Search Results (6,891)

Quantitative assessment of mitral regurgitation (MR) in dogs with myxomatous mitral valve disease (MMVD) is influenced by the method used to estimate left ventricular volume. This study aimed to evaluate the impact of different left ventricular volume estimation methods on quantitative MR assessment, using the modified Simpson’s method of discs (Disc method) as a reference. Echocardiographic data from 167 dogs with MMVD and 19 healthy control dogs were analyzed. Regurgitant volume (RVol), body size-normalized RVol, and regurgitant fraction (RF) were calculated using diameter-based methods (Cube, Gibson, Meyer, and Teichholz) and compared with values obtained using the Disc method. All diameter-based methods showed significant positive correlations with the Disc method. However, Bland–Altman analyses demonstrated wide limits of agreement and systematic bias. Between-method discrepancies increased with advancing disease stage, with diameter-based methods tending to overestimate RVol and RF, particularly in dogs classified as American College of Veterinary Internal Medicine (ACVIM) stages B2 and C/D. Although relative trends in regurgitant indices were consistent across methods, substantial differences were observed in absolute values. These findings indicate that diameter-based methods are not interchangeable with the Disc method for absolute quantification of MR severity in dogs with MMVD, especially in advanced disease stages. Full article

Macroeconomic conditions are widely believed to influence the direction of equity markets, yet most forecasting models either ignore macroeconomic information or incorporate it through a small set of ad hoc predictors. We propose XAttnFusion, a macro–market fusion architecture that jointly learns from high-frequency market data and lower-frequency macroeconomic time series for equity return prediction. The model comprises three branches: a 1D convolutional network that encodes 40-day market windows (price, volume, and technical indicators), a temporal convolutional network that encodes 24-month macro sequences, and a feedforward branch for volume-at-price structure features. These representations are integrated through multi-head cross-attention, in which the current market state queries the macro sequence to produce a fused representation for directional forecasting. We evaluate XAttnFusion on daily SPY returns from 2012 to 2024 using purged cross-validation with a 5-day embargo to prevent information leakage. To address potential look-ahead bias from macroeconomic publication lags, all macro inputs are lagged by two months. The model achieves a mean out-of-sample AUROC of $0.63\pm 0.05$, representing a 27% improvement over random and an 8.1% improvement over the best concatenation baseline. In a fair comparison where each model is independently hyperparameter-tuned, cross-attention fusion improves AUROC by 0.047 over concatenation ($p=0.031$, Wilcoxon signed-rank test). The model also generalizes to QQQ and IWM, where cross-attention consistently outperforms concatenation fusion. Crucially, the model’s discriminative ability is state-dependent, indicating that the value of macro–market fusion is itself conditioned on market structure. Permutation-based feature importance shows that macro and market branches contribute on a comparable scale (approximately 48% and 36%, respectively), so the gains come from jointly fusing two comparably weighted sources rather than from a single dominant input. Our results show that explicitly modeling macro–market interactions with interpretable attention improves predictive accuracy over naive fusion strategies and provides insight into the time-varying relevance of macroeconomic information in financial forecasting and equity market prediction. Full article

The reconstruction of complex orbitomaxillary defects requires biomaterials that can simultaneously provide structural stability, biocompatibility, and accurate restoration of facial volume and contour. While rigid polymers such as polyetheretherketone (PEEK) offer reliable mechanical support, they do not adequately replicate the viscoelastic behavior of soft tissues. This report presents a translational revision case employing a personalized hybrid biomaterial approach that combines a 3D-printed PEEK implant for structural orbital floor support with a patient-specific polydimethylsiloxane (PDMS) implant for malar volumetric augmentation. Reconstruction was planned using CT segmentation and contralateral mirroring. Patient-specific implants were subsequently designed using CAD/CAM techniques, combining a rigid PEEK implant for structural orbital support with a flexible PDMS implant for malar volumetric augmentation with complementary mechanical properties. Revision surgery included the removal of inadequately positioned titanium hardware, the release of incarcerated extraocular muscles, and the restoration of orbital anatomy and facial symmetry. Postoperative imaging demonstrated stable implant positioning and sustained orbitomaxillary stability. Despite successful anatomical reconstruction, residual functional sequelae, including strabismus related to the severity of the initial orbital trauma, persisted and were addressed separately in a staged manner, resulting in satisfactory ocular alignment and resolution of diplopia in primary gaze. This case underscores the complementary functional roles of rigid and elastic polymers and highlights the translational potential of PDMS as a permanent, patient-specific implant material for volumetric and contour restoration in craniofacial reconstruction. Full article

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

The sixth-generation (6G) mobile communication systems are anticipated to be operational by 2030, prompting extensive research efforts by governments and private entities. Designed to meet societal, economic, and technological demands unaddressed by fifth-generation (5G) networks, 6G integrates scalability, security, and reliability with ubiquity and resource-intensive artificial intelligence. Envisaged as multi-band, decentralized, autonomous, flexible, and user-centric, 6G networks incorporate innovative technologies, including cell-free (CF), three-dimensional heterogeneous networks (3D HetNet), reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), as well as artificial intelligence/machine learning (ML). In 6G 3D HetNets, the densification of access points (APs) continues, accommodating increased connections and traffic volumes, alongside the use of higher frequency bands. Although 6G networks are not fully standardized, they target demanding Quality of Service (QoS) standards, such as a peak data rate of 1.0 Tbps and latency of 0.1 ms. This paper conducts a comprehensive literature review on radio resource management (RRM) in 6G cell-free and 3D HetNet systems, emphasizing challenges such as interference mitigation. It presents a taxonomy of RRM approaches, systematically studying, categorizing, and qualitatively analyzing recent techniques, outlining the current state, and indicating future trends, technologies, and challenges shaping 6G systems. Full article

This paper investigates co-continuous structures in immiscible polymer blends through three-dimensional (3D) computational calculations based on a multiphase phase-field equation for fluid flow. The mathematical model describes phase separation with the Cahn–Hilliard (CH) equation and fluid motion with the incompressible Navier–Stokes (NS) equations. Both polymers are treated as Newtonian viscous fluids, and the model includes surface tension, viscosity, and volume fraction effects. A semi-implicit finite difference method (FDM) solves the CH equation, and a projection method maintains the incompressibility of the flow field. Multigrid techniques solve the nonlinear systems efficiently. In addition, a connectivity-based detection algorithm determines whether a phase forms a connected structure that reaches all boundaries of the numerical domain. The numerical results show that the morphology changes from a droplet–matrix structure to a co-continuous structure as the volume fraction increases. The interfacial area per unit volume reaches a local maximum near the transition between these two regimes. Full article

Given limited evidence on temporal changes in pathogen detection patterns and hospital-based inflammatory burden across the pandemic transition, this study examined their long-term relationship using respiratory multiplex PCR positivity and concurrent C-reactive protein (CRP) levels. We analyzed 19,002 episodes linking respiratory multiplex PCR (mPCR) results and concurrent CRP from October 2008 to December 2024. Pre-pandemic, pandemic, and post-pandemic changes in monthly testing volume, positivity rate, median CRP, high and extreme inflammation by mPCR status, and the correlation between positivity rate and median CRP were assessed. mPCR positivity decreased from 60.62% (pre-pandemic) to 22.45% (pandemic) and remained low at 25.95% thereafter, whereas the median CRP increased from 0.94 to 3.35 and 5.97 mg/dL, respectively. After January 2020, testing volume and positivity rate decreased, whereas the median CRP increased. High inflammation increased from 13.78% to 27.93% and 38.98% in mPCR-negative episodes, and from 4.61% to 7.20% and 27.66% in mPCR-positive episodes, remaining consistently lower in the latter. Monthly positivity rate was strongly negatively correlated with median CRP. Overall, respiratory virus positivity declined, whereas CRP-based inflammatory burden increased, indicating divergent temporal trends across the pandemic transition. These findings should be interpreted descriptively, not causally, as reflecting divergent temporal trajectories of pathogen detection and inflammatory burden. Full article

Introduction: Non-muscle-invasive bladder cancer (NMIBC) represents approximately 78% of newly diagnosed bladder cancers and is characterized by high recurrence rates and variable progression risk. While transurethral resection of bladder tumor (TURBT) followed by intravesical therapy remains standard management, optimal treatment of high-risk and Bacillus Calmette-Guerin (BCG)-unresponsive disease remains controversial. Radiotherapy (RT), particularly in combination with chemotherapy, has been explored as a bladder-preserving alternative. Material and Methods: We conducted a narrative review of published literature evaluating the role of definitive RT in high-risk NMIBC, with emphasis on T1 disease. Retrospective series, prospective trials, meta-analyses, and contemporary guideline recommendations were examined. For each included study, we extracted data on the extent of TURBT (maximal vs. incomplete/non-specified), use and type of concurrent chemotherapy, radiotherapy technique (3D-conformal, IMRT, or proton), treatment volume (bladder only vs. whole pelvis), and dose/fractionation schedule. Results: Early studies evaluating RT alone demonstrated modest complete response rates. More recent approaches incorporating maximal TURBT followed by concurrent chemoradiotherapy report improved outcomes, with complete response rates of approximately 80–88% and 5-year overall survival comparable to surgical series. The phase II NRG/RTOG 0926 trial in recurrent high-risk T1 disease after intravesical therapy failure demonstrated an 81% complete response rate and favorable bladder preservation outcomes. Meta-analytic data suggest 5-year recurrence-free survival around 54% and overall survival near 70%, although evidence remains limited and largely non-randomized. Advances in image-guided and hypofractionated RT may further improve therapeutic outcomes while limiting toxicity. Conclusions: while definitive chemoradiotherapy is a promising option for selected patients, it remains investigational and should be considered only in those who are unfit for or decline radical cystectomy. Prospective randomized studies are needed to better define its role in contemporary management. Full article

The accurate quantification of microplastics (MPs) in aquatic environments remains challenging due to the heterogeneous distribution of MPs in different environments, making representative sampling difficult, as well as methodological variabilities in sampling, sample processing, and detection. This study examined measurement fluctuations for MP analysis across four distinct water matrices: wastewater treatment plant (WWTP) effluent, tap water (TW), combined sewer overflow (CSO), and surface water (SW). Two sampling strategies were compared: grab sampling (0.5 L, n = 5) and large-volume filtration using a particle sampling unit (PSU; 100 L, 10 µm mesh, subsampled). Samples were processed through oxidative digestion, stained with fluorescent dye, and analyzed via fluorescence microscopy with automated particle counting. Recovery experiments using polyamide (PA) reference particles (357 ± 60 µm) were conducted to assess method accuracy. PSU sampling demonstrated higher precision (mean R.S.D. 41 ± 17%) compared to grab sampling (mean R.S.D. 64 ± 19%), despite additional variability introduced by subsampling. Recovery rates reached 93 ± 7% for grab samples and 88 ± 23% for PSU samples with complete filter analysis. Statistical modeling revealed that achieving a ±25% margin of error (95% CI) required 21 PSU samples versus 51 grab samples. The quadratic relationship between the margin of error and required sample size underscores the importance of methodological optimization for cost-effective monitoring. These findings provide practical guidance for designing MP monitoring campaigns and demonstrate that fluorescent labeling combined with large-volume sampling offers a reliable approach for MP quantification in diverse aquatic environments. Full article

This work introduces Structured 3D-SVD as a practical framework for the reconstruction, compression, and analysis of biological volumetric data. Inspired by the logic of matrix singular value decomposition (SVD), the proposed approach represents third-order volumetric data in the spatial domain and supports progressive reconstruction through ordered quasi-singular coefficients. The experimental evaluation was carried out on two biological volumetric datasets: one full-volume scan of a fish and another of a brain. The results show that Structured 3D-SVD achieves reconstruction quality close to that of Tucker decomposition while requiring shorter computation times and outperforms canonical polyadic decomposition (CPD) in both accuracy and runtime. In addition, a progressive reconstruction analysis shows that relatively low truncation levels are sufficient to preserve the main volumetric structures, while higher truncation levels lead to more detailed reconstructions. Full article

Reliable temperature distribution measurement in ultra-low temperature (ULT) chest freezers is crucial for preserving biospecimen integrity in cryopreservation, but dense sensor arrays required for accuracy are often impractical due to space constraints and cost limitations. To address this critical challenge, this work presents a systematic data-driven framework for optimal sensor placement in large-scale (3 m³) ULT chest freezers under stable operating conditions. To our knowledge, it is the first realization of high-fidelity cryogenic temperature field reconstruction coupled with sparse sensor layout optimization tailored to large-volume ULT chest freezers. First, high-resolution reference temperature fields were constructed via universal kriging interpolation, validated with leave-one-out cross-validation (LOOCV) to achieve mean absolute error (MAE) $\le 0.67 °\mathrm{C}$ and coefficient of determination $R^2>0.92$. Principal component analysis (PCA) was then applied to training data to extract a tailored proper orthogonal decomposition (POD) basis. The first three principal components captured 99.8% of cumulative energy. Optimal sensor locations were determined via QR-column pivoting on the rank-3 POD basis, converging to a minimal configuration of 3 sensors (a 94% reduction from the 48-sensor full-scale setup). This sparse sensor network achieved exceptional reconstruction performance: grid-level MAE $\le 0.079 °\mathrm{C}$ and root mean squared error (RMSE) $\le 0.093 °\mathrm{C}$ against reference fields ($R^2\ge 0.999$), while point-level validation against experimental measurements yielded MAE $\le 0.502 °\mathrm{C}$ and RMSE $\le 0.842 °\mathrm{C}$ ($R^2\ge 0.971$). The results demonstrate that, for large-scale ULT chest freezers, the proposed data-driven approach is capable of automatically determining an optimal sparse sensor subset and enabling reliable 3D cryogenic temperature field reconstruction for efficient thermal monitoring. By resolving the trade-off between monitoring accuracy, space efficiency, and cost-effectiveness, this framework provides a scientifically rigorous alternative to empirical sensor deployment standards, offering practical scalability for cryogenic biobanking applications. Full article

The progressive phase-out of coal-fired power plants in Portugal has significantly reduced the availability of fly ash (FA) as a supplementary cementitious material (SCM), reinforcing the need for sustainable alternatives. Waste glass powder (WGP), characterized by its high amorphous silica content, has emerged as a promising candidate; however, most studies focus on ultrafine particles or isolated performance indicators, lacking an integrated technical, environmental, and economic assessment. This study evaluates cement pastes incorporating 25% WGP (by volume) with different particle size distributions, including fineness levels comparable to cement and FA. Mechanical performance, grinding energy demand, carbon footprint, and cost were systematically analyzed. The results indicate that WGP is technically viable as an SCM, with a median particle size (D₅₀) of approximately 48 µm providing the most balanced performance. Although finer particles enhance pozzolanic reactivity, the associated increase in grinding energy and economic cost offsets these gains. The findings demonstrate that optimizing particle size, rather than maximizing fineness, enables a technically robust and industrially realistic use of WGP. This approach supports circular economic strategies and contributes to the decarbonization of the construction sector by identifying an efficient replacement pathway for FA under resource-scarcity conditions. Full article

This study utilized polysiloxane as the raw material to successfully prepare black spherical silica fillers with varying internal carbon content. Through different thermal treatment processes, a dense silica layer was formed on the particle surface, while the internal hydrocarbon groups were thermally decomposed into carbon. Four types of spherical silica with different carbon contents were systematically characterized in terms of particle size distribution (D50 ≈ 2.0 μm, D100 < 5 μm), scanning electron microscopy morphology, moisture content (<0.1%), specific surface area (~1.0–1.1 m²/g), true density (~1.90–1.97 g/cm³), carbon content, blackness (L* values), and volume resistivity. The results indicate that the prepared black spherical silica exhibits a narrow particle size distribution, low moisture content, and high electrical insulation properties. Furthermore, the prepared black spherical silica was used as a filler in a polyphenylene oxide-based binder system to fabricate copper-clad laminates (CCLs), and their dielectric properties were systematically investigated. The study found that at electric field frequencies of 1 GHz and 10 GHz, the dielectric constant (D_k) and dielectric loss (D_f) of CCLs prepared with fillers containing less than 5% carbon remained largely consistent with those of carbon-free control samples. However, the D_f of CCLs prepared with fillers containing 9.00% carbon increased nearly tenfold, indicating that when the internal carbon content of the filler exceeds a certain threshold, it adversely affects the high-frequency dielectric properties of the copper-clad laminate. Full article

This study examines the spatio-temporal evolution and restricting mechanisms of agricultural supply chain resilience in the Yangtze River Basin from a gradient perspective. An evaluation index system encompassing the dimensions of the supply side, demand side, circulation side, and support side was developed. The Entropy-Weighted TOPSIS method, kernel density estimation, and obstacle degree model were comprehensively applied to measure and dynamically analyze supply chain resilience across 11 provinces from 2013 to 2023. The findings reveal distinct spatio-temporal evolution patterns: while the overall resilience shows an upward trend, significant gradient disparities exist, with downstream areas exhibiting markedly higher resilience than the mid- and upstream regions. Regarding the restricting mechanisms, the circulation and support sides exhibit higher levels of obstacles, representing key constraints to resilience enhancement. Among these, express delivery volume, freight turnover, and local R&D personnel full-time equivalents are the core obstacle factors affecting resilience. Based on these findings, this study proposes targeted recommendations, including optimizing rural last-mile logistics, upgrading inter-provincial freight hubs, improving rail–water intermodal transport, and strengthening cold-chain infrastructure, as well as implementing differentiated regional strategies and establishing cross-regional coordination mechanisms. These recommendations aim to provide decision-making guidance for enhancing the risk-response capabilities of agricultural supply chains in the Yangtze River Basin and to promote balanced regional development. Full article

This article proposes and validates a hybrid structural reinforcement strategy for onshore wind turbine foundations in repowering projects, enabling the installation of higher-capacity units without demolishing the existing foundation. In a context of increasing demand for renewable energy and infrastructure optimization, the original foundation is reused as the primary element for global stability and serviceability limit state (SLS) requirements, while ultimate limit state (ULS) demands, arising from the replacement of approximately 1.5 MW turbines with 4.1 MW and 6.25 MW units with power ratings representative of various manufacturers’ models in the current market are resisted by a new peripheral reinforced concrete strengthening system. The study considers both shallow (gravity) and piled foundation typologies, which are the most common globally for wind turbines. This solution, applied to a commercially operating wind farm in southern Brazil with actual load data, demonstrated a substantial reduction in concrete volume–up to 80% for shallow foundations and 40% for piled foundations compared to constructing an entirely new foundation. Structural assessment was performed through numerical modeling in SAP2000, employing a shell-beam hybrid model validated against a 3D solid reference, combined with analytical verifications of limit states. Results confirm that the proposed solution ensures global serviceability and adequate ultimate limit state capacity, achieving significant material optimization. This offers a sustainable and efficient alternative for repowering wind turbine foundations, with notable economic and environmental benefits, including the elimination of demolition, transportation, and material disposal costs. Full article