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: Traditional higher-education classrooms allow lecturers to observe students- behaviours and responses to a particular pedagogy during learning in a way that can influence changes to the pedagogical approach. Within current e-learning systems it is difficult to perform continuous analysis of the cohort-s behavioural tendency, making real-time pedagogical decisions difficult. This paper presents a Virtual Learning Process Environment (VLPE) based on the Business Process Management (BPM) conceptual framework. Within the VLPE, course designers can model various education pedagogies in the form of learning process workflows using an intuitive flow diagram interface. These diagrams are used to visually track the learning progresses of a cohort of students. This helps assess the effectiveness of the chosen pedagogy, providing the information required to improve course design. A case scenario of a cohort of students is presented and quantitative statistical analysis of their learning process performance is gathered and displayed in realtime using dashboards.
Abstract: Rice seed expression (cDNA) library in the Lambda
Zap 11® phage constructed from the developing grain 10-20 days
after flowering was transformed into yeast for functional
complementation assays in three salt sensitive yeast mutants S.
cerevisiae strain CY162, G19 and Axt3K. Transformed cells of G19
and Axt3K with pYES vector with cDNA inserts showed enhance
tolerance than those with empty pYes vector. Sequencing of the
cDNA inserts revealed that they encode for the putative proteins with
the sequence homologous to rice putative protein PROLM24
(Os06g31070), a prolamin precursor. Expression of this cDNA did
not affect yeast growth in absence of salt. Axt3k and G19 strains
expressing the PROLM24 were able to grow upto 400 mM and 600
mM of NaCl respectively. Similarly, Axt3k mutant with PROLM24
expression showed comparatively higher growth rate in the medium
with excess LiCl (50 mM). The observation that expression of
PROLM24 rescued the salt sensitive phenotypes of G19 and Axt3k
indicates the existence of a regulatory system that ameliorates the
effect of salt stress in the transformed yeast mutants. However, the
exact function of the cDNA sequence, which shows partial sequence
homology to yeast UTR1 is not clear. Although UTR1 involved in
ferrous uptake and iron homeostasis in yeast cells, there is no
evidence to prove its role in Na+ homeostasis in yeast cells. Absence
of transmembrane regions in Os06g31070 protein indicates that salt
tolerance is achieved not through the direct functional
complementation of the mutant genes but through an alternative
mechanism.
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: Mapping between local and global coordinates is an
important issue in finite element method, as all calculations are
performed in local coordinates. The concern arises when subparametric
are used, in which the shape functions of the field variable
and the geometry of the element are not the same. This is particularly
the case for C* elements in which the extra degrees of freedoms
added to the nodes make the elements sub-parametric. In the present
work, transformation matrix for C1* (an 8-noded hexahedron
element with 12 degrees of freedom at each node) is obtained using
equivalent C0 elements (with the same number of degrees of
freedom). The convergence rate of 8-noded C1* element is nearly
equal to its equivalent C0 element, while it consumes less CPU time
with respect to the C0 element. The existence of derivative degrees
of freedom at the nodes of C1* element along with excellent
convergence makes it superior compared with it equivalent C0
element.
Abstract: Following the research in the Department of environmental engineering in Faculty of mechanical engineering on Technical University of Kosice and experiences with electrocoagulation style of disposal waste water, there were designed and partly examining the equipment of two stage revitalization on the standing and little fusible water of tenet electrolysis on the little tarns. With the cooperation with vet experts was that manners prove and it is innocuous for animals, during which time cyanobacteria are totally paralyzed. For the implementation of science and research results have been obtained by means EU funds for structural development.
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: In this paper, a alternative structure method for
continuous time sigma delta modulator is presented. In this
modulator for implementation of integrators in loop filter second
generation current conveyors are employed. The modulator is
designed in CMOS technology and features low power consumption
(65db),
and with 180khZ bandwidth. Simulation results confirm that this
design is suitable for data converters.
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: In Image processing the Image compression can improve
the performance of the digital systems by reducing the cost and
time in image storage and transmission without significant reduction
of the Image quality. This paper describes hardware architecture of
low complexity Discrete Cosine Transform (DCT) architecture for
image compression[6]. In this DCT architecture, common computations
are identified and shared to remove redundant computations
in DCT matrix operation. Vector processing is a method used for
implementation of DCT. This reduction in computational complexity
of 2D DCT reduces power consumption. The 2D DCT is performed
on 8x8 matrix using two 1-Dimensional Discrete cosine transform
blocks and a transposition memory [7]. Inverse discrete cosine
transform (IDCT) is performed to obtain the image matrix and
reconstruct the original image. The proposed image compression
algorithm is comprehended using MATLAB code. The VLSI design
of the architecture is implemented Using Verilog HDL. The proposed
hardware architecture for image compression employing DCT was
synthesized using RTL complier and it was mapped using 180nm
standard cells. . The Simulation is done using Modelsim. The
simulation results from MATLAB and Verilog HDL are compared.
Detailed analysis for power and area was done using RTL compiler
from CADENCE. Power consumption of DCT core is reduced to
1.027mW with minimum area[1].
Abstract: In this paper, a semi empirical formula is presented based on the experimental results to predict the first pick (maximum force) value in the instantaneous folding force- axial distance diagram of a square column. To achieve this purpose, the maximum value of the folding force was assumed to be a function of the average folding force. Using the experimental results, the maximum value of the force necessary to initiate the first fold in a square column was obtained with respect to the geometrical quantities and material properties. Finally, the results obtained from the semi empirical relation in this paper, were compared to the experimental results which showed a good correlation.
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: 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: This work aims to investigate a potential of
microalgae for utilizing industrial wastewater as a cheap nutrient for
their growth and oil accumulation. Wastewater was collected from
the effluent ponds of agro-industrial factories (cassava and ethanol
production plants). Only 2 microalgal strains were isolated and
identified as Scenedesmus quadricauda and Chlorella sp.. However,
only S. quadricauda was selected to cultivate in various wastewater
concentrations (10%, 20%, 40%, 60%, 80% and 100%). The highest
biomass obtained at 6.6×106 and 6.27×106 cells/ml when 60%
wastewater was used in flask and photo-bioreactor. The cultures gave
the highest lipid content at 18.58 % and 42.86% in cases of S.
quadricauda and S. obliquus. In addition, under salt stress (1.0 M
NaCl), S. obliquus demonstrated the highest lipid content at 50%
which was much more than the case of no NaCl adding. However, the
concentration of NaCl does not affect on lipid accumulation in case
of S. quadricauda.
Abstract: A new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephalographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. This paper presented the usage of statistics over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents of the EEG signals were used as inputs of the MLPNN trained with Levenberg- Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.