Search Results (448)

Background: The epidemiology of influenza was disrupted during the COVID-19 pandemic. Following the relaxation of non-pharmaceutical interventions, influenza viruses have re-emerged and caused epidemics with shifts in age distribution and seasonality. This study aimed to characterise the post-pandemic epidemiology of influenza infections among children in Xi’an, China. Methods: A retrospective analysis of laboratory-confirmed paediatric influenza cases spanning three periods [pre-pandemic (1 January 2010–22 January 2020), intra-pandemic (23 January 2020–8 January 2023), and post-pandemic (9 January 2023–31 August 2025)] was conducted. Age-specific incidences were determined by subtypes (lineage) and compared across periods. Seasonal parameters were estimated using a generalised linear model with harmonic terms. Associations between influenza infection and risk of co-detection with other respiratory pathogens were assessed using logistic regression models. Results: Influenza peak activity in the post-pandemic period was 10-fold higher than in the intra-pandemic period. The mean age of infected children increased by 1.4 years (95% CI: 1.2–1.7), shifting towards school-aged children (6–17 years). The seasonal pattern re-established with an earlier peak (13.9 weeks earlier than the pre-pandemic period, 95% CI: 10.4–15.2) and increased amplitude (10-fold and 4-fold higher than the intra- and pre-pandemic periods, respectively). It was observed that A(H1N1)pdm09 positivity was elevated in preschool and school-aged children, whereas B/Victoria infections showed renewed susceptibility among infants [0–5 months vs. 6–35 months vs. 3–5 years vs. 6–17 years: 11.0% (95% CI: 5.1–19.8) vs. 2.8% (1.9–4.0) vs. 4.0% (3.2–5.0) vs. 5.2% (4.5–6.0); p = 0.00014]. Influenza infection was associated with higher risk of bacterial co-detection with Streptococcus pneumoniae (aOR = 1.52, 95% CI: 1.22–1.91) and Haemophilus influenzae (aOR = 1.46, 95% CI: 1.19–1.80), but lower risk of co-detection with SARS-CoV-2 (aOR = 0.52, 95% CI: 0.27–0.99), RSV (aOR = 0.29, 95% CI: 0.11–0.79), and parainfluenza viruses (aOR = 0.16, 95% CI: 0.04–0.65). Conclusions: The post-pandemic landscape of paediatric influenza in Xi’an has undergone substantial reconfiguration, characterised by intensified activity, altered seasonality, and a marked shift in age distribution. The increased bacterial co-detection points out the potential for more severe respiratory co-infections. These findings highlight the importance of optimising vaccination timing and prompting school-aged-children-targeted immunisation programmes in the post-pandemic era. Full article

The paper presents measurements of frequency dependence of conductivity and real components of complex permittivity of a nanocomposite consisting of electrical pressboard, bio-insulating oil and water nanodroplets with moisture content ranging from 0.6 wt.% to 5 wt.%. Bio-oil meets high environmental requirements—it is fully biodegradable, and its combustion products are significantly less harmful than those of mineral oil. In addition, the use of bio-oil reduces the carbon footprint of power transformer production. The quantum mechanical phenomenon of electron tunnelling between potential wells created by water nanodroplets was used to analyze the experimental results obtained. The study determined the effect of moisture content on the relative relaxation time values. On this basis, the number of water molecules in nanodroplets, their diameters and the concentration of nanodroplets depending on moisture content were determined. The distances over which electrons tunnel in moist pressboard impregnated with bio-oil were determined. These values are the expected values of the probability distribution of the distance between neighbouring nanodroplets. The values of the number of water molecules in nanodroplets are also the expected values of the probability distribution of the number of molecules in nanodroplets. It has been established that during many years of transformer life, several parallel processes occur as the moisture content in bio-oil-impregnated pressboard increases. One of them involves the accumulation of water molecules collected in the pressboard in nanodroplets. The second is an increase in the concentration of nanodroplets. The third is an increase in the average number of water molecules in nanodroplets. Full article

Accurate evaluation of the severity of external short-circuit (ESC) faults in li-ion batteries is critical to ensuring the safety and reliability of battery systems. This study proposes a novel ESC fault assessment method based on electrochemical impedance spectroscopy (EIS) and differential feature extraction from relaxation time distributions. By comparing EIS responses before and after the short circuit, differential curves are constructed, and relevant peak descriptors are extracted to form physically interpretable feature vectors without requiring equivalent circuit modeling. Standardized feature data are further analyzed using principal component analysis (PCA) and K-Means clustering to perform unsupervised classification of fault severity. In addition, a differential evolution algorithm is employed to adaptively optimize the feature weights, enhancing the monotonic correlation between the weighted scores and actual short-circuit durations. The resulting SeverityScore provides an interpretable, mechanism-driven indicator of ESC fault severity. Experimental results demonstrate that the proposed method effectively distinguishes between mild and moderate short-circuit conditions and generalizes well across four independent battery groups. The model, trained on a single group, demonstrates strong robustness by accurately classifying the fault severity for three unseen validation groups. This data-driven framework offers a robust and model-free approach for fault evaluation, providing a promising tool for health monitoring and risk assessment in li-ion batteries. Full article

Although physics-informed neural networks (PINNs) offer a novel, mesh-free paradigm for computational fluid dynamics (CFD), existing models often suffer from poor stability and insufficient accuracy, particularly when dealing with complex flows at high Reynolds numbers. To address this limitation, we propose, for the first time, a novel hybrid architecture, PINN-MRT, which integrates the multi-relaxation-time lattice Boltzmann method (MRT-LBM) with PINNs. The model embeds the MRT-LBM evolution equation as a physical constraint within the loss function and employs a unique dual-network architecture to separately predict macroscopic conserved variables and non-equilibrium distribution functions, enabling both forward and inverse problem-solving through a composite loss function. Benchmark tests on the lid-driven cavity flow demonstrate the superior performance of PINN-MRT. In inverse problems, it remains stable at Reynolds numbers up to 5000 with parameter inversion errors below 15%, whereas standard PINN and single-relaxation-time PINN-LBM models fail at a Reynolds number of 1000 with errors exceeding 80%. In purely physics-driven forward problems, PINN-MRT also provides stable solutions at a Reynolds number of 400, while the other models completely collapse. This study confirms that incorporating mesoscopic kinetic theory into PINNs effectively overcomes the stability bottlenecks of conventional approaches, providing a more robust and accurate architecture for CFD and paving the way for solving more challenging fluid dynamics problems. Full article

This study addresses the joint optimization of full-length and short-turning operations for the Nanchang Metro Airport Line, aiming to balance operational efficiency and passenger service quality. A novel mathematical model is proposed, which integrates train schedule design, capacity allocation, and passenger flow assignment into a linear programming framework. The model features three key innovations: (1) precise calculation of passenger waiting times under strict capacity constraints by incorporating dynamic passenger flow distribution and train occupancy thresholds; (2) implicit treatment of train numbers as decision variables, enabling flexible adjustments to service frequency based on time-varying demand patterns; and (3) a linear formulation for direct optimal solution computation, avoiding the complexity of nonlinear constraints through variable substitution and constraint relaxation. The model is validated through a case study of the Nanchang Metro Line 1 (Airport Line), where passenger demand is derived from historical data and flight schedules. Numerical experiments demonstrate that the optimized strategy reduces the number of full-length trains by 53%, achieves a 22% power cost saving, and decreases the waiting time for all passengers by 3.4%. The relevant findings and recommendations can offer valuable guidance to metro companies in making operational decisions related to the full-length and short-turning service plans and schedules. Full article

Anion exchange membrane fuel cells (AEMFCs) represent a promising class of clean energy devices, with their performance being critically dependent on the efficiency of the cathode oxygen reduction reaction (ORR) catalyst. Manganese-cobalt spinel (Mn_1.5Co_1.5O₄, MCS) has been demonstrated to be a highly active ORR catalyst. Herein, we report a strategy of incorporating Cu (MCCS) and Fe (MCFS) into MCS to form ternary spinel oxides for tuning ORR activity. Among them, MCS exhibits the best ORR performance, with a half-wave potential (E_1/2) of 0.736 V vs. RHE in 0.1 M KOH and a peak power density (PPD) of 248.3 mW·cm⁻² for the fuel cell test. In contrast, MCCS and MCFS show divergent behaviors in a rotating disk-ring electrode (RRDE) and fuel cell tests. X-ray diffraction (XRD) analyses and X-ray photoelectron spectroscopy (XPS) analyses reveal that the introduction of Cu²⁺ and Fe³⁺ induces a phase transformation in the spinel structure, leading to a reduction in oxygen vacancies and an increase in the valence state of Mn, thereby degrading catalytic activity. However, the incorporation of these elements also modulates the hydration capability of the catalysts, which is critical for the ion and charge transfer in the fuel cell environment and has been validated in the distribution of relaxation time (DRT) analysis of the fuel cell test. This study provides a valuable strategy for designing and synthesizing low-cost, highly efficient, and stable ternary spinel electrocatalysts for AEMFC applications, and bridges the gap between RRDE evaluation and fuel cell testing through DRT analysis. Full article

The optimization of the microstructural homogeneity and residual stress distribution in Ti sputtering targets for OLED 6G applications is essential for improving dimensional stability, durability, and deposition performance. Herein, 3N Ti plates were hot-rolled at 730 °C and then annealed at 600 °C and 700 °C for different durations to investigate the effects of annealing parameters on microstructural evolution and stress relaxation. X-ray diffraction analysis revealed that hexagonal α-Ti with progressive development of the (002) orientation was produced during annealing under all the conditions. Electron backscatter diffraction analyses showed that short-time annealing at 600 °C (≤30 min) generated heterogeneous grains, high dislocation density, and mixed grain boundary character, whereas extended annealing (≥60 min) produced a more uniform microstructure. However, residual stress differences between the plate center and edge remained significant under this condition. Conversely, annealing at 700 °C promoted progressive recrystallization, as indicated by increased high-angle grain boundary fractions and decreased kernel average misorientation values, and facilitated grain growth stabilization across the plate. Prolonged annealing improved microstructural and residual stress uniformity significantly, and near-complete homogenization was achieved after 5 h. These findings demonstrate that annealing at 700 °C for sufficient time is optimal for producing homogeneous microstructures and uniform residual stress distributions, providing valuable guidelines for Ti sputtering target processing. Full article

Electrospinning is a versatile technique used to manufacture nanofibers by applying an electric field to a polymer solution. This method has gained significant interest in the biomedical, pharmaceutical, and materials engineering fields due to its ability to produce porous structures with a high specific surface area, making it ideal for applications such as wound dressings, controlled drug delivery systems, and tissue engineering. The materials used in electrospinning play a crucial role in determining the final properties of the obtained nonwoven nanofibers. Among the most studied substances are chitosan, collagen, and fish-derived gelatin, which are biopolymers with high biocompatibility. These materials are especially used in the medical and pharmaceutical fields due to their bioactive properties. In combination with synthetic polymers such as polyethylene glycol (PEG) and polyvinyl alcohol (PVA), these biopolymers can form electrospun fibers with improved mechanical characteristics and enhanced structural stability. The characterization of these materials was performed using modern characterization techniques, such as one-dimensional (1D) proton NMR spectroscopy (¹H), for which the spin–spin relaxation time distributions T₂ were characterized. Additionally, two-dimensional (2D) measurements were conducted, for which EXSY T₂-T₂ and COSY T₁-T₂ exchange maps were obtained. The characterization was complemented with FT-IR spectra measurements, and the nanofiber morphology was observed using SEM. As a novelty, machine learning methods, including artificial neural networks (ANNs), were applied to characterize the local structural order of the produced nanofibers. In this study, it was shown that the nanofiber nonwoven materials made from PVA are characterized by a degree of order in the range of 0.27 to 0.61, which are more ordered than the nanofibers made from chitosan and fish gelatin, characterized by an order degree ranging from 0.051 to 0.312, where 0 represents the completely unordered network and 1 a fully ordered fabric. Full article

Magnetic nanoparticles (MNPs) perturb magnetic field homogeneity, influencing transverse relaxation and the full width at half maximum (FWHM) of nuclear magnetic resonance (NMR) spectra. In Nuclear Magnetic Resonance (NMR), this appears as decay of the free induction decay (FID) signal, whose relaxation rate determines spectral FWHM. In D₂O containing MNPs, both nanoparticles and solvent molecules undergo Brownian motion and diffusion. Under a vertical main field ($B_0$), MNPs respond to their magnetization behavior, evolving toward a dynamic steady state in which the time-averaged distribution of local field fluctuations remains stable. The resulting spatial magnetic field can thus characterize field homogeneity. Within this framework, Monte Carlo simulations of spatial field distributions approximate the dynamic environment experienced by nuclear spins. NMR experiments confirm that increasing MNP concentration and particle size significantly broadens FWHM, while stronger $B_0$ enhances sensitivity to MNP-induced inhomogeneities. Full article

In this paper, the effects of TGase reaction times (0, 6, 12, 18, and 24 h) at 4 °C on the solubility, emulsion stability, cooking yield, gel properties and water distribution of reduced-fat and reduced-sodium chicken meat batter were studied. The results showed that the reaction time had a significant effect on the water fluidity and quality characteristics of reduced-fat and reduced-sodium chicken meat batter. The solubility, cooking yield and water-holding capacity of salt-soluble proteins initially increased then decreased with extended reaction time, reaching maximum values of 65.50%, 96.13% and 96.00%, respectively, at 12 h. The emulsifying stability and textural properties initially increased, then decreased with extended reaction time (p < 0.05), achieving optimal levels at 12 h. In contrast, the initial relaxation time of T21 and T22 initially decreased (p < 0.05) and then increased (p < 0.05) with longer reaction times; the minimum values were 12 h, especially the free water decreased from 17.97% to 6.69%, consistent with the finding on water-holding capacity and gel properties. In conclusion, the reaction time of the TGase affected its effect on improving the gel effect of reduced-fat and reduced-sodium chicken meat batter, and the best effect was achieved at 12 h. Full article

Under extreme operating conditions, the internal lubricating flow field of high-speed gear transmission systems exhibits a transient oil–gas multiphase flow, predominantly governed by cavitation-induced phase transitions and turbulent shear. This phenomenon involves complex mechanisms of nonlinear multi-physical coupling and energy dissipation. Traditional lubrication theories and single-phase flow simplified models show significant limitations in capturing microsecond-scale flow features, dynamic interface evolution, and turbulence modulation mechanisms. To address these challenges, this study developed a cross-scale coupled numerical framework based on the Lattice Boltzmann method and large eddy simulation (LBM-LES). By incorporating an adaptive time relaxation algorithm, the framework effectively enhances the computational accuracy and stability for high-speed rotational flow fields, enabling the precise characterization of lubricant splashing, distribution, and its interaction with air. The research systematically reveals the spatiotemporal evolution characteristics of the internal flow field within the gearbox and focuses on analyzing the nonlinear regulatory effect of baffle geometric parameters on the system’s energy transport and dissipation characteristics. Numerical results indicate that the baffle structure significantly influences the spatial distribution of the vorticity field and turbulence intensity by reconstructing the shear layer topology. Low-profile baffles optimize the energy transfer pathway, effectively reducing the flow enthalpy, whereas excessively tall baffles induce strong secondary recirculation flows, exacerbating vortex-induced energy losses. Simultaneously, appropriately increasing the spacing between double baffles helps enhance global lubricant transport efficiency and suppresses unsteady dissipation caused by localized momentum accumulation. Furthermore, the geometrically optimized double-baffle configuration can achieve synergistic improvements in lubrication performance, oil film stability, and system energy efficiency by guiding the main shear flow and mitigating localized high-momentum impacts. This study provides crucial theoretical foundations and design guidelines for developing the next generation of theory-driven, energy-efficient lubrication design strategies for gear transmissions. Full article

Reliable DC cable insulation is crucial for photovoltaic (PV) systems and high-voltage DC (HVDC) networks. However, conventional materials such as cross-linked polyethylene (XLPE) and polyvinyl chloride (PVC) face challenges under prolonged DC stress—notably space charge buildup, dielectric losses, and thermal aging. Cross-linked polyolefin (XLPO) has emerged as a halogen-free, thermally stable alternative, but its comparative DC performance remains underreported. Methods: We evaluated the insulations of virgin XLPO, XLPE, and PVC PV cables under ±1 kV DC using time-domain indices (IR, DAR, PI, Loss Index), supported by MATLAB and FTIR. Multi-layer cable geometries were modeled in MATLAB to simulate radial electric field distribution, and Fourier-transform infrared (FTIR) spectroscopy was employed to reveal polymer chemistry and functional groups. Results: XLPO exhibited an IR on the order of 10⁸–10⁹ Ω, and XLPE (IR ~ 10⁸ Ω) and PVC (IR ~ 10⁷ Ω, LI ≥ 1) at 60 s, with favorable polarization indices under both polarities. Notably, they showed high insulation resistance and low-to-moderate loss indices (≈1.3–1.5) under both polarities, indicating controlled relaxation with limited conduction contribution. XLPE showed good initial insulation resistance but revealed polarity-dependent relaxation and higher loss (especially under positive bias) due to trap-forming cross-linking byproducts. PVC had the lowest resistance (GΩ-range) and near-unit DAR/PI, dominated by leakage conduction and dielectric losses. Simulations confirmed a uniform electric field in XLPO insulation with no polarity asymmetry, while FTIR spectra linked XLPO’s low polarity and PVC’s chlorine content to their electrical behavior. Conclusions: XLPO outperforms XLPE and PVC in resisting DC leakage, charge trapping, and thermal stress, underscoring its suitability for long-term PV and HVDC applications. This study provides a comprehensive structure–property understanding to guide the selection of advanced, polarity-resilient cable insulation materials. Full article

Tuning structural heterogeneity in metallic glasses is key to improving their mechanical performance. Here we examine how carbon micro-alloying modulates the relaxation dynamics and creep of Fe-based amorphous ribbons. Increasing carbon content lowers the crystallization temperature, amplifies β-relaxation, and reduces hardness, consistent with enhanced atomic mobility. Nanoindentation creep, fitted with a stretched-exponential model, shows a decreasing exponent with carbon addition, indicating broader relaxation–time distributions and stronger dynamic heterogeneity. Nanoscale force-mapping further reveals a larger fraction of liquid-like regions and pronounced viscoelastic heterogeneity in carbon-rich samples. These changes facilitate the activation of shear-transformation zones and promote room-temperature creep flow. Together, the results establish a direct link between structural heterogeneity, relaxation processes, and mechanical response, providing guidance for the design of ductile metallic glasses. Full article

In this paper, Distribution of Relaxation Time (DRT) analysis is presented as a powerful tool for understanding the aging mechanisms in lithium-ion batteries, with a focus on its application to estimating the State of Health (SOH). A novel parameter, the characteristic relaxation time, derived from DRT analysis, is introduced to enhance SOH estimation. By analyzing the ratio of the central relaxation time (τ) between the charge transfer and diffusion peaks, the battery status can be determined without the need for historical data. Experimental data from lithium-ion batteries, including 18650 cells and LR2032 coin cells, were examined until the end of their life. Nyquist and DRT plots across various frequency ranges revealed consistent aging trends, particularly in the charge transfer and diffusion processes. These processes appeared as shifting and merging peaks in the DRT plots, signifying progressive degradation. A polynomial equation fitted to the τ ratio graph achieved a high accuracy (Adj. R² = 0.9994), enabling reliable battery lifespan prediction. Validation with a Samsung Galaxy S9+ battery demonstrated that the method could estimate its remaining life, predicting a total lifespan of approximately 2100 cycles (compared to 1000 cycles already completed). These results confirm that SOH estimation is feasible without prior data and highlight the potential of DRT analysis for accurate and quantitative prediction of battery longevity. Full article

This study investigated the physiological effects of seat vibration during prolonged sitting in a simulated autonomous driving environment. Eleven healthy participants (3 young adults and 8 older adults) viewed a 120-min highway driving video under two conditions: rhythmic seat vibration (2 Hz, mimicking natural respiration) and no vibration. Physiological and behavioral metrics—including Psychomotor Vigilance Task (PVT), seat pressure distribution, heart rate variability (HRV), body acceleration, and skin temperature—were assessed across three phases. Results demonstrated that seat vibration significantly enhanced parasympathetic activity, as evidenced by increased HF power and decreased LF/HF ratio (p < 0.05), suggesting reduced autonomic stress. Additionally, seated posture remained more stable under vibration, with reduced asymmetry and sway, while the no-vibration condition showed time-dependent postural degradation. Interestingly, skin surface temperature was lower in the vibration condition (p < 0.001), indicating a possible thermoregulatory mechanism. In contrast, PVT performance revealed more false starts in the vibration condition, particularly among older adults, suggesting that vibration may not enhance—and could slightly impair—cognitive alertness. These findings suggest that low-frequency seat vibration can support physiological stability and postural control during prolonged sedentary conditions, such as in autonomous vehicles. However, its effects on vigilance appear limited and age-dependent. Overall, rhythmic vibration may contribute to enhancing passenger comfort and reducing fatigue-related risks, particularly in older individuals. Future work should explore adaptive vibration strategies to balance physiological relaxation and cognitive alertness in mobility environments. Full article