Abstract: In this study, a computational fluid dynamics (CFD)
model has been developed for studying the effect of surface
roughness profile on the EHL problem. The cylinders contact
geometry, meshing and calculation of the conservation of mass and
momentum equations are carried out using the commercial software
packages ICEMCFD and ANSYS Fluent. The user defined functions
(UDFs) for density, viscosity and elastic deformation of the cylinders
as the functions of pressure and temperature are defined for the CFD
model. Three different surface roughness profiles are created and
incorporated into the CFD model. It is found that the developed CFD
model can predict the characteristics of fluid flow and heat transfer in
the EHL problem, including the main parameters such as pressure
distribution, minimal film thickness, viscosity, and density changes.
The results obtained show that the pressure profile at the center of the
contact area directly relates to the roughness amplitude. A rough
surface with kurtosis value of more than 3 has greater influence over
the fluctuated shape of pressure distribution than in other cases.
Abstract: Traditional document representation for classification
follows Bag of Words (BoW) approach to represent the term weights.
The conventional method uses the Vector Space Model (VSM) to
exploit the statistical information of terms in the documents and they
fail to address the semantic information as well as order of the terms
present in the documents. Although, the phrase based approach
follows the order of the terms present in the documents rather than
semantics behind the word. Therefore, a semantic concept based
approach is used in this paper for enhancing the semantics by
incorporating the ontology information. In this paper a novel method
is proposed to forecast the intraday stock market price directional
movement based on the sentiments from Twitter and money control
news articles. The stock market forecasting is a very difficult and
highly complicated task because it is affected by many factors such
as economic conditions, political events and investor’s sentiment etc.
The stock market series are generally dynamic, nonparametric, noisy
and chaotic by nature. The sentiment analysis along with wisdom of
crowds can automatically compute the collective intelligence of
future performance in many areas like stock market, box office sales
and election outcomes. The proposed method utilizes collective
sentiments for stock market to predict the stock price directional
movements. The collective sentiments in the above social media have
powerful prediction on the stock price directional movements as
up/down by using Granger Causality test.
Abstract: The arm length, hand length, hand breadth and middle
finger length of 1540 right-handed industrial workers of Haryana
state was used to assess the relationship between the upper limb
dimensions and stature. Initially, the data were analyzed using basic
univariate analysis and independent t-tests; then simple and multiple
linear regression models were used to estimate stature using SPSS
(version 17). There was a positive correlation between upper limb
measurements (hand length, hand breadth, arm length and middle
finger length) and stature (p < 0.01), which was highest for hand
length. The accuracy of stature prediction ranged from ± 54.897 mm
to ± 58.307 mm. The use of multiple regression equations gave better
results than simple regression equations. This study provides new
forensic standards for stature estimation from the upper limb
measurements of male industrial workers of Haryana (India). The
results of this research indicate that stature can be determined using
hand dimensions with accuracy, when only upper limb is available
due to any reasons likewise explosions, train/plane crashes, mutilated
bodies, etc. The regression formula derived in this study will be
useful for anatomists, archaeologists, anthropologists, design
engineers and forensic scientists for fairly prediction of stature using
regression equations.
Abstract: A three-dimensional numerical model of
thermoelectric generator (TEG) modules attached to a large chimney
plate is proposed and solved numerically using a control volume based
finite difference formulation. The TEG module consists of a
thermoelectric generator, an elliptical pin-fin heat sink, and a cold
plate for water cooling. In the chimney, the temperature of flue gases is
450-650K. Although the TEG hot-side temperature and thus the
electric power output can be increased by inserting an elliptical pin-fin
heat sink into the chimney tunnel to increase the heat transfer area, the
pin fin heat sink would cause extra pumping power at the same time.
The main purpose of this study is to analyze the effects of geometrical
parameters on the electric power output and chimney pressure drop
characteristics. The effects of different operating conditions, including
various inlet velocities (Vin= 1, 3, 5 m/s), inlet temperatures (Tgas = 450,
550, 650K) and different fin height (0 to 150 mm) are discussed in
detail. The predicted numerical data for the power vs. current (P-I)
curve are in good agreement (within 11%) with the experimental data.
Abstract: An artificial neural network is a mathematical model
inspired by biological neural networks. There are several kinds of
neural networks and they are widely used in many areas, such as:
prediction, detection, and classification. Meanwhile, in day to day life,
people always have to make many difficult decisions. For example,
the coach of a soccer club has to decide which offensive player
to be selected to play in a certain game. This work describes a
novel Neural Network using a combination of the General Regression
Neural Network and the Probabilistic Neural Networks to help a
soccer coach make an informed decision.
Abstract: Parabolic solar trough systems have seen limited
deployments in cold northern climates as they are more suitable for
electricity production in southern latitudes. A numerical dynamic
model is developed to simulate troughs installed in cold climates and
validated using a parabolic solar trough facility in Winnipeg. The
model is developed in Simulink and will be utilized to simulate a trigeneration
system for heating, cooling and electricity generation in
remote northern communities. The main objective of this simulation
is to obtain operational data of solar troughs in cold climates and use
the model to determine ways to improve the economics and address
cold weather issues.
In this paper the validated Simulink model is applied to simulate a
solar assisted absorption cooling system along with electricity
generation using Organic Rankine Cycle (ORC) and thermal storage.
A control strategy is employed to distribute the heated oil from solar
collectors among the above three systems considering the
temperature requirements. This modelling provides dynamic
performance results using measured meteorological data recorded
every minute at the solar facility location. The purpose of this
modeling approach is to accurately predict system performance at
each time step considering the solar radiation fluctuations due to
passing clouds. Optimization of the controller in cold temperatures is
another goal of the simulation to for example minimize heat losses in
winter when energy demand is high and solar resources are low.
The solar absorption cooling is modeled to use the generated heat
from the solar trough system and provide cooling in summer for a
greenhouse which is located next to the solar field.
The results of the simulation are presented for a summer day in
Winnipeg which includes comparison of performance parameters of
the absorption cooling and ORC systems at different heat transfer
fluid (HTF) temperatures.
Abstract: This study analyzes the innovative orientation of the
Croatian entrepreneurs. Innovative orientation is represented by the
perceived extent to which an entrepreneur’s product or service or
technology is new, and no other businesses offer the same product.
The sample is extracted from the GEM Croatia Adult Population
Survey dataset for the years 2003-2013. We apply descriptive
statistics, t-test, Chi-square test and logistic regression. Findings
indicate that innovative orientations vary with personal, firm, meso
and macro level variables, and between different stages in
entrepreneurship process. Significant predictors are occupation of the
entrepreneurs, size of the firm and export aspiration for both early
stage and established entrepreneurs. In addition, fear of failure,
expecting to start a new business and seeing an entrepreneurial career
as a desirable choice are predictors of innovative orientation among
early stage entrepreneurs.
Abstract: Groundwater inflow to the tunnels is one of the most
important problems in tunneling operation. The objective of this
study is the investigation of model dimension effects on tunnel inflow
assessment in discontinuous rock masses using numerical modeling.
In the numerical simulation, the model dimension has an important
role in prediction of water inflow rate. When the model dimension is
very small, due to low distance to the tunnel border, the model
boundary conditions affect the estimated amount of groundwater flow
into the tunnel and results show a very high inflow to tunnel. Hence,
in this study, the two-dimensional universal distinct element code
(UDEC) used and the impact of different model parameters, such as
tunnel radius, joint spacing, horizontal and vertical model domain
extent has been evaluated. Results show that the model domain extent
is a function of the most significant parameters, which are tunnel
radius and joint spacing.
Abstract: Using the first-principles full-potential linearized
augmented plane wave plus local orbital (FP-LAPW+lo) method
based on density functional theory (DFT), we have investigated the
electronic structure and magnetism of full Heusler alloys Co2ZrGe
and Co2NbB. These compounds are predicted to be half-metallic
ferromagnets (HMFs) with a total magnetic moment of 2.000 B per
formula unit, well consistent with the Slater-Pauling rule.
Calculations show that both the alloys have an indirect band gaps, in
the minority-spin channel of density of states (DOS), with values of
0.58 eV and 0.47 eV for Co2ZrGe and Co2NbB, respectively.
Analysis of the DOS and magnetic moments indicates that their
magnetism is mainly related to the d-d hybridization between the Co
and Zr (or Nb) atoms. The half-metallicity is found to be relatively
robust against volume changes. In addition, an atom inside molecule
AIM formalism and an electron localization function ELF were also
adopted to study the bonding properties of these compounds, building
a bridge between their electronic and bonding behavior.
As they have a good crystallographic compatibility with the lattice of
semiconductors used industrially and negative calculated cohesive
energies with considerable absolute values these two alloys could be
promising magnetic materials in the spintronic field.
Abstract: The main goal of this article is to describe the online
flood monitoring and prediction system Floreon+ primarily developed
for the Moravian-Silesian region in the Czech Republic and the basic
process it uses for running automatic rainfall-runoff and
hydrodynamic simulations along with their calibration and
uncertainty modeling. It takes a long time to execute such process
sequentially, which is not acceptable in the online scenario, so the use
of a high performance computing environment is proposed for all
parts of the process to shorten their duration. Finally, a case study on
the Ostravice River catchment is presented that shows actual
durations and their gain from the parallel implementation.
Abstract: The key role in phenomenological modelling of cyclic
plasticity is good understanding of stress-strain behaviour of given
material. There are many models describing behaviour of materials
using numerous parameters and constants. Combination of individual
parameters in those material models significantly determines whether
observed and predicted results are in compliance. Parameter
identification techniques such as random gradient, genetic algorithm
and sensitivity analysis are used for identification of parameters using
numerical modelling and simulation. In this paper genetic algorithm
and sensitivity analysis are used to study effect of 4 parameters of
modified AbdelKarim-Ohno cyclic plasticity model. Results
predicted by Finite Element (FE) simulation are compared with
experimental data from biaxial ratcheting test with semi-elliptical
loading path.
Abstract: The effects of hypertension are often lethal thus its
early detection and prevention is very important for everybody. In
this paper, a neural network (NN) model was developed and trained
based on a dataset of hypertension causative parameters in order to
forecast the likelihood of occurrence of hypertension in patients. Our
research goal was to analyze the potential of the presented NN to
predict, for a period of time, the risk of hypertension or the risk of
developing this disease for patients that are or not currently
hypertensive. The results of the analysis for a given patient can
support doctors in taking pro-active measures for averting the
occurrence of hypertension such as recommendations regarding the
patient behavior in order to lower his hypertension risk. Moreover,
the paper envisages a set of three example scenarios in order to
determine the age when the patient becomes hypertensive, i.e.
determine the threshold for hypertensive age, to analyze what
happens if the threshold hypertensive age is set to a certain age and
the weight of the patient if being varied, and, to set the ideal weight
for the patient and analyze what happens with the threshold of
hypertensive age.
Abstract: Power Regeneration in Refrigeration Plant concept
has been analyzed and has been shown to be capable of saving about
25% power in Cryogenic Plants with the Power Regeneration System
(PRS) running under nominal conditions. The innovative component
Compressor Expander Group (CEG) based on turbomachinery has
been designed and built modifying CETT compressor and expander,
both selected for optimum plant performance. Experiments have
shown the good response of the turbomachines to run with R404a as
working fluid. Power saving up to 12% under PRS derated conditions
(50% loading) has been demonstrated. Such experiments allowed
predicting a power saving up to 25% under CEG full load.
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: Bir El Djir is an important coastal township in Oran
department, located at 450 Km far away from Algiers on northwest of
Algeria. In this coastal area, the urban sprawl is one of the main
problems that reduce the limited highly fertile land. So, using the
remote sensing and GIS technologies have shown their great
capabilities to solve many earth resources issues.
The aim of this study is to produce land use and cover map for the
studied area at varied periods to monitor possible changes that may
occurred, particularly in the urban areas and subsequently predict
likely changes. For this, two spatial images SPOT and Landsat
satellites from 1987 and 2014 respectively were used to assess the
changes of urban expansion and encroachment during this period
with photo-interpretation and GIS approach.
The results revealed that the town of Bir El Djir has shown a
highest growth rate in the period 1987-2014 which is 1201.5 hectares
in terms of area. These expansions largely concern the new real estate
constructions falling within the social and promotional housing
programs launched by the government.
The most urban expansion is characterized by the new
construction in the form of spontaneous or peripheral precarious
habitat, but also unstructured slums settled especially in the
southeastern part of town.
Abstract: Pulmonary Function Tests are important non-invasive
diagnostic tests to assess respiratory impairments and provides
quantifiable measures of lung function. Spirometry is the most
frequently used measure of lung function and plays an essential role
in the diagnosis and management of pulmonary diseases. However,
the test requires considerable patient effort and cooperation,
markedly related to the age of patients resulting in incomplete data
sets. This paper presents, a nonlinear model built using Multivariate
adaptive regression splines and Random forest regression model to
predict the missing spirometric features. Random forest based feature
selection is used to enhance both the generalization capability and the
model interpretability. In the present study, flow-volume data are
recorded for N= 198 subjects. The ranked order of feature importance
index calculated by the random forests model shows that the
spirometric features FVC, FEF25, PEF, FEF25-75, FEF50 and the
demographic parameter height are the important descriptors. A
comparison of performance assessment of both models prove that, the
prediction ability of MARS with the `top two ranked features namely
the FVC and FEF25 is higher, yielding a model fit of R2= 0.96 and
R2= 0.99 for normal and abnormal subjects. The Root Mean Square
Error analysis of the RF model and the MARS model also shows that
the latter is capable of predicting the missing values of FEV1 with a
notably lower error value of 0.0191 (normal subjects) and 0.0106
(abnormal subjects) with the aforementioned input features. It is
concluded that combining feature selection with a prediction model
provides a minimum subset of predominant features to train the
model, as well as yielding better prediction performance. This
analysis can assist clinicians with a intelligence support system in the
medical diagnosis and improvement of clinical care.
Abstract: The development of allometric models is crucial to
accurate forest biomass/carbon stock assessment. The aim of this
study was to develop a set of biomass prediction models that will
enable the determination of total tree aboveground biomass for
savannah woodland area in Niger State, Nigeria. Based on the data
collected through biometric measurements of 1816 trees and
destructive sampling of 36 trees, five species specific and one site
specific models were developed. The sample size was distributed
equally between the five most dominant species in the study site
(Vitellaria paradoxa, Irvingia gabonensis, Parkia biglobosa,
Anogeissus leiocarpus, Pterocarpus erinaceous). Firstly, the
equations were developed for five individual species. Secondly these
five species were mixed and were used to develop an allometric
equation of mixed species. Overall, there was a strong positive
relationship between total tree biomass and the stem diameter. The
coefficient of determination (R2 values) ranging from 0.93 to 0.99 P
< 0.001 were realised for the models; with considerable low standard
error of the estimates (SEE) which confirms that the total tree above
ground biomass has a significant relationship with the dbh. F-test
values for the biomass prediction models were also significant at p
Abstract: Previous studies on financial distress prediction choose
the conventional failing and non-failing dichotomy; however, the
distressed extent differs substantially among different financial
distress events. To solve the problem, “non-distressed”, “slightlydistressed”
and “reorganization and bankruptcy” are used in our article
to approximate the continuum of corporate financial health. This paper
explains different financial distress events using the two-stage method.
First, this investigation adopts firm-specific financial ratios, corporate
governance and market factors to measure the probability of various
financial distress events based on multinomial logit models.
Specifically, the bootstrapping simulation is performed to examine the
difference of estimated misclassifying cost (EMC). Second, this work
further applies macroeconomic factors to establish the credit cycle
index and determines the distressed cut-off indicator of the two-stage
models using such index. Two different models, one-stage and
two-stage prediction models are developed to forecast financial
distress, and the results acquired from different models are compared
with each other, and with the collected data. The findings show that the
one-stage model has the lower misclassification error rate than the
two-stage model. The one-stage model is more accurate than the
two-stage model.
Abstract: In this paper, effects of using Alumina-water
nanofluid on the rate of heat transfer have been investigated
numerically. Physical model is a square enclosure with insulated top
and bottom horizontal walls, while the vertical walls are kept at
different constant temperatures. Two appropriate models are used to
evaluate the viscosity and thermal conductivity of nanofluid. The
governing stream-vorticity equations are solved using a second order
central finite difference scheme, coupled to the conservation of mass
and energy. The study has been carried out for the Richardson
number 0.1 to 10 and the solid volume fraction 0 to 0.04. Results are
presented by isotherms lines, average Nusselt number and normalized
Nusselt number in different range of φ and Ri for forced, combined
and natural convection dominated regime. It is found that higher heat
transfer rate is predicted when the effects of nanoparticle is taken into
account.
Abstract: The composite pavement system considered in this
paper is composed of a functional surface layer, a fiber reinforced
asphalt middle layer and a fiber reinforced lean concrete base layer.
The mix design of the fiber reinforced lean concrete corresponds to the
mix composition of conventional lean concrete but reinforced by
fibers. The quasi-absence of research on the durability or long-term
performances (fatigue, creep, etc.) of such mix design stresses the
necessity to evaluate experimentally the long-term characteristics of
this layer composition. This study tests the creep characteristics as one
of the long-term characteristics of the fiber reinforced lean concrete
layer for composite pavement using a new creep device. The test
results reveal that the lean concrete mixed with fiber reinforcement
and fly ash develops smaller creep than the conventional lean
concrete. The results of the application of the CEB-FIP prediction
equation indicate that a modified creep prediction equation should be
developed to fit with the new mix design of the layer.