Abstract: Forecasting electricity load plays a crucial role regards
decision making and planning for economical purposes. Besides, in
the light of the recent privatization and deregulation of the power
industry, the forecasting of future electricity load turned out to be a
very challenging problem. Empirical data about electricity load
highlights a clear seasonal behavior (higher load during the winter
season), which is partly due to climatic effects. We also emphasize
the presence of load periodicity at a weekly basis (electricity load is
usually lower on weekends or holidays) and at daily basis (electricity
load is clearly influenced by the hour). Finally, a long-term trend may
depend on the general economic situation (for example, industrial
production affects electricity load). All these features must be
captured by the model.
The purpose of this paper is then to build an hourly electricity load
model. The deterministic component of the model requires non-linear
regression and Fourier series while we will investigate the stochastic
component through econometrical tools.
The calibration of the parameters’ model will be performed by
using data coming from the Italian market in a 6 year period (2007-
2012). Then, we will perform a Monte Carlo simulation in order to
compare the simulated data respect to the real data (both in-sample
and out-of-sample inspection). The reliability of the model will be
deduced thanks to standard tests which highlight a good fitting of the
simulated values.
Abstract: It is not only the economic determinants that impact on life conditions, but maintaining a good level of life satisfaction (LS) may also be an important challenge currently. In Luxembourg, university students receive financial aid from the government. They are then registered at the Centre for Documentation and Information on Higher Education (CEDIES). Luxembourg is built on migration with almost half its population consisting of foreigners. It is upon this basis that our research aims to analyze the associations with mental health factors (health satisfaction, psychological quality of life, worry), perceived financial situation, career attitudes (adaptability, optimism, knowledge, planning) and LS, for non-Luxembourgish and native postgraduate students. Between 2012 and 2013, postgraduates registered at CEDIES were contacted by post and asked to participate in an online survey with either the option of English or French. The study population comprised of 644 respondents. Our statistical analysis excluded: those born abroad who had Luxembourgish citizenship, or those born in Luxembourg who did not have citizenship. Two groups were formed one consisting 147 non-Luxembourgish and the other 284 natives. A single item measured LS (1=not at all satisfied to 10=very satisfied). Bivariate tests, correlations and multiple linear regression models were used in which only significant relationships (p
Abstract: Makishima and Mackenzie model was used to
simulation of acoustic properties (longitudinal and shear ultrasonic
wave velocities, elastic moduli theoretically for many tellurite and
borate glasses. The model was proposed mainly depending on the
values of the experimentally measured density, which are obtained
before. In this search work, we are trying to obtain the values of
densities of amorphous glasses (as the density depends on the
geometry of the network structure of these glasses). In addition, the
problem of simulating the slope of linear regression between the
experimentally determined bulk modulus and the product of packing
density and experimental Young's modulus, were solved in this
search work. The results showed good agreement between the
experimentally measured values of densities and both ultrasonic wave
velocities, and those theoretically determined.
Abstract: This study was aimed to measure effective transverse
relaxation rates (R2*) in the liver and muscle of normal New Zealand
White (NZW) rabbits. R2* relaxation rate has been widely used in
various hepatic diseases for iron overload by quantifying iron contents
in liver. R2* relaxation rate is defined as the reciprocal of T2*
relaxation time and mainly depends on the constituents of tissue.
Different tissues would have different R2* relaxation rates. The signal
intensity decay in Magnetic resonance imaging (MRI) may be
characterized by R2* relaxation rates. In this study, a 1.5T GE Signa
HDxt whole body MR scanner equipped with an 8-channel high
resolution knee coil was used to observe R2* values in NZW rabbit’s
liver and muscle. Eight healthy NZW rabbits weighted 2 ~ 2.5 kg were
recruited. After anesthesia using Zoletil 50 and Rompun 2% mixture,
the abdomen of rabbit was landmarked at the center of knee coil to
perform 3-plane localizer scan using fast spoiled gradient echo
(FSPGR) pulse sequence. Afterwards, multi-planar fast gradient echo
(MFGR) scans were performed with 8 various echo times (TEs) to
acquire images for R2* measurements. Regions of interest (ROIs) at
liver and muscle were measured using Advantage workstation.
Finally, the R2* was obtained by a linear regression of ln(sı) on TE.
The results showed that the longer the echo time, the smaller the signal
intensity. The R2* values of liver and muscle were 44.8 ± 10.9 s-1 and
37.4 ± 9.5 s-1, respectively. It implies that the iron concentration of
liver is higher than that of muscle. In conclusion, the more the iron
contents in tissue, the higher the R2*. The correlations between R2*
and iron content in NZW rabbits might be valuable for further
exploration.
Abstract: High Performance Liquid Chromatography (HPLC)
method was developed and validated for simultaneous estimation of
6-Gingerol(6G) and 6-Shogaol(6S) in joint pain relief gel containing
ginger extract. The chromatographic separation was achieved by
using C18 column, 150 x 4.6mm i.d., 5μ Luna, mobile phase
containing acetonitrile and water (gradient elution). The flow rate
was 1.0 ml/min and the absorbance was monitored at 282 nm. The
proposed method was validated in terms of the analytical parameters
such as specificity, accuracy, precision, linearity, range, limit of
detection (LOD), limit of quantification (LOQ), and determined
based on the International Conference on Harmonization (ICH)
guidelines. The linearity ranges of 6G and 6S were obtained over 20-
60 and 6-18 μg/ml respectively. Good linearity was observed over the
above-mentioned range with linear regression equation Y= 11016x-
23778 for 6G and Y = 19276x-19604 for 6S (x is concentration of
analytes in μg/ml and Y is peak area). The value of correlation
coefficient was found to be 0.9994 for both markers. The limit of
detection (LOD) and limit of quantification (LOQ) for 6G were
0.8567 and 2.8555 μg/ml and for 6S were 0.3672 and 1.2238 μg/ml
respectively. The recovery range for 6G and 6S were found to be
91.57 to 102.36 % and 84.73 to 92.85 % for all three spiked levels.
The RSD values from repeated extractions for 6G and 6S were 3.43
and 3.09% respectively. The validation of developed method on
precision, accuracy, specificity, linearity, and range were also
performed with well-accepted results.
Abstract: Presently various computational techniques are used
in modeling and analyzing environmental engineering data. In the
present study, an intra-comparison of polynomial and radial basis
kernel functions based on Support Vector Regression and, in turn, an
inter-comparison with Multi Linear Regression has been attempted in
modeling mass transfer capacity of vertical (θ = 90O) and inclined (θ
multiple plunging jets (varying from 1 to 16 numbers). The data set
used in this study consists of four input parameters with a total of
eighty eight cases, forty four each for vertical and inclined multiple
plunging jets. For testing, tenfold cross validation was used.
Correlation coefficient values of 0.971 and 0.981 along with
corresponding root mean square error values of 0.0025 and 0.0020
were achieved by using polynomial and radial basis kernel functions
based Support Vector Regression respectively. An intra-comparison
suggests improved performance by radial basis function in
comparison to polynomial kernel based Support Vector Regression.
Further, an inter-comparison with Multi Linear Regression
(correlation coefficient = 0.973 and root mean square error = 0.0024)
reveals that radial basis kernel functions based Support Vector
Regression performs better in modeling and estimating mass transfer
by multiple plunging jets.
Abstract: Prediction of maximum local scour is necessary for
the safety and economical design of the bridges. A number of
equations have been developed over the years to predict local scour
depth using laboratory data and a few pier equations have also been
proposed using field data. Most of these equations are empirical in
nature as indicated by the past publications. In this paper attempts
have been made to compute local depth of scour around bridge pier in
dimensional and non-dimensional form by using linear regression,
simple regression and SVM (Poly & Rbf) techniques along with few
conventional empirical equations. The outcome of this study suggests
that the SVM (Poly & Rbf) based modeling can be employed as an
alternate to linear regression, simple regression and the conventional
empirical equations in predicting scour depth of bridge piers. The
results of present study on the basis of non-dimensional form of
bridge pier scour indicate the improvement in the performance of
SVM (Poly & Rbf) in comparison to dimensional form of scour.
Abstract: The knitted fabric suffers a deformation in its
dimensions due to stretching and tension factors, transverse and
longitudinal respectively, during the process in rectilinear knitting
machines so it performs a dry relaxation shrinkage procedure and
thermal action of prefixed to obtain stable conditions in the knitting.
This paper presents a dry relaxation shrinkage prediction of Bordeaux
fiber using a feed forward neural network and linear regression
models. Six operational alternatives of shrinkage were predicted. A
comparison of the results was performed finding neural network
models with higher levels of explanation of the variability and
prediction. The presence of different reposes is included. The models
were obtained through a neural toolbox of Matlab and Minitab
software with real data in a knitting company of Southern
Guanajuato. The results allow predicting dry relaxation shrinkage of
each alternative operation.
Abstract: The work reported through this paper is an
experimental work conducted on High Performance Concrete (HPC)
with super plasticizer with the aim to develop some models suitable
for prediction of compressive strength of HPC mixes. In this study,
the effect of varying proportions of fly ash (0% to 50% @ 10%
increment) on compressive strength of high performance concrete has
been evaluated. The mix designs studied were M30, M40 and M50 to
compare the effect of fly ash addition on the properties of these
concrete mixes. In all eighteen concrete mixes that have been
designed, three were conventional concretes for three grades under
discussion and fifteen were HPC with fly ash with varying
percentages of fly ash. The concrete mix designing has been done in
accordance with Indian standard recommended guidelines. All the
concrete mixes have been studied in terms of compressive strength at
7 days, 28 days, 90 days, and 365 days. All the materials used have
been kept same throughout the study to get a perfect comparison of
values of results. The models for compressive strength prediction
have been developed using Linear Regression method (LR), Artificial
Neural Network (ANN) and Leave-One-Out Validation (LOOV)
methods.
Abstract: Predicting earnings management is vital for the capital
market participants, financial analysts and managers. The aim of this
research is attempting to respond to this query: Is there a significant
difference between the regression model and neural networks’
models in predicting earnings management, and which one leads to a
superior prediction of it? In approaching this question, a Linear
Regression (LR) model was compared with two neural networks
including Multi-Layer Perceptron (MLP), and Generalized
Regression Neural Network (GRNN). The population of this study
includes 94 listed companies in Tehran Stock Exchange (TSE)
market from 2003 to 2011. After the results of all models were
acquired, ANOVA was exerted to test the hypotheses. In general, the
summary of statistical results showed that the precision of GRNN did
not exhibit a significant difference in comparison with MLP. In
addition, the mean square error of the MLP and GRNN showed a
significant difference with the multi variable LR model. These
findings support the notion of nonlinear behavior of the earnings
management. Therefore, it is more appropriate for capital market
participants to analyze earnings management based upon neural
networks techniques, and not to adopt linear regression models.
Abstract: Currently, planners try to have more green travel
options to decrease economic, social and environmental problems.
Therefore, this study tries to find significant urban travel factors to be
used to increase the usage of alternative urban travel modes. This
paper attempts to identify the relationship between prominent urban
mobility indicators and daily trips by public transport in 30 cities
from various parts of the world. Different travel modes,
infrastructures and cost indicators were evaluated in this research as
mobility indicators. The results of multi-linear regression analysis
indicate that there is a significant relationship between mobility
indicators and the daily usage of public transport.
Abstract: As a basic physiology need, threat to sufficient food
production is threat to human survival. Food security has been an
issue that has gained global concern. This paper looks at the food
security in Nigeria by assessing the availability of food and
accessibility of the available food. The paper employed multiple
linear regression technique and graphic trends of growth rates of
relevant variables to show the situation of food security in Nigeria.
Results of the tests revealed that population growth rate was higher
than the growth rate of food availability in Nigeria for the earlier
period of the study. Commercial bank credit to agricultural sector,
foreign exchange utilization for food and the Agricultural Credit
Guarantee Scheme Fund (ACGSF) contributed significantly to food
availability in Nigeria. Food prices grew at a faster rate than the
average income level, making it difficult to access sufficient food. It
implies that prior to the year 2012; there was insufficient food to feed
the Nigerian populace. However, continued credit to the food and
agricultural sector will ensure sustained and sufficient production of
food in Nigeria. Microfinance banks should make sufficient credit
available to smallholder farmer. Government should further control
and subsidize the rising price of food to make it more accessible by
the people.
Abstract: This paper seeks to assess the implications of bank
consolidation on the performance of small and medium scale
enterprises in the Nigerian economy. Multiple linear regression
technique and correlation matrix test were employed to measure the
extent to which small and medium scale enterprises asset size,
survival and access to credit were influenced. The result showed that
bank deposit (BD) and bank credit (L or BC) impacted on asset size
and survival of small and medium scale enterprises. None of the
variables had significant impact on SMEs access to credit. There is a
shift of focus by commercial banks away from small and medium
scale enterprises (small customers), which is evidenced by the
significant negative influence of bank credit to both the survival and
asset size of small and medium enterprises. While micro finance
banks work hard at providing funds to small and medium scale
entrepreneurs, their capacity to meet the needs of these entrepreneurs
is constrained. CBN should make policies that will boost micro
finance bank’s capital and also monitor closely the management of
the banks to ensure prudent financing of small and medium scale
investments.
Abstract: The present paper attempts to investigate the
prediction of air entrainment rate and aeration efficiency of a free
overfall jets issuing from a triangular sharp crested weir by using
regression based modelling. The empirical equations, Support vector
machine (polynomial and radial basis function) models and the linear
regression techniques were applied on the triangular sharp crested
weirs relating the air entrainment rate and the aeration efficiency to
the input parameters namely drop height, discharge, and vertex angle.
It was observed that there exists a good agreement between the
measured values and the values obtained using empirical equations,
Support vector machine (Polynomial and rbf) models and the linear
regression techniques. The test results demonstrated that the SVM
based (Poly & rbf) model also provided acceptable prediction of the
measured values with reasonable accuracy along with empirical
equations and linear regression techniques in modelling the air
entrainment rate and the aeration efficiency of a free overfall jets
issuing from triangular sharp crested weir. Further sensitivity analysis
has also been performed to study the impact of input parameter on the
output in terms of air entrainment rate and aeration efficiency.
Abstract: This paper seeks to assess the implications of
insurance to foreign direct investment inflow in Nigeria. Multiple
linear regression technique and correlation matrix test were employed
to measure the extent to which foreign direct investment was
influenced. The result showed that insurance premium (IP), asset size
of insurance industry (AS), and total investment of the industry (TI)
impacted significantly and positively on foreign direct investment
inflow in Nigeria. There should be effective risk transfer mechanism
and financial intermediation, which gives the investor confidence in
the risk management strength of the host country.
Abstract: Commercial banks in Nigeria adopted many strategies
to attract fresh deposits including the use of high deposit rate.
However, pricing of banking services moved in favor of the banks at
the expense of customers, resulting in their seeking other investment
alternatives rather than saving their money in the bank. Both deposit
and lending rates were greatly influenced by the Central Bank of
Nigeria (CBN) decision on interest rate. Therefore, commercial bank
effort to attract deposits via manipulation of her rates was greatly
limited, otherwise the banks will be giving out more than it earned.
The study aimed at examining the relationship between interest rate
and fixed fund deposit of commercial banks, how policy-controlled
interest rate affected commercial bank’s fixed fund deposit The
researcher employed ordinary least square technique, using, multiple
linear regression, unrestricted vector auto-regression, correlation
matrix test, granger causality and impulse response graph in the
analysis. Commercial bank’s interest rates affected commercial
bank’s fixed fund deposit significantly while policy-controlled
interest rate did not significantly transmit through the commercial
bank’s interest rates to affect fixed fund deposit. While commercial
banks seek creative ways to expand their fixed fund deposit, policy
authorities in Nigeria should better coordinate interest rate fluctuation
and induce competition in the entire financial sector.
Abstract: An approach was evaluated for the retrieval of soil
moisture of bare soil surface using bistatic scatterometer data in the
angular range of 200 to 700 at VV- and HH- polarization. The
microwave data was acquired by specially designed X-band (10
GHz) bistatic scatterometer. The linear regression analysis was done
between scattering coefficients and soil moisture content to select the
suitable incidence angle for retrieval of soil moisture content. The 250
incidence angle was found more suitable. The support vector
regression analysis was used to approximate the function described
by the input output relationship between the scattering coefficient and
corresponding measured values of the soil moisture content. The
performance of support vector regression algorithm was evaluated by
comparing the observed and the estimated soil moisture content by
statistical performance indices %Bias, root mean squared error
(RMSE) and Nash-Sutcliffe Efficiency (NSE). The values of %Bias,
root mean squared error (RMSE) and Nash-Sutcliffe Efficiency
(NSE) were found 2.9451, 1.0986 and 0.9214 respectively at HHpolarization.
At VV- polarization, the values of %Bias, root mean
squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were
found 3.6186, 0.9373 and 0.9428 respectively.
Abstract: Lead time is a critical measure of a supply chain's
performance. It impacts both the customer satisfactions as well as the
total cost of inventory. This paper presents the result of a study on the
analysis of the customer order lead-time for a multinational company.
In the study, the lead time was divided into three stages respectively:
order entry, order fulfillment, and order delivery. A sample of size 2,425 order lines was extracted from the
company's records to use for this study. The sample data entails
information regarding customer orders from the time of order entry
until order delivery. Data regarding the lead time of each stage for
different orders were also provided. Summary statistics on lead time
data reveals that about 30% of the orders were delivered later than the
scheduled due date. The result of the multiple linear regression
analysis technique revealed that component type, logistics parameter,
order size and the customer type have significant impacts on lead
time. Data analysis on the stages of lead time indicates that stage 2
consumed over 50% of the lead time. Pareto analysis was made to
study the reasons for the customer order delay in each stage.
Recommendation was given to resolve the problem.
Abstract: The fatigue life of tubular joints commonly found in
offshore industry is not only dependent on the value of hot-spot stress
(HSS), but is also significantly influenced by the through-thethickness
stress distribution characterized by the degree of bending
(DoB). The determination of DoB values in a tubular joint is essential
for improving the accuracy of fatigue life estimation using the stresslife
(S–N) method and particularly for predicting the fatigue crack
growth based on the fracture mechanics (FM) approach. In the
present paper, data extracted from finite element (FE) analyses of
tubular KT-joints, verified against experimental data and parametric
equations, was used to investigate the effects of geometrical
parameters on DoB values at the crown 0°, saddle, and crown 180°
positions along the weld toe of central brace in tubular KT-joints
subjected to axial loading. Parametric study was followed by a set of
nonlinear regression analyses to derive DoB parametric formulas for
the fatigue analysis of KT-joints under axial loads. The tubular KTjoint
is a quite common joint type found in steel offshore structures.
However, despite the crucial role of the DoB in evaluating the fatigue
performance of tubular joints, this paper is the first attempt to study
and formulate the DoB values in KT-joints.
Abstract: The purpose of the paper is to estimate the US small
wind turbines market potential and forecast the small wind turbines
sales in the US. The forecasting method is based on the application of
the Bass model and the generalized Bass model of innovations
diffusion under replacement purchases. In the work an exponential
distribution is used for modeling of replacement purchases. Only one
parameter of such distribution is determined by average lifetime of
small wind turbines. The identification of the model parameters is
based on nonlinear regression analysis on the basis of the annual
sales statistics which has been published by the American Wind
Energy Association (AWEA) since 2001 up to 2012. The estimation
of the US average market potential of small wind turbines (for
adoption purchases) without account of price changes is 57080
(confidence interval from 49294 to 64866 at P = 0.95) under average
lifetime of wind turbines 15 years, and 62402 (confidence interval
from 54154 to 70648 at P = 0.95) under average lifetime of wind
turbines 20 years. In the first case the explained variance is 90,7%,
while in the second - 91,8%. The effect of the wind turbines price
changes on their sales was estimated using generalized Bass model.
This required a price forecast. To do this, the polynomial regression
function, which is based on the Berkeley Lab statistics, was used. The
estimation of the US average market potential of small wind turbines
(for adoption purchases) in that case is 42542 (confidence interval
from 32863 to 52221 at P = 0.95) under average lifetime of wind
turbines 15 years, and 47426 (confidence interval from 36092 to
58760 at P = 0.95) under average lifetime of wind turbines 20 years.
In the first case the explained variance is 95,3%, while in the second
– 95,3%.