A multivariate morphometric investigation to delineate stock structure of gangetic whiting, Sillaginopsis panijus (Teleostei: Sillaginidae)

This study was conducted to delineate the stock structure of Sillaginopsis paniijus based on morphometric characters of the species. A total of 194 specimens were collected from the Meghna, Tentulia and Baleswar rivers located in the southern coastal zone of Bangladesh. Data were subjected to univariate ANOVA, multivariate ANOVA, discriminate function analysis (DFA), and principal component analysis. Mean variations of ten morphometric characters; HD, HBD, LBD, PsOL, ED, SnL, SPrDL, HAF, LSDB and LPB showed significant differences (p < 0.05) among 27 morphometric traits that were selected for the study. In DFA, the overall assignments of individuals into their correctly classified original groups were 71.1 and 70.6 % for male and female, respectively. A scatter plot of the first two discriminant functions was used to visually depict the discrimination among the populations. The results showed different stocks of S. panijus in the rivers of Baleswar, Tentulia and Meghna in southwest coast of Bangladesh

Predicting DHF Incidence in Northern Thailand using Time Series Analysis Technique

Abstract—This study aimed at developing a forecasting model on the number of Dengue Haemorrhagic Fever (DHF) incidence in Northern Thailand using time series analysis. We developed Seasonal Autoregressive Integrated Moving Average (SARIMA) models on the data collected between 2003-2006 and then validated the models using the data collected between January-September 2007. The results showed that the regressive forecast curves were consistent with the pattern of actual values. The most suitable model was the SARIMA(2,0,1)(0,2,0)12 model with a Akaike Information Criterion (AIC) of 12.2931 and a Mean Absolute Percent Error (MAPE) of 8.91713. The SARIMA(2,0,1)(0,2,0) 12 model fitting was adequate for the data with the Portmanteau statistic Q20 = 8.98644 ( = 27.5871, P>0.05). This indicated that there was no significant autocorrelation between residuals at different lag times in the SARIMA(2,0,1)(0,2,0)12 model