Search Results (1,963)

Accurate inversion of ionograms of the ionosonde is of great significance for studying ionospheric structure and radio wave propagation. Traditional inversion methods usually describe the electron density profile based on preset polynomial functions, but such functions are difficult to fully match the complex dynamic distribution characteristics of the ionosphere, especially in accurately representing special positions such as the F2 layer peak. To this end, this paper proposes an inversion model based on a Variational Autoencoder, named VSII-VAE, which realizes the mapping from ionograms to electron density profiles through an encoder–decoder structure. To enable the model to learn inversion patterns with physical significance, we introduced physical constraints into the latent variable space and the decoder, constructing a neural network inversion model that integrates data-driven approaches with physical mechanisms. Using multi-class ionograms as input and the electron density measured by Incoherent Scatter Radar as the training target, experimental results show that the electron density profiles retrieved by VSII-VAE are highly consistent with ISR observations, with errors between synthetic virtual heights and measured virtual heights generally below 5 km. On the independent test set, the model evaluation metrics reached ${\mathrm{R}}^2$ = 0.82, RMSE = 0.14 MHz, ${\mathrm{r}}_{\mathrm{p}}$ = 0.94, outperforming the ARTIST method and verifying the effectiveness and superiority of the model inversion. Full article

Mathematical models for infectious diseases, particularly autonomous ODE models, are generally known to possess simple dynamics, often converging to stable disease-free or endemic equilibria. This paper investigates the dynamic consequences of a crucial, yet often overlooked, component of pandemic response: the saturation of public health testing. We extend the standard SIR model to include compartments for ‘Confirmed’ (C) and ‘Monitored’ (M) individuals, resulting in a new SICMR model. By fitting the model to U.S. COVID-19 pandemic data (specifically the Omicron wave of late 2021), we demonstrate that capacity constraints in testing destabilize the testing-free endemic equilibrium ($E_1$). This equilibrium becomes an unstable saddle-focus. The instability is driven by a sociological feedback loop, where the rise in confirmed cases drive testing effort, modeled by a nonlinear Holling Type II functional response. We explicitly verify that the eigenvalues for the best-fit model satisfy the Shilnikov condition (${\lambda }_u>{\lambda }_s$), demonstrating the system possesses the necessary ingredients for complex, chaotic-like dynamics. Furthermore, we employ Stochastic Differential Equations (SDEs) to show that intrinsic noise interacts with this instability to generate ’noise-induced bursting,’ replicating the complex wave-like patterns observed in empirical data. Our results suggest that public health interventions, such as testing, are not merely passive controls but active dynamical variables that can fundamentally alter the qualitative stability of an epidemic. Full article

The use of ships and boats as sea-state (SS) measurement platforms has the potential to expand ocean observations while providing actionable information for real-time operational decision-making at sea. Within the framework of the Wave Buoy Analogy (WBA), this work develops an inverse approach in which efficient simulations of wave-induced motions of an advancing vessel are used to train a neural network (NN) to predict SS parameters across a broad range of wave climates. We show that a reduced set of novel motion discriminant variables (MDVs)—computed from short time series of heave, roll, and pitch motions measured by an onboard inertial measurement unit (IMU), together with the vessel’s forward speed—provides sufficient and robust information for accurate, near-real-time SS estimation. The methodology targets small, barge-like tugboats whose operations are SS-limited and whose motions can become large and strongly nonlinear near their upper operating limits. To accurately model such responses and generate training data, an efficient nonlinear time-domain seakeeping model is developed that includes nonlinear hydrostatic and viscous damping terms and explicitly accounts for forward-speed effects. The model is experimentally validated using a scaled physical model in laboratory wave-tank tests, demonstrating the necessity of these nonlinear contributions for this class of vessels. The validated model is then used to generate large, high-fidelity datasets for NN training. When applied to independent numerically simulated motion time series, the trained NN predicts SS parameters with errors typically below 5%, with slightly larger errors for SS directionality under relatively high measurement noise. Application to experimentally measured vessel motions yields similarly small errors, confirming the robustness and practical applicability of the proposed framework. In operational settings, the trained NN can be deployed onboard a tugboat and driven by IMU measurements to provide real-time SS estimates. While results are presented for a specific vessel, the methodology is general and readily transferable to other ship geometries given appropriate hydrodynamic coefficients. Full article

The peak height of the ionospheric F2 layer (hmF2) is a critical parameter in ionospheric physics and high-frequency radio wave propagation research. This study presents a backpropagation neural network (BPNN) enhanced by wavelet transform (WT) decomposition for one-hour-ahead hmF2 forecasting. The WT method decomposes and reconstructs the hmF2 time series, preserving its primary structural characteristics. Subsequently, the BPNN provides high-accuracy predictions. The model is trained and evaluated using 2014 hmF2 measurements from four observation stations in China. Utilizing only hmF2 data, the model produces accurate one-hour-ahead forecasts. The predicted values closely align with observed diurnal variations and exhibit lower fluctuations than those of the IRI and standalone BPNN models. On the test set, the proposed model achieves an average RMSE of 17.16 km, which is 10.10 km and 8.39 km lower than the IRI and BPNN models, respectively. The average RRMSE is 5.72%, representing reductions of 2.88% and 2.64% compared to the IRI and BPNN models, respectively. These findings indicate that the hybrid model is well-suited for the Chinese region and substantially enhances short-term hmF2 forecast accuracy. Full article

Prestack seismic waveform inversion starts with an initial model and computes synthetic or predicted seismic data using a wave equation-based approach. Then, by matching these predicted data with the observed seismic data, it iteratively modifies the initial model using an optimization method until the predicted and observed data reasonably match. This method has been demonstrated to be superior to amplitude-variation-with-angle inversion. Because of the wave equation-based approach, computational cost is, however, one major drawback of the method. In the presence of well-logs with borehole measurements of the subsurface properties such as the P-wave velocity, S-wave velocity, and density, it is possible to provide a good initial model, and the method quickly converges to the true model at well locations. However, for locations away from the wells, the initial models are obtained by interpolating the initial models at the well locations over the interpreted geological horizons. These models can be far from the true models and inverting prestack data for these locations using wave equation-based method is computationally challenging. Because of these computational challenges, amplitude-variation-with-angle inversion is the current state-of-the-art method for routine seismic inversion applications. In this work, we provide an attribute-guided framework to generate initial models and demonstrate its applicability, which can potentially overcome computational challenges of prestack seismic waveform inversion. Furthermore, we also discuss the feasibility of using this attribute-guided approach to characterize reservoirs for underground hydrogen storage. Full article

Background: The interplay between neuronutrition, physical activity, and mental health for enhancing brain resilience to stress and overall human health is widely recognized. The use of brain mapping via quantitative-EEG (qEEG) comparative analysis enables researchers to identify deviations or abnormalities and track the changes in neurological patterns when a targeted drug or specific nutrition is administered over time. High-functioning mild-to-borderline intellectual disorders (MBID) and autism spectrum disorder (ASD) constitute leading global public health challenges due to their high prevalence, chronicity, and profound cognitive and functional impact. Objective: The objectives of the present study were twofold: first, to characterize an extremely vulnerable group of children with functioning autism symptoms, disclosing their overall pattern of cognitive abilities and areas of difficulty, and second, to investigate the relevance of the effects of a mushroom (Hericium erinaceus) biomass dietary supplement on improvement on neurocognitive behavior. Methods: This study used qEEG to compare raw data with a normative database to track the changes in neurological brain patterns in 147 children with high-functioning autistic attributes when mushroom H. erinaceus biomass supplement was consumed over 6 and 12 months. Conclusions: H. erinaceus biomass in children with pervasive developmental disorders significantly improved the maturation of the CNS after 6 to 12 months of oral use, decreased the dominant slow-wave activity, and converted slow-wave activity to optimal beta1 frequency. Therefore, despite the lack of randomization, blinding, and risk of bias, due to a limited number of observations, it may be concluded that the H. erinaceus biomass may generate a complex effect on the deficits of the autism spectrum when applied to high-functioning MBID children, representing a safe and effective adjunctive strategy for supporting neurodevelopment in children. Full article

Timely and accurate crop yield estimation is vital for food security and management decision-making. Integrating remote sensing with machine learning provides an effective solution. In this study, based on canopy hyperspectral data collected by an ASD FieldSpec 3 handheld spectrometer during the critical growth stages of winter wheat, 18 vegetation indices (VIs) were systematically calculated, and their correlation with yield was analyzed. At the same time, a continuous projection algorithm, Successive Projections Algorithm (SPA), was used to screen the characteristic bands. Recursive Feature Elimination (RFE) was employed to select optimal features from VIs and characteristic spectral bands, facilitating the construction of a multi-temporal fusion feature set. To identify the superior yield estimation approach, a comparative analysis was conducted among four machine learning models: Deep Forest (DF), Support Vector Regression (SVR), Random Forest (RF), and Gaussian Process Regression (GPR). Performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). Results indicate that the highest correlations between VIs and grain yield were observed during the flowering and grain-filling stages. Independent analysis showed that VIs reached absolute correlations of 0.713 and 0.730 with winter wheat yield during the flowering and grain-filling stages, respectively, while the SPA further identified key bands primarily in the near-infrared and short-wave infrared regions. On this basis, integrating multi-temporal features through RFE significantly improved the accuracy of yield estimation. Among them, the DF model with the fusion of flowering and filling stage features performed best (R² = 0.786, RMSE = 641.470 kg·hm⁻², rRMSE = 15.67%). This study demonstrates that combining hyperspectral data and VIs from different growth stages provides complementary information. These findings provide an effective method for crop yield estimation in precision agriculture. Full article

We present several remarks on the spread of the COVID-19 epidemics in Bulgaria. The remarks are based on the hypothesis that the spread of the infection exhibits scaling properties similar to the scaling in urban dynamics. The corresponding mathematical theory leads us to a relationship for a power-law dependence of the number of infected in a certain region on the corresponding homochrony number. We prove the correctness of the mathematical theory on the basis of data for several Bulgarian regions for the first large COVID-19 wave in 2020. We observe a collapse of the real data along a single straight line. Full article

Significant wave height (SWH) is a key descriptor of sea state, yet providing accurate, site-specific forecasts at low computational cost remains challenging. This study proposes a transfer-learning-based framework for SWH forecasting that combines Seasonal and Trend decomposition using Loess (STL), a stacked long short-term memory (LSTM) network, and an efficient sliding-window updating scheme. First, STL is applied to decompose the SWH time series into trend, seasonal, and remainder components; the resulting sub-series are then fed into a transfer-learning architecture in which the parameters of the stacked LSTM backbone are kept fixed, and only a fully connected output layer is updated in each window. Using multi-year observations from five National Data Buoy Center (NDBC) buoys, the proposed STL-LSTM-T model is compared with a STL-LSTM configuration that is fully retrained after each STL decomposition. For example, the transfer-learning setup reduces MAE, MSE, and RMSE by up to 11.2%, 19.2%, and 14.5% at buoy 46244, respectively, while reducing the average training time per update to about one-fifth of the baseline. Parameter analyses indicate that a two-layer LSTM backbone and moderate continuous forecast step (6–12 steps) provide a good balance between predictive accuracy, error accumulation, and computational cost, making STL-LSTM-T suitable for SWH forecasting on resource-constrained platforms. Full article

Background/Objectives: This study aimed to determine the prevalence of persistent symptoms and the radiological and laboratory evolution at 6 months and 5 years after discharge in patients hospitalized for SARS-CoV-2 pneumonia during the first wave of the pandemic in Spain and to estimate the healthcare impact of their follow-up. Methods: A retrospective longitudinal observational study was conducted at the “Hospital Central de la Defensa”. A total of 200 patients aged >18 years with a diagnosis of SARS-CoV-2 pneumonia were screened. Clinical, radiological, and laboratory data were collected from electronic medical records. Patients with symptoms or radiological abnormalities at discharge underwent in-person evaluations, while the remainder were assessed by telephone. Results: A total of 182 patients met the inclusion and exclusion criteria. Of these, 112 were assessed in the outpatient setting; 60.7% required in-person evaluations, with normal pulmonary auscultation in 93.6%, complete radiological resolution in 85%, and normalized laboratory parameters in almost all cases. At 6 months, 26.5% presented at least one residual symptom, whereas only three patients (4.5%) reported symptoms at 5 years. No risk factors associated with symptom persistence were identified. The estimated cumulative healthcare cost was EUR 21,627.50. Conclusions: Among patients hospitalized for SARS-CoV-2 pneumonia during the first wave of the pandemic, 26.7% and 4.46% presented at least one persistent symptom at 6 months and 5 years after discharge, respectively. Full article

Objective: This study investigates the association between consumers’ perceived readability of Multiple Traffic Light (MTL) label print size—a theoretical structural gatekeeper for visual salience—and self-reported food consumption frequency in the United Kingdom. We aimed to disentangle the effect of label readability from label content. Using non-equivalent dependent variables (NEDVs), we tested whether the association is specific to unhealthy convenience foods and absent for healthy or unlabeled foods, while also examining heterogeneity across consumer subgroups. Methods: Data from 8948 adults across four waves (2012–2018) of the UK Food and You Survey were analyzed. Cumulative link ordinal logistic regressions were employed to model the association between self-reported print size readability and the consumption frequency of four product types: pre-packaged sandwiches and pre-cooked meat (unhealthy, labeled targets), dairy (nutritionally advisable, labeled control), and fresh meat (unlabeled control). Models were adjusted for sociodemographic covariates, health behaviors, and survey wave fixed effects. Results: The findings reveal a content-specific and significant dynamic relationship exclusively for pre-packaged sandwiches. In 2012, a one-unit increase in readability was associated with a 9% decrease in the odds of frequent consumption ($OR=0.91$), consistent with a warning effect. However, by 2018, this relationship reversed to a 4% increase ($OR=1.04$), indicating that higher readability became associated with more frequent consumption. In contrast, a persistent null association was observed for pre-cooked meat, dairy, and fresh meat. Subgroup analyses for sandwiches indicated that the association with readability was strongest among less-engaged consumers. Conclusions: Empirical evidence challenges the utility of a standardized approach to food labelling. The results suggest that the effectiveness of label salience is contingent not just on the consumer but on the product’s context and the content of its message, highlighting the need for adaptive rather than uniform policy standards. Full article

This study develops an immersive marine Information and Communication Technology (ICT) convergence framework designed to enhance coastal climate resilience by improving accessibility, visualization, and communication of scientific research on Dokdo (Dok Island) in the East Sea. High-resolution spatial datasets, multi-source marine observations, underwater imagery, and validated research outputs were integrated into an interactive virtual-reality (VR) and web-based three-dimensional (3D) platform that translates complex geophysical and ecological information into intuitive experiential formats. A geospatially accurate 3D virtual model of Dokdo was constructed from maritime and underwater spatial data and coupled with immersive VR scenarios depicting sea-level variability, coastal morphology, wave exposure, and ecological characteristics. To evaluate practical usability and pro environmental public engagement, a three-phase field survey (n = 174) and a System Usability Scale (SUS) assessment (n = 42) were conducted. The results indicate high satisfaction (88.5%), strong willingness to re-engage (97.1%), and excellent usability (mean SUS score = 80.18), demonstrating the effectiveness of immersive content for environmental education and science communication crucial for achieving Sustainable Development Goal 14 targets. The proposed platform supports stakeholder engagement, affective learning, early climate risk perception, conservation planning, and multidisciplinary science–policy dialogue. In addition, it establishes a foundation for a digital twin system capable of integrating real-time ecological sensor data for environmental monitoring and scenario-based simulation. Overall, this integrated ICT-driven framework provides a transferable model for visualizing marine research outputs, enhancing public understanding of coastal change, and supporting sustainable and adaptive decision-making in small island and coastal regions. Full article

Background/Objectives: Chronic total occlusion (CTO) is one of the most complex forms of coronary artery disease, and myocardial perfusion in patients with CTO largely depends on the adequacy of coronary collateral circulation (CCC). Identifying simple and non-invasive electrocardiographic markers associated with impaired collateralization remains clinically important. The R-wave peak time (RWPT), a surface electrocardiography (ECG) marker representing the time to peak R-wave deflection and an electrocardiographic surrogate of early intraventricular conduction, may provide insight into ischemia-related ventricular activation delay. The aim of this study was to evaluate whether RWPT is associated with poor CCC in patients with CTO. Methods: This cross-sectional observational study included 92 consecutive patients with CTO and complete clinical, angiographic, and 12-lead ECG data. Patients were categorized according to CCC adequacy into good (n = 52) and poor (n = 40) CCC groups. Demographic, laboratory, angiographic, and ECG parameters were compared. Variables showing significant differences were subjected to univariate analysis, followed by multivariate logistic regression using a backward stepwise selection method. Statistical significance was set at p < 0.05. Results: Patients with poor CCC were significantly older and exhibited longer QRS duration and prolonged RWPT, whereas triglyceride levels were significantly lower. In multivariate analysis, both age (OR: 1.058; 95% CI: 1.005–1.114; p = 0.033) and RWPT (OR: 1.069; 95% CI: 1.013–1.128; p = 0.015) were significantly associated with poor CCC. Conclusions: RWPT may provide adjunctive, non-invasive information regarding collateral adequacy rather than serving as a definitive predictive marker. As an easily obtainable ECG parameter, RWPT may offer incremental diagnostic information when interpreted alongside clinical and angiographic findings in patients with CTO. Full article

Background and Objectives: Healthcare-associated infections (HAIs) significantly increase morbidity and mortality in critically ill patients, and their burden became more pronounced during the COVID-19 pandemic. However, data describing the temporal evolution of HAIs, pathogen distribution, and associated risk factors across consecutive pandemic waves remain limited. This study aimed to characterize the epidemiology, microbiology, and outcomes of HAIs in COVID-19 intensive care units (ICU) patients and to identify clinical and laboratory predictors of mortality. Materials and Methods: This retrospective observational study included adult patients with RT-PCR–confirmed COVID-19 who developed at least one HAI ≥ 48 h after ICU admission between March 2020 and December 2020, encompassing the first three pandemic waves in Türkiye, in a tertiary-care ICU. Demographic, clinical, laboratory, and microbiological data were collected. Inflammatory markers and severity scores (SAPS-II, MCCI, and NLR) were analyzed. Receiver operating characteristic (ROC) curve analysis was used to determine optimal cut-off values for mortality prediction. Results: Among the 1656 ICU admissions, 145 patients (8.7%) developed HAIs; after exclusions, 136 patients were included in the final analysis. Bloodstream infections were the most frequent HAI (57%), followed by urinary tract infections (31%), ventilator-associated pneumonia (9%), and surgical site infections (1%). Klebsiella pneumoniae was the predominant pathogen, followed by Candida albicans and Acinetobacter baumannii. Multidrug-resistant organisms, including MRSA and VRE, showed variable distribution across pandemic periods. Overall in-hospital mortality was 74.3%. Non-survivors had significantly higher SAPS-II, MCCI, and NLR values. ROC analysis identified NLR > 38.8 and SAPS-II > 35.5 as mortality-predictive thresholds. Dynamic inflammatory marker patterns correlated with infection timing, and early peaks of CRP, WBC, and IL-6 were associated with worse outcomes. Conclusions: HAIs imposed a substantial clinical burden on critically ill COVID-19 patients, with high mortality driven predominantly by multidrug-resistant bloodstream infections. Severity indices and inflammation-based biomarkers demonstrated strong prognostic value. Temporal shifts in pathogen ecology across pandemic waves underscore the need for adaptive infection-prevention strategies, continuous microbiological surveillance, and strengthened antimicrobial stewardship in critical care settings. Full article

As offshore wind farms expand into deeper waters, the safe transportation of large jacket foundations presents a significant engineering challenge. This study utilizes the SESAM 2022 software suite, based on three-dimensional potential flow theory, to conduct a coupled numerical simulation and parametric analysis of a barge-jacket system. Finite element models of the barge and jacket are established, with mesh convergence verified. The influences of key parameters including wave frequencies (0.4–1.6 rad/s), wave directions (0–180°), forward speeds (0–8 knots) and jacket arrangement (vertical/horizontal) on the six degrees of freedom (6-DOF) dynamic responses of the coupled system are systematically investigated. Based on the observed response characteristics, optimized transportation configurations and practical engineering recommendations are proposed. The findings consolidate previous scattered parametric results into a single, repeatable SESAM-based benchmark data set, offering a reference against which future nonlinear or time-domain models can be validated. Furthermore, this work establishes a systematic parametric basis and offers practical guidance for assessing the safety of offshore wind turbine (OWT) foundation transportation in deep-water environments. Full article