impacts–responses (DPSIR) theory with 5 first-level indexes and 23 second-level indexes, and adopts the fuzzy hierarchical analysis method (FAHP) to calculate the subjective weights, and the improved Harris Hawks optimization–projection pursuit method (HHO-PPM) to determine the objective weights, combined with game theory to calculate the comprehensive weights of the indicators, and finally uses the improved cloud model of Bayesian feedback to determine the vulnerability level of subway station construction safety. The study found that the combined empowerment–improvement cloud model assessment method is reliable, and the case study verifies that the vulnerability level of the project is “very low risk”, and the investigations of safety hazards and the pressure of surrounding traffic are the key influencing factors, allowing for the proposal of more scientific and effective management strategies for the construction of subway stations. Full article

Artificial Intelligence (AI) and computer-aided diagnosis (CAD) have revolutionised various aspects of modern life, particularly in the medical domain. These technologies enable efficient solutions for complex challenges, such as accurately segmenting brain tumour regions, which significantly aid medical professionals in monitoring and treating patients. This research focuses on segmenting glioma brain tumour lesions in MRI images by analysing them at the pixel level. The aim is to develop a deep learning-based approach that enables ensemble learning to achieve precise and consistent segmentation of brain tumours. While many studies have explored ensemble learning techniques in this area, most rely on aggregation functions like the Weighted Arithmetic Mean (WAM) without accounting for the interdependencies between classifier subsets. To address this limitation, the Choquet integral is employed for ensemble learning, along with a novel evaluation framework for fuzzy measures. This framework integrates coalition game theory, information theory, and Lambda fuzzy approximation. Three distinct fuzzy measure sets are computed using different weighting strategies informed by these theories. Based on these measures, three Choquet integrals are calculated for segmenting different components of brain lesions, and their outputs are subsequently combined. The BraTS-2020 online validation dataset is used to validate the proposed approach. Results demonstrate superior performance compared with several recent methods, achieving Dice Similarity Coefficients of 0.896, 0.851, and 0.792 and 95% Hausdorff distances of 5.96 mm, 6.65 mm, and 20.74 mm for the whole tumour, tumour core, and enhancing tumour core, respectively. Full article

In the digital economy, sustainable development is based on digital technologies. However, information security issues arising from its use pose significant challenges to sustainable development. Assessing information security risks in the digital economy is crucial for sustainable development. This paper constructs an information security risk assessment indicator system for the digital economy based on information ecology theory. Using game theory to combine CRITIC weights and entropy weights, the information security risk values for the digital economy in 29 provinces of China from 2019 to 2021 are calculated. Quantitative analysis is conducted using Ward’s method and the obstacle degree model. The combined weighting results indicate that the information security risks of the digital economy are mostly influenced by information infrastructure. Additionally, the spatio–temporal evolution pattern shows that the risk values of provinces vary to different degrees over time, with a distribution pattern of southern regions > northern regions > northwestern regions. Furthermore, the clustering results indicate that information technology is the primary cause of risk gaps. Finally, the obstacle degree model indicates that digital criminal behavior is the greatest obstacle to information security in the digital economy. The research findings hold significant implications for addressing information security challenges in the global digital economy’s sustainable development process, particularly in terms of the replicability of the research methodology and the valuable case study of China. Full article

Classical epidemic models treat vaccine uptake as an exogenous parameter, yet real-world coverage emerges from strategic choices made by individuals facing uncertain risks. During the last two decades, vaccination games, which combine epidemic dynamics with game theory, behavioural economics, and network science, have become a very important tool for analysing this problem. Here, we synthesise more than 80 theoretical, computational, and empirical studies to clarify how population structure, psychological perception, pathogen complexity, and policy incentives interact to determine vaccination equilibria and epidemic outcomes. Papers are organised along five methodological axes: (i) population topology (well-mixed, static and evolving networks, multilayer systems); (ii) decision heuristics (risk assessment, imitation, prospect theory, memory); (iii) additional processes (information diffusion, non-pharmacological interventions, treatment, quarantine); (iv) policy levers (subsidies, penalties, mandates, communication); and (v) pathogen complexity (multi-strain, zoonotic reservoirs). Common findings across these studies are that voluntary vaccination is almost always sub-optimal; feedback between incidence and behaviour can generate oscillatory outbreaks; local network correlations amplify free-riding but enable cost-effective targeted mandates; psychological distortions such as probability weighting and omission bias materially shift equilibria; and mixed interventions (e.g., quarantine + vaccination) create dual dilemmas that may offset one another. Moreover, empirical work surveys, laboratory games, and field data confirm peer influence and prosocial motives, yet comprehensive model validation remains rare. Bridging the gap between stylised theory and operational policy will require data-driven calibration, scalable multilayer solvers, and explicit modelling of economic and psychological heterogeneity. This review offers a structured roadmap for future research on adaptive vaccination strategies in an increasingly connected and information-rich world. Full article

Current risk assessment in railway track maintenance operations faces challenges (low spatiotemporal accuracy, limited adaptability to various scenarios, and tendency of linear fuzzy comprehensive evaluation (FCE) methods to underestimate high-risk factors). To address these, this study proposes a novel risk assessment model that integrates subjective–objective weighting techniques with a nonlinear FCE approach. By incorporating spatiotemporal information, the model enables precise localization of risk occurrence in individual maintenance operations. A comprehensive risk index system is constructed across four dimensions: human, equipment, environment, and management. The game theory combined weighting method, integrating the G1 method and entropy weight method, is employed; it balances expert judgment with data-driven analysis. A cloud model is introduced to generate risk membership matrices, accounting for the fuzziness and randomness of risk data. The nonlinear FCE framework enhances the influence of high-risk factors. Risk levels are determined using the combined weights, membership matrices, and the maximum membership principle. A case study on the Lanzhou–Xinjiang Railway demonstrates that the proposed model achieves higher consistency with actual risk conditions than conventional methods, improving assessment accuracy and reliability. This model offers a practical and effective tool for risk prevention and control in railway maintenance operations. Full article

Urban flooding poses escalating threats to socioeconomic stability and human safety, exacerbated by urbanization and climate change. While urban flood resilience (UFR) has emerged as a critical framework for flood risk management, existing studies often overlook the systemic integration of post-disaster recovery capacity and multidimensional interactions in UFR assessment. This study develops a novel hazard–vulnerability–exposure–defense capacity–recovery capacity (HVEDR) framework to address research gaps. We employ a hybrid game theory combined weight method (GTCWM)-TOPSIS approach to evaluate UFR in China’s Taihu Lake Basin (TLB), a region highly vulnerable to monsoon- and typhoon-driven floods. Spanning 1999–2020, the analysis reveals three key insights: (1) weight allocation via GTCWM identifies defense capacity (0.224) and hazard (0.224) as dominant dimensions, with drainage pipeline density (0.091), flood-season precipitation (0.087), and medical capacity (0.085) ranking as the top three weighted indicators; (2) temporal trends show an overall upward trajectory in UFR, interrupted by a sharp decline in 2011 due to extreme hazard events, with Shanghai and Hangzhou exhibiting the highest UFR levels, contrasting Zhenjiang’s persistently low UFR; (3) spatial patterns reveal stronger UFR in southern and eastern areas and weaker resilience in northern and western regions. The proposed HVEDR framework and findings provide valuable insights for UFR assessments in other flood-prone basins and regions globally. Full article

With the acceleration of the oilfield development process during the high water content period, the contradiction between the increase in energy consumption and the decrease in the energy efficiency of the gathering and transportation system has become increasingly obvious. This paper develops a grey relational analysis model using a combination of AHP and EWM. Based on the characteristics of light oil production, a four-level evaluation indicator system is developed. Based on game theory, AHP can provide subjective weights, the EWM can provide objective weights, and subjective and objective combinations are used for a more reasonable assignment. Concurrently, the 0.05 distinguishing coefficient and the ideal reference values are selected as the GRA reference sequence to evaluate the energy consumption of the gathering and transportation system as a whole and each subsystem. The analysis of a light oil block indicates significant room for improvement in the energy efficiency correlation across the system. Taking the central processing station as an example, the grey relational degree of electricity consumption per unit of injected water is measured at 0.12, marking it as the weakest link in the system. This study supports efficiency enhancement by identifying energy consumption bottlenecks within the system. Full article