Abstract: Combined therapy using Interferon and Ribavirin is the standard treatment in patients with chronic hepatitis C. However, the number of responders to this treatment is low, whereas its cost and side effects are high. Therefore, there is a clear need to predict patient’s response to the treatment based on clinical information to protect the patients from the bad drawbacks, Intolerable side effects and waste of money. Different machine learning techniques have been developed to fulfill this purpose. From these techniques are Associative Classification (AC) and Decision Tree (DT). The aim of this research is to compare the performance of these two techniques in the prediction of virological response to the standard treatment of HCV from clinical information. 200 patients treated with Interferon and Ribavirin; were analyzed using AC and DT. 150 cases had been used to train the classifiers and 50 cases had been used to test the classifiers. The experiment results showed that the two techniques had given acceptable results however the best accuracy for the AC reached 92% whereas for DT reached 80%.
Abstract: A nonlinear model of two-beam free-electron laser
(FEL) in the absence of slippage is presented. The two beams are
assumed to be cold with different energies and the fundamental
resonance of the higher energy beam is at the third harmonic of lower
energy beam. By using Maxwell-s equations and full Lorentz force
equations of motion for the electron beams, coupled differential
equations are derived and solved numerically by the fourth order
Runge–Kutta method. In this method a considerable growth of third
harmonic electromagnetic field in the XUV and X-ray regions is
predicted.
Abstract: The Constraints imposed by non-thermal
leptogenesis on the survival of the neutrino mass models describing
the presently available neutrino mass patterns, are studied
numerically. We consider the Majorana CP violating phases coming
from right-handed Majorana mass matrices to estimate the baryon
asymmetry of the universe, for different neutrino mass models
namely quasi-degenerate, inverted hierarchical and normal
hierarchical models, with tribimaximal mixings. Considering two
possible diagonal forms of Dirac neutrino mass matrix as either
charged lepton or up-quark mass matrix, the heavy right-handed
mass matrices are constructed from the light neutrino mass matrix.
Only the normal hierarchical model leads to the best predictions of
baryon asymmetry of the universe, consistent with observations in
non-thermal leptogenesis scenario.
Abstract: Working memory (WM) can be defined as the system
which actively holds information in the mind to do tasks in spite of
the distraction. Contrary, short-term memory (STM) is a system that
represents the capacity for the active storing of information without
distraction. There has been accumulating evidence that these types of
memory are related to higher cognition (HC). The aim of this study
was to verify the relationship between HC and memory (visual STM
and WM, auditory STM and WM). 59 primary school children were
tested by intelligence test, mathematical tasks (HC) and memory
subtests. We have shown that visual but not auditory memory is a
significant predictor of higher cognition. The relevance of these
results are discussed.
Abstract: An incentive for performance, as one subsystem of a
new performance management system, has been implemented in the
Thai public sector since 2004. This research investigates the
development of organizational justice in the incentive allocation by
comparing the roles of distributive and procedural justice on national
personnel-s attitudinal outcomes (incentive satisfaction and job
performance) between 2 periods, i.e. 2006 and 2008. The data were
collected via self-administered questionnaires completed by national
government officers and employees. They were stratified using multistage
sampling with 2,600 usable samples or 72.0% response rate in
2006, and 1,969 usable samples or 59.3% in 2008. The findings are:
(1) There is no difference in means between the two periods relating
to distributive justice, procedural justice, incentive satisfaction and
job performance. (2) Distributive justice and procedural justice
played more important roles in predicting incentive satisfaction and
job performance in 2008 than in 2006.
Abstract: In order to enhance the aircraft survivability, the
infrared signatures emitted by hot engine parts should be determined
exactly. For its reduction it is necessary for the rear fuselage
temperature to be decreased. In this study, numerical modeling of flow
fields and heat transfer characteristics of an aircraft nozzle is
performed and its temperature distribution along each component wall
is predicted. The radiation shield is expected to reduce the skin
temperature of rear fuselage. The effect of material characteristic of
radiation shield on the heat transfer is also investigated. Through this
numerical analysis, design parameters related to the susceptibility of
aircraft are examined.
Abstract: BEAMnrc was used to calculate the spectrum and
HVL for X-ray Beam during low energy X-ray radiation using tube model: SRO 33/100 /ROT 350 Philips. The results of BEAMnrc
simulation and measurements were compared to the IPEM report
number 78 and SpekCalc software. Three energies 127, 103 and 84
Kv were used. In these simulation a tungsten anode with 1.2 mm for
Be window were used as source. HVLs were calculated from
BEAMnrc spectrum with air Kerma method for four different filters.
For BEAMnrc one billion particles were used as original particles for
all simulations. The results show that for 127 kV, there was
maximum 5.2 % difference between BEAMnrc and Measurements
and minimum was 0.7% .the maximum 9.1% difference between
BEAMnrc and IPEM and minimum was 2.3% .The maximum
difference was 3.2% between BEAMnrc and SpekCal and minimum
was 2.8%. The result show BEAMnrc was able to satisfactory predict
the quantities of Low energy Beam as well as high energy X-ray
radiation.
Abstract: When considering the development of constitutive
equations describing the behavior of materials under cyclic plastic
strains, different kinds of formulations can be adopted. The primary
intention of this study is to develop computer programming of
plasticity models to accurately predict the life of engineering
components. For this purpose, the energy or cyclic strain is computed
in multi-surface plasticity models in non-proportional loading and to
present their procedures and codes results.
Abstract: Fault-proneness of a software module is the
probability that the module contains faults. A correlation exists
between the fault-proneness of the software and the measurable
attributes of the code (i.e. the static metrics) and of the testing (i.e.
the dynamic metrics). Early detection of fault-prone software
components enables verification experts to concentrate their time and
resources on the problem areas of the software system under
development. This paper introduces Genetic Algorithm based
software fault prediction models with Object-Oriented metrics. The
contribution of this paper is that it has used Metric values of JEdit
open source software for generation of the rules for the classification
of software modules in the categories of Faulty and non faulty
modules and thereafter empirically validation is performed. The
results shows that Genetic algorithm approach can be used for
finding the fault proneness in object oriented software components.
Abstract: Until recently, researchers have developed various
tools and methodologies for effective clinical decision-making.
Among those decisions, chest pain diseases have been one of
important diagnostic issues especially in an emergency department. To
improve the ability of physicians in diagnosis, many researchers have
developed diagnosis intelligence by using machine learning and data
mining. However, most of the conventional methodologies have been
generally based on a single classifier for disease classification and
prediction, which shows moderate performance. This study utilizes an
ensemble strategy to combine multiple different classifiers to help
physicians diagnose chest pain diseases more accurately than ever.
Specifically the ensemble strategy is applied by using the integration
of decision trees, neural networks, and support vector machines. The
ensemble models are applied to real-world emergency data. This study
shows that the performance of the ensemble models is superior to each
of single classifiers.
Abstract: This paper investigates the problem of tracking spa¬tiotemporal changes of a satellite image through the use of Knowledge Discovery in Database (KDD). The purpose of this study is to help a given user effectively discover interesting knowledge and then build prediction and decision models. Unfortunately, the KDD process for spatiotemporal data is always marked by several types of imperfections. In our paper, we take these imperfections into consideration in order to provide more accurate decisions. To achieve this objective, different KDD methods are used to discover knowledge in satellite image databases. Each method presents a different point of view of spatiotemporal evolution of a query model (which represents an extracted object from a satellite image). In order to combine these methods, we use the evidence fusion theory which considerably improves the spatiotemporal knowledge discovery process and increases our belief in the spatiotemporal model change. Experimental results of satellite images representing the region of Auckland in New Zealand depict the improvement in the overall change detection as compared to using classical methods.
Abstract: Internal combustion engines rejects 30-40% of the
energy supplied by fuel to the environment through exhaust gas. thus, there is a possibility for further significant improvement of efficiency with the utilization of exhaust gas energy and its conversion to mechanical energy or electrical energy. The Thermo-Electric
Generator (TEG) will be located in the exhaust system and will make use of an energy flow between the warmer exhaust gas and the external environment. Predict to th optimum position of temperature
distribution and the performance of TEG through numerical analysis.
The experimental results obtained show that the power output significantly increases with the temperature difference between cold
and hot sides of a thermoelectric generator.
Abstract: In sport, human resources management gives special
attention to method of applying volunteers, their maintenance, and
participation of volunteers with each other and management
approaches for better operation of events celebrants. The recognition
of volunteers- characteristics and motives is important to notice,
because it makes the basis of their participation and commitment at
sport environment. The motivation and commitment of 281
volunteers were assessed using the organizational commitment scale,
motivation scale and personal characteristics questionnaire.The
descriptive results showed that; 64% of volunteers were women with
age average 21/24 years old. They were physical education student,
single (71/9%), without occupation (53%) and with average of 5
years sport experience. Their most important motivation was career
factor and the most important commitment factor was normative
factor. The results of examining the hypothesized showed that; age,
sport experience and education are effective in the amount of
volunteers- commitment. And the motive factors such as career,
material, purposive and protective factors also have the power to
predict the amount of sports volunteers- commitment value.
Therefore it is recommended to provide possible opportunities for
volunteers and carrying out appropriate instructional courses by
events executive managers.
Abstract: Purpose: To explore the use of Curvelet transform to
extract texture features of pulmonary nodules in CT image and support
vector machine to establish prediction model of small solitary
pulmonary nodules in order to promote the ratio of detection and
diagnosis of early-stage lung cancer. Methods: 2461 benign or
malignant small solitary pulmonary nodules in CT image from 129
patients were collected. Fourteen Curvelet transform textural features
were as parameters to establish support vector machine prediction
model. Results: Compared with other methods, using 252 texture
features as parameters to establish prediction model is more proper.
And the classification consistency, sensitivity and specificity for the
model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based
on texture features extracted from Curvelet transform, support vector
machine prediction model is sensitive to lung cancer, which can
promote the rate of diagnosis for early-stage lung cancer to some
extent.
Abstract: Scale defects are common surface defects in hot steel rolling. The modelling of such defects is problematic and their causes are not straightforward. In this study, we investigated genetic algorithms in search for a mathematical solution to scale formation. For this research, a high-dimensional data set from hot steel rolling process was gathered. The synchronisation of the variables as well as the allocation of the measurements made on the steel strip were solved before the modelling phase.
Abstract: The indoor airflow with a mixed natural/forced convection
was numerically calculated using the laminar and turbulent
approach. The Boussinesq approximation was considered for a simplification
of the mathematical model and calculations. The results
obtained, such as mean velocity fields, were successfully compared
with experimental PIV flow visualizations. The effect of the distance
between the cooled wall and the heat exchanger on the temperature
and velocity distributions was calculated. In a room with a simple
shape, the computational code OpenFOAM demonstrated an ability to
numerically predict flow patterns. Furthermore, numerical techniques,
boundary type conditions and the computational grid quality were
examined. Calculations using the turbulence model k-omega had a
significant effect on the results influencing temperature and velocity
distributions.
Abstract: This paper describes the application of a model
predictive controller to the problem of batch reactor temperature
control. Although a great deal of work has been done to improve
reactor throughput using batch sequence control, the control of the
actual reactor temperature remains a difficult problem for many
operators of these processes. Temperature control is important as
many chemical reactions are sensitive to temperature for formation of
desired products. This controller consist of two part (1) a nonlinear
control method GLC (Global Linearizing Control) to create a linear
model of system and (2) a Model predictive controller used to obtain
optimal input control sequence. The temperature of reactor is tuned
to track a predetermined temperature trajectory that applied to the
batch reactor. To do so two input signals, electrical powers and the
flow of coolant in the coil are used. Simulation results show that the
proposed controller has a remarkable performance for tracking
reference trajectory while at the same time it is robust against noise
imposed to system output.
Abstract: Constant upgrading of Enterprise Resource Planning
(ERP) systems is necessary, but can cause new defects. This paper
attempts to model the likelihood of defects after completed upgrades
with Weibull defect probability density function (PDF). A case study
is presented analyzing data of recorded defects obtained for one ERP
subsystem. The trends are observed for the value of the parameters
relevant to the proposed statistical Weibull distribution for a given
one year period. As a result, the ability to predict the appearance of
defects after the next upgrade is described.
Abstract: Methods of contemporary mathematical physics such
as chaos theory are useful for analyzing and understanding the
behavior of complex biological and physiological systems. The three
dimensional model of HIV/AIDS is the basis of active research since
it provides a complete characterization of disease dynamics and the
interaction of HIV-1 with the immune system. In this work, the
behavior of the HIV system is analyzed using the three dimensional
HIV model and a chaotic measure known as the Hurst exponent.
Results demonstrate that Hurst exponents of CD4, CD8 cells and
viral load vary nonlinearly with respect to variations in system
parameters. Further, it was observed that the three dimensional HIV
model can accommodate both persistent (H>0.5) and anti-persistent
(H
Abstract: This paper presents the investigation results of UV
measurement at different level of altitudes and the development of a
new portable instrument for measuring UV. The rapid growth of
industrial sectors in developing countries including Malaysia, brings
not only income to the nation, but also causes pollution in various
forms. Air pollution is one of the significant contributors to global
warming by depleting the Ozone layer, which would reduce the
filtration of UV rays. Long duration of exposure to high to UV rays
has many devastating health effects to mankind directly or indirectly
through destruction of the natural resources. This study aimed to
show correlation between UV and altitudes which indirectly can help
predict Ozone depletion. An instrument had been designed to
measure and monitors the level of UV. The instrument comprises of
two main blocks namely data logger and Graphic User Interface
(GUI). Three sensors were used in the data logger to detect changes
in the temperature, humidity and ultraviolet. The system has
undergone experimental measurement to capture data at two different
conditions; industrial area and high attitude area. The performance of
the instrument showed consistency in the data captured and the
results of the experiment drew a significantly high reading of UV at
high altitudes.