Search Results (350)

The dismantled components of retired oil-immersed power transformers, particularly the windings and core, have high recycling economic value. However, accurately estimating their weight based solely on nameplate information prior to disassembly remains a key challenge for recycling enterprises in formulating rational plans and enhancing bidding competitiveness. This paper proposes a knowledge distillation-based prediction method, where a categorical boosting algorithm (CatBoost) serves as the teacher model, and a tabular prior-data fitted network (TabPFN) acts as the student model for knowledge distillation. The nameplate information of transformers is used as initial input features, from which derived features are constructed to enrich the information representation and enhance the model’s predictive capability. Process-level weighing data (e.g., total and de-oiled weight) are used as privileged information during training. The distillation targets are constructed in a proportional space to account for physical differences between windings and cores, with sample-level transfer controlled to suppress negative transfer. The proposed method achieved R² values of 0.9593 and 0.9813, with RMSE of 11.49 kg and 16.56 kg, and MAPE of 8.50% and 5.46%, respectively. The proposed distilled TabPFN model enhances prediction accuracy, providing quantitative support for recycling pricing and dismantling decisions, and contributing to improved waste management and resource recovery. Full article

Amid growing divergence and confusion in Environmental, Social, and Governance (ESG) rating methodologies and results for assessing corporate sustainability, this study proposes a dual-dimensional framework that conceptualizes ESG rating quality through two distinct yet complementary lenses—validity (the rigor, transparency, and reproducibility of rating methodologies) and utility (the practical value and relevance of rating outputs for user-specific objectives). While the framework provides a structured approach for evaluating existing ratings, it also serves as prescriptive guidance for constructing user-oriented ESG assessment models. To demonstrate its operational value, the framework is implemented in the Chinese A-share market with two explicit utility targets—local adaptability (addressing the poor cross-regional transferability of international ESG standards) and entrepreneurial enablement (counteracting the systematic size-based ESG discrimination). The findings demonstrate that the dual-dimensional framework not only provides a coherent basis for assessing ESG rating quality from the bottom (an overall quality score of 71.67 assessed for the implemented model) but also yields meaningful empirical patterns that support the top objectives (corresponding ESG trends following China’s major policy events reflected in both rating distributions and market reactions, and significantly flattened ESG–size correlation from 0.27 to 0.18 and the reduced missing indicator ratio for smaller firms from 81% to 74%). Methodologically, the target alignment is benefited by four streams of data science techniques—event-based and location-based data, machine learning for carbon footprint estimation, generative AI for extracting and summarizing structured ESG information, and a hybrid analytic hierarchy process–entropy-weighting approach. This study contributes a replicable and user-interactive approach to ESG assessment, bridging macro-level policy influence and micro-level data validity, with practical implications for investors, regulators, rating agencies, and small enterprises navigating the “long tail” of sustainable development. Full article

Against China’s dual-carbon goals, carbon emissions and pollution transfer across supply chains hinder systemic low-carbon transformation; the prior literature on ESG and corporate environmental investment mainly focuses on individual firm-level effects, ignoring the unique lead goose governance function of core chain leader enterprises in supply chain networks and their internal transmission mechanisms. Drawing on stakeholder theory, this paper constructs a collaborative green supply chain governance framework led by chain leaders to fill the above research gaps. Based on a sample of Shanghai and Shenzhen A-share listed firms from 2011 to 2023, we identify chain leaders by combining official industrial chain leader lists and total asset threshold standards, with baseline ESG data from Wind and alternative Bloomberg ESG scores for robustness. Two-stage least squares instrumental variable regression addresses endogeneity, while omitted variable sensitivity analysis, indicator replacement and stepwise high-dimensional fixed effects ensure reliable empirical conclusions. The results provide evidence of a significant lead goose spillover effect: each one-unit improvement in chain leaders’ ESG performance is associated with an 8.33% increase in upstream suppliers’ environmental investment at the 1% significance level and downstream clients’ environmental investment by 3.54% at the 5% significance level, yet non-leader enterprises generate no meaningful spillover impacts, and the effect is stronger for upstream partners. Mechanism tests support two core mediating channels: chain leaders’ ESG performance stimulates supply-chain green investment by fostering environmental sensitivity salience and cutting inter-firm transaction costs. Heterogeneity analysis shows the spillover effect is amplified in polluting industries and highly concentrated supply chains. This research extends the emerging literature on supply chain ESG spillovers by documenting the lead goose effect of formally identified chain leaders on partners’ actual environmental investment, distinguishing directional asymmetry, and unveiling dual mediating mechanisms. Full article

Innovations in artificial intelligence are reshaping how tax administrations approach compliance and audit planning, yet existing AI-based fraud detection studies largely treat the taxpayer population as homogeneous or remain conceptual frameworks awaiting empirical validation. This gap is consequential because audit resources are limited, evasion tactics are increasingly sophisticated, and misallocating scarce audit capacity carries a direct fiscal cost. To address it, this study presents VERITAS, a machine learning-based decision support system operationalizing a segment- and sector-aware architecture for corporate income tax audit planning: a single-layer model for Large Taxpayer case selection, and a novel two-layered model for small and medium enterprises (SMEs) that filters evasion-suspect cases before prioritizing them by expected tax-recovery yield against a target threshold. Ten classification algorithms were compared across 4063 SME and 1903 Large Taxpayer financial statements, with correlation-ranked feature selection subsequently applied to each. Random Forest consistently outperformed all alternatives across every segment, sector, and task examined; feature selection improved performance in every case; sector-specific modeling outperformed a generic classifier in two of four SME sectors, matched it in a third, and was marginally outperformed in the fourth; and a novel business-activity-code feature was retained in most analyses. These findings position VERITAS as a practical innovation in tax audit practice: an architecture for AI-driven audit planning that is internally validated against verified audit outcomes within the historical Lebanese dataset examined, built entirely from data tax administrations already collect, and potentially transferable to comparable jurisdictions, though not yet operationally deployed. Full article

This paper presents the results of a study on the utilization of chromium-containing waste in the production of mineral wool based on natural basalt. It was shown that a mixture of low-dolomite chromate sludge and low-carbon ferrochrome slag in a 1:1 ratio is compositionally close to dolomite. When dolomite is equivalently replaced in the mineral wool batch, the physicochemical characteristics of the melt remain suitable for mineral wool production. The complete reduction of iron and chromium oxides is achieved in the presence of carbon, resulting in their transfer to the metallic phase, where iron and chromium cations are present in the form of carbides. Due to the cost difference between chromium waste and dolomite, implementing the proposed technology makes it possible to significantly improve the efficiency of chromium waste utilization by reducing the production cost of mineral wool. The Aktobe region has all the prerequisites for implementing the developed technology, with active sources of chromium-containing waste pollution (ACCP, AFP) and basalt deposits, as well as the operational experience of the mineral-wool-manufacturing enterprise Basalt-A LLP. Full article

Convective fruit drying is energy-intensive and a major greenhouse gas (GHG) source in the agri-food sector. Existing carbon footprint (CF) studies are fragmented, separating emissions accounting from economic performance and quality verification, limiting their usefulness for small and medium-sized enterprises (SMEs) seeking practical decarbonization actions. This study proposes an integrated, verification-oriented framework linking organizational carbon accounting, process engineering, and product quality within a unified decision platform. The framework combines (i) GHG quantification (ISO 14064-1:2018 and ISO 14067:2018); (ii) techno-economic modeling via process simulation; (iii) experimental validation of quality attributes (moisture, water activity, color, texture); and (iv) sensitivity analysis. The framework was applied to four energy configurations: baseline and three decarbonization scenarios (solar thermal, photovoltaic (PV), and combined). The baseline footprint was 139,090 kg CO₂e (±11.43%). Emission reductions ranged from 15.05% to 34.73%, with the combined solar-PV configuration achieving the highest mitigation while maintaining commercial quality. Emission intensity dropped from 1.19 to 0.78 kg CO₂e per functional unit. Economic performance showed payback periods of 5.78–10.54 years and IRRs of 15.8–24.2%. This framework provides a transferable tool for SMEs to prioritize decarbonization strategies based on integrated environmental, economic, and quality criteria. Full article

Sustainable development depends on whether emerging economies can convert foreign direct investment (FDI) into durable, knowledge-based capabilities rather than transient, low-skill employment. This paper asks how FDI catalyzes sustainable human-capital formation and whether multinational enterprises (MNEs) can help build the institutions that underpin a knowledge-economy transition. Using a single-equation instrumental-variables (2SLS) model for human-capital formation, with secondary-school enrolment as the dependent variable over 46 annual observations for Morocco (1975–2020), we find a positive and statistically significant FDI effect (β = 0.0029 per million USD of FDI inflow, p = 0.006), robust in sign and significance across OLS, 2SLS, and Dynamic OLS specifications. The result is consistent with a skill-demand channel through which knowledge-intensive MNEs are associated with higher private and social returns to education and with increased targeted public and private training investment. Sectoral evidence from Morocco’s aerospace and automotive clusters shows enrolment in vocationally oriented program growing 312% between 2005 and 2022, concentrated in curricula co-designed with foreign investors. Communications infrastructure carries a positive and significant coefficient across all four estimators but loses significance once urbanization is added as a control, so we do not treat it as a confirmed independent channel, while public education expenditure carries a negative and significant coefficient that is not stable in sign under Fully Modified OLS, indicating that its independent contribution is not well identified with the present instrument set. We interpret these findings through a sustainability lens, linking the mechanism to Sustainable Development Goals 4 (quality education), 8 (decent work and growth) and 9 (industry, innovation and infrastructure), and we specify three boundary conditions—skill specificity, investor commitment, and state coordination capacity—and argue that the mechanism should be expected to transfer to other developing economies, including in the MENA region and sub-Saharan Africa, only where these conditions co-occur, rather than as a general claim. The study advances the literature on MNE-led institutional co-creation and offers policy guidance for designing FDI strategies that support inclusive, sustainable knowledge-economy development across the MENA region and sub-Saharan Africa. Full article

The evolution of modern enterprise architecture has been strongly influenced by distributed web services, especially protocol-based standards such as Simple Object Access Protocol (SOAP) and resource-oriented architectural styles such as Representational State Transfer (REST). Cloud-native ecosystems, microservice architectures, and public API management commonly favor the lightweight, JSON-compatible, and horizontally scalable characteristics of RESTful services, whereas legacy configurations and highly regulated environments continue to use SOAP because of its formal contracts and compatibility with WS-* specifications for message-level security, reliable messaging, and transaction coordination. This paper presents a structured literature review that evaluates the architectural trade-offs, performance patterns, security boundaries, reliability considerations, and enterprise use cases of REST and SOAP. The review follows PRISMA-informed reporting practices and software-engineering review guidance, but it is not presented as an exhaustive systematic review because the original search strategy required REST and SOAP terms to appear together. IEEE Xplore, ACM Digital Library, ScienceDirect, and Scopus were searched for studies published between 2021 and 2026, resulting in 32 selected studies. The selected literature contains different evidence roles, including direct REST-SOAP empirical comparisons, REST-only and SOAP-only empirical studies, implementation studies, analytical papers, surveys, reviews, and contextual technical sources. The synthesis therefore separates direct empirical evidence from contextual and secondary evidence. The findings indicate that RESTful APIs generally show lower latency, smaller payloads, simpler parsing, and better horizontal scalability in the reported benchmark and web-facing settings, while SOAP remains relevant where formal service contracts, message-level protection, reliable messaging patterns, and transaction coordination are required. The paper identifies gaps in production-representative stress testing, empirical security comparison, reference-level traceability, and independent validation of hybrid REST-SOAP decision models. The resulting decision framework is presented as a provisional evidence-informed decision aid, not as an empirically validated tool. Full article

Energy-intensive foundries require methods to explore trade-offs among delivery performance, production horizon, electricity use, and cost when industrial data are incomplete. This paper presents an offline, process-level digital twin case study for the melting and casting area of a Danish cast-iron foundry. A multi-agent simulation represents production orders, enterprise resource planning and manufacturing execution system functions, induction furnaces, holding furnaces, crane-based transfer of molten metal, vertical moulding lines, the operating calendar, and electricity cost accounting for the induction furnaces. The model is assessed through boundary definition, assumption registration, implementation checks, material flow plausibility, a diagnostic comparison of furnace temperature, controlled scenario experiments, and local sensitivity analysis. These activities support internal consistency and bounded interpretation but do not constitute independent operational validation of the full production system. In the simulated 200-order monthly case, First-Come-First-Served and Earliest Deadline First complete the same 288,620 pieces and 5482.00 t. Earliest Deadline First increases the simulated on-time completion rate from 87.5% to 100%, while makespan, model-estimated electricity use by induction furnaces, and model-estimated electricity cost increase by 7.52%, 0.58%, and 3.42%, respectively. The case indicates that deadline-oriented sequencing may improve delivery performance but lead to a longer production horizon and higher energy use and cost within the defined model boundary. The contribution is an auditable foundry-specific modelling workflow that links heterogeneous data conditions to modelling choices, supporting evidence, and interpretation limits. The model is therefore intended for preliminary offline scenario exploration rather than validated operational decision support. Full article

Environmental noise is a major environmental and public health concern in rapidly urbanizing cities, yet integrated studies combining field measurements, GIS-based spatial analysis, and predictive modelling remain limited in Central Asia. This study assessed the spatial distribution of urban noise pollution in Samarkand (Uzbekistan) and explored its statistical association with selected public health indicators, forecasting future trends. Measurements were conducted at 50 georeferenced sites covering more than 300 streets. The measured data were processed and mapped using ArcGIS 10.5 (Esri, Redlands, CA, USA) to produce the spatial distribution of environmental noise across the study area. Official data on registered vehicles, industrial enterprises, and disease incidence (2014–2024) were analysed using Pearson correlation, Autoregressive Integrated Moving Average (ARIMA), and its extension incorporating exogenous variables (ARIMAX) models. Results revealed pronounced spatial heterogeneity in noise levels, highest along transport corridors and industrial zones. Industrial enterprises showed the strongest correlations with disease incidence; vehicle registrations were excluded from final models owing to collinearity with industrial activity. ARIMA projected continued industrial growth through 2030, while ARIMAX models identified significant associations between industrial activity and diseases of the ear and mastoid process and of the nervous system (MAPE 28.55% and 19.78%). As an ecological, exploratory study using infrastructural proxies rather than measured noise exposure, findings should be interpreted as associations rather than causation. The framework offers a transferable approach for environmental risk assessment and sustainable urban planning. Full article

Upstream small- and medium-sized enterprises (SMEs) in emerging economies often face severe credit constraints. These constraints hinder green transformation by limiting green R&D investment and production capacity. To address this, we develop a Stackelberg-based governance strategy selection framework. We first analyze cost-sharing (CS) and equity-sharing (ES) contracts and then extend them by incorporating external financing, where the retailer’s contractual commitment serves as an operational guarantee. This integration leads to two incentive-financing bundles, namely CS-F and ES-F. Three main findings emerge. First, capital constraints fundamentally shape the feasibility of green supply chain governance by creating a trade-off between green R&D and physical production. Specifically, the CS contract is feasible only within an intermediate capital range, whereas the ES contract is infeasible. Second, the incentive-financing bundles relax capital constraints and expand the feasible governance region. Although all feasible governance strategies promote green R&D investment, the ES-F bundle remains more sensitive to parameter variations. Third, the optimal governance strategy depends primarily on firms’ capital conditions, shifting across CS, CS-F, and ES-F. Notably, a distributive-efficiency paradox emerges: even when the ES-F bundle yields greater total surplus, a higher sharing ratio violates the retailer’s individual rationality and prevents its adoption. We introduce an asymmetric Nash bargaining mechanism to address this paradox. The mechanism determines transfer payments endogenously and restores the efficient governance outcome. Overall, our findings help supply chain managers select appropriate governance strategies based on observable firm-level capital conditions. Full article

Drawing on panel data from firms listed on the SME Board and Growth Enterprise Market (GEM) between 2010 and 2024, this study examines how patient capital influences SME innovation. It considers both the quantity and quality of innovation and investigates the underlying mechanisms. Using the China Industrial Enterprises Database (2000–2014), it further explores the innovation effects of patient capital on unlisted SMEs. The empirical findings are as follows. First, patient capital, measured by the proportion of relationship-based debt and stable equity, exhibits a significant and robust positive association with the output and quality of SME innovation, and this association gradually strengthens over time. Second, heterogeneity analyses show that relationship-based debt is more strongly associated with innovation in state-owned enterprises and national-level “Little Giant” firms (specialized, refined, distinctive, innovative SMEs), whereas stable equity is significantly associated with innovation only in private and ordinary enterprises. The association between stable equity and innovation is more pronounced in non-regulated industries, while the association for relationship-based debt remains consistent across industries. Third, mechanism tests reveal that patient capital is linked to SME innovation through four channels: alleviating financing constraints, fostering university–industry–research collaboration, improving knowledge conversion efficiency, and strengthening market power. Fourth, an extended analysis confirms that patient capital is also significantly associated with innovation among unlisted SMEs, indicating strong external validity of the study’s conclusions. Based on these findings, this paper advocates for establishing a long-term financing mechanism oriented toward patient capital, with differentiated allocation and optimization of institutional environments across industries. Such an approach should facilitate three transmission channels—university–industry–research collaboration, knowledge transfer, and market power—while extending policy coverage to unlisted SMEs, thereby nurturing a virtuous cycle ecosystem of “long-term capital → sustained R&D → high-quality innovation.” Full article

Small- and medium-sized enterprises (SMEs) face significant challenges in adopting robotic solutions due to limited financial resources, insufficient technical expertise, and uncertainty regarding operational and economic outcomes. Existing automation approaches are often technologydriven and provide limited support for systematic decisionmaking. This study proposes the Agile Robotics Implementation Model (ARIM), an iterative framework integrating Lean Manufacturing, Lean Robotics, and Lean Startup principles. ARIM combines process assessment, key performance indicator (KPI)-based evaluation, and iterative experimentation within the Robotic Startup Cycle, supported by a decision-support software tool. The framework was developed using a Design Science Research (DSR) approach and validated through an industrial case study. Results demonstrate strong agreement between predicted and realized KPI values. The implemented solution achieved a 24.5% return on investment (ROI), with a payback period of approximately 2.1 years, reduced labor demand by 3900 h, and improved productivity, ergonomics, and quality. The findings indicate that ARIM supports reliable and data-driven robotics implementation in the studied SMEs; broader transferability requires validation across multiple cases. Full article

Contact-rich manufacturing processes, such as surface cleaning, deburring, and polishing, require precise force regulation and complex trajectory tracking that are challenging to automate using conventional robot programming methods. Learning from Demonstration (LfD) offers a powerful alternative to transfer these expert skills from human operators to robotic systems. The objective of this study is to systematically map academic publications addressing LfD applications in robotic deburring and polishing between 2016 and 2026, classify the algorithmic structures, sensory modalities, and control configurations employed, and identify key industrial integration challenges. In accordance with the PRISMA 2020 guidelines, a systematic search was conducted across Scopus, Web of Science, IEEE Xplore, and Google Scholar databases. Out of the 288 initially retrieved records, duplicate removal and a two-stage screening process (Title/Abstract review, followed by full-text review) resulted in a final corpus of 24 primary studies included for qualitative synthesis. The included studies were classified into five algorithmic clusters: Dynamic Movement Primitives (DMPs) and variants (9 out of 24 studies, 38%), probabilistic and statistical models (8 out of 24 studies, 33%), deep learning and generative AI architectures (4 out of 24 studies, 17%), autonomous dynamical systems (2 out of 24 studies, 8%), and direct impedance control (1 out of 24 studies, 4%). Force/torque sensing remains the dominant modality; it was utilized exclusively in 71%—17 out of 24—of studies and in 87.5% of studies as any configuration (either as a sole modality or in multimodal setups). However, recent years have documented a trend toward multimodal perception and generative action policies (e.g., Diffusion Policies). The findings suggest that while LfD offers potential cost-reduction and flexibility benefits for small- and medium-sized enterprises (SMEs), technical barriers, such as the sim-to-real transfer gap, high-frequency impact dynamics in deburring, and the autonomous identification of local non-polishing areas (LNP areas), continue to limit widespread industrial deployment. Full article

The modern energy transition has outpaced the control and optimization frameworks built to govern it. As power and energy systems fragment into webs of renewable generators, storage operators, flexible loads, and carbon-constrained firms, the deterministic, single-optimizer models that once sufficed buckle against nonlinearity, bounded rationality, and strategic conflict among parties who learn and revise as they go. Evolutionary game theory (EGT), which traces how strategies propagate through populations by imitation and selection rather than instantaneous optimization, offers a route through this difficulty—one this review develops across three scales of low-carbon coordination central to cleaner production: enterprise-level industrial symbiosis, system-level smart energy operation, and market-level carbon governance. We synthesize three decades of theory alongside the recent fusion of EGT with artificial intelligence, where deep reinforcement learning approximates high-dimensional payoffs, federated learning lets rival firms co-train models without surrendering proprietary data, and blockchain underwrites decentralized mechanism execution. The synthesis is accompanied by two illustrative numerical case studies, constructed for this review rather than drawn from the surveyed literature, whose quantitative outputs are reported below as demonstrations of modeled behavior rather than as empirical measurements. In the first of these, cooperative emergence in industrial symbiosis hinges on critical thresholds that travel from 0.15 to 0.75 as subsidies and transaction costs vary, with anchor-enterprise targeting accelerating cooperation 2.4-fold while cutting outcome variance 3-fold. In smart energy coordination, AI-enhanced learning buys 32 to 41% faster convergence, yet pays 25 to 39% larger oscillations—a speed–stability tension whose resolution lives in a narrow learning-rate band near 0.08 to 0.12, outside which either sluggishness or instability takes hold. Carbon-market behavior turns on price thresholds: emitters switch abruptly from buying quotas toward investing in abatement once the clearing price clears firm-specific triggers, a discrete state switch that smooth equilibrium analysis misses entirely. Across all three domains, fragmented data, path dependence, and regime-switching dynamics recur as the binding constraints on modeling and on governance alike. Four mechanisms prove invariant to scale—the decisive weight of initial conditions, the catalytic leverage of well-positioned anchor agents, the equilibrium-shaping force of institutional design, and the computational reach added by AI integration—which suggests that insight earned in one domain transfers to the others. We close by mapping open problems in heterogeneity modeling, verification under deep uncertainty, and the still-unrealized coupling of digital twins with privacy-preserving learning. EGT emerges not as retrospective description but as prospective guidance for the cooperative transitions on which credible decarbonization depends. Full article