Search Results (6,943)

Background and Objectives: Endothelial dysfunction is an early indicator of cardiovascular disease and is commonly assessed using flow-mediated dilation (FMD). Although handheld ultrasound (HHUS) devices improve measurement accessibility, image analysis for conventional flow-mediated dilation (FMD) assessment remains time-consuming and highly operator-dependent. This study aimed to evaluate the between-day test–retest reliability of an AI-assisted brachial artery image analysis workflow integrating HHUS imaging with a YOLO_v12 deep learning model in postmenopausal women. Materials and Methods: Seventeen postmenopausal women aged 55–70 years completed two flow-mediated dilation assessments conducted seven days apart. Brachial artery images were acquired using a standardized FMD protocol with a handheld ultrasound system. An AI-assisted image analysis workflow based on a YOLO_v12 deep learning model was used to automatically measure baseline diameter (D_base), peak diameter (D_peak), absolute FMD (FMD_abs), and relative FMD (FMD%). Between-day reliability was evaluated using intraclass correlation coefficients (ICCs), coefficients of variation (CVs), and Bland–Altman analysis. Results: Good between-day repeatability was observed for baseline and peak diameters, with ICCs of 0.81 and 0.76 and low CVs (3.26% and 3.22%), respectively. Functional vascular outcomes also demonstrated good reliability, with ICCs of 0.81 for FMD_abs and 0.87 for FMD%. However, higher CVs were observed for FMD_abs (17.15%) and FMD% (19.09%), indicating substantial inter-individual variability. Bland–Altman analysis showed a small mean difference for FMD% (0.34%), with no evidence of systematic bias. Conclusions: An AI-assisted HHUS image analysis workflow integrating a YOLO_v12 deep learning model demonstrates acceptable between-day reliability for diameter-based and dilation-based measures of flow-mediated dilation in postmenopausal women. While variability in functional responses exists, the proposed system is feasible for research-oriented vascular assessment, providing a methodological foundation for future validation and clinical translation studies. Full article

Macrobrachium rosenbergii is one of the largest and most economically significant freshwater prawns worldwide. Understanding its population genetic structure is essential for optimizing cross-breeding strategies, conserving germplasm resources, and supporting sustainable aquaculture. However, progress in this area has been hindered by the limited availability of reliable molecular markers. In this study, we developed 20 polymorphic microsatellite primer pairs and applied them to assess the genetic diversity of 11 populations collected from China and Southeast Asia (including Jiangsu, Zhejiang, Taiwan, Myanmar, Bangladesh, Sri Lanka, and Thailand). All loci exhibited high levels of polymorphism. The number of alleles (Na) ranged from 5 to 27, while the mean observed heterozygosity (Ho), expected heterozygosity (He), and polymorphism information content (PIC) were 0.570, 0.720, and 0.686, respectively. The genetic differentiation coefficient (Fst) among populations ranged from 0.017 to 0.289. UPGMA clustering revealed that the Myanmar population formed an independent branch, whereas the remaining ten populations clustered together, indicating relatively close genetic relationships among them. Beyond enriching the currently limited molecular marker resources for M. rosenbergii, this study provides essential baseline data for evaluating genetic diversity in existing populations and establishes a solid molecular foundation for future genetic monitoring and breeding programs. Full article

Crop production in the desert Southwest of the United States, as well as in other arid and semi-arid regions, requires tools that provide accurate crop evapotranspiration (ET) estimates to support efficient irrigation management. Such tools include the web-based OpenET platform, which provides real-time ET data generated from six satellite-based models, their Ensemble, and a field-based system (LI-710, LI-COR Inc., Lincoln, NE, USA). This study evaluated simulated ET (ET_SIM) of cotton (Gossypium hirsutum L.) derived from OpenET models (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop), their Ensemble approach, and LI-710. Field data were utilized to estimate cotton ET using the soil water balance (SWB) method (ET_SWB) from June to October 2025 in Gila Bend, AZ, USA. Four evaluation metrics, the normalized root-mean-squared error (NRMSE), mean bias error (MBE), simulation error (S_e), and coefficient of determination (R²), were employed to evaluate the performance of OpenET models, their Ensemble, and the LI-710 in estimating cotton ET. Statistical analysis indicated that the ALEXI/DisALEXI, geeSEBAL, and PT-JPL models substantially underestimated ET_SWB, with simulation errors ranging from −26.92% to −20.57%. The eeMETRIC, SIMS, SSEBop, and Ensemble provided acceptable ET estimates (22.57% ≤ NRMSE ≤ 29.85%, −0.36 mm. day⁻¹ ≤ MBE ≤ 0.16 mm. day⁻¹, −7.58% ≤ S_e ≤ 3.42%, 0.57 ≤ R² ≤ 0.74). Meanwhile, LI-710 simulated cotton ET acceptably with a slight tendency to overestimate daily ET by 0.21 mm. A strong positive correlation was observed between daily ET_SIM from LI-710 and ET_SWB, with S_e and NRMSE of 4.40% and 23.68%, respectively. Based on our findings, using a singular OpenET model, such as eeMETRIC, SIMS, or SSEBop, the OpenET Ensemble, and the LI-710 can offer growers and decision-makers reliable guidance for efficient irrigation management of late-planted cotton in arid and semi-arid climates. Full article

Background: Teamwork and effective communication are widely recognized as essential pillars for the safety and quality of healthcare. However, in Portugal, no validated instrument had previously been available to assess healthcare professionals’ attitudes toward teamwork. This study aimed to translate, culturally adapt, and validate the TeamSTEPPS^® Teamwork Attitudes Questionnaire (T-TAQ) for the Portuguese context, resulting in the Portuguese version of the instrument. Methods: A methodological study with a quantitative approach was developed. The translation and cultural adaptation process followed internationally recognized guidelines. The sample consisted of 162 healthcare professionals (136 nurses and 26 physicians) from a hospital in Lisbon. Exploratory and confirmatory factor analysis techniques were used to assess construct validity. The internal consistency of the scale was analyzed using Cronbach’s alpha coefficient. Results: The Portuguese version comprises 30 items distributed across five dimensions: Effective Leadership Support, Team Functional Performance, Teamwork Coordination, Willingness to Engage in Teamwork, and Team Functioning Supervision. The scale demonstrated a total explained variance of 53.9% and an overall internal consistency coefficient (α) of 0.86, indicating good reliability. Confirmatory factor analysis supported the five-factor structure of the scale (χ²/df = 1.461; CFI = 0.900; GFI = 0.821; RMSEA = 0.054; MECVI = 4.731). Conclusions: The T-TAQ-PT proved to be a valid, reliable, and robust instrument for assessing healthcare professionals’ individual attitudes toward teamwork, contributing to the development of research and clinical practice in the Portuguese context. Full article

Thermal anomalies serve as potential earthquake precursors and are crucial for understanding the mechanisms underlying seismogenic mechanisms and geodynamic perturbations. To address the limited understanding of the polarity evolution of thermal anomalies, we developed a dynamic spatiotemporal adaptive framework to quantify global thermal anomaly responses. Four parameters—the coefficient of determination (R²), spatiotemporal uncertainty (SU), temporal–spatial uncertainty ratio (TSUR), and spatiotemporal correlation coefficient (SCC)—were established to characterize the spatiotemporal patterns of thermal anomaly responses. Additionally, the Anomaly Emphasis Proximity (AEP) was introduced to identify statistically significant thermal anomaly events. The results indicate that the spatiotemporal evolution of thermal anomalies exhibits a transition from pre-earthquake mixed anomalies (both positive and negative) to post-earthquake unipolar anomalies (TIB decreased from 92% to 49%), accompanied by pronounced sea–land differentiation (SST increased from 0.3% to 98.7%). The AEP reveals significant thermal anomaly clustering highly consistent with earthquake activity (e.g., the 2008 Mw 8.0 Wenchuan earthquake in the Qinghai–Tibet Plateau), showing strong correlations in structurally active regions (e.g., SCA and SWS; FDR < 18.5%, STCW > 3.7%) but weaker ones in stable regions (e.g., CNA and ECA). Overall, this framework significantly enhances the robustness and reliability of seismic thermal anomaly detection. Full article

Impairments in balance control are common across various clinical conditions and with aging, necessitating reliable methods for assessment. This study introduces a novel, low-cost posturographic system based on an unstable spring-supported platform that calculates center of pressure (COP) displacement using angular measurements in two horizontal axes. A heterogeneous sample of 105 participants underwent repeated trials on both the novel system and a traditional firm platform under eyes-open and eyes-closed conditions. COP velocity was recorded and analyzed for reliability using intraclass correlation coefficients (ICCs). The results showed significantly higher COP velocity on the unstable platform when visual input was removed, indicating greater reliance on visual control under unstable conditions. The novel system demonstrated comparable reliability to traditional platforms, with ICC values exceeding 0.90 when mean values from three trials were used. No learning effect was observed on the unstable platform, unlike the firm one. These findings suggest that the new system is a valid alternative for balance assessment, which is particularly effective in differentiating individuals with varying balance capabilities under eyes-closed conditions. Its affordability and methodological soundness make it suitable for clinical use and broader screening applications aimed at fall prevention. Full article

Accurate prediction of subway-induced environmental vibrations for unopened lines remains a significant challenge due to the difficulty in determining appropriate excitation inputs. To address this issue, this study proposes an excitation modification method based on field measurements and numerical simulations. First, field measurements were conducted on a subway line in Shanghai to analyze vibration propagation characteristics and validate a two-dimensional finite element model (FEM). Subsequently, based on the validated model, frequency-band excitation modification formulas were derived. Distinct from existing empirical approaches that often rely on simple statistical scaling, the proposed method utilizes parametric numerical analyses to determine frequency-dependent correction coefficients for four key parameters: tunnel burial depth, tunnel diameter, soil properties, and train speed. The reliability of the proposed method was verified through theoretical analysis and an engineering application. The results demonstrate that the proposed method improves prediction accuracy for tunnels in similar soft soil regions, reducing the prediction error from 10.1% to 5.2% in the engineering case study. Furthermore, parametric sensitivity analysis reveals that ground vibration levels generally decrease with increases in burial depth, tunnel diameter, and soil stiffness, while exhibiting an increase with train speed. This study improves the reliability of vibration prediction in the absence of direct measurements and provides a practical tool for early-stage design and vibration mitigation for unopened lines. Full article

Background: Quantitative assessment of proprioception in children with Developmental Coordination Disorder (DCD) is limited by methodological variability and the lack of developmentally appropriate protocols. Joint position sense (JPS) and force sense (FS) assessments are commonly used in adults; however, their reliability in pediatric populations has not been sufficiently established. The objective of this study was to evaluate the intra- and inter-rater reliability of adapted JPS and FS protocols in children with DCD and to determine whether the observed reliability supports the use of these methods in experimental research. Methods: A repeated-measurements reliability research design was employed. Twenty-eight children aged 10–15 years (mean age 12.86 years), with a mean body mass of 43.68 kg and a mean height of 149.32 cm, and with medically confirmed DCD, completed four proprioceptive tests: joint angle reproduction and differentiation, and force reproduction and differentiation. Absolute errors were calculated for each trial. Reliability was assessed using intraclass correlation coefficients (ICC2,k), standard error of measurement, and smallest detectable difference. Bland–Altman plots were used to evaluate agreement. Results: Reliability across all tests and movement directions ranged from good to excellent. Most ICC values exceeded 0.90, with only a small number falling between 0.86 and 0.90. Although differentiation tasks produced larger absolute errors than reproduction tasks, their reliability remained excellent. Bland–Altman analyses demonstrated acceptable bias, reasonable clustering around the mean difference, and only occasional outliers beyond the limits of agreement. Conclusions: The adapted JPS and FS protocols demonstrated high intra- and inter-rater reliability in children with DCD, supporting their use in experimental research. Full article

Surface water is essential for sustaining ecosystems and supporting human socio-economic development, yet pollution from urbanization increasingly threatens its ecological sustainability. The accurate prediction of dissolved oxygen (DO), as an important indicator of water quality, is crucial for water resource protection. To address the methodological gaps in current research, we propose a hybrid deep learning model (GCG) that integrates spatiotemporal correlations to enhance DO prediction accuracy through the systematic exploitation of latent data dependencies. This study proposes a three-stage modeling framework: (1) A novel adjacency matrix construction methodology based on Pearson correlation coefficients is developed to quantify spatial correlations between monitoring stations, enabling spatial feature aggregation via graph convolutional networks (GCNs); (2) the spatially enhanced features are subsequently processed through 1D convolutional neural networks (CNNs) to capture temporal local patterns; (3) model performance is comprehensively evaluated using four metrics: R², RMSE, MAE, and MAPE. The proposed model was implemented for DO prediction in Lake Taihu, China. Experimental results demonstrate that compared to conventional adjacency matrix construction methods, the Pearson correlation-based adjacency matrix confers advantages, achieving at least a 5% reduction in RMSE and over 10% improvement in MAE and MAPE. Furthermore, the GCG model outperformed the comparison model, with an R² enhancement of 8%, while reducing RMSE and MAE by over 70% and 60%, respectively. These results validate the model’s effectiveness in mining spatiotemporal correlations for regional water quality forecasting, offering a reliable tool toward sustainable water monitoring and ecosystem-based management. Full article

This study conducts systematic experimental and numerical investigations to address the parameter calibration issue in the discrete element model of seashells, aiming to establish a high-fidelity numerical model that accurately characterizes their macroscopic mechanical behavior, thereby providing a basis for optimizing parameters of seashell crushing equipment. Firstly, intrinsic parameters of seashells were determined through physical experiments: density of 2.2 kg/m³, Poisson’s ratio of 0.26, shear modulus of 1.57 × 10⁸ Pa, and elastic modulus of 6.5 × 10¹⁰ Pa. Subsequently, contact parameters between seashells and between seashells and 304 stainless steel, including static friction coefficient, rolling friction coefficient, and coefficient of restitution, were obtained via the inclined plane method and impact tests. The reliability of these contact parameters was validated by the angle of repose test, with a relative error of 5.1% between simulation and measured results. Based on this, using ultimate load as the response indicator, the PlackettBurman experimental design was employed to identify normal stiffness per unit area and tangential stiffness per unit area as the primary influencing parameters. The Bonding model parameters were then precisely calibrated through the steepest ascent test and design, resulting in an optimal parameter set. The error between simulation results and physical experiments was only 3.8%, demonstrating the high reliability and accuracy of the established model and parameter calibration methodology. Full article

Robust detection of emergency vehicle sirens remains difficult due to modern soundproofing, competing audio, and variable traffic noise. Although many simulation-based studies have been reported, relatively few systems have been realized in hardware, and many proposed approaches rely on complex or artificial intelligence-based processing with limited interpretability. This work presents a physical implementation of a low-complexity yelp siren detector that leverages the symmetries of the yelp signal, together with its characterization under realistic conditions. The design is not based on conventional signal processing or machine learning pipelines. Instead, it uses a simple analog envelope-based principle with threshold-crossing rate analysis and a fixed comparator threshold. Its performance was evaluated using an open dataset of more than 1000 real-world audio recordings spanning different road conditions. Detection accuracy, false-positive behavior, and robustness were systematically evaluated on a real hardware implementation using multiple deployable decision rules. Among the evaluated detection rules, a representative operating point achieved a true positive rate of 0.881 at a false positive rate of 0.01, corresponding to a Matthews correlation coefficient of 0.899. The results indicate that a fixed-threshold realization can provide reliable yelp detection with very low computational requirements while preserving transparency and ease of implementation. The study establishes a pathway from conceptual detection principle to deployable embedded hardware. Full article

This study proposes a framework, the RA_WLTE_River model, for quantifying the reliability of flood-altering water-level thresholds, considering rainfall and runoff-related uncertainties. The Keelung River in northern Taiwan is selected as the study area, and associated hydrological data from 2008 to 2016 are applied in the development and application of the model. According to the results from the model development and demonstration, the average and maximum rainfall intensities, roughness coefficients, and maximum tide depths exhibit a significant contribution to the reliability quantification of the estimated water-level thresholds. In addition, empirically based water-level thresholds can achieve the goal of rainfall-induced flood early warning, with a high likelihood of nearly 0.95. Additionally, the probabilistically based water-level thresholds derived from the described reliability can efficiently ensure consistent flood early warning performance at all control points along the river. Full article

This study examines the social determinants of the digital divide in pre-service teacher education through the design and validation of the Digital Hospitality Scale (DSBD-HD-FID). The instrument was developed to diagnose social inequalities across six key dimensions: socioeconomic status, geographic location, gender, age, disability status, and interculturality. These dimensions are understood as structural factors shaping access to, use of, and participation in digital environments within teacher education. The research followed a non-experimental, quantitative, and cross-sectional design, including content validation through expert judgment and statistical analysis based on a pilot sample of education students from Chile and Portugal. An exploratory factor analysis was conducted, and internal consistency was assessed using Cronbach’s alpha coefficient. The results confirm strong content and construct validity, as well as high reliability (α = 0.93). Empirical findings indicate that socioeconomic status and geographic location significantly condition access to connectivity and digital literacy, while gender differences emerge mainly in recreational uses and frequency of digital training. Beyond these results, the study highlights the relevance of addressing digital inequalities in teacher education through inclusive and equity-oriented training policies. The findings support the integration of digital hospitality, human rights education, and the Sustainable Development Goals into initial teacher training curricula as measurable and evaluable dimensions, providing an evidence-based framework to inform future teacher education policies aimed at reducing digital divides and promoting social cohesion. Full article

Gastrointestinal parasites are a major health and economic concern in small ruminants. The classic microscopic approach using the manual McMaster method serves to quantitatively count parasite eggs, which are labor-intensive and prone to variation. Artificial intelligence-based systems (Parasight^®, powered by Fecalsight AI™) could provide quicker and more objective alternatives; therefore, independent validation is necessary before clinical implementation. The objective of this study was to evaluate the agreement, classification consistency, and diagnostic performance of Parasight^® relative to the manual McMaster method, with a focus on its suitability as a screening and decision-support tool. Fecal samples from 44 Kiko goats over 3 sampling times were analyzed using both methods, with manual counts performed independently by 2 observers. Agreement between methods was assessed using Lin’s concordance correlation coefficient, Bland–Altman analysis, and Cohen’s Kappa for categorical classification. Diagnostic performance for identifying animals exceeding the clinical treatment threshold (>1000 eggs per gram) was evaluated using receiver operating characteristic (ROC) analysis, and regression modeling was used to characterize associations between methods. Manual observers showed high reliability, confirming the suitability of the McMaster method as a reference. Compared with manual counts, Parasight^® consistently underestimated egg counts, resulting in poor-to-moderate absolute agreement; however, it reliably ranked animals by parasite burden and showed excellent discrimination for identifying animals above the treatment threshold (AUC = 0.90–0.96). Regression analyses further demonstrated linear or curvilinear associations depending on egg counts. Overall, the Parasight^® device reliably captured relative parasite burden but required a lower operational threshold to match manual treatment decisions. Full article

Support Vector Machines (SVMs) are widely used in critical decision-making applications, such as precision agriculture, due to their strong theoretical foundations and their ability to construct an optimal separating hyperplane in high-dimensional spaces. However, the effectiveness of SVMs is highly dependent on the efficiency of the optimization algorithm used to solve their underlying dual problem, which is often complex and constrained. Classical solvers, such as Sequential Minimal Optimization (SMO) and Stochastic Gradient Descent (SGD), present inherent limitations: SMO ensures numerical stability but lacks scalability and is sensitive to heuristics, while SGD scales well but suffers from unstable convergence and limited suitability for nonlinear kernels. To address these challenges, this study proposes a novel hybrid optimization framework based on Open Competency Optimization and Particle Swarm Optimization (OCO–PSO) to enhance the training of SVMs. The proposed approach combines the global exploration capability of PSO with the adaptive competency-based learning mechanism of OCO, enabling efficient exploration of the solution space, avoidance of local minima, and strict enforcement of dual constraints on the Lagrange multipliers. Across multiple datasets spanning medical (diabetes), agricultural yield, signal processing (sonar and ionosphere), and imbalanced synthetic data, the proposed OCO-PSO–SVM consistently outperforms classical SVM solvers (SMO and SGD) as well as widely used classifiers, including decision trees and random forests, in terms of accuracy, macro-F1-score, Matthews correlation coefficient (MCC), and ROC-AUC. On the Ionosphere dataset, OCO-PSO achieves an accuracy of $95.71\%$, an F1-score of $0.954$, and an MCC of $0.908$, matching the accuracy of random forest while offering superior interpretability through its kernel-based structure. In addition, the proposed method yields a sparser model with only 66 support vectors compared to 71 for standard SVC (a reduction of approximately $7\%$), while strictly satisfying the dual constraints with a near-zero violation of $1.3\times {10}^{-3}$. Notably, the optimal hyperparameters identified by OCO-PSO ($\mathrm{C}=2$, $\gamma \approx 0.062$) differ substantially from those obtained via Bayesian optimization for SVC ($\mathrm{C}=10$, $\gamma \approx 0.012$), indicating that the proposed approach explores alternative yet equally effective regions of the hypothesis space. The statistical significance and robustness of these improvements are confirmed through extensive validation using 1000 bootstrap replications, paired Student’s t-tests, Wilcoxon signed-rank tests, and Holm–Bonferroni correction. These results demonstrate that the proposed metaheuristic hybrid optimization framework constitutes a reliable, interpretable, and scalable alternative for training SVMs in complex and high-dimensional classification tasks. Full article