Ahmad Mahir Razali

Speciation of heavy metals in paddy soils from selected areas in Kedah and Penang, Malaysia

This study was carried out with the intention of evaluating heavy metal contamination in cultivated paddy areas. The speciation of heavy metals in paddy soils was determined in order to gain insight regarding their origin and distribution in soils. Five sampling sites were randomly selected from Kedah (Yan, Kota Setar, Kubang Pasu district) and Penang (Bumbung Lima district), where the soils constituted marine alluvial deposits. A site in Langkawi, where organic paddy farming is practised was used as the control. The sequential extraction method was adopted in order to obtain the four heavy metal fractions namely the easily leachable and ion exchange (ELFE), acid reducible (AR), oxidizable organic (OO) and resistant (RR) fractions. This study shows that the soil samples were clayey (82 to 96% of grain size Mn>Cr>Cd>Pb>Zn>Cu. Heavy metals such as Pb, Cu, Cr and Zn predominantly occurred in the insoluble form (RR fraction), with the oxides of Fe and Mn incorporated into the clay minerals. Although, the fertilizers and pesticides studied contained low amounts of heavy metals, the elevated amount of amount of Mn and Cd in the soils (ELFE fraction) could possibly be attributed to the longterm and repeated application of these materials to the cultivated paddy areas.

Mixture Weibull distributions for fitting failure times data

Two and three-parameter Weibull distribution is considered a flexible and useful distribution for adequately representing unimodal frequency distribution of failure times, but sometimes these distributions do not accurately represent the failure times data set. In such cases mixture of two or three Weibull distributions developed here provide very good fits for these mixture distributions. In this paper a mixture of two and three Weibull distributions were used to analyze the data of failure times. The suitability of the distributions is judged from the various tests-of-fit commonly used in the specialized literature on failure times data. The shapes of the density and hazard functions were used in addition to another procedure using goodness of fit tests based on the empirical distribution function are used to find the suitability fits of the data of failure times. These measurements are; coefficient of determination R^2, sum of squares due to error SSE, mean square error MSE and root mean square error RMSE. Maximum likelihood estimation MLE was used to estimate the parameters. We found that two- and three-component mixture Weibull distribution provides suitable fits for the failure time data studied based on the shapes of density and hazard functions. A high value of R^2 and low SSE, MSE and RMSE were obtained for five cases indicating suitable fit. It was concluded that the mixture Weibull distributions provide very flexible models for the proposed failure times data. It was also found that increasing the number of components resulting in increasing the number of parameters can have a negative effect on the values of R^2, SSE, MSE and RMSE.

Modeling air quality in main cities of Peninsular Malaysia by using a generalized Pareto model

The air pollution index (API) is an important figure used for measuring the quality of air in the environment. The API is determined based on the highest average value of individual indices for all the variables which include sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and suspended particulate matter (PM10) at a particular hour. API values that exceed the limit of 100 units indicate an unhealthy status for the exposed environment. This study investigates the risk of occurrences of API values greater than 100 units for eight urban areas in Peninsular Malaysia for the period of January 2004 to December 2014. An extreme value model, known as the generalized Pareto distribution (GPD), has been fitted to the API values found. Based on the fitted model, return period for describing the occurrences of API exceeding 100 in the different cities has been computed as the indicator of risk. The results obtained indicated that most of the urban areas considered have a very small risk of occurrence of the unhealthy events, except for Kuala Lumpur, Malacca, and Klang. However, among these three cities, it is found that Klang has the highest risk. Based on all the results obtained, the air quality standard in urban areas of Peninsular Malaysia falls within healthy limits to human beings.

Developing treatment plan support in outpatient health care delivery with decision trees technique

This paper presents treatment plan support (TPS) development with the aim to support treatment decision making for physicians during outpatientcare giving to patients. Evidence-based clinical data from system database was used. The TPS predictive modeling was generated using decision trees technique, which incorporated predictor variables: patients age, gender, racial, marital status, occupation, visit complaint, clinical diagnosis and final diagnosed diseases; while dependent variable: treatment by drug, laboratory, imaging and/or procedure. Six common diseases which are coded as J02.9, J03.9, J06.9, J30.4, M62.6 and N39.0 in the International Classification of Diseases 10th Revision (ICD-10) by World Health Organization were selected as prototypes for this study. The good performance scores from experimental results indicate that this study can be used as guidance in developing support specifically on treatment plan in outpatient health care delivery.

Wafer dice process improvement using Six Sigma approach

Quality is an important objective in production or operational function. In mass production, a high accuracy manufacture line, better quality and less variance are inevitable. Six Sigma is customer oriented, improving the whole process by using data and statistical method for enhancing quality management. The main barrier in achieving targeted quality is product variation which causes by differences in machines, suppliers, raw materials and production batch. If the product variation is uncontrolled, the product needs to be reworked. Six Sigma approach focuses on quality improvement by eliminating variation in manufacturing process. In this paper, the use of quality techniques in Six Sigma methods for process improvement by electronic component manufacturing company is discussed. Six Sigma method by DMAIC approach is proposed to reduce defect rate of wafer dice processing. The optimum parameters were established through the design of experiment to ensure the process was stable.

The probability distribution model of air pollution index and its dominants in Kuala Lumpur

This paper focuses on the statistical modeling for the distributions of air pollution index (API) and its sub-indexes data observed at Kuala Lumpur in Malaysia. Five pollutants or sub-indexes are measured including, carbon monoxide (CO); sulphur dioxide (SO2); nitrogen dioxide (NO2), and; particulate matter (PM10). Four probability distributions are considered, namely log-normal, exponential, Gamma and Weibull in search for the best fit distribution to the Malaysian air pollutants data. In order to determine the best distribution for describing the air pollutants data, five goodness-of-fit criteria’s are applied. This will help in minimizing the uncertainty in pollution resource estimates and improving the assessment phase of planning. The conflict in criterion results for selecting the best distribution was overcome by using the weight of ranks method. We found that the Gamma distribution is the best distribution for the majority of air pollutants data in Kuala Lumpur.

Fitting a circular distribution based on nonnegative trigonometric sums for wind direction in Malaysia

Wind direction has a substantial effect on the environment and human lives. As examples, the wind direction influences the dispersion of particulate matter in the air and affects the construction of engineering structures, such as towers, bridges, and tall buildings. Therefore, a statistical analysis of the wind direction provides important information about the wind regime at a particular location. In addition, knowledge of the wind direction and wind speed can be used to derive information about the energy potential. This study investigated the characteristics of the wind regime of Mersing, Malaysia. A circular distribution based on Nonnegative Trigonometric Sums (NNTS) was fitted to a histogram of the average hourly wind direction data. The Newton-like manifold algorithm was used to estimate the parameter of each component of the NNTS model. Next, the suitability of each NNTS model was judged based on a graphical representation and Akaike’s Information Criteria. The study found that the NNTS model with...

Development of car theft crime index in peninsular Malaysia

Vehicle theft is classified as property crime and is considered as the most frequently reported crime in Malaysia. The rising number of vehicle thefts requires proper control by relevant authorities, especially through planning and implementation of strategic and effective measures. Nevertheless, the effort to control this crime would be much easier if there is an indication or index which is more specific to vehicle theft. This study aims to build an index crime which is specific to vehicle theft. The development of vehicle theft index proposed in this study requires three main steps; the first involves identification of criteria related to vehicle theft, the second requires calculation of degrees of importance, or weighting criteria, which involves application of correlation and entropy methods, and the final involves building of vehicle theft index using method of linear combination, or weighted arithmetic average. The results show that the two methods used for determining weights of vehicle theft inde...

Feature Selection in Extrusion Beltline Moulding Process Using Particle Swarm Optimization

Optimization is necessary for the control of any process to achieve better product quality, high productivity with low cost. The beltline moulding process is difficult task due to its low defects, making the material sensitive to reject. The efficient beltline moulding process involves the optimal selection of operating parameters to maximize the number of production while maintaining the required quality limiting beltline surface damage. In this research, objective is to obtain optimum process parameters, which satisfies given limit, minimizes number of defects and maximizes the productivity at the same time. A recently developed optimization algorithm called particle swarm optimization is used to find optimum process parameters. Accordingly, the results indicate that a system where multilayer perceptron is used to model and predict process outputs and particle swarm optimization is used to obtain optimum process parameters can be successfully applied to beltline moulding process through Particle Swarm Optimization (PSO). Results obtained are superior in comparison with Genetic Algorithm (GA) approach.

Assessing distributions for monthly mean wind speed data

Analysis of the wind speed behavior will contribute the vital information for the wind energy potential and its development. Hence, this study focuses on fitting several distributions to determine the most appropriate probability distribution that will describe the wind pattern in Kuala Terengganu and Mersing. Four different statistical distributions have been fitted to the monthly mean wind speed from eight different directions. Two stations of Kuala Terengganu and Mersing have been observed for the period 2000 to 2008. These distributions were tested using Kolmogorov-Smirnov statistic to find the best fit for describing the observed data. The Weibull distribution shows a clear fit for all wind speed directions in both locations.