Search Results (17,085)

Umbilical cord mesenchymal stromal cells (UC-MSCs) are a key element of regenerative medicine due to their ability to secrete growth factors that stimulate proliferation and angiogenesis, and modulate the inflammatory response. Despite their widespread use, the influence of the perinatal microenvironment on their biological properties remains poorly understood. The aim of this study was to assess the influence of pH and blood gas parameters in umbilical cord blood on the global transcriptomic profile of UC-MSCs and to analyze the correlation between the metabolic status of the newborn and the expression of key trophic factors: EGF, FGF2, FGFR1, FGFR3, GDNF, HGF, IGF1, NES, NGF, and PGF. Methods: The study was conducted in two stages. In the first phase, transcriptomic screening was performed using Affymetrix HuGene 2.0 ST microarray on cells isolated from three environmental groups defined by cord blood pH: acidic (pH < 7.35), physiological (7.35–7.39), and alkaline (pH ≥ 7.4). In the second phase, the results were validated using qPCR on an expanded study group (N = 50). Gene expression levels (RQ) were related to blood gas parameters (pH, pCO₂, pO₂, cHCO₃) and the presence of clinical features of threatened neonatal asphyxia. Results: Microarray analysis revealed that environmental pH acts as a molecular phenotypic switch. Under low pH conditions (<7.35), a shift in cell profile from proliferative to structural–migratory was observed. Significant overexpression of genes responsible for extracellular matrix (ECM) organization and adhesion (e.g., COMP, DCN, LUM, FMOD) was observed, while pathways related to cell cycle and cell division (↓CDK1, AURKA, TOP2A) were downregulated. qPCR validation confirmed these observations, demonstrating a strong positive correlation between blood pH and the expression of regenerative mediators: FGFR1 (r = 0.28), EGF (r = 0.30), NGF (r = 0.39), and IGF1 (r = 0.30). A negative correlation was also found between carbon dioxide pressure (pCO₂) and the expression of NGF, FGFR1, and EGF. A significant clinical finding was that in newborns diagnosed with threatened asphyxia, EGF, FGFR1, and NGF gene expression was significantly reduced, indicating impaired trophic potential of the cells in response to metabolic stress. Conclusions: These results indicate that cord blood gas parameters are critical regulators of the genetic activity of UC-MSCs. Metabolic and respiratory acidosis not only inhibit the cells’ proliferative potential but also force them into a matrix remodeling mode, permanently modifying their transcriptomic profile. This suggests that the neonatal acid–base status may serve as an objective indicator of the “biological quality” of isolated stromal cells, which has significant implications for their future applications in cell therapies. Full article

High-resolution, long-term gridded meteorological datasets from in situ observations are crucial for ecosystem monitoring, soil diagnostics, hydrological modelling, and Earth system model evaluation. This study presents two enhanced real-time adaptations of Thornton’s Daymet V4 interpolation method. Daymet4-r1 uses a traditional calibration strategy with exhaustive parameter search, while Daymet4-r2 applies a global optimization algorithm (find_min_global from the dlib library) to adjust parameters automatically at each time step. Both methods were tested over Tuscany using high-resolution terrain and a dense observation network. Validation with leave-one-out method was carried out for the period 1995–2011 for both versions, while Daymet4-r2 underwent extended evaluation from 1991 to 2024 to assess seasonal dynamics and long-term variability. Results show that Daymet4-r2 outperforms Daymet4-r1 and the original Daymet V4 for all variables (mean absolute error of 1.24 mm, 1.06 °C, 1.29 °C, 6.26%, 0.78 m/s, and 2.04 hPa for precipitation, maximum and minimum temperature, relative humidity, wind speed, and sea level pressure, respectively). The largest improvement was observed in minimum temperature due to an enhanced approach for detecting and modelling thermal inversions. The high performance, flexibility, and ability of Daymet4-r2 to operate without prior calibration highlight its potential for model verification, real-time environmental monitoring, and integration into climate services. Full article

High sound pressure level (SPL) acoustic sensing requires miniaturized microphones that can operate under large acoustic loading while maintaining mechanical linearity, sufficient sensing response, and broadband audio frequency behavior. This work targets high-SPL operation and numerically investigates a graphene piezoresistive MEMS microphone based on a membrane-supported beam–island diaphragm. The proposed structure retains a continuous membrane for acoustic load bearing, while the upper beam–island topology redirects deformation-induced strain toward beam root regions where graphene piezoresistors are placed. This design is intended to increase the local strain available for piezoresistive readout without simply relying on larger global diaphragm deflection. Finite-element analysis was used to optimize the diaphragm geometry and evaluate strain enhancement, pressure response linearity, modal behavior, and harmonic response. Under the 170 dB SPL reference condition, the optimized structure increases the peak structural strain from 47.83 με in a thickness-equivalent solid diaphragm to 562.53 με, achieving an approximately 11.8-fold enhancement in local sensing strain while maintaining a highly linear pressure response (R2 > 0.9999). Additionally, the results also show that the sensor exhibits a high first natural frequency of 64.07 kHz and a small response variation of approximately 0.94 dB within the 0–20 kHz target frequency range, indicating excellent dynamic stability and high-fidelity signal transduction characteristics. To connect the structural response with piezoresistive readout, first-order electromechanical output estimation was further performed using representative graphene gauge factors, quarter-bridge readout assumptions, contact resistance correction, and Johnson-noise-limited signal-to-noise ratio estimation. A ±5% geometric tolerance check further indicates that the membrane side length is the most fabrication-sensitive parameter, while the selected design remains generally robust except for reduced linearity margin under positive membrane side-length deviation. These results demonstrate the potential of the proposed graphene-based MEMS microphone for high-SPL broadband acoustic sensing applications in harsh and high-intensity acoustic environments. Full article

To solve the low-precision prediction problem of noisy non-stationary goaf subsidence sequences, this study aims to establish a high-accuracy hybrid prediction model for mining surface deformation monitoring. The Global Navigation Satellite System (GNSS) monitoring data of surface subsidence in goaf areas exhibits non-stationary and noisy characteristics, which limits the accuracy of traditional prediction models. In this paper, a hybrid prediction model, namely the Improved Radial Movement Optimization–Variational Mode Decomposition–Gated Recurrent Unit (IRMO-VMD-GRU) model, is proposed. The IRMO algorithm is employed to globally optimize the key parameters of VMD, achieving adaptive and stable decomposition of the settlement sequences. The obtained Intrinsic Mode Function (IMF) sub-sequences are input into the GRU network for independent training and prediction, followed by superposition and reconstruction. The model is validated using the GNSS monitoring data from three monitoring points at a coal mine in Shaanxi Province, China. The results show that the proposed model outperforms the comparison models in all four evaluation indicators, namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²), with all R² values exceeding 0.8. The model demonstrates superior fitting performance, correlation, and generalization ability, which provides important practical technical support for goaf subsidence early warning, geological disaster prevention and engineering safety management in mining areas. Full article

Numerous factors influence the formation of blasting craters in engineering blasting. Based on the actual parameters of the Daye Iron Mine, this study established six sets of single-hole blasting crater numerical models with different borehole diameters using ANSYS(19.0)/LS-DYNA(R13) software. The variation in blasting crater volume with the scaled depth was analyzed to determine the optimum scaled depth for each borehole diameter, and a functional relationship between the optimum scaled depth and borehole diameter was derived through curve fitting. Furthermore, using a borehole diameter of 0.076 m as a case study, a double-hole blasting crater was developed to investigate the effect of varying hole spacing on blasting crater volume and to determine the optimal hole spacing. The blasting parameters were optimized based on the numerical simulation results. The results show that within the range of borehole diameters considered, the blasting crater volume initially increases and then decreases with increasing scaled depth of the explosive charge. The fitted relationship between the optimum scaled depth and borehole diameter is y = −180.7197x³ + 86.3754x² − 9.5504x + 1.0782. For a borehole diameter of 0.076 m, the optimum scaled depth is 0.7278 m/kg^1/3, and the optimal hole spacing is 0.52 m. Based on blasting similarity theory, the calculated optimum burial depth of the explosive charge is 0.59 m, the critical burial depth is 1.1 m, and the recommended row spacing ranges from 0.95 m to 1.18 m. The findings of this study provide a theoretical basis for optimizing blasting parameters at the Daye Iron Mine and similar mining operations. Full article

Background: Isokinetic strength of the knee joint represents a significant determinant of athletic performance and injury prevention; however, its relationship with explosive performance in young female athletes remains insufficiently explored. The aim of the study was to analyze the relationships between isokinetic strength of the knee joint at different angular velocities and explosive jumping performance in young female athletes. Methods: The research sample consisted of 13 young female athletes enrolled in sport-oriented educational programs specializing in athletics. Explosive lower-limb power was assessed using performance tests for countermovement jump (CMJ), countermovement jump free arms (CMJ FAs) and squat jump (SJ) administered with the Chronojump system. Isokinetic strength of the knee flexors and extensors was assessed using the Humac Norm dynamometer in the concentric mode at angular velocities of 60°/s, 180°/s, and 300°/s. Peak torque, the ipsilateral H:Q ratio, and bilateral asymmetries were evaluated. Pearson’s correlation coefficient was used to analyze the relationships between the investigated parameters. Results: The strongest relationships with explosive performance were observed for hamstring strength at an angular velocity of 180°/s, where significant high correlations were identified with performance in the CMJ (r = 0.693), CMJ FA (r = 0.754), and SJ (r = 0.713). In contrast, quadriceps strength demonstrated predominantly low to moderate associations with jumping performance, while no significant correlations were confirmed at an angular velocity of 300°/s. Bilateral asymmetries of the knee extensors and flexors were generally low, ranging approximately between 7 and 10%, whereas the values of the ipsilateral H:Q ratio were within the physiological range of approximately 50–55%. Conclusions: The results suggest that the ability to generate force at higher contraction velocities, particularly in the hamstrings, is significantly associated with explosive performance in young female athletes. At the same time, isokinetic strength assessment appears to be an appropriate tool for evaluating muscular strength, muscle balance, and potential asymmetries in youth sports. However, explosive performance cannot be explained solely by the level of maximal muscular strength, but rather by a complex interaction of neuromuscular and biomechanical factors. Full article

In this study, Fe₇₀Ni₃₀ metal was deposited onto continuous glass fiber composites via magnetron sputtering, followed by surface coating with SiO₂. The effects of key process parameters-including Fe₇₀Ni₃₀ sputtering duration (2, 5, 10, 20, and 30 min) and SiO₂ surface coating-on the electromagnetic properties and microwave absorption performance of the materials were systematically investigated. Scanning electron microscopy (SEM) characterization revealed that as sputtering time increased, the metal coating evolved from discrete small particles into a continuous film. Cross-sectional SEM analysis further demonstrated the formation of a bilayer structure after SiO₂ introduction. X-ray diffraction (XRD) patterns confirmed the presence of diffraction peaks corresponding to the Fe₇₀Ni₃₀ alloy solid solution. Electromagnetic parameter measurements indicated that the influence of sputtering time on electromagnetic properties was primarily pronounced during the metal layer growth stage; once a continuous film was formed, the variation in electromagnetic parameters diminished. Concurrently, the SiO₂ coating exhibited a significant regulatory effect on dielectric parameters. Reflection coefficient calculations showed that the optimal absorption thickness for the single-layer material ranged from 2.5 to 3.0 mm, with the absorption peak shifting toward lower frequencies as thickness increased. However, the effective absorption bandwidth (EAB) was only 3–5 GHz, failing to meet wideband requirements. In contrast, the three-layer composite structure (total thickness: 3.8 mm) optimized via genetic algorithm achieved impedance gradient and loss synergy, expanding the EBW (R < −10 dB) from 4.8 GHz (single layer) to 10 GHz (8–18.0 GHz)-a substantial improvement over the single-layer configuration. This work provides experimental evidence and technical support for the structural design and process optimization of lightweight, high-efficiency, wideband microwave-absorbing materials. Full article

A disconnect remains between high-fidelity physical-characteristic simulation and upper-level validation in RF system design. High-fidelity simulations can accurately characterize key physical effects, such as frequency response, noise, and nonlinearity, but their results are difficult to directly transform into executable models for upper-level validation. In contrast, upper-level validation often relies on idealized or empirical parameters rather than real hardware characteristics. To address this issue, this paper proposes a dataflow-driven behavioral modeling method for RF systems, with system input–output characteristics as the modeling core. A behavioral model is constructed using characteristic blocks representing frequency response, noise, coupling, nonlinearity, and phase shift. Model parameters are configured from high-fidelity simulation results and/or hardware measurement data, thereby establishing a parameter-transfer path from physical-characteristic results to the executable behavioral model. Driven by baseband-equivalent input data streams, the model generates output data streams containing key physical effects and provides a reusable RF-link model for upper-level validation. The proposed method is instantiated and validated on the receive (Rx) channel of an X-band eight-channel phased-array transmit/receive module. Comparisons with circuit-level benchmark results demonstrate that the proposed method can effectively inherit underlying physical characteristics and exhibits good accuracy and practical feasibility. Full article

The adsorption of aromatic hydrocarbons from liquid paraffin is essential because of their harmful nature, long-lasting presence, and detrimental effects on the quality of the product. In this study, we investigated the adsorption of naphthalene from liquid paraffin by using a nanoclay-based adsorbent prepared from boron enrichment process waste. The characterization of the prepared adsorbent was carried out by using X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), X-ray Photoelectron Spectroscopy (XPS) and N₂ adsorption–desorption techniques, which confirmed the development of a layered nanostructure containing boron that possesses a porous and high-surface-area format appropriate for the adsorption. The hydrothermal treatment significantly increased the BET surface area from 35.42 to 112.15 m²/g, indicating the successful formation of a porous nanostructure. The kinetic and isotherm parameters of the adsorption process were calculated from experimental data. The adsorption of naphthalene followed pseudo-second-order kinetics and the isotherm fit well to the Langmuir model. Adsorption experiments revealed that the optimum adsorption performance was achieved at pH 4.0, and equilibrium was reached within 90 min. The adsorption kinetics were best described by the pseudo-second-order model (R² > 0.99), while the equilibrium data showed excellent agreement with the Langmuir isotherm model (R² = 0.995), suggesting monolayer adsorption. The maximum adsorption capacity of BNC was determined as 365.20 mg/g, which was more than twice that of the raw BEW (247.59 mg/g). Thermodynamic analysis indicated that the adsorption process was spontaneous at lower temperatures and exothermic, with a ΔH° value of −15.42 kJ/mol for BNC. The results suggest that the adsorption occurs through a multi-step process, beginning with external film diffusion, followed by pore diffusion and surface interaction. Based on the kinetic, isotherm, and spectroscopic data, a supramolecular adsorption mechanism is suggested, which encompasses π-π interactions, van der Waals forces, and surface complexation between naphthalene and the nanoclay structure. These results indicate that boron enrichment process waste-derived nanoclay is a sustainable, economical, and efficient adsorbent for removing naphthalene from liquid paraffin. Full article

Accurately obtaining aircraft aerodynamic parameters is essential for improving flight performance, optimizing design and control strategies, and ensuring flight safety. In this study, the improved Northern Goshawk Optimization (SPNGO) algorithm is used to optimize the kernel parameters and regularization coefficients of the Kernel Extreme Learning Machine (KELM). To address the defects of the original NGO algorithm, such as insufficient global optimization ability and being prone to falling into local optimums, two improvement strategies are proposed. The enhanced SPNGO algorithm is verified by 14 benchmark test functions, and the proposed SPNGO-KELM model is evaluated using open-source F-16 nonlinear simulation data for longitudinal aerodynamic parameter identification. The results demonstrate its effectiveness under the considered simulation conditions, while further validation with real flight-test data is required before application to actual flight environments. Comparative analysis with KELM, NGO-KELM, SSA-KELM, and WOA-KELM models shows that a single KELM is difficult to achieve high-precision aerodynamic parameter identification, and other comparison models have obvious fitting deviations in non-steady-state and strong nonlinear regions. Notably, the SPNGO-KELM model achieves the best identification performance, with a determination coefficient (R²) of 0.96537 and a mean absolute percentage error (MAPE) as low as 3.1574%. Its comprehensive identification accuracy is 1.81% to 37.98% higher than that of the comparison models, and it can effectively suppress error oscillations in nonlinear regions. Experimental results show that the proposed algorithm has excellent identification accuracy, generalization ability, and anti-interference performance. Full article

The ski-jump spillway is an energy-dissipating structure that discharges extra water beyond the dam’s capacity. The scour process occurs below spillways due to the collision of the water jet with high energy. It is critical to acquire information on scour holes to improve the dam’s safety and related components. Machine learning (ML) techniques have successfully demonstrated their effectiveness for modeling scour in hydraulic engineering. The present research considers novel approaches of ML models for estimating the scour hole geometries below ski-jump bucket spillways. This study investigates the capability of two novel feature-engineering approaches, namely Stronger Variable Creator Machine (SVCM) and High Correlated Variables Creator Machine (HCVCM), along with Gene Expression Programming (GEP) and their hybrid forms (SVCM+GEP and HCVCM+GEP), which were employed to predict normalized scour depth, scour length, and scour width below ski-jump spillways. Statistical metrics, graphical analyses, the Rank Mean (RM) method, the cross-validation approach, and $U_{95}$ index were used for the evaluation and reliability assessment of the proposed ML models. The results showed that hybrid ML models consistently outperformed individual algorithms. The results indicated that the SVCM+GEP method with $\mathrm{R}\mathrm{M}=1.83$ and $1.50$ had the highest performance compared to other methods for the prediction of ${\displaystyle \frac{Ds}{Dw}}$ and ${\displaystyle \frac{L_s}{D_w}}$, respectively. In addition, the HCVCM+GEP method with $\mathrm{R}\mathrm{M}=1.33$ was the best model for the prediction of ${\displaystyle \frac{Ws}{Dw}}$. In comparison with the conventional regression-based equations and previously reported ML methods, the proposed hybrid approaches improved the prediction results. In addition, the cross-validation method confirmed the robustness and generalization capability of the suggested hybrid ML models. The superior performance of the hybrid models is attributed to their ability to capture complex nonlinear interactions among hydraulic and geometric variables. The developed SVCM/HCVCM+GEP models provide accurate approaches for predicting scour parameters in hydraulic structures. Full article

Low-voltage electrical contacts are core components of power distribution systems, renewable energy installations, and industrial automation equipment. The electric arc generated during contact switching is the primary cause of contact erosion, material transfer, and equipment failure, posing significant threats to system reliability and operational safety. The accurate prediction of arc parameters is hindered by two challenges: the high scatter in available data undermines empirical models, and purely data-driven approaches risk physically implausible results. To address this, a Gaussian Mixture-enhanced Bayesian-optimized Physics-Informed Neural Network (GB-PINN) is proposed. Three core contributions are made: (1) High-fidelity MHD simulation foundation: A magnetohydrodynamic (MHD) multi-physics coupling model of the contact arc was constructed and validated against experiments, showing high fidelity with only 1.63% error in arc duration and 1.82% in arc energy. A multivariate simulation dataset was generated by varying key contact parameters based on this validated model. (2) GMM-based data augmentation: The measured and simulated data were modeled and sampled via Gaussian Mixture Model (GMM) to enrich the dataset while preserving physical consistency. (3) BOHB-optimized PINN prediction: The Bayesian Optimization and Hyperband (BOHB) algorithm was employed to optimize the PINN hyperparameters, enhancing training efficiency and predictive accuracy. Experimental results demonstrated that the proposed GB-PINN achieved superior performance in predicting arc duration and energy, with mean absolute errors (MAE) of 0.079 ms and 0.624 mJ, root mean square errors (RMSE) of 0.099 ms and 0.774 mJ, and coefficients of determination (R²) of 0.980 and 0.979, significantly outperforming grey model (GM (1, N)), long short-term memory (LSTM), and Transformer models. As a physics-informed data-driven tool, GB-PINN enables high-precision arc prediction, providing reliable support for electrical contact design. Full article

Five modifications of polytetrafluoroethylene (PTFE) are considered as a modern alternative to PTFE as sliding layers of bridge bearing parts. Radiation-modified PTFE without additives and with nano-additives as well as composites based on PTFE with bronze inclusions and nanomodified carbon fiber fillers were investigated. Ultra-high-molecular-weight polyethylene (UHMWPE) and classic pure PTFE were considered as control samples. The thermomechanical properties of the materials were studied within the framework of dynamic mechanical analysis in the operating temperature range of bridge structures [−40; +80] °C. The exit zones from the linear theory of viscoelasticity were established for all the materials considered. Temperature dependencies of the storage modulus and the loss modulus were determined. Thermoviscoelastic models of material behavior were constructed using a numerical identification procedure, experimental data, and simulation models. The thermomechanics of materials during the deformation of the spherical support part of the bridge were analyzed. Temperature dependencies of the parameters of the contact stress-strain state were determined with an average coefficient of determination R² = 0.97 and an average error size RMSE = 0.092. Full article

This study evaluates the aerodynamic performance of a modified Archimedes Spiral Wind Turbine (ASWT) using Computational Fluid Dynamics (CFD). A baseline model was compared with different designs, including surface dimples and a trailing-edge flap. Simulations were carried out in SolidWorks Flow Simulation 2025 under a constant inlet velocity of 12 m/s and rotational speeds ranging from 50 to 500 RPM. The performance of the modified ASWTs was evaluated using key parameters, including the power coefficient ($C_p$), torque, and tip speed ratio ($TSR$). The obtained results follow the expected $C_p-TSR$ behavior, with a peak of $C_p=0.24277$ for the smooth blades and $C_p=0.2565$ for the blades with the flap at $TSR=1.63625$. While the addition of dimples along the surface of the blades resulted in reduced $C_p$ values, the trailing-edge flap consistently improved performance, yielding increased $C_p$ values in comparison to the baseline configuration. Overall, the flap modification highlighted higher aerodynamic efficiency, recognizing it as the most successful improvement among all the tested configurations. These findings shed light on the relevance of geometry-specific optimization in improving ASWT productivity for small-scale wind energy applications. Full article

In our research, we developed a Distributed Relaxation Spectrum Delay Differential Equation (DRSDDE) model to simulate viscoelastic responses exhibited by materials with multiple-scale relaxation mechanisms and finite delay times. Our model expanded upon traditional integer-order viscoelastic models to include a continuum relaxation process using a log-time-space Gaussian distribution representing a continuum of relaxation processes, including a direct representation of the effect of delayed feedback via an explicit time delay term. Consequently, the resultant model can be viewed as a generalized Maxwell-type formulation where the viscoelastic behavior exhibits distributed relaxation dynamics and has finite signal propagation characteristics. We then used experimental data obtained from three representative materials: PDMS Sylgard 184, bovine brain white matter, and polyurethane foam to calibrate the model. Calibration was achieved by estimating model parameters through the use of Gauss-Legendre quadrature combined with non-linear optimization of the relaxation spectrum. The results indicate that the coefficients of determination for each of the materials exceeded R² > 0.83. Therefore, the proposed DRSDDE model outperformed the classical Zener model when simulating materials that exhibit a wide relaxation spectrum. The parameter values estimated for each of the examined materials provided additional insight into their physical behaviors. Specifically, the characteristic relaxation times for the studied materials were determined based upon \(\tau\)_c = 10^µ ranging from about 63 s to 158 s. These results illustrate different dominant relaxation regimes for the investigated materials. Additionally, both characteristic equations and frequency domain analyses were utilized to study the stability and bifurcation properties of the DRSDDE model. A significant finding resulted from identifying a delay-insensitive stability regime for materials with \(\tilde{K} < 1\) (as illustrated by bovine brain white matter). For materials with \(\tilde{K} > 1\), the analysis revealed Hopf bifurcation results illustrating critical delay thresholds and frequencies for the onset of oscillations. Further, it was established that all calibrated delay values were significantly less than these threshold values. This indicates that all identified models functioned well below the oscillation thresholds at realistic delay times. Ultimately, the proposed DRSDDE model represents a physically intuitive, robust, and flexible method for modeling complex viscoelastic systems. Future research will involve investigating temperature-dependent effects, nonlinear bifurcations, and experimental validations of predicted oscillatory dynamics Full article