Search Results (261)

This study focuses on the long-standing issue of θ-assignment in the generative enterprise literature. Despite the asymmetry of θ-assignment regarding structural positions (Head–Complement/Specifier–Head) being sanctioned by the Duality of Semantics, I argue that it is possible to eliminate the asymmetry in full accordance with third-factor principles by proposing two independent frameworks. In the first framework, I propose that θ-assignment is executed by applying Minimal Search to locate the assigner and the assignee, where both the external argument and the internal argument receive the θ-role in the same way. In the second framework, which does not hinge on the assumptions or results of the first one, I propose that θ-assignment is a postsyntactical operation; thus, the Duality of Semantics, as well as concepts like θ-assignment in the syntax or θ-position, may be disregarded. For a proper θ-interpretation to be possible, the assigner and the assignee must be in the same transfer domain. Nonetheless, the empirical coverage of the Duality of Semantics is largely retained, suggesting merge can and must be simplest with respect to θ. Full article

The management of transfer pricing operations is considered from the perspective of modeling financial and accounting processes for various organizations, using agricultural enterprises as an example. It is demonstrated that the execution of transfer pricing operations between related parties—which may function as responsibility centers within an organizational holding structure—serves as a managerial lever influencing the financial income and expenses of individual business units. It is revealed that the developed model of managerial accounting for transfer pricing operations, grounded in tax compliance and the balancing of stakeholder interests, is based on two key aspects: first, to ensure the balanced development of the company’s business units, a list of key performance indicators (KPIs) is developed and integrated into a balanced scorecard (BSC), promoting the sustainable and stable operation and growth of the company; second, with access to this list of KPIs, the manager of each business unit can exert indirect influence over a segment of the final product’s value chain by selecting transfer prices that adhere to the arm’s length principle. The practical application of the proposed model is illustrated using previously formed economic operations from the research base. Full article

Through the energy conversion of building skins, building-integrated photovoltaic (BIPV) technology, the core carrier of the smart city energy system, encourages the conversion of buildings into energy-generating units. However, the decommissioning of the module faces the challenge of physical dismantling and financial environmental damage because of the close coupling with the building itself. As the first tranche of BIPV projects will enter the end of their life cycle, it is urgent to establish a multi-dimensional collaborative recycling mechanism that meets the characteristics of building pv systems. Based on the theory of reverse logistics network, the research focuses on optimizing the reverse logistics network during the decommissioning stage of BIPV modules, and proposes a dual-objective optimization model that considers both cost and carbon emissions for BIPV. Meanwhile, the multi-level recycling network which covers “building points-regional transfer stations-specialized distribution centers” is designed in the research, the Pareto solution set is solved by the improved NSGA-II algorithm, a “1 + 1” du-al-core construction model of distribution center and transfer station is developed, so as to minimize the total cost and life cycle carbon footprint of the logistics network. At the same time, the research also reveals the driving effect of government reward and punishment policies on the collaborative behavior of enterprise recycling, and provides methodological support for the construction of a closed-loop supply chain of “PV-building-environment” symbiosis. The study concludes that in the process of constructing smart city energy system, the systematic control of resource circulation and environmental risks through the optimization of reverse logistics network can provide technical support for the sustainable development of smart city. Full article

The efficient simultaneous removal of NO_x and CO from sintering flue gas under low-temperature conditions (110–180 °C) in iron and steel enterprises remains a significant challenge in the field of environmental catalysis. In this study, we present an innovative strategy to enhance the performance of CuSmTi catalysts through silver modification, yielding a bifunctional system capable of oxygen structure regulation and demonstrating superior activity for the combined NH₃-SCR and CO oxidation reactions under low-temperature, oxygen-rich conditions. The modified AgCuSmTi catalyst achieves complete NO conversion at 150 °C, representing a 50 °C reduction compared to the unmodified CuSmTi catalyst (T_100% = 200 °C). Moreover, the catalyst exhibits over 90% N₂ selectivity across a broad temperature range of 150–300 °C, while achieving full CO oxidation at 175 °C. A series of characterization techniques, including XRD, Raman spectroscopy, N₂ adsorption, XPS, and O₂-TPD, were employed to elucidate the Ag-Cu interaction. These modifications effectively optimize the surface physical structure, modulate the distribution of acid sites, increase the proportion of Lewis acid sites, and enhance the activity of lattice oxygen species. As a result, they effectively promote the adsorption and activation of reactants, as well as electron transfer between active species, thereby significantly enhancing the low-temperature performance of the catalyst. Furthermore, in situ DRIFTS investigations reveal the reaction mechanisms involved in NH₃-SCR and CO oxidation over the Ag-modified CuSmTi catalyst. The NH₃-SCR process predominantly follows the L-H mechanism, with partial contribution from the E-R mechanism, whereas CO oxidation proceeds via the MvK mechanism. This work demonstrates that Ag modification is an effective approach for enhancing the low-temperature performance of CuSmTi-based catalysts, offering a promising technical solution for the simultaneous control of NO_x and CO emissions in industrial flue gases. Full article

The effective adoption of quality assurance and traceability systems is increasingly recognised as a critical enabler of circular economy (CE) outcomes in the plastics sector. This study examines the factors that influence the implementation of such systems within Australia’s recycled plastics industry, with a focus on how these factors vary by company size, supply chain role, and adoption of CE strategy. Recycled plastics are defined here as post-consumer or post-industrial polymers that have been reprocessed for reintegration into manufacturing applications. A mixed-methods survey was conducted with 65 stakeholders across the Australian plastics value chain, comprising recyclers, compounders, converters, and end-users. Respondents assessed a structured set of regulatory, technical, economic, and systemic factors, identifying whether each currently operates as an enabler or barrier in their organisational context. The analysis employed a comparative framework adapted from a 2022 European study, enabling a cross-regional interpretation of patterns and a comparison between CE-aligned and non-CE firms. The results show that firms with CE strategies report greater alignment with innovation-oriented enablers such as digital traceability, standardisation, and closed-loop models. However, these firms also express heightened sensitivity to systemic weaknesses, particularly in areas such as infrastructure limitations, inconsistent material quality, and data fragmentation. Small- and medium-sized enterprises (SMEs) highlighted compliance costs and operational uncertainty as primary barriers, while larger firms frequently cited frustration with regulatory inconsistency and infrastructure underperformance. These findings underscore the need for differentiated policy mechanisms that account for sectoral and organisational disparities in capacity, scale, and readiness for traceability. The study also cautions against the direct transfer of European circular economy models into the Australian context without consideration of local structural, regulatory, and geographic complexities. Full article

The construction of marine ranching is a crucial component of China’s Blue Granary strategy, yet the fragmented knowledge system in marine ranching equipment impedes intelligent management and operational efficiency. This study proposes the first knowledge graph (KG) framework tailored for marine ranching equipment, integrating hybrid ontology design, joint entity–relation extraction, and graph-based knowledge storage: (1) The limitations in existing KG are obtained through targeted questionnaires for diverse users and employees; (2) A domain ontology was constructed through a combination of the top-down and the bottom-up approach, defining seven key concepts and eight semantic relationships; (3) Semi-structured data from enterprises and standards, combined with unstructured data from the literature were systematically collected, cleaned via Scrapy and regular expression, and standardized into JSON format, forming a domain-specific corpus of 1456 annotated sentences; (4) A novel BERT-BiGRU-CRF model was developed, leveraging contextual embeddings from BERT, parameter-efficient sequence modeling via BiGRU (Bidirectional Gated Recurrent Unit), and label dependency optimization using CRF (Conditional Random Field). The TE + SE + R_i + BMESO tagging strategy was introduced to address multi-relation extraction challenges by linking theme entities to secondary entities; (5) The Neo4j-based KG encapsulated 2153 nodes and 3872 edges, enabling scalable visualization and dynamic updates. Experimental results demonstrated superior performance over BiLSTM-CRF and BERT-BiLSTM-CRF, achieving 86.58% precision, 77.82% recall, and 81.97% F1 score. This study not only proposes the first structured KG framework for marine ranching equipment but also offers a transferable methodology for vertical domain knowledge extraction. Full article

Patent licensing is essential for sustainable technological diffusion, fostering innovation and strengthening industrial resilience. However, the determinants influencing patent licensing decisions remain underexplored. This study investigates these factors at both the enterprise and patent levels, emphasizing their role in promoting sustainable industrial innovation and knowledge transfer. Given the low proportion of licensed patents, this research proposes a measurement framework to identify thematically similar but unlicensed patents and applies a conditional logistic regression model to analyze the factors affecting licensing decisions. Using patent abstracts from the intelligent connected vehicles (ICVs) sector, topic modeling is conducted to classify technological themes, and Kullback–Leibler divergence is applied to measure differences between licensed and unlicensed patents. The results indicate that technological prestige and depth negatively influence licensing, whereas technological breadth, advancement, and stability have a positive effect. From a sustainability perspective, enterprises should optimize technology management to support responsible knowledge transfer and green innovation. Universities should enhance patent quality and innovation impact to contribute more effectively to sustainable development. Policymakers should refine patent licensing frameworks to foster an efficient, inclusive, and sustainable intellectual property ecosystem, thereby facilitating cross-sectoral technology diffusion, advancing eco-friendly industrial transformation, and promoting sustainable economic growth. Full article

Green innovation has emerged as a key catalyst for the sustainable growth of logistics enterprises. Green innovation not only helps logistics enterprises reduce operating costs but also enhances their competitiveness and promotes the entire industry’s transformation towards environmental protection and efficiency. However, logistics enterprises encounter technical bottlenecks, capital shortages, and insufficient talent and infrastructure when implementing green innovation. Collaboration between universities and industries serves as a crucial method for logistics companies to access external resources and plays a significant role in promoting technological progress, knowledge transfer, and innovation capability enhancement of enterprises. This research, grounded in the theories of social capital and dynamic capabilities, explores the mechanism from the perspective of resources and capabilities, and examines how university–industry collaboration affects green innovation. This research employs a hierarchical regression model to evaluate the proposed hypotheses. The research results show that university–industry collaboration has a positive impact on social capital, slack resources, and dynamic capabilities, and social capital, slack resources, and dynamic capabilities positively influence green innovation. The research results have certain reference value for logistics enterprises to promote green innovation. Full article

This article explores the current state of collaboration between higher education institutions (HEIs) and small and medium-sized enterprises (SMEs), focusing on advancing the achievements of SDG 9. Using two bibliometric analyses, in Scopus and Web of the Science, this study examines resilient infrastructure, innovation, information and communication technology, and financial services as fundamental concepts within SDG 9’s targets to investigate how SMEs can contribute to meeting SDG 9 and what can be expected from higher education institutions to generate knowledge that supports SMEs’ efforts. The bibliometric analysis revealed trends and patterns that shape the state of the art regarding HEIs-SMEs collaboration for SDG 9. There is a subtle yet significant partnership between higher education institutions and SMEs centered around the key aspect of innovation within SDG 9. Even more significant is the insight into various mechanisms for strengthening knowledge transfer from higher education institutions to SMEs, as they substantially enhance the capabilities and skills of their workforce to innovate primarily through information and communication technologies. This suggests that higher education institutions emerge as strategic partners for SMEs to progress toward, if not all, some of the targets of SDG 9, which is essential for their sustainable future readiness. Full article

Our study investigates the drivers that foster the emergence of entrepreneurial responses to climate change among Indigenous communities. Indigenous peoples possess distinct worldviews and approaches to enterprise that prioritize community well-being and environmental stewardship over individual profit. Conventional entrepreneurship theories do not adequately capture Indigenous business approaches, leaving a limited understanding of how Indigenous communities merge traditional ecological knowledge with entrepreneurial activities to adapt to climate challenges. Through a systematic literature review (65 articles) and a case study of six Sri Lankan Vedda communities, we identified 15 key drivers that shape Indigenous climate-adaptive ventures and categorized them under five themes: (1) place-based relationships (resource stewardship, territorial connections, environmental risk factors); (2) intergenerational learning (traditional knowledge transfer, adaptation learning, collective experience); (3) community institutions (social networks, institutional support, overcoming the agency–structure paradox); (4) collective capacity (access to information, access to capital, community-oriented entrepreneurial traits); and (5) culturally aligned venture strategies (Indigenous business models, traditional products, local market relationships). Our study demonstrates how Vedda communities integrate entrepreneurship with cultural values to enhance climate resilience. Our research advances the field of Indigenous entrepreneurship while providing insights for policymakers and practitioners to support culturally appropriate climate adaptation strategies that enhance both community well-being and environmental sustainability. Full article

Small and medium-sized enterprises (SMEs) face unique challenges that can be effectively addressed through the adoption of data mining and business intelligence (BI) tools. This systematic literature review scrutinizes the deployment and efficacy of BI and data mining technologies across SME sectors, assessing their impact on operational efficiency, strategic decision-making, and market competitiveness. Therefore, drawing from a methodologically rigorous analysis of 93 scholarly articles published between 2014 and 2024, the review elucidates the evolving landscape of BI tools and techniques that have shaped SME practices. It reveals that advanced analytics such as predictive modeling and machine learning are increasingly being adopted, though significant gaps remain, particularly shaped by economic factors. The utilization of BI and data mining enhances decision-making processes and enables SMEs to adapt effectively to market dynamics. Despite these advancements, SMEs encounter barriers such as technological complexity, high implementation costs, and substantial skills gaps, impeding effective utilization. Our review, grounded in the analysis of business intelligence tools used indicates that dashboards (31.18%) and clustering techniques (10.75%) are predominantly utilized, highlighting their strategic importance in operational settings. However, a considerable number of studies (66.67%) do not specify the BI tools or data mining techniques employed, pointing to a need for more detailed methodological transparency in future research. The predominant focus on the ICT and manufacturing sectors underscores the industrial context sector specific applicability of these technologies, with ICT accounting for 45.16% and manufacturing 22.58% of the studies. We advocate for targeted educational programs, development of user-friendly and cost-effective BI solutions, and strategic partnerships to facilitate knowledge transfer and technological empowerment in SMEs. Empirical research validating the impacts of BI and data mining on SME performance is crucial, providing a directional pathway for future academic inquiries and policy formulation. Full article

In the current conditions of development of the country’s market economy, the methodological support for cadastral land valuation requires effective modernization and improvement of the existing mechanisms for determining cadastral value for a fair distribution of land tax among landowners. In this regard, the aim of the study was to develop a methodology for taking into account the qualitative state of soils in the cadastral valuation of agricultural lands in the conditions of an active land market, as well as to modernize the method for taking into account the quality of soils within the framework of the income approach in the conditions of a depressed land market. The study was conducted based on a set of scientific methods: the analytical method was used to conduct an analysis of the scientific review of the problem area and to substantiate the relevance of the study, a cycle of laboratory experiments was conducted using mechanical and chemical analyses, the construction of thematic maps was carried out using the dispersion method, the regression modeling method was used to determine the cadastral value of garden plots, and the land rent capitalization method was used to calculate the cadastral value of agricultural land. Research results were as follows: Methodological recommendations were provided for taking into account the quality of soils in the form of an integral indicator of physical and chemical properties in the model for calculating the specific indicator of cadastral value (SICV) of garden and vegetable lands in the conditions of an active land market. The method of accounting for the qualitative state of soil fertility in the form of a weighted quality score of an agricultural land plot was modernized when determining the specific gross income within the framework of the land rent capitalization method used to calculate the SICV. Based on field work and laboratory experiments, current indicators of soil fertility status were obtained, and soil quality scores for Saint Petersburg were calculated. The possibility of using an integral indicator (soil quality score) as a cost factor instead of a large number of fertility status indicators was proven. Also, models for calculating the SICV of garden and vegetable plots were built for the conditions of an active land market, according to which the cadastral value of land plots in Saint Petersburg was calculated for subsequent land taxation. For agricultural lands, using the example of a land plot of a high-commodity agricultural enterprise (Leningrad Region), the cadastral value was also calculated using the proposed income approach method. The scientific significance of the study lies in the improvement of the methodological foundations of cadastral valuation, as well as the technology of taking into account the quality of soils when calculating the cadastral value. The practical significance of the study lies in the applicability of the results of soil quality assessment and models for calculating the SICV for land taxation; individual market valuation for lending, purchase, and sale; lease of agricultural land; and allocation of land plots on account of a land share. In the area of developing a set of melioration measures on agricultural lands, including the development and implementation of agricultural technologies and technical means to improve soil fertility, the results of laboratory studies to determine the physical and chemical properties of soils can be used. The obtained soil quality scores for Saint Petersburg are also applicable to identifying unused and degraded lands for their transfer to other types of use. Full article

The demand for agriculture finance and investment for sustainable agriculture development has long been a concern for many years. However, the insufficient integration of the agricultural technology innovation chain and technology transfer impedes the enhancement of collaborative innovation capability in evolving total factor productivity. This paper utilizes Chinese agricultural companies’ investment in the Lancang-Mekong River region as an example to scrutinize key configuration factors fostering the integration of technical collaboration within agricultural industry chains. The results indicated that Chinese agricultural companies can be classified into two categories based on their approach to technical collaboration. The first category is strength-oriented, and companies in this category have the capability to transform technological investments, yielding relatively high returns. They also have optimistic expectations regarding favorable policies in the host country. This category accounts for about one-third of the companies studied. The second category is potential-oriented, in which firms possess the potential for technological investment transformation, with lower investment returns. They require effective contextual management and tax incentives from the host country to thrive. The impact of foreign direct investment decision-making diminishes, introducing new imperatives for the current host country’s market environment and the management of FDI enterprises in the host country. This study makes contributions to advance the exploration of technology’s impact on agricultural companies’ cross-border investment, stipulating new requirements for the transformative development of regional foreign direct investment, particularly for private enterprises. Full article

Efficient supply chain management (SCM) is essential for enterprises seeking to enhance operational efficiency, reduce costs, and mitigate risks while ensuring product quality and customer satisfaction. Addressing quality concerns within the supply chain proactively helps minimize rework, recalls, and returns, leading to significant cost savings and improved profitability. This study presents a machine learning (ML)-driven predictive analytics framework designed to forecast defect rates and optimize quality control processes. The research leverages a dataset sourced from a real-world fashion and beauty startup, hosted in a public repository. The framework employs advanced ML algorithms, including extreme gradient boosting (XGBoost), support vector machines (SVMs), and random forests (RFs), to accurately predict defect rates and derive actionable insights for supply chain optimization. Results demonstrate the effectiveness of predictive analytics in improving supply chain quality management, enabling enterprises to proactively reduce defect rates, minimize costs, and optimize return on investment (ROI). The proposed framework is designed to be scalable and transferable, ensuring adaptability across various industries, including fashion, e-commerce, and manufacturing. These findings underscore the economic and operational benefits of integrating machine learning into supply chain quality control, offering a data-driven, proactive approach to achieving high-efficiency, high-quality supply chain operations. Full article

Blockchain technology has emerged as a transformative solution for secure, immutable, and decentralized data management across diverse domains, including economics, healthcare, and supply chain management. Given its soaring adoption, it is crucial to assess the suitability of various blockchain platforms for specific applications. This study evaluates the performance of Hyperledger Fabric (HF) and private Ethereum (Geth) to analyze their scalability (node count), throughput (transactions per second (TPS)), and latency (measured in milliseconds). A benchmarking tool was developed in-house to assess the execution of key smart contract functions—QueryUser, CreateUser, TransferMoney, and IssueMoney—under varying transaction loads (10–1000 transactions) and network sizes (2–16 node count). The results indicate that HF performs significantly better than private Ethereum in terms of invoke functions, achieving up to 5× throughput and up to 26× lower latency. However, private Ethereum excels in query operations because of its account-based ledger model. While Hyperledger Fabric scales efficiently within moderate transaction volumes, it experiences concurrency limitations beyond 1000 transactions, whereas private Ethereum processes up to 10,000 transactions, albeit with performance fluctuations due to gas fees. The findings offer valuable insights into the strengths and tradeoffs of both platforms, informing optimal blockchain selection for enterprise applications that require high transaction efficiency. Full article