Search Results (23,792)

Background/Objectives: This study aimed to evaluate the additive effect of periodontitis and diabetes mellitus on systemic inflammation, measured by high-sensitivity C-reactive protein (hs-CRP), in a nationally representative Korean population. Methods: Data from 3178 adults (≥19 years) in the 2015 Korean National Health and Nutrition Examination Survey were analyzed. Periodontitis was assessed using the Community Periodontal Index (CPI), and diabetes mellitus was defined based on clinical criteria. Participants were classified into four groups according to the presence of periodontitis and diabetes. hs-CRP levels were analyzed by quartiles and ADA/CDC cardiovascular risk categories. ANCOVA and multivariable logistic regression, adjusted for socioeconomic status, oral health and health behaviors, and comorbidities, were used to examine the association between coexisting periodontitis and diabetes and elevated hs-CRP. Results: Mean hs-CRP increased progressively from G1 (1.11 ± 0.49 mg/L) to G4 (2.37 ± 0.38 mg/L). After adjustment, G4 retained the highest concentration (2.31 ± 0.39 mg/L) versus G1 (1.37 ± 0.11 mg/L; p = 0.020). High-risk hs-CRP prevalence (>3.0 mg/L) increased nearly threefold across groups (p < 0.001). Similarly, G4 had increased odds of being in the ADA/CDC high-risk category (>3.0 mg/L) (aOR = 2.73, 95% CI: 1.64–4.54), whereas no significant associations were observed for periodontitis or diabetes alone. Conclusions: The coexistence of periodontitis and diabetes mellitus is significantly associated with elevated systemic inflammation, as measured by hs-CRP, suggesting a synergistic effect beyond the impact of either condition alone. Full article

Smart contracts are central to blockchain ecosystems, yet their development remains technically demanding, error-prone, and tied to platform-specific programming languages. This paper introduces SCEditor-Web, a web-based modeling environment that combines model-driven engineering (MDE) with generative artificial intelligence (Gen-AI) to simplify contract design and code generation. Developers specify the structural and behavioral aspects of smart contracts through a domain-specific visual language grounded in a formal metamodel. The resulting contract model is exported as structured JSON and transformed into executable, platform-specific code using large language models (LLMs) guided by a tailored prompt engineering process. A prototype implementation was evaluated on Solidity contracts as a proof of concept, using representative use cases. Experiments with state-of-the-art LLMs assessed the generated contracts for compilability, semantic alignment with the contract model, and overall code quality. Results indicate that the visual-to-code workflow reduces manual effort, mitigates common programming errors, and supports developers with varying levels of expertise. The contributions include an abstract smart contract metamodel, a structured prompt generation pipeline, and a web-based platform that bridges high-level modeling with practical multi-language code synthesis. Together, these elements advance the integration of MDE and LLMs, demonstrating a step toward more accessible and reliable smart contract engineering. Full article

This study proposes a behavioral monitoring framework for newborn piglets based on SPMF-YOLO object detection and ByteTrack multi-object tracking, which enables precise quantification of early postnatal activity levels and health assessment. The method enhances small-object detection performance by incorporating the SPDConv module, the MFM module, and the NWD loss function into YOLOv11. When combined with the ByteTrack algorithm, it achieves stable tracking and maintains trajectory continuity for multiple targets. An annotated dataset containing both detection and tracking labels was constructed using video data from 10 piglet pens for evaluation. Experimental results indicate that SPMF-YOLO achieved a recognition accuracy rate of 95.3% for newborn piglets. When integrated with ByteTrack, it achieves 79.1% HOTA, 92.2% MOTA, and 84.7% IDF1 in multi-object tracking tasks, thereby outperforming existing methods. Building upon this foundation, this study further quantified the cumulative movement distance of each newborn piglet within 30 min after birth and proposed a health-assessment method based on statistical thresholds. The results demonstrated an overall consistency rate of 98.2% across pens and an accuracy rate of 92.9% for identifying abnormal individuals. The results validated the effectiveness of this method for quantifying individual behavior and assessing health status in newborn piglets within complex farming environments, providing a feasible technical pathway and scientific basis for health management and early intervention in precision animal husbandry. Full article

Additives such as surfactants (Tween-20) and cryoprotectants (glycerol and trehalose) are often used in enzymatic assays to improve the quality and long-term stabilization of proteins. However, these additives can affect the enzymatic activity and the enzyme’s affinity for active compounds, such as inhibitors, and must be considered during assay design since a slight shift in enzyme behavior may compromise the reliability of the results. In this study, the effects of Tween-20, glycerol, and trehalose on hyaluronidase (Hyal) were systematically evaluated by assessing their influence both directly—through microscale thermophoresis (MST) signals of the labeled enzyme (Hyal*)—and indirectly, by monitoring the formation of the final product of the degradation of hyaluronic acid, tetrasaccharide (Tet), using capillary electrophoresis (CE/UV). Hyal was labeled for the first time with ATTO-647 NHS ester, a commercial dye compatible with MST. Efficient labeling was achieved in a phosphate-based buffer without loss of catalytic activity. Tween-20 showed no impact on MST signals nor on enzymatic performance when used between 0.005 and 0.05% (v/v). Glycerol also did not interfere with MST measurements; however, it significantly reduced catalytic activity at concentrations above 2% (v/v). Trehalose affected Hyal* fluorescence in a concentration-dependent manner and enhanced catalytic activity even at 0.02% (v/v). Full article

Background and objectives: Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disorder with autosomal dominant forms most often linked to MAPT, GRN, and C9orf72. We aimed to evaluate the prevalence of pathogenic variants in these genes in a hospital-based cohort of FTD patients assessed at a tertiary referral center in southeastern Serbia. Methods: We studied 58 consecutive patients with FTD spectrum syndromes evaluated at a tertiary referral center. All underwent standardized neurological, neuropsychological, and imaging assessments, and family history was recorded. Genetic testing included validated assays for C9orf72 repeat expansions and next-generation sequencing of MAPT and GRN. Results: Women comprised 53.45% of the cohort. The mean age was 67.88 years, with mean onset at 61.70 years. Behavioral variant FTD predominated (75.87%), while language forms were less frequent. Positive family history was present in 16 patients (27.59%). Pathogenic variants were identified in three individuals (5.17%): two unrelated carriers of the intronic MAPT mutation c.1920+16C>T and one patient with a C9orf72 expansion. No GRN variants were detected. Mutation frequency was 18.75% in familial cases, while none were found among sporadic patients (p = 0.018). Four of nine relatives were asymptomatic MAPT mutation carriers. Conclusions: This first genetic study of FTD in southeastern Serbia revealed a lower mutation frequency than in Northern and Western Europe, but similar to cohorts from Southeastern Europe. The detection of MAPT c.1920+16C>T in two unrelated families extends the geographic range of this splice-site variant and underscores the importance of systematic genetic testing and larger collaborative studies in the Balkans. Full article

Alcohol misuse can lead to alcohol-related brain damage (ARBD), a condition linked to long-term cognitive impairment and considerable disease burden. The pharmacological characteristics of alpha-lipoic acid (ALA) make it a promising candidate for the treatment of ARBD. In this study, adult male Wistar rats were divided into eight experimental groups. Four groups received a 20% (v/v) ethanol–tap water solution ad libitum for 15 weeks to induce early-stage ARBD, while the remaining received only tap water. After 14 weeks, all groups were administered daily injections for one week with either ALA, rivastigmine, or memantine. Behavioral testing included the step-through passive avoidance and rotarod performance tests. Whole-brain biochemical analyses assessed acetylcholinesterase activity, brain-derived neurotrophic factor, and oxidative stress biomarkers. Brain weight, relative brain weight, and brain histopathological changes were also evaluated. Results showed that, similar to memantine and rivastigmine, ALA improved STL at both 24 h and 8 days and reduced ethanol-induced Purkinje cell damage. It also decreased lipid peroxidation levels by 44%, unlike the reference drugs, and superoxide dismutase activity by 33%, similar to them. No other significant changes were detected. Albeit several limitations, this is the first study comparing ALA with rivastigmine and memantine in this experimental context. Full article

In automated orchard operations, the straight-line locomotion stability of ground-based weeding robots is critical for ensuring path coverage efficiency and operational reliability. To address the response lag and high-frequency oscillations often observed in conventional PID and fixed-lookahead Pure Pursuit controllers, this study proposes an adaptive lookahead Pure Pursuit method incorporating angular velocity feedback. By dynamically adjusting the lookahead distance according to real-time attitude changes, the method enhances coordination between path curvature and robot stability. To enable systematic evaluation, three time-series-based metrics are introduced: mean absolute yaw error (MAYE), peak-to-peak fluctuation amplitude, and the standard deviation of angular velocity, with overshoot occurrences included as an additional indicator. Field experiments demonstrate that the proposed method outperforms baseline algorithms, achieving lower yaw errors (0.61–0.66°), reduced maximum deviation (≤3.7°), and smaller steady-state variance (<0.44°²), thereby suppressing high-frequency jitter and improving turning convergence. Under typical working conditions, the method achieved a mean yaw deviation of 0.6602°, a fluctuation of 5.59°, an angular velocity standard deviation of 10.79°/s, and 155 overshoot instances. The yaw angle remained concentrated around the target orientation, while angular velocity responses stayed stable without loss-of-control events, indicating a favorable balance between responsiveness and smoothness. Overall, the study validates the robustness and adaptability of the proposed strategy in complex orchard scenarios and establishes a reusable evaluation framework, offering theoretical insights and practical guidance for intelligent agricultural machinery optimization. Full article

Background: currently, there is a growing trend toward multifunctional cosmetics, which combine several active ingredients in a single product to enhance efficacy and user convenience. As ingredients may influence one another, it is important to study the behavior of mixing multiple compounds in complex formulations, especially regarding their interaction with the skin. Piceatannol, for instance, is a naturally occurring stilbene recognized for its in vitro potent antioxidant, anti-inflammatory, and anti-aging activities, making it a promising candidate for dermocosmetic use in suncare. But despite its beneficial biological activities, its cutaneous permeation remains poorly understood, particularly when delivered from complex formulations containing multiple ingredients. Objectives: in this sense, this study aimed to evaluate the in vitro skin diffusion profile of piceatannol from a passion fruit seed extract (P_ext) incorporated into a topical base (B_em) or an organic sunscreen emulsion (O_em), with or without a spilanthol-rich Acmella oleracea extract (J_ext) used as a natural permeation enhancer. Methods: due to ethical and variability issues with human and animal skins, the Strat-M™ synthetic membrane was chosen as a standardized model for the in vitro skin permeation assays. Piceatannol localization within membrane layers was examined by confocal Raman microscopy (CRM), while compound identification in donor and receptor compartments was performed via UHPLC-DAD. Results: piceatannol from B_em was detected up to 140 µm from the Strat-M™ surface and exceeded 180 µm in depth when J_ext and organic sunscreens were included in the formulation. Notably, formulations containing J_ext and those based on O_em promoted enhanced accumulation in both the stratum corneum and deeper skin layers, suggesting an improved delivery potential in lipid-rich vehicles. Conclusions: even though some instability issues were observed, piceatannol penetration into Strat-M™ from the proposed formulations was confirmed, and the results provide a foundation for further research on its topical delivery, supporting the rational development of formulations capable of harnessing its demonstrated biological properties. Full article

Chitosan-based nanoparticles were prepared using an eco-friendly chemical procedure that conjugates natural fatty acids to the backbone of chitosan. This consists of reacting two molecules in the absence of a solvent and using microwaves to promote the chemical transformation. Both conditions make the whole chemical process more eco-compatible in terms of reagents and energy consumption. The chemical structure and the self-association behavior of chitosan–fatty acid conjugates were characterized by FT-IR, NMR, and dynamic light scattering. The conjugates displayed an enhanced solubility and efficient self-assembly in aqueous solution. The antimicrobial activity of the resulting nanoparticles was evaluated against Escherichia coli (Gram-negative) and Bacillus subtilis (Gram-positive). The micelles significantly inhibited E. coli growth (35–60%), even at relatively low concentrations, whereas negligible activity was observed against B. subtilis. The antibacterial efficacy appears to arise primarily from the ability of the developed nanostructured conjugates to perturb bacterial membranes. These results support the potential of chitosan–fatty acid conjugates as sustainable nanomaterials for biomedical applications, particularly as eco-friendly antimicrobial agents. Future work will evaluate their activity against other Gram-positive pathogens and explore their use in drug delivery. Full article

In line with sustainable construction goals, this study investigates the synergistic use of amine-based grinding aids (GAs), triethanolamine (TEA), and triisopropanolamine (TIPA) to enhance grinding performance and cement properties. GAs were physically blended at varying TEA/TIPA ratios, and their effects on grinding efficiency, CO₂ emissions, and environmental footprint were assessed based on energy consumption per target Blaine fineness. The interaction of blended GAs with Ca²⁺ ions was modeled to understand adsorption behavior. Cement particle size distribution (PSD), Hausner ratio, Carr index, and angle of repose were analyzed to evaluate powder flowability. Scanning electron microscopy (SEM) was employed to examine microstructural changes. Finally, the Taguchi method statistically analyzed the effective parameters influencing system performance. Results demonstrated that the optimized blend containing 25% TEA and 75% TIPA improved grinding performance, enhanced polymer–ion interactions, refined PSD, and significantly increased powder flowability. Overall, the study underscores the potential of amine-based polymeric GAs in producing environmentally friendly, high-performance cement composites. Using a Taguchi design with the larger-is-better S/N criterion, the optimal formulation was determined to be 25% TEA and 75% TIPA at a dosage of 0.10%. ANOVA results indicated that the TEA content was the most significant factor, while the dosage had no statistically significant effect. Full article

Background: Preschool bedroom television placement represents an established risk factor for negative psychological and behavioral outcomes in adolescence. Girls and boys have different risk factors for developmental psychopathology. It is unclear if childhood sport participation can act as a protective factor for the potential maladaptive behaviors associated with having a bedroom television in early childhood. Methods: This study aims to evaluate the impact of having a bedroom television in early childhood on later externalizing behaviors while examining the potential beneficial role of extracurricular sport participation in middle childhood using the Quebec Longitudinal Study of Child Development (Canada). We examine subsequent teacher-reported psycho-social outcomes by the end of sixth grade. Linear regression is used to examine the interaction between child-reported bedroom television placement (age 4 years) and parent-reported childhood sport participation trajectories (ages 6 to 10 years) in predicting behavioral outcomes at age 12 years. Results: For boys, extracurricular sport amplified the relationship between having a preschool bedroom television and subsequent physical aggression (b = 0.95, SE = 0.32, p < 0.001) and ADHD symptoms (b = 0.59, SE = 0.30, p ≤ 0.05), beyond individual and family characteristics. No interaction results were found for girls; however, consistent sport participation between ages 6 and 10 years resulted in a decrease in ADHD symptoms in girls (b = −0.329, SE = 0.102, p ≤ 0.001). Conclusion: Unexpectedly, for boys exposed to early bedroom television, consistently participating in extracurricular sport in childhood exacerbated long-term behavioral risks. Social unpreparedness from bedroom television placement countered the intended benefits of sport. This private access to screens might influence sedentary, unsupervised, isolated activity that increases the chances of viewing violence and reduces opportunities for social interaction. Full article

Dynamic equivalence of active distribution networks (ADNs) is emerging as one of the most important issues for the backbone network security analysis due to high penetration of distributed generations (DGs) and electricity vehicles (EVs). The multiscale and multimodal fusion deep learning (MMFDL) method proposed in this paper contains two modalities, one of which is a CNN + attention module to simulate Newton Raphson power flow calculation (NRPFC) for the important feature extraction of a power system caused by disturbance, which is motivated by the similarities between NRPFC and convolution network computation. The other is a long short-term memory (LSTM) + fully connected (FC) module for load modeling based on the fact that LSTM + FC can represent a load′s differential algebraic equations (DAEs). Moreover, to better capture the relationship between voltage and power, the multiscale fusion method is used to aggregate load modeling models with different voltage input sizes and combined with CNN + attention, merging as MMFDL to represent the dynamic behaviors of ADNs. Then, the Kepler optimization algorithm (KOA) is applied to automatically tune the adjustable parameters of MMFLD (called KOA-MMFDL), especially the LSTM and FC hidden layer number, as they are important for load modeling and there is no human knowledge to set these parameters. The performance of the proposed method was evaluated by employing different electric power systems and various disturbance scenarios. The error analysis shows that the proposed method can accurately represent the dynamic response of ADNs. In addition, comparative experiments verified that the proposed method is more robust and generalizable than other advanced non-mechanism methods. Full article

Due to their potential to reduce greenhouse gas emissions, light-frame timber buildings (LFTBs) are widely used in seismically active regions. However, their construction in these areas remains limited, primarily due to the high costs associated with continuous anchor tie systems (ATSs), which are required to withstand significant seismic forces. To address this challenge, frictional seismic isolation offers an alternative by enhancing seismic protection. Although frictional base isolation is an effective mitigation strategy, its performance can be compromised by extreme ground motions that induce large lateral displacements, resulting in impacts between the sliders and the perimeter protection ring. The effects of these internal lateral impacts on base-isolated LFTBs remain largely unexplored. To fill this knowledge gap, this study evaluates the collapse capacity of a set of base-isolated LFTBs representative of Chilean real estate developments. Nonlinear numerical models were developed in the OpenSeesPy platform to capture the nonlinear behavior of the superstructure, including the impact effects within the frictional isolation system. Incremental dynamic analyses following the FEMA P695 methodology were performed using subduction ground motions. Collapse margin ratios (CMRs) and fragility curves were derived to quantify seismic performance. Results indicate that frictional base-isolated LFTBs can achieve acceptable collapse safety without ATS, even with compact-size bearings. Code-conforming archetypes achieved CMRs ranging from 1.24 to 1.55, indicating sufficient safety margins. These findings support the cost-effective implementation of frictional base isolation in mid-rise timber construction for high-seismic regions. Full article

The growing demand for precision and consistency in the forging industry has heightened the need for predictive simulation tools. While extensive research has focused on parameters such as flow stress, die wear, billet fracture, and residual stresses, the phenomenon of billet buckling, especially during cold upset forging, remains underexplored. Most existing models address only elastic buckling for slender billets using classical approaches like Euler and Rankine-Gordon formulae, which are not suitable for inelastic deformation in shorter billets. This study presents a numerical model developed to analyze inelastic buckling during cold forging and to determine associated stresses and deflection characteristics. The model was validated through finite element simulations across a range of billet geometries (10–40 mm diameter, 120 mm length), materials (aluminum, stainless steel, and copper), and friction coefficients (µ = 0.12, 0.16, and 0.35). Stress distributions were evaluated against die stroke, with particular emphasis on the influence of strain hardening and geometry. The results showed that billet geometry and strain-hardening exponent significantly affect buckling behavior, whereas friction had a secondary effect, mainly altering overall stress levels. A nonlinear regression approach incorporating material properties, geometric parameters, and friction was used to formulate the numerical model. The developed model effectively estimated buckling stresses across various conditions but could not precisely predict buckling points based on stress differentials. This work contributes a novel framework for integrating material, geometric, and process variables into stress prediction during forging, advancing defect control strategies in industrial metal forming. Full article

Proton spin–lattice and spin–spin NMR relaxation studies were conducted on lung tissue samples from 10 patients. For each case, relaxation properties of tumor tissue were compared with those of the corresponding reference tissue. The spin–lattice relaxation measurements were performed over a wide frequency range, from 10 kHz to 10 MHz, spanning three orders of magnitude. These were complemented by both spin–lattice and spin–spin relaxation data acquired at 18.7 MHz. Notably, the spin–spin relaxation process exhibited a bi-exponential character. This relaxation behavior was quantitatively analyzed using dedicated models to achieve two main goals: to evaluate the diagnostic potential of low-field NMR relaxometry, and to gain insights into the dynamics of water and macromolecules in tissue, in comparison with aqueous solutions of proteins and polymers. The frequency dependence of the spin–lattice relaxation rates was well described by a power-law function, with an exponent of approximately 0.3 closely matching the theoretical prediction for reptation dynamics in polymer systems, associated with the intermolecular relaxation contribution. The combined analysis of spin–lattice and spin–spin relaxation data revealed specific parameters (such as ratios between the relaxation rates or between the amplitudes of individual relaxation components) that can be considered as potential markers of pathological changes affecting molecular dynamics in tissues. Full article