Abstract: This contribution was developed from a research
within the doctoral thesis. Its object was to create multimedia
materials for sport gymnastics. Consequently we surveyed the
influence of its practical application on the efficiency of schooling at
a university. We verified the prescribed hypothesis of the efficiency
of the teaching process using the method of single-factor experiment,
where the entrance independent variable was the change of system of
tuition and the outgoing dependent variable was the change of level
of acquired motor skills. The results confirmed the positive impact of
using multimedia materials on the efficiency of the teaching process.
Further, with the aid of questionnaires, we evaluated how the tested
subjects perceive the innovative methods in sport gymnastics. The
responses showed that the students rate the application of multimedia
materials very positively.
Abstract: Grasslands of Iran are encountered with a vast
desertification and destruction. Some legumes are plants of forage
importance with high palatability. Studied legumes in this project are
Onobrychis, Medicago sativa (alfalfa) and Trifolium repens. Seeds
were cultivated in research field of Kaboutarabad (33 km East of
Isfahan, Iran) with an average 80 mm. annual rainfall. Plants were
cultivated in a split plot design with 3 replicate and two water
treatments (weekly irrigation, and under stress with same amount per
15 days interval). Water entrance to each plots were measured by
Partial flow. This project lasted 20 weeks. Destructive samplings
(1m2 each time) were done weekly. At each sampling plants were
gathered and weighed separately for each vegetative parts. An Area
Meter (Vista) was used to measure root surface and leaf area. Total
shoot and root fresh and dry weight, leaf area index and soil coverage
were evaluated too. Dry weight was achieved in 750c oven after 24
hours. Statgraphic and Harvard Graphic software were used to
formulate and demonstrate the parameters curves due to time. Our
results show that Trifolium repens has affected 60 % and Medicago
sativa 18% by water stress. Onobrychis total fresh weight was
reduced 45%. Dry weight or Biomass in alfalfa is not so affected by
water shortage. This means that in alfalfa fields we can decrease the
irrigation amount and have some how same amount of Biomass.
Onobrychis show a drastic decrease in Biomass. The increases in
total dry matter due to time in studied plants are formulated. For
Trifolium repens if removal or cattle entrance to meadows do not
occurred at perfect time, it will decrease the palatability and water
content of the shoots. Water stress in a short period could develop the
root system in Trifolium repens, but if it last more than this other
ecological and soil factors will affect the growth of this plant. Low
level of soil water is not so important for studied legume forges. But
water shortage affect palatability and water content of aerial parts.
Leaf area due to time in studied legumes is formulated. In fact leaf
area is decreased by shortage in available water. Higher leaf area
means higher forage and biomass production. Medicago and
Onobrychis reach to the maximum leaf area sooner than Trifolium
and are able to produce an optimum soil cover and inhibit the
transpiration of soil water of meadows. Correlation of root surface to
Total biomass in studied plants is formulated. Medicago under water
stress show a 40% decrease in crown cover while at optimum
condition this amount reach to 100%. In order to produce forage in
areas without soil erosion Medicago is the best choice even with a
shortage in water resources. It is tried to represent the growth
simulation of three famous Forage Legumes. By growth simulation
farmers and range managers could better decide to choose best plant
adapted to water availability without designing different time and
labor consuming field experiments.
Abstract: Email has become a fast and cheap means of online
communication. The main threat to email is Unsolicited Bulk Email
(UBE), commonly called spam email. The current work aims at
identification of unigrams in more than 2700 UBE that advertise
body-enhancement drugs. The identification is based on the
requirement that the unigram is neither present in dictionary, nor is a
slang term. The motives of the paper are many fold. This is an
attempt to analyze spamming behaviour and employment of wordmutation
technique. On the side-lines of the paper, we have
attempted to better understand the spam, the slang and their interplay.
The problem has been addressed by employing Tokenization
technique and Unigram BOW model. We found that the non-lexicon
words constitute nearly 66% of total number of lexis of corpus
whereas non-slang words constitute nearly 2.4% of non-lexicon
words. Further, non-lexicon non-slang unigrams composed of 2
lexicon words, form more than 71% of the total number of such
unigrams. To the best of our knowledge, this is the first attempt to
analyze usage of non-lexicon non-slang unigrams in any kind of
UBE.
Abstract: Particle damping is a technique to reduce the
structural vibrations by means of placing small metallic particles
inside a cavity that is attached to the structure at location of high
vibration amplitudes. In this paper, we have presented an analytical
model to simulate the particle damping of two dimensional transient
vibrations in structure operating under high centrifugal loads. The
simulation results show that this technique remains effective as long
as the ratio of the dynamic acceleration of the structure to the applied
centrifugal load is more than 0.1. Particle damping increases with the
increase of particle to structure mass ratio. However, unlike to the
case of particle damping in the absence of centrifugal loads where
the damping efficiency strongly depends upon the size of the cavity,
here this dependence becomes very weak. Despite the simplicity of
the model, the simulation results are considerably in good agreement
with the very scarce experimental data available in the literature for
particle damping under centrifugal loads.
Abstract: This study presents design of a carbon silicon electrode
for iontophorsis treatment towards alopecia. The alopecia is a medical
description means loss of hair from the body. For solving this problem,
the drug need to be delivered into the scalp, therefore, the
iontophoresis was chosen to use in this treatment. However, almost
common electrodes of iontophoresis device are made with metal
material, the electrodes could give patients hurt when they using it, and
it is hard to avoid the hair for attaching the hair. For this reason, an
electrode is made with silicon material to decrease the hurt from the
electrodes, and the carbon material is mixed in it for increasing
conductance. The several cones with stainless material on the
electrode make the electrode is able to void hair to attach the affected
part. According to the results of a vivo-experiment, the carbon silicon
electrode showed a good performance and in treatment comfortably.
Abstract: Image Edge Detection is one of the most important
parts of image processing. In this paper, by fuzzy technique, a new
method is used to improve digital image edge detection. In this
method, a 3x3 mask is employed to process each pixel by means of
vicinity. Each pixel is considered a fuzzy input and by examining
fuzzy rules in its vicinity, the edge pixel is specified and by utilizing
calculation algorithms in image processing, edges are displayed more
clearly. This method shows significant improvement compared to
different edge detection methods (e.g. Sobel, Canny).
Abstract: The use of neural networks for recognition application is generally constrained by their inherent parameters inflexibility after the training phase. This means no adaptation is accommodated for input variations that have any influence on the network parameters. Attempts were made in this work to design a neural network that includes an additional mechanism that adjusts the threshold values according to the input pattern variations. The new approach is based on splitting the whole network into two subnets; main traditional net and a supportive net. The first deals with the required output of trained patterns with predefined settings, while the second tolerates output generation dynamically with tuning capability for any newly applied input. This tuning comes in the form of an adjustment to the threshold values. Two levels of supportive net were studied; one implements an extended additional layer with adjustable neuronal threshold setting mechanism, while the second implements an auxiliary net with traditional architecture performs dynamic adjustment to the threshold value of the main net that is constructed in dual-layer architecture. Experiment results and analysis of the proposed designs have given quite satisfactory conducts. The supportive layer approach achieved over 90% recognition rate, while the multiple network technique shows more effective and acceptable level of recognition. However, this is achieved at the price of network complexity and computation time. Recognition generalization may be also improved by accommodating capabilities involving all the innate structures in conjugation with Intelligence abilities with the needs of further advanced learning phases.
Abstract: The goal of a network-based intrusion detection
system is to classify activities of network traffics into two major
categories: normal and attack (intrusive) activities. Nowadays, data
mining and machine learning plays an important role in many
sciences; including intrusion detection system (IDS) using both
supervised and unsupervised techniques. However, one of the
essential steps of data mining is feature selection that helps in
improving the efficiency, performance and prediction rate of
proposed approach. This paper applies unsupervised K-means
clustering algorithm with information gain (IG) for feature selection
and reduction to build a network intrusion detection system. For our
experimental analysis, we have used the new NSL-KDD dataset,
which is a modified dataset for KDDCup 1999 intrusion detection
benchmark dataset. With a split of 60.0% for the training set and the
remainder for the testing set, a 2 class classifications have been
implemented (Normal, Attack). Weka framework which is a java
based open source software consists of a collection of machine
learning algorithms for data mining tasks has been used in the testing
process. The experimental results show that the proposed approach is
very accurate with low false positive rate and high true positive rate
and it takes less learning time in comparison with using the full
features of the dataset with the same algorithm.
Abstract: In this paper, an overview of the radio over fiber (RoF) technology is provided. Obstacles for reducing the capital and operational expenses in the existing systems are discussed in various perspectives. Some possible RoF deployment scenarios for WiMAX
data transmission are proposed as a means for capital and operational
expenses reduction. IEEE 802.16a standard based end-to-end physical layer model is simulated including intensity modulated direct detection RoF technology. Finally the feasibility of RoF
technology to carry WiMAX signals between the base station and the
remote antenna units is demonstrated using the simulation results.
Abstract: This study aims to assess the potential of solar energy technology for improving access to water and hence the livelihood strategies of rural communities in Baja California Sur, Mexico. It focuses on livestock ranches and photovoltaic water-pumptechnology as well as other water extraction methods. The methodology used are the Sustainable Livelihoods and the Appropriate Technology approaches. A household survey was applied in June of 2006 to 32 ranches in the municipality, of which 22 used PV pumps; and semi-structured interviews were conducted. Findings indicate that solar pumps have in fact helped people improve their quality of life by allowing them to pursue a different livelihood strategy and that improved access to water -not necessarily as more water but as less effort to extract and collect it- does not automatically imply overexploitation of the resource; consumption is based on basic needs as well as on storage and pumping capacity. Justification for such systems lies in the avoidance of logistical problems associated to fossil fuels, PV pumps proved to be the most beneficial when substituting gasoline or diesel equipment but of dubious advantage if intended to replace wind or gravity systems. Solar water pumping technology-s main obstacle to dissemination are high investment and repairs costs and it is therefore not suitable for all cases even when insolation rates and water availability are adequate. In cases where affordability is not an obstacle it has become an important asset that contributes –by means of reduced expenses, less effort and saved time- to the improvement of livestock, the main livelihood provider for these ranches.
Abstract: Creation and maintenance of knowledge management
systems has been recognized as an important research area.
Consecutively lack of accurate results from knowledge management
systems limits the organization to apply their knowledge
management processes. This leads to a failure in getting the right
information to the right people at the right time thus followed by a
deficiency in decision making processes. An Intranet offers a
powerful tool for communication and collaboration, presenting data
and information, and the means that creates and shares knowledge,
all in one easily accessible place. This paper proposes an archetype
describing how a knowledge management system, with the support
of intranet capabilities, could very much increase the accuracy of
capturing, storing and retrieving knowledge based processes thereby
increasing the efficiency of the system. This system will expect a
critical mass of usage, by the users, for intranet to function as
knowledge management systems. This prototype would lead to a
design of an application that would impose creation and maintenance
of an effective knowledge management system through intranet. The
aim of this paper is to introduce an effective system to handle
capture, store and distribute knowledge management in a form that
may not lead to any failure which exists in most of the systems. The
methodology used in the system would require all the employees, in
the organization, to contribute the maximum to deliver the system to
a successful arena. The system is still in its initial mode and thereby
the authors are under the process to practically implement the ideas,
as mentioned in the system, to produce satisfactory results.
Abstract: This paper presents a resonant-based read-out circuit for capacitive pressure sensors. The proposed read-out circuit consists of an LC oscillator and a counter. The circuit detects the capacitance changes of a capacitive pressure sensor by means of frequency shifts from its nominal operation frequency. The proposed circuit is designed in 0.18 m CMOS with an estimated power consumption of 43.1mW. Simulation results show that the circuit has a capacitive resolution of 8.06kHz/fF, which enables it for high resolution pressure detection.
Abstract: This paper presents an optimal and unsupervised satellite image segmentation approach based on Pearson system and k-Means Clustering Algorithm Initialization. Such method could be considered as original by the fact that it utilised K-Means clustering algorithm for an optimal initialisation of image class number on one hand and it exploited Pearson system for an optimal statistical distributions- affectation of each considered class on the other hand. Satellite image exploitation requires the use of different approaches, especially those founded on the unsupervised statistical segmentation principle. Such approaches necessitate definition of several parameters like image class number, class variables- estimation and generalised mixture distributions. Use of statistical images- attributes assured convincing and promoting results under the condition of having an optimal initialisation step with appropriated statistical distributions- affectation. Pearson system associated with a k-means clustering algorithm and Stochastic Expectation-Maximization 'SEM' algorithm could be adapted to such problem. For each image-s class, Pearson system attributes one distribution type according to different parameters and especially the Skewness 'β1' and the kurtosis 'β2'. The different adapted algorithms, K-Means clustering algorithm, SEM algorithm and Pearson system algorithm, are then applied to satellite image segmentation problem. Efficiency of those combined algorithms was firstly validated with the Mean Quadratic Error 'MQE' evaluation, and secondly with visual inspection along several comparisons of these unsupervised images- segmentation.
Abstract: This paper summarizes and compares approaches to
solving the knapsack problem and its known application in capital
budgeting. The first approach uses deterministic methods and can be
applied to small-size tasks with a single constraint. We can also
apply commercial software systems such as the GAMS modelling
system. However, because of NP-completeness of the problem, more
complex problem instances must be solved by means of heuristic
techniques to achieve an approximation of the exact solution in a
reasonable amount of time. We show the problem representation and
parameter settings for a genetic algorithm framework.
Abstract: Motivated by the recent work of Herbert, Hayen, Macaskill and Walter [Interval estimation for the difference of two independent variances. Communications in Statistics, Simulation and Computation, 40: 744-758, 2011.], we investigate, in this paper, new confidence intervals for the difference between two normal population variances based on the generalized confidence interval of Weerahandi [Generalized Confidence Intervals. Journal of the American Statistical Association, 88(423): 899-905, 1993.] and the closed form method of variance estimation of Zou, Huo and Taleban [Simple confidence intervals for lognormal means and their differences with environmental applications. Environmetrics 20: 172-180, 2009]. Monte Carlo simulation results indicate that our proposed confidence intervals give a better coverage probability than that of the existing confidence interval. Also two new confidence intervals perform similarly based on their coverage probabilities and their average length widths.
Abstract: An important official knowledge in each country is to
have a comprehensive knowledge about markets of each group of
products. Drink markets are one the most important markets of each
country as a sub-group of nourishment markets. This paper is going
to study these markets in Iran. To do so, first, two drink products are
selected as pilot, including milk and concentrate. Then, for each
product, two groups of information are estimated for the last five
years, including 1) total consumption (demand) and 2) total
production. Finally, the two groups of productions are compared
statistically by means of two statistical tests called t test and Mann-
Whitney test. The implemented Different related tables and figures
are also illustrated to show the method more explicitly.
Abstract: Urban problems are problems of organized complexity. Thus, many models and scientific methods to resolve urban problems are failed. This study is concerned with proposing of a fuzzy system driven approach for classification and solving urban problems. The proposed study investigated mainly the selection of the inputs and outputs of urban systems for classification of urban problems. In this research, five categories of urban problems, respect to fuzzy system approach had been recognized: control, polytely, optimizing, open and decision making problems. Grounded Theory techniques were then applied to analyze the data and develop new solving method for each category. The findings indicate that the fuzzy system methods are powerful processes and analytic tools for helping planners to resolve urban complex problems. These tools can be successful where as others have failed because both incorporate or address uncertainty and risk; complexity and systems interacting with other systems.
Abstract: Integrins are a large family of multidomain α/β cell
signaling receptors. Some integrins contain an additional inserted I
domain, whose earliest expression appears to be with the chordates,
since they are observed in the urochordates Ciona intestinalis (vase
tunicate) and Halocynthia roretzi (sea pineapple), but not in integrins
of earlier diverging species. The domain-s presence is viewed as a
hallmark of integrins of higher metazoans, however in vertebrates,
there are clearly three structurally-different classes: integrins without
I domains, and two groups of integrins with I domains but separable
by the presence or absence of an additional αC helix. For example,
the αI domains in collagen-binding integrins from Osteichthyes
(bony fish) and all higher vertebrates contain the specific αC helix,
whereas the αI domains in non-collagen binding integrins from
vertebrates and the αI domains from earlier diverging urochordate
integrins, i.e. tunicates, do not. Unfortunately, within the early
chordates, there is an evolutionary gap due to extinctions between the
tunicates and cartilaginous fish. This, coupled with a knowledge gap
due to the lack of complete genomic data from surviving species,
means that the origin of collagen-binding αC-containing αI domains
remains unknown. Here, we analyzed two available genomes from
Callorhinchus milii (ghost shark/elephant shark; Chondrichthyes –
cartilaginous fish) and Petromyzon marinus (sea lamprey;
Agnathostomata), and several available Expression Sequence Tags
from two Chondrichthyes species: Raja erinacea (little skate) and
Squalus acanthias (dogfish shark); and Eptatretus burgeri (inshore
hagfish; Agnathostomata), which evolutionary reside between the
urochordates and osteichthyes. In P. marinus, we observed several
fragments coding for the αC-containing αI domain, allowing us to
shed more light on the evolution of the collagen-binding integrins.
Abstract: This paper used a fuzzy kohonen neural network for medical image segmentation. Image segmentation plays a important role in the many of medical imaging applications by automating or facilitating the diagnostic. The paper analyses the tumor by extraction of the features of (area, entropy, means and standard deviation).These measurements gives a description for a tumor.
Abstract: Due to the constant increase in the volume of information available to applications in fields varying from medical diagnosis to web search engines, accurate support of similarity becomes an important task. This is also the case of spam filtering techniques where the similarities between the known and incoming messages are the fundaments of making the spam/not spam decision. We present a novel approach to filtering based solely on layout, whose goal is not only to correctly identify spam, but also warn about major emerging threats. We propose a mathematical formulation of the email message layout and based on it we elaborate an algorithm to separate different types of emails and find the new, numerically relevant spam types.