Search Results (1,823)

Background: Auditory brainstem responses (ABRs) are widely used for objective hearing threshold estimation in both adults and children. Click and CE-Chirp stimuli differ substantially in cochlear activation and neural synchrony, yet their relative performance in patients with ski-sloping hearing loss remains insufficiently characterized, particularly with regard to pediatric diagnostic implications. Methods: This study compared ABRs elicited by click and CE-Chirp stimuli in adults with ski-sloping sensorineural hearing loss. The same comparison was also performed in a pediatric cohort including hearing-impaired and normal-hearing children. Adult subjects were further stratified according to audiometric configuration (DROP 1 kHz vs. DROP 2 kHz). ABR thresholds, wave V latency, amplitude, and detectability were analyzed across stimulus types and intensity levels. Associations between ABR thresholds and behavioral audiometric measures were also examined. Results: In adults with ski-sloping hearing loss, CE-Chirp stimulation yielded significantly lower ABR threshold estimates than click stimulation, particularly in the DROP 2 kHz subgroup, and showed stronger correlations with behavioral pure-tone averages across low-, mid-, and high-frequency ranges. Wave V latencies were consistently shorter with CE-Chirp stimulation, while wave V amplitudes did not differ significantly between stimuli at suprathreshold levels. In children, ABR thresholds obtained with CE-Chirp were generally equal to or lower than those obtained with clicks, although statistical significance was limited by sample size. CE-Chirp stimulation was associated with shorter wave V latencies in both hearing-impaired and normal-hearing children and produced larger wave V amplitudes at selected suprathreshold intensities in hearing-impaired children. Conclusions: Click and CE-Chirp stimuli provide complementary information in ABR assessment. While click stimulation remains essential for robust waveform identification, CE-Chirp stimulation appears to offer advantages in threshold estimation and neural synchrony, particularly in ski-sloping hearing loss and pediatric evaluations. Discrepancies between click- and CE-Chirp-derived ABR thresholds should not be attributed solely to maturational or synchrony-related factors but may warrant further frequency-specific audiological assessment to optimize diagnosis and rehabilitation strategies. Full article

Background: Myocardial infarction (MI) is the leading cause of morbidity and mortality globally. A major pathological progression of MI is the excess generation of reactive oxygen species (ROS), which results in oxidative stress and damage to the ischemic heart. Because damage to the myocardium is irreversible, the development of new therapeutic agents that can decrease the degree of ischemic damage following MI is crucial. The traditional Chinese medicine formulation, Qishen Yiqi dropping pills (QSYQ), has been clinically used in the treatment of various cardiovascular diseases; however, the precise mechanisms underlying its therapeutic effects remain unelucidated. Methods: In this study, we established murine models of MI via coronary artery ligation to investigate the protective effects and mechanisms of QSYQ following MI. Results: The administration of QSYQ significantly improved cardiac function, reduced infarct size, and attenuated ventricular remodeling in mice that underwent MI. Moreover, MI-induced oxidative stress and downregulated levels of antioxidant enzymes were prevented in mice administered QSYQ via upregulating the SIRT3/FOXO3a signaling pathway. Importantly, pretreatment with a selective SIRT3 inhibitor 3-TYP abolished the cardioprotective effects of QSYQ. Conclusions: Our findings elucidate the role and mechanism of QSYQ in protecting against oxidative damage and restoring redox homeostasis following myocardial infarction. This study provides support for the therapeutic potential of QSYQ in the clinical treatment of myocardial ischemic diseases. Full article

Extensive research and empirical evidence demonstrate that nanofluids enhance heat transfer efficiency in microchannels, but this improvement is often accompanied by increased pressure drop and particle clogging. This study aims to determine the optimal parameters for preparing stable nanofluids and to discuss the effects of different parameters on thermal and hydraulic performance. By analyzing the impact of varying ultrasonication time, particle concentration, particle size, surfactant type, and mixing ratios on stability, the most stable nanofluid was selected for evaluation of flow heat transfer and cost-effectiveness. Results indicate that a 1:1 mixed nanofluid of TiO₂ (20 nm)-SiO₂ (50 nm) exhibits optimal stability under conditions of 90 min ultrasonication, 0.20 vol% total particle concentration, and 0.15 wt% xanthan gum. At a Reynolds number of 550, this mixed nanofluid exhibits superior thermal performance. Compared with deionized water, its convective heat transfer coefficient and Nusselt number increase by 40.25% and 37.94%, respectively, while the pressure drop rises by only 17.18%. The performance evaluation criterion reaches 1.43, accompanied by a high cost–performance factor. These findings demonstrate that mixing large and small particles of TiO₂ and SiO₂ not only significantly enhances thermal performance but also positively impacts stability and hydraulic properties. A 90 min ultrasonic treatment time markedly improves stability and optimizes dynamic light scattering results. Full article

For safe and efficient human–robot interaction, lower-limb exoskeletons used for assistance and rehabilitation need to be precisely and energy-efficiently controlled. By creating an adaptive neuro-fuzzy fractional-order PID (ANFIS-FOPID) controller, this project seeks to improve tracking accuracy, robustness, and energy efficiency in a two-degree-of-freedom hip–knee exoskeleton. The Euler–Lagrange formulation is used to derive a nonlinear dynamic model, and a Lyapunov-based stability analysis is used to show that the closed-loop system remains uniformly ultimately bounded under disturbances and parameter uncertainties. The suggested controller performs noticeably better than traditional PID and fixed-parameter FOPID controllers, according to numerical simulations conducted under both normal and perturbed conditions. The ANFIS FOPID achieves root mean square errors below 0.028 rad and lowers the integral absolute errors at the hip and knee joints to 0.1454 and 0.1480, as opposed to 0.3496–0.3712 for PID controllers. Under ±10% parameter uncertainty, the total control-energy proxy drops from 2870.0 (PID) to 936.25, a 67.4% decrease, and stays at 1587.93. Statistically significant variations in energy consumption are confirmed by one-way ANOVA (p < 10⁻¹⁷⁶). Large effect sizes are found (η² = 0.237–0.314). These results demonstrate the superior tracking performance, robustness, and energy efficiency of the ANFIS-FOPID controller. The results set a quantitative standard for future experimental validation and hardware-in-the-loop implementation, despite being based on high-fidelity simulations. Full article

Identifying real-world saturation points and grid-hosting capacity in mixed-use urban Renewable Energy Communities (RECs) requires dynamic spatial evaluation. To address this, this paper introduces a novel simulation framework that integrates GIS spatial analysis with an iterative heuristic selection algorithm. The proposed method evaluates the energetic interaction between a primary generation node and surrounding consumers, utilizing a dynamic function to calculate the collective Self-Consumption Rate (SCR). Applied to the Flaminio Stadium in Rome, the model incrementally aggregates users to determine the optimal cluster size for economic feasibility. The results demonstrate that the heuristic selection algorithm successfully refined the community from an initial pool of 854 buildings to an optimal cluster of 734. This targeted selection eliminated energy surplus and achieved a near-perfect collective SCR of 99.8%. Furthermore, by strategically reducing the required installed PV capacity by 52.6%, the initial capital investment dropped from € 89.9 million to € 42.6 million, significantly de-risking the project while maintaining a competitive payback period of approximately 13 years. Ultimately, this study presents a scalable spatial optimization tool that empowers decision makers to transform large-scale urban infrastructure into the energetic and economic engines of district wide decarbonization Full article

At the current stage, water resource shortages and significant regional disparities in resource distribution severely restrict China’s food security. Existing research primarily focuses on resource use efficiency, while lacking a systematic framework to distinguish between equality and equity in the coupled distribution of irrigation water, grain production, and nitrogen pollution across major river basins. The core objective of this study is to utilize the Concentration Index (CI) to construct a unified equity assessment framework, quantify the evolution of equality and equity in irrigation water use, grain production, and nitrogen loss to surface water in different river basins in China from 1992 to 2017, and determine the key influencing factors. For positive production resources, a distribution that benefits low-income groups is equity, while for pollution burdens, this distribution pattern is inequity. The results show that water shortages in Northern China have intensified, and higher income groups have obtained excessive benefits. The distribution of grain production has shifted from favoring higher income groups to favoring low-income groups, with the Concentration Index changing from 0.214 to −0.052, indicating an enhancement in equity. Irrigation water use has shown a certain degree of improvement, with the CI dropping from 0.023 to 0.017. However, nitrogen loss to surface water has exacerbated environmental inequality, with the CI dropping from 0.10 to 0.03, indicating that pollution burdens have shifted to low-income groups. Changes in equity across the country are driven by a small number of high-intensity grain production areas, and the key influencing factors include food security policies, urbanization, population size, and nitrogen fertilizer application. An asymmetric coupling relationship exists between water resource shortages and equity, and the regional economic foundation determines the formation of synergy or trade-offs. The findings underscore the necessity of transitioning from efficiency-focused to equity-focused agricultural governance in China. Targeted policies should include cross-basin ecological compensation mechanisms, differentiated technology promotion strategies, and integrated water–food-pollution management systems to balance food security, environmental protection, and social justice. Full article

Particle deposition, rebound, and adhesion on rough surfaces play a crucial role in a wide range of powder handling, aerosol transport, and fouling-related processes. However, the underlying mechanisms governing single-particle interactions with rough surfaces, particularly those with complex surface morphologies, remain insufficiently understood. In this work, the deposition and elastic rebound behavior of an individual particle impacting superhydrophobic surfaces with ribbed and randomly distributed roughness structures are systematically investigated through a combined experimental and numerical approach. A coupled Lattice Boltzmann Method (LBM) and Discrete Particle Model (DPM) was developed, in which a new particle–surface contact model is proposed to account for adhesion, elastic deformation, and localized roughness effects through multi-node interactions. Randomly distributed rough surfaces are reconstructed using a Fast Fourier Transform (FFT)-based method, and single-particle impact experiments are conducted to validate the numerical predictions. Good agreement is achieved between simulated and measured values, with a relative error for the maximum rebound height of only 5.9% and a peak velocity deviation prior to impact of approximately 5.4%. Parametric analyses demonstrate that particle diameter, Young’s modulus, surface energy, surface roughness morphology, and flow Reynolds number all influence particle deposition outcomes. Larger particles exhibit significantly higher rebound heights due to increased stored elastic energy; specifically, when particle size increases from 20 μm to 100 μm, the maximum rebound height increases by a factor of 2.1. In contrast, smaller particles are more prone to adhesion after repeated impacts. The rebound height of particles decreases as surface energy increases. When surface energy rises from 0.01 J/m² to 0.05 J/m², rebound height drops from 53.65% to 38.66%. At 0.5 J/m², particles adhere immediately. Compared with ribbed surfaces, randomly distributed rough surfaces promote particle rebound by reducing effective contact area and inducing complex impact orientations. Particle rebound behavior is primarily governed by particle diameter, while material properties such as Young’s modulus and surface energy exhibit secondary and nonlinear effects. The proposed model provides a validated and transferable framework for analyzing particle–surface interactions on rough surfaces and offers physical insights relevant to the control of particle deposition in powder and particulate systems. Full article

Accurately estimating urban above-ground vegetation carbon density (UAGVCD) is crucial for assessing urban carbon sinks, but it is difficult due to varying spatial patterns, complex land covers, and differences caused by terrain. This study measures UAGVCD in Shenzhen, China, using an explainable remote sensing and machine-learning approach. We combined Landsat 8 spectral bands, vegetation indices, texture metrics, and terrain-based variables with 195 field measurements of carbon density to develop an Extreme Gradient Boosting (XGBoost) model. We evaluated model performance with spatial block cross-validation, using block sizes of 2 km, 5 km, and 10 km to account for spatial autocorrelation. The results show that the XGBoost model performed reliably during spatially independent validation, with the 5 km block showing the best accuracy (train R²= 0.917 ± 0.086, RMSE= 5.53 ± 3.97 Mg ha⁻¹; validation R² = 0.617 ± 0.055, RMSE = 10.25 ± 1.39 Mg ha⁻¹). Smaller blocks gave more varied results, while larger blocks led to a significant drop in accuracy (validation R² = 0.380 ± 0.297 at 10 km). Predictions showed clear differences in UAGVCD, with higher values in mountainous and green areas and lower values in highly developed regions. SHapley Additive exPlanations (SHAP) analyses suggested that both spectral and topographic factors play a significant role in UAGVCD. Additionally, the relationships between spectral data and carbon density showed strong nonlinear responses affected by terrain. These findings highlight the importance of spatially explicit validation and explainable machine learning for reliable urban vegetation carbon mapping. Full article

This study analyzes hydrodynamics and mass transfer in a packed-bed reactor (PBR) by comparing two representations of bed geometry. The first is a pseudo-homogeneous approach using effective parameters, such as a radial porosity distribution. The second is a heterogeneous approach with resolved particles in the CAD domain. Both models simulate single-phase flow and mass transfer of urea and NH₃ for an enzymatic reaction across a wide Reynolds number range $\left(5\le {\mathrm{Re}}_p\le 750\right)$. The pseudo-homogeneous model incorporated a detailed porosity distribution, derived from the heterogeneous model’s solids layout, which aligned well with literature, including classical correlations for radial porosity in packed beds. Additionally, hydrodynamic predictions were benchmarked against established pressure-drop correlations for confined packed beds, supporting the physical consistency of the particle-resolved framework. This non-uniform porosity informed local variations in permeability and dispersion coefficients. Velocity, pressure, and concentration fields from both approaches were compared to quantify predictive quality. Results indicate that a well-configured pseudo-homogeneous model can closely match heterogeneous model predictions, achieving similar accuracy in many flow regimes, with accumulated average relative errors below 8%. However, its performance varies with flow conditions. The optimal pseudo-homogeneous model (showing the highest predictive consistency with the particle-resolved simulations) was then used for transient simulations. These dynamic results support the preliminary sizing and conceptual design of a device for nutrient recovery from human urine for agricultural use, demonstrating the utility of simplified models for complex reactor design while acknowledging that full experimental validation under real urine-matrix conditions remains beyond the scope of the present study. Full article

This study numerically investigates the effects of red blood cell (RBC) volume fraction (hematocrit) and RBC diameter on cell distribution, cell-free layer (CFL) thickness and pressure drop in a microchannel with sudden expansion. Hematocrit levels of 0.2, 0.3, 0.4 and 0.5, together with RBC diameters of 4, 8 and 11 µm, are considered, where deviations from the physiological diameter of 8 μm represent pathological conditions. An Euler–Euler approach is employed to model the multiphase flow, treating RBCs as rigid spherical particles, while the non-Newtonian viscosity of blood is represented using a modified Carreau–Yasuda model. The numerical predictions are validated against existing experimental and numerical data. The effect of volumetric flow rate on RBC distribution is found to be limited; therefore, a representative flow rate of 100 μL/min is adopted for the subsequent analysis. The results show that RBC migration and the resulting cell distribution are strongly governed by RBC size and hematocrit. The pressure drop is primarily influenced by hematocrit, while the effect of RBC size is relatively weak. A minimum value for pressure drop is observed at a hematocrit of 0.3, indicating an optimal hematocrit level for minimizing flow resistance. A parabolic correlation is proposed for predicting the pressure drop as a function of hematocrit, with a maximum relative error of 1.13%. This study contributes to the understanding of pathological RBC size variations and their impact on microscale hemodynamics. Full article

Superhydrophilic micro/nano-porous media have extensive applications in electronic thermal management and energy storage systems. Predicting two-phase pressure drop in complex porous structures is of great importance for system design and optimization while remaining highly challenging. This study systematically investigates the two-phase flow resistance characteristics of bi-porous microstructures with multiple particle sizes and porosities under varying boiling regimes. Experimentally, porous samples were fabricated via vacuum sintering using nickel powders and pore-forming agents (CaCl₂), which exhibit superhydrophilicity and enhanced wicking characteristics. A visualized experimental platform was constructed to investigate the impact of pore size combinations, flow velocities, and boiling states on pressure drop. The dataset obtained through multi-factor saturated boiling experiments was further used to derive a semi-empirical model for the two-phase flow pressure drop based on the classic Kozeny-Carman (K-C) and Akagi-Chisholm (A-C) correlations. Results show that the pore size combinations and boiling states have a significant impact on the resistance performance. The proposed model achieves an average prediction deviation below 15.7%, confirming its reliability and its effectiveness as a design framework for optimizing high-capillary-force porous wicks in advanced thermal management systems. Full article

This study investigates the development and characterization of bioactive films incorporating silver nanoparticles (AgNPs) into biocompatible polymers, namely alginate and chitosan, fabricated using two methods, spin-coating and drop-casting, and aiming to enhance their antimicrobial properties. Dynamic light scattering (DLS) and electrophoretic mobility (EM) of the film precursor solutions revealed significant changes in the nanoparticles’ size and Zeta potential (ZP), reflecting the influence of polymer coatings. Alginate contributed to high electrostatic stability due to its negative charge, while chitosan facilitated specific interactions with negatively charged surfaces. Raman spectroscopy revealed that spin-coating conditions did not successfully result in film formation, highlighting the need for further optimization. Therefore, subsequent characterization studies were conducted only for the films formed by drop-casting. Topographical and nanomechanical assessments of these drop-cast films, using atomic force microscopy (AFM) and force spectroscopy, demonstrated that AgNPs reduced adhesion and elasticity in alginate films, while increasing rigidity and adhesion in chitosan-based films. Antimicrobial tests confirmed the efficacy of AgNPs in both precursor solutions and polymer films, with chitosan-based films that retained structural integrity, which makes them suitable for prolonged applications, while alginate films displayed rapid gelation upon hydration, potentially advantageous in short-term applications. The findings underscore the potential of these biopolymer-AgNP composites in creating antimicrobial materials for food packaging, wound dressings, and other biomedical applications. However, challenges related to film deposition methods, such as spin-coating, require further optimization to improve film formation and reproducibility. Full article

To assess the impacts of cascade dam development on riverine fish, this study investigated the population structure and spatiotemporal distribution of six rare and endemic fish species in the Hongshui River mainstream from 2022 to 2023. Results indicated a significant resource decline, with the proportion of rare species dropping to 18.33%. Populations exhibited pronounced characteristics of age rejuvenation and miniaturization, with mean body lengths failing to reach growth inflection points. Although growth parameter analysis indicated rapid growth patterns (k > 0.2), stock assessment revealed that Semilabeo obscurus and Onychostoma gerlachi were overexploited (E > E_max), while Ptychidio jordani maintained the highest biomass. Redundancy analysis (RDA) identified water chemistry (e.g., conductivity, TN) as the primary driver of seasonal distribution, whereas the proportion of natural free-flowing river segments significantly influenced community variation under dam regulation (corresponding 59.44% of variation). Conservation strategies should prioritize the protection of remnant natural river segments, implementation of ecological flow regulation to simulate natural hydrological rhythms, and strict enforcement of minimum capture size limits to ensure the sustainable utilization of these rare and endemic resources. Full article

How ordered and mutually independent are semiflexible ring polymers (RPs) confined to a spherical cavity of variable radius? By varying the cavity radius, we systematically investigate the effect of the confinement size on the conformations of RPs using the coarse-grained molecular dynamics simulations. The results reveal that as the bending energy increases, the RPs exhibit a transition from a purely flexible coil to an elongated oblate-shaped object and, eventually, to a disk-like conformation. Simultaneously, the stacked aggregates composed of adjacent, mutually nearly parallel, semiflexible RPs emerge for stiffer chains. We find that the structural modulation of the stacked aggregates is regulated by the confinement size. For the conditions of strong confinement ($R<2R_g$, where $R_g$ is the radius of gyration of an RP), the semiflexible RPs undergo peculiar deformations and twisting that lead to disruption of the stacked aggregates. At $R\approx 2R_g$, the average number of the RPs per stack reaches a maximum. Concurrently, the order of spatial alignment of all semiflexible RPs is maximized with the global orientational-order parameter reaching the value $S\approx 0.79$. As the cavity radius further increases, at $R>3R_g$, the semiflexible RPs gain greater mobility resulting in diverse orientations of the aggregates being formed, with the order parameter dropping to $S\approx 0.05$. These findings provide important quantitative insights for future applications of the RPs, i.e., in micro- and nanodevice assembly. Full article

This study combines multi-frequency dynamic mechanical analysis (DMA) with machine learning (ML) to characterize and predict the viscoelastic properties of a commercial polycarbonate/acrylonitrile–butadiene–styrene (PC/ABS) blend. DMA temperature sweeps at four frequencies (1–10 Hz) in single cantilever mode yielded a glass transition range of 115.8–$123.2$ °C ($E^{″}$ peak), frequency sensitivity of $7.18$ °C/$\mathrm{decade}$, and an apparent activation energy of $335\pm 85 \mathrm{k}\mathrm{J} {\mathrm{mol}}^{-1}$. Time–temperature superposition master curves were parameterized with a six-term Prony series ($R^2=0.998$). Four data-driven models (RF, XGB, SVR, MLP) and a physics-informed NeuralWLF model were evaluated through a hierarchical validation framework. Temperature-blocked CV ranked MLP ($\overline{R^2}=0.989$) above RF (0.950) for interpolation; LOFO validation revealed that NeuralWLF achieved the best cross-frequency generalization ($R^2>0.92$ for all targets) with interpretable WLF parameters ($C_1\approx 12.2$, $C_2\approx 51.7 °\mathrm{C}$). A systematic block size sweep (5–30 $°\mathrm{C}$) revealed a validation inflation effect in which MLP $tan\delta$$R^2$ dropped from 0.986 to 0.592 as the gap-to-FWHM ratio increased from 0.5 to 3.1, establishing the gap/FWHM ratio as a quantitative validation stringency criterion. A physics–data crossover was identified at gap/FWHM $\approx 2$: beyond this threshold, NeuralWLF outperformed all data-driven models in $tan\delta$ prediction by up to $+0.300$ in $R^2$, while curriculum learning (freezing the WLF layer for 300 epochs) further improved the most stringent 30 $°\mathrm{C}$ validation from $R^2=0.660$ to 0.731. The integrated framework demonstrates that honest evaluation of DMA–ML models requires validation gaps exceeding the characteristic feature width and introduces a quantifiable physics-data crossover criterion for selecting between data-driven and physics-informed architectures. Full article