Abstract: Our objectives were to evaluate the effects of sire
breed, type of protein supplement, level of supplementation and sex
on wool spinning fineness (SF), its correlations with other wool
characteristics and prediction accuracy in F1 Merino crossbred lambs.
Texel, Coopworth, White Suffolk, East Friesian and Dorset rams
were mated with 500 purebred Merino dams at a ratio of 1:100 in
separate paddocks within a single management system. The F1
progeny were raised on ryegrass pasture until weaning, before forty
lambs were randomly allocated to treatments in a 5 x 2 x 2 x 2
factorial experimental design representing 5 sire breeds, 2
supplementary feeds (canola or lupins), 2 levels of supplementation
(1% or 2% of liveweight) and sex (wethers or ewes). Lambs were
supplemented for six weeks after an initial three weeks of adjustment,
wool sampled at the commencement and conclusion of the feeding
trial and analyzed for SF, mean fibre diameter (FD), coefficient of
variation (CV), standard deviation, comfort factor (CF), fibre
curvature (CURV), and clean fleece yield. Data were analyzed using
mixed linear model procedures with sire fitted as a random effect,
and sire breed, sex, supplementary feed type, level of
supplementation and their second-order interactions as fixed effects.
Sire breed (P
Abstract: This paper proposes a novel approach to the question of lithofacies classification based on an assessment of the uncertainty in the classification results. The proposed approach has multiple neural networks (NN), and interval neutrosophic sets (INS) are used to classify the input well log data into outputs of multiple classes of lithofacies. A pair of n-class neural networks are used to predict n-degree of truth memberships and n-degree of false memberships. Indeterminacy memberships or uncertainties in the predictions are estimated using a multidimensional interpolation method. These three memberships form the INS used to support the confidence in results of multiclass classification. Based on the experimental data, our approach improves the classification performance as compared to an existing technique applied only to the truth membership. In addition, our approach has the capability to provide a measure of uncertainty in the problem of multiclass classification.
Abstract: The ability to predict an accurate temperature
distribution requires the knowledge of the losses, the thermal
characteristics of the materials, and the cooling conditions, all of
which are very difficult to quantify. In this paper, the impact of the
effects of iron and copper losses are investigated separately and
their effects on the heating in various points of the stator of an
induction motor, is highlighted by using two simple tests. In addition,
the effect of a defect, such as an open circuit in a phase of the stator,
on the heating is also obtained by a no-load test.
The squirrel cage induction motor is rated at 2.2 kW; 380 V; 5.2
A; Δ connected; 50 Hz; 1420 rpm and the class of insulation F, has
been thermally tested under several load conditions. Several
thermocouples were placed in strategic points of the stator.
Abstract: Lateral expansion is a factor defining the level of
confinement in reinforced concrete columns. Therefore, predicting
the lateral strain relationship with axial strain becomes an important
issue. Measuring lateral strains in experiments is difficult and only
few report experimental lateral strains. Among the existing analytical
formulations, two recent models are compared with available test
results in this paper with shortcomings highlighted. A new analytical
model is proposed here for lateral strain axial strain relationship and
is based on the supposition that the concrete behaves linear elastic in
the early stages of loading and then nonlinear hardening up to the
peak stress and then volumetric expansion. The proposal for the
lateral strain axial strain relationship after the peak stress is mainly
based on the hypothesis that the plastic lateral strain varies linearly
with the plastic axial strain and it is shown that this is related to the
lateral confinement level.
Abstract: Predicting protein-protein interactions represent a key step in understanding proteins functions. This is due to the fact that proteins usually work in context of other proteins and rarely function alone. Machine learning techniques have been applied to predict protein-protein interactions. However, most of these techniques address this problem as a binary classification problem. Although it is easy to get a dataset of interacting proteins as positive examples, there are no experimentally confirmed non-interacting proteins to be considered as negative examples. Therefore, in this paper we solve this problem as a one-class classification problem using one-class support vector machines (SVM). Using only positive examples (interacting protein pairs) in training phase, the one-class SVM achieves accuracy of about 80%. These results imply that protein-protein interaction can be predicted using one-class classifier with comparable accuracy to the binary classifiers that use artificially constructed negative examples.
Abstract: Stable bacterial polymorphism on a single limiting resource may appear if between the evolved strains metabolic interactions take place that allow the exchange of essential nutrients [8]. Towards an attempt to predict the possible outcome of longrunning evolution experiments, a network based on the metabolic capabilities of homogeneous populations of every single gene knockout strain (nodes) of the bacterium E. coli is reconstructed. Potential metabolic interactions (edges) are allowed only between strains of different metabolic capabilities. Bacterial communities are determined by finding cliques in this network. Growth of the emerged hypothetical bacterial communities is simulated by extending the metabolic flux balance analysis model of Varma et al [2] to embody heterogeneous cell population growth in a mutual environment. Results from aerobic growth on 10 different carbon sources are presented. The upper bounds of the diversity that can emerge from single-cloned populations of E. coli such as the number of strains that appears to metabolically differ from most strains (highly connected nodes), the maximum clique size as well as the number of all the possible communities are determined. Certain single gene deletions are identified to consistently participate in our hypothetical bacterial communities under most environmental conditions implying a pattern of growth-condition- invariant strains with similar metabolic effects. Moreover, evaluation of all the hypothetical bacterial communities under growth on pyruvate reveals heterogeneous populations that can exhibit superior growth performance when compared to the performance of the homogeneous wild-type population.
Abstract: The significant effects of the interactions between the
system boundaries and the near wall molecules in miniaturized
gaseous devices lead to the formation of the Knudsen layer in which
the Navier-Stokes-Fourier (NSF) equations fail to predict the correct
associated phenomena. In this paper, the well-known lattice
Boltzmann method (LBM) is employed to simulate the fluid flow and
heat transfer processes in rarefied gaseous micro media. Persuaded
by the problematic deficiency of the LBM in capturing the Knudsen
layer phenomena, present study tends to concentrate on the effective
molecular mean free path concept the main essence of which is to
compensate the incapability of this mesoscopic method in dealing
with the momentum and energy transport within the above mentioned
kinetic boundary layer. The results show qualitative and quantitative
accuracy comparable to the solutions of the linearized Boltzmann
equation or the DSMC data for the Knudsen numbers of O (1) .
Abstract: This study investigated the relationship between urban
and rural ozone concentrations and quantified the extent to which
ambient rural conditions and the concentrations of other pollutants
can be used to predict urban ozone concentrations. The study
describes the variations of ozone in weekday and weekends as well as
the daily maximum recorded at selected monitoring stations. The
results showed that Putrajaya station had the highest concentrations
of O3 on weekend due the titration of NO during the weekday.
Additionally, Jerantut had the lowest average concentration with a
reading value high on Wednesdays. The comparisons of average and
maximum concentrations of ozone for the three stations showed that
the strongest significant correlation is recorded in Jerantut station
with the value R2= 0.769. Ozone concentrations originating from a
neighbouring urban site form a better predictor to the urban ozone
concentrations than widespread rural ozone at some levels of
temporal averaging. It is found that in urban and rural of Malaysian
peninsular, the concentration of ozone depends on the concentration
of NOx and seasonal meteorological factors. The HYSPLIT Model
(the northeast monsoon) showed that the wind direction can also
influence the concentration of ozone in the atmosphere in the studied
areas.
Abstract: Classification is an interesting problem in functional
data analysis (FDA), because many science and application problems
end up with classification problems, such as recognition, prediction,
control, decision making, management, etc. As the high dimension
and high correlation in functional data (FD), it is a key problem to
extract features from FD whereas keeping its global characters, which
relates to the classification efficiency and precision to heavens. In this
paper, a novel automatic method which combined Genetic Algorithm
(GA) and classification algorithm to extract classification features is
proposed. In this method, the optimal features and classification model
are approached via evolutional study step by step. It is proved by
theory analysis and experiment test that this method has advantages in
improving classification efficiency, precision and robustness whereas
using less features and the dimension of extracted classification
features can be controlled.
Abstract: In this paper a new Joint Adaptive Block Matching
Search (JABMS) algorithm is proposed to generate motion vector
and search a best match macro block by classifying the motion vector
movement based on prediction error. Diamond Search (DS)
algorithm generates high estimation accuracy when motion vector is
small and Adaptive Rood Pattern Search (ARPS) algorithm can
handle large motion vector but is not very accurate. The proposed
JABMS algorithm which is capable of considering both small and
large motions gives improved estimation accuracy and the
computational cost is reduced by 15.2 times compared with
Exhaustive Search (ES) algorithm and is 1.3 times less compared
with Diamond search algorithm.
Abstract: Tailor-welded Blanks (TWBs) are tailor made for
different complex component designs by welding multiple metal
sheets with different thicknesses, shapes, coatings or strengths prior
to forming. In this study the Hemispherical Die Stretching (HDS) test
(out-of-plane stretching) of TWBs were simulated via
ABAQUS/Explicit to obtain the Forming Limit Diagrams (FLDs) of
Stainless steel (AISI 304) laser welded blanks with different
thicknesses. Two criteria were used to detect the start of necking to
determine the FLD for TWBs and parent sheet metals. These two
criteria are the second derivatives of the major and thickness strains
that are given from the strain history of simulation. In the other word,
in these criteria necking starts when the second derivative of
thickness or major strain reaches its maximum. With having the time
of onset necking, one can measure the major and minor strains at the
critical area and determine the forming limit curve.
Abstract: The study attempted to identify the dominant
intelligences of athletes by comparing the developmental differences
of multiple intelligences between athletes and non-athletes. The
weekly specialized training hours and years of specialized training
was examined to see how it can predict the dominant intelligence with
the age factor controlled. There were 355 participants in the research
(202 athletes and 153 non-athletes). Collected data were analyzed with
one-way MANOVA and multiple hierarchical regression. The results
suggested the dominant intelligences of athletes were Interpersonal
Intelligence, Bodily-Kinesthetic Intelligence, and Intrapersonal
Intelligence. The weekly specialized training hours and years of
specialized training could effectively predict the Interpersonal
Intelligence, Bodily-Kinesthetic Intelligence, and Intrapersonal
Intelligence of athletes. The author suggested the future studies could
focus on the theory construction of weekly specialized training and
years of specialized training. Also, the studies on using “Bridge
strategy" by the athletes to guide disadvantage intelligences with
dominant intelligences are highly valued.
Abstract: This paper presents the use of the predictive fuzzy logic controller (PFLC) applied to attitude control system for agile micro-satellite. In order to reduce the effect of unpredictable time delays and large uncertainties, the algorithm employs predictive control to predict the attitude of the satellite. Comparison of the PFLC and conventional fuzzy logic controller (FLC) is presented to evaluate the performance of the control system during attitude maneuver. The two proposed models have been analyzed with the same level of noise and external disturbances. Simulation results demonstrated the feasibility and advantages of the PFLC on the attitude determination and control system (ADCS) of agile satellite.
Abstract: The evaluation of energy release rate and centre Crack
Opening Displacement (COD) for circumferential Through-Wall
Cracked (TWC) pipes is an important issue in the assessment of
critical crack length for unstable fracture. The ability to predict crack
growth continues to be an important component of research for
several structural materials. Crack growth predictions can aid the
understanding of the useful life of a structural component and the
determination of inspection intervals and criteria. In this context,
studies were carried out at CSIR-SERC on Nuclear Power Plant
(NPP) piping components subjected to monotonic as well as cyclic
loading to assess the damage for crack growth due to low-cycle
fatigue in circumferentially TWC pipes.
Abstract: In this paper 2D Simulation of catalytic Fixed Bed Reactor in Fischer-Tropsch Synthesis of GTL technology has been performed utilizing computational fluid dynamics (CFD). Synthesis gas (a mixture of carbon monoxide and hydrogen) has been used as feedstock. The reactor was modeled and the model equations were solved employing finite volume method. The model was validated against the experimental data reported in literature. The comparison showed a good agreement between simulation results and the experimental data. In addition, the model was applied to predict the concentration contours of the reactants and products along the length of reactor.
Abstract: In this work, the results of mixing study by a jet mixer in a tank have been investigated in the laboratory scale. The tank dimensions are H/D=1 and the jet entrance have been considered in
the center of upper surface of tank. RNG-k-ε model is used as the
turbulent model for the prediction of the pattern of turbulent flow
inside the tank. For this purpose, a tank with volume of 110 liter is
simulated and it has been divided into 410,000 tetrahedral control
cells for performing the calculations. The grids at the vicinity of the
nozzle and suction pare are finer to get more accurate results. The
experimental results showed that in a vertical jet, the lowest mixing
time takes place at 35 degree. In addition, mixing time decreased by
increasing the Reynolds number. Furthermore, the CFD simulation
predicted the items as well a flow patterns precisely that validates the
experiments.
Abstract: Prediction of highly non linear behavior of suspended
sediment flow in rivers has prime importance in the field of water
resources engineering. In this study the predictive performance of
two Artificial Neural Networks (ANNs) namely, the Radial Basis
Function (RBF) Network and the Multi Layer Feed Forward (MLFF)
Network have been compared. Time series data of daily suspended
sediment discharge and water discharge at Pari River was used for
training and testing the networks. A number of statistical parameters
i.e. root mean square error (RMSE), mean absolute error (MAE),
coefficient of efficiency (CE) and coefficient of determination (R2)
were used for performance evaluation of the models. Both the models
produced satisfactory results and showed a good agreement between
the predicted and observed data. The RBF network model provided
slightly better results than the MLFF network model in predicting
suspended sediment discharge.
Abstract: In this paper, an ultrasonic technique is proposed to
predict oil content in a fresh palm fruit. This is accomplished by
measuring the attenuation based on ultrasonic transmission mode.
Several palm fruit samples with known oil content by Soxhlet
extraction (ISO9001:2008) were tested with our ultrasonic
measurement. Amplitude attenuation data results for all palm samples
were collected. The Feedforward Neural Networks (FNNs) are
applied to predict the oil content for the samples. The Root Mean
Square Error (RMSE) and Mean Absolute Error (MAE) of the FNN
model for predicting oil content percentage are 7.6186 and 5.2287
with the correlation coefficient (R) of 0.9193.
Abstract: This paper present a new way to find the aerodynamic characteristic equation of missile for the numerical trajectories prediction more accurate. The goal is to obtain the polynomial equation based on two missile characteristic parameters, angle of attack (α ) and flight speed (╬¢ ). First, the understudied missile is modeled and used for flow computational model to compute aerodynamic force and moment. Assume that performance range of understudied missile where range -10< α
Abstract: A dead leg is a typical subsea production system
component. CFD is required to model heat transfer within the dead
leg. Unfortunately its solution is time demanding and thus not
suitable for fast prediction or repeated simulations. Therefore there is
a need to create a thermal FEA model, mimicking the heat flows and
temperatures seen in CFD cool down simulations.
This paper describes the conventional way of tuning and a new
automated way using parametric model order reduction (PMOR)
together with an optimization algorithm. The tuned FE analyses
replicate the steady state CFD parameters within a maximum error in
heat flow of 6 % and 3 % using manual and PMOR method
respectively. During cool down, the relative error of the tuned FEA
models with respect to temperature is below 5% comparing to the
CFD. In addition, the PMOR method obtained the correct FEA setup
five times faster than the manually tuned FEA.