Abstract: Numerical studies on race car aerodynamics at wing
in ground effect have been carried out using a steady 3d, double
precision, pressure-based, and standard k-epsilon turbulence model.
Through various parametric analytical studies we have observed that
at a particular speed and ground clearance of the wings a favorable
negative lift was found high at a particular angle of attack for all the
physical models considered in this paper. The fact is that if the
ground clearance height to chord length (h/c) is too small, the
developing boundary layers from either side (the ground and the
lower surface of the wing) can interact, leading to an altered variation
of the aerodynamic characteristics at wing in ground effect. Therefore
a suitable ground clearance must be predicted throughout the racing
for a better performance of the race car, which obviously depends
upon the coupled effects of the topography, wing orientation with
respect to the ground, the incoming flow features and/or the race car
speed. We have concluded that for the design of high performance
and high speed race cars the adjustable wings capable to alter the
ground clearance and the angles of attack is the best design option for
any race car for racing safely with variable speeds.
Abstract: With the long-term objective of Critical Heat Flux (CHF) prediction, a Direct Numerical Simulation (DNS) framework for simulation of subcooled and saturated nucleate pool boiling is developed. One case of saturated, and three cases of subcooled boiling at different subcooling levels are simulated. Grid refinement study is also reported. Both boiling and condensation phenomena can be computed simultaneously in the proposed numerical framework. Computed bubble detachment diameters of the saturated nucleate pool boiling cases agree well with the experiment. The flow structures around the growing bubble are presented and the accompanying physics is described. The relation between heat flux evolution from the heated wall and the bubble growth is studied, along with investigations of temperature distribution and flow field evolutions.
Abstract: Hospital staff and managers are under pressure and
concerned for effective use and management of scarce resources. The
hospital admissions require many decisions that have complex and
uncertain consequences for hospital resource utilization and patient
flow. It is challenging to predict risk of admissions and length of stay
of a patient due to their vague nature. There is no method to capture
the vague definition of admission of a patient. Also, current methods
and tools used to predict patients at risk of admission fail to deal with
uncertainty in unplanned admission, LOS, patients- characteristics.
The main objective of this paper is to deal with uncertainty in
health system variables, and handles uncertain relationship among
variables. An introduction of machine learning techniques along with
statistical methods like Regression methods can be a proposed
solution approach to handle uncertainty in health system variables. A
model that adapts fuzzy methods to handle uncertain data and
uncertain relationships can be an efficient solution to capture the
vague definition of admission of a patient.
Abstract: High-velocity oxygen fuel (HVOF) thermal spraying
uses a combustion process to heat the gas flow and coating material.
A computational fluid dynamics (CFD) model has been developed to
predict gas dynamic behavior in a HVOF thermal spray gun in which
premixed oxygen and propane are burnt in a combustion chamber
linked to a parallel-sided nozzle. The CFD analysis is applied to
investigate axisymmetric, steady-state, turbulent, compressible,
chemically reacting, subsonic and supersonic flow inside and outside
the gun. The gas velocity, temperature, pressure and Mach number
distributions are presented for various locations inside and outside
the gun. The calculated results show that the most sensitive
parameters affecting the process are fuel-to-oxygen gas ratio and
total gas flow rate. Gas dynamic behavior along the centerline of the
gun depends on both total gas flow rate and fuel-to-oxygen gas ratio.
The numerical simulations show that the axial gas velocity and Mach
number distribution depend on both flow rate and ratio; the highest
velocity is achieved at the higher flow rate and most fuel-rich ratio.
In addition, the results reported in this paper illustrate that the
numerical simulation can be one of the most powerful and beneficial
tools for the HVOF system design, optimization and performance
analysis.
Abstract: A numerical prediction of flow in a tube bank is reported. The flow regimes considered cover a wide range of Reynolds numbers, which range from 380 to 99000 and which are equivalent to a range of inlet velocities from very low (0.072 m/s) to very high (60 m/s). In this study, calculations were made using the standard k-e model with standard wall function. The drag coefficient, skin friction drag, pressure drag, and pressure distribution around a tube were investigated. As the velocity increased, the drag coefficient decreased until the velocity exceeded 45 m/s, after which it increased. Furthermore, the pressure drag and skin friction drag depend on the velocity.
Abstract: To respond to the Kyoto Protocol, the policy of Payment for Environmental Service (PES), which was entitled “Plain Landscape Afforestation Program (PLAP)", was certified by Executive Yuan in Taiwan on 31 August 2001 and has been implementing for six years since 1 January 2002. Although the PLAP has received a lot of positive comments, there are still many difficulties during the process of implementation, such as insufficient technology for afforestation, private landowners- low interests in participating in PLAP, insufficient subsidies, and so on, which are potential threats that hinder the PLAP from moving forward in future. In this paper, selecting Ping-Tung County in Taiwan as a sample region and targeting those private landowners with and without intention to participate in the PLAP, respectively, we conduct an empirical analysis based on the Logit model to investigate the factors that determine whether those private landowners join the PLAP, so as to realize the incentive effects of the PLAP upon the personal decision on afforestation. The possible factors that might determine private landowner-s participation in the PLAP include landowner-s characteristics, cropland characteristics, as well as policy factors. Among them, the policy factors include afforestation subsidy amount (+), duration of afforestation subsidy (+), the rules on adjoining and adjacent areas (+), and so on, which do not reach the remarkable level in statistics though, but the directions of variable signs are consistent with the intuition behind the policy. As for the landowners- characteristics, each of age (+), education level (–), and annual household income (+) variables reaches 10% of the remarkable level in statistics; as for the cropland characteristics, each of cropland area (+), cropland price (–), and the number of cropland parcels (–) reaches 1% of the remarkable level in statistics. In light of the above, the cropland characteristics are the dominate factor that determines the probability of landowner-s participation in the PLAP. In the Logit model established by this paper, the probability of correctly estimating nonparticipants is 98%, the probability of correctly estimating the participants is 71.8%, and the probability for the overall estimation is 95%. In addition, Hosmer-Lemeshow test and omnibus test also revealed that the Logit model in this paper may provide fine goodness of fit and good predictive power in forecasting private landowners- participation in this program. The empirical result of this paper expects to help the implementation of the afforestation programs in Taiwan.
Abstract: Technological newness and innovativeness are
important aspects of small firm development, growth and wealth
creation. The contribution of the study to entrepreneurship
personality research and to technology-related research in
entrepreneurship is that the model of the general personality driven
technological development was developed and empirically tested.
Hypotheses relating the big five personality factors (OCEAN:
openness, conscientiousness, extraversion, agreeableness, and
neuroticism) and technological developments were tested by using
multiple regression analysis on survey data from a sample of 160
entrepreneurs from Slovenia. The model reveals two personality
factors, which are predictive of technological developments:
openness (positive impact) and neuroticism (negative impact). In
addition, a positive impact of firm age on technological
developments was found. Other personality factors
(conscientiousness, extraversion and agreeableness) of entrepreneurs
may not be considered important for their firm technological
developments.
Abstract: An artificial neural network (ANN) approach was used to model the energy consumption of wheat production. This study was conducted over 35,300 hectares of irrigated and dry land wheat fields in Canterbury in the 2007-2008 harvest year.1 In this study several direct and indirect factors have been used to create an artificial neural networks model to predict energy use in wheat production. The final model can predict energy consumption by using farm condition (size of wheat area and number paddocks), farmers- social properties (education), and energy inputs (N and P use, fungicide consumption, seed consumption, and irrigation frequency), it can also predict energy use in Canterbury wheat farms with error margin of ±7% (± 1600 MJ/ha).
Abstract: Some Chromium (III) complexes were synthesized
with three amino acids: L Glutamic Acid, Glycine, and L-cysteine as
the ligands, in order to provide a new supplement containing Cr(III)
for patients with type 2 diabetes mellitus. The complexes have been
prepared by refluxing a mixture of Chromium(III) chloride in
aqueous solution with L-glutamic acid, Glycine, and L-cysteine after
pH adjustment by sodium hydroxide. These complexes were
characterized by Infrared and Uv-Vis spectrophotometer and
Elemental analyzer. The product yields of four products were 87.50
and 56.76% for Cr-Glu complexes, 46.70% for Cr-Gly complex and
40.08% for Cr-Cys complex respectively. The predicted structure of
the complexes are [Cr(glu)2(H2O)2].xH2O, Cr(gly)3..xH2O and
Cr(cys)3.xH2O., respectively.
Abstract: The objective of this research was to investigate biodegradation of water hyacinth (Eichhornia crassipes) to produce bioethanol using dilute-acid pretreatment (1% sulfuric acid) results in high hemicellulose decomposition and using yeast (Pachysolen tannophilus) as bioethanol producing strain. A maximum ethanol yield of 1.14g/L with coefficient, 0.24g g-1; productivity, 0.015g l-1h-1 was comparable to predicted value 32.05g/L obtained by Central Composite Design (CCD). Maximum ethanol yield coefficient was comparable to those obtained through enzymatic saccharification and fermentation of acid hydrolysate using fully equipped fermentor. Although maximum ethanol concentration was low in lab scale, the improvement of lignocellulosic ethanol yield is necessary for large scale production.
Abstract: Series of experimental tests were conducted on a
section of a 660 kW wind turbine blade to measure the pressure
distribution of this model oscillating in plunging motion. In order to
minimize the amount of data required to predict aerodynamic loads
of the airfoil, a General Regression Neural Network, GRNN, was
trained using the measured experimental data. The network once
proved to be accurate enough, was used to predict the flow behavior
of the airfoil for the desired conditions.
Results showed that with using a few of the acquired data, the
trained neural network was able to predict accurate results with
minimal errors when compared with the corresponding measured
values. Therefore with employing this trained network the
aerodynamic coefficients of the plunging airfoil, are predicted
accurately at different oscillation frequencies, amplitudes, and angles
of attack; hence reducing the cost of tests while achieving acceptable
accuracy.
Abstract: Workflow Management Systems (WfMS) alloworganizations to streamline and automate business processes and reengineer their structure. One important requirement for this type of system is the management and computation of the Quality of Service(QoS) of processes and workflows. Currently, a range of Web processes and workflow languages exist. Each language can be characterized by the set of patterns they support. Developing andimplementing a suitable and generic algorithm to compute the QoSof processes that have been designed using different languages is a difficult task. This is because some patterns are specific to particular process languages and new patterns may be introduced in future versions of a language. In this paper, we describe an adaptive algorithm implemented to cope with these two problems. The algorithm is called adaptive since it can be dynamically changed as the patterns of a process language also change.
Abstract: The focus in this work is to assess which method
allows a better forecasting of malaria cases in Bujumbura ( Burundi)
when taking into account association between climatic factors and
the disease. For the period 1996-2007, real monthly data on both
malaria epidemiology and climate in Bujumbura are described and
analyzed. We propose a hierarchical approach to achieve our
objective. We first fit a Generalized Additive Model to malaria cases
to obtain an accurate predictor, which is then used to predict future
observations. Various well-known forecasting methods are compared
leading to different results. Based on in-sample mean average
percentage error (MAPE), the multiplicative exponential smoothing
state space model with multiplicative error and seasonality performed
better.
Abstract: One of the essential sectors of Myanmar economy is
agriculture which is sensitive to climate variation. The most
important climatic element which impacts on agriculture sector is
rainfall. Thus rainfall prediction becomes an important issue in
agriculture country. Multi variables polynomial regression (MPR)
provides an effective way to describe complex nonlinear input output
relationships so that an outcome variable can be predicted from the
other or others. In this paper, the modeling of monthly rainfall
prediction over Myanmar is described in detail by applying the
polynomial regression equation. The proposed model results are
compared to the results produced by multiple linear regression model
(MLR). Experiments indicate that the prediction model based on
MPR has higher accuracy than using MLR.
Abstract: In the paper an effective context based lossless coding
technique is presented. Three principal and few auxiliary contexts are
defined. The predictor adaptation technique is an improved CoBALP
algorithm, denoted CoBALP+. Cumulated predictor error combining
8 bias estimators is calculated. It is shown experimentally that
indeed, the new technique is time-effective while it outperforms the
well known methods having reasonable time complexity, and is
inferior only to extremely computationally complex ones.
Abstract: In this paper, we propose a Connect6 solver which
adopts a hybrid approach based on a tree-search algorithm and image
processing techniques. The solver must deal with the complicated
computation and provide high performance in order to make real-time
decisions. The proposed approach enables the solver to be
implemented on a single Spartan-6 XC6SLX45 FPGA produced by
XILINX without using any external devices. The compact
implementation is achieved through image processing techniques to
optimize a tree-search algorithm of the Connect6 game. The tree
search is widely used in computer games and the optimal search brings
the best move in every turn of a computer game. Thus, many
tree-search algorithms such as Minimax algorithm and artificial
intelligence approaches have been widely proposed in this field.
However, there is one fundamental problem in this area; the
computation time increases rapidly in response to the growth of the
game tree. It means the larger the game tree is, the bigger the circuit
size is because of their highly parallel computation characteristics.
Here, this paper aims to reduce the size of a Connect6 game tree using
image processing techniques and its position symmetric property. The
proposed solver is composed of four computational modules: a
two-dimensional checkmate strategy checker, a template matching
module, a skilful-line predictor, and a next-move selector. These
modules work well together in selecting next moves from some
candidates and the total amount of their circuits is small. The details of
the hardware design for an FPGA implementation are described and
the performance of this design is also shown in this paper.
Abstract: In this study, a fuzzy integrated logical forecasting method (FILF) is extended for multi-variate systems by using a vector autoregressive model. Fuzzy time series forecasting (FTSF) method was recently introduced by Song and Chissom [1]-[2] after that Chen improved the FTSF method. Rather than the existing literature, the proposed model is not only compared with the previous FTS models, but also with the conventional time series methods such as the classical vector autoregressive model. The cluster optimization is based on the C-means clustering method. An empirical study is performed for the prediction of the chartering rates of a group of dry bulk cargo ships. The root mean squared error (RMSE) metric is used for the comparing of results of methods and the proposed method has superiority than both traditional FTS methods and also the classical time series methods.
Abstract: In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.
Abstract: The objective of this research was to study the
influence of marketing mix on customers purchasing behavior. A
total of 397 respondents were collected from customers who were the
patronages of the Chatuchak Plaza market. A questionnaire was
utilized as a tool to collect data. Statistics utilized in this research
included frequency, percentage, mean, standard deviation, and
multiple regression analysis. Data were analyzed by using Statistical
Package for the Social Sciences. The findings revealed that the
majority of respondents were male with the age between 25-34 years
old, hold undergraduate degree, married and stay together. The
average income of respondents was between 10,001-20,000 baht. In
terms of occupation, the majority worked for private companies. The
research analysis disclosed that there were three variables of
marketing mix which included price (X2), place (X3), and product
(X1) which had an influence on the frequency of customer
purchasing. These three variables can predict a purchase about 30
percent of the time by using the equation; Y1 = 6.851 + .921(X2) +
.949(X3) + .591(X1). It also found that in terms of marketing mixed,
there were two variables had an influence on the amount of customer
purchasing which were physical characteristic (X6), and the process
(X7). These two variables are 17 percent predictive of a purchasing
by using the equation: Y2 = 2276.88 + 2980.97(X6) + 2188.09(X7).
Abstract: The aim of this study is to test the “work values"
inventory developed by Tevruz and Turgut and to utilize the concept
in a model, which aims to create a greater understanding of the work
experience. In the study multiple effects of work values, work-value
congruence and work centrality on organizational citizenship
behavior are examined. In this respect, it is hypothesized that work
values and work-value congruence predict organizational citizenship
behavior through work centrality. Work-goal congruence test, Tevruz
and Turgut-s work values inventory are administered along with
Kanungo-s work centrality and Podsakoff et al.-s [47] organizational
citizenship behavior test to employees working in Turkish SME-s.
The study validated that Tevruz and Turgut-s work values inventory
and the work-value congruence test were reliable and could be used
for future research. The study revealed the mediating role of work
centrality only for the relationship of work values and the
responsibility dimension of citizenship behavior. Most important, this
study brought in an important concept, work-value congruence,
which enables a better understanding of work values and their
relation to various attitudinal variables.