Search Results (5,033)

Objectives: The present study aimed to examine the relationships between screen time (ST), sleep time (SLT), physical activity engagement (PA), Mediterranean diet (MD) adherence, body mass index (BMI), self-esteem (SE) and academic performance (AP) in high-socioeconomic-status (SES) school-aged youth in southern Spain. Methods: A descriptive, comparative, non-experimental and cross-sectional research study was conducted with a total sample of 217 Spanish students (13.88 ± 1.32). Structural equation modelling (SEM) was employed to analyse relationships between study variables as a function of sex. Results: SLT was positively associated with MD adherence and negatively related with BMI. Both PA and MD adherence were positively linked to SE, whilst MD adherence and SE were also positively related to AP. Regarding sex differences, ST was a stronger determinant among girls, showing negative associations with PA (β = −0.378; p < 0.001) and MD adherence (β = −0.315; p < 0.001), with this pattern not being observed in boys. PA was positively associated with SE in both sexes, but more strongly in boys (β = 0.332; p < 0.001) than in girls (β = 0.190; p = 0.034). In girls, both MD adherence (β = 0.295; p < 0.001) and SE (β = 0.224; p = 0.008) were positively associated with AP, with these associations not being found in boys. Conclusions: The findings underscore the complex interplay between lifestyle behaviours, psychosocial factors, and AP in school-aged youth. Regardless of SES, interventions should focus on reducing ST, promoting PA and MD adherence, and enhancing SE whilst considering sex-specific patterns. Full article

This study investigated the preference behaviour of 24 four-week-old weaned piglets under different lighting conditions (0 lux with 0 Kelvin vs. 80 lux with 3000 Kelvin vs. 6500 Kelvin). Two trials with 12 piglets each were conducted over five weeks in a room with four interconnected pens, allowing free movement between the pens. Pens A and B were nearly dark (~0 lux), while pen C (80 lux, 3000 Kelvin) and pen D (80 lux, 6500 Kelvin) were illuminated. On three days in weeks 1, 3 and 5, behaviour (lying, eating and activity) was recorded using video observations and a 5 min time sampling method. Cleanliness was also monitored daily. In the first week, piglets in the first batch preferred the darkened pens, whereas piglets in the second batch preferred illuminated pens, especially when the colour temperature was 3000 Kelvin. By the third week, piglets in the second batch now preferred darker areas. In the fifth week, the piglets spent more time in the dark in the mornings and evenings but showed no preference for colour temperature. The darkened pens remained mostly clean, whereas pen D, which had a light colour temperature of 6500 Kelvin, was the most soiled. The results show that piglet behaviour changes with age and the time of day, suggesting that lighting concepts can be adapted to improve both animal welfare and pen hygiene. Full article

The primary objective of this study is to develop a machine learning-based predictive model using corrosion rate data for magnesium alloys compiled from the literature. Corrosion rates measured under different deformation rates and heat treatment parameters were analyzed using artificial intelligence algorithms. Variables such as chemical composition, heat treatment temperature and time, deformation state, pH, test method, and test duration were used as inputs in the dataset. Various regression algorithms were compared with the PyCaret AutoML library, and the models with the highest accuracy scores were analyzed with Gradient Extra Trees and AdaBoost regression methods. The findings of this study demonstrate that modelling corrosion behaviour by integrating chemical composition with experimental conditions and processing parameters substantially enhances predictive accuracy. The regression models, developed using the PyCaret library, achieved high accuracy scores, producing corrosion rate predictions that are remarkably consistent with experimental values reported in the literature. Detailed tables and figures confirm that the most influential factors governing corrosion were successfully identified, providing valuable insights into the underlying mechanisms. These results highlight the potential of AI-assisted decision systems as powerful tools for material selection and experimental design, and, when supported by larger databases, for predicting the corrosion life of magnesium alloys and guiding the development of new alloys. Full article

The rapid adoption of electric vehicles (BEVs) has increased the need to understand how fast-charging strategies influence long-distance travel times under real-world conditions. While most manufacturers specify maximum charging power and standardized driving ranges, these figures often fail to reflect actual highway operation, particularly in adverse weather. This study addresses this gap by analyzing the fast-charging behaviour, net battery capacity and highway energy consumption of 62 EVs from different market segments. Charging power curves were obtained experimentally at high-power DC stations, with data recorded through both the charging infrastructure and the vehicles’ battery management systems. Tests were conducted, under optimal conditions, between 10% and 90% state of charge (SoC), with additional sessions performed under both cold and preconditioned battery conditions to show thermal effects on the batteries’ fast-charging capabilities. Real-world highway consumption values were applied to simulate 1000 km journeys at 120 km/h under cold (−10 °C, cabin heating) and mild (23 °C, no AC) weather scenarios. An optimization model was developed to minimize total trip time by adjusting the number and duration of charging stops, including a 5 min detour for each charging session. Results show that the optimal charging cutoff point consistently emerges around 59% SoC, with a typical deviation of 10, regardless of ambient temperature. Charging beyond 70% SoC is generally inefficient unless dictated by charging station availability. The optimal strategy involves increasing the number of shorter stops—typically every 2–3 h of driving—thereby reducing total trip. Full article

Iron-based metallic glasses are gaining increased interest due to their good glass-forming ability, high compressive strength, high corrosion resistance, catalytic properties, excellent soft magnetic properties, and relatively low cost. Cold spraying was successfully used to produce amorphous coatings from commercially available powder without any crystallization due to its high cooling rate and short processing time, minimizing thermal influences. Thick and dense amorphous coatings were obtained. The effect of a substrate on the microstructure, phase composition, microhardness, flexural strength, and wear behaviour of the coatings was investigated. The cold sprayed coatings revealed an almost complete amorphous structure and negligible porosity. The coating deposited on the steel substrate showed higher microhardness, better resistance to loose abrasive wear, and a slightly lower wear index tested in the coating and Si₃N₄ ball tribological association than that cold sprayed on an Al alloy. The force required to destroy the durability of the coating–steel substrate system estimated during three-point bending tests was also much higher. Both coatings were characterized by a comparable friction coefficient. Full article

We build on the predictive processing framework to show that intelligent behaviour is adaptive control, driven by accurate prediction and uncertainty reduction in dynamic environments with limited information. We argue that adaptive control arises through a process of re-concretisation, where learned abstractions are grounded in new situations via embodiment. We use this as an explanation of why AI models often generalise at the cost of detail while biological systems manage to tailor their predictions towards specific environments over time. On this basis, we utilise the notion of embodied prediction to provide a new distinction between biological intelligence and the performance illustrated by AI systems. Full article

Assistance dogs have shown promise in alerting to epileptic seizures in their owners, but current approaches often lack consistency, standardisation, and objective validation. This proof-of-concept study presents the development and initial validation of a wearable behaviour-alert detection collar developed for trained assistance dogs. It demonstrates the technical feasibility for automated detection of trained signalling behaviours. The collar integrates an inertial sensor and machine learning pipeline to detect a specific, trained alert behaviour of two rapid clockwise spins used by dogs to signal a seizure event. Data were collected from six trained dogs, resulting in 135 labelled spin alerts. Although the dataset size is limited compared to other machine learning applications, this reflects the real-world constraint that it is not practical for assistance dogs to perform excessive spin signalling during their training. Four supervised machine learning models (Random Forest, Logistic Regression, Naïve Bayes, and SVM) were evaluated on segmented accelerometer and gyroscope data. Random Forest achieved the highest performance (F1-score = 0.65; accuracy = 92%) under a Leave-One-DOG-Out (LODO) protocol. The system represents a novel step toward combining intentional canine behaviours with wearable technology, aligning with trends on the Internet of Medical Things. This proof-of-concept demonstrates technical feasibility and provides a foundation for future development of real-time seizure-alerting systems, representing an important first step toward scalable animal-assisted healthcare innovation. Full article

Understanding the impact of hydric stress on medicinal plants in the context of climate change is becoming increasingly important. This study aimed to assess the quality of a seed lot of Agastache mexicana subsp. mexicana (Amm) through a novel calculation of the Vigour Index on time basis (${VI}_T$). The evaluation was based on relationships among plant height, leaf number, survival time, and plant density across six irrigation regimes, referred to as stages, which differed in the timing and quantity of water, designed to impose water stress from seedling emergence until plant death. To maximise growth and survival time, we utilised two input factors: Artificial Shade Levels (ASLs) of 38%, 87%, and 94%, as well as Silicon Dioxide Levels (SDLs) of 0.0%, 0.2%, 0.4%, and 0.8%. The effects of these treatments were measured using the Survival Index (SI) and the ${VI}_T$. The plants achieved their highest SI and ${VI}_T$ values influenced by minimum mortality and maximum height and leaf number in stage three. This behaviour aligned with the field capacity of the substrate, supporting the evaluation of stages one and two as waterlogging stress, while the remaining stages were classified as drought stress. The ${VI}_T$ results showed statistically significant effects from ASL, particularly at 94%. However, the ${VI}_T$ in relation to SDL was not statistically significant. The ${VI}_T$ measurements were visualised using spline interpolation, a method that provides an effective approach to quantify adverse conditions affecting Amm’s development and that it can support to identify the hydric stresses type. Full article

Sharks play a key role in coral reef ecosystems, but Caribbean populations are concerningly low. When monitoring endangered species, it is critical to use minimally invasive tools and protocols that are adequate for local species and the environment. This study investigated the adequate deployment time of baited remote underwater videos (BRUVs) for shark studies in the Cayman Islands and whether the use of photo-identification to recognise individuals (MaxIND) on BRUVs could improve abundance estimates (in comparison to MaxN) and the analysis of shark behaviour. From 2015 to 2018, a total of 557 BRUVs were deployed with recording times ranging from 3.8 to 211.03 min. The results showed that (1) of the total number of individual sharks recorded on videos, 95% of individuals were recorded within the first 110 min (slight variations between species), (2) MaxIND values were 1.1–1.5 times greater than that of MaxN (ratios varying with species) and (3) time of first arrival (Tarrive) was similar for all recorded species but time spent in front of the camera’s field of view (Tvisit) and activity levels (count of entries in camera’s field of view) varied between species. The results provide key information to improve the localised monitoring of rare/endangered species and can inform conservation management. Full article

Fully grouted rock bolts play a vital role in stabilising underground excavations, particularly in corrosive environments where material properties, geometric configuration, and installation conditions influence their load transfer performance. Although the practical importance of fully grouted fibreglass rock bolts is well recognised, quantitative evidence on their axial load transfer mechanisms remains limited. Prior work has primarily centred on steel rock bolts, with few studies on how embedment length, grout stiffness, interface roughness and confining stress govern bond mobilisation in fully grouted fibreglass rock bolts, indicating a clear need for further scientific investigation. This study examines the axial load transfer and shear behaviour of fully grouted fibreglass rock bolts, focusing on the effects of embedment length (EL), grout properties, and boundary conditions. A comprehensive series of laboratory pull-out tests were conducted on two widely used Australian glass fibre reinforced polymer (GFRP) rock bolts, TD22 and TD25, with diameters of 22 mm and 25 mm, respectively, under varying ELs and grout curing times to evaluate their axial performance. Additionally, single shear tests and uniaxial compressive strength (UCS) tests were conducted to assess the shear behaviour of the rock bolts and the mechanical properties of the grout. The results showed that increased EL, bolt diameter, and grout curing time generally enhance axial capacity. With grout curing from day 7 to the day 28, the influence of embedment length became increasingly pronounced, as the axial peak load rose from 35 kN (TD22-50, 7 days) to 116 kN (TD22-150, 28 days) and from 39 kN (TD25-50, 7 days) to 115 kN (TD25-150, 28 days), confirming that both longer bonded lengths and extended curing significantly enhance the axial load-bearing capacity of fully grouted GFRP rock bolts. However, the TD22 rock bolts exhibited superior shear strength and ductility compared to the TD25 rock bolts. Also, a calibrated distinct element model (DEM) was developed in 3DEC to simulate axial load transfer mechanisms and validated against experimental results. Parametric studies revealed that increasing the grout stiffness from 5 e7 N/m to 5 e8 N/m increased the peak load from 45 kN to 205 kN (approximately 350%), while reducing the peak displacement, indicating a shift toward a more brittle response. Similarly, increasing the grout-bolt interface roughness boosted the peak load by 150% (from 60 kN to 150 kN) and enhanced residual stability, raising the residual load from 12 kN to 93.5 kN. In contrast, confining stress (up to 5 MPa) did not affect the 110 kN peak load but reduced the residual load by up to 60% in isotropic conditions. These quantitative findings provide critical insights into the performance of GFRP bolts and support their optimised design for underground reinforcement applications. Full article

This article develops a method for detecting malware based on the multi-scale recurrent architecture (time-aware multi-scale LSTM) with salience gating, multi-headed attention, and a sequential statistical change detector (CUSUM) integration. The research aim is to create an algorithm capable of effectively detecting malicious activities in behavioural data streams of executable files with minimal delay and ensuring interpretability of the results for subsequent use in forensic audit and cyber defence systems. To implement the task, deep learning methods (training LSTM models with dynamic consideration of time intervals and adaptive attention mechanisms) and sequence statistical analysis (CUSUM, Kulback–Leibler divergence, and Wasserstein distances), as well as regularisation approaches to improve the model stability and explainability, were used. Experimental evaluation demonstrates the proposed approaches’ high efficiency, with the neural network model achieving competitive indicators of accuracy, recall, and classification balance with a low level of false positives and an acceptable detection delay. Attention and salience profile analysis confirmed the possibility of interpreting signals and early detection of abnormal events, which reduces the experts’ workload and reduces the number of false positives. This study introduces the new hybrid architecture development that combines the advantages of recurrent and statistical methods, the theoretical properties formalisation of gated cells for long-term memory, and the proposal of a practical approach to the model solutions’ explainability. The developed method implementation, implemented in the specialised software product form, is shown in a forensic audit. Full article

Family caregivers provide most daily care for people living with chronic illness or frailty, yet they remain under-recognized in health and social care systems. To address this gap, we co-designed the Caregiver-Centered Care Champions Education Program, which equips frontline providers with the competencies needed to lead caregiver-inclusive change. Guided by the Kirkpatrick-Barr Health Workforce Education Framework, we conducted a mixed methods interpretive description evaluation of learner satisfaction, knowledge and confidence gains, and self-reported behaviour change. Sixty-seven interdisciplinary participants completed three online modules. Quantitative results from pre/post surveys (Wilcoxon signed rank tests) showed significant improvements across all competencies (p < 0.001; large effect sizes) alongside high satisfaction (means 6.56–6.96/7). Qualitative findings revealed that 94% of participants applied program content within three months, and 61% implemented five or more distinct behaviour changes (e.g., collaborative care planning, system navigation support). The analysis illuminated how learners integrated caregiver-centred principles with change leadership strategies. Time constraints and staffing shortages emerged as key barriers. Our co-designed, theory-informed approach effectively bridged individual learning and system change, demonstrating the potential to transform caregiver inclusion practices when supported by organizational policies. Full article

The paper investigates disparities in food waste generation across European Union countries between 2020 and 2022, focusing on spatial and sustainability dimensions. It utilizes data for six key food waste parameters and a broad range of environmental, social and economic indicators. A combination of statistical methods, including correlation analysis, cluster analysis and Principal Component Analysis, uncovers multivariate patterns and identifies groups of countries with similar food waste characteristics and related factors. The paper highlights the temporal and spatial dynamics of food waste over the three-year period, particularly in light of the COVID-19 pandemic. While the total volume of food waste remained relatively stable across the EU, notable shifts occurred in waste sources. Household food waste peaked in 2021, likely due to increased time spent at home during pandemic-related lockdowns. Conversely, waste from retail, restaurants and food service sectors showed a consistent increase. The paper identifies non-trivial correlations between food waste and socio-economic variables, suggesting that differences in food waste generation across EU countries are influenced by a complex interplay of factors, including policy effectiveness, cultural practices, consumer behaviour and economic conditions. This comprehensive analysis of food waste patterns across EU countries and over time offers valuable insights for policymakers aiming to reduce waste and promote sustainability. Full article

Understanding the nonlinear mechanical behaviour of mitral valve chordae tendineae is critical for accurate biomechanical modelling in cardiac simulations. This study integrates high-resolution 3D finite element analysis with experimentally derived Cauchy stress–Green–Lagrange strain data to capture both material and geometric nonlinearities. A one-dimensional formulation incorporating strain-dependent elasticity and large deformation kinematics was developed and validated against 3D simulations in COMSOL Multiphysics. Calibrated using experimental stress–strain data and validated against high-fidelity 3D finite element simulations in COMSOL, it reveals that neglecting transverse deformation overestimates axial force by 7%. Cross-sectional area reduction during stretch remained consistently around 12%, underscoring the importance of Poisson effects. A polynomial fit to the strain-dependent modulus of elasticity enables efficient force prediction with excellent agreement to experimental data. These results advance the mathematical modelling of biological tissues with nonlinear hyperelastic behaviour, providing a foundation for patient-specific simulations and real-time predictive tools in cardiovascular engineering. Full article

Electrospun one-dimensional nanocomposites composed of polyvinylpyrrolidone (PVP) matrices reinforced with antimony sulphoiodide (SbSI) or antimony selenoiodide (SbSeI) nanowires were fabricated for the first time. Their properties were investigated for applications in piezoelectric sensors and nanogenerators. Precise control of the electrospinning parameters produced nanofibres with diameters comparable to the lateral dimensions of the nanowires, ensuring parallel alignment and a 1–3 composite structure. Structural analysis confirmed uniform nanowire distribution and stoichiometry retention. In both nanocomposites, the alignment of the nanowires enables clear observation of the anisotropy of their piezoelectric properties. PVP/SbSI nanocomposites exhibited a ferroelectric–paraelectric transition near 290 K. Under air-pressure excitation of 17.03 bar, they generated a maximum piezoelectric voltage of 2.09 V, with a sensitivity of 229 mV/bar and a surface power density of 12.0 µW/cm² for sandwich-type samples with nanowires aligned perpendicularly to the electrodes. PVP/SbSeI composites demonstrated stable semiconducting behaviour with a maximum piezoelectric voltage of 1.56 V, sensitivity of 130 mV/bar, and surface power density of 2.3 µW/cm² for the same type of sample and excitation. The high piezoelectric coefficients d₃₃ of 98 pC/N and 64 pC/N for PVP/SbSI and PVP/SbSeI, respectively, combined with mechanical flexibility, confirm the effectiveness of these nanocomposites as a practical solution for mechanical energy harvesting and pressure sensing in nanogenerators and sensors. Full article