Replicated linear functional relationship model is often used to describe
relationships between two circular variables where both variables have error terms and
replicate observations are available. We derive the estimate of the rotation parameter
of the model using the maximum likelihood method. The performance of the proposed
method is studied through simulation, and it is found that the biasness of the estimates
is small, thus implying the suitability of the method. Practical application of the
method is illustrated by using a real data set.
Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices from February 1999 up to May 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.
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.
In this work, a non-abelian metabelian group is represented by G while represents conjugacy class graph. Conjugacy class graph of a group is that graph associated with the conjugacy classes of the group. Its vertices are the non-central conjugacy classes of the group, and two distinct vertices are joined by an edge if their cardinalities are not coprime. A group is referred to as metabelian if there exits an abelian normal subgroup in which the factor group is also abelian. It has been proven earlier that 25 non-abelian metabelian groups which have order less than 24, which are considered in this work, exist. In this article, the conjugacy class graphs of non-abelian metabelian groups of order less than 24 are determined as well as examples of some finite groups associated to other graphs are given.
Medical diagnosis is the extrapolation of the future course and outcome of a disease and a sign of the likelihood of recovery from that disease. Diagnosis is important because it is used to guide the type and intensity of the medication to be administered to patients. A hybrid intelligent system that combines the fuzzy logic qualitative approach and Adaptive Neural Networks (ANNs) with the capabilities of getting a better performance is required. In this paper, a method for modeling the survival of diabetes patient by utilizing the application of the Adaptive NeuroFuzzy Inference System (ANFIS) is introduced with the aim of turning data into knowledge that can be understood by people. The ANFIS approach implements the hybrid learning algorithm that combines the gradient descent algorithm and a recursive least square error algorithm to update the antecedent and consequent parameters. The combination of fuzzy inference that will represent knowledge in an interpretable manner and the learning ability of neural network that can adjust the membership functions of the parameters and linguistic rules from data will be considered. The proposed framework can be applied to estimate the risk and survival curve between different diagnostic factors and survival time with the explanation capabilities.
In the recent economic crises, one of the precise uniqueness that all stock
markets have in common is the uncertainty. An attempt was made to forecast future
index of the Malaysia Stock Exchange Market using artificial neural network (ANN)
model and a traditional forecasting tool – Multiple Linear Regressions (MLR). This
paper starts with a brief introduction of stock exchange of Malaysia, an overview of
artificial neural network and machine learning models used for prediction. System
design and data normalization using MINITAB software were described. Training
algorithm, MLR Model and network parameter models were presented. Best training
graphs showing the training, validation, test and all regression values were analyzed.
The three dimensional free convection boundary layer flow near a stagnation point region is embedded in viscous nanofluid with the effect of g-jitter is studied in this paper. Copper (Cu) and aluminium oxide (Al2O3) types of water base nanofluid are cho- sen with the constant Prandtl number, Pr=6.2. Based on Tiwari-Das nanofluid model, the boundary layer equation used is converted into a non-dimensional form by adopting non- dimensional variables and is solved numerically by engaging an implicit finite-difference scheme known as Keller-box method. Behaviors of fluid flow such as skin friction and Nusset number are studied by the controlled parameters including oscillation frequency, amplitude of gravity modulation and nanoparticles volume fraction. The reduced skin friction and Nusset number are presented graphically and discussed for different values of principal curvatures ratio at the nodal point. The numerical results shows that, in- crement occurs in the values of Nusset number with the presence of solid nanoparticles together with the values of the skin friction. It is worth mentioning that for the plane stagnation point there is an absence of reduced skin friction along the y-direction where as for axisymmetric stagnation point, the reduced skin friction for both directions are the same. As nanoparticles volume fraction increased, the skin friction increased as well as the Nusset number. The results, indicated that skin frictions of copper are found higher than aluminium oxide.
A new method to construct the distinct Hamiltonian circuits in complete
graphs is called Half Butterfly Method. The Half Butterfly Method used the concept
of isomorphism in developing the distinct Hamiltonian circuits. Thus some theoretical
works are presented throughout developing this method.
The effect of oil shock on the global economy is evident through many studies. However, the effect is heterogeneous over time. One of the reasons that lead to such different impacts is due to the oil source that is either the oil shock is demand or supply- driven. Applying the structural vector autoregressive (SVAR) model to generate the three oil shocks based on the three oil sources (oil supply, oil demand and oil specific- demand), we extended the examination on the effect of oil shock on the global economy using the threshold regression. Our results reveal the threshold effects of oil directly and indirectly on the global economy. The impacts of oil shocks differ across sectors, implying oil intensity, as well as oil sources, are the factors that determine the impact of oil shocks on the global economy. Overall, the oil specific-demand shock is more influential among the three oil shocks. Hence, the global economy is oil demand-driven. Besides that, the impact of oil is relatively large in the energy sector when compared to the non-energy sector and precious metals industry. Despite that, the impact of oil shocks is small if compared to the non-oil shocks such as exchange rate changes and global consumer price inflation shock. Consequently, non-oil shocks are the main determinants of the global economic fluctuation. The study leads to a better understanding of the transmission of oil shock and its sources, the interaction between oil and economic indicators and the policy implication due to oil dependency/ intensity.
Price stability is one of the main policy objectives that is targeted by policymakers in many countries. Price uncertainty occurs due to the changes in market structure and consumer preference and expectation, which may affect price stability. In this study, the researchers aimed to examine the effects of price uncertainty of consumer price disaggregation in Malaysian sectors. To be specific, the researchers were seeking to discover on how domestic and global commodity prices can affect sectoral Consumer Price Index (CPI) on price inflation in Malaysia and most importantly, whether the effect is different for economic sectors in Malaysia. In addition, the effects of other factors (i.e., internal and external factors) on Malaysian sectoral CPI inflation were also studied. The threshold generalized autoregressive conditional heteroscedasticity (TGARCH) model was used to generate the price uncertainties. For the purpose of analysis, the threshold regression approach was applied based on time series of each single sector, followed by a combination of panel data of all sectors. The results differed across sectors, revealing that the impact of price uncertainties was determined by the sensitivity of each sector towards the price uncertainties. The effect of price increase is larger than the effect of price decrease. Price fluctuations were obvious in sectors that were dependent on consumer price or commodity price. Exchange rate and oil price inflation had also greatly influenced the CPI inflation.
Preventive maintenance (PM) planning becomes a crucial issue in the real world of the manufacturing process. It is important in the manufacturing industry to maintain the optimum level of production and minimize its investments. Thus, this paper focuses on multiple jobs with a single production line by considering stochastic machine breakdown time. The aim of this paper is to propose a good integration of production and PM schedule that will minimize total completion time. In this study, a hybrid method, which is a genetic algorithm (GA), is used with the Monte Carlo simulation (MCS) technique to deal with the uncertain behavior of machine breakdown time. A deterministic model is adopted and tested under different levels of complexity. Its performance is evaluated based on the value of average completion time. The result clearly shows that the proposed integrated production with PM schedule can reduce the average completion time by 11.68% compared to the production scheduling with machine breakdown time.
Abstract Demographers and actuaries are very much conscious of the trend of mortality in their own country or in the world in general. This is because mortality is the basis for longevity risk evaluation. Mortality is showing a declining trend and it is expected to further decline in the future. This will lead to continuous increase in life expectancy. Several stochastic models have been developed throughout the years to capture mortality and its variability. This includes Lee Carter (LC) model which has been extended by various researchers. This paper will be focusing on comparing LC model and another mortality model proposed by Cairns, Blake and Dowd (CBD). The LC uses the log of central rate of mortality and CBD uses logit of the mortality odds as dependent variable. Analysis of comparison is done using a few techniques including Akaike information criteria (AIC) and Bayesian information criterion (BIC). From the overall results, there is no model better than the other in every aspect tested. We illustrate this via visual inspection and in sample and outof sample analysis using Malaysian mortality data from 1980 to 2017.
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.
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.
In this paper, we consider the system of Volterra-Fredholm integral equations
of the second kind (SVFI-2). We proposed fixed point method (FPM) to solve
SVFI-2 and improved fixed point method (IFPM) for solving the problem. In addition,
a few theorems and two new algorithms are introduced. They are supported by
numerical examples and simulations using Matlab. The results are reasonably good
when compared with the exact solutions.
The selection criteria play an important role in the portfolio optimization
using any ratio model. In this paper, the authors have considered the mean return as
profit and variance of return as risk on the asset return as selection criteria, as the first
stage to optimize the selected portfolio. Furthermore, the sharp ratio (SR) has been
considered to be the optimization ratio model. In this regard, the historical data taken
from Shanghai Stock Exchange (SSE) has been considered. A metaheuristic technique
has been developed, with financial tool box available in MATLAB and the particle swarm
optimization (PSO) algorithm. Hence, called as the hybrid particle swarm optimization
(HPSO) or can also be called as financial tool box particle swarm optimization (FTBPSO).
In this model, the budgets as constraint, where as two different models i.e. with
and without short sale, have been considered. The obtained results have been compared
with the existing literature and the proposed technique is found to be optimum and better
in terms of profit.
In this paper, we look at the propagation of internal solitary waves over three
different types of slowly varying region, i.e. a slowly increasing slope, a smooth bump and
a parabolic mound in a two-layer fluid flow. The appropriate mathematical model for this
problem is the variable-coefficient extended Korteweg-de Vries equation. The governing
equation is then solved numerically using the method of lines. Our numerical simulations
show that the internal solitary waves deforms adiabatically on the slowly increasing slope.
At the same time, a trailing shelf is generated as the internal solitary wave propagates
over the slope, which would then decompose into secondary solitary waves or a wavetrain.
On the other hand, when internal solitary waves propagate over a smooth bump or a
parabolic mound, a trailing shelf of negative polarity would be generated as the results of
the interaction of the internal solitary wave with the decreasing slope of the bump or the
parabolic mound. The secondary solitary waves is observed to be climbing the negative
trailing shelf.
Markov map is one example of interval maps where it is a piecewise expanding
map and obeys the Markov property. One well-known example of Markov map is the
doubling map, a map which has two subintervals with equal partitions. In this paper, we
are interested to investigate another type of Markov map, the so-called skewed doubling
map. This map is a more generalized map than the doubling map. Thus, the aims of this
paper are to find the fixed points as well as the periodic points for the skewed doubling
map and to investigate the sensitive dependence on initial conditions of this map. The
method considered here is the cobweb diagram. Numerical results suggest that there exist
dense of periodic orbits for this map. The sensitivity of this map to initial conditions is
also verified where small differences in initial conditions give different behaviour of the
orbits in the map.
Pressurized water reactor (PWR) type AP1000 is a third generation of a nuclear
power plant. The primary system of PWR using uranium dioxide to generate heat energy
via fission process. The process influences temperature, pressure and pH value of water
chemistry of the PWR. The aim of this paper is to transform the primary system of PWR
using fuzzy autocatalytic set (FACS). In this work, the background of primary system
of PWR and the properties of the model are provided. The simulation result, namely
dynamic concentration of PWR is verified against published data.
A mechanistic model has been used to explain the effect of radiation. The
model consists of parameters which represent the biological process following ionizing
radiation. The parameters in the model are estimated using local and global optimiza-
tion algorithms. The aim of this study is to compare the efficiency between local and
global optimization method, which is Pattern Search and Genetic Algorithm respectively.
Experimental data from the cell survival of irradiated HeLa cell line is used to find the
minimum value of the sum of squared error (SSE) between experimental data and sim-
ulation data from the model. The performance of both methods are compared based on
the computational time and the value of the objective function, SSE. The optimization
process is carried out by using the built-in function in MATLAB software. The parameter
estimation results show that genetic algorithm is more superior than pattern search for
this problem.