Search Results (412)

Governments worldwide are mandating e-invoicing and real-time VAT reporting, yet many cash-intensive service SMEs continue to under-report VAT, eroding fiscal revenues. This study investigates whether financial technology (FinTech) adoption can reduce this under-reporting among tourism SMEs in Greece—an economy with high seasonal spending and a persistent shadow economy. This is the first micro-level empirical study to examine how FinTech tools affect VAT compliance in this sector, offering novel insights into how technology interacts with behavioral factors to influence fiscal behavior. Drawing on the Technology Acceptance Model, deterrence theory, and behavioral tax compliance frameworks, we surveyed 214 hotels, guesthouses, and tour operators across Greece’s main tourism regions. A structured questionnaire measured five constructs: FinTech adoption, VAT compliance behavior, tax morale, perceived audit probability, and financial performance. Using Partial Least Squares Structural Equation Modeling and bootstrapped moderation–mediation analysis, we find that FinTech adoption significantly improves declared VAT, with compliance fully mediating its impact on financial outcomes. The effect is especially strong among businesses led by owners with high tax morale or strong perceptions of audit risk. These findings suggest that FinTech tools function both as efficiency enablers and behavioral nudges. The results support targeted policy actions such as subsidies for e-invoicing, tax compliance training, and transparent audit communication. By integrating technological and psychological dimensions, the study contributes new evidence to the digital fiscal governance literature and offers a practical framework for narrowing the VAT gap in tourism-driven economies. Full article

Fast prediction of beam quality in SBS pulse compression for high-repetition-rate operation is urgently important for SBS experimental parameter acquisition. In this study, a fast computational prediction model for SBS beam profiles is developed using a convolutional neural network (CNN) method, which is trained and validated using experimental data from SBS pulse compression experiments. The CNN method can predict beam spot images for experimental conditions in the range of 100–500 Hz repetition rates and 5–40 mJ injection energy. The proposed CNN-based SBS beam profile prediction model has a fast convergence of the loss function and an average error of 15% with respect to the experimental results, indicating a high accuracy of the model. The CNN-based prediction model achieves an average error of 11.8% for beam profile prediction across various experimental conditions, demonstrating its potential for SBS beam profile characterization. The CNN method could provide a fast means for predicting the characteristic law of the beam intensity distribution in high-repetition-rate SBS pulse compression systems. Full article

The optimization of high-frequency circuits remains a computationally intensive task due to the need for repeated high-fidelity electromagnetic (EM) simulations. To address this challenge, we propose a novel integration of machine learning-generated inverse maps within the space mapping (SM) optimization framework to significantly accelerate circuit optimization while maintaining high accuracy. The proposed approach leverages Bayesian Neural Networks (BNNs) and surrogate modeling techniques to construct an inverse mapping function that directly predicts design parameters from target performance metrics, bypassing iterative forward simulations. The methodology was validated using a low-pass filter optimization scenario, where the inverse surrogate model was trained using electromagnetic simulations from COMSOL Multiphysics 2024 r6.3 and optimized using MATLAB R2024b r24.2 trust region algorithm. Experimental results demonstrate that our approach reduces the number of high-fidelity simulations by over 80% compared to conventional SM techniques while achieving high accuracy with a mean absolute error (MAE) of 0.0262 (0.47%). Additionally, convergence efficiency was significantly improved, with the inverse surrogate model requiring only 31 coarse model simulations, compared to 580 in traditional SM. These findings demonstrate that machine learning-driven inverse surrogate modeling significantly reduces computational overhead, accelerates optimization, and enhances the accuracy of high-frequency circuit design. This approach offers a promising alternative to traditional SM methods, paving the way for more efficient RF and microwave circuit design workflows. Full article

High-intensity interval training (HIIT) has emerged as a promising non-pharmacological intervention for improving cardiometabolic health. In populations with diabetes, cardiovascular disease, obesity, or metabolic dysfunction, redox imbalance—characterized by elevated oxidative stress and impaired antioxidant defense—is a key contributor to disease progression. This narrative review synthesizes current evidence on the effects of HIIT on oxidative stress and antioxidant capacity across diverse cardiometabolic disease cohorts. While findings are heterogeneous, the majority of studies demonstrate that HIIT intervention can reduce levels of oxidative stress markers and enhance antioxidant enzyme expression. These redox adaptations may underpin improvements in vascular endothelial function, inflammation, and metabolic regulation. Importantly, variations in intensity, duration, and health status influence these responses, highlighting the need for individualized exercise prescriptions. Safety considerations are emphasized, including the necessity for medical clearance, gradual progression, and individualized training prescriptions in higher-risk individuals. In conclusion, HIIT shows potential as a targeted strategy to restore redox homeostasis and improve cardiometabolic outcomes, although further research is needed to clarify optimal protocols and the underlying mechanisms. Full article

Quadricep weakness is frequently observed in patients following anterior cruciate ligament (ACL) injury or in those with knee osteoarthritis, often contributing to functional impairments and persistent symptoms. While high-intensity resistance training has been shown to effectively improve muscle strength, its application may be limited in certain populations due to pain or the risk of surgical complications. In recent years, blood flow restriction (BFR) training has emerged as a promising alternative. Growing evidence indicates that low-load BFR exercise can significantly improve muscle strength, induce hypertrophy, and enhance knee function, with outcomes comparable to those of high-intensity resistance training. When implemented using appropriate protocols, BFR training appears to be a safe and efficacious rehabilitation strategy for individuals with knee pathology. Full article

By utilizing daily precipitation data from 400 meteorological stations in the Yellow River Basin (YRB) of China, atmospheric and oceanic reanalysis data, this study investigates the climatological characteristics, leading modes, and relationships with atmospheric circulation and sea surface temperature (SST) of summer extreme precipitation in the YRB from 1981 to 2020 through the extreme precipitation metrics and Empirical Orthogonal Function (EOF) analysis. The results indicate that both the frequency and intensity of extreme precipitation exhibit an eastward and southward increasing pattern in terms of climate state, with regions of higher precipitation showing greater interannual variability. When precipitation in the YRB exhibits a spatially coherent enhancement pattern, high latitudes exhibits an Eurasian teleconnection wave train that facilitates the southward movement of cold air. Concurrently, the northward extension of the Western Pacific subtropical high (WPSH) enhances moisture transport from low latitudes to the YRB, against the backdrop of a transitioning SST pattern from El Niño to La Niña. When precipitation in the YRB shows a “south-increase, north-decrease” dipole pattern, the southward-shifted Ural high and westward-extended WPSH converge cold air and moist in the southern YRB region, with no dominant SST drivers identified. Full article

Background: Pre-season training is pivotal for optimizing athletic performance in collegiate basketball, yet the effectiveness of such programs in improving body composition (BC) under NCAA-mandated hourly restrictions remains underexplored. The aim of this study was to evaluate the impact of a four-week, NCAA Division II-compliant strength and conditioning (SC) program on BC in women’s basketball. Methods: Sixteen student athletes (20.6 ± 1.8 y; 173.9 ± 6.5 cm; 76.2 ± 20.2 kg) completed an eight-hour-per-week micro-cycle incorporating functional conditioning, Olympic-lift-centric resistance, and on-court skill development. Lean body mass (LBM) and body-fat percentage (BF%) were assessed using multi-frequency bioelectrical impedance on Day 1 and Day 28. Linear mixed-effects models were used to evaluate the fixed effect of Time (Pre, Post), including random intercepts for each athlete and covariate adjustment for age and height (α = 0.05). Results The LBM significantly increased by 1.49 kg (β = +1.49 ± 0.23 kg, t = 6.52, p < 0.001; 95% CI [1.02, 1.96]; R² semi-partial = 0.55), while BF% decreased by 1.27 percentage points (β = −1.27 ± 0.58%, t = −2.20, p = 0.044; 95% CI [−2.45, −0.08]; R² = 0.24). Height positively predicted LBM (β = +1.02 kg/cm, p < 0.001), whereas age showed no association (p > 0.64). Conclusions: A time-constrained, NCAA-compliant SC program meaningfully enhances lean mass and moderately reduces adiposity in collegiate women’s basketball athletes. These findings advocate for structured, high-intensity, mixed-modality training to maximize physiological readiness within existing regulatory frameworks. Future research should validate these results in larger cohorts and integrate performance metrics to further elucidate functional outcomes. Full article

While the interest and participation in general endurance training and recreational sports competitions have continuously increased in recent decades, the number of recreational master-level endurance athletes has additionally multiplied. Athletes, active men and women older than 40 years of age, who participate in competitive athletics are usually referred to by the term master athletes (MAs). Previous research revealed the significant benefits of regular moderate physical activity, i.e., its positive influence on cardiovascular risk factors and cardiovascular health; however, recent data have raised concerns that long-term endurance exercise participation is associated with cardiac remodeling and potential adverse cardiovascular outcomes. Previous research also indicated potential structural, functional, and electrical remodeling in MAs due to prolonged and repeated exposure to high-intensity endurance exercise—a condition known as athlete’s heart. In this review, we focus on the association between extreme levels of endurance exercise and potential cardiovascular controversies, such as arrhythmogenesis due to new-onset atrial fibrillation, accelerated coronary artery atherosclerosis, and exercise-induced cardiac remodeling. Additionally, the exercise-dependent modulation of immunological response, such as proteomic response and cytokine alterations, is discussed. Furthermore, we discuss the impact of nutritional supplements in MAs and their potential benefits and harmful interactions. We aim to provide sports medicine practitioners with knowledge of these contemporary longevity controversies in sports cardiology and to highlight the importance of shared decision making in situations of clinical uncertainty. Full article

Radar echo extrapolation is a critical forecasting tool in the field of meteorology, playing an especially vital role in nowcasting and weather modification operations. In recent years, spatiotemporal sequence prediction models based on deep learning have garnered significant attention and achieved notable progress in radar echo extrapolation. However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. Typically, radar echo intensity values ranging from −5 to 70 dBZ with a resolution of 5 dBZ are converted into 0–255 grayscale images from pseudo-color representations, which inevitably results in the loss of important echo details. Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. These variables are encoded jointly with high-resolution (0.5 dB) radar mosaic data to form multiple radar cells as input. A multi-channel radar echo extrapolation network architecture (MR-DCGAN) is then designed based on the DCGAN framework; (3) Since radar echo decay becomes more prominent over longer extrapolation horizons, this study departs from previous approaches that use a single model to extrapolate 120 min. Instead, it customizes time-specific loss functions for spatiotemporal attenuation correction and independently trains 20 separate models to achieve the full 120 min extrapolation. The dataset consists of radar composite reflectivity mosaics over North China within the range of 116.10–117.50°E and 37.77–38.77°N, collected from June to September during 2018–2022. A total of 39,000 data samples were matched with the initial zero-hour fields from RMAPS-NOW, with 80% (31,200 samples) used for training and 20% (7800 samples) for testing. Based on the ConvLSTM and the proposed MR-DCGAN architecture, 20 extrapolation models were trained using four different input encoding strategies. The models were evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). Compared to the baseline ConvLSTM-based extrapolation model without physical variables, the models trained with the MR-DCGAN architecture achieved, on average, 18.59%, 8.76%, and 11.28% higher CSI values, 19.46%, 19.21%, and 19.18% higher POD values, and 19.85%, 11.48%, and 9.88% lower FAR values under the 20 dBZ, 30 dBZ, and 35 dBZ reflectivity thresholds, respectively. Among all tested configurations, the model that incorporated three physical variables—relative humidity (rh), u-wind, and v-wind—demonstrated the best overall performance across various thresholds, with CSI and POD values improving by an average of 16.75% and 24.75%, respectively, and FAR reduced by 15.36%. Moreover, the SSIM of the MR-DCGAN models demonstrates a more gradual decline and maintains higher overall values, indicating superior capability in preserving echo structural features. Meanwhile, the comparative experiments demonstrate that the MR-DCGAN (u, v + rh) model outperforms the MR-ConvLSTM (u, v + rh) model in terms of evaluation metrics. In summary, the model trained with the MR-DCGAN architecture effectively enhances the accuracy of radar echo extrapolation. Full article

This paper presents FWStab, a specialized video stabilization dataset tailored for flapping-wing platforms. The dataset encompasses five typical flight scenarios, featuring 48 video clips with intense dynamic jitter. The corresponding Inertial Measurement Unit (IMU) sensor data are synchronously collected, which jointly provide reliable support for multimodal modeling. Based on this, to address the issue of poor image acquisition quality due to severe vibrations in aerial vehicles, this paper proposes a multi-modal signal fusion video stabilization framework. This framework effectively integrates image features and inertial sensor features to predict smooth and stable camera poses. During the video stabilization process, the true camera motion originally estimated based on sensors is warped to the smooth trajectory predicted by the network, thereby optimizing the inter-frame stability. This approach maintains the global rigidity of scene motion, avoids visual artifacts caused by traditional dense optical flow-based spatiotemporal warping, and rectifies rolling shutter-induced distortions. Furthermore, the network is trained in an unsupervised manner by leveraging a joint loss function that integrates camera pose smoothness and optical flow residuals. When coupled with a multi-stage training strategy, this framework demonstrates remarkable stabilization adaptability across a wide range of scenarios. The entire framework employs Long Short-Term Memory (LSTM) to model the temporal characteristics of camera trajectories, enabling high-precision prediction of smooth trajectories. Full article

Neuromuscular gait deficits in children with autism spectrum disorder (ASD) are often overlooked. High-intensity training protocols may improve running performance, but their efficacy in pediatric populations is underexplored. This study evaluates the impact of a high-intensity running protocol on locomotor performance in neurotypical and neurodivergent children (children with ASD). Spatiotemporal gait characteristics (speed, stride frequency, stride length, and duty factor), gait symmetry (symmetry ratio), and kinematics were assessed for ten neurodivergent children (10–15 years old) during a 15 m sprint. Locomotor costs (cost of locomotion, transport, and locomotion per stride) were analyzed in six neurodivergent participants (11–14 years old) via open-flow respirometry during treadmill running. Participants completed a 5–12 week, twice-weekly program; neurotypical participants served as a control group. Neurodivergent and neurotypical children exhibited baseline differences in spatiotemporal variables. Following training, neurodivergent participants demonstrated statistically significant improvements in spatiotemporal metrics and locomotor costs. Differences in symmetry between the two groups were not present pre- or post-program. These findings highlight the efficacy of high-intensity running programs in improving sensorimotor function and coordination in children with ASD. This program provides valuable insights into gross motor rehabilitation for neurodivergent children, supporting its potential as an effective intervention. Full article

This study investigated the effects of high-intensity interval training (HIIT) in elite youth soccer players using a novel multidomain correlational mapping approach. A four-week HIIT intervention was applied in a randomized controlled design, with physiological, cognitive, and neuromuscular data collected through laboratory, field-based, and biochemical tests. Metrics such as VO₂max, BDNF levels, lactate dynamics, and cognitive load were analyzed across time points and groups. HIIT elicited statistically significant improvements in aerobic capacity, buffering efficiency, and perceptual-cognitive function, with a notable emergence of cross-domain associations. Unlike the control group, HIIT participants showed strengthened correlations between metabolic, cognitive, and neuromuscular indices, such as lactate slope with exertion perception and BDNF response with cardiac recovery. Hierarchical clustering further revealed tightly integrated multidomain clusters in the HIIT group, absent in the controls, suggesting a reorganization of physiological networks. These findings support the concept that HIIT not only improves discrete capacities but fosters systemic adaptation through enhanced inter-domain coordination. These results align with emerging frameworks in network physiology and highlight the potential for using correlation structures as biomarkers of holistic training adaptation. This multidimensional perspective offers new insights into how targeted training reshapes performance-related systems and may inform individualized athletic programming. Full article

Background: Cystic fibrosis (CF) is a chronic genetic disease marked by progressive lung function decline and increased respiratory infections. Emerging evidence supports the role of physical exercise in improving lung function, aerobic capacity, and quality of life in pediatric CF patients. Methods: We reviewed randomized clinical trials and observational studies from the last ten years, sourced from PubMed and Google Scholar. Included studies involved children and adolescents (0–18 years) with CF and assessed physical exercise as a primary intervention to improve lung function, aerobic fitness, quality of life, or hospitalization rates. Results: Aerobic training, particularly when combined with strength training, improves cardiorespiratory fitness and muscle strength without compromising nutritional status. High-Intensity Interval Training and Inspiratory Muscle Training show potential but need further validation. Supervised, personalized exercise programs are key to promoting adherence and optimizing outcomes. Conclusions: Exercise-based interventions in pediatric CF should evolve toward personalized, technology-enhanced, and sustainable models. Integrating wearable devices, adapting programs to individual needs, and leveraging early parental involvement may enhance engagement and outcomes, especially in the era of CFTR modulator therapies. Full article

Background/Objectives: Physical activity is generally recommended, but youth with disabilities present increased sedentary behaviors. This is often due to limited or inaccessible exercise options. The aim of this systematic review was to report on the state of knowledge about the role of adapted physical activity (APA) in improving gross motor performance (query 1), participation and QoL (query 2) of children and adolescents with neurological motor disability. Methods: Pubmed, Scopus and Cinahl databases were enquired in October 2023 and updated in May 2025. Inclusion criteria were the following: any type of physical activity; pediatric subjects with any neuromotor disease; and any type of outcome measure regarding gross motor performance, participation or QoL. The risk of bias (RoB) was assessed by means of ROB 2, Robins-I and JBI tools. Results were synthetized focusing on the outcome measures and the type of activity proposed. Results: Thirteen and seven studies were included relative to queries 1 and 2, respectively. They all were RCTs, and some presented randomization RoB. Several types of APA (e.g., resistance, high-intensity circuit, running, cycling, aquatic and dance training) and of outcome measures were enquired, mostly focusing on subjects with cerebral palsy or Down syndrome. An increased time of moderate-to-vigorous physical activity, improvement in timed functional tests, muscle strength and stability were observed. Conclusions: APA may improve functioning, social participation and promote active lifestyle in pediatric persons with neuromotor disabilities, without adverse effects. In the future, more specific indications based on the functioning profile are advisable to orient professionals to define individualized safe training programs. Full article

Medical education is inherently demanding, requiring students to balance intense academic workload, clinical training, and emotional resilience. High levels of stress and burnout among medical students have been associated with decreased empathy, poorer academic performance, and increased risk of mental health problems. This cross-sectional, correlational study examined the relationships between emotional intelligence (EI), perceived stress, and burnout among undergraduate medical students at the Alexandria Faculty of Medicine. Participants completed self-report questionnaires: the Mind Tools Emotional Intelligence Test, the Perceived Stress Scale, and the Maslach Burnout Inventory. Descriptive statistics, bivariate correlations, and multivariate regression models were used for analysis. Among the 264 participants (88% response rate), the majority (73.4%) demonstrated average EI with no statistically significant differences across gender and academic year. Higher perceived stress was strongly correlated with emotional exhaustion and depersonalization, and it was also inversely correlated with personal accomplishment. Regression analysis indicated that gender, academic year, and academic grade were not independent predictors of stress or burnout (R² = 0.054). Approximately 30.3% of the students met the criteria for burnout. These findings highlight the complex interplay between emotional functioning and burnout, and they also suggest that interventions targeting emotional regulation and resilience may be beneficial in reducing stress and promoting well-being among medical students. Full article