Search Results (469)

In view of the fact that there are few reports on the preparation of NbC coating by high-power pulsed magnetron sputtering (HiPIMS) technology. In this study, the effects of NbC interlayer thickness on the microstructure, corrosion resistance and electrical conductivity of Nb/NbC/C multilayer coatings for proton exchange membrane fuel cell (PEMFC) bipolar plates were studied by using the high ionization characteristics of HiPIMS technology. A series of Nb/NbC/C multilayer coatings with varying NbC interlayer thicknesses was deposited via HiPIMS by modulating the deposition time (20, 40, and 60 min). The microstructure and properties of the coatings were characterized using scanning electron microscopy (SEM), Raman spectroscopy, interfacial contact resistance (ICR), and corrosion current, among other methods. The results indicate that as the NbC interlayer thickness increases, the total coating thickness increases from 0.43 μm to 1.42 μm. All coatings exhibit a uniform and dense microstructure lacking typical coarse columnar structures. Raman and XPS analyses show that the I_D/I_G ratio increases from 1.98 to 4.04, indicating an increase in sp²-hybridized bond content and a decrease in sp³ content. At a deposition time of 60 min, the coating achieved optimal performance, yielding a critical load (Lc1) of 31.9 N, the lowest average friction coefficient (0.27), the minimum corrosion current density, and an interfacial contact resistance of 7.5 mΩ·cm². These results demonstrate that the NbC interlayer thickness significantly governs the structure and properties of the Nb/NbC/C multilayer coatings. Specifically, an appropriate increase in the NbC interlayer thickness optimizes the sp²/sp³ hybrid bond ratio, thereby enhancing the overall coating performance. Full article

Methane combustion over palladium-based catalysts is a critical process for reducing greenhouse gas emissions from lean-burn engines and natural gas installations, yet the role of oxide support in controlling both the population and the intrinsic reactivity of Pd active centres remains incompletely understood. In this work, Pd catalysts at two series of higher and lower loading were prepared on five oxide supports—Al₂O₃, CeO₂, SiO₂, TiO₂, and ZrO₂—and characterised by a complementary suite of techniques including SEM-EDX, XRD, BET, AAS, in situ CO-FTIR, DRIFTS with methanol as a probe molecule, and Raman spectroscopy. Catalytic activity testing revealed the order Pd/CeO₂ > Pd/ZrO₂ > Pd/Al₂O₃ > Pd/TiO₂ > Pd/SiO₂. In situ CO-FTIR site quantification showed that active site density spans nearly an order of magnitude across the series, with Pd/CeO₂ reaching 105.44 µmol g⁻¹ and Pd/Al₂O₃ only 11.63 µmol g⁻¹. Turnover frequency analysis revealed a striking inversion: Pd/Al₂O₃ exhibited the highest TOF (0.1327 s⁻¹), approximately six times greater than Pd/CeO₂ (0.0226 s⁻¹). DRIFTS/methanol profiling demonstrated that CeO₂ and ZrO₂ expose cooperative redox and basic centres that promote methane activation, while SiO₂ supports only weakly bound methoxy species, consistent with its lowest activity. These results establish that the oxide support simultaneously governs Pd dispersion—and hence site density—and the electronic environment of each Pd centre, thereby modulating intrinsic reactivity. High specific surface area alone does not guarantee catalytic performance, and rational support selection is therefore the decisive lever for optimising methane combustion catalysts at ultra-low Pd loadings. In all, our findings provide a quantitative, molecular-level framework that disentangles support-controlled site density from intrinsic site reactivity under identical reaction conditions. By combining in situ CO-FTIR, DRIFTS, and Raman spectroscopy with kinetic analysis on well-defined, high-purity oxide supports, this work transforms previously qualitative “support effects” in Pd-catalysed methane combustion into predictive structure–activity relationships. Full article

As the offshore wind industry scales toward 15 MW capacity, the reliability of Direct-Drive Permanent Magnet Synchronous Generators (DD-PMSGs) becomes critical. However, real-world run-to-failure data for these massive, multi-pole machines is virtually non-existent, creating a barrier for developing effective data-driven diagnostic systems. This study proposes a high-fidelity framework for detecting permanent magnet faults in the International Energy Agency (IEA) 15 MW Reference Wind Turbine. Using Finite Element Analysis (FEA), a dataset (magnetic flux and back electromotive-force (EMF)) capturing the electromagnetic signatures of healthy and faulty states of a PMSG under varying severities is generated. To improve the power of computer vision, 1D time-series signals were transformed into 2D images. Specifically, Gramian Angular Fields (GAFs) and Recurrence Plots (RPs) were applied to magnetic flux density signals, while Markov Transition Fields (MTFs) were applied to back-EMF signals. These representations were then fused into multi-channel Red-Green-Blue (RGB) images and processed via a ResNet-18 Deep Transfer Learning model using a strictly non-overlapping, leakage-free dataset partitioning strategy. The proposed framework achieved a classification accuracy of 99.45% on noise-free data. Furthermore, robustness testing under varying levels of Additive White Gaussian Noise (AWGN) (30 dB, 40 dB, and 50 dB Signal-to-Noise Ratio (SNR)) demonstrated sustained high performance, maintaining over 90% accuracy even under severe 30 dB noise conditions. Comparative analysis proved that this multi-channel fusion significantly outperforms single-channel encoding methods, which collapse under heavy noise, validating the scalability of the framework and applicability for next-generation condition monitoring in harsh offshore environments. Full article

Addressing the critical challenges of insufficient precision in landslide hazard identification and the lack of dynamic stability assessment for high-steep slopes in open-pit mines, this study innovatively proposes an integrated technical framework that deeply fuses time-series Interferometric Synthetic Aperture Radar (InSAR) with machine learning (ML), achieving an intelligent analysis framework that integrates deformation monitoring and stability assessment for open-pit mine landslide hazards. The main contributions include: (1) overcoming the limitations of InSAR technology in low-coherence areas, an improved Small Baseline Subset InSAR (SBAS-InSAR) algorithm was adopted to extract slope deformation, increasing the monitoring point density on complex rock slopes by a factor of 2.18, obtaining high-precision deformation fields, and significantly enhancing the deformation capture capability of high-steep slopes; (2) a new paradigm of dynamic-static multi-factor coupled stability assessment was proposed, which deeply fuses time-series InSAR deformation characteristics with multi-source heterogeneous data, including geological, mining, and environmental factors, employing a dual-model collaborative strategy of Random Forest (RF) and XGBoost achieving an Area Under the Curve (AUC) exceeding 0.88, with the InSAR dynamic factor contributing the highest importance, thereby validating the core role of dynamic monitoring data in stability assessment. The empirical study at a large open-pit mine in northern China demonstrates that high- and very-high-risk zones are precisely localized to specific benches, providing an operational technical support system for mine safety production and offering significant demonstration value for promoting the deep application of InSAR technology in the field of mine disaster early warning. Full article

Ion-acoustic waves in dense quantum plasmas are strongly influenced by Fermi degeneracy, Landau quantization, and finite-temperature effects, and in many relevant environments, they also experience memory and nonlocal transport processes that cannot be captured within the planar integer Korteweg-de Vries (KdV) paradigm. In the present work, we revisit this problem by considering a two-fluid, partially degenerate electron-ion plasma in which electron trapping in the presence of a quantizing field and finite temperature is taken into account. Starting from the normalized fluid-Poisson system appropriate for such magnetized quantum plasmas, the reductive perturbation technique is used to derive the planar integer KdV equation for weakly nonlinear ion-acoustic disturbances. Within this integer-order KdV framework, we recast the evolution equation as a planar dynamical system, construct the associated Hamiltonian and effective Sagdeev-like potential, and demonstrate the existence of compressive solitary waves and nonlinear periodic modes via homoclinic and periodic phase-space orbits. Exact multi-soliton solutions and interaction states are then obtained by combining Hirota’s direct bilinear method with generalized Wronskian representations, allowing us to describe not only standard one-, two-, and three-soliton profiles but also positon-negaton interactions relevant to magnetized, partially degenerate plasmas. To incorporate hereditary and history-dependent effects that arise from anomalous transport and nonlocal temporal response in dense environments, we extend the model by introducing a Caputo time-fractional derivative, thereby obtaining a time-fractional KdV (FKdV) equation that continuously connects the classical KdV limit to fractional dynamics. The FKdV equation is analyzed using the Tantawy technique. This semi-analytical iterative scheme yields rapidly convergent series approximations for the fractional ion-acoustic soliton and provides explicit control of the approximation error. The fractional solutions show that varying the order of the Caputo derivative modifies the amplitude, width, and temporal relaxation of the solitary structures and can even split the pulse into two distinct lobes, in contrast with the nearly rigid propagation predicted by the integer-order KdV equation. Taken together, these results clarify how Landau quantization, finite electron temperature, and fractional-order memory jointly shape the morphology, robustness, and interaction properties of ion-acoustic structures in strongly magnetized quantum plasmas of astrophysical and high-energy-density laboratory interest. Full article

Soil compaction is a widespread yet insufficiently monitored form of agricultural land degradation, affecting approximately 25% of global soils and nearly 33% of European subsoils, with consequential reductions in soil physical functionality, crop performance, and long-term sustainability; however, approaches for national-scale compaction risk mapping remain limited. A geospatial decision support framework was developed to quantify and map susceptibility to compaction risk across Italy by integrating Earth observation products with multi-criteria decision analysis within a GIS-based Analytic Hierarchy Process. The model combined four indicators: (i) Soil Moisture Index derived from Sentinel 1 C band SAR time series (2018 to 2024), (ii) the Sentinel 2 Normalized Difference Tillage Index, (iii) clay fraction from SoilGrids 2.0, and (iv) an Intensity of Agricultural Practice Index derived from national census statistics. The approach was applied to 74,156 km² of bare soil surfaces across all 20 regions to generate 100 m seasonal and multi-year mean risk maps. Extreme risk (high plus very high) exhibited a bimodal seasonal behavior, occupying 53.6% in winter and 55.5% in autumn, while declining to 24.8% in spring and 26.5% in summer; Southern Italy showed the largest seasonal amplitude (40.7%), and Friuli Venezia Giulia persisted as a hotspot exceeding 50% in all seasons. Comparison with the independent bulk density observations yielded 31.24% accuracy, largely constrained by the temporal mismatch between dynamic processes and static reference data, which represents a constraint of this research. The framework provides an initial screening tool for mapping susceptibility to soil compaction aligned with the EU Soil Strategy 2023 to 2030, supporting targeted interventions by prioritizing spring (March to May) as a low-risk remediation window; however, local conditions must be checked because cultivated crop types are highly diverse, and cropping cycles vary significantly from one species to another. Full article

Reliable short-term wind power forecasting is crucial for smart grid stability. However, high-dimensional noise and stochastic fluctuations in wind sequences often degrade the accuracy of traditional forecasting models. Moreover, wind power time series typically exhibit asymmetric rising and decaying patterns, which further complicate accurate modeling. To address these challenges, this study proposes a hybrid intelligent system that integrates three components: data preprocessing, heterogeneous ensemble learning, and probabilistic interval forecasting. First, we build a multi-stage preprocessing workflow. Adaptive DBSCAN and Local Outlier Factor (LOF) remove spatial and density anomalies. Then multivariate variational mode decomposition (MVMD) synchronously separates multi-scale oscillatory patterns while preserving cross-channel correlations and frequency-domain symmetry across input variables. SHAP analysis quantifies feature importance, ensuring interpretability. The selected features are fed into a heterogeneous ensemble model consisting of Transformer, BPNN, ELM, XGBoost, and QRLSTM, which collectively capture multi-scale temporal dependencies and diverse data patterns. The ensemble weights are dynamically optimized by a modified multi-objective dragonfly algorithm (MMODA) that balances forecast accuracy and stability. Based on this ensemble, we apply MMODA to tune kernel density estimation for generating high-quality forecast intervals, maximizing coverage while minimizing interval width. Experiments on two wind farms in Shandong show that our MMODA-optimized ensemble reduces mean absolute percentage error by about 44.7% compared to single models, and ablations confirm that MVMD preprocessing adds a further 10.7% reduction. The proposed system provides an interpretable and reliable decision-support tool for sustainable grid operations. Full article

With the advancement of carbon peaking and carbon neutrality goals, the increasing penetration of renewable energy sources such as wind and photovoltaic power poses severe challenges to the power supply reliability of AC/DC hybrid distribution networks due to their fluctuating, intermittent, and stochastic outputs. This paper proposes a reliability assessment method for AC/DC hybrid distribution networks under large-scale renewable energy integration based on clustering of typical operating scenarios. The net load duration curve is adopted as the feature variable to characterize typical operating scenarios. An improved t-distributed Stochastic Neighbor Embedding (t-SNE) nonlinear dimensionality reduction method with Kullback–Leibler (KL) divergence elbow correction is proposed for effective reduction of high-dimensional time-series data. An adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) parameter optimization method based on the k-nearest-neighbor curve and a secondary K-means clustering method based on entropy-weighted multi-objective optimization are further developed, forming a hybrid t-SNE-DBSCAN–K-means clustering algorithm. The power supply reliability is then assessed based on the clustered typical operating scenarios. A typical AC/DC hybrid distribution network is used as the test system. Results show that the DB index of the proposed clustering method improves by at least 22% compared with conventional methods, the maximum relative error between the typical-day-based and full time-series simulation results is less than 6%, and the computational efficiency improves by about 8.8 times, achieving a good balance between accuracy and efficiency. Full article

This study presents an AI-assisted long-distance fiber Bragg grating (FBG)-based sensing approach for simultaneous temperature and vibration measurement using a single bare FBG sensor. To address the strong coupling between temperature- and vibration-induced effects in the wavelength time series, a signal processing framework based on adaptive variational mode decomposition (AVMD) is developed. With power-spectral-density-guided parameter selection, the mixed wavelength signal is separated into a low-frequency temperature-related component and a high-frequency vibration-related component, enabling stable temperature–vibration decoupling within a single-sensor architecture. Experiments conducted with a 10 km fiber link between the sensor and the interrogator demonstrate that the proposed method can stably track the dominant vibration frequency under various temperature and vibration conditions, while the reconstructed low-frequency component remains consistent with the thermal evolution trend even in the presence of vibration. Random vibration tests and low-frequency vibration resolution analysis further confirm the stability and practicality of the proposed approach under long-distance fiber transmission conditions. In addition, an AI-assisted condition-monitoring scheme is demonstrated using a one-dimensional convolutional autoencoder trained solely with normal wavelength time-series data. Rather than relying on raw reconstruction error alone, the diagnostic layer derives a latent transition score from encoder bottleneck features through temporal pooling, L2 normalization, cosine-distance evaluation, smoothing, and baseline removal. Deviations from steady operating conditions can thereby be preliminarily indicated, highlighting the potential for integrating physics-driven signal processing with data-driven artificial intelligence in long-distance fiber sensing systems. Full article

In ubiquitous Wi-Fi sensing, human motion interval segmentation is crucial for applications ranging from basic intrusion detection to advanced activity understanding. Existing methods often treat the Channel State Information (CSI) primarily as time series, overlooking its rich information in the spatial and frequency domains. To address this, we propose a training-free motion segmentation method that exploits the spatiotemporal features of CSI. We first analyze the discriminative spatial distributions of the CSI Ratio on the complex plane and construct a spatiotemporally dual-constrained local density estimator to characterize motion-induced perturbations. To overcome subcarrier selection challenges, we introduce a packet-level asymmetric truncation-based fusion algorithm, which yields a feature representation with a pronounced bimodal histogram. This enables the automatic determination of the optimal segmentation threshold based on the distribution characteristics of the truncated density image. Experiments in typical indoor environments demonstrate that the proposed method achieves high accuracy in both motion event detection and interval localization. Full article

Urban blue-green spaces play an important role in mitigating thermal environmental stress, yet their long-term configuration and relative thermal environmental benefits remain insufficiently understood at the metropolitan scale. This study examined Beijing from 2000 to 2020 by integrating Landsat time-series imagery, land-cover data, landscape metrics, land surface temperature retrieval, Geodetector analysis, and a configuration-oriented Blue-Green Environmental Benefit Index (BGEBI). The results showed that Beijing’s blue-green spaces experienced three stages: rapid decline during 2000–2003, gradual recovery during 2004–2012, and rapid expansion during 2013–2020. Spatially, low-temperature zones were mainly concentrated in the northwestern ecological conservation areas, whereas high-temperature zones were mainly distributed in the southeastern core and plain areas. Green-space landscape indicators, especially forest-related metrics, showed stronger explanatory associations with LST spatial heterogeneity than most wetland-related indicators at the metropolitan scale. The BGEBI results indicated an overall increase in relative thermal environmental benefits from 2000 to 2020, with high-value areas mainly located in the northwestern and central-western mountainous regions and low-value areas mainly distributed in southeastern urbanized areas. These findings suggest that blue-green space planning in high-density megacities should pay greater attention to landscape configuration, spatial connectivity, and scale-sensitive management. The proposed BGEBI framework provides a relative spatial-prioritization tool for identifying areas where blue-green configuration optimization may support thermal-environment improvement. Full article

In the classical Stochastic Subspace Identification (SSI) method, mode selection is primarily based on frequency stability, damping stability, and mode shape similarity using the Modal Assurance Criterion (MAC). However, these criteria are often insufficient for reliable modal identification in high-noise environments. This study advances beyond the classical approach by introducing a multi-criteria optimization framework for mode evaluation. In addition to the conventional frequency and damping assessments utilized in the classical SSI method, the proposed approach incorporates a range of supplementary structural metrics. These include Density, Cosine Similarity Difference (CSD), Damping Stability (DS), Spatial Roughness (SR), Mode Shape Complexity (MSC), Signal Energy Coherence (SEC), and Normalized Modal Difference (NMD). These metrics are computed within specifically optimized windows on the stabilization diagram. By integrating spatial, phase, and energy-based characteristics of mode shapes alongside traditional metrics such as the MAC, the method enables a more comprehensive and robust mode selection process that surpasses the limitations of relying solely on frequency and damping stability. Compared to the classical SSI, the optimized window approach provides a significant advantage by enabling the reliable selection of consistent modes by considering the continuity and multi-criteria coherence of modes across window transitions. As a result, the elimination of noise modes and the reliable separation of structural modes are established on a more systematic basis. To achieve this, a two-stage optimization strategy is implemented: the first stage determines the optimal frequency window width and minimum mode count threshold, while the second stage utilizes a Multi-Criteria Decision Making (MCDM) framework based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to assign optimized weights to the structural metrics and rank the candidate windows accordingly. As a result, the ideal frequency window is identified based on its TOPSIS score and subsequently validated using the MAC, confirming that the selected window corresponds to reliable structural modes. The framework is validated using long-term in situ measurements from a Roller Compacted Concrete (RCC) dam operating under significant environmental and operational noise. The dataset comprises continuous, high-resolution (200 Hz) vibration recordings collected between 1 July 2023 and 30 October 2024. While the calendar duration is limited to several weeks, the uninterrupted 24 h measurements yield a high-density time-series dataset with substantial information content, enabling a statistically meaningful and robust evaluation of modal identification performance under real-world and noisy conditions. The results reveal that relying solely on traditional selection criteria such as pole density and the MAC can often lead to the identification of spurious modes, particularly in noisy environments. In contrast, the proposed TOPSIS-based multi-criteria decision-making framework incorporates a broader range of structural indicators, balancing frequency, damping, spatial, and energy-related metrics to enhance the consistency and reliability of mode selection. This approach proved effective even under high-noise conditions, successfully distinguishing true structural modes from artificial ones. Application of the TOPSIS method to RCC dam data revealed consistent fundamental frequencies at approximately 5–10 Hz, 10 Hz, and 15 Hz, confirming its robustness and suitability for complex structural monitoring tasks. Full article

Background: Hospital-acquired multidrug-resistant organism (HA-MDRO) infections remain a major patient-safety threat linked to antimicrobial exposure, but long-term hospital-level evidence on whether integrated stewardship can reduce HA-MDRO burden remains limited. Methods: We conducted a quasi-experimental interrupted time-series study at a large multi-campus tertiary teaching hospital in China. A hospital-wide integrated intervention combining diagnostic stewardship and antimicrobial prescribing stewardship was implemented on 1 November 2021. Monthly aggregated hospital data from July 2018 to December 2024, including 2,145,489 hospitalizations, were analyzed. The primary outcome was HA-MDRO infection incidence density per 1000 patient-days. Results: HA-MDRO incidence density decreased immediately at the start of the COVID period (IRR = 0.246; p < 0.001) and then increased over time (IRR per month = 1.074; p < 0.001). After intervention implementation, the post-intervention trend declined significantly relative to the COVID-period trajectory (IRR per month = 0.938; p < 0.001). Microbiological testing increased immediately and continued to rise (OR = 1.381 and 1.016 per month, respectively), whereas restricted antibiotic use declined after implementation (OR = 0.979 per month; all p < 0.05). The control outcome showed no consistent post-intervention change. Counterfactual analysis estimated that 15,274 HA-MDRO cases were averted over follow-up. Conclusions: A hospital-wide integrated stewardship intervention was associated with reversal of the increasing HA-MDRO trajectory observed during the COVID period, together with improved microbiological testing and reduced restricted antibiotic use. These findings support the value of integrating diagnostic and prescribing stewardship in high-volume tertiary hospital settings. Full article

Despite the well-known strong influence of spatial resolution on the quality of burned area mapping and the need for timely environmental information, global wildfire monitoring services are commonly based on coarse spatial resolution (300–500 m) reflectance imagery and deliver products months or years after the present date. The paper presents, for the first time, an algorithm that provides highly accurate near-real-time medium spatial resolution burned area, from 20 m Sentinel-2 imagery. The paper exploits a pioneering sensor-independent potential of a mapping method, based on land surface reflectance modelling and machine learning, originally optimised for Sentinel-3 imagery. The mapping method uses predictions of time series of burned area from a neural network, which are combined with the spatio-temporal density of active fire detections. The mapping method was calibrated and validated using reference datasets for the years 2020 and 2019, respectively. The novelty of this method lies in its high accuracy and multi-latency flexibility: it achieves a Dice coefficient (DC) of 82.7% with zero-day latency, already surpassing the 81.8% accuracy of current state-of-the-art non-time critical methods. As reflectance data availability increases, accuracy scales to DC 84.7% and 85.4% with 5 and 10 days of latency, respectively, and to DC 87.2% for monthly composites with 45 days of latency. Full article

This study aims to evaluate the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) based on signal-to-noise ratio (SNR) analysis for monitoring crop structure and moisture. Data were collected using a GNSS antenna placed within an experimental meadow located in NW Italy. GNSS-IR exploits the interference between direct and ground-reflected signals to derive physical parameters such as the vegetation phase center height and soil moisture. In this work, by analyzing and modeling the oscillations in SNR time series, the sensitivity to crop growth dynamics was assessed. Vegetation height and dielectric parameters were compared against corresponding ground-surveyed values collected using a ruler and buried soil moisture sensors. Results suggest that GNSS-IR can detect canopy height with a high degree of consistency (Pearson’s r = 0.89, MAPE = 18%). Results also show that changes in the amplitude and phase of the interference pattern are sensitive to biomass density and dielectric properties of the reflecting surface (r = −0.81 and r = 0.86 respectively). GNSS-IR observables were analyzed across four representative measurement campaigns capturing distinct seasonal stages of meadow development. Despite the limited temporal sampling (n = 4), the selected observations correspond to contrasting vegetation and soil moisture conditions, allowing the identification of systematic variations in crop biophysical properties. These findings open promising perspectives for the development of innovative monitoring strategies in precision agriculture, leveraging existing GNSS infrastructure to obtain key biophysical parameters with minimal additional equipment and operational complexity. Full article