Universiti Teknologi Malaysia

About: Universiti Teknologi Malaysia is a education organization based out in Johor Bahru, Malaysia. It is known for research contribution in the topics: Membrane & Control theory. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.

Silver Nanoparticles in the Water Environment in Malaysia: Inspection, characterization, removal, modeling, and future perspective.

Abstract: The current status of silver nanoparticles (AgNPs) in the water environment in Malaysia was examined and reported. For inspection, two rivers and two sewage treatment plants (STPs) were selected. Two activated carbons derived from oil palm (ACfOPS) and coconut (ACfCS) shells were proposed as the adsorbent to remove AgNPs. It was found that the concentrations of AgNPs in the rivers and STPs are in the ranges of 0.13 to 10.16 mg L−1 and 0.13 to 20.02 mg L−1, respectively, with the highest concentration measured in July. ACfOPS and ACfCS removed up to 99.6 and 99.9% of AgNPs, respectively, from the water. The interaction mechanism between AgNPs and the activated carbon surface employed in this work was mainly the electrostatic force interaction via binding Ag+ with O− presented in the activated carbon to form AgO. Fifteen kinetic models were compared statistically to describe the removal of AgNPs. It was found that the experimental adsorption data can be best described using the mixed 1,2-order model. Therefore, this model has the potential to be a candidate for a general model to describe AgNPs adsorption using numerous materials, its validation of which has been confirmed with other material data from previous works.

Forecasting Gold Prices Using Multiple Linear Regression Method

Abstract: Problem statement: Forecasting is a function in management to assist decision making. It is also described as the process of estimation in u nknown future situations. In a more general term it is commonly known as prediction which refers to estimation of time series or longitudinal type data. Gold is a precious yellow commodity once used as money. It was made illegal in USA 41 years ago, but is now once again accepted as a potential curre ncy. The demand for this commodity is on the rise. Approach: Objective of this study was to develop a forecasti ng model for predicting gold prices based on economic factors such as inflation, currency pri ce movements and others. Following the melt-down of US dollars, investors are putting their money in to gold because gold plays an important role as a stabilizing influence for investment portfolios. Du e to the increase in demand for gold in Malaysian and other parts of the world, it is necessary to de velop a model that reflects the structure and patte rn of gold market and forecast movement of gold price. The most appropriate approach to the understanding of gold prices is the Multiple Linear Regression (M LR) model. MLR is a study on the relationship between a single dependent variable and one or more independent variables, as this case with gold price as the single dependent variable. The fitted model of MLR will be used to predict the future gol d prices. A naive model known as "forecast-1" was considered to be a benchmark model in order to evaluate the performance of the model. Results: Many factors determine the price of gold and based on "a hunch of experts", several economic factors h ad been identified to have influence on the gold prices. Variables such as Commodity Research Bureau future index (CRB); USD/Euro Foreign Exchange Rate (EUROUSD); Inflation rate (INF); Money Supply (M1); New York Stock Exchange (NYSE); Standard and Poor 500 (SPX); Treasury Bill (T-BILL) and US Dollar index (USDX) were considered to have influence on the prices. Paramet er estimations for the MLR were carried out using Statistical Packages for Social Science package (SP SS) with Mean Square Error (MSE) as the fitness function to determine the forecast accuracy. Conclusion: Two models were considered. The first model considered all possible independent variables . The model appeared to be useful for predicting the price of gold with 85.2% of sample variations i n monthly gold prices explained by the model. The second model considered the following four independent variables the (CRB lagged one), (EUROUSD lagged one), (INF lagged two) and (M1 lagged two) to be significant. In terms of prediction, the second model achieved high level of predictive accu racy. The amount of variance explained was about 70% and the regression coefficients also provide a means of assessing the relative importance of individual variables in the overall prediction of g old price.