Search Results (20,614)

Accumulating evidence indicates that a hypomagnetic field (HMF, <5 μT) has a significant impact on various organ systems in animals. However, the cellular and molecular mechanisms underlying these biological effects remain unclear. Understanding the molecular mechanisms underlying mammalian responses to a HMF is crucial for addressing health and safety concerns associated with HMF exposure. In this study, we investigated the changes in intracellular protein phosphorylation under HMF conditions and validated the functional mechanisms by which HMF-induced protein phosphorylation affects cell behavior. We found that U2OS cells can rapidly sense changes in magnetic fields, leading to alterations in protein phosphorylation levels within the cell. The quantitative phosphoproteomics results revealed that the exposure of U2OS cells to the HMF environment for 0.5 h and 3 days resulted in the alteration of 1101 and 1543 phosphosites, respectively. Notably, HMF exposure enhanced the phosphorylation of β-Catenin at Ser552, and this increased phosphorylation-promoted U2OS proliferation and migration. Furthermore, quantitative proteomics showed that exposure to a HMF for 3 days upregulated the expression of LOX and FN1, while the knockdown of LOX or FN1 suppressed the proliferation and migration of the U2OS cells. These results suggest that a HMF enhances U2OS cell proliferation and migration by promoting β-Catenin phosphorylation and upregulating FN1 and LOX expression. Full article

Titanium and its alloys are widely used for orthopedic implants, but their intrinsic bioinertness may hinder osseointegration. In this study, titanium dioxide nanotube (TNT) arrays were fabricated on Ti-6Al-4V scaffolds via anodization, and their effects on the adhesion behavior of human bone marrow mesenchymal stem cells (hBMSCs) were investigated. Surface characterization showed that anodization successfully generated ordered TNT layers, increased surface roughness, enhanced protein adsorption, and induced an apparent superhydrophilic wetting response. Compared to the untreated scaffold and TNT50, the small-diameter TNT10 surface significantly promoted hBMSC adhesion and proliferation. Microscope imaging further revealed enhanced cell spreading, F-actin organization, and vinculin expression on TNT surfaces, with the most prominent focal adhesion-related staining observed in TNT10. Quantitative proteomic analysis showed that TNT10 was associated with coordinated remodeling of adhesion- and cytoskeleton-related molecular programs, including focal adhesion, cell–substrate junction, and regulation of the actin cytoskeleton. In contrast, TNT50, despite supporting obvious cytoskeletal remodeling, was more compatible with a dynamic, higher-turnover adhesion state. Overall, these findings suggest that small-diameter TNTs provide a more favorable interfacial microenvironment for stable early hBMSC adhesion on porous titanium scaffolds. Full article

Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by heterogeneous behavioral symptoms and systemic comorbidities, including immune and gastrointestinal dysfunctions. Emerging studies suggest that glycosylation—a fundamental post-translational modification regulating cellular communication and immune responses—may play a role in ASD pathophysiology, yet its contribution remains underexplored. Methods: In this study, we developed an integrative transcriptomic and network analysis framework to investigate glycosylation-related gene expression changes and their functional associations in ASD. Using publicly available datasets from bulk and single-cell RNA sequencing of brain and blood tissues, we focused on four prior-knowledge gene subsets: glycogenes, extracellular matrix glycoproteins, immune response genes, and autism risk genes. Results: Differential expression and pathway enrichment analyses revealed consistent dysregulation of glycosylation pathways, including mucin-type O-glycan biosynthesis, glycosaminoglycan metabolism, GPI-anchor formation, and sialylation, across ASD tissues. These transcriptional changes were functionally linked to altered immune signaling (e.g., IL-17, Toll-like receptor, and complement pathways) and synaptic development pathways, forming a distinct glyco-immune axis. Network analysis identified key glycogenes such as GALNT10, NEU1, LMAN2L, and CHST1 as central molecular nodes, interacting with immune and neuronal regulators. Linkage disequilibrium analysis further revealed ASD-associated SNPs influencing the expression of these glycogenes in both blood and brain tissues. Conclusions: Together, these findings support a model in which disrupted glycosylation contributes to ASD pathophysiology by mediating immune dysregulation and altered neuronal connectivity. This study offers a systems-level framework to understand the molecular complexity of ASD and highlights glycogenes as potential biomarkers and targets for future therapeutic exploration. Full article

Energy efficiency improvement represents a central strategic objective of the European Union (EU), essential for mitigating climate change and facilitating the transition toward a sustainable energy system. In 2023, renewable energy sources generated approximately 46% of the electricity produced in the EU, becoming the dominant component of the regional energy mix. This progress has been supported by coherent public policies, dedicated investment programs, and regulatory mechanisms aimed at accelerating the adoption of sustainable technologies. However, the existing literature highlights a research gap regarding the relationship between the dynamics of the European energy transition, the operational challenges generated by the rapid increase in the share of renewable energy sources, and the potential for energy savings in the residential sector through non-technological interventions. This paper analyzes the structural transformations of the European energy mix, the limitations of energy systems in the context of accelerated renewable energy integration, and the role of behavioral interventions in supporting the stability of the energy system. The study examines the dynamics of residential energy consumption, behavioral determinants of energy use, and the effectiveness of instruments such as information campaigns, real-time feedback, dynamic pricing, and demand response programs. The results indicate that these interventions can reduce peak loads, increase consumption flexibility, and alleviate pressure on energy networks under conditions of variable renewable energy generation. The integration of energy storage systems and the implementation of low-cost behavioral measures can act as complementary instruments for maintaining the dynamic stability of the energy system and for achieving the EU’s sustainability and climate neutrality objectives. Full article

Thermal management is critical for maintaining the safety and performance of lithium-ion batteries. Phase change materials (PCMs) have been widely studied as passive cooling media due to their high latent heat capacity, but major technical challenges remain due to their relatively low thermal conductivity and nanoparticle sedimentation in composite systems. In this work, a composite phase change material (PCM) consisting of paraffin wax, a microencapsulated phase change material (MicroPCM 28D), and nano carbon black is developed to enhance thermal stability and suppress particle sedimentation through increased viscosity of the PCM matrix. Five capsule geometries fabricated by fused filament fabrication (FFF) 3D printing are experimentally investigated under airflow velocities ranging from 0 to 10 m s⁻¹. Wind tunnel experiments with infrared thermography are used to evaluate the thermal response of the PCM capsules. The results show that airflow velocity and capsule geometry strongly influence heat dissipation behavior. Compared with conventional wax composites, the MicroPCM 28D composite capsules reduce peak temperature by approximately 2–4 °C under airflow velocities of 0–10 m/s. These findings provide insights into geometry-regulated convection and stable composite PCM design for lithium-ion battery thermal management systems. Full article

As the integrated energy market evolves toward a multi-stakeholder coexistence model, balancing economic efficiency, user well-being, and system-level sustainability among interacting stakeholders has become a key challenge, particularly in the rapidly developing regional integrated energy markets in China. Thus, to satisfy user comfort and energy substitution requirements while achieving cost-effective electricity and heating supply, this study proposes a Stackelberg game-based market trading framework involving an integrated energy producer (IEP), an integrated energy operator (IEO), and a load aggregator (LA). First, the integrated energy market framework and transaction modes are established, and the profit models of IEP and IEO are formulated. Considering users’ energy substitution behavior, user comfort is quantified to explicitly reflect user welfare in market decision making, and a consumer surplus model is developed for LA participating in market transactions. Second, a Stackelberg game framework is constructed to coordinate the strategies of all participants by incorporating source–load energy flows, and the equilibrium solution is proven to be unique and solvable using quadratic programming. Finally, a case study based on historical data from Hebei Province, China, is conducted to validate the proposed strategy. The results demonstrate that the proposed method effectively coordinates the interests of all stakeholders, enhances demand response capability without reducing user comfort, and improves economic benefits for both supply and demand sides in regional integrated energy markets. Full article

Background/Objectives: Periodontitis represents a significant global health burden, yet preventive health literacy remains critically low among emerging adults—a developmental stage where lifelong health behaviors crystallize. This study evaluated the effectiveness of the GUM (an acronym of Gum Understanding Mission) game, an interactive gamified digital tool incorporating AI-informed or manual feedback, for improving periodontitis literacy among tenth-semester Software Engineering students at the University of Guayaquil. Methods: In a controlled pre-test/post-test experiment, 50 participants were randomly assigned to either the GUM game intervention or a traditional lecture. Both groups completed identical knowledge assessments immediately before and after their respective 50-min instructional sessions. The GUM game featured adaptive questioning, immediate elaborated feedback, and comprehensive performance analytics, while the control group received instructor-led didactic instruction with a subsequent question-and-answer session. Results: The GUM group improved from a baseline of 21% to 94% correct responses, while the lecture group increased from 22% to 67% ($p<0.001$). Error reduction was 74% in the GUM group versus 45% in the control group. However, the study’s scope is currently limited to a single, digitally literate cohort, and knowledge retention over time was not assessed. Conclusions: These findings suggest that a self-directed, feedback-driven serious game can substantially outperform traditional methods in fostering periodontitis literacy within this population. Further research is needed across diverse populations with extended follow-up periods to assess knowledge retention and generalizability. Full article

This study presents a comparison between the results of process parameter optimization for the deep drawing of an AA5754-O automotive fuel tank, which utilizes two different objective functions. The first objective function is the maximum thinning percentage (max. %Thinning) of the formed part, which is a conventional formability index. The second is Q-value, a metric derived from the Thinning Limit Diagram that accounts for both necking-prone (excessive thinning) and wrinkling-prone (thickening) regions. The experiments were conducted using finite element simulation to model the forming behavior under an inscribed central composite design within the response surface methodology. Three process parameters, which are well known to be important for controlling material flow and achieving a balance between wrinkling and excessive thinning in deep drawing, were varied: blank holder pressure, the height of the male drawbead, and the radius of the female drawbead. Refined second-order response surface models were developed for both objective functions. Optimization based on the response surface models showed that, for the max. %Thinning objective function, the final part exhibited 19.46% maximum thinning but suffered from substantially higher wrinkling, as indicated by a maximum thickening of 36.39%. In contrast, the Q-value-based optimization resulted in a more balanced formability condition, with maximum thinning of 21.74% and maximum thickening of 13.17%. Moreover, the normalized density of elements in the safe zone of the Thinning Limit Diagram was higher, indicating an improvement in formability robustness. Therefore, this study highlights the limitations of conventional thinning-based optimization and demonstrates the potential of the Q-value as an extended practical quantitative formability tool that can simultaneously address necking and wrinkling in sheet metal forming, as presented through the studied automotive fuel tank on behalf of complex components. Full article

With the increasing application of the Vertical Shaft Machine (VSM) method in ultra-deep shafts, accurate prediction of construction-induced structural stresses is vital for engineering safety. Currently, VSM is predominantly used in soft soils, where structural response analysis still relies on finite element (FE) simulations that are computationally intensive and complex to model. To improve analysis efficiency and understand the structural behavior of VSM shafts in granite composite strata, this study takes the first VSM shaft project in South China—the Guangzhou–Huadu Intercity Railway Shield Shaft—as a case study. A “monitoring-driven, large-sample data, machine learning substitution” framework is proposed for predicting structural stresses during construction. The framework calibrates an FE model using monitoring data. Through full factorial design, key design parameters—including main reinforcement diameter, stirrup diameter, concrete strength grade, and steel plate thickness—are systematically varied. Parametric FE simulations are then conducted to construct large-sample response databases (540 sets for ring 0 and 864 sets for the cutting edge ring). Genetic algorithm is introduced to optimize the hyperparameters of Random Forest, XGBoost, and Neural Network models, and their predictive performances are systematically compared. Results show that the proposed framework effectively substitutes traditional FE analysis and enables rapid multi-parameter comparison. Among the models, GA-XGBoost achieves the highest prediction accuracy across all stress indicators (R² > 0.999, where R² is the coefficient of determination, with values closer to 1 indicating better predictive performance), demonstrating the superiority of its gradient boosting and regularization mechanisms in handling tabular data with strong physical correlations. Moreover, the method exhibits good extensibility to other engineering response predictions beyond construction stresses. Full article

With the growing demand for hybrid and electric vehicles, the accurate prediction of NVH (Noise, Vibration, and Harshness) behavior in Permanent Magnet Synchronous Machines (PMSMs) has become a critical aspect of electric motor design. This paper presents a detailed modeling approach for electromagnetic-induced noise and vibrations in PMSMs, integrating both analytical and numerical methods. The model focuses on quantifying the contributions of radial and tangential electromagnetic forces, which are key drivers of vibro-acoustic responses. The analytical part employs curved beam theory and a simplified acoustic model, offering rapid insights during early design stages. In parallel, a detailed numerical model based on finite element analysis is developed using a physics-based approach that accounts for the actual geometry and material properties of the PMSM prototype. This allows for enhanced accuracy without relying on experimental material parameter identification. Moreover, the detailed model includes the fluid–structure interaction introduced by the channels of the cooling fluid of the electric machine, which, although poorly addressed by the existing literature, was found to play a key role in driving the vibrational behaviour of the structure. By combining analytical speed with numerical precision, the proposed approach enables consistent and physically-based NVH predictions across various design phases, ultimately supporting improved electric machine performance and reducing development time and costs. Validation against experimental data confirms the ability of the model to accurately predict both sound pressure levels and housing surface vibrations. The novelty of this work lies in its integration of fluid–structure interaction and material modeling without the need for empirical parameter tuning, offering a robust tool for NVH design in electric vehicle applications. Full article

Background/Objectives: Elevated central adiposity (ECA) in childhood is associated with early cardiometabolic risk and hemodynamic alterations. However, evidence in Spanish schoolchildren regarding the relationship between eating behavior traits and central adiposity is limited, particularly across developmental stages. This study aimed to examine the association between Children’s Eating Behaviour Questionnaire (CEBQ) subscales and ECA, and to explore potential differences by age group. Methods: A cross-sectional study was conducted in 496 rural schoolchildren aged 6–15 years. ECA was defined using the waist-to-height ratio (WHtR) and sex-specific cut-offs validated for the Spanish pediatric population. Eating behavior was assessed with the CEBQ (Z-scores), and diet quality was measured using the KIDMED index. Multivariable logistic regression models were adjusted for sex, KIDMED score, and maternal education. Analyses were subsequently stratified by age (6–9 and 10–15 years). Results: The prevalence of ECA was 45.90%. In fully adjusted models, higher Food Responsiveness (FR) was associated with increased odds of ECA, while Satiety Responsiveness (SR) acted as a protective factor; sex also showed an independent association. After stratification, sex remained the only significant predictor in children aged 6–9 years. Among those aged 10–15 years, FR was significantly associated with ECA (p = 0.008), while Slowness in Eating (SE) showed a borderline positive association in the adjusted model (p = 0.049) and was therefore interpreted cautiously. SR and Emotional Undereating (EU) showed protective trends near significance (p = 0.081 and p = 0.082, respectively). Conclusions: The association between eating behavior traits and ECA varies by age. In older children, FR showed a robust association with ECA, whereas no behavioral predictors were observed in younger children. The protective role of SR in the global model and the emergence of behavioral predictors in older participants highlight the importance of targeted interventions during late childhood. Full article

Background/Objectives: Mindfulness-based interventions are widely used to reduce psychological distress. Their long-term neurophysiological correlates remain insufficiently characterized. Using a portable Muse InteraXon^® EEG device, this study aimed to evaluate (i) the extent to which a 12-month combined mindfulness and gratitude-based intervention reduces anxiety, depression, and perceived stress, and (ii) whether these changes are accompanied by corresponding EEG-derived neurophysiological alterations, exploring longitudinal brain–behavior associations. Methods: Fifty participants completed psychological assessments at baseline, 6 months, and 12 months using validated scales (BDI-II, DASS-21, EMAS). A subcohort of 25 participants also underwent EEG recordings with a portable Muse device at the same time points. Longitudinal changes were analyzed using linear mixed-effect models, and exploratory brain–behavior associations were assessed with change-score analyses and Spearman’s correlations with false discovery rate correction. Results: Across the full cohort (n = 50), psychological outcomes showed longitudinal improvements over 12 months, with reductions in BDI-21, DASS-21 depression, anxiety, and stress subscales, and EMAS-State scores (all p < 0.001; linear mixed-effect models). In the EEG subcohort (n = 25), longitudinal analyses showed increased alpha power and reduced beta and gamma power in frontal and temporoparietal regions (pFDR < 0.05), along with a modest decrease in delta power at 12 months, while theta power remained stable. Exploratory analyses showed non-significant trends in the hypothesized directions that did not remain statistically significant after correction for multiple comparisons (e.g., Δalpha vs. Δstate anxiety: ρ ≈ −0.44; Δbeta vs. Δdepression: ρ ≈ 0.43) or after FDR correction. Conclusions: The mindfulness- and gratitude-based intervention was associated with sustained improvements in psychological outcomes and suggests accompanying dynamic modulation of neurophysiology. EEG appears to reflect time-dependent neural adaptation rather than a static predictor of treatment response. Full article

Starvation is a fundamental physiological stressor that triggers conserved adaptive responses across species, however, its effects are shaped by both developmental stage and prior nutritional history. This study aimed to investigate the effects of acute nutrient deprivation in Artemia franciscana, comparing newly hatched nauplii and adult individuals previously exposed to reduced caloric intake during development. Organisms were subjected to starvation for 24, 48, and 72 h, and mortality, morphometric parameters, and locomotor activity were assessed, complemented by in silico analysis of starvation-related pathways. Starvation induced distinct responses between groups, with markedly higher mortality in adults compared to nauplii. While these differences reflect developmental stage-associated responses, they are also influenced by prior nutritional history. Body length was significantly reduced under starvation in both developmental stages, while antennal length remained largely unchanged. Locomotor activity, including distance travelled and swimming velocity, was consistently decreased, indicating energy-conserving behavioral adaptation. Partial recovery of locomotor performance and antennal length was observed following restoration of feeding. Bioinformatic analysis suggested the presence of conserved autophagy-related genes and enrichment of pathways associated with autophagy and TOR signaling. However, these findings should be interpreted as hypothesis-generating, given the reliance on a proxy species for pathway inference. These findings indicate that starvation responses in A. franciscana are shaped by an interaction between developmental stage and prior nutritional history, supported by conserved stress–response pathways, highlighting the potential of this model for studying metabolic stress responses. Full article

Despite the growing adoption of Ti6Al4V-ELI made by Laser Powder Bed Fusion (LPBF) in tribologically demanding applications, the influence of hybrid nanoparticle additives on its lubrication behavior under starved contact conditions remains insufficiently explored. The tribological performance of Ti6Al4V was investigated under starved boundary-to-mixed lubrication conditions using engine oil modified with Ag-doped TiO₂ nanoparticles. Double-scan LPBF-fabricated discs were tested in a ball-on-disc configuration against AISI 52100 bearing steel using a TRB³ tribometer. Nanolubricants were prepared by dispersing TiO₂ and Ag–TiO₂ nanopowders with different Ag⁺/Ti⁴⁺ ratios (0.5%, 1.5%, and 2.5%) in SAE 10W-40 engine oil at a constant nanoparticle concentration of 0.05 wt%. Comprehensive physicochemical characterization of the nanopowders and nanolubricants was performed through structural, chemical, optical, morphological, rheological, and stability analyses. Tribological experiments were conducted following a full-factorial design combining three normal loads (5–15 N), three sliding speeds (0.10–0.20 m·s⁻¹), and four lubricant formulations. The steady-state coefficient of friction ranged between 0.281 and 0.359, while the specific wear rate varied from 2.81 × 10⁻⁴ to 4.83 × 10⁻⁴ mm³·N⁻¹·m⁻¹. The contact temperature rise remained relatively moderate, within the interval of 1.9–9.4 °C. Among the investigated formulations, the lubricant containing 1.5% Ag–TiO₂ exhibited the lowest friction coefficient, whereas the formulation with the highest Ag content showed improved stability of tribological performance across the investigated operating domain. These results indicate that Ag-modified TiO₂ nanoparticles are consistent with the formation of protective tribofilms and contribute to the stabilization of friction, wear, and thermal behavior under starved lubrication conditions. ANOVA confirmed that sliding speed and the load–lubricant interaction are the dominant factors governing friction and wear, while normal load controls the thermal response. These findings support the use of Ag–TiO₂ nanolubricants as a viable strategy for stabilizing interfacial behavior in LPBF-fabricated titanium components operating under starved lubrication conditions. Full article

Accurate rainfall information is essential for rainfall–runoff modeling in monsoon-influenced basins, where pronounced spatial variability and limited gauge coverage introduce significant uncertainty. Satellite precipitation products provide spatially continuous estimates but are affected by systematic biases, and improvements in statistical rainfall accuracy do not necessarily translate into hydrologically consistent model forcing. This study interpreted satellite rainfall bias correction through a rainfall–runoff framework in the Phetchaburi River Basin, Thailand, using the DWCM-AgWU hydrological model. Simulations were driven by gauge observations and multiple satellite-based rainfall products (GSMaP, CMORPH, CHIRPS, and PERSIANN-CCS), with bias correction applied using Linear Scaling and Quantile Mapping under rainfall-specific calibration. Results showed that bias correction significantly modified rainfall characteristics in distinct ways. Linear Scaling primarily preserved temporal and spatial structure while adjusting rainfall magnitude, whereas Quantile Mapping improved the distributional representation of rainfall intensities. These differences propagated through hydrological processes, leading to systematic variations in runoff responses across multiple metrics, including water balance consistency, peak magnitude, and timing errors. This suggests that each method performs differently depending on the aspect of system response. Rather than identifying a universally optimal method, the findings highlight trade-offs in how rainfall correction strategies influence hydrological system response. Runoff behavior is interpreted as a process-level indicator of rainfall representation, emphasizing that hydrological consistency depends not only on rainfall accuracy but also on its interaction with model structure. These results suggest a process-oriented perspective for interpreting the role of satellite rainfall products in regulated and monsoon-affected basins. Full article