Abstract: In this paper, a new trend for improvement in semianalytical
method based on scale boundaries in order to solve the 2D
elastodynamic problems is provided. In this regard, only the
boundaries of the problem domain discretization are by specific subparametric
elements. Mapping functions are uses as a class of higherorder
Lagrange polynomials, special shape functions, Gauss-Lobatto-
Legendre numerical integration, and the integral form of the weighted
residual method, the matrix is diagonal coefficients in the equations
of elastodynamic issues. Differences between study conducted and
prior research in this paper is in geometry production procedure of
the interpolation function and integration of the different is selected.
Validity and accuracy of the present method are fully demonstrated
through two benchmark problems which are successfully modeled
using a few numbers of DOFs. The numerical results agree very well
with the analytical solutions and the results from other numerical
methods.
Abstract: The present work aims to throw light on the effects of
arcing in air on the surface state of contact pastilles made of silvernickel
Ag-Ni (60/40). Also, the photoelectric emission from these
electrical contacts has been investigated in the spectral range of 196-
256 nm. In order to study the effects of arcing on the EWF, the
metallic samples were subjected to electrical arcs in air, at
atmospheric pressure and room temperature, after that, they have
been introduced into the vacuum chamber of an experimental UHV
set-up for EWF measurements. Both Fowler method of isothermal
curves and linearized Fowler plots were used for the measurement of
the EWF by the photoelectric effect.
It has been found that the EWF varies with the number of applied
arcs. Thus, after 500 arcs in air, the observed EWF increasing is
probably due to progressive inclusion of oxide on alloy surface.
Microscopic examination is necessary to get better understandings on
EWF of silver alloys, for both virgin and arced electrical contacts.
Abstract: This study is used as a definition method to the value
and function in manufacturing sector. In concurrence of discussion
about present condition of modeling method, until now definition of
1D-CAE is ambiguity and not conceptual. Across all the physic fields,
those methods are defined with the formulation of differential
algebraic equation which only applied time derivation and simulation.
At the same time, we propose semi-acausal modeling concept and
differential algebraic equation method as a newly modeling method
which the efficiency has been verified through the comparison of
numerical analysis result between the semi-acausal modeling
calculation and FEM theory calculation.
Abstract: Kazakhstan is currently one of the dynamically
developing states in its region. The stable growth in all sectors of the
economy leads to a corresponding increase in energy consumption.
Thus country consumes significant amount of energy due to the high
level of industrialisation and the presence of energy-intensive
manufacturing such as mining and metallurgy which in turn leads to
low energy efficiency. With allowance for this the Government has
set several priorities to adopt a transition of Republic of Kazakhstan
to a “green economy”. This article provides an overview of
Kazakhstan’s energy efficiency situation in for the period of 1991-
2014. First, the dynamics of production and consumption of
conventional energy resources are given. Second, the potential of
renewable energy sources is summarised followed by the description
of GHG emissions trends in the country. Third, Kazakhstan’ national
initiatives, policies and locally implemented projects in the field of
energy efficiency are described.
Abstract: Recent research in neural networks science and
neuroscience for modeling complex time series data and statistical
learning has focused mostly on learning from high input space and
signals. Local linear models are a strong choice for modeling local
nonlinearity in data series. Locally weighted projection regression is
a flexible and powerful algorithm for nonlinear approximation in
high dimensional signal spaces. In this paper, different learning
scenario of one and two dimensional data series with different
distributions are investigated for simulation and further noise is
inputted to data distribution for making different disordered
distribution in time series data and for evaluation of algorithm in
locality prediction of nonlinearity. Then, the performance of this
algorithm is simulated and also when the distribution of data is high
or when the number of data is less the sensitivity of this approach to
data distribution and influence of important parameter of local
validity in this algorithm with different data distribution is explained.
Abstract: Bacterial strains capable of degradation of malathion
from the domestic sewage were isolated by an enrichment culture
technique. Three bacterial strains were screened and identified as
Acinetobacter baumannii (AFA), Pseudomonas aeruginosa (PS1),
and Pseudomonas mendocina (PS2) based on morphological,
biochemical identification and 16S rRNA sequence analysis.
Acinetobacter baumannii AFA was the most efficient malathion
degrading bacterium, so used for further biodegradation study. AFA
was able to grow in mineral salt medium (MSM) supplemented with
malathion (100 mg/l) as a sole carbon source, and within 14 days,
84% of the initial dose was degraded by the isolate measured by high
performance liquid chromatography. Strain AFA could also degrade
other organophosphorus compounds including diazinon, chlorpyrifos
and fenitrothion. The effect of different culture conditions on the
degradation of malathion like inoculum density, other carbon or
nitrogen sources, temperature and shaking were examined.
Degradation of malathion and bacterial cell growth were accelerated
when culture media were supplemented with yeast extract, glucose
and citrate. The optimum conditions for malathion degradation by
strain AFA were; an inoculum density of 1.5x 10^12CFU/ml at 30°C
with shaking. A specific polymerase chain reaction primers were
designed manually using multiple sequence alignment of the
corresponding carboxylesterase enzymes of Acinetobacter species.
Sequencing result of amplified PCR product and phylogenetic
analysis showed low degree of homology with the other
carboxylesterase enzymes of Acinetobacter strains, so we suggested
that this enzyme is a novel esterase enzyme. Isolated bacterial strains
may have potential role for use in bioremediation of malathion
contaminated.
Abstract: In medical imaging, segmentation of different areas of
human body like bones, organs, tissues, etc. is an important issue.
Image segmentation allows isolating the object of interest for further
processing that can lead for example to 3D model reconstruction of
whole organs. Difficulty of this procedure varies from trivial for
bones to quite difficult for organs like liver. The liver is being
considered as one of the most difficult human body organ to segment.
It is mainly for its complexity, shape versatility and proximity of
other organs and tissues. Due to this facts usually substantial user
effort has to be applied to obtain satisfactory results of the image
segmentation. Process of image segmentation then deteriorates from
automatic or semi-automatic to fairly manual one. In this paper,
overview of selected available software applications that can handle
semi-automatic image segmentation with further 3D volume
reconstruction of human liver is presented. The applications are being
evaluated based on the segmentation results of several consecutive
DICOM images covering the abdominal area of the human body.
Abstract: In this work, we study the behavior of introducing
atomic size vacancy in a graphene nanoribbon superlattice. Our
investigations are based on the density functional theory (DFT) with
the Local Density Approximation in Atomistix Toolkit (ATK). We
show that, in addition to its shape, the position of vacancy has a
major impact on the electrical properties of a graphene nanoribbon
superlattice. We show that the band gap of an armchair graphene
nanoribbon may be tuned by introducing an appropriate periodic
pattern of vacancies. The band gap changes in a zig-zag manner
similar to the variation of band gap of a graphene nanoribbon by
changing its width.
Abstract: In this paper a real-time obstacle avoidance approach
for both autonomous and non-autonomous dynamical systems (DS) is
presented. In this approach the original dynamics of the controller
which allow us to determine safety margin can be modulated.
Different common types of DS increase the robot’s reactiveness in
the face of uncertainty in the localization of the obstacle especially
when robot moves very fast in changeable complex environments.
The method is validated by simulation and influence of different
autonomous and non-autonomous DS such as important
characteristics of limit cycles and unstable DS. Furthermore, the
position of different obstacles in complex environment is explained.
Finally, the verification of avoidance trajectories is described through
different parameters such as safety factor.
Abstract: In this paper, Bayesian online inference in models of
data series are constructed by change-points algorithm, which
separated the observed time series into independent series and study
the change and variation of the regime of the data with related
statistical characteristics. variation of statistical characteristics of time
series data often represent separated phenomena in the some
dynamical system, like a change in state of brain dynamical reflected
in EEG signal data measurement or a change in important regime of
data in many dynamical system. In this paper, prediction algorithm
for studying change point location in some time series data is
simulated. It is verified that pattern of proposed distribution of data
has important factor on simpler and smother fluctuation of hazard
rate parameter and also for better identification of change point
locations. Finally, the conditions of how the time series distribution
effect on factors in this approach are explained and validated with
different time series databases for some dynamical system.
Abstract: In this paper, we considered and applied parametric
modeling for some experimental data of dynamical system. In this
study, we investigated the different distribution of output
measurement from some dynamical systems. Also, with variance
processing in experimental data we obtained the region of
nonlinearity in experimental data and then identification of output
section is applied in different situation and data distribution. Finally,
the effect of the spanning the measurement such as variance to
identification and limitation of this approach is explained.
Abstract: In this paper, model order reduction method is used
for approximation in linear and nonlinearity aspects in some
experimental data. This method can be used for obtaining offline
reduced model for approximation of experimental data and can
produce and follow the data and order of system and also it can
match to experimental data in some frequency ratios. In this study,
the method is compared in different experimental data and influence
of choosing of order of the model reduction for obtaining the best and
sufficient matching condition for following the data is investigated in
format of imaginary and reality part of the frequency response curve
and finally the effect and important parameter of number of order
reduction in nonlinear experimental data is explained further.
Abstract: Performance of different filtering approaches depends
on modeling of dynamical system and algorithm structure. For
modeling and smoothing the data the evaluation of posterior
distribution in different filtering approach should be chosen carefully.
In this paper different filtering approaches like filter KALMAN,
EKF, UKF, EKS and smoother RTS is simulated in some trajectory
tracking of path and accuracy and limitation of these approaches are
explained. Then probability of model with different filters is
compered and finally the effect of the noise variance to estimation is
described with simulations results.
Abstract: In and around Erode District, it is estimated that more
than 1250 chemical and allied textile processing fabric industries are
affected, partially closed and shut off for various reasons such as poor
management, poor supplier performance, lack of planning for
productivity, fluctuation of output, poor investment, waste analysis,
labor problems, capital/labor ratio, accumulation of stocks, poor
maintenance of resources, deficiencies in the quality of fabric, low
capacity utilization, age of plant and equipment, high investment and
input but low throughput, poor research and development, lack of
energy, workers’ fear of loss of jobs, work force mix and work ethic.
The main objective of this work is to analyze the existing conditions
in textile fabric sector, validate the break even of Total Productivity
(TP), analyze, design and implement fuzzy sets and mathematical
programming for improvement of productivity and quality
dimensions in the fabric processing industry. It needs to be
compatible with the reality of textile and fabric processing industries.
The highly risk events from productivity and quality dimension were
found by fuzzy systems and results are wrapped up among the textile
fabric processing industry.
Abstract: Leukaemia is a blood cancer disease that contributes
to the increment of mortality rate in Malaysia each year. There are
two main categories for leukaemia, which are acute and chronic
leukaemia. The production and development of acute leukaemia cells
occurs rapidly and uncontrollable. Therefore, if the identification of
acute leukaemia cells could be done fast and effectively, proper
treatment and medicine could be delivered. Due to the requirement of
prompt and accurate diagnosis of leukaemia, the current study has
proposed unsupervised pixel segmentation based on clustering
algorithm in order to obtain a fully segmented abnormal white blood
cell (blast) in acute leukaemia image. In order to obtain the
segmented blast, the current study proposed three clustering
algorithms which are k-means, fuzzy c-means and moving k-means
algorithms have been applied on the saturation component image.
Then, median filter and seeded region growing area extraction
algorithms have been applied, to smooth the region of segmented
blast and to remove the large unwanted regions from the image,
respectively. Comparisons among the three clustering algorithms are
made in order to measure the performance of each clustering
algorithm on segmenting the blast area. Based on the good sensitivity
value that has been obtained, the results indicate that moving kmeans
clustering algorithm has successfully produced the fully
segmented blast region in acute leukaemia image. Hence, indicating
that the resultant images could be helpful to haematologists for
further analysis of acute leukaemia.
Abstract: Assembly line balancing problem is aimed to divide
the tasks among the stations in assembly lines and optimize some
objectives. In assembly lines the workload on stations is different
from each other due to different tasks times and the difference in
workloads between stations can cause blockage or starvation in some
stations in assembly lines. Buffers are used to store the semi-finished
parts between the stations and can help to smooth the assembly
production. The assembly line balancing and buffer sizing problem
can affect the throughput of the assembly lines. Assembly line
balancing and buffer sizing problems have been studied separately in
literature and due to their collective contribution in throughput rate of
assembly lines, balancing and buffer sizing problem are desired to
study simultaneously and therefore they are considered concurrently
in current research. Current research is aimed to maximize
throughput, minimize total size of buffers in assembly line and
minimize workload variations in assembly line simultaneously. A
multi objective optimization objective is designed which can give
better Pareto solutions from the Pareto front and a simple example
problem is solved for assembly line balancing and buffer sizing
simultaneously. Current research is significant for assembly line
balancing research and it can be significant to introduce optimization
approaches which can optimize current multi objective problem in
future.
Abstract: Motion Tracking and Stereo Vision are complicated,
albeit well-understood problems in computer vision. Existing
softwares that combine the two approaches to perform stereo motion
tracking typically employ complicated and computationally expensive
procedures. The purpose of this study is to create a simple and
effective solution capable of combining the two approaches. The
study aims to explore a strategy to combine the two techniques
of two-dimensional motion tracking using Kalman Filter; and depth
detection of object using Stereo Vision. In conventional approaches
objects in the scene of interest are observed using a single camera.
However for Stereo Motion Tracking; the scene of interest is
observed using video feeds from two calibrated cameras. Using two
simultaneous measurements from the two cameras a calculation for
the depth of the object from the plane containing the cameras is made.
The approach attempts to capture the entire three-dimensional spatial
information of each object at the scene and represent it through a
software estimator object. In discrete intervals, the estimator tracks
object motion in the plane parallel to plane containing cameras and
updates the perpendicular distance value of the object from the plane
containing the cameras as depth. The ability to efficiently track
the motion of objects in three-dimensional space using a simplified
approach could prove to be an indispensable tool in a variety of
surveillance scenarios. The approach may find application from high
security surveillance scenes such as premises of bank vaults, prisons
or other detention facilities; to low cost applications in supermarkets
and car parking lots.
Abstract: Shortfall of electrical energy in Pakistan is a challenge
adversely affecting its industrial output and social growth. As
elsewhere, Pakistan derives its electrical energy from a number of
conventional sources. The exhaustion of petroleum and conventional
resources, the rising costs coupled with extremely adverse climatic
effects are taking its toll especially on the under-developed countries
like Pakistan. As alternate, renewable energy sources like hydropower,
solar, wind, even bio-energy and a mix of some or all of them
could provide a credible alternative to the conventional energy
resources that would not only be cleaner but sustainable as well. As a
model, solar energy-based power grid for the near future has been
attempted to offset the energy shortfalls as a mix with our existing
sustainable natural energy resources. An assessment of solar energy
potential for electricity generation is being presented for fulfilling the
energy demands with higher level of reliability and sustainability.
This model is based on the premise that solar energy potential of
Pakistan is not only reliable but also sustainable. This research
estimates the present & future approaching renewable energy
resource specially the impact of solar energy based power grid for
mitigating energy shortage in Pakistan.
Abstract: The contact resistance between source/drain electrodes
and semiconductor layer is an important parameter affecting electron
transporting performance in the thin film transistor (TFT). In this
work, we introduced a transparent and the solution prossable
single-walled carbon nanotube (SWCNT)/Al-doped ZnO nano particle
(AZO NP) bilayer electrodes showing low contact resistance with
indium-oxide (In2O3) sol gel thin film. By inserting low work function
AZO NPs into the interface between the SWCNTs and the In2O3 which
has a high energy barrier, we could obtain an electrical Ohmic contact
between them. Finally, with the SWCNT-AZO NP bilayer electrodes,
we successfully fabricated a TFT showing a field effect mobility of
5.38 cm2/V·s at 250°C.
Abstract: Complexation of anthocyanins to mimic natural
copigmentation process was investigated. Cyanidin-rich extracts from
Zea mays L. ceritina Kulesh. and delphinidin-rich extracts from
Clitoria ternatea L. were used to form 4 anthocyanin complexes,
AC1, AC2, AC3 and AC4, in the presence of several polyphenols and
a trace metal. Characterizations of the ACs were conducted by UV,
FTIR, DSC/TGA and morphological observations. Bathochromic
shifts of the UV spectra of 4 formulas of ACs were observed at peak
wavelengths of about 510-620 nm by 10 nm suggesting complex
formation. FTIR spectra of the ACs indicate shifts of peaks from
1,733 cm-1 to 1,696 cm-1 indicating interactions and a decrease in the
peak areas within the wavenumber of 3,400-3,500 cm-1 indicating
changes in hydrogen bonding. Thermal analysis of all of the ACs
suggests increases in melting temperature after complexation. AC
with the highest melting temperature was morphologically observed
by SEM and TEM to be crystal-like particles within a range of 50 to
200 nm. Particle size analysis of the AC by laser diffraction gave a
range of 50-600 nm, indicating aggregation. This AC was shown to
have no cytotoxic effect on cultured HGEPp0.5 and HGF (all p>
0.05) by MTT. Therefore, complexation of anthocyanins was simple
and self-assembly process, potentially resulting in nanosized particles
of anthocyanin complex.