Search Results (99)

Background: Surgical site infections (SSIs) are healthcare-associated infections that can occur following surgical procedures, either at admission or within 30 days post-discharge. This study aimed to assess the incidence and associated risk factors for superficial SSI at a Tertiary Hospital in Ethiopia. Methods: A longitudinal study was conducted among patients undergoing surgery at Jimma University Specialized and Comprehensive Hospital (JUSCH) from 1 June to 30 September 2022. Pus, wound swab, or abscess samples were inoculated on Blood and MacConkey Agar for culture. Bacterial isolates were identified using MALDI-TOF, antimicrobial susceptibility testing was performed using the Kirby–Bauer disc diffusion method, and the results were interpreted according to EUCAST 2022 breakpoints. Incidence rates, Kaplan–Meier analysis, extended Cox regression, and violin plots were utilized to analyze and present the findings. Results: Among 1205 participants, 629 (52.2%) were male, and the median age was 27 years (IQR: 16–40). The incidence of SSI was 9.2 per 1000 person-days. Most SSIs occurred during hospitalization (81.1%), and the remaining primarily developed within the first week post-discharge. The culture positivity rate was 72.7%, yielding 252 isolates comprising 36 bacterial species. The most frequently identified organisms were E. coli (22.2%), Acinetobacter (20.2%), and Klebsiella (14.7%). Over 67% of Gram-negative bacteria were ESBL producers. Age, gender, residence, hospital ward, surgery area, emergency surgery, longer hospitalization, and the number of staff attending the surgery were identified as important risk factors. Conclusions: This study revealed a high incidence of SSI during hospitalization, with significant proportion identified post-discharge. The high rates of multidrug-resistant Gram-negative pathogens underscore the urgent need for comprehensive infection prevention and control measures. Full article

Background/Objectives: This pilot study employed a quasi-experimental, single-group, pre-post design to examine the acute effects of single music lessons on executive function and to explore whether affective changes are associated with cognitive improvement in trained musicians. Drawing on Fredrickson’s broaden-and-build theory and Eysenck’s processing efficiency theory, we hypothesized that changes in positive affect and state anxiety would be statistically associated with cognitive outcomes. Methods: Using purposive sampling, 60 musicians (34 female, 26 male; Mage = 26.0, SD = 9.8; range: 16–58 years) completed assessments before and after a 45–60 min instrumental lesson (guitar, n = 20; violin, n = 20; piano, n = 20). Executive function was measured using the Stroop Color-Word Test (Golden version, Greek-validated). Affective states were assessed using the Positive and Negative Affect Schedule (PANAS; 20 items) and State-Trait Anxiety Inventory-State (STAI-S; 20 items). Data were analyzed using paired t-tests, Pearson correlations, path analysis, and bootstrap mediation analysis (5000 resamples). Results: Music lessons were associated with improved executive function (Stroop interference: d = 0.59, p < 0.001), increased positive affect (d = 1.87, p < 0.001), and reduced negative affect (d = −2.34, p < 0.001) and state anxiety (d = −2.64, p < 0.001). Path analysis demonstrated excellent model fit (CFI = 1.00; RMSEA = 0.00), with affective changes associated with 61.3% of the total effect on cognitive improvement. Conclusions: Single music lessons were associated with both cognitive and affective benefits, with affective changes statistically linked to cognitive outcomes. As a pilot study, these exploratory findings require replication using controlled designs before generalization. Future research should incorporate neuroimaging methods and cross-cultural validation. Full article

It is known that a mare’s body temperature drops before parturition, but no research has yet applied this thermal change to the prediction of foaling. In this study, the ventral tail base surface temperature (VTB-ST) was recorded by a tail-attached device equipped with a thermistor in pregnant mares kept in an outdoor paddock all day. The objective of the present study was to make an algorithm for predicting nocturnal foaling (18:00 to 6:00) and to verify the accuracy of the algorithm. Prediction of nocturnal foaling was performed at 15:00 every day. The foaling prediction model was validated using 147 days of data recorded from 22 mares. The sensitivity of the foaling prediction model proposed in this study was 68.2 to 81.8% and the precision was 51.4 to 62.5%. To our knowledge, the present study is the first one to establish an algorithm for predicting nocturnal foaling at a specific time interval using VTB-ST. Further study will be necessary to improve the foaling prediction model, as the accuracy of the algorithm proposed in this study was considered to be insufficient for practical use in stud farms. Full article

Music performance anxiety (MPA) can be examined within the framework of music performance ecosystems, wherein performers’ musical self-concept is shaped through complex social and cultural interactions. This research aims to identify ecosystemic interactions contributing to the emergence of MPA, and to promote a shift in its understanding, from a focus on individual symptomatology to the recognition of collective influences. A qualitative analysis was conducted using biographical-narrative interviews with 11 established musical performers (six female, five male), aged 23 to 62, representing a range of instruments, including violin, viola, cello, flute, piano, harp, and voice. Their experience encompasses solo, chamber, and orchestral performance, as well as pedagogical work, all rooted in the traditional educational framework of Western classical music. The analysis, based on the grounded theory methodology, highlights four key dimensions: the demanding stage; the development of psychological resilience in performers; the musical self-concept as a reflection of the performer’s experience; and performance as a process of transformation. The findings suggest that an ecosystemic perspective may support preventive interventions for MPA, particularly by reframing the traditional virtuoso performance model. Often internalized early in music education, this prestigious ideal continues to shape professional careers, placing heavy self-reflective demands on performers. To support healthier artistic development, music performance ecosystems can bridge the gap between skill acquisition and performer attunement. This is not merely a divide between learning and performing, but a structural loop in which training-oriented processes—such as analysis, self-criticism, and control—may hinder stage performance if not transformed into holistic, embodied execution. Full article

Background: Accurate assessment of pediatric foot biomechanics is challenging due to growth-related variability and limited quantitative tools. The supination and pronation angles of the ankle are critical for understanding lower limb alignment and pathological gait patterns. Objectives: This study introduces a novel digital pedoscopic system designed to enhance the quantitative evaluation of foot alignment and to demonstrate its clinical utility through clustering analysis of pediatric ankle angles. Methods: Thirty-five pediatric patients (mean age = 6.17 ± 4.54 years) with neurological or developmental disorders were evaluated using a semi-automated digital pedoscopic system to obtain quantitative measurements of ankle alignment. Key anatomical landmarks, including the heel, calf, and knee centers, were manually identified from posterior images, and the system automatically calculated ankle pronation and supination angles. K-means clustering analysis was applied to classify participants based on their biomechanical profiles. Results: A total of thirty-five pediatric patients were assessed, and the revised abstract now explicitly reports this sample size to improve clarity. Data-driven k-means clustering of bilateral rearfoot angles identified three clearly defined alignment subgroups—neutral, pronated, and supinated—each exhibiting characteristic distribution patterns and degrees of inter-individual variability. These findings highlight the system’s ability to quantitatively distinguish biomechanical phenotypes within a heterogeneous pediatric population. Visualization through scatter, box, and violin plots demonstrated distinct cluster-specific distributions and inter-individual variability in rearfoot alignment, demonstrating the feasibility of objective biomechanical stratification in pediatric populations. Conclusions: The digital pedoscopic imaging system provides a reliable and reproducible approach for quantitative assessment of foot alignment in children. Clustering analysis enables stratification of biomechanical subtypes, supporting individualized rehabilitation strategies and longitudinal monitoring in pediatric clinical practice. Full article

This paper explores the dynamic behavior of different wood species in the form of violin boards, based on experimental modal analysis using a single-input, multiple-output configuration. Thus, two groups of species were studied: the first group for the violin top plates, being analyzed Picea abies (spruce), Taiwania cryptomerioides Hayata (Taiwania), and Cryptomeria japonica (Japanese cedar), and the second group, with species for the back plates, such as Acer pseudoplatanus (maple), Populus nigra (poplar), Salix alba (willow), and Firmiana simplex (Chinese parasol). The results highlighted the frequency spectrum and the dominant resonance frequency, as well as the frequency damping, the signal processing analysis being based on Fast Fourier Transform and Wigner–Ville distribution of signals. The results highlighted that the lowest values of acoustic radiation are recorded for maple wood (7.8 m⁴ kg⁻¹ s⁻¹) and Taiwania (10.08 m⁴ kg⁻¹ s⁻¹), and the highest values for spruce (14.7 m⁴ kg⁻¹ s⁻¹) and Chinese parasol (15.58 m⁴ kg⁻¹ s⁻¹). Regarding the resonance frequency, the Taiwania and Japanese cedar plates present the dominant frequency around 600–635 Hz in comparison with Norway spruce having 920 Hz. The ratios between dominant frequencies of the Chinese parasol, poplar, maple, and willow are 1:1.42:2.62:2.98. It can be concluded that spruce and maple wood present the best dynamic properties, but when using other species, Japanese cedar wood for the top plate and Chinese parasol wood for the back plate represent species with potential in the construction of stringed musical instruments. Either a mechano-thermal treatment or an appropriate finish can enhance the acoustic qualities of these wood species, research that can be undertaken in the future. Full article

Dimensionality reduction is fundamental for analyzing high-dimensional data, supporting visualization, denoising, and structure discovery. We present a systematic, large-scale benchmark of three widely used methods—Principal Component Analysis (PCA), Isometric Mapping (Isomap), and t-Distributed Stochastic Neighbor Embedding (t-SNE)—evaluated by average silhouette scores to quantify cluster preservation after embedding. Our full factorial simulation varies sample size $n\in \{100,200,300,400,500\}$, noise variance ${\sigma }^2\in \{0.25,0.5,0.75,1,1.5,2\}$, and feature count $p\in \{20,50,100,200,300,400\}$ under four generative regimes: (1) a linear Gaussian mixture, (2) a linear Student-t mixture with heavy tails, (3) a nonlinear Swiss-roll manifold, and (4) a nonlinear concentric-spheres manifold, each replicated 1000 times per condition. Beyond empirical comparisons, we provide mathematical results that explain the observed rankings: under standard separation and sampling assumptions, PCA maximizes silhouettes for linear, low-rank structure, whereas Isomap dominates on smooth curved manifolds; t-SNE prioritizes local neighborhoods, yielding strong local separation but less reliable global geometry. Empirically, PCA consistently achieves the highest silhouettes for linear structure (Isomap second, t-SNE third); on manifolds the ordering reverses (Isomap > t-SNE > PCA). Increasing ${\sigma }^2$ and adding uninformative dimensions (larger p) degrade all methods, while larger n improves levels and stability. To our knowledge, this is the first integrated study combining a comprehensive factorial simulation across linear and nonlinear regimes with distribution-based summaries (density and violin plots) and supporting theory that predicts method orderings. The results offer clear, practice-oriented guidance: prefer PCA when structure is approximately linear; favor manifold learning—especially Isomap—when curvature is present; and use t-SNE for the exploratory visualization of local neighborhoods. Complete tables and replication materials are provided to facilitate method selection and reproducibility. Full article

The segmentation and classification of color are crucial stages in image processing, computer vision, and pattern recognition, as they significantly impact the results. The diverse, hand-labeled datasets in the literature are applied for monochromatic or color segmentation in specific domains. On the other hand, synthetic datasets are generated using statistics, artificial intelligence algorithms, or generative artificial intelligence (AI). This last one includes Large Language Models (LLMs), Generative Adversarial Neural Networks (GANs), and Variational Autoencoders (VAEs), among others. In this work, we propose VitralColor-12, a synthetic dataset for color classification and segmentation, comprising twelve colors: black, blue, brown, cyan, gray, green, orange, pink, purple, red, white, and yellow. VitralColor-12 addresses the limitations of color segmentation and classification datasets by leveraging the capabilities of LLMs, including adaptability, variability, copyright-free content, and lower-cost data—properties that are desirable in image datasets. VitralColor-12 includes pixel-level classification and segmentation maps. This makes the dataset broadly applicable and highly variable for a range of computer vision applications. VitralColor-12 utilizes GPT-5 and DALL·E 3 for generating stained-glass images. These images simplify the annotation process, since stained-glass images have isolated colors with distinct boundaries within the steel structure, which provide easy regions to label with a single color per region. Once we obtain the images, we use at least one hand-labeled centroid per color to automatically cluster all pixels based on Euclidean distance and morphological operations, including erosion and dilation. This process enables us to automatically label a classification dataset and generate segmentation maps. Our dataset comprises 910 images, organized into 70 generated images and 12 pixel segmentation maps—one for each color—which include 9,509,524 labeled pixels, 1,794,758 of which are unique. These annotated pixels are represented by RGB, HSL, CIELAB, and YCbCr values, enabling a detailed color analysis. Moreover, VitralColor-12 offers features that address gaps in public resources such as violin diagrams with the frequency of colors across images, histograms of channels per color, 3D color maps, descriptive statistics, and standardized metrics, such as ΔE76, ΔE94, and CIELAB Chromacity, which prove the distribution, applicability, and realistic perceptual structures, including warm, neutral, and cold colors, as well as the high contrast between black and white colors, offering meaningful perceptual clusters, reinforcing its utility for color segmentation and classification. Full article

The sustainable management of water resources requires experts and also citizens who understand the hydrosphere and its key functions. To educate the public about water-related issues, various water literacy concepts have been developed. However, many of these concepts are too complex for people to understand. In contrast, the ocean literacy framework effectively translates knowledge into behavioral changes and actions. The Danube River, known as the world’s most international river, has a catchment area shared by 19 countries. This river basin has experienced unprecedented landscape alterations, floods, droughts, and pollution events, highlighting the need for a new approach to environmental education. Additionally, globally, more people live near rivers than by the ocean. To empower members of riverside communities with water literacy, we aimed to adapt the ocean literacy principles into river literacy principles. In this study, we introduce a novel concept of river literacy, consisting of seven principles. This framework aims to support sustainable development goals through education and to restore and revive damaged freshwater habitats more effectively. The principles were tested in formal education across five countries. The results indicate that participants in river literacy programs became more motivated to protect rivers, and their understanding of fluvial geography and riverine pollution improved. Full article

This paper introduces the Unit-Modified Weibull (UMW) distribution, a novel probability model defined on the unit interval $(0, 1)$. We derive its key statistical properties and estimate its parameters using the maximum likelihood method. The performance of the estimators is assessed via a simulation study based on mean squared error, coverage probability, and average confidence interval length. To evaluate the practical utility of the model, we analyze three real-world data sets. Both parametric and nonparametric goodness-of-fit techniques are employed to compare the UMW distribution with several well-established competing models. In addition, nonparametric diagnostic tools such as total time on test transform plots and violin plots are used to explore the data’s behavior and assess the adequacy of the proposed model. Results indicate that the UMW distribution offers a competitive and flexible alternative for modeling bounded data. Full article

Non-destructive evaluation techniques using infrared and terahertz waves were employed to examine an aged violin and an inlaid dish. The results suggest that active thermography can rapidly reveal the general features of deterioration, while optical coherence tomography and THz imaging visualise cross-sectional images by scanning. These techniques are complementary and provide useful information for conservation planning. Full article

Indonesia has 13.43 million hectares of tropical peatlands, the largest in Southeast Asia, which are crucial for carbon sequestration. This function is influenced by vegetation nutrient content, particularly carbon (C), nitrogen (N), phosphorus (P), and potassium (K), which regulate biogeochemical cycles and peat formation. This study analyzed stoichiometric profiles of tree species in South Sumatra peatlands based on (1) C:N ratios across roots, stems, twigs, and leaves, and identified species with traits associated with high carbon sequestration potential, and (2) leaf N:P:K stoichiometry to infer nutrient limitations. Research was conducted in a 1-hectare primary peatland plot within the PT. Tri Pupa Jaya conservation area. C, N, P, and K contents were measured using Kjeldahl distillation, spectrophotometry, flame photometry, and the Walkley–Black method following acid digestion. Stoichiometric distribution was visualized with violin-box plots and species grouped through hierarchical clustering. Among 153 identified species, stems showed the highest mean C:N ratio (314.9 ± 210.8), while leaves had the lowest (29.7 ± 13.0). Species were grouped into three clusters by C:N ratios across four organs, with six in clusters 1 and 2 showing high carbon sequestration potential. Leaf N:P:K stoichiometry suggested nitrogen, phosphorus, or combined N + P limitations. Full article

Plastic pollution in marine environments poses a new global threat. Microplastics (MPs) can bioaccumulate in marine organisms, leading to oxidative stress (OS). This study investigates MP accumulation and associated OS responses in six invertebrate species (Bivalvia, Gastropoda, and Malacostraca) and three key fish species of the Bulgarian Black Sea ecosystems. The target hydrobionts were collected from nine representative coastal habitats of the northern and southern aquatory. MPs were quantified microscopically, and OS biomarkers (lipid peroxidation, glutathione, and antioxidant enzymes) were analyzed spectrometrically in fish liver and gills and invertebrate soft tissues (STs). The specific OS (SOS) index was calculated as a composite indicator of the ecological impact, incl. MP effects. The results revealed species-specific MP bioaccumulation, with the highest concentrations in Palaemon adspersus, Rathke (1837) (0.99 ± 1.09 particles/g ST) and the least abundance in Bittium reticulatum (da Costa, 1778) (0.0033 ± 0.0025 particles/g ST). In Sprattus sprattus (Linnaeus, 1758), the highest accumulation of MPs was present (2.01 ± 2.56 particles/g muscle). The correlation analyses demonstrated a significant association between MP counts and catalase activity in all examined species. The SOS index varied among species, reflecting different stress responses, and this indicated that OS levels were linked to ecological conditions of the habitat and the species-specific antioxidant defense potential to overcome multiple stressors. These findings confirmed the importance of environmental conditions, including MP pollution and the evolutionarily developed capacity of marine organisms to tolerate and adapt to environmental stress. This study emphasizes the need for novel approaches in monitoring MPs and OS to better assess potential ecological risks. Full article

Hypocalcemia is common in dairy cows within the first 72 h post-calving, and can be either clinical or subclinical. Early detection is critical, but traditional laboratory tests are time-consuming and cow-side tests remain costly. A classic symptom of hypocalcemia is reduced ear skin temperature, which has been explored as a diagnostic tool in a previous study, but was not recommended at the end. Additionally, ambient temperature was found to strongly influence ear skin temperature, complicating diagnosis. The present study investigates infrared thermography of the ear as a potential non-invasive method for helping in the detection of hypocalcemia in Holstein cows. In order to differ from the previous study, with the goal of improving diagnosis accuracy, this research analyzed the entire ear temperature using infrared imaging software. Ambient temperature was factored in by categorizing samples into two groups based on air temperature: colder (−1.6 to 14.6 °C) and hotter (15.3 to 31.2 °C). Forty-two cows were monitored during the perinatal period, with blood samples and thermographic images taken twice a day until 48 h after calving. This study found that the median surface temperature of the ear correlated strongly with environmental temperature (r = 0.806, p < 0.001) and weakly with blood ionized calcium levels (r = 0.310, p < 0.01). In colder air temperatures, ear surface temperature was significantly different between healthy and hypocalcemic cows (p = 0.014). Logistic regression models were used to assess ionized calcium status based on different combinations of ear surface temperature, its difference from air temperature, and days in milk. In hotter air temperatures, only ear surface temperature, with no other covariates, was able to generate a valid model (p = 0.029). In colder air temperatures, multiple combinations of those variables generated valid models (p < 0.05), with the difference between ear and air temperature, together with days in milk, performing the best. Thus, this study concluded that ear surface temperature obtained through infrared thermography, while not promising for warmer environments, does show application potential for helping in the detection of hypocalcemia in colder environments. Full article

Antimicrobial resistance is a threat to probiotic microorganisms due to their potential role in harboring and transmitting resistance genes. This study focuses on two Bifidobacterium species (B. animalis subsp. lactis and B. longum) by analyzing 657 Minimal Inhibitory Concentration (MIC) values extracted from research articles indexed in Scopus, PubMed, and Web of Science, published since 2014, and considering 17 different antibiotics. MIC values were used for descriptive statistical analysis (boxplots and violin plots) to evaluate both inter- and intraspecies distributions. The results showed an overall increase in MIC values compared to historical data, with B. longum exhibiting high resistance to tetracyclines and streptomycin—approximately 25% to 50% of the strains had MIC values > EFSA cut-offs. The violin plots revealed the presence of resistant subpopulations, particularly within B. longum. These findings support the relevance of longitudinal MIC analysis as a tool for detecting early shifts in antimicrobial susceptibility and highlight the importance of data-driven approaches for microbiological risk assessment in probiotic applications. Full article