<p>The optimization of artificial neural networks (ANN) topology for predicting permeate flux of palm oil mill effluent (POME) in membrane bioreactor (MBR) filtration has been investigated using response surface methodology (RSM). A radial basis function neural network (RBFNN) model, trained by gradient descent with momentum (GDM) algorithms was developed to correlate output (permeate flux) to the four exogenous input variables (airflow rate, transmembrane pressure, permeate pump and aeration pump). A second-order polynomial model was developed from training results for natural log mean square error of 50 developed ANNs to generate 3D response surfaces. The optimum ANN topology had minimum ln MSE when the number of hidden neurons, spread, momentum coefficient, learning rate and number of epochs were 16, 1.4, 0.28, 0.3 and 1852, respectively. The MSE and regression coeffcient of the ANN model were determined as 0.0022 and 0.9906 for training, 0.0052 and 0.9839 for testing and 0.0217 and 0.9707 for validation data sets. These results confirmed that combining RSM and ANN was precise for predicting permeates flux of POME on MBR system. This development may have significant potential to improve model accuracy and reduce computational time.</p>

Purpose of the study: The purpose of this study is to investigate the relationship between teachers' knowledge and perceived skills in implementing inquiry-based science teaching at the secondary level. Methodology: This quantitative research involved 63 science teachers from nine secondary schools in Putrajaya. Respondents were selected using a random sampling technique. The data were analyzed using descriptive and inferential statistics methods. Main Findings: The analysis showed that the level of teachers' knowledge of inquiry-based science teaching and the level of teachers' perceived skills in implementing inquiry-based science teaching which was divided into four phases (conceptualization, investigation, conclusion, and discussion) were high. The Pearson correlation test found that there was a strong and significant relationship between teachers' knowledge in inquiry-based science teaching and teachers' skills in four phases of inquiry. Applications of this study: This study implies that teachers' knowledge and skills are essential aspects to be emphasized in implementing inquiry-based science teaching and teachers should be trained in both of these aspects as they are interrelated to each other. Novelty/Originality of this study: This study explores deeper on teachers' skills in implementing inquiry approach by dividing it into four phases to determine which phases of the inquiry that educators and scholars need to emphasize and give the training to improve teachers' implementation skills and determine whether the phases are interrelated to knowledge.

Nowadays, there are many breeding program to improve the quality of rice since the direct measurement (iodine colorimetric) is time consuming, complex and environmentally unfriendly. The objective of this study was to analyze the amylose content (AC) in several types of local rice and import rice in Malaysia. Next is to investigate suitable rice intake for diabetic patient. In this study, non-destructive method by using Near-Infrared Spectroscopy (NIRS) was used to measure the amylose content of single rice grain for milled rice and brown rice. The result showed that the AC for the brown rice was higher than basmati rice followed by local white rice. Therefore, the high amylose content is most suitable for the diabetic patient. Thus, NIRS was a convenient way to screen the quality of rice as well as increase the global competitive for farmers in agriculture field.

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This paper proposes an improved optimisation of sequencing batch reactors (SBR) for aerobic granular sludge (AGS) at high temperature-low humidity for domestic wastewater treatment using response surface methodology (RSM). The main advantages of RSM are less number of experiment required and suitable for complex process. The sludge from a conventional activated sludge wastewater treatment plant and three sequencing batch reactors (SBRs) were fed with synthetic wastewater. The experiment were carried out at different high temperatures (30, 40 and 50°C) and the formation of AGS for simultaneous organics and nutrients removal were examined in 60 days. RSM is used to model and to optimize the biological parameters for chemical oxygen demand (COD) and total phosphorus removal in SBR system. The simulation results showed that at temperature of 45.33°C give the optimum condition for the total removal of COD and phosphorus, which correspond to performance index R 2 of 0.955 and 0.91, respectively.