Search Results (5,428)

Water buffalo (Bubalus bubalis) are a primary source of milk in Pakistan, where bovine mastitis is a significant health issue among cattle, leading to substantial economic losses. Staphylococcus aureus is a predominant pathogen associated with mastitis; however, a detailed molecular characterization of the strains in the country remains limited. We previously characterized mastitis strains from the Hazara division of Khyber Pakhtunkhwa, Pakistan. In this study, we investigated mastitis cases in the Peshawar division, including samples from both animals and human farm workers for comparison. Higher rates of mastitis (67.27% of animals) and sub-clinical mastitis (91.03% of positive animals) were identified in Peshawar than for those (34.55% and 75.31%, respectively) previously observed in Hazara. Methicillin-susceptible S. aureus (MSSA) belonging to clonal complex 9 (ST2454) were predominant. Methicillin-resistant S. aureus (MRSA) belonging to ST22 and ST8 were also detected in the Nowshera district. While no S. aureus colonization was observed among animal handlers, evidence of hand contamination suggests a potential route for pathogen spread. Low levels of antibiotic resistance were noted amongst isolates, but higher rates were seen in MRSA. This study presents only the second comprehensive molecular investigation of S. aureus isolated from buffalo mastitis in Pakistan and indicates a concerning rise in mastitis within the province. Full article

Virtual reality (VR) can enrich neuropsychological testing, yet the ergonomic trade-offs of its input modes remain under-examined. Seventy-seven healthy volunteers—young (19–29 y) and middle-aged (35–56 y)—completed a VR Trail Making Test with three pointing methods: eye-tracking, head-gaze, and a six-degree-of-freedom hand controller. Completion time, spatial accuracy, and error counts for the simple (Trail A) and alternating (Trail B) sequences were analysed in 3 × 2 × 2 mixed-model ANOVAs; post-trial scales captured usability (SUS), user experience (UEQ-S), and acceptability. Age dominated behaviour: younger adults were reliably faster, more precise, and less error-prone. Against this backdrop, input modality mattered. Eye-tracking yielded the best spatial accuracy and shortened Trail A time relative to manual control; head-gaze matched eye-tracking on Trail A speed and became the quickest, least error-prone option on Trail B. Controllers lagged on every metric. Subjective ratings were high across the board, with only a small usability dip in middle-aged low-gamers. Overall, gaze-based ray-casting clearly outperformed manual pointing, but optimal choice depended on task demands: eye-tracking maximised spatial precision, whereas head-gaze offered calibration-free enhanced speed and error-avoidance under heavier cognitive load. TMT-VR appears to be accurate, engaging, and ergonomically adaptable assessment, yet it requires age-specific–stratified norms. Full article

Network security courses rely on hands-on labs where students configure virtual Linux networks to practice attack and defense. Automated feedback is scarce because no standard exists for exchanging detailed configurations—interfaces, bridging, routing tables, iptables policies—between exercise software and large language models (LLMs) that could serve as tutors. We address this interoperability gap with an exercise-oriented YANG profile that augments the Internet Engineering Task Force (IETF) ietf-network module with a new network-devices module. The profile expresses Linux interface settings, routing, and firewall rules, and tags each node with roles such as linux-server or linux-firewall. Integrated into our LiNeS Cloud platform, it enables LLMs to both parse and generate machine-readable network states. We evaluated the profile on four topologies—from a simple client–server pair to multi-subnet scenarios with dedicated security devices—using ChatGPT-4o, Claude 3.7 Sonnet, and Gemini 2.0 Flash. Across 1050 evaluation tasks covering profile understanding (n = 180), instance analysis (n = 750), and instance generation (n = 120), the three LLMs answered correctly in 1028 cases, yielding an overall accuracy of 97.9%. Even with only minimal follow-up cues (≦3 turns) —rather than handcrafted prompt chains— analysis tasks reached 98.1% accuracy and generation tasks 93.3%. To our knowledge, this is the first exercise-focused YANG profile that simultaneously captures Linux/iptables semantics and is empirically validated across three proprietary LLMs, attaining 97.9% overall task accuracy. These results lay a practical foundation for artificial intelligence (AI)-assisted security labs where real-time feedback and scenario generation must scale beyond human instructor capacity. Full article

In this research, a single composite-type stretchable triboelectric nanogenerator (TENG) is proposed for efficient energy harvesting and handwriting recognition. The composite TENGs were fabricated by blending dielectric barium titanate (BT) and conductive carbon nanotubes (CNTs) in varying amounts into a styrene–butadiene rubber matrix. The energy harvesting efficiency depends on the type and amount of fillers, as well as their dispersion within the matrix. Stearic acid modification of BT enables near-nanoscale filler distribution, resulting in high energy conversion efficiencies. The composite achieved power efficiency, power density, charge efficiency, and charge density values of 1.127 nW/N, 8.258 mW/m³, 0.146 nC/N, and 1.072 mC/m³, respectively, under only 2% cyclic compressive strain at 0.85 Hz. The material performs better at low stress–strain ranges, exhibiting higher charge efficiency. The generated charge in the TENG composite is well correlated with the compressive stress, which provides a minimum activation pressure of 0.144 kPa, making it suitable for low-pressure sensing applications. A flat composite with dimensions of 0.02 × 6 × 5 cm³ can produce a power density of 26.04 W/m³, a charge density of 0.205 mC/m³, and an output voltage of 10 V from a single hand pat. The rubber composite also demonstrates high accuracy in handwriting recognition across different individuals, with clear differences in sensitivity curves. Repeated attempts by the same person show minimal deviation (<5%) in writing time. Additionally, the presence of reinforcing fillers enhances mechanical strength and durability, making the composite suitable for long-term cyclic energy harvesting and wearable sensor applications. Full article

Inclusive higher education requires educators who are not only willing to teach inclusively but who also have the skills to do so. This preliminary study offers a blueprint on how to examine the effectiveness of a three-day professional development program to strengthen the attitudes, self-efficacy, and inclusive didactics of educators. We propose a quasi-experimental design with pre-, post-, and follow-up measures, to measure the effect of the professional development program at three levels: attitudes (SACIE-R), self-efficacy (TEIP), and inclusive teaching practices (adapted Teaching Practices Questionnaire). The results, although preliminary, show a small but significant decrease in concerns toward inclusive education over time. Self-efficacy, on the other hand, showed a non-significant but consistent increase, especially at follow-up. In terms of teaching practices, significant improvements were observed in the teaching of basic skills, but not in dealing with diversity or differentiating for individual students. These preliminary findings seem to underline that short professional development programs, while contributing to increased confidence and certain didactic skills, are not sufficient to achieve lasting changes in attitudes and inclusive teaching strategies. This suggests that lasting impact likely requires structural follow-up, practical support, and strengthening the inclusive learning climate within higher education institutions. Full article

Introduction: It is widely acknowledged that good quality relationships between social workers and children are essential to children’s non-formal inclusion in child protection processes. However, research exploring the perspective of children has shown this relationship to be highly complex, particularly when children are taken into care. Methods: This paper combines insights from two qualitative participatory studies conducted in Italy, both developed out of a collaboration between university, professional social work associations, and the Italian association of care leavers. The two projects explored, respectively, the perspectives of social workers on the one hand and of children in care and care leavers on the other regarding their relationship. Outcomes: The data reveal the complexity of the relationship between children and social workers, showing how both share a mirrored perception of it. Social workers recognise children’s negative feelings toward them and see them as inevitable, especially in cases of tense family dynamics. A relationship marked by hostile attitudes, anger, and distrust not only fuels social workers’ emotional struggles but also makes it harder to engage children effectively. While hostile attitudes and mistrust are often seen as obstacles to positive engagement, we advocate for recognising them not as barriers to be overcome but as realities to be acknowledged and addressed openly. This approach can create space for both children and practitioners to explore alternative forms of agency, fostering more meaningful participation. Full article

Aromatic compounds are vital in both natural and synthetic chemistry, and they are traditionally sourced from non-renewable petrochemicals. However, plant biomass, particularly lignin, offers a renewable alternative source of aromatic compounds. Lignin, a complex polymer found in plant cell walls, is the largest renewable source of aromatic compounds, though its degradation remains challenging. Lignin can be chemically degraded through oxidation, acid hydrolysis or solvolysis. As an alternative, microorganisms, including fungi, could offer a sustainable alternative for breaking down lignin. The aromatic compounds released from lignin, by either microbial, chemical or enzymatic degradation, can be used by microorganisms to produce valuable compounds. Fungi possess unique enzymes capable of converting aromatic compounds derived from lignin or other sources into chemical building blocks that can be used in several industries. However, their aromatic metabolic pathways are poorly studied compared to bacterial systems. In the past, only a handful of genes and enzymes involved in the aromatic metabolic pathways had been identified. Recent advances in genomics, proteomics, and metabolic engineering are helping to reveal these metabolic pathways and identify the involved genes. This review highlights recent progress in understanding fungal aromatic metabolism, focusing on how Aspergillus niger converts plant-derived aromatic compounds into potentially useful products and the versatility of aromatic metabolism within the Aspergillus genus. Addressing the current knowledge gaps in terms of fungal pathways could unlock their potential for use in sustainable technologies, promoting eco-friendly production of chemical building blocks from renewable resources or bioremediation. Full article

Disability in basic and instrumental activities of daily living (BADL and IADL) reflects functional decline in older adults and can be associated with chronic conditions like type 2 diabetes (T2DM) and hypertension (SAH). This cross-sectional study utilized data from the 2013 and 2019 Brazilian National Health Surveys to investigate the associations between T2DM, SAH, and disability levels. Exposures were self-reported diagnoses and outcomes were classified as independent, moderate, or severe. Multivariable Poisson regression models, with robust variance estimates, estimated adjusted prevalence ratios (PRa), accounting for sociodemographic variables and the survey design. In 2013, the absence of diabetes and hypertension was associated with a lower prevalence (PRa = 0.70; 95% CI: 0.58–0.85) of moderate disability in BADL when compared with the presence of only one of the conditions. On the other hand, the coexistence of T2DM and SAH was associated with a higher prevalence (PRa = 1.39; 95% CI: 1.01–1.91). A similar result was found in 2019 with the addition that coexistence was also associated with a higher prevalence of severe disability in BADLs (PRa = 1.82; 95% CI: 1.59–2.07). For IADL, the absence of T2DM and SAH was associated with a lower prevalence of severe disability in 2013 and 2019 and a lower prevalence of moderate disability only in 2019. However, coexistence showed a higher prevalence in both degrees of disability and both years of the survey. These findings highlight the impact of T2DM and SAH on disability in older people. Therefore, it is crucial to develop targeted strategies for vulnerable subgroups to enhance functional independence in aging populations. Full article

This work presents and compares different methodologies for the joint optimisation of the fractional derivative order and the parameters of the right-hand-side neural network in Neural Fractional Differential Equation models. The proposed strategies aim to tackle the training difficulties typically encountered when learning the fractional order $\alpha$ together with the network weights. One approach is based on regulating the gradient magnitude of the loss function with respect to $\alpha$, encouraging more stable and effective updates. Another strategy introduces an online pre-training scheme, where the network parameters are initially optimised over progressively longer time intervals, while $\alpha$ is updated more conservatively using the full time trajectory. The study focuses only on a foundational setting with one-dimensional problems, and numerical experiments demonstrate that the proposed techniques improve both training stability and accuracy. Nonetheless, the issue of non-uniqueness in the optimal derivative order remains, particularly in less well-posed scenarios, suggesting the need for further research in data-driven modelling of fractional-order systems. Full article

Monitoring Peripheral Oxygen Saturation (SpO₂) is an important vital sign both in Intensive Care Units (ICUs), during surgery and convalescence, and as part of remote medical consultations after of the COVID-19 pandemic. This has made the development of new SpO₂-measurement tools an area of active research and opportunity. In this paper, we present a new Deep Learning (DL) combined strategy to estimate SpO₂ without contact, using pre-magnified facial videos to reveal subtle color changes related to blood flow and with no calibration per subject required. We applied the Eulerian Video Magnification technique using the Hermite Transform (EVM-HT) as a feature detector to feed a Three-Dimensional Convolutional Neural Network (3D-CNN). Additionally, parameters and hyperparameter Bayesian optimization and an ensemble technique over the dataset magnified were applied. We tested the method on 18 healthy subjects, where facial videos of the subjects, including the automatic detection of the reference from a contact pulse oximeter device, were acquired. As performance metrics for the SpO₂-estimation proposal, we calculated the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and other parameters from the Bland–Altman (BA) analysis with respect to the reference. Therefore, a significant improvement was observed by adding the ensemble technique with respect to the only optimization, obtaining 14.32% in RMSE (reduction from 0.6204 to 0.5315) and 13.23% in MAE (reduction from 0.4323 to 0.3751). On the other hand, regarding Bland–Altman analysis, the upper and lower limits of agreement for the Mean of Differences (MOD) between the estimation and the ground truth were 1.04 and −1.05, with an MOD (bias) of −0.00175; therefore, MOD $\pm 1.96\sigma$ = −0.00175 ± 1.04. Thus, by leveraging Bayesian optimization for hyperparameter tuning and integrating a Bagging Ensemble, we achieved a significant reduction in the training error (bias), achieving a better generalization over the test set, and reducing the variance in comparison with the baseline model for SpO₂ estimation. Full article

Background: The global impact of the COVID-19 pandemic has significantly influenced elderly functionality, particularly in terms of balance, gait, and independence in daily activities. This study sought to evaluate how these aspects have changed over the course of the health crisis. Methods: We employed the Tinetti scale for assessing balance and gait, and the Barthel Index for measuring functional independence, conducting a comparative analysis of scores before and after the onset of the pandemic in a sample of elderly individuals. Results: Our findings indicated an increase in Tinetti scores, suggesting some improvement in balance and mobility, albeit with marked variability across participants. On the other hand, Barthel scores showed a significant decline, pointing to a reduction in functional independence. Conclusions: These results suggest that the impact of COVID-19 on elderly functionality is not uniform, highlighting the need for personalized rehabilitation strategies. Such strategies should not only focus on physical recovery but also consider the psychological and social repercussions of the pandemic to fully address the diverse needs of this vulnerable population. Full article

Aims: To systematically review and synthesize qualitative research exploring the Breast Cancer Survivors (BCSs)’ perception of health promotion interventions and informing strategies to mitigate recurrence risk within five years post-treatment and improve clinical outcomes. Specifically, this study addresses the question: “How do women diagnosed with breast cancer perceive health promotion interventions for recurrence prevention?” Design: A systematic review and qualitative meta-synthesis were performed. Data Sources: A systematic search of scientific databases (CINAHL, MEDLINE, and Scopus) was undertaken in November 2024. The reference list was cross-referenced and hand-searched to identify additional articles. Review Methods: Studies were included if they met the following criteria: they were primary qualitative studies focusing on BCSs within five years post-treatment, involving participants who had completed surgery, radiotherapy, or chemotherapy in the same time frame, as this period is critical for monitoring recurrence and implementing health promotion interventions. Only studies published in peer-reviewed journals and written in Italian, English, French, or Spanish were considered, provided that an abstract and the full text were available. Moreover, eligible studies had to be conducted in high-income or middle-income countries. Studies were excluded if they focused exclusively on advanced or metastatic breast cancer, if they involved mixed cancer populations without reporting separate data for BCSs, or if they were non-qualitative studies or gray literature. The review study protocol was registered in the PROSPERO database (CRD42024626033). Results: The literature search identified 490 records, 13 articles from databases, and 3 articles identified via other methods (web and citation searching) that met inclusion criteria. A narrative synthesis approach allowed the emerging five themes: (I) Challenges, (II) Self-motivation and empowerment, (III) The relationships as a facilitator, (IV) Barriers to change, and (V) Proactive support strategies. Conclusions: Addressing internal and external factors that influence health behaviors is essential to improve adherence, reduce recurrence risk, and enhance quality of life. Tailored interventions, social support, and healthcare engagement are crucial in this effort. Impact: Our meta-synthesis highlighted significant challenges as well as valuable resources for health promotion among BCSs, suggesting practical and tailored approaches to improving the adoption of healthy behaviors, supported by relationships and targeted support strategies. Full article

Urban commercial districts, with their unique characteristics, serve as a reflection of broader urban development patterns. However, only a handful of studies have harnessed point-of-interest (POI) data to model the intricate relationship between retail commercial space types and other factors. This paper endeavors to bridge this gap, focusing on the influence of urban development factors on retail commerce districts through the lens of POI data. Our exploration underscores how commercial zones impact the density of residential neighborhoods and the coherence of pedestrian pathways. To facilitate our investigation, we propose an ensemble clustering technique for identifying and outlining urban commercial areas, including Kernel Density Analysis (KDE), Density-based Spatial Clustering of Applications with Noise (DBSCAN), Geographically Weighted Regression (GWR). Our research uses the city of Manchester as a case study, unearthing the relationship between commercial retail catchment areas and a range of factors (retail commercial space types, land use function, walking coverage). These include land use function, walking coverage, and green park within the specified areas. As we explore the multiple impacts of different urban development factors on retail commerce models, we hope this study acts as a springboard for further exploration of the untapped potential of POI data in urban business development and planning. Full article

A rather novel approach for delivery of inertia-like grid services through energy storage devices is described and validated by physical experiments and on-site measurements. In this approach, denoted “amplified inertia response”, an actual inertial response from a grid-connected synchronous machine is amplified. This inertia emulation approach is contrasted by what is called synthetic inertia, which uses a frequency-locked loop in order to extract the grid frequency. The synthetic inertia faces the usual input signal filtering challenges if the signal-to-noise ratio is low. The amplified inertia controller avoids the input filtering since it only amplifies the natural inertial response from a synchronous machine. However, rotor angle oscillations lead to filtering requirements of the amplified version as well, but on the output signal of the controller. Experimental comparisons are conducted both on the measurement output from the physical experiments in a microgrid and on analysis based on input from on-site measurements from a 55 MVA hydropower generator connected to the Nordic grid. In the specific cases compared, we observe that the amplified inertia version is the better method for smaller power systems, with large frequency fluctuations. On the other hand, the synthetic inertia method is the better in larger power systems as compared to the amplification of the inertial response from a real production unit. Full article

Background/Objectives: Repetitive behaviors such as hand flapping, body rocking, and head shaking characterize Autism Spectrum Disorder (ASD) while functioning as early signs of neurodevelopmental variations. Traditional diagnostic procedures require extensive manual observation, which takes significant time, produces subjective results, and remains unavailable to many regions. The research introduces a real-time system for the detection of ASD-typical behaviors by analyzing body movements through the You Only Look Once (YOLOv11) deep learning model. Methods: The system’s multi-layered design integrates monitoring, network, cloud, and typical ASD behavior detection layers to facilitate real-time video acquisition, wireless data transfer, and cloud analysis along with ASD-typical behavior classification. We gathered and annotated our own dataset comprising 72 videos, yielding a total of 13,640 images representing four behavior classes that include hand flapping, body rocking, head shaking, and non_autistic. Results: YOLOv11 demonstrates superior performance compared to baseline models like the sub-sampling (CNN) (MobileNet-SSD) and Long Short-Term Memory (LSTM) by achieving 99% accuracy along with 96% precision and 97% in recall and the F1-score. Conclusions: The results indicate that our system provides a scalable solution for real-time ASD screening, which might help clinicians, educators, and caregivers with early intervention, as well as ongoing behavioral monitoring. Full article