Malaysia is one of the largest producers of natural rubber in the world. Among the various types of natural rubber which contribute to the country’s agricultural sector is the Standard Malaysian Rubber Grade 20 (S.M.R 20). Since 2008, the rubber price has received attention of investors and Malaysia Rubber Board due to price fluctuation. The price of rubber is characterized by the existence of heavy tails and volatility clustering. These properties play a significant impact on parameter estimation and forecasting performance resulting from S.M.R 20 rubber price data. The approach used in modeling S.M.R 20 rubber price data, is Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. The aims of this paper are to find the best ARMA-GARCH model by using different specifications structures and to forecast the daily price for 20 days ahead. There are 20 models produced from different specifications in ARMA(R,M) dan GARCH(p,q) models. In this study, 1953 daily price data of S.M.R 20 are taken into consideration. The validity comparison of diagnostic checking and forecasting performance are based on AIC, AICC, SBC, HQC, MSE, RMSE and MAPE. The results reveals that ARMA(1,0)-GARCH(1,2) model is the best volatility modeling in S.M.R 20 rubber price. Based on the implications of the results, the scope of the future research directions has been widen.

This paper describes the development of linear autoregressive moving average with exogenous input (ARMAX) models to monitor the progression of dengue infection based on hemoglobin status. Three differents ARMAX model order selection criteria namely Final Prediction Error (FPE), Akaike's Information Criteria (AIC) and Lipschitz number have been evaluated and analyzed. The results showed that Lipschitz number has better accuracy compared to FPE and AIC. Finally based on Lipschitz number, appropriate model orders have been selected to monitor the progression of dengue patients based on hemoglobin status. Further work is to apply this appropriate model orders to nonlinear Autoregressive (NARMAX) model.

An application of charge-coupled device (CCD) linear sensor and laser diode in an optical tomography (OPT) system is presented. The measurements are based on the final light intensity received by the sensor. The aim is to analyse and demonstrate the capability of laser with a CCD in an OPT system for detecting transparent objects in crystal clear water. The image reconstruction algorithms used were filtered images of linear back projection algorithms. These algorithms were programmed using LabVIEW programming software. Experiments in detecting transparent hollow straw were conducted. Based on the results, statistical analysis was performed to verify that the captured data were valid compared with the actual object data. The object's characteristics such as diameter also are observed. In conclusion, a non-intrusive and non-invasive OPT system that can detect transparent objects in crystal clear water is successfully developed. Introduction: The gas percentage in the liquid medium, gas flow rate, appearance and disappearance of gases, shape of gases and their diameters are imperative information for monitoring and process control. Petroleum refining systems, textile and fabric industries, oil and gas pipeline systems, geothermal wells, steam generation in boilers and burners and steam condensation all deal with two-phase flow which is in the form of gas bubbles and liquid [1]. Engineers need to monitor the condensation process or the distribution of steam bubbling to avoid any damage occurring in the high cost and high maintenance of their system. The optical tomography (OPT) system is considered as a hard-field sensor because the sensing field is based on the measurement of the attenuation or absorption of radiation [2]. For soft-field sensor, it depends on the changes of conductivity or permittivity of the objects that are being studied. OPT system provides many advantages compared with other soft-field sensors. OPT system gives a good spatial resolution of image reconstruction [3] and this sensor is appropriate for real-time monitoring system because it provides a high-speed data capture.

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The aim of bootstrapping is to approximate the sampling distribution of some estimator. An algorithm for combining method is given in SAS, along with applications and visualizations.