Search results for: Industrial clusters

Authors: M. V. Sudhamani, C. R. Venugopal

Abstract:

This paper deals with the application for contentbased image retrieval to extract color feature from natural images stored in the image database by segmenting the image through clustering. We employ a class of nonparametric techniques in which the data points are regarded as samples from an unknown probability density. Explicit computation of the density is avoided by using the mean shift procedure, a robust clustering technique, which does not require prior knowledge of the number of clusters, and does not constrain the shape of the clusters. A non-parametric technique for the recovery of significant image features is presented and segmentation module is developed using the mean shift algorithm to segment each image. In these algorithms, the only user set parameter is the resolution of the analysis and either gray level or color images are accepted as inputs. Extensive experimental results illustrate excellent performance.

Keywords: Segmentation, Clustering, Image Retrieval, Features.

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Authors: Paulo Gomes, Adelaide Figueiredo

Abstract:

We consider n individuals described by p standardized variables, represented by points of the surface of the unit hypersphere S_n-1. For a previous choice of n individuals we suppose that the set of observables variables comes from a mixture of bipolar Watson distribution defined on the hypersphere. EM and Dynamic Clusters algorithms are used for identification of such mixture. We obtain estimates of parameters for each Watson component and then a partition of the set of variables into homogeneous groups of variables. Additionally we will present a factor analysis model where unobservable factors are just the maximum likelihood estimators of Watson directional parameters, exactly the first principal component of data matrix associated to each group previously identified. Such alternative model it will yield us to directly interpretable solutions (simple structure), avoiding factors rotations.

Keywords: Dynamic Clusters algorithm, EM algorithm, Factor analysis model, Hierarchical Clustering, Watson distribution.

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Authors: M. Vivodík, Ž. Balážová, Z. Gálová

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The aim of this work was to detect genetic variability among the set of 40 castor genotypes using 8 RAPD markers. Amplification of genomic DNA of 40 genotypes, using RAPD analysis, yielded in 66 fragments, with an average of 8.25 polymorphic fragments per primer. Number of amplified fragments ranged from 3 to 13, with the size of amplicons ranging from 100 to 1200 bp. Values of the polymorphic information content (PIC) value ranged from 0.556 to 0.895 with an average of 0.784 and diversity index (DI) value ranged from 0.621 to 0.896 with an average of 0.798. The dendrogram based on hierarchical cluster analysis using UPGMA algorithm was prepared and analyzed genotypes were grouped into two main clusters and only two genotypes could not be distinguished. Knowledge on the genetic diversity of castor can be used for future breeding programs for increased oil production for industrial uses.

Keywords: Dendrogram, polymorphism, RAPD technique, Ricinus communis L.

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Authors: J. D. Mallapur, Supriya H., Santosh B. K., Tej H.

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The cellular network is one of the emerging areas of communication, in which the mobile nodes act as member for one base station. The cluster based communication is now an emerging area of wireless cellular multimedia networks. The cluster renders fast communication and also a convenient way to work with connectivity. In our scheme we have proposed an optimization technique for the fuzzy cluster nodes, by categorizing the group members into three categories like long refreshable member, medium refreshable member and short refreshable member. By considering long refreshable nodes as static nodes, we compute the new membership values for the other nodes in the cluster. We compare their previous and present membership value with the threshold value to categorize them into three different members. By which, we optimize the nodes in the fuzzy clusters. The simulation results show that there is reduction in the cluster computational time and iterational time after optimization.

Keywords: Clusters, fuzzy and optimization.

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Authors: Parvinder S. Sandhu, Sandeep Khimta, Kiranpreet Kaur

Abstract:

The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.

Keywords: Growing Neural Gas, data clustering, fault prediction.

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Authors: Ece Kaya

Abstract:

Industrial heritage reflects the traces of an industrial past that have contributed to the economic development of a country. This heritage should be included within the scope of preservation to remind of and to connect the city and its inhabitants to the past. Through adaptive conservation, industrial heritage can be reintroduced into contemporary urban life, with suitable functions and unique identities sustained. The conservation of industrial heritage should protect the material fabric of such heritage and maintain its cultural significance. Emphasising the historical and cultural significance of industrial areas, this research argues that industrial heritage is primarily impacted by political and economic thinking rather than by informed heritage and conservation issues. Waterfront redevelopment projects create similar landscapes around the world, transforming industrial identities and cultural significances. In the case of The Rocks and Darling Harbour, the goal of redevelopment was the creation of employment opportunities, and the provision of places to work, live and shop, through tourism promoted by the NSW State Government. The two case study areas were pivotal to the European industrial development of Sydney. Sydney Cove was one of the largest commercial wharves used to handle cargo in Australia. This paper argues, together with many historians, planners and heritage experts, that these areas have not received the due diligence deserved in regards to their significance to the industrial history of Sydney and modern Australia.

Keywords: Industrial heritage, post-industrial city, transformation of waterfronts, tourism, consumption.

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Authors: P. Vazan, P. Tanuska, M. Kebisek, S. Duchovicova

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The paper deals with communication standards for control and production system. The authors formulate the requirements for communication security protection. The paper is focused on application protocols of the industrial networks and their basic classification. The typical attacks are analysed and the safety protection, based on requirements for specific industrial network is suggested and defined in this paper.

Keywords: Application protocols, communication standards, industrial networks.

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Authors: Nikhil, Ari Visa, Chin-Chao Chen, Chiu-Yue Lin, Jaakko A. Puhakka, Olli Yli-Harja

Abstract:

A predictive clustering hybrid regression (pCHR) approach was developed and evaluated using dataset from H2- producing sucrose-based bioreactor operated for 15 months. The aim was to model and predict the H2-production rate using information available about envirome and metabolome of the bioprocess. Selforganizing maps (SOM) and Sammon map were used to visualize the dataset and to identify main metabolic patterns and clusters in bioprocess data. Three metabolic clusters: acetate coupled with other metabolites, butyrate only, and transition phases were detected. The developed pCHR model combines principles of k-means clustering, kNN classification and regression techniques. The model performed well in modeling and predicting the H2-production rate with mean square error values of 0.0014 and 0.0032, respectively.

Keywords: Biohydrogen, bioprocess modeling, clusteringhybrid regression.

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Authors: Samira Malekmohammadi Golsefid, Mehdi Ghazanfari, Somayeh Alizadeh

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The goal of this paper is to segment the countries based on the value of export from Iran during 14 years ending at 2005. To measure the dissimilarity among export baskets of different countries, we define Dissimilarity Export Basket (DEB) function and use this distance function in K-means algorithm. The DEB function is defined based on the concepts of the association rules and the value of export group-commodities. In this paper, clustering quality function and clusters intraclass inertia are defined to, respectively, calculate the optimum number of clusters and to compare the functionality of DEB versus Euclidean distance. We have also study the effects of importance weight in DEB function to improve clustering quality. Lastly when segmentation is completed, a designated RFM model is used to analyze the relative profitability of each cluster.

Keywords: Customers segmentation, Customer relationship management, Clustering, Data Mining

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Authors: Deepak Goswami, Padma Lochan Hazarika, Kandarpa Kumar Sarma

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The customary practice of identifying industrial sickness is a set traditional techniques which rely upon a range of manual monitoring and compilation of financial records. It makes the process tedious, time consuming and often are susceptible to manipulation. Therefore, certain readily available tools are required which can deal with such uncertain situations arising out of industrial sickness. It is more significant for a country like India where the fruits of development are rarely equally distributed. In this paper, we propose an approach based on Artificial Neural Network (ANN) to deal with industrial sickness with specific focus on a few such units taken from a less developed north-east (NE) Indian state like Assam. The proposed system provides decision regarding industrial sickness using eight different parameters which are directly related to the stages of sickness of such units. The mechanism primarily uses certain signals and symptoms of industrial health to decide upon the state of a unit. Specifically, we formulate an ANN based block with data obtained from a few selected units of Assam so that required decisions related to industrial health could be taken. The system thus formulated could become an important part of planning and development. It can also contribute towards computerization of decision support systems related to industrial health and help in better management.

Keywords: Industrial, Health, Classification, ANN, MLP, MSE.

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Authors: Mostafa Ahmed Shokshok

Abstract:

Libyan industrial companies face many challenges in today's competitive market. Quality management culture approaches is one of these challenges which may furnish the road to the Libyan industrial companies to effectively empower their employees and improve their ability to respond to the international competition. The primary objective of this paper is to design a practical approach to guide Libyan industrial companies toward successful quality culture implementation.

Keywords: Libyan manufacturing industries, TQM, Quality culture, Quality framework.

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Authors: Hemanth Kumar, A. R., Sudhakar G, Satyanarayana B. S.

Abstract:

In this paper, Land Marks for Unique Addressing( LMUA) algorithm is develped to generate unique ID for each and every node which leads to the formation of overlapping/Non overlapping clusters based on unique ID. To overcome the draw back of the developed LMUA algorithm, the concept of clustering is introduced. Based on the clustering concept a Land Marks for Unique Addressing and Clustering(LMUAC) Algorithm is developed to construct strictly non-overlapping clusters and classify those nodes in to Cluster Heads, Member Nodes, Gate way nodes and generating the Hierarchical code for the cluster heads to operate in the level one hierarchy for wireless communication switching. The expansion of the existing network can be performed or not without modifying the cost of adding the clusterhead is shown. The developed algorithm shows one way of efficiently constructing the

Keywords: Cluster Dimension, Cluster Basis, Metric Dimension, Metric Basis.

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Authors: Nida Rizvi, Deeksha Katyal, Varun Joshi

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River Hindon is an important river catering the demand of highly populated rural and industrial cluster of western Uttar Pradesh, India. Water quality of river Hindon is deteriorating at an alarming rate due to various industrial, municipal and agricultural activities. The present study aimed at identifying the pollution sources and quantifying the degree to which these sources are responsible for the deteriorating water quality of the river. Various water quality parameters, like pH, temperature, electrical conductivity, total dissolved solids, total hardness, calcium, chloride, nitrate, sulphate, biological oxygen demand, chemical oxygen demand, and total alkalinity were assessed. Water quality data obtained from eight study sites for one year has been subjected to the two multivariate techniques, namely, principal component analysis and cluster analysis. Principal component analysis was applied with the aim to find out spatial variability and to identify the sources responsible for the water quality of the river. Three Varifactors were obtained after varimax rotation of initial principal components using principal component analysis. Cluster analysis was carried out to classify sampling stations of certain similarity, which grouped eight different sites into two clusters. The study reveals that the anthropogenic influence (municipal, industrial, waste water and agricultural runoff) was the major source of river water pollution. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and elucidation of multifaceted data sets, recognition of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management.

Keywords: Cluster analysis, multivariate statistical technique, river Hindon, water Quality.

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Authors: Ikhwan Hakim, Bruno Parolin

Abstract:

This paper investigates the spatial structure of employment in the Jakarta Metropolitan Area (JMA), with reference to the concept of the Southeast Asian extended metropolitan region (EMR). A combination of factor analysis and local Getis-Ord (Gi*) hot-spot analysis is used to identify clusters of employment in the region, including those of the urban and agriculture sectors. Spatial statistical analysis is further used to probe the spatial association of identified employment clusters with their surroundings on several dimensions, including the spatial association between the central business district (CBD) in Jakarta city on employment density in the region, the spatial impacts of urban expansion on population growth and the degree of urban-rural interaction. The degree of spatial interaction for the whole JMA is measured by the patterns of commuting trips destined to the various employment clusters. Results reveal the strong role of the urban core of Jakarta, and the regional CBD, as the centre for mixed job sectors such as retail, wholesale, services and finance. Manufacturing and local government services, on the other hand, form corridors radiating out of the urban core, reaching out to the agriculture zones in the fringes. Strong associations between the urban expansion corridors and population growth, and urban-rural mix, are revealed particularly in the eastern and western parts of JMA. Metropolitan wide commuting patterns are focussed on the urban core of Jakarta and the CBD, while relatively local commuting patterns are shown to be prevalent for the employment corridors.

Keywords: Jakarta Metropolitan Area, Southeast Asian EMR, spatial association, spatial statistics, spatial structure.

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Authors: T. Chandrasekhar, K. Thangavel, E. N. Sathishkumar

Abstract:

Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins. This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this work K-Means algorithms has been applied for clustering of Gene Expression Data. Further, rough set based Quick reduct algorithm has been applied for each cluster in order to select the most similar genes having high correlation. Then the ACV measure is used to evaluate the refined clusters and classification is used to evaluate the proposed method. They could identify compact clusters with feature selection method used to genes are selected.

Keywords: Clustering, Feature selection, Gene expression data, Quick reduct.

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Authors: Monika Chuchro

Abstract:

This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain subgroups of time series data with normal distribution from the inflow into wastewater treatment plant data, composed of several groups differing by mean value. Two simple algorithms, K-mean and EM, were chosen as a clustering method. The Rand index was used to measure the similarity. After simple meta-clustering, a regression model was performed for each subgroups. The final model was a sum of the subgroups models. The quality of the obtained model was compared with the regression model made using the same explanatory variables, but with no clustering of data. Results were compared using determination coefficient (R2), measure of prediction accuracy- mean absolute percentage error (MAPE) and comparison on a linear chart. Preliminary results allow us to foresee the potential of the presented technique.

Keywords: Clustering, Data analysis, Data mining, Predictive models.

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Authors: Ebrahim Akbarzadeh, Josep A. Picas Barrachina, Maite Baile Puig

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This study focuses on a novel method for dispersion and distribution of reinforcement under high intensive shear stress to produce metal composites. The polyacrylonitrile (PAN)-based short carbon fiber (Csf) and Nextel 610 alumina fiber were dispersed under high intensive shearing at mushy zone in semi-solid of A356 by a novel method. The bundles and clusters were embedded by infiltration of slurry into the clusters, thus leading to a uniform microstructure. The fibers were embedded homogenously into the aluminum around 576-580°C with around 46% of solid fraction. Other experiments at 615°C and 568°C which are contained 0% and 90% solid respectively were not successful for dispersion and infiltration of aluminum into bundles of Csf. The alumina fiber has been cracked by high shearing load. The morphologies and crystalline phase were evaluated by SEM and XRD. The adopted thixo-process effectively improved the adherence and distribution of Csf into Al that can be developed to produce various composites by thixomixing.

Keywords: Aluminum, carbon fiber, alumina fiber, thixomixing, adhesion.

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Authors: S. Mulholland

Abstract:

Octree compression techniques have been used for several years for compressing large three dimensional data sets into homogeneous regions. This compression technique is ideally suited to datasets which have similar values in clusters. Oil engineers represent reservoirs as a three dimensional grid where hydrocarbons occur naturally in clusters. This research looks at the efficiency of storing these grids using octree compression techniques where grid cells are broken into active and inactive regions. Initial experiments yielded high compression ratios as only active leaf nodes and their ancestor, header nodes are stored as a bitstream to file on disk. Savings in computational time and memory were possible at decompression, as only active leaf nodes are sent to the graphics card eliminating the need of reconstructing the original matrix. This results in a more compact vertex table, which can be loaded into the graphics card quicker and generating shorter refresh delay times.

Keywords: 3D visualisation, compressed vertex tables, octree compression techniques, oil reservoir grids.

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Authors: M. Cizmesija, N. Erjavec, V. Bahovec

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While the European Union (EU) harmonized methodology is a benchmark of worldwide used business survey (BS) methodology, the choice of variables that are components of the confidence indicators, as the leading indicators, is not strictly determined and unique. Therefore, the aim of this paper is to investigate and to quantify the relationship between all business survey variables in manufacturing industry and industrial production as a reference macroeconomic series in Croatia. The assumption is that there are variables in the business survey, that are not components of Industrial Confidence Indicator (ICI) and which can accurately (and sometimes better then ICI) predict changes in Croatian industrial production. Empirical analyses are conducted using quarterly data of BS variables in manufacturing industry and Croatian industrial production over the period from the first quarter 2005 to the first quarter 2013. Research results confirmed the assumption: three BS variables which is not components of ICI (competitive position, demand and liquidity) are the best leading indicator then ICI, in forecasting changes in Croatian industrial production instantaneously, with one, two or three quarter ahead.

Keywords: Balance, Business Survey, Confidence Indicators, Industrial Production, Forecasting.

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Authors: Goran Djukanovic, Goran Popovic

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A special area in the study of Wireless Sensor Networks (WSNs) is how to move sensor nodes, as it expands the scope of application of wireless sensors and provides new opportunities to improve network performance. On the other side, it opens a set of new problems, especially if complete clusters are mobile. Node mobility can prolong the network lifetime. In such WSN, some nodes are possibly moveable or nomadic (relocated periodically), while others are static. This paper presents an idea of mobile, solar-powered CHs that relocate themselves inside clusters in such a way that the total energy consumption in the network reduces, and the lifetime of the network extends. Positioning of CHs is made in each round based on selfish herd hypothesis, where leader retreats to the center of gravity. Based on this idea, an algorithm, together with its modified version, has been presented and tested in this paper. Simulation results show that both algorithms have benefits in network lifetime, and prolongation of network stability period duration.

Keywords: CH-active algorithm, mobile cluster head, sensors, wireless sensor network.

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Authors: Naveen Chauhan, L.K. Awasthi, Narottam chand, Vivek Katiyar, Ankit Chug

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Mobile ad-hoc networks (MANETs) are a form of wireless networks which do not require a base station for providing network connectivity. Mobile ad-hoc networks have many characteristics which distinguish them from other wireless networks which make routing in such networks a challenging task. Cluster based routing is one of the routing schemes for MANETs in which various clusters of mobile nodes are formed with each cluster having its own clusterhead which is responsible for routing among clusters. In this paper we have proposed and implemented a distributed weighted clustering algorithm for MANETs. This approach is based on combined weight metric that takes into account several system parameters like the node degree, transmission range, energy and mobility of the nodes. We have evaluated the performance of proposed scheme through simulation in various network situations. Simulation results show that proposed scheme outperforms the original distributed weighted clustering algorithm (DWCA).

Keywords: MANETs, Clustering, Routing, WirelessCommunication, Distributed Clustering

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Authors: Mustahsan Mir, Ahmed Hassanin, Mohammed A. Al-Saleh

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The layout optimization of building blocks of unequal areas has applications in many disciplines including VLSI floorplanning, macrocell placement, unequal-area facilities layout optimization, and plant or machine layout design. A number of heuristics and some analytical and hybrid techniques have been published to solve this problem. This paper presents an efficient high-quality building-block layout design technique especially suited for solving large-size problems. The higher efficiency and improved quality of optimized solutions are made possible by introducing the concept of Promising Infant Clusters in a constructive placement procedure. The results presented in the paper demonstrate the improved performance of the presented technique for benchmark problems in comparison with published heuristic, analytic, and hybrid techniques.

Keywords: Block layout problem, building-block layout design, CAD, optimization, search techniques.

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Authors: César Camisón-Zornoza, Beatriz Forés-Julián, Alba Puig-Denia

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The literature has argued that firms based in industrial districts enjoy advantages for creating internal knowledge and absorbing external knowledge as a consequence of to the knowledge flows and spillovers that exist in the district. However, empirical evidence to show how belonging to an industrial district affects the business processes of creation and absorption of knowledge is scarce and, moreover, empirical research has not taken into account the influence of variations in the flows of knowledge circulating in each cluster. This study aims to extend empirical evidence on the effect that the stock of shared competencies in industrial districts has on the business processes of creation and absorption of knowledge, through data from an initial study on 952 firms and 35 industrial districts in Spain.

Keywords: Absorptive capacity, industrial district, knowledge creation, organisational learning

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Authors: JiEun Jeong, Se-Won Kang, Hee-Ju Hwang, Sung-Hwa Chae, Sang-Haeng Choi, Hong-SeogPark, YeonSoo Han, Bok-Reul Lee, Dae-Hyun Seog, Yong Seok Lee

Abstract:

Tofurther advance research on immune-related genes from T. molitor, we constructed acDNA library and analyzed expressed sequence taq (EST) sequences from 1,056 clones. After removing vector sequence and quality checkingthrough thePhred program (trim_alt 0.05 (P-score>20), 1039 sequences were generated. The average length of insert was 792 bp. In addition, we identified 162 clusters, 167 contigs and 391 contigs after clustering and assembling process using a TGICL package. EST sequences were searchedagainst NCBI nr database by local BLAST (blastx, EKeywords: EST, Innate immunity, Tenebriomolitor

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Authors: Ahmed H. Abdullah, Pavel Kroupa

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Globular clusters (GC) are important objects for tracing the early evolution of a galaxy. We study the correlation between the cluster population and the global properties of the host galaxy. We found that the correlation between cluster population (NGC) and the baryonic mass (Mb) of the host galaxy are best described as 10 −5.6038Mb. In order to understand the origin of the U -shape relation between the GC specific frequency (SN) and Mb (caused by the high value of SN for dwarfs galaxies and giant ellipticals and a minimum SN for intermediate mass galaxies≈ 1010M), we derive a theoretical model for the specific frequency (SNth). The theoretical model for SNth is based on the slope of the power-law embedded cluster mass function (β) and different time scale (Δt) of the forming galaxy. Our results show a good agreement between the observation and the model at a certain β and Δt. The model seems able to reproduce higher value of SNth of β = 1.5 at the midst formation time scale.

Keywords: Galaxies, dwarf, globular cluster, specific frequency, formation time scale.

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Authors: Abdelouahab Moussaoui, Nabila Ferahta, Victor Chen

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The development of aid's systems for the medical diagnosis is not easy thing because of presence of inhomogeneities in the MRI, the variability of the data from a sequence to the other as well as of other different source distortions that accentuate this difficulty. A new automatic, contextual, adaptive and robust segmentation procedure by MRI brain tissue classification is described in this article. A first phase consists in estimating the density of probability of the data by the Parzen-Rozenblatt method. The classification procedure is completely automatic and doesn't make any assumptions nor on the clusters number nor on the prototypes of these clusters since these last are detected in an automatic manner by an operator of mathematical morphology called skeleton by influence zones detection (SKIZ). The problem of initialization of the prototypes as well as their number is transformed in an optimization problem; in more the procedure is adaptive since it takes in consideration the contextual information presents in every voxel by an adaptive and robust non parametric model by the Markov fields (MF). The number of bad classifications is reduced by the use of the criteria of MPM minimization (Maximum Posterior Marginal).

Keywords: Clustering, Automatic Classification, SKIZ, MarkovFields, Image segmentation, Maximum Posterior Marginal (MPM).

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Authors: Summiya Moheuddin, Afzel Noore, Muhammad Choudhry

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This paper proposes a novel solution for optimizing the size and communication overhead of a distributed multiagent system without compromising the performance. The proposed approach addresses the challenges of scalability especially when the multiagent system is large. A modified spectral clustering technique is used to partition a large network into logically related clusters. Agents are assigned to monitor dedicated clusters rather than monitor each device or node. The proposed scalable multiagent system is implemented using JADE (Java Agent Development Environment) for a large power system. The performance of the proposed topologyindependent decentralized multiagent system and the scalable multiagent system is compared by comprehensively simulating different fault scenarios. The time taken for reconfiguration, the overall computational complexity, and the communication overhead incurred are computed. The results of these simulations show that the proposed scalable multiagent system uses fewer agents efficiently, makes faster decisions to reconfigure when a fault occurs, and incurs significantly less communication overhead.

Keywords: Multiagent system, scalable design, spectral clustering, reconfiguration.

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Authors: Rami H. Fouad

Abstract:

The purpose of this study is to identify and evaluate the scale of implementation of Just-In-Time (JIT) in the different industrial sectors in the Middle East. This study analyzes the empirical data collected by a questionnaire survey distributed to companies in three main industrial sectors in the Middle East, which are: food, chemicals and fabrics. The following main hypotheses is formulated and tested: (The requirements of JIT application differ according to the type of industrial sector).Descriptive statistics and Box plot analysis were used to examine the hypotheses. This study indicates a reasonable evidence for accepting the main hypotheses. It reveals that there is no standard way to adopt JIT as a production system. But each industrial sector should concentrate in the investment on critical requirements that differ according to the nature and strategy of production followed in that sector.

Keywords: Just-In-Time, JIT, Questionnaire, Types of Industrial Sectors.

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Authors: M. M. Islam, Rajib K. Saha, Abdullah A. Amin, M. Z. Haq

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The autonomous mobile robot was designed and implemented which was capable of navigating in the industrial environments and did a job of picking objects from variable height and delivering it to another location following a predefined trajectory. In developing country like Bangladesh industrial robotics is not very prevalent yet, due to the high installation cost. The objective of this project was to develop an autonomous mobile robot for industrial application using the available resources in the local market at lower manufacturing cost. The mechanical system of the robot was comprised of locomotion, gripping and elevation system. Grippers were designed to grip objects of a predefined shape. Cartesian elevation system was designed for vertical movement of the gripper. PIC18F452 microcontroller was the brain of the control system. The prototype autonomous robot was fabricated for relatively lower load than the industry and the performance was tested in a virtual industrial environment created within the laboratory to realize the effectiveness.

Keywords: Industrial application, autonomous mobile robot, appropriate technology.

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Authors: F. Titouna, S. Benferhat, K. Aksa, C. Titouna

Abstract:

A traffic light gives security from traffic congestion,reducing the traffic jam, and organizing the traffic flow. Furthermore,increasing congestion level in public road networks is a growingproblem in many countries. Using Intelligent Transportation Systemsto provide emergency vehicles a green light at intersections canreduce driver confusion, reduce conflicts, and improve emergencyresponse times. Nowadays, the technology of wireless sensornetworks can solve many problems and can offer a good managementof the crossroad. In this paper, we develop a new approach based onthe technique of clustering and the graphical possibilistic fusionmodeling. So, the proposed model is elaborated in three phases. Thefirst one consists to decompose the environment into clusters,following by the fusion intra and inter clusters processes. Finally, wewill show some experimental results by simulation that proves theefficiency of our proposed approach.KeywordsTraffic light, Wireless sensor network, Controller,Possibilistic network/Bayesain network.

Keywords: Traffic light, Wireless sensor network, Controller, Possibilistic network/Bayesain network.

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