Mahendran Shitan

Discrimination of Three Pegaga (Centella) Varieties and Determination of Growth-Lighting Effects on Metabolites Content Based on the Chemometry of 1H Nuclear Magnetic Resonance Spectroscopy

The metabolites of three species of Apiaceae, also known as Pegaga, were analyzed utilizing (1)H NMR spectroscopy and multivariate data analysis. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) resolved the species, Centella asiatica, Hydrocotyle bonariensis, and Hydrocotyle sibthorpioides, into three clusters. The saponins, asiaticoside and madecassoside, along with chlorogenic acids were the metabolites that contributed most to the separation. Furthermore, the effects of growth-lighting condition to metabolite contents were also investigated. The extracts of C. asiatica grown in full-day light exposure exhibited a stronger radical scavenging activity and contained more triterpenes (asiaticoside and madecassoside), flavonoids, and chlorogenic acids as compared to plants grown in 50% shade. This study established the potential of using a combination of (1)H NMR spectroscopy and multivariate data analyses in differentiating three closely related species and the effects of growth lighting, based on their metabolite contents and identification of the markers contributing to their differences.

Factors affecting the HIV/AIDS epidemic: An ecological analysis of global data

BACKGROUND All over the world the prevalence of Human Immunodeficiency Virus (HIV)/Acquired Immune Deficiency Syndrome (AIDS) has became a stumbling stone in progress of human civilization and is a huge concern for people worldwide. OBJECTIVES To determine the social and health factors which contribute to increase the size of HIV epidemic globally. METHODS The country level indicators of HIV prevalence rates, are contraceptive prevalence rate, physicians density, proportion of Muslim populations, adolescent fertility rate, and mean year of schooling were compiled of 187 countries from the United Nations (UN) agencies. To extract the major factors from those indicators of the later five categories, backward multiple regression analysis was used as the statistical tool. RESULTS The national HIV prevalence rate was significantly correlated with almost all the predictors. Backward multiple linear regression analysis identified the proportion of Muslims, physicians density, and adolescent fertility rate are as the three most prominent factors linked with the national HIV epidemic. CONCLUSION The findings support the hypotheses that a higher adolescent fertility rate in the population is the adverse effect of premarital and extramarital sex that leads to longer period of sexual activity which increases the risk of HIV infection. On the hand, and cultural restrictions of Muslims and sufficient physicians will decelerate the spread of HIV infections in the society.

Generalized Autoregressive (GAR) Model: A Comparison of Maximum Likelihood and Whittle Estimation Procedures Using a Simulation Study

This article evaluates the performance of two estimators namely, the Maximum Likelihood Estimator (MLE) and Whittles Estimator (WE), through a simulation study for the Generalised Autoregressive (GAR) model. As expected, it is found that for the parameters α and σ2, the MLE and WE have a better performance than Method of Moments (MOM) estimator. For the parameter δ, MOM sometimes appears to have a slightly better performance than MLE and WE, possibly due to truncation approximations associated with the hypergeometric functions for calculating the autocorrelation function. However, the MLE and WE can be used in practice without loss of efficiency.

Approximate Asymptotic Variance-Covariance Matrix for the Whittle Estimators of GAR(1) Parameters

Generalized Autoregressive (GAR) processes have been considered to model some features in time series. The Whittles estimates have been investigated for the GAR(1) process by a simulation study by Shitan and Peiris (2008). This article derives approximate theoretical expressions for the enteries of the asymptotic variance-covariance matrix for those estimates of GAR(1) parameters. These results are supported by a simulation study.

Time Series Properties of the Class of Generalized First-Order Autoregressive Processes with Moving Average Errors

A new class of time series models known as Generalized Autoregressive of order one with first-order moving average errors has been introduced in order to reveal some hidden features of certain time series data. The variance and autocovariance of the process is derived in order to study the behaviour of the process. It is shown that in special cases these new results reduce to the standard ARMA results. Estimation of parameters based on the Whittle procedure is discussed. We illustrate the use of this class of model by using two examples.

Moving holidays' effects on the Malaysian peak daily load

Malaysias yearly steady growth in electricity consumption as a result of fast development in various sectors of the Malaysian economy have increased the need to have a more robust, reliable and accurate load forecasting for short —, medium-, or long-term. A reliable method for short term load forecasting is crucial to any decision maker in a power utility company. Many studies have been made to improve the forecasting accuracy using various methods. The forecasting errors for the holiday seasons are known to be higher than those for weekends. This paper aims to determine which model would be a better model to estimate the holiday effects and therefore give a better forecasting accuracy for the peak daily load in Malaysia. Some of the holiday effects in Malaysia are from Eid ul-Fitr, Christmas, Independence Day and Chinese New Year. The seasonal ARIMA (SARIMA) and Dynamic Regression (DR) or Transfer function modelling are considered. Furthermore, the final selection of the models depends on the Mean Absolute Percentage Error (MAPE) and others such as the sample autocorrelation function (ACF), the sample partial autocorrelation function (PACF) and a bias-corrected version of the Akaikes information criterion (AICC) statistic. The Dynamic Regression (DR) model recorded 2.22% as the lowest MAPE value for the 2004 New Years Eve and 2.39% for the seven days ahead forecasting. And therefore, DR model is the most appropriate model to be considered for forecasting any public holidays in Malaysia.

Fractionally Integrated Separable Spatial Autoregressive (FISSAR) Model and Some of Its Properties

Spatial modelling has its applications in many fields. In time-series there exist a class of models known as long memory models where the autocorrelation function decays rather slowly. These types of time-series data are modelled as fractionally integrated ARMA processes. Spatial data may also exhibit a long memory structure and in order to model such a structure we introduce a new class of models called the fractionally integrated separable spatial autoregressive (FISSAR) model and discuss some of its properties. One way of estimating the parameters of the FISSAR model is also discussed in this article.

Temporal Water Quality Assessment of Langat River from 1995-2006

Water quality is generally described according to biological, chemical and physical properties (Coke et al 2005). Based on these properties, the quality of water can be expressed via a numerical index (i.e. Water Quality Index, WQI) by combining measurements of selected water quality variables. The index is important in evaluating the water quality of different sources and in observing the changes in the water quality as a function of time and other influencing factors (Sarkar and Abbasi 2006). The time when samples are taken is one of the contributing factors that can influence the concentration of a particular water quality variable (Coke et al 2005). Thus, temporal assessment is a good indication in determining the presence or absence of trend and seasonality to which water quality is responding to changes in the catchment and time.

A First-Order Spatial Integer-Valued Autoregressive SINAR(1, 1) Model

Binomial thinning operator has a major role in modeling one-dimensional integer-valued autoregressive time series models. The purpose of this article is to extend the use of such operator to define a new stationary first-order spatial non negative, integer-valued autoregressive SINAR(1, 1) model. We study some properties of this model like the mean, variance and autocorrelation function. Yule-Walker estimator of the model parameters is also obtained. Some numerical results of the model are presented and, moreover, this model is applied to a real data set.

Some Properties of the Generalized Autoregressive Moving Average (GARMA (1, 1; δ1, δ2)) Model

A new class of time series models known as Generalized Autoregressive Moving Average (1, 1; δ1, δ2) has been introduced in order to reveal some features of certain time series data. The authors derive the variance and autocovariance of the process in order to study the behavior of the process. It is shown that these new results reduce to the standard ARMA results. Some numerical results are also provided. Due to the generality of this model, it will be useful for modeling purposes.