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Background and aims: Root distribution over the soil profile is important for crop resource uptake. Using machine learning (ML), this study investigated whether measured square root of planar root length density (Sqrt_pRLD) at different soil depths were related to uptake of isotope tracer (15N) and drought stress indicator (13C) in wheat, to reveal root function. Methods: In the RadiMax semi-field root-screening facility 95 winter wheat genotypes were phenotyped for root growth in 2018 and 120 genotypes in 2019. Using the minirhizotron technique, root images were acquired across a depth range from 80 to 250 cm in May, June, and July and RL was extracted using a convolutional neural network. We developed ML models to explore whether the Sqrt_pRLD estimates at different soil depths were predictive of the uptake of deep soil nitrogen - using deep placement of 15N tracer as well as natural abundance of 13C isotope. We analyzed the correlations to tracer levels to both a parametrized root depth estimation and an ML approach. We further analyzed the genotypic effects on root function using mediation analysis. Results: Both parametrized and ML models demonstrated clear correlations between Sqrt_pRLD distribution and resource uptake. Further, both models demonstrated that deep roots at approx. 150 to 170 cm depth were most important for explaining the plant content of 15N and 13C isotopes. The correlations were higher in 2018. Conclusions: The results demonstrated that, parametrized models and ML-based analysis provided complementary insight into the importance of deep rooting for water and nitrogen uptake. [ABSTRACT FROM AUTHOR]

The stability analysis of breakwaters is very important to have a safe and economic design of these coastal protective structures and the damage level is one of the most important parameters in this context. In the recent past, machine learning techniques showed immense potential in transforming many industries and processes, for making them more efficient and accurate. In this study, five advanced machine learning algorithms, support vector regression, random forest, Adaboost, gradient boosting, and deep artificial neural network, were employed and analyzed on estimation of the damage level of rubble-mound breakwaters. A large experimental dataset, considering almost every stability variable with their whole ranges, was used in this purpose. Also, a detailed feature analysis is presented to have an insight into the relations between these variables. It was found that the present study had overcome all of the limitations of existing studies related to this field and delivered the highest level of accuracy. [ABSTRACT FROM AUTHOR]

An appropriate nonlinear blood flow model under the influence of periodic body acceleration through a multiple stenosed artery is investigated with the help of finite difference method. The arterial segment is simulated by a cylindrical tube filled with a viscous incompressible Newtonian fluid described by the Navier-Stokes equation. The nonlinear equation is solved numerically with the proper boundary conditions and pressure gradient that arise from the normal functioning of the heart. Results are discussed in comparison with the existing models. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of hybrid nanoparticles (Cu–TiO2) on a two-dimensional peristaltic blood flow pattern in a nonuniform cylindrical annulus in the presence of an external induced magnetic field with wall slip. Further, this study focuses on the flow dynamics of single and hybrid nanofluids through endoscopic or catheterized effects. The mathematical model consisting of continuity, linear momentum, thermal energy, and Maxwell's equations is simplified under the assumptions of long wavelength and negligible Reynolds number. The Homotopy perturbation method (HPM) is employed to get an approximate analytical solution of nonlinear dimensionless momentum equations. Based on the mathematical relationships and graphic visualization, the influence of the pertinent parameters described the velocity profile, temperature distribution, induced magnetic field, current density distribution, wall shear stress, and heat transfer coefficient. With the help of contours, the trapping phenomenon is also presented. The results reveal that the Lorentz force significantly reduces the Cu–TiO2/blood nanofluid velocity, whereas the elevating Grashof number does the opposite. Compared with copper nanoparticles, hybrid nanoparticles have a higher wall shear stress. The increasing values of Reynolds numbers amplify the induced magnetic field on annular surfaces. In the axial direction, Lorentz force significantly decreases the current density distribution for hybrid nanofluid. Moreover, hybrid nanoparticles (Cu–TiO2) exhibit superior heat transfer than Copper (Cu) nanoparticles in the blood-based fluid. According to the graphical outcomes, hybrid nanoparticles are comparatively more effective than unitary nanoparticles in the blood. [ABSTRACT FROM AUTHOR]

One of the current challenges of the coronary angioplasty technique for treating atherosclerosis in the artery is to target the desired area using nanoparticles. This study aims to investigate the hybrid Au-Fe 3 O 4 /blood nanofluid flow that passes through a narrowed section of a stenotic artery. The behavior of the Au-Fe 3 O 4 /blood flow in the stenosis region is mathematically examined, taking into account an inclined magnetic field and temperature-dependent thermal conductivity. Additionally, this work analyses the effect of nanolayers on nanofluid flow in the presence of a catheter with a balloon (angioplasty) on its wall. Tube and balloon catheter models are considered individually to compare the results of flow characteristics and heat transmission. Channel boundaries comprise wall properties with thermal slip and second-order velocity slip conditions. A similarity transformation procedure is used to convert the governing equations into highly nonlinear normal differential equations, which are analytically computed using the homotopy perturbation technique. The obtained results reveal an increased velocity profile due to the magnetic force, while an inverse trend occurs due to the thermal conductivity of the nanofluid. Furthermore, interfacial nanolayers of nanoparticles significantly alter the blood temperature to prevent and manage cardiovascular diseases. This rheological model may be useful for cancer diagnosis, tumor-selective photothermal therapy, medical simulation devices, and other applications. [Display omitted] • The current study presents advanced research on hybrid nanofluid flow dynamics. • The study aims to visually examine the hemodynamic characteristics of the fluid. • This model incorporates ferrous nanoparticles to enhance the heat transfer rate. • The study suggests that flow velocity is influenced by magnetic force and variable thermal conductivity. • Interfacial nanolayers alter blood temperature to prevent cardiovascular diseases. • Inserting a balloon catheter is more effective in a narrowed artery than using a catheter tube. [ABSTRACT FROM AUTHOR]