Search Results (10,165)

Research on ecosystem carbon sequestration services and ecological compensation is crucial for advancing carbon neutrality. As a public good, ecosystem carbon sequestration services inherently lead to externalities. Therefore, it is essential to consider externalities in the flow of sequestration services. However, few studies have examined intra- and inter-regional ecosystem carbon sequestration flows, making regional ecosystem carbon sequestration flows less comprehensive. Against this background, the research objectives of this paper are as follows. The flow of carbon sequestration services between Henan Province and out-of-province regions is studied. In addition, this study clarifies the beneficiary and supply areas of carbon sink services in Henan Province and the neighboring regions at the prefecture-level city scale to obtain a more systematic, comprehensive, and actual flow of carbon sequestration services for scientific and effective eco-compensation and to promote regional synergistic emission reductions. The research methodologies used in this paper are as follows. First, this study adopts a meta-coupling framework, designating Henan Province as the focal system, the Central Urban Agglomeration as the adjacent system, and eight surrounding provinces as remote systems. Regional carbon sequestration was assessed using net primary productivity (NEP), while carbon emissions were evaluated based on per capita carbon emissions and population density. A carbon balance analysis integrated carbon sequestration and emissions. Hotspot analysis identified areas of carbon sequestration service supply and associated benefits. Ecological radiation force formulas were used to quantify service flows, and compensation values were estimated considering the government’s payment capacity and willingness. A three-dimensional evaluation system—incorporating technology, talent, and fiscal capacity—was developed to propose a diversified ecological compensation scheme by comparing supply and beneficiary areas. By modeling the ecosystem carbon sequestration service flow, the main results of this paper are as follows: (1) Within Henan Province, Luoyang and Nanyang provided 521,300 tons and 515,600 tons of carbon sinks to eight cities (e.g., Jiaozuo, Zhengzhou, and Kaifeng), warranting an ecological compensation of CNY 262.817 million and CNY 263.259 million, respectively. (2) Henan exported 3.0739 million tons of carbon sinks to external provinces, corresponding to a compensation value of CNY 1756.079 million. Conversely, regions such as Changzhi, Xiangyang, and Jinzhong contributed 657,200 tons of carbon sinks to Henan, requiring a compensation of CNY 189.921 million. (3) Henan thus achieved a net ecological compensation of CNY 1566.158 million through carbon sink flows. (4) In addition to monetary compensation, beneficiary areas may also contribute through technology transfer, financial investment, and talent support. The findings support the following conclusions: (1) it is necessary to consider the externalities of ecosystem services, and (2) the meta-coupling framework enables a comprehensive assessment of carbon sequestration service flows, providing actionable insights for improving ecosystem governance in Henan Province and comparable regions. Full article

This study investigates how new R&D institutions mediate policy–market disjunctures to foster integrated innovation and entrepreneurship ecosystems. Employing a longitudinal case analysis (2013–2023) of the Jiangsu Industrial Technology Research Institute (JITRI), we delineate a three-phase evolutionary process: (1) an initial government-dominated phase, stimulating foundational capability development through contract R&D; (2) a subsequent marketization phase, enabling systemic resource integration via co-creation centers and global networks; and (3) a culminating synergy phase, where policy–market alignment facilitates ecosystem optimization through crowdsourced R&D and cross-domain collaboration. Three core mechanisms underpin this adaptation: policy–market coupling (providing external momentum), endogenous capability development (absorption to innovation), and dynamic resource orchestration (acquisition to optimization). JITRI’s hybrid governance model demonstrates that stage-contingent interventions—specifically, policy anchoring in early stages followed by market-responsive resource allocation—effectively transmute inherent tensions into productive synergies. These findings yield implementable frameworks for structuring innovative ecosystems and underscore the necessity for comparative studies to establish broader theoretical generalizability. Full article

This study extends our prior blockchain-based traceability framework, WEave, for application to a furniture supply chain scenario, while using the original multi-tier apparel supply chain as an anchoring use case. We integrate circular economy principles such as product reuse, recycling traceability, and full lifecycle transparency to bolster sustainability and resilience in supply chains by enabling data-driven accountability and tracking for closed-loop resource flows. The enhanced approach can track post-consumer returns, use of recycled materials, and second-life goods, all represented using a closed-loop supply chain topology. We describe the extended network architecture and smart contract logic needed to capture circular lifecycle events, while proposing new metrics for evaluating lifecycle traceability and reuse auditability. To validate the extended framework, we outline simulation experiments that incorporate circular flows and cross-industry scenarios. Results from these simulations indicate improved transparency on recycled content, audit trails for returned products, and acceptable performance overhead when scaling to different product domains. Finally, we offer conclusions and recommendations for implementing WEave functionality into real-world settings consistent with the goals of digital, resilient, and sustainable supply chains. Full article

Background: Antimicrobial resistance (AMR) is increasingly acknowledged as a critical global challenge, posing serious risks to human and animal health and potentially disrupting poultry production systems. Commensal bacteria such as Staphylococcus spp., Enterococcus spp., and Escherichia coli may serve as important reservoirs and vectors of resistance genes. Objectives: This study aimed to assess the AMR profiles of bacterial strains isolated from industrial chicken farms in the Dél-Alföld region of Hungary, providing region-specific insights into resistance dynamics. Methods: A total of 145 isolates, including Staphylococcus spp., Enterococcus spp., and E. coli isolates, were subjected to minimum inhibitory concentration (MIC) testing against 15 antimicrobial agents, following Clinical and Laboratory Standards Institute (CLSI) guidelines. Advanced multivariate statistics, machine learning algorithms, and network-based approaches were employed to analyze resistance patterns and co-resistance associations. Results Multidrug resistance (MDR) was identified in 43.9% of Staphylococcus spp. isolates, 28.8% of Enterococcus spp. isolates, and 75.6% of E. coli isolates. High levels of resistance to florfenicol, enrofloxacin, and potentiated sulfonamides were observed, whereas susceptibility to critical antimicrobials such as imipenem and vancomycin remained largely preserved. Discussion: Our findings underscore the necessity of implementing region-specific AMR monitoring programs and strengthening multidisciplinary collaboration within the “One Health” framework with proper animal hygiene and biosecurity measures to limit the spread of antimicrobial resistance and protect both animal and human health. Full article

In order to cope with the multimodal changes led by the digital era, enterprises urgently need to promote the construction of new-quality productive forces (NQPFs) through digital transformation. NQPFs take digital technology empowerment as the core driving force and emphasize the dynamic matching and synergy between the new-quality elements (digital infrastructure, digital talents, data resources, and diversified ecology) and the new-quality capabilities (digital dynamic capabilities) so as to unleash the innovation potentials of different production modes. Based on resource orchestration theory, this study constructs a “resource-capability-value creation” framework for digital empowerment (D-RCV) and employs fuzzy set qualitative comparative analysis (fsQCA) to examine 205 enterprise samples. Results reveal that enhanced innovation performance stems from digital empowerment at both resource and capability levels, generating three configurational paths: collaborative symbiosis, resource optimization, and data-driven approaches. These paths emerge through the interaction of resources and capabilities under different conditions. This study contributes by proposing a digital empowerment framework and exploring multiple pathway choices for new-quality productivity development. The findings provide theoretical insights for enterprise innovation research and practical guidance for innovation management strategies. Full article

Electric submersible pumps (ESPs) are critical for artificial lift operations; however, they are prone to frequent failures, often resulting in high operational costs and production downtime. Traditional ESP failure classifications are limited by lack of standardization and the conflation of failure modes with root causes. To address these limitations, this study proposes a new two-step integrated failure modes and root cause (IFMRC) classification system. The new framework clearly distinguishes between failure modes and root causes, providing a systematic, structured approach that enhances fault diagnosis and failure analysis and can lead to better failure prevention strategies. This methodology was validated using a case study of over 4000 ESP installations. The data came from Egypt’s Western Desert, covering a decade of operational data. The sources included ESP databases, workover records, and detailed failure investigation (DIFA) reports. The failure modes were categorized into electrical, mechanical, hydraulic, chemical, and operational types, while root causes were linked to environmental, design, operational, and equipment factors. Statistical analysis, in this case study, revealed that motor short circuits, low flow conditions, and cable short circuits were the most frequent failure modes, with excessive heat, scale deposition, and electrical grounding faults being the dominant root causes. This study underscores the importance of accurate root cause failure classification, robust data acquisition, and expanded failure diagnostics to improve ESP reliability. The proposed IFMRC framework addresses limitations in conventional taxonomies and facilitates ongoing enhancement of ESP design, operation, and maintenance in complex field conditions. Full article

This article presents an initial effort to systematize two interrelated research fronts within ecological economics (EE): ecological microeconomics and ecological macroeconomics. In response to the field’s transdisciplinary and plural nature—attributes that, while enriching, may limit its political influence—the article proposes a conceptual delineation of these two domains as a means to strengthen EE’s analytical identity and facilitate dialogue with other economic approaches. Ecological microeconomics focuses on the material and energy intensity of economic activity, the complementarity of natural capital in production processes, and the redesign of consumption and firm behavior under ecological constraints. Ecological macroeconomics, in turn, centers on the biophysical limits to growth, the concept of sustainable and optimal scale, and the integration of environmental variables into macroeconomic indicators and policy frameworks. The article argues that both fronts, despite their distinct emphases, are united by the need for long-term structural change and a normative commitment to sustainability. Together, they offer a coherent basis for rethinking prosperity within the ecological boundaries of the Earth system. Full article

Milk is a nutritionally rich food and a frequent target of economically motivated adulteration, particularly through substitution with lower-cost milk types. Over the past decade, significant progress has been made in the authentication of milk using advanced proteomic and chemometric approaches, with a focus on the discovery and application of protein and peptide biomarkers for species differentiation and fraud detection. Recent innovations in both top-down and bottom-up proteomics have markedly improved the sensitivity and specificity of detecting key molecular targets, including caseins and whey proteins. Peptide-based methods are especially valuable in processed dairy products due to their thermal stability and resilience to harsh treatment, although their species specificity may be limited when sequences are conserved across related species. Robust chemometric approaches are increasingly integrated with proteomic pipelines to handle high-dimensional datasets and enhance classification performance. Multivariate techniques, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), are frequently employed to extract discriminatory features and model adulteration scenarios. Despite these advances, key challenges persist, including the lack of standardized protocols, variability in sample preparation, and the need for broader validation across breeds, geographies, and production systems. Future progress will depend on the convergence of high-resolution proteomics with multi-omics integration, structured data fusion, and machine learning frameworks, enabling scalable, specific, and robust solutions for milk authentication in increasingly complex food systems. Full article

In the Kelameili volcanic gas reservoir, primary hydraulic fracturing treatments in some wells take place on a limited scale, resulting in a rapid decline in production post stimulation and necessitating re-fracturing operations. However, prolonged production has led to a significant evolution in the in situ stress field, which complicates the design of re-fracturing parameters. To address this, this study adopts an integrated geology–engineering approach to develop a formation-specific geomechanical model, using rock mechanical test results and well-log inversion to reconstruct the reservoir’s initial stress field. The dynamic stress field simulations and re-fracturing parameter optimization were performed for Block Dixi-14. The results show that stress superposition effects induced by multiple fracturing stages and injection–production cycles have significantly altered the current in situ stress distribution. For Well K6, the optimized re-fracturing parameters comprised a pump rate of 12 m³/min, total fluid volume of 1200 m³, prepad fluid ratio of 50–60%, and proppant volume of 75 m³, and the daily gas production increased by 56% correspondingly, demonstrating the effectiveness of the optimized re-fracturing design. This study not only provides a more realistic simulation framework for fracturing volcanic rock gas reservoirs but also offers a scientific basis for fracture design optimization and enhanced gas recovery. The geology–engineering integrated methodology enables the accurate prediction and assessment of dynamic stress field evolution during fracturing, thereby guiding field operations. Full article

Background and Objectives: Orbital floor fractures are challenging to treat, due to the complex orbital anatomy and limited surgical access. Emerging technologies—such as virtual surgical planning (VSP), 3D printing, patient-specific implants (PSIs), and intraoperative navigation—offer promising advancements to improve the surgical precision and clinical outcomes. This review systematically evaluates and synthesizes current technological modalities with respect to their accuracy, operative duration, cost-effectiveness, and postoperative functional outcomes. Materials and Methods: A systematic review was conducted according to the PRISMA 2020 guidelines. The PubMed, Scopus, and PRIMO databases were searched for clinical studies published between 2019 and September 2024. Out of 229 articles identified, 9 met the inclusion criteria and were analyzed using the PICO framework. Results: VSP and 3D printing enhanced diagnostics and presurgical planning, offering improved accuracy and reduced planning time. Pre-bent PSIs shaped on 3D models showed superior accuracy, lower operative times, and better cost efficiency compared to intraoperative mesh shaping. Custom-designed PSIs offered high precision and clinical benefit but required a longer production time. Intraoperative navigation improved implant positioning and reduced the complication rates, though a detailed cost analysis remains limited. Conclusions: VSP, 3D printing, and intraoperative navigation significantly improve surgical planning and outcomes in orbital floor reconstruction. Pre-bent PSIs provide a time- and cost-effective solution with strong clinical performance. While customized PSIs offer accuracy, they are less practical in time-sensitive settings. Navigation systems are promising tools that enhance outcomes and may serve as an alternative to custom implants when time or resources are limited. Full article

Cybersecurity represents a critical challenge for data-sharing platforms involving multiple stakeholders, particularly within complex and decentralized systems such as livestock supply chain networks. These systems demand novel approaches, robust security protocols, and advanced data management strategies to address key challenges such as data consistency, transparency, ownership, controlled access or exposure, and privacy-preserving analytics for value-added services. In this paper, we introduced the Framework for Framework for Livestock Empowerment and Decentralized Secure Data eXchange (FLEX), as a comprehensive solution grounded on five core design principles: (i) enhanced security and privacy, (ii) human-centric approach, (iii) decentralized and trusted infrastructure, (iv) system resilience, and (v) seamless collaboration across the supply chain. FLEX integrates interdisciplinary innovations, leveraging decentralized infrastructure-based protocols to ensure trust, traceability, and integrity. It employs secure data-sharing protocols and cryptographic techniques to enable controlled information exchange with authorized entities. Additionally, the use of data anonymization techniques ensures privacy. FLEX is designed and implemented using a microservices architecture and edge computing to support modularity and scalable deployment. These components collectively serve as a foundational pillar of the development of a digital product passport. The FLEX architecture adopts a layered design and incorporates robust security controls to mitigate threats identified using the STRIDE threat modeling framework. The evaluation results demonstrate the framework’s effectiveness in countering well-known cyberattacks while fulfilling its intended objectives. The performance evaluation of the implementation further validates its feasibility and stability, particularly as the volume of evidence associated with animal identities increases. All the infrastructure components, along with detailed deployment instructions, are publicly available as open-source libraries on GitHub, promoting transparency and community-driven development for wider public benefit. Full article

This study introduces a novel framework for fair resource allocation in self-governing, multi-agent systems, leveraging principles of interactional justice to enable agents to autonomously evaluate fairness in both individual and collective resource distribution. Central to our approach is the integration of Rescher’s canons of distributive justice, which provide a comprehensive, multidimensional framework encompassing equality, need, effort and productivity to assess legitimate claims on resources. In resource-constrained environments, multiagent systems require a balance between fairness and satisfaction. We compare the Borda Count (BC) method with K-means clustering, which group agents by similarity and allocate resources based on cluster averages. According to our findings, the BC method effectively prioritized the highest needs of the agents and resulted in higher satisfaction. On the other hand, K-means achieved higher fairness and facilitated a more equitable distribution of resources. The study showed that there was an intrinsic balance between fairness and satisfaction with the allocation of resources. The BC method is more suitable when individual needs are the main concern, while K-means is better when ensuring an equitable distribution between agents. In this work, we provide a refined understanding of the resource allocation strategies of multi-agent systems and emphasize the strengths and limitations of each approach to help system designers choose the appropriate methods. Full article

Early and accurate detection of mechanical damage in prunes is crucial for preserving postharvest quality and enabling automated sorting. This study proposes a practical and reproducible method for identifying cumulative bruising in prunes using visible–near-infrared (Vis–NIR) reflectance spectroscopy coupled with machine learning techniques. A self-developed impact simulation device was designed to induce progressive damage under controlled energy levels, simulating realistic postharvest handling conditions. Spectral data were collected from the equatorial region of each fruit and processed using a hybrid modeling framework comprising continuous wavelet transform (CWT) for spectral enhancement, uninformative variable elimination (UVE) for optimal wavelength selection, and support vector machine (SVM) for classification. The proposed CWT-UVE-SVM model achieved an overall classification accuracy of 93.22%, successfully distinguishing intact, mildly bruised, and cumulatively damaged samples. Notably, the results revealed nonlinear reflectance variations in the near-infrared region associated with repeated low-energy impacts, highlighting the capacity of spectral response patterns to capture progressive physiological changes. This research not only advances nondestructive detection methods for prune grading but also provides a scalable modeling strategy for cumulative mechanical damage assessment in soft horticultural products. Full article

Green hydrogen, produced via renewable-powered electrolysis, offers a promising path toward deep decarbonisation in energy systems. This study investigates the major technological, infrastructural, and economic challenges facing green hydrogen production in Jordan—a resource-constrained yet renewable-rich country. Key barriers were identified through a structured survey of 52 national stakeholders, including water scarcity, low electrolysis efficiency, limited grid compatibility, and underdeveloped transport infrastructure. Respondents emphasised that overcoming these challenges requires investment in smart grid technologies, seawater desalination, advanced electrolysers, and policy instruments such as subsidies and public–private partnerships. These findings are consistent with global assessments, which recognise similar structural and financial obstacles in scaling up green hydrogen across emerging economies. Despite the constraints, over 50% of surveyed stakeholders expressed optimism about Jordan’s potential to develop a competitive green hydrogen sector, especially for industrial and power generation uses. This paper provides empirical, context-specific insights into the conditions required to scale green hydrogen in developing economies. It proposes an integrated roadmap focusing on infrastructure modernisation, targeted financial mechanisms, and enabling policy frameworks. Full article

The field of metal additive manufacturing has witnessed significant growth in recent years, with technology offering the ability to produce complex geometries that are challenging to manufacture using the traditional methods. In situ monitoring and control of the manufacturing process are crucial for increasing the production capacity and improving the quality of manufactured parts. This article provides a comparative analysis of computational, indirect, and direct methods for in situ temperature monitoring during additive manufacturing of metal alloy components. Furthermore, it discusses the current status, recent improvements, and perspectives for in situ temperature measurements. The basic principles of thermal imaging, two-color pyrometry, and millimeter-wave radiometry are explored, highlighting their limitations for addressing challenges related to material emissivity and rapid changes in building material composition. Overcoming the challenges related to the inaccessibility of the chamber where the parts are formed, direct temperature measurements would allow for the integration of collected information into big data systems. Within the framework of Industry 4.0, this approach offers a viable alternative to the conventional metal shaping processes, improving the production capacity and part quality. This research aims to contribute to ongoing advancements in metal additive manufacturing and its potential to completely replace traditional metal casting practices in the Industry 4.0 era. Full article