This study investigates the impact of industrial emissions on the concentration of toxic elements, such as barium, strontium, arsenic, thorium, and uranium, in the biological tissues of pregnant women residing in Kazakhstan's industrial regions. The study focuses on the potential health risks to both the mothers and their developing fetuses, given the ongoing environmental contamination due to rapid industrialization. 67 pregnant women from various districts in the Akmola region were selected for this cross-sectional study. Biological samples, including placenta and umbilical cord blood, were collected and analyzed using instrumental neutron activation analysis and scanning electron microscopy techniques. Data on environmental and occupational exposure were gathered through questionnaires. The barium, strontium, arsenic, thorium, and uranium concentrations were statistically analyzed using Microsoft Excel and STATISTICA to assess correlations with health outcomes. The findings showed elevated concentrations of barium and strontium in both the placenta and umbilical cord blood, indicating significant exposure through environmental contamination. Arsenic and uranium were also detected in smaller amounts, with localized variations across different regions. The study found a strong association between higher concentrations of these elements and adverse pregnancy outcomes, such as anemia, preeclampsia, and developmental anomalies in the fetus. This study highlights the critical environmental health risks of industrial emissions in Kazakhstan's rapidly developing regions. The transplacental transfer of toxic elements poses serious risks to maternal and fetal health, increasing the incidence of pregnancy-related complications. These findings emphasize the need for stricter environmental regulations and public health interventions to mitigate industrial pollution and safeguard vulnerable populations.

Studying the spatial variation of soil properties is necessary to predict the productivity of agricultural land, food safety, and environmental status. Therefore, this study was carried out in Ramadi district, the center of Anbar Governorate - Iraq, to study the spatial variation of some soil properties (physical and chemical) using the geo statistical model. Using the predictive model of soil systems and Geographic information to prepare maps of the studied soil properties. The results showed the dominance of the moderately textured class with a percentage of 47.4%, followed by the moderately coarse texture class with a percentage of 34.2%. In addition, moderate variations have been recorded in the soil content of silt and clay, while unclear variation was recorded in the soil bulk density. As for the chemical characteristics, the soil content of organic matter, gypsum, and soil salinity showed very high variation, compared to the soil content of calcium carbonate and soil pH, which indicates the need to take them into consideration when planning the future use of the region’s lands to increase their soil capability.

Accurate traffic prediction is essential for optimizing urban mobility and mitigating congestion. Traditional deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), struggle to capture complex spatiotemporal dependencies and dynamic traffic variations across urban networks. To address these challenges, this study introduces DSTGN-ExpertNet, a novel Deep Spatio-Temporal Graph Neural Network (DSTGNN) framework that integrates Graph Neural Networks (GNNs) for spatial modeling and advanced deep learning techniques for temporal dynamics. The framework employs a Mixture of Experts (MoE) approach, where specialized expert models are dynamically assigned to distinct traffic patterns through a gating network, optimizing both prediction accuracy and interpretability. The proposed model is evaluated on large-scale real-world traffic datasets from Beijing and New York, demonstrating superior performance over conventional methods, including Spatio-Temporal Graph Convolutional Networks (ST-GCN) and attention-based models. With a mean absolute error (MAE) of 1.97 on the BikeNYC dataset and 9.70 on the TaxiBJ dataset, DSTGN-ExpertNet achieves state-of-the-art accuracy. These findings highlight the potential of GNN-based frameworks in revolutionizing traffic forecasting and intelligent transportation systems (ITS).

Diabetes mellitus (DM) is a major non-communicable metabolic disorder characterized by persistent hyperglycemia arising from impaired insulin secretion, insulin resistance, or a combination of both. As the global burden of DM continues to rise, understanding its prevalence and associated risk factors in specific populations is critical for the development of effective prevention and management strategies. A cross-sectional study was conducted among 150 residents (75 males and 75 females) attending a tertiary healthcare facility in Mardan, Pakistan. Sociodemographic characteristics, family history, body mass index (BMI), lifestyle behaviors, dietary patterns, psychological stress, and other potential risk factors were assessed using a structured questionnaire, while venous blood samples were collected to confirm the diagnosis of DM. Overall, the prevalence of DM was found to be 34.67% (n=52), with 29.33% (n=44) previously diagnosed and 5.33% (n=8) newly identified during the investigation. A significant sex-related disparity was observed, with prevalence rates of 26.67% (n=20) in males and 42.67% (n=32) in females. Rural residents exhibited a higher prevalence (42.86%, n=33) compared to urban residents (26.03%, n=19). Several risk factors demonstrated a notable association with DM, including advanced age (>60 years: 8.67%, n=13), obesity (12.67%, n=19), low physical activity (26.67%, n=40), smoking (11.33%, n=17), unhealthy dietary patterns (27.33%, n=41), high psychological stress (17.33%, n=26), hypertension (14%, n=21), and a positive family history (27.33%, n=41). The findings indicate an upward trend in the prevalence of DM in the Mardan region. Immediate implementation of targeted interventions, including public health education, lifestyle modification, dietary counseling, and risk factor management, is essential to mitigate the increasing burden of DM in this population.

This study investigates the recognition of seven primary human emotions—contempt, anger, disgust, surprise, fear, happiness, and sadness—based on facial expressions. A transfer learning approach was employed, utilizing three pre-trained convolutional neural network (CNN) architectures: AlexNet, VGG16, and ResNet50. The system was structured to perform facial expression recognition (FER) by incorporating three key stages: face detection, feature extraction, and emotion classification using a multiclass classifier. The proposed methodology was designed to enhance pattern recognition accuracy through a carefully structured training pipeline. Furthermore, the performance of the transfer learning models was compared using a multiclass support vector machine (SVM) classifier, and extensive testing was planned on large-scale datasets to further evaluate detection accuracy. This study addresses the challenge of spontaneous FER, a critical research area in human-computer interaction, security, and healthcare. A key contribution of this study is the development of an efficient feature extraction method, which facilitates FER with minimal reliance on extensive datasets. The proposed system demonstrates notable improvements in recognition accuracy compared to traditional approaches, significantly reducing misclassification rates. It is also shown to require less computational time and resources, thereby enhancing its scalability and applicability to real-world scenarios. The approach outperforms conventional techniques, including SVMs with handcrafted features, by leveraging the robust feature extraction capabilities of transfer learning. This framework offers a scalable and reliable solution for FER tasks, with potential applications in healthcare, security, and human-computer interaction. Additionally, the system’s ability to function effectively in the absence of a caregiver provides significant assistance to individuals with disabilities in expressing their emotional needs. This research contributes to the growing body of work on facial emotion recognition and paves the way for future advancements in artificial intelligence-driven emotion detection systems.

Drought, a complex natural phenomenon with profound global impacts, including the depletion of water resources, reduced agricultural productivity, and ecological disruption, has become a critical challenge in the context of climate change. Effective drought prediction models are essential for mitigating these adverse effects. This study investigates the contribution of various data preprocessing steps—specifically class imbalance handling and dimensionality reduction techniques—to the performance of machine learning models for drought prediction. Synthetic Minority Over-sampling Technique (SMOTE) and near miss sampling methods were employed to address class imbalances within the dataset. Additionally, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were applied for dimensionality reduction, aiming to improve computational efficiency while retaining essential features. Decision tree algorithms were trained on the preprocessed data to assess the impact of these preprocessing techniques on model accuracy, precision, recall, and F1-score. The results indicate that the SMOTE-based sampling approach significantly enhances the overall performance of the drought prediction model, particularly in terms of accuracy and robustness. Furthermore, the combination of SMOTE, PCA, and LDA demonstrates a substantial improvement in model reliability and generalizability. These findings underscore the critical importance of carefully selecting and applying appropriate data preprocessing techniques to address class imbalances and reduce feature space, thus optimizing the performance of machine learning models in drought prediction. This study highlights the potential of preprocessing strategies in improving the predictive capabilities of models, providing valuable insights for future research in climate-related prediction tasks.

Foggy road conditions present significant challenges for road monitoring systems and autonomous driving, as conventional defogging techniques often fail to accurately recover fine details of road structures, particularly under dense fog conditions, and may introduce undesirable artifacts. Furthermore, these methods typically lack the ability to dynamically adjust transmission maps, leading to imprecise differentiation between foggy and clear areas. To address these limitations, a novel approach to image dehazing is proposed, which combines an entropy-weighted Gaussian Mixture Model (EW-GMM) with Pythagorean fuzzy aggregation (PFA) and a level set refinement technique. The method enhances the performance of existing models by adaptively adjusting the influence of each Gaussian component based on entropy, with greater emphasis placed on regions exhibiting higher uncertainty, thereby enabling more accurate restoration of foggy images. The EW-GMM is further refined using PFA, which integrates fuzzy membership functions with entropy-based weights to improve the distinction between foggy and clear regions. A level set method is subsequently applied to smooth the transmission map, reducing noise and preserving critical image details. This process is guided by an energy functional that accounts for spatial smoothness, entropy-weighted components, and observed pixel intensities, ensuring a more robust and accurate dehazing effect. Experimental results demonstrate that the proposed model outperforms conventional methods in terms of feature similarity, image quality, and cross-correlation, while significantly reducing execution time. The results highlight the efficiency and robustness of the proposed approach, making it a promising solution for real-time image processing applications, particularly in the context of road monitoring and autonomous driving systems.

The impact of artificial hailstones on G300 steel sheets with varying thicknesses has been systematically investigated to evaluate the resulting dent depths. Two distinct methods for producing simulated hailstones were employed: one utilizing polyvinyl alcohol (PVA) adhesive and the other incorporating liquid nitrogen. Comparative analyses of these techniques revealed that the liquid nitrogen method, in conjunction with demineralized water, yielded more accurate results than the PVA adhesive-based method. Experimental findings were cross-referenced with theoretical predictions and finite element simulations, with model accuracy being validated against existing research in the field. The study focused on three hailstone diameters—38mm, 45mm, and 50mm—across various sheet thicknesses. Results indicate that dent depth is primarily influenced by the impact energy, sheet metal thickness, and hailstone diameter. Notably, the momentum of the hailstone plays a critical role, with smaller, higher-momentum hailstones inducing permanent deformations comparable to those of larger, lower-momentum hailstones, even when the impact energies are equivalent. The findings suggest that variations in hailstone momentum can lead to similar deformation patterns across different sizes, emphasizing the importance of momentum in the design of steel sheet materials for enhanced hailstone impact resistance. This study contributes valuable insights for the development of more resilient materials in industries subject to dynamic impact loading, such as automotive and aerospace engineering.