Search Results (7,560)

The short-circuit withstanding capability of a transformer is a critical indicator for evaluating its operational reliability. This study investigates the influence of the low-voltage winding height, a key structural parameter, on the electromagnetic forces induced by short-circuit currents and the resultant short-circuit withstand capability. First, theoretical calculation formulas for the transformer leakage magnetic field and winding electromagnetic forces were derived, establishing a foundation for subsequent analysis. Subsequently, two 110 kV transformers, identical in all structural parameters except for their low-voltage winding heights, were selected as case studies. Three-dimensional finite element models were constructed to perform detailed simulations and a comparative analysis of the leakage magnetic field distribution and electromagnetic forces under short-circuit conditions. Finally, practical short-circuit tests were conducted on both transformers for experimental validation, monitoring and comparison of their short-circuit reactance variation curves. Furthermore, a CNN-LSTM model, utilizing the winding axial height of a 110 kV three-phase three-limb transformer as the characteristic parameter, is developed to detect short-circuit fault damage in such transformers with varying winding heights. Through a combined approach of theoretical analysis, simulation, and experimental verification, this study confirms that the low-voltage winding height was a crucial factor affecting the transformer’s short-circuit withstand capability of the transformer. Studies have shown that with the increase in the height of low-voltage windings, the leakage magnetic flux of the low-voltage windings increases by 36%, the radial electromagnetic force increases by 37.5%, and the axial electromagnetic force increases by 8.5%. Excessively tall windings amplify radial electromagnetic forces, compromising mechanical stability and consequently increasing the risk of damage during short-circuit faults. Full article

In this study, we apply machine learning to model the spatiotemporal dynamics of gene expression during early Drosophila embryogenesis. By optimizing model architecture, feature selection, and spatial grid resolution, we developed a predictive pipeline capable of accurately classifying active nuclei and forecasting their future distribution in time. We evaluated the model on two reporter constructs for the short gastrulation (sog) gene, sogD and sogD_∆Su(H), allowing us to assess its performance across distinct genetic contexts. The model achieved high accuracy on the wild-type sogD dataset, particularly along the dorsal–ventral (DV) axis during nuclear cycle 14 (NC14), and accurately predicted expression in the central regions of both wild-type and Suppressor of Hairless (Su(H)) mutant enhancers, sogD_∆Su(H). Bootstrap analysis confirmed that the model performed better in the central region than at the edges, where prediction accuracy dropped. Our previous work showed that Su(H) can act both as a repressor at the borders and as a stabilizer of transcriptional bursts in the center of the sog expression domain. This dual function is not unique to Su(H); other broadly expressed transcription factors also exhibit context-dependent regulatory roles, functioning as activators in some regions and repressors in others. These results highlight the importance of spatial context in transcriptional regulation and demonstrate the ability of machine learning to capture such nuanced behavior. Looking ahead, incorporating mechanistic features such as transcriptional bursting parameters into predictive models could enable simulations that forecast not just where genes are expressed but also how their dynamics unfold over time. This form of in silico enhancer mutagenesis would make it possible to predict the effects of specific binding site changes on both spatial expression patterns and underlying transcriptional activity, offering a powerful framework for studying cis-regulatory logic and modeling early developmental processes across diverse genetic backgrounds. Full article

Objective: To evaluate the psychometric properties of the short version of the Token Test (SVTT) and the Rey–Osterrieth Complex Figure (ROCF) using an item response theory (IRT) framework and to establish normative data for Colombian children and adolescents based on ability scores. Methods: A total of 668 healthy participants aged 6–17 years took part in this study. Factorial structure was assessed through confirmatory factor analysis (CFA). Item parameters were estimated using a two-parameter logistic (2PL) model for the SVTT, which accounts for both item difficulty and discrimination in dichotomous responses, and a graded response model (GRM) for the ROCF, suitable for items scored on ordered categories reflecting increasing levels of performance accuracy and Differential Item Functioning (DIF) analysis was conducted to assess potential bias related to sex. Reliability was examined using the Test Information Function (TIF), internal consistency throughout Cronbach’s alpha, and the influence of sociodemographic variables was analyzed through regression models. Results: CFA confirmed unidimensionality for all measures. For most items, moderate-to-low ability was sufficient to achieve the highest scores in the ROCF, and low ability in the SVTT. DIF analysis indicated no meaningful sex-related bias in any of the subtests. Both tests showed excellent reliability and internal consistency. Copy scores were influenced by polynomial age and parents’ mean years of education (MPE), while both immediate recall in the ROCF and SVTT were affected by MPE and the interaction of logarithmic age. Conclusions: This study provides strong psychometric evidence and, together with the integration of digital tools for generating normative data, represents a meaningful advancement in neuropsychological assessment. Full article

Water inrush and mud burst disasters pose severe challenges to the safe and efficient construction of underground engineering. Water inflow prediction is closely related to drainage design, disaster prevention and control, and the safety of the surrounding ecological environment. Thus, assessing the water inflow accurately is of importance. This study proposes a Bayesian Optimization-Long Short-Term Memory (BOA-LSTM) recurrent neural network for predicting tunnel water inflow. The model is based on four input parameters, namely tunnel depth (H), groundwater level (h), rock quality designation (RQD), and water-richness (W), with water inflow (WI) as the single-output variable. The model first processes and analyzes the data, quantitatively characterizing the correlations between input parameters. The tunnel water inflow is predicted using the long short-term memory (LSTM) recurrent neural network, and the Bayesian optimization algorithm (BOA) is employed to select the hyperparameters of the LSTM, primarily including the number of hidden layer units, initial learning rate, and L2 regularization coefficient. The modeling process incorporates a five-fold cross-validation strategy for dataset partitioning, which effectively mitigates overfitting risks and enhances the model’s generalization capability. After a comprehensive comparison among a series of machine learning models, including a long short-term memory recurrent neural network (LSTM), random forest (RF), back propagation neural network (BP), extreme learning machine (ELM), radial basis function neural network (RBFNN), least squares support vector machine (LIBSVM), and convolutional neural network (CNN), BOA-LSTM performed excellently. The proposed BOA-LSTM model substantially surpasses the standard LSTM and other comparative models in tunnel water inflow prediction, demonstrating superior performance in both accuracy and generalization. Hence, it provides a reference basis for tunnel engineering water inflow prediction. Full article

Rockburst disasters have become increasingly prevalent as distinct forms of subsurface geotechnical engineering advanced to the deep earth. Confronted with such a threatening subsurface geopressure disaster that poses a risk to personnel and equipment safety, the microseismic monitoring technology has been employed to track signals generated from rock fracture and collapse in the field. To guide the prevention and control of the hazard, the investigation conducted an effective microseismic data mining method. Through deep feature engineering and interpretable intelligence, a practical and available short-term prediction approach for the rockburst intensity class was developed. On the basis of rockburst case database collected from various underground geotechnical engineering, the neural network-based feature extraction method was conducted in the process of model training. The optimized model was obtained by combining the K-fold cross-validation approach with the structural parameter search methodology. The evaluation among the considered artificial intelligence models on the testing dataset was conducted and compared. Through analyses, the interpretable coupling intelligent model combining convolutional and recurrent neural networks for rockburst prediction were demonstrated with the most robust performance by evaluation metrics. Among them, the proposed adaptive feature extraction method leads the benchmark method by 6% for both accuracy and precision; meanwhile, the proposed metric generalization loss rate (GLR) for accuracy and precision in the validation–testing process reached 1.5% and 0.2%. Furthermore, the Shapley additive explanations (SHAP) approach was employed to verify the model interpretability by deciphering the model prediction from the perspective of the fined impact of input features. Therefore, the investigation demonstrates that the proposed method can predict rockburst intensity with robust generalization and feature extraction capabilities, which possess substantial engineering significance and academic worth. Full article

This study focuses on human awareness, a critical component in human–robot interaction, particularly within agricultural environments where interactions are enriched by complex contextual information. The main objective is identifying human activities occurring during collaborative harvesting tasks involving humans and robots. To achieve this, we propose a novel and lightweight deep learning model, named 1D-ResNeXt, designed explicitly for recognizing activities in agriculture-related human–robot collaboration. The model is built as an end-to-end architecture incorporating feature fusion and a multi-kernel convolutional block strategy. It utilizes residual connections and a split–transform–merge mechanism to mitigate performance degradation and reduce model complexity by limiting the number of trainable parameters. Sensor data were collected from twenty individuals with five wearable devices placed on different body parts. Each sensor was embedded with tri-axial accelerometers, gyroscopes, and magnetometers. Under real field conditions, the participants performed several sub-tasks commonly associated with agricultural labor, such as lifting and carrying loads. Before classification, the raw sensor signals were pre-processed to eliminate noise. The cleaned time-series data were then input into the proposed deep learning network for sequential pattern recognition. Experimental results showed that the chest-mounted sensor achieved the highest F1-score of 99.86%, outperforming other sensor placements and combinations. An analysis of temporal window sizes (0.5, 1.0, 1.5, and 2.0 s) demonstrated that the 0.5 s window provided the best recognition performance, indicating that key activity features in agriculture can be captured over short intervals. Moreover, a comprehensive evaluation of sensor modalities revealed that multimodal fusion of accelerometer, gyroscope, and magnetometer data yielded the best accuracy at 99.92%. The combination of accelerometer and gyroscope data offered an optimal compromise, achieving 99.49% accuracy while maintaining lower system complexity. These findings highlight the importance of strategic sensor placement and data fusion in enhancing activity recognition performance while reducing the need for extensive data and computational resources. This work contributes to developing intelligent, efficient, and adaptive collaborative systems, offering promising applications in agriculture and beyond, with improved safety, cost-efficiency, and real-time operational capability. Full article

Background/Objectives: Occlusal trauma does not initiate periodontitis but may accelerate its progression when inflammation is present. Chlorhexidine (CHX) mouthwash is widely used as an adjunct to periodontal therapy, although its effectiveness in patients with occlusal trauma is insufficiently documented. This study aimed to evaluate the effect of CHX mouthwash on periodontal status in patients with deflective occlusal contacts, in the absence of occlusal adjustments. Materials and Methods: This observational prospective study analyzed data from 52 patients (20 males, 32 females; mean age 41.35 years). Periodontal status was assessed using the Community Periodontal Index of Treatment Needs (CPITN) at baseline, 3 months, and 6 months. Patients were divided into groups based on CHX use and concentration. Statistical analysis evaluated intra- and intergroup variations. Results: Patients using CHX demonstrated statistically significant improvements in CPITN scores at 3 months for all sextants except sextant 5 (p < 0.05). Between 3 and 6 months, further significant improvement was observed only for sextant 6 and for the overall score. In contrast, patients without CHX showed a slight trend toward worsening CPITN values, with no statistically significant differences over the same period. Conclusions: CHX mouthwash significantly improved periodontal parameters in patients with occlusal trauma during the first three months of use. However, improvements plateaued after this period, highlighting the short-term benefits and limitations of CHX. These findings support the adjunctive role of CHX in managing periodontal disease associated with occlusal trauma but reinforce the necessity of mechanical occlusal correction for long-term stability. The CPITN index provides moderate clinical utility compared with full-mouth clinical periodontal measurements. Full article

We present the ML-CALMO framework, which integrates machine learning with queueing theory for last-mile delivery optimization under dynamic conditions. The system combines Long Short-Term Memory (LSTM) demand forecasting, Convolutional Neural Network (CNN) traffic prediction, and Deep Q-Network (DQN)-based routing with theoretical stability guarantees. Evaluation on modern benchmarks, including the 2022 Multi-Depot Dynamic VRP with Stochastic Road Capacity (MDDVRPSRC) dataset and real-world compatible data from OSMnx-based spatial extraction, demonstrates measurable improvements: 18.5% reduction in delivery time and +8.9 pp (≈12.2% relative) gain in service efficiency compared to current state-of-the-art methods, with statistical significance (p < 0.01). Critical limitations include (1) computational requirements that necessitate mid-range GPU hardware, (2) performance degradation under rapid parameter changes (drift rate > 0.5/min), and (3) validation limited to simulation environments. The framework provides a foundation for integrating predictive machine learning with operational guarantees, though field deployment requires addressing identified scalability and robustness constraints. All code, data, and experimental configurations are publicly available for reproducibility. Full article

Periodontal diseases are inflammatory conditions that destroy the periodontal attachment apparatus. Hyaluronic acid (HA) has anti-inflammatory properties that make it a candidate for the adjuvant treatment of gingivitis and periodontitis. Our objective was to observe the role of HA in the variability of clinical parameters indicative of gingivitis/periodontitis by comparing it with conventional treatments or placebo. This systematic review and meta-analysis was conducted according to Cochrane guidelines, and searches were performed in PubMed, Embase, Cochrane Central, Scopus, and Web of Science (WOS) to identify eligible studies. Review Manager 5.4.1 and SPSS Statistics 30.0^® were used to calculate standardized mean differences (SMDs) and 95% confidence intervals (CIs). The outcomes assessed were probing depth (PPD), bleeding on probing (BOP), clinical attachment level (CAL), plaque index (PI), and gingival index (GI). Sixteen randomized clinical trials (RCTs) with 947 subjects were included. HA as an adjunct to periodontal treatment improves the clinical parameters of PPD in the short and medium term (1–24 months, 12.5 average) (−0.51; 95% CI [−0.85 to −0.17]; p = 0.004), BOP, CAL and GI. Plaque indices (PI) approached statistical significance. Despite limitations and heterogeneity, the evidence reveals that only two of the included studies on severe periodontitis reported significant improvements in CAL gain and PPD reduction, with attachmet gains greater than 1 mm at 12 months of follow-up. Full article

Background: Schizophrenia is a chronic disorder requiring long-term pharmacological treatment. Many patients experience inadequate response and adverse effects, often leading to poor adherence and need for antipsychotic switch or polypharmacotherapy. In this context, brexpiprazole, an atypical antipsychotic with favorable tolerability profile, may offer clinical benefits following previous treatment failure or intolerance. However, real-world evidence after treatment switch remains limited. Methods: This retrospective, observational study included 50 outpatients with schizophrenia switched to brexpiprazole (2–4 mg/day) via cross-titration and evaluated over 12 weeks. Primary outcomes were changes in Patient Life Engagement, assessed through a 14-item subset of the Positive and Negative Syndrome Scale (PANSS), along with response/remission rates. Secondary outcomes included changes in subjective well-being, quality of life, sexual functioning (based on Subjective Well-being under Neuroleptics—Short Form [SWN-S], WHO-5 Well-Being Index [WHO-5], and Arizona Sexual Experience Scale [ASEX] scores, respectively), metabolic parameters, and prolactin levels. Results: Life engagement improved significantly (p < 0.001) across all domains, and clinical response was achieved in 40% of patients. Significant improvements were observed in SWN-S and WHO-5 scores (both p < 0.001). Weight and BMI significantly decreased (–2.64 kg, p = 0.013, and –0.91 kg/m², p = 0.006, respectively). Numerical non-significant reductions were found in ASEX (p = 0.067) and prolactin levels (–30.7 ng/mL, p = 0.077). Overall, treatment was well-tolerated. Conclusions: Switching to brexpiprazole was associated with improvements in psychopathological, functional, and physical health domains. These findings support its potential role in real-world, personalized therapeutic strategies for patients with schizophrenia following suboptimal outcomes with prior antipsychotic treatments. Full article

Background/Objectives: Oral candidiasis is frequently encountered in pediatric populations, particularly in infants and toddlers, where the development of immunity and inconsistent oral hygiene contribute to disease susceptibility. While topical antifungal agents remain the standard of care, treatment challenges persist, especially regarding adherence and recurrence. Aloe vera, recognized for its antimicrobial, anti-inflammatory, and mucosal healing properties, may offer therapeutic benefits when used in conjunction with standard regimens. This study aimed to evaluate the adjunctive effect of topical Aloe vera gel, when added to standard antifungal therapy, on reducing fungal load and improving treatment adherence in children with moderate oral candidiasis. Methods: A prospective controlled study was conducted among 54 children diagnosed with moderate oral candidiasis. Participants were randomly assigned to receive either standard topical nystatin or nystatin in conjunction with Aloe vera gel over a 7 day treatment duration. Fungal load was assessed using colony-forming units (CFU) counts from oral swabs collected at baseline and day 7, analyzed via ANCOVA. Additional parameters included treatment adherence, compared using an independent t-test, and clinical recurrence at a 14-day post-treatment follow-up, assessed through logistic regression. Results: Baseline characteristics were similar across groups. By day 7, children in the Aloe vera group exhibited a greater reduction in fungal load compared to those receiving standard therapy alone. Adherence was significantly higher in the aloe group (92.73% vs. 89.21%; p < 0.0001). Regression analysis identified both baseline fungal burden and adherence as factors associated with an increased risk of recurrence. Conclusions: The addition of Aloe vera gel to standard therapy may support a more effective fungal clearance and improved treatment adherence in children with moderate oral candidiasis, suggesting its potential as a complementary treatment option. Given the single-center design, short follow-up, and underpowered recurrence analysis, these findings should be considered preliminary, pending confirmation in larger studies with symptom-anchored endpoints. Full article

Background: Sow reproductive performance is a critical parameter for the productivity of commercial pig farms. Gut microbiota is associated with performance in sow reproduction. At least, under healthy conditions, microbial metabolites from the gut microbiota are considered major contributors to host physiological regulation and productivity. However, information on the differences in gut-derived metabolites related to the sow reproductive performance remain scarce. Our aim was to investigate the relationship between the reproductive performance and microbial metabolite levels in sow’s feces and plasma. Methods: We selected four commercial farms: two with high- (group H) and two with low-reproductive performance (group L). Sows had their feces and blood collected. Results: Except for the iso-butyrate concentration, fecal short-chain fatty acid concentrations remained unchanged between groups. Among intestinal putrefactive metabolites, the indole concentration was higher (p < 0.05) in group H. The concentrations of plasma metabolites p-cresyl sulfate, p-cresyl glucuronide and trimethylamine N-oxide (TMAO) were higher (p < 0.05) in group L than in group H, while the opposite was true for the acetate concentration (p < 0.05). Among plasma biochemicals, tumor necrosis factor (TNF)-alpha and potassium concentrations were higher (p < 0.05) in group L. Conclusions: Blood metabolites, especially gut microbiota-derived metabolites, seemed to be associated with the performance related to sow reproduction. Particularly, harmful metabolites such as p-cresyl glucuronide, p-cresyl sulfate and TMAO were of importance, because they are potentially inflammation factors. In fact, TNF-alpha was stimulated in group L. According to our results, we estimated that p-cresyl glucuronide, p-cresyl sulfate, TMAO and TNF-alpha could be useful physiological indicators to understand sow reproductive performance. Full article

This study systematically examined shock wave velocity induced by millisecond–nanosecond combined-pulse laser (ms–ns CPL) at a fixed ns laser energy density of 6 J/cm², exploring the effects of varying pulse delays of 0 to 3 ms and ms laser energy densities of 226.13 J/cm², 301 J/cm² and 376.89 J/cm². The temporal evolution of shock wave velocity induced by varying laser parameters was predicted by an attention mechanism-based long short-term memory algorithm (Attention-LSTM). The dependence between laser parameters and the evolution of shock wave velocity was captured by the LSTM layer. An attention mechanism was utilized to adaptively increase the weights of important time points during the propagation of the shock wave, thereby improving prediction accuracy. The experimental data corresponding to ms laser energy densities of 226.13 J/cm² and 301 J/cm² were set as the training set. The ms laser energy density of 376.89 J/cm² experimental data was set as test set to evaluate the generalization ability of the model under unknown ms laser energy. The results indicate that when ms laser energy density is 376.8 J/cm², the pulse delay is 2.2 ms. The shock wave velocity induced by the CPL increased by 50.77% compared with that induced by a single ns laser. The proposed Attention-LSTM model effectively predicts the evolutionary characteristics of shock wave velocity. The mean absolute error (MAE), root mean square error (RMSE), mean bias error (MBE) and the correlation coefficient (R²) of the test set are 7.65, 9.01, 1.47 and 0.98, respectively. This study provides a new data-driven approach for predicting the shock wave behavior induced by combined laser parameters and provides valuable guidance for optimizing laser process parameter combinations. Full article

Objective: As the survival of individuals born with congenital heart disease (CHD) improves into adulthood, the focus has shifted from traditional clinical outcomes to patient-reported outcome measures that better reflect the impact of the disease on daily life. Our aim was to assess the quality of life (QoL) of adult patients with congenital heart disease (ACHD) followed in a tertiary centre and to evaluate the parameters that influence QoL in this population. Methods: This cross-sectional observational study included patients followed up at the national referral ACHD centre between April and September 2022. Sociodemographic and clinical data were collected from medical records and self-report questionnaires. Quality of life (QoL) was assessed using the validated Short Form–36 (SF-36) and Euro Quality of Life–5 Dimension (EQ-5D) questionnaires, including the EQ Visual Analogue Scale (VAS). Results: A total of 123 ACHD patients were included (median age 34 (29–41) years; 43.9% male). Most participants had moderate CHD (61%), and 14.6% were cyanotic. Overall, SF-36 Physical Component Summary scores were higher than Mental Component Summary scores. Almost half of the patients (48.8%) reported no problems in all five domains of the EQ-5D, with most problems reported in anxiety/depression domain. Patients with severe CHD, cyanosis, or HF reported lower QoL scores across multiple SF-36 domains, particularly general health, role–physical, and physical functioning domains. Conclusions: QoL among ACHD patients in our cohort was generally high in most domains as assessed by the SF-36 and EQ-5D. Patients with HF reported lower QoL scores, emphasizing the importance of close clinical follow-up and the need for tailored QoL assessment tools for this complex population. Full article

Background and Objectives: Ongoing efforts to develop early diagnostic tools for Acute Ischemic Stroke (AIS) point out the advantages of accessible biomarkers such as Immature Platelet Fraction (IPF). This is particularly important for emergency department (EDs), especially those that are overcrowded and have limited resources. The present study aimed to evaluate the diagnostic, prognostic, and therapeutic significance of IPF in patients with AIS presenting to the ED. Materials and Methods: This prospective case–control study was conducted in an ED. Participants aged 18-years and older who presented with complaints of numbness, weakness, diplopia or visual disturbances, speech or comprehension impairment, confusion, imbalance, impaired coordination and gait, or dizziness were included in the study. The diagnostic value of IPF in AIS and its relationship with short-term prognosis (STP) were investigated. Additional variables potentially associated with parameters such as infarct localization, number of lesions, affected hemisphere, main artery status, carotid status and treatment method were also analyzed. Results: The median age of the study participants was 67 years (Q1 = 54, Q3 = 76), with 48.9% (n = 88) being female and 51.1% (n = 92) male. Receiver operating characteristic curve analysis demonstrated that IPF was statistically significantly superior to other complete blood count parameters in the diagnostic evaluation of AIS. The diagnostic cutoff value of IPF for AIS was calculated as 2.45. An increase of 1 unit in IPF was found to raise the likelihood of AIS by 2.599 times. The Ratio of Red Cell Distribution Width (RDW) to IPF and NEU to IPF, mean corpuscular volume, and infarct volume were found to be significant predictors in STP assessment. Conclusions: Although not definitive alone, IPF may aid early stroke recognition, support treatment monitoring, and inform targeted therapies. The use of IPF, a biomarker that can be rapidly obtained, in the diagnosis of AIS is expected to yield beneficial outcomes in patient management, particularly in emergency departments and other clinical settings. Full article