Abstract: The paper deals with the analysis of triggering
conditions and evolution processes of piping phenomena, in relation
to both mechanical and hydraulic aspects. In particular, the aim of
the study is to predict slope instabilities triggered by piping,
analysing the conditions necessary for a flow failure to occur. Really,
the mechanical effect involved in the loads redistribution around the
pipe is coupled to the drainage process arising from higher
permeability of the pipe. If after the pipe formation, the drainage
goes prevented for pipe clogging, the porewater pressure increase can
lead to the failure or even the liquefaction, with a subsequent flow
slide. To simulate the piping evolution and to verify relevant stability
conditions, a iterative coupled modelling approach has been pointed
out. As example, the proposed tool has been applied to the Stava
Valley disaster (July, 1985), demonstrating that piping might be one
of triggering phenomena of the tailings dams collapse.
Abstract: This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of Pulping of Sugar Maple problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified problem where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.
Abstract: One part of the total employee’s reward is apart from basic wages or salary, employee’s benefits and intangible remuneration also so called contingent (variable) pay. Contingent pay is connected to performance, contribution, cap competency or skills of individual employees, and to team’s or company-wide performance or to combination of few of the mentioned possibilities. Sometimes among the contingent pay is also incorporated the remuneration based on length of employment, when the financial reward is not connected to performance or skills, but to length of continuous employment either on one working position or in one level of remuneration scale. Main aim of this article is to define, based on available information, contingent pay, describe individual forms, its advantages and disadvantages and possibilities to utilization in practice; but also bring information not only about its extent and level of utilization of contingent pay by companies in one of the Czech Republic’s regions, but also mention their practical experience with this type of remuneration.
Abstract: The lack of inclusive housing in Australia contributes
to the marginalization and exclusion of people with disability and
older people from family and community life. The Australian
government has handed over the responsibility of increasing the
supply of inclusive housing to the housing industry through an agreed
national access standard and a voluntary strategy. Voluntary
strategies have not been successful in other constituencies and little is
known about what would work in Australia today. Findings from a
research project into the voluntariness of the housing industry
indicate that a reliable and consistent supply is unlikely without an
equivalent increase in demand. The strategy has, however, an
important role to play in the task of changing housing industry
practices towards building more inclusive communities.
Abstract: Experimental liquid-liquid equilibra of butan-2-ol -
ethanol -water; pentan-1-ol - ethanol - water and toluene - acetone -
water ternary systems were investigated at (25oC). The reliability of
the experimental tie-line data was ascertained by using Othmer-Tobias
and Hand plots. The distribution coefficients (D) and separation
factors (S) of the immiscibility region were evaluated for the three
systems.
Abstract: In this contribution is presented a complex design of
individual objects identification in the workplace of intelligent
assembly cell. Intelligent assembly cell is situated at Institute of
Manufacturing Systems and Applied Mechanics and is used for
pneumatic actuator assembly. Pneumatic actuator components are
pneumatic roller, cover, piston and spring. Two identification objects
alternatives for assembly are designed in the workplace of industrial
robot. In the contribution is evaluated and selected suitable
alternative for identification – 2D codes reader. The complex design
of individual object identification is going out of intelligent
manufacturing systems knowledge.
Intelligent assembly and manufacturing systems as systems of
new generation are gradually loaded in to the mechanical production,
when they are removeing human operation out of production process
and they also short production times.
Abstract: Data Mining aims at discovering knowledge out of
data and presenting it in a form that is easily comprehensible to
humans. One of the useful applications in Egypt is the Cancer
management, especially the management of Acute Lymphoblastic
Leukemia or ALL, which is the most common type of cancer in
children.
This paper discusses the process of designing a prototype that can
help in the management of childhood ALL, which has a great
significance in the health care field. Besides, it has a social impact
on decreasing the rate of infection in children in Egypt. It also
provides valubale information about the distribution and
segmentation of ALL in Egypt, which may be linked to the possible
risk factors.
Undirected Knowledge Discovery is used since, in the case of this
research project, there is no target field as the data provided is
mainly subjective. This is done in order to quantify the subjective
variables. Therefore, the computer will be asked to identify
significant patterns in the provided medical data about ALL. This
may be achieved through collecting the data necessary for the
system, determimng the data mining technique to be used for the
system, and choosing the most suitable implementation tool for the
domain.
The research makes use of a data mining tool, Clementine, so as to
apply Decision Trees technique. We feed it with data extracted from
real-life cases taken from specialized Cancer Institutes. Relevant
medical cases details such as patient medical history and diagnosis
are analyzed, classified, and clustered in order to improve the disease
management.
Abstract: There are many real world problems in which
parameters like the arrival time of new jobs, failure of resources, and
completion time of jobs change continuously. This paper tackles the
problem of scheduling jobs with random due dates on multiple
identical machines in a stochastic environment. First to assign jobs to
different machine centers LPT scheduling methods have been used,
after that the particular sequence of jobs to be processed on the
machine have been found using simple stochastic techniques. The
performance parameter under consideration has been the maximum
lateness concerning the stochastic due dates which are independent
and exponentially distributed. At the end a relevant problem has been
solved using the techniques in the paper..
Abstract: The new idea of analyze of power system failure with
use of artificial neural network is proposed. An analysis of the
possibility of simulating phenomena accompanying system faults and
restitution is described. It was indicated that the universal model for
the simulation of phenomena in whole analyzed range does not exist.
The main classic method of search of optimal structure and
parameter identification are described shortly. The example with
results of calculation is shown.
Abstract: In this paper, we explore the applicability of the Sinc-
Collocation method to a three-dimensional (3D) oceanography model.
The model describes a wind-driven current with depth-dependent
eddy viscosity in the complex-velocity system. In general, the
Sinc-based methods excel over other traditional numerical methods
due to their exponentially decaying errors, rapid convergence and
handling problems in the presence of singularities in end-points.
Together with these advantages, the Sinc-Collocation approach that
we utilize exploits first derivative interpolation, whose integration
is much less sensitive to numerical errors. We bring up several
model problems to prove the accuracy, stability, and computational
efficiency of the method. The approximate solutions determined by
the Sinc-Collocation technique are compared to exact solutions and
those obtained by the Sinc-Galerkin approach in earlier studies. Our
findings indicate that the Sinc-Collocation method outperforms other
Sinc-based methods in past studies.
Abstract: A model is presented to find the optimal design of the
mixed renewable warranty policy for non-repairable Weibull life
products. The optimal design considers the conflict of interests
between the customer and the manufacturer: the customer interests
are longer full rebate coverage period and longer total warranty
coverage period, the manufacturer interests are lower warranty cost
and lower risk. The design factors are full rebate and total warranty
coverage periods. Results showed that mixed policy is better than full
rebate policy in terms of risk and total warranty coverage period in all
of the three bathtub regions. In addition, results showed that linear
policy is better than mixed policy in infant mortality and constant
failure regions while the mixed policy is better than linear policy in
ageing region of the model. Furthermore, the results showed that
using burn-in period for infant mortality products reduces warranty
cost and risk.
Abstract: The increasing usage of antibiotics in the animal
farming industry is an emerging worldwide problem contributing to
the development of antibiotic resistance. The purpose of this work was
to investigate the prevalence and antibiotic resistance profile of
bacterial isolates collected from aquatic environments and meats in a
peri-urban community in Daejeon, Korea. In an antibacterial
susceptibility test, the bacterial isolates showed a high incidence of
resistance (~ 26.04 %) to cefazolin, tetracycline, gentamycin,
norfloxacin, erythromycin and vancomycin. The results from a test for
multiple antibiotic resistance indicated that the isolates were
displaying an approximately 5-fold increase in the incidence of
multiple antibiotic resistance to combinations of two different
antibiotics compared to combinations of three or more antibiotics.
Most of the isolates showed multi-antibiotic resistance, and the
resistance patterns were similar among the sampling groups.
Sequencing data analysis of 16S rRNA showed that most of the
resistant isolates appeared to be dominated by the classes
Betaproteobacteria and Gammaproteobacteria in the phylum
Proteobacteria.
Abstract: Socioeconomic stability and development of a
country, can be describe by housing affordability. It is aimed to
ensure the housing provided as one of the key factors that is
affordable by every income earner group whether low-income,
middle income and high income group. This research carried out is to
find out affordability of home ownership level for first medium cost
landed-house by the middle-income group in Selangor, Malaysia. It is
also hope that it could be seen as able to contribute to the knowledge
and understanding on housing affordability level for the middleincome
group and variables that influenced the medium income
group-s ability to own first medium-cost houses.
Abstract: The aim of this paper is to determine the stress levels
at the end of a long slender shaft such as a drilling assembly used in
the oil or gas industry using a mathematical model in real-time. The
torsional deflection experienced by this type of drilling shaft (about 4
KM length and 20 cm diameter hollow shaft with a thickness of 1
cm) can only be determined using a distributed modeling technique.
The main objective of this project is to calculate angular velocity and
torque at the end of the shaft by TLM method and also analyzing of
the behavior of the system by transient response. The obtained result
is compared with lumped modeling technique the importance of these
results will be evident only after the mentioned comparison. Two
systems have different transient responses and in this project because
of the length of the shaft transient response is very important.
Abstract: Nowadays, there is little information, concerning the
heat shield systems, and this information is not completely reliable to
use in so many cases. for example, the precise calculation cannot be
done for various materials. In addition, the real scale test has two
disadvantages: high cost and low flexibility, and for each case we
must perform a new test. Hence, using numerical modeling program
that calculates the surface recession rate and interior temperature
distribution is necessary. Also, numerical solution of governing
equation for non-charring material ablation is presented in order to
anticipate the recession rate and the heat response of non-charring
heat shields. the governing equation is nonlinear and the Newton-
Rafson method along with TDMA algorithm is used to solve this
nonlinear equation system. Using Newton- Rafson method for
solving the governing equation is one of the advantages of the
solving method because this method is simple and it can be easily
generalized to more difficult problems. The obtained results
compared with reliable sources in order to examine the accuracy of
compiling code.
Abstract: Fluid flow and heat transfer of vertical full cone
embedded in porous media is studied in this paper. Nonlinear
differential equation arising from similarity solution of inverted cone
(subjected to wall temperature boundary conditions) embedded in
porous medium is solved using a hybrid neural network- particle
swarm optimization method.
To aim this purpose, a trial solution of the differential equation is
defined as sum of two parts. The first part satisfies the initial/
boundary conditions and does contain an adjustable parameter and
the second part which is constructed so as not to affect the
initial/boundary conditions and involves adjustable parameters (the
weights and biases) for a multi-layer perceptron neural network.
Particle swarm optimization (PSO) is applied to find adjustable
parameters of trial solution (in first and second part). The obtained
solution in comparison with the numerical ones represents a
remarkable accuracy.
Abstract: Thyroid cancer-s overall contribution to the
worldwide cancer burden is relatively small, but incidence rates have increased over the last three decades throughout the world. This trend has been hypothesised to reflect a combination of technological advances enabling increased detection, but also changes in
environmental factors, including population exposure to ionising radiation from fallout, diagnostic tests and treatment for benign and
malignant conditions. The Thyroid dose received apparently shielded
by cerrobend blocks was about 8cGy in 100cGy Expose
Abstract: This paper discusses the applicability of the Data
Distribution Service (DDS) for the development of automated and modular manufacturing systems which require a flexible and robust
communication infrastructure. DDS is an emergent standard for datacentric publish/subscribe middleware systems that provides an
infrastructure for platform-independent many-to-many
communication. It particularly addresses the needs of real-time systems that require deterministic data transfer, have low memory
footprints and high robustness requirements. After an overview of the
standard, several aspects of DDS are related to current challenges for the development of modern manufacturing systems with distributed architectures. Finally, an example application is presented based on a modular active fixturing system to illustrate the described aspects.
Abstract: Most scientific programs have large input and output
data sets that require out-of-core programming or use virtual memory
management (VMM). Out-of-core programming is very error-prone
and tedious; as a result, it is generally avoided. However, in many
instance, VMM is not an effective approach because it often results
in substantial performance reduction. In contrast, compiler driven I/O
management will allow a program-s data sets to be retrieved in parts,
called blocks or tiles. Comanche (COmpiler MANaged caCHE) is a
compiler combined with a user level runtime system that can be used
to replace standard VMM for out-of-core programs. We describe
Comanche and demonstrate on a number of representative problems
that it substantially out-performs VMM. Significantly our system
does not require any special services from the operating system and
does not require modification of the operating system kernel.
Abstract: We study the spatial design of experiment and we want to select a most informative subset, having prespecified size, from a set of correlated random variables. The problem arises in many applied domains, such as meteorology, environmental statistics, and statistical geology. In these applications, observations can be collected at different locations and possibly at different times. In spatial design, when the design region and the set of interest are discrete then the covariance matrix completely describe any objective function and our goal is to choose a feasible design that minimizes the resulting uncertainty. The problem is recast as that of maximizing the determinant of the covariance matrix of the chosen subset. This problem is NP-hard. For using these designs in computer experiments, in many cases, the design space is very large and it's not possible to calculate the exact optimal solution. Heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective and exact solution not possible. We developed a GA algorithm to take advantage of the exploratory power of this algorithm. The successful application of this method is demonstrated in large design space. We consider a real case of design of experiment. In our problem, design space is very large and for solving the problem, we used proposed GA algorithm.