Search Results (10,039)

Atmospheric refractivity governs the propagation behavior of electromagnetic waves in the lower troposphere. Accurate spatial characterization of this parameter is essential for optimizing communication, radar, and navigation systems. This study presents a geostatistical framework for generating high-resolution refractivity maps using Universal Kriging (UK) applied to meteorological observations from a dense network of automatic weather stations in the Galician region (NW Spain). The methodology explicitly models the non-stationary vertical structure of the atmosphere by decomposing the refractivity field into a deterministic altitude-dependent drift and a stochastic residual component characterized by an exponential variogram. Validation, performed using independent test stations bounding the regional vertical profile, demonstrates that the UK approach significantly outperforms Ordinary Kriging (OK). UK not only reduces mean errors and improves linear agreement, but critically minimizes systematic bias and extreme outlier occurrences ($P_{95}$). Beyond accurate spatial interpolation, the dynamically estimated vertical drift retrieves the macroscopic refractivity gradient, serving as a direct, real-time diagnostic tool to classify anomalous radio-frequency (RF) propagation regimes (e.g., super-refraction and ducting) and supporting robust decision-making in complex topographies. Full article

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault detection sensitivity across various rotational speeds and load conditions. Using an accelerometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphone positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), kurtosis and Crest Factor (CF) values, with advanced diagnostic techniques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spectrum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, kurtosis, CF) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the maximum sensor distance. The study concludes that optimal microphone placement is essential for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications. Full article

Background/Objectives: The objective assessment of natural tooth mobility remains challenging in clinical practice. This pilot study aimed to investigate the feasibility, repeatability, and agreement of a modified implant stability measurement system adapted for natural teeth using a custom-fabricated titanium bracket and a modified SmartPeg. Methods: Sixteen systemically healthy patients (10 males, six females) and 94 single-rooted permanent teeth with varying mobility grades were included. The tooth mobility was assessed using the Miller Mobility Index, Periotest M, and resonance frequency analysis (RFA) with the Osstell Beacon device. For the Osstell measurements, a custom titanium bracket bonded to the buccal tooth surface allowed for the placement of a modified SmartPeg. Each tooth was measured twice under standardized conditions, and mean values were recorded. The statistical analyses included Spearman correlation analysis, Cohen’s kappa for agreement with Miller categories, and intraclass correlation coefficients (ICCs) to assess the measurement repeatability. Results: The mean Periotest value was 12.70 ± 13.69, and the mean ISQ (implant stability quotient) value was 69.45 ± 19.37. The repeated measurements demonstrated excellent intra-examiner repeatability for both devices (ICC > 0.95). The Periotest values showed substantial agreement with the Miller mobility grades (κ = 0.763; p < 0.001), whereas the Osstell values demonstrated weak agreement with these ordinal categories (κ = 0.094; p = 0.048). A strong negative correlation was observed between the Periotest and Osstell measurements irrespective of the scales (r = −0.865; p < 0.001). Conclusions: In natural dentition, the resonance frequency analysis demonstrated reproducible measurements under controlled experimental conditions and showed measurable associations with conventional mobility assessments. However, the method remains investigational. The findings do not establish clinical validity for the routine assessment of natural tooth mobility. Further studies with larger sample sizes and statistical models accounting for patient-level clustering are required before clinical implementation can be considered. This study is registered at ClinicalTrials.gov (NCT07188168). Full article

Background: Restless legs syndrome (RLS) is a prevalent neurological sleep disorder that often impairs sleep maintenance. This single-arm, open-label study evaluated the efficacy, safety, and tolerability of extended-duration tonic motor activation (XD-TOMAC) in adults with RLS who experience frequent awakenings with symptoms. Methods: The study comprised three stages: Stage 1 (2 weeks of no intervention), Stage 2 (8 weeks XD-TOMAC), and Stage 3 (2 weeks of no intervention). XD-TOMAC consisted of bilateral high-frequency peroneal nerve stimulation programmed to 180 min duration and administered nightly at bedtime. Nineteen adults with moderate–severe RLS were enrolled, each reporting at least three nights per week of RLS symptoms causing increased awakenings or interfering with returning to sleep after waking. Results: The intent-to-treat analysis population included all patients who began Stage 2 (n = 15). After 8 weeks of XD-TOMAC, the mean change in International RLS Study Group Rating Scale (IRLS) score was −10.6 points (p < 0.001), and the mean change in Medical Outcomes Study Sleep Problems Index II (MOS-II) was −29.5 points (p < 0.001). The mean change in the number of nocturnal awakenings was −1.1 per night (p = 0.009), and the mean change in sleep efficiency was +8.5% (p = 0.001). The mean change in time awake with RLS symptoms after sleep onset was −28.1 min (p = 0.009). Each of these improvements was sustained at the end of Stage 3 (p < 0.01). There were no serious or severe device-related adverse events. Conclusions: Compared with prior 30 min TOMAC studies, XD-TOMAC demonstrated greater efficacy and similar tolerability, supporting its potential as a nonpharmacological therapy for RLS patients whose symptoms frequently disrupt sleep. Full article

Background: Chorioamnionitis (CA) is a major pathological cause of preterm labor and is associated with both short- and long-term adverse outcomes in neonates, including early-onset sepsis (EOS) and late-onset sepsis (LOS). Neonatal sepsis remains a significant contributor to morbidity and mortality in neonatal intensive care units (NICUs). Aim: This study aimed to evaluate the association between maternal chorioamnionitis and the incidence of early-onset and late-onset neonatal sepsis in preterm neonates born at <32 weeks’ gestation. Furthermore, the study investigated maternal and neonatal factors affecting the presentation of sepsis. Methods: A retrospective cohort study was conducted on the medical records of preterm neonates born between 2020 and 2022. Inclusion criteria were gestational age < 32 weeks, available microbiological or histological examination for chorioamnionitis, and complete maternal medical records. Infants were categorized into two groups based on the presence (CA group) or absence (non-CA group) of histological and/or microbial chorioamnionitis. Descriptive statistical analyses were performed, including calculation of frequencies and percentages for categorical variables and means with standard deviations and ranges for continuous variables. Results: A total of 189 neonates were included, with a mean birth weight of 1286 ± 405 g and a mean gestational age of 29.2 ± 2.1 weeks. The CA group consisted of 55 neonates (29.1%), while 134 (70.9%) were in the non-CA group. Early-onset sepsis (EOS) occurred in 23 neonates (12.2%), with a significantly higher incidence in the CA group compared to the non-CA group (21% vs. 8%, p = 0.014). Late-onset sepsis (LOS) developed in 66 neonates (34.9%), but no significant difference in incidence was observed between the two groups (p = 0.402). Parsimonious logistic regression analysis identified maternal chorioamnionitis as an independent predictor of EOS (Odds Ratio 2.07, 95% CI 1.85–5.08; p = 0.009). Conclusions: Intrauterine infection and inflammation caused by chorioamnionitis are linked to an increased risk of early-onset sepsis in neonates born before 32 weeks’ gestation. However, chorioamnionitis does not appear to significantly influence the incidence of late-onset sepsis, which appears to be more closely associated with postnatal factors. Full article

Irritable bowel syndrome (IBS) and mental health disorders represent common and significant health concerns in pediatric populations. Objectives: This study aimed to evaluate psychological and social risk factors associated with IBS in children and to identify correlations with their gastrointestinal symptoms. Materials and Methods: Children aged 4 to 18 years diagnosed with IBS according to Rome IV criteria were eligible for inclusion. Both patients and parents completed a comprehensive questionnaire detailing gastrointestinal symptom characteristics. Additionally, all children underwent psychological assessment. Results: The study included 24 children with IBS, with a mean age of 12.7 ± 3.4 years. Anxiety was present in 54.2% of cases, and depression in 12.5%. Comparing children with IBS and anxiety to those without these, no statistically significant differences emerged regarding the duration and frequency of abdominal pain; however, abdominal pain intensity was significantly higher in children without anxiety (p = 0.04). The duration of IBS symptoms did not significantly differ in children with or without anxiety (p = 0.21). Impaired emotional self-regulation was identified in 54.2% of participants, and 41.6% exhibited vegetative symptoms in response to stress. Furthermore, 70.8% of parents and/or children reported experiencing a negative family event. Conclusions: The findings suggest that psychological characteristics and adverse family events are important risk factors associated with pediatric IBS. These factors should be systematically considered as integral components of clinical assessment and management. Full article

Background and Objectives: Visual evoked potentials (VEPs) are a simple, noninvasive method for detecting subclinical visual pathway involvement in multiple sclerosis. This study investigated the frequency of VEP abnormalities and their associations with baseline clinical, radiological, and cerebrospinal fluid (CSF) features in treatment-naïve patients with clinically isolated syndrome (CIS) or early relapsing-remitting multiple sclerosis (RRMS) without prior optic neuritis. Materials and Methods: We retrospectively reviewed newly diagnosed, treatment-naïve CIS/early RRMS patients evaluated between January 2022 and July 2024 who underwent CSF analysis. Pattern-reversal VEPs were recorded under standardized conditions. VEP abnormalities were analyzed as any or bilateral, and associations were assessed using group comparisons and multivariable logistic regression. Results: In 101 patients (mean age 31.8 ± 9.7 years; 72% female; median EDSS 1.0), latency prolongation occurred in 69 (42 any,27 bilateral) and amplitude reduction in 33 (22 any, 11 bilateral). Among patients with latency prolongation, both the number of OCB bands and the IgG index were higher (bilateral p = 0.032; any p = 0.007). In multivariable analysis, male sex (p = 0.032) and pyramidal/brainstem-onset presentation (p = 0.006) were independently associated with any amplitude reduction; neither was associated with latency abnormalities. Conclusions: VEP abnormalities are common early in the disease, even without a history of optic neuritis. Male sex and pyramidal/brainstem-onset presentation were associated with reduced amplitude, suggesting that amplitude decrease may reflect early tissue dysfunction and may be related to adverse baseline clinical features. Associations between intrathecal immune activation and prolonged latency may indicate subclinical demyelination of the visual pathways related to inflammatory activity. Larger longitudinal studies are needed to clarify the clinical significance of VEP abnormalities in early RRMS. Full article

Silicon carbide (SiC) power converters offer superior switching performance but generate severe broadband electromagnetic interference (EMI) that challenges regulatory compliance. Existing prediction methods face a fundamental trade-off between physical fidelity and computational efficiency, while conventional suppression strategies lack adaptability to varying operating conditions. This paper proposes a frequency-band-aware physics-informed generative adversarial network (FBA-PIGAN) that integrates electromagnetic domain knowledge into data-driven generative modeling for joint EMI prediction and adaptive suppression in SiC power converters. The framework employs a Wasserstein GAN with gradient penalty as the adversarial backbone and introduces feature-wise linear modulation (FiLM) to inject converter operating parameters into the generator through learned affine transformations. A hierarchical physics-informed loss function enforces three frequency-dependent constraints, namely, harmonic structure consistency, parasitic resonance characterization, and high-frequency envelope regularization, coordinated by a curriculum-based weight-scheduling strategy. An end-to-end differentiable suppression module maps predicted spectra to optimal passive filter parameters through an analytically embedded transfer function. Experimental validation on a 10 kW SiC inverter platform with 5120 measured spectra across 32 operating conditions demonstrates that FBA-PIGAN achieves a mean spectral error of 2.1 dB, 93.8% peak frequency accuracy, and a physical consistency score of 0.93, improving prediction accuracy by 56% over conventional conditional GANs while maintaining sub-millisecond inference latency. The integrated suppression pipeline attains 19.2 dB average attenuation with 98.5% CISPR 25 compliance, and the framework generalizes to unseen operating conditions with only 19% performance degradation, compared with 56% for data-driven baselines. Full article

Background and Objectives: Systemic mastocytosis (SM) is a clonal mast cell disorder characterized by abnormal mast cell accumulation and activation in multiple organs, leading to mediator-related symptoms, including anaphylaxis. Drug hypersensitivity reactions (DHRs) are a major clinical challenge in SM, but their frequency and characteristics remain undefined. This study aimed to evaluate the frequency of drug allergy, identify high-risk drug groups, investigate reaction characteristics, and examine the relationship between drug reactions, baseline serum tryptase levels, and SM subtypes in patients with SM. Materials and Methods: We retrospectively analyzed 34 patients diagnosed with SM between 2009 and 2024 at Ege University Faculty of Medicine. Clinical features, SM subtypes, baseline serum tryptase levels, and DHR characteristics were recorded. Reactions to antibiotics, nonsteroidal anti-inflammatory drugs (NSAIDs), paracetamol, anesthetics, radiocontrast media (RCM), and COVID-19 vaccines were graded using the Ring and Messmer anaphylaxis classification. Results: Among 34 patients, the mean age was 48.6 ± 13.3 years, 53% were male, and 10 (29.4%) had DHRs. The most common culprit drugs were NSAIDs (17.6%) and β-lactam antibiotics (14.7%). Anaphylaxis was the predominant reaction, frequently associated with hypotension. In 5 patients (14.7%), drug-induced anaphylaxis was the initial and only manifestation of SM. No hypersensitivity reactions occurred to quinolones, general anesthetics, or COVID-19 vaccines. Median baseline tryptase was 50.25 µg/L (min–max: 8.59–200.00) overall, and 41.85 µg/L (min–max: 19.00–200.00) among those with DHRs. Conclusions: Patients with SM are at increased risk of severe DHRs, particularly to NSAIDs and beta-lactam antibiotics. In some patients, drug allergy may be the first and only manifestation of SM. Measurement of baseline serum tryptase is essential in patients with drug-induced anaphylaxis. A comprehensive allergy assessment, including tolerance testing and individualized counseling, is crucial to ensure safe pharmacological management. Full article

Modeling the dynamic response of saturated marine soils is crucial yet computationally challenging for traditional methods. Meanwhile, purely data-driven models suffer from sparse data and lack of physical interpretability. To overcome these limitations, this study proposes an intelligent engineering framework based on a frequency-domain physics-informed neural network (FD-PINN) for the forward simulation and inverse parameter identification of saturated seabed soils. Constrained directly by physical laws during the learning process, FD-PINN remains highly reliable even when training data is sparse. By formulating the governing equations in the frequency domain, it directly predicts complex-valued displacement and pore-pressure phasors. Multiscale Fourier feature mappings mitigate spectral bias and capture boundary layers and high-frequency effects. For inverse problems, a phase-sensitive lock-in extraction strategy transforms time-domain measurements into robust frequency-domain targets, enabling the accurate and noise-tolerant identification of poroelastic parameters with clear physical meaning (nondimensional storage parameter S and permeability parameter $\Gamma$). Numerical experiments show that FD-PINN substantially outperforms conventional time-domain PINN, achieving relative $L^2$ errors of ${10}^{-2}\sim {10}^{-3}$ for single- and multi-frequency excitations typical of wave-induced loadings. In particular, $\Gamma$ is consistently recovered with sub-percent relative error, while S can be reliably identified with multi-frequency data. The framework offers a data-efficient, noise-robust approach for high-fidelity modeling and robust parameter inversion, which is particularly valuable in offshore environments where high-quality data is scarce. Full article

The cathode and anode differential pressure control of a proton exchange membrane fuel cell (PEMFC) directly affects its service life and operating efficiency. Existing control methods find it difficult to cope with strong nonlinear perturbations, and fixed differential pressure control is prone to pressure overshoot and threshold exceedance, resulting in unstable pressure regulation. In order to solve the current research problems, a reinforcement learning method based on hybrid experience replay (HP-TD3) is proposed. A CART-based algorithm is first used to classify the states of the test load, and a load-related segmented reward function is designed. In addition, a hindsight experience replay (HER) mechanism is incorporated into the Priority Experience Replay Twin Delayed Deep Deterministic Policy Gradient (PER-TD3) framework to improve sample utilization efficiency and training stability. Finally, the performance of HP-TD3 and its ability to cope with nonlinear disturbances are verified on a fuel cell control unit hardware-in-the-loop (FCU-HIL) platform. In the A test load (frequent switching and high low-load proportion), the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the degradation index of the fuel cell dynamic performance (Δfc) of HP-TD3 are respectively reduced by 17.4%, 20.5%, and 13.3% compared to P-TD3; in the B test load (high-load operation and low switching frequency), these indicators are reduced by 25.7%, 29.4%, and 15.4% respectively. Full article

This study develops a data-driven framework for predictive maintenance and prognostic health management in industrial systems using edge-enabled predictive algorithms. The objective is to support early identification of abnormal operating conditions and improve maintenance decision making under real production environments. The proposed approach combines edge-level monitoring, anomaly detection, and predictive modeling to analyze operational signals and estimate system health conditions from high-frequency industrial data. Empirical validation was conducted using operational datasets collected from two industrial production facilities between 2024 and 2025. The model evaluates patterns associated with operational instability and degradation-related anomalies and translates them into interpretable health indicators that can support proactive intervention. The empirical results show strong predictive performance, with R² reaching 0.989, a mean absolute percentage error of 3.67%, and a root mean square error of 0.79. In addition, the mitigation of early anomaly signals was associated with an observed improvement of approximately 3.99% in system stability. Unlike many existing studies that treat anomaly detection, predictive modeling, and prognostic analysis as separate tasks, the proposed framework connects these stages within a unified analytical structure designed for deployment in industrial environments. The findings indicate that edge-generated anomaly signals can provide meaningful early information about potential system deterioration and can assist in planning timely maintenance actions even when explicit failure labels are limited. The study contributes to the development of scalable predictive maintenance solutions that integrate artificial intelligence with edge-based industrial monitoring systems. Full article

The reliability of high-frequency (HF) frequency selection technology relies on the prediction accuracy of the Maximum Usable Frequency of the ionospheric F2 layer (MUF-F2). To improve its short-term prediction performance, a novel hybrid deep learning prediction model is proposed, which achieves accurate modeling of the complex spatiotemporal variation patterns of MUF-F2 by integrating a feature enhancement mechanism, a dual-branch feature extraction structure, and a bidirectional temporal dependency capture network. The hybrid prediction model integrates the Channel Attention mechanism (CA), Dual-Branch Convolutional Neural Network (DCNN), and Bidirectional Long Short-Term Memory network (BiLSTM). The model is trained and validated using MUF-F2 data from 5 communication links over China during geomagnetically quiet periods and 4 during geomagnetic storm periods, with the difference in the number of links attributed to experimental constraints and the disruptive effects of geomagnetic storms. Its performance is evaluated via multiple metrics, and a comparative analysis is conducted with commonly used prediction models such as the Long Short-Term Memory (LSTM) network. Experimental results show that during geomagnetically quiet periods, the proposed model achieves lower prediction errors (Root Mean Square Error (RMSE) < 1.1 MHz, Mean Absolute Percentage Error (MAPE) < 3.8%) and a higher goodness of fit (coefficient of determination (R²) > 0.94), with the average error reduction across all links ranging 8 from 6.2% to 46.9% compared with the baseline model. Under geomagnetic storm disturbance conditions, the model still maintains robust prediction performance, with R² > 0.89 for all communication links, as well as RMSE < 0.6 MHz, Mean Absolute Error (MAE) < 0.4 MHz, and MAPE < 3.3%. The study demonstrates that the proposed CA-DCNN-BiLSTM model exhibits excellent prediction accuracy and anti-interference capability under different geomagnetic activity conditions, which can effectively improve the short-term prediction accuracy of MUF-F2 and provide more reliable technical support for HF communication frequency decision-making. Full article

In estuarine environments, machine learning (ML) methods have been widely applied to predict water-level variations prone to flooding. However, most studies have focused on low-frequency components driven by tides and surges, neglecting high-frequency oscillations such as seiches. This study addresses this gap by assessing the ability of ML methods to predict seiche-influenced water levels. The application was conducted in the upper Elorn estuary (France), where seiches exceeded 0.6 m in height, with first-mode periods of 45–70 min. The ML procedure relied on a series of recurrent neural networks (RNNs, LSTM, and GRUs) and was implemented in a two-step framework to separately predict (i) low-frequency water-level variations and (ii) high-frequency seiche oscillations. The model accurately reproduced low-frequency dynamics (with a coefficient of determination of 0.98) and captured a substantial portion of seiches-related variability during major events. The integration of seiches improved peak total water-level predictions, reducing the mean absolute error by 30% during tidal cycles characterized by strong seiches (amplitude exceeding 0.1 m). Furthermore, the inclusion of seiches enhanced the estimation of the highest 10% peak water levels while reducing the tendency to underestimate measurements. These findings emphasize the importance of integrating seiche-generating physical processes into ML-based forecasting frameworks. Full article

Background/Objectives: Mental fatigue in adolescents is a growing concern in educational contexts, positioning physical education (PE) teachers as key agents in designing effective mitigation strategies. This study examined the perceptions of Spanish high school PE teachers regarding the causes, consequences, and potential countermeasures for students’ mental fatigue. Methods: A total of 116 in-service teachers (81 males and 35 females; mean teaching experience 7.8 ± 5.3 years) from 12 autonomous communities throughout Spain completed a comprehensive 34-item electronic questionnaire. The instrument assessed the perceived existence, etiology, and outcomes of mental fatigue through multiple-choice, dichotomous (yes/no), and five-point Likert scale questions, with particular attention given to the role of physical activity (PA) in symptom alleviation. A quantitative frequency analysis was conducted to examine the data. Results: The main findings reveal a strong consensus among the teachers (77.6% to 87.9%) on the prevalence of mental fatigue, with its primary causes attributed to academic pressure and sedentarism. The consequences were identified as increased irritability and reduced cognitive performance. The teachers overwhelmingly endorsed moderate intensity PA as the most effective countermeasure. However, a significant gap was identified between this theoretical awareness and the systematic implementation of targeted strategies within schools. Conclusions: These results underscore the critical need for professional development programs and structural support to translate teacher knowledge into practical intervention, suggesting important directions for future research. Full article