Abstract: A model of vortex wake is suggested to determine the
induced power during animal hovering flight. The wake is modeled
by a series of equi-spaced rigid rectangular vortex plates, positioned
horizontally and moving vertically downwards with identical speeds;
each plate is generated during powering of the functionally wing
stroke. The vortex representation of the wake considered in the
current theory allows a considerable loss of momentum to occur. The
current approach accords well with the nature of the wingbeat since it
considers the unsteadiness in the wake as an important fluid
dynamical characteristic. Induced power in hovering is calculated as
the aerodynamic power required to generate the vortex wake system.
Specific mean induced power to mean wing tip velocity ratio is
determined by solely the normal spacing parameter (f) for a given
wing stroke amplitude. The current theory gives much higher specific
induced power estimate than anticipated by classical methods.
Abstract: Algae-based fuel are considered a promising sources
of clean energy, and because it has many advantages over traditional
biofuel, research and business ventures have driven into developing
and producing Algal-biofuel. But its production stages create a cost
structure that it is not competitive with traditional fuels. Therefore,
cost becomes the main obstacle in commercial production purpose.
However, the present research which aims at using cost structure
model, and designed MS-Dose program, to investigate the a mount of
production cost and determined the parameter had great effect on it,
second to measured the amount of contribution rate of algae in
process the pollution by capturing Co2 from air . The result generated
from the model shows that the production cost of biomass is between
$0.137 /kg for 100 ha and $0.132 /kg for 500 ha which was less than
cost of other studies, while gallon costs between $3.4 - 3.5, more
than traditional sources of oil about $1 ,which regarded as a rate of
contribution of algal in capturing CO2 from air.
Abstract: In this paper, we seek to determine one reasonable
local hub port and optimal routes for a containership fleet,
performing pick-ups and deliveries, between the hub and spoke ports
in a same region. The relationship between a hub port, and traffic in
feeder lines is analyzed. A new network planning method is proposed,
an integrated hub port location and route design, a capacitated vehicle
routing problem with pick-ups, deliveries and time deadlines are
formulated and solved using an improved genetic algorithm for
positioning the hub port and establishing routes for a containership
fleet. Results on the performance of the algorithm and the feasibility
of the approach show that a relatively small fleet of containerships
could provide efficient services within deadlines.
Abstract: The aim of our work is to study phase composition,
particle size and magnetic response of Fe2O3/TiO2 nanocomposites
with respect to the final annealing temperature. Those nanomaterials
are considered as smart catalysts, separable from a liquid/gaseous
phase by applied magnetic field. The starting product was obtained
by an ecologically acceptable route, based on heterogeneous
precipitation of the TiO2 on modified g-Fe2O3 nanocrystals dispersed
in water. The precursor was subsequently annealed on air at
temperatures ranging from 200 oC to 900 oC. The samples were
investigated by synchrotron X-ray powder diffraction (S-PXRD),
magnetic measurements and Mössbauer spectroscopy. As evidenced
by S-PXRD and Mössbauer spectroscopy, increasing the annealing
temperature causes evolution of the phase composition from
anatase/maghemite to rutile/hematite, finally above 700 oC the
pseudobrookite (Fe2TiO5) also forms. The apparent particle size of
the various Fe2O3/TiO2 phases has been determined from the highquality
S-PXRD data by using two different approaches: the Rietveld
refinement and the Debye method. Magnetic response of the samples
is discussed in considering the phase composition and the particle
size.
Abstract: This paper presents a dynamic adaptation scheme for
the frequency of inter-deme migration in distributed genetic algorithms
(GA), and its VLSI hardware design. Distributed GA,
or multi-deme-based GA, uses multiple populations which evolve
concurrently. The purpose of dynamic adaptation is to improve
convergence performance so as to obtain better solutions. Through
simulation experiments, we proved that our scheme achieves better
performance than fixed frequency migration schemes.
Abstract: Fourier transform infrared (FT-IR) spectroscopic imaging
is an emerging technique that provides both chemically and
spatially resolved information. The rich chemical content of data
may be utilized for computer-aided determinations of structure and
pathologic state (cancer diagnosis) in histological tissue sections for
prostate cancer. FT-IR spectroscopic imaging of prostate tissue has
shown that tissue type (histological) classification can be performed to
a high degree of accuracy [1] and cancer diagnosis can be performed
with an accuracy of about 80% [2] on a microscopic (≈ 6μm)
length scale. In performing these analyses, it has been observed
that there is large variability (more than 60%) between spectra from
different points on tissue that is expected to consist of the same
essential chemical constituents. Spectra at the edges of tissues are
characteristically and consistently different from chemically similar
tissue in the middle of the same sample. Here, we explain these
differences using a rigorous electromagnetic model for light-sample
interaction. Spectra from FT-IR spectroscopic imaging of chemically
heterogeneous samples are different from bulk spectra of individual
chemical constituents of the sample. This is because spectra not
only depend on chemistry, but also on the shape of the sample.
Using coupled wave analysis, we characterize and quantify the nature
of spectral distortions at the edges of tissues. Furthermore, we
present a method of performing histological classification of tissue
samples. Since the mid-infrared spectrum is typically assumed to
be a quantitative measure of chemical composition, classification
results can vary widely due to spectral distortions. However, we
demonstrate that the selection of localized metrics based on chemical
information can make our data robust to the spectral distortions
caused by scattering at the tissue boundary.
Abstract: Numerical analysis for the aerodynamic characteristics
of the WIG (wing-in ground effect) craft with highly cambered and
aspect ratio of one is performed to predict the ground effect for the
case of with- and without- lower-extension endplate. The analysis is
included varying angles of attack from 0 to10 deg. and ground
clearances from 5% of chord to 50%. Due to the ground effect, the lift
by rising in pressure on the lower surface is increased and the
influence of wing-tip vortices is decreased. These two significant
effects improve the lift-drag ratio. On the other hand, the endplate
prevents the high-pressure air escaping from the air cushion at the
wing tip and causes to increase the lift and lift-drag ratio further. It is
found from the visualization of computation results that two wing-tip
vortices are generated from each surface of the wing tip and their
strength are weak and diminished rapidly. Irodov-s criteria are also
evaluated to investigate the static height stability. The comparison of
Irodov-s criteria shows that the endplate improves the deviation of the
static height stability with respect to pitch angles and heights. As the
results, the endplate can improve the aerodynamic characteristics and
static height stability of wings in ground effect, simultaneously.
Abstract: Documents clustering become an essential technology
with the popularity of the Internet. That also means that fast and
high-quality document clustering technique play core topics. Text
clustering or shortly clustering is about discovering semantically
related groups in an unstructured collection of documents. Clustering
has been very popular for a long time because it provides unique
ways of digesting and generalizing large amounts of information.
One of the issues of clustering is to extract proper feature (concept)
of a problem domain. The existing clustering technology mainly
focuses on term weight calculation. To achieve more accurate
document clustering, more informative features including concept
weight are important. Feature Selection is important for clustering
process because some of the irrelevant or redundant feature may
misguide the clustering results. To counteract this issue, the proposed
system presents the concept weight for text clustering system
developed based on a k-means algorithm in accordance with the
principles of ontology so that the important of words of a cluster can
be identified by the weight values. To a certain extent, it has resolved
the semantic problem in specific areas.
Abstract: A mathematical model for the Dynamics of Economic
Profit is constructed by proposing a characteristic differential oneform
for this dynamics (analogous to the action in Hamiltonian
dynamics). After processing this form with exterior calculus, a pair of
characteristic differential equations is generated and solved for the
rate of change of profit P as a function of revenue R (t) and cost C (t).
By contracting the characteristic differential one-form with a vortex
vector, the Lagrangian is obtained for the Dynamics of Economic
Profit.
Abstract: Economically transformers constitute one of the largest investments in a Power system. For this reason, transformer condition assessment and management is a high priority task. If a transformer fails, it would have a significant negative impact on revenue and service reliability. Monitoring the state of health of power transformers has traditionally been carried out using laboratory Dissolved Gas Analysis (DGA) tests performed at periodic intervals on the oil sample, collected from the transformers. DGA of transformer oil is the single best indicator of a transformer-s overall condition and is a universal practice today, which started somewhere in the 1960s. Failure can occur in a transformer due to different reasons. Some failures can be limited or prevented by maintenance. Oil filtration is one of the methods to remove the dissolve gases and prevent the deterioration of the oil. In this paper we analysis the DGA data by regression method and predict the gas concentration in the oil in the future. We bring about a comparative study of different traditional methods of regression and the errors generated out of their predictions. With the help of these data we can deduce the health of the transformer by finding the type of fault if it has occurred or will occur in future. Additional in this paper effect of filtration on the transformer health is highlight by calculating the probability of failure of a transformer with and without oil filtrating.
Abstract: Lately, significant work in the area of Intelligent
Manufacturing has become public and mainly applied within the
frame of industrial purposes. Special efforts have been made in the
implementation of new technologies, management and control
systems, among many others which have all evolved the field. Aware
of all this and due to the scope of new projects and the need of
turning the existing flexible ideas into more autonomous and
intelligent ones, i.e.: Intelligent Manufacturing, the present paper
emerges with the main aim of contributing to the design and analysis
of the material flow in either systems, cells or work stations under
this new “intelligent" denomination. For this, besides offering a
conceptual basis in some of the key points to be taken into account
and some general principles to consider in the design and analysis of
the material flow, also some tips on how to define other possible
alternative material flow scenarios and a classification of the states a
system, cell or workstation are offered as well. All this is done with
the intentions of relating it with the use of simulation tools, for which
these have been briefly addressed with a special focus on the Witness
simulation package. For a better comprehension, the previous
elements are supported by a detailed layout, other figures and a few
expressions which could help obtaining necessary data. Such data and
others will be used in the future, when simulating the scenarios in the
search of the best material flow configurations.
Abstract: Candida albicans ATCC 10231 had low endogenous activity of the alternative oxidase compared with that of C. albicans ATCC 10261. In C. albicans ATCC 10231 the endogenous activity declined as the cultures aged. Alternative oxidase activity could be induced in C. albicans ATCC 10231 by treatment with cyanide, but the induction of this activity required the presence of oxygen which could be replaced, at least in part, with high concentrations of potassium ferricyanide. We infer from this that the expression of the gene encoding the alternative oxidase is under the control of a redoxsensitive transcription factor.
Abstract: The effect of beak trimming on behavior of two strains
of Thai native pullets kept in floor pens was studied. Six general
activities (standing, crouching, moving, comforting, roosting, and
nesting), 6 beak related activities (preening, feeding, drinking,
pecking at inedible object, feather pecking, and litter pecking), and 4
agonistic activities (head pecking, threatening, avoiding, and fighting)
were measured twice a for 15 consecutive days, started when the
pullets were 19 wk old. It was found that beak trimmed pullets drank
more frequent (P
Abstract: In this paper we present semantic assistant agent
(SAA), an open source digital library agent which takes user query
for finding information in the digital library and takes resources-
metadata and stores it semantically. SAA uses Semantic Web to
improve browsing and searching for resources in digital library. All
metadata stored in the library are available in RDF format for
querying and processing by SemanSreach which is a part of SAA
architecture. The architecture includes a generic RDF-based model
that represents relationships among objects and their components.
Queries against these relationships are supported by an RDF triple
store.
Abstract: Group-III nitride material as particularly AlxGa1-xN is
one of promising optoelectronic materials to require for shortwavelength
devices. To achieve the high-quality AlxGa1-xN films for
a high performance of such devices, AlN-nucleation layers are the
important factor. To improve the AlN-nucleation layers with a
variation of Ga-addition, XRD measurements were conducted to
analyze the crystalline quality of the subsequent Al0.1Ga0.9N with the
minimum ω-FWHMs of (0002) and (10-10) reflections of 425 arcsec
and 750 arcsec, respectively. SEM and AFM measurements were
performed to observe the surface morphology and TEM
measurements to identify the microstructures and orientations.
Results showed that the optimized Ga-atoms in the Al(Ga)Nnucleation
layers improved the surface diffusion to form moreuniform
crystallites in structure and size, better alignment of each
crystallite, and better homogeneity of island distribution. This, hence,
improves the orientation of epilayers on the Si-surface and finally
improves the crystalline quality and reduces the residual strain of
subsequent Al0.1Ga0.9N layers.
Abstract: Pharmaceutical industries and effluents of sewage treatment plants are the main sources of residual pharmaceuticals in water resources. These emergent pollutants may adversely impact the biophysical environment. Pharmaceutical industries often generate wastewater that changes in characteristics and quantity depending on the used manufacturing processes. Carbamazepine (CBZ), {5Hdibenzo [b,f]azepine-5-carboxamide, (C15H12N2O)}, is a significant non-biodegradable pharmaceutical contaminant in the Jordanian pharmaceutical wastewater, which is not removed by the activated sludge processes in treatment plants. Activated carbon may potentially remove that pollutant from effluents, but the high cost involved suggests that more attention should be given to the potential use of low-cost materials in order to reduce cost and environmental contamination. Powders of Jordanian non-metallic raw materials namely, Azraq Bentonite (AB), Kaolinite (K), and Zeolite (Zeo) were activated (acid and thermal treatment) and evaluated by removing CBZ. The results of batch and column techniques experiments showed around 46% and 67% removal of CBZ respectively.
Abstract: An accurate and proficient artificial neural network
(ANN) based genetic algorithm (GA) is developed for predicting of
nanofluids viscosity. A genetic algorithm (GA) is used to optimize
the neural network parameters for minimizing the error between the
predictive viscosity and the experimental one. The experimental
viscosity in two nanofluids Al2O3-H2O and CuO-H2O from 278.15
to 343.15 K and volume fraction up to 15% were used from
literature. The result of this study reveals that GA-NN model is
outperform to the conventional neural nets in predicting the viscosity
of nanofluids with mean absolute relative error of 1.22% and 1.77%
for Al2O3-H2O and CuO-H2O, respectively. Furthermore, the results
of this work have also been compared with others models. The
findings of this work demonstrate that the GA-NN model is an
effective method for prediction viscosity of nanofluids and have
better accuracy and simplicity compared with the others models.
Abstract: The performance of schedules released to a shop floor may greatly be affected by unexpected disruptions. Thus, this paper considers the flexible job shop scheduling problem when processing times of some operations are represented by a uniform distribution with given lower and upper bounds. The objective is to find a predictive schedule that can deal with this uncertainty. The paper compares two genetic approaches to obtain predictive schedule. To determine the performance of the predictive schedules obtained by both approaches, an experimental study is conducted on a number of benchmark problems.
Abstract: Clustering in high dimensional space is a difficult
problem which is recurrent in many fields of science and
engineering, e.g., bioinformatics, image processing, pattern
reorganization and data mining. In high dimensional space some of
the dimensions are likely to be irrelevant, thus hiding the possible
clustering. In very high dimensions it is common for all the objects in
a dataset to be nearly equidistant from each other, completely
masking the clusters. Hence, performance of the clustering algorithm
decreases.
In this paper, we propose an algorithmic framework which
combines the (reduct) concept of rough set theory with the k-means
algorithm to remove the irrelevant dimensions in a high dimensional
space and obtain appropriate clusters. Our experiment on test data
shows that this framework increases efficiency of the clustering
process and accuracy of the results.