Abstract: Elastic scattering of α-particles from 9Be and 11B
nuclei at different alpha energies have been analyzed. Optical model
parameters (OMPs) of α-particles elastic scattering by these nuclei at
different energies have been obtained. In the present calculations, the
real part of the optical potential are derived by folding of nucleonnucleon
(NN) interaction into nuclear matter density distribution of
the projectile and target nuclei using computer code FRESCO. A
density-dependent version of the M3Y interaction (CDM3Y6), which
is based on the G-matrix elements of the Paris NN potential, has been
used. Volumetric integrals of the real and imaginary potential depth
(JR, JW) have been calculated and found to be energy dependent.
Good agreement between the experimental data and the theoretical
predictions in the whole angular range. In double folding (DF)
calculations, the obtained normalization coefficient Nr is in the range
0.70–1.32.
Abstract: Patient-specific models are instance-based learning
algorithms that take advantage of the particular features of the patient
case at hand to predict an outcome. We introduce two patient-specific
algorithms based on decision tree paradigm that use AUC as a
metric to select an attribute. We apply the patient specific algorithms
to predict outcomes in several datasets, including medical datasets.
Compared to the patient-specific decision path (PSDP) entropy-based
and CART methods, the AUC-based patient-specific decision path
models performed equivalently on area under the ROC curve (AUC).
Our results provide support for patient-specific methods being a
promising approach for making clinical predictions.
Abstract: Production fluids are transported from the platform to
tankers or process facilities through transfer pipelines. Water being
one of the heavier phases tends to settle at the bottom of pipelines
especially at low flow velocities and this has adverse consequences
for pipeline integrity. On restart after a shutdown, this could result in
corrosion and issues for process equipment, thus the need to have the
heavier liquid dispersed into the flowing lighter fluid. This study
looked at the flow regime of low water cut and low flow velocity oil
and water flow using conductive film thickness probes in a large
diameter 4-inch pipe to obtain oil and water interface height and the
interface structural velocity. A wide range of 0.1–1.0 m/s oil and
water mixture velocities was investigated for 0.5–5% water cut. Two
fluid model predictions were used to compare with the experimental
results.
Abstract: The aim of the study is to improve the understanding
of latent and sensible thermal energy storage within a paraffin wax
media by an array of cylindrical tubes arranged both in in-line and
staggered layouts. An analytical and experimental study is carried out
in a horizontal shell-and-tube type system during melting process.
Pertamina paraffin-wax was used as a phase change material (PCM),
while the tubes are embedded in the PCM. From analytical study we
can obtain the useful information in designing a thermal energy
storage such as: the motion of interface, amount of material melted at
any time in the process, and the heat storage characteristic during
melting. The use of staggered tubes is proposed compared to in-line
layout in a heat exchanger as thermal storage. The experimental study
is used to verify the validity of the analytical predictions. From the
comparisons, the analytical and experimental data are in a good
agreement.
Abstract: An industrial system for the production of white
liquor of a paper industry, Klabin Paraná Papéis, formed by ten
reactors was modeled, simulated, and analyzed. The developed model
considered possible water losses by evaporation and reaction, in
addition to variations in volumetric flow of lime mud across the
reactors due to composition variations. The model predictions agreed
well with the process measurements at the plant and the results
showed that the slaking reaction is nearly complete at the third
causticizing reactor, while causticizing ends by the seventh reactor.
Water loss due to slaking reaction and evaporation occurs more
pronouncedly in the slaking reaction than in the final causticizing
reactors; nevertheless, the lime mud flow remains nearly constant
across the reactors.
Abstract: When high strength reinforced concrete is exposed to
high temperature due to a fire, deteriorations occur such as loss in
strength and elastic modulus, cracking and spalling of the concrete.
Therefore, it is important to understand risk of structural safety in
building structures by studying structural behaviors and rehabilitation
of fire damaged high strength concrete structures. This paper aims at
investigating rehabilitation effect on fire damaged high strength
concrete beams using experimental and analytical methods. In the
experiments, flexural specimens with high strength concrete are
exposed to high temperatures according to ISO 834 standard time
temperature curve. From four-point loading test, results show that
maximum loads of the rehabilitated beams are similar to or higher than
those of the non-fire damaged RC beam. In addition, structural
analyses are performed using ABAQUS 6.10-3 with same conditions
as experiments to provide accurate predictions on structural and
mechanical behaviors of rehabilitated RC beams. The parameters are
the fire cover thickness and strengths of repairing mortar. Analytical
results show good rehabilitation effects, when the results predicted
from the rehabilitated models are compared to structural behaviors of
the non-damaged RC beams. In this study, fire damaged high strength concrete beams are
rehabilitated using polymeric cement mortar. The predictions from the
finite element (FE) models show good agreements with the
experimental results and the modeling approaches can be used to
investigate applicability of various rehabilitation methods for further
study.
Abstract: This paper presents a study on the effect of
second-order slip and jump on forced convection through a long
isothermally heated or cooled planar microchannel. The fully
developed solutions of thermal flow fields are analytically obtained on
the basis of the second-order Maxwell-Burnett slip and Smoluchowski
jump boundary conditions. Results reveal that the second-order term in
the Karniadakis slip boundary condition is found to contribute a
negative velocity slip and then to lead to a higher pressure drop as well
as a higher fluid temperature for the heated-wall case or to a lower
fluid temperature for the cooled-wall case. These findings are contrary
to predictions made by the Deissler model. In addition, the role of
second-order slip becomes more significant when the Knudsen
number increases.
Abstract: In this work, the plastic behaviour of cold-rolled zinc
coated dual-phase steel sheets DP600 and DP800 grades is firstly
investigated with the help of uniaxial, hydraulic bulge and Forming
Limit Curve (FLC) tests. The uniaxial tensile tests were performed in
three angular orientations with respect to the rolling direction to
evaluate the strain-hardening and plastic anisotropy. True stressstrain
curves at large strains were determined from hydraulic bulge
testing and fitted to a work-hardening equation. The limit strains are
defined at both localized necking and fracture conditions according to
Nakajima’s hemispherical punch procedure. Also, an elasto-plastic
localization model is proposed in order to predict strain and stress
based forming limit curves. The investigated dual-phase sheets
showed a good formability in the biaxial stretching and drawing FLC
regions. For both DP600 and DP800 sheets, the corresponding
numerical predictions overestimated and underestimated the
experimental limit strains in the biaxial stretching and drawing FLC
regions, respectively. This can be attributed to the restricted failure
necking condition adopted in the numerical model, which is not
suitable to describe the tensile and shear fracture mechanisms in
advanced high strength steels under equibiaxial and biaxial stretching
conditions.
Abstract: Particle size distribution, the most important
characteristics of aerosols, is obtained through electrical
characterization techniques. The dynamics of charged nanoparticles
under the influence of electric field in Electrical Mobility
Spectrometer (EMS) reveals the size distribution of these particles.
The accuracy of this measurement is influenced by flow conditions,
geometry, electric field and particle charging process, therefore by
the transfer function (transfer matrix) of the instrument. In this work,
a wire-cylinder corona charger was designed and the combined fielddiffusion
charging process of injected poly-disperse aerosol particles
was numerically simulated as a prerequisite for the study of a
multichannel EMS. The result, a cloud of particles with no uniform
charge distribution, was introduced to the EMS. The flow pattern and
electric field in the EMS were simulated using Computational Fluid
Dynamics (CFD) to obtain particle trajectories in the device and
therefore to calculate the reported signal by each electrometer.
According to the output signals (resulted from bombardment of
particles and transferring their charges as currents), we proposed a
modification to the size of detecting rings (which are connected to
electrometers) in order to evaluate particle size distributions more
accurately. Based on the capability of the system to transfer
information contents about size distribution of the injected particles,
we proposed a benchmark for the assessment of optimality of the
design. This method applies the concept of Von Neumann entropy
and borrows the definition of entropy from information theory
(Shannon entropy) to measure optimality. Entropy, according to the
Shannon entropy, is the ''average amount of information contained in
an event, sample or character extracted from a data stream''.
Evaluating the responses (signals) which were obtained via various
configurations of detecting rings, the best configuration which gave
the best predictions about the size distributions of injected particles,
was the modified configuration. It was also the one that had the
maximum amount of entropy. A reasonable consistency was also
observed between the accuracy of the predictions and the entropy
content of each configuration. In this method, entropy is extracted
from the transfer matrix of the instrument for each configuration.
Ultimately, various clouds of particles were introduced to the
simulations and predicted size distributions were compared to the
exact size distributions.
Abstract: This study utilizes a frequency domain approach over
the period of 1996 to 2013 to examine the causal relationship between
governance and economic growth in ten Asian countries, which have
different levels of democracy; classified as “Free”, “Partly Free”, and
“Not Free” countries. The empirical results show that there is no
Granger causality running from governance to economic growth in
“Not Free” countries and “Partly Free” countries with the exception of
Singapore. As for “Free” countries such as South Korea and Taiwan,
there is a one-way causality running from governance to economic
growth. The findings of this study indicate that policy makers in South
Korea, Taiwan, and Singapore could use governance index to improve
their predictions of the future economic growth.
Abstract: Numerical investigations were conducted to study the
influence of flexural reinforcement ratio on the diagonal cracking
strength and ultimate shear strength of reinforced concrete (RC)
beams without stirrups. Three-dimensional nonlinear finite element
analyses (FEAs) of the beams with flexural reinforcement ratios
ranging from 0.58% to 2.20% subjected to a mid-span concentrated
load were carried out. It is observed that the load-deflection and loadstrain
curves obtained from the numerical analyses agree with those
obtained from the experiments. It is concluded that flexural
reinforcement ratio has a significant effect on the shear strength and
deflection capacity of RC beams without stirrups. The predictions of
diagonal cracking strength and ultimate shear strength of beams
obtained by using the equations defined by a number of codes and
researchers are compared with each other and with the experimental
values.
Abstract: The sea waves carry thousands of GWs of power
globally. Although there are a number of different approaches to
harness offshore energy, they are likely to be expensive, practically
challenging, and vulnerable to storms. Therefore, this paper considers
using the near shore waves for generating mechanical and electrical
power. It introduces two new approaches, the wave manipulation and
using a variable duct turbine, for intercepting very wide wave fronts
and coping with the fluctuations of the wave height and the sea level,
respectively. The first approach effectively allows capturing much
more energy yet with a much narrower turbine rotor. The second
approach allows using a rotor with a smaller radius but captures
energy of higher wave fronts at higher sea levels yet preventing it
from totally submerging. To illustrate the effectiveness of the first
approach, the paper contains a description and the simulation results
of a scale model of a wave manipulator. Then, it includes the results
of testing a physical model of the manipulator and a single duct, axial
flow turbine in a wave flume in the laboratory. The paper also
includes comparisons of theoretical predictions, simulation results,
and wave flume tests with respect to the incident energy, loss in wave
manipulation, minimal loss, brake torque, and the angular velocity.
Abstract: Software fault prediction models are created by using
the source code, processed metrics from the same or previous version
of code and related fault data. Some company do not store and keep
track of all artifacts which are required for software fault prediction.
To construct fault prediction model for such company, the training
data from the other projects can be one potential solution. Earlier we
predicted the fault the less cost it requires to correct. The training
data consists of metrics data and related fault data at function/module
level. This paper investigates fault predictions at early stage using the
cross-project data focusing on the design metrics. In this study,
empirical analysis is carried out to validate design metrics for cross
project fault prediction. The machine learning techniques used for
evaluation is Naïve Bayes. The design phase metrics of other projects
can be used as initial guideline for the projects where no previous
fault data is available. We analyze seven datasets from NASA
Metrics Data Program which offer design as well as code metrics.
Overall, the results of cross project is comparable to the within
company data learning.
Abstract: The polymer foil used for manufacturing of
laminated glass members behaves in a viscoelastic manner with
temperature dependance. This contribution aims at incorporating
the time/temperature-dependent behavior of interlayer to our earlier
elastic finite element model for laminated glass beams. The model
is based on a refined beam theory: each layer behaves according
to the finite-strain shear deformable formulation by Reissner and
the adjacent layers are connected via the Lagrange multipliers
ensuring the inter-layer compatibility of a laminated unit. The
time/temperature-dependent behavior of the interlayer is accounted
for by the generalized Maxwell model and by the time-temperature
superposition principle due to the Williams, Landel, and Ferry.
The resulting system is solved by the Newton method with
consistent linearization and the viscoelastic response is determined
incrementally by the exponential algorithm. By comparing the model
predictions against available experimental data, we demonstrate that
the proposed formulation is reliable and accurately reproduces the
behavior of the laminated glass units.
Abstract: Behavioral aspects of experience such as will power
are rarely subjected to quantitative study owing to the numerous
complexities involved. Will is a phenomenon that has puzzled
humanity for a long time. It is a belief that will power of an individual
affects the success achieved by them in life. It is also thought that a
person endowed with great will power can overcome even the most
crippling setbacks in life while a person with a weak will cannot make
the most of life even the greatest assets. This study is an attempt
to subject the phenomena of will to the test of an artificial neural
network through a computational model. The claim being tested is
that will power of an individual largely determines success achieved
in life. It is proposed that data pertaining to success of individuals
be obtained from an experiment and the phenomenon of will be
incorporated into the model, through data generated recursively using
a relation between will and success characteristic to the model.
An artificial neural network trained using part of the data, could
subsequently be used to make predictions regarding data points in
the rest of the model. The procedure would be tried for different
models and the model where the networks predictions are found to
be in greatest agreement with the data would be selected; and used
for studying the relation between success and will.
Abstract: Rainfall runoff models play important role in
hydrological predictions. However, the model is only one part of the
process for creation of flood prediction. The aim of this paper is to
show the process of successful prediction for flood event (May 15 –
May 18 2014). Prediction was performed by rainfall runoff model
HEC–HMS, one of the models computed within Floreon+ system.
The paper briefly evaluates the results of automatic hydrologic
prediction on the river Olše catchment and its gages Český Těšín and
Věřňovice.
Abstract: We proposed a Hyperbolic Gompertz Growth Model
(HGGM), which was developed by introducing a shape parameter
(allometric). This was achieved by convoluting hyperbolic sine
function on the intrinsic rate of growth in the classical gompertz
growth equation. The resulting integral solution obtained
deterministically was reprogrammed into a statistical model and used
in modeling the height and diameter of Pines (Pinus caribaea). Its
ability in model prediction was compared with the classical gompertz
growth model, an approach which mimicked the natural variability of
height/diameter increment with respect to age and therefore provides
a more realistic height/diameter predictions using goodness of fit
tests and model selection criteria. The Kolmogorov Smirnov test and
Shapiro-Wilk test was also used to test the compliance of the error
term to normality assumptions while the independence of the error
term was confirmed using the runs test. The mean function of top
height/Dbh over age using the two models under study predicted
closely the observed values of top height/Dbh in the hyperbolic
gompertz growth models better than the source model (classical
gompertz growth model) while the results of R2, Adj. R2, MSE and
AIC confirmed the predictive power of the Hyperbolic Gompertz
growth models over its source model.
Abstract: A model was constructed to predict the amount of
solar radiation that will make contact with the surface of the earth in
a given location an hour into the future. This project was supported
by the Southern Company to determine at what specific times during
a given day of the year solar panels could be relied upon to produce
energy in sufficient quantities. Due to their ability as universal
function approximators, an artificial neural network was used to
estimate the nonlinear pattern of solar radiation, which utilized
measurements of weather conditions collected at the Griffin, Georgia
weather station as inputs. A number of network configurations and
training strategies were utilized, though a multilayer perceptron with
a variety of hidden nodes trained with the resilient propagation
algorithm consistently yielded the most accurate predictions. In
addition, a modeled direct normal irradiance field and adjacent
weather station data were used to bolster prediction accuracy. In later
trials, the solar radiation field was preprocessed with a discrete
wavelet transform with the aim of removing noise from the
measurements. The current model provides predictions of solar
radiation with a mean square error of 0.0042, though ongoing efforts
are being made to further improve the model’s accuracy.
Abstract: Customer churn prediction is one of the most useful
areas of study in customer analytics. Due to the enormous amount
of data available for such predictions, machine learning and data
mining have been heavily used in this domain. There exist many
machine learning algorithms directly applicable for the problem of
customer churn prediction, and here, we attempt to experiment on
a novel approach by using a cognitive learning based technique in
an attempt to improve the results obtained by using a combination
of supervised learning methods, with cognitive unsupervised learning
methods.
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.