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The term ‘biomedical engineering’ refers to the application of the principles and problem-solving techniques of engineering to biology and medicine. Biomedical engineering is an interdisciplinary branch, as many of the problems health professionals are confronted with have traditionally been of interest to engineers because they involve processes that are fundamental to engineering practice. Biomedical engineers employ common engineering methods to comprehend, modify, or control biological systems, and to design and manufacture devices that can assist in the diagnosis and therapy of human diseases. This Special Issue of Fluids aims to be a forum for scientists and engineers from academia and industry to present and discuss recent developments in the field of biomedical engineering. It contains papers that tackle, both numerically (Computational Fluid Dynamics studies) and experimentally, biomedical engineering problems, with a diverse range of studies focusing on the fundamental understanding of fluid flows in biological systems, modelling studies on complex rheological phenomena and molecular dynamics, design and improvement of lab-on-a-chip devices, modelling of processes inside the human body as well as drug delivery applications. Contributions have focused on problems associated with subjects that include hemodynamical flows, arterial wall shear stress, targeted drug delivery, FSI/CFD and Multiphysics simulations, molecular dynamics modelling and physiology-based biokinetic models.

risk assessment --- n/a --- stability study --- inclined ?-channel --- lab-on-a-chip --- pipette Petri dish single-cell trapping (PP-SCT) --- Abdominal Aortic Aneurysm --- drug delivery --- human biomonitoring --- abdominal aortic aneurysm --- shikonin --- hyaluronic --- Computational Fluid Dynamics (CFD) --- exposure reconstruction --- doxorubicin --- biokinetics --- blood flow --- gelation --- hyperbranched polyester --- single cell analysis --- capillary --- liposomes --- meniscus --- small vessel --- spreading --- alkannin --- hydrogel --- single-cell trapping --- drug delivery system --- microfluidics --- viscoelastic --- CFD --- FFMR --- computational fluid dynamics simulations --- biochemical processes --- hematocrit --- pressure drop --- passive trapping --- dipalmitoylphosphatidylglycerol (DPPG) --- arterial wall shear stress --- cell capture --- free-flowing film --- falling film microreactor --- non-Newtonian --- pulsatile flow --- tilt trapping --- haematocrit --- ?-PIV --- viscous --- hydrodynamics --- gravitational --- fluid–structure interaction --- blood --- physiology-based biokinetics --- simulations --- droplet spreading --- human bio-monitoring --- shear thinning --- Fluid-Structure Interaction (FSI) --- cancer --- bisphenol A --- Casson fluid --- fluid-structure interaction

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The analysis of Big Data in biomedical as well as business and financial research has drawn much attention from researchers worldwide. This book provides a platform for the deep discussion of state-of-the-art statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions are showcased.

Humanities --- Social interaction --- high-dimensional --- nonlocal prior --- strong selection consistency --- estimation consistency --- generalized linear models --- high dimensional predictors --- model selection --- stepwise regression --- deep learning --- financial time series --- causal and dilated convolutional neural networks --- nuisance --- post-selection inference --- missingness mechanism --- regularization --- asymptotic theory --- unconventional likelihood --- high dimensional time-series --- segmentation --- mixture regression --- sparse PCA --- entropy-based robust EM --- information complexity criteria --- high dimension --- multicategory classification --- DWD --- sparse group lasso --- L2-consistency --- proximal algorithm --- abdominal aortic aneurysm --- emulation --- Medicare data --- ensembling --- high-dimensional data --- Lasso --- elastic net --- penalty methods --- prediction --- random subspaces --- ant colony system --- bayesian spatial mixture model --- inverse problem --- nonparamteric boostrap --- EEG/MEG data --- feature representation --- feature fusion --- trend analysis --- text mining

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The analysis of Big Data in biomedical as well as business and financial research has drawn much attention from researchers worldwide. This book provides a platform for the deep discussion of state-of-the-art statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions are showcased.

Humanities --- Social interaction --- high-dimensional --- nonlocal prior --- strong selection consistency --- estimation consistency --- generalized linear models --- high dimensional predictors --- model selection --- stepwise regression --- deep learning --- financial time series --- causal and dilated convolutional neural networks --- nuisance --- post-selection inference --- missingness mechanism --- regularization --- asymptotic theory --- unconventional likelihood --- high dimensional time-series --- segmentation --- mixture regression --- sparse PCA --- entropy-based robust EM --- information complexity criteria --- high dimension --- multicategory classification --- DWD --- sparse group lasso --- L2-consistency --- proximal algorithm --- abdominal aortic aneurysm --- emulation --- Medicare data --- ensembling --- high-dimensional data --- Lasso --- elastic net --- penalty methods --- prediction --- random subspaces --- ant colony system --- bayesian spatial mixture model --- inverse problem --- nonparamteric boostrap --- EEG/MEG data --- feature representation --- feature fusion --- trend analysis --- text mining --- high-dimensional --- nonlocal prior --- strong selection consistency --- estimation consistency --- generalized linear models --- high dimensional predictors --- model selection --- stepwise regression --- deep learning --- financial time series --- causal and dilated convolutional neural networks --- nuisance --- post-selection inference --- missingness mechanism --- regularization --- asymptotic theory --- unconventional likelihood --- high dimensional time-series --- segmentation --- mixture regression --- sparse PCA --- entropy-based robust EM --- information complexity criteria --- high dimension --- multicategory classification --- DWD --- sparse group lasso --- L2-consistency --- proximal algorithm --- abdominal aortic aneurysm --- emulation --- Medicare data --- ensembling --- high-dimensional data --- Lasso --- elastic net --- penalty methods --- prediction --- random subspaces --- ant colony system --- bayesian spatial mixture model --- inverse problem --- nonparamteric boostrap --- EEG/MEG data --- feature representation --- feature fusion --- trend analysis --- text mining