Search Results (4,571)

In the context of large-scale renewable integration and increasing demand for power-system flexibility, energy-storage systems are indispensable components of modern grids, and their safe, reliable operation is a decisive factor in investment decisions. To mitigate lifecycle degradation and cost increases caused by frequent charge–discharge cycles, this study puts forward a two-layer energy storage capacity configuration optimization approach with explicit integration of cycle life restrictions. The upper-level model uses time-of-use pricing to economically dispatch storage, balancing power shortfalls while maximizing daily operational revenue. Based on the upper-level dispatch schedule, the lower-level model computes storage degradation and optimizes storage capacity as the decision variable to minimize degradation costs. Joint optimization of the two levels thus enhances overall storage operating efficiency. To overcome limitations of the conventional Whale Optimization Algorithm (WOA)—notably slow convergence, limited accuracy, and susceptibility to local optima—an Improved WOA (IWOA) is developed. IWOA integrates circular chaotic mapping for population initialization, a golden-sine search mechanism to improve the exploration–exploitation trade-off, and a Cauchy-mutation strategy to increase population diversity. Comparative tests against WOA, Gray Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) show IWOA’s superior convergence speed and solution quality. A case study using measured load data from an industrial park in Zhuzhou City validates that the proposed approach significantly improves economic returns and alleviates capacity degradation. Full article

Life course exposure estimates developed using geospatial datasets must address issues of individual mobility, missing and incorrect data, and incompatible scaling of the datasets. We propose methods to assess and resolve these issues by developing individual exposure histories for an adult cohort of patients with amyotrophic lateral sclerosis (ALS) and matched controls using residence history and PM_2.5, black carbon, NO₂, and traffic intensity estimates. The completeness of the residence histories was substantially improved by adding both date and age questions to the survey and by accounting for the preceding and following residence. Information for the past five residences fully captured a 20-year exposure window for 95% of the cohort. A novel spatial multiple imputation approach dealt with missing or incomplete address data and avoided biases associated with centroid approaches. These steps boosted the time history completion to 99% and the geocoding success to 92%. PM_2.5 and NO₂, but not black carbon, had moderately high agreement with observed data; however, the 1 km resolution of the pollution datasets did not capture fine scale spatial heterogeneity and compressed the range of exposures. This appears to be the first study to examine the mobility of an older cohort for long exposure windows and to utilize spatial imputation methods to estimate exposure. The recommended methods are broadly applicable and can improve the completeness, reliability, and accuracy of life course exposure estimates. Full article

Distributed temperature sensing (DTS) has long been employed in the oil and gas industry to characterize reservoirs, optimize production, and extend well life. More recently, its application has expanded to geothermal energy development, where DTS provides critical insights into transient wellbore temperature profiles and flow behavior. A comprehensive understanding of such field measurements can be achieved by systematically comparing and interpreting DTS data in conjunction with robust numerical models. However, many existing wellbore models rely on steady-state heat transfer assumptions that fail to capture transient dynamics, while fully coupled wellbore–reservoir simulations are often computationally demanding and mathematically complex. This study aims to address this gap by developing a transient wellbore heat transfer model validated with DTS data. The model was formulated using a thermal-analogy approach based on the theoretical framework of Eickmeier et al. and implemented with a finite-difference scheme. Validation was performed by comparing thermal slug velocities predicted by the model with those extracted from DTS measurements. The results demonstrated strong agreement between modeled and measured slug velocities, confirming the model’s reliability. In addition, the modeled thermal slug velocity was lower than the corresponding fluid velocity, indicating that thermal front propagates more slowly than the fluid front. Consequently, this computationally efficient approach enhances the interpretation of DTS data and offers a practical tool for improved monitoring and management of geothermal operations. Full article

The ageing of polymer-modified bitumen (PMB) significantly affects its rheological performance and service life in asphalt pavements. In this study, experimental data PMB 25/55–60 aged under a modified Thin Film Oven Test (TFOT) were restructured into a tidy dataset and analyzed using machine learning techniques. The input variables included temperature, angular frequency, and ageing condition, while the output variable was the complex shear modulus (G*). Two state-of-the-art regression models, Random Forest (RF) and Gradient Boosting Regressor (GBR), were trained and evaluated. Performance assessment revealed that GBR outperformed RF, achieving R² = 0.992, MAE = 1.07 × 10⁶ Pa, and RMSE = 2.04 × 10⁶ Pa, compared to RF with R² = 0.962. Condition-wise analysis further confirmed the robustness of GBR across different TFOT scenarios. Feature importance analysis identified temperature as the dominant factor influencing rheological behavior, followed by frequency and ageing condition. These findings demonstrate the potential of gradient boosting approaches for accurately predicting the rheological properties of aged PMB, providing a reliable tool for performance evaluation and supporting the development of predictive frameworks for pavement materials. Full article

The tensile behavior of cold recycled cement-treated mixtures (CRCTMs), typically produced through full-depth reclamation (FDR), is critical for pavement design. Since no universal design method exists, different tests are applied, leading to varying results. In this context, this study aimed (a) to statistically analyze the flexural tensile strength (FTS) and indirect tensile strength (ITS) of CRCTMs incorporating reclaimed asphalt pavement (RAP) and lateritic soil (LS); (b) to evaluate how using FTS or ITS influences the design of CRCTM layers. FTS and ITS tests were conducted with different cement (1–7%) and RAP (7–93%) contents at multiple curing times (3–28 days), and results were used for statistical and mechanistic analyses. Results showed that cement and RAP contents significantly increased FTS and ITS. RAP exhibited the strongest influence on ITS. This indicates that CRCTMs with similar materials benefit from higher RAP contents. Mechanistic analysis revealed that lower RAP contents require thicker pavement structures, suggesting that increasing RAP can reduce costs and environmental impacts. FTS was about 65% higher than ITS, but using ITS in design led to structures 1.7–3.3 times thicker for the same service life. These findings highlight the need for proper CRCTM characterization, with flexural tests recommended for more reliable and cost-effective pavement design. Full article

The introduction of Poland (2004) and Croatia (2013) into the European Union presented the challenge of modernising ageing rail rolling stock equipped with DC traction motors, operating under limited financial and technical resources. In both countries, older and modernised vehicles remain largely equipped with DC traction motors: in Poland, about 86% of electric locomotives, 77% of EMUs, 68% of trams, 29% of metro trains (expected to fall to 0% by 2025), and 8% of trolleybuses use this technology. Although these numbers have declined rapidly over the last decade, DC traction motors have played a crucial transitional role, enabling effective modernisation and extending vehicle life while postponing the costly purchase of new AC-motor rolling stock. In 2022, Ukraine became an EU candidate country and faced similar challenges in aligning its transport sector with European standards. This review analyses the re-engineering strategies adopted in Poland and Croatia, focusing on the technical, organisational, and policy measures that supported sustainable fleet renewal. Using a comparative method based on documentation, case studies, and reports (2004–2024), this study shows that re-engineering can extend service life by 15–25 years, reduce energy use by up to 20%, and improve reliability by 30–40%. Recommendations are outlined for Ukraine’s future modernisation strategy. Full article

This study provides a comprehensive quantitative comparison of three structurally distinct epoxy prepolymers—cycloaliphatic, novolac, and bis-aromatic (BADGE)—cured with a single hardener, methyl nadic anhydride (MNA), and catalyzed by 1-methylimidazole under strictly identical stoichiometric and thermal conditions. Each formulation was optimized in terms of epoxy/anhydride ratio and catalyst concentration to ensure meaningful cross-comparison under representative cure conditions. A multi-technique approach combining differential scanning calorimetry (DSC), dynamic rheometry, and thermogravimetric analysis (TGA) was employed to jointly assess cure kinetics, network build-up, and long-term thermal stability. DSC analyses provided reaction enthalpies and glass transition temperatures (Tg) ranging from 145 °C (BADGE-MNA) to 253 °C (cycloaliphatic ECy-MNA) after stabilization of the curing reaction under the chosen thermal protocol, enabling experimental fine-tuning of stoichiometry beyond the theoretical 1:1 ratio. Isothermal rheology revealed gel times of approximately 14 s for novolac, 16 s for BADGE, and 20 s for the cycloaliphatic system at 200 °C, defining a clear hierarchy of reactivity (Novolac > BADGE > ECy). Post-cure thermomechanical performance and thermal aging resistance (100 h at 250 °C) were assessed via rheometry and TGA under both dynamic and isothermal conditions. They demonstrated that the novolac-based resin retained approximately 93.7% of its initial mass, confirming its outstanding thermo-oxidative stability. The three systems exhibited distinct trade-offs between reactivity and thermal resistance: the novolac resin showed superior thermal endurance but, owing to its highly aromatic and rigid structure, limited flowability, while the cycloaliphatic resin exhibited greater molecular mobility and longer pot life but reduced stability. Overall, this work provides a comprehensive and quantitatively consistent benchmark, consolidating stoichiometric control, DSC and rheological reactivity, Tg evolution, thermomechanical stability, and degradation behavior within a single unified experimental framework. The results offer reliable reference data for modeling, formulation, and possible use of epoxy–anhydride thermosets at temperatures above 200 °C. Full article

Bumblebees (Bombus spp.) are essential pollinators in natural and agricultural ecosystems but face increasing threats across Europe from habitat loss, climate change, and intensive land use. Austria hosts 42 recognized bumblebee species, yet comprehensive molecular data have been lacking. Here, we present the first complete DNA barcode reference library for the Austrian bumblebee fauna, generated as part of the Austrian Barcode of Life initiative. This reference library includes 586 partial mitochondrial COI sequences. DNA barcoding successfully identified all species, with distinct Barcode Index Numbers (BINs) and no BIN sharing observed, demonstrating its reliability as a complementary method to traditional morphology-based identification. Intraspecific genetic diversity was generally low, though B. jonellus exhibited notable mitochondrial structure with a complex biogeographic pattern. Our results underscore the value of DNA barcoding as a straightforward tool for accurate species identification and biodiversity monitoring, even for non-experts, while also highlighting cryptic genetic variation within widely distributed species. This reference library provides a robust framework for taxonomic, ecological, and conservation research, and supports future metabarcoding-based monitoring efforts in Austria and beyond. Full article

The increasing complexity of the Edge–Cloud Continuum (ECC), driven by the rapid expansion of the Internet of Things (IoT) and data-intensive applications, necessitates implementing innovative methods for automated and efficient system management. In this context, recent studies focused on the utilization of self-* capabilities that can be used to enhance system autonomy and increase operational proactiveness. Separately, anomaly detection and adaptive sampling techniques have been explored to optimize data transmission and improve systems’ reliability. The integration of those techniques within a single, lightweight, and extendable self-optimization module is the main subject of this contribution. The module was designed to be well suited for distributed systems, composed of highly resource-constrained operational devices (e.g., wearable health monitors, IoT sensors in vehicles, etc.), where it can be utilized to self-adjust data monitoring and enhance the resilience of critical processes. The focus is put on the implementation of two core mechanisms, derived from the current state-of-the-art: (1) density-based anomaly detection in real-time resource utilization data streams, and (2) a dynamic adaptive sampling technique, which employs Probabilistic Exponential Weighted Moving Average. The performance of the proposed module was validated using both synthetic and real-world datasets, which included a sample collected from the target infrastructure. The main goal of the experiments was to showcase the effectiveness of the implemented techniques in different, close to real-life scenarios. Moreover, the results of the performed experiments were compared with other state-of-the-art algorithms in order to examine their advantages and inherent limitations. With the emphasis put on frugality and real-time operation, this contribution offers a novel perspective on resource-aware autonomic optimization for next-generation ECC. Full article

During the actual service process, water-lubricated bearings on ships are often in complex operating environments such as low speed, heavy load and salt-sand water areas. To meet the requirements of high load-bearing capacity, long service life and the ability to discharge sand and dissipate heat during the service of bearings, research has been conducted on water-lubricated bearings made of polyetheretherketone (PEEK) with a semi-groove structure. Mathematical and physical models based on the averaged Reynolds equation have been established. By adopting the method of multi-physics field coupling, the lubrication characteristics of the bearings under the coupling influence of multiple factors in the salt-sand water environment (lubrication interface (the surface roughness of the bearing bush), different working conditions (water supply pressure, rotational speed, eccentricity)) are analyzed. Finally, a water-lubricated bearing test bench is set up to conduct bearing lubrication performance tests under multiple factors. The research shows that compared with liquid water, the salt-sand water environment exhibits better lubrication characteristics. The maximum water film pressure, the deformation amount of the bearing bush and the bearing capacity of the bearings increase with the increase of the rotational speed, water supply pressure and eccentricity, while the friction coefficient decreases. With the increase of the roughness of the bearing bush, these parameters decrease slightly and the friction coefficient increases. The presence of salt-sand particles can weaken the influence of roughness on the lubrication characteristics of the bearings. After considering the thermal effect, the mechanical load and thermal load act on the surface of the bearing bush together, resulting in an increase in the deformation amount of the bearing bush, a 0.11% drop in the water film pressure, and the highest temperature of the water film being concentrated at the outlet of the groove. The local semi-groove structure of PEEK can make the friction coefficient as low as 0.019. The comparison errors between the simulation and the experiment are within 10% (for water film pressure) and 2.6% (for friction coefficient), which verifies the reliability of the model. Full article

Alzheimer’s disease (AD), the most prevalent form of dementia, is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, ultimately leading to loss of independence and reduced quality of life. Since current treatments are most effective in early stages, the development of reliable and noninvasive biomarkers for early diagnosis and monitoring is crucial. Abnormal tau protein aggregation is a key pathological hallmark of AD, disrupting neuronal integrity, accelerating progression, and associating closely with cognitive decline and the transition to mild cognitive impairment, a prodromal stage of AD. Currently, tau pathology is evaluated mainly by cerebrospinal fluid analysis and tau positron emission tomography (tau PET), which are invasive or costly, limiting their clinical applicability. This systematic review and meta-analysis synthesized evidence on tau as a blood-based biomarker for dementia, with emphasis on its relationship to tau PET, the gold standard for in vivo tau assessment. Findings indicate that elevated plasma tau levels such as p-tau181, p-tau217 and p-tau231 consistently reflect brain tau pathology, supporting their role as surrogate markers. Large-scale longitudinal validation is warranted to establish blood-based tau as a practical, accessible tool for early detection and disease monitoring, thereby improving therapeutic outcomes in AD. Full article

Weld lines in injection-molded plastics often act as structural weak points that reduce mechanical performance. Enhancing weld line strength is therefore essential to improve product reliability and service life. This study aims to develop and validate an injection mold system capable of localized cavity temperature control to strengthen weld line regions. A specialized mold with an integrated cooling layer was designed to enable rapid thermal response during molding. The Taguchi method was applied to optimize three key parameters—part thickness, melt temperature, and injection pressure—to maximize weld line flexural strength. Experiments based on an L25 orthogonal array revealed that weld line stress varied significantly across parameter combinations, with a maximum of 109.23 MPa. A subsequent validation test conducted under the optimal conditions (250 °C melt temperature, 1.5 mm part thickness, and 16 MPa injection pressure) achieved an enhanced weld line stress of 121.88 MPa, confirming the reliability of the Taguchi-based optimization. Among the factors studied, part thickness had the greatest influence, followed by injection pressure, while melt temperature had the smallest effect. These results demonstrate that combining cavity temperature control with systematic parameter optimization provides an effective strategy to enhance weld line strength in high-performance plastic components. Full article

The starting torque of Self-lubricating Spherical Plain Bearings (SSPBs) has a significant impact on the reliability and service life of aircraft. Due to the low accuracy of the dynamic detection of the starting torque of the bearing, the starting torque cannot be measured accurately under high-frequency swinging conditions. Therefore, the problem of the dynamic detection accuracy of the starting torque of the bearing on a high-frequency swinging friction and wear tester was proposed to be investigated in this paper, and a dynamic simulation model of the swinging system of the tester was constructed. With the combination of the inertia torque test and the least square method, a mathematical model of the inertia torque was developed and the influence of the inertia torque on the results of the dynamic detection of the starting torque was revealed. At the same time, an error compensation procedure for the on-line dynamic detection of the starting torque was written. This research shows that the inertia torque of the swing system of the tester has a great influence on the detection accuracy of the starting torque. As the swing frequency increases, the inertia torque increases, and the dynamic detection accuracy of the starting torque is reduced. The dynamic detection error of the starting torque of the bearing can be efficiently compensated by the error compensation procedure, and then the detection accuracy can be improved. This research provides a good theory for the design of SSPBs and the reasonable control of the starting torque during the use of the bearings, and it is valuable for engineering practice. Full article

This paper proposes a novel statistical test for evaluating exponentiality against the recently introduced EBUCL (Exponential Better than Used in Convex Laplace transform order) class of life distributions. The EBUCL class generalizes classical aging concepts and provides a flexible framework for modeling various non-exponential aging behaviors. The test is constructed using Laplace transform ordering and is shown to be effective in distinguishing exponential distributions from EBUCL alternatives. We derive the test statistic, establish its asymptotic properties, and assess its performance using Pitman’s asymptotic efficiency under standard alternatives, including Weibull, Makeham, and linear failure rate distributions. Critical values are obtained through extensive Monte Carlo simulations, and the power of the proposed test is evaluated and compared with existing methods. Furthermore, the test is extended to handle right-censored data, demonstrating its robustness and practical applicability. The effectiveness of the procedure is illustrated through several real-world datasets involving both censored and uncensored observations. The results confirm that the proposed test is a powerful and versatile tool for reliability and survival analysis. Full article

This paper presents a comprehensive study on the estimation of multicomponent stress–strength reliability under progressively censored data, assuming the inverse Rayleigh distribution. Both maximum likelihood estimation and Bayesian estimation methods are considered. The loss function and prior distribution play crucial roles in Bayesian inference. Therefore, Bayes estimators of the unknown model parameters are obtained under symmetric (squared error loss function) and asymmetric (linear exponential and general entropy) loss functions using gamma priors. Lindley and MCMC approximation methods are used for Bayesian calculations. Additionally, asymptotic confidence intervals based on maximum likelihood estimators and Bayesian credible intervals constructed via Markov Chain Monte Carlo methods are presented. An extensive Monte Carlo simulation study compares the efficiencies of classical and Bayesian estimators, revealing that Bayesian estimators outperform classical ones. Finally, a real-life data example is provided to illustrate the practical applicability of the proposed methods. Full article