Abstract: The problems associated with wind predictions of
WAsP model in complex terrain are already the target of several
studies in the last decade. In this paper, the influence of surrounding
orography on accuracy of wind data analysis of a train is
investigated. For the case study, a site with complex surrounding
orography is considered. This site is located in Manjil, one of the
windiest cities of Iran. For having precise evaluation of wind regime
in the site, one-year wind data measurements from two metrological
masts are used. To validate the obtained results from WAsP, the
cross prediction between each mast is performed. The analysis
reveals that WAsP model can estimate the wind speed behavior
accurately. In addition, results show that this software can be used
for predicting the wind regime in flat sites with complex surrounding
orography.
Abstract: This paper aims to improve a fine lapping process of
hard disk drive (HDD) lapping machines by removing materials from
each slider together with controlling the strip height (SH) variation to
minimum value. The standard deviation is the key parameter to
evaluate the strip height variation, hence it is minimized. In this
paper, a design of experiment (DOE) with factorial analysis by twoway
analysis of variance (ANOVA) is adopted to obtain a
statistically information. The statistics results reveal that initial stripe
height patterns affect the final SH variation. Therefore, initial SH
classification using a radial basis function neural network is
implemented to achieve the proportional gain prediction.
Abstract: This paper presents a novel method for prediction of
the mechanical behavior of proximal femur using the general
framework of the quantitative computed tomography (QCT)-based
finite element Analysis (FEA). A systematic imaging and modeling
procedure was developed for reliable correspondence between the
QCT-based FEA and the in-vitro mechanical testing. A speciallydesigned
holding frame was used to define and maintain a unique
geometrical reference system during the analysis and testing. The
QCT images were directly converted into voxel-based 3D finite
element models for linear and nonlinear analyses. The equivalent
plastic strain and the strain energy density measures were used to
identify the critical elements and predict the failure patterns. The
samples were destructively tested using a specially-designed gripping
fixture (with five degrees of freedom) mounted within a universal
mechanical testing machine. Very good agreements were found
between the experimental and the predicted failure patterns and the
associated load levels.
Abstract: Attempt was made to improve certain characteristics of bio-oil derived from palm kernel pyrolysis by blending it with diesel fuel and alcohols. Two types of alcohol, ethanol or butanol, was used as cosolvent to stabilize the phase of ternary systems. Phase behaviors and basic fuel properties of palm kernel bio-oildiesel- alcohol systems were investigated in this study. Alcohol types showed a significant influence on the phase characteristics with palm kernel bio-oil-diesel-butanol system giving larger soluble area than that of palm kernel bio-oil-diesel-ethanol system. For fuel properties, blended fuels showed superior properties including lower values of density (~860 kg/m3 at 25°C), viscosity (~4.12 mm2/s at 40°C), carbon residue (1.02-2.53 wt%), ash (0.018-0.034 wt%) and pour point (
Abstract: The prediction of transmembrane helical segments
(TMHs) in membrane proteins is an important field in the
bioinformatics research. In this paper, a method based on discrete
wavelet transform (DWT) has been developed to predict the number
and location of TMHs in membrane proteins. PDB coded as 1F88 was
chosen as an example to describe the prediction of the number and
location of TMHs in membrane proteins by using this method. One
group of test data sets that contain total 19 protein sequences was
utilized to access the effect of this method. Compared with the
prediction results of DAS, PRED-TMR2, SOSUI, HMMTOP2.0 and
TMHMM2.0, the obtained results indicate that the presented method
has higher prediction accuracy.
Abstract: Researchers have long had trouble in measurement of
Exchangeable Sodium Ratio (ESR) at salt-affected soils. this
parameter are often determined using laborious and time consuming
laboratory tests, but it may be more appropriate and economical to
develop a method which uses a more simple soil salinity index. The
aim of this study was to determine the relationship between
exchangeable sodium ratio (ESR) and sodium adsorption ratio (SAR)
in some salt-affected soils of Khuzestan plain. To this purpose, two
experimental areas (S1, S2) of Khuzestan province-IRAN were
selected and four treatments with three replications by series of
double rings were applied. The treatments were included 25cm,
50cm, 75cm and 100cm water application. The statistical results of
the study indicated that in order to predict soil ESR based on soil
SAR the linear regression model ESR=0.2048+0.0066 SAR
(R2=0.53) & ESR=0.0564+0.0171 SAR (R2=0.76) can be
recommended in Pilot S1 and S2 respectively.
Abstract: A procedural-animation-based approach which rapidly
synthesize the adaptive locomotion for quadruped characters that they
can walk or run in any directions on an uneven terrain within a
dynamic environment was proposed. We devise practical motion
models of the quadruped animals for adapting to a varied terrain in a
real-time manner. While synthesizing locomotion, we choose the
corresponding motion models by means of the footstep prediction of
the current state in the dynamic environment, adjust the key-frames of
the motion models relying on the terrain-s attributes, calculate the
collision-free legs- trajectories, and interpolate the key-frames
according to the legs- trajectories. Finally, we apply dynamic time
warping to each part of motion for seamlessly concatenating all desired
transition motions to complete the whole locomotion. We reduce the
time cost of producing the locomotion and takes virtual characters to
fit in with dynamic environments no matter when the environments are
changed by users.
Abstract: Artificial Neural Networks (ANNs) have been used successfully in many scientific, industrial and business domains as a method for extracting knowledge from vast amounts of data. However the use of ANN techniques in the sporting domain has been limited. In professional sport, data is stored on many aspects of teams, games, training and players. Sporting organisations have begun to realise that there is a wealth of untapped knowledge contained in the data and there is great interest in techniques to utilise this data. This study will use player data from the elite Australian Football League (AFL) competition to train and test ANNs with the aim to predict the onset of injuries. The results demonstrate that an accuracy of 82.9% was achieved by the ANNs’ predictions across all examples with 94.5% of all injuries correctly predicted. These initial findings suggest that ANNs may have the potential to assist sporting clubs in the prediction of injuries.
Abstract: Identifying protein coding regions in DNA sequences is a basic step in the location of genes. Several approaches based on signal processing tools have been applied to solve this problem, trying to achieve more accurate predictions. This paper presents a new predictor that improves the efficacy of three techniques that use the Fourier Transform to predict coding regions, and that could be computed using an algorithm that reduces the computation load. Some ideas about the combination of the predictor with other methods are discussed. ROC curves are used to demonstrate the efficacy of the proposed predictor, based on the computation of 25 DNA sequences from three different organisms.
Abstract: Cosmic showers, from their places of origin in space,
after entering earth generate secondary particles called Extensive Air
Shower (EAS). Detection and analysis of EAS and similar High
Energy Particle Showers involve a plethora of experimental setups
with certain constraints for which soft-computational tools like
Artificial Neural Network (ANN)s can be adopted. The optimality
of ANN classifiers can be enhanced further by the use of Multiple
Classifier System (MCS) and certain data - dimension reduction
techniques. This work describes the performance of certain data
dimension reduction techniques like Principal Component Analysis
(PCA), Independent Component Analysis (ICA) and Self Organizing
Map (SOM) approximators for application with an MCS formed
using Multi Layer Perceptron (MLP), Recurrent Neural Network
(RNN) and Probabilistic Neural Network (PNN). The data inputs are
obtained from an array of detectors placed in a circular arrangement
resembling a practical detector grid which have a higher dimension
and greater correlation among themselves. The PCA, ICA and SOM
blocks reduce the correlation and generate a form suitable for real
time practical applications for prediction of primary energy and
location of EAS from density values captured using detectors in a
circular grid.
Abstract: This paper presents the prediction of kidney
dysfunction using different neural network (NN) approaches. Self
organization Maps (SOM), Probabilistic Neural Network (PNN) and
Multi Layer Perceptron Neural Network (MLPNN) trained with Back
Propagation Algorithm (BPA) are used in this study. Six hundred and
sixty three sets of analytical laboratory tests have been collected from
one of the private clinical laboratories in Baghdad. For each subject,
Serum urea and Serum creatinin levels have been analyzed and tested
by using clinical laboratory measurements. The collected urea and
cretinine levels are then used as inputs to the three NN models in
which the training process is done by different neural approaches.
SOM which is a class of unsupervised network whereas PNN and
BPNN are considered as class of supervised networks. These
networks are used as a classifier to predict whether kidney is normal
or it will have a dysfunction. The accuracy of prediction, sensitivity
and specificity were found for each type of the proposed networks
.We conclude that PNN gives faster and more accurate prediction of
kidney dysfunction and it works as promising tool for predicting of
routine kidney dysfunction from the clinical laboratory data.
Abstract: In this paper, we study the application of Extreme
Learning Machine (ELM) algorithm for single layered feedforward
neural networks to non-linear chaotic time series problems. In this
algorithm the input weights and the hidden layer bias are randomly
chosen. The ELM formulation leads to solving a system of linear
equations in terms of the unknown weights connecting the hidden
layer to the output layer. The solution of this general system of
linear equations will be obtained using Moore-Penrose generalized
pseudo inverse. For the study of the application of the method we
consider the time series generated by the Mackey Glass delay
differential equation with different time delays, Santa Fe A and
UCR heart beat rate ECG time series. For the choice of sigmoid,
sin and hardlim activation functions the optimal values for the
memory order and the number of hidden neurons which give the
best prediction performance in terms of root mean square error are
determined. It is observed that the results obtained are in close
agreement with the exact solution of the problems considered
which clearly shows that ELM is a very promising alternative
method for time series prediction.
Abstract: The physical methods for RNA secondary structure prediction are time consuming and expensive, thus methods for computational prediction will be a proper alternative. Various algorithms have been used for RNA structure prediction including dynamic programming and metaheuristic algorithms. Musician's behaviorinspired harmony search is a recently developed metaheuristic algorithm which has been successful in a wide variety of complex optimization problems. This paper proposes a harmony search algorithm (HSRNAFold) to find RNA secondary structure with minimum free energy and similar to the native structure. HSRNAFold is compared with dynamic programming benchmark mfold and metaheuristic algorithms (RnaPredict, SetPSO and HelixPSO). The results showed that HSRNAFold is comparable to mfold and better than metaheuristics in finding the minimum free energies and the number of correct base pairs.
Abstract: Two-phase frictional pressure drop data were
obtained for condensation of carbon dioxide in single horizontal
micro tube of inner diameter ranged from 0.6 mm up to 1.6 mm over
mass flow rates from 2.5*10-5 to 17*10-5 kg/s and vapor qualities
from 0.0 to 1.0. The inlet condensing pressure is changed from 33.5
to 45 bars. The saturation temperature ranged from -1.5 oC up to 10
oC. These data have then been compared against three (two-phase)
frictional pressure drop prediction methods. The first method is by
Muller-Steinhagen and Heck (Muller-Steinhagen H, Heck K. A
simple friction pressure drop correlation for two-phase flow in pipes.
Chem. Eng. Process 1986;20:297–308) and that by Gronnerud R.
Investigation of liquid hold-up, flow-resistance and heat transfer in
circulation type evaporators, part IV: two-phase flow resistance in
boiling refrigerants, Annexe 1972. Then the method used by
FriedelL. Improved friction pressures drop in horizontal and vertical
two-phase pipe flow. European Two-Phase Flow Group Meeting,
Paper E2; 1979 June, Ispra, Italy. The methods are used by M.B Ould
Didi et al (2001) “Prediction of two-phase pressure gradients of
refrigerant in horizontal tubes". Int.J.of Refrigeration 25(2002) 935-
947. The best available method for annular flow was that of Muller-
Steinhagen and Heck. It was observed that the peak in the two-phase
frictional pressure gradient is at high vapor qualities.
Abstract: For a long time as a result of accommodating car
traffic, planning ideologies in the past put a low priority on public
space, pedestrianism and the role of city space as a meeting place for
urban dwellers. In addition, according to authors such as Jan Gehl,
market forces and changing architectural perceptions began to shift
the focus of planning practice from the integration of public space in
various pockets around the contemporary city to individual buildings.
Eventually, these buildings have become increasingly more isolated
and introverted and have turned their backs to the realm of the public
space adjoining them. As a result of this practice, the traditional
function of public space as a social forum for city dwellers has in
many cases been reduced or even phased out. Author Jane Jacobs
published her seminal book “The Death and Life of Great American
Cities" more than fifty years ago, but her observations and
predictions at the time still ring true today, where she pointed out
how the dramatic increase in car traffic and its accommodation by the
urban planning ideology that was brought about by the Modern
movement has prompted a separation of the uses of the city. At the
same time it emphasizes free standing buildings that threaten urban
space and city life and result in underutilized and lifeless urban cores.
In this discussion context, the aim of this paper is to showcase a
reversal of just such a situation in the case of the Dasoupolis
neighborhood in Strovolos, Cyprus, where enlightened urban design
practice has see the reclamation of pedestrian space in a car
dominated area.
Abstract: In most cases, it is considerably difficult to directly measure structural vibration with a lot of sensors because of complex
geometry, time and equipment cost. For this reason, this paper deals
with the problem of locating sensors on a plate model by four advanced sensor placement optimization (S.P.O) techniques. It also
suggests the evaluation index representing the characteristic of orthogonal between each of natural modes. The index value provides the assistance to selecting of proper S.P.O technique and optimal
positions for monitoring of dynamic systems without the experiment.
Abstract: The purpose of this study is to investigate the
relationship between corporate governance and pricing for initial
public offerings (IPOs). Empirical result finds that the prediction of
pricing of IPOs with corporate governance added can have a rather
higher degree of predicting accuracy than that of non governance
added during the training and testing samples. Therefore, it can be
observed that corporate governance mechanism can affect the pricing
of IPOs
Abstract: An ordered porous antimony-doped tin oxide (ATO) film was successfully prepared using a simple coating process with colloidal templates. The facile production was effective when a combination of 16-nm ATO (as a model of an inorganic nanoparticle) and polystyrene (PS) spheres (as a model of the template) weresimply coated to produce a composite ATO/PS film. Heat treatment was then used to remove the PS and produce the porous film. The porous film with a spherical pore shape and a highly ordered porous structure could be obtained. A potential way for the control of pore size could be also achieved by changing initial template size. The theoretical explanation and mechanism of porous formation were also added, which would be important for the scaling-up prediction and estimation.
Abstract: The compatibility of optical resonators with microfluidic systems may be relevant for chemical and biological applications. Here, a fluorescent-core microcavity (FCM) is investigated as a refractometric sensor for heavy oils. A high-index film of silicon quantum dots (QDs) was formed inside the capillary, supporting cylindrical fluorescence whispering gallery modes (WGMs). A set of standard refractive index oils was injected into a capillary, causing a shift of the WGM resonances toward longer wavelengths. A maximum sensitivity of 240 nm/RIU (refractive index unit) was found for a nominal oil index of 1.74. As well, a sensitivity of 22 nm/RIU was obtained for a lower index of 1.48, more typical of fuel hydrocarbons. Furthermore, the observed spectra and sensitivities were compared to theoretical predictions and reproduced via FDTD simulations, showing in general an excellent agreement. This work demonstrates the potential use of FCMs for oil sensing applications and the more generally for detecting liquid solutions with a high refractive index or high viscosity.
Abstract: The flow field over a flat roof model building has been numerically investigated in order to determine threedimensional CFD guidelines for the calculation of the turbulent flow over a structure immersed in an atmospheric boundary layer. To this purpose, a complete validation campaign has been performed through a systematic comparison of numerical simulations with wind tunnel experimental data. Wind tunnel measurements and numerical predictions have been compared for five different vertical positions, respectively from the upstream leading edge to the downstream bottom edge of the analyzed model. Flow field characteristics in the neighborhood of the building model have been numerically investigated, allowing a quantification of the capabilities of the CFD code to predict the flow separation and the extension of the recirculation regions. The proposed calculations have allowed the development of a preliminary procedure to be used as guidance in selecting the appropriate grid configuration and corresponding turbulence model for the prediction of the flow field over a three-dimensional roof architecture dominated by flow separation.