Abstract: Oxidative stress and overwhelming free radicals
associated with diabetes mellitus are likely to be linked with
development of certain complication such as retinopathy,
nephropathy and neuropathy. Treatment of diabetic subjects with
antioxidant may be of advantage in attenuating these complications.
Olive leaf (Oleaeuropaea), has been endowed with many beneficial
and health promoting properties mostly linked to its antioxidant
activity. This study aimed to evaluate the significance of
supplementation of Olive leaves extract (OLE) in reducing oxidative
stress, hyperglycemia and hyperlipidemia in Sterptozotocin (STZ)-
induced diabetic rats. After induction of diabetes, a significant rise in
plasma glucose, lipid profiles except High density lipoproteincholestrol
(HDLc), malondialdehyde (MDA) and significant decrease
of plasma insulin, HDLc and Plasma reduced glutathione GSH as
well as alteration in enzymatic antioxidants was observed in all
diabetic animals. During treatment of diabetic rats with 0.5g/kg body
weight of Olive leaves extract (OLE) the levels of plasma (MDA)
,(GSH), insulin, lipid profiles along with blood glucose and
erythrocyte enzymatic antioxidant enzymes were significantly
restored to establish values that were not different from normal
control rats. Untreated diabetic rats on the other hand demonstrated
persistent alterations in the oxidative stress marker (MDA), blood
glucose, insulin, lipid profiles and the antioxidant parameters. These
results demonstrate that OLE may be of advantage in inhibiting
hyperglycemia, hyperlipidemia and oxidative stress induced by
diabetes and suggest that administration of OLE may be helpful in
the prevention or at least reduced of diabetic complications
associated with oxidative stress.
Abstract: The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.
Abstract: An attempt has been made to investigate the
machinability of zirconia toughened alumina (ZTA) inserts while
turning AISI 4340 steel. The insert was prepared by powder
metallurgy process route and the machining experiments were
performed based on Response Surface Methodology (RSM) design
called Central Composite Design (CCD). The mathematical model of
flank wear, cutting force and surface roughness have been developed
using second order regression analysis. The adequacy of model has
been carried out based on Analysis of variance (ANOVA) techniques.
It can be concluded that cutting speed and feed rate are the two most
influential factor for flank wear and cutting force prediction. For
surface roughness determination, the cutting speed & depth of cut
both have significant contribution. Key parameters effect on each
response has also been presented in graphical contours for choosing
the operating parameter preciously. 83% desirability level has been
achieved using this optimized condition.
Abstract: It is important to remove manganese from water
because of its effects on human and the environment. Human
activities are one of the biggest contributors for excessive manganese
concentration in the environment. The proposed method to remove
manganese in aqueous solution by using adsorption as in carbon
nanotubes (CNT) at different parameters: The parameters are CNT
dosage, pH, agitation speed and contact time. Different pHs are pH
6.0, pH 6.5, pH 7.0, pH 7.5 and pH 8.0, CNT dosages are 5mg,
6.25mg, 7.5mg, 8.75mg or 10mg, contact time are 10 min, 32.5 min,
55 min, 87.5 min and 120 min while the agitation speeds are 100rpm,
150rpm, 200rpm, 250rpm and 300rpm. The parameters chosen for
experiments are based on experimental design done by using Central
Composite Design, Design Expert 6.0 with 4 parameters, 5 levels and
2 replications. Based on the results, condition set at pH 7.0, agitation
speed of 300 rpm, 7.5mg and contact time 55 minutes gives the
highest removal with 75.5%. From ANOVA analysis in Design
Expert 6.0, the residual concentration will be very much affected by
pH and CNT dosage. Initial manganese concentration is 1.2mg/L
while the lowest residual concentration achieved is 0.294mg/L,
which almost satisfy DOE Malaysia Standard B requirement.
Therefore, further experiments must be done to remove manganese
from model water to the required standard (0.2 mg/L) with the initial
concentration set to 0.294 mg/L.
Abstract: This paper presents the effectiveness of artificial
intelligent technique to apply for pattern recognition and
classification of Partial Discharge (PD). Characteristics of PD signal
for pattern recognition and classification are computed from the
relation of the voltage phase angle, the discharge magnitude and the
repeated existing of partial discharges by using statistical and fractal
methods. The simplified fuzzy ARTMAP (SFAM) is used for pattern
recognition and classification as artificial intelligent technique. PDs
quantities, 13 parameters from statistical method and fractal method
results, are inputted to Simplified Fuzzy ARTMAP to train system
for pattern recognition and classification. The results confirm the
effectiveness of purpose technique.
Abstract: This paper presents three-phase evolution search methodology to automatically design fuzzy logic controllers (FLCs) that can work in a wide range of operating conditions. These include varying load, parameter variations, and unknown external disturbances. The three-phase scheme consists of an exploration phase, an exploitation phase and a robustness phase. The first two phases search for FLC with high accuracy performances while the last phase aims at obtaining FLC providing the best compromise between the accuracy and robustness performances. Simulations were performed for direct-drive two-axis robot arm. The evolved FLC with the proposed design technique found to provide a very satisfactory performance under the wide range of operation conditions and to overcome problem associated with coupling and nonlinearities characteristics inherent to robot arms.
Abstract: A mathematical model for the transmission of SARS is developed. In addition to dividing the population into susceptible (high and low risk), exposed, infected, quarantined, diagnosed and recovered classes, we have included a class called untraced. The model simulates the Gompertz curves which are the best representation of the cumulative numbers of probable SARS cases in Hong Kong and Singapore. The values of the parameters in the model which produces the best fit of the observed data for each city are obtained by using a differential evolution algorithm. It is seen that the values for the parameters needed to simulate the observed daily behaviors of the two epidemics are different.
Abstract: The LHP is a two-phase device with extremely high
effective thermal conductivity that utilizes the thermodynamic
pressure difference to circulate a cooling fluid. A thermodynamics
analytical model is developed to explore different parameters effects
on a Loop Heat Pipe (LHP).. The effects of pipe length, pipe
diameter, condenser temperature, and heat load are reported. As pipe
length increases and/or pipe diameter decreases, a higher temperature
is expected in the evaporator.
Abstract: The three-species food web model proposed and investigated by Gakkhar and Naji is known to have chaotic behaviour for a choice of parameters. An attempt has been made to synchronize the chaos in the model using bidirectional coupling. Numerical simulations are presented to demonstrate the effectiveness and feasibility of the analytical results. Numerical results show that for higher value of coupling strength, chaotic synchronization is achieved. Chaos can be controlled to achieve stable synchronization in natural systems.
Abstract: In this paper, we carry over some of the results which
are valid on a certain class of Moufang-Klingenberg planes M(A)
coordinatized by an local alternative ring A := A(ε) = A+Aε of
dual numbers to finite projective Klingenberg plane M(A) obtained
by taking local ring Zq (where prime power q = pk) instead of A.
So, we show that the collineation group of M(A) acts transitively
on 4-gons, and that any 6-figure corresponds to only one inversible
m ∈ A.
Abstract: This paper presents PSS (Power system stabilizer) design based on optimal fuzzy PID (OFPID). OFPID based PSS design is considered for single-machine power systems. The main motivation for this design is to stabilize or to control low-frequency oscillation on power systems. Firstly, describing the linear PID control then to combine this PID control with fuzzy logic control mechanism. Finally, Fuzzy PID parameters (Kp. Kd, KI, Kupd, Kui) are tuned by Genetic Algorthm (GA) to reach optimal global stability. The effectiveness of the proposed PSS in increasing the damping of system electromechanical oscillation is demonstrated in a one-machine-infinite-bus system
Abstract: In this paper we propose a novel method for human
face segmentation using the elliptical structure of the human head. It
makes use of the information present in the edge map of the image.
In this approach we use the fact that the eigenvalues of covariance
matrix represent the elliptical structure. The large and small
eigenvalues of covariance matrix are associated with major and
minor axial lengths of an ellipse. The other elliptical parameters are
used to identify the centre and orientation of the face. Since an
Elliptical Hough Transform requires 5D Hough Space, the Circular
Hough Transform (CHT) is used to evaluate the elliptical parameters.
Sparse matrix technique is used to perform CHT, as it squeeze zero
elements, and have only a small number of non-zero elements,
thereby having an advantage of less storage space and computational
time. Neighborhood suppression scheme is used to identify the valid
Hough peaks. The accurate position of the circumference pixels for
occluded and distorted ellipses is identified using Bresenham-s
Raster Scan Algorithm which uses the geometrical symmetry
properties. This method does not require the evaluation of tangents
for curvature contours, which are very sensitive to noise. The method
has been evaluated on several images with different face orientations.
Abstract: A magnetohydrodynamic mixed convective flow in a
cavity was studied in this paper. The lower surface of cavity was
heated from below whereas other walls of the cavity were thermally
isolated. The governing two-dimensional flow equations have been
solved by using finite volume code. The effects of magnetic field
were studied on flow and temperature field and heat transfer
performance at a wide range of parameters, Such as Hartmann
(0≤Ha≤100) and Reynolds (1≤Re≤100) numbers. The results showed
that as Hartman number increases the Nusselt number, representing
heat transfer from the cavity decreases.
Abstract: In this paper a comprehensive model of a fossil fueled
power plant (FFPP) is developed in order to evaluate the
performance of a newly designed turbine follower controller.
Considering the drawbacks of previous works, an overall model is
developed to minimize the error between each subsystem model
output and the experimental data obtained at the actual power plant.
The developed model is organized in two main subsystems namely;
Boiler and Turbine. Considering each FFPP subsystem
characteristics, different modeling approaches are developed. For
economizer, evaporator, superheater and reheater, first order models
are determined based on principles of mass and energy conservation.
Simulations verify the accuracy of the developed models. Due to the
nonlinear characteristics of attemperator, a new model, based on a
genetic-fuzzy systems utilizing Pittsburgh approach is developed
showing a promising performance vis-à-vis those derived with other
methods like ANFIS. The optimization constraints are handled
utilizing penalty functions. The effect of increasing the number of
rules and membership functions on the performance of the proposed
model is also studied and evaluated. The turbine model is developed
based on the equation of adiabatic expansion. Parameters of all
evaluated models are tuned by means of evolutionary algorithms.
Based on the developed model a fuzzy PI controller is developed. It
is then successfully implemented in the turbine follower control
strategy of the plant. In this control strategy instead of keeping
control parameters constant, they are adjusted on-line with regard to
the error and the error rate. It is shown that the response of the
system improves significantly. It is also shown that fuel consumption
decreases considerably.
Abstract: The separation efficiency of a hydrocyclone has
extensively been considered on the rigid particle assumption. A
collection of experimental studies have demonstrated their
discrepancies from the modeling and simulation results. These
discrepancies caused by the actual particle elasticity have generally
led to a larger amount of energy consumption in the separation
process. In this paper, the influence of particle elasticity on the
separation efficiency of a hydrocyclone system was investigated
through the Finite Element (FE) simulations using crude oil droplets
as the elastic particles. A Reitema-s design hydrocyclone with a
diameter of 8 mm was employed to investigate the separation
mechanism of the crude oil droplets from water. The cut-size
diameter eter of the crude oil was 10 - Ðçm in order to fit with the
operating range of the adopted hydrocylone model. Typical
parameters influencing the performance of hydrocyclone were varied
with the feed pressure in the range of 0.3 - 0.6 MPa and feed
concentration between 0.05 – 0.1 w%. In the simulation, the Finite
Element scheme was applied to investigate the particle-flow
interaction occurred in the crude oil system during the process. The
interaction of a single oil droplet at the size of 10 - Ðçm to the flow
field was observed. The feed concentration fell in the dilute flow
regime so the particle-particle interaction was ignored in the study.
The results exhibited the higher power requirement for the separation
of the elastic particulate system when compared with the rigid
particulate system.
Abstract: The objective of this study is to propose a statistical
modeling method which enables simultaneous term structure
estimation of the risk-free interest rate, hazard and loss given default,
incorporating the characteristics of the bond issuing company such as
credit rating and financial information. A reduced form model is used
for this purpose. Statistical techniques such as spline estimation and
Bayesian information criterion are employed for parameter estimation
and model selection. An empirical analysis is conducted using the
information on the Japanese bond market data. Results of the
empirical analysis confirm the usefulness of the proposed method.
Abstract: Today technological process makes possible surface
control of producing parts which is needful for product quality
guarantee. Geometrical structure of part surface includes form,
proportion, accuracy to shape, accuracy to size, alignment and
surface topography (roughness, waviness, etc.). All these parameters
are dependence at technology, production machine parameters,
material properties, but also at human, etc. Every parameters
approves at total part accuracy, it is means at accuracy to shape. One
of the most important accuracy to shape element is roundness. This
paper will be deals by comparison of roughness deviations at
coordination measuring machines and at special single purpose
machines. Will describing measuring by discreet method
(discontinuous) and scanning method (continuous) at coordination
measuring machines and confrontation with reference method using
at single purpose machines.
Abstract: This paper provides a scheme to improve the read efficiency of anti-collision algorithm in EPCglobal UHF Class-1 Generation-2 RFID standard. In this standard, dynamic frame slotted ALOHA is specified to solve the anti-collision problem. Also, the Q-algorithm with a key parameter C is adopted to dynamically adjust the frame sizes. In the paper, we split the C parameter into two parameters to increase the read speed and derive the optimal values of the two parameters through simulations. The results indicate our method outperforms the original Q-algorithm.
Abstract: Combined conduction-free convection heat transfer in
vertical eccentric annuli is numerically investigated using a finitedifference
technique. Numerical results, representing the heat transfer
parameters such as annulus walls temperature, heat flux, and heat
absorbed in the developing region of the annulus, are presented for a
Newtonian fluid of Prandtl number 0.7, fluid-annulus radius ratio 0.5,
solid-fluid thermal conductivity ratio 10, inner and outer wall
dimensionless thicknesses 0.1 and 0.2, respectively, and
dimensionless eccentricities 0.1, 0.3, 0.5, and 0.7. The annulus walls
are subjected to thermal boundary conditions, which are obtained by
heating one wall isothermally whereas keeping the other wall at inlet
fluid temperature. In the present paper, the annulus heights required
to achieve thermal full development for prescribed eccentricities are
obtained. Furthermore, the variation in the height of thermal full
development as function of the geometrical parameter, i.e.,
eccentricity is also investigated.
Abstract: There are several ways of improving the performance of a vapor compression refrigeration cycle. Use of an ejector as expansion device is one of the alternative ways. The present paper aims at evaluate the performance improvement of a vapor compression refrigeration cycle under a wide range of operating conditions. A numerical model is developed and a parametric study of important parameters such as condensation (30-50°C) and evaporation temperatures (-20-5°C), nozzle and diffuser efficiencies (0.75-0.95), subcooling and superheating degrees (0-15K) are investigated. The model verification gives a good agreement with the literature data. The simulation results revealed that condensation temperature has the highest effect (129%) on the performance improvement ratio while superheating has the lowest one (6.2%). Among ejector efficiencies, the diffuser efficiency has a significant effect on the COP of ejector expansion refrigeration cycle. The COP improvement percentage decreases from 10.9% to 4.6% as subcooling degrees increases by 15K.