Search Results (64,112)

With the growing trend toward insulator hybridization, higher requirements are imposed on the synergistic improvement of interfacial durability and pollution flashover performance. Machining annular grooves at the green-body stage and embedding silicone rubber enables the construction of an embedded structure with improved durability, forming hydrophilic/hydrophobic alternating surfaces. However, the outdoor insulation characteristics of such hybrid surfaces remain insufficiently investigated, and their engineering feasibility requires further validation. In this study, a series of hydrophilic/hydrophobic alternating surfaces were fabricated, and artificial pollution tests were conducted. The results show that the AC pollution flashover voltage exhibits a saturated increasing trend as the hydrophobic interfaces become more dispersed. When twenty 4 mm wide hydrophobic interfaces were distributed along a 16 cm creepage distance, the flashover voltage was 12.4% higher than that of a fully hydrophobic surface. These results indicate that appropriate design of hydrophobic interface distribution can achieve excellent pollution flashover performance even at relatively low hydrophobic coverage (≤50%). High-speed imaging combined with infrared thermography reveals the discharge mechanism governed by hydrophobic interface distribution from an electro–thermal coupling perspective. The coexistence of multiple dry bands induced by discrete hydrophobic interfaces is identified as the key factor enhancing flashover withstand capability. A static pollution flashover model was established to quantitatively estimate the AC flashover voltage, confirming the external insulation feasibility of the embedded hybrid concept. Full article

Ecological zoning is a critical instrument for coordinating economic development with environmental conservation, ensuring regional ecological security, and fostering sustainable development. Using the Western Sichuan Plateau (WSP) as a case study and taking 2000, 2010, and 2020 as the time nodes, this research employed an optimized landscape ecological risk assessment model to comprehensively evaluate the spatiotemporal evolution of regional landscape ecological risk (LER) and ecosystem services (ESs). By analyzing the spatial correlation between LER and ESs, we constructed an ecological zoning framework, identified key driving factors, and proposed differentiated management strategies. The results showed the following: (1) The LER generally declined from 2000 to 2020, with the high-risk areas mainly distributed in the high-elevation meadow belt in the west and north, and the low and lowest-risk areas concentrated in the eastern part of the plateau continued to expand in area. (2) The overall level of ESs showed a downward and then upward trend, with a spatial pattern of “high in the east and low in the west”. (3) LER was significantly negatively correlated with most ESs (except WY), and localized agglomeration was clearly characterized. (4) Based on the four-quadrant model, the study area was categorized into four types of ecological zones, high LER–high ES, low LER–high ES, low LER–low ES and high LER–low ES, whose spatial patterns were mainly significantly influenced by factors such as elevation, per capita GDP and precipitation (PRE). The “risk-service” synergistic zoning framework proposed in this study provides a spatially explicit decision-making basis for ecological protection and restoration on the WSP, and is useful for the sustainable management of similar ecologically fragile areas. Full article

In response to global climate change, virtual power plants (VPPs) have emerged as critical entities for integrating distributed energy resources and enabling demand response. However, the design of multi-energy collaborative pricing mechanisms for VPPs remains a significant challenge, particularly under carbon trading regulation. This paper addresses this gap by proposing a bi-level optimization model that captures the real-time interactions between users and energy suppliers. The model is designed to simultaneously maximize user utility and minimize supplier costs, explicitly accounting for energy costs, equipment operation and maintenance (O&M) costs, carbon emission costs, and power generation structure constraints. A particle swarm optimization (PSO) algorithm is employed to solve the formulated problem. The results of a case study demonstrate that the proposed mechanism effectively guides users toward peak shaving and valley filling, achieving a real-time balance between supply and demand. Furthermore, the simulation results indicate that the model significantly enhances power system operational efficiency and economic benefits while reducing carbon emissions. This work offers a practical approach for improving renewable energy integration and overall system performance within a carbon-constrained environment. Full article

As a widely used computing substrate, the side-channel security of FPGAs has attracted considerable attention, yet a systematic understanding of how FPGA device types contribute to exploitable leakage remains limited. This work presents a device-centric evaluation that maps an S-box-like function onto common FPGA primitives, including look-up table (LUT), flip-flop (FF), block RAM (BRAM), and distributed RAM (LUTRAM), and assesses Correlation Power Analysis (CPA) outcomes under the Hamming Weight (HW) and Hamming Distance (HD) power models. The results show pronounced leakage differences across device types: FF- and BRAM-based implementations exhibit substantially stronger leakage than LUT- and LUTRAM-based ones, and they frequently achieve $\mathrm{GE}=0$ in our configurations, while the HD model is generally more effective than the HW model in the performed CPA evaluations. Notably, FF-, BRAM-, and LUTRAM-based implementations can already be breakable starting from one instance under the HD model in our device-level tests, indicating that exploitable leakage may manifest in real FPGA applications. These device-level observations are further validated on a practical cipher by analyzing two SM4 encryption modules that differ only in the S-box implementation style; the BRAM-based design shows significantly stronger leakage than the LUT-based design, achieving $\mathrm{GE}=2.58$ versus $\mathrm{GE}=78.3$ at 10,000 traces. This work highlights the critical role of device selection and implementation style in FPGA side-channel security, and it provides practical insights for designing secure FPGA applications against power side-channel analysis. Full article

Species distribution models (SDMs) are widely used to define climatic constraints on species ranges, yet their ability to reflect demographic processes remains poorly understood. We integrated annually calibrated SDMs (1981–2005) with tree-ring width data from 15 European forest species in the Iberian Peninsula to evaluate if climatic suitability mirrors tree growth, particularly for populations at their climatic tolerance limits. Our results show that higher suitability consistently relates to reduced growth decline, acting as a reliable proxy for forest vigor. Notably, interannual variability in climatic suitability was positively associated with growth, suggesting that climatic fluctuations may enhance physiological resilience. We also found that Mediterranean species exhibit higher growth sensitivity to climatic suitability changes than Eurosiberian species. These findings demonstrate that SDMs can capture functional constraints beyond mere presence, positioning annual climatic suitability as a key predictor of radial growth and offering valuable insights for forest management under climate change. Full article

Background/Objectives: Tablet development requires simultaneous optimization of multiple quality attributes under limited experimental budgets, yet formulation–property relationships are highly nonlinear in mixture systems. To support pre-formulation decision-making prior to extensive tablet prototyping, this study proposes an AI framework that organizes formulation and process data together with raw-material property records into a reusable database, and enriches conventional composition/process features with physically motivated mixture descriptors derived from raw-material properties and formulation/process settings. Methods: Mixture-level scalar descriptors are constructed by composition-weighted aggregation of material properties, and particle size distribution (PSD) is incorporated via a compact set of summary statistics computed from composition-weighted mixture PSDs. Three feature sets are compared: (i) Materials + Processes (MP), (ii) MP with scalar Descriptors (MPD), and (iii) MPD with PSD summaries (MPDD). Five target properties are modeled: hardness, disintegration time, flow function, cohesion, and thickness. We train and evaluate Random Forest, Extra Trees Regressor, Lasso, Partial Least Squares, Support Vector Regression, and a multi-branch neural network that processes the three feature blocks separately and concatenates them for prediction. For interpolation assessment, repeated Train/Dev/Test splitting (5:3:2) across multiple random seeds is used, and the effect of feature augmentation is quantified by paired RMSE improvements with bootstrap confidence intervals and paired Wilcoxon signed-rank tests. To assess robustness under practical formulation updates, rolling-origin time-series splits are employed and Applicability Domain indicators are computed to characterize out-of-distribution coverage. Results: Across interpolation evaluations, mixture-descriptor augmentation (MPD/MPDD) improves hardness and disintegration time in most settings, whereas gains for flow function are smaller and cohesion/thickness show mixed effects under limited sample sizes. Conclusions: Under extrapolation-oriented evaluation, the descriptors can improve hardness but may degrade disintegration-time prediction under covariate shift, emphasizing the need for careful descriptor selection and dimensionality control when deploying pre-formulation predictors. Full article

To investigate the characteristics and health risks of heavy metal (HM) contamination in soils of typical industrial sites during urban renewal, this study selected Pudong New District, Shanghai, as a case. Seven HMs (Cd, Pb, Cu, Zn, Ni, Hg, and As) were analyzed for their concentrations, ecological risks, spatial patterns, and potential sources. Inverse Distance Weighted (IDW) interpolation was used to assess spatial distribution, Random Forest (RF) regression to predict HM concentrations, and a two-dimensional Monte Carlo simulation to evaluate human health risks. The results showed that all HMs except As exceeded Shanghai background values in surface soils, with varying levels observed in subsoil and saturated layers. The Index of Geoaccumulation (Igeo) and Risk Index (RI) indicated low contamination and moderate ecological risk. Pearson correlation combined with Positive Matrix Factorization (PMF) identified four major sources: traffic emissions dominated by Cd and Zn, combustion-related sources dominated by Pb and Hg, industry-related inputs dominated by Cu and Ni, and a natural source dominated by As. The RF model demonstrated strong predictive accuracy for Cd, Pb, Hg, and As (R² = 0.80–0.94), and predicted values were consistent with observations. Monte Carlo results showed that non-carcinogenic risks for children and adults were within acceptable limits, while carcinogenic risks reached “notable” levels with probabilities of 62.06%, 55.65%, and 22.49% for children, adult females, and adult males, respectively. Cd and As were identified as key contributors. This work provides scientific support for soil pollution prevention and remediation during urban renewal. Full article

As the key technology of space exploration, space power has been a major area of international research focus. A lot of research work has been carried out around the world for the space nuclear reactor using the heat pipe, liquid metal and gas cooling methods. With the development of molten salt reactor in the Generation IV reactor system, molten salt dissolving fissile material and acting as a coolant at the same time has become a new cooling scheme, which provides new ideas for the design of space nuclear reactors. In this study, a novel reactor, the liquid-solid dual-fuel space nuclear reactor (LSSNR) was preliminarily proposed, combining the molten salt fuel and cross-shaped spiral solid fuel to achieve the design goals of 30-year lifetime and an active core weight of less than 200 kg. Monte Carlo neutron transport code OpenMC based on ENDF/B-VII.1 library was employed for neutronics design in the aspect of fuel type, cladding material, reflector material and the spectral shift absorber. Then, the thickness of the control drum absorber was optimized to meet the requirement of the sufficient shutdown margin, lower solid fuel enrichment, and 30-effective-full power-years (EFPY) operation lifetime. Finally, UC solid fuel with U-235 enrichment of 80.98 wt.% and B₄C thickness of 0.75 cm were adopted in LSSNR, and BeO was adopted as the reflector and the matrix material of the control drum. A spectral shift absorber Gd₂O₃ was used to avoid the subcritical LSSNR returning to criticality in a launch accident. The k_eff with the control drum in the innermost position is 0.954949, and the k_eff reaches 1.00592 after 30 EFPY of operation. The total mass of the active core is 158.11 kg. In addition, the thermal-hydraulic feasibility of LSSNR using cross-shaped spiral fuel was analyzed based on a 4/61 reactor core model. The structure of cross-shaped spiral fuel achieves enhanced heat transfer by generating turbulence, which leads to a uniform temperature distribution of the coolant flow field and reduces local temperature peaks. Based on the LSSNR scheme, some neutronic characteristics were analyzed. Results demonstrate that the LSSNR has strongly negative reactivity coefficients due to the thermal expansion of liquid fuel, and the fission gas-induced pressure meets safety requirements. One hundred years after the end of core life, the total radioactivity of reactor core is reduced by 99% and is 7.1305 Ci. Full article

The aim of this study is to identify logistics supply chain criteria by considering the sustainability factor and to conduct a performance evaluation based on these criteria. The application analyzes 15 sub-criteria under the five main criteria of sustainable logistics: procurement logistics, production logistics, reverse logistics, distribution logistics, and disposal logistics. Accordingly, the importance weights of the logistics criteria were determined using the Fuzzy AHP (Analytic Hierarchy Process) method. Based on the determined criterion weights, an integrated model for performance evaluation was proposed using the Spherical Fuzzy MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the Full Multiplicative Form) and Heuristic Fuzzy COPRAS (Complex Proportional Assessment) methods. The application ranked the sustainable logistics performance of three major logistics firms, and the results obtained from both methods were consistent. The findings highlight that the three criteria with the highest importance levels are, in order, as follows: green purchasing strategies (0.356), green design (0.151), and integration of supplier into environmental management processes (0.125). This demonstrates that firms aim to foster environmental responsibility not only in their internal processes but also throughout the supply chain. This study provides a reliable model for evaluating and improving sustainable logistics performance, contributing both to the academic literature and to practical applications in logistics firms. Full article

With the increasing penetration of converter-based devices, harmonic distortion has become a major challenge for power quality monitoring in large-scale power systems. This study presents a systematic review of methods for modeling harmonic sources and their applicability to real-time monitoring of power distribution systems. The review was conducted following PRISMA guidelines, considering literature published between 2000 and 2026. Searches were performed across Scopus, IEEE Xplore, Web of Science, ScienceDirect, and MDPI using predefined keywords. A total of 128 peer-reviewed journal articles were included. Potential sources of bias were qualitatively assessed, including selection, retrieval, and classification bias; however, residual bias may still arise from database selection, keyword design, and study classification. A structured comparative framework is introduced, based on a six-dimension coverage scoring scheme and maturity analysis, enabling consistent evaluation across both methodological and deployment aspects. The robustness of this framework was evaluated using leave-one-out and perturbation analyses, indicating low variability in coverage scores and stable rankings across both corpora. A taxonomy of harmonic source modeling approaches is proposed. Comparative synthesis indicates that measurement-based approaches, particularly those leveraging distribution-level PMUs, show strong potential for real-time monitoring. Key challenges include D-PMU placement, data integration, and computational scalability. Future work should focus on physics-informed AI and digital twin-based monitoring. Full article

The increasing penetration of distributed energy resources (DERs), electric vehicles (EVs), and dynamic loads introduces significant operational challenges in modern distribution networks, including voltage violations, reverse power flows, and congestion. Distribution network reconfiguration (DNR) is widely used to improve network performance; however, most existing approaches focus primarily on radial topology optimization and rarely consider practical switching feasibility or adaptive transitions between radial and meshed configurations. This paper proposes an operational framework for congestion management based on adaptive radial–mesh reconfiguration. The framework integrates radial network optimization, temporary mesh reinforcement for congestion mitigation, and safe switching sequence validation to ensure operational feasibility. A comprehensive operational cost model incorporating power losses, time-of-use energy imports, switching operations, and on-load tap-changer actions is also developed. The proposed method is validated on a real 22 kV distribution feeder operated by the Provincial Electricity Authority in Thailand. The results demonstrate that the framework effectively mitigates congestion and reduces operational costs by 1.57–9.18% relative to baseline operation, highlighting its practical applicability in active distribution networks. Full article

The accuracy of Digital Elevation Models (DEMs) plays a crucial role in determining their reliability for geoscientific and engineering applications. Next-generation distributed interferometric synthetic aperture radar (SAR) constellations, such as the PIESAT-1 wheel constellation with its “one primary, three secondary” setup, provide a novel method for efficiently acquiring high-precision DEMs. However, a comprehensive and systematic performance evaluation of DEMs derived from such an innovative constellation is lacking, particularly in the context of comparative studies under complex terrain conditions. This study uses PIESAT-1 SAR imagery to generate a 10 m resolution DEM through multi-baseline interferometric processing. The ICESat-2 ATL08 dataset serves as the reference baseline, and mainstream products, including ZY-3, GLO-30, TanDEM-X DEM, and AW3D30, are incorporated for a multidimensional vertical accuracy evaluation, considering land cover, slope, aspect, and topographic profiles. The results indicate that, in three representative mountainous regions, the PIESAT-1 DEM achieves optimal overall accuracy (RMSE = 3.25 m). Furthermore, in regions with significant radar geometric distortions, such as south-facing slopes, vegetation-covered areas, and regions with noticeable anthropogenic topographic changes, the PIESAT-1 DEM demonstrates superior stability and information capture capabilities relative to conventional single- or dual-baseline SAR systems. This study validates the technological potential of the PIESAT-1 wheel constellation in enhancing DEM accuracy and terrain adaptability, and provides insights for the scientific selection of high-resolution topographic data and the design of future spaceborne interferometric missions. Full article

Accurate and rapid transient voltage stability assessment is crucial for the safe and stable operation of new energy bases in desert and grassland regions. Existing deep learning methods fail to adequately capture the high-dimensional dynamic coupling features of transient voltage signals in large-scale renewable energy bases with UHVDC transmission, and suffer from poor performance under class-imbalanced sample conditions. This paper proposes a transient voltage stability assessment method utilizing continuous wavelet transform (CWT) time–frequency images and a deep residual network (ResNet-50). CWT with the Morlet wavelet basis converts voltage time-series signals into multi-scale time–frequency images to simultaneously capture temporal and frequency-domain transient features. An improved focal loss (FL) function is introduced to dynamically adjust category weights based on actual sample distribution, enhancing model robustness under extreme class imbalance. The proposed method is validated on a modified IEEE 39-bus system incorporating the Qishao UHVDC line and wind/photovoltaic integration in Northwest China, using 1490 simulation samples under diverse fault scenarios. Results demonstrate that the proposed CWT-ResNet achieves 98.88% accuracy, 94.74% precision, 100% recall, and 97.29% F1-score, outperforming SVM, 1D-CNN, and 1D-ResNet baselines. Under 5 dB noise conditions, the method maintains over 90% accuracy, demonstrating strong noise robustness. Full article

Exploring public awareness, participation, and emotional inclination toward intangible cultural heritage (ICH) clarifies public attitudes and demands toward traditional culture, providing a crucial basis for targeted ICH protection and inheritance. Based on ICH text big data collected from China’s mainstream social media platform Weibo, this study improves the TF-IDF algorithm, integrates LDA topic analysis for semantic feature mining, and trains a new sentiment analysis model to explore public emotional attitudes and their formation mechanisms. The study is geographically limited to China and covers the entire year of 2023. The results show that: (1) Public ICH perception is multi-dimensional, with close attention to crafts like paper-cutting and traditional Chinese medicine; action-oriented terms reflect dynamic inheritance demands. Public discussions focus on three dimensions: ICH inheritance and development (39%), introduction and promotion (45%), and public experience and participation (16%), with the latter accounting for a low proportion. (2) Public sentiment toward ICH is predominantly positive, with all regions scoring above 0.730 (full score = 1), and Zhejiang (0.751) and Jiangsu (0.750) ranking significantly higher. (3) Spatial econometric analysis reveals marked regional differences in ICH sentiment distribution, mainly affected by three key factors—the number of ICH projects, the number of inheritors, and regional GDP—with regression coefficients of 0.699, 0.632, and 0.458 (p < 0.01). This finding provides a basis for formulating targeted ICH protection strategies. Full article

This study aims to investigate the static performance of water-lubricated rubber bearings (WLRBs) with axial grooves. To achieve this objective, an analytical approach is employed that combines a modified Reynolds equation, accounting for surface groove effects and rubber deformation, with a Winkler model and finite element analysis of pressure distribution. By developing a fluid–structure interaction model that incorporates rubber liner deformation, this research reveals the interaction between WLRB geometry and steady-state performance parameters. The investigation evaluates the influence of geometric characteristics, including groove shape, number, and size, on the performance of elastomeric liner WLRBs, while assessing optimal groove depths under various conditions. The study analyzes five distinct groove geometries, including semi-cylindrical, rectangular prism, and three pyramidal types with different apex positions, in a six-groove bearing configuration, presenting their qualitative effects on the behavior of the examined bearings. The key findings indicate that increasing groove size or quantity reduces maximum pressure and load-carrying capacity while elevating friction coefficients. As groove count rises, supporting surfaces diminish, causing pressure distribution to intensify and minimum film thickness to decrease under a specified external load. A notable result reveals that when groove depth exceeds film thickness, performance becomes geometry-independent; however, shallower grooves exhibit significant geometric effects. Additionally, the study identifies groove ends as critical functional zones where film thickness reduction substantially enhances pressure distribution and static performance. Comparative analysis shows that longitudinal grooves with triangular cross sections outperform semi-circular and rectangular variants, with the backward triangular configuration demonstrating superior characteristics due to optimal end-film properties. In conclusion, this research provides a detailed understanding of how groove geometry influences the static performance of WLRBs, highlighting the importance of groove design, particularly at the groove ends, in optimizing bearing functionality. The findings offer valuable insights for the design and selection of groove configurations in water-lubricated rubber bearing applications. Full article