Search Results (1,443)

Magnetic Resonance Imaging (MRI) serves as a pivotal medical diagnostic technique widely deployed in clinical practice, yet high-resolution reconstruction frequently introduces motion artifacts and degrades signal-to-noise ratios. To enhance imaging efficiency and improve reconstruction quality, this study proposes a Transformer network-based super-resolution framework for MRI images. The methodology integrates Nonuniform Fast Fourier Transform (NUFFT) with a hybrid-attention Transformer network to achieve high-fidelity reconstruction. The embedded NUFFT module adaptively applies density compensation to k-space data based on sampling trajectories, while the Mixed Attention Block (MAB) activates broader pixel engagement to amplify feature extraction capabilities. The Interactive Attention Block (IAB) facilitates cross-window information fusion via overlapping windows, effectively suppressing artifacts. Evaluated on the fastMRI dataset under 4× radial undersampling, the network demonstrates 3.52 dB higher PSNR and 0.21 SSIM improvement over baselines, outperforming state-of-the-art methods across quantitative metrics. Visual assessments further confirm superior detail preservation and artifact suppression. This work establishes an effective pipeline for high-quality radial MRI reconstruction, providing a novel technical pathway for low-field MRI systems with significant research and application value. Full article

The study of the destruction mechanisms of mineral component particles during processing in grinding units is a relevant scientific problem that requires further theoretical and experimental solutions. This work is dedicated to determining the kinetic characteristics of ferromagnetic bodies moving under the influence of an electromagnetic field within a vortex mill. Dependencies of the velocity of these bodies on the radial coordinate for various values of magnetic induction and its gradient were obtained, establishing that velocities can reach approximately 50 m/s. A model for the disintegration of Portland cement particles, caused by their interaction during mechanical processing in a vortex mill, has been developed. It is shown that the average number of disintegration events for the predominant portion of the studied particles is two, which is significantly lower than the total number of collisions. An analysis of the key factors influencing the intensity and nature of particle destruction was conducted, including the magnitude of magnetic induction, the switching frequency of electromagnets, and the magnetic susceptibility of the processed materials. Based on a statistical analysis of the particle size distributions of the mineral raw material after dispersion, a principle for dividing the space within the working volume of the unit into functional zones was formulated: (1) a zone of mixing, grinding, and particle activation (at ferromagnetic element speeds of 0–12 m/s); (2) a zone of intensive grinding and particle activation (with speeds of 12–50 m/s). Full article

This study presents a reduced-order modeling framework for the shape optimization of a centrifugal pump. A database of CFD solutions is generated using Latin Hypercube Sampling over five design parameters to construct a reduced-order model based on proper orthogonal decomposition with radial basis function interpolation. The model predicts the flow field at the impeller–diffuser interface and pump outlet, enabling the estimation of impeller torque and total pressure rise. The active subspaces method is applied to reduce the dimensionality of the input space from five to four modified parameters. The sensitivity of the ROM is assessed with respect to further dimensionality reductions in the parameter space, POD mode truncation, and adaptive sampling. The model is then used to perform pump shape optimization via a quasi-Newton method, identifying the combination of the parameters that minimizes the impeller torque while satisfying a constraint on the head. The optimal result is validated through CFD analysis and compared against the Pareto front generated by a genetic algorithm. The work highlights the potential of model-order reduction techniques in centrifugal pump optimization. Full article

Accurate simulation of boundary layer wind field structures is essential for evaluating tropical cyclone (TC) wind hazards and supporting engineering design in coastal regions. However, existing models often assume radially symmetric and homogeneous surface conditions, leading to limited accuracy near landfall where surface roughness varies significantly. This study conducts a comprehensive evaluation of four representative TC boundary layer models of M95, K01, Y21a, and Y21b, under both idealized and real TC case conditions. The idealized experiments are used to clarify the role of vertical advection and turbulent diffusion in shaping the TC boundary layer, while the landfalling case of Typhoon Mangkhut (2018) is simulated to examine the impacts of surface roughness parameterization. Results show that Y21a, which incorporates nonlinear vertical advection, produces stronger and more realistic super-gradient phenomenon than linear models of M95 and K01. Furthermore, the model of Y21b, which accounts for spatially varying drag coefficients and using a terrain-following coordinate system, successfully reproduces the asymmetric wind patterns observed in the WRF simulations during landfall, achieving the highest correlation (R = 0.93). When the spatially varying drag coefficients incorporated into the linear models, their correlation with WRF improved markedly by about 37%. These findings highlight the necessity of incorporating nonlinear advection, dynamic turbulence, and surface heterogeneity for physically consistent TC boundary layer simulations. The results provide valuable guidance for improving parametric wind field models and enhancing TC wind hazard assessments over complex coastal terrains. Full article

This study examines Lijiang Juniper from Yunnan Province. Through visual inspection, cross-section analysis, and separation tests, it analyzes the macroscopic and microscopic characteristics of Juniper wood from Baoshan Township, Lijiang City. The annual ring width of Juniper wood was determined using a tree-ring analyzer. The results showed that: macroscopically, the heartwood and sapwood of Juniper were clearly differentiated; the transition from earlywood to latewood was slow; the boundary of the growth rings was obvious, and the air-dry density was 0.771 g/cm³. Microscopically, the number of rimmed pores on the tubular cells was large, the lumen of the tubular cells contained invaginations, and the cross-field pores were of platyrrhizal type; the wood rays were uniseriate, with a height of two to five cells; axial thin-walled tissues were scattered; and large traumatic resin tracts were present. On the morphological characteristics of cellular fibers, the characteristic values of Juniper wood, such as tubular cell length, tubular cell width, tubular cell wall thickness, internal diameter of tubular cells, and height of wood rays, were determined. Based on their mean, standard deviation, and extreme deviation, it was concluded that the Lijiang Juniper was an excellent raw material for paper making. The ages of seven Juniper trees in Baoshan Township were also analyzed and determined by the tree annual ring analyzer. The response relationship between annual ring width and precipitation was discussed, with Juniper BSX-2 and BSX-3 as examples. It was found that the fit between annual ring width and precipitation was high, and the correlation coefficients were 0.605 and 0.678. The correlation between the annual ring width of Juniper and the amount of precipitation was strong at the 0.05 level. This indicates that Juniper’s radial growth is more sensitive to water supply. Full article

Efficient and accurate prediction of transient temperature fields is critical for thermal management of electronic devices with multiple heat sources. In this study, a reduced-order surrogate modeling approach is developed based on proper orthogonal decomposition (POD) and radial basis function (RBF) neural networks. The method maps time-conditioned modal coefficients in a parameter–time space, enabling robust temporal extrapolation beyond the training horizon. A multi-heat-source conduction model typical of electronic packages is used as the application scenario. Numerical experiments demonstrate that the proposed POD–RBF surrogate achieves high predictive accuracy (global MRE < 3%) with significantly reduced computational cost, offering strong potential for real-time thermal monitoring and management in electronic systems. Full article

This study aims to qualitatively and quantitatively assess the ability of the flow solver “reactingFoam” of the open-source OpenFOAM software v.2506 for a control-volume-based computational fluid dynamics (CFD) solver in treating the reacting flow problem of a popular benchmarking bluff-body-stabilized turbulent diffusion (non-premixed) flame, that is, the HM1 flame. The HM1 flame has a fuel stream composed of 50% hydrogen (H₂) and 50% methane (CH₄) by mole. Thus, the acronym “HM1” stands for “hydrogen–methane, with level 1 of jet speed”. This fuel stream is surrounded by a coflow of oxidizing air jet. This flame was studied experimentally at the University of Sydney. A measurement dataset of flow and chemical fields was compiled and made available freely for validating relevant computational models. We simulate the HM1 flame using the reactingFoam solver and report here various comparisons between the simulation results and the experimental results to aid in judging the feasibility of this open-source CFD solver. The computational modeling was conducted using the specialized wedge geometry, suitable for axisymmetric problems. The turbulence–chemistry interaction (TCI) was based on the Chalmers’ partially stirred reactor (CPaSR) model. The two-equation k-epsilon framework is used in modeling the small eddy scales. The four-step Jones-Lindstedt (JL) reaction mechanism is used to describe the chemical kinetics. Two meshes (coarse and fine) were attempted, and a converged (mesh-independent) solution was nearly attained. Overall, we notice good agreement with the experimental data in terms of resolved profiles of the axial velocity, mass fractions, and temperature. For either mesh resolution, the overall deviation between the computational results and the experimental results is approximately 8% (mean absolute deviation) and 10% (root mean square deviation). These are favorably low. The current study, and the presented details about the reactingFoam solver and its implementation, can be viewed as a good case study in CFD modeling of reacting flows. In addition, the information we provide about the measurement dataset, the emphasized recirculation zone, the entrainment phenomena, and the irregularity in the radial velocity can help other researchers who may use the same HM1 data. Full article

Soil electrical resistivity is a fundamental parameter in various geotechnical, agricultural, environmental, and engineering applications, as it directly depends on the soil’s moisture content and physical properties. Understanding this relationship is crucial for improving the safety and efficiency of electrical installations. This study analyzes the relationship between soil electrical resistivity and gravimetric moisture content in three soil types, sandy, clayey, and silty, with the aim of comparing the performance of different predictive models under controlled laboratory conditions. Seven fitting models were evaluated, Logarithmic Spline, Radial Basis Function (RBF), Locally Estimated Scatterplot Smoothing (LOESS), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge Regression (RIDGE), Power Law and a segmented equation, using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination $R^2$ . The Spline and RBF models showed excellent accuracy and near-zero errors in all soils, although their applicability is limited by the lack of an explicit formulation and by the fact that, as interpolation methods, they do not guarantee predictive capacity outside the experimental dataset. Therefore, their use should be restricted to controlled laboratory conditions, as field variability factors can significantly alter soil resistivity responses. Among the explicit models, the Segmented Equation obtained a MAPE of 6.14% (sandy), 15.1% (clayey), and 13.16% (silty), with $R^2$ values of 0.91, 0.93, and 0.89, respectively, demonstrating good performance and functionality. The Power Law model, although showing an $R^2$ close to 0.96, presented significant overestimations, especially in silty soils (MAPE > 187%). The LASSO model yielded inconsistent predictions with percentage errors exceeding 120% in silty soils. In conclusion, nonparametric models provide excellent accuracy, while the segmented equation stands out as the best explicit alternative for estimating resistivity with reasonable precision. Full article

Understanding the thermal behaviour of radioisotope generators under Martian conditions is essential for the safe and efficient operation of planetary exploration rovers. This study investigates the heat transfer and flow mechanisms around a simplified full-scale model of the Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) by means of Computational Fluid Dynamics (CFD) simulations performed with ANSYS Fluent 2023 R1. The model consists of a central cylindrical core and eight radial fins, operating under pure CO₂ at a pressure of approximately 600 Pa, representative of the Martian atmosphere. Four cases were simulated, varying both the reactor surface temperature (373–453 K) and the ambient temperature (248 to 173 K) to reproduce typical diurnal and seasonal scenarios on Mars. The results show the formation of a buoyancy-driven plume rising above the generator, with peak velocities between 1 and 3.5 m/s depending on the thermal load. Temperature fields reveal that the fins generate multiple localized hot spots that merge into a single vertical plume at higher elevations. The calculated dimensionless numbers (Grashof ≈ 10⁵, Rayleigh ≈ 10⁵, Reynolds ≈ 10², Prandtl ≈ 0.7, Nusselt ≈ 4) satisfy the expected range for natural convection in low-density CO₂ atmospheres, confirming the laminar regime. These results contribute to a better understanding of heat dissipation processes in Martian environments and may guide future design improvements of thermoelectric generators and passive thermal management systems for space missions. Full article

Tunnel construction in karst formations faces significant geological uncertainties, which pose challenges for quantifying construction risks using traditional deterministic methods. This paper proposes a probabilistic reliability analysis framework that integrates the Stochastic Finite Element Method (SFEM), a Radial Basis Function Neural Network (RBFNN) surrogate model, and Monte Carlo Simulation (MCS) method. The probability distributions of rock mass mechanical parameters and karst geometric parameters were established based on field investigation and geophysical prospecting data. The accuracy of the finite element model was verified through existing physical model tests, with the lateral karst condition identified as the most unfavorable scenario. Limit state functions with control indices, including tunnel crown settlement, invert uplift, ground surface settlement and convergence, were defined. A high-precision surrogate model was constructed using RBFNN (average R² > 0.98), and the failure probabilities of displacement indices were quantitatively evaluated via MCS (10,000 samples). Results demonstrate that the overall failure probability of tunnel construction is 3.31%, with the highest failure probability observed for crown settlement (3.26%). Sensitivity analysis indicates that the elastic modulus of the disturbed rock mass and the clear distance between the karst cavity and the tunnel are the key parameters influencing deformation. This study provides a probabilistic risk assessment tool and a quantitative decision-making basis for tunnel construction in karst areas. Full article

The Discrete Element Method (DEM) has become a cornerstone for analysing granular flow and mixing phenomena in static mixers. This review provides a comprehensive synthesis that distinguishes it from previous studies by: (i) covering a broad range of static mixer geometries, including Kenics, SMX, and Sulzer designs; (ii) integrating experimental validation methods, such as particle tracking, high-speed imaging, Particle Image Velocimetry (PIV), and X-ray tomography, to assess DEM predictions; and (iii) systematically analyzing computational strategies, including advanced contact models, hybrid DEM-CFD/FEM frameworks, machine learning surrogates, and GPU-accelerated simulations. Recent advances in contact mechanics—such as improved cohesion, rolling resistance, and nonspherical particle modelling—have enhanced simulation realism, while adaptive time-stepping and coarse-graining improve computational efficiency. DEM studies have revealed several non-obvious relationships between mixer geometry and particle dynamics. Variations in blade pitch, helix angle, and element arrangement significantly affect local velocity fields, mixing uniformity, and energy dissipation. Alternating left–right element orientations promote cross-sectional particle exchange and reduce stagnant regions, whereas higher pitch angles enhance axial transport but can weaken radial mixing. Particle–wall friction and surface roughness strongly govern shear layer formation and segregation intensity, demonstrating the need for geometry-specific optimization. Comparative analyses elucidate how particle–wall interactions and channel structure influence segregation, residence time, and energy dissipation. The review also identifies current limitations, highlights validation and scale-up challenges, and outlines key directions for developing faster, more physically grounded DEM models, providing practical guidance for industrial mixer design and optimization. Full article

The mechanized harvesting of peanut crops results in both visible and invisible losses. Therefore, monitoring and accurately determining pod maturation are essential to minimizing such losses. The objectives of this study were to (i) identify the most relevant variables for estimating peanut pod maturation and (ii) estimate two maturation indices (brown and black classes; orange, brown, and black classes) using Remote Sensing (RS) and Artificial Neural Networks (ANN), while assessing the generalization potential of the models across different areas. The experiment was carried out in two commercial peanut fields in the state of São Paulo, Brazil, during the 2021/2022 and 2022/2023 growing seasons, using the IAC 503 cultivar. Data collection began one month before the expected harvest date, with weekly intervals. Spectral variables and vegetation indices were obtained from orbital remote sensing (PlanetScope), while climatic data were retrieved from NASA POWER. For analysis, two ANN architectures were employed: Multilayer Perceptron (MLP) and Radial Basis Function (RBF). The dataset from the Cândido Rodrigues site was split into 80% for training and 20% for testing. The model was then evaluated and generalized using data from the Guariba site. Variable selection involved filtering via Principal Component Analysis (PCA) followed by the Stepwise method. Both models demonstrated high accuracy (R² ≥ 0.90; MAE between 0.06 and 0.07). Generalization tests yielded promising results (R² between 0.59 and 0.64; MAE between 0.13 and 0.17), confirming the robustness of the approach under different conditions. Full article

The fractional thermoelasticity theory is presented for the thermal response of a circular cylinder. The basic equations of the cylinder are derived from a fractional theory in the context of the generalized Lord and Shulman theory. It is taken into consideration the variable thermal conductivity of the circular cylinder. A temperature-mapping function is used for this purpose. The cylinder is subjected to an exponential decay of temperature mapping over time at its outer surface. The governing equations are solved by using the Laplace transform technique, and its inversion is carried out numerically. Numerical outcomes are computed and represented graphically for the field variables along the radial direction of the cylinder. The effects of many parameters on all thermoelastic fields are investigated. The analysis highlights the relationship between the field quantities and the radial direction of the circular cylinder, the impact of the exponential decay time, the impact of the thermal conductivity parameter, the inclusion of the fractional parameter, and the difference between the refined thermoelasticity theories. Full article

The ODYSEA (Ocean DYnamics and Surface Exchange with the Atmosphere) mission will provide simultaneous two-dimensional measurements of currents and winds for the first time. According to the ODYSEA radar concept, with a high incidence angle, current noise is primarily driven by backscattered power, which is triggered by wind speed. Therefore, random noise will affect the quality of observations. In low wind conditions, the absence of surface roughness increases the noise level considerably, to the point where the measurement becomes unusable, as the error can exceed 3 m/s at 5 km posting compared to mean current amplitudes of tens of cm/s. Winds higher than 7.5 m/s enable current measurements at 5 km posting with an RMS accuracy below 50 cm/s, but derivatives of currents will amplify noise, hampering the understanding of ocean dynamics and the interaction between the ocean and the atmosphere. In this context, this study shows the advantages and limitations of using noise-reduction algorithms. A convolutional neural network, a UNet inspired by the work of the SWOT (Surface Water and Ocean Topography) mission, is trained and tested on simulated radial velocities that are representative of the global ocean. The results are compared with those of classical smoothing: an Adaptive Gaussian Smoother whose filtering transfer function is optimized based on local wind speed (e.g., more smoothing in regions of low wind). The UNet outperforms the kernel smoother everywhere with our simulated dataset, especially in low wind conditions (SNR << 1) where the smoother essentially removes all velocities whereas the UNet mitigates random noise while preserving most of the signal of interest. Error is reduced by a factor of 30 and structures down to 30 km are reconstructed accurately. The UNet also enables the reconstruction of the main eddies and fronts in the relative vorticity field. It shows good robustness and stability in new scenarios. Full article

Background/Objectives: Magnetisation Transfer (MT) MRI is used for neuro-degenerative disorders, including Multiple Sclerosis (MS), providing an indirect measure of large biomolecular MR signal sources which cannot be observed directly because their typical T2 is usually much shorter than the echo time (TE) of conventional MR sequences. We investigated a 3D-radial Zero Time of Echo (ZTE) MT-weighted sequence with potentially enhanced sensitivity to short-T2 MR signals indirectly (via MT weighting) and directly (due to the short TE). Methods: The sequence runs on a human 7T MR scanner, producing whole-brain MT-weighted images with isotropic 0.8 mm resolution in 6.5 minutes. One RF pulse is used to suppress the fat signal and generate MT weighting, reducing RF power deposition to moderate levels. The small excitation pulses and the “quasi-adiabatic” MT pulse mitigate the negative effects of inhomogeneous transmit RF fields observed at 7T in the human head, facilitating the generation of uniform Magnetisation Transfer Ratio (MTR) maps. Results: Results from a biologic phantom, a healthy volunteer, and an MS patient illustrate important imaging features of the “SilentMT” sequence. When the MS patient images were compared with Fluid Attenuated Inversion Recovery (FLAIR) images taken on the same patient at 1.5T and 7T, SilentMT was able to detect all the MS lesions observed on the “reference truth” 1.5T FLAIR; 7T FLAIR, however, failed to detect some lesions in the temporal lobe and brain stem. SilentMT detected a lesion which was not immediately apparent on either FLAIR image. Increased MTR was observed in some regions of the brain of the MS patient, notably the left temporal lobe. Conclusions: This initial investigation of an MT-weighted ZTE sequence shows evidence that it may be more sensitive to pathology in a patient with MS. Full article