Reliable before-fabrication forecasting of MEMS piezoresistive pressure sensor: mathematical modelling and numerical simulation

Microsystem Technologies - Micro-mechanical systems (MEMS) based piezoresistive pressure sensors have significant importance in several pressure sensor devices in real world, i.e., aviation, IoT...     

Automatic Parallelization of Recursive Procedures

Parallelizing compilers have traditionally focussed mainly on parallelizing loops. This paper presents a new framework for automatically parallelizing recursive procedures that typically appear in divide-and-conquer algorithms. We present compile-time analysis, using powerful, symbolic array section analysis, to detect the independence of multiple recursive calls in a procedure. This allows exploitation of a scalable form of nested parallelism, where each parallel task can further spawn off parallel work in subsequent recursive calls. We describe a runtime system which efficiently supports this kind of nested parallelism without unnecessarily blocking tasks. We have implemented this framework in a parallelizing compiler, which is able to automatically parallelize programs like quicksort and mergesort, written in C. For cases where even the advanced compile-time analysis we describe is not able to prove the independence of procedure calls, we propose novel techniques for speculative runtime parallelization, which are more efficient and powerful in this context than analogous techniques proposed previously for speculatively parallelizing loops. Our experimental results on an IBM G30 SMP machine show good speedups obtained by following our approach.     

Dramatic reduction of dimensionality in large biochemical networks owing to strong pair correlations

Large multi-dimensionality of high-throughput datasets pertaining to cell signalling and gene regulation renders it difficult to extract mechanisms underlying the complex kinetics involving various biochemical compounds (e.g. proteins and lipids). Data-driven models often circumvent this difficulty by using pair correlations of the protein expression levels to produce a small number (fewer than 10) of principal components, each a linear combination of the concentrations, to successfully model how cells respond to different stimuli. However, it is not understood if this reduction is specific to a particular biological system or to nature of the stimuli used in these experiments. We study temporal changes in pair correlations, described by the covariance matrix, between concentrations of different molecular species that evolve following deterministic mass-action kinetics in large biologically relevant reaction networks and show that this dramatic reduction of dimensions (from hundreds to less than five) arises from the strong correlations between different species at any time and is insensitive to the form of the nonlinear interactions, network architecture, and to a wide range of values of rate constants and concentrations. We relate temporal changes in the eigenvalue spectrum of the covariance matrix to low-dimensional, local changes in directions of the system trajectory embedded in much larger dimensions using elementary differential geometry. We illustrate how to extract biologically relevant insights such as identifying significant timescales and groups of correlated chemical species from our analysis. Our work provides for the first time, to our knowledge, a theoretical underpinning for the successful experimental analysis and points to a way to extract mechanisms from large-scale high-throughput datasets.     

Fully developed hydrodynamic and thermal transport in combined pressure and electrokinetically driven flow in a microchannel with asymmetric boundary conditions

Thermally and hydrodynamically fully developed combined pressure-driven and electroosmotic flow through a channel has been simulated for isoflux wall boundary conditions. Effects of asymmetries in wall zeta potential and heat flux have been considered and closed form expressions have been obtained for transverse distribution of electric potential, velocity and temperature. The results indicate that both flow and heat transfer characteristics are significantly affected by the asymmetries in wall boundary conditions for both purely electroosmotic and combined pressure-driven and electroosmotic flow. These findings have important implications for flow and heat transfer control in microfluidics through alteration of surface conditions.