Abstract: This article presents an alternative collapse capacity
intensity measure in the three elements form which is influenced by
the spectral ordinates at periods longer than that of the first mode
period at near and far source sites. A parameter, denoted by β, is
defined by which the spectral ordinate effects, up to the effective
period (2T1), on the intensity measure are taken into account. The
methodology permits to meet the hazard-levelled target extreme
event in the probabilistic and deterministic forms. A MATLAB code
is developed involving OpenSees to calculate the collapse capacities
of the 8 archetype RC structures having 2 to 20 stories for regression
process. The incremental dynamic analysis (IDA) method is used to
calculate the structure’s collapse values accounting for the element
stiffness and strength deterioration. The general near field set
presented by FEMA is used in a series of performing nonlinear
analyses. 8 linear relationships are developed for the 8structutres
leading to the correlation coefficient up to 0.93. A collapse capacity
near field prediction equation is developed taking into account the
results of regression processes obtained from the 8 structures. The
proposed prediction equation is validated against a set of actual near
field records leading to a good agreement. Implementation of the
proposed equation to the four archetype RC structures demonstrated
different collapse capacities at near field site compared to those of
FEMA. The reasons of differences are believed to be due to
accounting for the spectral shape effects.
Abstract: It has been known that a characteristic
Burst-Suppression (BS) pattern appears in EEG during the early
recovery period following Cardiac Arrest (CA). Here, to explore the
relationship between cortical and subcortical neural activities
underlying BS, extracellular activity in the parietal cortex and the
centromedian nucleus of the thalamus and extradural EEG were
recorded in a rodent CA model. During the BS, the cortical firing rate
is extraordinarily high, and that bursts in EEG correlate to dense spikes
in cortical neurons. Newly observed phenomena are that 1) thalamic
activity reemerges earlier than cortical activity following CA, and 2)
the correlation coefficient of cortical and thalamic activities rises
during BS period. These results would help elucidate the underlying
mechanism of brain recovery after CA injury.
Abstract: Based on Business and Consumer Survey (BCS) data,
the European Commission (EC) regularly publishes the monthly
Economic Sentiment Indicator (ESI) for each EU member state. ESI
is conceptualized as a leading indicator, aimed ad tracking the overall
economic activity. In calculating ESI, the EC employs arbitrarily
chosen weights on 15 BCS response balances. This paper raises the
predictive quality of ESI by applying nonlinear programming to find
such weights that maximize the correlation coefficient of ESI and
year-on-year GDP growth. The obtained results show that the highest
weights are assigned to the response balances of industrial sector
questions, followed by questions from the retail trade sector. This
comes as no surprise since the existing literature shows that the
industrial production is a plausible proxy for the overall Croatian
economic activity and since Croatian GDP is largely influenced by
the aggregate personal consumption.
Abstract: Several parameters are established in order to measure
biodiesel quality. One of them is the iodine value, which is an
important parameter that measures the total unsaturation within a
mixture of fatty acids. Limitation of unsaturated fatty acids is
necessary since warming of higher quantity of these ones ends in
either formation of deposits inside the motor or damage of lubricant.
Determination of iodine value by official procedure tends to be very
laborious, with high costs and toxicity of the reagents, this study uses
artificial neural network (ANN) in order to predict the iodine value
property as an alternative to these problems. The methodology of
development of networks used 13 esters of fatty acids in the input
with convergence algorithms of back propagation of back
propagation type were optimized in order to get an architecture of
prediction of iodine value. This study allowed us to demonstrate the
neural networks’ ability to learn the correlation between biodiesel
quality properties, in this caseiodine value, and the molecular
structures that make it up. The model developed in the study reached
a correlation coefficient (R) of 0.99 for both network validation and
network simulation, with Levenberg-Maquardt algorithm.
Abstract: Most of the oil palm plantations have been threatened
by Basal Stem Rot (BSR) disease which causes serious economic
impact. This study was conducted to identify the healthy and BSRinfected
oil palm tree using thirteen color indices. Multispectral and
thermal camera was used to capture 216 images of the leaves taken
from frond number 1, 9 and 17. Indices of normalized difference
vegetation index (NDVI), red (R), green (G), blue (B), near infrared
(NIR), green – blue (GB), green/blue (G/B), green – red (GR),
green/red (G/R), hue (H), saturation (S), intensity (I) and thermal
index (T) were used. From this study, it can be concluded that G
index taken from frond number 9 is the best index to differentiate
between the healthy and BSR-infected oil palm trees. It not only gave
high value of correlation coefficient (R=-0.962), but also high value
of separation between healthy and BSR-infected oil palm tree.
Furthermore, power and S model developed using G index gave the
highest R2 value which is 0.985.
Abstract: The main objective of this article is to examine the
impact of interest rates on investments in Poland in the context of
financial crisis. The paper also investigates the dependence of bank
loans to enterprises on interbank market rates. The article studies the
impact of interbank market rate on the level of investments in Poland.
Besides, this article focuses on the research of the correlation
between the level of corporate loans and the amount of investments
in Poland in order to determine the indirect impact of central bank
interest rates through the transmission mechanism of monetary policy
on the real economy. To achieve the objective we have used
econometric and statistical research methods like: econometric model
and Pearson correlation coefficient.
This analysis suggests that the central bank reference rate
inversely proportionally affects the level of investments in Poland
and this dependence is moderate. This is also important issue because
it is related to preparing of Poland to accession to euro area. The
research is important from both theoretical and empirical points of
view. The formulated conclusions and recommendations determine
the practical significance of the paper which may be used in the
decision making process of monetary and economic authorities of the
country.
Abstract: This paper presents a rank correlation curve. The
traditional correlation coefficient is valid for both continuous
variables and for integer variables using rank statistics. Since
the correlation coefficient has already been established in rank
statistics by Spearman, such a calculation can be extended to
the correlation curve.
This paper presents two survey questions. The survey
collected non-continuous variables. We will show weak to
moderate correlation. Obviously, one question has a negative
effect on the other. A review of the qualitative literature
can answer which question and why. The rank correlation
curve shows which collection of responses has a positive
slope and which collection of responses has a negative slope.
Such information is unavailable from the flat, ”first-glance”
correlation statistics.
Abstract: The Standard Penetration Test (SPT) is the most
common in situ test for soil investigations. On the other hand, the
Cone Penetration Test (CPT) is considered one of the best
investigation tools. Due to the fast and accurate results that can be
obtained it complaints the SPT in many applications like field
explorations, design parameters, and quality control assessments.
Many soil index and engineering properties have been correlated to
both of SPT and CPT. Various foundation design methods were
developed based on the outcome of these tests. Therefore it is vital to
correlate these tests to each other so that either one of the tests can be
used in the absence of the other, especially for preliminary evaluation
and design purposes.
The primary purpose of this study was to investigate the
relationships between the SPT and CPT for different type of sandy
soils in Florida. Data for this research were collected from number of
projects sponsored by the Florida Department of Transportation
(FDOT), six sites served as the subject of SPT-CPT correlations. The
correlations were established between the cone resistance (qc), sleeve
friction (fs) and the uncorrected SPT blow counts (N) for various
soils.
A positive linear relationship was found between qc, fs and N for
various sandy soils. In general, qc versus N showed higher
correlation coefficients than fs versus N. qc/N ratios were developed
for different soil types and compared to literature values, the results
of this research revealed higher ratios than literature values.
Abstract: Digital image correlation (DIC) is a contactless fullfield
displacement and strain reconstruction technique commonly
used in the field of experimental mechanics. Comparing with
physical measuring devices, such as strain gauges, which only
provide very restricted coverage and are expensive to deploy widely,
the DIC technique provides the result with full-field coverage and
relative high accuracy using an inexpensive and simple experimental
setup. It is very important to study the natural patterns effect on the
DIC technique because the preparation of the artificial patterns is
time consuming and hectic process. The objective of this research is
to study the effect of using images having natural pattern on the
performance of DIC. A systematical simulation method is used to
build simulated deformed images used in DIC. A parameter (subset
size) used in DIC can have an effect on the processing and accuracy
of DIC and even cause DIC to failure. Regarding to the picture
parameters (correlation coefficient), the higher similarity of two
subset can lead the DIC process to fail and make the result more
inaccurate. The pictures with good and bad quality for DIC methods
have been presented and more importantly, it is a systematic way to
evaluate the quality of the picture with natural patterns before they
install the measurement devices.
Abstract: In this study, we propose a novel technique for acoustic
echo suppression (AES) during speech recognition under barge-in
conditions. Conventional AES methods based on spectral subtraction
apply fixed weights to the estimated echo path transfer function
(EPTF) at the current signal segment and to the EPTF estimated until
the previous time interval. However, the effects of echo path changes
should be considered for eliminating the undesired echoes. We
describe a new approach that adaptively updates weight parameters in
response to abrupt changes in the acoustic environment due to
background noises or double-talk. Furthermore, we devised a voice
activity detector and an initial time-delay estimator for barge-in speech
recognition in communication networks. The initial time delay is
estimated using log-spectral distance measure, as well as
cross-correlation coefficients. The experimental results show that the
developed techniques can be successfully applied in barge-in speech
recognition systems.
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: The present study involved analysis of certain
characteristics of the perennial ryegrass (Lolium perenne L.)
genotypes collected from the natural flora of Ankara, and explores a
correlation among them. In order to evaluate the plants for breeding
purpose as per Turkey's environmental conditions, the perennial
ryegrass plants were collected from natural pasture of Ankara in 2004
and were utilized for the study. Seeds of the collected plants were
sown in pots and seedlings were prepared in a greenhouse. In 2005,
the seedlings were transplanted at 50 × 50 cm2 intervals in
Randomized Complete Blocks Design in an experimental field. In
2007 and 2008, data were recorded from the observations and
measurements of 568 perennial ryegrasses. The plant characteristics,
which were investigated, included re-growth time in spring, color,
density, growth habit, tendency to form inflorescence, time of
inflorescence, plant height, length of upper internode, spike length,
leaf length, leaf width, leaf area, leaf shape, number of spikelets per
spike, seed yield per spike and 1000 grain weight and the correlation
analyses were made using this data. Correlation coefficients were
estimated between all paired combinations of the studied traits. The
yield components exhibited varying trends of association among
themselves. Seed yield per spike showed significant and positive
association with the number of spikelets per spike, 1000 grain weight,
plant height, length of upper internode, spike length, leaf length, leaf
width, leaf area and color, but significant and negative association
with the growth habit and re-growth time in spring.
Abstract: Cement-based grouts has been used successfully to
repair cracks in many concrete structures such as bridges, tunnels,
buildings and to consolidate soils or rock foundations. In the present
study the rheological characterization of cement grout with
water/binder ratio (W/B) is fixed at 0.5. The effect of the replacement
of cement by bentonite (2 to 10% wt) in presence of superplasticizer
(0.5% wt) was investigated. Several rheological tests were carried out
by using controlled-stress rheometer equipped with vane geometry in
temperature of 20°C. To highlight the influence of bentonite and
superplasticizer on the rheological behavior of grout cement, various
flow tests in a range of shear rate from 0 to 200 s-1 were observed.
Cement grout showed a non-Newtonian viscosity behavior at all
concentrations of bentonite. Three parameter model Herschel-
Bulkley was chosen for fitting of experimental data. Based on the
values of correlation coefficients of the estimated parameters, The
Herschel-Bulkley law model well described the rheological behavior
of the grouts. Test results showed that the dosage of bentonite
increases the viscosity and yield stress of the system and introduces
more thixotropy. While the addition of both bentonite and
superplasticizer with cement grout improve significantly the fluidity
and reduced the yield stress due to the action of dispersion of SP.
Abstract: The study investigated the implementation of the
Neural Network (NN) techniques for prediction of the loading of Cu
ions onto clinoptilolite. The experimental design using analysis of
variance (ANOVA) was chosen for testing the adequacy of the
Neural Network and for optimizing of the effective input parameters
(pH, temperature and initial concentration). Feed forward, multi-layer
perceptron (MLP) NN successfully tracked the non-linear behavior of
the adsorption process versus the input parameters with mean squared
error (MSE), correlation coefficient (R) and minimum squared error
(MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed
that NN modeling techniques could effectively predict and simulate
the highly complex system and non-linear process such as ionexchange.
Abstract: This paper presents a comparative study between two
neural network models namely General Regression Neural Network
(GRNN) and Back Propagation Neural Network (BPNN) are used
to estimate radial overcut produced during Electrical Discharge
Machining (EDM). Four input parameters have been employed:
discharge current (Ip), pulse on time (Ton), Duty fraction (Tau) and
discharge voltage (V). Recently, artificial intelligence techniques, as
it is emerged as an effective tool that could be used to replace
time consuming procedures in various scientific or engineering
applications, explicitly in prediction and estimation of the complex
and nonlinear process. The both networks are trained, and the
prediction results are tested with the unseen validation set of the
experiment and analysed. It is found that the performance of both the
networks are found to be in good agreement with average percentage
error less than 11% and the correlation coefficient obtained for the
validation data set for GRNN and BPNN is more than 91%. However,
it is much faster to train GRNN network than a BPNN and GRNN is
often more accurate than BPNN. GRNN requires more memory space
to store the model, GRNN features fast learning that does not require
an iterative procedure, and highly parallel structure. GRNN networks
are slower than multilayer perceptron networks at classifying new
cases.
Abstract: The objectives of this project are to study on the work
efficiency of the employees, sorted by their profiles, and to study on
the relation between job attributes and work efficiency of employees
of Suan Sunandha Rajabhat University. The samples used for this
study are 292 employees. The statistics used in this study are
frequencies, standard deviations, One-way ANOVA and Pearson’s
correlation coefficient. Majority of respondent were male with an
undergraduate degree, married and lives together. The average age of
respondents was between 31-41 years old, married and the
educational background are higher than bachelor’s degree. The job
attribute is correlated to the work efficiency with the statistical
significance level of.o1. This concurs with the predetermined
hypothesis. The correlation between the two main factors is in the
moderate level. All the categories of job attributes such as the variety
of skills, job clarity, job importance, freedom to do work are
considered separately.
Abstract: Chromium is one of the most common heavy metals which exist in very high concentrations in wastewater. The removal is very expensive due to the high cost of normal adsorbents. Lignocellulosic materials and mainly treated materials have proven to be a good solution for this problem.
Adsorption tests were performed at different pH, different times and with varying concentrations.
Results show that is at pH 3 that treated wood absorbs more chromium ranging from 70% (2h treatment) to almost 100% (12 h treatment) much more than untreated wood with less than 40%. Most of the adsorption is made in the first 2-3 hours for untreated and heat treated wood. Modified wood adsorbs more chromium throughout the time. For all the samples, adsorption fitted relatively well the Langmuir model with correlation coefficient ranging from 0.85 to 0.97.
The results show that heat treated wood is a good adsorbent ant that this might be a good utilization for sawdust from treating companies.
Abstract: The purpose of the study is to investigate the education faculty students’ attitudes towards e-learning according to different variables. In current study, the data were collected from 393 students of an education faculty in Turkey. In this study, theattitude towards e‐learning scale and the demographic information form were used to collect data. The collected data were analyzed by t-test, ANOVA and Pearson correlation coefficient. It was found that there is a significant difference in students’ tendency towards e-learning and avoidance from e-learning based on gender. Male students have more positive attitudes towards e-learning than female students. Also, the students who used the internet lesshave higher levels of avoidance from e-learning. Additionally, it is found that there is a positive and significant relationship between the number of personal mobile learning devices and tendency towards e-learning. On the other hand, there is a negative and significant relationship between the number of personal mobile learning devices and avoidance from e-learning. Also, suggestions were presented according to findings.
Abstract: The aim of this paper is image encryption using Genetic Algorithm (GA). The proposed encryption method consists of two phases. In modification phase, pixels locations are altered to reduce correlation among adjacent pixels. Then, pixels values are changed in the diffusion phase to encrypt the input image. Both phases are performed by GA with binary chromosomes. For modification phase, these binary patterns are generated by Local Binary Pattern (LBP) operator while for diffusion phase binary chromosomes are obtained by Bit Plane Slicing (BPS). Initial population in GA includes rows and columns of the input image. Instead of subjective selection of parents from this initial population, a random generator with predefined key is utilized. It is necessary to decrypt the coded image and reconstruct the initial input image. Fitness function is defined as average of transition from 0 to 1 in LBP image and histogram uniformity in modification and diffusion phases, respectively. Randomness of the encrypted image is measured by entropy, correlation coefficients and histogram analysis. Experimental results show that the proposed method is fast enough and can be used effectively for image encryption.
Abstract: Complex sensitivity analysis of stresses in a concrete slab of the real type of rigid pavement made from recycled materials is performed. The computational model of the pavement is designed as a spatial (3D) model, is based on a nonlinear variant of the finite element method that respects the structural nonlinearity, enables to model different arrangements of joints, and the entire model can be loaded by the thermal load. Interaction of adjacent slabs in joints and contact of the slab and the subsequent layer are modeled with the help of special contact elements. Four concrete slabs separated by transverse and longitudinal joints and the additional structural layers and soil to the depth of about 3m are modeled. The thickness of individual layers, physical and mechanical properties of materials, characteristics of joints, and the temperature of the upper and lower surface of slabs are supposed to be random variables. The modern simulation technique Updated Latin Hypercube Sampling with 20 simulations is used. For sensitivity analysis the sensitivity coefficient based on the Spearman rank correlation coefficient is utilized. As a result, the estimates of influence of random variability of individual input variables on the random variability of principal stresses s1 and s3 in 53 points on the upper and lower surface of the concrete slabs are obtained.