Search Results (203)

Background: The widespread use of cardiac magnetic resonance imaging (MRI) in clinical practice has enabled the identification of numerous patients with evident damage from previous myocarditis, whether known or unknown. For years, myocardial fibrosis has been a topic of interest due to its established correlation with arrhythmic events in various clinical settings, including ischemic heart disease, dilated cardiomyopathy, and hypertrophic cardiomyopathy. MIBG scintigraphy is a method widely used in patients who are candidates for defibrillator implantation or have experienced heart failure. This examination evaluates the sympathetic innervation of the myocardium. Objective: To assess the real arrhythmogenic risk of non-ischemic scars identified in symptomatic or asymptomatic patients through the use of MIBG. Methods: Patients were retrospectively selected based on the presence of non-ischemic myocardial fibrosis detected by cardiac MRI, consistent with a myocarditis outcome (even in the absence of a clear history of myocarditis). These patients underwent myocardial scintigraphy with MIBG using a tomographic technique. Results: A total of 50 patients (41 males, mean age 51 ± 16 years) who underwent MRI from 2019 to June 2024 were selected. The primary indication for MRI was ventricular ectopic extrasystoles detected on Holter ECG (n = 12, 54%), while five patients underwent MRI following a known acute infectious event (23%, including three cases of COVID-19 infection). All symptomatic patients presented with chest pain in the acute phase, accompanied by elevated hsTNI levels (mean value: 437 pg/mL). The MRI findings showed normal ventricular volumes (LV: 80 mL/m², RV: 81 mL/m²) and normal ejection fractions (56% and 53%, respectively). The mean native T1 mapping value was 1013 ms (normal range: 950–1050). T2 mapping values were altered in the 5 patients who underwent MRI during the acute phase (mean value: 57 ms), without segmentation. Additionally, three patients had non-tamponade pericardial effusion. All patients exhibited LGE (nine subepicardial, seven midwall, six patchy). All patients underwent myocardial scintigraphy with MIBG at least 6 months after the acute event, with only one case yielding a positive result. This patient, a 57-year-old male, had the most severe clinical presentation, including more than 65,000 premature ventricular beats (PVBs) and multiple episodes of paroxysmal supraventricular tachycardia (PSVT) recorded on Holter ECG. MRI findings showed severe left ventricular dysfunction, a slightly dilated LV, and midwall LGE at the septum, coinciding with hypokinetic areas. Conclusions: MIBG scintigraphy could be a useful tool in assessing arrhythmic risk in patients with previous myocarditis. It could help reduce the clinical burden of incidental findings of non-ischemic LGE, which does not appear to be independently associated with an increased risk profile. Full article

The impact mechanism of long-term creep in gas-containing coal on coal and gas outbursts has not been fully elucidated and remains insufficiently understood for the purpose of disaster engineering control. This investigation conducted triaxial creep experiments on raw coal specimens under controlled confining pressures, axial stresses, and gas pressures. Through systematic analysis of coal’s physical responses across different loading conditions, we developed and validated a novel creep damage constitutive model for gas-saturated coal through laboratory data calibration. The key findings reveal three characteristic creep regimes: (1) a decelerating phase dominates under low stress conditions, (2) progressive transitions to combined decelerating–steady-state creep with increasing stress, and (3) triphasic decelerating–steady–accelerating behavior at critical stress levels. Comparative analysis shows that gas-free specimens exhibit lower cumulative strain than the 0.5 MPa gas-saturated counterparts, with gas presence accelerating creep progression and reducing the time to failure. Measured creep rates demonstrate stress-dependent behavior: primary creep progresses at 0.002–0.011%/min, decaying exponentially to secondary creep rates below 0.001%/min. Steady-state creep rates follow a power law relationship when subject to deviatoric stress (R² = 0.96). Through the integration of Burgers viscoelastic model with the effective stress principle for porous media, we propose an enhanced constitutive model, incorporating gas adsorption-induced dilatational stresses. This advancement provides a theoretical foundation for predicting time-dependent deformation in deep coal reservoirs and informs monitoring strategies concerning gas-bearing strata stability. This study contributes to the theoretical understanding and engineering monitoring of creep behavior in deep coal rocks. Full article

CT scanners are essential tools in modern medical imaging. Sudden failures of their X-ray tubes can lead to equipment downtime, affecting healthcare services and patient diagnosis. However, existing prediction methods based on a single model struggle to adapt to the multi-stage variation characteristics of tube lifespan and have limited modeling capabilities for temporal features. To address these issues, this paper proposes an intelligent prediction architecture for CT tubes’ remaining useful life based on a dual-branch neural network. This architecture consists of two specialized branches: a residual self-attention BiLSTM (RSA-BiLSTM) and a multi-layer dilation temporal convolutional network (D-TCN). The RSA-BiLSTM branch extracts multi-scale features and also enhances the long-term dependency modeling capability for temporal data. The D-TCN branch captures multi-scale temporal features through multi-layer dilated convolutions, effectively handling non-linear changes in the degradation phase. Furthermore, a dynamic phase detector is applied to integrate the prediction results from both branches. In terms of optimization strategy, a dynamically weighted triplet mixed loss function is designed to adjust the weight ratios of different prediction tasks, effectively solving the problems of sample imbalance and uneven prediction accuracy. Experimental results using leave-one-out cross-validation (LOOCV) on six different CT tube datasets show that the proposed method achieved significant advantages over five comparison models, with an average MSE of 2.92, MAE of 0.46, and R² of 0.77. The LOOCV strategy ensures robust evaluation by testing each tube dataset independently while training on the remaining five, providing reliable generalization assessment across different CT equipment. Ablation experiments further confirmed that the collaborative design of multiple components is significant for improving the accuracy of X-ray tubes remaining life prediction. Full article

Background/Objectives: Recent changes in obstetric practices and population demographics have prompted a re-evaluation of labor patterns. This study aimed to characterize labor patterns in a Greek pregnant population using ultrasound and compare them with established labor curves. Methods: A prospective cohort study was conducted at the Third Department of Obstetrics and Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece, over a two-year period (December 2022 to June 2024). Transabdominal ultrasound was used to determine the fetal head position and transperineal ultrasound was used to measure angle of progression (AoP) and head–perineum distance (HPD) during labor. Maternal and labor characteristics, including body mass index (BMI), parity, labor duration, and mode of delivery, were recorded. Statistical analysis included mixed linear models to assess the relationship between AoP, HPD, and cervical dilatation. Results: In total, 500 parturients were included in this study. Women entered the active phase of labor approximately 5 h before delivery, with AoP increasing sharply and HPD decreasing rapidly at this point. Cesarean section (CS) cases showed a slower increase in AoP compared to vaginal deliveries (VDs), with CS cases having a mean AoP of 117.9° (95% CI: 111.6–124.2°) at full dilation, compared to 133.4° (95% CI: 130.6–136.2°) in VD. HPD values declined more slowly in CS cases, with a mean HPD of 45.1 mm (95% CI: 40.6–49.6 mm) at full dilation, compared to 36.4 mm (95% CI: 34.3–38.5 mm) in VD. Epidural analgesia was associated with steeper increases in AoP and decreases in HPD in the final 2.5 h before delivery, while oxytocin administration accelerated these changes in the last 3–4 h. The mean time to delivery was 3.19 h (95% CI: 2.80–3.59 h) when AoP reached 125° and 3.92 h when HPD was 40 mm (95% CI: 3.53–4.30 h). BMI in women who gave birth via CS was significantly higher compared to VD (32.03 vs. 29.94 kg/m², p-value: 0.008), and the total duration of labor was shorter in VD compared to CS and operative vaginal delivery (OVD) (8 h vs. 15 h, p-value < 0.001 and 8 h vs. 12 h, p-value < 0.001, respectively). Birthweight was also lower in VD compared to CS (3103.09 g vs. 3267.88 g, p-value: 0.05). Conclusions: This study provides the first ultrasonographic characterization of labor patterns in a Greek population, highlighting the utility of ultrasound in objectively assessing labor progression. Full article

Atmospheric climate science lacks the capacity to integrate thermodynamics with the gravitational potential of air in a classical quantum theory. To what extent can we identify Carnot’s ideal heat engine cycle in reversible isothermal and isentropic phases between dual temperatures partitioning heat flow with coupled work processes in the atmosphere? Using statistical action mechanics to describe Carnot’s cycle, the maximum rate of work possible can be integrated for the working gases as equal to variations in the absolute Gibbs energy, estimated as sustaining field quanta consistent with Carnot’s definition of heat as caloric. His treatise of 1824 even gave equations expressing work potential as a function of differences in temperature and the logarithm of the change in density and volume. Second, Carnot’s mechanical principle of cooling caused by gas dilation or warming by compression can be applied to tropospheric heat–work cycles in anticyclones and cyclones. Third, the virial theorem of Lagrange and Clausius based on least action predicts a more accurate temperature gradient with altitude near 6.5–6.9 °C per km, requiring that the Gibbs rotational quantum energies of gas molecules exchange reversibly with gravitational potential. This predicts a diminished role for the radiative transfer of energy from the atmosphere to the surface, in contrast to the Trenberth global radiative budget of ≈330 watts per square metre as downwelling radiation. The spectral absorptivity of greenhouse gas for surface radiation into the troposphere enables thermal recycling, sustaining air masses in Lagrangian action. This obviates the current paradigm of cooling with altitude by adiabatic expansion. The virial-action theorem must also control non-reversible heat–work Carnot cycles, with turbulent friction raising the surface temperature. Dissipative surface warming raises the surface pressure by heating, sustaining the weight of the atmosphere to varying altitudes according to latitude and seasonal angles of insolation. New predictions for experimental testing are now emerging from this virial-action hypothesis for climate, linking vortical energy potential with convective and turbulent exchanges of work and heat, proposed as the efficient cause setting the thermal temperature of surface materials. Full article

Infrared small-target detection encounters significant challenges due to a low image signal-to-noise ratio, limited target size, and complex background noise. To address the issues of sparse feature loss for small targets during the down-sampling phase of the traditional U-Net network and the semantic gap in the feature fusion process, a multilevel feature extraction and fusion attention network (MEFA-Net) is designed. Specifically, the dilated direction-sensitive convolution block (DDCB) is devised to collaboratively extract local detail features, contextual features, and Gaussian salient features via ordinary convolution, dilated convolution and parallel strip convolution. Furthermore, the encoder attention fusion module (EAF) is employed, where spatial and channel attention weights are generated using dual-path pooling to achieve the adaptive fusion of deep and shallow layer features. Lastly, an efficient up-sampling block (EUB) is constructed, integrating a hybrid up-sampling strategy with multi-scale dilated convolution to refine the localization of small targets. The experimental results confirm that the proposed algorithm model surpasses most existing recent methods. Compared with the baseline, the intersection over union (IoU) and probability of detection $P_d$ of MEFA-Net on the IRSTD-1k dataset are increased by 2.25% and 3.05%, respectively, achieving better detection performance and a lower false alarm rate in complex scenarios. Full article

Background/Objectives: Anatomical and functional damage of the mitral valve (MV) apparatus in patients with dilated cardiomyopathy (DCM) is secondary to left ventricular (LV) injury, leading to functional mitral regurgitation (FMR). Real-time four-dimensional echocardiography (RT 4DE) is a useful imaging technique in different pathologies, including DCM. Left atrial (LA) strain, as measured by left atrium quantification software, is an accurate technique for evaluating increased filling pressure. The MV has a complex three-dimensional morphology and motion. Four-dimensional echocardiography (4DE) has revolutionized clinical imaging of the mitral valve apparatus. This study aims (1) to characterize the mitral annulus (MA) parameters in patients with DCM and advanced-stage heart failure (HF) according to etiology and (2) to find correlations between left atrial function and MA remodeling in this group of patients, using 4DE quantification software. Methods: A total of 82 patients with DCM and an LV ejection fraction ≤ 40% were recruited. Conventional 2DE and RT 4DE were conducted in DCM patients with a compensated phase of HF before discharge. The measured parameters were left atrial reservoir strain (LASr), annular area (AA), annular perimeter (AP), anteroposterior diameter (A-Pd), posteromedial to anterolateral diameter (PM-ALd), commissural distance (CD), interregional distance (ITD), annular height (AH), nonplanar angle (NPA), tenting height (TH), tenting area (TA), and tenting volume (TV). Results: Measured parameters revealed more advanced damage of LA and MA parameters in ischemic compared to nonischemic etiology. Univariate analysis identified AA, AP, A-Pd, PM-ALd, CD, ITD, TH, TA, and TV (p < 0.0001) as determinants of LASr. Including these parameters in a stepwise multivariate logistic regression, PM-ALd (p = 0.03), TH (p = 0.043), and TV (p = 0.0001) were the best predictors of LAsr in these patients. Conclusions: The results of this study revealed the correlation between LA function depression and MA remodeling in patients with DCM. Full article

The South Shetland Islands and Antarctic Peninsula (SHETPENANT region) constitute a geodynamically active area shaped by the interaction of major tectonic plates and active magmatic systems. This study analyzes GNSS time series spanning from 2017 to 2024 to investigate surface deformation associated with the 2020–2021 seismic swarm near the Orca submarine volcano. Horizontal and vertical displacement velocities were estimated for the preseismic, coseismic, and postseismic phases using the CATS method. Results reveal significant coseismic displacements exceeding 20 mm in the horizontal components near Orca, associated with rapid magmatic pressure release and dike intrusion. Postseismic velocities indicate continued, though slower, deformation attributed to crustal relaxation. Stations located near the Orca exhibit nonlinear, transient behavior, whereas more distant stations display stable, linear trends, highlighting the spatial heterogeneity of crustal deformation. Stress and strain fields derived from the velocity models identify zones of extensional dilatation in the central Bransfield Basin and localized compression near magmatic intrusions. Maximum strain rates during the coseismic phase exceeded 200 $\mathrm{\nu }$strain/year, supporting a scenario of crustal thinning and fault reactivation. These patterns align with the known structural framework of the region. The integration of GNSS-based displacement and strain modeling proves essential for resolving active volcano-tectonic interactions. The findings enhance our understanding of back-arc deformation processes in polar regions and support the development of more effective geohazard monitoring strategies. Full article

To address the challenges of extracting rolling bearing degradation information and the insufficient performance of conventional convolutional networks, this paper proposes a rolling bearing degradation state identification method based on the improved monopulse feature extraction and a one-dimensional dilated residual convolutional neural network (1D-DRCNN). First, the fault pulse envelope waveform features are extracted through phase scanning and synchronous averaging, and a two-stage grid search strategy is employed to achieve FCC calibration. Subsequently, a 1D-DRCNN model is constructed to identify rolling bearing degradation states under different working conditions. The experimental study collects the vibration signals of nine degradation states, including the different sizes of inner and outer ring local faults as well as normal conditions, to comparatively analyze the proposed method’s rapid calibration capability and feature extraction quality. Furthermore, t-SNE visualization is utilized to analyze the network response to bearing degradation features. Finally, the degradation state identification performance across different network architectures is compared in pattern recognition experiments. The results show that the proposed improved feature extraction method significantly reduces the iterative calibration computational burden while effectively extracting local fault degradation information and overcoming complex working condition influence. The established 1D-DRCNN model integrates the advantages of dilated convolution and residual connections and can deeply mine sensitive features and accurately identify different bearing degradation states. The overall recognition accuracy can reach 97.33%. Full article

This paper presents a novel deep learning-based automatic modulation recognition (AMR) model, designed to classify ten modulation types from complex I/Q signal data. The proposed architecture, named CV-CNN-TCN, integrates Complex-Valued Convolutional Neural Networks (CV-CNNs) with Temporal Convolutional Networks (TCNs) to jointly extract spatial and temporal features while preserving the inherent phase information of the signal. An enhanced variant, CV-CNN-TCN-DCC, incorporates dilated causal convolutions to further strengthen temporal representation. The models are trained and evaluated on the benchmark RadioML2016.10b dataset. At SNR = −10 dB, the CV-CNN-TCN achieves a classification accuracy of 37%, while the CV-CNN-TCN-DCC improves to 40%. In comparison, ResNet reaches 33%, and other models such as CLDNN (convolutional LSTM dense neural network) and SCRNN (Sequential Convolutional Recurrent Neural Network) remain below 30%. At 0 dB SNR, the CV-CNN-TCN-DCC achieves a Jaccard index of 0.58 and an MCC of 0.67, outperforming ResNet (0.55, 0.64) and CNN (0.53, 0.61). Furthermore, the CV-CNN-TCN-DCC achieves 75% accuracy at SNR = 10 dB and maintains over 90% classification accuracy for SNRs above 2 dB. These results demonstrate that the proposed architectures, particularly with dilated causal convolutional enhancements, significantly improve robustness and generalization under low-SNR conditions, outperforming state-of-the-art models in both accuracy and reliability. Full article

Women have a lower age-matched cardiovascular risk than men, largely due to estrogen’s protective role in endothelial function. While exercise improves vascular health, acute vascular responses are influenced by factors such as age, fitness level, metabolic status, and exercise modality. In premenopausal women, fluctuations in estrogen levels during the menstrual cycle may further affect vascular reactivity. Here, we review current evidence on acute exercise-induced vascular responses in women, emphasizing menstrual phase influences and key biomarkers such as flow-mediated dilation (FMD), along with others including vascular conductance and pulse wave velocity (PWV). Despite limited and heterogeneous evidence, shear-induced vascular responses, (including FMD) following acute exercise, appear to be relatively stable across menstrual cycle phase, suggesting that strict phasic control may not always be necessary. However, future high-quality studies are needed to further clarify this response. In contrast, other vascular assessments that rely more heavily on neural components—such as vascular conductance and PWV—show greater estrogen sensitivity. Nonetheless, the inconsistencies between studies again underscore the need for future research with hormonal verification. Morever, adequate sample sizes, and standardized exercise protocols will improve both consistency and help develop and promote the inclusion of women in vascular research. Full article

Small-target detection in remote sensing presents three fundamental challenges: limited pixel representation of targets, multi-angle imaging-induced appearance variance, and complex background interference. This paper introduces a dual-component neural architecture comprising Hierarchical Dynamic Refinement Attention (HiDRA) and Densely Connected Dilated Block (DCDBlock) to address these challenges systematically. The HiDRA mechanism implements a dual-phase feature enhancement process: channel competition through bottleneck compression for discriminative feature selection, followed by spatial-semantic reweighting for foreground–background decoupling. The DCDBlock architecture synergizes multi-scale dilated convolutions with cross-layer dense connections, establishing persistent feature propagation pathways that preserve critical spatial details across network depths. Extensive experiments on AI-TOD, VisDrone, MAR20, and DOTA-v1.0 datasets demonstrate our method’s consistent superiority, achieving average absolute gains of +1.16% (mAP₅₀), +0.93% (mAP₉₅), and +1.83% (F1-score) over prior state-of-the-art approaches across all benchmarks. With 8.1 GFLOPs computational complexity and 2.6 ms inference speed per image, our framework demonstrates practical efficacy for real-time remote sensing applications, achieving superior accuracy–efficiency trade-off compared to existing approaches. Full article

Interrupted sampling repeater jamming (ISRJ) is characterized by its coherent processing gains and flexible modulation techniques. ISRJ generates spurious targets along the range, which presents significant challenges to the radar systems. However, existing ISRJ countermeasure methods struggle to eliminate ISRJ signals without compromising the integrity of the real target signal, especially under low-signal-to-noise-ratio (SNR) conditions, resulting in a deteriorated sidelobe and diminished detection performance. We propose a complex-valued encoder–decoder network (CVEDNet) to address these challenges based on signal decomposition. This network offers an end-to-end ISRJ suppression approach, working on complex-valued time-domain signals without the need for additional preprocessing. The encoding and decoding structure suppresses noise components and obtains more compact echo feature representations through layer-by-layer compression and reconstruction. A stacked dual-branch structure and multi-scale dilated convolutions are adopted to further separate the echo signal and ISRJ based on high-dimensional features. A multi-domain combined loss function integrates the waveform and range-pulse-compression information to ensure the amplitude and phase integrity of the reconstructed echo waveform during the training process. The effectiveness of the proposed method was validated in terms of its jamming suppression capability, echo fidelity, and detection performance indicators under low-SNR conditions compared to conventional methods. Full article

Background/Objectives: Fruits and vegetables (F&Vs) are major dietary sources of phytochemicals, crucial for preventing non-communicable diseases. However, barriers such as preparation inconvenience and a short shelf life hinder their consumption. F&V-coated foods have emerged as an alternative. This human nutrition intervention study assessed the effects of a blended F&Vs mixture versus an F&V-coated food on phytochemical absorption and chronic disease risk markers. It also explored how genetic variation influences physiological responses to these F&V products. Methods: In this randomized-controlled trial, participants were assigned to one of three dietary interventions: a blended F&V mixture (“F&V Blend”), a rice-based cereal product coated with this blend (“Coated Pearl”), or the same product without the F&V mixture (“Uncoated Pearl”). The four-week study included a two-week run-in and a two-week intervention phase, each followed by a test day. Measurements included DNA damage resistance (comet assay), plasma antioxidant status (Trolox capacity and superoxide levels), microvasculature health (retinal analysis), and plasma phytochemical concentrations (colorimetric analyses or HPLC). To assess group differences, a linear mixed model was used. Fifteen polymorphic genes related to phytochemical metabolism and oxidative stress were tested using TaqMan and PCR, with outcomes analyzed via ANOVA. Results: The F&V Blend and Coated Pearl products increased plasma carotenoid levels versus the Uncoated Pearl product. Only the F&V Blend improved retinal dilation and DNA resistance. Surprisingly, the Uncoated Pearl product enhanced antioxidant capacity, lowered superoxide levels, and improved retinal microvasculature. Genotype effects were minimal, except for HNF1A, where wildtypes in the Uncoated Pearl group showed a higher antioxidant capacity. Conclusions: Fresh F&Vs were more effective than coated alternatives in improving vascular health and DNA protection. Full article

To investigate the effects of deformation parameters on the phase transformation of B1500HS high-strength steel, non-isothermal deformation tests were conducted on a Thermomaster-Z thermal mechanical simulator under different conditions in this study. Qualitative and quantitative investigations were carried out by analyzing the dilatation curves, color metallograph, and hardness data of deformed specimens. The results indicate that deformation can promote the formation of non-martensite. Higher initial deformation temperature and lower strain are beneficial for obtaining more martensite in the deformed high-strength steel and leading to higher martensite transformation temperatures. Meanwhile, the variation of strain rate has relatively small effects on the content and transformation temperature of martensite, and the effects do not show a singular trend. Full article