Abstract: This paper describes text mining technique for automatically extracting association rules from collections of textual documents. The technique called, Extracting Association Rules from Text (EART). It depends on keyword features for discover association rules amongst keywords labeling the documents. In this work, the EART system ignores the order in which the words occur, but instead focusing on the words and their statistical distributions in documents. The main contributions of the technique are that it integrates XML technology with Information Retrieval scheme (TFIDF) (for keyword/feature selection that automatically selects the most discriminative keywords for use in association rules generation) and use Data Mining technique for association rules discovery. It consists of three phases: Text Preprocessing phase (transformation, filtration, stemming and indexing of the documents), Association Rule Mining (ARM) phase (applying our designed algorithm for Generating Association Rules based on Weighting scheme GARW) and Visualization phase (visualization of results). Experiments applied on WebPages news documents related to the outbreak of the bird flu disease. The extracted association rules contain important features and describe the informative news included in the documents collection. The performance of the EART system compared with another system that uses the Apriori algorithm throughout the execution time and evaluating extracted association rules.
Abstract: Different problems may causes distortion of the rotor,
and hence vibration, which is the most severe damage of the turbine
rotors. In many years different techniques have been developed for
the straightening of bent rotors. The method for straightening can be
selected according to initial information from preliminary inspections
and tests such as nondestructive tests, chemical analysis, run out tests
and also a knowledge of the shaft material. This article covers the
various causes of excessive bends and then some applicable common
straightening methods are reviewed. Finally, hot spotting is opted for
a particular bent rotor. A 325 MW steam turbine rotor is modeled and
finite element analyses are arranged to investigate this straightening
process. Results of experimental data show that performing the exact
hot spot straightening process reduced the bending of the rotor
significantly.
Abstract: An important step in studying the statistics of
fingerprint minutia features is to reliably extract minutia features from
the fingerprint images. A new reliable method of computation for
minutiae feature extraction from fingerprint images is presented. A
fingerprint image is treated as a textured image. An orientation flow
field of the ridges is computed for the fingerprint image. To
accurately locate ridges, a new ridge orientation based computation
method is proposed. After ridge segmentation a new method of
computation is proposed for smoothing the ridges. The ridge skeleton
image is obtained and then smoothed using morphological operators
to detect the features. A post processing stage eliminates a large
number of false features from the detected set of minutiae features.
The detected features are observed to be reliable and accurate.
Abstract: A structural study of an aqueous electrolyte whose
experimental results are available. It is a solution of LiCl-6H2O type
at glassy state (120K) contrasted with pure water at room temperature
by means of Partial Distribution Functions (PDF) issue from neutron
scattering technique. Based on these partial functions, the Reverse
Monte Carlo method (RMC) computes radial and angular correlation
functions which allow exploring a number of structural features of
the system. The obtained curves include some artifacts. To remedy
this, we propose to introduce a screened potential as an additional
constraint. Obtained results show a good matching between
experimental and computed functions and a significant improvement
in PDFs curves with potential constraint. It suggests an efficient fit of
pair distribution functions curves.
Abstract: Background: Widespread use of chemotherapeutic
drugs in the treatment of cancer has lead to higher health hazards
among employee who handle and administer such drugs, so nurses
should know how to protect themselves, their patients and their work
environment against toxic effects of chemotherapy. Aim of this study
was carried out to examine the effect of chemotherapy safety protocol
for oncology nurses on their protective measure practices. Design: A
quasi experimental research design was utilized. Setting: The study
was carried out in oncology department of Menoufia university
hospital and Tanta oncology treatment center. Sample: A
convenience sample of forty five nurses in Tanta oncology treatment
center and eighteen nurses in Menoufiya oncology department.
Tools: 1. an interviewing questionnaire that covering sociodemographic
data, assessment of unit and nurses' knowledge about
chemotherapy. II: Obeservational check list to assess nurses' actual
practices of handling and adminestration of chemotherapy. A base
line data were assessed before implementing Chemotherapy Safety
protocol, then Chemotherapy Safety protocol was implemented, and
after 2 monthes they were assessed again. Results: reveled that 88.9%
of study group I and 55.6% of study group II improved to good total
knowledge scores after educating on the safety protocol, also 95.6%
of study group I and 88.9% of study group II had good total practice
score after educating on the safety protocol. Moreover less than half
of group I (44.4%) reported that heavy workload is the most barriers
for them, while the majority of group II (94.4%) had many barriers
for adhering to the safety protocol such as they didn’t know the
protocol, the heavy work load and inadequate equipment.
Conclusions: Safety protocol for Oncology Nurses seemed to have
positive effect on improving nurses' knowledge and practice.
Recommendation: chemotherapy safety protocol should be instituted
for all oncology nurses who are working in any oncology unit and/ or
center to enhance compliance, and this protocol should be done at
frequent intervals.
Abstract: This paper is concerned with the delay-distributiondependent
stability criteria for bidirectional associative memory
(BAM) neural networks with time-varying delays. Based on the
Lyapunov-Krasovskii functional and stochastic analysis approach,
a delay-probability-distribution-dependent sufficient condition is derived
to achieve the globally asymptotically mean square stable of
the considered BAM neural networks. The criteria are formulated in
terms of a set of linear matrix inequalities (LMIs), which can be
checked efficiently by use of some standard numerical packages. Finally,
a numerical example and its simulation is given to demonstrate
the usefulness and effectiveness of the proposed results.
Abstract: Clean air in subway station is important to passengers. The Platform Screen Doors (PSDs) can improve indoor air quality in the subway station; however the air quality in the subway tunnel is degraded. The subway tunnel has high CO2 concentration and indoor particulate matter (PM) value. The Indoor Air Quality (IAQ) level in subway environment degrades by increasing the frequency of the train operation and the number of the train. The ventilation systems of the subway tunnel need improvements to have better air-quality. Numerical analyses might be effective tools to analyze the performance of subway twin-track tunnel ventilation systems. An existing subway twin-track tunnel in the metropolitan Seoul subway system is chosen for the numerical simulations. The ANSYS CFX software is used for unsteady computations of the airflow inside the twin-track tunnel when the train moves. The airflow inside the tunnel is simulated when one train runs and two trains run at the same time in the tunnel. The piston-effect inside the tunnel is analyzed when all shafts function as the natural ventilation shaft. The supplied air through the shafts is mixed with the pollutant air in the tunnel. The pollutant air is exhausted by the mechanical ventilation shafts. The supplied and discharged airs are balanced when only one train runs in the twin-track tunnel. The pollutant air in the tunnel is high when two trains run simultaneously in opposite direction and all shafts functioned as the natural shaft cases when there are no electrical power supplies in the shafts. The remained pollutant air inside the tunnel enters into the station platform when the doors are opened.
Abstract: The role of entrepreneurs in generating the economy is
very important. Thus, nurturing entrepreneurship skills among
society is very crucial and should start from the early age. One of the
methods is to teach through game such as board game. Game
provides a fun and interactive platform for players to learn and play.
Besides that as today-s world is moving towards Islamic approach in
terms of finance, banking and entertainment but Islamic based game
is still hard to find in the market especially games on
entrepreneurship. Therefore, there is a gap in this segment that can be
filled by learning entrepreneurship through game. The objective of
this paper is to develop an entrepreneurship digital-based game
entitled “Catur Bistari" that is based on Islamic business approach.
Knowledge and skill of entrepreneurship and Islamic business
approach will be learned through the tasks that are incorporated
inside the game.
Abstract: In this paper, based on the estimation of the Cauchy matrix of linear impulsive differential equations, by using Banach fixed point theorem and Gronwall-Bellman-s inequality, some sufficient conditions are obtained for the existence and exponential stability of almost periodic solution for Cohen-Grossberg shunting inhibitory cellular neural networks (SICNNs) with continuously distributed delays and impulses. An example is given to illustrate the main results.
Abstract: Airport capacity has always been perceived in the
traditional sense as the number of aircraft operations during a
specified time corresponding to a tolerable level of average delay and
it mostly depends on the airside characteristics, on the fleet mix
variability and on the ATM. The adoption of the Directive
2002/30/EC in the EU countries drives the stakeholders to conceive
airport capacity in a different way though. Airport capacity in this
sense is fundamentally driven by environmental criteria, and since
acoustical externalities represent the most important factors, those are
the ones that could pose a serious threat to the growth of airports and
to aviation market itself in the short-medium term. The importance of
the regional airports in the deregulated market grew fast during the
last decade since they represent spokes for network carriers and a
preferential destination for low-fares carriers. Not only regional
airports have witnessed a fast and unexpected growth in traffic but
also a fast growth in the complaints for the nuisance by the people
living near those airports. In this paper the results of a study
conducted in cooperation with the airport of Bologna G. Marconi are
presented in order to investigate airport acoustical capacity as a defacto
constraint of airport growth.
Abstract: A complex valued neural network is a neural network
which consists of complex valued input and/or weights and/or thresholds
and/or activation functions. Complex-valued neural networks
have been widening the scope of applications not only in electronics
and informatics, but also in social systems. One of the most important
applications of the complex valued neural network is in signal
processing. In Neural networks, generalized mean neuron model
(GMN) is often discussed and studied. The GMN includes a new
aggregation function based on the concept of generalized mean of all
the inputs to the neuron. This paper aims to present exhaustive results
of using Generalized Mean Neuron model in a complex-valued neural
network model that uses the back-propagation algorithm (called
-Complex-BP-) for learning. Our experiments results demonstrate the
effectiveness of a Generalized Mean Neuron Model in a complex
plane for signal processing over a real valued neural network. We
have studied and stated various observations like effect of learning
rates, ranges of the initial weights randomly selected, error functions
used and number of iterations for the convergence of error required on
a Generalized Mean neural network model. Some inherent properties
of this complex back propagation algorithm are also studied and
discussed.
Abstract: Overcurrent (OC) relays are the major protection
devices in a distribution system. The operating time of the OC relays
are to be coordinated properly to avoid the mal-operation of the
backup relays. The OC relay time coordination in ring fed
distribution networks is a highly constrained optimization problem
which can be stated as a linear programming problem (LPP). The
purpose is to find an optimum relay setting to minimize the time of
operation of relays and at the same time, to keep the relays properly
coordinated to avoid the mal-operation of relays.
This paper presents two phase simplex method for optimum time
coordination of OC relays. The method is based on the simplex
algorithm which is used to find optimum solution of LPP. The
method introduces artificial variables to get an initial basic feasible
solution (IBFS). Artificial variables are removed using iterative
process of first phase which minimizes the auxiliary objective
function. The second phase minimizes the original objective function
and gives the optimum time coordination of OC relays.
Abstract: The reduction in vehicle exhaust emissions achieved
in the last two decades is offset by the growth in traffic, as well as by
changes in the composition of emitted pollutants. The present
investigation illustrates the emissions of in-use gasoline and diesel
passenger cars using the official European driving cycle and the
ARTEMIS real-world driving cycle. It was observed that some of the
vehicles do not comply with the corresponding regulations.
Significant differences in emissions were observed between driving
cycles. Not all pollutants showed a tendency to decrease from Euro 3
to Euro 5.
Abstract: Antimicrobial resistant is becoming a major factor in
virtually all hospital acquired infection may soon untreatable is a
serious public health problem. These concerns have led to major
research effort to discover alternative strategies for the treatment of
bacterial infection. Nanobiotehnology is an upcoming and fast
developing field with potential application for human welfare. An
important area of nanotechnology for development of reliable and
environmental friendly process for synthesis of nanoscale particles
through biological systems In the present studies are reported on the
use of fungal strain Aspergillus species for the extracellular synthesis
of bionanoparticles from 1 mM silver nitrate (AgNO3) solution. The
report would be focused on the synthesis of metallic bionanoparticles
of silver using a reduction of aqueous Ag+ ion with the
culture supernatants of Microorganisms. The bio-reduction of the
Ag+ ions in the solution would be monitored in the aqueous
component and the spectrum of the solution would measure through
UV-visible spectrophotometer The bionanoscale particles were
further characterized by Atomic Force Microscopy (AFM), Fourier
Transform Infrared Spectroscopy (FTIR) and Thin layer
chromatography. The synthesized bionanoscale particle showed a
maximum absorption at 385 nm in the visible region. Atomic Force
Microscopy investigation of silver bionanoparticles identified that
they ranged in the size of 250 nm - 680 nm; the work analyzed the
antimicrobial efficacy of the silver bionanoparticles against various
multi drug resistant clinical isolates. The present Study would be
emphasizing on the applicability to synthesize the metallic
nanostructures and to understand the biochemical and molecular
mechanism of nanoparticles formation by the cell filtrate in order to
achieve better control over size and polydispersity of the
nanoparticles. This would help to develop nanomedicine against
various multi drug resistant human pathogens.
Abstract: Localized surface plasmon resonance (LSPR) is the
coherent oscillation of conductive electrons confined in noble
metallic nanoparticles excited by electromagnetic radiation, and
nanosphere lithography (NSL) is one of the cost-effective methods to
fabricate metal nanostructures for LSPR. NSL can be categorized
into two major groups: dispersed NSL and closely pack NSL. In
recent years, gold nanocrescents and gold nanoholes with vertical
sidewalls fabricated by dispersed NSL, and silver nanotriangles and
gold nanocaps on silica nanospheres fabricated by closely pack NSL,
have been reported for LSPR biosensing. This paper introduces
several novel gold nanostructures fabricated by NSL in LSPR
applications, including 3D nanostructures obtained by evaporating
gold obliquely on dispersed nanospheres, nanoholes with slant
sidewalls, and patchy nanoparticles on closely packed nanospheres,
all of which render satisfactory sensitivity for LSPR sensing. Since
the LSPR spectrum is very sensitive to the shape of the metal
nanostructures, formulas are derived and software is developed for
calculating the profiles of the obtainable metal nanostructures by
NSL, for different nanosphere masks with different fabrication
conditions. The simulated profiles coincide well with the profiles of
the fabricated gold nanostructures observed under scanning electron
microscope (SEM) and atomic force microscope (AFM), which
proves that the software is a useful tool for the process design of
different LSPR nanostructures.
Abstract: Chua’s circuit is one of the most important electronic devices that are used for Chaos and Bifurcation studies. A central role of secure communication is devoted to it. Since the adaptive control is used vastly in the linear systems control, here we introduce a new trend of application of adaptive method in the chaos controlling field. In this paper, we try to derive a new adaptive control scheme for Chua’s circuit controlling because control of chaos is often very important in practical operations. The novelty of this approach is for sake of its robustness against the external perturbations which is simulated as an additive noise in all measured states and can be generalized to other chaotic systems. Our approach is based on Lyapunov analysis and the adaptation law is considered for the feedback gain. Because of this, we have named it NAFT (Nonlinear Adaptive Feedback Technique). At last, simulations show the capability of the presented technique for Chua’s circuit.
Abstract: Palladium-catalyzed hydrodechlorination is a
promising alternative for the treatment of environmentally relevant
water bodies, such as groundwater, contaminated with chlorinated
organic compounds (COCs). In the aqueous phase
hydrodechlorination of COCs, Pd-based catalysts were found to have
a very high catalytic activity. However, the full utilization of the
catalyst-s potential is impeded by the sensitivity of the catalyst to
poisoning and deactivation induced by reduced sulfur compounds
(e.g. sulfides). Several regenerants have been tested before to recover
the performance of sulfide-fouled Pd catalyst. But these only
delivered partial success with respect to re-establishment of the
catalyst activity. In this study, the deactivation behaviour of
Pd/Al2O3 in the presence of sulfide was investigated. Subsequent to
total deactivation the catalyst was regenerated in the aqueous phase
using potassium permanganate. Under neutral pH condition,
oxidative regeneration with permanganate delivered a slow recovery
of catalyst activity. However, changing the pH of the bulk solution to
acidic resulted in the complete recovery of catalyst activity within a
regeneration time of about half an hour. These findings suggest the
superiority of permanganate as regenerant in re-activating Pd/Al2O3
by oxidizing Pd-bound sulfide.
Abstract: Image convolution similar to the receptive fields
found in mammalian visual pathways has long been used in
conventional image processing in the form of Gabor masks.
However, no VLSI implementation of parallel, multi-layered pulsed
processing has been brought forward which would emulate this
property. We present a technical realization of such a pulsed image
processing scheme. The discussed IC also serves as a general testbed
for VLSI-based pulsed information processing, which is of interest
especially with regard to the robustness of representing an analog
signal in the phase or duration of a pulsed, quasi-digital signal, as
well as the possibility of direct digital manipulation of such an
analog signal. The network connectivity and processing properties
are reconfigurable so as to allow adaptation to various processing
tasks.
Abstract: Grid networks provide the ability to perform higher throughput computing by taking advantage of many networked computer-s resources to solve large-scale computation problems. As the popularity of the Grid networks has increased, there is a need to efficiently distribute the load among the resources accessible on the network. In this paper, we present a stochastic network system that gives a distributed load-balancing scheme by generating almost regular networks. This network system is self-organized and depends only on local information for load distribution and resource discovery. The in-degree of each node is refers to its free resources, and job assignment and resource discovery processes required for load balancing is accomplished by using fitted random sampling. Simulation results show that the generated network system provides an effective, scalable, and reliable load-balancing scheme for the distributed resources accessible on Grid networks.