Tobacco use is one of the largest causes of preventable diseases across the world. It is interesting to note that second-hand smoke exposure is a major public health problem in Malaysia. The objectives of this study are first, to explore the perspectives of pregnant mothers whose partners are smokers, and second, to explore barriers and facilitators to creating a smoke-free environment in their home. This study uses a qualitative method in analysing the experiences of 15 pregnant mothers taken from a specialist healthcare centre in Sungai Buloh, Malaysia. It discusses the perspectives of these pregnant mothers towards their partners’ smoking habits, as well as the barriers and facilitators in creating a smoke-free home. From the interviews, it became apparent that pregnant mothers despised their partners’ smoking habits due to smoke odour, lack of money, and poor health. The barriers in creating a smoke-free home include the attitude of the smoking spouse, difficulty in weaning off the habit, excessive encouragement by a spouse, difficultly in advising a smoking spouse, influences from friends, and wrong perceptions. Meanwhile, the facilitators towards creating a smoke-free home are illnesses developed through smoking, multiple reminders from family members, and nicotine replacement therapy (NRT) were perceived to work on certain smokers and habitual cues in quitting smoking. The transition process towards making homes smoke-free is complex and individualistic; healthcare professionals may need to tailor strategies to take into account the specific contexts of each individual. More education programmes on the impacts of smoking and second-hand smoke exposures to family members and smoking cessation are needed for both pregnant mothers and their smoking partners.
Study site: Gynaecology Clinic, public teaching hospital, Malaysia
In this work, sensors were prepared by depositing the pH sensitive indicator (bromothymol blue)
entrapped in polyaniline sol-gel onto the un-cladded middle potion of optical fiber. Polyaniline is sensitive
to pH. However, it is important to study ways to increase sensitivity of the indicator by improving a
combination of the materials used and pH sensor fabrication method. The fabrication and characterisation
of optical fiber pH sensor on absorption intensity in arbitrary unit (a.u) were evaluated and optimised.
The better sensitivity of the optical pH sensor was used to identify the optimum setting for number of
layers deposited, coated length, and withdrawal rate. Thickness of the membrane film depends on the
number of deposited layers and withdrawal speed which mainly affects sensitivity. The sensitivity of
the optical pH sensor represents the slope (a.u/pH) of the absorbance intensities in pH 4, 5, 7, 9 and 10.
Results obtained herein suggest that the optimised setting for bromothymol blue sol-gel coated optical
fiber with thickness of 285.4 nm is 4 deposited layers, 20mm/s withdrawal rate and 0.5 cm coated length.
Bismuth oxide nanoparticles (Bi2O3 NPs) have gained a spot in the development of novel molecular
probes for in vivo biomedical imaging. It exists in six polymorphic forms and each of them exerts with
different stabilities according to its synthetisation temperature. The aim of this preliminary study is to
determine effect of different synthetiation temperatures on cellular viability in vitro. One hundred µg/ml
Bi2O3 NPs synthesised at 60, 90 and 120°C were characterised using scanning electron microscope (SEM)
and their cytotoxicity was evaluated using cell viability assay (MTT assay) upon 24 hours exposure to
Chang liver cells. Images captured by SEM showed an average diameter of 300 nm monoclinic-shaped
with high crystalline formation of all three Bi2O3 NPs. MTT assay revealed increase in liver cell viability
as the synthetisation temperature of Bi2O3 NPs increase. The outcomes suggested that synthetisation
temperature of Bi2O3 NPs plays a role in cellular viability, hence predictive to the biocompatibility of
these nanoparticles to be applied as in vivo radiographic contrast medium.
The aim of this paper is to interrogate the principle of heat gain by the Overall Thermal Transfer Value
(OTTV) through residential building facades. This study proposes three façade configurations as case
studies to determine their capability of achieving the OTTV set by the current residential standards.
Utilising the OTTV formula provided by the Malaysian Standards, the OTTV of each case study was
calculated using parameters including Window-to-Wall Ratio (WWR), Shading Coefficient (SC), U-values
and solar absorption (α). Results showed that each of the façade generated OTTV exceeding the regulation
of 50 Wm-2. The study uncovered the increase in WWR leading to the increase in OTTV. The OTTV
increased alongside window areas due primarily to the high amount of heat gained through windows,
a constituent component of OTTV. Simultaneously, high Shading Coefficient (SC) and U-values were
found to cause the high amount of solar heat gained through windows. The result underpins the impacts
of high solar heat gain particularly from windows of a building envelope on OTTV. Recommendations
for improvement of OTTV of the residential façades are also discussed..
Personality represents the mixture of features and qualities that built an individual’s distinctive characters including thinking, feeling and behaving. Traditionally, self-assessment method via questionnaire is the most common means to identify personality. Since recommender systems and advertisement
campaigns have evolved rapidly, personality computing has become a popular research field to provide personalisation to users. Currently, researchers have utilised social media data for automatically predicting personality. However, it is complex to mine the social media data as they are noisy, free-format, and
of varying length and multimedia. This paper proposes a decision tree C4.5 algorithm to automatically predict personality based on Big Five model. The Big Five Inventory and ZeroR algorithm were included to be served as the baseline for performance evaluation. Experimental evaluation demonstrated that C4.5
performs better than ZeroR in terms of accuracy.
Keywords: Big Five, decision tree, personality, social media
Applying an intelligent system means the machine is capable of making its own unique decision.
Fuzzy logic is a form of logic that acknowledges other forms of true and false values. With fuzzy logic,
propositions may be diagrammatic with degrees of truthfulness and falsehood. The main aims of this
study are to demonstrate the method to apply fuzzy logic in a structure that uses Arduino as its brain,
and apply it in a line follower mobile robot’s decision making algorithm. This mobile robot consists of
two front wheels and a nub caster at the back. It uses a line follower array that consists of eight infrared
(IR) sensors. The system control is based on the input from IR sensors, which measures the intensity
of light reflected by the track. Data were then transmitted to the microcontroller and will then be sent
the correct command to the motor driver so that the trail can be followed. The performance of the line
follower robot when using the fuzzy logic algorithm was compared to the line follower algorithm, which
uses simple if-else commands. Analysis is primarily on the time taken to complete the track, along
with the behaviour of the robot while manoeuvring. From the results, fuzzy logic is shown to provide a
better performance in terms of the time taken to complete the track compared to the other set of rules. In
addition, from the video recordings, fuzzys are moving smoother as compared to the non-fuzzy logics.
In this work, two types of controller were designed for the nonlinear air blower system PT326 used at
the Instrumentation Laboratory Faculty Electrical Engineering, UiTM, Shah Alam. This work began with
collection of data from the experimental work. Once the S-shape of the system response was obtained,
the procedure of getting the process dead time, τD and time constant τC was applied to the S-shape form.
By determining these two values, the optimum values of PI and PID controllers can be calculated. From
the acquired data, the simulation model was developed in MATLAB/Simulink R2013a software using
the transfer function obtained from the open-loop control system. The modelling system is based on
the transfer function of open-loop air blower system PT326 before the design state of finding a suitable
controller can be suggested. The controller design of PI and PID was obtained using the first method
Ziegler-Nichols tuning rules. The result from the simulation shows that the Ziegler-Nichols first tuning
rules can be applied in designing the PI and PID controller based on S-shape response obtained in
open-loop test.
Pineapple (Ananas Comosus), which has been identified as high-value non-seasonal tropic fruit, is one
of the top five fruits in Malaysia that shows a promising demand in the local and export markets. The
current study was conducted to analyse the efficiency energy inputs used in every activity involved in the
process of pineapple production. The study also aimed to contribute in initiating a database for energy
used in producing pineapples in Malaysia, which would hopefully become a guideline for applications
in related policies in pineapple plantations. Data were collected based on direct field observations at a
pineapple plantation under the management of Koperasi Serbaguna Anak-Anak Selangor Bhd. (KOSAS
Bhd.) at Kg. Kundang in Selangor, Malaysia. Oral interviews were also carried out to gather any relevant
information. The highest energy input used in pineapple production is fertiliser, contributing to 45.65%
from the total energy equivalents used in the production. Fuel is the second highest energy source with
a segment of 20.21% of the total energy, followed by planting material, agrochemicals, human labour
and machine at 17.33%, 12.76%, 3.34 % and 0.69%, respectively. The ratio of energy output/inputs in
pineapple production in the study area was 3.56. Conclusively, it means the energy input is effectively
used in the pineapple production in the study area since the energy output/inputs ratio is greater than 1.
Involvement of machinery in the pineapple cultivation practices in Malaysia is recommended since it
reduces human drudgery and optimises farm’s capability to do work more efficiently and also to offset
the labour shortage problem.
In this paper, a multi-objective analytical method to evaluate the impacts of optimal location and sizing
of distributed generation is presented. This method is based on an analysis of the exact loss formula and
continuous power flow in a radial distribution system. Based on two methods of analysis, power loss
and weakest voltage buses and lines are calculated and then the optimal size of distributed generation
is determined. After that, by considering the minimum power losses and the maximisation of voltage
stability, the proposed index determines and ranks positions to decide the optimal distributed generation
location in the system. This method allows us to find the best places and size to connect a number of
distributed generation units by optimising the objective functions. The simulation results were obtained
using a 33-bus radial distribution system to determine the location and size of the distributed generation
units. The results show the effectiveness of voltage profile improvement, loading factor improvement
and power loss reduction. Further, the problems of a single objective function and the placement of the
distributed generation unit using analytical methods are solved by the proposed approach.
Adaptive Neuro Fuzzy Inference System (ANFIS) is among the most efficient classification and prediction
modelling techniques used to develop accurate relationship between input and output parameters in
different processes. This paper reports the design and evaluation of the classification performances of
two discrete Adaptive Neuro Fuzzy Inference System models, ANFIS Matlab’s built-in model (ANFIS_
LSGD) and a newly ANFIS model with Levenberg-Marquardt algorithm (ANFIS_LSLM). Major steps
were performed, which included classification using grid partitioning method, the ANFIS trained with
least square estimates and backpropagation gradient descent method, as well as the ANFIS trained with
Levenberg-Marquardt algorithm using finite difference technique for computation of a Jacobian matrix.
The proposed ANFIS_LSLM model predicts the degree of patient’s heart disease with better, reliable
and more accurate results. This is due to its new feature of index membership function that determines
the unique membership functions in an ANFIS structure, which indexes them into a row-wise vector. In
addition, an attempt was also done to specify the effectiveness of the model’s performance measuring
accuracy, sensitivity and specificity. A comparison of the two models in terms of training and testing
with the Statlog-Cleveland Heart Disease dataset have also been done.
Credit risk assessment has become an important topic in financial risk administration. Fuzzy clustering
analysis has been applied in credit scoring. Gustafson-Kessel (GK) algorithm has been utilised to cluster
creditworthy customers as against non-creditworthy ones. A good clustering analysis implemented by
good Initial Centres of clusters should be selected. To overcome this problem of Gustafson-Kessel (GK)
algorithm, we proposed a modified version of Kohonen Network (KN) algorithm to select the initial
centres. Utilising similar degree between points to get similarity density, and then by means of maximum
density points selecting; the modified Kohonen Network method generate clustering initial centres to get
more reasonable clustering results. The comparative was conducted using three credit scoring datasets:
Australian, German and Taiwan. Internal and external indexes of validity clustering are computed and
the proposed method was found to have the best performance in these three data sets.
This paper presents the technology of Active Radio Frequency Identification (RFID) and Wireless Mesh
Sensor Network (WMSN) that will be used in agriculture. In this paper, ZigBee technology platform is
applied in 2.45 GHz and active RFID to sustain the WSN by developing a fully automated IoT solution
in agriculture for irrigation system. The system includes a plurality of sensor nodes installed in a crop
field sending an ID, which are embedded sensor and WSN that work on ZigBee 2.4 GHz platform. The
ID was sent to act as a signal of soil in dry condition of a specific area to a reader at base station. The
pump stations will use information from base station to sprinkling water in the specific area of the dry
state automatically. The automatic control system is very practical in agriculture but most of it is based
on schedule and timer regardless of soil condition and temperature. Therefore, wireless automated
irrigation system for efficient water use and production is proposed.
An incorporation of waste tyre particles in concrete has been established to produce a green concrete.
However, despite its advantages, strength reduction is an obvious handicap. To improve the strength,
pre-treatments of the waste tyre particles and addition of Oil Palm Fruit Fibre (OPFF) were chosen and
reported in this study. The addition of OPFF was to influence the internal structure in order to improve
shrinkage and other strength properties. Performance of the composites in compressive, split tensile and
flexural strengths, as well as shrinkage and microstructure were observed. Results showed better behaviour
of the treated tyre crumb mortar rather compared to the untreated tyre, with the replacement of up to 40%
by volume of the treated tyre crumb particles and 0.5- 1.0% OPFF addition by mass of cement content.
The present study aimed to examine the levels of diabetes knowledge, attitudes, self-management and quality of life (QoL) between two countries with different cultural and language backgrounds. Data collection was conducted in two hospitals in Melbourne, Australia, and a hospital in Kelantan, Malaysia. Participants with type 2 diabetes mellitus (T2DM) were asked to complete four questionnaires, measuring diabetes knowledge, attitudes, self-management, and QoL. The differences between the samples were
examined using chi-square and independent samples t-tests. The variables of gender and type of treatment (using insulin or not using insulin for treatment) based on groups were analysed using one-way ANOVA. All analyses were conducted using SPSS 22.0. The results highlighted some similarities and differences between the Australia-based sample and the Malaysia-based sample. In general, the Australia-based participants scored significantly higher in diabetes knowledge and reported more regular self-management of T2DM in exercise, blood glucose testing and foot care. The Australia-based sample also scored higher on attitudes compared to the Malaysia-based participants. On the other hand, Malaysia-based participants reported a lower level of impacts of T2DM on QoL. There was no significant difference between self-management of T2DM in terms of diet and satisfaction as an aspect of QoL related to living with diabetes between the two samples. The present study highlighted the levels of diabetes knowledge, attitudes, self-management, and QoL among Australia-based and Malaysia-based people with T2DM.
Study site: Alfred Hospital and Western Hospital, Australia; Diabetes Health Clinic, Hospital
Universiti Sains Malaysia (HUSM), Kelantan, Malaysia
In this study, a hybrid approach that employs Hopfield neural network and a genetic algorithm in
doing k-SAT problems was proposed. The Hopfield neural network was used to minimise logical
inconsistency in interpreting logic clauses or programme. Hybrid optimisation made use of the global
convergence advantage of the genetic algorithm to deal with learning complexity in the Hopfield
network. The simulation incorporated with genetic algorithm and exhaustive search method with different
k-Satisfiability (k-SAT) problems, namely, the Horn-Satisfiability (HORN-SAT), 2-Satisfiability (2-SAT)
and 3-Satisfiability (3-SAT) will be developed by using Microsoft Visual C++ 2010 Express Software.
The performance of both searching techniques was evaluated based on global minima ratio, hamming
distance and computation time. Simulated results suggested that the genetic algorithm outperformed
exhaustive search in doing k-SAT logic programming in the Hopfield network.
Renewable energy projects in many developing countries need financial and legal back up from
governments and other supportive bodies. There is a viable alternative to finite energy via usage of
biomass waste a renewable energy source. The electrical energy production analysis on biomass waste
presented in this paper is based on the experimental analysis carried out using the laboratory and pilot
scale bioreactors. Electrical energy generated with oscillatory flow bioreactor (OFBR) was 10.12 kWh or
up to 91% higher than the 10 L lab scale bioreactor (0.9 kWh), demonstrating that the novel OFBR has a
great potential for renewable electricity. Also, the pilot scale plant achieves a value of 12.3 kWh, which
the difference is not quite significant with that of OFBR. These results illustrate that the generation of
the renewable electricity is feasible especially with the OFBR thereby achieving high methane potential
during the treatment of manure and food waste. Nevertheless, energy recoveries should be enhanced to
improve the entire operational performance.
Preventive maintenance (PM) is a predetermined task that constitutes any maintenance actions performed
before the quantity or quality of product equipment deteriorates. PM is primarily aimed at protecting assets,
improving system reliability, and decreasing system downtime. Recently, the implementation of PM is
becoming more challenging due to the increase in complexity of manufacturing systems. A case study on
the effect of different parameters from a maintenance perspective on industrial shop floor performance is
presented. The parameters include number of technicians, number of operations in each machine before
maintenance actions, and volume of parts ordered by customers. This present research demonstrates the
application of WITNESS simulation software to develop shop floor simulation model and the use of
analysis of variance (ANOVA) to measure the significance of such parameters on performance.
A Structural Equation Model (SEM) is often used to test whether a hypothesised theoretical model agrees with data by examining the model fit. This study investigates the effect of sample size and distribution of data (normal and non-normal) on goodness of fit measures in structural equation model. Simulation results confirm that the GoF measures are affected by sample size, whereas they are quite robust when data are not normal. Absolute measures (GFI, AGFI, RMSEA) are more affected by sample size while incremental fit measures such as TLI and CFI are less affected by sample size and non-normality.
This paper offers a technique to construct a prediction interval for the future value of the last variable in the vector r of m variables when the number of observed values of r is small. Denoting r(t) as the time-t value of r, we model the time-(t+1) value of the m-th variable to be dependent on the present and l-1 previous values r(t), r(t-1), …, r(t-l+1) via a conditional distribution which is derived from an (ml+1)-dimensional power-normal distribution. The 100(α / 2)% and 100(1−α / 2)% points of the conditional distribution may then be used to form a prediction interval for the future value of the m-th variable. A method is introduced to estimate the above (ml+1)-dimensional power-normal distribution such that the coverage probability of the resulting prediction interval is nearer to the target value 1- α .
Portfolio optimisation is one of the most crucial issues in investment decision-making and has received considerable attention from researchers and practitioners. Traditionally, the portfolio optimisation models are formulated based on the assumption that investors have complete information on the distribution of random returns. However, in real life case, this is not possible since decisions have to be made under uncertainty. This paper deals with a fuzzy portfolio optimisation problem in which returns and turnover rates of securities are represented by fuzzy variables. A goal programming model is proposed to optimise three objectives: maximisation of portfolio return, maximisation of liquidity and minimisation of the portfolio risk. The cardinality constraints, floor and ceiling constraints are also taken into consideration. Finally, a numerical experiment using real data is conducted to demonstrate the applicability of the model.