Successful oil palm plantation should have high profit, clean and environmental friendly. Since oil palm trees have a long life and it takes years to be fully grown, controlling the felling rate of the oil palm trees is a fundamental challenge. It needs to be addressed in order to maximize oil production. However, a good arrangement of the felling of the oil palm trees may also affect the amount of carbon absorption. The objec- tive of this study is to develop an optimal felling model of the oil palm plantation system taking into account both oil production and carbon absorption. The model facilitates in providing the optimal control of felling rate that results in maximizing both oil produc- tion and carbon absorption. With this aim, the model is formulated considering oil palm biomass, carbon absorption rate, oil production rate and the average prices of carbon and oil palm. A set of real data is used to estimate the parameters of the model and numerical simulation is conducted to highlight the application of the proposed model. The resulting parameter estimation that leads to an optimal control of felling rate problem is solved.
The well-known geostatistics method (variance-reduction method) is commonly used to determine the optimal rain gauge network. The main problem in geostatistics method to determine the best semivariogram model in order to be used in estimating the variance. An optimal choice of the semivariogram model is an important point for a good data evaluation process. Three different semivariogram models which are Spherical, Gaussian and Exponential are used and their performances are compared in this study. Cross validation technique is applied to compute the errors of the semivariograms. Rain-fall data for the period of 1975 – 2008 from the existing 84 rain gauge stations covering the state of Johor are used in this study. The result shows that the exponential model is the best semivariogram model and chosen to determine the optimal number and location of rain gauge station.
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.
This journal renders the random walk behaviour of the Malaysian daily share return, through tests of efficient market hypothesis (EMH) based on three different financial periods, namely growth, financial crisis, and recovery period. This review also covers the behaviour of extreme return for weekly and monthly series generated from Block maxima-minima method. Autocorrelation Function test (ACF) and Ljung-Box test had been employed to measure average correlation between observations, while Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), Kwiatkowski Phillips Schmidt Shin (KPSS) test had been used to scan the unit root and the stationarity. Multiple variance ratio tests had also been conducted to examine the random walk behaviour. Serial correlation test indicated that the movement of daily return during the financial crisis period was weak-form efficiency. The unit root and stationary tests suggested that each daily series was stationary, but trend stationary for extreme cases. Variance ratio tests indicated that the return during the recovery period was weak-form inefficiency due to the short lag autocorrelation in series.
Rainfall is an interesting phenomenon to investigate since it is directly related
to all aspects of life on earth. One of the important studies is to investigate and under-
stand the rainfall patterns that occur throughout the year. To identify the pattern, it
requires a rainfall curve to represent daily observation of rainfall received during the year.
Functional data analysis methods are capable to convert discrete data intoa function that
can represent the rainfall curve and as a result, try to describe the hidden patterns of the
rainfall. This study focused on the distribution of daily rainfall amount using functional
data analysis. Fourier basis functions are used for periodic rainfall data. Generalized
cross-validation showed 123 basis functions were sufficient to describe the pattern of daily
rainfall amount. North and west areas of the peninsula show a significant bimodal pattern
with the curve decline between two peaks at the mid-year. Meanwhile,the east shows uni-
modal patterns that reached a peak in the last three months. Southern areas show more
uniform trends throughout the year. Finally, the functional spatial method is introduced
to overcome the problem of estimating the rainfall curve in the locations with no data
recorded. We use a leave one out cross-validation as a verification method to compare
between the real curve and the predicted curve. We used coefficient of basis functions
to get the predicted curve. It was foundthatthe methods ofspatial prediction can match
up with theexistingspatialpredictionmethodsin terms of accuracy,but it isbetterasthe new
approach provides a simpler calculation.
In this paper, the problem of forced convection flow of micropolar fluid of
lighter density impinging orthogonally on another heavier density of micropolar fluid
on a stretching surface is investigated. The boundary layer governing equations are
transformed from partial differential equations into a system of nonlinear ordinary
differential equations using similarity transformation and solved numerically using dsolve
function in maple software version 2016. The velocity, microrotation and temperature of
micropolar fluid are analyzed. It is found that both upper fluid and lower fluid display
opposite behaviour when micropolar parameter k various with strong concentration
n = 0, pr = 7 and stretching parameter = 0.5. The results also show that stretching
surface exert the force that increasing the velocity of micropolar fluid.
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.
In this paper, the application of the method of lines (MOL) to the Forced
Korteweg-de Vries-Burgers equation with variable coefficient (FKdVB) is presented.
The MOL is a powerful technique for solving partial differential equations by typically
using finite-difference approximations for the spatial derivatives and ordinary differential
equations (ODEs) for the time derivative. The MOL approach of the FKdVB
equation leads to a system of ODEs. The solution of the system of ODEs is obtained
by applying the Fourth-Order Runge-Kutta (RK4) method. The numerical solution
obtained is then compared with its progressive wave solution in order to show the
accuracy of the MOL method.
Invadopodia are finger-like protrusions located at subcellular membrane which can lead to cancer cell invasion. The formation of invadopodia involves several steps such as actin polymerizations, degradation of extracellular matrix which produce ligand and signal stimulation that is occurred from the binding of ligand with epidermal growth factor receptor. In this paper, a mathematical model of signal transduction is investigated. Both signal and ligand are represented by Laplace equation with Dirichlet boundary condition for each region. The cell membrane is treated as free boundary surface to separate any activity that occurred in intracellular and extracellular regions. The motion of the interface is taken as gradient of interior signal and the cell membrane is set as zero level set function. The problem is solved numerically using finite difference scheme of upwind, interpolation and extrapolation methods. The results showed that the formation of invadopodia is formed when protrusions exist on the cell membrane.
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.
In this paper, extended Runge-Kutta fourth order method for directly solving the fuzzy logistic problem is presented. The extended Runge-Kutta method has lower number of function evaluations, compared with the classical Runge-Kutta method. The numerical robustness of the method in parameter estimation is enhanced via error minimization in predicting growth rate and carrying capacity. The results of fuzzy logistic model with the estimated parameters have been compared with population growth data in Malaysia, which indicate that this method is more accurate that the data population. Numerical example is given to illustrate the efficiency of the proposed model. It is concluded that robust parameter estimation technique is efficient in modelling population growth.
Tumour cells behave differently than normal cells in the body. They grow and
divide in an uncontrolled manner (actively proliferating) and fail to respond to signal.
However, there are cells that become inactive and reside in quiescent phase (G0). These
cells are known as quiescence cells that are less sensitive to drug treatments (radiotherapy
and chemotherapy) than actively proliferation cells. This paper proposes a new mathe-
matical model that describes the interaction of tumour growth and immune response by
considering tumour population that is divided into three different phases namely inter-
phase, mitosis and G0. The model consists of a system of delay differential equations
where the delay, represents the time for tumour cell to reside interphase before entering
mitosis phase. Stability analysis of the equilibrium points of the system was performed
to determine the dynamics behaviour of system. Result showed that the tumour popu-
lation depends on number of tumour cells that enter active (interphase and mitosis) and
G0phases. This study is important for treatment planning since tumour cell can resist
treatment when they refuge in a quiescent state.
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.
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.
Recently, oil refining industry is facing with lower profit margin due to un-
certainty. This causes oil refinery to include stochastic optimization in making a decision
to maximize the profit. In the past, deterministic linear programming approach is widely
used in oil refinery optimization problems. However, due to volatility and unpredictability
of oil prices in the past ten years, deterministic model might not be able to predict the
reality of the situation as it does not take into account the uncertainties thus, leads to
non-optimal solution. Therefore, this study will develop two-stage stochastic linear pro-
gramming for the midterm production planning of oil refinery to handle oil price volatility.
Geometric Brownian motion (GBM) is used to describe uncertainties in crude oil price,
petroleum product prices, and demand for petroleum products. This model generates the
future realization of the price and demands with scenario tree based on the statistical
specification of GBM using method of moment as input to the stochastic programming.
The model developed in this paper was tested for Malaysia oil refinery data. The result
of stochastic approach indicates that the model gives better prediction of profit margin.
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.
Subsea cable laying is a risky and challenging operation faced by engineers, due to many uncertainties arise during the operation. In order to ensure that subsea cables are laid out diligently, the analysis of subsea cable tension during the laying operation is crucial. This study focuses on the fatigue failure of cables that will cause large hang-off loads based on catenary configuration after laying operation. The presented problem was addressed using mathematical modelling with consideration for a number of defining parameters, which include external forces such as current velocity and design parameters such as cable diameter. There were two types of subsea cable tension analyses studied: tensional analysis of catenary configurations and tensional analysis of lazy wave configurations. The latter involved a buoyancy module that was incorporated in the current catenary configuration that reduced subsea cable tension and enhanced subsea cable lifespan. Both analyses were solved using minimization through the gradient- based approach concerning on the tensional analysis of the subsea cable in different configurations. Lazy wave configurations were shown to successfully reduce cable tension, especially at the hang-off section.
The hydromagnetic mixed convection flow of Cassonnano fluid under the influence of chemical reaction,thermal radiation and heat generation or absorption is investigated. The flow is induced due to unsteady nonlinearly stretching sheet saturated in a porous medium. The governing nonlinear coupled partial differential equations are converted into the system of coupled ordinary differential equations using similarity transformations and then solved numerically via Keller box method. The effects of pertinent parameters on velocity, temperature and nanoparticles concentration as well as wall shear stress, heat and mass transfer rate are analyzed and displayed graphically. The results for skin friction coefficient and local Nusselt number are compared with previously published work and found to be in good agreement. Findings demonstrate that increase in Casson parameter enhanced the friction factor and heat transfer rate. It is noticed that the heat transfer rate is declined with increment in Brownian motion and thermophoresis parameters. The nanoparticles concentration is seen to be higher in generative chemical reaction and opposite effect is observed in destructive chemical reaction. Increase in unsteadiness parameter decreased the fluid velocity, temperature and nanoparticles concentration. The magnitude of wall shear stress is also reduced with increase in unsteadiness and porous medium parameters.
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.
Life table is a table that shows mortality experience of a nation. However, in Malaysia, the information in this table is provided in the five-years age groups (abridged) instead of every one-year age. Hence, this study aims to estimate the one-year age mor- tality rates from the abridged mortality rates using several interpolation methods. We applied Kostaki method and the Akima spline method to five sets of Malaysian group mortality rates ranging from period of 2012 to 2016. The result were then compared with the one-year mortality rates. We found that the method by Akima is the best method for Malaysian mortality experience as it gives the least minimum of sum of square errors. The method does not only provide a good fit but also, shows a smooth mortality curve.