Abstract: Levan, an exopolysaccharide, was produced by
Microbacterium laevaniformans and its yield was characterized as a
function of concentrations of date syrup, sucrose and the fermentation
time. The optimum condition for levan production from sucrose was
at concentration of 20% sucrose for 48 h and for date syrup was 25%
for 48 h. The results show that an increase in fermentation time
caused a decrease in the levan production at all concentrations of date
syrup tested. Under these conditions after 48 h in sucrose medium,
levan production reached 48.9 g/L and for date syrup reached 10.48
g/L . The effect of pH on the yield of the purified levan was examined
and the optimum pH for levan production was determined to be 6.0.
Levan was composed mainly of fructose residues when analyzed by
TLC and FT-IR spectroscopy. Date syrup is a cheap substrate widely
available in Iran and has potential for levan production. The thermal
stability of levan was assessed by Thermo Gravimetric Analysis
(TGA) that revealed the onset of decomposition near to 49°C for the
levan produced from sucrose and 51°C for the levan from date syrup.
DSC results showed a single Tg at 98°C for levan produced from
sucrose and 206 °C for levan from date syrup.
Abstract: Chaos and fractals are novel fields of physics and mathematics showing up a new way of universe viewpoint and creating many ideas to solve several present problems. In this paper, a novel algorithm based on the chaotic sequence generator with the highest ability to adapt and reach the global optima is proposed. The adaptive ability of proposal algorithm is flexible in 2 steps. The first one is a breadth-first search and the second one is a depth-first search. The proposal algorithm is examined by 2 functions, the Camel function and the Schaffer function. Furthermore, the proposal algorithm is applied to optimize training Multilayer Neural Networks.
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: In this paper, a new time-delay estimation
technique based on the cross IB-energy operator [5] is
introduced. This quadratic energy detector measures how
much a signal is present in another one. The location of the
peak of the energy operator, corresponding to the maximum of
interaction between the two signals, is the estimate of the
delay. The method is a fully data-driven approach. The
discrete version of the continuous-time form of the cross IBenergy
operator, for its implementation, is presented. The
effectiveness of the proposed method is demonstrated on real
underwater acoustic signals arriving from targets and the
results compared to the cross-correlation method.
Abstract: This paper presents the results of the experimental
tests of the cooling performance of a 12,000-Btu/h modified air
conditioner (referred to as M-AC) that use the ground as a heat sink
of a condenser. In the tests, cooling capacity of M-AC with an
optimal length of a condensing coil as well as life expectancy of
copper coil buried underground were investigated. The lengths of
copper coil fabricated and used as condenser coil of M-AC were set
at 67, 50, 40 and 30 m whereas that of a 12,000-Btu/h conventional
split-type air conditioner (referred to as C-AC) was about 22 m. The
results showed that the ground can absorb heat rejected from a
condenser of M-AC. The coefficient of performance (COP) of C-AC
was about 2.5 whereas those of M-AC were found to be higher. It
was found that the values of COP of M-AC with condensing coils of
67, 50 and 40 m long were about 6.9, 5.5 and 3.3, respectively, while
that of 30-m-long one was found to be about 2.1. The electrical
consumptions of M-AC were found lower than that of C-AC in the
range of 11.5 – 15.5%. Additionally, life expectancy of underground
condensing coil of M-AC was found to be over 7 years.
Abstract: Human identification at a distance has recently gained
growing interest from computer vision researchers. Gait recognition
aims essentially to address this problem by identifying people based
on the way they walk [1]. Gait recognition has 3 steps. The first step
is preprocessing, the second step is feature extraction and the third
one is classification. This paper focuses on the classification step that
is essential to increase the CCR (Correct Classification Rate).
Multilayer Perceptron (MLP) is used in this work. Neural Networks
imitate the human brain to perform intelligent tasks [3].They can
represent complicated relationships between input and output and
acquire knowledge about these relationships directly from the data
[2]. In this paper we apply MLP NN for 11 views in our database and
compare the CCR values for these views. Experiments are performed
with the NLPR databases, and the effectiveness of the proposed
method for gait recognition is demonstrated.
Abstract: 17α-ethynylestradiol (EE2) is a synthetic estrogen
used as a key ingredient in an oral contraceptives pill. EE2 is an
endocrine disrupting compound, high in estrogenic potency.
Although EE2 exhibits low degree of biodegradability with common
microorganisms in wastewater treatment plants (WWTPs), this
compound can be biotransformed by ammonia-oxidizing bacteria
(AOB) via a co-metabolism mechanism in WWTPs. This study
aimed to investigate the effect of real wastewater on
biotransformation of EE2 by AOB. A preliminary experiment on the
effect of nitrite and pH levels on abiotic transformation of EE2
suggested that the abiotic transformation occurred at only pH
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: We aimed to investigate how can target and optimize
pulmonary delivery distribution by changing physicochemical
characteristics of instilled liquid.Therefore, we created a new liquids
group:
a. eligible for desired distribution within lung because of
assorted physicochemical characteristics
b. capable of being augmented with a broad range of
chemicals inertly
c. no interference on respiratory function
d. compatible with airway surface liquid
We developed forty types of new liquid,were composed of
Carboxymethylcellulose sodium,Glycerin and different types of
Polysorbates.Viscosity was measured using a Programmable
Rheometer and surface tension by KRUSS Tensiometer.We
subsequently examined the liquids and delivery protocols by simple
and branched glass capillary tube models of airways.Eventually,we
explored pulmonary distribution of liquids being augmented with
technetium-99m in mechanically ventilated rabbits.We used a single
head large field of view gamma camera.Kinematic viscosity between
0.265Stokes and 0.289Stokes,density between 1g/cm3 and 1.5g/cm3
and surface tension between 25dyn/cm and 35dyn/cm were the most
acceptable.
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: The paper deals with the estimation of amplitude and phase of an analogue multi-harmonic band-limited signal from irregularly spaced sampling values. To this end, assuming the signal fundamental frequency is known in advance (i.e., estimated at an independent stage), a complexity-reduced algorithm for signal reconstruction in time domain is proposed. The reduction in complexity is achieved owing to completely new analytical and summarized expressions that enable a quick estimation at a low numerical error. The proposed algorithm for the calculation of the unknown parameters requires O((2M+1)2) flops, while the straightforward solution of the obtained equations takes O((2M+1)3) flops (M is the number of the harmonic components). It is applied in signal reconstruction, spectral estimation, system identification, as well as in other important signal processing problems. The proposed method of processing can be used for precise RMS measurements (for power and energy) of a periodic signal based on the presented signal reconstruction. The paper investigates the errors related to the signal parameter estimation, and there is a computer simulation that demonstrates the accuracy of these algorithms.
Abstract: Let R be a ring and n a fixed positive integer, we
investigate the properties of n-strongly Gorenstein projective, injective
and flat modules. Using the homological theory , we prove that
the tensor product of an n-strongly Gorenstein projective (flat) right
R -module and projective (flat) left R-module is also n-strongly
Gorenstein projective (flat). Let R be a coherent ring ,we prove that
the character module of an n -strongly Gorenstein flat left R -module
is an n-strongly Gorenstein injective right R -module . At last, let
R be a commutative ring and S a multiplicatively closed set of R ,
we establish the relation between n -strongly Gorenstein projective
(injective , flat ) R -modules and n-strongly Gorenstein projective
(injective , flat ) S−1R-modules. All conclusions in this paper is
helpful for the research of Gorenstein dimensions in future.
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: In this paper, two very different optimization
algorithms, Genetic and DIRECT algorithms, are used to history
match a bottomhole pressure response for a reservoir with wellbore
storage and skin with the best possible analytical model. No initial
guesses are available for reservoir parameters. The results show that
the matching process is much faster and more accurate for DIRECT
method in comparison with Genetic algorithm. It is furthermore
concluded that the DIRECT algorithm does not need any initial
guesses, whereas Genetic algorithm needs to be tuned according to
initial guesses.
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: Mining sequential patterns from large customer transaction databases has been recognized as a key research topic in database systems. However, the previous works more focused on mining sequential patterns at a single concept level. In this study, we introduced concept hierarchies into this problem and present several algorithms for discovering multiple-level sequential patterns based on the hierarchies. An experiment was conducted to assess the performance of the proposed algorithms. The performances of the algorithms were measured by the relative time spent on completing the mining tasks on two different datasets. The experimental results showed that the performance depends on the characteristics of the datasets and the pre-defined threshold of minimal support for each level of the concept hierarchy. Based on the experimental results, some suggestions were also given for how to select appropriate algorithm for a certain datasets.
Abstract: Experimental investigations were carried out in the
Manchester Tidal flow Facility (MTF) to study the flow patterns in
the region around and adjacent to a hypothetical headland in tidal
(oscillatory) ambient flow. The Planar laser-induced fluorescence
(PLIF) technique was used for visualization, with fluorescent dye
released at specific points around the headland perimeter and in its
adjacent recirculation zone. The flow patterns can be generalized into
the acceleration, stable flow and deceleration stages for each halfcycle,
with small variations according to location, which are more
distinct for low Keulegan-Carpenter number (KC) cases. Flow
patterns in the mixing region are unstable and complex, especially in
the recirculation zone. The flow patterns are in agreement with
previous visualizations, and support previous results in steady
ambient flow. It is suggested that the headland lee could be a viable
location for siting of pollutant outfalls.
Abstract: The purpose of this paper is to solve the problem of protecting aerial lines from high impedance faults (HIFs) in distribution systems. This investigation successfully applies 3I0 zero sequence current to solve HIF problems. The feature extraction system based on discrete wavelet transform (DWT) and the feature identification technique found on statistical confidence are then applied to discriminate effectively between the HIFs and the switch operations. Based on continuous wavelet transform (CWT) pattern recognition of HIFs is proposed, also. Staged fault testing results demonstrate that the proposed wavelet based algorithm is feasible performance well.
Abstract: Trust management and Reputation models are
becoming integral part of Internet based applications such as CSCW,
E-commerce and Grid Computing. Also the trust dimension is a
significant social structure and key to social relations within a
collaborative community. Collaborative Decision Making (CDM) is
a difficult task in the context of distributed environment (information
across different geographical locations) and multidisciplinary
decisions are involved such as Virtual Organization (VO). To aid
team decision making in VO, Decision Support System and social
network analysis approaches are integrated. In such situations social
learning helps an organization in terms of relationship, team
formation, partner selection etc. In this paper we focus on trust
learning. Trust learning is an important activity in terms of
information exchange, negotiation, collaboration and trust
assessment for cooperation among virtual team members. In this
paper we have proposed a reinforcement learning which enhances the
trust decision making capability of interacting agents during
collaboration in problem solving activity. Trust computational model
with learning that we present is adapted for best alternate selection of
new project in the organization. We verify our model in a multi-agent
simulation where the agents in the community learn to identify
trustworthy members, inconsistent behavior and conflicting behavior
of agents.
Abstract: We have previously introduced an ultrasonic imaging
approach that combines harmonic-sensitive pulse sequences with a
post-beamforming quadratic kernel derived from a second-order
Volterra filter (SOVF). This approach is designed to produce images
with high sensitivity to nonlinear oscillations from microbubble
ultrasound contrast agents (UCA) while maintaining high levels of
noise rejection. In this paper, a two-step algorithm for computing the
coefficients of the quadratic kernel leading to reduction of tissue
component introduced by motion, maximizing the noise rejection and
increases the specificity while optimizing the sensitivity to the UCA
is presented. In the first step, quadratic kernels from individual
singular modes of the PI data matrix are compared in terms of their
ability of maximize the contrast to tissue ratio (CTR). In the second
step, quadratic kernels resulting in the highest CTR values are
convolved. The imaging results indicate that a signal processing
approach to this clinical challenge is feasible.