Abstract: The procurement and cost management approach adopted for mechanical and electrical (M&E) services in Malaysian construction industry have been criticized for its inefficiency. The study examined early cost estimating practices adopted for mechanical and electrical services (M&E) in Malaysia so as to understand the level of compliance of the current techniques with best practices. The methodology adopted for the study is a review of bidding documents used on both completed and on – going building projects awarded between 2008 – 2010 under 9th Malaysian Plan. The analysis revealed that, M&E services cost cannot be reliably estimated at pre-contract stage; the bidding techniques adopted for M&E services failed to provide uniform basis for contractors to submit tender; detailed measurement of items were not made which could complicate post contract cost control and financial management. The paper concluded that, there is need to follow a structured approach in determining the pre-contract cost estimate for M&E services which will serve as a virile tool for post contract cost control.
Abstract: Fundamental sensor-motor couplings form the backbone
of most mobile robot control tasks, and often need to be implemented
fast, efficiently and nevertheless reliably. Machine learning
techniques are therefore often used to obtain the desired sensor-motor
competences.
In this paper we present an alternative to established machine
learning methods such as artificial neural networks, that is very fast,
easy to implement, and has the distinct advantage that it generates
transparent, analysable sensor-motor couplings: system identification
through nonlinear polynomial mapping.
This work, which is part of the RobotMODIC project at the
universities of Essex and Sheffield, aims to develop a theoretical understanding
of the interaction between the robot and its environment.
One of the purposes of this research is to enable the principled design
of robot control programs.
As a first step towards this aim we model the behaviour of the
robot, as this emerges from its interaction with the environment, with
the NARMAX modelling method (Nonlinear, Auto-Regressive, Moving
Average models with eXogenous inputs). This method produces
explicit polynomial functions that can be subsequently analysed using
established mathematical methods.
In this paper we demonstrate the fidelity of the obtained NARMAX
models in the challenging task of robot route learning; we present a
set of experiments in which a Magellan Pro mobile robot was taught
to follow four different routes, always using the same mechanism to
obtain the required control law.
Abstract: Oxidative stress is considered to be the cause for onset
and the progression of type 2 diabetes mellitus (T2DM) and
complications including neuropathy. It is a deleterious process that
can be an important mediator of damage to cell structures: protein,
lipids and DNA. Data suggest that in patients with diabetes and
diabetic neuropathy DNA repair is impaired, which prevents effective
removal of lesions. Objective: The aim of our study was to evaluate
the association of the hOGG1 (326 Ser/Cys) and XRCC1 (194
Arg/Trp, 399 Arg/Gln) gene polymorphisms whose protein is
involved in the BER pathway with DNA repair efficiency in patients
with diabetes type 2 and diabetic neuropathy compared to the healthy
subjects. Genotypes were determined by PCR-RFLP analysis in 385
subjects, including 117 with type 2 diabetes, 56 with diabetic
neuropathy and 212 with normal glucose metabolism. The
polymorphisms studied include codon 326 of hOGG1 and 194, 399
of XRCC1 in the base excision repair (BER) genes. Comet assay was
carried out using peripheral blood lymphocytes from the patients and
controls. This test enabled the evaluation of DNA damage in cells
exposed to hydrogen peroxide alone and in the combination with the
endonuclease III (Nth). The results of the analysis of polymorphism
were statistically examination by calculating the odds ratio (OR) and
their 95% confidence intervals (95% CI) using the ¤ç2-tests. Our data
indicate that patients with diabetes mellitus type 2 (including those
with neuropathy) had higher frequencies of the XRCC1 399Arg/Gln
polymorphism in homozygote (GG) (OR: 1.85 [95% CI: 1.07-3.22],
P=0.3) and also increased frequency of 399Gln (G) allele (OR: 1.38
[95% CI: 1.03-1.83], P=0.3). No relation to other polymorphisms
with increased risk of diabetes or diabetic neuropathy. In T2DM
patients complicated by neuropathy, there was less efficient repair of
oxidative DNA damage induced by hydrogen peroxide in both the
presence and absence of the Nth enzyme. The results of our study
suggest that the XRCC1 399 Arg/Gln polymorphism is a significant
risk factor of T2DM in Polish population. Obtained data suggest a
decreased efficiency of DNA repair in cells from patients with
diabetes and neuropathy may be associated with oxidative stress.
Additionally, patients with neuropathy are characterized by even
greater sensitivity to oxidative damage than patients with diabetes,
which suggests participation of free radicals in the pathogenesis of
neuropathy.
Abstract: This paper describes a proposed support system which
enables applications designers to effectively create VR applications
using multiple haptic APIs. When the VR designers create
applications, it is often difficult to handle and understand many
parameters and functions that have to be set in the application program
using documentation manuals only. This complication may disrupt
creative imagination and result in inefficient coding. So, we proposed
the support application which improved the efficiency of VR
applications development and provided the interactive components of
confirmation of operations with haptic sense previously.
In this paper, we describe improvements of our former proposed
support application, which was applicable to multiple APIs and haptic
devices, and evaluate the new application by having participants
complete VR program. Results from a preliminary experiment suggest
that our application facilitates creation of VR applications.
Abstract: The objective of this research was to identify the
vegetation-soil relationships in Nodushan arid rangelands of Yazd. 5
sites were selected for measuring the cover of plant species and soil
attributes. Soil samples were taken in 0-10 and 10-80 cm layers. The
species studied were Salsola tomentosa, Salsola arbuscula, Peganum
harmala, Zygophylum eurypterum and Eurotia ceratoides. Canonical
correspondence analysis (CCA) was used to analyze the data. Based
on the CCA results, 74.9 % of vegetation-soil variation was explained
by axis 1-3. Axis 1, 2 and 3 accounted for 27.2%, 24.9 % and 22.8%
of variance respectively. Correlation between axis 1, 2, 3 and speciesedaphic
variables were 0.995, 0.989, 0.981 respectively. Soil texture,
lime, salinity and organic matter significantly influenced the
distribution of these plant species. Determination of soil-vegetation
relationships will be useful for managing and improving rangelands
in arid and semi arid environments.
Abstract: In this paper is described a new conception of the
Cartesian robot for automated assembly and also disassembly
process. The advantage of this conception is the utilization the
Cartesian assembly robot with its all peripheral automated devices for
assembly of the assembled product. The assembly product in the end
of the lifecycle can be disassembled with the same Cartesian
disassembly robot with the use of the same peripheral automated
devices and equipment. It is a new approach to problematic solving
and development of the automated assembly systems with respect to
lifecycle management of the assembly product and also assembly
system with Cartesian robot. It is also important to develop the
methodical process for design of automated assembly and
disassembly system with Cartesian robot. Assembly and disassembly
system use the same Cartesian robot input and output devices,
assembly and disassembly units in one workplace with different
application. Result of design methodology is the verification and
proposition of real automated assembly and disassembly workplace
with Cartesian robot for known verified model of assembled actuator.
Abstract: In this paper, image compression using hybrid vector
quantization scheme such as Multistage Vector Quantization
(MSVQ) and Pyramid Vector Quantization (PVQ) are introduced. A
combined MSVQ and PVQ are utilized to take advantages provided
by both of them. In the wavelet decomposition of the image, most of
the information often resides in the lowest frequency subband.
MSVQ is applied to significant low frequency coefficients. PVQ is
utilized to quantize the coefficients of other high frequency
subbands. The wavelet coefficients are derived using lifting scheme.
The main aim of the proposed scheme is to achieve high compression
ratio without much compromise in the image quality. The results are
compared with the existing image compression scheme using MSVQ.
Abstract: The objective from this paper is to design a solar
thermal engine for space vehicles orbital control and electricity
generation. A computational model is developed for the prediction of
the solar thermal engine performance for different design parameters and conditions in order to enhance the engine efficiency. The engine is divided into two main subsystems. First, the concentrator dish
which receives solar energy from the sun and reflects them to the
cavity receiver. The second one is the cavity receiver which receives
the heat flux reflected from the concentrator and transfers heat to the
fluid passing over. Other subsystems depend on the application required from the engine. For thrust application, a nozzle is
introduced to the system for the fluid to expand and produce thrust.
Hydrogen is preferred as a working fluid in the thruster application.
Results model developed is used to determine the thrust for a
concentrator dish 4 meters in diameter (provides 10 kW of energy),
focusing solar energy to a 10 cm aperture diameter cavity receiver.
The cavity receiver outer length is 50 cm and the internal cavity is 47
cm in length. The suggested design material of the internal cavity is
tungsten to withstand high temperature. The thermal model and
analysis shows that the hydrogen temperature at the plenum reaches
2000oK after about 250 seconds for hot start operation for a flow rate
of 0.1 g/sec.Using solar thermal engine as an electricity generation
device on earth is also discussed. In this case a compressor and
turbine are used to convert the heat gained by the working fluid (air)
into mechanical power. This mechanical power can be converted into
electrical power by using a generator.
Abstract: In this work, the primary compressive strength
components of human femur trabecular bone are qualitatively
assessed using image processing and wavelet analysis. The Primary
Compressive (PC) component in planar radiographic femur trabecular
images (N=50) is delineated by semi-automatic image processing
procedure. Auto threshold binarization algorithm is employed to
recognize the presence of mineralization in the digitized images. The
qualitative parameters such as apparent mineralization and total area
associated with the PC region are derived for normal and abnormal
images.The two-dimensional discrete wavelet transforms are utilized
to obtain appropriate features that quantify texture changes in medical
images .The normal and abnormal samples of the human femur are
comprehensively analyzed using Harr wavelet.The six statistical
parameters such as mean, median, mode, standard deviation, mean
absolute deviation and median absolute deviation are derived at level
4 decomposition for both approximation and horizontal wavelet
coefficients. The correlation coefficient of various wavelet derived
parameters with normal and abnormal for both approximated and
horizontal coefficients are estimated. It is seen that in almost all cases
the abnormal show higher degree of correlation than normals. Further
the parameters derived from approximation coefficient show more
correlation than those derived from the horizontal coefficients. The
parameters mean and median computed at the output of level 4 Harr
wavelet channel was found to be a useful predictor to delineate the
normal and the abnormal groups.
Abstract: The clinical usefulness of heart rate variability is
limited to the range of Holter monitoring software available. These
software algorithms require a normal sinus rhythm to accurately
acquire heart rate variability (HRV) measures in the frequency
domain. Premature ventricular contractions (PVC) or more
commonly referred to as ectopic beats, frequent in heart failure,
hinder this analysis and introduce ambiguity. This investigation
demonstrates an algorithm to automatically detect ectopic beats by
analyzing discrete wavelet transform coefficients. Two techniques
for filtering and replacing the ectopic beats from the RR signal are
compared. One technique applies wavelet hard thresholding
techniques and another applies linear interpolation to replace ectopic
cycles. The results demonstrate through simulation, and signals
acquired from a 24hr ambulatory recorder, that these techniques can
accurately detect PVC-s and remove the noise and leakage effects
produced by ectopic cycles retaining smooth spectra with the
minimum of error.
Abstract: The study investigated the effects of Teaching Games
for Understanding approach on students ‘cognitive learning outcome.
The study was a quasi-experimental non-equivalent pretest-posttest
control group design whereby 10 year old primary school students
(n=72) were randomly assigned to an experimental and a control
group. The experimental group students were exposed with TGfU
approach and the control group with the Traditional Skill approach of
handball game. Game Performance Assessment Instrument (GPAI)
was used to measure students' tactical understanding and decision
making in 3 versus 3 handball game situations. Analysis of
covariance (ANCOVA) was used to analyze the data. The results
reveal that there was a significant difference between the TGfU
approach group and the traditional skill approach group students on
post test score (F (1, 69) = 248.83, p < .05). The findings of this
study suggested the importance of TGfU approach to improve
primary students’ tactical understanding and decision making in
handball game.
Abstract: Yeast cells live in a constantly changing environment that requires the continuous adaptation of their genomic program in order to sustain their homeostasis, survive and proliferate. Due to the advancement of high throughput technologies, there is currently a large amount of data such as gene expression, gene deletion and protein-protein interactions for S. Cerevisiae under various environmental conditions. Mining these datasets requires efficient computational methods capable of integrating different types of data, identifying inter-relations between different components and inferring functional groups or 'modules' that shape intracellular processes. This study uses computational methods to delineate some of the mechanisms used by yeast cells to respond to environmental changes. The GRAM algorithm is first used to integrate gene expression data and ChIP-chip data in order to find modules of coexpressed and co-regulated genes as well as the transcription factors (TFs) that regulate these modules. Since transcription factors are themselves transcriptionally regulated, a three-layer regulatory cascade consisting of the TF-regulators, the TFs and the regulated modules is subsequently considered. This three-layer cascade is then modeled quantitatively using artificial neural networks (ANNs) where the input layer corresponds to the expression of the up-stream transcription factors (TF-regulators) and the output layer corresponds to the expression of genes within each module. This work shows that (a) the expression of at least 33 genes over time and for different stress conditions is well predicted by the expression of the top layer transcription factors, including cases in which the effect of up-stream regulators is shifted in time and (b) identifies at least 6 novel regulatory interactions that were not previously associated with stress-induced changes in gene expression. These findings suggest that the combination of gene expression and protein-DNA interaction data with artificial neural networks can successfully model biological pathways and capture quantitative dependencies between distant regulators and downstream genes.
Abstract: Cement, the most widely used construction material
is very brittle and characterized by low tensile strength and strain
capacity. Macro to nano fibers are added to cement to provide
tensile strength and ductility to it. Carbon Nanotube (CNT), one of
the nanofibers, has proven to be a promising reinforcing material in
the cement composites because of its outstanding mechanical
properties and its ability to close cracks at the nano level. The
experimental investigations for CNT reinforced cement is costly,
time consuming and involves huge number of trials. Mathematical
modeling of CNT reinforced cement can be done effectively and
efficiently to arrive at the mechanical properties and to reduce the
number of trials in the experiments. Hence, an attempt is made to
numerically study the effective mechanical properties of CNT
reinforced cement numerically using Representative Volume
Element (RVE) method. The enhancement in its mechanical
properties for different percentage of CNTs is studied in detail.
Abstract: The aim of this study is to find out and analyze the
role of gender and age on the perceptions of students to the distant
online program offered by Vocational High School in Sakarya
University. The research is based on a questionnaire as a mean of
data collection method to find out the role of age and gender on the
student-s perceptions toward online education, and the study
progressed through finding relationships between the variables used
in the data collection instrument. The findings of the analysis
revealed that although the students registered to the online program
by will, they preferred the traditional face-to-face education due to
the difficulty of the nonverbal communication, their incompetence of
using the technology required, and their belief in traditional face-toface
learning more than online education.
Regarding gender, the results showed that the female students
have a better perception of the online education as opposed to the
male students. Regarding age, the results showed that the older the
students are the more is their preference towards attending face-toface
classes.
Abstract: The 2008 Candlelight Protests of Korea was very
significant to portray the political environment among the South
Korean youth. Many challenges and new advanced technologies have
driven the youth community to be engaged in the political arena that
has shifted them from traditional Korean youth to a very greater
community. Due to historical perspective with the people of North
Korea, the young generation has embraced different view of ethnic
nationalism. This study examines the youth involvement in politics in
line with their level of acceptance the practice of democracy. The
increase usage of new media has shown great results in the survey
results whereby the youth used as a platform to gain political
information and brought higher degree of their sociopolitical interests
among them. Furthermore, the rise of nationalism and patriotism will
be discussed in this paper to the dynamism of the political approaches
used by the Korea government
Abstract: Breast skin-line estimation and breast segmentation is an important pre-process in mammogram image processing and computer-aided diagnosis of breast cancer. Limiting the area to be processed into a specific target region in an image would increase the accuracy and efficiency of processing algorithms. In this paper we are presenting a new algorithm for estimating skin-line and breast segmentation using fast marching algorithm. Fast marching is a partial-differential equation based numerical technique to track evolution of interfaces. We have introduced some modifications to the traditional fast marching method, specifically to improve the accuracy of skin-line estimation and breast tissue segmentation. Proposed modifications ensure that the evolving front stops near the desired boundary. We have evaluated the performance of the algorithm by using 100 mammogram images taken from mini-MIAS database. The results obtained from the experimental evaluation indicate that this algorithm explains 98.6% of the ground truth breast region and accuracy of the segmentation is 99.1%. Also this algorithm is capable of partially-extracting nipple when it is available in the profile.
Abstract: Knowledge bases are basic components of expert
systems or intelligent computational programs. Knowledge bases
provide knowledge, events that serve deduction activity,
computation and control. Therefore, researching and developing of
models for knowledge representation play an important role in
computer science, especially in Artificial Intelligence Science and
intelligent educational software. In this paper, the extensive
deduction computational model is proposed to design knowledge
bases whose attributes are able to be real values or functional values.
The system can also solve problems based on knowledge bases.
Moreover, the models and algorithms are applied to produce the
educational software for solving alternating current problems or
solving set of equations automatically.
Abstract: Many natural language expressions are ambiguous, and
need to draw on other sources of information to be interpreted.
Interpretation of the e word تعاون to be considered as a noun or a verb
depends on the presence of contextual cues. To interpret words we
need to be able to discriminate between different usages. This paper
proposes a hybrid of based- rules and a machine learning method for
tagging Arabic words. The particularity of Arabic word that may be
composed of stem, plus affixes and clitics, a small number of rules
dominate the performance (affixes include inflexional markers for
tense, gender and number/ clitics include some prepositions,
conjunctions and others). Tagging is closely related to the notion of
word class used in syntax. This method is based firstly on rules (that
considered the post-position, ending of a word, and patterns), and
then the anomaly are corrected by adopting a memory-based learning
method (MBL). The memory_based learning is an efficient method to
integrate various sources of information, and handling exceptional
data in natural language processing tasks. Secondly checking the
exceptional cases of rules and more information is made available to
the learner for treating those exceptional cases. To evaluate the
proposed method a number of experiments has been run, and in
order, to improve the importance of the various information in
learning.
Abstract: The overall objective of this research is a strain
improvement technology for efficient pectinase production. A novel
cells cultivation technology by immobilization of fungal cells has
been studied in long time continuous fermentations. Immobilization
was achieved by using of new material for absorption of stores of
immobilized cultures which was for the first time used for
immobilization of microorganisms. Effects of various conditions of
nitrogen and carbon nutrition on the biosynthesis of pectolytic
enzymes in Aspergillus awamori 1-8 strain were studied. Proposed
cultivation technology along with optimization of media components
for pectinase overproduction led to increased pectinase productivity
in Aspergillus awamori 1-8 from 7 to 8 times. Proposed technology
can be applied successfully for production of major industrial
enzymes such as α-amylase, protease, collagenase etc.
Abstract: Video-on-demand (VOD) is designed by using content delivery networks (CDN) to minimize the overall operational cost and to maximize scalability. Estimation of the viewing pattern (i.e., the relationship between the number of viewings and the ranking of VOD contents) plays an important role in minimizing the total operational cost and maximizing the performance of the VOD systems. In this paper, we have analyzed a large body of commercial VOD viewing data and found that the viewing rank distribution fits well with the parabolic fractal distribution. The weighted linear model fitting function is used to estimate the parameters (coefficients) of the parabolic fractal distribution. This paper presents an analytical basis for designing an optimal hierarchical VOD contents distribution system in terms of its cost and performance.