Search Results (7,944)

Background: Vitamin D has been associated with blood pressure across the life course in observational studies, although effect sizes are generally modest and findings are not fully consistent. We examined the association between serum 25-hydroxyvitamin D [25(OH)D] concentrations and multiple blood pressure indices in a community-based cohort of young adults. Methods: We conducted a cross-sectional analysis within the Young Taiwanese Adults (YOTA) cohort, restricting the sample to adults aged 18–45 years with complete serum 25(OH)D and blood pressure data. Serum 25(OH)D was modeled as a continuous variable and additionally examined using predefined concentration categories. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were assessed using standardized protocols. Multivariable linear regression models evaluated associations, with sequential adjustment for demographic, anthropometric, cardiometabolic, and lifestyle covariates. Restricted cubic spline models assessed potential nonlinearity. Results: Among 923 participants, higher serum 25(OH)D concentrations were modestly and linearly associated with lower SBP, DBP, and MAP after multivariable adjustment. Each 10 ng/mL increase in serum 25(OH)D was associated with a 1.07 mmHg lower SBP, a 1.19 mmHg lower DBP, and a 1.22 mmHg lower MAP. Associations remained consistent in sensitivity and subgroup analyses. Conclusions: In young adults, higher serum 25(OH)D concentrations were modestly associated with lower blood pressure indices. These findings confirm prior observational evidence of modest inverse associations between serum 25-hydroxyvitamin D concentrations and blood pressure and extend these observations to a relatively healthy young adult population. Prospective studies are required to clarify temporality and clinical relevance. Full article

CoNi layered double hydroxides (CoNiLDHs) were successfully synthesized on nickel foam (NF) using a hydrothermal method. X-ray diffraction (XRD) analysis confirmed the formation of a well-defined hydrotalcite-like phase, including a strong (003) peak, indicating layered stacking. Scanning electron microscopy (SEM) revealed a 3D hierarchical nanosheet structure resembling flower-like arrays, which was further supported by EDS mapping showing a uniform distribution of Co, Ni, and O. Electrochemical studies demonstrated excellent OER activity, with a low overpotential of 188 mV at 10 mA/cm² and a Tafel slope of 97.48 mV/dec, inferring rapid reaction kinetics. Furthermore, the material exhibited a significant electrochemical surface area (ECSA) compared to bare NF. Chronoamperometry over 24 h confirmed the operational durability catalyst, stabilizing around 7–8 mA/cm², validating its potential as a cost-effective and efficient OER electrocatalyst in alkaline media. Full article

For Autonomous Vehicles (AVs), recognizing traffic lights and signs is critical for safety because perception errors directly affect navigation decisions. Real-world disturbances such as glare, rain, dirt, and graffiti, as well as digital adversarial attacks, can lead to dangerous misclassifications. Current research lacks (i) temporal continuity (stable detection across consecutive frames to prevent flickering misclassifications), (ii) multi-field-of-view (FoV) sensing, and (iii) integrated defenses against both digital and natural degradation. This paper presents two principal contributions: (1) a three-layer defense framework integrating feature squeezing, inference-time temperature scaling (softmax $\tau$ = 3 without distillation training), and entropy-based anomaly detection with sequence-level temporal voting; (2) a 500 sequence dual-FoV benchmark (30k base frames, 150k with perturbations) from aiMotive, Waymo, Udacity, and Texas sources across four operational design domains. The unified defense stack achieves 79.8% mAP on a 100-sequence test set (6k base frames, 30k with perturbations), reducing attack success rate from 37.4% to 18.2% (51% reduction) and high-risk misclassifications by 32%. Cross-FoV validation and temporal voting enhance stability under lighting changes (+3.5% mAP) and occlusions (+2.7% mAP). Defense improvements (+9.5–9.6% mAP) remain consistent across native 3D (aiMotive, Waymo) and projected 2D (Udacity, Texas) annotations. Preliminary recapture experiments (n = 15 scenarios) show 2.5% synthetic–physical ASR gap (p = 0.18), though larger validation is needed. Code, models, and dataset reconstruction tools are publicly available. Full article

Phase-shifting profilometry (PSP) suffers from motion-induced phase-step variations in dynamic scenes. The breakdown of the fixed phase shift assumption results in issues such as ripples, distortions and accuracy decline in PSP systems. To reduce motion-induced phase errors, we propose a wavelet-assisted adaptive extended Kalman filter (WAEKF) to estimate varied pixel-wise phase shift. A wavelet-based strategy is presented to extract an initial spatial carrier frequency at each row from fringe patterns for EKF estimation. A state-space model employing the quadrature phase component and carrier frequency is established in this paper. The unknown phase shifts can be evaluated by using a forward–backward filter. Experiments show that the proposed method can acquire an accurate initial carrier frequency and phase shift map, which effectively reduces 3D reconstruction error and can be extended to N-step PSP systems. Full article

Accurate time and frequency acquisition is essential for deploying Orthogonal Time–Frequency Space (OTFS) modulation in non-terrestrial networks (NTNs), where severe Doppler shifts and low-SNR conditions are common. We propose a practical synchronization method that inserts an m-sequence-based pilot (illustrated using the 5G NR PSS) periodically in the delay–Doppler grid. Leveraging OTFS mapping properties, the method enables robust matched-filter detection for joint coarse time and frequency acquisition and continuous phase-drift tracking without increasing transmission redundancy. Numerical simulations show that the proposed method achieves a slightly lower PAPR and approximately a 3 dB improvement in detection threshold compared to a recent practical baseline. The algorithm is suitable for 5G/6G NTN links such as LEO constellations and operates reliably at low and negative SNR values. Full article

Background/Objectives: Operator experience, the implementation of low frame rates during both fluoroscopy and digital subtraction angiography (DSA), and the use of modern angiographic systems are essential for maintaining diagnostic image quality while minimizing ionizing radiation exposure during stent-assisted endovascular treatment of intracranial aneurysms. At the study center, a low-dose protocol is employed, using the lowest available fluoroscopy frame rate (3.125 frames per second) and a nominal acquisition rate of 2 frames per second for DSA, three-dimensional (3D) rotational angiography, 2D/3D mapping, and roadmapping. Methods: A retrospective analysis was performed on 132 stent-assisted procedures conducted at a single tertiary center between 2018 and 2024. For each procedure, data were collected for dose-area product (DAP), reference air kerma (Ka,r), fluoroscopy time (FT), and the total number of DSA frames. Local diagnostic reference levels (DRLs; 75th percentile [P75]) and typical values (50th percentile [P50]) were established and compared with values reported in the literature. Results: For all patients the P75 values, representing DRLs, were 19.89 Gy·cm² for DAP, 332 mGy for Ka,r, 25 min 32 s for FT, and 354 DSA frames. The P50 values were 13.71 Gy·cm² for DAP, 219.5 mGy for Ka,r, 20 min 36 s for FT, and 277 DSA frames. Conclusions: In this single-center cohort, dose metrics for stent-assisted coil embolization were within the lower range of published values. Cross-study comparisons remain descriptive and require cautious interpretation. The proposed local DRLs may support quality assurance, dose optimization, and patient safety in similar clinical settings. Further multicenter and multi-operator studies are necessary to assess transferability and applicability beyond coil-only procedures. Limitations include the retrospective single-center design (single operator) and the lack of a contemporaneous control group and formal image-quality/outcome assessment. Full article

Thermal hydrolysis pretreatment (THP) increases the solubilization of sewage sludge, while bioelectrochemically assisted anaerobic digestion (BEAD) enhances the conversion of the solubilized organic matter into methane and improves reactor stability in the presence of inhibitory compounds. In this study, by mapping methane production in a BEAD reactor against the soluble organic loading rate (sOLR), determined from soluble chemical oxygen demand (sCOD) measurements, distinct operational regimes corresponding to different THP temperatures were identified. With the 120 °C pretreated feedstock, the BEAD reactor operated in a hydrolysis-limited regime, where increasing sOLR increased methane production but reduced conversion efficiency. Accordingly, at an sOLR of 4.5 g (L_R d)⁻¹, a volumetric methane production rate of 0.8 L L_R⁻¹ was achieved. Increasing THP severity to 150 °C improved solids solubilization and shifted the system into a kinetically enhanced regime, in which methane production was directly proportional to sOLR, indicating improved substrate accessibility and reaction kinetics. Consequently, at an sOLR of 7.75 g (L_R d)⁻¹, methane production reached 1.46 L L_R⁻¹. This regime-based analysis provides quantitative guidance for selecting pretreatment severity and loading strategies to maximize methane production, while maintaining stable BEAD reactor operation at high organic loads. Full article

Object pose estimation is fundamental for robotic manipulation, autonomous driving, and augmented reality, yet recovering the full 9-DoF state (rotation, translation, and anisotropic 3D scale) from RGB-D observations remains challenging for previously unseen objects. Existing methods either rely on instance-specific CAD models, predefined category boundaries, or suffer from scale ambiguity under sparse observations. We propose ProM-Pose, a unified cross-modal temporal perception framework for zero-shot 9-DoF object pose estimation. By integrating language-conditioned generative 3D shape priors as canonical geometric references, an asymmetric cross-modal attention mechanism for spatially aware fusion, and a decoupled pose decoding strategy with temporal refinement, ProM-Pose constructs metrically consistent and semantically grounded representations without relying on category-specific pose priors or instance-level CAD supervision. Extensive experiments on CAMERA25 and REAL275 benchmarks demonstrate that ProM-Pose achieves competitive or superior performance compared to category-level methods, with mAP of $75.0\%$ at $5^{°},2\mathrm{cm}$ and $90.5\%$ at ${10}^{°},5\mathrm{cm}$ on CAMERA25, and $42.2\%$ at $5^{°},2\mathrm{cm}$ and $76.0\%$ at ${10}^{°},5\mathrm{cm}$ on REAL275 under zero-shot cross-domain evaluation. Qualitative results on real-world logistics scenarios further validate temporal stability and robustness under occlusion and lighting variations. ProM-Pose effectively bridges semantic grounding and metric geometric reasoning within a unified formulation, enabling stable and scale-aware 9-DoF pose estimation for previously unseen objects under open-vocabulary conditions. Full article

While anisotropic extrudate swell in polymer processing is fundamentally driven by physical viscoelastic recovery, this paper proposes a theoretical framework to explicitly isolate and map the purely geometric and kinematic components of this phenomenon. Serving as a mathematical proof-of-concept, a multi-velocity-centre hypothesis is proposed. By introducing a semi-empirical, lumped material-flow calibration parameter, the macroscopic diameter swell ratio is mathematically extended to the discrete local flow field of a rectangular slit die. To evaluate its validity, the analytical framework is subjected to a numerical test for kinematic consistency utilizing isothermal, inelastic power-law fluid CFD simulations, thereby separating geometric mapping from complex viscoelastic stress relaxation. Results indicate that analytical predictions show good agreement with CFD data (error < 5%) strictly within the core zone of high-aspect-ratio dies. However, due to the infinite-slit assumption, 3D flow kinematics near die edges induce velocity decay, leading to local deviations that require future empirical corrections. Although comprehensive physical extrusion experiments and non-isothermal viscoelastic coupling are required for industrial deployment, this semi-empirical kinematic mapping provides a foundational mathematical basis that could potentially inform future inverse die-profile design and shape distortion compensation. Full article

Formerly considered nonfunctional “junk DNA,” pseudogenes have been re-evaluated in light of technological advances in bioinformatics and high-throughput sequencing. The limited research to date on pseudogenes in hexaploid common wheat (Triticum aestivum L.) is largely confined to individual gene families, thus hindering our understanding of the long-term evolutionary dynamics among the A, B, and D subgenomes. Using the IWGSC RefSeq v2.1 genome assembly, we performed genome-wide identification, classification, and analysis of pseudogenes in wheat, including their distribution, evolutionary history, and parental gene functions. Marked asymmetries in pseudogene abundance, distribution, evolutionary dynamics, and constraints were detected among the subgenomes. The B subgenome harbored significantly more pseudogenes and showed distinct evolutionary patterns compared with the A and D subgenomes. Pseudogenes were strongly associated with transposable elements and peaks in their formation were incongruent with ancient genomic upheavals of wheat ancestral lineages. The parent genes were predominantly enriched in non-core functions and showed tissue-specific expression. The findings provide direct evidence for long-term asymmetric, post-polyploidization evolution in wheat, enhance our understanding of polyploid genome evolution, and offer a methodological framework for research on other genetically complex polyploid crops. Full article

High-temperature, high-pressure (HTHP) hard-rock drilling frequently causes chamfer spalling of polycrystalline diamond compact (PDC) cutters, leading to ~20% loss in the rate of penetration (ROP) and large torque oscillations. We propose a surface-gradient chamfer comprising a thin SiC interlayer (t_SiC ≈ 0.7 μm) and a nanocrystalline diamond topcoat (t_D ≈ 5 μm, d_D ~100 nm), combined with shallow cobalt leaching (L_deCo ≈ 100 μm). The structure was verified by microscopy/spectroscopy and evaluated by scratch adhesion, SEVNB toughness, instrumented impact, thermal shock, 400 °C pin-on-disc wear, and bench-scale granite drilling with vibration/torque monitoring. A coupled thermo-mechanical finite-element model, calibrated with Raman stress maps and thermal measurements, was used to interpret failure trends. Relative to untreated cutters, the gradient design reduced peak tensile residual stress by ~45% and lowered high-temperature wear volume by ~40%. In the present impact dataset (limited cutters per condition), the observed spall incidence at 1.0 J decreased from 2/3 (baseline) to 1/5 (gradient-treated). Short bench drilling runs suggested improved signal separability between healthy and pre-spall states (ROC-AUC ≈ 0.85 vs. ~0.65 for baseline, evaluated using a leave-one-cutter-out protocol); these drilling results should be interpreted as trend-level evidence given the limited number of cutters. These gains arise from mitigated thermal mismatch and residual stresses at the chamfer. Full article

Automotive vision systems depend critically on front-view cameras, whose image quality frequently degrades under adverse conditions such as rain, fog, low illumination, and rapid motion. To address this challenge, we propose VMESR, a variable mamba-enhanced super-resolution network that integrates a selective state-space model into a lightweight super-resolution architecture. By serializing 2D feature maps and applying variable-depth mamba blocks, VMESR captures long-range dependencies with linear complexity. A multi-scale feature extractor, enhanced residual modules equipped with a convolutional block attention module, and dense fusion connections work together to improve the recovery of high-frequency details. Extensive experiments demonstrate that VMESR achieves competitive performance in both objective metrics and perceptual quality compared to state-of-the-art methods, while significantly reducing parameter counts and computational cost. VMESR provides a practical balance between efficiency and reconstructive accuracy, offering a deployable super-resolution solution for embedded automotive sensors and enhancing the robustness of autonomous driving perception pipelines. Full article

4D millimeter-wave radar provides a promising solution for robust perception in adverse weather. Existing detectors still struggle with sparse and noisy point clouds, and maintaining real-time inference while achieving competitive accuracy remains challenging. We propose SGE-Flow, a streamlined PointPillars-based 4D radar 3D detector that embeds lightweight spatiotemporal geometric enhancements into the voxelization front-end. Velocity Displacement Compensation (VDC) leverages compensated radial velocity to align accumulated points in physical space and improve geometric consistency. Distribution-Aware Density (DAD) enables fast density feature extraction by estimating per-pillar density from simple statistical moments, which also restores vertical distribution cues lost during pillarization. To compensate for the absence of tangential velocity measurements, a Transformer-based Inter-frame Flow (IFF) module infers latent motion from frame-to-frame pillar occupancy changes. Evaluations on the View-of-Delft (VoD) dataset show that SGE-Flow achieves 53.23% 3D mean Average Precision (mAP) while running at 72 frames per second (FPS) on an NVIDIA RTX 3090. The proposed modules are plug-and-play and can also improve strong baselines such as MAFF-Net. Full article

Extracting standardized digital design patterns from real knitted fabric images is critical for textile reverse engineering and digital archiving. Unlike smooth graphics, knitted fabrics exhibit high-frequency textures from yarn loop interlacing, introducing significant grayscale variations within same-color regions. Existing algorithms struggle to distinguish these from pattern edges, causing color quantization and segmentation failures. To suppress yarn texture while preserving edges between color blocks, we propose an adaptive pattern extraction method using Bayesian-optimized bilateral filtering. The primary contribution lies in providing a domain-specific, application-focused integrated framework. Specifically, (1) a knitting-texture-aware multidimensional evaluation parameter is constructed by integrating physical-cause-based texture features (gray-level co-occurrence matrix (GLCM) contrast, homogeneity, and Laplacian variance) with perception-based edge preservation metrics (the Sobel operator and the structural similarity index (SSIM)), enabling accurate discrimination between yarn-level texture noise and pattern-level color block boundaries—a distinction that generic image quality metrics cannot make. (2) Then, this domain-specific objective function is embedded within a Bayesian optimization framework to achieve automatic, zero-shot, per-image parameter adaptation across different knitting processes, without requiring any external training data. K-means color quantization maps in continuous tones to discrete classes, generating standardized patterns meeting knitting requirements. Experiments on 316 samples covering six processes show our method outperforms standard denoising and advanced algorithms like relative total variation (RTV), achieving an average SSIM of 0.83 and PSNR of 26.92 dB, reducing processing time from 15–30 min to 21 s per image, providing efficient automation for knitted Computer-Aided Design (CAD) systems. Full article

The increasing frequency and intensity of extreme climate events have posed more geohazards worldwide. It is therefore crucial to quantify and map risk to reduce disaster-related losses. The main objective of this study is to propose a quantitative framework to conduct risk assessment of buildings and infrastructures impacted by geohazards. A debris flow hazard in Tianjin, North China was taken as a case study. A physically based model and the Gumbel extreme value distribution were utilized to construct a range of extreme rainfall and runoff scenarios. The FLO-2D and ABAQUS software were subsequently employed to simulate the surging behavior of the debris flow and assess the structural vulnerability of buildings, respectively. Furthermore, the number of elements at risk and economic values were estimated to generate risk maps. The results revealed that variations in peak discharge in the channel evidently affected flow velocity and depth, thus elevating the debris flow intensity and the likelihood of the materials threatening buildings. The stiffness degradation of concrete was strategically used as the indicator to quantify structure vulnerability and effectively present the dynamic responses under the impacts of the debris flow. Under a 100-year return period rainfall scenario, the proportion of very high- and high-risk areas reached 31%, with the estimated economic loss approximately ¥167.7 million. This highlighted the critical role that extreme rainfall played in shaping both the spatial distribution and severity of debris flow risks. The proposed method provides a scientific basis for enhancing the resilience of mountainous regions to compound natural disasters exacerbated by climate change. Full article