Search Results (16,087)

Hyaluronic acid (HA)–based nanocapsules containing plant-derived bioactives are promising formulations for dermatological applications. In this study, nanocapsules containing extracts of Arnica montana, Calendula officinalis and Aesculus hippocastanum were synthesized and their structural and functional properties were characterized. Scanning electron microscopy confirmed the formation of spherical nanostructures with uniform morphology, while rheological analyses demonstrated stable viscoelastic behavior suitable for topical application. Their antimicrobial potential was assessed on microorganisms isolated from multiple regions of healthy human skin and opportunistic pathogens. A diverse panel of approx. 100 bacterial and fungal isolates was identified using MALDI-TOF MS. The antimicrobial activity of formulations was compared with commonly used disinfectants: H₂O₂, octenidine, isopropanol and topical ophthalmic antiseptic. Arnica-based formulations showed the strongest inhibitory effect against both Gram-positive and Gram-negative bacteria, whereas chestnut extract demonstrated selective activity against Candida spp. Calendula-based formulations exhibited limited antimicrobial activity. These findings demonstrate that plant-extract-loaded HA nanocapsules exhibit selective antimicrobial properties dependent on extract type and microbial group, supporting their potential as multifunctional components of future dermatological formulations. Full article

Background and Objectives: Determining the mechanical stability of osteosynthesis in pertrochanteric fractures remains a critical challenge in orthopedic biomechanics. The aim of this study was to develop a mathematical model for quantifying the stability of osteosynthesis and to establish criteria for its evaluation under physiological loading conditions. Materials and Methods: A mathematical model describing the biomechanical behavior of a proximal femur with a pertrochanteric fracture stabilized using a cephalomedullary nail (CMN) was developed. The model integrates force equilibrium, stress–strain relationships, and loading conditions representative of early functional rehabilitation. The theoretical framework was implemented in MATLAB/Simulink R2025b and complemented by finite element analysis to determine stress distribution, deformation patterns, and stability-related parameters of the bone–implant system. Results: The developed mathematical model enabled a quantitative assessment of osteosynthesis stability through the evaluation of key mechanical indicators, including displacement, stress distribution, and safety factor within the fixation system. Critical stress zones in the implant and surrounding bone were identified, allowing analysis of load transfer mechanisms. Finite element simulations showed that improved fixation mechanics reduced peak implant stresses, limited displacement at the fracture site, and increased the safety factor of the fixation construct, resulting in a more uniform load distribution in the surrounding bone and enhanced overall stability of the osteosynthesis system. Conclusions: The proposed mathematical model provides a systematic approach for determining the stability of osteosynthesis in pertrochanteric fractures. It offers a theoretical basis for optimizing implant design and fixation strategies, with potential applications in preclinical evaluation and surgical planning. Full article

Traditional cloud pricing models are allocation-centric, where users are charged based on reserved resources rather than workload energy consumption. However, modern AI workloads exhibit substantial and heterogeneous power behavior, limiting the effectiveness of such allocation-centric pricing. This paper presents a comprehensive experimental study of nine widely used workloads across 50 controlled configurations, including standalone and concurrent executions under varying resource partitions. Our results show that total system power is largely unaffected by how resources are divided among co-located workloads, except in cases of explicit resource under-provisioning or severe resource contention. Across 45 workload–core groups, 41 exhibit a coefficient of variation below 3% across different co-located workloads, demonstrating structural stability of workload-level power profiles under heterogeneous execution environments. In contrast, deployment choice (e.g., CPU versus GPU execution) can shift the same model into distinct power regimes. Based on measured power decomposition and scaling behavior, we derive an empirical categorization framework distinguishing GPU-dominant and CPU-dominant workloads, further characterized by utilization and memory dimensions. From an energy perspective, CPU utilization (for CPU-dominant workloads) and SM utilization (for GPU-dominant workloads) emerge as the primary determinants of power magnitude, while memory-related parameters contribute marginally to overall power. These findings provide empirical evidence that allocation-based pricing is a weak proxy for actual energy cost and motivate energy-aligned cloud management strategies grounded in workload power profiles. As our findings are derived from a controlled single-node experiment, evaluations under more realistic data center environments will be required for further generalization. Full article

Artificial intelligence (AI)-mediated tools have rapidly penetrated student life and become a valuable resource for seeking help with academic assignments/tasks, psychological problems, and social interactions. This study aims to investigate the levels and associations of AI-help-seeking behavior (AI-HSB), anxiety, stress, and depression among university students in the United Arab Emirates (UAE). In addition, it examines the factors associated with AI-HSB based on the selected demographic (gender, marital status, age, academic year, employment status, major, and nationality), as well as anxiety, stress, and depression. This study employed a descriptive cross-sectional design among 433 university students, who were recruited via an online Google Form between 1 October 2025 and 10 December 2025. The study utilized validated Arabic versions of the AI-HSB scale and the anxiety, stress, and depression scale. Descriptive statistics, Pearson correlation, and predictive analyses were conducted using SPSS v 25. Results indicated that students reported moderate reliance on AI-HSB despite moderate to severe levels of psychological distress, with particular emphasis on anxiety. The AI-HSB was positively associated with anxiety, stress, and depression amongst the participants. Furthermore, both depression and the students’ academic year emerged as the only significant predictors of AI-HSB, explaining a modest but meaningful proportion of variance with an exact percentage of 18.1%. AI tools may partially circumvent stigma by offering privacy and anonymity; however, cultural expectations around interpersonal support, trust, and authority may simultaneously limit students’ willingness to rely on non-human agents for emotional care. Full article

Aims: To conduct an exploratory analysis the psychometric properties of the Spanish version of the Soft Skills Questionnaire for Nurses (SSQN) and examine its conceptual coherence and its preliminary empirical behavior among nursing professionals and students. The aim is to critically assess the instrument’s suitability as a tool for exploring perceptions and self-reported soft skills rather than to establish its psychometric validity. Design: Exploratory methodological study focused on analyzing the empirical performance and conceptual adequacy of the SSQN within a Spanish sample, with particular attention to the internal patterns of responses and the coherence between the instrument’s items and its proposed dimensions. Methods: The process included the translation of the questionnaire and an empirical application in a sample of nursing professionals and students. Exploratory analyses were performed, including exploratory factor analysis and reliability assessment (Cronbach’s alpha and McDonald’s omega), using Jeffreys’s Amazing Statistics Program (JASP) (version 1.18.3), in order to examine the structural performance of the instrument and detect possible conceptual and methodological limitations. Results: The SSQN showed notable inconsistencies in its empirical structure, with dimensions that did not display clear or theoretically coherent patterns. Factor inconsistencies and low internal consistency suggest that the instrument does not adequately capture the multidimensionality of interpersonal skills, reflecting weaknesses inherent in its original formulation rather than in the adaptation process. Conclusions: The Spanish version of the SSQN cannot be considered valid or reliable in its current form. The results underscore the need for a thorough revision of the questionnaire and a conceptual rethinking to develop more robust tools for assessing soft skills. Impact: This study highlights the need for a solid methodological evaluation before introducing instruments designed to measure complex and subjective competencies in the healthcare field. Full article

We investigate when LLM-based data augmentation can mitigate temporal collapse in web intrusion detection under extreme cross-temporal distribution shift. Under a strict hold-out protocol—training on CSIC-2010 and evaluating exclusively on the temporally separated SRBH-2020 golden set—the legacy-trained baseline exhibits near-collapse in balanced correlation (MCC ≈ 0) despite retaining high recall, revealing severe false-positive bias under drift. Rather than assuming uniform benefits from synthetic data, we analyze how augmentation effects vary with model scale. Using a fixed DeBERTa-v3-base backbone and five random seeds, we compare synthetic training corpora generated by multiple LLMs under identical schema-guided structured decoding and filtering constraints. The results reveal a scale-dependent threshold effect. Models below 12B parameters (i.e., the 4–8B settings in our experiments) frequently introduce structural artifacts that amplify false-positive bias and further destabilize cross-temporal performance. In contrast, models at or above 12B parameters consistently produce modest but statistically reliable recovery from correlation collapse (p < 0.001), with balanced metrics shifting toward the target-domain distribution. Although the absolute performance remains limited under extreme temporal separation, a confusion-matrix analysis shows that large-scale generation reduces false-positive skew and moves decision-boundary behavior closer to the modern-domain regime. These findings indicate that LLM-based augmentation is not inherently robustness-enhancing; rather, its effect depends critically on model scale and disciplined generation control. When properly constrained, ≥12B-scale models can partially stabilize cross-temporal behavior, whereas smaller-scale generation may exacerbate distributional fragility. Full article

Existing studies have extensively investigated sand-rich shallow-water deltas. However, the sedimentary characteristics and internal architecture of mud-rich deltas remain poorly understood. In this study, two comparative flume experiments were conducted with sand–mud ratio as the key variable. High-resolution topographic data were acquired using a laser scanner to extract geometric parameters of the architectural elements. Three-dimensional architectural models were established and validated against the Ganjiang Delta (sand-rich) and the Ouchi River Delta (mud-rich) in China. The results reveal contrasting depositional styles: sand-rich deltas develop dense, laterally migrating braided channels with broad fan-shaped morphologies, forming blanket-like geometries that consist of vertically stacked and laterally amalgamated channel complexes with good connectivity; mud-rich deltas are characterized by stable channels with limited bifurcation, forming elongated finger-like morphologies with isolated, ribbon-like channel–mouth bar complexes that exhibit strong lateral heterogeneity and poor connectivity. These contrasting behaviors are governed by sediment cohesion: non-cohesive sands promote channel migration and dispersion, whereas cohesive silt and mud stabilize channels and focus sediment transport along main conduits. The experimental models successfully reproduce natural delta end-members, confirming the universal control of the sand–mud ratio. The established quantitative relationships provide a predictive basis for subsurface reservoir characterization and the formulation of differentiated development strategies. Full article

Background: Endovascular aneurysm repair (EVAR) requires lifelong imaging surveillance because endoleaks, aneurysm sac expansion, and severe adverse events occur in up to one-third of the patients. Conventional follow-up based on sac diameter and visual assessment may fail to detect early microstructural changes that precede clinical deterioration. Methods: This narrative review summarizes the current evidence on texture-based radiomics and artificial intelligence (AI) applied to computed tomography (CT) and CT angiography (CTA) for post-EVAR outcome prediction and surveillance. Original studies evaluating radiomic features and AI-based models for endoleak detection, aneurysm sac behavior, and EVAR-related adverse events were included and qualitatively synthesized. Results: Ten studies were included. Radiomic features describing texture heterogeneity, gray-level nonuniformity, entropy, and spatial complexity were extracted from the aneurysm sac, intraluminal thrombus, and perivascular adipose tissue. Machine learning and deep learning models achieved good to excellent performance, with reported AUC values ranging from 0.78 to 0.95 for predicting endoleaks, sac expansion, and severe adverse events. Texture-based radiomics consistently outperformed morphology-only assessments and showed complementary value to deep learning, including applications on non-contrast CT. Conclusions: CT texture radiomics combined with AI represents an emerging research approach with potential relevance for post-EVAR surveillance, although current evidence remains limited. By capturing tissue heterogeneity beyond conventional morphology, radiomics may enable the earlier detection of complications and support risk-adapted follow-up. However, the heterogeneity of methods limited external validation, and reproducibility issues remain major barriers to clinical translation. Full article

Three-dimensional spatial effects in deep excavations critically govern the mechanical response of retaining structures and adjacent soils, yet their quantitative characterization remains a challenge. This study systematically investigates the spatial behavior of row-pile-supported foundation pits through an integrated approach combining model tests, theoretical analysis, and numerical simulations. A novel formulation for the spatial effect influence coefficient K is derived from limit equilibrium principles and subsequently validated via ABAQUS-based finite element simulations. Model test results reveal pronounced spatial heterogeneity in earth pressure and bending moment distributions along the pit perimeter: lateral earth pressure at corner regions exceeds that at mid-side locations at equivalent depths, whereas bending moments in mid-side piles are substantially larger than those at corners. Displacement field measurements further demonstrate that corner zones, constrained bidirectionally, undergo minimal deformation, while maximum displacement occurs at the midpoints of the long sides. These observations collectively confirm the existence of a marked corner effect and a subdued side-midpoint effect under three-dimensional confinement. Complementary numerical analyses indicate that the coefficient K decreases monotonically with increasing half-angle corners and distance from the corner, thereby quantitatively capturing the decay of spatial constraint intensity. Together, these findings establish a theoretical framework for assessing excavation-induced spatial effects and provide actionable guidance for the rational design of deep foundation pit support systems. Full article

The mineralogical composition, textural characteristics, and uranium occurrence of sandstone-hosted uranium ores significantly influence the leaching performance during in situ recovery. This study investigates ore samples from the Zhenyuan uranium deposit, China, utilizing SEM, EPMA, XRD, and XRF to characterize their texture and mineralogy. Combined with thin-section leaching tests, batch stirring experiments, and pressurized column leaching experiments, the leaching behavior of pitchblende, associated gangue minerals, and the whole rocks were evaluated. The results indicate that: Uranium mainly occurs as nano-spherical and film-like pitchblende distributed along the edges of detrital grains and Ti-oxides. Minor uranium is incorporated into Ti-oxides and dolomite lattices via isomorphic substitution or adsorbed by chlorite. Under CO₂ + O₂ leaching conditions, pitchblende was almost completely dissolved, while U-bearing Ti-oxides experienced slight corrosion. Dolomite underwent partial dissolution, providing bicarbonate ions and improving rock permeability. Pyrite dissolution was limited during the early stage of leaching. The high dolomite content, low clay abundance, favorable pore structure, and easily leachable pitchblende suggest that the Zhenyuan deposit is well suited for CO₂ + O₂ in situ recovery. Increasing CO₂ pressure is recommended to enhance dolomite dissolution and improve uranium recovery efficiency. 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

Fault Detection and Diagnosis (FDD) in complex industrial systems requires methods that can handle uncertain operating conditions, soft thresholds, evolving sensor behavior, and increasing volumes of heterogeneous data. Traditional model-based or rule-driven approaches offer interpretability but lack adaptability, while purely data-driven and Large Language Model (LLM)-based methods often struggle with consistency, traceability, and causal grounding. Dynamic Master Logic (DML) provides a causal and temporal reasoning structure with fuzzy rules that capture gradual drift, soft limits, and asynchronous sensor signals while preserving traceability and deterministic evidence propagation. Building on this foundation, this paper presents a DML–LLM hybrid architecture that integrates targeted LLM inference to interpret unstructured information such as logs, notes, or retrieved documents under controlled prompts that maintain domain constraints. The combined system integrates Bayesian updating, deterministic routing, and semantic interpretation into a unified FDD pipeline. In a semiconductor manufacturing case study, the proposed framework reduced time to detection (TTD) from 7.4 h to 1.2 h and improved the F1 score from 0.59 to 0.83 when compared with conventional Statistical Process Control (SPC) and Fault Detection and Classification (FDC) workflows. Provenance completeness increased from 18% to 96%, while engineer triage time was reduced from 72 min to 18 min per event. These results demonstrate that the hybrid framework provides a scalable and explainable approach to anomaly detection and fault diagnosis in sensor-rich industrial environments. Full article

Sports participation is a widely recognized facilitator of physical health, mental well-being, and social inclusion, but persistent and substantial disparities have been observed across socioeconomic groups. Focusing on Japan, this study examined the socioeconomic determinants of sports participation, particularly the roles of gender, age, employment, and caregiving responsibilities. It used nationally representative repeated cross-sectional data to analyze participation rates and annual participation days across multiple sports at the population-segment level, defined by combinations of demographic and social attributes. Results revealed prominent sport-specific gender differences, heterogeneous age effects across sports, significant age–gender interaction effects, and distinctive behavioral changes during the COVID-19 pandemic. During the pandemic, participation in competitive and group sports declined with age, but walking increased among middle-aged and older adults. In addition, constraints in employment and caregiving had limited overall effects but significantly reduced engagement in walking. These findings suggest the crucial influence of the interaction among social roles, life-stage transitions, and historical context, rather than biological sex differences alone, on sports participation patterns, highlighting the urgency of designing sports policies as inclusive social interventions that consider diverse motivations and limitations across population groups. Full article

Background: Activity trackers support physical activity, yet evidence on their effectiveness among older adults and care partners is limited. This study assesses the relationship between activity-tracking frequency and engagement in consistent physical activity among older adults and care partners. Methods: For this cross-sectional study, 615 older adults and care partners completed online surveys assessing the frequency of activity tracking (predictor) and the regularity in physical activity engagement (outcome). Using multivariable logistic regression, we assessed the association between the predictors and the outcome across the entire population and separately among older adults (n = 310) and care partners (n = 305), adjusting for sociodemographic, mobility, and health-related covariates. We reported the adjusted odds ratio (aOR) and 95% confidence intervals (CI). Results: Older adult (OA) and care partner (CP) respondents were predominantly female (OA: 57%, CP: 53%) and non-Hispanic White (OA: 51%, CP: 43%). Across the entire population, frequent tracking of physical activity was associated with a 2.4-fold increase in the odds of engaging in consistent physical activity (aOR: 2.40; 95% CI: 1.45–3.96). Older adults who frequently track their physical activity were 2.5 times more likely to engage in consistent physical activity (aOR: 2.47; 95% CI: 1.08–5.64). Care partners who occasionally tracked their physical activity were 3.5 times more likely to engage in consistent physical activity (aOR: 3.54; 95% CI: 1.54–8.11). Conclusions: Physical activity tracking is associated with greater physical activity engagement among older adults and care partners. These findings contribute to understanding factors associated with physical activity behavior in this population. Full article