Search Results (402)

Breast cancer remains a major global health problem, characterized by high incidence and mortality rates. Developing accurate prediction models is essential to improving early detection and treatment outcomes. Machine learning (ML) has become a valuable resource in breast cancer prediction; however, the complexities inherent in medical data, including biases and imbalances, can hinder the effectiveness of these models. This paper explores combining the Box–Cox transformation with ML models to normalize data distributions and stabilize variance, thereby enhancing prediction accuracy. Two datasets were analyzed: a synthetic gamma-distributed dataset that simulates skewed real-world data and the Surveillance, Epidemiology, and End Results (SEER) breast cancer dataset, which displays imbalanced real-world data. Four distinct experimental scenarios were conducted on the ML models with a synthetic dataset, the SEER dataset with the Box–Cox transformation, a SEER dataset with the logarithmic transformation, and with Synthetic Minority Over-sampling Technique (SMOTE) augmentation to evaluate the impact of the Box–Cox transformation through different lambda values. The results show that the Box–Cox transformation significantly improves the performance of Artificial Intelligence (AI) models, particularly the stacking model, achieving the highest accuracy with 94.53% and 94.74% of the F1 score. This study demonstrates the importance of feature transformation in healthcare analytics, offering a scalable framework for improving breast cancer prediction and potentially applicable to other medical datasets with similar challenges. Full article

This study is focused on the analysis of the level of geometric thinking of 15-year-old Slovak pupils in relation to the difficulty of geometric problems, their gender, and their assessment in mathematics. The main aim of this study was to determine the level of geometric thinking of 15-year-old Slovak pupils, to examine the relationship between their mathematics assessment and the level of geometric thinking, and to find out gender differences in relation to the different levels of geometric thinking. The van Hiele test was adapted and applied to a representative sample of 15-year-old Slovak pupils to determine the level of geometric thinking. We used reliability/item analysis. The reliability of the knowledge test (after adaptation) was assessed using Cronbach’s alpha (0.64). The validity of the test was demonstrated by the correlation of the Usiskin test results with pupils’ mathematics grades (Goodman–Kruskal’s gamma, p < 0.05). Statistical analysis showed that 15-year-old Slovak pupils achieve different levels of geometric thinking depending on the difficulty of the tasks. Pupil achievement declined significantly as task difficulty increased. Pupils had the greatest difficulty with tasks classified as the fifth (rigorous) and partly the fourth (deductive) van Hiele level, which require a deep understanding of geometric systems and the ability to prove logically. The lower-level tasks (visualization, analysis, and abstraction) were able to differentiate students according to different levels of geometric thinking. The results showed a significant positive relationship (Goodman–Kruskal’s gamma, p < 0.05) between the pupils’ overall mathematics scores (expressed as a grade) and their level of geometric thinking as detected by the van Hiele test. The analysis of gender differences (Duncan’s test, p < 0.05) showed that in the less challenging tasks, corresponding to the first three van Hiele levels (visualization, analysis, abstraction), girls performed statistically significantly better than boys. In the more challenging tasks, classified as the fourth (deductive) and fifth (rigorous) levels of geometric thinking, there were no statistically significant differences between boys and girls. In the more challenging tasks, the performances of both genders were comparable. The presented study identifies significant deficits in the development of higher levels of geometric thinking among 15-year-old Slovak pupils. These findings strongly imply the necessity for the transformation of the curriculum, textbooks, and didactic approaches with the aim of systematically developing deductive and rigorous reasoning, while it is essential to account for the demonstrated gender differences in performance. Full article

The paper presents an enhanced numerical framework for computing the one-dimensional fast Fractional Fourier Transform (FRFT) by integrating closed-form Composite Newton-Cotes quadrature rules. We show that a FRFT of a $QN$-length weighted sequence can be decomposed analytically into two mathematically commutative compositions: one involving the composition of a FRFT of an N-length sequence and a FRFT of a Q-length weighted sequence, and the other in reverse order. The composite FRFT approach is applied to the inversion of Fourier and Laplace transforms, with a focus on estimating probability densities for distributions with complex-valued characteristic functions. Numerical experiments on the Variance-Gamma (VG) and Generalized Tempered Stable (GTS) models show that the proposed scheme significantly improves accuracy over standard (non-weighted) fast FRFT and classical Newton-Cotes quadrature, while preserving computational efficiency. The findings suggest that the composite FRFT framework offers a robust and mathematically sound tool for transform-based numerical approximations, particularly in applications involving oscillatory integrals and complex-valued characteristic functions. Full article

Underwater inspection images of hydraulic structures often suffer from haze, severe color distortion, low contrast, and blurred textures, impairing the accuracy of automated crack, spalling, and corrosion detection. However, many existing enhancement methods fail to preserve structural details and suppress noise in turbid environments. To address these limitations, we propose a compact image enhancement framework called Wavelet Fusion with Sobel-based Weighting (WWSF). This method first corrects global color and luminance distributions using multiscale Retinex and gamma mapping, followed by local contrast enhancement via CLAHE in the L channel of the CIELAB color space. Two preliminarily corrected images are decomposed using discrete wavelet transform (DWT); low-frequency bands are fused based on maximum energy, while high-frequency bands are adaptively weighted by Sobel edge energy to highlight structural features and suppress background noise. The enhanced image is reconstructed via inverse DWT. Experiments on real-world sluice gate datasets demonstrate that WWSF outperforms six state-of-the-art methods, achieving the highest scores on UIQM and AG while remaining competitive on entropy (EN). Moreover, the method retains strong robustness under high turbidity conditions (T ≥ 35 NTU), producing sharper edges, more faithful color representation, and improved texture clarity. These results indicate that WWSF is an effective preprocessing tool for downstream tasks such as segmentation, defect classification, and condition assessment of hydraulic infrastructure in complex underwater environments. Full article

Monitoring soil moisture (SM) using permanently installed gamma radiation (GR) detectors is a promising non-invasive method based on the inverse relationship between SM and soil-emitted GR. In a previous study, we successfully estimated SM from environmental gamma radiation (EGR) measured by a low-cost counter-tube detector. Since this detector type provides a bulk GR response across a wide energy range, EGR signals are influenced by several confounding factors, e.g., soil radon emanation, biomass. To what extent these confounding factors deteriorate the accuracy of SM estimates obtained from EGR is not fully understood. Therefore, the aim of this study was to compare the accuracy of SM estimates from EGR with those from reference ⁴⁰K GR (1460 keV) measurements which are much less influenced by these factors. For this, a Geiger–Mueller counter (G–M), which is commonly used for EGR monitoring, and a gamma spectrometer were installed side by side in an agricultural field equipped with in situ sensors to measure reference SM and a meteorological station. The EGR_G–M and spectrometry-based ⁴⁰K measurements were related to reference SM using a functional relationship derived from theory. We found that daily SM can be predicted with an RMSE of 3.39 vol. % from ⁴⁰K using the theoretical value of α = 1.11 obtained from the effective ratio of GR mass attenuation coefficients for the water and solid phase. A lower accuracy was achieved for the EGR_G–M measurements (RMSE = 6.90 vol. %). Wavelet coherence analysis revealed that the EGR_G–M measurements were influenced by radon-induced noise in winter. Additionally, biomass shielding had a stronger impact on EGR_G–M than on ⁴⁰K GR estimates of SM during summer. In summary, our study provides a better understanding on the lower prediction accuracy of EGR_G–M and suggests that correcting for biomass can improve SM estimation from the bulk EGR data of operational radioactivity monitoring networks. Full article

This study estimates the population median through stratified random sampling, which enhances accuracy by ensuring the proper representation of key population groups. The proposed class of estimators based on transformations effectively handles data variability and enhances estimation efficiency. We examine bias and mean square error expressions up to the first-order approximation for both existing and newly introduced estimators, establishing theoretical conditions for their applicability. Moreover, to assess the effectiveness of the suggested estimators, five simulated datasets derived from distinct asymmetric distributions (gamma, log-normal, Cauchy, uniform, and exponential), along with actual datasets, are used for numerical analysis. These estimators are designed to significantly enhance the precision and effectiveness of median estimation, resulting in more reliable and consistent outcomes. Comparative analysis using percent relative efficiency (PRE) reveals that the proposed estimators perform better than conventional approaches. Full article

Background: Previously, we found that the pathological changes in the corpus spongiosum (CS) in hypospadias were mainly localized within smooth muscle tissue, presenting as a transformation from the contraction phenotype to synthesis. The role of low-intensity pulsed ultrasound (LIPUS) in regulating smooth muscle cells (SMCs) and angiogenesis has been confirmed. Objectives: To demonstrate the feasibility of regulating the phenotypic transformation of corpus spongiosum smooth muscle cells (CSSMCs) in hypospadias using LIPUS and to explore the potential mechanisms. Materials and Methods: The CSSMCs were extracted from CS in patients with proximal hypospadias. In vitro experiments were conducted to explore the appropriate LIPUS irradiation intensity and duration which could promote the phenotypic transformation of CSSMCs. A total of 71 patients with severe hypospadias were randomly divided into a control group and a LIPUS group to verify the in vivo transition effect of LIPUS. Consequently, the potential mechanisms by which LIPUS regulates the phenotypic transformation of CSSMCs were explored in vitro. Results: In vitro experiments showed that LIPUS with an intensity of 100 mW/cm² and a duration of 10 min could significantly increase the expression of contraction markers in CSSMCs and decrease the expression of synthesis markers. Moreover, LIPUS stimulation could alter the phenotype of CSSMCs in patients with proximal hypospadias. RNA sequencing results revealed that peroxisome proliferator-activated receptor gamma (PPAR-γ) significantly increased after LIPUS stimulation. Overexpression of PPAR-γ significantly increased the expression of contraction markers in CSSMCs, and the knockdown of PPAR-γ blocked this effect. Conclusions: LIPUS can regulate the transition of CSSMCs from a synthetic to a contractile phenotype in hypospadias. The PPAR-γ-mediated signaling pathway is a possible mechanism involved in this process. Full article

In recent years, the continuous advancement of deep learning technology and its integration into the domain of low-light image enhancement have led to a steady improvement in enhancement effects. However, this progress has been accompanied by an increase in model complexity, imposing significant constraints on applications that demand high real-time performance. To address this challenge, inspired by the state-of-the-art Zero-DCE approach, we introduce a novel method that transforms the low-light image enhancement task into a curve estimation task tailored to each individual image, utilizing a lightweight shallow neural network. Specifically, we first design a novel curve formula based on Gamma correction, which we call the Gamma-based light-enhancement (GLE) curve. This curve enables outstanding performance in the enhancement task by directly mapping the input low-light image to the enhanced output at the pixel level, thereby eliminating the need for multiple iterative mappings as required in the Zero-DCE algorithm. As a result, our approach significantly improves inference speed. Additionally, we employ a lightweight network architecture to minimize computational complexity and introduce a novel global channel attention (GCA) module to enhance the nonlinear mapping capability of the neural network. The GCA module assigns distinct weights to each channel, allowing the network to focus more on critical features. Consequently, it enhances the effectiveness of low-light image enhancement while incurring a minimal computational cost. Finally, our method is trained using a set of zero-reference loss functions, akin to the Zero-DCE approach, without relying on paired or unpaired data. This ensures the practicality and applicability of our proposed method. The experimental results of both quantitative and qualitative comparisons demonstrate that, despite its lightweight design, the images enhanced using our method not only exhibit perceptual quality, authenticity, and contrast comparable to those of mainstream state-of-the-art (SOTA) methods but in some cases even surpass them. Furthermore, our model demonstrates very fast inference speed, making it suitable for real-time inference in resource-constrained or mobile environments, with broad application prospects. Full article

Since water contamination has become a serious concern, more effective environmental remediation methods are required. Chitosan (CHT)-based adsorbents have demonstrated high efficacy in removing pollutants due to their unique chemical and structural properties. However, their utilization remains limited by low environmental stability and the absence of effective adsorption sites. The functional moieties of CHT can be altered to improve its performance via graft modification and crosslinking. Compared to conventional hydrogel synthesis techniques, ionizing radiation-induced fabrication, using gamma or electron-beam irradiation, offers a promising platform for innovation across diverse fields. The prime focus of this review is on ionizing radiation developed CHT-based hydrogels to remove toxic heavy metals, dyes, organic contaminants, radionuclides, and humic substances. The fabrication strategy, adsorption mechanism, and factors affecting the adsorption capacity of CHT-based hydrogels are presented. This review aims to underscore the transformative potential of ionizing radiation-induced CHT hydrogels in environmental remediation by examining current research trends and identifying future prospects. Full article

Introduction: The clinical implementation of noninvasive tests for liver fibrosis assessment has attracted increasing attention, particularly for diagnosing advanced fibrosis (≥F3). This observational study aimed to evaluate the stratification accuracy of nine direct and seven indirect biomarkers across four etiologies: chronic hepatitis B (CHB), chronic hepatitis C (CHC), alcoholic liver cirrhosis (ALC), and nonalcoholic liver cirrhosis (NALC). Materials and Methods: Our study was conducted on 116 participants, including 96 with chronic liver disease (16 CHB, 15 CHC, 49 ALC, and 16 NALC) and 20 healthy controls. The values of direct (aspartate aminotransferase, alanine aminotransferase, total bilirubin, serum albumin, platelet count, international normalized ratio, gamma-glutamyl transpeptidase, CD5 antigen-like, and transforming growth factor-beta 1) and indirect non-serological biomarkers (De Ritis ratio, albumin–bilirubin score, gamma-glutamyl transpeptidase-to-platelet ratio, aspartate aminotransferase-to-platelet-ratio index, fibrosis-4 index, INR-to-platelet ratio, and fibrosis quotient) were analyzed for their discriminative power in fibrosis stratification. Results: Statistical analyses revealed a significant correlation (0.05 level; two-tailed), and AUC 95% CI ranged within 0.50–1.00 between the direct and indirect biomarker values across all etiologies. Among the evaluated biomarkers, the recorded AUC was 0.998 in CHB for APRI, 0.981 in CHC for FIB-4, and 1.000 in ALC and NALC for APRI and AST, respectively, while CD5L consistently achieved an AUC of 1.000 across all etiologies. Conclusions: These findings suggest that applying a multifactorial approach in liver pathology may improve diagnosis accuracy compared to the use of individual biomarkers and can provide data that may inform the development of clinically applicable mathematical models. Full article

Background/Objectives: Sleep disorders are prevalent among aging populations and are often linked to cognitive decline, chronic conditions, and reduced quality of life. Traditional diagnostic methods, such as polysomnography, are resource-intensive and limited in accessibility. Meanwhile, individuals frequently describe their sleep experiences through unstructured narratives in clinical notes, online forums, and telehealth platforms. This study proposes a machine learning pipeline (sleepCare) that classifies sleep-related narratives into clinically meaningful categories, including stress-related, neurodegenerative, and breathing-related disorders. The proposed framework employs natural language processing (NLP) and machine learning techniques to support remote applications and real-time patient monitoring, offering a scalable solution for the early identification of sleep disturbances. Methods: The sleepCare consists of a three-tiered classification pipeline to analyze narrative sleep reports. First, a baseline model used a Multinomial Naïve Bayes classifier with n-gram features from a Bag-of-Words representation. Next, a Support Vector Machine (SVM) was trained on GloVe-based word embeddings to capture semantic context. Finally, a transformer-based model (BERT) was fine-tuned to extract contextual embeddings, using the [CLS] token as input for SVM classification. Each model was evaluated using stratified train-test splits and 10-fold cross-validation. Hyperparameter tuning via GridSearchCV optimized performance. The dataset contained 475 labeled sleep narratives, classified into five etiological categories relevant for clinical interpretation. Results: The transformer-based model utilizing BERT embeddings and an optimized Support Vector Machine classifier achieved an overall accuracy of 81% on the test set. Class-wise F1-scores ranged from 0.72 to 0.91, with the highest performance observed in classifying normal or improved sleep (F1 = 0.91). The macro average F1-score was 0.78, indicating balanced performance across all categories. GridSearchCV identified the optimal SVM parameters (C = 4, kernel = ‘rbf’, gamma = 0.01, degree = 2, class_weight = ‘balanced’). The confusion matrix revealed robust classification with limited misclassifications, particularly between overlapping symptom categories such as stress-related and neurodegenerative sleep disturbances. Conclusions: Unlike generic large language model applications, our approach emphasizes the personalized identification of sleep symptomatology through targeted classification of the narrative input. By integrating structured learning with contextual embeddings, the framework offers a clinically meaningful, scalable solution for early detection and differentiation of sleep disorders in diverse, real-world, and remote settings. Full article

We introduce a novel logarithmic approach within the Baumgarte–Shapiro–Shibata–Nakamura (BSSN) formalism for self-consistently solving the equations of general relativistic hydrodynamics (GRHD) in evolving curved spacetimes. This method employs a “3 + 1” decomposition of spacetime, complemented by the “1 + log” slicing condition and Gamma-driver shift conditions, which have been shown to improve numerical stability in spacetime evolution. A key innovation of our work is the logarithmic transformation applied to critical variables such as rest-mass density, energy density, and pressure, thus preserving physical positivity and mitigating numerical issues associated with extreme variations. Our formulation is fully compatible with advanced numerical techniques, including spectral methods and Fourier-based algorithms, and it is particularly suited for simulating highly nonlinear regimes in which gravitational fields play a significant role. This approach aims to provide a solid foundation for future numerical implementations and investigations of relativistic hydrodynamics, offering promising new perspectives for modeling complex astrophysical phenomena in strong gravitational fields, including matter evolution around compact objects like neutron stars and black holes, turbulent flows in the early universe, and the nonlinear evolution of cosmic structures. Full article

The instability of the geopolitical situation due to the high terrorist danger leads to the need to take into account at the planning stage the capabilities of intruders to perform UAV flight missions. A general method for analyzing the process of cargo delivery by UAVs (Unmanned Aerial Vehicles) to hard-to-reach areas during emergencies has been proposed. This method allows for the evaluation of UAV effectiveness based on the probability of successful cargo delivery within a specified time limit. The method is based on applying topological transformation techniques to stochastic networks. The cargo delivery process is modeled as a stochastic network, followed by the determination of its equivalent function and the use of Heaviside decomposition to calculate the distribution function and the expected delivery time. This presentation of the studied process for the first time made it possible to take into account the impact on the flight mission of the UAV of the destructive impact from the attacker. This approach allows the destructive effects on the UAV from malicious interference to be considered. The input data used for the analysis are parameters that characterize the properties of individual processes within the stochastic network, represented as branches, which are computed using methodologies published in earlier studies. It has been demonstrated that the resulting distribution function of the mission completion time can be accurately approximated by a gamma distribution with a level of precision suitable for practical applications. In this case, the gamma distribution parameters are determined using the equivalent function of the stochastic network. The proposed method can be used by flight planners when scheduling UAV operations in emergency zones, especially in scenarios where there is a risk of malicious interference. Full article

Background/Objectives: Gamma oscillations (30–100 Hz), which are essential for memory, attention, and cortical synchronization, remain underexplored in Alzheimer’s disease (AD) research. While resting-state EEG studies have predominantly examined lower frequency bands (delta to beta), gamma activity may more accurately reflect early synaptic dysfunction and other mechanisms relevant to AD pathophysiology. AD is a common age-related neurodegenerative disorder frequently associated with altered resting-state EEG (rEEG) patterns. This study analyzed gamma power spectral density (PSD) during eyes-open (EOR) and eyes-closed (ECR) resting-state EEG in AD patients compared to cognitively normal (CN) individuals. Methods: rEEG data from 534 participants (269 CN, 265 AD) aged 40–90 were analyzed. Quantitative EEG (qEEG) analysis focused on the gamma band (30–100 Hz) using PSD estimation with the Welch method, coherence matrices, and coherence-based functional connectivity. Data preprocessing and analysis were performed using EEGLAB and Brainstorm in MATLAB R2024b. Group comparisons were conducted using ANOVA for unadjusted models and linear regression with age adjustment using log₁₀-transformed PSD values in Python (version 3.13.2, 2025). Results: AD patients exhibited significantly elevated gamma PSD in frontal and temporal regions during EOR and ECR states compared to CN. During ECR, gamma PSD was markedly higher in the AD group (Mean = 0.0860 ± 0.0590) than CN (Mean = 0.0042 ± 0.0010), with a large effect size (Cohen’s d = 1.960, p < 0.001). Conversely, after adjusting for age, the group difference was no longer statistically significant (β = −0.0047, SE = 0.0054, p = 0.391), while age remained a significant predictor of gamma power (β = −0.0008, p = 0.019). Pairwise coherence matrix and coherence-based functional connectivity were increased in AD during ECR but decreased in EOR relative to CN. Conclusions: Gamma oscillatory activity in the 30–100 Hz range differed significantly between AD and CN individuals during resting-state EEG, particularly under ECR conditions. However, age-adjusted analyses revealed that these differences are not AD-specific, suggesting that gamma band changes may reflect aging-related processes more than disease effects. These findings contribute to the evolving understanding of gamma dynamics in dementia and support further investigation of gamma PSD as a potential, age-sensitive biomarker. Full article

The challenge of accurately representing diverse visual experiences from the real world through image rendering, especially in High Dynamic Range (HDR) imaging, persists due to limitations in conveying luminosity and colour depth on standard displays. In this study, we explore luminosity and Wide Colour Gamut (WCG) in HDR and investigate prevalent HDR/WCG frameworks like hybrid log-gamma (HLG). The focus lies in overcoming the hurdle of displaying transformed HDR images on Standard Dynamic Range (SDR) screens through HDR tone mapping (TM). Despite numerous TM operators available, the need for a detailed comparative analysis remains the same. This study aims to convert HDR images into HLG-transformed images using ISO 22028-5 and transform these to SDR using various TM methods, followed by encoding them into standard displays. Another objective of the study is to also identify the optimal TM method for preserving image quality and artistic integrity on SDR screens, complemented by evaluating content dependencies and optimizing visualization using gain maps. This paper’s comprehensive evaluation involves subjective experiments to discern the most effective TM methodology, providing insights into the transformative potential of HDR images for broader display compatibility. The results indicate that content-aware TM methods combined with gain map optimization provide superior visual fidelity and are recommended for high-quality HDR-to-SDR rendering. Full article