Search Results (369)

Background/Objectives: Maternal overweight and obesity are critical factors increasing the risk of various pregnancy complications. Maternal obesity can lead to fetal macrosomia and a heightened risk of intrauterine death, with long-term implications for the child’s health. This study aimed to analyze the incidence of obesity and its impact on pregnancy outcomes in women who delivered by cesarean section at the University Hospital “St. George”, Plovdiv. Methods: A single-center retrospective cohort study was conducted. The documentary method was used for gathering data. Records were randomly selected. The statistical methods used included mean values, confidence intervals (of mean), frequency, and the Kolmogorov–Smirnov test for normality of distribution. Data comparisons were performed using the Mann–Whitney test. Mean values of numerical variables were compared using the independent samples t-test. Results: In total, 46.36% of women in this study were affected by obesity to varying degrees, and the proportion of women who were overweight at the end of their pregnancy was 37.85%. In the studied cohort, 15.99% of women were affected by hypertensive complications. This significant prevalence of obesity highlights concerns regarding body weight among women of reproductive age. This study emphasized a strong correlation between maternal obesity, particularly severe obesity, and the occurrence of preeclampsia. Conclusions: In this study among women who delivered by cesarean section, a significant proportion of them were affected by overweight and obesity. Data for our country are insufficient, and a more in-depth study of this problem is needed. Future research should explore the long-term impacts of maternal obesity on the health of the mother and the newborn. Full article

The corrosion of high-strength steel wires is a key factor impacting the durability and reliability of cable-stayed bridges. In this study, the corrosion pit features on a high-strength steel wire, which had been in service for 27 years, were extracted and modeled using three-dimensional point cloud data obtained through 3D surface scanning. The Otsu method was applied for image binarization, and each corrosion pit was geometrically represented as an ellipse. Key pit parameters—including length, width, depth, aspect ratio, and a defect parameter—were statistically analyzed. Results of the Kolmogorov–Smirnov (K–S) test at a 95% confidence level indicated that the directional angle component (θ) did not conform to any known probability distribution. In contrast, the pit width (b) and defect parameter (Φ) followed a generalized extreme value distribution, the aspect ratio (b/a) matched a Beta distribution, and both the pit length (a) and depth (d) were best described by a Gaussian mixture model. The obtained results provide valuable reference for assessing the stress state, in-service performance, and predicted remaining service life of operational stay cables. Full article

Deep learning models rely heavily on extensive training data, but obtaining sufficient real-world data remains a major challenge in clinical fields. To address this, we explore methods for generating realistic synthetic multivariate fall data to supplement limited real-world samples collected from three fall-related datasets: SmartFallMM, UniMib, and K-Fall. We apply three conventional time-series augmentation techniques, a Diffusion-based generative AI method, and a novel approach that extracts fall segments from public video footage of older adults. A key innovation of our work is the exploration of two distinct approaches: video-based pose estimation to extract fall segments from public footage, and Diffusion models to generate synthetic fall signals. Both methods independently enable the creation of highly realistic and diverse synthetic data tailored to specific sensor placements. To our knowledge, these approaches and especially their application in fall detection represent rarely explored directions in this research area. To assess the quality of the synthetic data, we use quantitative metrics, including the Fréchet Inception Distance (FID), Discriminative Score, Predictive Score, Jensen–Shannon Divergence (JSD), and Kolmogorov–Smirnov (KS) test, and visually inspect temporal patterns for structural realism. We observe that Diffusion-based synthesis produces the most realistic and distributionally aligned fall data. To further evaluate the impact of synthetic data, we train a long short-term memory (LSTM) model offline and test it in real time using the SmartFall App. Incorporating Diffusion-based synthetic data improves the offline F1-score by 7–10% and boosts real-time fall detection performance by 24%, confirming its value in enhancing model robustness and applicability in real-world settings. Full article

This paper presents the exponentiated power shanker (EPS) distribution, a fresh three-parameter extension of the standard Shanker distribution with the ability to extend a wider class of data behaviors, from right-skewed and heavy-tailed phenomena. The structural properties of the distribution, namely complete and incomplete moments, entropy, and the moment generating function, are derived and examined in a formal manner. Maximum likelihood estimation (MLE) techniques are used for estimation of parameters, as well as a Monte Carlo simulation study to account for estimator performance across varying sample sizes and parameter values. The EPS model is also generalized to a regression paradigm to include covariate data, whose estimation is also conducted via MLE. Practical utility and flexibility of the EPS distribution are demonstrated through two real examples: one for the duration of repairs and another for HIV/AIDS mortality in Germany. Comparisons with some of the existing distributions, i.e., power Zeghdoudi, power Ishita, power Prakaamy, and logistic-Weibull, are made through some of the goodness-of-fit statistics such as log-likelihood, AIC, BIC, and the Kolmogorov–Smirnov statistic. Graphical plots, including PP plots, QQ plots, TTT plots, and empirical CDFs, further confirm the high modeling capacity of the EPS distribution. Results confirm the high goodness-of-fit and flexibility of the EPS model, making it a very good tool for reliability and biomedical modeling. Full article

Currently, the visual study of the structure of building materials and products is gradually supplemented by intelligent algorithms based on computer vision technologies. These algorithms are powerful tools for the visual diagnostic analysis of materials and are of great importance in analyzing the quality of production processes and predicting their mechanical properties. This paper considers the process of analyzing the visual structure of non-autoclaved aerated concrete products, namely their porosity, using the YOLOv11 convolutional neural network, with a subsequent prediction of one of the most important properties—thermal conductivity. The object of this study is a database of images of aerated concrete samples obtained under laboratory conditions and under the same photography conditions, supplemented by using the author’s augmentation algorithm (up to 100 photographs). The results of the porosity analysis, obtained in the form of a log-normal distribution of pore sizes, show that the developed computer vision model has a high accuracy of analyzing the porous structure of the material under study: Precision = 0.86 and Recall = 0.88 for detection; precision = 0.86 and recall = 0.91 for segmentation. The Hellinger and Kolmogorov–Smirnov statistical criteria, for determining the belonging of the real distribution and the one obtained using the intelligent algorithm to the same general population show high significance. Subsequent modeling of the material using the ANSYS 2024 R2 Material Designer module, taking into account the stochastic nature of the pore size, allowed us to predict the main characteristics—thermal conductivity and density. Comparison of the predicted results with real data showed an error less than 7%. Full article

To investigate the sintering shrinkage behavior of multigeneration, multilayer zirconia materials using geometrical measurements. Seven zirconia CAD/CAM materials were analyzed, comprising two mono-generation zirconia (HTML: Katana Zr, HTML Plus, 3Y-TZP; UTML: Katana Zr, UTML, 5Y-TZP) and five strength-gradient multilayer zirconia (AIDI: optimill 3D PRO Zir; PRIT: Priti multidisc ZrO₂ multicolor; UPCE: Explore Esthetic; ZCPC: IPS e.max ZirCAD Prime; ZYML: Katana YML) materials. Cubes (10 × 10 × 10 mm³) were milled in varying positions within the disks. Geometrical measurements were applied before and after dense sintering using a micrometer screw gauge, light microscopy, as well as surface scans and shrinkages were calculated. Data were analyzed using Kolmogorov–Smirnov, five-way ANOVA followed by the Scheffé post hoc test, and partial eta squared, as well as the Kruskal–Wallis test, including Bonferroni correction (p < 0.05). The highest influence on the shrinkage was exerted by the zirconia material (^η_P² = 0.893, p < 0.001), followed by the test method (^η_P² = 0.175, p < 0.001), while the vertical and horizontal position and measurement point showed no impact on the shrinkage results (p = 0.195–0.763) in the global analysis. Depending on the test method, the pooled shrinkage values of all tested zirconia materials varied between 17.7 and 20.2% for micrometer screw gauge, 17.7 and 20.1% for light microscopy, and 17.8 and 21.1% for surface scan measurements. The shrinkage values measured in the upper, middle, and lower multilayered vertical direction did not differ significantly in the global analysis for the multilayer materials. Therefore, a uniform shrinkage of these strength-gradient multilayer zirconia materials within clinically relevant restorations can be assumed. Full article

To investigate the relationship between the seeding performance of a novel pre-cut sugarcane planter designed by South China Agricultural University and operational settings, field seeding tests was conducted with the following protocol: First, the John Deere M1654 tractor’s forward velocity was calibrated, and the planter’s safe loading capacity was determined. Subsequently, eight experimental treatments (A–H) were designed to quantify the relationships between the three performance indicators: seeding density N, the seeding efficiency E and seeding uniformity (coefficient of variation, CV), and three key operational parameters: forward speed of planter v, the discharging sprocket rotational speed n, and the hopper outlet size w. Mathematical models (R20.979) between three key operational parameters with two performance indicators (N, E) was developed through analysis of variance (ANOVA) and regression analysis. The seeding rate per meter was confirmed to follow a Poisson distribution based on Kolmogorov–Smirnov (K–S) tests. When the CV was below 40%, the mean relative error remained within 3%. These findings provide a theoretical foundation for seeding performance prediction under field conditions. Full article

This paper introduces the record-based transmuted Rayleigh distribution of order 3 (rbt-R), a three-parameter extension of the classical Rayleigh model designed to address data characterized by high skewness and heavy tails. While traditional generalizations of the Rayleigh distribution enhance model flexibility, they often lack sufficient adaptability to capture the complexity of empirical distributions encountered in applied statistics. The rbt-R model incorporates two additional shape parameters, a and b, enabling it to represent a wider range of distributional shapes. Parameter estimation for the rbt-R model is performed using the maximum likelihood method. Simulation studies are conducted to evaluate the asymptotic properties of the estimators, including bias and mean squared error. The performance of the rbt-R model is assessed through empirical applications to four datasets: nicotine yields and carbon monoxide emissions from cigarette data, as well as breaking stress measurements from carbon-fiber materials. Model fit is evaluated using standard goodness-of-fit criteria, including AIC, AIC_c, BIC, and the Kolmogorov–Smirnov statistic. In all cases, the rbt-R model demonstrates a superior fit compared to existing Rayleigh-based models, indicating its effectiveness in modeling highly skewed and heavy-tailed data. Full article

Background/Objectives: War conflicts impact public health and patient hospital presentations. We aimed to assess the incidence and severity of acute appendicitis (AA) in children during the 2023 Israeli–Hamas–Hezbollah war. Methods: This multicenter retrospective cohort study included children (<18 years) admitted with AA in six medical centers in a 2-month period during the war (7 October–30 November 2023) and a comparable period in 2022. Demographic, clinical, laboratory, imaging, treatment, and outcome data were collected at individual medical centers and analyzed, with subgroup analysis based on proximity to conflict zones. Statistical tests used were Kolmogorov–Smirnov test, Student’s t-test, Mann–Whitney U, and Pearson chi square. p < 0.05 was considered significant. Results: Among 209 patients (106 in 2023, 103 in 2022), a higher rate of complicated AA during wartime was observed, although not statistically significant (27% vs. 18%, p = 0.11). The median symptom-to-presentation time remained 24 h (p = 0.64). The overall incidence of AA decreased by 20% in medical centers near conflict zones but increased by 28% in centers distant from conflict zones. The proportion of complicated AA doubled during the war in hospitals close to conflict zones as compared to during pre-war time (16% vs. 9%, respectively, p = 0.016), with a trend toward higher C-reactive protein (CRP) levels [26.5 (5.3–107.0) vs. 13 (3.4–40.9), respectively, p = 0.075], although symptom-to-presentation times remained unchanged (24 h in both groups, p = 0.32). Conclusions: Proximity to war zones was associated with an increase in the rate of complicated appendicitis in children. While the causes remain unclear, this finding highlights the complex impact of war on healthcare in general and on the well-being of children in particular. Full article

Background: Dilated Cardiomyopathy (DCM) is one of the leading causes of non-ischemic cardiomyopathy in the United States (US). The aim of our study is to analyze the general trends in DCM admissions between 2016 and 2021, and analyze social and healthcare disparities in terms of treatments and outcomes. Methods: National Inpatient Sample (NIS) data for the years 2016 to 2021 were used for the analysis. General population trends were analyzed. Normality of data distribution was tested using the Kolmogorov–Smirnov test and homogeneity was assessed using Levine’s test. One-way ANOVA was used after confirmation of normality of distribution to analyze social and healthcare disparities. Subgroup analysis was conducted, with the paired t-test for continuous variables and Fischer’s exact t-test for categorical variables to analyze statistical differences. Multivariate regression analysis was conducted to analyze the association of factors that were significant in the one-way ANOVA and paired t/chi square tests. A two-tailed p-value < 0.05 was used to determine statistical significance. Results: A total of 5262 admissions for DCM were observed between 2016 and 2021. A general declining trend was observed in the total number of DCM admissions, with a 33.51% decrease in total admissions in 2021 compared to 2016. All-cause in-hospital mortality remained stable across the years (between 3.5% and 4.5%). A total of 15.3% of admissions had CRT/ICD devices in place. A total of 425 patients (8.07%) for DCM underwent HT, and 214 admissions for DCM (4.06%) underwent LVAD placements between 2016 and 2021 In terms of interventions for DCM, namely Cardiac Resynchronization Therapy (CRT), Left Ventricular Assist Devices (LVADs) and Heart Transplantations (HTs), significant variance was observed in the mean age of the admissions with admissions over the mean age of 55 had lower number of interventions. Significant variance in terms of sex was observed for DCM admissions receiving HT, with lower rates observed for females. In terms of quarterly income, patients belonging to the lowest fourth quartile had higher rates of LVAD and HT compared to general DCM admissions. In the multivariate regression analysis, age at admission had significant association with lower chances of receiving LVADs and HT among DCM admissions, and significant association with higher chances of all-cause mortality during the hospital stay. Conclusions: A general declining trend in the total number of DCM admissions was observed between 2016 and 2021. Significant gender disparities were seen with lower rates of females with DCM receiving LVADs and HT. DCM admissions with mean age of 55 and above were found to have significantly lower rates of receiving LVADs and HT, and higher chances of all-cause mortality during the admission. Full article

The emergence of 6G wireless networks presents new challenges, for which cell-free massive MIMO combined with machine learning (ML) offers a promising solution. A key requirement for practical deployment is the generalizability of ML models—their ability to maintain robust performance across varying propagation conditions, user distributions, and network topologies. However, achieving generalizability typically demands large, diverse training datasets and high model complexity, which can hinder practical feasibility. This study analyzes the robustness of a low-complexity deep neural network (DNN) trained for power control under a single network configuration. The model’s robustness is assessed by testing it across a wide range of unseen scenarios, including changes in the number of access points, user equipment, and propagation environments. The DNN is trained to emulate three power control schemes: max-min spectral efficiency (SE) fairness, sum SE maximization, and fractional power control. To rigorously evaluate robustness, we compare the cumulative distribution functions of performance metrics quantitatively using the Kolmogorov–Smirnov test. Results show strong robustness, particularly for the sum SE scheme, with D statistics below 0.05 and p-values above 0.001. This work provides a reproducible framework and dataset to support further research into practical ML-based power control in cell-free massive MIMO systems. Full article

Background/Objectives: Temporomandibular joint disorders (TMDs) affect the temporomandibular joint and associated structures of the stomatognathic system. Joint Vibration Analysis (JVA) is a non-invasive method used to assess TMJ dysfunction through vibration frequencies. This study aimed to explore how age and gender influence TMJ vibration characteristics, hypothesizing that these factors may affect diagnostic accuracy in TMJ evaluations. Methods: This cross-sectional study includes 251 participants (143 females and 108 males) aged 10 to 30 years. TMJ evaluation used JVA to assess range of motion, integral values, and frequency distributions over and under 300 Hz. Participants with a history of TMJ disorders or significant maxillofacial trauma were excluded. Statistical analysis was conducted using employing Kolmogorov–Smirnov tests for data distribution, Kruskal–Wallis test for group comparisons, and Pearson correlation test for variable relationships. Results: Significant gender differences in range of motion (ROM) were observed, with males exhibiting higher values (p = 0.005). Age notably influenced vibration frequencies, particularly in total integral values (TIL and TIR) and frequency distributions around 300 Hz, suggesting links to degenerative changes. Females showed more pronounced age-related effects on vibration parameters. However, gender did not greatly affect vibration characteristics across all frequency bands, indicating that other factors also impact TMJ function. Conclusions: Age and gender significantly influence TMJ vibrations and the interpretation of JVA in clinical settings. Personalized approaches considering these demographic factors may enhance the accuracy of TMJ dysfunction diagnoses. Full article

Background/Objectives: Sexual attitudes, particularly those on the erotophilia (positive openness) to erotophobia (negative fear) scales, play a critical role in shaping behaviors and health decisions. While associations between sexual behavior and substance use have been documented, limited research has explored how sexual attitudes relate to mental health and substance use among Latin American university populations. This study aimed to examine the associations among erotophilic attitudes, mental health symptoms (anxiety and depression), substance use risk, and sexual behaviors in Mexican university students. Methods: A cross-sectional observational study was conducted between 2019 and 2023 with 1475 undergraduate students aged 17–25 years. Participants completed the Revised Sexual Opinion Survey (R-SOS) to assess sexual attitudes, the Hospital Anxiety and Depression Scale (HADS) for mental health evaluation, and adapted items from the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) to measure substance use risk. Erotophilic attitudes were defined as R-SOS scores ≥ 70. Statistical tests included the Kolmogorov–Smirnov test for normality, t-tests or Mann–Whitney U tests for group comparisons, Fisher’s exact test for categorical variables, and Spearman’s correlations. Multivariate binary logistic regression was used to calculate adjusted odds ratios (AdORs) and 95% confidence intervals (CIs), with a significance level of p < 0.05. Results: Erotophilic students were more likely to be male, older, initiate sexual activity earlier, and report a greater number of sexual partners. Erotophilia was positively associated with anxiety and tobacco, alcohol and marijuana use, and negatively associated with depressive symptoms. Multivariate analysis indicated that erotophilia was independently associated with male sex, age ≥ 20, higher anxiety, lower depression, low socioeconomic status, and increased risk of tobacco and marijuana use. Lower rates of consistent condom use were also reported among erotophilic individuals. Conclusions: Erotophilia may serve as a behavioral risk marker linked to anxiety symptoms and increased substance use, but not to depression. These findings highlight the need for integrated interventions addressing sexual health, substance use, and mental well-being in university populations. Full article

This study aimed to propose a probability-guaranteed spectrum method to enhance the reliability of seismic building designs, thereby addressing the inadequacy of the current code-specified response spectrum based on mean fortification levels. This study systematically evaluated the fitting performance of dynamic coefficient spectra under normal, log-normal, and gamma distribution assumptions based on 288 ground motion records from type II sites. MATLAB(2010) parameter fitting and the Kolmogorov–Smirnov test were used, revealing that the gamma distribution optimally characterized spectral characteristics across all period ranges (p < 0.05). This study innovatively established dynamic coefficient spectra curves for various probability guarantee levels (50–80%), quantitatively revealing the insufficient probability assurance of code spectra in the long-period range. Furthermore, this study proposed an evaluation framework for load safety levels of spectral values over the design service period, demonstrating that increasing probability guarantee levels significantly improved safety margins over a 50-year reference period. This method provides probabilistic foundations for the differentiated seismic design of important structures and offers valuable insights for revising current code provisions based on mean spectra. Full article

We propose the financial generative adversarial network–bidirectional long short-term memory (FINGAN-BiLSTM) model to accurately reproduce the complex statistical properties and stylized facts, namely, heavy-tailed behavior, volatility clustering, and leverage effects observed in the log returns of the foreign exchange (FX) market. The proposed model integrates a bidirectional LSTM (BiLSTM) into the conventional FINGAN framework so that the generator, discriminator, and predictor networks simultaneously incorporate both past and future information, thereby overcoming the information loss inherent in unidirectional LSTM architectures. Experimental results, assessed using metrics such as the Kolmogorov–Smirnov statistic, demonstrate that FINGAN-BiLSTM effectively mimics the distributional and dynamic patterns of actual FX data. In particular, the model significantly reduces the maximum cumulative distribution discrepancy in assets with high standard deviations and extreme values, such as the Canadian dollar (CAD) and the Mexican Peso (MXN), while precisely replicating dynamic features like volatility clustering and leverage effects, thereby outperforming conventional models. The findings suggest that the proposed deep learning–based forecasting model holds significant promise for practical applications in financial risk assessment, derivative pricing, and portfolio optimization, and they highlight the need for further research to enhance its generalization capabilities through the integration of exogenous economic variables. Full article