Search Results (8,350)

High-pressure pipelines in nuclear power plants (NPPs) are prone to structural failures, and the study of their failure behavior is essential to analyze and minimize damage to the surrounding structures and components. The prediction of the extent of damage is also a key parameter when designing the surrounding structures. This prediction holds significant importance, since a substantial number of NPPs globally are approaching the 60-year mark in their operational lifespan. Consequently, it becomes imperative to formulate sophisticated methodologies for assessing damage behavior of structures and components under dynamic loading conditions with a more realistic representation of the behavior. This study investigates the damage response resulting from the pipe whip phenomenon in high-pressure pipelines of nuclear power plants through numerical simulations that incorporate damage models for both concrete and steel. The proposed modeling approach was also verified with the results of a ballistics impact study. The finite element modeling (FEM) of the pipe-on-wall-impact (POWI) scenario using ABAQUS helps to implement the damage models of Johnson–Cook (J–C) and Cowper–Symonds (C–S) to steel and the Concrete Damaged Plasticity (CDP) model to concrete using a damage-based approach to determine the extent of damage and failure possibilities. The maximum stresses of the pipe attained 450 MPa for the C–S model and 387 MPa for the J–C model, with the C–S model predicting higher stresses due to its high strain rate sensitivity at extreme loads. By incorporating the damage parameters for the POWI model, a better understanding of the mechanical behavior under impact conditions can be attained. Full article

Rapid evolution in electrified transportation and, in general, sustainability of electrical and electronic assets is turning the traditional power supply and utilization into something more complex and less known. This transition involves increasing operating voltage and specific power, as well as various types of power supply sources, from AC sinusoidal to DC and power electronics. This revolution, beneficial for asset efficiency and resilience, does come at the cost of increased risk of failure for electrical insulation systems. Intrinsic and extrinsic aging mechanisms are not completely known under DC and power electronics, and the risk of inception of partial discharges, PD, which is the most harmful extrinsic aging factor for electrical insulation, is as high, or even higher, compared with AC. To complicate the picture, electrical and electronic components can be used at different pressure levels, such as in aerospace, and it is known that partial discharge inception voltage, PDIV, drops down, and PD magnitude increases, lowering pressure. Models to predict PDIV for surface and internal discharges, as function of pressure, have been proposed recently, but they cannot be applied straightforwardly on practical asset components where type and locations of defects generating PD is unknown. This paper wants to close this application gap. Derivation and validation of an approximate, heuristic model able to predict PDIV at various pressure levels below and above the standard atmospheric pressure, SAP, are dealt with in this paper, referring to typical asset components such as cables, motors, printed circuit-boards, PCB, and under sinusoidal AC voltage. The good capability of the model to predict PDIV and any investigated pressure, from 3 to 0.05 bar, is validated by PD measurements performed using an innovative, automatic PD analytics software able to identify the typology of defect generating PD, i.e., whether surface or internal. Full article

The rapid digitalization of banking services has transformed consumer interactions, necessitating a deeper understanding of the factors influencing online banking adoption. This research investigates the factors influencing consumer adoption in a country undergoing rapid digital transformation but still facing gaps in digital skills and infrastructure—Romania. The objective of the study is to analyze how key variables such as ease of use, security, speed, usefulness, and social pressure influence online banking behavior of Romanian consumers, especially the most digitally engaged ones. The study utilizes a multi-method empirical approach, hypothesis testing, binary logistic regression for prediction modeling, and segmentation analysis to offer a comprehensive view of customer behavior. The findings identify essential adoption drivers and separate customer profiles, providing useful information for financial organizations aiming to enhance their digital strategy. Perceived ease of use and perceived security are primary factors influencing adoption; nevertheless, decision tree analysis indicates that speed and usefulness have a more significant impact than logistic regression implies, but social pressure unexpectedly serves as an impediment. These results highlight the necessity for banks to customize their digital services, harmonizing security and user-friendliness with improved efficiency and usefulness to promote broader adoption in emerging digital economies like Romania. Full article

The Yangtze River Basin serves as the socioeconomic core of China, and rapid development in recent years has intensified the conflict in the area between economic growth and ecological conservation. This study evaluated the spatiotemporal evolution of the land ecological security (LES) across 11 provinces and municipalities in the Yangtze River Basin from 2008 to 2023 by using the framework of the drivers, pressures, state, impact, and response model of intervention. We forecasted the trends of LES (2024–2033) by using a grey prediction model and identified the key obstacles to it through an obstacle degree model. The findings revealed the following: (1) Economic density (D3) and per capita water resources (S4) had significantly high weights, disproportionately impacting LES. Shanghai scored highest for Drivers, Impact, and Response subsystems, while Tibet led in Pressures and State. (2) Basin-wide LES scores improved from “less safe” to “critical safe” but saw no fundamental breakthrough. LES exhibited a three-tier spatial pattern: higher in the middle-lower reaches (e.g., Shanghai, Jiangsu) and lower in the upper reaches (e.g., Qinghai). Tibet remained “critical safe” with minor fluctuations; other regions improved gradually yet mostly remained “less safe” or “critical safe”. (3) Forecasts (2024–2033) indicate continued overall LES improvement. Shanghai and Jiangsu are projected to reach “safe” status, Qinghai will remain “unsafe”, while most others persist as “critical safe”. Basin LES remains fragile, requiring intervention. (4) The Drivers (D) and State (S) subsystems were the primary constraints on LES. Critical obstacle indicators included economic pressure (per capita GDP (D2), D3), resource availability (S4, ratio of effectively irrigated area (I1)), land productivity (agricultural/forestry output per unit area (I3)), and forest coverage rate (R6). Enhancing LES necessitates implementing regionally tailored policies addressing spatial variations, prioritizing urban economic optimization, strengthening water resource management, and ensuring effective cross-regional governance. Full article

This study investigates hydrogen storage enhancement through adsorption in porous materials by coupling the Dubinin–Astakhov (D-A) adsorption model with H₂ conservation equations (mass, momentum, and energy). The resulting system of partial differential equations (PDEs) was solved numerically using the finite element method (FEM). Experimental work using activated carbon as an adsorbent was carried out to validate the model. The comparison showed good agreement in terms of temperature distribution, average pressure of the system, and the amount of adsorbed hydrogen (H₂). Further simulations with different adsorbents indicated that compact metal–organic framework 5 (MOF-5) is the most effective material in terms of H₂ adsorption. Additionally, the pair (273 K, 800 s) remains the optimal combination of injection temperature and time. The findings underscore the prospective advantages of optimized MOF-5-based systems for enhanced hydrogen storage. These systems offer increased capacity and safety compared to traditional adsorbents. Subsequent research should investigate multi-objective optimization of material properties and system geometry, along with evaluating dynamic cycling performance in practical operating conditions. Additionally, experimental validation on MOF-5-based storage prototypes would further reinforce the model’s predictive capabilities for industrial applications. Full article

Vaporous cavitating flow may occur in pipelines when a water hammer causes pressure to drop to saturated vapor pressure. This paper presents a two-phase homogeneous flow model for transient vaporous cavitating flows. The homogeneous flow model consists of continuity and momentum balance equations and an equation describing the volume fraction of vapor. A two-step finite difference MacCormack scheme is used to solve the model. The calculated results obtained from the model are compared with those of the classical discrete gas cavity model (DGCM) and with experimental data from the literature. For all test cases, the model converged at a similar number of grids. The numerical results indicate that the model can reproduce cavitation events well, especially for the prediction of the first maximum pressure peak after cavity collapse. The model also provides direct access to the vapor volume fraction at each location as a function of time. Through numerical analyses, the initial vapor volume fraction in the model is selected as 10⁻⁷; with this selection, the numerical results are in good agreement with experimental data. The model also exhibits comparable predictive capability with respect to the DGCM and superior performance under some operating conditions. Nevertheless, neither of these two models can appropriately estimate the pressure phase in severe cavitation events. Full article

This study aimed to elucidate the vasorelaxant mechanism of action for apigenin 7-glucoside (A7G) by integrating computational and ex vivo pharmacological approaches. Molecular docking simulations were conducted to predict the binding affinities and interactions of A7G with key vascular proteins, specifically human endothelial nitric oxide synthase (eNOS-PDB ID: 1M9M), and human intermediate (IKCa-PDB ID: 9ED1) and small-conductance (SKCa-PDB ID: 6CNN) Ca2+-activated K+ channels. The vasodilatory properties of A7G were subsequently evaluated in isolated mesenteric vascular beds (MVBs) from normotensive Wistar Kyoto (WKY) and spontaneously hypertensive rats (SHR). The in silico analysis indicated that A7G possesses favorable binding affinities for the 1M9M, 9ED1, and 6CNN protein targets. Pharmacological assessments demonstrated that A7G induced a dose- and endothelium-dependent reduction in perfusion pressure in MVBs from WKY and SHR rats. The vasodilatory response to A7G was completely abrogated by perfusion with a high-potassium solution or a non-selective K+ channelblocker. Furthermore, co-administration of apamin and TRAM-34, selective inhibitors of SKCa and IKCa, respectively, also abolished the vasorelaxant effects of A7G. Collectively, these findings suggest that the vascular effects of A7G in both WKY and SHR rats involve an endothelium-dependent mechanism, likely initiated by the activation of the NO/cGMP pathway, which culminates in the opening of IKCa and SKCa channels. Full article

Background/Objectives: Hippocampal atrophy indicates Alzheimer’s disease (AD) progression and guides follow-up and trial enrichment. Identifying high-risk patients is crucial for optimizing care, but accessible, interpretable machine-learning models (ML) are limited. We developed an explainable ML model using clinical data and CSF erythrocyte load (CTRED) to classify adults with AD as high- or low-risk based on hippocampal volume decline. Methods: Included ADNI participants with ≥2 MRIs, baseline lumbar puncture, and vital signs within 6 months of MRI (n = 26). The outcome was the Annual Percentage Change (APC) in hippocampal volume, classified as low or high risk. Predictors were standardized; models included SVM, logistic regression, and Ridge Classifier, tuned and tested on a set (n = 6). Thresholds were based on out-of-fold predictions under a 10–90% positive rate. Explainability used PFI and SHAP for per-patient contributions. Results: All models gave identical classifications, but discrimination varied: Ridge AUC = 1.00, logistic = 0.889, and SVM = 0.667. PFI highlighted MAPres and sex as main signals; CTRED contributed, and age had a minor impact. Conclusions: The explainable ML model with clinical data and CTRED can stratify AD patients by hippocampal atrophy risk, aiding follow-up and vascular assessment planning rather than treatment decisions. Validation in larger cohorts is needed. This is the first ML study to use CSF erythrocyte load to predict hippocampal atrophy risk in AD. Full article

Unconventional reservoirs are critical for future energy supply, but present major challenges for predictions of production due to their ultra-low permeability, strong pressure sensitivity, and non-Darcy flow. Mechanistically grounded physics-based models depend on uncertain parameters derived from laboratory tests or empirical correlations, limiting their field reliability. A data-enhanced variable start-up pressure gradient framework is developed herein, integrating flow physics with physics-informed neural networks (PINNs), surrogate models, and Bayesian optimization. The framework adaptively refines key parameters to represent spatial and temporal variability in reservoir behavior. Validation with field production data shows significantly improved accuracy and robustness compared to baseline physics-based and purely data-driven approaches. Sensitivity and uncertainty analyses confirm the physical consistency of the corrected parameters and the model’s stable predictive performance under perturbations. Comparative results demonstrate that the data-enhanced model outperforms conventional models in accuracy, generalization, and interpretability. This study provides a unified and scalable approach that bridges physics and data, offering a reliable tool for prediction, real-time adaptation, and decision support in unconventional reservoir development. Full article

The decarbonization of shipping and shipbuilding is a critical challenge under the Inter-national Maritime Organization’s (IMO) 2030 greenhouse gas (GHG) reduction target and 2050 net-zero strategy, requiring effective coordination between policy and technology. This study investigates how Japan, China, and Korea respond to these regulatory pressures by systematically analyzing their policy–technology linkages. A four-stage design was applied, combining qualitative case studies, policy–technology mapping, theoretical interpretation, and comparative analysis, to trace how national strategies shape eco-friendly transitions. Japan employs an innovation-led, institution-convergent model in which technological demonstrations drive institutional adaptation and diffusion, China follows a policy-designated, execution-oriented model where state-led interventions accelerate commercialization, and Korea adopts a coordination-based, cyclical model balancing public demonstrations, financial support, and international standardization to reduce transition costs. These findings demonstrate that sequencing between policy–technology linkage is context-dependent, shaped by technological maturity, economic feasibility and infrastructure, institutional predictability, and socio-environmental acceptance. The study contributes a cyclic co-evolutionary perspective that moves beyond technological or institutional determinism, reconceptualizes regulation as enabling infra-structure, and identifies implications for global standard-setting and industrial competitiveness. The insights inform practical strategies for major shipbuilding nations to reduce costs while sustaining competitiveness under the IMO’s decarbonization framework. Full article

Rupture discs are critical safety devices for pressure vessels, yet defining replacement intervals for discs that have not ruptured remains challenging due to limited quantitative life-prediction methods. This study investigates forward-acting rupture discs made of 316 L stainless steel and Inconel 600 under three test conditions: low pressure at room temperature, low pressure at elevated temperature, and ultra-high pressure at elevated temperature. Static hold life and fatigue life were measured over a range of operating ratios R = P_w/P_b. To model life–ratio relationships while avoiding far-reaching extrapolation, static life was fitted with a log-normal accelerated-life (AFT) model and fatigue life with a Basquin relation following ASTM E739, reporting 95% prediction bands. Predictions were restricted to validated domains (static: R ≥ 0.86) and truncated at five times the groupwise maximum observed life/cycles. Results show a consistent trend for both materials and all conditions: life decreases as R increases, with steep sensitivities within the observed range. At matched R, Inconel 600 generally exhibits longer life than 316 L. Qualitative failure analysis under constant and cyclic loading indicates progressive plastic deformation, local thinning, and a concomitant reduction in bursting pressure until failure. The proposed in-range predictive framework provides actionable guidance for determining conservative replacement intervals for rupture discs. Full article

Nanostructured oxide coatings play a critical role in determining molecular adsorption and desorption behavior on solid surfaces. In this study, we propose a rapid and simple method to estimate the apparent vapor pressure of fragrance compounds using a quartz crystal microbalance (QCM) sensor modified with a nanostructured silica surface. Here, the term “apparent vapor pressure” refers to the vapor pressure values predicted from the QCM response characteristics, which correlate quantitatively with reference data obtained from conventional thermodynamic calculations. The QCM responses of various fragrances were analyzed in relation to the adsorption–desorption dynamics occurring at the nanostructured interface. We found a quantitative relationship between the sensor responses and the reference vapor pressure values, with a mean absolute percentage error (MAPE) ranging from 19.3% to 220% depending on the compound. This correlation enables rapid evaluation of vapor pressure-related behavior without relying on conventional vapor pressure measurement methods. The results suggest that the surface nanostructure influences the adsorption–desorption balance governed by vapor pressure. This approach provides a practical and efficient means of evaluating the apparent vapor pressure of volatile compounds on nanostructured materials, offering insights into interfacial phenomena relevant to materials science and applied nanosciences. Full article

The aerodynamic pressure of a car is crucial for its shape design. To overcome the time-consuming and costly bottleneck of wind tunnel tests and computational fluid dynamics (CFD) simulations, deep learning-based surrogate models have emerged as highly promising alternatives. However, existing methods that only predict on the surface of objects only learn the mapping of pressure. In contrast, a physically realistic field has values and gradients that are structurally unified and self-consistent. Therefore, existing methods ignore the crucial differential structure and intrinsic continuity of the physical field as a whole. This oversight leads to their predictions, even if locally numerically close, often showing unrealistic gradient distributions and high-frequency oscillations macroscopically, greatly limiting their reliability and practicality in engineering decisions. To address this, this study proposes the Geo-PhysNet model, a graph neural network framework specifically designed for complex surface manifolds with strong physical constraints. This framework learns a differential representation, and its network architecture is designed to simultaneously predict the pressure scalar field and its tangential gradient vector field on the surface manifold within a unified framework. By making the gradient an explicit learning target, we force the network to understand the local mechanical causes leading to pressure changes, thereby mathematically ensuring the self-consistency of the field’s intrinsic structure, rather than merely learning the numerical mapping of pressure. Finally, to solve the common noise problem in the predictions of existing methods, we introduce a physical regularization term based on the surface Laplacian operator to penalize non-smooth solutions, ensuring the physical rationality of the final output field. Experimental verification results show that Geo-PhysNet not only outperforms existing benchmark models in numerical accuracy but, more importantly, demonstrates superior advantages in the physical authenticity, field continuity, and gradient smoothness of the generated pressure fields. Full article

Onshore and offshore wind energy are two of the best options from an environmental point of view. Nevertheless, the volatile and intermittent nature of the wind resource hampers its integration into the power system. Accurate wind speed forecasting facilitates the operation of the electric grid, guaranteeing its stability and safety. However, most existing studies focus on very-short- and short-term time horizons, typically ranging from a few minutes to six hours, and rely exclusively on data measured at the prediction site. In contrast, only a few works address medium-term horizons or incorporate offshore data. Therefore, the main objective of this study is to predict medium-term (24 h ahead) onshore wind speed using the most influential offshore predictors, which are water surface temperature, atmospheric pressure, air temperature, wave direction, and spectral significant height. A new methodology based on twenty-seven machine learning regression models was developed and compared using the root mean squared error (RMSE) as the main evaluation metric. Unlike most existing studies that focus on very-short- or short-term horizons (typically below 6 h), this work addresses the medium-term (24 h ahead) forecast. After hyperparameter tuning, the CatBoost regressor achieved the best performance, with a root mean squared error of 2.06 m/s and a mean absolute error of 1.62 m/s—an improvement of around 40% compared to the simplest regression models. This approach opens new possibilities for wind speed estimation in regions where in situ measurements are not available. This will potentially reduce the cost, time, and environmental impacts derived from onshore wind resource characterisation campaigns. It also serves as a basis for future applications using combined offshore data from several locations. Full article

With the continuous increase in power capacity of spacecraft radio frequency payloads, low-pressure discharge effects have become a significant factor threatening the safe operation of spacecraft payloads. Clarifying the low-pressure discharge effects and their plasma evolution mechanisms is of great importance for elucidating the underlying discharge processes and proposing effective preventive measures. Based on the characteristics of the actual operating environment of spacecraft microwave payloads, this paper proposes a global simulation model for low-pressure discharge plasma in humid air. The validity of the model was verified through online diagnostic experiments on low-pressure discharge plasma. Using the constructed global plasma model, the influence of key parameters such as pressure and humidity on electron and ion densities in the plasma was investigated, revealing the impact mechanisms of initial discharge conditions on plasma characteristics. The potential hazards of these factors to spacecraft microwave payloads were also discussed. This model provides a foundation for improving the accurate prediction of key parameters in low-pressure discharge. Full article