The fast-growing urbanization has contributed to the construction sector be- coming one of the major sectors traded in the world stock market. In general, non- stationarity is highly related to most of the stock market price pattern. Even though stationarity transformation is a common approach, yet this may prompt to originality loss of the data. Hence, the non-transformation technique using a generalized dynamic principal component (GDPC) were considered for this study. Comparison of GDPC was performed with two transformed principal component techniques. This is pertinent as to observe a larger perspective of both techniques. Thus, the latest weekly two-years observations of nine constructions stock market price from seven different countries were applied. The data was tested for stationarity before performing the analysis. As a re- sult, the mean squared error in the non-transformed technique shows eight lowest values. Similarly, eight construction stock market prices had the highest percentage of explained variance. In conclusion, a non-transformed technique can also present a better result outcome without the stationarity transformation.
Conjugate Gradient (CG) methods have an important role in solving large
scale unconstrained optimization problems. Nowadays, the Three-Term CG method has
become a research trend of the CG methods. However, the existing Three-Term CG
methods could only be used with the inexact line search. When the exact line search
is applied, this Three-Term CG method will be reduced to the standard CG method.
Hence in this paper, a new Three-Term CG method that could be used with the exact
line search is proposed. This new Three-Term CG method satisfies the descent condition
using the exact line search. Performance profile based on numerical results show that
this proposed method outperforms the well-known classical CG method and some related
hybrid methods. In addition, the proposed method is also robust in term of number of
iterations and CPU time.
In numerical methods, boundary element method has been widely used to solve
acoustic problems. However, it suffers from certain drawbacks in terms of computational
efficiency. This prevents the boundary element method from being applied to large-scale
problems. This paper presents proposal of a new multiscale technique, coupled with
boundary element method to speed up numerical calculations. Numerical example is
given to illustrate the efficiency of the proposed method. The solution of the proposed
method has been validated with conventional boundary element method and the proposed
method is indeed faster in computation.
Regression is one of the basic relationship models in statistics. This paper focuses on the formation of regression models for the rice production in Malaysia by analysing the effects of paddy population, planted area, human population and domestic consumption. In this study, the data were collected from the year 1980 until 2014 from the website of the Department of Statistics Malaysia and Index Mundi. It is well known that the regression model can be solved using the least square method. Since least square problem is an unconstrained optimisation, the Conjugate Gradient (CG) was chosen to generate a solution for regression model and hence to obtain the coefficient value of independent variables. Results show that the CG methods could produce a good regression equation with acceptable Root Mean-Square Error (RMSE) value.
The proposed modified methods of Cramer's rule consider the column vector as well as the coefficient matrix concurrently in the linear system. The modified methods can be applied since Cramer's rule is typically known for solving the linear systems in $WZ$ factorization to yield Z-matrix. Then, we presented our results to show that there is no tangible difference in performance time between Cramer's rule and the modified methods in the factorization from improved versions of MATLAB. Additionally, the Frobenius norm of the modified methods in the factorization is better than using Cramer's rule irrespective of the version of MATLAB used.
Riverbank filtration (RBF) system is a surface water technology that is based
on the natural treatment of filtration instead of the use of chemicals, to pre-treat sur-
face water and provides public water supplies. Hydraulic conductivity value is one of the
significant factors affecting the water quality in RBF systems. In this article, an analyti-
cal modelling is developed to investigate the effect of this parameter on one dimensional
contaminant transport in RBF system. The model is solved by using Green’s function
approach. The model is applied for the first RBF system conducted in Malaysia. Gener-
ally, the results show that increasing the hydraulic conductivity value lead to an increase
in contaminant concentration in pumping well area.
Johor Bahru with its rapid development where pollution is an issue that needs to be considered because it has contributed to the number of asthma cases in this area. Therefore, the goal of this study is to investigate the behaviour of asthma disease in Johor Bahru by count analysis approach namely; Poisson Integer Generalized Autoregressive Conditional Heteroscedasticity (Poisson-INGARCH) and Negative Binomial INGARCH (NB-INGARCH) with identity and log link function. Intervention analysis was conducted since the outbreak in the asthma data for the period of July 2012 to July 2013. This occurs perhaps due to the extremely bad haze in Johor Bahru from Indonesian fires. The estimation of the parameter will be done by quasi-maximum likelihood estimation. Model assessment was evaluated from the Pearson residuals, cumulative periodogram, the probability integral transform (PIT) histogram, log-likelihood value, Akaike’s Information Criterion (AIC) and Bayesian information criterion (BIC). Our result shows that NB-INGARCH with identity and log link function is adequate in representing the asthma data with uncorrelated Pearson residuals, higher in log likelihood, the PIT exhibits normality yet the lowest AIC and BIC. However, in terms of forecasting accuracy, NB-INGARCH with identity link function performed better with the smaller RMSE (8.54) for the sample data. Therefore, NB-INGARCH with identity link function can be applied as the prediction model for asthma disease in Johor Bahru. Ideally, this outcome can assist the Department of Health in executing counteractive action and early planning to curb asthma diseases in Johor Bahru.
Coxmodel is popular in survival analysis. In the case of time-varying covariate;
several subject-specific attributes possibly to change more frequently than others. This
paper deals with that issue. This study aims to analyze survival data with time-varying
covariate using a time-dependent covariate Cox model. The two case studies employed in
this work are (1) delisting time of companies from IDX and (2) delisting time of company
from LQ45 (liquidity index). The survival time is the time until a company is delisted
from IDX or LQ45. The determinants are eighteen quarterly financial ratios and two
macroeconomics indicators, i.e., the Jakarta Composite Index (JCI) and BI interest rate
that changes more frequent. The empirical results show that JCI is significant for both
delisting and liquidity whereas BI rate is significant only for liquidity. The significant
firm-specific financial ratios vary for delisting and liquidity.
In irradiation process, instead of traverse on the targeted cells, there is side
effect happens to non-targeted cells. The targeted cells that had been irradiated with
ionizing radiation emits damaging signal molecules to the surrounding and then, dam-
age the bystander cells. The type of damage considered in this work is the number of
double-strand breaks (DSBs) of deoxyribonucleic acid (DNA) in cell’s nucleus. By us-
ing mathematical approach, a mechanistic model that can describe this phenomenon is
developed based on a structured population approach. Then, the accuracy of the model
is validated by its ability to match the experimental data. The Particle Swarm (PS)
optimization is employed for the data fitting procedure. PS optimization searches the
parameter value that minimize the errors between the model simulation data and exper-
imental data. It is obtained that the mathematical modelling proposed in this paper is
strongly in line with the experimental data.
Subsea cable laying process is a difficult task for an engineer due to many
uncertain situations which occur during the operation. It is very often that the cable being
laid out is not perfectly fit on the route being planned, which results in the formation of
slack. In order to control wastages during installation, the slack needs to be minimized
and the movement of a ship/vessel needs to be synchronized with the cable being laid out.
The current problem was addressed using a mathematical model by considering a number
of defining parameters such as the external forces, the cable properties and geometry. Due
to the complexity, the model is developed for a steady-state problem assuming velocity
of the vessel is constant, seabed is flat and the effect of wind and wave is insignificant.
Non-dimensional system is used to scale the engineering parameters and grouped them
into only two main parameters which are the hydrodynamic drag of the fluid and the
bending stiffness of the cable. There are two solutions generated in this article; numerical
and asymptotic solutions. The result of these solutions suggests that the percentage of
slack can be reduced by the increase of the prescribed cable tension, and also the increase
in either the drag coefficient of the sea water or the bending stiffness of the cable, similarly
will result in lower slack percentage
Inventory Routing Problem (IRP) has been continuously developed and improved due to pressure from global warming issue particularly related to greenhouse gases (GHGs) emission. The burning of fossil fuel for transportations such as cars, trucks, ships, trains, and planes primarily emits GHGs. Carbon dioxide (CO2) from burning of fossil fuel to power transportation and industrial process is the largest contributor to global GHGs emission. Therefore, the focus of this study is on solving a multi-period inventory routing problem (MIRP) involving carbon emission consideration based on carbon cap and offset policy. Hybrid genetic algorithm (HGA) based on allocation first and routing second is used to compute a solution for the MIRP in this study. The objective of this study is to solve the proposed MIRP model with HGA then validate the effectiveness of the proposed HGA on data of different sizes. Upon validation, the proposed MIRP model and HGA is applied on real-world data. The HGA is found to be able to solve small size and large size instances effectively by providing near optimal solution in relatively short CPU execution time.
Hantaviruses are etiological agents of zoonotic diseases and certain other dis-
eases, which pose a serious threat to human health. When rodent and predator popula-
tions share in an ecology, the competitive force of the populations can lead to a reduction
or elimination of a hantavirus outbreak. The effect of the predator eliminating rodents
and predator populations that tends to reduce or eliminate hantavirus infection is investi-
gated. The existence of several equilibrium points of the model is identified and local and
global stabilities of the model at these equilibrium points are analysed in detail. Numerical
simulations are carried out to illustrate our model results.
Recent studies have shown that independent identical distributed Gaussian
random variables is not suitable for modelling extreme values observed during extremal
events. However, many real life data on extreme values are dependent and stationary
rather than the conventional independent identically distributed data. We propose a stationary
autoregressive (AR) process with Gumbel distributed innovation and characterise
the short-term dependence among maxima of an (AR) process over a range of sample
sizes with varying degrees of dependence. We estimate the maximum likelihood of the
parameters of the Gumbel AR process and its residuals, and evaluate the performance
of the parameter estimates. The AR process is fitted to the Gumbel-generalised Pareto
(GPD) distribution and we evaluate the performance of the parameter estimates fitted
to the cluster maxima and the original series. Ignoring the effect of dependence leads to
overestimation of the location parameter of the Gumbel-AR (1) process. The estimate
of the location parameter of the AR process using the residuals gives a better estimate.
Estimate of the scale parameter perform marginally better for the original series than the
residual estimate. The degree of clustering increases as dependence is enhance for the AR
process. The Gumbel-AR(1) fitted to the threshold exceedances shows that the estimates
of the scale and shape parameters fitted to the cluster maxima perform better as sample
size increases, however, ignoring the effect of dependence lead to an underestimation of
the parameter estimates of the scale parameter. The shape parameter of the original
series gives a superior estimate compare to the threshold excesses fitted to the Gumbel
distributed Generalised Pareto ditribution.
Cardiovascular disease (CVD) includes coronary heart disease, cerebrovascular disease (stroke), peripheral artery disease, and atherosclerosis of the aorta. All females face the threat of CVD. But becoming aware of symptoms and signs is a great challenge since most adults at increased risk of cardiovascular disease (CVD) have no symptoms or obvious signs especially in females. The symptoms may be identified by the assessment of their risk factors. The Bayesian approach is a specific way in dealing with this kind of problem by formalizing a priori beliefs and of combining them with the available observations. This study aimed to identify associated risk factors in CVD among female patients presenting with ST Elevation Myocardial Infarction (STEMI) using Bayesian logistic regression and obtain a feasible model to describe the data. A total of 874 STEMI female patients in the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry year 2006-2013 were analysed. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied in the univariate and multivariate analysis. Model performance was assessed through the model calibration and discrimination. The final multivariate model of STEMI female patients consisted of six significant variables namely smoking, dyslipidaemia, myocardial infarction (MI), renal disease, Killip class and age group. Females aged 65 years and above have higher incidence of CVD and mortality is high among female patients with Killip class IV. Also, renal disease was a strong predictor of CVD mortality. Besides, performance measures for the model was considered good. Bayesian logistic regression model provided a better understanding on the associated risk factors of CVD for female patients which may help tailor prevention or treatment plans more effectively.
Heat and mass transfer of MHD boundary-layer flow of a viscous incompress-
ible fluid over an exponentially stretching sheet in the presence of radiation is investi-
gated. The two-dimensional boundary-layer governing partial differential equations are
transformed into a system of nonlinear ordinary differential equations by using similarity
variables. The transformed equations of momentum, energy and concentration are solved
by Homotopy Analysis Method (HAM). The validity of HAM solution is ensured by com-
paring the HAM solution with existing solutions. The influence of physical parameters
such as magnetic parameter, Prandtl number, radiation parameter, and Schmidt num-
ber on velocity, temperature and concentration profiles are discussed. It is found that
the increasing values of magnetic parameter reduces the dimensionless velocity field but
enhances the dimensionless temperature and concentration field. The temperature dis-
tribution decreases with increasing values of Prandtl number. However, the temperature
distribution increases when radiation parameter increases. The concentration boundary
layer thickness decreases as a result of increase in Schmidt number.
Blood flow through a bifurcated artery with the presence of an overlapping stenosis located at parent’s arterial lumen under the action of a uniform external magnetic field is studied in this paper. Blood is treated as an electrically conducting fluid which exhibits the Magnetohydrodynamics principle and it is characterized by a Newtonian fluid model. The governing equations are discretized using a stabilization technique of finite element known as Galerkin least-squares. The maximum velocity and pressure drop evaluated in this present study are compared with the results found in previous literature and COMSOL Multiphysics. The solutions found in a satisfactory agreement, thus verify the source code is working properly. The effects of dimensionless parameters of Hartmann and Reynolds numbers in the fluid’s velocity and pressure are examined in details with further scientific discussions.
This study presents a two-strain deterministic model which incorporates Dengvaxia vaccine and insecticide (adulticide) control strategies to forecast the dynamics of transmission and control of dengue in Madeira Island if there is a new outbreak with a different virus serotypes after the first outbreak in 2012. We construct suitable Lyapunov functions to investigate the global stability of the disease-free and boundary equilibrium points. Qualitative analysis of the model which incorporates time-varying controls with the specific goal of minimizing dengue disease transmission and the costs related to the control implementation by employing the optimal control theory is carried out. Three strategies, namely the use of Dengvaxia vaccine only, application of adulticide only, and the combination of Dengvaxia vaccine and adulticide are considered for the controls implementation. The necessary conditions are derived for the optimal control of dengue. We examine the impacts of the control strategies on the dynamics of infected humans and mosquito population by simulating the optimality system. The disease-free equilibrium is found to be globally asymptotically stable whenever the basic reproduction numbers associated with virus serotypes 1 and j (j ∈ {2,3,4}), respectively, satisfy R01,R0j ≤ 1, and the boundary equilibrium is globally asymptotically stable when the related R0i (i = 1,j) is above one. It is shown that the strategy based on the combination of Dengvaxia vaccine and adulticide helps in an effective control of dengue spread in the Island.
In DNA splicing system, the potential effect of sets of restriction enzymes and
a ligase that allow DNA molecules to be cleaved and re-associated to produce further
molecules is modelled mathematically. This modelling is done in the framework of formal
language theory, in which the nitrogen bases, nucleotides and restriction sites are modelled
as alphabets, strings and rules respectively. The molecules resulting from a splicing system
is depicted as the splicing language. In this research, the splicing language resulting from
DNA splicing systems with one palindromic restriction enzyme for one and two (nonoverlapping)
cutting sites are generalised as regular expressions.
A domination polynomial is a type of graph polynomial in which its coefficients represent the number of dominating sets in the graph. There are many researches being done on the domination polynomial of some common types of graphs but not yet for graphs associated to finite groups. Two types of graphs associated to finite groups are the conjugate graph and the conjugacy class graph. A graph of a group G is called a conjugate graph if the vertices are non-central elements of G and two distinct vertices are adjacent if they are conjugate to each other. Meanwhile, a conjugacy class graph of a group G is a graph in which its vertices are the non-central conjugacy classes of G and two distinct vertices are connected if and only if their class cardinalities are not coprime. The conjugate and conjugacy class graph of dihedral groups can be expressed generally as a union of complete graphs on some vertices. In this paper, the domination polynomials are computed for the conjugate and conjugacy class graphs of the dihedral groups.
An accurate forecasting of tropospheric ozone (O3) concentration is benefi-
cial for strategic planning of air quality. In this study, various forecasting techniques are
used to forecast the daily maximum O3 concentration levels at a monitoring station
in the Klang Valley, Malaysia. The Box-Jenkins autoregressive integrated movingaverage
(ARIMA) approach and three types of neural network models, namely, backpropagation
neural network, Elman recurrent neural network and radial basis function
neural network are considered. The daily maximum data, spanning from 1 January
2011 to 7 August 2011, was obtained from the Department of Environment, Malaysia.
The performance of the four methods in forecasting future values of ozone concentrations
is evaluated based on three criteria, which are root mean square error (RMSE),
mean absolute error (MAE) and mean absolute percentage error (MAPE). The findings
show that the Box-Jenkins approach outperformed the artificial neural network
methods.