Search Results (82)

This systematic review examines how Extended Reality (XR) technologies, i.e., Virtual (VR), Augmented (AR), Mixed (MR), and Spatial Augmented Reality (SAR) are designed, implemented, and evaluated in cultural heritage (CH) applications, addressing five research questions: (RQ1) How were XR technologies applied in CH between 2021 and 2025? (RQ2) What interaction paradigms are used, and how do they shape engagement and meaning making? (RQ3) What user experience outcomes are reported in XR CH applications? (RQ4) What evaluation methods are employed and what methodological gaps remain? (RQ5) What challenges persist across XR heritage implementations? Peer-reviewed, English-language studies reporting on implemented XR systems in CH contexts with empirical or evaluative data were included; conceptual articles without a described implementation, non-English publications, and studies published before January 2020 were excluded. Scopus, Web of Science, IEEE Xplore, and the ACM Digital Library were searched for publications dated January 2020 through March 2025, complemented by manual proceedings screening (SIGGRAPH, CHI, IMX, VRCAI) and backward/forward citation tracking. All databases were last searched in March 2025. Two independent researchers screened all records and extracted data; disagreements were resolved through structured discussion. Bias toward positive novelty outcomes was mitigated by including conference proceedings alongside journal articles to broaden the evidence base. A qualitative thematic synthesis was employed, as methodological heterogeneity across studies precluded statistical meta-analysis. Findings were organized inductively into four thematic domains through iterative coding and inter-author consensus. From an initial corpus of 359 records, 287 unique records were retained after deduplication; following title/abstract screening and full-text eligibility assessment, 64 studies were included in the final synthesis. The majority (60/64) were published between 2021 and 2025, with study sample sizes ranging from small expert cohorts (n ≈ 6) to large public deployments (n > 125). The thematic analysis across technology, interaction design, user experience, and evaluation reveals trends toward participatory, multiuser, and multimodal XR designs, reporting benefits including immersion, engagement, learning, and accessibility, alongside recurring challenges such as cost, usability, cybersickness, content authenticity, and lack of longitudinal evaluation. Beyond thematic description, using a cross-domain analytical synthesis, we identify the Design Coherence Framework for XR Heritage (DCF-XR); this is a four-dimensional interpretive model spanning technology, interaction design, user experience, and evaluation, which provides an original diagnostic lens for understanding the conditions under which XR effectively serves cultural heritage goals. A typology of four recurring design failure modes, derived inductively from the corpus, demonstrates that the most persistent shortcomings in the field arise not from the weakness of individual dimensions but from their misalignment with one another. Evidence is limited by the predominance of small convenience samples, single-session laboratory evaluations, and the absence of domain-specific standardized assessment instruments for XR in CH, which constrains the generalizability of reported outcomes. Targeted recommendations for rigorous, ethical, and inclusive XR practice in CH are presented, highlighting the need for longitudinal studies, open datasets, and standardized evaluation frameworks. This review received no external funding. This review was not pre-registered in a prospective register. Full article

The present paper proposes an adaptive steering weight allocation strategy based on a non-cooperative Stackelberg game and Model Predictive Control (MPC) for dynamic steering authority allocation in human–machine shared control of intelligent vehicles. First, the human–machine steering interaction is modelled as a Stackelberg game, and the steering control problem is formulated as an MPC optimization problem. The optimal control sequences of the driver and the Advanced Driver Assistance System (ADAS) under game equilibrium are then derived through backward induction. Subsequently, driver behaviour is classified as aggressive, moderate, or conservative according to lateral preview error and lateral acceleration, and the driver state is quantified using parametric indicators. Furthermore, by integrating potential field-based driving risk assessment with human–machine conflict intensity, a fuzzy logic-based dynamic weight adjustment mechanism is constructed. Simulation results show that when the steering intentions of the driver and the ADAS are highly consistent, the proposed strategy can effectively reduce driver workload and improve driving safety. In high-risk driving situations, the strategy automatically transfers more steering authority to the ADAS to enhance safety, whereas under low-risk conditions with strong human–machine steering conflict, greater driver authority is preserved to ensure that the vehicle follows the intended path. Hardware-in-the-loop experiments in lane-changing assistance scenarios further verify the effectiveness of the proposed strategy under different driving styles. Quantitative results show that, compared with manual driving, the proposed strategy reduces the maximum lateral overshoot by 98.75%, 85.54%, and 98.58% for aggressive, moderate, and conservative drivers, respectively. In addition, the peak yaw rate and driver control effort are significantly reduced, indicating smoother vehicle dynamic response and lower steering workload. These results demonstrate that the proposed strategy can effectively improve lane-change stability, reduce driver burden, and maintain safe and coordinated human–machine shared control. Full article

In the context of the rapid growth of the Internet of Things and mobile health services, sensors and smart wearable devices are continuously collecting and uploading dynamic health data. Together with the long-term accumulated electronic medical records and multi-source heterogeneous clinical data from healthcare institutions, these data form the cornerstone of intelligent healthcare. In the context of medical data sharing, previous studies have mainly focused on privacy protection and secure data transmission, while relatively few have addressed the issue of incentive mechanisms. However, relying solely on technical means is insufficient to solve the problem of individuals’ willingness to share their data. To address this challenge, this paper proposes a three-party Stackelberg-game-based incentive mechanism for medical data sharing. The mechanism captures the hierarchical interactions among the intermediator, electronic device users, and data consumers. In this framework, the intermediator acts as the leader, setting the transaction fee; electronic device users serve as the first-level followers, determining the data price; and data consumers function as the second-level followers, deciding on the purchase volume. A social network externality is incorporated into the model to reflect the diffusion effect of data demand, and the optimal strategies and system equilibrium are derived through backward induction. Theoretical analysis and numerical experiments demonstrate that the proposed mechanism effectively enhances users’ willingness to share data and improves the overall system utility, achieving a balanced benefit among the cloud platform, electronic device users, and data consumers. This study not only enriches the game-theoretic modeling approaches to medical data sharing but also provides practical insights for designing incentive mechanisms in IoT-based healthcare systems. Full article

Against the background of carbon neutrality, the impact of carbon quota allocation mechanism on supply chain’s decision-making of emission reduction has received increasing attention. This study analyzes the optimal decision-making behavior of manufacturing supply chains under three mechanisms: completely free, complete auction and hybrid. Meanwhile, the abatement cost-sharing contract is introduced and the backward induction method is applied to solve the optimal equilibrium solution under each mechanism. Combined with numerical simulation, this study further investigates the impacts of market demand and cost-sharing coefficient changes on the system profit. The result shows that the abatement cost-sharing contract can significantly improve the level of manufacturers’ abatement and the total profit of the supply chain. Among the mechanisms analyzed, the hybrid mechanism realizes the balance between efficiency and incentives and demonstrates stronger adaptability and policy flexibility. Full article

This paper develops a tractable method to solve a general equilibrium model with bank runs and exogenous leverage ratio restrictions, enabling welfare analysis of macroprudential policy across the business cycle. By computing bankers’ value functions via backward induction from steady state, the framework quantifies how leverage caps affect capital allocation, asset prices, and run probabilities during recovery from crises. Calibrated simulations show that welfare-enhancing policy is time-varying—lenient when households’ marginal utility of consumption is high, and restrictive in low-marginal-utility states. The results highlight a trade-off: tighter leverage restrictions improve stability but risk persistent efficiency losses if imposed too harshly after crises. Full article

Against the backdrop of the growing popularity of blockchain technology, this study investigates blockchain adoption strategies for the fresh food supply chain (FFSC) under a carbon cap-and-trade (CAT) policy. Taking a two-echelon supply chain consisting of a supplier and a retailer as an example, we designed four blockchain adoption modes based on the supplier’s strategy (adopt or not) and the retailer’s strategy (adopt or not). Combining influencing factors such as consumers’ low-carbon preference, consumers’ freshness preference, and carbon trading price (CTP), we established four game-theoretic models. Using backward induction, we derived the equilibrium strategies for the supplier and retailer under different modes and analyzed the impact of key factors on these equilibrium strategies. The analysis yielded four key findings: (1) BB mode (both adopt blockchain) is the optimal adoption strategy for both FFSC parties when carbon prices are high, and consumers exhibit strong dual preferences. It most effectively mitigates the negative price impact of rising carbon prices by synergistically enhancing emission reduction efforts and freshness preservation efforts, thereby increasing overall profits and achieving a Pareto improvement in the benefits for both parties. (2) Consumers’ low-carbon preference and freshness preference exhibit an interaction effect. These two preferences mutually reinforce each other’s incentive effect on FFSC efforts (emission reduction/freshness preservation). Blockchain’s information transparency makes these efforts more perceptible to consumers, forming a synergistic “emission reduction-freshness preservation” cycle that further drives sales and profit growth. (3) The adoption of blockchain by either the supplier or the retailer significantly lowers the cost threshold for the other party to adopt blockchain, thereby increasing their willingness to adopt. (4) CAT and consumer preferences jointly influence the adoption strategies of suppliers and retailers. Additionally, the adoption strategies of FFSC participants are also affected by the other party’s blockchain adoption status. Drawing on the above conclusions, this study provides actionable guidance for suppliers and retailers in selecting optimal blockchain adoption strategies. Full article

Amidst escalating concerns about climate change, manufacturers are increasingly pressured to adopt a low-carbon supply chain (LCSC). Financial constraints deter numerous companies from embracing low-carbon initiatives in a competitive landscape. Inclusive internal financing (IIF) provides operational funds from capital-abundant members to capital-constrained members, resolving funding shortages internally within the system. However, when dominant members cannot support all such enterprises, selective internal financing (SIF) or hybrid financing (HF) becomes necessary. This paper studies the operation and financing strategies of a competitive LCSC. Within the framework of an LCSC where two capital-constrained retailers compete, using Stackelberg game theory and the backward induction method, three game-theoretical models are developed under IIF, SIF, and HF. The results indicate that increased competition intensity reduces product sales price, the manufacturer’s carbon emission reduction level, and profit. When competition intensity is high, SIF more effectively enhances carbon emission reduction level, product sales quantity, and profit acquisition. HF reduces profits for the allied retailer and diminishes its competitiveness, yet enhances the competitive strength of the rival retailer. Numerical analysis demonstrates that when equity financing in HF exceeds 0.546, the allied retailer becomes unprofitable and is driven out of the market. This study complements LCSC finance research and provides references for supply chain operations and financing strategy formulation. Full article

This study examines how a low-carbon manufacturer strategically adopts blockchain technology and selects sales modes with a dual-purpose e-commerce platform that focuses on both profit and consumer surplus. We develop six game-theoretic models by combining three sales modes (agency, reselling, and dual modes) with two blockchain scenarios (adoption vs. non-adoption). Using backward induction, we derive equilibrium strategies for supply chain members and analyze the impacts of key parameters. Building on these analyses, we further investigate the joint decision-making of blockchain adoption and sales mode selection, exploring how the platform’s consumer surplus concern influences manufacturer decisions, and evaluating the economic value created by blockchain under alternative sales modes, ultimately leading to three key findings: (1) The agency mode is generally preferred in most cases, especially when the platform has a moderate level of concern for consumer surplus. Blockchain adoption is only recommended when its unit operational cost is below certain thresholds, and it can significantly impact the choice between agency and dual modes based on the platform’s consumer surplus concern. (2) Platform’s degree of consumer surplus concern exerts a negligible effect on manufacturer’s sales mode selection without blockchain, but it becomes crucial and can trigger a shift to the dual mode when blockchain is adopted. (3) Blockchain generates the greatest economic value for the manufacturer under the dual mode, regardless of cost thresholds. For platforms, the optimal strategy depends on blockchain’s unit operational cost, with the reselling mode being optimal for low cost and the agency mode preferred for higher cost. Full article

To elucidate the compositional characteristics and sources of heavy metals in aerosols at China’s Great Wall Station in Antarctica, high-volume aerosol sampling was conducted from 4 January to 26 December 2022, on Fildes Peninsula, King George Island. Ten heavy metals (V, Cr, Mn, Fe, Ni, Cu, Zn, As, Cd, and Pb) in total suspended particulates (TSPs) were quantified via inductively coupled plasma mass spectrometry (ICP-MS). Enrichment factor (EF) analysis, correlation metrics, and backward trajectory clustering were integrated to identify potential sources. The results revealed pronounced enrichment (EF > 10) for Cr, As, Zn, Cd, and Pb, indicating dominant non-crustal contributions. Source apportionment identified three pathways: (1) long-range transported anthropogenic emissions, including Southern Hemisphere marine traffic (e.g., V and Ni from ship fuel combustion) and industrial pollutants from South America (Pb and Cd); (2) local anthropogenic sources, primarily diesel generators and tourism-related gasoline combustion (Cu and Zn); and (3) crustal inputs via glacial melt and weathering (Fe and Mn). This study pioneers the quantification of direct anthropogenic impacts (e.g., power generation and tourism) on aerosol heavy metals in Antarctic research zones, offering critical insights into transboundary pollutant dynamics and regional mitigation strategies. Full article

With the improvement of living standards, consumer demand for fresh produce has witnessed a remarkable upsurge. Fresh products present significant preservation challenges, as their freshness directly correlates with sales performance and ultimately impacts the sustainable development of the fresh supply chain. Enhancing freshness preservation standards, boosting market demand, strengthening brand reputation, and promoting the development of a fresh supply chain are urgent problems that need to be solved. This paper delves into the dynamic optimal decision-making processes within a fresh food supply chain, which is composed of a supplier and a retailer, under different government subsidy scenarios: centralized decision-making, decentralized decision-making under supplier myopia, and decentralized decision-making under supplier’s foresight. Herein, a differential game model is constructed, and through the utilization of dynamic optimization and backward induction techniques, feedback strategies for various decision-making paradigms are derived. A comparative evaluation of decision-making models is conducted, grounded in theoretical frameworks and substantiated through numerical simulations, to assess critical parameter impacts. The results indicate the following: (1) the effect coefficients of consumer preference and preservation efforts show significant positive correlations with suppliers’ preservation strategies and retailers’ promotional tactics, respectively; (2) supply chain profitability reaches its Pareto-optimal state under centralized decision-making structures, while myopic supplier behavior leads to systematically lower profits than foresighted supplier strategies; (3) retailers demonstrate stronger collaboration preferences toward foresighted suppliers, as such partnerships ensure enhanced quality consistency and supply continuity; and (4) government agencies should implement dynamically adjusted subsidy schemes during preservation initiatives to facilitate sustainable operational frameworks in fresh produce supply chains. Full article

We study a duel game in which each player has incomplete knowledge of the game parameters. We present a simple, heuristically motivated and easily implemented algorithm by which, in the course of repeated plays, each player estimates the missing parameters and consequently learns his optimal strategy. Full article

Cap-and-trade is widely recognized as an effective mechanism for curbing carbon emissions, and it significantly influences the operational decisions within supply chains. This study investigates a three-echelon closed-loop supply chain (CLSC) consisting of one original equipment manufacturer, one traditional retailer, and one independent third-party collector. The manufacturer invests in cleaner technologies to produce green products and remanufactures new products from used items recycled by the third-party collector. Considering different channel power structures, three Stackelberg game models are developed, and their optimal solutions are derived using the backward induction. Additionally, the combined effects of remanufacturing-related and carbon-related parameters on economic and environmental benefits as well as social welfare are investigated under different settings. Moreover, the derived results are validated via numerical simulation. The findings indicate that: (1) Each channel member is incentivized to act as the leader role within the CLSC to maximize profits. (2) A loose cap-and-trade regulation is conducive to enhancing the emission abatement rate, collection rate, and overall performance for the CLSC. (3) The retailer-led model is the best option for capturing more economic benefits and social welfare, while the third party-led model can always achieve the best environmental performance regardless of carbon trading price. These research findings can provide valuable insights for policymakers and decision makers engaged in CLSC. Full article

The growth in carbon emissions is increasingly exacerbating global warming. As the principal source of carbon emissions, companies can effectively enhance their emission reduction levels through the vertical spillover of emission reduction technologies to investigate the impact of vertical spillover rates, consumers’ low-carbon preference coefficients, emission reduction cost coefficients on optimal supply chain decisions, and profits in centralized and decentralized decision-making environments. Considering consumers’ preferences for low-carbon options, this paper constructs a Stackelberg game model under centralized and decentralized supply chain decision-making scenarios. It examines the effects of considering the vertical spillover effects of emission reduction versus not taking them into account. Using backward induction, this study optimizes the emission reduction levels of leading suppliers and manufacturers. The results indicate that an increase in consumers’ low-carbon preference levels and vertical spillover rate not only enhances the emission reduction levels of suppliers and manufacturers but also increases their profits. Conversely, an increase in emission reduction cost coefficients impedes emission reductions, with a more pronounced effect under vertical spillover conditions. In centralized decision-making, increases in vertical spillover rates, consumers’ low-carbon preferences, and decreases in emission reduction cost coefficients create a synergistic effect, resulting in greater increases in emission reduction levels and profits for suppliers and manufacturers compared to the sum of the effects of changes in individual coefficients. This finding provides new insights for governments in formulating relevant policies to promote corporate emission reductions. Full article

We study Asynchronous Dynamic games and show that in games with a finite state space and finite action sets, one can obtain the pure strategy Markov perfect equilibrium by using a simple backward induction method when the time period for the game is finite. The equilibrium strategies for games with an infinite horizon are then obtained as the point-wise limit of the equilibrium strategies of a sequence of finite horizon games, where the finite horizon games are truncated versions of the original game with successively longer time periods. We also show that if the game has a fixed K-period cycle, then there is a stationary Markov equilibrium. Using these results, we derive an algorithm to compute the equilibrium strategies. We test the algorithm in three experiments. The first is a two-player asynchronous game with three states and three actions. In the second experiment, we compute the equilibrium of a cybersecurity game in which there are two players, an attacker and a defender. In the third experiment, we compute the stationary equilibrium of a duopoly game with two firms that choose an output in alternate periods. Full article

Under the background of the Energy Internet and the shared economy, it is of great significance to explore the collaborative planning strategies of multi-energy microgrids (MEMGs) and a shared energy storage operator (SESO) supported by shared energy storage resources. In this context, a joint planning method of SESO and MEMG alliances based on a dynamic game with perfect information is proposed in this paper. First, an upper-level model for energy storage capacity configuration and pricing strategy planning of SESO is proposed to maximize the total planning and operational income of SESO. Then, a lower-level model for the optimal configuration of MEMGs’ alliance considering SES is proposed to minimize the total planning and operational costs of the MEMG alliance. On this basis, a solving algorithm based on the dynamic game theory with perfect information and the backward induction method is proposed to obtain the Nash equilibrium solution of the proposed bi-level optimization models. Finally, a case study with one SESO and an alliance consisting of five MEMGs is conducted, and the simulation results show that the proposed bi-level optimization method can increase SESO’s net income by 1.47%, reduce the average planning costs for each MEMG at least by 1.7%, and reduce model solving time by 62.9% compared with other counterpart planning methods. Full article