Abstract: The network of delivering commodities has been an important design problem in our daily lives and many transportation applications. The delivery performance is evaluated based on the system reliability of delivering commodities from a source node to a sink node in the network. The system reliability is thus maximized to find the optimal routing. However, the design problem is not simple because (1) each path segment has randomly distributed attributes; (2) there are multiple commodities that consume various path capacities; (3) the optimal routing must successfully complete the delivery process within the allowable time constraints. In this paper, we want to focus on the design optimization of the Multi-State Flow Network (MSFN) for multiple commodities. We propose an efficient approach to evaluate the system reliability in the MSFN with respect to randomly distributed path attributes and find the optimal routing subject to the allowable time constraints. The delivery rates, also known as delivery currents, of the path segments are evaluated and the minimal-current arcs are eliminated to reduce the complexity of the MSFN. Accordingly, the correct optimal routing is found and the worst-case reliability is evaluated. It has been shown that the reliability of the optimal routing is at least higher than worst-case measure. Two benchmark examples are utilized to demonstrate the proposed method. The comparisons between the original and the reduced networks show that the proposed method is very efficient.
Abstract: A novel low-cost flight simulator with the development
goals cost effectiveness and high performance has been realized for
meeting the huge pilot training needs of airlines. The simulator
consists of an aircraft dynamics model, a sophisticated designed
low-profile electrical driven motion system with a subsided cabin, a
mixed reality based semi-virtual cockpit system, a control loading
system and some other subsystems. It shows its advantages over
traditional flight simulator by its features achieved with open
architecture, software solutions and low-cost hardware.
Abstract: Recent years have instance that there is a invigoration
of interest in drug discovery from medicinal plants for the support of
health in all parts of the world . This study was designed to examine
the in vitro antimicrobial activities of the flowers and leaves
methanolic and ethanolic extracts of Chenopodium album L.
Chenopodium album Linn. flowers and leaves were collected from
East Esfahan, Iran. The effects of methanolic and ethanolic extracts
were tested against 4 bacterial strains by using disc,well-diffusion
method. Results showed that flowers and leaves methanolic and
ethanolic extracts of C.album don-t have any activity against the
selected bacterial strains. Our study has indicated that ,there are
effective different factors on antimicrobial properties of plant extracts
Abstract: This paper presents an online method that learns the
corresponding points of an object from un-annotated grayscale images
containing instances of the object. In the first image being
processed, an ensemble of node points is automatically selected
which is matched in the subsequent images. A Bayesian posterior
distribution for the locations of the nodes in the images is formed.
The likelihood is formed from Gabor responses and the prior assumes
the mean shape of the node ensemble to be similar in a translation
and scale free space. An association model is applied for separating
the object nodes and background nodes. The posterior distribution is
sampled with Sequential Monte Carlo method. The matched object
nodes are inferred to be the corresponding points of the object
instances. The results show that our system matches the object nodes
as accurately as other methods that train the model with annotated
training images.
Abstract: This study aims to specify to what extent students
understand topology during the lesson and to determine possible
misconceptions. 14 teacher trainees registered at Secondary School
Mathematics education department were observed in the topology
lessons throughout a semester and data collected at the first topology
lesson is presented here. Students- knowledge was evaluated using a
written test right before and after the topology lesson. Thus, what the
students learnt in terms of the definition and examples of topologic
space were specified as well as possible misconceptions. The
findings indicated that students did not fully comprehend the topic
and misunderstandings were due to insufficient pre-requisite
knowledge of abstract mathematical topics and mathematical
notation.
Abstract: The principal purpose of this article is to present a new method based on Adaptive Neural Network Fuzzy Inference System (ANFIS) to generate additional artificial earthquake accelerograms from presented data, which are compatible with specified response spectra. The proposed method uses the learning abilities of ANFIS to develop the knowledge of the inverse mapping from response spectrum to earthquake records. In addition, wavelet packet transform is used to decompose specified earthquake records and then ANFISs are trained to relate the response spectrum of records to their wavelet packet coefficients. Finally, an interpretive example is presented which uses an ensemble of recorded accelerograms to demonstrate the effectiveness of the proposed method.
Abstract: Numerical analysis of flow characteristics and
separation efficiency in a high-efficiency cyclone has been performed.
Several models based on the experimental observation for a design
purpose were proposed. However, the model is only estimated the
cyclone's performance under the limited environments; it is difficult to
obtain a general model for all types of cyclones. The purpose of this
study is to find out the flow characteristics and separation efficiency
numerically. The Reynolds stress model (RSM) was employed instead
of a standard k-ε or a k-ω model which was suitable for isotropic
turbulence and it could predict the pressure drop and the Rankine
vortex very well. For small particles, there were three significant
components (entrance of vortex finder, cone, and dust collector) for
the particle separation. In the present work, the particle re-entraining
phenomenon from the dust collector to the cyclone body was observed
after considerable time. This re-entrainment degraded the separation
efficiency and was one of the significant factors for the separation
efficiency of the cyclone.
Abstract: The aim of this study was to evaluate the effect of preexercise glycerol hyperhydration on endurance performance in a heat chamber designed to simulate the World Championship Distance (WCD) duathlon (10km run, 40km ride, 5 km run). Duathlons are often performed in hot and humid conditions and as a result hydration is a major issue. Glycerol enhances the body’s capacity for fluid retention by inducing hyperhydration, which is theorized to improve thermoregulatory and cardiovascular responses, and thereby improve performance. Six well-trained athletes completed the testing protocol in a heat chamber at the La Trobe University Exercise Physiology Laboratory. Each testing session was approximately 4.5 hours in duration (2 hours of pre-exercise glycerol hyper-hydration followed by approximately 2.5 hours of exercise). The results showed an increased water retention pre-exercise and an improved overall performance of 2.04% was achieved by subjects ingesting the glycerol solution.
Abstract: recurrent neural network (RNN) is an efficient tool for
modeling production control process as well as modeling services. In
this paper one RNN was combined with regression model and were
employed in order to be checked whether the obtained data by the
model in comparison with actual data, are valid for variable process
control chart. Therefore, one maintenance process in workshop of
Esfahan Oil Refining Co. (EORC) was taken for illustration of
models. First, the regression was made for predicting the response
time of process based upon determined factors, and then the error
between actual and predicted response time as output and also the
same factors as input were used in RNN. Finally, according to
predicted data from combined model, it is scrutinized for test values
in statistical process control whether forecasting efficiency is
acceptable. Meanwhile, in training process of RNN, design of
experiments was set so as to optimize the RNN.
Abstract: The environmental performance of rapeseed oil (RO)
and rapeseed methyl ester(RME) from winter rape as fuels produced
in Romanian agroclimate is analyzed in this paper. The proposed
methodology is life cycle assessment (LCA) and takes into
consideration the influence of grain production and agroclimatic
conditions. This study shows favorable results first for RO and then
for RME. When compared to diesel fuel, both studied biofuels show
better results in the following impact categories: Abiotic depletion
potential (ADP), Ozone layer depletion (ODP) and Photochemical
ozone creation potential (POCP).Furthermore, the environmental
performance of the two biofuels studied can be improved by
changing the type of fertilizer used and also by using biofuels instead
of diesel in the field works.
Abstract: In this paper, the problem of finding the optimal
topological configuration of a deregulated distribution network is
considered. The new features of this paper are proposing a multiobjective
function and its application on deregulated distribution
networks for finding the optimal configuration. The multi-objective
function will be defined for minimizing total Energy Supply Costs
(ESC) and energy losses subject to load flow constraints. The
optimal configuration will be obtained by using Binary Genetic
Algorithm (BGA).The proposed method has been tested to analyze a
sample and a practical distribution networks.
Abstract: There is a world-wide need for the development of sustainable management strategies to control pest infestation and the development of phosphine (PH3) resistance in lesser grain borer (Rhyzopertha dominica). Computer simulation models can provide a relatively fast, safe and inexpensive way to weigh the merits of various management options. However, the usefulness of simulation models relies on the accurate estimation of important model parameters, such as mortality. Concentration and time of exposure are both important in determining mortality in response to a toxic agent. Recent research indicated the existence of two resistance phenotypes in R. dominica in Australia, weak and strong, and revealed that the presence of resistance alleles at two loci confers strong resistance, thus motivating the construction of a two-locus model of resistance. Experimental data sets on purified pest strains, each corresponding to a single genotype of our two-locus model, were also available. Hence it became possible to explicitly include mortalities of the different genotypes in the model. In this paper we described how we used two generalized linear models (GLM), probit and logistic models, to fit the available experimental data sets. We used a direct algebraic approach generalized inverse matrix technique, rather than the traditional maximum likelihood estimation, to estimate the model parameters. The results show that both probit and logistic models fit the data sets well but the former is much better in terms of small least squares (numerical) errors. Meanwhile, the generalized inverse matrix technique achieved similar accuracy results to those from the maximum likelihood estimation, but is less time consuming and computationally demanding.
Abstract: Recently many research has been conducted to
retrieve pertinent parameters and adequate models for automatic
music genre classification. In this paper, two measures based upon
information theory concepts are investigated for mapping the features
space to decision space. A Gaussian Mixture Model (GMM) is used
as a baseline and reference system. Various strategies are proposed
for training and testing sessions with matched or mismatched
conditions, long training and long testing, long training and short
testing. For all experiments, the file sections used for testing are
never been used during training. With matched conditions all
examined measures yield the best and similar scores (almost 100%).
With mismatched conditions, the proposed measures yield better
scores than the GMM baseline system, especially for the short testing
case. It is also observed that the average discrimination information
measure is most appropriate for music category classifications and on
the other hand the divergence measure is more suitable for music
subcategory classifications.
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: Sowing date and density are two important factors in
produce of coriander. A field experiment was conducted with
treatments: sowing time (5 May, 20 May, 4 June and 19 June 2009)
and plant density (10, 30, 50 and 70 plants m-2). The experimental
plots were laid out in a factorial according to a RCBD with three
replications. Results showed that the effect of sowing dates and
densities were significant on grain yield and yield components, but
interaction effects between sowing time and density were non
significant for all of traits in this trial. At sowing times 5 May, 20
May, 4 June and 19 June, grain yield obtained 736.9, 837.8, 1003.1
and 1299.6 kg ha-1, respectively. At 10, 30, 50 and 70 plants m-2,
grain yield were 794.9, 1031.0, 1092.3 and 959.3 kg ha-1,
respectively. In this experiment, sowing at 19 June and 50 and 30
plants m-2 had the most grain yield.
Abstract: 15 strains of oil-destructing microorganisms were
isolated from oil polluted soil of Western Kazakhstan. Strains 2-A
and 41-3 with the highest oil-destructing activities were chosen from
them. It was shown that these strains oxidized n-alkanes very well,
but isoalkanes, isoparaffin, cycloparaffin and heavy aromatic
compounds were destructed very slowly. These both strains were
tested as preparations for bioremediation of oil-polluted soil in model
and field experiments. The degree of utilizing of soil oil by this
preparation was 79-84 % in field experiments.
Abstract: (Bi0.5Na0.5)TiO3 doped with 8 mol % BaTiO3 powder
(BNT-BT0.08), prepared by sol-gel method was compacted and
sintered by Spark Plasma Sintering (SPS) process. The influence of
SPS temperature on the densification of BNT-BT0.08 ceramic was
investigated. Starting from sol-gel nanopowder of BNT-BT
containing 8 mol % BaTiO3 with an average particles size of about
30 nm, were obtained ceramics with density around 98 % of the
theoretical density value when the SPS temperature used was about
850 °C. The average grain size of the resulting ceramics was 80 nm.
The BNT-BT0.08 ceramic sample obtained by SPS method has shown
good electric properties at various frequencies.
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: In this study, a fuzzy similarity approach for Arabic web pages classification is presented. The approach uses a fuzzy term-category relation by manipulating membership degree for the training data and the degree value for a test web page. Six measures are used and compared in this study. These measures include: Einstein, Algebraic, Hamacher, MinMax, Special case fuzzy and Bounded Difference approaches. These measures are applied and compared using 50 different Arabic web-pages. Einstein measure was gave best performance among the other measures. An analysis of these measures and concluding remarks are drawn in this study.
Abstract: In this paper a one-dimension Self Organizing Map
algorithm (SOM) to perform feature selection is presented. The
algorithm is based on a first classification of the input dataset on a
similarity space. From this classification for each class a set of
positive and negative features is computed. This set of features is
selected as result of the procedure. The procedure is evaluated on an
in-house dataset from a Knowledge Discovery from Text (KDT)
application and on a set of publicly available datasets used in
international feature selection competitions. These datasets come
from KDT applications, drug discovery as well as other applications.
The knowledge of the correct classification available for the training
and validation datasets is used to optimize the parameters for positive
and negative feature extractions. The process becomes feasible for
large and sparse datasets, as the ones obtained in KDT applications,
by using both compression techniques to store the similarity matrix
and speed up techniques of the Kohonen algorithm that take
advantage of the sparsity of the input matrix. These improvements
make it feasible, by using the grid, the application of the
methodology to massive datasets.