Abstract: The study of proteomics reached unexpected levels of
interest, as a direct consequence of its discovered influence over some
complex biological phenomena, such as problematic diseases like
cancer. This paper presents the latest authors- achievements regarding
the analysis of the networks of proteins (interactome networks), by
computing more efficiently the betweenness centrality measure. The
paper introduces the concept of betweenness centrality, and then
describes how betweenness computation can help the interactome net-
work analysis. Current sequential implementations for the between-
ness computation do not perform satisfactory in terms of execution
times. The paper-s main contribution is centered towards introducing
a speedup technique for the betweenness computation, based on
modified shortest path algorithms for sparse graphs. Three optimized
generic algorithms for betweenness computation are described and
implemented, and their performance tested against real biological
data, which is part of the IntAct dataset.
Abstract: The Emergency Department of a medical center in
Taiwan cooperated to conduct the research. A predictive model of
triage system is contracted from the contract procedure, selection of
parameters to sample screening. 2,000 pieces of data needed for the
patients is chosen randomly by the computer. After three
categorizations of data mining (Multi-group Discriminant Analysis,
Multinomial Logistic Regression, Back-propagation Neural
Networks), it is found that Back-propagation Neural Networks can
best distinguish the patients- extent of emergency, and the accuracy
rate can reach to as high as 95.1%. The Back-propagation Neural
Networks that has the highest accuracy rate is simulated into the triage
acuity expert system in this research. Data mining applied to the
predictive model of the triage acuity expert system can be updated
regularly for both the improvement of the system and for education
training, and will not be affected by subjective factors.
Abstract: This paper presents the design, fabrication and
evaluation of magneto-rheological damper. Semi-active control
devices have received significant attention in recent years because
they offer the adaptability of active control devices without requiring
the associated large power sources. Magneto-Rheological (MR)
dampers are semi- active control devices that use MR fluids to
produce controllable dampers. They potentially offer highly reliable
operation and can be viewed as fail-safe in that they become passive
dampers if the control hardware malfunction. The advantage of MR
dampers over conventional dampers are that they are simple in
construction, compromise between high frequency isolation and
natural frequency isolation, they offer semi-active control, use very
little power, have very quick response, has few moving parts, have a
relax tolerances and direct interfacing with electronics. Magneto-
Rheological (MR) fluids are Controllable fluids belonging to the
class of active materials that have the unique ability to change
dynamic yield stress when acted upon by an electric or magnetic
field, while maintaining viscosity relatively constant. This property
can be utilized in MR damper where the damping force is changed by
changing the rheological properties of the fluid magnetically. MR
fluids have a dynamic yield stress over Electro-Rheological fluids
(ER) and a broader operational temperature range. The objective of
this papert was to study the application of an MR damper to vibration
control, design the vibration damper using MR fluids, test and
evaluate its performance. In this paper the Rheology and the theory
behind MR fluids and their use on vibration control were studied.
Then a MR vibration damper suitable for vehicle suspension was
designed and fabricated using the MR fluid. The MR damper was
tested using a dynamic test rig and the results were obtained in the
form of force vs velocity and the force vs displacement plots. The
results were encouraging and greatly inspire further research on the
topic.
Abstract: Nowadays, new home appliances and office appliances
have been developed that communicate with users through the
Internet, for remote monitor and remote control. However, developments
and sales of these new appliances are just started, then,
many products in our houses and offices do not have these useful
functions. In few years, we add these new functions to the outlet,
it means multifunctional electrical power socket plug adapter. The
outlet measure power consumption of connecting appliances, and it
can switch power supply to connecting appliances, too. Using this
outlet, power supply of old appliances can be control and monitor.
And we developed the interface system using web browser to operate
it from users[1]. But, this system need to set up LAN cables between
outlets and so on. It is not convenience that cables around rooms. In
this paper, we develop the system that use wireless mobile ad hoc
network instead of wired LAN to communicate with the outlets.
Abstract: The Petri net tool INA is a well known tool by the
Petri net community. However, it lacks a graphical environment to
cerate and analyse INA models. Building a modelling tool for the
design and analysis from scratch (for INA tool for example) is
generally a prohibitive task. Meta-Modelling approach is useful to
deal with such problems since it allows the modelling of the
formalisms themselves. In this paper, we propose an approach based
on the combined use of Meta-modelling and Graph Grammars to
automatically generate a visual modelling tool for INA for analysis
purposes. In our approach, the UML Class diagram formalism is
used to define a meta-model of INA models. The meta-modelling
tool ATOM3 is used to generate a visual modelling tool according to
the proposed INA meta-model. We have also proposed a graph
grammar to automatically generate INA description of the
graphically specified Petri net models. This allows the user to avoid
the errors when this description is done manually. Then the INA tool
is used to perform the simulation and the analysis of the resulted INA
description. Our environment is illustrated through an example.
Abstract: Flexible macroblock ordering (FMO), adopted in the
H.264 standard, allows to partition all macroblocks (MBs) in a frame
into separate groups of MBs called Slice Groups (SGs). FMO can not
only support error-resilience, but also control the size of video packets
for different network types. However, it is well-known that the number
of bits required for encoding the frame is increased by adopting FMO.
In this paper, we propose a novel algorithm that can reduce the bitrate
overhead caused by utilizing FMO. In the proposed algorithm, all MBs
are grouped in SGs based on the similarity of the transform
coefficients. Experimental results show that our algorithm can reduce
the bitrate as compared with conventional FMO.
Abstract: The main goal of the study is to analyze all relevant properties of the electro hydraulic systems and based on that to make a proper choice of the neural network control strategy that may be used for the control of the mechatronic system. A combination of electronic and hydraulic systems is widely used since it combines the advantages of both. Hydraulic systems are widely spread because of their properties as accuracy, flexibility, high horsepower-to-weight ratio, fast starting, stopping and reversal with smoothness and precision, and simplicity of operations. On the other hand, the modern control of hydraulic systems is based on control of the circuit fed to the inductive solenoid that controls the position of the hydraulic valve. Since this circuit may be easily handled by PWM (Pulse Width Modulation) signal with a proper frequency, the combination of electrical and hydraulic systems became very fruitful and usable in specific areas as airplane and military industry. The study shows and discusses the experimental results obtained by the control strategy of neural network control using MATLAB and SIMULINK [1]. Finally, the special attention was paid to the possibility of neuro-controller design and its application to control of electro-hydraulic systems and to make comparative with other kinds of control.
Abstract: In this paper the Analytic Network Process (ANP) is
applied to the selection of photovoltaic (PV) solar power projects.
These projects follow a long management and execution process
from plant site selection to plant start-up. As a consequence, there are
many risks of time delays and even of project stoppage.
In the case study presented in this paper a top manager of an
important Spanish company that operates in the power market has to
decide on the best PV project (from four alternative projects) to
invest based on risk minimization. The manager identified 50 project
execution delay and/or stoppage risks.
The influences among elements of the network (groups of risks
and alternatives) were identified and analyzed using the ANP
multicriteria decision analysis method. After analyzing the results the
main conclusion is that the network model can manage all the
information of the real-world problem and thus it is a decision
analysis model recommended by the authors. The strengths and
weaknesses ANP as a multicriteria decision analysis tool are also
described in the paper.
Abstract: MRAM technology provides a combination of fast
access time, non-volatility, data retention and endurance. While a
growing interest is given to two-terminal Magnetic Tunnel Junctions
(MTJ) based on Spin-Transfer Torque (STT) switching as the
potential candidate for a universal memory, its reliability is
dramatically decreased because of the common writing/reading path.
Three-terminal MTJ based on Spin-Orbit Torque (SOT) approach
revitalizes the hope of an ideal MRAM. It can overcome the
reliability barrier encountered in current two-terminal MTJs by
separating the reading and the writing path. In this paper, we study
two possible writing schemes for the SOT-MTJ device based on
recently fabricated samples. While the first is based on precessional
switching, the second requires the presence of permanent magnetic
field. Based on an accurate Verilog-A model, we simulate the two
writing techniques and we highlight advantages and drawbacks of
each one. Using the second technique, pioneering logic circuits based
on the three-terminal architecture of the SOT-MTJ described in this
work are under development with preliminary attractive results.
Abstract: Nowadays the asynchronous learning has granted the permission to the anywhere and anything learning via the technology and E-media which give the learner more convenient. This research is about the design of the blended and online learning for the asynchronous learning of the process management subject in order to create the prototype of this subject asynchronous learning which will create the easiness and increase capability in the learning. The pattern of learning is the integration between the in-class learning and online learning via the internet. This research is mainly focused on the online learning and the online learning can be divided into 5 parts which are virtual classroom, online content, collaboration, assessment and reference material. After the system design was finished, it was evaluated and tested by 5 experts in blended learning design and 10 students which the user’s satisfaction level is good. The result is as good as the assumption so the system can be used in the process management subject for a real usage.
Abstract: The goal of Gene Expression Analysis is to understand the processes that underlie the regulatory networks and pathways controlling inter-cellular and intra-cellular activities. In recent times microarray datasets are extensively used for this purpose. The scope of such analysis has broadened in recent times towards reconstruction of gene networks and other holistic approaches of Systems Biology. Evolutionary methods are proving to be successful in such problems and a number of such methods have been proposed. However all these methods are based on processing of genotypic information. Towards this end, there is a need to develop evolutionary methods that address phenotypic interactions together with genotypic interactions. We present a novel evolutionary approach, called Phenomic algorithm, wherein the focus is on phenotypic interaction. We use the expression profiles of genes to model the interactions between them at the phenotypic level. We apply this algorithm to the yeast sporulation dataset and show that the algorithm can identify gene networks with relative ease.
Abstract: Web usage mining is an interesting application of data
mining which provides insight into customer behaviour on the Internet. An important technique to discover user access and navigation trails is based on sequential patterns mining. One of the
key challenges for web access patterns mining is tackling the problem
of mining richly structured patterns. This paper proposes a novel
model called Web Access Patterns Graph (WAP-Graph) to represent all of the access patterns from web mining graphically. WAP-Graph
also motivates the search for new structural relation patterns, i.e. Concurrent Access Patterns (CAP), to identify and predict more
complex web page requests. Corresponding CAP mining and modelling methods are proposed and shown to be effective in the
search for and representation of concurrency between access patterns
on the web. From experiments conducted on large-scale synthetic
sequence data as well as real web access data, it is demonstrated that
CAP mining provides a powerful method for structural knowledge discovery, which can be visualised through the CAP-Graph model.
Abstract: The first and basic cause of the failure of concrete is repeated freezing (thawing) of moisture contained in the pores, microcracks, and cavities of the concrete. On transition to ice, water existing in the free state in cracks increases in volume, expanding the recess in which freezing occurs. A reduction in strength below the initial value is to be expected and further cycle of freezing and thawing have a further marked effect. By using some experimental parameters like nuclear magnetic resonance variation (NMR), enthalpy-temperature (or heat capacity) variation, we can resolve between the various water states and their effect on concrete properties during cooling through the freezing transition temperature range. The main objective of this paper is to describe the principal type of water responsible for the reduction in strength and structural damage (frost damage) of concrete following repeated freeze –thaw cycles. Some experimental work was carried out at the institute of cryogenics to determine what happens to water in concrete during the freezing transition.
Abstract: This paper presents a part of research on the
rheological properties of bitumen modified by thermoplastic namely
linear low density polyethylene (LLDPE), high density polyethylene
(HDPE) and polypropylene (PP) and its interaction with 80 pen base
bitumen. As it is known that the modification of bitumen by the use
of polymers enhances its performance characteristics but at the same
time significantly alters its rheological properties. The rheological
study of polymer modified bitumen (PMB) was made through
penetration, ring & ball softening point and viscosity test. The results
were then related to the changes in the rheological properties of
polymer modified bitumen. It was observed that thermoplastic
copolymer shows profound effect on penetration rather than
softening point. The viscoelastic behavior of polymer modified
bitumen depend on the concentration of polymer, mixing
temperature, mixing technique, solvating power of base bitumen and
molecular structure of polymer used. PP offer better blend in
comparison to HDPE and LLDPE. The viscosity of base bitumen was
also enhanced with the addition of polymer. The pseudoplastic
behavior was more prominent for HDPE and LLDPE than PP. Best
results were obtained when polymer concentration was kept below
3%
Abstract: Optical properties of sputter-deposited ZnS thin films
were investigated as potential replacements for CBD(chemical bath
deposition) CdS buffer layers in the application of CIGS solar cells.
ZnS thin films were fabricated on glass substrates at RT, 150oC, 200oC,
and 250oC with 50 sccm Ar gas using an RF magnetron sputtering
system. The crystal structure of the thin film is found to be zinc blende
(cubic) structure. Lattice parameter of ZnS is slightly larger than CdS
on the plane and thus better matched with that of CIGS. Within a
400-800 nm wavelength region, the average transmittance was larger
than 75%. When the deposition temperature of the thin film was
increased, the blue shift phenomenon was enhanced. Band gap energy
of the ZnS thin film tended to increase as the deposition temperature
increased. ZnS thin film is a promising material system for the CIGS
buffer layer, in terms of ease of processing, low cost, environmental
friendliness, higher transparency, and electrical properties
Abstract: During recent years, the traditional learning
approaches have undergone fundamental changes due to the
emergence of new technologies such as multimedia, hypermedia and
telecommunication. E-learning is a modern world phenomenon that
has come into existence in the information age and in a knowledgebased
society. E-learning has developed significantly within a short
period of time. Thus it is of a great significant to secure information,
allow a confident access and prevent unauthorized accesses. Making
use of individuals- physiologic or behavioral (biometric) properties is
a confident method to make the information secure. Among the
biometrics, fingerprint is more acceptable and most countries use it as
an efficient methods of identification. This article provides a new
method to compare the fingerprint comparison by pattern recognition
and image processing techniques. To verify fingerprint, the shortest
distance method is used together with perceptronic multilayer neural
network functioning based on minutiae. This method is highly
accurate in the extraction of minutiae and it accelerates comparisons
due to elimination of false minutiae and is more reliable compared
with methods that merely use directional images.
Abstract: The Internet telephony employs a new type of Internet communication on which a mutual communication is realized by establishing sessions. Session Initiation Protocol (SIP) is used to establish sessions between end-users. For unreliable transmission (UDP), SIP message should be retransmitted when it is lost. The retransmissions increase a load of the SIP signaling network, and sometimes lead to performance degradation when a network is overloaded. The paper proposes an overload control for a SIP signaling network to protect from a performance degradation. Introducing two thresholds in a queue of a SIP proxy server, the SIP proxy server detects a congestion. Once congestion is detected, a SIP signaling network restricts to make new calls. The proposed overload control is evaluated using the network simulator (ns-2). With simulation results, the paper shows the proposed overload control works well.
Abstract: Fundamental sensor-motor couplings form the backbone
of most mobile robot control tasks, and often need to be implemented
fast, efficiently and nevertheless reliably. Machine learning
techniques are therefore often used to obtain the desired sensor-motor
competences.
In this paper we present an alternative to established machine
learning methods such as artificial neural networks, that is very fast,
easy to implement, and has the distinct advantage that it generates
transparent, analysable sensor-motor couplings: system identification
through nonlinear polynomial mapping.
This work, which is part of the RobotMODIC project at the
universities of Essex and Sheffield, aims to develop a theoretical understanding
of the interaction between the robot and its environment.
One of the purposes of this research is to enable the principled design
of robot control programs.
As a first step towards this aim we model the behaviour of the
robot, as this emerges from its interaction with the environment, with
the NARMAX modelling method (Nonlinear, Auto-Regressive, Moving
Average models with eXogenous inputs). This method produces
explicit polynomial functions that can be subsequently analysed using
established mathematical methods.
In this paper we demonstrate the fidelity of the obtained NARMAX
models in the challenging task of robot route learning; we present a
set of experiments in which a Magellan Pro mobile robot was taught
to follow four different routes, always using the same mechanism to
obtain the required control law.
Abstract: The use of new technologies such internet (e-mail, chat
rooms) and cell phones has steeply increased in recent years.
Especially among children and young people, use of technological
tools and equipments is widespread. Although many teachers and
administrators now recognize the problem of school bullying, few are
aware that students are being harassed through electronic
communication. Referred to as electronic bullying, cyber bullying, or
online social cruelty, this phenomenon includes bullying through email,
instant messaging, in a chat room, on a website, or through
digital messages or images sent to a cell phone. Cyber bullying is
defined as causing deliberate/intentional harm to others using internet
or other digital technologies. It has a quantitative research design nd
uses relational survey as its method. The participants consisted of
300 secondary school students in the city of Konya, Turkey. 195
(64.8%) participants were female and 105 (35.2%) were male. 39
(13%) students were at grade 1, 187 (62.1%) were at grade 2 and 74
(24.6%) were at grade 3. The “Cyber Bullying Question List"
developed by Ar─▒cak (2009) was given to students. Following
questions about demographics, a functional definition of cyber
bullying was provided. In order to specify students- human values,
“Human Values Scale (HVS)" developed by Dilmaç (2007) for
secondary school students was administered. The scale consists of 42
items in six dimensions. Data analysis was conducted by the primary
investigator of the study using SPSS 14.00 statistical analysis
software. Descriptive statistics were calculated for the analysis of
students- cyber bullying behaviour and simple regression analysis was
conducted in order to test whether each value in the scale could
explain cyber bullying behaviour.
Abstract: Yeast cells live in a constantly changing environment that requires the continuous adaptation of their genomic program in order to sustain their homeostasis, survive and proliferate. Due to the advancement of high throughput technologies, there is currently a large amount of data such as gene expression, gene deletion and protein-protein interactions for S. Cerevisiae under various environmental conditions. Mining these datasets requires efficient computational methods capable of integrating different types of data, identifying inter-relations between different components and inferring functional groups or 'modules' that shape intracellular processes. This study uses computational methods to delineate some of the mechanisms used by yeast cells to respond to environmental changes. The GRAM algorithm is first used to integrate gene expression data and ChIP-chip data in order to find modules of coexpressed and co-regulated genes as well as the transcription factors (TFs) that regulate these modules. Since transcription factors are themselves transcriptionally regulated, a three-layer regulatory cascade consisting of the TF-regulators, the TFs and the regulated modules is subsequently considered. This three-layer cascade is then modeled quantitatively using artificial neural networks (ANNs) where the input layer corresponds to the expression of the up-stream transcription factors (TF-regulators) and the output layer corresponds to the expression of genes within each module. This work shows that (a) the expression of at least 33 genes over time and for different stress conditions is well predicted by the expression of the top layer transcription factors, including cases in which the effect of up-stream regulators is shifted in time and (b) identifies at least 6 novel regulatory interactions that were not previously associated with stress-induced changes in gene expression. These findings suggest that the combination of gene expression and protein-DNA interaction data with artificial neural networks can successfully model biological pathways and capture quantitative dependencies between distant regulators and downstream genes.