Length-weight relationship and relative condition factor in Macrobrachium (Hellex)

The length-weight relationship was calculated for the freshwater prawn Macrobrachium idae. About 150 specimens of M. idae (males 50, females 50 and 50 juveniles) were utilised for this study. The length-weight relationship was assessed separately for males, females and indeterminants. The regression equation for males, females and indeterminants showed significant differences whereas it was insignificant for males and females. The variations in length-weight relation between sexes and indeterminants were compared and discussed. The relationship between total length with carapace length and total length with rostral length were also determined.

Length-weight relationship and condition factor of black carp (Mylopharyngodon spiceus Richardson) in Bangladesh

The length-weight relationship and condition factor of Mylopharyngodon spiceus were determined. The result of the study showed the dependence of weight (W) on the total length (L) in the following form: W= 0.006L(super 3.156) or in the logarithmic form Log W=- 2.1851 + 3.156 Log L. Standard errors of length and weight were 0.674 cm and 3.214 g respectively. The co-efficient correlation "r" was found to be 0.972 which indicated that the relationship between length and body weight of the fish was highly significant. The t-test also indicated that the correlation between length and weight was significant. The range and mean value of condition factor (K) were 0.865 to 1.041 and 0.958 respectively.

Nonparametric Bayesian sparse factor models with application to gene expression modeling

A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data $\mathbf{Y}$ is modeled as a linear superposition, $\mathbf{G}$, of a potentially infinite number of hidden factors, $\mathbf{X}$. The Indian Buffet Process (IBP) is used as a prior on $\mathbf{G}$ to incorporate sparsity and to allow the number of latent features to be inferred. The model's utility for modeling gene expression data is investigated using randomly generated data sets based on a known sparse connectivity matrix for E. Coli, and on three biological data sets of increasing complexity.

Robust control of plants with saturation nonlinearity based on coprime factor representations

We consider the robust control of plants with saturation nonlinearities from an input/output viewpoint. First, we present a parameterization for anti-windup control based on coprime factorizations of the controller. Second, we propose a synthesis method which exploits the freedom to choose a particular coprime factorization.