Abstract: The ever increasing product diversity and competition on the market of goods and services has dictated the pace of growth in the number of advertisements. Despite their admittedly diminished effectiveness over the recent years, advertisements remain the favored method of sales promotion. Consequently, the challenge for an advertiser is to explore every possible avenue of making an advertisement more noticeable, attractive and impellent for consumers. One way to achieve this is through invoking celebrity endorsements. On the one hand, the use of a celebrity to endorse a product involves substantial costs, however, on the other hand, it does not immediately guarantee the success of an advertisement. The question of how celebrities can be used in advertising to the best advantage is therefore of utmost importance. Celebrity endorsements have become commonplace: empirical evidence indicates that approximately 20 to 25 per cent of advertisements feature some famous person as a product endorser. The popularity of celebrity endorsements demonstrates the relevance of the topic, especially in the context of the current global economic downturn, when companies are forced to save in order to survive, yet simultaneously to heavily invest in advertising and sales promotion. The issue of the effective use of celebrity endorsements also figures prominently in the academic discourse. The study presented below is thus aimed at exploring what qualities (characteristics) of a celebrity endorser have an impact on the ffectiveness of the advertisement in which he/she appears and how.
Abstract: This paper introduces a low cost INS/GPS algorithm for
land vehicle navigation application. The data fusion process is done
with an extended Kalman filter in cascade configuration mode. In
order to perform numerical simulations, MATLAB software has been
developed. Loosely coupled configuration is considered. The results
obtained in this work demonstrate that a low-cost INS/GPS navigation
system is partially capable of meeting the performance requirements
for land vehicle navigation. The relative effectiveness of the kalman
filter implementation in integrated GPS/INS navigation algorithm is
highlighted. The paper also provides experimental results; field test
using a car is carried out.
Abstract: Transportation is one of the most fundamental
challenges of urban development in contemporary world. On the
other hand, sustainable urban development has received tremendous
public attention in the last few years. This trend in addition to other
factors such as energy cost, environmental concerns, traffic
congestion and the feeling of lack of belonging have contributed to
the development of pedestrian areas. The purpose of this paper is to
study the role of walkable streets in sustainable development of
cities. Accordingly, a documentary research through valid sources
has been utilized to substantiate this study. The findings demonstrate
that walking can lead to sustainable urban development from
physical, social, environmental, cultural, economic and political
aspects. Also, pedestrian areas –which are the main context of
walking- act as focal points of development in cities and have a great
effect on modifying and stimulating of their adjacent urban spaces.
Abstract: The usual correctness condition for a schedule of
concurrent database transactions is some form of serializability of
the transactions. For general forms, the problem of deciding whether
a schedule is serializable is NP-complete. In those cases other approaches
to proving correctness, using proof rules that allow the steps
of the proof of serializability to be guided manually, are desirable.
Such an approach is possible in the case of conflict serializability
which is proved algebraically by deriving serial schedules using
commutativity of non-conflicting operations. However, conflict serializability
can be an unnecessarily strong form of serializability restricting
concurrency and thereby reducing performance. In practice,
weaker, more general, forms of serializability for extended models of
transactions are used. Currently, there are no known methods using
proof rules for proving those general forms of serializability. In this
paper, we define serializability for an extended model of partitioned
transactions, which we show to be as expressive as serializability
for general partitioned transactions. An algebraic method for proving
general serializability is obtained by giving an initial-algebra specification
of serializable schedules of concurrent transactions in the
model. This demonstrates that it is possible to conduct algebraic
proofs of correctness of concurrent transactions in general cases.
Abstract: Text Mining is around applying knowledge discovery
techniques to unstructured text is termed knowledge discovery in text
(KDT), or Text data mining or Text Mining. In decision tree
approach is most useful in classification problem. With this
technique, tree is constructed to model the classification process.
There are two basic steps in the technique: building the tree and
applying the tree to the database. This paper describes a proposed
C5.0 classifier that performs rulesets, cross validation and boosting
for original C5.0 in order to reduce the optimization of error ratio.
The feasibility and the benefits of the proposed approach are
demonstrated by means of medial data set like hypothyroid. It is
shown that, the performance of a classifier on the training cases from
which it was constructed gives a poor estimate by sampling or using a
separate test file, either way, the classifier is evaluated on cases that
were not used to build and evaluate the classifier are both are large. If
the cases in hypothyroid.data and hypothyroid.test were to be
shuffled and divided into a new 2772 case training set and a 1000
case test set, C5.0 might construct a different classifier with a lower
or higher error rate on the test cases. An important feature of see5 is
its ability to classifiers called rulesets. The ruleset has an error rate
0.5 % on the test cases. The standard errors of the means provide an
estimate of the variability of results. One way to get a more reliable
estimate of predictive is by f-fold –cross- validation. The error rate of
a classifier produced from all the cases is estimated as the ratio of the
total number of errors on the hold-out cases to the total number of
cases. The Boost option with x trials instructs See5 to construct up to
x classifiers in this manner. Trials over numerous datasets, large and
small, show that on average 10-classifier boosting reduces the error
rate for test cases by about 25%.
Abstract: This paper deals with the design of a moving sliding
surface in a variable structure plant for a second order system. The
chattering phenomena is also dealt with during the switching process
for an unstable sliding surface condition. The simulation examples
considered in this paper shows the effectiveness of the sliding mode
control method used for the design of the moving sliding surfaces. A
simulink model of the continuous system was also developed in
MATLAB-SIMULINK for the design and hence demonstrated. The
phase portraits and the state plots shows the demonstration of
the powerful control technique which can be applied for second
order systems.
Abstract: The ideal sinc filter, ignoring the noise statistics, is often
applied for generating an arbitrary sample of a bandlimited signal by
using the uniformly sampled data. In this article, an optimal interpolator is proposed; it reaches a minimum mean square error (MMSE)
at its output in the presence of noise. The resulting interpolator is
thus a Wiener filter, and both the optimal infinite impulse response
(IIR) and finite impulse response (FIR) filters are presented. The
mean square errors (MSE-s) for the interpolator of different length
impulse responses are obtained by computer simulations; it shows that
the MSE-s of the proposed interpolators with a reasonable length are
improved about 0.4 dB under flat power spectra in noisy environment with signal-to-noise power ratio (SNR) equal 10 dB. As expected,
the results also demonstrate the improvements for the MSE-s with various fractional delays of the optimal interpolator against the ideal
sinc filter under a fixed length impulse response.
Abstract: In the recent past, there has been an increasing interest
in applying evolutionary methods to Knowledge Discovery in
Databases (KDD) and a number of successful applications of Genetic
Algorithms (GA) and Genetic Programming (GP) to KDD have been
demonstrated. The most predominant representation of the
discovered knowledge is the standard Production Rules (PRs) in the
form If P Then D. The PRs, however, are unable to handle
exceptions and do not exhibit variable precision. The Censored
Production Rules (CPRs), an extension of PRs, were proposed by
Michalski & Winston that exhibit variable precision and supports an
efficient mechanism for handling exceptions. A CPR is an
augmented production rule of the form:
If P Then D Unless C, where C (Censor) is an exception to the rule.
Such rules are employed in situations, in which the conditional
statement 'If P Then D' holds frequently and the assertion C holds
rarely. By using a rule of this type we are free to ignore the exception
conditions, when the resources needed to establish its presence are
tight or there is simply no information available as to whether it
holds or not. Thus, the 'If P Then D' part of the CPR expresses
important information, while the Unless C part acts only as a switch
and changes the polarity of D to ~D.
This paper presents a classification algorithm based on evolutionary
approach that discovers comprehensible rules with exceptions in the
form of CPRs.
The proposed approach has flexible chromosome encoding, where
each chromosome corresponds to a CPR. Appropriate genetic
operators are suggested and a fitness function is proposed that
incorporates the basic constraints on CPRs. Experimental results are
presented to demonstrate the performance of the proposed algorithm.
Abstract: Principle component analysis is often combined with
the state-of-art classification algorithms to recognize human faces.
However, principle component analysis can only capture these
features contributing to the global characteristics of data because it is a
global feature selection algorithm. It misses those features
contributing to the local characteristics of data because each principal
component only contains some levels of global characteristics of data.
In this study, we present a novel face recognition approach using
non-negative principal component analysis which is added with the
constraint of non-negative to improve data locality and contribute to
elucidating latent data structures. Experiments are performed on the
Cambridge ORL face database. We demonstrate the strong
performances of the algorithm in recognizing human faces in
comparison with PCA and NREMF approaches.
Abstract: Earthmoving operations are a major part of many
construction projects. Because of the complexity and fast-changing
environment of such operations, the planning and estimating are
crucial on both planning and operational levels. This paper presents
the framework ofa microscopic discrete-event simulation system for
modeling earthmoving operations and conducting productivity
estimations on an operational level.A prototype has been developed
to demonstrate the applicability of the proposed framework, and this
simulation system is presented via a case study based on an actual
earthmoving project. The case study shows that the proposed
simulation model is capable of evaluating alternative operating
strategies and resource utilization at a very detailed level.
Abstract: Classes on creativity, innovation, and entrepreneurship
are becoming quite popular at universities throughout the world.
However, it is not easy for business students to get involved to
innovative activities, especially patent application. The present study
investigated how to enhance business students- intention to participate
in innovative activities and which incentives universities should
consider. A 22-item research scale was used, and confirmatory factor
analysis was conducted to verify its reliability and validity. Multiple
regression and discriminant analyses were also conducted. The results
demonstrate the effect of growth-need strength on innovative behavior
and indicate that the theory of planned behavior can explain and
predict business students- intention to participate in innovative
activities. Additionally, the results suggest that applying our proposed
model in practice would effectively strengthen business students-
intentions to engage in innovative activities.
Abstract: Recently research on human wayfinding has focused
mainly on mental representations rather than processes of
wayfinding. The objective of this paper is to demonstrate the
rationality behind applying multi-agent simulation paradigm to the
modeling of rescuer team wayfinding in order to develop
computational theory of perceptual wayfinding in crisis situations
using image schemata and affordances, which explains how people
find a specific destination in an unfamiliar building such as a
hospital. The hypothesis of this paper is that successful navigation is
possible if the agents are able to make the correct decision through
well-defined cues in critical cases, so the design of the building
signage is evaluated through the multi-agent-based simulation. In
addition, a special case of wayfinding in a building, finding one-s
way through three hospitals, is used to demonstrate the model.
Thereby, total rescue time for rescue operation during building fire is
computed. This paper discuses the computed rescue time for various
signage localization and provides experimental result for
optimization of building signage design. Therefore the most
appropriate signage design resulted in the shortest total rescue time in
various situations.
Abstract: In this paper, a new approach is introduced to solve
Blasius equation using parameter identification of a nonlinear
function which is used as approximation function. Bees Algorithm
(BA) is applied in order to find the adjustable parameters of
approximation function regarding minimizing a fitness function
including these parameters (i.e. adjustable parameters). These
parameters are determined how the approximation function has to
satisfy the boundary conditions. In order to demonstrate the
presented method, the obtained results are compared with another
numerical method. Present method can be easily extended to solve a
wide range of problems.
Abstract: This paper proposes an efficient learning method for the layered neural networks based on the selection of training data and input characteristics of an output layer unit. Comparing to recent neural networks; pulse neural networks, quantum neuro computation, etc, the multilayer network is widely used due to its simple structure. When learning objects are complicated, the problems, such as unsuccessful learning or a significant time required in learning, remain unsolved. Focusing on the input data during the learning stage, we undertook an experiment to identify the data that makes large errors and interferes with the learning process. Our method devides the learning process into several stages. In general, input characteristics to an output layer unit show oscillation during learning process for complicated problems. The multi-stage learning method proposes by the authors for the function approximation problems of classifying learning data in a phased manner, focusing on their learnabilities prior to learning in the multi layered neural network, and demonstrates validity of the multi-stage learning method. Specifically, this paper verifies by computer experiments that both of learning accuracy and learning time are improved of the BP method as a learning rule of the multi-stage learning method. In learning, oscillatory phenomena of a learning curve serve an important role in learning performance. The authors also discuss the occurrence mechanisms of oscillatory phenomena in learning. Furthermore, the authors discuss the reasons that errors of some data remain large value even after learning, observing behaviors during learning.
Abstract: Validity is an overriding consideration in language testing. If a test score is intended for a particular purpose, this must be supported through empirical evidence. This article addresses the validity of a multiple-choice achievement test (MCT). The test is administered at the end of each semester to decide about students' mastery of a course in general English. To provide empirical evidence pertaining to the validity of this test, two criterion measures were used. In so doing, a Cloze test and a C-test which are reported to gauge general English proficiency were utilized. The results of analyses show that there is a statistically significant correlation among participants' scores on the MCT, Cloze, and Ctest. Drawing on the findings of the study, it can be cautiously deduced that these tests measure the same underlying trait. However, allowing for the limitations of using criterion measures to validate tests, we cannot make any absolute claim as to the validity of this MCT test.
Abstract: Knowledge about the magnetic quantities in a magnetic circuit is always of great interest. On the one hand, this information is needed for the simulation of a transformer. On the other hand, parameter studies are more reliable, if the magnetic quantities are derived from a well established model. One possibility to model the 3-phase transformer is by using a magnetic equivalent circuit (MEC). Though this is a well known system, it is often not an easy task to set up such a model for a large number of lumped elements which additionally includes the nonlinear characteristic of the magnetic material. Here we show the setup of a solver for a MEC and the results of the calculation in comparison to measurements taken. The equations of the MEC are based on a rearranged system of the nodal analysis. Thus it is possible to achieve a minimum number of equations, and a clear and simple structure. Hence, it is uncomplicated in its handling and it supports the iteration process. Additional helpful tasks are implemented within the solver to enhance the performance. The electric circuit is described by an electric equivalent circuit (EEC). Our results for the 3-phase transformer demonstrate the computational efficiency of the solver, and show the benefit of the application of a MEC.
Abstract: Corrugated wire mesh laminates (CWML) are a class
of engineered open cell structures that have potential for applications
in many areas including aerospace and biomedical engineering. Two
different methods of fabricating corrugated wire mesh laminates from
stainless steel, one using a high temperature Lithobraze alloy and the
other using a low temperature Eutectic solder for joining the
corrugated wire meshes are described herein. Their implementation is
demonstrated by manufacturing CWML samples of 304 and 316
stainless steel (SST). It is seen that due to the facility of employing
wire meshes of different densities and wire diameters, it is possible to
create CWML laminates with a wide range of effective densities. The
fabricated laminates are tested under uniaxial compression. The
variation of the compressive yield strength with relative density of the
CWML is compared to the theory developed by Gibson and Ashby for
open cell structures [22]. It is shown that the compressive strength of
the corrugated wire mesh laminates can be described using the same
equations by using an appropriate value for the linear coefficient in the
Gibson-Ashby model.
Abstract: This paper deals with the synthesis of fuzzy state feedback controller of induction motor with optimal performance. First, the Takagi-Sugeno (T-S) fuzzy model is employed to approximate a non linear system in the synchronous d-q frame rotating with electromagnetic field-oriented. Next, a fuzzy controller is designed to stabilise the induction motor and guaranteed a minimum disturbance attenuation level for the closed-loop system. The gains of fuzzy control are obtained by solving a set of Linear Matrix Inequality (LMI). Finally, simulation results are given to demonstrate the controller-s effectiveness.
Abstract: Twenty - nine Holstein cows were used to evaluate the effects of different dry period (DP) lengths on milk yield and composition, some blood metabolites, and complete blood count (CBC). Cows were assigned to one of 2 treatments: 1) 60-d dry period, 2) 35-d DP. Milk yield, from calving to 60 days, was not different for cows on the treatments (p =0.130). Cows in the 35-d DP produced more milk protein and SNF compare with cows in treatment 1 (p ≤ 0.05). Serum glucose, non-esterified fatty acids (NEFA), beta hydroxyl butyrate acid (BHBA), blood urea nitrogen (BUN), urea, and glutamic oxaloacetic transaminase (GOT) were all similar among the treatments. Body condition score (BCS), body weight (BW), complete blood count (CBC) and health problems were similar between the treatments. The results of this study demonstrated we can reduce the dry period length to 35 days with no problems.
Abstract: The aim of the present study was to develop and
validate an inexpensive and simple high performance liquid
chromatographic (HPLC) method for the determination of colistin
sulfate. Separation of colistin sulfate was achieved on a ZORBAX
Eclipse XDB-C18 column using UV detection at λ=215 nm. The
mobile phase was 30 mM sulfate buffer (pH 2.5):acetonitrile(76:24).
An excellent linearity (r2=0.998) was found in the concentration
range of 25 - 400 μg/mL. Intra- day and inter-day precisions of
method (%RSD, n=3) were less than 7.9%.The developed and
validated method was applied to determination of the content of
colistin sulfate in medicated premix and animal feed sample.The
recovery of colistin from animal feed was satisfactorily ranged from
90.92 to 93.77%. The results demonstrated that the HPLC method
developed in this work is appropriate for direct determination of
colistin sulfate in commercial medicated premixes and animal feed.