Parametrical modeling and design optimization of blood plasma separation device with microchannel mechanism

This paper presents an analysis of biofluid behavior in a T-shaped microchannel device and a design optimization for improved biofluid performance in terms of particle liquid separation. The biofluid is modeled with single phase shear rate non-Newtonian flow with blood property. The separation of red blood cell from plasma is evident based on biofluid distribution in the microchannels against various relevant effects and findings, including Zweifach-Fung bifurcation law, Fahraeus effect, Fahraeus-Lindqvist effect and cell free phenomenon. The modeling with the initial device shows that this T-microchannel device can separate red blood cell from plasma but the separation efficiency among different bifurcations varies largely. In accordance with the imbalanced performance, a design optimization is conducted. This includes implementing a series of simulations to investigate the effect of the lengths of the main and branch channels to biofluid behavior and searching an improved design with optimal separation performance. It is found that changing relative lengths of branch channels is effective to both uniformity of flow rate ratio among bifurcations and reduction of difference of the flow velocities between the branch channels, whereas extending the length of the main channel from bifurcation region is only effective for uniformity of flow rate ratio

BMC Public Health

BACKGROUND: In 2009, the World Health Organization's Commission on Social Determinants of Health set out its recommendations for action, which included establishing equity from early childhood onwards by enabling all children and their mothers to benefit from a comprehensive package of quality programmes. In order to address social inequalities in health, it is recommended that action be taken from early childhood, and actions providing support for parenting are an effective lever in this respect. The aim of this review of systematic reviews is to analyse, on the one hand, the components and characteristics of effective interventions in parenting support and, on the other, the extent to which the reviews took into account social inequalities in health. METHODS: A total of 796 reviews were selected from peer-reviewed journals published between 2009 and 2016 in French or English. Of these, 21 reviews responding to the AMSTAR and selected ROBIS criteria were retained. These were analysed in relation to the consideration they gave to social inequalities in health according to PRISMA-equity. RESULTS: The reviews confirmed that parenting support programmes improved infants' sleep, increased mothers' self-esteem and reduced mothers' anger, anxiety and stress levels. The mainly authors noted that the contexts in which the interventions had taken place were described either scantly or not at all, making it difficult to evaluate them. Only half of the reviews had addressed the question of social inequalities in health. In particular, there had been little research conducted on the relational aspect and the social link. CONCLUSION: In terms of addressing social inequalities in perinatal health, the approach remains both modest and reductive. Understanding how, for whom and in what conditions interventions operate is one way of optimising their results. Further research is needed to study the interactions between the interventions and their contexts

Combining Polynomial Chaos Expansions and Kriging

Computer simulation has emerged as a key tool for designing and assessing engineeringsystems in the last two decades. Uncertainty quantification has becomepopular more recently as a way to model all the uncertainties affecting the systemand their impact onto its performance.In this respect meta-models (a.k.a. surrogate models) have gained interest. Indeeddealing with uncertainties requires running the computer model many times,which may not be affordable for complex models. Surrogate models mimic the behaviourof the original model while being cheap to evaluate.Polynomial chaos expansion (PCE) and Kriging are two popular techniques, whichhave been developed with very little interaction so far. In this report we present a newapproach, called PC-Kriging, that combines the two tools. The algorithm is based onthe universal Kriging model where the trend is represented by a set or orthonormalpolynomials.Various aspects of the new metamodelling technique are presented and investigatedin details. The discussion starts with a survey on methods for generating anoptimal design of experiments (DOE). The PC-Kriging algorithm inherits many parametersand sub-methods such as the number of polynomial terms and the choiceof the autocorrelation kernel. A variety of kernels are presented and discussed.The methods are compared on analytical benchmark functions. The conclusionof this report is that PC-Kriging performs better or at least as well as PCE or Krigingtaken separately in terms of relative generalized error (L2-error)

Combining Polynomial Chaos Expansions and Kriging

Computer simulation has emerged as a key tool for designing and assessing engineeringsystems in the last two decades. Uncertainty quantification has becomepopular more recently as a way to model all the uncertainties affecting the systemand their impact onto its performance.In this respect meta-models (a.k.a. surrogate models) have gained interest. Indeeddealing with uncertainties requires running the computer model many times,which may not be affordable for complex models. Surrogate models mimic the behaviourof the original model while being cheap to evaluate.Polynomial chaos expansion (PCE) and Kriging are two popular techniques, whichhave been developed with very little interaction so far. In this report we present a newapproach, called PC-Kriging, that combines the two tools. The algorithm is based onthe universal Kriging model where the trend is represented by a set or orthonormalpolynomials.Various aspects of the new metamodelling technique are presented and investigatedin details. The discussion starts with a survey on methods for generating anoptimal design of experiments (DOE). The PC-Kriging algorithm inherits many parametersand sub-methods such as the number of polynomial terms and the choiceof the autocorrelation kernel. A variety of kernels are presented and discussed.The methods are compared on analytical benchmark functions. The conclusionof this report is that PC-Kriging performs better or at least as well as PCE or Krigingtaken separately in terms of relative generalized error (L2-error)

Statistical analysis of scattered field by building facades using polynomial chaos expansion

International audienc

Statistical analysis of scattered field by building facades

International audienc