Search Results (2,009)

This study presents a novel approach to addressing the challenges associated with wind turbine blade fatigue, focusing on the development of a simplified method for dynamic load measurement and life estimation. Wind turbine blades are subjected to complex and varied loads during their operational life, leading to fatigue-induced damage that can significantly impact the overall performance and longevity of the turbine. The proposed method integrates advanced sensor technologies and data analytics to capture dynamic loads on the blades more effectively. Dynamic load measurement and fatigue estimation for a wind turbine blade are quite challenging tasks, since the real-time wind-induced load is irregular and stochastic, and the associated load history affects blade fatigue life in complex ways. This paper shows the implementation of a simplified method for damage and life estimation of a 1.5 kW wind turbine blade with an aerodynamic stall-limiting system. The findings from this research contribute to advancing the field of wind energy by providing a streamlined and efficient approach to addressing blade fatigue issues, ultimately promoting the sustainable and economic utilization of wind power resources. The proposed method simplifies the processes of dynamic load measurement and fatigue life estimation by employing a resonance-based approach. This reduces energy and cost requirements compared to forced displacement methods, while maintaining accuracy in replicating damage equivalent loads. Additionally, it avoids the complexities of simulating real-world turbulence by using controlled conditions, ensuring reproducibility. Full article

Municipal solid waste incineration fly ash (MSWI FA) is recognized as a hazardous solid waste due to its enrichment in toxic heavy metals and high leaching potential. This review systematically summarizes the current understanding of heavy metal occurrence in MSWI FA and associated environmental risks. Solidification and stabilization methods, such as cement-based curing and chemical immobilization, are widely applied due to their cost-effectiveness and operability, though their long-term stability and recovery potential remain limited. Thermal treatment technologies, including sintering, vitrification, thermal separation, and molten salt processes, have shown excellent performance in reducing volume and enhancing the immobilization or recovery of heavy metals. However, these methods are often limited by high energy demands and operational complexity. Recently, emerging technologies such as electrodialysis, bioleaching, and electrokinetic remediation have demonstrated promising capabilities for selective metal recovery under relatively mild conditions. Nevertheless, these novel approaches remain at an early stage of development and have thus far been validated only at the laboratory or pilot scale. Overall, integrating multiple treatment technologies while advancing resource-oriented and low-carbon approaches will be essential for the sustainable management of MSWI FA. Full article

This review focuses on the advancements of CO₂ transcritical cycle technology in building indoor environmental regulation, particularly in combined heating and cooling applications. The paper highlights the energy efficiency and environmental benefits of CO₂ as a natural refrigerant, which has zero ozone depletion potential (ODP) and very low global warming potential (GWP). It provides a comprehensive overview of recent optimization strategies, including distributed compression, the integration of ejectors and expanders, and the design improvements of key components such as gas coolers, compressors, and throttling valves. Through optimization strategies such as dual-system cycles, this technology can achieve a COP improvement of 15.3–46.96% in heating scenarios; meanwhile, with the help of distributed compression technology, its cooling capacity can be enhanced by up to 26.5%. The review also examines various operating conditions such as discharge pressure and subcooling, which significantly affect system performance. The paper concludes by identifying the current challenges in the application of CO₂ systems, such as high initial costs and system stability under extreme conditions, and suggests future research directions to overcome these limitations and improve the practical application of CO₂ transcritical cycles in the building industry. Overall, it is concluded that the development of expander-compressors holds great potential for achieving better performance and represents a promising direction for future advancements in this field. 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

This study presents the proof-of-concept design and preliminary implementation of a bioelectric biosensor based on an Arduino platform for real-time monitoring of gel-immobilized N2a neuroblastoma cells using dopamine as a model neurotransmitter. The sensor operates on the principle of bioelectric recognition assay (BERA), and uses a two-electrode set-up as a simple, cost-efficient way to capture electrophysiological responses following dopamine exposure, while at the same time mimicking the in vivo cellular environment. Cellular ohmic resistance was assessed under increasing dopamine concentrations and temperatures (24 °C and 37 °C). The results showed that temperature significantly affected cell responses to increasing dopamine concentrations, possibly because of differences in dopamine diffusion in gel, which may in turn have affected membrane polarization and overall cell electric resistance. Pending further testing against a wider range of dopamine concentrations along with various dopamine agonists/antagonists, as well as optimization in terms of specificity, selectivity, and sensitivity, the biosensor could be applied in bioscreening and neuropharmacological studies in a user-friendly, scalable way. Full article

This study investigates the optimal sizing of photovoltaic (PV) and battery storage (BAT) systems for Estonian households operating under grid constraints that prevent selling surplus energy. We develop and compare three sizing models of increasing complexity, ranging from a simple heuristic to a full simulation based optimization. Their performance is evaluated using a multi-criteria decision analysis (MCDA) framework that integrates Net Present Value (NPV), Internal Rate of Return (IRR), Profitability Index Ratio (PIR), and payback period. Sensitivity analyses are used to test the robustness of each configuration against electricity price shifts and market volatility. Our findings reveal that standalone PV-only systems are the most economically robust investment. They consistently outperform combined PV + BAT and BAT-only configurations in terms of investment efficiency and overall financial attractiveness. Key results demonstrate that the simplest heuristic-based model (Model 1) identifies configurations with a better balance of financial returns and capital efficiency than the more complex simulation-based approach (Model 3). While the optimization model achieves the highest absolute NPV, it requires significantly higher investment and results in lower overall efficiency. The economic case for batteries remains weak, with viability depending heavily on price volatility and arbitrage potential. These results provide practical guidance, suggesting that for grid constrained households, a well-sized PV-only system identified with a simple model offers the most effective path to cost savings and energy self-sufficiency. Full article

In printed circuit board (PCB) assembly, the volume of dispensed glue is closely related to the PCB’s durability, production costs, and the overall product reliability. Currently, quality inspection is performed manually by operators, inheriting the limitations of human-performed procedures. To address this, we propose an automatic optical inspection framework that utilizes convolutional neural networks (CNNs) and post-hoc explainable methods. Our methodology handles glue quality inspection as a three-fold procedure. Initially, a detection system based on CenterNet MobileNetV2 is developed to localize PCBs, thus, offering a flexible lightweight tool for targeting and cropping regions of interest. Consequently, a CNN is proposed to classify PCB images into three classes based on the placed glue volume achieving 92.2% accuracy. This classification step ensures that varying glue volumes are accurately assessed, addressing potential quality issues that appear early in the production process. Finally, the Deep SHAP and Grad-CAM methods are applied to the CNN classifier to produce explanations of the decision making and further increase the interpretability of the proposed approach, targeting human-centered artificial intelligence. These post-hoc explainable methods provide visual explanations of the model’s decision-making process, offering insights into which features and regions contribute to each classification decision. The proposed method is validated with real industrial data, demonstrating its practical applicability and robustness. The evaluation procedure indicates that the proposed framework offers increased accuracy, low latency, and high-quality visual explanations, thereby strengthening quality assurance in PCB manufacturing. Full article

Cement production is a carbon-intensive process that contributes significantly to global greenhouse gas emissions. Approximately 50–60% of these emissions result from limestone calcination, while 30–40% result from fossil fuel combustion in kilns. This study presents a multi-objective optimization (MOO) framework that integrates raw mix design and alternative fuel blending to simultaneously reduce production costs and carbon dioxide (CO₂) emissions while maintaining clinker quality. A hybrid Genetic Algorithm–Linear Programming (GA-LP) model was developed to navigate the balance between economic and environmental objectives under stringent chemical and operational constraints. The approach models the impact of raw materials and fuel ash on critical clinker quality indices: the Lime Saturation Factor (LSF), Silica Modulus (SM), and Alumina Modulus (AM). It incorporates practical constraints such as maximum substitution rates and specific fuel compositions. A case study inspired by a medium-sized African cement plant demonstrates the utility of the model. The results reveal a Pareto front of optimal solutions, highlighting that a 20% reduction in CO₂ emissions from 928 to 740 kg/ton clinker is achievable with only a 24% cost increase. Optimal strategies include 10% fly ash and 30–50% alternative fuels, such as biomass, tire-derived fuel (TDF), and dynamic raw mix adjustments based on fuel ash contributions. Sensitivity analysis further illustrates how biomass cost and LSF targets affect clinker performance, emissions, and fuel shares. The GA-LP hybrid model is validated through process simulation and benchmarked against African case studies. Overall, the findings provide cement producers and policymakers with a robust decision-support tool to evaluate and adopt sustainable production strategies aligned with net-zero targets and emerging carbon regulations. Full article

Additive Manufacturing is a rapidly developing technology that enables the fabrication of objects with complex geometries and high levels of customization while keeping the prototyping costs relatively low. In recent years, its application has grown to include the fabrication of end-use parts, creating new opportunities in industries such as the automotive, aerospace, mechanical, and hydraulic engineering industries. The present research paper focuses on the fabrication and evaluation of 3D-printed operational end-use parts of a water pump, which were originally made from cast iron. This approach aims to determine whether AM can be an alternative for metal parts in operational systems such as water pumps. In particular, the impeller of a centrifugal pump is remanufactured using material extrusion AM technology with PPS-CF composite polymer as a fabrication material. Subsequently, the surface roughness of the two parts is measured, and the performance of each part is predicted by creating a CFD model. Additionally, the printed part is compared to the original part by conducting a centrifugal pump performance test for each impeller. The results show that the 3D-printed impeller achieves an approximate 15% increase in overall efficiency compared to the original impeller. Full article

Under the global decarbonization trend, combined cooling, heating, and power (CCHP) systems are critical for improving regional energy efficiency. However, the integration of high-penetration variable renewable energy (RE) sources introduces significant volatility and multi-dimensional uncertainties, challenging conventional operation strategies designed for stable energy inputs. This review systematically examines recent advances in CCHP optimization under high-RE scenarios, with a focus on flexibility-enabled operation mechanisms and uncertainty-aware optimization strategies. It first analyzes the evolving architecture of variable RE-driven CCHP systems and core challenges arising from RE intermittency, demand volatility, and multi-energy coupling. Subsequently, it categorizes key flexibility resources and clarifies their roles in mitigating uncertainties. The review further elaborates on optimization methodologies tailored to high-RE contexts, along with their comparative analysis and selection criteria. Additionally, it details the formulation of optimization models, model formulation, and solution techniques. Key findings include the following: Generalized energy storage, which integrates physical and virtual storage, increases renewable energy utilization by 12–18% and reduces costs by 45%. Hybrid optimization strategies that combine robust optimization and deep reinforcement learning lower operational costs by 15–20% while strengthening system robustness against renewable energy volatility by 30–40%. Multi-energy synergy and exergy-efficient flexibility resources collectively improve system efficiency by 8–15% and reduce carbon emissions by 12–18%. Overall, this work provides a comprehensive technical pathway for enhancing the efficiency, stability, and low-carbon performance of CCHP systems in high-RE environments, supporting their scalable contribution to global decarbonization efforts. Full article

Enhancing system durability and fuel economy stands as a crucial factor in the energy management of fuel cell hybrid vehicles. This paper proposes an Equivalent Consumption Minimization Strategy (ECMS) based on the Genetic Algorithm (GA), aiming to minimize the overall operating cost of the system. First, this study establishes a dynamic model of the hydrogen–electric hybrid vehicle, a static input–output model of the hybrid power system, and an aging model. Next, a speed prediction method based on an Autoregressive Integrated Moving Average (ARIMA) model is designed. This method fits a predictive model by collecting historical speed data in real time, ensuring the robustness of speed prediction. Finally, based on the speed prediction results, an adaptive Equivalence Factor (EF) method using a GA is proposed. This method comprehensively considers fuel consumption and the economic costs associated with the aging of the hydrogen–electric hybrid system, forming a total operating cost function. The GA is then employed to dynamically search for the optimal EF within the cost function, optimizing the system’s economic performance while ensuring real-time feasibility. Simulation outcomes demonstrate that the proposed energy management strategy significantly enhances both the durability and fuel economy of the fuel cell hybrid vehicle. Full article

Cryogenic upgrading represents a promising route for the production of high-purity biomethane, aligning with current decarbonization goals and the increasing demand for renewable gases. This review provides a critical assessment of cryogenic technologies applied to biogas purification, focusing on process fundamentals, technological configurations, energy and separation performance, and their industrial integration potential. The analysis covers standalone cryogenic systems as well as hybrid configurations combining cryogenic separation with membrane or chemical pretreatment to enhance efficiency and reduce operating costs. A comparative evaluation of key performance indicators—including methane recovery, specific energy demand, product purity, and technology readiness level—is presented, along with a discussion of representative industrial applications. In addition, recent techno-economic studies are examined to contextualize cryogenic upgrading within the broader landscape of CO₂ separation technologies. Environmental trade-offs, investment thresholds, and sensitivity to gas prices and CO₂ taxation are also discussed. The review identifies existing technical and economic barriers, outlines research and innovation priorities, and highlights the relevance of process integration with natural gas networks. Overall, cryogenic upgrading is confirmed as a technically viable and environmentally competitive solution for biomethane production, particularly in contexts requiring liquefied biomethane or CO₂ recovery. Strategic deployment and regulatory support will be key to accelerating its industrial adoption. The objectives of this review have been met by consolidating the current state of knowledge and identifying specific gaps that warrant further investigation. Future work is expected to address these gaps through targeted experimental studies and technology demonstrations. Full article

The decrease in employee performance that occurs during organizational change is one of the main problems that this study attempts to address. This phenomenon, which is known as resistance to change, has been directly linked to the failure or abandonment of change initiatives when performance drops to critical levels. This study proposes an innovative approach to organizational change management based on a model that integrates real-time performance monitoring and employee reassignment to tasks. This approach contributes to improving overall system performance and stabilizing costs by achieving a reduction in resistance to change through staff training and dynamic reallocation of human resources. The method utilizes Evolutionary Dynamic Multi-Objective Optimization with the aim of both maximizing performance and minimizing costs. It incorporates the performance of employees in each task and the associated costs, enabling continuous adjustment of task assignments in accordance with temporal variability in the factors that affect the success of organizational change. Experimental simulations show that the proposed method leads to a considerable enhancement in overall system performance, cost stabilization, and a significant reduction in the risk of change abandonment. More specifically, the proposed method demonstrates an improvement in total performance from 55% to over 200% in comparison to three reference methods. Furthermore, it achieves faster recovery and a lower performance drop, especially in critical stages, providing optimized decision-making during the change process and leading to the new desired and improved state being achieved in a time that is up to 27% shorter, consequently reducing the risk of abandonment. The proposed method operates as both an optimization tool and a real-time decision support system. The continuous analysis of employee performance and cost provides actionable indications of the current state of change, allowing for timely detection and intervention. Full article

This study explores practical approaches to improving the reliability of power supply systems through the expansion and optimization of substation power lines. As electricity demand steadily increases, ensuring a stable and efficient power delivery network has become essential to support industrial growth and socio-economic development. This study focuses on challenges such as vulnerability to single-line faults, limited transmission capacity, and complex coordination in system operation. To address these issues, the proposed strategy includes building redundant transmission lines, improving network configuration, and applying modern transmission technologies to enhance operational flexibility. Notably, a Deep Q-Learning algorithm is introduced during the planning and optimization process. Its ability to accelerate convergence and streamline decision making significantly reduces computation time while maintaining solution accuracy, thereby increasing overall efficiency in evaluating large-scale network configurations. Simulation results and case studies confirm that such improvements lead to shorter outage durations, enhanced fault tolerance, and better adaptability to future load demands. The findings highlight strong practical value for industrial applications, offering a scalable and cost-conscious solution for strengthening the reliability of modern power systems. Full article

This study proposes a hierarchical framework with task priority perception for mission planning, to enhance multi-UAV coordination in maritime emergency search and rescue. By establishing a hierarchical decoupling optimization mechanism, the complex multi-region coverage problem is decomposed into two stages: task allocation and path planning. First, a coverage voyage estimation model is constructed based on regional geometric features to provide basic data for subsequent task allocation. Second, an improved multi-objective, multi-population grey wolf optimizer (IM2GWO) is designed to solve the task allocation problem; this integrates adaptive genetic operations and the multi-population coevolutionary mechanism. Finally, a globally optimal coverage path is generated based on the improved dynamic programming (DP). Simulation results indicate that the proposed method effectively reduces total task duration while boosting overall coverage benefits through the aggregation of high-value regions. IM2GWO demonstrates statistically superior performance with respect to the Pareto front distribution index across all test scenarios. Meanwhile, the path planning module based on DP can effectively reduce the overall coverage path cost. Full article