Abstract: The grey oyster mushroom, Pleurotus sajor-caju
(PSC), is a common edible mushroom and is now grown
commercially around the world for food. This fungus has been
broadly used as food or food ingredients in various food products for
a long time. To enhance the nutritional quality and sensory attributes
of bakery-based products, PSC powder is used in the present study to
partially replace wheat flour in baked product formulations. The
nutrient content and sensory properties of rice-porridge and
unleavened bread (paratha) incorporated with various levels of PSC
powder were studied. These food items were formulated with either
0%, 2%, 4% or 6% of PSC powder. Results show PSC powder
recorded β-glucan at 3.57g/100g. In sensory evaluation, consumers
gave higher score to both rice-porridge and paratha bread containing
2-4% PSC compared to those that are not added with PSC powder.
The paratha containing 4% PSC powder can be formulated with the
intention in improving overall acceptability of paratha bread.
Meanwhile, for rice-porridge, consumers prefer the formulated
product added with 4% PSC powder. In conclusion, the addition of
PSC powder to partially wheat flour can be recommended for the
purpose of enhancing nutritional composition and maintaining the
acceptability of carbohydrate-based products.
Abstract: Grid computing provides a virtual framework for
controlled sharing of resources across institutional boundaries.
Recently, trust has been recognised as an important factor for
selection of optimal resources in a grid. We introduce a new method
that provides a quantitative trust value, based on the past interactions
and present environment characteristics. This quantitative trust value
is used to select a suitable resource for a job and eliminates run time
failures arising from incompatible user-resource pairs. The proposed
work will act as a tool to calculate the trust values of the various
components of the grid and there by improves the success rate of the
jobs submitted to the resource on the grid. The access to a resource
not only depend on the identity and behaviour of the resource but
also upon its context of transaction, time of transaction, connectivity
bandwidth, availability of the resource and load on the resource. The
quality of the recommender is also evaluated based on the accuracy
of the feedback provided about a resource. The jobs are submitted for
execution to the selected resource after finding the overall trust value
of the resource. The overall trust value is computed with respect to
the subjective and objective parameters.
Abstract: The COSvd Ciphers has been proposed by Filiol and others (2004). It is a strengthened version of COS stream cipher family denoted COSvd that has been adopted for at least one commercial standard. We propose a distinguish attack on this version, and prove that, it is distinguishable from a random stream. In the COSvd Cipher used one S-Box (10×8) on the final part of cipher. We focus on S-Box and use weakness this S-Box for distinguish attack. In addition, found a leak on HNLL that the sub s-boxes don-t select uniformly. We use this property for an Improve distinguish attack.
Abstract: Organ motion, especially respiratory motion, is a technical challenge to radiation therapy planning and dosimetry. This motion induces displacements and deformation of the organ tissues within the irradiated region which need to be taken into account when simulating dose distribution during treatment. Finite element modeling (FEM) can provide a great insight into the mechanical behavior of the organs, since they are based on the biomechanical material properties, complex geometry of organs, and anatomical boundary conditions. In this paper we present an original approach that offers the possibility to combine image-based biomechanical models with particle transport simulations. We propose a new method to map material density information issued from CT images to deformable tetrahedral meshes. Based on the principle of mass conservation our method can correlate density variation of organ tissues with geometrical deformations during the different phases of the respiratory cycle. The first results are particularly encouraging, as local error quantification of density mapping on organ geometry and density variation with organ motion are performed to evaluate and validate our approach.
Abstract: This work deals with aspects of support vector machine learning for large-scale data mining tasks. Based on a decomposition algorithm for support vector machine training that can be run in serial as well as shared memory parallel mode we introduce a transformation of the training data that allows for the usage of an expensive generalized kernel without additional costs. We present experiments for the Gaussian kernel, but usage of other kernel functions is possible, too. In order to further speed up the decomposition algorithm we analyze the critical problem of working set selection for large training data sets. In addition, we analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our tests and conclusions led to several modifications of the algorithm and the improvement of overall support vector machine learning performance. Our method allows for using extensive parameter search methods to optimize classification accuracy.
Abstract: This essay endeavors to read Ama Ata Aidoo-s Our Sister Killjoy with a postocolonially-inflected consciousness. It aims at demonstrating how her work could be read as a sophisticated postcolonial revision of the colonial travel narrative whereby the protagonist-s black-eyed squint operates as 'the all-seeing-eye' to subvert the historically unbroken legacy of the Orientalist ideology. It tries to demonstrate how Sissie assumes authority and voice in an act that destabilizes the traditionally established modes of western representation. It is also an investigation into how Aidoo-s text adopts processes which disengage the Eurocentric view produced by the discursive itineraries of western institutions through diverse acts of resistance and 'various strategies of subversion and appropriation'. Her counter discursive strategies of resistance are shaped up in various ways by a feminist consciousness that attempts to articulate a distinct African version of identity and preserve cultural distinctiveness.
Abstract: Stock portfolio selection is a classic problem in finance,
and it involves deciding how to allocate an institution-s or an individual-s
wealth to a number of stocks, with certain investment objectives
(return and risk). In this paper, we adopt the classical Markowitz
mean-variance model and consider an additional common realistic
constraint, namely, the cardinality constraint. Thus, stock portfolio
optimization becomes a mixed-integer quadratic programming problem
and it is difficult to be solved by exact optimization algorithms.
Chemical Reaction Optimization (CRO), which mimics the molecular
interactions in a chemical reaction process, is a population-based
metaheuristic method. Two different types of CRO, named canonical
CRO and Super Molecule-based CRO (S-CRO), are proposed to solve
the stock portfolio selection problem. We test both canonical CRO
and S-CRO on a benchmark and compare their performance under
two criteria: Markowitz efficient frontier (Pareto frontier) and Sharpe
ratio. Computational experiments suggest that S-CRO is promising
in handling the stock portfolio optimization problem.
Abstract: Wimax (Worldwide Interoperability for Microwave Access)
is a promising technology which can offer high speed data,
voice and video service to the customer end, which is presently, dominated
by the cable and digital subscriber line (DSL) technologies.
The performance assessment of Wimax systems is dealt with. The
biggest advantage of Broadband wireless application (BWA) over its
wired competitors is its increased capacity and ease of deployment.
The aims of this paper are to model and simulate the fixed OFDM
IEEE 802.16d physical layer under variant combinations of digital
modulation (BPSK, QPSK, and 16-QAM) over diverse combination
of fading channels (AWGN, SUIs). Stanford University Interim (SUI)
Channel serial was proposed to simulate the fixed broadband wireless
access channel environments where IEEE 802.16d is to be deployed.
It has six channel models that are grouped into three categories
according to three typical different outdoor Terrains, in order to give
a comprehensive effect of fading channels on the overall performance
of the system.
Abstract: This paper investigates the use of mobile phones and
tablets for learning purposes among university students in Saudi
Arabia. For this purpose, an extended Technology Acceptance Model
(TAM) is proposed to analyze the adoption of mobile devices and
smart phones by Saudi university students for accessing course
materials, searching the web for information related to their
discipline, sharing knowledge, conducting assignments etc.
Abstract: Regular physical activity contributes positively to physiological and psychological health. This study aimed to identify exercise behavior changes, self efficacy and decisional balance in nursing and midwifery students. This was a cross-sectional study carried out in Iran.300undergraduate nursing and midwifery students participated in this study. Data were collected using a questionnaire including demographic information, exercise stages of change, exercise self efficacy and pros and cons exercise decisional balance. The analysis was performed using the SPSS.A p-value of less than 0.05 was considered as statistically significant.
Abstract: Predict daily global solar radiation (GSR) based on meteorological variables, using Multi-layer perceptron (MLP) neural networks is the main objective of this study. Daily mean air temperature, relative humidity, sunshine hours, evaporation, wind speed, and soil temperature values between 2002 and 2006 for Dezful city in Iran (32° 16' N, 48° 25' E), are used in this study. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data.
Abstract: This paper presents an approach for the design of
fuzzy logic power system stabilizers using genetic algorithms. In the
proposed fuzzy expert system, speed deviation and its derivative
have been selected as fuzzy inputs. In this approach the parameters of
the fuzzy logic controllers have been tuned using genetic algorithm.
Incorporation of GA in the design of fuzzy logic power system
stabilizer will add an intelligent dimension to the stabilizer and
significantly reduces computational time in the design process. It is
shown in this paper that the system dynamic performance can be
improved significantly by incorporating a genetic-based searching
mechanism. To demonstrate the robustness of the genetic based
fuzzy logic power system stabilizer (GFLPSS), simulation studies on
multimachine system subjected to small perturbation and three-phase
fault have been carried out. Simulation results show the superiority
and robustness of GA based power system stabilizer as compare to
conventionally tuned controller to enhance system dynamic
performance over a wide range of operating conditions.
Abstract: Customarily, the LMTD correction factor, FT, is used
to screen alternative designs for a heat exchanger. Designs with
unacceptably low FT values are discarded. In this paper, authors have
proposed a more fundamental criterion, based on feasibility of a
multipass exchanger as the only criteria, followed by economic
optimization. This criterion, coupled with asymptotic energy targets,
provide the complete optimization space in a heat exchanger network
(HEN), where cost-optimization of HEN can be performed with only
Heat Recovery Approach temperature (HRAT) and number-of-shells
as variables.
Abstract: Measures of complexity and entropy have not converged to a single quantitative description of levels of organization of complex systems. The need for such a measure is increasingly necessary in all disciplines studying complex systems. To address this problem, starting from the most fundamental principle in Physics, here a new measure for quantity of organization and rate of self-organization in complex systems based on the principle of least (stationary) action is applied to a model system - the central processing unit (CPU) of computers. The quantity of organization for several generations of CPUs shows a double exponential rate of change of organization with time. The exact functional dependence has a fine, S-shaped structure, revealing some of the mechanisms of self-organization. The principle of least action helps to explain the mechanism of increase of organization through quantity accumulation and constraint and curvature minimization with an attractor, the least average sum of actions of all elements and for all motions. This approach can help describe, quantify, measure, manage, design and predict future behavior of complex systems to achieve the highest rates of self organization to improve their quality. It can be applied to other complex systems from Physics, Chemistry, Biology, Ecology, Economics, Cities, network theory and others where complex systems are present.
Abstract: The increase on the demand of IT resources diverts
the enterprises to use the cloud as a cheap and scalable solution.
Cloud computing promises achieved by using the virtual machine as a
basic unite of computation. However, the virtual machine pre-defined
settings might be not enough to handle jobs QoS requirements. This
paper addresses the problem of mapping jobs have critical start
deadlines to virtual machines that have predefined specifications.
These virtual machines hosted by physical machines and shared a
fixed amount of bandwidth. This paper proposed an algorithm that
uses the idle virtual machines bandwidth to increase the quote of other
virtual machines nominated as executors to urgent jobs. An algorithm
with empirical study have been given to evaluate the impact of the
proposed model on impatient jobs. The results show the importance
of dynamic bandwidth allocation in virtualized environment and its
affect on throughput metric.
Abstract: The impact force of a rockfall is mainly determined by
its moving behavior and velocity, which are contingent on the rock
shape, slope gradient, height, and surface roughness of the moving
path. It is essential to precisely calculate the moving path of the
rockfall in order to effectively minimize and prevent damages caused
by the rockfall. By applying the Colorado Rockfall Simulation
Program (CRSP) program as the analysis tool, this research studies the
influence of three shapes of rock (spherical, cylindrical and discoidal)
and surface roughness on the moving path of a single rockfall. As
revealed in the analysis, in addition to the slope gradient, the geometry
of the falling rock and joint roughness coefficient ( JRC ) of the slope
are the main factors affecting the moving behavior of a rockfall. On a
single flat slope, both the rock-s bounce height and moving velocity
increase as the surface gradient increases, with a critical gradient value
of 1:m = 1 . Bouncing behavior and faster moving velocity occur more
easily when the rock geometry is more oval. A flat piece tends to cause
sliding behavior and is easily influenced by the change of surface
undulation. When JRC
Abstract: Segmenting the lungs in medical images is a
challenging and important task for many applications. In particular,
automatic segmentation of lung cavities from multiple magnetic
resonance (MR) images is very useful for oncological applications
such as radiotherapy treatment planning. However, distinguishing of
the lung areas is not trivial due to largely changing lung shapes, low
contrast and poorly defined boundaries. In this paper, we address
lung segmentation problem from pulmonary magnetic resonance
images and propose an automated method based on a robust regionaided
geometric snake with a modified diffused region force into the
standard geometric model definition. The extra region force gives the
snake a global complementary view of the lung boundary
information within the image which along with the local gradient
flow, helps detect fuzzy boundaries. The proposed method has been
successful in segmenting the lungs in every slice of 30 magnetic
resonance images with 80 consecutive slices in each image. We
present results by comparing our automatic method to manually
segmented lung cavities provided by an expert radiologist and with
those of previous works, showing encouraging results and high
robustness of our approach.
Abstract: In this paper we present the deep study about the Bio-
Medical Images and tag it with some basic extracting features (e.g.
color, pixel value etc). The classification is done by using a nearest
neighbor classifier with various distance measures as well as the
automatic combination of classifier results. This process selects a
subset of relevant features from a group of features of the image. It
also helps to acquire better understanding about the image by
describing which the important features are. The accuracy can be
improved by increasing the number of features selected. Various
types of classifications were evolved for the medical images like
Support Vector Machine (SVM) which is used for classifying the
Bacterial types. Ant Colony Optimization method is used for optimal
results. It has high approximation capability and much faster
convergence, Texture feature extraction method based on Gabor
wavelets etc..
Abstract: In this study, we used shape memory alloys as
actuators to build a biomorphic robot which can imitate the motion of
an earthworm. The robot can be used to explore in a narrow space.
Therefore we chose shape memory alloys as actuators. Because of the
small deformation of a wire shape memory alloy, spiral shape memory
alloys are selected and installed both on the X axis and Y axis (each
axis having two shape memory alloys) to enable the biomorphic robot
to do reciprocating motion. By the mechanism we designed, the robot
can increase the distance as it moves in a duty cycle. In addition, two
shape memory alloys are added to the robot head for controlling right
and left turns. By sending pulses through the I/O card from the
controller, the signals are then amplified by a driver to heat the shape
memory alloys in order to make the SMA shrink to pull the mechanism
to move.
Abstract: In this paper a genetic algorithms approach for solving the linear and quadratic fuzzy equations Ãx̃=B̃ and Ãx̃2 + B̃x̃=C̃ , where Ã, B̃, C̃ and x̃ are fuzzy numbers is proposed by genetic algorithms. Our genetic based method initially starts with a set of random fuzzy solutions. Then in each generation of genetic algorithms, the solution candidates converge more to better fuzzy solution x̃b . In this proposed method the final reached x̃b is not only restricted to fuzzy triangular and it can be fuzzy number.