Abstract: One of the determinants of a firm-s prosperity is the
customers- perceived service quality and satisfaction. While service
quality is wide in scope, and consists of various dimensions, there
may be differences in the relative importance of these dimensions in
affecting customers- overall satisfaction of service quality.
Identifying the relative rank of different dimensions of service quality
is very important in that it can help managers to find out which
service dimensions have a greater effect on customers- overall
satisfaction. Such an insight will consequently lead to more effective
resource allocation which will finally end in higher levels of
customer satisfaction. This issue –despite its criticality- has not
received enough attention so far. Therefore, using a sample of 240
bank customers in Iran, an artificial neural network is developed to
address this gap in the literature. As customers- evaluation of service
quality is a subjective process, artificial neural networks –as a brain
metaphor- may appear to have a potentiality to model such a
complicated process. Proposing a neural network which is able to
predict the customers- overall satisfaction of service quality with a
promising level of accuracy is the first contribution of this study. In
addition, prioritizing the service quality dimensions in affecting
customers- overall satisfaction –by using sensitivity analysis of
neural network- is the second important finding of this paper.
Abstract: The present study has been carried out with a view to calculate the coastal vulnerability index (CVI) to know the high and low sensitive areas and area of inundation due to future SLR. Both conventional and remotely sensed data were used and analyzed through the modelling technique. Out of the total study area, 8.26% is very high risk, 14.21% high, 9.36% medium, 22.46% low and 7.35% in the very low vulnerable category, due to costal components. Results of the inundation analysis indicate that 225.2 km² and 397 km² of the land area will be submerged by flooding at 1m and 10m inundation levels. The most severely affected sectors are expected to be the residential, industrial and recreational areas. As this coast is planned for future coastal developmental activities, measures such as industrializations, building regulation, urban growth planning and agriculture, development of an integrated coastal zone management, strict enforcement of the Coastal Regulation Zone (CRZ) Act, monitoring of impacts and further research in this regard are recommended for the study area.
Abstract: Undoubtedly, chassis is one of the most important
parts of a vehicle. Chassis that today are produced for vehicles are
made up of four parts. These parts are jointed together by screwing.
Transverse parts are called cross member.
This study reviews the stress generated by cyclic laboratory loads
in front cross member of Peugeot 405. In this paper the finite element
method is used to simulate the welding process and to determine the
physical response of the spot-welded joints. Analysis is done by the
Abaqus software.
The Stresses generated in cross member structure are generally
classified into two groups: The stresses remained in form of residual
stresses after welding process and the mechanical stress generated by
cyclic load. Accordingly the total stress must be obtained by
determining residual stress and mechanical stress separately and then
sum them according to the superposition principle.
In order to improve accuracy, material properties including
physical, thermal and mechanical properties were supposed to be
temperature-dependent. Simulation shows that maximum Von Misses
stresses are located at special points. The model results are then
compared to the experimental results which are reported by
producing factory and good agreement is observed.
Abstract: Bone remodeling occurs by the balanced action of
bone resorbing osteoclasts (OC) and bone-building osteoblasts.
Increased bone resorption by excessive OC activity contributes
to malignant and non-malignant diseases including osteoporosis.
To study OC differentiation and function, OC formed in
in vitro cultures are currently counted manually, a tedious
procedure which is prone to inter-observer differences. Aiming
for an automated OC-quantification system, classification of
OC and precursor cells was done on fluorescence microscope
images based on the distinct appearance of fluorescent nuclei.
Following ellipse fitting to nuclei, a combination of eight
features enabled clustering of OC and precursor cell nuclei.
After evaluating different machine-learning techniques, LOGREG
achieved 74% correctly classified OC and precursor cell
nuclei, outperforming human experts (best expert: 55%). In
combination with the automated detection of total cell areas,
this system allows to measure various cell parameters and most
importantly to quantify proteins involved in osteoclastogenesis.
Abstract: In this paper, a new technique for fast painting with
different colors is presented. The idea of painting relies on applying
masks with different colors to the background. Fast painting is
achieved by applying these masks in the frequency domain instead of
spatial (time) domain. New colors can be generated automatically as a
result from the cross correlation operation. This idea was applied
successfully for faster specific data (face, object, pattern, and code)
detection using neural algorithms. Here, instead of performing cross
correlation between the input input data (e.g., image, or a stream of
sequential data) and the weights of neural networks, the cross
correlation is performed between the colored masks and the
background. Furthermore, this approach is developed to reduce the
computation steps required by the painting operation. The principle of
divide and conquer strategy is applied through background
decomposition. Each background is divided into small in size subbackgrounds
and then each sub-background is processed separately by
using a single faster painting algorithm. Moreover, the fastest painting
is achieved by using parallel processing techniques to paint the
resulting sub-backgrounds using the same number of faster painting
algorithms. In contrast to using only faster painting algorithm, the
speed up ratio is increased with the size of the background when using
faster painting algorithm and background decomposition. Simulation
results show that painting in the frequency domain is faster than that in
the spatial domain.
Abstract: This paper is mainly concerned with the application of a novel technique of data interpretation to the characterization and classification of measurements of plasma columns in Tokamak reactors for nuclear fusion applications. The proposed method exploits several concepts derived from soft computing theory. In particular, Artifical Neural Networks have been exploited to classify magnetic variables useful to determine shape and position of the plasma with a reduced computational complexity. The proposed technique is used to analyze simulated databases of plasma equilibria based on ITER geometry configuration. As well as demonstrating the successful recovery of scalar equilibrium parameters, we show that the technique can yield practical advantages compares with earlier methods.
Abstract: The present paper proposes high performance nonlinear
force controllers for a servopneumatic real-time fatigue test
machine. A CompactRIO® controller was used, being fully
programmed using LabVIEW language. Fuzzy logic control
algorithms were evaluated to tune the integral and derivative
components in the development of hybrid controllers, namely a FLC
P and a hybrid FLC PID real-time-based controllers. Their
behaviours were described by using state diagrams. The main
contribution is to ensure a smooth transition between control states,
avoiding discrete transitions in controller outputs. Steady-state errors
lower than 1.5 N were reached, without retuning the controllers.
Good results were also obtained for sinusoidal tracking tasks from
1/¤Ç to 8/¤Ç Hz.
Abstract: Artificial Neural Network (ANN) has been
extensively used for classification of heart sounds for its
discriminative training ability and easy implementation. However, it
suffers from overparameterization if the number of nodes is not
chosen properly. In such cases, when the dataset has redundancy
within it, ANN is trained along with this redundant information that
results in poor validation. Also a larger network means more
computational expense resulting more hardware and time related
cost. Therefore, an optimum design of neural network is needed
towards real-time detection of pathological patterns, if any from heart
sound signal. The aims of this work are to (i) select a set of input
features that are effective for identification of heart sound signals and
(ii) make certain optimum selection of nodes in the hidden layer for a
more effective ANN structure. Here, we present an optimization
technique that involves Singular Value Decomposition (SVD) and
QR factorization with column pivoting (QRcp) methodology to
optimize empirically chosen over-parameterized ANN structure.
Input nodes present in ANN structure is optimized by SVD followed
by QRcp while only SVD is required to prune undesirable hidden
nodes. The result is presented for classifying 12 common
pathological cases and normal heart sound.
Abstract: Integration of system process information obtained
through an image processing system with an evolving knowledge
database to improve the accuracy and predictability of wear particle
analysis is the main focus of the paper. The objective is to automate
intelligently the analysis process of wear particle using classification
via self organizing maps. This is achieved using relationship
measurements among corresponding attributes of various
measurements for wear particle. Finally, visualization technique is
proposed that helps the viewer in understanding and utilizing these
relationships that enable accurate diagnostics.
Abstract: For the last decade, statistics show traumatic brain
injury (TBI) is a growing concern in our legal system. In an effort to
obtain data regarding the influence of neuropsychological expert
witness testimony in a criminal case, this study tested three
hypotheses. H1: The majority of jurors will vote not guilty, due to
mild head injury. H2: The jurors will give more credence to the
testimony of the neuropsychologist rather than the psychiatrist. H3:
The jurors will be more lenient in their sentencing, given the
testimony of the neuropsychologist-s testimony. The criterion for
inclusion in the study as a participant is identical to those used for
inclusion in the eligibility for jury duty in the United States. A chisquared
test was performed to analyze the data for the three
hypotheses. The results supported all of the hypotheses; however
statistical significance was seen in H1 and H2 only.
Abstract: The development of competences and practical
capacities of students is getting an important incidence into the
guidelines of the European Higher Education Area (EHEA). The
methodology applied in this work is based on the education through
directed resolution of practical cases. All cases are related to
professional tasks that the students will have to develop in their
future career. The method is intended to form the necessary
competences of students of the Marine Engineering and Maritime
Transport Degree in the matter of “Physics".
The experience was applied in the course of 2011/2012. Students
were grouped, and a practical task was assigned to them, that should
be developed and solved within the team. The aim was to realize
students learning by three ways: their own knowledge, the
contribution of their teammates and the teacher's direction. The
results of the evaluation were compared with those obtained
previously by the traditional teaching method.
Abstract: In this paper we present an efficient system for
independent speaker speech recognition based on neural network
approach. The proposed architecture comprises two phases: a
preprocessing phase which consists in segmental normalization and
features extraction and a classification phase which uses neural
networks based on nonparametric density estimation namely the
general regression neural network (GRNN). The relative
performances of the proposed model are compared to the similar
recognition systems based on the Multilayer Perceptron (MLP), the
Recurrent Neural Network (RNN) and the well known Discrete
Hidden Markov Model (HMM-VQ) that we have achieved also.
Experimental results obtained with Arabic digits have shown that the
use of nonparametric density estimation with an appropriate
smoothing factor (spread) improves the generalization power of the
neural network. The word error rate (WER) is reduced significantly
over the baseline HMM method. GRNN computation is a successful
alternative to the other neural network and DHMM.
Abstract: The effect of antifungal compound from Bacillus
subtilis strain LB5 was tested against conidial germination of
Colletotrichum gloeosporioides and Pestalotiopsis eugeniae, causal
agent of anthracnose and fruit rot of wax apple, respectively.
Observation under scanning electron microscope and light compound
microscope revealed that conidial germination was completely
inhibited when treated with culture broth, culture filtrate, or crude
extract from strain LB5. Identification of purified antifungal
compound produced by strain LB5 in cell-free supernatant by nuclear
magnetic resonance and fast atom bombardment showed that the
active compound was iturin A-2.
Abstract: Time full of changes which is associated with globalization, tougher competition, changes in the structures of markets and economic downturn, that all force companies to think about their competitive advantages. These changes can bring the company a competitive advantage and that can help improve competitive position in the market. Policy of the European Union is focused on the fast growing innovative companies which quickly respond to market demands and consequently increase its competitiveness. To meet those objectives companies need the right conditions and support of their state.
Abstract: The paper makes part from a complex research project
on Romanian Grey Steppe, a unique breed in terms of biological and
cultural-historical importance, on the verge of extinction and which
has been included in a preservation programme of genetic resources
from Romania. The study of genetic polymorphism of protean
fractions, especially kappa-casein, and the genotype relations of
these lactoproteins with some quantitative and qualitative features of
milk yield represents a current theme and a novelty for this breed. In
the estimation of the genetic parameters we used R.E.M.L.
(Restricted Maximum Likelihood) method.
The main lactoprotein from milk, kappa - casein (K-cz),
characterized in the specialized literature as a feature having a high
degree of hereditary transmission, behaves as such in the nucleus under
study, a value also confirmed by the heritability coefficient (h2 = 0.57
%). We must mention the medium values for milk and fat quantity
(h2=0.26, 0.29 %) and the fat and protein percentage from milk
having a high hereditary influence h2 = 0.71 - 0.63 %.
Correlations between kappa-casein and the milk quantity are
negative and strong. Between kappa-casein and other qualitative
features of milk (fat content 0.58-0.67 % and protein content 0.77-
0.87%), there are positive and very strong correlations. At the same
time, between kappa-casein and β casein (β-cz), β lactoglobulin (β-
lg) respectively, correlations are positive having high values (0.37 –
0.45 %), indicating the same causes and determining factors for the
two groups of features.
Abstract: A dynamic stall-corrected Blade Element-Momentum algorithm based on a hybrid polar is validated through the comparison with Sandia experimental measurements on a 5-m diameter wind turbine of Troposkien shape. Different dynamic stall models are evaluated. The numerical predictions obtained using the extended aerodynamic coefficients provided by both Sheldal and Klimas and Raciti Castelli et al. are compared to experimental data, determining the potential of the hybrid database for the numerical prediction of vertical-axis wind turbine performances.
Abstract: The existence of maximal durations drastically modifies the performance evaluation in Discrete Event Systems (DES). The same particularity may be found on systems where the associated constraints do not concern the time. For example weight measures, in chemical industry, are used in order to control the quantity of consumed raw materials. This parameter also takes a fundamental part in the product quality as the correct transformation process is based upon a given percentage of each essence. Weight regulation therefore increases the global productivity of the system by decreasing the quantity of rejected products. In this paper we present an approach based on mixing different characteristics theories, the fuzzy system and Petri net system to describe the behaviour. An industriel application on a tobacco manufacturing plant, where the critical parameter is the weight is presented as an illustration.