Search Results (1,055)

This paper proposes a unified energy management, fault detection, and fault-tolerant control (EMS–FDI–FTC) framework for Hybrid Hydrogen Electric Vehicles (HHEVs) integrating a fuel cell (FC), battery (Bat), and supercapacitor (SC). While such multi-source architectures enable high-efficiency propulsion under dynamic driving conditions, actuator and state faults such as FC voltage sag, Bat internal resistance increase, and SC capacitance degradation can compromise safety, availability, and component lifetime. The proposed framework converts real-world GPS-recorded vehicle speed profiles into route-aware traction power demand and combines interpretable model-based indicators with an AI-based fault detection and classification module. Based on the diagnosis outcome, a fault-tolerant supervisory strategy performs online power reallocation among the FC, Bat, and SC while enforcing operational constraints. Validation is conducted in a MATLAB-based software-in-the-loop (SIL) environment using three urban driving routes collected from on-road measurements in Tunisia with injected ground-truth faults. The results demonstrate reliable fault classification performance and effective service continuity during fault intervals, supplying over 94% of the demanded energy across all routes, with energy-not-served remaining below 0.02 kWh. In addition, processor-in-the-loop (PIL) implementation on an STM32F407VG controller confirms real-time feasibility with a 10 Hz supervisory sampling rate and execution time margins compatible with embedded automotive deployment. Overall, the proposed closed-loop framework provides a practical route-aware diagnosis-to-control solution for robust and fault-resilient HHEV operation under realistic driving variability. All energy and efficiency indicators reported in this study are derived from control-oriented component models and are intended for consistent comparative evaluation across routes and operating scenarios, rather than absolute representation of a specific commercial vehicle. Full article

Doubly Fed Induction Generators (DFIGs) are widely employed in variable-speed wind turbine systems due to their high efficiency, enhanced controllability, and economic viability. This paper presents an intelligent neural-network-based control strategy aimed at maximizing wind energy extraction while ensuring accurate speed regulation of a DFIG by continuously tracking the maximum power point under fluctuating wind conditions. Two independent control schemes are developed for the decoupled regulation of active and reactive power in a grid-connected DFIG wind turbine. The first scheme is based on conventional field-oriented control using proportional integral regulators (FOC–PI), while the second employs an Artificial Neural Network Controller (ANNC). The effectiveness of both controllers is evaluated through MATLAB/Simulink 2020 Version simulations of a 1.5 MW DFIG-based wind energy conversion system and experimentally validated using a real wind profile implemented on an eZdsp TMS320F28335 digital signal processor. The proposed control approach achieves low output ripple, a steady-state error below 0.16%, total harmonic distortion of 0.38%, and a limited overshoot of 5%. The obtained results confirm the robustness and reliability of the implemented control strategies in enhancing power capture and improving overall system stability under variable wind conditions. Full article

Avoiding highway infrastructure construction cost overruns and reducing associated claims and disputes continues to be a challenge in many countries. Research is needed in identifying notable project planning and management deficiencies that are likely to cause cost overruns. The literature suggests numerous potential causes of cost overrun but the clustering of cause variables and relative importance of clusters has not been researched. The research reported here addresses this knowledge gap using predictive models developed with data contributed by several agencies in participating countries and suggests mitigation measures. Following a review of methods and data sources, a methodological framework is advanced that encompasses statistical methods well suited for providing a scientific basis for identifying important clusters of cost overrun variables. Fifty-three completed questionnaires contributed by knowledge experts and experienced managers from Canada, the United States, the Middle East, and Australia met the sample requirements of statistical methods. Starting from 53 variables, the principal component-supported factor analysis method identified clusters of cost overrun variables and their relative importance was inferred with developed logistic regression models. Deeper insights into the causes of cost overruns obtained from this research suggest mitigation measures (e.g., improved qualification and experience of personnel, enhanced planning and design practices, risk analysis of inputs to cost estimation process) that are within reach of managers. The results can enhance infrastructure planning and management practice including a reduction in claims and disputes. Full article

To address the inherent mismatch between the fluctuating power output of renewable energy and the continuous production requirements of ammonia in off-grid wind–solar–hydrogen–ammonia systems, this paper proposes a “day-ahead–intraday–real-time” multi-time-scale coordinated optimization scheduling strategy. In the day-ahead layer, Wasserstein Distributionally Robust Optimization (WDRO) is employed to determine a conservative and stable baseline plan for ammonia load under high uncertainty of wind and solar output. The intraday layer utilizes Model Predictive Control (MPC) with a 2-h prediction horizon and 15-min rolling steps to correct short-term forecast deviations. The real-time layer achieves minute-level power balancing through priority dispatch and deadband control. Furthermore, hydrogen storage tanks serve as a material buffer between hydrogen production and ammonia synthesis, with their state variables transmitting across layers to achieve flexible multi-time-scale coupling. Simulation results demonstrate that, although this strategy slightly reduces the theoretical maximum ammonia yield, it completely avoids load-shedding risks. Compared with the deterministic scheduling (Scheme 1), which suffers a net loss due to severe penalty costs, the proposed strategy achieves a positive daily profit of CNY 277,700, representing an absolute increase of CNY 429,300. Furthermore, it provides an additional daily profit of CNY 65,800 compared to the stochastic optimization approach (Scheme 2), demonstrating superior economic robustness in off-grid environments. Full article

Background/Objectives: Wearable devices capable of capturing heart rate variability (HRV) enable continuous assessment of autonomic nervous system function in real-world settings. Because systemic inflammation disrupts autonomic balance through vagal withdrawal and sympathetic activation, HRV has been proposed as a non-invasive digital biomarker of inflammatory activity. Despite the rapid expansion of wearable sensor technologies, the accuracy and consistency for detecting systemic inflammatory states remain unclear. This systematic review aimed to evaluate the clinical relevance of wearable-derived HRV indices in relation to established inflammatory biomarkers. Methods: A systematic search of PubMed, Scopus, Web of Science, and the Cochrane Library was conducted through April 2025. Due to methodological heterogeneity, findings were synthesized using the Synthesis Without Meta-analysis (SWiM) framework with vote counting, effect-direction plots, and sign tests. Results: Eleven studies involving 2419 participants met the inclusion criteria. Vote counting demonstrated that SDNN showed a predominantly inverse association with CRP, with 83% of comparisons indicating reduced SDNN in the presence of elevated CRP (sign test p = 0.031). In contrast, associations between RMSSD and inflammatory cytokines were heterogeneous and largely non-significant. ECG-based wearable devices yielded more consistent associations than photoplethysmography-based devices, while recording duration and population characteristics contributed to variability across studies. Conclusions: Wearable-derived HRV, particularly SDNN from ECG-based devices, shows a consistent inverse association with CRP, supporting its role as a non-invasive physiological correlate of systemic inflammation. However, heterogeneity and the lack of diagnostic accuracy metrics limit conclusions regarding clinical utility. At present, wearable HRV should be considered an exploratory or adjunctive biomarker, pending validation in standardized longitudinal studies with formal diagnostic performance assessment. Full article

Impaired glucose metabolism, a known precursor to type 2 diabetes, is associated with dysregulation of the autonomic nervous system. To assess such autonomic states, consumer wearable devices provide continuous, non-invasive physiological monitoring and may capture autonomic signatures related to metabolic status. This exploratory study examined whether dynamic features of heart rate (HR) and heart rate variability (HRV) during sleep—derived from a consumer wrist-worn device (Fitbit)—are associated with glucose metabolism status in free-living adults. We analyzed 189 nights from 18 participants (7 participants in the higher-glycemic-risk group, estimated glycated hemoglobin (HbA1c) ≥ 5.5%; 11 participants in the lower-glycemic-risk group, estimated HbA1c < 5.5%). From 28 candidate HR/HRV variables, Elastic Net regression ($\alpha =0.5$) was applied to identify features associated with nocturnal mean glucose. Fourteen features retained non-zero coefficients; notably, dynamic features capturing overnight trends and variability patterns showed stronger associations than conventional static mean values. The nocturnal trends of within-window standard deviation and variance of $ln\left(\mathrm{RMSSD}\right)$ (root mean square of successive differences between consecutive RR intervals, estimated here from PPG-derived inter-beat intervals; RMSSD) emerged as prominent candidates, alongside HR variability indices. Independent between-group comparisons further confirmed that two dynamic HRV features differed significantly between the lower- and higher-glycemic-risk groups (both $\mathit{p}<0.05$; Cohen’s $\left|d\right|>1.1$). Specifically, the lower-glycemic-risk group exhibited decreasing overnight trends in HRV variability, consistent with progressive autonomic stabilization during sleep. In contrast, the higher-glycemic-risk group showed increasing variability trends, suggestive of persistent autonomic instability. These directional patterns are consistent with prior evidence linking autonomic dysfunction to impaired glucose metabolism. We characterize these findings as hypothesis-generating. The identified dynamic HR/HRV features represent physiologically plausible candidate correlates of glycemic status and warrant confirmatory investigation in larger, independent cohorts with laboratory-measured HbA1c. More broadly, this work highlights the potential of widely available, consumer-grade wearable devices to move beyond activity tracking and support continuous, real-world assessment of cardiometabolic health, thereby expanding their utility in everyday health monitoring and preventive medicine. Full article

The expected fossil fuel reserves, along with the continuous environmental degradation, underline the necessity to turn to more environmentally friendly energy resources. However, the significant exploitation of variable or even stochastic solar and wind potential challenges the market balance and the stability of most electrical networks. On the other hand, hydropower stands out, for almost one century, with its contribution to the global annual electricity consumption being nowadays almost 15%. In Greece, acknowledging the stagnation noted in the last fifteen years in the exploitation of the existing hydropower potential through large hydropower plants, small hydropower applications come to the forefront for revitalizing the pertinent total hydropower growth rates. Nevertheless, the relevant potential remains strongly under-exploited. The present work investigates the current status of the small hydropower potential exploitation with special focus on Greece, considering aspects such as the installed capacity and the geographical distribution of the corresponding power plants, their annual energy generation, and their pertinent Capacity Factors’ time evolution. The financial support schemes offered in the past by the Greek State are also investigated in comparison with the current status. The current progress and challenges of small hydropower installations are thoroughly scrutinized, thus revealing the future prospects and substantial benefits that could occur from both carbon-free, relatively stable electricity generation and a financial perspective. Full article

Background/Objectives: Patient Support Programs (PSPs) are increasingly used to support treatment adherence and continuity of care in chronic, high-cost conditions. In hemophilia A, consistent prophylaxis is essential to prevent bleeding episodes and long-term joint damage. In Mexico, disparities in access to treatment have encouraged the development of public–industry collaborative models. The objective of this study was to describe the structure, implementation, and operational characteristics of a PSP delivering home-based prophylactic treatment for individuals with hemophilia A in Mexico, and to compare annual bleeding rates according to factor VIII dosing adequacy. Methods: A cross-sectional, retrospective analysis was conducted using fully anonymized operational data from the PSP registry between January 2023 and March 2024. Variables included infusion location and administrator, prescribed and used doses, weekly infusion frequency, program incorporation and discontinuation, geographic coverage, and bleeding events. Annual bleeding rates were compared across dosing categories using Poisson regression models with patient-years as an offset. Results: A total of 1173 patients contributed 16,331 infusion records. Participants were predominantly male (99.8%), with a median age of 26 years; 71.8% had severe hemophilia. Home infusion accounted for 92.0% of administrations, primarily self-administered or caregiver-delivered. The median prescribed and used monthly doses were 18,000 IU and 16,000 IU, respectively, with dose concordance observed in 66.8% of records. Only 40.7% of patients achieved the recommended prophylactic frequency of three infusions per week. Geographic coverage increased from 62.5% to 71.9% of states. The overall annualized bleeding rate was 2.24 bleeds per patient-year. When stratified by dosing adequacy, patients receiving doses consistent with clinical recommendations showed the lowest bleeding rate (0.18 bleeds per patient-year), compared with those with overdosing (3.84) and underdosing (6.68), with statistically significant differences between groups. Knees, elbows, and ankles were the most frequently affected sites. Conclusions: This PSP achieved broad national reach and high adoption of home-based infusion. The observed dose-dependent differences in bleeding rates underscore the clinical relevance of appropriate prophylactic dosing within structured support programs and support the value of PSPs in strengthening treatment continuity in middle-income settings. Full article

Semiconductor manufacturing equipment such as vacuum pumps, wafer handling mechanisms, etching machines, and deposition systems operates for a long time under high vacuum, high temperature, strong electromagnetic, and high-precision continuous production environments. Its reliability is directly related to the yield and stability of the production line. During equipment operation, the fault signals are often weak, the noise is strong, and the working conditions are variable, so traditional methods are difficult to achieve high-precision recognition. To solve this problem, this paper proposes a fault diagnosis and health monitoring method for semiconductor manufacturing equipment based on deep learning and subspace transfer. Firstly, considering the cyclostationary characteristics of the operating signals of key equipment, the cyclic spectral analysis technology is used to obtain the cyclic spectral coherence map, which effectively reveals the feature differences under different health states. Then, a deep fault diagnosis model based on the convolutional neural network (CNN) is constructed to extract deep feature representations. Furthermore, the subspace transfer learning technology is introduced, and group normalization and correlation alignment unsupervised adaptation layers are designed to achieve automatic alignment and enhancement of the statistical characteristics of deep features between the source domain and the target domain, which effectively improves the generalization and adaptability of the model. Finally, simulation experiments based on the public bearing dataset verify that the proposed method has strong feature representation ability and high classification accuracy under different working conditions and different loads. Because the key components and experimental scenarios of semiconductor manufacturing equipment have similar signal characteristics, this method can be directly transferred to the early fault diagnosis and health monitoring of semiconductor production line equipment, which has important engineering application value. Full article

This study develops a mathematical optimization framework to determine optimal inspection policies for imperfect production systems subject to stochastic deterioration. System degradation is modeled using a Weibull power law process, which captures the increasing likelihood of transitions from in-control to out-of-control states over time. When deterioration occurs, a reverse-order inspection strategy based on negative binomial sampling is employed, wherein an inspection continues until a predefined number of conforming items is obtained. The proposed model integrates inspection decisions with production learning effects and Bayesian demand updating. Learning-by-doing is incorporated through an experience-dependent production cost function, while demand uncertainty is addressed using Bayesian posterior estimation. A comprehensive expected total cost function is formulated, including production, inspection, inventory holding, warranty, and rework costs. The analytical properties of the model are examined, demonstrating that the expected total cost function is strictly convex with respect to the inspection decision variable. This convexity guarantees the existence and uniqueness of the optimal solution. Numerical experiments and sensitivity analyses illustrate the effects of defect rates, learning parameters, warranty periods, and demand uncertainty on the optimal inspection policy. The results show that jointly optimizing inspection intensity, learning effects, and demand information leads to significant cost reductions and robust decision-making in deteriorating production systems. Full article

This systematic review explores the incorporation of Environmental, Social, and Governance (ESG) factors within financial risk prediction models, with a particular focus on Machine Learning (ML), Natural Language Processing (NLP), and Large Language Models (LLM). Adhering to the Preferred Reporting Items for Systematic Reviews and the Meta-Analyses (PRISMA) and PICOC frameworks, we identified 74 peer-reviewed publications disseminated between 2009 and March 2025 from the Scopus database. After excluding 10 systematic and literature reviews to avoid double-counting of evidence, we conducted quantitative analysis on 64 empirical studies. The findings indicate that traditional econometric methodologies continue to prevail (48%), followed by ML strategies (39%), NLP methodologies (8%), and Other (5%). Research that concurrently focuses on all three dimensions of ESG constitutes the most substantial category (44%), whereas the Social dimension, in isolation, receives minimal focus (5%). A geographic analysis reveals a concentration of research activity in China (13 studies), Italy (10), and the United States and India (6 each). Chi-square tests reveal no statistically significant relationship between the methodological approaches employed and the ESG dimensions examined (p = 0.62). The principal findings indicate that ML models—particularly ensemble methodologies and neural networks—exhibit enhanced predictive accuracy in the context of credit risk and default probability, whereas NLP methodologies reveal significant potential for the analysis of unstructured ESG disclosures. The review highlighted ongoing challenges, including inconsistencies in ESG data, variability in ratings across different providers, insufficient coverage of emerging markets, and the disparity between academic research and practical application in model implementation. Full article

To improve solar energy utilization efficiency, address control precision issues in solar panel tracking systems, and strengthen the sustainable supply capacity of clean renewable energy, this study proposes an innovative variable universe fuzzy adaptive PID control algorithm for high-precision solar tracking systems. Based on this algorithm, a fusion scheme combining a high-precision four-quadrant detector and GPS positioning is employed to achieve real-time and precise positioning of the tracking system. The azimuth and elevation angle deviations between the real-time solar rays and the system’s actual position are calculated and used as input signals for the tracking control system. These deviations are dynamically corrected by the variable universe fuzzy adaptive PID controller, which drives a stepper motor to achieve high-precision solar tracking. The results demonstrate that, under ideal operating conditions, the proposed algorithm reduces the steady-state error by 3.5–4.9°, shortens the settling time by 4.4–5.8 s, decreases the rise time by 0.6 s, lowers the overshoot by 18–19%, and reduces the disturbance recovery time by 1.3 s. These improvements significantly enhance tracking accuracy and dynamic response efficiency. Under complex operating conditions, the algorithm reduces the steady-state error by 3.2–5.9°, shortens the settling time by 5.4–6.2 s, decreases the rise time by 0.7 s, lowers the overshoot by 17.5–19%, and reduces the disturbance recovery time by 1.5 s, thereby ensuring stable and efficient solar tracking and maintaining continuous energy capture. By quantitatively optimizing multiple performance metrics, this algorithm significantly enhances the control precision of solar panel tracking and improves solar energy utilization efficiency. It holds substantial significance for promoting the transition of the energy structure toward cleaner and more sustainable sources. Full article

Orthotropic materials are increasingly employed in advanced thermal systems due to their direction-dependent heat transfer characteristics. Accurate numerical modeling of heat conduction in such media remains challenging, particularly for 3D geometries with nonlinear boundary conditions and internal heat generation. In this study, conventional boundary element method (BEM) and isogeometric boundary element method (IGABEM) formulations are developed and compared for steady-state orthotropic heat conduction problems. A coordinate transformation is adopted to map the anisotropic governing equation onto an equivalent isotropic form, enabling the use of classical Laplace fundamental solutions. Volumetric heat generation is incorporated via the radial integration method (RIM), preserving the boundary-only discretization, while nonlinear Robin boundary conditions are treated using variable condensation and a Newton–Raphson iterative scheme. The performance of both methods is evaluated using a hollow ellipsoidal benchmark problem with available analytical solutions. The results demonstrate that IGABEM provides higher accuracy and smoother convergence than conventional BEM, particularly for higher-order discretizations, which is owing to its exact geometric representation and higher continuity. Although IGABEM involves additional computational overhead due to NURBS evaluations, both methods exhibit similar quadratic scaling with respect to the degrees of freedom. Full article

Background: This equity audit assessed enrolment and completion of a state-funded cardiometabolic risk-reduction programme for women with prior gestational diabetes in Victoria, Australia. The analyses compared completion rates between the standard prevention programme Life! with one specifically adapted for women with prior gestational diabetes (Life! GDM) using the PROGRESS equity framework. Methods: Women with a history of GDM in the Life! GDM or the mainstream Life! programme in 2022–2025 were included. Multinomial logistic regression was used to impute categorical variables, logistic regression for binary variables, and linear regression for continuous variables. Estimates were combined across imputed datasets using Rubin’s rules. Results: A total of 2261 women were included: 370 in Life! GDM, and 1891 in Life! from 2022 to 2025, with completion rates of 36.7% and 52.2%, respectively. Compared with women in Life!, women in Life! GDM were more likely to come from non-English-speaking backgrounds, particularly South and Central Asian (30.5% vs. 17.0%) and South-East Asian backgrounds (13.0% vs. 4.3%). After multiple imputation, multivariable logistic regression showed that none of the examined participant characteristics were significantly associated with programme completion in Life! GDM. In the Life! cohort, completion was significantly associated with marital status, with single participants having lower odds of completion (OR = 0.59, 95% CI: 0.41–0.85), and with referral channel, with self-referral associated with higher odds of completion (OR = 1.71, 95% CI: 1.39–2.12). Conclusions: The adapted programme appeared to have reached more culturally and linguistically diverse women; however, lower completion among those experiencing disadvantage highlights the need for enhanced support and retention strategies to ensure equitable postpartum diabetes prevention. Full article

The continuous evolution of power systems and increasing integration of power electronic devices cause a progressive expansion of the system scale, which generates timeout issues in real-time simulation applications. Multi-rate parallel simulation stands as one of the key technologies for enhancing the simulation speed of complex power systems in real-time applications. However, this approach is often constrained by limited simulation accuracy. To improve the precision of multi-rate simulation, this paper proposes a novel multi-rate real-time simulation method that integrates the state-space branch-cutting method with a variable slope interpolation interface algorithm. In the proposed method, the system is divided into multiple subsystems through the cutting branch with each subsystem modeled by the state-space method. The time-step of each subsystem is determined by its inherent dynamic characteristic, and a variable slope interpolation interface algorithm is presented to facilitate data exchange between subsystems operating at different rates. Finally, real-time simulation cases demonstrate that the proposed method achieves an improvement in simulation accuracy exceeding 14.27% over conventional approaches. Full article