Abstract: The effects of irrigation with dairy factory wastewater
on soil properties were investigated at two sites that had received
irrigation for > 60 years. Two adjoining paired sites that had never
received DFE were also sampled as well as another seven fields from
a wider area around the factory. In comparison with paired sites that
had not received effluent, long-term wastewater irrigation resulted in
an increase in pH, EC, extractable P, exchangeable Na and K and
ESP. These changes were related to the use of phosphoric acid,
NaOH and KOH as cleaning agents in the factory. Soil organic C
content was unaffected by DFE irrigation but the size (microbial
biomass C and N) and activity (basal respiration) of the soil
microbial community were increased. These increases were
attributed to regular inputs of soluble C (e.g. lactose) present as milk
residues in the wastewater. Principal component analysis (PCA) of
the soils data from all 11sites confirmed that the main effects of DFE
irrigation were an increase in exchangeable Na, extractable P and
microbial biomass C, an accumulation of soluble salts and a liming
effect. PCA analysis of soil bacterial community structure, using
PCR-DGGE of 16S rDNA fragments, generally separated individual
sites from one another but did not group them according to irrigation
history. Thus, whilst the size and activity of the soil microbial
community were increased, the structure and diversity of the
bacterial community remained unaffected.
Abstract: To create a solution for a specific problem in machine
learning, the solution is constructed from the data or by use a search
method. Genetic algorithms are a model of machine learning that can
be used to find nearest optimal solution. While the great advantage of
genetic algorithms is the fact that they find a solution through
evolution, this is also the biggest disadvantage. Evolution is inductive,
in nature life does not evolve towards a good solution but it evolves
away from bad circumstances. This can cause a species to evolve into
an evolutionary dead end. In order to reduce the effect of this
disadvantage we propose a new a learning tool (criteria) which can be
included into the genetic algorithms generations to compare the
previous population and the current population and then decide
whether is effective to continue with the previous population or the
current population, the proposed learning tool is called as Keeping
Efficient Population (KEP). We applied a GA based on KEP to the
production line layout problem, as a result KEP keep the evaluation
direction increases and stops any deviation in the evaluation.
Abstract: Precast concrete has been widely adopted in public
housing construction of Hong Kong since the mid-1980s. While
pre-casting is considered an environmental friendly solution, there is
lack of study to investigate the life cycle performance of precast
concrete units. This study aims to bridge the knowledge gap by
providing a comprehensive life cycle assessment (LCA) study for two
precast elements namely façade and bathroom. The results show that
raw material is the most significant contributor of environmental
impact accounting for about 90% to the total impact. Furthermore,
human health is more affected by the production of precast concrete
than the ecosystems.
Abstract: This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, depth of cut and engagement) leading to minimum surface roughness in face milling of high silicon stainless steel by coupling neural network (NN) and Electromagnetism-like Algorithm (EM). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and Electromagnetism-like optimization method are exploited in an integrated manner. To this end, numerous experiments on this stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness is created by using a back propogation neural network, then the optimization problem was solved by using EM optimization. Additional experiments were performed to validate optimum surface roughness value predicted by EM algorithm. It is clearly seen that a good agreement is observed between the predicted values by EM coupled with feed forward neural network and experimental measurements. The obtained results show that the EM algorithm coupled with back propogation neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.
Abstract: In the present analysis an unsteady laminar
forced convection water boundary layer flow is considered.
The fluid properties such as viscosity and Prandtl number are
taken as variables such that those are inversely proportional to
temperature. By using quasi-linearization technique the nonlinear
coupled partial differential equations are linearized and
the numerical solutions are obtained by using implicit finite
difference scheme with the appropriate selection of step sizes.
Non-similar solutions have been obtained from the starting
point of the stream-wise coordinate to the point where skin
friction value vanishes. The effect non-uniform mass transfer
along the surface of the cylinder through slot is studied on the
skin friction and heat transfer coefficients.
Abstract: Khatunabad area is situated geologically in Urmieh-
Dokhtar magmatic belt in NW of Iran. In this research, studied area
has been investigated in order to recognize the potential copper and
molybdenum-bearing target areas. The survey layers include the
lithologic units, alteration, geochemical result, tectonics and copper
and molybdenum occurrence. As an accurate decision can have a
considerable effect on exploration plans, so in this efforts have been
made to make use of new combination method. For this purpose, the
analytical hierarchy process was used and revealed highly potential
copper and molybdenum mineralization areas. Based on achieved
results, geological perspective in north of studied area is appropriate
for advance stage, especially for subsurface exploration in future.
Abstract: In this paper the effects of top management commitment on knowledge management activities has been analyzed. This research has been conducted as a case study in an academic environment. The data collection was carried out in the form of semi-structured interview with an interview guide. This study shows the effects of knowledge management strategic plan developing in academia strategic plan on knowledge management success. This paper shows the importance top management commitment factors including strategic plan, communication, and training on knowledge management success in academia. In particular the most important role of Strategic planning in knowledge management success is clarified. This study explores one of the necessary organizational infrastructures of successful implementation of knowledge management. The idea of this research could be applied in the other context especially in the industrial organizations.
Abstract: The paper presents a novel method for the 3D shaping
of different materials using a high-pressure abrasive water jet and a
flat target image. For steering movement process of the jet a principle
similar to raster image way of record and readout was used.
However, respective colors of pixel of such a bitmap are connected
with adequate jet feed rate that causes erosion of material with
adequate depth. Thanks to that innovation, one can observe spatial
imaging of the object. Theoretical basis as well as spatial model of
material shaping and experimental stand including steering program
are presented in. There are also presented methodic and some
experimental erosion results as well as practical example of object-s
bas-relief made of metal.
Abstract: Program slicing is the task of finding all statements in a program that directly or indirectly influence the value of a variable occurrence. The set of statements that can affect the value of a variable at some point in a program is called a program slice. In several software engineering applications, such as program debugging and measuring program cohesion and parallelism, several slices are computed at different program points. In this paper, algorithms are introduced to compute all backward and forward static slices of a computer program by traversing the program representation graph once. The program representation graph used in this paper is called Program Dependence Graph (PDG). We have conducted an experimental comparison study using 25 software modules to show the effectiveness of the introduced algorithm for computing all backward static slices over single-point slicing approaches in computing the parallelism and functional cohesion of program modules. The effectiveness of the algorithm is measured in terms of time execution and number of traversed PDG edges. The comparison study results indicate that using the introduced algorithm considerably saves the slicing time and effort required to measure module parallelism and functional cohesion.
Abstract: Recently, a great number of theoretical frameworks
have been proposed to develop the linkages between knowledge
management (KM) and organizational strategies. However, while
there has been much theorizing and case study in the area, validated
research models integrating KM and information technology
strategies for empirical testing of these theories have been scarce. In
this research, we try to develop a research model for explaining the
relationship between KM strategy and IT strategy and their effects on
performance. Finally, meaningful propositions and conclusions are
derived, and suggestions for future research are proposed and
discussed.
Abstract: Clustering techniques have received attention in many areas including engineering, medicine, biology and data mining. The purpose of clustering is to group together data points, which are close to one another. The K-means algorithm is one of the most widely used techniques for clustering. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called PSO-ACO, for optimally clustering N object into K clusters. The new PSO-ACO algorithm is tested on several data sets, and its performance is compared with those of ACO, PSO and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handing data clustering.
Abstract: People nowadays love to travel around the world.
Regardless of their location and time, they especially Muslims still need to perform their five times prayer. Normally for travelers, they
need to bring maps, compass and for Muslim, they even have to bring Qibla pointer when they travel. It is slightly difficult to
determine the Qibla direction and to know the time for each prayer. In this paper we present a new electronic device called Universal Qibla and Prayer Time Finder to locate the Qibla direction and to determine each prayer time based on the current user-s location. This device use PIC microcontroller equipped with digital compass and
Global Positioning System (GPS) where it will display the exact Qibla direction and prayer time automatically at any place in the world. This device is reliable, user friendly and accurate in
determining the Qibla direction and prayer time.
Abstract: With the advent of emerging personal computing paradigms such as ubiquitous and mobile computing, Web contents are becoming accessible from a wide range of mobile devices. Since these devices do not have the same rendering capabilities, Web contents need to be adapted for transparent access from a variety of client agents. Such content adaptation results in better rendering and faster delivery to the client device. Nevertheless, Web content adaptation sets new challenges for semantic markup. This paper presents an advanced components platform, called MorfeoSMC, enabling the development of mobility applications and services according to a channel model based on Services Oriented Architecture (SOA) principles. It then goes on to describe the potential for integration with the Semantic Web through a novel framework of external semantic annotation of mobile Web contents. The role of semantic annotation in this framework is to describe the contents of individual documents themselves, assuring the preservation of the semantics during the process of adapting content rendering, as well as to exploit these semantic annotations in a novel user profile-aware content adaptation process. Semantic Web content adaptation is a way of adding value to and facilitates repurposing of Web contents (enhanced browsing, Web Services location and access, etc).
Abstract: Computer-mediated communication technologies which provide for virtual communities have typically evolved in a cross-dichotomous manner, such that technical constructs of the technology have evolved independently from the social environment of the community. The present paper analyses some limitations of current implementations of computer-mediated communication technology that are implied by such a dichotomy, and discusses their inhibiting effects on possible developments of virtual communities. A Socio-Technical Indicator Model is introduced that utilizes integrated feedback to describe, simulate and operationalise increasing representativeness within a variety of structurally and parametrically diverse systems. In illustration, applications of the model are briefly described for financial markets and for eco-systems. A detailed application is then provided to resolve the aforementioned technical limitations of moderation on the evolution of virtual communities. The application parameterises virtual communities to function as self-transforming social-technical systems which are sensitive to emergent and shifting community values as products of on-going communications within the collective.
Abstract: A Novel fuzzy neural network combining with support vector learning mechanism called support-vector-based fuzzy neural networks (SVBFNN) is proposed. The SVBFNN combine the capability of minimizing the empirical risk (training error) and expected risk (testing error) of support vector learning in high dimensional data spaces and the efficient human-like reasoning of FNN.
Abstract: We proposed the use of a Toda-Rayleigh ring as a
central pattern generator (CPG) for controlling hexapodal robots. We
show that the ring composed of six Toda-Rayleigh units coupled to
the limb actuators reproduces the most common hexapodal gaits. We
provide an electrical circuit implementation of the CPG and test our
theoretical results obtaining fixed gaits. Then we propose a method
of incorporation of the actuator (motor) dynamics in the CPG. With
this approach we close the loop CPG – environment – CPG, thus
obtaining a decentralized model for the leg control that does not
require higher level intervention to the CPG during locomotion in
a nonhomogeneous environments. The gaits generated by the novel
CPG are not fixed, but adapt to the current robot bahvior.
Abstract: An alternative approach to the use of Discrete Fourier
Transform (DFT) for Magnetic Resonance Imaging (MRI) reconstruction
is the use of parametric modeling technique. This method
is suitable for problems in which the image can be modeled by
explicit known source functions with a few adjustable parameters.
Despite the success reported in the use of modeling technique as an
alternative MRI reconstruction technique, two important problems
constitutes challenges to the applicability of this method, these are
estimation of Model order and model coefficient determination. In
this paper, five of the suggested method of evaluating the model
order have been evaluated, these are: The Final Prediction Error
(FPE), Akaike Information Criterion (AIC), Residual Variance (RV),
Minimum Description Length (MDL) and Hannan and Quinn (HNQ)
criterion. These criteria were evaluated on MRI data sets based on the
method of Transient Error Reconstruction Algorithm (TERA). The
result for each criterion is compared to result obtained by the use of a
fixed order technique and three measures of similarity were evaluated.
Result obtained shows that the use of MDL gives the highest measure
of similarity to that use by a fixed order technique.
Abstract: Inspired by the recent experiments [1]-[3] indicating
unusual doubly magic nucleus 24O which lies just at the neutron
drip-line and encouraged by the success of our relativistic mean-field
(RMF) plus state dependent BCS approach for the description of
the ground state properties of the drip-line nuclei [23]-[27], we have
further employed this approach, across the entire periodic table, to
explore the unusual shell closures in exotic nuclei. In our RMF+BCS
approach the single particle continuum corresponding to the RMF is
replaced by a set of discrete positive energy states for the calculations
of pairing energy. Detailed analysis of the single particle spectrum,
pairing energies and densities of the nuclei predict the unusual proton
shell closures at Z = 6, 14, 16, 34, and unusual neutron shell closures
at N = 6, 14, 16, 34, 40, 70, 112.
Abstract: Electricity market activities and a growing demand for electricity have led to heavily stressed power systems. This requires operation of the networks closer to their stability limits. Power system operation is affected by stability related problems, leading to unpredictable system behavior. Voltage stability refers to the ability of a power system to sustain appropriate voltage levels through large and small disturbances. Steady-state voltage stability is concerned with limits on the existence of steady-state operating points for the network. FACTS devices can be utilized to increase the transmission capacity, the stability margin and dynamic behavior or serve to ensure improved power quality. Their main capabilities are reactive power compensation, voltage control and power flow control. Among the FACTS controllers, Static Var Compensator (SVC) provides fast acting dynamic reactive compensation for voltage support during contingency events. In this paper, voltage stability assessment with appropriate representations of tap-changer transformers and SVC is investigated. Integrating both of these devices is the main topic of this paper. Effect of the presence of tap-changing transformers on static VAR compensator controller parameters and ratings necessary to stabilize load voltages at certain values are highlighted. The interrelation between transformer off nominal tap ratios and the SVC controller gains and droop slopes and the SVC rating are found. P-V curves are constructed to calculate loadability margins.
Abstract: The rapid expansion of the web is causing the
constant growth of information, leading to several problems such as
increased difficulty of extracting potentially useful knowledge. Web
content mining confronts this problem gathering explicit information
from different web sites for its access and knowledge discovery.
Query interfaces of web databases share common building blocks.
After extracting information with parsing approach, we use a new
data mining algorithm to match a large number of schemas in
databases at a time. Using this algorithm increases the speed of
information matching. In addition, instead of simple 1:1 matching,
they do complex (m:n) matching between query interfaces. In this
paper we present a novel correlation mining algorithm that matches
correlated attributes with smaller cost. This algorithm uses Jaccard
measure to distinguish positive and negative correlated attributes.
After that, system matches the user query with different query
interfaces in special domain and finally chooses the nearest query
interface with user query to answer to it.