Search Results (26,743)

Quantitative evaluation of the interfacial contact characteristics between the cathode active material (CAM) and solid electrolyte (SE) in all-solid-state battery (ASSB) composite cathodes is essential for improving electrochemical performance. In this study, a previously proposed integrated galvanostatic method (GM)-electrochemical impedance spectroscopy (EIS) framework for analyzing the electrochemically active area (EAA) was applied to particle-size-controlled composite cathodes to examine how particle-size design influences interfacial contact in practical ASSB composite cathodes. Specifically, three cathodes were examined: a small-particle Ni-rich layered oxide cathode (SP), a large-particle Ni-rich layered oxide cathode (LP), and a bimodal cathode containing an equal-weight mixture of the two particle fractions (BP). An area-independent lithium diffusion coefficient was first determined from the Warburg-blocking transition in the impedance response. The EAA of each cathode was then obtained by combining this reference value with the area-sensitive galvanostatic response in a one-step constraining procedure. Although bimodal particle-size distributions are often expected to improve interfacial contact by combining the advantages of small and large particles, the EAA increased in the order of SP < BP < LP. This result indicates that under the present electrode configuration, the LP cathode secured the most effective CAM–SE interfacial contact and the highest effective surface coverage. Consistent with this trend, the LP cathode exhibited the best rate capability under high-rate conditions. These results demonstrate that the GM–EIS-based EAA analysis framework provides a practical quantitative tool for evaluating particle-size-dependent interfacial contact and guiding microstructure optimization in ASSB composite cathodes. Full article

Reinforced concrete (RC) columns in cold regions are often exposed to combined seismic actions and cryogenic environments, which can significantly alter their structural response. This study examines the seismic performance of RC columns over a temperature range of 20 °C to −90 °C using numerical simulations, with axial load ratios of 0.0–0.6 and stirrup ratios of 1.0–3.0% considered. The results reveal that failure modes remain generally consistent across temperatures, while damage becomes more pronounced at lower temperatures. A decrease in temperature leads to higher peak load and initial stiffness, accompanied by a reduction in ductility. Taking the specimens with ρ_sv = 1.0% as an example, as the temperature decreases from 20 °C to −30 °C, −60 °C, and −90 °C, the peak load increases by 10.9%, 17.1%, and 32.7%, respectively. As the temperature decreased from 20 °C to −90 °C, the ductility coefficient decreased by 33.3%, and the total dissipated energy increased by 6.4%. Increasing the stirrup ratio enhances deformation capacity and partially mitigates ductility loss. Furthermore, the influence of axial load ratio on hysteretic response follows a similar pattern to that at ambient temperature, but with greater sensitivity under cryogenic conditions. Based on the numerical findings, predictive expressions are proposed to estimate the plastic hinge length and flexural strength considering temperature effects. Full article

Array Gm-APD LiDAR is highly vulnerable to strong backscattering caused by dynamic smoke. Conventional depth imaging methods cannot rapidly identify the smoke occlusion state, which greatly reduces the target recovery quality of the reconstructed depth image. To solve this problem, this paper presents a non-parametric algorithm for rapid smoke detection and depth imaging for array Gm-APD LiDAR. The proposed method does not rely on parameter estimation of the echo model. Instead, it determines the presence of smoke occlusion by calculating the Pearson correlation coefficient between the echo signal obtained from the superposition of all array pixels and the instrument response function. In this way, the method rapidly identifies smoke interference in a single depth image, performs fast denoising, and reconstructs the depth image. In a dynamic smoke environment with an average attenuation length of no more than 5.1, the proposed algorithm achieves 100% accuracy in occlusion discrimination based on 250 frames of array data. When the smoke occlusion rate reaches 96% and the average attenuation length is 2.29, the method obtains a target recovery of 0.71, which is 86.8% higher than that of the conventional algorithm. These results indicate that the proposed method has strong practical value for array Gm-APD LiDAR, especially for high-speed depth imaging in harsh atmospheric environments with severe obscuration. Full article

This study examines the potential of sustainable, biobased paper-based structures as panel/membrane sound absorbers. Although intact paper is naturally impermeable and a poor sound absorber, transforming it into complex three-dimensional origami geometries, specifically the Miura-ori pattern, could produce effective panel/membrane absorbers. Three distinct Miura-ori samples (A, B, and C) were fabricated with increasing geometric complexity, ranging from a simple triangular prism to a complex labyrinthine waveguide. The random incidence sound absorption coefficients of these samples were measured in a validated small-scale reverberation room. The underlying absorption mechanisms were further investigated through modal analysis and non-contact vibration velocity measurements. The results indicate that increased geometric complexity enhances acoustic performance. Sample C, the most complex structure, demonstrated the most consistent broadband absorption. The analysis confirmed a significant positive correlation between acoustic pressure modes, surface vibration velocity, and sound absorption peaks, indicating that acoustic energy dissipation is driven by the vibrational response of the paper membrane coupled with resonant modes in the air gap. This research demonstrates that tunable origami folding techniques using intact paper can be used to design lightweight acoustic treatments for diffuse sound fields in the mid-frequency range. Full article

Although SARS-CoV-2 has been extensively studied from clinical, virological, and diagnostic perspectives, the problem of accurate automatic semantic segmentation of SARS-CoV-2 particles in electron microscopy images remains inadequately explored. Existing studies have largely focused on virus detection, classification, morphometry, or conventional image analysis, while comparatively little attention has been paid to pixel-level delineation of viral structures using specialised deep learning segmentation frameworks. To address this gap, we propose here a deep learning system based on convolutional neural networks (CNNs) combined with image processing techniques to establish semantic segmentation tools for the automatic identification of SARS-CoV-2. Our approach utilises the super-Euclidean pixels method as an intermediate layer within the CNN for semantic segmentation. We then compare its performance against the gradient vector flow (GVF) and Poisson inverse gradient (PIG) segmenters. The proposed CNN model surpassed the traditional GVF and PIG segmentation models, achieving the following metrics (mean ± variance): Dice similarity coefficient (DSC) = 0.9345 ± 0.0006; intersection over union (IoU) = 0.8782 ± 0.0018; sensitivity/true positive rate (TPR) = 0.9373 ± 0.0018; specificity/true negative rate (SPC) = 0.9517 ± 0.0012; accuracy = 0.9449 ± 0.0004; area under the ROC curve (AUC) = 0.9446 ± 0.0431; and Cohen’s Kappa = 0.9137 ± 0.0011. This method enables virologists to employ an automatic CNN-based segmentation tool for detecting SARS-CoV-2 and demonstrates superiority over GVF and PIG. Full article

This paper presents a comprehensive investigation of the effects of atmospheric plasma treatment (APT) on the surface morphology, microhardness, chemical composition, and tribological performance of alloy steel used in railway applications. A novel mathematical model is proposed to describe the dependence of the maximum surface asperity height on the APT parameters and material properties. Experimental validation was performed using a series of alloy steel specimens treated under controlled APT conditions. The surfaces were characterized by roughness measurements, microhardness testing, scanning electron microscopy, and energy-dispersive spectroscopy. Tribological properties were evaluated under dry sliding conditions using ball-on-disk tests with steel counterbodies (grades 1.3529 and 1.3505). Tribological testing showed that APT leads to a 6–7% reduction in the steady-state friction coefficient, eliminates the long running-in stage, and improves stability by lowering the coefficient of variation by up to 43%. Overall, this study demonstrates that APT provides a dual benefit: improving tribological performance through surface smoothing and stabilization of the friction regime, and preserving the mechanical and chemical integrity of the material. Full article

This study examined whether barbell velocity measured under standardized loading conditions during routine resistance training may provide useful contextual information for athlete monitoring in elite track-and-field athletes. Although velocity-based training has been widely used for load prescription, its utility as contextual monitoring information under fixed external loads remains unclear. Eight national-level jump and throw athletes were observed over a six-week in-season period. Mean concentric velocity (MCV) was recorded during back squat exercises performed at a consistent external load (~60% 1RM) across 95 regularly scheduled training sessions. The overall mean MCV was 0.783 ± 0.057 m·s⁻¹ (range: 0.68–0.89 m·s⁻¹). Within-athlete session-to-session fluctuations ranged from 6.13% to 11.20%, exceeding both the coefficient of variation (2.06–3.80%) and the pooled typical error of measurement (0.019 m·s⁻¹), suggesting that the observed variability was unlikely to be explained solely by measurement noise. Distinct individual velocity trajectories were observed under fixed individualized loading conditions, reflecting notable intra- and inter-individual variability within an ecologically valid training environment. These findings suggest that barbell velocity may provide contextual information regarding session-to-session fluctuations relevant to athlete monitoring under standardized loading conditions. Collectively, the findings suggest that barbell velocity measured under standardized loading conditions may provide contextual information regarding day-to-day variability during routine elite training. Full article

Wire Arc Additive Manufacturing (WAAM) has emerged as a cost-effective and high-deposition-rate technique for fabricating large-scale metallic components; however, the complex thermal history inherent to the process leads to heterogeneous microstructures that can significantly influence tribological performance. In this study, the dry sliding wear behavior of WAAM-fabricated austenitic stainless steel 308L (SS308L) was systematically investigated using a pin-on-disk configuration. The influence of applied normal load (1.5–15 N) and sliding speed (0.03–0.229 m/s) on wear volume, specific wear rate, coefficient of friction (COF), and tangential force was evaluated. Optical microstructural observations indicated features consistent with a ferritic–austenitic solidification structure, including regions resembling polygonal ferrite, Widmanstätten ferrite, and austenitic dendritic morphologies. Wear results showed that wear volume and cross-sectional area increased monotonically with increasing load, while the effect of sliding speed was comparatively less significant. The specific wear rate remained on the order of 10⁻⁴ mm³/N·m with minor variations across test conditions. The COF decreased with increasing load up to 10 N, followed by a speed-dependent response at higher loads. The findings demonstrate that load is the dominant factor governing wear behavior in WAAM SS308L, while microstructural heterogeneity may contribute to frictional stability and wear resistance. This study provides valuable insight into the structure–tribology relationship of WAAM stainless steels and supports the optimization of process parameters for wear-critical applications. Full article

Aiming at the problems of extremely scarce measured samples and significant domain shift between simulated and measured data in automatic target recognition (ATR) of air targets for spaceborne radar, this paper proposes an inverse synthetic aperture radar (ISAR) image recognition method for air targets combining physics-driven data augmentation guided by detection prior information with domain adversarial transfer learning. First, the mapping relationship between scattering point projection and ISAR images is established by using the target 3D point cloud and radar observation geometric priors, and a 2D sinc kernel function is introduced for energy distribution rendering. Then, under the unsupervised transfer learning paradigm, aiming at the distribution inconsistency between augmented data (source domain) and unlabeled simulated data (target domain), this paper designs a cross-domain recognition task experiment including six types of typical aircraft targets, and compares the cross-domain recognition performance of three transfer learning methods (model fine-tuning, deep domain confusion (DDC) and domain-adversarial neural networks (DANN)) on the target domain. Meanwhile, t-distributed stochastic neighbor embedding (t-SNE) visualization is used to analyze the feature distribution alignment ability of the models. Simulation experiments show that the DANN model with a dynamic inversion coefficient introduced in the gradient reversal layer (GRL) achieves a recognition accuracy of 99.5% on the unlabeled target domain, which is significantly superior to the model fine-tuning and DDC methods. Moreover, it makes the feature distributions of source and target domain samples highly overlapping, and maintains a strong inter-class discriminability while eliminating the domain shift. The proposed scheme provides a physically interpretable and robust technical path for few-shot radar target image recognition. Full article

Brush seals, as a fundamental dynamic sealing technology in the aerospace and energy propulsion industries, require performance enhancement through instantaneous adjustment of the friction coefficient and force analysis of brush filaments. This paper establishes an instantaneous friction coefficient correction method based on the open volume between bristles and the backing plate. The downstream section of the double-row brush wire (2.6 mm) was quantitatively identified as the maximum leakage point, and it was found that the vortex characteristic length in the downstream area is approximately 1–3 times the bristle gap, with an increasing pressure ratio enhancing downstream turbulence and reducing gas leakage. A cantilever beam structural model was developed to assess the motion, force, and hysteresis properties of a single filament. Additionally, a porous medium model was utilized to elucidate the flow field and temperature distribution within the seal. The results suggest that the lag angle increases linearly over the first one-third of the brush wire’s length from the free end to the fixed end and is directly proportional to the pressure difference ΔP, reaching a maximum of 10.18°. The viscous drag causes the radial force y-component F_xy to increase and then decrease near the free end. The rear baffle contact force, F_b, shows variable peaks at two-thirds of the filament length. The displacement at the brush filament’s free end, the deflection angle, and the bending moment are directly proportional to the pressure differential. As pressure increases, the deformed region propagates toward the fixed end, and the maximum displacement at the free end of the brush wire reaches 13.04 mm. The leakage rate increases nearly linearly with ΔP and its deformation, reaching a maximum of 0.00849 m²/s. The pressure gradient growth rates of 164%, 73%, and 29% at the front baffle corner demonstrate that adding pressure chambers on front and rear baffles is optimal for high-pressure scenarios (ΔP > 0.3 MPa), while the formation of vortices between bristles and rotor reduces tip friction force and front-row turbulent disturbance, providing design guidance for extending seal service life. Full article

To address the mismatch between spatial form and wind environment of residential districts in cold-region valley-type cities, which leads to poor thermal comfort, low ventilation efficiency and high building energy consumption, this study takes Hongyun Runyuan, a typical large-scale residential district in Lanzhou, as the research case. Using orthogonal experimental design, nine spatial schemes were developed with three core morphological parameters (building orientation, spacing coefficient, enclosure degree), each set with three levels. CFD simulations via PHOENICS were performed to analyze the influence weight of each parameter on the winter wind environment at 1.5 m pedestrian height. Results show that building orientation exerts an extremely significant effect on the winter wind environment (p = 0.006), while the spacing coefficient and enclosure degree have no significant independent effects (all p > 0.05). The optimal scheme, featuring 10° east of south orientation, 1.1 spacing coefficient and 0.3 enclosure degree, can effectively meet the winter wind protection demand. The quantitative optimization strategies proposed in this study provide scientific support for wind-friendly residential planning and building energy efficiency improvement in cold-region valley-type cities. Full article

Background: Cardiac magnetic resonance (MRI) images often exhibit low contrast, anatomical variability, and indistinct boundaries, particularly in the myocardium (MYO) and right ventricle (RV). These challenges can reduce the reliability of both manual and automated segmentation, highlighting the need for more robust and boundary-aware approaches. Methods: In this study, an Aspect-Aware Boundary-Resilient UNet3D (ABR-UNet3D) architecture is proposed for cardiac MRI segmentation. The model incorporates an Aspect-Aware Complementary Attention (AAC) module that combines multi-planar contextual information with a complementary gating mechanism to enhance boundary representation. The method was evaluated on the ACDC dataset under consistent training conditions. In addition to Dice Similarity Coefficient (DSC) and Intersection over Union (IoU), boundary-based metrics, including the 95th percentile Hausdorff Distance (HD95), Average Surface Distance (ASD), and Surface Dice, were employed. Furthermore, a five-fold cross-validation protocol and detailed ablation studies were conducted to assess robustness and analyze the contribution of individual AAC components. Results: The proposed method achieved a mean DSC of 0.9603 in single-run experiments on the ACDC dataset and showed consistent performance in anatomically challenging regions, particularly for RV and MYO segmentation. In addition, five-fold cross-validation experiments resulted in an average DSC of 0.952 ± 0.009 and IoU of 0.908 ± 0.012, indicating stable performance across different data splits within the evaluated dataset. Boundary-based metrics also showed improved surface agreement and lower boundary errors compared with the evaluated baseline models. Ablation studies further indicated that the combined use of multi-planar contextual information and complementary gating contributes more effectively to segmentation performance than the individual components used separately. Conclusions: The results suggest that the proposed ABR-UNet3D architecture provides a stable and competitive segmentation framework for cardiac MRI images within the scope of the ACDC dataset. By jointly modeling contextual information and boundary refinement, the method improves segmentation reliability in challenging regions while maintaining competitive and consistent performance with respect to existing approaches. Full article

The horizontal bearing mechanism of pile–soil composite foundations adjacent to retaining walls is significantly affected by asymmetric lateral constraints caused by retaining wall movement, a scenario that remains inadequately explored in conventional design. This study employs a validated three-dimensional finite element model to investigate the response of such foundations to rotational displacement of a nearby wall. A comprehensive parametric analysis quantifies the influence of pile configuration, cushion properties, soil modulus, and loading conditions. The results demonstrate that rotational displacement (RB mode) induces a highly non-uniform load distribution within the pile group. The middle-front row piles emerge as critical load-bearing components, experiencing significant load amplification (load-transfer coefficients η_p up to 2.3). Key parameters, including pile length and cushion stiffness, selectively regulate system stiffness or optimize load sharing. Increasing the pile–wall distance is identified as an effective measure to reduce load concentration on front-row piles. The findings provide quantitative insights and practical guidance for the performance-based design of composite foundations under asymmetric constraints. Full article

Polymer–ceramic piezoelectric composites are widely investigated to combine the high piezoelectric performance of ferroelectric ceramics with the flexibility and processability of electroactive polymers. However, achieving enhanced dielectric properties while preserving the intrinsic piezoelectric response of the polymer matrix remains challenging, particularly due to dielectric mismatch between the constituent phases and interfacial effects. In this work, barium titanate (BaTiO₃) loaded poly(vinylidene fluoride-trifluoroethylene) (PVDF-TrFE) nanocomposites were fabricated by solvent casting using polyvinylpyrrolidone (PVP) and polysorbate 80 (PS80) as dispersing agents, aiming to obtain polarizable materials capable of retaining high piezoelectric strain coefficient (d₃₃) values and potentially exploiting the opposite polarity of matrix and filler through tailored poling strategies. Morphological, crystallographic, structural, thermal, thermomechanical, dielectric, and piezoelectric characterizations were performed by SEM/EDXS, XRD, FTIR, DSC, TGA, DMTA, dielectric spectroscopy, and d₃₃ measurements. Both dispersants improved filler dispersion and film densification, increasing the crystalline fraction of the matrix, without altering the relative fraction of β-phase (up to 93%). PVP enabled moderate and stable permittivity enhancement with weak frequency dependence, whereas PS80 introduced an electrically active interfacial contribution that amplified low-frequency permittivity at high filler loadings but made the permittivity more frequency-dependent. The piezoelectric response (between −20 pC/N and −25 pC/N) remained predominantly governed by the polymer phase, suggesting limited polarization played by BaTiO₃. These results underlined the critical role of interfacial electrical properties in designing stable high-performance flexible PVDF-TrFE/BaTiO₃ composites. Full article

This study examines the relationship between Environmental, Social, and Governance (ESG) and financial performance in the United Kingdom context, an area of increasing importance in both academic and practical domains. ESG is measured using the London Stock Exchange Group (LSEG) disclosure score. Using a dataset of firms in the FTSE 350 index over a 5-year period from 2020 to 2024, a panel regression model is employed to analyse the relationship between ESG and financial performance. The results of this study are mixed. When using ROA as a proxy for financial performance, the results suggest a statistically significant and positive relationship between ESG and financial performance, although the ESG coefficient indicates a relatively modest effect. However, when ROE is used as a proxy, the results are insignificant, suggesting that the impact of ESG may vary depending on the financial performance measure used. These findings contribute to the literature by providing evidence from the UK during a period of economic disruption, highlighting ESG’s role in operational performance rather than shareholder returns. However, the results should be interpreted with caution due to the use of disclosure-based ESG measures and a limited set of control variables. Full article