Search Results (13,314)

Background/Objective: Nurse educators are central to consolidating nursing as a discipline and shaping professional identity, yet their preparation is heterogeneous. This study aimed to identify profiles of nurse educators based on the value they assign to teaching competencies and to analyze factors influencing these profiles. Methods: A cross-sectional, descriptive research design was applied, using convenience sampling to recruit 326 nurse educators from Spanish universities. Data were collected through a self-administered online questionnaire distributed to nursing faculty from public, private, and affiliated (semi-private) universities across Spain. The instrument included sociodemographic and academic variables, along with nine teaching competencies. Descriptive statistics, cluster analysis, ANOVA, chi-square tests, and multinomial logistic regression were conducted using SPSS. Results: Three distinct profiles of nursing faculty were identified. The academic–pedagogical profile assigned the highest importance to all competencies (means 4.78–4.91), the clinical–pragmatic profile assigned the lowest (3.61–4.04), and the intermediate–researcher profile showed moderate values (4.26–4.50). Doctoral degree (χ² = 65.36, p < 0.001) and pedagogical training (χ² = 33.89, p < 0.001) were the strongest predictors of membership in the academic–pedagogical group, confirmed in multivariate regression (OR for doctorate = 0.07; OR for pedagogical training = 0.13, both p < 0.001). Conclusion: This study delineates three coherent and statistically robust profiles of nursing faculty based on their appraisal of teaching competencies. Academic qualifications and pedagogical training emerged as key determinants of these profiles. Tailored faculty development strategies that reinforce doctoral-level preparation and pedagogical expertise are critical to advancing the quality and consistency of nursing education. Full article

Accurate subsurface velocity modeling is of fundamental scientific and practical significance for seismic data processing and interpretation. However, conventional depth-domain methods still face limitations in physical consistency and inversion accuracy. To overcome these challenges, this study proposes a deep learning-based seismic velocity modeling approach in the time domain. The method establishes an end-to-end mapping between seismic records and velocity models directly in the time domain, reducing the nonlinear complexity of mapping time-domain data to depth-domain models and improving prediction stability and accuracy. Synthetic aquifer velocity models were constructed from representative stratigraphic features, and multi-shot seismic records were generated through forward modeling. A U-Net network was employed, taking multi-shot seismic records as input and time-domain velocity fields as output, with training guided by a mean squared error (MSE) loss function. Experimental results show that the proposed strategy outperforms conventional depth-domain approaches in aquifer structure identification, velocity recovery, and interlayer contrast depiction. Quantitatively, significant improvements in MSE, peak signal-to-noise ratio, and structural similarity index indicate higher reconstruction reliability. Overall, the results confirm the effectiveness and potential of the proposed time-domain framework for aquifer velocity inversion and its promise for intelligent seismic velocity modeling. Full article

Background: Previous longitudinal evidence suggested that screen exposure at age 4 was associated with dysregulation symptoms and lower academic achievement up to age 8. Yet, it remains unclear whether these effects persist in preadolescence and extend to higher-order developmental outcomes such as the capacity to be alone, a marker of self-regulation and autonomy within the developmental psychopathology framework. Aim: This follow-up study re-contacted the original cohort at age 12 (T3) to examine whether early screen time predicted dysregulation, academic achievement, and capacity to be alone, testing the mediating role of dysregulation at ages 6 (T1) and 8 (T2), and the moderating role of maternal scaffolding at age 4. Methods: A community sample of N = 323 children and their mothers, previously assessed at T0–T2, was re-evaluated at T3 (mean age = 12.2 years, SD = 0.7). At T0, screen exposure and maternal scaffolding were measured using the StimQ (PIDA subscale). Dysregulation at T1–T3 was assessed with the Teacher Report Form (TRF). Academic achievement in mathematics and literacy was rated by teachers using the Teacher Academic Ratings. At T3, children also completed the Capacity to Be Alone Scale for Children (CBASC). Structural Equation Modeling (SEM) tested longitudinal direct, indirect, and moderated pathways, adjusting for sex, maternal education, and socioeconomic status. Results: Screen time at age 4 was associated with elevated dysregulation at T1 and T2, which in turn mediated poorer mathematics and literacy outcomes and reduced capacity to be alone at age 12 (all p < 0.01). Maternal scaffolding buffered early dysregulation but did not prevent long-term academic or self-regulatory impairments. Conclusions: Findings indicate that early excessive screen use contributes to a cumulative cascade of dysregulation, undermining both academic achievement and the developmental capacity to be alone by preadolescence. Preventive strategies should integrate screen-time guidelines with parental scaffolding interventions. Full article

Background: Pediatric heart failure (PHF) is a heterogeneous syndrome with high morbidity, but existing classification systems inadequately capture its developmental and pathophysiological complexity due to reliance on adult-centric parameters. Using machine learning, we aimed to identify clinically distinct PHF phenotypes with unique outcomes and therapeutic implications. Methods: In this multicenter retrospective study, we analyzed 2903 consecutive PHF patients (≤18 years) from 30 Chinese tertiary centers from 20 provinces (2013–2022). Unsupervised machine learning (k-means clustering with PCA) evaluated 99 clinical, biomarker, and echocardiographic variables to derive phenotypes, which were compared for mortality, hospitalization, and treatment responses. Results: Three phenotypically distinct clusters emerged. Cluster 1 (Chronic Hypertensive and Cardiorenal Profile, 30.1%) predominantly affected older children (78%) with hypertension (54.4%), renal dysfunction (creatinine 45.8 μmol/L), and ventricular tachycardia (53.8%). This cluster showed the lowest in-hospital mortality (2.5%) but frequent 7–14 day hospitalizations (35.8%) and the highest beta-blocker use (54.5%). Cluster 2 (Preterm and CHD-Associated HF, 43.4%) comprised preterm infants (71.4%) with congenital heart disease (72.2%) and preserved LVEF (67%), demonstrating the highest mortality (5.1%) and prolonged stays (>30 days: 10.6%) with predominant diuretic (40.6%) and antibiotic use (54.3%). Cluster 3 (Fulminant Myocarditis Profile, 26.5%) exhibited cardiogenic shock with severely reduced LVEF (33%) and elevated BNP (3234 pg/mL), showing bimodal outcomes (4.8% LOS < 3 days vs. 32.2% LOS 15–30 days) and the highest IVIG utilization (46.5%) with intermediate mortality (3.8%). The majority of between-group differences were statistically significant (p < 0.001). Conclusions: Machine learning identified three PHF phenotypes with distinct in-hospital risk profiles and therapeutic implications, challenging current classification systems. These findings highlight the potential for phenotype-specific management strategies and provide a rationale for future research into arrhythmia prevention in hypertensive profiles and early immunomodulation in fulminant myocarditis, while highlighting the need for specialized care pathways for preterm/CHD patients. Prospective validation is warranted to translate this framework into clinical practice. Full article

The use of simulated environments for the development and validation of Automated Guided Vehicles (AGVs) has proven to be an effective approach for reducing costs and accelerating the testing process. Simulated environments offer a safe and controlled means for performance analysis and controller parameter adjustment. However, most simulators employed for AGVs and mobile robots rely on kinematic models, which limits the fidelity of the tests. This work introduces a physics-driven Unity framework that leverages the NVIDIA PhysX engine to model AGV dynamics—including payload variation, wheel–ground interactions, and suspension effects—addressing a critical gap in surveyed studies. A factory-floor virtual environment was developed, and a holonomic AGV was implemented with RigidBody and WheelCollider components. PID controllers were tuned via Exhaustive Search and Ziegler–Nichols methods across loads from 0 kg to 100 kg. Exhaustive Search achieved a mean lateral error of just 0.0069 cm and a standard deviation of 1.33 cm at 50 kg—58% lower variability than Ziegler–Nichols. Meanwhile, controller tuning using Ziegler–Nichols required only up to 40 min per load but exhibited up to 84% inter-operator gain variability. Performance was validated on infinity-shaped track, demonstrating Unity’s utility for quantitative performance benchmarking. As contributions, this study (i) presents a novel dynamic AGV simulation framework, (ii) proposes a dual validation workflow combining on-site tuning and systematic optimization, and (iii) integrates an embedded evaluation suite for reproducible control- strategy comparisons. Full article

Freshwater scarcity in arid regions forces farmers to use saline water, reducing durum wheat (Triticum turgidum L. subsp. durum) productivity, particularly during early growth stages. This study evaluated two Moroccan varieties, Faraj and Nachit, on silty clay soil under five salinity levels (0.2, 4, 8, 12, and 16 dS m⁻¹) in a randomized complete block design with three replications, aiming to identify tolerance thresholds and characterize physiological and agronomic responses. Key traits measured included germination percentage, germination stress index, mean germination time, root and coleoptile length, plant height, leaf number, chlorophyll fluorescence, grain yield, weight of 200 grains, and straw yield. Germination percentage declined from 8 dS m⁻¹, with delayed germination and inhibited vegetative growth at higher salinity. Both varieties maintained grain yield up to 8 dS m⁻¹ and weight of 200 grains and straw yield up to 12 dS m⁻¹, with Nachit showing higher tolerance. Multivariate analyses, including principal component analysis and heatmaps, linked soil sodium, chloride, and electrical conductivity negatively to growth and yield, whereas potassium, calcium, and magnesium supported plant growth and physiological activity. These findings provide insights for breeding and irrigation strategies to sustain durum wheat under salinity stress. Full article

In response to the challenges posed by multifactorial nonlinear relationships and uncertainties, and to address the limitations of the existing Belief Rule Base (BRB) in nonlinear fitting, uncertainty representation, and parameter optimization, this paper presents an improved reliable modeling method using a nonlinear belief rule base (R-NBRB). First, the linear inference mechanism is replaced by a smooth nonlinear S-function. This replacement better adapts to nonlinear dynamics in complex industrial systems. Second, attribute reliability is quantified through a reliability assessment method. Data, reliability, and expert knowledge are integrated using the Evidential Reasoning (ER) algorithm. Uncertainty is expressed in the form of belief degrees. Finally, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm is applied to optimize the inference parameters. Decision bias caused by insufficient expert knowledge is thereby reduced. Experiments were conducted on a task involving the detection of a petroleum pipeline leak. The mean squared error (MSE) of the R-NBRB model is only 0.2569. This represents a 28.24% reduction compared with the BRB model. The proposed method’s effectiveness and adaptability in complex industrial situations are confirmed. Full article

Peach firmness is a critical quality attribute, yet conventional destructive measurement methods are unsuitable for batch detection in industrial settings. This study investigated a noncontact method for firmness assessment across multiple peach cultivars based on acoustic vibration technology. Three peach cultivars were mechanically excited via a controlled air jet, and the resulting acoustic vibration responses were captured noninvasively using a laser Doppler vibrometer. The frequency-domain acoustic vibration spectra were used as input for firmness prediction models developed using partial least squares regression (PLSR), support vector regression (SVR), and a one-dimensional convolutional neural network (ISNet-1D) that incorporated Inception and squeeze-and-excitation modules. Comparative analysis demonstrated that the ISNet-1D substantially outperformed the conventional linear and nonlinear methods on an independent test set, achieving superior predictive accuracy, with a coefficient of determination ( $R_P^2)$ of 0.8069, a root mean square error (RMSEP) of 0.9206 N/mm, and a residual prediction deviation ( ${RPD}_P)$ of 2.2879. The good performance of the ISNet-1D can be attributed to the integration of multi-scale convolutional filters with a channel-wise attention mechanism. This integration allows the network to adaptively prioritize discriminative spectral features, thereby enhancing its prediction accuracy. A hierarchical transfer learning strategy was proposed to improve model generalizability, offering a practical and cost-effective means to adapt to diverse cultivars. In summary, the combination of noncontact acoustic vibration and deep learning presents a robust, accurate, and nondestructive methodology for assessing peach firmness, demonstrating considerable potential for cross-cultivar application in industrial sorting and quality control. Full article

Objectives: The aim of this study is to evaluate the safety and efficacy of repeat transcatheter aortic valve implantation (redo-TAVI) in the polish population. Methods: In this multicentre nationwide registry (ClinicalTrials.gov identifier, NCT03361046), we provide characteristics, periprocedural variables and long-term outcomes of high-risk patients who underwent redo-TAVI. Results: The mean age among 32 individuals who underwent redo-TAVI was 75 ± 13 years, and 62.5% were male. The mean time from index TAVI to redo-TAVI was 4.7 ± 3.5 years, with failed procedures (up to 1 year) occurring in 7 (21.9%) and failed transcatheter heart valve (THV, beyond 1 year) in the remaining majority of the 25 (78.1%) patients. Computed tomography-based native bicuspid aortic anatomy was found frequently in 37.5% of cases (58.3% in failed procedures and 41.7% in failed THV). The mean failed THV size was large (27.7 ± 3 mm) and predominantly presenting with pure regurgitation (59.4%). In more than two-thirds (68.7%), balloon-expandable or self-expandable THV was the most common strategy of redo-TAVI. None or mild regurgitation was found in 90.6%, and the mean transvalvular gradient was 13.1 ± 5.5 mmHg, with only three cases with >20 mmHg of the residual gradient (9.4%). Peri-procedural and 30-day complications were low, and cardiovascular and all-cause mortality at 1 year was 9.4 and 15.6%, respectively. There was a relatively high incidence of non-procedural stroke after redo-TAVI (n = 5, 15.6%), with all cases observed after 30 days. Conclusions: Initial data of redo-TAVI in Poland suggest that the procedure is safe and characterized by favourable efficacy and low rates of short-term adverse outcomes. A high frequency of baseline native bicuspid anatomy and late stroke occurrence after the redo-procedure warrants further investigation in larger cohorts. Full article

Accurate prediction of drilling speed is essential in mechanical drilling operations, as it improves operational efficiency, enhances safety, and reduces overall costs. Traditional prediction methods, however, are often constrained by delayed responsiveness, limited exploitation of real-time parameters, and inadequate capability to model complex temporal dependencies, ultimately resulting in suboptimal performance. To overcome these limitations, this study introduces a novel model termed CTLSF (CNN-TCN-LSTM with Self-Attention), which integrates multiple neural network architectures within a symmetry-aware framework. The model achieves architectural symmetry through the coordinated interplay of spatial and temporal learning modules, each contributing complementary strengths to the prediction task. Specifically, Convolutional Neural Networks (CNNs) extract localized spatial features from sequential drilling data, while Temporal Convolutional Networks (TCNs) capture long-range temporal dependencies through dilated convolutions and residual connections. In parallel, Long Short-Term Memory (LSTM) networks model unidirectional temporal dynamics, and a self-attention mechanism adaptively highlights salient temporal patterns. Furthermore, a sliding window strategy is employed to enable real-time prediction on streaming data. Comprehensive experiments conducted on the Volve oilfield dataset demonstrate that the proposed CTLSF model substantially outperforms conventional data-driven approaches, achieving a low Mean Absolute Error (MAE) of 0.8439, a Mean Absolute Percentage Error (MAPE) of 2.19%, and a high coefficient of determination (R²) of 0.9831. These results highlight the effectiveness, robustness, and symmetry-aware design of the CTLSF model in predicting mechanical drilling speed under complex real-world conditions. Full article

The detrimental effects of anthropogenic pollution are often magnified across ecosystems due to the interconnected nature of land, rivers, and oceans. Phytoremediation is an accessible technique that leverages the ability of plants to absorb and sequester pollutants and can potentially mitigate contaminants entering the ocean. It is a cost-effective and minimally invasive alternative to traditional water treatment methods. This study investigates the potential of the grapevine species Cissus verticillata (L.), a native plant from Puerto Rico, to be used in the phytoremediation of a creek in a highly urbanized site impacted by contaminated runoff due to heavy rainfall and sanitary waters. A mesocosm experiment was conducted using distilled water mixed with nutrients and known concentrations of cadmium (Cd) and lead (Pb) salts to assess whether C. verticillata could accumulate heavy metals in its tissues. Results showed that C. verticillata successfully absorbed heavy metals, with removal efficiencies of 80.13% (±0.16 SE) for Pb and 44% (±1 SE) for Cd. Results indicated a translocation factor <1 for both cadmium and lead, meaning C. verticillata is not a hyperaccumulator, but a metal stabilizer, as evident by the below detection limit (BDL) of the metals in Juan Mendez Creek. Despite evidence of new vegetative growth among individuals, no significant changes in total biomass or chlorophyll concentration were detected, indicating that C. verticillata maintained physiological stability under heavy metal exposure. Therefore, C. verticillata’s wide availability, adaptability to various environments, and climbing nature—which makes it less vulnerable to runoff and strong currents during rainy seasons—position it as a promising candidate for conservation initiatives and pollution management strategies. Full article

Zero-shot action recognition remains challenging due to the visual–semantic gap and the persistent bias toward seen classes, particularly under the generalized setting where both seen and unseen categories appear during inference. To address these issues, we propose Multi-Scale Semantic Alignment framework for Zero-Shot Sports Action Recognition (MSA-ZSAR), a framework that integrates a multi-scale spatiotemporal feature extractor to capture both coarse and fine-grained motion dynamics, a dual-branch semantic alignment strategy that adapts to different levels of semantic availability, and a bias-suppression mechanism to improve the balance between seen and unseen recognition. This design ensures that the model can effectively align visual features with semantic representations while alleviating overfitting to source classes. Extensive experiments demonstrate the effectiveness of the proposed framework. MSA-ZSAR achieves 52.8% unseen accuracy, 69.7% seen accuracy, and 61.3% harmonic mean, consistently surpassing prior approaches. These results confirm that the proposed framework delivers balanced and superior performance in realistic generalized zero-shot scenarios. Full article

To address the problems of low inversion accuracy and poor noise resistance in pulsed eddy current (PEC) grounding grid depth detection, this study proposes a novel inversion model (IE-CBiLSTM). This model integrates the Informer Encoder with the CNN-BiLSTM for the first time to detect the depth of the PEC grounding grid and conducts experimental verification based on an independently designed pulsed eddy current detection device and a dedicated coil sensor. The model design employs a two-dimensional convolutional neural network (CNN) to extract local spatial features, combines a bidirectional long short-term memory network (Bi-LSTM) to model temporal dependencies, and introduces a multi-head attention mechanism along with the Informer structure to enhance the expression of key features. In terms of data construction, the design integrates both forward simulation data and measured data to improve the model’s generalization capability. Experimental validation includes self-burial experiments and field tests at a substation. In the self-burial test, the IE-CBiLSTM inversion results show high consistency with actual burial depths under various conditions (1.0 m, 1.2 m, and 1.5 m), significantly outperforming other optimization algorithms, achieving a coefficient of determination (R²) of 0.861, along with root mean square error (E_RMS) and mean relative error (E_MR) values of 17.54 Ω·m and 0.061 Ω·m, respectively. In the field test, the inversion results also closely match the design depths from engineering drawings, with an R² of 0.933, E_RMS of 11.30 Ω·m, and E_MR of 0.046 Ω·m. These results are significantly better than those obtained using traditional Occam and LSTM methods. At the same time, based on the inversion results, a three-dimensional inversion map of the grounding grid and a buried depth profile were drawn, and the spatial direction and buried depth distribution of the underground flat steel were clearly displayed, proving the visualization ability of the model and its engineering practicality under complex working conditions. This method provides an efficient and reliable inversion strategy for deep PEC nondestructive testing of grounding grid laying. Full article

The field of hard milling has recently witnessed growing interest in environmentally sustainable machining practices. Among these, Minimum Quantity Lubrication (MQL) has emerged as an effective strategy, offering not only reduced environmental impact but also economic benefits and enhanced cooling performance compared to conventional flood cooling methods. In hard milling operations, cutting temperature is a critical factor that significantly influences the quality of the finished component. Proper control of this parameter is essential for producing high-precision workpieces, yet measuring cutting temperature is often complex, time-consuming, and costly. These challenges can be effectively addressed by predicting cutting temperature using advanced Machine Learning (ML) models, which offer a faster and more efficient alternative to direct measurement. In this context, the present study investigates and compares the performance of Conventional Minimum Quantity Lubrication (CMQL) and Graphene-Enhanced MQL (GEMQL), with sesame oil serving as the base fluid, in terms of their effect on cutting temperature. The experiments are structured using a Taguchi L36 orthogonal array, with key variables including cutting speed, feed rate, MQL jet pressure, and the type of cooling applied. Additionally, the study explores the predictive capabilities of various advanced ML models, including Decision Tree, XGBoost Regressor, K-Nearest Neighbor, Random Forest Regressor, and CatBoost Regressor, along with a Hybrid Stacking Machine Learning Model (HSMLM) for estimating cutting temperature. The results demonstrate that the GEMQL setup reduced cutting temperature by 36.8% compared to the CMQL environment. Among all the ML models tested, HSMLM exhibited superior predictive performance, achieving the best evaluation metrics with a mean absolute error of 3.15, root mean squared error (RMSE) of 5.3, mean absolute percentage error of 3.9, coefficient of determination (R²) of 0.91, and an overall accuracy of 96%. Full article

Management zones (MZs) are a key precision agriculture strategy for managing spatial variability in crops, but conventional delineation methods are costly, time-consuming, and rely on specialized equipment. Previous studies in potato production have primarily relied on single-year NDVI or proximal soil sensor data analyses, limiting their ability to capture temporal stability and variability across multiple fields. This study addresses this gap by applying multi-year, multi-source NDVI composites to characterize spatial and temporal patterns of agricultural potential across 17 commercial potato fields at McCain’s Farm of the Future, Florenceville-Bristol, New Brunswick. A total of 230 NDVI images from Sentinel-2 and Landsat 8 (2015–2023) were processed into composite metrics (mean, standard deviation, skewness) to delineate three agricultural potential (AP) MZs. Validation was conducted using 2023 potato tuber yield and soil physicochemical properties. The results showed statistically significant correlations between NDVI metrics and key soil nutrients (total carbon: |r| < 0.19; total nitrogen: |r| < 0.28), with tuber yield (|r| < 0.41). Spatial patterns of total carbon and nitrogen corresponded with delineated MZs, and tuber yield variability partially aligned with these zones. These findings demonstrate that multi-year NDVI composites provide a cost-effective and scalable approach for mapping agricultural potential, capturing both spatial and temporal variability, and supporting data-driven management decisions in potato production systems. Full article