Abstract: The numerical simulation of the slip effect via
vicoelastic fluid for 4:1 contraction problem is investigated with
regard to kinematic behaviors of streamlines and stress tensor by
models of the Navier-Stokes and Oldroyd-B equations. Twodimensional
spatial reference system of incompressible creeping flow
with and without slip velocity is determined and the finite element
method of a semi-implicit Taylor-Galerkin pressure-correction is
applied to compute the problem of this Cartesian coordinate system
including the schemes of velocity gradient recovery method and the
streamline-Upwind / Petrov-Galerkin procedure. The slip effect at
channel wall is added to calculate after each time step in order to
intend the alteration of flow path. The result of stress values and the
vortices are reduced by the optimum slip coefficient of 0.1 with near
the outcome of analytical solution.
Abstract: Human middle-ear is the key component of the
auditory system. Its function is to transfer the sound waves through
the ear canal to provide sufficient stimulus to the fluids of the inner
ear. Degradation of the ossicles that transmit these sound waves from
the eardrum to the inner ear leads to hearing loss. This problem can
be overcome by replacing one or more of these ossicles by middleear
prosthesis. Designing such prosthesis requires a comprehensive
knowledge of the biomechanics of the middle-ear. There are many
finite element modeling approaches developed to understand the
biomechanics of the middle ear. The available models in the
literature, involve high computation time. In this paper, we propose a
simplified model which provides a reasonably accurate result with
much less computational time. Simulation results indicate a
maximum sound pressure gain of 10 dB at 5500 Hz.
Abstract: Distributed Computing Systems are usually considered the most suitable model for practical solutions of many parallel algorithms. In this paper an enhanced distributed system is presented to improve the time complexity of Binary Indexed Trees (BIT). The proposed system uses multi-uniform processors with identical architectures and a specially designed distributed memory system. The analysis of this system has shown that it has reduced the time complexity of the read query to O(Log(Log(N))), and the update query to constant complexity, while the naive solution has a time complexity of O(Log(N)) for both queries. The system was implemented and simulated using VHDL and Verilog Hardware Description Languages, with xilinx ISE 10.1, as the development environment and ModelSim 6.1c, similarly as the simulation tool. The simulation has shown that the overhead resulting by the wiring and communication between the system fragments could be fairly neglected, which makes it applicable to practically reach the maximum speed up offered by the proposed model.
Abstract: Digital broadcasting has been an area of active
research, development, innovation and business models development
in recent years. This paper presents a survey on the characteristics of
the digital terrestrial television broadcasting (DTTB) standards, and
implementation status of DTTB worldwide showing the standards
adopted. It is clear that only the developed countries and some in the
developing ones shall be able to beat the ITU set analogue to digital
broadcasting migration deadline because of the challenges that these
countries faces in digitizing their terrestrial broadcasting. The
challenges to keep on track the DTTB migration plan are also
discussed in this paper. They include financial, technology gap,
policies alignment with DTTB technology, etc. The reported
performance comparisons for the different standards are also
presented. The interesting part is that the results for many
comparative studies depends to a large extent on the objective behind
such studies, hence counter claims are common.
Abstract: This paper presents the methodology from machine
learning approaches for short-term rain forecasting system. Decision
Tree, Artificial Neural Network (ANN), and Support Vector Machine
(SVM) were applied to develop classification and prediction models
for rainfall forecasts. The goals of this presentation are to
demonstrate (1) how feature selection can be used to identify the
relationships between rainfall occurrences and other weather
conditions and (2) what models can be developed and deployed for
predicting the accurate rainfall estimates to support the decisions to
launch the cloud seeding operations in the northeastern part of
Thailand. Datasets collected during 2004-2006 from the
Chalermprakiat Royal Rain Making Research Center at Hua Hin,
Prachuap Khiri khan, the Chalermprakiat Royal Rain Making
Research Center at Pimai, Nakhon Ratchasima and Thai
Meteorological Department (TMD). A total of 179 records with 57
features was merged and matched by unique date. There are three
main parts in this work. Firstly, a decision tree induction algorithm
(C4.5) was used to classify the rain status into either rain or no-rain.
The overall accuracy of classification tree achieves 94.41% with the
five-fold cross validation. The C4.5 algorithm was also used to
classify the rain amount into three classes as no-rain (0-0.1 mm.),
few-rain (0.1- 10 mm.), and moderate-rain (>10 mm.) and the overall
accuracy of classification tree achieves 62.57%. Secondly, an ANN
was applied to predict the rainfall amount and the root mean square
error (RMSE) were used to measure the training and testing errors of
the ANN. It is found that the ANN yields a lower RMSE at 0.171 for
daily rainfall estimates, when compared to next-day and next-2-day
estimation. Thirdly, the ANN and SVM techniques were also used to
classify the rain amount into three classes as no-rain, few-rain, and
moderate-rain as above. The results achieved in 68.15% and 69.10%
of overall accuracy of same-day prediction for the ANN and SVM
models, respectively. The obtained results illustrated the comparison
of the predictive power of different methods for rainfall estimation.
Abstract: There is a complex situation on the transport environment in the cities of the world. For the analysis and prevention of environmental problems an accurate calculation hazardous substances concentrations at each point of the investigated area is required. In the turbulent atmosphere of the city the wellknown methods of mathematical statistics for these tasks cannot be applied with a satisfactory level of accuracy. Therefore, to solve this class of problems apparatus of mathematical physics is more appropriate. In such models, because of the difficulty as a rule the influence of uneven land surface on streams of air masses in the turbulent atmosphere of the city are not taken into account. In this paper the influence of the surface roughness, which can be quite large, is mathematically shown. The analysis of this problem under certain conditions identified the possibility of areas appearing in the atmosphere with pressure tending to infinity, i.e. so-called "wall effect".
Abstract: Stochastic models of biological networks are well established in systems biology, where the computational treatment of such models is often focused on the solution of the so-called chemical master equation via stochastic simulation algorithms. In contrast to this, the development of storage-efficient model representations that are directly suitable for computer implementation has received significantly less attention. Instead, a model is usually described in terms of a stochastic process or a "higher-level paradigm" with graphical representation such as e.g. a stochastic Petri net. A serious problem then arises due to the exponential growth of the model-s state space which is in fact a main reason for the popularity of stochastic simulation since simulation suffers less from the state space explosion than non-simulative numerical solution techniques. In this paper we present transition class models for the representation of biological network models, a compact mathematical formalism that circumvents state space explosion. Transition class models can also serve as an interface between different higher level modeling paradigms, stochastic processes and the implementation coded in a programming language. Besides, the compact model representation provides the opportunity to apply non-simulative solution techniques thereby preserving the possible use of stochastic simulation. Illustrative examples of transition class representations are given for an enzyme-catalyzed substrate conversion and a part of the bacteriophage λ lysis/lysogeny pathway.
Abstract: The recent advances in computational fluid dynamics
(CFD) can be useful in observing the detailed hemodynamics in
cerebral aneurysms for understanding not only their formation and
rupture but also for clinical evaluation and treatment. However,
important hemodynamic quantities are difficult to measure in vivo. In
the present study, an approximate model of normal middle cerebral
artery (MCA) along with two cases consisting broad and narrow
saccular aneurysms are analyzed. The models are generated in
ANSYS WORKBENCH and transient analysis is performed in
ANSYS-CFX. The results obtained are compared for three cases and
agree well with the available literature.
Abstract: A method has been developed for preparing load
models for power flow and stability. The load modeling
(LOADMOD) computer software transforms data on load class mix,
composition, and characteristics into the from required for
commonly–used power flow and transient stability simulation
programs. Typical default data have been developed for load
composition and characteristics. This paper defines LOADMOD
software and describes the dynamic and static load modeling
techniques used in this software and results of initial testing for
BAKHTAR power system.
Abstract: The full length mitochondrial small subunit ribosomal
(mt-rns) gene has been characterized for Ophiostoma novo-ulmi
subspecies americana. The gene was also characterized for
Ophiostoma ulmi and a group II intron was noted in the mt-rns gene
of O. ulmi. The insertion in the mt-rns gene is at position S952 and it
is a group IIB1 intron that encodes a double motif LAGLIDADG
homing endonuclease from an open reading frame located within a
loop of domain III. Secondary structure models for the mt-rns RNA
of O. novo-ulmi subsp. americana and O. ulmi were generated to
place the intron within the context of the ribosomal RNA. The in vivo
splicing of the O.ul-mS952 group II intron was confirmed with
reverse transcription-PCR. A survey of 182 strains of Dutch Elm
Diseases causing agents showed that the mS952 intron was absent in
what is considered to be the more aggressive species O. novo-ulmi
but present in strains of the less aggressive O. ulmi. This observation
suggests that the O.ul-mS952 intron can be used as a PCR-based
molecular marker to discriminate between O. ulmi and O. novo-ulmi
subsp. americana.
Abstract: Road rage is an increasingly prevalent expression of
aggression in our society. Its dangers are apparent and understanding
its causes may shed light on preventative measures. This study
involved a fifteen-minute survey administered to 147 undergraduate
students at a North Eastern suburban university. The survey
consisted of a demographics section, questions regarding financial
investment in respondents- vehicles, experience driving, habits of
driving, experiences witnessing role models driving, and an
evaluation of road rage behavior using the Driving Vengeance
Questionnaire. The study found no significant differences in driving
aggression between respondents who were financially invested in
their vehicle compared to those who were not, or between
respondents who drove in heavy traffic hours compared to those who
did not, suggesting internal factors correlate with aggressive driving
habits. The study also found significant differences in driving
aggression between males versus females, those with more points on
their license versus fewer points, and those who witnessed parents
driving aggressively very often versus rarely or never. Additional
studies can investigate how witnessing parents driving aggressively
is related to future driving behaviors.
Abstract: This paper describes an experimental investigation of
the drying behavior and conditions of rosehip in a convective
cyclone-type dryer. Drying experiments were conducted at air inlet
temperatures of 50, 60 and 70 o C and air velocities of 0.5, 1 and 1.5
ms–1. The parametric values obtained from the experiments were
fitted to the Newton mathematical models. Consequently, the drying
model developed by Newton model showed good agreement with the
data obtained from the experiments. Concluding, it was obtained that;
(i) the temperature is the major effect on the drying process, (ii) air
velocity has low effect on the drying of rosehip, (iii) the C-vitamin is
observed to change according to the temperature, moisture, drying
time and flow types. The changing ratio is found to be in the range of
0.70-0.74.
Abstract: Many supervised induction algorithms require discrete
data, even while real data often comes in a discrete
and continuous formats. Quality discretization of continuous
attributes is an important problem that has effects on speed,
accuracy and understandability of the induction models. Usually,
discretization and other types of statistical processes are applied
to subsets of the population as the entire population is practically
inaccessible. For this reason we argue that the discretization
performed on a sample of the population is only an estimate of
the entire population. Most of the existing discretization methods,
partition the attribute range into two or several intervals using
a single or a set of cut points. In this paper, we introduce a
technique by using resampling (such as bootstrap) to generate
a set of candidate discretization points and thus, improving the
discretization quality by providing a better estimation towards
the entire population. Thus, the goal of this paper is to observe
whether the resampling technique can lead to better discretization
points, which opens up a new paradigm to construction of
soft decision trees.
Abstract: Availability and mobilization of revenue is the main
essential with which an economy is managed and run. While
planning or while making the budgets nations set revenue targets to
be achieved. But later when the accounts are closed the actual
collections of revenue through taxes or even the non-tax revenue
collection would invariably be different as compared to the initial
estimates and targets set to be achieved. This revenue-gap distorts the
whole system and the economy disturbing all the major macroeconomic
indicators. This study is aimed to find out short and long
term impact of revenue gap on budget deficit, debt burden and
economic growth on the economy of Pakistan. For this purpose the
study uses autoregressive distributed lag approach to cointegration
and error correction mechanism on three different models for the
period 1980 to 2009. The empirical results show that revenue gap has
a short and long run relationship with economic growth and budget
deficit. However, revenue gap has no impact on debt burden.
Abstract: This paper presents parametric probability density
models for call holding times (CHTs) into emergency call center
based on the actual data collected for over a week in the public
Emergency Information Network (EIN) in Mongolia. When the set of
chosen candidates of Gamma distribution family is fitted to the call
holding time data, it is observed that the whole area in the CHT
empirical histogram is underestimated due to spikes of higher
probability and long tails of lower probability in the histogram.
Therefore, we provide the Gaussian parametric model of a mixture of
lognormal distributions with explicit analytical expressions for the
modeling of CHTs of PSNs. Finally, we show that the CHTs for
PSNs are fitted reasonably by a mixture of lognormal distributions
via the simulation of expectation maximization algorithm. This result
is significant as it expresses a useful mathematical tool in an explicit
manner of a mixture of lognormal distributions.
Abstract: Study of soil properties like field capacity (F.C.) and permanent wilting point (P.W.P.) play important roles in study of soil moisture retention curve. Although these parameters can be measured directly, their measurement is difficult and expensive. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. In this investigation, 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. The data set was divided into two subsets for calibration (80%) and testing (20%) of the models and their normality were tested by Kolmogorov-Smirnov method. Both multivariate regression and artificial neural network (ANN) techniques were employed to develop the appropriate PTFs for predicting soil parameters using easily measurable characteristics of clay, silt, O.C, S.P, B.D and CaCO3. The performance of the multivariate regression and ANN models was evaluated using an independent test data set. In order to evaluate the models, root mean square error (RMSE) and R2 were used. The comparison of RSME for two mentioned models showed that the ANN model gives better estimates of F.C and P.W.P than the multivariate regression model. The value of RMSE and R2 derived by ANN model for F.C and P.W.P were (2.35, 0.77) and (2.83, 0.72), respectively. The corresponding values for multivariate regression model were (4.46, 0.68) and (5.21, 0.64), respectively. Results showed that ANN with five neurons in hidden layer had better performance in predicting soil properties than multivariate regression.
Abstract: Recommender systems are usually regarded as an
important marketing tool in the e-commerce. They use important
information about users to facilitate accurate recommendation. The
information includes user context such as location, time and interest
for personalization of mobile users. We can easily collect information
about location and time because mobile devices communicate with the
base station of the service provider. However, information about user
interest can-t be easily collected because user interest can not be
captured automatically without user-s approval process. User interest
usually represented as a need. In this study, we classify needs into two
types according to prior research. This study investigates the
usefulness of data mining techniques for classifying user need type for
recommendation systems. We employ several data mining techniques
including artificial neural networks, decision trees, case-based
reasoning, and multivariate discriminant analysis. Experimental
results show that CHAID algorithm outperforms other models for
classifying user need type. This study performs McNemar test to
examine the statistical significance of the differences of classification
results. The results of McNemar test also show that CHAID performs
better than the other models with statistical significance.
Abstract: In Thailand, the practice of pre-hospital Emergency
Medical Service (EMS) in each area reveals the different growth
rates and effectiveness of the practices. Those can be found as the
diverse quality and quantity. To shorten the learning curve prior to
speed-up the practices in other areas, story telling and lessons learnt
from the effective practices are valued as meaningful knowledge. To
this paper, it was to ascertain the factors, lessons learnt and best
practices that have impact as contributing to the success of prehospital
EMS system. Those were formulized as model prior to
speedup the practice in other areas. To develop the model, Malcolm
Baldrige National Quality Award (MBNQA), which is widely
recognized as a framework for organizational quality assessment and
improvement, was chosen as the discussion framework. Remarkably,
this study was based on the consideration of knowledge capture;
however it was not to complete the loop of knowledge activities.
Nevertheless, it was to highlight the recognition of knowledge
capture, which is the initiation of knowledge management.
Abstract: In this paper a nonlinear model is presented to
demonstrate the relation between production and marketing
departments. By introducing some functions such as pricing cost and
market share loss functions it will be tried to show some aspects of
market modelling which has not been regarded before. The proposed
model will be a constrained signomial geometric programming
model. For model solving, after variables- modifications an iterative
technique based on the concept of geometric mean will be introduced
to solve the resulting non-standard posynomial model which can be
applied to a wide variety of models in non-standard posynomial
geometric programming form. At the end a numerical analysis will
be presented to accredit the validity of the mentioned model.
Abstract: This work proposes an accurate crosstalk noise estimation method in the presence of multiple RLC lines for the use in design automation tools. This method correctly models the loading effects of non switching aggressors and aggressor tree branches using resistive shielding effect and realistic exponential input waveforms. Noise peak and width expressions have been derived. The results obtained are at good agreement with SPICE results. Results show that average error for noise peak is 4.7% and for the width is 6.15% while allowing a very fast analysis.