Abstract: This paper examines the interplay of policy options
and cost-effective technology in providing sustainable distance
education. A case study has been conducted among the learners and
teachers. The emergence of learning technologies through CD,
internet, and mobile is increasingly adopted by distance institutes for
quick delivery and cost-effective factors. Their sustainability is
conditioned by the structure of learners and well as the teaching
community. The structure of learners in terms of rural and urban
background revealed similarity in adoption and utilization of mobile
learning. In other words, the technology transcended the rural-urban
dichotomy. The teaching community was divided into two groups on
policy issues. This study revealed both cost-effective as well as
sustainability impacts on different learners groups divided by rural
and urban location.
Abstract: In this paper, we have presented the effect of varying
time-delays on performance and stability in the single-channel multirate
sampled-data system in hard real-time (RT-Linux) environment.
The sampling task require response time that might exceed the
capacity of RT-Linux. So a straight implementation with RT-Linux is
not feasible, because of the latency of the systems and hence,
sampling period should be less to handle this task. The best sampling
rate is chosen for the sampled-data system, which is the slowest rate
meets all performance requirements. RT-Linux is consistent with its
specifications and the resolution of the real-time is considered 0.01
seconds to achieve an efficient result. The test results of our
laboratory experiment shows that the multi-rate control technique in
hard real-time operating system (RTOS) can improve the stability
problem caused by the random access delays and asynchronization.
Abstract: Data Mining aims at discovering knowledge out of
data and presenting it in a form that is easily comprehensible to
humans. One of the useful applications in Egypt is the Cancer
management, especially the management of Acute Lymphoblastic
Leukemia or ALL, which is the most common type of cancer in
children.
This paper discusses the process of designing a prototype that can
help in the management of childhood ALL, which has a great
significance in the health care field. Besides, it has a social impact
on decreasing the rate of infection in children in Egypt. It also
provides valubale information about the distribution and
segmentation of ALL in Egypt, which may be linked to the possible
risk factors.
Undirected Knowledge Discovery is used since, in the case of this
research project, there is no target field as the data provided is
mainly subjective. This is done in order to quantify the subjective
variables. Therefore, the computer will be asked to identify
significant patterns in the provided medical data about ALL. This
may be achieved through collecting the data necessary for the
system, determimng the data mining technique to be used for the
system, and choosing the most suitable implementation tool for the
domain.
The research makes use of a data mining tool, Clementine, so as to
apply Decision Trees technique. We feed it with data extracted from
real-life cases taken from specialized Cancer Institutes. Relevant
medical cases details such as patient medical history and diagnosis
are analyzed, classified, and clustered in order to improve the disease
management.
Abstract: One field experiment was conducted on corn (Zea
mays L.Var. SC 704) to study the effect of three different basic levels
of nitrogen (90, 140and 190 Kg/ha as urea) with 0.01% and 0.02%
pyridoxine pre-sowing seed soaking for 8 hours. Water-soaked seeds
were treated as controled. biomass production was recorded on 45,
70 and 95 days after sowing. Total dry material (TDM), leaf area
index (LAI), crop growth rate (CGR), relative growth rate (RGR) and
net assimilation rate (NAR) was calculated form 45until 95 days after
sowing. Yield and its components such as kernel yield, grain weight,
biologic yield, harvest index and protein percentage was measured at
harvest. In general, 0.02% pyridoxine and 190 Kg pure nitrogen/ha
was shown gave maximum value for growth and yield parameters.
N190 + 0.02 % pyridoxine enhanced seed yield and biologic yield by
57.15% and 62.98% compared to 90kg N and water – soaked
treatment.
Abstract: Robust face recognition under various illumination
environments is very difficult and needs to be accomplished for
successful commercialization. In this paper, we propose an improved
illumination normalization method for face recognition. Illumination
normalization algorithm based on anisotropic smoothing is well known
to be effective among illumination normalization methods but
deteriorates the intensity contrast of the original image, and incurs less
sharp edges. The proposed method in this paper improves the previous
anisotropic smoothing-based illumination normalization method so
that it increases the intensity contrast and enhances the edges while
diminishing the effect of illumination variations. Due to the result of
these improvements, face images preprocessed by the proposed
illumination normalization method becomes to have more distinctive
feature vectors (Gabor feature vectors) for face recognition. Through
experiments of face recognition based on Gabor feature vector
similarity, the effectiveness of the proposed illumination
normalization method is verified.
Abstract: Natural frequencies and dynamic response of a spur
gear sector are investigated using a two dimensional finite element
model that offers significant advantages for dynamic gear analyses.
The gear teeth are analyzed for different operating speeds. A primary
feature of this modeling is determination of mesh forces using a
detailed contact analysis for each time step as the gears roll through
the mesh. ANSYS software has been used on the proposed model to
find the natural frequencies by Block Lanczos technique and
displacements and dynamic stresses by transient mode super position
method. The effect of rotational speed of the gear on the dynamic
response of gear tooth has been studied and design limits have been
discussed.
Abstract: In this work, several ASP solutions were flooded into
fractured models initially saturated with heavy oil at a constant flow
rate and different geometrical characteristics of fracture. The ASP
solutions are constituted from 2 polymers i.e. a synthetic polymer,
hydrolyzed polyacrylamide as well as a biopolymer, a surfactant and
2types of alkaline. The results showed that using synthetic
hydrolyzed polyacrylamide polymer increases ultimate oil recovery;
however, type of alkaline does not play a significant rule on oil
recovery. In addition, position of the injection well respect to the
fracture system has remarkable effects on ASP flooding. For instance
increasing angle of fractures with mean flow direction causes more
oil recovery and delays breakthrough time. This work can be
accounted as a comprehensive survey on ASP flooding which
considers most of effective factors in this chemical EOR method.
Abstract: A novel nanofinishing process using improved ball
end magnetorheological (MR) finishing tool was developed for finishing of flat as well as 3D surfaces of ferromagnetic and non ferromagnetic workpieces. In this process a magnetically controlled
ball end of smart MR polishing fluid is generated at the tip surface of
the tool which is used as a finishing medium and it is guided to
follow the surface to be finished through computer controlled 3-axes
motion controller. The experiments were performed on ferromagnetic
workpiece surface in the developed MR finishing setup to study the effect of finishing time on final surface roughness. The performance
of present finishing process on final finished surface roughness was studied. The surface morphology was observed under scanning
electron microscopy and atomic force microscope. The final surface finish was obtained as low as 19.7 nm from the initial surface
roughness of 142.9 nm. The outcome of newly developed finishing process can be found useful in its applications in aerospace,
automotive, dies and molds manufacturing industries, semiconductor and optics machining etc.
Abstract: Semantic Web Technologies enable machines to
interpret data published in a machine-interpretable form on the web.
At the present time, only human beings are able to understand the
product information published online. The emerging semantic Web
technologies have the potential to deeply influence the further
development of the Internet Economy. In this paper we propose a
scenario based research approach to predict the effects of these new
technologies on electronic markets and business models of traders
and intermediaries and customers. Over 300 million searches are
conducted everyday on the Internet by people trying to find what
they need. A majority of these searches are in the domain of
consumer ecommerce, where a web user is looking for something to
buy. This represents a huge cost in terms of people hours and an
enormous drain of resources. Agent enabled semantic search will
have a dramatic impact on the precision of these searches. It will
reduce and possibly eliminate information asymmetry where a better
informed buyer gets the best value. By impacting this key
determinant of market prices semantic web will foster the evolution
of different business and economic models. We submit that there is a
need for developing these futuristic models based on our current
understanding of e-commerce models and nascent semantic web
technologies. We believe these business models will encourage
mainstream web developers and businesses to join the “semantic web
revolution."
Abstract: Green house effect has becomes a serious concern in
many countries due to the increase consumption of the fossil fuel.
There have been many studies to find an alternative power source.
Wind energy found to be one of the most useful solutions to help in
overcoming the air pollution and global. There is no agreed solution
to conversion of wind energy to electrical energy. In this paper, the
advantages of using a Switched Reluctance Generator (SRG) for
wind energy applications. The theoretical study of the self excitation
of a SRG and the determination of the variable parameters in a SRG
design are discussed. The design parameters for the maximum power
output of the SRG are computed using Matlab simulation. The
designs of the circuit to control the variable parameters in a SRG to
provide the maximum power output are also discussed.
Abstract: In this paper, subtractive clustering based fuzzy inference system approach is used for early detection of faults in the function oriented software systems. This approach has been tested with real time defect datasets of NASA software projects named as PC1 and CM1. Both the code based model and joined model (combination of the requirement and code based metrics) of the datasets are used for training and testing of the proposed approach. The performance of the models is recorded in terms of Accuracy, MAE and RMSE values. The performance of the proposed approach is better in case of Joined Model. As evidenced from the results obtained it can be concluded that Clustering and fuzzy logic together provide a simple yet powerful means to model the earlier detection of faults in the function oriented software systems.
Abstract: The amounts of radioactivity in the igneous rocks
have been investigated; samples were collected from the total of eight
basalt rock types in the northeastern of Kurdistan region/Iraq. The
activity concentration of 226Ra (238U) series, 228Ac (232Th) series, 40K
and 137Cs were measured using Planar HPGe and NaI(Tl) detectors.
Along the study area the radium equivalent activities Raeq in Bq/Kg
of samples under investigation were found in the range of 22.16 to
77.31 Bq/Kg with an average value of 44.8 Bq/Kg, this value is much
below the internationally accepted value of 370 Bq/Kg. To estimate
the health effects of this natural radioactive composition, the average
values of absorbed gamma dose rate D (55 nGyh-1), Indoor and
outdoor annual effective dose rates Eied (0.11 mSvy-1) . and Eoed
(0.03 mSvy-1), External hazard index Hex (0.138) and internal hazard
index Hin(0.154), and representative level index Iγr (0.386) have been
calculated and found to be lower than the worldwide average values.
Abstract: Arvia®, a spin-out company of University of Manchester, UK is commercialising a water treatment technology for the removal of low concentrations of organics from water. This technology is based on the adsorption of organics onto graphite based adsorbents coupled with their electrochemical regeneration in a simple electrochemical cell. In this paper, the potential of the process to adsorb microorganisms and electrochemically disinfect them present in water has been demonstrated. Bench scale experiments have indicated that the process of adsorption using graphite adsorbents with electrochemical regeneration can be used for water disinfection effectively. The most likely mechanisms of disinfection of water through this process include direct electrochemical oxidation and electrochemical chlorination.
Abstract: In order to improve the effect of isolation structure, the
principles and behaviours of the base-isolation system are studied, and
the types and characteristics of the base-isolation are also discussed.
Compared to the traditional aseismatic structures, the base isolation
structures decrease the seismic response obviously: the total structural
aseismatic value decreases to 1/4-1/32 and the seismic shear stress in
the upper structure decreases to 1/14-1/23. In the huge seism, the
structure can have an obvious aseismatic effect.
Abstract: This paper aims at developing a multilevel fuzzy
decision support model for urban rail transit planning schemes in
China under the background that China is presently experiencing an
unprecedented construction of urban rail transit. In this study, an
appropriate model using multilevel fuzzy comprehensive evaluation
method is developed. In the decision process, the followings are
considered as the influential objectives: traveler attraction,
environment protection, project feasibility and operation. In addition,
consistent matrix analysis method is used to determine the weights
between objectives and the weights between the objectives-
sub-indictors, which reduces the work caused by repeated
establishment of the decision matrix on the basis of ensuring the
consistency of decision matrix. The application results show that
multilevel fuzzy decision model can perfectly deal with the
multivariable and multilevel decision process, which is particularly
useful in the resolution of multilevel decision-making problem of
urban rail transit planning schemes.
Abstract: The network traffic data provided for the design of
intrusion detection always are large with ineffective information and
enclose limited and ambiguous information about users- activities.
We study the problems and propose a two phases approach in our
intrusion detection design. In the first phase, we develop a
correlation-based feature selection algorithm to remove the worthless
information from the original high dimensional database. Next, we
design an intrusion detection method to solve the problems of
uncertainty caused by limited and ambiguous information. In the
experiments, we choose six UCI databases and DARPA KDD99
intrusion detection data set as our evaluation tools. Empirical studies
indicate that our feature selection algorithm is capable of reducing the
size of data set. Our intrusion detection method achieves a better
performance than those of participating intrusion detectors.
Abstract: Nowadays, there is little information, concerning the
heat shield systems, and this information is not completely reliable to
use in so many cases. for example, the precise calculation cannot be
done for various materials. In addition, the real scale test has two
disadvantages: high cost and low flexibility, and for each case we
must perform a new test. Hence, using numerical modeling program
that calculates the surface recession rate and interior temperature
distribution is necessary. Also, numerical solution of governing
equation for non-charring material ablation is presented in order to
anticipate the recession rate and the heat response of non-charring
heat shields. the governing equation is nonlinear and the Newton-
Rafson method along with TDMA algorithm is used to solve this
nonlinear equation system. Using Newton- Rafson method for
solving the governing equation is one of the advantages of the
solving method because this method is simple and it can be easily
generalized to more difficult problems. The obtained results
compared with reliable sources in order to examine the accuracy of
compiling code.
Abstract: Fluid flow and heat transfer of vertical full cone
embedded in porous media is studied in this paper. Nonlinear
differential equation arising from similarity solution of inverted cone
(subjected to wall temperature boundary conditions) embedded in
porous medium is solved using a hybrid neural network- particle
swarm optimization method.
To aim this purpose, a trial solution of the differential equation is
defined as sum of two parts. The first part satisfies the initial/
boundary conditions and does contain an adjustable parameter and
the second part which is constructed so as not to affect the
initial/boundary conditions and involves adjustable parameters (the
weights and biases) for a multi-layer perceptron neural network.
Particle swarm optimization (PSO) is applied to find adjustable
parameters of trial solution (in first and second part). The obtained
solution in comparison with the numerical ones represents a
remarkable accuracy.
Abstract: Most scientific programs have large input and output
data sets that require out-of-core programming or use virtual memory
management (VMM). Out-of-core programming is very error-prone
and tedious; as a result, it is generally avoided. However, in many
instance, VMM is not an effective approach because it often results
in substantial performance reduction. In contrast, compiler driven I/O
management will allow a program-s data sets to be retrieved in parts,
called blocks or tiles. Comanche (COmpiler MANaged caCHE) is a
compiler combined with a user level runtime system that can be used
to replace standard VMM for out-of-core programs. We describe
Comanche and demonstrate on a number of representative problems
that it substantially out-performs VMM. Significantly our system
does not require any special services from the operating system and
does not require modification of the operating system kernel.
Abstract: We study the spatial design of experiment and we want to select a most informative subset, having prespecified size, from a set of correlated random variables. The problem arises in many applied domains, such as meteorology, environmental statistics, and statistical geology. In these applications, observations can be collected at different locations and possibly at different times. In spatial design, when the design region and the set of interest are discrete then the covariance matrix completely describe any objective function and our goal is to choose a feasible design that minimizes the resulting uncertainty. The problem is recast as that of maximizing the determinant of the covariance matrix of the chosen subset. This problem is NP-hard. For using these designs in computer experiments, in many cases, the design space is very large and it's not possible to calculate the exact optimal solution. Heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective and exact solution not possible. We developed a GA algorithm to take advantage of the exploratory power of this algorithm. The successful application of this method is demonstrated in large design space. We consider a real case of design of experiment. In our problem, design space is very large and for solving the problem, we used proposed GA algorithm.