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In this study, a novel neuro heuristic approach is designed to investigate the flow properties of magnetohydrodynamic (MHD) nanofluid along an exponentially extending sheet with a permeable medium with the impact of radiation as well as fluctuating heat source/sink. The designed scheme to handle the suggested problem is established through the well‐known biologically inspired neural networks (BINNs) by exploiting the inverse multiquadric kernel (IMQK), that is, BINNs‐IMQK which is quite a new approach. The partial differential equations (PDEs) which govern the fluidic flow are reformed into a nonlinear system of ordinary differential equations (ODEs) using the most fitted similarity transformations rules and numerically solved by varying the parametric values including unsteady parameter, Brownian motion parameter, suction/injection parameter, radiation parameter, Schmidt number together with Prandtl number to visualize the velocity, thermal gradient, and mass transfer in the suggested fluid problem. It is noticed that nanofluid temperature hikes by uplifting the value of the Brownian motion parameter but this effect is reversed in case of unsteady parameter. The obtained numerical results are verified through reference solution using the well‐known Adams method and the efficacy of the suggested solver is endorsed using a variety of statistical operators. [ABSTRACT FROM AUTHOR]

A complete shape factor investigation of water‐based mixture type hybrid nanofluid in a permeable boundary with the impact of magnetic field, thick dissemination, and warm radiation is presented in this article. A computational convection analysis of an inverted semi vertical cone with a porous surface in the form of SiO2$$ {\mathrm{SiO}}_2 $$/water nanofluid and MoS2‐SiO2$$ {\mathrm{MoS}}_2\hbox{-} {\mathrm{SiO}}_2 $$/water hybrid nanofluid transport is developed. The system of differential equations is presented and solved numerically by the Lobatto IIIA method. The temperature distributions and fluid velocity are studied along with the coefficient of skin friction and the Nusselt number, taking into account the form of distinct nano‐particles. The flow problem's results are approximated by using several embedding variables. Tables and graphs are constructed for a variety of scenarios including maximum residual error, mesh points, and Nusselt numbers. We conclude that boundary film thickness reduces and the fluid flow is resisted by magnetic field presence. Fluid flow slows down as λ$$ \lambda $$ increases, and this reduction is more evident in nanofluids than in hybrid nanofluids. With an increment in S$$ S $$, velocity drops. A detailed analysis of the proposed ordinary differential equations, boundary conditions, and numerical data of skin friction is given both in tabular and graphical forms. Additionally, it is observed that the fluid flow slows down more for the hybrid nanofluid than for the SiO2$$ {\mathrm{SiO}}_2 $$/water nanofluid. Moreover, it is clear that the temperature increase for the SiO2$$ {\mathrm{SiO}}_2 $$/water nanofluid is substantially greater. The authors deduce that the existence of a magnetic field resists fluid flow for hybrid nanofluid forms and decreases the thickness of the viscous boundary layer. [ABSTRACT FROM AUTHOR]

A stochastic computing approach is implemented in the present work to solve the nonlinear nanofluidics system that occurs in the model of atomic physics. The process converts the partial differential nanofluidics system with suitable level of similarities transformation into nonlinear systems of differential equations. For the construction of datasets, finite difference scheme (Lobatto IIIA) is applied through different selection of collocation points for nonlinear nanofluidics system having accuracy of order four. Lobatto IIIA has a strong point to tackle extremely nonlinear systems of ordinary differential equations in smooth way. For different scenarios, datasets are well trained through computing scheme to investigate the heat transfer and thermal performance of nanofluidic transportation system of nanofluids and hybrid nanofluids toward stretching surfaces with variation of Biot number, Nusselt number and skin fraction. Furthermore, the reliability, accuracy and efficiency are endorsed through various statistical analysis and graphical illustrations of proposed computing scheme. [ABSTRACT FROM AUTHOR]

Background: Thalassemia major is the severe form of β thalassemia characterized by severe anaemia, hepatosplenomegaly and facioskeletal changes due to increased haemolysis of defective red blood cells. In iron overload states, high levels of iron exceed the iron-carrying capacity of transferrin within the plasma, leading to the formation of nontransferrin-bound iron form. These nontransferrin-bound iron forms can be taken up into cells, including liver, heart, and endocrine cells leading to organ damage. To prevent complications associated with hemosiderosis, iron chelation therapy remains one of the main objectives of clinical management of the patients affected by Thalassemia Major. Methods: Thirty-seven patients were enrolled using non randomized convenience sampling technique after the written consent from patients. Patients age 2--30 years were enrolled in this study. Serum Ferritin, ALT, Serum Creatinine were checked at the start of the study, 3 months, 6months and then at the end of the study, i.e., at 9 months of the commencement of the study. They were also assessed for other side effects pertaining to oral tolerability of the drug like vomiting, nausea, GI upset, diarrhoea, urinary complaints or any other subjective complaint. Results: Of the 37 patients, 20 were male (54.1%) and 17 were female (45.9%). Mean age of the patients was 10.2 years (Min. 3 years, Max 21 years). The average serum Ferritin at baseline was noted as 3440 which increased after a period of 3 months, 6 months and 9 months with average of 3359, 3677 and 4394 respectively. After the period of 9 months largest 95% confidence interval of serum Ferritin levels was observed in the range of 3420.17 to 5368.63. In our study, 17 patients required alternative chelation (46%). These patients needed IV Deferioxamine because of the rising trend of Serum Ferritin after the study. Conclusion: From the results of our study, we infer that oral Deferasirox is not an effective iron chelator. If the patients are taking oral deferasirox, their Serum Ferritin should be checked 3 monthlies. The drug is effective only in maintaining Serum Ferritin levels with levels less than 1500ng/ml. Intravenous Deferioxamine still should be preferred over oral iron chelators for effective control of iron overload and its complications. [ABSTRACT FROM AUTHOR]

In this research, a novel design stochastic numerical technique is presented to investigate the unsteady form magnetohydrodynamic (MHD) slip flow along the boundary layer to analyze the transportation and heat transfer in a solar collector through nano liquids which is a revolution in the field of neurocomputing. Thermal conductivity in variable form is dependent on temperature and wall slips are assumed over the boundary. For mathematical modeling, the solar collector is assumed in the form of a nonlinear stretching sheet and a quite new artificial neural networks (ANNs) based approach is used to solve the current problem in which inverse multiquadric radial basis (IMRB) kernel is sandwiched between a global search solver named genetic algorithms (GAs) and a highly effective local solver named sequential quadratic programming (SQP) i.e. IMRB-GASQP solver. The governing boundary value problem is altered in the form of a system of nonlinear ordinary differential equations (ODEs) through the utilization of similarity transformation and then the obtained system of ODEs is solved using IMRB-GASQP solver by altering the values of distinguished parameters involved in it to observe the fluctuation in the velocity and temperature profiles of nanofluid. The obtained results are effectively compared with the reference solutions using the Adams numerical technique in graphical and tabulated form. An exhaustive error analysis using performance operators is presented while the efficacy of the designed solver using various statistical operators is also part of this research. [ABSTRACT FROM AUTHOR]

Illness related brain effects of neuropsychiatric disorders are not regionally uniform, with some regions showing large pathological effects while others are relatively spared. Presently, Big Data meta-analytic studies tabulate these effects using structural and/or functional brain atlases that are based on the anatomical boundaries, landmarks and connectivity patterns in healthy brains. These patterns are then translated to individual level predictors using approaches such as Regional Vulnerability Index (RVI), which quantifies the agreement between individual brain patterns and the canonical pattern found in the illness. However, the atlases from healthy brains are unlikely to align with deficit pattern expressed in specific disorders such as Major Depressive Disorder (MDD), thus reducing the statistical power for individualized predictions. Here, we evaluated a novel approach, where disorder specific templates are constructed using the Kullback-Leibler (KL) distance to balance granularity, signal-to-noise ratio and the contrast between regional effect sizes to maximize translatability of the population-wide illness pattern at the level of the individual. We used regional homogeneity (ReHo) maps extracted from resting state functional MRI for N = 2, 289 MDD sample (mean age ± s.d.: 63.2 ± 7.2 years) and N = 6104 control subjects (mean age ± s.d.: 62.9 ± 7.2 years) who were free of MDD and any other mental condition. The cortical effects of MDD were analyzed on the 3D spherical surfaces representing cerebral hemispheres. KL-distance was used to organize the cortical surface into 28 regions of interest based on effect sizes, connectivity and signal-to-noise ratio. The RVI values calculated using this novel approach showed significantly higher effect size of the illness than these calculated using standard Desikan brain atlas.