Search Results (4,689)

Accurate assessment of mitral valve (MV) and pulmonary vein (PV) flow velocities is important for left ventricular diastolic function testing. This study investigated the scan–rescan reproducibility of 4D Flow MRI-assessed MV and PV flow velocities in 21 healthy volunteers (25 ± 4 years). Participants underwent repeated whole-heart 3T 4D Flow MRI involving repositioning and different respiratory compensation strategies (motion-uncompensated free-breathing vs. respiratory motion-compensated navigator gating). MV parameters (net flow volume (NFV), E-wave velocity, A-wave velocity, E/A ratio, E deceleration time (DT), annular e’ velocity, E/e’ ratio) and PV parameters (NFV, S-wave velocity, D-wave velocity, S/D ratio, atrial reversal (AR) wave velocity) were derived from velocity–time curves and compared using intraclass correlation coefficients (ICCs), Bland–Altman analysis, and Pearson’s correlation (r). Results showed significant moderate-to-strong scan–rescan agreement and correlation for most MV and PV parameters (ICC = 0.51–0.92; r = 0.51–0.92; all p < 0.05), except E DT, e’ velocity, E/e’ ratio, PV NFV, and AR velocity (ICC = −0.13–0.47; r = −0.14–0.47). Subanalysis of respiratory motion strategies showed moderate-to-strong agreement and correlation for MV and PV parameters (ICC = 0.61–0.99; r = 0.52–0.99; all p < 0.05 excluding E DT), except E DT (ICC = 0.44) and PV NFV (ICC = 0.46; r = 0.46). While intraobserver agreement was mostly moderate-to-excellent (ICC = 0.58–0.97; ICC = 0.41 for E DT), interobserver agreement was poor for E DT and PV parameters (ICC = −0.12–0.34). Overall, 4D Flow MRI shows acceptable reproducibility for selected diastolic flow parameters, particularly mitral inflow indices, but substantial variability and limited robustness for key indices currently restrict its clinical applicability. Full article

We investigate the effects of dark matter on the properties of strange quark stars within the framework of general relativity with two fluids coupled only by gravity. Adopting the color–flavor-locked model for strange quark matter and considering both fermionic (free fermion gas) and bosonic (polytropic) equations of state for dark matter, we systematically study the structure and tidal deformability of dark matter-admixed strange stars. Our results show that the presence of dark matter significantly modifies the mass–radius relations, with the maximum mass of dark matter-admixed strange stars exhibiting a non-monotonic dependence on the dark matter mass fraction $\chi$, which reaches a minimum at an intermediate value of $\chi$. The tidal deformability $\Lambda$ of dark matter-admixed strange stars shows complex behavior depending on both the stellar mass and dark matter fraction, with $\Lambda -\beta$ (the compactness parameter) relations deviating from the universal relations observed for pure strange stars or dark stars. Our findings demonstrate that dark matter-admixed strange stars with different configurations but identical masses and radii can be distinguished by their tidal deformabilities, providing potential observational signatures for detecting dark matter in compact astrophysical objects. The results are compared with current astrophysical constraints from gravitational wave observations and pulsar measurements. Full article

The electrocardiogram (ECG) is a central tool in cardiovascular diagnostics, yet interpretation requires expertise and remains subject to variability. Multimodal large language models (MLLMs) have shown emerging capabilities in medical image analysis, but their performance in ECG interpretation remains insufficiently characterized. This study evaluated the diagnostic accuracy and inter-run reliability of five MLLMs across ECG interpretation tasks. Thirteen standard 12-lead ECGs were presented to five models (ChatGPT-5.3, Gemini 3.1 Pro, Claude Opus 4.6, Grok 4.1, and ERNIE 5.0) across five independent runs per case, yielding 2275 task-level assessments. Six categorical interpretation tasks (rhythm, electrical axis, PR/P-wave morphology, QRS duration, ST/T-wave morphology, and QTc interval) were compared with expert-consensus ground truth, while heart rate estimation was evaluated using mean absolute error (MAE). Overall categorical accuracy ranged from 52.3% to 64.9%. QRS duration classification achieved the highest accuracy (66.2–90.8%), whereas ST/T-wave assessment showed the lowest performance (20.0–41.5%). Heart rate MAE ranged from 14.8 to 46.7 bpm. A dissociation between diagnostic accuracy and inter-run reliability was observed across models. These findings indicate that current MLLMs do not achieve clinically reliable ECG interpretation performance and highlight the importance of assessing diagnostic accuracy and inter-run reliability when evaluating artificial intelligence systems in biomedical diagnostics. Full article

High gain and low sidelobe level remain challenges for 5G millimeter-wave antenna systems. This paper presents a low-sidelobe, high-gain microstrip array antenna based on non-uniformly slotted identical-sized radiating patch, designed to simultaneously enhance gain and suppress sidelobe levels for 5G millimeter-wave (mmWave) communication systems. The key innovation lies in the use of an intermediate-deep, edge-shallow non-uniform slotting technique to precisely control the surface current distribution of the radiating elements, thereby achieving significant sidelobe level (SLL) suppression and antenna isolation enhancement without increasing the physical footprint of each element. The final design operates at a center frequency of 78.5 GHz, achieving a maximum gain of 15 dB and suppressing the first sidelobe below −20 dB, outperforming conventional linear arrays. It is noteworthy that, compared with a Chebyshev-distributed array, the patch width is reduced to only 1 mm, thereby enabling a compact array layout. The unit width dimension is reduced by over 40%, while in a densely packed array configuration, the inter-antenna isolation is increased by more than 18 dB. This current-distribution engineering approach offers a novel, structure-efficient pathway for designing high-performance, densely packed mmWave antenna arrays, circumventing the need for additional decoupling structures or enlarging the antenna spacing. Simulation results show that the average isolation has increased by more than 5 dB from 76 GHz to 79 GHz. Finally, the same design method was used to design a 24 GHz antenna, which was then fabricated and tested. The antenna achieved a sidelobe suppression of −17 dB. Full article

Low-frequency broadband electromagnetic wave absorption is a critical challenge for radar stealth materials, as traditional absorbent-based coatings often suffer from poor low-frequency performance or severe high-frequency degradation when optimized for low frequencies. This study proposes a novel ultra-thin broadband low-frequency absorber fabricated by depositing a periodic resistive layer onto a conventional absorbent-based wave-absorbing layer, which forms a tailored low-frequency conductive metasurface structure. The integrated coating achieves an ultra-thin total thickness of merely 0.4 mm while exhibiting excellent broadband absorption performance across multiple radar bands: it delivers an average reflection loss of −0.6 dB in the L-band (1–2 GHz), −2 dB in the S-band (2–4 GHz), −3.6 dB in the C-band (4–8 GHz), and maintains a stable average reflection loss of −2.8 dB in the X to Ku bands. Compared with single-layer absorbing materials of the same thickness, this material exhibits significantly improved absorbing performance in the S-band and C-band, and achieves a breakthrough from zero to effective absorption in the L-band. Meanwhile, it can be integrated with structural design to reduce radar cross section (RCS), showing excellent engineering application value. The key mechanism underlying the performance enhancement lies in the periodic resistive layer, which optimizes the broadband impedance matching of the entire coating system, effectively elevates the surface current density, and augments resistive loss and eddy current loss within the structure. This design strategy enables an effectively boost in S-band wave-absorbing performance with minimal compromise to the high-frequency absorption characteristics, thus meeting the stringent requirements for broadband radar wave absorption in practical engineering applications. Full article

To ensure the safe operation of floating LNG offloading hoses in bow-loading systems, this study investigates the key factors affecting the dynamic response of an LNG offloading hose string. A fully coupled dynamic model of the hose string, the LNG carrier (LNGC), and the FLNG is established based on the lumped-mass method. The sensitivities of hose loads and deformation indicators to the hose-string length, vessel stand-off distance, tanker-heading offset, internal flow velocity, ocean current speed, and wave height are quantified. Based on these results, a low-load operating configuration is identified and a preliminary operational envelope is proposed. The results show that, under the considered operational sea state, a hose-string length of 170.6 m and an FLNG–LNGC distance of 80 m yield the minimum effective tension. The recommended limiting environmental conditions for safe operations are a surface current speed of 1.1 m/s and a maximum wave height of 7.0 m. The present study provides a practical basis for preliminary configuration design, response assessment, and operational-limit determination of floating LNG export hoses in bow-loading applications. The main contributions of this study are threefold. First, a coupled time-domain framework combining AQWA-based vessel motions and OrcaFlex hose dynamics is established. Second, the effects of key configuration and environmental parameters are systematically quantified. Third, a preliminary operating envelope and a recommended configuration are proposed based on effective tension, bending moment, and curvature. These contributions distinguish the present study from previous work focusing mainly on local hose mechanics or qualitative system description. Full article

Background: Women face underdiagnosed cardiovascular disease (CVD)/stroke risks due to sex-specific pathophysiological mechanisms, including hormonal variations such as oestrogen decline, adverse pregnancy outcomes (APOs), endothelial dysfunction, autoimmune-mediated factors, and sexual dimorphism in cardiac remodelling. Conventional risk assessment tools, predominantly calibrated to male pathophysiology, lack sensitivity in detecting these female-specific determinants. We hypothesise that artificial intelligence (AI), machine learning (ML) and deep learning (DL) may offer a transformative approach by integrating multimodal data, including pathological biomarkers, clinical history, and vascular imaging, to enable precision CVD/stroke risk stratification, pending rigorous external validation in sex-stratified cohorts. Method: This narrative review adopts a PRISMA-informed study selection framework and oversees gender-specific biomarkers, including vasoactive peptides (adrenomedullin), adipocytokines (adiponectin), inflammatory mediators (hs-CRP, IL-6), and thrombogenic factors (homocysteine, D-dimer), alongside clinical variables (APOs, autoimmune disorders) and ultrasonographic markers, carotid intima-media thickness (cIMT), plaque burden and plaque area (PA). Advanced ML/DL algorithms were employed to synthesise these heterogeneous datasets, identifying nonlinear interactions for better outcomes. Findings: Key insights reveal that hormonal dynamics (e.g., hypoestrogenism post-menopause) modulate CVD risk, while APOs induce persistent endothelial dysfunction and subclinical atherosclerosis. Biomarker sexual dimorphism is evident; hs-CRP exhibits higher baseline levels in women, whereas adiponectin declines with metabolic dysfunction. Radiomic features (cIMT progression, plaque morphology) are a well-established biomarker for CVD risk stratification. Conclusions: The integration of AI-driven multimodal systems holds the potential to enable a paradigm shift from population-based to personalised risk assessment, addressing critical gaps in female CVD health. However, this potential is currently at the early validation stage, and widespread clinical implementation requires prospective, externally validated, and ethnically diverse studies. Future applications should incorporate longitudinal biomarker profiling and advanced imaging, namely shear wave elastography and plaque radiomics, to optimise predictive models. Full article

The diagnosis of spinal cord injury (SCI) remains associated with a poor prognosis due to limited treatment options and the absence of curative therapies. Optimizing treatment strategies is therefore crucial to enhance patients’ quality of life, reduce mortality and re-hospitalization rates, and lower overall therapy costs. Shock wave therapy (SWT) is a well-established regenerative treatment option for pathologies of the musculoskeletal system that delivers high-energy acoustic waves. SWT is non-invasive, safe and cost-effective. Preclinical and clinical evidence is emerging, showing the efficacy of SWT in the treatment of both traumatic and ischemic SCI. This systematic review synthesizes evidence on SWT in SCI from 2000 to 2025, excluding case reports and non-regenerative applications. Results were categorized into preclinical and clinical studies, with preclinical findings further divided into functional, histological, cellular, and molecular outcomes. Promising preclinical results led to initial clinical studies, which demonstrated the safety and feasibility of SWT, with a randomized controlled trial currently ongoing (ClinicalTrials.gov: NCT04474106). Overall, the encouraging evidence suggests that SWT is a promising novel regenerative treatment option for SCI, although further research is needed to define optimal treatment protocols and to establish its role in standard clinical care. Full article

Introduction: Caffeine is the most widely consumed psychoactive stimulant worldwide and acts primarily through antagonism of adenosine A1 and A2A receptors, thereby reducing sleep pressure and promoting wakefulness. Although its alerting and performance-enhancing effects are well established, its influence on sleep-related electroencephalography (EEG) has been investigated across diverse paradigms with substantial methodological heterogeneity. This systematic and mechanistic review aimed to synthesize human evidence on how caffeine affects sleep architecture, quantitative sleep EEG, and neurophysiological markers of sleep homeostasis, and to interpret these findings within current models of adenosine-mediated sleep–wake regulation. Materials and methods: A systematic search of PubMed/MEDLINE, Web of Science, Scopus, Embase, PsycINFO, ResearchGate, and Google Scholar was conducted for studies published between January 1980 and January 2026, with the final search performed on 10 January 2026. Eligible studies were original human investigations examining caffeine exposure or administration and reporting sleep-related EEG outcomes, including polysomnographic sleep staging, spectral EEG analyses, or other EEG-derived sleep metrics. Two reviewers independently screened records and assessed eligibility, with disagreements resolved by consensus. Data on study design, participant characteristics, caffeine interventions, EEG methodology, and outcomes were extracted using a predefined form. Risk of bias was evaluated using the RoB 2 and ROBINS-I tools. Owing to marked heterogeneity across studies, findings were synthesized narratively within a mechanistic interpretive framework. Results: Thirty-two studies were included. Across highly heterogeneous paradigms—including acute bedtime or evening dosing, daytime or repeated caffeine use before nocturnal sleep, administration during prolonged wakefulness followed by recovery sleep, withdrawal protocols, and ambulatory/home EEG monitoring—the most consistent finding was suppression of low-frequency NREM EEG activity, particularly slow-wave activity and the lowest delta frequencies. Caffeine frequently increased faster EEG activity, including sigma/spindle and beta ranges, producing a lighter, more aroused, and more wake-like sleep EEG profile. These effects were especially prominent during early-night NREM sleep and in recovery sleep after sleep deprivation, where caffeine attenuated the expected homeostatic rebound in low-frequency power. REM-related effects were less consistent, but some studies reported delayed REM timing and subtler alterations in REM EEG. Emerging evidence further suggests that caffeine increases EEG complexity and shifts sleep dynamics toward a more excitation-dominant state. Several studies indicated that quantitative EEG measures were more sensitive than conventional sleep-stage variables in detecting caffeine-related sleep disruption. Dose, timing, habitual caffeine use, withdrawal state, age, circadian context, and adenosinergic genetic variation, particularly involving ADORA2A, moderated the magnitude of effects. We also highlighted the connection between current results and sports and sports science. Conclusions: Caffeine reliably alters the neurophysiological architecture of human sleep in a direction consistent with reduced sleep depth and weakened homeostatic recovery. The overall evidence supports a mechanistic model centered on adenosine receptor antagonism, attenuation of sleep-pressure build-up and expression, and a shift toward greater cortical arousal during sleep. Sleep EEG appears to be a sensitive marker of these effects, often revealing physiological disruption even when conventional sleep architecture changes are modest. Future research should prioritize larger and more diverse samples, pharmacokinetic and pharmacogenetic characterization, and ecologically valid high-resolution sleep monitoring to clarify the real-world and functional consequences of caffeine-induced EEG changes. Full article

Detonation phenomena in reactive flow systems continue to pose significant challenges for accurate simulation, particularly in 3D validation against experiments and achieving community standardization for schemes. Among the primary difficulties is the selection of suitable convective schemes, which are essential for capturing the complex dynamics of wave propagation and reaction fronts. This study provides a comprehensive historical review of the development and implementation of convective schemes in OpenFOAM, covering the period from 2013 to the present. In addition to documenting the evolution of these methods, we present a detailed technical description of various convective approximation techniques used in detonation simulations within OpenFOAM. This includes an exploration of their underlying principles, advantages, and limitations. Our analysis synthesizes key findings from recent studies and offers practical guidance to researchers when choosing schemes for specific detonation scenarios. It is found that currently, within OpenFOAM, the dominant schemes for convection are the HLLC and KN. Full article

Background/Objectives: Seasonal waves of respiratory viruses—including SARS-CoV-2, influenza A virus (IAV), and respiratory syncytial virus (RSV)—continue to pose a global health burden and highlight the need for antiviral agents that are effective, safe, broadly active, affordable, and widely accessible. Current interventions are limited by the need for their early administration, the risk of resistance, their costs, and the restricted availability in large parts of the world. For certain natural products, such as European black elderberry (Sambucus nigra L.) fruit extract (ElderCraft^®; EC) and the seaweed-derived sulfated polymer iota-carrageenan (IC), antiviral activities against respiratory viruses, particularly IAV and SARS-CoV-2, have previously been shown. Here, we assessed the antiviral activity of IC and an anthocyanin-standardized EC extract against SARS-CoV-2, IAV, and RSV, either as monotherapy or in multiple-dose combinations. Methods: MDCKII cells were infected with IAV_PR8, human Calu-3 lung epithelial cells with the SARS-CoV-2 Omicron variant, and HEp-2 cells with RSV (A2 strain). Inhibitors were administered either by pre-incubation of cell-free virions prior to infection or, in separate time-of-addition experiments, during or post-infection. Viral replication was quantified by qRT-PCR or intracellular immunostaining. Cytotoxicity was evaluated using a neutral red uptake assay. Results: Most intriguingly, both EC and IC are able to neutralize virions derived from SARS-CoV-2, IAV, or RSV extracellularly in a dose-dependent manner. Notably, EC and IC alone exhibited strong anti-RSV activity, which was not reported previously. Most importantly, combined treatment with IC and EC caused a pronounced synergistic antiviral effect against the tested viruses, as confirmed by the Bliss independence model, without any detectable impact on cell viability. Finally, solutions prepared from matrix-standardized mono- or combi-lozenges, containing IC and/or EC in high or low doses, reproduced the antiviral and synergistic combination effects observed with the pure compounds. Conclusions: In summary, these findings support further development of EC and IC as a topically accessible, virion-neutralizing combination (e.g., lozenges) to provide additional protection against major respiratory viruses and potentially strengthen pandemic preparedness. Full article

When a line short-circuit fault occurs in a DC distribution network, the fault current rises quickly and affects a wide range, jeopardizing the safe operation of the system. In order to locate the fault quickly and accurately, this study proposes a fault localization method based on the Variational Mode Decomposition (VMD) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. First, the nonlinear relationship between the intrinsic principal frequency and fault distance is analyzed; then, the intrinsic principal frequency of the faulty traveling wave is extracted by using VMD, and the nonlinear relationship between the spectral energy of the principal frequency of the intrinsic frequency and the fault distance is fitted by training the Bi-LSTM network incorporating the attention mechanism. Finally, in response to the issue that a small amount of fault data in practical engineering is difficult to support the amount of data required for deep learning, a transfer learning method is used to locate the fault in the target domain. A small sample test of the target domain is carried out using the migration learning method. The experimental results show that the proposed method has high localization accuracy and good resistance to over-resistance and noise; compared with the traditional network training, the localization error based on migration learning is smaller, and the network convergence effect is better. Full article

This paper proposes a Hybrid-Field DD (HFDD) coupler designed for wireless power transfer (WPT) in mobile robots within smart manufacturing environments, utilizing a dual-coupling mechanism of magnetic and electric fields. The proposed coupler integrates Double-D coils for vertical magnetic field concentration with a split metal plate structure for enhanced electric field coupling in a compact, low-profile design. To evaluate the electromagnetic performance and the impact of inevitable eddy current interference, two distinct configurations—Front Plate Arrangement (FPA) and Back Plate Arrangement (BPA)—are analyzed through both theoretical modeling and 3D full-wave simulations (HFSSs). The comparative results demonstrate that the FPA model reduces the peak induced current intensity by 56.23 A/m compared to the BPA and achieves a peak leakage magnetic field intensity of 1.12 A/m, which is 28% lower than the 1.56 A/m observed in the BPA, offering a superior solution for suppressing leakage magnetic field and contributing to robust coupling stability. The high consistency between the proposed analytical methodology and numerical simulations underscores the theoretical robustness of the HFDD structure, establishing a clear design framework for efficient power transfer in robotic applications. Full article

The 795 nm wavelength corresponds to the D1 transition of rubidium atoms and is widely used in atomic optical pumping, atomic clocks, magnetometers, and precision spectroscopy. For compact free-space collimation, beam shaping, and efficient fiber coupling, edge-emitting semiconductor lasers with reduced fast-axis (vertical) divergence are highly desirable, yet low-divergence designs at 795 nm remain limited. Here, we propose and demonstrate low-divergence photonic-crystal epitaxy (LD–PC) for 795 nm edge-emitting lasers. By engineering a periodic n-side photonic-crystal stack to place the fundamental vertical mode near the photonic band edge, the vertical mode is expanded while maintaining effective modal discrimination. Narrow-ridge Fabry–Pérot lasers based on GaAsP/AlGaAs single-quantum-well epitaxy were fabricated and characterized. The optimized LD–PC device (3 μm ridge width, 1 mm cavity length) delivers 227 mW at 200 mA with a threshold current of 23 mA, a slope efficiency of 1.28 W/A, and a peak wall-plug efficiency of 55% under continuous-wave operation at 25 °C. The measured far-field divergences (FWHMs) are 7.16° and 18.83° in the lateral and vertical directions, respectively, corresponding to a reduction in the vertical divergence from >40° in the reference structure to <20° with LD–PC. These results validate photonic-crystal epitaxy as an effective route toward compact, high-performance, low-divergence 795 nm semiconductor laser sources for rubidium-based atomic systems. Full article

In the present work a new regime of periodical energy exchange between pump, signal and idler waves, under the influence of the process of four-wave mixing (FWM), with additional consideration of the effects of self-phase modulation (SPM) and cross-phase modulation (XPM), is presented. In our previous papers a theoretical model which successfully describes the amplification and periodic energy exchange between the three optical waves in CW regime of laser source propagation (short-cut equations) was developed. Exact analytical solutions, describing the periodic changes in the intensities of pump, signal and idler waves, were found and expressed by the Jacobi elliptic functions. The period of the energy exchange between the waves can be presented by elliptic integral of the first kind. In the current research, the periods of energy exchange between the pump, signal and idler waves in the process of FWM, additionally taking into account the effects of SPM and XPM, are investigated. A comparison between the obtained results has been made. It is shown that the effects of self-phase modulation and cross-phase modulation increase the period of energy exchange. Full article