Search Results (26,820)

Background: High-intensity interval training (HIT) is a time-efficient strategy to improve metabolic health in children, but its impact on inflammatory markers is still unclear. Therefore, we conducted a meta-analysis to examine the role of HIT on pro-inflammatory cytokines including C-reactive protein (CRP), interleukin 6 (IL-6), and tumor necrosis factor-alpha (TNF-α) in children with overweight/obesity. Methods: A meta-analysis was conducted following PRISMA guidelines. PubMed, Web of Science, Scopus, and Embase were searched up to 31 July 2025, for studies involving children with overweight/obesity aged 6 to 18 years. Randomized controlled trials and non-randomized controlled trials with outcome measurements that included CRP, IL-6, and TNF-α were included. Random-effects models were used to aggregate a mean effect size (ES) with 95% confidence intervals (CI), and potential moderators were explored. Results: In total, 768 participants from 15 studies were included. HIT significantly improved CRP (574 participants, 13 studies, SMD= −0.63, 95% CI: −1.02 to −0.24, p < 0.01) when compared to control group/pre-intervention. There were no significant effects on IL-6 and TNF-α, and no differences when compared to moderate-intensity training. Subgroup analyses indicated greater effectiveness in intervention duration, work-and-rest ratio, and work time were the significant moderators (p < 0.05). Conclusions: High-intensity interval training is effective for reducing CRP levels in children with obesity. Intervention duration, work-and-rest ratio, and work time can affect the intervention effects of HIT. Full article

In this paper, an anisotropic object electromagnetic image reconstruction system based on a two-stage multi-frequency extended network is developed by deep learning techniques. We obtain the scattered field information by irradiating the TM different polarization waves to uniaxial objects located in free space. We input the measured single-frequency scattered field into the Deep Residual Convolutional Neural Network (DRCNN) for training and to be further extended to multi-frequency data by the trained model. In the second stage, we feed the multi-frequency data into the Deep Convolutional Encoder–Decoder (DCED) architecture to reconstruct an accurate distribution of the dielectric constants. We focus on EMIS applications using Transverse Magnetic (TM) and Transverse Electric (TE) waves in 2D scenes. Numerical findings confirm that our method can effectively reconstruct high-contrast uniaxial objects under limited information. In addition, the TM/TE scattering from uniaxial anisotropic objects is governed by polarization-dependent Lippmann–Schwinger integral equations, yielding a nonlinear and severely ill-posed inverse operator that couples the dielectric tensor components with multi-frequency field responses. Within this mathematical framework, the proposed two-stage DRCNN–DCED architecture serves as a data-driven approximation to the anisotropic inverse scattering operator, providing improved stability and representational fidelity under limited-aperture measurement constraints. Full article

Condition monitoring and diagnosis in a permanent magnet synchronous generator (PMSG) are crucial for ensuring its service continuity and reliability. Recent advancements have introduced innovative, non-invasive techniques for detecting mechanical and electrical faults in this machine. This paper proposes a novel application of the discrete wavelet transform and binary classifiers for diagnosing interturn short-circuit faults in a PMSG with high accuracy and low computational burden. The objective of fault diagnosis is to detect the presence of an interturn short-circuit fault (fault vs. no-fault) under different fault severities and operating speeds. Multiple binary models were trained separately for each fault scenario. The three-phase currents from the PMSG are processed using the discrete wavelet transform to extract features, which are then fed into a binary classifier based on a Random Forest algorithm. Optimization techniques are used to improve the performance of the binary classifiers. Experimental results obtained under various stator fault conditions in the PMSG are presented. Metrics such as accuracy and confusion matrices are used to evaluate the performance of binary classifiers. Full article

Background/Objectives: Accurate assessment of lymph nodes is of paramount importance for correct cN staging in head and neck cancer; however, it is very time-consuming for radiologists, and lymph node metastases of head and neck cancers may show distinct characteristics, such as central necrosis or very large size. Here, we evaluate the performance of a previously developed generic cervical lymph node segmentation model in a cohort of patients with head and neck cancer. Methods: In our retrospective single-center, multi-vendor study, we included 125 patients with head and neck cancer with at least one untreated lymph node metastasis. On the respective cervical CT scan, an experienced radiologist segmented lymph nodes semi-automatically. All 3D segmentations were confirmed by a second reader. These manual segmentations were compared to segmentations generated by an AI model previously trained on a different dataset of varying cancers. Results: In cervical CT scans from 125 patients (61.9 years ± 10.6, 100 men), 3656 lymph nodes were segmented as ground-truth, including 544 clinical metastases. The AI achieved an average recall of 0.70 with 6.5 false positives per CT scan. The average global Dice accounts for 0.73 per scan, with an average Hausdorff distance of 0.88 mm. When analyzing the individual nodes, segmentation accuracy was similar for non-metastatic and metastatic lymph nodes, with a sensitivity of 0.89 and 0.85. Localization performance was lower for metastatic than for non-metastatic lymph nodes, with a recall of 0.65 and 0.74, respectively. Model performance was worse for enlarged nodes (short-axis diameter ≥ 15 mm), with a recall of 0.36 and a sensitivity of 0.67. Conclusions: The AI model for generic cervical lymph node segmentation shows good performance for smaller nodes (SAD ≤ 15 mm) with respect to localization and segmentation accuracy. However, for clearly enlarged and necrotic nodes, a retraining of the generic AI algorithm seems to be required for accurate cN staging. Full article

Accurate indoor positioning and navigation remain significant challenges, with audio sensor-based sound source localization emerging as a promising sensing modality. Conventional methods, often reliant on multi-channel processing or time-delay estimation techniques such as Generalized Cross-Correlation, encounter difficulties regarding computational complexity, hardware synchronization, and reverberant environments where time difference in arrival cues are masked. While machine learning approaches have shown potential, their performance depends heavily on the discriminative power of input features. This paper proposes a novel feature extraction method named Short-Time Homomorphic Deconvolution, which transforms multi-channel audio signals into a 2D Time × Time-of-Flight representation. Unlike prior 1D methods, this feature effectively captures the temporal evolution and stability of time-of-flight differences between microphone pairs, offering a rich and robust input for deep learning models. We validate this feature using a lightweight Convolutional Neural Network integrated with a dual-stage channel attention mechanism, designed to prioritize reliable spatial cues. The system was trained on a large-scale dataset generated via simulations and rigorously tested using real-world data acquired in an ISO-certified anechoic chamber. Experimental results demonstrate that the proposed model achieves precise Direction of Arrival estimation with a Mean Absolute Error of 1.99 degrees in real-world scenarios. Notably, the system exhibits remarkable consistency between simulation and physical experiments, proving its effectiveness for robust indoor navigation and positioning systems. Full article

The integration of unmanned aerial vehicles (UAVs) and Federated Learning (FL) enables distributed model training while preserving data privacy. To overcome the challenges caused by centralized and synchronous model updates, we integrate Directed Acyclic Graph (DAG) blockchain-based FL into UAV networks. In this decentralized and asynchronous framework, UAVs can independently and autonomously participate in the FL process according to their own requirement. To achieve the high FL performance, it is essential for UAVs to actively contribute their computational and data resources to the FL process. However, it is challenging to ensure that UAVs consistently contribute their resources, as they may have a propensity to prioritize their own self-interest. Therefore, it is crucial to design effective incentive mechanisms that encourage UAVs to actively participate in the FL process and contribute their computational and data resources. Currently, research on effective incentive mechanisms for DAG blockchain-based FL framework in UAV networks remains limited. To address these challenges, this paper proposes a novel incentive mechanism that integrates both rewards and punishments to encourage UAVs to actively contribute to FL and to deter free riding under incomplete information. We formulate the interactions among UAVs as an evolutionary game, and the aspiration-driven rule is employed to imitate the UAV’s decision-making processes. We evaluate the proposed mechanism for UAVs within a DAG blockchain-based FL framework. Experimental results show that the proposed incentive mechanism substantially increases the average UAV contribution rate from $77.{04}_{\pm 0.84}\%$ (without incentive mechanism) to $97.{48}_{\pm 1.29}\%$. Furthermore, the higher contribution rate results in an approximate 2.23% improvement in FL performance. Additionally, we evaluate the impact of different parameter configurations to analyze how they affect the performance and efficiency of the FL system. Full article

Background: Role clarity is a persistent challenge among Patient Care Technicians (PCTs), contributing to inconsistent task performance and safety risks. In Saudi Arabia, little is known about PCTs’ understanding of their responsibilities. This study evaluated the impact of a targeted educational program designed to improve PCTs’ role clarity, safety practices, and communication. Methods: A quasi-experimental pre-post study was conducted in September 2025 with 35 PCTs from the Hail Health Cluster. The one-day intervention included lectures, discussions, role-play, and case scenarios. Outcomes were measured using a validated instrument across four domains: role clarity; core clinical tasks and safety; communication and ethics; and objective knowledge. Pre-post changes were analyzed using paired t-tests (Cohen’s d), and subgroup differences in change scores were examined using one-way ANOVA (η²) in SPSS v29. Results: Baseline scores were lowest in objective knowledge (41.4%) and role clarity (62.8%). Post-training, total composite scores improved significantly (+10.88%, p < 0.001, d = 1.63), with the most significant gain in objective knowledge (+19.8%, p < 0.001, d = 0.99). Role clarity showed only a modest, non-significant increase (+3.98%, p = 0.088, d = 0.30). No demographic differences were found. Conclusions: Targeted training was effective in reducing knowledge gaps; however, improving role clarity may require organizational reinforcement beyond brief training. Full article

Metaphor is ubiquitous in daily communication and makes language expression more vivid. Identifying metaphorical words, known as metaphor detection, is crucial for capturing the real meaning of a sentence. As an important step of metaphorical understanding, the correct interpretation of metaphorical words directly affects metaphor detection. This article investigates how to use metaphor interpretation to enhance metaphor detection. Since previous approaches for metaphor interpretation are coarse-grained or constrained by ambiguous meanings of substitute words, we propose a different interpretation mechanism that explains metaphorical words by means of gloss-based interpretations. To comprehensively explore the optimal joint strategy, we go beyond previous work by designing diverse model architectures. We investigate both classification and sequence labeling paradigms, incorporating distinct component designs based on MIP and SPV theories. Furthermore, we integrate Part-of-Speech tags and external knowledge to further refine the feature representation. All methods utilize pre-trained language models to encode text and capture semantic information of the text. Since this mechanism involves both metaphor detection and metaphor interpretation but there is a lack of datasets annotated for both tasks, we have enhanced three datasets with glosses for metaphor detection: one Chinese dataset (PSUCMC) and two English datasets (TroFi and VUA). Experimental results demonstrate that the proposed joint methods are superior to or at least comparable to state-of-the-art methods on the three enhanced datasets. Results confirm that joint learning of metaphor detection and gloss-based interpretation makes metaphor detection more accurate. Full article

Visual search behavior, influenced by expertise, prior knowledge, training, and visual fatigue, is crucial in ophthalmic diagnostics. This study investigates differences in eye-tracking strategies between novice and experienced eye care practitioners during fundus image interpretation. Forty-seven participants, including 37 novices (first- to fourth-year optometry students) and 10 experienced optometrists (≥2 years of experience), viewed 20 fundus images (10 normal, 10 abnormal) while their eye movements were recorded using an Eyelink1000 Plus gaze tracker (2000 Hz). Diagnostic and laterality accuracy were assessed, and statistical analyses were conducted using Sigma Plot 12.0. Results showed that experienced practitioners had significantly higher diagnostic accuracy (83 ± 6.3%) than novices (70 ± 12.9%, p < 0.005). Significant differences in oculomotor behavior were observed, including median latency (p < 0.001), while no significant differences were found in median peak velocity (p = 0.11) or laterality accuracy (p = 0.97). Diagnostic accuracy correlated with fixation count in novices (r = 0.54, p < 0.001), while laterality accuracy correlated with total dwelling time (r = −0.62, p < 0.005). The experienced practitioners demonstrated systematic and focused visual search patterns, whereas the novices exhibited unorganized scan paths. Enhancing training with visual feedback could improve fundus image analysis accuracy in novice clinicians. Full article

There are many studies showing fitness and/or sporting skill is associated with better cognitive performance. One mechanism proposed to explain these effects is increased neural plasticity, meaning better cognitive performance could be the result of a more trainable brain. This theory was tested by looking at the initial performance and the performance following training on a visual search task for individuals engaged in sports and for control individuals. Analysis of variance for speed of task performance with factors of group, gender and sport for both speed of responses on the task and improvement in response times with practice showed no significant effects (analysis of variance F < 1.9, p > 0.175 for all group effects). The only significant difference was a reduced difference between specific and non-specific learning on the task, likely indicative of reduced non-specific learning in some of the sports groups (runners/controls, ANOVA F(1, 43) = 5.484, p = 0.024, η_p² = 0.113 a medium effect size; and male baseball, runners and controls F(2, 33) = 3.427, p = 0.044, η_p² = 0.172 a large effect size), possibly due to previous improvement because of fitness or sporting skill. These findings suggest a need for specificity in terms of selecting sport training when trying to produce cognitive benefits and a need for better assessment of sport-specific and sport/fitness-general effects on cognitive performance. Full article

This study presents the first application of machine learning (ML) to detect and map mowed tidal wetlands in the Chesapeake Bay region of Maryland and Virginia, focusing on emergent estuarine intertidal (E2EM) wetlands. Monitoring human disturbances like mowing is essential because repeated mowing stresses wetland vegetation, reducing habitat quality and diminishing other ecological services wetlands provide, including shoreline stabilization and water filtration. Traditional field-based monitoring is labor-intensive and impractical for large-scale assessments. To address these challenges, this study utilized 2021 and 2022 Sentinel-2 satellite imagery and a time-series analysis of the Normalized Difference Vegetation Index (NDVI) to distinguish between mowed and unmowed (control) wetlands. A bidirectional Long Short-Term Memory (BiLSTM) neural network was created to predict NDVI patterns associated with mowing events, such as rapid decreases followed by slow vegetation regeneration. The training dataset comprised 204 field-verified and desktop-identified samples, accounting for under 0.002% of the research area’s herbaceous E2EM wetlands. The model obtained 97.5% accuracy on an internal test set and was verified at eight separate Chesapeake Bay locations, indicating its promising generality. This work demonstrates the potential of remote sensing and machine learning for scalable, automated monitoring of tidal wetland disturbances to aid in conservation, restoration, and resource management. Full article

Background: Microsaccades are small fixational eye movements tightly linked to attention and oculomotor control. Although diabetes mellitus is associated with retinal and neural alterations that may impair visuomotor function, the influence of physical activity on microsaccade behaviour in individuals with type 2 diabetes mellitus (T2DM) remains unknown. This study investigated whether habitual physical activity modulates microsaccade characteristics during fixation under different optic flow stimuli. Given that optic flow engages motion processing and gaze stabilisation pathways that may be affected by diabetes-related microvascular/neural changes, it can reveal subtle visuomotor alterations during fixation. Methods: Twenty-eight adults with T2DM and no diagnosed retinopathy performed a fixation task while viewing optic flow stimuli made of moving dots. Eye movements were recorded using an EyeLink system. Physical activity behaviour was assessed at baseline and at a 6-month follow-up after a low-threshold aerobic circuit training programme. Classification as physically active (≥600 MET-min/week) or inactive (<600 MET-min/week) was based on the 6-month assessment. Microsaccade characteristics were analysed by repeated-measures ANOVA. Results: Microsaccade rate was modulated by optic flow (p = 0.044, η²p = 0.106) and showed a significant stimulus × group × sex interaction (p = 0.005, η²p = 0.163), indicating sex-dependent differences in how optic flow modulated microsaccade rate across physically active and inactive participants. A time × stimulus interaction effect was found in peak velocity (p = 0.03, η²p = 0.114) and amplitude (p = 0.02, η²p = 0.127), consistent with modest context-dependent changes over time. Conclusions: These findings suggest that physical activity modulates microsaccade generation and supports the potential of microsaccade metrics as sensitive indicators of oculomotor function in diabetes. Full article

This study presents a standardized method for multimodal monitoring of exercise execution in isolated, confined, and extreme (ICE) environments, addressing the need for reproducible assessment of neuromuscular and cardiovascular responses under space- and equipment-limited conditions. The method integrates wearable surface electromyography (sEMG), inertial measurement units (IMU), and electrocardiography (ECG) to capture muscle activation, movement, and cardiac dynamics during space-efficient exercise. Ten exercises suitable for confined habitats were implemented during analog missions conducted in the DeepLabH03 facility, with feasibility evaluated in a seven-day campaign involving three adult participants. Signals were synchronized using video-verified repetition boundaries, sEMG was normalized to maximum voluntary contraction, and sEMG amplitude- and frequency-domain features were extracted alongside heart rate variability indices. The protocol enabled stable real-time data acquisition, reliable repetition-level segmentation, and consistent detection of muscle-specific activation patterns across exercises. While amplitude-based sEMG indices showed no uniform main effect of exercise, robust exercise-by-muscle interactions were observed, and sEMG mean frequency demonstrated sensitivity to differences in movement strategy. Cardiac measures showed limited condition-specific modulation, consistent with short exercise bouts and small sample size. As a proof-of-concept feasibility study, the proposed protocol provides a practical and reproducible framework for multimodal physiological monitoring of exercise in ICE analogs and other constrained environments, supporting future studies on exercise quality, training load, and adaptive feedback systems. The protocol is designed to support near-real-time monitoring and forms a technical basis for future exercise-quality feedback in confined habitats. Full article

Background/Objectives: Functional limitations are common among older cancer survivors and tend to increase with age and survivorship duration. Physical activity (PA) associates with better functional outcomes, but little is known about how these associations vary as time passes post-diagnosis. This study examined how years since diagnosis, three types of physical activity, and their interactions associate with functional limitations in older cancer survivors. Methods: Data drawn from the 2021 National Health Interview Survey (NHIS), representing adults aged 55+ and with a prior cancer diagnosis (n = 9356; mean age = 72.17 ± 8.5 years), were studied. A four-item self-reported difficulty index (i.e., washing/dressing, walking one block, climbing stairs, and picking up/opening objects) was summed to measure functional limitations. PA was assessed using the items aligned with the United States PA Guidelines. Hierarchical regression was used to evaluate associations between functional limitations and years since diagnosis, vigorous physical activity, moderate physical activity, and strength training. Interaction effects of years since diagnosis and each activity type were also examined. Covariates were age, sex, BMI, and educational attainment. Results: Elapsed time since cancer diagnosis positively associated with functional limitations in interaction with physical behaviors, while moderate physical activity and strength training negatively associated with functional limitations. Interactions of years since diagnosis and both moderate physical activity and strength training revealed smaller increases in functional limitations. No interaction effects were observed for vigorous physical activity. Conclusions: Among older cancer survivors, the association between survivorship duration and functional limitations differs by engagement in moderate and resistance-based physical activity. These findings support the clinical importance of promoting sustainable, non-vigorous physical activity in long-term survivorship care. Full article

This study examined whether the Ringelmann and Köhler effects emerge in adolescent rowing by assessing how crew size influences performance, physiological responses and perceived exertion in youth rowers aged 14–17 years. A total of 136 competitive rowers (mean age = 15.79 ± 1.14 years) completed four three-minute maximal-effort trials on a rowing ergometer under four conditions: individual trials, two-person crews, four-person crews and eight-person crews. Objective performance indicators, including stroke rate, heart rate and perceived exertion (Borg scale), were recorded. Repeated-measures ANOVA indicated that objective performance indicators (distance and power output) remained largely stable across conditions and age groups, although some isolated and non-systematic differences with large intra-subject effect sizes emerged in the younger category (14–15 years), particularly in the two-person crew condition. In contrast, the stroke rate differed consistently across crew sizes, with higher values observed in the eight-person crew condition in both age groups. Cardiovascular responses showed minimal and transient variation between conditions. Perceived exertion differed markedly by age, with older rowers (16–17 years) reporting significantly higher effort during individual trials compared with crew-based conditions, without corresponding gains in objective performance. Overall, although crew size influenced the regulation and perception of effort, the findings do not provide support for a consistent expression of either the Ringelmann or Köhler effects in adolescent rowing, as no systematic performance losses or motivational gains among weaker crew members were evident. These results suggest that developmental differences in self-regulation and effort perception may play a more prominent role than crew size alone in shaping performance responses, with practical implications for training design and crew configuration in youth rowing. Full article