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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]

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.