Abstract: Electron back-scattered diffraction was used to follow the evolution of microstructure from the base metal to the stir zone (SZ) in a duplex stainless steel subjected to friction stir welding. In the stir zone (SZ), a continuous dynamic recrystallization (CDRX) was evidenced for ferrite, while it was suggested that a static recrystallization together with CDRX may occur for austenite. It was found that ferrite and austenite grains in the SZ take a typical shear texture of bcc and fcc materials respectively.
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: Biometrics, which refers to identifying an individual
based on his or her physiological or behavioral characteristics, has
the capability to reliably distinguish between an authorized person
and an imposter. Signature verification systems can be categorized as
offline (static) and online (dynamic). This paper presents a neural
network based recognition of offline handwritten signatures system
that is trained with low-resolution scanned signature images.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. After a preprocessing
step, the documents are typically represented as large sparse vectors.
When training classifiers on large collections of documents, both the
time and memory restrictions can be quite prohibitive. This justifies
the application of feature selection methods to reduce the
dimensionality of the document-representation vector. In this paper,
we present three feature selection methods: Information Gain,
Support Vector Machine feature selection called (SVM_FS) and
Genetic Algorithm with SVM (called GA_SVM). We show that the
best results were obtained with GA_SVM method for a relatively
small dimension of the feature vector.
Abstract: This paper present lease agreement regulations in
selected European countries. The lease agreement has a long history
and now is one of the main ways to manage agricultural lands in
Europe. The analysis of individual regulations, which has been done,
indicates that this agreement is very important to build social
relations in agriculture and society. This article provides an analysis
of the legal regulations concerning the lease in France, Spain,
Switzerland, Ukraine and Italy. Article is example of study of the
legal regulations and can be used for legal changes in individual
countries.
Abstract: Fine-grained data replication over the Internet allows duplication of frequently accessed data objects, as opposed to entire sites, to certain locations so as to improve the performance of largescale content distribution systems. In a distributed system, agents representing their sites try to maximize their own benefit since they are driven by different goals such as to minimize their communication costs, latency, etc. In this paper, we will use game theoretical techniques and in particular auctions to identify a bidding mechanism that encapsulates the selfishness of the agents, while having a controlling hand over them. In essence, the proposed game theory based mechanism is the study of what happens when independent agents act selfishly and how to control them to maximize the overall performance. A bidding mechanism asks how one can design systems so that agents- selfish behavior results in the desired system-wide goals. Experimental results reveal that this mechanism provides excellent solution quality, while maintaining fast execution time. The comparisons are recorded against some well known techniques such as greedy, branch and bound, game theoretical auctions and genetic algorithms.
Abstract: Synchronous cooperative systems (SCS) bring together users that are geographically distributed and connected through a network to carry out a task. Examples of SCS include Tele- Immersion and Tele-Conferences. In SCS, the coordination is the core of the system, and it has been defined as the act of managing interdependencies between activities performed to achieve a goal. Some of the main problems that SCS present deal with the management of constraints between simultaneous activities and the execution ordering of these activities. In order to resolve these problems, orderings based on Lamport-s happened-before relation have been used, namely, causal, Δ-causal, and causal-total orderings. They mainly differ in the degree of asynchronous execution allowed. One of the most important orderings is the causal order, which establishes that the events must be seen in the cause-effect order as they occur in the system. In this paper we show that for certain SCS (e.g. videoconferences, tele-immersion) where some degradation of the system is allowed, ensuring the causal order is still rigid, which can render negative affects to the system. In this paper, we illustrate how a more relaxed ordering, which we call Fuzzy Causal Order (FCO), is useful for such kind of systems by allowing a more asynchronous execution than the causal order. The benefit of the FCO is illustrated by applying it to a particular scenario of intermedia synchronization of an audio-conference system.
Abstract: The orthogonal processes to shape the triangle steel plate into a equilateral vertical steel are examined by an incremental elasto-plastic finite-element method based on an updated Lagrangian formulation. The highly non-linear problems due to the geometric changes, the inelastic constitutive behavior and the boundary conditions varied with deformation are taken into account in an incremental manner. On the contact boundary, a modified Coulomb friction mode is specially considered. A weighting factor r-minimum is employed to limit the step size of loading increment to linear relation. In particular, selective reduced integration was adopted to formulate the stiffness matrix. The simulated geometries of verticality could clearly demonstrate the vertical processes until unloading. A series of experiments and simulations were performed to validate the formulation in the theory, leading to the development of the computer codes. The whole deformation history and the distribution of stress, strain and thickness during the forming process were obtained by carefully considering the moving boundary condition in the finite-element method. Therefore, this modeling can be used for judging whether a equilateral vertical steel can be shaped successfully. The present work may be expected to improve the understanding of the formation of the equilateral vertical steel.
Abstract: To derive the fractional flow equation oil
displacement will be assumed to take place under the so-called
diffusive flow condition. The constraints are that fluid saturations at
any point in the linear displacement path are uniformly distributed
with respect to thickness; this allows the displacement to be described
mathematically in one dimension. The simultaneous flow of oil and
water can be modeled using thickness averaged relative permeability,
along the centerline of the reservoir. The condition for fluid potential
equilibrium is simply that of hydrostatic equilibrium for which the
saturation distribution can be determined as a function of capillary
pressure and therefore, height. That is the fluids are distributed in
accordance with capillary-gravity equilibrium.
This paper focused on the fraction flow of water versus
cumulative oil recoveries using Buckley Leverett method. Several
field cases have been developed to aid in analysis. Producing watercut
(at surface conditions) will be compared with the cumulative oil
recovery at breakthrough for the flowing fluid.
Abstract: The central recirculation zone (CRZ) in a swirl
stabilized gas turbine combustor has a dominant effect on the fuel air
mixing process and flame stability. Most of state of the art swirlers
share one disadvantage; the fixed swirl number for the same swirler
configuration. Thus, in a mathematical sense, Reynolds number
becomes the sole parameter for controlling the flow characteristics
inside the combustor. As a result, at low load operation, the
generated swirl is more likely to become feeble affecting the flame
stabilization and mixing process. This paper introduces a new swirler
concept which overcomes the mentioned weakness of the modern
configurations. The new swirler introduces air tangentially and
axially to the combustor through tangential vanes and an axial vanes
respectively. Therefore, it provides different swirl numbers for the
same configuration by regulating the ratio between the axial and
tangential flow momenta. The swirler aerodynamic performance was
investigated using four CFD simulations in order to demonstrate the
impact of tangential to axial flow rate ratio on the CRZ. It was found
that the length of the CRZ is directly proportional to the tangential to
axial air flow rate ratio.
Abstract: IP networks are evolving from data communication
infrastructure into many real-time applications such as video
conferencing, IP telephony and require stringent Quality of Service
(QoS) requirements. A rudimentary issue in QoS routing is to find a
path between a source-destination pair that satisfies two or more endto-
end constraints and termed to be NP hard or complete. In this
context, we present an algorithm Multi Constraint Path Problem
Version 3 (MCPv3), where all constraints are approximated and
return a feasible path in much quicker time. We present another
algorithm namely Delay Coerced Multi Constrained Routing
(DCMCR) where coerce one constraint and approximate the
remaining constraints. Our algorithm returns a feasible path, if exists,
in polynomial time between a source-destination pair whose first
weight satisfied by the first constraint and every other weight is
bounded by remaining constraints by a predefined approximation
factor (a). We present our experimental results with different
topologies and network conditions.
Abstract: The purpose of this study was to investigate the religious behavior of students in high school and universality in Lamerd , a town in the south of Iran, with respect to increase in their level of education and age. The participants were 450 high school and university students in all levels from first year of junior high school
to the senior university students who were chosen through multistage
cluster sampling method and their religious behavior was
studied. Through the revised questionnaire by Nezar Alany from the University of Bahrain (r = 0/797), the religious behavior of the subjects were analyzed. Results showed that students in high school
in religious behavior were superior to the students of university (003/0>p) and there was a decline of religious behavior in junior high school third year students to second students of the same school
(042/0>p). More important is that the decrease in religious behavior was associated with increase in educational levels (017/0>p) and age (043/0>p).
Abstract: The steady coupled dissipative layers, called
Marangoni mixed convection boundary layers, in the presence of a
magnetic field and solute concentration that are formed along the
surface of two immiscible fluids with uniform suction or injection
effects is examined. The similarity boundary layer equations are
solved numerically using the Runge-Kutta Fehlberg with shooting
technique. The Marangoni, buoyancy and external pressure gradient
effects that are generated in mixed convection boundary layer flow
are assessed. The velocity, temperature and concentration boundary
layers thickness decrease with the increase of the magnetic field
strength and the injection to suction. For buoyancy-opposed flow, the
Marangoni mixed convection parameter enhances the velocity
boundary layer but decreases the temperature and concentration
boundary layers. However, for the buoyancy-assisted flow, the
Marangoni mixed convection parameter decelerates the velocity but
increases the temperature and concentration boundary layers.
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: L-asparaginase was extracted from pathogenic
Escherichia coli which was isolated from urinary tract infection
patients. L-asparaginase was purified 96-fold by ultrafiltration, ion
exchange and gel filtration giving 39.19% yield with final specific
activity of 178.57 IU/mg. L-asparaginase showed 138,356±1,000
Dalton molecular weight with 31024±100 Dalton molecular mass.
Kinetic properties of enzyme resulting 1.25×10-5 mM Km and
2.5×10-3 M/min Vmax. L-asparaginase showed a maximum activity
at pH 7.5 when incubated at 37 ºC for 30 min and illustrated its full
activity (100%) after 15 min incubation at 20-37 ºC, while 70% of its
activity was lost when incubated at 60 ºC. L-asparaginase showed
cytotoxicity to U937 cell line with IC50 0.5±0.19 IU/ml, and
selectivity index (SI=7.6) about 8 time higher selectivity over the
lymphocyte cells. Therefore, the local pathogenic E. coli strains may
be used as a source of high yield of L-asparaginase to produce anti
cancer agent with high selectivity.
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: Information systems practitioners are frequently
required to master new technology, often without the aid of formal
training. They require the skill to manage their own learning and,
when this skill is developed in their formal training, their adaptability
to new technology may be improved. Self- directed learning is the
ability of the learner to manage his or her own learning experience
with some guidance from a facilitator. Self-directed learning skills
are best improved when practiced. This paper reflects on a critical
social research project to improve the self-directed learning skills of
fourth year Information Systems students. Critical social research
differs from other research paradigms in that the researcher is viewed
as the agent of change to achieve the desired outcome in the problem
situation.
Abstract: The move from cash accounting to accrual accounting, or rule-based to principle-based accounting, by many governments is part of an ongoing efforts in promoting a more business-like and performance-focused public sector. Using questionnaire responses from preparers of financial statements of public universities in Malaysia, this study examines the implementation challenges and benefits of principle-based accounting. Results from these responses suggest that most respondents perceived significant costs would be incurred in relation to staff training and recruitment of staffs with relevant technical knowledge. In addition, most respondents also perceived that there will be significant changes in the current accounting system and structure in order to comply with the principle-based accounting requirements. However, most respondents perceived that these changes might not result in significant benefits for management purposes, for example, financial management, budgeting and allocation of resources. Nevertheless, most respondents perceived that principle-based accounting information would facilitate the monitoring function of the board. The general perception is that adoption of principle-based accounting information is not significantly useful than rule-based accounting information is expected to change over time as preparers of the financial statements gradually understand and appreciate the benefits of principle-based accounting information. This infers that the perceived usefulness of different accounting system is a function of familiarity by the preparers.
Abstract: In this paper we propose a simple adaptive algorithm
iteratively solving the unit-norm constrained optimization problem.
Instead of conventional parameter norm based normalization,
the proposed algorithm incorporates scalar normalization which is
computationally much simpler. The analysis of stationary point is
presented to show that the proposed algorithm indeed solves the
constrained optimization problem. The simulation results illustrate
that the proposed algorithm performs as good as conventional ones
while being computationally simpler.
Abstract: This paper presents the development of recurrent neural network based fuzzy inference system for identification and control of dynamic nonlinear plant. The structure and algorithms of fuzzy system based on recurrent neural network are described. To train unknown parameters of the system the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The neuro-fuzzy system is used for the identification and control of nonlinear dynamic plant. The simulation results of identification and control systems based on recurrent neuro-fuzzy network are compared with the simulation results of other neural systems. It is found that the recurrent neuro-fuzzy based system has better performance than the others.