Abstract: Environmental studies have expanded dramatically all
over the world in the past few years. Nowadays businesses interact
with society and the environment in ways that put their mark on both
sides. Efforts improving human standard living, through the control
of nature and the development of new products, have also resulted in
contamination of the environment. Consequently companies play an
important role in environmental sustainability of a region or country.
Therefore we can say that a company's sustainable development is
strictly dependent on the environment. This article presents a fuzzy
model to evaluate a company's environmental impact. Article
illustrates an example of the automotive industry in order to prove the
usefulness of using such a model.
Abstract: Ti-6Al-4V alloy has demonstrated a high strength to
weight ratio as well as good properties at high temperature. The
successful application of the alloy in some important areas depends
on suitable joining techniques. Friction welding has many
advantageous features to be chosen for joining Titanium alloys. The
present work investigates the feasibility of producing similar metal
joints of this Titanium alloy by rotary friction welding method. The
joints are produced at three different speeds and the performances of
the welded joints are evaluated by conducting microstructure studies,
Vickers Hardness and tensile tests at the joints. It is found that the
weld joints produced are sound and the ductile fractures in the tensile
weld specimens occur at locations away from the welded joints. It is
also found that a rotational speed of 1500 RPM can produce a very
good weld, with other parameters kept constant.
Abstract: Artificial Neural Networks (ANNs) have been used successfully in many scientific, industrial and business domains as a method for extracting knowledge from vast amounts of data. However the use of ANN techniques in the sporting domain has been limited. In professional sport, data is stored on many aspects of teams, games, training and players. Sporting organisations have begun to realise that there is a wealth of untapped knowledge contained in the data and there is great interest in techniques to utilise this data. This study will use player data from the elite Australian Football League (AFL) competition to train and test ANNs with the aim to predict the onset of injuries. The results demonstrate that an accuracy of 82.9% was achieved by the ANNs’ predictions across all examples with 94.5% of all injuries correctly predicted. These initial findings suggest that ANNs may have the potential to assist sporting clubs in the prediction of injuries.
Abstract: This paper illustrates why existing technology
acceptance models are only of limited use for predicting and
explaining the adoption of future information and communication
technologies. It starts with a general overview over technology
adoption processes, and presents several theories for the acceptance
as well as adoption of traditional information technologies. This is
followed by an overview over the recent developments in the area of
information and communication technologies. Based on the
arguments elaborated in these sections, it is shown why the factors
used to predict adoption in existing systems, will not be sufficient for
explaining the adoption of future information and communication
technologies.
Abstract: In the forming of ceramic materials the plasticity
concept is commonly used. This term is related to a particular
mechanical behavior when clay is mixed with water. A plastic
ceramic material shows a permanent strain without rupture
when a compressive load produces a shear stress that exceeds
the material-s yield strength. For a plastic ceramic body it
observes a measurable elastic behavior before the yield
strength and when the applied load is removed. In this work, a
mathematical model was developed from applied concepts of
the plasticity theory by using the stress/strain diagram under
compression.
Abstract: Identifying protein coding regions in DNA sequences is a basic step in the location of genes. Several approaches based on signal processing tools have been applied to solve this problem, trying to achieve more accurate predictions. This paper presents a new predictor that improves the efficacy of three techniques that use the Fourier Transform to predict coding regions, and that could be computed using an algorithm that reduces the computation load. Some ideas about the combination of the predictor with other methods are discussed. ROC curves are used to demonstrate the efficacy of the proposed predictor, based on the computation of 25 DNA sequences from three different organisms.
Abstract: The home in these days has not one computer connected to the Internet but rather a network of many devices within the home, and that network might be connected to the Internet. In such an environment, the potential for attacks is greatly increased. The general security technology can not apply because of the use of various wired and wireless network, middleware and protocol in digital home environment and a restricted system resource of home information appliances. To offer secure home services home network environments have need of access control for various home devices and information when users want to access. Therefore home network access control for user authorization is a very important issue. In this paper we propose access control model using RBAC in home network environments to provide home users with secure home services.
Abstract: This paper presents unified theory for local (Savitzky-
Golay) and global polynomial smoothing. The algebraic framework
can represent any polynomial approximation and is seamless from
low degree local, to high degree global approximations. The representation
of the smoothing operator as a projection onto orthonormal
basis functions enables the computation of: the covariance matrix
for noise propagation through the filter; the noise gain and; the
frequency response of the polynomial filters. A virtually perfect Gram
polynomial basis is synthesized, whereby polynomials of degree
d = 1000 can be synthesized without significant errors. The perfect
basis ensures that the filters are strictly polynomial preserving. Given
n points and a support length ls = 2m + 1 then the smoothing
operator is strictly linear phase for the points xi, i = m+1. . . n-m.
The method is demonstrated on geometric surfaces data lying on an
invariant 2D lattice.
Abstract: The survival of publicly listed companies largely
depends on their stocks being liquidly traded. This goal can be
achieved when new investors are attracted to invest on companies-
stocks. Among different groups of investors, individual investors are
generally less able to objectively evaluate companies- risks and
returns, and tend to be emotionally biased in their investing
decisions. Therefore their decisions may be formed as a result of
perceived risks and returns, and influenced by companies- images.
This study finds that perceived risk, perceived returns and trust
directly affect individual investors- trading decisions while attitude
towards brand partially mediates the relationships. This finding
suggests that, in courting individual investors, companies still need to
perform financially while building a good image can result in their
stocks being accepted quicker than the stocks of good performing
companies with hidden images.
Abstract: Protection of slope and embankment from erosion has
become an important issue in Bangladesh. The constructions of
strong structures require large capital, integrated designing, high
maintenance cost. Strong structure methods have negative impact on
the environment and sometimes not function for the design period.
Plantation of vetiver system along the slopes is an alternative
solution. Vetiver not only serves the purpose of slope protection but
also adds green environment reducing pollution. Vetiver is available
in almost all the districts of Bangladesh. This paper presents the
application of vetiver system with geo-jute, for slope protection and
erosion control of embankments and slopes. In-situ shear tests have
been conducted on vetiver rooted soil system to find the shear
strength. The shear strength and effective soil cohesion of vetiver
rooted soil matrix are respectively 2.0 times and 2.1 times higher than
that of the bared soil. Similar trends have been found in direct shear
tests conducted on laboratory reconstituted samples. Field trials have
been conducted in road embankment and slope protection with
vetiver at different sites. During the time of vetiver root growth the
soil protection has been accomplished by geo-jute. As the geo-jute
degrades with time, vetiver roots grow and take over the function of
geo-jutes. Slope stability analyses showed that vegetation increase
the factor of safety significantly.
Abstract: Linear cryptanalysis methods are rarely used to improve the security of chaotic stream ciphers. In this paper, we apply linear cryptanalysis to a chaotic stream cipher which was designed by strictly using the basic design criterion of cryptosystem – confusion and diffusion. We show that this well-designed chaos-based stream cipher is still insecure against distinguishing attack. This distinguishing attack promotes the further improvement of the cipher.
Abstract: Cosmic showers, from their places of origin in space,
after entering earth generate secondary particles called Extensive Air
Shower (EAS). Detection and analysis of EAS and similar High
Energy Particle Showers involve a plethora of experimental setups
with certain constraints for which soft-computational tools like
Artificial Neural Network (ANN)s can be adopted. The optimality
of ANN classifiers can be enhanced further by the use of Multiple
Classifier System (MCS) and certain data - dimension reduction
techniques. This work describes the performance of certain data
dimension reduction techniques like Principal Component Analysis
(PCA), Independent Component Analysis (ICA) and Self Organizing
Map (SOM) approximators for application with an MCS formed
using Multi Layer Perceptron (MLP), Recurrent Neural Network
(RNN) and Probabilistic Neural Network (PNN). The data inputs are
obtained from an array of detectors placed in a circular arrangement
resembling a practical detector grid which have a higher dimension
and greater correlation among themselves. The PCA, ICA and SOM
blocks reduce the correlation and generate a form suitable for real
time practical applications for prediction of primary energy and
location of EAS from density values captured using detectors in a
circular grid.
Abstract: This paper presents a new adaptive DMC controller
that improves the controller performance in case of plant-model
mismatch. The new controller monitors the plant measured output,
compares it with the model output and calculates weights applied to
the controller move. Simulations show that the new controller can
help improve control performance and avoid instability in case of
severe model mismatches.
Abstract: This paper presents the prediction of kidney
dysfunction using different neural network (NN) approaches. Self
organization Maps (SOM), Probabilistic Neural Network (PNN) and
Multi Layer Perceptron Neural Network (MLPNN) trained with Back
Propagation Algorithm (BPA) are used in this study. Six hundred and
sixty three sets of analytical laboratory tests have been collected from
one of the private clinical laboratories in Baghdad. For each subject,
Serum urea and Serum creatinin levels have been analyzed and tested
by using clinical laboratory measurements. The collected urea and
cretinine levels are then used as inputs to the three NN models in
which the training process is done by different neural approaches.
SOM which is a class of unsupervised network whereas PNN and
BPNN are considered as class of supervised networks. These
networks are used as a classifier to predict whether kidney is normal
or it will have a dysfunction. The accuracy of prediction, sensitivity
and specificity were found for each type of the proposed networks
.We conclude that PNN gives faster and more accurate prediction of
kidney dysfunction and it works as promising tool for predicting of
routine kidney dysfunction from the clinical laboratory data.
Abstract: In this paper, we study the application of Extreme
Learning Machine (ELM) algorithm for single layered feedforward
neural networks to non-linear chaotic time series problems. In this
algorithm the input weights and the hidden layer bias are randomly
chosen. The ELM formulation leads to solving a system of linear
equations in terms of the unknown weights connecting the hidden
layer to the output layer. The solution of this general system of
linear equations will be obtained using Moore-Penrose generalized
pseudo inverse. For the study of the application of the method we
consider the time series generated by the Mackey Glass delay
differential equation with different time delays, Santa Fe A and
UCR heart beat rate ECG time series. For the choice of sigmoid,
sin and hardlim activation functions the optimal values for the
memory order and the number of hidden neurons which give the
best prediction performance in terms of root mean square error are
determined. It is observed that the results obtained are in close
agreement with the exact solution of the problems considered
which clearly shows that ELM is a very promising alternative
method for time series prediction.
Abstract: The physical methods for RNA secondary structure prediction are time consuming and expensive, thus methods for computational prediction will be a proper alternative. Various algorithms have been used for RNA structure prediction including dynamic programming and metaheuristic algorithms. Musician's behaviorinspired harmony search is a recently developed metaheuristic algorithm which has been successful in a wide variety of complex optimization problems. This paper proposes a harmony search algorithm (HSRNAFold) to find RNA secondary structure with minimum free energy and similar to the native structure. HSRNAFold is compared with dynamic programming benchmark mfold and metaheuristic algorithms (RnaPredict, SetPSO and HelixPSO). The results showed that HSRNAFold is comparable to mfold and better than metaheuristics in finding the minimum free energies and the number of correct base pairs.
Abstract: In this paper, we consider nested sliding mode control of SISO nonlinear systems, perturbed by bounded matched and unmatched uncertainties. The systems are assumed to be in strict-feedback form. A step wise procedure is introduced to obtain the controller. In each step, a continuous sliding mode controller is designed as virtual control law. Then the next step sliding surface is defined by using this virtual controller. These sliding surfaces are selected as nonlinear static functions of the system states. Finally in the last step, smooth static state feedback control law is determined such that the output reaches the desired set-point while the system is forced arbitrary close to the intersection of sliding surfaces and the states remain bounded.
Abstract: A pilot project was carried out in 2007 by the senior
students of Cyprus International University, aiming to minimize the
total cost of waste collection in Northern Cyprus. Many developed
and developing countries have cut their transportation costs – which
lies between 30-40% – down at a rate of 40% percent, by
implementing network models for their route assignments.
Accordingly, a network model was implemented at Göçmenköy
district, to optimize and standardize waste collection works. The
work environment of the employees were also redesigned to provide
maximum ergonomy and to increase productivity, efficiency and
safety. Following the collection of the required data including waste
densities, lengths of roads and population, a model was constructed
to allocate the optimal route assignment for the waste collection
trucks at Göçmenköy district.
Abstract: Two-phase frictional pressure drop data were
obtained for condensation of carbon dioxide in single horizontal
micro tube of inner diameter ranged from 0.6 mm up to 1.6 mm over
mass flow rates from 2.5*10-5 to 17*10-5 kg/s and vapor qualities
from 0.0 to 1.0. The inlet condensing pressure is changed from 33.5
to 45 bars. The saturation temperature ranged from -1.5 oC up to 10
oC. These data have then been compared against three (two-phase)
frictional pressure drop prediction methods. The first method is by
Muller-Steinhagen and Heck (Muller-Steinhagen H, Heck K. A
simple friction pressure drop correlation for two-phase flow in pipes.
Chem. Eng. Process 1986;20:297–308) and that by Gronnerud R.
Investigation of liquid hold-up, flow-resistance and heat transfer in
circulation type evaporators, part IV: two-phase flow resistance in
boiling refrigerants, Annexe 1972. Then the method used by
FriedelL. Improved friction pressures drop in horizontal and vertical
two-phase pipe flow. European Two-Phase Flow Group Meeting,
Paper E2; 1979 June, Ispra, Italy. The methods are used by M.B Ould
Didi et al (2001) “Prediction of two-phase pressure gradients of
refrigerant in horizontal tubes". Int.J.of Refrigeration 25(2002) 935-
947. The best available method for annular flow was that of Muller-
Steinhagen and Heck. It was observed that the peak in the two-phase
frictional pressure gradient is at high vapor qualities.
Abstract: The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-based encoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the next generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which Genetic Algorithm (GA) was applied.