Search Results (24,279)

Background/Objectives: Mindfulness-based interventions (MBIs) have emerged in recent years as a strong candidate for the treatment of a range of difficulties faced by individuals with autism spectrum disorder (ASD), including cognitive, emotional, and social aspects. Therefore, we aimed to conduct a review that systematically examined the efficacy of MBIs for individuals with ASD and their caregivers. Methods: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our literature search was conducted within the MEDLINE database. We included in the review only longitudinal or intervention studies focusing mainly on mindfulness principles, while we excluded mixed intervention studies. We only included studies that explicitly utilized quantitative methodologies for evaluating the outcomes of the interventions, including mental health indices (e.g., stress, anxiety, depression) and assessments of cognitive and social skills (e.g., attention, prosociality). We conducted also a risk of bias assessment through the method of the Cochrane risk of bias tool for intervention studies ROBINS-I. Results: Thirty-seven studies were included in the review, and we grouped the studies by the targets of the interventions, i.e., adults (n = 12), children and adolescents (n = 9), caregivers and medical staff (n = 13), and combined intervention for both children/adolescents and their parents/caregivers (n 5). The reviewed papers seem to support the feasibility and utility of mindfulness interventions for persons with ASD and their caregivers, but any recommendations based on this body of evidence should be made with caution due to the overall low quality of the studies conducted so far. Conclusions: The review reveals a positive outcome, including the alleviation of psychological distress, reduced behavioral problems, and enhanced cognitive and social skills in individuals with ASD. Despite such promising results, the review notes a limitation in the scarcity of MBIs for young patients, emphasizing caution in universally endorsing the existing literature. Moreover, the results underline the urgency of the exploration of tailored interventions for different ASD subgroups, considering varying levels of autism, and expanding support to teachers in educational settings. Full article

Klebsiella pneumoniae is an opportunistic pathogen known for two main pathotypes: classical K. pneumoniae (cKp), often multidrug-resistant and common in hospitals, and hypervirulent K. pneumoniae (hvKp), associated with severe community-acquired infections. The recent emergence of strains combining hypervirulence and resistance is alarming. This study investigates the distribution of sequence types (STs), resistance, and virulence factors in K. pneumoniae strains causing bloodstream and urinary tract infections in Croatia. In 2022, 200 consecutive K. pneumoniae isolates were collected from blood and urine samples across several Croatian hospitals. Whole genome sequencing was performed on 194 isolates. Within the analyzed K. pneumoniae population, the distribution of sequence types was determined with multi-locus sequence typing (MLST) and capsule loci, resistance, and virulence determinants were assessed with the bioinformatics tool Kleborate. The analysis identified 77 different STs, with ST101 (24.6%) being the most prevalent, predominantly linked to the K17 capsular type (CT), invasive device usage, high antimicrobial resistance, and low virulence scores. The highest virulence scores were recorded in ST86 isolates, which were predominantly linked to the K2 CT and included some strains with medium resistance scores. String tests were positive in 19 strains, but only four of those harbored hypermucoviscous genetic determinants. The most prevalent ST101 clone in Croatia demonstrated a diverging association between resistance and virulence. An alarming co-existence of resistance and virulence was recorded in the ST86 strains. Full article

Sustainability reporting is an essential tool for companies and organizations to assess and communicate their sustainability performance. Its importance derives from the need for transparency and accountability to stakeholders, including investors, customers, employees and local communities. The principles underlying an effective sustainability report include integrity, accuracy, comparability and clarity. The main purpose of the sustainability report is to provide a clear view of the economic, social and environmental impact of the organization’s activities, contributing to continuous performance improvement and alignment with international standards. The objective of our study is to analyze sustainability reporting and ESG (environment, social, governance) reporting, key steps and methods for measuring and reporting sustainability, and the sustainability policy criteria and reporting frameworks used internationally (GRI, IIRC, SASB, TCFD, ISO 26000, ISO 14016) in order to identify areas for further development to improve the relevance and effectiveness of sustainability reporting. The results obtained from our study enable a better understanding of how an organization reports its social, economic, and environmental impact, the differences in compliance with the international standards used, the main steps, and sustainability criteria followed. Our research highlights the necessary actions and steps through which sustainability reporting can become a more effective and relevant tool, contributing to real sustainable development and more responsible resource management. The usefulness of this report is reflected in many aspects: it facilitates informed decision making, enhances trust and reputation, helps identify risks and opportunities, and supports sustainable business strategies. The sustainability report is not only a means of reporting but also a catalyst for positive change, promoting responsible business practices and contributing to global sustainable development. Full article

Abstract: Background: Precise continuous feeding of active pharmaceutical ingredients (APIs) and excipients is crucial in a continuous powder-to-tablet manufacturing setup, as any inconsistency can affect the final tablet quality. Method: This study investigated the impact of various materials on the performance of a continuous twin-screw loss-in-weight (LIW) feeder. The materials tested included spray-dried lactose, anhydrous lactose, granulated lactose, microcrystalline cellulose (MCC), an MCC–lactose preblend (50%:50% w/w ratio), and a co-processed excipient (lactose–lactitol at a 95%:5% w/w ratio). The feeding performance of these excipients was systematically assessed, focusing on powder densification and screw layering within the LIW feeder. Results: The results demonstrated densification for the spray-dried lactose and preblend. Densification was more pronounced during the initial feeding cycles for spray-dried lactose, but decreased gradually over time. In contrast, the densification remained relatively constant throughout the feeding process for the preblend. Notably, minor screw layering was observed for both spray-dried lactose and anhydrous lactose, with the extent of this issue reducing over time for the spray-dried lactose. Interestingly, granulated lactose grades did not show screw layering, making them preferable for blending with APIs prone to severe screw layering. The LIW feeder control system successfully managed powder densification and minor screw layering, maintaining the mass flow rate at the set point for all investigated materials. Conclusions: These findings inform the selection of optimal excipients, appropriate tooling for LIW feeders, and the enhancement of control strategies to shorten startup times. By addressing these factors, the precision and reliability of continuous feeding processes can be improved. Full article

Low-intensity, diversified agricultural land use is needed to counteract the current decline in agrobiodiversity. Landscape ecology tools can support agrobiodiversity assessment efforts by investigating biodiversity-related ecological functions (pattern–process paradigm). In this study, we test a toolkit of landscape ecology analyses to compare different farm management models: polyculture agroforestry (POLY) vs. conventional monoculture crop management (CV). Farm-scale analyses are applied on temperate alluvial sites (Po Plain, Northern Italy), as part of a broader multi-scale analytical approach. We analyze the landscape ecological quality through landscape matrix composition, patch shape complexity, diversity, metastability, and connectivity indices. We assess farm differences through multivariate analyses and t-tests and test a farm classification tool, namely, a scoring system based on the relative contributions of POLY farms, considering their deviation from a local CV baseline. The results showed a separate ecological behavior of the two models. The POLY model showed better performance, with significant positive contributions to the forest and semi-natural component equipment and diversity; agricultural component diversity, metastability; total farm diversity, metastability, connectivity, and circuitry. A reference matrix for the ecological interpretation of the results is provided. Farm classification provides a quick synthesis of such contributions, facilitating farm comparisons. The methodology has a low cost and quickly provides information on ongoing ecological processes resulting from specific farm management practices; it is intended to complement field-scale assessments and could help to meet the need for a partially outcome-based assessment of good farm practice. Full article

The modern analytical technique of Fourier-transform mid-infrared spectroscopy (FT-MIR) has found its place in routine wine quality control. It allows rapid and nondestructive analysis, with easy sample preparation and without the need for chemical pretreatment or expensive reagents. The objective of this research was to apply these advantages to fruit wines in order to create a tool for the authentication of fruit wines produced from different fruit species (chokeberry, blackberry, and raspberry). The aim of this work was to establish a chemometric model from FT-MIR spectra and to find a “fingerprint” of specific fruit wines, enabling the classification of fruit wines by plant species. Physicochemical analysis of 111 Croatian fruit wine samples (38 liqueur fruit wines and 73 fruit wines) revealed content levels of the following parameters: alcoholic strength (5.0–15.2% vol.), total dry extract (60.4–253.3 g/L), total sugars (1.2–229.9 g/L), pH (3.13–4.98), total acidity (4.2–18.3 g/L) and volatile acidity (0.2–1.5 g/L). For statistical data processing, spectral ranges between 926 and 1450 cm⁻¹ and between 1801 and 2951 cm⁻¹ were used. The first principal component (PC1) explained 70.4% of the observed variation, and the second component (PC2) explained 16.7%, clearly separating chokeberry fruit wines from blackberry and raspberry fruit wines. Soft Independent Modeling Class Analogy (SIMCA) was performed following the development of a PCA model showing that the chokeberry and blackberry wine samples form clearly separated clusters. Key discriminators for classifying chokeberry vs. blackberry wines were identified at 1157, 1304, and 1435 cm⁻¹, demonstrating high discrimination power (DP 26, 17, and 14, respectively). FT-MIR spectroscopy, in combination with chemometric methods, has shown promising potential for the authenticity assessment of fruit wines. Full article

The aerospace industry faces the challenge of maintaining product excellence amidst intricate processes and demands for cost and time efficiency. Mathematical modeling emerges as a valuable tool for optimizing the engineering process and enhancing quality, with potential applications extending beyond aerospace to other sectors with high quality and safety standards. This study develops and validates a mathematical model specific to the aerospace industry, aiming to assess the impact of human resource expertise on product quality. Through a case study within an aerospace organization, an IDEF0-methodology-based mathematical model, coupled with weighted averages, was constructed to depict the comprehensive engineering process and quantify knowledge’s impact on deliverable quality. Simulation data, gathered through human resource knowledge assessments and non-conformity analyses, revealed a direct correlation between technical knowledge levels and deliverable quality, consequently impacting final product quality. The proposed model serves as a tool for estimating potential deliverable error rates and pinpointing critical areas within the process that necessitate refinement. The research underscores the significance of knowledge investment and effective knowledge management strategies in upholding quality and competitiveness across industries with stringent quality requirements. Full article

Background/Objectives: Topologically associating domains (TADs) are key structural units of the genome, playing a crucial role in gene regulation. TAD boundaries are enriched with specific biological markers and have been linked to genetic diseases, making consistent TAD detection essential. However, accurately identifying TADs remains challenging due to the lack of a definitive validation method. This study aims to develop a novel algorithm, termed coiTAD, which introduces an innovative approach for preprocessing Hi-C data to improve TAD prediction. This method employs a proposed “circle of influence” (COI) approach derived from Hi-C contact matrices. Methods: The coiTAD algorithm is based on the creation of novel features derived from the circle of influence in input contact matrices, which are subsequently clustered using the HDBSCAN clustering algorithm. The TADs are extracted from the clustered features based on intra-cluster interactions, thereby providing a more accurate method for identifying TADs. Results: Rigorous tests were conducted using both simulated and real Hi-C datasets. The algorithm’s validation included analysis of boundary proteins such as H3K4me1, RNAPII, and CTCF. coiTAD consistently matched other TAD prediction methods. Conclusions: The coiTAD algorithm represents a novel approach for detecting TADs. At its core, the circle-of-influence methodology introduces an innovative strategy for preparing Hi-C data, enabling the assessment of interaction strengths between genomic regions. This approach facilitates a nuanced analysis that effectively captures structural variations within chromatin. Ultimately, the coiTAD algorithm enhances our understanding of chromatin organization and offers a robust tool for genomic research. The source code for coiTAD is publicly available, and the URL can be found in the Data Availability Statement section. Full article

Visual Impact Assessment (VIA) is a critical tool in managing cultural heritage, evaluating the impacts of development and construction projects on the visual aspects of heritage values. However, VIA is often constrained by subjectivity, low public participation, and a lack of generalizability. This study aims to develop a methodological framework for a more objective and comprehensive assessment of visual impacts on cultural heritage. The study establishes criteria for indicators based on the value attributes of cultural heritage, develops an assessment indicator system, and integrates visual sensitivity assessment through multi-factor calculations with visual perception assessment using the AHP method and questionnaire surveys. This constructs an assessment framework that combines both objectivity and subjectivity. The Shangri-La Hotel East Building project at the World Heritage site of the West Lake Cultural Landscape of Hangzhou, China, is employed as a case study to empirically demonstrate the framework’s practicality and effectiveness. The results indicate that the Shangri-La East Building significantly impacts the attributes of the West Lake. The conclusion demonstrates that the indicator system, grounded in the attributes of cultural heritage, enhances the framework’s applicability across different contexts. The integrated assessment framework, which includes both a quantitative assessment of visual sensitivity and a public-participation-based assessment of visual perception, is shown to be effective in predicting the visual impacts of proposed projects on heritage values. The study also underscores the importance of Heritage Impact Assessment as a preliminary evaluation. Full article

As we transition into the era of Web 3.0, where decentralized information and user privacy are paramount, emerging technologies are reshaping the way in which personal data are managed. This paper focuses on decentralized identity management (DID) in the metaverse, particularly within the education sector, which has rapidly embraced digital tools for e-learning, especially since the COVID-19 pandemic. Technologies such as blockchain, artificial intelligence (AI), and virtual and augmented reality (VR/AR) are increasingly integrated into educational platforms, raising questions about privacy, security, and interoperability. This literature review examines the current landscape of DID in metaverse-based educational applications. Through a systematic methodology, relevant academic papers were identified, filtered, and analyzed based on four key criteria: standardization, interoperability, application scalability, and security/privacy considerations. The paper provides a comparative analysis of these papers to assess the maturity of DID implementations, highlight existing challenges, and suggest future research directions in the intersection of decentralized identity and educational metaverse applications. Full article

Background: With the increasing prevalence of pregnant women adhering to a vegan diet, gaining insight into their nutritional intake and its association with maternal and fetal outcomes is essential to providing recommendations and developing guidelines for general practice. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review of the available scientific literature in Medline, Embase, and Cochrane was conducted in January 2024. Results: The titles and abstracts of 2211 unique articles were screened. Only six studies were eligible for inclusion and assessed for methodological quality using the (National Institutes of Health Study Quality (NIHSQ) Assessment Tool. The intake of protein and various micronutrients was significantly lower among vegan pregnant women compared to omnivorous women. Vitamin B12 supplements seemed sufficient in optimizing maternal and umbilical cord vitamin B12 levels amongst vegan mothers. Further, women on a vegan diet less often showed excessive pregnancy weight gain. However, children from women on a vegan diet had a significantly lower birth weight than those from women on an omnivorous diet. Conclusion: So far, only a few studies, with a large diversity of (assessment of) outcomes and insufficient power, have been published on this topic, limiting our ability to make firm conclusions about the effects of a vegan diet during pregnancy on maternal and fetal outcomes. Full article

This study aimed to identify the characteristics and challenges faced by children with disabilities and their families living in rural Thailand, and understand the factors that bring changes in their lives through intervention. In a 2021 online survey on the quality of life of school-aged children with disabilities, approximately half of the respondents indicated that they were not enrolled in school. However, the factors responsible were not identified. Therefore, in 2023, we interviewed three children with disabilities in one of the target areas for the abovementioned survey to explore the correlation between the level of independence in the activities of daily living (ADL) and educational and rehabilitation opportunities. This study did not identify a clear correlation between the two factors. Furthermore, a hypothesis emerged that the changes in their lives are influenced more by environmental factors, especially personal factors such as social group awareness and attitudes, caregivers’ understanding and support, and individual relationships within the community. Further research is needed to develop environmental factor assessment tools for problem-solving in accordance with the regional characteristics of rural areas, assess the degree of functional independence of children with disabilities in daily life, and build regional support models. Full article

Today’s adolescents represent an elective target in addressing environmental challenges. Education is a key factor in achieving a sustainable future for them. However, formal education can represent a challenge when youths are its target, as they are considered the “interactive generation”. Game-based learning, and, in particular, Digital Educational Escape Rooms (DERs), have emerged as innovative methods in education, with promising applications in environmental sustainability studies. In this study, we developed 14 Digital Escape Rooms using the Social Cognitive Theory of Bandura as a theoretical framework. These were focused on environmental education and we tested them on a sample of 411 students (aged 12–18 years; 158 female, 38.4%). A one-group quasi-experimental research design was adopted, carrying out a pre-test post-test analysis. Each participant completed assessments at two time points: before engaging in the escape rooms (T0) and after (T1). The assessment tools included the Goal Assessment Scale (GAS) and the Perceived Climate Self-Efficacy Scale. Our findings revealed no significant gender differences in goal achievement. However, at T0, females exhibited higher levels of perceived climate self-efficacy in both individual and collective dimensions. Instead, the Digital Escape Rooms appeared particularly effective in increasing self-efficacy among male participants. These results suggest that Digital Escape Rooms hold potential for enhancing environmental self-efficacy, although gender differences in baseline efficacy levels warrant further exploration. Full article

Mycoplasma (M.) hyopneumoniae is the key pathogen of the porcine respiratory disease complex (PRDC) and contributes to pleurisy in pigs. Due to its limited metabolism and laborious cultivation, molecular tools are useful for diagnosis. This study investigated the genetic diversity of M. hyopneumoniae in slaughter pigs with pneumonia and pleurisy, and it assessed co-infections by Pasteurella multocida type A (PM), Actinobacillus pleuropneumoniae (APP), and swine influenza virus A (sIVA). Lungs (n = 70) with different pleurisy scores and lesions compatible with M. hyopneumoniae infection were collected for convenience. Macroscopic and microscopic evaluations were performed. M. hyopneumoniae was detected using qPCR, and MLST was used for genetic characterization. Co-infections with PM and APP were also evaluated by qPCR, while the immunohistochemistry assessed sIVA infection. All lungs were positive for M. hyopneumoniae. Histopathology confirmed M. hyopneumoniae-associated lesions. MLST characterization was possible in 25 lungs and revealed 10 distinct allelic profiles, with none matching known sequence types in the public database. Co-infections were detected in 40% of the samples with APP and 32% with PM, with 12% showing both pathogens and 52% of the samples presenting microscopic lesions compatible with sIVA infection. The diverse genetic profiles found underscore the need for research on isolation and potential pathogenic variations. Full article

The COVID-19 pandemic has significantly disrupted traditional medical training, particularly in critical areas such as the injection process, which require expert supervision. To address the challenges posed by reduced face-to-face interactions, this study introduces a multi-modal fusion network designed to evaluate the timing and motion aspects of the injection training process in medical education. The proposed framework integrates 3D reconstructed data and 2D images of hand movements during the injection process. The 3D data are preprocessed and encoded by a Long Short-Term Memory (LSTM) network to extract temporal features, while a Convolutional Neural Network (CNN) processes the 2D images to capture detailed image features. These encoded features are then fused and refined through a proposed multi-head self-attention module, which enhances the model’s ability to capture and weigh important temporal and image dynamics in the injection process. The final classification of the injection process is conducted by a classifier module. The model’s performance was rigorously evaluated using video data from 255 subjects with assessments made by professional physicians according to the Objective Structured Assessment of Technical Skill—Global Rating Score (OSATS-GRS)[B] criteria for time and motion evaluation. The experimental results demonstrate that the proposed data fusion model achieves an accuracy of 0.7238, an F1-score of 0.7060, a precision of 0.7339, a recall of 0.7238, and an AUC of 0.8343. These findings highlight the model’s potential as an effective tool for providing objective feedback in medical injection training, offering a scalable solution for the post-pandemic evolution of medical education. Full article