Search Results (760)

Conveyor belts are critical equipment in mining operations, where continuous and reliable material transport is essential for production efficiency. This systematic review aims to analyze computer vision-based techniques applied to conveyor belt condition monitoring. Following PRISMA guidelines, a search was conducted in the Scopus and Web of Science databases, and 80 studies were selected after applying predefined eligibility criteria. These studies were synthesized through quantitative bibliometric methods and structured qualitative thematic categorization. The findings reveal a significant increase in scientific output after 2020, as well as its geographic distribution and potentially the most influential contributions. The main research lines focus on damage detection, deviation detection, and foreign object detection. A clear transition is also observed from traditional image processing methods—such as filtering, segmentation, and geometric analysis—toward deep learning models, including YOLO, CenterNet, and hybrid architectures, with improvements in precision, speed, and stability. Nevertheless, challenges remain related to datasets representativeness, the heterogeneity of evaluation protocols, and variability in operational conditions. Finally, opportunities for advancement are identified through multimodal datasets, adaptive models, and lightweight solutions that facilitate integration into asset management systems and support scalable industrial adoption. Full article

This study examines the accounting determinants of the capital structure of Portuguese firms in the textile, clothing, and leather sectors, based on a sample of 6469 firms over the period 2010–2022, using panel data models. The relevance of this study lies in its focus on specific industrial sectors characterized by a high predominance of small and medium-sized enterprises (SMEs) and a strong dependence on bank financing. In addition to the traditional analysis of leverage determinants, this study introduces a conditional approach to accounting variables based on firms’ structural characteristics, namely size and age. Robustness checks and data treatment procedures were conducted to mitigate the potential impact of outliers in the financial variables. The results show that profitability, liquidity, and risk negatively affect indebtedness, whereas asset structure and growth exert positive effects. The effective tax rate has a negative impact on debt. Firm size and age significantly condition the relationship between variables. SMEs’ financing decisions exhibit differentiated patterns depending on firm size and age. The findings support the predictions of the Pecking Order Theory and, to a lesser extent, the Trade-Off Theory. The study highlights the importance of considering firm heterogeneity when designing financing policies and strategies for Portuguese SMEs. Full article

This study examines dynamic interdependencies and risk transmission among major cryptocurrencies and traditional financial assets, including Bitcoin, Ethereum, U.S. equities, and gold, over the period 2017–2024. Particular attention is given to the structural shift associated with the 2024 U.S. spot Bitcoin exchange-traded fund (ETF) approval, which marked a significant milestone in the institutionalization of cryptocurrency markets. Using daily data, the analysis distinguishes volatility-driven co-movement from structural spillover effects across markets. Dependence structures are modeled using tail-sensitive Student-t copulas applied to GARCH-filtered returns to capture nonlinear and extreme co-movements, while a vector autoregressive framework combined with generalized impulse response functions and Diebold–Yilmaz connectedness measures is employed to evaluate order-invariant shock transmission dynamics across pre- and post-ETF regimes. The results reveal three main findings. First, cryptocurrencies display strong internal dependence and short-horizon contagion, with Bitcoin consistently acting as the dominant transmitter of shocks to Ethereum over an approximately three-day transmission window. Second, linkages between cryptocurrencies and equity markets remain moderate and largely regime-dependent rather than indicative of persistent structural spillovers. Third, gold remains weakly connected throughout the sample, maintaining its role as a diversification asset. Portfolio analysis further indicates that including Bitcoin can reduce portfolio variance by 4–7% and Value-at-Risk by up to 5%, although economic gains are sensitive to transaction costs. Overall, the findings suggest that cryptocurrencies function as a partially segmented asset class, offering conditional diversification benefits despite increasing institutional adoption. Full article

Distributed generation (DG) exhibits inherent volatility and intermittency, and its grid-integration expansion presents formidable challenges to microgrid regulation and control. Conventional control strategies often neglect the uncertainties associated with renewable energy generation and the coordinated management of flexible resources. This paper proposes a multi-time-scale optimal control strategy for microgrids that explicitly accounts for uncertainty. The strategy integrates a collaborative scheduling framework for assets, including electric vehicles (EVs) and energy storage systems, alongside a stochastic optimization model for microgrids that comprehensively incorporates uncertainties from wind and solar power generation, EV operations, and load forecasting errors. The improved Archimedean chaotic adaptive whale optimization algorithm is utilized to solve the optimal scheduling model, while the Latin hypercube sampling (LHS) technique is employed to address uncertainty-related problems in the optimization process. Case study results demonstrate that, in comparison with traditional optimal scheduling strategies, the proposed approach more effectively mitigates uncertainties in real-world operations, reduces microgrid operational risks, achieves a significant reduction in scheduling costs, and concurrently fulfills the dual objectives of microgrid economic efficiency and operational security. Full article

The transition toward sustainable food systems requires innovative approaches to managing perishable products, where inefficient inventory practices contribute significantly to global food loss and environmental degradation. This study develops a circular-economy-oriented inventory optimisation framework for dairy supply chains that integrates environmental externalities and waste valorisation pathways into operational decision-making. Departing from traditional linear “produce–consume–dispose” models, this study embeds three core sustainability mechanisms into a stochastic dynamic-programming framework: (1) progressive environmental cost internalisation aligned with EU Emissions-Trading System carbon pricing, capturing both waste-related emissions and cold-chain energy footprints; (2) circular-economy value-recovery channels that redirect near-expiry products to secondary applications (animal feed, biogas production, industrial processing) rather than disposal; and (3) deterioration-aware demand management that minimises resource throughput while maintaining service levels. Empirical calibration using Ukrainian dairy industry data demonstrates that sustainability-integrated inventory policies reduce waste generation by 4.8–10% relative to conventional approaches, with high-deterioration products showing the greatest potential for improvement. The authors identify a critical threshold in the circular economy: when salvage recovery rates exceed 35%, waste becomes an economic and ecological asset, fundamentally altering the sustainability calculus of inventory decisions. Environmental costs account for 4.6% of total operating expenses at current carbon prices, a share projected to increase substantially as climate regulations tighten. The findings provide actionable guidance for dairy supply chain stakeholders pursuing the Sustainable Development Goals (SDGs 2, 12, 13): processors should establish circular-economy partnerships that achieve salvage rates above 35%, implement product-specific policies for high-deterioration items, and proactively integrate carbon pricing into inventory optimisation. The framework bridges sustainable operations theory and circular economy practice, offering a replicable model for transitioning perishable food supply chains toward closed-loop, low-waste configurations that simultaneously reduce environmental impact and enhance economic performance. Full article

The proliferation of digital assets has catalyzed a profound decoupling between intangible property and traditional inheritance jurisprudence. Under the existing legal framework in Taiwan, practitioners must rely on the testamentary forms prescribed in Article 1189 of the Civil Code, which are fundamentally ill equipped to handle cryptographic assets. Specifically, Notarized Wills (Article 1191) necessitate full disclosure to a notary, creating a “Privacy–Security Paradox” where revealing private keys exposes assets to misappropriation. Conversely, while Sealed Wills (Article 1192) offer confidentiality, they are plagued by risks of physical degradation and technical non-executability. This study proposes zkWill, an EVM-compatible decentralized testamentary framework designed to bridge these structural gaps. By leveraging Zero-Knowledge Proofs (ZKPs), zkWill achieves a state of “blind compliance,” verifying that a sealed will meets the statutory requirements of the Civil Code without disclosing its underlying content. The system integrates the Permit2 protocol for secure asset migration and combines AES-256 encryption with IPFS to immunize testaments against centralized storage failures. Unlike conventional services that demand custodial trust, zkWill employs decentralized oracles to trigger automated execution, ensuring legacy distribution without compromising wallet private keys. Empirical data from the Arbitrum Sepolia testnet confirms that the framework maintains constant verification efficiency and a judicially resilient audit trail, providing a paradigm that harmonizes legal pragmatism with cryptographic security for digital inheritance. Full article

A major connectivity gap arises from the misalignment between the forward-looking climate disclosures required by IFRS S2 and the historically rooted asset valuations mandated by IAS 36. This misalignment can cause the overvaluation of carbon-intensive assets and disrupt capital allocation decisions. This research specifically examines transition risks, such as carbon pricing, regulatory shocks, and technological disruption, and quantifies the financial externality using a combination of deterministic impairment testing and stochastic climate scenarios. We create a constructive framework and develop a model of a Synthetic Representative Firm, calibrated to major integrated steel producers in Europe. To generate nonlinear Green Swan shocks for Value-in-Use, the process combines Monte Carlo simulation with the Merton Jump-Diffusion model. This comparison shows the difference between the steady Management View and the volatile Market View. Empirical results reveal a material Sustainability Discount, representing a substantial erosion in the recoverable amount under IFRS S2 transition risk scenarios compared to the IAS 36 Deterministic Baseline. Simulations show a strong probability of asset stranding due to restricted cost pass-through, indicating that older assets may face elevated impairment risks under disorderly transition scenarios. Traditional deterministic models may not fully capture aspects of Double Materiality, potentially leaving balance sheets less responsive to transition risks. Integrating digitalization and the Circular Carbon Economy (CCE) framework presents a strategic method for averting value destruction. Therefore, this research supports the integration of stochastic transition risk modeling into impairment testing to achieve faithful financial representation. Full article

The platformization of urban space is opening new frontiers of capital accumulation, particularly through short-term rentals. Airbnb plays a central role in this process by commodifying housing in tourist destinations. Despite its rapid growth, research on Airbnb in Latin America—especially in small tourist cities—remains limited and largely focused on metropolitan contexts. This article addresses this gap with the objective of analyzing how platform-mediated short-term rentals reorient housing markets beyond traditional urban cores. It is hypothesized that Airbnb expands housing commodification by extending tourism-oriented uses into new residential areas and by redistributing returns unevenly across actors. Using a quantitative and geospatial approach, the results reveal a strong presence of Airbnb in rural and natural areas, from which the highest returns are extracted, as well as a high concentration of accommodation supply among professional hosts. These dynamics reconfigure housing use toward asset-based logics, posing challenges for housing security and social and territorial sustainability in small tourist cities. Full article

Crypto-asset markets have been rapidly evolving during the past years, being under the spotlight of a diverse set of actors in the financial ecosystem, including investors, financial institutions, regulators and academics. Their potential interconnections with the traditional financial markets are important, and identifying them can provide useful insight in a diversity of areas such as risk contagion and mitigation, price formation, portfolio management and regulatory framework design. In order to identify such interconnections, various lines of research are followed. Specifically, the correlation between prominent stock market indices and crypto-assets from 2018 to 2025 is examined, while their volatility is also evaluated. Furthermore, the relevant effect of news, events and announcements is explored. The results are based on both daily and high-frequency datasets, with the use of the latter focusing on intra-day variation. The analysis of the results identifies existing interconnections between 2020 and 2025, as well as the important respective impact of news and announcements. An additional generic outcome is the usefulness of high-frequency datasets in the crypto-asset context. The conclusions are useful for all actors in the financial ecosystem. Future work can focus on the extension of the research to additional markets or crypto-assets. Full article

Asset discovery is a fundamental but inherently flawed capability in cybersecurity, as current methodologies frequently confuse preliminary discovery observations with definitive asset inventories, thereby obscuring uncertainty, restricting auditability, and eroding trust in security-critical decision-making. This work addresses the issue of inconsistent asset identification in dynamic IT settings by presenting an evidence-based architectural paradigm that clearly distinguishes observation, identity resolution, and inventory representation. The principal research aim is to develop and authenticate an architecture that maintains discovery evidence, facilitates deterministic, verifiable identity resolution, and supports interpretable inventory derivation. In contrast to state-centric and model-driven methodologies, the proposed architecture enhances (i) traceability through the preservation of time-scoped, method-attributed observations, (ii) identity continuity amidst dynamic conditions such as IP reassignment and infrastructure modifications, and (iii) auditability by facilitating the reconstruction of inventory claims from foundational evidence. An examined proof-of-concept implementation in a controlled yet realistic network environment shows superior identity stability, greater discovery traceability, and retention of historical context relative to traditional inventory models. The results validate the practicality and architectural benefits of the strategy; nevertheless, the evaluation is constrained by a lack of formalised performance indicators and adversarial robustness, which are recognised as priorities for further investigation. Full article

Developing regions rich in ethnic cultures face structural tensions between cultural heritage preservation, ecological conservation, and economic development. Yet existing research analyzes village types in isolation, overlooks non-additive factor interactions, and lacks frameworks connecting spatial heterogeneity to differentiated sustainability pathways. This study addresses these three gaps through integrated spatial analysis of 4083 characteristic villages across five nationally designated types in Southwest China, a region harboring over 40% of China’s Traditional Villages and high densities of Forest Villages, Key Tourism Villages, Ethnic Minority Characteristic Villages, and Historic and Cultural Villages. Kernel Density Estimation, Average Nearest Neighbor analysis, Standard Deviational Ellipse, and Geographical Detector methods are employed in a three-stage analytical progression. Spatial characterization reveals pronounced heterogeneity with “large-scale dispersion, small-scale agglomeration” patterns and systematic cross-type spatial co-location in high-heritage, high-vulnerability zones. Mechanism quantification shows that intangible cultural heritage (q-values 0.66–0.78) and GDP per capita (q-values 0.68–0.82) are dominant drivers whose pairwise interactions exceed individual effects by 40–60%. Sustainability classification translates q-value-weighted composite indices into four context zones across 506 counties, Culture-Ecology Tension Zones (22.7%), Economic Isolation Nodes (17.0%), Tourism-Driven Development Corridors (19.6%), and Balanced Development Potentials (40.7%), each exhibiting a distinct configuration of cultural, ecological, and economic conditions that necessitates differentiated pathways. The “culture-ecology-economy” tripartite framework advances sustainability science in three ways: it empirically identifies non-additive spatial interactions as generative mechanisms of heterogeneity, achieves a methodological progression from pattern description to sustainability diagnosis, and reconceptualizes cultural heritage from a development constraint into a measurable sustainability asset. The framework is transferable to analogous mountain regions globally where heritage-rich communities confront coupled ecological and economic vulnerabilities. Full article

Agricultural enterprises serve as the cornerstone of food security. However, they operate under significant resource constraints and environmental risks. Adopting a systems lens, this study examines digitalization as a critical variable reshaping the input–output logic of agribusinesses. Using a longitudinal panel dataset of Chinese listed agricultural firms from 2013 to 2022 and Ordinary Least Squares regression, the study empirically identifies the mechanisms driving ESG performance. The results demonstrate that digitalization significantly enhances overall ESG performance, functioning as a governance mechanism that improves internal resource integration and transparency. Critically, the moderation analysis reveals a dynamic substitution relationship among system elements. Traditional inputs, specifically management expenses, financial slack, and intangible assets, exert significant negative moderating effects. This confirms the logic of factor substitution, suggesting that as digitalization advances, traditional governance modes relying on high administrative costs face diminishing marginal returns. In the environmental dimension, digitalization facilitates a transition from post-event remediation to whole-process control through intelligent traceability, effectively internalizing external constraints and reducing waste emissions. Additionally, heterogeneity analysis highlights significant structural variations. The ESG-enhancing effect of digitalization is more pronounced in firms characterized by high financial leverage, low long-term debt, and low industry concentration. Spatially, the marginal improvement is stronger in Western regions compared to the East, underscoring the Hu Huanyong Line as a critical structural boundary. Ultimately, digitalization serves as a core governance element that drives the structural transformation from traditional operating paradigms to digital governance architectures, thereby providing a robust pathway for corporate sustainability. Full article

Road maintenance remains one of the most resource-intensive and hazardous operations in infrastructure management. Traditional inspection practices rely heavily on manual labour and discrete procedures, often resulting in limited scalability, operator exposure to traffic hazards, and inefficiencies in data collection. This paper presents a novel automated methodology that integrates Unmanned Aerial Vehicles (UAVs) and autonomous mobile robots (AMRs) to enable automated inspection and measurement of road assets through a digital twin (DT) system. The system leverages data fusion and real-time synchronisation between field agents and a centralised digital twin to monitor the retro-reflectivity of vertical and horizontal signage, detect obstacles and vegetation, and support data-driven maintenance planning. A case study conducted on the Italian highway network demonstrated improvements in operational safety, inspection efficiency, and measurement consistency. The results confirm that the integration of UAVs and AMRs within a digital twin framework can significantly improve sustainability, productivity, and workers’ safety in road maintenance operations. Full article

Efficiently converting natural capital into economic assets is a critical challenge in urban–rural transformation, yet the interactive mechanism between institutional land reform and physical spatial restructuring remains underexplored. While traditional frameworks emphasize institutional design, this study identifies a “Spatial-First” mechanism where physical reconfiguration serves as a spatial mediator to catalyze property rights breakthroughs. Using an entropy-weighted coupling coordination model, we analyzed policy dynamics in Nanhai District, China, a unique “dual-pilot” zone, from 2020 to 2024. The results indicate a nonlinear leap in the Coupling Coordination Degree (D) from 0.100 to 0.978. We interpret this surge as a policy-driven shock during the intensive pilot phase, where substantive spatial integration (0.719) effectively bypassed high transaction costs inherent in collective tenure, outpacing institutional progress (0.281). However, an Ecological Lag was observed; the disproportionately low weighting of the ecological carrier index (7.09%) suggests that current gains are primarily driven by green industrialization rather than the expansion of absolute ecological stock. This study concludes that while spatial tools can effectively unlock natural capital value in the short term, long-term sustainability necessitates a strategic shift from administrative-led economic efficiency to market-based ecological restoration. Full article

As secondary educational environments face increasing volatility due to systemic resource constraints and pedagogical uncertainty, understanding the behavioral mechanisms of teacher agency has become paramount. While traditional teacher education has emphasized the execution of standardized curricula, the current era demands a fundamental shift toward adaptive expertise and psychological resilience. This study investigates the processes by which 28 secondary pre-service physical education teachers (PSTs) navigate instructional resource deficits through the lens of adaptive behavior (bricolage) and ecological teacher agency. Utilizing a qualitative case study design, I collected data from two universities in Seoul, South Korea, through reflective journals, revised lesson plans, and micro-teaching video analysis reports over a full 15-week semester. The results identified five coordinates of an adaptive instructional design compass: (1) Facing Constraints, (2) Resource Mining, (3) Contextual Engineering, (4) Simulation, and (5) Reflective Participation. These coordinates represent a transformative behavioral process where PSTs convert environmental deficits into professional assets. The findings reveal distinct adaptation styles based on psychological dispositions: the analytically oriented group (Group A) prioritized structural redesign through digital tools, while the narratively oriented group (Group B) utilized human-centric somatic metaphors and virtual rehearsals to bridge the epistemic void. Crucially, this research suggests that teacher adaptation is not a mere technical adjustment but a dynamic behavioral achievement of agency that ensures the long-term instructional quality of physical education. I propose that teacher education programs should incorporate “Safe Deficit” simulations—carefully calibrated instructional constraints—to trigger adaptive behavior and ensure that future educators can thrive in unpredictable pedagogical contexts without the risk of professional burnout. Full article