Abstract: The presented work is motivated by a french law regarding nuclear waste management. In order to avoid the limitation coming with the usage of the existing scenario codes, as COSI, VISION or FAMILY, the Core Library for Advance Scenario Simulation (CLASS) is being develop. CLASS is an open source tool, which allows any user to simulate an electronuclear scenario. The main CLASS asset, is the possibility to include any type of reactor, even a complitely new concept, through the generation of its ACSII evolution database. In the present article, the CLASS working basis will be presented as well as a simple exemple in order to show his potentiel. In the considered exemple, the effect of the transmutation will be assessed on Minor Actinide Inventory produced by PWR reactors.
Abstract: Airbag deployment has been known to be responsible
for huge death, incidental injuries and broken bones due to low crash
severity and wrong deployment decisions. Therefore, the authorities
and industries have been looking for more innovative and intelligent
products to be realized for future enhancements in the vehicle safety
systems (VSSs). Although the VSSs technologies have advanced
considerably, they still face challenges such as how to avoid
unnecessary and untimely airbag deployments that can be hazardous
and fatal. Currently, most of the existing airbag systems deploy
without regard to occupant size and position. As such, this paper will
focus on the occupant and crash sensing performances due to frontal
collisions for the new breed of so called smart airbag systems. It
intends to provide a thorough discussion relating to the occupancy
detection, occupant size classification, occupant off-position
detection to determine safe distance zone for airbag deployment,
crash-severity analysis and airbag decision algorithms via a computer
modeling. The proposed system model consists of three main
modules namely, occupant sensing, crash severity analysis and
decision fusion. The occupant sensing system module utilizes the
weight sensor to determine occupancy, classify the occupant size,
and determine occupant off-position condition to compute safe
distance for airbag deployment. The crash severity analysis module is
used to generate relevant information pertinent to airbag deployment
decision. Outputs from these two modules are fused to the decision
module for correct and efficient airbag deployment action. Computer
modeling work is carried out using Simulink, Stateflow,
SimMechanics and Virtual Reality toolboxes.
Abstract: The paper describes a self supervised parallel self organizing neural network (PSONN) architecture for true color image segmentation. The proposed architecture is a parallel extension of the standard single self organizing neural network architecture (SONN) and comprises an input (source) layer of image information, three single self organizing neural network architectures for segmentation of the different primary color components in a color image scene and one final output (sink) layer for fusion of the segmented color component images. Responses to the different shades of color components are induced in each of the three single network architectures (meant for component level processing) by applying a multilevel version of the characteristic activation function, which maps the input color information into different shades of color components, thereby yielding a processed component color image segmented on the basis of the different shades of component colors. The number of target classes in the segmented image corresponds to the number of levels in the multilevel activation function. Since the multilevel version of the activation function exhibits several subnormal responses to the input color image scene information, the system errors of the three component network architectures are computed from some subnormal linear index of fuzziness of the component color image scenes at the individual level. Several multilevel activation functions are employed for segmentation of the input color image scene using the proposed network architecture. Results of the application of the multilevel activation functions to the PSONN architecture are reported on three real life true color images. The results are substantiated empirically with the correlation coefficients between the segmented images and the original images.
Abstract: In the hardening energy context, the transport sector
which constitutes a large worldwide energy demand has to be
improving for decrease energy demand and global warming impacts.
In a controversial situation where subsists an increasing demand for
long-distance and high-speed travels, high-speed trains offer many
advantages, as consuming significantly less energy than road or air
transports.
At the project phase of new rail infrastructures, it is nowadays
important to characterize accurately the energy that will be induced
by its operation phase, in addition to other more classical criteria as
construction costs and travel time.
Current literature consumption models used to estimate railways
operation phase are obsolete or not enough accurate for taking into
account the newest train or railways technologies.
In this paper, an updated model of consumption for high-speed is
proposed, based on experimental data obtained from full-scale tests
performed on a new high-speed line. The assessment of the model
is achieved by identifying train parameters and measured power
consumptions for more than one hundred train routes. Perspectives
are then discussed to use this updated model for accurately assess
the energy impact of future railway infrastructures.
Abstract: In the framework of adaptive parametric modelling of images, we propose in this paper a new technique based on the Chandrasekhar fast adaptive filter for texture characterization. An Auto-Regressive (AR) linear model of texture is obtained by scanning the image row by row and modelling this data with an adaptive Chandrasekhar linear filter. The characterization efficiency of the obtained model is compared with the model adapted with the Least Mean Square (LMS) 2-D adaptive algorithm and with the cooccurrence method features. The comparison criteria is based on the computation of a characterization degree using the ratio of "betweenclass" variances with respect to "within-class" variances of the estimated coefficients. Extensive experiments show that the coefficients estimated by the use of Chandrasekhar adaptive filter give better results in texture discrimination than those estimated by other algorithms, even in a noisy context.
Abstract: Semantic query optimization consists in restricting the
search space in order to reduce the set of objects of interest for a
query. This paper presents an indexing method based on UB-trees
and a static analysis of the constraints associated to the views of the
database and to any constraint expressed on attributes. The result of
the static analysis is a partitioning of the object space into disjoint
blocks. Through Space Filling Curve (SFC) techniques, each
fragment (block) of the partition is assigned a unique identifier,
enabling the efficient indexing of fragments by UB-trees. The search
space corresponding to a range query is restricted to a subset of the
blocks of the partition. This approach has been developed in the
context of a KB-DBMS but it can be applied to any relational
system.
Abstract: In terms of total online audience, newspapers are the most successful form of online content to date. The online audience for newspapers continues to demand higher-quality services, including personalized news services. News providers should be able to offer suitable users appropriate content. In this paper, a news article recommender system is suggested based on a user-s preference when he or she visits an Internet news site and reads the published articles. This system helps raise the user-s satisfaction, increase customer loyalty toward the content provider.
Abstract: Searching similar documents and document
management subjects have important place in text mining. One of the
most important parts of similar document research studies is the
process of classifying or clustering the documents. In this study, a
similar document search approach that includes discussion of out the
case of belonging to multiple categories (multiple categories
problem) has been carried. The proposed method that based on Fuzzy
Similarity Classification (FSC) has been compared with Rocchio
algorithm and naive Bayes method which are widely used in text
mining. Empirical results show that the proposed method is quite
successful and can be applied effectively. For the second stage,
multiple categories vector method based on information of categories
regarding to frequency of being seen together has been used.
Empirical results show that achievement is increased almost two
times, when proposed method is compared with classical approach.
Abstract: The study examined the influence of pay differentials on employee retention in the State Colleges of Education in the South-South Region of Nigeria. 275 subjects drawn from members of the wage negotiating teams in the Colleges were administered questionnaires constructed for study. Analysis of Variance revealed that the observed pay differentials significantly influenced retainership, f(5,269 = 6.223, P< 0.05). However, the Multiple Classification Analysis and Post-Hoc test indicated that employees in two of the Colleges with slightly lower and higher pay levels may probably remain with their employers while employees in other Colleges with the least and highest pay levels suggested quitting. Based on these observations, the influence of pay on employee retention seems inconclusive. Generally, employees in the colleges studied are dissatisfied with current pay levels. Management should confront these challenges by improving pay packages to encourage employees to remain and be dedicated to duty.
Abstract: The Minimum Vertex Cover (MVC) problem is a classic
graph optimization NP - complete problem. In this paper a competent
algorithm, called Vertex Support Algorithm (VSA), is designed to
find the smallest vertex cover of a graph. The VSA is tested on a
large number of random graphs and DIMACS benchmark graphs.
Comparative study of this algorithm with the other existing methods
has been carried out. Extensive simulation results show that the VSA
can yield better solutions than other existing algorithms found in the
literature for solving the minimum vertex cover problem.
Abstract: A synchronous network-on-chip using wormhole packet switching
and supporting guaranteed-completion best-effort with low-priority (LP)
and high-priority (HP) wormhole packet delivery service is presented in
this paper. Both our proposed LP and HP message services deliver a good
quality of service in term of lossless packet completion and in-order message
data delivery. However, the LP message service does not guarantee minimal
completion bound. The HP packets will absolutely use 100% bandwidth of
their reserved links if the HP packets are injected from the source node with
maximum injection. Hence, the service are suitable for small size messages
(less than hundred bytes). Otherwise the other HP and LP messages, which
require also the links, will experience relatively high latency depending on the
size of the HP message. The LP packets are routed using a minimal adaptive
routing, while the HP packets are routed using a non-minimal adaptive routing
algorithm. Therefore, an additional 3-bit field, identifying the packet type,
is introduced in their packet headers to classify and to determine the type
of service committed to the packet. Our NoC prototypes have been also
synthesized using a 180-nm CMOS standard-cell technology to evaluate the
cost of implementing the combination of both services.
Abstract: Cloud Computing has recently emerged as a
compelling paradigm for managing and delivering services over the
internet. The rise of Cloud Computing is rapidly changing the
landscape of information technology, and ultimately turning the longheld
promise of utility computing into a reality. As the development
of Cloud Computing paradigm is speedily progressing, concepts, and
terminologies are becoming imprecise and ambiguous, as well as
different technologies are interfering. Thus, it becomes crucial to
clarify the key concepts and definitions. In this paper, we present the
anatomy of Cloud Computing, covering its essential concepts,
prominent characteristics, its affects, architectural design and key
technologies. We differentiate various service and deployment
models. Also, significant challenges and risks need are tackled in
order to guarantee the long-term success of Cloud Computing. The
aim of this paper is to provide a better understanding of the anatomy
of Cloud Computing and pave the way for further research in this
area.
Abstract: Microarrays have become the effective, broadly used tools in biological and medical research to address a wide range of problems, including classification of disease subtypes and tumors. Many statistical methods are available for analyzing and systematizing these complex data into meaningful information, and one of the main goals in analyzing gene expression data is the detection of samples or genes with similar expression patterns. In this paper, we express and compare the performance of several clustering methods based on data preprocessing including strategies of normalization or noise clearness. We also evaluate each of these clustering methods with validation measures for both simulated data and real gene expression data. Consequently, clustering methods which are common used in microarray data analysis are affected by normalization and degree of noise and clearness for datasets.
Abstract: We propose a method for discrimination and
classification of ovarian with benign, malignant and normal tissue
using independent component analysis and neural networks. The
method was tested for a proteomic patters set from A database, and
radial basis functions neural networks. The best performance was
obtained with probabilistic neural networks, resulting I 99% success
rate, with 98% of specificity e 100% of sensitivity.
Abstract: In this era of globalization, the role of the State in all aspects of development is widely debated. Some scholars contend the 'demise' and diminishing role of the State whilst others claim that the State is still “de facto developmental". Clearly, it is vital to ascertain which of these two contentions are reflective of the role of the State as nations ascend their development trajectories. Based on the findings of this paper, the perception that the Malaysian State plays an active and committed role towards distributing equitable educational opportunities and enhancing employability of Malaysian PWDs is actually a myth and not reality. Thus, in order to fulfill the promise of Vision 2020 to transform Malaysia into a caring and socially-inclusive society; this paper calls for a more interventionist and committed role by the Malaysian State to translate the universal rights of education and employment opportunities for PWDs from mere policy rhetoric into inclusive realities.
Abstract: Axisymmetric vibration of an infinite Pyrocomposite
circular hollow cylinder made of inner and outer pyroelectric layer of
6mm-class bonded together by a Linear Elastic Material with Voids
(LEMV) layer is studied. The exact frequency equation is obtained
for the traction free surfaces with continuity condition at the
interfaces. Numerical results in the form of data and dispersion
curves for the first and second mode of the axisymmetric vibration of
the cylinder BaTio3 / Adhesive / BaTio3 by taking the Adhesive layer
as an existing Carbon Fibre Reinforced Polymer (CFRP) are
compared with a hypothetical LEMV layer with and without voids
and as well with a pyroelectric hollow cylinder. The damping is
analyzed through the imaginary parts of the complex frequencies.
Abstract: Mobile payments have been deployed by businesses for more than a decade. Customers use mobile payments if they trust in this relatively new payment method, have a belief and confidence in, as well as reliance on its services and applications. Despite its potential, the current literature shows that there is lack of customer trust in B2C mobile payments, and a lack of studies that determine the factors that influence their trust in these payments; which make these factors yet to be understood, especially in the Middle East region. Thus, this study aims to explore the factors that influence customer trust in mobile payments. The empirical data for this explorative study was collected by establishing four focus group sessions in the UAE. The results indicate that the explored significant factors can be classified into five main groups: customer characteristics, environmental (social and cultural) influences, provider characteristics, mobile-device characteristics, and perceived risks.
Abstract: In synchronized games players make their moves simultaneously
rather than alternately. Synchronized Quadromineering is
the synchronized version of Quadromineering, a variants of a classical
two-player combinatorial game called Domineering. Experimental
results for small m × n boards (with m + n < 15) and some
theoretical results for general k × n boards (with k = 4, 5, 6) are
presented. Moreover, some Synchronized Quadromineering variants
are also investigated.
Abstract: The environmental impact caused by industries is an issue that, in the last 20 years, has become very important in terms of society, economics and politics in Colombia. Particularly, the tannery process is extremely polluting because of uneffective treatments and regulations given to the dumping process and atmospheric emissions. Considering that, this investigation is intended to propose a management model based on the integration of Lean Supply Chain, Green Supply Chain, Cleaner Production and ISO 14001-2004, that prioritizes the strategic components of the organizations. As a result, a management model will be obtained and it will provide a strategic perspective through a systemic approach to the tanning process. This will be achieved through the use of Multicriteria Decision tools, along with Quality Function Deployment and Fuzzy Logic. The strategic approach that embraces the management model using the alignment of Lean Supply Chain, Green Supply Chain, Cleaner Production and ISO 14001-2004, is an integrated perspective that allows a gradual frame of the tactical and operative elements through the correct setting of the information flow, improving the decision making process. In that way, Small Medium Enterprises (SMEs) could improve their productivity, competitiveness and as an added value, the minimization of the environmental impact. This improvement is expected to be controlled through a Dashboard that helps the Organization measure its performance along the implementation of the model in its productive process.
Abstract: This paper aims to improve a fine lapping process of
hard disk drive (HDD) lapping machines by removing materials from
each slider together with controlling the strip height (SH) variation to
minimum value. The standard deviation is the key parameter to
evaluate the strip height variation, hence it is minimized. In this
paper, a design of experiment (DOE) with factorial analysis by twoway
analysis of variance (ANOVA) is adopted to obtain a
statistically information. The statistics results reveal that initial stripe
height patterns affect the final SH variation. Therefore, initial SH
classification using a radial basis function neural network is
implemented to achieve the proportional gain prediction.