Abstract: Music has a great effect on human body and mind; it
can have a positive effect on hormone system. Objective of this study
is to analysis the effect of music (carnatic, hard rock and jazz) on
brain activity during mental work load using electroencephalography
(EEG). Eight healthy subjects without special musical education
participated in the study. EEG signals were acquired at frontal (Fz),
parietal (Pz) and central (Cz) lobes of brain while listening to music
at three experimental condition (rest, music without mental task and
music with mental task). Spectral powers features were extracted at
alpha, theta and beta brain rhythms. While listening to jazz music, the
alpha and theta powers were significantly (p < 0.05) high for rest as
compared to music with and without mental task in Cz. While
listening to Carnatic music, the beta power was significantly (p <
0.05) high for with mental task as compared to rest and music
without mental task at Cz and Fz location. This finding corroborates
that attention based activities are enhanced while listening to jazz and
carnatic as compare to Hard rock during mental task.
Abstract: Principal Component Analysis (PCA) has many
different important applications especially in pattern detection
such as face detection / recognition. Therefore, for real time
applications, the response time is required to be as small as
possible. In this paper, new implementation of PCA for fast
face detection is presented. Such new implementation is
designed based on cross correlation in the frequency domain
between the input image and eigenvectors (weights).
Simulation results show that the proposed implementation of
PCA is faster than conventional one.
Abstract: The study was carried out to evaluated effect of S-gridling on fruit growth and quality of wax apple. The study was laid in Random completed block design with four replicated. Four treatment were applied as follows: S-girdling, fruit thinning plus bagging with 2,4-D sprayed, fruit thinning plus bagging and the control treatment. 2,4D was sprayed at the small bud and petal fall stage. Girdling was applied three week before flowering. The effect of all treatments on fruit growth was measured weekly. Number of flower, fruit set, fruit drop, fruit crack, and fruit quality were recorded. The result indicated that S-girdling, 2,4D application produced the lowest bud drop, fruit drop compared to untreated control. S-girdling improved faster fruit growth producing the best final fruit length and diameter compared to untreated control. S-girdling also markedly enhanced fruit set, fruit weight, and total soluble solid, reduced fruit crack, titratable acidity. On the other hand, it was noticed that with 2,4-D application also increased the fruit growth rate, improved physiological and biochemical characters of fruit than control treatment. It was concluded that S-girdling was recommended as the industry norm to increase fruit set, fruit quality in wax apple. 2,4D application had a distinctive and significant effect on most of the fruit quality characteristics assessed.
Abstract: The healthcare environment is generally perceived as
being information rich yet knowledge poor. However, there is a lack
of effective analysis tools to discover hidden relationships and trends
in data. In fact, valuable knowledge can be discovered from
application of data mining techniques in healthcare system. In this
study, a proficient methodology for the extraction of significant
patterns from the Coronary Heart Disease warehouses for heart
attack prediction, which unfortunately continues to be a leading cause
of mortality in the whole world, has been presented. For this purpose,
we propose to enumerate dynamically the optimal subsets of the
reduced features of high interest by using rough sets technique
associated to dynamic programming. Therefore, we propose to
validate the classification using Random Forest (RF) decision tree to
identify the risky heart disease cases. This work is based on a large
amount of data collected from several clinical institutions based on
the medical profile of patient. Moreover, the experts- knowledge in
this field has been taken into consideration in order to define the
disease, its risk factors, and to establish significant knowledge
relationships among the medical factors. A computer-aided system is
developed for this purpose based on a population of 525 adults. The
performance of the proposed model is analyzed and evaluated based
on set of benchmark techniques applied in this classification problem.
Abstract: Global warming and continental changes have been
one of the people's issues in the recent years and its consequences
have appeared in the most parts of the earth planet or will appear in
the future. Temperature and Precipitation are two main parameters in
climatology. Any changes in these two parameters in this region
cause widespread changes in the ecosystem and its natural and
humanistic structure. One of the important consequences of this
procedure is change in surface and underground water resources.
Zayanderood watershed basin which is the main central river in Iran
has faced water shortage in the recent years and also it has resulted in
drought in Gavkhuni swamp and the river itself. Managers and
experts in provinces which are the Zayanderood water consumers
believe that global warming; raining decrease and continental
changes are the main reason of water decrease. By statistical
investigation of annual Precipitation and 46 years temperature of
internal and external areas of Zayanderood watershed basin's stations
and by using Kendal-man method, Precipitation and temperature
procedure changes have been analyzed in this basin. According to
obtained results, there was not any noticeable decrease or increase
procedure in Precipitation and annual temperature in the basin during
this period. However, regarding to Precipitation, a noticeable
decrease and increase have been observed in small part of western
and some parts of eastern and southern basin, respectively.
Furthermore, the investigation of annual temperature procedure has
shown that a noticeable increase has been observed in some parts of
western and eastern basin, and also a noticeable increasing procedure
of temperature in the central parts of metropolitan Esfahan can be
observed.
Abstract: In this paper, a benchmarking framework is presented
for the performance assessment of irrigations systems. Firstly, a data
envelopment analysis (DEA) is applied to measure the technical
efficiency of irrigation systems. This method, based on linear
programming, aims to determine a consistent efficiency ranking of
irrigation systems in which known inputs, such as water volume
supplied and total irrigated area, and a given output corresponding to
the total value of irrigation production are taken into account
simultaneously. Secondly, in order to examine the irrigation
efficiency in more detail, a cross – system comparison is elaborated
using a performance indicators set selected by IWMI. The above
methodologies were applied in Thessaloniki plain, located in
Northern Greece while the results of the application are presented and
discussed. The conjunctive use of DEA and performance indicators
seems to be a very useful tool for efficiency assessment and
identification of best practices in irrigation systems management.
Abstract: The paper presents an approach for handling uncertain
information in deductive databases using multivalued logics. Uncertainty
means that database facts may be assigned logical values other
than the conventional ones - true and false. The logical values represent
various degrees of truth, which may be combined and propagated
by applying the database rules. A corresponding multivalued database
semantics is defined. We show that it extends successful conventional
semantics as the well-founded semantics, and has a polynomial time
data complexity.
Abstract: This research aims to develop and evaluate a training
course to promote learning activities of 2nd year, Suan Sunandha
Rajabhat University, faculty of education students using multiple
intelligences theory. The process is divided into two phases: Phase 1
development of training course to promote learning activities
consisting of principles, objectives of the course, structure, training
duration, content, training materials, training activities, media
training, monitoring, measurement and evaluation quality of the
course. Phase 2 evaluation efficiency of training course was to use
the improved curriculum with experimental group which is 2nd year,
Suan Sunandha Rajabhat University, faculty of education students
was drawn randomly 152 students. The experimental pattern was
randomized Control Group Pre-Test Post-Test Design, Analysis Data
by t-Test with the software SPFSS for Windows. Research has shown
that: 1). the ability of teaching and learning according to the theory of
multiple intelligences after training is higher than before training
significantly in statistic at .01 level, 2). The satisfaction of students
to the training courses was overall at the highest level.
Abstract: In this paper, the implementation of a rule-based
intuitive reasoner is presented. The implementation included two
parts: the rule induction module and the intuitive reasoner. A large
weather database was acquired as the data source. Twelve weather
variables from those data were chosen as the “target variables"
whose values were predicted by the intuitive reasoner. A “complex"
situation was simulated by making only subsets of the data available
to the rule induction module. As a result, the rules induced were
based on incomplete information with variable levels of certainty.
The certainty level was modeled by a metric called "Strength of
Belief", which was assigned to each rule or datum as ancillary
information about the confidence in its accuracy. Two techniques
were employed to induce rules from the data subsets: decision tree
and multi-polynomial regression, respectively for the discrete and the
continuous type of target variables. The intuitive reasoner was tested
for its ability to use the induced rules to predict the classes of the
discrete target variables and the values of the continuous target
variables. The intuitive reasoner implemented two types of
reasoning: fast and broad where, by analogy to human thought, the
former corresponds to fast decision making and the latter to deeper
contemplation. . For reference, a weather data analysis approach
which had been applied on similar tasks was adopted to analyze the
complete database and create predictive models for the same 12
target variables. The values predicted by the intuitive reasoner and
the reference approach were compared with actual data. The intuitive
reasoner reached near-100% accuracy for two continuous target
variables. For the discrete target variables, the intuitive reasoner
predicted at least 70% as accurately as the reference reasoner. Since
the intuitive reasoner operated on rules derived from only about 10%
of the total data, it demonstrated the potential advantages in dealing
with sparse data sets as compared with conventional methods.
Abstract: This paper presents the development of an electricity simulation model taking into account electrical network constraints, applied on the Belgian power system. The base of the model is optimizing an extensive Unit Commitment (UC) problem through the use of Mixed Integer Linear Programming (MILP). Electrical constraints are incorporated through the implementation of a DC load flow. The model encloses the Belgian power system in a 220 – 380 kV high voltage network (i.e., 93 power plants and 106 nodes). The model features the use of pumping storage facilities as well as the inclusion of spinning reserves in a single optimization process. Solution times of the model stay below reasonable values.
Abstract: In this paper, we present a novel statistical approach to
corpus-based speech synthesis. Classically, phonetic information is
defined and considered as acoustic reference to be respected. In this
way, many studies were elaborated for acoustical unit classification.
This type of classification allows separating units according to their
symbolic characteristics. Indeed, target cost and concatenation cost
were classically defined for unit selection.
In Corpus-Based Speech Synthesis System, when using large text
corpora, cost functions were limited to a juxtaposition of symbolic
criteria and the acoustic information of units is not exploited in the
definition of the target cost.
In this manuscript, we token in our consideration the unit phonetic
information corresponding to acoustic information. This would be realized
by defining a probabilistic linguistic Bi-grams model basically
used for unit selection. The selected units would be extracted from
the English TIMIT corpora.
Abstract: Minor law breaking seems more and more to be a part
of adolescence behavior. An important risk factor which seems to
influence delinquency appears to be the socio-economic one.
According to Romanian statistics, during the first six months of 2012,
1,378 minors have committed various crimes, the most common
being theft, sexual offenses and violent assaults. Drug-related
offenses did not reach the gravity of those from high income
countries of the European Union, but have a continuous upward
during the last years.
The aim of our research was to examine whether delinquency in
adolescence is correlated to mental disorders or socio-economic and
familial factors. Forensic psychiatric expertise was performed to 79
adolescents who committed offenses between 01 January 2012 and
31 December 2012. Teenagers, with ages between 12 and 17, were
examined by day hospitalization in the University Clinic of
Psychiatry Craiova.
Abstract: This paper focuses on testing database of existing
information system. At the beginning we describe the basic problems
of implemented databases, such as data redundancy, poor design of
database logical structure or inappropriate data types in columns of
database tables. These problems are often the result of incorrect
understanding of the primary requirements for a database of an
information system. Then we propose an algorithm to compare the
conceptual model created from vague requirements for a database
with a conceptual model reconstructed from implemented database.
An algorithm also suggests steps leading to optimization of
implemented database. The proposed algorithm is verified by an
implemented prototype. The paper also describes a fuzzy system
which works with the vague requirements for a database of an
information system, procedure for creating conceptual from vague
requirements and an algorithm for reconstructing a conceptual model
from implemented database.
Abstract: Saudi Arabia in recent years has seen drastic increase
in traffic related crashes. With population of over 29 million, Saudi
Arabia is considered as a fast growing and emerging economy. The
rapid population increase and economic growth has resulted in rapid
expansion of transportation infrastructure, which has led to increase
in road crashes. Saudi Ministry of Interior reported more than 7,000
people killed and 68,000 injured in 2011 ranking Saudi Arabia to be
one of the worst worldwide in traffic safety. The traffic safety issues
in the country also result in distress to road users and cause and
economic loss exceeding 3.7 billion Euros annually. Keeping this in
view, the researchers in Saudi Arabia are investigating ways to
improve traffic safety conditions in the country. This paper presents a
multilevel approach to collect traffic safety related data required to do
traffic safety studies in the region. Two highway corridors including
King Fahd Highway 39 kilometre and Gulf Cooperation Council
Highway 42 kilometre long connecting the cities of Dammam and
Khobar were selected as a study area. Traffic data collected included
traffic counts, crash data, travel time data, and speed data. The
collected data was analysed using geographic information system to
evaluate any correlation. Further research is needed to investigate the
effectiveness of traffic safety related data when collected in a
concerted effort.
Abstract: A new design of a planar passive T-micromixer with fin-shaped baffles in the mixing channel is presented. The mixing efficiency and the level of pressure loss in the channel have been investigated by numerical simulations in the range of Reynolds number (Re) 1 to 50. A Mixing index (Mi) has been defined to quantify the mixing efficiency, which results over 85% at both ends of the Re range, what demonstrates the micromixer can enhance mixing using the mechanisms of diffusion (lower Re) and convection (higher Re). Three geometric dimensions: radius of baffle, baffles pitch and height of the channel define the design parameters, and the mixing index and pressure loss are the performance parameters used to optimize the micromixer geometry with a multi-criteria optimization method. The Pareto front of designs with the optimum trade-offs, maximum mixing index with minimum pressure loss, is obtained. Experiments for qualitative and quantitative validation have been implemented.
Abstract: Several models of vulnerability assessment have been proposed. The selection of one of these models depends on the objectives of the study. The classical methodologies for seismic vulnerability analysis, as a part of seismic risk analysis, have been formulated with statistical criteria based on a rapid observation. The information relating to the buildings performance is statistically elaborated. In this paper, we use the European Macroseismic Scale EMS-98 to define the relationship between damage and macroseismic intensity to assess the seismic vulnerability. Applying to Algiers area, the first step is to identify building typologies and to assign vulnerability classes. In the second step, damages are investigated according to EMS-98.
Abstract: Disordered function of maniphalanx and difficulty with
ambulation will occur insofar as a human has a failure in the spinal
marrow. Cervical spondylotic myelopathy as one of the myelopathy
emanates from not only external factors but also increased age. In
addition, the diacrisis is difficult since cervical spondylotic
myelopathy is evaluated by a doctor-s neurological remark and
imaging findings. As a quantitative method for measuring the degree
of disability, hand-operated triangle step test (for short, TST) has
formulated. In this research, a full automatic triangle step counter
apparatus is designed and developed to measure the degree of
disability in an accurate fashion according to the principle of TST. The
step counter apparatus whose shape is a low triangle pole displays the
number of stepping upon each corner. Furthermore, the apparatus has
two modes of operation. Namely, one is for measuring the degree of
disability and the other for rehabilitation exercise. In terms of
usefulness, clinical practice should be executed before too long.
Abstract: The ElectroEncephaloGram (EEG) is useful for
clinical diagnosis and biomedical research. EEG signals often
contain strong ElectroOculoGram (EOG) artifacts produced
by eye movements and eye blinks especially in EEG recorded
from frontal channels. These artifacts obscure the underlying
brain activity, making its visual or automated inspection
difficult. The goal of ocular artifact removal is to remove
ocular artifacts from the recorded EEG, leaving the underlying
background signals due to brain activity. In recent times,
Independent Component Analysis (ICA) algorithms have
demonstrated superior potential in obtaining the least
dependent source components. In this paper, the independent
components are obtained by using the JADE algorithm (best
separating algorithm) and are classified into either artifact
component or neural component. Neural Network is used for
the classification of the obtained independent components.
Neural Network requires input features that exactly represent
the true character of the input signals so that the neural
network could classify the signals based on those key
characters that differentiate between various signals. In this
work, Auto Regressive (AR) coefficients are used as the input
features for classification. Two neural network approaches
are used to learn classification rules from EEG data. First, a
Polynomial Neural Network (PNN) trained by GMDH (Group
Method of Data Handling) algorithm is used and secondly,
feed-forward neural network classifier trained by a standard
back-propagation algorithm is used for classification and the
results show that JADE-FNN performs better than JADEPNN.
Abstract: This study aimed at developing visualization tools for integrating CloudSat images and Water Vapor Satellite images. KML was used for integrating data from CloudSat Satellite and GMS-6 Water Vapor Satellite. CloudSat 2D images were transformed into 3D polygons in order to achieve 3D images. Before overlaying the images on Google Earth, GMS-6 water vapor satellite images had to be rescaled into linear images. Web service was developed using webMathematica. Shoreline from GMS-6 images was compared with shoreline from LandSat images on Google Earth for evaluation. The results showed that shoreline from GMS-6 images was highly matched with the shoreline in LandSat images from Google Earth. For CloudSat images, the visualizations were compared with GMS-6 images on Google Earth. The results showed that CloudSat and GMS-6 images were highly correlated.
Abstract: The objective of our work is to develop a new approach for discovering knowledge from a large mass of data, the result of applying this approach will be an expert system that will serve as diagnostic tools of a phenomenon related to a huge information system. We first recall the general problem of learning Bayesian network structure from data and suggest a solution for optimizing the complexity by using organizational and optimization methods of data. Afterward we proposed a new heuristic of learning a Multi-Entities Bayesian Networks structures. We have applied our approach to biological facts concerning hereditary complex illnesses where the literatures in biology identify the responsible variables for those diseases. Finally we conclude on the limits arched by this work.