On the fatigue life of dental implants: Numerical and experimental investigation on configuration effect.

Dental implants have seen widespread and successful use in recent years. Given their long-term application and the critical role of geometry in determining fracture and fatigue characteristics, fatigue assessments are of utmost importance for implant systems. In this study, nine dental implant system samples were subjected to testing in accordance with ISO 14801 standards.

Coronary artery lipid accumulation prevention through vibrating piezo electric nano plates embedded in smart stent.

Considering the lipid concentration and side effects regarding the stents used by surgeons, a new heart stent model is proposed. In the new stent, a few piezo plates are designed and attached to the stents by which release of the lipids can take place due to the applied alternative voltages. Due to the vibrations of small-scale piezoelectric plates, the deposition of low-density-lipoproteins (LDL) floating in the blood flow in the coronary arteries is prevented.

Experimental and numerical study on micro-blasting process of 3A dental implant titanium alloy: A comparison between finite element method and smoothed particle hydrodynamics.

In the present study, solid particle erosion due to micro-blasting of dental implants (3A) made of titanium alloy under the impact of multiple alumina particles with an average diameter of 85 μm was analyzed, experimentally and numerically. The numerical investigation was conducted using finite element (FE) and smoothed particle hydrodynamics (SPH) methods. The erosive behavior of this alloy was simulated as impacts in micro-scale based on Johnson-Cook constitutive equations.

Global sensitivity analysis for assessing the parameters importance and setting a stopping criterion in a biomedical inverse problem.

This paper shows how to obtain in addition to the standard deviations available after a data assimilation procedure based on the ensemble Kalman filter, an apportioning of the total uncertainty in the outputs of a patient-specific blood flow model into small portions of uncertainty due to input parameters. Statistical indicators generally used for identifying the importance of numerical parameters, namely the Sobol' first order and total indices, are introduced and discussed. These allow the identification of the importance rank of the different input parameters for the patient-specific blood flow model, as well as the influence of the interactions between these parameters on the model output variance.

Augmented patient-specific functional medical imaging by implicit manifold learning.

This paper uses machine learning to enrich magnetic resonance angiography and magnetic resonance imaging acquisitions. A convolutional neural network is built and trained over a synthetic database linking geometrical parameters and mechanical characteristics of the arteries to blood flow rates and pressures in an arterial network. Once properly trained, the resulting neural network can be used in order to predict blood pressure in cerebral arteries noninvasively in nearly real-time.

Photothermal Effect of Modulating Laser Irradiation on the Thermal Diffusivity of AlO Nanofluids.

Modulated continuous wave (CW) lasers cause photothermal effect that leads to rapid optical absorption and generation of thermal waves around the irradiated nanostructures. In this work, we examined the effect of modulated CW laser irradiation on the particle fragmentation process to enhance the thermal diffusivity of nanofluids. A facile and cost-effective diode laser was applied to reduce the agglomerated size of AlO nanoparticles in deionized water.

Backward sensitivity analysis and reduced-order covariance estimation in noninvasive parameter identification for cerebral arteries.

Using a previously developed inversion platform for functional cerebral medical imaging with ensemble Kalman filters, this work analyzes the sensitivity of the results with respect to different parameters entering the physical model and inversion procedure, such as the inlet flow rate from the heart, the choice of the boundary conditions, and the nonsymmetry in the network terminations. It also proposes an alternative low complexity construction for the covariance matrix of the hemodynamic parameters of a network of arteries including the circle of Willis. The platform takes as input patient-specific blood flow rates extracted from magnetic resonance angiography and magnetic resonance imaging (dicom files) and is applied to several available patients data.

Thermal Wavelength Measurement of Nanofluid in an Optical-Fiber Thermal Wave Cavity Technique to Determine the Thermal Diffusivity.

The application of optical-fiber thermal wave cavity (OF-TWC) technique was investigated to measure the thermal diffusivity of Ag nanofluids. The thermal diffusivity was obtained by measuring the thermal wavelength of sample in a cavity scan mode. The spherical Ag nanoparticles samples were prepared at various sizes using the microwave method.

Data assimilation for identification of cardiovascular network characteristics.

A method to estimate the hemodynamics parameters of a network of vessels using an Ensemble Kalman filter is presented. The elastic moduli (Young's modulus) of blood vessels and the terminal boundary parameters are estimated as the solution of an inverse problem. Two synthetic test cases and a configuration where experimental data are available are presented.

Exact and approximate Fourier rebinning algorithms for the solution of the data truncation problem in 3-D PET.

This paper presents an extended 3-D exact rebinning formula in the Fourier space that leads to an iterative reprojection algorithm (iterative FOREPROJ), which enables the estimation of unmeasured oblique projection data on the basis of the whole set of measured data. In first approximation, this analytical formula also leads to an extended Fourier rebinning equation that is the basis for an approximate reprojection algorithm (extended FORE). These algorithms were evaluated on numerically simulated 3-D positron emission tomography (PET) data for the solution of the truncation problem, i.

Optimization of a microfluidic mixer for studying protein folding kinetics.

We have applied an optimization method in conjunction with numerical simulations to minimize the mixing time of a microfluidic mixer developed for protein folding studies. The optimization method uses a semideterministic algorithm to find the global minimum of the mixing time by varying the mixer geometry and flow conditions. We describe the minimization problem and constraints and give a brief overview of the optimization algorithm.

Design and optimization of on-chip capillary electrophoresis.

We present a systematic, experimentally validated method of designing electrokinetic injections for on-chip capillary electrophoresis applications. This method can be used to predict point-wise and charge-coupled device (CCD)-imaged electropherograms using estimates of species mobilities, diffusivities and initial sample plug parameters. A simple Taylor dispersion model is used to characterize electrophoretic separations in terms of resolution and signal-to-noise ratio (SNR).