Search Results (9,596)

Background and Objectives: Cardiac Syndrome X (CSX), a clinical entity within the Ischaemia with Non-Obstructive Coronary Arteries (INOCA) spectrum, is increasingly recognised as an inflammatory and systemic vascular disorder. Remnant cholesterol (RC) and inflammation are emerging contributors to residual cardiovascular risk; however, their combined role in microvascular angina remains unclear. This study aimed to evaluate the association between the remnant cholesterol inflammation index (RCII), integrating RC and high-sensitivity C-reactive protein (hs-CRP), and the clinical presence of CSX. Methods: This single-centre, retrospective observational study included 392 individuals who underwent coronary angiography between January 2023 and January 2025. The study population comprised 197 patients diagnosed with CSX and 195 control subjects with normal coronary anatomy and no objective evidence of myocardial ischaemia. RC was calculated as total cholesterol minus the sum of LDL-C and HDL-C, and RCII was derived as RC × hs-CRP. Importantly, invasive microvascular testing (e.g., CFR or IMR) was not performed. Logistic regression analyses were performed to identify independent predictors of CSX, and receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic performance. Results: Patients with CSX exhibited significantly higher levels of hs-CRP, SII, and RCII compared with controls (all p < 0.001). In the multivariable logistic regression analysis, RCII demonstrated an independent association with CSX (odds ratio 1.095, 95% confidence interval 1.060–1.131; p < 0.001). ROC curve analysis showed that RCII provided moderate but significant discrimination for CSX (area under the curve [AUC] 0.765, 95% CI 0.695–0.795). Pairwise comparisons confirmed that RCII had a significantly higher AUC than RC, hs-CRP, or SII individually. Conclusions: Higher RCII levels appear to be significantly associated with the clinical diagnosis of CSX. By integrating atherogenic remnant cholesterol burden and systemic inflammation, RCII may serve as a valuable composite biomarker for identifying residual inflammatory lipid risk. Rather than acting as a definitive diagnostic tool, these findings warrant further validation in large-scale prospective cohort studies. Full article

Polyhydroxyalkanoates (PHAs) emerge as promising biodegradable polymers for sustainable applications, yet predicting their biodegradation behavior under different environmental conditions remains challenging. In this study, we propose a novel data-driven computational framework for predicting biodegradation-induced weight/mass loss in PHA-based materials. A comprehensive database of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV)-based formulations was manually curated by systematically collecting and harmonizing material descriptors, environmental parameters, and experimental biodegradation outcomes from laboratory- and large-scale studies conducted in soil, marine, freshwater, and compost environments. Multiple regression-based quantitative structure–activity relationship (QSAR) models were developed and rigorously validated, demonstrating high predictive performance and strong correlations between polymer structure, environmental conditions and degradation behavior. “Exposure time”, “degradation environment” and “hydroxybutyrate (HB) ratio” were identified as the most important features for weight loss. Finally, the predictive model was integrated into the Jaqpot computational platform, enabling open access and facilitating data-driven assessment and design of biodegradable polymer systems. Full article

Azimuth multi-channel (AMC) Synthetic Aperture Ladar (SAL) is a promising technique for overcoming the inherent trade-off between azimuth resolution and swath width in single-channel SAL, by replacing temporal sampling with spatial sampling. However, due to the micron-scale wavelength, AMC SAL is extremely sensitive to non-cooperative target motion: even millimeter-level radial velocities can induce significant inter-channel phase deviations, leading to severe azimuth ambiguities (false targets). To address this critical issue, a motion-compensated reconstruction framework for AMC SAL is proposed for micro-motion targets. The relationship between target radial motion and inter-channel phase deviations is theoretically derived, and a parametric strategy based on a Minimum Azimuth Ambiguity-to-Signal Ratio (MAASR) criterion is proposed to estimate the radial velocity. Simulation results demonstrate that the uncompensated processing suffers from strong ambiguities (AASR = −2.90 dB) and a notable azimuth position shift (−42 samples), whereas the proposed method suppresses false targets to the noise floor (<−40 dB) and corrects the position error. These simulation results indicate that the proposed method enables AMC SAL imaging for the non-cooperative moving target with millimeter-level radial velocity. Full article

Background/Objections: One of the most common surgical procedures performed internationally is the cesarean section. It is known to be associated with intense postoperative pain and a slow recovery process. Focusing on surgical techniques, especially the type of fascial closure, is an area that has received very little attention when it comes to postoperative pain and rapid recovery. Using a mixed-methods approach, the primary objective of this study was to assess the impact of guide-suture-assisted modified fascial closure on postoperative pain and early mobilization after cesarean sections. Methods: Women undergoing elective cesarean sections with Pfannenstiel’s incision were the study participants of this prospective, single-center, randomized mixed-methods study. Participants were enrolled in the study and randomized to either classical continuous fascial closure or guide-suture-assisted modified fascial closure, which was carried out in a 1:1 ratio. Quantitative data assessed postoperative pain through the Visual Analog Scale (VAS), a Numeric Rating Scale (NRS), and the Short-Form McGill Pain Questionnaire (SF-MPQ), and functional recovery was assessed through walking distances at postoperative 6, 12, 24, and 48 h. Qualitative data were collected via semi-structured interviews and analyzed through conventional content analysis to understand the patients’ perceptions of pain and recovery experiences. Results: The first 24 h postoperative period pain levels were significantly lower for the modified fascial closure group versus the classical closure group (p < 0.05). Moreover, the modified closure group had a significantly better functional recovery, evidenced by walking greater distances at 12, 24, and 48 h postoperative. Qualitative results indicated improved comfort and stronger early mobilization confidence, in addition to less movement apprehension, consistent with the above results, among those with the modified technique. Conclusions: The modified fascial closure technique with guide suture was linked to less pain in the early postoperative period and better functional recovery after cesarean section. This technique is a good candidate for addition to standard obstetric procedures since it is cost effective, easily added, and surgical practice will improve comfort for mothers and assist with early mobilization. Full article

This study represents the first comprehensive investigation examining how oven temperature and drawing ratios, two key tow stretch-breaking parameters, influence the properties of high-bulk acrylic yarns. Only the tow parameters were altered, while all other production parameters involved in converting from tow to yarn remained constant. Two experimental sets were conducted. In the first, oven temperatures (100 °C, 120 °C, 130 °C, 150 °C, and 170 °C) and the ratios (1.3, 1.47, 1.59, and 1.64) in the drawing zone (E₁) were altered. In the second, oven temperatures (130 °C and 150 °C) and the ratios (1.3, 1.35, 1.49, 1.54, 1.62, 1.66, 1.70, 1.81, and 1.90) in the break-draw zone (E₅) were altered. The samples, produced on industrial-scale machines, were evaluated for shrinkage of fiber slivers in water steam, yarn hairiness, unevenness, tensile strength and strain, and hand-feel rating of yarn balls. The highest shrinkage was obtained at 130 °C and 150 °C with the drawing ratio of 1.47, while the lowest occurred at 130 °C with the drawing ratio of 1.3. The lowest tensile strength and strain were obtained at 150 °C, while the highest values were obtained at 130 °C with 1.59. The yarn hairiness and unevenness were lowest at 130 °C and increased at both lower and higher temperatures. Full article

Reliable precipitation data are fundamental for climate and hydrological research, especially in regions with sparse ground-based observations. This study evaluates and compares the accuracy of two satellite-based precipitation products—CMORPH and GPCP—across daily, monthly, and annual scales over Iran. Daily, monthly, and annual precipitation estimates from CMORPH and GPCP were validated against observations from 128 meteorological stations distributed throughout the country. The assessment employed two statistical indices—correlation coefficient (CC) and root mean square error (RMSE)—alongside three categorical indices: probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). At the daily scale, CMORPH outperformed GPCP in terms of CC, RMSE, POD, and CSI, while GPCP exhibited a lower FAR. At the monthly scale, correlations between satellite-derived and station-based precipitation were stronger than those at the daily scale; CMORPH achieved the highest correlation (CC = 0.84), whereas GPCP yielded a lower RMSE, with a mean value of 26.2 mm. At the annual scale, GPCP demonstrated better performance in CC, while CMORPH showed superior accuracy in RMSE. CMORPH consistently underestimated precipitation, whereas GPCP tended to overestimate rainfall across Iran. Although both datasets provided reliable precipitation estimates at the national scale, CMORPH demonstrated higher overall accuracy and efficiency. Its superior performance across most indices makes CMORPH the more suitable dataset for precipitation monitoring in Iran, despite its tendency to underestimate rainfall relative to ground observations. Full article

In recent years, image denoising has seen a shift from traditional non-local self-similarity methods like BM3D to deep-learning based approaches that use learnable convolutions and attention mechanisms. While pixel-level attention is effective at capturing long-range relationships similar to non-local self-similarity based methods, it incurs extremely high computational costs that scale quadratically with image resolution. As an alternative, channel-wise attention is resolution-independent and computationally efficient but may miss crucial spatial details. In this paper, an adjustable attention mechanism is introduced that bridges the gap between pixel and channel attentions. In the proposed model, average pooling and variable-size convolutions are added before attention calculation to adjust spatial resolution and, thus, allow dynamical adjustment of computational complexity. This adjustable attention is applied in a transformer-based U-Net architecture and achieves performance comparable to state-of-the-art methods in both real and Gaussian blind denoising tasks. To be more concrete, the proposed method achieves a Peak Signal-to-Noise Ratio of 39.65 dB and a Structural Similarity Index Measure of 0.913 on the Smartphone Image Denoising Dataset. Therefore, the proposed method demonstrates a balance between efficiency and denoising quality. Full article

Unmanned aerial vehicles (UAVs) provide a flexible platform for surface reflectance measurement at spatial scales between ground observations and satellite remote sensing. This study develops a UAV-based spectroradiometric system for surface reflectance retrieval under natural illumination conditions using non-imaging hyperspectral sensors. The system integrates two stabilized spectroradiometers mounted on a UAV to simultaneously measure hemispherical downwelling irradiance and upwelling surface radiance at flight altitude, enabling reflectance retrieval through a radiance–irradiance ratio framework without relying on ground calibration targets or radiative transfer model inversion. Field experiments were conducted over agricultural plots, and the UAV-derived reflectance was quantitatively validated against ground-based dual-spectroradiometer measurements. The results demonstrate stable irradiance measurements during flight and good agreement between UAV- and ground-derived reflectance across the 400–900 nm spectral range. The proposed system offers a practical and reliable solution for hyperspectral reflectance retrieval using UAV platforms. Full article

Large-scale pavement maintenance generates substantial amounts of reclaimed base course material (RBM), whose high-value reuse presents a critical challenge. Although cement is commonly used for stabilization, its high carbon footprint and shrinkage issues limit sustainability. This study proposes a 100% solid waste-based cementitious material (SWC) as an alternative stabilizer for pavement base layers containing high proportions of RBM. A comparative investigation was conducted between SWC-stabilized RBM (SSRBM) and ordinary Portland cement-stabilized RBM (CSRBM) regarding key road performance indicators. The results indicate that with 100% RBM, the 7-day compressive strength of SSRBM containing 4% SWC reaches 1.88 MPa, meeting the Chinese specification JTG/T 5521-2019. By incorporating 15% natural coarse aggregate, this strength can be increased by 35.4%. Furthermore, SSRBM demonstrates superior freeze–thaw resistance, with a freeze–thaw-retained unconfined compressive strength ratio of 93.9%, compared to 89.6% for CSRBM, and exhibits a lower drying shrinkage coefficient. Carbon emission analysis shows that the emissions per cubic meter of SSRBM are approximately 73% lower than those of CSRBM, presenting a viable and environmentally advantageous alternative for sustainable pavement construction. Full article

Distributed acoustic sensing (DAS) has been widely applied to injection–production profile monitoring in horizontal wells because it provides continuous full-wellbore coverage, real-time acquisition, and straightforward long-term deployment. In practical downhole operations, however, DAS measurements are frequently compromised by optical-signal attenuation, loss of fiber–casing/formation coupling, and environmental noise. Meanwhile, the mechanisms governing flow-induced acoustic responses remain insufficiently understood, which continues to impede quantitative diagnosis and interpretation of injection–production profiles based on DAS data. To address these challenges, this study performed controlled laboratory-scale physical simulation experiments of single-phase flow in a horizontal wellbore, systematically investigating DAS acoustic responses under two wellbore diameters (25 mm and 50 mm) and a range of flow velocities. Power spectral density (PSD) was derived using the fast Fourier transform to identify flow-sensitive characteristic frequency bands, and frequency-band energy (FBE) was further used to establish an optimal quantitative relationship with flow velocity. The results show that: (1) DAS energy is dominated by low-frequency components (<100 Hz), with the total energy increasing nonlinearly as flow velocity rises, accompanied by a progressive broadening of the characteristic bands; (2) the feature bands identified using an adaptive method based on energy difference statistics applied to PSD frequency-domain features exhibit a higher signal-to-noise ratio and greater physical clarity than traditional wide frequency bands; furthermore, by employing a feature band merging strategy, the distribution characteristics of flow energy can be captured more comprehensively; and (3) FBE exhibits a strong nonlinear dependence on flow velocity, with a power-law model delivering the best theoretical fit, whereas a cubic model (FBE ∝ V³) achieves high accuracy and robustness for practical applications. The proposed workflow—“PSD peak identification–characteristic band delineation–FBE regression”—establishes a methodological foundation for quantitative DAS-based monitoring of horizontal-well injection–production profiles in both laboratory and field settings, and it provides a basis for subsequent intelligent monitoring and interpretation under multiphase-flow conditions. Full article

With the rapid evolution of urbanization and artificial intelligence technology in China, small, intelligent road sweepers have emerged as a highly promising technical solution to address urban cleaning challenges. The development and breakthrough of high-performance dust collection trays (DCT) stand as the core prerequisite for the large-scale practical application of such sweepers. Although blowing–suction integration technology theoretically offers substantial potential for improving dust removal efficiency, it has not received adequate attention in the sweeper field, particularly in the research on its application in unmanned, small-sized models. In this study, a fresh concept of an efficient DCT was proposed, and its numerical method was verified by experiment. Then, the design work for this efficient DCT was efficiently carried out by combining computational fluid dynamics (CFD) numerical simulation with response surface methodology (RSM). Finally, the influence mechanisms of three key operational parameters of nozzle airflow velocity, suction negative pressure, and vehicle travel speed on the dust removal effect were numerically investigated. The results indicated that the parameter combination of DCT with an 18° blowing angle, 20° shoulder angle, and 0.2 diameter-to-length ratio was recommended, and its dust removal efficiency could reach a peak level of 98.7% when the nozzle blowing velocity, negative pressure at suction port, and travel speed were respectively 14 m/s, −1800 Pa, and 1.4 m/s. This research provides important theoretical support and a feasible technical pathway for the design of high-performance DCTs. Full article

With the widespread adoption of various types of Unmanned Aerial Vehicles (UAVs), their non-compliant operations pose a severe challenge to public safety, necessitating the urgent identification and detection of UAV targets. However, in complex backgrounds, UAV targets exhibit small-scale dimensions and low contrast, coupled with extremely low signal-to-noise ratios. This forces conventional target detection methods to confront issues such as feature convergence, missed detections, and false alarms. To address these challenges, we propose a Multi-View Feature Fusion Network (MVFF) that achieves precise identification of small, low-contrast UAV targets by leveraging complementary multi-view information. First, we design a collaborative view alignment fusion module. This module employs a cross-map feature fusion attention mechanism to establish pixel-level mapping relationships and perform deep fusion, effectively resolving geometric distortion and semantic overlap caused by imaging angle differences. Furthermore, we introduce a view feature smoothing module that employs displacement operators to construct a lightweight long-range modeling mechanism. This overcomes the limitations of traditional convolutional local receptive fields, effectively eliminating ghosting artifacts and response discontinuities arising from multi-view fusion. Additionally, we developed a small object binary cross-entropy loss function. By incorporating scale-adaptive gain factors and confidence-aware weights, this function enhances the learning capability of edge features in small objects, significantly reducing prediction uncertainty caused by background noise. Comparative experiments conducted on a multi-perspective UAV dataset demonstrate that our approach consistently outperforms existing state-of-the-art methods across multiple performance metrics. Specifically, it achieves a Structure-measure of 91.50% and an F-measure of 85.14%, validating the effectiveness and superiority of the proposed method. Full article

Proppant transport in complex fracture networks strongly influences the effectiveness of volumetric hydraulic fracturing in deep shale reservoirs; however, experimental investigations remain limited by the scale and structural complexity of existing laboratory models. In this study, large-scale physical experiments were conducted using a self-designed fracture system consisting of a main fracture and multi-level tertiary branch fractures to investigate proppant transport and placement behavior under different operational conditions. Twelve experimental cases were performed by varying injection rate, fracturing fluid viscosity, proppant concentration, proppant type, and particle-size pumping sequence. The results show that increasing the injection rate and fluid viscosity improves the proppant transport capacity and promotes proppant migration into tertiary branch fractures, increasing the proppant distribution ratio by 6.58%, while the placement proportion in the main fracture decreases by 15.92%. Increasing the proppant concentration enhances proppant placement in all fracture levels, with the placement ratio of quartz sand increasing by 10–15%, but excessive concentration causes accumulation and bridging near the fracture entrance. Under identical conditions, ceramic proppant exhibits better overall placement performance than quartz sand, with a 22.81% higher placement ratio in the main fracture. In addition, the pumping sequence significantly affects proppant distribution; the large–small–large particle-size sequence achieves the highest placement ratio of 74.52%. These results provide quantitative experimental evidence for optimizing proppant injection strategies and fracturing parameters in deep shale reservoirs. Full article

Storm-driven turbidity is a major water-quality concern in urban watersheds, reflecting the mobilization and transport of fine sediment during runoff events. This study examines how seasonal storm characteristics influence turbidity and associated sediment transport responses in the Middle Fork of Beargrass Creek, Louisville, Kentucky, over a two-year period. Forty-one erosive storm events were identified and characterized using high-resolution rainfall data to capture storm magnitude and structure. Study objectives were to: (1) quantify event-scale turbidity responses to erosive storms, (2) compare upstream and downstream turbidity behavior to assess spatial variability, (3) evaluate seasonal variation in these relationships, and (4) assess implications for sediment-focused best management practice (BMP) design. Event-based regression models related downstream turbidity to lagged upstream turbidity and downstream erosivity. Turbidity ratios and turbidity–discharge hysteresis characterized spatial and temporal sediment transport dynamics. Results showed that winter and spring storms exhibited longer durations, stronger upstream–downstream turbidity coupling, and more stable lag relationships, indicating integrated sediment transport. Short-duration, high-intensity summer storms produced elevated turbidity ratios, pronounced clockwise hysteresis, and greater model sensitivity, consistent with localized sediment mobilization. Findings support seasonally adaptive BMP strategies, with volume-reduction approaches most effective during winter–spring and source control measures critical during summer-fall. Full article

CoFe₂O₄ nanoparticles were prepared using a hydrothermal method. All the studied samples were single-phase and were crystallized in a cubic Fd-3m structure. XRD and TEM analyses revealed that the particles had average sizes between 5 and 22 nm. It has been shown that, by using the PVP of different molecular masses, trends of growth and crystallization can be established, obtaining elongated 40 k, cubical 58 k, and rhomboidal 360 kg/mol nanoparticles. While using Ethylene glycol as solvent, the formation of separated “raspberry”-like nanostructures was revealed. The saturation magnetizations are somewhat smaller compared with crystalline CoFe₂O₄ saturation magnetization, but are high enough to have possible biomedical applications. FC and ZFC measurements show that the blocking temperature was around 100 K for the CF5 sample and around 20 K for the FC6 sample. The calculated anisotropy constants were between 7 and 10 kJ/m³, being close to previously reported values. The calculated blocking temperatures are in good agreement with experimental ones. The M_r/Ms ratio at room temperature was lower than 0.5, confirming the predominance of magnetostatic interactions. This paper serves as a good starting point for researchers seeking to synthesize a CoFe₂O₄ system with a desired size and growth tendency at the nanometer scale. Full article