Search Results (6,618)

Background/Objectives: Accurate detection of thyroid cancer using ultrasound remains a challenge, as malignant nodules can be microscopic and heterogeneous, easily confused with point clusters and borderline-featured tissues. Current studies in deep learning demonstrate good performance with convolutional neural networks (CNNs) and clustering; however, many approaches focus on local tissue and provide limited, non-quantitative interpretation, reducing clinical confidence. This study proposes an integrated framework combining enhanced convolutional feature encoders (DenseNet169 and VGG19) with an enhanced vision transformer (ViT-E) to integrate local feature and global relational context during learning, rather than delayed integration. Methods: The proposed framework integrates enhanced convolutional feature encoders (DenseNet169 and VGG19) with an enhanced vision transformer (ViT-E), enabling simultaneous learning of local feature representations and global relational context. This design allows feature fusion during the learning stage instead of delayed integration, aiming to improve diagnostic performance and interpretability in thyroid ultrasound image analysis. Results: The best-performing model, ViT-E–DenseNet169, achieved 98.5% accuracy, 98.9% sensitivity, 99.15% specificity, and 97.35% AUC, surpassing the robust basic hybrid model (CNN–XGBoost/ANN) and existing systems. A second contribution is improved interpretability, moving from mere illustration to validation. Gradient-weighted class activation mapping (Grad-CAM) maps demonstrated distinct and clinically understandable concentration patterns across various thyroid cancers: precise intralesional concentration for high-confidence malignancies (PTC = 0.968), edge/interface concentration for capsule risk patterns (PTC = 0.957), and broader-field activation consistent with infiltration concerns (PTC = 0.984), while benign scans showed low and diffuse activation (PTC = 0.002). Spatial audits reinforced this behavior (IoU/PAP: 0.72/91%, 0.65/78%, 0.58/62%). Conclusions: The integrated ViT-E–DenseNet169 framework provides highly accurate thyroid cancer detection while offering clinically meaningful interpretability through Grad-CAM-based spatial validation, supporting improved confidence in AI-assisted ultrasound diagnosis. Full article

Foodborne diseases remain a major public health challenge. Among them, salmonellosis is one of the most frequently reported illnesses, associated with clusters and outbreaks and with considerable morbidity, potentially severe in vulnerable populations. Children are more susceptible due to biological, behavioral, and dietary factors. This study aimed to summarize and describe national surveillance data from the Mandatory Notification System, combined with serotyping data, on reported salmonellosis cases in Greece during the period 2005–2024, with a focus on children aged 0–14 years. During the study period, a total of 7340 salmonellosis cases were reported among children aged 0–14 years. Notification rates declined gradually until 2021, followed by an increase through 2024. The mean annual notification rate was 23.0 cases per 100,000 population, with the highest incidence observed among children aged 0–4 years. A clear seasonal pattern was observed, with a peak during summer months, alongside notable geographical variation. The most frequently identified serovars were Salmonella Enteritidis and Salmonella Typhimurium. These findings indicate that salmonellosis remains a public health concern in the pediatric population, highlighting the need for enhanced surveillance, improved food hygiene practices, and targeted prevention strategies to reduce disease burden. Full article

Monkeypox virus (MPXV) is emerging as a global public health concern due to its nature of spread. There are limited treatment options, as the sole drug for treatment is lacking, highlighting the need for new therapeutic options. The use of computer-aided drugs discovery such as molecular docking, molecular dynamic (MD) simulations and post-simulation analysis are important tools in identifying potential compounds that can target specific proteins of the virus, such as DNA polymerase to stop virus replication. This study employed molecular docking and molecular simulation with the aim to identify potential inhibitors for MPXV treatment from the ZINC Database. Molecular docking was performed using PyRx 0.8 version after virtual screening of the ZINC database using the Tranches tool; then, toxicity prediction of the selected compounds was performed using the ProTox-3.0 web server. Molecular dynamics simulation was conducted using GROMACS version 4.5 to evaluate the structural stability and dynamic behavior of the protein–ligand complex for the best interacting compound. Furthermore, post-simulation analysis was conducted using standard GROMACS utilities for visualizing time-dependent properties from MD simulations. A total of 16 compounds were shortlisted based on their molecular docking scores and interaction profiles with the monkeypox virus DNA polymerase (PDB ID: 8HG1). The leading compound, ZINC000019418450, demonstrated strong binding affinity (−7.4 kcal/mol). According to post-simulation analysis, all top compounds formed between one and five hydrogen bonds and up to eleven hydrophobic contacts with residues within the active site, thus providing strong geometric and energetic evidence for binding stability. Notably, our identification of ZINC000104288636 as a Class 6 compound with an LD₅₀ of 23,000 mg/kg adds translational value by highlighting candidates with low predicted acute toxicity. Overall, this study lays a solid foundation for the rational design of next-generation monkeypox antiviral therapeutics. Future work is needed for experimental validation of prioritized compounds to assess their biochemical efficacy and pharmacological potential. Full article

Understanding the socio-demographic drivers of residential energy-saving behavior is critical for designing effective energy policies and technologies. This study applies a Multiple Indicators, Multiple Causes (MIMIC) model to examine how individual characteristics are associated with two latent constructs: energy concern and smart-home interest and usage. Using survey data collected from urban residents in Guangdong Province (N = 261), structural equation modeling was employed to assess both measurement and structural components of the model. The results show that income is positively associated with both energy concern and smart-home interest and usage, whereas being a bill payer is negatively related to both. Gender also plays a role, with females reporting higher energy concern. Other factors, such as age, education, and time spent at home, did not show significant effects. The model showed acceptable global fit indices; however, the reliability and convergent validity of the latent constructs were limited. Accordingly, the findings should be interpreted as exploratory associations observed within this sample and may serve as a basis for future research on segmentation in similar urban contexts. Full article

Volatile organic compounds (VOCs, dominated by xylene, toluene, and benzene) and paint mist emissions from ship painting represent a major environmental and health concern, posing a critical bottleneck to the green transformation of the shipbuilding industry. To tackle this challenge, this study presents an integrated recovery system designed specifically for ship automatic-spraying robots. Guided by the synergistic principle of “air-curtain containment, multi-stage adsorption, and negative-pressure recovery,” the system features a modular design that ensures full compatibility with the robots’ spraying trajectory without operational interference. Core adsorption materials, namely glass fiber filter cotton and honeycomb activated carbon fiber, were selected to suit the high-humidity and high-pollutant-concentration environment typical of ship painting. An appropriately matched axial flow fan maintains stable negative pressure throughout the system. Furthermore, the design integrates an air curtain isolation subsystem and an automated control subsystem, enabling coordinated operation and real-time adjustment. Using ANSYS Fluent, geometric and flow field simulation models were established to analyze airflow distribution and pollutant adsorption behavior, which led to the optimization of key structural and material parameters. Field experiments conducted in shipyard environments demonstrated the system’s superior performance: it achieved a VOC removal efficiency of 88.4% and a paint mist capture efficiency of 85.7% under optimal working conditions, with a maximum simulated paint mist capture efficiency of 86.2%. The system maintained stable performance under complex vertical and overhead spraying conditions, with an efficiency attenuation of less than 1.5%, and its outlet emissions fully complied with the mandatory limits specified in the Emission Standard of Air Pollutants for the Shipbuilding Industry (GB 30981.2-2025). The relative error between experimental data and simulation results is less than 2%, confirming the reliability and practicality of the proposed system. This research provides an efficient and adaptable pollution control solution for green shipbuilding and offers valuable technical insights for the sustainable upgrading of automated painting processes in heavy industries. Full article

Finned tubes contribute to the heat transfer performance of heat exchangers by increasing the surface area; they also modify patterns within the flow around the tubes and thus increase the likelihood of flow-induced vibrations (FIVs), which can undermine structural integrity. The tradeoff between improved heat transfer and minimized vibration risks is thus of concern in the optimization of finned tube designs. This paper examines the vibration behavior of circular finned tubes fitted in a parallel triangular configuration when subjected to crossflow conditions with particular reference to the structural response as opposed to thermal performance. In this study, two tube bundles arranged in a 30° parallel triangular layout were tested. The test tube has pitch-to-diameter (P/D) ratios of 1.16 and 1.37 and fin densities of 3, 6, and 9. In this study, experiments were conducted in a low-speed closed-loop water tunnel, which also involved the fabrication of circular finned tubes, the preparation of test bundles, and vibration response measurements. The key parameters analyzed in this experiment were the vibration amplitude, damping, pitch ratio, and fin density. Based on the free-stream velocity range of 0.13–0.28 m/s in a 300 mm × 300 mm closed-circuit water tunnel (hydraulic diameter $D_h=0.3$ m), the Reynolds number ranged from 3.9 × 10⁴ to 8.4 × 10⁴ (water at 20 °C). The results of this experiment demonstrate that by increasing the fin density, the vibration amplitudes can be reduced, which also raises the critical velocities. Reducing the pitch ratio from 1.37 to 1.16 produced an onset of instability approximately 53% earlier than the onset of instability at the ratio of 1.37. The bandwidth of the pitch ratio of 1.16 at the same fin density of 9 was almost 45% lower than that at 1.37, which confirms that the system at 1.16 is much more unstable. In general, the 1.37 pitch ratio offers 3 times higher stability margins than those of 1.16 for the fin densities under study. The development of optimal finned tube heat exchanger designs that reduce flow-induced vibrations without sacrificing thermal performance is aided by these findings, which provide information on the relationship between the fin density, pitch ratio and vibration behavior. Full article

Systemic inflammation leading to neuroinflammation is a matter of concern in recent years because of its implication with neurological disorders. Selective peroxisome proliferator-activated receptor gamma (PPAR-γ) agonists have shown promising anti-inflammatory effects in various neurodegenerative diseases. With pioglitazone being one such PPAR-γ agonist, our study was aimed at investigating the role of pioglitazone on oxidative stress and cognitive changes against LPS-induced neuroinflammation in rats. In-house-bred male Wistar rats, about six weeks old, were utilized for the present study. They were categorized as A (preventive) and B (curative) groups, each with five subgroups: control (1A and 1B), neuro-inflammatory (2A and 2B), and three different dosages of pioglitazone treatment (3A, 3B, 4A, 4B, and 5A, 5B). After the experimental period, cognitive changes were examined by behavioral tests. Brain homogenate was used for biochemical parameters. Deteriorated memory, superoxide dismutase activity and increase in lipid peroxidation in the brain tissue induced by LPS exposure were substantially alleviated (p < 0.001) by pioglitazone treatment. These results suggest that pioglitazone may be neuroprotective against LPS-induced neuroinflammation. Full article

The detection of anomaly energy consumption patterns in smart grid metering systems remains a critical issue. This is due to data imbalance, privacy constraints, and the dynamic nature of consumption patterns. To address these concerns, we present a privacy-preserving and scalable anomaly detection framework named as FedTheftDetect framework. The proposed framework integrates deep learning algorithms into a federated learning (FL) architecture through the incorporation of advanced ensemble classifiers to detect behavioral anomalies in daily consumption patterns. A real-world smart meter dataset with significant class imbalance is used to assess the suggested framework. The dataset had significant preprocessing to identify consumption-related anomalies in behavior. Experimental results demonstrate that the suggested framework outperforms the competitive centralized and distributed models. It achieves significant improvements in Accuracy, Precision, Recall, and F1-score, all of which are close to 0.95, which indicates a great predictive capability and reliability. Full article

Background/Objectives: Dietary behavior in eating disorders (EDs) is often framed through either nutritional or psychological perspectives, yet emerging evidence suggests that eating may involve a transdiagnostic, emotionally embedded, and relationally negotiated process. While research highlights the role of emotion regulation difficulties, perfectionism, control, and overvaluation of weight and shape in ED maintenance, less is known about how these processes are interpreted and managed in clinical practice across different cultural contexts. This study explored psychotherapists’ perspectives on dietary behavior, nutritional assessment, and interdisciplinary collaboration in ED treatment in Greece and the United Kingdom. Methods: Eighteen psychotherapists (9 Greek and 9 British) with experience in treating individuals with EDs participated in in-depth semi-structured interviews. Data were analyzed using reflexive thematic analysis. Results: Three themes were developed. First, therapists conceptualized dietary behavior as reflecting broader transdiagnostic psychological processes, particularly perfectionism, control, emotion regulation difficulties, and body image concerns. Second, nutritional assessment and intervention (e.g., food diaries and meal plans) were experienced as emotionally significant practices that required negotiation of timing, meaning, and clients’ readiness for change. Third, interdisciplinary collaboration was described as involving ongoing negotiation of nutritional authority, with therapists balancing nutritional considerations and psychological safety, influenced by contextual differences between UK and Greek mental health systems. Conclusions: Findings suggest that dietary behavior in ED treatment may benefit from approaches that integrate psychological and nutritional perspectives. Clinicians may consider attending to clients’ emotional readiness, the symbolic meanings of food, and the dynamics of multidisciplinary collaboration, offering insights that can inform clinical practice and future research. Full article

AI travel assistants are increasingly designating hotels as “eco”, yet when the evidence is not independently verifiable, these recommendations may serve as persuasive cues or credible decision support. We present a preregistered 2 × 2 between-subject laboratory experiment (n = 63) that manipulates autonomy framing (Recommend vs. Plan) and evidence verifiability (verifiable vs. non-verifiable) in a realistic hotel-booking workflow with a standardized “Verify eco-claim” drawer. Phasic arousal was recorded at recommendation onset (E1) and verification initiation (E3), employing eye-tracking indexed verification behavior (verify clicks, time-to-verify, verification depth) and event-locked galvanic skin response (GSR). Verifiability did not directly speed up or deepen verification (H1 not supported), but verification was common (74.6% clicked Verify). Rather, autonomy influenced checking: Plan slowed verification and altered verification depth. E1 SCR revealed an Evidence × Autonomy interaction, which is consistent with an autonomy-boundary account (H4), rather than credibility stress emerging as a simple evidence main effect at E1 (H2 not supported as stated). Verification served as a repair moment: depending on the availability of diagnostic cues, arousal dynamics from E1 to E3 supported differential “repair” (H3). SCR dynamics explained incremental variance in perceived manipulation/greenwashing concern beyond condition and eye-tracking indices (H5b supported), but verification depth did not mediate effects on trust or delegation (H5a not supported). Overall, users’ interpretation of AI sustainability advice is influenced by autonomy, and multimodal process measures offer useful signals for auditing eco-recommendation designs in travel platforms. Full article

Against the backdrop of increasing global emphasis on sustainable development and ecological conservation, green seafood has emerged as a key component of sustainable marine food consumption. However, the discrepancy between consumers’ intention to consume and their consumption behavior remains a critical issue requiring in-depth investigation. Herein, based on survey data collected from 415 consumers in China in 2025, this study employs structural equation modeling (SEM) to analyze the determinants and mechanisms influencing green seafood consumption intention and behavior. The findings indicate that heightened concerns regarding dietary health, food safety, and nutrition significantly enhance consumer intention, driven primarily by ecological awareness and the pursuit of a higher quality of life. Individual and household characteristics, along with consumers’ cognitive status of green seafood, exert significant positive effects on consumption intention, with cognitive status demonstrating the strongest influence. Nevertheless, a notable gap exists between consumption intention and actual behavior. Among respondents with consumption intention, only 48.7% had ever purchased green seafood, and the consumption frequency remained generally low. SEM path coefficients further reveal that marketing factors play a dominant role in actualizing consumption behavior. Compared to marketing factors, consumption intention shows a relatively weaker effect in facilitating consumption behavior. This finding further confirms the intention–behavior gap in green seafood consumption. The intention–behavior gap in green seafood consumption is jointly driven by asymmetric information on product quality, an underdeveloped certification system, a relatively undiversified supply structure, and elevated prices. Accordingly, this study proposes an integrated strategy that includes establishing a unified certification and traceability system, optimizing supply structures and pricing mechanisms, and strengthening science communication and targeted marketing. These measures aim to bridge the intention–behavior gap and promote the transition toward sustainable consumption patterns. Full article

The growing integration of chatbots and large language models (LLMs) into society raises important concerns about their potential to reproduce toxic human behaviors. As a result, it is essential to investigate these models to mitigate or eliminate such risks. This paper presents an experimental study evaluating the responses of the Llama and Alpaca models to scenarios involving verbal harassment. The methodology involved using harassment dialogues generated by an LLM as prompts to elicit responses from both models. The responses were then analyzed for levels of toxicity, sexually explicit content, and flirtatiousness. The results indicate that although both models reduce explicit offensive terms, they exhibit limitations in identifying and intercepting abusive behavior from users. Statistical analysis reveals that general-purpose instruction tuning in Alpaca does not provide a robust safety barrier compared to the Llama base model for most variables investigated in the experiment. However, a significant difference was observed concerning flirting, where Llama proved more prone to validation and encouragement than Alpaca. Furthermore, the study identifies critical vulnerabilities, such as a “self-deprecation” bias in Llama and “mirroring” behavior in Alpaca. We also report a complementary triangulation with GPT-family models as a secondary point of reference. This paper discusses and contains content that can be offensive or upsetting. Full article

Personality recognition and emotion recognition, two core tasks within affective computing, are fundamentally constrained by data scarcity as collecting and annotating human behavioral data is expensive and restricted by privacy concerns. Under these limited data conditions, existing models tend to rely on superficial shortcut features such as background appearance, lighting conditions, or color variations, rather than behavior-relevant cues including facial expressions, posture, and motion dynamics. To address this issue, we propose Style-Abstraction-based Data Augmentation, a style transfer-based augmentation strategy that reduces dependency on low-level appearance information while preserving high-level semantic cues. Specifically, we employ cartoonization to generate stylized variants of training videos that retain expressive characteristics but remove stylistic bias. We validate our approach on three diverse personality benchmarks (First Impression v2, UDIVA v0.5, and KETI) and emotion benchmark(Emotion Dataset) using state-of-the-art models including ViViT (Video Vision Transformer), TimeSformer, and VST (Video Swin Transformer). Our experiments indicate that increasing the proportion of style-abstracted data in the training set can improve performance on the evaluated datasets. Notably, our method yields consistent gains across all benchmarks: a 0.0893 reduction in MSE on UDIVA v0.5 (with VST), a 0.0023 improvement in 1-MAE on KETI (with TimeSformer), and a 0.0051 improvement on First Impression v2 (with TimeSformer). Furthermore, extending style-abstraction-based data augmentation to a four-class categorical emotion recognition task demonstrates similar performance gains, achieving up to a 3.44% accuracy increase with the TimeSformer backbone. These findings verify that our style-abstraction-based data augmentation facilitates learning of behavior-relevant features by reducing reliance on superficial shortcuts. Overall, cartoonization-based style abstraction for data augmentation functions as both an effective augmentation strategy and a regularization mechanism, encouraging the model to learn more stable and generalizable representations for affective computing applications. Full article

Background: Systemic arterial hypertension (SAH) shows a high prevalence among postmenopausal women and represents an important public health concern. Objective: To evaluate factors associated with SAH in postmenopausal women participating in a resistance training program. Methods: This observational, cross-sectional study included 55 postmenopausal women (66.0 ± 4.9 years) recruited from the “More Active Women” research project, an umbrella experimental and longitudinal study involving resistance training interventions. Cross-sectional data were collected during the baseline assessment (April–May 2025). Sociodemographic variables, nutritional status (body mass index and waist circumference), and behavioral and health-related variables obtained through structured interviews and anthropometric assessments were analyzed. Associations were tested using Pearson’s chi-square test or Fisher’s exact test, with effect size estimated by Phi or Cramer’s V when appropriate, and binary logistic regression was performed for adjusted analyses. Results: Significant associations were observed between SAH and elevated BMI (p = 0.03; φ = 0.30), waist circumference > 88 cm (p = 0.006; φ = 0.40), and lower educational level (p = 0.003; V = 0.47). In the adjusted analysis, waist circumference ≤ 88 cm was associated with a lower likelihood of SAH (OR = 5.54; 95% CI: 0.965–31.872; p = 0.007), whereas lower educational level was associated with a higher likelihood of hypertension (OR = 13.98; 95% CI: 1.505–129.833; p = 0.004). Conclusion: Excess central adiposity and lower educational level are associated with SAH in postmenopausal women, highlighting the importance of integrated health promotion strategies that address both cardiometabolic risk factors and social determinants of health during aging. Full article