Search Results (3,398)

This paper presents a comprehensive analysis of the dynamic properties of low-power current transformers (LPCTs) in the context of their application in both power systems and electromechanical systems. Momentary changes in external loads occurring in the mechanical parts of systems, affecting their correct operation, cause the appropriate monitoring and control systems, including LPCTs, to operate in transient states where dynamic errors are significant. The issues discussed in this article are therefore important from both an electrical and mechanical engineering perspective. The study focuses on the evaluation of dynamic errors using two complementary performance criteria: the mean squared error and the absolute dynamic error. An equivalent circuit model of the LPCT is formulated and employed to investigate its response under transient conditions representative of modern energy networks as well as electromechanical devices, including drives, converters, and rotating machines operating under variable loads. A key contribution of this work is the determination of the upper bounds of dynamic errors, which establish the ultimate accuracy constraints of LPCTs when subjected to rapid current variations. The obtained results provide quantitative evidence of the impact of dynamic properties on the reliability of current measurements, thereby reinforcing the importance of the proposed error evaluation framework. In this context, the study demonstrates that a rigorous assessment of dynamic errors is essential for improving the functional performance of LPCTs, particularly in applications where steady-state accuracy must be complemented by a reliable transient response. Full article

Plant phenotyping plays a vital role in connecting genotype to environmental adaptability, with important applications in crop breeding and precision agriculture. Traditional leaf measurement methods are laborious and destructive, while modern 3D sensing technologies like LiDAR provide high-resolution point clouds but face challenges in automatic leaf segmentation due to occlusion, geometric similarity, and uneven point density. To address these challenges, we propose MRSliceNet, an end-to-end deep learning framework inspired by human visual cognition. The network integrates three key components: a Multi-scale Recursive Slicing Module (MRSM) for detailed local feature extraction, a Context Fusion Module (CFM) that combines local and global features through attention mechanisms, and an Instance-Aware Clustering Head (IACH) that generates discriminative embeddings for precise instance separation. Extensive experiments on two challenging datasets show that our method establishes new state-of-the-art performance, achieving AP of 55.04%/53.78%, AP₅₀ of 65.37%/64.00%, and AP₂₅ of 74.68%/73.45% on Dataset A and Dataset B, respectively. The proposed framework not only produces clear boundaries and reliable instance identification but also provides an effective solution for automated plant phenotyping, as evidenced by its successful implementation in real-world agricultural research pipelines. Full article

Replication is an important aspect of experimental research and it is therefore crucial that participant-level measures (e.g., judgment scores) are reliable. Reliability refers to the precision of measurement and thus informs the replicability of experiments: more precise measurements are more dependable for future reference. Formally defined as the ratio of true score variance to the total variance, reliability can be achieved by fine-tuning the measurement instrument or by collecting a sufficiently large number of observations per participant, as averaging over more items reduces the influence of random item-specific noise and yields a more precise estimate of participants’ true scores. The present paper uses Generalizability Theory to estimate the reliability of participant scores in 52 distinct datasets from studies that used comparable experimental designs to investigate different types of island effects in different languages. Effect sizes (DD-scores) are commonly reported and used for comparative purposes in discussions on island effects. The present paper argues that caution is warranted when island effect sizes are compared: the analyses reveal that participant-level reliability in island experiments is moderate, but that increasing the number of items to six per condition enhances measurement precision. Full article

Background: Vegetables are consumed worldwide in various forms, including raw, as green leaves in salads, and as ingredients in a wide range of dishes, such as curries, sauces, and burgers. They are rich in carbohydrates and dietary fiber (DF), and also provide moderate amounts of protein, fat, oils, essential micronutrients, minerals, vitamins, and phytochemicals. Among their carbohydrate components, simple sugars such as monosaccharides/hexoses significantly influence postprandial blood glucose responses. The glycemic index (GI) is critical for managing chronic conditions, such as diabetes, obesity, hyperglycemia, and other metabolic diseases. The influence of individual carbohydrate fractions, such as hexoses, on GI and glycemic load (GL) has not been extensively investigated. Methods: This retrospective study analyzed the carbohydrates in vegetables (n = 65), focusing on hexoses and fibers, their carbohydrate-to-fiber ratio, and their effect on the GI and GL. Carbohydrate data were obtained from publicly accessible databases, including the U.S. Department of Agriculture (USDA), FooDB, European and Australian food databases, and PubMed. The study assessed total carbohydrates (TC), hexoses, dietary starch (DS), total sugars (TS), and DF, and examined their correlations with GI using regression analysis. Results: Our analysis revealed that fiber ratios are a more reliable predictor of GI than conventional net carbohydrate measures. Among the carbohydrates analyzed, TC exhibited the highest positive correlation with GI, both in absolute terms and when normalized to fiber, while TS showed a weak correlation. Among the ratios studied, TC demonstrated a stronger correlation with the GI, followed by DS. Conclusions: Comparative evaluation revealed that DF exerts a buffering effect on glycemic response (GR) and supports the use of fiber ratios as a more stable and intrinsic parameter for predicting GI than standard estimation methods. Traditional approaches that rely on net carbohydrates may overlook important factors affecting glycemic impact, particularly the buffering effects of dietary fiber. This study advocates for the incorporation of carbohydrate-to-fiber ratios into GI estimation models. Our research may help evaluate the carbohydrate content in vegetables for further in vitro and in vivo studies aimed at clarifying the mechanisms and validating these metrics in glycemic regulation. Full article

Optically pumped magnetometers (OPMs) present a promising opportunity to advance magnetoencephalography (MEG), enhancing the accuracy of neuronal activity recordings due to their high spatiotemporal resolution. However, to fully realize the potential of OPM-MEG as an emerging brain functional imaging technology, it is essential to measure key indicators of neural dynamics, particularly phase–amplitude coupling (PAC). PAC is a fundamental mechanism for integrating information across different frequency bands and plays an important role in various cognitive functions and neurological disorders. Therefore, measuring PAC with OPM-MEG is a crucial step toward expanding its applications. In this study, brain signals under pitch sequence stimulation were recorded using OPM-MEG to analyze the PAC effect in the primary auditory cortex (Aud) and the inferior frontal gyrus (IFG), as well as the functional connectivity between brain regions. The findings were validated through EEG control experiments. The results indicated that the PAC effect measured by OPM-MEG was largely consistent with that measured by EEG, with OPM-MEG appearing to detect PAC more prominently under the current experimental conditions. The PAC of Aud exhibited a trend of initially increasing and then decreasing centered on the target pitch, showing hemispheric symmetry. The PAC of IFG showed variations under different pitch conditions and displayed right hemisphere lateralization. Functional connectivity analysis provided convergent evidence for the mechanisms underlying the PAC effect and suggested the reliability of the OPM-MEG system in capturing cross-frequency neural dynamics. To our knowledge, this study provides the first task-based evidence that OPM-MEG can measure PAC effects in cortical regions, offering an initial foundation for future investigations of brain dynamics using this technology. Full article

Background: The COVID-19 pandemic disrupted school life worldwide, heightening risks to students’ psychosocial well-being and mental health, and creating an urgent need for sustainable support strategies during crises. Drama-based interventions, as participatory arts-based approaches, are proposed as flexible interventions that can strengthen resilience, social interaction, and emotional expression in school communities. Objective: This study evaluated the impact of a large-scale, short-term, remote drama-based intervention on the psychosocial adjustment and well-being of primary school students during the pandemic. Methods: An embedded mixed methods design with a pre-post measurement was employed, involving 239 teachers and 719 students aged 9–13 years from schools across various regions of Greece. Psychosocial functioning was assessed using a standardized instrument measuring levels of social, school, and emotional competence, as well as behavioral difficulties. The intervention, totaling 700 min over seven weeks, followed a five-day weekly structure that combined health-focused and psychosocial activities. Results: Quantitative findings indicated improvements across several dimensions of psychosocial adaptation and well-being, while Reliable Change Index analysis revealed important individual-level changes. Qualitative data corroborated these results, highlighting enhanced peer collaboration, increased emotional expression, and stronger classroom cohesion, while also emphasizing the adaptability and scalability of the approach under restrictive conditions. Conclusions: The findings suggest that such artful interventions can make a meaningful contribution to promoting well-being and sustaining the educational and social life of school communities during public health emergencies, thereby adding to the applied psychology evidence based on effective school health interventions. Full article

Sialic acids are terminal monosaccharides that cap glycans on glycoconjugates. Accumulating clinical and experimental evidence shows that obesity, insulin resistance, and diabetes are accompanied by changes in sialic-acid levels. In these conditions, the sialic-acid axis is also broadly remodeled: writers (sialyltransferases), erasers (neuraminidases), and readers (Siglecs) are dysregulated across adipose tissue, liver, pancreas, endothelium, and blood, shifting insulin signaling and inflammatory tone. This review summarizes relevant studies from the perspectives of disease clinical indicators, molecular mechanisms, and interventions targeting sialic acid. Taken together, these results confirm that sialic acids and related molecules play important roles in multiple metabolic diseases; however, controversies remain due to differences in glycan structure, isoforms, and tissue specificity, particularly regarding the precise roles of neuraminidases. Future studies should build on advanced, standardized glycomic and glycoproteomic measures to define molecule- and tissue-specific roles of sialic acids in metabolic disease, enabling reliable biomarkers and guiding targeted therapy. Full article

The emergence of mobile private networks (MPNs) has enabled tailored communication solutions for industries, enterprises, and specialized applications, fostering improved control, security, and flexibility. With the rapid advancements in software-defined radio (SDR) platforms, implementing MPNs using cost-effective and versatile hardware has become increasingly feasible. Analyzing the radio coverage of such networks is critical for optimizing performance, ensuring reliable connectivity, and addressing site-specific challenges in deployment. This paper investigates the radio coverage of a 4G MPN implemented using as radio front-end an SDR platform from the Universal Software Radio Peripheral (USRP) family and the srsRAN-4G open-source software suite. Using the HTZ Communication software as simulation tool and field-test measurements performed using an off-the-shelf mobile phone as user equipment (UE), an analysis is made to evaluate the accuracy of various propagation models in predicting network coverage, in several different frequency bands. The results provide valuable insights into the design and deployment of MPNs, highlighting the importance of accurate coverage estimation in achieving robust and efficient network operation. Full article

There are many types of probes available on the market for measuring ambient dose equivalent rates (ADERs), which makes intercomparison exercises essential to ensure data comparability and reliability. This study evaluated the performance of four widely used and similarly priced probes—the Reuter-Stokes ionization chamber, the RX04L from BITT, the MIRA from ENVINET, and the LB9360 from Berthold. The Reuter-Stokes ionization chamber was also taken as reference. Measurements were continuously conducted in Bilbao, northern Spain, during the period 2017–2021 under background conditions as well as during episodes of heavy rainfall and extreme temperatures. Results show that the BITT proportional counter exhibited the highest consistency with the Reuter-Stokes chamber under all meteorological conditions, and excellent stability even during extreme conditions. The Berthold probe displayed similar trends, but systematically overestimated dose rates, while the Geiger–Müller-based detector showed acceptable agreement under rainfall, but clear instability during temperature extremes. These findings highlight the importance of probe selection in environmental radioactivity networks as well as the use of reliable instruments for integration into modernized radiological surveillance systems. Full article

Access to accurate and reliable information on the cost of COVID-19 is essential for informed socio-economic policy decisions. This paper analyses the economic costs associated with the COVID-19/SARS-CoV-2 pandemic, with a particular focus on Australia. This study examined both the macroeconomic costs measured as the foregone gross domestic product attributable to the pandemic and the direct and indirect costs to society. Using a bottom-up costing approach and the WHO-CHOICE model, this study estimates the direct and indirect economic impacts of COVID-19 on the Australian economy. The analysis draws on quarterly and fortnightly data from 2020 to 2022, the period during which the pandemic exerted its most severe economic effects. The results indicate that the per-day inpatient unit cost is estimated at AUD 836, representing the minimum benchmark for direct health costs. The WHO-CHOICE model identifies key determinants of inpatient hospital costs, including hospital bed occupancy, GDP per capita, and hospital admissions, which are found to be highly responsive to changes in inpatient costs. In terms of indirect effects, GDP fell by 1.9 percent below its projected no-COVID level in the first quarter of 2021. Based on these empirical findings, this study proposes several important policy recommendations to enhance economic resilience and healthcare preparedness in future public health crises. Full article

Maglev trains represent an advanced form of modern rail transportation. The guideway irregularity presents a common disturbance to the safe and reliable operation of the maglev train. Variations in the air gap between the train and the guideway, induced by the guideway irregularities, exert a significant influence on the train’s dynamic performance, thereby impacting both ride comfort and operational safety. Although previous studies have acknowledged the importance of guideway irregularity, the stochastic effects on the car body vibration across different speeds have not been quantitatively assessed. To fill in this gap, this paper presents a 10-degree-of-freedom maglev train model based on multibody dynamics. The guideway is modelled via the finite element method using Euler–Bernoulli beam theory, and a linearized electromagnetic force equation is employed to couple the guideway and the train dynamics. Furthermore, the measurement data of guideway irregularity from the Shanghai Maglev commercial line are incorporated to evaluate their stochastic effect. Analysis results under varying speeds and irregularity wavelengths identify a resonance speed of 127.34 km/h, attributed to the interplay between guideway periodicity and the train’s natural frequency. When the ratio of the train speed versus irregularity wavelength satisfies the train’s natural frequency, a significant resonance can be observed, leading to an increase in train vibration. Based on the Monte Carlo method, stochastic analysis is conducted using 150 simulations per speed in 200–600 km/h. The maximum vertical acceleration remains relatively stable at 200–400 km/h but increases significantly at higher speeds. When the irregularity is present, greater dispersion is observed with increasing speed, with the standard deviation at 600 km/h reaching 2.7 times that at 200 km/h. Across all tested cases, acceleration values are consistently higher than those without irregularities within the corresponding confidence intervals. Full article

Eight diffusion couples were fabricated to systematically investigate the composition-dependent interdiffusion behavior in hcp Dy-Y, Dy-Nd, Sm-Nd, and Sm-Tb binary alloys. The interdiffusion coefficients were determined at two representative temperatures using the Sauer–Freise method based on concentration–distance profiles measured by electron probe microanalysis (EPMA). These experimentally obtained diffusivities, together with available thermodynamic data, were subsequently employed to assess the atomic mobilities of each system by means of the CALTPP (CALculation of Thermo Physical Properties) program within the CALPHAD (CALculation of PHAse Diagrams) framework. The optimized mobility parameters provide a reliable description of the diffusion behavior in all investigated alloys. This reliability is confirmed by the close agreement between the calculated and experimentally measured interdiffusion coefficients, as well as by the strong consistency between the model-predicted and experimental concentration profiles. The present work thus establishes the first set of critically evaluated atomic mobility parameters for these hcp rare-earth binary systems. These results fill an important gap in the kinetic database of rare-earth alloys and lay a robust foundation for future multi-component CALPHAD-based simulations, thereby supporting the design and optimization of advanced rare-earth permanent magnets with improved coercivity and thermal stability. Full article

Background and Objectives: Evaluating osteoarthritis (OA) care quality is increasingly relevant for service improvement and benchmarking purposes. The Osteoarthritis Quality Indicator questionnaire (OA-QI) measures patient-reported guideline-concordant care; however, no version has been tested in Italian primary care or bilingual contexts. This study aimed to introduce the OA-QI version 3 (OA-QI v3) in German and Italian, assess its applicability in practice, and examine its acceptability and reliability. Materials and Methods: A cross-sectional survey was conducted using the South Tyrolean General Practice Research Network. Thirty-eight general practitioners recruited 266 patients with hip or knee OA. Patients completed the OA-QI v3 in German or Italian, with subsamples for comprehensibility testing (n = 38) and retest reliability after 14 days (n = 36). Test–retest reliability was analyzed using percent agreement, Cohen’s κ, intraclass correlation coefficients (ICC), and standard error of measurement. The smallest detectable change was analyzed to estimate factual change. Results: Response rate reached 95% of the targeted patients. Patient feedback showed good comprehensibility and ease of use in both languages. Adherence to recommended quality indicators varied, with strengths in physical activity advice, NSAID prescription, and pain assessment, but gaps in weight management, occupational counseling, and assistive devices. Test–retest reliability ranged from fair to substantial at the item level (κ = 0.33–0.69) and was moderate for the total score (ICC = 0.55, 95% CI 0.28–0.74). While measurement error restricted individual-level interpretation, reliability at the practice or institutional level supports application for benchmarking and quality monitoring. Conclusions: The OA-QI v3 was feasible, acceptable, and reliable for group-level assessments in South Tyrol. These findings position OA-QI v3 as a practical tool for identifying care gaps and guiding quality improvement, while providing important lessons for the full validation of the German and Italian versions in larger cross-national samples. Full article

Accurate and efficient estimation of Sobol’ sensitivity indices is a cornerstone of variance-based global sensitivity analysis, providing critical insights into how uncertainties in input parameters affect model outputs. This is particularly important for large-scale environmental, engineering, and financial models, where understanding parameter influence is essential for improving model reliability, guiding calibration, and supporting informed decision-making. However, computing Sobol’ indices requires evaluating high-dimensional integrals, presenting significant numerical and computational challenges. In this study, we present a comparative analysis of two of the best available Quasi-Monte Carlo (QMC) techniques: polynomial lattice rules (PLRs) and modified Sobol’ sequences. The performance of both approaches is systematically assessed in terms of performance and accuracy. Extensive numerical experiments demonstrate that the proposed PLR-based framework achieves superior precision for several sensitivity measures, while modified Sobol’ sequences remain competitive for lower-dimensional indices. Our results show that IPLR-α3 outperforms traditional QMC methods in estimating both dominant and weak sensitivity indices, offering a robust framework for high-dimensional models. These findings provide practical guidelines for selecting optimal QMC strategies, contributing to more reliable sensitivity analysis and enhancing the predictive power of complex computational models. Full article

This study evaluated the integration of non-invasive remote sensing and colorimetry to classify the maturity stages of Coffea arabica fruits across four varieties: Caturra Amarillo, Excelencia, Milenio, and Típica. Multispectral signatures were captured using a Parrot Sequoia camera at wavelengths of 550 nm, 660 nm, 735 nm, and 790 nm, while colorimetric parameters L*, a*, and b* were measured with a high-precision colorimeter. We conducted multivariate analyses, including Principal Component Analysis (PCA) and multiple linear regression (MLR), to identify color patterns and develop predictors for fruit maturity. Spectral curve analysis revealed consistent changes related to ripening: a decrease in reflectance in the green band (550 nm), a progressive increase in the red band (660 nm), and relative stability in the RedEdge and near-infrared regions (735–790 nm). Colorimetric analysis confirmed systematic trends, indicating that the a* component (green to red) was the most reliable indicator of ripeness. Additionally, L* (lightness) decreased with maturity, and the b* component (yellowness to blue) showed varying importance depending on the variety. PCA accounted for over 98% of the variability across all varieties, demonstrating that these three parameters effectively characterize maturity. MLR models exhibited strong predictive performance, with adjusted R² values ranging between 0.789 and 0.877. Excelencia achieved the highest predictive accuracy, while Milenio demonstrated the lowest, highlighting varietal differences in pigmentation dynamics. These findings show that combining multispectral imaging, colorimetry, and statistical modeling offers a non-destructive, accessible, and cost-effective method for objectively classifying coffee maturity. Integrating this approach into computer vision or remote sensing systems could enhance harvest planning, reduce variability in specialty coffee lots, and improve competitiveness by ensuring greater consistency in cup quality. Full article