Cratojoppa maculata Cameron (Hymenoptera, Ichneumonidae, Ichneumoninae) new record to Malaysia

Cratojoppa maculata is reported as new record to the fauna of Ichneumoninae from Malaysia. Five specimens was collected from malaise trap during field work sampling in Pasoh Forest Reserved in Negeri Sembilan between May 2002 until March 2003. The species was first time described by Cameron in 1907 based on specimen from Sikkim, India. The discovery of this species contributed to the fauna of the genus Cratojoppa in Malaysia apart from two species namely Cratojoppa ornaticeps and Cratojoppa maculiceps. In oriental region it has previously been found in China, India and Myanmar only. Key to three species of genus Cratojoppa found in Malaysia is provided

A catalogue of ichneumonidae ( hymenoptera: inchneumonidae) from Malaysia

A catalogue of ichneumoninae (hymenoptera:inchneumoninae) from Malaysia which listing 34 species under 10 tribes (Alomyini(=phaeogini), compsophorini,goedartiini, heresiarchini,ichneumonini,ischnojoppini,joppocryptini,listrodromini oedicephalini platylabini and 27 genera are presentes. The tribe heresiarchini has the greatest number of species (12) folled by ichneumonini with 6 species. Imeria is the largest genus which contains 3 species recorded from Malaysia. Type specimens of all species recorded in Malaysia was deposited in various museums or depositories around the world

Improvement of artificial neural network model for the prediction of wastewater treatment plant performance

A statistical modeling tool called artificial neural network (ANN) is used in this work to predict the performance of wastewater treatment plant (WWTP). Extensive influent and effluent parameters database containing measured data spanning over two years of period was used to develop and train ANN using ANN toolbox in commercially available software, MATLAB. The data were obtained from one of Sewage Treatment Plant in Malaysia. The input parameters for the ANN were BOD, SS, and COD of the influent, while the output parameters were combination of the effluent characteristics. The networks for single input-single output were compared with those of single input-multiple output. The ANN was developed for raw and screened data and the results were compared for both networks. It was found that the use of data screening is essential to come up with a better ANNs model. From the regression analysis, networks with one hidden layer and 20 neurons were found to be the best one for single input-single output approach. While the best network for the multiple inputs-single output approach was with BOD as outputs and 30 neurons. The second approach which showed a lower RMSE and higher R values was selected