Abstract: Sensor networks are often deployed in unattended
environments, thus leaving these networks vulnerable to false data
injection attacks in which an adversary injects forged reports into the
network through compromised nodes, with the goal of deceiving the
base station or depleting the resources of forwarding nodes. Several
research solutions have been recently proposed to detect and drop such
forged reports during the forwarding process. Each design can provide
the equivalent resilience in terms of node compromising. However,
their energy consumption characteristics differ from each other. Thus,
employing only a single filtering scheme for a network is not a
recommendable strategy in terms of energy saving. It's very important
the threshold determination for message authentication to identify. We
propose the recursive contract net protocols which less energy level of
terminal node in wireless sensor network.
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: In rail vehicles, air springs are very important isolating component, which guarantee good ride comfort for passengers during their trip. In the most new rail–vehicle models, developed by researchers, the thermo–dynamical effects of air springs are ignored and secondary suspension is modeled by simple springs and dampers. As the performance of suspension components have significant effects on rail–vehicle dynamics and ride comfort of passengers, a complete nonlinear thermo–dynamical air spring model, which is a combination of two different models, is introduced. Result from field test shows remarkable agreement between proposed model and experimental data. Effects of air suspension parameters on the system performances are investigated here and then these parameters are tuned to minimize Sperling ride comfort index during the trip. Results showed that by modification of air suspension parameters, passengers comfort is improved and ride comfort index is reduced about 10%.
Abstract: The quality and shelf life of foods of containing lipids (fats and oils) significantly reduces due to rancidity.Applications of natural antioxidants are one of the most effective manners to prevent the oxidation of oils and lipids. The antioxidant properties of juice extracted from barberry fruit (Berberris vulgaris.L) using maceration and SWE (10 bars and 120 - 180°C) methods were investigated and compared with conventional method. The amount of phenolic compound and reduction power of all samples were determined and the data were statistically analyzed using multifactor design. The results showed that the total amount of phenolic compound increased with increasing of pressure and temprature from 1861.9 to 2439.1 (mg Gallic acid /100gr Dry matter). The ability of reduction power of SWE obtained antioxidant extract compared with BHA (synthetic antioxidant) and ascorbic acid (natural antioxidant). There were significant differences among reduction power of extracts and there were remarkable difference with BHA and Ascorbic acid (P
Abstract: The compression-absorption heat pump (C-A HP), one
of the promising heat recovery equipments that make process hot
water using low temperature heat of wastewater, was evaluated by
computer simulation. A simulation program was developed based on
the continuity and the first and second laws of thermodynamics. Both
the absorber and desorber were modeled using UA-LMTD method. In
order to prevent an unfeasible temperature profile and to reduce
calculation errors from the curved temperature profile of a mixture,
heat loads were divided into lots of segments. A single-stage
compressor was considered. A compressor cooling load was also
taken into account. An isentropic efficiency was computed from the
map data. Simulation conditions were given based on the system
consisting of ordinarily designed components. The simulation results
show that most of the total entropy generation occurs during the
compression and cooling process, thus suggesting the possibility that
system performance can be enhanced if a rectifier is introduced.
Abstract: In this paper we propose a Multiple Description Image Coding(MDIC) scheme to generate two compressed and balanced rates descriptions in the wavelet domain (Daubechies biorthogonal (9, 7) wavelet) using pairwise correlating transform optimal and application method for Generalized Multiple Description Coding (GMDC) to image coding in the wavelet domain. The GMDC produces statistically correlated streams such that lost streams can be estimated from the received data. Our performance test shown that the proposed method gives more improvement and good quality of the reconstructed image when the wavelet coefficients are normalized by Gaussian Scale Mixture (GSM) model then the Gaussian one ,.
Abstract: Medical Tourism is a new development in Taiwan
recently. The willingness and barriers of potential tourists from
China to participate medical tourism are studied. A questionnaire
survey is conducted and the SPSS software is used to analyze data.
The results show that under one fifth of respondents express full
medical tourism participation willingness. Among travel barriers
toward medical tourism, “insufficient information of medical tourism
trip", “not enough time", “no companion", “worrying about
unsatisfied itinerary." are perceived the most important barriers.
Abstract: With the advance of information technology in the
new era the applications of Internet to access data resources has
steadily increased and huge amount of data have become accessible
in various forms. Obviously, the network providers and agencies,
look after to prevent electronic attacks that may be harmful or may
be related to terrorist applications. Thus, these have facilitated the
authorities to under take a variety of methods to protect the special
regions from harmful data. One of the most important approaches is
to use firewall in the network facilities. The main objectives of
firewalls are to stop the transfer of suspicious packets in several
ways. However because of its blind packet stopping, high process
power requirements and expensive prices some of the providers are
reluctant to use the firewall. In this paper we proposed a method to
find a discriminate function to distinguish between usual packets and
harmful ones by the statistical processing on the network router logs.
By discriminating these data, an administrator may take an approach
action against the user. This method is very fast and can be used
simply in adjacent with the Internet routers.
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: Leptospirosis occurs worldwide (except the
poles of the earth), urban and rural areas, developed and
developing countries, especially in Thailand. It can be
transmitted to the human by rats through direct and indirect
ways. Human can be infected by either touching the infected rats
or contacting with water, soil containing urine from the infected
rats through skin, eyes and nose. The data of the people who
are infected with this disease indicates that most of the
patients are adults. The transmission of this disease is studied
through mathematical model. The population is separated into human
and rat. The human is divided into two classes, namely juvenile
and adult. The model equation is constructed for each class. The
standard dynamical modeling method is then used for
analyzing the behaviours of solutions. In addition, the
conditions of the parameters for the disease free and endemic
states are obtained. Numerical solutions are shown to support the
theoretical predictions. The results of this study guide the way to
decrease the disease outbreak.
Abstract: In the proposed method for Web page-ranking, a
novel theoretic model is introduced and tested by examples of order
relationships among IP addresses. Ranking is induced using a
convexity feature, which is learned according to these examples
using a self-organizing procedure. We consider the problem of selforganizing
learning from IP data to be represented by a semi-random
convex polygon procedure, in which the vertices correspond to IP
addresses. Based on recent developments in our regularization
theory for convex polygons and corresponding Euclidean distance
based methods for classification, we develop an algorithmic
framework for learning ranking functions based on a Computational
Geometric Theory. We show that our algorithm is generic, and
present experimental results explaining the potential of our approach.
In addition, we explain the generality of our approach by showing its
possible use as a visualization tool for data obtained from diverse
domains, such as Public Administration and Education.
Abstract: The purpose of this paper is to study Database Models
to use them efficiently in E-commerce websites. In this paper we are
going to find a method which can save and retrieve information in Ecommerce
websites. Thus, semantic web applications can work with,
and we are also going to study different technologies of E-commerce
databases and we know that one of the most important deficits in
semantic web is the shortage of semantic data, since most of the
information is still stored in relational databases, we present an
approach to map legacy data stored in relational databases into the
Semantic Web using virtually any modern RDF query language, as
long as it is closed within RDF. To achieve this goal we study XML
structures for relational data bases of old websites and eventually we
will come up one level over XML and look for a map from relational
model (RDM) to RDF. Noting that a large number of semantic webs
get advantage of relational model, opening the ways which can be
converted to XML and RDF in modern systems (semantic web) is
important.
Abstract: A large amount of valuable information is available in
plain text clinical reports. New techniques and technologies are
applied to extract information from these reports. In this study, we
developed a domain based software system to transform 600
Otorhinolaryngology discharge notes to a structured form for
extracting clinical data from the discharge notes. In order to decrease
the system process time discharge notes were transformed into a data
table after preprocessing. Several word lists were constituted to
identify common section in the discharge notes, including patient
history, age, problems, and diagnosis etc. N-gram method was used
for discovering terms co-Occurrences within each section. Using this
method a dataset of concept candidates has been generated for the
validation step, and then Predictive Apriori algorithm for Association
Rule Mining (ARM) was applied to validate candidate concepts.
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: In this paper, we address the problem of reducing the
switching activity (SA) in on-chip buses through the use of a bus
binding technique in high-level synthesis. While many binding
techniques to reduce the SA exist, we present yet another technique for
further reducing the switching activity. Our proposed method
combines bus binding and data sequence reordering to explore a wider
solution space. The problem is formulated as a multiple traveling
salesman problem and solved using simulated annealing technique.
The experimental results revealed that a binding solution obtained
with the proposed method reduces 5.6-27.2% (18.0% on average) and
2.6-12.7% (6.8% on average) of the switching activity when compared
with conventional binding-only and hybrid binding-encoding
methods, respectively.
Abstract: Fruits and vegetables are the essentials of a healthy
diet, mainly because of their antioxidant properties contributing to
disease blockage especially for some certain types of cancer. Being a
favourite fruit, citrus are produced for economic and commercial
purposes worldwide. Particularly, lemon fruit (Citrus limon L.), has
an important place in export products of Turkey. Lemon has a great
importance on human nutrition with regard to being a source of
nutrients, flavonoids, vitamin C and minerals. It is used for food
flavouring and pickling and also processed for lemonade. By
processing citrus into fruit juices, consumption may increase and also
become easier. Like many fruits and vegetables lemons are cheap and
abundant during harvesting period, while they are quite expensive in
other seasons. Lemon juice and concentrate production allows
consumers to get benefits from lemon fruit in any time of the year.
Lemonade is getting in to the focus of consumers’ attention
preferring non-carbonated drinks. The demand of healthy, convenient
functional foods affects consumer trends through innovative
products. For this reason, lemonade could be enriched with different
natural herb extracts such as ginger (Zingiber officinale), linden (Tilia
cordata), and mint (Mentha piperita).
Abstract: Names are important in many societies, even in technologically oriented ones which use e.g. ID systems to identify individual people. Names such as surnames are the most important as they are used in many processes, such as identifying of people and genealogical research. On the other hand variation of names can be a major problem for the identification and search for people, e.g. web search or security reasons. Name matching presumes a-priori that the recorded name written in one alphabet reflects the phonetic identity of two samples or some transcription error in copying a previously recorded name. We add to this the lode that the two names imply the same person. This paper describes name variations and some basic description of various name matching algorithms developed to overcome name variation and to find reasonable variants of names which can be used to further increasing mismatches for record linkage and name search. The implementation contains algorithms for computing a range of fuzzy matching based on different types of algorithms, e.g. composite and hybrid methods and allowing us to test and measure algorithms for accuracy. NYSIIS, LIG2 and Phonex have been shown to perform well and provided sufficient flexibility to be included in the linkage/matching process for optimising name searching.
Abstract: This paper presents a hybrid association control
scheme that can maintain load balancing among access points in the
wireless LANs and can satisfy the quality of service requirements of
the multimedia traffic applications. The proposed model is
mathematically described as a linear programming model. Simulation
study and analysis were conducted in order to demonstrate the
performance of the proposed hybrid load balancing and association
control scheme. Simulation results shows that the proposed scheme
outperforms the other schemes in term of the percentage of blocking
and the quality of the data transfer rate providing to the multimedia
and real-time applications.
Abstract: Recently, grid computing has been widely focused on
the science, industry, and business fields, which are required a vast
amount of computing. Grid computing is to provide the environment
that many nodes (i.e., many computers) are connected with each
other through a local/global network and it is available for many
users. In the environment, to achieve data processing among nodes
for any applications, each node executes mutual authentication by
using certificates which published from the Certificate Authority
(for short, CA). However, if a failure or fault has occurred in the
CA, any new certificates cannot be published from the CA. As
a result, a new node cannot participate in the gird environment.
In this paper, an off-the-shelf scheme for dependable grid systems
using virtualization techniques is proposed and its implementation is
verified. The proposed approach using the virtualization techniques
is to restart an application, e.g., the CA, if it has failed. The system
can tolerate a failure or fault if it has occurred in the CA. Since
the proposed scheme is implemented at the application level easily,
the cost of its implementation by the system builder hardly takes
compared it with other methods. Simulation results show that the
CA in the system can recover from its failure or fault.
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.