Abstract: To understand life as biological system, evolutionary
understanding is indispensable. Protein interactions data are rapidly
accumulating and are suitable for system-level evolutionary analysis.
We have analyzed yeast protein interaction network by both
mathematical and biological approaches. In this poster presentation,
we inferred the evolutionary birth periods of yeast proteins by
reconstructing phylogenetic profile. It has been thought that hub
proteins that have high connection degree are evolutionary old. But
our analysis showed that hub proteins are entirely evolutionary new.
We also examined evolutionary processes of protein complexes. It
showed that member proteins of complexes were tend to have
appeared in the same evolutionary period. Our results suggested that
protein interaction network evolved by modules that form the
functional unit. We also reconstructed standardized phylogenetic trees
and calculated evolutionary rates of yeast proteins. It showed that
there is no obvious correlation between evolutionary rates and
connection degrees of yeast proteins.
Abstract: In this paper, a target signal detection method using
multiple signal classification (MUSIC) algorithm is proposed. The
MUSIC algorithm is a subspace-based direction of arrival (DOA)
estimation method. The algorithm detects the DOAs of multiple
sources using the inverse of the eigenvalue-weighted eigen spectra. To
apply the algorithm to target signal detection for GSC-based
beamforming, we utilize its spectral response for the target DOA in
noisy conditions. For evaluation of the algorithm, the performance of
the proposed target signal detection method is compared with that of
the normalized cross-correlation (NCC), the fixed beamforming, and
the power ratio method. Experimental results show that the proposed
algorithm significantly outperforms the conventional ones in receiver
operating characteristics(ROC) curves.
Abstract: Our objectives were to evaluate the effects of sire
breed, type of protein supplement, level of supplementation and sex
on wool spinning fineness (SF), its correlations with other wool
characteristics and prediction accuracy in F1 Merino crossbred lambs.
Texel, Coopworth, White Suffolk, East Friesian and Dorset rams
were mated with 500 purebred Merino dams at a ratio of 1:100 in
separate paddocks within a single management system. The F1
progeny were raised on ryegrass pasture until weaning, before forty
lambs were randomly allocated to treatments in a 5 x 2 x 2 x 2
factorial experimental design representing 5 sire breeds, 2
supplementary feeds (canola or lupins), 2 levels of supplementation
(1% or 2% of liveweight) and sex (wethers or ewes). Lambs were
supplemented for six weeks after an initial three weeks of adjustment,
wool sampled at the commencement and conclusion of the feeding
trial and analyzed for SF, mean fibre diameter (FD), coefficient of
variation (CV), standard deviation, comfort factor (CF), fibre
curvature (CURV), and clean fleece yield. Data were analyzed using
mixed linear model procedures with sire fitted as a random effect,
and sire breed, sex, supplementary feed type, level of
supplementation and their second-order interactions as fixed effects.
Sire breed (P
Abstract: The passive electrical properties of a tissue depends
on the intrinsic constituents and its structure, therefore by measuring
the complex electrical impedance of the tissue it might be possible to
obtain indicators of the tissue state or physiological activity [1].
Complete bio-impedance information relative to physiology and
pathology of a human body and functional states of the body tissue or
organs can be extracted by using a technique containing a fourelectrode
measurement setup. This work presents the estimation
measurement setup based on the four-electrode technique. First, the
complex impedance is estimated by three different estimation
techniques: Fourier, Sine Correlation and Digital De-convolution and
then estimation errors for the magnitude, phase, reactance and
resistance are calculated and analyzed for different levels of
disturbances in the observations. The absolute values of relative
errors are plotted and the graphical performance of each technique is
compared.
Abstract: Given a bivariate normal sample of correlated variables,
(Xi, Yi), i = 1, . . . , n, an alternative estimator of Pearson’s correlation
coefficient is obtained in terms of the ranges, |Xi − Yi|.
An approximate confidence interval for ρX,Y is then derived, and
a simulation study reveals that the resulting coverage probabilities
are in close agreement with the set confidence levels. As well, a
new approximant is provided for the density function of R, the
sample correlation coefficient. A mixture involving the proposed
approximate density of R, denoted by hR(r), and a density function
determined from a known approximation due to R. A. Fisher is shown
to accurately approximate the distribution of R. Finally, nearly exact
density approximants are obtained on adjusting hR(r) by a 7th degree
polynomial.
Abstract: In this paper, an automatic determination algorithm for nuclear magnetic resonance (NMR) spectra of the metabolites in the living body by magnetic resonance spectroscopy (MRS) without human intervention or complicated calculations is presented. In such method, the problem of NMR spectrum determination is transformed into the determination of the parameters of a mathematical model of the NMR signal. To calculate these parameters efficiently, a new model called modified Hopfield neural network is designed. The main achievement of this paper over the work in literature [30] is that the speed of the modified Hopfield neural network is accelerated. This is done by applying cross correlation in the frequency domain between the input values and the input weights. The modified Hopfield neural network can accomplish complex dignals perfectly with out any additinal computation steps. This is a valuable advantage as NMR signals are complex-valued. In addition, a technique called “modified sequential extension of section (MSES)" that takes into account the damping rate of the NMR signal is developed to be faster than that presented in [30]. Simulation results show that the calculation precision of the spectrum improves when MSES is used along with the neural network. Furthermore, MSES is found to reduce the local minimum problem in Hopfield neural networks. Moreover, the performance of the proposed method is evaluated and there is no effect on the performance of calculations when using the modified Hopfield neural networks.
Abstract: The aim of this study is to develop mathematical
relationships for the performance parameter brake thermal efficiency
(BTE) and emission parameter nitrogen oxides (NOx) for the various
esters of vegetable oils used as CI engine fuel. The BTE is an
important performance parameter defining the ability of engine to
utilize the energy supplied and power developed similarly it is
indication of efficiency of fuels used. The esters of cottonseed oil,
soybean oil, jatropha oil and hingan oil are prepared using
transesterification process and characterized for their physical and
main fuel properties including viscosity, density, flash point and
higher heating value using standard test methods. These esters are
tried as CI engine fuel to analyze the performance and emission
parameters in comparison to diesel. The results of the study indicate
that esters as a fuel does not differ greatly with that of diesel in
properties. The CI engine performance with esters as fuel is in line
with the diesel where as the emission parameters are reduced with the
use of esters.
The correlation developed between BTE and brake power(BP),
gross calorific value(CV), air-fuel ratio(A/F), heat carried away by
cooling water(HCW). Another equation is developed between the
NOx emission and CO, HC, smoke density (SD), exhaust gas
temperature (EGT). The equations are verified by comparing the
observed and calculated values which gives the coefficient of
correlation of 0.99 and 0.96 for the BTE and NOx equations
respectively.
Abstract: This study reports results of a meta-analytic path analysis e-learning Acceptance Model with k = 27 studies, Databases searched included Information Sciences Institute (ISI) website. Variables recorded included perceived usefulness, perceived ease of use, attitude toward behavior, and behavioral intention to use e-learning. A correlation matrix of these variables was derived from meta-analytic data and then analyzed by using structural path analysis to test the fitness of the e-learning acceptance model to the observed aggregated data. Results showed the revised hypothesized model to be a reasonable, good fit to aggregated data. Furthermore, discussions and implications are given in this article.
Abstract: This study investigated the relationship between urban
and rural ozone concentrations and quantified the extent to which
ambient rural conditions and the concentrations of other pollutants
can be used to predict urban ozone concentrations. The study
describes the variations of ozone in weekday and weekends as well as
the daily maximum recorded at selected monitoring stations. The
results showed that Putrajaya station had the highest concentrations
of O3 on weekend due the titration of NO during the weekday.
Additionally, Jerantut had the lowest average concentration with a
reading value high on Wednesdays. The comparisons of average and
maximum concentrations of ozone for the three stations showed that
the strongest significant correlation is recorded in Jerantut station
with the value R2= 0.769. Ozone concentrations originating from a
neighbouring urban site form a better predictor to the urban ozone
concentrations than widespread rural ozone at some levels of
temporal averaging. It is found that in urban and rural of Malaysian
peninsular, the concentration of ozone depends on the concentration
of NOx and seasonal meteorological factors. The HYSPLIT Model
(the northeast monsoon) showed that the wind direction can also
influence the concentration of ozone in the atmosphere in the studied
areas.
Abstract: Classification is an interesting problem in functional
data analysis (FDA), because many science and application problems
end up with classification problems, such as recognition, prediction,
control, decision making, management, etc. As the high dimension
and high correlation in functional data (FD), it is a key problem to
extract features from FD whereas keeping its global characters, which
relates to the classification efficiency and precision to heavens. In this
paper, a novel automatic method which combined Genetic Algorithm
(GA) and classification algorithm to extract classification features is
proposed. In this method, the optimal features and classification model
are approached via evolutional study step by step. It is proved by
theory analysis and experiment test that this method has advantages in
improving classification efficiency, precision and robustness whereas
using less features and the dimension of extracted classification
features can be controlled.
Abstract: Different pseudo-random or pseudo-noise (PN) as well as orthogonal sequences that can be used as spreading codes for code division multiple access (CDMA) cellular networks or can be used for encrypting speech signals to reduce the residual intelligence are investigated. We briefly review the theoretical background for direct sequence CDMA systems and describe the main characteristics of the maximal length, Gold, Barker, and Kasami sequences. We also discuss about variable- and fixed-length orthogonal codes like Walsh- Hadamard codes. The equivalence of PN and orthogonal codes are also derived. Finally, a new PN sequence is proposed which is shown to have certain better properties than the existing codes.
Abstract: This paper deals with econometric analysis of real
retail trade turnover. It is a part of an extensive scientific research
about modern trends in Croatian national economy. At the end of the
period of transition economy, Croatia confronts with challenges and
problems of high consumption society. In such environment as
crucial economic variables: real retail trade turnover, average
monthly real wages and household loans are chosen for consequence
analysis. For the purpose of complete procedure of multiple
econometric analysis data base adjustment has been provided.
Namely, it has been necessary to deflate original national statistics
data of retail trade turnover using consumer price indices, as well as
provide process of seasonally adjustment of its contemporary
behavior. In model establishment it has been necessary to involve the
overcoming procedure for the autocorrelation and colinearity
problems. Moreover, for case of time-series shift a specific
appropriate econometric instrument has been applied. It would be
emphasize that the whole methodology procedure is based on the real
Croatian national economy time-series.
Abstract: This paper systematically investigates the timedependent
health outcomes for office staff during computer work
using the developed mathematical model. The model describes timedependent
health outcomes in multiple body regions associated with
computer usage. The association is explicitly presented with a doseresponse
relationship which is parametrized by body region
parameters. Using the developed model we perform extensive
investigations of the health outcomes statically and dynamically. We
compare the risk body regions and provide various severity rankings
of the discomfort rate changes with respect to computer-related
workload dynamically for the study population. Application of the
developed model reveals a wide range of findings. Such broad
spectrum of investigations in a single report literature is lacking.
Based upon the model analysis, it is discovered that the highest
average severity level of the discomfort exists in neck, shoulder, eyes,
shoulder joint/upper arm, upper back, low back and head etc. The
biggest weekly changes of discomfort rates are in eyes, neck, head,
shoulder, shoulder joint/upper arm and upper back etc. The fastest
discomfort rate is found in neck, followed by shoulder, eyes, head,
shoulder joint/upper arm and upper back etc. Most of our findings are
consistent with the literature, which demonstrates that the developed
model and results are applicable and valuable and can be utilized to
assess correlation between the amount of computer-related workload
and health risk.
Abstract: In this paper, an ultrasonic technique is proposed to
predict oil content in a fresh palm fruit. This is accomplished by
measuring the attenuation based on ultrasonic transmission mode.
Several palm fruit samples with known oil content by Soxhlet
extraction (ISO9001:2008) were tested with our ultrasonic
measurement. Amplitude attenuation data results for all palm samples
were collected. The Feedforward Neural Networks (FNNs) are
applied to predict the oil content for the samples. The Root Mean
Square Error (RMSE) and Mean Absolute Error (MAE) of the FNN
model for predicting oil content percentage are 7.6186 and 5.2287
with the correlation coefficient (R) of 0.9193.
Abstract: This study investigates the performance of radial basis function networks (RBFN) in forecasting the monthly CO2 emissions of an electric power utility. We also propose a method for input variable selection. This method is based on identifying the general relationships between groups of input candidates and the output. The effect that each input has on the forecasting error is examined by removing all inputs except the variable to be investigated from its group, calculating the networks parameter and performing the forecast. Finally, the new forecasting error is compared with the reference model. Eight input variables were identified as the most relevant, which is significantly less than our reference model with 30 input variables. The simulation results demonstrate that the model with the 8 inputs selected using the method introduced in this study performs as accurate as the reference model, while also being the most parsimonious.
Abstract: In this paper we proposed comparison of four content based objective metrics with results of subjective tests from 80 video sequences. We also include two objective metrics VQM and SSIM to our comparison to serve as “reference” objective metrics because their pros and cons have already been published. Each of the video sequence was preprocessed by the region recognition algorithm and then the particular objective video quality metric were calculated i.e. mutual information, angular distance, moment of angle and normalized cross-correlation measure. The Pearson coefficient was calculated to express metrics relationship to accuracy of the model and the Spearman rank order correlation coefficient to represent the metrics relationship to monotonicity. The results show that model with the mutual information as objective metric provides best result and it is suitable for evaluating quality of video sequences.
Abstract: This paper proposes a new approach for image encryption
using a combination of different permutation techniques.
The main idea behind the present work is that an image can be
viewed as an arrangement of bits, pixels and blocks. The intelligible
information present in an image is due to the correlations among the
bits, pixels and blocks in a given arrangement. This perceivable information
can be reduced by decreasing the correlation among the bits,
pixels and blocks using certain permutation techniques. This paper
presents an approach for a random combination of the aforementioned
permutations for image encryption. From the results, it is observed
that the permutation of bits is effective in significantly reducing the
correlation thereby decreasing the perceptual information, whereas
the permutation of pixels and blocks are good at producing higher
level security compared to bit permutation. A random combination
method employing all the three techniques thus is observed to be
useful for tactical security applications, where protection is needed
only against a casual observer.
Abstract: Natural convection heat transfer from a heated
horizontal semi-circular cylinder (flat surface upward) has been
investigated for the following ranges of conditions; Grashof number,
and Prandtl number. The governing partial differential equations
(continuity, Navier-Stokes and energy equations) have been solved
numerically using a finite volume formulation. In addition, the role of
the type of the thermal boundary condition imposed at cylinder
surface, namely, constant wall temperature (CWT) and constant heat
flux (CHF) are explored. Natural convection heat transfer from a
heated horizontal semi-circular cylinder (flat surface upward) has
been investigated for the following ranges of conditions; Grashof
number, and Prandtl number, . The governing partial differential
equations (continuity, Navier-Stokes and energy equations) have
been solved numerically using a finite volume formulation. In
addition, the role of the type of the thermal boundary condition
imposed at cylinder surface, namely, constant wall temperature
(CWT) and constant heat flux (CHF) are explored. The resulting flow
and temperature fields are visualized in terms of the streamline and
isotherm patterns in the proximity of the cylinder. The flow remains
attached to the cylinder surface over the range of conditions spanned
here except that for and ; at these conditions, a separated flow
region is observed when the condition of the constant wall
temperature is prescribed on the surface of the cylinder. The heat
transfer characteristics are analyzed in terms of the local and average
Nusselt numbers. The maximum value of the local Nusselt number
always occurs at the corner points whereas it is found to be minimum
at the rear stagnation point on the flat surface. Overall, the average
Nusselt number increases with Grashof number and/ or Prandtl
number in accordance with the scaling considerations. The numerical
results are used to develop simple correlations as functions of
Grashof and Prandtl number thereby enabling the interpolation of the
present numerical results for the intermediate values of the Prandtl or
Grashof numbers for both thermal boundary conditions.
Abstract: This work is to study a roll of the fluctuating density
gradient in the compressible flows for the computational fluid dynamics
(CFD). A new anisotropy tensor with the fluctuating density
gradient is introduced, and is used for an invariant modeling technique
to model the turbulent density gradient correlation equation derived
from the continuity equation. The modeling equation is decomposed
into three groups: group proportional to the mean velocity, and that
proportional to the mean strain rate, and that proportional to the mean
density. The characteristics of the correlation in a wake are extracted
from the results by the two dimensional direct simulation, and shows
the strong correlation with the vorticity in the wake near the body.
Thus, it can be concluded that the correlation of the density gradient
is a significant parameter to describe the quick generation of the
turbulent property in the compressible flows.
Abstract: The intrusion detection problem has been frequently studied, but intrusion detection methods are often based on a single point of view, which always limits the results. In this paper, we introduce a new intrusion detection model based on the combination of different current methods. First we use a notion of distance to unify the different methods. Second we combine these methods using the Pearson correlation coefficients, which measure the relationship between two methods, and we obtain a combined distance. If the combined distance is greater than a predetermined threshold, an intrusion is detected. We have implemented and tested the combination model with two different public data sets: the data set of masquerade detection collected by Schonlau & al., and the data set of program behaviors from the University of New Mexico. The results of the experiments prove that the combination model has better performances.