Higher heating value estimation of wastes and fuels from ultimate and proximate analysis by using artificial neural networks.

Higher heating value (HHV) is one of the most important parameters in determining the quality of the fuels. In this study, comparatively large datasets of ultimate and proximate analysis are constructed to be used in HHV estimation of several classes of fuels, including char & fossil fuels, agricultural wastes, manure (chicken, cow, horse, sheep, llama, and pig), sludge (like paper, paper-mil, sewage, and pulp), micro/macro-algae's, wastes (RDF and MSW), treated woods, untreated woods, and others (non-fossil pyrolysis oils) between the HHV range of 4.22-55.

Development and characterization of edible films based on modified corn starch and grape juice.

Chemically modified corn starch with sodium trimetaphosphate (STMP) or citric acid (CA) and grape juice was used to produce edible films. Modification reactions were discussed by results of FT-IR scan, water solubility, swelling power, viscosity and degree of cross-linking properties. Mechanical, barrier, physical (solubility, color, transparency, microstructure) and glass transition temperature properties of films were studied to understand the effects of grape juice and modified starch usage in films.

The role of flexibility and optimality in the prediction of intracellular fluxes of microbial central carbon metabolism.

Prediction of intracellular metabolic fluxes based on optimal biomass assumption is a well-known computational approach. While there has been a significant emphasis on the optimality, cellular flexibility, the co-occurrence of suboptimal flux distributions in a microbial population, has hardly been considered in the related computational methods. We have implemented a flexibility-incorporated optimization framework to calculate intracellular fluxes based on a few extracellular measurement constraints.

Sparsity as cellular objective to infer directed metabolic networks from steady-state metabolome data: a theoretical analysis.

Since metabolome data are derived from the underlying metabolic network, reverse engineering of such data to recover the network topology is of wide interest. Lyapunov equation puts a constraint to the link between data and network by coupling the covariance of data with the strength of interactions (Jacobian matrix). This equation, when expressed as a linear set of equations at steady state, constitutes a basis to infer the network structure given the covariance matrix of data.