Search Results (3,610)

With the advancement of industrial systems toward greater complexity and higher asset value, unexpected equipment failures now risk severe production interruptions, substantial economic costs, and critical safety hazards. Conventional maintenance strategies, which are primarily reactive or schedule-based, have proven inadequate in preventing unplanned downtime, underscoring a pressing demand for more intelligent monitoring solutions. In this context, intelligent spectral detection has arisen as a transformative methodology to bridge this gap. This review explores the integration of spectroscopic techniques with machine learning for equipment defect diagnosis and prognosis, with a particular focus on applications such as hydrogen leak detection and transformer oil aging assessment. Key aging indicators derived from spectral data are systematically evaluated to establish a robust basis for condition monitoring. The paper also identifies prevailing challenges in the field, including spectral data scarcity, limited model interpretability, and poor generalization across different operational scenarios. Future research directions emphasize the construction of large-scale, annotated spectral databases, the development of multimodal data fusion frameworks, and the optimization of lightweight algorithms for practical, real-time deployment. Ultimately, this work aims to provide a clear roadmap for implementing predictive maintenance paradigms, thereby contributing to safer, more reliable, and more efficient industrial operations. Full article

Stock price prediction is a core challenge in quantitative finance. While machine learning has advanced the modeling of complex financial time series, existing methods often rely on single-target predictions, underutilize multidimensional market information, and are disconnected from practical trading systems. To address these gaps, this research develops a hybrid machine learning framework for flexible target forecasting and systematic trading of major American technology stocks. The framework integrates Ensemble Models (AdaBoost, Decision Tree, LightGBM, Random Forest, XGBoost) with Fusion Models (Voting, Stacking, Blending) and introduces a Transfer Learning method enhanced by Dynamic Time Warping to facilitate knowledge sharing across assets, improving robustness. Focusing on ten key stocks, we forecast three distinct momentum indicators: next-day Closing Price Difference, Moving Average Difference, and Exponential Moving Average Difference. Empirical results demonstrate that the proposed Transfer Learning approach achieves superior predictive performance and trading simulations confirm that strategies based on these predicted momentum signals generate substantial returns. This research demonstrates that the proposed hybrid machine learning framework can mitigate the high information entropy inherent in financial markets, offering a systematic and practical method for integrating machine learning with quantitative trading. Full article

As core infrastructure of power transmission networks, power towers require high-precision 3D models, which are critical for intelligent inspection and digital twin applications of power transmission lines. Traditional reconstruction methods, such as LiDAR scanning and oblique photogrammetry, suffer from issues including high operational risks, low modeling efficiency, and loss of fine details. To address these limitations, this paper proposes a 3D Gaussian Splatting (3DGS)-based method for power tower 3D reconstruction to enhance reconstruction efficiency and detail preservation capability. First, a multi-view data acquisition scheme combining “unmanned aerial vehicle + oblique photogrammetry” was designed to capture RGB images acquired by Unmanned Aerial Vehicle (UAV) platforms, which are used as the primary input for 3D reconstruction. Second, a sparse point cloud was generated via Structure from Motion. Finally, based on 3DGS, Gaussian model initialization, differentiable rendering, and adaptive density control were performed to produce high-precision 3D models of power towers. Taking two typical power tower types as experimental subjects, comparisons were made with the oblique photogrammetry + ContextCapture method. Experimental results demonstrate that 3DGS not only achieves high model completeness (with the reconstructed model nearly indistinguishable from the original images) but also excels in preserving fine details such as angle steels and cables. Additionally, the final modeling time is reduced by over 70% compared to traditional oblique photogrammetry. 3DGS enables efficient and high-precision reconstruction of power tower 3D models, providing a reliable technical foundation for digital twin applications in power transmission lines. By significantly improving reconstruction efficiency and reducing operational costs, the proposed method supports sustainable power infrastructure inspection, asset lifecycle management, and energy-efficient digital twin applications. Full article

Despite growing interest in sustainable supply chains, we still know relatively little about how environmental requirements transmitted from key customers along the supply chain affect firms’ productivity and long-run economic sustainability. To address this gap, we introduce the notion of green supply chain pressure, downstream customers’ explicit green and low-carbon requirements on suppliers, and examine its implications for firm-level productivity and the mechanisms involved. Using a panel of Chinese A-share listed firms over 2014–2024, we construct a novel text-based index of green supply chain pressure by combining supply-chain relationship data with MD&A disclosures of major customers. Firm-level economic sustainability is measured by Levinsohn–Petrin total factor productivity, with Olley–Pakes estimates used for robustness. Fixed-effects regressions with industry–year and city–year controls show that stronger green supply chain pressure is associated with significantly higher productivity. Mediation analysis reveals that this effect operates partly through three resource-intensive adjustment channels: (i) a higher share of green patents in total innovation, (ii) capital deepening via a higher share of digital and intelligent fixed assets in total net fixed assets, and (iii) human capital upgrading through a larger proportion of highly educated employees. Interaction models further indicate that financing constraints critically condition these gains: the productivity effect of green supply chain pressure is stronger for firms with greater financial slack, and for high-tech, green-attribute and larger firms. Overall, the results highlight supply chain-based governance as a powerful complement to formal regulation for promoting long-run economic sustainability at the firm level. Full article

Industrial Internet of Things (IIoT) networks, Microsoft Azure Internet of Things (IoT) Hub, and device twins (DvT) are increasingly recognized as core enablers of adaptive, data-driven manufacturing. This paper proposes a hierarchical IIoT framework that integrates industrial IoT networking, DvT for asset-level virtualisation, system-level digital twins (DT) for cell orchestration, and cloud-native services to support the digital transformation of brownfield, programmable logic controller (PLC)-centric modular automation (MA) environments. Traditional PLC/supervisory control and data acquisition (SCADA) paradigms struggle to meet interoperability, observability, and adaptability requirements at scale, motivating architectures in which DvT and IoT Hub underpin real-time orchestration, virtualisation, and predictive-maintenance workflows. Building on and extending a previously introduced conceptual model, the present work instantiates a multilayered, end-to-end design that combines a federated Message Queuing Telemetry Transport (MQTT) mesh on the on-premises side, a ZigBee-based backup mesh, and a secure bridge to Azure IoT Hub, together with a systematic DvT modelling and orchestration strategy. The methodology is supported by a structured analysis of relevant IIoT and DvT design choices and by a concrete implementation in a nine-cell MA laboratory featuring a robotic arm predictive-maintenance scenario. The resulting framework sustains closed-loop monitoring, anomaly detection, and control under realistic workloads, while providing explicit envelopes for telemetry volume, buffering depth, and latency budgets in edge-cloud integration. Overall, the proposed architecture offers a transferable blueprint for evolving PLC-centric automation toward more adaptive, secure, and scalable IIoT systems and establishes a foundation for future extensions toward full DvT ecosystems, tighter artificial intelligence/machine learning (AI/ML) integration, and fifth/sixth generation (5G/6G) and time-sensitive networking (TSN) support in industrial networks. Full article

Between 2018 and 2025, alternative finance expanded while micro-, small- and medium-sized enterprises in emerging economies continued to face a substantial funding gap. This study examines how entrepreneurial frugality articulates frugal ecosystems, access to alternative finance, resilience and SME performance within a single explanatory framework. Following PRISMA 2020 and PRISMA-S, we conduct a systematic review of Scopus, Web of Science and Cairn; out of 1483 records, 106 peer-reviewed studies are retained and assessed using the Mixed Methods Appraisal Tool and a narrative synthesis approach. The findings show that frugal ecosystems characterized by pooled assets, norms of repair and modularity, and lightweight digital tools reduce experimentation costs and develop frugal innovation as an organizational capability. This capability enhances access to alternative finance by generating readable quality signals, while non-bank channels provide a financial buffer that aligns liquidity with operating cycles and strengthens entrepreneurial resilience. The article proposes an operationalized conceptual model, measurement guidelines for future quantitative surveys, and public policy and managerial implications to support frugal and inclusive innovation trajectories in emerging contexts. Full article

Green innovation is important for environmental sustainability and long-term ecological balance. Using 1129 observations of Chinese listed firms spanning 2014–2024, combined with text mining method to quantify data assets, this paper empirically examines the impact of customer data assets on suppliers’ green innovation. Our model is integrated with fixed effects for both industry and year. We find that there is a significant improvement in suppliers’ green innovation when customers have more data assets, with a one-notch improvement in the customer data assets of a customer firm. This results in an overall 0.06 increase in supplier green innovation output. Specifically, the spillover effect is more pronounced when there is a shorter geographic distance between suppliers and customers, as well as higher customer concentration. After conducting a variety of endogeneity tests, our results are robust. The mechanism analysis shows that customer data assets facilitate supplier digital transformation and improve supplier operational capacity. The heterogeneity analysis also reveals stronger effects when (1) customers are located in eastern regions, (2) customers belong to technology-intensive industries, (3) suppliers are state-owned enterprises (SOEs), and (4) suppliers face lower financial constraints. Further analysis suggests that customers with more data assets also increase suppliers’ R&D investment and improve green innovation quality. Our research contributes to understanding the spillover effect of customer data assets along the supply chain. Full article

This paper develops a quantitative framework for climate–financial risk measurement that combines a spatially explicit jump–diffusion asset–loss model with prudentially aligned risk metrics. The approach connects regional physical hazards and transition variables derived from climate-consistent pathways to asset returns and credit parameters through the use of climate-adjusted volatilities and jump intensities. Fat tails and geographic heterogeneity are captured by it, which conventional diffusion-based or purely narrative stress tests fail to reflect. The framework delivers portfolio-level Spatial Climate Value-at-Risk (SCVaR) and Expected Shortfall (ES) across scenario–horizon matrices and incorporates an explicit robustness layer (block bootstrap confidence intervals, unconditional/conditional coverage backtests, and structural-stability tests). All ES measures are understood as Conditional Expected Shortfall (CES), i.e., tail expectations evaluated conditional on climate stress scenarios. Applications to bank loan books, pension portfolios, and sovereign exposures show how climate shocks reprice assets, alter default and recovery dynamics, and amplify tail losses in a region- and sector-dependent manner. The resulting, statistically validated outputs are designed to be decision-useful for Internal Capital Adequacy Assessment Process (ICAAP) and Pillar 2: climate-adjusted capital buffers, scenario-based stress calibration, and disclosure bridges that complement alignment metrics such as the Green Asset Ratio (GAR). Overall, the framework operationalises a move from exposure tallies to forward-looking, risk-sensitive, and auditable measures suitable for supervisory dialogue and internal risk appetite. Full article

Amid the rapid growth of the digital economy and increasing global competition, the role of digital–intelligent technologies in enabling corporate internationalization has gained significant attention. From the perspective of “digital–intelligent synergy,” this study constructs a mediated moderation model to explore the impact mechanism of digital–intelligent synergy on corporate internationalization. The findings indicate that data assets, artificial intelligence, and digital–intelligent coupling coordination significantly enhance overseas revenue. Green technology innovation mediates this relationship, while investor stability exerts an asymmetrical moderating effect. This strengthens both the direct effect of digital–intelligent synergy on internationalization and its impact on green innovation, though not the path from green innovation to international performance. Further analysis indicates that self-use data assets significantly promote firm internationalization, while transactional data assets do not. Both AI technology and applications markedly enhance overseas expansion. For digital–intelligent coupling coordination, the level of coordination—not merely coupling intensity—positively affects internationalization level. By integrating green innovation and investor behavior perspectives, this study reveals the complex mechanisms through which digital–intelligent synergy empowers internationalization, offering theoretical and policy insights for corporate global expansion in the digital–green transition era. Full article

Nobel Laureate Markowitz originates portfolio selection as the birth of modern finance. Nobel Laureate Sharpe implements portfolio selection and originates capital asset pricing models. Nobel Laureate Fama also implements portfolio selection and originates zero-covariance capital asset pricing models. After these feats, researchers have gradually realized additional objectives and have promisingly extended portfolio selection into multiple-objective portfolio selection. However, there hardly exists research to leap from multiple-objective portfolio selection to multiple-objective capital asset pricing models (as initiated by Markowitz and Sharpe in finance). Moreover, the extension is basically confined to the branches of mathematics, operations research, optimization, and computer sciences. Many researchers sufficiently review multiple-objective portfolio selection. However, the reviews are extensive. Instead, we intensively criticize and envision the research on multiple-objective portfolio selection from the perspective of capital asset pricing models by crystallizing the research limitations and heralding future directions. Specifically, we emphasize seven research limitations for multiple-objective portfolio optimization, multiple-objective capital asset pricing models, and multiple-objective zero-covariance capital asset pricing models. We also generalize from common three-objective portfolio selection to k-objective portfolio selection. Visually, we orchestrate figures to delineate the complexity. Theoretically, this paper heralds challenging but encouraging future directions. Pragmatically, this paper proposes a formulation for the multiple-objective nature of practical convolution in finance. Full article

Unmanned helicopters play a critical role in various fields including defense, disaster response, and infrastructure inspection. Military platforms such as the MQ-8C Fire Scout represent high-value assets exceeding $40 million per unit including development costs, particularly when compared to expendable multicopter drones costing approximately $500–2000 per unit. Unexpected failures of these high-value assets can lead to substantial economic losses and mission failures, making the implementation of Health and Usage Monitoring Systems (HUMS) essential. However, the scarcity of failure data in unmanned helicopters presents significant challenges for HUMS development, while the economic feasibility of investing resources comparable to manned helicopter programs remains questionable. This study presents a novel cross-scale transfer learning framework for vibration-based anomaly detection in unmanned helicopters. The framework successfully transfers knowledge from a source domain (Airbus large manned helicopter) using publicly available data to a target domain (Stanford small RC helicopter), achieving excellent anomaly detection performance without labeled target domain data. The approach consists of three key processes. First, we developed a multi-task learning transformer model achieving an F-β score of 0.963 (β = 0.3) using only Airbus vibration data. Second, we applied CORAL (Correlation Alignment) domain adaptation techniques to reduce the distribution discrepancy between source and target domains by 79.7%. Third, we developed a Control Effort Score (CES) based on control input data as a proxy labeling metric for 20 flight maneuvers in the target domain, achieving a Spearman correlation coefficient ρ of 0.903 between the CES and the Anomaly Index measured by the transfer-learned model. This represents a 95.5% improvement compared to the non-transfer learning baseline of 0.462. Full article

Climate-driven sea ice decline is accelerating the commercial use of Arctic routes and raising the need for Green Shipping Corridors that couple decarbonization with safety and ecosystem protection. This study introduces the concept of Arctic Green Maritime Data—environmental, meteorological, operational, and emission datasets generated in polar navigation—and examines how the EU Data Act can serve as a legal–institutional backbone. Using a multilayered integrative analysis, we (i) interpret core provisions on user access, portability, compensation, public-interest requests, cloud switching, and interoperability; (ii) map the Act’s roles of data holder, user, and recipient onto shipping stakeholders; (iii) assess whether polar operational datasets qualify as “data generated through the use of a product”; and (iv) derive a contractual architecture for corridor operations. We propose a three-layer governance model: firm-level instruments (a Standard Arctic Green Maritime Data Transaction Agreement, enterprise data governance architecture, and FRAND (Fair, Reasonable, and Non-Discriminatory) based contracting), association-level tools (industry model terms, public-purpose data protocols, and a neutral data-trust intermediary), and IMO-level integration aligning EU Data Act principles with Polar Code and MARPOL. The analysis showed that structured rights and obligations reduce vendor lock-in, enable safe public-interest data flows (with emergency access and fair compensation), and improve interoperability across clouds and jurisdictions. The results provide implementable pathways for shipping companies to turn Arctic Green Maritime Data into strategic assets while supporting sustainable and resilient green shipping corridor operations. Full article

This paper investigates the factors of sustainability of the financial performance of Islamic insurance (Takaful) windows in Pakistan. A large body of literature has examined Takaful providers across many countries; however, there is little research on the dynamics of Takaful windows. This study uses an analytical approach to investigate the effects of various operational and financial measures on Takaful window performance. It is one of the earliest works to examine the profitability of Takaful windows with a dynamic PVAR model, providing new evidence on the peculiar financial forces in hybrid Islamic–conventional insurance frameworks. It explores the effects of the retention ratio, Wakalah fees, commission ratio, gross written contributions, and underwriting surplus on profitability, measured by return on assets (ROA) and return on equity (ROE). It uses annual data from 18 Pakistani Takaful window insurers, employs a panel vector autoregressive framework to capture dynamic interdependencies and endogeneity, and conducts a variance decomposition with impulse response analysis. The findings indicate that the retention ratio and underwriting surplus have significant positive effects on ROA, whereas Wakalah fees have a negative impact. In the case of ROE, the underwriting surplus and commission ratio are associated with positive effects; meanwhile, the retention ratio and gross written contributions are related to negative effects. Variance decomposition emphasizes the commission and retention ratios as the main sources of profitability, with Wakalah fees and underwriting surplus being insignificant. The regulators need to ensure proper fund separation and establish the most optimal rules regarding Wakalah fees. The operation of Takaful windows should focus on commission management and business retention strategies to enhance profitability and financial sustainability. The increase in the financial performance of Takaful windows contributes to the expansion of Shariah-compliant insurance, facilitating the financial inclusion of Muslim communities in mixed markets. Full article

Workforce adjustments, such as mass layoffs, are significant corporate events that can influence stock returns and volatility, yet their broader asset-pricing implications remain underexplored. We examine the impact of such workforce shocks on stock performance from an asset-pricing perspective. Grounded in production-based asset-pricing theory, incorporating labor adjustment costs and search-and-matching frictions, our study posits that disruptions in the labor force significantly affect firm risk and value. This focus addresses a clear gap. Previous research has not comprehensively evaluated workforce shocks as systematic risk factors in a cross-sectional asset-pricing model. Using an extensive dataset spanning 1990–2023 and covering thousands of layoff events, we construct a novel “workforce shock” factor and conduct the first large-scale empirical tests of its pricing relevance. Our analysis reveals that workforce shocks lead to lower stock returns and heightened volatility, effects especially pronounced in labor-intensive firms. Moreover, exposure to workforce shock risk carries a significant premium, indicating that these disruptions act as a systematic risk factor priced in the cross-section of equity returns. Overall, our study provides the first comprehensive evidence linking labor force disturbances to equity risk premia, underscoring the importance of incorporating labor market considerations into asset-pricing models. Full article

As a cornerstone of the modern financial safety net, the Lender of Last Resort (LOLR) is essential in mitigating liquidity crises and containing financial contagion. However, during periods of economic stability, risk-taking incentives in the banking sector may undermine its effectiveness. Using quarterly panel data from 55 countries over the period 2010–2023, this study employs a two-way fixed effects model to assess the impact of LOLR support on systemic financial risk and its transmission mechanisms. We find that LOLR support significantly increases systemic risk during stable economic periods. Mechanism analysis indicates that this effect is channeled through the erosion of bank asset liquidity, expansion of financial leverage, and deterioration in asset quality. Moreover, the adverse impact is more pronounced in emerging economies, bank-dominated financial systems, countries with low capital adequacy ratios, underdeveloped regulatory frameworks, and lower levels of digital technology adoption. This study provides cross-country evidence on the potential negative consequences of central bank rescue functions during calm periods and offers important policy insights for optimizing the LOLR framework and building a more resilient financial safety net. Full article