Zalila Ali

Fast Dynamic Programming Based Sequence Alignment Algorithm

Protein sequence alignment is basic operation mostly used in protein sequence analysis. The most optimal algorithm used in sequence alignment is based on the dynamic programming method. Smith-Waterman algorithm is the most commonly used dynamic programming based sequence alignment algorithm. However the algorithm uses quadratic time and space. Heuristic algorithm such as FASTA and BLAST were introduced to speed up the sequence alignment algorithm. FASTA is based on word search whereas BLAST is based on maximum segment pairs. In word search algorithm, lists of words from the query and database sequence are being compared to determine if two sequences have a region of sufficient similarity to merit further alignment using the Smith-Waterman Algorithm. All the different algorithms use the substitutions matrix based on the twenty alphabet amino acids. However research shows that reducing the number of amino acids to 10 does not affect the similarity measure. Our proposed algorithm uses the reduced amino acids alphabet to transform the protein sequences into a sequence of integer and uses n-gram to reduce the length of the sequence. Then the Smith-Waterman algorithm is used to get the similarity measure between two sequences. Result shows that the new proposed algorithm is as sensitive as the Smith-Waterman algorithm yet uses less space and performs better

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test of the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. ...

Modelling the breeding of Aedes Albopictus species in an urban area in Pulau Pinang using polynomial regression

Polynomial regression is used to model a curvilinear relationship between a response variable and one or more predictor variables. It is a form of a least squares linear regression model that predicts a single response variable by decomposing the predictor variables into an nth order polynomial. In a curvilinear relationship, each curve has a number of extreme points equal to the highest order term in the polynomial. A quadratic model will have either a single maximum or minimum, whereas a cubic model has both a relative maximum and a minimum. This study used quadratic modeling techniques to analyze the effects of environmental factors: temperature, relative humidity, and rainfall distribution on the breeding of Aedes albopictus, a type of Aedes mosquito. Data were collected at an urban area in south-west Penang from September 2010 until January 2011. The results indicated that the breeding of Aedes albopictus in the urban area is influenced by all three environmental characteristics. The number of mosqui...

Trends in the air temperature of transitional spaces of a high-rise office building: The effects of season and location

Recent increases in the awareness of sustainable green design have resulted in the creation of transitional spaces in high-rise buildings. Incorporating green design in high-rise buildings has been associated with the reduction of energy consumption in buildings and the provision of open spaces for occupants. Achieving thermal comfort in the outdoor environment is crucial, particularly in tropical climate settings where it is highly affected by microclimatic conditions such as the air temperature, wind, humidity and solar radiation. This paper examines the air temperature at three different transitional spaces of a high-rise office building in Penang, Malaysia. The field measurements were conducted in both dry and wet seasons, and the trend in the air temperature was assessed during these two seasons. Air temperature was measured using a temperature sensor and recorded with a data logger, BABUC-A. The findings indicated that there are significant differences among the three transitional spaces in both seasons. It was found that the different locations and the different seasons had substantial effects on the pattern of air temperature over time. Thus, designing transitional spaces specifically requires an understanding of the local microclimate of the area to achieve thermal comfort.

Maintenance modelling of human behaviour on epidemic transmission

Various studies in epidemiology have shown interesting developments in recent years. Models with different approached have successfully described the nature of epidemic transmission and the one of it is the role of human behaviour. Essential to note that changes in behaviour can lead to health condition status of an individual, therefore, maintenance of individual health (eating habits, daily activities, lifestyle, etc.) and environment’s condition are important. This paper presented on the development of maintenance modelling in human behaviour on the epidemic transmission. New developing areas in maintenance modelling such as inspection maintenance and condition based maintenance are systematically modelled and discussed according to the disease dynamic and human behaviour. This effort is hoped to give insight and understanding for future studies in epidemic transmission.

A weighted least squares estimation of the polynomial regression model on paddy production in the area of Kedah and Perlis

The linear regression model assumes that all random error components are identically and independently distributed with constant variance. Hence, each data point provides equally precise information about the deterministic part of the total variation. In other words, the standard deviations of the error terms are constant over all values of the predictor variables. When the assumption of constant variance is violated, the ordinary least squares estimator of regression coefficient lost its property of minimum variance in the class of linear and unbiased estimators. Weighted least squares estimation are often used to maximize the efficiency of parameter estimation. A procedure that treats all of the data equally would give less precisely measured points more influence than they should have and would give highly precise points too little influence. Optimizing the weighted fitting criterion to find the parameter estimates allows the weights to determine the contribution of each observation to the final parame...

A quadratic regression modelling on paddy production in the area of Perlis

Polynomial regression models are useful in situations in which the relationship between a response variable and predictor variables is curvilinear. Polynomial regression fits the nonlinear relationship into a least squares linear regression model by decomposing the predictor variables into a kth order polynomial. The polynomial order determines the number of inflexions on the curvilinear fitted line. A second order polynomial forms a quadratic expression (parabolic curve) with either a single maximum or minimum, a third order polynomial forms a cubic expression with both a relative maximum and a minimum. This study used paddy data in the area of Perlis to model paddy production based on paddy cultivation characteristics and environmental characteristics. The results indicated that a quadratic regression model best fits the data and paddy production is affected by urea fertilizer application and the interaction between amount of average rainfall and percentage of area defected by pest and disease. Urea fer...

Effect of visible laser light on ATP level of anaemic red blood cell

In this work we present influence of visible laser light on ATP level and viability of anaemic red blood cell (RBC). The visible laser lights used in this work are 460nm and 532nm. The responses of ATP level in anaemic and normal RBC before and after laser irradiation at different exposure time (30, 40, 50 and 60s) were observed. Three aliquots were prepared from the ethylenediaminetetraacetic acid (EDTA) blood sample. One served as a control (untreated) and another two were irradiated with 460nm and 560nm lasers. Packed RBC was prepared to study ATP level in the RBC using CellTiter-GloLuminescent cell Viability Assay kit. The assay generates a glow type signal produced by luciferase reaction, which is proportional to the amount of ATP present in RBCs. Paired t-test were done to analyse ATP level before and after laser irradiation. The results revealed laser irradiation improve level of ATP in anaemic RBC. Effect of laser light on anaemic RBCs were significant over different exposure time for both 460nm (p=0.000) and 532nm (p=0.003). The result of ATP level is further used as marker for RBC viability. The influence of ATP level and viability were studied. Optical densities obtained from the data were used to determine cell viability of the samples. Results showed that laser irradiation increased viability of anaemic RBC compared to normal RBC.

Multinomial logistic regression modelling of stress level among secondary school teachers in Kubang Pasu District, Kedah

Multinomial logistic regression is used to model the outcomes of a categorical dependent variable with more than two categories and predicts the probabilities of the different possible outcomes based on several independent variables. Mathematically, for k categories of the response variable, the multinomial logit model consists of k-1 binary logit model that estimate the effect of the predictors on the probability of success in that category, in comparison to the reference category. The development of the model consists of selection procedures used in selecting important predictor variable and diagnostics tools used to examine for multicollinearity and to detect strongly influential outliers. The overall model is evaluated using the goodness of fit tests and the pseudo R-squares. The significance of each predictor variable is tested using the likelihood ratio test and the odds ratio is used to assess the contribution of individual predictors. This study used multinomial logistic regression model to determ...

Modeling multinomial logistic regression on characteristics of smokers after the smoke-free campaign in the area of Melaka

Multinomial logistic regression is used to model the outcome of a polytomous variable with categorical more than two categories and the predictors are nominal, ordinal, interval and ratio. This model is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables. Multinomial logistic regression model also estimates a separate binary logistic regression model for each indicator variable. The result is j-1 binary logistic regression models. Each one tells the effect of the predictors on the probability of success in that category, in comparison to the reference category. The model is validated by selection of predictor variables, test of regression coefficients, a significance test of the overall model, goodness-of-fit measures and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression techniques to examine smokers’ status that were affected by the Smoke-Free ...