Abstract: The effects of basil and/or chamomile seed
supplementation on the growth of Hubbard broiler chicks were
evaluated. The antioxidant effects of these supplements were also
assessed. 120 1-day-old broiler chicks were randomly divided into
four equal groups. The control group (group 1) was fed a basal diet
(BD) without supplementation. Groups 2, 3, and 4 were fed the BD
supplemented with 10g basil, 10g chamomile, and 5g basil plus 5g
chamomile per kg of food, respectively. Basil supplementation alone
or in combination with chamomile non-significantly (P≥0.05)
increased final body weight (3.2% and 0.3%, respectively) and
weight gain (3.5% and 3.6%, respectively) over the experimental
period. Chamomile supplementation alone non-significantly (P≥0.05)
reduced final body weight and weight gain over the experimental
period by 1.7% and 1.7%, respectively. In comparison to the control
group, herbal seed supplementation reduced feed intake and
improved the feed conversion and protein efficiency ratios. In
general, basil seed supplementation stimulated chicken growth and
improved the feed efficiency more effectively than chamomile seed
supplementation. The antioxidant activities of basil and/or chamomile
supplementation were examined in the thymus, bursa, and spleen. In
chickens that received supplements, the level of malondialdehyde
was significantly decreased, whereas the activities of glutathione,
superoxide dismutase, and catalase were significantly increased
(P
Abstract: Estimation of model parameters is necessary to predict
the behavior of a system. Model parameters are estimated using
optimization criteria. Most algorithms use historical data to estimate
model parameters. The known target values (actual) and the output
produced by the model are compared. The differences between the
two form the basis to estimate the parameters. In order to compare
different models developed using the same data different criteria are
used. The data obtained for short scale projects are used here. We
consider software effort estimation problem using radial basis
function network. The accuracy comparison is made using various
existing criteria for one and two predictors. Then, we propose a new
criterion based on linear least squares for evaluation and compared
the results of one and two predictors. We have considered another
data set and evaluated prediction accuracy using the new criterion.
The new criterion is easy to comprehend compared to single statistic.
Although software effort estimation is considered, this method is
applicable for any modeling and prediction.
Abstract: An analysis is carried out to investigate the effect of
magnetic field and heat source on the steady boundary layer flow and
heat transfer of a Casson nanofluid over a vertical cylinder stretching
exponentially along its radial direction. Using a similarity
transformation, the governing mathematical equations, with the
boundary conditions are reduced to a system of coupled, non –linear
ordinary differential equations. The resulting system is solved
numerically by the fourth order Runge – Kutta scheme with shooting
technique. The influence of various physical parameters such as
Reynolds number, Prandtl number, magnetic field, Brownian motion
parameter, thermophoresis parameter, Lewis number and the natural
convection parameter are presented graphically and discussed for non
– dimensional velocity, temperature and nanoparticle volume
fraction. Numerical data for the skin – friction coefficient, local
Nusselt number and the local Sherwood number have been tabulated
for various parametric conditions. It is found that the local Nusselt
number is a decreasing function of Brownian motion parameter Nb
and the thermophoresis parameter Nt.
Abstract: This study attempts to elicit the perceptions and
attitudes of EFL learners of the Preparatory Year Program at KSU
towards dialogue journal writing as an EFL learning strategy. The
descriptive research design used incorporated both qualitative and
quantitative instruments to accomplish the objectives of the study. A
learners’ attitude questionnaire and follow-up interviewswith learners
from a randomly selected representative sample of the participants
were employed. The participants were 55 female Saudi university
students in the Preparatory Year Program at King Saud University.
The analysis of the results indicated that the PYP learners had highly
positive attitudes towards dialogue journal writing in their EFL
classes and positive perceptions of the benefits of the use of dialogue
journal writing as an EFL learning strategy. The results also revealed
that dialogue journals are considered an effective EFL learning
strategy since they fulfill various needs for both learners and
instructors. Interestingly, the analysis of the results also revealed that
Saudi university level students tend to write about personal topics in
their dialogue journals more than academic ones.
Abstract: Background and aim: It has not been well studied
whether fentanyl-thiopental (FT) is effective and safe for PSA in
orthopedic procedures in Emergency Department (ED). The aim of
this trial was to evaluate the effectiveness of intravenous FT versus
fentanyl-midazolam (FM) in patients who suffered from shoulder
dislocation or distal radial fracture-dislocation.
Methods: In this randomized double-blinded study, Seventy-six
eligible patients were entered the study and randomly received
intravenous FT or FM. The success rate, onset of action and recovery
time, pain score, physicians’ satisfaction and adverse events were
assessed and recorded by treating emergency physicians. The
statistical analysis was intention to treat.
Results: The success rate after administrating loading dose in FT
group was significantly higher than FM group (71.7% vs. 48.9%,
p=0.04); however, the ultimate unsuccessful rate after 3 doses of
drugs in the FT group was higher than the FM group (3 to 1) but it
did not reach to significant level (p=0.61). Despite near equal onset
of action time in two study group (P=0.464), the recovery period in
patients receiving FT was markedly shorter than FM group
(P
Abstract: This paper presents an evolutionary algorithm for
solving multi-objective optimization problems-based artificial neural
network (ANN). The multi-objective evolutionary algorithm used in
this study is genetic algorithm while ANN used is radial basis
function network (RBFN). The proposed algorithm named memetic
elitist Pareto non-dominated sorting genetic algorithm-based RBFN
(MEPGAN). The proposed algorithm is implemented on medical
diseases problems. The experimental results indicate that the
proposed algorithm is viable, and provides an effective means to
design multi-objective RBFNs with good generalization capability
and compact network structure. This study shows that MEPGAN
generates RBFNs coming with an appropriate balance between
accuracy and simplicity, comparing to the other algorithms found in
literature.
Abstract: It is difficult to study the effect of various variables on
cycle fitting through actual experiment. To overcome such difficulty,
the forward dynamics of a musculoskeletal model was applied to cycle
fitting in this study. The measured EMG data weres compared with the
muscle activities of the musculoskeletal model through forward
dynamics. EMG data were measured from five cyclists who do not
have musculoskeletal diseases during three minutes pedaling with a
constant load (150 W) and cadence (90 RPM). The muscles used for
the analysis were the Vastus Lateralis (VL), Tibialis Anterior (TA),
Bicep Femoris (BF), and Gastrocnemius Medial (GM). Person’s
correlation coefficients of the muscle activity patterns, the peak timing
of the maximum muscle activities, and the total muscle activities were
calculated and compared. BIKE3D model of AnyBody (Anybodytech,
Denmark) was used for the musculoskeletal model simulation. The
comparisons of the actual experiments with the simulation results
showed significant correlations in the muscle activity patterns (VL:
0.789, TA: 0.503, BF: 0.468, GM: 0.670). The peak timings of the
maximum muscle activities were distributed at particular phases. The
total muscle activities were compared with the normalized muscle
activities, and the comparison showed about 10% difference in the VL
(+10%), TA (+9.7%), and BF (+10%), excluding the GM (+29.4%).
Thus, it can be concluded that muscle activities of model &
experiment showed similar results. The results of this study indicated
that it was possible to apply the simulation of further improved
musculoskeletal model to cycle fitting.
Abstract: Currently, thorium fuel has been especially noticed
because of its proliferation resistance than long half-life alpha emitter
minor actinides, breeding capability in fast and thermal neutron flux
and mono-isotopic naturally abundant. In recent years, efficiency of
minor actinide burning up in PWRs has been investigated. Hence, a
minor actinide-contained thorium based fuel matrix can confront both
proliferation resistance and nuclear waste depletion aims. In the
present work, minor actinide depletion rate in a CANDU-type nuclear
core modeled using MCNP code has been investigated. The obtained
effects of minor actinide load as mixture of thorium fuel matrix on
the core neutronics has been studied with comparing presence and
non-presence of minor actinide component in the fuel matrix.
Depletion rate of minor actinides in the MA-contained fuel has been
calculated using different power loads. According to the obtained
computational data, minor actinide loading in the modeled core
results in more negative reactivity coefficients. The MA-contained
fuel achieves less radial peaking factor in the modeled core. The
obtained computational results showed 140 kg of 464 kg initial load
of minor actinide has been depleted in during a 6-year burn up in 10
MW power.
Abstract: Red blood cells (RBC) are the most common types of
blood cells and are the most intensively studied in cell biology. The
lack of RBCs is a condition in which the amount of hemoglobin level
is lower than normal and is referred to as “anemia”. Abnormalities in
RBCs will affect the exchange of oxygen. This paper presents a
comparative study for various techniques for classifying the RBCs as
normal or abnormal (anemic) using WEKA. WEKA is an open
source consists of different machine learning algorithms for data
mining applications. The algorithms tested are Radial Basis Function
neural network, Support vector machine, and K-Nearest Neighbors
algorithm. Two sets of combined features were utilized for
classification of blood cells images. The first set, exclusively consist
of geometrical features, was used to identify whether the tested blood
cell has a spherical shape or non-spherical cells. While the second
set, consist mainly of textural features was used to recognize the
types of the spherical cells. We have provided an evaluation based on
applying these classification methods to our RBCs image dataset
which were obtained from Serdang Hospital - Malaysia, and
measuring the accuracy of test results. The best achieved
classification rates are 97%, 98%, and 79% for Support vector
machines, Radial Basis Function neural network, and K-Nearest
Neighbors algorithm respectively.
Abstract: Map is a powerful and convenient tool in helping us to
navigate to different places, but the use of indirect devices often
makes its usage cumbersome. This study intends to propose a new
map navigation dialogue that uses hand gesture. A set of dialogue
was developed from users’ perspective to provide users complete
freedom for panning, zooming, rotate, tilt and find direction
operations. A participatory design experiment was involved here
where one hand gesture and two hand gesture dialogues had been
analysed in the forms of hand gestures to develop a set of usable
dialogues. The major finding was that users prefer one-hand gesture
compared to two-hand gesture in map navigation.
Abstract: Urban public spaces are sutured with a range of
surveillance and sensor technologies that claim to enable new forms
of ‘data based citizen participation’, but also increase the tendency
for ‘function-creep’, whereby vast amounts of data are gathered,
stored and analysed in a broad application of urban surveillance. This
kind of monitoring and capacity for surveillance connects with
attempts by civic authorities to regulate, restrict, rebrand and reframe
urban public spaces. A direct consequence of the increasingly
security driven, policed, privatised and surveilled nature of public
space is the exclusion or ‘unfavourable inclusion’ of those considered
flawed and unwelcome in the ‘spectacular’ consumption spaces of
many major urban centres. In the name of urban regeneration,
programs of securitisation, ‘gentrification’ and ‘creative’ and ‘smart’
city initiatives refashion public space as sites of selective inclusion
and exclusion. In this context of monitoring and control procedures,
in particular, children and young people’s use of space in parks,
neighbourhoods, shopping malls and streets is often viewed as a
threat to the social order, requiring various forms of remedial action.
This paper suggests that cities, places and spaces and those who
seek to use them, can be resilient in working to maintain and extend
democratic freedoms and processes enshrined in Marshall’s concept
of citizenship, calling sensor and surveillance systems to account.
Such accountability could better inform the implementation of public
policy around the design, build and governance of public space and
also understandings of urban citizenship in the sensor saturated urban
environment.
Abstract: This paper proposes a backward/forward sweep
method to analyze the power flow in radial distribution systems. The
distribution system has radial structure and high R/X ratios. So the
newton-raphson and fast decoupled methods are failed with
distribution system. The proposed method presents a load flow study
using backward/forward sweep method, which is one of the most
effective methods for the load-flow analysis of the radial distribution
system. By using this method, power losses for each bus branch and
voltage magnitudes for each bus node are determined. This method
has been tested on IEEE 33-bus radial distribution system and
effective results are obtained using MATLAB.
Abstract: Predicting earthquakes is an important issue in the
study of geography. Accurate prediction of earthquakes can help
people to take effective measures to minimize the loss of personal
and economic damage, such as large casualties, destruction of
buildings and broken of traffic, occurred within a few seconds.
United States Geological Survey (USGS) science organization
provides reliable scientific information about Earthquake Existed
throughout history & the Preliminary database from the National
Center Earthquake Information (NEIC) show some useful factors to
predict an earthquake in a seismic area like Aleutian Arc in the U.S.
state of Alaska. The main advantage of this prediction method that it
does not require any assumption, it makes prediction according to the
future evolution of the object's time series. The article compares
between simulation data result from trained BP and RBF neural
network versus actual output result from the system calculations.
Therefore, this article focuses on analysis of data relating to real
earthquakes. Evaluation results show better accuracy and higher
speed by using radial basis functions (RBF) neural network.
Abstract: This paper proposes the designing direct adaptive
neural controller to apply for a class of a nonlinear pendulum
dynamic system. The radial basis function (RBF) neural adaptive
controller is robust in presence of external and internal uncertainties.
Both the effectiveness of the controller and robustness against
disturbances are importance of this paper. The simulation results
show the promising performance of the proposed controller.
Abstract: The handwriting is a physical demonstration of a
complex cognitive process learnt by man since his childhood. People
with disabilities or suffering from various neurological diseases are
facing so many difficulties resulting from problems located at the
muscle stimuli (EMG) or signals from the brain (EEG) and which
arise at the stage of writing. The handwriting velocity of the same
writer or different writers varies according to different criteria: age,
attitude, mood, writing surface, etc. Therefore, it is interesting to
reconstruct an experimental basis records taking, as primary
reference, the writing speed for different writers which would allow
studying the global system during handwriting process. This paper
deals with a new approach of the handwriting system modeling based
on the velocity criterion through the concepts of artificial neural
networks, precisely the Radial Basis Functions (RBF) neural
networks. The obtained simulation results show a satisfactory
agreement between responses of the developed neural model and the
experimental data for various letters and forms then the efficiency of
the proposed approaches.
Abstract: reliability-based methodology for the assessment
and evaluation of reinforced concrete (R/C) structural elements of
concrete structures is presented herein. The results of the reliability
analysis and assessment for R/C structural elements were verified by
the results obtained through deterministic methods. The outcomes of
the reliability-based analysis were compared against currently
adopted safety limits that are incorporated in the reliability indices
β’s, according to international standards and codes. The methodology
is based on probabilistic analysis using reliability concepts and
statistics of the main random variables that are relevant to the subject
matter, and for which they are to be used in the performance-function
equation(s) associated with the structural elements under study.
These methodology techniques can result in reliability index β, which
is commonly known as the reliability index or reliability measure
value that can be utilized to assess and evaluate the safety, human
risk, and functionality of the structural component. Also, these
methods can result in revised partial safety factor values for certain
target reliability indices that can be used for the purpose of
redesigning the R/C elements of the building and in which they could
assist in considering some other remedial actions to improve the
safety and functionality of the member.
Abstract: Load Forecasting plays a key role in making today's
and future's Smart Energy Grids sustainable and reliable. Accurate
power consumption prediction allows utilities to organize in advance
their resources or to execute Demand Response strategies more
effectively, which enables several features such as higher
sustainability, better quality of service, and affordable electricity
tariffs. It is easy yet effective to apply Load Forecasting at larger
geographic scale, i.e. Smart Micro Grids, wherein the lower available
grid flexibility makes accurate prediction more critical in Demand
Response applications. This paper analyses the application of
short-term load forecasting in a concrete scenario, proposed within the
EU-funded GreenCom project, which collect load data from single
loads and households belonging to a Smart Micro Grid. Three
short-term load forecasting techniques, i.e. linear regression, artificial
neural networks, and radial basis function network, are considered,
compared, and evaluated through absolute forecast errors and training
time. The influence of weather conditions in Load Forecasting is also
evaluated. A new definition of Gain is introduced in this paper, which
innovatively serves as an indicator of short-term prediction
capabilities of time spam consistency. Two models, 24- and
1-hour-ahead forecasting, are built to comprehensively compare these
three techniques.
Abstract: One of the major goals of Spoken Dialog Systems
(SDS) is to understand what the user utters.
In the SDS domain, the Spoken Language Understanding (SLU)
Module classifies user utterances by means of a pre-definite
conceptual knowledge. The SLU module is able to recognize only the
meaning previously included in its knowledge base. Due the vastity
of that knowledge, the information storing is a very expensive
process.
Updating and managing the knowledge base are time-consuming
and error-prone processes because of the rapidly growing number of
entities like proper nouns and domain-specific nouns. This paper
proposes a solution to the problem of Name Entity Recognition
(NER) applied to a SDS domain. The proposed solution attempts to
automatically recognize the meaning associated with an utterance by
using the PANKOW (Pattern based Annotation through Knowledge
On the Web) method at runtime.
The method being proposed extracts information from the Web to
increase the SLU knowledge module and reduces the development
effort. In particular, the Google Search Engine is used to extract
information from the Facebook social network.
Abstract: Control of a semi-batch polymerization reactor using
an adaptive radial basis function (RBF) neural network method is
investigated in this paper. A neural network inverse model is used to
estimate the valve position of the reactor; this method can identify the
controlled system with the RBF neural network identifier. The
weights of the adaptive PID controller are timely adjusted based on
the identification of the plant and self-learning capability of RBFNN.
A PID controller is used in the feedback control to regulate the actual
temperature by compensating the neural network inverse model
output. Simulation results show that the proposed control has strong
adaptability, robustness and satisfactory control performance and the
nonlinear system is achieved.
Abstract: In this paper, an analytical study is made for the dynamic behavior of human brain tissue under transient loading. In this analytical model the Mooney-Rivlin constitutive law is coupled with visco-elastic constitutive equations to take into account both the nonlinear and time-dependent mechanical behavior of brain tissue. Five ordinary differential equations representing the relationships of five main parameters (radial stress, circumferential stress, radial strain, circumferential strain, and particle velocity) are obtained by using the characteristic method to transform five partial differential equations (two continuity equations, one motion equation, and two constitutive equations). Analytical expressions of the attenuation properties for spherical wave in brain tissue are analytically derived. Numerical results are obtained based on the five ordinary differential equations. The mechanical responses (particle velocity and stress) of brain are compared at different radii including 5, 6, 10, 15 and 25 mm under four different input conditions. The results illustrate that loading curves types of the particle velocity significantly influences the stress in brain tissue. The understanding of the influence by the input loading cures can be used to reduce the potentially injury to brain under head impact by designing protective structures to control the loading curves types.