Search Results (7,172)

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

Introduction: Three-dimensional (3D) bioprinting is a promising approach for endodontic tissue engineering, enabling scaffolds with controlled architecture and bioactivity to support pulp regeneration. Objectives: This systematic review assessed the following: “What 3D bioprinting applications are reported in endodontics-related studies?” Materials and Methods: Following PRISMA 2020 guidelines, PubMed/MEDLINE, Scopus, Embase, Cochrane Library, Web of Science, SciELO, LILACS, and Google Scholar were searched up to January 2026 with no date or language limits. Two reviewers independently screened studies; risk of bias in in vitro studies was assessed with the QUIN tool. As only one study reported complete antimicrobial outcomes, an intra-study quantitative comparison (MD, 95% CI) of inhibition halos was performed (not a meta-analysis). Results: From 518 records, nine studies were included. Outcomes mainly addressed physicochemical properties (n = 9), cell viability (n = 7), biocompatibility (n = 5), and cell differentiation (n = 5); antimicrobial activity was evaluated in two studies. Most used hDPSCs and extrusion-based printing, testing calcium silicate composites, alginate hydrogels, functionalized PCL, and modified PLA. Modified PLA scaffolds showed greater antimicrobial activity, strongest with naringin and nHA formulations. Overall risk of bias was moderate (58.33%), largely due to limited reporting of randomization, blinding, and sampling. Conclusion: 3D-bioprinted scaffolds/bioinks generally improved cellular responses and bioactivity, especially with MTA, Biodentine, nHA, or naringin; antimicrobial effects were most evident in functionalized PLA (PLA/NAR and PLA/nHA/NAR). Full article

Large-scale integration of photovoltaics (PV) introduces complex source-load temporal volatility and grid-connection/off-grid transitions. Traditional static reliability assessments fail to capture these dynamics, resulting in “considerable deviations” in system indices. This paper proposes a reliability evaluation framework that couples temporal source-load trajectories with a multi-stage fault recovery process. Unlike traditional methods that rely on a single static snapshot, the proposed model evaluates the system state across a continuous 5-h restoration window. The novelty lies in the unique integration of a Dynamic Time Warping (DTW)–Kmedoids method to preserve temporal phase-shifts and a multi-stage Mixed-Integer Linear Programming (MILP) model to simulate PV grid-connection transitions throughout this window. By capturing the intra-outage evolution of sources and loads, the framework fundamentally corrects the “considerable deviations” of static assessments. Case studies demonstrate high precision with an error of less than 0.71% and a 20-fold speedup. Crucially, the framework corrects the 22.31% risk underestimation bias inherent in static models by tracking real-time source-load evolution. This confirms that temporal coordination performance is the primary determinant of the reliability ceiling in active distribution networks. The findings reveal that the precise alignment of intermittent generation and fluctuating demand defines the actual operational safety margin, providing a superior quantitative foundation for grid resilience enhancement. 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

Accurate wind speed forecasting is critical for reliable wind-power integration, yet it remains challenging due to the strongly non-stationary and inherently multi-scale nature of atmospheric processes. While deep learning models—such as LSTM, GRU, and Transformer architectures—achieve competitive short- and medium-term performance, they frequently suffer from spectral bias, hyperparameter sensitivity, and reduced generalization under heterogeneous operating regimes. To address these limitations, we propose a multi-scale spectral–recurrent framework, termed RFF-RNN, which integrates multi-band Random Fourier Feature (RFF) encodings with parameterizable recurrent backbones. A key innovation of our approach is the deliberate relaxation of strict shift-invariance constraints; by jointly optimizing spectral frequencies, phase biases, and bandwidth scales alongside the neural weights, the framework dynamically shapes a fully data-driven spectral embedding. To ensure robust adaptation, we employ a two-stage optimization strategy combining gradient-based inner-loop learning with outer-loop Bayesian hyperparameter tuning. Our extensive evaluations on a controlled synthetic benchmark and six geographically diverse real-world wind datasets (spanning the USA, China, and the Netherlands) demonstrate the superiority of the proposed framework. Statistical validation via the Friedman test confirms that RFF-enhanced models—particularly RFF-GRU and RFF-LSTM—systematically outperform standard recurrent networks and state-of-the-art Transformer architectures (Autoformer and FEDformer). The proposed approach yields significantly lower error metrics (MAE and RMSE) and higher explained variance ($R^2$), while exhibiting remarkable resilience against error accumulation at extended forecasting horizons. Full article

Hydrogen production is gaining increasing importance as a key component of the transition toward carbon-neutral energy systems. In this study, the prediction of hydrogen generation in membraneless alkaline water electrolyzers (MAWEs) is investigated using deep-learning-based time-series modeling. A single-input modeling framework is adopted, where only the system current is used as the input variable. Experimental current signals obtained from long-duration tests conducted at electrolyte concentrations between 5 and 35 g KOH (7200 s per experiment) are employed as the model inputs, while mass-based hydrogen production (in grams) is used as the output variable. Two recurrent neural network architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), are implemented, and their predictive performance is comparatively evaluated using RMSE, MAE, and R² metrics. In addition to deep learning models, classical approaches including Linear Regression, ARIMA, and Naïve Forecast are also considered for comparison. The results show that both models are capable of accurately reproducing the hydrogen-production dynamics across the entire concentration range. In particular, the prediction accuracy improves notably at medium and high electrolyte concentrations, where the coefficient of determination (R²) approaches 0.98. The residual distributions remain narrow and symmetric around zero, indicating the absence of systematic estimation bias. The results also show that classical models can achieve comparable performance under stable operating conditions, while deep learning models provide advantages in capturing nonlinear and dynamic behavior. While LSTM and GRU exhibit comparable accuracy, each architecture provides complementary advantages under different operating conditions. These findings indicate that deep-learning-based time-series modeling constitutes a lightweight and reliable framework for prediction and control applications in MAWE systems. Overall, this study demonstrates the applicability of data-driven models for the dynamic characterization of membraneless water electrolysis. Full article

Objective: This study investigates the prognostic value and clinical utility of the neutrophil percentage-to-albumin ratio (NPAR) in patients with resected non-small-cell lung cancer (NSCLC). Methods: We retrospectively included 335 patients with NSCLC who underwent lung resection at our institution between January 2017 and October 2018. Optimal cutoffs for preoperative and postoperative day 1 (D1) NPAR were determined using X-tile (version 3.6.1; Yale University, New Haven, CT, USA) to define high and low groups. Overall survival (OS) was evaluated using Kaplan–Meier analysis and Cox proportional hazards models. A perioperative NPAR trajectory (low–low, low–high, high–low, high–high) was constructed to characterize dynamic risk patterns. To mitigate potential bias associated with postoperative measurements, a D1 landmark analysis was performed. A nomogram was developed based on the multivariable model and assessed by calibration at 1, 3, and 5 years. Incremental clinical value beyond TNM stage and surgical approach was evaluated using decision curve analysis (DCA), as well as by 5-year continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results: The optimal cutoffs for preoperative and postoperative D1 NPAR were 14.5 and 23.1, respectively. In univariate analyses, sex, smoking history, preoperative NPAR, postoperative D1 NPAR, pathologic type, TNM stage, surgical approach, and adjuvant therapy were associated with OS (all p < 0.01). In multivariable Cox regression, high preoperative NPAR (HR 1.896, 95% CI 1.135–3.168; p = 0.014) and high postoperative D1 NPAR (HR 1.905, 95% CI 1.097–3.305; p = 0.014) were independent risk factors, along with TNM stage (Stage II: HR 2.824, 95% CI 1.209–6.595; p = 0.016; Stage III: HR 9.470, 95% CI 4.935–18.171; p < 0.001) and open surgery (HR 2.350, 95% CI 1.341–4.117; p = 0.003). Trajectory analysis further stratified risk, with the high–high group showing the poorest survival (adjusted HR 3.48, 95% CI 1.43–8.47; p = 0.006). The association of postoperative NPAR persisted in the D1 landmark analysis (HR 1.836, 95% CI 1.071–3.148; p = 0.027). Adding NPAR to TNM stage and surgical approach improved 5-year risk reclassification (continuous NRI 0.377, 95% CI 0.094–0.659; IDI 0.028, 95% CI −0.002–0.054) and increased net benefit on DCA. The nomogram demonstrated acceptable calibration at 1, 3, and 5 years. Conclusions: This study demonstrates that NPAR serves as an independent prognostic marker for long-term outcomes in patients with NSCLC. The use of NPAR offers clinicians a comprehensive and precise tool for assessing patient prognosis. Full article

Background/Objectives: Measures of excess body fat are often more informative for predicting health risk than body mass index (BMI) alone. With obesity prevalence increasing among young adults, this study evaluated whether adding dominant handgrip strength improves prediction of body fat percentage (BF%) and BF%-defined obesity in a university population. Methods: Cross-sectional data from 895 students (401 women, 494 men; mean age 19.9 years; fall 2015–spring 2016) in Kentucky, USA were analyzed. BMI was calculated from self-reported height and weight. BF% was estimated using bioelectrical impedance analysis (BIA), and dominant handgrip strength was measured with a hydraulic hand grip dynamometer. Sex-specific linear and logistic regression models assessed associations among BMI, grip strength, relative grip strength, and BF%. Results: BMI was a strong predictor of BF% in linear models (R² = 0.74 in women; 0.68 in men). Grip strength alone was not associated with BF% but showed an inverse association when combined with BMI. For BF%-defined obesity, BMI remained the most influential predictor, with grip strength contributing additional predictive value. Among men, age significantly modified these relationships, with marked differences between those aged 18–19 years versus older participants. Conclusions: BMI strongly predicted BF% and BF%-based obesity in this cross-sectional study of a predominantly white young adult population. Incorporating handgrip strength modestly improved classification, particularly among women, suggesting that a functional measure like hand grip strength may enhance obesity screening and health communication in young adults. Full article

Objectives: We aimed to evaluate the reliability and validity of the Arabic version of the Chronic Heart Failure Health-Related Quality of Life Questionnaire (CHFQOLQ-20) among patients with heart failure in Jordan. Methods: A cross-sectional study was conducted among 399 adults with heart failure recruited from a tertiary hospital in Jordan (median age 68 years; 55.9% male). The CHFQOLQ-20 was translated using forward–backward procedures. Construct validity was examined using confirmatory factor analysis (CFA) and a multidimensional Partial Credit Model. Differential item functioning by sex and internal consistency were assessed. Results: CFA supported the original four-domain structure (physical, cognitive, mental, and general health), with all items showing significant factor loadings. Item-level analyses demonstrated acceptable model fit, ordered response thresholds, and minimal sex-related bias. Physical health scores were lower than other domains. Conclusions: The Arabic CHFQOLQ-20 is a valid, reliable, and multidimensional measure of HRQoL in patients with heart failure, supporting its use in clinical practice and research. Full article

Vector–matrix multiplication (VMM), implemented through multiply–accumulate (MAC) operations, represents the dominant computational primitive in many artificial intelligence (AI) workloads. When executed on conventional von Neumann architectures, VMM operations suffer from important energy consumption and latency due to the separation between memory and processing units. To overcome these limitations, crossbar arrays built from Resistive Random Access Memory (RRAM) cells have been proposed for accelerating VMM computations. In this work, we investigate the key optimization trade-offs associated with implementing RRAM-based neural networks for classification applications. A simple two-layer neural network is first defined and trained in software to generate the weight matrices and bias parameters. Next, three hardware implementation scenarios are evaluated depending on whether negative floating-point numbers are used: Positive Weights Only (PWO), Positive and Negative Weights Only (PNWO), and Positive and Negative Weights with Biases (PNWB). The different implementations are analyzed at the hardware level by examining classification accuracy, energy efficiency, latency, and area overhead. The study further incorporates important RRAM limitations, including restricted conductance range and device variability. Hardware results show that the PWO scenario offers the lowest energy consumption (189 fJ/MAC) and area overhead but results in the lowest accuracy. PNWO and PNWB significantly improve accuracy (+177% and +180%) but increase energy consumption (+63% and +87%) and area (×2 and ×2.1). Under variability effects, PWO achieves better accuracy (94.65%), followed by PNWO (93.11%) and PNWB (92.11%). Full article

Micro-public space (MPS) regeneration is typically evaluated at the point of delivery, yet long-term performance depends on whether everyday stewardship can be sustained thereafter. This study reframes neighbourhood social capital as a governance–environment signal reflecting coordination capacity and examines whether residents’ willingness to participate in post-regeneration co-management is primarily appraisal-driven (perceived value, attitude, and perceived behavioural control) or coordination-driven via a residual direct channel consistent with routine governance. A cross-sectional survey of adults residing within walkable catchments of five regenerated MPS sites in Nan’an District, Chongqing, China ($N=477$), was conducted. An integrated Stimulus–Organism–Response × TPB model was estimated using WLSMV with ordered categorical indicators; indirect effects were assessed via bias-corrected bootstrap confidence intervals. Coordination capacity was strongly associated with perceived value, participation attitude, and perceived behavioural control. In the joint model, only perceived value retained a statistically reliable positive association with stewardship willingness, whereas the incremental contributions of attitude and perceived behavioural control were negligible once the stimulus was included. A residual direct association from coordination capacity to willingness persisted beyond the appraisal block, supporting a direct-dominant interpretation; bootstrap analyses yielded no robust evidence for mediation (BCa 95% CIs crossed zero). These findings suggest that sustaining regenerated micro-spaces requires low-friction governance designs that minimise coordination costs, reinforce soft accountability, and render institutional responsiveness visible to residents. Full article

Background/Objectives: Influenza remains a primary cause of severe illness and death in adults over 60. In this group, immunosenescence and existing health conditions make infections more dangerous and traditional vaccines less effective. The MF59-adjuvanted vaccine was specifically designed to overcome these limitations by enhancing the body’s immune activation and antigen presentation. While the vaccine shows clear benefits, some recent concerns regarding vaccine safety have been raised without supporting scientific evidence. Therefore, this systematic review focuses on providing a comprehensive evaluation of its safety outcomes compared to standard vaccines. Methods: Following the PRISMA guidelines, a systematic review and meta-analysis were conducted; two researchers independently assessed the eligibility of the studies, and the risk of bias was assessed using RoB2 and ROBINS tools for randomized clinical trials and observational studies, respectively. Pooled risk estimates were calculated using a random-effects model. Results: Ten RCTs and three non-RCTs meeting the inclusion criteria were included. No significant differences were found for severe systemic outcomes: Guillain–Barré syndrome (RR 1.01, 95% CI 0.64–1.80) and encephalitis (RR 1.23, 95% CI 0.85–1.78). For other systemic adverse effects, there were no significant differences between adjuvanted and non-adjuvanted vaccines; only myalgia showed a small but significant increase with adjuvanted vaccines (RR 1.35, 95% CI 1.02–1.78) compared with non-adjuvanted vaccines. Conclusions: MF59-adjuvanted influenza vaccines have a favorable and well-characterized safety profile in adults aged 60 years and older. Adverse events are predominantly mild and transient, with no evidence of increased risk of serious or immune-mediated outcomes compared with non-adjuvanted vaccines. Full article

Spaceborne LiDAR systems, such as ICESat-2, provide critical data for global land cover and topography; however, their performance in rugged, vegetated landscapes requires rigorous local validation. This study evaluates the vertical accuracy of ICESat-2 ATL08 terrain height metrics in the complex Turkish Western Black Sea region, utilizing a reference dataset of high-precision terrestrial GNSS measurements. Following strict IQR-based outlier detection and photon density filtering, 1637 spatially matched segments were analyzed. The h_te_best_fit terrain height metric showed the best agreement with the terrestrial GNSS reference data, yielding an RMSE of 3.37 m and a mean bias of −0.42 m, indicating a slight underestimation of the terrain surface. The univariate analysis revealed a strong positive correlation between terrain slope and vertical error, indicating that slope is the prominent degradation factor contributing to pulse broadening. Additionally, dense forest cover was found to limit ground photon retrieval, leading to increased error margins, whereas nighttime acquisitions offered slightly improved precision. These findings suggest that while ATL08 is a valuable topographic source, slope-dependent corrections are essential for applications in mountainous environments. Full article

Background/Objectives: Stroke is a sudden neurological event caused by disrupted cerebral blood flow and represents a leading cause of acquired disability worldwide. Motor impairments and spasticity are among the most prevalent sequelae, significantly limiting functional independence and quality of life. Non-invasive electrotherapy has emerged as a complementary strategy in neurorehabilitation aimed at enhancing neuroplasticity and improving motor recovery. To systematically review current evidence regarding the effectiveness of non-invasive electrotherapy modalities in the rehabilitation of motor sequelae and spasticity following stroke, and to examine their theoretical and clinical rationale. Methods: A systematic literature review was conducted in accordance with PRISMA 2020 guidelines. The protocol was prospectively registered in the Open Science Framework (OSF). A comprehensive search was performed in Pubmed, Web of Science (WoS), and Scopus for studies published up to 14 November 2023, using the terms “Electric Stimulation Therapy” and “Stroke”. The methodological quality was assessed using the PEDro scale. The levels of evidence were classified according to the Oxford Centre for Evidence-Based Medicine criteria, and the risk of bias was evaluated using the Cochrane Risk of Bias tool (RoB 2). Results: Sixteen studies were included in the review. The analyzed interventions comprised neuromuscular electrical stimulation (NMES), transcutaneous electrical nerve stimulation (TENS), functional electrical stimulation (FES), neuromuscular electrical stimulation combined with transcranial magnetic stimulation (NMES + rTMS), transcranial direct current stimulation (tDCS), and afferent electrical stimulation (AES). Overall, the methodological quality of the included studies ranged from moderate to high, with PEDro scores between 6 and 9 out of 10. According to the Oxford Centre for Evidence-Based Medicine classification, most studies corresponded to level 1b evidence, while a smaller proportion were classified as level 2b. A risk of bias assessment using the Cochrane RoB 2 tool showed that the majority of the included studies presented a low risk of bias across most domains, although some concerns were identified in the domains of randomization and measurement in a limited number of trials. Across modalities, consistency within group improvement in motor function and spasticity was reported. However, between group comparisons with conventional rehabilitation were often inconsistent and did not consistently demonstrate superiority. The variability in stimulation parameters, intervention duration, and outcome measures further limited direct comparisons across studies. Conclusions: Non-invasive electrotherapy appears to be a safe and promising adjunct to conventional stroke rehabilitation. Nevertheless, further high-quality studies are required to clarify the underlying neurophysiological mechanisms and to establish standardized treatment protocols. Full article

This paper introduces a new family of distributions called exponentiated Harris-G. This new distribution is a weighted distribution of the well established exponentiated-G distributions. The model allows for easy derivation of statistical properties based on the exponentiated-G distribution. Several statistical properties for the new model were derived. The paper considered different parameter estimation techniques and the maximum likelihood estimation technique emerged as the best technique. This was evaluated via Monte Carlo simulation studies of the proposed family. Estimation techniques were ranked based on the lowest values of the root mean square error and average bias. The proposed model showed enhanced flexibility in data modeling when compared to some selected competing models. This was demonstrated through application of the special case to two real-world datasets. Full article