Abstract: Analysis of the properties of coconut (Cocos nucifera)
and its oil was evaluated in this work using standard analytical
techniques. The analyses carried out include proximate composition
of the fruit, extraction of oil from the fruit using different process
parameters and physicochemical analysis of the extracted oil. The
results showed the percentage (%) moisture, crude lipid, crude
protein, ash and carbohydrate content of the coconut as 7.59, 55.15,
5.65, 7.35 and 19.51 respectively. The oil from the coconut fruit was
odourless and yellowish liquid at room temperature (30oC). The
treatment combinations used (leaching time, leaching temperature
and solute: solvent ratio) showed significant differences (P
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 urban area, several landmarks may affect housing
price and rents, and hedonic analysis should employ distance variables
corresponding to each landmarks. Unfortunately, the effects of
distances to landmarks on housing prices are generally not consistent
with the true price. These distance variables may cause magnitude
error in regression, pointing a problem of spatial multicollinearity. In
this paper, we provided some approaches for getting the samples with
less bias and method on locating the specific sampling area to avoid
the multicollinerity problem in two specific landmarks case.
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: Non-linear FEM calculations are indispensable when
important technical information like operating performance of a
rubber component is desired. For example rubber bumpers built into
air-spring structures may undergo large deformations under load,
which in itself shows non-linear behavior. The changing contact
range between the parts and the incompressibility of the rubber
increases this non-linear behavior further. The material
characterization of an elastomeric component is also a demanding
engineering task.
The shape optimization problem of rubber parts led to the study of
FEM based calculation processes. This type of problems was posed
and investigated by several authors. In this paper the time demand of
certain calculation methods are studied and the possibilities of time
reduction is presented.
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 well been of human beings on construction site is
very important, many man power had been lost through accidents
which kills or make workers physically unfit to carry out construction
activities, these in turn have multiple effects on the whole economy.
Thus it is necessary to put all safety items and regulations in place
before construction activities can commence. This study was carried
out in Ondo state of Nigeria to known and analyse the state of health
and safety of construction workers in the state. The study was done
using first hand observation method, 50 construction project sites
were visited in 10 major towns of Ondo state, questionnaires were
distributed and the results were analysed. The result show that
construction workers are being exposed to a lot of construction site
hazards due to lack of inadequate safety programmes and nonprovision
of appropriate safety materials for workers on site. From the
data gotten for each site visited and the statistical analysis, it can be
concluded that occurrence of accident on construction sites depends
significantly on the available safety facilities on the sites. The result of
the regression statistics show that the level of significant of the
dependence of occurrence of accident on the availability of safety
items on site is 0.0362 which is less than 0.05 maximum significant
level required. Therefore a vital way of sustaining our building
strategy is by given a detail attention to provision of adequate health
and safety items on construction sites which will reduce the
occurrence of accident, loss of man power and death of skilled
workers among others.
Abstract: The purpose of this study is to forecast the influences
of information and communication technology (ICT) on the structural
changes of Japanese economies. In this study, input-output (IO) and
statistical approaches are used as analysis instruments. More
specifically, this study employs Leontief IO coefficients and
constrained multivariate regression (CMR) model in order to achieve
the purpose. The periods of initial and forecast in this study are 2005
and 2015, respectively. In this study, ICT is represented by ICT capital
stocks. This study conducts two levels of analysis, namely macro and
micro. The results of macro level analysis show that the dynamics of
Japanese economies on the forecast period, relative to the initial period,
are not so high. We focus on (1) commerce, (2) business services and
office supplies, and (3) personal services sectors when conducting the
analysis of the micro level. Further, we analyze its specific IO
coefficients when doing this analysis. The results of the analysis
explain that ICT gives a strong influence on the changes of these
coefficients from initial to forecast periods.
Abstract: Recent research in neural networks science and
neuroscience for modeling complex time series data and statistical
learning has focused mostly on learning from high input space and
signals. Local linear models are a strong choice for modeling local
nonlinearity in data series. Locally weighted projection regression is
a flexible and powerful algorithm for nonlinear approximation in
high dimensional signal spaces. In this paper, different learning
scenario of one and two dimensional data series with different
distributions are investigated for simulation and further noise is
inputted to data distribution for making different disordered
distribution in time series data and for evaluation of algorithm in
locality prediction of nonlinearity. Then, the performance of this
algorithm is simulated and also when the distribution of data is high
or when the number of data is less the sensitivity of this approach to
data distribution and influence of important parameter of local
validity in this algorithm with different data distribution is explained.
Abstract: Many organizations bring e-Learning to use as a tool
in their training and human development department. It is getting
more popular because it is easy to access to get knowledge all the
time and also it provides a rich content, which can develop the
employees’ skill efficiently. This study is focused on the factors that
affect using e-Learning efficiently, so it will make job satisfaction
increasing. The questionnaires were sent to employees in large
commercial banks, which use e-Learning located in Bangkok, the
results from multiple linear regression analysis showed that
employee’s characteristics, characteristics of e-Learning, learning and
growth have influence on job satisfaction.
Abstract: In the last few decades, many southeast-Asia women
migrate to Taiwan by marriage, and it usually takes several years for
them to acquire Taiwanese citizenship. This study investigates the
relationship between their citizenship acquisition and whether they
develop Taiwanese identities, and how does it affect their ethnical
identity towards their original ethnics. Furthermore, the present study
also explores that whether citizenship acquisition help the immigrant
women to explore the host society further and make commitment to it,
or the identification towards mainstream Taiwanese society is only
symbolic and superficial? One hundred and ninety-two immigrant
women were measured using Multigroup Ethnic Identity
Measure-Revised and a global 10-point ethnic identity question.
Correlation tests, t-test, and hierarchical regression were performed to
answer the above questions. The results revealed that citizenship
acquisition does help immigrant women to identify with Taiwanese
society, but it does not affect how they identify with their own ethnics.
Furthermore, the results also indicated that acquiring citizenship
would not help these immigrant women become involved in deeper
cultural exploration of Taiwan nor would it encourage them to make
commitments to the host society.
Abstract: This paper focuses on the assessment of the air
pollution and morbidity relationship in Tunisia. Air pollution is
measured by ozone air concentration and the morbidity is measured
by the number of respiratory-related restricted activity days during
the 2-week period prior to the interview. Socioeconomic data are also
collected in order to adjust for any confounding covariates. Our
sample is composed by 407 Tunisian respondents; 44.7% are women,
the average age is 35.2, near 69% are living in a house built after
1980, and 27.8% have reported at least one day of respiratory-related
restricted activity. The model consists on the regression of the
number of respiratory-related restricted activity days on the air
quality measure and the socioeconomic covariates. In order to correct
for zero-inflation and heterogeneity, we estimate several models
(Poisson, negative binomial, zero inflated Poisson, Poisson hurdle,
negative binomial hurdle and finite mixture Poisson models).
Bootstrapping and post-stratification techniques are used in order to
correct for any sample bias. According to the Akaike information
criteria, the hurdle negative binomial model has the greatest goodness
of fit. The main result indicates that, after adjusting for
socioeconomic data, the ozone concentration increases the probability
of positive number of restricted activity days.
Abstract: The purpose of this study is to examine the possible
link between employee and customer satisfaction. The service
provided by employees, help to build a good relationship with
customers and can help at increasing their loyalty. Published data for
job satisfaction and indicators of customer services of banks were
gathered from relevant published works which included data from
five different countries. The scores of customers and employees
satisfaction of the different published works were transformed and
normalized to the scale of 1 to 100. The data were analyzed and a
regression analysis of the two parameters was used to describe the
link between employee’s satisfaction and customer’s satisfaction.
Assuming that employee satisfaction has a significant influence on
customer’s service and the resulting customer satisfaction, the
reviewed data indicate that employee’s satisfaction contributes
significantly on the level of customer satisfaction in the Banking
sector. There was a significant correlation between the two
parameters (Pearson correlation R2=0.52 P
Abstract: Geopolymer concretes are new class of construction
materials that have emerged as an alternative to Ordinary Portland
cement concrete. Considerable researches have been carried out on
material development of geopolymer concrete; however, a few studies
have been reported on the structural use of them. This paper presents
the bond behaviors of reinforcement embedded in fly ash based
geopolymer concrete. The development lengths of reinforcement for
various compressive strengths of concrete, 20, 30 and 40 MPa, and
reinforcement diameters, 10, 16 and 25 mm, are investigated. Total 27
specimens were manufactured and pull-out test according to EN 10080
was applied to measure bond strength and slips between concrete and
reinforcements. The average bond strengths decreased from 23.06MPa
to 17.26 MPa, as the diameters of reinforcements increased from
10mm to 25mm. The compressive strength levels of geopolymer
concrete showed no significant influence on bond strengths in this
study. Also, the bond-slip relations between geopolymer concrete and
reinforcement are derived using non-linear regression analysis for
various experimental conditions.
Abstract: Near infrared (NIR) spectroscopy has always been of
great interest in the food and agriculture industries. The development
of prediction models has facilitated the estimation process in recent
years. In this study, 110 crude palm oil (CPO) samples were used to
build a free fatty acid (FFA) prediction model. 60% of the collected
data were used for training purposes and the remaining 40% used for
testing. The visible peaks on the NIR spectrum were at 1725 nm and
1760 nm, indicating the existence of the first overtone of C-H bands.
Principal component regression (PCR) was applied to the data in
order to build this mathematical prediction model. The optimal
number of principal components was 10. The results showed
R2=0.7147 for the training set and R2=0.6404 for the testing set.
Abstract: Enterprise Architecture (EA) is a strategy that is
employed by enterprises in order to align their business and
Information Technology (IT). EA is managed, developed, and
maintained through Enterprise Architecture Implementation
Methodology (EAIM). Effectiveness of EA implementation is the
degree in which EA helps to achieve the collective goals of the
organization. This paper analyzes the results of a survey that aims to
explore the factors that affect the effectiveness of EAIM and
specifically the relationship between factors and effectiveness of the
output and functionality of EA project. The exploratory factor
analysis highlights a specific set of five factors: alignment,
adaptiveness, support, binding, and innovation. The regression
analysis shows that there is a statistically significant and positive
relationship between each of the five factors and the effectiveness of
EAIM. Consistent with theory and practice, the most prominent
factor for developing an effective EAIM is innovation. The findings
contribute to the measuring the effectiveness of EA implementation
project by providing an indication of the measurement
implementation approaches which is used by the Enterprise
Architects, and developing an effective EAIM.
Abstract: A new relative efficiency in linear model in reference is
instructed into the linear weighted regression, and its upper and lower
bound are proposed. In the linear weighted regression model, for the
best linear unbiased estimation of mean matrix respect to the
least-squares estimation, two new relative efficiencies are given, and
their upper and lower bounds are also studied.
Abstract: One of the most important tasks in the risk
management is the correct determination of probability of default
(PD) of particular financial subjects. In this paper a possibility of
determination of financial institution’s PD according to the creditscoring
models is discussed. The paper is divided into the two parts.
The first part is devoted to the estimation of the three different
models (based on the linear discriminant analysis, logit regression
and probit regression) from the sample of almost three hundred US
commercial banks. Afterwards these models are compared and
verified on the control sample with the view to choose the best one.
The second part of the paper is aimed at the application of the chosen
model on the portfolio of three key Czech banks to estimate their
present financial stability. However, it is not less important to be able
to estimate the evolution of PD in the future. For this reason, the
second task in this paper is to estimate the probability distribution of
the future PD for the Czech banks. So, there are sampled randomly
the values of particular indicators and estimated the PDs’ distribution,
while it’s assumed that the indicators are distributed according to the
multidimensional subordinated Lévy model (Variance Gamma model
and Normal Inverse Gaussian model, particularly). Although the
obtained results show that all banks are relatively healthy, there is
still high chance that “a financial crisis” will occur, at least in terms
of probability. This is indicated by estimation of the various quantiles
in the estimated distributions. Finally, it should be noted that the
applicability of the estimated model (with respect to the used data) is
limited to the recessionary phase of the financial market.
Abstract: The current study explored the effect of economic
development, financial development and institutional quality on
environmental destruction in upper-middle income countries during
the time period of 1999-2011. The dependent variable is logarithm of
carbon dioxide emissions that can be considered as an index for
destruction or quality of the environment given to its effects on the
environment. Financial development and institutional development
variables as well as some control variables were considered. In order
to study cross-sectional correlation among the countries under study,
Pesaran and Friz test was used. Since the results of both tests show
cross-sectional correlation in the countries under study, seemingly
unrelated regression method was utilized for model estimation. The
results disclosed that Kuznets’ environmental curve hypothesis is
confirmed in upper-middle income countries and also, financial
development and institutional quality have a significant effect on
environmental quality. The results of this study can be considered by
policy makers in countries with different income groups to have
access to a growth accompanied by improved environmental quality.
Abstract: Stochastic User Equilibrium (SUE) model is a widely
used traffic assignment model in transportation planning, which is
regarded more advanced than Deterministic User Equilibrium (DUE)
model. However, a problem exists that the performance of the SUE
model depends on its error term parameter. The objective of this
paper is to propose a systematic method of determining the
appropriate error term parameter value for the SUE model. First, the
significance of the parameter is explored through a numerical
example. Second, the parameter calibration method is developed
based on the Logit-based route choice model. The calibration process
is realized through multiple nonlinear regression, using sequential
quadratic programming combined with least square method. Finally,
case analysis is conducted to demonstrate the application of the
calibration process and validate the better performance of the SUE
model calibrated by the proposed method compared to the SUE
models under other parameter values and the DUE model.