Search Results (4,432)

Metamaterials possess high freedom on structural design, yet their ability to modulate electromagnetic waves is subject to intrinsic constraints that are independent of specific meta-atom geometries. The constraints are revealed by analyzing the statistical amplitudes and phases of transmission and reflection wave in some representative metamaterials. Based on scattering theory, a reconstructed and more general description of the electromagnetic modulation process in metamaterials is established. Two explicit and geometry-independent corollaries concerning the coupling between transmission and reflection waves are further obtained and verified. The results provide a new perspective on the fundamental modulation mechanism of metamaterials on electromagnetic waves. Full article

Wind turbine blades are subjected to complex environmental conditions during long-term operation, which may lead to structural degradation and performance loss. To ensure structural integrity, fatigue testing prior to deployment is essential. This paper proposes a vision-based method for measuring the full-cycle breathing deformation of wind turbine blades during fatigue testing. The method captures dynamic image sequences of the blade’s hotspot cross-section using industrial cameras and employs a feature-based template matching approach to reconstruct the three-dimensional coordinates of target points. Through coordinate transformation, the deformation trajectories are obtained, enabling quantitative analysis of the blade’s dynamic responses in both flapwise and edgewise directions. A dedicated hardware–software system was developed and validated through full-scale fatigue experiments. Quantitative comparison with strain gage measurements shows that the proposed method achieves mean absolute deviations of 0.84 mm and 0.93 mm in two independent experiments, respectively, with closely matched deformation trends under typical loading conditions. These results demonstrate that the proposed method can reliably capture the global deformation behavior of the blade with millimeter-level accuracy, while significantly reducing instrumentation complexity compared to conventional contact-based approaches. The proposed method provides an effective and practical solution for full-field dynamic deformation measurement in blade fatigue testing, offering strong potential for structural health monitoring and early damage detection in wind turbine systems. Full article

Background: In Greece, nutrition education is not taught as an independent subject but is incorporated across multiple disciplines. Objective: To evaluate the extent, distribution, and thematic orientation of nutrition-related content in Greek secondary school textbooks. Methods: A systematic content analysis was conducted on all officially approved textbooks used in Greek secondary education during the academic year 2022–2023. A total of 164 textbooks (26,914 pages) were analyzed. Nutrition-related references were systematically identified using predefined keywords and categorized into thematic domains. Coding was performed independently by two researchers (Cohen’s κ = 0.84). Results: A total of 1426 nutrition-related references were identified. Nutrition-related content was unevenly distributed, with 57.1% in Gymnasium textbooks and 42.9% in Lyceum textbooks. When normalized, Gymnasium textbooks contained 7.47 references per 100 pages compared with 3.82 in Lyceum. Content was primarily focused on biological concepts (27.1%) and health/disease prevention (20.3%), while behavioral (16.5%), psychosocial (16.2%), and sustainability-related (19.8%) dimensions were less represented. No references addressed skills-based nutrition education. Conclusions: Nutrition education in Greek textbooks is present but fragmented and predominantly biologically oriented. The lack of behavioral and skills-based content suggests the need for more comprehensive and interdisciplinary educational approaches. Full article

The electrocardiogram (ECG) is a central tool in cardiovascular diagnostics, yet interpretation requires expertise and remains subject to variability. Multimodal large language models (MLLMs) have shown emerging capabilities in medical image analysis, but their performance in ECG interpretation remains insufficiently characterized. This study evaluated the diagnostic accuracy and inter-run reliability of five MLLMs across ECG interpretation tasks. Thirteen standard 12-lead ECGs were presented to five models (ChatGPT-5.3, Gemini 3.1 Pro, Claude Opus 4.6, Grok 4.1, and ERNIE 5.0) across five independent runs per case, yielding 2275 task-level assessments. Six categorical interpretation tasks (rhythm, electrical axis, PR/P-wave morphology, QRS duration, ST/T-wave morphology, and QTc interval) were compared with expert-consensus ground truth, while heart rate estimation was evaluated using mean absolute error (MAE). Overall categorical accuracy ranged from 52.3% to 64.9%. QRS duration classification achieved the highest accuracy (66.2–90.8%), whereas ST/T-wave assessment showed the lowest performance (20.0–41.5%). Heart rate MAE ranged from 14.8 to 46.7 bpm. A dissociation between diagnostic accuracy and inter-run reliability was observed across models. These findings indicate that current MLLMs do not achieve clinically reliable ECG interpretation performance and highlight the importance of assessing diagnostic accuracy and inter-run reliability when evaluating artificial intelligence systems in biomedical diagnostics. Full article

Background: The nasolabial angle is widely used in orthodontic diagnosis, but its composite geometric nature complicates interpretation. Whether it primarily reflects skeletal or soft-tissue influences remains unclear, as no previous study has quantitatively partitioned its angular components using multivariable regression in a large adult sample. Objective: The objective of this study is to quantify the relative angular contributions of skeletal classification and soft-tissue parameters to NLA morphology using a multivariate cephalometric model. Methods: A retrospective analysis of 504 adult lateral cephalograms was conducted. Subjects were categorized by sagittal (Class I, II, or III) and vertical skeletal patterns (hyperdivergent, normodivergent, and hypodivergent). Measured variables included the NLA, nasal inclination (N/FH), upper lip inclination (L/FH), upper lip thickness, and maxillary incisor inclination. Correlation and multivariable regression analyses were performed. Results: Although the NLA differed across sagittal classes (p = 0.001; η² = 0.047), upper lip inclination demonstrated the strongest association with the NLA (r = 0.766; R² = 0.588). In multivariable analysis, L/FH exhibited the largest standardized contribution (β = 0.752, p < 0.001), while vertical pattern and lip thickness were not independently associated. Class II showed a modest independent association (β = 0.083, p = 0.006). Conclusions: Nasolabial angle variation largely reflects upper lip orientation rather than skeletal classification alone. These findings support component-based interpretation, emphasizing separate evaluation of lip inclination during esthetic orthodontic assessment. Full article

Background: R wave amplitude in the aVL ECG lead (R_aVL) has been identified as a marker of cardiovascular (CV) risk, hypertension-mediated target organ damage (HMOD), and mortality in patients with arterial hypertension (AH), where R_aVL > 1.1 mV suggests left ventricular hypertrophy. However, the exact threshold for identifying high-risk patients has yet to be determined. Therefore, we compared R_aVL values among hypertensive patients with and without hypertensive urgencies (HUs) and healthy subjects, aiming to identify the predictors of elevated R_aVL and to compare its prognostic value with the SCORE 2 model. Methods: This cross-sectional study included 339 participants divided into three groups according to ambulatory blood pressure monitoring results: 100 patients with AH and HU from the emergency department, 134 patients with AH without HU, and 105 healthy subjects recruited from four family medicine practices. Basic laboratory parameters were determined, SCORE 2 risk was calculated, PWV was measured using oscillometry, and a standard 12-lead ECG was recorded in all participants. Results: Participants with AH and HU had the highest R_aVL values compared to those with AH without HU and healthy subjects (averages of 0.76 ± 0.24 mV, 0.49 ± 0.27, 0.22 ± 0.25, respectively; p < 0.001). Significantly higher R_aVL values were observed in males compared to females (0.56 ± 0.31 vs. 0.41 ± 0.34 mV; p < 0.001) and in non-dippers compared to dippers (0.56 ± 0.34 mV vs. 0.41 ± 0.31 in dippers; p < 0.001). Age, mean arterial pressure, PWV, and SCORE 2 were shown as independent predictors of R_aVL. Compared with SCORE 2, individuals with R_aVL > 0.40 mV had high CV risk (sensitivity of 58.16%, specificity of 73.68%; p < 0.001). Conclusions: In this study, R_aVL demonstrated good prognostic value for CV risk stratification. However, larger studies are needed to determine a precise high-risk threshold to improve CV risk estimation and HMOD detection in patients with marginal SCORE 2 CV risk. Full article

Post-exercise appetite loss may interfere with adequate recovery nutrition in athletes; however, the substantial inter-individual variability in appetite responses remains insufficiently understood. This exploratory cross-sectional study investigated lifestyle- and health-related factors associated with post-exercise appetite loss in 35 female university athletes. Appetite loss was assessed as a self-reported binary outcome (often, sometimes/never). Associations with subjective sleep quality and other lifestyle-related variables were examined using contingency analysis, followed by exploratory logistic regression. Post-exercise appetite loss was reported by 74.3% of participants and did not differ across sports disciplines, indicating that the sport type alone did not explain the observed variability. Poor/fair subjective sleep quality was associated with appetite loss (OR = 11.6, 95% CI: 1.9–73.6) and remained associated in the multivariate model. Other lifestyle-related variables were not independently associated. These findings imply a potential connection linking post-exercise appetite responses in female university athletes to broader lifestyle-related factors, particularly subjective sleep quality, rather than exercise characteristics alone. Monitoring sleep quality may therefore help identify athletes who may be at risk of insufficient post-exercise energy intake and compromised recovery. Further studies with larger samples and longitudinal designs are needed to clarify these relationships. Full article

Botulinum neurotoxin (BoNT) treatment outcomes are commonly assessed through visual evaluation of facial wrinkle patterns, a process that remains inherently subjective despite structured grading systems. This study evaluated whether contemporary multimodal artificial intelligence (AI) systems can identify facial changes associated with BoNT treatment, using region-specific wrinkle patterns as surrogate markers of underlying muscle activity. A dataset of 46 facial images (23 pre-treatment, 23 post-treatment) was analyzed using four multimodal models, each assessed across five independent runs. Models were tasked with classifying treatment state from single images, detecting wrinkle presence in the forehead, glabella, and periorbital regions, and generating exploratory severity scores and age estimates. Two models achieved 100% accuracy in distinguishing pre- from post-treatment images in this dataset, while region-specific wrinkle detection was variable and frequently did not exceed majority-class baselines. Inter-run reliability varied substantially across models. Exploratory wrinkle severity scores showed directional differences between treatment states, whereas apparent age estimates demonstrated minimal systematic variation. These findings suggest that global facial changes associated with BoNT treatment appear to be detectable in model outputs, but region-specific assessment remains limited, underscoring the need for cautious interpretation and further validation. Full article

Background: The incidence and mortality rates of cancer among females aged 65 years or older in the Republic of Korea are increasing; however, the national cancer screening rate (50.4%) remains low. Therefore, this study aimed to identify predictors of participation in the National Cancer Screening Program (NCSP) among women aged 65 years or older using data from the 8th Korea National Health and Nutrition Examination Survey (KNHANES VIII, 2019–2021). Methods: This cross-sectional study utilized data from the 8th Korea National Health and Nutrition Examination Survey (KNHANES VIII, 2019–2021). Study variables were selected based on Andersen’s healthcare utilization model. Participation in the National Cancer Screening Program (NCSP) was defined as the dependent variable, and independent variables included predisposing, enabling, and need factors. Descriptive analyses were conducted to examine participants’ characteristics. Chi-square tests were used to assess differences in NCSP participation according to participant characteristics. Multivariable logistic regression analysis was conducted to identify factors associated with participation, with all independent variables simultaneously included in the model to adjust for potential confounding. A p-value of <0.05 was considered statistically significant. Results: A total of 2105 women aged 65 years or older were included in the analysis. Of the 2105 women aged 65 years or older, 1429 (67.9%) reported participation in cancer screening within the past two years. NCSP participation was significantly associated with being married (OR = 1.540; 95% CI: 1.263–1.879), being a middle school (OR = 1.357; 95% CI: 1.022–1.801) or college graduate or higher (OR = 2.012; 95% CI: 1.199–3.378), having private insurance (OR = 1.930; 95% CI: 1.573–2.368), average subjective health (OR = 1.332; 95% CI: 1.004–1.766), dyslipidemia (OR = 1.347; 95% CI: 1.110–1.636), and physical activity participation (OR = 1.252; 95% CI: 1.029–1.524). In contrast, urban residence, income level, being employed, medical coverage type, hypertension, diabetes, monthly drinking status and current smoking status were not statistically significantly correlated with NCSP participation. Conclusions: These findings highlight the need for tailored outreach strategies and health education programs targeting women aged 65 years and older to enhance participation in the NCSP and ultimately improve public health outcomes. Full article

Electroencephalography (EEG) provides a non-invasive and high-temporal-resolution modality for decoding cognitive states, but high-density recordings remain challenging for Transformer-based models because self-attention scales quadratically with the number of channels. In addition, conventional Euclidean representations do not fully capture the intrinsic geometry of EEG covariance features, which may limit robustness in cross-subject settings. To address these issues, we propose EEG-RCformer, a Riemannian geometry-informed channel clustering Transformer for EEG decoding. The model first computes per-channel symmetric positive definite (SPD) covariance matrices from windowed EEG features and uses the affine-invariant Riemannian metric (AIRM) to identify trial-specific functional hubs. These hubs are then integrated with capacity-constrained spatial clustering to generate anatomically plausible and computationally efficient channel groups, which are encoded as tokens for a Transformer classifier. We evaluated EEG-RCformer on the MODMA and SEED datasets under both subject-dependent and -independent paradigms, achieving area under the curve (AUC) values of 0.9802 and 0.7154 on MODMA and 0.8541 and 0.8011 on SEED, respectively. Paired statistical tests further showed significant gains for MODMA in both the subject-dependent and -independent settings and for SEED in the subject-dependent setting, while SEED still showed a positive but non-significant mean improvement in the subject-independent setting. Full article

This study proposes a morphological convolutional neural network (MCNN) architecture that integrates morphological operations with CNN layers for facial expression recognition (FER). Conventional CNN-based FER models primarily rely on appearance features and may be sensitive to illumination and demographic variations. This work investigates whether morphological structural representations provide complementary information to convolutional features. A multi-source and multi-ethnic FER dataset was constructed by combining CK+, JAFFE, KDEF, TFEID, and a newly collected Indonesian Facial Expression dataset, resulting in 3684 images from 326 subjects across seven expression classes. Subject-independent data splitting with 10-fold cross-validation was applied to ensure reliable evaluation. Experimental results show that the proposed MCNN1 model achieves an average accuracy of 88.16%, while the best MCNN2 variant achieves 88.7%, demonstrating competitive performance compared to MobileNetV2 (88.27%), VGG19 (87.58%), and the morphological baseline MNN (50.73%). The proposed model also demonstrates improved computational efficiency, achieving lower inference latency (21%) and reduced GPU memory usage (64%) compared to baseline models. These results indicate that integrating morphological representations into convolutional architectures provides a modest but consistent improvement in FER performance while enhancing generalization and efficiency under heterogeneous data conditions. Full article

Rapid and reliable tea classification is valuable for routine product screening, yet conventional sensory or physicochemical methods are subjective or time-consuming. Electronic nose (E-nose) sensing provides a fast alternative, but performance often degrades under domain shifts caused by different tea types, commercial categories, or acquisition conditions. This study proposes MGDA-Net, a multi-granularity domain adversarial network for cross-domain tea classification using E-nose time-series signals. MGDA-Net learns local temporal dynamics via a CNN branch and global contextual dependencies via a self-attention branch, and fuses them through an adaptive gating module. A branch-level adversarial alignment strategy is introduced to reduce source–target discrepancy at both local and global feature levels. A three-stage training procedure, consisting of source pretraining, adversarial alignment, and target fine-tuning, enables knowledge transfer from a labeled green tea source-domain to two target tasks. Experiments on oolong tea commercial-category classification (6 classes) and jasmine tea retail price-level classification (8 classes) show that MGDA-Net achieves mean accuracies of 99.31 ± 0.69% and 99.38 ± 0.51% over 10 independent runs, substantially outperforming all compared baseline methods. Ablation studies, feature-space analyses, and label-efficiency experiments further confirm the contribution of each component and show that MGDA-Net maintains mean accuracies above 87% when only 40% of the target-domain labels are used for fine-tuning. These findings suggest that MGDA-Net is a promising approach for cross-domain tea classification using E-nose data. Full article

Encephalomyocarditis virus (EMCV) belongs to the genus Cardiovirus of the family Picornaviridae. It is a non-enveloped, positive-sense, single-stranded RNA virus and an important pathogen causing encephalomyocarditis (EMC). Tripartite motif 13 (TRIM13) is a member of the tripartite motif (TRIM) family and serves as an important effector molecule in antiviral innate immunity. However, its antiviral activity and underlying molecular mechanisms during EMCV infection remain unknown. In this study, we identified TRIM13 as a regulator of NF-κB activation. TRIM13, dependent on its E3 ubiquitin ligase activity, directly binds to IκBα and dose-dependently increases its phosphorylation level. To determine the chain type of IκBα polyubiquitination, antibodies specific for K48-linked and K63-linked ubiquitin were used. Our data indicated that IκBα was subjected to polyubiquitination independent of K48 and K63 linkages. This interaction promotes non-K48/K63-linked polyubiquitination of IκBα, thereby inducing NF-κB nuclear translocation. Subsequently, nuclear NF-κB activates the secretion of pro-inflammatory cytokines, exacerbating inflammatory responses and ultimately facilitating EMCV infection. Full article

Introduction: Nerve-sparing (NS) during robot-assisted radical prostatectomy (RARP) plays a critical role in postoperative functional recovery, particularly urinary continence and erectile function. Despite the importance of precise neurovascular bundle (NVB) preservation, intraoperative assessment of NS quality remains largely subjective and lacks standardized evaluation tools. The aim of this study was to develop and preliminarily evaluate a structured intraoperative scoring system designed specifically for assessing NS quality during RARP. Methods: A novel 10-point intraoperative NS scoring system (NSQ Score) based on five domains was developed: dissection plane, bleeding control, bundle manipulation, continuity of dissection, and symmetry. Each parameter was rated on a 0–2 scale. Thirty robot-assisted radical prostatectomy (RARP) procedures performed in 2024 were randomly selected from a prospectively maintained institutional surgical video archive. Cases were not pre-filtered based on tumor stage, surgical difficulty, or intraoperative complexity. High-definition video recordings of the nerve-sparing phase were anonymized and independently evaluated by three experienced observers blinded to patient outcomes and to each other’s assessments. Inter-rater agreement was analyzed using weighted Cohen’s kappa statistics with quadratic weights, complemented by exact and near-agreement proportions. Cluster bootstrap resampling was applied to account for bilateral observations. Results: A total of 48 evaluable observations were analyzed. The overall inter-rater agreement demonstrated a weighted kappa of 0.41 (95% CI 0.36–0.48), indicating fair-to-moderate agreement among reviewers. Exact agreement occurred in 43% of observations, while near-agreement (allowing one ordinal level difference) reached 98%. Among individual parameters, symmetry demonstrated the highest reliability with substantial agreement (κ = 0.70; 95% CI 0.58–0.81). Other domains showed fair agreement, including intraoperative bleeding (κ = 0.36), continuity of dissection (κ = 0.39), bundle manipulation (κ = 0.34), and dissection plane (κ = 0.27). Agreement levels were comparable between left- and right-sided dissections. Conclusions: We propose a novel structured intraoperative scoring system for evaluating nerve-sparing quality during RARP. The scale is simple, procedure-specific, and feasible for structured postoperative or video-based assessment. Preliminary results demonstrate fair-to-moderate inter-rater reliability with very high near-agreement, supporting the feasibility of this tool for clinical use. The proposed scoring system may facilitate standardized training, objective performance assessment, and future studies correlating intraoperative NS quality with functional outcomes. Full article

Background: Polysomnography (PSG) is the gold standard for diagnosing sleep disorders. However, the subjectivity of manual scoring can lead to inter-scorer variability, undermining diagnostic accuracy and subsequent clinical decisions. This study aims to quantitatively assess scoring concordance among multiple scorers across various clinical subgroups to identify the factors that contribute to interpretive discrepancies. Methods: We conducted a retrospective analysis of overnight diagnostic PSG data from adult patients at a tertiary university hospital sleep center. Interrater reliability was evaluated by three independent expert scorers for 30 subjects selected through stratified random sampling. The polysomnographic data were independently and blindly scored according to the American Academy of Sleep Medicine criteria, focusing on sleep stages, arousals, respiratory events, and leg movements, all scored in 30 s epochs. Interrater agreement was measured using Fleiss’ κ, along with 95% confidence intervals, and included subgroup analyses by diagnostic category. Results: The analysis included a total of 28,291 epochs from 30 adults across normal, insomnia, obstructive sleep apnea (OSA) [mild–severe], and periodic limb movement (PLM) disorder subgroups. The overall interrater agreement for sleep staging among the three scorers was nearly perfect (Fleiss’ κ = 0.932), with the highest concordance observed in stages W, N2, and R, and excellent agreement in stages N1 and N3. Respiratory events showed particularly high reliability, with near-perfect agreement for apnea (κ = 0.955) and substantial agreement for hypopnea, arousals, and PLMs. Pairwise analyses indicated the highest concordance between scorer 1 and scorer 3, while the agreement between scorer 1 and scorer 2 was lower, particularly for detecting arousals and limb movements. Subgroup analyses showed the highest and most stable agreement in moderate OSA, whereas severe OSA exhibited reduced reliability for sleep staging and arousal scoring, indicating increased scoring complexity with greater sleep fragmentation. Conclusions: Although expert PSG scoring demonstrates high overall reliability, significant variability persists in complex cases like severe OSA. These findings underscore the necessity for structured quality assurance and automated tools to improve diagnostic consistency in clinical practice. Full article