Search Results (8,990)

Background/Objectives: Chronic obstructive pulmonary disease (COPD) is a significant public health concern, affecting millions of people worldwide. This study aims to use Surface-Enhanced Raman Scattering (SERS) technology to detect the presence of respiratory conditions, with a focus on COPD. Methods: The samples of human serum from 41 patients with respiratory diseases (11 patients with COPD, 20 with bronchial asthma (BA), and 10 with asthma–COPD overlap syndrome) and 103 patients with ischemic heart disease, complicated by chronic heart failure (CHF), were analyzed using SERS. A multivariate analysis of the SERS characteristics of human serum was performed using Partial Least Squares Discriminant Analysis (PLS-DA) to classify the following groups: (1) all respiratory disease patients versus the pathological referent group, which included CHF patients, and (2) patients with COPD versus those with BA. Results: We found that a combination of SERS characteristics at 638 and 1051 cm⁻¹ could help to identify respiratory diseases. The PLS-DA model achieved a mean predictive accuracy of 0.92 for classifying respiratory diseases and the pathological referent group (0.85 sensitivity, 0.97 specificity). However, in the case of differentiating between COPD and BA, the mean predictive accuracy was only 0.61. Conclusions: Therefore, the metabolic and proteomic composition of human serum shows significant differences in respiratory disease patients compared to the pathological referent group, but the differences between patients with COPD and BA are less significant, suggesting a similarity in the serum and general pathogenetic mechanisms of these two conditions. Full article

Musicians have an advantage in recognizing vocal emotions compared to non-musicians, a performance advantage often attributed to enhanced early auditory sensitivity to pitch. Yet a previous ERP study only detected group differences from 500 ms onward, suggesting that conventional ERP analyses might not be sensitive enough to detect early neural effects. To address this, we re-analyzed EEG data from 38 musicians and 39 non-musicians engaged in a vocal emotion perception task. Stimuli were generated using parameter-specific voice morphing to preserve emotional cues in either the pitch contour (F0) or timbre. By employing a neural decoding framework with a Linear Discriminant Analysis classifier, we tracked the evolution of emotion representations over time in the EEG signal. Converging with the previous ERP study, our findings reveal that musicians—but not non-musicians—exhibited significant emotion decoding between 500 and 900 ms after stimulus onset, a pattern observed for F0-Morphs only. These results suggest that musicians’ superior vocal emotion recognition arises from more effective integration of pitch information during later processing stages rather than from enhanced early sensory encoding. Our study also demonstrates the potential of neural decoding approaches using EEG brain activity as a biological sensor for unraveling the temporal dynamics of voice perception. Full article

Background/Objectives: The Chinese soft-shelled turtle (Pelodiscus sinensis) is an important species in freshwater aquaculture. Genetic admixture and degradation due to rapid industry expansion threaten sustainable development. This study aims to assess the genetic diversity and structure of six P. sinensis populations for better management. Methods: We combined morphological analysis and microsatellite markers to evaluate the genetic diversity of six populations. A discriminant function based on morphology was developed, achieving 71.4% classification accuracy. Two SSR markers were identified to specifically distinguish the HS population. Results: The six populations were classified into three subgroups. Frequent gene flow was observed among the CY, W, and DT populations, with most genetic variation occurring within individuals. However, significant genetic differentiation was detected between populations. While gene flow enhanced diversity, it suppressed differentiation. Conclusions: This study provides insights into the genetic structure and diversity of six P. sinensis populations. The discriminant function and SSR markers offer a basis for germplasm conservation and management, supporting sustainable aquaculture development. Full article

For potato external defect detection, ordinary spectral technology has limitations in detail detection and processing accuracy, while the machine vision method has the limitation of a long feedback time. To realize accurate and rapid external defect detection for red-skin potatoes, a non-destructive detection method using hyperspectral imaging and a machine learning model was explored in this study. Firstly, Savitzky–Golay (SG), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), the normalization algorithm, and different preprocessing algorithms combined with SG were used to preprocess the hyperspectral data. Then, principal component regression (PCR), support vector machine (SVM), partial least squares regression (PLSR), and least squares support vector machine (LSSVM) algorithms were used to establish quantitative models to find the most suitable preprocessing algorithm. The successive projections algorithm (SPA) was used to obtain various characteristic wavelengths. Finally, the qualitative models were established to detect the external defects of potatoes using the machine learning algorithms of backpropagation neural network (BPNN), k-nearest neighbors (KNN), classification and regression tree (CART), and linear discriminant analysis (LDA). The experimental results showed that the SG–SNV fusion hyperspectral data preprocessing algorithm and the KNN machine learning model were the most suitable for the detection of external defects in red-skin potatoes. Moreover, multiple external defects can be detected without multiple models. For healthy potatoes, black/green-skin potatoes, and scab/mechanical-damage/broken-skin potatoes, the detection accuracy was 93%,93%, and 83%, which basically meets the production requirements. However, enhancing the prediction accuracy of the scab/mechanical-damage/broken-skin potatoes is still a challenge. The results also demonstrated the feasibility of using hyperspectral imaging technology and machine learning technology to detect potato external defects and provided new insights for potato external defect detection. Full article

Bench-scale geotechnical characterization often suffers from high uncertainty, reducing confidence in geotechnical analysis on account of expensive resource development drilling and mapping. The Measure-While-Drilling (MWD) system uses sensors to collect the drilling data from open-pit blast hole drill rigs. Historically, the focus of MWD studies was on penetration rates to identify rock formations during drilling. This study explores the effectiveness of Artificial Intelligence (AI) classification models using MWD data to predict geotechnical categories, including stratigraphic unit, rock/soil strength, rock type, Geological Strength Index, and weathering properties. Feature importance algorithms, Minimum Redundancy Maximum Relevance and ReliefF, identified all MWD responses as influential, leading to their inclusion in Machine Learning (ML) models. ML algorithms tested included Decision Trees, Support Vector Machines (SVMs), Naive Bayes, Random Forests (RFs), K-Nearest Neighbors (KNNs), Linear Discriminant Analysis. KNN, SVMs, and RFs achieved up to 97% accuracy, outperforming other models. Prediction performance varied with class distribution, with balanced datasets showing wider accuracy ranges and skewed datasets achieving higher accuracies. The findings demonstrate a robust framework for applying AI to real-time orebody characterization, offering valuable insights for geotechnical engineers and geologists in improving orebody prediction and analysis Full article

Bacterial biofilms play an important role in the pathogenesis of infectious diseases but are also very relevant in other fields such as the food industry. This fact has led to an increased focus on the early identification of these structures as prophylaxes to prevent biofilm-related contaminations or infections. One of the objectives of the present study was to assess the effectiveness of NIR (Near Infrared) spectroscopy in the detection and differentiation of biofilms from different bacterial species, namely Staphylococcus epidermidis, Staphylococcus aureus, Enterococcus faecium, Salmonella Typhymurium, Escherichia coli, Listeria monocytogenes, and Lactiplantibacillus plantarum. Additionally, we aimed to examine the capability of this technology to specifically identify S. aureus biofilms on glass surfaces commonly used as storage containers and processing equipment. We developed a detailed methodology for data acquisition and processing that takes into consideration the biochemical composition of these biofilms. To improve the quality of the spectral data, SNV (Standard Normal Variate) and Savitzky–Golay filters were applied, which correct systematic variations and eliminate random noise, followed by an exploratory analysis that revealed significant spectral differences in the NIR range. Then, we performed principal component analysis (PCA) to reduce data dimensionality and, subsequently, a Random Forest discriminant statistical analysis was used to classify biofilms accurately and reliably. The samples were organized into two groups, a control set and a test set, for the purpose of performing a comparative analysis. Model validation yielded an accuracy of 80.00% in the first analysis (detection and differentiation of biofilm) and 93.75% in the second (identification of biofilm on glass surfaces), thus demonstrating the efficacy of the proposed method. These results demonstrate that this technique is effective and reliable, indicating great potential for its application in the field of biofilm detection. Full article

Recently, fault detection has played a crucial role in ensuring the safety and reliability of inverter operation. Switch failures are primarily classified into Open-Circuit (OC) and short-circuit faults. While OC failures have limited negative impacts, prolonged system operation under such conditions may lead to further malfunctions. This paper demonstrates the effectiveness of employing Artificial Intelligence (AI) approaches for detecting single OC faults in a Packed E-Cell (PEC) inverter. Two promising strategies are considered: Random Forest Decision Tree (RFDT) and Feed-Forward Neural Network (FFNN). A comprehensive literature review of various fault detection approaches is first conducted. The PEC inverter’s modulation scheme and the significance of OC fault detection are highlighted. Next, the proposed methodology is introduced, followed by an evaluation based on five performance metrics, including an in-depth comparative analysis. This paper focuses on improving the robustness of fault detection strategies in PEC inverters using MATLAB/Simulink software. Simulation results show that the RFDT classifier achieved the highest accuracy of 93%, the lowest log loss value of 0.56, the highest number of correctly predicted estimations among the total samples, and nearly perfect ROC and PR curves, demonstrating exceptionally high discriminative ability across all fault categories. Full article

Distance protection is widely applied in AC transmission systems. It may operate incorrectly under power swings, so a power swing blocking unit (PSBU) is needed to work with the distance protection relay. Such a unit should not only block the protection relay in time when a power swing occurs, but also deblock the protection relay after detecting a fault during the power swing. In this paper, a method that satisfies these requirements is proposed. To discriminate between power swings and faults, the characteristics of three-phase voltage under a power swing and fault situation are used. Principal Component Analysis (PCA) is applied to extract and quantify the characteristics. To detect faults during power swings, an index is proposed, and the change rate of the index is used to form the criterion. Simulations for different kinds of power swing and fault situations are conducted based on a two-end system and a nine-bus system in PSCAD/EMTDC. The simulation test results indicate that the proposed method can block the protection relay reliably under a power swing and deblock the relay quickly after detecting a fault during the power swing. Moreover, the proposed method is compared with other methods. The comparison results show that the proposed method has an advantage in terms of response speed and is less affected by measurement noise. Full article

Refugees face post-migration stressors during resettlement in host countries, including forced separation from loved ones. This qualitative study aimed to examine the impact of family separation on refugee men living in the United Kingdom. Data were collected through in-depth interviews and analysed following the Interpretative Phenomenological Analysis framework. Participants described the emotional burden of family separation, alongside a perceived responsibility to support their families practically, emotionally, and financially. Men shared experiences of powerlessness, discrimination, and acculturation in the UK, and associated their experiences with time and context. Participants’ stories were embedded in their intersectional identities of masculinity, race, sexuality, religion, and migration status. Policymakers should consider the unique challenges male refugees separated from their families face in the UK in order to implement positive changes in the asylum system. Clinicians working with refugees and asylum-seekers should inform their assessment, formulation, and intervention approaches. Full article

Terminal drought is the major constraint for chickpea production, leading to yield losses of up to 90% in tropical environments. Understanding the morphological, phenological, and physiological traits underlying drought tolerance is crucial for developing resilient chickpea genotypes. This study elucidates the drought-tolerant traits of eight kabuli chickpea genotypes under a controlled environment using polyvinyl chloride (PVC) lysimeters. Terminal drought was imposed after the flowering stage, and the response was assessed against non-stress (well-watered) treatment. Drought stress significantly impacted gas-exchange parameters, reducing the stomatal conductance (16–35%), chlorophyll content (10–22%), carbon assimilation rate (21–40%) and internal carbon concentration (7–14%). Principal component analysis (PCA) indicated three groups among these eight genotypes. The drought-tolerant group included two genotypes (AVTCPK#6 and AVTCPK#19) with higher water use efficiency (WUE), deep-rooted plants, longer maturity, and seed yield stability under drought stress. In contrast, the drought-susceptible group included two genotypes (AVTCPK#1 and AVTCPK#12) that were early-maturing and low-yielding with poor assimilation rates. The intermediate group included four genotypes (AVTCPK#3, AVTCPK8, AVTCPK#24, and AVTCPK#25) that exhibited medium maturity and medium yield, conferring intermediate tolerance to terminal drought. A significantly strong positive correlation was observed between seed yield and key physiological traits (stomatal conductance (gsw), leaf chlorophyll content (SPAD) and carbon assimilation rate (A_sat)) and morphological traits (plant height, number of pods, and root biomass). Conversely, carbon discrimination (Δ¹³C) and intrinsic WUE (iWUE) showed a strong negative correlation with seed yield, supporting Δ¹³C as a surrogate for WUE and drought tolerance and a trait suitable for the selection of kabuli chickpea genotypes for drought resilience. Full article

Chemicals in personal care and consumer products are suspected to disrupt endocrine function and affect reproductive health. However, the link between mixed exposure and long-term amenorrhea is not well understood. This study analyzed data from 684 women (2013–2018 National Health and Nutrition Examination Survey) to assess exposure to eight polyfluorinated alkyl substances (PFASs), 15 phthalates (PAEs), six phenols, and four parabens. Various statistical models for robustness tests and mediation analysis were used to explore associations with long-term amenorrhea and the role of serum globulin. Biological mechanisms were identified through an integrated strategy involving target analysis of key chemicals and long-term amenorrhea intersections, pathway analysis, and target validation. Results showed that women with long-term amenorrhea had higher exposure levels of Perfluorodecanoic acid, Perfluorohexane sulfonic acid (PFHxS), Perfluorononanoic acid, n-perfluorooctanoic acid (n_PFOA), n-perfluorooctane sulfonic acid, and Perfluoromethylheptane sulfonic acid isomers. Logistic regression with different adjustments consistently found significant associations between elevated PFAS concentrations and increased long-term amenorrhea risk, confirmed by Partial Least Squares Discriminant Analysis. Mediation analysis revealed that serum globulin partially mediated the relationship between PFAS exposure and long-term amenorrhea. Network and target analysis suggested that PFHxS and n_PFOA may interact with Signal Transducer and Activator of Transcription 3 (STAT3). This study highlights significant associations between PFAS exposure, particularly PFHxS and n_PFOA, and long-term amenorrhea, with serum globulin and STAT3 serving as mediators in the underlying mechanisms. Full article

Cardiovascular diseases (CVDs) remain a leading cause of global morbidity and mortality. Metabolomics allows for the identification of important biomarkers for CVDs, essential for early detection and risk assessment. This cross-sectional study aimed to identify novel metabolic biomarkers associated with CVDs using non-targeted metabolomics. We compared the metabolic profiles of 112 patients with confirmed CVDs diagnosis and 112 gender- and age-matched healthy controls from the Qatar Biobank database. Orthogonal partial least square discriminate analysis and linear models were used to analyze differences in the level of metabolites between the two groups. We report here a significant association between the indoleacetylglutamine pathway and cardiovascular diseases, expanding the repertoire of gut microbiota metabolites linked to CVDs. Our findings suggest that alterations in gut microbiota metabolism, potentially resulting in increased production of indoleacetate, indoleacetylglutamine, and related compounds at the expense of the cardioprotective indolepropionate, may contribute to this association. Our findings may pave the way for novel approaches in CVD risk assessment and potential therapeutic interventions targeting the gut-heart axis. Full article

Individuals with high psychological resilience cope with stress more effectively. It is crucial to select a suitable psychological resilience tool for workers in high-risk industries to identify and help those with lower resilience early on, protecting their health and reducing accidents. The CD-RISC-10 is widely used, and this study assessed its validity and reliability among Chinese construction workers, focusing on workers on elevated platforms. A total of 325 valid CD-RISC-10 scales were collected and analyzed using statistical methods, such as exploratory factor analysis, confirmatory factor analysis, and K-means cluster analysis. The results show that the CD-RISC-10 can effectively measure psychological resilience with a high scale reliability of 0.857, and it had an acceptable model fit (CFI = 0.947) and good item discrimination. About 17.23% of the measured sample of Chinese workers working at height were identified as having resilience impairments, and demographic variables such as age, length of service, educational level, and accident experience had a significant impact on the level of resilience, revealing the heterogeneity of the workers. This study validated the measurement validity of the CD-RISC-10 scale among Chinese high-place workers, and the analysis results were conducive to conducting psychological resilience assessments, improving workers’ occupational health, and promoting the sustainable development of construction enterprises. Full article

The acquisition of kinetic data in QD-based PL sensing methodologies has been revealed to be an auspicious alternative in applying these nanomaterials in analytical chemistry, enabling enhanced discrimination and quantification of analytes, even in complex sample matrices. The accessibility of kinetic measurements, which use routine laboratory instrumentation, is a significant advantage that increases the practicality of this methodology. The simple acquisition of these kinds of second-order data combined with chemometric analysis can ensure accurate results in environmental, biomedical, and food monitoring applications. These developments emphasize the vital importance of kinetic approaches in increasing sensitivity, improving analyte discrimination, and making the application of QDs in complex samples possible. Full article

Background/Objectives: Maternal nutrition during pregnancy exerts a significant influence on both maternal and foetal health, as well as long-term child development. Despite its importance, adherence to dietary guidelines among pregnant women remains low. The present study aimed to develop and validate the Italian Pregnancy Nutrition Knowledge Questionnaire (ItPreNKQ), based on national dietary guidelines for the pregnant Italian population, assessing its reliability and validity. Methods: The ItPreNKQ comprised 15 questions covering key topics on nutrition during pregnancy. The questionnaire was validated through item analysis (difficulty and discrimination indices), construct validity, internal consistency, and reliability tests. Results: A total of 145 pregnant Italian women participated in the study. The reliability of the questionnaire was confirmed through a Pearson’s correlation of R = 0.790 and a Cronbach’s alpha of 0.682, indicating strong temporal stability and acceptable internal consistency. Despite good overall performance, the mean knowledge score was 10.6 ± 2.5 out of 15, indicating significant knowledge gaps in specific topics. Conclusions: The ItPreNKQ has been demonstrated to be a reliable and valid tool for the assessment of nutrition knowledge among pregnant Italian women. The tool could be used for assessing nutritional knowledge in prenatal education settings and could be administered in future studies aimed at evaluating the impact of nutritional interventions among pregnant women. Full article