A reverse predictive model towards design automation of microfluidic droplet generators

This work has been presented in the 10th IWBDA workshop.Droplet-based microfluidic devices in comparison to test tubes can reduce reaction volumes 10^9 times and more due to the encapsulation of reactions in micro-scale droplets [4]. This volume reduction, alongside higher accuracy, higher sensitivity and faster reaction time made droplet microfluidics a superior platform particularly in biology, biomedical, and chemical engineering. However, a high barrier of entry prevents most of life science laboratories to exploit the advantages of microfluidics. There are two main obstacles to the widespread adoption of microfluidics, high fabrication costs, and lack of design automation tools. Recently, low-cost fabrication methods have reduced the cost of fabrication significantly [7]. Still, even with a low-cost fabrication method, due to lack of automation tools, life science research groups are still reliant on a microfluidic expert to develop any new microfluidic device [3, 5]. In this work, we report a framework to develop reverse predictive models that can accurately automate the design process of microfluidic droplet generators. This model takes prescribed performance metrics of droplet generators as the input and provides the geometry of the microfluidic device and the fluid and flow settings that result in the desired performance. We hope this automation tool makes droplet-based microfluidics more accessible, by reducing the time, cost, and knowledge needed for developing a microfluidic droplet generator that meets certain performance requirement

Optimal predictive model selection

Often the goal of model selection is to choose a model for future prediction, and it is natural to measure the accuracy of a future prediction by squared error loss. Under the Bayesian approach, it is commonly perceived that the optimal predictive model is the model with highest posterior probability, but this is not necessarily the case. In this paper we show that, for selection among normal linear models, the optimal predictive model is often the median probability model, which is defined as the model consisting of those variables which have overall posterior probability greater than or equal to 1/2 of being in a model. The median probability model often differs from the highest probability model

A predictive model of Dirac Neutrinos

Assuming lepton number conservation, hermiticity of the neutrino mass matrix and $\nu_{\mu} - \nu_{\tau}$ exchange symmetry, we show that we can determine the neutrino mass matrix completely from the existing data. Comparing with the existing data, our model predicts an inverted mass hierarchy (close to a degenerate pattern) with the three neutrino mass values, $8.91 \times 10^{-2}$ eV, $8.95 \times 10^{-2}$ eV, $7.50 \times 10^ {-2}$ eV, a large value for the CP violating phase, $\delta = 110^0$, and of course, the absence of neutrinoless $\beta \beta$ decay. All of these predictions can be tested in the forthcoming or future precision neutrino experiments.Comment: 10 page

Valve recession: From experiment to predictive model

Increasing demands on engine performance and cost reductions have meant that advances made in materials and production technology are often outpaced This frequently results in wear problems occurring with engine components. Few models exist for predicting wear, and consequently each wear problem has to be investigated, the cause isolated and remedial action taken. The objective of this work was to carry out experimental studies to investigate valve and seat insert wear mechanisms and use the test results to develop a recession prediction tool to assess the potential for valve recession and solve problems that occur more quickly. Experimental apparatus has been developed that is capable of providing a valid simulation of the wear of diesel automotive inlet valves and seats. Test methodologies developed have isolated the effects of impact and sliding. A semi-empirical wear model for predicting valve recession has been developed based on data gathered during the bench testing. A software program, RECESS, was developed to run the model. Model predictions are compared with engine dynamometer tests and bench tests. The model can be used to give a quantitative prediction of the valve recession to be expected with a particular material pair or a qualitative assessment of how parameters need to be altered in order to reduce recession. The valve recession model can be integrated into an industrial environment in order to help reduce costs and timescales involved in solving valve/seat wear problems

Projection predictive model selection for Gaussian processes

We propose a new method for simplification of Gaussian process (GP) models by projecting the information contained in the full encompassing model and selecting a reduced number of variables based on their predictive relevance. Our results on synthetic and real world datasets show that the proposed method improves the assessment of variable relevance compared to the automatic relevance determination (ARD) via the length-scale parameters. We expect the method to be useful for improving explainability of the models, reducing the future measurement costs and reducing the computation time for making new predictions.Comment: A few minor changes in tex

Active Learning of Gaussian Processes for Spatial Functions in Mobile Sensor Networks

This paper proposes a spatial function modeling approach using mobile sensor networks, which potentially can be used for environmental surveillance applications. The mobile sensor nodes are able to sample the point observations of an 2D spatial function. On the one hand, they will use the observations to generate a predictive model of the spatial function. On the other hand, they will make collective motion decisions to move into the regions where high uncertainties of the predictive model exist. In the end, an accurate predictive model is obtained in the sensor network and all the mobile sensor nodes are distributed in the environment with an optimized pattern. Gaussian process regression is selected as the modeling technique in the proposed approach. The hyperparameters of Gaussian process model are learned online to improve the accuracy of the predictive model. The collective motion control of mobile sensor nodes is based on a locational optimization algorithm, which utilizes an information entropy of the predicted Gaussian process to explore the environment and reduce the uncertainty of predictive model. Simulation results are provided to show the performance of the proposed approach. © 2011 IFAC