Abstract: Female breast cancer is the second in frequency after cervical cancer. Surgery is the most common treatment for breast cancer, followed by chemotherapy as a treatment of choice. Although effective, it causes serious side effects. Controlled-release drug delivery is an alternative method to improve the efficacy and safety of the treatment. It can release the dosage of drug between the minimum effect concentration (MEC) and minimum toxic concentration (MTC) within tumor tissue and reduce the damage of normal tissue and the side effect. Because an in vivo experiment of this system can be time-consuming and labor-intensive, a mathematical model is desired to study the effects of important parameters before the experiments are performed. Here, we describe a 3D mathematical model to predict the release of doxorubicin from pluronic gel to treat human breast cancer. This model can, ultimately, be used to effectively design the in vivo experiments.
Abstract: We review a knowledge extractor model in
constructing 3G Killer Applications. The success of 3G is essential
for Government as it became part of Telecommunications National
Strategy. The 3G wireless technologies may reach larger area and
increase country-s ICT penetration. In order to understand future
customers needs, the operators require proper information
(knowledge) lying inside. Our work approached future customers as
complex system where the complex knowledge may expose regular
behavior. The hidden information from 3G future customers is
revealed by using fractal-based questionnaires. Afterward, further
statistical analysis is used to match the results with operator-s
strategic plan. The developments of 3G applications also consider its
saturation time and further improvement of the application.
Abstract: The standard investigational method for obstructive
sleep apnea syndrome (OSAS) diagnosis is polysomnography (PSG),
which consists of a simultaneous, usually overnight recording of
multiple electro-physiological signals related to sleep and
wakefulness. This is an expensive, encumbering and not a readily
repeated protocol, and therefore there is need for simpler and easily
implemented screening and detection techniques. Identification of
apnea/hypopnea events in the screening recordings is the key factor
for the diagnosis of OSAS. The analysis of a solely single-lead
electrocardiographic (ECG) signal for OSAS diagnosis, which may
be done with portable devices, at patient-s home, is the challenge of
the last years. A novel artificial neural network (ANN) based
approach for feature extraction and automatic identification of
respiratory events in ECG signals is presented in this paper. A
nonlinear principal component analysis (NLPCA) method was
considered for feature extraction and support vector machine for
classification/recognition. An alternative representation of the
respiratory events by means of Kohonen type neural network is
discussed. Our prospective study was based on OSAS patients of the
Clinical Hospital of Pneumology from Iaşi, Romania, males and
females, as well as on non-OSAS investigated human subjects. Our
computed analysis includes a learning phase based on cross signal
PSG annotation.
Abstract: Uncertainties of a serial production line affect on the
production throughput. The uncertainties cannot be prevented in a
real production line. However the uncertain conditions can be
controlled by a robust prediction model. Thus, a hybrid model
including autoregressive integrated moving average (ARIMA) and
multiple polynomial regression, is proposed to model the nonlinear
relationship of production uncertainties with throughput. The
uncertainties under consideration of this study are demand, breaktime,
scrap, and lead-time. The nonlinear relationship of production
uncertainties with throughput are examined in the form of quadratic
and cubic regression models, where the adjusted R-squared for
quadratic and cubic regressions was 98.3% and 98.2%. We optimized
the multiple quadratic regression (MQR) by considering the time
series trend of the uncertainties using ARIMA model. Finally the
hybrid model of ARIMA and MQR is formulated by better adjusted
R-squared, which is 98.9%.
Abstract: In this study, aeroelastic response and performance
analyses have been conducted for a 5MW-Class composite wind
turbine blade model. Advanced coupled numerical method based on
computational fluid dynamics (CFD) and computational flexible
multi-body dynamics (CFMBD) has been developed in order to
investigate aeroelastic responses and performance characteristics of
the rotating composite blade. Reynolds-Averaged Navier-Stokes
(RANS) equations with k-ω SST turbulence model were solved for
unsteady flow problems on the rotating turbine blade model. Also,
structural analyses considering rotating effect have been conducted
using the general nonlinear finite element method. A fully implicit
time marching scheme based on the Newmark direct integration
method is applied to solve the coupled aeroelastic governing equations
of the 3D turbine blade for fluid-structure interaction (FSI) problems.
Detailed dynamic responses and instantaneous velocity contour on the
blade surfaces which considering flow-separation effects were
presented to show the multi-physical phenomenon of the huge rotating
wind- turbine blade model.
Abstract: Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.
Abstract: Self-compacting concrete (SCC), a new kind of high
performance concrete (HPC) have been first developed in Japan in
1986. The development of SCC has made casting of dense
reinforcement and mass concrete convenient, has minimized noise.
Fresh self-compacting concrete (SCC) flows into formwork and
around obstructions under its own weight to fill it completely and
self-compact (without any need for vibration), without any
segregation and blocking. The elimination of the need for
compaction leads to better quality concrete and substantial
improvement of working conditions. SCC mixes generally have a
much higher content of fine fillers, including cement, and produce
excessively high compressive strength concrete, which restricts its
field of application to special concrete only. To use SCC mixes in
general concrete construction practice, requires low cost materials to
make inexpensive concrete.
Rice husk ash (RHA) has been used as a highly reactive
pozzolanic material to improve the microstructure of the interfacial
transition zone (ITZ) between the cement paste and the aggregate in
self compacting concrete. Mechanical experiments of RHA blended
Portland cement concretes revealed that in addition to the pozzolanic
reactivity of RHA (chemical aspect), the particle grading (physical
aspect) of cement and RHA mixtures also exerted significant
influences on the blending efficiency.
The scope of this research was to determine the usefulness of Rice
husk ash (RHA) in the development of economical self compacting
concrete (SCC). The cost of materials will be decreased by reducing
the cement content by using waste material like rice husk ash instead
of.
This paper presents a study on the development of Mechanical
properties up to 180 days of self compacting and ordinary concretes
with rice-husk ash (RHA), from a rice paddy milling industry in
Rasht (Iran). Two different replacement percentages of cement by
RHA, 10%, and 20%, and two different water/cementicious material
ratios (0.40 and 0.35), were used for both of self compacting and
normal concrete specimens. The results are compared with those of
the self compacting concrete without RHA, with compressive,
flexural strength and modulus of elasticity. It is concluded that RHA
provides a positive effect on the Mechanical properties at age after
60 days.
Base of the result self compacting concrete specimens have higher
value than normal concrete specimens in all test except modulus of
elasticity. Also specimens with 20% replacement of cement by RHA
have the best performance.
Abstract: Chronic hepatitis B can evolve to cirrhosis and liver
cancer. Interferon is the only effective treatment, for carefully selected
patients, but it is very expensive. Some of the selection criteria are
based on liver biopsy, an invasive, costly and painful medical procedure.
Therefore, developing efficient non-invasive selection systems,
could be in the patients benefit and also save money. We investigated
the possibility to create intelligent systems to assist the Interferon
therapeutical decision, mainly by predicting with acceptable accuracy
the results of the biopsy. We used a knowledge discovery in integrated
medical data - imaging, clinical, and laboratory data. The resulted
intelligent systems, tested on 500 patients with chronic hepatitis
B, based on C5.0 decision trees and boosting, predict with 100%
accuracy the results of the liver biopsy. Also, by integrating the other
patients selection criteria, they offer a non-invasive support for the
correct Interferon therapeutic decision. To our best knowledge, these
decision systems outperformed all similar systems published in the
literature, and offer a realistic opportunity to replace liver biopsy in
this medical context.
Abstract: In this article the authors are researching cultural
differences between rural and urban characters in case of
contemporary Kazakh cinema.Two motion pictures are analyzed:
“Strizh" (2007) by AbaiKulbai and “Seker" (2009) by
SabitKurmanbekov.According to the authors- opinion ateenage girl
characters in these two films reflect two cultures (urban and rural) of
Kazakh society, which displays complicated socio-cultural processes
of modern Kazakhstan.
Abstract: The indistinctness of the manufacturing processes makes that a parts cannot be realized in an absolutely exact way towards the specifications on the dimensions. It is thus necessary to assume that the effectively realized product has to belong in a very strict way to compatible intervals with a correct functioning of the parts. In this paper we present an approach based on mixing tow different characteristics theories, the fuzzy system and Petri net system. This tool has been proposed to model and control the quality in an assembly system. A robust command of a mechanical assembly process is presented as an application. This command will then have to maintain the specifications interval of parts in front of the variations. It also illustrates how the technique reacts when the product quality is high, medium, or low.
Abstract: Kuwait-s electric power system is vertically integrated
organization owned and operated by the government. For more than
five decades, the government of Kuwait has provided relatively
reliable electric services to consumers with subsidized electric
service fees. Given the country-s rapid socio-economical
development and consequently the increase of electricity demand, a
question that inflicts itself: Is it necessary to reform the power system
to face the fast growing demand? This paper recommends that the
government should consider the private sector as a partner in
operating the power system. Therefore, power system restructuring is
needed to allow such partnership. There are challenges that prevent
such restructuring. Abstract recommendations toward resolving these
challenges are proposed.
Abstract: In the previous multi-solid models,¤ò approach is
used for the calculation of fugacity in the liquid phase. For the first
time, in the proposed multi-solid thermodynamic model,γ approach
has been used for calculation of fugacity in the liquid mixture.
Therefore, some activity coefficient models have been studied that
the results show that the predictive Wilson model is more appropriate
than others. The results demonstrate γ approach using the predictive
Wilson model is in more agreement with experimental data than the
previous multi-solid models. Also, by this method, generates a new
approach for presenting stability analysis in phase equilibrium
calculations. Meanwhile, the run time in γ approach is less than the
previous methods used ¤ò approach. The results of the new model
present 0.75 AAD % (Average Absolute Deviation) from the
experimental data which is less than the results error of the previous
multi-solid models obviously.
Abstract: Intelligent schools are those which use IT devices and
technologies as media software, hardware and networks to improve
learning process. On the other hand management improvement is
best described as the process from which managers learn and improve
their skills not only to benefit themselves but also their employing
organizations Here, we present a model Management improvement
System that has been applied on some schools and have made strict
improvement.
Abstract: Quantitative precipitation forecast (QPF) from
atmospheric model as input to hydrological model in an integrated
hydro-meteorological flood forecasting system has been operational
in many countries worldwide. High-resolution numerical weather
prediction (NWP) models with grid cell sizes between 2 and 14 km
have great potential in contributing towards reasonably accurate QPF.
In this study the potential of two NWP models to forecast
precipitation for a flood-prone area in a tropical region is examined.
The precipitation forecasts produced from the Fifth Generation Penn
State/NCAR Mesoscale (MM5) and Weather Research and
Forecasting (WRF) models are statistically verified with the observed
rain in Kelantan River Basin, Malaysia. The statistical verification
indicates that the models have performed quite satisfactorily for low
and moderate rainfall but not very satisfactory for heavy rainfall.
Abstract: The main purpose of this study is to analyze the
relationship of leisure agriculture park visitors on tourist destination
image, environmental perception, travel experiences and revisiting
willingness. This study used questionnaires to Xinshe leisure
agriculture park visitors- targeted convenience sampling manner total
of 636 valid questionnaires. Valid questionnaires by descriptive
statistics, correlation analysis and multiple regression analysis, the
study found that: 1. The agricultural park visitors- correlations exist
between the destination image, perception of the environment, tourism
experience and revisiting willingness. 2."Excellent facilities and
services", "space atmosphere comfortable" and "the spacious paternity
outdoor space" imagery, of visitors- "revisiting willingness predict. 3.
Visitors- in leisure agriculture park "environmental perception" and
"travel experience, future revisiting willingness predict. According to
the analysis of the results, the study not only operate on the
recommendations of the leisure farm owners also provide follow-up
study direction for future researchers.
Abstract: This paper analyzes the patterns of the Monte Carlo
data for a large number of variables and minterms, in order to
characterize the circuit path length behavior. We propose models
that are determined by training process of shortest path length
derived from a wide range of binary decision diagram (BDD)
simulations. The creation of the model was done use of feed forward
neural network (NN) modeling methodology. Experimental results
for ISCAS benchmark circuits show an RMS error of 0.102 for the
shortest path length complexity estimation predicted by the NN
model (NNM). Use of such a model can help reduce the time
complexity of very large scale integrated (VLSI) circuitries and
related computer-aided design (CAD) tools that use BDDs.
Abstract: Lack of resources for road infrastructure financing is a
problem that currently affects not only eastern European economies
but also many other countries especially in relation to the impact of
global financial crisis. In this context, we are talking about the socalled
short-investment problem as a result of long-term lack of
investment resources. Based on an analysis of road infrastructure
financing in the Czech Republic this article points out at weaknesses
of current system and proposes a long-term planning methodology
supported by system approach. Within this methodology and using
created system dynamic model the article predicts the development of
short-investment problem in the Country and in reaction on the
downward trend of certain sources the article presents various
scenarios resulting from the change of the structure of financial
sources. In the discussion the article focuses more closely on the
possibility of introduction of tax on vehicles instead of taxes with
declining revenue streams and estimates its approximate price in
relation to reaching various solutions of short-investment in time.
Abstract: We propose a novel graphical technique (SVision) for
intrusion detection, which pictures the network as a community of
hosts independently roaming in a 3D space defined by the set of
services that they use. The aim of SVision is to graphically cluster
the hosts into normal and abnormal ones, highlighting only the ones
that are considered as a threat to the network. Our experimental
results using DARPA 1999 and 2000 intrusion detection and
evaluation datasets show the proposed technique as a good candidate
for the detection of various threats of the network such as vertical
and horizontal scanning, Denial of Service (DoS), and Distributed
DoS (DDoS) attacks.
Abstract: The general idea behind the filter is to average a pixel
using other pixel values from its neighborhood, but simultaneously to
take care of important image structures such as edges. The main
concern of the proposed filter is to distinguish between any variations
of the captured digital image due to noise and due to image structure.
The edges give the image the appearance depth and sharpness. A
loss of edges makes the image appear blurred or unfocused.
However, noise smoothing and edge enhancement are traditionally
conflicting tasks. Since most noise filtering behaves like a low pass
filter, the blurring of edges and loss of detail seems a natural
consequence. Techniques to remedy this inherent conflict often
encompass generation of new noise due to enhancement.
In this work a new fuzzy filter is presented for the noise reduction
of images corrupted with additive noise. The filter consists of three
stages. (1) Define fuzzy sets in the input space to computes a fuzzy
derivative for eight different directions (2) construct a set of IFTHEN
rules by to perform fuzzy smoothing according to
contributions of neighboring pixel values and (3) define fuzzy sets in
the output space to get the filtered and edged image.
Experimental results are obtained to show the feasibility of the
proposed approach with two dimensional objects.
Abstract: This research was to study effect of rotational speed
and eccentric factors, which were affected on looseness of bearing.
The experiment was conducted on three rotational speeds and five
eccentric distances with 5 replications. The results showed that
influenced factor affected to looseness of bearing was rotational
speed and eccentric distance which showed statistical significant.
Higher rotational speed would cause on high looseness. Moreover,
more eccentric distance, more looseness of bearing. Using bearing at
high rotational with high eccentric of shaft would be affected
bearing fault more than lower rotational speed. The prediction
equation of looseness was generated by regression analysis. The
prediction has an effected to the looseness of bearing at 91.5%.