Abstract: This study sought to uncover the complex role of
stress in the workplace by investigating both positive (eustress) and
negative (distress) stress responses. In particular, the study tested a
mediation model in which organisational stressors (person-job fit and
role overload) influence employee affective wellbeing, both directly
and indirectly through stress responses. Participants were recruited
from retail and finance organisations in Australia and New Zealand,
and asked to complete an anonymous online questionnaire. A total of
140 individuals returned completed questionnaires. The results show
that person-job fit influenced eustress, which in turn had a positive
effect on employee affective wellbeing; and role overload impacted
distress, which in turn held a negative influence on affective
wellbeing. These findings indicate that different organisational
stressors have unique relationships with eustress and distress
responses. Limitations and implications of the study are discussed.
Abstract: The research purpose was to evaluate the effect of
Active Imagination Technique (AIT) for bruxism treatment. This
project was approved by the Ethics Committee on Human Research
(CAAE: 05619512.9.0000.0109). Twenty-one volunteers using
interocclusal splint completed the study. Initially they filled in a
questionnaire about their condition, composed of objective questions
on signs and symptoms. Following they were underwent asingle
session of AIT. After 15 days, the volunteers met again the same
initial questionnaire. The results were compared and showed that the
vast majority had pain symptoms, difficulty opening the mouth, pain
when chewing, reduced, some of the participants abandoned the
interocclusal splint during the evaluate period. It is concluded that the
technique can be used in bruxism treatment. Results seem to be
promising and demonstrates the need of highlighting Active
Imagination Technique since it points a possibility of bruxism cure
and that is unprecedented.
Abstract: Removing noise from the any processed images is very important. Noise should be removed in such a way that important information of image should be preserved. A decisionbased nonlinear algorithm for elimination of band lines, drop lines, mark, band lost and impulses in images is presented in this paper. The algorithm performs two simultaneous operations, namely, detection of corrupted pixels and evaluation of new pixels for replacing the corrupted pixels. Removal of these artifacts is achieved without damaging edges and details. However, the restricted window size renders median operation less effective whenever noise is excessive in that case the proposed algorithm automatically switches to mean filtering. The performance of the algorithm is analyzed in terms of Mean Square Error [MSE], Peak-Signal-to-Noise Ratio [PSNR], Signal-to-Noise Ratio Improved [SNRI], Percentage Of Noise Attenuated [PONA], and Percentage Of Spoiled Pixels [POSP]. This is compared with standard algorithms already in use and improved performance of the proposed algorithm is presented. The advantage of the proposed algorithm is that a single algorithm can replace several independent algorithms which are required for removal of different artifacts.
Abstract: Power system stabilizers (PSS) are now routinely used in the industry to damp out power system oscillations. In this paper, real-coded genetic algorithm (RCGA) optimization technique is applied to design robust power system stabilizer for both singlemachine infinite-bus (SMIB) and multi-machine power system. The design problem of the proposed controller is formulated as an optimization problem and RCGA is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved; stability performance of the system is improved. The non-linear simulation results are presented under wide range of operating conditions; disturbances at different locations as well as for various fault clearing sequences to show the effectiveness and robustness of the proposed controller and their ability to provide efficient damping of low frequency oscillations.
Abstract: Optimal supplementary damping controller design for Thyristor Controlled Series Compensator (TCSC) is presented in this paper. For the proposed controller design, a multi-objective fitness function consisting of both damping factors and real part of system electromachanical eigenvalue is used and Real- Coded Genetic Algorithm (RCGA) is employed for the optimal supplementary controller parameters. The performance of the designed supplementary TCSC-based damping controller is tested on a weakly connected power system with different disturbances and loading conditions with parameter variations. Simulation results are presented and compared with a conventional power system stabilizer and also with the TCSC-based supplementary controller when the controller parameters are not optimized to show the effectiveness and robustness of the proposed approach over a wide range of loading conditions and disturbances.
Abstract: This paper aims to scale up Dye-sensitized Solar Cell
(DSSC) production using a commonly available industrial material –
stainless steel - and industrial plasma equipment. A working DSSC
electrode formed by (1) coating titania nanotube (TiO2 NT) film on
304 stainless steel substrate using a plasma spray technique; then, (2)
filling the nano-pores of the TiO2 NT film using a TiF4 sol-gel method.
A DSSC device consists of an anode absorbed photosensitive dye
(N3), a transparent conductive cathode with platinum (Pt)
nano-catalytic particles adhered to its surface, and an electrolytic
solution sealed between the anode and the transparent conductive
cathode. The photo-current conversion efficiency of the DSSC sample
was tested under an AM 1.5 Solar Simulator. The sample has a short
current (Isc) of 0.83 mA cm-2, open voltage (Voc) of 0.81V, filling
factor (FF) of 0.52, and conversion efficiency (η) of 2.18% on a 0.16
cm2 DSSC work-piece.
Abstract: Image compression can improve the performance of
the digital systems by reducing time and cost in image storage
and transmission without significant reduction of the image quality.
Furthermore, the discrete cosine transform has emerged as the new
state-of-the art standard for image compression. In this paper, a
hybrid image compression technique based on reversible blockade
transform coding is proposed. The technique, implemented over
regions of interest (ROIs), is based on selection of the coefficients
that belong to different transforms, depending on the coefficients is
proposed. This method allows: (1) codification of multiple kernals
at various degrees of interest, (2) arbitrary shaped spectrum,and (3)
flexible adjustment of the compression quality of the image and the
background. No standard modification for JPEG2000 decoder was
required. The method was applied over different types of images.
Results show a better performance for the selected regions, when
image coding methods were employed for the whole set of images.
We believe that this method is an excellent tool for future image
compression research, mainly on images where image coding can
be of interest, such as the medical imaging modalities and several
multimedia applications. Finally VLSI implementation of proposed
method is shown. It is also shown that the kernal of Hartley and
Cosine transform gives the better performance than any other model.
Abstract: This paper features the modeling and design of a
Robust Decentralized Fast Output Sampling (RDFOS) Feedback
control technique for the active vibration control of a smart flexible
multimodel Euler-Bernoulli cantilever beams for a multivariable
(MIMO) case by retaining the first 6 vibratory modes. The beam
structure is modeled in state space form using the concept of
piezoelectric theory, the Euler-Bernoulli beam theory and the Finite
Element Method (FEM) technique by dividing the beam into 4 finite
elements and placing the piezoelectric sensor / actuator at two finite
element locations (positions 2 and 4) as collocated pairs, i.e., as
surface mounted sensor / actuator, thus giving rise to a multivariable
model of the smart structure plant with two inputs and two outputs.
Five such multivariable models are obtained by varying the
dimensions (aspect ratios) of the aluminium beam. Using model
order reduction technique, the reduced order model of the higher
order system is obtained based on dominant Eigen value retention
and the Davison technique. RDFOS feedback controllers are
designed for the above 5 multivariable-multimodel plant. The closed
loop responses with the RDFOS feedback gain and the magnitudes of
the control input are obtained and the performance of the proposed
multimodel smart structure system is evaluated for vibration control.
Abstract: TiO2/Ag composite films were prepared by
incorporating Ag in the pores of mesoporous TiO2 films using a
photoreduction method. The Ag nanoparticle sizes were in a range of
3.66-38.56 nm. The TiO2/Ag composite films were characterized by
X-ray diffraction (XRD), scanning electron microscopy (SEM) and
transmission electron microscropy (TEM). The TiO2 films and
TiO2/Ag composite films were immersed in a 0.3 mM N719 dye
solution and characterized by UV-Vis spectrophotometer. The
TiO2/Ag/N719 composite film showed that an optimal size of Ag
nanoparticles was 19.12 nm and, hence, gave the maximum optical
absorption spectra. The improved absorption was due to surface
plasmon resonance induced by the Ag nanoparticles to enhance the
absorption coefficient of the dye.
Abstract: This essay presents applicative methods to reduce human exposure levels in the area around base transceiver stations in a environment with multiple sources based on ITU-T recommendation K.70. An example is presented to understand the mitigation techniques and their results and also to learn how they can be applied, especially in developing countries where there is not much research on non-ionizing radiations.
Abstract: The paper proposes a methodology to process the signals coming from the Transcranial Magnetic Stimulation (TMS) in order to identify the pathology and evaluate the therapy to treat the patients affected by demency diseases. In particular, a fuzzy model is developed to identify the demency of the patients affected by Subcortical Ischemic Vascular Dementia and to measure the positive effect, if any, of a repetitive TMS on their motor performances. A tool is also presented to support the mentioned analysis.
Abstract: In this paper the supersonic ejectors are
experimentally and analytically studied. Ejector is a device that
uses the energy of a fluid to move another fluid. This device works
like a vacuum pump without usage of piston, rotor or any other
moving component. An ejector contains an active nozzle, a passive
nozzle, a mixing chamber and a diffuser. Since the fluid viscosity
is large, and the flow is turbulent and three dimensional in the
mixing chamber, the numerical methods consume long time and
high cost to analyze the flow in ejectors. Therefore this paper
presents a simple analytical method that is based on the precise
governing equations in fluid mechanics. According to achieved
analytical relations, a computer code has been prepared to analyze
the flow in different components of the ejector. An experiment has
been performed in supersonic regime 1.5
Abstract: In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.
Abstract: This paper presents a novel method for inferring the
odor based on neural activities observed from rats- main olfactory
bulbs. Multi-channel extra-cellular single unit recordings were done
by micro-wire electrodes (tungsten, 50μm, 32 channels) implanted in
the mitral/tufted cell layers of the main olfactory bulb of anesthetized
rats to obtain neural responses to various odors. Neural response
as a key feature was measured by substraction of neural firing rate
before stimulus from after. For odor inference, we have developed a
decoding method based on the maximum likelihood (ML) estimation.
The results have shown that the average decoding accuracy is about
100.0%, 96.0%, 84.0%, and 100.0% with four rats, respectively. This
work has profound implications for a novel brain-machine interface
system for odor inference.
Abstract: The performance of high-resolution schemes is investigated for unsteady, inviscid and compressible multiphase flows. An Eulerian diffuse interface approach has been chosen for the simulation of multicomponent flow problems. The reduced fiveequation and seven equation models are used with HLL and HLLC approximation. The authors demonstrated the advantages and disadvantages of both seven equations and five equations models studying their performance with HLL and HLLC algorithms on simple test case. The seven equation model is based on two pressure, two velocity concept of Baer–Nunziato [10], while five equation model is based on the mixture velocity and pressure. The numerical evaluations of two variants of Riemann solvers have been conducted for the classical one-dimensional air-water shock tube and compared with analytical solution for error analysis.
Abstract: Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients- changes are analysed. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes- dynamics of GTE work parameters allows drawing conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values- changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stageby- stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made.
Abstract: This paper deals with the design of a periodic output
feedback controller for a flexible beam structure modeled with
Timoshenko beam theory, Finite Element Method, State space
methods and embedded piezoelectrics concept. The first 3 modes are
considered in modeling the beam. The main objective of this work is
to control the vibrations of the beam when subjected to an external
force. Shear piezoelectric sensors and actuators are embedded into
the top and bottom layers of a flexible aluminum beam structure, thus
making it intelligent and self-adaptive. The composite beam is
divided into 5 finite elements and the control actuator is placed at
finite element position 1, whereas the sensor is varied from position 2
to 5, i.e., from the nearby fixed end to the free end. 4 state space
SISO models are thus developed. Periodic Output Feedback (POF)
Controllers are designed for the 4 SISO models of the same plant to
control the flexural vibrations. The effect of placing the sensor at
different locations on the beam is observed and the performance of
the controller is evaluated for vibration control. Conclusions are
finally drawn.
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: Decision feedback equalizers are commonly employed to reduce the error caused by intersymbol interference. Here, an adaptive decision feedback equalizer is presented with a new adaptation algorithm. The algorithm follows a block-based approach of normalized least mean square (NLMS) algorithm with set-membership filtering and achieves a significantly less computational complexity over its conventional NLMS counterpart with set-membership filtering. It is shown in the results that the proposed algorithm yields similar type of bit error rate performance over a reasonable signal to noise ratio in comparison with the latter one.
Abstract: In the course of the present work, plain (nonencapsulated)
and microencapsulated polyphenols were produced
using olive mill wastewater (OMW) as raw material, in order to be
used for enrichment of yogurt and dairy products. The OMW was
first clarified by using membrane technology and subsequently the
contained poly-phenols were isolated by adsorption-desorption
technique using selective macro-porous resins and finally recovered
in dry form after been processed by RO membrane technique
followed by freeze drying. Moreover, the polyphenols were
encapsulated in modified starch by freeze drying in order to mask the
color and bitterness effect and improve their functionality. The two
products were used successfully as additives in yogurt preparations
and the produced products were acceptable by the consumers and
presented with certain advantage to the plain yogurt. For the herein
proposed production scheme a patent application was already
submitted.