Search Results (92)

Sterlet is a perishable product; therefore, its freshness monitoring and shelf-life evaluation are important. In this study, a series of analytical techniques named physicochemical, microbiological, sensory, colorimetric, and mid-infrared and fluorescence spectroscopies were applied on Sterlet (Acipenser ruthenus) samples during 18 days of storage at 4 °C. The water content increased from 72.8 g/100 g on day 1 to 77.81 g/100 on day 14. Regarding the peroxide value (PV), the initial value was 4.17 meq/kg of Sterlet on day 1, reaching a maximum on day 4 (4.9 meq/kg of Sterlet), and then it decreased gradually, attaining a value of 0.7 meq/kg of Sterlet on day 18. Generally, the thiobarbituric acid reactive substance (TBARS), total viable count (TVC) and psychrotrophic count (PTC) increased during the storage time and increased from 0.03 to 0.13 MDA eq./kg of Sterlet sample, 2.27 to 9.09 log10 CFU/g, and 2.18 to 9.15 log10 CFU/g, respectively, on day 1 and 18, respectively. The microbiological and sensory analyses indicated that Sterlet samples were acceptable for human consumption up to 7 days of storage at 4 °C. This result was confirmed by fluorescence measurements, since the principal component analysis (PCA) applied to the NADH and MIR spectra allowed for a clear differentiation between Sterlet samples aged 7 days or less from the others. This trend was confirmed by the factorial discriminant analysis (FDA) applied to the NADH and MIR spectra, since a correct classification with leave-one cross-validation of 94.44% was observed. In addition, the heatmap of the Pearson correlation coefficients showed high correlations between overall acceptability and microbiology parameters and the structural properties of Sterlet samples during storage, indicating that the modifications observed at the macroscopic level were related to those notedat the molecular scale. Full article

Emotional exhaustion in children and adolescents has become a significant concern in post-pandemic educational settings, with increased risks of anxiety, depression, and academic disengagement. Despite the growing prevalence of burnout symptoms in school-aged populations, few psychometrically validated tools exist to assess this construct in younger cohorts. This study aimed to validate the factorial structure and psychometric properties of the Emotional Exhaustion Scale (EES-CA) for use in children and adolescents, focusing on reliability, internal structure, and convergent validity. An instrumental, cross-sectional study was conducted with a sample of 543 Chilean students aged 10 to 18 (M = 13.00, SD = 1.77). The EES-CA, adapted from the university-level Emotional Exhaustion Scale, was administered along with the Depression, Anxiety, and Stress Scale (DASS-21). Data were analyzed in the following four phases: descriptive analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and convergent validity. Reliability was estimated via Cronbach’s alpha and McDonald’s omega. EFA supported the following two-factor solution: Scholar Stress and Emotional Fatigue, explaining 58.49% of the total variance. CFA confirmed the superiority of the bifactorial model (χ² = 91.74, df = 34; CFI = 0.960; RMSEA = 0.072) over the unifactorial model (χ² = 133.20, df = 35; CFI = 0.932; RMSEA = 0.093). The internal consistency was strong (α = 0.888; ω = 0.883). The convergent validity for the EES-CA showed low correlations with wellbeing (PWI) and non-significant correlations with emotional intelligence (TMMS-24), supporting discriminant validity. The EES-CA exhibits a robust bifactorial structure with high reliability and valid associations with psychological distress measures. This scale is an appropriate and psychometrically sound instrument for assessing emotional exhaustion in school-aged populations, providing a valuable tool for early detection and intervention in educational and mental health contexts. Full article

This study examines urban flood vulnerability in Tetouan city, Northern Morocco, using four machine learning models—Classification and Regression Tree (CART), Support Vector Machine (SVM), Logistic Regression (LR), and Factorial Discriminant Analysis (FDA)—to identify and map flood-prone areas. The primary goal is to enhance flood prevention efforts and minimize losses by determining the most vulnerable zones. The analysis highlights consistent flood risk along the Martil River and eastern plains, areas characterized by low-lying topography, dense drainage, proximity to canals, and recent urban development. Despite some spatial variation among the models, all consistently indicate low and very high vulnerability zones, with FDA identifying the highest proportion of very high risk areas (58%), followed by CART, SVM, and LR (39%, 38%, and 37%, respectively). In terms of model accuracy, SVM and LR outperform others, demonstrating their effectiveness in flood risk delineation. The findings offer valuable insights for urban planners and decision-makers in flood risk management, contributing to more informed resource allocation in Tetouan-Martil and potentially guiding similar strategies in comparable regions globally. Full article

The present study aimed to translate, culturally adapt, and present a psychometric validation for the Everyday Memory Questionnaire—Revised (EMQ-R) to the Portuguese population. The study involved 267 participants aged between 18 and 75 years (M = 39.32; SD = 14.8), recruited online. Self-report measures of anxiety and depression symptoms were administered to assess the instrument’s convergent validity. To examine the factorial structure of the measure, a two-step validation process was employed. Given the uncertainty about the optimal measurement model, the sample was randomly divided into two independent subsamples. First, a principal component analysis (PCA) was conducted to explore the factorial structure. Next, a confirmatory factor analysis (CFA) was performed to validate the identified structure. The results supported a unidimensional structure consisting of 12 items, suggesting that perceived memory difficulties are best represented as a single overarching factor. High reliability was observed for this structure (Cronbach’s alpha and McDonald’s omega values ≥ 0.90). The results also indicated that general memory complaints were moderately correlated with symptoms of anxiety and depression. Furthermore, the study highlighted the promising potential of the measure as a screening tool for detecting subjective memory complaints, with an optimal cut-off score of 16 points. Future studies should focus on validating the EMQ-R with clinical samples, exploring its discriminative ability, and examining the stability of the cut-off score across different populations and contexts. Full article

This study was conducted to assess the antioxidant potential of polyphenolic extracts from Rhizophora mucronata and Avicennia marina as natural preservatives in comparison with synthetic butylated hydroxytoluene (BHT) and rosemary extract. Antioxidant activities were assessed by 2,2-diphenyl-1-picrylhydrazyl (DPPH), oxygen radical absorbance capacity (ORAC), and total phenolic content (TPC). Extracts were blended into linseed oil and evaluated for oxidative stability using a 15-day Schaal oven test. Physicochemical analyses, including peroxide value (PV), acid index (AI), p-anisidine value (p-AnV), and thiobarbituric acid reactive substances (TBARS), showed that mangrove-treated oils exhibited the highest stability against oxidation compared to the negative and positive controls. R. mucronata mature leaves presented the highest DPPH inhibition (93.40%) and the lowest TBARS value (0.33 ± 0.0 mg MDA/kg of oil) on day 11. Fluorescence spectroscopy provided complementary and valuable information. Statistical analysis using factorial discriminant analysis (FDA) achieved a classification accuracy of 91.43%, underlining the different oxidative profiles of the treated samples. These findings demonstrated the potential of extracts from mangrove plants as a sustainable alternative to synthetic antioxidants for food preservation. Future studies should explore broader food applications using advanced analytical techniques to optimize their efficiency and performing a series of toxicity evaluations. Full article

Objectives: The objective of this study was to develop and validate the short version of The Joint Assessment of Equilibrium and Neuromotor Status Scale (JAEN scale) for use in women with Fibromyalgia Syndrome (FMS) to make the balance disorder measurement process more efficient. Methods: A cross-sectional observational validation study was conducted. Fifty-six women with FMS and forty-four healthy controls were included. Certain items from the original tool were selected with the aim of (1) improving internal consistency by reducing item redundancy and (2) obtaining a diagnostic capacity with an area under the ROC curve (AUC) greater than 0.70 for discriminating FMS patients and fallers. The internal consistency, factorial validity, concurrent validity and diagnostic capacity of the new tool were analyzed. Results: Factorial analysis showed a two-factor structure that explained 72% of the variance. Cronbach alpha coefficients of 0.904 were obtained for the total score of the JAEN-10 items. Concurrent validity analysis showed strong correlations of the JAEN-10 with other instruments that measured quality of life, postural balance or disability related to dizziness. The score of the JAEN-10 items showed an AUC of 0.858 with a sensitivity of 64.29 and a specificity of 95.45 for discriminating between FMS and healthy controls, and an AUC of 0.835 with a sensitivity of 90.48 and a specificity of 67.24 for discriminating between fallers and non-fallers. Conclusions: The 10-item JAEN scale is a valid instrument for discriminating between subjects with or without FMS and between fallers and non-fallers. Its psychometric properties are good and are similar to those of the original 20-item scale. Moreover, it is quicker to complete, which may be relevant for subjects with a tendency to experience fatigue. Full article

This study aimed to evaluate the influence in torrefaction of the chemical structure of biomasses from nine poplar commercial SRC clones, evaluated through analytical pyrolysis. The chemical data were integrated into a dataset including LHV gain, representative of torrefaction aptitude and six chemical variables obtained through analytical pyrolysis, which were: (i) CH₂Cl₂ extractives; (ii) total extractives; (iii) Py-lignin; (iv) holocellulose; (v) (syringil/guaiacyl) ratio; and (vi) (pentosan/hexosan) ratio. Significant univariate and bivariate linear relations were obtained with LHV gain from torrefaction as dependent variable vs. Py-lignin, CH₂Cl₂ extractives and (cP/cH) ratio. Representative results were: (i) a negative correlation of −0.82 and −0.76 between LHV gain and the (pentosan/hexosan) ratio and Py-lignin, respectively, and (ii) a positive correlation of 0.79 between LHV gain and CH₂Cl₂ extractive amounts. Factorial and discriminant analysis allowed for clustering the tested clones in three groups, evidencing relevant influences of (S/G) ratio, Py-lignin, and, to a lesser extent, (cP/cH) ratio in the classification of these groups, clearly showing the impact of chemical variables of feedstock in torrefaction. The results contribute: (i) to the validation of use of the expedite analytical pyrolysis technique for classification of biomasses in accordance with their torrefaction aptitude and, thereby, (ii) to optimizing strategies in technological issues as diverse as poplar clone genetic breeding and modeling biomass torrefaction and pyrolysis. Full article

Automated sandbox-based analysis systems are dominantly focused on sequences of API calls, which are widely acknowledged as discriminative and easily extracted features. In this paper, we argue that an extension of the feature set beyond API calls may improve the malware detection performance. For this purpose, we apply the Cuckoo open-source sandbox system, carefully configured for the production of a novel dataset for dynamic malware analysis containing 22,200 annotated samples (11,735 benign and 10,465 malware). Each sample represents a full-featured report generated by the Cuckoo sandbox when a corresponding binary file is submitted for analysis. To support our position that the discriminative power of the full-featured sandbox reports is greater than the discriminative power of just API call sequences, we consider samples obtained from binary files whose execution induced API calls. In addition, we derive an additional dataset from samples in the full-featured dataset, whose samples contain only information on API calls. In a three-way factorial design experiment (considering the feature set, the feature representation technique, and the random forest model hyperparameter settings), we trained and tested a set of random forest models in a two-class classification task. The obtained results demonstrate that resorting to full-featured sandbox reports improves malware detection performance. The accuracy of 95.56 percent obtained for API call sequences was increased to 99.74 percent when full-featured sandbox reports were considered. Full article

Olive leaves are a rich source of polyphenols with healthful properties and represent one of the most abundant waste products of olive oil production. The aims of this study were to explore the phenolic composition of olive leaves from the three main Tuscan cultivars (Leccino, Moraiolo and Frantoio) collected in Siena and Grosseto provinces and to investigate the possible use of these compounds as varietal and geographic origin markers. Discriminant factorial analysis (DFA) was used for distinguishing between different cultivars and locations. Apigenin and caffeoyl-secologanoside showed significant differences between cultivars. DFA showed that ligstroside, apigenin and luteolin have the most influence in determining the differences between sites, whereas total polyphenols, olacein and hydroxytyrosol acetate allowed for separation between leaves from the same province. The results of the present study indicate that concentrations of phenolic compounds, measured through high-resolution mass spectrometry, can be used as a marker for both the cultivar and of geographical origin of olive leaves, and possibly of olive-related products, as well as across small geographic scales (less than 50 km distance between sites). Full article

(1) Background: Parents of children with autism spectrum disorders often experience psychological distress, which can affect the quality of childcare they provide. It is crucial to screen for psychiatric symptoms among these parents. The core symptom index (CSI) is a widely recognized tool used to assess general symptoms, including depression, anxiety, and somatic issues. It has proven validity and reliability across diverse Thai populations. Given the cultural similarities between Thai and Chinese populations, the CSI has been successfully implemented within the Chinese population. Nevertheless, it is crucial to research its validity and reliability in the general Chinese population. This study aimed to investigate the psychometric properties of the Chinese version of the CSI among parents of children with autism spectrum disorders using confirmatory factor analysis (CFA). (2) Methods: A total of 794 Chinese parents raising children with autism participated in this study. All completed the CSI, along with the social inhibition subscale of the Interpersonal Problems Inventory and the Couple Satisfaction Index. Factorial validity was assessed using CFA to determine how well the bifactor three-factor model fits the data. Various structural models were compared using model fit indices. Convergent and discriminant validity were examined by exploring correlations with the social inhibition subscale and the Couple Satisfaction Index. Invariance testing of the CSI was conducted across multiple groups based on gender, age, and education using CFA. The reliability of the CSI was evaluated using McDonald’s omega coefficients. (3) Results: The bifactor model emerged as the best-fitting model for the data, suggesting that the total score of the CSI adequately represents overall psychiatric symptoms. The CSI exhibited significant correlations with the social inhibition subscale (r = 0.41, p < 0.01) and smaller correlation coefficients with the Couple Satisfaction Index (r = −0.16, p < 0.05), indicating both convergent and discriminant validity. The invariant test results support scalar invariance levels based on gender and age but only partial invariance for education. The Chinese version of the CSI demonstrated high consistency, with McDonald’s omega coefficients ranging between 0.86 and 0.95. (4) Conclusions: The bifactor model of the Chinese version of the CSI is validated, making it a suitable tool for measuring depression, anxiety, and somatization symptoms among parent(s) of children with autism spectrum disorders. Further research on other Chinese populations is encouraged. Full article

Developing valid and reliable measures of psychological responses to climate change is of high importance, as this facilitates our understanding of people’s psychological responses, including their pro-environmental behavior. Recently, the Climate Change Worry Scale (CCWS) was introduced. This study aimed to develop the first Polish version of the CCWS and explore its psychometric properties. Our sample comprised 420 Polish adults aged 18–70, with a mean age of 26.20 (standard deviation = 10.61) years. The CCWS’s factor structure was assessed with confirmatory factor analysis. McDonald’s omega and Cronbach’s alpha coefficients were computed to assess internal consistency reliability. Pearson correlations between climate change worry (CCW) and experience of climate change (i.e., an individual’s level of perception of being affected by climate change), pro-environmental behavior, ill-being (i.e., anxiety and depression symptoms), and well-being were calculated. Our results support the strong factorial validity of the CCWS, conforming to its intended one-factor solution, with excellent internal consistency reliability for the total scale score (i.e., McDonald’s omega and Cronbach’s alpha values of 0.93). We noted large positive correlations between CCW and experiences of climate change, as well as pro-environmental behavior, and medium positive correlations with psychopathology symptoms. CCW scores were not associated with well-being. As the CCWS represents a measure of a specific manifestation of worry, we also examined its discriminant validity against more general psychological distress markers, and it evidenced strong validity in this regard. Overall, the Polish version of the CCWS appears to have strong psychometric properties, and will therefore be a useful tool to use in research on psychological responses to climate change. Full article

Fleas (Siphonaptera) are ectoparasitic hematophagous insects responsible for causing bites and itchy skin conditions in both humans and animals. Furthermore, they can act as vectors of different pathogens of a wide variety of diseases worldwide, including bartonellosis, rickettsiosis, and bubonic plague. Accurate identification of fleas is necessary for the study of their epidemiology, prevention, and control. In addition to traditional morphological classification approaches and molecular biology techniques, geometric morphometrics is increasingly proving to be a useful complementary tool for discriminating between Siphonaptera taxa. With the objective of determining the capacity of this technique to identify and differentiate synanthropic fleas, a principal component analysis was carried out on populations of Ctenocephalides felis, Pulex irritans, and Archaeopsylla erinacei collected in distinct regions of Andalusia (Spain). The analysis carried out on 81 male and female specimens revealed factorial maps that allowed the differentiation of the populations under study, with only partial overlaps that did not prevent their correct identification. Global size differences were also detected, with a slightly larger size in P. irritans males and a bigger size in A. erinacei females. Therefore, the present study emphasizes the role of geometric morphometrics as a useful complementary technique in taxonomic studies of arthropods, especially in the case of flea specimens lacking representative morphological features. Full article

Background: Medical education represents a complex field of study, influenced by various psychological, demographic, and contextual factors. Academic motivation, essential for educational success, has been linked to critical decisions in medical careers and can be modulated by contextual elements such as socioeconomic and geographical environments. The theory of self-determination has provided a solid framework for understanding the multidimensional nature of motivation. Objective: To evaluate the psychometric properties of a Spanish version of the Short Scale of Academic Motivation among Peruvian medical students. Methods: Using an instrumental design, the factorial structure, reliability, and gender invariance of the SAMS-S were assessed. A confirmatory factor analysis (CFA) was conducted to validate the scale’s structure based on seven dimensions. Additionally, reliability was assessed through Cronbach’s alpha coefficient and omega coefficient, and gender invariance was determined through multi-group confirmatory factor analysis. Results: The Peruvian version of the SAMS-S showed a good fit in the CFA with satisfactory goodness-of-fit indices. However, challenges in discriminant validity among certain dimensions were detected, suggesting the presence of a second-order factor. The proposed second-order model yielded an adequate fit (χ² = 198.26, df = 70, CFI = 0.92, TLI = 0.90, RMSEA = 0.08 [90% CI: 0.07–0.1], SRMR = 0.07), validating the factorial structure of the SAMS-S. The scale’s reliability and its subscales were within acceptable ranges. Furthermore, the gender invariance of the SAMS-S was confirmed at all levels, from configural to strict. Conclusions: The second-order model of the SAMS-S presents as a valid and reliable tool for measuring academic motivation among medical students in Peru. Its robustness and adaptability make it relevant for future research in similar educational contexts and can serve as a basis for interventions aimed at improving academic motivation in this specific group. Full article

The coronavirus disease 2019 (COVID-19) pandemic had a meaningful impact on several areas of human activity. With respect to psychological assessment, the requirements to study the fear of vaccination as a means to diminish negative behaviour towards vaccination had been reported. This study aimed to evaluate the factorial invariance of the six-item Vaccination Fear Scale (VFS-6) across individuals and cultures. To achieve this goal, a sample of university students was recruited (n = 2535; mean age = 20.59, SD = 2.04; males: 26.75%, females: 73.25%) from Spain (n = 388; 15.3%), Italy (n = 376; 14.83%), Lebanon (n = 487; 19.21%), Nigeria (n = 561; 22.13%), Turkey (n = 410; 16.17%), and Ukraine (n = 313; 12.34%). The results showed that the most appropriate factorial structure, exhibiting excellent fit indices, was a model with two correlated factors (cognitive symptoms: items 1, 2, and 4; somatic symptoms: items 3, 5, and 6) for both the total sample and individual samples from each country and language (Spanish, Italian, Arabic, English, Turkish, and Ukrainian). Notably, the VFS-6 demonstrated configural, metric, scalar, and strict invariance across sex. Regarding countries and languages, configural invariance was observed between them. Also, metric invariance was observed between Spain, Italy, and Ukraine and between Lebanon, Nigeria, and Turkey, which indicates the presence of two well-differentiated groups of countries and the possibility of inferential analysis between them. Item Response Theory analysis suggested an appropriate level of discrimination and difficulty of the test. These significant findings lay the groundwork for future investigations into vaccination fear across diverse cultural backgrounds, providing valuable insights for addressing vaccination-related concerns worldwide. Full article

The number and diversity of groundwater bodies (GWBs) in large French administrative regions pose challenges to their monitoring and protection by regional health agencies. To overcome this obstacle, we propose, for the Auvergne-Rhône-Alpes region (about 70,000 km²), a grouping of GWBs into homogeneous groups based on the sources of variability within a large dataset of groundwater physico-chemical and bacteriological characteristics (8078 observations and 13 parameters). This grouping involved a dimensional reduction in the data hyperspace by principal component analysis (PCA) and a clustering based on the mean values of each GWB on the factorial axes. The information lost when clustering from the sample point scale to the GWB scale and then to that of the GWB group was quantified by analysis of variance and showed that grouping GWBs is accompanied by a small loss of information. A discriminant analysis confirmed the high spatial and temporal variability within the dataset, as well as the effectiveness of the proposed method for establishing homogeneous sets. Some roadmaps for more targeted monitoring of water resources were briefly proposed. Full article