Abstract: Studies in economics domain tried to reveal the correlation between stock markets. Since the globalization era, interdependence between stock markets becomes more obvious. The Dynamic Interaction Network (DIN) algorithm, which was inspired by a Gene Regulatory Network (GRN) extraction method in the bioinformatics field, is applied to reveal important and complex dynamic relationship between stock markets. We use the data of the stock market indices from eight countries around the world in this study. Our results conclude that DIN is able to reveal and model patterns of dynamic interaction from the observed variables (i.e. stock market indices). Furthermore, it is also found that the extracted network models can be utilized to predict movement of the stock market indices with a considerably good accuracy.
Abstract: In the present study, computational fluid dynamics
(CFD) simulation has been executed to investigate the transition
boundaries of different flow patterns for moderately viscous oil-water
(viscosity ratio 107, density ratio 0.89 and interfacial tension of 0.032
N/m.) two-phase flow through a horizontal pipeline with internal
diameter and length of 0.025 m and 7.16 m respectively. Volume of
Fluid (VOF) approach including effect of surface tension has been
employed to predict the flow pattern. Geometry and meshing of the
present problem has been drawn using GAMBIT and ANSYS
FLUENT has been used for simulation. A total of 47037 quadrilateral
elements are chosen for the geometry of horizontal pipeline. The
computation has been performed by assuming unsteady flow,
immiscible liquid pair, constant liquid properties, co-axial flow and a
T-junction as entry section. The simulation correctly predicts the
transition boundaries of wavy stratified to stratified mixed flow.
Other transition boundaries are yet to be simulated. Simulated data
has been validated with our own experimental results.
Abstract: In this paper, we have presented a new multivariate fuzzy time series forecasting method. This method assumes mfactors with one main factor of interest. History of past three years is used for making new forecasts. This new method is applied in forecasting total number of car accidents in Belgium using four secondary factors. We also make comparison of our proposed method with existing methods of fuzzy time series forecasting. Experimentally, it is shown that our proposed method perform better than existing fuzzy time series forecasting methods. Practically, actuaries are interested in analysis of the patterns of causalities in road accidents. Thus using fuzzy time series, actuaries can define fuzzy premium and fuzzy underwriting of car insurance and life insurance for car insurance. National Institute of Statistics, Belgium provides region of risk classification for each road. Thus using this risk classification, we can predict premium rate and underwriting of insurance policy holders.
Abstract: Measuring the effect of perinatal lead exposure on learning ability of offspring is considered as a sensitive and selective index for providing an early marker for central nervous system damage produced by this toxic metal. A total of 35 Sprague-Dawley adult rats were used to investigate the effect of lead acetate toxicity on behavioral patterns of adult female rats and learning ability of offspring. Rats were allotted into 4 groups, group one received 1g/l lead acetate (n=10), group two received 1.5g/l lead acetate (n=10), group three received 2g/l lead acetate in drinking water (n=10) and control group did not receive lead acetate (n=5) from 8th day of pregnancy till weaning of pups.
The obtained results revealed a dose dependent increase in the feeding time, drinking frequency, licking frequency, scratching frequency, licking litters, nest building and retrieving frequencies, while standing time increased significantly in rats treated with 1.5g/l lead acetate than other treated groups and control, on contrary lying time decreased gradually in a dose dependent manner. Moreover, movement activities were higher in rats treated with 1g/l lead acetate than other treated groups and control. Furthermore, time spent in closed arms was significantly lower in rats given 2g/l lead acetate than other treated groups, while, they spent significantly much time spent in open arms than other treated groups which could be attributed to occurrence of adaptation. Furthermore, number of entries in open arms was dose dependent. However, the ratio between open/closed arms revealed a significant decrease in rats treated with 2g/l lead acetate than control group.
Abstract: The level of visual abilities, language, memory
processes and intellectual functioning development affects the quality
of a written text. The following analysis will present the results of
diagnostic tests indicating the most common criterion for a group and
stating whether a person has been diagnosed with having cognitive
developmental level below the group-s average or not.The study-s
aim is to determine whether there are specific patterns of cognitive
deficits, which can be distinguished among the group of young
people with spelling disorders.
Abstract: In this study, static batch fermentation was used for bacterial cellulose production in date syrup solution (Bx. 10%) at 28°C using Gluconacetobacter. xylinus (PTCC 1734). The physicochemical properties of standard Sigma CMC and the produced carboxymethyl bacterial cellulose (CMBC) were studied using FT-IR spectroscopy, X-ray diffractometry (XRD) and Scanning Electron Microscopy (SEM). According to the FT-IR spectra the bands at 1664 and 1431 cm-1 indicate that carboxylic acid groups and carboxylate groups exist on the surface. The SEM imaging of CMBC and CMC carried out in magnification of 1K. Comparing the SEM imaging obviously showed that the ribbon shape in CMC remained but the length of ribbons became shorter while that shape changed to flake shape for CMBC. Determination of the area under XRD patterns demonstrated that the crystallinity amount of CMC was more than that for CMBC (51.08% and 81.84% for CMBC and CMC, respectively).
Abstract: Learning using labeled and unlabelled data has
received considerable amount of attention in the machine learning
community due its potential in reducing the need for expensive
labeled data. In this work we present a new method for combining
labeled and unlabeled data based on classifier ensembles. The model
we propose assumes each classifier in the ensemble observes the
input using different set of features. Classifiers are initially trained
using some labeled samples. The trained classifiers learn further
through labeling the unknown patterns using a teaching signals that is
generated using the decision of the classifier ensemble, i.e. the
classifiers self-supervise each other. Experiments on a set of object
images are presented. Our experiments investigate different classifier
models, different fusing techniques, different training sizes and
different input features. Experimental results reveal that the proposed
self-supervised ensemble learning approach reduces classification
error over the single classifier and the traditional ensemble classifier
approachs.
Abstract: In this paper, a new design technique for enhancing
bandwidth that improves the performance of a conventional
microstrip patch antenna is proposed. This paper presents a novel
wideband probe fed inverted slotted microstrip patch antenna. The
design adopts contemporary techniques; coaxial probe feeding,
inverted patch structure and slotted patch. The composite effect of
integrating these techniques and by introducing the proposed patch,
offer a low profile, broadband, high gain, and low cross-polarization
level. The results for the VSWR, gain and co-and cross-polarization
patterns are presented. The antenna operating the band of 1.80-2.36
GHz shows an impedance bandwidth (2:1 VSWR) of 27% and a gain
of 10.18 dBi with a gain variation of 1.12 dBi. Good radiation
characteristics, including a cross-polarization level in xz-plane less
than -42 dB, have been obtained.
Abstract: Because road traffic accidents are a major source of death worldwide, attempts have been made to create Advanced Driver Assistance Systems (ADAS) able to detect vehicle, driver and
environmental conditions that are cues for possible potential accidents. This paper presents continued work on a novel Nonintrusive
Intelligent Driver Assistance and Safety System (Ni-DASS)
for assessing driver attention and hazard awareness. It uses two onboard
CCD cameras – one observing the road and the other observing
the driver-s face. The windscreen is divided into cells and analysis of
the driver-s eye-gaze patterns allows Ni-DASS to determine the windscreen cell the driver is focusing on using eye-gesture templates.
Intersecting the driver-s field of view through the observed
windscreen cell with subsections of the camera-s field of view containing a potential hazard allows Ni-DASS to estimate the
probability that the driver has actually observed the hazard. Results
have shown that the proposed technique is an accurate enough
measure of driver observation to be useful in ADAS systems.
Abstract: German electricity European options on futures using
Lévy processes for the underlying asset are examined. Implied
volatility evolution, under each of the considered models, is
discussed after calibrating for the Merton jump diffusion (MJD),
variance gamma (VG), normal inverse Gaussian (NIG), Carr, Geman,
Madan and Yor (CGMY) and the Black and Scholes (B&S) model.
Implied volatility is examined for the entire sample period, revealing
some curious features about market evolution, where data fitting
performances of the five models are compared. It is shown that
variance gamma processes provide relatively better results and that
implied volatility shows significant differences through time, having
increasingly evolved. Volatility changes for changed uncertainty, or
else, increasing futures prices and there is evidence for the need to
account for seasonality when modelling both electricity spot/futures
prices and volatility.
Abstract: The various types of frequent pattern discovery
problem, namely, the frequent itemset, sequence and graph mining
problems are solved in different ways which are, however, in certain
aspects similar. The main approach of discovering such patterns can
be classified into two main classes, namely, in the class of the levelwise
methods and in that of the database projection-based methods.
The level-wise algorithms use in general clever indexing structures
for discovering the patterns. In this paper a new approach is proposed
for discovering frequent sequences and tree-like patterns efficiently
that is based on the level-wise issue. Because the level-wise
algorithms spend a lot of time for the subpattern testing problem, the
new approach introduces the idea of using automaton theory to solve
this problem.
Abstract: Here we have considered non uniform microstrip
leaky-wave antenna implemented on a dielectric waveguide by a
sinusoidal profile of periodic metallic grating. The non distribution of
the attenuation constant α along propagation axis, optimize the
radiating characteristics and performances of such antennas. The
method developped here is based on an integral method where the
formalism of the admittance operator is combined to a BKW
approximation. First, the effect of the modeling in the modal analysis
of complex waves is studied in detail. Then, the BKW model is used
for the dispersion analysis of the antenna of interest. According to
antenna theory, a forced continuity of the leaky-wave magnitude at
discontinuities of the non uniform structure is established. To test the
validity of our dispersion analysis, computed radiation patterns are
presented and compared in the millimeter band.
Abstract: In neural networks, when new patterns are learned by a network, the new information radically interferes with previously stored patterns. This drawback is called catastrophic forgetting or catastrophic interference. In this paper, we propose a biologically inspired neural network model which overcomes this problem. The proposed model consists of two distinct networks: one is a Hopfield type of chaotic associative memory and the other is a multilayer neural network. We consider that these networks correspond to the hippocampus and the neocortex of the brain, respectively. Information given is firstly stored in the hippocampal network with fast learning algorithm. Then the stored information is recalled by chaotic behavior of each neuron in the hippocampal network. Finally, it is consolidated in the neocortical network by using pseudopatterns. Computer simulation results show that the proposed model has much better ability to avoid catastrophic forgetting in comparison with conventional models.
Abstract: This paper presents the prediction of air flow,
humidity and temperature patterns in a co-current pilot plant spray
dryer fitted with a pressure nozzle using a three dimensional model.
The modelling was done with a Computational Fluid Dynamic
package (Fluent 6.3), in which the gas phase is modelled as
continuum using the Euler approach and the droplet/ particle phase is
modelled by the Discrete Phase model (Lagrange approach).Good
agreement was obtained with published experimental data where the
CFD simulation correctly predicts a fast downward central flowing
core and slow recirculation zones near the walls. In this work, the
effects of the air flow pattern on droplets trajectories, residence time
distribution of droplets and deposition of the droplets on the wall also
were investigated where atomizing of maltodextrin solution was
used.
Abstract: Supplement use is common in athletes. Besides their cost, they may have side effects on health and performance. 250 questionnaires were distributed among female athletes (mean age 27.08 years). The questionnaire aimed to explore the frequency, type, believes, attitudes and knowledge regarding dietary supplements. Knowledge was good in 30.3%, fair in 60.2%, and poor in 9.1% of respondents. 65.3% of athletes did not use supplements regularly. The most widely used supplements were vitamins (48.4%), minerals (42.9%), energy supplements (21.3%), and herbals (20.9%). 68.9% of athletes believed in their efficacy. 34.4% experienced performance enhancement and 6.8% of reported side effects. 68.2% reported little knowledge and 60.9% were eager to learn more. In conclusion, many of the female athletes believe in the efficacy of supplements and think they are an unavoidable part of competitive sports. However, their information is not sufficient. We have to stress on education, consulting sessions, and rational prescription.
Abstract: This study had two goals. First, it investigated marital
interaction variables as predictors of treatment outcome in panic
disorder with agoraphobia (PDA) in sixty-five couples with one
spouse suffering from PDA. Second, it analyzed the impact of PDA
improvement, following therapy, on marital interaction patterns of
both spouses. The partners were observed during a problem-solving
task, before and after treatment. Negative behaviors at the outset of
therapy, both in the PDA and the NPDA partners, predicted less
improvement at post-test. It also appears that improvement in some
PDA symptoms following therapy is linked to increase in the
dominant behavior of the NPDA spouse and to an improvement in
terms of his intrusiveness.
Abstract: The refueling of a transparent rectangular fuel tank
fitted with a standard filler pipe and roll-over valve was
experimentally studied. A fuel-conditioning cart, capable of
handling fuels of different Reid vapor pressure at a constant
temperature, was used to dispense fuel at the desired rate. The
experimental protocol included transient recording of the tank and
filler tube pressures while video recording the flow patterns in the
filler tube and tank during the refueling process. This information
was used to determine the effect of changes in the vent tube
diameter, fuel-dispense flow rate and fuel Reid vapor pressure on the
pressure-time characteristics and the occurrence of premature fuel
filling shut-off and fuel spill-back. Pressure-time curves for the case
of normal shut-off demonstrated the classic, three-phase
characteristic noted in the literature. The variation of the maximum
values of tank dome and filler tube pressures are analyzed in relation
to the occurrence of premature shut-off.
Abstract: Workflow Management Systems (WfMS) alloworganizations to streamline and automate business processes and reengineer their structure. One important requirement for this type of system is the management and computation of the Quality of Service(QoS) of processes and workflows. Currently, a range of Web processes and workflow languages exist. Each language can be characterized by the set of patterns they support. Developing andimplementing a suitable and generic algorithm to compute the QoSof processes that have been designed using different languages is a difficult task. This is because some patterns are specific to particular process languages and new patterns may be introduced in future versions of a language. In this paper, we describe an adaptive algorithm implemented to cope with these two problems. The algorithm is called adaptive since it can be dynamically changed as the patterns of a process language also change.
Abstract: Naïve Bayes classifiers are simple probabilistic
classifiers. Classification extracts patterns by using data file with a set
of labeled training examples and is currently one of the most
significant areas in data mining. However, Naïve Bayes assumes the
independence among the features. Structural learning among the
features thus helps in the classification problem. In this study, the use
of structural learning in Bayesian Network is proposed to be applied
where there are relationships between the features when using the
Naïve Bayes. The improvement in the classification using structural
learning is shown if there exist relationship between the features or
when they are not independent.
Abstract: All-to-all personalized communication, also known as complete exchange, is one of the most dense communication patterns in parallel computing. In this paper, we propose new indirect algorithms for complete exchange on all-port ring and torus. The new algorithms fully utilize all communication links and transmit messages along shortest paths to completely achieve the theoretical lower bounds on message transmission, which have not be achieved among other existing indirect algorithms. For 2D r × c ( r % c ) all-port torus, the algorithm has time complexities of optimal transmission cost and O(c) message startup cost. In addition, the proposed algorithms accommodate non-power-of-two tori where the number of nodes in each dimension needs not be power-of-two or square. Finally, the algorithms are conceptually simple and symmetrical for every message and every node so that they can be easily implemented and achieve the optimum in practice.