Search Results (71)

This paper introduces a novel four-party evolutionary game model to analyze cooperation dynamics in High-Value Mixed Cropping (HMC) systems integrating non-pesticide cacao, cashew nut, and free-range chicken farming within circular and sharing economy frameworks. The model uniquely examines strategic interactions among local government and three farming family types (cacao, cashew, and chicken), incorporating both regulatory mechanisms and cooperative behaviors. Through rigorous stability analysis and MATLAB simulations based on empirical data from Southeast Vietnam, we identify precise conditions for Evolutionarily Stable Strategies (ESSs) that sustain long-term cooperation. Our results demonstrate that government incentives (subsidies, technical support) and reputational sanctions critically shape farmers’ and consumers’ payoffs, thereby steering the system toward collective action equilibria. In particular, increasing the strength of positive incentives or reputational benefits enlarges the basin of attraction for full-cooperation ESSs, regardless of initial strategy distributions. Conversely, overly punitive sanctions can destabilize collaborative outcomes. These findings underscore the pivotal role of well-balanced policy instruments in fostering resilience, innovation, and resource circulation within rural agroecosystems. Finally, we propose targeted policy recommendations, such as graduated subsidy schemes, participatory monitoring platforms, and cooperative branding initiatives, to reinforce circular economy practices and accelerate progress toward the United Nations Sustainable Development Goals. Full article

The incentive mechanism functions as a core safeguard to ensure the efficient execution of consortium-based Public–Private Partnership (PPP) projects and the realization of value-added outcomes. The heterogeneity of consortium members, their reciprocal preferences, and the collaborative dynamics of the team collectively contribute to the formation of project alliances characterized by resource synergy, complementary advantages, and risk sharing. However, these same factors also contribute to the multi-layered structure of principal–agent relationships and the inherent complexity of incentive pathways and mechanisms in consortium-based PPP settings. Drawing upon the team collaboration effect and reciprocal preferences among consortium members, this study incorporated the member heterogeneity and developed three incentive models for such projects, such as the Dual-Performance (DP) mode, the Total-Performance (TP) mode, and the Individual-Performance (IP) mode. This study examined the conditions under which these incentive modes were established, the relationship between incentive intensity and optimal effort levels of consortium members, and the influence of reciprocal preferences on incentive effectiveness. Further, the selection criteria and appropriate application scenarios for each of the three incentive models were analyzed according to a comparative analysis, thereby putting forward effective suggestions for improving the effort levels of private investors in consortium-based PPP projects. The study results indicate that team synergy effects play an imperative role in improving the optimal effort levels under all three modes, whereas reciprocity preferences exhibit a negative relationship with effort in the DP and TP modes. When reciprocity remains within a moderate range, the DP mode achieves highest aggregate effort levels, whereas the IP mode induces positive incentive effects only under extreme reciprocity conditions. Thus, the application of dual incentive coefficients can enhance operational adaptability and allocative efficiency and governments should establish a multidimensional collaborative incentive for consortium-based PPP projects to strengthen effectiveness and project quality. This comprehensive evaluation provides crucial insights for policymakers, emphasizing the strategic selection of incentive mechanisms to enhance the sustainability and effectiveness of consortium-based PPP Projects. Full article

As global energy systems shift to low-carbon models, microgrid systems play an increasingly vital role in decentralized energy management. This study proposes a collaborative scheduling strategy, incorporating both power and carbon contribution for multi-microgrid systems. Through the utilization of a cooperative Stackelberg game and a Nash bargaining model, a bi-level game framework is established between grid operators and microgrid alliances, enabling efficient resource sharing and equitable benefit distribution. To accurately assess each microgrid’s impacts, a VCG (Vickrey–Clarke–Groves)-based mechanism is introduced to quantify its marginal contribution to both power supply and carbon mitigation. The contribution factors are then embedded into the bargaining process, guiding incentive-compatible allocation. Furthermore, to improve computational efficiency and enable distributed problem-solving, an enhanced analytical target cascading (ATC) algorithm is applied. Experimental results reveal that this approach improves both economic and environmental performance, effectively reducing carbon emissions and dependence on the main grid. Full article

As a new type of production factor, data possesses multidimensional application value, and its pivotal role is becoming increasingly prominent in the aviation sector. Data sharing can significantly enhance the utilization efficiency of data resources and serves as one of the key tasks in building smart civil aviation. However, currently, data silos are pervasive, with vast amounts of data only being utilized and analyzed within limited scopes, leaving their full potential untapped. The challenges in data sharing primarily stem from three aspects: (1) Data owners harbor concerns regarding data security and privacy. (2) The highly dynamic and real-time nature of aviation operations imposes stringent requirements on the timeliness, stability, and reliability of data sharing, thereby constraining its scope and extent. (3) The lack of reasonable incentive mechanisms results in insufficient motivation for data owners to share. Consequently, addressing the issue of aviation big data sharing holds significant importance. Since the release of the Bitcoin whitepaper in 2008, blockchain technology has achieved continuous breakthroughs in the fields of data security and collaborative computing. Its unique characteristics—decentralization, tamper-proofing, traceability, and scalability—lay the foundation for its integration with aviation. Blockchain can deeply integrate with air traffic management (ATM) operations, effectively resolving trust, efficiency, and collaboration challenges in distributed scenarios for ATM data. To address the heterogeneous data usage requirements of different ATM stakeholders, this paper constructs a blockchain-based multi-level data security sharing architecture, enabling fine-grained management and secure collaboration. Furthermore, to meet the stringent timeliness demands of aviation operations and the storage pressure posed by massive data, this paper optimizes blockchain storage deployment and consensus mechanisms, thereby enhancing system scalability and processing efficiency. Additionally, a dual-mode data-sharing solution combining raw data sharing and model sharing is proposed, offering a novel approach to aviation big data sharing. Security and formal analyses demonstrate that the proposed solution is both secure and effective. Full article

Industrial tree plantations (ITPs) are increasingly recognized as a sustainable response to deforestation and the decline in native wood resources in the Philippines. This study presents LUNTIAN (Labor, UNiversity, Timber Investment, and Agent-based Nexus), an agent-based model that simulates an experimental ITP operation within a mountain forest managed by University of the Philippines Los Baños. The model integrates biophysical processes—such as tree growth, hydrology, and stand dynamics—with socio-economic components such as investment decision making based on risk preferences, employment allocation influenced by local labor availability, and informal harvesting behavior driven by job scarcity. These are complemented by institutional enforcement mechanisms such as forest patrolling, reflecting the complex interplay between financial incentives and rule compliance. To assess the model’s validity, its outputs were compared to those of the 3PG forest growth model, with results demonstrating alignment in growth trends and spatial distributions, thereby supporting LUNTIAN’s potential to represent key ecological dynamics. Sensitivity analysis identified investor earnings share and community member count as significant factors influencing net earnings and management costs. Parameter calibration using the Non-dominated Sorting Genetic Algorithm yielded an optimal configuration that ensured profitability for resource managers, investors, and community-hired laborers while minimizing unauthorized independent harvesting. Notably, even with continuous harvesting during a 17-year rotation, the final tree population increased by 55%. These findings illustrate the potential of LUNTIAN to support the exploration of sustainable ITP management strategies in the Philippines by offering a robust framework for analyzing complex social–ecological interactions. Full article

Introduction and Objectives: Risk-sharing agreements (RSAs) have emerged as a key strategy for financing high-cost medical technologies while ensuring financial sustainability. These payment mechanisms mitigate clinical and financial uncertainties, optimizing pricing and reimbursement decisions. Despite their widespread adoption globally, Latin America has reported limited implementation, particularly for high-cost medical devices. This study aims to share insights from designing RSAs in the form of Outcome Protection Programs (OPPs) for medical devices in Latin America from the perspective of a medical devices company. Methods: The report follows a structured approach, defining key OPP dimensions: payment base, access criteria, pricing schemes, risk assessment, and performance incentives. Risks were categorized as financial, clinical, and operational. The framework applied principles from prior models, emphasizing negotiation, program design, implementation, and evaluation. A multidisciplinary task force analyzed patient needs, provider motivations, and payer constraints to ensure alignment with health system priorities. Results: Over two semesters, a panel of seven experts from the manufacturer designed n = 105 innovative payment programs implemented in Argentina (n = 7), Brazil (n = 7), Colombia (n = 75), Mexico (n = 9), Panama (n = 4), and Puerto Rico (n = 3). The programs targeted eight high-burden conditions, including Coronary Artery Disease, atrial fibrillation, Heart Failure, and post-implantation arrhythmias, among others. Private providers accounted for 80% of experiences. Challenges include clinical inertia and operational complexities, necessitating structured training and monitoring mechanisms. Conclusions: Outcome Protection Programs offer a viable and practical risk-sharing approach to financing high-cost medical devices in Latin America. Their implementation requires careful stakeholder alignment, clear eligibility criteria and endpoints, and robust monitoring frameworks. These findings contribute to the ongoing dialogue on sustainable healthcare financing, emphasizing the need for tailored approaches in resource-constrained settings. Full article

The logistics industry faces challenges from evolving consumer expectations, technological advances, sustainability demands, and market disruptions. Logistics collaboration is in theory perceived as one of the most promising solutions to solve these issues, but here are still a lot of challenges that needs to be better understood and addressed. While vertical collaboration among supply chain actors is well advanced, horizontal collaboration among competing service providers remains under-explored. This study developed a novel methodology based on the COM-B behaviour-change framework to better understand the main challenges, opportunities, capabilities and drivers that would motivate competing companies to exploit the potential of horizontal logistics collaboration. A survey was designed and conducted among 71 logistics service providers in Slovenia, chosen for its fragmented market and low willingness to collaborate. Statistical analysis reveals cost reduction (M = 4.21/5) and improved vehicle utilization (M = 4.29/5) as the primary motivators. On the other hand, maintaining company reputation (M = 4.64/5), fair resource sharing (M = 4.20/5), and transparency of logistics processes (M = 4.17/5) all persist as key enabling conditions. These findings underscore the pivotal role of behavioural drivers and suggest strategies that combine economic incentives with targeted trust-building measures. Future research should employ experimental designs in diverse national contexts and integrate vertical–horizontal approaches to validate causal pathways and advance theory. Full article

Applying the resource-based view and dynamic capability theory, this study employs panel data analysis to examine how financial flexibility influences corporate innovation efficiency from an integrated resource-capability perspective. Analyzing data from Chinese A-share listed companies during 2013–2022, we discovered three key results. First, as an organizational liquidity buffer, financial flexibility reduces transaction costs, enhances incentives for technical talent retention, and better aligns executive compensation with innovation objectives. Second, as a manifestation of financial dynamic capabilities, financial flexibility significantly boosts a firm’s overall dynamic capabilities, thereby increasing innovation efficiency. Third, institutional investor engagement positively moderates this relationship through enhanced governance oversight. These investors strengthen governance oversight and reduce information asymmetry. Our findings advance the financial flexibility literature and offer actionable strategies to optimize innovation resource allocation and sustain R&D competitiveness. Companies should strategically build financial reserves to enhance innovation efficiency and achieve sustainable development. Full article

Decentralized blockchains have grown into massive and Internet-scale ecosystems, collectively securing hundreds of billions of dollars in value. The complex interplay of technology and economic incentives within blockchain systems creates a delicate balance that is susceptible to significant shifts even from minor changes. This paper underscores the importance of conducting thorough, data-driven studies to monitor and understand the impacts of significant shifts in blockchain systems, particularly focusing on Ethereum’s groundbreaking builder–proposer separation (PBS) as a pivotal innovation reshaping the ecosystem. PBS revolutionizes Ethereum’s block production, entrusting builders with block construction and proposers with validation via blockchain consensus, with significant impacts on Ethereum decentralization, fairness, and security. Our empirical study reveals key insights, including the following: (a) A substantial 261% increase in proposer revenue underscores the effectiveness of PBS in promoting widespread adoption, significantly enhancing block rewards and proposer incomes. (b) The small profits garnered by builders, comprising only a 3.5% share of block rewards, raise concerns that the security assumptions based on builder reputation may introduce new threats to the system. (c) PBS promotes a more equitable distribution of resources among network participants by reducing proposer centralization and preventing centralization trends among builders and relays, thereby significantly enhancing fairness and decentralization in the Ethereum ecosystem. This study provides a comprehensive analysis of the dynamics of Ethereum PBS adoption, exploring its effects on revenue redistribution among various participants and highlighting its implications for the Ethereum ecosystem’s decentralization. Full article

Prostate cancer (PCa) is one of the most common malignancies among men worldwide. While prostate-specific antigen (PSA) screening has improved early detection, it has also led to significant challenges regarding overdiagnosis and overtreatment. Overdiagnosis involves identifying indolent tumors unlikely to affect a patient’s lifespan, while overtreatment refers to unnecessary interventions that can cause adverse effects such as urinary incontinence, erectile dysfunction, and a reduced quality of life. This review highlights contributing factors, including the limitations of PSA testing, advanced imaging techniques like multi-parametric MRI (mpMRI), medical culture, and patient expectations. The analysis emphasizes the need for refining screening protocols, integrating novel biomarkers (e.g., PCA3, TMPRSS2-ERG), and adopting conservative management strategies such as active surveillance to minimize harm. Risk-based screening and shared decision-making are critical to balancing the benefits of early detection with the risks of unnecessary treatment. Additionally, systemic healthcare factors like financial incentives and malpractice concerns exacerbate overuse. This review advocates for updated clinical guidelines and personalized approaches to optimizing patient outcomes while reducing the strain on healthcare resources. Addressing overdiagnosis and overtreatment through targeted interventions will improve the quality of life for PCa patients and enhance the efficiency of healthcare systems. Full article

While green infrastructure (GI) offers numerous benefits, its implementation in low-resource settings remains constrained by limited policy support and upfront costs, highlighting the need for context-sensitive strategies. This paper highlights the value of integrating GI within sustainable agricultural systems and the effectiveness of various GI techniques in improving soil microbial communities and reducing greenhouse gas emissions. The transition to sustainable agricultural systems requires innovative strategies that balance productivity, environmental conservation, and resilience to climate change. Sustainable agriculture increasingly leverages technological innovations in GI to enhance productivity, biodiversity, and microclimate resilience. Green infrastructure has found direct application in agroforestry, conservation buffers, precision agriculture, soil health monitoring systems, and nature-based solutions such as regenerative soil management. These applications are crucial in enhancing soil health, water retention, and biodiversity, while mitigating microclimatic impacts. Precision agriculture tools, like IoT sensors, drones, and AI-driven analytics, allow farmers to optimize water, nutrient, and pesticide use, boosting yields and efficiency while minimizing environmental impact. Simultaneously, advanced soil health monitoring technologies track soil moisture, nutrients, and biological activity in real time, informing practices that maintain long-term soil fertility and carbon sequestration. This integrated approach yields practical on-farm benefits, such as higher crop stability during droughts and enhanced habitats for beneficial species. In conclusion, there is a need for supportive frameworks, like subsidies for GI adoption, application of precision tools, incentives for improving soil microclimate, development of innovative GI programs, and knowledge-sharing initiatives, to encourage farmer adoption. Full article

Water planning and governance strategies must adapt to challenges associated with population growth, climate change, and projected water shortages. In the Western United States, agriculture is the dominant water use, and agricultural water users are being asked to conserve or share their water with other uses. Managing scarce water supplies at the local level often involves creative solutions, many of which are not well documented, especially in the agricultural sector. It is therefore critical to understand ideas to manage scarce water resources from the perspective of agricultural water users and those who work with them. In our research, we used interviews to explore how agricultural water users are managing increasing water scarcity in the Middle Rio Grande basin of central New Mexico and what enables or prevents them from taking innovative action to manage water scarcity. We hypothesized that we would find undocumented water use innovations born out of water users’ responses to lower and more variable water availability in recent years. We primarily recruited interviewees through snowball sampling, with a total of 42 (47%) agricultural water users, decision makers, and non-profit leaders influencing agricultural water governance in the basin accepting our invitation to participate. Our approximately one-hour, semi-structured and open-ended interviews explored agricultural water users’ lived experiences with water governance and opportunities to manage water scarcity. The interviews were recorded, transcribed, coded, and analyzed using HyperRESEARCH software (version 4.5.4). Our results did not support our hypothesis. Instead, we found that agricultural water users struggled to implement well-known innovations amid the pressures of water scarcity, supply uncertainty, administrative complexity, and constraints on their time, labor, and money. Water users and decision makers were mutually interested in implementing innovations in crop choice, flexibility in water storage, use, and management, stricter enforcement of water use efficiency, and access to more efficient irrigation equipment. However, high costs, a lack of knowledge, education, and training, and challenges related to water distribution and scheduling prevented agricultural water users from accessing these and other innovations. Recommendations include incentive-based policies to promote agricultural water use innovations that require high initial costs, improved water accounting at the basin and regional levels to promote flexible and reliable access to agricultural water, targeted education and outreach programming on alternative irrigation methods and cropping patterns, and improved access to irrigation scheduling information to support agricultural water users in planning for water scarcity. Full article

Value co-creation in online communities refers to a process in which all participants within a platform’s ecosystem exchange and integrate resources while engaging in mutually beneficial interactive processes to generate perceived value-in-use. User behavior plays a crucial role in influencing value co-creation in human–computer interaction. However, existing research contains controversies, and there is a lack of comprehensive studies exploring which factors of user behavior influence it and the mechanisms through which they operate. This paper employs meta-analysis to examine the factors and mechanisms based on 42 studies from 2006 to 2023 with a sample size of 30,016. It examines the relationships at the individual, interaction, and environment layers and explores moderating effects through subgroup analysis. The results reveal a positive overall effect between user behavior and value co-creation performance. Factors including self-efficacy, social identity, enjoyment, and belonging (individual layer); information support, social interaction, trust, and reciprocity (interaction layer); as well as shared values, incentives, community culture, and subjective norms (environment layer) positively influence value co-creation. The moderating effect of situational and measurement factors indicates that Chinese communities and monocultural environments have more significant effects than international and multicultural ones, while community type is not significant. Structural equation models and subjective collaboration willingness have a stronger moderating effect than linear regression and objective behavior, which constitutes a counterintuitive finding. This study enhances theoretical research on user behavior and provides insights for managing value co-creation in human–computer interaction. Full article

Efficient and secure data sharing is paramount for advancing modern digital ecosystems, especially within edge computing environments characterized by resource-constrained nodes and dynamic network topologies. In such settings, privacy preservation, computational efficiency, and system resilience are critical for user engagement and overall system performance. However, existing approaches face three primary challenges: (i) limited optimization of privacy protection and absence of dynamic privacy budget scheduling for resource-constrained scenarios, (ii) static incentive mechanisms that overlook individual differences in data quality and resource consumption, and (iii) inadequate strategies to ensure resilience in environments with limited resources and unstable networks. This paper introduces the Federated Learning-based Dynamic Incentive Allocation Framework (FL-DIAF) to address these issues. FL-DIAF integrates differential privacy into the federated learning paradigm deployed on edge nodes, enabling collaborative model training that safeguards individual data privacy while maintaining computational efficiency and system resilience. Additionally, the framework employs a Shapley value-based dynamic incentive allocation model to ensure equitable and transparent distribution of incentives by accurately quantifying each participant’s contribution within an elastic edge computing infrastructure. Comprehensive experimental evaluations on diverse datasets demonstrate that FL-DIAF achieves a 9.573% reduction in the objective function value under typical conditions and attains a 100% task completion rate across all tested resilient edge scenarios. Full article

To enhance the participation enthusiasm of distributed energy resources (DERs) and DER aggregators in their demand response, this paper develops a three-level distributed scheduling model for the distribution network operators (DNO), DER aggregators, and DERs based on the conditional value-at-risk (CVaR) theory. First, a demand response model is established for the DNO, DER aggregators, and DERs. Next, we employ the analytical target cascading (ATC) method to construct a three-level distributed scheduling model, where incentive and compensation prices are shared as consensus variables across the model levels to amplify the influence of DER aggregators on incentive prices and DERs on compensation prices. Then, the photovoltaic output model is restructured using the CVaR theory to effectively measure the risk associated with photovoltaic output uncertainty. Finally, an analysis is conducted using the IEEE 33-node distribution network to validate the effectiveness of the proposed model. Full article