Abstract: Game theory is the study of how people interact and
make decisions to handle competitive situations. It has mainly been
developed to study decision making in complex situations. Humans
routinely alter their behaviour in response to changes in their social
and physical environment. As a consequence, the outcomes of
decisions that depend on the behaviour of multiple decision makers
are difficult to predict and require highly adaptive decision-making
strategies. In addition to the decision makers may have preferences
regarding consequences to other individuals and choose their actions
to improve or reduce the well-being of others. Nash equilibrium is a
fundamental concept in the theory of games and the most widely used
method of predicting the outcome of a strategic interaction in the
social sciences. A Nash Equilibrium exists when there is no unilateral
profitable deviation from any of the players involved. On the other
hand, no player in the game would take a different action as long as
every other player remains the same.
Abstract: The aim of this paper is to select the most accurate
forecasting method for predicting the future values of the
unemployment rate in selected European countries. In order to do so,
several forecasting techniques adequate for forecasting time series
with trend component, were selected, namely: double exponential
smoothing (also known as Holt`s method) and Holt-Winters` method
which accounts for trend and seasonality. The results of the empirical
analysis showed that the optimal model for forecasting
unemployment rate in Greece was Holt-Winters` additive method. In
the case of Spain, according to MAPE, the optimal model was double
exponential smoothing model. Furthermore, for Croatia and Italy the
best forecasting model for unemployment rate was Holt-Winters`
multiplicative model, whereas in the case of Portugal the best model
to forecast unemployment rate was Double exponential smoothing
model. Our findings are in line with European Commission
unemployment rate estimates.
Abstract: Due to the rapid increase of Internet, web opinion
sources dynamically emerge which is useful for both potential
customers and product manufacturers for prediction and decision
purposes. These are the user generated contents written in natural
languages and are unstructured-free-texts scheme. Therefore, opinion
mining techniques become popular to automatically process customer
reviews for extracting product features and user opinions expressed
over them. Since customer reviews may contain both opinionated and
factual sentences, a supervised machine learning technique applies
for subjectivity classification to improve the mining performance. In
this paper, we dedicate our work is the task of opinion
summarization. Therefore, product feature and opinion extraction is
critical to opinion summarization, because its effectiveness
significantly affects the identification of semantic relationships. The
polarity and numeric score of all the features are determined by
Senti-WordNet Lexicon. The problem of opinion summarization
refers how to relate the opinion words with respect to a certain
feature. Probabilistic based model of supervised learning will
improve the result that is more flexible and effective.
Abstract: Absorptive capacity generally facilitates the adoption
of innovation. How does this relationship change when economic
return is not the sole driver of innovation uptake? We investigate
whether absorptive capacity facilitates the adoption of green
innovation based on a survey of 79 construction companies in
Scotland. Based on the results of multiple regression analyses, we
confirm that existing knowledge utilisation (EKU), knowledge
building (KB) and external knowledge acquisition (EKA) are
significant predictors of green process GP), green administrative
(GA) and green technical innovation (GT), respectively. We discuss
the implications for theories of innovation adoption and knowledge
enhancement associated with environmentally-friendly practices.
Abstract: Livestock is one of the fastest-growing sectors in
agriculture. If carefully managed, have potential opportunities for
economic growth, food sovereignty and food security. In this study
we mainly analyse and compare long-term i.e. for year 2030 climate
variability impact on predicted productivity of meat i.e. beef, mutton
and poultry for the Kingdom of Saudi Arabia w.r.t three factors i.e. i)
climatic-change vulnerability ii) CO2 fertilization and iii) water
scarcity and compare the results with two countries of the region i.e.
Iraq and Yemen. We do the analysis using data from diverse sources,
which was extracted, transformed and integrated before usage. The
collective impact of the three factors had an overall negative effect on
the production of meat for all the three countries, with adverse impact
on Iraq. High similarity was found between CO2 fertilization
(effecting animal fodder) and water scarcity i.e. higher than that
between production of beef and mutton for the three countries
considered. Overall, the three factors do not seem to be favorable for
the three Middle-East countries considered. This points to possibility
of a vegetarian year 2030 based on dependency on indigenous livestock
population.
Abstract: The Cone Penetration Test (CPT) is a common in-situ
test which generally investigates a much greater volume of soil more
quickly than possible from sampling and laboratory tests. Therefore,
it has the potential to realize both cost savings and assessment of soil
properties rapidly and continuously. The principle objective of this
paper is to demonstrate the feasibility and efficiency of using
artificial neural networks (ANNs) to predict the soil angle of internal
friction (Φ) and the soil modulus of elasticity (E) from CPT results
considering the uncertainties and non-linearities of the soil. In
addition, ANNs are used to study the influence of different
parameters and recommend which parameters should be included as
input parameters to improve the prediction. Neural networks discover
relationships in the input data sets through the iterative presentation
of the data and intrinsic mapping characteristics of neural topologies.
General Regression Neural Network (GRNN) is one of the powerful
neural network architectures which is utilized in this study. A large
amount of field and experimental data including CPT results, plate
load tests, direct shear box, grain size distribution and calculated data
of overburden pressure was obtained from a large project in the
United Arab Emirates. This data was used for the training and the
validation of the neural network. A comparison was made between
the obtained results from the ANN's approach, and some common
traditional correlations that predict Φ and E from CPT results with
respect to the actual results of the collected data. The results show
that the ANN is a very powerful tool. Very good agreement was
obtained between estimated results from ANN and actual measured
results with comparison to other correlations available in the
literature. The study recommends some easily available parameters
that should be included in the estimation of the soil properties to
improve the prediction models. It is shown that the use of friction
ration in the estimation of Φ and the use of fines content in the
estimation of E considerable improve the prediction models.
Abstract: In this paper, an effective non-destructive, noninvasive
approach for leak detection was proposed. The process relies
on analyzing thermal images collected by an IR viewer device that
captures thermo-grams. In this study a statistical analysis of the
collected thermal images of the ground surface along the expected
leak location followed by a visual inspection of the thermo-grams
was performed in order to locate the leak. In order to verify the
applicability of the proposed approach the predicted leak location
from the developed approach was compared with the real leak
location. The results showed that the expected leak location was
successfully identified with an accuracy of more than 95%.
Abstract: Experimental & numeral study of temperature
distribution during milling process, is important in milling quality
and tools life aspects. In the present study the milling cross-section
temperature is determined by using Artificial Neural Networks
(ANN) according to the temperature of certain points of the work
piece and the point specifications and the milling rotational speed of
the blade. In the present work, at first three-dimensional model of the
work piece is provided and then by using the Computational Heat
Transfer (CHT) simulations, temperature in different nods of the
work piece are specified in steady-state conditions. Results obtained
from CHT are used for training and testing the ANN approach. Using
reverse engineering and setting the desired x, y, z and the milling
rotational speed of the blade as input data to the network, the milling
surface temperature determined by neural network is presented as
output data. The desired points temperature for different milling
blade rotational speed are obtained experimentally and by
extrapolation method for the milling surface temperature is obtained
and a comparison is performed among the soft programming ANN,
CHT results and experimental data and it is observed that ANN soft
programming code can be used more efficiently to determine the
temperature in a milling process.
Abstract: This study presents the moisture variations of
unbound layers from April 2012 to January 2014 in the Interstate 40
(I-40) pavement section in New Mexico. Three moisture probes were
installed at different layers inside the pavement which measure the
continuous moisture variations of the unbound layers. Data show that
the moisture contents of unbound layers are typically constant
throughout the day and month unless there is rainfall. Moisture
contents of all unbound layers change with rainfall. Change in ground
water table may affect the moisture content of unbound layers which
has not been investigated in this study. In addition, the Level 3
predictions of moisture contents using the Pavement Mechanistic-
Empirical (ME) Design software were compared and found quite
reasonable. However, results presented in the current study may not
be applicable for pavement in other regions.
Abstract: Our goal is development of an algorithm capable of
predicting the directional trend of the Standard and Poor’s 500 index
(S&P 500). Extensive research has been published attempting to
predict different financial markets using historical data testing on an
in-sample and trend basis, with many authors employing excessively
complex mathematical techniques. In reviewing and evaluating these
in-sample methodologies, it became evident that this approach was
unable to achieve sufficiently reliable prediction performance for
commercial exploitation. For these reasons, we moved to an out-ofsample
strategy based on linear regression analysis of an extensive
set of financial data correlated with historical closing prices of the
S&P 500. We are pleased to report a directional trend accuracy of
greater than 55% for tomorrow (t+1) in predicting the S&P 500.
Abstract: A computational fluid dynamics (CFD) model is
developed for rechargeable non-aqueous electrolyte lithium-air
batteries with a partial opening for oxygen supply to the cathode.
Multi-phase transport phenomena occurred in the battery are
considered, including dissolved lithium ions and oxygen gas in the
liquid electrolyte, solid-phase electron transfer in the porous
functional materials and liquid-phase charge transport in the
electrolyte. These transport processes are coupled with the
electrochemical reactions at the active surfaces, and effects of
discharge reaction-generated solid Li2O2 on the transport properties
and the electrochemical reaction rate are evaluated and implemented
in the model. The predicted results are discussed and analyzed in terms
of the spatial and transient distribution of various parameters, such as
local oxygen concentration, reaction rate, variable solid Li2O2 volume
fraction and porosity, as well as the effective diffusion coefficients. It
is found that the effect of the solid Li2O2 product deposited at the solid
active surfaces is significant on the transport phenomena and the
overall battery performance.
Abstract: The air transport impact on environment is more than
ever a limitative obstacle to the aeronautical industry continuous
growth. Over the last decades, considerable effort has been carried
out in order to obtain quieter aircraft solutions, whether by changing
the original design or investigating more silent maneuvers. The
noise propagated by rotating surfaces is one of the most important
sources of annoyance, being present in most aerial vehicles. Bearing
this is mind, CEIIA developed a new computational chain for
noise prediction with in-house software tools to obtain solutions in
relatively short time without using excessive computer resources. This
work is based on the new acoustic tool, which aims to predict the
rotor noise generated during steady and maneuvering flight, making
use of the flexibility of the C language and the advantages of GPU
programming in terms of velocity. The acoustic tool is based in the
Formulation 1A of Farassat, capable of predicting two important
types of noise: the loading and thickness noise. The present work
describes the most important features of the acoustic tool, presenting
its most relevant results and framework analyses for helicopters and
UAV quadrotors.
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: Biofuels production has come forth as a future
technology to combat the problem of depleting fossil fuels. Bio-based
ethanol production from enzymatic lignocellulosic biomass
degradation serves an efficient method and catching the eye of
scientific community. High cost of the enzyme is the major obstacle
in preventing the commercialization of this process. Thus main
objective of the present study was to optimize composition of
medium components for enhancing cellulase production by newly
isolated strain of Bacillus tequilensis. Nineteen factors were taken
into account using statistical Plackett-Burman Design. The significant
variables influencing the cellulose production were further employed
in statistical Response Surface Methodology using Central
Composite Design for maximizing cellulase production. The
optimum medium composition for cellulase production was: peptone
(4.94 g/L), ammonium chloride (4.99 g/L), yeast extract (2.00 g/L),
Tween-20 (0.53 g/L), calcium chloride (0.20 g/L) and cobalt chloride
(0.60 g/L) with pH 7, agitation speed 150 rpm and 72 h incubation at
37oC. Analysis of variance (ANOVA) revealed high coefficient of
determination (R2) of 0.99. Maximum cellulase productivity of 11.5
IU/ml was observed against the model predicted value of 13 IU/ml.
This was found to be optimally active at 60oC and pH 5.5.
Abstract: Load modeling is one of the central functions in
power systems operations. Electricity cannot be stored, which means
that for electric utility, the estimate of the future demand is necessary
in managing the production and purchasing in an economically
reasonable way. A majority of the recently reported approaches are
based on neural network. The attraction of the methods lies in the
assumption that neural networks are able to learn properties of the
load. However, the development of the methods is not finished, and
the lack of comparative results on different model variations is a
problem. This paper presents a new approach in order to predict the
Tunisia daily peak load. The proposed method employs a
computational intelligence scheme based on the Fuzzy neural
network (FNN) and support vector regression (SVR). Experimental
results obtained indicate that our proposed FNN-SVR technique gives
significantly good prediction accuracy compared to some classical
techniques.
Abstract: The aim of this paper is to perform experimental
modal analysis (EMA) of reinforced concrete (RC) square slabs.
EMA is the process of determining the modal parameters (Natural
Frequencies, damping factors, modal vectors) of a structure from a
set of frequency response functions FRFs (curve fitting). Although,
experimental modal analysis (or modal testing) has grown steadily in
popularity since the advent of the digital FFT spectrum analyzer in
the early 1970’s, studying all types of members and materials using
such method have not yet been well documented. Therefore, in this
work, experimental tests were conducted on RC square slab
specimens of dimensions 600mm x 600mmx 40mm. Experimental
analysis was based on freely supported boundary condition.
Moreover, impact testing as a fast and economical means of finding
the modes of vibration of a structure was used during the
experiments. In addition, Pico Scope 6 device and MATLAB
software were used to acquire data, analyze and plot Frequency
Response Function (FRF). The experimental natural frequencies
which were extracted from measurements exhibit good agreement
with analytical predictions. It is showed that EMA method can be
usefully employed to investigate the dynamic behavior of RC slabs.
Abstract: As the use of geothermal energy grows internationally
more effort is required to monitor and protect areas with rare and
important geothermal surface features. A number of approaches are
presented for developing and calibrating numerical geothermal
reservoir models that are capable of accurately representing
geothermal surface features. The approaches are discussed in the
context of cases studies of the Rotorua geothermal system and the
Orakei-korako geothermal system, both of which contain important
surface features. The results show that models are able to match the
available field data accurately and hence can be used as valuable
tools for predicting the future response of the systems to changes in
use.
Abstract: Urban areas have been expanded throughout the
globe. Monitoring and modelling urban growth have become a
necessity for a sustainable urban planning and decision making.
Urban prediction models are important tools for analyzing the causes
and consequences of urban land use dynamics. The objective of this
research paper is to analyze and model the urban change, which has
been occurred from 1990 to 2000 using CORINE land cover maps.
The model was developed using drivers of urban changes (such as
road distance, slope, etc.) under an Artificial Neural Network
modelling approach. Validation was achieved using a prediction map
for 2006 which was compared with a real map of Urban Atlas of
2006. The accuracy produced a Kappa index of agreement of 0,639
and a value of Cramer's V of 0,648. These encouraging results
indicate the importance of the developed urban growth prediction
model which using a set of available common biophysical drivers
could serve as a management tool for the assessment of urban
change.
Abstract: These days, the field of tissue engineering is getting
serious attention due to its usefulness. Bone tissue engineering helps
to address and sort-out the critical sized and non-healing orthopedic
problems by the creation of manmade bone tissue. We will design
and validate an efficient numerical model, which will simulate the
effective diffusivity in bone tissue engineering. Our numerical model
will be based on the finite element analysis of the diffusion-reaction
equations. It will have the ability to optimize the diffusivity, even
at multi-scale, with the variation of time. It will also have a special
feature “parametric sweep”, with which we will be able to predict
the oxygen, glucose and cell density dynamics, more accurately. We
will fix these problems by modifying the governing equations, by
selecting appropriate spatio-temporal finite element schemes and by
transient analysis.
Abstract: Since columns are the most important elements of the
structures, failure of one column in a critical location can cause a
progressive collapse. In this respect, the repair and strengthening of
columns is a very important subject to reduce the building failure and
to keep the columns capacity. Twenty columns with different
parameters is tested and analysis. Eleven typical confined reinforced
concrete (RC) columns with different types of techniques are
assessment. And also, four confined concrete columns with plastic
tube (PVC) are tested with and with four paralleling tested of
unconfined plain concrete. The techniques of confined RC columns
are mortar strengthening, Steel rings strengthening, FRP
strengthening. Moreover, the technique of confined plain concrete
(PC) column is used PVC tubes. The columns are tested under
uniaxial compressive loads studied the effect of confinement on the
structural behavior of circular RC columns. Test results for each
column are presented in the form of crack patterns, stress-strain
curves. Test results show that confining of the RC columns using
different techniques of strengthening results significant improvement
of the general behavior of the columns and can used in construction.
And also, tested confined PC columns with PVC tubes results shown
that the confined PC with PVC tubes can be used in economical
building. The theoretical model for predicted column capacity is
founded with experimental factor depends on the confined techniques
used and the strain reduction.