Abstract: Semiconductor nanomaterials like TiO2 nanoparticles
(TiO2-NPs) approximately less than 100 nm in diameter have become
a new generation of advanced materials due to their novel and
interesting optical, dielectric, and photo-catalytic properties. With the
increasing use of NPs in commerce, to date few studies have
investigated the toxicological and environmental effects of NPs.
Motivated by the importance of TiO2-NPs that may contribute to the
cancer research field especially from the treatment prospective
together with the fractal analysis technique, we have investigated the
effect of TiO2-NPs on colony morphology in the dark condition
using fractal dimension as a key morphological characterization
parameter. The aim of this work is mainly to investigate the cytotoxic
effects of TiO2-NPs in the dark on the growth of human cervical
carcinoma (HeLa) cell colonies from morphological aspect. The in
vitro studies were carried out together with the image processing
technique and fractal analysis. It was found that, these colonies were
abnormal in shape and size. Moreover, the size of the control
colonies appeared to be larger than those of the treated group. The
mean Df +/- SEM of the colonies in untreated cultures was
1.085±0.019, N= 25, while that of the cultures treated with TiO2-NPs
was 1.287±0.045. It was found that the circularity of the control
group (0.401±0.071) is higher than that of the treated group
(0.103±0.042). The same tendency was found in the diameter
parameters which are 1161.30±219.56 μm and 852.28±206.50 μm
for the control and treated group respectively. Possible explanation of
the results was discussed, though more works need to be done in
terms of the for mechanism aspects. Finally, our results indicate that
fractal dimension can serve as a useful feature, by itself or in
conjunction with other shape features, in the classification of cancer
colonies.
Abstract: Stage bus operated in the mixed traffic might always meet many problems about low quality and reliability of services. The low quality and reliability of bus service can make the system not attractive and directly reduce the interest of using bus service. This paper presents the result of field investigation and analysis of on-time performance and service regularity of stage bus in mixed traffic. Data for analysis was collected from the field by on-board observation along the Ipoh-Lumut corridor in Perak, Malaysia. From analysis and discussion, it can be concluded that on-time performance and service regularity varies depend on station, typical day, time period, operation characteristics of bus and characteristics of traffic. The on-time performance and service regularity of stage bus in mixed traffic can be derived by using data collected by onboard survey. It is clear that on-time performance and service regularity of the existing stage bus system was low.
Abstract: The various applications of VLSI circuits in highperformance
computing, telecommunications, and consumer
electronics has been expanding progressively, and at a very hasty
pace. This paper describes a new model for partitioning a circuit
using DBSCAN and fuzzy ARTMAP neural network. The first step
is concerned with feature extraction, where we had make use
DBSCAN algorithm. The second step is the classification and is
composed of a fuzzy ARTMAP neural network. The performance of
both approaches is compared using benchmark data provided by
MCNC standard cell placement benchmark netlists. Analysis of the
investigational results proved that the fuzzy ARTMAP with
DBSCAN model achieves greater performance then only fuzzy
ARTMAP in recognizing sub-circuits with lowest amount of
interconnections between them The recognition rate using fuzzy
ARTMAP with DBSCAN is 97.7% compared to only fuzzy
ARTMAP.
Abstract: Ability of accurate and reliable location estimation in
indoor environment is the key issue in developing great number of
context aware applications and Location Based Services (LBS).
Today, the most viable solution for localization is the Received
Signal Strength (RSS) fingerprinting based approach using wireless
local area network (WLAN). This paper presents two RSS
fingerprinting based approaches – first we employ widely used
WLAN based positioning as a reference system and then investigate
the possibility of using GSM signals for positioning. To compare
them, we developed a positioning system in real world environment,
where realistic RSS measurements were collected. Multi-Layer
Perceptron (MLP) neural network was used as the approximation
function that maps RSS fingerprints and locations. Experimental
results indicate advantage of WLAN based approach in the sense of
lower localization error compared to GSM based approach, but GSM
signal coverage by far outreaches WLAN coverage and for some
LBS services requiring less precise accuracy our results indicate that
GSM positioning can also be a viable solution.
Abstract: The supply chains (SCs) have to appeal to new management paradigms to improve their ability to respond rapidly and cost effectively to unpredictable changes in markets and increasing levels of environmental turbulence, both in terms of volume and variety. In this highly demanded context, the Agile paradigm provides the capabilities to SC quickly adapt to changes in the market requirements. The purpose of this paper is to suggest an Agile Index to assess the agility of the automotive companies and corresponding SCs. The proposed integrated assessment model incorporates Agile practices weighted according to their importance to the automotive SC competitiveness and obtained from the Delphi technique.
Abstract: Ion-acoustic solitary and shock waves in dense
quantum plasmas whose constituents are electrons, positrons, and
positive ions are investigated. We assume that ion velocity is weakly
relativistic and also the effects of kinematic viscosity among the
plasma constituents is considered. By using the reductive
perturbation method, the Korteweg–deVries–Burger (KdV-B)
equation is derived.
Abstract: Periphyton development and composition were
studied in three different treatments: (i) two fishpond units of
wetland-type wastewater treatment pond systems, (ii) two fishponds
in combined intensive-extensive fish farming systems and (iii) three
traditional polyculture fishponds. Results showed that amounts of
periphyton developed in traditional polyculture fishponds (iii) were
different compared to the other treatments (i and ii), where the main
function of ponds was stated wastewater treatment. Negative
correlation was also observable between water quality parameters
and periphyton production. The lower trophity, halobity and
saprobity level of ponds indicated higher amount of periphyton. The
dry matter content of periphyton was significantly higher in the
samples, which were developed in traditional polyculture fishponds
(2.84±3.02 g m-2 day-1, whereby the ash content in dry matter 74%),
than samples taken from (i) (1.60±2.32 g m-2 day-1, 61%) and (ii)
fishponds (0.65±0.45 g m-2 day-1, 81%).
Abstract: Vinegar is a precious food additive and complement as well as effective preservative against food spoilage. Recently traditional vinegar production has been improved using various natural substrates and fruits such as grape, palm, cherry, coconut, date, sugarcane, rice and balsam. These neoclassical fermentations resulted in several vinegar types with different tastes, fragrances and nutritional values because of applying various acetic acid bacteria as starters. Acetic acid bacteria include genera Acetobacter, Gluconacetobacter and Gluconobacter according to latest edition of Bergy-s Manual of Systematic Bacteriology that classifies genera on the basis of their 16s RNA differences. Acetobacter spp as the main vinegar starters belong to family Acetobacteraceae that are gram negative obligate aerobes, chemoorganotrophic bacilli that are oxidase negative and oxidize ethanol to acetic acid. In this research we isolated and identified a native Acetobacter strain with high acetic acid productivity and tolerance against high ethanol concentrations from Iranian peach as a summer delicious fruit that is very susceptible to food spoilage and decay. We used selective and specific laboratorial culture media such as Standard GYC, Frateur and Carr medium. Also we used a new industrial culture medium and a miniature fermentor with a new aeration system innovated by Pars Yeema Biotechnologists Co., Isfahan Science and Technology Town (ISTT), Isfahan, Iran. The isolated strain was successfully cultivated in modified Carr media with 2.5% and 5% ethanol simultaneously in high temperatures, 34 - 40º C after 96 hours of incubation period. We showed that the increase of ethanol concentration resulted in rising of strain sensitivity to high temperature. In conclusion we isolated and characterized a new Acetobacter strain from Iranian peach that could be considered as a potential strain for production of a new vinegar type, peach vinegar, with a delicious taste and advantageous nutritional value in food biotechnology and industrial microbiology.
Abstract: In this paper, a Dynamic Economic Dispatch (DED) model is developed for the system consisting of both thermal generators and wind turbines. The inclusion of a significant amount of wind energy into power systems has resulted in additional constraints on DED to accommodate the intermittent nature of the output. The probability of stochastic wind power based on the Weibull probability density function is included in the model as a constraint; A Here-and-Now Approach. The Environmental Protection Agency-s hourly emission target, which gives the maximum emission during the day, is used as a constraint to reduce the atmospheric pollution. A 69-bus test system with non-smooth cost function is used to illustrate the effectiveness of the proposed model compared with static economic dispatch model with including the wind power.
Abstract: High redundancy and strong uncertainty are two main characteristics for underwater robotic manipulators with unlimited workspace and mobility, but they also make the motion planning and control difficult and complex. In order to setup the groundwork for the research on control schemes, the mathematical representation is built by using the Denavit-Hartenberg (D-H) method [9]&[12]; in addition to the geometry of the manipulator which was studied for establishing the direct and inverse kinematics. Then, the dynamic model is developed and used by employing the Lagrange theorem. Furthermore, derivation and computer simulation is accomplished using the MATLAB environment. The result obtained is compared with mechanical system dynamics analysis software, ADAMS. In addition, the creation of intelligent artificial skin using Interlink Force Sensing ResistorTM technology is presented as groundwork for future work
Abstract: In this note, a theoretical model for analyzing of
normal penetration of the ogive – nose projectile into metallic targets
is presented .The failure is assumed to be asymmetry petalling and
the analysis is performed by using the energy balance and work done
.The work done consist of the work required for plastic deformation
Wp, the work for transferring the matter to new position Wd and the
work for bending of the petals Wb. In several studies, it has been
shown that we can neglect the loss of energy by temperature.
In this present study, in first, by assuming the crater formation
after perforation, the value of work done is calculated during the
normal penetration of conical projectiles into thin metallic targets.
Then the value of residual velocity and ballistic limit of the projectile
is predicated by using the energy balance. In final, theoretical and
experimental results is compared.
Abstract: In this paper, a simple heuristic genetic algorithm is
used for Multistage Multiuser detection in fast fading environments.
Multipath channels, multiple access interference (MAI) and near far
effect cause the performance of the conventional detector to degrade.
Heuristic Genetic algorithms, a rapidly growing area of artificial
intelligence, uses evolutionary programming for initial search, which
not only helps to converge the solution towards near optimal
performance efficiently but also at a very low complexity as
compared with optimal detector. This holds true for Additive White
Gaussian Noise (AWGN) and multipath fading channels.
Experimental results are presented to show the superior performance
of the proposed techque over the existing methods.
Abstract: Thepurpose of the research is to characterize the levels
of satisfaction of the students in e-learning post-graduate courses,
taking into account specific dimensions of the course which were
considered as benchmarks for the quality of this type of online
learning initiative, as well as the levels of satisfaction towards each
specific indicator identified in each dimension. It was also an aim of
this study to understand how thesedimensions relate to one another.
Using a quantitative research approach in the collection and analysis
of the data, the study involves the participation of the students who
attended on e-learning course in 2010/2011. The conclusions of this
study suggest that online students present relatively high levels of
satisfaction, which points towards a positive experience during the
course. It is possible to note that there is a correlation between the
different dimensions studied, consequently leading to different
improvement strategies. Ultimately, this investigation aims to
contribute to the promotion of quality and the success of e-learning
initiatives in Higher Education.
Abstract: In this paper, a comparative study of application of
supervised and unsupervised learning algorithms on illumination
invariant face recognition has been carried out. The supervised
learning has been carried out with the help of using a bi-layered
artificial neural network having one input, two hidden and one output
layer. The gradient descent with momentum and adaptive learning
rate back propagation learning algorithm has been used to implement
the supervised learning in a way that both the inputs and
corresponding outputs are provided at the time of training the
network, thus here is an inherent clustering and optimized learning of
weights which provide us with efficient results.. The unsupervised
learning has been implemented with the help of a modified
Counterpropagation network. The Counterpropagation network
involves the process of clustering followed by application of Outstar
rule to obtain the recognized face. The face recognition system has
been developed for recognizing faces which have varying
illumination intensities, where the database images vary in lighting
with respect to angle of illumination with horizontal and vertical
planes. The supervised and unsupervised learning algorithms have
been implemented and have been tested exhaustively, with and
without application of histogram equalization to get efficient results.
Abstract: Complex assemblies of interacting proteins carry out
most of the interesting jobs in a cell, such as metabolism, DNA
synthesis, mitosis and cell division. These physiological properties
play out as a subtle molecular dance, choreographed by underlying
regulatory networks that control the activities of cyclin-dependent
kinases (CDK). The network can be modeled by a set of nonlinear
differential equations and its behavior predicted by numerical
simulation. In this paper, an innovative approach has been proposed
that uses genetic algorithms to mine a set of behavior data output by
a biological system in order to determine the kinetic parameters of
the system. In our approach, the machine learning method is
integrated with the framework of existent biological information in a
wiring diagram so that its findings are expressed in a form of system
dynamic behavior. By numerical simulations it has been illustrated
that the model is consistent with experiments and successfully shown
that such application of genetic algorithms will highly improve the
performance of mathematical model of the cell division cycle to
simulate such a complicated bio-system.
Abstract: The issue of real-time and reliable report delivery is extremely important for taking effective decision in a real world mission critical Wireless Sensor Network (WSN) based application. The sensor data behaves differently in many ways from the data in traditional databases. WSNs need a mechanism to register, process queries, and disseminate data. In this paper we propose an architectural framework for data placement and management. We propose a reliable and real time approach for data placement and achieving data integrity using self organized sensor clusters. Instead of storing information in individual cluster heads as suggested in some protocols, in our architecture we suggest storing of information of all clusters within a cell in the corresponding base station. For data dissemination and action in the wireless sensor network we propose to use Action and Relay Stations (ARS). To reduce average energy dissipation of sensor nodes, the data is sent to the nearest ARS rather than base station. We have designed our architecture in such a way so as to achieve greater energy savings, enhanced availability and reliability.
Abstract: Image clustering is a process of grouping images
based on their similarity. The image clustering usually uses the color
component, texture, edge, shape, or mixture of two components, etc.
This research aims to explore image clustering using color
composition. In order to complete this image clustering, three main
components should be considered, which are color space, image
representation (feature extraction), and clustering method itself. We
aim to explore which composition of these factors will produce the
best clustering results by combining various techniques from the
three components. The color spaces use RGB, HSV, and L*a*b*
method. The image representations use Histogram and Gaussian
Mixture Model (GMM), whereas the clustering methods use KMeans
and Agglomerative Hierarchical Clustering algorithm. The
results of the experiment show that GMM representation is better
combined with RGB and L*a*b* color space, whereas Histogram is
better combined with HSV. The experiments also show that K-Means
is better than Agglomerative Hierarchical for images clustering.
Abstract: A new numerical method for simultaneously updating mass and stiffness matrices based on incomplete modal measured data is presented. By using the Kronecker product, all the variables that are to be modified can be found out and then can be updated directly. The optimal approximation mass matrix and stiffness matrix which satisfy the required eigenvalue equation and orthogonality condition are found under the Frobenius norm sense. The physical configuration of the analytical model is preserved and the updated model will exactly reproduce the modal measured data. The numerical example seems to indicate that the method is quite accurate and efficient.
Abstract: Protective effect of ethanolic extract of polyherbal formulation (PHF) was studied on carbon tetrachloride induced liver damage on carbon tetrachloride induced liver damage. Treatment of rats with 250mg /kg body weight of ethanolic extract of PHF protected rats against carbon tetrachloride liver injury by significant lowerering 5’ nucleotidase (5’NT), Gamma Glutamyl transferase (GGT), Glutamate dehdyrogenasse (GDH) and Succinate Dehydrogenase (SDH) levels compared to control. Normalization in these enzyme levels indicates strong hepatoprotective property of PHF extract.
Abstract: Selecting the routes and the assignment of link flow in a computer communication networks are extremely complex combinatorial optimization problems. Metaheuristics, such as genetic or simulated annealing algorithms, are widely applicable heuristic optimization strategies that have shown encouraging results for a large number of difficult combinatorial optimization problems. This paper considers the route selection and hence the flow assignment problem. A genetic algorithm and simulated annealing algorithm are used to solve this problem. A new hybrid algorithm combining the genetic with the simulated annealing algorithm is introduced. A modification of the genetic algorithm is also introduced. Computational experiments with sample networks are reported. The results show that the proposed modified genetic algorithm is efficient in finding good solutions of the flow assignment problem compared with other techniques.