Search Results (6,906)

In a conventional elastic beam with steady boundary stiffness, vibrational energy tends to concentrate at specific modal frequencies, often resulting in significant resonance phenomena. To address this issue, a novel control strategy is proposed in this study, in which the stiffness of boundary springs is dynamically modulated to alter the resonance characteristics of the beam. The Newmark–Beta method is employed to compute the transient response of the beam with time-varying stiffness in the time domain. A series of numerical simulations is conducted to analyze the vibration behavior of the structure under single-model frequency, multimodal frequency, narrowband, and broadband random excitations. The results indicate that time-varying stiffness effectively redistributes energy from resonance frequencies to other frequency bands, thereby suppressing resonance peaks and reducing displacement amplitudes. Furthermore, parametric analysis reveals that increasing the range of stiffness variation enhances spectral dispersion and improves vibration attenuation performance, and increasing the average stiffness level helps improve energy dispersion; however, it may lead to a slight increase in vibration response at low frequencies. Full article

This article comprehensively reviews the tribological behavior of a Ti-6Al-4V alloy manufactured via electron beam powder bed fusion (EB-PBF), an additive manufacturing process for aerospace and biomedical applications. EB-PBF Ti-6Al-4V demonstrates wear resistance that is superior or comparable to conventional Ti-6Al-4V. The reported average friction coefficient ranges between ~0.22 and ~0.75 during sliding wear in dry and lubricated conditions against metallic and ceramic counterparts when loading 1–50 N under varied surface and heat treatment conditions, and between 1.29 and 2.2 during fretting wear against EB-PBF Ti-6Al-4V itself. The corresponding average specific wear rates show a broad range between ~8.20 × 10⁻⁵ mm³/Nm and ~1.30 × 10⁻³ mm³/Nm during sliding wear. Lubrication reduces the wear rates and/or the friction coefficient. Wear resistance can be improved via machining and heat treatment. Wear anisotropy is reported and primarily attributed to microhardness variations, which can be mitigated through lubrication and post-processing. The effects of applied load and frequency on EB-PBF Ti-6Al-4V are also discussed. The wear resistance at elevated temperatures shows a mixed trend that depends on the counterpart material and the testing methods. Wear mechanisms involve oxide tribo-layer formation, abrasive wear, and adhesive wear. Current limitations, future research directions, and a standardization framework are also discussed. Full article

Electrochemical Impedance Spectroscopy (EIS) measurements are highly sensitive to the fixturing, temperature, and state of charge (SoC) of batteries. For 10 kWh automotive battery modules, we show that variations in SoC and temperature introduce significant errors at low-to-medium frequencies (<100 Hz), while improper fixture wiring affects mainly higher-frequency accuracy, with errors up to 100% in the imaginary part at 1 kHz. In addition, we study repeatability across various tester-module configurations. EIS results remain highly consistent (±100 µΩ) across three different modules. Comparing the same module across two different testers, deviations are even lower (±30 µΩ up to 1 kHz). The EIS evolution is studied with respect to the cycle numbers, where a strong correlation of low-frequency impedance features is demonstrated. A new combined quotient feature is introduced and suggested as a reliable and efficient state of health (SoH) indicator, solely based on a model-free and phenomenological approach. The study demonstrates the potential of EIS as a powerful tool for battery module characterization, provided that its requirements and limitations are carefully addressed through well-defined experimental setups. Accurate and repeatable EIS measurements are particularly important for obtaining accurate electrochemical insights, especially in the low-to-mid frequency domain, where impedance variations are most sensitive to battery states and ageing effects. Full article

Soil organic carbon (SOC) is a critical component of soil health, influencing soil structure, soil water retention capacity, and nutrient cycling while playing a key role in the global carbon cycle. Accurate SOC estimation over croplands is essential for sustainable land management and climate change mitigation. This study explores a novel approach to SOC estimation using multi-frequency synthetic aperture radar (SAR) data, specifically Sentinel-1 and ALOS-2/PALSAR-2 imagery, combined with advanced machine learning techniques for cropland SOC estimation. Diverse agricultural practices, with major crop types such as rice (Oryza sativa), finger millet (Eleusine coracana), Niger (Guizotia abyssinica), maize (Zea mays), and vegetable cultivation, characterize the study region. By integrating C-band (Sentinel-1) and L-band (ALOS-2/PALSAR-2) SAR data with key polarimetric features such as the C2 matrix, entropy, and degree of polarization, this study enhances SOC estimation. These parameters help distinguish variations in soil moisture, texture, and mineral composition, reducing their confounding effects on SOC estimation. An ensemble model incorporating Random Forest (RF) and neural networks (NNs) was developed to capture the complex relationships between SAR data and SOC. The NN component effectively models complex non-linear relationships, while the RF model helps prevent overfitting. The proposed model achieved a correlation coefficient (r) of 0.64 and a root mean square error (RMSE) of 0.18, demonstrating its predictive capability. In summary, our results offer an efficient approach for enhanced SOC mapping in diverse agricultural landscapes, with ongoing work targeting challenges in data availability to facilitate large-scale SOC mapping. Full article

The widespread integration of renewable energy sources (RESs) into the grid through inertia-less power converters is reducing the overall system inertia leading to large frequency variations. To mitigate this issue, grid-forming (GFM) control strategies in bidirectional battery chargers have emerged as a promising solution, since the inertial response of synchronous generators (SGs) can be emulated by power converters. However, unlike SGs, which can withstand currents above their rated values, the output current of a power converter is limited to its nominal design value. Therefore, the estimation of the power delivered by the GFM power converter during frequency events, called Virtual Inertia (VI) support, is essential to prevent exceeding the rated current. This article analyzes the VI response of GFM power converters, classifying the dynamic behavior as underdamped, critically damped, or overdamped according to the selected inertia constant and damping coefficient, parameters of the GFM control strategy. Subsequently, the transient power response under step-shaped and ramp-shaped frequency deviations is quantified. The proposed analysis is validated using a 1.2 KW single-phase power converter. The simulation and experimental results confirm that the overdamped response under a ramp-shaped frequency event shows higher fidelity to the theorical model. Full article

For urban rail vehicles operating at speeds ranging from 60 to 250 km/h, the dominant source of radiated noise is the wheel–rail interaction. Finite element modal analysis was conducted on the wheelset, rails, and track slab. A multibody dynamics model under straight-line condition was established. It was a rigid–flexible coupling dynamics model, including the rigid vehicle body, flexible wheelsets, flexible rails, and flexible track slabs. Dynamic simulation calculations were carried out in this model to obtain the wheel–rail forces. The finite element and boundary element models of wheels and rails were established using simulation software to obtain the results of wheel–rail noise. The sound pressure levels on the surfaces of wheels and rails were calculated under the operating conditions of 120 km/h, 140 km/h, 160 km/h, and 200 km/h in the straight-line condition. The variation law of the frequency distribution of wheel–rail noise with the change in speed was obtained. The variation fitting function of wheel–rail noise SPL with speeds was obtained. Within the speed of 200 km/h, as the speed increased, the total value of wheel–rail SPL basically shows a linear growth. The simulation analysis results were compared with the experiment results. It indicated that the simulation results were reasonable. The simulation models are of great significance for the noise prediction in train design and manufacturing. Full article

Wavelength separation coatings can effectively separate the fundamental frequency (1ω) and third harmonic (3ω) laser beams. However, the laser-induced damage threshold (LIDT) of the surface defect-free WS coatings for the 3ω laser is 1.68 J/cm² (obtained in the preliminary experiment), significantly lower than the ideal LIDT of the fused silica substrate (80 J/cm²). This is directly correlated with extrinsic defects such as nanoscale defects and nodular defects introduced during the coating manufacturing process. Moreover, the damage in WS coatings caused by extrinsic defects is a complex physical process involving multiple physical phenomena such as material melting, vaporization, and ejection. The mechanism by which extrinsic defects interact with lasers to form damage is not yet fully elucidated. To address this, a multi-physics coupling model considering photoelectric, thermal and stress was established to simulate the incident laser propagation within coatings, the temperature distribution and thermal stress distribution of the coating material. This model systematically investigates the influence of defect location, type, and size on the laser-induced damage process. It is found that when a 10 nm-diameter defect is located at the 32nd layer of the coatings, the light intensity enhancement factor (LIEF) for 3ω laser can reach up to 5 times that for the 1ω laser. The variation in thermal stress induced by changes in defect size is jointly determined by the defect-induced modulation effect and the interference effect realized by the coating. This work theoretically reveals the mechanism of extrinsic defects in the laser damage. It provides effective guidance for establishing control standards for extrinsic defects during the optical coating process. Full article

Background: Type 1 diabetes mellitus (T1DM) is one of the most frequently occurring chronic metabolic conditions in the pediatric and adolescent population. That is why our aim in this study was to compare metabolic control, eating habits, activity, and mental health in patients using insulin pumps with predictive low glucose suspend (PLGS) and advanced hybrid closed loop (AHCL) systems. Methods: We selected 37 patients and collected clinical, continuous glucose monitoring (CGM), and question-naire data (food frequency questionnaire (FFQ-6), physical activity questionnaire for children (PAQ-C), pediatric quality of life inventory (PedsQL). Additionally, all pa-tients participated in culinary workshops, which included education on a low-glycemic-index diet. Results: We observed a significant difference between the PLGS and the AHCL groups for mean glucose, coefficient of variation, and Time in Range (≤54, 70–140, 70–180, ≥180, and ≥250 mg/dL). Patients with higher Time Below Range consumed juices or sugary drinks more frequently. All participants had incor-rect eating habits and engaged in irregular physical activity. Conclusions: We observed no significant differences in the diabetes-specific quality of life scores between the PLGS and AHCL groups. Full article

The extraction of geothermal energy is of great significance for sustainable energy development. The destruction of hard rock masses during geothermal well exploitation generates seismic waves that can compromise wellbore stability and operational sustainability. Seismic waves are known to be affected by rock structures like cracks and interfaces. However, a quantitative understanding of these effects on wave parameters is still lacking. This study addresses this gap by experimentally investigating the effect of crack geometry (angle and width) and rock interfaces on seismic wave propagation. Using a synchronous system for rock loading and seismic wave acquisition, we analyzed wave propagation through carbonate rock samples with pre-defined cracks and interfaces under unconfined, dry laboratory conditions. Key wave parameters (amplitude, frequency, and energy) were extracted using the fast Fourier transform (FFT) and the Hilbert–Huang transform (HHT). Our primary findings show the following: (1) Increasing the crack angle from 35° to 75° and the width from 1 mm to 3 mm leads to significant attenuation, reducing peak amplitude by up to 94.0% and energy by over 99.8%. (2) A tightly pressed rock interface also causes severe attenuation (94.2% in amplitude and 99.9% in energy) but can increase the main frequency by up to 8.5%, a phenomenon attributed to a “boundary effect”. (3) Seismic wave parameters exhibit significant spatial variations depending on the propagation path relative to the source and rock structures. This study provides a fundamental, quantitative baseline for how rock structures govern seismic wave attenuation and parameter shifts, which is crucial to improving microseismic monitoring and wellbore integrity assessment in geothermal engineering. Full article

Background: To prospectively evaluate the safety and clinical impact of SARS-CoV-2 vaccines in patients with systemic lupus erythematosus (SLE). Methods: Subanalysis of the Brazilian multicenter observational study “Safety, Effectiveness and Duration of Immunity after Vaccination against SARS-CoV-2 in Patients with Immune-Mediated Inflammatory Diseases (SAFER)”, which included SLE patients vaccinated with CoronaVac, ChAdOx1, or BNT162b2. Patients with HIV infection, pregnant women, or those with immunosuppression not related to SLE were excluded. Safety data related to adverse events and underlying disease activity were assessed. Additionally, COVID-19 cases were monitored throughout the follow-up period. Results: The study included 373 patients with systemic lupus erythematosus (SLE), with a mean age of 36 years, the majority being women (89.8%). The most common adverse events after SARS-CoV-2 vaccination were injection site reactions and headache, observed both after the first and subsequent doses. The ChAdOx-1 vaccine was associated with a higher frequency of adverse events compared to CoronaVac. At baseline, 38.3% of patients were in remission, 32.8% had low disease activity, and 28.9% had moderate to high activity. Following CoronaVac vaccination, there was an increase in remission rates (from 34.6% to 51.1%) and a significant reduction in moderate to high activity (from 37.6% to 15.0%) after the first dose, with this reduction partially maintained after the second dose. In contrast, patients vaccinated with ChAdOx-1 showed an increase in moderate to high activity (from 14.5% to 38.2% after the first dose), a trend that persisted after the second dose. No statistically significant changes in disease activity were observed among those who received BNT162b2. During follow-up, 44 cases of COVID-19 were reported, all mild, with no deaths or need for intensive care unit admission. Conclusions: Vaccination against SARS-CoV-2 demonstrated a favorable safety profile in patients with SLE, with a low frequency of serious adverse events. While analysis of disease activity revealed variations across vaccine platforms, most notably an increased proportion of moderate to high disease activity among those receiving ChAdOx-1 compared with CoronaVac and BNT162b2, the overall occurrence of COVID-19 during follow-up was limited to mild cases, with no severe outcomes. These findings highlight that, despite potential risks of disease exacerbation, the clear protection against severe COVID-19 supports vaccination as a beneficial strategy for this immunocompromised population. Full article

Transformer load is a key factor influencing its aging and service life. Accurately predicting load trends is crucial for assisting load redistribution. This study proposes a hybrid model called RIME-VMD-TCN-iTransformer to forecast the trend of transformer load. In this model, RIME (Randomized Improved Marine Predators Algorithm) is employed to enhance decomposition stability, VMD (Variational Mode Decomposition) is used to address the non-stationary characteristics of the load sequence, TCN (Temporal Convolutional Network) extracts local temporal dependencies, and iTransformer (Inverted Transformer) captures global inter-variable correlations. First, the variational mode decomposition algorithm is applied to mitigate the non-stationary characteristics of the signal, followed by the RIME to further enhance the orderliness of the intrinsic mode functions. Subsequently, the TCN-iTransformer model is utilized to predict each intrinsic mode function individually, and the prediction results of all intrinsic mode functions are reconstructed to obtain the final forecast. The findings indicate that the intrinsic mode functions obtained through RIME-VMD exhibit no spectral aliasing and can decompose abrupt time-series signals into stable and regular frequency components. Compared to other hybrid models, the proposed model demonstrates superior responsiveness to changes in time-series trends and achieves the lowest prediction error across various transformer capacity scenarios. These results highlight the model’s superior accuracy and generalization capability in handling abrupt signals, underscoring its potential for preventing unexpected transformer events. Full article

Mixed eccentricity faults in squirrel cage induction motors (SCIMs) are challenging to diagnose due to their subtle influence on the stator-current signal. Several research gaps remain in this field, including the limited investigation of fault severity levels and the scarcity of studies addressing fault detection under full-load conditions. Motivated by these gaps, this study proposes a diagnostic approach based on the variational mode decomposition (VMD) of the stator current. This paper proposes a diagnostic approach based on VMD of the stator current. The current signal is decomposed into intrinsic mode components, which are further separated into approximated and detailed signals. By focusing on the detailed signals and removing the fundamental frequency, the proposed algorithm highlights the spectral components associated with the mixed eccentricity. Experimental validation was carried out on a 1.5 kW SCIM connected directly to the power grid and tested under three loading levels (12.5%, 50%, and 100% of the rated load). In all nine experimental scenarios, the method successfully distinguished the healthy motor from faulty conditions with 20% and 30% mixed eccentricity severities. These results demonstrate that the proposed VMD-based method provides a reliable and quantitative tool for rotor fault diagnosis under varying load conditions. Full article

Francis turbines are widely used due to their large capacity and broad head adaptability, placing higher demands on the internal flow characteristics and runner performance of the units. In this paper, numerical simulations of a Francis turbine model were conducted using ANSYS CFX 2022 R1. The SST turbulence model, ZGB cavitation model, and VOF multiphase flow model were selected for the calculations. The internal flow characteristics and pressure pulsations in the runner and draft tube under different operating conditions were analyzed, and the variations in normal and tangential forces acting on the runner blades during operation were investigated. The results indicate significant differences in the internal flow within the runner and draft tube under various guide vane opening conditions. The pressure pulsation in the unit is influenced by both the internal flow in the draft tube and the rotation of the runner. The mechanical load on the runner blades is affected by multiple factors, including the wake from upstream fixed guide vanes, rotor–stator interaction, and downstream vortex ropes. Under low-flow conditions, the variation in forces acting on the runner blades is relatively small, whereas under high-flow conditions, the runner blades are prone to abrupt force fluctuations at 0.6–0.8 times the rotational frequency. This is manifested as periodic abrupt force changes in both the X and Y directions of the runner blades under high-flow conditions. The normal force in the Z-direction of the runner blades increases instantaneously and then decreases immediately, while the tangential force decreases instantaneously and then increases promptly. Full article

The Qinling Mountains, situated in the climatic transition zone between northern and southern China, represent a critical region for climate and ecological studies due to their unique transitional characteristics and the rising frequency of extreme climate events. As net primary productivity (NPP) is a key indicator of ecosystem stability, clarifying its response to extreme climate events is essential for understanding ecological resilience in this region. In this study, daily observational data from 123 meteorological stations (1960–2023) were used to derive eight extreme temperature and precipitation indices. Combined with MODIS NPP data (2001–2023), we applied Theil–Sen slope estimation, Mann–Kendall significance testing, ridge regression, Pearson correlation analysis, and Moran’s I spatial autocorrelation to systematically investigate the spatiotemporal dynamics and driving mechanisms of NPP. The main findings are as follows: (1) From 2001 to 2023, the mean annual NPP in the Qinling region was 558.43 ± 134.27 gC·m⁻²·year⁻¹, showing a significant increasing trend of 5.44 gC·m⁻²·year⁻¹ (p < 0.05). (2) Extreme temperature indices exhibited significant changes, whereas among the precipitation indices, only the number of days with daily precipitation ≥ 20 mm (R20) showed a significant trend, suggesting that extreme temperatures exert a stronger influence in the region. (3) Correlation analysis indicated that temperature-related indices were generally positively correlated, precipitation-related indices displayed even stronger associations, and covariation existed among extreme precipitation events of varying intensities. Moreover, precipitation indices demonstrated relatively stable spatial distributions, while temperature indices fluctuated considerably. (4) Absolute contribution analysis further revealed that the number of days with daily minimum temperature below the 10th percentile (TN10p) contributed up to 3.53 gC·m⁻²·year⁻¹ to annual NPP variation in the Henan subregion, whereas maximum rainfall over five consecutive days (Rx5day) exerted an overall negative effect on NPP (−0.77 gC·m⁻²·year⁻¹). By integrating long-term meteorological observations with remote sensing products, this study quantitatively evaluates the differential impacts of extreme climate events on vegetation within a climatic transition zone, offering important implications for ecological conservation and adaptive management in the Qinling Mountains. Full article

The segmentation and classification of color are crucial stages in image processing, computer vision, and pattern recognition, as they significantly impact the results. The diverse, hand-labeled datasets in the literature are applied for monochromatic or color segmentation in specific domains. On the other hand, synthetic datasets are generated using statistics, artificial intelligence algorithms, or generative artificial intelligence (AI). This last one includes Large Language Models (LLMs), Generative Adversarial Neural Networks (GANs), and Variational Autoencoders (VAEs), among others. In this work, we propose VitralColor-12, a synthetic dataset for color classification and segmentation, comprising twelve colors: black, blue, brown, cyan, gray, green, orange, pink, purple, red, white, and yellow. VitralColor-12 addresses the limitations of color segmentation and classification datasets by leveraging the capabilities of LLMs, including adaptability, variability, copyright-free content, and lower-cost data—properties that are desirable in image datasets. VitralColor-12 includes pixel-level classification and segmentation maps. This makes the dataset broadly applicable and highly variable for a range of computer vision applications. VitralColor-12 utilizes GPT-5 and DALL·E 3 for generating stained-glass images. These images simplify the annotation process, since stained-glass images have isolated colors with distinct boundaries within the steel structure, which provide easy regions to label with a single color per region. Once we obtain the images, we use at least one hand-labeled centroid per color to automatically cluster all pixels based on Euclidean distance and morphological operations, including erosion and dilation. This process enables us to automatically label a classification dataset and generate segmentation maps. Our dataset comprises 910 images, organized into 70 generated images and 12 pixel segmentation maps—one for each color—which include 9,509,524 labeled pixels, 1,794,758 of which are unique. These annotated pixels are represented by RGB, HSL, CIELAB, and YCbCr values, enabling a detailed color analysis. Moreover, VitralColor-12 offers features that address gaps in public resources such as violin diagrams with the frequency of colors across images, histograms of channels per color, 3D color maps, descriptive statistics, and standardized metrics, such as ΔE76, ΔE94, and CIELAB Chromacity, which prove the distribution, applicability, and realistic perceptual structures, including warm, neutral, and cold colors, as well as the high contrast between black and white colors, offering meaningful perceptual clusters, reinforcing its utility for color segmentation and classification. Full article