Search Results (304)

To address the issue where information asymmetry in third-party logistics (3PL)-led low-carbon supply chain coordination undermines coordination efficiency, thereby threatening the sustainability and resilience of supply chain cooperation, this paper develops a Stackelberg dynamic game model with the 3PL as the leader. This model is constructed within the context where consumers’ low-carbon preferences influence product demand, and a government carbon cap policy is implemented. By comparing decentralized and centralized equilibria, we verify that centralized collaboration achieves dual gains: higher carbon reduction levels and greater overall supply chain profits, which strengthens sustainability and resilience. To address efficiency losses from three types of information asymmetry, we propose a two-stage dynamic coordination mechanism adapted to evolving cooperation transparency. At the initial stage with opaque information, a bargaining-power-weighted profit-sharing contract is adopted, where negotiation weights are quantified by enterprise scale, resource control and industry influence. After data transparency improves, the system switches to a Nash bargaining framework supported by blockchain carbon data sharing to realize stable long-term collaboration. Numerical cases and sensitivity analysis demonstrate that manufacturer cost information asymmetry is the primary constraint on coordination efficiency. The proposed dynamic coordination scheme effectively mitigates systemic complexity, balancing economic benefits and carbon reduction targets. This study provides practical pathways for supply chain participants to navigate complex low-carbon environments and advance sustainable, resilient supply chain operation. Full article

Environmental sustainability has become an increasingly critical issue in the inland shipping sector. The adoption of green-fuelled vessels, data-driven speed optimization enabled by digital and intelligent technologies, and the use of shore power during berthing can substantially reduce harmful emissions from shipping activities and enhance environmental performance. This study investigates the evolutionary stable strategy (ESS) of inland shipowners’ green initiatives under government subsidy schemes. Firstly, a decision-making framework is developed by incorporating price elasticity, market competition, green investment, and subsidy intensity, through which pricing, subsidies, demand, and profit decisions are jointly modeled. Secondly, a game-theoretic model involving two market participants under three alternative strategies is constructed, together with an effective solution approach. Thirdly, based on evolutionary game theory, the equilibrium strategies ultimately adopted by the majority of inland shipowners are derived, and sensitivity analyses of key parameters are conducted. The results indicate that: (1) shipowners implementing green strategies can achieve higher economic returns, and green strategies are expected to be adopted by approximately 71.55% of inland shipowners in the long-term; (2) governments should increase subsidy intensity in the early stage of green strategy development, while gradually reducing subsidies once the market reaches a stable equilibrium. The findings provide theoretical insights for inland shipowners’ strategic decisions in environmentally conscious markets and offer policy implications for governments seeking to design stable and effective subsidy mechanisms to promote the green transition of inland shipping services. Full article

A pursuer and an evader can exploit low-energy, non-Keplerian trajectories in cislunar space, making it difficult to obtain the saddle point for impulsive orbital pursuit–evasion games (OPEG). To address this problem, this paper first establishes a zero-sum differential game model based on the circular restricted three-body problem (CR3BP), where the terminal interception time is taken as the performance objective. The necessary optimality conditions for impulsive maneuvers are then derived using Pontryagin’s Maximum Principle (PMP), which transforms the optimal control problem into multipoint boundary value problems (MPBVPs). Subsequently, to overcome the high sensitivity of the MPBVPs to initial costate vectors in shooting methods, a two-layer hybrid initial-guess strategy combining a genetic algorithm with a time-domain coarse-grid search method is proposed for the single-impulse case. Furthermore, a receding-horizon strategy is introduced to generate the initial impulse sequence guess stage by stage for multiple-impulse cases. Finally, numerical simulations demonstrate that the proposed initial-guess strategy can effectively obtain the Stackelberg equilibrium solution for representative cislunar scenarios, including distant retrograde orbits (DROs) and Halo orbits. Meanwhile, the effects of observation delay and three-dimensional orbital characteristics on the game outcomes are also discussed based on dynamic game theory. Full article

International shipping decarbonization has expanded beyond technical and operational regulation to include contested market-based bargaining. The proposed International Maritime Organization (IMO) Net-Zero Framework (NZF) combines a marine fuel standard with an economic compliance mechanism. Drawing on IMO submissions, voting records, official statements, and domestic and sectoral materials, this two-level game analysis examines why support sufficient to approve the draft for circulation in April 2025 did not translate into timely adoption later that year. The evidence suggests that the initial agreement space was not durable enough to support adoption on the planned timetable. At Level I, bargaining involved three linked dimensions: rule-making authority, responsibility allocation, and transition pathway design. At Level II, domestic, regional, and sectoral constraints included regulatory credibility and investment signals for the European Union; sovereignty and cost concerns for the United States; trade exposure and disproportionate impacts for trade-exposed emerging economies; ambition and revenue-supported transition for small island developing States; and fuel availability, fleet competitiveness, and legal certainty for energy exporters and open registries. A coalition sufficient at one procedural stage may therefore not remain sufficient at the next. A more durable agreement space would likely require an IMO-centered regulatory core, targeted enabling support, an ambition floor with compliance flexibility, and sequenced implementation and review. Full article

In the current paper, pulmonary emphysema is hypothesized to emerge from a nonlinear breakdown of cooperation across two tightly coupled systems: the extracellular matrix (ECM) crosslink network and the cellular populations responsible for its maintenance. To formalize this concept, we construct a game-theoretic model that unifies the mechanical failure, inflammatory changes, and percolation-driven tissue collapse that are recognized features of the disease. At the ECM level, elastin and collagen crosslinks are modeled as players in an iterated Prisoner’s Dilemma, where cooperation corresponds to maintaining structural integrity, and defection corresponds to rupture under mechanical stress. At the cellular level, fibroblasts, macrophages, and neutrophils engage in a parallel strategic game in which repair reflects cooperative activity, and protease- or oxidant-producing phenotypes are indicative of defection. These parallel games are coupled through bidirectional payoff modulation, generating a dynamical system with bistability, tipping points, and runaway positive feedback. As the fraction of intact crosslinks falls below a critical percolation threshold, global network connectivity collapses and lung function drops precipitously. This framework explains the characteristic features of pulmonary emphysema, including spatial heterogeneity, abrupt acceleration, and irreversibility as emergent properties of coupled cooperation–defection dynamics, and identifies new leverage points for stabilizing cooperation and preventing catastrophic network failure in early disease. In support of this hypothesis, we present previously published studies from our laboratory involving measurements of elastin-specific desmosine crosslinks in human postmortem emphysematous lungs showing a marked increase in tissue crosslink density at the early stage of the disease, and accelerating loss of these crosslinks as airspace enlargement progresses, consistent with initial cooperation followed by defection. This conceptual framework is then applied to the poorly understood lung disease, Combined Pulmonary Fibrosis and Emphysema, to provide a potential mechanism for its pathogenesis. Full article

This study investigates whether holder-side RMB-related exposure is systematically associated with macroeconomic performance efficiency. Using a multi-objective efficiency framework rather than single macroeconomic indicators, we construct economy-year efficiency scores for 25 economies over 2005–2018 based on data envelopment analysis (DEA) and cross-efficiency evaluation. We then examine how these scores vary with RMB-related exchange-rate exposure and China-related value-added linkage proxies, while distinguishing RMB-specific exposure from broader trade integration and structural conditions. The results suggest conditional associations between holder-side RMB-related exposure and macroeconomic performance efficiency. The exchange-rate channel is positive and statistically significant under the baseline DEA specification (p < 0.01) when using PCSE. The estimated magnitude is economically modest and sensitive to alternative ICT proxies, efficiency benchmarks, and lag structures. By contrast, the GVC channel provides suggestive rather than confirmatory evidence, as China-related value-added linkages are not robustly significant under the revised fixed-effects specifications, augmented controls, or two-way fixed effects. The positive ERPT association is consistent in sign across the pooled CCR and genuine Game Cross-efficiency benchmarks. The VRS/BCC results are used as a complementary first-stage sensitivity check on the returns-to-scale assumption. The magnitude and statistical inference remain sensitive to alternative specifications and variance estimators. Overall, the findings should be interpreted as reduced-form, mechanism-consistent associations rather than causal effects of RMB internationalization. Full article

With the acceleration of port electrification, the recycling of retired batteries has become an emerging challenge for green port development and sustainable maritime logistics. Digitalized recycling technologies can improve battery traceability, condition assessment, and recycling coordination, but their implementation requires substantial investment. This study investigates how government subsidy mechanisms influence digitalized retired battery recycling decisions in green ports. A digitalized reverse supply chain consisting of a battery manufacturer, a retailer, and green ports as end users is considered. Based on a two-stage Stackelberg game framework, two policy scenarios are developed and compared: a no-government-subsidy (NG) scenario and a government-subsidy (TG) scenario. The analytical results demonstrate that government subsidies can promote digital technology adoption, enhance recycling demand, and improve supply chain profitability when green ports have strong preferences for digitalized recycling services and when digital investment costs remain within a reasonable range. The numerical simulations further indicate that the effectiveness of subsidies depends on the interaction between green port preference and the manufacturer’s digital investment cost coefficient. The findings provide theoretical and managerial implications by revealing how government subsidies, green port digital preferences, and digital investment costs jointly influence recycling decisions and supply chain performance. Specifically, the results suggest that governments should design differentiated subsidy mechanisms according to digital technology maturity and market conditions, manufacturers should optimize digital investment strategies in reverse supply chains, and green ports should strengthen digital traceability and coordination capabilities to improve retired battery recycling efficiency. Full article

Supply chain greening has become an important direction for sustainable development. This paper develops a two-stage game model involving a traditional manufacturer, a green manufacturer, and a retailer, to investigate the fairness concerns of the green manufacturer in a supply chain. Different from previous fairness concern models that focus on a single comparison relationship, this study incorporates both horizontal fairness concern with the competing traditional manufacturer and vertical fairness concern with the retailer into a unified framework for green supply chains. The results show that when traditional and green consumers hold equal market shares, the demand and profit of the green manufacturer are consistently higher than those of the traditional manufacturer, although this advantage diminishes as green R&D efficiency decreases. Moreover, increasing bidirectional fairness concern reduces the greenness of green products and significantly affects the retailer’s profit, while its impact on manufacturers’ profits is relatively limited. The effects of bidirectional fairness concern on product pricing depend on green R&D efficiency and the competitive relationship between green and traditional products. Based on these findings, managerial insights are provided for supply chain participants. Full article

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

To address three major industry pain points—the lack of quantified profit-sharing standards in the two-stage pricing under the separation model of military research and production, the absence of stable Nash equilibrium in single-layer synchronous optimization, and insufficient incentives for full-cycle process optimization in design units—this paper constructs a two-level Stackelberg leader–follower game model with the general contracting unit as the leader and design and general contracting units as followers. This aligns with current prototype incentives and phased pricing policies for production rewards and penalties. At the theoretical level, it improves the complete proof system for the two-stage concave profit two-level Stackelberg Nash equilibrium, distinguishes the mathematical differences in equilibrium existence between sequential decision-making and synchronous optimization, and extracts general rules for phased differentiated profit sharing: high-innovation segments should be allocated more profit weight; simply maximizing total alliance profit may cause imbalanced interests, while introducing a minimum net profit-weighted objective can achieve Pareto improvements without profit loss. This conclusion can be applied to multi-stage general contracting scenarios across industries, such as EPC and military–civil collaborative innovation, enriching the basic theory of profit sharing and hierarchical games. Theoretically, the existence of the lower-level Nash equilibrium is proven using Brouwer’s fixed-point theorem, and combining it with the strictly monotonically decreasing feature of the best response function, uniqueness of the equilibrium is derived. Multiple sets of differentiated initial values are simulated to rule out multi-equilibrium bifurcation risk. The model incorporates the military’s reward and penalty policies as rigid exogenous constraints, sets dual individual rationality constraints of ‘cooperative profit greater than baseline profit with no allocation, and both parties’ net profit non-negative,’ and introduces differentiated cost-reduction efficiency and quadratic increasing effort costs to characterize the heterogeneous input of the two types of development entities. For models with piecewise nonlinearity and multi-constraint nonconvex structures, this paper modifies the standard PSO into a Bi-PSO solving framework through hierarchical temporal adaptation. It does not innovate the underlying particle update mechanism and is only used to match the sequential decision order of the leader–follower game. By comparing five algorithms—IPM, GA, SA, DE, and adaptive PSO—through 20 repeated simulations: gradient-based interior point methods easily get stuck in locally invalid solutions that violate cooperation thresholds; differential evolution has the best numerical global search performance, but all general evolutionary algorithms optimize allocation and effort variables simultaneously, disrupting the Stackelberg hierarchical timing. Only Bi-PSO maintains consistent game logic. Using a pricing case for a certain type of equipment and jointly calibrating all parameters with policy documents, three simulation scenarios were set up: no allocation, equal 50/50 split, and single-layer profit maximization. Under the no-allocation mode, R&D investment from the design unit drops to zero and alliance benefits plummet; a blanket equal split ignores differences in technical contributions across two stages, leading to clear efficiency losses; single-layer optimization only pursues total profit maximization, causing a severe imbalance in profit distribution. The two-layer basic framework can achieve the upper limit of alliance benefits, and by adding a weighted optimization goal that considers both total profit and cooperation fairness, it can achieve equal net profits for both parties without reducing overall profit. Through single-parameter sweeps and two-factor heatmap simulations, the study further revealed the coupled effects of main party efficiency and mass production rewards and penalties on equilibrium input and optimal sharing ranges. A robust check was performed by replacing the logarithmic concave output function, producing a standardized allocation range resilient to parameter perturbations: optimal split for the prototype stage is 0.4–0.6 for the design unit, and for mass production stage 0.7–0.9. The findings suggest that high-contribution stages in multi-phase collaboration contracts should receive more benefits, and a weighted fairness objective can achieve Pareto improvements. These conclusions can extend to multi-stage collaboration scenarios such as EPC and military–civilian cooperation. Theoretically, this research further completes the equilibrium proof system for two-party concave payoff two-layer games, providing a new reference for the theory of phased differentiated benefit-sharing contracts in the military sector. Methodologically, it proposes a two-layer intelligent solving tool adapted to leader–follower sequential decisions, effectively mitigating issues where single-layer model equilibria fail or analytical algorithms struggle with multi-constraint nonconvex games. The results can provide quantitative support for the military, general contracting unit, and design unit in drafting equipment incentive pricing contracts and managing full-cycle cost collaboration. Full article

When marine chillers operate under complex marine conditions, they suffer from severe cross-equipment feature distribution shifts, scarce labeled fault samples in the target domain, industrial vibration noise mixed in sensor signals, and difficulties in accurately identifying subtle faults with varying severity levels. To tackle these issues, this paper improves upon the domain difference perception network (DDPN) and proposes a dual-hybrid attention feature discriminant domain-Adversarial network (DAFDAN) to realize intelligent fault diagnosis across different equipment and working conditions under few-shot scenarios. The proposed method constructs a dual-branch feature encoder consisting of a source domain compressor and a target domain extender to accommodate the distinct sensor dimensions of two heterogeneous chiller types. A hybrid attention module is formed by integrating squeeze-and-excitation efficient channel attention (SE-ECA, a module for screening channel-wise features) and spatial attention, which adaptively amplifies time-series features sensitive to faults and suppresses irrelevant noise. Residual connections (shortcut paths in deep neural networks to mitigate the vanishing gradient problem during deep-layer training) are introduced to optimize feature transmission. A dual-layer domain alignment framework is built with gradient reversal layers and maximum mean discrepancy (MMD). Combined with adversarial training (a training paradigm that learns domain-agnostic features through a game between a feature extractor and a domain discriminator), the framework achieves joint optimization of implicit feature confusion and explicit distance constraints. Meanwhile, a five-stage progressive training strategy is designed, which activates multiple loss functions, including weighted cross-entropy, mean square error (MSE), binary cross-entropy (BCE), and Kullback–Leibler (KL) divergence stage by stage. Class weighting and early stopping strategies are adopted to alleviate sample imbalance and model overfitting. In this paper, the public ASHRAE RP-1043 centrifugal chiller dataset is used as the source domain, and time-series measurement data collected from a self-developed laboratory marine screw chiller serves as the target domain. Verification experiments are carried out covering one normal steady-state operating condition and 15 gradient faults falling into five major categories with different severity degrees. Results from ablation experiments (controlled-variable comparative experiments that quantify the independent contribution of each component by comparing model performance with or without a specific module/loss), multi-algorithm comparisons, and confusion matrix visualization demonstrate that the cross-domain fault diagnosis accuracy of the proposed DAFDAN approaches is 100%, outperforming mainstream transfer learning algorithms such as support vector machine (SVM), deep neural network (DNN), MMD, correlation alignment (CORAL), and domain-adversarial neural network (DANN). Multiple ablation experiments verify that the three core components—hybrid attention, adversarial training, and semi-supervised learning—jointly boost the model’s diagnosis accuracy and operational stability. The loss curves of the complete five-stage training process converge smoothly. The confusion matrix reveals zero misjudgments and zero false alarms across all 16 refined operating states, enabling precise identification of subtle incipient faults of all severity levels. This study proves that DAFDAN can effectively address the pain points of few-shot cross-equipment fault diagnosis for marine chillers and provides a reliable algorithmic reference for the intelligent operation and maintenance of ship refrigeration equipment. Full article

Cross-border remanufacturing trade is developing steadily. Carbon tariffs and carbon cap-and-trade regulation have emerged as two important instruments for carbon governance, yet their independent and combined effects on supply chain operations remain insufficiently discussed, which delivers supplementary analytical space for cross-border remanufacturing supply chain research. This paper examines a cross-border supply chain consisting of an exporting manufacturer and an importing retailer that distributes both new and remanufactured products. Four research scenarios are established: no regulation, carbon tariffs only, cap-and-trade only, and dual mixed regulation. Adopting a two-stage Stackelberg game, we analyze equilibrium pricing, production, and carbon abatement decisions, and further evaluate environmental performance and overall social welfare. Firms with different initial carbon emission levels respond differently to regulatory stringency. The results show that carbon tariffs reduce total emissions but erode corporate profits and social welfare, while cap-and-trade regulation can mitigate such adverse effects. When carbon tariffs are stringent and carbon quotas are sufficient, dual regulation improves all participants’ profitability alongside better environmental quality and higher consumer surplus only within this paper’s simplified analytical context. This study offers tentative operational references for remanufacturing firms and theoretical and analytical implications for governments to design compatible cross-border carbon regulatory systems. Full article

This paper investigates the dynamic coopetition and capacity-sharing strategies between an Integrated Manufacturer and a Developer under R&D uncertainty, focusing on the governance of strategically scarce capacity. By constructing a two-stage game model, we analyze how government intervention and risk-hedging mechanisms influence the allocation of idle strategically scarce capacity in innovation-driven industries. The findings reveal a two-sided paradoxical behavioral pattern: in the low-probability R&D interval, rather than relying on safe contract manufacturing, the Integrated Manufacturer counter-intuitively reduces collaborative duration to aggressively gamble on its immature product. Conversely, in the high-probability R&D interval, where conventional wisdom predicts an aggressive pivot to self-production, the manufacturer paradoxically extends or maintains the contract manufacturing duration, driven by the partner’s full cost-sharing incentive mechanism. Furthermore, To maximize the total supply output of strategically scarce resources during collaboration, we uncover a non-linear ‘counterproductive subsidy trap’ and propose a binary ‘critical mass’ policy rule: governments should either withhold subsidies entirely or commit sufficient funding to bypass the supply deficit zone. This framework provides a theoretical foundation for managing scarcity in capital-intensive sectors such as biopharmaceuticals and semiconductors. Full article

Varroa destructor is the most devastating ectoparasite of Apis mellifera, and early detection is critical for colony survival. This study systematically investigated how image preprocessing, model architecture, and feature map resolution jointly affect classification accuracy and Grad-CAM++ explainability in deep-learning-based Varroa detection. From comb-surface images of 20 A. mellifera colonies, 3400 region-of-interest images were processed through 12 preprocessing pipelines combining deblurring, histogram normalization, morphology-preserving resizing, and non-morphological resizing. Nineteen CNN architectures, including VarroaNet — a custom lightweight model with configurable channel attention — were screened across all pipelines, and the top six further evaluated at four feature-map resolutions (7 × 7 to 56 × 56); the two stages together comprised 1,548 classification training runs across 516 configurations. Resizing consistently improved classification accuracy, whereas histogram normalization degraded it. VarroaNet (r = 8) achieved the highest mean accuracy across configurations (97.28%) with the lowest cross-configuration variability (CV = 1.47%). The 28 × 28 resolution was jointly optimal for classification and localization at minimal computational overhead, whereas 56 × 56 degraded performance. Notably, classification accuracy and localization quality did not always coincide—the highest-accuracy configuration (ShuffleNet-V2-x1.0 at 14 × 14, 97.34%) achieved an IoU@30 of only 0.160, underscoring the need for explicit localization evaluation. Morphology-preserving resizing achieved higher localization efficiency with zero morphological distortion. The recommended configuration—VarroaNet (r = 8) at 28 × 28 with deblurred MR preprocessing—achieved the highest localization performance (Pointing Game = 0.927), indicating correct attention to the mite region in 92.7% of infested test images. Full article

Bit allocation is a core design problem in spatially correlated sensor fields under limited communication resources since per-sensor bit depth determines quantization fidelity and thus the quality of acquired information. In this paper, we investigate several regime-dependent bit allocation strategies and compare them under various deployment geometries, bit budgets, and performance metrics. We consider a per-reporting-round integer bit-allocation problem in which a total bit budget is distributed among sensors as nonnegative quantization bits, allowing zero-bit allocation to represent sensor silencing. To examine different allocation principles, we compare five strategies: Shapley-value-based contribution-aware allocation and four other heuristic approaches—uniform allocation, Voronoi-based geometry-aware allocation, greedy mutual information-driven allocation, and conditional variance-based allocation. We implement the contribution-aware allocation as a two-stage framework: a mutual information-based cooperative game first quantifies each sensor’s spatial redundancy-aware contribution using Shapley value, and the value is then mapped to integer bit allocations. To mitigate the intractability of this formulation in larger networks, we approximate Shapley values via Neyman stratified sampling. Numerical experiments on sampled random fields show that reconstruction performance is context-dependent: geometry-aware allocation often performs best under tight budgets, particularly on boundary and tail errors, while Shapley-value-based allocation yields the best performance in stringent small-scale fields and becomes competitive under high budgets for global and tail errors. Furthermore, mutual information and weighted posterior trace provide complementary rankings, highlighting trade-offs between information-centric objectives and reconstruction-error objectives under heterogeneous spatial redundancy. These results show trade-offs among allocation strategies in accordance with different regimes and performance metrics. Full article