Search Results (976)

Under severe ice and snow weather, ice-covered pantograph–catenary arcs affect the safe operation of high-speed trains. This study investigates the impact of ice-covered arc electrical characteristics, plasma parameters, and material ablation mechanisms. By constructing a comprehensive pantograph–catenary icing experimental platform, arc voltage, current signals, high-speed dynamic images, and emission spectra were synchronously collected under different icing thicknesses ranging from 0 to 15 mm. Research indicates that ice coverture causes frequent “extinction–reignition” phenomena during the arc initiation stage due to the latent heat absorbed by melting ice, significantly reducing the initial stability of arc combustion. Spectral analysis confirms that the arc excitation temperature and energy density are positively correlated with the concentration of hydrogen ions produced by water vapor ionization, reaching a peak under the 5 mm icing condition. Experimental results show that the average energy density of ice-covered arcs is approximately double that of the non-iced condition, causing the ablation pits on the carbon strip to exhibit characteristics of greater depth and wider copper deposition zones. This study reveals the unique mechanisms and damage characteristics of icing pantograph–catenary arcs, providing an important basis for the safe design and maintenance of pantograph–catenary systems in high-cold railway environments. Full article

In X-ray imaging, tissue scattering is an important factor that degrades image clarity, especially using a portable gridless X-ray imaging device. This study focuses on using Monte Carlo simulation to quantify the effect of scatter radiation on image resolution, by analyzing the point spread function (PSF) and the corresponding modulation transfer function (MTF). Lateral energy absorption profiles in tissue and a cesium iodide (CsI) scintillator were calculated at different X-ray tube voltages (70–90 kV) and filter configurations. Results showed that 85.7% of the total scattered radiation is concentrated at a distance of 4 cm from the central axis for the tissue and 67.37% for the CsI scintillator. The MTF remained high at low spatial frequencies (23% at 0.04 cycles/cm) but dropped at mid frequencies (0.015–0.025 at 0.3–0.6 cycles/cm) and was almost zero at high frequencies (0.004 at 0.8 cycles/cm), indicating loss of detail due to scattering. Increasing the thickness of the filter or adding a copper (Cu) filter reduced the contrast at low spatial frequencies (from 23% to 21%). The study quantitatively investigated the MTF degradation in portable X-ray imaging devices without grid, due to scatter. These results may aid in the development of scatter correction algorithms to improve image quality without the need for an anti-scatter grid. Full article

Motivated by altitude-induced fluctuations in oxygen partial pressure (pO₂) and their impacts on PCS off-line arc motion and erosion response, this study proposes a comparative experimental approach featuring single-variable control under constant total pressure and coordinated multi-source electrical-signal observation. A reciprocating current-carrying arc-generation rig was established, in which pO₂ was equivalently regulated via a constant-pressure gas substitution and mixing approach. High-speed imaging–based quantitative vision analysis was integrated with synchronized voltage–current measurements to evaluate the net effects of five O₂ volumetric fraction levels (6, 11, 14, 17, and 21 vol%) under a DC supply of 120 V/25 A on arc dynamics, electrochemical processes, and contact pair erosion. Based on repeated-test results, the 14 vol% case exhibited the poorest stability (maximum fluctuation coefficient 20.306%), whereas the 17 vol% case showed the lowest current-carrying efficiency (minimum 56.070%) together with the most severe erosion damage. Moreover, with increasing pO₂, the erosion morphology evolved in a staged manner, transitioning from localized central ablation accompanied by melt-related traces to adhesive wear-induced delamination, and ultimately to electrochemical oxidative wear. Overall, pO₂ imposes a pronounced non-monotonic “window effect” on arc stability and erosion, providing key evidence for PCS structural optimization and risk assessment in open operating environments. Full article

Insulators are essential components in high-voltage transmission lines and require regular inspection to ensure reliable power delivery. Traditional manual inspection methods are inefficient and labor intensive, highlighting the need for intelligent and automated solutions. In this study, we propose a missing insulator detection method that integrates Unmanned Aerial Vehicle (UAV) imaging with deep learning techniques. Firstly, an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) is employed to detect and localize insulators in aerial images. Secondly, the localized insulators are segmented using an improved U-Net to reduce background interference. A bounding box regression approach is adopted to obtain the minimum enclosing rectangles, and the insulators are aligned vertically. Adaptive thresholding is then applied to extract binary images of the insulators. These binary images are further transformed into defect curves, from which missing insulators are identified based on curve distribution. To address the limited availability of labeled samples, a transfer learning-based strategy is adopted to improve model generalization. A dataset of glass insulators was collected using a DJI M300 UAV equipped with an H20T camera along a 330 kV overhead transmission line. On the collected UAV insulator dataset, the proposed method achieved an AP@0.5 of 99.85% and an average IoU of 88.56% for insulator string detection, while the improved U-Net achieved an mIoU of 89.73% for insulator string segmentation. Outdoor flight experiments further verified performance under varying backgrounds and illumination conditions in our UAV inspection scenarios. Full article

Background: Myricetin (MYR) is a natural flavonol with antioxidant, neuroprotective, anti-inflammatory, antidiabetic, and cardioprotective activities. Still, its pharmaceutical use is limited by very low aqueous solubility (~16.6 µg/mL) and poor oral bioavailability (<10%). This study aimed to enhance the solubility and potentially improve the bioavailability of MYR by developing an amorphous nanofibrous delivery system. Methods: Electrospinning was applied to fabricate MYR-loaded nanofibers using polyvinylpyrrolidone K30 (PVP30), and the influence of key processing parameters on MYR solubility was evaluated. Nanofibers produced under selected electrospinning conditions were characterized in terms of morphology, encapsulation efficiency, and physicochemical properties. Results: X-ray powder diffraction confirmed complete amorphization of MYR within the BB5 fiber structure (distance: 12 cm, voltage: 25 kV, flow rate: 1.5 mL/h). FTIR analysis indicated hydrogen-bonding interactions between MYR hydroxyl groups and PVP30 carbonyl groups, contributing to stabilization of the amorphous form. SEM images revealed homogeneous, defect-free fibers with diameters below 400 nm, although localized MYR agglomerates were observed. Solubility and release studies demonstrated a characteristic spring-and-parachute effect, enabling rapid MYR release and maintenance of a supersaturated state. Enhanced solubility resulted in significantly improved antioxidant activity in DPPH and CUPRAC assays compared with crystalline MYR. Conclusions: Electrospun PVP30 nanofibers represent a promising platform for improving the solubility, dissolution behavior, and functional activity of poorly soluble bioactive compounds such as myricetin, supporting their potential application in pharmaceutical formulations. Full article

Electrical impedance tomography (EIT) provides noninvasive, high-temporal-resolution imaging for medical and industrial applications. However, accurate image reconstruction remains challenging due to the severe ill-posedness and nonlinearity of the inverse problem, as well as the limited robustness of existing single-source learning-based methods in real measurement scenarios. To address these limitations, a data-constrained and physics-guided Multi-Source Conditional Diffusion Model (MS-CDM) is proposed for EIT image reconstruction. Unlike conventional conditional diffusion methods that rely on a single measurement or an image prior, MS-CDM utilizes boundary voltage measurements as data-driven constraints and incorporates coarse reconstructions as physics-guided structural priors. This multi-source conditioning strategy provides complementary guidance during the reverse diffusion process, enabling balanced recovery of fine boundary details and global topological consistency. To support this framework, a Hybrid Swin–Mamba Denoising U-Net is developed, combining hierarchical window-based self-attention for local spatial modeling with bidirectional state-space modeling for efficient global dependency capture. Extensive experiments on simulated datasets and three real EIT experimental platforms demonstrate that MS-CDM consistently outperforms state-of-the-art numerical, supervised, and diffusion-based methods in terms of reconstruction accuracy, structural consistency, and noise robustness. Moreover, the proposed model exhibits robust cross-system applicability without system-specific retraining under multi-protocol training, highlighting its practical applicability in diverse real-world EIT scenarios. Full article

This paper presents a process-controlled study of illumination engineering in single-photon avalanche diodes (SPADs) fabricated in a 110 nm standard CMOS image sensor (CIS) technology. Front-illuminated (FI) and back-illuminated (BI) SPADs were implemented with identical front-end-of-line (FEOL) structures, including the junction and guard-ring configurations, enabling the isolation of the effects of illumination direction and back-end-of-line (BEOL) configuration without modifying the junction structure. Through TCAD simulations and comprehensive experimental characterizations, including current–voltage, light-emission, dark count rate (DCR), photon detection probability (PDP), and timing-jitter measurements, we systematically analyze the performance trade-offs introduced by the BI configuration. The BI SPAD exhibits enhanced near-infrared PDP and a broader spectral response due to its deeper absorption region and the incorporation of a metal reflector, while maintaining identical avalanche characteristics, as evidenced by an unchanged 72 ps full-width-at-half-maximum (FWHM) timing jitter. However, the backside illumination increases the diffusion tail, indicating a trade-off between near-infrared sensitivity and diffusion-related timing performance. These results provide design guidelines for optimizing SPAD performance through illumination-direction and BEOL engineering while preserving the FEOL design and demonstrate a useful approach for SPAD integration in standard CMOS technology. Full article

Background: Cardiovascular disease (CVD) remains a major global health concern and the leading cause of mortality and disability. Early detection and prevention strategies rely heavily on evaluating coronary artery calcification, traditionally assessed using the coronary artery calcium score (CACS). However, CACS is limited by its dependence on strictly fixed tube voltage and slice thickness, sensitivity to changes in scanning parameters, and the need for an additional dedicated coronary calcium scan that increases radiation exposure. Methods: To address these challenges, we developed a novel two-compartment coronary artery calcium score system (TACS) for quantitative calcium assessment. TACS was established and validated using a QRM Thorax phantom scanned on a GE Revolution CT at 70–140 kVp. Volumetric calcium density (VCD) derived from TACS was compared with conventional CACS under varying slice thickness, pitch, and iterative reconstruction algorithms. Additionally, coronary artery calcium scans from 15 patients were retrospectively analyzed to assess correlations between TACS and CACS. Results: TACS demonstrated stable performance across tube voltages, with VCD errors ranging from 3.8% to −19.0% and maintained consistency under different slice thicknesses (23.9% to −2.3%) and reconstruction algorithms, showing near-zero residual percentages. Patient analyses revealed a strong correlation between TACS and CACS (r = 0.932). Conclusions: These findings suggest that TACS provides robust and reliable quantification of coronary calcium, supporting its potential use for opportunistic coronary artery disease screening, particularly in routine CT imaging. Further studies with larger cohorts are warranted to confirm its clinical applicability. Full article

Capacitive micromachined ultrasonic transducers (CMUTs) are a promising alternative to conventional piezoelectric transducers, offering superior design flexibility and broadband operational characteristics. However, their clinical and practical deployment is constrained by elevated driving voltages and limited acoustic power output, particularly when producing CMUTs based on polymers. This paper presents an end-to-end, measurement-driven experimental validation strategy for designing passive broadband impedance-matching networks that enhance transmitted acoustic power in immersed CMUT arrays while preserving bandwidth. Matching topologies are synthesized to operate near the theoretical Bode–Fano limit, and robustness to component tolerances is quantified through Monte Carlo yield analysis using realistic off-the-shelf component variations. The matching networks are then implemented and experimentally validated under representative unipolar pulse excitation, with far-field acoustic pressure characterized in both time and frequency domains and compared against numerical predictions. The results show that the optimized impedance matching increases transmit power by a factor of 2.1 at the cost of a 40% fractional-bandwidth reduction. These findings establish a directly applicable, validated framework for broadband impedance matching in polymer CMUT arrays and support its use as a cost-effective approach for ultrasound imaging and therapeutic systems. Full article

The influence of different voltage measurement and current injection configurations on the quality of image reconstruction in electrical impedance tomography (EIT) was investigated using numerical simulations. Adjacent and opposing techniques were systematically used to examine their effectiveness in voltage acquisition and current delivery. The simulation model employed 16 equally spaced electrodes arranged around a circular domain, with an injected alternating current of 1 mA at a frequency of 50 kHz. A circular object with a conductivity of 0.9 units was sequentially positioned at five distinct locations within the imaging domain, each spaced 0.05 units apart. The reconstructed images were analyzed for positional accuracy and contrast resolution. While each configuration offers specific advantages, they exhibit inherent limitations depending on the application. The results of this study enable the understanding of the trade-offs involved in selecting electrode drive and measurement strategies for optimizing image quality in EIT systems. Full article

We explore the differences between real and absolute values of signals in Electrical Impedance Tomography image reconstruction, with a focus on their impact on image quality and accuracy. Simulations were conducted using a finite element mesh model containing three inclusions with varying conductivity values. The inclusions representing regions with moderate, poor, and high conductivity were carefully chosen to create sharp contrasts in conductivity. In the experiment, 16 electrodes were placed around a circle, a current injection pattern was applied, and the resulting boundary voltages were recorded. The reconstruction based on absolute signal values, depicted in the center image, tended to smooth out sharp conductivity contrasts, leading to significant artifacts and reduced accuracy in localizing the inclusions. In contrast, the reconstruction based on real signal values provided an accurate representation of the true conductivity distribution, improving the localization of the inclusions. The results underscore the critical role of considering the real component of the signal in electrical impedance tomography image reconstruction to achieve improved accuracy and higher fidelity in the resulting images. Full article

Intracortical microstimulation (ICMS) is a promising approach for visual prostheses. We recently proposed using retinal neuromorphic spike trains derived from visual images as ICMS pulse sequences, and preliminarily recorded cortical voltage-sensitive dye (VSD) responses to such stimulation. To examine whether these cortical responses contain image information, we explore the feasibility of machine-learning–based decoding. However, constructing such a decoder requires large-scale datasets linking visual images, spike trains, and cortical responses, which are not yet experimentally available. Therefore, we generated surrogate data with a Wiener-system model that simulates VSD responses of the visual cortex to ICMS pulse trains. A convolutional neural network trained on these synthetic datasets successfully reconstructed images from the simulated cortical responses. This simulation work serves as a proof-of-concept study, demonstrating the computational feasibility of estimating visual information contained in neuromorphic ICMS-evoked cortical activity and providing a foundation for future physiological validation. Full article

Underwater electrical explosions of single metallic wires driven by microsecond current pulses are investigated and compared with previously reported sub-microsecond experiments. Current and voltage waveforms, streak camera shadow imaging, and one-dimensional hydrodynamic simulations are employed to characterize how the energy density, energy density deposition rate, and the generated shock waves in water depend on the wire parameters. It was found that, similar to the sub-microsecond timescale, the solid–liquid phase transition occurs later than thermodynamic calculations predicted, while the liquid–vapor phase transition happens sooner than expected, leading to a two-phase coexistence. Additionally, most materials show a notable resistance peak (Ti, Fe, Ni, Zn, Ag, Sn, Ta, Au) compared to a quasi-plateau for Cu and Mo or a continuous increase for Al and Pt. Moreover, the specific action integral values are significantly smaller than those observed in wire explosion experiments in vacuum. Finally, the plasma formed at peak resistive voltage is non-ideal but exhibits lower electron density, ionization degree, and temperature compared to the sub-microsecond case. Full article

In this paper we present a low-noise, high-frame-rate global shutter CMOS image sensor with UHD resolution (3840 × 2160), targeting high-speed machine vision applications. The sensor (ForzaFAST581) supports video capture at up to 1141 FPS at 12 bits and 1694 FPS at 8 bits at full resolution, consuming a total power of 5.5 W. Fabricated in a 65 nm, four-metal BSI process, the imager features a 5 µm voltage-domain global shutter pixel with dual-gain capability for improved dynamic range and a read noise of 3.04 e⁻ in global shutter and 2.15 e⁻ in rolling shutter mode for high-gain at maximum frame rate operation. For compact camera integration and low power consumption, the sensor is designed to stream video through 16 CML data ports, each operating at 7.44 Gbps, achieving a total aggregate throughput of 119 Gbps. Additionally, the sensor supports selectable output bit depths—8-bit, 10-bit, and 12-bit—allowing frame rate optimization based on application-specific requirements. Full article

Accurate current–voltage (IV) characterization is essential for assessing photovoltaic (PV) module performance, yet conventional IV tracing requires physical contact and controlled conditions, limiting large-scale deployment. Electroluminescence (EL) imaging, while highly effective for detecting localized defects, remains largely qualitative and indirect in estimating actual PV module power loss. This study introduces a deep learning framework that directly predicts complete IV curves from EL images, transforming EL inspection into a quantitative, non-contact diagnostic tool. In this work, we propose a convolutional neural network (CNN) that learns the nonlinear mapping between paired EL images captured at 20% and 80% of the short-circuit current and the corresponding IV response. A total of 438 PV modules were used for model development, with performance evaluated on unseen data. The trained CNN reconstructs IV curves with high fidelity, achieving a validation accuracy of approximately 95% and low parameter deviations (<2% for key metrics such as maximum power point and fill factor). The model maintains consistent accuracy even when a single EL image is provided, supporting flexible field operation. Inference is rapid, requiring less than 0.5 s per PV module inspection, enabling real-time analysis. Full article