Abstract: In this paper, the concepts of dichotomous logistic
regression (DLR) with leave-one-out (L-O-O) were discussed. To
illustrate this, the L-O-O was run to determine the importance of the
simulation conditions for robust test of spread procedures with good
Type I error rates. The resultant model was then evaluated. The
discussions included 1) assessment of the accuracy of the model, and
2) parameter estimates. These were presented and illustrated by
modeling the relationship between the dichotomous dependent
variable (Type I error rates) with a set of independent variables (the
simulation conditions). The base SAS software containing PROC
LOGISTIC and DATA step functions can be making used to do the
DLR analysis.
Abstract: Since hyaluronic acid (HA) receptor such as CD44 is
over-expressed at sites of cancer cells, HA can be used as a targeting
vehicles for anti-cancer drugs. The aim of this study is to synthesize
block copolymer composed of hyaluronic acid and
poly(ε-caprolactone) (HAPCL) and to fabricate polymeric micelles for
anticancer drug targeting against CD44 receptor of tumor cells.
Chemical composition of HAPCL was confirmed using 1H NMR
spectroscopy. Doxorubicin (DOX) was incorporated into polymeric
micelles of HAPCL. The diameters of HAPHS polymeric micelles
were changed around 80nm and have spherical shapes. Targeting
potential was investigated using CD44-overexpressing. When
DOX-incorporated polymeric micelles was added to KB cells, they
revealed strong red fluorescence color while blocking of CD44
receptor by pretreatment of free HA resulted in reduced intensity,
indicating that HAPCL polymeric micelles have targetability against
CD44 receptor.
Abstract: This paper proposes the stochastic tabu search (STS)
for improving the measurement scheme for power system state
estimation. If the original measured scheme is not observable, the
additional measurements with minimum number of measurements are
added into the system by STS so that there is no critical measurement
pair. The random bit flipping and bit exchanging perturbations are
used for generating the neighborhood solutions in STS. The Pδ
observable concept is used to determine the network observability.
Test results of 10 bus, IEEE 14 and 30 bus systems are shown that
STS can improve the original measured scheme to be observable
without critical measurement pair. Moreover, the results of STS are
superior to deterministic tabu search (DTS) in terms of the best
solution hit.
Abstract: Array signal processing involves signal enumeration and source localization. Array signal processing is centered on the ability to fuse temporal and spatial information captured via sampling signals emitted from a number of sources at the sensors of an array in order to carry out a specific estimation task: source characteristics (mainly localization of the sources) and/or array characteristics (mainly array geometry) estimation. Array signal processing is a part of signal processing that uses sensors organized in patterns or arrays, to detect signals and to determine information about them. Beamforming is a general signal processing technique used to control the directionality of the reception or transmission of a signal. Using Beamforming we can direct the majority of signal energy we receive from a group of array. Multiple signal classification (MUSIC) is a highly popular eigenstructure-based estimation method of direction of arrival (DOA) with high resolution. This Paper enumerates the effect of missing sensors in DOA estimation. The accuracy of the MUSIC-based DOA estimation is degraded significantly both by the effects of the missing sensors among the receiving array elements and the unequal channel gain and phase errors of the receiver.
Abstract: The goal of data mining algorithms is to discover
useful information embedded in large databases. One of the most
important data mining problems is discovery of frequently occurring
patterns in sequential data. In a multidimensional sequence each
event depends on more than one dimension. The search space is quite
large and the serial algorithms are not scalable for very large
datasets. To address this, it is necessary to study scalable parallel
implementations of sequence mining algorithms.
In this paper, we present a model for multidimensional sequence
and describe a parallel algorithm based on data parallelism.
Simulation experiments show good load balancing and scalable and
acceptable speedup over different processors and problem sizes and
demonstrate that our approach can works efficiently in a real parallel
computing environment.
Abstract: This paper examines the concept of simulation from
a modelling viewpoint. How can one Mealy machine simulate the other one? We create formalism for simulation of Mealy machines.
The injective s–morphism of the machine semigroups induces the simulation of machines [1]. We present the example of s–morphism
such that it is not a homomorphism of semigroups. The story for the
surjective s–morphisms is quite different. These are homomorphisms
of semigroups but there exists the surjective s–morphism such that it does not induce the simulation.
Abstract: The Economic factors are leading to the rise of
infrastructures provides software and computing facilities as a
service, known as cloud services or cloud computing. Cloud services
can provide efficiencies for application providers, both by limiting
up-front capital expenses, and by reducing the cost of ownership over
time. Such services are made available in a data center, using shared
commodity hardware for computation and storage. There is a varied
set of cloud services available today, including application services
(salesforce.com), storage services (Amazon S3), compute services
(Google App Engine, Amazon EC2) and data services (Amazon
SimpleDB, Microsoft SQL Server Data Services, Google-s Data
store). These services represent a variety of reformations of data
management architectures, and more are on the horizon.
Abstract: In this paper a class of analog algorithms based on the
concept of Cellular Neural Network (CNN) is applied in some
processing operations of some important medical images, namely
retina images, for detecting various symptoms connected with
diabetic retinopathy. Some specific processing tasks like
morphological operations, linear filtering and thresholding are
proposed, the corresponding template values are given and
simulations on real retina images are provided.
Abstract: The main goal in this paper is to quantify the quality of
different techniques for radiation treatment plans, a back-propagation
artificial neural network (ANN) combined with biomedicine theory
was used to model thirteen dosimetric parameters and to calculate
two dosimetric indices. The correlations between dosimetric indices
and quality of life were extracted as the features and used in the ANN
model to make decisions in the clinic. The simulation results show
that a trained multilayer back-propagation neural network model can
help a doctor accept or reject a plan efficiently. In addition, the
models are flexible and whenever a new treatment technique enters
the market, the feature variables simply need to be imported and the
model re-trained for it to be ready for use.
Abstract: This paper focuses on PSS/E modeling of wind farms
of Doubly-fed Induction Generator (DFIG) type and their impact on
issues of power system operation. Since Wind Turbine Generators
(WTG) don-t have the same characteristics as synchronous
generators, the appropriate modeling of wind farms is essential for
transmission system operators to analyze the best options of
transmission grid reinforcements as well as to evaluate the wind
power impact on reliability and security of supply. With the high
excepted penetration of wind power into the power system a
simultaneous loss of Wind Farm generation will put at risk power
system security and reliability. Therefore, the main wind grid code
requirements concern the fault ride through capability and frequency
operation range of wind turbines. In case of grid faults wind turbines
have to supply a definite reactive power depending on the
instantaneous voltage and to return quickly to normal operation.
Abstract: Data Structures and Algorithms is a module in most
Computer Science or Information Technology curricula. It is one of
the modules most students identify as being difficult. This paper
demonstrates how programming a solution for Sudoku can make
abstract concepts more concrete. The paper relates concepts of a
typical Data Structures and Algorithms module to a step by step
solution for Sudoku in a human type as opposed to a computer
oriented solution.
Abstract: The purpose of this paper is to conceptualize a futureoriented
human work environment and organizational activity in
deep mines that entails a vision of good and safe workplace. Futureoriented
technological challenges and mental images required for
modern work organization design were appraised. It is argued that an
intelligent-deep-mine covering the entire value chain, including
environmental issues and with work organization that supports good
working and social conditions towards increased human productivity
could be designed. With such intelligent system and work
organization in place, the mining industry could be seen as a place
where cooperation, skills development and gender equality are key
components. By this perspective, both the youth and women might
view mining activity as an attractive job and the work environment
as a safe, and this could go a long way in breaking the unequal
gender balance that exists in most mines today.
Abstract: This study presents a hybrid neural network and Gravitational Search Algorithm (HNGSA) method to solve well known Wessinger's equation. To aim this purpose, gravitational search algorithm (GSA) technique is applied to train a multi-layer perceptron neural network, which is used as approximation solution of the Wessinger's equation. A trial solution of the differential equation is written as sum of two parts. The first part satisfies the initial/ boundary conditions and does not contain any adjustable parameters and the second part which is constructed so as not to affect the initial/boundary conditions. The second part involves adjustable parameters (the weights and biases) for a multi-layer perceptron neural network. In order to demonstrate the presented method, the obtained results of the proposed method are compared with some known numerical methods. The given results show that presented method can introduce a closer form to the analytic solution than other numerical methods. Present method can be easily extended to solve a wide range of problems.
Abstract: The concept of flexible manufacturing is highly
appealing in gaining a competitive edge in the market by quickly
adapting to the changing customer needs. Scheduling jobs on flexible
manufacturing systems (FMSs) is a challenging task of managing the
available flexibility on the shop floor to react to the dynamics of the
environment in real-time. In this paper, an agent-oriented scheduling
framework that can be integrated with a real or a simulated FMS is
proposed. This framework works in stochastic environments with a
dynamic model of job arrival. It supports a hierarchical cooperative
scheduling that builds on the available flexibility of the shop floor.
Testing the framework on a model of a real FMS showed the
capability of the proposed approach to overcome the drawbacks of
the conventional approaches and maintain a near optimal solution
despite the dynamics of the operational environment.
Abstract: Teaching and learning about sustainability is a pedagogical endeavour with various innate difficulties and increased demands. Higher education has a dual role to play in addressing this challenge: to identify and explore innovative approaches and tools for addressing the complex and value-laden nature of sustainability in more meaningful ways, and to help teachers to integrate these approaches into their practice through appropriate professional development programs. The study reported here was designed and carried out within the context of a Masters course in Environmental Education. Eight teachers were collaboratively engaged in reconstructing a digital game microworld which was deliberately designed by the researchers to be questioned and evoke critical discussion on the idea of ‘sustainable city’. The study was based on the design-based research method. The findings indicate that the teachers’ involvement in processes of co-constructing the microworld initiated discussion and reflection upon the concepts of sustainability and sustainable lifestyles.
Abstract: The rapid growth of e-Commerce services is
significantly observed in the past decade. However, the method to
verify the authenticated users still widely depends on numeric
approaches. A new search on other verification methods suitable for
online e-Commerce is an interesting issue. In this paper, a new online
signature-verification method using angular transformation is
presented. Delay shifts existing in online signatures are estimated by
the estimation method relying on angle representation. In the
proposed signature-verification algorithm, all components of input
signature are extracted by considering the discontinuous break points
on the stream of angular values. Then the estimated delay shift is
captured by comparing with the selected reference signature and the
error matching can be computed as a main feature used for verifying
process. The threshold offsets are calculated by two types of error
characteristics of the signature verification problem, False Rejection
Rate (FRR) and False Acceptance Rate (FAR). The level of these two
error rates depends on the decision threshold chosen whose value is
such as to realize the Equal Error Rate (EER; FAR = FRR). The
experimental results show that through the simple programming,
employed on Internet for demonstrating e-Commerce services, the
proposed method can provide 95.39% correct verifications and 7%
better than DP matching based signature-verification method. In
addition, the signature verification with extracting components
provides more reliable results than using a whole decision making.
Abstract: It is difficult to judge ripeness by outward
characteristics such as size or external color. In this paper a nondestructive
method was studied to determine watermelon (Crimson
Sweet) quality. Responses of samples to excitation vibrations were
detected using laser Doppler vibrometry (LDV) technology. Phase
shift between input and output vibrations were extracted overall
frequency range. First and second were derived using frequency
response spectrums. After nondestructive tests, watermelons were
sensory evaluated. So the samples were graded in a range of ripeness
based on overall acceptability (total desired traits consumers).
Regression models were developed to predict quality using obtained
results and sample mass. The determination coefficients of the
calibration and cross validation models were 0.89 and 0.71
respectively. This study demonstrated feasibility of information
which is derived vibration response curves for predicting fruit
quality. The vibration response of watermelon using the LDV method
is measured without direct contact; it is accurate and timely, which
could result in significant advantage for classifying watermelons
based on consumer opinions.
Abstract: In the present paper, a set of parametric FE stress
analyses is carried out for two-planar welded tubular DKT-joints
under two different axial load cases. Analysis results are used to
present general remarks on the effect of geometrical parameters on
the stress concentration factors (SCFs) at the inner saddle, outer
saddle, toe, and heel positions on the main (outer) brace. Then a new
set of SCF parametric equations is developed through nonlinear
regression analysis for the fatigue design of two-planar DKT-joints.
An assessment study of these equations is conducted against the
experimental data; and the satisfaction of the criteria regarding the
acceptance of parametric equations is checked. Significant effort has
been devoted by researchers to the study of SCFs in various uniplanar
tubular connections. Nevertheless, for multi-planar joints
covering the majority of practical applications, very few
investigations have been reported due to the complexity and high
cost involved.
Abstract: The aim of this study is to emphasize the opportunities in space design under the aspect of HCI as performance areas. HCI is a multidisciplinary approach that could be identified in many different areas. The aesthetical reflections of HCI by virtual reality in space design are the high-tech solutions of the new innovations as computational facilities by artistic features. The method of this paper is to identify the subject in 3 main parts. In the first part a general approach and definition of interactivity on the basis of space design; in the second part the concept of multimedia interactive theater by some chosen samples from the world and interactive design aspects; in the third part the samples from Turkey will be identified by stage designing principles. In the results it could be declared that the multimedia database is the virtual approach of theatre stage designing regarding interactive means by computational facilities according to aesthetical aspects. HCI is mostly identified in theatre stages as computational intelligence under the affect of interactivity.
Abstract: This study investigated students- perception of self
efficacy and anxiety in acquiring English language, and consequently
examined the relationship existing among the independent variables,
confounding variables and students- performances in the English
language. The researcher tested the research hypotheses using a
sample group of 318 respondents out of the population size of 400
students. The results obtained revealed that there was a significant
moderate negative relationship between English language anxiety
and performance in English language, but no significant relationship
between self-efficacy and English language performance, among the
middle-school students. There was a significant moderate negative
relationship between English language anxiety and self-efficacy. It
was discovered that general self-efficacy and English language
anxiety represented a significantly more powerful set of predictors
than the set of confounding variables. Thus, the study concluded that
English language anxiety and general self-efficacy were significant
predictors of English language performance among middle-school
students in Satri Si Suriyothai School.