Search Results (480)

Background: Oxidative liver damage, fibrosis, cirrhosis and liver failure are caused by reactive oxygen species and inflammatory responses triggered by bile retention during prolonged cholestasis. Pomegranate and persimmon fruits, which are loaded with bioactive compounds that have anti-inflammatory and antioxidant properties, were evaluated separately for their efficacy in preventing oxidative stress and inflammation in cholestasis. Methods: Pomegranate and persimmon juices were analyzed for their vitamin C, carotenoids and organic acid levels, phenolic profile, and antioxidant activity. Liver protection against oxidative stress and inflammation brought on by cyclosporine-induced cholestasis in rats was verified by biochemical measurements, metabolite identification, and histopathologic examination. To forecast the mechanism of pomegranate and persimmon anti-inflammatory action, an in silico assessment was also carried out. Results: Vitamin C levels in pomegranate and persimmon juices were 99.55 and 51.75 µg/g, respectively. In both pomegranate and persimmon juices, gallic acid was the most prevalent phenolic compound (123.20 and 50.69 µg/g, respectively). Pomegranate and persimmon juices significantly (p < 0.05) reduced the rise in liver values of MDA, NO, TNF-α, IL-6, IL-1β, and TLR4, as well as serum values of total and direct bilirubin caused by cyclosporine. Additionally, the alteration of metabolites, particularly amino acids, demonstrated the inhibitory effect of pomegranate and persimmon juices on liver damage. Gallic acid’s and catechin’s substantial binding affinities with target inflammatory cytokines (TNF-α and TLR4) were further validated by molecular docking. Conclusions: These results showed that pomegranate and persimmon juices mainly modulated inflammation and oxidative stress to provide hepato-protective benefits against cyclosporine-induced cholestatic liver injury. Full article

Driven by renewable energy, the operating temperatures of alkaline water electrolyzers (AWEs) exhibit significant dynamic variations. Conventional control strategies rely on fixed startup parameters, causing dispatch plans to deviate from actual physical states, which leads to transient over-temperature or startup failures. To address this issue, this paper proposes a dual-layer optimization strategy for multi-electrolyzer systems based on temperature–power adaptation. First, a thermo-electro-hydrogen coupling model is established to quantitatively reveal the dynamic relationship among the initial temperature, startup power, and transition time. This relationship is utilized to construct a dynamic startup boundary, overcoming the limitations of traditional static constraints. Within the proposed framework, the upper layer utilizes a Mixed-Integer Linear Programming (MILP) model to formulate state-switching and baseline power allocation plans derived from short-term forecasts. Concurrently, the lower layer employs the Mongoose Optimization Algorithm (MOA) for real-time rolling optimization, enabling the system to actively perceive temperature variations and adaptively schedule power allocation. Simulations across typical seasonal scenarios validate the strategy’s superiority. In a typical spring scenario, compared to the traditional Daisy Chain and Rotation Control strategies, as well as the Equal Allocation strategy, the proposed approach reduces total startup time and energy consumption by 59.2% and 54.6%, respectively. Furthermore, it increases wind power accommodation rates by 17.7% and 14.2%, and total hydrogen production by 20.0% and 14.9%, respectively. These superior renewable energy utilization and production efficiencies are robustly maintained across typical seasonal scenarios. By actively perceiving actual temperatures for adaptive scheduling, the proposed strategy ultimately ensures synergy and reliability between the control strategy and actual operational constraints under fluctuating conditions. Full article

As a crucial bridge in the global transition toward sustainable and low-carbon energy systems, the resilient operation of natural gas pipeline networks (NGPNs) is essential for ensuring socio-economic stability and energy security. Gas supply reliability serves as a fundamental metric to quantify and monitor the sustainability of energy delivery infrastructure. However, the existing optimization-based model and maximum-flow algorithm often neglect the impact of linepack, defined here as the volume of gas stored within pipelines, particularly end-section linepack, on system state evolution. To address these limitations, a gas supply reliability assessment method, consisting of six interrelated components, is developed. Gas supply reliability indicators were defined from both the quantity and time perspectives at first. Second, system uncertainties were quantified by incorporating supply uncertainties, network units, and demand fluctuations. Third, the sequential Monte Carlo method was employed to simulate the state transitions by capturing the transition times, affected units, and post-transition states. Fourth, a calculation model for the gas supply capacity of the NGPN was established that incorporated the linepack effect and treated the forecasted gas consumption of downstream consumers as boundary conditions. Finally, a method was developed for gas supply reliability assessment, which incorporated the coefficient of variation to improve its engineering applicability for evaluating the NGPN dependability. By applying the proposed approach to a real NGPN in China, the system gas supply reliability increased by 0.379% and 0.051% in the time and quantity dimensions, respectively, compared with the method neglecting linepack. These results indicate that linepack provides an effective short-term supplementary gas source during unit failures or demand fluctuations and should be considered in practical NGPN reliability assessment. Full article

The Taoping paleo-landslide poses a significant risk to local residents and critical infrastructure. However, traditional field surveys and deformation monitoring methods are often inadequate for capturing subtle, localized deformation characteristics—particularly at the head scarp and lateral margins—thereby limiting comprehensive assessments of slope instability. Surface strain data offer direct insights into internal stress redistribution during slope evolution and are essential for interpreting landslide mechanisms and forecasting failure. Given the current limitations in dense and wide-area strain monitoring technologies, this study proposes a novel method for modeling landslide strain fields based on Interferometric Synthetic Aperture Radar (InSAR) phase gradients. Using the phase gradient stacking approach, InSAR-derived phase gradients are transformed into strain-related parameters, enabling estimation of shear strain rates, principal strain rates, and their directional distributions. The application to the Taoping paleo-landslide reveals clear spatial patterns of compressive and tensile strain across the landslide body. Field investigations corroborate the InSAR-derived strain features through corresponding geomorphological evidence observed in both compressional and extensional zones. The proposed method enhances the understanding of landslide deformation behavior, supports evaluation of shear surface continuity and evolution, and offers a robust framework for early warning and risk mitigation in complex landslide-prone areas. Full article

This study develops and evaluates three complementary predictive modeling frameworks for gearbox oil temperature forecasting: Stochastic Differential Equation (SDE) modeling with iterative Markov correction, multi-objective genetic algorithm-enhanced grey modeling (MOGA-GM(1,N)), and multi-output Gaussian Process Regression (MO-GPR). The study used supervisory control and data acquisition (SCADA) data from a 1.5 MW wind turbine gearbox, comprising 14 temperature measurements spanning 789 operational hours. The SDE framework partitions temperature evolution into deterministic aging effects and stochastic environmental perturbations, achieving a fitting accuracy of 2.5% and testing accuracy of 8.0% after thirty iterative corrections. The MOGA-GM(1,N) approach optimizes weight coefficients through the dual objective of minimizing the posterior difference ratio and maximizing small error probability, attaining first-class accuracy classification ($C=0.06$; $P=0.99$) while identifying mechanical loads and rotational speeds as dominant thermal drivers. MO-GPR demonstrates competitive performance with uncertainty quantification capabilities, achieving RMSE values of 2.51–7.48 depending on training SCADA data proportions. Comparative analysis shows that the iteratively refined SDE methodachieves the best prediction accuracy in this case study for continuous thermal trajectory forecasting, while MOGA-GM(1,N) excels at wear source diagnostics and operational factor analysis. The proposed framework addresses persistent challenges in wind turbine condition monitoring, including extreme nonlinearity, discontinuous data, and unpredictable thermal spikes. The results suggest potential for implementation in preventive maintenance systems, enabling timely intervention before critical thermal thresholds that precipitate component failure. Full article

In classical reliability engineering, failure is a probabilistic structural failure based on lifetime distributions of Weibull models. However, in the control-critical mechanical systems, it is possible that functional failure of the system happens before material failure occurs as a result of control power loss. This paper proposes a Controllability–Reliability Coupling (CRC) model, which redefines the concept of reliability as the stabilizability in the face of progressive degradation. The actuators’ deterioration is modeled using the time-varying input effectiveness factor $\alpha \left(t\right)$, and the actuator is said to be in failure when the minimum singular value of the finite-horizon controllability Gramian becomes less than a stabilizability threshold $\epsilon$. The performance of the simulation indicates that the functional failure is a precursor of structural failure in several degradation conditions. A baseline comparison shows that the CRC metric forecasts loss of controllability at $T_{CRC}=17.0$ s, but the classical Weibull reliability never attains the structural failure threshold even in the time horizon of 20 s. The system retains margins of Lyapunov stability and H infinity robustness are not lost, and it is still stable and attenuates disturbances even when control authority is lost. In practical degradation scenarios, the forecasted CRC failure times are 21.5 s (linear wear), 13.1 s (accelerated fatigue), 23.7 s (intermittent faults), and 24.4 s (shock damage), whereas maintenance recovery abated functional failure completely. In a case study of an industrial robotic joint, at 27.0 s, functional collapse occurred, and at the same time, structural reliability was still above the failure threshold. The findings support the hypothesis that structural survival and functional controllability are distinct concepts. The proposed CRC framework is an approach to control-conscious reliability measure, which can detect early failures and offer proactive maintenance advice in the context of a cyber–physical system. Full article

The evolution towards “more-electric” aircraft has accelerated in the last decade, motivated by environmental concerns and the development of new market frontiers such as urban air mobility. This transition is affecting both propulsion and aircraft systems, with electro-mechanical brakes (E-Brakes) representing a promising alternative to traditional hydraulic solutions. While E-Brakes offer advantages such as reduced system complexity and elimination of hydraulic leakage issues, they remain a relatively unproven technology in civil aviation. In this context, the development of Prognostics and Health Management (PHM) solutions aligns with the need for continuous monitoring of novel components while also providing the benefits typically associated with prognostic techniques. This paper presents the preliminary stages of the development of a PHM framework for an E-Brake intended for future executive-class aircraft. Since experimental activities are not yet available, the analysis was carried out on simulated data generated through a high-fidelity model of the system. The study focuses on brake pad wear as the primary degradation mechanism and proposes a particle-filtering approach to estimate the health state and predict the Remaining Useful Life (RUL). Early results obtained from simulated fault-to-failure trajectories prove the ability of the algorithm to track degradation and to provide reliable prognostic forecasts, paving the way for future validation with real-world data. Full article

Reliable bearing-failure prognosis in induction machines remains a critical research challenge, as it directly impacts system availability, maintenance efficiency, and overall operational safety. To address this challenge, it is essential to develop an online prognostic system capable of continuously assessing bearing health and predicting future failures in real time. This paper proposes a novel unsupervised data-driven prognostic framework for induction machine bearings that integrates advanced signal processing techniques for the preprocessing step, data clustering to construct bearing health stage (HS), artificial neural network (ANN) forecasting using a designed health indicator (HI) based on the latest historical observations, and a fine-tuning model to improve the estimation of remaining useful life (RUL) for induction machine bearings using vibration and temperature signals provided by the PRONOSTIA and NASA-IMS experimentation platform. The results show that the proposed approach is an effective way for bearing RUL estimation. Full article

Nowadays, the emergence of Foundation Models as a Service enables mobile users to access powerful capabilities such as inference and fine-tuning on demand and without incurring local computational overhead. This paper introduces a software-aware offloading framework for FMaaS that allows edge nodes to forecast software aging and prevent service degradation. Each node employs a lightweight Echo State Network to predict its software age, with tasks dynamically assigned based on communication cost, inference delay, and forecast reliability. Simulation results including ablation studies confirm the effectiveness of software age forecasting in reducing task failures and improving session continuity. Full article

A vulnerability surface modeling method based on dual intensity metrics is proposed to assess the impact of typhoons and heavy rainfall disasters on the distribution pole-tower conductor system. A three-dimensional finite-element model is developed for a typical “three-pole four-conductor” distribution line, considering the uncertainties in both load-side and structural-side parameters. A spatially coherent turbulent wind field is generated using the Davenport spectrum and harmonic superposition method, while an equivalent rain load is derived based on raindrop spectrum integration. Nonlinear dynamic time-history analysis is then conducted under multiple combinations of basic wind speeds and rainfall intensities, extracting engineering demand parameters such as conductor axial tension and pole-base bending moments. Based on probabilistic demand analysis, the relationship between engineering demand parameters and dual intensity measures is regressed in the logarithmic domain to construct bivariate fragility surfaces for both the conductors and the poles. Critical failure curves are obtained by intersecting the fragility surfaces with the 10% exceedance probability level, enabling rapid classification of structural risk under the joint effects of wind and rain. The results show that the regression model provides a high fit, effectively revealing that wind speed is the dominant control factor, while rainfall intensity serves as a secondary amplifying factor. The resulting critical failure curves can be directly used as operation and maintenance warning thresholds and can be coupled with observed and forecast meteorological data for time-varying risk assessment. These findings provide methodological support and engineering guidance for risk assessment, operation and maintenance decision-making, and resilience enhancement of distribution networks under multi-hazard coupling. Full article

Since the emergence of big data analytics, forecasting future population distributions in specific regions has become a popular prominent research topic. Accurate predictions offer benefits for urban planners, traffic management authorities, and commercial stakeholders. However, most studies have concentrated on population dynamics within isolated locations, often overlooking broader spatial fluctuations across larger geographic areas. This narrow scope limits the practical utility of such predictions. Therefore, generative adversarial networks (GANs) have been employed to estimate population counts across multiple locations within expansive regions. Despite their potential, many GAN-based models encounter significant challenges when tasked with predicting numerous locations simultaneously, resulting in prolonged training times or failure to achieve convergence. To address these limitations, we developed a novel random number generation method to improve the training efficiency and convergence stability of GANs. We also set a new identification criterion to ensure that the large-scale population distributions generated by GAN closely reflect real-world conditions. The developed model in this study was validated using actual telecommunications-based pedestrian flow data from Taiwan, demonstrating its effectiveness and practical feasibility. Full article

Notably, one of the key points to address low accuracy and delayed responsiveness of dam deformation prediction models lies in the timely detection of the outliers caused by environmental disturbances, sensor failures, or operational anomalies of dam monitoring sequences. Therefore, our work offers an unambiguous method for overcoming this challenge. In this paper, a robust prediction framework that integrates Complete Ensemble Empirical Mode Decomposition (CEEMD) and Isolation Forest (iForest) for effective outlier detection, followed by a Multi-Head Attention Bidirectional Gated Recurrent Unit (MHA-BiGRU) model for dam deformation prediction, is presented. The original deformation time series is first decomposed using CEEMD into a set of intrinsic mode functions (IMFs). This decomposition separates the series into trend-related components and noise components. Subsequently, the iForest algorithm is applied in outlier detection for noise components. Then, the BiGRU model is enhanced with an MHA mechanism to give more weight to the features that affect the sequences of monitoring dam deformation. By enabling the proposed model to focus on the key factors affecting dam deformation, the accuracy of the prediction results has been enhanced. Finally, a case study introducing monitoring data from a practical project in China demonstrates the performance of the proposed method. The proposed MHA-BiGRU model demonstrates superior performance across all tested scenarios. Notably, the coefficient of determination is consistently maintained above 0.98, peaking at 0.9880. In terms of error control, the model exhibits a maximum mean absolute error of 0.1789, thereby substantiating its exceptional prediction accuracy and robustness. In comparison with classical time series forecasting models, including LSTM, GRU and BiGRU, the proposed approach demonstrates enhanced robustness and delivers greater prediction accuracy. The findings provide a promising reference framework for dam structural characteristics prediction in similar projects. Full article

In this study, a comprehensive mathematical method for modeling the operational reliability of mine hoisting equipment under conditions of incomplete and heavily censored data is developed. The analyzed dataset includes 259 observations collected over a five-year period for six critical components, with the overall level of censoring reaching 62% and exceeding 70% for long life mechanical subsystems. Considering right, left, and interval censoring, the paper proposes a unified statistical procedure that combines empirical estimation of failure rates with parametric identification using Weibull, exponential, normal, and lognormal distributions. Model parameters are estimated using censored data–aware fitting procedures, while model selection is performed based on likelihood-based criteria, supplemented by correlation analysis to assess agreement between empirical and fitted reliability curves. The methodology is implemented computationally in the Mathcad Prime environment and is supplemented with mathematical tools for reconstructing survival curves, analyzing parameter sensitivity, and evaluating robustness at different censoring levels. In addition, an economic optimization model is formulated to determine cost-effective maintenance intervals by minimizing an integral functional that accounts for preventive maintenance, repair, and downtime costs. The results demonstrate that the proposed approach provides stable reliability estimates and reliable forecast intervals, enabling the construction of generalized life cycle curves for individual subsystems. The study establishes a rigorous mathematical basis for the transition from fixed-interval maintenance to adaptive, reliability-oriented maintenance strategies in industrial mine hoisting systems. Full article

Current international standards (EN 12004; SI 4004) are testing ceramic tile adhesives under post-cure thermal aging. However, the standards omit UV radiation exposure during the fresh-adhesive phase. This research investigated three commercial polymer-modified cement adhesives (C2TE, C2TE-S2, C2T) bonding porcelain stoneware tiles under simulated Eastern Mediterranean and desert conditions. Three commercial adhesives were exposed during the initial (uncured) period to elevated temperature (30 °C), humidity variation (40–65% RH), and UV radiation (295–365 nm, 1.5–2.0 mW/cm²) for 20 min, followed by 28 days of curing. Pull-off testing and scanning electron microscopy, combined with quantitative directionality analysis, were used to characterize the mechanical performance and microstructural degradation. UV exposure of adhesives during tiling working time caused a drop of mean bond strength from 1.77 to 0.26 MPa (85% reduction) compared with 1.77 to 0.64 MPa (36% reduction) under hot-arid conditions. Microstructural analysis of the hardened pull-off adhesives revealed that exposure of the fresh adhesive to UV radiation causes thinning and degradation of the interfacial layer (15–40 µm), leading to a drop in macroscopic strength. In contrast, hot-arid exposure induces adhesive bulk cracking while preserving interface integrity. Fracture surface directionality (goodness parameter), crack density, and delamination percentage together distinguish interface failure from adhesive bulk degradation and provide a forecast of long-term durability. This combined SEM-mechanical approach identified critical gaps in testing protocols and enables evidence-based adhesive selection, as current EN 12004 classifications based solely on mechanical properties prove insufficient. Full article

Against the backdrop of global climate change, remote subtropical mountainous power grids face severe operational challenges due to their fragile infrastructure and complex climatic conditions. However, existing research has insufficiently addressed load forecasting in data-sparse regions, particularly lacking systematic analysis of the “meteorology–load–failure” coupling mechanism. To address this gap, this study focused on 10 kV distribution lines in a typical subtropical monsoon region of southern China. Based on hourly load and meteorological data from 2016 to 2025, we propose a two-stage hybrid model combining “Random Forest (RF) feature selection + Long Short-Term Memory (LSTM) time series forecasting”. Through deep feature engineering, composite, lagged, and interactive features were constructed. Using the RF algorithm, we quantitatively identified the core drivers of load variation across different time scales: at the hourly scale, variations are dominated by historical inertia (with weights of 0.5915 and 0.3757 for 1-h and 24-h lagged loads, respectively); at the daily scale, the logic shifts to meteorological triggering and cumulative effects, where the composite feature load_lag1_hi_product emerged as the most critical driver (weight of 0.8044). Experimental results demonstrate that the hybrid model significantly improved forecasting accuracy compared to the full-feature LSTM benchmark: on a daily scale, RMSE decreased by 13.29% and MAE by 16.67%, with R² reaching 0.8654; on an hourly scale, R² reached 0.9687. Furthermore, correlation analysis with failure data revealed that most grid faults occurred during intervals of extremely low load variation (0–5%), suggesting that “chronic stress” from environmental exposure in hot and humid conditions is the primary cause, with lightning identified as the leading external threat (26.90%). The interpretable forecasting framework proposed in this study transcends regional limitations. It provides a strategic “low-cost, high-resilience” prototype applicable to power systems in humid-subtropical zones worldwide, particularly for developing regions facing the dual challenges of data sparsity and climate vulnerability. Full article