Tracking and predicting growth areas in science

growth of science in iran

Predicting encounter and colocation events in metropolitan areas

Despite an extensive literature has been devoted to mine and model mobility features, forecasting where, when and whom people will encounter/colocate still deserve further research efforts. Forecasting people’s encounter and colocation features is the key point for the success of many applications ranging from epidemiology to the design of new networking paradigms and services such as delay tol...

Qualitatively predicting acetylation and methylation areas in DNA sequences.

Eukaryotic genomes are packaged by the wrapping of DNA around histone octamers to form nucleosomes. Nucleosome occupancy, acetylation, and methylation, which have a major impact on all nuclear processes involving DNA, have been recently mapped across the yeast genome using chromatin immunoprecipitation and DNA microarrays. However, this experimental protocol is laborious and expensive. Moreover...

Tracking the Best Predicting Model

According to standard MDL and Bayesian model selection, we should (roughly) prefer the model that minimises overall prediction error. But if the goal is to predict well, it may well depend on the sample size which model is most useful to predict the next outcome. By re-interpreting the Bayesian prediction strategies associated with the models as “experts”, we can use the various algorithms for ...

Macroscopic Model for Predicting Columnar to Equiaxed Transitions using Columnar Front Tracking and Average Equiaxed Growth

A macroscopic model of Columnar-to-Equiaxed Transition (CET) formation is presented. The growth of a columnar zone and an equiaxed zone are treated separately and modeled on a fixed grid. The model uses a columnar Front Tracking (FT) formulation to compute the motion of the columnar front and the solidification of the dendritic columnar mushy zone. The model for the equiaxed zone calculates the...