Abstract: Growing world population has fundamental impacts
and often catastrophic on natural habitat. The immethodical
consumption of energy, destruction of the forests and extinction of
plant and animal species are the consequence of this experience.
Urban sustainability and sustainable urban development, that is so
spoken these days, should be considered as a strategy, goal and
policy, beyond just considering environmental issues and protection.
The desert-s climate has made a bunch of problems for its residents.
Very hot and dry climate in summers of the Iranian desert areas,
when there was no access to modern energy source and mechanical
cooling systems in the past, made Iranian architects to design a
natural ventilation system in their buildings. The structure, like a
tower going upward the roof, besides its ornamental application and
giving a beautiful view to the building, was used as a spontaneous
ventilation system. In this paper, it has been tried to name the
problems of the area and it-s inconvenience, then some answers has
pointed out in order to solve the problems and as an alternative
solution BADGIR (wind-catcher) has been introduced as a solution
knowing that it has been playing a major role in dealing with the
problems.
Abstract: While financial institutions have faced difficulties
over the years for a multitude of reasons, the major cause of serious
banking problems continues to be directly related to lax credit
standards for borrowers and counterparties, poor portfolio risk
management, or a lack of attention to changes in economic or other
circumstances that can lead to a deterioration in the credit standing of
a bank's counterparties. Credit risk is most simply defined as the
potential that a bank borrower or counterparty will fail to meet its
obligations in accordance with agreed terms. The goal of credit risk
management is to maximize a bank's risk-adjusted rate of return by
maintaining credit risk exposure within acceptable parameters. Banks
need to manage the credit risk inherent in the entire portfolio as well
as the risk in individual credits or transactions. Banks should also
consider the relationships between credit risk and other risks. The
effective management of credit risk is a critical component of a
comprehensive approach to risk management and essential to the
long-term success of any banking organization. In this research we
also study the relationship between credit risk indices and borrower-s
timely payback in Karafarin bank.
Abstract: The information revealed by derivatives can help to
better characterize digital near-end crosstalk signatures with the
ultimate goal of identifying the specific aggressor signal.
Unfortunately, derivatives tend to be very sensitive to even low
levels of noise. In this work we approximated the derivatives of both
quiet and noisy digital signals using a wavelet-based technique. The
results are presented for Gaussian digital edges, IBIS Model digital
edges, and digital edges in oscilloscope data captured from an actual
printed circuit board. Tradeoffs between accuracy and noise
immunity are presented. The results show that the wavelet technique
can produce first derivative approximations that are accurate to
within 5% or better, even under noisy conditions. The wavelet
technique can be used to calculate the derivative of a digital signal
edge when conventional methods fail.
Abstract: Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.
Abstract: Lignocellulosic materials are new targeted source to
produce second generation biofuels like biobutanol. However, this
process is significantly resisted by the native structure of biomass.
Therefore, pretreatment process is always essential to remove
hemicelluloses and lignin prior to the enzymatic hydrolysis.
The goals of pretreatment are removing hemicelluloses and
lignin, increasing biomass porosity, and increasing the enzyme
accessibility. The main goal of this research is to study the important
variables such as pretreatment temperature and time, which can give
the highest total sugar yield in pretreatment step by using dilute
phosphoric acid. After pretreatment, the highest total sugar yield of
13.61 g/L was obtained under an optimal condition at 140°C for 10
min of pretreatment time by using 1.75% (w/w) H3PO4 and at 15:1
liquid to solid ratio. The total sugar yield of two-stage process
(pretreatment+enzymatic hydrolysis) of 27.38 g/L was obtained.
Abstract: The goal of this paper is to develop a model to
integrate “pricing" and “advertisement" for short life cycle products,
such as branded fashion clothing products. To achieve this goal, we
apply the concept of “Dynamic Pricing". There are two classes of
advertisements, for the brand (regardless of product) and for a
particular product. Advertising the brand affects the demand and
price of all the products. Thus, the model considers all these products
in relation with each other. We develop two different methods to
integrate both types of advertisement and pricing. The first model is
developed within the framework of dynamic programming. However,
due to the complexity of the model, this method cannot be applicable
for large size problems. Therefore, we develop another method,
called hieratical approach, which is capable of handling the real
world problems. Finally, we show the accuracy of this method, both
theoretically and also by simulation.
Abstract: As the Textile Industry is the second largest industry
in Egypt and as small and medium-sized enterprises (SMEs) make up
a great portion of this industry therein it is essential to apply the
concept of Cleaner Production for the purpose of reducing pollution.
In order to achieve this goal, a case study concerned with ecofriendly
stone-washing of jeans-garments was investigated. A raw
material-substitution option was adopted whereby the toxic
potassium permanganate and sodium sulfide were replaced by the
environmentally compatible hydrogen peroxide and glucose
respectively where the concentrations of both replaced chemicals
together with the operating time were optimized. In addition, a
process-rationalization option involving four additional processes
was investigated. By means of criteria such as product quality,
effluent analysis, mass and heat balance; and cost analysis with the
aid of a statistical model, a process optimization treatment revealed
that the superior process optima were 50%, 0.15% and 50min for
H2O2 concentration, glucose concentration and time, respectively.
With these values the superior process ought to reduce the annual
cost by about EGP 105 relative to the currently used conventional
method.
Abstract: The quality of Ribbed Smoked Sheets
(RSS) primarily based on color, dryness, and the presence or
absence of fungus and bubbles. This quality is strongly
influenced by the drying and fumigation process namely
smoking process. Smoking that is held in high temperature
long time will result scorched dark brown sheets, whereas if
the temperature is too low or slow drying rate would resulted
in less mature sheets and growth of fungus. Therefore need to
find the time and temperature for optimum quality of sheets.
Enhance, unmonitored heat and mass transfer during smoking
process lead to high losses of energy balance. This research
aims to generate simple empirical mathematical model
describing the effect of smoking time and temperature to RSS
quality of color, water content, fungus and bubbles. The
second goal of study was to analyze energy balance during
smoking process. Experimental study was conducted by
measuring temperature, residence time and quality parameters
of 16 sheets sample in smoking rooms. Data for energy
consumption balance such as mass of fuel wood, mass of
sheets being smoked, construction temperature, ambient
temperature and relative humidity were taken directly along
the smoking process. It was found that mathematical model
correlating smoking temperature and time with color is Color
= -169 - 0.184 T4 - 0.193 T3 - 0.160 0.405 T1 + T2 + 0.388 t1
+3.11 t2 + 3.92t3 + 0.215 t4 with R square 50.8% and with
moisture is Moisture = -1.40-0.00123 T4 + 0.00032 T3 +
0.00260 T2 - 0.00292 T1 - 0.0105 t1 + 0.0290 t2 + 0.0452 t3
+ 0.00061 t4 with R square of 49.9%. Smoking room energy
analysis found useful energy was 27.8%. The energy stored in
the material construction 7.3%. Lost of energy in conversion
of wood combustion, ventilation and others were 16.6%. The
energy flowed out through the contact of material construction
with the ambient air was found to be the highest contribution
to energy losses, it reached 48.3%.
Abstract: Improvement in CAE methods has an important role for shortening of the vehicle product development time. It is provided that validation of the design and improvements in terms of durability can be done without hardware prototype production. In recent years, several different methods have been developed in order to investigate fatigue damage of the vehicle. The intended goal among these methods is prediction of fatigue damage in a short time with reduced costs. This study developed a new fatigue damage prediction method in the automotive sector using power spectrum densities of accelerations. This study also confirmed that the weak region in vehicle can be easily detected with the method developed in this study which results were compared with conventional method.
Abstract: A computer model of Quantum Theory (QT) has been
developed by the author. Major goal of the computer model was
support and demonstration of an as large as possible scope of QT.
This includes simulations for the major QT (Gedanken-) experiments
such as, for example, the famous double-slit experiment.
Besides the anticipated difficulties with (1) transforming exacting
mathematics into a computer program, two further types of problems
showed up, namely (2) areas where QT provides a complete mathematical
formalism, but when it comes to concrete applications the
equations are not solvable at all, or only with extremely high effort;
(3) QT rules which are formulated in natural language and which do
not seem to be translatable to precise mathematical expressions, nor
to a computer program.
The paper lists problems in all three categories and describes also
the possible solutions or circumventions developed for the computer
model.
Abstract: In this work a new method for low complexity
image coding is presented, that permits different settings and great
scalability in the generation of the final bit stream. This coding
presents a continuous-tone still image compression system that
groups loss and lossless compression making use of finite arithmetic
reversible transforms. Both transformation in the space of color and
wavelet transformation are reversible. The transformed coefficients
are coded by means of a coding system in depending on a
subdivision into smaller components (CFDS) similar to the bit
importance codification. The subcomponents so obtained are
reordered by means of a highly configure alignment system
depending on the application that makes possible the re-configure of
the elements of the image and obtaining different importance levels
from which the bit stream will be generated. The subcomponents of
each importance level are coded using a variable length entropy
coding system (VBLm) that permits the generation of an embedded
bit stream. This bit stream supposes itself a bit stream that codes a
compressed still image. However, the use of a packing system on the
bit stream after the VBLm allows the realization of a final highly
scalable bit stream from a basic image level and one or several
improvement levels.
Abstract: Rarefied gas flows are often occurred in micro electro
mechanical systems and classical CFD could not precisely anticipate
the flow and thermal behavior due to the high Knudsen number.
Therefore, the heat transfer and the fluid dynamics characteristics of
rarefied gas flows in both a two-dimensional simple microchannel
and geometry similar to single Knudsen compressor have been
investigated with a goal of increasing performance of a actual
Knudsen compressor by using a particle simulation method. Thermal
transpiration and thermal creep, which are rarefied gas dynamic
phenomena, that cause movement of the flow from less to higher
temperature is generated by using two different longitude temperature
gradients (Linear, Step) along the walls of the flow microchannel. In
this study the influence of amount of temperature gradient and
governing pressure in various Knudsen numbers and length-to-height
ratios have been examined.
Abstract: Multi-agent system is composed by several agents
capable of reaching the goal cooperatively. The system needs an agent
platform for efficient and stable interaction between intelligent agents.
In this paper we propose a flexible and scalable agent platform by
composing the containers with multiple hierarchical agent groups. It
also allows efficient implementation of multiple domain presentations
of the agents unlike JADE. The proposed platform provides both
group management and individual management of agents for
efficiency. The platform has been implemented and tested, and it can
be used as a flexible foundation of the dynamic multi-agent system
targeting seamless delivery of ubiquitous services.
Abstract: How to coordinate the behaviors of the agents through
learning is a challenging problem within multi-agent domains.
Because of its complexity, recent work has focused on how
coordinated strategies can be learned. Here we are interested in using
reinforcement learning techniques to learn the coordinated actions of a
group of agents, without requiring explicit communication among
them. However, traditional reinforcement learning methods are based
on the assumption that the environment can be modeled as Markov
Decision Process, which usually cannot be satisfied when multiple
agents coexist in the same environment. Moreover, to effectively
coordinate each agent-s behavior so as to achieve the goal, it-s
necessary to augment the state of each agent with the information
about other existing agents. Whereas, as the number of agents in a
multiagent environment increases, the state space of each agent grows
exponentially, which will cause the combinational explosion problem.
Profit sharing is one of the reinforcement learning methods that allow
agents to learn effective behaviors from their experiences even within
non-Markovian environments. In this paper, to remedy the drawback
of the original profit sharing approach that needs much memory to
store each state-action pair during the learning process, we firstly
address a kind of on-line rational profit sharing algorithm. Then, we
integrate the advantages of modular learning architecture with on-line
rational profit sharing algorithm, and propose a new modular
reinforcement learning model. The effectiveness of the technique is
demonstrated using the pursuit problem.
Abstract: This research presented in this paper is an on-going
project of an application of neural network and fuzzy models to
evaluate the sociological factors which affect the educational
performance of the students in Sri Lanka. One of its major goals is to
prepare the grounds to device a counseling tool which helps these
students for a better performance at their examinations, especially at
their G.C.E O/L (General Certificate of Education-Ordinary Level)
examination. Closely related sociological factors are collected as raw
data and the noise of these data are filtered through the fuzzy
interface and the supervised neural network is being utilized to
recognize the performance patterns against the chosen social factors.
Abstract: The objective of the present study was to determine
the effect of different concentration of spermatozoa and length of
storage in 5 0C on sperm motility. Semen was collected using
artificial vagina from goat aged 2 to 2.5 years. Fresh goat semen
with sperm motility ≥ 70% was used as material. Semen was
divided into 4 treatments of concentration (40 x 10 6 / ml, 50 x
106/ml, 60x106/ml, 70x106/ml) with length of storage 0,12,24,36 h. in
5 0C. There were interactions (P
Abstract: In this paper, we present C@sa, a multiagent system aiming at modeling, controlling and simulating the behavior of an intelligent house. The developed system aims at providing to architects, designers and psychologists a simulation and control tool for understanding which is the impact of embedded and pervasive technology on people daily life. In this vision, the house is seen as an environment made up of independent and distributed devices, controlled by agents, interacting to support user's goals and tasks.
Abstract: The study aimed to evaluated the reproductive performance response to short term oestrus synchronization during the transition period. One hundred and sixty-five indigenous multiparous non-lactating goats were subdivided into the following six treatment groups for oestrus synchronization: NT control Group (N= 30), Fe-21d, FGA vaginal sponge for 21days+eCG at 19thd; FPe- 11d, FGA 11d + PGF2α and eCG at 9th d; FPe-10d, FGA 10d+ PGF2α and eCG at 8th d; FPe-9d, FGA 9d +PGF2α and eCG at 7thd; PFe-5d, PGF2α at d0 + FGA 5d + eCG at 5thd. The goats were natural mated (1 male/6 females). Fecundity rates (n. births /n. females treated x 100) were statistically higher (P < 0.05) in short term FPe-9d (157.9%), FPe- 11d (115.4%), FPe-10d (111.1%) and PFe-5d (107.7%) groups compared to the NT control Group (66.7%).
Abstract: Logistics outsourcing is a growing trend and measuring its performance, a challenge. It must be consistent with the objectives set for logistics outsourcing, but we have found no objective-based performance measurement system. We have conducted a comprehensive review of the specialist literature to cover this gap, which has led us to identify and define these objectives. The outcome is that we have obtained a list of the most relevant objectives and their descriptions. This will enable us to analyse in a future study whether the indicators used for measuring logistics outsourcing performance are consistent with the objectives pursued with the outsourcing. If this is not the case, a proposal will be made for a set of financial and operational indicators to measure performance in logistics outsourcing that take the goals being pursued into account.
Abstract: The ElectroEncephaloGram (EEG) is useful for
clinical diagnosis and biomedical research. EEG signals often
contain strong ElectroOculoGram (EOG) artifacts produced
by eye movements and eye blinks especially in EEG recorded
from frontal channels. These artifacts obscure the underlying
brain activity, making its visual or automated inspection
difficult. The goal of ocular artifact removal is to remove
ocular artifacts from the recorded EEG, leaving the underlying
background signals due to brain activity. In recent times,
Independent Component Analysis (ICA) algorithms have
demonstrated superior potential in obtaining the least
dependent source components. In this paper, the independent
components are obtained by using the JADE algorithm (best
separating algorithm) and are classified into either artifact
component or neural component. Neural Network is used for
the classification of the obtained independent components.
Neural Network requires input features that exactly represent
the true character of the input signals so that the neural
network could classify the signals based on those key
characters that differentiate between various signals. In this
work, Auto Regressive (AR) coefficients are used as the input
features for classification. Two neural network approaches
are used to learn classification rules from EEG data. First, a
Polynomial Neural Network (PNN) trained by GMDH (Group
Method of Data Handling) algorithm is used and secondly,
feed-forward neural network classifier trained by a standard
back-propagation algorithm is used for classification and the
results show that JADE-FNN performs better than JADEPNN.