Search Results (653)

In the context of Industry 4.0 and the proliferation of smart buildings, elevators represent critical assets whose performance is often inadequately measured by traditional indicators that overlook energy consumption. This study addresses the need for a more holistic Key Performance Indicator (KPI) by developing the Overall Equipment Effectiveness for Elevators (OEEE), an index designed to integrate operational effectiveness with energy efficiency. The methodology involves adapting the classical OEE framework through a comprehensive literature review and an analysis of elevator energy standards. This leads to a novel structure that incorporates a dedicated energy efficiency dimension alongside the traditional pillars of availability, performance, and quality. The framework further refines the performance and energy efficiency dimensions, resulting in six distinct sub-indicators that specifically measure operational uptime, speed adherence, electromechanical conversion, fault-free cycles (as a proxy for operational quality), and energy use during both movement and standby modes. The primary result is the complete mathematical formulation of the OEEE, a single, integrated KPI derived from these six metrics and designed for implementation using data from modern IoT-enabled elevators. The study concludes that the OEEE provides a more accurate and comprehensive tool for asset management, enabling data-driven decisions to enhance reliability, optimise energy consumption, and reduce operational costs in smart vertical transportation systems. Full article

Banks in the United States face persistent challenges from non-performing loans (NPLs), despite conducting thorough client evaluations before issuing loans. To mitigate the impact of NPLs and support both local and global growth, banks must adopt effective risk management strategies. This study investigates the effect of NPLs on bank growth and the moderating of bank size and Capital Adequacy Ratio (CAR) through the lens of the Resource-Based View (RBV) theory. A sample of 253 banks listed on the New York Stock Exchange from 2006 to 2023 was selected using specific inclusion criteria from the Thomson Reuters Eikon DataStream. To address cross-sectional dependence and endogeneity, advanced estimation techniques—Feasible Generalized Least Squares (FGLS), Driscoll and Kraay standard errors, and the Generalized Method of Moments (GMM)—were employed. The results show that NPLs have a significant negative impact on banks’ asset and income growth. Furthermore, bank size and capital adequacy ratio (CAR) negatively and significantly moderate this relationship. These findings underscore the need for banks to enhance credit risk management by strengthening loan approval processes and leveraging advanced analytics to assess borrower risk more accurately. Full article

Ensemble learning techniques continue to show greater interest in forecasting the volatility of cryptocurrency assets. In particular, XGBoost, an ensemble learning technique, has been shown in recent studies to provide the most accurate forecast of Bitcoin volatility. However, the performance of XGBoost largely depends on the tuning of its hyperparameters. In this study, we examine the effectiveness of the Bayesian optimization method for tuning the XGBoost hyperparameters for Bitcoin volatility forecasting. We chose to explore this method rather than the most commonly used manual, grid, and random hyperparameter choices due to its ability to predict the most promising areas of hyperparameter spaces through exploitation and exploration using acquisition functions, as well as its ability to minimize error with a reduced amount of time and resources required to find an optimal configuration. The obtained XGBoost configuration improves the forecast accuracy of Bitcoin volatility. Our empirical results, based on letting the data speak for itself, could be used for a comparative study on Bitcoin volatility forecasting. This would also be important for volatility trading, option pricing, and managing portfolios related to Bitcoin. Full article

Digital Twins (DTs) have become central to intelligent asset management within Industry 4.0, enabling real-time monitoring, diagnostics, and predictive maintenance. However, implementing Prognostics and Health Management (PHM) strategies within DT frameworks remains a significant challenge, particularly in systems experiencing multi-component degradation (MCD). MCD occurs when several components degrade simultaneously or in interaction, complicating detection and isolation processes. Traditional data-driven fault detection models often require extensive historical degradation data, which is costly, time-consuming, or difficult to obtain in many real-world scenarios. This paper proposes a model-based, control-driven approach to MCD detection, which reduces the need for large training datasets by leveraging reference tracking performance in closed-loop control systems. We benchmark the accuracy of four control strategies—Proportional-Integral (PI), Linear Quadratic Regulator (LQR), Model Predictive Control (MPC), and a hybrid model—within a Digital Twin-enabled hydraulic system testbed comprising multiple components, including pumps, valves, nozzles, and filters. The control strategies are evaluated under various MCD scenarios for their ability to accurately detect and isolate degradation events. Simulation results indicate that the hybrid model consistently outperforms the individual control strategies, achieving an average accuracy of 95.76% under simultaneous pump and nozzle degradation scenarios. The LQR model also demonstrated strong predictive performance, especially in identifying degradation in components such as nozzles and pumps. Also, the sequence and interaction of faults were found to influence detection accuracy, highlighting how the complexities of fault sequences affect the performance of diagnostic strategies. This work contributes to PHM and DT research by introducing a scalable, data-efficient methodology for MCD detection that integrates seamlessly into existing DT architectures using containerized RESTful APIs. By shifting from data-dependent to model-informed diagnostics, the proposed approach enhances early fault detection capabilities and reduces deployment timelines for real-world DT-enabled PHM applications. Full article

This paper presents a cluster analysis of 20 spa enterprises operating in Slovakia, based on key financial indicators for the years 2018 and 2023. A comparative time-based approach was adopted to capture changes in financial performance over time. The primary objective is to group the spas into homogeneous clusters to better understand their financial performance and strategic positioning. Ten financial indicators were selected across five dimensions: profitability (return on assets, return on sales), efficiency (assets turnover), cost efficiency (personnel cost ratio, cost-to-sales ratio, return on costs), liquidity (net working capital, current ratio), and indebtedness (equity to total liabilities ratio, debt ratio). Hierarchical cluster analysis—a widely used statistical method in unsupervised machine learning and a foundational technique in artificial intelligence—was employed, serving as a robust tool for data-driven decision making. The analysis identified distinct clusters of spas with similar financial characteristics. The results reveal meaningful segmentation that can inform resource allocation, performance benchmarking, and strategic planning. The findings provide spa managers, investors, and policy makers with a clearer understanding of financial patterns in the Slovak spa sector and offer practical implications for enhancing competitiveness and operational effectiveness. Full article

Mechanised agricultural operations are often performed individually, under minimal supervision and across a wide range of unfavourable working conditions, resulting in a complex mixture of hazards and external stressors that severely affect safety conditions. Socio-technical and environmental constraints significantly affect safety culture and require continuous performance adjustments to overcome timing pressures, resource limitations, and unstable weather conditions. This study introduces a FRAM-based safety culture model that embeds the thoroughness-efficiency trade-off (ETTO) in four distinct operational modes that adhere to specific safety cultures, namely, thoroughness, risk awareness, compliance, and efficiency. This model has been instantiated for mechanised ploughing: foreground task functions were coupled with background functions that represent socio-technical constraints and environmental variability, while severity classes for potential incidents were derived from the US OSHA accident database. The framework was also supported by a semi-quantitative Resonance Index based on severity and coupling strength, the Total Resonance Index (TRI), to assess how variability propagates in foreground functions and to identify hot-spot functions where small adjustments can escalate into high resonance and hazardous conditions. Results showed that the negative effects on functional resonance generated by safety detriment on TRI observed between compliance and effective working modes were three times larger than the drift between risk awareness and compliance, demonstrating that efficiency comes with a much higher cost than keeping safety at compliance levels. Extending the proposed approach with quantitative assessments could further support the management of socio-technical and environmental drivers in mechanised farming, strengthening the role of safety as a competitive asset for enhancing resilience and service quality. Full article

Equipment renewal and replacement strategy as well as smart capital investment is a vital focus in engineering asset management, particularly for water utilities aiming to improve asset reliability, water quality, service continuity and affordability. This study presents a novel decision support model that integrates whole-life costing principles across all asset lifecycle phases—from capital delivery and daily operations to long-term maintenance. The proposed model uniquely combines asset degradation and failure patterns, operating and maintenance costs, and the impact of technological advancements to provide a holistic and comprehensive asset management decision-making tool. These dimensions are jointly analysed using a hybrid approach that combines optimisation with stochastic dynamic programming, allowing for the determination of optimal asset renewal and replacement timing. The model’s performance was validated using historical data from eight critical wastewater pump stations within a township’s sewerage network. This was performed by comparing the model’s cost-saving results to those achieved by the water utility’s current strategy. Results revealed that the proposed model achieved an average cost saving of 12%, demonstrating its effectiveness in supporting sustainable and cost-efficient asset renewal decisions. Full article

This study examines the impact of environmental, social, and governance (ESG) practices on green innovation and financial performance among 174 publicly listed firms across ASEAN countries over the period from 2019 to 2023. Utilizing an unbalanced panel dataset of firms from key ASEAN economies, the analysis employs panel regression techniques. Green innovation performance is measured through innovation disclosures related to environmental technologies, while financial success is assessed via return on assets (ROA) and Tobin’s Q. The findings reveal that environmental and governance disclosure scores positively influence green innovation, whereas social scores exert a more immediate impact on financial performance. Moreover, green innovation is found to partially mediate the relationship between overall ESG practices and long-term market valuation. These results highlight the strategic role of ESG transparency in enhancing innovation-driven competitiveness, responsible business conduct, and sustainable employment across Southeast Asian markets. Implications are discussed for corporate managers, policymakers, and socially responsible investors. The study reinforces the case for ESG-aligned strategy as a pathway to both innovation, inclusive economic growth, and long-term competitiveness in ASEAN markets. Full article

The digitization and semantic enrichment of built environments traditionally rely on costly and labor-intensive processes, which hinder scalability, adaptability, and real-time deployment in real-world applications. This research presents a novel, fully automated approach that transforms single RGB images directly into semantically enriched, Building Information Modeling (BIM)-compatible 3D representations via an innovative domain adaptation and multi-task learning pipeline. The proposed method simultaneously leverages depth estimation and semantic segmentation from single-image inputs, using high-capacity 2D neural networks, thereby enabling accurate 3D mesh reconstruction and semantic labeling without manual annotation or specialized sensors. The developed pipeline segments and reconstructs both common architectural elements and previously unrepresented object classes, such as stairs, balustrades, railings, people, and furniture items, expanding the coverage of existing 3D indoor datasets. Experimental evaluations demonstrate remarkable reconstruction precision, with an RMSE as low as 0.02 and a per-point semantic accuracy of 81.89% on the TUM CMS Indoor Point Clouds dataset. The resulting 3D models are directly exportable to BIM, OBJ, and CAD formats, supporting a wide range of applications including digital documentation, asset management, and digital twins. By achieving high accuracy and semantic richness with minimal input, the proposed framework offers a scalable, efficient, and automated solution for the rapid digitization of complex built environments, addressing critical limitations in traditional scan-to-BIM workflows and setting new performance standards for future research in the field. Full article

The global rise in population, increased life expectancy, and heightened international mobility have escalated disease prevalence and pharmaceutical demand. This growth intensifies energy consumption and chemical waste production within the pharmaceutical industry, challenging environmental sustainability and operational efficiency. Chromatography, a vital analytical technique for ensuring product quality and regulatory compliance, can also contribute to material waste and energy inefficiencies if not properly maintained and optimized. This study applies Failure Mode and Effects Analysis (FMEA) to chromatographic equipment maintenance within Hovione’s Engineering and Maintenance Department, aiming to identify and mitigate failure risks. By integrating environmental metrics derived from Life Cycle Assessment (LCA) into the FMEA framework, a hybrid risk evaluation tool was developed that prioritizes both equipment reliability and sustainability performance. The findings demonstrate how this integrated approach reduces unplanned downtime, lowers solvent waste, and improves energy efficiency. Additionally, the study proposes a conceptual dashboard to support proactive, sustainability-driven asset management in pharmaceutical laboratories. By bridging reliability engineering and environmental sustainability, this research offers a strategic model for optimizing resource use, minimizing chemical waste, and enhancing long-term operational resilience in regulated pharmaceutical environments. Full article

This study explores how FinTech and artificial intelligence (AI) adoption shape efficiency and financial stability in dual-banking systems. It focuses on 26 listed Islamic and conventional banks across 11 countries in the MENA and Southeast Asia regions between 2020 and 2024. To measure digital adoption, we create a seven-component FinTech Adoption Index. We use fixed-effects regressions to examine its impact on cost efficiency, profitability, solvency stability, and credit risk. This analysis also controls bank size, capitalization, and macroeconomic conditions. The results show a clear adoption gap. Conventional banks consistently score 0.5–0.8 points higher on the FinTech Index compared to Islamic banks. Each additional FinTech component raised operating costs by about 0.8%, but improved profitability slightly by only 0.03%. This suggests that technological integration creates upfront costs before any real efficiency gains are seen. However, the stability benefits are stronger. FinTech adoption increases the Z-score by 3.6 points and lowers the non-performing loan ratio by 0.1%. Islamic banks gain more stability benefits due to their risk-sharing contracts and asset-backed financing structures. Overall, an efficiency–stability trade-off emerges. Conventional banks focus more on profitability, while Islamic banks gain resilience, but face slower efficiency improvements. By combining the Resource-Based View and Financial Stability Theory, this study provides the first multi-country evidence of how governance structures shape digital transformation in dual-banking markets. The findings offer practical guidance for regulators and bank managers around balancing innovation, efficiency, and stability. Full article

Power flow analysis is essential for managing power systems, helping grid operators ensure reliability and efficiency. This paper explores the use of machine learning (ML) techniques as surrogates for computationally intensive power flow calculations to evaluate the effects of distributed energy resources, such as battery energy storage systems (BESSs), on grid performance. In this paper, a case study is presented where XGBoost (eXtreme Gradient Boosting) and Artificial Neural Networks (ANNs) are trained to simulate power flows in a medium-voltage grid in Norway. The impact of BESS units on line loading, transformer loading, and bus voltages is estimated across thousands of configurations, with results compared in terms of simulation time, error metrics, and robustness. In this paper it is proven that while ML models require considerable data and training time, they offer speed-up factors of up to 45×, depending on the predicted parameter. The proposed methodology can also be used to assess the impact of other grid-connected assets, such as small-scale solar plants and electric vehicle chargers, whose presence in distribution networks continues to grow. Full article

Asset discovery in critical infrastructures, and in particular within industrial control systems, constitutes a fundamental cybersecurity function. Ensuring accurate and comprehensive asset visibility while maintaining operational continuity represents an ongoing challenge. Existing methodologies rely on deterministic tools that apply fixed fingerprinting strategies and lack the capacity for contextual reasoning. Such approaches often fail to adapt to the heterogeneous architectures and dynamic configurations characteristic of modern critical infrastructures. This work introduces an architecture based on a Mixture of Experts model designed to overcome these limitations. The proposed framework combines multiple specialized modules to perform automated asset discovery, integrating passive and active software probes with physical sensors. This design enables the system to adapt to different operational scenarios and to classify discovered assets according to functional and security-relevant attributes. A proof-of-concept implementation is also presented, along with experimental results that demonstrate the feasibility of the proposed approach. The outcomes indicate that our LLM-based approach can support the development of non-intrusive asset management solutions, strengthening the cybersecurity posture of critical infrastructure systems. Full article

This study examines the drivers and heterogeneous impacts of platformization—a form of digital transformation involving systems such as supply chain management, product data management, and integrated information technology solutions—on firm performance in a developing economy. Drawing on the Resource-Based View and Dynamic Capabilities View, we analyze data from 5542 Vietnamese firms across four sectors using an endogenous switching regression model, complemented by quantile regression. Platformization decisions are shaped by firm resources and managerial expectations, with strong sectoral variation. In manufacturing and construction, larger assets and a lower leverage promote adoption, while, in wholesale and retail, workforce size and perceived competitiveness are key drivers. Platformization enhances the returns to assets and cash flow—especially among high-performing firms—while reducing the negative effects of high debt and geographic disadvantages. The findings offer three practical implications: (1) prioritize digital adoption in asset-heavy sectors when financial conditions are stable; (2) invest in coordination- and customer-focused platforms in labor-intensive sectors; and (3) use digital tools to convert liquidity into performance gains. These insights support inclusive digitalization policies and contribute to Sustainable Development Goals 8 and 9 by linking digital transformation to resilience, adaptability, and innovation-led growth in transitional economies. Full article

The pronounced heterogeneity and geological complexity of low-permeability reservoirs pose significant challenges to parameter optimization and performance prediction during the development of CO₂ water-alternating-gas (CO₂-WAG) injection processes. This study introduces a predictive model based on the Extreme Gradient Boosting (XGBoost) algorithm, trained on 1225 multivariable numerical simulation cases of CO₂-WAG injection. To enhance the model’s performance, four advanced metaheuristic algorithms—Collective Parallel Optimization (CPO), Grey Wolf Optimization (GWO), Artificial Hummingbird Algorithm (AHA), and Black Kite Algorithm (BKA)—were applied for hyperparameter tuning. Among these, the CPO algorithm demonstrated superior performance due to its ability to balance global exploration with local exploitation in high-dimensional, complex optimization problems. Additionally, the integration of Chebyshev chaotic mapping and Elite Opposition-Based Learning (EOBL) strategies further improved the algorithm’s efficiency and adaptability, leading to the development of the ICPO (Improved Crowned Porcupine Optimization)-XGBoost model. Rigorous evaluation of the model, including comparative analyses, cross-validation, and real-case simulations, demonstrated its exceptional predictive capacity, with a coefficient of determination of 0.9894, a root mean square error of 2.894, and errors consistently within ±2%. These results highlight the model’s robustness, reliability, and strong generalization capabilities, surpassing traditional machine learning methods and other state-of-the-art boosting-based ensemble algorithms. In conclusion, the ICPO-XGBoost model represents an efficient and reliable tool for optimizing the CO₂-WAG process in low-permeability reservoirs. Its exceptional predictive accuracy, robustness, and generalization capability make it a highly valuable asset for practical reservoir management and strategic decision-making in the oil and gas industry. Full article