Radiative Blood-Based Hybrid Copper-Graphene Nanoliquid Flows along a Source-Heated Leaning Cylinder

Variant graphene,graphene oxides(GO),and graphene nanoplatelets(GNP)dispersed in blood-based copper(Cu)nanoliquids over a leaning permeable cylinder are the focus of this study.These forms of graphene are highly beneficial in the biological and medical fields for cancer therapy,anti-infection measures,and drug delivery.The non-Newtonian Sutterby(blood-based)hybrid nanoliquid flows are generalized within the context of the Tiwari-Das model to simulate the effects of radiation and heating sources.The governing partial differential equations are reformulated into a nonlinear set of ordinary differential equations using similar transformational expressions.These equations are then transformed into boundary value problems through a shooting technique,followed by the implementation of the bvp4c tool in MATLAB.The influences of various parameters on the model’s nondimensional velocity and temperature profiles,reduced skin friction,and reduced Nusselt number are presented for detailed discussions.The results indicated that Cu-GNP/blood and Cu-GO/blood hybrid nanofluids exhibit the lowest and highest velocity distributions,respectively,for increased nanoparticles volume fraction,curvature parameter,Sutterby fluid parameter,Hartmann number,and wall permeability parameter.Conversely,opposite trends are observed for the temperature distribution for all considered parameters,except the mixed convection parameter.Increases in the reduced skin friction magnitude and the reduced Nusselt number with higher values of graphene/GO/GNP nanoparticle volume fraction are also reported.Finally,GNP is identified as the superior heat conductor,with an average increase of approximately 5%and a peak of 7.8%in the reduced Nusselt number compared to graphene and GO nanoparticles in the Cu/blood nanofluids.

Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

Artificial intelligence(AI)continues to transform data analysis in many domains.Progress in each domain is driven by a growing body of annotated data,increased computational resources,and technological innovations.In medicine,the sensitivity of the data,the complexity of the tasks,the potentially high stakes,and a requirement of accountability give rise to a particular set of challenges.In this review,we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making.1)Explainable AI aims to produce a human-interpretable justification for each output.Such models increase confidence if the results appear plausible and match the clinicians expectations.However,the absence of a plausible explanation does not imply an inaccurate model.Especially in highly non-linear,complex models that are tuned to maximize accuracy,such interpretable representations only reflect a small portion of the justification.2)Domain adaptation and transfer learning enable AI models to be trained and applied across multiple domains.For example,a classification task based on images acquired on different acquisition hardware.3)Federated learning enables learning large-scale models without exposing sensitive personal health information.Unlike centralized AI learning,where the centralized learning machine has access to the entire training data,the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates,not personal health data.This narrative review covers the basic concepts,highlights relevant corner-stone and stateof-the-art research in the field,and discusses perspectives.

Slip effects on unsteady mixed convection of hybrid nanofluid flow near the stagnation point

The unsteady mixed convection of the Al_(2)O_(3)-Cu/H_(2)O hybrid nanofluid flow near the stagnation point past a vertical plate is analyzed.The bvp4c technique is used to solve the resulting ordinary differential equations.The combined effects of the velocity and thermal slip are addressed.The effects of different relevant physical parameters are studied numerically.The results show that the heat transfer rate is reduced when the volume fraction of the nanoparticles increases,while the unsteadiness parameter has an opposite effect in the opposing flow.The presence of the slip parameter is proven to increase the skin friction coefficient while reduce the local Nusselt number in the buoyancy opposing flow.A contradictory result is observed in the buoyancy assisting flow.Meanwhile,the heat transfer rate is reduced in the buoyancy of the assisting and opposing flows when the thermal slip effect is considered.