Testing and Diagnosis of Realistic Defects in Digital Microfluidic Biochips

Microfluidics-based biochips are soon expected to revolutionize biosensing, clinical diagnostics and drug discovery. Robust off-line and on-line test techniques are required to ensure system dependability as these biochips are deployed for safety-critical applications. Due to the underlying mixed-technology and mixed-energy domains, biochips exhibit unique failure mechanisms and defects. We first relate some realistic defects to fault models and observable errors. We next set up an experiment to evaluate the manifestations of electrode-short faults. Motivated by the experimental results, we present a testing and diagnosis methodology to detect catastrophic faults and locate faulty regions. The proposed method is evaluated using a biochip performing real-life multiplexed bioassays.     

Support vector machines for spatiotemporal tornado prediction

The use of support vector machines (SVMs) for predicting the location and time of tornadoes is presented. In this paper, we extend the work by Lakshmanan et al. (Proceedings of 2005 IEEE international joint conference on neural networks (Montreal, Canada), 3, 2005a, 1642–1647) to use a set of 33 storm days and introduce some variations that improve the results. The goal is to estimate the probability of a tornado event at a particular spatial location within a given time window. We utilize a least-squares methodology to estimate shear, quality control of radar reflectivity, morphological image processing to estimate gradients, fuzzy logic to generate compact measures of tornado possibility and SVM classification to generate the final spatiotemporal probability field. On the independent test set, this method achieves a Heidke's skill score of 0.60 and a critical success index of 0.45.     

Stochastic contraction-based observer and controller design algorithm with application to a flight vehicle

This paper focuses on a stochastic version of contraction theory to construct observer-controller structure for a flight dynamic system with noisy velocity measurement. A nonlinear stochastic observer is designed to estimate the pitch rate, the pitch angle, and the velocity of an aircraft example model using stochastic contraction theory. Estimated states are used to compute feedback control for solving a tracking problem. The structure and gain selection of the observer is carried out using Itô's stochastic differential equations and the contraction theory. The contraction property of integrated observer-controller structure is derived to ensure the exponential convergence of the trajectories of closed-loop nonlinear system. The upper bound of the state estimation error is explicitly derived and the efficacy of the proposed observer-controller structure has been shown through the numerical simulations.     

Modeling of vial and ball motions for an effective mechanical milling process

Mechanical milling (MM) is referred to a solid state size reduction process where work materials in the form of coarse particulates are broken into the ultimate fineness by means of mechanical impact created by collisions of the work materials and the milling media which are placed inside a reciprocating vial. Many milling techniques have been so far developed to improve the process. However, the efficiency of MM process is still below satisfactory in terms of energy balance, where the energy consumed by the process of reduction is still very low compared to the energy supplied to perform the milling process itself. This contributes to high energy losses and proportionally to the span of processing time. Other major problems inherent in the process are contamination by the balls and the vial materials into the work materials, and process temperature that could influence the properties of milled materials. Since MM process utilizes the energy generated by impact upon the collisions of the balls against the work materials, it is important to understand the motions of the balls, the work materials, and the vial, which are the sources of the generation of impact energy. To obtain an optimized processing condition, the motions of vial and ball in relationship with the work materials should be designed in such a way to ensure the optimum impact energy is consumed by the work materials for the size reduction purposes. This paper presents a physical model for work materials, balls, and vial collisions based on different ways of motions. Using this model, higher impact could be achieved. These would lead to the reduction of milling time, contamination, as well as milling temperature.     

Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: Theory

The generation of multi-decade long Earth System Data Records (ESDRs) of Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) from remote sensing measurements of multiple sensors is key to monitoring long-term changes in vegetation due to natural and anthropogenic influences. Challenges in developing such ESDRs include problems in remote sensing science (modeling of variability in global vegetation, scaling, atmospheric correction) and sensor hardware (differences in spatial resolution, spectral bands, calibration, and information content). In this paper, we develop a physically based approach for deriving LAI and FPAR products from the Advanced Very High Resolution Radiometer (AVHRR) data that are of comparable quality to the Moderate resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products, thus realizing the objective of producing a long (multi-decadal) time series of these products. The approach is based on the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). The methodology permits decoupling of the structural and radiometric components and obeys the energy conservation law. The approach is applicable to any optical sensor, however, it requires selection of sensor-specific values of configurable parameters, namely, the single scattering albedo and data uncertainty. According to the theory of spectral invariants, the single scattering albedo is a function of the spatial scale, and thus, accounts for the variation in BRF with sensor spatial resolution. Likewise, the single scattering albedo accounts for the variation in spectral BRF with sensor bandwidths. The second adjustable parameter is data uncertainty, which accounts for varying information content of the remote sensing measurements, i.e., Normalized Difference Vegetation Index (NDVI, low information content), vs. spectral BRF (higher information content). Implementation of this approach indicates good consistency in LAI values retrieved from NDVI (AVHRR-mode) and spectral BRF (MODIS-mode). Specific details of the implementation and evaluation of the derived products are detailed in the second part of this two-paper series.     

Monte Carlo simulation of diffusive-to-ballistic transition in phonon transport

Heat conduction properties in Si nanostructures are analyzed using a Monte Carlo method developed for solving the phonon Boltzmann transport equation. The thermal resistances are evaluated for the systems with various sizes, and the transition from the diffusive to the ballistic heat conduction are investigated. We compare the two different phonon dispersion models (the realistic dispersion relation based on the adiabatic bond charge model and the analytically approximated model), and it is shown that the correct implementation of the phonon dispersion relation is essential to accurately simulate the quasi-ballistic heat conduction properties, which becomes obvious in the structures smaller than the phonon mean free path.     

Comparative studies on the nutritive and anti-nutritive properties of fruits in selected Berberis species of West Himalaya, India

Nutritional and anti-nutritional factors of five Berberis species, widely known for their wild edible fruits and medicinal properties, were investigated in the Indian west Himalaya. These fruits contained high content of fiber (pulp 7.0-8.1%; seeds 4.4-5.3%), protein (pulp 4.7-7.2%; seeds 5.9-8.5%) and fat (pulp 2.6-4.0%; seeds 4.6-5.3%) as compared to most of the known wild edibles in the region. They, however, contained reasonably lower food energy, largely due to low carbohydrate content. All the five species emerged as good source of minerals, especially Ca and K. The values of various nutrient and mineral elements varied significantly among species, which implies potential of different species can be harnessed for diverse attributes. The fruits, however, possessed anti-nutritional factors like tannins and phytic acid, which need to be tackled appropriately while considering these fruits for value addition as health food.     

Stochastic multiscale fracture analysis of three-dimensional functionally graded composites

A new moment-modified polynomial dimensional decomposition (PDD) method is presented for stochastic multiscale fracture analysis of three-dimensional, particle-matrix, functionally graded materials (FGMs) subject to arbitrary boundary conditions. The method involves Fourier-polynomial expansions of component functions by orthonormal polynomial bases, an additive control variate in conjunction with Monte Carlo simulation for calculating the expansion coefficients, and a moment-modified random output to account for the effects of particle locations and geometry. A numerical verification conducted on a two-dimensional FGM reveals that the new method, notably the univariate PDD method, produces the same crude Monte Carlo results with a five-fold reduction in the computational effort. The numerical results from a three-dimensional, edge-cracked, FGM specimen under a mixed-mode deformation demonstrate that the statistical moments or probability distributions of crack-driving forces and the conditional probability of fracture initiation can be efficiently generated by the univariate PDD method. There exist significant variations in the probabilistic characteristics of the stress-intensity factors and fracture-initiation probability along the crack front. Furthermore, the results are insensitive to the subdomain size from concurrent multiscale analysis, which, if selected judiciously, leads to computationally efficient estimates of the probabilistic solutions.     

A fluctuation-based characterization of athermal phase transitions: Application to shape memory alloys

We employ a fluctuation-based technique to investigate the athermal component associated with martensite phase transition, which is a prototype of temperature-driven structural transformation. Statistically, when the phase transition is purely athermal, we find that the temporal sequence of avalanches under constant drive is insensitive to the drive rate. We have used fluctuations in electrical resistivity or “noise” in nickel titanium shape memory alloys in three different forms: a thin film exhibiting well-defined transition temperatures, a highly disordered film, and a bulk wire of rectangular cross-section. Noise is studied in the realm of dynamic transition, viz., while the temperature is being ramped, which probes into the kinetics of the transformation at real time scales, and could probably stand out as a promising tool for material testing in various other systems, including nanoscale devices.