Abstract: The shortest path routing problem is a multiobjective
nonlinear optimization problem with constraints. This problem has
been addressed by considering Quality of service parameters, delay
and cost objectives separately or as a weighted sum of both
objectives. Multiobjective evolutionary algorithms can find multiple
pareto-optimal solutions in one single run and this ability makes them
attractive for solving problems with multiple and conflicting
objectives. This paper uses an elitist multiobjective evolutionary
algorithm based on the Non-dominated Sorting Genetic Algorithm
(NSGA), for solving the dynamic shortest path routing problem in
computer networks. A priority-based encoding scheme is proposed
for population initialization. Elitism ensures that the best solution
does not deteriorate in the next generations. Results for a sample test
network have been presented to demonstrate the capabilities of the
proposed approach to generate well-distributed pareto-optimal
solutions of dynamic routing problem in one single run. The results
obtained by NSGA are compared with single objective weighting
factor method for which Genetic Algorithm (GA) was applied.
Abstract: The mechanical behavior of porous media is governed by the interaction between its solid skeleton and the fluid existing inside its pores. The interaction occurs through the interface of gains and fluid. The traditional analysis methods of porous media, based on the effective stress and Darcy's law, are unable to account for these interactions. For an accurate analysis, the porous media is represented in a fluid-filled porous solid on the basis of the Biot theory of wave propagation in poroelastic media. In Biot formulation, the equations of motion of the soil mixture are coupled with the global mass balance equations to describe the realistic behavior of porous media. Because of irregular geometry, the domain is generally treated as an assemblage of fmite elements. In this investigation, the numerical formulation for the field equations governing the dynamic response of fluid-saturated porous media is analyzed and employed for the study of transient wave motion. A finite element model is developed and implemented into a computer code called DYNAPM for dynamic analysis of porous media. The weighted residual method with 8-node elements is used for developing of a finite element model and the analysis is carried out in the time domain considering the dynamic excitation and gravity loading. Newmark time integration scheme is developed to solve the time-discretized equations which are an unconditionally stable implicit method Finally, some numerical examples are presented to show the accuracy and capability of developed model for a wide variety of behaviors of porous media.
Abstract: The growth of the aquaculture industry has been
associated with negative environmental impacts through the
discharge of raw effluents into the adjacent receiving water bodies.
Macrophytes from natural saline lakes, which have adaptability to the
high salinity, can be suitable for saline effluent treatment. Eight
emergent species from natural saline area were planted in an
experimental gravel bed hydroponic mesocosm (GBH) which was
treated with effluent water from an intensive fish farm using
geothermal water. In order to examine the applicability of the
halophytes in treatment processes, we tested the relative efficacy of
total nitrogen (TN), total phosphorus (TP), potassium (K), sodium
(Na), magnesium (Mg) and calcium (Ca) removal for the saline
wastewater treatment. Four of the eight species, which were
Phragmites australis, Typha angustifolia, Glyceria maxima, Scirpus
lacustris spp. tabernaemontani could survive and contribute the
experimental treatment.
Abstract: This paper proposes a Particle Swarm Optimization
(PSO) based technique for the optimal allocation of Distributed
Generation (DG) units in the power systems. In this paper our aim is
to decide optimal number, type, size and location of DG units for
voltage profile improvement and power loss reduction in distribution
network. Two types of DGs are considered and the distribution load
flow is used to calculate exact loss. Load flow algorithm is combined
appropriately with PSO till access to acceptable results of this
operation. The suggested method is programmed under MATLAB
software. Test results indicate that PSO method can obtain better
results than the simple heuristic search method on the 30-bus and 33-
bus radial distribution systems. It can obtain maximum loss reduction
for each of two types of optimally placed multi-DGs. Moreover,
voltage profile improvement is achieved.
Abstract: Most of the real queuing systems include special properties and constraints, which can not be analyzed directly by using the results of solved classical queuing models. Lack of Markov chains features, unexponential patterns and service constraints, are the mentioned conditions. This paper represents an applied general algorithm for analysis and optimizing the queuing systems. The algorithm stages are described through a real case study. It is consisted of an almost completed non-Markov system with limited number of customers and capacities as well as lots of common exception of real queuing networks. Simulation is used for optimizing this system. So introduced stages over the following article include primary modeling, determining queuing system kinds, index defining, statistical analysis and goodness of fit test, validation of model and optimizing methods of system with simulation.
Abstract: Innovations in technology have created new ethical
challenges. Essential use of electronic communication in the
workplace has escalated at an astronomical rate over the past decade.
As such, legal and ethical dilemmas confronted by both the employer
and the employee concerning managerial control and ownership of einformation
have increased dramatically in the USA. From the
employer-s perspective, ownership and control of all information
created for the workplace is an undeniable source of economic
advantage and must be monitored zealously. From the perspective of
the employee, individual rights, such as privacy, freedom of speech,
and freedom from unreasonable search and seizure, continue to be
stalwart legal guarantees that employers are not legally or ethically
entitled to abridge in the workplace. These issues have been the
source of great debate and the catalyst for legal reform. The fine line
between ethical and legal has been complicated by emerging
technologies. This manuscript will identify and discuss a number of
specific legal and ethical issues raised by the dynamic electronic
workplace and conclude with suggestions that employers should
follow to respect the delicate balance between employees- legal
rights to privacy and the employer's right to protect its knowledge
systems and infrastructure.
Abstract: Artificial Immune System is adopted as a Heuristic
Algorithm to solve the combinatorial problems for decades.
Nevertheless, many of these applications took advantage of the benefit
for applications but seldom proposed approaches for enhancing the
efficiency. In this paper, we continue the previous research to develop
a Self-evolving Artificial Immune System II via coordinating the T
and B cell in Immune System and built a block-based artificial
chromosome for speeding up the computation time and better
performance for different complexities of problems. Through the
design of Plasma cell and clonal selection which are relative the
function of the Immune Response. The Immune Response will help
the AIS have the global and local searching ability and preventing
trapped in local optima. From the experimental result, the significant
performance validates the SEAIS II is effective when solving the
permutation flows-hop problems.
Abstract: This study has investigated the antidiabetic and
antioxidant potential of Pseudovaria macrophylla bark extract on
streptozotocin–nicotinamide induced type 2 diabetic rats. LCMSQTOF
and NMR experiments were done to determine the chemical
composition in the methanolic bark extract. For in vivo experiments,
the STZ (60 mg/kg/b.w, 15 min after 120 mg/kg/1 nicotinamide, i.p.)
induced diabetic rats were treated with methanolic extract of
Pseuduvaria macrophylla (200 and 400 mg/kg·bw) and
glibenclamide (2.5 mg/kg) as positive control respectively.
Biochemical parameters were assayed in the blood samples of all
groups of rats. The pro-inflammatory cytokines, antioxidant status
and plasma transforming growth factor βeta-1 (TGF-β1) were
evaluated. The histological study of the pancreas was examined and
its expression level of insulin was observed by
immunohistochemistry. In addition, the expression of glucose
transporters (GLUT 1, 2 and 4) were assessed in pancreas tissue by
western blot analysis. The outcomes of the study displayed that the
bark methanol extract of Pseuduvaria macrophylla has potentially
normalized the elevated blood glucose levels and improved serum
insulin and C-peptide levels with significant increase in the
antioxidant enzyme, reduced glutathione (GSH) and decrease in the
level of lipid peroxidation (LPO). Additionally, the extract has
markedly decreased the levels of serum pro-inflammatory cytokines
and transforming growth factor beta-1 (TGF-β1). Histopathology
analysis demonstrated that Pseuduvaria macrophylla has the
potential to protect the pancreas of diabetic rats against peroxidation
damage by downregulating oxidative stress and elevated
hyperglycaemia. Furthermore, the expression of insulin protein,
GLUT-1, GLUT-2 and GLUT-4 in pancreatic cells was enhanced.
The findings of this study support the anti-diabetic claims of
Pseudovaria macrophylla bark.
Abstract: In this paper, we introduce an e-collaborative learning circles methodology which utilizes the information and communication technologies (ICTs) in e-educational processes. In e-collaborative learning circles methodology, the teachers and students announce their research projects on various mailing lists and discussion boards using available ICTs. The teachers & moderators and students who are already members of the e-forums, discuss the project proposals in their classrooms sent out by the potential global partner schools and return the requested feed back to the proposing school(s) about their level of the participation and contribution in the research. In general, an e-collaborative learning circle project is implemented with a small and diverse group (usually 8-10 participants) from around the world. The students meet regularly over a period of weeks/months through the ICTs during the ecollaborative learning process. When the project is completed, a project product (e-book / DVD) is prepared and sent to the circle members. In this research, when taking into account the interests and motivation of the participating students with the facilitating role of the teacher(s), the students in each circle do research to obtain new data and information, thus enabling them to have the opportunity to meet both different cultures and international understandings across the globe. However, while the participants communicate along with the members in the circle they also practice and develop their communication language skills. Finally, teachers and students find the possibility to develop their skills in using the ICTs as well.
Abstract: The purpose of this study was to present a reliable mean for human-computer interfacing based on finger gestures made in two dimensions, which could be interpreted and adequately used in controlling a remote robot's movement. The gestures were captured and interpreted using an algorithm based on trigonometric functions, in calculating the angular displacement from one point of touch to another as the user-s finger moved within a time interval; thereby allowing for pattern spotting of the captured gesture. In this paper the design and implementation of such a gesture based user interface was presented, utilizing the aforementioned algorithm. These techniques were then used to control a remote mobile robot's movement. A resistive touch screen was selected as the gesture sensor, then utilizing a programmed microcontroller to interpret them respectively.
Abstract: Cosmic showers, during the transit through space, produce
sub - products as a result of interactions with the intergalactic
or interstellar medium which after entering earth generate secondary
particles called Extensive Air Shower (EAS). Detection and analysis
of High Energy Particle Showers involve a plethora of theoretical and
experimental works with a host of constraints resulting in inaccuracies
in measurements. Therefore, there exist a necessity to develop a
readily available system based on soft-computational approaches
which can be used for EAS analysis. This is due to the fact that soft
computational tools such as Artificial Neural Network (ANN)s can be
trained as classifiers to adapt and learn the surrounding variations. But
single classifiers fail to reach optimality of decision making in many
situations for which Multiple Classifier System (MCS) are preferred
to enhance the ability of the system to make decisions adjusting
to finer variations. This work describes the formation of an MCS
using Multi Layer Perceptron (MLP), Recurrent Neural Network
(RNN) and Probabilistic Neural Network (PNN) with data inputs
from correlation mapping Self Organizing Map (SOM) blocks and
the output optimized by another SOM. The results show that the setup
can be adopted for real time practical applications for prediction
of primary energy and location of EAS from density values captured
using detectors in a circular grid.
Abstract: Tubular linear induction motor (TLIM) can be used as a capsule pump in a large pneumatic capsule pipeline (PCP) system. Parametric performance evaluation of the designed 1-meter diameter PCP-TLIM system yields encouraging results for practical implementation. The capsule thrust and speed inside the TLIM pump can be calculated from the combination of the PCP fluid mechanics and the TLIM equations. The TLIM equivalent circuits derived from those of the conventional three-phase induction motor are used as a model to predict the static test results of a small-scale PCP-TLIM system. In this paper, additional dynamic tests are performed on the same small-scale PCP-TLIM system with two capsules of different diameters. The behaviors of the capsule inside the pump are observed and analyzed. The dynamic performances from the dynamic tests are compared with the theoretical predictions based on the TLIM equivalent circuit model.
Abstract: The design of technological procedures for
manufacturing certain products demands the definition and
optimization of technological process parameters. Their
determination depends on the model of the process itself and its
complexity. Certain processes do not have an adequate mathematical
model, thus they are modeled using heuristic methods. First part of
this paper presents a state of the art of using soft computing
techniques in manufacturing processes from the perspective of
applicability in modern CAx systems. Methods of artificial
intelligence which can be used for this purpose are analyzed. The
second part of this paper shows some of the developed models of
certain processes, as well as their applicability in the actual
calculation of parameters of some technological processes within the
design system from the viewpoint of productivity.
Abstract: Raman spectroscopy are used to characterize the
chemical changes in normoxic polyhydroxyethylacrylate gel
dosimeter (PHEA) induced by radiation. Irradiations in the low dose
region are performed and the polymerizations of PHEA gels are
monitored by the observing the changes of Raman shift intensity of
the carbon covalent bond of PHEA originated from both monomer
and the cross-linker. The variation in peak intensities with absorbed
dose was observed. As the dose increase, the peak intensities of
covalent bond of carbon in the polymer gels decrease. This point out
that the amount of absorbed dose affect the polymerization of
polymer gels. As the absorbed dose increase, the polymerizations
also increase. Results verify that PHEA gel dosimeters are sensitive
even in lower dose region.
Abstract: The proposed multiplexer-based novel 1-bit full
adder cell is schematized by using DSCH2 and its layout is generated
by using microwind VLSI CAD tool. The adder cell layout
interconnect analysis is performed by using BSIM4 layout analyzer.
The adder circuit is compared with other six existing adder circuits
for parametric analysis. The proposed adder cell gives better
performance than the other existing six adder circuits in terms of
power, propagation delay and PDP. The proposed adder circuit is
further analyzed for interconnect analysis, which gives better
performance than other adder circuits in terms of layout thickness,
width and height.
Abstract: In synchronized games players make their moves simultaneously
rather than alternately. Synchronized Triomineering
and Synchronized Tridomineering are respectively the synchronized
versions of Triomineering and Tridomineering, two variants of a
classic two-player combinatorial game called Domineering. Experimental
results for small m × n boards (with m + n ≤ 12 for
Synchronized Triomineering and m + n ≤ 10 for Synchronized
Tridomineering) and some theoretical results for general k×n boards
(with k = 3, 4, 5 for Synchronized Triomineering and k = 3
for Synchronized Tridomineering) are presented. Future research is
indicated.
Abstract: Abstract–The objectives of the current study are to determine the
prevalence, etiological agents, drug susceptibility pattern and plasmid
profile of Acinetobacter baumannii isolates from Hospital-Acquired
Infections (HAI) at Community Hospital, Al Jouf Province, Saudi
Arabia. A total of 1890 patients had developed infection during
hospital admission and were included in the study. Among those who
developed nosocomial infections, 15(9.4), 10(2.7) and 118 (12.7) had
respiratory tract infection (RTI), blood stream infections (BSI) and
urinary tract (UTI) respectively. A total of 268 bacterial isolates were
isolated from nosocomial infection. S. aureus was reported in 23.5%
for of the total isolates followed by Klebsiella pneumoniae (17.5%), E.
coli (17.2%), P. aeruginosa (11.9%), coagulase negative
staphylococcus (9%), A. baumannii (7.1%), Enterobacter spp.
(3.4%), Citrobacter freundii (3%), Proteus mirabilis (2.6%), and
Proteus vulgaris and Enterococcous faecalis (0.7%). Isolated
organisms are multi-drug resistant, predominantly Gram-positive
pathogens with a high incidence of methicillin-resistant S. aureus,
extended spectrum beta lactamase and vancomycin resistant
enterococci organisms. The RFLP (Fragment Length Polymorphisms)
patterns of plasmid preparations from isolated A. baumannii isolates
had altered RFLP patterns, possibly due to the presence of plasmid(s).
Five A. baumannii isolates harbored plasmids all of which were not
less than 2.71kbp in molecular weight. Hence, it showed that the gene
coding for the isolates were located on the plasmid DNA while the
remaining isolates which have no plasmid might showed gene coding
for antibiotic resistance being located on chromosomal DNA.
Nosocomial infections represent a current problem in Community
Hospital, Al Jouf Province, Saudi Arabia. Problems associated with
SSI include infection with multidrug resistant pathogens which are
difficult to treat and are associated with increased mortality.
Abstract: We consider a network of two M/M/1 parallel queues having the same poisonnian arrival stream with rate λ. Upon his arrival to the system a customer heads to the shortest queue and stays until being served. If the two queues have the same length, an arriving customer chooses one of the two queues with the same probability. Each duration of service in the two queues is an exponential random variable with rate μ and no jockeying is permitted between the two queues. A new numerical method, based on linear programming and convex optimization, is performed for the computation of the steady state solution of the system.
Abstract: Since primary school trips usually start from home,
attention by many scholars have been focused on the home end for
data gathering. Thereafter category analysis has often been relied
upon when predicting school travel demands. In this paper, school
end was relied on for data gathering and multivariate regression for
future travel demand prediction. 9859 pupils were surveyed by way
of questionnaires at 21 primary schools. The town was divided into 5
zones. The study was carried out in Skudai Town, Malaysia. Based
on the hypothesis that the number of primary school trip ends are
expected to be the same because school trips are fixed, the choice of
trip end would have inconsequential effect on the outcome. The
study compared empirical data for home and school trip end
productions and attractions. Variance from both data results was
insignificant, although some claims from home based family survey
were found to be grossly exaggerated. Data from the school trip ends
was relied on for travel demand prediction because of its
completeness. Accessibility, trip attraction and trip production were
then related to school trip rates under daylight and dry weather
conditions. The paper concluded that, accessibility is an important
parameter when predicting demand for future school trip rates.
Abstract: The design of a pattern classifier includes an attempt
to select, among a set of possible features, a minimum subset of
weakly correlated features that better discriminate the pattern classes.
This is usually a difficult task in practice, normally requiring the
application of heuristic knowledge about the specific problem
domain. The selection and quality of the features representing each
pattern have a considerable bearing on the success of subsequent
pattern classification. Feature extraction is the process of deriving
new features from the original features in order to reduce the cost of
feature measurement, increase classifier efficiency, and allow higher
classification accuracy. Many current feature extraction techniques
involve linear transformations of the original pattern vectors to new
vectors of lower dimensionality. While this is useful for data
visualization and increasing classification efficiency, it does not
necessarily reduce the number of features that must be measured
since each new feature may be a linear combination of all of the
features in the original pattern vector. In this paper a new approach is
presented to feature extraction in which feature selection, feature
extraction, and classifier training are performed simultaneously using
a genetic algorithm. In this approach each feature value is first
normalized by a linear equation, then scaled by the associated weight
prior to training, testing, and classification. A knn classifier is used to
evaluate each set of feature weights. The genetic algorithm optimizes
a vector of feature weights, which are used to scale the individual
features in the original pattern vectors in either a linear or a nonlinear
fashion. By this approach, the number of features used in classifying
can be finely reduced.