The gathering and sharing of information lay the groundwork for decision-making, while large-scale group decision-making (LSGDM) strategies address biases, promoting a more comprehensive evaluation of alternatives. Regarding information representation, incomplete multi-scale information systems (MSISs), as an application of granular computing, combine inputs from decision-makers (DMs) and tackle data gaps through multi-level analysis to foster LSGDM. Furthermore, given the interference effect among DMs, quantum social networks (SNs) and three-way decisions (TWD) are vital for effective decision-making. Quantum SNs provide a framework for modeling complex trust relationships among DMs, while TWD offers a structured approach to manage uncertainty. Therefore, this paper seeks to investigate quantum SN-guided three-way LSGDM under incomplete MSISs. First, MSISs are designed to gather information across spatial dimensions. Second, trust propagation paths within SNs are aggregated using quantum theory. Following community clustering through the Leiden algorithm, each community is further divided into core and fringe regions by three-way clustering (TWC), where core alternatives reflect the central members and fringe alternatives represent uncertain members. Third, to achieve intra-group consensus, the weights of DMs in fringe regions and those with low consensus levels are adjusted, while for intergroup consensus, the weight and decision information of community representatives with low consensus levels are modified. Fourth, alternatives are classified using the TWD method, which is grounded in the Dempster-Shafer theory and incorporates the enhanced belief Jensen-Sharma-Mittal (EBJSM) divergence. Finally, air quality datasets are used to validate the practicality of this method through sensitivity analysis, simulation analysis, comparative analysis, and statistical analysis.

This study develops a risk contagion framework for identifying systemically important banks (SIBs), focusing on interbank lending and common asset holdings. Credit easing intensity is incorporated into the model to evaluate its impact on risk contagion, and a novel approach for identifying SIBs is proposed. A case study of China’s banking system reveals three key results: (1) several large banks are identified as SIBs due to their capacity to generate substantial systemic risk, and a joint analysis of both contagion channels reveals higher systemic risk than separate single-channel analysis; (2) the rising average DebtRank from 2020 to 2023 reflects increased risk accumulation and heightened systemic vulnerability in the banking system; (3) systemic risk declines with greater credit easing, but contagion through common asset holdings remains more volatile than through interbank lending. These findings provide forward-looking insights for maintaining financial stability and strengthening the prudential regulation of SIBs.

the increasing penetration of renewable energy sources into modern power systems is accelerating the global energy transition but also introducing greater operational variability and uncertainty, especially under extreme weather events. This paper analyzes the necessity of enhancing resilience in high-renewable penetration power systems, identifies limitations in conventional protective measures, and proposes a dual-layer resilience framework. The framework integrates long-term planning strategies, such as infrastructure hardening, technical standard unification, and advanced technology deployment, with short-term emergency operational strategies, including pre-event prevention, real-time response, and post-event recovery. The proposed approach addresses renewable variability, compound hazards, and interdependent infrastructure vulnerabilities. Implementation guidelines, supported by recent case studies, demonstrate how coordinated strategies can improve system adaptability, recovery capability, and service continuity under extreme conditions. This work provides practical guidance for policymakers and utilities to ensure secure, sustainable power supply in the context of climate change.

This study develops a model to analyse risk contagion and feedback effects between the banking sector and industry sectors. To quantify the systemic impact of exogenous shocks, a set of systemic risk metrics is introduced. The results, derived from a case study using data from China's banking and industry sectors, indicate that neglecting the bidirectional and dynamic contagion between banks and industries may lead to an underestimation of systemic risks triggered by exogenous shocks. Moreover, exogenous shocks to banks propagate to industry sectors primarily through lending relationships, with the contagion extent and pattern varying according to the type of bank affected. Additionally, preventing large banks and core industries from such shocks is crucial for maintaining macroeconomic stability. These findings highlight the importance of coordinated cross-sectoral risk monitoring and provide policy-relevant insights for improving the resilience of the financial and real sectors.

Electromagnetic flowmeter has advantages such as non-invasiveness, no moving parts and high reliability. And they are not affected by physical parameters like fluid density, temperature, pressure, and viscosity. However, they can be influenced by non-uniform distribution of medium conductivity. In this paper, the theoretical model of electromagnetic flowmeter under annular conductivity distribution was established based on the fundamental control equation and the annular domain weight function. To verify the effectiveness of the theoretical model, COMSOL Multiphysics numerical simulation and experimental validation study were both conducted. The relative deviations between the theoretical value and the numerical simulation, as well as the experimental result, are +/- 4 % and +/- 1 %, respectively. This study provides a theoretical basis for expanding the application of electromagnetic flowmeter to annular conductivity distribution.

The construction of smart grids is crucial in responding to rapid urbanization and growing energy demand. However, assessing the benefits of their investment is one of the main challenges in the construction process. This study presents a new decision-making model aimed at addressing the smart grid investment benefit assessment problem from a dynamic perspective. First, dynamic Fermatean fuzzy Schweizer–Sklar aggregation operators with specific properties are proposed to aggregate the dynamic evaluation information. Then, we construct an evaluation index system with economic, technical, environmental, and social criteria. Furthermore, a dynamic multicriteria decision-making model, referred to as the dynamic Fermatean fuzzy combined compromise solution method, is proposed based on the proposed aggregation operators. Finally, to validate the practicality of the proposed framework, we conducted a detailed assessment of the investment benefits of four smart grid projects. The primary novelty of this study lies in its simultaneous consideration of dynamic temporal factors and high-order fuzzy uncertainty within the investment evaluation process, which has been largely overlooked in prior work. Our approach can adjust to changes in information reliability and importance over time, improving the adaptability of decision outcomes. The findings reveal that the proposed framework can effectively integrate various complex evaluation data and accurately reflect the performance of each smart grid project across different dimensions. Sensitivity and comparative analyses verify their credibility and applicability in practical applications, demonstrating their superiority in handling dynamic decision-making scenarios. This model provides power companies with a set of tools and methods to help them conduct more accurate investment assessments.

Pre-disaster protection strategies are essential to enhance electric power system resilience against uncertain natural disasters. Considering budget constraints, interdependencies with other infrastructure systems, and uncertainty in disaster scenarios, this study proposes a two-stage stochastic optimization model to maximize the expected value of resilience (EVR) for integrated infrastructure systems. A case study on energy infrastructures in the Greater Toronto Area (GTA) demonstrates the effectiveness of the proposed model. Results indicate that increasing the protection budget significantly improves system resilience, yet the marginal benefits diminish beyond a certain threshold. Additionally, optimal protection strategies differ according to available budgets, emphasizing the importance of strategic resource allocation. Sensitivity analysis further highlights the necessity of considering disaster intensity when determining optimal budget allocations.

Large-scale real estate loan defaults have frequently occurred in China in recent years. This study proposes a model to describe the risk contagion process within the banking system under such loan default shocks. The model incorporates both interbank debt default contagion and asset liquidity contagion arising from fire sales of bank assets. Using data from listed banks in China, numerical experiments systematically examine the impact of different real estate loan default scenarios on banking system stability. The results show that: (i) although the banking system is resilient to small-scale shocks, it becomes vulnerable to large-scale shocks stemming from real estate loan defaults. Preventing major shocks in key banks is crucial for maintaining financial stability. (ii) Developing asset sale strategies for illiquid assets from a system-wide perspective can mitigate the negative impact of loan defaults on the banking system. (iii) Adjustments to monetary policy, such as changes to banks' statutory leverage ratios and the price sensitivity of illiquid assets, can enhance banks' ability to withstand loan defaults. These findings offer valuable insights for policymakers in developing effective response strategies to reduce the impact of real estate loan defaults on the banking system.

We propose a dynamic asset pricing model that features asymmetric extrapolative beliefs and a dynamic trading population to investigate the behavioral drivers of asset bubbles. In the model, extrapolators place more weight on past negative returns than on positive ones when forming their beliefs about future returns, and the trading population varies as investors enter or exit the market in response to past performance. Exit is defined as a complete withdrawal from the market, while previously nonparticipating investors enter through an uncertain process, with an entry probability reflecting this uncertainty. The model generates classical price bubbles with high trading volume as well as negative bubbles following a crash. The results show that the interaction between asymmetric extrapolative beliefs and a dynamic trading population plays a critical role in bubble formation. Moreover, when extrapolators have prospect theory preferences rather than constant absolute risk aversion preference, the bubble effects become more pronounced.

We propose a dynamic asset pricing model that features asymmetric extrapolative beliefs and a dynamic trading population to investigate the behavioral drivers of asset bubbles. In the model, extrapolators place more weight on past negative returns than on positive ones when forming their beliefs about future returns, and the trading population varies as investors enter or exit the market in response to past performance. Exit is defined as a complete withdrawal from the market, while previously nonparticipating investors enter through an uncertain process, with an entry probability reflecting this uncertainty. The model generates classical price bubbles with high trading volume as well as negative bubbles following a crash. The results show that the interaction between asymmetric extrapolative beliefs and a dynamic trading population plays a critical role in bubble formation. Moreover, when extrapolators have prospect theory preferences rather than constant absolute risk aversion preference, the bubble effects become more pronounced.

The rapid development of edge artificial intelligence (AI) has brought about revolutionary changes in supply chain management (SCM). It not only provides real-time data processing capabilities but also accelerates the decision-making process, injecting more innovative elements into SCM. In this context, SCM platforms require new technological developments and effective evaluations, as they collectively drive the efficient coordination of complex business processes. SCM platforms enable the seamless coordination of various supply chain elements, facilitating streamlined operations and enhanced decision-making processes. The evaluation of these platforms not only serves to validate their performance and effectiveness, but also contributes to continual improvements in design and application, and addressing the evolving demands within the realms of global commerce and dynamic market conditions. The ongoing assessment and enhancement of SCM platforms play a crucial role in optimizing resource allocation, improving production efficiency, and fostering adaptability to changing market dynamics. However, the acquisition of assessment data often introduces imprecise data. Additionally, the large-scale nature and bounded rationality of decision-makers (DMs) significantly impact the evaluation of SCM platforms. This article aims to explore a collaborative large-scale information fusion approach and provide a robust fuzzy framework for evaluating SCM platforms. This approach employs a combination of spherical fuzzy sets (SFSs), large-scale group decision-making (LSGDM), behavioral theories and three-way decisions (TWD) to thoroughly explore collaborative large-scale information fusion and its practical application in assessing SCM platforms. It introduces an innovative spherical fuzzy (SF) LSGDM technique and integrates TWD with the inclusion of prospect theory (PT) and regret theory (RT) to mitigate potential decision risks. The developed collaborative large-scale information fusion approach is assessed for validity, effectiveness and practicality in the context of evaluating SCM platforms using online data. Experimental results demonstrate that this approach provides reasonable evaluation outcomes, considering uncertain information processing capabilities, large-scale characteristics, bounded rationality and decision risks.

Set pair analysis is a valuable tool for managing uncertain systems and can effectively handle missing information in the decision-making process. In addition, regret theory can objectively describe the impact of decision-maker psychology on decision-making. However, traditional decision-making methods rarely combine set pair analysis and regret theory to discuss decision problems. Thus, this article aims to integrate regret theory into the three-way decision theory based on set pair analysis, presenting a novel approach to address multi-attribute decision-making problems in real life. Specifically, we first propose a data-driven approach based on set pair analysis to determine attribute weights. Then, a similarity relationship is established based on the weighted distance to calculate conditional probability. Meanwhile, the average value of regret and joy under each attribute is selected as a reference point to determine the relative utility function using regret theory. Subsequently, we develop a new three-way decision method in incomplete information systems and apply it to solve practical problems. Finally, the effectiveness and superiority of our method are demonstrated by case analysis and comparison. We also conduct sensitivity analysis to examine the stability of the new method.

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Scientific and effective transmission mechanisms are the basis for optimizing the allocation of power resources on a larger scale. Studying the transmission rights mechanism of multi-level power markets will help to efficiently utilize transmission and distribution channels at all levels, scientifically handle congestion surplus, and ultimately contribute to the construction of a unified national power market system. This paper innovatively proposes an initial allocation and settlement model for transmission rights based on the principle of consistent responsibility and rights. The effectiveness of the model is verified through examples. This research can provide a theoretical research basis for the construction of China's transmission rights market and serve the needs of the construction of a unified national power market system.

Missing traffic data imputation is an important step in the intelligent transportation systems. Low rank approximation is an important method for the missing traffic data imputation, especially the low rank matrix/tensor completion. However, the low-rank matrix completion has a huge time cost and the low-rank tensor completion cannot fully extract the information and maintain non-negativity. Therefore, we propose a novel non-negative temporal dimension preserved tensor completion (NT-DPTC) model with ideas: a) proposing a novel dimension preserved (DP) tensor decomposition method, which decomposes a tensor into three latent factor tensors to fully extract the intrinsic feature, b) using a Sigmoid mapper for releasing non-negative constraint from the training process and increasing flexibility, c) exploiting temporal constrains and AdamW schemes for obtaining a higher accuracy and faster convergence. Experiments on four industrial application scenarios highlight the superiority of our proposed model when compared with the existing state-of-the-art models.

The foundation of intelligent computing and expert systems relies on the data processing conducted within the realm of multi-scale information systems (MSISs). Data processing within MSISs caters to diverse analytical needs, where the preference hierarchy among various factors plays a vital role in the consensus reaching process (CRP). Additionally, the three-way decisions (TWD) theory serves as an efficient tool, while regret theory (RT) quantifies decision-makers' risk inclinations associated with various psychological behaviors. Thus, the paper aims to introduce an innovative approach known as the CRP-TWD-RT-MSIS method. The study draws inspiration from spatial geometry, incorporating concepts like points, lines, surfaces, and bodies to create MSISs. This process generates a matrix of fuzzy preference relations (FPRs) among distinct decision-makers through data preprocessing. In addition, the paper explores feedback mechanisms based on identification and directional rules, as well as those rooted in minimal adjustments or cost considerations. Simultaneously, it takes negotiation and discussion time into account, culminating in the development of a personalized adjustment optimization model that focuses on minimizing time costs. In the end, the method's effectiveness and superiority are validated through a case study and a comparative analysis of real data from the Chinese Weather and Meteorological Bureau's website.

Pre-disaster protection strategies are essential for enhancing the resilience of electric power systems against natural disasters. Considering the budgets for protection strategies, the dependency of other infrastructure systems on electricity, and the uncertainty of disaster scenarios, this paper develops risk-neutral and risk management models of strategies for pre-disaster protection. The risk-neutral model is a stochastic model designed to maximize the expected value of resilience (EVR) of the integrated system. The risk management model is a multi-objective model prioritizing the minimization of risk metrics as a secondary goal alongside maximizing the EVR. A case study conducted on the energy infrastructure systems in the Greater Toronto Area (GTA) validates the effectiveness of the models. The findings reveal the following: (i) increasing the budget enhances the EVR of the integrated system; however, beyond a certain budget threshold, the incremental benefits to the EVR significantly diminish; (ii) reducing the value of the downside risk often results in an increase in the EVR, with the variation in Pareto-optimal solutions between the two objectives being non-linear; and (iii) whether for the risk-neutral or risk management protection strategies, there are reasonable budgets when considering disaster intensity and the cost of protection measures. The models can help decision-makers to select effective protection measures for natural disasters.

Enhancing decision-making under risks is crucial in various fields, and three-way decision (3WD) methods have been extensively utilized and proven to be effective in numerous scenarios. However, traditional methods may not be sufficient when addressing intricate decision-making scenarios characterized by uncertain and ambiguous information. In response to this challenge, the generalized intuitionistic fuzzy set (IFS) theory extends the conventional fuzzy set theory by introducing two pivotal concepts, i.e., membership degrees and non-membership degrees. These concepts offer a more comprehensive means of portraying the relationship between elements and fuzzy concepts, thereby boosting the ability to model complex problems. The generalized IFS theory brings about heightened flexibility and precision in problem-solving, allowing for a more thorough and accurate description of intricate phenomena. Consequently, the generalized IFS theory emerges as a more refined tool for articulating fuzzy phenomena. The paper offers a thorough review of the research advancements made in 3WD methods within the context of generalized intuitionistic fuzzy (IF) environments. First, the paper summarizes fundamental aspects of 3WD methods and the IFS theory. Second, the paper discusses the latest development trends, including the application of these methods in new fields and the development of new hybrid methods. Furthermore, the paper analyzes the strengths and weaknesses of research methods employed in recent years. While these methods have yielded impressive outcomes in decision-making, there are still some limitations and challenges that need to be addressed. Finally, the paper proposes key challenges and future research directions. Overall, the paper offers a comprehensive and insightful review of the latest research progress on 3WD methods in generalized IF environments, which can provide guidance for scholars and engineers in the intelligent decision-making field with situations characterized by various uncertainties.