Abstract: The colonic tissue is a complicated dynamic system
and the colonic activities it generates are composed of irregular
segmental waves, which are referred to as erratic fluctuations or spikes.
They are also highly irregular with subunit fractal structure. The
traditional time-frequency domain statistics like the averaged
amplitude, the motility index and the power spectrum, etc. are
insufficient to describe such fluctuations. Thus the fractal
box-counting dimension is proposed and the fractal scaling behaviors
of the human colonic pressure activities under the physiological
conditions are studied. It is shown that the dimension of the resting
activity is smaller than that of the normal one, whereas the clipped
version, which corresponds to the activity of the constipation patient,
shows with higher fractal dimension. It may indicate a practical
application to assess the colonic motility, which is often indicated by
the colonic pressure activity.
Abstract: This paper proposes an efficient method to classify
inverse synthetic aperture (ISAR) images. Because ISAR images can
be translated and rotated in the 2-dimensional image place, invariance
to the two factors is indispensable for successful classification. The
proposed method achieves invariance to translation and rotation of
ISAR images using a combination of two-dimensional Fourier
transform, polar mapping and correlation-based alignment of the
image. Classification is conducted using a simple matching score
classifier. In simulations using the real ISAR images of five scaled
models measured in a compact range, the proposed method yields
classification ratios higher than 97 %.
Abstract: This paper features the proposed modeling and design
of a Robust Decentralized Periodic Output Feedback (RDPOF)
control technique for the active vibration control of smart flexible
multimodel Euler-Bernoulli cantilever beams for a multivariable
(MIMO) case by retaining the first 6 vibratory modes. The beam
structure is modeled in state space form using the concept of
piezoelectric theory, the Euler-Bernoulli beam theory and the Finite
Element Method (FEM) technique by dividing the beam into 4 finite
elements and placing the piezoelectric sensor / actuator at two finite
element locations (positions 2 and 4) as collocated pairs, i.e., as
surface mounted sensor / actuator, thus giving rise to a multivariable
model of the smart structure plant with two inputs and two outputs.
Five such multivariable models are obtained by varying the
dimensions (aspect ratios) of the aluminum beam, thus giving rise to
a multimodel of the smart structure system. Using model order
reduction technique, the reduced order model of the higher order
system is obtained based on dominant eigen value retention and the
method of Davison. RDPOF controllers are designed for the above 5
multivariable-multimodel plant. The closed loop responses with the
RDPOF feedback gain and the magnitudes of the control input are
observed and the performance of the proposed multimodel smart
structure system with the controller is evaluated for vibration control.
Abstract: Non-uniform current distribution in polymer
electrolyte membrane fuel cells results in local over-heating,
accelerated ageing, and lower power output than expected. This
issue is very critical when fuel cell experiences water flooding. In
this work, the performance of a PEM fuel cell is investigated under
cathode flooding conditions. Two-dimensional partially flooded
GDL models based on the conservation laws and electrochemical
relations are proposed to study local current density distributions
along flow fields over a wide range of cell operating conditions.
The model results show a direct association between cathode inlet
humidity increases and that of average current density but the
system becomes more sensitive to flooding. The anode inlet
relative humidity shows a similar effect. Operating the cell at
higher temperatures would lead to higher average current densities
and the chance of system being flooded is reduced. In addition,
higher cathode stoichiometries prevent system flooding but the
average current density remains almost constant. The higher anode
stoichiometry leads to higher average current density and higher
sensitivity to cathode flooding.
Abstract: In this paper, a study on the applications of the
optimization and regression techniques for optimal calculation of
partial ratios of helical gearboxes with second-step double gear-sets
for minimal cross section dimension is introduced. From the condition
of the moment equilibrium of a mechanic system including three gear
units and their regular resistance condition, models for calculation of
the partial ratios of helical gearboxes with second-step double
gear-sets were given. Especially, by regression analysis, explicit
models for calculation of the partial ratios are introduced. These
models allow determining the partial ratios accurately and simply.
Abstract: The effect of the discontinuity of the rail ends and the
presence of lower modulus insulation material at the gap to the
variations of stresses in the insulated rail joint (IRJ) is presented. A
three-dimensional wheel – rail contact model in the finite element
framework is used for the analysis. It is shown that the maximum stress
occurs in the subsurface of the railhead when the wheel contact occurs
far away from the rail end and migrates to the railhead surface as the
wheel approaches the rail end; under this condition, the interface
between the rail ends and the insulation material has suffered
significantly increased levels of stress concentration. The ratio of the
elastic modulus of the railhead and insulation material is found to alter
the levels of stress concentration. Numerical result indicates that a
higher elastic modulus insulating material can reduce the stress
concentration in the railhead but will generate higher stresses in the
insulation material, leading to earlier failure of the insulation material
Abstract: Method of determining of moisture diffusivity on two types of autoclaved aerated concretes with different bulk density is represented in the paper. On the specimens were measured one dimensional water transport only on liquid phase. Ever evaluation was done from moisture profiles measured in specific times by capacitance moisture meter. All values from capacitance meter were recalculated to moisture content by mass. Moisture diffusivity was determined in dependence on both moisture and temperature. The experiment temperatures were set at values 55, 65, 75 and 85°C.
Abstract: In this paper, a three dimensional autonomous chaotic system is considered. The existence of Hopf bifurcation is investigated by choosing the appropriate bifurcation parameter. Furthermore, formulas for determining the direction of the Hopf bifurcation and the stability of bifurcating periodic solutions are derived with the help of normal form theory. Finally, a numerical example is given.
Abstract: We present a discussion of three adaptive filtering
algorithms well known for their one-step termination property, in
terms of their relationship with the minimal residual method. These
algorithms are the normalized least mean square (NLMS), Affine
Projection algorithm (APA) and the recursive least squares algorithm
(RLS). The NLMS is shown to be a result of the orthogonality
condition imposed on the instantaneous approximation of the Wiener
equation, while APA and RLS algorithm result from orthogonality
condition in multi-dimensional minimal residual formulation. Further
analysis of the minimal residual formulation for the RLS leads to
a triangular system which also possesses the one-step termination
property (in exact arithmetic)
Abstract: In this paper, we propose a robust disease detection
method, called adaptive orientation code matching (Adaptive OCM),
which is developed from a robust image registration algorithm:
orientation code matching (OCM), to achieve continuous and
site-specific detection of changes in plant disease. We use two-stage
framework for realizing our research purpose; in the first stage,
adaptive OCM was employed which could not only realize the
continuous and site-specific observation of disease development, but
also shows its excellent robustness for non-rigid plant object searching
in scene illumination, translation, small rotation and occlusion changes
and then in the second stage, a machine learning method of support
vector machine (SVM) based on a feature of two dimensional (2D)
xy-color histogram is further utilized for pixel-wise disease
classification and quantification. The indoor experiment results
demonstrate the feasibility and potential of our proposed algorithm,
which could be implemented in real field situation for better
observation of plant disease development.
Abstract: The two-dimensional gel electrophoresis method
(2-DE) is widely used in Proteomics to separate thousands of proteins
in a sample. By comparing the protein expression levels of proteins in
a normal sample with those in a diseased one, it is possible to identify
a meaningful set of marker proteins for the targeted disease. The major
shortcomings of this approach involve inherent noises and irregular
geometric distortions of spots observed in 2-DE images. Various
experimental conditions can be the major causes of these problems. In
the protein analysis of samples, these problems eventually lead to
incorrect conclusions. In order to minimize the influence of these
problems, this paper proposes a partition based pair extension method
that performs spot-matching on a set of gel images multiple times and
segregates more reliable mapping results which can improve the
accuracy of gel image analysis. The improved accuracy of the
proposed method is analyzed through various experiments on real
2-DE images of human liver tissues.
Abstract: Using Dynamic Bayesian Networks (DBN) to model genetic regulatory networks from gene expression data is one of the major paradigms for inferring the interactions among genes. Averaging a collection of models for predicting network is desired, rather than relying on a single high scoring model. In this paper, two kinds of model searching approaches are compared, which are Greedy hill-climbing Search with Restarts (GSR) and Markov Chain Monte Carlo (MCMC) methods. The GSR is preferred in many papers, but there is no such comparison study about which one is better for DBN models. Different types of experiments have been carried out to try to give a benchmark test to these approaches. Our experimental results demonstrated that on average the MCMC methods outperform the GSR in accuracy of predicted network, and having the comparable performance in time efficiency. By proposing the different variations of MCMC and employing simulated annealing strategy, the MCMC methods become more efficient and stable. Apart from comparisons between these approaches, another objective of this study is to investigate the feasibility of using DBN modeling approaches for inferring gene networks from few snapshots of high dimensional gene profiles. Through synthetic data experiments as well as systematic data experiments, the experimental results revealed how the performances of these approaches can be influenced as the target gene network varies in the network size, data size, as well as system complexity.
Abstract: The present microfluidic study is emphasizing the flow behavior within a Y shape micro-bifurcation in two similar flow configurations. We report here a numerical and experimental investigation on the velocity profiles evolution and secondary flows, manifested at different Reynolds numbers (Re) and for two different boundary conditions. The experiments are performed using special designed setup based on optical microscopic devices. With this setup, direct visualizations and quantitative measurements of the path-lines are obtained. A Micro-PIV measurement system is used to obtain velocity profiles distributions in a spatial evolution in the main flows domains. The experimental data is compared with numerical simulations performed with commercial computational code FLUENT in a 3D geometry with the same dimensions as the experimental one. The numerical flow patterns are found to be in good agreement with the experimental manifestations.
Abstract: Microarray data profiles gene expression on a whole
genome scale, therefore, it provides a good way to study associations
between gene expression and occurrence or progression of cancer.
More and more researchers realized that microarray data is helpful
to predict cancer sample. However, the high dimension of gene
expressions is much larger than the sample size, which makes this
task very difficult. Therefore, how to identify the significant genes
causing cancer becomes emergency and also a hot and hard research
topic. Many feature selection algorithms have been proposed in
the past focusing on improving cancer predictive accuracy at the
expense of ignoring the correlations between the features. In this
work, a novel framework (named by SGS) is presented for stable gene
selection and efficient cancer prediction . The proposed framework
first performs clustering algorithm to find the gene groups where
genes in each group have higher correlation coefficient, and then
selects the significant genes in each group with Bayesian Lasso and
important gene groups with group Lasso, and finally builds prediction
model based on the shrinkage gene space with efficient classification
algorithm (such as, SVM, 1NN, Regression and etc.). Experiment
results on real world data show that the proposed framework often
outperforms the existing feature selection and prediction methods,
say SAM, IG and Lasso-type prediction model.
Abstract: The aim of this paper is to know the sociodemographic
and operational-financial determinants of the services
quality perceived by users of the national health services. Through
the use of an inquiry conducted by the Ministry of Health,
comprehending 16.936 interviews in 2006, we intend to find out if
there is any characteristic that determines the 2006 inquiry results.
With the revision of the literature we also want to know if the
operational-financial results have implications in hospitals users-
perception on the quality of the received services. In order to achieve
our main goals we will make use of the regression analysis to find out
the possible dimensions that determine those results.
Abstract: The reliable results of an insulated oval duct
considering heat radiation are obtained basing on accurate oval
perimeter obtained by integral method as well as one-dimensional
Plane Wedge Thermal Resistance (PWTR) model. This is an extension
study of former paper of insulated oval duct neglecting heat radiation.
It is found that in the practical situations with long-short-axes ratio a/b
4.5% while t/R2
Abstract: The curriculum of the primary school science course was redesigned on the basis of constructivism in 2005-2006 academic years, in Turkey. In this context, the name of this course has been changed as “Science and Technology"; and both content and course books, students workbooks for this course have been redesigned in light of constructivism. The aim of this study is to determine whether the Science and Technology course books and student work books for primary school 5th grade are appropriate for the constructivism by evaluating them in terms of the fundamental principles of constructivism. In this study, out of qualitative research methods, documentation technique (i.e. document analysis) is applied; while selecting samples, criterion-sampling is used out of purposeful sampling techniques. When the Science and Technology course book and workbook for the 5th grade in primary education are examined, it is seen that both books complete each other in certain areas. Consequently, it can be claimed that in spite of some inadequate and missing points in the course book and workbook of the primary school Science and Technology course for the 5th grade students, these books are attempted to be designed in terms of the principles of constructivism. To overcome the inadequacies in the books, it can be suggested to redesign them. In addition to them, not to ignore the technology dimension of the course, the activities that encourage the students to prepare projects using technology cycle should be included.
Abstract: Developing an accurate classifier for high dimensional microarray datasets is a challenging task due to availability of small sample size. Therefore, it is important to determine a set of relevant genes that classify the data well. Traditionally, gene selection method often selects the top ranked genes according to their discriminatory power. Often these genes are correlated with each other resulting in redundancy. In this paper, we have proposed a hybrid method using feature ranking and wrapper method (Genetic Algorithm with multiclass SVM) to identify a set of relevant genes that classify the data more accurately. A new fitness function for genetic algorithm is defined that focuses on selecting the smallest set of genes that provides maximum accuracy. Experiments have been carried on four well-known datasets1. The proposed method provides better results in comparison to the results found in the literature in terms of both classification accuracy and number of genes selected.
Abstract: The feature of HIV genome is in a wide range because
of it is highly heterogeneous. Hence, the infection ability of the virus changes related with different chemokine receptors. From this point,
R5 and X4 HIV viruses use CCR5 and CXCR5 coreceptors respectively while R5X4 viruses can utilize both coreceptors. Recently, in Bioinformatics, R5X4 viruses have been studied to
classify by using the coreceptors of HIV genome.
The aim of this study is to develop the optimal Multilayer
Perceptron (MLP) for high classification accuracy of HIV sub-type viruses. To accomplish this purpose, the unit number in hidden layer
was incremented one by one, from one to a particular number. The statistical data of R5X4, R5 and X4 viruses was preprocessed by the
signal processing methods. Accessible residues of these virus sequences were extracted and modeled by Auto-Regressive Model
(AR) due to the dimension of residues is large and different from each other. Finally the pre-processed dataset was used to evolve MLP with various number of hidden units to determine R5X4
viruses. Furthermore, ROC analysis was used to figure out the optimal MLP structure.
Abstract: The objective of the research was to study of foot
anthropometry of children aged 7-12 years in the South of Thailand Thirty-three dimensions were measured on 305 male and 295 female
subjects with 3 age ranges (7-12 years old). The instrumentation consists of four types of anthropometer, digital vernier caliper, digital
height gauge and measuring tape. The mean values and standard
deviations of average age, height, and weight of the male subjects were 9.52(±1.70) years, 137.80(±11.55) cm, and 37.57(±11.65) kg.
Female average age, height, and weight subjects were 9.53(±1.70) years, 137.88(±11.55) cm, and 34.90(±11.57) kg respectively. The
comparison of the 33 comparison measured anthropometric. Between
male and female subjects were sexual differences in size on women in almost all areas of significance (p