Search Results (291)

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

In practice, platforms are likely to target popular or niche product markets to introduce store brands (SBs). However, existing studies on horizontal SB positioning mainly focus on product similarity or attribute differentiation and do not clarify how such positioning should be chosen when national brand manufacturers (NBMs) strategically respond through innovation. Motivated by the conflict between the NBs and SBs, as well as the upstream–downstream co-opetition induced by the platform’s dual role, we develop a game-theoretic model to analyze the interplay between the platform’s SB positioning strategy and the NBM’s core category innovation decisions to provide new insights for promoting supply chain coordination. We find that first, when consumers prefer the SB product intended for the popular market, the platform should introduce an SB targeting the popular market if the NBM is expected to either refrain from innovation or allocate innovation efforts to the popular NB product. However, this decision may change if the NBM directs innovation efforts toward the niche NB product instead. Second, when confronting the invasion of SB, the NBM should reduce the wholesale price of the affected NB product and increase innovation efforts for that product. Additionally, under the reselling mode, a “win-win” outcome can only be achieved when the NBM directs innovation efforts to the product categories affected by SB invasion. In contrast, under the agency mode, Pareto optimality can be achieved regardless of whether the NBM allocates innovation efforts to affected or unaffected product categories. Full article

Efficient resource allocation is a central challenge in multi-tenant cloud, fog, and edge environments, where heterogeneous tenants compete for shared resources under dynamic and uncertain workloads. Static or purely heuristic methods often fail to capture strategic tenant behavior, whereas many existing game-theoretic approaches overlook stochastic demand variability, fairness, or scalability. This paper proposes a Dynamic and Stochastic Game-Theoretic Allocation (DSGTA) model that jointly models non-cooperative tenant interactions, repeated strategy adaptation, and random workload fluctuations. The framework combines a Nash-like dynamic equilibrium, achieved via a lightweight best-response update rule, with an approximate Shapley-value-based fairness mechanism that remains tractable for large tenant populations. The model is evaluated on synthetic scenarios, with a trace-driven setup built from the Google 2019 Cluster dataset, and a scalability study is conducted with up to $K=500$ heterogeneous tenants. Using a consistent set of core metrics (tenant utility, resource cost, fairness index, and SLA satisfaction rate), DSGTA is compared against a static game-theoretic allocation (SGTA) and a dynamic pricing-based allocation (DPBA). The results, supported by statistical significance tests, show that DSGTA achieves higher utility, lower average cost, improved fairness and competitive utilization across diverse strategy profiles and stochastic conditions, thereby demonstrating its practical relevance for scalable, fair, and economically efficient resource allocation in realistic multi-tenant cloud environments. Full article

Against the backdrop of the rapid growth in the scale of the prepared food market, safety issues have gradually become prominent. Establishing a traceability system has become crucial to safeguarding consumer rights and promoting the sustainable development of the industry, with traceability information sharing serving as the core link. However, affected by differences in interest demands and information asymmetry between manufacturers and retailers in the prepared food supply chain, there are obstacles to traceability information sharing. To explore the coordination mechanism of traceability information-sharing behavior in the prepared food supply chain under different decision-making models and its impact on profit distribution, this paper constructs a two-level supply chain model including manufacturers and retailers, comprehensively considers the online–offline dual-channel sales model, and distinguishes four scenarios: centralized decision-making, decentralized decision-making, retailer-led cost-sharing contract decision-making, and manufacturer-led cost-sharing contract decision-making. Using a differential game model, the equilibrium results under different decision-making models are discussed. The validity of the model is verified through fitting with empirical analysis and numerical example analysis. The research results show the following: (1) The centralized decision-making model has the best effect on increasing the market share of the prepared food supply chain, and although the cost-sharing contract model can improve it, there is still a gap. (2) The centralized decision-making model is not the one with the maximum profit, and manufacturer-led cost-sharing decision-making basically achieves Pareto optimality. The main reasons are the insufficient incentive mechanism, high coordination costs, and uneven profit distribution in centralized decision-making. (3) The impact of manufacturers’ offline channel traceability information-sharing behavior on profits is more significant than that of online channels. (4) In a market environment with information asymmetry, the impact of goodwill on the profits of prepared foods is more prominent. This research provides a theoretical basis for the management of the prepared food supply chain, helps optimize the traceability information-sharing mechanism and profit distribution plan, and promotes the healthy development of the industry. (5) When the coefficient measuring the intensity of traceability information sharing’s impact on product quality across manufacturers’ online and offline channels increases, only under the retailer-led model does product quality and goodwill exhibit a fluctuating trend of “rising from the bottom to the second place and then falling back to the bottom,” while the profits of all subjects increase simultaneously. (6) As the system attenuation coefficient increases, the evolution of product quality and goodwill under different cooperation models shows significant differences; in terms of profits, the profits of manufacturers’ online channels increase over time, while those of other subjects decrease. (7) When the discount rate rises, the manufacturer-led model presents distinct characteristics: both the ranking and absolute value of product quality decline synchronously, the ranking of goodwill falls, but its absolute value rises against the trend, the evolution of product quality and goodwill shows obvious model heterogeneity, and the profits of all subjects generally decrease. Full article

The rapid advancement of artificial intelligence has driven a surge in demand for computing power. As the core computing infrastructure, data centers have expanded in scale, escalating electricity consumption and magnifying a regional mismatch between computing capacity and energy resources: facilities are concentrated in the energy-constrained East, while the renewable-rich West possesses vast, untapped hosting capacity. Focusing on cross-regional data-center migration under the “Eastern Data, Western Computing” initiative, this study constructs a tripartite evolutionary game model comprising the Eastern Local Government, the Western Local Government, and data-center enterprises. The central government is modeled as an external regulator that indirectly shapes players’ strategies through policies such as energy-efficiency constraints and carbon-quota mechanisms. First, we introduce key parameters—including energy efficiency, carbon costs, green revenues, coordination subsidies, and migration losses—and analyze the system’s evolutionary stability using replicator-dynamics equations. Second, we conduct numerical simulations in MATLAB 2024a and perform sensitivity analyses with respect to energy and green constraints, central rewards and penalties, regional coordination incentives, and migration losses. The results show the following: (1) Multiple equilibria can arise, including coordinated optima, policy-failure states, and coordination-impeded outcomes. These coordinated optima do not emerge spontaneously but rather depend on a precise alignment of payoff structures across central government, local governments, and enterprises. (2) The eastern regulatory push—centered on energy efficiency and carbon emissions—is generally more effective than western fiscal subsidies or stand-alone energy advantages at reshaping firm payoffs and inducing relocation. Central penalties and coordination subsidies serve complementary and constraining roles. (3) Commercial risks associated with full migration, such as service interruption and customer attrition, remain among the key barriers to shifting from partial to full migration. These risks are closely linked to practical relocation and connectivity constraints—such as logistics and commissioning effort, and cross-regional network latency/bandwidth—thereby potentially trapping firms in a suboptimal partial-migration equilibrium. This study provides theoretical support for refining the “Eastern Data, Western Computing” policy mix and offers generalized insights for other economies facing similar spatial energy–demand asymmetries. Full article

Non-Fungible Tokens (NFTs) have transformed digital ownership, offering unique representations of assets such as art, collectibles, and virtual property. However, pricing NFTs remains a complex and underexplored issue. This study addresses two core questions: what determines NFT prices? And how are prices set in NFT markets? We conduct a comprehensive literature review and market analysis to identify both endogenous and exogenous price determinants. Trait rarity emerges as the most influential intrinsic factor, while cryptocurrency value stands out as a major external influence, albeit with ambiguous effects. Other factors include visual aesthetics, scarcity, utility in games, social media engagement, and broader market sentiment. As to pricing mechanisms, aside from fixed pricing (which is accepted in all marketplaces), NFT marketplaces primarily utilise auctions for art pieces and collectibles— especially English and Dutch formats—which are effective at capturing the buyer’s willingness-to-pay. Full article

Eye-tracking for user experience analysis has traditionally relied on dedicated hardware, which is often costly and imposes restrictive operating conditions. As an alternative, solutions utilizing ordinary webcams have attracted significant interest due to their affordability and ease of use. However, a major limitation persists in these vision-based methods: sensitivity to head movements. Therefore, users are often required to maintain a rigid head position, leading to discomfort and potentially skewed results. To address this challenge, this paper proposes a robust eye-tracking methodology designed to accommodate head motion. Our core technique involves mapping the displacement of the pupil center from a dynamically updated reference point to estimate the gaze point. When head movement is detected, the system recalculates the head-pointing coordinate using estimated head pose and user-to-screen distance. This new head position and the corresponding pupil center are then established as the fresh benchmark for subsequent gaze point estimation, creating a continuous and adaptive correction loop. We conducted accuracy tests with 22 participants. The results demonstrate that our method surpasses the performance of many current methods, achieving mean gaze errors of 1.13 and 1.37 degrees in two testing modes. Further validation in a smooth pursuit task confirmed its efficacy in dynamic scenarios. Finally, we applied the method in a real-world gaming context, successfully extracting fixation counts and gaze heatmaps to analyze visual behavior and UX across different game modes, thereby verifying its practical utility. Full article

The metaverse represents a transformative integration of virtual and physical worlds, offering unprecedented opportunities for social interaction, commerce, education, healthcare, and entertainment. Establishing trust in these expansive and decentralized environments remains a critical challenge. Blockchain technology, with its decentralized, secure, and immutable nature, is emerging as an essential pillar of trust and digital asset ownership within the metaverse. This paper provides an extensive review of blockchain-enabled trust and reputation frameworks specifically tailored to metaverse ecosystems. We present an in-depth analysis of existing blockchain solutions across diverse metaverse domains, including gaming, virtual real estate, healthcare, and education. Our core contributions include a comprehensive taxonomy that classifies current trust and reputation schemes by their underlying mechanisms, threat models addressed, and their architectural strategies. We provide a comparative benchmark analysis evaluating key performance metrics such as security robustness, scalability, user privacy, and cross-platform interoperability, revealing critical trade-offs inherent in current designs. Our analysis finds that score-based designs trade scalability for nuanced reputation representation, while SSI- and SBT-based approaches improve Sybil-resistance but introduce significant privacy governance challenges. Finally, we outline unresolved research challenges, including cross-platform reputation portability, privacy-preserving computation, real-time trust management, and standardized governance structures. Full article

As the demand for maritime transportation of power battery shipping containers grows rapidly, the incidence of fire accidents has increased in tandem. However, most studies focus on analyzing fire causes through the thermal runaway mechanism; few analyze fire risk across the full maritime transportation process from a safety science perspective. To fill this gap, based on the thermal runaway mechanism of lithium-ion batteries, this study couples the loading characteristics of shipping containers with maritime operating conditions and employs the Fault Tree (FT) model, Bayesian Network (BN) model, and Attack–Defense Game Theory for investigation. The results are as follows: Starting from three core factors—battery thermal runaway mechanism, scenario characteristics of shipping container maritime transportation, and failure of initial emergency response—and combining the FT model, it qualitatively identified and systematically sorted accident-causing factors. Via the FT-BN conversion criteria and expert assessment results, the fire probability of po’wer battery shipping containers on the target route was calculated to be 35%. According to Attack–Defense Game Theory, two key risk evolution pathways were identified with occurrence probabilities of 3.77% and 4.35%, respectively. Meanwhile, their action mechanisms were elaborated on, and the targeted preventive measures were proposed. This study provides theoretical support and methodological reference for the systematic assessment of fire risks associated with power battery shipping containers in maritime scenarios. Full article

Regional economic diversity and unevenly allocated space-based resources have created unprecedented difficulties for collaborative and innovative supply chain construction. This paper sets up a tripartite evolutionary model of the government, upstream companies, and downstream companies to explore dynamic processes of regional supply chain collaborative innovation with bounded rationality. Through incorporation of hierarchical space organizations and policy incentive differentiation mechanisms, the model discerns actors’ behavioral evolution and strategic adjustment in a geographically divided structure. Adopting evolutionary game theory and numerical simulation, this paper includes crucial parameters like the conversion efficiency of return conversion, information-sharing coefficient, mutual trust coefficient, and fiscal subsidy coefficient for examining policy and spatial heterogeneity effects on information collaborative innovations. The results reveal that fiscal incentives are the primary driving factor for collaborative evolution across local supply chains. Adaptive profit-sharing and subsidy intensities both stimulate upstream innovation investments and downstream cooperation adoption efficiently, stimulating a shift out of inefficient equilibrium states towards sustainable high-cooperation states. Furthermore, the restructuring of space accelerates hierarchical differentiation—core region companies are able to act like initiators and leaders for collaborative innovations, while periphery companies encounter participatory barriers in terms of elevated coordination costs and incentive shortages. In light of this, it is therefore crucial to have a “core-driven, periphery-subsidized” policy system for eliminating spatial gaps, stimulating cross-regional information exchange, and building systemic robustness. These findings contribute to enhancing the overall efficiency, stability, and innovation capacity of regional supply chain systems. They also provide a theoretical basis for policy decision making and industrial upgrading across regions of varying scales and environments. Full article

Hand rehabilitation requires consistent, repetitive exercises that can often reduce patient motivation, especially in home-based therapy. This study introduces ReHAb Playground, a deep learning-based system that merges real-time gesture recognition with 3D hand tracking to create an engaging and adaptable rehabilitation experience built in the Unity Game Engine. The system utilizes a YOLOv10n model for hand gesture classification and MediaPipe Hands for 3D hand landmark extraction. Three mini-games were developed to target specific motor and cognitive functions: Cube Grab, Coin Collection, and Simon Says. Key gameplay parameters, namely repetitions, time limits, and gestures, can be tuned according to therapeutic protocols. Experiments with healthy participants were conducted to establish reference performance ranges based on average completion times and standard deviations. The results showed a consistent decrease in both task completion and gesture times across trials, indicating learning effects and improved control of gesture-based interactions. The most pronounced improvement was observed in the more complex Coin Collection task, confirming the system’s ability to support skill acquisition and engagement in rehabilitation-oriented activities. ReHAb Playground was conceived with modularity and scalability at its core, enabling the seamless integration of additional exercises, gesture libraries, and adaptive difficulty mechanisms. While preliminary, the findings highlight its promise as an accessible, low-cost rehabilitation platform suitable for home use, capable of monitoring motor progress over time and enhancing patient adherence through engaging, game-based interactions. Future developments will focus on clinical validation with patient populations and the implementation of adaptive feedback strategies to further personalize the rehabilitation process. Full article

A strategic fulcrum for leading high-quality economic development and shaping the nation’s future. Core competitiveness lies in how governments can effectively stimulate consumer demand for green consumption and motivate enterprises to pursue green technology innovation through the development of precise and efficient innovative regulation models. In this paper, a tripartite evolutionary game model is constructed based on evolutionary game theory, encompassing the government, enterprises, and consumers. We analyze the strategic interactions and evolutionary path among these three entities under conditions of bounded rationality and information asymmetry. The research reveals the following: (1) the government can effectively guide enterprises towards genuine green innovation through enhanced rewards for substantive innovation and increased penalties for strategic innovation; (2) consumer purchasing decisions are significantly shaped by economic benefits, perceived social value, and government subsidies, with their market choices forming a critical external supervisory force; and (3) government regulatory strategies are dynamically adjusted in response to market integrity levels and social welfare, with a tendency to implement innovative regulation when “greenwashing” risk is elevated. In conclusion, simulation analysis is conducted using MATLAB 2018a, and governance recommendations are offered based on three dimensions: precise government regulation, enhanced corporate responsibility, and enhanced consumer capabilities. These recommendations offer both a theoretical basis and a practical path for establishing an integrated green innovation governance system based on incentive constraint empowerment. Full article

This study focuses on highway service areas. Building upon prior research that identified key influencing factors through surveys and ISM–MICMAC analysis, it constructs a tripartite evolutionary game model involving the government, service area operators, and carbon reduction technology providers based on stakeholder theory. Combined with MATLAB simulations, the model reveals the dynamic patterns of the carbon reduction system. The results indicate that government strategies exert the strongest influence on the system and catalyze the other two parties, followed by service area operators. Carbon reduction technology providers adopt a more cautious stance in decision-making. Government actions shape system evolution through a “cost-benefit-incentive” triple mechanism, with its strategies exhibiting significant spillover effects on other actors. Enterprise behavior is markedly influenced by Xinjiang’s regional characteristics, where the core barriers to corporate carbon reduction lie in the costs of proactive equipment and technological investments. The willingness of technology providers to cooperate primarily depends on two drivers: incremental baseline benefits and enhanced economies of scale. The core trade-off in government decision-making lies between the cost of strong regulation (Cg1) and the cost of environmental governance under weak regulation (Cg2). An increase in Cg1 prolongs the government’s convergence time by 233.3% and indirectly suppresses the willingness of enterprises and technology providers due to weakened subsidy capacity. Enterprises are relatively sensitive to the investment costs of carbon reduction equipment and technology, with convergence time extending by 120%. Technology providers are highly sensitive to incremental baseline returns (Rt), with stabilization time extending by 500%. Compared to existing research, this model quantitatively reveals the “cost-benefit-incentive” triple transmission mechanism for carbon reduction coordination in “grid-end” regions, identifying key parameters for strategic shifts among stakeholders. Based on this, corresponding policy recommendations are provided for all three parties, offering precise and actionable directions for the sustainable advancement of carbon reduction efforts in service areas. The research conclusions can provide a replicable collaborative framework for decarbonizing transportation infra-structure in grid-end regions with high clean energy endowments. Full article

A growing international consensus holds that science education must advance beyond content coverage to cultivate robust understanding of the Nature of Science (NoS)—how scientific knowledge is generated, justified, revised, and socially negotiated. Yet naïve conceptions persist among students and teachers, and effective, scalable classroom strategies remain contested. This narrative review synthesizes research and practice on games and playful activities that make epistemic features of science visible and discussable. We organize the repertoire into six families—(i) observation–inference and discrepant-event tasks; (ii) pattern discovery and rule-finding puzzles; (iii) black-box and model-based inquiry; (iv) activities that dramatize tentativeness and anomaly management; (v) deliberately underdetermined mysteries that cultivate warrant-based explanations; and (vi) moderately contextualized games. Across these designs, we analyze how specific mechanics afford core NoS dimensions (e.g., observation vs. inference, creativity, plurality of methods, theory-ladenness and subjectivity, tentativeness) and what scaffolds transform playful engagement into explicit, reflective learning. We conclude with pragmatic guidance for teacher education and curriculum design, highlighting the importance of language supports, structured debriefs, and calibrated contextualization, and outline priorities for future research on equity, assessment, and digital extensions. Full article

Initial attitude calibration is a critical yet challenging phase in hardware-in-the-loop (HIL) testing for space docking, often hindered by cumbersome procedures, safety concerns, and reliance on external equipment. This paper introduces a human–robot collaborative calibration method based on H∞ robust control. The core objective is to achieve symmetric pose alignment between docking mechanisms by allowing the operator to manually guide the test device, thereby rapidly obtaining initial attitude calibration results. An interactive model incorporating a time delay is established. Using H∞ synthesis, a stabilizing controller is designed to accurately track low-frequency operator commands while strongly suppressing high-frequency disturbances. Notably, the H∞ framework reconstructs an ideal interactive symmetry in human–robot collaboration by compensating for delays and disturbances. The solution to the Riccati equation within a game-theoretic framework effectively achieves symmetric optimization that balances tracking accuracy with safety constraints. Experimental results demonstrate that the method successfully compensates for system delays, enabling symmetric pose alignment while maintaining smooth and continuous motion of the docking mechanism. It also faithfully translates the operator’s low-frequency traction intent into motion. By retaining contact forces/torques within safe thresholds, the method balances interaction safety with operational precision, ultimately providing a reliable solution for initial attitude calibration in space docking HIL tests. Full article