Parameter estimation in Generalized Autoregressive Conditional Heteroscedastic (GARCH) model has received much attention in the literature. Commonly used quasi maximum likelihood estimator (QMLE) may not be suitable if the model is misspecified. Alternatively, we can consider using variance targeting estimator (VTE) as it seems to be a better fit for misspecified initial parameters. This paper extends the application to see how both QMLE and VTE perform under error distribution misspecifications. Data are simulated under two error distribution conditions: one is to have a true normal error distribution and the other is to have a true student-t error distribution with degree of freedom equals to 3. The error distribution assumption that has been selected for this study are: normal distribution, student-t distribution, skewed normal distribution and skewed student-t. In addition, this study also includes the effect of initial parameter specification.
Data mining processes such as clustering, classification, regression and outlier detection are developed based on similarity between two objects. Data mining processes of categorical data is found to be most challenging. Earlier similarity measures are context-free. In recent years, researchers have come up with context-sensitive similarity measure based on the relationships of objects. This paper provides an in-depth review of context-based similarity measures. Descriptions of algorithm for four context-based similarity measure, namely Association-based similarity measure, DILCA, CBDL and the hybrid context-based similarity measure, are described. Advantages and limitations of each context-based similarity measure are identified and explained. Context-based similarity measure is highly recommended for data-mining tasks for categorical data. The findings of this paper will help data miners in choosing appropriate similarity measures to achieve more accurate classification or clustering results.
The study of stock market volatility has been the focus of market participants primarily because most
of the applications in financial economics are concerned with volatility. The economic structure in
Malaysia is divided into three sectors: primary, secondary and tertiary. As the stability of the stock
market is important for businesses, this paper carefully reviews the concept of volatility and analyses
how different business sectors in Malaysia are affected by stock market volatility.
This paper presents a mathematical approach to solve railway rescheduling problems. The approach assumes that the trains are able to resume their journey after a given time frame of disruption whereby The train that experiences disruption and trains affected by the incident are rescheduled. The approach employed mathematical model to prioritise certain types of train according the railway operator’s requirement. A pre-emptive goal programming model was adapted to find an optimal solution that satisfies the operational constraints and the company’s stated goals. Initially, the model minimises the total service delay of all trains while adhering to the minimum headway requirement and track capacity. Subsequently, it maximises the train service reliability by only considering the trains with delay time window of five minutes or less. The model uses MATLAB R2014a software which automatically generates the optimal solution of the problem based on the input matrix of constraints. An experiment with three incident scenarios on a double-track railway of local network was conducted to evaluate the performance of the proposed model. The new provisional timetable was produced in short computing time and the model was able to prioritise desired train schedule.
Non-invasive imaging modalities for wound assessment have become increasingly popular over the past
two decades. The wounds can be developed superficially or from within deep tissues, depending on the
nature of the dominant risk factors. Developing a reproducible quantitative method to assess woundhealing
status has demonstrated to be a convoluted task. Advances in High-Frequency Ultrasound (HFU)
skin scanners have expanded their application as they are cost-effective and reproducible diagnostic tools
in dermatology, including for the measurement of skin thickness, the assessment of skin tumours, the
estimation of the volume of melanoma and non-melanoma skin cancers, the visualisation of skin structure
and the monitoring of the healing of acute and chronic wounds. Previous studies have revealed that HFU
images carry dominant parameters and depict the phenomena occurring within deep tissue layers during
the wound-healing process. However, the investigations have mostly focussed on the validation of HFU
images, and few studies have utilised HFU imaging in quantitative assessment of wound generation and
healing. This paper is an introductory review of the
important studies proposed by the researchers in
the context of wound assessment. The principles
of dermasonography are briefly explained,
followed by a review of the relevant literature that
investigated the wound-healing process and tissue
structures within the wound using HFU imaging.
The enormous attention and interest by both academics and industrial field for greener, biodegradable
and renewable materials implicate a persuasive trends towards the encroachment of nano-materials
science and technology in the polymer composite field. Nanocomposites creates high impacts on the
development of nano materials with advanced features to solve potential risks with their wider industrial
applications. Nano fibres are highly engineered fibres with diameters less than 100 nm that offer several
advantages over conventional fibres. One dimensional (1D) nanostructure fillers such as carbon nano
fibre and cellulose nano fibre are the most common, promising and unique for developing multifunctional
nanocomposites with better properties and extensive applications compared to micro size fibres. Nano
fibre technology brings revolution by providing products that are completely safe, truly greener, reliable
and environmentally friendly for industries, researchers and users. This review article is intended to
present valuable literature data on research and trend in the fields of carbon and cellulose nano fiber,
nanocomposites with specific focus on various applications for a sustainable and greener environment.
The feasibility of an anaerobic digestion of cattle manure for biogas production is studied in this paper.
A batch thermophilic oscillatory flow anaerobic bioreactor (OFBR) operated in thermophilic (55o
C)
condition was used. Within the experimental conditions set in this study, the effect of mixing intensity on
volatile solids removal was found out to be significant. Results demonstrated that increasing the level of
mixing decreased the digester performance. Low intensity mixing at oscillatory Reynolds number (Reo)
of 100 achieved an increase of 37% in biogas yields compared to high mixing intensity, Reo of 500. It
was observed that the mixing intensity effect interacts with the methane composition in the biogas. The
benefit of decreasing mixing intensity emerges to significantly increase the methane composition in the
biogas. These experiments established that high intensity mixing was not essential for good performance
of oscillatory flow anaerobic bioreactor. In addition, the effect of mixing intensity might be reduced
through the use of a slightly lower total solid concentration, hence, lowering the operational cost of the
process. Although the study was lab scale a pilot-scale system where mixing retention times are longer
would be useful.
In 2013, 632 602 private cars were involved in road crashes, this amounting to 64% of all road crashes on
Malaysian roads. Risk is often used to quantifying the level of road safety whilst exposure is an essential
component of risk measurement. The calculation of VKT (vehicle kilometre travel) in this study using
odometer reading data obtained from automobile manufacturer is used for cars. This average in 2013
was 24,129 kilometres, the highest recorded in Selangor with 28,575 kilometres and the lowest at
16,342 kilometres a year, in Johor. This method is believed to be reliable with a high yielding number
of samples as well as a good representative set of samples for Malaysia.
CCTV surveillance systems are widely used as a street monitoring tool in public and private areas. This
paper presents a novel approach of an intelligent surveillance system that consists of adaptive background
modelling, optimal trade-off features tracking and detected moving objects classification. The proposed
system is designed to work in real-time. Experimental results show that the proposed background
modelling algorithms are able to reconstruct the background correctly and handle illumination and adverse
weather that modifies the background. For the tracking algorithm, the effectiveness between colour,
edge and texture features for target and candidate blobs were analysed. Finally, it is also demonstrated
that the proposed object classification algorithm performs well with different classes of moving objects
such as, cars, motorcycles and pedestrians.
Experimental data from a physical model of scouring around a cylindrical wide pier embedded in two
types of uniform sediment beds are presented. The effects of sediment sizes and various pier widths on
scour development and equilibrium scour depth of wide bridge piers are described. Existing literature
suggest that the empirical scour prediction equations based on laboratory data over-predict scour depths
for large structures. The present study has attempted to fill this gap for a cylindrical wide pier. Further,
equations for the estimation of non-dimensional maximum scour depth for a wide cylindrical pier
embedded in uniform sediment were proposed as functions of the sediment coarseness.
Manual Material Handling (MMH) involves lifting, bending, and twisting motions of the torso. Poor
lifting technique is often considered a major risk factor in low back injury associated with manual lifting
tasks. Currently, there is little work on the effects of lifting on the Malaysian population. The MMH
activities that was designed with the different lifting heights, frequency, weight of loads and the effect
on of biceps and triceps muscle contraction of the subjects during the lifting tasks were studied. The
parameters involved are weight of the loads lifted, height of the loads lifted and lifting frequency as the
independent variables. Whereas the dependent variable is Electromyography (EMG) signal. The weight
loads are varying from 10kg up to 24kg and the heights of the loads travels from the floor to 70cm
and 130cm heights. The frequency of lifting is set to 1 lift and 6 lifts per minute. 14 healthy male and
female subjects were recruited in this study. The questionnaires and consent form were used to identify
the health condition of the subjects before performing the lifting tasks. The EMG activity was recorded
and collected from biceps and triceps muscles using the Shimmer EMG system. This method is used
in determining the maximum acceptable weight limit (MAWL) that can be lifted by the subjects in the
lifting tasks. This research aims to design a lifting equation that suits for Malaysian people. Therefore,
the effects of different manual lifting tasks on Malaysian physiological limits need to be identified.
The effect of osmotic stress was carried out to determine the resistance to salt toxicity using 4 Continuous
Stirred Tank Reactor (CSTR). A CSTR digestion study revealed that digesters seeded with an inoculum
from a conventional mesophilic digester treating municipal wastewater and fed on domestic wastewater
(DW) plus salts were able to acclimate successfully to a final salt concentration of 10 g l-1. The digesters
showed some disturbances during the acclimatisation period as indicated by reductions in specific methane
production (SMP), specific biogas production (SBP), pH and increases in Intermediate Alkalinity /Partial
Alkalinity (1A/PA) ratio and Volatile Fatty Acid (VFA) concentration. This study revealed the order
of disturbance was Sodium Chloride (NaCl) > Potassium Chloride (KCl) > KCl + NaCl. The average
values for SMP after stabilisation were below those in the controlled digester, at 0.335 (controlled),
0.323 (NaCl), 0.316 (KCl + NaCl) and 0.308 l CH4 g-1 COD added (KCl).
This paper focuses on examining the ‘cutting zone temperature’ while performing turning operation
on AZ91Mg alloy using cemented carbide tools. The regression model is developed by using the RSM
techniques based on experimental results. It is revealed that the cutting speed (v) is the most dominant
factor affecting cutting zone temperature. The developed models of cutting zone temperature sufficiently
map within the range of the turning conditions considered. The adequacy and accuracy of the regression
equation is justified through ANOVA. It is found that the optimal combinations of machining parameters
minimize the cutting temperature.
Swarm intelligence is a research area that models the population of swarm that is able to self-organise effectively. Honey bees that gather around their hive with a distinctive behaviour is another example of swarm intelligence. In fact, the artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic algorithm introduced by Karaboga in order to optimise numerical problems. 2SAT can be treated as a constrained optimisation problem which represents any problem by using clauses containing 2 literals each. Most of the current researchers represent their problem by using 2SAT. Meanwhile, the Hopfield neural network incorporated with the ABC has been utilised to perform randomised 2SAT. Hence, the aim of this study is to investigate the performance of the solutions produced by HNN2SAT-ABC and compared it with the traditional HNN2SAT-ES. The comparison of both algorithms has been examined by using Microsoft Visual Studio 2013 C++ Express Software. The detailed comparison on the performance of the ABC and ES in performing 2SAT is discussed based on global minima ratio, hamming distance, CPU time and fitness landscape. The results obtained from the computer simulation depict the beneficial features of ABC compared to ES. Moreover, the findings have led to a significant implication on the choice of determining an alternative method to perform 2SAT.
The synthesis of carbon nanotubes (CNTs) using a chemical vapour deposition (CVD) method requires the use of hydrocarbon as the carbon precursor. Among the commonly used hydrocarbons are methane and acetylene, which are both light gas-phase substances. Besides that, other carbon-rich sources, such as carbon monoxide and coal, have also been reportedly used. Nowadays, researches have also been conducted into utilising heavier hydrocarbons and petrochemical products for the production of CNTs, such as kerosene and diesel oil. Therefore, this article reviews the different kind of hydrocarbon sources for CNTs production using a CVD method. The method is used for it allows the decomposition of the carbon-rich source with the aid of a catalyst at a temperature in the range 600-1200°C. This synthesis technique gives an advantage as a high yield and high-quality CNTs can be produced at a relatively low cost process. As compared to other processes for CNTs production such as arc discharge and laser ablation, they may produce high quality CNTs but has a disadvantage for use as large scale synthesis routes.
Medical diagnosis is the process of determining which disease or medical condition explains a person’s determinable signs and symptoms. Diagnosis of most diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). It incorporates hybrid learning algorithms least square estimates with Levenberg-Marquardt algorithm using analytic derivation for computation of Jacobian matrix, as well as code optimisation technique, which indexes membership functions. The goal is to investigate how certain diseases are affected by patient’s characteristics and measurement such as abnormalities or a decision about the presence or absence of a disease. In order to achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent technique was tested with Statlog heart disease and Hepatitis disease datasets obtained from the University of California at Irvine’s (UCI) machine learning repository. The robustness of the performance measuring total accuracy, sensitivity and specificity was examined. In comparison, the proposed method was found to achieve superior
performance when compared to some other related existing methods.
Despite wide applications in industries, phenol pollution leads to many health effects, and one of the technologies used to clean up phenol pollution is phytoremediation. The aim of this research was to assess the remediation ability of Ipomoea aquatica Forssk., which is easy to handle and and has a fast growth rate. Plantlet was grown in water spiked with 0.05, 0.10, 0.20, 0.30 and 0.40 g/L phenol, followed by daily observation of the plantlets morphology and tracking of phenol concentration in the water and plantlet extracts via 4-aminoantipyrine (4-AAP) assay. Plantlet’s roots in 0.10 g/L phenol (57.42 ± 1.41 mm) were significantly longer (p < 0.05) than those of the control plantlets (43.57 ± 3.87 mm) in contrast to other phenol concentrations which had stunted roots growth. I. aquatica Forssk. was able to survive with 0.30 g/L phenol despite exhibiting yellowing of leaves and increased sensitivity to scarring on the stems. The plantlets were able to completely remove the phenol from the water spiked with phenol at 0.05 g/L after 12 days of growth. However, the highest average rate of phenol removal was 0.021 g/L/day from water spiked with 0.30 g/L phenol. Phenol analysis on the plantlets’ extracts revealed that I. aquatica Forssk. had degraded the absorbed phenol. This observation is of significant interest as it highlights the
potential of I. aquatica Forssk. for use as a phytoremediator to clean up phenol contaminated water.
Dispersibility of nanoparticles is the key problem in nanotechnology industries, and thus warrants
attention on the techniques of dispersion. This review paper presents dispersibility of treated nanoparticles
in polymer resin. Dispersibility of nanoparticles in polymer media is crucial in order to enhance the
mechanical and thermal properties of nanocomposite. This paper concentrates on several preparations
on how to incorporate nanoparticles in polymer to overcome the problem described in this review. A
few techniques are discussed in this paper such as by using ultra sonication or even directly mixing
nanoparticles into polymer matrix.
The growing interest, environmental consciousness and high performance demands on engineering have
led to extensive research and development of new and improved materials. Among the most commonly
used natural fibres are kenaf, oil palm, sugar palm, pineapple leaf fibre, flax, hemp, sisal, coir and jute.
These fibres are used to reinforce thermoplastic polymer matrices such as polystyrene (PS), polypropylene
(PP), polyethylene (PE) and polyvinyl chloride (PVC). Meanwhile, phenolic, unsaturated polyester vinyl
ester and epoxy resin are for thermosetting polymer matrices. The objective of this paper is to solicit
works that cover major class of natural fibres, thermosetting polymers matrices, which detail about
unsaturated polyester resin and hybrid biocomposites industry.
Environmental issues have motivated researchers to replace synthetic fibres with natural fibres in the
fabrication of polymer composites. However, natural fibres demonstrate weak mechanical or thermal
properties which limit their different applications. Researchers have suggested fabrication of hybrid
composites in order to improve the mechanical and thermal properties of natural fibre-based composites.
Hybrid composites are made up by two or more fibres in one matrix or two polymer blends and with
one natural fibre reinforcement. By hybridising one
natural fibre with another natural fibre/synthetic
fibre in one matrix, the resulting composite is a
unique product (hybrid composites) that displays
better mechanical and thermal properties in
comparison with individual fibre-reinforced
polymer composites. The advantages of developing
hybrid composites are that they are more reliable
for different applications and more environmental
friendly. In this review paper, we present some
recently published works related to mechanical
and thermal properties of natural/natural fibres, and
natural/synthetic fibre-based hybrid composites. Hybrid composites are one of the emerging fields in material science which has attracted attention for
their different engineering applications.