Abstract: Demolitions of buildings have created a lot of waste
and one of it is clay bricks. The waste clay bricks were ground to
roughly cement fineness and used to partially replaced cement at
10%, 20% and 30% with w/b ratio of 0.6 and tested at 7, 28, 60, 90
and 120 days. The result shows that the compressive strength of GCB
concrete increases over age however, decreases as the level of
replacements increases. It was also found that 10% replacement of
GCB gave the highest compressive strength, however for optimum
replacement, 30% was chosen as it still attained strength of grade 30
concrete. In terms of durability performances, results show that GCB
replacement up to 30% was found to be efficient in reducing water
absorption as well as water permeability. These studies show that
GCB has the potential to be used as partial cement replacement in
making concrete.
Abstract: Non-viral gene carriers composed of biodegradable
polymers or lipids have been considered as a safer alternative for gene
carriers over viral vectors. We have developed multi-functional
nano-micelles for both drug and gene delivery application.
Polyethyleneimine (PEI) was modified by grafting stearic acid (SA)
and formulated to polymeric micelles (PEI-SA) with positive surface
charge for gene and drug delivery. Our results showed that PEI-SA
micelles provided high siRNA binding efficiency. In addition, siRNA
delivered by PEI-SA carriers also demonstrated significantly high
cellular uptake even in the presence of serum proteins. The
post-transcriptional gene silencing efficiency was greatly improved by
the polyplex formulated by 10k PEI-SA/siRNA. The amphiphilic
structure of PEI-SA micelles provided advantages for multifunctional
tasks; where the hydrophilic shell modified with cationic charges can
electrostatically interact with DNA or siRNA, and the hydrophobic
core can serve as payloads for hydrophobic drugs, making it a
promising multifunctional vehicle for both genetic and chemotherapy
application.
Abstract: EDF (Early Deadline First) algorithm is a very important scheduling algorithm for real- time systems . The EDF algorithm assigns priorities to each job according to their absolute deadlines and has good performance when the real-time system is not overloaded. When the real-time system is overloaded, many misdeadlines will be produced. But these misdeadlines are not uniformly distributed, which usually focus on some tasks. In this paper, we present an adaptive fuzzy control scheduling based on EDF algorithm. The improved algorithm can have a rectangular distribution of misdeadline ratios among all real-time tasks when the system is overloaded. To evaluate the effectiveness of the improved algorithm, we have done extensive simulation studies. The simulation results show that the new algorithm is superior to the old algorithm.
Abstract: In the recent past Learning Classifier Systems have
been successfully used for data mining. Learning Classifier System
(LCS) is basically a machine learning technique which combines
evolutionary computing, reinforcement learning, supervised or
unsupervised learning and heuristics to produce adaptive systems. A
LCS learns by interacting with an environment from which it
receives feedback in the form of numerical reward. Learning is
achieved by trying to maximize the amount of reward received. All
LCSs models more or less, comprise four main components; a finite
population of condition–action rules, called classifiers; the
performance component, which governs the interaction with the
environment; the credit assignment component, which distributes the
reward received from the environment to the classifiers accountable
for the rewards obtained; the discovery component, which is
responsible for discovering better rules and improving existing ones
through a genetic algorithm. The concatenate of the production rules
in the LCS form the genotype, and therefore the GA should operate
on a population of classifier systems. This approach is known as the
'Pittsburgh' Classifier Systems. Other LCS that perform their GA at
the rule level within a population are known as 'Mitchigan' Classifier
Systems. The most predominant representation of the discovered
knowledge is the standard production rules (PRs) in the form of IF P
THEN D. The PRs, however, are unable to handle exceptions and do
not exhibit variable precision. The Censored Production Rules
(CPRs), an extension of PRs, were proposed by Michalski and
Winston that exhibit variable precision and supports an efficient
mechanism for handling exceptions. A CPR is an augmented
production rule of the form: IF P THEN D UNLESS C, where
Censor C is an exception to the rule. Such rules are employed in
situations, in which conditional statement IF P THEN D holds
frequently and the assertion C holds rarely. By using a rule of this
type we are free to ignore the exception conditions, when the
resources needed to establish its presence are tight or there is simply
no information available as to whether it holds or not. Thus, the IF P
THEN D part of CPR expresses important information, while the
UNLESS C part acts only as a switch and changes the polarity of D
to ~D. In this paper Pittsburgh style LCSs approach is used for
automated discovery of CPRs. An appropriate encoding scheme is
suggested to represent a chromosome consisting of fixed size set of
CPRs. Suitable genetic operators are designed for the set of CPRs
and individual CPRs and also appropriate fitness function is proposed
that incorporates basic constraints on CPR. Experimental results are
presented to demonstrate the performance of the proposed learning
classifier system.
Abstract: A Comparison and evaluation of the different
condition monitoring (CM) techniques was applied experimentally
on RC e.g. Dynamic cylinder pressure and crankshaft Instantaneous
Angular Speed (IAS), for the detection and diagnosis of valve faults
in a two - stage reciprocating compressor for a programme of
condition monitoring which can successfully detect and diagnose a
fault in machine. Leakage in the valve plate was introduced
experimentally into a two-stage reciprocating compressor. The effect
of the faults on compressor performance was monitored and the
differences with the normal, healthy performance noted as a fault
signature been used for the detection and diagnosis of faults.
The paper concludes with what is considered to be a unique
approach to condition monitoring. First, each of the two most useful
techniques is used to produce a Truth Table which details the
circumstances in which each method can be used to detect and
diagnose a fault. The two Truth Tables are then combined into a
single Decision Table to provide a unique and reliable method of
detection and diagnosis of each of the individual faults introduced
into the compressor. This gives accurate diagnosis of compressor
faults.
Abstract: Data objects are usually organized hierarchically, and
the relations between them are analyzed based on a corresponding
concept hierarchy. The relation between data objects, for example how
similar they are, are usually analyzed based on the conceptual distance
in the hierarchy. If a node is an ancestor of another node, it is enough
to analyze how close they are by calculating the distance vertically.
However, if there is not such relation between two nodes, the vertical
distance cannot express their relation explicitly. This paper tries to fill
this gap by improving the analysis method for data objects based on
hierarchy. The contributions of this paper include: (1) proposing an
improved method to evaluate the vertical distance between concepts;
(2) defining the concept horizontal distance and a method to calculate
the horizontal distance; and (3) discussing the methods to confine a
range by the horizontal distance and the vertical distance, and
evaluating the relation between concepts.
Abstract: Dredged sediment (DS) was utilized as source of
silt-clay and organic matter in artificially prepared eelgrass substrates with mountain sand (MS) as the sand media. Addition of DS showed
improved growth of eelgrass in the mixed substrates. Increase in added
DS up to 15% silt-clay showed increased shoot growth but additional
DS in 20% silt-clay mixture didn-t result to further increase in eelgrass
growth. Improved root establishment were also found for plants in pots
with added DS as shown by the increased resistance to uprooting, increased number of rhizome nodes and longer roots. Results demonstrated that addition of DS may be beneficial to eelgrass up to a
certain extent only and too much of it might be harmful to eelgrass plants.
Abstract: Three alumina-supported Pt-Sn catalysts have been
prepared by means of co-impregnation and characterized by XRD and
N2 adsorption. The influence of catalyst composition and reaction
conditions on the conversion and selectivity were investigated in the
hydrogenation of acetic acid in an isothermal integral fixed bed
reactor. The experiments were performed on the temperature interval
468-548 K, liquid hourly space velocity (LHSV) of 0.3-0.7h-1,
pressures between 1.0 and 5.0Mpa. A good compromise of
0.75%Pt-1.5%Sn can act as an optimized acetic acid hydrogenation
catalyst, and the conversion and selectivity can be tuned through the
variation of reaction conditions.
Abstract: The technology usages of high speed Internet leads to
establish and start new era of online education. With the
advancement of the information technology and communication
systems new opportunities have been created. This leads universities
to have various online education channels to meet the demand of
different learners- needs. One of these channels is M-learning, which
can be used to improve the online education environment. With using
such mobile technology in learning both students and instructors can
easily access educational courses anytime from anywhere. The paper
first presents literature about mobile learning and to what extent this
approach can be utilized to enhance the overall learning system. It
provides a comparison between mobile learning and traditional elearning
showing the wide array of benefits of the new generation of
technology. The possible challenges and potential advantages of Mlearning
in the online education system are also discussed.
Abstract: In recent years, scanning probe atomic force
microscopy SPM AFM has gained acceptance over a wide spectrum
of research and science applications. Most fields focuses on physical,
chemical, biological while less attention is devoted to manufacturing
and machining aspects. The purpose of the current study is to assess
the possible implementation of the SPM AFM features and its
NanoScope software in general machining applications with special
attention to the tribological aspects of cutting tool. The surface
morphology of coated and uncoated as-received carbide inserts is
examined, analyzed, and characterized through the determination of
the appropriate scanning setting, the suitable data type imaging
techniques and the most representative data analysis parameters
using the MultiMode SPM AFM in contact mode. The NanoScope
operating software is used to capture realtime three data types
images: “Height", “Deflection" and “Friction". Three scan sizes are
independently performed: 2, 6, and 12 μm with a 2.5 μm vertical
range (Z). Offline mode analysis includes the determination of three
functional topographical parameters: surface “Roughness", power
spectral density “PSD" and “Section". The 12 μm scan size in
association with “Height" imaging is found efficient to capture every
tiny features and tribological aspects of the examined surface. Also,
“Friction" analysis is found to produce a comprehensive explanation
about the lateral characteristics of the scanned surface. Configuration
of many surface defects and drawbacks has been precisely detected
and analyzed.
Abstract: This paper describes the Multilingual Virtual Simulated Patient framework. It has been created to train the social skills and testing the knowledge of primary health care medical students. The framework generates conversational agents which perform in serveral languages as virtual simulated patients that help to improve the communication and diagnosis skills of the students complementing their training process.
Abstract: This paper discusses the designing of knowledge
integration of clinical information extracted from distributed medical
ontologies in order to ameliorate a machine learning-based multilabel
coding assignment system. The proposed approach is
implemented using a decision tree technique of the machine learning
on the university hospital data for patients with Coronary Heart
Disease (CHD). The preliminary results obtained show a satisfactory
finding that the use of medical ontologies improves the overall
system performance.
Abstract: A traffic light gives security from traffic congestion,reducing the traffic jam, and organizing the traffic flow. Furthermore,increasing congestion level in public road networks is a growingproblem in many countries. Using Intelligent Transportation Systemsto provide emergency vehicles a green light at intersections canreduce driver confusion, reduce conflicts, and improve emergencyresponse times. Nowadays, the technology of wireless sensornetworks can solve many problems and can offer a good managementof the crossroad. In this paper, we develop a new approach based onthe technique of clustering and the graphical possibilistic fusionmodeling. So, the proposed model is elaborated in three phases. Thefirst one consists to decompose the environment into clusters,following by the fusion intra and inter clusters processes. Finally, wewill show some experimental results by simulation that proves theefficiency of our proposed approach.KeywordsTraffic light, Wireless sensor network, Controller,Possibilistic network/Bayesain network.
Abstract: Finding effective ways of improving university quality assurance requires, as well, a retraining of the staff. This article illustrates an Online Programme of Excellence Model (OPEM), based on the European quality assurance model, for improving participants- formative programme standards. The results of applying this OPEM indicate the necessity of quality policies that support the evaluators- competencies to improve formative programmes. The study concludes by outlining how faculty and agency staff can use OPEM for the internal and external quality assurance of formative programmes.
Abstract: The main objective of this paper is to analyse the influence of preparation and control of orders on performance. The focused activities explored in this research are: procurement, production and distribution. These changes in performance were obtained through improvement of the supply chain. It is proved using all the company activities that it is possible to increase de efficiency and do services in an adequate way, placing the products in the market efficiently. For that, it was explored the importance of the supply chain, with privilege to the practical environment and the quantification of the obtained results.
Abstract: Heat-inducible gene expression vectors are useful for hyperthermia-induced cancer gene therapy, because the combination
of hyperthermia and gene therapy can considerably improve the therapeutic effects. In the present study, we developed an enhanced
heat-inducible transgene expression system in which a heat-shock
protein (HSP) promoter and tetracycline-responsive transactivator
were combined. When the transactivator plasmid containing the
tetracycline-responsive transactivator gene was co-transfected with
the reporter gene expression plasmid, a high level of heat-induced gene expression was observed compared with that using the HSP
promoter without the transactivator. In vitro evaluation of the
therapeutic effect using HeLa cells showed that heat-induced therapeutic gene expression caused cell death in a high percentage of
these cells, indicating that this strategy is promising for cancer gene therapy.
Abstract: In this paper, the modified optimal sliding mode control with a proposed method to design a sliding surface is presented. Because of the inability of the previous approach of the sliding mode method to design a bounded and suitable input, the new variation is proposed in the sliding manifold to obviate problems in a structural system. Although the sliding mode control is a powerful method to reject disturbances and noises, the chattering problem is not good for actuators. To decrease the chattering phenomena, the optimal control is added to the sliding mode control. Not only the proposed method can decline the intense variations in the inputs of the system but also it can produce the efficient responses respect to the sliding mode control and optimal control that are shown by performing some numerical simulations.
Abstract: High quality requirements analysis is one of the most
crucial activities to ensure the success of a software project, so that
requirements verification for software system becomes more and more
important in Requirements Engineering (RE) and it is one of the most
helpful strategies for improving the quality of software system.
Related works show that requirement elicitation and analysis can be
facilitated by ontological approaches and semantic web technologies.
In this paper, we proposed a hybrid method which aims to verify
requirements with structural and formal semantics to detect
interactions. The proposed method is twofold: one is for modeling
requirements with the semantic web language OWL, to construct a
semantic context; the other is a set of interaction detection rules which
are derived from scenario-based analysis and represented with
semantic web rule language (SWRL). SWRL based rules are working
with rule engines like Jess to reason in semantic context for
requirements thus to detect interactions. The benefits of the proposed
method lie in three aspects: the method (i) provides systematic steps
for modeling requirements with an ontological approach, (ii) offers
synergy of requirements elicitation and domain engineering for
knowledge sharing, and (3)the proposed rules can systematically assist
in requirements interaction detection.
Abstract: Due to the complex network architecture, the mobile
adhoc network-s multihop feature gives additional problems to the
users. When the traffic load at each node gets increased, the
additional contention due its traffic pattern might cause the nodes
which are close to destination to starve the nodes more away from the
destination and also the capacity of network is unable to satisfy the
total user-s demand which results in an unfairness problem. In this
paper, we propose to create an algorithm to compute the optimal
MAC-layer bandwidth assigned to each flow in the network. The
bottleneck links contention area determines the fair time share which
is necessary to calculate the maximum allowed transmission rate used
by each flow. To completely utilize the network resources, we
compute two optimal rates namely, the maximum fair share and
minimum fair share. We use the maximum fair share achieved in
order to limit the input rate of those flows which crosses the
bottleneck links contention area when the flows that are not allocated
to the optimal transmission rate and calculate the following highest
fair share. Through simulation results, we show that the proposed
protocol achieves improved fair share and throughput with reduced
delay.
Abstract: This paper describes an optimal approach for feature
subset selection to classify the leaves based on Genetic Algorithm
(GA) and Kernel Based Principle Component Analysis (KPCA). Due
to high complexity in the selection of the optimal features, the
classification has become a critical task to analyse the leaf image
data. Initially the shape, texture and colour features are extracted
from the leaf images. These extracted features are optimized through
the separate functioning of GA and KPCA. This approach performs
an intersection operation over the subsets obtained from the
optimization process. Finally, the most common matching subset is
forwarded to train the Support Vector Machine (SVM). Our
experimental results successfully prove that the application of GA
and KPCA for feature subset selection using SVM as a classifier is
computationally effective and improves the accuracy of the classifier.