This study aims at developing a novel effluent removal management tool for septic sludge treatment plants (SSTP) using a clonal selection algorithm (CSA). The proposed CSA articulates the idea of utilizing an artificial immune system (AIS) to identify the behaviour of the SSTP, that is, using a sequence batch reactor (SBR) technology for treatment processes. The novelty of this study is the development of a predictive SSTP model for effluent discharge adopting the human immune system. Septic sludge from the individual septic tanks and package plants will be desuldged and treated in SSTP before discharging the wastewater into a waterway. The Borneo Island of Sarawak is selected as the case study. Currently, there are only two SSTPs in Sarawak, namely the Matang SSTP and the Sibu SSTP, and they are both using SBR technology. Monthly effluent discharges from 2007 to 2011 in the Matang SSTP are used in this study. Cross-validation is performed using data from the Sibu SSTP from April 2011 to July 2012. Both chemical oxygen demand (COD) and total suspended solids (TSS) in the effluent were analysed in this study. The model was validated and tested before forecasting the future effluent performance. The CSA-based SSTP model was simulated using MATLAB 7.10. The root mean square error (RMSE), mean absolute percentage error (MAPE), and correction coefficient (R) were used as performance indexes. In this study, it was found that the proposed prediction model was successful up to 84 months for the COD and 109 months for the TSS. In conclusion, the proposed CSA-based SSTP prediction model is indeed beneficial as an engineering tool to forecast the long-run performance of the SSTP and in turn, prevents infringement of future environmental balance in other towns in Sarawak.
Adult intussusception is rare. It represents only 5% of all intussusceptions and 1% of bowel obstruction. Clinical presentations are usually variable with a variety of acute, intermittent and chronic symptoms. It is associated with an underlying pathologic process in 90% of cases. A lack of Malaysia data prompted review of the Sarawak experience with this uncommon entity, focusing on the clinical features, diagnostic procedure and treatment. During the last 5 years, there were 14 cases of surgically proven adult intussusception. Mean age was 45.9 years. There were 9 enteric and 5 colonic intussusceptions. Ninety-three percent of the intussusceptions were associated with a pathologic lesion. Thirty-three percent of the enteric lesions were malignant and 67% were benign. Eighty percent of the colonic lesions were malignant and 20% were benign. Computed tomography scan has a good diagnostic accuracy of 83% and should be considered for all patients with nonspecific abdominal symptoms or suspected bowel obstruction. Treatment of choice for colonic intussusception in adults is en bloc resection without reduction whenever possible, whereas a more selective approach for enteric lesions.
This study applies the clonal selection algorithm (CSA) in an artificial immune system (AIS) as an alternative method to predicting future rainfall data. The stochastic and the artificial neural network techniques are commonly used in hydrology. However, in this study a novel technique for forecasting rainfall was established. Results from this study have proven that the theory of biological immune systems could be technically applied to time series data. Biological immune systems are nonlinear and chaotic in nature similar to the daily rainfall data. This study discovered that the proposed CSA was able to predict the daily rainfall data with an accuracy of 90% during the model training stage. In the testing stage, the results showed that an accuracy between the actual and the generated data was within the range of 75 to 92%. Thus, the CSA approach shows a new method in rainfall data prediction.