Simulated annealing based automatic fuzzy clustering combined with ANN classification for analyzing microarray data

Microarray technology has made it possible to monitor the expression levels of many genes simultaneously across a number of experimental conditions. Fuzzy clustering is an important tool for analyzing microarray gene expression data. In this article, a real-coded Simulated Annealing (VSA) based fuzzy clustering method with variable length configuration is developed and combined with popular Artificial Neural Network (ANN) based classifier. The idea is to refine the clustering produced by VSA using ANN classifier to obtain improved clustering performance. The proposed technique is used to cluster three publicly available real life microarray data sets. The superior performance of the proposed technique has been demonstrated by comparing with some widely used existing clustering algorithms. Also statistical significance test has been conducted to establish the statistical significance of the superior performance of the proposed clustering algorithm. Finally biological relevance of the clustering solutions are established.     

Fairness of Transitions in Diagnosability of Discrete Event Systems

Failure diagnosability has been widely studied for discrete event system (DES) models because of modeling simplicity and computational efficiency due to abstraction. In the literature it is often held that for diagnosability, such models can be used not only for systems that fall naturally in the class of DES but also for the ones traditionally treated as continuous variable dynamic systems. A class of algorithms for failure diagnosability of DES models has been successfully developed for systems where fairness is not a part of the model. These algorithms are based on detecting cycles in the normal and the failure model that look identical. However, there exist systems with all transitions fair where the diagnosability condition that hinges upon this feature renders many failures non-diagnosable although they may actually be diagnosable by transitions out of a cycle. Hence, the diagnosability conditions based on cycle detection need to be modified to hold for many real-world systems where all transitions are fair. In this work, however, it is shown by means of an example that a system may have some transitions fair and some unfair. A new failure diagnosability mechanism is proposed for DES models with both fair and unfair transitions. Time complexity for deciding diagnosability of DES models with fair and unfair transitions is analyzed and compared with the time complexities of other DES diagnosability analysis methods reported in the literature.     

Learning Sparse Feature Representations Using Probabilistic Quadtrees and Deep Belief Nets

Learning sparse feature representations is a useful instrument for solving an unsupervised learning problem. In this paper, we present three labeled handwritten digit datasets, collectively called n-MNIST by adding noise to the MNIST dataset, and three labeled datasets formed by adding noise to the offline Bangla numeral database. Then we propose a novel framework for the classification of handwritten digits that learns sparse representations using probabilistic quadtrees and Deep Belief Nets. On the MNIST, n-MNIST and noisy Bangla datasets, our framework shows promising results and outperforms traditional Deep Belief Networks.     

Lattice Boltzmann method simulation of electroosmotic stirring in a microscale cavity

The suitable surface modification of microfluidic channels can enable a neutral electrolyte solution to develop an electric double layer (EDL). The ions contained within the EDL can be moved by applying an external electric field, inducing electroosmotic flows (EOFs) that results in associated stirring. This provides a solution for the rapid mixing required for many microfluidic applications. We have investigated EOFs generated by applying a steady electric field across a square cavity that has homogenous electric potentials along its walls. The flowfield is simulated using the lattice Boltzmann method. The extent of mixing is characterized for different electrode configurations and electric field strengths. We find that rapid mixing can be achieved by using this simple configuration which increases with increasing electric field strength. The mixing time for water-soluble organic molecules can be decreased by four orders of magnitude by suitable choice of wall zeta potential and electric field. We dedicate this paper to the memory of our colleagues Professors Kevin Granata and Liviu Librescu who fell tragically on April 16, 2007 while answering their call to serve higher education. They continue to inspire us. AM gratefully acknowledges support from Jadavpur University under the World Bank funded Technical Education Quality Improvement Programme of the Government of India and the hospitality of the Virginia Tech ESM Department where he conducted a portion of this work.     

Boundedness analysis of finitely recursive processes. I. Concurrentprocesses

In the finitely recursive process (FRP) model of discrete event systems (DES), concepts about processes and process operators have been introduced. An infinite set of process expressions or functions can be built recursively through function composition using a few elementary operators. Given any process realization, it is important to find out whether the process is bounded, i.e., whether it has a finite state realization. In the FRP setting this translates to the problem of finding out whether the set of post-process expressions is finite or not. In Cieslak and Varaiya (1990) it has been shown that the boundedness problem is undecidable for general FRPs. This paper investigates the decidability of the problem for subclasses of FRP. In Inan and Varaiya (1988), it was conjectured that the set of functions that can be recursively generated using the parallel composition operator and different change operators (i.e. without using the sequential composition operator) will be finite and FRPs constructed over this set of functions will naturally be bounded. In the present work a counterexample has been provided to disprove the conjecture about the finiteness of the above set of functions. However, using a suitable post-process computation procedure, it has been shown here that the FRPs, built recursively over this set of functions, are bounded     

Effect of shielding gas mixture on gas metal arc welding of HSLA steel using solid and flux-cored wires

In this work, gas metal arc welding of high strength-low alloy (HSLA) steel with solid- and flux-cored arc welding wires using different shielding gas compositions was performed. The composition of filler wire and shielding gas in gas metal arc welds of HSLA steel determines the inclusion characteristics, microstructure and mechanical properties. Thus, acceptable weld metal properties in HSLA steel using gas metal arc welding (GMAW) process could be achieved with the proper combination of filler wire and shielding gas composition.     

Formation of corn fiber gum-milk protein conjugates and their molecular characterization

Corn fiber arabinoxylan is hemicellulose B isolated from the fibrous portions (pericarp, tip cap, and endosperm cell wall fractions) of corn kernels and is commonly referred to as corn fiber gum (CFG). Our previous studies showed that CFG isolated from corn bran (a byproduct of corn dry milling) contains very little protein and is an inferior emulsifier for oil-in-water emulsion systems as compared to corn fiber gum isolated from corn fiber derived from the corn wet-milling process. The protein deficient CFG isolated from corn bran was covalently conjugated with byproducts of cheese processing, β-lactoglobulin (β-LG) and whey protein isolate (WPI) using an economical food-grade Maillard-type heating reaction for the purpose of increasing their commercialization potential as a food emulsifier and soluble nutritional additive in beverages. The formation of the CFG-protein conjugates has been confirmed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). It has also been demonstrated that CFG-protein conjugates are capable of producing fine emulsions with better stability than either CFG or protein alone. The molecular characterization of CFG-protein conjugates was performed by high-performance size-exclusion chromatography (HPSEC) coupled to on-line multi-angle laser light scattering and viscometric detection. The analysis by HPSEC revealed that CFG-protein conjugates had higher weight-average molar mass (M_w, 340-359 kDa) and polydispersity (M_w/M_n, 1.74) than the corresponding unconjugated CFG (M_w, 290 kDa and M_w/M_n, 1.35). The z-average root-mean-square radius of gyration (R_gz) of CFG-protein conjugates was slightly higher (30.5-33.5 nm) in comparison to CFG (29.5 nm) but their weight-average-intrinsic viscosity (η) remained unchanged (about 1.32 dL g⁻¹). The Mark-Houwink exponent “a” of conjugates (0.40) was lower than the unconjugated CFG (0.53) indicating the formation of a more compact structure after conjugation with protein. These findings should benefit the beverage industry, which can use this information to produce a high quality emulsifier from the low-value byproducts of corn dry milling and cheese processing.     

Predictive Thermal Inactivation Model for Effects and Interactions of Temperature, NaCl, Sodium Pyrophosphate, and Sodium Lactate on Listeria monocytogenes in Ground Beef

The effects and interactions of heating temperature (60 °C to 73.9 °C), salt (0.0 % to 4.5 %?w/v), sodium pyrophosphate (0.0 % to 0.5 %?w/v), and sodium lactate (0.0 % to 4.5 %?w/v) on the heat resistance of a five-strain mixture of Listeria monocytogenes in 75 % lean ground beef were examined. Meat samples in sterile filtered stomacher bags were heated in a temperature controlled waterbath to determine thermal death times. The recovery medium was tryptic soy agar supplemented with 0.6 % yeast extract and 1 % sodium pyruvate. Weibull survival functions were employed to model the primary survival curves. Then, survival curve-specific estimated parameter values obtained from the Weibull model were used for determining a secondary model. The results indicate that temperature and salt have a large impact on the inactivation kinetics of L. monocytogenes, while sodium lactate (NaL) has an impact in the presence of salt. The model presented in this paper for predicting inactivation of L. monocytogenes can be used as an aid in designing lethality treatments meant to control the presence of this pathogen in ready-to-eat products.