Search Results (28,994)

Computer vision seeks to mimic the human visual system and plays an essential role in artificial intelligence. It is based on different signal reprocessing techniques; therefore, developing efficient techniques becomes essential to achieving fast and reliable processing. Various signal preprocessing operations have been used for computer vision, including smoothing techniques, signal analyzing, resizing, sharpening, and enhancement, to reduce reluctant falsifications, segmentation, and image feature improvement. For example, to reduce the noise in a disturbed signal, smoothing kernels can be effectively used. This is achievedby convolving the distributed signal with smoothing kernels. In addition, orthogonal moments (OMs) are a crucial technique in signal preprocessing, serving as key descriptors for signal analysis and recognition. OMs are obtained by the projection of orthogonal polynomials (OPs) onto the signal domain. However, when dealing with 3D signals, the traditional approach of convolving kernels with the signal and computing OMs beforehand significantly increases the computational cost of computer vision algorithms. To address this issue, this paper develops a novel mathematical model to embed the kernel directly into the OPs functions, seamlessly integrating these two processes into a more efficient and accurate approach. The proposed model allows the computation of OMs for smoothed versions of 3D signals directly, thereby reducing computational overhead. Extensive experiments conducted on 3D objects demonstrate that the proposed method outperforms traditional approaches across various metrics. The average recognition accuracy improves to 83.85% when the polynomial order is increased to 10. Experimental results show that the proposed method exhibits higher accuracy and lower computational costs compared to the benchmark methods in various conditions for a wide range of parameter values. Full article

The current study examines the neural mechanisms underlying facial recognition, focusing on how emotional expression and mouth display modulate event-related potential (ERP) waveforms. 42 participants categorized faces by gender in one of two experimental setups: one featuring full-face images and another with cropped faces presented against neutral gray backgrounds. The stimuli included 288 images balanced across gender, race/ethnicity, emotional expression (“Fearful”, “Happy”, “Neutral”), and mouth display (“closed mouth” vs. “open mouth with exposed teeth”). Results revealed that N170 amplitude was significantly greater for open-mouth (exposed teeth) conditions (p < 0.01), independent of emotional expression, and no interaction between emotional expression and mouth display was found. However, the P100 amplitude exhibited a significant interaction between these variables (p < 0.05). Monte Carlo simulations analyzing N170 latency differences showed that fearful faces elicited a faster response than happy and neutral faces, with a 2 ms delay unlikely to occur by chance (p < 0.01). While these findings challenge prior research suggesting that N170 is directly influenced by emotional expression, they also highlight the potential role of emotional intensity as an alternative explanation. This underscores the importance of further studies to disentangle these effects. This study highlights the critical need to control for mouth display when investigating emotional face processing. The results not only refine our understanding of the neural dynamics of face perception but also confirm that the brain processes fearful expressions more rapidly than happy or neutral ones. These insights offer valuable methodological considerations for future neuroimaging research on emotion perception. Full article

Human activity recognition (HAR) has become a crucial research area for many applications, such as Healthcare, surveillance, etc. With the development of artificial intelligence (AI) and Internet of Things (IoT), sensor-based HAR has gained increasing attention and presents great advantages to existing work. Relying solely on existing labeled data may not adequately address the challenge of ensuring the model’s generalization ability to new data. The ’CLEAR’ method is designed to improve the accuracy of multimodal human activity recognition. This approach employs data augmentation, multimodal feature fusion, and contrastive learning techniques. These strategies are utilized to refine and extract highly discriminative features from various data sources, thereby significantly enhancing the model’s capacity to identify and classify diverse human activities accurately. CLEAR achieves high generalization performance on unknown datasets using only training data. Furthermore, CLEAR can be directly applied to various target domains without retraining or fine-tuning. Specifically, CLEAR consists of two parts. First, it employs data augmentation techniques in both the time and frequency domains to enrich the training data. Second, it optimizes feature extraction using attention-based multimodal fusion techniques and employs supervised contrastive learning to improve feature discriminability. We achieved accuracy rates of 81.09%, 90.45%, and 82.75% on three public datasets USC-HAD, DSADS, and PAMAP2, respectively. Additionally, when the training data are reduced from 100% to 20%, the model’s accuracy on the three datasets decreases by only about 5%, demonstrating that our model possesses strong generalization capabilities. Additionally, when the training data are reduced from 100% to 20%, the model’s accuracy on the three datasets decreases by only about 5%, demonstrating that our model possesses strong generalization capabilities. Full article

This study investigates the determinants of women empowerment among refugee women living in Nairobi, Kenya. First, the study constructs an index to examine empowerment drivers using data from the Refugee and Host Household Survey (RHHS) 2021. A fractional logit regression model was employed in the study. The results obtained show that the incidence of refugee women empowerment among refugees living in Nairobi was six percent. In addition, the study finds evidence that age; the gender of the household head; the education level of the refugee woman; employment status; and the education of the household head play substantial roles in enabling women empowerment. Conversely, marital statuses (divorced/separated/widowed and single/never married) and religious affiliations (Muslim) hinder women empowerment. Efforts geared towards improving wage employment and education are likely to empower refugee women. The study emphasizes the recognition of the role played by women in household income through care work. Full article

Epigallocatechin gallate (EGCG), the predominant catechin in green tea, comprises approximately 50% of its total polyphenol content and has garnered widespread recognition for its significant therapeutic potential. As the principal bioactive component of Camellia sinensis, EGCG is celebrated for its potent antioxidant, anti-inflammatory, cardioprotective, and antitumor properties. The bioavailability and metabolism of EGCG within the gut microbiota underscore its systemic effects, as it is absorbed in the intestine, metabolized into bioactive compounds, and transported to target organs. This compound has been shown to influence key physiological pathways, particularly those related to lipid metabolism and inflammation, offering protective effects against a variety of diseases. EGCG’s ability to modulate cell signaling pathways associated with oxidative stress, apoptosis, and immune regulation highlights its multifaceted role in health promotion. Emerging evidence underscores EGCG’s therapeutic potential in preventing and managing a range of chronic conditions, including cancer, cardiovascular diseases, neurodegenerative disorders, and metabolic syndromes. Given the growing prevalence of lifestyle-related diseases and the increasing interest in natural compounds, EGCG presents a promising avenue for novel therapeutic strategies. This review aims to summarize current knowledge on EGCG, emphasizing its critical role as a versatile natural bioactive agent with diverse clinical applications. Further exploration in both experimental and clinical settings is essential to fully unlock its therapeutic potential. Full article

Background/Objectives: Viral gastroenteritis can have a potentially fatal outcome at young ages and the recognition of severe cases could be aided by clinically derived severity scores. Methods: This observational study intended to conduct a comparative assessment of the utility of the Vesikari and modified Vesikari score in the evaluation of viral gastroenteritis severity and for the possible prediction of the dehydration degree. A total number of 113 children diagnosed with gastroenteritis were retrospectively enrolled and divided based on viral etiology into group 1 (34 children with unknown viral etiology), group 2 (60 children with rotavirus) and group 3 (19 children with adenovirus). Results: The highest mean Vesikari and modified Vesikari scores were found in group 2 (p<0.01; p=0.01). A significant increase in liver enzymes was also identified in patients infected with rotavirus. The highest mean diarrhea, vomiting duration and body temperature were found in group 3 (p<0.01; p<0.01; p=0.02), as well as the highest mean inflammatory markers, such as C-reactive protein (CRP; p=0.01) and the erythrocyte sedimentation rate (p<0.01). Significant linear associations were found between pH, bicarbonate level, base excess and the Vesikari scores, whereas urea, CRP and aspartate aminotransferase levels were associated with both severity scores. ROC curve analysis revealed a significant correlation between the Vesikari scores and dehydration degree (p<0.01), with numeric cut-off values of 11.5 being proposed for the differentiation between mild and moderate gastroenteritis and 13.5 for the distinction between moderate and severe gastroenteritis. Conclusions: Both severity scores are useful in clinical settings, but more studies enrolling populations with various enteral infections could provide more insight into their etiology-based performance and reflection of paraclinical changes. Full article

By developing a conceptual framework that integrates the use of fire in agricultural activities, the occurrence of wildfires, and the perception of wildfire risk, this article examines the interplay among these three elements within both wet and dry Puna grasslands. The analysis focuses on two peasant and agropastoral communities, Vilcabamba and Apachaco, both located in the Cusco region—an area with the highest incidence of wildfires in Peru. This study highlights the sociocultural significance and persistence of agricultural burnings within Puna agropastoral communities and the necessity of considering changes in agricultural activity, mutual aid systems, and communal institutions—particularly regarding land ownership—to understand the factors contributing to wildfire occurrence. Furthermore, it reveals the widespread recognition of wildfire risk among community members, who are acutely aware of both the likelihood and potential severity of wildfire events, while governmental policies aimed at addressing this hazard predominantly focus on raising awareness and enforcing bans on agricultural burning, with limited consideration of these complex sociocultural dynamics. Full article

Autism spectrum disorder (ASD) is an early-onset neurodevelopmental condition that now impacts 1 in 36 children in the United States and is characterized by deficits in social communication, repetitive behaviors, and restricted interests. Children with ASD also frequently experience co-morbidities including anxiety and ADHD, and up to 80% experience gastrointestinal (GI) symptoms such as constipation, diarrhea, and/or abdominal pain. Systemic immune activation and dysregulation, including increased pro-inflammatory cytokines, are frequently observed in ASD. Evidence has shown that the innate immune system may be impacted in ASD, as altered monocyte gene expression profiles and cytokine responses to pattern recognition ligands have been observed compared to typically developing (TD) children. In humans, circulating monocytes are often categorized into three subpopulations—classical, transitional (or “intermediate”), and nonclassical monocytes, which can vary in functions, including archetypal inflammatory and/or reparative functions, as well as their effector locations. The potential for monocytes to contribute to immune dysregulation in ASD and its comorbidities has so far not been extensively studied. This study aims to determine whether these monocyte subsets differ in frequency in children with ASD and if the presence of GI symptoms alters subset distribution, as has been seen for T cell subsets. Whole blood from ASD children with (ASD⁺GI⁺) and without gastrointestinal symptoms (ASD⁺GI⁻) and their TD counterparts was collected from children enrolled in the Childhood Autism Risk from Genetics and Environment (CHARGE) study. Peripheral blood mononuclear cells were isolated and stained for commonly used subset identifiers CD14 and CD16 as well as activation state markers CCR2, HLA-DR, PD-1, and PD-L1 for flow cytometry analysis. We identified changes in monocyte subpopulations and their expression of surface markers in children with ASD compared to TD children. These differences in ASD appear to be dependent on the presence or absence of GI symptoms. We found that the ASD⁺GI⁺ group have a different monocyte composition, evident in their classical, transitional, and nonclassical populations, compared to the ASD⁺GI⁻ and TD groups. Both the ASD⁺GI⁺ and ASD⁺GI⁻ groups exhibited greater frequencies of classical monocytes compared to the TD group. However, the ASD⁺GI⁺ group demonstrated lower frequencies of transitional and nonclassical monocytes than their ASD⁺GI⁻ and TD counterparts. CCR2⁺ classical monocyte frequencies were highest in the ASD⁺GI⁻ group. HLA-DR⁺ classical, transitional, and nonclassical monocytes were statistically comparable between groups, however, HLA-DR⁻ nonclassical monocyte frequencies were lower in both ASD groups compared to TD. The frequency of classical monocytes displaying exhaustion markers PD-1 and PD-L1 were increased in the ASD⁺GI⁺ group compared to ASD⁺GI⁻ and TD, suggesting potentially impaired ability for clearance of foreign pathogens or debris, typically associated with worsened inflammation. Taken together, the findings of differential proportions of the monocyte subpopulations and altered surface markers may explain some of the characteristics of immune dysregulation, such as in the gastrointestinal tract, observed in ASD. Full article

This study evaluates the potential of an electronic tongue (E-tongue) as an innovative and alternative method for detecting and classifying lead concentrations in wastewater generated by coal mining activities in North Santander, Colombia. The E-tongue aims to complement traditional environmental monitoring techniques with a more efficient and accurate solution. A total of 110 wastewater samples were collected from two locations at a coal mine in the municipality of Toledo: one inside the mine (Point 2) and another outside the mine (Point 1). This research involved the physicochemical analysis of parameters such as pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), hardness, and alkalinity, conducted at the University of Pamplona’s laboratories. The integration of PCA with machine learning algorithms highlighted the E-tongue’s capability for the real-time, on-site detection and discrimination of lead concentrations in coal mining wastewater. Achieving a precision and accuracy above 90%, the SVM classifier outperformed alternative models such as the k-NN, Random Forest, Naïve Bayes, and Quadratic Discriminant Analysis. This demonstrates the system’s robustness and reliability in environmental monitoring, enabling the accurate classification of lead concentrations within the critical range of 0.05 to 1 ppm, essential for assessing contamination levels and ensuring water safety. These findings highlight the E-tongue system’s capability as a rapid, cost-effective tool for monitoring lead contamination in mining wastewater, presenting a viable alternative to conventional methods such as atomic absorption spectroscopy. Full article

Aging is associated with decreased neuronal sensitivity and activity that creates deficits in cognitive processes, including learning, memory, motivation, general activity, and other behaviors. These effects are due in part to decreased intracellular Ca²⁺ homeostasis, increasing hyperpolarization of the resting potential in aged neurons and therefore decreasing their excitability. To reduce hyperpolarization in aged mice, we used apamin, a selective small conductance Ca²⁺-activated K⁺ (sK_Ca) channel blocker. By blocking the sK_Ca channels, apamin decreases the egress of the K⁺ out of the cell, reducing its hyperpolarization and causing it to be closer to threshold potential. As a result, neurons should be more sensitive to excitatory stimuli and more active. We evaluated the performance of aged mice in a selection of cognitive and behavioral tests prior to and after systemic applications of apamin or the vehicle saline. Apamin improved performance in short-term memory, increased attention to tasks, and decreased anhedonia. Apamin had no significant effect on long-term spatial and recognition memory, risk-taking behavior, sociability, and anxiety. Our results are compatible with the known effects of sK_Ca channel blockade on neuronal sensitivity and activity; however, these short-term effects were not reflected in longer-term alterations of neural plasticity responsible for long-term spatial and recognition memory or other more complex cognitive processes we evaluated. Full article

Background/Objectives: Despite its long-standing recognition, the etiopathogenesis of idiopathic granulomatous mastitis (IGM) remains poorly understood. This study aims to investigate the relationship between IGM and food intolerance, allergies, and immunological factors to shed light on its etiology. Materials and Methods: This case–control study included 32 patients with IGM and 32 healthy women. In order to examine their potential relevance to allergy and immunology, serum interleukin (IL)-4, IL-4 receptor, histamine, and histamine-releasing factor (HRF) were measured by ELISA. Furthermore, serum IgG antibodies against specific food allergens were measured to evaluate food intolerance. Results: The patient group exhibited significantly higher intolerance values for lentils and curry compared to the control group (p = 0.023 and p = 0.012, respectively). Histamine (p < 0.001) and IL-4 (p = 0.003) levels were elevated in IGM patients compared to the control group, while HRF and IL-4R outcomes did not show any significant differences (p > 0.05). Conclusions: Elevated histamine and IL-4 levels may suggest the involvement of allergy and immunological factors in IGM’s etiopathogenesis. The integration of anti-histamine medications for IGM patients with elevated histamine levels could provide an alternative therapeutic strategy. Full article

Blind Source Separation (BSS) is a significant field of study in signal processing, with many applications in various fields such as audio processing, speech recognition, biomedical signal analysis, image processing and communication systems. Traditional methods, such as Independent Component Analysis (ICA), often rely on statistical independence assumptions, which may limit their performance in systems with significant temporal dynamics. This paper introduces an extension of the dynamic mode decomposition (DMD) approach by using time-delayed coordinates to implement BSS. Time-delay embedding enhances the capability of the method to handle complex, nonstationary signals by incorporating their temporal dependencies. We validate the approach through numerical experiments and applications, including audio signal separation, image separation and EEG artifact removal. The results demonstrate that modification achieves superior performance compared to conventional techniques, particularly in scenarios where sources exhibit dynamic coupling or non-stationary behavior. Full article

Recognizing human actions through video analysis has gained significant attention in applications like surveillance, sports analytics, and human–computer interaction. While deep learning models such as 3D convolutional neural networks (CNNs) and recurrent neural networks (RNNs) deliver promising results, they often struggle with computational inefficiencies and inadequate spatial–temporal feature extraction, hindering scalability to larger datasets or high-resolution videos. To address these limitations, we propose a novel model combining a two-dimensional convolutional restricted Boltzmann machine (2D Conv-RBM) with a long short-term memory (LSTM) network. The 2D Conv-RBM efficiently extracts spatial features such as edges, textures, and motion patterns while preserving spatial relationships and reducing parameters via weight sharing. These features are subsequently processed by the LSTM to capture temporal dependencies across frames, enabling effective recognition of both short- and long-term action patterns. Additionally, a smart frame selection mechanism minimizes frame redundancy, significantly lowering computational costs without compromising accuracy. Evaluation on the KTH, UCF Sports, and HMDB51 datasets demonstrated superior performance, achieving accuracies of 97.3%, 94.8%, and 81.5%, respectively. Compared to traditional approaches like 2D RBM and 3D CNN, our method offers notable improvements in both accuracy and computational efficiency, presenting a scalable solution for real-time applications in surveillance, video security, and sports analytics. Full article

Semantic segmentation of remotely sensed images for building footprint recognition has been extensively researched, and several supervised and unsupervised approaches have been presented and adopted. The capacity to do real-time mapping and precise segmentation on a significant scale while considering the intrinsic diversity of the urban landscape in remotely sensed data has significant consequences. This study presents a novel approach for delineating building footprints by utilizing the compressed sensing and radial basis function technique. At the feature extraction stage, a small set of random features of the built-up areas is extracted from local image windows. The random features are used to train a radial basis neural network to perform building classification; thus, learning and classification are carried out in the compressed sensing domain. By virtue of its ability to represent characteristics in a reduced dimensional space, the scheme shows promise in being robust in the face of variability inherent in urban remotely sensed images. Through a comparison of the proposed method with numerous state-of-the-art approaches utilizing remotely sensed data of different spatial resolutions and building clutter, we establish its robustness and prove its viability. Accuracy assessment is performed for segmented footprints, and comparative analysis is carried out in terms of intersection over union, overall accuracy, precision, recall, and F1 score. The proposed method achieved scores of 93% in overall accuracy, 90.4% in intersection over union, and 91.1% in F1 score, even when dealing with drastically different image features. The results demonstrate that the proposed methodology yields substantial enhancements in classification accuracy and decreases in feature dimensionality. Full article

The incidence of cognitive disorders is increasing globally, with a reported prevalence of over 50 million individuals affected, and current interventions offer limited efficacy. This study investigates the effects of sunflower oil fortified with sunflower lecithin, vitamin D, and vitamin A on scopolamine-induced cognitive dysfunction in mice and explores the underlying mechanisms. The incidence of cognitive disorders, such as Alzheimer’s disease, is increasing yearly, and current interventions offer limited efficacy. Therefore, this research aims to evaluate the cognitive improvement effects of the three added functional factors on mice with learning and memory impairments, along with the associated molecular mechanisms. Behavioral tests, biochemical assays, and real-time quantitative polymerase chain reaction (RT-qPCR) were utilized to examine the intervention effects of these functional factors on scopolamine-induced cognitive impairment in mice. The results revealed that the groups treated with sunflower lecithin and vitamin D significantly enhanced the mice’s exploratory behavior, working memory, and spatial memory, with increases of 1.6 times and 4.5 times, respectively, in the open field and novel object recognition tests (VD group). Additionally, these treatments reduced levels of inflammatory markers and IL-6, increased antioxidant GSH levels, and decreased oxidative stress marker MDA levels, with all effects showing significant differences (p < 0.01). The effects were further enhanced when vitamin A was combined with these treatments. Transcriptomic analysis demonstrated that the intervention groups had markedly improved learning and memory abilities through upregulation of key gene expression levels in the PI3K-AKT signaling pathway, cholinergic pathway, and folate biosynthesis pathway. These findings provide a theoretical basis for the development of nutritionally fortified edible oils with added sunflower lecithin, vitamin D, and vitamin A, which may help prevent and ameliorate cognitive disorders. Full article